diff --git a/README.md b/README.md index 916cd085a9cd499fc11276c16160218ea1c325e7..d04243917c7cefd8588dafb6eb2397db9f2698a5 100644 --- a/README.md +++ b/README.md @@ -3,6 +3,7 @@ license: mit task_categories: - other tags: +- smoldataenvs - rl-environment - agent - data-analysis @@ -12,54 +13,85 @@ tags: - openenv --- -[![View tasks in Harbor Visualiser](https://img.shields.io/badge/%F0%9F%A4%97%20Harbor%20Visualiser-View%20tasks-FFD21F?style=for-the-badge)](https://huggingface.co/spaces/HuggingFaceH4/harbor-visualiser?dataset=FineEnvs/data-agent-harbor-train) -# 📊 Data Agent — Harbor (train) +
-Teach an agent to *actually do data science*. This is a suite of **5,000 hands-on -data-analysis tasks**: each one drops your agent into a sandbox with a real dataset and a -question, and asks it to explore the data, compute the answer, and write it down. Every answer is -checked **deterministically — no LLM judge, no guesswork**. +SmolDataEnvs -It's packaged in [**Harbor**](https://github.com/huggingface/OpenEnv) format, so it runs as a -ready-made agentic environment. +# 📊 SmolDataEnvs — Harbor (train) -## Where it comes from -Built from the [**jupyter-agent dataset**](https://huggingface.co/datasets/jupyter-agent/jupyter-agent-dataset) -— real data-science notebooks over Kaggle datasets. We extracted each question–answer pair and -then **verified every task**: strong agent models solve it in a live sandbox and must reproduce -the gold answer under deterministic grading. Tasks that couldn't be verified cleanly (ambiguous -or un-checkable answers) were dropped. So **every task here is known-solvable and unambiguously -gradable.** +**5.5K+ RL tasks for hill-climbing small models in code and data science.** + +[![Collection](https://img.shields.io/badge/%F0%9F%A4%97%20Collection-SmolDataEnvs-FFD21E?style=for-the-badge&labelColor=1a1a1a)](https://huggingface.co/collections/FineEnvs/smoldataenvs) +[![Harbor Visualiser](https://img.shields.io/badge/%F0%9F%A4%97%20Harbor%20Visualiser-Browse%20tasks-FFD21E?style=for-the-badge&labelColor=1a1a1a)](https://huggingface.co/spaces/HuggingFaceH4/harbor-visualiser?dataset=FineEnvs/SmolDataEnvs-harbor-train) + +
+ + +The training suite: **5,000 hands-on data-analysis tasks**. Each one drops an agent into a sandbox with a +real dataset and a question, and asks it to explore the data, compute the answer, and write it down. +Every answer is checked deterministically. + + +Packaged in [Harbor](https://github.com/huggingface/OpenEnv) format, so each task is a ready-made +agentic environment: its own container, its own data, its own verifier. ## What's inside -- **5,000 verified tasks** -- **Difficulty** — easy **1,433** · medium **2,845** · hard **722** (`difficulty_tier`; also `difficulty_level` 1–4) -- **Answer types** — numeric 2,906 · short-label 1,409 · list 367 · flexible 152 · yes/no 127 · csv-list 39 + +- **5,000 verified tasks** — the RL training set +- **Difficulty** — easy **1,433** · medium **2,845** · hard **722** (`difficulty_tier`, plus `difficulty_level` 1–5) ## How a task is laid out + ``` tasks// - task.toml # metadata + the question, gold answer, and grading tolerances + task.toml # metadata, the question, the gold answer, grading tolerances instruction.md # the prompt the agent sees - environment/ # Dockerfile (shared base image) + data-pull hook + environment/ # Dockerfile (shared base image) + the data-pull hook tests/ # grader.py (deterministic) + test.sh -registry.json # index of every task -manifest.parquet # the same metadata as a flat table +registry.json # the suite manifest +manifest.parquet # one row per task, for filtering without walking the tree ``` -The dataset's CSV/SQLite files are pulled into `/home/user/input/` when the task starts. -## How grading works -The agent writes its final answer to `/workdir/answer.txt`. `grader.py` then scores it through a -ladder of deterministic checks — **exact match → numeric tolerance → list/percent normalization → -symbolic (math-verify)** — and returns `1.0` (correct) or `0.0`. No network, no model calls. +## Serve it -## Run it ```bash -# see what resolves and how many tasks load -openenv harbor info --dataset HuggingEnvs/data-agent-harbor-train +openenv harbor serve \ + --dataset FineEnvs/SmolDataEnvs-harbor-train \ + --llm-url http://127.0.0.1:8000/v1 --model \ + --port 8000 --capture-port 8100 +``` -# run your agent/model against the suite -openenv harbor run --dataset HuggingEnvs/data-agent-harbor-train --model +Pass several with `--dataset a,b` and each arrives as its own split, which is how you train against +`-train` and validate against `-eval` from one server. + +## One rollout, no trainer + +```bash +openenv harbor rollout \ + --dataset FineEnvs/SmolDataEnvs-harbor-train \ + --llm-url http://127.0.0.1:8000/v1 --model \ + --harness opencode --sandbox e2b --task-index 0 ``` -Each task gives the agent one shell/code tool, so **any tool-calling model works**, and grading -is completely model-agnostic and offline. + +## Where it comes from + +Built from the [jupyter-agent dataset](https://huggingface.co/datasets/jupyter-agent/jupyter-agent-dataset) +— real data-science notebooks over 471 Kaggle datasets. Every question–answer pair was extracted and +then **verified**: strong agent models had to solve the task in a live sandbox and reproduce the gold +answer under deterministic grading. Anything ambiguous or un-checkable was dropped. So every task +here is known-solvable and unambiguously gradable. + +**Verified by a checker, not judged by a model.** Grading is an exact comparison against a known +answer, through a ladder of checks: exact match → numeric with tolerances → list and percent +normalisation → symbolic equivalence. No LLM sits in the reward path, so the signal does not drift +when you change the grader's model, because there isn't one. + +## The family + +| Repo | What it is | +|---|---| +| [`SmolDataEnvs`](https://huggingface.co/datasets/FineEnvs/SmolDataEnvs) | the tasks as plain rows — load it and prompt any model | +| [`SmolDataEnvs-sft`](https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-sft) | 4,677 verified agent trajectories, TRL-ready | +| [`SmolDataEnvs-harbor-train`](https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-harbor-train) | 5,000 tasks as Harbor environments | +| [`SmolDataEnvs-harbor-test`](https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-harbor-test) | 250 held-out, deliberately harder | +| [`SmolDataEnvs-harbor-eval`](https://huggingface.co/datasets/FineEnvs/SmolDataEnvs-harbor-eval) | 144 for quick validation during a run | diff --git a/banner.png b/banner.png new file mode 100644 index 0000000000000000000000000000000000000000..b065e7c3ec9ef2ce4606a35c6b3755d354b8937c --- /dev/null +++ b/banner.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b5a8a6613c85b436d5624937cbbd61d6cf827df7cb0db04905cc1d9c7c5d8081 +size 1991711 diff --git a/registry.json b/registry.json index 39d5607fbd3cfd2189c050d3c690c49a1d8e0fcb..701ddcdb76d791221b86e34afe40b8a0c86a0f61 100644 --- a/registry.json +++ b/registry.json @@ -1,6 +1,6 @@ [ { - "name": "data-agent-harbor-train", + "name": "smoldataenvs-harbor-train", "version": "2.0", "description": "5000 deterministic data-analysis tasks (no LLM judge).", "tasks": [ @@ -20006,4 +20006,4 @@ } ] } -] \ No newline at end of file +] diff --git a/tasks/0000_324_324276_qa_3/task.toml b/tasks/0000_324_324276_qa_3/task.toml index 1ee15f710603b607e4e039d7dea0423192804b10..0bcd9bf122858bce8469ad86d7379c2d76ec174e 100644 --- a/tasks/0000_324_324276_qa_3/task.toml +++ b/tasks/0000_324_324276_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0000_324_324276_qa_3" +name = "smoldataenvs-train/0000_324_324276_qa_3" description = "What is the most common job role interest among new coders?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Web Development" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0000_369_369503_qa_1/task.toml b/tasks/0000_369_369503_qa_1/task.toml index 3ea7f9a117e571da64265fcf3400d7e04cc38c43..fb0a3dfc79aa9b12e81310eb28ef862c88d761db 100644 --- a/tasks/0000_369_369503_qa_1/task.toml +++ b/tasks/0000_369_369503_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0000_369_369503_qa_1" +name = "smoldataenvs-train/0000_369_369503_qa_1" description = "What percentage of all matches have a goal difference of zero (i.e., draws)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "25.4%" reward_mode_initial = "flexible" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0000_455_455459_qa_4/task.toml b/tasks/0000_455_455459_qa_4/task.toml index ce06c48248d3d6043e0e76def3f88a078905e19d..3f68b8b30f201e1ef15e7671885b6da86959f263 100644 --- a/tasks/0000_455_455459_qa_4/task.toml +++ b/tasks/0000_455_455459_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0000_455_455459_qa_4" +name = "smoldataenvs-train/0000_455_455459_qa_4" description = "What is the error rate (as a percentage) for non-legendary Pokémon in the logistic regression model's predictions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0000_465_465850_qa_5/task.toml b/tasks/0000_465_465850_qa_5/task.toml index 374d4313e41bf2d3fc12ef44c406349a68041831..6e6938a8331604e117442d92731ca97592f52381 100644 --- a/tasks/0000_465_465850_qa_5/task.toml +++ b/tasks/0000_465_465850_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0000_465_465850_qa_5" +name = "smoldataenvs-train/0000_465_465850_qa_5" description = "Which species exhibits the highest average sepal length according to the aggregated dataset statistics?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "virginica" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0000_526_526258_qa_2/task.toml b/tasks/0000_526_526258_qa_2/task.toml index e14c41a93579302169df9a3a41140fee07663a12..dba8236b8a0931c61e0217f74dcdac8941d5ff13 100644 --- a/tasks/0000_526_526258_qa_2/task.toml +++ b/tasks/0000_526_526258_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0000_526_526258_qa_2" +name = "smoldataenvs-train/0000_526_526258_qa_2" description = "How many entries are categorized into the non-NA group, edge-NA group, and interrupted group based on missing value patterns?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "141205, 32568, 4824" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0000_539_539873_qa_3/task.toml b/tasks/0000_539_539873_qa_3/task.toml index e346661a7aaa3695ef385b170d6e88b1e729677c..53bff422d0ccdb22d79c96b6dcc07592a98aad16 100644 --- a/tasks/0000_539_539873_qa_3/task.toml +++ b/tasks/0000_539_539873_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0000_539_539873_qa_3" +name = "smoldataenvs-train/0000_539_539873_qa_3" description = "Which city has the lowest crime ratio, and what is the value of this ratio?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Imperial3, 0.003403" reward_mode_initial = "list" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0000_582_582934_qa_4/task.toml b/tasks/0000_582_582934_qa_4/task.toml index 8ac7fec4445a5624fba446b58ccd758b5fdae47c..e5cac7e82491187102ea9cdf5294f1c78f55976d 100644 --- a/tasks/0000_582_582934_qa_4/task.toml +++ b/tasks/0000_582_582934_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0000_582_582934_qa_4" +name = "smoldataenvs-train/0000_582_582934_qa_4" description = "Which state has the lowest proportion of shootings involving individuals with signs of mental illness?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Kentucky (KY)" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0000_587_587336_qa_5/task.toml b/tasks/0000_587_587336_qa_5/task.toml index a0f8736d4935cc24987ba7eae71419b4206ea861..5b2f8ec3e47626cf0f9e12ce05bf4020f31f8ebd 100644 --- a/tasks/0000_587_587336_qa_5/task.toml +++ b/tasks/0000_587_587336_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0000_587_587336_qa_5" +name = "smoldataenvs-train/0000_587_587336_qa_5" description = "What is the percentage of total gun-related shootings in Washington state attributed to individuals with mental illness compared to those without?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "45%" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0000_641_641256_qa_1/task.toml b/tasks/0000_641_641256_qa_1/task.toml index 7a6e01ac714c7f6aa4ca72e9d98e29caa272e11f..f70d3a374e195f789be983bd08c48d5d3c6fecde 100644 --- a/tasks/0000_641_641256_qa_1/task.toml +++ b/tasks/0000_641_641256_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0000_641_641256_qa_1" +name = "smoldataenvs-train/0000_641_641256_qa_1" description = "Which state has the highest average effective literacy rate, and what is that rate?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Mizoram, 98.8" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0000_656_656399_qa_2/task.toml b/tasks/0000_656_656399_qa_2/task.toml index eb78720113845ac684add08f9c1f8758e42f7497..7668813e45b132c9a0dd2240de4389ebc523657b 100644 --- a/tasks/0000_656_656399_qa_2/task.toml +++ b/tasks/0000_656_656399_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0000_656_656399_qa_2" +name = "smoldataenvs-train/0000_656_656399_qa_2" description = "Which pair of numerical features in the dataset shows the strongest positive correlation according to the correlation analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "raisedhands, VisITedResources" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0000_767_767688_qa_4/task.toml b/tasks/0000_767_767688_qa_4/task.toml index 5cf8969d586cf83be95c4cc431231563811295e9..c5abc681388d898b1963e508280453932f71580e 100644 --- a/tasks/0000_767_767688_qa_4/task.toml +++ b/tasks/0000_767_767688_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0000_767_767688_qa_4" +name = "smoldataenvs-train/0000_767_767688_qa_4" description = "According to the scatter matrix analysis, which three features exhibited the highest linear correlation with each other in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "perimeter_mean, area_mean, radius_mean" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0000_780_780974_qa_4/task.toml b/tasks/0000_780_780974_qa_4/task.toml index 81c540cad23ef7080428368742106d3ae9e0f75c..94259f78002896a274b4874334a6ae5230e96a9a 100644 --- a/tasks/0000_780_780974_qa_4/task.toml +++ b/tasks/0000_780_780974_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0000_780_780974_qa_4" +name = "smoldataenvs-train/0000_780_780974_qa_4" description = "What was the maximum number of arrests recorded at the Southwest border and in which year?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1643679 in 2000" reward_mode_initial = "list" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0000_804_804467_qa_1/task.toml b/tasks/0000_804_804467_qa_1/task.toml index b771a8f302e1edaa73eb8747ed9495edb547553d..8c2c9884fb24eccb41efc8706918f64200fe4c05 100644 --- a/tasks/0000_804_804467_qa_1/task.toml +++ b/tasks/0000_804_804467_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0000_804_804467_qa_1" +name = "smoldataenvs-train/0000_804_804467_qa_1" description = "Which model achieved the highest accuracy using KFold cross-validation, and what was the accuracy score?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "RandomForest, 1.0" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0000_804_804467_qa_3/task.toml b/tasks/0000_804_804467_qa_3/task.toml index 0249c177b4c5e7b05557904f739f5652e1d4ebcf..dba551df63c9dbe329e60440a558bfa3b345265c 100644 --- a/tasks/0000_804_804467_qa_3/task.toml +++ b/tasks/0000_804_804467_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0000_804_804467_qa_3" +name = "smoldataenvs-train/0000_804_804467_qa_3" description = "What is the lowest RMSE value observed in the train_test_split results, and which models achieved it?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0, DecisionTree, RandomForest, SVM" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0000_806_806826_qa_3/task.toml b/tasks/0000_806_806826_qa_3/task.toml index 473f94b0ef7dc0f939ca477bd8abcd8251bdf02d..486a0803859ca8832747de44efb85ef3dfdfc51f 100644 --- a/tasks/0000_806_806826_qa_3/task.toml +++ b/tasks/0000_806_806826_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0000_806_806826_qa_3" +name = "smoldataenvs-train/0000_806_806826_qa_3" description = "How does the number of years with above-average temperature changes from February to March compare to the number of years with below-average changes in the dataset spanning 1895-2016?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "62 above, 60 below" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0000_849_849952_qa_4/task.toml b/tasks/0000_849_849952_qa_4/task.toml index 9d13a7dd86100592a93a2dfad5b5437e27a3bfe8..3b1611d8854cb889164d59fce7399d1d5d5e5996 100644 --- a/tasks/0000_849_849952_qa_4/task.toml +++ b/tasks/0000_849_849952_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0000_849_849952_qa_4" +name = "smoldataenvs-train/0000_849_849952_qa_4" description = "Which group of Western European countries exhibited synchronized fluctuations in Christian adherents over the five decades of analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Western European countries" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0000_886_886039_qa_2/task.toml b/tasks/0000_886_886039_qa_2/task.toml index fa1da7df5d035354726d8d5a8fc47bba7d58eda5..56ab89f3f49bddb208f9e804bb4cddecb47dd484 100644 --- a/tasks/0000_886_886039_qa_2/task.toml +++ b/tasks/0000_886_886039_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0000_886_886039_qa_2" +name = "smoldataenvs-train/0000_886_886039_qa_2" description = "Which defender was defeated the most times in the dataset, and how many times were they defeated?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Robb Stark, 13" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0000_981_981197_qa_1/task.toml b/tasks/0000_981_981197_qa_1/task.toml index 95160e6be5bb592d559bcb8eece1badfdf7eb3e9..f2e3da92f9361b9b83421b0460b48e0804ac28c9 100644 --- a/tasks/0000_981_981197_qa_1/task.toml +++ b/tasks/0000_981_981197_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0000_981_981197_qa_1" +name = "smoldataenvs-train/0000_981_981197_qa_1" description = "Which four features were identified as the top-performing predictors for diabetes classification using the chi-square ($\\chi^2$) feature selection method?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose, Insulin, BMI, Age" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0000_982_982280_qa_2/task.toml b/tasks/0000_982_982280_qa_2/task.toml index cd65f1ef4a496da21df44ac56ce3ed09e306c333..268519057b4ede8990c590dfdc97ccd53eb0689f 100644 --- a/tasks/0000_982_982280_qa_2/task.toml +++ b/tasks/0000_982_982280_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0000_982_982280_qa_2" +name = "smoldataenvs-train/0000_982_982280_qa_2" description = "What is the item with the highest Trans Fat content, and what is its Trans Fat value in grams?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Double Quarter Pounder with Cheese, 2.5" reward_mode_initial = "list" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0000_992_992184_qa_3/task.toml b/tasks/0000_992_992184_qa_3/task.toml index 9c42d4137bd8f7ddf629f8aeac29380ec96dd0d0..db75bb4ba1e80982bc226631346490b5d25b04d1 100644 --- a/tasks/0000_992_992184_qa_3/task.toml +++ b/tasks/0000_992_992184_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0000_992_992184_qa_3" +name = "smoldataenvs-train/0000_992_992184_qa_3" description = "What are the keys present in the loaded PETCT dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ct_data, label_data, pet_data" reward_mode_initial = "list" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_042_1042725_qa_5/task.toml b/tasks/0001_042_1042725_qa_5/task.toml index 34fa2e31a5120dc0a63a5aaefe5416df63f4c5ed..9181c21b097f1d62c0454794973a8bc9cf0931df 100644 --- a/tasks/0001_042_1042725_qa_5/task.toml +++ b/tasks/0001_042_1042725_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_042_1042725_qa_5" +name = "smoldataenvs-train/0001_042_1042725_qa_5" description = "Which two countries have the most top 100 male marathon runners after the USA in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Kenya, Ethiopia" reward_mode_initial = "list" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_074_1074738_qa_1/task.toml b/tasks/0001_074_1074738_qa_1/task.toml index 5de8e664f002c0ef4dde5b9d509f99bdfd364ea5..7eebe78ba06151812cedcc1c194a2ff015c0e779 100644 --- a/tasks/0001_074_1074738_qa_1/task.toml +++ b/tasks/0001_074_1074738_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_074_1074738_qa_1" +name = "smoldataenvs-train/0001_074_1074738_qa_1" description = "Which U.S. state has the highest number of recorded \"Murder or Manslaughter\" cases, and what is the exact count of such incidents in that state?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "California, 98994" reward_mode_initial = "list" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_074_1074738_qa_4/task.toml b/tasks/0001_074_1074738_qa_4/task.toml index 04ffe658dda18d872ab2185c99ba26daf31137ab..ce9c67197ade438c4eb3a02217aa7b4cd94e4109 100644 --- a/tasks/0001_074_1074738_qa_4/task.toml +++ b/tasks/0001_074_1074738_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_074_1074738_qa_4" +name = "smoldataenvs-train/0001_074_1074738_qa_4" description = "Which relationship category between victims and perpetrators is most prevalent in the dataset, and what percentage of homicide cases fall into this category?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Unknown, 42.76%" reward_mode_initial = "list" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_085_1085629_qa_2/task.toml b/tasks/0001_085_1085629_qa_2/task.toml index a5b734d3b6864d33dee59f4fa8cc918de3a76a63..d0c010d7e48199ca019abac65f62ff0ac7267112 100644 --- a/tasks/0001_085_1085629_qa_2/task.toml +++ b/tasks/0001_085_1085629_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0001_085_1085629_qa_2" +name = "smoldataenvs-train/0001_085_1085629_qa_2" description = "What are the top three features according to the feature importance ranking provided by the Extra Trees Classifier?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "fnlwgt, age, hours.per.week" reward_mode_initial = "list" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0001_085_1085629_qa_4/task.toml b/tasks/0001_085_1085629_qa_4/task.toml index 2bd774764b8aac45f6f83db6b84fe9ccd907c8a7..65d9270b75aa2ef0832371c2e165e6c2de8e2803 100644 --- a/tasks/0001_085_1085629_qa_4/task.toml +++ b/tasks/0001_085_1085629_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_085_1085629_qa_4" +name = "smoldataenvs-train/0001_085_1085629_qa_4" description = "What is the accuracy of the KNN model when using only the top two features from the Feature Importance ranking (fnlwgt and age) with the optimal K value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.762" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0001_090_1090499_qa_1/task.toml b/tasks/0001_090_1090499_qa_1/task.toml index be740c89c282aeaaa6672f162ef3cf4b61c0eb19..2f8d05f5c2ba5813379df60703803ffb92871c64 100644 --- a/tasks/0001_090_1090499_qa_1/task.toml +++ b/tasks/0001_090_1090499_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_090_1090499_qa_1" +name = "smoldataenvs-train/0001_090_1090499_qa_1" description = "Which two nationalities have the highest representation in the dataset based on the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Kuwait, Jordan" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_133_1133625_qa_4/task.toml b/tasks/0001_133_1133625_qa_4/task.toml index 73a0a80b236beb7bf793401bd836cb7215d039b8..402377af2aefdfa685d8101192435f1e15cd8fb1 100644 --- a/tasks/0001_133_1133625_qa_4/task.toml +++ b/tasks/0001_133_1133625_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_133_1133625_qa_4" +name = "smoldataenvs-train/0001_133_1133625_qa_4" description = "In which phase did BJP+ achieve the highest percentage of total votes, and what was that percentage?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Phase 1, 45.48" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_137_1137361_qa_2/task.toml b/tasks/0001_137_1137361_qa_2/task.toml index ee38d741bd2ab58805e5db64554c0fb5bc2191da..0f2f3fa5459aab9391eb1ab80e820f03388e498a 100644 --- a/tasks/0001_137_1137361_qa_2/task.toml +++ b/tasks/0001_137_1137361_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_137_1137361_qa_2" +name = "smoldataenvs-train/0001_137_1137361_qa_2" description = "What is the total number of check-ins recorded in the New York City dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "227428" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_137_1137537_qa_2/task.toml b/tasks/0001_137_1137537_qa_2/task.toml index 2275c9bdf44347f2d8183ac215e41120eaa82611..ea442051b6fd8dd2d45086dcb371dc39546a5681 100644 --- a/tasks/0001_137_1137537_qa_2/task.toml +++ b/tasks/0001_137_1137537_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_137_1137537_qa_2" +name = "smoldataenvs-train/0001_137_1137537_qa_2" description = "What are the geographic coordinates of the closest check-in point to the convex hull centroid in New York City?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "40.77607305, -73.98191214" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0001_155_1155051_qa_5/task.toml b/tasks/0001_155_1155051_qa_5/task.toml index 841b8bb5f4a73554439813bd2e205bfb36b008c8..ba74042868e25a6ea8b30cc4d4c6f9c8e2a331ff 100644 --- a/tasks/0001_155_1155051_qa_5/task.toml +++ b/tasks/0001_155_1155051_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_155_1155051_qa_5" +name = "smoldataenvs-train/0001_155_1155051_qa_5" description = "What is the average age for customers who defaulted compared to those who did not?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Defaulters: 35.73, Non-defaulters: 35.42" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_155_1155264_qa_5/task.toml b/tasks/0001_155_1155264_qa_5/task.toml index e0bfdfdd31b52d4e7f1754c34200aaed2cb7b488..656853854de3d74d54de35a33fbb60ccde6eeb76 100644 --- a/tasks/0001_155_1155264_qa_5/task.toml +++ b/tasks/0001_155_1155264_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0001_155_1155264_qa_5" +name = "smoldataenvs-train/0001_155_1155264_qa_5" description = "What is the most common instance type in the south zone identified through the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "m4.large" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_160_1160639_qa_1/task.toml b/tasks/0001_160_1160639_qa_1/task.toml index ef7bf2aa646d03839fd5d4e95e4cd09bb9c4749c..2ea9ac97ed4f34a1729b722622ccdc472ac3f44a 100644 --- a/tasks/0001_160_1160639_qa_1/task.toml +++ b/tasks/0001_160_1160639_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_160_1160639_qa_1" +name = "smoldataenvs-train/0001_160_1160639_qa_1" description = "What is the average opening price of Nifty 50 across all recorded dates in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7374.52" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_170_1170198_qa_3/task.toml b/tasks/0001_170_1170198_qa_3/task.toml index 9450e5d6cfafb0d17e598d4bcf43743c09b3b179..c0d18524726f96b1154e8b6a3d317746d4654efb 100644 --- a/tasks/0001_170_1170198_qa_3/task.toml +++ b/tasks/0001_170_1170198_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_170_1170198_qa_3" +name = "smoldataenvs-train/0001_170_1170198_qa_3" description = "After imputing missing values with the column mean, how many missing values remain in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_170_1170198_qa_4/task.toml b/tasks/0001_170_1170198_qa_4/task.toml index b6d476c6825f2d3d8165746be42c03d7c1e5f3e8..5ab04b7084f469421fdf9276a61cd103f24bf8ab 100644 --- a/tasks/0001_170_1170198_qa_4/task.toml +++ b/tasks/0001_170_1170198_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_170_1170198_qa_4" +name = "smoldataenvs-train/0001_170_1170198_qa_4" description = "What is the frequency of the most populated bin in the 'huml' histogram, and what is the bin range?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "234, 9.85 to 50.865" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_173_1173665_qa_3/task.toml b/tasks/0001_173_1173665_qa_3/task.toml index 8bd5b5217487dea8fd597e74226886d6d59852f0..993c3fd9d9b0b1957e98fa6d8fee4794c0bc08dc 100644 --- a/tasks/0001_173_1173665_qa_3/task.toml +++ b/tasks/0001_173_1173665_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_173_1173665_qa_3" +name = "smoldataenvs-train/0001_173_1173665_qa_3" description = "After normalization, what is the mean value of the 'Balance' feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.3048" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_173_1173665_qa_5/task.toml b/tasks/0001_173_1173665_qa_5/task.toml index e76df01a7e52fd60b80611a2c22f9fb64924626e..fa4358b480eabe92f6124b2ba61231b53cb13ef6 100644 --- a/tasks/0001_173_1173665_qa_5/task.toml +++ b/tasks/0001_173_1173665_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_173_1173665_qa_5" +name = "smoldataenvs-train/0001_173_1173665_qa_5" description = "Which feature, 'Balance' or 'EstimatedSalary', has a higher standard deviation after normalization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "EstimatedSalary" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_175_1175291_qa_4/task.toml b/tasks/0001_175_1175291_qa_4/task.toml index 8ae6a6630b119a8ba493b2a3178954bb0b760324..fe28d0615d4b4a8603a1a3717f3a6170a3120c51 100644 --- a/tasks/0001_175_1175291_qa_4/task.toml +++ b/tasks/0001_175_1175291_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_175_1175291_qa_4" +name = "smoldataenvs-train/0001_175_1175291_qa_4" description = "What is the maximum earthquake magnitude recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9.1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_181_1181828_qa_4/task.toml b/tasks/0001_181_1181828_qa_4/task.toml index b408184a760dc2252e247f489e0ead188c94813a..4c776fcaec30cea2faf397ad317aba63be80e9ec 100644 --- a/tasks/0001_181_1181828_qa_4/task.toml +++ b/tasks/0001_181_1181828_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_181_1181828_qa_4" +name = "smoldataenvs-train/0001_181_1181828_qa_4" description = "Which model demonstrated the highest training accuracy but the lowest test accuracy in the comparison analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Decision Tree" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0001_182_1182948_qa_1/task.toml b/tasks/0001_182_1182948_qa_1/task.toml index 73159b21cfd6b56c894f93608ac664320ebc6e54..f6f7192efd537ee80dfe007e69329fbaea7b35d7 100644 --- a/tasks/0001_182_1182948_qa_1/task.toml +++ b/tasks/0001_182_1182948_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_182_1182948_qa_1" +name = "smoldataenvs-train/0001_182_1182948_qa_1" description = "What is the minimum recorded solar radiation value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.11" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_188_1188925_qa_1/task.toml b/tasks/0001_188_1188925_qa_1/task.toml index 25770bc00de963da3427792b23b3d41e3222f950..300e758a99f3d4d9d3d33eeb80a0ae771f3de340 100644 --- a/tasks/0001_188_1188925_qa_1/task.toml +++ b/tasks/0001_188_1188925_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_188_1188925_qa_1" +name = "smoldataenvs-train/0001_188_1188925_qa_1" description = "What is the mean age of individuals who defaulted on their credit card payments compared to those who did not?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Default: 35.73, Non-Default: 35.42" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_188_1188925_qa_3/task.toml b/tasks/0001_188_1188925_qa_3/task.toml index 569a3cdea8114b21d446750f38e04a32acb7d9fc..4aafefa256bf6d4ca3dbafd547eb566874fa5410 100644 --- a/tasks/0001_188_1188925_qa_3/task.toml +++ b/tasks/0001_188_1188925_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_188_1188925_qa_3" +name = "smoldataenvs-train/0001_188_1188925_qa_3" description = "What percentage of the dataset consists of credit card defaults?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "22" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_189_1189227_qa_1/task.toml b/tasks/0001_189_1189227_qa_1/task.toml index 29322c2ad35044264045b72510c42c94a69f032e..70856b5b3d80ccf1fa319c147661e4a13881da25 100644 --- a/tasks/0001_189_1189227_qa_1/task.toml +++ b/tasks/0001_189_1189227_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_189_1189227_qa_1" +name = "smoldataenvs-train/0001_189_1189227_qa_1" description = "What percentage of the dataset represents credit card defaults?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "22" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_189_1189227_qa_2/task.toml b/tasks/0001_189_1189227_qa_2/task.toml index b452c73d3d2291fae2c7ed81fc6a6862f3cc8880..b5329b225582a93af56acef97c1d0f97e19fbbc1 100644 --- a/tasks/0001_189_1189227_qa_2/task.toml +++ b/tasks/0001_189_1189227_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_189_1189227_qa_2" +name = "smoldataenvs-train/0001_189_1189227_qa_2" description = "What is the mean age of credit card holders who defaulted?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "35.73" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_189_1189227_qa_5/task.toml b/tasks/0001_189_1189227_qa_5/task.toml index bf8fe56386854049f17e79fdf9b87a10347355fc..89191aa6b336b1e113c2b5972d963ab1bf66716f 100644 --- a/tasks/0001_189_1189227_qa_5/task.toml +++ b/tasks/0001_189_1189227_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_189_1189227_qa_5" +name = "smoldataenvs-train/0001_189_1189227_qa_5" description = "How many samples were allocated to the training set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "24000" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_191_1191057_qa_4/task.toml b/tasks/0001_191_1191057_qa_4/task.toml index 0e8f4d0fa1369d2f0936b0154358351a1893d4df..9a504860afedd28fd9b98425c80f91b2c0b7309b 100644 --- a/tasks/0001_191_1191057_qa_4/task.toml +++ b/tasks/0001_191_1191057_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_191_1191057_qa_4" +name = "smoldataenvs-train/0001_191_1191057_qa_4" description = "Which original features were removed from the dataset because they contained only a single unique value across all observations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "EmployeeCount, Over18, StandardHours" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_193_1193343_qa_3/task.toml b/tasks/0001_193_1193343_qa_3/task.toml index d48649078432297c3a9d821ca4620dc1cdf9e92b..eae62d4038c8838fe3c3c2664af345b56ad73034 100644 --- a/tasks/0001_193_1193343_qa_3/task.toml +++ b/tasks/0001_193_1193343_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_193_1193343_qa_3" +name = "smoldataenvs-train/0001_193_1193343_qa_3" description = "Which cuisine type is mentioned most frequently in the \"fav_cuisine\" column of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "italian" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_196_1196803_qa_3/task.toml b/tasks/0001_196_1196803_qa_3/task.toml index 3e3f9e62bf59667a10ec6fb43854447313603209..d0a9dff2169014cca7b855fbf5152399a5b74cca 100644 --- a/tasks/0001_196_1196803_qa_3/task.toml +++ b/tasks/0001_196_1196803_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_196_1196803_qa_3" +name = "smoldataenvs-train/0001_196_1196803_qa_3" description = "What is the most common ownership type among all Starbucks stores in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Company Owned" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_197_1197721_qa_2/task.toml b/tasks/0001_197_1197721_qa_2/task.toml index b4dfb2184812e6134f95722ce20bc99c82f27401..dee13a159d108d33050babe218f23b8c3c821635 100644 --- a/tasks/0001_197_1197721_qa_2/task.toml +++ b/tasks/0001_197_1197721_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_197_1197721_qa_2" +name = "smoldataenvs-train/0001_197_1197721_qa_2" description = "Which two variables exhibit the strongest positive correlation with the number of games owned in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "geek_rating, num_votes" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_202_1202888_qa_1/task.toml b/tasks/0001_202_1202888_qa_1/task.toml index b0f0680548875385c5df8ad60acdd895e8e1c5b5..174a97ba09f3259f13ab3b28249000a70667ee2a 100644 --- a/tasks/0001_202_1202888_qa_1/task.toml +++ b/tasks/0001_202_1202888_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_202_1202888_qa_1" +name = "smoldataenvs-train/0001_202_1202888_qa_1" description = "Which generation has the highest probability of producing a legendary Pokémon in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Generation 3" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_221_1221016_qa_1/task.toml b/tasks/0001_221_1221016_qa_1/task.toml index 0ee657f64e6c76a4a1fc86240414df3f8e6cde00..15d5c675249b1b8e425968fe03efb64e6416147f 100644 --- a/tasks/0001_221_1221016_qa_1/task.toml +++ b/tasks/0001_221_1221016_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_221_1221016_qa_1" +name = "smoldataenvs-train/0001_221_1221016_qa_1" description = "Who is the tallest player in NBA history based on the dataset, and what is their height in centimeters?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Manute Bol, 231.0" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_231_1231918_qa_1/task.toml b/tasks/0001_231_1231918_qa_1/task.toml index c4601875a872dc83d2fafdb5207590ebde62d54b..e84961025f74b14e6e7c758f6521e6fff16abb35 100644 --- a/tasks/0001_231_1231918_qa_1/task.toml +++ b/tasks/0001_231_1231918_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_231_1231918_qa_1" +name = "smoldataenvs-train/0001_231_1231918_qa_1" description = "What percentage of McDonald's menu items contain zero sugar based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9.61" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_231_1231918_qa_2/task.toml b/tasks/0001_231_1231918_qa_2/task.toml index 2a6b2e389d3a938250305115dd79fa337a9b936f..bc52b720d715755114cee9be2bd1701939385a8b 100644 --- a/tasks/0001_231_1231918_qa_2/task.toml +++ b/tasks/0001_231_1231918_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_231_1231918_qa_2" +name = "smoldataenvs-train/0001_231_1231918_qa_2" description = "How many menu items in the dataset have zero sugar content?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "25" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_233_1233959_qa_2/task.toml b/tasks/0001_233_1233959_qa_2/task.toml index 72bdccb22ab65683df71d684100b868d279ea610..7975a988ecb3fe22c1fbb99e5489ccc19e50d1fe 100644 --- a/tasks/0001_233_1233959_qa_2/task.toml +++ b/tasks/0001_233_1233959_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_233_1233959_qa_2" +name = "smoldataenvs-train/0001_233_1233959_qa_2" description = "What is the most common cap shape in the dataset based on the feature frequency analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "convex" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_233_1233959_qa_5/task.toml b/tasks/0001_233_1233959_qa_5/task.toml index 1517386d2ea3ebc937bd7ba1247a06ef5a90cae9..006ca47dc44f9acd4ea31e12b265139bb259aaf9 100644 --- a/tasks/0001_233_1233959_qa_5/task.toml +++ b/tasks/0001_233_1233959_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_233_1233959_qa_5" +name = "smoldataenvs-train/0001_233_1233959_qa_5" description = "What is the most common cap color in the dataset based on the feature frequency analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "brown" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_234_1234901_qa_3/task.toml b/tasks/0001_234_1234901_qa_3/task.toml index c21e4f65134da4770bd0336eb204d258561de604..90311c119ebccb8382fd18513650ce27a1ba8ed3 100644 --- a/tasks/0001_234_1234901_qa_3/task.toml +++ b/tasks/0001_234_1234901_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_234_1234901_qa_3" +name = "smoldataenvs-train/0001_234_1234901_qa_3" description = "Which undergraduate major has the highest mid-career median salary, and what is that value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Chemical Engineering, 107000" reward_mode_initial = "list" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_234_1234901_qa_5/task.toml b/tasks/0001_234_1234901_qa_5/task.toml index 503fb449c1ae087e14667858e056a0b8c572e41e..fa9d819787d4f00be58dcce4aeca0f258916fd49 100644 --- a/tasks/0001_234_1234901_qa_5/task.toml +++ b/tasks/0001_234_1234901_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_234_1234901_qa_5" +name = "smoldataenvs-train/0001_234_1234901_qa_5" description = "Which undergraduate major has the highest starting median salary, and what is that value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Physician Assistant, 74300" reward_mode_initial = "list" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_238_1238370_qa_1/task.toml b/tasks/0001_238_1238370_qa_1/task.toml index cbcc1e276e3a043ee25634af35a27dcd3c82dd21..f924e7c98f7b65b193918f284ecc562d51e143cf 100644 --- a/tasks/0001_238_1238370_qa_1/task.toml +++ b/tasks/0001_238_1238370_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_238_1238370_qa_1" +name = "smoldataenvs-train/0001_238_1238370_qa_1" description = "How many unique Netflix shows are present in the dataset, considering duplicate titles?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "496" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_239_1239559_qa_4/task.toml b/tasks/0001_239_1239559_qa_4/task.toml index 98c8450f06658933e914823ae3e258cd346d4e8a..bef53a439d53619d8dfc22bca1533ef8d4418b2c 100644 --- a/tasks/0001_239_1239559_qa_4/task.toml +++ b/tasks/0001_239_1239559_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_239_1239559_qa_4" +name = "smoldataenvs-train/0001_239_1239559_qa_4" description = "How much does the average food pinching efficiency decrease from the optimal length (240mm) to the next length tested (270mm)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.999" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_240_1240535_qa_1/task.toml b/tasks/0001_240_1240535_qa_1/task.toml index 0af76afa524389ca1aa2f3bf333c45a444c06ecf..bc2f2aec892dc74070362e0912ea3f08995f3d61 100644 --- a/tasks/0001_240_1240535_qa_1/task.toml +++ b/tasks/0001_240_1240535_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_240_1240535_qa_1" +name = "smoldataenvs-train/0001_240_1240535_qa_1" description = "What is the mean price of computers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2219.58" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_243_1243037_qa_1/task.toml b/tasks/0001_243_1243037_qa_1/task.toml index fd579f9026eee8eae37e83b478a2223839cec55d..abdda4f02ffaedb1b199811ea260d7675f757e2d 100644 --- a/tasks/0001_243_1243037_qa_1/task.toml +++ b/tasks/0001_243_1243037_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_243_1243037_qa_1" +name = "smoldataenvs-train/0001_243_1243037_qa_1" description = "What percentage of individuals in the dataset have diabetes (Outcome=1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.90" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_243_1243037_qa_2/task.toml b/tasks/0001_243_1243037_qa_2/task.toml index 4a119378b7bf7e60e9b355324fd76ff89977f92b..ee95a62a77087440d00a990eb4ae09771c9260bb 100644 --- a/tasks/0001_243_1243037_qa_2/task.toml +++ b/tasks/0001_243_1243037_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_243_1243037_qa_2" +name = "smoldataenvs-train/0001_243_1243037_qa_2" description = "Which feature exhibits the strongest positive correlation with the Outcome variable (diabetes status)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_244_1244861_qa_4/task.toml b/tasks/0001_244_1244861_qa_4/task.toml index 506a6b88b53aaa9cb8928e7815000d9ac851e7a2..ca5aeb2ebe106819343e9429ba68fa13b4bf2a81 100644 --- a/tasks/0001_244_1244861_qa_4/task.toml +++ b/tasks/0001_244_1244861_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_244_1244861_qa_4" +name = "smoldataenvs-train/0001_244_1244861_qa_4" description = "What was the average family score in 2015 compared to 2016?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9910, 0.7936" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_247_1247152_qa_1/task.toml b/tasks/0001_247_1247152_qa_1/task.toml index 1b9ea18f32050cdde8ea0a4a80b033ef1984ec80..6c1333d41a2102814f150204c7271dd20e5b663d 100644 --- a/tasks/0001_247_1247152_qa_1/task.toml +++ b/tasks/0001_247_1247152_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_247_1247152_qa_1" +name = "smoldataenvs-train/0001_247_1247152_qa_1" description = "Which U.S. state has the highest number of breweries based on the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Colorado" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_250_1250826_qa_3/task.toml b/tasks/0001_250_1250826_qa_3/task.toml index 1da9d22addb8928a66e0e3eacf680761a641d834..2bc1990102a030ae98600c68b4b684641309e71a 100644 --- a/tasks/0001_250_1250826_qa_3/task.toml +++ b/tasks/0001_250_1250826_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_250_1250826_qa_3" +name = "smoldataenvs-train/0001_250_1250826_qa_3" description = "What is the average salary of users who use both R and Python compared to those who use neither language?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "63584.37, 54166.61" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_257_1257061_qa_1/task.toml b/tasks/0001_257_1257061_qa_1/task.toml index b9c55515247f46e02cc6097029ed2c3ff728e7fe..7aab89fa78f16308686993cac6c70df1a85894b3 100644 --- a/tasks/0001_257_1257061_qa_1/task.toml +++ b/tasks/0001_257_1257061_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_257_1257061_qa_1" +name = "smoldataenvs-train/0001_257_1257061_qa_1" description = "What is the skewness of the original SalePrice distribution before any transformation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.024069" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_257_1257061_qa_2/task.toml b/tasks/0001_257_1257061_qa_2/task.toml index fe3c0b22a057f6719f5757504d2e65a4d4fcad8c..fc8a0ae182d4310dfd74f7fc4ee57896ac5cb801 100644 --- a/tasks/0001_257_1257061_qa_2/task.toml +++ b/tasks/0001_257_1257061_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0001_257_1257061_qa_2" +name = "smoldataenvs-train/0001_257_1257061_qa_2" description = "Which feature has the highest absolute correlation with SalePrice, and what is the magnitude of that correlation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "sqft_living with 0.7" reward_mode_initial = "flexible" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_257_1257061_qa_3/task.toml b/tasks/0001_257_1257061_qa_3/task.toml index 9d06210fca52a6c1d6849f3baea66ce0ca7646c1..dc4e2fcb95e29998e2ee2ed695527e1c0e7cb629 100644 --- a/tasks/0001_257_1257061_qa_3/task.toml +++ b/tasks/0001_257_1257061_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_257_1257061_qa_3" +name = "smoldataenvs-train/0001_257_1257061_qa_3" description = "What transformation was applied to the SalePrice and sqft_living features to achieve a more normal distribution?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "log transformation" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_257_1257756_qa_1/task.toml b/tasks/0001_257_1257756_qa_1/task.toml index bad2204e56ddf7b849651473bd5b4412537450a0..86e70a59fd1f469d0da8e15b15fa8a26bfce74b6 100644 --- a/tasks/0001_257_1257756_qa_1/task.toml +++ b/tasks/0001_257_1257756_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_257_1257756_qa_1" +name = "smoldataenvs-train/0001_257_1257756_qa_1" description = "What is the average percentage of matches won by the home team across all seasons in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "51.16" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_257_1257756_qa_2/task.toml b/tasks/0001_257_1257756_qa_2/task.toml index e3e7a85da7f111e4afa5f8c50a9175c8ccd21c30..7beccb345e96dceac897efc1facbf1861a502434 100644 --- a/tasks/0001_257_1257756_qa_2/task.toml +++ b/tasks/0001_257_1257756_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_257_1257756_qa_2" +name = "smoldataenvs-train/0001_257_1257756_qa_2" description = "Which team has the highest number of home wins in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Real Madrid" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_257_1257756_qa_3/task.toml b/tasks/0001_257_1257756_qa_3/task.toml index 7fccc3aef7d92885f35042bcd6506683302e1562..ee2718e5ec15b761eed24b64f976d896bfee0b81 100644 --- a/tasks/0001_257_1257756_qa_3/task.toml +++ b/tasks/0001_257_1257756_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_257_1257756_qa_3" +name = "smoldataenvs-train/0001_257_1257756_qa_3" description = "What is the average total number of goals scored per match in the dataset (local + visitor goals)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.45" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_261_1261978_qa_5/task.toml b/tasks/0001_261_1261978_qa_5/task.toml index c15ee232ea34914cc8a5cc43b1ffa8d0e92ce2be..14c46a1ccd9f5466ddbba4370d5d41f2b25f25e6 100644 --- a/tasks/0001_261_1261978_qa_5/task.toml +++ b/tasks/0001_261_1261978_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0001_261_1261978_qa_5" +name = "smoldataenvs-train/0001_261_1261978_qa_5" description = "Which chopstick length has the lowest mean food pinching efficiency based on the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "330" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_262_1262014_qa_4/task.toml b/tasks/0001_262_1262014_qa_4/task.toml index 449f17cf2165c6a03d19fd25cb76411dcde776ad..d92174b68750230a0b1a3a19728baddf70953cd8 100644 --- a/tasks/0001_262_1262014_qa_4/task.toml +++ b/tasks/0001_262_1262014_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_262_1262014_qa_4" +name = "smoldataenvs-train/0001_262_1262014_qa_4" description = "How many of the female-on-female homicides had the weapon listed as unknown?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1507" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_272_1272652_qa_1/task.toml b/tasks/0001_272_1272652_qa_1/task.toml index 9048d9614710a01e246824a20df206e929f14e4c..a9a089ea6c4c382ff1fd50692cab0e54c187d3c2 100644 --- a/tasks/0001_272_1272652_qa_1/task.toml +++ b/tasks/0001_272_1272652_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_272_1272652_qa_1" +name = "smoldataenvs-train/0001_272_1272652_qa_1" description = "What is the total length of the first six chromosomes listed in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "110357861" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_273_1273208_qa_1/task.toml b/tasks/0001_273_1273208_qa_1/task.toml index 21f2843d34de82950afbf388012911a149d2ca83..7e6828019250b88fb3b28ef9e04cf7eedb863dee 100644 --- a/tasks/0001_273_1273208_qa_1/task.toml +++ b/tasks/0001_273_1273208_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_273_1273208_qa_1" +name = "smoldataenvs-train/0001_273_1273208_qa_1" description = "What percentage of the original dataset consists of fraudulent transactions (Class 1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.1727485630620034" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_273_1273208_qa_2/task.toml b/tasks/0001_273_1273208_qa_2/task.toml index ca7a5e020afee0c8efe6292a8bfc7afd6a6c2b1d..b22fa498657c77fe783a81fa13a870a6da37f051 100644 --- a/tasks/0001_273_1273208_qa_2/task.toml +++ b/tasks/0001_273_1273208_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0001_273_1273208_qa_2" +name = "smoldataenvs-train/0001_273_1273208_qa_2" description = "After undersampling, how many total rows are present in the balanced dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "984" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_275_1275171_qa_3/task.toml b/tasks/0001_275_1275171_qa_3/task.toml index d9dbfced9d8b1c0ae8ef2c0963bb1ab79d56548a..800a85d2a004b166dcf86c65139dc5806e658b1a 100644 --- a/tasks/0001_275_1275171_qa_3/task.toml +++ b/tasks/0001_275_1275171_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_275_1275171_qa_3" +name = "smoldataenvs-train/0001_275_1275171_qa_3" description = "How many Indian states have more than 100 cities listed in the dataset based on the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_275_1275171_qa_4/task.toml b/tasks/0001_275_1275171_qa_4/task.toml index e2d330762b515e4edba154c07485815dbefdcf04..62d2d5811dff3def82266fc319dccb07bed5c57d 100644 --- a/tasks/0001_275_1275171_qa_4/task.toml +++ b/tasks/0001_275_1275171_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_275_1275171_qa_4" +name = "smoldataenvs-train/0001_275_1275171_qa_4" description = "What is the total number of cities listed for India in the dataset according to the filtered data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2443" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_277_1277058_qa_1/task.toml b/tasks/0001_277_1277058_qa_1/task.toml index ad1c89436ce19ce03fb76880a430043e1827dad6..5eb84262333d8f6591db6711a36c956e29d2ff95 100644 --- a/tasks/0001_277_1277058_qa_1/task.toml +++ b/tasks/0001_277_1277058_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_277_1277058_qa_1" +name = "smoldataenvs-train/0001_277_1277058_qa_1" description = "How many unique countries are represented in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_277_1277058_qa_2/task.toml b/tasks/0001_277_1277058_qa_2/task.toml index 2ee45a6287c2ddddc478824a30a942d979be0a26..a87e0a4a0a802daa6829beb0b0a12b2e3c54bb64 100644 --- a/tasks/0001_277_1277058_qa_2/task.toml +++ b/tasks/0001_277_1277058_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_277_1277058_qa_2" +name = "smoldataenvs-train/0001_277_1277058_qa_2" description = "Which year had the highest number of companies founded, and how many companies were founded that year?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2011, 51" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_277_1277058_qa_5/task.toml b/tasks/0001_277_1277058_qa_5/task.toml index 85b36a8581feec0bc05a35901008c632de6e0fc9..1fbc40009955fd60c9dda19819831db78257d81d 100644 --- a/tasks/0001_277_1277058_qa_5/task.toml +++ b/tasks/0001_277_1277058_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0001_277_1277058_qa_5" +name = "smoldataenvs-train/0001_277_1277058_qa_5" description = "Which year had the second-highest number of companies founded, and how many companies were founded that year?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2010 and 50" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_282_1282413_qa_5/task.toml b/tasks/0001_282_1282413_qa_5/task.toml index e4b972e61a95b4c13d47b48b7b410dd565308465..fe6c6982de7c98b4639703cb871832ff1cfd9912 100644 --- a/tasks/0001_282_1282413_qa_5/task.toml +++ b/tasks/0001_282_1282413_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_282_1282413_qa_5" +name = "smoldataenvs-train/0001_282_1282413_qa_5" description = "What percentage of matches in the English Premier League ended in draws according to the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "25.76" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_289_1289812_qa_4/task.toml b/tasks/0001_289_1289812_qa_4/task.toml index b57ca25c397d671d4aaf0e5d7f24f0b483ceb982..9d9c406a740261ff97680b1517d143319583cd64 100644 --- a/tasks/0001_289_1289812_qa_4/task.toml +++ b/tasks/0001_289_1289812_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_289_1289812_qa_4" +name = "smoldataenvs-train/0001_289_1289812_qa_4" description = "How many Netflix shows in the dataset contain missing values in at least one column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "426" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_293_1293142_qa_5/task.toml b/tasks/0001_293_1293142_qa_5/task.toml index ba39b503963c2090689e661e6bbdf1afa9c93e3c..3b1cad28e3dd79ab7ed3ac0deceaeb42bc765e4b 100644 --- a/tasks/0001_293_1293142_qa_5/task.toml +++ b/tasks/0001_293_1293142_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_293_1293142_qa_5" +name = "smoldataenvs-train/0001_293_1293142_qa_5" description = "What is the correlation coefficient between the sqft_living feature and the log-transformed price variable in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.70" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_312_1312239_qa_2/task.toml b/tasks/0001_312_1312239_qa_2/task.toml index 0b51b77aeb8f4569e30a5fd5151b00412dfd2183..307516768f1b8a47ad8b1c604f0698f75d10ee86 100644 --- a/tasks/0001_312_1312239_qa_2/task.toml +++ b/tasks/0001_312_1312239_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_312_1312239_qa_2" +name = "smoldataenvs-train/0001_312_1312239_qa_2" description = "Which region has the highest average Happiness Score when grouping by geographic regions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Australia and New Zealand" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_315_1315610_qa_2/task.toml b/tasks/0001_315_1315610_qa_2/task.toml index 956da314c2dd4ce4b00d8fca5dc9ad1eec726978..5531a50aeeda7998d99e80232133a0cdbaee87af 100644 --- a/tasks/0001_315_1315610_qa_2/task.toml +++ b/tasks/0001_315_1315610_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_315_1315610_qa_2" +name = "smoldataenvs-train/0001_315_1315610_qa_2" description = "After MinMax scaling, what is the range (maximum value minus minimum value) of the SepalWidthCm feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_323_1323152_qa_1/task.toml b/tasks/0001_323_1323152_qa_1/task.toml index 88017c790385fc21ec0b5b1276f85fed15eac88c..7ec079ad1560d18ebb29659c0c62f6619db47fce 100644 --- a/tasks/0001_323_1323152_qa_1/task.toml +++ b/tasks/0001_323_1323152_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_323_1323152_qa_1" +name = "smoldataenvs-train/0001_323_1323152_qa_1" description = "Which movie generated the highest revenue in the dataset, and what was the exact revenue amount?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Star Wars: Episode VII - The Force Awakens, 936.63" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_325_1325110_qa_5/task.toml b/tasks/0001_325_1325110_qa_5/task.toml index 3789220cb531bdb971adafce027561ec2a365c9e..e6227e78a230d15f2e880cd1f588de3eb35337d5 100644 --- a/tasks/0001_325_1325110_qa_5/task.toml +++ b/tasks/0001_325_1325110_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_325_1325110_qa_5" +name = "smoldataenvs-train/0001_325_1325110_qa_5" description = "How many distinct latitude-based groups were created based on the arbitrary thresholds defined in the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_330_1330281_qa_3/task.toml b/tasks/0001_330_1330281_qa_3/task.toml index 8fdd3d0948b2f35336d6c41cb0217bcc6b8f16fe..38631a5ab3bf94133bd6a3c57b5178d22017792f 100644 --- a/tasks/0001_330_1330281_qa_3/task.toml +++ b/tasks/0001_330_1330281_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_330_1330281_qa_3" +name = "smoldataenvs-train/0001_330_1330281_qa_3" description = "What is the base shot success rate across all shots in the dataset, regardless of contextual factors?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "45.2139" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_330_1330281_qa_4/task.toml b/tasks/0001_330_1330281_qa_4/task.toml index 40889015676a15ff0d24cc768edccb9dfc260d43..bbabc5da30994862a0940187242809262c38a7c7 100644 --- a/tasks/0001_330_1330281_qa_4/task.toml +++ b/tasks/0001_330_1330281_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_330_1330281_qa_4" +name = "smoldataenvs-train/0001_330_1330281_qa_4" description = "How does the 1st period’s shot success rate compare to the 4th period’s shot success rate during regulation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1st period 46.0528%, 4th period 44.0099%" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_341_1341821_qa_1/task.toml b/tasks/0001_341_1341821_qa_1/task.toml index 92d09f27e25c995ba3fee1c5ac188f35a5fa7da8..61503efde143dbadd459d3fed51956b3fd72fbd4 100644 --- a/tasks/0001_341_1341821_qa_1/task.toml +++ b/tasks/0001_341_1341821_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_341_1341821_qa_1" +name = "smoldataenvs-train/0001_341_1341821_qa_1" description = "What is the most common dual-type Pokémon combination across all generations, and what is its total count?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Normal/Flying, 24" reward_mode_initial = "list" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_349_1349978_qa_4/task.toml b/tasks/0001_349_1349978_qa_4/task.toml index ca9433d7e143905aacf4a8d104cc3865ab6d6d98..125913d9a2bcca9a286f5d3feeb9535fadfa3daf 100644 --- a/tasks/0001_349_1349978_qa_4/task.toml +++ b/tasks/0001_349_1349978_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_349_1349978_qa_4" +name = "smoldataenvs-train/0001_349_1349978_qa_4" description = "How many categorical features were originally present in the mushroom dataset before numerical encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "23" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_351_1351211_qa_2/task.toml b/tasks/0001_351_1351211_qa_2/task.toml index d0cb9a44c4068020bbe0b07bc7ce48a9ed1fb484..105f08cc2869a05f99e0ef2511c779236259ab1b 100644 --- a/tasks/0001_351_1351211_qa_2/task.toml +++ b/tasks/0001_351_1351211_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_351_1351211_qa_2" +name = "smoldataenvs-train/0001_351_1351211_qa_2" description = "What is the most common degree of endangerment among languages in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Definitely endangered" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_352_1352372_qa_1/task.toml b/tasks/0001_352_1352372_qa_1/task.toml index 36fac8a7f7b8847815340646f6b8eddadbd7881b..cd9ccf00d3a1524e3ff9fdb75b5461f5109868b8 100644 --- a/tasks/0001_352_1352372_qa_1/task.toml +++ b/tasks/0001_352_1352372_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_352_1352372_qa_1" +name = "smoldataenvs-train/0001_352_1352372_qa_1" description = "Which year had the highest number of celebrity deaths based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2016" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_352_1352372_qa_5/task.toml b/tasks/0001_352_1352372_qa_5/task.toml index 4cf48c9d4cba5ee5e14ee34e356631682898b699..6f32b8df66f1320929ec9db68accb4cecd967f04 100644 --- a/tasks/0001_352_1352372_qa_5/task.toml +++ b/tasks/0001_352_1352372_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_352_1352372_qa_5" +name = "smoldataenvs-train/0001_352_1352372_qa_5" description = "How many celebrities in the dataset died as a result of accidents?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "141" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_353_1353632_qa_3/task.toml b/tasks/0001_353_1353632_qa_3/task.toml index 6bfb9f4d77c0834247a14c5beb4a7c9bae6c849a..82e7a021af2f9ba615c3bd56130898f6287e7494 100644 --- a/tasks/0001_353_1353632_qa_3/task.toml +++ b/tasks/0001_353_1353632_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_353_1353632_qa_3" +name = "smoldataenvs-train/0001_353_1353632_qa_3" description = "What is the difference between the number of \"run\" samples collected on the left wrist versus \"walk\" samples on the same wrist?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5086" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_353_1353930_qa_1/task.toml b/tasks/0001_353_1353930_qa_1/task.toml index c17d44cf3367d5cacc190c8d6a530fb23b568fb2..b334f16f7d03b9e710780283b7af0f79092cd649 100644 --- a/tasks/0001_353_1353930_qa_1/task.toml +++ b/tasks/0001_353_1353930_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_353_1353930_qa_1" +name = "smoldataenvs-train/0001_353_1353930_qa_1" description = "Which language in the dataset has the highest number of speakers, and what is its speaker count?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "South Italian, 7500000" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_354_1354131_qa_1/task.toml b/tasks/0001_354_1354131_qa_1/task.toml index 7e7acd1186a65cfd662e2050b5a84dba0de73a1f..ddc62abe6d88be585f0493e9c5b8a08cae17aec3 100644 --- a/tasks/0001_354_1354131_qa_1/task.toml +++ b/tasks/0001_354_1354131_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_354_1354131_qa_1" +name = "smoldataenvs-train/0001_354_1354131_qa_1" description = "Is the distribution of species in the Iris dataset balanced across all classes?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_361_1361614_qa_1/task.toml b/tasks/0001_361_1361614_qa_1/task.toml index 91f5bdd8229932ec72cae2053b1daaed895f02aa..1f467d6dff501a0efd215fd3cabac76ed5193e0a 100644 --- a/tasks/0001_361_1361614_qa_1/task.toml +++ b/tasks/0001_361_1361614_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_361_1361614_qa_1" +name = "smoldataenvs-train/0001_361_1361614_qa_1" description = "Which Indian city has the highest temperature difference between its maximum and minimum recorded temperatures in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "New Delhi" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_364_1364936_qa_3/task.toml b/tasks/0001_364_1364936_qa_3/task.toml index b6d96bc4698d5b5f4220718230d29e1c69e8c74d..50bb7897510e0b390a5fe510a558c5177c380ef3 100644 --- a/tasks/0001_364_1364936_qa_3/task.toml +++ b/tasks/0001_364_1364936_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0001_364_1364936_qa_3" +name = "smoldataenvs-train/0001_364_1364936_qa_3" description = "Which movie has the lowest total count of entries in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Kill Bill: Vol. 2" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_364_1364936_qa_4/task.toml b/tasks/0001_364_1364936_qa_4/task.toml index 2956d1f54bada12a4efd5882d1e3cb23ddd1ffad..626cc4fd91ee82290a35d88b37fe556e206bc66a 100644 --- a/tasks/0001_364_1364936_qa_4/task.toml +++ b/tasks/0001_364_1364936_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0001_364_1364936_qa_4" +name = "smoldataenvs-train/0001_364_1364936_qa_4" description = "How many movies in the dataset were released after the year 2004?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_367_1367107_qa_1/task.toml b/tasks/0001_367_1367107_qa_1/task.toml index 008d8ac833a7123a044d62773e1739494a59ec36..307e726577a7a390370323189be00132ae96cc96 100644 --- a/tasks/0001_367_1367107_qa_1/task.toml +++ b/tasks/0001_367_1367107_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_367_1367107_qa_1" +name = "smoldataenvs-train/0001_367_1367107_qa_1" description = "Which pair of variables in the dataset has the strongest positive correlation according to the correlation matrix?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "battle_number, year" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_367_1367483_qa_4/task.toml b/tasks/0001_367_1367483_qa_4/task.toml index 27aba6774869dcdb66ee20f12f48129e1b866780..f11858fab9c9d3cf42b903d3dcdda948ae04e820 100644 --- a/tasks/0001_367_1367483_qa_4/task.toml +++ b/tasks/0001_367_1367483_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_367_1367483_qa_4" +name = "smoldataenvs-train/0001_367_1367483_qa_4" description = "Did any attacker king achieve a 100% win rate in all battles fought according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Balon/Euron Greyjoy" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_368_1368371_qa_2/task.toml b/tasks/0001_368_1368371_qa_2/task.toml index 13d6851d014f163a917a56adc0b59fdd9a120685..b9e37da3d41dc27a78b301b026c2996f349d873b 100644 --- a/tasks/0001_368_1368371_qa_2/task.toml +++ b/tasks/0001_368_1368371_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_368_1368371_qa_2" +name = "smoldataenvs-train/0001_368_1368371_qa_2" description = "What is the Pearson correlation coefficient between Death Year and Book of Death in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.831684" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_374_1374329_qa_2/task.toml b/tasks/0001_374_1374329_qa_2/task.toml index bcc4ac1a5495719f473e88e31c76bf38afd73a19..62d14e6cef8cb70f8bb248eeab47a0ba3a61b17a 100644 --- a/tasks/0001_374_1374329_qa_2/task.toml +++ b/tasks/0001_374_1374329_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_374_1374329_qa_2" +name = "smoldataenvs-train/0001_374_1374329_qa_2" description = "What is the most common cause of suicide in the dataset according to the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Family problems" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_374_1374329_qa_3/task.toml b/tasks/0001_374_1374329_qa_3/task.toml index 8d0c2ad76d44cb9ccb3c28d307c41fa5a11b817e..caed0eb6e9489c404bb04ae91be38d24659b4be3 100644 --- a/tasks/0001_374_1374329_qa_3/task.toml +++ b/tasks/0001_374_1374329_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_374_1374329_qa_3" +name = "smoldataenvs-train/0001_374_1374329_qa_3" description = "Which demographic group (based on social status) has the highest total number of suicides in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Married" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_379_1379568_qa_1/task.toml b/tasks/0001_379_1379568_qa_1/task.toml index bde0ba8be3854d8fc5049a610f572e7d4316989d..9c4cebebf76e95344037e96d2bdb835dd2e9f654 100644 --- a/tasks/0001_379_1379568_qa_1/task.toml +++ b/tasks/0001_379_1379568_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_379_1379568_qa_1" +name = "smoldataenvs-train/0001_379_1379568_qa_1" description = "Which subdivision has the highest average annual rainfall according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ARUNACHAL PRADESH" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_379_1379568_qa_4/task.toml b/tasks/0001_379_1379568_qa_4/task.toml index 3ab8f34dcfc093520744fe7f06e50d308959db1e..e0f8c9919d906cab40de4e2dbdb5ed917961d614 100644 --- a/tasks/0001_379_1379568_qa_4/task.toml +++ b/tasks/0001_379_1379568_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0001_379_1379568_qa_4" +name = "smoldataenvs-train/0001_379_1379568_qa_4" description = "What is the average annual rainfall for the subdivision with the lowest average annual rainfall in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "292.673043" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_380_1380018_qa_1/task.toml b/tasks/0001_380_1380018_qa_1/task.toml index d04a91d0bd095cd2d75f9119aaf977331574cba8..9e30677d8385e0a6dcc662296ec04197250f570c 100644 --- a/tasks/0001_380_1380018_qa_1/task.toml +++ b/tasks/0001_380_1380018_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_380_1380018_qa_1" +name = "smoldataenvs-train/0001_380_1380018_qa_1" description = "Which Indian subdivision has the highest average annual rainfall, and what is the value in millimeters?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Arunachal Pradesh, 3418.86" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_380_1380018_qa_3/task.toml b/tasks/0001_380_1380018_qa_3/task.toml index ff196688b6253d5920da54b4dfd34686a099630e..4cd3c82312781f6a1246d659ad78d6e1e09c51d7 100644 --- a/tasks/0001_380_1380018_qa_3/task.toml +++ b/tasks/0001_380_1380018_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_380_1380018_qa_3" +name = "smoldataenvs-train/0001_380_1380018_qa_3" description = "What is the average monthly rainfall in the month with the highest average rainfall across all Indian subdivisions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "348.57" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_392_1392811_qa_3/task.toml b/tasks/0001_392_1392811_qa_3/task.toml index 94ce7754bace70d0151ce3e35f68688626cc58b8..41a99b4ee3d0e8d08d6998c8af4b1e8332bb7028 100644 --- a/tasks/0001_392_1392811_qa_3/task.toml +++ b/tasks/0001_392_1392811_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_392_1392811_qa_3" +name = "smoldataenvs-train/0001_392_1392811_qa_3" description = "What is the earliest year for which data is available for Czechia in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1993" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_400_1400936_qa_5/task.toml b/tasks/0001_400_1400936_qa_5/task.toml index 8d53edae37e62560411f54beead3fdd77b565921..6fa515313a699758d1ef8d32f115b616051d67a5 100644 --- a/tasks/0001_400_1400936_qa_5/task.toml +++ b/tasks/0001_400_1400936_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_400_1400936_qa_5" +name = "smoldataenvs-train/0001_400_1400936_qa_5" description = "What is the range (max - min) of the property tax rate per $10,000 (TAX) variable in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "524.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_404_1404790_qa_3/task.toml b/tasks/0001_404_1404790_qa_3/task.toml index d00c890590dba6196787d8232ea7d8bb0bc280b3..ea09fd9d4e8a701c5facbb517f54c3440d95df33 100644 --- a/tasks/0001_404_1404790_qa_3/task.toml +++ b/tasks/0001_404_1404790_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0001_404_1404790_qa_3" +name = "smoldataenvs-train/0001_404_1404790_qa_3" description = "How many transactions are present in the resampled dataset after applying under-sampling to balance the classes?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "984" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_404_1404839_qa_4/task.toml b/tasks/0001_404_1404839_qa_4/task.toml index a3bed17d49ed34c91b6bec3182583ea272462d7c..0a12a84858dc2dd3d1d420eede64ed4de02c0914 100644 --- a/tasks/0001_404_1404839_qa_4/task.toml +++ b/tasks/0001_404_1404839_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_404_1404839_qa_4" +name = "smoldataenvs-train/0001_404_1404839_qa_4" description = "What is the most common toss decision made in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "field" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_413_1413239_qa_1/task.toml b/tasks/0001_413_1413239_qa_1/task.toml index 6622938626c6509f59bf19be3793a5bd89f42908..57698b52a29c6fd473903ed5938bd34f4ccbaf74 100644 --- a/tasks/0001_413_1413239_qa_1/task.toml +++ b/tasks/0001_413_1413239_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_413_1413239_qa_1" +name = "smoldataenvs-train/0001_413_1413239_qa_1" description = "How many teams in the dataset have missing FIFA API IDs?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_413_1413239_qa_2/task.toml b/tasks/0001_413_1413239_qa_2/task.toml index d077e5d240534ea99330927c78f05e11172af10b..7d8e5f5d747defdd976d3004f2afe2c7b75540ee 100644 --- a/tasks/0001_413_1413239_qa_2/task.toml +++ b/tasks/0001_413_1413239_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_413_1413239_qa_2" +name = "smoldataenvs-train/0001_413_1413239_qa_2" description = "What is the average height of players in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "181.87" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_413_1413239_qa_3/task.toml b/tasks/0001_413_1413239_qa_3/task.toml index 776e8ec5ca0fda48ba9808c106bdd020acd08eff..3679ee9ca1cc19eafe152c71e8aeafc8115d1d46 100644 --- a/tasks/0001_413_1413239_qa_3/task.toml +++ b/tasks/0001_413_1413239_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_413_1413239_qa_3" +name = "smoldataenvs-train/0001_413_1413239_qa_3" description = "How many unique player names are present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10848" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_425_1425114_qa_5/task.toml b/tasks/0001_425_1425114_qa_5/task.toml index 31dc08350995228ae420fc204ad1f389eba2be45..ffe037d3ac3fc6dd81d39e60e71a2f96091cab11 100644 --- a/tasks/0001_425_1425114_qa_5/task.toml +++ b/tasks/0001_425_1425114_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_425_1425114_qa_5" +name = "smoldataenvs-train/0001_425_1425114_qa_5" description = "What is the number of principal components used in the PCA analysis for dimensionality reduction?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_426_1426219_qa_2/task.toml b/tasks/0001_426_1426219_qa_2/task.toml index 9f601e14fd684c5a0e81a714049188280f8b70a6..64a50149c87b83f58523ddb56a2e01045adbfa28 100644 --- a/tasks/0001_426_1426219_qa_2/task.toml +++ b/tasks/0001_426_1426219_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_426_1426219_qa_2" +name = "smoldataenvs-train/0001_426_1426219_qa_2" description = "What is the difference between the highest and lowest opening prices in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "47830" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_426_1426219_qa_4/task.toml b/tasks/0001_426_1426219_qa_4/task.toml index f17124e95656e9a04e0f85f844f58127c89e8c2b..e8b47d0ac3319b9052326f286447e9250e4637b9 100644 --- a/tasks/0001_426_1426219_qa_4/task.toml +++ b/tasks/0001_426_1426219_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_426_1426219_qa_4" +name = "smoldataenvs-train/0001_426_1426219_qa_4" description = "What is the total number of trading days recorded in the year 2012?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "248" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_426_1426833_qa_1/task.toml b/tasks/0001_426_1426833_qa_1/task.toml index 2cde99c5e185ed4c94add529a2fdf8004aabd1af..f31dabe9a4ea8303ec02abda2b1059211615480e 100644 --- a/tasks/0001_426_1426833_qa_1/task.toml +++ b/tasks/0001_426_1426833_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_426_1426833_qa_1" +name = "smoldataenvs-train/0001_426_1426833_qa_1" description = "What is the maximum closing price recorded in the Bitcoin price dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2958.11" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_435_1435960_qa_3/task.toml b/tasks/0001_435_1435960_qa_3/task.toml index eabe1493eebf9f71f8e45e3cfaacbc7628fc7c1c..94f4bf47e6834c5c528aefc6a2047747ea38d698 100644 --- a/tasks/0001_435_1435960_qa_3/task.toml +++ b/tasks/0001_435_1435960_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_435_1435960_qa_3" +name = "smoldataenvs-train/0001_435_1435960_qa_3" description = "Which workclass category has the lowest average hours per week worked?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Never-worked" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_435_1435960_qa_4/task.toml b/tasks/0001_435_1435960_qa_4/task.toml index d25124011d6e2bf71fec1b5c6e73b066c9a8bdac..79d3b8a2d66837fb05193d3c6578f9b0e483031e 100644 --- a/tasks/0001_435_1435960_qa_4/task.toml +++ b/tasks/0001_435_1435960_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_435_1435960_qa_4" +name = "smoldataenvs-train/0001_435_1435960_qa_4" description = "What is the average hours per week for individuals in the 'Federal-gov' workclass category?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "41.38" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_445_1445407_qa_4/task.toml b/tasks/0001_445_1445407_qa_4/task.toml index 84fd0b990f34b14acd595ecfc8194b80ebeabd4a..2c7b40ecc5fb6112b88e4d55b757e70d53a44ca2 100644 --- a/tasks/0001_445_1445407_qa_4/task.toml +++ b/tasks/0001_445_1445407_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_445_1445407_qa_4" +name = "smoldataenvs-train/0001_445_1445407_qa_4" description = "What is the total number of patients in the test set used for evaluating the KNN model's performance?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_448_1448587_qa_3/task.toml b/tasks/0001_448_1448587_qa_3/task.toml index 08d40e63c98368e67926fdbbd8e587d378af79a6..08be3c9a71ae2e09e92d493d0fa84e775860fc67 100644 --- a/tasks/0001_448_1448587_qa_3/task.toml +++ b/tasks/0001_448_1448587_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_448_1448587_qa_3" +name = "smoldataenvs-train/0001_448_1448587_qa_3" description = "What were the FDI inflows for the METALLURGICAL INDUSTRIES sector in the four years 2013, 2014, 2015, and 2016?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "567.63, 359.34, 456.31, 1440.18" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_451_1451212_qa_2/task.toml b/tasks/0001_451_1451212_qa_2/task.toml index da0172d7bfe24b6ca390cabcb7e067ab45ab1242..7f24094ec9245e06d77c158ca9d900b1b2f6874e 100644 --- a/tasks/0001_451_1451212_qa_2/task.toml +++ b/tasks/0001_451_1451212_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_451_1451212_qa_2" +name = "smoldataenvs-train/0001_451_1451212_qa_2" description = "What is the exact difference between upvotes and downvotes for the definition of the word \"love,\" which has the largest positive difference in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "75688" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_452_1452536_qa_1/task.toml b/tasks/0001_452_1452536_qa_1/task.toml index 39bcadfdfeec0a28b80535a7e4cb2a8aad0e3808..11e494f692baa0eef63c34e06c9e349d50346506 100644 --- a/tasks/0001_452_1452536_qa_1/task.toml +++ b/tasks/0001_452_1452536_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0001_452_1452536_qa_1" +name = "smoldataenvs-train/0001_452_1452536_qa_1" description = "Which team had the lowest True Performance in the dataset, and what was their True Performance value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Borussia Dortmund in the 2014/2015 season with -24.03 points." reward_mode_initial = "flexible" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0001_464_1464229_qa_3/task.toml b/tasks/0001_464_1464229_qa_3/task.toml index d46bc00e5206686b6ce379f1d81788a38b01036c..eb840eb47cb09fdebf5d968e92df4f116f3ea384 100644 --- a/tasks/0001_464_1464229_qa_3/task.toml +++ b/tasks/0001_464_1464229_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_464_1464229_qa_3" +name = "smoldataenvs-train/0001_464_1464229_qa_3" description = "How many Starbucks stores are located in China according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2734" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_464_1464229_qa_4/task.toml b/tasks/0001_464_1464229_qa_4/task.toml index 9b064f74928759dcf21274d9f85bc193a0830467..ab9a0f73b9248618bb914ebe66525ab3ce473ca2 100644 --- a/tasks/0001_464_1464229_qa_4/task.toml +++ b/tasks/0001_464_1464229_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_464_1464229_qa_4" +name = "smoldataenvs-train/0001_464_1464229_qa_4" description = "What are the five countries with the most Starbucks stores, listed in order from highest to lowest number of stores?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "United States, China, Canada, Japan, South Korea" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_471_1471274_qa_1/task.toml b/tasks/0001_471_1471274_qa_1/task.toml index 5055f8c237beff2d7a439c6d276bf22f3bc8f3b9..5adf1320f4616f1c3a331dbb57086db6b1ce1b05 100644 --- a/tasks/0001_471_1471274_qa_1/task.toml +++ b/tasks/0001_471_1471274_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_471_1471274_qa_1" +name = "smoldataenvs-train/0001_471_1471274_qa_1" description = "What is the highest correlation coefficient between any two numerical features in the Iris dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.96" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_473_1473187_qa_1/task.toml b/tasks/0001_473_1473187_qa_1/task.toml index 0a5e4d027f645ba8d82307e3c7012d37fae4b377..827a7a0c7fd7a587e128ceaedff31f14311ff37e 100644 --- a/tasks/0001_473_1473187_qa_1/task.toml +++ b/tasks/0001_473_1473187_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_473_1473187_qa_1" +name = "smoldataenvs-train/0001_473_1473187_qa_1" description = "What is the highest correlation coefficient among the features in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.96" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_473_1473187_qa_2/task.toml b/tasks/0001_473_1473187_qa_2/task.toml index 69d02f6c9a65d4eb2b73d74815f05e1eebbbba75..c96655c73f8619ae59c60ebe9bffa715f1b021aa 100644 --- a/tasks/0001_473_1473187_qa_2/task.toml +++ b/tasks/0001_473_1473187_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_473_1473187_qa_2" +name = "smoldataenvs-train/0001_473_1473187_qa_2" description = "After splitting the dataset into training and testing sets with a 70% training split, how many samples are in the testing set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "45" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_473_1473187_qa_4/task.toml b/tasks/0001_473_1473187_qa_4/task.toml index 7c4724b822ff978d551c2f038a361cb0948144ba..aa92a2c4afd6f50074e2c24e4da3a2d8da0fd3a4 100644 --- a/tasks/0001_473_1473187_qa_4/task.toml +++ b/tasks/0001_473_1473187_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_473_1473187_qa_4" +name = "smoldataenvs-train/0001_473_1473187_qa_4" description = "What is the correlation coefficient between Petal Length and Petal Width in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.96" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_480_1480111_qa_3/task.toml b/tasks/0001_480_1480111_qa_3/task.toml index 8cd2053f7d43a458abeb1cbd22f0fe4e72bd1a52..49ce03f59031c908e4daecc80b031cdc95bb3f24 100644 --- a/tasks/0001_480_1480111_qa_3/task.toml +++ b/tasks/0001_480_1480111_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_480_1480111_qa_3" +name = "smoldataenvs-train/0001_480_1480111_qa_3" description = "Based on the histogram visualization of concrete compressive strength, what is the range containing the majority of samples (34.438 to 42.465 MPa) indicating the central tendency of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.438 to 42.465" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_483_1483764_qa_5/task.toml b/tasks/0001_483_1483764_qa_5/task.toml index 32527f5090922259b7ad696ca025127e022795e9..63bd71257ea3e14974300473933e252ca75c21e3 100644 --- a/tasks/0001_483_1483764_qa_5/task.toml +++ b/tasks/0001_483_1483764_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_483_1483764_qa_5" +name = "smoldataenvs-train/0001_483_1483764_qa_5" description = "What is the ratio of ham to spam messages in the original dataset before any preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.5:1" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_487_1487950_qa_1/task.toml b/tasks/0001_487_1487950_qa_1/task.toml index 7d40ec005223005bafe57c6fa6f4ca6386054a4c..c1f2d3d497e4e3d5781b33e516817de1c5655f42 100644 --- a/tasks/0001_487_1487950_qa_1/task.toml +++ b/tasks/0001_487_1487950_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0001_487_1487950_qa_1" +name = "smoldataenvs-train/0001_487_1487950_qa_1" description = "What is the percentage of correct bets when betting on the safest outcome (lowest odds) for every match in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "45.86%" reward_mode_initial = "flexible" package_tier = 3 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_487_1487950_qa_3/task.toml b/tasks/0001_487_1487950_qa_3/task.toml index 9c587f364693a851c7974d8b3294b9319b42592c..394f62a5b85747d416f1069f949cfbcc5b3a07c5 100644 --- a/tasks/0001_487_1487950_qa_3/task.toml +++ b/tasks/0001_487_1487950_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_487_1487950_qa_3" +name = "smoldataenvs-train/0001_487_1487950_qa_3" description = "What is the total net investment required for betting $10 on every match in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "259790" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_494_1494820_qa_3/task.toml b/tasks/0001_494_1494820_qa_3/task.toml index f80519f7b95312edc2dd7e84ce4fd4a1da6b43b2..0fbfc20edb29a8cb14f2b0ed8c8d62c9cc2589d7 100644 --- a/tasks/0001_494_1494820_qa_3/task.toml +++ b/tasks/0001_494_1494820_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_494_1494820_qa_3" +name = "smoldataenvs-train/0001_494_1494820_qa_3" description = "What is the average rating of all players (both white and black) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1592.73" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_518_1518668_qa_3/task.toml b/tasks/0001_518_1518668_qa_3/task.toml index c0445a965075e454b3ad4967dacb576cfc071355..5d4365a2a1225d71251e0f20923d68f09b8dda93 100644 --- a/tasks/0001_518_1518668_qa_3/task.toml +++ b/tasks/0001_518_1518668_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_518_1518668_qa_3" +name = "smoldataenvs-train/0001_518_1518668_qa_3" description = "What is the range of critic scores present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "85.0" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_520_1520172_qa_1/task.toml b/tasks/0001_520_1520172_qa_1/task.toml index 8c7ced5b3a943091330d1834cc45ec71a178d257..8819e63758edc556909c1f05d2adb06c806d45ea 100644 --- a/tasks/0001_520_1520172_qa_1/task.toml +++ b/tasks/0001_520_1520172_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_520_1520172_qa_1" +name = "smoldataenvs-train/0001_520_1520172_qa_1" description = "How many unique patients are in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "62299" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_520_1520172_qa_4/task.toml b/tasks/0001_520_1520172_qa_4/task.toml index 908dc4e77457eca0e7c267d6abe8064686659bfe..b0bfce6d03d998b52d81e1cc97db388ec0fbaabc 100644 --- a/tasks/0001_520_1520172_qa_4/task.toml +++ b/tasks/0001_520_1520172_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0001_520_1520172_qa_4" +name = "smoldataenvs-train/0001_520_1520172_qa_4" description = "What is the total number of unique appointment dates in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "27" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_521_1521206_qa_1/task.toml b/tasks/0001_521_1521206_qa_1/task.toml index 15e09237c122cf55b15a17017ba710ee4ed7940e..5b6da86bb01708f8fa46db511b3acedfeb5017a8 100644 --- a/tasks/0001_521_1521206_qa_1/task.toml +++ b/tasks/0001_521_1521206_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_521_1521206_qa_1" +name = "smoldataenvs-train/0001_521_1521206_qa_1" description = "What is the total number of passengers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "891" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_521_1521206_qa_2/task.toml b/tasks/0001_521_1521206_qa_2/task.toml index 095ef436dd77ad3969931cf82ea17572533da028..c2c09fcc16a4b283eb2b868e57301a9a7943ce85 100644 --- a/tasks/0001_521_1521206_qa_2/task.toml +++ b/tasks/0001_521_1521206_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_521_1521206_qa_2" +name = "smoldataenvs-train/0001_521_1521206_qa_2" description = "How many distinct Pclass values are present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_522_1522371_qa_2/task.toml b/tasks/0001_522_1522371_qa_2/task.toml index 9783fdbc3fbadb0e896c0b1f4ed2054c9d23b7e3..bc85290cc62ee974652584a91ffb163b410bd5a3 100644 --- a/tasks/0001_522_1522371_qa_2/task.toml +++ b/tasks/0001_522_1522371_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_522_1522371_qa_2" +name = "smoldataenvs-train/0001_522_1522371_qa_2" description = "Who was the highest-paid player in the 2017 season, and what was their guaranteed compensation amount?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Kaka, 7167500" reward_mode_initial = "list" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_522_1522371_qa_5/task.toml b/tasks/0001_522_1522371_qa_5/task.toml index 3b3cb7c3b8eddbf0eca4c3f359b0df2d7050beff..7fc73f1194b885f006b0b575e7762526941f392b 100644 --- a/tasks/0001_522_1522371_qa_5/task.toml +++ b/tasks/0001_522_1522371_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_522_1522371_qa_5" +name = "smoldataenvs-train/0001_522_1522371_qa_5" description = "Which goalkeeper had the highest guaranteed compensation in the dataset, and what was the amount?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Tim Howard, 2575000" reward_mode_initial = "list" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_526_1526706_qa_4/task.toml b/tasks/0001_526_1526706_qa_4/task.toml index 2e5764ff9145d7090c38095b76114fb99ff6a3fb..dbb947ea52110e51517e9e0307244c0048cf6744 100644 --- a/tasks/0001_526_1526706_qa_4/task.toml +++ b/tasks/0001_526_1526706_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_526_1526706_qa_4" +name = "smoldataenvs-train/0001_526_1526706_qa_4" description = "How many distinct target classes remain in the dataset after filtering out classes with fewer than 30 instances?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_527_1527039_qa_2/task.toml b/tasks/0001_527_1527039_qa_2/task.toml index 8e3946f3ac9a0ead1f04b0ec84e0179249160e8b..dd06788def4b2f067811b1011eb13c8b379c457b 100644 --- a/tasks/0001_527_1527039_qa_2/task.toml +++ b/tasks/0001_527_1527039_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_527_1527039_qa_2" +name = "smoldataenvs-train/0001_527_1527039_qa_2" description = "What is the total number of recorded deaths across all Tarantino movies in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "190" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_527_1527039_qa_3/task.toml b/tasks/0001_527_1527039_qa_3/task.toml index adcc0f3c34e8db07c4d5a1088fb87b2ab6985529..150ba622049582a71b947ed8cc94fbfb7262b081 100644 --- a/tasks/0001_527_1527039_qa_3/task.toml +++ b/tasks/0001_527_1527039_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_527_1527039_qa_3" +name = "smoldataenvs-train/0001_527_1527039_qa_3" description = "Which specific expletive appears most frequently in Tarantino's films according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "fucking" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_527_1527054_qa_2/task.toml b/tasks/0001_527_1527054_qa_2/task.toml index 64d89aa91d4fff616435c7599f37c1057ea90725..fb320460a2184c80ed061a21d56fa478d6291057 100644 --- a/tasks/0001_527_1527054_qa_2/task.toml +++ b/tasks/0001_527_1527054_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0001_527_1527054_qa_2" +name = "smoldataenvs-train/0001_527_1527054_qa_2" description = "In which calendar year did the number of games receiving critic scores first exceed 10, marking the beginning of widespread critic score data collection?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1996" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_527_1527054_qa_5/task.toml b/tasks/0001_527_1527054_qa_5/task.toml index 98e060b5a86f033685fef84fc8c7a294a0e01568..e11baa8cb852febaf1d60fcfab6d0dbb87b2d8e5 100644 --- a/tasks/0001_527_1527054_qa_5/task.toml +++ b/tasks/0001_527_1527054_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_527_1527054_qa_5" +name = "smoldataenvs-train/0001_527_1527054_qa_5" description = "What is the highest global sales value achieved by a Nintendo-published game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "82.53" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_531_1531545_qa_2/task.toml b/tasks/0001_531_1531545_qa_2/task.toml index 6dc7be383d18c25edb7ee26602f7410fd26df77c..3a4a3a205bb8cb0c99a54e4e33623d7d88602810 100644 --- a/tasks/0001_531_1531545_qa_2/task.toml +++ b/tasks/0001_531_1531545_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_531_1531545_qa_2" +name = "smoldataenvs-train/0001_531_1531545_qa_2" description = "What is the modal release month for GPU models according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "June" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_531_1531545_qa_3/task.toml b/tasks/0001_531_1531545_qa_3/task.toml index 056c419a184c6ea2f237e5ebbaee97eee7bafa70..5513fdc5501f8ec69f9a99847a4e2392b2787068 100644 --- a/tasks/0001_531_1531545_qa_3/task.toml +++ b/tasks/0001_531_1531545_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_531_1531545_qa_3" +name = "smoldataenvs-train/0001_531_1531545_qa_3" description = "Which manufacturer has released the highest number of GPU models overall?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Nvidia" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_531_1531559_qa_2/task.toml b/tasks/0001_531_1531559_qa_2/task.toml index 78a03c5e2ffad028a38b32bca956b4679949ae08..b63c1fe9ecc7b2d855290d9fbae3f85def3c32ab 100644 --- a/tasks/0001_531_1531559_qa_2/task.toml +++ b/tasks/0001_531_1531559_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_531_1531559_qa_2" +name = "smoldataenvs-train/0001_531_1531559_qa_2" description = "What percentage of the dataset represents non-liver patient records (Dataset = 2)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "28.64" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_531_1531776_qa_2/task.toml b/tasks/0001_531_1531776_qa_2/task.toml index 4bc3ce8aafef36b6dc8c3adeacba6e8ea5e1b29b..8bfed26d5eeedf4d8da90e8537d8cc705cbf7e79 100644 --- a/tasks/0001_531_1531776_qa_2/task.toml +++ b/tasks/0001_531_1531776_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_531_1531776_qa_2" +name = "smoldataenvs-train/0001_531_1531776_qa_2" description = "Which year had the highest number of award nominations for the director in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2016" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_532_1532154_qa_1/task.toml b/tasks/0001_532_1532154_qa_1/task.toml index 459797aa54657c13cdcd2e953278208067449e33..70890f9e603117c75567d0341aecd34ed9de427c 100644 --- a/tasks/0001_532_1532154_qa_1/task.toml +++ b/tasks/0001_532_1532154_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_532_1532154_qa_1" +name = "smoldataenvs-train/0001_532_1532154_qa_1" description = "What percentage of the dataset contains missing values in the Albumin_and_Globulin_Ratio column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.686106" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_532_1532154_qa_3/task.toml b/tasks/0001_532_1532154_qa_3/task.toml index 9ca1cc9e0ef7cc132858b9a73f0c89101751a6e5..37942747cb939712a53dfc2377c489d3e79eb530 100644 --- a/tasks/0001_532_1532154_qa_3/task.toml +++ b/tasks/0001_532_1532154_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_532_1532154_qa_3" +name = "smoldataenvs-train/0001_532_1532154_qa_3" description = "What is the median Alkaline Phosphotase level for all patients in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "208.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_532_1532154_qa_4/task.toml b/tasks/0001_532_1532154_qa_4/task.toml index 9d6257ca2cb4853a888b1f2d58e6f0c51ddc29ab..a7ff0890513a7768e0531d0f0d5096e9483ab5b3 100644 --- a/tasks/0001_532_1532154_qa_4/task.toml +++ b/tasks/0001_532_1532154_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0001_532_1532154_qa_4" +name = "smoldataenvs-train/0001_532_1532154_qa_4" description = "How many patients in the dataset have been recorded as liver disease cases (Dataset=1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "416" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_532_1532154_qa_5/task.toml b/tasks/0001_532_1532154_qa_5/task.toml index 3f28473fd0e10f08ed40c87ec339214da049396d..259fedaa60b2a2b03aab06f50bf608142ff2387b 100644 --- a/tasks/0001_532_1532154_qa_5/task.toml +++ b/tasks/0001_532_1532154_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_532_1532154_qa_5" +name = "smoldataenvs-train/0001_532_1532154_qa_5" description = "What is the average age of all patients in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "44.746141" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_532_1532619_qa_4/task.toml b/tasks/0001_532_1532619_qa_4/task.toml index 3f217937b6b5c619760d272c82db7fb66754d32c..e6d946d3e273fa68ac9fdb0ce4c8b7d2141f9bc0 100644 --- a/tasks/0001_532_1532619_qa_4/task.toml +++ b/tasks/0001_532_1532619_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_532_1532619_qa_4" +name = "smoldataenvs-train/0001_532_1532619_qa_4" description = "What is the difference in ROC AUC scores between the optimized SVM model (0.577) and the optimized Random Forest model (0.692) on the test dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.115" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_532_1532619_qa_5/task.toml b/tasks/0001_532_1532619_qa_5/task.toml index 230f3f2bd481b2ecd0ad0a9fd097ec60090245f6..085aedccad682ebc5f061676dcdf60a59fe6d9d1 100644 --- a/tasks/0001_532_1532619_qa_5/task.toml +++ b/tasks/0001_532_1532619_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_532_1532619_qa_5" +name = "smoldataenvs-train/0001_532_1532619_qa_5" description = "How many missing values were present in the Albumin_and_Globulin_Ratio column before imputation with zeros?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_533_1533644_qa_1/task.toml b/tasks/0001_533_1533644_qa_1/task.toml index 03b00d47e0529f8245589ca413638a4014cf24c0..92f690c362c3926aa12bc3855a8fdf2a468e59db 100644 --- a/tasks/0001_533_1533644_qa_1/task.toml +++ b/tasks/0001_533_1533644_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_533_1533644_qa_1" +name = "smoldataenvs-train/0001_533_1533644_qa_1" description = "What percentage of the dataset represents patients with liver disease (target label = 1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "71.35506" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_538_1538781_qa_4/task.toml b/tasks/0001_538_1538781_qa_4/task.toml index 8497d489fb07605f21fd33399c096ed8be915b62..468eed83f8a51afbef7e2aa57afa1701ba9be396 100644 --- a/tasks/0001_538_1538781_qa_4/task.toml +++ b/tasks/0001_538_1538781_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_538_1538781_qa_4" +name = "smoldataenvs-train/0001_538_1538781_qa_4" description = "Which professional category has the highest number of female suicides according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "House Wife" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_540_1540077_qa_4/task.toml b/tasks/0001_540_1540077_qa_4/task.toml index b51bf976d37250d30094d06f60419eeed08ae97d..78ba0be71fcab45a51a2da313190ac012e43d3fa 100644 --- a/tasks/0001_540_1540077_qa_4/task.toml +++ b/tasks/0001_540_1540077_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_540_1540077_qa_4" +name = "smoldataenvs-train/0001_540_1540077_qa_4" description = "Does the random sample of 1000 mushrooms retain all the distinct cap color categories present in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_541_1541002_qa_1/task.toml b/tasks/0001_541_1541002_qa_1/task.toml index 786905b51bf0342ecf30977d6f895d11c834d25a..aa00d9315012b0b569825005a1f6bce7587308ba 100644 --- a/tasks/0001_541_1541002_qa_1/task.toml +++ b/tasks/0001_541_1541002_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_541_1541002_qa_1" +name = "smoldataenvs-train/0001_541_1541002_qa_1" description = "Which five platforms have the highest total global sales according to the bar plot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PS2, PS3, Wii, Xbox360, DS" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_541_1541002_qa_2/task.toml b/tasks/0001_541_1541002_qa_2/task.toml index 3fbc417b6cd3a8248171daf70c54b9e21bc3709d..249b51f9e0e234304cc61b57af354e9f22d2b7f9 100644 --- a/tasks/0001_541_1541002_qa_2/task.toml +++ b/tasks/0001_541_1541002_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_541_1541002_qa_2" +name = "smoldataenvs-train/0001_541_1541002_qa_2" description = "What is the most profitable genre by total global sales as shown in the genre sales analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_541_1541002_qa_4/task.toml b/tasks/0001_541_1541002_qa_4/task.toml index db90cdfc47752985d02ffd163ae0c5767592fbf3..44560f5718dbcbac05f308cfcd9d0181a3e76b80 100644 --- a/tasks/0001_541_1541002_qa_4/task.toml +++ b/tasks/0001_541_1541002_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_541_1541002_qa_4" +name = "smoldataenvs-train/0001_541_1541002_qa_4" description = "In the stacked bar plot comparing platform sales by genre, which platform has the highest sales in the Sports category?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_541_1541084_qa_4/task.toml b/tasks/0001_541_1541084_qa_4/task.toml index 7fd5a668f0ccabcd9e8f0b64b397472b31e54244..8fdcb58bdbb3d4a317f6bdf5eca5d252b50ab3dc 100644 --- a/tasks/0001_541_1541084_qa_4/task.toml +++ b/tasks/0001_541_1541084_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_541_1541084_qa_4" +name = "smoldataenvs-train/0001_541_1541084_qa_4" description = "What is the most frequently used domain name ending for restaurant email addresses in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = ".com" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_545_1545221_qa_3/task.toml b/tasks/0001_545_1545221_qa_3/task.toml index a88caf2eb907d63b67ec6b47513985dd8b3a0b4b..3ae6dd3bcf4f025b4561783b0e8856468576713a 100644 --- a/tasks/0001_545_1545221_qa_3/task.toml +++ b/tasks/0001_545_1545221_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0001_545_1545221_qa_3" +name = "smoldataenvs-train/0001_545_1545221_qa_3" description = "What is the average starting median salary across all undergraduate majors?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "44310.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_545_1545776_qa_1/task.toml b/tasks/0001_545_1545776_qa_1/task.toml index 72259af4caec5894851478423efcc18f0d7f4455..918a29a3d950a32934048d87ac64bc6e69808d92 100644 --- a/tasks/0001_545_1545776_qa_1/task.toml +++ b/tasks/0001_545_1545776_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_545_1545776_qa_1" +name = "smoldataenvs-train/0001_545_1545776_qa_1" description = "Which meteorological feature has the strongest correlation with solar radiation according to the Pearson correlation analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Temperature" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_548_1548830_qa_1/task.toml b/tasks/0001_548_1548830_qa_1/task.toml index fe14ef5ea5e948533aa6869cd94a8b0c46e72f2f..914cec321cea5a06b1ac01ac061087288196d6f6 100644 --- a/tasks/0001_548_1548830_qa_1/task.toml +++ b/tasks/0001_548_1548830_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_548_1548830_qa_1" +name = "smoldataenvs-train/0001_548_1548830_qa_1" description = "What percentage of clients in the dataset defaulted on their next month payment?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "22.12" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_551_1551019_qa_2/task.toml b/tasks/0001_551_1551019_qa_2/task.toml index edc5993c2245aeebc097fc1fee575e9bd6873817..7cb7877284f29548fad2483065f3fc18cfef37fe 100644 --- a/tasks/0001_551_1551019_qa_2/task.toml +++ b/tasks/0001_551_1551019_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_551_1551019_qa_2" +name = "smoldataenvs-train/0001_551_1551019_qa_2" description = "How many genes are included in the gene expression dataset after data processing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7129" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_570_1570948_qa_4/task.toml b/tasks/0001_570_1570948_qa_4/task.toml index a7b5aac2607d996c99330e5059e70beca0d8ed82..6a6482e22b63743799c203e8fb60133e17341e8f 100644 --- a/tasks/0001_570_1570948_qa_4/task.toml +++ b/tasks/0001_570_1570948_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_570_1570948_qa_4" +name = "smoldataenvs-train/0001_570_1570948_qa_4" description = "Based on the violin plot, which species has the most variable petal length?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-virginica" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_575_1575012_qa_4/task.toml b/tasks/0001_575_1575012_qa_4/task.toml index 3e1dd497c23f7f48726e37959330627c50bcdd97..81b1a3a24297ccb9d308f8e38da81fe6f10e73ac 100644 --- a/tasks/0001_575_1575012_qa_4/task.toml +++ b/tasks/0001_575_1575012_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_575_1575012_qa_4" +name = "smoldataenvs-train/0001_575_1575012_qa_4" description = "How many Iris entries have a Petal Width of exactly 1.3 cm?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_576_1576253_qa_2/task.toml b/tasks/0001_576_1576253_qa_2/task.toml index 20f4abb602081321bc520f8918fabf20c695406e..e6dc7274f9bdd8f9540540d5257ca281d5e22f90 100644 --- a/tasks/0001_576_1576253_qa_2/task.toml +++ b/tasks/0001_576_1576253_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_576_1576253_qa_2" +name = "smoldataenvs-train/0001_576_1576253_qa_2" description = "Which gender is associated with the highest number of mass shooting incidents in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Male" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_576_1576253_qa_5/task.toml b/tasks/0001_576_1576253_qa_5/task.toml index 13a2ca959e75e91ded1581b0050317f342e62936..302631ea898caecbb79dd09fdcbf5832e5ef213e 100644 --- a/tasks/0001_576_1576253_qa_5/task.toml +++ b/tasks/0001_576_1576253_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_576_1576253_qa_5" +name = "smoldataenvs-train/0001_576_1576253_qa_5" description = "Which racial group has the highest total number of injured individuals across all incidents in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "White American or European American" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_579_1579979_qa_2/task.toml b/tasks/0001_579_1579979_qa_2/task.toml index 9492e9660ee5c716ade896cf4d11907ac02c6728..ec77cd837a972fbf004521bbba50d299e2c4b1bb 100644 --- a/tasks/0001_579_1579979_qa_2/task.toml +++ b/tasks/0001_579_1579979_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_579_1579979_qa_2" +name = "smoldataenvs-train/0001_579_1579979_qa_2" description = "What percentage of Pokémon in the dataset are dual-type (have both type1 and type2) compared to single-type (only type1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "52.1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_580_1580188_qa_4/task.toml b/tasks/0001_580_1580188_qa_4/task.toml index 81622f29814118b9c7a54c26712cb1de57ff5092..7afbf290a34fb1c28ed2731e970a39b8fe2f9691 100644 --- a/tasks/0001_580_1580188_qa_4/task.toml +++ b/tasks/0001_580_1580188_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0001_580_1580188_qa_4" +name = "smoldataenvs-train/0001_580_1580188_qa_4" description = "How many speeches were delivered in the year 1993 according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "35" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_580_1580188_qa_5/task.toml b/tasks/0001_580_1580188_qa_5/task.toml index ded926bbba8251ca29a8b95396425c01052924e8..ee67c24c7af777af535d4271bc0dd2143eb1d713 100644 --- a/tasks/0001_580_1580188_qa_5/task.toml +++ b/tasks/0001_580_1580188_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_580_1580188_qa_5" +name = "smoldataenvs-train/0001_580_1580188_qa_5" description = "How many unique High Commissioners are represented in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_580_1580621_qa_1/task.toml b/tasks/0001_580_1580621_qa_1/task.toml index 14ae290fef2628376a7dcde8162a0be17e92b5fa..ba4bdf64c8c302eb206f38e9f6b4844d3f89ee19 100644 --- a/tasks/0001_580_1580621_qa_1/task.toml +++ b/tasks/0001_580_1580621_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_580_1580621_qa_1" +name = "smoldataenvs-train/0001_580_1580621_qa_1" description = "How many samples in the dataset have a sepal length greater than their petal length?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "150" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_580_1580621_qa_2/task.toml b/tasks/0001_580_1580621_qa_2/task.toml index d974c2e590f9b68e958311b81fda08bd2f2eed5c..94ecbcfba3c19c2de13675351a6324510e49b7f4 100644 --- a/tasks/0001_580_1580621_qa_2/task.toml +++ b/tasks/0001_580_1580621_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_580_1580621_qa_2" +name = "smoldataenvs-train/0001_580_1580621_qa_2" description = "What is the average sepal length for samples where sepal length exceeds petal length?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5.84" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_580_1580621_qa_4/task.toml b/tasks/0001_580_1580621_qa_4/task.toml index c1b008d2d7aa05603bdfd748c38a5b44222d8cde..03542140f8b488525ca5f46c7d9fc400b1add117 100644 --- a/tasks/0001_580_1580621_qa_4/task.toml +++ b/tasks/0001_580_1580621_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_580_1580621_qa_4" +name = "smoldataenvs-train/0001_580_1580621_qa_4" description = "What is the median petal width in the entire dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_580_1580621_qa_5/task.toml b/tasks/0001_580_1580621_qa_5/task.toml index 932503ddde2de96ac81cdaff66ac81601dcd7700..8d04451d8f14591b4b2d110e7851f87b8f8fb9f8 100644 --- a/tasks/0001_580_1580621_qa_5/task.toml +++ b/tasks/0001_580_1580621_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_580_1580621_qa_5" +name = "smoldataenvs-train/0001_580_1580621_qa_5" description = "How many species in the dataset have exactly 50 samples?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_583_1583897_qa_3/task.toml b/tasks/0001_583_1583897_qa_3/task.toml index 63f600d0e5bf04c66efbfae1fbfa55f8e15a731b..5324f833092c60588dc2a41c51b5e9008ca1c252 100644 --- a/tasks/0001_583_1583897_qa_3/task.toml +++ b/tasks/0001_583_1583897_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_583_1583897_qa_3" +name = "smoldataenvs-train/0001_583_1583897_qa_3" description = "What is the total number of students in the medium performance category (M) according to the Class distribution derived from the crosstab?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "211" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_586_1586397_qa_2/task.toml b/tasks/0001_586_1586397_qa_2/task.toml index 57b05046feff4788ff1a5534dda21a9651d214b1..eed3eb4393f3c4ca7b71e572e35672e5fe6c4c66 100644 --- a/tasks/0001_586_1586397_qa_2/task.toml +++ b/tasks/0001_586_1586397_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_586_1586397_qa_2" +name = "smoldataenvs-train/0001_586_1586397_qa_2" description = "What percentage of total fatalities in the dataset are attributed to attackers with mental health issues according to the pie chart analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "48.5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_593_1593034_qa_5/task.toml b/tasks/0001_593_1593034_qa_5/task.toml index b4581b722a51ba32df4db95264458b6ba66f1ac2..f390b90cdc9906de5d2da975352116e81028d7ae 100644 --- a/tasks/0001_593_1593034_qa_5/task.toml +++ b/tasks/0001_593_1593034_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_593_1593034_qa_5" +name = "smoldataenvs-train/0001_593_1593034_qa_5" description = "What is the mean alcohol content of all wines in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10.422983" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_593_1593609_qa_5/task.toml b/tasks/0001_593_1593609_qa_5/task.toml index cd738b35acd727dfa90ca72ead0b747cc3033fe5..69092d917c546b42c6803e6c24788bae2b2bd347 100644 --- a/tasks/0001_593_1593609_qa_5/task.toml +++ b/tasks/0001_593_1593609_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_593_1593609_qa_5" +name = "smoldataenvs-train/0001_593_1593609_qa_5" description = "How many missing values were present in the 'type2' column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "384" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_598_1598064_qa_3/task.toml b/tasks/0001_598_1598064_qa_3/task.toml index 5f865f27fb09abad54982496850ad053fd878c25..37de68d021a0ea449de9558e5802bf76a204c4a9 100644 --- a/tasks/0001_598_1598064_qa_3/task.toml +++ b/tasks/0001_598_1598064_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_598_1598064_qa_3" +name = "smoldataenvs-train/0001_598_1598064_qa_3" description = "Which region has the highest median Total Household Income according to the boxplot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "NCR" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_598_1598981_qa_3/task.toml b/tasks/0001_598_1598981_qa_3/task.toml index d087aeb99b708c84ec8b1504286490075b50e39c..c99db4fe9900b9c584f188ba578e6be7031814c3 100644 --- a/tasks/0001_598_1598981_qa_3/task.toml +++ b/tasks/0001_598_1598981_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_598_1598981_qa_3" +name = "smoldataenvs-train/0001_598_1598981_qa_3" description = "Which student address type (urban 'U' or rural 'R') has the highest count in the dataset, and what is the specific number of students for that address type?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "U, 307" reward_mode_initial = "list" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_599_1599781_qa_5/task.toml b/tasks/0001_599_1599781_qa_5/task.toml index 67547b7a0b6f2331770b83dc0f1219c2e4370b6f..e6b6fefc9bf8a2e40cf894169e7b02806341d08a 100644 --- a/tasks/0001_599_1599781_qa_5/task.toml +++ b/tasks/0001_599_1599781_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_599_1599781_qa_5" +name = "smoldataenvs-train/0001_599_1599781_qa_5" description = "What percentage of all UNHCR speeches in the dataset were delivered by Sadako Ogata?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "38.3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_599_1599947_qa_3/task.toml b/tasks/0001_599_1599947_qa_3/task.toml index 7d7bfe8e51d4d16609f3ee037a0f1a33a80fab15..a3055e53c8328640c35b3a4e0064fb67dccbf92f 100644 --- a/tasks/0001_599_1599947_qa_3/task.toml +++ b/tasks/0001_599_1599947_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_599_1599947_qa_3" +name = "smoldataenvs-train/0001_599_1599947_qa_3" description = "What is the correlation coefficient between father's education level and weekly alcohol consumption?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.0071" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_604_1604140_qa_2/task.toml b/tasks/0001_604_1604140_qa_2/task.toml index 0594eefb98a4b549bba8ec5431778c0abe201034..29705e119ba3dafc0e1e21dac5eec0c3081ddfaf 100644 --- a/tasks/0001_604_1604140_qa_2/task.toml +++ b/tasks/0001_604_1604140_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0001_604_1604140_qa_2" +name = "smoldataenvs-train/0001_604_1604140_qa_2" description = "Which U.S. state has the highest number of recorded mass shootings between 2010 and 2017 according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "California" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_604_1604140_qa_4/task.toml b/tasks/0001_604_1604140_qa_4/task.toml index 28ba0d5661b48936e4dec85a532cdcd940acb5b9..d7ef8332c15dc1d9469ffc6d250367f31c1f8bd9 100644 --- a/tasks/0001_604_1604140_qa_4/task.toml +++ b/tasks/0001_604_1604140_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0001_604_1604140_qa_4" +name = "smoldataenvs-train/0001_604_1604140_qa_4" description = "Which weekday experiences the highest frequency of mass shootings based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Thursday" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_604_1604140_qa_5/task.toml b/tasks/0001_604_1604140_qa_5/task.toml index cf4916c2ef792306bdc4072759cf64a7688c518c..26ed4106adef74785ff5e3f228f1a87e69ac2acb 100644 --- a/tasks/0001_604_1604140_qa_5/task.toml +++ b/tasks/0001_604_1604140_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_604_1604140_qa_5" +name = "smoldataenvs-train/0001_604_1604140_qa_5" description = "What is the average number of injured individuals per mass shooting incident according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_623_1623388_qa_1/task.toml b/tasks/0001_623_1623388_qa_1/task.toml index eed79e663e481a53617b02e16b5dc88b3385ab68..a77ea6138addb6a0c49eeb96e6ea7e9e4d281daa 100644 --- a/tasks/0001_623_1623388_qa_1/task.toml +++ b/tasks/0001_623_1623388_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_623_1623388_qa_1" +name = "smoldataenvs-train/0001_623_1623388_qa_1" description = "How many unique audio classes are represented in the collected samples?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_623_1623388_qa_5/task.toml b/tasks/0001_623_1623388_qa_5/task.toml index 6ffe8316ef7065399ac3727f91782f7ff366b2c0..248a75c0af3c32ed36850793205dd1f4c4f2acf0 100644 --- a/tasks/0001_623_1623388_qa_5/task.toml +++ b/tasks/0001_623_1623388_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_623_1623388_qa_5" +name = "smoldataenvs-train/0001_623_1623388_qa_5" description = "What is the length in samples of each audio segment in the collected dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "80000" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_628_1628992_qa_4/task.toml b/tasks/0001_628_1628992_qa_4/task.toml index 5d6d3b26a447b89cea9ffb97a3b7034bea2b8516..ab0c5c141eaa82bf9aa43addadc2c03066f9475e 100644 --- a/tasks/0001_628_1628992_qa_4/task.toml +++ b/tasks/0001_628_1628992_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_628_1628992_qa_4" +name = "smoldataenvs-train/0001_628_1628992_qa_4" description = "What is the number of features used in the XGBoost model after preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_632_1632608_qa_2/task.toml b/tasks/0001_632_1632608_qa_2/task.toml index db7cf82a5ac8a305c23e39df79cf9b2bb62b349d..3c80249d437bb6446f514ca0149ab52891ea5c36 100644 --- a/tasks/0001_632_1632608_qa_2/task.toml +++ b/tasks/0001_632_1632608_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0001_632_1632608_qa_2" +name = "smoldataenvs-train/0001_632_1632608_qa_2" description = "During which hour of the day were traffic-related deaths most frequent on Fridays?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "15:00" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_633_1633600_qa_1/task.toml b/tasks/0001_633_1633600_qa_1/task.toml index 5bb37406b7a43a52c1d66d9b0bf824ec3727614e..05e7691034b74e3b259880eee48f6fad278fdcc6 100644 --- a/tasks/0001_633_1633600_qa_1/task.toml +++ b/tasks/0001_633_1633600_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_633_1633600_qa_1" +name = "smoldataenvs-train/0001_633_1633600_qa_1" description = "What is the highest positive correlation coefficient between SalePrice and any numeric feature in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.697199" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_633_1633600_qa_4/task.toml b/tasks/0001_633_1633600_qa_4/task.toml index 87d7fec2f8efe6595fead96e2fac9274b1bb4eec..43b18235e16902116977d52b9ef866631d2ee23a 100644 --- a/tasks/0001_633_1633600_qa_4/task.toml +++ b/tasks/0001_633_1633600_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_633_1633600_qa_4" +name = "smoldataenvs-train/0001_633_1633600_qa_4" description = "Which numeric feature shows the strongest positive correlation with N_FacilitiesInApt?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "N_Parkinglot(Basement)" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_636_1636611_qa_2/task.toml b/tasks/0001_636_1636611_qa_2/task.toml index b6683a94596457daefa45b5b720b43eaf61059ba..d9943611cb42bb470acf66d2c34dca14a55b5012 100644 --- a/tasks/0001_636_1636611_qa_2/task.toml +++ b/tasks/0001_636_1636611_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_636_1636611_qa_2" +name = "smoldataenvs-train/0001_636_1636611_qa_2" description = "What is the most commonly assigned review rating in the ICLR 2017 dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6: Marginally above acceptance threshold" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_637_1637985_qa_3/task.toml b/tasks/0001_637_1637985_qa_3/task.toml index 6e7a91db595965b884d9919756af6ddfdcd2dc01..9176aedab0e3b3913e1964edc0cbac42b74e82d5 100644 --- a/tasks/0001_637_1637985_qa_3/task.toml +++ b/tasks/0001_637_1637985_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_637_1637985_qa_3" +name = "smoldataenvs-train/0001_637_1637985_qa_3" description = "What is the average number of total victims per attack for perpetrators with mental health issues compared to those without?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12.3, 4.7" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_638_1638617_qa_4/task.toml b/tasks/0001_638_1638617_qa_4/task.toml index 73ee82a81d6f5a31c040c88f380afaa841e051ad..35759a10c2caa3104f15b9b38363d422e8992738 100644 --- a/tasks/0001_638_1638617_qa_4/task.toml +++ b/tasks/0001_638_1638617_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_638_1638617_qa_4" +name = "smoldataenvs-train/0001_638_1638617_qa_4" description = "How many unique ticket numbers are shared between the training dataset and test dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "115" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_645_1645214_qa_2/task.toml b/tasks/0001_645_1645214_qa_2/task.toml index 65064b59245803116cff6dae35839db11162c1a9..820b5e6eb2552659d9ea8c290da865eb77461941 100644 --- a/tasks/0001_645_1645214_qa_2/task.toml +++ b/tasks/0001_645_1645214_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_645_1645214_qa_2" +name = "smoldataenvs-train/0001_645_1645214_qa_2" description = "Which feature in the Iris dataset has the highest standard deviation according to the descriptive statistics?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_646_1646297_qa_2/task.toml b/tasks/0001_646_1646297_qa_2/task.toml index f18d68f183ffe871a24fb7796cb6051a56ebd323..878eab92c61bba24b15a13ad1283565f5a1d09d8 100644 --- a/tasks/0001_646_1646297_qa_2/task.toml +++ b/tasks/0001_646_1646297_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_646_1646297_qa_2" +name = "smoldataenvs-train/0001_646_1646297_qa_2" description = "Which feature in the dataset has the highest number of unique categories, and how many are there?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "gill-color, 12" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_656_1656907_qa_2/task.toml b/tasks/0001_656_1656907_qa_2/task.toml index 13011eb755a45cc274769f4bddf41fefaeed8c89..30189855c6e981fa9702c33bc881e7e38a30570b 100644 --- a/tasks/0001_656_1656907_qa_2/task.toml +++ b/tasks/0001_656_1656907_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_656_1656907_qa_2" +name = "smoldataenvs-train/0001_656_1656907_qa_2" description = "What is the average (mean) Level 50 Attack value for all Digimon in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "124.52" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_656_1656907_qa_4/task.toml b/tasks/0001_656_1656907_qa_4/task.toml index eb1deeadd1fe3031e414c292636773ba809b4df5..43fe1c935055f451d95b9d127c451ad6d41e5e02 100644 --- a/tasks/0001_656_1656907_qa_4/task.toml +++ b/tasks/0001_656_1656907_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_656_1656907_qa_4" +name = "smoldataenvs-train/0001_656_1656907_qa_4" description = "What is the highest Memory value required by any Digimon in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "25" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_656_1656907_qa_5/task.toml b/tasks/0001_656_1656907_qa_5/task.toml index ebff1069d67d0746094ccd011ecc4ee623500cf5..f7420e47433e12c4a4e0881d253d84fbbcf5d434 100644 --- a/tasks/0001_656_1656907_qa_5/task.toml +++ b/tasks/0001_656_1656907_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_656_1656907_qa_5" +name = "smoldataenvs-train/0001_656_1656907_qa_5" description = "What is the average number of Equip Slots for all Digimon in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.57" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_657_1657214_qa_3/task.toml b/tasks/0001_657_1657214_qa_3/task.toml index 38ce96189007eeabffc581c65291ff227f56f71b..793556605c13fc41d9d4efd84fcfe78860a41126 100644 --- a/tasks/0001_657_1657214_qa_3/task.toml +++ b/tasks/0001_657_1657214_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_657_1657214_qa_3" +name = "smoldataenvs-train/0001_657_1657214_qa_3" description = "What is the range (maximum minus minimum) of the Level 50 Attack values across all Digimon?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "266" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_659_1659062_qa_3/task.toml b/tasks/0001_659_1659062_qa_3/task.toml index 22d2c88a26cdd070cb3c97cadd0ed191aa17ef1b..15298bb870cbccfc74ca6ee161aa17de4acf046d 100644 --- a/tasks/0001_659_1659062_qa_3/task.toml +++ b/tasks/0001_659_1659062_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_659_1659062_qa_3" +name = "smoldataenvs-train/0001_659_1659062_qa_3" description = "What is the maximum revenue value recorded for history museums after removing duplicates and missing values from the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5.121523e9" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_659_1659621_qa_3/task.toml b/tasks/0001_659_1659621_qa_3/task.toml index 69173c743344efe2f23029909abc98a8b7cc835d..9ef903e82d31c6e04b9a10c4dbbd6b109b3a40e0 100644 --- a/tasks/0001_659_1659621_qa_3/task.toml +++ b/tasks/0001_659_1659621_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_659_1659621_qa_3" +name = "smoldataenvs-train/0001_659_1659621_qa_3" description = "What are the standard deviations of sugar content for cold and hot cereals respectively?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.333, 2.082" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_660_1660748_qa_2/task.toml b/tasks/0001_660_1660748_qa_2/task.toml index b4cab9c5e640b8314e027c2296920ae8507faa2f..491c3924eeb9cc2c22002d30c1a3b5d06f06ee8f 100644 --- a/tasks/0001_660_1660748_qa_2/task.toml +++ b/tasks/0001_660_1660748_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_660_1660748_qa_2" +name = "smoldataenvs-train/0001_660_1660748_qa_2" description = "After correcting typos and removing unreadable characters, what is the most frequent pet preference in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Dogs" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_660_1660748_qa_4/task.toml b/tasks/0001_660_1660748_qa_4/task.toml index 8fcb484d033cc1a9b83334274af7c7adcd538fdb..3572ef810e58b28903950b12750da38879476a3b 100644 --- a/tasks/0001_660_1660748_qa_4/task.toml +++ b/tasks/0001_660_1660748_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_660_1660748_qa_4" +name = "smoldataenvs-train/0001_660_1660748_qa_4" description = "What is the primary stated interest in data science for respondents with \"quite a bit of programming experience\"?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "I want to get a job where I use data science" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_660_1660748_qa_5/task.toml b/tasks/0001_660_1660748_qa_5/task.toml index c5540fe9a67405b87d568e86fab0dbde26873cd0..4fdfa299e799625b12c1226dc46d379ed5bac1c4 100644 --- a/tasks/0001_660_1660748_qa_5/task.toml +++ b/tasks/0001_660_1660748_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_660_1660748_qa_5" +name = "smoldataenvs-train/0001_660_1660748_qa_5" description = "Which pet preference category shows the highest frequency of respondents with \"quite a bit of programming experience\"?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Dogs" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_661_1661005_qa_1/task.toml b/tasks/0001_661_1661005_qa_1/task.toml index de5760d97b622f7c6b315f3fb0f14dcfacc7ebfc..2cfb4ddc6ab52a473ea47b1b09f1f79f7da210c8 100644 --- a/tasks/0001_661_1661005_qa_1/task.toml +++ b/tasks/0001_661_1661005_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_661_1661005_qa_1" +name = "smoldataenvs-train/0001_661_1661005_qa_1" description = "What is the maximum HP at level 50 among all Digimon in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2080" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_661_1661005_qa_3/task.toml b/tasks/0001_661_1661005_qa_3/task.toml index 1eefc94966577846ac24f483b5a8071cc246336c..8e2e378e450f12bb22892c1d7bd67296522999e9 100644 --- a/tasks/0001_661_1661005_qa_3/task.toml +++ b/tasks/0001_661_1661005_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0001_661_1661005_qa_3" +name = "smoldataenvs-train/0001_661_1661005_qa_3" description = "What is the average SP cost of all moves in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "14.03" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_662_1662961_qa_3/task.toml b/tasks/0001_662_1662961_qa_3/task.toml index 4321abfa71d92c340d81ff756608b61575d64179..34059a3ef88a07a8d862d4a4d6ed71dd74b82736 100644 --- a/tasks/0001_662_1662961_qa_3/task.toml +++ b/tasks/0001_662_1662961_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_662_1662961_qa_3" +name = "smoldataenvs-train/0001_662_1662961_qa_3" description = "What is the average sugar content in hot cereals?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.6667" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_662_1662961_qa_5/task.toml b/tasks/0001_662_1662961_qa_5/task.toml index d295ccdddb919433c18af87bfe6bff4fbc79db4b..9dce647b6bed2c5a6b1e21a09bbacb04b79394a2 100644 --- a/tasks/0001_662_1662961_qa_5/task.toml +++ b/tasks/0001_662_1662961_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0001_662_1662961_qa_5" +name = "smoldataenvs-train/0001_662_1662961_qa_5" description = "What is the median sugar content in cold cereals?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_664_1664478_qa_1/task.toml b/tasks/0001_664_1664478_qa_1/task.toml index 223e585ed0c894d92d398216bea48c76738c73d5..0377020a272f3e9b4985335254c30a1123413d9a 100644 --- a/tasks/0001_664_1664478_qa_1/task.toml +++ b/tasks/0001_664_1664478_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_664_1664478_qa_1" +name = "smoldataenvs-train/0001_664_1664478_qa_1" description = "Which manufacturer has the highest number of cereal products in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "K" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_666_1666101_qa_5/task.toml b/tasks/0001_666_1666101_qa_5/task.toml index fec060f835f34772075e47cd8ca5c708d3fd868a..d99d207b9b2968b467609ed249d643b557c42178 100644 --- a/tasks/0001_666_1666101_qa_5/task.toml +++ b/tasks/0001_666_1666101_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_666_1666101_qa_5" +name = "smoldataenvs-train/0001_666_1666101_qa_5" description = "What is the average cereal rating for manufacturer K?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "44.04" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_667_1667317_qa_1/task.toml b/tasks/0001_667_1667317_qa_1/task.toml index a1d38ce13629e60dac89867046ed5be8ba80ff3f..2b69c9159548bdeea9744bc1252d9f4598bb7998 100644 --- a/tasks/0001_667_1667317_qa_1/task.toml +++ b/tasks/0001_667_1667317_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_667_1667317_qa_1" +name = "smoldataenvs-train/0001_667_1667317_qa_1" description = "What is the maximum zoom range (difference between maximum Zoom tele and minimum Zoom wide) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "518.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_667_1667317_qa_4/task.toml b/tasks/0001_667_1667317_qa_4/task.toml index d6190682e1b91f2b105244ba430516026f7cbb39..bc13954bc951a7fc6c133c89af7bec8afd6b0716 100644 --- a/tasks/0001_667_1667317_qa_4/task.toml +++ b/tasks/0001_667_1667317_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_667_1667317_qa_4" +name = "smoldataenvs-train/0001_667_1667317_qa_4" description = "What is the minimum Zoom wide (W) value recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_667_1667317_qa_5/task.toml b/tasks/0001_667_1667317_qa_5/task.toml index 5f11d57d0657533bdbde118e6a1599f9bc9ad06f..55721486ca619169d73bf992a8163ad7972bdd06 100644 --- a/tasks/0001_667_1667317_qa_5/task.toml +++ b/tasks/0001_667_1667317_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_667_1667317_qa_5" +name = "smoldataenvs-train/0001_667_1667317_qa_5" description = "What is the maximum Storage included capacity recorded for any camera in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "450" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_668_1668567_qa_3/task.toml b/tasks/0001_668_1668567_qa_3/task.toml index 574310e9e4845a1b483ebcaa6228623e481181ee..52cf36df12047db8eaada4edc558abb9ca99c67a 100644 --- a/tasks/0001_668_1668567_qa_3/task.toml +++ b/tasks/0001_668_1668567_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_668_1668567_qa_3" +name = "smoldataenvs-train/0001_668_1668567_qa_3" description = "How many unique cereal manufacturers are represented in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_672_1672543_qa_1/task.toml b/tasks/0001_672_1672543_qa_1/task.toml index affb0ce6c65dbfe6a4df21ff4abc45d4b8f983a0..5357ca379a48aea1295b589fcf38dfd733ea7f9e 100644 --- a/tasks/0001_672_1672543_qa_1/task.toml +++ b/tasks/0001_672_1672543_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_672_1672543_qa_1" +name = "smoldataenvs-train/0001_672_1672543_qa_1" description = "What is the critical value of the chi-square distribution at 95% confidence level with 12 degrees of freedom used to determine statistical significance in the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "21.026" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_672_1672543_qa_3/task.toml b/tasks/0001_672_1672543_qa_3/task.toml index c2ced35cb0753f02c7344c4914154c0f25d0f8bb..425586a5129690f3b4b859c19d1296e361f3248e 100644 --- a/tasks/0001_672_1672543_qa_3/task.toml +++ b/tasks/0001_672_1672543_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_672_1672543_qa_3" +name = "smoldataenvs-train/0001_672_1672543_qa_3" description = "What are the degrees of freedom for the chi-square test of independence between cereal manufacturers (mfr) and shelf positions in this dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_673_1673862_qa_4/task.toml b/tasks/0001_673_1673862_qa_4/task.toml index 15c9c0d3fb6b6a4029f5fa0fd039ef5871aafa74..ceef8855e53b2c29e6b59fa6df5ad37b13e42efc 100644 --- a/tasks/0001_673_1673862_qa_4/task.toml +++ b/tasks/0001_673_1673862_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_673_1673862_qa_4" +name = "smoldataenvs-train/0001_673_1673862_qa_4" description = "What is the maximum Delta value observed for any cryptocurrency in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34513.333333" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_674_1674081_qa_1/task.toml b/tasks/0001_674_1674081_qa_1/task.toml index 82128dc8ffd71f6224b3a100112c2f97e87b8a81..acc52fe7be47d5a3e500c8da668a9e142ee67864 100644 --- a/tasks/0001_674_1674081_qa_1/task.toml +++ b/tasks/0001_674_1674081_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_674_1674081_qa_1" +name = "smoldataenvs-train/0001_674_1674081_qa_1" description = "What is the maximum value in the 'Balance' field before outlier removal in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "98417" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_683_1683631_qa_1/task.toml b/tasks/0001_683_1683631_qa_1/task.toml index 796710b5c74a7441d6a34769ae62dc0ed4c19234..6e4ca5a58a880e7aa960298cab931936b249170a 100644 --- a/tasks/0001_683_1683631_qa_1/task.toml +++ b/tasks/0001_683_1683631_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_683_1683631_qa_1" +name = "smoldataenvs-train/0001_683_1683631_qa_1" description = "Which athlete has the highest median total score across all apparatuses?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Xiao Ruoteng" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_683_1683832_qa_2/task.toml b/tasks/0001_683_1683832_qa_2/task.toml index ec072179c519826405f1cc0b4c41bc7ecaa03f17..c00a5224f6a8c2fbdfe5f0b6a1c0e8961c7b12d5 100644 --- a/tasks/0001_683_1683832_qa_2/task.toml +++ b/tasks/0001_683_1683832_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_683_1683832_qa_2" +name = "smoldataenvs-train/0001_683_1683832_qa_2" description = "Which character class is most commonly used in the Harbinger division according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Pathfinder" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_690_1690621_qa_2/task.toml b/tasks/0001_690_1690621_qa_2/task.toml index bb5a92de00e52f5ea7c2dc448cf90d7ab5bb2d06..baa65bf3b49b671bc56cd28e646235c8afc99a87 100644 --- a/tasks/0001_690_1690621_qa_2/task.toml +++ b/tasks/0001_690_1690621_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_690_1690621_qa_2" +name = "smoldataenvs-train/0001_690_1690621_qa_2" description = "Which college type has the highest median salary growth from starting to mid-career (Mid_50th percentile) based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Ivy League" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_690_1690622_qa_2/task.toml b/tasks/0001_690_1690622_qa_2/task.toml index dccd569f9b0cbc70bd1cdb63f8bd27a23f8ef070..66a088b065558728c6e3c84a4f4305da48188ca0 100644 --- a/tasks/0001_690_1690622_qa_2/task.toml +++ b/tasks/0001_690_1690622_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_690_1690622_qa_2" +name = "smoldataenvs-train/0001_690_1690622_qa_2" description = "What is the median mid-career salary for the undergraduate major with the highest starting salary?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "91700" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_690_1690622_qa_4/task.toml b/tasks/0001_690_1690622_qa_4/task.toml index a4f9e320cb276a96d2310e38a62de1b7bc100002..119d90f49f03fc73f7929d8cd2c272651ea2e209 100644 --- a/tasks/0001_690_1690622_qa_4/task.toml +++ b/tasks/0001_690_1690622_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_690_1690622_qa_4" +name = "smoldataenvs-train/0001_690_1690622_qa_4" description = "What is the average percentage change from starting to mid-career salary across all majors in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "69.27" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_690_1690622_qa_5/task.toml b/tasks/0001_690_1690622_qa_5/task.toml index 7f0829209551c2a0b55b9df594d1e64f1f722df1..bed329b3526c4a59795ec91b37e5f3fd1f579869 100644 --- a/tasks/0001_690_1690622_qa_5/task.toml +++ b/tasks/0001_690_1690622_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_690_1690622_qa_5" +name = "smoldataenvs-train/0001_690_1690622_qa_5" description = "What is the most common range for the percentage change from starting to mid-career salary based on the histogram visualization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "60% to 70%" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_690_1690859_qa_5/task.toml b/tasks/0001_690_1690859_qa_5/task.toml index 6de9157718ca67f5fb6d129c4981f4d1e9c643db..8dd1f683ca267929243b5897730ea199c91a0be9 100644 --- a/tasks/0001_690_1690859_qa_5/task.toml +++ b/tasks/0001_690_1690859_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_690_1690859_qa_5" +name = "smoldataenvs-train/0001_690_1690859_qa_5" description = "What is the baseline accuracy achieved by the most frequent class in the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "62.42" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_692_1692435_qa_3/task.toml b/tasks/0001_692_1692435_qa_3/task.toml index 0a15e4d877dea3f1f318006d004dfda88217f9a5..68e6ddf3c35cb4d7c3ede2805ec50351e4624a0c 100644 --- a/tasks/0001_692_1692435_qa_3/task.toml +++ b/tasks/0001_692_1692435_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0001_692_1692435_qa_3" +name = "smoldataenvs-train/0001_692_1692435_qa_3" description = "Which species has the smallest average sepal width in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-versicolor" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_692_1692435_qa_4/task.toml b/tasks/0001_692_1692435_qa_4/task.toml index b879d70ec1b24f032a94176027b815b02b45da37..c6526285e8dc032a628d41eacec3790b3d37c9a4 100644 --- a/tasks/0001_692_1692435_qa_4/task.toml +++ b/tasks/0001_692_1692435_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_692_1692435_qa_4" +name = "smoldataenvs-train/0001_692_1692435_qa_4" description = "What is the highest positive correlation between any two measured features in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.962757" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_692_1692435_qa_5/task.toml b/tasks/0001_692_1692435_qa_5/task.toml index 97513ce6bfd66d9abd58fece4913ed7478a1ba3a..c5e88bd2e9cab8b66b7f4bc08c0dd7d0d4efcd34 100644 --- a/tasks/0001_692_1692435_qa_5/task.toml +++ b/tasks/0001_692_1692435_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_692_1692435_qa_5" +name = "smoldataenvs-train/0001_692_1692435_qa_5" description = "Which species exhibits the largest average sepal length in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-virginica" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_699_1699079_qa_1/task.toml b/tasks/0001_699_1699079_qa_1/task.toml index 65c931a8e992af78c1f3dab2292b3fa9413c58db..b405a2e89d9c6aea6c057bdb90cb81766b2dd774 100644 --- a/tasks/0001_699_1699079_qa_1/task.toml +++ b/tasks/0001_699_1699079_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_699_1699079_qa_1" +name = "smoldataenvs-train/0001_699_1699079_qa_1" description = "What is the average IMDb score for movies in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.44" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_725_1725548_qa_1/task.toml b/tasks/0001_725_1725548_qa_1/task.toml index 22499b7629cd16ce00f5caed2a1f12db915df29f..f01f8dd3e971990704a00c06a9d9a8f4e1b81831 100644 --- a/tasks/0001_725_1725548_qa_1/task.toml +++ b/tasks/0001_725_1725548_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_725_1725548_qa_1" +name = "smoldataenvs-train/0001_725_1725548_qa_1" description = "How many verses (Ayah) have non-missing values in the dataset after processing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4871" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_725_1725695_qa_5/task.toml b/tasks/0001_725_1725695_qa_5/task.toml index 4476e06f406e553bc4a1467b017bf8da3f5be694..d3ba5eff07ad506dac3afddf15e94395e42b9742 100644 --- a/tasks/0001_725_1725695_qa_5/task.toml +++ b/tasks/0001_725_1725695_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_725_1725695_qa_5" +name = "smoldataenvs-train/0001_725_1725695_qa_5" description = "What is the percentage of no-shows for patients with diabetes?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "18.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_733_1733851_qa_3/task.toml b/tasks/0001_733_1733851_qa_3/task.toml index 36813d5ed87b155c9d07e8cb954427495b6115a2..f0fb5a16ba020b556cf020260765f1db94edb710 100644 --- a/tasks/0001_733_1733851_qa_3/task.toml +++ b/tasks/0001_733_1733851_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_733_1733851_qa_3" +name = "smoldataenvs-train/0001_733_1733851_qa_3" description = "How many columns in the dataset are explicitly identified as containing compensation-related information?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_733_1733851_qa_5/task.toml b/tasks/0001_733_1733851_qa_5/task.toml index b48c00dee4932a86396af0da2bd58039b839ef43..d3f38f609d75ffaa496e1e169068180c3d2bf75e 100644 --- a/tasks/0001_733_1733851_qa_5/task.toml +++ b/tasks/0001_733_1733851_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_733_1733851_qa_5" +name = "smoldataenvs-train/0001_733_1733851_qa_5" description = "How many columns in the dataset are explicitly of numerical data type (float64)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_735_1735855_qa_1/task.toml b/tasks/0001_735_1735855_qa_1/task.toml index 0dd1e8afd1301e116519329e95b4d9eb46ccab62..b3267d39dca4e2a2513a3540b7ab82b1b89866e1 100644 --- a/tasks/0001_735_1735855_qa_1/task.toml +++ b/tasks/0001_735_1735855_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_735_1735855_qa_1" +name = "smoldataenvs-train/0001_735_1735855_qa_1" description = "What is the most frequently occurring word in the dataset after removing common stopwords?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "allah" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_735_1735855_qa_3/task.toml b/tasks/0001_735_1735855_qa_3/task.toml index 29d1ca53246b9cd286dae83680049e058ac9f7f7..a87b095000962f1548d3b01f76d23435b3e69c16 100644 --- a/tasks/0001_735_1735855_qa_3/task.toml +++ b/tasks/0001_735_1735855_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0001_735_1735855_qa_3" +name = "smoldataenvs-train/0001_735_1735855_qa_3" description = "Which word from the list ['paradise', 'hellfire', 'heaven', 'hell'] occurs most frequently in the text?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "hell" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_735_1735855_qa_5/task.toml b/tasks/0001_735_1735855_qa_5/task.toml index a2c7d245fe1529626c87c7a07cb45de6a3612d68..e156c7bcbf0134d4a249999255b304e0071a11ef 100644 --- a/tasks/0001_735_1735855_qa_5/task.toml +++ b/tasks/0001_735_1735855_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_735_1735855_qa_5" +name = "smoldataenvs-train/0001_735_1735855_qa_5" description = "Between the words 'good' and 'evil', which has a higher count in the text?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "good" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_736_1736876_qa_4/task.toml b/tasks/0001_736_1736876_qa_4/task.toml index 233ea627fec0e11377e7e89a962b615f870da552..f436431d2f4b3f691e7e1f549ff402d5d9212526 100644 --- a/tasks/0001_736_1736876_qa_4/task.toml +++ b/tasks/0001_736_1736876_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_736_1736876_qa_4" +name = "smoldataenvs-train/0001_736_1736876_qa_4" description = "How many numeric columns are present in the dataset before additional processing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_737_1737901_qa_1/task.toml b/tasks/0001_737_1737901_qa_1/task.toml index 2e1812ec13cfa283248e7b4a7ec76de58877e907..c0a6881d17486fdd09cb1f45b1ace6a9bf79b209 100644 --- a/tasks/0001_737_1737901_qa_1/task.toml +++ b/tasks/0001_737_1737901_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_737_1737901_qa_1" +name = "smoldataenvs-train/0001_737_1737901_qa_1" description = "What is the median age of respondents in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "30" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_737_1737901_qa_2/task.toml b/tasks/0001_737_1737901_qa_2/task.toml index 52b654c5462cf1f07269b9c417af1cb1f40b92c3..459cda11cb7c713c6f60ba08473a4932b877920d 100644 --- a/tasks/0001_737_1737901_qa_2/task.toml +++ b/tasks/0001_737_1737901_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_737_1737901_qa_2" +name = "smoldataenvs-train/0001_737_1737901_qa_2" description = "What is the most frequently recommended programming language among respondents?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Python" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_739_1739101_qa_3/task.toml b/tasks/0001_739_1739101_qa_3/task.toml index d3a89eb3ccd6f4b7bfefc1fe52f61a5dc1a453c5..b977472cef2850178154272976ac9525ffb2835c 100644 --- a/tasks/0001_739_1739101_qa_3/task.toml +++ b/tasks/0001_739_1739101_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_739_1739101_qa_3" +name = "smoldataenvs-train/0001_739_1739101_qa_3" description = "What is the median percentage of time spent on model building according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20.00" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_739_1739101_qa_5/task.toml b/tasks/0001_739_1739101_qa_5/task.toml index c6bc363c7157fa0d77106624471ac1b3ea16764a..35c299d07af4fe6541b6e14fc5d5b7249dcd64aa 100644 --- a/tasks/0001_739_1739101_qa_5/task.toml +++ b/tasks/0001_739_1739101_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_739_1739101_qa_5" +name = "smoldataenvs-train/0001_739_1739101_qa_5" description = "What is the average percentage of time spent on finding insights according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13.09" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_741_1741634_qa_2/task.toml b/tasks/0001_741_1741634_qa_2/task.toml index 605f1c0c6c94b4fe33572f88e797d2242512430d..f1453bc8385fdc44f906925120a9f18c5d6b79a4 100644 --- a/tasks/0001_741_1741634_qa_2/task.toml +++ b/tasks/0001_741_1741634_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_741_1741634_qa_2" +name = "smoldataenvs-train/0001_741_1741634_qa_2" description = "After excluding compensation outliers (values below $1 or above $2,000,000), what is the maximum compensation value retained in the dataset for analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2000000" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_741_1741873_qa_4/task.toml b/tasks/0001_741_1741873_qa_4/task.toml index 2893a3e19e33effbe234dee8313d2950613d1e3a..c395bdefec6d37e8be53615412ab7e6b6f6db719 100644 --- a/tasks/0001_741_1741873_qa_4/task.toml +++ b/tasks/0001_741_1741873_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_741_1741873_qa_4" +name = "smoldataenvs-train/0001_741_1741873_qa_4" description = "Which country has produced the highest number of movies according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "United States" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_746_1746593_qa_2/task.toml b/tasks/0001_746_1746593_qa_2/task.toml index d0104b9809f5ba833258e178b497f0150029ee85..573a4e09cb52c688aaef37dbbdd7818ebc0185c2 100644 --- a/tasks/0001_746_1746593_qa_2/task.toml +++ b/tasks/0001_746_1746593_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_746_1746593_qa_2" +name = "smoldataenvs-train/0001_746_1746593_qa_2" description = "What is the most common reason for 911 calls according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "EMS" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_750_1750005_qa_1/task.toml b/tasks/0001_750_1750005_qa_1/task.toml index 9c3823b9b02b93a8bbc571f8a5e0fd8a692585f6..4efa3efa1944f48005492bd82b3aa4bc5ebffe0e 100644 --- a/tasks/0001_750_1750005_qa_1/task.toml +++ b/tasks/0001_750_1750005_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_750_1750005_qa_1" +name = "smoldataenvs-train/0001_750_1750005_qa_1" description = "What percentage of the CompensationAmount data is missing before filtering for USD currency in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "68.75" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_754_1754320_qa_2/task.toml b/tasks/0001_754_1754320_qa_2/task.toml index c015283c0183003fcea832b13fbaf95949c98aa8..5933d12572c9855d32f1eeb896f429a1640e23eb 100644 --- a/tasks/0001_754_1754320_qa_2/task.toml +++ b/tasks/0001_754_1754320_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_754_1754320_qa_2" +name = "smoldataenvs-train/0001_754_1754320_qa_2" description = "How many distinct operational bike stations are present in the dataset after removing closed stations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "17" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_755_1755036_qa_1/task.toml b/tasks/0001_755_1755036_qa_1/task.toml index b4d4e173f965290de62e651f7ac965a00a15702a..0d2e57ea5a3c20f383542fce5e27089d3348548a 100644 --- a/tasks/0001_755_1755036_qa_1/task.toml +++ b/tasks/0001_755_1755036_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0001_755_1755036_qa_1" +name = "smoldataenvs-train/0001_755_1755036_qa_1" description = "Which feature has the strongest negative correlation with survival (Survived) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Pclass" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_755_1755036_qa_2/task.toml b/tasks/0001_755_1755036_qa_2/task.toml index 2f2f2a6af74339c08a742f46d9aec06a07e5d4ab..7fb5ba9facabe9fcdbfc2b9fbb5f4b3dec0a14f0 100644 --- a/tasks/0001_755_1755036_qa_2/task.toml +++ b/tasks/0001_755_1755036_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_755_1755036_qa_2" +name = "smoldataenvs-train/0001_755_1755036_qa_2" description = "What is the correlation coefficient between passenger class (Pclass) and survival (Survived)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.338481" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_761_1761668_qa_2/task.toml b/tasks/0001_761_1761668_qa_2/task.toml index 4028013d00d40b863402a8ac6c3e19c1b3bff339..4240db66707ea3a0f3d7f4733a10627f3acf4261 100644 --- a/tasks/0001_761_1761668_qa_2/task.toml +++ b/tasks/0001_761_1761668_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_761_1761668_qa_2" +name = "smoldataenvs-train/0001_761_1761668_qa_2" description = "How many columns containing the term \"call\" were removed from the training dataset during preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "38" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_761_1761668_qa_3/task.toml b/tasks/0001_761_1761668_qa_3/task.toml index aa09aa44667bded8e6d136269993bc1ee2357bd9..42495cc0b5217ec29d1374b10357765752765314 100644 --- a/tasks/0001_761_1761668_qa_3/task.toml +++ b/tasks/0001_761_1761668_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0001_761_1761668_qa_3" +name = "smoldataenvs-train/0001_761_1761668_qa_3" description = "What is the median value of the gene expression for the gene 'X64594_at' in the training dataset's sample subset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-134.0" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_781_1781985_qa_1/task.toml b/tasks/0001_781_1781985_qa_1/task.toml index 9794cc3257d82afa1f605c621b6f3c5836599de1..bbedf936f7da495d30222a2a6abad5701649eff9 100644 --- a/tasks/0001_781_1781985_qa_1/task.toml +++ b/tasks/0001_781_1781985_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_781_1781985_qa_1" +name = "smoldataenvs-train/0001_781_1781985_qa_1" description = "Which player has the highest total number of match wins, and what is that number?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Serena Williams, 708" reward_mode_initial = "list" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_784_1784765_qa_5/task.toml b/tasks/0001_784_1784765_qa_5/task.toml index 3d4466ab650b2b4517caf051a12fc663cfb34fc5..44d96d255766dc168d71baa45251e938ba597327 100644 --- a/tasks/0001_784_1784765_qa_5/task.toml +++ b/tasks/0001_784_1784765_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0001_784_1784765_qa_5" +name = "smoldataenvs-train/0001_784_1784765_qa_5" description = "What is the highest weekly wage reported for female workers across all specific jobs in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1836.0" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_789_1789575_qa_1/task.toml b/tasks/0001_789_1789575_qa_1/task.toml index 219fac1176c0a3bb8d7ef3771fc4fdfd834afcab..8be9b5d293705e3dd68d68b01ad41526bba57c69 100644 --- a/tasks/0001_789_1789575_qa_1/task.toml +++ b/tasks/0001_789_1789575_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_789_1789575_qa_1" +name = "smoldataenvs-train/0001_789_1789575_qa_1" description = "Which movie in the dataset has the highest profit (calculated as revenue minus budget)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Avatar" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_789_1789575_qa_2/task.toml b/tasks/0001_789_1789575_qa_2/task.toml index f9748fa676af4e17d4540c11d23ab104cabe97ba..873de13a9126281e0fdbfa85d80c5d4cc7a8fef4 100644 --- a/tasks/0001_789_1789575_qa_2/task.toml +++ b/tasks/0001_789_1789575_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_789_1789575_qa_2" +name = "smoldataenvs-train/0001_789_1789575_qa_2" description = "What is the maximum budget value recorded for any movie in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "380000000" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_825_1825877_qa_3/task.toml b/tasks/0001_825_1825877_qa_3/task.toml index b2d0dc17ebc90ac610c0bd6ccb053c6d04fd33f6..d7f3d1347618d940ed4b551f9bf9004366b596c7 100644 --- a/tasks/0001_825_1825877_qa_3/task.toml +++ b/tasks/0001_825_1825877_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_825_1825877_qa_3" +name = "smoldataenvs-train/0001_825_1825877_qa_3" description = "What is the standard deviation of the year of operation for all patients in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.249405" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_825_1825877_qa_5/task.toml b/tasks/0001_825_1825877_qa_5/task.toml index 695fde9422778542268cef7030250d11ce2e3fdd..dfaca3ed5b3eabdc703084aeb554011f9142bf8c 100644 --- a/tasks/0001_825_1825877_qa_5/task.toml +++ b/tasks/0001_825_1825877_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_825_1825877_qa_5" +name = "smoldataenvs-train/0001_825_1825877_qa_5" description = "What is the 75th percentile value for the age of patients in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "60.75" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_840_1840182_qa_2/task.toml b/tasks/0001_840_1840182_qa_2/task.toml index 00a58a31fd80d2a0c940a9b1e21fca13c44ff52c..408d6bb2a10ddef1f875cd68befe4e6832ac27da 100644 --- a/tasks/0001_840_1840182_qa_2/task.toml +++ b/tasks/0001_840_1840182_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_840_1840182_qa_2" +name = "smoldataenvs-train/0001_840_1840182_qa_2" description = "Which wine taster has assigned the highest average rating to wines they reviewed?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Anne Krebiehl MW" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_840_1840182_qa_3/task.toml b/tasks/0001_840_1840182_qa_3/task.toml index 8d9efb7a538759972f7b71228ad6d702c2d13766..933aa0162c124d35f1b35b74a856fc860748f912 100644 --- a/tasks/0001_840_1840182_qa_3/task.toml +++ b/tasks/0001_840_1840182_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_840_1840182_qa_3" +name = "smoldataenvs-train/0001_840_1840182_qa_3" description = "Which country is represented in the most expensive wines (highest price) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "France" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_840_1840182_qa_4/task.toml b/tasks/0001_840_1840182_qa_4/task.toml index f23a29c53dc27b5f330c3a26e36796c9fed0b657..111c86de5730dd2d49a2ecd75c8f9d70a45701e0 100644 --- a/tasks/0001_840_1840182_qa_4/task.toml +++ b/tasks/0001_840_1840182_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_840_1840182_qa_4" +name = "smoldataenvs-train/0001_840_1840182_qa_4" description = "What is the wine variety with the largest number of reviews in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Pinot Noir" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_850_1850034_qa_2/task.toml b/tasks/0001_850_1850034_qa_2/task.toml index 976bd0394567bc13625ab70d9acde8a6c36c2e67..bf77bdb82db730d1bb49cf378e1d9886464870fc 100644 --- a/tasks/0001_850_1850034_qa_2/task.toml +++ b/tasks/0001_850_1850034_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0001_850_1850034_qa_2" +name = "smoldataenvs-train/0001_850_1850034_qa_2" description = "What is the mean value of the 'Total_Bilirubin' feature for the entire dataset after preprocessing but before scaling?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.298799" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_850_1850034_qa_5/task.toml b/tasks/0001_850_1850034_qa_5/task.toml index e0265644c9ba2a7be1770768b03e75ba768a8e99..c784aa4b64b2ff3df6fd97e4e3bb3b9e0a33cbed 100644 --- a/tasks/0001_850_1850034_qa_5/task.toml +++ b/tasks/0001_850_1850034_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_850_1850034_qa_5" +name = "smoldataenvs-train/0001_850_1850034_qa_5" description = "What is the 75th percentile (third quartile) of the 'Albumin' feature in the dataset after preprocessing but before scaling?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.8" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_860_1860651_qa_4/task.toml b/tasks/0001_860_1860651_qa_4/task.toml index e24b43322c87832bd0f8d991c5724c947e5fe468..df60799dbf82062f95079e090f4e0711feca6765 100644 --- a/tasks/0001_860_1860651_qa_4/task.toml +++ b/tasks/0001_860_1860651_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_860_1860651_qa_4" +name = "smoldataenvs-train/0001_860_1860651_qa_4" description = "How many distinct types of items are available in the Chipotle menu based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_864_1864781_qa_3/task.toml b/tasks/0001_864_1864781_qa_3/task.toml index 3aca8a1107a4916d139634d6ca21d5c0a1b47239..ddc2852faab673076d4c3479e6c7df8a97fac95a 100644 --- a/tasks/0001_864_1864781_qa_3/task.toml +++ b/tasks/0001_864_1864781_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_864_1864781_qa_3" +name = "smoldataenvs-train/0001_864_1864781_qa_3" description = "What is the standard deviation of the total number of cyclists per day?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5569.17" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_864_1864781_qa_4/task.toml b/tasks/0001_864_1864781_qa_4/task.toml index f6472440bc6140c6497daab379c4fd45d2ec06e3..cede351f7b4f21c5dd85d0d50decc2f8bc27fa83 100644 --- a/tasks/0001_864_1864781_qa_4/task.toml +++ b/tasks/0001_864_1864781_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_864_1864781_qa_4" +name = "smoldataenvs-train/0001_864_1864781_qa_4" description = "What is the average total number of cyclists per day?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "14534.5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_866_1866353_qa_1/task.toml b/tasks/0001_866_1866353_qa_1/task.toml index a3050efccac6f81f97f29127067aaf190e63af48..9d3af2fe20a10cd3a185c3af598ba1fa51d794d4 100644 --- a/tasks/0001_866_1866353_qa_1/task.toml +++ b/tasks/0001_866_1866353_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_866_1866353_qa_1" +name = "smoldataenvs-train/0001_866_1866353_qa_1" description = "What is the age group with the highest number of participants in the survey dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "25.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_866_1866353_qa_2/task.toml b/tasks/0001_866_1866353_qa_2/task.toml index 52feb5b275e1650b028ec41ff4e6a82167867fbd..5cbd855eb1f4581c5720f36ee0807c99472283e3 100644 --- a/tasks/0001_866_1866353_qa_2/task.toml +++ b/tasks/0001_866_1866353_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_866_1866353_qa_2" +name = "smoldataenvs-train/0001_866_1866353_qa_2" description = "How many participants in the dataset are aged 25 years?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "969" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_869_1869308_qa_4/task.toml b/tasks/0001_869_1869308_qa_4/task.toml index 8a0063226865dba5959e3950d17e1e86101789b7..57a6dcf77e7e934f94d8b313ee81a642058498d0 100644 --- a/tasks/0001_869_1869308_qa_4/task.toml +++ b/tasks/0001_869_1869308_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_869_1869308_qa_4" +name = "smoldataenvs-train/0001_869_1869308_qa_4" description = "What is the median value of the carbohydrate content (carbo) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "14.0" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_869_1869526_qa_5/task.toml b/tasks/0001_869_1869526_qa_5/task.toml index 8794f3405328bca97bfeb5bac170d06fde285cc6..7e37b46d802403b0bea4bc404b7d074111ee0e00 100644 --- a/tasks/0001_869_1869526_qa_5/task.toml +++ b/tasks/0001_869_1869526_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_869_1869526_qa_5" +name = "smoldataenvs-train/0001_869_1869526_qa_5" description = "How many times was the true label \"bed\" mispredicted as \"bad\"?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "92" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_869_1869604_qa_2/task.toml b/tasks/0001_869_1869604_qa_2/task.toml index 2dc69e73d29065190a393403da5adc6421f44407..2039810537cf80760cdedc04f1ff82e0ecb9594e 100644 --- a/tasks/0001_869_1869604_qa_2/task.toml +++ b/tasks/0001_869_1869604_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_869_1869604_qa_2" +name = "smoldataenvs-train/0001_869_1869604_qa_2" description = "What is the average transaction amount across all records in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "88.35" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_871_1871102_qa_2/task.toml b/tasks/0001_871_1871102_qa_2/task.toml index 2907db403a54c7af1210ddf33a20084a79f5ff35..75f28b76c6c2f0e964be132409d4669b5a86de53 100644 --- a/tasks/0001_871_1871102_qa_2/task.toml +++ b/tasks/0001_871_1871102_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_871_1871102_qa_2" +name = "smoldataenvs-train/0001_871_1871102_qa_2" description = "How many unique classes are present in the categorical labels after conversion to categorical format?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_875_1875604_qa_1/task.toml b/tasks/0001_875_1875604_qa_1/task.toml index 7d90687522a9e9030bdfae114bba8961d2f5ec20..90511d5487a1d988238cf7d5f18ea9c6511ead97 100644 --- a/tasks/0001_875_1875604_qa_1/task.toml +++ b/tasks/0001_875_1875604_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_875_1875604_qa_1" +name = "smoldataenvs-train/0001_875_1875604_qa_1" description = "Which cereal has the highest health rating score in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "All-Bran with Extra Fiber" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_876_1876377_qa_2/task.toml b/tasks/0001_876_1876377_qa_2/task.toml index 7cc38fd4c17519704ce1b948876c51009e6d84ab..dd7dd2b1913e95d5802027cf3647d8b62d105ac2 100644 --- a/tasks/0001_876_1876377_qa_2/task.toml +++ b/tasks/0001_876_1876377_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_876_1876377_qa_2" +name = "smoldataenvs-train/0001_876_1876377_qa_2" description = "Which gender category has the highest default payment probability, and what is its value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Male, 24.17%" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_878_1878667_qa_2/task.toml b/tasks/0001_878_1878667_qa_2/task.toml index 387bdb8e7c385bc85972679c1c2136bb6c19f109..ba3dd61f0cf24e40f38c84b1185ce9c54f6d9ba5 100644 --- a/tasks/0001_878_1878667_qa_2/task.toml +++ b/tasks/0001_878_1878667_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_878_1878667_qa_2" +name = "smoldataenvs-train/0001_878_1878667_qa_2" description = "What is the most common flight phase associated with bird strike incidents in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Approach" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_878_1878667_qa_5/task.toml b/tasks/0001_878_1878667_qa_5/task.toml index d88820c7fb46dc7fda266ee6e64386daf7c368b7..9bdfbe0beec8115185ab8f11e3b3559f96ebfe22 100644 --- a/tasks/0001_878_1878667_qa_5/task.toml +++ b/tasks/0001_878_1878667_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_878_1878667_qa_5" +name = "smoldataenvs-train/0001_878_1878667_qa_5" description = "What is the most frequently reported visibility condition during bird strike incidents?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Day" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_878_1878746_qa_1/task.toml b/tasks/0001_878_1878746_qa_1/task.toml index 483b405a1e2bd8339e28d5423c32f91954b20026..81e3948044dec91be3e2ae0eb0aa84560b6910bd 100644 --- a/tasks/0001_878_1878746_qa_1/task.toml +++ b/tasks/0001_878_1878746_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_878_1878746_qa_1" +name = "smoldataenvs-train/0001_878_1878746_qa_1" description = "What is the highest rating given to any cereal in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "93.704912" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_878_1878746_qa_2/task.toml b/tasks/0001_878_1878746_qa_2/task.toml index 68cabe1d3e3ce0add9865e24fedb4b7921fb6256..6685c51f64c34eaefc6e26dac7d9cfdf990a36ba 100644 --- a/tasks/0001_878_1878746_qa_2/task.toml +++ b/tasks/0001_878_1878746_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_878_1878746_qa_2" +name = "smoldataenvs-train/0001_878_1878746_qa_2" description = "What is the standard deviation of sugar content across all cereals in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.444885" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_878_1878746_qa_4/task.toml b/tasks/0001_878_1878746_qa_4/task.toml index 4ecb2a899a7c07418a7677b51ed19145fa77089a..ebc6d748af4475430fc981c7eb4fa89a3781c0d6 100644 --- a/tasks/0001_878_1878746_qa_4/task.toml +++ b/tasks/0001_878_1878746_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_878_1878746_qa_4" +name = "smoldataenvs-train/0001_878_1878746_qa_4" description = "What is the maximum sodium content found in any cereal in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "320" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_882_1882893_qa_3/task.toml b/tasks/0001_882_1882893_qa_3/task.toml index f0364b61a5ed3e3f1c631c0b6146e3b4a0db0a91..8fb844fb6b152879f58133937dafb5cd5e807dc4 100644 --- a/tasks/0001_882_1882893_qa_3/task.toml +++ b/tasks/0001_882_1882893_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_882_1882893_qa_3" +name = "smoldataenvs-train/0001_882_1882893_qa_3" description = "What is the leading known cause of suicide in the dataset when excluding \"Causes Not known\"?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Family problems" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_885_1885477_qa_5/task.toml b/tasks/0001_885_1885477_qa_5/task.toml index 5d4c78727e7d4f42a1fe22ab1c115158a4459188..164b5d7ee58f56eb117ba7c3f154aa103dd446d8 100644 --- a/tasks/0001_885_1885477_qa_5/task.toml +++ b/tasks/0001_885_1885477_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_885_1885477_qa_5" +name = "smoldataenvs-train/0001_885_1885477_qa_5" description = "What is the average vitamin content in cereals rated 50 or below?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "32.589285714285715" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_888_1888068_qa_2/task.toml b/tasks/0001_888_1888068_qa_2/task.toml index 1dc55d50862c780b60d463b5f878c3a3914a8e73..1ecdd1e233adcdf27a2e38ea37388560199a988b 100644 --- a/tasks/0001_888_1888068_qa_2/task.toml +++ b/tasks/0001_888_1888068_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_888_1888068_qa_2" +name = "smoldataenvs-train/0001_888_1888068_qa_2" description = "What are the mean sodium contents (in mg) for hot and cold cereals respectively?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26.67, 165.07" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_892_1892691_qa_2/task.toml b/tasks/0001_892_1892691_qa_2/task.toml index d55c8d419d12a3be32c36092daa31e45426ef372..2a68dc831956bf441386cba336fa6eb8a48324aa 100644 --- a/tasks/0001_892_1892691_qa_2/task.toml +++ b/tasks/0001_892_1892691_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_892_1892691_qa_2" +name = "smoldataenvs-train/0001_892_1892691_qa_2" description = "What is the maximum sodium content observed in hot cereals compared to cold cereals?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "hot=80, cold=320" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_892_1892691_qa_3/task.toml b/tasks/0001_892_1892691_qa_3/task.toml index 6dfc134459184e1513d5233508d72d5eb7060d3c..151ca5754835915a66e08dfa62cf1c46a507e1eb 100644 --- a/tasks/0001_892_1892691_qa_3/task.toml +++ b/tasks/0001_892_1892691_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_892_1892691_qa_3" +name = "smoldataenvs-train/0001_892_1892691_qa_3" description = "What is the mean potassium content for hot cereals versus cold cereals?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "hot=68.0, cold=97.22" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_892_1892776_qa_1/task.toml b/tasks/0001_892_1892776_qa_1/task.toml index bbd81631a29dd632ac9c71148d776af331169d6e..df069ecb25bd7db25afc729627b276807ad8d18f 100644 --- a/tasks/0001_892_1892776_qa_1/task.toml +++ b/tasks/0001_892_1892776_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_892_1892776_qa_1" +name = "smoldataenvs-train/0001_892_1892776_qa_1" description = "What is the most frequently occurring bet type in the dataset and how many times does it occur?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Win, 30417" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_892_1892776_qa_2/task.toml b/tasks/0001_892_1892776_qa_2/task.toml index 2d456ec2895072cb7f33d5b26786fd7bcbfab1ab..dd6771e68ecbed53956722be7c58e1e28a073b55 100644 --- a/tasks/0001_892_1892776_qa_2/task.toml +++ b/tasks/0001_892_1892776_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_892_1892776_qa_2" +name = "smoldataenvs-train/0001_892_1892776_qa_2" description = "Which tipster has the highest number of entries in the dataset and how many entries do they have?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Tipster X, 4383" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_892_1892776_qa_4/task.toml b/tasks/0001_892_1892776_qa_4/task.toml index e25f20568759fd96fe9a9a1bcbd7b1e38ac5b311..3e2287297f874272c98e9357232b97331cdf3f57 100644 --- a/tasks/0001_892_1892776_qa_4/task.toml +++ b/tasks/0001_892_1892776_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_892_1892776_qa_4" +name = "smoldataenvs-train/0001_892_1892776_qa_4" description = "How many unique tracks are represented in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "116" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_893_1893246_qa_3/task.toml b/tasks/0001_893_1893246_qa_3/task.toml index acc8ce777424e02f7bcfa7397bf304b05863b723..b0ab34d612d8923006ca374060c9f2c58938705c 100644 --- a/tasks/0001_893_1893246_qa_3/task.toml +++ b/tasks/0001_893_1893246_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_893_1893246_qa_3" +name = "smoldataenvs-train/0001_893_1893246_qa_3" description = "What is the range of the health rating metric (difference between maximum and minimum values) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "75.662061" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_894_1894014_qa_2/task.toml b/tasks/0001_894_1894014_qa_2/task.toml index 1ecbd8c0098052a74b65438808578d3511742b28..fdc83614f8d902bae2073263abc9669beeb854be 100644 --- a/tasks/0001_894_1894014_qa_2/task.toml +++ b/tasks/0001_894_1894014_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_894_1894014_qa_2" +name = "smoldataenvs-train/0001_894_1894014_qa_2" description = "What is the mean revenue for zoo-type museums after removing duplicate and null records?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5483602" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_897_1897174_qa_5/task.toml b/tasks/0001_897_1897174_qa_5/task.toml index 687d8ce76d41e37e49d89537182f0978b95c9830..19c39d75b44eeb1118eb9235e854014e2dd63646 100644 --- a/tasks/0001_897_1897174_qa_5/task.toml +++ b/tasks/0001_897_1897174_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_897_1897174_qa_5" +name = "smoldataenvs-train/0001_897_1897174_qa_5" description = "How many museums in the dataset have a revenue value of exactly zero before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10783" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_898_1898542_qa_4/task.toml b/tasks/0001_898_1898542_qa_4/task.toml index 22d4381d65b103b652577daf68facced303a6e90..0d1a6c7de6f3038151a25bbaef1d60ce501e93db 100644 --- a/tasks/0001_898_1898542_qa_4/task.toml +++ b/tasks/0001_898_1898542_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_898_1898542_qa_4" +name = "smoldataenvs-train/0001_898_1898542_qa_4" description = "How many entries in the training dataset have missing values for the 'Embarked' feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_902_1902242_qa_4/task.toml b/tasks/0001_902_1902242_qa_4/task.toml index b5f03dd0599aa7c4ff061bbc2ce9e83a97f0d5de..ad702706881d64c1d05a78ea0c12ee649ae42ca0 100644 --- a/tasks/0001_902_1902242_qa_4/task.toml +++ b/tasks/0001_902_1902242_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_902_1902242_qa_4" +name = "smoldataenvs-train/0001_902_1902242_qa_4" description = "What is the total number of samples in each class (male and female) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "male: 1584, female: 1584" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_902_1902890_qa_5/task.toml b/tasks/0001_902_1902890_qa_5/task.toml index 752f6e5a09593a8bc1dbd0b0bc43c59b07b28feb..6e61d28de8835e321a9ad2ca420547cc033f7cfd 100644 --- a/tasks/0001_902_1902890_qa_5/task.toml +++ b/tasks/0001_902_1902890_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_902_1902890_qa_5" +name = "smoldataenvs-train/0001_902_1902890_qa_5" description = "What is the most common experience category among players in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1 Season" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_903_1903160_qa_2/task.toml b/tasks/0001_903_1903160_qa_2/task.toml index a13f425027e178305c04b49a862a71134728cd77..32db2165ff481791973d405a15092d1f5dffd569 100644 --- a/tasks/0001_903_1903160_qa_2/task.toml +++ b/tasks/0001_903_1903160_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_903_1903160_qa_2" +name = "smoldataenvs-train/0001_903_1903160_qa_2" description = "Which nutritional attribute in the dataset has the highest coefficient of variation (standard deviation relative to the mean)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "fiber" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_903_1903160_qa_3/task.toml b/tasks/0001_903_1903160_qa_3/task.toml index e8b50647b7e184de412e69289c4a57684fb18e45..4fc05d3a95c1597574609ea784859a7187622153 100644 --- a/tasks/0001_903_1903160_qa_3/task.toml +++ b/tasks/0001_903_1903160_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_903_1903160_qa_3" +name = "smoldataenvs-train/0001_903_1903160_qa_3" description = "What is the median rating value for cereals in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "40.400208" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_904_1904660_qa_4/task.toml b/tasks/0001_904_1904660_qa_4/task.toml index deedf788ff4589f7dd9d9e3c829577d5ab60c349..e096963374238eae5dccf6d7614866a15ee6084d 100644 --- a/tasks/0001_904_1904660_qa_4/task.toml +++ b/tasks/0001_904_1904660_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_904_1904660_qa_4" +name = "smoldataenvs-train/0001_904_1904660_qa_4" description = "How many distinct wine quality scores are present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_906_1906064_qa_4/task.toml b/tasks/0001_906_1906064_qa_4/task.toml index 4e8be7f72771cc1c8a2f83618688b2d8e48ab9cf..d2d713c75f6132b01535429ab2531a756c75ea18 100644 --- a/tasks/0001_906_1906064_qa_4/task.toml +++ b/tasks/0001_906_1906064_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_906_1906064_qa_4" +name = "smoldataenvs-train/0001_906_1906064_qa_4" description = "What is the standard deviation of sugar content for cold cereals in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.333" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_908_1908198_qa_1/task.toml b/tasks/0001_908_1908198_qa_1/task.toml index 964aaa614d92c4dd0ed502bc555e1f5cd62696d2..c884aa4bf5fc5faee33b0685f15a5c523a00f7c1 100644 --- a/tasks/0001_908_1908198_qa_1/task.toml +++ b/tasks/0001_908_1908198_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_908_1908198_qa_1" +name = "smoldataenvs-train/0001_908_1908198_qa_1" description = "Which dog breed had the highest number of reported bites in the dataset after filtering to top breeds with over 100 incidents?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Pit Bull" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_909_1909933_qa_4/task.toml b/tasks/0001_909_1909933_qa_4/task.toml index ab396eee246f89492366573137e7b2ffc0ce0554..bcd78897a4939150d61ed31226fd98c01d6e4dfa 100644 --- a/tasks/0001_909_1909933_qa_4/task.toml +++ b/tasks/0001_909_1909933_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_909_1909933_qa_4" +name = "smoldataenvs-train/0001_909_1909933_qa_4" description = "What is the mean sodium content for cold cereals?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "165.07" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_916_1916491_qa_4/task.toml b/tasks/0001_916_1916491_qa_4/task.toml index 64c1f48f467827ce816cf669794a951dae3b414b..db85f971171b094d80be1a3b01d17470d155817d 100644 --- a/tasks/0001_916_1916491_qa_4/task.toml +++ b/tasks/0001_916_1916491_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_916_1916491_qa_4" +name = "smoldataenvs-train/0001_916_1916491_qa_4" description = "Which team has won the most number of IPL finals?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Mumbai Indians" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_918_1918012_qa_2/task.toml b/tasks/0001_918_1918012_qa_2/task.toml index f3be83734a0c657be92294c5f45fd5f2760e208c..13d829bcdf847596fb475f958b82d6f1128d4ca4 100644 --- a/tasks/0001_918_1918012_qa_2/task.toml +++ b/tasks/0001_918_1918012_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0001_918_1918012_qa_2" +name = "smoldataenvs-train/0001_918_1918012_qa_2" description = "What is the average sodium content in cold cereals according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "165.07" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_918_1918012_qa_3/task.toml b/tasks/0001_918_1918012_qa_3/task.toml index 92a0f2cc05105f46331db88266c7fa0cf69487f1..fc0fedae712e012281379a66fe3049971b8df156 100644 --- a/tasks/0001_918_1918012_qa_3/task.toml +++ b/tasks/0001_918_1918012_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_918_1918012_qa_3" +name = "smoldataenvs-train/0001_918_1918012_qa_3" description = "How many cereals in the dataset are classified as hot versus cold based on the type column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3 hot, 74 cold" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_918_1918012_qa_5/task.toml b/tasks/0001_918_1918012_qa_5/task.toml index 145fbfa8a75ab55990135e31568f0d105cef7015..d330e470c510a72f00b103369c0dd28ef486df57 100644 --- a/tasks/0001_918_1918012_qa_5/task.toml +++ b/tasks/0001_918_1918012_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_918_1918012_qa_5" +name = "smoldataenvs-train/0001_918_1918012_qa_5" description = "What is the average rating of all cereals in the dataset based on the computed summary statistics?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "42.67" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_918_1918045_qa_2/task.toml b/tasks/0001_918_1918045_qa_2/task.toml index 8a8db73046b6b608544ae9b99e5a47534e8e78ff..64ec934927ed4bb02c8f81048228b494f26af1b4 100644 --- a/tasks/0001_918_1918045_qa_2/task.toml +++ b/tasks/0001_918_1918045_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_918_1918045_qa_2" +name = "smoldataenvs-train/0001_918_1918045_qa_2" description = "Which PCA variable exhibits the most significant positive skew in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "V28" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_918_1918045_qa_4/task.toml b/tasks/0001_918_1918045_qa_4/task.toml index b73bd4c66fd32179926180c7581f75f9181c741c..8f362c99f8e11df7d3a39c8ec4bc6766835c0aea 100644 --- a/tasks/0001_918_1918045_qa_4/task.toml +++ b/tasks/0001_918_1918045_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_918_1918045_qa_4" +name = "smoldataenvs-train/0001_918_1918045_qa_4" description = "Which PCA variable has the largest negative skew in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "V8" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_930_1930122_qa_2/task.toml b/tasks/0001_930_1930122_qa_2/task.toml index 4cd84bb3e33c368f6a4d055c1d9cf5a9ac583ec1..68628b1ae65ad457f4fdd3cd3a7e4964af6fbfd8 100644 --- a/tasks/0001_930_1930122_qa_2/task.toml +++ b/tasks/0001_930_1930122_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_930_1930122_qa_2" +name = "smoldataenvs-train/0001_930_1930122_qa_2" description = "What is the median Item Weight after imputation using the median method in the combined dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12.6" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_930_1930899_qa_1/task.toml b/tasks/0001_930_1930899_qa_1/task.toml index 0555a5133fe41c2f14084646debd7d0b26b60436..ecbfdb7eb7209dc966366ac3eae1bbc57c39c2b3 100644 --- a/tasks/0001_930_1930899_qa_1/task.toml +++ b/tasks/0001_930_1930899_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_930_1930899_qa_1" +name = "smoldataenvs-train/0001_930_1930899_qa_1" description = "Which racial group has the highest average age of victims in gun-related deaths according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "White" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_930_1930899_qa_5/task.toml b/tasks/0001_930_1930899_qa_5/task.toml index b002e9e1ad6345d9037d0708eaf837def6278b6a..abd38397f61d471c0d82422af50f6f372a8c2917 100644 --- a/tasks/0001_930_1930899_qa_5/task.toml +++ b/tasks/0001_930_1930899_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_930_1930899_qa_5" +name = "smoldataenvs-train/0001_930_1930899_qa_5" description = "What is the average age difference between male and female victims in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.19" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_935_1935106_qa_2/task.toml b/tasks/0001_935_1935106_qa_2/task.toml index 54a4357a81e7015c067255f8732558d18d232a9f..0c7988626353f73a329fd63c3d2852b622967f04 100644 --- a/tasks/0001_935_1935106_qa_2/task.toml +++ b/tasks/0001_935_1935106_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_935_1935106_qa_2" +name = "smoldataenvs-train/0001_935_1935106_qa_2" description = "Which legislator received the highest total contributions, and what was the amount?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "John McCain, 2554784" reward_mode_initial = "list" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_937_1937476_qa_1/task.toml b/tasks/0001_937_1937476_qa_1/task.toml index 390a2b7182590b80a36d21720eba1bd497528e6b..1ea1b230606aec65bf598575b16872790f0e8f90 100644 --- a/tasks/0001_937_1937476_qa_1/task.toml +++ b/tasks/0001_937_1937476_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_937_1937476_qa_1" +name = "smoldataenvs-train/0001_937_1937476_qa_1" description = "Which country has the highest average points score for its wines among the first five countries listed in the groupby mean output?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Austria" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_937_1937476_qa_2/task.toml b/tasks/0001_937_1937476_qa_2/task.toml index 2a9275345e8122bf58a67cdbca2a4affe4c14718..e9be3d92e759d7c5c229e0a2b98479c41f15f3b5 100644 --- a/tasks/0001_937_1937476_qa_2/task.toml +++ b/tasks/0001_937_1937476_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_937_1937476_qa_2" +name = "smoldataenvs-train/0001_937_1937476_qa_2" description = "What is the average price of wines from Australia as shown in the groupby mean output?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "31.258480" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_938_1938182_qa_3/task.toml b/tasks/0001_938_1938182_qa_3/task.toml index cb3f2a99d22e8d625e943703698b7dc3ea4d7b18..7e248820f5b7e002c822f98627a5ba1b5557c6ee 100644 --- a/tasks/0001_938_1938182_qa_3/task.toml +++ b/tasks/0001_938_1938182_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_938_1938182_qa_3" +name = "smoldataenvs-train/0001_938_1938182_qa_3" description = "What is the most common interest in data science among participants with a whole lot of programming experience?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "I want to get a job where I use data science" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_938_1938182_qa_5/task.toml b/tasks/0001_938_1938182_qa_5/task.toml index 5728a2ec5e4945274a37b6b7634b04e41865e44b..7d6c72f96b8b810dee16e9e61b808603329ca986 100644 --- a/tasks/0001_938_1938182_qa_5/task.toml +++ b/tasks/0001_938_1938182_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_938_1938182_qa_5" +name = "smoldataenvs-train/0001_938_1938182_qa_5" description = "What is the most common pet preference in the overall dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Dogs" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_955_1955907_qa_3/task.toml b/tasks/0001_955_1955907_qa_3/task.toml index ebbf4559d0008b95e82b530d4ab88c36e9fa2a4d..1b5f6df8e5594f8a17aad9cba23dcf7096e02061 100644 --- a/tasks/0001_955_1955907_qa_3/task.toml +++ b/tasks/0001_955_1955907_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_955_1955907_qa_3" +name = "smoldataenvs-train/0001_955_1955907_qa_3" description = "What are the earliest and latest release years of movies in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1916, 2017" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_955_1955907_qa_5/task.toml b/tasks/0001_955_1955907_qa_5/task.toml index df8eaa866babd9d73fa8b3da2f21e18791a95925..7e24b03fe0e9f964518f42bb62008054f9637615 100644 --- a/tasks/0001_955_1955907_qa_5/task.toml +++ b/tasks/0001_955_1955907_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0001_955_1955907_qa_5" +name = "smoldataenvs-train/0001_955_1955907_qa_5" description = "What are the four most frequent movie genres in the dataset, accounting for more than half of all genre representations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Drama, Comedy, Thriller, Action" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_956_1956536_qa_4/task.toml b/tasks/0001_956_1956536_qa_4/task.toml index dd005c4e7ad2850d544ccf14acd67f5f4a1f402c..c3de146c17639341e0459f734316739193606b50 100644 --- a/tasks/0001_956_1956536_qa_4/task.toml +++ b/tasks/0001_956_1956536_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_956_1956536_qa_4" +name = "smoldataenvs-train/0001_956_1956536_qa_4" description = "Which job role has the lowest median job satisfaction according to the box plots?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Human Resources" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_959_1959663_qa_3/task.toml b/tasks/0001_959_1959663_qa_3/task.toml index de7db8bb14c7c78d6aa782e24da6dabfad15928e..1db2b92d9c89190969f13e8021a6404739cc68c6 100644 --- a/tasks/0001_959_1959663_qa_3/task.toml +++ b/tasks/0001_959_1959663_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_959_1959663_qa_3" +name = "smoldataenvs-train/0001_959_1959663_qa_3" description = "After removing outliers in 'Item_Outlet_Sales' using the 0.95 quantile threshold, what was the new maximum value of this variable?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5522.81" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_969_1969476_qa_2/task.toml b/tasks/0001_969_1969476_qa_2/task.toml index ca3136615af6a3170f2d1186714b1fa764684725..fb2ef60db033c2bcc6cd8f7f13356bc0661bb2cc 100644 --- a/tasks/0001_969_1969476_qa_2/task.toml +++ b/tasks/0001_969_1969476_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_969_1969476_qa_2" +name = "smoldataenvs-train/0001_969_1969476_qa_2" description = "How many dog bite records in the dataset resulted in a confirmed rabies case after filtering out records with unknown outcomes?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_969_1969476_qa_3/task.toml b/tasks/0001_969_1969476_qa_3/task.toml index 2596ed0d22875d742971abfb9c7ee6b0cf139b14..7f6f0471669ad0d4cc41c16dcdaacf528e016383 100644 --- a/tasks/0001_969_1969476_qa_3/task.toml +++ b/tasks/0001_969_1969476_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_969_1969476_qa_3" +name = "smoldataenvs-train/0001_969_1969476_qa_3" description = "What percentage of the original dataset consists of dog bite records (excluding cat bites and other species)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "78.07" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_973_1973830_qa_4/task.toml b/tasks/0001_973_1973830_qa_4/task.toml index 1b11b7bd538e801ae7ccffe4e632e98e62e5205f..725f8d131f4aec05963800b6eac1d2921e6a5512 100644 --- a/tasks/0001_973_1973830_qa_4/task.toml +++ b/tasks/0001_973_1973830_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_973_1973830_qa_4" +name = "smoldataenvs-train/0001_973_1973830_qa_4" description = "In which year did West Bengal record the highest number of incest rape cases, and what was the count?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2007, 114" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_975_1975169_qa_3/task.toml b/tasks/0001_975_1975169_qa_3/task.toml index 92c70427b395b1a45a52820a73b93a0d39438701..27254f66e9580569fc3b4ac2feabcc04026320a9 100644 --- a/tasks/0001_975_1975169_qa_3/task.toml +++ b/tasks/0001_975_1975169_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_975_1975169_qa_3" +name = "smoldataenvs-train/0001_975_1975169_qa_3" description = "Which job level has the highest attrition rate based on the boxplot analysis of attrition factors?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_975_1975497_qa_3/task.toml b/tasks/0001_975_1975497_qa_3/task.toml index 3847e94f4587ff5c914bd1d5502dd7ce11965509..27a02816ac2e4cb0aefd7a6c75ca6db0ad3815a6 100644 --- a/tasks/0001_975_1975497_qa_3/task.toml +++ b/tasks/0001_975_1975497_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_975_1975497_qa_3" +name = "smoldataenvs-train/0001_975_1975497_qa_3" description = "Which platform has the highest number of games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PS2" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_975_1975497_qa_4/task.toml b/tasks/0001_975_1975497_qa_4/task.toml index fe0f3049312dee2b91268f4ffd10d6a8bc777e0e..37a6a1d9e4f73ab2ec970930fc40c799cb23cc05 100644 --- a/tasks/0001_975_1975497_qa_4/task.toml +++ b/tasks/0001_975_1975497_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_975_1975497_qa_4" +name = "smoldataenvs-train/0001_975_1975497_qa_4" description = "What is the top-selling game in Europe based on EU sales?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii Sports" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_989_1989309_qa_2/task.toml b/tasks/0001_989_1989309_qa_2/task.toml index c05ab48a1359d1df7e0c9ddb963ba016c4f5cfce..89dbc5dcae17f05784876d123b11a96aebea5544 100644 --- a/tasks/0001_989_1989309_qa_2/task.toml +++ b/tasks/0001_989_1989309_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_989_1989309_qa_2" +name = "smoldataenvs-train/0001_989_1989309_qa_2" description = "How many cereals in the dataset have negative values in the carbo, sugars, or potass columns?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_990_1990392_qa_4/task.toml b/tasks/0001_990_1990392_qa_4/task.toml index 9069c01a8a9f8c2c998001c9c411eb6192a6d4ce..efe67bd8c35a52b14b598052db011498a49db9c7 100644 --- a/tasks/0001_990_1990392_qa_4/task.toml +++ b/tasks/0001_990_1990392_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_990_1990392_qa_4" +name = "smoldataenvs-train/0001_990_1990392_qa_4" description = "What is the average score of wines that exceed 95 points in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "96.66" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_990_1990794_qa_2/task.toml b/tasks/0001_990_1990794_qa_2/task.toml index 74ca44e830e12246e8a62f12c88bff1e28387401..6facbaff4c5f28e2291b29c0389eb8d81c5060e2 100644 --- a/tasks/0001_990_1990794_qa_2/task.toml +++ b/tasks/0001_990_1990794_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_990_1990794_qa_2" +name = "smoldataenvs-train/0001_990_1990794_qa_2" description = "Which priority level has the highest percentage of calls in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Medium" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_990_1990794_qa_3/task.toml b/tasks/0001_990_1990794_qa_3/task.toml index 55c99e78bf472af30f78163ed73e70cf03457c20..8e418a111c15d61fdefc1087cb30d7d871efaa1c 100644 --- a/tasks/0001_990_1990794_qa_3/task.toml +++ b/tasks/0001_990_1990794_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_990_1990794_qa_3" +name = "smoldataenvs-train/0001_990_1990794_qa_3" description = "What is the most common call description based on the count of occurrences?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "911/NO VOICE" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0001_990_1990794_qa_5/task.toml b/tasks/0001_990_1990794_qa_5/task.toml index bcef41bb7e3a7d73e0e40236a27fcc931460038f..ccec979c0c169bf150337b71bbf0efadcb00473d 100644 --- a/tasks/0001_990_1990794_qa_5/task.toml +++ b/tasks/0001_990_1990794_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_990_1990794_qa_5" +name = "smoldataenvs-train/0001_990_1990794_qa_5" description = "What is the average number of 911 calls received per month in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "87497.31" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_991_1991376_qa_4/task.toml b/tasks/0001_991_1991376_qa_4/task.toml index e4f0ff3e44c61be540dec475b264038ca1b9a959..b668b077e0de902bd012b76e03acdfc5e653f346 100644 --- a/tasks/0001_991_1991376_qa_4/task.toml +++ b/tasks/0001_991_1991376_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0001_991_1991376_qa_4" +name = "smoldataenvs-train/0001_991_1991376_qa_4" description = "What percentage of individuals in the dataset maintain a credit card balance greater than zero?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "77.5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0001_998_1998202_qa_2/task.toml b/tasks/0001_998_1998202_qa_2/task.toml index db54c06bdf90a9fa0898e1d3c931279a8fdaa553..cf5d211227069faf01a5abd1f0f754def796b954 100644 --- a/tasks/0001_998_1998202_qa_2/task.toml +++ b/tasks/0001_998_1998202_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0001_998_1998202_qa_2" +name = "smoldataenvs-train/0001_998_1998202_qa_2" description = "What was the total number of entries in the Points_Rank column that originally contained the value 'a' before replacement with 0?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "209" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_081_2081941_qa_2/task.toml b/tasks/0002_081_2081941_qa_2/task.toml index 2a39446cd690b152089279ef2de1e391324900ed..eccc4f7d1010c44199f6414bd22f60ada0c2e301 100644 --- a/tasks/0002_081_2081941_qa_2/task.toml +++ b/tasks/0002_081_2081941_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0002_081_2081941_qa_2" +name = "smoldataenvs-train/0002_081_2081941_qa_2" description = "Which variable has the strongest positive correlation with the number of views in the TED talks dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "comments" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_081_2081941_qa_4/task.toml b/tasks/0002_081_2081941_qa_4/task.toml index 67ed58910d1edbdd07f803e2d1bc9f8a6860ff5a..fed015775e46344798ae21a5f24fc3211faa2ba2 100644 --- a/tasks/0002_081_2081941_qa_4/task.toml +++ b/tasks/0002_081_2081941_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0002_081_2081941_qa_4" +name = "smoldataenvs-train/0002_081_2081941_qa_4" description = "How many TED talks in the dataset have missing values in the 'speaker_occupation' column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0002_084_2084371_qa_5/task.toml b/tasks/0002_084_2084371_qa_5/task.toml index 92556fcadff196c0a03cd38dcefd921bb9f924f3..6fb65b3ff6762f10d549ce24b88bae843ef92e33 100644 --- a/tasks/0002_084_2084371_qa_5/task.toml +++ b/tasks/0002_084_2084371_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0002_084_2084371_qa_5" +name = "smoldataenvs-train/0002_084_2084371_qa_5" description = "What are the mean values of fixed acidity and volatile acidity for the \"good wine\" class (class 0) in the preprocessed dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8.735795454545455, 0.40863636363636363" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_092_2092240_qa_1/task.toml b/tasks/0002_092_2092240_qa_1/task.toml index a6af1d15c0047805719df45c2befdbcb1bf6eb5f..e91efabd7ee2f3bfc4bbf6d4900c3c9547585720 100644 --- a/tasks/0002_092_2092240_qa_1/task.toml +++ b/tasks/0002_092_2092240_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0002_092_2092240_qa_1" +name = "smoldataenvs-train/0002_092_2092240_qa_1" description = "What is the median number of axillary nodes detected for patients who survived more than 5 years?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_092_2092240_qa_4/task.toml b/tasks/0002_092_2092240_qa_4/task.toml index 9c897d7b18970a378e250263c706314c2aa071a5..a84417cda562b811da161178fec194ccdeeaf1a9 100644 --- a/tasks/0002_092_2092240_qa_4/task.toml +++ b/tasks/0002_092_2092240_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0002_092_2092240_qa_4" +name = "smoldataenvs-train/0002_092_2092240_qa_4" description = "What is the mean number of axillary nodes detected for patients who died within 5 years?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7.456790" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_092_2092240_qa_5/task.toml b/tasks/0002_092_2092240_qa_5/task.toml index 1377ff9fae3fd70f1f0a2684b1411b731d01cb86..86d9ed1c43a61e06d7c42cfc014bcc7c388b6e6c 100644 --- a/tasks/0002_092_2092240_qa_5/task.toml +++ b/tasks/0002_092_2092240_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0002_092_2092240_qa_5" +name = "smoldataenvs-train/0002_092_2092240_qa_5" description = "What is the standard deviation of axillary nodes detected for patients who died within 5 years?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9.185654" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_093_2093580_qa_1/task.toml b/tasks/0002_093_2093580_qa_1/task.toml index 92bfd14a32578609a01511d6cdaf0ab8e5715cf8..ec1256521a2bde87e67e47689f6f9a80f0b5872f 100644 --- a/tasks/0002_093_2093580_qa_1/task.toml +++ b/tasks/0002_093_2093580_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0002_093_2093580_qa_1" +name = "smoldataenvs-train/0002_093_2093580_qa_1" description = "Which five features show the highest correlation with the mushroom class (edible/poisonous) according to the correlation heatmap?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "bruises, gill-color, stalk-root, ring-type, gill-size" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_094_2094789_qa_1/task.toml b/tasks/0002_094_2094789_qa_1/task.toml index 78571a3a7991fbf67b094601462f9fb78415c457..a5062db01708fa6d0979ab4571b04fc21da56c35 100644 --- a/tasks/0002_094_2094789_qa_1/task.toml +++ b/tasks/0002_094_2094789_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0002_094_2094789_qa_1" +name = "smoldataenvs-train/0002_094_2094789_qa_1" description = "Which variable has the highest positive correlation with the Happiness Score according to the 2017 dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Economy (GDP per Capita)" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_094_2094789_qa_3/task.toml b/tasks/0002_094_2094789_qa_3/task.toml index e5da9e083e02024eb133ca96917273389e0a9826..ffd6da417d99659dbb52917a4d04ecacb2d67e18 100644 --- a/tasks/0002_094_2094789_qa_3/task.toml +++ b/tasks/0002_094_2094789_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0002_094_2094789_qa_3" +name = "smoldataenvs-train/0002_094_2094789_qa_3" description = "Which variable shows the least positive correlation with the Happiness Score based on the dataset's correlation analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Generosity" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_094_2094844_qa_1/task.toml b/tasks/0002_094_2094844_qa_1/task.toml index 75171bfc74ae93e0bfdb656a2db5f8dae5a7871d..003863e9d8c2372c5e3e9cb7d51c846705e88f35 100644 --- a/tasks/0002_094_2094844_qa_1/task.toml +++ b/tasks/0002_094_2094844_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0002_094_2094844_qa_1" +name = "smoldataenvs-train/0002_094_2094844_qa_1" description = "Which organization achieved the highest total retweets across all tweets in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ActiveSpaces" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_094_2094844_qa_3/task.toml b/tasks/0002_094_2094844_qa_3/task.toml index 0f78373a0237bb53124c8b94cfda6b36e44d98d9..8daa2d2aeb23b9b663f3d2eb1036824865b981e3 100644 --- a/tasks/0002_094_2094844_qa_3/task.toml +++ b/tasks/0002_094_2094844_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0002_094_2094844_qa_3" +name = "smoldataenvs-train/0002_094_2094844_qa_3" description = "What is the maximum number of retweets achieved by any single tweet in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "79537" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0002_105_2105396_qa_1/task.toml b/tasks/0002_105_2105396_qa_1/task.toml index 4dfb52b7a1f216d15346d1b45d844dd130f68a87..15acce9c4da6948f9ec61c99de89c823a817c7ee 100644 --- a/tasks/0002_105_2105396_qa_1/task.toml +++ b/tasks/0002_105_2105396_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0002_105_2105396_qa_1" +name = "smoldataenvs-train/0002_105_2105396_qa_1" description = "How many missing values were present in the 'Item_Weight' column of the training dataset before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1463" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0002_105_2105769_qa_3/task.toml b/tasks/0002_105_2105769_qa_3/task.toml index 12a2e20bf95f94b2e8ac0109fad6d67d0d22500b..9a7a1434a4fbb85ee6582869183a4cd2534817ef 100644 --- a/tasks/0002_105_2105769_qa_3/task.toml +++ b/tasks/0002_105_2105769_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0002_105_2105769_qa_3" +name = "smoldataenvs-train/0002_105_2105769_qa_3" description = "Which month has the highest number of TED Talks filmed according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "February" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_109_2109152_qa_1/task.toml b/tasks/0002_109_2109152_qa_1/task.toml index 2cbc86b5d71c14d08a3f51d7dc6479c0d13e4345..e748353da31fe56f2721ff732591e9ded487fdf0 100644 --- a/tasks/0002_109_2109152_qa_1/task.toml +++ b/tasks/0002_109_2109152_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0002_109_2109152_qa_1" +name = "smoldataenvs-train/0002_109_2109152_qa_1" description = "Which country contributed the most respondents to the mental health survey, and how many respondents did it contribute?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "United States, 751" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_128_2128926_qa_4/task.toml b/tasks/0002_128_2128926_qa_4/task.toml index 807c5cfdacdf441e780f4405847cdc715742e93a..e9b89b98d7fc7f7b58f96ed4d8293ff63332e752 100644 --- a/tasks/0002_128_2128926_qa_4/task.toml +++ b/tasks/0002_128_2128926_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0002_128_2128926_qa_4" +name = "smoldataenvs-train/0002_128_2128926_qa_4" description = "How many unique values does the 'veil-type' feature have after encoding in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_133_2133400_qa_2/task.toml b/tasks/0002_133_2133400_qa_2/task.toml index 9a26f0dda87309b5cb13696d2d6d48ea4a297b73..198f44334e6086e37e7cf1841f3dab1ac8cfa971 100644 --- a/tasks/0002_133_2133400_qa_2/task.toml +++ b/tasks/0002_133_2133400_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0002_133_2133400_qa_2" +name = "smoldataenvs-train/0002_133_2133400_qa_2" description = "What is the standard deviation of goals scored by the winning team across all matches in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.170" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_144_2144947_qa_1/task.toml b/tasks/0002_144_2144947_qa_1/task.toml index 2017513b42e5ad7242713a761a748b2501b74a69..2b47530ba635c2a23c0845ec7afb25e519efd938 100644 --- a/tasks/0002_144_2144947_qa_1/task.toml +++ b/tasks/0002_144_2144947_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0002_144_2144947_qa_1" +name = "smoldataenvs-train/0002_144_2144947_qa_1" description = "What percentage of Age data was missing in the original dataset before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0002_152_2152103_qa_1/task.toml b/tasks/0002_152_2152103_qa_1/task.toml index ce030460187f4bbc90322d03dcdae26b89e3efdd..a59368e992fcb1087854110c81ccdf486f75fa9e 100644 --- a/tasks/0002_152_2152103_qa_1/task.toml +++ b/tasks/0002_152_2152103_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0002_152_2152103_qa_1" +name = "smoldataenvs-train/0002_152_2152103_qa_1" description = "Which video game genre has the highest total global sales according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_156_2156667_qa_2/task.toml b/tasks/0002_156_2156667_qa_2/task.toml index 1171e584527122bd308af5bcff12e4391780c9a3..115373b4e515febdc9c4553d186d094fb9e3c81a 100644 --- a/tasks/0002_156_2156667_qa_2/task.toml +++ b/tasks/0002_156_2156667_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0002_156_2156667_qa_2" +name = "smoldataenvs-train/0002_156_2156667_qa_2" description = "Which Pokemon type (Type 2) has the highest median Defense stat in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Rock, Ground" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_157_2157761_qa_1/task.toml b/tasks/0002_157_2157761_qa_1/task.toml index e90d191ccc1b9e1d12485ab7f5117ec54264eb9c..9a506ee2bee47a917dc482d6c40a83749684bdf3 100644 --- a/tasks/0002_157_2157761_qa_1/task.toml +++ b/tasks/0002_157_2157761_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0002_157_2157761_qa_1" +name = "smoldataenvs-train/0002_157_2157761_qa_1" description = "Which year had the highest total number of crimes in Vancouver according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2003" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_158_2158528_qa_4/task.toml b/tasks/0002_158_2158528_qa_4/task.toml index 53e29cf6779f2cb7ebfd624a8c558ec62bf6e0c8..2ffd7d89df0dc0dff39fc7a3cccf2154377c1ba4 100644 --- a/tasks/0002_158_2158528_qa_4/task.toml +++ b/tasks/0002_158_2158528_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0002_158_2158528_qa_4" +name = "smoldataenvs-train/0002_158_2158528_qa_4" description = "How many distinct values does the x80 feature have in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "37" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0002_163_2163197_qa_3/task.toml b/tasks/0002_163_2163197_qa_3/task.toml index 5a4de656f7ce5b206fc6cb319ffb0c39b77bfc11..e94ac7c31bd01a655a637cd12079038087179879 100644 --- a/tasks/0002_163_2163197_qa_3/task.toml +++ b/tasks/0002_163_2163197_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0002_163_2163197_qa_3" +name = "smoldataenvs-train/0002_163_2163197_qa_3" description = "Which cricket stadium has hosted the most IPL matches according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "M Chinnaswamy Stadium" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0002_163_2163197_qa_5/task.toml b/tasks/0002_163_2163197_qa_5/task.toml index 2205abd140af2265942b13bee9c8ded3b8682beb..8c468fed87acab7df180720b9391e6d42d363821 100644 --- a/tasks/0002_163_2163197_qa_5/task.toml +++ b/tasks/0002_163_2163197_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0002_163_2163197_qa_5" +name = "smoldataenvs-train/0002_163_2163197_qa_5" description = "What is the most common type of dismissal in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Caught" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0002_170_2170781_qa_1/task.toml b/tasks/0002_170_2170781_qa_1/task.toml index f81142a1cd5c5c2896a8cb0ea83d17202533604e..d906c662fc6cad1db915ccdb40248483fb7d9e95 100644 --- a/tasks/0002_170_2170781_qa_1/task.toml +++ b/tasks/0002_170_2170781_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0002_170_2170781_qa_1" +name = "smoldataenvs-train/0002_170_2170781_qa_1" description = "What percentage of the variation in player salaries is explained by their wins contribution (WINS_RPM) based on the regression analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "32.7" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0002_182_2182549_qa_3/task.toml b/tasks/0002_182_2182549_qa_3/task.toml index 673d713a8fd4aadf5f31df07850bbfd815ce705c..ef5426807b97e8688d2998aaa9e712f33df9b40c 100644 --- a/tasks/0002_182_2182549_qa_3/task.toml +++ b/tasks/0002_182_2182549_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0002_182_2182549_qa_3" +name = "smoldataenvs-train/0002_182_2182549_qa_3" description = "How many dummy variables were created when applying one-hot encoding to the 'Sex' feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_203_2203836_qa_3/task.toml b/tasks/0002_203_2203836_qa_3/task.toml index 4ecff06d2b1fc1b18e78a603dd8a7e0fdca06ca9..be25a573383845873c437caf657287f4cae79a8d 100644 --- a/tasks/0002_203_2203836_qa_3/task.toml +++ b/tasks/0002_203_2203836_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0002_203_2203836_qa_3" +name = "smoldataenvs-train/0002_203_2203836_qa_3" description = "How many non-spam messages (class 0) in the dataset contain the special symbol pattern \"<#>\" in their text before final preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "214" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_203_2203836_qa_4/task.toml b/tasks/0002_203_2203836_qa_4/task.toml index 8cffb5508b8c8c29a246dc29087812bbcd5ef3e3..fdc799ce3376ebb59365172bdc2f1b51e7a9bbb9 100644 --- a/tasks/0002_203_2203836_qa_4/task.toml +++ b/tasks/0002_203_2203836_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0002_203_2203836_qa_4" +name = "smoldataenvs-train/0002_203_2203836_qa_4" description = "What percentage of the dataset was allocated as the test split size in the ShuffleSplit cross-validation configuration used for learning curve analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0002_203_2203836_qa_5/task.toml b/tasks/0002_203_2203836_qa_5/task.toml index e341d8874034fd6e53f2226d8b09c85c1a85ab0a..df3ee463ad6d2eabf518ccf6e0c1e6ed471f8ecc 100644 --- a/tasks/0002_203_2203836_qa_5/task.toml +++ b/tasks/0002_203_2203836_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0002_203_2203836_qa_5" +name = "smoldataenvs-train/0002_203_2203836_qa_5" description = "How many SMS messages in the dataset contain angle bracket symbols (\"<\" or \">\") and are classified as spam (class 1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "19" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_203_2203940_qa_1/task.toml b/tasks/0002_203_2203940_qa_1/task.toml index 8075a8bb1db5bfdeddc7486c00c6cd7ef85ac1a7..36f89148ff73b2a2363f4af52ca168ff2206ceb0 100644 --- a/tasks/0002_203_2203940_qa_1/task.toml +++ b/tasks/0002_203_2203940_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0002_203_2203940_qa_1" +name = "smoldataenvs-train/0002_203_2203940_qa_1" description = "What is the total number of unique wine classes present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0002_203_2203940_qa_5/task.toml b/tasks/0002_203_2203940_qa_5/task.toml index a3c3365682ec5da3f1ccc09ab15058bcc988fb4b..5e799e5ddb13a93768f9e3baca245788f386d025 100644 --- a/tasks/0002_203_2203940_qa_5/task.toml +++ b/tasks/0002_203_2203940_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0002_203_2203940_qa_5" +name = "smoldataenvs-train/0002_203_2203940_qa_5" description = "What is the total number of samples in the original wine dataset before any data splitting?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "178" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0002_211_2211506_qa_1/task.toml b/tasks/0002_211_2211506_qa_1/task.toml index c9ce6c566a90d01ee14fc5155bf8448819aad92c..1092760a3cdef5cba7697dc78b3fb4694a8c8088 100644 --- a/tasks/0002_211_2211506_qa_1/task.toml +++ b/tasks/0002_211_2211506_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0002_211_2211506_qa_1" +name = "smoldataenvs-train/0002_211_2211506_qa_1" description = "According to the analysis, which generations are considered overpowered in terms of attack, defense, hp, and speed based on the boxplot visualization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1, 4" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_217_2217508_qa_3/task.toml b/tasks/0002_217_2217508_qa_3/task.toml index 75c5d610bc87736661114d67d3a876eb779c6d12..8b0081cc3fd46ba693dd8978e44bb2f521c7e6a3 100644 --- a/tasks/0002_217_2217508_qa_3/task.toml +++ b/tasks/0002_217_2217508_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0002_217_2217508_qa_3" +name = "smoldataenvs-train/0002_217_2217508_qa_3" description = "What is the average Total stat of all Pokémon in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "435.1025" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0002_217_2217977_qa_1/task.toml b/tasks/0002_217_2217977_qa_1/task.toml index e5725cb136fed8c392b56f5e0d8728faed5ec150..f1efad25be03f803b7c261301c4bb2e3312ca0b1 100644 --- a/tasks/0002_217_2217977_qa_1/task.toml +++ b/tasks/0002_217_2217977_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0002_217_2217977_qa_1" +name = "smoldataenvs-train/0002_217_2217977_qa_1" description = "How many wines in the original dataset have missing price values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13695" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0002_219_2219893_qa_3/task.toml b/tasks/0002_219_2219893_qa_3/task.toml index fe7b96f49f61536cce843eed9ad9a16666bf0df3..9a523a5d164bc73e40b5b89efeb244763c3eabf6 100644 --- a/tasks/0002_219_2219893_qa_3/task.toml +++ b/tasks/0002_219_2219893_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0002_219_2219893_qa_3" +name = "smoldataenvs-train/0002_219_2219893_qa_3" description = "Which country has the highest average wine rating (points) according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "England" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_234_2234436_qa_1/task.toml b/tasks/0002_234_2234436_qa_1/task.toml index bd145d270f75f9b6220720901ff7375366254e4a..0bac88e8fe510354c7cf1bf4ff7e3f83d9cc42f5 100644 --- a/tasks/0002_234_2234436_qa_1/task.toml +++ b/tasks/0002_234_2234436_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0002_234_2234436_qa_1" +name = "smoldataenvs-train/0002_234_2234436_qa_1" description = "What is the median TotalPay for full-time (FT) employees based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "94271.735" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_240_2240465_qa_4/task.toml b/tasks/0002_240_2240465_qa_4/task.toml index 81b74e5b6f1fb6fff6475ebfee37423a34150241..ed77ad1a9b37b619b69eec1fa40258dac1adb8e6 100644 --- a/tasks/0002_240_2240465_qa_4/task.toml +++ b/tasks/0002_240_2240465_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0002_240_2240465_qa_4" +name = "smoldataenvs-train/0002_240_2240465_qa_4" description = "What is the 90th percentile of USD pledged amount for technology category projects?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "21223.50" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_246_2246634_qa_4/task.toml b/tasks/0002_246_2246634_qa_4/task.toml index a71c4a686c6173f95c79c305bfc9ce552821513b..e818c7a157dca927cc8edbd3905c2950c8194f2b 100644 --- a/tasks/0002_246_2246634_qa_4/task.toml +++ b/tasks/0002_246_2246634_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0002_246_2246634_qa_4" +name = "smoldataenvs-train/0002_246_2246634_qa_4" description = "What is the highest points per game (POINTS) value recorded for any player in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "31.6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_253_2253632_qa_1/task.toml b/tasks/0002_253_2253632_qa_1/task.toml index 3a2db977aa5406d46c32ee8b17ea62352572e790..32a53edf75a8d69b37669b238efe226a60e7e799 100644 --- a/tasks/0002_253_2253632_qa_1/task.toml +++ b/tasks/0002_253_2253632_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0002_253_2253632_qa_1" +name = "smoldataenvs-train/0002_253_2253632_qa_1" description = "Which diamond characteristic (carat, x, y, or z) has the strongest linear correlation with price according to the correlation matrix visualization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "carat" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_253_2253838_qa_5/task.toml b/tasks/0002_253_2253838_qa_5/task.toml index 668868433fce5a977967d53d119f074b226ea441..00d5b9f3a1b2af48f62160ddfc7d21b9f22e4645 100644 --- a/tasks/0002_253_2253838_qa_5/task.toml +++ b/tasks/0002_253_2253838_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0002_253_2253838_qa_5" +name = "smoldataenvs-train/0002_253_2253838_qa_5" description = "Which neckline type has the highest proportion among recommended dresses according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "o-neck" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_264_2264461_qa_1/task.toml b/tasks/0002_264_2264461_qa_1/task.toml index f962dc3012f9f374339391f3eed0a9d3ea321d9e..e53039d538fb8b527af2c525553fe5d4c14688d1 100644 --- a/tasks/0002_264_2264461_qa_1/task.toml +++ b/tasks/0002_264_2264461_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0002_264_2264461_qa_1" +name = "smoldataenvs-train/0002_264_2264461_qa_1" description = "Which state has the highest number of counties in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Texas" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_276_2276545_qa_2/task.toml b/tasks/0002_276_2276545_qa_2/task.toml index 179cf27a77396e6a40343953d1576000762a71f5..7a6a79fd63f10558be36471e88f8de5b6cc3ebea 100644 --- a/tasks/0002_276_2276545_qa_2/task.toml +++ b/tasks/0002_276_2276545_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0002_276_2276545_qa_2" +name = "smoldataenvs-train/0002_276_2276545_qa_2" description = "What was the standard deviation of precipitation (PRCP) in the dataset before handling missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.239031" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0002_325_2325668_qa_5/task.toml b/tasks/0002_325_2325668_qa_5/task.toml index 97447ce05caba202fa5b4b4df83e66250035799d..a9d84b8c1a0ce0805ef89883a4dc5a598201aa23 100644 --- a/tasks/0002_325_2325668_qa_5/task.toml +++ b/tasks/0002_325_2325668_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0002_325_2325668_qa_5" +name = "smoldataenvs-train/0002_325_2325668_qa_5" description = "What is the highest recorded March temperature in Pennsylvania based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "47.70" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0002_349_2349296_qa_2/task.toml b/tasks/0002_349_2349296_qa_2/task.toml index 141a80a13718513faad40be5330110595bab13a4..1cf16a8191e4dab587891d25862e3fdfe3123c74 100644 --- a/tasks/0002_349_2349296_qa_2/task.toml +++ b/tasks/0002_349_2349296_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0002_349_2349296_qa_2" +name = "smoldataenvs-train/0002_349_2349296_qa_2" description = "Which taster has reviewed the most wines, and how many reviews did they contribute?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Roger Voss, 25514" reward_mode_initial = "list" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0002_349_2349296_qa_3/task.toml b/tasks/0002_349_2349296_qa_3/task.toml index 7fd627cdb6035173ff388805e8c3b98854338536..7527d723a3aae70a4a97974f43b94141b8ca45e4 100644 --- a/tasks/0002_349_2349296_qa_3/task.toml +++ b/tasks/0002_349_2349296_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0002_349_2349296_qa_3" +name = "smoldataenvs-train/0002_349_2349296_qa_3" description = "What is the highest score (points) assigned to any wine in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "100" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0002_350_2350400_qa_3/task.toml b/tasks/0002_350_2350400_qa_3/task.toml index a535020e6d226ca2b25d1d1dc4470f5679c6f9fd..c4e5e8203aa3e1d542a84e35be69ac4b4941f293 100644 --- a/tasks/0002_350_2350400_qa_3/task.toml +++ b/tasks/0002_350_2350400_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0002_350_2350400_qa_3" +name = "smoldataenvs-train/0002_350_2350400_qa_3" description = "What is the most common configuration of bedrooms and bathrooms in the dataset, based on the distribution analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3 bedrooms, 1 bathroom" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_351_2351047_qa_1/task.toml b/tasks/0002_351_2351047_qa_1/task.toml index 88d3c9f7c9db4140aa01c5ff6f9813e09fe55a44..8b63de26b503089caba999051eb0913a23a98554 100644 --- a/tasks/0002_351_2351047_qa_1/task.toml +++ b/tasks/0002_351_2351047_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0002_351_2351047_qa_1" +name = "smoldataenvs-train/0002_351_2351047_qa_1" description = "Which movie has the highest star rating in the R-rated category in the trimmed dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "The Shawshank Redemption" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_375_2375931_qa_1/task.toml b/tasks/0002_375_2375931_qa_1/task.toml index cf4dd0222d687e78daaca3e26b74cdf1eab5c856..af5323f396c98ca1eb6bd5620f01efebe85f2cae 100644 --- a/tasks/0002_375_2375931_qa_1/task.toml +++ b/tasks/0002_375_2375931_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0002_375_2375931_qa_1" +name = "smoldataenvs-train/0002_375_2375931_qa_1" description = "Which industry vertical has the highest number of startups receiving funding according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Consumer Internet" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_375_2375931_qa_3/task.toml b/tasks/0002_375_2375931_qa_3/task.toml index 6cc415c98d23aa34c8f7f0e6892fe181fb5bebfd..84d76c27ce420269ef47795aa3794d793c441670 100644 --- a/tasks/0002_375_2375931_qa_3/task.toml +++ b/tasks/0002_375_2375931_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0002_375_2375931_qa_3" +name = "smoldataenvs-train/0002_375_2375931_qa_3" description = "Which city has the highest number of startup funding events recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Bangalore" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0002_376_2376035_qa_1/task.toml b/tasks/0002_376_2376035_qa_1/task.toml index 42d568a14898c16e7b876f1a51218b14d9935182..6134c2f60bb87f405034594e807e780d581e13f9 100644 --- a/tasks/0002_376_2376035_qa_1/task.toml +++ b/tasks/0002_376_2376035_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0002_376_2376035_qa_1" +name = "smoldataenvs-train/0002_376_2376035_qa_1" description = "What is the data type of the `points` column in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "int64" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0010_098_10098449_qa_1/task.toml b/tasks/0010_098_10098449_qa_1/task.toml index 8393cde0f94eb1dae14e7f2655552210f4723265..101d4170e12b41429201cafba7cacb46acdb779c 100644 --- a/tasks/0010_098_10098449_qa_1/task.toml +++ b/tasks/0010_098_10098449_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0010_098_10098449_qa_1" +name = "smoldataenvs-train/0010_098_10098449_qa_1" description = "What is the highest Pearson correlation coefficient between any feature and house price in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.702" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0010_191_10191909_qa_1/task.toml b/tasks/0010_191_10191909_qa_1/task.toml index f9c6cd4df6bafb5d312e211abeffb9721a11423b..fc2f9d0b65063b5a52bdda0920622627ec4fa3f0 100644 --- a/tasks/0010_191_10191909_qa_1/task.toml +++ b/tasks/0010_191_10191909_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0010_191_10191909_qa_1" +name = "smoldataenvs-train/0010_191_10191909_qa_1" description = "What is the percentage of benign tumors in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "62.7417" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0010_191_10191909_qa_4/task.toml b/tasks/0010_191_10191909_qa_4/task.toml index 668f20667fdc5395edc3d573919cf037f6b02a74..2abd2e6d74a5ff2799117921555b00fed3b9a1c2 100644 --- a/tasks/0010_191_10191909_qa_4/task.toml +++ b/tasks/0010_191_10191909_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0010_191_10191909_qa_4" +name = "smoldataenvs-train/0010_191_10191909_qa_4" description = "What is the total number of samples (rows) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "569" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0010_361_10361003_qa_5/task.toml b/tasks/0010_361_10361003_qa_5/task.toml index 85ddbe8955f74dabaf2d31cff126b4fcf7d93e75..cc4abff5f555cceb89692a4713dd12b3bca26b39 100644 --- a/tasks/0010_361_10361003_qa_5/task.toml +++ b/tasks/0010_361_10361003_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0010_361_10361003_qa_5" +name = "smoldataenvs-train/0010_361_10361003_qa_5" description = "Which class was transformed to numerical labels (0 for Abnormal, 1 for Normal) in the preprocessing step?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "class" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0010_376_10376491_qa_3/task.toml b/tasks/0010_376_10376491_qa_3/task.toml index 81cd9a0fd309991f8b54f0c9a1a89ecb1f936107..53e100b6aec01fed78661428d98a835ff34bd1db 100644 --- a/tasks/0010_376_10376491_qa_3/task.toml +++ b/tasks/0010_376_10376491_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0010_376_10376491_qa_3" +name = "smoldataenvs-train/0010_376_10376491_qa_3" description = "What is the average texture_mean for benign and malignant tumors in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Benign=17.9148, Malignant=21.6049" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0010_391_10391673_qa_1/task.toml b/tasks/0010_391_10391673_qa_1/task.toml index bccf1145e7fc571048c4787a3095a0254564abe3..999e69c1e6f4590e12355ee8b88079aa37135b3b 100644 --- a/tasks/0010_391_10391673_qa_1/task.toml +++ b/tasks/0010_391_10391673_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0010_391_10391673_qa_1" +name = "smoldataenvs-train/0010_391_10391673_qa_1" description = "What is the correlation coefficient between cocoa percentage and chocolate bar rating in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.1648" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0010_482_10482813_qa_1/task.toml b/tasks/0010_482_10482813_qa_1/task.toml index b5c5cfec54aed9972a31c826058c544f88f42370..c5d276b9e3f61da13fc55cb207a0b99bdcac345e 100644 --- a/tasks/0010_482_10482813_qa_1/task.toml +++ b/tasks/0010_482_10482813_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0010_482_10482813_qa_1" +name = "smoldataenvs-train/0010_482_10482813_qa_1" description = "What are the Petal Length and Petal Width values of the cluster centroid with the highest Petal Length in the 5-cluster K-means model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.3, 2.05" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0010_489_10489077_qa_1/task.toml b/tasks/0010_489_10489077_qa_1/task.toml index 67c68743dbddc02bf141decf30c5e0482201eb07..b1c8687fab844abd419bd0689216379d19c5e01a 100644 --- a/tasks/0010_489_10489077_qa_1/task.toml +++ b/tasks/0010_489_10489077_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0010_489_10489077_qa_1" +name = "smoldataenvs-train/0010_489_10489077_qa_1" description = "Which five countries had the lowest happiness scores in 2015 according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Togo, Burundi, Syria, Benin, Rwanda" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0010_489_10489077_qa_4/task.toml b/tasks/0010_489_10489077_qa_4/task.toml index 49cb4505afce4dcdcc0342955c5d1bed94fd48e2..57bae3fa2d5b576e3df887a44f4d898a8cd2ffd5 100644 --- a/tasks/0010_489_10489077_qa_4/task.toml +++ b/tasks/0010_489_10489077_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0010_489_10489077_qa_4" +name = "smoldataenvs-train/0010_489_10489077_qa_4" description = "What is the difference in the number of countries between 2015 and 2017 datasets?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0010_506_10506952_qa_2/task.toml b/tasks/0010_506_10506952_qa_2/task.toml index 2665a4e15e08f995117962710c90fce0d6c158a7..d9b62c25919185a87c4184f99c3f30c406f7b0ee 100644 --- a/tasks/0010_506_10506952_qa_2/task.toml +++ b/tasks/0010_506_10506952_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0010_506_10506952_qa_2" +name = "smoldataenvs-train/0010_506_10506952_qa_2" description = "Which species has the highest median value for PetalLengthCm based on the violin plot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-virginica" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0010_506_10506952_qa_4/task.toml b/tasks/0010_506_10506952_qa_4/task.toml index 354a9b544feed3754f0a9b162703818087ad8445..4b6cb2420dfc043d01181378c38d8d8a690eb844 100644 --- a/tasks/0010_506_10506952_qa_4/task.toml +++ b/tasks/0010_506_10506952_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0010_506_10506952_qa_4" +name = "smoldataenvs-train/0010_506_10506952_qa_4" description = "Which feature combination provides the best visual separation of species according to the scatter plot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm, PetalWidthCm" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0010_515_10515511_qa_1/task.toml b/tasks/0010_515_10515511_qa_1/task.toml index fe42423b4e5d98b5587f3e77f69bf690046d5372..d670901bbd3d1a47a2a08f0661a7640c1f896651 100644 --- a/tasks/0010_515_10515511_qa_1/task.toml +++ b/tasks/0010_515_10515511_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0010_515_10515511_qa_1" +name = "smoldataenvs-train/0010_515_10515511_qa_1" description = "Which web browser was most frequently used by guests when submitting hotel reviews?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Firefox" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0010_614_10614011_qa_1/task.toml b/tasks/0010_614_10614011_qa_1/task.toml index 704df09d8620dfbbc1980e7aa4d1ac481ae01a42..e321dd3e6380888a3e070af35751396d3e89699a 100644 --- a/tasks/0010_614_10614011_qa_1/task.toml +++ b/tasks/0010_614_10614011_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0010_614_10614011_qa_1" +name = "smoldataenvs-train/0010_614_10614011_qa_1" description = "What is the median number of positive axillary nodes for patients who survived more than 5 years?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0010_624_10624322_qa_3/task.toml b/tasks/0010_624_10624322_qa_3/task.toml index 67f8b22067b3e2a0e62539f5e72601900284b487..cbccd06b62fe8ed5f31345e147dbfaf55c1ceafd 100644 --- a/tasks/0010_624_10624322_qa_3/task.toml +++ b/tasks/0010_624_10624322_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0010_624_10624322_qa_3" +name = "smoldataenvs-train/0010_624_10624322_qa_3" description = "Which species has the smallest petal length and width according to the analysis in the notebook?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-setosa" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0010_624_10624322_qa_4/task.toml b/tasks/0010_624_10624322_qa_4/task.toml index 7b79426a9bf48d0bc20a60d88644ba51e6fb912e..2ddddc92e5438e8aebd1cdec222084108371805d 100644 --- a/tasks/0010_624_10624322_qa_4/task.toml +++ b/tasks/0010_624_10624322_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0010_624_10624322_qa_4" +name = "smoldataenvs-train/0010_624_10624322_qa_4" description = "Which species has the largest petal length and width according to the analysis in the notebook?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-virginica" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0010_631_10631903_qa_1/task.toml b/tasks/0010_631_10631903_qa_1/task.toml index 7fabe00a8bac07154284cc5e6e0b30ce4edd5ad7..3fe918adc2a6ba8e47fcf9be4fa02c155ab11f44 100644 --- a/tasks/0010_631_10631903_qa_1/task.toml +++ b/tasks/0010_631_10631903_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0010_631_10631903_qa_1" +name = "smoldataenvs-train/0010_631_10631903_qa_1" description = "Which country has the highest Happiness Score in the 2017 dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Norway" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0010_637_10637554_qa_1/task.toml b/tasks/0010_637_10637554_qa_1/task.toml index 885c534da7306dcbb9592e8731103b4184b9e9a7..06d95860b11c796364d7f26043d9592a1298c6ac 100644 --- a/tasks/0010_637_10637554_qa_1/task.toml +++ b/tasks/0010_637_10637554_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0010_637_10637554_qa_1" +name = "smoldataenvs-train/0010_637_10637554_qa_1" description = "Which wine taster provided the highest average rating score in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Anne Krebiehl MW" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0010_708_10708335_qa_2/task.toml b/tasks/0010_708_10708335_qa_2/task.toml index c128338fa1637319591fa2f11623bb2d71282fef..e0c81dc789875389b4d9d1605f969e644544c9b8 100644 --- a/tasks/0010_708_10708335_qa_2/task.toml +++ b/tasks/0010_708_10708335_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0010_708_10708335_qa_2" +name = "smoldataenvs-train/0010_708_10708335_qa_2" description = "What is the average number of publications for authors who have contributed to multiple papers (i.e., authors with more than one publication)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.388571428571429" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0010_821_10821945_qa_3/task.toml b/tasks/0010_821_10821945_qa_3/task.toml index e646baae3b41a93af40843bee88a0ddb3a3575c7..64864c909b77797eceb62897368248317403a6f3 100644 --- a/tasks/0010_821_10821945_qa_3/task.toml +++ b/tasks/0010_821_10821945_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0010_821_10821945_qa_3" +name = "smoldataenvs-train/0010_821_10821945_qa_3" description = "What percentage of patients in the dataset are diagnosed with diabetes (Outcome=1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0010_841_10841985_qa_3/task.toml b/tasks/0010_841_10841985_qa_3/task.toml index 0bd287012606f8bf14d97084b33ae190f0a5d0c0..fe9a9f91b7dc668063d350da1e705f0f02cd4c2b 100644 --- a/tasks/0010_841_10841985_qa_3/task.toml +++ b/tasks/0010_841_10841985_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0010_841_10841985_qa_3" +name = "smoldataenvs-train/0010_841_10841985_qa_3" description = "What is the average area_mean for malignant tumors in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "978.376415" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0010_842_10842977_qa_3/task.toml b/tasks/0010_842_10842977_qa_3/task.toml index 71df4341258c41bf780c17ba2fa968b872b9daf6..8d3cf9260747c4a3a83a95d9ac9b8836c52fc460 100644 --- a/tasks/0010_842_10842977_qa_3/task.toml +++ b/tasks/0010_842_10842977_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0010_842_10842977_qa_3" +name = "smoldataenvs-train/0010_842_10842977_qa_3" description = "What is the median Axillary nodes detected value for deceased patients according to the boxplot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0010_842_10842977_qa_4/task.toml b/tasks/0010_842_10842977_qa_4/task.toml index c27da75365c01df835a1d5966465e8ad5ee27733..2d20778647a5970cff6e26bf7e9b459e92da8b5b 100644 --- a/tasks/0010_842_10842977_qa_4/task.toml +++ b/tasks/0010_842_10842977_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0010_842_10842977_qa_4" +name = "smoldataenvs-train/0010_842_10842977_qa_4" description = "What is the 25th percentile Axillary nodes detected value for deceased patients compared to survivors based on the boxplot observations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1 for deceased, 0 for survivors" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0010_843_10843239_qa_1/task.toml b/tasks/0010_843_10843239_qa_1/task.toml index ca434c23968b146dc639dd69f939a04f16f615aa..1c95f62f15be533288d7a99e7c892192e0c36874 100644 --- a/tasks/0010_843_10843239_qa_1/task.toml +++ b/tasks/0010_843_10843239_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0010_843_10843239_qa_1" +name = "smoldataenvs-train/0010_843_10843239_qa_1" description = "What is the 90th percentile value of axillary nodes for patients who survived compared to those who did not survive?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Survived: 8, Did not survive: 20" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0010_843_10843239_qa_2/task.toml b/tasks/0010_843_10843239_qa_2/task.toml index e297ecb29e046b8233754d6bf0f80b8a959f1026..b70b9150ad405f7d28b39f498ed734e0af4c7972 100644 --- a/tasks/0010_843_10843239_qa_2/task.toml +++ b/tasks/0010_843_10843239_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0010_843_10843239_qa_2" +name = "smoldataenvs-train/0010_843_10843239_qa_2" description = "What is the median value of axillary nodes for patients who survived versus those who did not survive?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Survived=0, Did not survive=4" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0010_860_10860955_qa_1/task.toml b/tasks/0010_860_10860955_qa_1/task.toml index 0db32cc8e9f46a2ddd65e1ac9de6de691c2f3e92..7a6d17b0f03509e4746f3017b6906fb8c12a26fc 100644 --- a/tasks/0010_860_10860955_qa_1/task.toml +++ b/tasks/0010_860_10860955_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0010_860_10860955_qa_1" +name = "smoldataenvs-train/0010_860_10860955_qa_1" description = "Which four features were identified as the most important based on the Random Forest feature importance analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose, BMI, Age, DiabetesPedigreeFunction" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0010_909_10909980_qa_3/task.toml b/tasks/0010_909_10909980_qa_3/task.toml index 90d3737f59c4204f20e5eb178314586fd72d3ecd..e11224203397dae1c773316319eb3882e27d6612 100644 --- a/tasks/0010_909_10909980_qa_3/task.toml +++ b/tasks/0010_909_10909980_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0010_909_10909980_qa_3" +name = "smoldataenvs-train/0010_909_10909980_qa_3" description = "Which factor has the strongest negative correlation with the Happiness Rank in the 2017 dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Economy (GDP per Capita)" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0011_015_11015604_qa_2/task.toml b/tasks/0011_015_11015604_qa_2/task.toml index 0bec94963d80fb7aec5fd72d876c833993203d63..0d1380c61f9582866b0c7c7297d6ce5f2a9ede0e 100644 --- a/tasks/0011_015_11015604_qa_2/task.toml +++ b/tasks/0011_015_11015604_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0011_015_11015604_qa_2" +name = "smoldataenvs-train/0011_015_11015604_qa_2" description = "What is the correlation coefficient between the Attack stat and the Total stats of Pokémon in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.736211" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0011_015_11015604_qa_4/task.toml b/tasks/0011_015_11015604_qa_4/task.toml index 23e370e02f2d08cc34e4e0f58906bebf7b5cc10d..107d96c85b6c6f98936abcffa30d5b086139c67e 100644 --- a/tasks/0011_015_11015604_qa_4/task.toml +++ b/tasks/0011_015_11015604_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0011_015_11015604_qa_4" +name = "smoldataenvs-train/0011_015_11015604_qa_4" description = "What is the correlation coefficient between the Defense stat and the Total stats of Pokémon in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.612787" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0011_015_11015604_qa_5/task.toml b/tasks/0011_015_11015604_qa_5/task.toml index 8ceba5f28303ce9771eebafab026de0ec83f9e57..f2b3dcafc98a18f410cc2d9c197856f17c79a7bd 100644 --- a/tasks/0011_015_11015604_qa_5/task.toml +++ b/tasks/0011_015_11015604_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0011_015_11015604_qa_5" +name = "smoldataenvs-train/0011_015_11015604_qa_5" description = "What is the correlation coefficient between the HP stat and the Legendary status of Pokémon in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.273620" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0011_054_11054618_qa_1/task.toml b/tasks/0011_054_11054618_qa_1/task.toml index 8468c24f5525f2ee70d533677d82b762016145fd..ba2e5cd53fd1711fd2ba918e926d6578d18342e7 100644 --- a/tasks/0011_054_11054618_qa_1/task.toml +++ b/tasks/0011_054_11054618_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0011_054_11054618_qa_1" +name = "smoldataenvs-train/0011_054_11054618_qa_1" description = "How many patients in the dataset have more than one appointment scheduled?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "24379" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0011_054_11054618_qa_5/task.toml b/tasks/0011_054_11054618_qa_5/task.toml index 7540df7210e9c614832bcacc2c946df7eece730e..a4f04aa1317630959cee708d0a077b2852100e05 100644 --- a/tasks/0011_054_11054618_qa_5/task.toml +++ b/tasks/0011_054_11054618_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0011_054_11054618_qa_5" +name = "smoldataenvs-train/0011_054_11054618_qa_5" description = "How many patients in the dataset have had more than 3 medical appointments scheduled?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4984" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0011_060_11060050_qa_4/task.toml b/tasks/0011_060_11060050_qa_4/task.toml index 7abff39377f97b0351b301426c51d5afff23a52f..144afbc578c2ac12ddcec0505ab9660de24d06dc 100644 --- a/tasks/0011_060_11060050_qa_4/task.toml +++ b/tasks/0011_060_11060050_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0011_060_11060050_qa_4" +name = "smoldataenvs-train/0011_060_11060050_qa_4" description = "What data type was the 'horsepower' column converted to after handling missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "float64" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0011_088_11088784_qa_2/task.toml b/tasks/0011_088_11088784_qa_2/task.toml index 1ca38cc51070eb6feaab27a3fae6ef4431b8ace5..bae7306e48067e347a6c12f1ddd06c5ab03e2460 100644 --- a/tasks/0011_088_11088784_qa_2/task.toml +++ b/tasks/0011_088_11088784_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0011_088_11088784_qa_2" +name = "smoldataenvs-train/0011_088_11088784_qa_2" description = "What is the most common wine variety among top-rated (≥90 points) Australian wines in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Shiraz" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0011_130_11130431_qa_2/task.toml b/tasks/0011_130_11130431_qa_2/task.toml index 12e8e52f467da453e842dd6e237965302756cda9..97a57f07fa086209ba73e5af31ce065d3b1e5b76 100644 --- a/tasks/0011_130_11130431_qa_2/task.toml +++ b/tasks/0011_130_11130431_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0011_130_11130431_qa_2" +name = "smoldataenvs-train/0011_130_11130431_qa_2" description = "What is the 75th percentile of positive lymph nodes for surviving patients as observed in the box plot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0011_130_11130431_qa_4/task.toml b/tasks/0011_130_11130431_qa_4/task.toml index a49589c2e7b33513a7ae43f4906fc6bdd7e4d563..61771f81d24f54eeca6331962b0cdae3c7ab88af 100644 --- a/tasks/0011_130_11130431_qa_4/task.toml +++ b/tasks/0011_130_11130431_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0011_130_11130431_qa_4" +name = "smoldataenvs-train/0011_130_11130431_qa_4" description = "What is the 90th percentile of positive lymph nodes for patients who did not survive, as calculated from the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0011_224_11224334_qa_1/task.toml b/tasks/0011_224_11224334_qa_1/task.toml index c3d68ece097326848a27d2f81f9f73697d62f1c6..503b61c063e21adce29fae2d8504030462c8a936 100644 --- a/tasks/0011_224_11224334_qa_1/task.toml +++ b/tasks/0011_224_11224334_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0011_224_11224334_qa_1" +name = "smoldataenvs-train/0011_224_11224334_qa_1" description = "Which generation contains the highest number of Pokémon species in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0011_271_11271761_qa_2/task.toml b/tasks/0011_271_11271761_qa_2/task.toml index 2a537d08cb63ae74b08276b3ebf48fddbdfbac7e..e94e45404b62d5ef9b822e2f016d926e0f3f8c77 100644 --- a/tasks/0011_271_11271761_qa_2/task.toml +++ b/tasks/0011_271_11271761_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0011_271_11271761_qa_2" +name = "smoldataenvs-train/0011_271_11271761_qa_2" description = "What is the ratio of abnormal cases to normal cases in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.1:1" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0011_355_11355842_qa_3/task.toml b/tasks/0011_355_11355842_qa_3/task.toml index c838441288a873cf8f4e172aa9f426c2aab7da41..92d52ace8b304a83334cda40545498528c5479cb 100644 --- a/tasks/0011_355_11355842_qa_3/task.toml +++ b/tasks/0011_355_11355842_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0011_355_11355842_qa_3" +name = "smoldataenvs-train/0011_355_11355842_qa_3" description = "Which feature in the dataset has the highest standard deviation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "degree_spondylolisthesis" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0011_355_11355842_qa_4/task.toml b/tasks/0011_355_11355842_qa_4/task.toml index 996f005f48f4ecde84ebfe0f1cec307fe972f895..ca88cf897228dcd020badf74956bdfcb11f51000 100644 --- a/tasks/0011_355_11355842_qa_4/task.toml +++ b/tasks/0011_355_11355842_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0011_355_11355842_qa_4" +name = "smoldataenvs-train/0011_355_11355842_qa_4" description = "Which feature has the highest mean value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "pelvic_radius" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0011_450_11450569_qa_4/task.toml b/tasks/0011_450_11450569_qa_4/task.toml index e0ae29de89625ae811ca6a983fb5a0058bde3a30..3faa6a03ba37e7738c4f038e42471af66c36f8db 100644 --- a/tasks/0011_450_11450569_qa_4/task.toml +++ b/tasks/0011_450_11450569_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0011_450_11450569_qa_4" +name = "smoldataenvs-train/0011_450_11450569_qa_4" description = "Which weekday for the appointment has the highest number of no-shows?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Tuesday" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0011_506_11506450_qa_4/task.toml b/tasks/0011_506_11506450_qa_4/task.toml index af240c340aff262658cead41d53673246b964588..cabe6d54d0d490a3b68652f459ee943189efb51c 100644 --- a/tasks/0011_506_11506450_qa_4/task.toml +++ b/tasks/0011_506_11506450_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0011_506_11506450_qa_4" +name = "smoldataenvs-train/0011_506_11506450_qa_4" description = "How many movies have a budget exceeding 75 million and revenue exceeding 200 million?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "325" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0011_508_11508075_qa_1/task.toml b/tasks/0011_508_11508075_qa_1/task.toml index 793c4b744e83a43d409d6968e438a963e93c03c8..4801443a1fc1feeff138e145009e9612136d5ef7 100644 --- a/tasks/0011_508_11508075_qa_1/task.toml +++ b/tasks/0011_508_11508075_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0011_508_11508075_qa_1" +name = "smoldataenvs-train/0011_508_11508075_qa_1" description = "How many unique companies in the dataset eventually become financially distressed based on the threshold of financial_distress < -0.5?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "136" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0011_544_11544512_qa_4/task.toml b/tasks/0011_544_11544512_qa_4/task.toml index d9c02efd61eea402cc4a5180f6feeca3e1beb319..5cd6080dfd20445f21b1912aef8dff706883c92e 100644 --- a/tasks/0011_544_11544512_qa_4/task.toml +++ b/tasks/0011_544_11544512_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0011_544_11544512_qa_4" +name = "smoldataenvs-train/0011_544_11544512_qa_4" description = "What is the mean value of the derived `num_rooms` feature (total rooms per household) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5.4" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0011_548_11548867_qa_2/task.toml b/tasks/0011_548_11548867_qa_2/task.toml index ca47af4a704a5507c18fcc31603b0b0b744896fc..c40c3a20d5055fac41812634d74fbe2fa86f6074 100644 --- a/tasks/0011_548_11548867_qa_2/task.toml +++ b/tasks/0011_548_11548867_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0011_548_11548867_qa_2" +name = "smoldataenvs-train/0011_548_11548867_qa_2" description = "What is the median age difference between survivors and non-survivors in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0011_587_11587305_qa_5/task.toml b/tasks/0011_587_11587305_qa_5/task.toml index b360cabd9fad2882398fa9e0f329eaabe13a6ac3..e0e0818a41c6718d5cced2a65f68c4b243b767c5 100644 --- a/tasks/0011_587_11587305_qa_5/task.toml +++ b/tasks/0011_587_11587305_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0011_587_11587305_qa_5" +name = "smoldataenvs-train/0011_587_11587305_qa_5" description = "What percentage of patients who did not survive had fewer than 10 positive lymph nodes based on the CDF analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "70" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0011_637_11637182_qa_3/task.toml b/tasks/0011_637_11637182_qa_3/task.toml index 3280d63427a0c68e7ddfac84b507e18a21da96bd..e9c62e84a566b02e70bc66bf64ba93d10ed45dcd 100644 --- a/tasks/0011_637_11637182_qa_3/task.toml +++ b/tasks/0011_637_11637182_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0011_637_11637182_qa_3" +name = "smoldataenvs-train/0011_637_11637182_qa_3" description = "What is the difference in the number of patients between the non-diabetic (Outcome = 0) group and the diabetic (Outcome = 1) group?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "232" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0011_654_11654148_qa_3/task.toml b/tasks/0011_654_11654148_qa_3/task.toml index 7b3bbc3a1330c422fc6cf72a956b11af76a2f7f6..35ab4af9752781f2b4c1269ce7d8a7ddb3d71d1e 100644 --- a/tasks/0011_654_11654148_qa_3/task.toml +++ b/tasks/0011_654_11654148_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0011_654_11654148_qa_3" +name = "smoldataenvs-train/0011_654_11654148_qa_3" description = "How many missing values are present in the 'Type 2' column of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "386" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0011_763_11763257_qa_1/task.toml b/tasks/0011_763_11763257_qa_1/task.toml index 7b1fbd65998b267b4021fa0abcf800a231370909..08b6d11f81792423b5c1af8d8b1355a6c23c5707 100644 --- a/tasks/0011_763_11763257_qa_1/task.toml +++ b/tasks/0011_763_11763257_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0011_763_11763257_qa_1" +name = "smoldataenvs-train/0011_763_11763257_qa_1" description = "What percentage of employees in the dataset have experienced attrition (Attrition = Yes)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16.12" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0011_763_11763257_qa_2/task.toml b/tasks/0011_763_11763257_qa_2/task.toml index cbf29a050d38c36f8f056dacf8ce9986c793b15c..0ccd02f33a0bfca2d1256e94e87701a834fea191 100644 --- a/tasks/0011_763_11763257_qa_2/task.toml +++ b/tasks/0011_763_11763257_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0011_763_11763257_qa_2" +name = "smoldataenvs-train/0011_763_11763257_qa_2" description = "How many categorical variables are present in the original dataset before any transformations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0011_763_11763257_qa_4/task.toml b/tasks/0011_763_11763257_qa_4/task.toml index 4e1464eb9cf1a96f68d57092f7035ba07adace81..f97da3691d999a9874f225515d505cd6a0c888c9 100644 --- a/tasks/0011_763_11763257_qa_4/task.toml +++ b/tasks/0011_763_11763257_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0011_763_11763257_qa_4" +name = "smoldataenvs-train/0011_763_11763257_qa_4" description = "Which column in the dataset is identified as having no analytical value due to its constant value across all records?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "EmployeeCount" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0011_821_11821121_qa_2/task.toml b/tasks/0011_821_11821121_qa_2/task.toml index d8dc4ac9220879a075036a49cce1b0fbcd4217cb..8e671b2a811549c5f6455f1a6a554c17df83829f 100644 --- a/tasks/0011_821_11821121_qa_2/task.toml +++ b/tasks/0011_821_11821121_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0011_821_11821121_qa_2" +name = "smoldataenvs-train/0011_821_11821121_qa_2" description = "How many training samples were used after the 50-day lookback period was applied to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1208" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0011_842_11842614_qa_1/task.toml b/tasks/0011_842_11842614_qa_1/task.toml index 01eb2b9334d500339c15c8e0ab8748e8de836a17..77b89976ddc26b297736edec6731c40f28ca869a 100644 --- a/tasks/0011_842_11842614_qa_1/task.toml +++ b/tasks/0011_842_11842614_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0011_842_11842614_qa_1" +name = "smoldataenvs-train/0011_842_11842614_qa_1" description = "How many Pokémon in the dataset have an HP (Hit Points) greater than 200?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0011_842_11842614_qa_2/task.toml b/tasks/0011_842_11842614_qa_2/task.toml index adbbe096f685ab2bc9b94e8be548a7fe23c32d8c..ec492247736c0fdecad8d8fffda33e5c18dc2ce5 100644 --- a/tasks/0011_842_11842614_qa_2/task.toml +++ b/tasks/0011_842_11842614_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0011_842_11842614_qa_2" +name = "smoldataenvs-train/0011_842_11842614_qa_2" description = "How many Pokémon in the dataset simultaneously satisfy two conditions: HP greater than 150 and Speed greater than 35?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0011_954_11954276_qa_3/task.toml b/tasks/0011_954_11954276_qa_3/task.toml index 7548457df7241e8fab6908cedfa405febad79081..05dc6dca65bcb6ea46ba7aa4aa6adbf175be2321 100644 --- a/tasks/0011_954_11954276_qa_3/task.toml +++ b/tasks/0011_954_11954276_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0011_954_11954276_qa_3" +name = "smoldataenvs-train/0011_954_11954276_qa_3" description = "What percentage of patients in the dataset have zero Positive_axillary_nodes (Positive_axillary_nodes=0)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "44" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0011_970_11970593_qa_2/task.toml b/tasks/0011_970_11970593_qa_2/task.toml index 3dc95aaeb57d8036c41e4fe398711e16a9f599f0..8a52f4fb967b93f04285cb721624ed01d5681d82 100644 --- a/tasks/0011_970_11970593_qa_2/task.toml +++ b/tasks/0011_970_11970593_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0011_970_11970593_qa_2" +name = "smoldataenvs-train/0011_970_11970593_qa_2" description = "How many entries in the dataset have a spam_score value of exactly 0?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11632" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0011_998_11998157_qa_3/task.toml b/tasks/0011_998_11998157_qa_3/task.toml index c240cc394b0524be60b75705ab7655174240840f..828be6a7b37c5fc61c4db05e70f4bc60907e24fe 100644 --- a/tasks/0011_998_11998157_qa_3/task.toml +++ b/tasks/0011_998_11998157_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0011_998_11998157_qa_3" +name = "smoldataenvs-train/0011_998_11998157_qa_3" description = "What is the average USD goal amount for all projects in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "45454.40" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0011_998_11998157_qa_4/task.toml b/tasks/0011_998_11998157_qa_4/task.toml index b1ded89d379fd753b052ff581c541db3300090a1..4aef2ea8d2f9c905d0a895dba7b0cbbc51017b9e 100644 --- a/tasks/0011_998_11998157_qa_4/task.toml +++ b/tasks/0011_998_11998157_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0011_998_11998157_qa_4" +name = "smoldataenvs-train/0011_998_11998157_qa_4" description = "What is the most frequent project state in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "failed" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0011_998_11998157_qa_5/task.toml b/tasks/0011_998_11998157_qa_5/task.toml index 0a93f2222472a55a9f6f14ac76808766b79e3d64..f9358b77b2b22418db6068a8e29383239804050d 100644 --- a/tasks/0011_998_11998157_qa_5/task.toml +++ b/tasks/0011_998_11998157_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0011_998_11998157_qa_5" +name = "smoldataenvs-train/0011_998_11998157_qa_5" description = "What is the total number of projects included in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "378661" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0012_075_12075481_qa_1/task.toml b/tasks/0012_075_12075481_qa_1/task.toml index 1351bd0cd4804931b4b0caad4a8316ff725591af..061782e7c6a84566b6ff2015a507db3f0eef8096 100644 --- a/tasks/0012_075_12075481_qa_1/task.toml +++ b/tasks/0012_075_12075481_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0012_075_12075481_qa_1" +name = "smoldataenvs-train/0012_075_12075481_qa_1" description = "What is the 95th percentile of axillary nodes for patients with a survival status of 2?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "23.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0012_075_12075481_qa_2/task.toml b/tasks/0012_075_12075481_qa_2/task.toml index 4256710d3cf2c8376f49eed092d7dd34658c1e73..a44b689c6a62e5e683bb3d47f1cd9627369af037 100644 --- a/tasks/0012_075_12075481_qa_2/task.toml +++ b/tasks/0012_075_12075481_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0012_075_12075481_qa_2" +name = "smoldataenvs-train/0012_075_12075481_qa_2" description = "What is the interquartile range (IQR) of axillary nodes for patients with a survival status of 1?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0012_075_12075481_qa_5/task.toml b/tasks/0012_075_12075481_qa_5/task.toml index 8e8f32401d220ed34d1ca3cecfcffbabad95e737..48ef33ff67772486638aaf44485b970c50733dd7 100644 --- a/tasks/0012_075_12075481_qa_5/task.toml +++ b/tasks/0012_075_12075481_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0012_075_12075481_qa_5" +name = "smoldataenvs-train/0012_075_12075481_qa_5" description = "What is the difference between the 75th percentile of axillary nodes for survival status 2 patients and survival status 1 patients?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0012_101_12101308_qa_2/task.toml b/tasks/0012_101_12101308_qa_2/task.toml index 25afaea6136bb15fa3ef311bde7eb07d3c26c953..60de78e83154041a852afad11f113e27839e2296 100644 --- a/tasks/0012_101_12101308_qa_2/task.toml +++ b/tasks/0012_101_12101308_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0012_101_12101308_qa_2" +name = "smoldataenvs-train/0012_101_12101308_qa_2" description = "Which department has the highest attrition rate according to the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Sales" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0012_101_12101336_qa_5/task.toml b/tasks/0012_101_12101336_qa_5/task.toml index 1143817bf61441e21c948a03e15cd7092417428f..42eb13a764567fba61ea85ed26ee5642b8c50a54 100644 --- a/tasks/0012_101_12101336_qa_5/task.toml +++ b/tasks/0012_101_12101336_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0012_101_12101336_qa_5" +name = "smoldataenvs-train/0012_101_12101336_qa_5" description = "What is the most common secondary type (Type 2) among all Pokémon in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Flying" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0012_112_12112066_qa_4/task.toml b/tasks/0012_112_12112066_qa_4/task.toml index 4f8396144f6de514016e9d6fa60e1ffda1bbd2d9..661bbfe4f332f8b831c2092c871ecf82fd068b85 100644 --- a/tasks/0012_112_12112066_qa_4/task.toml +++ b/tasks/0012_112_12112066_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0012_112_12112066_qa_4" +name = "smoldataenvs-train/0012_112_12112066_qa_4" description = "Which non-legendary Pokémon has the highest HP value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Blissey" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0012_112_12112975_qa_1/task.toml b/tasks/0012_112_12112975_qa_1/task.toml index 7f7dc9e6ed93a7ec43960f455eeae3fa39389f27..3e3de79b6987a7db773268313125369931958d2e 100644 --- a/tasks/0012_112_12112975_qa_1/task.toml +++ b/tasks/0012_112_12112975_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0012_112_12112975_qa_1" +name = "smoldataenvs-train/0012_112_12112975_qa_1" description = "What percentage of students in the dataset are female?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "59.01" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0012_112_12112975_qa_3/task.toml b/tasks/0012_112_12112975_qa_3/task.toml index 3b87d6507034c463f4a67cf5347206c8820ba66b..d9662ea0e0440db15f10f64ae9931eaa430215c4 100644 --- a/tasks/0012_112_12112975_qa_3/task.toml +++ b/tasks/0012_112_12112975_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0012_112_12112975_qa_3" +name = "smoldataenvs-train/0012_112_12112975_qa_3" description = "What is the size of the training dataset after splitting 20% of the data for testing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "519" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0012_161_12161282_qa_4/task.toml b/tasks/0012_161_12161282_qa_4/task.toml index f37d57736c7cea64b9ad36f1fe913da02346e7d5..e5bb4d7aab1a26121d13967fba29af4a4704e0c3 100644 --- a/tasks/0012_161_12161282_qa_4/task.toml +++ b/tasks/0012_161_12161282_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0012_161_12161282_qa_4" +name = "smoldataenvs-train/0012_161_12161282_qa_4" description = "What percentage of the dataset corresponds to diabetic outcomes (Outcome=1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0012_179_12179865_qa_4/task.toml b/tasks/0012_179_12179865_qa_4/task.toml index 9c6b4daa1153be0b3a459fdf79088cd5d1f4e6c9..7062fedd80303b2d8b0e165f3847de08e4146daf 100644 --- a/tasks/0012_179_12179865_qa_4/task.toml +++ b/tasks/0012_179_12179865_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0012_179_12179865_qa_4" +name = "smoldataenvs-train/0012_179_12179865_qa_4" description = "What is the most frequent purpose of satellites in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Communications" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0012_222_12222108_qa_2/task.toml b/tasks/0012_222_12222108_qa_2/task.toml index 4b6cb2d47827232ab5812b6295b13247f0a5147c..b458f09dbb241fb9aa33e706c8d89aacc8381800 100644 --- a/tasks/0012_222_12222108_qa_2/task.toml +++ b/tasks/0012_222_12222108_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0012_222_12222108_qa_2" +name = "smoldataenvs-train/0012_222_12222108_qa_2" description = "What is the R² score of the linear regression model predicting sacral slope from pelvic incidence in abnormal patients?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.6458" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0012_330_12330544_qa_3/task.toml b/tasks/0012_330_12330544_qa_3/task.toml index b3af49cafce0fcab69c99bffac3647065457decf..bbd9e8187f68175b92fcfe43859f4a6dd3840311 100644 --- a/tasks/0012_330_12330544_qa_3/task.toml +++ b/tasks/0012_330_12330544_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0012_330_12330544_qa_3" +name = "smoldataenvs-train/0012_330_12330544_qa_3" description = "Are the classes uniformly distributed in the training dataset based on the provided analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0012_556_12556896_qa_1/task.toml b/tasks/0012_556_12556896_qa_1/task.toml index 3bb49cdc391a5ad31b500a3d28b0dd40fe7ae183..bf209f92e34cf68f41840cc8e81e6f996899b614 100644 --- a/tasks/0012_556_12556896_qa_1/task.toml +++ b/tasks/0012_556_12556896_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0012_556_12556896_qa_1" +name = "smoldataenvs-train/0012_556_12556896_qa_1" description = "Which chemical feature shows the highest correlation with wine quality based on the correlation matrix analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "alcohol" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0012_556_12556896_qa_5/task.toml b/tasks/0012_556_12556896_qa_5/task.toml index ddf5e2183c51e29319bbce114022b9cbc8de45f2..687ff4fa7e631df98ab200ebad9fe727878e252a 100644 --- a/tasks/0012_556_12556896_qa_5/task.toml +++ b/tasks/0012_556_12556896_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0012_556_12556896_qa_5" +name = "smoldataenvs-train/0012_556_12556896_qa_5" description = "What is the most significant factor identified in the correlation analysis that could potentially influence wine quality according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "alcohol" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0012_593_12593899_qa_2/task.toml b/tasks/0012_593_12593899_qa_2/task.toml index 55dc61bdaf8bcbc73d55d1b2826d3195d239fc38..fd38650e56733cc08755df08283572f3dda1d5ee 100644 --- a/tasks/0012_593_12593899_qa_2/task.toml +++ b/tasks/0012_593_12593899_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0012_593_12593899_qa_2" +name = "smoldataenvs-train/0012_593_12593899_qa_2" description = "Which day of the month has the highest crime rate based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0012_689_12689524_qa_2/task.toml b/tasks/0012_689_12689524_qa_2/task.toml index 0358f1b11be6ab7695c620f59c85f7e7f5d6170a..7382c1062a21ad6de2d999a9733fca20c215afee 100644 --- a/tasks/0012_689_12689524_qa_2/task.toml +++ b/tasks/0012_689_12689524_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0012_689_12689524_qa_2" +name = "smoldataenvs-train/0012_689_12689524_qa_2" description = "Which pair of features exhibits the strongest positive correlation in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "radius_mean, perimeter_mean" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0012_689_12689524_qa_4/task.toml b/tasks/0012_689_12689524_qa_4/task.toml index 0d42acf29d3086de4286ad70e91217448ac8fcc4..03672186e8b9a33f8b0955735f59bf6e6af87e8f 100644 --- a/tasks/0012_689_12689524_qa_4/task.toml +++ b/tasks/0012_689_12689524_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0012_689_12689524_qa_4" +name = "smoldataenvs-train/0012_689_12689524_qa_4" description = "Which feature in the dataset shows the highest skewness in its distribution?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "area_se" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0012_748_12748314_qa_1/task.toml b/tasks/0012_748_12748314_qa_1/task.toml index 27ab98af9b271af207039a1c2ef77373ef87f018..b308472d8cabd06c24e01f3716bffa26704d8c95 100644 --- a/tasks/0012_748_12748314_qa_1/task.toml +++ b/tasks/0012_748_12748314_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0012_748_12748314_qa_1" +name = "smoldataenvs-train/0012_748_12748314_qa_1" description = "What is the survival rate percentage of patients in the Haberman Cancer Dataset based on the Surv_status class distribution?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "73.53" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0012_748_12748314_qa_3/task.toml b/tasks/0012_748_12748314_qa_3/task.toml index 054b0f703f119e9be8e035a6a85790c01d15e95c..3e6a7186148616d138871cbefbbbc62a651325c3 100644 --- a/tasks/0012_748_12748314_qa_3/task.toml +++ b/tasks/0012_748_12748314_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0012_748_12748314_qa_3" +name = "smoldataenvs-train/0012_748_12748314_qa_3" description = "What is the maximum axil_nodes count observed for a survived patient versus a not-survived patient?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "survived=46, not_survived=52" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0012_748_12748314_qa_4/task.toml b/tasks/0012_748_12748314_qa_4/task.toml index 63015e1d453b4f1cb18c8e1215b4b9efdf656a2a..027fffc9ba084f9ad9db69bef3f232fc8ae9e89c 100644 --- a/tasks/0012_748_12748314_qa_4/task.toml +++ b/tasks/0012_748_12748314_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0012_748_12748314_qa_4" +name = "smoldataenvs-train/0012_748_12748314_qa_4" description = "What is the age threshold below which 75% of survived patients fall, compared to the 75% age threshold for not-survived patients?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "60, 62" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0012_761_12761066_qa_2/task.toml b/tasks/0012_761_12761066_qa_2/task.toml index 5f7039d0c06daacf3fa55693572dd86cc080a8eb..6a66170832357910c0cda6aa49b12440aa7425d9 100644 --- a/tasks/0012_761_12761066_qa_2/task.toml +++ b/tasks/0012_761_12761066_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0012_761_12761066_qa_2" +name = "smoldataenvs-train/0012_761_12761066_qa_2" description = "Which gender has a higher median annual income based on the boxplot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Male" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0012_834_12834655_qa_4/task.toml b/tasks/0012_834_12834655_qa_4/task.toml index 9fd77463e9bd7fa06916a79f1a8f6d547db8af5c..2ae310d383086f25da305a4433df2342b95ba8b8 100644 --- a/tasks/0012_834_12834655_qa_4/task.toml +++ b/tasks/0012_834_12834655_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0012_834_12834655_qa_4" +name = "smoldataenvs-train/0012_834_12834655_qa_4" description = "What is the test set accuracy and AUC score of the decision tree model with max_depth=2?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Accuracy=0.7268, AUC=0.7880" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0012_840_12840531_qa_5/task.toml b/tasks/0012_840_12840531_qa_5/task.toml index 15ff776bd4d0f80d3a6d9570826f67e308c9e813..efb37ea736cf78616648648b077cd323a7bffa9a 100644 --- a/tasks/0012_840_12840531_qa_5/task.toml +++ b/tasks/0012_840_12840531_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0012_840_12840531_qa_5" +name = "smoldataenvs-train/0012_840_12840531_qa_5" description = "After normalization, what is the range of pixel intensity values in the training data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0 to 1" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0012_856_12856368_qa_3/task.toml b/tasks/0012_856_12856368_qa_3/task.toml index 583773b66735682d8151d606f966b33eb3db6a93..a7ee58dcf1cbd8997ebfa0d7c75444bb579bc344 100644 --- a/tasks/0012_856_12856368_qa_3/task.toml +++ b/tasks/0012_856_12856368_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0012_856_12856368_qa_3" +name = "smoldataenvs-train/0012_856_12856368_qa_3" description = "What percentage of \"Love\" categorized poems remained in the dataset after removing long poems (those with more than 245 words)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "90.8" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0012_920_12920904_qa_2/task.toml b/tasks/0012_920_12920904_qa_2/task.toml index 20ed2f176720986b3f1d83ff546934bbef1843ad..45af69214df12fd3f1e5fa447619383b993e617e 100644 --- a/tasks/0012_920_12920904_qa_2/task.toml +++ b/tasks/0012_920_12920904_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0012_920_12920904_qa_2" +name = "smoldataenvs-train/0012_920_12920904_qa_2" description = "Which Minkowski metric parameter (p) value resulted in the lowest test error rate across all evaluated values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0012_920_12920904_qa_4/task.toml b/tasks/0012_920_12920904_qa_4/task.toml index 31c50e28d402607693c9a4d1425d7ae109b810e6..b83ae98981214569094f1358b07a702dabbf8f27 100644 --- a/tasks/0012_920_12920904_qa_4/task.toml +++ b/tasks/0012_920_12920904_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0012_920_12920904_qa_4" +name = "smoldataenvs-train/0012_920_12920904_qa_4" description = "Which two features were identified as having the highest permutation importance in the trained KNN model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm, PetalWidthCm" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0012_920_12920904_qa_5/task.toml b/tasks/0012_920_12920904_qa_5/task.toml index 450fa65edf5b3cda201ccaa02e384020fed0d6a0..b3768fac4ff84a84492bdc98f018f9f49e332e96 100644 --- a/tasks/0012_920_12920904_qa_5/task.toml +++ b/tasks/0012_920_12920904_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0012_920_12920904_qa_5" +name = "smoldataenvs-train/0012_920_12920904_qa_5" description = "According to the error analysis, how does the test error rate change as the number of neighbors increases beyond the optimal point?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "increases" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0012_941_12941575_qa_2/task.toml b/tasks/0012_941_12941575_qa_2/task.toml index 9c3058dfbedd974a654253728e6330bb6fd24bd0..5d0eb2fb30a5e406370841534a0890475c20a510 100644 --- a/tasks/0012_941_12941575_qa_2/task.toml +++ b/tasks/0012_941_12941575_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0012_941_12941575_qa_2" +name = "smoldataenvs-train/0012_941_12941575_qa_2" description = "What is the highest attrition rate observed among employees with different job involvement levels?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "33.73" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0012_949_12949007_qa_3/task.toml b/tasks/0012_949_12949007_qa_3/task.toml index 1452133a82cafd6de0d226f72e65c610d27b36c4..b33c0caf0d1c87d93d3ee0421960ca2654c7069a 100644 --- a/tasks/0012_949_12949007_qa_3/task.toml +++ b/tasks/0012_949_12949007_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0012_949_12949007_qa_3" +name = "smoldataenvs-train/0012_949_12949007_qa_3" description = "What is the correlation coefficient between SepalLengthCm and PetalLengthCm in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.871754" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0012_949_12949007_qa_4/task.toml b/tasks/0012_949_12949007_qa_4/task.toml index 8a98b29b91127727b9858f8b1c9a4f6f3140211c..525a4d43bbd704d9d31065c9043b62c5315f9b2e 100644 --- a/tasks/0012_949_12949007_qa_4/task.toml +++ b/tasks/0012_949_12949007_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0012_949_12949007_qa_4" +name = "smoldataenvs-train/0012_949_12949007_qa_4" description = "Which feature has the highest absolute skewness value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "SepalWidthCm" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0012_972_12972339_qa_3/task.toml b/tasks/0012_972_12972339_qa_3/task.toml index 607eaefb1f0c5835b384ca65361a228c253ad672..d3825ca4d5c9d0158518801d15c5fa4372152926 100644 --- a/tasks/0012_972_12972339_qa_3/task.toml +++ b/tasks/0012_972_12972339_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0012_972_12972339_qa_3" +name = "smoldataenvs-train/0012_972_12972339_qa_3" description = "What is the kurtosis value for the Petal Length feature in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-1.401921" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0012_992_12992267_qa_3/task.toml b/tasks/0012_992_12992267_qa_3/task.toml index eb06b333eb56391c1336f8ae8770be12e87d95d5..43c90234421065a9db6d74be76517542f99ac6b9 100644 --- a/tasks/0012_992_12992267_qa_3/task.toml +++ b/tasks/0012_992_12992267_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0012_992_12992267_qa_3" +name = "smoldataenvs-train/0012_992_12992267_qa_3" description = "What is the highest accuracy achieved by the KNN model using the optimal k value (13) on the test dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0012_992_12992267_qa_5/task.toml b/tasks/0012_992_12992267_qa_5/task.toml index 99117b2a91908dc5578fe88828aba8eba1a1c249..e138589c4dcf64f3027b3b9e9e4d3b01958bb120 100644 --- a/tasks/0012_992_12992267_qa_5/task.toml +++ b/tasks/0012_992_12992267_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0012_992_12992267_qa_5" +name = "smoldataenvs-train/0012_992_12992267_qa_5" description = "Which feature in the Iris dataset has the most platykurtic distribution (most negative kurtosis value), and what is this kurtosis value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm, -1.401921" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0013_067_13067540_qa_4/task.toml b/tasks/0013_067_13067540_qa_4/task.toml index e810b1d136a5af82e2529bb94d82c321fef46752..f7735b79a50081bf228521371ccd9df17c04082e 100644 --- a/tasks/0013_067_13067540_qa_4/task.toml +++ b/tasks/0013_067_13067540_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0013_067_13067540_qa_4" +name = "smoldataenvs-train/0013_067_13067540_qa_4" description = "What method was used to handle the 4 missing values in the 'Albumin_and_Globulin_Ratio' column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Replaced with the column mean" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0013_072_13072460_qa_4/task.toml b/tasks/0013_072_13072460_qa_4/task.toml index b14a8b59259b644087414f7a5b64f78072147b6b..c8f49b911ab3cf713d6072a464033af81049a3e1 100644 --- a/tasks/0013_072_13072460_qa_4/task.toml +++ b/tasks/0013_072_13072460_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0013_072_13072460_qa_4" +name = "smoldataenvs-train/0013_072_13072460_qa_4" description = "What is the average waiting time (in days) for the 'quarter' (31-90 days) waiting time category?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "45.45" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0013_081_13081151_qa_1/task.toml b/tasks/0013_081_13081151_qa_1/task.toml index 4fb86924b7032f8b7f2b5ac09fd669b4e642ee8a..ecfdbee7bb3ed82a9a49a178ccbc366f869fd0d6 100644 --- a/tasks/0013_081_13081151_qa_1/task.toml +++ b/tasks/0013_081_13081151_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0013_081_13081151_qa_1" +name = "smoldataenvs-train/0013_081_13081151_qa_1" description = "How many diamonds in the dataset have a carat weight greater than 3.5 carats?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0013_081_13081725_qa_3/task.toml b/tasks/0013_081_13081725_qa_3/task.toml index 223441005ca38ee28cdc42795e9ee68ce36f856d..5881e22b425f0e7c4982cd407706e31b1362d851 100644 --- a/tasks/0013_081_13081725_qa_3/task.toml +++ b/tasks/0013_081_13081725_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0013_081_13081725_qa_3" +name = "smoldataenvs-train/0013_081_13081725_qa_3" description = "How many countries in the dataset have an average temperature above 15°C from 1970 to 2013?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0013_126_13126909_qa_2/task.toml b/tasks/0013_126_13126909_qa_2/task.toml index 9b3ec9495de9c8f53041ce2aa29af0212358d674..18e8808e94e21da46f6d825690052e2a19d7e351 100644 --- a/tasks/0013_126_13126909_qa_2/task.toml +++ b/tasks/0013_126_13126909_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0013_126_13126909_qa_2" +name = "smoldataenvs-train/0013_126_13126909_qa_2" description = "What percentage of the dataset's variance is captured by the first principal component after PCA transformation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "72.77" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0013_126_13126909_qa_3/task.toml b/tasks/0013_126_13126909_qa_3/task.toml index eef41af9fbb864b054d590317e79b158e52f715b..d90c2ee6f687f57bc1bac6dc190e81146c990802 100644 --- a/tasks/0013_126_13126909_qa_3/task.toml +++ b/tasks/0013_126_13126909_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0013_126_13126909_qa_3" +name = "smoldataenvs-train/0013_126_13126909_qa_3" description = "How many principal components are required to capture at least 95% of the total variance in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0013_126_13126909_qa_4/task.toml b/tasks/0013_126_13126909_qa_4/task.toml index c49c3a0a61fd6c623b0d0cd2ef94573ee72e874d..b5acc6298e24cf8266504ca183e61b6f97e5e2f9 100644 --- a/tasks/0013_126_13126909_qa_4/task.toml +++ b/tasks/0013_126_13126909_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0013_126_13126909_qa_4" +name = "smoldataenvs-train/0013_126_13126909_qa_4" description = "Are the three Iris species well-separated in the 2D PCA projection visualization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0013_169_13169934_qa_5/task.toml b/tasks/0013_169_13169934_qa_5/task.toml index 9becf6dcb87b3dabe0f31e26c871bc3dd84958c8..393301c1e3c6b5a5a9eac64ed700dc81a329ae90 100644 --- a/tasks/0013_169_13169934_qa_5/task.toml +++ b/tasks/0013_169_13169934_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0013_169_13169934_qa_5" +name = "smoldataenvs-train/0013_169_13169934_qa_5" description = "Which team has a higher average number of inhibitor kills when they win the game according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Team 1" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0013_170_13170004_qa_5/task.toml b/tasks/0013_170_13170004_qa_5/task.toml index cfa9de80c57a07addbbb80c342d43a30346c4515..acebd439dd446d4f3aee9d44c2d515395a76a954 100644 --- a/tasks/0013_170_13170004_qa_5/task.toml +++ b/tasks/0013_170_13170004_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0013_170_13170004_qa_5" +name = "smoldataenvs-train/0013_170_13170004_qa_5" description = "What is the average engine displacement measured in liters (DispLitr) after feature engineering and data normalization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.1858" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0013_225_13225334_qa_2/task.toml b/tasks/0013_225_13225334_qa_2/task.toml index 1e185ada4aee6293e6a84c746e64e7ff6b303357..9bf8a520efd0af8fa364add3eb867f8d36f41135 100644 --- a/tasks/0013_225_13225334_qa_2/task.toml +++ b/tasks/0013_225_13225334_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0013_225_13225334_qa_2" +name = "smoldataenvs-train/0013_225_13225334_qa_2" description = "What is the total number of missing values in the 'Item_Weight' column before imputation in the combined dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2439" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0013_310_13310368_qa_1/task.toml b/tasks/0013_310_13310368_qa_1/task.toml index b99c9d928c2f38ea5f9c15f72a3929ed27319ac3..782d108e801b8cb651b763668ac3bf13f94661d5 100644 --- a/tasks/0013_310_13310368_qa_1/task.toml +++ b/tasks/0013_310_13310368_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0013_310_13310368_qa_1" +name = "smoldataenvs-train/0013_310_13310368_qa_1" description = "What is the difference in the number of wins between the Blue team and the Red team in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "664" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0013_355_13355821_qa_2/task.toml b/tasks/0013_355_13355821_qa_2/task.toml index 2cf570e5b27f7303b0f67726999084b23df12f08..7293362f13a916dc3f6609d78f9f6d453e02ca6f 100644 --- a/tasks/0013_355_13355821_qa_2/task.toml +++ b/tasks/0013_355_13355821_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0013_355_13355821_qa_2" +name = "smoldataenvs-train/0013_355_13355821_qa_2" description = "How many countries have both a Happiness.Score greater than 7.0 and a Family score greater than 1.5?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0013_408_13408860_qa_5/task.toml b/tasks/0013_408_13408860_qa_5/task.toml index 5cddeb4cba7af8d972bdb9160f08bf89eb22ef18..7ad3188d00a4a0c6a28ac8205024b72a2b4f763e 100644 --- a/tasks/0013_408_13408860_qa_5/task.toml +++ b/tasks/0013_408_13408860_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0013_408_13408860_qa_5" +name = "smoldataenvs-train/0013_408_13408860_qa_5" description = "What is the range of 'alcohol' content (maximum minus minimum) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0013_444_13444633_qa_2/task.toml b/tasks/0013_444_13444633_qa_2/task.toml index 25597e912aea16b16609db5e61d89d12e6dbb6ed..9648c308ccb2751924bff08424506ba8e92d4d24 100644 --- a/tasks/0013_444_13444633_qa_2/task.toml +++ b/tasks/0013_444_13444633_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0013_444_13444633_qa_2" +name = "smoldataenvs-train/0013_444_13444633_qa_2" description = "Which three features have the highest positive correlation with house prices?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "sqft_living, grade, sqft_above" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0013_444_13444633_qa_4/task.toml b/tasks/0013_444_13444633_qa_4/task.toml index 58384868b495725b1517316f77d289f70033c14d..4208db6698cd6f4dbdba80d8e02bf4841acb60b6 100644 --- a/tasks/0013_444_13444633_qa_4/task.toml +++ b/tasks/0013_444_13444633_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0013_444_13444633_qa_4" +name = "smoldataenvs-train/0013_444_13444633_qa_4" description = "How many houses were sold in the month with the highest average price?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2231" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0013_457_13457318_qa_3/task.toml b/tasks/0013_457_13457318_qa_3/task.toml index a7cb8ef6d64065a8016a595d3379af2f7f9ca42c..105e7eebc079fe9279c07a94c9319b3bac55c767 100644 --- a/tasks/0013_457_13457318_qa_3/task.toml +++ b/tasks/0013_457_13457318_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0013_457_13457318_qa_3" +name = "smoldataenvs-train/0013_457_13457318_qa_3" description = "Which loan purpose category has the highest number of \"good\" risk credit holders among female applicants?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "radio/TV" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0013_462_13462877_qa_1/task.toml b/tasks/0013_462_13462877_qa_1/task.toml index a51bec600270cb51908c09632e6b2c08a7e1d810..05c2b3afc5a6365783ae96e8b718e86b2e34c7ea 100644 --- a/tasks/0013_462_13462877_qa_1/task.toml +++ b/tasks/0013_462_13462877_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0013_462_13462877_qa_1" +name = "smoldataenvs-train/0013_462_13462877_qa_1" description = "What percentage of the 'Unnamed: 32' column in the original dataset contained missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "100" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0013_523_13523088_qa_4/task.toml b/tasks/0013_523_13523088_qa_4/task.toml index 434ffa80598e2d85d6f653b2ea7ec02140ac99ef..4251904fc12965c8ce6d41b2a9b6f35dab2809e4 100644 --- a/tasks/0013_523_13523088_qa_4/task.toml +++ b/tasks/0013_523_13523088_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0013_523_13523088_qa_4" +name = "smoldataenvs-train/0013_523_13523088_qa_4" description = "What is the interquartile range (IQR) for the SepalWidthCm feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.5" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0013_523_13523088_qa_5/task.toml b/tasks/0013_523_13523088_qa_5/task.toml index 3d0b110f8ba6b0a36bdd544eee15bae1542bc7ee..26bbfdd393604a8e9241d5cf175e61eec018596b 100644 --- a/tasks/0013_523_13523088_qa_5/task.toml +++ b/tasks/0013_523_13523088_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0013_523_13523088_qa_5" +name = "smoldataenvs-train/0013_523_13523088_qa_5" description = "What is the correlation coefficient between Sepal Length and Petal Length in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.871754" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0013_604_13604091_qa_2/task.toml b/tasks/0013_604_13604091_qa_2/task.toml index 955ddbbf6fa3baf72af2ede71570ef76e774f8ca..3ca408b55c761dd6428a96c29c7837dd7e9c09c3 100644 --- a/tasks/0013_604_13604091_qa_2/task.toml +++ b/tasks/0013_604_13604091_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0013_604_13604091_qa_2" +name = "smoldataenvs-train/0013_604_13604091_qa_2" description = "What proportion of all user interactions in the dataset represent completed purchase transactions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.008148" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0013_613_13613426_qa_5/task.toml b/tasks/0013_613_13613426_qa_5/task.toml index dd20c19e64df41e24ed77c666d479e31888dafe2..ae4833c6ed284beb7de63dd7bc58140d2d60dab9 100644 --- a/tasks/0013_613_13613426_qa_5/task.toml +++ b/tasks/0013_613_13613426_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0013_613_13613426_qa_5" +name = "smoldataenvs-train/0013_613_13613426_qa_5" description = "What is the class distribution of the Species attribute in the Iris dataset, as determined by the group size analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-setosa: 50, Iris-versicolor: 50, Iris-virginica: 50" reward_mode_initial = "list" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0013_655_13655596_qa_1/task.toml b/tasks/0013_655_13655596_qa_1/task.toml index c7b2f8db8b8bc5e151506d9d61cf00099a15a771..967cc6c09b1a20a7b728f7e7eb48c36f612180de 100644 --- a/tasks/0013_655_13655596_qa_1/task.toml +++ b/tasks/0013_655_13655596_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0013_655_13655596_qa_1" +name = "smoldataenvs-train/0013_655_13655596_qa_1" description = "What is the predicted sentiment for the user input \"Not happy with the flight, too boring and late\"?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0013_708_13708498_qa_4/task.toml b/tasks/0013_708_13708498_qa_4/task.toml index 0d6254e114bf1c9734efd71c5c35ce296831c61c..057343563e720d367c6bed2dc7a57cdf550df03c 100644 --- a/tasks/0013_708_13708498_qa_4/task.toml +++ b/tasks/0013_708_13708498_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0013_708_13708498_qa_4" +name = "smoldataenvs-train/0013_708_13708498_qa_4" description = "What is the total number of sentences in the dataset after processing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "35177" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0013_733_13733964_qa_1/task.toml b/tasks/0013_733_13733964_qa_1/task.toml index 3a7dee364f29f72d50bbabbfde0ef1d879dd860f..3dcd6d72606be1413ba67dc55ba94614ced35e73 100644 --- a/tasks/0013_733_13733964_qa_1/task.toml +++ b/tasks/0013_733_13733964_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0013_733_13733964_qa_1" +name = "smoldataenvs-train/0013_733_13733964_qa_1" description = "What is the highest range among the continuous variables in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "18497" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0013_733_13733964_qa_2/task.toml b/tasks/0013_733_13733964_qa_2/task.toml index 949388c7fc082905ea4040edabb9b2211fd11560..5551a808dd331dbfcffe757d390838512338b180 100644 --- a/tasks/0013_733_13733964_qa_2/task.toml +++ b/tasks/0013_733_13733964_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0013_733_13733964_qa_2" +name = "smoldataenvs-train/0013_733_13733964_qa_2" description = "Which continuous variable's distribution is described as having the highest positive skewness based on the histogram analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "price" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0013_733_13733964_qa_3/task.toml b/tasks/0013_733_13733964_qa_3/task.toml index 70a851d61fde3f7bd64e38258b8ffd1988f787ab..610d299de1d9bdb4f35f2ac0bcc25755bd72947d 100644 --- a/tasks/0013_733_13733964_qa_3/task.toml +++ b/tasks/0013_733_13733964_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0013_733_13733964_qa_3" +name = "smoldataenvs-train/0013_733_13733964_qa_3" description = "What is the minimum value of the carat variable in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0013_733_13733964_qa_4/task.toml b/tasks/0013_733_13733964_qa_4/task.toml index 872315afdbee5d7c0a7eb6f241c9d9bb76a7c66c..47763f52221fef06ca08732b8d7ba9e6a615b3ca 100644 --- a/tasks/0013_733_13733964_qa_4/task.toml +++ b/tasks/0013_733_13733964_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0013_733_13733964_qa_4" +name = "smoldataenvs-train/0013_733_13733964_qa_4" description = "What is the maximum value of the table variable in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "95.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0013_733_13733964_qa_5/task.toml b/tasks/0013_733_13733964_qa_5/task.toml index 0f3d0524342af6619a5e4a898d0f364f3b615938..33e7cfd2d6dfb89178e87c406d6d70c1e6f4574e 100644 --- a/tasks/0013_733_13733964_qa_5/task.toml +++ b/tasks/0013_733_13733964_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0013_733_13733964_qa_5" +name = "smoldataenvs-train/0013_733_13733964_qa_5" description = "What is the range of the carat variable in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.81" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0013_748_13748223_qa_1/task.toml b/tasks/0013_748_13748223_qa_1/task.toml index d4a3fbbb9eb5ceee63d7f6ecac0203801a2de8d4..eed08d137b2a2f502ef630bd5af23475268292f0 100644 --- a/tasks/0013_748_13748223_qa_1/task.toml +++ b/tasks/0013_748_13748223_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0013_748_13748223_qa_1" +name = "smoldataenvs-train/0013_748_13748223_qa_1" description = "Which video game has the highest global sales according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii Sports" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0013_769_13769567_qa_2/task.toml b/tasks/0013_769_13769567_qa_2/task.toml index e053ff500b9596e176f9c266a170f6b0e93c185e..e83515cc2c3221b60d5715cb9e20e6f78204ff24 100644 --- a/tasks/0013_769_13769567_qa_2/task.toml +++ b/tasks/0013_769_13769567_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0013_769_13769567_qa_2" +name = "smoldataenvs-train/0013_769_13769567_qa_2" description = "What year had the highest global sales according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1989" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0013_769_13769567_qa_5/task.toml b/tasks/0013_769_13769567_qa_5/task.toml index d206b8fe539182f0bb64006c62a0038bf8b2d2e8..d2124f5e6fb6f444e72b02216976953644fcad85 100644 --- a/tasks/0013_769_13769567_qa_5/task.toml +++ b/tasks/0013_769_13769567_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0013_769_13769567_qa_5" +name = "smoldataenvs-train/0013_769_13769567_qa_5" description = "What is the most frequently occurring video game genre in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0013_791_13791869_qa_3/task.toml b/tasks/0013_791_13791869_qa_3/task.toml index 9f68d0c8c36fad794c766f22d5a2975aad79cef8..04688ab9f5e266d158805a0de07e0027a40d03e5 100644 --- a/tasks/0013_791_13791869_qa_3/task.toml +++ b/tasks/0013_791_13791869_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0013_791_13791869_qa_3" +name = "smoldataenvs-train/0013_791_13791869_qa_3" description = "Which job role category shows the highest correlation with employee attrition?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Sales Representative" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0013_811_13811868_qa_3/task.toml b/tasks/0013_811_13811868_qa_3/task.toml index 604bf584e7f90662ad89aefe406d1c0df05afa6c..7d6a2a9fb9608a11f1f895ac18e9e84f17387ced 100644 --- a/tasks/0013_811_13811868_qa_3/task.toml +++ b/tasks/0013_811_13811868_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0013_811_13811868_qa_3" +name = "smoldataenvs-train/0013_811_13811868_qa_3" description = "What is the lowest total score among all non-Legendary Pokémon, and which Pokémon has this score?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "180, Sunkern" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0013_811_13811868_qa_5/task.toml b/tasks/0013_811_13811868_qa_5/task.toml index 4ed3e9e0984f7f52b4cb1bad8bb4e7c2301e986f..555c3cc1326846ebddf6ce84cf6a1fb928b0d7d6 100644 --- a/tasks/0013_811_13811868_qa_5/task.toml +++ b/tasks/0013_811_13811868_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0013_811_13811868_qa_5" +name = "smoldataenvs-train/0013_811_13811868_qa_5" description = "Which generation has the highest average total score?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0013_821_13821679_qa_4/task.toml b/tasks/0013_821_13821679_qa_4/task.toml index 6333d35ef000dc27176f2da8fb87a830cdd7f940..ad1c3a3545cb1d25cdaacea3a909c26e99ca114a 100644 --- a/tasks/0013_821_13821679_qa_4/task.toml +++ b/tasks/0013_821_13821679_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0013_821_13821679_qa_4" +name = "smoldataenvs-train/0013_821_13821679_qa_4" description = "How many non-legendary Pokémon have both Type 1 as Water and Type 2 as Flying?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0013_826_13826694_qa_5/task.toml b/tasks/0013_826_13826694_qa_5/task.toml index 5f07343c01c8052c3f57fb7dad3d039dda46dfbe..094668aa5d4cfa004e37c4bae02a8992c037a5c8 100644 --- a/tasks/0013_826_13826694_qa_5/task.toml +++ b/tasks/0013_826_13826694_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0013_826_13826694_qa_5" +name = "smoldataenvs-train/0013_826_13826694_qa_5" description = "How many features in the dataset are categorized as standard error (SE) measurements?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0013_839_13839617_qa_5/task.toml b/tasks/0013_839_13839617_qa_5/task.toml index 58a906e5eb6d2c25b0a8852159860e1956e00a49..afb993550648b1f4545bcd0b0dc4a8b2eaf7017e 100644 --- a/tasks/0013_839_13839617_qa_5/task.toml +++ b/tasks/0013_839_13839617_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0013_839_13839617_qa_5" +name = "smoldataenvs-train/0013_839_13839617_qa_5" description = "What is the median age of patients in the dataset after removing missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "45" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0013_884_13884693_qa_1/task.toml b/tasks/0013_884_13884693_qa_1/task.toml index 81ecf993b647225856554cce56b065188fa9d786..06451b5e4d57424e93e9aebf1d27ffb4c2bfb2cd 100644 --- a/tasks/0013_884_13884693_qa_1/task.toml +++ b/tasks/0013_884_13884693_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0013_884_13884693_qa_1" +name = "smoldataenvs-train/0013_884_13884693_qa_1" description = "What is the number of diabetic patients in the dataset based on the Outcome distribution?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "268" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0013_911_13911248_qa_1/task.toml b/tasks/0013_911_13911248_qa_1/task.toml index 09a0ea7d7427c3db8cba7f18f96beec63cd4b455..9ea26d1f8350f87013247e876e1f31e3874b9f43 100644 --- a/tasks/0013_911_13911248_qa_1/task.toml +++ b/tasks/0013_911_13911248_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0013_911_13911248_qa_1" +name = "smoldataenvs-train/0013_911_13911248_qa_1" description = "What is the difference in mean values of positive_axillary_nodes between patients who did not survive (class \"no\") and those who survived (class \"yes\") after 5 years of treatment?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.6657" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0014_089_14089673_qa_5/task.toml b/tasks/0014_089_14089673_qa_5/task.toml index 5bb4652767ff21df4e6f94e3483e6d72577b10fe..9014787a2a9339930bb18601659be358a8a7b4b4 100644 --- a/tasks/0014_089_14089673_qa_5/task.toml +++ b/tasks/0014_089_14089673_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0014_089_14089673_qa_5" +name = "smoldataenvs-train/0014_089_14089673_qa_5" description = "What is the total number of bombing missions recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "178281" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0014_115_14115737_qa_1/task.toml b/tasks/0014_115_14115737_qa_1/task.toml index 450d8ac7e6fb87abe8997cb1c4bf7c8e3bacbd94..20f52d101953da49d24abb819a336f4e5b95a70a 100644 --- a/tasks/0014_115_14115737_qa_1/task.toml +++ b/tasks/0014_115_14115737_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0014_115_14115737_qa_1" +name = "smoldataenvs-train/0014_115_14115737_qa_1" description = "What is the highest global sales value recorded for any video game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "82.74" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0014_124_14124969_qa_5/task.toml b/tasks/0014_124_14124969_qa_5/task.toml index 9157ab657aba0ddc0d3595fb35dbb8ef76d79b78..5b1b9e943142e1a739a51843d25ae7c90f8c5041 100644 --- a/tasks/0014_124_14124969_qa_5/task.toml +++ b/tasks/0014_124_14124969_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0014_124_14124969_qa_5" +name = "smoldataenvs-train/0014_124_14124969_qa_5" description = "What is the maximum 'Age' value among the detected outliers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "81" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0014_126_14126772_qa_4/task.toml b/tasks/0014_126_14126772_qa_4/task.toml index 1a66ead4ae845873d81c4578b6089b5005ff6297..b641efcee40c94a929f9256039f46ef71495882c 100644 --- a/tasks/0014_126_14126772_qa_4/task.toml +++ b/tasks/0014_126_14126772_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0014_126_14126772_qa_4" +name = "smoldataenvs-train/0014_126_14126772_qa_4" description = "What is the total number of neutral sentiment tweets in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3099" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0014_153_14153015_qa_3/task.toml b/tasks/0014_153_14153015_qa_3/task.toml index 3838221a1640f1ed45a68926a4687ff82795e88b..7170d73f0fa8ae63b2c5f29175e01f4ebad95770 100644 --- a/tasks/0014_153_14153015_qa_3/task.toml +++ b/tasks/0014_153_14153015_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0014_153_14153015_qa_3" +name = "smoldataenvs-train/0014_153_14153015_qa_3" description = "What is the maximum recorded number of suicides for any single category in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "63343" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0014_164_14164509_qa_1/task.toml b/tasks/0014_164_14164509_qa_1/task.toml index 4d0fe82b31d54e2c17dc77803005b61fa3012ddc..94b307632723c7bc12958f3085bd4b81254699af 100644 --- a/tasks/0014_164_14164509_qa_1/task.toml +++ b/tasks/0014_164_14164509_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0014_164_14164509_qa_1" +name = "smoldataenvs-train/0014_164_14164509_qa_1" description = "What is the correlation coefficient between wine scores and description lengths in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.56" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0014_211_14211731_qa_3/task.toml b/tasks/0014_211_14211731_qa_3/task.toml index 25b192ea5cbad1768b909660fcc274ce91013c9b..c3adb4b95882dfd6221b63a7dacf61fb1bcec271 100644 --- a/tasks/0014_211_14211731_qa_3/task.toml +++ b/tasks/0014_211_14211731_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0014_211_14211731_qa_3" +name = "smoldataenvs-train/0014_211_14211731_qa_3" description = "What is the standard deviation of the number of axillary nodes in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7.18" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0014_211_14211731_qa_5/task.toml b/tasks/0014_211_14211731_qa_5/task.toml index cb5211d8e1e34bf9faaa942b98aeb002e11bd6c0..713019ca36a6f9727fb6d2a72f304673a68213c0 100644 --- a/tasks/0014_211_14211731_qa_5/task.toml +++ b/tasks/0014_211_14211731_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0014_211_14211731_qa_5" +name = "smoldataenvs-train/0014_211_14211731_qa_5" description = "What is the median age of patients in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "52.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0014_221_14221675_qa_1/task.toml b/tasks/0014_221_14221675_qa_1/task.toml index ece2466ba39ea411e5413aba1f9ed53d241dab0c..1f050d4286d3b915373fe63058c7efd8e01f6760 100644 --- a/tasks/0014_221_14221675_qa_1/task.toml +++ b/tasks/0014_221_14221675_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0014_221_14221675_qa_1" +name = "smoldataenvs-train/0014_221_14221675_qa_1" description = "What is the highest correlation coefficient between any feature and the price range in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.917046" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0014_312_14312130_qa_4/task.toml b/tasks/0014_312_14312130_qa_4/task.toml index 73dd79252d81eb7c8091a08425ce63a76374acaa..bac8220ec8bfea75c2d6ae15d5d8cb199bef3603 100644 --- a/tasks/0014_312_14312130_qa_4/task.toml +++ b/tasks/0014_312_14312130_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0014_312_14312130_qa_4" +name = "smoldataenvs-train/0014_312_14312130_qa_4" description = "Which country was specifically identified for experimental testing due to having both high infant deaths and low HepatitisB immunization coverage?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "India" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0014_312_14312432_qa_4/task.toml b/tasks/0014_312_14312432_qa_4/task.toml index 114286976302e64dd1b1efb257b6f99b220fe09b..191bd83fd8e12d4b43fb4d79fed85df7c4db173a 100644 --- a/tasks/0014_312_14312432_qa_4/task.toml +++ b/tasks/0014_312_14312432_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0014_312_14312432_qa_4" +name = "smoldataenvs-train/0014_312_14312432_qa_4" description = "Which country was selected for the A/B testing experiment based on the analysis of HepatitisB immunization coverage and infant death rates?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "India" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0014_341_14341598_qa_1/task.toml b/tasks/0014_341_14341598_qa_1/task.toml index 13f119c4e25a4487a1a129cc87faf34728d82ca2..3092576d0058c3b06ca579774ad5b71f73450773 100644 --- a/tasks/0014_341_14341598_qa_1/task.toml +++ b/tasks/0014_341_14341598_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0014_341_14341598_qa_1" +name = "smoldataenvs-train/0014_341_14341598_qa_1" description = "What is the percentage of Malignant (M) cases in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "37.3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0014_351_14351012_qa_2/task.toml b/tasks/0014_351_14351012_qa_2/task.toml index 0a7199f133a7b47fd6e2ec0b20ff9c2918946830..01a3a86fe61b6798175da3dec0d05571d4150a12 100644 --- a/tasks/0014_351_14351012_qa_2/task.toml +++ b/tasks/0014_351_14351012_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0014_351_14351012_qa_2" +name = "smoldataenvs-train/0014_351_14351012_qa_2" description = "What was the threshold value used to classify days as \"high humidity\" based on the relative humidity measurement at 3pm?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "24.99" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0014_351_14351012_qa_5/task.toml b/tasks/0014_351_14351012_qa_5/task.toml index 3a9f1e3ea1bf1bf588b53d87ee3c02e045726404..7fef5699cb0497acda106769cac2bc0e9313ffa3 100644 --- a/tasks/0014_351_14351012_qa_5/task.toml +++ b/tasks/0014_351_14351012_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0014_351_14351012_qa_5" +name = "smoldataenvs-train/0014_351_14351012_qa_5" description = "What is the proportion of high humidity days in the training set as indicated by the \"high_humidity_label\"?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.494" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0014_358_14358635_qa_1/task.toml b/tasks/0014_358_14358635_qa_1/task.toml index 5335177764da559b135a672a93c492ecf313399c..8ed17de128f2ad1266cfc1992d56aee7734b28f8 100644 --- a/tasks/0014_358_14358635_qa_1/task.toml +++ b/tasks/0014_358_14358635_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0014_358_14358635_qa_1" +name = "smoldataenvs-train/0014_358_14358635_qa_1" description = "Which feature has the highest Pearson correlation coefficient with median house value after creating the rooms_per_household derived feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "median_income" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0014_358_14358635_qa_4/task.toml b/tasks/0014_358_14358635_qa_4/task.toml index bc43760cd9cbad034dec99e3f07ff114a44afe4b..301729c73ee8eedf14a94d6d7b6d0d37a9bc1fc5 100644 --- a/tasks/0014_358_14358635_qa_4/task.toml +++ b/tasks/0014_358_14358635_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0014_358_14358635_qa_4" +name = "smoldataenvs-train/0014_358_14358635_qa_4" description = "Which feature shows the strongest linear relationship with median_house_value in the scatter matrix analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "median_income" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0014_367_14367970_qa_1/task.toml b/tasks/0014_367_14367970_qa_1/task.toml index 26265548437becb650f79412a4f3eb3091db1011..19ef39e628bf6ea4115ea2d7b77ea14a3ee57939 100644 --- a/tasks/0014_367_14367970_qa_1/task.toml +++ b/tasks/0014_367_14367970_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0014_367_14367970_qa_1" +name = "smoldataenvs-train/0014_367_14367970_qa_1" description = "What percentage of patients did not show up for their appointments in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20.19" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0014_390_14390255_qa_4/task.toml b/tasks/0014_390_14390255_qa_4/task.toml index 9abec6eeb2330e1b14efdae2b6ef1a4ae3a7f303..431ff5901c0c984469f38bce080ffb166baac38c 100644 --- a/tasks/0014_390_14390255_qa_4/task.toml +++ b/tasks/0014_390_14390255_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0014_390_14390255_qa_4" +name = "smoldataenvs-train/0014_390_14390255_qa_4" description = "What percentage of students in the Portuguese dataset attend the Gabriel Pereira school (GP)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "65.18" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0014_390_14390255_qa_5/task.toml b/tasks/0014_390_14390255_qa_5/task.toml index 0588c247f18eb8f0a33781aa28076321723f7024..c1e507a8ab0f67e8c7ae5e9deee86b5d48f263de 100644 --- a/tasks/0014_390_14390255_qa_5/task.toml +++ b/tasks/0014_390_14390255_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0014_390_14390255_qa_5" +name = "smoldataenvs-train/0014_390_14390255_qa_5" description = "What percentage of students in the Portuguese dataset have parents who live together (Pstatus=T)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "87.67" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0014_394_14394486_qa_3/task.toml b/tasks/0014_394_14394486_qa_3/task.toml index 97027b6ae58b77470d223c9bf0de68cd3a92f856..79233bef500a2ba815809845d44a45030ef0bb11 100644 --- a/tasks/0014_394_14394486_qa_3/task.toml +++ b/tasks/0014_394_14394486_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0014_394_14394486_qa_3" +name = "smoldataenvs-train/0014_394_14394486_qa_3" description = "What is the total number of samples in the merged training and test datasets?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "70000" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0014_428_14428721_qa_1/task.toml b/tasks/0014_428_14428721_qa_1/task.toml index 384de4a62ce92709d5ee214bce22eea6b0c144b5..2a9d960ec3a6a7c9606ab7371c8e180dae8d143e 100644 --- a/tasks/0014_428_14428721_qa_1/task.toml +++ b/tasks/0014_428_14428721_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0014_428_14428721_qa_1" +name = "smoldataenvs-train/0014_428_14428721_qa_1" description = "Which Pokémon generation has the highest average total stats (HP, Attack, Defense, Sp. Atk, Sp. Def, Speed)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0014_428_14428721_qa_5/task.toml b/tasks/0014_428_14428721_qa_5/task.toml index 37622be3ab352b6e4d8c74ae07c3e8046cdf33ea..bbe9073204f7d2a9aca9cee513a9669fc9c91eea 100644 --- a/tasks/0014_428_14428721_qa_5/task.toml +++ b/tasks/0014_428_14428721_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0014_428_14428721_qa_5" +name = "smoldataenvs-train/0014_428_14428721_qa_5" description = "Which Pokémon generation has the highest proportion of legendary Pokémon?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0014_432_14432687_qa_3/task.toml b/tasks/0014_432_14432687_qa_3/task.toml index e2251ce6b20efef0e60413371aac2d42da928283..558c4bdfb467c2412ee11c927fb67a53d8de8c3e 100644 --- a/tasks/0014_432_14432687_qa_3/task.toml +++ b/tasks/0014_432_14432687_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0014_432_14432687_qa_3" +name = "smoldataenvs-train/0014_432_14432687_qa_3" description = "What is the standard deviation of the Petal Length feature before standardization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.764" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0014_469_14469283_qa_1/task.toml b/tasks/0014_469_14469283_qa_1/task.toml index a5c4776020a2e7b38a6041dc88ee0d07d8faaa80..2b8c98e525bc3aa79d2ae96447571db144934d88 100644 --- a/tasks/0014_469_14469283_qa_1/task.toml +++ b/tasks/0014_469_14469283_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0014_469_14469283_qa_1" +name = "smoldataenvs-train/0014_469_14469283_qa_1" description = "Does the Iris dataset contain any missing values in its features?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0014_469_14469283_qa_3/task.toml b/tasks/0014_469_14469283_qa_3/task.toml index d1a6f62efdb3f105fe34cd2459b5b512ff853681..f90c2196de5d442d7188d254cf008bc9361da9c3 100644 --- a/tasks/0014_469_14469283_qa_3/task.toml +++ b/tasks/0014_469_14469283_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0014_469_14469283_qa_3" +name = "smoldataenvs-train/0014_469_14469283_qa_3" description = "What is the shape of the standardized training input features after data preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "(112, 4)" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0014_469_14469283_qa_4/task.toml b/tasks/0014_469_14469283_qa_4/task.toml index 4da9ea0fb4c564722692c1262e6403cfdd41d782..c794193cf443580e5de844d622ca423f0d928022 100644 --- a/tasks/0014_469_14469283_qa_4/task.toml +++ b/tasks/0014_469_14469283_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0014_469_14469283_qa_4" +name = "smoldataenvs-train/0014_469_14469283_qa_4" description = "What is the distribution of the three iris species in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50, 50, 50" reward_mode_initial = "list" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0014_565_14565651_qa_5/task.toml b/tasks/0014_565_14565651_qa_5/task.toml index c33541088b1787cfef1aa53c4616bb442799190c..5f365eb6d6b378af51824f10e8400b674fd500f3 100644 --- a/tasks/0014_565_14565651_qa_5/task.toml +++ b/tasks/0014_565_14565651_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0014_565_14565651_qa_5" +name = "smoldataenvs-train/0014_565_14565651_qa_5" description = "After log-transformation and min-max scaling, what is the new minimum value for the 'absences' feature in the processed dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0014_579_14579474_qa_3/task.toml b/tasks/0014_579_14579474_qa_3/task.toml index 25aaa460d2c3b46ae868346df45a4e61ea1515b7..1f5f2a1a0f6adca94098272d38110b487025df2e 100644 --- a/tasks/0014_579_14579474_qa_3/task.toml +++ b/tasks/0014_579_14579474_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0014_579_14579474_qa_3" +name = "smoldataenvs-train/0014_579_14579474_qa_3" description = "Which feature is most strongly correlated with area_mean in the mean feature group?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "radius_mean" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0014_590_14590396_qa_2/task.toml b/tasks/0014_590_14590396_qa_2/task.toml index 1b78c49a6077ba816738b07dd43a091c58612704..24e55826d123faebd7536b04f0c9490a470057f0 100644 --- a/tasks/0014_590_14590396_qa_2/task.toml +++ b/tasks/0014_590_14590396_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0014_590_14590396_qa_2" +name = "smoldataenvs-train/0014_590_14590396_qa_2" description = "Among students whose parents do not answer surveys (ParentAnsweringSurvey='No'), what percentage are classified as having low academic performance (Class='L')?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "47.14" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0014_590_14590396_qa_5/task.toml b/tasks/0014_590_14590396_qa_5/task.toml index ba07ccda00b28130e2606728dd2ee1b379544b1e..02f32619e2f5c8ef5433c37e3f22d7051d3d1f9f 100644 --- a/tasks/0014_590_14590396_qa_5/task.toml +++ b/tasks/0014_590_14590396_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0014_590_14590396_qa_5" +name = "smoldataenvs-train/0014_590_14590396_qa_5" description = "What percentage of students with absence days above 7 are classified as having low academic performance (Class='L')?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "60.73" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0014_613_14613843_qa_3/task.toml b/tasks/0014_613_14613843_qa_3/task.toml index 4c19f4341da08d1243a77309581bdcc4ee234140..194793cb6a9fcb7651d0f1593630654fbb90f432 100644 --- a/tasks/0014_613_14613843_qa_3/task.toml +++ b/tasks/0014_613_14613843_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0014_613_14613843_qa_3" +name = "smoldataenvs-train/0014_613_14613843_qa_3" description = "What percentage of the training data samples are classified as positive (target == 1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50.3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0014_613_14613843_qa_4/task.toml b/tasks/0014_613_14613843_qa_4/task.toml index 7419b196c131d1f11e04dcefe210a796e9967cc4..3066fc8e348a97ddb67f5218942dc4468b69d77f 100644 --- a/tasks/0014_613_14613843_qa_4/task.toml +++ b/tasks/0014_613_14613843_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0014_613_14613843_qa_4" +name = "smoldataenvs-train/0014_613_14613843_qa_4" description = "How many distinct audio features are used as input variables in the decision tree and random forest models?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0014_667_14667230_qa_5/task.toml b/tasks/0014_667_14667230_qa_5/task.toml index c11e959dcce1369b0d35a0ccb8a6a319f00c222b..e851cdb2e871b7c26e08c6e6a0c1e72407e0307f 100644 --- a/tasks/0014_667_14667230_qa_5/task.toml +++ b/tasks/0014_667_14667230_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0014_667_14667230_qa_5" +name = "smoldataenvs-train/0014_667_14667230_qa_5" description = "What is the highest absolute correlation coefficient between any numerical feature and YearsWithCurrManager in the final processed dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.769" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0014_701_14701455_qa_3/task.toml b/tasks/0014_701_14701455_qa_3/task.toml index 68c685dbcd6de5d20a951b947a606cae7d82cc28..420ebd197a560133e22403ca3e16f250da96e17f 100644 --- a/tasks/0014_701_14701455_qa_3/task.toml +++ b/tasks/0014_701_14701455_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0014_701_14701455_qa_3" +name = "smoldataenvs-train/0014_701_14701455_qa_3" description = "What is the most frequently occurring stock code in the cleaned dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "85123A" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0014_933_14933483_qa_2/task.toml b/tasks/0014_933_14933483_qa_2/task.toml index c5bbd25651725f3a227700f7a8571a24b8d8d864..3e22b2704819ee0ead95b6661a21da72f7f50b39 100644 --- a/tasks/0014_933_14933483_qa_2/task.toml +++ b/tasks/0014_933_14933483_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0014_933_14933483_qa_2" +name = "smoldataenvs-train/0014_933_14933483_qa_2" description = "What is the minimum PetalWidthCm value recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0014_933_14933483_qa_3/task.toml b/tasks/0014_933_14933483_qa_3/task.toml index 103376e331b7480346510d8650403280bc6ec6a0..0d31a36bcd23380cd74fdc8c524a5474ecf2b688 100644 --- a/tasks/0014_933_14933483_qa_3/task.toml +++ b/tasks/0014_933_14933483_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0014_933_14933483_qa_3" +name = "smoldataenvs-train/0014_933_14933483_qa_3" description = "Which feature exhibits the highest mean value across all samples, and what is that mean?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "SepalLengthCm with a mean of 5.843333." reward_mode_initial = "flexible" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0014_952_14952543_qa_1/task.toml b/tasks/0014_952_14952543_qa_1/task.toml index 26f6f35d28e9391962f8ef893c07571f5739e3c0..4d11bbc2bbd2261dbe04290c7a414d8277596a81 100644 --- a/tasks/0014_952_14952543_qa_1/task.toml +++ b/tasks/0014_952_14952543_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0014_952_14952543_qa_1" +name = "smoldataenvs-train/0014_952_14952543_qa_1" description = "How many distinct price range categories are present in the mobile phone dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0014_952_14952543_qa_3/task.toml b/tasks/0014_952_14952543_qa_3/task.toml index 792fd66d7beb3233ad9612b3cb8717d2b8592dca..5b0c3c33bd5c854a5bfaec2573f7de7887a73d8f 100644 --- a/tasks/0014_952_14952543_qa_3/task.toml +++ b/tasks/0014_952_14952543_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0014_952_14952543_qa_3" +name = "smoldataenvs-train/0014_952_14952543_qa_3" description = "Do the training data contain any missing or null values across all features?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0015_048_15048236_qa_3/task.toml b/tasks/0015_048_15048236_qa_3/task.toml index 9c0ec596e77512818056d155944a0f21b73db5a0..2aaa1749f998dc8a7c95e45687242cefdf5ee717 100644 --- a/tasks/0015_048_15048236_qa_3/task.toml +++ b/tasks/0015_048_15048236_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0015_048_15048236_qa_3" +name = "smoldataenvs-train/0015_048_15048236_qa_3" description = "Do all Customer user type records have missing gender information?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0015_050_15050710_qa_1/task.toml b/tasks/0015_050_15050710_qa_1/task.toml index ac986b13d0c1f9b8aea102d5447062593fe09d9b..a2c9035b24428290591fc577b38c4d542fdc7375 100644 --- a/tasks/0015_050_15050710_qa_1/task.toml +++ b/tasks/0015_050_15050710_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0015_050_15050710_qa_1" +name = "smoldataenvs-train/0015_050_15050710_qa_1" description = "Which feature has the highest positive correlation with the 'Outcome' variable in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0015_050_15050710_qa_2/task.toml b/tasks/0015_050_15050710_qa_2/task.toml index 7b1f0c54257184ec094cfdea3fd8050aa747776a..5ce5e24b215231844a4289ec307a1802806ca50b 100644 --- a/tasks/0015_050_15050710_qa_2/task.toml +++ b/tasks/0015_050_15050710_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0015_050_15050710_qa_2" +name = "smoldataenvs-train/0015_050_15050710_qa_2" description = "What is the total number of missing values in the 'Insulin' column after replacing zeros with NaN values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "374" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0015_153_15153699_qa_3/task.toml b/tasks/0015_153_15153699_qa_3/task.toml index 4560445cda61fd199f234f17f42001e076182293..47012c5142558bafef70cbee3f740612dd10fa97 100644 --- a/tasks/0015_153_15153699_qa_3/task.toml +++ b/tasks/0015_153_15153699_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0015_153_15153699_qa_3" +name = "smoldataenvs-train/0015_153_15153699_qa_3" description = "What is the total number of messages in the dataset after removing the unused columns?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5572" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0015_157_15157151_qa_5/task.toml b/tasks/0015_157_15157151_qa_5/task.toml index ad50fb7a2359d725f5019c0bcafecd2bfc8a01b3..f44ef18e4bd1c3668d7bd63a2651fe296db1a754 100644 --- a/tasks/0015_157_15157151_qa_5/task.toml +++ b/tasks/0015_157_15157151_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0015_157_15157151_qa_5" +name = "smoldataenvs-train/0015_157_15157151_qa_5" description = "How many districts have zero First Referral Units (FRUs) as calculated by summing Community Health Centres, Area Hospitals, and District Hospitals?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0015_211_15211665_qa_1/task.toml b/tasks/0015_211_15211665_qa_1/task.toml index 9572c34c995b226170f27b632f9f5f7784b44538..5425073c03d56e902f577214d7cb6b5460dffc86 100644 --- a/tasks/0015_211_15211665_qa_1/task.toml +++ b/tasks/0015_211_15211665_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0015_211_15211665_qa_1" +name = "smoldataenvs-train/0015_211_15211665_qa_1" description = "How many categories were present in the 'ocean_proximity' column after applying one-hot encoding to the housing dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0015_211_15211665_qa_2/task.toml b/tasks/0015_211_15211665_qa_2/task.toml index 6fb31418289ef7a56dd4a07248e95b0a457c80cd..762763bb012cea456e0e2a27a30b3daff6513001 100644 --- a/tasks/0015_211_15211665_qa_2/task.toml +++ b/tasks/0015_211_15211665_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0015_211_15211665_qa_2" +name = "smoldataenvs-train/0015_211_15211665_qa_2" description = "What is the number of samples in the test set after splitting the housing dataset into train, validation, and test sets?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4128" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0015_256_15256843_qa_3/task.toml b/tasks/0015_256_15256843_qa_3/task.toml index e882d53f6ce7f95cf2e0dffdf691aa151ef43499..2da4d8bc961bbec43be1c4d7d6f94b2723716933 100644 --- a/tasks/0015_256_15256843_qa_3/task.toml +++ b/tasks/0015_256_15256843_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0015_256_15256843_qa_3" +name = "smoldataenvs-train/0015_256_15256843_qa_3" description = "Between the categories 'Feed' and 'Food', which has a higher count in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Food" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0015_256_15256843_qa_5/task.toml b/tasks/0015_256_15256843_qa_5/task.toml index f83e44454e2f73b4275dd0cb6d518c785b63b29d..f694373cc35e4670781c27fb9dd9c46b9049833e 100644 --- a/tasks/0015_256_15256843_qa_5/task.toml +++ b/tasks/0015_256_15256843_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0015_256_15256843_qa_5" +name = "smoldataenvs-train/0015_256_15256843_qa_5" description = "Which year between 1961 and 2013 recorded the highest global total production across all countries?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2013" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0015_285_15285404_qa_3/task.toml b/tasks/0015_285_15285404_qa_3/task.toml index 249b7f217cf2f1cd53132a1e948a0b52803e7662..f97c2b00c3f4c00db189ff8ac9462ec10cc9bd24 100644 --- a/tasks/0015_285_15285404_qa_3/task.toml +++ b/tasks/0015_285_15285404_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0015_285_15285404_qa_3" +name = "smoldataenvs-train/0015_285_15285404_qa_3" description = "What is the average account length of customers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "101.06" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0015_404_15404226_qa_1/task.toml b/tasks/0015_404_15404226_qa_1/task.toml index c03e43916aa85a298db36202c29994fb6d0c6509..a818fab0584cea8b8d496865aa4931d595db29cf 100644 --- a/tasks/0015_404_15404226_qa_1/task.toml +++ b/tasks/0015_404_15404226_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0015_404_15404226_qa_1" +name = "smoldataenvs-train/0015_404_15404226_qa_1" description = "Which year between 2011-2014 had the highest average BasePay, and what was the average value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2013, 69630.03" reward_mode_initial = "list" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0015_404_15404226_qa_2/task.toml b/tasks/0015_404_15404226_qa_2/task.toml index 2187d4095048f0a3c44e31dd6ef5088f84d6d064..e5878f2c1d7847616a9f9f6b09e303996d60db04 100644 --- a/tasks/0015_404_15404226_qa_2/task.toml +++ b/tasks/0015_404_15404226_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0015_404_15404226_qa_2" +name = "smoldataenvs-train/0015_404_15404226_qa_2" description = "How many unique job titles were represented by only one person in 2013?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "202" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0015_404_15404226_qa_3/task.toml b/tasks/0015_404_15404226_qa_3/task.toml index 63f3283cd1fe2bf870f7e7ff292d9b7f7635343a..0b1646f516266d16e9dfbf9f68c302e997b8ebf3 100644 --- a/tasks/0015_404_15404226_qa_3/task.toml +++ b/tasks/0015_404_15404226_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0015_404_15404226_qa_3" +name = "smoldataenvs-train/0015_404_15404226_qa_3" description = "How many employees have the word \"Chief\" in their job title?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "627" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0015_404_15404226_qa_4/task.toml b/tasks/0015_404_15404226_qa_4/task.toml index cc2f2650c6b999fd640763a434c28c6603f51c93..7761158a04912f0d16fbaec7e0b4230bdc1712f7 100644 --- a/tasks/0015_404_15404226_qa_4/task.toml +++ b/tasks/0015_404_15404226_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0015_404_15404226_qa_4" +name = "smoldataenvs-train/0015_404_15404226_qa_4" description = "What is the highest OvertimePay amount recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "245131.88" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0015_404_15404226_qa_5/task.toml b/tasks/0015_404_15404226_qa_5/task.toml index 2220cca198f3385932e6824680adb5d7beb1995d..572db15dbfb46e534018dd38ac7a3c2792192ab4 100644 --- a/tasks/0015_404_15404226_qa_5/task.toml +++ b/tasks/0015_404_15404226_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0015_404_15404226_qa_5" +name = "smoldataenvs-train/0015_404_15404226_qa_5" description = "What is the name of the highest-paid employee (including benefits)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "NATHANIEL FORD" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0015_420_15420402_qa_1/task.toml b/tasks/0015_420_15420402_qa_1/task.toml index e057e5005634295af117c22cfb63453efb534fcd..d684821a4b8003b6c2fe324fe5d551b523b0d46b 100644 --- a/tasks/0015_420_15420402_qa_1/task.toml +++ b/tasks/0015_420_15420402_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0015_420_15420402_qa_1" +name = "smoldataenvs-train/0015_420_15420402_qa_1" description = "What is the correlation coefficient between Glucose levels and Outcome in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.47" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0015_420_15420402_qa_4/task.toml b/tasks/0015_420_15420402_qa_4/task.toml index ea4c88d7ce75910e26804fa306ae60d631922bc6..f4d78b20d836ba474dc4fc40f45f6b5bf9f3bee8 100644 --- a/tasks/0015_420_15420402_qa_4/task.toml +++ b/tasks/0015_420_15420402_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0015_420_15420402_qa_4" +name = "smoldataenvs-train/0015_420_15420402_qa_4" description = "What is the mean glucose level for non-diabetic patients (Outcome=0) after excluding zero values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "110.64386317907444" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0015_456_15456455_qa_4/task.toml b/tasks/0015_456_15456455_qa_4/task.toml index e37cb29a2d23d79865ecf5182a56d5501b36c2cf..32625a7cf7b14f7b9718ddffae4219c93ee52bea 100644 --- a/tasks/0015_456_15456455_qa_4/task.toml +++ b/tasks/0015_456_15456455_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0015_456_15456455_qa_4" +name = "smoldataenvs-train/0015_456_15456455_qa_4" description = "What percentage of patients in the dataset survived the five-year period?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "73.5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0015_494_15494676_qa_1/task.toml b/tasks/0015_494_15494676_qa_1/task.toml index 6cd1a984f69f2a3c7a8dbd6aeae47d10896db381..561d77b00238c50c58c733e4f1edd093cb731589 100644 --- a/tasks/0015_494_15494676_qa_1/task.toml +++ b/tasks/0015_494_15494676_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0015_494_15494676_qa_1" +name = "smoldataenvs-train/0015_494_15494676_qa_1" description = "What are the dimensions of the dataset after removing the 'Id' column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "150, 5" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0015_604_15604957_qa_3/task.toml b/tasks/0015_604_15604957_qa_3/task.toml index 3c1a07d21bf892b6177ff434ca81b12e1fa68ad9..254e03d6a0cfee90e0e0cf2481cd20ad65d735f0 100644 --- a/tasks/0015_604_15604957_qa_3/task.toml +++ b/tasks/0015_604_15604957_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0015_604_15604957_qa_3" +name = "smoldataenvs-train/0015_604_15604957_qa_3" description = "How many rows remain in the training dataset after removing missing values in the target variable (y)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "699" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0015_624_15624986_qa_4/task.toml b/tasks/0015_624_15624986_qa_4/task.toml index 99b4bfc8eb2707a79de11a1997138b558e9938aa..06f2e13a8d57d0f4475a232588199fee176d5f51 100644 --- a/tasks/0015_624_15624986_qa_4/task.toml +++ b/tasks/0015_624_15624986_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0015_624_15624986_qa_4" +name = "smoldataenvs-train/0015_624_15624986_qa_4" description = "What is the average sepal length across all 150 samples in the Iris dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5.84" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0015_684_15684716_qa_5/task.toml b/tasks/0015_684_15684716_qa_5/task.toml index b60cb6845daf6ba79320361d9a518d69307d7da2..0f76fa52d5f1a54080c1ef72a2149aa403f52fb4 100644 --- a/tasks/0015_684_15684716_qa_5/task.toml +++ b/tasks/0015_684_15684716_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0015_684_15684716_qa_5" +name = "smoldataenvs-train/0015_684_15684716_qa_5" description = "What is the most frequently occurring word in the wine description text after completing all preprocessing steps?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "wine" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0015_808_15808353_qa_1/task.toml b/tasks/0015_808_15808353_qa_1/task.toml index 878acb4a006a9354b808fbd9a95e589f211c98eb..c26f62663d08019ab6c4eeb448b9d4778cfb79f0 100644 --- a/tasks/0015_808_15808353_qa_1/task.toml +++ b/tasks/0015_808_15808353_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0015_808_15808353_qa_1" +name = "smoldataenvs-train/0015_808_15808353_qa_1" description = "What is the percentage of patients in the Haberman dataset who survived after 5 years (Surv_status = \"yes\")?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "73.44" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0015_817_15817041_qa_2/task.toml b/tasks/0015_817_15817041_qa_2/task.toml index 5a6875efdc4bbc18b624d77459c5338b11c76ade..31e6f42b4971e8e520ed2b60118261256746e9dd 100644 --- a/tasks/0015_817_15817041_qa_2/task.toml +++ b/tasks/0015_817_15817041_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0015_817_15817041_qa_2" +name = "smoldataenvs-train/0015_817_15817041_qa_2" description = "How many pairs of features in the dataset have a correlation coefficient greater than 0.6 based on the correlation matrix analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0015_861_15861774_qa_1/task.toml b/tasks/0015_861_15861774_qa_1/task.toml index 62bf9979cdf91ea066f83a6cddd6dc56fb991d26..f8d4e2f91042d89592b5837bcb8e9e88c572445e 100644 --- a/tasks/0015_861_15861774_qa_1/task.toml +++ b/tasks/0015_861_15861774_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0015_861_15861774_qa_1" +name = "smoldataenvs-train/0015_861_15861774_qa_1" description = "Which year had the highest total number of fatalities in aviation history according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1972" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0015_881_15881525_qa_2/task.toml b/tasks/0015_881_15881525_qa_2/task.toml index e7c6da5fb5aa59dcd11c8d28ab81653cc8957c5a..1018e7e4174403fe45ae98a47b82aacc7b84e032 100644 --- a/tasks/0015_881_15881525_qa_2/task.toml +++ b/tasks/0015_881_15881525_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0015_881_15881525_qa_2" +name = "smoldataenvs-train/0015_881_15881525_qa_2" description = "What is the most frequent cover type in the dataset, and how many instances does it contain?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Cover_Type 2, 283301" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0016_039_16039611_qa_1/task.toml b/tasks/0016_039_16039611_qa_1/task.toml index 5915ccf5d52474ac2dbcdbc61a99e96f6ef0b952..c472506c7d8fa53efa72d26a08237b34c98f8a3a 100644 --- a/tasks/0016_039_16039611_qa_1/task.toml +++ b/tasks/0016_039_16039611_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0016_039_16039611_qa_1" +name = "smoldataenvs-train/0016_039_16039611_qa_1" description = "Which health parameter shows the strongest statistical correlation with the diabetes outcome according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0016_135_16135360_qa_2/task.toml b/tasks/0016_135_16135360_qa_2/task.toml index 424d7abb706c9238f2a7a37684481488d4a07a07..3c748d4e464fbde07586411b316bb719ad3c6c92 100644 --- a/tasks/0016_135_16135360_qa_2/task.toml +++ b/tasks/0016_135_16135360_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0016_135_16135360_qa_2" +name = "smoldataenvs-train/0016_135_16135360_qa_2" description = "Which state ranks highest based on the composite parameter combining normalized graduate ratio, sex ratio, and literacy rate?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Manipur" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0016_135_16135360_qa_3/task.toml b/tasks/0016_135_16135360_qa_3/task.toml index 354422d1aafed9c74f0007bef003e32f5726beca..3dce2a0c982fd8579e3d88fdf4daa54409985aee 100644 --- a/tasks/0016_135_16135360_qa_3/task.toml +++ b/tasks/0016_135_16135360_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0016_135_16135360_qa_3" +name = "smoldataenvs-train/0016_135_16135360_qa_3" description = "Which state has the highest average literacy rate calculated from total literates and population?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "KERALA" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0016_136_16136283_qa_1/task.toml b/tasks/0016_136_16136283_qa_1/task.toml index b8aca5328b5148276825d6618e518b80ac44940f..29d00d6151af28f2fd266ed5a5cdad7644da5203 100644 --- a/tasks/0016_136_16136283_qa_1/task.toml +++ b/tasks/0016_136_16136283_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0016_136_16136283_qa_1" +name = "smoldataenvs-train/0016_136_16136283_qa_1" description = "What is the percentage of reviews classified as \"Positively Rated\" after filtering out neutral reviews (rating = 3)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "74.8" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0016_369_16369869_qa_1/task.toml b/tasks/0016_369_16369869_qa_1/task.toml index d9f64f540546ba9b5117787a440db0d85db93fa8..ee8c9b52412aafc84f198742e670fe772b31f9a7 100644 --- a/tasks/0016_369_16369869_qa_1/task.toml +++ b/tasks/0016_369_16369869_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0016_369_16369869_qa_1" +name = "smoldataenvs-train/0016_369_16369869_qa_1" description = "Which year had the highest maximum camera resolution according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2007" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0016_498_16498602_qa_3/task.toml b/tasks/0016_498_16498602_qa_3/task.toml index 0e280e1b32f6eace15c10d7906cceb4a1b3b1ed9..a329337b37dc416d0eafa084223baa4cbdff31e6 100644 --- a/tasks/0016_498_16498602_qa_3/task.toml +++ b/tasks/0016_498_16498602_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0016_498_16498602_qa_3" +name = "smoldataenvs-train/0016_498_16498602_qa_3" description = "What is the average carat weight of diamonds in the dataset after removing entries with zero dimensions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.7977" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0016_498_16498602_qa_5/task.toml b/tasks/0016_498_16498602_qa_5/task.toml index 5f017aafed0a503379fdf1180484150d84b45443..1168b7016bd0a21a8de3303d947cf630869231d6 100644 --- a/tasks/0016_498_16498602_qa_5/task.toml +++ b/tasks/0016_498_16498602_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0016_498_16498602_qa_5" +name = "smoldataenvs-train/0016_498_16498602_qa_5" description = "What is the minimum diamond depth in the dataset after removing entries with zero dimensions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "43.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0016_558_16558367_qa_1/task.toml b/tasks/0016_558_16558367_qa_1/task.toml index 433e142b3d40b632f2c1db90a2c69f8f93d72cad..ceb9879733ec49491825f097923398729bcf5110 100644 --- a/tasks/0016_558_16558367_qa_1/task.toml +++ b/tasks/0016_558_16558367_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0016_558_16558367_qa_1" +name = "smoldataenvs-train/0016_558_16558367_qa_1" description = "Which user has the highest total positive sentiment score according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "mituamin" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0016_573_16573161_qa_2/task.toml b/tasks/0016_573_16573161_qa_2/task.toml index 3176c84046cc8ce9205a5afec9413ca4f9f9ba27..a0189d088ad6ff494fca35995bd4f156744f7f8e 100644 --- a/tasks/0016_573_16573161_qa_2/task.toml +++ b/tasks/0016_573_16573161_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0016_573_16573161_qa_2" +name = "smoldataenvs-train/0016_573_16573161_qa_2" description = "What is the proportion of legendary Pokémon among the Flying primary type?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1/3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0016_673_16673727_qa_2/task.toml b/tasks/0016_673_16673727_qa_2/task.toml index b24746c9d867dc2f7ff3064681ff549db3432947..b20d088e905c7170cbc618d21161896c6cc6490b 100644 --- a/tasks/0016_673_16673727_qa_2/task.toml +++ b/tasks/0016_673_16673727_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0016_673_16673727_qa_2" +name = "smoldataenvs-train/0016_673_16673727_qa_2" description = "What is the interquartile range (IQR) for petal width in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0016_712_16712977_qa_1/task.toml b/tasks/0016_712_16712977_qa_1/task.toml index fbd9c6feeec7c6c37fca0bb17e117a166068f8e6..639a94d2bf61011a07f8e31702c0ca822e7fa306 100644 --- a/tasks/0016_712_16712977_qa_1/task.toml +++ b/tasks/0016_712_16712977_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0016_712_16712977_qa_1" +name = "smoldataenvs-train/0016_712_16712977_qa_1" description = "Which airline has the highest number of negative sentiment tweets in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "United" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0016_712_16712977_qa_5/task.toml b/tasks/0016_712_16712977_qa_5/task.toml index b17f47af8c7c7c0eea1abf170cbf0bc4061f935f..311611d00615e388d5b069ae312ac7e35f175f6f 100644 --- a/tasks/0016_712_16712977_qa_5/task.toml +++ b/tasks/0016_712_16712977_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0016_712_16712977_qa_5" +name = "smoldataenvs-train/0016_712_16712977_qa_5" description = "How many negative sentiment tweets are attributed to \"Late Flight\" for US Airways?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "453" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0016_714_16714217_qa_1/task.toml b/tasks/0016_714_16714217_qa_1/task.toml index 98bd0b093549007b4eb63a4b27cc531eef09382e..efe2c57dcc63dd8eb3ba773761e9531f4fc3fc03 100644 --- a/tasks/0016_714_16714217_qa_1/task.toml +++ b/tasks/0016_714_16714217_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0016_714_16714217_qa_1" +name = "smoldataenvs-train/0016_714_16714217_qa_1" description = "How many rows were removed from the dataset due to missing 'horsepower' values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0016_714_16714217_qa_3/task.toml b/tasks/0016_714_16714217_qa_3/task.toml index 07129d77b0dcf3df553931fdfe8843b0649340d9..b9f71e030646587c2f187c109624ca5ae483117d 100644 --- a/tasks/0016_714_16714217_qa_3/task.toml +++ b/tasks/0016_714_16714217_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0016_714_16714217_qa_3" +name = "smoldataenvs-train/0016_714_16714217_qa_3" description = "After data scaling, what is the minimum value in the 'model year' column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0016_714_16714217_qa_4/task.toml b/tasks/0016_714_16714217_qa_4/task.toml index 2f2318c38d55c9939d683279e9e06720333f6288..62dd56cef1e35826f315568a17e44ad20c461107 100644 --- a/tasks/0016_714_16714217_qa_4/task.toml +++ b/tasks/0016_714_16714217_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0016_714_16714217_qa_4" +name = "smoldataenvs-train/0016_714_16714217_qa_4" description = "What is the shape of the dataset after removing rows with missing 'horsepower' values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "392, 9" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0016_770_16770911_qa_2/task.toml b/tasks/0016_770_16770911_qa_2/task.toml index 72b5ad396607087781f183a37945e5169d12cde7..5ef933d0eaf712917368da6e0eb1197650e358ca 100644 --- a/tasks/0016_770_16770911_qa_2/task.toml +++ b/tasks/0016_770_16770911_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0016_770_16770911_qa_2" +name = "smoldataenvs-train/0016_770_16770911_qa_2" description = "What is the most frequently picked champion in the dataset according to the exploratory data analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Thresh" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0016_795_16795888_qa_4/task.toml b/tasks/0016_795_16795888_qa_4/task.toml index 0f730b39f6323b8ec39eaeed937dcc1aa4ddff2e..c0978b6faf057362604c456d1ac212e5fa2487ca 100644 --- a/tasks/0016_795_16795888_qa_4/task.toml +++ b/tasks/0016_795_16795888_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0016_795_16795888_qa_4" +name = "smoldataenvs-train/0016_795_16795888_qa_4" description = "What was the median value of the total_bedrooms column used for imputing missing values in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "433.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0016_945_16945752_qa_1/task.toml b/tasks/0016_945_16945752_qa_1/task.toml index ca085741f2818f89519ec9beda839dfb5d40af8b..6e42f887e1cafae597da93f18d4757eed50ece39 100644 --- a/tasks/0016_945_16945752_qa_1/task.toml +++ b/tasks/0016_945_16945752_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0016_945_16945752_qa_1" +name = "smoldataenvs-train/0016_945_16945752_qa_1" description = "What is the mean axillary node count difference between patients who survived more than 5 years and those who did not?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.67" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0016_945_16945752_qa_5/task.toml b/tasks/0016_945_16945752_qa_5/task.toml index e003913268e23a0dff423abe4e654c392404d47e..710ddaad77fe842e4d434c28269c9c45b96a6bc9 100644 --- a/tasks/0016_945_16945752_qa_5/task.toml +++ b/tasks/0016_945_16945752_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0016_945_16945752_qa_5" +name = "smoldataenvs-train/0016_945_16945752_qa_5" description = "What is the standard deviation of axillary node counts for patients who survived versus those who did not?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5.86, 9.13" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0016_952_16952441_qa_1/task.toml b/tasks/0016_952_16952441_qa_1/task.toml index 5d54f465d92a697686b5e094437f95b6698cbe4b..62c0fdf4c7911d308ea023cf1a464791b0b99e94 100644 --- a/tasks/0016_952_16952441_qa_1/task.toml +++ b/tasks/0016_952_16952441_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0016_952_16952441_qa_1" +name = "smoldataenvs-train/0016_952_16952441_qa_1" description = "Which year (2015, 2016, or 2017) had the highest number of countries with both Freedom and Generosity scores above 0.5?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2017" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0016_952_16952441_qa_2/task.toml b/tasks/0016_952_16952441_qa_2/task.toml index 4eb65d85e8676ba5a7ddc2c5174a7eff7e4d540a..eb857e0511248d7011595cc438bc37ce6ea6ec25 100644 --- a/tasks/0016_952_16952441_qa_2/task.toml +++ b/tasks/0016_952_16952441_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0016_952_16952441_qa_2" +name = "smoldataenvs-train/0016_952_16952441_qa_2" description = "Which year (2015, 2016, or 2017) had the highest number of countries with Economy (GDP per Capita) above 1.3 and Trust above 0.4?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2016" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0016_952_16952441_qa_4/task.toml b/tasks/0016_952_16952441_qa_4/task.toml index cd3615a769b64aa89a8f92a62fca0a951bc8f864..5b0031e713f20fbc760240ecbd8ae03152b37bd0 100644 --- a/tasks/0016_952_16952441_qa_4/task.toml +++ b/tasks/0016_952_16952441_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0016_952_16952441_qa_4" +name = "smoldataenvs-train/0016_952_16952441_qa_4" description = "How many countries in the Middle East and Northern Africa region had a happiness score above 7 in 2015?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0016_971_16971389_qa_1/task.toml b/tasks/0016_971_16971389_qa_1/task.toml index bfde3ed14265c4a73483159d305d2f3aacac6ef1..c2d29f5995b9a0a942d70afd93d694f217591119 100644 --- a/tasks/0016_971_16971389_qa_1/task.toml +++ b/tasks/0016_971_16971389_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0016_971_16971389_qa_1" +name = "smoldataenvs-train/0016_971_16971389_qa_1" description = "What is the most predictive feature for distinguishing edible from poisonous mushrooms based on value counts analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "odor" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0016_985_16985676_qa_3/task.toml b/tasks/0016_985_16985676_qa_3/task.toml index 0492cc5160992ff9b146eee67aacc1d5ad79db1a..2964b0549d99128c8c8700878e7d1f4577ece60f 100644 --- a/tasks/0016_985_16985676_qa_3/task.toml +++ b/tasks/0016_985_16985676_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0016_985_16985676_qa_3" +name = "smoldataenvs-train/0016_985_16985676_qa_3" description = "Which game is most similar to \"Pokemon Red/Pokemon Blue\" based on genre similarity, excluding the game itself?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Pokemon Gold/Pokemon Silver" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0017_247_17247673_qa_2/task.toml b/tasks/0017_247_17247673_qa_2/task.toml index 35911210df910c1355b193781798def7becf54d9..a68798ece226fcc6d64bfe9858d7665f74914f16 100644 --- a/tasks/0017_247_17247673_qa_2/task.toml +++ b/tasks/0017_247_17247673_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0017_247_17247673_qa_2" +name = "smoldataenvs-train/0017_247_17247673_qa_2" description = "How many countries in 2017 had a Family score below 0.5?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0017_257_17257933_qa_3/task.toml b/tasks/0017_257_17257933_qa_3/task.toml index 413e85d8bfbf48b1617de3b9112b7711cdebc77d..8b600879876cfa37dfbbab2f72bcff485e0ca162 100644 --- a/tasks/0017_257_17257933_qa_3/task.toml +++ b/tasks/0017_257_17257933_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0017_257_17257933_qa_3" +name = "smoldataenvs-train/0017_257_17257933_qa_3" description = "What is the interquartile range (IQR) for the Age feature in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "17" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0017_257_17257933_qa_4/task.toml b/tasks/0017_257_17257933_qa_4/task.toml index e75786447cbe446ed42ce85dc4753a8e90fc8828..e0a60482dbb42075e0738cdcbba1b152ef7b9138 100644 --- a/tasks/0017_257_17257933_qa_4/task.toml +++ b/tasks/0017_257_17257933_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0017_257_17257933_qa_4" +name = "smoldataenvs-train/0017_257_17257933_qa_4" description = "How many outliers are present in the Insulin column based on the IQR method?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0017_257_17257933_qa_5/task.toml b/tasks/0017_257_17257933_qa_5/task.toml index 3492b5c67350fb713f05c310ae262789946bdc97..18dbb87746138342c646e66a8619d614e74835d6 100644 --- a/tasks/0017_257_17257933_qa_5/task.toml +++ b/tasks/0017_257_17257933_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0017_257_17257933_qa_5" +name = "smoldataenvs-train/0017_257_17257933_qa_5" description = "How many outliers are present in the Glucose column based on the IQR method?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0017_291_17291057_qa_1/task.toml b/tasks/0017_291_17291057_qa_1/task.toml index 01f7123243eb2051082e5cacbaae511fea266046..a98a51941211d1008a0ee84b6ecebc692fec493c 100644 --- a/tasks/0017_291_17291057_qa_1/task.toml +++ b/tasks/0017_291_17291057_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0017_291_17291057_qa_1" +name = "smoldataenvs-train/0017_291_17291057_qa_1" description = "Which feature in the dataset shows the strongest positive correlation with wine quality according to the correlation matrix analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "alcohol" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0017_291_17291057_qa_5/task.toml b/tasks/0017_291_17291057_qa_5/task.toml index d38c7eb24498fee62a9ff3397ef0cca6e65872e9..e136f38624ac832bf5eac015d96f4342f628c2ff 100644 --- a/tasks/0017_291_17291057_qa_5/task.toml +++ b/tasks/0017_291_17291057_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0017_291_17291057_qa_5" +name = "smoldataenvs-train/0017_291_17291057_qa_5" description = "Based on the correlation matrix analysis, which feature has the highest negative correlation with wine quality?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "volatile acidity" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0017_311_17311492_qa_2/task.toml b/tasks/0017_311_17311492_qa_2/task.toml index 7b30aa247899969a417680f8ad057f605db780a2..29b0086d7355d10f72fa3cd868e48aff89da9ead 100644 --- a/tasks/0017_311_17311492_qa_2/task.toml +++ b/tasks/0017_311_17311492_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0017_311_17311492_qa_2" +name = "smoldataenvs-train/0017_311_17311492_qa_2" description = "Which year had the highest maximum happiness score across the 2015–2017 dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2015" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0017_339_17339042_qa_3/task.toml b/tasks/0017_339_17339042_qa_3/task.toml index fa525fa565a63b8c7511449aefc15427d43a0b51..ff28b4062b930fcd60b5a75c3d6e6c51d05126b2 100644 --- a/tasks/0017_339_17339042_qa_3/task.toml +++ b/tasks/0017_339_17339042_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0017_339_17339042_qa_3" +name = "smoldataenvs-train/0017_339_17339042_qa_3" description = "What is the frequency of the most common ham message in the training data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "22" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0017_359_17359385_qa_4/task.toml b/tasks/0017_359_17359385_qa_4/task.toml index 070aeb69606c33fee8b5847afbb02e1cfdff3403..b85b0d5a97161d0196cb3e6d2225922729faec78 100644 --- a/tasks/0017_359_17359385_qa_4/task.toml +++ b/tasks/0017_359_17359385_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0017_359_17359385_qa_4" +name = "smoldataenvs-train/0017_359_17359385_qa_4" description = "What is the median tenure (in months) of all customers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "29" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0017_359_17359385_qa_5/task.toml b/tasks/0017_359_17359385_qa_5/task.toml index db62875c156b4de8bd3ebcc85fedff6ec1c01397..e9e3b34115e478de66fa1b4961fb1d94a8190bde 100644 --- a/tasks/0017_359_17359385_qa_5/task.toml +++ b/tasks/0017_359_17359385_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0017_359_17359385_qa_5" +name = "smoldataenvs-train/0017_359_17359385_qa_5" description = "What is the average monthly charge (in USD) for all customers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "64.76" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0017_565_17565404_qa_1/task.toml b/tasks/0017_565_17565404_qa_1/task.toml index 6676ff1df5c44feabd8b0df361cf2ee29c2632b2..b677c58e7519a8abb370d0348602e0f4ede02aba 100644 --- a/tasks/0017_565_17565404_qa_1/task.toml +++ b/tasks/0017_565_17565404_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0017_565_17565404_qa_1" +name = "smoldataenvs-train/0017_565_17565404_qa_1" description = "What is the average satisfaction level of employees who stayed in the company?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.666810" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0017_565_17565404_qa_2/task.toml b/tasks/0017_565_17565404_qa_2/task.toml index 0edfd5b6e852b15029ef155fbc001d72f09c4e8d..594beee0f54a428baa0788ebb66005db35a3648c 100644 --- a/tasks/0017_565_17565404_qa_2/task.toml +++ b/tasks/0017_565_17565404_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0017_565_17565404_qa_2" +name = "smoldataenvs-train/0017_565_17565404_qa_2" description = "Which department has the highest average employee satisfaction level?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "management" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0017_693_17693429_qa_4/task.toml b/tasks/0017_693_17693429_qa_4/task.toml index 886d909534f2e0c7e4e999b2de78f3029d4dfa7c..971657c3d7b27e3f39ed087354d234db66033bf7 100644 --- a/tasks/0017_693_17693429_qa_4/task.toml +++ b/tasks/0017_693_17693429_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0017_693_17693429_qa_4" +name = "smoldataenvs-train/0017_693_17693429_qa_4" description = "What is the variance of the feature with the largest spread in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "324167.385102" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0017_703_17703063_qa_2/task.toml b/tasks/0017_703_17703063_qa_2/task.toml index 372fa4c3c47408e5bf6f726cdc782621789a802b..1109043daa391738198d52513b32066a71ec7a42 100644 --- a/tasks/0017_703_17703063_qa_2/task.toml +++ b/tasks/0017_703_17703063_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0017_703_17703063_qa_2" +name = "smoldataenvs-train/0017_703_17703063_qa_2" description = "Which state has the lowest per hectare cost for sugarcane according to the cultivation_data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Uttar Pradesh" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0017_740_17740333_qa_1/task.toml b/tasks/0017_740_17740333_qa_1/task.toml index 8ccbc57cf9d668fd05d6f714fe0175dacf91d574..e7949a4e4f17adbfbd609d26d4eb7a31e9b293f4 100644 --- a/tasks/0017_740_17740333_qa_1/task.toml +++ b/tasks/0017_740_17740333_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0017_740_17740333_qa_1" +name = "smoldataenvs-train/0017_740_17740333_qa_1" description = "What is the correlation coefficient between average monthly hours worked and time spent at the company in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.12775491036185835" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0017_795_17795348_qa_5/task.toml b/tasks/0017_795_17795348_qa_5/task.toml index c01a2cc90308c92476c2ea53921539e7e197507c..f0c9a051d5a9b092b584703ba04e52d40b646972 100644 --- a/tasks/0017_795_17795348_qa_5/task.toml +++ b/tasks/0017_795_17795348_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0017_795_17795348_qa_5" +name = "smoldataenvs-train/0017_795_17795348_qa_5" description = "Which team won the most tosses across all seasons in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Mumbai Indians" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0017_802_17802867_qa_4/task.toml b/tasks/0017_802_17802867_qa_4/task.toml index d8a1a696ecf5fa9ea8ee6ea6c635de52208929c1..34a3c11a9726f3bb8e4d4152ed7ec625c5dda545 100644 --- a/tasks/0017_802_17802867_qa_4/task.toml +++ b/tasks/0017_802_17802867_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0017_802_17802867_qa_4" +name = "smoldataenvs-train/0017_802_17802867_qa_4" description = "Which feature exhibits the highest skewness in the dataset according to the skewness distribution?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "area_se" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0017_808_17808830_qa_5/task.toml b/tasks/0017_808_17808830_qa_5/task.toml index 85da67cc35ca9cbaa2184b8b69745113d54e2a85..92dc85e3eec15d2d97204ef1738a98d67e988232 100644 --- a/tasks/0017_808_17808830_qa_5/task.toml +++ b/tasks/0017_808_17808830_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0017_808_17808830_qa_5" +name = "smoldataenvs-train/0017_808_17808830_qa_5" description = "What percentage of employees in the dataset have the highest job involvement level (level 3)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "59.05" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0017_810_17810500_qa_2/task.toml b/tasks/0017_810_17810500_qa_2/task.toml index 86d6956496d0d6f790bccfae7bba9caee57be77b..3dbf5ed35756738f1ae22664c1f0a5fb99e17a43 100644 --- a/tasks/0017_810_17810500_qa_2/task.toml +++ b/tasks/0017_810_17810500_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0017_810_17810500_qa_2" +name = "smoldataenvs-train/0017_810_17810500_qa_2" description = "What is the coefficient value for the 'satisfaction_level' variable in the trained logistic regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-4.128237323420271" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0017_859_17859052_qa_1/task.toml b/tasks/0017_859_17859052_qa_1/task.toml index b90990d1c1bb1015e99e456bf251ebfbb468a0ba..2eb7b0c114b14d450c0f5967f2e87731c8228e39 100644 --- a/tasks/0017_859_17859052_qa_1/task.toml +++ b/tasks/0017_859_17859052_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0017_859_17859052_qa_1" +name = "smoldataenvs-train/0017_859_17859052_qa_1" description = "Which pair of features in the Iris dataset exhibits the strongest pairwise relationship based on the scatter plot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalWidthCm, PetalLengthCm" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0017_914_17914820_qa_1/task.toml b/tasks/0017_914_17914820_qa_1/task.toml index 98c9406b6080c4235f645d9239ffd6021acd9be6..52b8e19efa8a7f2f6b342703ae719eb611ad3697 100644 --- a/tasks/0017_914_17914820_qa_1/task.toml +++ b/tasks/0017_914_17914820_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0017_914_17914820_qa_1" +name = "smoldataenvs-train/0017_914_17914820_qa_1" description = "Which month in the year 2018 had the highest number of Amazon job openings, and how many openings were there?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "January, 907" reward_mode_initial = "list" package_tier = 3 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0017_914_17914820_qa_5/task.toml b/tasks/0017_914_17914820_qa_5/task.toml index 8e175e0a793ad0177da87803abe59b46731340ea..150df9fc0836d2c6a3addd07c45dc50a66723bda 100644 --- a/tasks/0017_914_17914820_qa_5/task.toml +++ b/tasks/0017_914_17914820_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0017_914_17914820_qa_5" +name = "smoldataenvs-train/0017_914_17914820_qa_5" description = "In which year did Amazon post the highest number of job openings specifically related to Java development?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2018" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0017_920_17920359_qa_1/task.toml b/tasks/0017_920_17920359_qa_1/task.toml index 89b441824564c471f4c2e69d4b148a9054b527d5..d06132871138ff56d95f7295b5353624699847c0 100644 --- a/tasks/0017_920_17920359_qa_1/task.toml +++ b/tasks/0017_920_17920359_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0017_920_17920359_qa_1" +name = "smoldataenvs-train/0017_920_17920359_qa_1" description = "Which video game genre has the highest number of published games, and what is the total count?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action, 3252" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0017_920_17920359_qa_2/task.toml b/tasks/0017_920_17920359_qa_2/task.toml index f8492f5195fb55dff7de81fde623630cf4631cca..c0188735485debc2248440ee9a0ae1a5008d22cb 100644 --- a/tasks/0017_920_17920359_qa_2/task.toml +++ b/tasks/0017_920_17920359_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0017_920_17920359_qa_2" +name = "smoldataenvs-train/0017_920_17920359_qa_2" description = "Which publisher has released the most games in the dataset, and what is the total count?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic Arts, 1339" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0018_020_18020173_qa_3/task.toml b/tasks/0018_020_18020173_qa_3/task.toml index 632fa9157d87c2effdff78d4e97b4f35a10a70eb..f513f820b8df534ffb271654d4c6e0b49f809fe2 100644 --- a/tasks/0018_020_18020173_qa_3/task.toml +++ b/tasks/0018_020_18020173_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0018_020_18020173_qa_3" +name = "smoldataenvs-train/0018_020_18020173_qa_3" description = "What is the correlation coefficient between TotalPay and TotalPayBenefits columns in the cleaned dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.977312" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0018_020_18020173_qa_5/task.toml b/tasks/0018_020_18020173_qa_5/task.toml index 43353b673a17cfd72f1fd2eeb4a70c1923fb6390..661b45594e9a6992b14088b361c4c7698b49ce76 100644 --- a/tasks/0018_020_18020173_qa_5/task.toml +++ b/tasks/0018_020_18020173_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0018_020_18020173_qa_5" +name = "smoldataenvs-train/0018_020_18020173_qa_5" description = "Which calendar year has the highest number of salary records in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2014" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0018_038_18038519_qa_5/task.toml b/tasks/0018_038_18038519_qa_5/task.toml index d6e19e8d62117839ebd743ef345865cfb416171b..4fb38468450f151ac2ff00962d47d9f9a88b8e8c 100644 --- a/tasks/0018_038_18038519_qa_5/task.toml +++ b/tasks/0018_038_18038519_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0018_038_18038519_qa_5" +name = "smoldataenvs-train/0018_038_18038519_qa_5" description = "What is the variance of the PURCHASES feature in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4565208.191108808" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0018_278_18278432_qa_1/task.toml b/tasks/0018_278_18278432_qa_1/task.toml index 61b09c0063a72d713c3a9d4fbe0f2dd5c62eb5e5..a0cec4bb32134dc978d907f53f5f956de1a6bb52 100644 --- a/tasks/0018_278_18278432_qa_1/task.toml +++ b/tasks/0018_278_18278432_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0018_278_18278432_qa_1" +name = "smoldataenvs-train/0018_278_18278432_qa_1" description = "Which animal class has the highest number of domesticated species, and how many are there?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Mammal, 8" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0018_278_18278542_qa_3/task.toml b/tasks/0018_278_18278542_qa_3/task.toml index 5a231096f00abb0e423aa76cdc8482019a4641ae..fd1ecc17abf40dada719207cdd99956448e7a569 100644 --- a/tasks/0018_278_18278542_qa_3/task.toml +++ b/tasks/0018_278_18278542_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0018_278_18278542_qa_3" +name = "smoldataenvs-train/0018_278_18278542_qa_3" description = "How many diamonds in the 1.75 carat and above category have both 'IF' clarity and 'J' color?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0018_395_18395380_qa_5/task.toml b/tasks/0018_395_18395380_qa_5/task.toml index 5b4cbc87c12b464a1f97b68e2e76ae9c2a0caa73..6374b5424bd6fcc97f7550d361e31b8a44aff7e4 100644 --- a/tasks/0018_395_18395380_qa_5/task.toml +++ b/tasks/0018_395_18395380_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0018_395_18395380_qa_5" +name = "smoldataenvs-train/0018_395_18395380_qa_5" description = "What is the range of the year of operation for patients who survived?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0018_456_18456045_qa_5/task.toml b/tasks/0018_456_18456045_qa_5/task.toml index ac7bba7c0efe5e939f9f39b65f74bc700f321b78..d236e8180cf41304544adc10231f898a79ae934c 100644 --- a/tasks/0018_456_18456045_qa_5/task.toml +++ b/tasks/0018_456_18456045_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0018_456_18456045_qa_5" +name = "smoldataenvs-train/0018_456_18456045_qa_5" description = "What is the difference in test accuracy between the kNN and SVM models when using the 80/20 train/test split configuration?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0018_479_18479259_qa_4/task.toml b/tasks/0018_479_18479259_qa_4/task.toml index 47a8a91958874be613a5f8150939a7ad20ae95c2..23e3808972ab097b3e428ad5a675c44d211ac9e3 100644 --- a/tasks/0018_479_18479259_qa_4/task.toml +++ b/tasks/0018_479_18479259_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0018_479_18479259_qa_4" +name = "smoldataenvs-train/0018_479_18479259_qa_4" description = "How many genres are classified as having 'Successful' average global sales (mean > 0.5)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0018_479_18479259_qa_5/task.toml b/tasks/0018_479_18479259_qa_5/task.toml index 18c070d2c1737e23e9aeb29b9ca7a73d89ae91a5..a74eb6382f25fc17f4f1ebbfee1d486310266dcd 100644 --- a/tasks/0018_479_18479259_qa_5/task.toml +++ b/tasks/0018_479_18479259_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0018_479_18479259_qa_5" +name = "smoldataenvs-train/0018_479_18479259_qa_5" description = "Which genre has the highest number of games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0018_493_18493579_qa_1/task.toml b/tasks/0018_493_18493579_qa_1/task.toml index 2af6728e45e158572f1a56ce93ace6cf8b76ff5d..1b29abe8608fba98cd23af84de01d9d744e60d06 100644 --- a/tasks/0018_493_18493579_qa_1/task.toml +++ b/tasks/0018_493_18493579_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0018_493_18493579_qa_1" +name = "smoldataenvs-train/0018_493_18493579_qa_1" description = "How many races did Lewis Hamilton win in his Formula 1 career based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "62" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0018_493_18493579_qa_2/task.toml b/tasks/0018_493_18493579_qa_2/task.toml index 1c4ea118c4ec23407d283e0695d5354adae33f0f..1b5ccd458b858f68ebc9f05ac817235d6d76fae5 100644 --- a/tasks/0018_493_18493579_qa_2/task.toml +++ b/tasks/0018_493_18493579_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0018_493_18493579_qa_2" +name = "smoldataenvs-train/0018_493_18493579_qa_2" description = "What was the total points awarded to the winner of the 2015 Japanese Grand Prix?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "25" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0018_510_18510968_qa_2/task.toml b/tasks/0018_510_18510968_qa_2/task.toml index a0ae2a1e896efb5edc1f9e64a10c6f2def6170b6..369f4ca4b6f8b2b283fb49dfa1cf4bb33a7ee0d8 100644 --- a/tasks/0018_510_18510968_qa_2/task.toml +++ b/tasks/0018_510_18510968_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0018_510_18510968_qa_2" +name = "smoldataenvs-train/0018_510_18510968_qa_2" description = "Which passenger class (pclass) has the lowest survival rate according to the countplot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0018_519_18519406_qa_1/task.toml b/tasks/0018_519_18519406_qa_1/task.toml index 797ac12c093397038e88e9f3263567550bc46ded..567dd85fe2166047dddfb589f428a4787ac157c5 100644 --- a/tasks/0018_519_18519406_qa_1/task.toml +++ b/tasks/0018_519_18519406_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0018_519_18519406_qa_1" +name = "smoldataenvs-train/0018_519_18519406_qa_1" description = "What is the maximum number of games played by any player in the 2016-2017 WNBA season according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "32" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0018_519_18519406_qa_3/task.toml b/tasks/0018_519_18519406_qa_3/task.toml index b4bc5eb23ecc4d3a91e10a0ed9d068abb8da8d87..027d0b6747d807b554ad9ef6d6f780a4ef1fb6a1 100644 --- a/tasks/0018_519_18519406_qa_3/task.toml +++ b/tasks/0018_519_18519406_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0018_519_18519406_qa_3" +name = "smoldataenvs-train/0018_519_18519406_qa_3" description = "Which player position (Guard, Forward, Center, etc.) has the highest average points per game according to stratified sampling analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Guard" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0018_550_18550354_qa_2/task.toml b/tasks/0018_550_18550354_qa_2/task.toml index 22c2769a7878ed3decad14a5e59ea4e532b349de..e395fc94f980098e7b76d8abdb729d355775815a 100644 --- a/tasks/0018_550_18550354_qa_2/task.toml +++ b/tasks/0018_550_18550354_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0018_550_18550354_qa_2" +name = "smoldataenvs-train/0018_550_18550354_qa_2" description = "Which feature has the highest importance in predicting ViolentCrimesPerPop according to the Gradient Boosting model trained on the filled dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "assaultPerPop" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0018_550_18550354_qa_4/task.toml b/tasks/0018_550_18550354_qa_4/task.toml index c34fbbc6c6901a11a8022f9347205453612a3211..a4a78d5f7bd6b64e5166c306974ada72bb4b2266 100644 --- a/tasks/0018_550_18550354_qa_4/task.toml +++ b/tasks/0018_550_18550354_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0018_550_18550354_qa_4" +name = "smoldataenvs-train/0018_550_18550354_qa_4" description = "Which feature exhibits the highest correlation with ViolentCrimesPerPop based on Pearson correlation analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "assaultPerPop" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0018_563_18563893_qa_5/task.toml b/tasks/0018_563_18563893_qa_5/task.toml index 31f35ac415a8476aefe0aa2a5d1f124ee964bced..f95b2714446b1f057ccef04735c7fa4e66ae5cf8 100644 --- a/tasks/0018_563_18563893_qa_5/task.toml +++ b/tasks/0018_563_18563893_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0018_563_18563893_qa_5" +name = "smoldataenvs-train/0018_563_18563893_qa_5" description = "How many training samples were used after stratified splitting for model evaluation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26010" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0018_822_18822167_qa_1/task.toml b/tasks/0018_822_18822167_qa_1/task.toml index dc5f09e15356a8c48810bf3a431607e45e3cf5d9..6a20641e17a22719b3cfa95ea2a9acbd43c5dd68 100644 --- a/tasks/0018_822_18822167_qa_1/task.toml +++ b/tasks/0018_822_18822167_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0018_822_18822167_qa_1" +name = "smoldataenvs-train/0018_822_18822167_qa_1" description = "What are the average conversion rates for 2-point and 3-point field goals across the entire 2014-2015 NBA season?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "49%, 35%" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0018_823_18823003_qa_5/task.toml b/tasks/0018_823_18823003_qa_5/task.toml index 4be039a01813574ed539c12f69a1249f73bb49ac..dddb048c85deb8be9a5d4dd4ef0fd2b9f958af49 100644 --- a/tasks/0018_823_18823003_qa_5/task.toml +++ b/tasks/0018_823_18823003_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0018_823_18823003_qa_5" +name = "smoldataenvs-train/0018_823_18823003_qa_5" description = "What is the recall score of the random forest classifier on the test set for spam detection?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.797" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0019_106_19106342_qa_1/task.toml b/tasks/0019_106_19106342_qa_1/task.toml index b2be844c7879e13db28cae120e283fed5e6134ba..80fc4e8bd97c450cb509b58c3d35d9f2e21d132f 100644 --- a/tasks/0019_106_19106342_qa_1/task.toml +++ b/tasks/0019_106_19106342_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0019_106_19106342_qa_1" +name = "smoldataenvs-train/0019_106_19106342_qa_1" description = "How many distinct years are covered in the global religious population dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "14" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0019_205_19205019_qa_1/task.toml b/tasks/0019_205_19205019_qa_1/task.toml index 684c6334bb585de19ca669ae3e7a77cbbeaffb7e..1c64eacdb840547df5abb69ea09385043dfb22ff 100644 --- a/tasks/0019_205_19205019_qa_1/task.toml +++ b/tasks/0019_205_19205019_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0019_205_19205019_qa_1" +name = "smoldataenvs-train/0019_205_19205019_qa_1" description = "What is the specificity of the Bernoulli Naive Bayes model on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0019_267_19267356_qa_5/task.toml b/tasks/0019_267_19267356_qa_5/task.toml index 95a980bdb626a5d21eda6b8c983e8127d84160f8..8e65460a67b0267d6a3e7001c51cd2b26b4586bf 100644 --- a/tasks/0019_267_19267356_qa_5/task.toml +++ b/tasks/0019_267_19267356_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0019_267_19267356_qa_5" +name = "smoldataenvs-train/0019_267_19267356_qa_5" description = "Which feature selection method results in the highest model overfitting based on the difference between training and test R² scores?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Recursive Feature Elimination" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0019_288_19288397_qa_1/task.toml b/tasks/0019_288_19288397_qa_1/task.toml index 9658a837af676318ee2a1c06cc8ae02360723d4b..a9ab6278f762ccc789885c359f344444bb99c10e 100644 --- a/tasks/0019_288_19288397_qa_1/task.toml +++ b/tasks/0019_288_19288397_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0019_288_19288397_qa_1" +name = "smoldataenvs-train/0019_288_19288397_qa_1" description = "What proportion of employees who left the company were dissatisfied with their job (JobSatisfaction level 1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "27" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0019_309_19309067_qa_5/task.toml b/tasks/0019_309_19309067_qa_5/task.toml index bdbfe9c4073a72bd8aebdbce9a388702667fc0df..20e94036573d128d6136cc7a7ab2fe8e30d191c6 100644 --- a/tasks/0019_309_19309067_qa_5/task.toml +++ b/tasks/0019_309_19309067_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0019_309_19309067_qa_5" +name = "smoldataenvs-train/0019_309_19309067_qa_5" description = "What is the percentage of positive examples in the training set after down-sampling to balance the classes?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50%" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0019_416_19416448_qa_4/task.toml b/tasks/0019_416_19416448_qa_4/task.toml index 50f7c4e3f493ae0cc5fee202643635cbfa72a144..e143d6ed0f3d626afb39afba9d5bcf8d2df51c4e 100644 --- a/tasks/0019_416_19416448_qa_4/task.toml +++ b/tasks/0019_416_19416448_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0019_416_19416448_qa_4" +name = "smoldataenvs-train/0019_416_19416448_qa_4" description = "What is the precision score for predicting candidates who did not win (class 0.0) in the test data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.92" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0019_434_19434702_qa_1/task.toml b/tasks/0019_434_19434702_qa_1/task.toml index d3e66efdfe5bb0ddead00adfe7fe37799cc50cd0..6a08fab2ed7b6f0a695dc8685de8b2a78caf5350 100644 --- a/tasks/0019_434_19434702_qa_1/task.toml +++ b/tasks/0019_434_19434702_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0019_434_19434702_qa_1" +name = "smoldataenvs-train/0019_434_19434702_qa_1" description = "How many rows were removed from the dataset after handling missing values in the TotalCharges column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0019_469_19469309_qa_5/task.toml b/tasks/0019_469_19469309_qa_5/task.toml index e7636f69bc7d319f768a6d7c4b73e548921c8ffb..793da0695468898ecd4f0e903a1505b531c834e0 100644 --- a/tasks/0019_469_19469309_qa_5/task.toml +++ b/tasks/0019_469_19469309_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0019_469_19469309_qa_5" +name = "smoldataenvs-train/0019_469_19469309_qa_5" description = "What is the F1-score for the 'Malignant' class in the classification report of the SVM model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.97" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0019_517_19517366_qa_3/task.toml b/tasks/0019_517_19517366_qa_3/task.toml index 7f7e04563b7d2c4e16e312ae19d56ca8ed507c30..1634464aecf4e6d7f68f523123a9a6f17c888baa 100644 --- a/tasks/0019_517_19517366_qa_3/task.toml +++ b/tasks/0019_517_19517366_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0019_517_19517366_qa_3" +name = "smoldataenvs-train/0019_517_19517366_qa_3" description = "What is the precision score of the Random Forest model on the test dataset after training and prediction?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0019_517_19517811_qa_1/task.toml b/tasks/0019_517_19517811_qa_1/task.toml index feac17e7701c37394f6bb082158ceb0ff761fbbc..0e609b1c763b519172b8dfc2300bea14b1bd5663 100644 --- a/tasks/0019_517_19517811_qa_1/task.toml +++ b/tasks/0019_517_19517811_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0019_517_19517811_qa_1" +name = "smoldataenvs-train/0019_517_19517811_qa_1" description = "Which passenger class (Pclass) had the highest survival rate according to the training data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0019_521_19521236_qa_1/task.toml b/tasks/0019_521_19521236_qa_1/task.toml index d8db43f99102cc64851feb6ca3430bf5e6b8479d..64e46c3ffefaeab83c3d4d060c8a1711110201be 100644 --- a/tasks/0019_521_19521236_qa_1/task.toml +++ b/tasks/0019_521_19521236_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0019_521_19521236_qa_1" +name = "smoldataenvs-train/0019_521_19521236_qa_1" description = "Which menu item has the highest calorie count according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Chicken McNuggets (40 piece)" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0019_521_19521236_qa_2/task.toml b/tasks/0019_521_19521236_qa_2/task.toml index 61120a7ec1495809edfb7e6dfb3fa4d8e0f08947..f42f5af0ef0fc35203070259d4b78b256bd3ffcf 100644 --- a/tasks/0019_521_19521236_qa_2/task.toml +++ b/tasks/0019_521_19521236_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0019_521_19521236_qa_2" +name = "smoldataenvs-train/0019_521_19521236_qa_2" description = "What is the name of the food item with the highest trans fat content in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Double Quarter Pounder with Cheese" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0019_521_19521236_qa_3/task.toml b/tasks/0019_521_19521236_qa_3/task.toml index 0a5861f49d30a8806135c8ba18022ab64424e457..6a6a04ba0f36ad07c06241bbbb2d395dd285a9a5 100644 --- a/tasks/0019_521_19521236_qa_3/task.toml +++ b/tasks/0019_521_19521236_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0019_521_19521236_qa_3" +name = "smoldataenvs-train/0019_521_19521236_qa_3" description = "Which menu item provides the highest amount of protein in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Chicken McNuggets (40 piece)" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0019_570_19570997_qa_1/task.toml b/tasks/0019_570_19570997_qa_1/task.toml index e3b19bbcfa1e62657bed6a49c10ea4c15b360bcb..c136af21e485cd2089c6d5237bd241a842b6442a 100644 --- a/tasks/0019_570_19570997_qa_1/task.toml +++ b/tasks/0019_570_19570997_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0019_570_19570997_qa_1" +name = "smoldataenvs-train/0019_570_19570997_qa_1" description = "How many districts in the dataset had missing values in the total_bedrooms column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "207" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0019_570_19570997_qa_5/task.toml b/tasks/0019_570_19570997_qa_5/task.toml index 456e9bffbfe77fc795fa79f2c31f4c3da923e727..7c9869a30f3cc751f61c6e9e31ced625030d355f 100644 --- a/tasks/0019_570_19570997_qa_5/task.toml +++ b/tasks/0019_570_19570997_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0019_570_19570997_qa_5" +name = "smoldataenvs-train/0019_570_19570997_qa_5" description = "After one-hot encoding, how many binary columns were added to the dataset to represent ocean_proximity categories?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0019_685_19685428_qa_1/task.toml b/tasks/0019_685_19685428_qa_1/task.toml index de2d661994488b1daae20ec095f5496c8d16199b..2a53eaa0826e0c2d602e5682b93b5f20cc7b9ef2 100644 --- a/tasks/0019_685_19685428_qa_1/task.toml +++ b/tasks/0019_685_19685428_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0019_685_19685428_qa_1" +name = "smoldataenvs-train/0019_685_19685428_qa_1" description = "What percentage of employees in the dataset left the company?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "23.8" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0019_706_19706257_qa_2/task.toml b/tasks/0019_706_19706257_qa_2/task.toml index 156a14898fb7fbbe2c2585de428491207de67bd1..f1cdc8c47aa20d194781cc4f432bcb703c513ee9 100644 --- a/tasks/0019_706_19706257_qa_2/task.toml +++ b/tasks/0019_706_19706257_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0019_706_19706257_qa_2" +name = "smoldataenvs-train/0019_706_19706257_qa_2" description = "Which gender has a higher average credit amount in the cleaned dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "male" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0019_787_19787626_qa_3/task.toml b/tasks/0019_787_19787626_qa_3/task.toml index efcf8be6aaf2518a8efef8ab34d63f20ec28680d..56815a5cf592b8068e1cbc3eae3972879bd6c5c9 100644 --- a/tasks/0019_787_19787626_qa_3/task.toml +++ b/tasks/0019_787_19787626_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0019_787_19787626_qa_3" +name = "smoldataenvs-train/0019_787_19787626_qa_3" description = "What is the intercept value of the trained linear regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-2640159.796851911" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0019_787_19787626_qa_4/task.toml b/tasks/0019_787_19787626_qa_4/task.toml index cb18d02886316b8de24e6e36c3765d35bdd3e669..e33f5e03b899acba0a41d1eac094cdc1e0a0507d 100644 --- a/tasks/0019_787_19787626_qa_4/task.toml +++ b/tasks/0019_787_19787626_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0019_787_19787626_qa_4" +name = "smoldataenvs-train/0019_787_19787626_qa_4" description = "What is the average housing price in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1232073.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0019_978_19978878_qa_2/task.toml b/tasks/0019_978_19978878_qa_2/task.toml index bbea39045c1ff37e10f6b46047495e552e6274cd..7b256e27a46d7554cb85b6bd86de25f971bc06e8 100644 --- a/tasks/0019_978_19978878_qa_2/task.toml +++ b/tasks/0019_978_19978878_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0019_978_19978878_qa_2" +name = "smoldataenvs-train/0019_978_19978878_qa_2" description = "Which Iris species is linearly separable from the other two according to the pairplot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-setosa" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0019_978_19978878_qa_3/task.toml b/tasks/0019_978_19978878_qa_3/task.toml index 19441f888a574b83c945ca63ab441ae93fd26e98..3c5ade4c23cc15128c0eed02ce357b6f0f7167ae 100644 --- a/tasks/0019_978_19978878_qa_3/task.toml +++ b/tasks/0019_978_19978878_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0019_978_19978878_qa_3" +name = "smoldataenvs-train/0019_978_19978878_qa_3" description = "Which combination of features in the 2D scatter plot provides the best separation between the three Iris species?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Petal length and petal width" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0020_001_20001575_qa_5/task.toml b/tasks/0020_001_20001575_qa_5/task.toml index a9711bebacdcef0b7eb09230055a1087f11d7df2..9148fd482b6d2a478ef77e5aac1e6eea847fd579 100644 --- a/tasks/0020_001_20001575_qa_5/task.toml +++ b/tasks/0020_001_20001575_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0020_001_20001575_qa_5" +name = "smoldataenvs-train/0020_001_20001575_qa_5" description = "After replacing '?' with NaN, how many missing values are present in the 'IUD (years)' column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "117" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0020_098_20098950_qa_1/task.toml b/tasks/0020_098_20098950_qa_1/task.toml index f1f2861bb4091b58102ea74e807ca029c3e6bcef..0d4664f68405b59266bd01295fc73c0b13b50d63 100644 --- a/tasks/0020_098_20098950_qa_1/task.toml +++ b/tasks/0020_098_20098950_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0020_098_20098950_qa_1" +name = "smoldataenvs-train/0020_098_20098950_qa_1" description = "Which feature (age, bmi, or children) has the highest coefficient in the trained linear regression model for predicting insurance charges?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "children" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0020_143_20143569_qa_3/task.toml b/tasks/0020_143_20143569_qa_3/task.toml index c528f056ac36949d43c02e6c0d5a0d4ebb2c48fa..d55bcd9b8406e137260257b9bdc9cdbf212858c1 100644 --- a/tasks/0020_143_20143569_qa_3/task.toml +++ b/tasks/0020_143_20143569_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0020_143_20143569_qa_3" +name = "smoldataenvs-train/0020_143_20143569_qa_3" description = "What is the median value of the 'magnesium' feature across all wine samples?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "98.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0020_162_20162109_qa_3/task.toml b/tasks/0020_162_20162109_qa_3/task.toml index 1700298a3f47d8e0211c98bc5ca9259794d62e02..44c52cc8da2e6982745cfa59e6176b64820eb028 100644 --- a/tasks/0020_162_20162109_qa_3/task.toml +++ b/tasks/0020_162_20162109_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0020_162_20162109_qa_3" +name = "smoldataenvs-train/0020_162_20162109_qa_3" description = "Do cold and hot cereals have statistically significantly different average ratings in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0020_216_20216126_qa_1/task.toml b/tasks/0020_216_20216126_qa_1/task.toml index aa53e50aa541b693696cfcaaadc17c553621e6f6..5f99914ed1a4f4e3838c4d118e47fc1931dc4428 100644 --- a/tasks/0020_216_20216126_qa_1/task.toml +++ b/tasks/0020_216_20216126_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0020_216_20216126_qa_1" +name = "smoldataenvs-train/0020_216_20216126_qa_1" description = "What is the difference in the number of edible and poisonous mushrooms in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "292" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0020_216_20216126_qa_2/task.toml b/tasks/0020_216_20216126_qa_2/task.toml index 3e09111de63f5de3d0c0ce175c61c797a974421c..c218eedcbfeb4c98e03b152aea22f479d20aebee 100644 --- a/tasks/0020_216_20216126_qa_2/task.toml +++ b/tasks/0020_216_20216126_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0020_216_20216126_qa_2" +name = "smoldataenvs-train/0020_216_20216126_qa_2" description = "Which odor is most commonly associated with poisonous mushrooms in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Foul" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0020_395_20395932_qa_4/task.toml b/tasks/0020_395_20395932_qa_4/task.toml index b6b36bfeb376b9dfafc19634cb011dd20910d65c..9403858dbd11c6ccdc724ff219022a4b6367d17d 100644 --- a/tasks/0020_395_20395932_qa_4/task.toml +++ b/tasks/0020_395_20395932_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0020_395_20395932_qa_4" +name = "smoldataenvs-train/0020_395_20395932_qa_4" description = "Is the number of ham messages significantly higher than spam messages in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0020_397_20397857_qa_2/task.toml b/tasks/0020_397_20397857_qa_2/task.toml index 82b6e51279ddb3539bab202ee1cc0e38f2e5aaf0..906b04a224bfe38d29206824b533ce7442a1afac 100644 --- a/tasks/0020_397_20397857_qa_2/task.toml +++ b/tasks/0020_397_20397857_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0020_397_20397857_qa_2" +name = "smoldataenvs-train/0020_397_20397857_qa_2" description = "What percentage of the dataset is classified as bad credit?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "30" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0020_397_20397857_qa_5/task.toml b/tasks/0020_397_20397857_qa_5/task.toml index 983f8d5e87b2e9a5748bfda1886a0dae148bd909..8ecc04581c928d844e406352f185d2707f3d08ab 100644 --- a/tasks/0020_397_20397857_qa_5/task.toml +++ b/tasks/0020_397_20397857_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0020_397_20397857_qa_5" +name = "smoldataenvs-train/0020_397_20397857_qa_5" description = "What is the total number of missing values in the Saving accounts column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "183" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0020_400_20400458_qa_3/task.toml b/tasks/0020_400_20400458_qa_3/task.toml index b3f3033c65eb8bcd0d835115a36b16082ad95ed6..633320cf5b412c3b4351aac836733e0987acf5fa 100644 --- a/tasks/0020_400_20400458_qa_3/task.toml +++ b/tasks/0020_400_20400458_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0020_400_20400458_qa_3" +name = "smoldataenvs-train/0020_400_20400458_qa_3" description = "What is the Pearson correlation coefficient between job satisfaction and salary for professional developers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.121" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0020_470_20470916_qa_1/task.toml b/tasks/0020_470_20470916_qa_1/task.toml index b4164a0a0cf3a2458f95ca8cea280bda7f5d5ed3..37b21d0efb551b6b73c2f32b07a9dd9707e9e36d 100644 --- a/tasks/0020_470_20470916_qa_1/task.toml +++ b/tasks/0020_470_20470916_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0020_470_20470916_qa_1" +name = "smoldataenvs-train/0020_470_20470916_qa_1" description = "What percentage of patients in the dataset have diabetes (Outcome = 1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0020_511_20511877_qa_1/task.toml b/tasks/0020_511_20511877_qa_1/task.toml index b0c24615bf00734c655f7a2922f58e52bd52ea58..e31cc782c766262c6b3836b4d8654a643f9a2d63 100644 --- a/tasks/0020_511_20511877_qa_1/task.toml +++ b/tasks/0020_511_20511877_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0020_511_20511877_qa_1" +name = "smoldataenvs-train/0020_511_20511877_qa_1" description = "What is the maximum global sales value among games released after 2016?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.29" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0020_511_20511877_qa_4/task.toml b/tasks/0020_511_20511877_qa_4/task.toml index e873732bd88f881f55a18c6861192a472f3729e4..ccfeb03a3dd77bb09dfeaf5af6303e1cf8c2a70b 100644 --- a/tasks/0020_511_20511877_qa_4/task.toml +++ b/tasks/0020_511_20511877_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0020_511_20511877_qa_4" +name = "smoldataenvs-train/0020_511_20511877_qa_4" description = "What is the highest NA_Sales value among games released after 2010 with global sales over 12 million?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9.66" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0020_539_20539860_qa_3/task.toml b/tasks/0020_539_20539860_qa_3/task.toml index 553e3cd4f766575f317141c5b65444bb0d5279b9..933a406e0170e750c1ba6af747ae01820b3a623b 100644 --- a/tasks/0020_539_20539860_qa_3/task.toml +++ b/tasks/0020_539_20539860_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0020_539_20539860_qa_3" +name = "smoldataenvs-train/0020_539_20539860_qa_3" description = "What is the minimum price for a wine rated 99 points?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "44.0" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0020_539_20539860_qa_5/task.toml b/tasks/0020_539_20539860_qa_5/task.toml index 31d82980e2c36687a3453314e0fbc7ac2c01f8d9..06c010b5259d4cb8b87d28f3e118a2c6a38dcf0a 100644 --- a/tasks/0020_539_20539860_qa_5/task.toml +++ b/tasks/0020_539_20539860_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0020_539_20539860_qa_5" +name = "smoldataenvs-train/0020_539_20539860_qa_5" description = "What is the minimum price for a 91-point rated wine?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7.0" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0020_610_20610563_qa_5/task.toml b/tasks/0020_610_20610563_qa_5/task.toml index 3a52014563eb8bf3201e601be4cc5ddd92d87b4b..415cdf1d24bc4388cf628c34dd39038ea9a7de7c 100644 --- a/tasks/0020_610_20610563_qa_5/task.toml +++ b/tasks/0020_610_20610563_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0020_610_20610563_qa_5" +name = "smoldataenvs-train/0020_610_20610563_qa_5" description = "What is the F1-score for ham messages in the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.99" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0020_611_20611134_qa_1/task.toml b/tasks/0020_611_20611134_qa_1/task.toml index ab98428a1927d8350c89b9aed5f2bc09207e7101..7f48d851eb9097f85bf69674f4aafd9d810c449b 100644 --- a/tasks/0020_611_20611134_qa_1/task.toml +++ b/tasks/0020_611_20611134_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0020_611_20611134_qa_1" +name = "smoldataenvs-train/0020_611_20611134_qa_1" description = "What is the accuracy of the spam classification model on the test data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "98.38" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0020_613_20613845_qa_4/task.toml b/tasks/0020_613_20613845_qa_4/task.toml index 91243c75b2b941f785e8ca1c02cedefdc6e963e8..fd816ea216506d1cadee9b1241abddb899cace5e 100644 --- a/tasks/0020_613_20613845_qa_4/task.toml +++ b/tasks/0020_613_20613845_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0020_613_20613845_qa_4" +name = "smoldataenvs-train/0020_613_20613845_qa_4" description = "What is the maximum value of the 'radius_mean' feature in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "28.11" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0020_756_20756026_qa_2/task.toml b/tasks/0020_756_20756026_qa_2/task.toml index 65cdfec09287b1055b5df70a95cf275e19629486..377aea272a8c39221cb868a705328dbf9b289603 100644 --- a/tasks/0020_756_20756026_qa_2/task.toml +++ b/tasks/0020_756_20756026_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0020_756_20756026_qa_2" +name = "smoldataenvs-train/0020_756_20756026_qa_2" description = "How many missing values were present in the Outlet_Size column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4016" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0020_764_20764174_qa_3/task.toml b/tasks/0020_764_20764174_qa_3/task.toml index 95b8c131d474fd2198384d05168643ea1718e932..f8abfa39ae7b99e91b5fc2073c7c4e9140acf25b 100644 --- a/tasks/0020_764_20764174_qa_3/task.toml +++ b/tasks/0020_764_20764174_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0020_764_20764174_qa_3" +name = "smoldataenvs-train/0020_764_20764174_qa_3" description = "What is the median area population value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "36199.406689" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0020_815_20815404_qa_2/task.toml b/tasks/0020_815_20815404_qa_2/task.toml index e46f739b1a691dc58fac90a7547263f2cb7289da..e5a4ade59295ac6be23131062065d0c2a538754c 100644 --- a/tasks/0020_815_20815404_qa_2/task.toml +++ b/tasks/0020_815_20815404_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0020_815_20815404_qa_2" +name = "smoldataenvs-train/0020_815_20815404_qa_2" description = "What is the correlation coefficient between the \"Apps\" and \"Accept\" features in the original dataset before preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.943450572" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0020_828_20828784_qa_1/task.toml b/tasks/0020_828_20828784_qa_1/task.toml index 856360f78216a7f0e691b5c906d9df8b9f46ee16..ab3b59dbf0f8bb03d6df082d009840cf2b0a3fe9 100644 --- a/tasks/0020_828_20828784_qa_1/task.toml +++ b/tasks/0020_828_20828784_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0020_828_20828784_qa_1" +name = "smoldataenvs-train/0020_828_20828784_qa_1" description = "Which feature in the dataset had the highest number of missing zeros before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Insulin" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0020_895_20895805_qa_2/task.toml b/tasks/0020_895_20895805_qa_2/task.toml index a319759073ad87f69996046eb27577a0d5c93e55..b50135542153b17befe313098aa588633b162fe1 100644 --- a/tasks/0020_895_20895805_qa_2/task.toml +++ b/tasks/0020_895_20895805_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0020_895_20895805_qa_2" +name = "smoldataenvs-train/0020_895_20895805_qa_2" description = "What percentage of tweets have a valid negative reason recorded after data preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "63.12" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0020_904_20904261_qa_2/task.toml b/tasks/0020_904_20904261_qa_2/task.toml index 557be129c2dca3d226b4fd9d0829a7dd4e19dc2a..a0bc45e6c29bb3c58d4d3711b519f539f8c1ad72 100644 --- a/tasks/0020_904_20904261_qa_2/task.toml +++ b/tasks/0020_904_20904261_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0020_904_20904261_qa_2" +name = "smoldataenvs-train/0020_904_20904261_qa_2" description = "What is the most frequent negative reason for United Airlines based on the processed data analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Customer Service Issue" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0020_991_20991108_qa_5/task.toml b/tasks/0020_991_20991108_qa_5/task.toml index 856ef09c927d86033970845893aebf041cdc663f..b0c776bab2a92e6dca3397af251595a948b6a073 100644 --- a/tasks/0020_991_20991108_qa_5/task.toml +++ b/tasks/0020_991_20991108_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0020_991_20991108_qa_5" +name = "smoldataenvs-train/0020_991_20991108_qa_5" description = "What is the total number of test samples evaluated in the MLP model's confusion matrix output?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1637 samples" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0020_991_20991289_qa_3/task.toml b/tasks/0020_991_20991289_qa_3/task.toml index b371055eea46f8f1595590ca334b27d66f4eb268..3de5df1331b215309c9fd0be20d66e60ded3e640 100644 --- a/tasks/0020_991_20991289_qa_3/task.toml +++ b/tasks/0020_991_20991289_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0020_991_20991289_qa_3" +name = "smoldataenvs-train/0020_991_20991289_qa_3" description = "What is the accuracy score of the Random Forest classifier on the test set compared to the Decision Tree classifier?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Random Forest: 0.87, Decision Tree: 0.79" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0021_030_21030697_qa_5/task.toml b/tasks/0021_030_21030697_qa_5/task.toml index e311a88554c281f1a90b819456396aaadebe82f9..8e96d5e45e973faaead755d6bf8f9e23ac4fe862 100644 --- a/tasks/0021_030_21030697_qa_5/task.toml +++ b/tasks/0021_030_21030697_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0021_030_21030697_qa_5" +name = "smoldataenvs-train/0021_030_21030697_qa_5" description = "What percentage of projects in the original dataset were either failed or successful (excluding canceled, undefined, live, and suspended states)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "87.6%" reward_mode_initial = "flexible" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_046_21046589_qa_1/task.toml b/tasks/0021_046_21046589_qa_1/task.toml index d7b7bf7dd9996a9b1c6cc548995513cf39959a33..c9bb1833e4ae9ccdb6e571996d6367800a80cd4d 100644 --- a/tasks/0021_046_21046589_qa_1/task.toml +++ b/tasks/0021_046_21046589_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0021_046_21046589_qa_1" +name = "smoldataenvs-train/0021_046_21046589_qa_1" description = "How many instances in the dataset originally had zero values for the SkinThickness feature before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "227" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0021_063_21063235_qa_1/task.toml b/tasks/0021_063_21063235_qa_1/task.toml index 9ca5f4341cc4718ed7315265565bf3d3a92d71e9..189e7899838f560212d4e793c0ef8755876ddb83 100644 --- a/tasks/0021_063_21063235_qa_1/task.toml +++ b/tasks/0021_063_21063235_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0021_063_21063235_qa_1" +name = "smoldataenvs-train/0021_063_21063235_qa_1" description = "How many outliers were identified in the 'radius_mean' column before replacement with median values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "14" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_093_21093883_qa_2/task.toml b/tasks/0021_093_21093883_qa_2/task.toml index 865f69684cf1c534be8a7f3ae2bff054800ad08f..74ceba70aaa7dbe5f56716a2d52da0ae0a09e705 100644 --- a/tasks/0021_093_21093883_qa_2/task.toml +++ b/tasks/0021_093_21093883_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0021_093_21093883_qa_2" +name = "smoldataenvs-train/0021_093_21093883_qa_2" description = "Which stadium has hosted the most IPL matches from 2008 to 2017?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "M Chinnaswamy Stadium" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_093_21093883_qa_3/task.toml b/tasks/0021_093_21093883_qa_3/task.toml index 287abc33a16789628cf2e847318e735c768e5614..602f636a60e824dd689a9ad9ec747368a4949e67 100644 --- a/tasks/0021_093_21093883_qa_3/task.toml +++ b/tasks/0021_093_21093883_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0021_093_21093883_qa_3" +name = "smoldataenvs-train/0021_093_21093883_qa_3" description = "Which player received the most Man of the Match awards across all 9 IPL seasons?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Chris Gayle" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_121_21121885_qa_3/task.toml b/tasks/0021_121_21121885_qa_3/task.toml index 631358eb096e8eb633a121e06b14d8874269e750..e4440a499d9c5e0f85c7bdd2feb5948ce5d445f9 100644 --- a/tasks/0021_121_21121885_qa_3/task.toml +++ b/tasks/0021_121_21121885_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0021_121_21121885_qa_3" +name = "smoldataenvs-train/0021_121_21121885_qa_3" description = "Which four topics emerged as most prominent in the word cloud analysis of the larger dataset of unpopular TED talks (205 entries with high negative rating ratios)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Technology, design, culture, global issues" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0021_135_21135636_qa_3/task.toml b/tasks/0021_135_21135636_qa_3/task.toml index 620b586dbc0285f5e4e945b6d3b6f2c702de63c7..4dd2f7e25b7d620d87d67525b03eb5762b535da7 100644 --- a/tasks/0021_135_21135636_qa_3/task.toml +++ b/tasks/0021_135_21135636_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0021_135_21135636_qa_3" +name = "smoldataenvs-train/0021_135_21135636_qa_3" description = "What is the highest price recorded for any wine in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3300" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0021_145_21145195_qa_3/task.toml b/tasks/0021_145_21145195_qa_3/task.toml index 9a6f48010e773968a890c384049b8b2d87d526a9..843a787bbc3189f5c35cd0e5ef373b28c3c964a1 100644 --- a/tasks/0021_145_21145195_qa_3/task.toml +++ b/tasks/0021_145_21145195_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0021_145_21145195_qa_3" +name = "smoldataenvs-train/0021_145_21145195_qa_3" description = "Which month has the highest average rainfall in the Western Ghats across all subdivisions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "July" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_147_21147512_qa_4/task.toml b/tasks/0021_147_21147512_qa_4/task.toml index 494b9b64dfb325f25b42d434cd57cc8102d247af..0ac53252ae6c19aa8379779a590b3c1e486e550e 100644 --- a/tasks/0021_147_21147512_qa_4/task.toml +++ b/tasks/0021_147_21147512_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0021_147_21147512_qa_4" +name = "smoldataenvs-train/0021_147_21147512_qa_4" description = "What is the most common victory method (by count) for top 10% rated white players, and what percentage of their total wins does it represent?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Resignation (42%)" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_193_21193214_qa_1/task.toml b/tasks/0021_193_21193214_qa_1/task.toml index b58092eaa698661e9dbcf3aeeb005604fcd3c19f..9de8f6b91221bad13901f385bc4ba6ac904ba037 100644 --- a/tasks/0021_193_21193214_qa_1/task.toml +++ b/tasks/0021_193_21193214_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0021_193_21193214_qa_1" +name = "smoldataenvs-train/0021_193_21193214_qa_1" description = "Which exoplanet identified in the habitable zone has the shortest distance from Earth, and what is its distance in parsecs?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Proxima Centauri b, 1.2950" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_193_21193214_qa_3/task.toml b/tasks/0021_193_21193214_qa_3/task.toml index 4adfd27eb72c5bef88604efde80095a48249f82c..30d67f4e1ca94284dd65a3b9f11e07cb8ab3766a 100644 --- a/tasks/0021_193_21193214_qa_3/task.toml +++ b/tasks/0021_193_21193214_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0021_193_21193214_qa_3" +name = "smoldataenvs-train/0021_193_21193214_qa_3" description = "What is the slope of the linear regression line fitted to the log-transformed Kepler's third law data (log(PeriodDays) vs log(SemiMajorAxisAU))?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.46539408" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_193_21193214_qa_5/task.toml b/tasks/0021_193_21193214_qa_5/task.toml index 38de3edde4e8bf97461fe0a045e6b7c6a11d51bc..96340f78ad360e8b76413c5123c59b43bc69ff84 100644 --- a/tasks/0021_193_21193214_qa_5/task.toml +++ b/tasks/0021_193_21193214_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0021_193_21193214_qa_5" +name = "smoldataenvs-train/0021_193_21193214_qa_5" description = "What is the coefficient of determination (R²) for the linear regression model analyzing the relationship between log(PlanetaryMass) and log(Radius) of exoplanets?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.7419577761281136" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_222_21222912_qa_1/task.toml b/tasks/0021_222_21222912_qa_1/task.toml index c26c83cac577f58fc6033be7b7fadf7aadc678b3..b846297ecdd42d5c181630e2462946adac227e46 100644 --- a/tasks/0021_222_21222912_qa_1/task.toml +++ b/tasks/0021_222_21222912_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0021_222_21222912_qa_1" +name = "smoldataenvs-train/0021_222_21222912_qa_1" description = "How many rows were removed from the dataset due to missing values after data cleaning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3737" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_225_21225981_qa_3/task.toml b/tasks/0021_225_21225981_qa_3/task.toml index a8a6082c60cec34312ce55fa699b6ebad1deb96e..95c78fec9fdafad5610f374e2016ec707c01792c 100644 --- a/tasks/0021_225_21225981_qa_3/task.toml +++ b/tasks/0021_225_21225981_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0021_225_21225981_qa_3" +name = "smoldataenvs-train/0021_225_21225981_qa_3" description = "Which theme name appears most frequently in the themes dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Supplemental" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0021_225_21225981_qa_4/task.toml b/tasks/0021_225_21225981_qa_4/task.toml index 749a1182c585a127655f04b527f35bbe3e77370a..42a74d7197b9e422514a7fdbf76df04554390548 100644 --- a/tasks/0021_225_21225981_qa_4/task.toml +++ b/tasks/0021_225_21225981_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0021_225_21225981_qa_4" +name = "smoldataenvs-train/0021_225_21225981_qa_4" description = "How many colors in the dataset are classified as transparent?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "28" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0021_299_21299002_qa_3/task.toml b/tasks/0021_299_21299002_qa_3/task.toml index 7c7d07328d07dc67a5cf01ce2886530be248e044..56a946a4999f6179e88976317fea2c16d3c2cc96 100644 --- a/tasks/0021_299_21299002_qa_3/task.toml +++ b/tasks/0021_299_21299002_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0021_299_21299002_qa_3" +name = "smoldataenvs-train/0021_299_21299002_qa_3" description = "Following the second train-test split with a test size of 20%, how many samples were allocated to the training set for the CNN model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1649" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_336_21336465_qa_1/task.toml b/tasks/0021_336_21336465_qa_1/task.toml index a43d96b506725dd69356ca76a9f21d1db0b23282..0f9cf38e47aec15dbaa55dd95f7493d2d450d991 100644 --- a/tasks/0021_336_21336465_qa_1/task.toml +++ b/tasks/0021_336_21336465_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0021_336_21336465_qa_1" +name = "smoldataenvs-train/0021_336_21336465_qa_1" description = "How many rows had missing TotalCharges values before they were removed from the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_336_21336465_qa_2/task.toml b/tasks/0021_336_21336465_qa_2/task.toml index 59938e9b5eb940d6b6e9ba2cd6f455d4aafe73d7..b5e5c2af84a2f754abf798729fbd46bbf484c5bd 100644 --- a/tasks/0021_336_21336465_qa_2/task.toml +++ b/tasks/0021_336_21336465_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0021_336_21336465_qa_2" +name = "smoldataenvs-train/0021_336_21336465_qa_2" description = "What percentage of the dataset was removed due to missing TotalCharges values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.16" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_353_21353656_qa_1/task.toml b/tasks/0021_353_21353656_qa_1/task.toml index 4b5d03d71e2061ce0e98984d5980e01022d61fb7..aa23e67daa49e17f41de28d6154fb3ac09cb9044 100644 --- a/tasks/0021_353_21353656_qa_1/task.toml +++ b/tasks/0021_353_21353656_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0021_353_21353656_qa_1" +name = "smoldataenvs-train/0021_353_21353656_qa_1" description = "What is the highest average alcohol content percentage among the three wine quality categories (bad, ok, good)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11.52" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_358_21358195_qa_1/task.toml b/tasks/0021_358_21358195_qa_1/task.toml index d2c3b0f371eb5098e826c8553ab77b4bba4227ca..9f0fa8e7240b5e945024ddf5cec4e85dd702344b 100644 --- a/tasks/0021_358_21358195_qa_1/task.toml +++ b/tasks/0021_358_21358195_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0021_358_21358195_qa_1" +name = "smoldataenvs-train/0021_358_21358195_qa_1" description = "What is the correlation coefficient between PetalLengthCm and PetalWidthCm in the Iris dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.96" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0021_358_21358195_qa_4/task.toml b/tasks/0021_358_21358195_qa_4/task.toml index b7e0870d365cc0c59cc57d87e999d483d2a00f7d..2ab0a00753e654adf6615aad3262746879d19c78 100644 --- a/tasks/0021_358_21358195_qa_4/task.toml +++ b/tasks/0021_358_21358195_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0021_358_21358195_qa_4" +name = "smoldataenvs-train/0021_358_21358195_qa_4" description = "What is the maximum PetalWidthCm observed in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0021_358_21358195_qa_5/task.toml b/tasks/0021_358_21358195_qa_5/task.toml index 25038293fbcb40e82ac9e033e2605e8d084e2619..2835c4ad141b04c61be0e3b1ea4340d4536df31b 100644 --- a/tasks/0021_358_21358195_qa_5/task.toml +++ b/tasks/0021_358_21358195_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0021_358_21358195_qa_5" +name = "smoldataenvs-train/0021_358_21358195_qa_5" description = "How many instances are there for each species in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0021_384_21384330_qa_2/task.toml b/tasks/0021_384_21384330_qa_2/task.toml index 1c83ec8a32cb9548b719d261b42f40537e2591f2..ef9a42428f68bf337142fcc377a4b24df1ff011a 100644 --- a/tasks/0021_384_21384330_qa_2/task.toml +++ b/tasks/0021_384_21384330_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0021_384_21384330_qa_2" +name = "smoldataenvs-train/0021_384_21384330_qa_2" description = "Which airline has the highest total number of aircraft across all periods?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "American Airlines" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_389_21389737_qa_4/task.toml b/tasks/0021_389_21389737_qa_4/task.toml index bc594808d6ffbb7e2884fc9c53dd79d1edcf213e..77ad6d2fca66735581ffd086247ee02402d28f61 100644 --- a/tasks/0021_389_21389737_qa_4/task.toml +++ b/tasks/0021_389_21389737_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0021_389_21389737_qa_4" +name = "smoldataenvs-train/0021_389_21389737_qa_4" description = "Which South American country had the highest life expectancy in 2015 according to the cleaned dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Chile" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_453_21453047_qa_2/task.toml b/tasks/0021_453_21453047_qa_2/task.toml index 7ad4aa363eb675795258a31e44f62601dd65f06b..d6ae8e0d2fd5486779995724e6fdee7a99900795 100644 --- a/tasks/0021_453_21453047_qa_2/task.toml +++ b/tasks/0021_453_21453047_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0021_453_21453047_qa_2" +name = "smoldataenvs-train/0021_453_21453047_qa_2" description = "What is the average number of legs for animals in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.84" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0021_453_21453047_qa_4/task.toml b/tasks/0021_453_21453047_qa_4/task.toml index 345da80b6782848ecd68c40a4125e5272772ee7b..060547ffc853d483fde0855de9ca68f964d72c48 100644 --- a/tasks/0021_453_21453047_qa_4/task.toml +++ b/tasks/0021_453_21453047_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0021_453_21453047_qa_4" +name = "smoldataenvs-train/0021_453_21453047_qa_4" description = "What percentage of animals in the dataset are classified as aquatic?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "35.64" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_470_21470555_qa_4/task.toml b/tasks/0021_470_21470555_qa_4/task.toml index 2b0ce2318c647ff61ae96ae618f3f6867ce3287d..1eb6488641f6be41f7d6ad22ec5aa08f4d837373 100644 --- a/tasks/0021_470_21470555_qa_4/task.toml +++ b/tasks/0021_470_21470555_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0021_470_21470555_qa_4" +name = "smoldataenvs-train/0021_470_21470555_qa_4" description = "What is the predicted salary for an individual with 6 years of experience according to the regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "82384.22921357" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_562_21562813_qa_3/task.toml b/tasks/0021_562_21562813_qa_3/task.toml index a4459f00fa20c7497508087c070fdca85387f4b5..26acd70f78f5f4f86faacec085db0f99a5d46c7c 100644 --- a/tasks/0021_562_21562813_qa_3/task.toml +++ b/tasks/0021_562_21562813_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0021_562_21562813_qa_3" +name = "smoldataenvs-train/0021_562_21562813_qa_3" description = "What are the unique numerical values assigned to the species categories after label encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "[0, 1, 2]" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_636_21636109_qa_2/task.toml b/tasks/0021_636_21636109_qa_2/task.toml index 77b560a41c680dbdf061568dc8e4c51130625008..ba81c4aa56bec968712ab41367643abbbfc76d1c 100644 --- a/tasks/0021_636_21636109_qa_2/task.toml +++ b/tasks/0021_636_21636109_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0021_636_21636109_qa_2" +name = "smoldataenvs-train/0021_636_21636109_qa_2" description = "How many entries in the dataset show a discrepancy between the \"Rape_Cases_Reported\" and \"Victims_of_Rape_Total\" columns?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "87" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_636_21636109_qa_3/task.toml b/tasks/0021_636_21636109_qa_3/task.toml index 220206d46b62f87d8941234360b512f514c5836a..9305c19b758844868b07a96ee3b13bf122c0b9a7 100644 --- a/tasks/0021_636_21636109_qa_3/task.toml +++ b/tasks/0021_636_21636109_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0021_636_21636109_qa_3" +name = "smoldataenvs-train/0021_636_21636109_qa_3" description = "Which subgroup of rape cases has a higher cumulative total across all states and years: \"Victims of Incest Rape\" or \"Victims of Other Rape\"?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Victims of Other Rape" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_643_21643452_qa_5/task.toml b/tasks/0021_643_21643452_qa_5/task.toml index d1d6510427b572fbb5f621a426cb14cf05c34dda..56d1237ae0c9711aaa486fd75c4df67ad1440083 100644 --- a/tasks/0021_643_21643452_qa_5/task.toml +++ b/tasks/0021_643_21643452_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0021_643_21643452_qa_5" +name = "smoldataenvs-train/0021_643_21643452_qa_5" description = "Which currency has the highest proportion of successful projects in the dataset based on the heatmap visualization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "USD" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_656_21656365_qa_5/task.toml b/tasks/0021_656_21656365_qa_5/task.toml index 75256791456b0648665dbbd223fde0118a2bd189..89103aa3506c7050e2ccec01d5b625e1b77518e3 100644 --- a/tasks/0021_656_21656365_qa_5/task.toml +++ b/tasks/0021_656_21656365_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0021_656_21656365_qa_5" +name = "smoldataenvs-train/0021_656_21656365_qa_5" description = "Which feature (by original variable name) is used for the first split in the decision tree model based on Gini impurity reduction?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PAY_0" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0021_681_21681967_qa_1/task.toml b/tasks/0021_681_21681967_qa_1/task.toml index cc46043fd79972124733554f18ca241c65f3aed9..8c6f6158a6d88e9b8948d8d6a090140d4bda22a2 100644 --- a/tasks/0021_681_21681967_qa_1/task.toml +++ b/tasks/0021_681_21681967_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0021_681_21681967_qa_1" +name = "smoldataenvs-train/0021_681_21681967_qa_1" description = "What is the percentage of spam messages in the SMS dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13.41" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_687_21687757_qa_1/task.toml b/tasks/0021_687_21687757_qa_1/task.toml index a4af6ce0ba400b84535d40c75b87df8d283b0844..55d55e0a89a809c9e8d2b3dd08af454a5dfc8164 100644 --- a/tasks/0021_687_21687757_qa_1/task.toml +++ b/tasks/0021_687_21687757_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0021_687_21687757_qa_1" +name = "smoldataenvs-train/0021_687_21687757_qa_1" description = "Which four features were selected as the most important using the KBest method based on chi-squared scores?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ram, px_height, battery_power, px_width" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_688_21688932_qa_1/task.toml b/tasks/0021_688_21688932_qa_1/task.toml index 053f98531e3b0afbae76cda0e88220d298138f2a..312e9a7e13f9924b04c237ba0e652fc0bd0ac1a3 100644 --- a/tasks/0021_688_21688932_qa_1/task.toml +++ b/tasks/0021_688_21688932_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0021_688_21688932_qa_1" +name = "smoldataenvs-train/0021_688_21688932_qa_1" description = "What is the most frequently occurring cuisine type in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Mexican" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0021_698_21698205_qa_2/task.toml b/tasks/0021_698_21698205_qa_2/task.toml index 3e8a856336e52a90f9e0541e79914ba4f8b7d7fc..6bdc0127c9d1ccf97687d169e17fdb0403a5204c 100644 --- a/tasks/0021_698_21698205_qa_2/task.toml +++ b/tasks/0021_698_21698205_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0021_698_21698205_qa_2" +name = "smoldataenvs-train/0021_698_21698205_qa_2" description = "Which feature extraction technique achieves 100% accuracy while reducing the number of features from 117 to 1?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "LDA" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0021_699_21699891_qa_1/task.toml b/tasks/0021_699_21699891_qa_1/task.toml index e4e2f72004ae92a937c6933d0294aa44644efa11..005f987b5b7ec4a6ad0f567558e529752aecc36f 100644 --- a/tasks/0021_699_21699891_qa_1/task.toml +++ b/tasks/0021_699_21699891_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0021_699_21699891_qa_1" +name = "smoldataenvs-train/0021_699_21699891_qa_1" description = "What percentage of students in the dataset dropped out according to the 'continue_drop' classification?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.7" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_719_21719622_qa_1/task.toml b/tasks/0021_719_21719622_qa_1/task.toml index 3e7f7bcd3e37a071f950f52df0936f7d950563f5..a3eb4a95e8e08194294962944c27273df2111068 100644 --- a/tasks/0021_719_21719622_qa_1/task.toml +++ b/tasks/0021_719_21719622_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0021_719_21719622_qa_1" +name = "smoldataenvs-train/0021_719_21719622_qa_1" description = "Which European city has the highest average number of reviews per restaurant after accounting for the total number of restaurants in each city?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Rome" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_719_21719622_qa_2/task.toml b/tasks/0021_719_21719622_qa_2/task.toml index 677187c4446e5c37a097b62cd9a4ac12869e6a5d..c7d19c73add907bcb29fb11ae7d384088a248519 100644 --- a/tasks/0021_719_21719622_qa_2/task.toml +++ b/tasks/0021_719_21719622_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0021_719_21719622_qa_2" +name = "smoldataenvs-train/0021_719_21719622_qa_2" description = "In cities where higher-priced restaurants exist, does the average rating for higher-priced restaurants exceed the average rating for medium- and lower-priced restaurants?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_719_21719622_qa_4/task.toml b/tasks/0021_719_21719622_qa_4/task.toml index 0ce71a7221810a6c82995039d3cf32bf4247f492..8cf7c2847c9bbd929f64bccd24935e4c461cf33f 100644 --- a/tasks/0021_719_21719622_qa_4/task.toml +++ b/tasks/0021_719_21719622_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0021_719_21719622_qa_4" +name = "smoldataenvs-train/0021_719_21719622_qa_4" description = "Which price range (cheaper, medium, or higher) has the highest average number of reviews across all cities in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "higher" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_719_21719622_qa_5/task.toml b/tasks/0021_719_21719622_qa_5/task.toml index dead937794c59b1161a4149f8ab9cb45140f1770..e96c65ace91d3c6900f3448fef4bd028725afc69 100644 --- a/tasks/0021_719_21719622_qa_5/task.toml +++ b/tasks/0021_719_21719622_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0021_719_21719622_qa_5" +name = "smoldataenvs-train/0021_719_21719622_qa_5" description = "What is the total number of unique cuisine styles identified in the dataset after expanding multi-cuisine restaurants into individual rows?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "127" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_751_21751462_qa_3/task.toml b/tasks/0021_751_21751462_qa_3/task.toml index 58103bf8955a1723ccc9aa77c3a3e0f279b32d17..ba65f8b6f3e2162aaecff0600b2124ca397062ca 100644 --- a/tasks/0021_751_21751462_qa_3/task.toml +++ b/tasks/0021_751_21751462_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0021_751_21751462_qa_3" +name = "smoldataenvs-train/0021_751_21751462_qa_3" description = "Which zipcode had the highest number of property sales in the dataset before any outlier removal, and how many transactions occurred in that area?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "98103, 602" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_751_21751462_qa_4/task.toml b/tasks/0021_751_21751462_qa_4/task.toml index c18511eb2a974114bded0b2f9b0af6ec4e38c4b5..052f814e5b5b4d551ff5fe43181efe06e773b51a 100644 --- a/tasks/0021_751_21751462_qa_4/task.toml +++ b/tasks/0021_751_21751462_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0021_751_21751462_qa_4" +name = "smoldataenvs-train/0021_751_21751462_qa_4" description = "How many duplicate property records were identified and removed based on the unique combination of location, date, and property characteristics?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_794_21794618_qa_2/task.toml b/tasks/0021_794_21794618_qa_2/task.toml index aa0898ab9b5004e2287386cb73a773e780dba05e..d8a51d402e25fc26fd52891b327db401247d90ac 100644 --- a/tasks/0021_794_21794618_qa_2/task.toml +++ b/tasks/0021_794_21794618_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0021_794_21794618_qa_2" +name = "smoldataenvs-train/0021_794_21794618_qa_2" description = "After outlier removal using the IQR method, what was the skewness value of the 'so2' pollutant column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.888" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_803_21803688_qa_1/task.toml b/tasks/0021_803_21803688_qa_1/task.toml index a68a10185deade316d9a04d97d9cc2c1dab1ad03..c929bdcc12094a930062c07a0a01ddc7d1a44000 100644 --- a/tasks/0021_803_21803688_qa_1/task.toml +++ b/tasks/0021_803_21803688_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0021_803_21803688_qa_1" +name = "smoldataenvs-train/0021_803_21803688_qa_1" description = "Which two features are identified as the most important predictors of diamond price according to the RandomForestRegressor model based on feature importance scores?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "carat and y" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0021_803_21803688_qa_2/task.toml b/tasks/0021_803_21803688_qa_2/task.toml index 2ff428bce346e6cc6031009d3fbae86b34d81d2f..5620f8956a8c60159a648c1a80d320cf7a9e29d7 100644 --- a/tasks/0021_803_21803688_qa_2/task.toml +++ b/tasks/0021_803_21803688_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0021_803_21803688_qa_2" +name = "smoldataenvs-train/0021_803_21803688_qa_2" description = "What is the coefficient of determination (R² score) achieved by the Random Forest model on the test set for predicting diamond prices?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9803271929837977" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0021_804_21804957_qa_3/task.toml b/tasks/0021_804_21804957_qa_3/task.toml index 050ae641300d640ac1b271b86ee95950fd815541..75849f6d2787b3570b0a3c28bdf99e7b9f08ce6b 100644 --- a/tasks/0021_804_21804957_qa_3/task.toml +++ b/tasks/0021_804_21804957_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0021_804_21804957_qa_3" +name = "smoldataenvs-train/0021_804_21804957_qa_3" description = "What is the most common sentiment category in the dataset after deduplication?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Positive" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_828_21828791_qa_2/task.toml b/tasks/0021_828_21828791_qa_2/task.toml index c8573c9da260962fc19c0a8eb183c5ad8e25b7c6..b6444b966e95c050aff952cd9ce18122f6dd5470 100644 --- a/tasks/0021_828_21828791_qa_2/task.toml +++ b/tasks/0021_828_21828791_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0021_828_21828791_qa_2" +name = "smoldataenvs-train/0021_828_21828791_qa_2" description = "What classification accuracy was achieved using the correlation-reduced feature set with a Random Forest classifier?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "95.32" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0021_847_21847371_qa_3/task.toml b/tasks/0021_847_21847371_qa_3/task.toml index caa2643c6386c9d196f2b5c6f4e539bc3f76d94f..c7e2c3b894eb0b68d62c9a3e91b97519df258f40 100644 --- a/tasks/0021_847_21847371_qa_3/task.toml +++ b/tasks/0021_847_21847371_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0021_847_21847371_qa_3" +name = "smoldataenvs-train/0021_847_21847371_qa_3" description = "Which sentiment category is most frequently represented in the training-validation split of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "negative" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_868_21868084_qa_1/task.toml b/tasks/0021_868_21868084_qa_1/task.toml index 930393e83f2a6527cce81f75cc92a3eef2266bb7..47f3f237f8f7dabd1f44f32ccae24e776bb86631 100644 --- a/tasks/0021_868_21868084_qa_1/task.toml +++ b/tasks/0021_868_21868084_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0021_868_21868084_qa_1" +name = "smoldataenvs-train/0021_868_21868084_qa_1" description = "Which column in the dataset has the highest number of missing values, and what is the count?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Low Wind NE, 19750" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_873_21873405_qa_2/task.toml b/tasks/0021_873_21873405_qa_2/task.toml index 1259d469a3777cae0edaee030a6a623180746b45..43e4dcbbb942f6e34fcc6f0e2440e41b4b5bcab4 100644 --- a/tasks/0021_873_21873405_qa_2/task.toml +++ b/tasks/0021_873_21873405_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0021_873_21873405_qa_2" +name = "smoldataenvs-train/0021_873_21873405_qa_2" description = "What is the mean monthly charge for customers in the tenure-0-10 group who churned?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "65.86" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_893_21893685_qa_1/task.toml b/tasks/0021_893_21893685_qa_1/task.toml index 9ab9cb70484669ef9efa53177746af23409db5c6..c7ab1f12ba6deef287747852e53436554c55913b 100644 --- a/tasks/0021_893_21893685_qa_1/task.toml +++ b/tasks/0021_893_21893685_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0021_893_21893685_qa_1" +name = "smoldataenvs-train/0021_893_21893685_qa_1" description = "What percentage of total variance in the breast cancer dataset is explained by the first five principal components selected using PCA?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "84.73" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0021_893_21893685_qa_5/task.toml b/tasks/0021_893_21893685_qa_5/task.toml index 2adba8fd6d2017cdd61ea7e8a3476650b449011d..4fd8d9902470eb784aca3db5ae9a6e022c59a39d 100644 --- a/tasks/0021_893_21893685_qa_5/task.toml +++ b/tasks/0021_893_21893685_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0021_893_21893685_qa_5" +name = "smoldataenvs-train/0021_893_21893685_qa_5" description = "What is the proportion of benign (B) vs malignant (M) tumors in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "62.7% benign, 37.3% malignant" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0021_901_21901400_qa_3/task.toml b/tasks/0021_901_21901400_qa_3/task.toml index 87867198e034b44498316d70cd9af76e2d58418b..e2c4bec774814378ac022064db8c8978afcc726f 100644 --- a/tasks/0021_901_21901400_qa_3/task.toml +++ b/tasks/0021_901_21901400_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0021_901_21901400_qa_3" +name = "smoldataenvs-train/0021_901_21901400_qa_3" description = "Which category type (main category vs. subcategory) shows greater influence on project success based on the feature importance analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "subcategory" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0021_960_21960570_qa_3/task.toml b/tasks/0021_960_21960570_qa_3/task.toml index 373da908a6823f935a3f216f34db54a2ab384301..c31560981e8b38a3f7cae39a5006e78bc7672ea3 100644 --- a/tasks/0021_960_21960570_qa_3/task.toml +++ b/tasks/0021_960_21960570_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0021_960_21960570_qa_3" +name = "smoldataenvs-train/0021_960_21960570_qa_3" description = "What is the skewness of the mpg distribution before any transformation is applied?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.46" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0021_960_21960570_qa_4/task.toml b/tasks/0021_960_21960570_qa_4/task.toml index 802c9de0cc685a1a2741259290f7412c2a31369e..351fd6d44c78725749393e73d88980977b270e77 100644 --- a/tasks/0021_960_21960570_qa_4/task.toml +++ b/tasks/0021_960_21960570_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0021_960_21960570_qa_4" +name = "smoldataenvs-train/0021_960_21960570_qa_4" description = "What is the skewness of the mpg distribution after applying the Box-Cox transformation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.02" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0022_056_22056321_qa_5/task.toml b/tasks/0022_056_22056321_qa_5/task.toml index 8e18b4a4478207f654dc2b4bdd8a04696b0b11a6..ad124817627415c0e9d4c73bc33018273427ac25 100644 --- a/tasks/0022_056_22056321_qa_5/task.toml +++ b/tasks/0022_056_22056321_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0022_056_22056321_qa_5" +name = "smoldataenvs-train/0022_056_22056321_qa_5" description = "What is the rank of the video game with the highest Global_Sales value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0022_076_22076548_qa_2/task.toml b/tasks/0022_076_22076548_qa_2/task.toml index 66aa63d8c825997aa13884e6b9515210f1799563..f193cdded31cf19a86298db418a820f0ca92a390 100644 --- a/tasks/0022_076_22076548_qa_2/task.toml +++ b/tasks/0022_076_22076548_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0022_076_22076548_qa_2" +name = "smoldataenvs-train/0022_076_22076548_qa_2" description = "Which payment method is associated with the highest churn rate, and what is the percentage?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic check, 45.29" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0022_076_22076548_qa_3/task.toml b/tasks/0022_076_22076548_qa_3/task.toml index 016ed90e04a5dbce9ca849ea9d5aa4551f5ecc5b..20871d5708ba440cba33b6c7dda8ee13f5dd571b 100644 --- a/tasks/0022_076_22076548_qa_3/task.toml +++ b/tasks/0022_076_22076548_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0022_076_22076548_qa_3" +name = "smoldataenvs-train/0022_076_22076548_qa_3" description = "What is the negative correlation coefficient between tenure and churn rate in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.34" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0022_122_22122831_qa_2/task.toml b/tasks/0022_122_22122831_qa_2/task.toml index b9efc83c5c3558fc3d8f0d4ed5807263cb272128..f3137f45f2128ea66295f78503dddbec3f322bb8 100644 --- a/tasks/0022_122_22122831_qa_2/task.toml +++ b/tasks/0022_122_22122831_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0022_122_22122831_qa_2" +name = "smoldataenvs-train/0022_122_22122831_qa_2" description = "What is the average yearly balance of all clients in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1528.5385235620856" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0022_169_22169428_qa_5/task.toml b/tasks/0022_169_22169428_qa_5/task.toml index 6e34b50a8e78563aafb1f76dffcc14292280ef62..bcdae910575d80524eac8b2d4813a18fbe8be849 100644 --- a/tasks/0022_169_22169428_qa_5/task.toml +++ b/tasks/0022_169_22169428_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0022_169_22169428_qa_5" +name = "smoldataenvs-train/0022_169_22169428_qa_5" description = "What is the mean absolute error (MAE) of the logistic regression model's predictions for shot make percentage between 22 and 27.3 feet?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.0199" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0022_193_22193578_qa_1/task.toml b/tasks/0022_193_22193578_qa_1/task.toml index c8bb85a137cc6f64a60549a04be0976737860961..ef66f74721129c58710e7a4a08ab183a63b1152b 100644 --- a/tasks/0022_193_22193578_qa_1/task.toml +++ b/tasks/0022_193_22193578_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0022_193_22193578_qa_1" +name = "smoldataenvs-train/0022_193_22193578_qa_1" description = "Which feature in the glass dataset exhibits the highest skewness before any data transformation is applied?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "K" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0022_193_22193578_qa_5/task.toml b/tasks/0022_193_22193578_qa_5/task.toml index 4bcdfe83cfb1b14513c43836a114f029df164d30..c7952ccb5860bb805c73d036aa253efb0a9b812c 100644 --- a/tasks/0022_193_22193578_qa_5/task.toml +++ b/tasks/0022_193_22193578_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0022_193_22193578_qa_5" +name = "smoldataenvs-train/0022_193_22193578_qa_5" description = "Which feature in the original dataset (pre-transformation) has the highest kurtosis value, and what is that value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "K with 54.689699" reward_mode_initial = "flexible" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0022_210_22210529_qa_2/task.toml b/tasks/0022_210_22210529_qa_2/task.toml index 93fd000b0b6677dc90c7c99cef7945a747540a49..f6b673d6085480ed77ff2da016c840ee100dc6db 100644 --- a/tasks/0022_210_22210529_qa_2/task.toml +++ b/tasks/0022_210_22210529_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0022_210_22210529_qa_2" +name = "smoldataenvs-train/0022_210_22210529_qa_2" description = "How many entries in the TotalCharges column initially contained non-numeric values that required conversion to numeric type during data preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0022_258_22258436_qa_1/task.toml b/tasks/0022_258_22258436_qa_1/task.toml index 126566f545034572e9e9e1de0aec62fa3aea52ce..298df023963be3106cbb43182435640a20d104b4 100644 --- a/tasks/0022_258_22258436_qa_1/task.toml +++ b/tasks/0022_258_22258436_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0022_258_22258436_qa_1" +name = "smoldataenvs-train/0022_258_22258436_qa_1" description = "What percentage of the messages in the dataset are classified as spam?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13.41" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0022_258_22258436_qa_2/task.toml b/tasks/0022_258_22258436_qa_2/task.toml index 5baa70e987b84fa221a2ac4ac221b49683031d65..9481bf6f5a5427b7971c7fc8deb36f2eedd1eba1 100644 --- a/tasks/0022_258_22258436_qa_2/task.toml +++ b/tasks/0022_258_22258436_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0022_258_22258436_qa_2" +name = "smoldataenvs-train/0022_258_22258436_qa_2" description = "What is the maximum length of any message in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "910" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0022_258_22258436_qa_3/task.toml b/tasks/0022_258_22258436_qa_3/task.toml index 5842c191c96b7a9083ded29a2ab28b95d9034a46..f4f792adaa16a3d2b2355d94caf4925771de82fc 100644 --- a/tasks/0022_258_22258436_qa_3/task.toml +++ b/tasks/0022_258_22258436_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0022_258_22258436_qa_3" +name = "smoldataenvs-train/0022_258_22258436_qa_3" description = "What is the most frequently occurring message in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Sorry, I'll call later" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0022_258_22258436_qa_4/task.toml b/tasks/0022_258_22258436_qa_4/task.toml index 3eba4dee564f41acf28806e61a74c1f0f7b3b5c0..3a63bc4ae474b9f23d66daf4bdfa6969aa582ad4 100644 --- a/tasks/0022_258_22258436_qa_4/task.toml +++ b/tasks/0022_258_22258436_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0022_258_22258436_qa_4" +name = "smoldataenvs-train/0022_258_22258436_qa_4" description = "How many unique messages are present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5169" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0022_272_22272220_qa_3/task.toml b/tasks/0022_272_22272220_qa_3/task.toml index 1ff499919287ce55007d14ffc8f616930a39b971..455bf5a2636601f3d5b5c012a655c80d09e84bbf 100644 --- a/tasks/0022_272_22272220_qa_3/task.toml +++ b/tasks/0022_272_22272220_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0022_272_22272220_qa_3" +name = "smoldataenvs-train/0022_272_22272220_qa_3" description = "What is the most common primary type among the top 10 Pokémon with the highest total stats?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Dragon" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0022_383_22383259_qa_5/task.toml b/tasks/0022_383_22383259_qa_5/task.toml index 5e3bfc1d8a931fcf98822c012618798e3b8c6d31..47f82eac6b1833e67d38e9c5f91b23d47278ba20 100644 --- a/tasks/0022_383_22383259_qa_5/task.toml +++ b/tasks/0022_383_22383259_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0022_383_22383259_qa_5" +name = "smoldataenvs-train/0022_383_22383259_qa_5" description = "What is the AUC score achieved by the LightGBM model after incorporating count-encoded features for categorical variables?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.7452610748007213" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0022_423_22423880_qa_4/task.toml b/tasks/0022_423_22423880_qa_4/task.toml index 09320f76f0d27d603725c112d2fc5ed89ea49bc9..76dd6133df5c55caf6d6583d221802ccc28ec4cc 100644 --- a/tasks/0022_423_22423880_qa_4/task.toml +++ b/tasks/0022_423_22423880_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0022_423_22423880_qa_4" +name = "smoldataenvs-train/0022_423_22423880_qa_4" description = "How many samples are in the test dataset after splitting the data with a 20% test size?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1625" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0022_457_22457642_qa_2/task.toml b/tasks/0022_457_22457642_qa_2/task.toml index 3339b887457f5eafa48400d720adf8ea79f0b771..fae3361e151b9d1f5b8dfe7b8bc56bd93de79490 100644 --- a/tasks/0022_457_22457642_qa_2/task.toml +++ b/tasks/0022_457_22457642_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0022_457_22457642_qa_2" +name = "smoldataenvs-train/0022_457_22457642_qa_2" description = "What is the average total population of all cities in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "448112" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0022_457_22457642_qa_5/task.toml b/tasks/0022_457_22457642_qa_5/task.toml index 7001f8bf75d8ef25ff45f7f49ea47e42673d90b6..f20893f0c6cc9f3f7820a1be98782821fc680752 100644 --- a/tasks/0022_457_22457642_qa_5/task.toml +++ b/tasks/0022_457_22457642_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0022_457_22457642_qa_5" +name = "smoldataenvs-train/0022_457_22457642_qa_5" description = "What is the highest effective literacy rate recorded for any city in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "98.8" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0022_482_22482992_qa_2/task.toml b/tasks/0022_482_22482992_qa_2/task.toml index 93aeb5c4ba86a85e70abe8de7755e94a783dbf75..330208384ed737ee51e0694fa801607e00bc82fc 100644 --- a/tasks/0022_482_22482992_qa_2/task.toml +++ b/tasks/0022_482_22482992_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0022_482_22482992_qa_2" +name = "smoldataenvs-train/0022_482_22482992_qa_2" description = "What is the maximum petal length in centimeters observed for the Iris-setosa species in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.9" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0022_555_22555686_qa_2/task.toml b/tasks/0022_555_22555686_qa_2/task.toml index 681b507deb181ff2284d3032ad37b2e9ca086322..d3bdb6d05f700cd402f5082353887b1f2b151eb4 100644 --- a/tasks/0022_555_22555686_qa_2/task.toml +++ b/tasks/0022_555_22555686_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0022_555_22555686_qa_2" +name = "smoldataenvs-train/0022_555_22555686_qa_2" description = "How many features were retained in the dataset after preprocessing steps including removal of non-informative columns and normalization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "30" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0022_609_22609620_qa_3/task.toml b/tasks/0022_609_22609620_qa_3/task.toml index 02c899e37b4f7438c2bef82038c4804aa33f1ed8..dc4efeebe4432dccae02513dd5400876fc550bfe 100644 --- a/tasks/0022_609_22609620_qa_3/task.toml +++ b/tasks/0022_609_22609620_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0022_609_22609620_qa_3" +name = "smoldataenvs-train/0022_609_22609620_qa_3" description = "What p-value was obtained from the Dickey-Fuller test for stationarity of the original time series?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.991880" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0022_668_22668393_qa_3/task.toml b/tasks/0022_668_22668393_qa_3/task.toml index 63960f0e4974f9a67785aa48ba1e7f385466ee3a..cab49d23cc10c3da38136275fa9cf44e71ad0550 100644 --- a/tasks/0022_668_22668393_qa_3/task.toml +++ b/tasks/0022_668_22668393_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0022_668_22668393_qa_3" +name = "smoldataenvs-train/0022_668_22668393_qa_3" description = "What is the average number of countries per region when considering the combined 2015-2017 dataset, and which region has the highest number of countries represented?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16.4, Sub-Saharan Africa" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0022_704_22704559_qa_4/task.toml b/tasks/0022_704_22704559_qa_4/task.toml index b8d6b981c7cf32ae561fcc205f0bf43b0f5a1c77..9178fd718b84ab41fc96ed1d210922e759946d78 100644 --- a/tasks/0022_704_22704559_qa_4/task.toml +++ b/tasks/0022_704_22704559_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0022_704_22704559_qa_4" +name = "smoldataenvs-train/0022_704_22704559_qa_4" description = "Which region in 2016 has the lowest median GDP per Capita according to the grouped median analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Sub-Saharan Africa" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0022_712_22712745_qa_5/task.toml b/tasks/0022_712_22712745_qa_5/task.toml index 943717f49e7f8991a105055c4a24ca8eace1f810..6e8b27d37a773888f32feddbbe360dee616d67e7 100644 --- a/tasks/0022_712_22712745_qa_5/task.toml +++ b/tasks/0022_712_22712745_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0022_712_22712745_qa_5" +name = "smoldataenvs-train/0022_712_22712745_qa_5" description = "Based on the analysis, which features (Petal or Sepal) provide better class separation and model accuracy?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Petal" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0022_788_22788694_qa_2/task.toml b/tasks/0022_788_22788694_qa_2/task.toml index f494a2078ed4902098352e8d8f465c47569a64c7..52bc50b5b8c6e0a59c482848b8be98a60d9f5c6a 100644 --- a/tasks/0022_788_22788694_qa_2/task.toml +++ b/tasks/0022_788_22788694_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0022_788_22788694_qa_2" +name = "smoldataenvs-train/0022_788_22788694_qa_2" description = "What is the average petal width in centimeters for the Iris-setosa species?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.244" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0022_790_22790047_qa_2/task.toml b/tasks/0022_790_22790047_qa_2/task.toml index a08ca270c058f06c884d1ce75fe9c69f4fe3dd27..436fac045af924a925c6ac60dad453c74c223c5d 100644 --- a/tasks/0022_790_22790047_qa_2/task.toml +++ b/tasks/0022_790_22790047_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0022_790_22790047_qa_2" +name = "smoldataenvs-train/0022_790_22790047_qa_2" description = "What is the highest household income recorded in the dataset after removing missing values in the \"Household Head Occupation\" column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11815988" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0022_860_22860039_qa_1/task.toml b/tasks/0022_860_22860039_qa_1/task.toml index 584cfe8dda38afa955dc0d7ec5b67af0b5fcbd0a..55fda25733ff5b873fcd5872c1671e585c5ad917 100644 --- a/tasks/0022_860_22860039_qa_1/task.toml +++ b/tasks/0022_860_22860039_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0022_860_22860039_qa_1" +name = "smoldataenvs-train/0022_860_22860039_qa_1" description = "Which feature in the Mushroom Classification dataset exhibits the strongest negative Pearson correlation with the 'class' attribute (edible/poisonous)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "gill-color" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0022_886_22886038_qa_4/task.toml b/tasks/0022_886_22886038_qa_4/task.toml index e8016f7566b1123e92724933a4143ae04a13a9be..c5c29ca458da3fdc1f128855903ca98cf5ac19ab 100644 --- a/tasks/0022_886_22886038_qa_4/task.toml +++ b/tasks/0022_886_22886038_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0022_886_22886038_qa_4" +name = "smoldataenvs-train/0022_886_22886038_qa_4" description = "What is the highest cross-validation accuracy achieved through the grid search process?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9812" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0022_916_22916338_qa_1/task.toml b/tasks/0022_916_22916338_qa_1/task.toml index 72461273bb92f79b78cffc2d8b7fec9aabd5ee70..b224a7c8efd29287c9a81a5e43359534deedaa3d 100644 --- a/tasks/0022_916_22916338_qa_1/task.toml +++ b/tasks/0022_916_22916338_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0022_916_22916338_qa_1" +name = "smoldataenvs-train/0022_916_22916338_qa_1" description = "Is the original raw time series (number of Uber pickups per hour) stationary according to the ADF test at a 5% significance level?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0022_916_22916338_qa_5/task.toml b/tasks/0022_916_22916338_qa_5/task.toml index 6f1f4d5e7452d3895f74fcf8424208834e284f85..304ec33b6dc8742f5ab9ad3735a0ae684ebdf163 100644 --- a/tasks/0022_916_22916338_qa_5/task.toml +++ b/tasks/0022_916_22916338_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0022_916_22916338_qa_5" +name = "smoldataenvs-train/0022_916_22916338_qa_5" description = "What is the highest number of Uber pickups recorded in a single hour according to the first 20 entries of the hourly data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1262" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0022_928_22928479_qa_2/task.toml b/tasks/0022_928_22928479_qa_2/task.toml index 0344f5edd5da6e1a5d2c21e1c05c38c2128bd6eb..0bc547b998a643baaeb07b025b298daae815ed2f 100644 --- a/tasks/0022_928_22928479_qa_2/task.toml +++ b/tasks/0022_928_22928479_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0022_928_22928479_qa_2" +name = "smoldataenvs-train/0022_928_22928479_qa_2" description = "How many features in the dataset contain missing values according to the null value analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0022_941_22941609_qa_2/task.toml b/tasks/0022_941_22941609_qa_2/task.toml index cf13561d81a2f62c3a684cd276124d8450449ba8..9c4ad06504dcb1becab1d9d0a3c268619882e28c 100644 --- a/tasks/0022_941_22941609_qa_2/task.toml +++ b/tasks/0022_941_22941609_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0022_941_22941609_qa_2" +name = "smoldataenvs-train/0022_941_22941609_qa_2" description = "How many dummy variables were created for the \"drive-wheels\" categorical variable during one-hot encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0023_186_23186300_qa_1/task.toml b/tasks/0023_186_23186300_qa_1/task.toml index 0b4bdc77f4d6d59ba13e2d0bbd708fb5cc2f6d11..8dada47668d904c3a25641249762088ed7db1677 100644 --- a/tasks/0023_186_23186300_qa_1/task.toml +++ b/tasks/0023_186_23186300_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0023_186_23186300_qa_1" +name = "smoldataenvs-train/0023_186_23186300_qa_1" description = "Which of the top 10 highest-paid NBA players has the highest win percentage when they are on the court, and what is the exact percentage?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Kevin Durant, 82.3" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0023_251_23251738_qa_1/task.toml b/tasks/0023_251_23251738_qa_1/task.toml index cbea6bdd94b7b7c1400650cfcade6405bee7f24c..994ee74f5dfc2eeb82c613237b08386fb25b6f36 100644 --- a/tasks/0023_251_23251738_qa_1/task.toml +++ b/tasks/0023_251_23251738_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0023_251_23251738_qa_1" +name = "smoldataenvs-train/0023_251_23251738_qa_1" description = "How many samples belong to the original glass type with the highest frequency in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "76" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0023_251_23251738_qa_3/task.toml b/tasks/0023_251_23251738_qa_3/task.toml index dbe34cf20b0bf886e4b0fb6912ec707feef5197e..690c617817d6406b7b4b4491fd2c3a13113d993c 100644 --- a/tasks/0023_251_23251738_qa_3/task.toml +++ b/tasks/0023_251_23251738_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0023_251_23251738_qa_3" +name = "smoldataenvs-train/0023_251_23251738_qa_3" description = "What is the maximum observed value of the 'K' chemical composition feature in the glass samples?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.21" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0023_425_23425201_qa_4/task.toml b/tasks/0023_425_23425201_qa_4/task.toml index 93e0744761708b2fe22b358620d6cd997ebd6182..ca0b91715cd868d6df84c3f96b3a0d0978e6065f 100644 --- a/tasks/0023_425_23425201_qa_4/task.toml +++ b/tasks/0023_425_23425201_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0023_425_23425201_qa_4" +name = "smoldataenvs-train/0023_425_23425201_qa_4" description = "What is the average house price across all records in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1232073" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0023_426_23426961_qa_3/task.toml b/tasks/0023_426_23426961_qa_3/task.toml index ae933d6ec92928ff57b2ebc16e466a592aedb406..af3d329f9e59b971819f328b215dc845ad251170 100644 --- a/tasks/0023_426_23426961_qa_3/task.toml +++ b/tasks/0023_426_23426961_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0023_426_23426961_qa_3" +name = "smoldataenvs-train/0023_426_23426961_qa_3" description = "How many columns in the training dataset contain missing values that were identified during preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0023_468_23468316_qa_2/task.toml b/tasks/0023_468_23468316_qa_2/task.toml index 4bb6075e025bceb1d651891f898da6e8cc28f882..897e24814a01947475b1069c26ef469856daae95 100644 --- a/tasks/0023_468_23468316_qa_2/task.toml +++ b/tasks/0023_468_23468316_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0023_468_23468316_qa_2" +name = "smoldataenvs-train/0023_468_23468316_qa_2" description = "What is the most frequent occupation category in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Prof-specialty" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0023_468_23468316_qa_3/task.toml b/tasks/0023_468_23468316_qa_3/task.toml index 9550777657abe87cffc8763174cfc9a05f181a9f..440c6355e9b20deb2a700c3aa024be347989a9e6 100644 --- a/tasks/0023_468_23468316_qa_3/task.toml +++ b/tasks/0023_468_23468316_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0023_468_23468316_qa_3" +name = "smoldataenvs-train/0023_468_23468316_qa_3" description = "How many unique countries are present in the nativeCountry column of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "42" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0023_531_23531492_qa_4/task.toml b/tasks/0023_531_23531492_qa_4/task.toml index 64d528a93ead7d4287abf6f911d2e7a488ecc74f..110f71962e9659618fd4d8b4abca0b3b8591254e 100644 --- a/tasks/0023_531_23531492_qa_4/task.toml +++ b/tasks/0023_531_23531492_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0023_531_23531492_qa_4" +name = "smoldataenvs-train/0023_531_23531492_qa_4" description = "What is the mean value of the 'Sp. Def' stat for all Pokémon in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "71.9025" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0023_531_23531492_qa_5/task.toml b/tasks/0023_531_23531492_qa_5/task.toml index b3ee93daf471f3b168bf89fd783e46701e2f0d19..c78a243f4b23e2cee583837cc539e851ab5535ed 100644 --- a/tasks/0023_531_23531492_qa_5/task.toml +++ b/tasks/0023_531_23531492_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0023_531_23531492_qa_5" +name = "smoldataenvs-train/0023_531_23531492_qa_5" description = "What is the exact correlation coefficient between the 'Legendary' status and HP in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.2736" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0023_580_23580177_qa_2/task.toml b/tasks/0023_580_23580177_qa_2/task.toml index cd06006cbad37ab3e542b8e1d1f04186ae324ab3..d38dd2ff3f13dbb149678c52cee77fdd9015a78b 100644 --- a/tasks/0023_580_23580177_qa_2/task.toml +++ b/tasks/0023_580_23580177_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0023_580_23580177_qa_2" +name = "smoldataenvs-train/0023_580_23580177_qa_2" description = "Which variable in the regression model has the strongest positive effect on house prices in the sample dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "grade" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0023_583_23583710_qa_4/task.toml b/tasks/0023_583_23583710_qa_4/task.toml index 0e38541ede518dfe04357fc328081887ebe5518a..9f62cbd68a5cc66a2fb78efa16edf1c0354516b8 100644 --- a/tasks/0023_583_23583710_qa_4/task.toml +++ b/tasks/0023_583_23583710_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0023_583_23583710_qa_4" +name = "smoldataenvs-train/0023_583_23583710_qa_4" description = "How many Mega Pokémon in the dataset are classified as legendary?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0023_598_23598239_qa_1/task.toml b/tasks/0023_598_23598239_qa_1/task.toml index 3b5b4477deca4da80ff8ace41660356d32d68f98..09509d94b42be40f83afdef090d63b4c1e11166b 100644 --- a/tasks/0023_598_23598239_qa_1/task.toml +++ b/tasks/0023_598_23598239_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0023_598_23598239_qa_1" +name = "smoldataenvs-train/0023_598_23598239_qa_1" description = "What is the most common crime category in the dataset, and how many incidents does it account for?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "LARCENY/THEFT, 223" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0023_598_23598239_qa_4/task.toml b/tasks/0023_598_23598239_qa_4/task.toml index 1548b096c2dc0b6825f4023499a515d961b1cf52..94321fee87a20f61a70074690254d2ceb13b8b49 100644 --- a/tasks/0023_598_23598239_qa_4/task.toml +++ b/tasks/0023_598_23598239_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0023_598_23598239_qa_4" +name = "smoldataenvs-train/0023_598_23598239_qa_4" description = "For LARCENY/THEFT cases, what is the most common resolution outcome, and how many cases have this resolution?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "NONE, 220" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0023_626_23626093_qa_1/task.toml b/tasks/0023_626_23626093_qa_1/task.toml index 5409cb1c7c3b773a976211e8dd9fea8b5fb6327e..98841b6ad82909edd68b55603ce3fab8e1ef1d5b 100644 --- a/tasks/0023_626_23626093_qa_1/task.toml +++ b/tasks/0023_626_23626093_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0023_626_23626093_qa_1" +name = "smoldataenvs-train/0023_626_23626093_qa_1" description = "Which team has the highest goal difference (GD) in the partial standings of the 2015/2016 Italian Serie A up to stage 19?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Napoli" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0023_626_23626093_qa_3/task.toml b/tasks/0023_626_23626093_qa_3/task.toml index fddb216b606ef789c13ae9113158b6a6be8ed469..fc79064deb2b2e68ea9aba4b5c5ab53843ea8fcd 100644 --- a/tasks/0023_626_23626093_qa_3/task.toml +++ b/tasks/0023_626_23626093_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0023_626_23626093_qa_3" +name = "smoldataenvs-train/0023_626_23626093_qa_3" description = "Which team has the highest total goals scored (GF) in the partial standings of the 2015/2016 Italian Serie A up to stage 19?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Napoli" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0023_670_23670397_qa_1/task.toml b/tasks/0023_670_23670397_qa_1/task.toml index bef575440e951b647b897f7c0e119d34445dfa63..569c172561360c3ea4d0c299b150f9339c0b2515 100644 --- a/tasks/0023_670_23670397_qa_1/task.toml +++ b/tasks/0023_670_23670397_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0023_670_23670397_qa_1" +name = "smoldataenvs-train/0023_670_23670397_qa_1" description = "Which publisher has the highest total global sales according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Nintendo" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0023_684_23684693_qa_1/task.toml b/tasks/0023_684_23684693_qa_1/task.toml index c5ff80ca61be022d623540e9c86169c44d742f9b..582bf575172c329074ce53d8db24bb45a0a9dbf8 100644 --- a/tasks/0023_684_23684693_qa_1/task.toml +++ b/tasks/0023_684_23684693_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0023_684_23684693_qa_1" +name = "smoldataenvs-train/0023_684_23684693_qa_1" description = "Which feature shows the strongest negative correlation with customer churn in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "tenure" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0023_715_23715177_qa_1/task.toml b/tasks/0023_715_23715177_qa_1/task.toml index a0978555e764f80b68bfd2dd3e2bcfd2390e0b88..2a65850ab4433a89fb9cbe36f95be78463445b95 100644 --- a/tasks/0023_715_23715177_qa_1/task.toml +++ b/tasks/0023_715_23715177_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0023_715_23715177_qa_1" +name = "smoldataenvs-train/0023_715_23715177_qa_1" description = "Which country has the highest average ramen rating based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Brazil" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0023_715_23715177_qa_2/task.toml b/tasks/0023_715_23715177_qa_2/task.toml index 4c99fecb152f5e60e1ddb7afe7f50e25833db04b..741aab29ab8ffae2b3af6e65e6a984d5f3c24e16 100644 --- a/tasks/0023_715_23715177_qa_2/task.toml +++ b/tasks/0023_715_23715177_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0023_715_23715177_qa_2" +name = "smoldataenvs-train/0023_715_23715177_qa_2" description = "How many brands have an average rating of 4.5 or higher and at least 2 reviews?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0023_721_23721314_qa_1/task.toml b/tasks/0023_721_23721314_qa_1/task.toml index c142a8700dad9cd720c85ac059f0c07fe512bb51..6b21973cdf9040cc25ef5ea7090231c2e881f426 100644 --- a/tasks/0023_721_23721314_qa_1/task.toml +++ b/tasks/0023_721_23721314_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0023_721_23721314_qa_1" +name = "smoldataenvs-train/0023_721_23721314_qa_1" description = "What is the highest single-season passing yardage achieved by a player in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5477" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0023_721_23721684_qa_4/task.toml b/tasks/0023_721_23721684_qa_4/task.toml index bc16d248189d6b8a0a87b779ab1c7b21eb3ed335..3ab4218ea7aee1a1b8779d084bc8504416af57b8 100644 --- a/tasks/0023_721_23721684_qa_4/task.toml +++ b/tasks/0023_721_23721684_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0023_721_23721684_qa_4" +name = "smoldataenvs-train/0023_721_23721684_qa_4" description = "How many cereals in the dataset are classified as hot cereals?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0023_721_23721684_qa_5/task.toml b/tasks/0023_721_23721684_qa_5/task.toml index 9c44b877a8ed2fd3b676d01c43fa6ac7d703c5a3..d016dab0e0d890ea2766237713df959aa87afec6 100644 --- a/tasks/0023_721_23721684_qa_5/task.toml +++ b/tasks/0023_721_23721684_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0023_721_23721684_qa_5" +name = "smoldataenvs-train/0023_721_23721684_qa_5" description = "What is the average rating of cereals produced by Nabisco (manufacturer N)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "67.97" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0023_757_23757075_qa_3/task.toml b/tasks/0023_757_23757075_qa_3/task.toml index ea9aaf0116113152a1e1bd2d7217a9a4e6249c1a..060c98e01c45ee32f2f04d8587cb8d4c434c9a90 100644 --- a/tasks/0023_757_23757075_qa_3/task.toml +++ b/tasks/0023_757_23757075_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0023_757_23757075_qa_3" +name = "smoldataenvs-train/0023_757_23757075_qa_3" description = "After applying MinMaxScaler, what is the maximum value for any numerical feature in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0023_757_23757075_qa_4/task.toml b/tasks/0023_757_23757075_qa_4/task.toml index db0bd67b6897e68579e95a9b9abeaab52eb72bdc..11cdb206196a67581f7d2b955438d352abbc02cf 100644 --- a/tasks/0023_757_23757075_qa_4/task.toml +++ b/tasks/0023_757_23757075_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0023_757_23757075_qa_4" +name = "smoldataenvs-train/0023_757_23757075_qa_4" description = "What is the mean value of the 'capital.gain' feature after applying the log1p transformation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.7346" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0023_772_23772737_qa_1/task.toml b/tasks/0023_772_23772737_qa_1/task.toml index ceaefdd843995c3ba714c47ea9f19ff666e50667..555e171c0223104dc453850a6a5677ff3334d24a 100644 --- a/tasks/0023_772_23772737_qa_1/task.toml +++ b/tasks/0023_772_23772737_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0023_772_23772737_qa_1" +name = "smoldataenvs-train/0023_772_23772737_qa_1" description = "Which feature shows the highest absolute correlation with wine quality in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "alcohol" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0023_774_23774958_qa_1/task.toml b/tasks/0023_774_23774958_qa_1/task.toml index 7eba0c12cb7166afff3cf1cc549ad09c33d3e249..e4a1a7b0c08257522cc98cc0cd1292840f48805b 100644 --- a/tasks/0023_774_23774958_qa_1/task.toml +++ b/tasks/0023_774_23774958_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0023_774_23774958_qa_1" +name = "smoldataenvs-train/0023_774_23774958_qa_1" description = "What is the total number of patients in the dataset after removing duplicate patient records?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "71518" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0023_813_23813502_qa_1/task.toml b/tasks/0023_813_23813502_qa_1/task.toml index 5543527c47d09c07f1e9cc8b8aff2aa45d96afa7..6fa4078743ba7733fb505647b1058ae38ac30eba 100644 --- a/tasks/0023_813_23813502_qa_1/task.toml +++ b/tasks/0023_813_23813502_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0023_813_23813502_qa_1" +name = "smoldataenvs-train/0023_813_23813502_qa_1" description = "Which feature in the dataset shows the highest correlation with the diabetes outcome (Outcome=1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0023_813_23813502_qa_4/task.toml b/tasks/0023_813_23813502_qa_4/task.toml index 9d592eb88aca2da8a1f1203b4ad36e6cd88bebf7..0d5f79deba6ed15fd5fe8a508136a8ceb5b8580d 100644 --- a/tasks/0023_813_23813502_qa_4/task.toml +++ b/tasks/0023_813_23813502_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0023_813_23813502_qa_4" +name = "smoldataenvs-train/0023_813_23813502_qa_4" description = "What percentage of patients have missing values in the SkinThickness feature (original zero-values before imputation)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "29.6" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0023_845_23845805_qa_3/task.toml b/tasks/0023_845_23845805_qa_3/task.toml index b923a030ebe874c1cbaead0166a797307bf3bc74..71c251e39a31eacf837fdc6570e5582dbf119d70 100644 --- a/tasks/0023_845_23845805_qa_3/task.toml +++ b/tasks/0023_845_23845805_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0023_845_23845805_qa_3" +name = "smoldataenvs-train/0023_845_23845805_qa_3" description = "What is the mean value of the StandardScaler scaled 'Purchase' column after preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.878241e-16" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0023_846_23846255_qa_1/task.toml b/tasks/0023_846_23846255_qa_1/task.toml index dd0e35fa371a0fc0ef965d7e7274727c2e2ed720..627b346c712f3869bf31a0aa2615b4dfbc1058ed 100644 --- a/tasks/0023_846_23846255_qa_1/task.toml +++ b/tasks/0023_846_23846255_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0023_846_23846255_qa_1" +name = "smoldataenvs-train/0023_846_23846255_qa_1" description = "What is the absolute difference between the maximum purchase amount and the 75th percentile purchase amount in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11907.00" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0023_908_23908999_qa_2/task.toml b/tasks/0023_908_23908999_qa_2/task.toml index 314e565d6700d10584189250e8db568a324aacb6..0a6d25e54650aeb21f06980c7ccb4cafeb17913e 100644 --- a/tasks/0023_908_23908999_qa_2/task.toml +++ b/tasks/0023_908_23908999_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0023_908_23908999_qa_2" +name = "smoldataenvs-train/0023_908_23908999_qa_2" description = "What is the most frequently used opening move in the dataset, and how many times does it appear?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "e4 with 12598 occurrences" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0023_908_23908999_qa_4/task.toml b/tasks/0023_908_23908999_qa_4/task.toml index 81843fb953f71b379d84e8f19f8c228f075d9f05..bec639c70cbdd14eacecc0f82047c4dbcd73afe3 100644 --- a/tasks/0023_908_23908999_qa_4/task.toml +++ b/tasks/0023_908_23908999_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0023_908_23908999_qa_4" +name = "smoldataenvs-train/0023_908_23908999_qa_4" description = "How many unique chess openings (opening names) are recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1477" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0023_913_23913406_qa_1/task.toml b/tasks/0023_913_23913406_qa_1/task.toml index e639ce2a1fbb66da97344316f215e244ed717148..029a7844a40e13d922689ad4094ba989f2fb9ab0 100644 --- a/tasks/0023_913_23913406_qa_1/task.toml +++ b/tasks/0023_913_23913406_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0023_913_23913406_qa_1" +name = "smoldataenvs-train/0023_913_23913406_qa_1" description = "How many instances remain in the dataset after removing all rows with missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "36423" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0023_913_23913406_qa_2/task.toml b/tasks/0023_913_23913406_qa_2/task.toml index 96ef939e109af56e9d22745b4b554f1f4bde6694..46c5f98a79fbd5c1d125ede9c35126293f68523e 100644 --- a/tasks/0023_913_23913406_qa_2/task.toml +++ b/tasks/0023_913_23913406_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0023_913_23913406_qa_2" +name = "smoldataenvs-train/0023_913_23913406_qa_2" description = "What is the most frequent loan purpose in the dataset after data cleaning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Debt Consolidation" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0023_931_23931431_qa_1/task.toml b/tasks/0023_931_23931431_qa_1/task.toml index fbee3c2bd7f8b63214a7f510772810ea4fb9e02c..ccc111276b8bc44a15a46f9e9006ff64da4a8802 100644 --- a/tasks/0023_931_23931431_qa_1/task.toml +++ b/tasks/0023_931_23931431_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0023_931_23931431_qa_1" +name = "smoldataenvs-train/0023_931_23931431_qa_1" description = "What percentage of messages in the dataset are classified as spam (Category 0)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13.4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0023_931_23931431_qa_2/task.toml b/tasks/0023_931_23931431_qa_2/task.toml index efcf522fba3f68a54d073f999c0a4691f2f68b52..e56067b49206448c6a1294d352a4d7a9bb88d944 100644 --- a/tasks/0023_931_23931431_qa_2/task.toml +++ b/tasks/0023_931_23931431_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0023_931_23931431_qa_2" +name = "smoldataenvs-train/0023_931_23931431_qa_2" description = "What is the average character length of messages in the dataset before preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "80.37" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0023_980_23980311_qa_3/task.toml b/tasks/0023_980_23980311_qa_3/task.toml index 396764b9ede90edf268cedb53948c1bfe164d00f..a8808225042e08cd2008080048da0b8ca00ad11e 100644 --- a/tasks/0023_980_23980311_qa_3/task.toml +++ b/tasks/0023_980_23980311_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0023_980_23980311_qa_3" +name = "smoldataenvs-train/0023_980_23980311_qa_3" description = "What is the accuracy of the logistic regression model on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9701" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0023_990_23990837_qa_5/task.toml b/tasks/0023_990_23990837_qa_5/task.toml index 53d835ba0ee6d13984b693c50d224a0d817ca221..4c33b9190472d4eb3cb3b797c2e4ffc0ac1496b6 100644 --- a/tasks/0023_990_23990837_qa_5/task.toml +++ b/tasks/0023_990_23990837_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0023_990_23990837_qa_5" +name = "smoldataenvs-train/0023_990_23990837_qa_5" description = "What is the 25th percentile value for the 'smoothness_mean' feature in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.086370" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0024_025_24025935_qa_1/task.toml b/tasks/0024_025_24025935_qa_1/task.toml index 1f679a1a13af99b52cb4e31295764cce96b7c685..11e8329ac2cc67ea13c39ec00be4aa84af00b3f2 100644 --- a/tasks/0024_025_24025935_qa_1/task.toml +++ b/tasks/0024_025_24025935_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0024_025_24025935_qa_1" +name = "smoldataenvs-train/0024_025_24025935_qa_1" description = "What is the total number of drug-related death cases recorded in the dataset between 2012 and 2017?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3583" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0024_025_24025935_qa_3/task.toml b/tasks/0024_025_24025935_qa_3/task.toml index 32ea316cd90e5a4d92edae86552e728e0e6605bf..bbae696f8521191f2854dbc5b53761ba366f1e8e 100644 --- a/tasks/0024_025_24025935_qa_3/task.toml +++ b/tasks/0024_025_24025935_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0024_025_24025935_qa_3" +name = "smoldataenvs-train/0024_025_24025935_qa_3" description = "Which drug was involved in the highest number of accidental death cases according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Heroin" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0024_025_24025935_qa_4/task.toml b/tasks/0024_025_24025935_qa_4/task.toml index 2493ca9819e7cbe13f146b16b8352e8c99d25847..360da9c0fc4cef77dfcca9a84dd25e12cb250828 100644 --- a/tasks/0024_025_24025935_qa_4/task.toml +++ b/tasks/0024_025_24025935_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0024_025_24025935_qa_4" +name = "smoldataenvs-train/0024_025_24025935_qa_4" description = "How many cases in the dataset have missing data for the residence state?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1920" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0024_028_24028302_qa_2/task.toml b/tasks/0024_028_24028302_qa_2/task.toml index 3d0ffaf4b3f6c56cadf40590c0441e991f22cf09..3c55b077665732380da5c547eda7f917ecb3d382 100644 --- a/tasks/0024_028_24028302_qa_2/task.toml +++ b/tasks/0024_028_24028302_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0024_028_24028302_qa_2" +name = "smoldataenvs-train/0024_028_24028302_qa_2" description = "What is the total number of drug-related deaths recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3583" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0024_069_24069916_qa_1/task.toml b/tasks/0024_069_24069916_qa_1/task.toml index afb7f1be5ec9ff201a377b2f87491924f9c49aab..1187c7c2c3dea5cf9b04fbe90818762be4164933 100644 --- a/tasks/0024_069_24069916_qa_1/task.toml +++ b/tasks/0024_069_24069916_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0024_069_24069916_qa_1" +name = "smoldataenvs-train/0024_069_24069916_qa_1" description = "Which nationality has the highest number of students in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "KW" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0024_069_24069916_qa_3/task.toml b/tasks/0024_069_24069916_qa_3/task.toml index 4f16bc5ed8e9c6e61aa19283edf28c031749a288..4bd1a3330224ad1dc3bc112602ef804a87e715aa 100644 --- a/tasks/0024_069_24069916_qa_3/task.toml +++ b/tasks/0024_069_24069916_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0024_069_24069916_qa_3" +name = "smoldataenvs-train/0024_069_24069916_qa_3" description = "Which class category (H, L, M) contains the largest number of students?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "M" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0024_069_24069916_qa_5/task.toml b/tasks/0024_069_24069916_qa_5/task.toml index 50a302f891f1f2212adb9c253b4c60553021f32d..e02c5f34d12ab2f366761b08f73e248a8a43cac0 100644 --- a/tasks/0024_069_24069916_qa_5/task.toml +++ b/tasks/0024_069_24069916_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0024_069_24069916_qa_5" +name = "smoldataenvs-train/0024_069_24069916_qa_5" description = "What is the percentage of students with \"Good\" ParentschoolSatisfaction?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "60.83" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0024_081_24081260_qa_3/task.toml b/tasks/0024_081_24081260_qa_3/task.toml index a57c4cdb84f33a38a4baadd4a53e2b50d9ba0f0a..aef3d1519eb213cf9c7d9e67c5645e05b91531a6 100644 --- a/tasks/0024_081_24081260_qa_3/task.toml +++ b/tasks/0024_081_24081260_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0024_081_24081260_qa_3" +name = "smoldataenvs-train/0024_081_24081260_qa_3" description = "Which feature pair (Sepal Length vs Sepal Width or Petal Length vs Petal Width) shows better separation between species based on the scatter plots?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Petal Length vs Petal Width" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0024_141_24141442_qa_3/task.toml b/tasks/0024_141_24141442_qa_3/task.toml index 79cc54df09b9d9d0664676ea0af29faab555a02a..ac45b6f7cc75b00ee7180a083e048e43f4518dd6 100644 --- a/tasks/0024_141_24141442_qa_3/task.toml +++ b/tasks/0024_141_24141442_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0024_141_24141442_qa_3" +name = "smoldataenvs-train/0024_141_24141442_qa_3" description = "How many unique sentences are present in the spam messages compared to ham messages?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "653 spam, 4516 ham" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0024_158_24158944_qa_1/task.toml b/tasks/0024_158_24158944_qa_1/task.toml index cc1cf71a9e188fea2d595b1741c3b8a4befb206e..d8958e4d669040a35ade5b98fb94747e2638df5a 100644 --- a/tasks/0024_158_24158944_qa_1/task.toml +++ b/tasks/0024_158_24158944_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0024_158_24158944_qa_1" +name = "smoldataenvs-train/0024_158_24158944_qa_1" description = "What is the percentage of benign (B) cases compared to malignant (M) cases in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "62.8% benign, 37.2% malignant" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0024_251_24251009_qa_3/task.toml b/tasks/0024_251_24251009_qa_3/task.toml index 5e260d2d22bea5f4acb4b3cbdc7ab6c7df6230c6..e735e028bb03174839ab01e353dc86c99867fe15 100644 --- a/tasks/0024_251_24251009_qa_3/task.toml +++ b/tasks/0024_251_24251009_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0024_251_24251009_qa_3" +name = "smoldataenvs-train/0024_251_24251009_qa_3" description = "What is the range of vote averages (maximum minus minimum) across all movies?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10.0" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0024_252_24252070_qa_2/task.toml b/tasks/0024_252_24252070_qa_2/task.toml index ab0ff0d80c53a266268e0b83276af789bf20e37b..cba0e08a6a27b93fc5d2a9ae9a3e127a7b7cdf61 100644 --- a/tasks/0024_252_24252070_qa_2/task.toml +++ b/tasks/0024_252_24252070_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0024_252_24252070_qa_2" +name = "smoldataenvs-train/0024_252_24252070_qa_2" description = "How many of the top 5 movie recommendations for \"Fast Five\" are identical between the two content-based filtering methods (Method 1 and Method 2)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0024_275_24275657_qa_5/task.toml b/tasks/0024_275_24275657_qa_5/task.toml index f9866bbbb21d50a3e9c6b64667e047af5a013e49..7429c2ae4adb5221ffd6be7228d904a4acb7f16d 100644 --- a/tasks/0024_275_24275657_qa_5/task.toml +++ b/tasks/0024_275_24275657_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0024_275_24275657_qa_5" +name = "smoldataenvs-train/0024_275_24275657_qa_5" description = "What percentage of variance is explained by the second principal component in the PCA analysis of the Iris dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "23.03" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0024_285_24285301_qa_2/task.toml b/tasks/0024_285_24285301_qa_2/task.toml index 0eb2f96ab2481c88b933cc297956b48c3839b297..d71368ddb987319c101daa5ed7f2af8a3572b41c 100644 --- a/tasks/0024_285_24285301_qa_2/task.toml +++ b/tasks/0024_285_24285301_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0024_285_24285301_qa_2" +name = "smoldataenvs-train/0024_285_24285301_qa_2" description = "What is the highest correlation coefficient observed between any two numeric variables in the dataset, excluding perfect correlations (1.0)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.975094" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0024_315_24315517_qa_5/task.toml b/tasks/0024_315_24315517_qa_5/task.toml index fcb331b5bce8c1db176607d3bf501abfa8d2e269..d881e252f71e3d804b21592d08516efcc0eabc0b 100644 --- a/tasks/0024_315_24315517_qa_5/task.toml +++ b/tasks/0024_315_24315517_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0024_315_24315517_qa_5" +name = "smoldataenvs-train/0024_315_24315517_qa_5" description = "What is the difference in median age between the two survival status categories in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0024_315_24315892_qa_1/task.toml b/tasks/0024_315_24315892_qa_1/task.toml index 6619b03b4e2a936555539c13cdf40f1c203545c4..6b8ee6751485a4da728b947f29fb34d1639fecf2 100644 --- a/tasks/0024_315_24315892_qa_1/task.toml +++ b/tasks/0024_315_24315892_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0024_315_24315892_qa_1" +name = "smoldataenvs-train/0024_315_24315892_qa_1" description = "What is the 75th percentile value of the axillary nodes (axil_nodes) feature for patients who survived 5 years or longer (Surv_status = 1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0024_315_24315892_qa_3/task.toml b/tasks/0024_315_24315892_qa_3/task.toml index ed679371e2907622f61e2a035e50a49a468bbea7..0a8a5ea7c15fed4f6db038b8cc323449af21fcd2 100644 --- a/tasks/0024_315_24315892_qa_3/task.toml +++ b/tasks/0024_315_24315892_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0024_315_24315892_qa_3" +name = "smoldataenvs-train/0024_315_24315892_qa_3" description = "What are the median values of axillary nodes (axil_nodes) for patients with survival status 1 (5+ years) and survival status 2 (<5 years)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0, 4" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0024_357_24357086_qa_3/task.toml b/tasks/0024_357_24357086_qa_3/task.toml index b6ba520f9fadba519fd1ce7f0f5a6a4581f90552..867b68925bf86d01264c18211ebdaab66dac98f9 100644 --- a/tasks/0024_357_24357086_qa_3/task.toml +++ b/tasks/0024_357_24357086_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0024_357_24357086_qa_3" +name = "smoldataenvs-train/0024_357_24357086_qa_3" description = "Which city category (A, B, or C) has the highest average purchase value based on the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "C" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0024_471_24471564_qa_5/task.toml b/tasks/0024_471_24471564_qa_5/task.toml index 22cd45db94ea392a41893e0d75195b272c7804b1..d794c2beb1e57f8073f20ebc128df5fb653dfed5 100644 --- a/tasks/0024_471_24471564_qa_5/task.toml +++ b/tasks/0024_471_24471564_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0024_471_24471564_qa_5" +name = "smoldataenvs-train/0024_471_24471564_qa_5" description = "Which category has received the most awards according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Politics" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0024_556_24556014_qa_1/task.toml b/tasks/0024_556_24556014_qa_1/task.toml index 3ed8f0e4dcc48bd64bde8a4fd21d6ea97cc41267..e36a3c938cb187324fdea675d9be500db2b23082 100644 --- a/tasks/0024_556_24556014_qa_1/task.toml +++ b/tasks/0024_556_24556014_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0024_556_24556014_qa_1" +name = "smoldataenvs-train/0024_556_24556014_qa_1" description = "What is the percentage of customers who churned in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26.537" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0024_602_24602868_qa_1/task.toml b/tasks/0024_602_24602868_qa_1/task.toml index 97601fb12c193acc58a91e18be99124419cd7593..00a4dd8b4f36fd8deac35a3ca688a26c02d025b9 100644 --- a/tasks/0024_602_24602868_qa_1/task.toml +++ b/tasks/0024_602_24602868_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0024_602_24602868_qa_1" +name = "smoldataenvs-train/0024_602_24602868_qa_1" description = "What is the highest Total stat points among non-legendary Pokémon in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "700" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0024_602_24602868_qa_2/task.toml b/tasks/0024_602_24602868_qa_2/task.toml index 07058285075c31ade75b8b816179c360e32c50b5..b4c9338d5c2c438326015d2a9c216cf86d42194e 100644 --- a/tasks/0024_602_24602868_qa_2/task.toml +++ b/tasks/0024_602_24602868_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0024_602_24602868_qa_2" +name = "smoldataenvs-train/0024_602_24602868_qa_2" description = "Which non-legendary Pokémon has the highest Attack stat in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "HeracrossMega Heracross" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0024_610_24610752_qa_3/task.toml b/tasks/0024_610_24610752_qa_3/task.toml index e33cc19698253c27513bc61465443773012b957f..4c1ba510551fa695da3228a17199b554bcacac7b 100644 --- a/tasks/0024_610_24610752_qa_3/task.toml +++ b/tasks/0024_610_24610752_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0024_610_24610752_qa_3" +name = "smoldataenvs-train/0024_610_24610752_qa_3" description = "What is the total number of non-legendary Pokémon in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "735" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0024_633_24633797_qa_1/task.toml b/tasks/0024_633_24633797_qa_1/task.toml index 4e7e9665c07d02fc3691482ca73e59bb5b1b67b2..224b3f851f04ddbe372de28aef0b360f4ac1a8ed 100644 --- a/tasks/0024_633_24633797_qa_1/task.toml +++ b/tasks/0024_633_24633797_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0024_633_24633797_qa_1" +name = "smoldataenvs-train/0024_633_24633797_qa_1" description = "Which feature in the cleaned dataset shows the strongest positive correlation with the diabetes outcome (Outcome=1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0024_650_24650942_qa_2/task.toml b/tasks/0024_650_24650942_qa_2/task.toml index 35711d4cb2c8b4f3422976353cd441c555034052..b6fcc865a725f27df6c89a071dea5f9f3160c957 100644 --- a/tasks/0024_650_24650942_qa_2/task.toml +++ b/tasks/0024_650_24650942_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0024_650_24650942_qa_2" +name = "smoldataenvs-train/0024_650_24650942_qa_2" description = "After relabeling the weather event classes into three categories (Clear, Rain, Thunderstorm), what is the distribution of instances across these classes in the final dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Clear=924, Rain=207, Thunderstorm=188" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0024_654_24654257_qa_2/task.toml b/tasks/0024_654_24654257_qa_2/task.toml index 38a685733ca274e71e6641dd466993b29a3df9d5..5e3902c76f44369142756c0b2d89ca7da54d5ccb 100644 --- a/tasks/0024_654_24654257_qa_2/task.toml +++ b/tasks/0024_654_24654257_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0024_654_24654257_qa_2" +name = "smoldataenvs-train/0024_654_24654257_qa_2" description = "Is the median age of customers who exited higher than those who did not exit?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0024_654_24654257_qa_3/task.toml b/tasks/0024_654_24654257_qa_3/task.toml index 2187a525ec548d60446e01320f3d980e8069de6f..bc47f0f9c8b32c33837fdca6b6c07f611ebb8032 100644 --- a/tasks/0024_654_24654257_qa_3/task.toml +++ b/tasks/0024_654_24654257_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0024_654_24654257_qa_3" +name = "smoldataenvs-train/0024_654_24654257_qa_3" description = "What percentage of customers in the dataset have exited?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20.37" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0024_658_24658460_qa_4/task.toml b/tasks/0024_658_24658460_qa_4/task.toml index 07573529e72ab13d711d4bb7d9127f802de8861e..1386f0e9089748fd2a0aed06da45b36e2a5c6606 100644 --- a/tasks/0024_658_24658460_qa_4/task.toml +++ b/tasks/0024_658_24658460_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0024_658_24658460_qa_4" +name = "smoldataenvs-train/0024_658_24658460_qa_4" description = "What is the memory usage reported for the dataset as shown in the notebook's output?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.4+ MB" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0024_658_24658460_qa_5/task.toml b/tasks/0024_658_24658460_qa_5/task.toml index 9c64162799dd8ea44853b8bb322b6893fd4ca5ad..171b1426b845c0782ad7a063399df0885c154a80 100644 --- a/tasks/0024_658_24658460_qa_5/task.toml +++ b/tasks/0024_658_24658460_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0024_658_24658460_qa_5" +name = "smoldataenvs-train/0024_658_24658460_qa_5" description = "How many unique data columns are present in the dataset according to the provided information?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0024_675_24675509_qa_1/task.toml b/tasks/0024_675_24675509_qa_1/task.toml index 8141d56c66db250e6f79dd52477158a6274c809f..08df506dc898fe458246fb36a6358961eea3d346 100644 --- a/tasks/0024_675_24675509_qa_1/task.toml +++ b/tasks/0024_675_24675509_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0024_675_24675509_qa_1" +name = "smoldataenvs-train/0024_675_24675509_qa_1" description = "What percentage of customers in the dataset have exited?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20.37" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0024_675_24675509_qa_3/task.toml b/tasks/0024_675_24675509_qa_3/task.toml index f23849f2b3db7548d7041f2fce32798453206a5a..21f53b67df685f5037b8d778d8a68ba4ef339df5 100644 --- a/tasks/0024_675_24675509_qa_3/task.toml +++ b/tasks/0024_675_24675509_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0024_675_24675509_qa_3" +name = "smoldataenvs-train/0024_675_24675509_qa_3" description = "Which country has the highest proportion of customers who have exited?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Germany" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0024_675_24675509_qa_4/task.toml b/tasks/0024_675_24675509_qa_4/task.toml index de9a413c9bd59363a662d576f6723d0acd543bf2..1ce2f98b40e262f06068989c30f5f43ba1243d17 100644 --- a/tasks/0024_675_24675509_qa_4/task.toml +++ b/tasks/0024_675_24675509_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0024_675_24675509_qa_4" +name = "smoldataenvs-train/0024_675_24675509_qa_4" description = "Which gender has more outliers in Age among customers who have exited?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Female" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0024_796_24796579_qa_1/task.toml b/tasks/0024_796_24796579_qa_1/task.toml index 49cf445ea3e0ae87a741b6c15b1b12e54aff013e..887136204fbe1d4069326cc8bdb7148fc39f983e 100644 --- a/tasks/0024_796_24796579_qa_1/task.toml +++ b/tasks/0024_796_24796579_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0024_796_24796579_qa_1" +name = "smoldataenvs-train/0024_796_24796579_qa_1" description = "What is the ratio of ham to spam messages in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.5" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0024_796_24796579_qa_2/task.toml b/tasks/0024_796_24796579_qa_2/task.toml index da0dc24323e7b0ef4c0c50bdc59f074cee2e81bd..64fc9753eedb856e504931509da45ab2b1ec9a30 100644 --- a/tasks/0024_796_24796579_qa_2/task.toml +++ b/tasks/0024_796_24796579_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0024_796_24796579_qa_2" +name = "smoldataenvs-train/0024_796_24796579_qa_2" description = "What is the difference in the proportion of unique messages between ham and spam (as a percentage point)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.1" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0024_796_24796579_qa_3/task.toml b/tasks/0024_796_24796579_qa_3/task.toml index 96f8174f4bd382125f034c18dc61a1156aaeca4a..603da33738707f76c1649de6232e2c6cd42380b2 100644 --- a/tasks/0024_796_24796579_qa_3/task.toml +++ b/tasks/0024_796_24796579_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0024_796_24796579_qa_3" +name = "smoldataenvs-train/0024_796_24796579_qa_3" description = "What is the difference in mean absolute sentiment between spam and ham messages?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.03" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0024_829_24829447_qa_4/task.toml b/tasks/0024_829_24829447_qa_4/task.toml index eeb0ca60562ec91995f3647f475fd6b46f931f81..c27e7359f06c04de1445871765f2b8a7b7bcff46 100644 --- a/tasks/0024_829_24829447_qa_4/task.toml +++ b/tasks/0024_829_24829447_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0024_829_24829447_qa_4" +name = "smoldataenvs-train/0024_829_24829447_qa_4" description = "Which feature was removed from the dataset due to low variance (minimal difference between mean, min, 25%, 50%, 75% values)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "density" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0024_893_24893903_qa_4/task.toml b/tasks/0024_893_24893903_qa_4/task.toml index 26edb15c128f9c1de23fd7eb46828558dfd12918..d2b283d3edb6d740fd46e90275ad4d4beee782d6 100644 --- a/tasks/0024_893_24893903_qa_4/task.toml +++ b/tasks/0024_893_24893903_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0024_893_24893903_qa_4" +name = "smoldataenvs-train/0024_893_24893903_qa_4" description = "What is the difference in average years at company between employees who stayed and those who left the company?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.238" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0024_893_24893903_qa_5/task.toml b/tasks/0024_893_24893903_qa_5/task.toml index b6f2e13a704add5186e728787229fb29fa2e299c..a719046518e070d55af1d40cc73616292c72b93d 100644 --- a/tasks/0024_893_24893903_qa_5/task.toml +++ b/tasks/0024_893_24893903_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0024_893_24893903_qa_5" +name = "smoldataenvs-train/0024_893_24893903_qa_5" description = "What is the difference in average job satisfaction scores between employees who stayed and those who left the company?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.3102" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0024_894_24894091_qa_1/task.toml b/tasks/0024_894_24894091_qa_1/task.toml index 2a477e21f5f10e23a652ee55aa58270d807399b2..a0a12e8c3ad84370764ae695b114744d12aad2a7 100644 --- a/tasks/0024_894_24894091_qa_1/task.toml +++ b/tasks/0024_894_24894091_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0024_894_24894091_qa_1" +name = "smoldataenvs-train/0024_894_24894091_qa_1" description = "How many employees in the dataset have experienced attrition (Attrition = Yes)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "237" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0024_991_24991983_qa_2/task.toml b/tasks/0024_991_24991983_qa_2/task.toml index a28c696748d3a2b1c782e9fbe4fb3629e6894783..0c6c873296a3da57b5e82b296e61a5c9eebbdbb2 100644 --- a/tasks/0024_991_24991983_qa_2/task.toml +++ b/tasks/0024_991_24991983_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0024_991_24991983_qa_2" +name = "smoldataenvs-train/0024_991_24991983_qa_2" description = "What is the highest correlation coefficient between any two features in the dataset according to the heatmap visualization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.98" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0025_024_25024460_qa_1/task.toml b/tasks/0025_024_25024460_qa_1/task.toml index 80e79334d925c3fb5ec193534298c51898b868d6..097d01bfd3d60bae5d307f99917a6b17fd371c85 100644 --- a/tasks/0025_024_25024460_qa_1/task.toml +++ b/tasks/0025_024_25024460_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0025_024_25024460_qa_1" +name = "smoldataenvs-train/0025_024_25024460_qa_1" description = "Which feature is ranked as the most important by the Extra-Trees Regressor model in predicting insurance charges?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "smoker" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0025_160_25160247_qa_2/task.toml b/tasks/0025_160_25160247_qa_2/task.toml index 35139cc67c7afa481a111dd181b9a474a2c6a352..11827b60ef0c4a1aec0f517c9da5be670cc051db 100644 --- a/tasks/0025_160_25160247_qa_2/task.toml +++ b/tasks/0025_160_25160247_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0025_160_25160247_qa_2" +name = "smoldataenvs-train/0025_160_25160247_qa_2" description = "Which feature was removed during preprocessing due to having no correlation with other variables?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "veil-type" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0025_160_25160247_qa_3/task.toml b/tasks/0025_160_25160247_qa_3/task.toml index c26497d69d4e1aa64c0930cde353ed330dd109be..9b96a1b24ffa45750f856d66df81ab7fee991204 100644 --- a/tasks/0025_160_25160247_qa_3/task.toml +++ b/tasks/0025_160_25160247_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0025_160_25160247_qa_3" +name = "smoldataenvs-train/0025_160_25160247_qa_3" description = "Which feature exhibits the highest correlation with the target class according to the correlation heatmap?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "gill-size" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0025_163_25163859_qa_4/task.toml b/tasks/0025_163_25163859_qa_4/task.toml index fe9f179ebf9f1b1714866760a731a47b62d0d075..12217cb35362e041149fe5246476ee264a78a3b0 100644 --- a/tasks/0025_163_25163859_qa_4/task.toml +++ b/tasks/0025_163_25163859_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0025_163_25163859_qa_4" +name = "smoldataenvs-train/0025_163_25163859_qa_4" description = "Which specific months show the highest average number of passengers across all years in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "July, August" reward_mode_initial = "list" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0025_173_25173804_qa_3/task.toml b/tasks/0025_173_25173804_qa_3/task.toml index bbe4fd5f25cb3241910ebcaea14e907e979982b9..4239fa1921b1b32fb501ed88cfac3e5b93f6b112 100644 --- a/tasks/0025_173_25173804_qa_3/task.toml +++ b/tasks/0025_173_25173804_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0025_173_25173804_qa_3" +name = "smoldataenvs-train/0025_173_25173804_qa_3" description = "What is the highest correlation coefficient between Total Ecological Footprint and any other numeric variable in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.993230" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0025_204_25204322_qa_4/task.toml b/tasks/0025_204_25204322_qa_4/task.toml index 24114425b30d132019138944ee4f98d9e200b681..3b4ed59be5d88a0908e9f838e4027f271164a8eb 100644 --- a/tasks/0025_204_25204322_qa_4/task.toml +++ b/tasks/0025_204_25204322_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0025_204_25204322_qa_4" +name = "smoldataenvs-train/0025_204_25204322_qa_4" description = "How many missing values were present in the Type 2 column before data cleaning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "386" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0025_207_25207458_qa_2/task.toml b/tasks/0025_207_25207458_qa_2/task.toml index b7ae74cffe46e630a2c9a6d7b524b764ed3297c7..1fc8daf1d4817f8e6b166053347d56d424cfdf30 100644 --- a/tasks/0025_207_25207458_qa_2/task.toml +++ b/tasks/0025_207_25207458_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0025_207_25207458_qa_2" +name = "smoldataenvs-train/0025_207_25207458_qa_2" description = "Which location has experienced the most airplane crashes according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Sao Paulo, Brazil" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0025_211_25211872_qa_4/task.toml b/tasks/0025_211_25211872_qa_4/task.toml index 5cebfc14a70b4b1ea8aab75ef6bbfbaa7a7fa24e..5fd6a77ca137c69d4444d7acd5929860490d7966 100644 --- a/tasks/0025_211_25211872_qa_4/task.toml +++ b/tasks/0025_211_25211872_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0025_211_25211872_qa_4" +name = "smoldataenvs-train/0025_211_25211872_qa_4" description = "What percentage of missing values existed in the Insulin column of the original dataset before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "48.69" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0025_241_25241426_qa_2/task.toml b/tasks/0025_241_25241426_qa_2/task.toml index df1369cfd34fdb32349b7f230b88fd3925cda865..5ed7316795c0dac8edb1c75790cb8c0d55695a0e 100644 --- a/tasks/0025_241_25241426_qa_2/task.toml +++ b/tasks/0025_241_25241426_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0025_241_25241426_qa_2" +name = "smoldataenvs-train/0025_241_25241426_qa_2" description = "What is the F1-score for the spam class (class 1) in the classification report?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.86" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0025_241_25241426_qa_3/task.toml b/tasks/0025_241_25241426_qa_3/task.toml index ec05b05bb5414524fd589de1ad9ab21cca866287..e84443ae0d9b1ac78e312ad15d84eddfdacdbd86 100644 --- a/tasks/0025_241_25241426_qa_3/task.toml +++ b/tasks/0025_241_25241426_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0025_241_25241426_qa_3" +name = "smoldataenvs-train/0025_241_25241426_qa_3" description = "Do spam messages have more words than ham messages based on the KDE plot?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Yes" reward_mode_initial = "exact_bool" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] build_timeout_sec = 600.0 diff --git a/tasks/0025_273_25273223_qa_3/task.toml b/tasks/0025_273_25273223_qa_3/task.toml index eae44e54eac439ddffa3650d11ef53cdc5325dfc..ab6f54f2a94c0b6f25280f530b0cf6674e0c32e8 100644 --- a/tasks/0025_273_25273223_qa_3/task.toml +++ b/tasks/0025_273_25273223_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0025_273_25273223_qa_3" +name = "smoldataenvs-train/0025_273_25273223_qa_3" description = "How many malignant cases were misclassified as benign (false negatives) in the test set predictions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0025_273_25273223_qa_4/task.toml b/tasks/0025_273_25273223_qa_4/task.toml index 055f39416dd9436891491efd7131c3bc3be5a521..89f84a330c61c9c94d0978b1788bee45fa960bf5 100644 --- a/tasks/0025_273_25273223_qa_4/task.toml +++ b/tasks/0025_273_25273223_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0025_273_25273223_qa_4" +name = "smoldataenvs-train/0025_273_25273223_qa_4" description = "What percentage of the original dataset consists of malignant cases before splitting into training and test sets?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "37.26" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0025_274_25274016_qa_4/task.toml b/tasks/0025_274_25274016_qa_4/task.toml index 0e083ce04b6a76ac9d3c12d80e64669d8238ddbd..a5662e3b6b87363a41e8ba85d969c11436f01f7d 100644 --- a/tasks/0025_274_25274016_qa_4/task.toml +++ b/tasks/0025_274_25274016_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0025_274_25274016_qa_4" +name = "smoldataenvs-train/0025_274_25274016_qa_4" description = "What percentage of tweets in the dataset are classified as neutral (target 2)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0025_491_25491400_qa_4/task.toml b/tasks/0025_491_25491400_qa_4/task.toml index ffb4fbc9669a51ec672f2883cd8eaf0eb512c6ba..145b1186e3acf1244b91a9eb843a5a5a80b5d3f0 100644 --- a/tasks/0025_491_25491400_qa_4/task.toml +++ b/tasks/0025_491_25491400_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0025_491_25491400_qa_4" +name = "smoldataenvs-train/0025_491_25491400_qa_4" description = "What percentage of employees in the dataset have left the company, as shown in the target variable distribution?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16.1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0025_564_25564899_qa_4/task.toml b/tasks/0025_564_25564899_qa_4/task.toml index 34b952f99f6227a2bd40836685c9caa18f64902a..1404a8bd3fd78aa378ee87f2cef447eb54416a7d 100644 --- a/tasks/0025_564_25564899_qa_4/task.toml +++ b/tasks/0025_564_25564899_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0025_564_25564899_qa_4" +name = "smoldataenvs-train/0025_564_25564899_qa_4" description = "Which model achieved the highest accuracy when using only the tweet text as input?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Logistic Regression" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0025_744_25744450_qa_1/task.toml b/tasks/0025_744_25744450_qa_1/task.toml index 16ad05d2b262aa2a5eadd714cef023a9b2c4acad..6491e29765a9e270337050536e3cab9e04a74678 100644 --- a/tasks/0025_744_25744450_qa_1/task.toml +++ b/tasks/0025_744_25744450_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0025_744_25744450_qa_1" +name = "smoldataenvs-train/0025_744_25744450_qa_1" description = "What is the strongest positive correlation with median house value in the dataset after creating derived features like rooms_per_household?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "median_income" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0026_141_26141545_qa_1/task.toml b/tasks/0026_141_26141545_qa_1/task.toml index a2ad1a12b44dc5007e992c8dc4e25d158b93dc6c..e767cefba056fdacdd611f1b558bae2cddb287b8 100644 --- a/tasks/0026_141_26141545_qa_1/task.toml +++ b/tasks/0026_141_26141545_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0026_141_26141545_qa_1" +name = "smoldataenvs-train/0026_141_26141545_qa_1" description = "What percentage of patients who survived had a number of axillary nodes ≤ 5 based on the cumulative distribution function analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "82" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0026_141_26141545_qa_5/task.toml b/tasks/0026_141_26141545_qa_5/task.toml index 46007f1963f9e1799dad9eb0382298b71919850b..8a31086394182e85e3064c1de9789091838db699 100644 --- a/tasks/0026_141_26141545_qa_5/task.toml +++ b/tasks/0026_141_26141545_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0026_141_26141545_qa_5" +name = "smoldataenvs-train/0026_141_26141545_qa_5" description = "Which operation year range was associated with the highest failure rate based on the violin plot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1962-1965" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0026_168_26168351_qa_5/task.toml b/tasks/0026_168_26168351_qa_5/task.toml index 4ba56405eee98907660edca0021a6c343ce71110..caf0ddae453db1eebc300bba530fa199efa87557 100644 --- a/tasks/0026_168_26168351_qa_5/task.toml +++ b/tasks/0026_168_26168351_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0026_168_26168351_qa_5" +name = "smoldataenvs-train/0026_168_26168351_qa_5" description = "What is the first interaction value created by combining category and country in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Poetry_GB" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0026_168_26168363_qa_1/task.toml b/tasks/0026_168_26168363_qa_1/task.toml index 096aa36e7ccaefacca056b34cb6f5a1c4ebfe81c..312ce9e2fdab22128c06a143db3d7e208ca6699f 100644 --- a/tasks/0026_168_26168363_qa_1/task.toml +++ b/tasks/0026_168_26168363_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0026_168_26168363_qa_1" +name = "smoldataenvs-train/0026_168_26168363_qa_1" description = "What is the AUC score of the LightGBM model on the test set after training?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.7476" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0026_168_26168363_qa_4/task.toml b/tasks/0026_168_26168363_qa_4/task.toml index f393ae72004142c61d243ce611ceb26776b9bce5..c603022b12926a2613884d09395d528a46123225 100644 --- a/tasks/0026_168_26168363_qa_4/task.toml +++ b/tasks/0026_168_26168363_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0026_168_26168363_qa_4" +name = "smoldataenvs-train/0026_168_26168363_qa_4" description = "How many unique project states were present in the original dataset before preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0026_293_26293551_qa_3/task.toml b/tasks/0026_293_26293551_qa_3/task.toml index f445347dc4414175a869362b1f51d89ec2473799..8a7443831350fc56d1ca6452a9163d13e4127c59 100644 --- a/tasks/0026_293_26293551_qa_3/task.toml +++ b/tasks/0026_293_26293551_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0026_293_26293551_qa_3" +name = "smoldataenvs-train/0026_293_26293551_qa_3" description = "What is the average age of patients in the dataset based on the preprocessed data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "33.24" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0026_354_26354042_qa_1/task.toml b/tasks/0026_354_26354042_qa_1/task.toml index ca02d0644f0bd2f48b3ca381c3422b7608a4dc48..c140b7d11537a878b6d2d1053e64043bccac8837 100644 --- a/tasks/0026_354_26354042_qa_1/task.toml +++ b/tasks/0026_354_26354042_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0026_354_26354042_qa_1" +name = "smoldataenvs-train/0026_354_26354042_qa_1" description = "What is the interquartile range (IQR) of years of experience in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0026_354_26354042_qa_4/task.toml b/tasks/0026_354_26354042_qa_4/task.toml index ff6430455765cd3f2a98372635a86ec01a67d88c..5f43bf0dbbfc21d0482c8fdb870420bd29e11f0f 100644 --- a/tasks/0026_354_26354042_qa_4/task.toml +++ b/tasks/0026_354_26354042_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0026_354_26354042_qa_4" +name = "smoldataenvs-train/0026_354_26354042_qa_4" description = "What is the difference between the maximum salary and the average salary in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "46388" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0026_376_26376215_qa_4/task.toml b/tasks/0026_376_26376215_qa_4/task.toml index 45384c3b8687e2e1a5e197a51cc22d3820fdbb13..21e0bece78732f321fc243b88e657e7b62ed008e 100644 --- a/tasks/0026_376_26376215_qa_4/task.toml +++ b/tasks/0026_376_26376215_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0026_376_26376215_qa_4" +name = "smoldataenvs-train/0026_376_26376215_qa_4" description = "What is the maximum number of positive axillary nodes where at least 80% of patients who survived 5+ years had values less than or equal to this threshold?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0026_494_26494658_qa_1/task.toml b/tasks/0026_494_26494658_qa_1/task.toml index e799a0a9ae860dcbf747275d9fdfcba36587ded3..600cabf93f82987d5dc7eb6fb2e4137d8cbb4d04 100644 --- a/tasks/0026_494_26494658_qa_1/task.toml +++ b/tasks/0026_494_26494658_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0026_494_26494658_qa_1" +name = "smoldataenvs-train/0026_494_26494658_qa_1" description = "Which digit class in the training dataset has the highest number of samples, and how many samples does it have?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1, 6742" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0026_494_26494658_qa_4/task.toml b/tasks/0026_494_26494658_qa_4/task.toml index e1db966b43350ad07317d32781bd460165097fd3..4c1f2e037cf7d1dd0c877e130bc488a723ec3b73 100644 --- a/tasks/0026_494_26494658_qa_4/task.toml +++ b/tasks/0026_494_26494658_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0026_494_26494658_qa_4" +name = "smoldataenvs-train/0026_494_26494658_qa_4" description = "What is the median number of samples per digit class in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5936" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0026_494_26494658_qa_5/task.toml b/tasks/0026_494_26494658_qa_5/task.toml index d26aab8505d45a9b7a0f47df2a075020277cf536..d22aab56d69d84d9a11770406d91d4679f6d6751 100644 --- a/tasks/0026_494_26494658_qa_5/task.toml +++ b/tasks/0026_494_26494658_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0026_494_26494658_qa_5" +name = "smoldataenvs-train/0026_494_26494658_qa_5" description = "What is the total number of pixels in all training images combined?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "47040000" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0026_514_26514302_qa_2/task.toml b/tasks/0026_514_26514302_qa_2/task.toml index c9b4169f427698d1a997229f4e8528252e067827..842f524d40aadd642982fa1767e64acd56aec110 100644 --- a/tasks/0026_514_26514302_qa_2/task.toml +++ b/tasks/0026_514_26514302_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0026_514_26514302_qa_2" +name = "smoldataenvs-train/0026_514_26514302_qa_2" description = "Which U.S. state has the highest average high school graduation rate according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Massachusetts" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0026_514_26514302_qa_4/task.toml b/tasks/0026_514_26514302_qa_4/task.toml index 0909665de0521769558de53ab9bdbf103174ba1d..b73e88a65f33d0853efceb923b2bb9d462e80e28 100644 --- a/tasks/0026_514_26514302_qa_4/task.toml +++ b/tasks/0026_514_26514302_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0026_514_26514302_qa_4" +name = "smoldataenvs-train/0026_514_26514302_qa_4" description = "What is the most common name or surname among victims in the police shootings dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Michael" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0026_528_26528881_qa_3/task.toml b/tasks/0026_528_26528881_qa_3/task.toml index f47323f6c370fc0eda642396b19bdfd0a7de8114..43d2619b777bc62fdb84f6484bce600115443b0d 100644 --- a/tasks/0026_528_26528881_qa_3/task.toml +++ b/tasks/0026_528_26528881_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0026_528_26528881_qa_3" +name = "smoldataenvs-train/0026_528_26528881_qa_3" description = "Is the Mean Absolute Error (MAE) on the test dataset higher than the MAE on the training dataset after model evaluation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0026_546_26546219_qa_2/task.toml b/tasks/0026_546_26546219_qa_2/task.toml index 3ca5ca10560f5642aaf6d94b1cfe3e5f7a3cee00..36099d96614c6a0c03f5ed5b390524eddcf436aa 100644 --- a/tasks/0026_546_26546219_qa_2/task.toml +++ b/tasks/0026_546_26546219_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0026_546_26546219_qa_2" +name = "smoldataenvs-train/0026_546_26546219_qa_2" description = "What is the median price of houses in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "450000" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0026_546_26546219_qa_3/task.toml b/tasks/0026_546_26546219_qa_3/task.toml index c1223a3a5e9793293c295b25d35a975459c15bae..bdba90f796d50e6de8498bc236142911e86674ca 100644 --- a/tasks/0026_546_26546219_qa_3/task.toml +++ b/tasks/0026_546_26546219_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0026_546_26546219_qa_3" +name = "smoldataenvs-train/0026_546_26546219_qa_3" description = "What is the highest price recorded for any house in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7700000" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0026_546_26546219_qa_5/task.toml b/tasks/0026_546_26546219_qa_5/task.toml index 4a4960179287664dd2a0bd162fd7e88a6d462bf8..f16ce44a41d4baaa113b2e992f0865530d522522 100644 --- a/tasks/0026_546_26546219_qa_5/task.toml +++ b/tasks/0026_546_26546219_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0026_546_26546219_qa_5" +name = "smoldataenvs-train/0026_546_26546219_qa_5" description = "What is the average number of bedrooms in the houses included in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.37" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0026_551_26551206_qa_3/task.toml b/tasks/0026_551_26551206_qa_3/task.toml index 41ae8103aaf15b3d6dddf9021315927aef63aada..3cd07622986147a067db663192afa513a2d38edf 100644 --- a/tasks/0026_551_26551206_qa_3/task.toml +++ b/tasks/0026_551_26551206_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0026_551_26551206_qa_3" +name = "smoldataenvs-train/0026_551_26551206_qa_3" description = "What is the precision score for class 1 (malignant tumors) when using K=11 in the KNN model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.98" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0026_643_26643260_qa_1/task.toml b/tasks/0026_643_26643260_qa_1/task.toml index bcbc1216cfa7421c716e77ad5611fd749bbc0f28..6283ae7534839f678c84a0d460a4d3313a818478 100644 --- a/tasks/0026_643_26643260_qa_1/task.toml +++ b/tasks/0026_643_26643260_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0026_643_26643260_qa_1" +name = "smoldataenvs-train/0026_643_26643260_qa_1" description = "Which Pokémon type has the highest average attack and which has the highest average defense?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "dragon, steel" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0026_643_26643260_qa_5/task.toml b/tasks/0026_643_26643260_qa_5/task.toml index bf1c054f4c4307cb27b2ffca00407adb91437895..c098bb7ec97c77a265a6352837a26693fd5a5306 100644 --- a/tasks/0026_643_26643260_qa_5/task.toml +++ b/tasks/0026_643_26643260_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0026_643_26643260_qa_5" +name = "smoldataenvs-train/0026_643_26643260_qa_5" description = "What is the most effective type against Fairy-type Pokémon based on battle advantage analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "poison" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0026_651_26651353_qa_3/task.toml b/tasks/0026_651_26651353_qa_3/task.toml index af099f6e97a12e262ac2af341d09d41e018aef4d..593f6b9196281efa1500809584b320529c3c393f 100644 --- a/tasks/0026_651_26651353_qa_3/task.toml +++ b/tasks/0026_651_26651353_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0026_651_26651353_qa_3" +name = "smoldataenvs-train/0026_651_26651353_qa_3" description = "What is the difference in skewness between the original SalePrice distribution and its log-transformed distribution?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.759730" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0026_818_26818249_qa_5/task.toml b/tasks/0026_818_26818249_qa_5/task.toml index df1c0a618eb76b270b19abbf4872ccd7f658d572..abbf892fc421e7d2cb274d73be765afbacd13d89 100644 --- a/tasks/0026_818_26818249_qa_5/task.toml +++ b/tasks/0026_818_26818249_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0026_818_26818249_qa_5" +name = "smoldataenvs-train/0026_818_26818249_qa_5" description = "What is the recall of the malignant class (labeled 1) after hyperparameter tuning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.98" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0026_826_26826730_qa_1/task.toml b/tasks/0026_826_26826730_qa_1/task.toml index c2d371572b041e52bc09e2228adad833012724cf..8027be71baddbd39e6fc1cc0444f0cda51ac1cd7 100644 --- a/tasks/0026_826_26826730_qa_1/task.toml +++ b/tasks/0026_826_26826730_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0026_826_26826730_qa_1" +name = "smoldataenvs-train/0026_826_26826730_qa_1" description = "Which feature has the highest chi-square score for predicting employee attrition according to the feature selection analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "MonthlyIncome" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0026_826_26826730_qa_2/task.toml b/tasks/0026_826_26826730_qa_2/task.toml index afc901475a3c6e48eb8607e407847632276cbd36..e15d955b9d6bcce3297cd1b351df26eec8f05293 100644 --- a/tasks/0026_826_26826730_qa_2/task.toml +++ b/tasks/0026_826_26826730_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0026_826_26826730_qa_2" +name = "smoldataenvs-train/0026_826_26826730_qa_2" description = "How many features were retained after applying the chi-square feature selection method with the top 20 scores?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0026_826_26826730_qa_3/task.toml b/tasks/0026_826_26826730_qa_3/task.toml index d40dbd0f9978512f9ab6d3708fad3c2483addae0..bbd1856fd798c32f2ebab8a22af6f6801ae4b6e4 100644 --- a/tasks/0026_826_26826730_qa_3/task.toml +++ b/tasks/0026_826_26826730_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0026_826_26826730_qa_3" +name = "smoldataenvs-train/0026_826_26826730_qa_3" description = "What percentage of the MonthlyIncome data was removed as outliers during the outlier filtering process?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7.7551" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0026_836_26836336_qa_1/task.toml b/tasks/0026_836_26836336_qa_1/task.toml index a813bcc5aca486813ee3ef2799e9a4577ecb6d85..336e39c152ccd0b5b1846361e6f5b856a083dd95 100644 --- a/tasks/0026_836_26836336_qa_1/task.toml +++ b/tasks/0026_836_26836336_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0026_836_26836336_qa_1" +name = "smoldataenvs-train/0026_836_26836336_qa_1" description = "How many features remain in the dataset after removing the 'id' and 'Unnamed: 32' columns?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "31" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0026_836_26836336_qa_2/task.toml b/tasks/0026_836_26836336_qa_2/task.toml index 9b23255b8b1f5a2922ebeacfc66db62d1d2dff62..8c363fcd066c3308c3ff4f96b0e308cb59863a06 100644 --- a/tasks/0026_836_26836336_qa_2/task.toml +++ b/tasks/0026_836_26836336_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0026_836_26836336_qa_2" +name = "smoldataenvs-train/0026_836_26836336_qa_2" description = "What is the number of samples in the training set after an 80% train-test split?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "455" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0026_878_26878814_qa_5/task.toml b/tasks/0026_878_26878814_qa_5/task.toml index 912169cef0d1690cc4590786048f0aef3f54484d..520ac783ec153d31ab31a9dbf025d4250684364f 100644 --- a/tasks/0026_878_26878814_qa_5/task.toml +++ b/tasks/0026_878_26878814_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0026_878_26878814_qa_5" +name = "smoldataenvs-train/0026_878_26878814_qa_5" description = "What is the total number of missing values in the 'Insulin' column after replacing zero values with NaN during data preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "374" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0026_887_26887570_qa_4/task.toml b/tasks/0026_887_26887570_qa_4/task.toml index 7ced7069de272dd5d13719b6307d71e4ce07870e..ee0eb3cb34a90364a06b8d6f3ff7b1ff5415ef85 100644 --- a/tasks/0026_887_26887570_qa_4/task.toml +++ b/tasks/0026_887_26887570_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0026_887_26887570_qa_4" +name = "smoldataenvs-train/0026_887_26887570_qa_4" description = "Which outlet size category has the highest median Item_Outlet_Sales according to the pivot table analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Medium" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0026_895_26895005_qa_1/task.toml b/tasks/0026_895_26895005_qa_1/task.toml index 31ac14f4c69653f983edce127f73a5b0db687e10..a039baa25db02ad95806dcf5df5ac365408e95b1 100644 --- a/tasks/0026_895_26895005_qa_1/task.toml +++ b/tasks/0026_895_26895005_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0026_895_26895005_qa_1" +name = "smoldataenvs-train/0026_895_26895005_qa_1" description = "What is the ratio of class 0 samples to class 1 samples in the original income dataset before applying any imbalance correction techniques?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.18:1" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0026_920_26920486_qa_1/task.toml b/tasks/0026_920_26920486_qa_1/task.toml index 0a74131f25049e0fc165731d7f1b8c4aa940737d..baa28afc9c3c2bd9e8884f598dc143fe88d620e5 100644 --- a/tasks/0026_920_26920486_qa_1/task.toml +++ b/tasks/0026_920_26920486_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0026_920_26920486_qa_1" +name = "smoldataenvs-train/0026_920_26920486_qa_1" description = "Which feature has the highest positive coefficient in the linear regression model predicting solar radiation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Temperature" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0026_947_26947069_qa_5/task.toml b/tasks/0026_947_26947069_qa_5/task.toml index 847e2d3b82e218438eb1eea443a066b0d905773a..4751b641fa386e62fec7f92ceadb58969d05e7c8 100644 --- a/tasks/0026_947_26947069_qa_5/task.toml +++ b/tasks/0026_947_26947069_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0026_947_26947069_qa_5" +name = "smoldataenvs-train/0026_947_26947069_qa_5" description = "What is the maximum value of the charges variable after applying MinMaxScaler with the range [3, 7]?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0026_997_26997571_qa_2/task.toml b/tasks/0026_997_26997571_qa_2/task.toml index b34a1b45f28d8aeaf44cf4146195928c320e87f3..2b91ca7ce6b8820e9dae1795a9fa4d4d59d2e0dd 100644 --- a/tasks/0026_997_26997571_qa_2/task.toml +++ b/tasks/0026_997_26997571_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0026_997_26997571_qa_2" +name = "smoldataenvs-train/0026_997_26997571_qa_2" description = "What is the Pearson correlation coefficient between wine price and points in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.459863" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0026_997_26997571_qa_4/task.toml b/tasks/0026_997_26997571_qa_4/task.toml index 55064da6fbd566ae6abfdb4dab4e0870a799e4b2..ab6d7421a9a99e59724bfd761eedbcd86280f49a 100644 --- a/tasks/0026_997_26997571_qa_4/task.toml +++ b/tasks/0026_997_26997571_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0026_997_26997571_qa_4" +name = "smoldataenvs-train/0026_997_26997571_qa_4" description = "What is the lowest average wine points score among all countries represented in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "82" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0026_997_26997571_qa_5/task.toml b/tasks/0026_997_26997571_qa_5/task.toml index 0d56cf4d0dd69fc098bc030ac7a9bffb7e8a6828..a003467c23602100b3ab427a78aa6756721aa18c 100644 --- a/tasks/0026_997_26997571_qa_5/task.toml +++ b/tasks/0026_997_26997571_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0026_997_26997571_qa_5" +name = "smoldataenvs-train/0026_997_26997571_qa_5" description = "Which country ranks third in the average wine points hierarchy?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "France" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0027_004_27004450_qa_2/task.toml b/tasks/0027_004_27004450_qa_2/task.toml index 2734557595ba7b1f62260acd955770f1e8464b3b..1ff30b17c112a063ab62fa6240592d0b7b21312f 100644 --- a/tasks/0027_004_27004450_qa_2/task.toml +++ b/tasks/0027_004_27004450_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0027_004_27004450_qa_2" +name = "smoldataenvs-train/0027_004_27004450_qa_2" description = "Which opening for white has the highest win percentage among the top 10 most common openings in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Philiodor Defense #3" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0027_089_27089896_qa_3/task.toml b/tasks/0027_089_27089896_qa_3/task.toml index e23a076f2556ca6d5c659e78f95490026b23f400..faa06185f866408afb6e5b8cca3abd6723bff116 100644 --- a/tasks/0027_089_27089896_qa_3/task.toml +++ b/tasks/0027_089_27089896_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0027_089_27089896_qa_3" +name = "smoldataenvs-train/0027_089_27089896_qa_3" description = "What is the intercept value of the linear regression model after training?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-2640159.796853739" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0027_117_27117791_qa_2/task.toml b/tasks/0027_117_27117791_qa_2/task.toml index 32cc804639f231df3ef4af2e4711424b92d052a3..9ccb31554f0bea852d54b2f94993d7730c819612 100644 --- a/tasks/0027_117_27117791_qa_2/task.toml +++ b/tasks/0027_117_27117791_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0027_117_27117791_qa_2" +name = "smoldataenvs-train/0027_117_27117791_qa_2" description = "What is the average number of words per ironic comment in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "29.63" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0027_151_27151900_qa_1/task.toml b/tasks/0027_151_27151900_qa_1/task.toml index 5f26c9228b4cffa74fecaa12c82365ae2f2c0b32..2002566294568544fde9fbbb97038612616d9ecd 100644 --- a/tasks/0027_151_27151900_qa_1/task.toml +++ b/tasks/0027_151_27151900_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0027_151_27151900_qa_1" +name = "smoldataenvs-train/0027_151_27151900_qa_1" description = "Which feature has the highest negative correlation with the mushroom class (poisonous/edible) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "gill-color" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0027_151_27151900_qa_5/task.toml b/tasks/0027_151_27151900_qa_5/task.toml index 2b17aeff3ec6791502390f7ba52474f78699e476..5eba1b3ee8fbe8cb9f2fccfeba4ec48e8bd9a1c8 100644 --- a/tasks/0027_151_27151900_qa_5/task.toml +++ b/tasks/0027_151_27151900_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0027_151_27151900_qa_5" +name = "smoldataenvs-train/0027_151_27151900_qa_5" description = "What is the F1 score of the Logistic Regression model on the training data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.94559" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0027_179_27179583_qa_2/task.toml b/tasks/0027_179_27179583_qa_2/task.toml index d1a6d74c41ea21bb10c965f37fafed5581376c1f..f3f3ee0f6e5b82ba2b94aef1b0f6d70f7d2ead0d 100644 --- a/tasks/0027_179_27179583_qa_2/task.toml +++ b/tasks/0027_179_27179583_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0027_179_27179583_qa_2" +name = "smoldataenvs-train/0027_179_27179583_qa_2" description = "What is the average number of comments for Ask HN posts posted at 15:00?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "28.68" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0027_179_27179583_qa_3/task.toml b/tasks/0027_179_27179583_qa_3/task.toml index bb161834a75c3fbbd2eb804c019a26ecd7228dcb..0490eecc4353b9a29e2a0b638988db8efa1aef08 100644 --- a/tasks/0027_179_27179583_qa_3/task.toml +++ b/tasks/0027_179_27179583_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0027_179_27179583_qa_3" +name = "smoldataenvs-train/0027_179_27179583_qa_3" description = "What is the average number of comments for the second-top hour of Ask HN posts?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16.32" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0027_179_27179583_qa_4/task.toml b/tasks/0027_179_27179583_qa_4/task.toml index 5a081b9e1f9f55e84359430960e7cde3985884e3..bc6c8f1a5a2bd5174bc70a179cb64de8b096b0ea 100644 --- a/tasks/0027_179_27179583_qa_4/task.toml +++ b/tasks/0027_179_27179583_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0027_179_27179583_qa_4" +name = "smoldataenvs-train/0027_179_27179583_qa_4" description = "What is the average number of comments for Show HN posts?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.89" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0027_223_27223315_qa_1/task.toml b/tasks/0027_223_27223315_qa_1/task.toml index ea7eb77f269bc88d5f5245fcae64ebad6d0eb0f5..8820519ea176e2171b21015fb20a84f3c43df4aa 100644 --- a/tasks/0027_223_27223315_qa_1/task.toml +++ b/tasks/0027_223_27223315_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0027_223_27223315_qa_1" +name = "smoldataenvs-train/0027_223_27223315_qa_1" description = "What is the average rating of all reviews in the dataset after removing neutral ratings (rating = 3)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.8946" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0027_246_27246299_qa_4/task.toml b/tasks/0027_246_27246299_qa_4/task.toml index 684262e73aeb291fab290804b3a52112d204c50a..07336894ecaec828866bacbe36cee0ccd006a9d0 100644 --- a/tasks/0027_246_27246299_qa_4/task.toml +++ b/tasks/0027_246_27246299_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0027_246_27246299_qa_4" +name = "smoldataenvs-train/0027_246_27246299_qa_4" description = "Which feature in the dataset had the highest standard deviation before any data preprocessing steps?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Insulin" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0027_270_27270164_qa_2/task.toml b/tasks/0027_270_27270164_qa_2/task.toml index 3f91ea5b01baa9632be0e29db873d81396f7fb94..7fada0a2c0cfc9e488c4b7a65e0c1be91aba126c 100644 --- a/tasks/0027_270_27270164_qa_2/task.toml +++ b/tasks/0027_270_27270164_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0027_270_27270164_qa_2" +name = "smoldataenvs-train/0027_270_27270164_qa_2" description = "What is the percentage of malignancy cases in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "37.26" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0027_503_27503967_qa_5/task.toml b/tasks/0027_503_27503967_qa_5/task.toml index 3c9b516ffbdc458819712d60f9feb8d58ee55e0b..96f6edd6a7d85b5f4e9c7468b9b90df1ff364aa0 100644 --- a/tasks/0027_503_27503967_qa_5/task.toml +++ b/tasks/0027_503_27503967_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0027_503_27503967_qa_5" +name = "smoldataenvs-train/0027_503_27503967_qa_5" description = "What was the highest test set accuracy achieved by any of the models evaluated (kNN, Random Forest, Naive Bayes) using their optimized parameters?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.95" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0027_515_27515535_qa_5/task.toml b/tasks/0027_515_27515535_qa_5/task.toml index 5e0a4d08d7458e8dbfe6d5e66cd11c94fb022244..11f1779dd16e3d84c200b0d028b0d3ab2569df3e 100644 --- a/tasks/0027_515_27515535_qa_5/task.toml +++ b/tasks/0027_515_27515535_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0027_515_27515535_qa_5" +name = "smoldataenvs-train/0027_515_27515535_qa_5" description = "What is the correlation coefficient between JobLevel and MonthlyIncome in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9503" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0027_583_27583327_qa_3/task.toml b/tasks/0027_583_27583327_qa_3/task.toml index 8fdea79a94f0d3213f66cbfc85634b240646b0d5..9fc91f083a76647a82fd365c412c67f2b8e09775 100644 --- a/tasks/0027_583_27583327_qa_3/task.toml +++ b/tasks/0027_583_27583327_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0027_583_27583327_qa_3" +name = "smoldataenvs-train/0027_583_27583327_qa_3" description = "What is the numerical value assigned to \"Vodka\" in the \"Favorite Beverage\" column after label encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0027_583_27583327_qa_4/task.toml b/tasks/0027_583_27583327_qa_4/task.toml index 043604a05598343f6fff6c513f5210e4604af7de..1fa8ae53282626e0e4f81bda479d35f9b359a2b2 100644 --- a/tasks/0027_583_27583327_qa_4/task.toml +++ b/tasks/0027_583_27583327_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0027_583_27583327_qa_4" +name = "smoldataenvs-train/0027_583_27583327_qa_4" description = "What is the numerical value assigned to \"Whiskey\" in the \"Favorite Beverage\" column after label encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0027_660_27660903_qa_2/task.toml b/tasks/0027_660_27660903_qa_2/task.toml index efc8bc9935c623955ad5ef056cbe97ee43effd9b..1c59648c13cba6b13b661e5617b098cd4147eae8 100644 --- a/tasks/0027_660_27660903_qa_2/task.toml +++ b/tasks/0027_660_27660903_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0027_660_27660903_qa_2" +name = "smoldataenvs-train/0027_660_27660903_qa_2" description = "What is the ratio of the average Q25 value between female and male voices in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.430826" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0027_804_27804225_qa_2/task.toml b/tasks/0027_804_27804225_qa_2/task.toml index d0a32e5ff578ea906872f1e45901a602cd1be8e3..cc51b48862ae6fb8cf400266a1e4b25940cb9879 100644 --- a/tasks/0027_804_27804225_qa_2/task.toml +++ b/tasks/0027_804_27804225_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0027_804_27804225_qa_2" +name = "smoldataenvs-train/0027_804_27804225_qa_2" description = "Is there at least one approved credit card application (Class 1) located in a neuron with a high distance value, indicating potential fraud?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0027_893_27893972_qa_4/task.toml b/tasks/0027_893_27893972_qa_4/task.toml index 15d97bee28efbaf89c425727570ace8f2e2e48ae..cdfc2e0135fd77e31df4b3e20999e408b10843e7 100644 --- a/tasks/0027_893_27893972_qa_4/task.toml +++ b/tasks/0027_893_27893972_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0027_893_27893972_qa_4" +name = "smoldataenvs-train/0027_893_27893972_qa_4" description = "How many missing values are present in the total_bedrooms column of the dataset before imputation or removal?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "207" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0027_986_27986516_qa_1/task.toml b/tasks/0027_986_27986516_qa_1/task.toml index 708bd7003511879aefebcdd9f638b1d7bfd117df..a210b92c1f965e75c721daf06eef102fa740b6e9 100644 --- a/tasks/0027_986_27986516_qa_1/task.toml +++ b/tasks/0027_986_27986516_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0027_986_27986516_qa_1" +name = "smoldataenvs-train/0027_986_27986516_qa_1" description = "What is the average number of words in the abstracts of NIPS 2015 papers?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "148.65" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0027_992_27992214_qa_3/task.toml b/tasks/0027_992_27992214_qa_3/task.toml index 896df3ec0812c515dddd548564bceb781c492d9d..5a66abbfa73d50366e4e5dd811ad4cccbd44d4e2 100644 --- a/tasks/0027_992_27992214_qa_3/task.toml +++ b/tasks/0027_992_27992214_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0027_992_27992214_qa_3" +name = "smoldataenvs-train/0027_992_27992214_qa_3" description = "What is the difference between the training and testing R² scores for the random forest model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.186" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0027_992_27992214_qa_5/task.toml b/tasks/0027_992_27992214_qa_5/task.toml index f49c3b8256fd7723be405f9b22e393b814d16bbe..851ba76d8c28ac4e619ae3474101771a118440be 100644 --- a/tasks/0027_992_27992214_qa_5/task.toml +++ b/tasks/0027_992_27992214_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0027_992_27992214_qa_5" +name = "smoldataenvs-train/0027_992_27992214_qa_5" description = "Which ocean proximity category has the highest frequency in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "<1H OCEAN" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0028_007_28007632_qa_1/task.toml b/tasks/0028_007_28007632_qa_1/task.toml index 54e43d9dfd9d6eeaabca2f6263a0a8117f6467a2..759f61ea265c37dd3ec7414736df8a11cd9a3534 100644 --- a/tasks/0028_007_28007632_qa_1/task.toml +++ b/tasks/0028_007_28007632_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0028_007_28007632_qa_1" +name = "smoldataenvs-train/0028_007_28007632_qa_1" description = "Which classification model achieved the highest test accuracy, and what was the accuracy score?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "GaussianNB, 1.0" reward_mode_initial = "list" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0028_007_28007632_qa_3/task.toml b/tasks/0028_007_28007632_qa_3/task.toml index 7f302079b052d134c994a8e3e1ee27c509532c14..6815647cc9700480544e2c910cda85bbd71bb46a 100644 --- a/tasks/0028_007_28007632_qa_3/task.toml +++ b/tasks/0028_007_28007632_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0028_007_28007632_qa_3" +name = "smoldataenvs-train/0028_007_28007632_qa_3" description = "Based on the violin plots, which feature exhibits the greatest separation in distributions between the three species?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0028_007_28007632_qa_4/task.toml b/tasks/0028_007_28007632_qa_4/task.toml index e1b342936708ca905144209b2650e7c03abe75b7..e93fb9eefa63a60a0343f516ddec99dd4ba7694d 100644 --- a/tasks/0028_007_28007632_qa_4/task.toml +++ b/tasks/0028_007_28007632_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0028_007_28007632_qa_4" +name = "smoldataenvs-train/0028_007_28007632_qa_4" description = "What is the percentage of samples for each species in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "33.33" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0028_054_28054736_qa_2/task.toml b/tasks/0028_054_28054736_qa_2/task.toml index a42a330e4090073cb4de489fac813f5cc9c54f81..dd9a48afe0f70203f2e90e12b5a64b6675d6b40c 100644 --- a/tasks/0028_054_28054736_qa_2/task.toml +++ b/tasks/0028_054_28054736_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0028_054_28054736_qa_2" +name = "smoldataenvs-train/0028_054_28054736_qa_2" description = "Are there any missing values present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0028_150_28150461_qa_1/task.toml b/tasks/0028_150_28150461_qa_1/task.toml index 0fbbd99c7ebbf71d6fba8eb3ff3dd6cf9265fcaa..22cbfc6b19ac8a559aeeed1f7c2af75ba61b8b32 100644 --- a/tasks/0028_150_28150461_qa_1/task.toml +++ b/tasks/0028_150_28150461_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0028_150_28150461_qa_1" +name = "smoldataenvs-train/0028_150_28150461_qa_1" description = "What is the median fruit label in the dataset based on the statistical description?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0028_150_28150461_qa_3/task.toml b/tasks/0028_150_28150461_qa_3/task.toml index 9876f56fabae2bd9fcd2b43b23a0a4e792812237..ee8fa1a4ef7418e21b1b48207aca078bfc40ef84 100644 --- a/tasks/0028_150_28150461_qa_3/task.toml +++ b/tasks/0028_150_28150461_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0028_150_28150461_qa_3" +name = "smoldataenvs-train/0028_150_28150461_qa_3" description = "How many distinct fruit categories are present in the dataset according to the unique values in the fruit_label column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0028_150_28150461_qa_4/task.toml b/tasks/0028_150_28150461_qa_4/task.toml index e726a3f45ae6052cc015aaeab67bf168bbe67dab..96c2cdcea5350e2637da82c72a3d05db8cc7a352 100644 --- a/tasks/0028_150_28150461_qa_4/task.toml +++ b/tasks/0028_150_28150461_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0028_150_28150461_qa_4" +name = "smoldataenvs-train/0028_150_28150461_qa_4" description = "What is the standard deviation of the color_score feature across all samples in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.076857" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0028_183_28183894_qa_1/task.toml b/tasks/0028_183_28183894_qa_1/task.toml index 644f533e641cf3c1402fb3364cbd558eb87b5389..da2784890bb58949a9cdd27ed84afdff17c69863 100644 --- a/tasks/0028_183_28183894_qa_1/task.toml +++ b/tasks/0028_183_28183894_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0028_183_28183894_qa_1" +name = "smoldataenvs-train/0028_183_28183894_qa_1" description = "How many distinct original glass types were classified as the positive class (label 1) in the binary classification?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0028_198_28198239_qa_1/task.toml b/tasks/0028_198_28198239_qa_1/task.toml index acdf8e0149e026ab8f3cb7ab3b6c6c5996ada501..919d2761dd145d24dfed16d2a594558be30f0d21 100644 --- a/tasks/0028_198_28198239_qa_1/task.toml +++ b/tasks/0028_198_28198239_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0028_198_28198239_qa_1" +name = "smoldataenvs-train/0028_198_28198239_qa_1" description = "What is the number of samples in the training set after an 85-15 train-test split of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1714" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0028_198_28198239_qa_3/task.toml b/tasks/0028_198_28198239_qa_3/task.toml index f990354fad6671d07281f2c20f2049d98c6f4ee3..4e189e41962506d62048fb3c0e01d5788de68425 100644 --- a/tasks/0028_198_28198239_qa_3/task.toml +++ b/tasks/0028_198_28198239_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0028_198_28198239_qa_3" +name = "smoldataenvs-train/0028_198_28198239_qa_3" description = "What is the average loudness (in dB) of all songs in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-7.085624" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0028_299_28299086_qa_3/task.toml b/tasks/0028_299_28299086_qa_3/task.toml index b4fc50e7fd8ddb8362bd2e7c0964cb6f4b9ca0c7..b336b1089d74a04cd16f7141c60c91e77518fb8b 100644 --- a/tasks/0028_299_28299086_qa_3/task.toml +++ b/tasks/0028_299_28299086_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0028_299_28299086_qa_3" +name = "smoldataenvs-train/0028_299_28299086_qa_3" description = "After feature engineering, which classification model achieved the highest training accuracy on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Decision Tree" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0028_299_28299086_qa_4/task.toml b/tasks/0028_299_28299086_qa_4/task.toml index 3e1b769faaa9aaa8e129072f1d5aa2ff51154081..ecdb2d6758322ec86e6c5a9c71f2cd957427a1b1 100644 --- a/tasks/0028_299_28299086_qa_4/task.toml +++ b/tasks/0028_299_28299086_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0028_299_28299086_qa_4" +name = "smoldataenvs-train/0028_299_28299086_qa_4" description = "What percentage of the dataset consists of songs in a major key (mode = 1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "61.23" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0028_443_28443310_qa_4/task.toml b/tasks/0028_443_28443310_qa_4/task.toml index 0b8658ab7f167ad7e4e287f153f80e6ca33985d8..4e076fb3b4b921f6d91e98d669fe29a31d818644 100644 --- a/tasks/0028_443_28443310_qa_4/task.toml +++ b/tasks/0028_443_28443310_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0028_443_28443310_qa_4" +name = "smoldataenvs-train/0028_443_28443310_qa_4" description = "What is the calculated Catch Rate percentage for Charizard after normalization to a 0-100% scale?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "18.37" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0028_443_28443310_qa_5/task.toml b/tasks/0028_443_28443310_qa_5/task.toml index e437600d8f62ca5a282ade052235713e077e5d62..7e32c5e107151e357b670d405c1a78845490dda2 100644 --- a/tasks/0028_443_28443310_qa_5/task.toml +++ b/tasks/0028_443_28443310_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0028_443_28443310_qa_5" +name = "smoldataenvs-train/0028_443_28443310_qa_5" description = "What is the correlation coefficient between \"Height_m\" and \"Weight_kg\" in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.661" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0028_488_28488702_qa_1/task.toml b/tasks/0028_488_28488702_qa_1/task.toml index a7069adbc45ffdac411389e858d5c7266816b289..a445f77a75dd3f9c80b05d1f4caa92e2a36ad8b6 100644 --- a/tasks/0028_488_28488702_qa_1/task.toml +++ b/tasks/0028_488_28488702_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0028_488_28488702_qa_1" +name = "smoldataenvs-train/0028_488_28488702_qa_1" description = "What percentage of variance is explained by the first linear discriminant after applying LDA to the Iris dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "99.128" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0028_492_28492741_qa_1/task.toml b/tasks/0028_492_28492741_qa_1/task.toml index b1613087fe941795d20e70fb78df7b778896e11a..536b52bc2bde70d218a64c815bafb4596520d103 100644 --- a/tasks/0028_492_28492741_qa_1/task.toml +++ b/tasks/0028_492_28492741_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0028_492_28492741_qa_1" +name = "smoldataenvs-train/0028_492_28492741_qa_1" description = "What is the accuracy of the Logistic Regression model on the test set after training with TF-IDF features?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9784758580570099" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0028_492_28492741_qa_5/task.toml b/tasks/0028_492_28492741_qa_5/task.toml index f3c1218bac876491e02c526cc7c8b3387944e64f..51bf78e4352a52ea93d4c907adb6a5a2bce6c550 100644 --- a/tasks/0028_492_28492741_qa_5/task.toml +++ b/tasks/0028_492_28492741_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0028_492_28492741_qa_5" +name = "smoldataenvs-train/0028_492_28492741_qa_5" description = "How many samples are in the training set after splitting the data with a test size of 0.3?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4009" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0028_513_28513384_qa_1/task.toml b/tasks/0028_513_28513384_qa_1/task.toml index b24a61d16a6bd91b081e618bef12f60e86bd6b2e..505051392b69fb7dde32ce184d6ef9fae6bf67bb 100644 --- a/tasks/0028_513_28513384_qa_1/task.toml +++ b/tasks/0028_513_28513384_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0028_513_28513384_qa_1" +name = "smoldataenvs-train/0028_513_28513384_qa_1" description = "Which month shows the highest average house price based on the sales data analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "April" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0028_513_28513384_qa_3/task.toml b/tasks/0028_513_28513384_qa_3/task.toml index 8ddf0461fa7e904f089fb171605a5e3f4ddf37e3..7dab6804f895a13c332f8245ae417d8b4356fc6f 100644 --- a/tasks/0028_513_28513384_qa_3/task.toml +++ b/tasks/0028_513_28513384_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0028_513_28513384_qa_3" +name = "smoldataenvs-train/0028_513_28513384_qa_3" description = "Which feature exhibits the strongest positive linear relationship with house prices according to the correlation analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "sqft_living" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0028_513_28513384_qa_4/task.toml b/tasks/0028_513_28513384_qa_4/task.toml index 1fd2ae3ca0a5030e8408f637b4065b7206d3713a..50a2e3be3c37f17d8ebed145fd904cbfa1bf5993 100644 --- a/tasks/0028_513_28513384_qa_4/task.toml +++ b/tasks/0028_513_28513384_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0028_513_28513384_qa_4" +name = "smoldataenvs-train/0028_513_28513384_qa_4" description = "What is the most frequent value in the yr_renovated column of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0028_521_28521768_qa_3/task.toml b/tasks/0028_521_28521768_qa_3/task.toml index fb6744078da8b30c2cba86cd5d46151a2e1483cc..550c4ea2767518982536f363333f36992736bf0a 100644 --- a/tasks/0028_521_28521768_qa_3/task.toml +++ b/tasks/0028_521_28521768_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0028_521_28521768_qa_3" +name = "smoldataenvs-train/0028_521_28521768_qa_3" description = "What is the median movie rating in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.8" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0028_571_28571042_qa_1/task.toml b/tasks/0028_571_28571042_qa_1/task.toml index 7d456f645531c921ead459f3f473f128016a7ed1..145035044e07eb360b2bad7aa40ae3ae4edb349c 100644 --- a/tasks/0028_571_28571042_qa_1/task.toml +++ b/tasks/0028_571_28571042_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0028_571_28571042_qa_1" +name = "smoldataenvs-train/0028_571_28571042_qa_1" description = "Which feature has the highest positive correlation with house price, and what is the correlation value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "sqft_living, 0.702035" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0028_571_28571042_qa_4/task.toml b/tasks/0028_571_28571042_qa_4/task.toml index 975d56b21815224a01283c8575b388ce8114fc1c..5fbc7c40885cc098b7ce2649b1770ad0b79c48f8 100644 --- a/tasks/0028_571_28571042_qa_4/task.toml +++ b/tasks/0028_571_28571042_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0028_571_28571042_qa_4" +name = "smoldataenvs-train/0028_571_28571042_qa_4" description = "What is the most frequent zipcode in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "98103" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0028_586_28586559_qa_4/task.toml b/tasks/0028_586_28586559_qa_4/task.toml index 9de3d7d1e16869a18c35f0f199703a43fb739186..d406eea34ee31796fe445d6c734d11df39bc7a23 100644 --- a/tasks/0028_586_28586559_qa_4/task.toml +++ b/tasks/0028_586_28586559_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0028_586_28586559_qa_4" +name = "smoldataenvs-train/0028_586_28586559_qa_4" description = "What percentage of patients with positive axillary nodes ≤2 fall into the 50-56 age group?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20.30" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0028_601_28601781_qa_4/task.toml b/tasks/0028_601_28601781_qa_4/task.toml index fb1b1ebf76cb122a5717ad8c34b02cfb4f31b99f..034479bf53b867727662107501e0458b476d5d66 100644 --- a/tasks/0028_601_28601781_qa_4/task.toml +++ b/tasks/0028_601_28601781_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0028_601_28601781_qa_4" +name = "smoldataenvs-train/0028_601_28601781_qa_4" description = "What is the accuracy score of the updated model after selecting features contributing to 90% of cumulative importance?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9763406940063092" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0028_685_28685202_qa_1/task.toml b/tasks/0028_685_28685202_qa_1/task.toml index d3e8005178875d3815b07501d3f6fb4cbb571161..fd928dfccff33f007c2f18adbe88c5a82d9816f6 100644 --- a/tasks/0028_685_28685202_qa_1/task.toml +++ b/tasks/0028_685_28685202_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0028_685_28685202_qa_1" +name = "smoldataenvs-train/0028_685_28685202_qa_1" description = "Which country has the highest number of distinct ramen brands represented in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Japan" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0028_862_28862512_qa_4/task.toml b/tasks/0028_862_28862512_qa_4/task.toml index 0f067d2f5ed6f5ecf272f4fc7c7992f0349c1b12..93d605c08c067692f7a4042aeceda35d1a461865 100644 --- a/tasks/0028_862_28862512_qa_4/task.toml +++ b/tasks/0028_862_28862512_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0028_862_28862512_qa_4" +name = "smoldataenvs-train/0028_862_28862512_qa_4" description = "Which genre(s) are most common among the top-selling video games (global sales >20 million)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Sports, Platform" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0028_913_28913339_qa_4/task.toml b/tasks/0028_913_28913339_qa_4/task.toml index d25b2d13d82108272df8ea460dd3eba2448a6e03..5f3e075132efecb72a0515912c8f6316605e852c 100644 --- a/tasks/0028_913_28913339_qa_4/task.toml +++ b/tasks/0028_913_28913339_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0028_913_28913339_qa_4" +name = "smoldataenvs-train/0028_913_28913339_qa_4" description = "Which month had no free items (zero unit price) given to customers?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "June 2011" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0028_913_28913339_qa_5/task.toml b/tasks/0028_913_28913339_qa_5/task.toml index d2f1beeb57e544cde0d43e4f738fdcea2a0ebc99..da0db2a8fed8fff1882abfe232e1d7eba091afc3 100644 --- a/tasks/0028_913_28913339_qa_5/task.toml +++ b/tasks/0028_913_28913339_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0028_913_28913339_qa_5" +name = "smoldataenvs-train/0028_913_28913339_qa_5" description = "Which country has the highest number of orders in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "United Kingdom" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0028_934_28934534_qa_4/task.toml b/tasks/0028_934_28934534_qa_4/task.toml index d8b10345e3f602bb1140801eec989267b2a01189..048780850a0ec153b8012ee1218cd41da00c6f32 100644 --- a/tasks/0028_934_28934534_qa_4/task.toml +++ b/tasks/0028_934_28934534_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0028_934_28934534_qa_4" +name = "smoldataenvs-train/0028_934_28934534_qa_4" description = "Is there a statistically significant correlation between the number of exclamation marks and sentiment polarity in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0028_938_28938151_qa_5/task.toml b/tasks/0028_938_28938151_qa_5/task.toml index 57cb43a19b435166532735100b412861f6650803..cb8b4b2b92b3fd43b4307a01559bd1dcdfa274cd 100644 --- a/tasks/0028_938_28938151_qa_5/task.toml +++ b/tasks/0028_938_28938151_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0028_938_28938151_qa_5" +name = "smoldataenvs-train/0028_938_28938151_qa_5" description = "What is the most common result type (white win, black win, draw) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "white win" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0029_108_29108182_qa_1/task.toml b/tasks/0029_108_29108182_qa_1/task.toml index 01d3114a6867a737df3842e75f3be9de51041c75..4de579a36fae591eb2a84da5cc5d101b5f13fbc8 100644 --- a/tasks/0029_108_29108182_qa_1/task.toml +++ b/tasks/0029_108_29108182_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0029_108_29108182_qa_1" +name = "smoldataenvs-train/0029_108_29108182_qa_1" description = "Which feature in the dataset shows the highest correlation with the Outcome variable indicating diabetes diagnosis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0029_136_29136731_qa_2/task.toml b/tasks/0029_136_29136731_qa_2/task.toml index c4031222eb72886fb59d802d851e1ac0c63ae156..bcb640549bf1f6dcb2911f1bf8bd2d7f587e37eb 100644 --- a/tasks/0029_136_29136731_qa_2/task.toml +++ b/tasks/0029_136_29136731_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0029_136_29136731_qa_2" +name = "smoldataenvs-train/0029_136_29136731_qa_2" description = "How many samples and features are present in the preprocessed dataset used for modeling?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "768, 8" reward_mode_initial = "list" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0029_184_29184728_qa_1/task.toml b/tasks/0029_184_29184728_qa_1/task.toml index 0f62c214a961a9395c72b501ba53c6f54f5e5e40..a2873daa0c3fc115c1881bb46bc60ac57841d70a 100644 --- a/tasks/0029_184_29184728_qa_1/task.toml +++ b/tasks/0029_184_29184728_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0029_184_29184728_qa_1" +name = "smoldataenvs-train/0029_184_29184728_qa_1" description = "Which species of iris has the highest average sepal width according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-setosa" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0029_200_29200364_qa_1/task.toml b/tasks/0029_200_29200364_qa_1/task.toml index 448601d3cf4f9bbfb52150f42f80d07107bd6bf6..3deeb001384f5d8a8f8fcbeaad1ec0636e1dddc9 100644 --- a/tasks/0029_200_29200364_qa_1/task.toml +++ b/tasks/0029_200_29200364_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0029_200_29200364_qa_1" +name = "smoldataenvs-train/0029_200_29200364_qa_1" description = "How many unique species are present in the Iris dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0029_200_29200364_qa_2/task.toml b/tasks/0029_200_29200364_qa_2/task.toml index c363961b7829bae7ec1860e7ad25884698449bfb..8448fb4278e17f962eec084d23baac6e6028a51d 100644 --- a/tasks/0029_200_29200364_qa_2/task.toml +++ b/tasks/0029_200_29200364_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0029_200_29200364_qa_2" +name = "smoldataenvs-train/0029_200_29200364_qa_2" description = "What is the data type of the target variable 'Species' in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "object" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0029_200_29200364_qa_3/task.toml b/tasks/0029_200_29200364_qa_3/task.toml index 5127d7cd06dfdc2f2006947e1d511a8aecb76d7a..7ba5c6aa09208b6037be84fca99ece19aaa5c34b 100644 --- a/tasks/0029_200_29200364_qa_3/task.toml +++ b/tasks/0029_200_29200364_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0029_200_29200364_qa_3" +name = "smoldataenvs-train/0029_200_29200364_qa_3" description = "Does the dataset contain any missing values in any of its columns?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0029_204_29204719_qa_1/task.toml b/tasks/0029_204_29204719_qa_1/task.toml index bc9262296bcb92fdf17563abbabb3e91d2e05303..9159156a5429637d5d4474b064d15b2b5c8eeebd 100644 --- a/tasks/0029_204_29204719_qa_1/task.toml +++ b/tasks/0029_204_29204719_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0029_204_29204719_qa_1" +name = "smoldataenvs-train/0029_204_29204719_qa_1" description = "What is the accuracy of the Naive Bayes classifier on the test set based on the confusion matrix?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "73" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0029_204_29204719_qa_2/task.toml b/tasks/0029_204_29204719_qa_2/task.toml index b47dd6aa56ea3abeace6b7810113484c04d1ada0..49c443614dbc0b07bf19571b22f56a2e65908ddd 100644 --- a/tasks/0029_204_29204719_qa_2/task.toml +++ b/tasks/0029_204_29204719_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0029_204_29204719_qa_2" +name = "smoldataenvs-train/0029_204_29204719_qa_2" description = "How many true positive predictions did the Naive Bayes classifier make on the test set according to the confusion matrix?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "91" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0029_212_29212385_qa_2/task.toml b/tasks/0029_212_29212385_qa_2/task.toml index 30bb584c10cfd3341ed5d828ed779545a8cc016d..d4fe193665f340fee351a461ec77c3fd33995c51 100644 --- a/tasks/0029_212_29212385_qa_2/task.toml +++ b/tasks/0029_212_29212385_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0029_212_29212385_qa_2" +name = "smoldataenvs-train/0029_212_29212385_qa_2" description = "What is the optimal ARIMA order (p,d,q) selected by the auto_arima parameter tuning process?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "(2,1,1)" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0029_212_29212385_qa_5/task.toml b/tasks/0029_212_29212385_qa_5/task.toml index 43ffb6c2648cc587c716627f8076c254e6d796d1..65712078f4f5358bf11dbf8dcac711424b2434a9 100644 --- a/tasks/0029_212_29212385_qa_5/task.toml +++ b/tasks/0029_212_29212385_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0029_212_29212385_qa_5" +name = "smoldataenvs-train/0029_212_29212385_qa_5" description = "Which model achieved a lower RMSE on the test data: the ARIMA model or the SARIMA model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "SARIMA" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0029_245_29245024_qa_1/task.toml b/tasks/0029_245_29245024_qa_1/task.toml index ea133f345b50d56c6bd5f52d4e6f2e7004081baf..e5258cb2e8c6c8bd8027724b6d0c54ed072eafe4 100644 --- a/tasks/0029_245_29245024_qa_1/task.toml +++ b/tasks/0029_245_29245024_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0029_245_29245024_qa_1" +name = "smoldataenvs-train/0029_245_29245024_qa_1" description = "Which wine taster has the highest mean rating across all their reviews in the US dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Matt Kettmann" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0029_340_29340222_qa_5/task.toml b/tasks/0029_340_29340222_qa_5/task.toml index ba80228b746bc85c5bc8b5ff503b18ce21b6de89..ea00e1b844e7554592ea85fb811aa4c3332974b5 100644 --- a/tasks/0029_340_29340222_qa_5/task.toml +++ b/tasks/0029_340_29340222_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0029_340_29340222_qa_5" +name = "smoldataenvs-train/0029_340_29340222_qa_5" description = "What is the first decision node feature and split value in the decision tree rules for wine rating prediction?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "taster_twitter_handleimputed_value, 24.50" reward_mode_initial = "list" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0029_411_29411994_qa_3/task.toml b/tasks/0029_411_29411994_qa_3/task.toml index 14c6c8f9d7c118b2f55629dc21adf314d93e37ba..ab9a99166280fface81e52bf00f28c9ab74fb496 100644 --- a/tasks/0029_411_29411994_qa_3/task.toml +++ b/tasks/0029_411_29411994_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0029_411_29411994_qa_3" +name = "smoldataenvs-train/0029_411_29411994_qa_3" description = "After filtering out age outliers, which age group has the highest number of patients?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Adult (40-50)" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0029_448_29448999_qa_3/task.toml b/tasks/0029_448_29448999_qa_3/task.toml index ef210fe7c2e97ba7ca1d5ff20e72f184e2ebd755..f8715261c549c81cb18b6fef9950be4b3869e164 100644 --- a/tasks/0029_448_29448999_qa_3/task.toml +++ b/tasks/0029_448_29448999_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0029_448_29448999_qa_3" +name = "smoldataenvs-train/0029_448_29448999_qa_3" description = "How many validation samples were allocated to each fold during the k-fold cross-validation process?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "101" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0029_448_29448999_qa_5/task.toml b/tasks/0029_448_29448999_qa_5/task.toml index ef36631020989be6aedc6833d228cd583b242b32..cc4500dbad045141e835db3296e2ecfab1d58b55 100644 --- a/tasks/0029_448_29448999_qa_5/task.toml +++ b/tasks/0029_448_29448999_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0029_448_29448999_qa_5" +name = "smoldataenvs-train/0029_448_29448999_qa_5" description = "What is the median value of the RM (average number of rooms per dwelling) feature in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.2275" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0029_449_29449296_qa_3/task.toml b/tasks/0029_449_29449296_qa_3/task.toml index 02b2f5c66e43a7904cf0322cca7867572ba97751..6765550fe07a633f3ec9aa75667c9009e4f085c6 100644 --- a/tasks/0029_449_29449296_qa_3/task.toml +++ b/tasks/0029_449_29449296_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0029_449_29449296_qa_3" +name = "smoldataenvs-train/0029_449_29449296_qa_3" description = "How many samples belong to each species in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0029_467_29467495_qa_2/task.toml b/tasks/0029_467_29467495_qa_2/task.toml index c2e1921419f3e13a185492e974b3b5dd5f23ed68..ecc26759c2a584b88a168299da74bbe0263b04a1 100644 --- a/tasks/0029_467_29467495_qa_2/task.toml +++ b/tasks/0029_467_29467495_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0029_467_29467495_qa_2" +name = "smoldataenvs-train/0029_467_29467495_qa_2" description = "How many principal components are required to retain at least 90% of the total variance in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0029_536_29536756_qa_3/task.toml b/tasks/0029_536_29536756_qa_3/task.toml index 2f10fdbdb44204d586d827e90c6b884d4638ffde..23f296cda7143153762517aa4a1a548a2cb62a2d 100644 --- a/tasks/0029_536_29536756_qa_3/task.toml +++ b/tasks/0029_536_29536756_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0029_536_29536756_qa_3" +name = "smoldataenvs-train/0029_536_29536756_qa_3" description = "What is the most frequent cap color category in the dataset based on the descriptive statistics?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "n" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] build_timeout_sec = 600.0 diff --git a/tasks/0029_558_29558970_qa_2/task.toml b/tasks/0029_558_29558970_qa_2/task.toml index 9a40d9e22235836f2d284f646522a4453305c9f2..9a088147586e90966f7bfc8e09c3184ae2af38ce 100644 --- a/tasks/0029_558_29558970_qa_2/task.toml +++ b/tasks/0029_558_29558970_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0029_558_29558970_qa_2" +name = "smoldataenvs-train/0029_558_29558970_qa_2" description = "Which Pokémon has the highest win percentage in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Mega Aerodactyl" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0029_630_29630344_qa_4/task.toml b/tasks/0029_630_29630344_qa_4/task.toml index 31bd683fdca2eebcd5fcd9baa1472e903dc6f5ee..acc5c497299f17ae048aeaf91070c72dc4f01ef3 100644 --- a/tasks/0029_630_29630344_qa_4/task.toml +++ b/tasks/0029_630_29630344_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0029_630_29630344_qa_4" +name = "smoldataenvs-train/0029_630_29630344_qa_4" description = "How many transactions were removed as duplicates from the original dataset during preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5268" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0029_630_29630344_qa_5/task.toml b/tasks/0029_630_29630344_qa_5/task.toml index ab2f764bd4449cda7d0428535637342a4f7a0507..6daad163a88e7f7dde5b8f1dea613ff68342e139 100644 --- a/tasks/0029_630_29630344_qa_5/task.toml +++ b/tasks/0029_630_29630344_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0029_630_29630344_qa_5" +name = "smoldataenvs-train/0029_630_29630344_qa_5" description = "What is the most frequently sold product by transaction count in the dataset after data cleaning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "WHITE HANGING HEART T-LIGHT HOLDER" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0029_650_29650445_qa_3/task.toml b/tasks/0029_650_29650445_qa_3/task.toml index 60ff8868741531343a23f6be4e791f685241503f..7bc7da93c14b96cc690a8ae462c1e0de1b842148 100644 --- a/tasks/0029_650_29650445_qa_3/task.toml +++ b/tasks/0029_650_29650445_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0029_650_29650445_qa_3" +name = "smoldataenvs-train/0029_650_29650445_qa_3" description = "What is the average North American sales value across all video games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.264667" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0029_784_29784165_qa_1/task.toml b/tasks/0029_784_29784165_qa_1/task.toml index b3380929a695288859ef3f94f6ee5e31fee706a2..0d2c059250eb277f1c4150484d8640f6b0bc3de8 100644 --- a/tasks/0029_784_29784165_qa_1/task.toml +++ b/tasks/0029_784_29784165_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0029_784_29784165_qa_1" +name = "smoldataenvs-train/0029_784_29784165_qa_1" description = "Which two features in the Iris dataset exhibit the highest positive correlation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm, PetalWidthCm" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0029_902_29902659_qa_3/task.toml b/tasks/0029_902_29902659_qa_3/task.toml index 88ab8eaca974b4beab9268a08916cca64c2a564d..7c44c083cea62c81858fbb1abc6250f622d883e4 100644 --- a/tasks/0029_902_29902659_qa_3/task.toml +++ b/tasks/0029_902_29902659_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0029_902_29902659_qa_3" +name = "smoldataenvs-train/0029_902_29902659_qa_3" description = "How many samples in the dataset belong to the 'Iris-virginica' species?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0029_908_29908654_qa_2/task.toml b/tasks/0029_908_29908654_qa_2/task.toml index 5c26e1fc7a5e01250b5a53ce10048ba90f5487c3..a116e253d57dc2af925eb3352ab99bd6a6d71846 100644 --- a/tasks/0029_908_29908654_qa_2/task.toml +++ b/tasks/0029_908_29908654_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0029_908_29908654_qa_2" +name = "smoldataenvs-train/0029_908_29908654_qa_2" description = "Among the selected samples (43, 12, 39), which one has the highest Grocery spending value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "43" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0029_908_29908654_qa_4/task.toml b/tasks/0029_908_29908654_qa_4/task.toml index 67434cbc2a9e43c6b355d97ed146316e2777a67a..a26c882ad0f3e7d1d2f90e79b84fc9f583e4245f 100644 --- a/tasks/0029_908_29908654_qa_4/task.toml +++ b/tasks/0029_908_29908654_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0029_908_29908654_qa_4" +name = "smoldataenvs-train/0029_908_29908654_qa_4" description = "What is the maximum Fresh spending value among the selected samples (43, 12, 39)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "56159" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0030_006_30006119_qa_3/task.toml b/tasks/0030_006_30006119_qa_3/task.toml index e31758d59a55d1862a9540bf34356bde12274e7c..f0bdf5af82a559bf707f96f0c45fa386d2440ee0 100644 --- a/tasks/0030_006_30006119_qa_3/task.toml +++ b/tasks/0030_006_30006119_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0030_006_30006119_qa_3" +name = "smoldataenvs-train/0030_006_30006119_qa_3" description = "How many unique property IDs are present in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7109" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0030_144_30144601_qa_2/task.toml b/tasks/0030_144_30144601_qa_2/task.toml index d9b52795a65bb31f6b2dabc6c476ae7130ce68cb..f80fef0d2d8384fc9736e994c519bec1d7df7499 100644 --- a/tasks/0030_144_30144601_qa_2/task.toml +++ b/tasks/0030_144_30144601_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0030_144_30144601_qa_2" +name = "smoldataenvs-train/0030_144_30144601_qa_2" description = "What is the highest correlation coefficient between any two features in the Iris dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.962757" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0030_144_30144601_qa_5/task.toml b/tasks/0030_144_30144601_qa_5/task.toml index 62fd42f36446ce5919558a4f7f74ff7a7cf96578..853f5fbd94beea51584c6907d3a23bf49a542148 100644 --- a/tasks/0030_144_30144601_qa_5/task.toml +++ b/tasks/0030_144_30144601_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0030_144_30144601_qa_5" +name = "smoldataenvs-train/0030_144_30144601_qa_5" description = "Based on visual analysis, which feature pair provides better class separation for clustering purposes?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm, PetalWidthCm" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0030_152_30152211_qa_2/task.toml b/tasks/0030_152_30152211_qa_2/task.toml index 1155bb515c85c4360e01450e7b2b5a9592daa698..8bd0ec3c45d0214365bb213a66f157e1c4b4e30f 100644 --- a/tasks/0030_152_30152211_qa_2/task.toml +++ b/tasks/0030_152_30152211_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0030_152_30152211_qa_2" +name = "smoldataenvs-train/0030_152_30152211_qa_2" description = "Which customer segment has the highest churn rate based on contract type?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Month-to-month" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0030_284_30284375_qa_1/task.toml b/tasks/0030_284_30284375_qa_1/task.toml index 32a3dac332db78d9cd9cbeeae30b25966110f42a..6156c5cccb6a8572537a59a04b274ea38a1cbfb1 100644 --- a/tasks/0030_284_30284375_qa_1/task.toml +++ b/tasks/0030_284_30284375_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0030_284_30284375_qa_1" +name = "smoldataenvs-train/0030_284_30284375_qa_1" description = "What was the average cross-validation accuracy before removing potential leaky features in the credit card dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9802915082382764" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0030_284_30284375_qa_3/task.toml b/tasks/0030_284_30284375_qa_3/task.toml index dbbb44885748c074193a60d2bedca8f947201fef..ef77788fc13fd4849694051255fdbcdcbf675365 100644 --- a/tasks/0030_284_30284375_qa_3/task.toml +++ b/tasks/0030_284_30284375_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0030_284_30284375_qa_3" +name = "smoldataenvs-train/0030_284_30284375_qa_3" description = "Which four features were identified as potential sources of target leakage and subsequently removed from the model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "expenditure, share, active, majorcards" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0030_304_30304394_qa_5/task.toml b/tasks/0030_304_30304394_qa_5/task.toml index 542ad0c5c5c20e1ccecd2296c94045bc14db9a9b..8edaeaf63ed33b0c84e685eb6a2d06f1b03421cc 100644 --- a/tasks/0030_304_30304394_qa_5/task.toml +++ b/tasks/0030_304_30304394_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0030_304_30304394_qa_5" +name = "smoldataenvs-train/0030_304_30304394_qa_5" description = "What is the most frequent gender category in the dataset based on the initial data description?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Male" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0030_348_30348412_qa_2/task.toml b/tasks/0030_348_30348412_qa_2/task.toml index dfdd020164cbd8b63e7cf66541810e738764c997..6978492c073e3cf88c8bc8b137975dc7562f7ce8 100644 --- a/tasks/0030_348_30348412_qa_2/task.toml +++ b/tasks/0030_348_30348412_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0030_348_30348412_qa_2" +name = "smoldataenvs-train/0030_348_30348412_qa_2" description = "What is the property area with the highest number of loan applications?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Semiurban" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0030_471_30471502_qa_1/task.toml b/tasks/0030_471_30471502_qa_1/task.toml index 15a26dd1f29acfa2a121818b93d461f02c5466e3..3746ad8069249388f61141373f7a48c077a22f65 100644 --- a/tasks/0030_471_30471502_qa_1/task.toml +++ b/tasks/0030_471_30471502_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0030_471_30471502_qa_1" +name = "smoldataenvs-train/0030_471_30471502_qa_1" description = "Which feature in the dataset has the highest chi-square value indicating the strongest statistical association with mushroom edibility (class)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "odor" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0030_471_30471502_qa_3/task.toml b/tasks/0030_471_30471502_qa_3/task.toml index 6a2b58daa382a4ab5cbb69521f21e84bb5282219..f5e80bed383bdf8304a982c2fa2b7648a20a058c 100644 --- a/tasks/0030_471_30471502_qa_3/task.toml +++ b/tasks/0030_471_30471502_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0030_471_30471502_qa_3" +name = "smoldataenvs-train/0030_471_30471502_qa_3" description = "How many mushrooms in the dataset are classified as edible (class = 'e')?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4208" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0030_544_30544921_qa_1/task.toml b/tasks/0030_544_30544921_qa_1/task.toml index afd4e240a3491e48e843e39e38ae4f8151d9c02f..0a3b45cd4820162e279ce87c423cc7dc75b566ec 100644 --- a/tasks/0030_544_30544921_qa_1/task.toml +++ b/tasks/0030_544_30544921_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0030_544_30544921_qa_1" +name = "smoldataenvs-train/0030_544_30544921_qa_1" description = "What is the most common marital status among employees who attrited?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Single" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0030_588_30588724_qa_4/task.toml b/tasks/0030_588_30588724_qa_4/task.toml index d9917c9c63fc800342cf3328a13a4a28ecc955dc..5f4944ff9a617319efb5d55f606b245b7c19a836 100644 --- a/tasks/0030_588_30588724_qa_4/task.toml +++ b/tasks/0030_588_30588724_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0030_588_30588724_qa_4" +name = "smoldataenvs-train/0030_588_30588724_qa_4" description = "What is the minimum training loss achieved by the model during the 50 epochs?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.00020267" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0030_688_30688656_qa_2/task.toml b/tasks/0030_688_30688656_qa_2/task.toml index e5cc087e6f4a72a66a1fc580590d93565bfc1bba..70e9b490fbd0ad1be3ae92d64cddc37f332c8b8a 100644 --- a/tasks/0030_688_30688656_qa_2/task.toml +++ b/tasks/0030_688_30688656_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0030_688_30688656_qa_2" +name = "smoldataenvs-train/0030_688_30688656_qa_2" description = "How many of the top 20 words most similar to 'bank' are proper nouns (e.g., company names like \"ubs\" or \"citigroup\")?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0030_688_30688656_qa_3/task.toml b/tasks/0030_688_30688656_qa_3/task.toml index 7717db91295032a6e2f92fd3cd79fd2ab78200aa..8d7e13e910d3512df621ecee5c1ea1e89e8b1adc 100644 --- a/tasks/0030_688_30688656_qa_3/task.toml +++ b/tasks/0030_688_30688656_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0030_688_30688656_qa_3" +name = "smoldataenvs-train/0030_688_30688656_qa_3" description = "What is the highest cosine similarity score among the top 20 words for 'bank' in the GloVe dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.8057132959365845" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0030_751_30751961_qa_1/task.toml b/tasks/0030_751_30751961_qa_1/task.toml index 512ccdcbcb66354649441168fcb20986909149f2..3ab4f748bd09f737864eb7d1708e00a12f45b6f3 100644 --- a/tasks/0030_751_30751961_qa_1/task.toml +++ b/tasks/0030_751_30751961_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0030_751_30751961_qa_1" +name = "smoldataenvs-train/0030_751_30751961_qa_1" description = "Which customer segment has the highest churn rate based on internet service type in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Fiber optic" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0030_791_30791695_qa_4/task.toml b/tasks/0030_791_30791695_qa_4/task.toml index 8946900e717eeab4a4d17d6fe6ca4f5c282394d2..bf73efc6d29e06d2f414799748943655ab108737 100644 --- a/tasks/0030_791_30791695_qa_4/task.toml +++ b/tasks/0030_791_30791695_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0030_791_30791695_qa_4" +name = "smoldataenvs-train/0030_791_30791695_qa_4" description = "What is the maximum quantity ordered in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "97" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0030_804_30804903_qa_1/task.toml b/tasks/0030_804_30804903_qa_1/task.toml index b44190b542f4d54e667a259e7671c5f229885847..fd686793c685890900ddb5106d7f05c3bca5e766 100644 --- a/tasks/0030_804_30804903_qa_1/task.toml +++ b/tasks/0030_804_30804903_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0030_804_30804903_qa_1" +name = "smoldataenvs-train/0030_804_30804903_qa_1" description = "Which physical property of red wine has the strongest positive correlation with its quality according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Alcohol" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0030_817_30817038_qa_2/task.toml b/tasks/0030_817_30817038_qa_2/task.toml index 07f4206f078624fa3f300df8a911677197bbb268..995c8891051318f9c85ff8aef172f1d328d43433 100644 --- a/tasks/0030_817_30817038_qa_2/task.toml +++ b/tasks/0030_817_30817038_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0030_817_30817038_qa_2" +name = "smoldataenvs-train/0030_817_30817038_qa_2" description = "What is the total number of missing values across all columns in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "207" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0030_856_30856788_qa_1/task.toml b/tasks/0030_856_30856788_qa_1/task.toml index 7dde709306a26852a84dde5c5d5ac3b252679233..0ca0764311c8e9c0edd5441b8773d594bcb10398 100644 --- a/tasks/0030_856_30856788_qa_1/task.toml +++ b/tasks/0030_856_30856788_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0030_856_30856788_qa_1" +name = "smoldataenvs-train/0030_856_30856788_qa_1" description = "How many rows were removed from the dataset after applying outlier filtering to the SALES, MSRP, and QUANTITYORDERED columns?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "104" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0030_858_30858491_qa_3/task.toml b/tasks/0030_858_30858491_qa_3/task.toml index 9442f8ea9b17b333549675f0325bdeb39825e07d..5ca5a7eceb56e332dc90c7a3fd58435396872a19 100644 --- a/tasks/0030_858_30858491_qa_3/task.toml +++ b/tasks/0030_858_30858491_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0030_858_30858491_qa_3" +name = "smoldataenvs-train/0030_858_30858491_qa_3" description = "Does the `SALES` column still contain outliers in the dataset after IQR-based filtering, as indicated by the box plot visualization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 3 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0030_890_30890976_qa_1/task.toml b/tasks/0030_890_30890976_qa_1/task.toml index 33e69d4e577a01d40d567f2ac91d4c4ec02b4541..6f3d094de75523b4e1dd49ffa82e090288e6c68b 100644 --- a/tasks/0030_890_30890976_qa_1/task.toml +++ b/tasks/0030_890_30890976_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0030_890_30890976_qa_1" +name = "smoldataenvs-train/0030_890_30890976_qa_1" description = "What is the overall accuracy of the XGBoost model on the validation set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.84" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0030_890_30890976_qa_2/task.toml b/tasks/0030_890_30890976_qa_2/task.toml index 842a1cb28dcd31506ddf4f83e828b37f2ccf9077..24ac2a857db95aaffcef7f3a2bdde7bdcdf95af5 100644 --- a/tasks/0030_890_30890976_qa_2/task.toml +++ b/tasks/0030_890_30890976_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0030_890_30890976_qa_2" +name = "smoldataenvs-train/0030_890_30890976_qa_2" description = "What is the maximum age value in the combined training and test datasets used for normalization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "80.0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0030_890_30890976_qa_4/task.toml b/tasks/0030_890_30890976_qa_4/task.toml index 57dc9f55228658215b8a334231562334aa9b8c08..53cd60adf5169532d1f984268e216143e3a4345a 100644 --- a/tasks/0030_890_30890976_qa_4/task.toml +++ b/tasks/0030_890_30890976_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0030_890_30890976_qa_4" +name = "smoldataenvs-train/0030_890_30890976_qa_4" description = "Which embarkation port (S, C, or Q) has the highest frequency in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "S" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0030_890_30890976_qa_5/task.toml b/tasks/0030_890_30890976_qa_5/task.toml index e91471a7ca4eaad31ac71454f1089f69f69679a9..92abdf8d7031c6a9c133bed2f717b46504d93225 100644 --- a/tasks/0030_890_30890976_qa_5/task.toml +++ b/tasks/0030_890_30890976_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0030_890_30890976_qa_5" +name = "smoldataenvs-train/0030_890_30890976_qa_5" description = "What is the macro average F1-score of the XGBoost model on the validation set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.83" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0030_894_30894225_qa_2/task.toml b/tasks/0030_894_30894225_qa_2/task.toml index d9b358a9dc441273c897ae0d03983d938a939f38..d09349fb8c75939a301912d1a8c9314356c90702 100644 --- a/tasks/0030_894_30894225_qa_2/task.toml +++ b/tasks/0030_894_30894225_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0030_894_30894225_qa_2" +name = "smoldataenvs-train/0030_894_30894225_qa_2" description = "What is the standard deviation of the age distribution in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9.22" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0030_894_30894225_qa_3/task.toml b/tasks/0030_894_30894225_qa_3/task.toml index 0e243ca675a8758de1046fe2158b4256dc8e168d..58a06c5fadfe2285c94ad2ec873e990b1c99348a 100644 --- a/tasks/0030_894_30894225_qa_3/task.toml +++ b/tasks/0030_894_30894225_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0030_894_30894225_qa_3" +name = "smoldataenvs-train/0030_894_30894225_qa_3" description = "What is the age range that covers the middle 50% of the population in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "28 to 41" reward_mode_initial = "list" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0030_991_30991339_qa_2/task.toml b/tasks/0030_991_30991339_qa_2/task.toml index c9c0c7d965f4132cd0a15cdf49c036f38dd9db26..b5f31cf646725ece63a31e011a8208283e68de99 100644 --- a/tasks/0030_991_30991339_qa_2/task.toml +++ b/tasks/0030_991_30991339_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0030_991_30991339_qa_2" +name = "smoldataenvs-train/0030_991_30991339_qa_2" description = "Which feature has the highest feature importance score according to the ExtraTreesClassifier model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ram" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0030_991_30991339_qa_3/task.toml b/tasks/0030_991_30991339_qa_3/task.toml index c8afe9e21e5ba31004815eb0f497aeabd32f96b4..2111a620d0df5399b88dd98b103d63643429ff91 100644 --- a/tasks/0030_991_30991339_qa_3/task.toml +++ b/tasks/0030_991_30991339_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0030_991_30991339_qa_3" +name = "smoldataenvs-train/0030_991_30991339_qa_3" description = "What is the chi-squared score for the feature \"ram\" in the Univariate Selection analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "931267.519053" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0030_991_30991339_qa_4/task.toml b/tasks/0030_991_30991339_qa_4/task.toml index eaac3dbf89addbaefe43627030ea18a82985cc11..1dd5b32e4a71aedaaad9165c5ca342e0c15e8552 100644 --- a/tasks/0030_991_30991339_qa_4/task.toml +++ b/tasks/0030_991_30991339_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0030_991_30991339_qa_4" +name = "smoldataenvs-train/0030_991_30991339_qa_4" description = "What is the feature importance score for the feature \"clock_speed\" according to the ExtraTreesClassifier model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.03285815" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0031_033_31033882_qa_1/task.toml b/tasks/0031_033_31033882_qa_1/task.toml index e58f35c8c9acb4b85474cc3bc52f21ace1930575..c840961aa6ea456df65d8ea0cdb2ffff85a5147d 100644 --- a/tasks/0031_033_31033882_qa_1/task.toml +++ b/tasks/0031_033_31033882_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0031_033_31033882_qa_1" +name = "smoldataenvs-train/0031_033_31033882_qa_1" description = "What is the correlation coefficient between the duration of contact and the deposit outcome in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.451919" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_057_31057767_qa_5/task.toml b/tasks/0031_057_31057767_qa_5/task.toml index ab6a0ea9637df640cdcd06cb58049e219f2d0d50..c9d38fbad05f6d3e2eaf97d6a86166f888691074 100644 --- a/tasks/0031_057_31057767_qa_5/task.toml +++ b/tasks/0031_057_31057767_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0031_057_31057767_qa_5" +name = "smoldataenvs-train/0031_057_31057767_qa_5" description = "What is the mean pelvic_radius value for patients classified as Normal?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "123.89" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_074_31074287_qa_3/task.toml b/tasks/0031_074_31074287_qa_3/task.toml index 4ed2817529dc1e5476fcd8fadb0fd0bdc95eac09..ba1dd554cc25317eb5ab9c02ec6fa3a4ec430eab 100644 --- a/tasks/0031_074_31074287_qa_3/task.toml +++ b/tasks/0031_074_31074287_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0031_074_31074287_qa_3" +name = "smoldataenvs-train/0031_074_31074287_qa_3" description = "What is the average vintage year of the wines in the dataset after removing entries with missing years?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2010.67" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_089_31089222_qa_2/task.toml b/tasks/0031_089_31089222_qa_2/task.toml index 85ec57019cbde88136e58493c9a4f8c4cffc4f6d..e323c83062e702e9f188fb1617c05340f14d9970 100644 --- a/tasks/0031_089_31089222_qa_2/task.toml +++ b/tasks/0031_089_31089222_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0031_089_31089222_qa_2" +name = "smoldataenvs-train/0031_089_31089222_qa_2" description = "After applying the 90th percentile outlier treatment, what was the skewness value for the 'residual sugar' feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.811384" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_106_31106192_qa_2/task.toml b/tasks/0031_106_31106192_qa_2/task.toml index a7bd95a42f97773106d18b428f9c7e9fd2b87d0d..0a82b9525f97314bb2ce85ff4783ef2dab201d6e 100644 --- a/tasks/0031_106_31106192_qa_2/task.toml +++ b/tasks/0031_106_31106192_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0031_106_31106192_qa_2" +name = "smoldataenvs-train/0031_106_31106192_qa_2" description = "Which regression model achieved the lowest RMSE in K-fold cross-validation (7 splits) for predicting IMDb scores?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "RandomForestRegressor" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0031_106_31106192_qa_5/task.toml b/tasks/0031_106_31106192_qa_5/task.toml index c64c6682bbc2aa6a759c2d00764030e0fd69aea8..31459afa4dc6399c4372e177edd7e25aa2b6b535 100644 --- a/tasks/0031_106_31106192_qa_5/task.toml +++ b/tasks/0031_106_31106192_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0031_106_31106192_qa_5" +name = "smoldataenvs-train/0031_106_31106192_qa_5" description = "Based on the Shapiro-Wilk test, is the distribution of IMDb scores in the dataset normal?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0031_128_31128554_qa_1/task.toml b/tasks/0031_128_31128554_qa_1/task.toml index 9ff21951d392405d4eae790eb7f2dee4545033a6..fe57d6986b35de0b6d9383b068b359a1e8cf49fc 100644 --- a/tasks/0031_128_31128554_qa_1/task.toml +++ b/tasks/0031_128_31128554_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0031_128_31128554_qa_1" +name = "smoldataenvs-train/0031_128_31128554_qa_1" description = "Which independent variable shows the strongest positive linear relationship with the diabetes outcome (Outcome=1) based on Pearson's correlation coefficient?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_128_31128554_qa_2/task.toml b/tasks/0031_128_31128554_qa_2/task.toml index 81e836dac3ecdcb5d1b8e98df540b10a621decf9..033ccab353c93638374e7d994ff084d026b65ff4 100644 --- a/tasks/0031_128_31128554_qa_2/task.toml +++ b/tasks/0031_128_31128554_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0031_128_31128554_qa_2" +name = "smoldataenvs-train/0031_128_31128554_qa_2" description = "What is the median BMI value for individuals in the dataset according to the descriptive statistics?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "32.0" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0031_128_31128554_qa_3/task.toml b/tasks/0031_128_31128554_qa_3/task.toml index bb2df39acbe8dabd3c669ffcf120cb87953451dd..9ac409f11d9e00b9f39a6b817736449708ba6b42 100644 --- a/tasks/0031_128_31128554_qa_3/task.toml +++ b/tasks/0031_128_31128554_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0031_128_31128554_qa_3" +name = "smoldataenvs-train/0031_128_31128554_qa_3" description = "Which variable exhibits the highest degree of positive skewness in its distribution?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Insulin" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_128_31128554_qa_5/task.toml b/tasks/0031_128_31128554_qa_5/task.toml index 9c6f12a233f831b0baca66c45aaa2a3732c8d934..971dc2f5972ca07ab353503f1c497d9f53adb074 100644 --- a/tasks/0031_128_31128554_qa_5/task.toml +++ b/tasks/0031_128_31128554_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0031_128_31128554_qa_5" +name = "smoldataenvs-train/0031_128_31128554_qa_5" description = "Which pair of variables demonstrates the strongest negative linear correlation in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "SkinThickness, Age" reward_mode_initial = "list" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_326_31326046_qa_5/task.toml b/tasks/0031_326_31326046_qa_5/task.toml index d20e6fd21962599d752706d9f7f14caa8b6b3c1e..043a2da0e5f6ff1e76d5b944b6611af97b5b523e 100644 --- a/tasks/0031_326_31326046_qa_5/task.toml +++ b/tasks/0031_326_31326046_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0031_326_31326046_qa_5" +name = "smoldataenvs-train/0031_326_31326046_qa_5" description = "What is the original proportion of defaulters (before SMOTE application) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "22.12%" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0031_326_31326436_qa_3/task.toml b/tasks/0031_326_31326436_qa_3/task.toml index 23361d7d60f7529007cfae12fb11accf2108d2af..71925ff606075acbc288ab8377fbf1fe2ff9e0df 100644 --- a/tasks/0031_326_31326436_qa_3/task.toml +++ b/tasks/0031_326_31326436_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0031_326_31326436_qa_3" +name = "smoldataenvs-train/0031_326_31326436_qa_3" description = "Is the mean age of patients with diabetes statistically higher than those without diabetes in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0031_374_31374028_qa_1/task.toml b/tasks/0031_374_31374028_qa_1/task.toml index 450d7ad982e324874dc036a587b742ab65b38d17..6097334efb9ed7d7a14289aaddfa57f1cd7bd14e 100644 --- a/tasks/0031_374_31374028_qa_1/task.toml +++ b/tasks/0031_374_31374028_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0031_374_31374028_qa_1" +name = "smoldataenvs-train/0031_374_31374028_qa_1" description = "What is the highest absolute correlation coefficient between any two features in the red wine quality dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.683" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_374_31374028_qa_4/task.toml b/tasks/0031_374_31374028_qa_4/task.toml index 38644c32e4f5267cd7185d43e81ea9b2e5f7c7da..fada73eb52600ec1cb1e833e45bcb6d8cb3939f6 100644 --- a/tasks/0031_374_31374028_qa_4/task.toml +++ b/tasks/0031_374_31374028_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0031_374_31374028_qa_4" +name = "smoldataenvs-train/0031_374_31374028_qa_4" description = "Which pair of features shows the strongest negative correlation in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "pH, fixed acidity" reward_mode_initial = "list" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_395_31395077_qa_1/task.toml b/tasks/0031_395_31395077_qa_1/task.toml index 365de9d3cdfdde4d24a3d36a60738f6e0e7f1485..eb132f4f54eb0513bc192bf4b10bbae854a1f4d3 100644 --- a/tasks/0031_395_31395077_qa_1/task.toml +++ b/tasks/0031_395_31395077_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0031_395_31395077_qa_1" +name = "smoldataenvs-train/0031_395_31395077_qa_1" description = "Which feature in the dataset exhibits the highest positive skewness based on the skewness values calculated?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Insulin" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_402_31402162_qa_4/task.toml b/tasks/0031_402_31402162_qa_4/task.toml index 222fd03ad07425a43860ca315c521ff019400aeb..189858a7adb264b27a5e7ce8a9ee2f30016e692e 100644 --- a/tasks/0031_402_31402162_qa_4/task.toml +++ b/tasks/0031_402_31402162_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0031_402_31402162_qa_4" +name = "smoldataenvs-train/0031_402_31402162_qa_4" description = "What is the maximum average churn rate observed across all Contract types?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.4271" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_434_31434625_qa_1/task.toml b/tasks/0031_434_31434625_qa_1/task.toml index 42b7815e10a4ce1123e092315afbcc5b5747957a..5520a0a2453c8bfce754a6a8833bd4e5f3c35a08 100644 --- a/tasks/0031_434_31434625_qa_1/task.toml +++ b/tasks/0031_434_31434625_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0031_434_31434625_qa_1" +name = "smoldataenvs-train/0031_434_31434625_qa_1" description = "Which feature has the highest absolute correlation with the median house value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "median_income" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_523_31523942_qa_1/task.toml b/tasks/0031_523_31523942_qa_1/task.toml index 6fbdd8e843c216cde1034a89edbaca1d439f30d9..f047c445e7306335c20cf6a5a344e88bdd680e8c 100644 --- a/tasks/0031_523_31523942_qa_1/task.toml +++ b/tasks/0031_523_31523942_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0031_523_31523942_qa_1" +name = "smoldataenvs-train/0031_523_31523942_qa_1" description = "Which class in the data3c dataset has the highest average pelvic_incidence value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Spondylolisthesis" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_523_31523942_qa_3/task.toml b/tasks/0031_523_31523942_qa_3/task.toml index 223234742e4195816f49e90fbf191f80f963221e..3efbc695450f50fc7b12d5ba2861c3cc2977a6fd 100644 --- a/tasks/0031_523_31523942_qa_3/task.toml +++ b/tasks/0031_523_31523942_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0031_523_31523942_qa_3" +name = "smoldataenvs-train/0031_523_31523942_qa_3" description = "Which class in the data3c dataset exhibits the highest mean degree_spondylolisthesis value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Spondylolisthesis" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_523_31523942_qa_4/task.toml b/tasks/0031_523_31523942_qa_4/task.toml index 9d90cf62ba592b45e2af43bcb9e9a47cc303afd7..8a58b15a723d5eaa5d3292618b7e9796a5ea5f52 100644 --- a/tasks/0031_523_31523942_qa_4/task.toml +++ b/tasks/0031_523_31523942_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0031_523_31523942_qa_4" +name = "smoldataenvs-train/0031_523_31523942_qa_4" description = "What is the difference in average pelvic_radius between the Normal and Hernia classes in the data3c dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7.42" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_566_31566788_qa_1/task.toml b/tasks/0031_566_31566788_qa_1/task.toml index db858102158e73027ea6e21228678c211c8b6ad5..b86eb77770722e1522033397bbc0882a7bcedf33 100644 --- a/tasks/0031_566_31566788_qa_1/task.toml +++ b/tasks/0031_566_31566788_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0031_566_31566788_qa_1" +name = "smoldataenvs-train/0031_566_31566788_qa_1" description = "Which two numerical features in the dataset show the strongest positive correlation according to the Pandas Profiling report?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm, PetalWidthCm" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_566_31566788_qa_5/task.toml b/tasks/0031_566_31566788_qa_5/task.toml index 0bd31f9d8417beb52d917e48289eba1e644aae24..c33731111dd9fd3f9ae7c147476af3ba955e20a3 100644 --- a/tasks/0031_566_31566788_qa_5/task.toml +++ b/tasks/0031_566_31566788_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0031_566_31566788_qa_5" +name = "smoldataenvs-train/0031_566_31566788_qa_5" description = "What is the maximum recorded SepalLengthCm value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0031_576_31576713_qa_1/task.toml b/tasks/0031_576_31576713_qa_1/task.toml index ddacd1a99530f2721b7b66eb111aecfccc3ccf4a..ba0156dc64b46d9a42cbf4839efa2f3f4da156ab 100644 --- a/tasks/0031_576_31576713_qa_1/task.toml +++ b/tasks/0031_576_31576713_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0031_576_31576713_qa_1" +name = "smoldataenvs-train/0031_576_31576713_qa_1" description = "Which feature has the highest importance in predicting diabetes according to the random forest model analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0031_600_31600148_qa_2/task.toml b/tasks/0031_600_31600148_qa_2/task.toml index 51953de0760c92be8ecc08018bc926a9f965a688..e33cbc3623f4167fe7139c4f474a8927692221d6 100644 --- a/tasks/0031_600_31600148_qa_2/task.toml +++ b/tasks/0031_600_31600148_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0031_600_31600148_qa_2" +name = "smoldataenvs-train/0031_600_31600148_qa_2" description = "What is the highest recorded \"energy\" value in the dataset, and which song has this value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.998, No Absolution" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0031_625_31625575_qa_3/task.toml b/tasks/0031_625_31625575_qa_3/task.toml index 03d0f6889595dac814b063119092b18fe384a123..bc2d93507b925091a9e9f441aeaf0c799ebce4b4 100644 --- a/tasks/0031_625_31625575_qa_3/task.toml +++ b/tasks/0031_625_31625575_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0031_625_31625575_qa_3" +name = "smoldataenvs-train/0031_625_31625575_qa_3" description = "How many movies in the dataset meet the minimum vote threshold (90th percentile) for inclusion in the IMDB weighted rating calculation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "481" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_625_31625575_qa_5/task.toml b/tasks/0031_625_31625575_qa_5/task.toml index 59a9597923a557daef604d0831b039811c1f3a9a..06340c3b71b0ab899344052f4feac4e61d9691ff 100644 --- a/tasks/0031_625_31625575_qa_5/task.toml +++ b/tasks/0031_625_31625575_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0031_625_31625575_qa_5" +name = "smoldataenvs-train/0031_625_31625575_qa_5" description = "What is the minimum number of votes required (m) for a movie to qualify for the IMDB weighted rating calculation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1838.4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_700_31700793_qa_5/task.toml b/tasks/0031_700_31700793_qa_5/task.toml index 43427287c52d75e9e7510b239c0ce958ea22ada0..78f69cc8411fd45b9cf1f9e09fd04d06333181fd 100644 --- a/tasks/0031_700_31700793_qa_5/task.toml +++ b/tasks/0031_700_31700793_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0031_700_31700793_qa_5" +name = "smoldataenvs-train/0031_700_31700793_qa_5" description = "What is the direction of the relationship between Years of Experience and Salary based on the linear regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "positive" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_757_31757076_qa_1/task.toml b/tasks/0031_757_31757076_qa_1/task.toml index 40430882cfac49968174bdb42c6b98527d623b5c..8ca9daf41cba44f2270cf8ee3feca3532e4f8be7 100644 --- a/tasks/0031_757_31757076_qa_1/task.toml +++ b/tasks/0031_757_31757076_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0031_757_31757076_qa_1" +name = "smoldataenvs-train/0031_757_31757076_qa_1" description = "What is the highest average apparent temperature recorded in any April from 2006 to 2016 in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "14.267076" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_757_31757076_qa_3/task.toml b/tasks/0031_757_31757076_qa_3/task.toml index 186c7d71ee3f1e16f6979bbc1384af61b58f9372..28a1b103282621d86ef90f0f4ae37fe800b1ce30 100644 --- a/tasks/0031_757_31757076_qa_3/task.toml +++ b/tasks/0031_757_31757076_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0031_757_31757076_qa_3" +name = "smoldataenvs-train/0031_757_31757076_qa_3" description = "What is the average apparent temperature in April 2006 according to the monthly resampled data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12.098827" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_776_31776194_qa_2/task.toml b/tasks/0031_776_31776194_qa_2/task.toml index 674db934b9dff79b3c33a8b4a7b6af7af63915c3..a00260be18133f2774217bee4e99661c6bf37fef 100644 --- a/tasks/0031_776_31776194_qa_2/task.toml +++ b/tasks/0031_776_31776194_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0031_776_31776194_qa_2" +name = "smoldataenvs-train/0031_776_31776194_qa_2" description = "What is the most common ramen style in countries with over 50 reviewed products based on the stacked percentage bar chart?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Pack" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_780_31780833_qa_2/task.toml b/tasks/0031_780_31780833_qa_2/task.toml index 7a52fa9554cd5d9c8a44a642525e29cf2b00ab36..1282dd0ec58ec325272f7169315391d24d391eb0 100644 --- a/tasks/0031_780_31780833_qa_2/task.toml +++ b/tasks/0031_780_31780833_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0031_780_31780833_qa_2" +name = "smoldataenvs-train/0031_780_31780833_qa_2" description = "Which feature has the highest skewness value according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Insulin" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0031_780_31780833_qa_3/task.toml b/tasks/0031_780_31780833_qa_3/task.toml index 60440ea4875f3ddd3bb3fd46e5beb7e17c500225..203e9348108c2d2dc6595a11eaaf60e0cbcfc577 100644 --- a/tasks/0031_780_31780833_qa_3/task.toml +++ b/tasks/0031_780_31780833_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0031_780_31780833_qa_3" +name = "smoldataenvs-train/0031_780_31780833_qa_3" description = "What percentage of patients in the dataset have diabetes (Outcome=1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0031_780_31780833_qa_4/task.toml b/tasks/0031_780_31780833_qa_4/task.toml index 16f051676fd6bb74f27eaa3e198b8d12361f48b3..a60facdccca9c6d0e278f6153217d66a2acec835 100644 --- a/tasks/0031_780_31780833_qa_4/task.toml +++ b/tasks/0031_780_31780833_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0031_780_31780833_qa_4" +name = "smoldataenvs-train/0031_780_31780833_qa_4" description = "Which feature demonstrates the most negative kurtosis value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Outcome" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_829_31829521_qa_1/task.toml b/tasks/0031_829_31829521_qa_1/task.toml index dffc969eee36ae7459f2f8cbd6f66fc4e568456a..86d2e5adf771bb3231bcdaa70dd369c7c0de70f5 100644 --- a/tasks/0031_829_31829521_qa_1/task.toml +++ b/tasks/0031_829_31829521_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0031_829_31829521_qa_1" +name = "smoldataenvs-train/0031_829_31829521_qa_1" description = "What is the highest Attack stat among first generation legendary Pokémon?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "190" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_829_31829521_qa_2/task.toml b/tasks/0031_829_31829521_qa_2/task.toml index 804cd87326d65b86ee3c36ff25723b852680e77e..5baa8a4cade8d883123dbca4045c4e98e8b85b3c 100644 --- a/tasks/0031_829_31829521_qa_2/task.toml +++ b/tasks/0031_829_31829521_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0031_829_31829521_qa_2" +name = "smoldataenvs-train/0031_829_31829521_qa_2" description = "Which generation has the highest number of Pokémon in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "First Generation" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0031_829_31829521_qa_4/task.toml b/tasks/0031_829_31829521_qa_4/task.toml index ad1c48b6e94530f95b55c5c2adc2073fd0a176dd..ab4965da0535c2bf26e82c5126242e20e49985ea 100644 --- a/tasks/0031_829_31829521_qa_4/task.toml +++ b/tasks/0031_829_31829521_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0031_829_31829521_qa_4" +name = "smoldataenvs-train/0031_829_31829521_qa_4" description = "What is the highest Defense value among third generation legendary Pokémon?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "200" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_829_31829521_qa_5/task.toml b/tasks/0031_829_31829521_qa_5/task.toml index b8b1b6a770e72bec863f9447f55ddf0721730f6e..f7c8d279e3f7456d89a1e2b0c18364430a6d743b 100644 --- a/tasks/0031_829_31829521_qa_5/task.toml +++ b/tasks/0031_829_31829521_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0031_829_31829521_qa_5" +name = "smoldataenvs-train/0031_829_31829521_qa_5" description = "What is the highest Attack value among first generation Pokémon (including non-legendary)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "190" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_852_31852116_qa_2/task.toml b/tasks/0031_852_31852116_qa_2/task.toml index 5ae4d47ea6274daf98e203875d8892218cb795a2..d6cfe341781997e648924dbcd5d2262a13ce26c6 100644 --- a/tasks/0031_852_31852116_qa_2/task.toml +++ b/tasks/0031_852_31852116_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0031_852_31852116_qa_2" +name = "smoldataenvs-train/0031_852_31852116_qa_2" description = "What is the most important feature in predicting employee attrition according to SHAP feature importance analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "OverTime" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0031_872_31872499_qa_4/task.toml b/tasks/0031_872_31872499_qa_4/task.toml index bf967e8cfea4770de302fe22ab493796c635f39c..846aed30cdd368d32955b3ea2617c20ee4be289d 100644 --- a/tasks/0031_872_31872499_qa_4/task.toml +++ b/tasks/0031_872_31872499_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0031_872_31872499_qa_4" +name = "smoldataenvs-train/0031_872_31872499_qa_4" description = "After applying upsampling, what is the ratio of non-readmitted to readmitted patients in the resampled dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1:1" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_872_31872884_qa_1/task.toml b/tasks/0031_872_31872884_qa_1/task.toml index 0b6cbf41dbbba105dc45088eed6cd0dd69fae0c6..6f6c3a72bb878a0181f873f18caff367c1fb8fb7 100644 --- a/tasks/0031_872_31872884_qa_1/task.toml +++ b/tasks/0031_872_31872884_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0031_872_31872884_qa_1" +name = "smoldataenvs-train/0031_872_31872884_qa_1" description = "What is the highest correlation coefficient between any two sales regions (NA, EU, JP, Other, and Global) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.941499" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_889_31889886_qa_2/task.toml b/tasks/0031_889_31889886_qa_2/task.toml index 28e01766d71572e618601b0da739c0312d2d6424..c39330df1515e2353fc4f5c4d46326074cbe993a 100644 --- a/tasks/0031_889_31889886_qa_2/task.toml +++ b/tasks/0031_889_31889886_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0031_889_31889886_qa_2" +name = "smoldataenvs-train/0031_889_31889886_qa_2" description = "What is the ratio of healthy individuals (Outcome=0) to sick individuals (Outcome=1) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "500:268" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0031_906_31906980_qa_1/task.toml b/tasks/0031_906_31906980_qa_1/task.toml index e23d90f2b9dd34c26b8409070b4ae9a64685a4f9..8a980e198c8b6653193cb84737be51a1d599a7fe 100644 --- a/tasks/0031_906_31906980_qa_1/task.toml +++ b/tasks/0031_906_31906980_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0031_906_31906980_qa_1" +name = "smoldataenvs-train/0031_906_31906980_qa_1" description = "What is the accuracy of the SVM classifier on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0031_908_31908862_qa_5/task.toml b/tasks/0031_908_31908862_qa_5/task.toml index 8158b82bde0b2ed2320b6bde894b3823dfb9423a..90721bcb825982879d8cf277c61402e86e860e41 100644 --- a/tasks/0031_908_31908862_qa_5/task.toml +++ b/tasks/0031_908_31908862_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0031_908_31908862_qa_5" +name = "smoldataenvs-train/0031_908_31908862_qa_5" description = "What is the number of unique fare values present in the dataset after processing missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "248" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_927_31927038_qa_4/task.toml b/tasks/0031_927_31927038_qa_4/task.toml index 70f77966e1183c41ba7c9a5791f3cfb07bef3c4a..8d6b3b76e41c5bbc05b7dfcf7aab2fdee8cfb598 100644 --- a/tasks/0031_927_31927038_qa_4/task.toml +++ b/tasks/0031_927_31927038_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0031_927_31927038_qa_4" +name = "smoldataenvs-train/0031_927_31927038_qa_4" description = "What is the median age of patients in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "52" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0031_927_31927038_qa_5/task.toml b/tasks/0031_927_31927038_qa_5/task.toml index a331d7c7c4e58c2c0a0b03645ab98f90eeb6db8b..95735a2ff8617ef776c0e04ddfd5d949889e5ae3 100644 --- a/tasks/0031_927_31927038_qa_5/task.toml +++ b/tasks/0031_927_31927038_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0031_927_31927038_qa_5" +name = "smoldataenvs-train/0031_927_31927038_qa_5" description = "What is the median number of axillary lymph nodes detected among the patients?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0031_931_31931380_qa_5/task.toml b/tasks/0031_931_31931380_qa_5/task.toml index 9e2f6ff4b20fd5148381bc8da5696a54f4ab28fc..f499e03eacdec5f37442d8222f7f73ec043d9048 100644 --- a/tasks/0031_931_31931380_qa_5/task.toml +++ b/tasks/0031_931_31931380_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0031_931_31931380_qa_5" +name = "smoldataenvs-train/0031_931_31931380_qa_5" description = "What is the total number of medals awarded to female athletes in the Summer Olympics dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8419" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_950_31950412_qa_5/task.toml b/tasks/0031_950_31950412_qa_5/task.toml index b11d64419eca516a74bc6ef0a6e73bee919c256b..b011df6dbe71f42f76818a7a388a43c092b8a6bf 100644 --- a/tasks/0031_950_31950412_qa_5/task.toml +++ b/tasks/0031_950_31950412_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0031_950_31950412_qa_5" +name = "smoldataenvs-train/0031_950_31950412_qa_5" description = "What is the average sepal width in centimeters for the Iris-versicolor species according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.770" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_951_31951515_qa_3/task.toml b/tasks/0031_951_31951515_qa_3/task.toml index 5bad4c34af196b74ff6a168e1a96b2a695dc4295..28774430f57752a31d744b037fe0033d3bb5630c 100644 --- a/tasks/0031_951_31951515_qa_3/task.toml +++ b/tasks/0031_951_31951515_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0031_951_31951515_qa_3" +name = "smoldataenvs-train/0031_951_31951515_qa_3" description = "What is the difference in average monthly charges between customers who churned and those who did not?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13.18" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_954_31954480_qa_3/task.toml b/tasks/0031_954_31954480_qa_3/task.toml index 3c1aa60864ee7e2afa5633db87efe8bc6a258e99..4d3184c7b62456f01f23f2cb78c87a76eb9f5d36 100644 --- a/tasks/0031_954_31954480_qa_3/task.toml +++ b/tasks/0031_954_31954480_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0031_954_31954480_qa_3" +name = "smoldataenvs-train/0031_954_31954480_qa_3" description = "Which species exhibits the highest average petal width according to the feature averages analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-virginica" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_954_31954480_qa_5/task.toml b/tasks/0031_954_31954480_qa_5/task.toml index 37c80ae9093914e6585fc5bfd0e36042eff1396a..8bc4fc4f9712ce46379229f38817544a0ee58d3f 100644 --- a/tasks/0031_954_31954480_qa_5/task.toml +++ b/tasks/0031_954_31954480_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0031_954_31954480_qa_5" +name = "smoldataenvs-train/0031_954_31954480_qa_5" description = "Which species has the highest average petal length based on the statistical summary visualization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-virginica" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_954_31954799_qa_2/task.toml b/tasks/0031_954_31954799_qa_2/task.toml index df213e2069e3392520f81af4862ea912ed18bee6..f50a92f8d921fedf6b8cbd1f972ffd8dd3d00853 100644 --- a/tasks/0031_954_31954799_qa_2/task.toml +++ b/tasks/0031_954_31954799_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0031_954_31954799_qa_2" +name = "smoldataenvs-train/0031_954_31954799_qa_2" description = "What is the most frequently mentioned Python version in titles using the format \"Python X\"?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Python 3" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_987_31987709_qa_1/task.toml b/tasks/0031_987_31987709_qa_1/task.toml index 36beedb02d94021678b8a3ff06773cfd43654fc1..065118483924c7d6676f158d5082f7a9b53b9e48 100644 --- a/tasks/0031_987_31987709_qa_1/task.toml +++ b/tasks/0031_987_31987709_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0031_987_31987709_qa_1" +name = "smoldataenvs-train/0031_987_31987709_qa_1" description = "What is the highest positive correlation between any feature and the diabetes outcome in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.46" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0031_987_31987709_qa_5/task.toml b/tasks/0031_987_31987709_qa_5/task.toml index 78f17abe218417b56f4d760fae8cb55ae57a4ae7..ec2e711429b034e88db1527bd3bcfb1c9d602822 100644 --- a/tasks/0031_987_31987709_qa_5/task.toml +++ b/tasks/0031_987_31987709_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0031_987_31987709_qa_5" +name = "smoldataenvs-train/0031_987_31987709_qa_5" description = "Which feature has the second-highest positive correlation with the diabetes outcome after Glucose?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "BMI" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_014_32014924_qa_3/task.toml b/tasks/0032_014_32014924_qa_3/task.toml index 39b313f09cedf0e11d19be41570c1d10e7aa7377..70c9c34b305d204fe1b9bbd26ce770a04aecb0c3 100644 --- a/tasks/0032_014_32014924_qa_3/task.toml +++ b/tasks/0032_014_32014924_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0032_014_32014924_qa_3" +name = "smoldataenvs-train/0032_014_32014924_qa_3" description = "What is the mean pelvic incidence angle for patients diagnosed with Spondylolisthesis in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "71.51" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_044_32044463_qa_1/task.toml b/tasks/0032_044_32044463_qa_1/task.toml index 99b56ba7891df3baaa5b48e2d4a42258e957af79..6522a8054e54e5dd5f43318f408bbaf0b9d37311 100644 --- a/tasks/0032_044_32044463_qa_1/task.toml +++ b/tasks/0032_044_32044463_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0032_044_32044463_qa_1" +name = "smoldataenvs-train/0032_044_32044463_qa_1" description = "What is the highest starting median salary among all undergraduate majors, and which major achieves this salary?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Physician Assistant, 74300" reward_mode_initial = "list" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_054_32054124_qa_5/task.toml b/tasks/0032_054_32054124_qa_5/task.toml index ecc1bae207e03d24e99cf0db5c461942d999f1ba..bfa65c86a0f8b2a149fd4f9af23432997b208733 100644 --- a/tasks/0032_054_32054124_qa_5/task.toml +++ b/tasks/0032_054_32054124_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0032_054_32054124_qa_5" +name = "smoldataenvs-train/0032_054_32054124_qa_5" description = "Which Pokémon type has the lowest average Speed stat compared to the overall mean of the selected types?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Fairy" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_057_32057827_qa_4/task.toml b/tasks/0032_057_32057827_qa_4/task.toml index 43ee0dfc71c4dc04bc01e7a57209fca1b4e71eec..c96ae3ab29b3b48e8b7f1004424a2d9dbb717881 100644 --- a/tasks/0032_057_32057827_qa_4/task.toml +++ b/tasks/0032_057_32057827_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0032_057_32057827_qa_4" +name = "smoldataenvs-train/0032_057_32057827_qa_4" description = "Which nationality has the most players in the dataset, and how many players does it represent?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Canada, 289" reward_mode_initial = "list" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_069_32069437_qa_1/task.toml b/tasks/0032_069_32069437_qa_1/task.toml index fc1079382c8afaebddeb447ab55f99f2e162325f..5286b18913618da07cf8aa3ce88d0ab27ee09af7 100644 --- a/tasks/0032_069_32069437_qa_1/task.toml +++ b/tasks/0032_069_32069437_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0032_069_32069437_qa_1" +name = "smoldataenvs-train/0032_069_32069437_qa_1" description = "Which food category has the highest average calorie count, and what is that average value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Chicken & Fish, 552.96" reward_mode_initial = "list" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_069_32069437_qa_5/task.toml b/tasks/0032_069_32069437_qa_5/task.toml index cee4c2435f94888c23e27505ddd1c30d130f07ea..7a61249539bf177a9ca9e47409e32622b7dddd1f 100644 --- a/tasks/0032_069_32069437_qa_5/task.toml +++ b/tasks/0032_069_32069437_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0032_069_32069437_qa_5" +name = "smoldataenvs-train/0032_069_32069437_qa_5" description = "What is the average calorie count for breakfast items in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "526.67" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_071_32071401_qa_3/task.toml b/tasks/0032_071_32071401_qa_3/task.toml index 1c132e05099ec8b0b6775013e5b0695e03e5dc81..39d9112525238db72ff52b33a932a11cd28eb615 100644 --- a/tasks/0032_071_32071401_qa_3/task.toml +++ b/tasks/0032_071_32071401_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0032_071_32071401_qa_3" +name = "smoldataenvs-train/0032_071_32071401_qa_3" description = "Which species has the highest median sepal width as indicated by the violin plot?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-setosa" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_071_32071401_qa_5/task.toml b/tasks/0032_071_32071401_qa_5/task.toml index c57c5d9c8a2a3c2841fe618094684b9a974cd02b..7d0aafc9413522ee083c169154bd54ad98feed6c 100644 --- a/tasks/0032_071_32071401_qa_5/task.toml +++ b/tasks/0032_071_32071401_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0032_071_32071401_qa_5" +name = "smoldataenvs-train/0032_071_32071401_qa_5" description = "Which species has the highest median petal width based on the violin plot visualization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-virginica" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_092_32092871_qa_2/task.toml b/tasks/0032_092_32092871_qa_2/task.toml index d6dc5e912f8fc220d4201a1d7f78195dfeff0594..66628949846b9d85275114434d334d44b45a1775 100644 --- a/tasks/0032_092_32092871_qa_2/task.toml +++ b/tasks/0032_092_32092871_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0032_092_32092871_qa_2" +name = "smoldataenvs-train/0032_092_32092871_qa_2" description = "What is the predicted price for the first property in the preprocessed dataset using the decision tree model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1035000" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0032_093_32093418_qa_4/task.toml b/tasks/0032_093_32093418_qa_4/task.toml index 4b71c1acbca8aaedf8b3145f27e3e37791b90159..4b2dfe5248478671fe1a6aca8d96e3f55288b1de 100644 --- a/tasks/0032_093_32093418_qa_4/task.toml +++ b/tasks/0032_093_32093418_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0032_093_32093418_qa_4" +name = "smoldataenvs-train/0032_093_32093418_qa_4" description = "Which cuisine type is most correlated with \"Tortas Locas Hipocampo\" based on Pearson correlation analysis of user ratings?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Fast_Food" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0032_101_32101676_qa_1/task.toml b/tasks/0032_101_32101676_qa_1/task.toml index 5e2009ec2af721cc33836bfc6de04699742f88f3..0d5b6d91348876b3918d0fa1f61b33f16d4cb9e6 100644 --- a/tasks/0032_101_32101676_qa_1/task.toml +++ b/tasks/0032_101_32101676_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0032_101_32101676_qa_1" +name = "smoldataenvs-train/0032_101_32101676_qa_1" description = "Which outlet identifier has the highest total Item_Outlet_Sales in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "OUT027" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_101_32101676_qa_2/task.toml b/tasks/0032_101_32101676_qa_2/task.toml index 6939d2fc395c3a7e01c3fb277ba044228b8f879f..3768d96a07c6184878ab8764f11716231a6bda88 100644 --- a/tasks/0032_101_32101676_qa_2/task.toml +++ b/tasks/0032_101_32101676_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0032_101_32101676_qa_2" +name = "smoldataenvs-train/0032_101_32101676_qa_2" description = "Which outlet type has the highest total Item_Outlet_Sales across all records in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Supermarket Type1" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_103_32103943_qa_4/task.toml b/tasks/0032_103_32103943_qa_4/task.toml index d96ce2cc7d28b1f5e0e1ef23823a5d1cfa62a137..1c457a19c5bff2f54767df76db9714bcbcb8c879 100644 --- a/tasks/0032_103_32103943_qa_4/task.toml +++ b/tasks/0032_103_32103943_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0032_103_32103943_qa_4" +name = "smoldataenvs-train/0032_103_32103943_qa_4" description = "What is the largest eigenvalue calculated from the covariance matrix of the standardized Iris dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.93035378" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_178_32178295_qa_2/task.toml b/tasks/0032_178_32178295_qa_2/task.toml index 0637addf28a08aec6e303e84958f1af3cd0d44f0..9ea867a32717336568394fe003fede99952797e7 100644 --- a/tasks/0032_178_32178295_qa_2/task.toml +++ b/tasks/0032_178_32178295_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0032_178_32178295_qa_2" +name = "smoldataenvs-train/0032_178_32178295_qa_2" description = "What is the maximum price of a diamond in the dataset before removing rows with zero dimensions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "18823" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0032_205_32205594_qa_1/task.toml b/tasks/0032_205_32205594_qa_1/task.toml index 92493754fd646eeec66ca9483bc56b636d12018a..7ca8828993ceb011a89cbd3a95e8eeca0896bf72 100644 --- a/tasks/0032_205_32205594_qa_1/task.toml +++ b/tasks/0032_205_32205594_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0032_205_32205594_qa_1" +name = "smoldataenvs-train/0032_205_32205594_qa_1" description = "What is the average difference in message length between spam and ham messages in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "66.54 characters" reward_mode_initial = "flexible" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_205_32205594_qa_3/task.toml b/tasks/0032_205_32205594_qa_3/task.toml index e764f66397c02b618459ccd3b642e06d39bdb668..dea058108ba7abcbd28fdec2ff2313fc0832d87d 100644 --- a/tasks/0032_205_32205594_qa_3/task.toml +++ b/tasks/0032_205_32205594_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0032_205_32205594_qa_3" +name = "smoldataenvs-train/0032_205_32205594_qa_3" description = "What is the ratio of ham messages to spam messages in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.46:1" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_205_32205594_qa_4/task.toml b/tasks/0032_205_32205594_qa_4/task.toml index bb1b31f28812cd2766e76f42464e5205579396c8..165dbed1747f3125f9cddaaf29319f60e4b7b3ca 100644 --- a/tasks/0032_205_32205594_qa_4/task.toml +++ b/tasks/0032_205_32205594_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0032_205_32205594_qa_4" +name = "smoldataenvs-train/0032_205_32205594_qa_4" description = "What percentage of the dataset consists of spam messages?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13.4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_211_32211559_qa_1/task.toml b/tasks/0032_211_32211559_qa_1/task.toml index 5ba17afa13ec363eea3b3748c10c69c867b2b79d..2e883a5a394fdabede9f5739b7b313455872fabc 100644 --- a/tasks/0032_211_32211559_qa_1/task.toml +++ b/tasks/0032_211_32211559_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0032_211_32211559_qa_1" +name = "smoldataenvs-train/0032_211_32211559_qa_1" description = "Which car feature demonstrates the strongest negative correlation with miles per gallon (mpg) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "weight" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_211_32211559_qa_5/task.toml b/tasks/0032_211_32211559_qa_5/task.toml index ae364065133604b116dfd0f0e1248f400a22e027..d4c34db80932ea3e07f64487a9d89c4439504b21 100644 --- a/tasks/0032_211_32211559_qa_5/task.toml +++ b/tasks/0032_211_32211559_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0032_211_32211559_qa_5" +name = "smoldataenvs-train/0032_211_32211559_qa_5" description = "How many vehicles in the dataset originally had missing values in the horsepower column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_216_32216997_qa_2/task.toml b/tasks/0032_216_32216997_qa_2/task.toml index 9eb413a1dec4c7db508f9fad0b7e673a81f8d831..ca1fa0ace309e949cdb183db4cfb37a07d246bc5 100644 --- a/tasks/0032_216_32216997_qa_2/task.toml +++ b/tasks/0032_216_32216997_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0032_216_32216997_qa_2" +name = "smoldataenvs-train/0032_216_32216997_qa_2" description = "What is the correlation coefficient between movie budget and revenue, and is this relationship statistically significant based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.72, yes" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0032_222_32222371_qa_1/task.toml b/tasks/0032_222_32222371_qa_1/task.toml index b3788638cbf33fb22b0bd58940ba0eefdb8743ca..382ce49851a8cc2c65b366d15059b564d70842cb 100644 --- a/tasks/0032_222_32222371_qa_1/task.toml +++ b/tasks/0032_222_32222371_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0032_222_32222371_qa_1" +name = "smoldataenvs-train/0032_222_32222371_qa_1" description = "What percentage of patients did not show up for their scheduled medical appointments in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20.19" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_281_32281132_qa_4/task.toml b/tasks/0032_281_32281132_qa_4/task.toml index ea5e7e3d63acc363661dce12626b0b076c93092a..2e655e9942996d543836fa7657ddf21141281f4d 100644 --- a/tasks/0032_281_32281132_qa_4/task.toml +++ b/tasks/0032_281_32281132_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0032_281_32281132_qa_4" +name = "smoldataenvs-train/0032_281_32281132_qa_4" description = "Which cap-shape is most frequently observed in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "x" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0032_281_32281132_qa_5/task.toml b/tasks/0032_281_32281132_qa_5/task.toml index 3cf0ee2aac2165ab59623f29892153eb3a6a83a1..d2ce84afdf349e753feb50fe903687c1f556d8a7 100644 --- a/tasks/0032_281_32281132_qa_5/task.toml +++ b/tasks/0032_281_32281132_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0032_281_32281132_qa_5" +name = "smoldataenvs-train/0032_281_32281132_qa_5" description = "Which feature has the highest number of unique categories in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "gill-color" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0032_310_32310373_qa_3/task.toml b/tasks/0032_310_32310373_qa_3/task.toml index f0e109ccfe713048c3bbbfecf840a312d522fa47..4c9474043d1fe5f0af40c893193a5abeb558c806 100644 --- a/tasks/0032_310_32310373_qa_3/task.toml +++ b/tasks/0032_310_32310373_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0032_310_32310373_qa_3" +name = "smoldataenvs-train/0032_310_32310373_qa_3" description = "What was the percentage of missing values in the 'Insulin' column before imputation with median values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "48.7" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_310_32310373_qa_5/task.toml b/tasks/0032_310_32310373_qa_5/task.toml index 3935f6c004f20fe04938427a7526bc1fea83c229..ff488194b3f95d14f552551c160e24ab132afee6 100644 --- a/tasks/0032_310_32310373_qa_5/task.toml +++ b/tasks/0032_310_32310373_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0032_310_32310373_qa_5" +name = "smoldataenvs-train/0032_310_32310373_qa_5" description = "What percentage of patients in the dataset are diagnosed with diabetes (Outcome=1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0032_314_32314190_qa_3/task.toml b/tasks/0032_314_32314190_qa_3/task.toml index 4984a60448773b695e77e36feb1957199cb1d44c..9601a55c8bca40004af32072c1702490974865c6 100644 --- a/tasks/0032_314_32314190_qa_3/task.toml +++ b/tasks/0032_314_32314190_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0032_314_32314190_qa_3" +name = "smoldataenvs-train/0032_314_32314190_qa_3" description = "What is the most common frequency of weekly alcohol consumption (Walc) among students based on the distribution analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0032_314_32314190_qa_4/task.toml b/tasks/0032_314_32314190_qa_4/task.toml index 4a6fd3f7d5abd457a12eb827c80c24eda3f0ca97..9b6cb093a5d082f730bfaae68d258ba1549ead78 100644 --- a/tasks/0032_314_32314190_qa_4/task.toml +++ b/tasks/0032_314_32314190_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0032_314_32314190_qa_4" +name = "smoldataenvs-train/0032_314_32314190_qa_4" description = "Which Walc frequency group shows the highest range of first-grade scores (G1), and what is the observed grade range?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1, 15" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_317_32317961_qa_1/task.toml b/tasks/0032_317_32317961_qa_1/task.toml index ff19e8e1bd56373a1f752ae86e87b20b34422ca9..775743b15e2ab012c90a22be2eaa1d91f0df576a 100644 --- a/tasks/0032_317_32317961_qa_1/task.toml +++ b/tasks/0032_317_32317961_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0032_317_32317961_qa_1" +name = "smoldataenvs-train/0032_317_32317961_qa_1" description = "Which U.S. state has the highest average poverty rate based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Mississippi" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_317_32317961_qa_2/task.toml b/tasks/0032_317_32317961_qa_2/task.toml index 1adecbe00c12a993c2f60c1ae8b194b79f6f6054..24161ab436d1d7654d6ff6e94b9e8efba2ed373f 100644 --- a/tasks/0032_317_32317961_qa_2/task.toml +++ b/tasks/0032_317_32317961_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0032_317_32317961_qa_2" +name = "smoldataenvs-train/0032_317_32317961_qa_2" description = "What is the most common first name among fatally shot individuals in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Michael" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_337_32337163_qa_5/task.toml b/tasks/0032_337_32337163_qa_5/task.toml index 0b4600adcf0130956d12bbe09f41e17dd73b6f89..1fbd0fa99bd25d82f9103bfaf4adc25553f8e141 100644 --- a/tasks/0032_337_32337163_qa_5/task.toml +++ b/tasks/0032_337_32337163_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0032_337_32337163_qa_5" +name = "smoldataenvs-train/0032_337_32337163_qa_5" description = "Is the model more specific or more sensitive in its predictions based on the confusion matrix evaluation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "more specific" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0032_337_32337518_qa_4/task.toml b/tasks/0032_337_32337518_qa_4/task.toml index ca4a1fe726a969f1c6e1661475ea547b313e448a..3b3672a021309285f91c5d8c2e9d3b0a3c1b4f5f 100644 --- a/tasks/0032_337_32337518_qa_4/task.toml +++ b/tasks/0032_337_32337518_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0032_337_32337518_qa_4" +name = "smoldataenvs-train/0032_337_32337518_qa_4" description = "Which anime title has received the highest number of user ratings in the dataset according to the EDA analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Death Note" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_340_32340135_qa_5/task.toml b/tasks/0032_340_32340135_qa_5/task.toml index 9f7a3fb6c2dc3a9f51eb45c88e68434b02b143d0..4568182bef271b52deda3161b4eddbd5cdec350d 100644 --- a/tasks/0032_340_32340135_qa_5/task.toml +++ b/tasks/0032_340_32340135_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0032_340_32340135_qa_5" +name = "smoldataenvs-train/0032_340_32340135_qa_5" description = "What is the total number of datasets in the original Kaggle dataset collection analyzed in this study?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2150" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0032_340_32340712_qa_1/task.toml b/tasks/0032_340_32340712_qa_1/task.toml index 06f5abe6de3f3864f30004793da7d03e044b3633..057e4b5c3ca6b65d6e6e5a915aeb6f89ddeed4b9 100644 --- a/tasks/0032_340_32340712_qa_1/task.toml +++ b/tasks/0032_340_32340712_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0032_340_32340712_qa_1" +name = "smoldataenvs-train/0032_340_32340712_qa_1" description = "What percentage of reviews in the dataset contain missing latitude and longitude information?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.63" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_350_32350811_qa_5/task.toml b/tasks/0032_350_32350811_qa_5/task.toml index 01a6ab405eecac19469a8e46834bef67ca352527..d79a888a36e32680faf8b509f57d79ae09b49838 100644 --- a/tasks/0032_350_32350811_qa_5/task.toml +++ b/tasks/0032_350_32350811_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0032_350_32350811_qa_5" +name = "smoldataenvs-train/0032_350_32350811_qa_5" description = "What percentage of mobile phones in the dataset support 3G connectivity?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "76.15" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0032_386_32386426_qa_2/task.toml b/tasks/0032_386_32386426_qa_2/task.toml index c41ffda70e1059ab327784e3d50ae62aa43a75c0..3c903d51e030bc1429fccbc118ecd48a9bffda52 100644 --- a/tasks/0032_386_32386426_qa_2/task.toml +++ b/tasks/0032_386_32386426_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0032_386_32386426_qa_2" +name = "smoldataenvs-train/0032_386_32386426_qa_2" description = "What specific age ranges show the highest likelihood of term deposit subscription based on the KDE analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "below 30 and above 60" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_386_32386426_qa_3/task.toml b/tasks/0032_386_32386426_qa_3/task.toml index 7824d8524b9c01dc8eda9c35b8243658c0419430..6782fac56033a8fa9fcd6ef5e385a82ed51fa969 100644 --- a/tasks/0032_386_32386426_qa_3/task.toml +++ b/tasks/0032_386_32386426_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0032_386_32386426_qa_3" +name = "smoldataenvs-train/0032_386_32386426_qa_3" description = "Which marital status category demonstrates the highest proportion of term deposit subscriptions according to the visual analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Single" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_431_32431999_qa_1/task.toml b/tasks/0032_431_32431999_qa_1/task.toml index 64bda8329dd736b016fbb6fbbc51374cbe7970dd..6556a81633f9a214d6c036e998b806601852a61a 100644 --- a/tasks/0032_431_32431999_qa_1/task.toml +++ b/tasks/0032_431_32431999_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0032_431_32431999_qa_1" +name = "smoldataenvs-train/0032_431_32431999_qa_1" description = "Which year in the dataset had the lowest average monthly car sales quantity?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2009" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_431_32431999_qa_2/task.toml b/tasks/0032_431_32431999_qa_2/task.toml index cacb7aab278f5779c6588a688cfea758c93b3fd4..d27b74ec9b1b919b984c5d191ce93c68f23f8f4a 100644 --- a/tasks/0032_431_32431999_qa_2/task.toml +++ b/tasks/0032_431_32431999_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0032_431_32431999_qa_2" +name = "smoldataenvs-train/0032_431_32431999_qa_2" description = "What was the highest monthly car sales quantity recorded in the dataset, and in which year and month did it occur?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "June 2015" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0032_431_32431999_qa_5/task.toml b/tasks/0032_431_32431999_qa_5/task.toml index 4bc9e2e8820f38d516db4f7fee462fa81766a45b..19014afb45abad8bab576640369eef6b67e780ca 100644 --- a/tasks/0032_431_32431999_qa_5/task.toml +++ b/tasks/0032_431_32431999_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0032_431_32431999_qa_5" +name = "smoldataenvs-train/0032_431_32431999_qa_5" description = "Is the autocorrelation of the car sales data significant in the early periods of the time series?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_442_32442771_qa_3/task.toml b/tasks/0032_442_32442771_qa_3/task.toml index 567d69717be96a3a4f8e3c6f98205d011c4410c8..6823126462c5d803f967b0a077760539d95aa41e 100644 --- a/tasks/0032_442_32442771_qa_3/task.toml +++ b/tasks/0032_442_32442771_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0032_442_32442771_qa_3" +name = "smoldataenvs-train/0032_442_32442771_qa_3" description = "How many chocolate bars in the dataset use blended bean types in their production?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "103" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_458_32458821_qa_4/task.toml b/tasks/0032_458_32458821_qa_4/task.toml index 4dae86a89df39f122a3009ad7ca02aa5281102b6..0180d594d11c20369a483268a8c3cc7894f3f83a 100644 --- a/tasks/0032_458_32458821_qa_4/task.toml +++ b/tasks/0032_458_32458821_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0032_458_32458821_qa_4" +name = "smoldataenvs-train/0032_458_32458821_qa_4" description = "What is the most common Item_Type_Category in the dataset based on the feature engineering steps?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Food" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_458_32458821_qa_5/task.toml b/tasks/0032_458_32458821_qa_5/task.toml index 388db5b823c67f2ac5df0c5744c6cef4d890ac02..36e615f7df27b524a7fddb12a166cbdca9325c6a 100644 --- a/tasks/0032_458_32458821_qa_5/task.toml +++ b/tasks/0032_458_32458821_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0032_458_32458821_qa_5" +name = "smoldataenvs-train/0032_458_32458821_qa_5" description = "Which Outlet_Size has the highest median Item_Outlet_Sales according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Medium" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_488_32488845_qa_3/task.toml b/tasks/0032_488_32488845_qa_3/task.toml index 02b772ea0cc8f12723900eec3e3529a1bdd41ace..d64a9b13acf982d7a2ddd96e14a7745e57c1bf95 100644 --- a/tasks/0032_488_32488845_qa_3/task.toml +++ b/tasks/0032_488_32488845_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0032_488_32488845_qa_3" +name = "smoldataenvs-train/0032_488_32488845_qa_3" description = "Which gender has a higher attrition rate according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Male" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_515_32515991_qa_5/task.toml b/tasks/0032_515_32515991_qa_5/task.toml index 5299b5f2e0b1a067124800041e5db7bb28734e55..14f00cd5db70042a3506c93e530df7c3a863052c 100644 --- a/tasks/0032_515_32515991_qa_5/task.toml +++ b/tasks/0032_515_32515991_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0032_515_32515991_qa_5" +name = "smoldataenvs-train/0032_515_32515991_qa_5" description = "How many features were included in the final machine learning models?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_535_32535593_qa_1/task.toml b/tasks/0032_535_32535593_qa_1/task.toml index ece147d22aa578acfc66d602abef302bcc21644b..d09a91fbf6b7a1e379efba8d94b556f159c4293e 100644 --- a/tasks/0032_535_32535593_qa_1/task.toml +++ b/tasks/0032_535_32535593_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0032_535_32535593_qa_1" +name = "smoldataenvs-train/0032_535_32535593_qa_1" description = "Which team has the highest number of wins in the IPL dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Mumbai Indians" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0032_553_32553847_qa_1/task.toml b/tasks/0032_553_32553847_qa_1/task.toml index 51236e4d8f62c8e763b045255d9f86b5a6e238cd..69757eb826c422971f8dadffdc4ee909b06600de 100644 --- a/tasks/0032_553_32553847_qa_1/task.toml +++ b/tasks/0032_553_32553847_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0032_553_32553847_qa_1" +name = "smoldataenvs-train/0032_553_32553847_qa_1" description = "What is the maximum length (in characters) of any message in the SMS Spam dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "910" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0032_553_32553847_qa_3/task.toml b/tasks/0032_553_32553847_qa_3/task.toml index 93f319447f3bd8d13efcf088c1e4ad0d9a1d4502..30bfee8d6fe248ee03715a214aae5cba17a26bbe 100644 --- a/tasks/0032_553_32553847_qa_3/task.toml +++ b/tasks/0032_553_32553847_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0032_553_32553847_qa_3" +name = "smoldataenvs-train/0032_553_32553847_qa_3" description = "What is the most frequently occurring message in the ham category?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Sorry, I'll call later" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_553_32553847_qa_5/task.toml b/tasks/0032_553_32553847_qa_5/task.toml index 5a21861af8b1a41f6e7e88afc28fb03fdbc22eb5..acb13cb957977aefc238c4d9b322d6825633a5e4 100644 --- a/tasks/0032_553_32553847_qa_5/task.toml +++ b/tasks/0032_553_32553847_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0032_553_32553847_qa_5" +name = "smoldataenvs-train/0032_553_32553847_qa_5" description = "Do the histograms of message lengths indicate that spam messages tend to have longer character counts than ham messages?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_573_32573322_qa_2/task.toml b/tasks/0032_573_32573322_qa_2/task.toml index 7ad21ebd89e3ab172b52dd516267c3fe3e57568b..68b4906480ce28f543d32cfcb4b2c56c7e3146aa 100644 --- a/tasks/0032_573_32573322_qa_2/task.toml +++ b/tasks/0032_573_32573322_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0032_573_32573322_qa_2" +name = "smoldataenvs-train/0032_573_32573322_qa_2" description = "Which contract type has the highest proportion of customers, and what is the churn rate for that contract type?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Month-to-month, 42%" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_583_32583396_qa_2/task.toml b/tasks/0032_583_32583396_qa_2/task.toml index 287f2a8158557c41388f89f649aeffb5da03bf55..c25573fab954dafc3990c752183f9dac76eb2bf6 100644 --- a/tasks/0032_583_32583396_qa_2/task.toml +++ b/tasks/0032_583_32583396_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0032_583_32583396_qa_2" +name = "smoldataenvs-train/0032_583_32583396_qa_2" description = "How many missing data points were present in the 'Insulin' feature before the imputation process?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "374" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_614_32614408_qa_1/task.toml b/tasks/0032_614_32614408_qa_1/task.toml index 07589efa267a57a9bc502a7d161a6b1e9e238dfc..9cc28b1b0b5d1c9831fc3b0a96d778681e206b7a 100644 --- a/tasks/0032_614_32614408_qa_1/task.toml +++ b/tasks/0032_614_32614408_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0032_614_32614408_qa_1" +name = "smoldataenvs-train/0032_614_32614408_qa_1" description = "Which wine color type (red or white) has a higher average quality score, and by how much does it exceed the other type's average quality?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "white, 0.24" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_631_32631784_qa_4/task.toml b/tasks/0032_631_32631784_qa_4/task.toml index 8c8153b469203ca7fd23c61b652b215bf99ba27a..31da3d6a95d31553d27e01bb0c0befeb38360da1 100644 --- a/tasks/0032_631_32631784_qa_4/task.toml +++ b/tasks/0032_631_32631784_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0032_631_32631784_qa_4" +name = "smoldataenvs-train/0032_631_32631784_qa_4" description = "Which marital status group has the lowest attrition rate according to the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Divorced" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_680_32680563_qa_2/task.toml b/tasks/0032_680_32680563_qa_2/task.toml index b745949c2efbe86f2ae3bd2a32cb02285e12ef97..c83777450915e216036336cb4217f49ce2739b28 100644 --- a/tasks/0032_680_32680563_qa_2/task.toml +++ b/tasks/0032_680_32680563_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0032_680_32680563_qa_2" +name = "smoldataenvs-train/0032_680_32680563_qa_2" description = "What is the total number of training samples for the author with the highest representation in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7900" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0032_686_32686448_qa_1/task.toml b/tasks/0032_686_32686448_qa_1/task.toml index 34f7040f7ed96d918d2dd322f0b2c2057c290a3e..476f5d4fef5e787fbb05f83b97c7c2a46fa25587 100644 --- a/tasks/0032_686_32686448_qa_1/task.toml +++ b/tasks/0032_686_32686448_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0032_686_32686448_qa_1" +name = "smoldataenvs-train/0032_686_32686448_qa_1" description = "Which feature exhibits the strongest negative correlation with the glass type classification?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Mg" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_686_32686448_qa_2/task.toml b/tasks/0032_686_32686448_qa_2/task.toml index cc646ad10f1532ef132177d39db6be4ae00decdf..b1cbae433f249f52e42cf3cfbc7f594a6fa5724e 100644 --- a/tasks/0032_686_32686448_qa_2/task.toml +++ b/tasks/0032_686_32686448_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0032_686_32686448_qa_2" +name = "smoldataenvs-train/0032_686_32686448_qa_2" description = "What is the standard deviation of the Magnesium (Mg) concentration in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.442" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0032_686_32686448_qa_4/task.toml b/tasks/0032_686_32686448_qa_4/task.toml index 076a01a88b8600a60b0db679caba9536bac2366f..5b34dd8ef1d84853fb9f7d066d8f987e58ad1b47 100644 --- a/tasks/0032_686_32686448_qa_4/task.toml +++ b/tasks/0032_686_32686448_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0032_686_32686448_qa_4" +name = "smoldataenvs-train/0032_686_32686448_qa_4" description = "What is the highest positive correlation between any feature and the glass type?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.598" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_699_32699731_qa_4/task.toml b/tasks/0032_699_32699731_qa_4/task.toml index 1eb65641d8777b7d6f74bd10610bf3240e121ffc..cccbd8ed4c9e5c6486d7fbf4a8cff6e0715658d3 100644 --- a/tasks/0032_699_32699731_qa_4/task.toml +++ b/tasks/0032_699_32699731_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0032_699_32699731_qa_4" +name = "smoldataenvs-train/0032_699_32699731_qa_4" description = "How many times does the digit '3' appear in the first Sudoku quiz (excluding zeros)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0032_714_32714901_qa_2/task.toml b/tasks/0032_714_32714901_qa_2/task.toml index 696cf16704fd59b1eccda59249873439b8532a85..8bc06a81b8a0c0767935aa734758a22ad33fc777 100644 --- a/tasks/0032_714_32714901_qa_2/task.toml +++ b/tasks/0032_714_32714901_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0032_714_32714901_qa_2" +name = "smoldataenvs-train/0032_714_32714901_qa_2" description = "What percentage of the training data consists of individuals who made a donation in March 2007?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "23.96" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_714_32714901_qa_3/task.toml b/tasks/0032_714_32714901_qa_3/task.toml index 14c8deda436b2092795332a826f99da536aab929..b523e5f9d4a0adfa1c559a5689717dcac09502d0 100644 --- a/tasks/0032_714_32714901_qa_3/task.toml +++ b/tasks/0032_714_32714901_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0032_714_32714901_qa_3" +name = "smoldataenvs-train/0032_714_32714901_qa_3" description = "What is the interquartile range (IQR) for the \"Months since Last Donation\" feature in the training data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_734_32734784_qa_3/task.toml b/tasks/0032_734_32734784_qa_3/task.toml index e8e2e661c688b71e3633110acb9a9cfc1736a2ad..b4429f5985fb19da5bb1de5d81d99ec466d394c0 100644 --- a/tasks/0032_734_32734784_qa_3/task.toml +++ b/tasks/0032_734_32734784_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0032_734_32734784_qa_3" +name = "smoldataenvs-train/0032_734_32734784_qa_3" description = "What is the difference in mean area_mean between malignant and benign tumors?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "515.59" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_769_32769596_qa_1/task.toml b/tasks/0032_769_32769596_qa_1/task.toml index 8c8e2c0b60922e509552eaa0f5c842ec2cb2dbd3..4b5ebb6348c2cde24edca551c6e10150ef4e0cbd 100644 --- a/tasks/0032_769_32769596_qa_1/task.toml +++ b/tasks/0032_769_32769596_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0032_769_32769596_qa_1" +name = "smoldataenvs-train/0032_769_32769596_qa_1" description = "What is the highest classification accuracy achieved across different k values in the kNN model using the breast cancer dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9649" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0032_769_32769596_qa_3/task.toml b/tasks/0032_769_32769596_qa_3/task.toml index 48857697cf746edc9ba46ad590071ba6043c22fc..a5398933fab740f6f98e9efc1bd50ec2552d1a52 100644 --- a/tasks/0032_769_32769596_qa_3/task.toml +++ b/tasks/0032_769_32769596_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0032_769_32769596_qa_3" +name = "smoldataenvs-train/0032_769_32769596_qa_3" description = "Based on the confusion matrix for the k=15 kNN model, how many malignant tumors were misclassified as benign in the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0032_782_32782251_qa_1/task.toml b/tasks/0032_782_32782251_qa_1/task.toml index f688bdbb535008c33bb5a42158e1db8e12277b70..87de532e14c4df637d780b43179842ec219acc7f 100644 --- a/tasks/0032_782_32782251_qa_1/task.toml +++ b/tasks/0032_782_32782251_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0032_782_32782251_qa_1" +name = "smoldataenvs-train/0032_782_32782251_qa_1" description = "Which feature was the first to be removed during backward elimination based on the p-value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "floors" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0032_842_32842401_qa_1/task.toml b/tasks/0032_842_32842401_qa_1/task.toml index 525fc0740b007e75846fcf1eaa415ce5f5e9364c..34a8bf7712083c8b1d6442e766d33fd9b321bcb0 100644 --- a/tasks/0032_842_32842401_qa_1/task.toml +++ b/tasks/0032_842_32842401_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0032_842_32842401_qa_1" +name = "smoldataenvs-train/0032_842_32842401_qa_1" description = "What is the correlation coefficient between smoker status and insurance charges in the dataset after encoding categorical variables?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.787251" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_842_32842401_qa_5/task.toml b/tasks/0032_842_32842401_qa_5/task.toml index 421f38e3b9445ec27a5bf83a97def70c41174df5..73282a20637dbb0a6862a0bb949e079c9775acc0 100644 --- a/tasks/0032_842_32842401_qa_5/task.toml +++ b/tasks/0032_842_32842401_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0032_842_32842401_qa_5" +name = "smoldataenvs-train/0032_842_32842401_qa_5" description = "What is the correlation coefficient between age and insurance charges in the processed dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.299008" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0032_867_32867580_qa_1/task.toml b/tasks/0032_867_32867580_qa_1/task.toml index 47596a8b45290f2b5f305516d7a3072c01e92244..3745671401beb59b2c7d56cbbb142bea196dcd15 100644 --- a/tasks/0032_867_32867580_qa_1/task.toml +++ b/tasks/0032_867_32867580_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0032_867_32867580_qa_1" +name = "smoldataenvs-train/0032_867_32867580_qa_1" description = "What is the skewness of the 'children' variable after applying the log transformation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.264" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_867_32867580_qa_4/task.toml b/tasks/0032_867_32867580_qa_4/task.toml index fbf2f08d0d2fb39c386863e9dec6510d8e0f6bdc..0cfd64d2d8f0d260a0348798f5deee9fc5bdfaba 100644 --- a/tasks/0032_867_32867580_qa_4/task.toml +++ b/tasks/0032_867_32867580_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0032_867_32867580_qa_4" +name = "smoldataenvs-train/0032_867_32867580_qa_4" description = "What is the skewness of the 'charges' variable after applying the log transformation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.0898" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_867_32867580_qa_5/task.toml b/tasks/0032_867_32867580_qa_5/task.toml index 09befe8e2b10efc20f1ca041c6f5e591f181d372..39e8cacbfa774dd48ab6ae00e30132dac336e9ba 100644 --- a/tasks/0032_867_32867580_qa_5/task.toml +++ b/tasks/0032_867_32867580_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0032_867_32867580_qa_5" +name = "smoldataenvs-train/0032_867_32867580_qa_5" description = "What is the reduction in skewness for the 'children' variable after the log transformation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.674" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_945_32945624_qa_2/task.toml b/tasks/0032_945_32945624_qa_2/task.toml index fb6f0c7166cadac4c8eddbafffdb0d6a2b5146d1..2685e7f617f2df8f78361a6180f5e0870b5f186f 100644 --- a/tasks/0032_945_32945624_qa_2/task.toml +++ b/tasks/0032_945_32945624_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0032_945_32945624_qa_2" +name = "smoldataenvs-train/0032_945_32945624_qa_2" description = "For the Iris-virginica species, which measured feature shows the least skewness in its distribution?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalWidthCm" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0032_988_32988907_qa_4/task.toml b/tasks/0032_988_32988907_qa_4/task.toml index a1e09ea5f7b96661f015c12f59e10d1373981a47..4a9478ffc0a591bc4c4ef82b6e2124b1eaf0cc4d 100644 --- a/tasks/0032_988_32988907_qa_4/task.toml +++ b/tasks/0032_988_32988907_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0032_988_32988907_qa_4" +name = "smoldataenvs-train/0032_988_32988907_qa_4" description = "What is the most common education field among employees in the dataset based on absolute counts?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Life Sciences" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0033_057_33057679_qa_1/task.toml b/tasks/0033_057_33057679_qa_1/task.toml index 79826af77cf5b77d9538860c9e6ec3a77cf6b287..11dc268657bff09563789a9f381379665421dcab 100644 --- a/tasks/0033_057_33057679_qa_1/task.toml +++ b/tasks/0033_057_33057679_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0033_057_33057679_qa_1" +name = "smoldataenvs-train/0033_057_33057679_qa_1" description = "What percentage of the original dataset represents individuals diagnosed with diabetes before any preprocessing steps?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0033_061_33061669_qa_1/task.toml b/tasks/0033_061_33061669_qa_1/task.toml index ed761bdfe030e8967df7722f14a2de1ea6b9ff96..e9dfdbb64119c885cac78ffa819b81da8f9f2197 100644 --- a/tasks/0033_061_33061669_qa_1/task.toml +++ b/tasks/0033_061_33061669_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0033_061_33061669_qa_1" +name = "smoldataenvs-train/0033_061_33061669_qa_1" description = "Which flower species has the highest predicted probability for the input features (SL=4.7, SW=3.7, PL=2, PW=0.3) using Gaussian Naive Bayes calculations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-setosa" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0033_076_33076783_qa_3/task.toml b/tasks/0033_076_33076783_qa_3/task.toml index e03f0cc9bb0e2bd0d83bbfd4d52e28d47df7eec4..51dfaff3ed80113f8420f23e33f7530bf5ce0cbc 100644 --- a/tasks/0033_076_33076783_qa_3/task.toml +++ b/tasks/0033_076_33076783_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0033_076_33076783_qa_3" +name = "smoldataenvs-train/0033_076_33076783_qa_3" description = "Which feature in the dataset has the highest number of unique categories?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "gill-color" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0033_076_33076783_qa_4/task.toml b/tasks/0033_076_33076783_qa_4/task.toml index 250952395b3f5aa5dba1e40d287f4d9e88763d1d..7e1b209a943bb4a10f99dc9b64001a85f03fb28e 100644 --- a/tasks/0033_076_33076783_qa_4/task.toml +++ b/tasks/0033_076_33076783_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0033_076_33076783_qa_4" +name = "smoldataenvs-train/0033_076_33076783_qa_4" description = "What is the frequency of the most common 'stalk-root' category in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3776" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0033_076_33076783_qa_5/task.toml b/tasks/0033_076_33076783_qa_5/task.toml index bddb4e317bd899b77571a4fd3f545b5610c451e3..cc8ff3dfac9f2727a1d5d6d7bdc6e0e6be0831db 100644 --- a/tasks/0033_076_33076783_qa_5/task.toml +++ b/tasks/0033_076_33076783_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0033_076_33076783_qa_5" +name = "smoldataenvs-train/0033_076_33076783_qa_5" description = "What is the frequency of the most common 'spore-print-color' category in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2388" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0033_083_33083379_qa_3/task.toml b/tasks/0033_083_33083379_qa_3/task.toml index 7bce5587c797995b4f1cb8da78cff92526c0ede9..c6666203c8dcbb2da8bbf17fdf5f1e6a6120288c 100644 --- a/tasks/0033_083_33083379_qa_3/task.toml +++ b/tasks/0033_083_33083379_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0033_083_33083379_qa_3" +name = "smoldataenvs-train/0033_083_33083379_qa_3" description = "What is the calculated probability density for Petal Length=2.0 in the Iris-virginica distribution using Gaussian Naive Bayes?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.857770086427337e-10" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0033_083_33083379_qa_4/task.toml b/tasks/0033_083_33083379_qa_4/task.toml index d8e5e6398d5bc7e928d180c0cb54888e40dcb726..f0b6c0b2e9193b9cdf29b8a3a0ab1c2359e3a5e9 100644 --- a/tasks/0033_083_33083379_qa_4/task.toml +++ b/tasks/0033_083_33083379_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0033_083_33083379_qa_4" +name = "smoldataenvs-train/0033_083_33083379_qa_4" description = "What is the prior probability assigned to each species in this Naive Bayes implementation based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.3333333333333333" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0033_142_33142865_qa_4/task.toml b/tasks/0033_142_33142865_qa_4/task.toml index 023972fa0cdf58bfb438d446d75b09988c4ade10..dd6f70efb676938ee72081a3af78d44bdf662017 100644 --- a/tasks/0033_142_33142865_qa_4/task.toml +++ b/tasks/0033_142_33142865_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0033_142_33142865_qa_4" +name = "smoldataenvs-train/0033_142_33142865_qa_4" description = "How many Pokémon entries originally had missing values in their secondary type (Type 2) column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "386" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0033_183_33183014_qa_4/task.toml b/tasks/0033_183_33183014_qa_4/task.toml index 2281cc610e96259a5ac8216a77ff43813703e117..c3c752e92131f31f286a79f4930cf132cf22b2a4 100644 --- a/tasks/0033_183_33183014_qa_4/task.toml +++ b/tasks/0033_183_33183014_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0033_183_33183014_qa_4" +name = "smoldataenvs-train/0033_183_33183014_qa_4" description = "What is the average tenure duration for customers with a Two-year contract?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "56.74" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0033_183_33183014_qa_5/task.toml b/tasks/0033_183_33183014_qa_5/task.toml index 94b3045e108c16e39f1297295019493478e577fa..060c01ccd98f032c36ecdf0e562d85c38b4ef032 100644 --- a/tasks/0033_183_33183014_qa_5/task.toml +++ b/tasks/0033_183_33183014_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0033_183_33183014_qa_5" +name = "smoldataenvs-train/0033_183_33183014_qa_5" description = "What is the churn rate for customers with Fiber optic internet service?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "41.89" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0033_198_33198049_qa_4/task.toml b/tasks/0033_198_33198049_qa_4/task.toml index e7b5f57daed0bdca652527d8267378a56c0ac9b6..c093a535b1bb051d76ad848a6397229ce94bc6ff 100644 --- a/tasks/0033_198_33198049_qa_4/task.toml +++ b/tasks/0033_198_33198049_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0033_198_33198049_qa_4" +name = "smoldataenvs-train/0033_198_33198049_qa_4" description = "What is the highest maximum rating given by any user to a joke in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10.00" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0033_273_33273125_qa_1/task.toml b/tasks/0033_273_33273125_qa_1/task.toml index 1f7530fba67499a8e96b295d3f3d8c7ef6666b91..6bad232d99880698b010b1660bebc4f696a9c2f8 100644 --- a/tasks/0033_273_33273125_qa_1/task.toml +++ b/tasks/0033_273_33273125_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0033_273_33273125_qa_1" +name = "smoldataenvs-train/0033_273_33273125_qa_1" description = "What is the most common wine quality rating in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0033_273_33273125_qa_3/task.toml b/tasks/0033_273_33273125_qa_3/task.toml index 20c5e99137412591bc329c1973eb5cf09f84d8fe..ea50236570ab8287f6cc36190bbff7c89cd1f981 100644 --- a/tasks/0033_273_33273125_qa_3/task.toml +++ b/tasks/0033_273_33273125_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0033_273_33273125_qa_3" +name = "smoldataenvs-train/0033_273_33273125_qa_3" description = "What is the correlation coefficient between alcohol content and wine quality?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.48" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0033_273_33273125_qa_5/task.toml b/tasks/0033_273_33273125_qa_5/task.toml index e567ccc6bffd3439a7db4c75dd2fe863edd3e67f..d71c2f2de6d429f5187b8ad6c11af8ff8f8b9fa9 100644 --- a/tasks/0033_273_33273125_qa_5/task.toml +++ b/tasks/0033_273_33273125_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0033_273_33273125_qa_5" +name = "smoldataenvs-train/0033_273_33273125_qa_5" description = "What is the range (maximum minus minimum) of density values in the entire dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.01362" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0033_290_33290847_qa_1/task.toml b/tasks/0033_290_33290847_qa_1/task.toml index 53c1e118182a93e1ae98a52450e2aa2c5d54a9f4..2ca83a46fc34d1972f92e7cc715925a78c7ea506 100644 --- a/tasks/0033_290_33290847_qa_1/task.toml +++ b/tasks/0033_290_33290847_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0033_290_33290847_qa_1" +name = "smoldataenvs-train/0033_290_33290847_qa_1" description = "Which number of bedrooms is most frequently observed in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0033_303_33303111_qa_3/task.toml b/tasks/0033_303_33303111_qa_3/task.toml index 3342e9320bb43f508151e74fcc70742332174440..9acef1690a5e5aeb4c4fef1d1cbb3a61458bfa1a 100644 --- a/tasks/0033_303_33303111_qa_3/task.toml +++ b/tasks/0033_303_33303111_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0033_303_33303111_qa_3" +name = "smoldataenvs-train/0033_303_33303111_qa_3" description = "Which species has the highest standard deviation in Petal Length?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-virginica" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0033_308_33308132_qa_3/task.toml b/tasks/0033_308_33308132_qa_3/task.toml index c1d974219e942f8f36409c46b36ec195390bef3c..bcbb1051d2fd5ec724f3bbd0f0d4f9b077d872b2 100644 --- a/tasks/0033_308_33308132_qa_3/task.toml +++ b/tasks/0033_308_33308132_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0033_308_33308132_qa_3" +name = "smoldataenvs-train/0033_308_33308132_qa_3" description = "What is the correlation coefficient between monthly income and employee attrition as shown in the distribution plot?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.13" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0033_326_33326125_qa_3/task.toml b/tasks/0033_326_33326125_qa_3/task.toml index 91306187278c97fc20d78e0fa106723bda11aba9..834037bd940249a31c4b6bf6a68834910bc8b905 100644 --- a/tasks/0033_326_33326125_qa_3/task.toml +++ b/tasks/0033_326_33326125_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0033_326_33326125_qa_3" +name = "smoldataenvs-train/0033_326_33326125_qa_3" description = "How many total features are present in the dataset after one-hot encoding and preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0033_326_33326125_qa_4/task.toml b/tasks/0033_326_33326125_qa_4/task.toml index 53dee3e7675527409a5ca7e4410e8dbe60a59b04..059096c2020f91d92aabb1a7d269b5913b054b32 100644 --- a/tasks/0033_326_33326125_qa_4/task.toml +++ b/tasks/0033_326_33326125_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0033_326_33326125_qa_4" +name = "smoldataenvs-train/0033_326_33326125_qa_4" description = "How many dummy variables were created for the 'hour' feature after one-hot encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "24" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0033_420_33420599_qa_1/task.toml b/tasks/0033_420_33420599_qa_1/task.toml index 877892a9dd46c854dd74e0e89ac8fc267a33f396..355533f625da2873384ae2c736dd67f246e0d0f7 100644 --- a/tasks/0033_420_33420599_qa_1/task.toml +++ b/tasks/0033_420_33420599_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0033_420_33420599_qa_1" +name = "smoldataenvs-train/0033_420_33420599_qa_1" description = "Which video game achieved the highest global sales in the dataset, and what was the exact global sales figure?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii Sports, 82.74" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0033_420_33420599_qa_5/task.toml b/tasks/0033_420_33420599_qa_5/task.toml index 26ff0c9b7fd5762938eb5ce745996c57b432b105..9578dd187d94f903bb05bf3939f357728f7840e0 100644 --- a/tasks/0033_420_33420599_qa_5/task.toml +++ b/tasks/0033_420_33420599_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0033_420_33420599_qa_5" +name = "smoldataenvs-train/0033_420_33420599_qa_5" description = "Which publisher has the highest total global sales across all their games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Nintendo" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0033_423_33423582_qa_4/task.toml b/tasks/0033_423_33423582_qa_4/task.toml index df5d11930a9afb91c382ffa2ac5267c4aa2b2c7c..3cfb5cb6986732e3b456d1e72783eb6055ea79c1 100644 --- a/tasks/0033_423_33423582_qa_4/task.toml +++ b/tasks/0033_423_33423582_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0033_423_33423582_qa_4" +name = "smoldataenvs-train/0033_423_33423582_qa_4" description = "What is the average rating (C) across all movies in the dataset before applying the weighted rating formula?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5.618207215133889" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0033_448_33448868_qa_2/task.toml b/tasks/0033_448_33448868_qa_2/task.toml index 99e679d0d31d849fcd061653a5bc840f5bd9e3ae..777f44c10147185d6c731f53226a5577b819de78 100644 --- a/tasks/0033_448_33448868_qa_2/task.toml +++ b/tasks/0033_448_33448868_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0033_448_33448868_qa_2" +name = "smoldataenvs-train/0033_448_33448868_qa_2" description = "What percentage of messages in the dataset are classified as ham?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "86.59" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0033_509_33509634_qa_1/task.toml b/tasks/0033_509_33509634_qa_1/task.toml index ad706f270ec58a1023679f55e8fa1c96618d2b12..82d3fa709f41dfecff05bb96e3f1a09d27b8288d 100644 --- a/tasks/0033_509_33509634_qa_1/task.toml +++ b/tasks/0033_509_33509634_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0033_509_33509634_qa_1" +name = "smoldataenvs-train/0033_509_33509634_qa_1" description = "Which two variables, when used together, achieve a perfect R² (1.0) in predicting the sqft_living feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "sqft_above, sqft_basement" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0033_509_33509634_qa_4/task.toml b/tasks/0033_509_33509634_qa_4/task.toml index 6f5ed49cb6ca699d7238fa033d4f25c0783079f2..bdfaaffd47923356c22fb6a36f047c813ad914a1 100644 --- a/tasks/0033_509_33509634_qa_4/task.toml +++ b/tasks/0033_509_33509634_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0033_509_33509634_qa_4" +name = "smoldataenvs-train/0033_509_33509634_qa_4" description = "What is the Mean Squared Error (MSE) achieved when using the optimal variable pair (sqft_above and sqft_basement) to predict sqft_living?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.00" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0033_558_33558610_qa_3/task.toml b/tasks/0033_558_33558610_qa_3/task.toml index c02f6ba41f4bbc032ed6f32a5a2dd2a95fd7b78b..e95e9fb471b4c1568b3a6bda243b2fbde56c0be0 100644 --- a/tasks/0033_558_33558610_qa_3/task.toml +++ b/tasks/0033_558_33558610_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0033_558_33558610_qa_3" +name = "smoldataenvs-train/0033_558_33558610_qa_3" description = "Based on the boxplot analysis, does non-domestic chocolate receive statistically significantly higher median ratings compared to domestic chocolate?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0033_589_33589892_qa_1/task.toml b/tasks/0033_589_33589892_qa_1/task.toml index 7c086f434d70491074146dd642c5a7e6b14153bd..290261de4615eb176b19f1a06611af9f296d3c94 100644 --- a/tasks/0033_589_33589892_qa_1/task.toml +++ b/tasks/0033_589_33589892_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0033_589_33589892_qa_1" +name = "smoldataenvs-train/0033_589_33589892_qa_1" description = "After standardizing the 'Item_Fat_Content' column, which category has the highest count in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Low Fat" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0033_589_33589892_qa_4/task.toml b/tasks/0033_589_33589892_qa_4/task.toml index 17f10b3117cabf376f714813511c232db065d77e..b541ca6544ba395c15abfe709661fc92abb330e3 100644 --- a/tasks/0033_589_33589892_qa_4/task.toml +++ b/tasks/0033_589_33589892_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0033_589_33589892_qa_4" +name = "smoldataenvs-train/0033_589_33589892_qa_4" description = "What percentage of missing values are present in the 'Outlet_Size' column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "28.3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0033_652_33652373_qa_2/task.toml b/tasks/0033_652_33652373_qa_2/task.toml index 1d448362a2f23c2bcec5d48dc8b372a653b9871c..2a8969dde460af4ce434cd9c86b01dcad31f0dd8 100644 --- a/tasks/0033_652_33652373_qa_2/task.toml +++ b/tasks/0033_652_33652373_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0033_652_33652373_qa_2" +name = "smoldataenvs-train/0033_652_33652373_qa_2" description = "Which BMI category has the highest average medical charges according to the distribution analysis in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Obese" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0033_652_33652373_qa_3/task.toml b/tasks/0033_652_33652373_qa_3/task.toml index 78a1b330a7e5d38723ed41157649a26f0072bb64..b38927acaf7a254c696b06e148c9bccfd1b9bf54 100644 --- a/tasks/0033_652_33652373_qa_3/task.toml +++ b/tasks/0033_652_33652373_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0033_652_33652373_qa_3" +name = "smoldataenvs-train/0033_652_33652373_qa_3" description = "How many individuals in the dataset have exactly 5 children?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "18" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0033_668_33668266_qa_2/task.toml b/tasks/0033_668_33668266_qa_2/task.toml index d9e99a5f99c1a97f490486b6d1af3eb9fbe17881..b573ce5d19686337396a5a33a4fad8af748640d5 100644 --- a/tasks/0033_668_33668266_qa_2/task.toml +++ b/tasks/0033_668_33668266_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0033_668_33668266_qa_2" +name = "smoldataenvs-train/0033_668_33668266_qa_2" description = "Which three months have the highest median profit margins based on the boxplot analysis of release months?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "May, June, December" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0033_668_33668266_qa_3/task.toml b/tasks/0033_668_33668266_qa_3/task.toml index b45010396835caf970273bc756a0cda341e02269..c738fa7be0b4b70b81d4cd115a3f7659dd950d9a 100644 --- a/tasks/0033_668_33668266_qa_3/task.toml +++ b/tasks/0033_668_33668266_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0033_668_33668266_qa_3" +name = "smoldataenvs-train/0033_668_33668266_qa_3" description = "Is there a statistically detectable positive correlation between movie runtime and average rating (vote_average)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0033_684_33684962_qa_2/task.toml b/tasks/0033_684_33684962_qa_2/task.toml index 9f3033a8d5e94272c8dc73c671a8a225ec875e1d..07cc4a7484fa25347323daee4f2c1c081ce940f2 100644 --- a/tasks/0033_684_33684962_qa_2/task.toml +++ b/tasks/0033_684_33684962_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0033_684_33684962_qa_2" +name = "smoldataenvs-train/0033_684_33684962_qa_2" description = "What is the standard deviation of the cross-validation accuracy scores for the Random Forest model when using 100 estimators?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.0939" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0033_684_33684962_qa_3/task.toml b/tasks/0033_684_33684962_qa_3/task.toml index 13f3bfa1d811e4660980c57a9b50dd32bce303a1..eb7889949ddce9504e6c89271fe3658875eb9e71 100644 --- a/tasks/0033_684_33684962_qa_3/task.toml +++ b/tasks/0033_684_33684962_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0033_684_33684962_qa_3" +name = "smoldataenvs-train/0033_684_33684962_qa_3" description = "Are the test accuracies of the Decision Tree and Random Forest models identical based on the analysis of the normalized dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0033_684_33684962_qa_5/task.toml b/tasks/0033_684_33684962_qa_5/task.toml index 987387606e21d467ef1eab45fff75b93d197a37b..11685232c902657a7db51c9e3db5a60ad87c033c 100644 --- a/tasks/0033_684_33684962_qa_5/task.toml +++ b/tasks/0033_684_33684962_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0033_684_33684962_qa_5" +name = "smoldataenvs-train/0033_684_33684962_qa_5" description = "Which classification algorithm demonstrated the lowest test accuracy on the Glass dataset after all preprocessing steps?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Naive Bayes" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0033_697_33697266_qa_2/task.toml b/tasks/0033_697_33697266_qa_2/task.toml index 8d23d7278a977d626196ed6edbb5d01da51232ba..a4ed8beaaa2b5369e25d3c680b00b8e58fc395cf 100644 --- a/tasks/0033_697_33697266_qa_2/task.toml +++ b/tasks/0033_697_33697266_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0033_697_33697266_qa_2" +name = "smoldataenvs-train/0033_697_33697266_qa_2" description = "Which gender has the highest absolute count of liver disease cases in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Male" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0033_700_33700089_qa_1/task.toml b/tasks/0033_700_33700089_qa_1/task.toml index 84b00a865b5a5f617eaafd43000cd3278922863b..bdd745930979d1d58037d82fda8d0b2144b93688 100644 --- a/tasks/0033_700_33700089_qa_1/task.toml +++ b/tasks/0033_700_33700089_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0033_700_33700089_qa_1" +name = "smoldataenvs-train/0033_700_33700089_qa_1" description = "How many samples are included in the training set after the train-test split with a test size of 20%?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "120" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0033_703_33703584_qa_3/task.toml b/tasks/0033_703_33703584_qa_3/task.toml index 925bc58c2203fa4ee855b57f652910491d468e97..db66093c269c48b423bcf7fb8e927878cc0d0d62 100644 --- a/tasks/0033_703_33703584_qa_3/task.toml +++ b/tasks/0033_703_33703584_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0033_703_33703584_qa_3" +name = "smoldataenvs-train/0033_703_33703584_qa_3" description = "How many training samples are present in the Sign Language MNIST dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "27455" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0033_703_33703584_qa_5/task.toml b/tasks/0033_703_33703584_qa_5/task.toml index 4e19d717834c9c2ded73713a0de19f2f548938b8..de366942316134e4b89904a96e172cd4fb21c411 100644 --- a/tasks/0033_703_33703584_qa_5/task.toml +++ b/tasks/0033_703_33703584_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0033_703_33703584_qa_5" +name = "smoldataenvs-train/0033_703_33703584_qa_5" description = "What are the dimensions of the images after preprocessing and data augmentation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "28x28x1" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0033_717_33717801_qa_3/task.toml b/tasks/0033_717_33717801_qa_3/task.toml index 1e039209b5f882bae15e8d0fd6964484169ca877..2484e91042917a8065d3e262de5e938dde9fd281 100644 --- a/tasks/0033_717_33717801_qa_3/task.toml +++ b/tasks/0033_717_33717801_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0033_717_33717801_qa_3" +name = "smoldataenvs-train/0033_717_33717801_qa_3" description = "What is the regression coefficient for years of experience, representing the change in predicted salary for each additional year of experience?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9423.815" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0033_717_33717801_qa_5/task.toml b/tasks/0033_717_33717801_qa_5/task.toml index a50492f83637373e4743a1c6a91be337f28c50ea..be8b3dec7eaff8a5988b1a90ca35d8fdc935c94a 100644 --- a/tasks/0033_717_33717801_qa_5/task.toml +++ b/tasks/0033_717_33717801_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0033_717_33717801_qa_5" +name = "smoldataenvs-train/0033_717_33717801_qa_5" description = "What is the correlation coefficient between years of experience and salary in the dataset, measuring the strength and direction of their linear relationship?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.978" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0033_717_33717850_qa_2/task.toml b/tasks/0033_717_33717850_qa_2/task.toml index 98ccd479a1233e0fa9208a1c50a2fe6db2a5a27a..e9b46582962294bafe067f02ba2acbb39d410209 100644 --- a/tasks/0033_717_33717850_qa_2/task.toml +++ b/tasks/0033_717_33717850_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0033_717_33717850_qa_2" +name = "smoldataenvs-train/0033_717_33717850_qa_2" description = "How many rows were removed from the dataset during data cleaning to eliminate entries with zero values in the x, y, or z dimensions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0033_726_33726120_qa_2/task.toml b/tasks/0033_726_33726120_qa_2/task.toml index b3157df34e3d9d1af86eb78f19c7823e0fd4d924..1566371a9f7bbb295ecd76feef198d0ff9d1a471 100644 --- a/tasks/0033_726_33726120_qa_2/task.toml +++ b/tasks/0033_726_33726120_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0033_726_33726120_qa_2" +name = "smoldataenvs-train/0033_726_33726120_qa_2" description = "What is the percentage of diamond instances in the dataset that are considered outliers based on the price distribution (values exceeding the calculated maximum threshold)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.55" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0033_726_33726120_qa_4/task.toml b/tasks/0033_726_33726120_qa_4/task.toml index ff62cb89c2f6a8be598f6f8fe70f3e0c65d109ff..4485bd013f6e4a792b4c44403042925bb58a1499 100644 --- a/tasks/0033_726_33726120_qa_4/task.toml +++ b/tasks/0033_726_33726120_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0033_726_33726120_qa_4" +name = "smoldataenvs-train/0033_726_33726120_qa_4" description = "What is the absolute difference between the maximum carat value and the mean carat value in the cleaned diamonds dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.21" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0033_726_33726120_qa_5/task.toml b/tasks/0033_726_33726120_qa_5/task.toml index 479972738e206668ec528c91eed2881348f650a4..8a5bfcbdd04aa2160cf2c2eabe1846a5e33f70a6 100644 --- a/tasks/0033_726_33726120_qa_5/task.toml +++ b/tasks/0033_726_33726120_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0033_726_33726120_qa_5" +name = "smoldataenvs-train/0033_726_33726120_qa_5" description = "How many rows were removed from the original dataset during the data cleaning process to eliminate instances with zero values in the x, y, or z dimensions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0033_745_33745374_qa_2/task.toml b/tasks/0033_745_33745374_qa_2/task.toml index 741a5784f3eb95d346dee509431805eda9a40fae..da0b7b38ca1e27294349a7778d781e46feffddbd 100644 --- a/tasks/0033_745_33745374_qa_2/task.toml +++ b/tasks/0033_745_33745374_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0033_745_33745374_qa_2" +name = "smoldataenvs-train/0033_745_33745374_qa_2" description = "What is the most significant positive contributor to Life Expectancy according to the LassoCV model coefficients?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "infant deaths" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0033_752_33752341_qa_4/task.toml b/tasks/0033_752_33752341_qa_4/task.toml index 3275a807ce7c610b506eccf1be927fc7f581d846..600fd8698e42ad3955f16c4c54a9e0a29a4e5918 100644 --- a/tasks/0033_752_33752341_qa_4/task.toml +++ b/tasks/0033_752_33752341_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0033_752_33752341_qa_4" +name = "smoldataenvs-train/0033_752_33752341_qa_4" description = "Which feature had the highest score in the Mutual Information feature selection method?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "odor_n" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0033_755_33755094_qa_3/task.toml b/tasks/0033_755_33755094_qa_3/task.toml index c411652dbc2cc110d5c9cca5f8da3680999066e8..7107b138c946660949b87e76ae4b7be782a1e510 100644 --- a/tasks/0033_755_33755094_qa_3/task.toml +++ b/tasks/0033_755_33755094_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0033_755_33755094_qa_3" +name = "smoldataenvs-train/0033_755_33755094_qa_3" description = "Which feature among 'price' and 'bedrooms' has the stronger linear relationship with 'sqft_living' based on the correlation matrix?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "price" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0033_760_33760253_qa_4/task.toml b/tasks/0033_760_33760253_qa_4/task.toml index 49809f89dba9e272e2a82b3bf332bd5d917ba882..5cef81f2576eaac91687039f121c311c89c58c2c 100644 --- a/tasks/0033_760_33760253_qa_4/task.toml +++ b/tasks/0033_760_33760253_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0033_760_33760253_qa_4" +name = "smoldataenvs-train/0033_760_33760253_qa_4" description = "What is the mean death year in the balanced dataset after stratified sampling by age quartiles?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2012.35" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0033_787_33787812_qa_1/task.toml b/tasks/0033_787_33787812_qa_1/task.toml index 5a89309673e34af637ea89894d4fbbc8b197d13b..046cc84db8500bbf86a76f3f34509339f6fa8707 100644 --- a/tasks/0033_787_33787812_qa_1/task.toml +++ b/tasks/0033_787_33787812_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0033_787_33787812_qa_1" +name = "smoldataenvs-train/0033_787_33787812_qa_1" description = "How many data points are included in the height and weight dataset analyzed in the notebook?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "15" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0033_963_33963401_qa_1/task.toml b/tasks/0033_963_33963401_qa_1/task.toml index bff36b46e8297cc6c351853504eca109786146de..cc825729fe7aa4cbaebb5f32947e5cb088570e32 100644 --- a/tasks/0033_963_33963401_qa_1/task.toml +++ b/tasks/0033_963_33963401_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0033_963_33963401_qa_1" +name = "smoldataenvs-train/0033_963_33963401_qa_1" description = "How many unique numerical values were assigned to the \"Geography\" column after label encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0033_963_33963401_qa_3/task.toml b/tasks/0033_963_33963401_qa_3/task.toml index 4f56d7efcafd50e2a21daaabf71bd6096b699a4a..e4879b0a8cfe45b2f031ba1bf03c0e75ad1466a0 100644 --- a/tasks/0033_963_33963401_qa_3/task.toml +++ b/tasks/0033_963_33963401_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0033_963_33963401_qa_3" +name = "smoldataenvs-train/0033_963_33963401_qa_3" description = "How many customers in the first 10 rows of the dataset exited?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0033_988_33988731_qa_4/task.toml b/tasks/0033_988_33988731_qa_4/task.toml index a339e9f9c3eca074c70026da7404717c69879e54..52d13c9a60220f58424974b4b46eac918b3b40c9 100644 --- a/tasks/0033_988_33988731_qa_4/task.toml +++ b/tasks/0033_988_33988731_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0033_988_33988731_qa_4" +name = "smoldataenvs-train/0033_988_33988731_qa_4" description = "What percentage of individuals in the original dataset are diabetic (Outcome=1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0034_023_34023168_qa_1/task.toml b/tasks/0034_023_34023168_qa_1/task.toml index 12d66341a5a84dcebc5d80b13fe6c519253ee168..6c42514093333936f5d31a7e440b336c2ba8ed1c 100644 --- a/tasks/0034_023_34023168_qa_1/task.toml +++ b/tasks/0034_023_34023168_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0034_023_34023168_qa_1" +name = "smoldataenvs-train/0034_023_34023168_qa_1" description = "Which country has the highest number of wines reviewed in the dataset, and how many reviews does it have?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "US, 54504" reward_mode_initial = "list" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0034_023_34023168_qa_2/task.toml b/tasks/0034_023_34023168_qa_2/task.toml index aed9f60687c15a9d0dd6119e49e03185de932e80..dad7f9f1cb4399acf9d76c171010783bada182f5 100644 --- a/tasks/0034_023_34023168_qa_2/task.toml +++ b/tasks/0034_023_34023168_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0034_023_34023168_qa_2" +name = "smoldataenvs-train/0034_023_34023168_qa_2" description = "What is the highest price of a wine in the dataset, and which specific wine has this price?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3300.0, Château les Ormes Sorbet 2013 Médoc" reward_mode_initial = "flexible" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0034_043_34043351_qa_3/task.toml b/tasks/0034_043_34043351_qa_3/task.toml index 565dbc63c70daf52a123c524ca900945c7356d8c..2a294464493800fc6ccf2c1615bdf6a64586f1d6 100644 --- a/tasks/0034_043_34043351_qa_3/task.toml +++ b/tasks/0034_043_34043351_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0034_043_34043351_qa_3" +name = "smoldataenvs-train/0034_043_34043351_qa_3" description = "What is the p-value for the 'LSTAT' variable in the F-regression feature selection analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.15640294e-28" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0034_108_34108193_qa_1/task.toml b/tasks/0034_108_34108193_qa_1/task.toml index 94bd46b57617de3bce5fa00d1a2233e14099f20d..c7a4bfcbe25f4e1d4e26bf4ac929a40ebf6aa9bb 100644 --- a/tasks/0034_108_34108193_qa_1/task.toml +++ b/tasks/0034_108_34108193_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0034_108_34108193_qa_1" +name = "smoldataenvs-train/0034_108_34108193_qa_1" description = "Which two features achieved the highest cross-validation accuracy when used alone in the model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalWidthCm, PetalLengthCm" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0034_170_34170800_qa_2/task.toml b/tasks/0034_170_34170800_qa_2/task.toml index e64d3505a874e53641f9863d3d4b685c6c28e756..74453d34b8ee31ac80aa7a1f4f5a8c90444ff37a 100644 --- a/tasks/0034_170_34170800_qa_2/task.toml +++ b/tasks/0034_170_34170800_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0034_170_34170800_qa_2" +name = "smoldataenvs-train/0034_170_34170800_qa_2" description = "What percentage of the dataset had missing values in the Outlet_Size column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "28.28" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0034_193_34193226_qa_2/task.toml b/tasks/0034_193_34193226_qa_2/task.toml index c757e0c2582e7beeb585924d5a166791fa341125..4b66724b86157d35198ef0a851e93453944611e1 100644 --- a/tasks/0034_193_34193226_qa_2/task.toml +++ b/tasks/0034_193_34193226_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0034_193_34193226_qa_2" +name = "smoldataenvs-train/0034_193_34193226_qa_2" description = "Which house grade has the highest median price based on the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0034_193_34193226_qa_3/task.toml b/tasks/0034_193_34193226_qa_3/task.toml index 783a960525dd059e6b8a18d77d8a8b1808df955b..07356f9326fe7b932b24293159f1b954a3deddde 100644 --- a/tasks/0034_193_34193226_qa_3/task.toml +++ b/tasks/0034_193_34193226_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0034_193_34193226_qa_3" +name = "smoldataenvs-train/0034_193_34193226_qa_3" description = "What is the most common number of bedrooms in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0034_200_34200101_qa_1/task.toml b/tasks/0034_200_34200101_qa_1/task.toml index d0e7d04919e4998efbf15848bae3fa37cff4f5fd..375fec4106537587bc070dbb06d56943aa182f10 100644 --- a/tasks/0034_200_34200101_qa_1/task.toml +++ b/tasks/0034_200_34200101_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0034_200_34200101_qa_1" +name = "smoldataenvs-train/0034_200_34200101_qa_1" description = "Which variable (age, bmi, or children) has the highest coefficient in the linear regression model for predicting medical charges?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "children" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0034_233_34233505_qa_1/task.toml b/tasks/0034_233_34233505_qa_1/task.toml index 3a0dd8e48ce1096764c3f6f5afa42ab2c714d1f6..7e5d155fb8630ad0ea98e0e25a4479b7cdc468ae 100644 --- a/tasks/0034_233_34233505_qa_1/task.toml +++ b/tasks/0034_233_34233505_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0034_233_34233505_qa_1" +name = "smoldataenvs-train/0034_233_34233505_qa_1" description = "What percentage of the original filtered dataset was retained after deduplication?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "69.25" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0034_233_34233505_qa_2/task.toml b/tasks/0034_233_34233505_qa_2/task.toml index 24a49453c24f9f55f99227a75dccf85be329734b..c20d605d7d7874335ff7e8bfdbbc88ac200b15e2 100644 --- a/tasks/0034_233_34233505_qa_2/task.toml +++ b/tasks/0034_233_34233505_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0034_233_34233505_qa_2" +name = "smoldataenvs-train/0034_233_34233505_qa_2" description = "What is the ratio of positive reviews to negative reviews in the final cleaned dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5.38" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0034_233_34233505_qa_4/task.toml b/tasks/0034_233_34233505_qa_4/task.toml index 10d475d18665bbaf73966465378c3e320c218804..7712d53626ede4da5a31c49c7cc8a8236748fef3 100644 --- a/tasks/0034_233_34233505_qa_4/task.toml +++ b/tasks/0034_233_34233505_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0034_233_34233505_qa_4" +name = "smoldataenvs-train/0034_233_34233505_qa_4" description = "What percentage of the dataset was removed due to invalid HelpfulnessNumerator/HelpfulnessDenominator values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.00055%" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0034_265_34265100_qa_1/task.toml b/tasks/0034_265_34265100_qa_1/task.toml index 810c8f752da570efea0aa9932d851b40bb35881d..61140630a7126960cf9e5c630f6953103bea340e 100644 --- a/tasks/0034_265_34265100_qa_1/task.toml +++ b/tasks/0034_265_34265100_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0034_265_34265100_qa_1" +name = "smoldataenvs-train/0034_265_34265100_qa_1" description = "What is the overall churn rate percentage in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26.6" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0034_265_34265100_qa_2/task.toml b/tasks/0034_265_34265100_qa_2/task.toml index 26378a5270afea6e340e8256d7127aa8a98a8812..2e41430a44a66005e8b3fb82b477889df9a080f4 100644 --- a/tasks/0034_265_34265100_qa_2/task.toml +++ b/tasks/0034_265_34265100_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0034_265_34265100_qa_2" +name = "smoldataenvs-train/0034_265_34265100_qa_2" description = "What is the ratio of non-churned to churned customers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.76" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0034_295_34295424_qa_1/task.toml b/tasks/0034_295_34295424_qa_1/task.toml index 6c59316f31304c7eb0e00fea09b70dea35cc0100..87726caf4e7d2c5446fcabb6dcb8594ee742bdd9 100644 --- a/tasks/0034_295_34295424_qa_1/task.toml +++ b/tasks/0034_295_34295424_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0034_295_34295424_qa_1" +name = "smoldataenvs-train/0034_295_34295424_qa_1" description = "Which game has the highest global sales according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii Sports" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0034_339_34339717_qa_1/task.toml b/tasks/0034_339_34339717_qa_1/task.toml index 1d6e01e6b90c7409f4c37ea2e859cd7763743152..0f7041034593fd369175aecc5d14a10fcd6509a0 100644 --- a/tasks/0034_339_34339717_qa_1/task.toml +++ b/tasks/0034_339_34339717_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0034_339_34339717_qa_1" +name = "smoldataenvs-train/0034_339_34339717_qa_1" description = "What percentage of total variance is explained by the first four principal components in the PCA model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "79.24" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0034_339_34339717_qa_3/task.toml b/tasks/0034_339_34339717_qa_3/task.toml index 8d4de3d27e2bbb5a3faed3d9a518bab4891b8a35..0e2c557217037a72bd6ccaa327c66bd165311828 100644 --- a/tasks/0034_339_34339717_qa_3/task.toml +++ b/tasks/0034_339_34339717_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0034_339_34339717_qa_3" +name = "smoldataenvs-train/0034_339_34339717_qa_3" description = "What is the F1-score for malignant (M) class predictions in the logistic regression model's validation set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.94" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0034_339_34339717_qa_5/task.toml b/tasks/0034_339_34339717_qa_5/task.toml index 94f5632a88d6bad70e2247849b9a3a46e36b336c..2a0aa3fae03a139f8a2cb682cde7b1784452b491 100644 --- a/tasks/0034_339_34339717_qa_5/task.toml +++ b/tasks/0034_339_34339717_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0034_339_34339717_qa_5" +name = "smoldataenvs-train/0034_339_34339717_qa_5" description = "Which mean feature in the dataset has the highest standard deviation, and what is its value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "area_mean, 351.914" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0034_376_34376500_qa_4/task.toml b/tasks/0034_376_34376500_qa_4/task.toml index 1555f039e34331df26954ad0dc81b926213935ea..08514dfc5b200fb455f6620bb2f6e9e824703ab3 100644 --- a/tasks/0034_376_34376500_qa_4/task.toml +++ b/tasks/0034_376_34376500_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0034_376_34376500_qa_4" +name = "smoldataenvs-train/0034_376_34376500_qa_4" description = "What is the best cross-validation score achieved by GridSearchCV during parameter tuning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.772184" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0034_380_34380054_qa_1/task.toml b/tasks/0034_380_34380054_qa_1/task.toml index 52f10e3f173362a9315cac668dde6a6a3adc0535..6123255d964dbd91d0987764a4b997c519892c42 100644 --- a/tasks/0034_380_34380054_qa_1/task.toml +++ b/tasks/0034_380_34380054_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0034_380_34380054_qa_1" +name = "smoldataenvs-train/0034_380_34380054_qa_1" description = "What is the predicted number of wins for a team with a run difference (RD) of 133 according to the linear regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "94.99" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0034_380_34380054_qa_2/task.toml b/tasks/0034_380_34380054_qa_2/task.toml index df3b56b56280c9a26923d4d33fc68d7ad23238e3..57125da295ec22844e823ef0b57c08f5196a6cf6 100644 --- a/tasks/0034_380_34380054_qa_2/task.toml +++ b/tasks/0034_380_34380054_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0034_380_34380054_qa_2" +name = "smoldataenvs-train/0034_380_34380054_qa_2" description = "What is the coefficient of the run difference (RD) in the linear regression model predicting wins from RD?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.1058" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0034_457_34457545_qa_2/task.toml b/tasks/0034_457_34457545_qa_2/task.toml index db03397f72ebfd94ad3fd57a01bc03edbc96efd9..d41ea576020f7be16ac9381010d8bea89b748eaa 100644 --- a/tasks/0034_457_34457545_qa_2/task.toml +++ b/tasks/0034_457_34457545_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0034_457_34457545_qa_2" +name = "smoldataenvs-train/0034_457_34457545_qa_2" description = "What is the median number of times a product (StockCode) was sold across the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "62" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0034_513_34513680_qa_5/task.toml b/tasks/0034_513_34513680_qa_5/task.toml index db58fd7dece51cb15de4e93a3ffcb9c24a8e377d..3c420f2a80208b012c4fe20aeda1ddf6588d35e2 100644 --- a/tasks/0034_513_34513680_qa_5/task.toml +++ b/tasks/0034_513_34513680_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0034_513_34513680_qa_5" +name = "smoldataenvs-train/0034_513_34513680_qa_5" description = "What percentage of the dataset's \"Price\" values fall below the 25th percentile threshold of $997,577.10?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "25" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0034_568_34568948_qa_3/task.toml b/tasks/0034_568_34568948_qa_3/task.toml index 7e40893b4e7a980d970c5428eab53c3c60abe172..8d864c7559a0a054d3ab19a9648c4ad99f58b380 100644 --- a/tasks/0034_568_34568948_qa_3/task.toml +++ b/tasks/0034_568_34568948_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0034_568_34568948_qa_3" +name = "smoldataenvs-train/0034_568_34568948_qa_3" description = "What is the median value of the 'acceleration' feature across all vehicles in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "15.5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0034_568_34568948_qa_5/task.toml b/tasks/0034_568_34568948_qa_5/task.toml index d15acccf3501e44a4c2d9631e5532c92cf272409..8e02f4cb6995723c3306e4a81ad53f6af605d10b 100644 --- a/tasks/0034_568_34568948_qa_5/task.toml +++ b/tasks/0034_568_34568948_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0034_568_34568948_qa_5" +name = "smoldataenvs-train/0034_568_34568948_qa_5" description = "What is the mean weight of vehicles in the dataset, rounded to two decimal places?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2970.42" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0034_588_34588161_qa_5/task.toml b/tasks/0034_588_34588161_qa_5/task.toml index 7e74bc0dce9388bce55ba5af7ec21e7064dd1178..66014a8dd4ce0c4ff3bfb287388d9afcbaa35428 100644 --- a/tasks/0034_588_34588161_qa_5/task.toml +++ b/tasks/0034_588_34588161_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0034_588_34588161_qa_5" +name = "smoldataenvs-train/0034_588_34588161_qa_5" description = "What is the highest predicted closing price for Apple stock in the test set using the linear regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "177.84" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0034_651_34651965_qa_2/task.toml b/tasks/0034_651_34651965_qa_2/task.toml index f42b797e74c2996a00020ad564f02af811cb2f1b..963ece13633b70868b923f6322534d794ba3810f 100644 --- a/tasks/0034_651_34651965_qa_2/task.toml +++ b/tasks/0034_651_34651965_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0034_651_34651965_qa_2" +name = "smoldataenvs-train/0034_651_34651965_qa_2" description = "What is the R-squared score of the model's performance on the test dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0034_705_34705944_qa_4/task.toml b/tasks/0034_705_34705944_qa_4/task.toml index 382957231f8c1ddaa486cb88090fa1c527c11c58..2dfd4305f0c4cde272b8d9e2822f9f2e29a47d8d 100644 --- a/tasks/0034_705_34705944_qa_4/task.toml +++ b/tasks/0034_705_34705944_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0034_705_34705944_qa_4" +name = "smoldataenvs-train/0034_705_34705944_qa_4" description = "What is the coefficient value for the Global_intensity variable in the linear regression model predicting Global_active_power?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.23668058" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0034_726_34726167_qa_2/task.toml b/tasks/0034_726_34726167_qa_2/task.toml index bb1ac19bc2ff29f5ce8f99bdca45f8aca7cd2842..3ff36f89e5d1bc9ebf6f96aaf65049c2fd58e295 100644 --- a/tasks/0034_726_34726167_qa_2/task.toml +++ b/tasks/0034_726_34726167_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0034_726_34726167_qa_2" +name = "smoldataenvs-train/0034_726_34726167_qa_2" description = "How many features in the dataset have a minimum value of exactly 0.0000?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0034_748_34748003_qa_1/task.toml b/tasks/0034_748_34748003_qa_1/task.toml index 969d772e55fa0a9fde764402693474796c9c0ab6..7c2518e7bbfd089c4eb829ec059bf4e00caed8f5 100644 --- a/tasks/0034_748_34748003_qa_1/task.toml +++ b/tasks/0034_748_34748003_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0034_748_34748003_qa_1" +name = "smoldataenvs-train/0034_748_34748003_qa_1" description = "Which primary Pokémon type has the highest average Attack value according to the box plot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Dragon" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0034_748_34748003_qa_3/task.toml b/tasks/0034_748_34748003_qa_3/task.toml index cae7cd2118c0c8b5efaf7bb7460ba47d1c3c481c..d482c5f2258718f1c5018aad6243b79bc20f4f18 100644 --- a/tasks/0034_748_34748003_qa_3/task.toml +++ b/tasks/0034_748_34748003_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0034_748_34748003_qa_3" +name = "smoldataenvs-train/0034_748_34748003_qa_3" description = "Which primary Pokémon types have no Legendary Pokémon as shown in the stacked bar plot and count plots?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Bug, Fighting, Poison" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0034_768_34768850_qa_2/task.toml b/tasks/0034_768_34768850_qa_2/task.toml index 6be30db71cf8fe29ede05d53ebebb5ced78d6b17..8e5f45bd25b43059359a5d9d816465d86b0029e2 100644 --- a/tasks/0034_768_34768850_qa_2/task.toml +++ b/tasks/0034_768_34768850_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0034_768_34768850_qa_2" +name = "smoldataenvs-train/0034_768_34768850_qa_2" description = "Which clothing class has the highest number of customer reviews in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Dresses" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0034_768_34768850_qa_3/task.toml b/tasks/0034_768_34768850_qa_3/task.toml index f8e11361d8f32509048dbdf1f8d32039839e75cc..4172022b022d7548596af1120ca894eae5706818 100644 --- a/tasks/0034_768_34768850_qa_3/task.toml +++ b/tasks/0034_768_34768850_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0034_768_34768850_qa_3" +name = "smoldataenvs-train/0034_768_34768850_qa_3" description = "What is the total count of the word \"love\" across all customer reviews in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8951" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0034_773_34773515_qa_1/task.toml b/tasks/0034_773_34773515_qa_1/task.toml index 8bda434fe12000964b0e982ad84c7ffe8fccd3b0..cd9f14d64b1f43b7b23a787b39a4e2ed5dbacdcf 100644 --- a/tasks/0034_773_34773515_qa_1/task.toml +++ b/tasks/0034_773_34773515_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0034_773_34773515_qa_1" +name = "smoldataenvs-train/0034_773_34773515_qa_1" description = "Which country has the highest mean wine review score (points) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "England" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0034_773_34773515_qa_2/task.toml b/tasks/0034_773_34773515_qa_2/task.toml index 6914b7e602c608e6a24d0175d5f71e68b8e9fc4e..20bfb09df631186263ab226306f3f388413e0190 100644 --- a/tasks/0034_773_34773515_qa_2/task.toml +++ b/tasks/0034_773_34773515_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0034_773_34773515_qa_2" +name = "smoldataenvs-train/0034_773_34773515_qa_2" description = "What percentage of all wine reviews in the dataset are for wines from the United States?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "41.96" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0034_773_34773515_qa_3/task.toml b/tasks/0034_773_34773515_qa_3/task.toml index 0ee22436b1d187fd43b89cd38b0d55feaad8242e..5465905c95af88ab6fb72e69c398173d7dd76dac 100644 --- a/tasks/0034_773_34773515_qa_3/task.toml +++ b/tasks/0034_773_34773515_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0034_773_34773515_qa_3" +name = "smoldataenvs-train/0034_773_34773515_qa_3" description = "How many wines in the dataset are priced above $1000?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "14" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0034_773_34773515_qa_5/task.toml b/tasks/0034_773_34773515_qa_5/task.toml index b6783044e00637d949c159a9c2c7c35de8d4dcf7..a687f766ff34630cdad459b9a31602760769628f 100644 --- a/tasks/0034_773_34773515_qa_5/task.toml +++ b/tasks/0034_773_34773515_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0034_773_34773515_qa_5" +name = "smoldataenvs-train/0034_773_34773515_qa_5" description = "What percentage of wines in the dataset are priced above the 75th percentile price ($42)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "22" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0034_777_34777395_qa_3/task.toml b/tasks/0034_777_34777395_qa_3/task.toml index 2f3097f6583d7fa0d3bcb21f048aedd33a2e3fcf..615096dae113d6c330d21cbd4630f42d05ef1ef9 100644 --- a/tasks/0034_777_34777395_qa_3/task.toml +++ b/tasks/0034_777_34777395_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0034_777_34777395_qa_3" +name = "smoldataenvs-train/0034_777_34777395_qa_3" description = "Do the predictions generated by the second linear regression model include negative values for diamond price?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0034_793_34793224_qa_1/task.toml b/tasks/0034_793_34793224_qa_1/task.toml index 05ae491f4ba6b51e74aaaef7c38ee722d7511471..feeb09201d34c4cea0c207e4fb707660ebd2cdd1 100644 --- a/tasks/0034_793_34793224_qa_1/task.toml +++ b/tasks/0034_793_34793224_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0034_793_34793224_qa_1" +name = "smoldataenvs-train/0034_793_34793224_qa_1" description = "What percentage of customers in the dataset have churned after data preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26.6" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0034_793_34793224_qa_3/task.toml b/tasks/0034_793_34793224_qa_3/task.toml index a76e73951d3f8a95acf9f7e31967e7c730953765..7596f3f79e793dd7814587b6bae64e174863ee94 100644 --- a/tasks/0034_793_34793224_qa_3/task.toml +++ b/tasks/0034_793_34793224_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0034_793_34793224_qa_3" +name = "smoldataenvs-train/0034_793_34793224_qa_3" description = "After data preprocessing, how many distinct payment methods are present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0034_886_34886359_qa_3/task.toml b/tasks/0034_886_34886359_qa_3/task.toml index 63aadd30e82540dc1d64e1348c371ccafcb93cdb..1c852658a67b11d392ca9160b31cd8d2fa877344 100644 --- a/tasks/0034_886_34886359_qa_3/task.toml +++ b/tasks/0034_886_34886359_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0034_886_34886359_qa_3" +name = "smoldataenvs-train/0034_886_34886359_qa_3" description = "What is the R-squared value of the linear regression model when evaluated on the test dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9888014444327563" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0034_886_34886359_qa_5/task.toml b/tasks/0034_886_34886359_qa_5/task.toml index 3c35dbfb552d11b8d81825c98e2cbe91e4f40ef6..25d7aae112cfea6364608ee899d1c03364448394 100644 --- a/tasks/0034_886_34886359_qa_5/task.toml +++ b/tasks/0034_886_34886359_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0034_886_34886359_qa_5" +name = "smoldataenvs-train/0034_886_34886359_qa_5" description = "What is the mean squared error (MSE) of the linear regression model on the training data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7.867752733487686" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0034_890_34890721_qa_3/task.toml b/tasks/0034_890_34890721_qa_3/task.toml index a1bf657d1e61b2661ae61d48fd1cb91d0cdcee0d..56e7d4fcf5d426e6d2303040fb20fdfd5a7c41b7 100644 --- a/tasks/0034_890_34890721_qa_3/task.toml +++ b/tasks/0034_890_34890721_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0034_890_34890721_qa_3" +name = "smoldataenvs-train/0034_890_34890721_qa_3" description = "What is the best max_depth value for the Random Forest classifier that maximizes accuracy according to the hyperparameter tuning results?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0034_890_34890721_qa_4/task.toml b/tasks/0034_890_34890721_qa_4/task.toml index a96feb87180745132bf39037a4f54d3f8d8aba39..9eab1374f386b18e86e70f9fb12b710021a55e8f 100644 --- a/tasks/0034_890_34890721_qa_4/task.toml +++ b/tasks/0034_890_34890721_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0034_890_34890721_qa_4" +name = "smoldataenvs-train/0034_890_34890721_qa_4" description = "Which model (KNN or Random Forest) achieved the higher ROC AUC score on the test set after hyperparameter optimization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "KNN" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0034_912_34912092_qa_3/task.toml b/tasks/0034_912_34912092_qa_3/task.toml index c53d1125d43685642123cb142b817b8a067e51b8..ca0f58604ed66c29bfa4e355686e338942b624cc 100644 --- a/tasks/0034_912_34912092_qa_3/task.toml +++ b/tasks/0034_912_34912092_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0034_912_34912092_qa_3" +name = "smoldataenvs-train/0034_912_34912092_qa_3" description = "What percentage of the dataset has missing values in the \"Checking account\" column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "39.4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0034_912_34912092_qa_5/task.toml b/tasks/0034_912_34912092_qa_5/task.toml index 98868dcf4259849dc29b164dc0780a7104c46c43..231a16dc8a4357b51943fe86f87857883956431e 100644 --- a/tasks/0034_912_34912092_qa_5/task.toml +++ b/tasks/0034_912_34912092_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0034_912_34912092_qa_5" +name = "smoldataenvs-train/0034_912_34912092_qa_5" description = "What is the most common credit purpose among applicants with \"free\" housing status?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "car" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0034_916_34916115_qa_2/task.toml b/tasks/0034_916_34916115_qa_2/task.toml index ba23a6d8f61b65e3cfc974c26998bd19f161b534..1b6bc36361f5d5548e810c38f59feb1744126561 100644 --- a/tasks/0034_916_34916115_qa_2/task.toml +++ b/tasks/0034_916_34916115_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0034_916_34916115_qa_2" +name = "smoldataenvs-train/0034_916_34916115_qa_2" description = "Which country produces wines with the highest average price according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Switzerland" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0034_945_34945606_qa_2/task.toml b/tasks/0034_945_34945606_qa_2/task.toml index 5d3c145682f414d6f859ba5308f0cfa4802d3457..d174c85f477522d93e1367c71c21e7b45d0329c3 100644 --- a/tasks/0034_945_34945606_qa_2/task.toml +++ b/tasks/0034_945_34945606_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0034_945_34945606_qa_2" +name = "smoldataenvs-train/0034_945_34945606_qa_2" description = "Which education field has the highest attrition percentage among employees?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Human Resources" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0034_945_34945606_qa_5/task.toml b/tasks/0034_945_34945606_qa_5/task.toml index cc67fffbd9b474c7aa08d10d85034073906abdd3..ee81d96c44ab3162404ce93f83e219f833913b0b 100644 --- a/tasks/0034_945_34945606_qa_5/task.toml +++ b/tasks/0034_945_34945606_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0034_945_34945606_qa_5" +name = "smoldataenvs-train/0034_945_34945606_qa_5" description = "What job satisfaction level is most strongly correlated with employee attrition?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_022_35022939_qa_2/task.toml b/tasks/0035_022_35022939_qa_2/task.toml index b84d3ca4e4d2fc854f087dfa1a38538e38b120aa..f4973bf85b013d5847a0bd13414bc6e9cd947048 100644 --- a/tasks/0035_022_35022939_qa_2/task.toml +++ b/tasks/0035_022_35022939_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0035_022_35022939_qa_2" +name = "smoldataenvs-train/0035_022_35022939_qa_2" description = "Which dog breed appears most frequently in the Austin Animal Shelter dataset after filtering for dogs only?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Pit Bull Mix" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_025_35025726_qa_2/task.toml b/tasks/0035_025_35025726_qa_2/task.toml index f8c5cda591515085351d3329d8b48eaf2509644d..d8beb857f83c2fd5eb3f964b090506a415129791 100644 --- a/tasks/0035_025_35025726_qa_2/task.toml +++ b/tasks/0035_025_35025726_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0035_025_35025726_qa_2" +name = "smoldataenvs-train/0035_025_35025726_qa_2" description = "Which species of Iris has the highest median petal length according to the boxplot visualization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-virginica" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_070_35070602_qa_2/task.toml b/tasks/0035_070_35070602_qa_2/task.toml index fd60c6b384c53d9301436ebef4ec2c30d9b2e2ec..9346aff27691a3cce927ae3d25be6e7b636768a5 100644 --- a/tasks/0035_070_35070602_qa_2/task.toml +++ b/tasks/0035_070_35070602_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0035_070_35070602_qa_2" +name = "smoldataenvs-train/0035_070_35070602_qa_2" description = "After preprocessing, how many missing values remain in both 'Item_Weight' and 'Outlet_Size'?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_074_35074138_qa_2/task.toml b/tasks/0035_074_35074138_qa_2/task.toml index a58b9b8d9f6e72587cb2134860d105887fc101f4..b46567a3a63107d55a0ffc6cb8d8bcaf9047f107 100644 --- a/tasks/0035_074_35074138_qa_2/task.toml +++ b/tasks/0035_074_35074138_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0035_074_35074138_qa_2" +name = "smoldataenvs-train/0035_074_35074138_qa_2" description = "Which department had the highest number of employees who left the company?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Sales" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_121_35121535_qa_1/task.toml b/tasks/0035_121_35121535_qa_1/task.toml index 29711413561a427253fdcd9beae1bd2c37cd79c6..248da0bdd5141c200e635843fe637cc413b92df8 100644 --- a/tasks/0035_121_35121535_qa_1/task.toml +++ b/tasks/0035_121_35121535_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0035_121_35121535_qa_1" +name = "smoldataenvs-train/0035_121_35121535_qa_1" description = "Which outlet has the highest average Item_Outlet_Sales according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "OUT027" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_122_35122628_qa_1/task.toml b/tasks/0035_122_35122628_qa_1/task.toml index bd3b04f615b6dc4e3028e1ea6e8fc663b68f4da5..4551d6ad1f62f934e75d821596498a298156ff15 100644 --- a/tasks/0035_122_35122628_qa_1/task.toml +++ b/tasks/0035_122_35122628_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0035_122_35122628_qa_1" +name = "smoldataenvs-train/0035_122_35122628_qa_1" description = "How many unique words (features) were identified in the spam dataset after preprocessing text data for the bag-of-words model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8615" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_152_35152468_qa_2/task.toml b/tasks/0035_152_35152468_qa_2/task.toml index 3715d9fdc2bda233ac6602e72cf3716969d6c942..397804ffaa0e1b180cd6a7a29e283db617ec03ec 100644 --- a/tasks/0035_152_35152468_qa_2/task.toml +++ b/tasks/0035_152_35152468_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0035_152_35152468_qa_2" +name = "smoldataenvs-train/0035_152_35152468_qa_2" description = "How many unique countries are represented in the customer data after cleaning missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "37" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_171_35171975_qa_3/task.toml b/tasks/0035_171_35171975_qa_3/task.toml index 0c2b3859b9fcf79389298f4b4ebfe8ffc2566624..b37a47722fe1a4f39ccaa435480c028570517b09 100644 --- a/tasks/0035_171_35171975_qa_3/task.toml +++ b/tasks/0035_171_35171975_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0035_171_35171975_qa_3" +name = "smoldataenvs-train/0035_171_35171975_qa_3" description = "What percentage of individuals in the dataset are diagnosed with diabetes based on the Outcome variable?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.8958" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0035_171_35171975_qa_4/task.toml b/tasks/0035_171_35171975_qa_4/task.toml index 0e603986f5ba120942ff45f52bcc4f2a1b7f9735..b5e3222be5f07069dc0f9f2f764d7b45787220ad 100644 --- a/tasks/0035_171_35171975_qa_4/task.toml +++ b/tasks/0035_171_35171975_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0035_171_35171975_qa_4" +name = "smoldataenvs-train/0035_171_35171975_qa_4" description = "Which variable shows the most extreme leptokurtic distribution, and what is its kurtosis value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Insulin, 7.214260" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_188_35188217_qa_4/task.toml b/tasks/0035_188_35188217_qa_4/task.toml index ecea9bec6cf7bb721e56a920b214057f29d52d28..3806f0a78e27614418a2669b247e1810bf4f7310 100644 --- a/tasks/0035_188_35188217_qa_4/task.toml +++ b/tasks/0035_188_35188217_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0035_188_35188217_qa_4" +name = "smoldataenvs-train/0035_188_35188217_qa_4" description = "How many hourly data points were included in the analysis after aggregating the dataset by month, day, and hour?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2,777" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_225_35225660_qa_3/task.toml b/tasks/0035_225_35225660_qa_3/task.toml index 7073eb67b22edb78db33825aa0c5c6a0aff5869b..b120736ae8d8a0a114069bad6441b66aec937b20 100644 --- a/tasks/0035_225_35225660_qa_3/task.toml +++ b/tasks/0035_225_35225660_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0035_225_35225660_qa_3" +name = "smoldataenvs-train/0035_225_35225660_qa_3" description = "Which social status category had the highest total number of suicide cases across all genders in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Married" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_228_35228737_qa_5/task.toml b/tasks/0035_228_35228737_qa_5/task.toml index 0ffb632660ad53057389f50180fe10e436791d18..cb4d1b246697ffefe9dbf53306af1cab832002e0 100644 --- a/tasks/0035_228_35228737_qa_5/task.toml +++ b/tasks/0035_228_35228737_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0035_228_35228737_qa_5" +name = "smoldataenvs-train/0035_228_35228737_qa_5" description = "Which feature selection method (chi-squared test or Random Forest feature importance) yields higher mean cross-validation accuracy when using the top 7 features?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "chi-squared test" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0035_236_35236430_qa_3/task.toml b/tasks/0035_236_35236430_qa_3/task.toml index 012c8d8abbd8a793c1a1b08b19727905bb82d47a..af64210d83dd861589f0c8b81fc155ad873fe26e 100644 --- a/tasks/0035_236_35236430_qa_3/task.toml +++ b/tasks/0035_236_35236430_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0035_236_35236430_qa_3" +name = "smoldataenvs-train/0035_236_35236430_qa_3" description = "What is the root mean squared error (RMSE) of the Linear Regression model built using sklearn on the test data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5786.98" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0035_236_35236553_qa_1/task.toml b/tasks/0035_236_35236553_qa_1/task.toml index ecdcb7eb0251328e2d88c7e16a685b0aba549773..40df7ec6cefb6344e99739507aaf956dc356f515 100644 --- a/tasks/0035_236_35236553_qa_1/task.toml +++ b/tasks/0035_236_35236553_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0035_236_35236553_qa_1" +name = "smoldataenvs-train/0035_236_35236553_qa_1" description = "What is the average game length in the 2015 LCK season according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "40.41" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_236_35236553_qa_4/task.toml b/tasks/0035_236_35236553_qa_4/task.toml index 5b6e5ac71aef68ef9b0df5e573392683f65c0368..750a7d473b2c4d7b91dfbca2cf769bb2785cb0ab 100644 --- a/tasks/0035_236_35236553_qa_4/task.toml +++ b/tasks/0035_236_35236553_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0035_236_35236553_qa_4" +name = "smoldataenvs-train/0035_236_35236553_qa_4" description = "What is the average game length in the 2018 LCK season included in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "40.98" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_242_35242928_qa_2/task.toml b/tasks/0035_242_35242928_qa_2/task.toml index 16b4c4a861443a5981cbe2f72c9369c47f3ffc52..5b4f661ad44c7c47f3ee04440dc58507fc617fca 100644 --- a/tasks/0035_242_35242928_qa_2/task.toml +++ b/tasks/0035_242_35242928_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0035_242_35242928_qa_2" +name = "smoldataenvs-train/0035_242_35242928_qa_2" description = "Which player has won the most \"Player of the Match\" awards in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "CH Gayle" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0035_247_35247886_qa_1/task.toml b/tasks/0035_247_35247886_qa_1/task.toml index 37a75da012d307a7bffb874422f04ff0f67ea286..16922534423220417fff355d6ba8707bc371e07f 100644 --- a/tasks/0035_247_35247886_qa_1/task.toml +++ b/tasks/0035_247_35247886_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0035_247_35247886_qa_1" +name = "smoldataenvs-train/0035_247_35247886_qa_1" description = "What is the prior probability of each species (Iris-setosa, Iris-versicolor, Iris-virginica) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.3333" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0035_269_35269567_qa_1/task.toml b/tasks/0035_269_35269567_qa_1/task.toml index 4acc97496609ac1d8d1b2a3d96013ed4b47772b7..c35b71ed6829d0a1da43963919f353cc9e68cdf7 100644 --- a/tasks/0035_269_35269567_qa_1/task.toml +++ b/tasks/0035_269_35269567_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0035_269_35269567_qa_1" +name = "smoldataenvs-train/0035_269_35269567_qa_1" description = "What is the highest Pearson correlation coefficient between any selected variable and SalePrice in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.790982" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_269_35269567_qa_2/task.toml b/tasks/0035_269_35269567_qa_2/task.toml index b1728446dc6bbf1da968a340a582989c99f87cf7..018394cee473d18ba4609ad373bdd2ca98bda8cc 100644 --- a/tasks/0035_269_35269567_qa_2/task.toml +++ b/tasks/0035_269_35269567_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0035_269_35269567_qa_2" +name = "smoldataenvs-train/0035_269_35269567_qa_2" description = "Which variable among OverallQual, GrLivArea, and GarageCars has the strongest statistically significant correlation with SalePrice?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "OverallQual" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_273_35273491_qa_3/task.toml b/tasks/0035_273_35273491_qa_3/task.toml index c06a11689335ed52c6baa0343ce4f713a6214942..1ef8565831e2ead429b4e4e7f353f05984596df8 100644 --- a/tasks/0035_273_35273491_qa_3/task.toml +++ b/tasks/0035_273_35273491_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0035_273_35273491_qa_3" +name = "smoldataenvs-train/0035_273_35273491_qa_3" description = "What is the average insurance charge for the entire dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13270.422265" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0035_303_35303628_qa_1/task.toml b/tasks/0035_303_35303628_qa_1/task.toml index 697c7431f563737e9e72f148692320052a30d70a..d97bd72d2c640f51b4329f91aac0216cba7be210 100644 --- a/tasks/0035_303_35303628_qa_1/task.toml +++ b/tasks/0035_303_35303628_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0035_303_35303628_qa_1" +name = "smoldataenvs-train/0035_303_35303628_qa_1" description = "After normalization, what is the number of patients in each survival status group?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "81, 81" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_303_35303628_qa_2/task.toml b/tasks/0035_303_35303628_qa_2/task.toml index 474951c404e4ff3cfd87197bac3a05c20b63af40..b5f81359962d11814d9a1be461552c4d66f00df8 100644 --- a/tasks/0035_303_35303628_qa_2/task.toml +++ b/tasks/0035_303_35303628_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0035_303_35303628_qa_2" +name = "smoldataenvs-train/0035_303_35303628_qa_2" description = "What is the median number of Axil nodes for patients who survived?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_332_35332466_qa_2/task.toml b/tasks/0035_332_35332466_qa_2/task.toml index b8fe587268ea7f9902162ae533dbd99c2a2e442b..85a56bf9e416c07f81c4ce95df72022476ab3387 100644 --- a/tasks/0035_332_35332466_qa_2/task.toml +++ b/tasks/0035_332_35332466_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0035_332_35332466_qa_2" +name = "smoldataenvs-train/0035_332_35332466_qa_2" description = "What is the number of samples per class in the test dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1000" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_332_35332810_qa_3/task.toml b/tasks/0035_332_35332810_qa_3/task.toml index 3134067e20d93ab6c4cdc308f5fa12a210e7eb52..91b125dcc8e5b78d0b805773042925e4b5ea59f3 100644 --- a/tasks/0035_332_35332810_qa_3/task.toml +++ b/tasks/0035_332_35332810_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0035_332_35332810_qa_3" +name = "smoldataenvs-train/0035_332_35332810_qa_3" description = "What is the shape of the training data (X_train) after applying the train-test split with a test size of 0.2?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "(455, 30)" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_336_35336797_qa_5/task.toml b/tasks/0035_336_35336797_qa_5/task.toml index 240a8a419ed90e605f1f79c6a7515dc3e11da9eb..089ff731991ad596c6b2ef01e40e7345e2165db8 100644 --- a/tasks/0035_336_35336797_qa_5/task.toml +++ b/tasks/0035_336_35336797_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0035_336_35336797_qa_5" +name = "smoldataenvs-train/0035_336_35336797_qa_5" description = "What is the coefficient value for OverallQual in the multiple linear regression model using OverallQual, GrLivArea, GarageCars, GarageArea, TotalBsmtSF, and 1stFlrSF as predictors?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "23997.0394" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0035_337_35337905_qa_1/task.toml b/tasks/0035_337_35337905_qa_1/task.toml index 8532bf7429c4bfd242296f5e3572eb22398dafac..499849fad81f4c58fbc50d60ec6861c0ed9933d4 100644 --- a/tasks/0035_337_35337905_qa_1/task.toml +++ b/tasks/0035_337_35337905_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0035_337_35337905_qa_1" +name = "smoldataenvs-train/0035_337_35337905_qa_1" description = "What is the correlation coefficient between the poverty rate and the high school graduation rate across states in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.861672" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_350_35350224_qa_5/task.toml b/tasks/0035_350_35350224_qa_5/task.toml index 6a47237ad2d10903bfe8ce91fbb65f6f65d59883..e302ef4d9a520d26d430b92a6393b29a1fce3e6a 100644 --- a/tasks/0035_350_35350224_qa_5/task.toml +++ b/tasks/0035_350_35350224_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0035_350_35350224_qa_5" +name = "smoldataenvs-train/0035_350_35350224_qa_5" description = "What is the maximum number of items in a frequent itemset identified by the Apriori algorithm in this dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0035_362_35362667_qa_4/task.toml b/tasks/0035_362_35362667_qa_4/task.toml index 4c66f645d2e7e7c4d59791cfdbd89ff0b0d160a6..799dda5e81c6bf89664193dfe1c67f926e9ddfd9 100644 --- a/tasks/0035_362_35362667_qa_4/task.toml +++ b/tasks/0035_362_35362667_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0035_362_35362667_qa_4" +name = "smoldataenvs-train/0035_362_35362667_qa_4" description = "Which mushroom class (edible/poisonous) has the most imbalanced feature distribution in the 'stalk-root' attribute?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "edible" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_362_35362667_qa_5/task.toml b/tasks/0035_362_35362667_qa_5/task.toml index 725fcb110c9f304dd787c77711942a5d8c83f521..79b5b601c55d183f2dfa52a7ad59b8eb643a09dd 100644 --- a/tasks/0035_362_35362667_qa_5/task.toml +++ b/tasks/0035_362_35362667_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0035_362_35362667_qa_5" +name = "smoldataenvs-train/0035_362_35362667_qa_5" description = "What is the percentage of mushrooms with 'g' cap-color in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "22.65" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_370_35370774_qa_3/task.toml b/tasks/0035_370_35370774_qa_3/task.toml index 4b7ce1a11888258c267c68b77475a3b58f2ffa4d..27904c20bea27ba1b6e9bdc37240ddacf62eca05 100644 --- a/tasks/0035_370_35370774_qa_3/task.toml +++ b/tasks/0035_370_35370774_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0035_370_35370774_qa_3" +name = "smoldataenvs-train/0035_370_35370774_qa_3" description = "What is the minimum average number of reported cases for any cancer type across U.S. states?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "309.33" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_391_35391770_qa_1/task.toml b/tasks/0035_391_35391770_qa_1/task.toml index abd15db9034b6dff5719bf5673108fcaa03e0d22..3ff4a93df1ccbb81559f84afcc4e8663627b8a9f 100644 --- a/tasks/0035_391_35391770_qa_1/task.toml +++ b/tasks/0035_391_35391770_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0035_391_35391770_qa_1" +name = "smoldataenvs-train/0035_391_35391770_qa_1" description = "What is the coefficient of determination (R²) for the linear regression model trained on this dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9663144321580247" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_442_35442155_qa_2/task.toml b/tasks/0035_442_35442155_qa_2/task.toml index 2496de82fbb547d78e4425517af3f947cecdcdd8..f359bf64d82ac47d6fa84f591b0f6dff1e64d26e 100644 --- a/tasks/0035_442_35442155_qa_2/task.toml +++ b/tasks/0035_442_35442155_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0035_442_35442155_qa_2" +name = "smoldataenvs-train/0035_442_35442155_qa_2" description = "What is the correlation between Sepal Width and Petal Length in the Iris dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.42" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0035_456_35456170_qa_5/task.toml b/tasks/0035_456_35456170_qa_5/task.toml index 440773f94565dce7fe30f8e3e55a6d52e08d75cb..a2ccab6dbd7be8fc167e67c243729c82b60c3a42 100644 --- a/tasks/0035_456_35456170_qa_5/task.toml +++ b/tasks/0035_456_35456170_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0035_456_35456170_qa_5" +name = "smoldataenvs-train/0035_456_35456170_qa_5" description = "Which feature has the highest skewness in the dataset before transformation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "area_se" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_513_35513650_qa_5/task.toml b/tasks/0035_513_35513650_qa_5/task.toml index 843da05a5b78103e308b636915a3d0d567c81cec..b14a20f9350d8f1d794166497d9481f8cae5cd3d 100644 --- a/tasks/0035_513_35513650_qa_5/task.toml +++ b/tasks/0035_513_35513650_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0035_513_35513650_qa_5" +name = "smoldataenvs-train/0035_513_35513650_qa_5" description = "What is the skewness coefficient of the original Order_Demand distribution before any transformations were applied?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "31.506" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_539_35539946_qa_3/task.toml b/tasks/0035_539_35539946_qa_3/task.toml index 73726e92401d316931d480d543782e150281e7ed..b381c0a516280d6c307c07df88cc90ab95088774 100644 --- a/tasks/0035_539_35539946_qa_3/task.toml +++ b/tasks/0035_539_35539946_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0035_539_35539946_qa_3" +name = "smoldataenvs-train/0035_539_35539946_qa_3" description = "What proportion of the dataset has an income greater than $50K annually?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "24.23%" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_546_35546963_qa_4/task.toml b/tasks/0035_546_35546963_qa_4/task.toml index 2cf01225a0a6bff7e476be40291ea6e2df230259..9a3e646c7c7a49d9b49610060167068244f57798 100644 --- a/tasks/0035_546_35546963_qa_4/task.toml +++ b/tasks/0035_546_35546963_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0035_546_35546963_qa_4" +name = "smoldataenvs-train/0035_546_35546963_qa_4" description = "What is the total number of observations in the final combined dataset after merging New York and Los Angeles pumpkin price data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "174" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_558_35558843_qa_1/task.toml b/tasks/0035_558_35558843_qa_1/task.toml index 53af56ba6767ee4bf228e8aea552292d7a8bb964..e911f28b300c44b20e2c303b3c01c3237279fcb4 100644 --- a/tasks/0035_558_35558843_qa_1/task.toml +++ b/tasks/0035_558_35558843_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0035_558_35558843_qa_1" +name = "smoldataenvs-train/0035_558_35558843_qa_1" description = "Which customerID generated the highest total revenue, and what was the amount?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "14646, 279489.02" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_586_35586526_qa_2/task.toml b/tasks/0035_586_35586526_qa_2/task.toml index 2402c2906431672c8209f133b5361ca66b0368bb..0704699ebdd874d1d9fd3e18606ba97711ac0381 100644 --- a/tasks/0035_586_35586526_qa_2/task.toml +++ b/tasks/0035_586_35586526_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0035_586_35586526_qa_2" +name = "smoldataenvs-train/0035_586_35586526_qa_2" description = "What is the feature with the highest importance in the first Decision Tree model based on feature importance scores?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "grade" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0035_609_35609748_qa_1/task.toml b/tasks/0035_609_35609748_qa_1/task.toml index 5ce5e5fb130a8962ccc71bde982960c57415b355..cd43cd749cbcefac33fe6181ab243330a8c0938e 100644 --- a/tasks/0035_609_35609748_qa_1/task.toml +++ b/tasks/0035_609_35609748_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0035_609_35609748_qa_1" +name = "smoldataenvs-train/0035_609_35609748_qa_1" description = "How many cars in the dataset have missing values in the horsepower column after data cleaning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_610_35610325_qa_3/task.toml b/tasks/0035_610_35610325_qa_3/task.toml index a25e9cfe97dd185506c65a3973a1e244f389680d..c1556c00c45c782f648e288018c9421546159626 100644 --- a/tasks/0035_610_35610325_qa_3/task.toml +++ b/tasks/0035_610_35610325_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0035_610_35610325_qa_3" +name = "smoldataenvs-train/0035_610_35610325_qa_3" description = "How many features were retained in the final preprocessed dataset used for training the model after removing irrelevant columns?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "30" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_638_35638998_qa_1/task.toml b/tasks/0035_638_35638998_qa_1/task.toml index ab36dae75185957d175584aae0c6d9eba824f468..481e1353f10f5323307c92cb7d47a10f498d495f 100644 --- a/tasks/0035_638_35638998_qa_1/task.toml +++ b/tasks/0035_638_35638998_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0035_638_35638998_qa_1" +name = "smoldataenvs-train/0035_638_35638998_qa_1" description = "What is the most important feature according to the Decision Tree feature importance analysis in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "degree_spondylolisthesis" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_638_35638998_qa_4/task.toml b/tasks/0035_638_35638998_qa_4/task.toml index 9aa5c02ac349cda892f8619a4a473b92546d910b..44fc6d3f7d30c7e7c302528d7f7aa9b3d4f50392 100644 --- a/tasks/0035_638_35638998_qa_4/task.toml +++ b/tasks/0035_638_35638998_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0035_638_35638998_qa_4" +name = "smoldataenvs-train/0035_638_35638998_qa_4" description = "Which feature was identified as having the highest importance by both Decision Tree and Random Forest feature importance analyses?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "degree_spondylolisthesis" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0035_645_35645519_qa_3/task.toml b/tasks/0035_645_35645519_qa_3/task.toml index a69f83be7f8c6198bdd7309959a928e17be8ee0a..8423b8f68141f9df884fee159aab22754640b08f 100644 --- a/tasks/0035_645_35645519_qa_3/task.toml +++ b/tasks/0035_645_35645519_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0035_645_35645519_qa_3" +name = "smoldataenvs-train/0035_645_35645519_qa_3" description = "What is the mean pixel intensity value of the first pixel (pixel1) across all images in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "145.419377" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0035_645_35645519_qa_4/task.toml b/tasks/0035_645_35645519_qa_4/task.toml index 0ada80e1276bf36bd2b11cf94631285cecb844e2..809a4ddf8642d6fa15b33408a41e4b7ccac26fec 100644 --- a/tasks/0035_645_35645519_qa_4/task.toml +++ b/tasks/0035_645_35645519_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0035_645_35645519_qa_4" +name = "smoldataenvs-train/0035_645_35645519_qa_4" description = "What is the standard deviation of pixel intensity values for the last pixel (pixel784) in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "64.396846" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0035_645_35645519_qa_5/task.toml b/tasks/0035_645_35645519_qa_5/task.toml index f01de90b019df60bda0321eb7b0caf5cf66321a1..9216314532fa4c2dc826ac6c5531fa3e567becdc 100644 --- a/tasks/0035_645_35645519_qa_5/task.toml +++ b/tasks/0035_645_35645519_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0035_645_35645519_qa_5" +name = "smoldataenvs-train/0035_645_35645519_qa_5" description = "What is the median pixel intensity value for the last pixel (pixel784) in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "182.0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0035_700_35700149_qa_1/task.toml b/tasks/0035_700_35700149_qa_1/task.toml index dfc251ac403671be0b18138f5cfacb1e0f0c7e69..0e95bdfd532937e8b3484208b3d326137d77a82d 100644 --- a/tasks/0035_700_35700149_qa_1/task.toml +++ b/tasks/0035_700_35700149_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0035_700_35700149_qa_1" +name = "smoldataenvs-train/0035_700_35700149_qa_1" description = "Which variable (age, BMI, smoker, children, region) is identified as the strongest predictor of insurance charges based on the analysis in the notebook?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "smoker" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_700_35700149_qa_3/task.toml b/tasks/0035_700_35700149_qa_3/task.toml index c986735e4e9eb2d24221adcef02196f8e3b1697c..5099a54f56414b7d5c4e89565f9cb03661f174a6 100644 --- a/tasks/0035_700_35700149_qa_3/task.toml +++ b/tasks/0035_700_35700149_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0035_700_35700149_qa_3" +name = "smoldataenvs-train/0035_700_35700149_qa_3" description = "Among the categorical variables (sex, smoker, region), which one shows the most significant visual difference in average insurance charges according to the swarm plot and bar chart analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Smoker" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_712_35712790_qa_3/task.toml b/tasks/0035_712_35712790_qa_3/task.toml index d69b23f76f2ea76e172a0e6999f55de86a198d26..60aa0c8a169c2317cbbe9b5e18ec8e09f0016785 100644 --- a/tasks/0035_712_35712790_qa_3/task.toml +++ b/tasks/0035_712_35712790_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0035_712_35712790_qa_3" +name = "smoldataenvs-train/0035_712_35712790_qa_3" description = "What is the p-value from the ADF test for the Global_intensity variable in the monthly aggregated data before any differencing was applied?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.255263e-07" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0035_717_35717551_qa_2/task.toml b/tasks/0035_717_35717551_qa_2/task.toml index caf0a1f53d362426da56a6ab537887877d4acca2..f2602c9206577de499c6c7cd3d50bd3c45daf894 100644 --- a/tasks/0035_717_35717551_qa_2/task.toml +++ b/tasks/0035_717_35717551_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0035_717_35717551_qa_2" +name = "smoldataenvs-train/0035_717_35717551_qa_2" description = "What is the interquartile range (IQR) of the number of positive axillary nodes for all patients?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_717_35717551_qa_4/task.toml b/tasks/0035_717_35717551_qa_4/task.toml index b0eaefa60cea495fcb5e82751a404f5eedde6b82..2ea7824a8f89308dcc0999cbfb6f72ecbffc9485 100644 --- a/tasks/0035_717_35717551_qa_4/task.toml +++ b/tasks/0035_717_35717551_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0035_717_35717551_qa_4" +name = "smoldataenvs-train/0035_717_35717551_qa_4" description = "What is the average age of all patients in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "52.457516" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0035_745_35745803_qa_1/task.toml b/tasks/0035_745_35745803_qa_1/task.toml index 0b835d9e03ffb3adc79888d013c498d73fb29f26..8a9975b952b300b1945a808af1a4e4673a4b6dba 100644 --- a/tasks/0035_745_35745803_qa_1/task.toml +++ b/tasks/0035_745_35745803_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0035_745_35745803_qa_1" +name = "smoldataenvs-train/0035_745_35745803_qa_1" description = "What is the highest correlation coefficient between log_price and any other feature in the cleaned dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.69" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_755_35755658_qa_3/task.toml b/tasks/0035_755_35755658_qa_3/task.toml index 799c6c74cd4fe6d297499e64bd3146f3269e00e6..357d0ddc17c0982268f9be483227f311266a2e8e 100644 --- a/tasks/0035_755_35755658_qa_3/task.toml +++ b/tasks/0035_755_35755658_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0035_755_35755658_qa_3" +name = "smoldataenvs-train/0035_755_35755658_qa_3" description = "What is the highest accuracy achieved by the Decision Tree Classifier when predicting the author of the poetry using TF-IDF vectorization with different criteria?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.41578947368421054" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0035_789_35789775_qa_3/task.toml b/tasks/0035_789_35789775_qa_3/task.toml index 4b6694f87135cdc07ec0606980d39e9f3aa315be..926e960a04b2ffe349d1716e03b0fd8acbb7657b 100644 --- a/tasks/0035_789_35789775_qa_3/task.toml +++ b/tasks/0035_789_35789775_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0035_789_35789775_qa_3" +name = "smoldataenvs-train/0035_789_35789775_qa_3" description = "Which publisher achieved the highest total sales in the Japanese region?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Nintendo" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_789_35789775_qa_5/task.toml b/tasks/0035_789_35789775_qa_5/task.toml index 0722e8e812cbe3d9ff183691c1e00340d4bc7192..46a355241f4916641a6586a27584c770c9862859 100644 --- a/tasks/0035_789_35789775_qa_5/task.toml +++ b/tasks/0035_789_35789775_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0035_789_35789775_qa_5" +name = "smoldataenvs-train/0035_789_35789775_qa_5" description = "What is the platform with the highest average sales in the European region?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "GB" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_812_35812389_qa_2/task.toml b/tasks/0035_812_35812389_qa_2/task.toml index 98a2d519a2a9f9b457b056f48dd3c953e62ea935..f0f2322bd5d2185380ebbf6b0246367dddcefe88 100644 --- a/tasks/0035_812_35812389_qa_2/task.toml +++ b/tasks/0035_812_35812389_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0035_812_35812389_qa_2" +name = "smoldataenvs-train/0035_812_35812389_qa_2" description = "How many distinct named entity tags are present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "17" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0035_835_35835284_qa_2/task.toml b/tasks/0035_835_35835284_qa_2/task.toml index 41ccf2c1a9c5d7867594a513051355d1ff0a0ddb..abdf74989f3a0c7d04e3c5ae8dfd8743bbc07d7b 100644 --- a/tasks/0035_835_35835284_qa_2/task.toml +++ b/tasks/0035_835_35835284_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0035_835_35835284_qa_2" +name = "smoldataenvs-train/0035_835_35835284_qa_2" description = "What is the hour of the day with the highest average solar radiation based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_835_35835284_qa_3/task.toml b/tasks/0035_835_35835284_qa_3/task.toml index b0cef8dc68a450c1e478e00f0cc53bcc86dea07d..c176f2d0112ee8e1e51371d405403af1fc594c39 100644 --- a/tasks/0035_835_35835284_qa_3/task.toml +++ b/tasks/0035_835_35835284_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0035_835_35835284_qa_3" +name = "smoldataenvs-train/0035_835_35835284_qa_3" description = "Does the dataset show a statistically significant positive correlation between temperature and solar radiation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0035_901_35901074_qa_5/task.toml b/tasks/0035_901_35901074_qa_5/task.toml index b712fc321789eee934dd9082372cb28f18aad933..92a45c04b1e2cfc25e7cc2b673b7cb6af024979f 100644 --- a/tasks/0035_901_35901074_qa_5/task.toml +++ b/tasks/0035_901_35901074_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0035_901_35901074_qa_5" +name = "smoldataenvs-train/0035_901_35901074_qa_5" description = "What is the R² score of the AdaBoost Regressor model on the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.6863749384485794" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0035_911_35911748_qa_2/task.toml b/tasks/0035_911_35911748_qa_2/task.toml index 9cf61cd19d7348780f61cd59e3dbb30d42a7bfe2..7e9f9db33eee7c6535a3447f07c2bff1dfd91394 100644 --- a/tasks/0035_911_35911748_qa_2/task.toml +++ b/tasks/0035_911_35911748_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0035_911_35911748_qa_2" +name = "smoldataenvs-train/0035_911_35911748_qa_2" description = "What are the three most important features identified by the Random Forest model for predicting glass type?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Al, Mg, RI" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0035_921_35921172_qa_1/task.toml b/tasks/0035_921_35921172_qa_1/task.toml index 09387a10bd83dabdc6408b954f9e5a5500198d18..b33afbccc44dca07b32a52c5b0db976fa34ffabb 100644 --- a/tasks/0035_921_35921172_qa_1/task.toml +++ b/tasks/0035_921_35921172_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0035_921_35921172_qa_1" +name = "smoldataenvs-train/0035_921_35921172_qa_1" description = "Which metric among number of characters, number of words, number of difficult words, and Dale-Chall readability scores shows the strongest correlation with the year of the inaugural address?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Dale-Chall readability score" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0035_921_35921172_qa_4/task.toml b/tasks/0035_921_35921172_qa_4/task.toml index d2ac9ddf2fbc5c521225dd38c435abf1d4a71473..6d3e9a004eed694861c834a6b64c78a62367fd59 100644 --- a/tasks/0035_921_35921172_qa_4/task.toml +++ b/tasks/0035_921_35921172_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0035_921_35921172_qa_4" +name = "smoldataenvs-train/0035_921_35921172_qa_4" description = "What is the highest Dale-Chall readability score recorded for any inaugural address in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11.73" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_921_35921172_qa_5/task.toml b/tasks/0035_921_35921172_qa_5/task.toml index 4d7bef427d1c90e39147c67ae43574ac8bcc4922..bd140844ec1cff1ead82afadfc3f2a77cfe2e978 100644 --- a/tasks/0035_921_35921172_qa_5/task.toml +++ b/tasks/0035_921_35921172_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0035_921_35921172_qa_5" +name = "smoldataenvs-train/0035_921_35921172_qa_5" description = "What is the word count of the longest inaugural address in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8464" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_974_35974226_qa_4/task.toml b/tasks/0035_974_35974226_qa_4/task.toml index 56b83adbf0f72c503492ef0e88b5a8f0d43f1b9b..d7741a79485ce6fcc6d3298c44503f7c9a2c8d06 100644 --- a/tasks/0035_974_35974226_qa_4/task.toml +++ b/tasks/0035_974_35974226_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0035_974_35974226_qa_4" +name = "smoldataenvs-train/0035_974_35974226_qa_4" description = "What is the average price of houses grouped by their construction quality grade, and which grade shows the highest mean price?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0035_980_35980933_qa_5/task.toml b/tasks/0035_980_35980933_qa_5/task.toml index 7f902fb2711b0d4af08b421851734e7cba8ce292..b2653499d83d84f1c65a7f0fac7883e5a0a95059 100644 --- a/tasks/0035_980_35980933_qa_5/task.toml +++ b/tasks/0035_980_35980933_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0035_980_35980933_qa_5" +name = "smoldataenvs-train/0035_980_35980933_qa_5" description = "Which feature has the lowest mean value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "smoothness_worst" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0035_999_35999601_qa_3/task.toml b/tasks/0035_999_35999601_qa_3/task.toml index 8cc1cfe280cf8130c05495eeaf80899c1b050666..a07bbf52134ae80aceb679152655d6ef55fc4422 100644 --- a/tasks/0035_999_35999601_qa_3/task.toml +++ b/tasks/0035_999_35999601_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0035_999_35999601_qa_3" +name = "smoldataenvs-train/0035_999_35999601_qa_3" description = "What is the difference in average number of pregnancies between diabetic and non-diabetic women in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0036_001_36001422_qa_1/task.toml b/tasks/0036_001_36001422_qa_1/task.toml index 75e50af0642443b9b1ae544f22b7d8f1d861fa24..bfe59d4836466f6f68860e041d706d695af68488 100644 --- a/tasks/0036_001_36001422_qa_1/task.toml +++ b/tasks/0036_001_36001422_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0036_001_36001422_qa_1" +name = "smoldataenvs-train/0036_001_36001422_qa_1" description = "Is the 'horsepower' column in the dataset stored as a numerical data type?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0036_030_36030056_qa_2/task.toml b/tasks/0036_030_36030056_qa_2/task.toml index 5810c05925f663d807e65ef400faed3bc629e66a..815472e073e42ea5a115702068db581af7708ddb 100644 --- a/tasks/0036_030_36030056_qa_2/task.toml +++ b/tasks/0036_030_36030056_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0036_030_36030056_qa_2" +name = "smoldataenvs-train/0036_030_36030056_qa_2" description = "What is the weighted average precision of the baseline model that predicts match outcomes based solely on the rating difference?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.5933" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0036_141_36141043_qa_2/task.toml b/tasks/0036_141_36141043_qa_2/task.toml index 4a0c84cb3d702c15bb8da774adeb1f588a97d365..bc91b6e45174809ef48cca15b3ceb759c6e313ab 100644 --- a/tasks/0036_141_36141043_qa_2/task.toml +++ b/tasks/0036_141_36141043_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0036_141_36141043_qa_2" +name = "smoldataenvs-train/0036_141_36141043_qa_2" description = "What data normalization technique was applied to the pixel values before model training?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Divide pixel values by 255.0" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0036_186_36186441_qa_2/task.toml b/tasks/0036_186_36186441_qa_2/task.toml index 197265fd97c135e0fb92b737a780dde28fb04ad2..0f29387059389d87390c9d84657d5cabeb98f216 100644 --- a/tasks/0036_186_36186441_qa_2/task.toml +++ b/tasks/0036_186_36186441_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0036_186_36186441_qa_2" +name = "smoldataenvs-train/0036_186_36186441_qa_2" description = "What is the threshold value used to classify Pokémon as having a \"powerful\" Attack stat?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "79.00125" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0036_230_36230590_qa_1/task.toml b/tasks/0036_230_36230590_qa_1/task.toml index 63e1650981b79b6c3d1a6da2f1741d254a979fc0..b16023e29ff4b25681d2149b98d8a3b1d22b2129 100644 --- a/tasks/0036_230_36230590_qa_1/task.toml +++ b/tasks/0036_230_36230590_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0036_230_36230590_qa_1" +name = "smoldataenvs-train/0036_230_36230590_qa_1" description = "What is the third most used female name in the year 2002?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Hannah" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0036_275_36275138_qa_4/task.toml b/tasks/0036_275_36275138_qa_4/task.toml index f6adf80818af116e35b25daf6037f3f1ddebc4d9..73ab97d7d82c5545c84d0985405e938d2f0929b2 100644 --- a/tasks/0036_275_36275138_qa_4/task.toml +++ b/tasks/0036_275_36275138_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0036_275_36275138_qa_4" +name = "smoldataenvs-train/0036_275_36275138_qa_4" description = "Which habitat type has the lowest proportion of poisonous mushrooms according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Waste" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0036_275_36275741_qa_5/task.toml b/tasks/0036_275_36275741_qa_5/task.toml index 6f3e00b0aec771206b344fb40563fa1dfcb6cefa..42e521f6d29c1b0e632e89cb6ea303f517469cd9 100644 --- a/tasks/0036_275_36275741_qa_5/task.toml +++ b/tasks/0036_275_36275741_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0036_275_36275741_qa_5" +name = "smoldataenvs-train/0036_275_36275741_qa_5" description = "How many features are included in the balanced dataset used for model training?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0036_294_36294724_qa_1/task.toml b/tasks/0036_294_36294724_qa_1/task.toml index a6581473cd272103758e772e5a7b7b33ff9735cb..b1fadba999f061f8ddffb306f29b4649761cc45b 100644 --- a/tasks/0036_294_36294724_qa_1/task.toml +++ b/tasks/0036_294_36294724_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0036_294_36294724_qa_1" +name = "smoldataenvs-train/0036_294_36294724_qa_1" description = "How many missing values were present in the 'total_bedrooms' column before imputation, and what value was used to replace them?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "207, 435.0" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0036_305_36305200_qa_3/task.toml b/tasks/0036_305_36305200_qa_3/task.toml index a755abbe08180b5321761027281d4558639f9b27..2e536215596d064ae7c621d0959eec2037f92ef0 100644 --- a/tasks/0036_305_36305200_qa_3/task.toml +++ b/tasks/0036_305_36305200_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0036_305_36305200_qa_3" +name = "smoldataenvs-train/0036_305_36305200_qa_3" description = "What is the cross-validation accuracy of the tuned Random Forest model using GridSearchCV parameters?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "80.15" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0036_324_36324294_qa_1/task.toml b/tasks/0036_324_36324294_qa_1/task.toml index 8f1393e952eeb1898516fbac5c83dbbb5c4934f9..7e992d9fc299094895d99360d452d0d57260b251 100644 --- a/tasks/0036_324_36324294_qa_1/task.toml +++ b/tasks/0036_324_36324294_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0036_324_36324294_qa_1" +name = "smoldataenvs-train/0036_324_36324294_qa_1" description = "What is the survival rate (as a percentage) of patients in the Haberman's Survival dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "73.53" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0036_430_36430763_qa_1/task.toml b/tasks/0036_430_36430763_qa_1/task.toml index 855571c10db8d7555e6193e6bdd8c8677453d1f6..d8a4b40982070b8c9bacbdea63da5cc758c640a8 100644 --- a/tasks/0036_430_36430763_qa_1/task.toml +++ b/tasks/0036_430_36430763_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0036_430_36430763_qa_1" +name = "smoldataenvs-train/0036_430_36430763_qa_1" description = "Do medical insurance charges show significant differences between smokers and non-smokers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0036_430_36430763_qa_5/task.toml b/tasks/0036_430_36430763_qa_5/task.toml index 909d9aca15a88dfdbf1c26ccd138efee7cd4bf8d..dd4c074cb4dcd35b47bf35b45dc3c87234f8ca98 100644 --- a/tasks/0036_430_36430763_qa_5/task.toml +++ b/tasks/0036_430_36430763_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0036_430_36430763_qa_5" +name = "smoldataenvs-train/0036_430_36430763_qa_5" description = "What is the skewness value for the medical insurance charges distribution in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.5142" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0036_436_36436182_qa_5/task.toml b/tasks/0036_436_36436182_qa_5/task.toml index d20fd573732be29a4347b731fd23a477c6a9f6c2..6bd6435534f6bd519fc94676626df00d101f9696 100644 --- a/tasks/0036_436_36436182_qa_5/task.toml +++ b/tasks/0036_436_36436182_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0036_436_36436182_qa_5" +name = "smoldataenvs-train/0036_436_36436182_qa_5" description = "How many missing values were present in the 'horsepower' column before the KNN imputation was applied?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0036_510_36510950_qa_3/task.toml b/tasks/0036_510_36510950_qa_3/task.toml index 93223d447a2c205db800cb6a8e7f7ccf1fcdf7a4..c46ff5c33adc75aeb6b21361b34b03c7220ccf62 100644 --- a/tasks/0036_510_36510950_qa_3/task.toml +++ b/tasks/0036_510_36510950_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0036_510_36510950_qa_3" +name = "smoldataenvs-train/0036_510_36510950_qa_3" description = "What percentage of the dataset was allocated to the training set during data splitting?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "80" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0036_510_36510950_qa_4/task.toml b/tasks/0036_510_36510950_qa_4/task.toml index c02d5b2f4bb45c0f977a4b925ba3dcc2588607e8..f43918751a45e970c42f0c13310aa308eefc6fbf 100644 --- a/tasks/0036_510_36510950_qa_4/task.toml +++ b/tasks/0036_510_36510950_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0036_510_36510950_qa_4" +name = "smoldataenvs-train/0036_510_36510950_qa_4" description = "What imputation strategy was applied to handle missing values in the total bedrooms feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "median" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0036_510_36510950_qa_5/task.toml b/tasks/0036_510_36510950_qa_5/task.toml index 553dce473a04c921456b38beca246d6369971e8b..812a2528cf0eec51a155d3c785ff9ef0d780e4de 100644 --- a/tasks/0036_510_36510950_qa_5/task.toml +++ b/tasks/0036_510_36510950_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0036_510_36510950_qa_5" +name = "smoldataenvs-train/0036_510_36510950_qa_5" description = "What is the exact correlation coefficient between median income and median house value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.688075" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0036_579_36579520_qa_4/task.toml b/tasks/0036_579_36579520_qa_4/task.toml index ec9b9c30fd0c3228926dd90c012b9ff3a1a1557e..cce6ff48551bfa9dc1be1f784231c67eb6a9c3ba 100644 --- a/tasks/0036_579_36579520_qa_4/task.toml +++ b/tasks/0036_579_36579520_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0036_579_36579520_qa_4" +name = "smoldataenvs-train/0036_579_36579520_qa_4" description = "How many principal components are needed to explain at least 80% of the total variance in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "78" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0036_629_36629065_qa_2/task.toml b/tasks/0036_629_36629065_qa_2/task.toml index 430c82470ac47e5a25ff42c24ebfc4169ecc139a..591b08acee3f8c96b88c6c41d201f4d8b214e7a0 100644 --- a/tasks/0036_629_36629065_qa_2/task.toml +++ b/tasks/0036_629_36629065_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0036_629_36629065_qa_2" +name = "smoldataenvs-train/0036_629_36629065_qa_2" description = "How many missing values were present in the 'Year' column of the dataset before data cleaning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "271" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0036_637_36637598_qa_2/task.toml b/tasks/0036_637_36637598_qa_2/task.toml index 7e347ee428102dfcd53f5e30c804000208a6ad2a..a22797af2b6122f7b56c3bf63f11f06aa7d3e256 100644 --- a/tasks/0036_637_36637598_qa_2/task.toml +++ b/tasks/0036_637_36637598_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0036_637_36637598_qa_2" +name = "smoldataenvs-train/0036_637_36637598_qa_2" description = "What are the top three most frequently reviewed grape varieties in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Pinot Noir, Chardonnay, Cabernet Sauvignon" reward_mode_initial = "list_csv" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0036_637_36637598_qa_4/task.toml b/tasks/0036_637_36637598_qa_4/task.toml index 32008ecaab5d58432f8b189f1b5fe0a83d40d2fd..3ac9d8947d9a262733aa5d47e4041165eec316d6 100644 --- a/tasks/0036_637_36637598_qa_4/task.toml +++ b/tasks/0036_637_36637598_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0036_637_36637598_qa_4" +name = "smoldataenvs-train/0036_637_36637598_qa_4" description = "Which grape variety is most similar to Pinot Noir according to the first result of the recommendation system?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Grenache" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0036_800_36800377_qa_3/task.toml b/tasks/0036_800_36800377_qa_3/task.toml index ea4d2114e4af41b11c02d408a71e94b7d168fd62..3b75456e34a487df53895a3077ccec9d205cb02f 100644 --- a/tasks/0036_800_36800377_qa_3/task.toml +++ b/tasks/0036_800_36800377_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0036_800_36800377_qa_3" +name = "smoldataenvs-train/0036_800_36800377_qa_3" description = "What is the most common embarkation point (Embarked) for passengers after replacing missing values with the most frequent port?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "S" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0036_800_36800377_qa_4/task.toml b/tasks/0036_800_36800377_qa_4/task.toml index a71332f16230c11e4f33084c5f85681913051e8d..9278014fc601bb74a84fe594b8b5ce1383855cdf 100644 --- a/tasks/0036_800_36800377_qa_4/task.toml +++ b/tasks/0036_800_36800377_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0036_800_36800377_qa_4" +name = "smoldataenvs-train/0036_800_36800377_qa_4" description = "Which passenger class (Pclass) had the highest number of passengers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0036_800_36800377_qa_5/task.toml b/tasks/0036_800_36800377_qa_5/task.toml index bda193359adcba0444a2bc32301d8006114c1af9..f32387ae44e8c644c96db2a82950c83796fce908 100644 --- a/tasks/0036_800_36800377_qa_5/task.toml +++ b/tasks/0036_800_36800377_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0036_800_36800377_qa_5" +name = "smoldataenvs-train/0036_800_36800377_qa_5" description = "Among the available cabin levels (C, B, D, etc.), which level had the highest number of passengers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "C" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0036_817_36817810_qa_1/task.toml b/tasks/0036_817_36817810_qa_1/task.toml index 1a9d0dca930f250469477d363a4c573c7f2ad95a..e0a337bcdf11edc69ebd27aa2941a94b8d29fe65 100644 --- a/tasks/0036_817_36817810_qa_1/task.toml +++ b/tasks/0036_817_36817810_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0036_817_36817810_qa_1" +name = "smoldataenvs-train/0036_817_36817810_qa_1" description = "What is the ratio of average medical charges between smokers and non-smokers in the customized dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.809472" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0036_817_36817810_qa_4/task.toml b/tasks/0036_817_36817810_qa_4/task.toml index 879ec6f35b8ec6b696a400397b6f68f14cbfce56..329e46605c12e1d695d23d57de02f3fae8f0323c 100644 --- a/tasks/0036_817_36817810_qa_4/task.toml +++ b/tasks/0036_817_36817810_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0036_817_36817810_qa_4" +name = "smoldataenvs-train/0036_817_36817810_qa_4" description = "What is the difference between the maximum and minimum medical charges in the customized dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "63275.039651" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0036_818_36818972_qa_1/task.toml b/tasks/0036_818_36818972_qa_1/task.toml index d2a2e757b5c5d881fcfeb576a39ef8bbc06c8263..fdd659a57b12e99a476541597d60d812a7278757 100644 --- a/tasks/0036_818_36818972_qa_1/task.toml +++ b/tasks/0036_818_36818972_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0036_818_36818972_qa_1" +name = "smoldataenvs-train/0036_818_36818972_qa_1" description = "What is the ratio of total purchase amounts between male and female customers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0036_847_36847235_qa_1/task.toml b/tasks/0036_847_36847235_qa_1/task.toml index 85738d016ff8e88da54c471cbe014540aeb22bca..818b8a4c5a35d6dafb801eb1e6f190365d4737ed 100644 --- a/tasks/0036_847_36847235_qa_1/task.toml +++ b/tasks/0036_847_36847235_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0036_847_36847235_qa_1" +name = "smoldataenvs-train/0036_847_36847235_qa_1" description = "What is the difference in the number of edible and poisonous mushrooms in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "292" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0036_916_36916974_qa_1/task.toml b/tasks/0036_916_36916974_qa_1/task.toml index e2430b6df6a3cfe89bd45676b06e8f36d2cad208..882192ca9e3c6e51414ae5f9fc1f48a9990bcdee 100644 --- a/tasks/0036_916_36916974_qa_1/task.toml +++ b/tasks/0036_916_36916974_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0036_916_36916974_qa_1" +name = "smoldataenvs-train/0036_916_36916974_qa_1" description = "What is the F1 score achieved by the XGBoost model using the specified beta parameter (β=0.5)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0036_916_36916974_qa_5/task.toml b/tasks/0036_916_36916974_qa_5/task.toml index ad153ba6daf2adc1469ed16b79afa13086bbf7bd..a82e5e225670259ea68b37ef2e22f6dec47068e6 100644 --- a/tasks/0036_916_36916974_qa_5/task.toml +++ b/tasks/0036_916_36916974_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0036_916_36916974_qa_5" +name = "smoldataenvs-train/0036_916_36916974_qa_5" description = "What is the Pearson correlation coefficient between the 'bruises' feature and the target class in the factorized dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.501530" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0036_927_36927955_qa_1/task.toml b/tasks/0036_927_36927955_qa_1/task.toml index 09bbcd99d05426c196c913184d3e23aac6a80c81..773b2e2b5ca818b577c1fc13b485ca80b5c197bb 100644 --- a/tasks/0036_927_36927955_qa_1/task.toml +++ b/tasks/0036_927_36927955_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0036_927_36927955_qa_1" +name = "smoldataenvs-train/0036_927_36927955_qa_1" description = "What is the total loan amount distributed in the Rwanda region according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16616750.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0036_949_36949181_qa_1/task.toml b/tasks/0036_949_36949181_qa_1/task.toml index 89096d0ef3a1456d63ae882ad65811e6363443cb..f848ff7c982ccbc0909e591cde923581d1c2051c 100644 --- a/tasks/0036_949_36949181_qa_1/task.toml +++ b/tasks/0036_949_36949181_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0036_949_36949181_qa_1" +name = "smoldataenvs-train/0036_949_36949181_qa_1" description = "What is the calculated sample mean of the pH values in the wine dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.311113195747343" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0037_011_37011559_qa_5/task.toml b/tasks/0037_011_37011559_qa_5/task.toml index 23faae210cdc235d71723e518d0958a1b8898849..04a15c7d4f6999135be76f11370e00c1cc3067cf 100644 --- a/tasks/0037_011_37011559_qa_5/task.toml +++ b/tasks/0037_011_37011559_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0037_011_37011559_qa_5" +name = "smoldataenvs-train/0037_011_37011559_qa_5" description = "Which feature in the correlation matrix has the highest positive correlation with the 'PURCHASES' feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ONEOFF_PURCHASES" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0037_052_37052709_qa_5/task.toml b/tasks/0037_052_37052709_qa_5/task.toml index ca98ea8f8b3f878413b3d52e683204593ce2a9fb..3ff52b8a67ad0121a2c1767e153c500fd5ef22c2 100644 --- a/tasks/0037_052_37052709_qa_5/task.toml +++ b/tasks/0037_052_37052709_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0037_052_37052709_qa_5" +name = "smoldataenvs-train/0037_052_37052709_qa_5" description = "What is the mean age of respondents after restricting the age range to 12-100 years in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "32.09650582362729" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0037_084_37084203_qa_1/task.toml b/tasks/0037_084_37084203_qa_1/task.toml index c45636d9193ce241bdc20849edde4a50f362431f..ec14d43d49d702bae396f2be4d446d3622b32483 100644 --- a/tasks/0037_084_37084203_qa_1/task.toml +++ b/tasks/0037_084_37084203_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0037_084_37084203_qa_1" +name = "smoldataenvs-train/0037_084_37084203_qa_1" description = "Which generation has the highest median value across all six stats (Attack, Defense, Speed, Sp. Atk, Sp. Def, HP) based on the boxplots?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0037_093_37093290_qa_1/task.toml b/tasks/0037_093_37093290_qa_1/task.toml index 31cd0d1db7b1ff67187ad909b6ac279c591fcabd..5e6cf4b335a71778b4f3196087e84f86518ff365 100644 --- a/tasks/0037_093_37093290_qa_1/task.toml +++ b/tasks/0037_093_37093290_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0037_093_37093290_qa_1" +name = "smoldataenvs-train/0037_093_37093290_qa_1" description = "Which feature in the dataset shows the strongest linear correlation with the diabetes outcome variable?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0037_139_37139079_qa_2/task.toml b/tasks/0037_139_37139079_qa_2/task.toml index 93c742e75748a655b8fe7892bfd473436e1336ee..d1648f2829d3cbcfd516b0eb0855cff17af6b356 100644 --- a/tasks/0037_139_37139079_qa_2/task.toml +++ b/tasks/0037_139_37139079_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0037_139_37139079_qa_2" +name = "smoldataenvs-train/0037_139_37139079_qa_2" description = "How many entries in the poverty dataset had missing values represented by '-' before they were replaced with 0?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "201" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0037_168_37168956_qa_5/task.toml b/tasks/0037_168_37168956_qa_5/task.toml index 2d1ca02fce0b25e8617eaca3552606d638b77c4e..66ea563f3b53a198fd9ebe348eff1f1a305df874 100644 --- a/tasks/0037_168_37168956_qa_5/task.toml +++ b/tasks/0037_168_37168956_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0037_168_37168956_qa_5" +name = "smoldataenvs-train/0037_168_37168956_qa_5" description = "What is the predicted salary for an individual with 10 years of experience according to the model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "120291.82" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0037_173_37173260_qa_1/task.toml b/tasks/0037_173_37173260_qa_1/task.toml index 88c846ab6ea713d6e8f581755aa4f779e069b7bc..4bc15c648405ef0b0e94c1bcb919ca90db2b77be 100644 --- a/tasks/0037_173_37173260_qa_1/task.toml +++ b/tasks/0037_173_37173260_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0037_173_37173260_qa_1" +name = "smoldataenvs-train/0037_173_37173260_qa_1" description = "Which non-military operator has the highest number of crashes in the dataset, and what is the most common aircraft type associated with their crashes?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Aeroflot, Yakovlev YAK-40" reward_mode_initial = "list" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0037_209_37209957_qa_5/task.toml b/tasks/0037_209_37209957_qa_5/task.toml index dfb0fc1a92e15ecaabf77d47db1d3858dc7ca2ac..8fb499bace429a3eb7ae268b0c6ebc9b5a5c168a 100644 --- a/tasks/0037_209_37209957_qa_5/task.toml +++ b/tasks/0037_209_37209957_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0037_209_37209957_qa_5" +name = "smoldataenvs-train/0037_209_37209957_qa_5" description = "What is the most common genre directed by Adam McKay in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Comedy" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0037_236_37236169_qa_4/task.toml b/tasks/0037_236_37236169_qa_4/task.toml index 0d42231ec48b6d938eefffe6adf797db1a97bdbe..c6735df0db204243bd5a3d665043c0ad99f58090 100644 --- a/tasks/0037_236_37236169_qa_4/task.toml +++ b/tasks/0037_236_37236169_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0037_236_37236169_qa_4" +name = "smoldataenvs-train/0037_236_37236169_qa_4" description = "What is the total number of unique names and surnames combined in the police killings dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4972" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0037_280_37280696_qa_2/task.toml b/tasks/0037_280_37280696_qa_2/task.toml index 2236129f446249beea58a7f4567f8932d1ac2527..a305aece1d57834a2fd0d4e39b798f4ea0b56343 100644 --- a/tasks/0037_280_37280696_qa_2/task.toml +++ b/tasks/0037_280_37280696_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0037_280_37280696_qa_2" +name = "smoldataenvs-train/0037_280_37280696_qa_2" description = "For the Action game genre, which platform achieves the highest total sales in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PS3" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0037_280_37280696_qa_5/task.toml b/tasks/0037_280_37280696_qa_5/task.toml index 2e6ed6fcb41656045170de8f69e8b8d58c10feaf..6785aba93178c8eece56a5c8e946bce16d1616a5 100644 --- a/tasks/0037_280_37280696_qa_5/task.toml +++ b/tasks/0037_280_37280696_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0037_280_37280696_qa_5" +name = "smoldataenvs-train/0037_280_37280696_qa_5" description = "Which platform is most associated with Sports games in terms of maximum sales for that genre?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0037_292_37292636_qa_3/task.toml b/tasks/0037_292_37292636_qa_3/task.toml index 983b83c4db090a1cad53132c4f2a331cf5121508..a51521b39bdc884875d685aab795d10261478a60 100644 --- a/tasks/0037_292_37292636_qa_3/task.toml +++ b/tasks/0037_292_37292636_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0037_292_37292636_qa_3" +name = "smoldataenvs-train/0037_292_37292636_qa_3" description = "What is the ratio of non-defaulters to defaulters among college students who paid their credit card in full during April 2005 (PAY_6=-1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.8:1" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0037_308_37308262_qa_3/task.toml b/tasks/0037_308_37308262_qa_3/task.toml index 33ec935f82c121dc97e1d1a0fb415b2f86011cf0..5f993904a2e0a487e98a0ebaf800ad972e6c51a3 100644 --- a/tasks/0037_308_37308262_qa_3/task.toml +++ b/tasks/0037_308_37308262_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0037_308_37308262_qa_3" +name = "smoldataenvs-train/0037_308_37308262_qa_3" description = "How many rows were removed from the dataset during the data cleaning step to address missing values in the \"TotalCharges\" column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0037_308_37308262_qa_4/task.toml b/tasks/0037_308_37308262_qa_4/task.toml index 3e5edd61539f2fbb53e0fbfdce24f9e0b455ac20..3971f58de8196164c04e310f38602e92c9c162de 100644 --- a/tasks/0037_308_37308262_qa_4/task.toml +++ b/tasks/0037_308_37308262_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0037_308_37308262_qa_4" +name = "smoldataenvs-train/0037_308_37308262_qa_4" description = "What is the correlation coefficient between \"MonthlyCharges\" and \"TotalCharges\" in the dataset after completing all data cleaning steps?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.651065" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0037_513_37513711_qa_2/task.toml b/tasks/0037_513_37513711_qa_2/task.toml index 8178c006d8a6af425087c101c1fd3d6dc0ee2d76..ed0157a77724b0e1daf2d21c8545a8013e23fd57 100644 --- a/tasks/0037_513_37513711_qa_2/task.toml +++ b/tasks/0037_513_37513711_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0037_513_37513711_qa_2" +name = "smoldataenvs-train/0037_513_37513711_qa_2" description = "Which individual product has the highest support value in the frequent itemsets?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "BREAD" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0037_551_37551034_qa_1/task.toml b/tasks/0037_551_37551034_qa_1/task.toml index 8f48fb6af654564da38f1b85f5cc0211b6c23c8b..8b0c1435bc4de9392187e103d59f17981699f6e3 100644 --- a/tasks/0037_551_37551034_qa_1/task.toml +++ b/tasks/0037_551_37551034_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0037_551_37551034_qa_1" +name = "smoldataenvs-train/0037_551_37551034_qa_1" description = "What is the most frequent wine quality rating in the dataset based on the 'quality' column value counts?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0037_554_37554731_qa_1/task.toml b/tasks/0037_554_37554731_qa_1/task.toml index 3fbc67a4e368fd8a38091a28fdf2d29173710a93..d3127f31483b668ad42a88c8edb9aae17a6da2e5 100644 --- a/tasks/0037_554_37554731_qa_1/task.toml +++ b/tasks/0037_554_37554731_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0037_554_37554731_qa_1" +name = "smoldataenvs-train/0037_554_37554731_qa_1" description = "Which platform has the highest average global sales according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "GB" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0037_594_37594747_qa_1/task.toml b/tasks/0037_594_37594747_qa_1/task.toml index d97f734cc3e6491291b5df10ae9c59a3060794b7..9906d93126cb170a06770c1cccc8aa41e16c9f99 100644 --- a/tasks/0037_594_37594747_qa_1/task.toml +++ b/tasks/0037_594_37594747_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0037_594_37594747_qa_1" +name = "smoldataenvs-train/0037_594_37594747_qa_1" description = "What is the highest correlation coefficient between any independent variable and home price in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.702" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0037_631_37631084_qa_2/task.toml b/tasks/0037_631_37631084_qa_2/task.toml index b01464bfb3366af859c6984e5caeeff5e87361b4..f138f6ed4f426c2ffaf21c30823fb608919b78e3 100644 --- a/tasks/0037_631_37631084_qa_2/task.toml +++ b/tasks/0037_631_37631084_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0037_631_37631084_qa_2" +name = "smoldataenvs-train/0037_631_37631084_qa_2" description = "In which decade did the highest proportion of Nobel laureates originate from the United States of America?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2000s" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0037_631_37631084_qa_3/task.toml b/tasks/0037_631_37631084_qa_3/task.toml index b64ee3a95de6383650eda87be8b915a248219562..43e36b4e20ac8e3f2486c37c0b35974311ea8f33 100644 --- a/tasks/0037_631_37631084_qa_3/task.toml +++ b/tasks/0037_631_37631084_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0037_631_37631084_qa_3" +name = "smoldataenvs-train/0037_631_37631084_qa_3" description = "Who was the first woman to win a Nobel Prize, and in which category and year did she win?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Marie Curie, Physics, 1903" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0037_718_37718370_qa_1/task.toml b/tasks/0037_718_37718370_qa_1/task.toml index abe432b618318e2aa000199d877a56a6daa1db19..273ecec7cacf3c4e721790e61e5db741896fafa0 100644 --- a/tasks/0037_718_37718370_qa_1/task.toml +++ b/tasks/0037_718_37718370_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0037_718_37718370_qa_1" +name = "smoldataenvs-train/0037_718_37718370_qa_1" description = "What percentage of customers in the dataset have churned?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26.5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0037_718_37718370_qa_2/task.toml b/tasks/0037_718_37718370_qa_2/task.toml index 831e68bc2911e7211dc91eba98f6850b04ed6347..0748d57de1d5960624cd4ce6b20b7db1d0db54c9 100644 --- a/tasks/0037_718_37718370_qa_2/task.toml +++ b/tasks/0037_718_37718370_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0037_718_37718370_qa_2" +name = "smoldataenvs-train/0037_718_37718370_qa_2" description = "Which contract type is most frequently associated with churned customers?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Month-to-month" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0037_718_37718370_qa_3/task.toml b/tasks/0037_718_37718370_qa_3/task.toml index 5f2cd0f82954e6431167e32a3f6fa142ca04510f..6175e291c16a47d00660e1d5cfdffa749980deff 100644 --- a/tasks/0037_718_37718370_qa_3/task.toml +++ b/tasks/0037_718_37718370_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0037_718_37718370_qa_3" +name = "smoldataenvs-train/0037_718_37718370_qa_3" description = "What is the most common payment method used by churned customers with Month-to-month contracts?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic check" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0037_725_37725403_qa_5/task.toml b/tasks/0037_725_37725403_qa_5/task.toml index cbe25fa5a765fa93282eaf4022db06ce9a09dc03..40b9146f3c47d61f31e92713efee0809c079103d 100644 --- a/tasks/0037_725_37725403_qa_5/task.toml +++ b/tasks/0037_725_37725403_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0037_725_37725403_qa_5" +name = "smoldataenvs-train/0037_725_37725403_qa_5" description = "How many samples were retained in the dataset after removing rows with missing descriptions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16306" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0037_728_37728227_qa_2/task.toml b/tasks/0037_728_37728227_qa_2/task.toml index 465239805a23211807d730e1a7028ee6256ef8c5..0cfe3c3e7e998eebdd81314c91ed845c2185f850 100644 --- a/tasks/0037_728_37728227_qa_2/task.toml +++ b/tasks/0037_728_37728227_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0037_728_37728227_qa_2" +name = "smoldataenvs-train/0037_728_37728227_qa_2" description = "What is the ratio of non-exoplanet-labeled samples (class 1) to exoplanet-labeled samples (class 2) in the training dataset based on the confusion matrix output?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "136.2" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0037_799_37799845_qa_4/task.toml b/tasks/0037_799_37799845_qa_4/task.toml index d64cf97d24cb2f65d22d55418f0b4dfff6ad31ea..838f5257b3537a3bd72264aec2bd40411d880770 100644 --- a/tasks/0037_799_37799845_qa_4/task.toml +++ b/tasks/0037_799_37799845_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0037_799_37799845_qa_4" +name = "smoldataenvs-train/0037_799_37799845_qa_4" description = "What is the percentage of defaults among customers who paid only the minimum amount in the first month (PAY_1 = 0)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12.81" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0037_799_37799845_qa_5/task.toml b/tasks/0037_799_37799845_qa_5/task.toml index 4f883bd876ddacc7da77c125cbf9243cb8e26092..260da61c0c6300fed124af713bdad756f1d8630f 100644 --- a/tasks/0037_799_37799845_qa_5/task.toml +++ b/tasks/0037_799_37799845_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0037_799_37799845_qa_5" +name = "smoldataenvs-train/0037_799_37799845_qa_5" description = "What is the percentage of defaults among customers who paid their bills in full (PAY_1 = -1) in the most recent month?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16.78" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0037_859_37859442_qa_1/task.toml b/tasks/0037_859_37859442_qa_1/task.toml index bd3f70c3a4cb93667d7e66bdca6f9fa156ff444e..7b82f05fe576003a7ef5e3ef6ce22ceece43171e 100644 --- a/tasks/0037_859_37859442_qa_1/task.toml +++ b/tasks/0037_859_37859442_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0037_859_37859442_qa_1" +name = "smoldataenvs-train/0037_859_37859442_qa_1" description = "What is the average Recency score across all customers in the dataset based on their transaction history?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "81.54" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0037_895_37895109_qa_2/task.toml b/tasks/0037_895_37895109_qa_2/task.toml index 260e840a7c626115187e46a97ca2ba5ad3620633..f45d1593fa25171477a7be859d4ba7e4c3c066aa 100644 --- a/tasks/0037_895_37895109_qa_2/task.toml +++ b/tasks/0037_895_37895109_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0037_895_37895109_qa_2" +name = "smoldataenvs-train/0037_895_37895109_qa_2" description = "What is the highest confidence value for any association rule where BREAD is the antecedent item?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.307692" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0037_918_37918087_qa_1/task.toml b/tasks/0037_918_37918087_qa_1/task.toml index bdf4200750f59e33d24cd9f0c12258383ca57da6..e74a856e90ad17864cc3632fe3d0c3cbf77f6ab3 100644 --- a/tasks/0037_918_37918087_qa_1/task.toml +++ b/tasks/0037_918_37918087_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0037_918_37918087_qa_1" +name = "smoldataenvs-train/0037_918_37918087_qa_1" description = "Which feature has the highest importance according to the ExtraTreesRegressor model in predicting the price range?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ram" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0037_918_37918087_qa_4/task.toml b/tasks/0037_918_37918087_qa_4/task.toml index 162d4d76d3aa4589199ab68fcb55ebf54916c89a..6172c4bcaea93fe2fba53a98fd89e4508141745f 100644 --- a/tasks/0037_918_37918087_qa_4/task.toml +++ b/tasks/0037_918_37918087_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0037_918_37918087_qa_4" +name = "smoldataenvs-train/0037_918_37918087_qa_4" description = "Which price range class has the lowest precision according to the classification report?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0037_933_37933355_qa_2/task.toml b/tasks/0037_933_37933355_qa_2/task.toml index 7a06e797a2f9e4c8e0d89ce328e7a40c25db4bd3..d7339beae12738325c607b869f9f8c6ee7223d56 100644 --- a/tasks/0037_933_37933355_qa_2/task.toml +++ b/tasks/0037_933_37933355_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0037_933_37933355_qa_2" +name = "smoldataenvs-train/0037_933_37933355_qa_2" description = "Which hobbit character spoke the most total words across all three films combined?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Sam" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0037_995_37995698_qa_1/task.toml b/tasks/0037_995_37995698_qa_1/task.toml index 67e36676664baad4e008279424f7a887d1c68dee..021f939bd1bceff938e425813f072930d700ea31 100644 --- a/tasks/0037_995_37995698_qa_1/task.toml +++ b/tasks/0037_995_37995698_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0037_995_37995698_qa_1" +name = "smoldataenvs-train/0037_995_37995698_qa_1" description = "Are the three Iris species classes balanced in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0038_031_38031736_qa_4/task.toml b/tasks/0038_031_38031736_qa_4/task.toml index fc20d84f17097db9c991d431764f77d0836091ab..563993ee3485366455c76c27773626fc7e9e4904 100644 --- a/tasks/0038_031_38031736_qa_4/task.toml +++ b/tasks/0038_031_38031736_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0038_031_38031736_qa_4" +name = "smoldataenvs-train/0038_031_38031736_qa_4" description = "Which feature had the most missing values before any data imputation was performed?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "QS_OVERALL" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0038_159_38159473_qa_2/task.toml b/tasks/0038_159_38159473_qa_2/task.toml index 265dc48418e81e1eda0161a243c969734b047b75..a989b352da8a0e3e3504c8dc48da43ab513b1502 100644 --- a/tasks/0038_159_38159473_qa_2/task.toml +++ b/tasks/0038_159_38159473_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0038_159_38159473_qa_2" +name = "smoldataenvs-train/0038_159_38159473_qa_2" description = "What is the most common location type for party-related incidents across all boroughs?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Residential Building/House" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0038_159_38159473_qa_4/task.toml b/tasks/0038_159_38159473_qa_4/task.toml index fa74c42981c6f768cced265fbb4d1c18e8180454..b7c423721bf5dcae7cb43fc647aab1cc73afa19a 100644 --- a/tasks/0038_159_38159473_qa_4/task.toml +++ b/tasks/0038_159_38159473_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0038_159_38159473_qa_4" +name = "smoldataenvs-train/0038_159_38159473_qa_4" description = "What day of the week had the highest number of party-related incidents?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Saturday" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_165_38165504_qa_3/task.toml b/tasks/0038_165_38165504_qa_3/task.toml index 8c68c563da11aac422503a8d8ef4d0fd95ab3d1d..5f2896c024cc29dfbeea9434fbb62d349fcf7cdf 100644 --- a/tasks/0038_165_38165504_qa_3/task.toml +++ b/tasks/0038_165_38165504_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0038_165_38165504_qa_3" +name = "smoldataenvs-train/0038_165_38165504_qa_3" description = "What is the test accuracy achieved by the Artificial Neural Network (ANN) model after training?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9730861244019139" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0038_178_38178392_qa_1/task.toml b/tasks/0038_178_38178392_qa_1/task.toml index 18faed799334b287767b83e41b60845207b462e7..603e83952a277b9ea40d77db311912acba71a24b 100644 --- a/tasks/0038_178_38178392_qa_1/task.toml +++ b/tasks/0038_178_38178392_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0038_178_38178392_qa_1" +name = "smoldataenvs-train/0038_178_38178392_qa_1" description = "Which column in the dataset contains entirely missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Unnamed: 32" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0038_189_38189340_qa_1/task.toml b/tasks/0038_189_38189340_qa_1/task.toml index 2f97b2d071a4bb0bf2fe55701a8dab4a14815543..52db0550d4ba81babb0cee6eb8056e603176af52 100644 --- a/tasks/0038_189_38189340_qa_1/task.toml +++ b/tasks/0038_189_38189340_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0038_189_38189340_qa_1" +name = "smoldataenvs-train/0038_189_38189340_qa_1" description = "What is the highest test accuracy achieved among the different gradient descent implementations in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.86" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0038_189_38189340_qa_2/task.toml b/tasks/0038_189_38189340_qa_2/task.toml index 6b88f8140e6d24efaca5272ace141a89cd71e111..043367a28d04e931e93f13532d35789836c2d481 100644 --- a/tasks/0038_189_38189340_qa_2/task.toml +++ b/tasks/0038_189_38189340_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0038_189_38189340_qa_2" +name = "smoldataenvs-train/0038_189_38189340_qa_2" description = "What is the difference in test accuracy between the gradient descent implementation and the sklearn LogisticRegression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.04" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0038_194_38194359_qa_1/task.toml b/tasks/0038_194_38194359_qa_1/task.toml index 26d18f5e17ff15e71fbfd11d9d6d754841a08ea6..9f1d00e380ffccfb41af76c4beecbc79c07a7720 100644 --- a/tasks/0038_194_38194359_qa_1/task.toml +++ b/tasks/0038_194_38194359_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0038_194_38194359_qa_1" +name = "smoldataenvs-train/0038_194_38194359_qa_1" description = "What percentage of the dataset consists of individuals earning more than $50K?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "24.08" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_225_38225070_qa_5/task.toml b/tasks/0038_225_38225070_qa_5/task.toml index 59f6d2991530a67117b843c3ba3e891d4c79038d..9ecda638b194199238e6a2adb858b9bc81d64a8f 100644 --- a/tasks/0038_225_38225070_qa_5/task.toml +++ b/tasks/0038_225_38225070_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0038_225_38225070_qa_5" +name = "smoldataenvs-train/0038_225_38225070_qa_5" description = "What is the support for the itemset consisting of CORNFLAKES and COFFEE?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.20" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_280_38280511_qa_5/task.toml b/tasks/0038_280_38280511_qa_5/task.toml index 781fe30758dbe3910522848109082e7ebe12b6ec..726f65356ff173214ded8434069778d9a9629c10 100644 --- a/tasks/0038_280_38280511_qa_5/task.toml +++ b/tasks/0038_280_38280511_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0038_280_38280511_qa_5" +name = "smoldataenvs-train/0038_280_38280511_qa_5" description = "Is there a statistically significant relationship between geographic region and insurance charges in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0038_316_38316301_qa_1/task.toml b/tasks/0038_316_38316301_qa_1/task.toml index 86384a593475356543080401bd27415895b7aaf3..f6198dcab775cc5a27298eabd071456f7580dee2 100644 --- a/tasks/0038_316_38316301_qa_1/task.toml +++ b/tasks/0038_316_38316301_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0038_316_38316301_qa_1" +name = "smoldataenvs-train/0038_316_38316301_qa_1" description = "What was the total number of missing values in the 'bare_nucleoli' column before imputation in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_322_38322925_qa_1/task.toml b/tasks/0038_322_38322925_qa_1/task.toml index e5d6da8786800c396191ce5cb011b89b2188323f..71a9d0499ac5f78f44f2a269d4e05890d0bc00f8 100644 --- a/tasks/0038_322_38322925_qa_1/task.toml +++ b/tasks/0038_322_38322925_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0038_322_38322925_qa_1" +name = "smoldataenvs-train/0038_322_38322925_qa_1" description = "Which type of post (ask or show) has a higher average number of comments on Hacker News?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ask" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_322_38322925_qa_2/task.toml b/tasks/0038_322_38322925_qa_2/task.toml index 3ee50cf13456867646003239b5f0da459a805534..b1ee46e9758bb9207e1dd88a8ec564b41550a792 100644 --- a/tasks/0038_322_38322925_qa_2/task.toml +++ b/tasks/0038_322_38322925_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0038_322_38322925_qa_2" +name = "smoldataenvs-train/0038_322_38322925_qa_2" description = "What is the highest average number of comments per ask post on Hacker News based on the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "28.68" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_325_38325022_qa_3/task.toml b/tasks/0038_325_38325022_qa_3/task.toml index 76204341121111095d241dc655703ef213d39d79..7da91985e4c778da2b7b1e5cbdec714db1a3cf15 100644 --- a/tasks/0038_325_38325022_qa_3/task.toml +++ b/tasks/0038_325_38325022_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0038_325_38325022_qa_3" +name = "smoldataenvs-train/0038_325_38325022_qa_3" description = "What is the maximum value of North American (NA) sales recorded for any video game?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "41.49" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0038_408_38408993_qa_2/task.toml b/tasks/0038_408_38408993_qa_2/task.toml index 556d0eb3d2d21172d51351876f41410b90279d4f..a6536db9df8162e2a5d5473740994f07181b55a3 100644 --- a/tasks/0038_408_38408993_qa_2/task.toml +++ b/tasks/0038_408_38408993_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0038_408_38408993_qa_2" +name = "smoldataenvs-train/0038_408_38408993_qa_2" description = "How many missing values were present in the SkinThickness column before imputation with median/mean values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "227" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_412_38412209_qa_2/task.toml b/tasks/0038_412_38412209_qa_2/task.toml index 4ae5b3d8549ebdd240672618b1ab4621c3e8737d..b3f935700b6d69f01e792d31ba759462e5e28800 100644 --- a/tasks/0038_412_38412209_qa_2/task.toml +++ b/tasks/0038_412_38412209_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0038_412_38412209_qa_2" +name = "smoldataenvs-train/0038_412_38412209_qa_2" description = "Which publisher holds the largest market share in global sales according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Nintendo" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_412_38412209_qa_3/task.toml b/tasks/0038_412_38412209_qa_3/task.toml index b2bfbf6f2cac95f8239f6e44277595a62dd3f335..5a07f75d742112274891e3af988fac2254224390 100644 --- a/tasks/0038_412_38412209_qa_3/task.toml +++ b/tasks/0038_412_38412209_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0038_412_38412209_qa_3" +name = "smoldataenvs-train/0038_412_38412209_qa_3" description = "Which gaming platform achieved the highest total global sales across all years?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PS2" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_412_38412209_qa_5/task.toml b/tasks/0038_412_38412209_qa_5/task.toml index 0c8edc144b1a086f87b509dcfa745b84917fc1dc..10a72f06d125ff879e8e974c2d7bc23c1da91a4c 100644 --- a/tasks/0038_412_38412209_qa_5/task.toml +++ b/tasks/0038_412_38412209_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0038_412_38412209_qa_5" +name = "smoldataenvs-train/0038_412_38412209_qa_5" description = "Which video game achieved the highest sales specifically in Japan according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Pokemon Red/Pokemon Blue" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0038_417_38417457_qa_1/task.toml b/tasks/0038_417_38417457_qa_1/task.toml index ecd54e400268d055689c78cad5f9470b2f780f79..4e9d319c2f5f9133dd91a54fb67535f4c8248801 100644 --- a/tasks/0038_417_38417457_qa_1/task.toml +++ b/tasks/0038_417_38417457_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0038_417_38417457_qa_1" +name = "smoldataenvs-train/0038_417_38417457_qa_1" description = "Which feature has the highest F-value in the SelectKBest feature selection analysis for predicting house prices?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "sqft_living" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_439_38439618_qa_2/task.toml b/tasks/0038_439_38439618_qa_2/task.toml index e5ca72c9c3b6271e70c360015124db8a735570d8..0ceedad88e1ba7fd4ed94d95eb0810014e7def5c 100644 --- a/tasks/0038_439_38439618_qa_2/task.toml +++ b/tasks/0038_439_38439618_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0038_439_38439618_qa_2" +name = "smoldataenvs-train/0038_439_38439618_qa_2" description = "Among the three models (Lasso, Ridge, Random Forest), which achieved the highest test R² score for predicting European sales (eu_sales)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Ridge" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0038_439_38439618_qa_5/task.toml b/tasks/0038_439_38439618_qa_5/task.toml index e25e5f5b1139e9e36069bb058b25ede7490ca5d3..b5602551b979b77afb1adbb32fc4f70c1f6fc9dc 100644 --- a/tasks/0038_439_38439618_qa_5/task.toml +++ b/tasks/0038_439_38439618_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0038_439_38439618_qa_5" +name = "smoldataenvs-train/0038_439_38439618_qa_5" description = "Which sales region (North American, European, or Japanese) had the highest average sales value in the test dataset after outlier removal?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "North American" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_466_38466860_qa_2/task.toml b/tasks/0038_466_38466860_qa_2/task.toml index 734d55886204813d0ae3ec72f9df4449b9d13056..7f9001f4b1bddddf23c86da3cc3539e954811e05 100644 --- a/tasks/0038_466_38466860_qa_2/task.toml +++ b/tasks/0038_466_38466860_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0038_466_38466860_qa_2" +name = "smoldataenvs-train/0038_466_38466860_qa_2" description = "What is the highest confidence value among all association rules generated with a minimum support threshold of 0.20?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.8" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0038_484_38484065_qa_2/task.toml b/tasks/0038_484_38484065_qa_2/task.toml index acb1c512713653640e5a8ee1972be92a5ae21782..e5f81cf9be85bd2a56e251a40baed17284c370e9 100644 --- a/tasks/0038_484_38484065_qa_2/task.toml +++ b/tasks/0038_484_38484065_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0038_484_38484065_qa_2" +name = "smoldataenvs-train/0038_484_38484065_qa_2" description = "What is the percentage contribution to total sales by the country with the highest sales contribution?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "84.61" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_509_38509914_qa_4/task.toml b/tasks/0038_509_38509914_qa_4/task.toml index 651ae8c73354033c897398ed590dbc0dd990e0a4..b51082f63af5456fadc405211a2ec2480887a021 100644 --- a/tasks/0038_509_38509914_qa_4/task.toml +++ b/tasks/0038_509_38509914_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0038_509_38509914_qa_4" +name = "smoldataenvs-train/0038_509_38509914_qa_4" description = "How many false positives (ham messages incorrectly classified as spam) are present in the test set using the Naive Bayes model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0038_547_38547552_qa_1/task.toml b/tasks/0038_547_38547552_qa_1/task.toml index 3490f705e473d34d84cd93d071711f8bbc3087fe..89d4e2e16474feebcb4fa4bcd5f0fea7a68e00b9 100644 --- a/tasks/0038_547_38547552_qa_1/task.toml +++ b/tasks/0038_547_38547552_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0038_547_38547552_qa_1" +name = "smoldataenvs-train/0038_547_38547552_qa_1" description = "Which department has the highest employee attrition rate according to the cross-tabulation analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Sales" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_547_38547552_qa_3/task.toml b/tasks/0038_547_38547552_qa_3/task.toml index a4e7a566f754abfe82961703ffc9d7b29bb49430..be50ce4ed9c2df154f37785678c188b9cc7d6544 100644 --- a/tasks/0038_547_38547552_qa_3/task.toml +++ b/tasks/0038_547_38547552_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0038_547_38547552_qa_3" +name = "smoldataenvs-train/0038_547_38547552_qa_3" description = "What is the correlation coefficient between Monthly Income and Age according to the correlation matrix analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.50" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_547_38547552_qa_4/task.toml b/tasks/0038_547_38547552_qa_4/task.toml index c18e28887ec41241de3bce28ce29f6951d351b3b..5188ea15c99caa3cfcab6c03e825501bb7f159a9 100644 --- a/tasks/0038_547_38547552_qa_4/task.toml +++ b/tasks/0038_547_38547552_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0038_547_38547552_qa_4" +name = "smoldataenvs-train/0038_547_38547552_qa_4" description = "Which gender group has a higher attrition rate according to the normalized cross-tabulation analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Male" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_553_38553601_qa_2/task.toml b/tasks/0038_553_38553601_qa_2/task.toml index e64af63c411ebf0e123d79157b7bc3630cd5c8cb..ce383dae43913ba4a7d57c8dcafc3b48f6b4085f 100644 --- a/tasks/0038_553_38553601_qa_2/task.toml +++ b/tasks/0038_553_38553601_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0038_553_38553601_qa_2" +name = "smoldataenvs-train/0038_553_38553601_qa_2" description = "What is the maximum waiting time in days between scheduled and appointment dates after data cleaning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "179" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_553_38553601_qa_3/task.toml b/tasks/0038_553_38553601_qa_3/task.toml index 6bb14b4bcfd512f2f259c293000c6996741b11fc..a04efd40137558e50e70d96338e71351d56d2a40 100644 --- a/tasks/0038_553_38553601_qa_3/task.toml +++ b/tasks/0038_553_38553601_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0038_553_38553601_qa_3" +name = "smoldataenvs-train/0038_553_38553601_qa_3" description = "How many unique scheduled weekdays (0-6 mapping) are present in the dataset after processing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_570_38570610_qa_3/task.toml b/tasks/0038_570_38570610_qa_3/task.toml index ec005882d910ac3f197554178347896f4955a778..6ab90a0df2a780d062c680e29be1362407749af7 100644 --- a/tasks/0038_570_38570610_qa_3/task.toml +++ b/tasks/0038_570_38570610_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0038_570_38570610_qa_3" +name = "smoldataenvs-train/0038_570_38570610_qa_3" description = "Which pair of features in the Iris dataset shows the highest positive correlation according to the correlation matrix?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm, PetalWidthCm" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_606_38606903_qa_2/task.toml b/tasks/0038_606_38606903_qa_2/task.toml index 4a9475d7a89cd0cbe3d981edbea77dbdce1f7c53..1db9e7d0c8f2098858ad67be6f3a2bcdf5a1cf7f 100644 --- a/tasks/0038_606_38606903_qa_2/task.toml +++ b/tasks/0038_606_38606903_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0038_606_38606903_qa_2" +name = "smoldataenvs-train/0038_606_38606903_qa_2" description = "How many video game entries are present in the dataset after removing missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16291" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0038_606_38606903_qa_3/task.toml b/tasks/0038_606_38606903_qa_3/task.toml index 7452724f2045730fd5a5718eabc0765041a237b4..19e0de641a9a7db495bac880139628c4056c843d 100644 --- a/tasks/0038_606_38606903_qa_3/task.toml +++ b/tasks/0038_606_38606903_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0038_606_38606903_qa_3" +name = "smoldataenvs-train/0038_606_38606903_qa_3" description = "Which video game has the highest sales in North America (NA_Sales) based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii Sports" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0038_672_38672361_qa_1/task.toml b/tasks/0038_672_38672361_qa_1/task.toml index 074d38cf0f1106db34c58bcc31de47df7939455e..b8329d24868299657a51479086da300fb7a3d832 100644 --- a/tasks/0038_672_38672361_qa_1/task.toml +++ b/tasks/0038_672_38672361_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0038_672_38672361_qa_1" +name = "smoldataenvs-train/0038_672_38672361_qa_1" description = "What is the difference in the proportion of missing Cabin values between non-survivors and survivors in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "27.4" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_672_38672361_qa_3/task.toml b/tasks/0038_672_38672361_qa_3/task.toml index a0b97f2162654dd33252fa56f72802cefc983fd1..3b49ed1e9f2c82494fa82f1abf73bb9358ec7458 100644 --- a/tasks/0038_672_38672361_qa_3/task.toml +++ b/tasks/0038_672_38672361_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0038_672_38672361_qa_3" +name = "smoldataenvs-train/0038_672_38672361_qa_3" description = "What is the median Age value used for imputation in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "28.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0038_672_38672361_qa_4/task.toml b/tasks/0038_672_38672361_qa_4/task.toml index 013171895bf6a1f8c06d48a735a4eade670b5d28..9adab495858a2ad5a4f5418fc562dbe1599dead7 100644 --- a/tasks/0038_672_38672361_qa_4/task.toml +++ b/tasks/0038_672_38672361_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0038_672_38672361_qa_4" +name = "smoldataenvs-train/0038_672_38672361_qa_4" description = "How many passengers had missing Age values before the imputation process was applied?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "177" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0038_673_38673777_qa_1/task.toml b/tasks/0038_673_38673777_qa_1/task.toml index ec02fcca60d0c17b5fa945d8f3320c5c35671cb1..14d19f43bb1356fe1ae731f503b1241681a294e9 100644 --- a/tasks/0038_673_38673777_qa_1/task.toml +++ b/tasks/0038_673_38673777_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0038_673_38673777_qa_1" +name = "smoldataenvs-train/0038_673_38673777_qa_1" description = "What is the total global sales (in millions) of Action and Shooter games on Xbox and PS platforms that sold over 3 million copies in both North America and Europe?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "231.98" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_681_38681339_qa_3/task.toml b/tasks/0038_681_38681339_qa_3/task.toml index 0d7bedf4698e71e1919c0ed9eb697cd56dfb18a8..48a7ec8ab3ed66b490be88d3dfb62d4c6f5e4f7a 100644 --- a/tasks/0038_681_38681339_qa_3/task.toml +++ b/tasks/0038_681_38681339_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0038_681_38681339_qa_3" +name = "smoldataenvs-train/0038_681_38681339_qa_3" description = "Which model demonstrated the best combination of high accuracy (over 94%) and acceptable recall (over 70%) for the churn prediction task?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Gradient Boosting" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0038_688_38688219_qa_1/task.toml b/tasks/0038_688_38688219_qa_1/task.toml index cdd7e83cc1f0fbc1ce7922ac6843b28a485d0c35..db86f895464bc3b8f0bd7d201b1ecb35bda0c06d 100644 --- a/tasks/0038_688_38688219_qa_1/task.toml +++ b/tasks/0038_688_38688219_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0038_688_38688219_qa_1" +name = "smoldataenvs-train/0038_688_38688219_qa_1" description = "Which feature had the highest chi-square importance score in the feature selection process?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "gill-color" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_692_38692630_qa_4/task.toml b/tasks/0038_692_38692630_qa_4/task.toml index 74f1b8586043586ceb29e6160820b32a34471547..629a60cc5c6f0be286900c25db1fee01fd205188 100644 --- a/tasks/0038_692_38692630_qa_4/task.toml +++ b/tasks/0038_692_38692630_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0038_692_38692630_qa_4" +name = "smoldataenvs-train/0038_692_38692630_qa_4" description = "Which K-means cluster exhibits the lowest average review word count based on distribution analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Cluster 2" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0038_705_38705868_qa_4/task.toml b/tasks/0038_705_38705868_qa_4/task.toml index 0496813d0218761cad146ae93fd79a7f80285793..f0e206d9e47c7a28e62c64480bf0ec337754a57c 100644 --- a/tasks/0038_705_38705868_qa_4/task.toml +++ b/tasks/0038_705_38705868_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0038_705_38705868_qa_4" +name = "smoldataenvs-train/0038_705_38705868_qa_4" description = "How many features were selected as optimal by the RFECV method?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "14" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0038_708_38708794_qa_3/task.toml b/tasks/0038_708_38708794_qa_3/task.toml index 8b789b7484ed5e8b1016ed60d7563197b939baf8..2b04e565e9d31706414ac47d3fb562a58be1434a 100644 --- a/tasks/0038_708_38708794_qa_3/task.toml +++ b/tasks/0038_708_38708794_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0038_708_38708794_qa_3" +name = "smoldataenvs-train/0038_708_38708794_qa_3" description = "How many instances of missing values were present in the SkinThickness column before data imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "227" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_779_38779670_qa_5/task.toml b/tasks/0038_779_38779670_qa_5/task.toml index d58805d992008621df7c2277eebe568c45fdc578..1d2f25a6c2b16bff74691ecd958c348c9b88049e 100644 --- a/tasks/0038_779_38779670_qa_5/task.toml +++ b/tasks/0038_779_38779670_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0038_779_38779670_qa_5" +name = "smoldataenvs-train/0038_779_38779670_qa_5" description = "What is the number of unique values present in the 'against_psychic' column after completing all preprocessing steps?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0038_790_38790514_qa_3/task.toml b/tasks/0038_790_38790514_qa_3/task.toml index 14aa449f773cb670edd41fe7791facc9a5d44327..e0a853f675a0d5918d7949c90a3c1aadaefea5d6 100644 --- a/tasks/0038_790_38790514_qa_3/task.toml +++ b/tasks/0038_790_38790514_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0038_790_38790514_qa_3" +name = "smoldataenvs-train/0038_790_38790514_qa_3" description = "What percentage of variance in wine quality is explained by the linear regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "36.71" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0038_821_38821052_qa_5/task.toml b/tasks/0038_821_38821052_qa_5/task.toml index 9662cc4278549b5b07bada23e76cdb73484b06ff..d87b0df490014b74ba71569b60578c1db55fb60c 100644 --- a/tasks/0038_821_38821052_qa_5/task.toml +++ b/tasks/0038_821_38821052_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0038_821_38821052_qa_5" +name = "smoldataenvs-train/0038_821_38821052_qa_5" description = "After outlier handling, which ocean proximity category has the highest number of housing records in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "<1H OCEAN" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_821_38821925_qa_1/task.toml b/tasks/0038_821_38821925_qa_1/task.toml index c5cb4431a880604ec23500c66a33b53e9efdd04e..68e4708e4e3dbcee9c9f1c9388ee438ef4e907c7 100644 --- a/tasks/0038_821_38821925_qa_1/task.toml +++ b/tasks/0038_821_38821925_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0038_821_38821925_qa_1" +name = "smoldataenvs-train/0038_821_38821925_qa_1" description = "Which features have the strongest correlation (absolute value ≥ 0.2) with wine quality in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "alcohol, sulphates, citric acid, volatile acidity" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_845_38845267_qa_1/task.toml b/tasks/0038_845_38845267_qa_1/task.toml index b1c270dd9c9d2419b1c12864c070f7b551aeed38..3842a47277d633db2b61901a4bfa6d203e5706c5 100644 --- a/tasks/0038_845_38845267_qa_1/task.toml +++ b/tasks/0038_845_38845267_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0038_845_38845267_qa_1" +name = "smoldataenvs-train/0038_845_38845267_qa_1" description = "Which publisher has the highest number of produced games in the Sports genre?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic Arts" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_884_38884588_qa_5/task.toml b/tasks/0038_884_38884588_qa_5/task.toml index 067460f906c1b07ec1a6a33f08966dcd076baf8f..cc771d1dc8aaff0fd82543988a09a4e9998c358c 100644 --- a/tasks/0038_884_38884588_qa_5/task.toml +++ b/tasks/0038_884_38884588_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0038_884_38884588_qa_5" +name = "smoldataenvs-train/0038_884_38884588_qa_5" description = "Which three numerical values are explicitly missing from the final grade (G3) categories in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1, 2, 3" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_899_38899462_qa_2/task.toml b/tasks/0038_899_38899462_qa_2/task.toml index 6db6ca5ead85cf45bd80772955360c81cb942a83..0ec40d37f36f403fa386f8934a90a3bbd07e43d0 100644 --- a/tasks/0038_899_38899462_qa_2/task.toml +++ b/tasks/0038_899_38899462_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0038_899_38899462_qa_2" +name = "smoldataenvs-train/0038_899_38899462_qa_2" description = "What is the p-value from the Augmented Dickey-Fuller test?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.99188" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0038_900_38900780_qa_4/task.toml b/tasks/0038_900_38900780_qa_4/task.toml index 8690059a96c76bbb5155f679582e807a2a703c70..28bb97a9434778f83b069d678fba47fa2b5b3788 100644 --- a/tasks/0038_900_38900780_qa_4/task.toml +++ b/tasks/0038_900_38900780_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0038_900_38900780_qa_4" +name = "smoldataenvs-train/0038_900_38900780_qa_4" description = "Which car make has the lowest average risk factor based on the symboling score?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "volvo" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_900_38900780_qa_5/task.toml b/tasks/0038_900_38900780_qa_5/task.toml index 2a63dec6853574ac399568ead692dd61e22d642a..021910cbc8bc474d856dcea51d6ec97acc3f1ad6 100644 --- a/tasks/0038_900_38900780_qa_5/task.toml +++ b/tasks/0038_900_38900780_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0038_900_38900780_qa_5" +name = "smoldataenvs-train/0038_900_38900780_qa_5" description = "Which body style has the highest median price according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "hardtop" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_910_38910811_qa_2/task.toml b/tasks/0038_910_38910811_qa_2/task.toml index 75dbcc5519cf7e746b8e998df02087458f0a2311..5fd47b9ec67520d07ee41ab6d128e958229f0ac7 100644 --- a/tasks/0038_910_38910811_qa_2/task.toml +++ b/tasks/0038_910_38910811_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0038_910_38910811_qa_2" +name = "smoldataenvs-train/0038_910_38910811_qa_2" description = "What is the exact difference in total Global Sales between the X360 and PS3 platforms as calculated in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "22.12" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_910_38910811_qa_5/task.toml b/tasks/0038_910_38910811_qa_5/task.toml index 016575cbd0d34794705005b7133f0cc9f962c2cd..cbdafc02b2733d3018a95299cd066c3e9ff6469d 100644 --- a/tasks/0038_910_38910811_qa_5/task.toml +++ b/tasks/0038_910_38910811_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0038_910_38910811_qa_5" +name = "smoldataenvs-train/0038_910_38910811_qa_5" description = "What is the combined total of North American Sales for all three platforms (X360, PS3, and Wii) presented in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1501.02" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_949_38949847_qa_2/task.toml b/tasks/0038_949_38949847_qa_2/task.toml index 51d4e27fb6800bc00e29624efcba230ff85c286d..8bd89932e264997722f59d780c4539fcc0be5cd3 100644 --- a/tasks/0038_949_38949847_qa_2/task.toml +++ b/tasks/0038_949_38949847_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0038_949_38949847_qa_2" +name = "smoldataenvs-train/0038_949_38949847_qa_2" description = "What percentage of the dataset represents malignant cases (Severity = 1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "48.55" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_951_38951341_qa_1/task.toml b/tasks/0038_951_38951341_qa_1/task.toml index 7c48fab5e2191e120b959efba747856838037e72..75c5465e28bd7412d596572348be9bf0710a24f9 100644 --- a/tasks/0038_951_38951341_qa_1/task.toml +++ b/tasks/0038_951_38951341_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0038_951_38951341_qa_1" +name = "smoldataenvs-train/0038_951_38951341_qa_1" description = "What percentage of co-applicants in the dataset have zero income?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "44.46" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_970_38970193_qa_5/task.toml b/tasks/0038_970_38970193_qa_5/task.toml index 9b0be682e2d64f013c64917c800bef5683c28291..b11f4b9967e54817e3c34971f700658ad6449daa 100644 --- a/tasks/0038_970_38970193_qa_5/task.toml +++ b/tasks/0038_970_38970193_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0038_970_38970193_qa_5" +name = "smoldataenvs-train/0038_970_38970193_qa_5" description = "Which video game genre has the highest total sales in the Japanese market?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Role-Playing" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0038_970_38970844_qa_5/task.toml b/tasks/0038_970_38970844_qa_5/task.toml index 3bcd9b0d299af162d7bf359c3e5f0eb1a65ce2c9..51a33ec0d9b54cd90a46cedce53035047f464620 100644 --- a/tasks/0038_970_38970844_qa_5/task.toml +++ b/tasks/0038_970_38970844_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0038_970_38970844_qa_5" +name = "smoldataenvs-train/0038_970_38970844_qa_5" description = "What is the highest vote average score achieved by any movie in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0038_984_38984979_qa_4/task.toml b/tasks/0038_984_38984979_qa_4/task.toml index 788af7fc472b5fb4c97032fc38fbf0cd65be32fa..f113a6f900979c2eb9a299c7668e2a55545ec468 100644 --- a/tasks/0038_984_38984979_qa_4/task.toml +++ b/tasks/0038_984_38984979_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0038_984_38984979_qa_4" +name = "smoldataenvs-train/0038_984_38984979_qa_4" description = "In which calendar year was the absolute difference between North American and European sales the greatest?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2008" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_006_39006547_qa_4/task.toml b/tasks/0039_006_39006547_qa_4/task.toml index e97cb75981acdb526551d256ad455290cf0a64bf..aee669894e7beb7122a91471f7714af0a0f81b0e 100644 --- a/tasks/0039_006_39006547_qa_4/task.toml +++ b/tasks/0039_006_39006547_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_006_39006547_qa_4" +name = "smoldataenvs-train/0039_006_39006547_qa_4" description = "What is the reduction in standard deviation of BloodPressure after replacing zero values with non-zero means during data cleaning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7.26" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_008_39008775_qa_5/task.toml b/tasks/0039_008_39008775_qa_5/task.toml index 055dddda6828c1a712a3fbd9f302b743f773a3b7..8ee51b1c0be5d3cbd6a7849c05dd3bde47e693bb 100644 --- a/tasks/0039_008_39008775_qa_5/task.toml +++ b/tasks/0039_008_39008775_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_008_39008775_qa_5" +name = "smoldataenvs-train/0039_008_39008775_qa_5" description = "Which solver achieved the highest cross-validation accuracy for logistic regression when using NCA(2) on data without pelvic incidence during grid search?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "newton-cg" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0039_033_39033983_qa_3/task.toml b/tasks/0039_033_39033983_qa_3/task.toml index 200ab4a17205274631294b4663ec5925bf87913d..15b6a50d73706d8fd174c19fd840558a77a0aec7 100644 --- a/tasks/0039_033_39033983_qa_3/task.toml +++ b/tasks/0039_033_39033983_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_033_39033983_qa_3" +name = "smoldataenvs-train/0039_033_39033983_qa_3" description = "What percentage of the original dataset is allocated to the training set after splitting into 70% training and 30% testing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "70" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0039_100_39100190_qa_5/task.toml b/tasks/0039_100_39100190_qa_5/task.toml index e9d6f5767433f445dcf7b3c472d6cba270a32d63..5cebed325ca1c0a834e0d2128e8fa340964aa8c7 100644 --- a/tasks/0039_100_39100190_qa_5/task.toml +++ b/tasks/0039_100_39100190_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_100_39100190_qa_5" +name = "smoldataenvs-train/0039_100_39100190_qa_5" description = "What is the percentage of benign (B) cases in the dataset before any data transformations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "62.7" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0039_111_39111308_qa_1/task.toml b/tasks/0039_111_39111308_qa_1/task.toml index cfd3b2c509743bdb4feae8aba45e1d61bdc786f9..4079ee72822689b0010bd2dd9c1734920df51e6f 100644 --- a/tasks/0039_111_39111308_qa_1/task.toml +++ b/tasks/0039_111_39111308_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_111_39111308_qa_1" +name = "smoldataenvs-train/0039_111_39111308_qa_1" description = "What is the distribution of students across the three performance classes (Low, Medium, High) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Low: 127, Medium: 211, High: 142" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0039_126_39126060_qa_2/task.toml b/tasks/0039_126_39126060_qa_2/task.toml index 45b5dc0131d23f360ce24d02a6ecce40f9631716..ce922da9843a4aa62f143cebae9b05dde7d74b20 100644 --- a/tasks/0039_126_39126060_qa_2/task.toml +++ b/tasks/0039_126_39126060_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_126_39126060_qa_2" +name = "smoldataenvs-train/0039_126_39126060_qa_2" description = "What is the standard deviation of sepal width in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.433594" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0039_131_39131612_qa_1/task.toml b/tasks/0039_131_39131612_qa_1/task.toml index 6c47d792ae9d9ac69700d5a4dc2504cd3c407a57..07781345e3760ba327f1ee9c9f484828d1a49d2e 100644 --- a/tasks/0039_131_39131612_qa_1/task.toml +++ b/tasks/0039_131_39131612_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_131_39131612_qa_1" +name = "smoldataenvs-train/0039_131_39131612_qa_1" description = "Which customer segment has the highest churn rate when combining tenure duration and contract type analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Month-to-month contracts with tenure less than 12 months" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_154_39154395_qa_2/task.toml b/tasks/0039_154_39154395_qa_2/task.toml index 8aeb6cae81d8d7ecdc36253b01afa067e80dc66a..4d5a6572d094a00f63d25e999f30758d5a7206e6 100644 --- a/tasks/0039_154_39154395_qa_2/task.toml +++ b/tasks/0039_154_39154395_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_154_39154395_qa_2" +name = "smoldataenvs-train/0039_154_39154395_qa_2" description = "What is the difference in the number of students whose fathers have 'other' as their job compared to those whose mothers have 'other' as their job?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "76" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_156_39156457_qa_3/task.toml b/tasks/0039_156_39156457_qa_3/task.toml index 3d62bf1cbfbf23b8bf5e79c500bf67dd96db584f..2caa2e5d394968f8653de6860b90b806e12c2575 100644 --- a/tasks/0039_156_39156457_qa_3/task.toml +++ b/tasks/0039_156_39156457_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_156_39156457_qa_3" +name = "smoldataenvs-train/0039_156_39156457_qa_3" description = "What is the highest p-value observed in any Augmented Dickey-Fuller test during the stationarity analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.99188" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0039_159_39159070_qa_2/task.toml b/tasks/0039_159_39159070_qa_2/task.toml index 4659251cdc77441b9a7ea795eb04858044fafffe..737791323ddbc40507452e4e1de92dcb93a46516 100644 --- a/tasks/0039_159_39159070_qa_2/task.toml +++ b/tasks/0039_159_39159070_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_159_39159070_qa_2" +name = "smoldataenvs-train/0039_159_39159070_qa_2" description = "Which gaming platform has the highest average Global_Sales according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "GB" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_161_39161872_qa_5/task.toml b/tasks/0039_161_39161872_qa_5/task.toml index ee127506bc6139b8a963100f52dfb18900c81ea4..cd293bd363a5a876a2a21e8369dd876a877cd307 100644 --- a/tasks/0039_161_39161872_qa_5/task.toml +++ b/tasks/0039_161_39161872_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_161_39161872_qa_5" +name = "smoldataenvs-train/0039_161_39161872_qa_5" description = "Which species has the smallest average Petal Length, and what is that average value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-setosa, 1.464" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_221_39221041_qa_2/task.toml b/tasks/0039_221_39221041_qa_2/task.toml index 67b345f84f0250d5f1e673432b24ba628dba8f3c..4f9d0985a283099c846c8cee3adfd37c24dc5d0a 100644 --- a/tasks/0039_221_39221041_qa_2/task.toml +++ b/tasks/0039_221_39221041_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0039_221_39221041_qa_2" +name = "smoldataenvs-train/0039_221_39221041_qa_2" description = "What percentage of the original dataset was retained after removing rows with missing values during data cleaning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "78.18" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_234_39234687_qa_1/task.toml b/tasks/0039_234_39234687_qa_1/task.toml index 932f1d221e76744d16e06580b3e7da417fd61464..8e705e0ff88e194f95654cf134e9d3793d25a5d2 100644 --- a/tasks/0039_234_39234687_qa_1/task.toml +++ b/tasks/0039_234_39234687_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_234_39234687_qa_1" +name = "smoldataenvs-train/0039_234_39234687_qa_1" description = "Which variable in the dataset has the strongest negative correlation with the final grade (G3)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "failures" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_234_39234687_qa_2/task.toml b/tasks/0039_234_39234687_qa_2/task.toml index 63acdd1898ed34ef62a086ecc98d9ef05497ffb5..878f840ba57d0de126a8a3b45a20e0b3c98704d8 100644 --- a/tasks/0039_234_39234687_qa_2/task.toml +++ b/tasks/0039_234_39234687_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_234_39234687_qa_2" +name = "smoldataenvs-train/0039_234_39234687_qa_2" description = "What is the highest correlation coefficient between any two variables in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.904868" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_243_39243552_qa_4/task.toml b/tasks/0039_243_39243552_qa_4/task.toml index 392dddccbdbeb118652295c0b07711a1243d93cd..a85c013507080fbe37b529d9dc18336989f3d561 100644 --- a/tasks/0039_243_39243552_qa_4/task.toml +++ b/tasks/0039_243_39243552_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0039_243_39243552_qa_4" +name = "smoldataenvs-train/0039_243_39243552_qa_4" description = "What percentage of the total monthly charges is lost due to customer churn?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "30.5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_289_39289264_qa_1/task.toml b/tasks/0039_289_39289264_qa_1/task.toml index 50e7f1256180e7562b47821951d84d105ccaced4..456a8f7f95285e333d5287599e45989b1da12836 100644 --- a/tasks/0039_289_39289264_qa_1/task.toml +++ b/tasks/0039_289_39289264_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_289_39289264_qa_1" +name = "smoldataenvs-train/0039_289_39289264_qa_1" description = "What is the correlation coefficient between the first period grade (G1) and the second period grade (G2) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.852" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0039_293_39293599_qa_4/task.toml b/tasks/0039_293_39293599_qa_4/task.toml index 3fd3171bed232ae4c37460d9392d48bae00ffb62..5daad096b97df73789d4b74c553e496871293ffc 100644 --- a/tasks/0039_293_39293599_qa_4/task.toml +++ b/tasks/0039_293_39293599_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0039_293_39293599_qa_4" +name = "smoldataenvs-train/0039_293_39293599_qa_4" description = "Which customer cluster exhibits the highest average number of transactions per customer?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Cluster 1" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0039_302_39302156_qa_3/task.toml b/tasks/0039_302_39302156_qa_3/task.toml index cbd69e15e8ece4e7c0f190cb83011839e352e020..ff0d2a283cc1f34c5c8b6161766ff817c86db963 100644 --- a/tasks/0039_302_39302156_qa_3/task.toml +++ b/tasks/0039_302_39302156_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_302_39302156_qa_3" +name = "smoldataenvs-train/0039_302_39302156_qa_3" description = "Which employment status category has the highest number of respondents according to the survey data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Employed full-time" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0039_302_39302156_qa_5/task.toml b/tasks/0039_302_39302156_qa_5/task.toml index 7e9fa3d95fa9ec0eb59ddb47f88ea57b919ffbde..4637ee80af08e0bad7d12f4f3a7b57710ca9de3f 100644 --- a/tasks/0039_302_39302156_qa_5/task.toml +++ b/tasks/0039_302_39302156_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_302_39302156_qa_5" +name = "smoldataenvs-train/0039_302_39302156_qa_5" description = "Which learning platform is most frequently recommended by respondents for learning data science?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Kaggle" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_318_39318025_qa_4/task.toml b/tasks/0039_318_39318025_qa_4/task.toml index 173ab6576a4fe3ca356a21c5b7b9d0b0a1133f31..9e10db8db915894f849ba09920a28b35ff90b5a0 100644 --- a/tasks/0039_318_39318025_qa_4/task.toml +++ b/tasks/0039_318_39318025_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_318_39318025_qa_4" +name = "smoldataenvs-train/0039_318_39318025_qa_4" description = "What is the average number of numeric digits in spam messages compared to non-spam messages in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Spam: 15.76, Non-spam: 0.3" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_354_39354291_qa_1/task.toml b/tasks/0039_354_39354291_qa_1/task.toml index a46d7fe11294af3de0f14912b5fd0bb5aadf3567..513a1ce6a8be8ce59c0a7d7ec78fab8e3fa37e35 100644 --- a/tasks/0039_354_39354291_qa_1/task.toml +++ b/tasks/0039_354_39354291_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_354_39354291_qa_1" +name = "smoldataenvs-train/0039_354_39354291_qa_1" description = "What is the percentage of malignant cases in the breast cancer dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "37.26" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_354_39354291_qa_3/task.toml b/tasks/0039_354_39354291_qa_3/task.toml index 6d837e2be0becc6b440f7029de9be49de5ef5a89..7eb2c6a5f174532d41d7e69649a5ce5a7c5e4e4d 100644 --- a/tasks/0039_354_39354291_qa_3/task.toml +++ b/tasks/0039_354_39354291_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0039_354_39354291_qa_3" +name = "smoldataenvs-train/0039_354_39354291_qa_3" description = "What is the test accuracy of the Random Forest Classifier on the breast cancer dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.965" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0039_357_39357103_qa_3/task.toml b/tasks/0039_357_39357103_qa_3/task.toml index a0457b0eb4c8ed1cbf649c9c92ba9cab77398437..2ba94160203c23e1c770eead8afc771216d5e8c8 100644 --- a/tasks/0039_357_39357103_qa_3/task.toml +++ b/tasks/0039_357_39357103_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_357_39357103_qa_3" +name = "smoldataenvs-train/0039_357_39357103_qa_3" description = "What is the median value of the 'concave points_worst' feature across all samples in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.09993" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0039_370_39370613_qa_3/task.toml b/tasks/0039_370_39370613_qa_3/task.toml index d9a9d7f0951528cf7baf3dd81ed8fa71280adb9a..0c5f8285364ed31b67a0ad3fc425e8a4e9cb67d6 100644 --- a/tasks/0039_370_39370613_qa_3/task.toml +++ b/tasks/0039_370_39370613_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_370_39370613_qa_3" +name = "smoldataenvs-train/0039_370_39370613_qa_3" description = "What was the skewness value of the \"Credit amount\" column before applying any transformation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.9496276798326209" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0039_376_39376908_qa_1/task.toml b/tasks/0039_376_39376908_qa_1/task.toml index 7ce70962b0678e7392669a0a6f405a2d301b5a77..22cf9ec3970774c4ee34158e7df595956a333c30 100644 --- a/tasks/0039_376_39376908_qa_1/task.toml +++ b/tasks/0039_376_39376908_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_376_39376908_qa_1" +name = "smoldataenvs-train/0039_376_39376908_qa_1" description = "Which feature in the dataset has the highest positive correlation with wine quality?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "alcohol" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_392_39392401_qa_1/task.toml b/tasks/0039_392_39392401_qa_1/task.toml index a6188c5f9534f631f273495d7c3d8239f77024f9..4a3f6d297e8056ceba417e51e83a2dd198c7ce18 100644 --- a/tasks/0039_392_39392401_qa_1/task.toml +++ b/tasks/0039_392_39392401_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_392_39392401_qa_1" +name = "smoldataenvs-train/0039_392_39392401_qa_1" description = "What is the test accuracy of the logistic regression model on the breast cancer dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.98" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0039_392_39392401_qa_3/task.toml b/tasks/0039_392_39392401_qa_3/task.toml index e5051ea2f9c7531778a8ac2ab5e77d233fe09b69..ecdb66c637a417f53c91afb0b96adcdc842c4a58 100644 --- a/tasks/0039_392_39392401_qa_3/task.toml +++ b/tasks/0039_392_39392401_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_392_39392401_qa_3" +name = "smoldataenvs-train/0039_392_39392401_qa_3" description = "What is the highest test accuracy achieved by the random forest classifier using the original 30 features?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.93" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0039_398_39398269_qa_1/task.toml b/tasks/0039_398_39398269_qa_1/task.toml index 0c2eb42d192cbd43f1a005c50c4fa97ccd9fa9a7..c993fd5b47474e9d09ff0bb78c2fb946f95136ef 100644 --- a/tasks/0039_398_39398269_qa_1/task.toml +++ b/tasks/0039_398_39398269_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0039_398_39398269_qa_1" +name = "smoldataenvs-train/0039_398_39398269_qa_1" description = "How many unique questions remain in the dataset after removing duplicates based on the \"Question\" column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1033" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_409_39409376_qa_2/task.toml b/tasks/0039_409_39409376_qa_2/task.toml index a3a60e025fdc6ca8193f3dfab46a8585fbbb2a49..ced4eac8918204e4926ccb72cb46bee784fcbb74 100644 --- a/tasks/0039_409_39409376_qa_2/task.toml +++ b/tasks/0039_409_39409376_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_409_39409376_qa_2" +name = "smoldataenvs-train/0039_409_39409376_qa_2" description = "What is the highest correlation coefficient between any two numerical features in the iris dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.962757" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_450_39450914_qa_1/task.toml b/tasks/0039_450_39450914_qa_1/task.toml index 3812a67ef0127318688179246fd8de58f359e58e..1fbf9405554bbddc08b15cc8d70814b5ee87833a 100644 --- a/tasks/0039_450_39450914_qa_1/task.toml +++ b/tasks/0039_450_39450914_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0039_450_39450914_qa_1" +name = "smoldataenvs-train/0039_450_39450914_qa_1" description = "What is the correlation coefficient between North American sales and European Union sales in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.76" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0039_450_39450914_qa_3/task.toml b/tasks/0039_450_39450914_qa_3/task.toml index 0fc425ac83f9c89b0b314998d14bf347f5c7705f..554ae95d80f18a24f2feb90d42321851e3980c0e 100644 --- a/tasks/0039_450_39450914_qa_3/task.toml +++ b/tasks/0039_450_39450914_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_450_39450914_qa_3" +name = "smoldataenvs-train/0039_450_39450914_qa_3" description = "What is the most common platform among games with the lowest global sales (0.01 million)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PC" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_450_39450914_qa_4/task.toml b/tasks/0039_450_39450914_qa_4/task.toml index f84bd87ae31294e7b9b3747dcdbbc565f992eeff..91186ec40a342863476c14b4e4e8d0ab661ea0e6 100644 --- a/tasks/0039_450_39450914_qa_4/task.toml +++ b/tasks/0039_450_39450914_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_450_39450914_qa_4" +name = "smoldataenvs-train/0039_450_39450914_qa_4" description = "Which platform has the highest number of games listed in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "DS" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0039_458_39458303_qa_5/task.toml b/tasks/0039_458_39458303_qa_5/task.toml index 69198b41c5ee53938bf44e56a3526b82c8ff038c..0f9cfb0c7162f103959f3ef846603e3eac24abc9 100644 --- a/tasks/0039_458_39458303_qa_5/task.toml +++ b/tasks/0039_458_39458303_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_458_39458303_qa_5" +name = "smoldataenvs-train/0039_458_39458303_qa_5" description = "What is the difference in accuracy between the manually tuned Random Forest model and the best RandomizedSearchCV model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.006467004967004967" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0039_470_39470080_qa_2/task.toml b/tasks/0039_470_39470080_qa_2/task.toml index 84d8375f5863ebe8b9cbf6e425070ad17f81f1f9..8657eb66c1510c30b0b3a902adb112a51068d622 100644 --- a/tasks/0039_470_39470080_qa_2/task.toml +++ b/tasks/0039_470_39470080_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_470_39470080_qa_2" +name = "smoldataenvs-train/0039_470_39470080_qa_2" description = "What is the average base total statistic across all Pokémon in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "428.38" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0039_470_39470080_qa_3/task.toml b/tasks/0039_470_39470080_qa_3/task.toml index 21dc2a305b08a7027644c54c04483446e21791e1..84705d4f417cd766a4b425c9ef9a5a7e59d5c124 100644 --- a/tasks/0039_470_39470080_qa_3/task.toml +++ b/tasks/0039_470_39470080_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_470_39470080_qa_3" +name = "smoldataenvs-train/0039_470_39470080_qa_3" description = "What is the 75th percentile value for the special attack (sp_attack) attribute among all Pokémon?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "91" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0039_477_39477592_qa_5/task.toml b/tasks/0039_477_39477592_qa_5/task.toml index b40a8958d84129e8c36594ff5e87b1a14c4aa88d..8ea5634f1c24edd127781871964e484ba9440934 100644 --- a/tasks/0039_477_39477592_qa_5/task.toml +++ b/tasks/0039_477_39477592_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_477_39477592_qa_5" +name = "smoldataenvs-train/0039_477_39477592_qa_5" description = "What is the training accuracy of the Random Forest model trained on the PCA-reduced dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0039_489_39489585_qa_1/task.toml b/tasks/0039_489_39489585_qa_1/task.toml index 5aed8e54d425ea03fc91a7f9676fde9864bac91a..6c6f2ac84ec7ca6bfed32cf6f88ec5ee3baff8c1 100644 --- a/tasks/0039_489_39489585_qa_1/task.toml +++ b/tasks/0039_489_39489585_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0039_489_39489585_qa_1" +name = "smoldataenvs-train/0039_489_39489585_qa_1" description = "Which generation has the highest number of Pokémon entries in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0039_489_39489585_qa_2/task.toml b/tasks/0039_489_39489585_qa_2/task.toml index 0c68748d57a57c7113070b2b9dfab50405139404..1c02b520a0975ea0a977cedd7052f23322ee583f 100644 --- a/tasks/0039_489_39489585_qa_2/task.toml +++ b/tasks/0039_489_39489585_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0039_489_39489585_qa_2" +name = "smoldataenvs-train/0039_489_39489585_qa_2" description = "Which generation has the fewest Pokémon entries in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_510_39510919_qa_3/task.toml b/tasks/0039_510_39510919_qa_3/task.toml index e03f2a0c6b51f37bea0e4536afb703fe33cdca1f..71957008e2e97ef56225bbf4ead433f67541f142 100644 --- a/tasks/0039_510_39510919_qa_3/task.toml +++ b/tasks/0039_510_39510919_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_510_39510919_qa_3" +name = "smoldataenvs-train/0039_510_39510919_qa_3" description = "Which feature in the dataset contains the highest number of distinct numerical values according to the nunique() analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "density" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0039_510_39510919_qa_4/task.toml b/tasks/0039_510_39510919_qa_4/task.toml index 0cd74308140a57ef85cca535efcf775041f4a43d..0af88fe48cddd3f8ec51038d53843650cb698639 100644 --- a/tasks/0039_510_39510919_qa_4/task.toml +++ b/tasks/0039_510_39510919_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_510_39510919_qa_4" +name = "smoldataenvs-train/0039_510_39510919_qa_4" description = "What is the exact number of unique values present in the 'alcohol' content feature of the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "65" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0039_510_39510919_qa_5/task.toml b/tasks/0039_510_39510919_qa_5/task.toml index 0bfeef2bc9b68782b9430189557c561a53a35e0b..0e9aef30b79dcdee84cf438c56b9bf4c1cec0583 100644 --- a/tasks/0039_510_39510919_qa_5/task.toml +++ b/tasks/0039_510_39510919_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_510_39510919_qa_5" +name = "smoldataenvs-train/0039_510_39510919_qa_5" description = "Which feature exhibits the lowest diversity in its values as measured by the number of unique entries in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "quality" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_512_39512601_qa_2/task.toml b/tasks/0039_512_39512601_qa_2/task.toml index 7418e385af53cbf7a173656f06efb10eabdce904..c1cc0d86b7513fbb1be37162d0197b9a013b192f 100644 --- a/tasks/0039_512_39512601_qa_2/task.toml +++ b/tasks/0039_512_39512601_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_512_39512601_qa_2" +name = "smoldataenvs-train/0039_512_39512601_qa_2" description = "Which error metric has the highest value in the model evaluation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Mean Squared Error" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_513_39513695_qa_3/task.toml b/tasks/0039_513_39513695_qa_3/task.toml index 6da5dc46cd98fe1fd8c007fa5ba5ceb9ea75afaa..71e11968972dfe52ac58fc8a329a653255f4e96b 100644 --- a/tasks/0039_513_39513695_qa_3/task.toml +++ b/tasks/0039_513_39513695_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_513_39513695_qa_3" +name = "smoldataenvs-train/0039_513_39513695_qa_3" description = "What is the frequency of pumpkins originating from Texas in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "115" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_513_39513695_qa_4/task.toml b/tasks/0039_513_39513695_qa_4/task.toml index 26b82667114b9641e56890085f2dcd1f00e6bf03..be2c70cb11348e5f9c712866b56feb1dbcbb390b 100644 --- a/tasks/0039_513_39513695_qa_4/task.toml +++ b/tasks/0039_513_39513695_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_513_39513695_qa_4" +name = "smoldataenvs-train/0039_513_39513695_qa_4" description = "Which state ranks third in the frequency of pumpkin origins in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "CALIFORNIA" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_560_39560761_qa_1/task.toml b/tasks/0039_560_39560761_qa_1/task.toml index 59fc1fb93392452c98788337829e3abec21a88f2..f17acb4e6420187bf1271293899ac3b539fdf4b2 100644 --- a/tasks/0039_560_39560761_qa_1/task.toml +++ b/tasks/0039_560_39560761_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0039_560_39560761_qa_1" +name = "smoldataenvs-train/0039_560_39560761_qa_1" description = "What is the proportion of diabetic patients (Outcome=1) in the PIMA Indians diabetes dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.9%" reward_mode_initial = "flexible" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0039_560_39560761_qa_3/task.toml b/tasks/0039_560_39560761_qa_3/task.toml index bac8219ee88bac60b17fa53df4f409a8631a46ac..3cedba49ac6e6504ae52c9757c8f46632f6e2989 100644 --- a/tasks/0039_560_39560761_qa_3/task.toml +++ b/tasks/0039_560_39560761_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_560_39560761_qa_3" +name = "smoldataenvs-train/0039_560_39560761_qa_3" description = "How many missing values were present in the SkinThickness feature before the imputation process was applied?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "227" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_581_39581749_qa_4/task.toml b/tasks/0039_581_39581749_qa_4/task.toml index 24542d180d6689e12657ddf09bf1aaf5608b280d..6868c0c72e5a2765019855370af6d11b98c95d6c 100644 --- a/tasks/0039_581_39581749_qa_4/task.toml +++ b/tasks/0039_581_39581749_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0039_581_39581749_qa_4" +name = "smoldataenvs-train/0039_581_39581749_qa_4" description = "What is the total number of unique job categories in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0039_581_39581749_qa_5/task.toml b/tasks/0039_581_39581749_qa_5/task.toml index 1cf5197289df7620d45a2c7f2392af4615b917c9..e353a3d14c84635bd129f2f54db55d6141e7497d 100644 --- a/tasks/0039_581_39581749_qa_5/task.toml +++ b/tasks/0039_581_39581749_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0039_581_39581749_qa_5" +name = "smoldataenvs-train/0039_581_39581749_qa_5" description = "Which column in the dataset has the highest number of unique values based on the data summary?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "balance" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0039_599_39599874_qa_1/task.toml b/tasks/0039_599_39599874_qa_1/task.toml index c43615863b86bf063574075522027d36d4a92a32..70c4a8a80b6e701dda6a509c756274926037ce02 100644 --- a/tasks/0039_599_39599874_qa_1/task.toml +++ b/tasks/0039_599_39599874_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_599_39599874_qa_1" +name = "smoldataenvs-train/0039_599_39599874_qa_1" description = "Which video game genre has the highest number of entries in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0039_599_39599874_qa_4/task.toml b/tasks/0039_599_39599874_qa_4/task.toml index e58e473573b541ec92daff7eb77e1a1a835cbc4b..2073ab86421f75302498061934248c973a1547c8 100644 --- a/tasks/0039_599_39599874_qa_4/task.toml +++ b/tasks/0039_599_39599874_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_599_39599874_qa_4" +name = "smoldataenvs-train/0039_599_39599874_qa_4" description = "Which video game holds the highest global sales rank between 1990 and 2000?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Pokemon Red/Pokemon Blue" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_613_39613543_qa_3/task.toml b/tasks/0039_613_39613543_qa_3/task.toml index 7b835608d9b33338fd0432197fd576ac371a1db5..0f550a65196d96d1e84b9441682977744b547ae1 100644 --- a/tasks/0039_613_39613543_qa_3/task.toml +++ b/tasks/0039_613_39613543_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_613_39613543_qa_3" +name = "smoldataenvs-train/0039_613_39613543_qa_3" description = "What is the R² score of the Linear Regression model on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.7916924162472252" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0039_655_39655291_qa_2/task.toml b/tasks/0039_655_39655291_qa_2/task.toml index e27647c74dfdd1736e1bf4653ee2b4699de9e661..f99d3905dacdb856d8ec3c2a39dd8c020242c125 100644 --- a/tasks/0039_655_39655291_qa_2/task.toml +++ b/tasks/0039_655_39655291_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_655_39655291_qa_2" +name = "smoldataenvs-train/0039_655_39655291_qa_2" description = "What is the mean value of the 'quality' target variable in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5.636" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0039_655_39655291_qa_4/task.toml b/tasks/0039_655_39655291_qa_4/task.toml index 54db89c1c20467d4f8e531fbbd4a4151cf5f92b5..515612d790016d10e1f9e4e3c576122d3842217d 100644 --- a/tasks/0039_655_39655291_qa_4/task.toml +++ b/tasks/0039_655_39655291_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0039_655_39655291_qa_4" +name = "smoldataenvs-train/0039_655_39655291_qa_4" description = "How many data points are present in the wine quality dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1599" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0039_667_39667959_qa_5/task.toml b/tasks/0039_667_39667959_qa_5/task.toml index 7120cbfe2096119adc26ec94787d5a3c02ee2bf0..c028c687d3d450d52c6ad312d64fbec5130ece64 100644 --- a/tasks/0039_667_39667959_qa_5/task.toml +++ b/tasks/0039_667_39667959_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_667_39667959_qa_5" +name = "smoldataenvs-train/0039_667_39667959_qa_5" description = "What is the average sepal width for the Iris-setosa species in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.418" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_671_39671080_qa_2/task.toml b/tasks/0039_671_39671080_qa_2/task.toml index d74430edb81c4b3bb9ad37d625fe7e7ec2ab2bb0..b9fd3b41a12904e5b5e2623498e7aaffdb4443b3 100644 --- a/tasks/0039_671_39671080_qa_2/task.toml +++ b/tasks/0039_671_39671080_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_671_39671080_qa_2" +name = "smoldataenvs-train/0039_671_39671080_qa_2" description = "What is the correlation coefficient between Petal Length and Petal Width in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9628654314222122" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0039_708_39708837_qa_1/task.toml b/tasks/0039_708_39708837_qa_1/task.toml index b8efdfc1ae9f95c1674a259d3f50cc329388edb3..d0e5e79e7944262ce19a1296e00704153bb9d86b 100644 --- a/tasks/0039_708_39708837_qa_1/task.toml +++ b/tasks/0039_708_39708837_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_708_39708837_qa_1" +name = "smoldataenvs-train/0039_708_39708837_qa_1" description = "Which MBTI personality type has the highest upper bound in the 95% confidence interval for the average number of words per post?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ESFJ" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0039_714_39714962_qa_1/task.toml b/tasks/0039_714_39714962_qa_1/task.toml index 00e3362f371bfd1e7b7192a2bdb330951e33820e..55b3ea230291a8513b677366d60ffab76178aeda 100644 --- a/tasks/0039_714_39714962_qa_1/task.toml +++ b/tasks/0039_714_39714962_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_714_39714962_qa_1" +name = "smoldataenvs-train/0039_714_39714962_qa_1" description = "Which fuel type has a higher median car price based on the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "diesel" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_714_39714962_qa_2/task.toml b/tasks/0039_714_39714962_qa_2/task.toml index 5a087d3886826f0b86112b530d72db1a136dc1bb..822b96b2c8f87c5e08b864d333ea86ecfdac0745 100644 --- a/tasks/0039_714_39714962_qa_2/task.toml +++ b/tasks/0039_714_39714962_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_714_39714962_qa_2" +name = "smoldataenvs-train/0039_714_39714962_qa_2" description = "Are the median prices of two-door and four-door cars statistically significantly different according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0039_714_39714962_qa_3/task.toml b/tasks/0039_714_39714962_qa_3/task.toml index 7ab5d4e5816d9e9fa49a14d71b19a7f1228a3954..9a696a9a40aa47f8709ed559e4b0a73474814ecc 100644 --- a/tasks/0039_714_39714962_qa_3/task.toml +++ b/tasks/0039_714_39714962_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0039_714_39714962_qa_3" +name = "smoldataenvs-train/0039_714_39714962_qa_3" description = "What is the correlation direction between horsepower and average mileage (calculated as average of city and highway mpg) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "negative" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_715_39715876_qa_1/task.toml b/tasks/0039_715_39715876_qa_1/task.toml index 3c861c514a5292d1d5058eb8f34e6c25e7a33505..d826da611a54208111abb61b2e9229787a5f6f14 100644 --- a/tasks/0039_715_39715876_qa_1/task.toml +++ b/tasks/0039_715_39715876_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_715_39715876_qa_1" +name = "smoldataenvs-train/0039_715_39715876_qa_1" description = "Which Iris species exhibits the highest number of features with positive skewness based on the mean-median analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-setosa" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_729_39729863_qa_2/task.toml b/tasks/0039_729_39729863_qa_2/task.toml index 870eaf3788f97ed7f54f6d97fb962dd41885d3a2..0bc8c31369520732ea50f8e04e152e33a78d63be 100644 --- a/tasks/0039_729_39729863_qa_2/task.toml +++ b/tasks/0039_729_39729863_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0039_729_39729863_qa_2" +name = "smoldataenvs-train/0039_729_39729863_qa_2" description = "What percentage of the total messages in the dataset are classified as spam?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13.4" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_729_39729863_qa_4/task.toml b/tasks/0039_729_39729863_qa_4/task.toml index f4b1b1e0fe4fdf6341b135d1d5ed48f20f2b6aa3..a269e046cb11df112c0ccde77f33142e6cc97d79 100644 --- a/tasks/0039_729_39729863_qa_4/task.toml +++ b/tasks/0039_729_39729863_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_729_39729863_qa_4" +name = "smoldataenvs-train/0039_729_39729863_qa_4" description = "How many non-null entries exist in the \"Unnamed: 2\" column of the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0039_729_39729863_qa_5/task.toml b/tasks/0039_729_39729863_qa_5/task.toml index da5788cb2af78b40d5199e7b11a98822b695b33e..087039ccc04f836e8c31c9e60405f463b510e93d 100644 --- a/tasks/0039_729_39729863_qa_5/task.toml +++ b/tasks/0039_729_39729863_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_729_39729863_qa_5" +name = "smoldataenvs-train/0039_729_39729863_qa_5" description = "Which message classification (ham/spam) has the highest frequency in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ham" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0039_741_39741233_qa_5/task.toml b/tasks/0039_741_39741233_qa_5/task.toml index 8d692173b6503b1d1081e6e027b6295e9f66fb05..9cd07440b33499cc03181c3025cae176cc7f7561 100644 --- a/tasks/0039_741_39741233_qa_5/task.toml +++ b/tasks/0039_741_39741233_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0039_741_39741233_qa_5" +name = "smoldataenvs-train/0039_741_39741233_qa_5" description = "Which education level has the highest median account balance according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "unknown" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_742_39742099_qa_4/task.toml b/tasks/0039_742_39742099_qa_4/task.toml index 8235904ba50e67ca23dc1b607d14ab1ac4817ec0..3265a33812e4b15c8c30988f8f641d4bfc441378 100644 --- a/tasks/0039_742_39742099_qa_4/task.toml +++ b/tasks/0039_742_39742099_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0039_742_39742099_qa_4" +name = "smoldataenvs-train/0039_742_39742099_qa_4" description = "Which U.S. region has the highest number of patients in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Southeast" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0039_742_39742099_qa_5/task.toml b/tasks/0039_742_39742099_qa_5/task.toml index 412665d7bbe3860f06a90b048be25185616f8e81..fda3c2209adae256daa5bdb202af630c3d603a2c 100644 --- a/tasks/0039_742_39742099_qa_5/task.toml +++ b/tasks/0039_742_39742099_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0039_742_39742099_qa_5" +name = "smoldataenvs-train/0039_742_39742099_qa_5" description = "Is the overall multiple linear regression model statistically significant at the 0.05 significance level?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0039_768_39768515_qa_1/task.toml b/tasks/0039_768_39768515_qa_1/task.toml index 8e63825685b0dea7ad93a780895016bad38b18c1..2cbbfcbc38faa8dc05320e9c3c55b25c25a2d7bd 100644 --- a/tasks/0039_768_39768515_qa_1/task.toml +++ b/tasks/0039_768_39768515_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_768_39768515_qa_1" +name = "smoldataenvs-train/0039_768_39768515_qa_1" description = "What is the highest correlation coefficient between house price and any other feature in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.702035" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_769_39769016_qa_3/task.toml b/tasks/0039_769_39769016_qa_3/task.toml index 4d66637a534327474d2c12193f1dc20b2c15e803..1ee974c38a9144082b9d6268484fcc8f90a6b08e 100644 --- a/tasks/0039_769_39769016_qa_3/task.toml +++ b/tasks/0039_769_39769016_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0039_769_39769016_qa_3" +name = "smoldataenvs-train/0039_769_39769016_qa_3" description = "Calculate the range (maximum minus minimum) of petal length measurements in the dataset." authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0039_782_39782405_qa_2/task.toml b/tasks/0039_782_39782405_qa_2/task.toml index d2f1e52b27964b7eb33946e3bbe962a1241912b9..42628d439fae37ef1fbe1fbb5c47832d88347cdb 100644 --- a/tasks/0039_782_39782405_qa_2/task.toml +++ b/tasks/0039_782_39782405_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_782_39782405_qa_2" +name = "smoldataenvs-train/0039_782_39782405_qa_2" description = "What is the shape of the preprocessed training images dataset after normalization and reshaping?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "(27455, 28, 28, 1)" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_785_39785108_qa_1/task.toml b/tasks/0039_785_39785108_qa_1/task.toml index 0e8468b31ef91cff23baa99bb9e3b2fbbc45a628..8b1e3a8f7a6bbe70f07591335a18c857fe45a1d7 100644 --- a/tasks/0039_785_39785108_qa_1/task.toml +++ b/tasks/0039_785_39785108_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_785_39785108_qa_1" +name = "smoldataenvs-train/0039_785_39785108_qa_1" description = "What is the highest test accuracy achieved when varying the n_neighbors parameter, and which value of K achieved this maximum accuracy?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.964912, K=9" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0039_794_39794889_qa_1/task.toml b/tasks/0039_794_39794889_qa_1/task.toml index 2009fd75fb59b3dfcba35b6622454a84378fd0d5..5356177a950d8caa26faba9aadc1c6402a963cbd 100644 --- a/tasks/0039_794_39794889_qa_1/task.toml +++ b/tasks/0039_794_39794889_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_794_39794889_qa_1" +name = "smoldataenvs-train/0039_794_39794889_qa_1" description = "What is the most frequently assigned score in the wine reviews dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "88" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0039_794_39794889_qa_3/task.toml b/tasks/0039_794_39794889_qa_3/task.toml index 0725d10be90dfd52bb6e71d865c749d9ee013378..b38db276c1be202427619ca2b8aa05cd36653f83 100644 --- a/tasks/0039_794_39794889_qa_3/task.toml +++ b/tasks/0039_794_39794889_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_794_39794889_qa_3" +name = "smoldataenvs-train/0039_794_39794889_qa_3" description = "What is the maximum price recorded for any wine variety, and which variety has this maximum price?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Bordeaux-style Red Blend, 3300" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_810_39810401_qa_1/task.toml b/tasks/0039_810_39810401_qa_1/task.toml index ece791d632a24569bf390a4f665a3a509129f02b..7cc62b8e28c2556991e7a153ebc56708afdc54ef 100644 --- a/tasks/0039_810_39810401_qa_1/task.toml +++ b/tasks/0039_810_39810401_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_810_39810401_qa_1" +name = "smoldataenvs-train/0039_810_39810401_qa_1" description = "Which job title in the dataset recommends Python as the primary programming language the most frequently?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Data Scientist" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_810_39810401_qa_2/task.toml b/tasks/0039_810_39810401_qa_2/task.toml index 80e6c69a834d08f363803b750d26dad213c7aed1..18c96b3d3cd1c027bf694d2646e60ff54b96c510 100644 --- a/tasks/0039_810_39810401_qa_2/task.toml +++ b/tasks/0039_810_39810401_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0039_810_39810401_qa_2" +name = "smoldataenvs-train/0039_810_39810401_qa_2" description = "What is the most frequently selected machine learning tool among respondents planning to learn new tools/technologies in the future?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "TensorFlow" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_810_39810505_qa_1/task.toml b/tasks/0039_810_39810505_qa_1/task.toml index cc88b1536792ea8358a0edf50d9f573a25ad18fc..66de8b1204dbf9510c896292b3a18b5ae6ebb10d 100644 --- a/tasks/0039_810_39810505_qa_1/task.toml +++ b/tasks/0039_810_39810505_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0039_810_39810505_qa_1" +name = "smoldataenvs-train/0039_810_39810505_qa_1" description = "Which country had the highest number of survey respondents, and what was the number?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "United States with 4197 respondents" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_815_39815076_qa_3/task.toml b/tasks/0039_815_39815076_qa_3/task.toml index 691573f0ec0c25ee3e12f43c262e385b893135b3..027027df7379ca2f1f9494af084618a0c6b8f44c 100644 --- a/tasks/0039_815_39815076_qa_3/task.toml +++ b/tasks/0039_815_39815076_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0039_815_39815076_qa_3" +name = "smoldataenvs-train/0039_815_39815076_qa_3" description = "Which algorithm parameter ('auto', 'ball_tree', 'kd_tree', or 'brute') yields the highest model accuracy for the KNN classifier according to the experimental results?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "auto" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0039_818_39818487_qa_1/task.toml b/tasks/0039_818_39818487_qa_1/task.toml index 7630ae09a4d4f87e7bb93118ab7bd8a82c8db9a1..e26213773b3f75430aa571f14560873b91bca342 100644 --- a/tasks/0039_818_39818487_qa_1/task.toml +++ b/tasks/0039_818_39818487_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_818_39818487_qa_1" +name = "smoldataenvs-train/0039_818_39818487_qa_1" description = "Is there a positive correlation between a vehicle's horsepower and its price in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_818_39818487_qa_3/task.toml b/tasks/0039_818_39818487_qa_3/task.toml index 762865f3c9694886d7efb2134eca1a2aa69afbc0..18eccc29ec5c3f3f450a609c3b4fbc300dab668a 100644 --- a/tasks/0039_818_39818487_qa_3/task.toml +++ b/tasks/0039_818_39818487_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_818_39818487_qa_3" +name = "smoldataenvs-train/0039_818_39818487_qa_3" description = "Which transmission type is more common in the dataset after removing duplicates and missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "AUTOMATIC" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_839_39839532_qa_1/task.toml b/tasks/0039_839_39839532_qa_1/task.toml index fb14d68a4839558813eb758878864b150fba5d8f..aee0d4c861492c7a6114fe883f57f8fee0053667 100644 --- a/tasks/0039_839_39839532_qa_1/task.toml +++ b/tasks/0039_839_39839532_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_839_39839532_qa_1" +name = "smoldataenvs-train/0039_839_39839532_qa_1" description = "What is the proportion of diabetic patients (Outcome=1) among individuals with Skin Thickness values greater than the dataset mean?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.3908" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_839_39839532_qa_2/task.toml b/tasks/0039_839_39839532_qa_2/task.toml index 823a6e879a0f10fe72e364c7cab405f6f284d134..8adbfdb081c8650cdda91a313ed0ed63c60248ae 100644 --- a/tasks/0039_839_39839532_qa_2/task.toml +++ b/tasks/0039_839_39839532_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0039_839_39839532_qa_2" +name = "smoldataenvs-train/0039_839_39839532_qa_2" description = "How many patients in the dataset have the maximum recorded number of pregnancies (17)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0039_839_39839532_qa_5/task.toml b/tasks/0039_839_39839532_qa_5/task.toml index 581475e3dffb2ea55e118295ec63798fe77838de..11de652406a92e49b026343f4bf61095e46842ea 100644 --- a/tasks/0039_839_39839532_qa_5/task.toml +++ b/tasks/0039_839_39839532_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0039_839_39839532_qa_5" +name = "smoldataenvs-train/0039_839_39839532_qa_5" description = "What is the maximum Glucose value observed in the dataset, and how many patients exhibit this value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "199, 1" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_850_39850014_qa_2/task.toml b/tasks/0039_850_39850014_qa_2/task.toml index 2d33f6b991affa7edf3d3d3ecf0b5eb1a179984b..7a4ce5ea34b3ab91849a5a7e0aa4794d36cbdbdf 100644 --- a/tasks/0039_850_39850014_qa_2/task.toml +++ b/tasks/0039_850_39850014_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_850_39850014_qa_2" +name = "smoldataenvs-train/0039_850_39850014_qa_2" description = "How many missing values are present in the 'event_type' column of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4819" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0039_850_39850014_qa_3/task.toml b/tasks/0039_850_39850014_qa_3/task.toml index 6cb529191e89a6865105e16e60dff0ad10937987..82daa38db083d8294441c9b1a596ca2dc47e08b1 100644 --- a/tasks/0039_850_39850014_qa_3/task.toml +++ b/tasks/0039_850_39850014_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_850_39850014_qa_3" +name = "smoldataenvs-train/0039_850_39850014_qa_3" description = "How many papers in the dataset have fewer than 50 words in their text content?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_873_39873778_qa_5/task.toml b/tasks/0039_873_39873778_qa_5/task.toml index dea9b438a08189fbd42dfbdb52d5b24f4ca2366a..2705b5558c97e0306ab11a95d4a32af5cdbd1bdd 100644 --- a/tasks/0039_873_39873778_qa_5/task.toml +++ b/tasks/0039_873_39873778_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_873_39873778_qa_5" +name = "smoldataenvs-train/0039_873_39873778_qa_5" description = "Which wine quality score has the highest number of false negatives in the test set predictions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0039_890_39890497_qa_1/task.toml b/tasks/0039_890_39890497_qa_1/task.toml index 7193e9e5277e115c1ea4951fa8c34b06895d4eff..6b5328793f1a4c60a3aa83f4ee6994da9610b2e6 100644 --- a/tasks/0039_890_39890497_qa_1/task.toml +++ b/tasks/0039_890_39890497_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_890_39890497_qa_1" +name = "smoldataenvs-train/0039_890_39890497_qa_1" description = "After applying the filtering criteria (BMI > 10, Blood Pressure > 20, Glucose > 25), what percentage of the original dataset remains?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "94.23" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_908_39908824_qa_4/task.toml b/tasks/0039_908_39908824_qa_4/task.toml index db2c9d636fb3815ecbef3a15f9790a3cb0e1bf56..58b5d1685193f57dd7a645471fe9d474a5cdedd4 100644 --- a/tasks/0039_908_39908824_qa_4/task.toml +++ b/tasks/0039_908_39908824_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_908_39908824_qa_4" +name = "smoldataenvs-train/0039_908_39908824_qa_4" description = "What percentage of the dataset consists of spam messages?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13.4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_963_39963214_qa_3/task.toml b/tasks/0039_963_39963214_qa_3/task.toml index f7a49bafc7f8b8e742995a1a3a47f0feac6fa652..d2ce6f4d74099f2eb5e4cf3333c41605c805eb20 100644 --- a/tasks/0039_963_39963214_qa_3/task.toml +++ b/tasks/0039_963_39963214_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0039_963_39963214_qa_3" +name = "smoldataenvs-train/0039_963_39963214_qa_3" description = "What is the proportion of malignant cases in the validation set used for model evaluation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.3567" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0039_964_39964259_qa_2/task.toml b/tasks/0039_964_39964259_qa_2/task.toml index 789f97155eee8f5e362cee600f75ebe55c23d01b..a63f8ca702b54e055e88a64dc5b5d53a5839b6aa 100644 --- a/tasks/0039_964_39964259_qa_2/task.toml +++ b/tasks/0039_964_39964259_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_964_39964259_qa_2" +name = "smoldataenvs-train/0039_964_39964259_qa_2" description = "What is the F-value from the ANOVA test comparing insurance charges between males and females?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.3997" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0039_968_39968264_qa_1/task.toml b/tasks/0039_968_39968264_qa_1/task.toml index f9d6ab7d77d9e557a00e61f19ba9d3617a1dc5f9..7cda6310a509f303b66bbe858b81fd749bd82b0c 100644 --- a/tasks/0039_968_39968264_qa_1/task.toml +++ b/tasks/0039_968_39968264_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0039_968_39968264_qa_1" +name = "smoldataenvs-train/0039_968_39968264_qa_1" description = "What is the highest test accuracy achieved using the optimal n_neighbors value (K=9) when all other parameters are set to their default values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.964912" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0039_968_39968264_qa_3/task.toml b/tasks/0039_968_39968264_qa_3/task.toml index 998c020fd23819f03b279632aaa2f68f352814d3..7e1d976bc651f9162c0e0bf7bcc244cf9d0f13b5 100644 --- a/tasks/0039_968_39968264_qa_3/task.toml +++ b/tasks/0039_968_39968264_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0039_968_39968264_qa_3" +name = "smoldataenvs-train/0039_968_39968264_qa_3" description = "Does adjusting the weights parameter from 'uniform' to 'distance' result in a statistically significant improvement in test accuracy when all other parameters are set to their default values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0040_008_40008810_qa_1/task.toml b/tasks/0040_008_40008810_qa_1/task.toml index 7ce0925e7b669056145eb2e56308f6caa218bac4..7f04c7147afb4bbbf5661d99950e2e39639aaa15 100644 --- a/tasks/0040_008_40008810_qa_1/task.toml +++ b/tasks/0040_008_40008810_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0040_008_40008810_qa_1" +name = "smoldataenvs-train/0040_008_40008810_qa_1" description = "Which feature has the highest importance in the Random Forest model and what is its importance score?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PBRAND, 0.051858" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0040_067_40067366_qa_4/task.toml b/tasks/0040_067_40067366_qa_4/task.toml index ed576786b36d62dc29df46764e5e33257c063c6f..1cf5fd62c53ac6ec4732564688de0a219b75aa44 100644 --- a/tasks/0040_067_40067366_qa_4/task.toml +++ b/tasks/0040_067_40067366_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0040_067_40067366_qa_4" +name = "smoldataenvs-train/0040_067_40067366_qa_4" description = "What are the exact names of the top 3 most sold video games based on global sales figures?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii Sports, Super Mario Bros., Mario Kart Wii" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0040_178_40178940_qa_3/task.toml b/tasks/0040_178_40178940_qa_3/task.toml index eceb29e959e03620ec52a71d1f4e0c47bac083d8..d15db559083878b0c7c63040454ea307929f154e 100644 --- a/tasks/0040_178_40178940_qa_3/task.toml +++ b/tasks/0040_178_40178940_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0040_178_40178940_qa_3" +name = "smoldataenvs-train/0040_178_40178940_qa_3" description = "What percentage of individuals who work the minimum number of hours per week (1 hour) are classified as earning more than 50K?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10.0" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0040_178_40178940_qa_4/task.toml b/tasks/0040_178_40178940_qa_4/task.toml index b88ca06500dc65bdc497c9252d072c211d4d8e09..e82e084365f1519aea5121f3e006087498d256ff 100644 --- a/tasks/0040_178_40178940_qa_4/task.toml +++ b/tasks/0040_178_40178940_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0040_178_40178940_qa_4" +name = "smoldataenvs-train/0040_178_40178940_qa_4" description = "What is the most common occupation among individuals earning more than 50K in India?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Prof-specialty" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0040_288_40288007_qa_1/task.toml b/tasks/0040_288_40288007_qa_1/task.toml index b6af90b9ea760ef6b11bbc8536d6557b9994e414..ca383467e1b1071d97f3e34a1b306402fcf47f90 100644 --- a/tasks/0040_288_40288007_qa_1/task.toml +++ b/tasks/0040_288_40288007_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0040_288_40288007_qa_1" +name = "smoldataenvs-train/0040_288_40288007_qa_1" description = "Is there a statistically significant association between employee attrition and job satisfaction in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0040_288_40288007_qa_3/task.toml b/tasks/0040_288_40288007_qa_3/task.toml index 5ceac55152068a2b435ca9f9fb37db14da491314..f12958ff371365cc0834be4e3bf07d29a7d6c335 100644 --- a/tasks/0040_288_40288007_qa_3/task.toml +++ b/tasks/0040_288_40288007_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0040_288_40288007_qa_3" +name = "smoldataenvs-train/0040_288_40288007_qa_3" description = "Does the analysis indicate a statistically significant relationship between education level and employee attrition?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "No" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] build_timeout_sec = 600.0 diff --git a/tasks/0040_288_40288007_qa_4/task.toml b/tasks/0040_288_40288007_qa_4/task.toml index a9998b072bbac81ed1024e8a1ceb6b89468ed3f0..0ab2bb71af904428b10196bad9bdfb3a1314776f 100644 --- a/tasks/0040_288_40288007_qa_4/task.toml +++ b/tasks/0040_288_40288007_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0040_288_40288007_qa_4" +name = "smoldataenvs-train/0040_288_40288007_qa_4" description = "Which department shows a statistically significant association between work-life balance and employee attrition?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Research & Development" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0040_298_40298967_qa_4/task.toml b/tasks/0040_298_40298967_qa_4/task.toml index aba3be895c33093b894f3e5147c738677596da66..c65d15f6585a97064ba9ea61d346e5e91f458668 100644 --- a/tasks/0040_298_40298967_qa_4/task.toml +++ b/tasks/0040_298_40298967_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0040_298_40298967_qa_4" +name = "smoldataenvs-train/0040_298_40298967_qa_4" description = "Which number of children corresponds to the lowest average medical charges in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0040_298_40298967_qa_5/task.toml b/tasks/0040_298_40298967_qa_5/task.toml index dbccea39d2af63dc2d902cb28460013ef595aec3..37af393e9fdbf68f76271f157a58d598d11fcb37 100644 --- a/tasks/0040_298_40298967_qa_5/task.toml +++ b/tasks/0040_298_40298967_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0040_298_40298967_qa_5" +name = "smoldataenvs-train/0040_298_40298967_qa_5" description = "What is the coefficient of determination (R-squared) for the multiple linear regression model on the test dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.7979" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0040_340_40340401_qa_2/task.toml b/tasks/0040_340_40340401_qa_2/task.toml index 141815d97fe6f7a738d8319ad7426b2e6b4d6409..0a87841084ece1b30af4514659184232f2371ab7 100644 --- a/tasks/0040_340_40340401_qa_2/task.toml +++ b/tasks/0040_340_40340401_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0040_340_40340401_qa_2" +name = "smoldataenvs-train/0040_340_40340401_qa_2" description = "What is the Pearson correlation coefficient between alcohol content and wine quality as observed in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.47" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0040_340_40340401_qa_3/task.toml b/tasks/0040_340_40340401_qa_3/task.toml index d4309292eea44643bc277939bd7e12ff06041287..a0ecae0619de65c96f96f54eeed6c2e150965849 100644 --- a/tasks/0040_340_40340401_qa_3/task.toml +++ b/tasks/0040_340_40340401_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0040_340_40340401_qa_3" +name = "smoldataenvs-train/0040_340_40340401_qa_3" description = "Is the correlation between total acidity and pH in the dataset statistically significant at the 0.05 significance level?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0040_340_40340401_qa_4/task.toml b/tasks/0040_340_40340401_qa_4/task.toml index 1ef62579e5b72cf66222e0d0f68207ac868be26c..9e36d49837167b9e140044b2f1cb145a54ab6feb 100644 --- a/tasks/0040_340_40340401_qa_4/task.toml +++ b/tasks/0040_340_40340401_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0040_340_40340401_qa_4" +name = "smoldataenvs-train/0040_340_40340401_qa_4" description = "What is the median alcohol content of the wines in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10.2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0040_418_40418668_qa_4/task.toml b/tasks/0040_418_40418668_qa_4/task.toml index e99164eec626895628791e09de03fee2a3f877bd..89c35f46e8d01d2c161558e71da458d42809f3fb 100644 --- a/tasks/0040_418_40418668_qa_4/task.toml +++ b/tasks/0040_418_40418668_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0040_418_40418668_qa_4" +name = "smoldataenvs-train/0040_418_40418668_qa_4" description = "What percentage of patients who died within 5 years had more than 5 axillary nodes?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "40" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0040_418_40418668_qa_5/task.toml b/tasks/0040_418_40418668_qa_5/task.toml index b2cd14e0d39d0981783595906829f6d5e81f3bdf..378117808b3273147ff7a8d289c17f8dc13b0b95 100644 --- a/tasks/0040_418_40418668_qa_5/task.toml +++ b/tasks/0040_418_40418668_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0040_418_40418668_qa_5" +name = "smoldataenvs-train/0040_418_40418668_qa_5" description = "What is the most significant feature identified for predicting patient survival in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "axillary nodes" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0040_423_40423274_qa_5/task.toml b/tasks/0040_423_40423274_qa_5/task.toml index b5c923fadc56fd3c4dcd8fb93df2656be8938754..2b00c2d1996842538a439e9742d655f6ea13c973 100644 --- a/tasks/0040_423_40423274_qa_5/task.toml +++ b/tasks/0040_423_40423274_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0040_423_40423274_qa_5" +name = "smoldataenvs-train/0040_423_40423274_qa_5" description = "How many samples were included in the test set after splitting the data with a 70-30 train/test ratio?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1102" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0040_528_40528853_qa_4/task.toml b/tasks/0040_528_40528853_qa_4/task.toml index 2d439821436f4b8b1ed1ea6b65c8ff9705ff12fe..a895cd029185d35cd1a97f45687df6c5037e3a62 100644 --- a/tasks/0040_528_40528853_qa_4/task.toml +++ b/tasks/0040_528_40528853_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0040_528_40528853_qa_4" +name = "smoldataenvs-train/0040_528_40528853_qa_4" description = "Is the correlation between age and charges in the dataset statistically significant based on the model results?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0040_543_40543827_qa_1/task.toml b/tasks/0040_543_40543827_qa_1/task.toml index c833a6b85786bdb85976731f4241f7f67ca2d493..25ea49ae9ec43f157832ef63fb6f3ccdcc59d1ed 100644 --- a/tasks/0040_543_40543827_qa_1/task.toml +++ b/tasks/0040_543_40543827_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0040_543_40543827_qa_1" +name = "smoldataenvs-train/0040_543_40543827_qa_1" description = "What is the highest mean RAM value among the four price ranges in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3449.232" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0040_576_40576526_qa_2/task.toml b/tasks/0040_576_40576526_qa_2/task.toml index 708b12152d5a0f56246f89172feba8e29b506510..0ce31dd562625e63aeae28b0d3b3e3576fc3a0f4 100644 --- a/tasks/0040_576_40576526_qa_2/task.toml +++ b/tasks/0040_576_40576526_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0040_576_40576526_qa_2" +name = "smoldataenvs-train/0040_576_40576526_qa_2" description = "Which glass type has the highest proportion of samples concentrated in a single k-means cluster?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Type 7" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0040_587_40587072_qa_1/task.toml b/tasks/0040_587_40587072_qa_1/task.toml index 761396e4e41db73ab13651b7f5bb2e03e03a2bec..0e689e5db5537578bd5607d1591e131391b36567 100644 --- a/tasks/0040_587_40587072_qa_1/task.toml +++ b/tasks/0040_587_40587072_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0040_587_40587072_qa_1" +name = "smoldataenvs-train/0040_587_40587072_qa_1" description = "What is the highest F-value obtained from the ANOVA tests comparing categorical variables (sex, region, smoker) against insurance charges?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2177.614868056519" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0040_604_40604616_qa_1/task.toml b/tasks/0040_604_40604616_qa_1/task.toml index e2afed50f0284407f1a26d4b3daacb0d58561a87..e9f72af57d674569a88c15df446c7980c1a5f0c4 100644 --- a/tasks/0040_604_40604616_qa_1/task.toml +++ b/tasks/0040_604_40604616_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0040_604_40604616_qa_1" +name = "smoldataenvs-train/0040_604_40604616_qa_1" description = "What was the most frequently observed quality rating in the original dataset before the binary transformation was applied?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0040_620_40620313_qa_1/task.toml b/tasks/0040_620_40620313_qa_1/task.toml index 4e2bb1ca10fc7b01185c55f1ffa5318b5d198528..b3ac479e1417373689f344efb3d7a700cfffb29a 100644 --- a/tasks/0040_620_40620313_qa_1/task.toml +++ b/tasks/0040_620_40620313_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0040_620_40620313_qa_1" +name = "smoldataenvs-train/0040_620_40620313_qa_1" description = "What percentage of the dataset consists of benign tumors?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "62.7" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0040_620_40620313_qa_3/task.toml b/tasks/0040_620_40620313_qa_3/task.toml index 320a90e5b48505bad95e443aaacab36b689d642a..25e540ae9e34f2017c7690c2f5d05069c62594db 100644 --- a/tasks/0040_620_40620313_qa_3/task.toml +++ b/tasks/0040_620_40620313_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0040_620_40620313_qa_3" +name = "smoldataenvs-train/0040_620_40620313_qa_3" description = "How many samples are allocated to the test set after splitting the data with a 20% test size?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "114" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0040_620_40620313_qa_5/task.toml b/tasks/0040_620_40620313_qa_5/task.toml index 009cc5d140330e293fd43cffe50f5fe538c519a8..a67c6872f2c5a8c2e7971b20d7b6527b743fb1fc 100644 --- a/tasks/0040_620_40620313_qa_5/task.toml +++ b/tasks/0040_620_40620313_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0040_620_40620313_qa_5" +name = "smoldataenvs-train/0040_620_40620313_qa_5" description = "How many missing values are present in the 'Unnamed: 32' column of the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "569" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0040_627_40627093_qa_1/task.toml b/tasks/0040_627_40627093_qa_1/task.toml index 1e44af73affa241e26b0181c1468279be181dbe5..4d04b4b706e70d44d8a75076a815bdbf716c2089 100644 --- a/tasks/0040_627_40627093_qa_1/task.toml +++ b/tasks/0040_627_40627093_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0040_627_40627093_qa_1" +name = "smoldataenvs-train/0040_627_40627093_qa_1" description = "Which feature shows the strongest positive correlation with the diabetes outcome (Outcome=1) in the preprocessed dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0040_627_40627093_qa_4/task.toml b/tasks/0040_627_40627093_qa_4/task.toml index 08a504ae503a5086c5be76ca87ac181350a79448..30e481d5a4f6d13457cd1b536b5218a235ba8ea3 100644 --- a/tasks/0040_627_40627093_qa_4/task.toml +++ b/tasks/0040_627_40627093_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0040_627_40627093_qa_4" +name = "smoldataenvs-train/0040_627_40627093_qa_4" description = "Which data scaling method (MinMaxScaler vs Z-score) produced the highest test accuracy score for SVM models with C=10 parameter?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "MinMaxScaler" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0040_644_40644570_qa_1/task.toml b/tasks/0040_644_40644570_qa_1/task.toml index 7781dd088e75a832f7ce54a763161b35d4359794..e2feaba546d769647ed304c94b78db9ea3dea287 100644 --- a/tasks/0040_644_40644570_qa_1/task.toml +++ b/tasks/0040_644_40644570_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0040_644_40644570_qa_1" +name = "smoldataenvs-train/0040_644_40644570_qa_1" description = "What is the percentage of the minority class in the target variable after removing outliers from the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12.3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0040_673_40673055_qa_1/task.toml b/tasks/0040_673_40673055_qa_1/task.toml index 87cec521fa784146e17394bc99691e92dd97fe42..370dd60b44d7a9fd4af7cf7059f91fa7700c1e7a 100644 --- a/tasks/0040_673_40673055_qa_1/task.toml +++ b/tasks/0040_673_40673055_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0040_673_40673055_qa_1" +name = "smoldataenvs-train/0040_673_40673055_qa_1" description = "What is the most common genre of video games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0040_673_40673055_qa_4/task.toml b/tasks/0040_673_40673055_qa_4/task.toml index 7868ba70370d7da6f4c7cb5640a70897a897ccd6..1a3f1643b976c17ad6bc9529a147eceb497ea2c4 100644 --- a/tasks/0040_673_40673055_qa_4/task.toml +++ b/tasks/0040_673_40673055_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0040_673_40673055_qa_4" +name = "smoldataenvs-train/0040_673_40673055_qa_4" description = "What is the highest global sales figure recorded for a single video game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "82.74" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0040_713_40713386_qa_2/task.toml b/tasks/0040_713_40713386_qa_2/task.toml index 8a46d1f45536417309b4e1bd7bc942b7f09da9a1..fc4b55d7af1035470a243bb938707d3cc5ac2b01 100644 --- a/tasks/0040_713_40713386_qa_2/task.toml +++ b/tasks/0040_713_40713386_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0040_713_40713386_qa_2" +name = "smoldataenvs-train/0040_713_40713386_qa_2" description = "What is the correlation coefficient between poverty rate and high school graduation rate across states in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.86" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0040_713_40713386_qa_3/task.toml b/tasks/0040_713_40713386_qa_3/task.toml index 0ad83c66393419489b3ea891e41fb09101f6a56f..ffa273678e03ba705a491e013595967009bd6aa1 100644 --- a/tasks/0040_713_40713386_qa_3/task.toml +++ b/tasks/0040_713_40713386_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0040_713_40713386_qa_3" +name = "smoldataenvs-train/0040_713_40713386_qa_3" description = "What percentage of police killing victims in the dataset were identified as male?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "95.9" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0040_717_40717045_qa_2/task.toml b/tasks/0040_717_40717045_qa_2/task.toml index 97d28548521a4dce54606a6d02e5a999b40b82dc..a36109124e747268dfbfe1215e5ffc3959d6751e 100644 --- a/tasks/0040_717_40717045_qa_2/task.toml +++ b/tasks/0040_717_40717045_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0040_717_40717045_qa_2" +name = "smoldataenvs-train/0040_717_40717045_qa_2" description = "Which numerical feature has the highest chi-squared test score when analyzing its relationship with customer churn?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "TotalCharges" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0040_736_40736477_qa_1/task.toml b/tasks/0040_736_40736477_qa_1/task.toml index 9a01e7fc368b308c373c6cda1864120145d74d9f..a76b64850fd8640658fc728a7f2e813c49748572 100644 --- a/tasks/0040_736_40736477_qa_1/task.toml +++ b/tasks/0040_736_40736477_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0040_736_40736477_qa_1" +name = "smoldataenvs-train/0040_736_40736477_qa_1" description = "What is the highest global sales value achieved by any video game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "82.74" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0040_736_40736477_qa_3/task.toml b/tasks/0040_736_40736477_qa_3/task.toml index 17c9172e8db0bb12884a1fba883c2d5073a42fd5..ddd7a253c284345b529f750a14153465ab0d59b8 100644 --- a/tasks/0040_736_40736477_qa_3/task.toml +++ b/tasks/0040_736_40736477_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0040_736_40736477_qa_3" +name = "smoldataenvs-train/0040_736_40736477_qa_3" description = "What is the maximum year value in the dataset after correcting the anomalous entry?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2017" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0040_738_40738491_qa_1/task.toml b/tasks/0040_738_40738491_qa_1/task.toml index 70f53c2196ba68a6dfd7ba280ca6f2124ea5e097..ee3e125993e3a7a47993577c948164d6d70b3ed5 100644 --- a/tasks/0040_738_40738491_qa_1/task.toml +++ b/tasks/0040_738_40738491_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0040_738_40738491_qa_1" +name = "smoldataenvs-train/0040_738_40738491_qa_1" description = "How many sentences were retained in the dataset after removing entries with mismatched sentence and tag lengths?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "47955" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0040_738_40738491_qa_3/task.toml b/tasks/0040_738_40738491_qa_3/task.toml index 93536baeb8a476db6ff0df55d04df78f39e21832..3474a3f9174b6b4a18b515dffdcf55e0cf81ec95 100644 --- a/tasks/0040_738_40738491_qa_3/task.toml +++ b/tasks/0040_738_40738491_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0040_738_40738491_qa_3" +name = "smoldataenvs-train/0040_738_40738491_qa_3" description = "How many unique named entity tags are present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "17" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0040_738_40738491_qa_4/task.toml b/tasks/0040_738_40738491_qa_4/task.toml index e0a14cad396785df53eba7808f5f437df36b32d1..4fcc460d9cb2a330abaabd313ef46bdb1eb7b044 100644 --- a/tasks/0040_738_40738491_qa_4/task.toml +++ b/tasks/0040_738_40738491_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0040_738_40738491_qa_4" +name = "smoldataenvs-train/0040_738_40738491_qa_4" description = "What is the maximum sentence length in terms of word count before padding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "104" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0040_748_40748661_qa_2/task.toml b/tasks/0040_748_40748661_qa_2/task.toml index 9db89cbea1d7d6030d1312f4838c16318c3c15c8..555f36c37433a888a787523b7c895820627f2313 100644 --- a/tasks/0040_748_40748661_qa_2/task.toml +++ b/tasks/0040_748_40748661_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0040_748_40748661_qa_2" +name = "smoldataenvs-train/0040_748_40748661_qa_2" description = "What is the average spending score for female customers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "51.53" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0040_748_40748661_qa_3/task.toml b/tasks/0040_748_40748661_qa_3/task.toml index 823ef516d5dfc07b6b920260e1a79847f4d639d4..7d2f26c8f2b49aabbd8201460a638b2a4412c6fc 100644 --- a/tasks/0040_748_40748661_qa_3/task.toml +++ b/tasks/0040_748_40748661_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0040_748_40748661_qa_3" +name = "smoldataenvs-train/0040_748_40748661_qa_3" description = "What is the interquartile range (IQR) of customer ages in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20.25" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0040_748_40748661_qa_4/task.toml b/tasks/0040_748_40748661_qa_4/task.toml index 412bedaf0985f0c3eb9e476523c6e135f3968826..58957563eb10ef1bbc11ee0958bd6f44345fc817 100644 --- a/tasks/0040_748_40748661_qa_4/task.toml +++ b/tasks/0040_748_40748661_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0040_748_40748661_qa_4" +name = "smoldataenvs-train/0040_748_40748661_qa_4" description = "How many clusters were determined to be optimal for the hierarchical clustering analysis based on the dendrogram?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0040_756_40756003_qa_2/task.toml b/tasks/0040_756_40756003_qa_2/task.toml index 53d4e3ad0a9cad1f3a7daf8c82c874908e964e52..149dcae2d60e5356b0ccdbc30ed1e0116dac0580 100644 --- a/tasks/0040_756_40756003_qa_2/task.toml +++ b/tasks/0040_756_40756003_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0040_756_40756003_qa_2" +name = "smoldataenvs-train/0040_756_40756003_qa_2" description = "What is the total number of benign (B) and malignant (M) tumors in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "357 benign, 212 malignant" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0040_776_40776095_qa_2/task.toml b/tasks/0040_776_40776095_qa_2/task.toml index 766e15f7e8a25bf981e47c1cd8c12f224942c273..030b233b54549d09e59e05e18c31c83e8b893a68 100644 --- a/tasks/0040_776_40776095_qa_2/task.toml +++ b/tasks/0040_776_40776095_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0040_776_40776095_qa_2" +name = "smoldataenvs-train/0040_776_40776095_qa_2" description = "What is the F1-score for malignant (M) cases in the Random Forest model's predictions on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.94" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0040_783_40783338_qa_3/task.toml b/tasks/0040_783_40783338_qa_3/task.toml index d1ad2a820d3f3c85164ce0a04c2d29651de5ec35..af3791a78a723aad3e683ac69a994bf94255e207 100644 --- a/tasks/0040_783_40783338_qa_3/task.toml +++ b/tasks/0040_783_40783338_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0040_783_40783338_qa_3" +name = "smoldataenvs-train/0040_783_40783338_qa_3" description = "Which dataset, the initial test split or the external test dataset, results in a higher Mean Absolute Error (MAE) for the model predictions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "External test dataset" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0040_785_40785152_qa_2/task.toml b/tasks/0040_785_40785152_qa_2/task.toml index 40906a86ed4fc83a548e973d951f5266fee249ba..a3661ee9b98bdc169a4abcddb32b02bf8d28e481 100644 --- a/tasks/0040_785_40785152_qa_2/task.toml +++ b/tasks/0040_785_40785152_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0040_785_40785152_qa_2" +name = "smoldataenvs-train/0040_785_40785152_qa_2" description = "What is the interquartile range (IQR) for the 'total sulfur dioxide' feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "40" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0040_785_40785152_qa_5/task.toml b/tasks/0040_785_40785152_qa_5/task.toml index bf8d2aae0b50f42a6cf684c3d3689ee9e0c2607b..5d28aa4becabd3f2b908e4a2d8735a7c7666b6a1 100644 --- a/tasks/0040_785_40785152_qa_5/task.toml +++ b/tasks/0040_785_40785152_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0040_785_40785152_qa_5" +name = "smoldataenvs-train/0040_785_40785152_qa_5" description = "What is the minimum value of 'fixed acidity' in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0040_788_40788057_qa_3/task.toml b/tasks/0040_788_40788057_qa_3/task.toml index 96484f5ae60def6c3a618a354cfb3f5a85870350..5220eaf85d16d0d9ccccde02599feeb5f741ff8f 100644 --- a/tasks/0040_788_40788057_qa_3/task.toml +++ b/tasks/0040_788_40788057_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0040_788_40788057_qa_3" +name = "smoldataenvs-train/0040_788_40788057_qa_3" description = "What is the median value of the predictor (MEDV) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "21.20" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0040_788_40788057_qa_5/task.toml b/tasks/0040_788_40788057_qa_5/task.toml index 0622fca8b6a634a575de805d62a493d488090029..bfd7c1e40b92947f148aaf113667d012644ae716 100644 --- a/tasks/0040_788_40788057_qa_5/task.toml +++ b/tasks/0040_788_40788057_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0040_788_40788057_qa_5" +name = "smoldataenvs-train/0040_788_40788057_qa_5" description = "What is the R² score of the Random Forest model after hyperparameter tuning on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.8768" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0040_788_40788908_qa_4/task.toml b/tasks/0040_788_40788908_qa_4/task.toml index 2ba358a8d30a552c309d10a8a60922a369f656b7..2f32473de3b747ac60db642145f21db785e901ec 100644 --- a/tasks/0040_788_40788908_qa_4/task.toml +++ b/tasks/0040_788_40788908_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0040_788_40788908_qa_4" +name = "smoldataenvs-train/0040_788_40788908_qa_4" description = "What is the percentage of individuals in the dataset who have an income greater than $50K per year?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "24.081" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0040_789_40789473_qa_3/task.toml b/tasks/0040_789_40789473_qa_3/task.toml index dc0e1c5793bd25266f8490f7de471b19cdc54373..60956bd1872bf39b666e270118cf66d2ceff06f8 100644 --- a/tasks/0040_789_40789473_qa_3/task.toml +++ b/tasks/0040_789_40789473_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0040_789_40789473_qa_3" +name = "smoldataenvs-train/0040_789_40789473_qa_3" description = "How many distinct item types are present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0040_789_40789473_qa_5/task.toml b/tasks/0040_789_40789473_qa_5/task.toml index 18eb93b646ffbce99878f537b7d541141b7367d3..e0fd8fd2ff520166f05e30d9e49c506600f38980 100644 --- a/tasks/0040_789_40789473_qa_5/task.toml +++ b/tasks/0040_789_40789473_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0040_789_40789473_qa_5" +name = "smoldataenvs-train/0040_789_40789473_qa_5" description = "What is the mean 'Item_Weight' used to impute missing values in the 'Item_Weight' column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12.857645" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0040_805_40805436_qa_1/task.toml b/tasks/0040_805_40805436_qa_1/task.toml index c600b0ef625548b92f7fea5e4f200cef3a095ed3..86ccc6ed3f939a1417246e9fc2eeb075724544e2 100644 --- a/tasks/0040_805_40805436_qa_1/task.toml +++ b/tasks/0040_805_40805436_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0040_805_40805436_qa_1" +name = "smoldataenvs-train/0040_805_40805436_qa_1" description = "What percentage of the dataset was missing in the 'Unnamed: 32' column before it was dropped?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "100" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0040_808_40808132_qa_5/task.toml b/tasks/0040_808_40808132_qa_5/task.toml index 267ce8a0b91c6f8f5c1b1966d4180bd8dec047ef..79fa6da5180dafcd14348f30b9de33a0cb09f518 100644 --- a/tasks/0040_808_40808132_qa_5/task.toml +++ b/tasks/0040_808_40808132_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0040_808_40808132_qa_5" +name = "smoldataenvs-train/0040_808_40808132_qa_5" description = "Which machine learning model achieved the highest median accuracy through k-fold cross-validation, and what was this median accuracy value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Decision trees (entropy-based) with median accuracy of 70%" reward_mode_initial = "flexible" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0040_826_40826860_qa_1/task.toml b/tasks/0040_826_40826860_qa_1/task.toml index 23a5a162b64a2a769e7a6c17ad9b6b4cfdc40acb..77d6e661d4335d6a9882ca2a1452669bd7fd3d7d 100644 --- a/tasks/0040_826_40826860_qa_1/task.toml +++ b/tasks/0040_826_40826860_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0040_826_40826860_qa_1" +name = "smoldataenvs-train/0040_826_40826860_qa_1" description = "Which gaming platform has the highest number of games developed based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "DS" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0040_826_40826860_qa_2/task.toml b/tasks/0040_826_40826860_qa_2/task.toml index c0ac4c5e24cea1f0d5ee488a81a7a8158dd6147d..75463f93b5c73c9f107d5c5e59b9de33c8d6ecb7 100644 --- a/tasks/0040_826_40826860_qa_2/task.toml +++ b/tasks/0040_826_40826860_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0040_826_40826860_qa_2" +name = "smoldataenvs-train/0040_826_40826860_qa_2" description = "Which video game genre has the largest number of games developed in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0040_843_40843458_qa_3/task.toml b/tasks/0040_843_40843458_qa_3/task.toml index 6d1a33226470377a235107c3d8ed4e0de9bb195c..389c3eaf766015db39e41f761bcefb87e12dd08f 100644 --- a/tasks/0040_843_40843458_qa_3/task.toml +++ b/tasks/0040_843_40843458_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0040_843_40843458_qa_3" +name = "smoldataenvs-train/0040_843_40843458_qa_3" description = "What is the highest price recorded for a wine in Argentina?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "230" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0040_843_40843458_qa_4/task.toml b/tasks/0040_843_40843458_qa_4/task.toml index 6ba55f5170ccfdb138355053692c409dbf65e6db..1005957e93c8ddacabfd214a3ca444784c9b5719 100644 --- a/tasks/0040_843_40843458_qa_4/task.toml +++ b/tasks/0040_843_40843458_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0040_843_40843458_qa_4" +name = "smoldataenvs-train/0040_843_40843458_qa_4" description = "Which US province has the most wine reviews in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "California" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0040_853_40853543_qa_3/task.toml b/tasks/0040_853_40853543_qa_3/task.toml index 689576555c2a612ef63c1eb0bdecb215c574c2f8..e747e988186b0e61904dd8e0c482802add4aa7cc 100644 --- a/tasks/0040_853_40853543_qa_3/task.toml +++ b/tasks/0040_853_40853543_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0040_853_40853543_qa_3" +name = "smoldataenvs-train/0040_853_40853543_qa_3" description = "What is the interquartile range (IQR) of the number of positive axillary nodes detected for patients who died?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0040_880_40880494_qa_3/task.toml b/tasks/0040_880_40880494_qa_3/task.toml index 841cca64eeb7a1db84614373082b3130d93c5a95..236e0d37fae11882a246d51ceebf6b2c4f250d3e 100644 --- a/tasks/0040_880_40880494_qa_3/task.toml +++ b/tasks/0040_880_40880494_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0040_880_40880494_qa_3" +name = "smoldataenvs-train/0040_880_40880494_qa_3" description = "Is the mean of the x values in the training data higher than the mean of the y values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] build_timeout_sec = 600.0 diff --git a/tasks/0040_896_40896108_qa_3/task.toml b/tasks/0040_896_40896108_qa_3/task.toml index 5ace2cd1c070cc47fc1e592ab380bdeb5e1dcf98..99d92ec4a585ef14a391a96c128c58c521dd3e99 100644 --- a/tasks/0040_896_40896108_qa_3/task.toml +++ b/tasks/0040_896_40896108_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0040_896_40896108_qa_3" +name = "smoldataenvs-train/0040_896_40896108_qa_3" description = "What was the total number of missing values in the Albumin_and_Globulin_Ratio column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0040_916_40916847_qa_3/task.toml b/tasks/0040_916_40916847_qa_3/task.toml index f0d4c3cb05e7309da29a4707c705dcb774772953..b26ac7443881a19dd1f66bfd09762b8270f24aa0 100644 --- a/tasks/0040_916_40916847_qa_3/task.toml +++ b/tasks/0040_916_40916847_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0040_916_40916847_qa_3" +name = "smoldataenvs-train/0040_916_40916847_qa_3" description = "What is the most frequent region in the dataset based on the value counts analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "southeast" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0040_921_40921599_qa_1/task.toml b/tasks/0040_921_40921599_qa_1/task.toml index 0903fcbfb22a2ba3ec94d3f2dc1c094e5674772f..f9f0704ee6052990a469bc496d17c9ad36777881 100644 --- a/tasks/0040_921_40921599_qa_1/task.toml +++ b/tasks/0040_921_40921599_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0040_921_40921599_qa_1" +name = "smoldataenvs-train/0040_921_40921599_qa_1" description = "Which type of post (ask or show) has a higher average number of comments in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ask" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0040_929_40929685_qa_2/task.toml b/tasks/0040_929_40929685_qa_2/task.toml index 7ecc8b929e914c3696962b8d1be46b8cd1ec828f..92372acaeebce20c4a500e3de09ac01a1878e374 100644 --- a/tasks/0040_929_40929685_qa_2/task.toml +++ b/tasks/0040_929_40929685_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0040_929_40929685_qa_2" +name = "smoldataenvs-train/0040_929_40929685_qa_2" description = "Which ocean proximity category has the highest frequency of entries in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "<1H OCEAN" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0040_929_40929691_qa_1/task.toml b/tasks/0040_929_40929691_qa_1/task.toml index 61b3160807f9facf74acfd28050b611566cd5440..9ad011151372e1f5be96307952bef3364ff6d3bb 100644 --- a/tasks/0040_929_40929691_qa_1/task.toml +++ b/tasks/0040_929_40929691_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0040_929_40929691_qa_1" +name = "smoldataenvs-train/0040_929_40929691_qa_1" description = "Which machine learning model achieved the highest cross-validation accuracy score before hyperparameter tuning in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Logistic Regression" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0040_930_40930969_qa_4/task.toml b/tasks/0040_930_40930969_qa_4/task.toml index 4a6f9445f99111518d4b2e37883eb3363f97a3c6..c11ef0d25fba901dabbe4fdbc70406255e70f479 100644 --- a/tasks/0040_930_40930969_qa_4/task.toml +++ b/tasks/0040_930_40930969_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0040_930_40930969_qa_4" +name = "smoldataenvs-train/0040_930_40930969_qa_4" description = "How many categorical variables were converted into dummy variables during the preprocessing phase?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0040_981_40981860_qa_1/task.toml b/tasks/0040_981_40981860_qa_1/task.toml index aa4bfcd572312064ab3e8a0a1d10d140e708a0a2..e04453e54bf2a8c09d56ff2364888d5a822c5495 100644 --- a/tasks/0040_981_40981860_qa_1/task.toml +++ b/tasks/0040_981_40981860_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0040_981_40981860_qa_1" +name = "smoldataenvs-train/0040_981_40981860_qa_1" description = "How many movies are recommended when using the content-based filtering approach for the movie 'Spectre'?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0040_981_40981860_qa_5/task.toml b/tasks/0040_981_40981860_qa_5/task.toml index 32adae761002cee6b7b4ac7d942eb991c5607ec3..718336b069d8d001c3f40164b1162b7c96af3ada 100644 --- a/tasks/0040_981_40981860_qa_5/task.toml +++ b/tasks/0040_981_40981860_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0040_981_40981860_qa_5" +name = "smoldataenvs-train/0040_981_40981860_qa_5" description = "What is the average rating of all movies in the dataset according to the 'vote_average' column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.092171559442011" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0040_983_40983485_qa_1/task.toml b/tasks/0040_983_40983485_qa_1/task.toml index 79a00d79c425def48b576d35b961efee3f4642bb..dbeb20e7a8ecb450a0b12881059bf1c394b43aea 100644 --- a/tasks/0040_983_40983485_qa_1/task.toml +++ b/tasks/0040_983_40983485_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0040_983_40983485_qa_1" +name = "smoldataenvs-train/0040_983_40983485_qa_1" description = "What is the 4-itemset with the highest support count in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "BISCUIT, COCK, COFFEE, CORNFLAKES" reward_mode_initial = "list" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0040_983_40983485_qa_4/task.toml b/tasks/0040_983_40983485_qa_4/task.toml index b510da0fa000593341c64ab93824f65ebebc3416..34e8f21f1619bba55094c254d9cade737cffa555 100644 --- a/tasks/0040_983_40983485_qa_4/task.toml +++ b/tasks/0040_983_40983485_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0040_983_40983485_qa_4" +name = "smoldataenvs-train/0040_983_40983485_qa_4" description = "What is the 3-itemset with the highest support count in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "BISCUIT, BREAD, MILK" reward_mode_initial = "list" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_009_41009075_qa_4/task.toml b/tasks/0041_009_41009075_qa_4/task.toml index 358d3b7d9a13e35023335f0594e1c5e929ba03be..fd7ef975a3978a6463e9e8e42f98f7261094d807 100644 --- a/tasks/0041_009_41009075_qa_4/task.toml +++ b/tasks/0041_009_41009075_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0041_009_41009075_qa_4" +name = "smoldataenvs-train/0041_009_41009075_qa_4" description = "What is the correlation coefficient between the funded loan amount and the number of lenders per loan in the Brazilian dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.976" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_015_41015677_qa_2/task.toml b/tasks/0041_015_41015677_qa_2/task.toml index 671dc1a7803052922c8a0e1949b589f4c70532cd..34575b529a6b01581a96367f0b58711db358eb8e 100644 --- a/tasks/0041_015_41015677_qa_2/task.toml +++ b/tasks/0041_015_41015677_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0041_015_41015677_qa_2" +name = "smoldataenvs-train/0041_015_41015677_qa_2" description = "Which two input features have the strongest positive and negative correlations with wine quality (excluding the target itself)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Alcohol, volatile acidity" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_015_41015677_qa_4/task.toml b/tasks/0041_015_41015677_qa_4/task.toml index f4b025ae7ff734cf0db82eb97d1ba82266d5cac0..3a9b618531151587187e9734e1d055a7f8714fa7 100644 --- a/tasks/0041_015_41015677_qa_4/task.toml +++ b/tasks/0041_015_41015677_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0041_015_41015677_qa_4" +name = "smoldataenvs-train/0041_015_41015677_qa_4" description = "Which machine learning model achieved the highest test accuracy on the balanced wine quality dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Random Forest" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0041_018_41018707_qa_1/task.toml b/tasks/0041_018_41018707_qa_1/task.toml index e6c9e5e51054eb98a2f3df6b6fc1960ed2ee5fa5..0f5a8fcb3ee434d277f949abf3dfe095d93ce3e4 100644 --- a/tasks/0041_018_41018707_qa_1/task.toml +++ b/tasks/0041_018_41018707_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0041_018_41018707_qa_1" +name = "smoldataenvs-train/0041_018_41018707_qa_1" description = "What is the highest test accuracy achieved after performing GridSearchCV for parameter optimization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0041_018_41018707_qa_4/task.toml b/tasks/0041_018_41018707_qa_4/task.toml index a5e8fe1e64a48fafa819525696c59e7f9c759eff..e984de885c602e9404a121e582a496ceb58656b2 100644 --- a/tasks/0041_018_41018707_qa_4/task.toml +++ b/tasks/0041_018_41018707_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0041_018_41018707_qa_4" +name = "smoldataenvs-train/0041_018_41018707_qa_4" description = "Which kernel type, when used with the highest gamma value (500), results in the most overfit model based on the visualizations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "rbf" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0041_020_41020843_qa_1/task.toml b/tasks/0041_020_41020843_qa_1/task.toml index 6e0d7d77237e8ec8b16d6bf1b6b1e73d1b563697..9a887b9187e4eb6f0f6909409b13ba953f19768a 100644 --- a/tasks/0041_020_41020843_qa_1/task.toml +++ b/tasks/0041_020_41020843_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0041_020_41020843_qa_1" +name = "smoldataenvs-train/0041_020_41020843_qa_1" description = "Which feature in the dataset shows the strongest linear relationship with the price_range based on the pairplot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "RAM" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_020_41020843_qa_4/task.toml b/tasks/0041_020_41020843_qa_4/task.toml index 668d25507078a37b4ca28aabe48371de04613c61..37e1be1204ea167bfcaaff5f2de74dd2ca01b161 100644 --- a/tasks/0041_020_41020843_qa_4/task.toml +++ b/tasks/0041_020_41020843_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0041_020_41020843_qa_4" +name = "smoldataenvs-train/0041_020_41020843_qa_4" description = "How many devices in the dataset have a price_range classification of 3?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "500" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0041_034_41034683_qa_2/task.toml b/tasks/0041_034_41034683_qa_2/task.toml index 020afaf42ed9a41d45ddf3f25aae1108bf0b9da6..eff0548f9cb252acd90f699310f15da781f19f85 100644 --- a/tasks/0041_034_41034683_qa_2/task.toml +++ b/tasks/0041_034_41034683_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0041_034_41034683_qa_2" +name = "smoldataenvs-train/0041_034_41034683_qa_2" description = "What is the median number of positive axillary nodes detected for patients who survived versus those who did not survive?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Survived=0, Did not survive=4" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_035_41035825_qa_1/task.toml b/tasks/0041_035_41035825_qa_1/task.toml index 4bf4488956d136fb5c7150e02180a8e69be08f17..414dc6d34c8d5af240b36387b0960ef868d0609f 100644 --- a/tasks/0041_035_41035825_qa_1/task.toml +++ b/tasks/0041_035_41035825_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0041_035_41035825_qa_1" +name = "smoldataenvs-train/0041_035_41035825_qa_1" description = "What percentage of respondents in the dataset reported having a family history of mental illness and also sought treatment for a mental health condition?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "28.4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_074_41074160_qa_1/task.toml b/tasks/0041_074_41074160_qa_1/task.toml index 08a3152e22ca4451b17d122bda11f22a6bbe4dff..4f79e23a59bd62678d4c3cb8fb7b8d11fa4f1e0b 100644 --- a/tasks/0041_074_41074160_qa_1/task.toml +++ b/tasks/0041_074_41074160_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0041_074_41074160_qa_1" +name = "smoldataenvs-train/0041_074_41074160_qa_1" description = "Which chemical feature has the highest absolute correlation with wine quality according to the correlation analysis in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "alcohol" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_084_41084912_qa_1/task.toml b/tasks/0041_084_41084912_qa_1/task.toml index a10b477bcccb2c765af7f2a9060e20282854ca02..4d1db110f4572cb444bfb3a05d95b2456df7a6b5 100644 --- a/tasks/0041_084_41084912_qa_1/task.toml +++ b/tasks/0041_084_41084912_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0041_084_41084912_qa_1" +name = "smoldataenvs-train/0041_084_41084912_qa_1" description = "What is the percentage of missing values in the Outlet_Size column for the test dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "28.27" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0041_143_41143507_qa_1/task.toml b/tasks/0041_143_41143507_qa_1/task.toml index d50aca7e591acfb516cb95818bdeca12c8d60872..3bf02211b1c99f519cd7ab21e5225c9ed06a63ce 100644 --- a/tasks/0041_143_41143507_qa_1/task.toml +++ b/tasks/0041_143_41143507_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0041_143_41143507_qa_1" +name = "smoldataenvs-train/0041_143_41143507_qa_1" description = "Which feature has the highest permutation importance in the trained model based on the test set evaluation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "capital.gain" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0041_145_41145764_qa_1/task.toml b/tasks/0041_145_41145764_qa_1/task.toml index 9c52bde38edcab375c86b9b6f2080008a56a43e9..ee3169d1df64d0ca9fdcee8bdb89cdecea0d5dd6 100644 --- a/tasks/0041_145_41145764_qa_1/task.toml +++ b/tasks/0041_145_41145764_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0041_145_41145764_qa_1" +name = "smoldataenvs-train/0041_145_41145764_qa_1" description = "What is the correlation coefficient between height and BMI in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.928574" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_145_41145764_qa_2/task.toml b/tasks/0041_145_41145764_qa_2/task.toml index 2542010babe9690a40060e22c9526b30fcda6f3d..bf4b361098dd827b57fed501bb6bedd53130a81d 100644 --- a/tasks/0041_145_41145764_qa_2/task.toml +++ b/tasks/0041_145_41145764_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0041_145_41145764_qa_2" +name = "smoldataenvs-train/0041_145_41145764_qa_2" description = "What is the correlation coefficient between weight and BMI in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.885171" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_156_41156002_qa_2/task.toml b/tasks/0041_156_41156002_qa_2/task.toml index 99193bf8271fc8fcf0d68f707fe6ac924f199962..ea52af19a79b597b757ae6c5ff7e8697f26812db 100644 --- a/tasks/0041_156_41156002_qa_2/task.toml +++ b/tasks/0041_156_41156002_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0041_156_41156002_qa_2" +name = "smoldataenvs-train/0041_156_41156002_qa_2" description = "What is the upper threshold value for the credit amount after applying the IQR method?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7882.375" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_156_41156002_qa_4/task.toml b/tasks/0041_156_41156002_qa_4/task.toml index 546bdd2073706ae6763a257cfcd0bb307817b767..5e5f915e753d967c4230f5886f6199ca4247bf65 100644 --- a/tasks/0041_156_41156002_qa_4/task.toml +++ b/tasks/0041_156_41156002_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0041_156_41156002_qa_4" +name = "smoldataenvs-train/0041_156_41156002_qa_4" description = "Which age category has the highest count in the transformed 'katAge' variable?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Young" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_159_41159247_qa_2/task.toml b/tasks/0041_159_41159247_qa_2/task.toml index 288d54a64412771458ecd3f8ae0ba5dc33c97da3..4903257525f168ab3a7a4befea326cddac206fa2 100644 --- a/tasks/0041_159_41159247_qa_2/task.toml +++ b/tasks/0041_159_41159247_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0041_159_41159247_qa_2" +name = "smoldataenvs-train/0041_159_41159247_qa_2" description = "What is the most common number of cylinders in the original dataset before outlier removal?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0041_172_41172395_qa_3/task.toml b/tasks/0041_172_41172395_qa_3/task.toml index 07a7c9ad9b5853f8bc4ffe74455c416f8f6f42fe..4cc0fda6bce0fa83ab78847e839fe9560aa4bbaa 100644 --- a/tasks/0041_172_41172395_qa_3/task.toml +++ b/tasks/0041_172_41172395_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0041_172_41172395_qa_3" +name = "smoldataenvs-train/0041_172_41172395_qa_3" description = "Which sales region (NA, EU, JP, or Other) shows the strongest positive correlation with Global_Sales according to the dataset's correlation matrix?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "NA" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_186_41186066_qa_2/task.toml b/tasks/0041_186_41186066_qa_2/task.toml index 33aed47517e3fc0e82ceae9539683520d2af385e..3a63fde112e1fd5a8357479025f3ee5806b070b6 100644 --- a/tasks/0041_186_41186066_qa_2/task.toml +++ b/tasks/0041_186_41186066_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0041_186_41186066_qa_2" +name = "smoldataenvs-train/0041_186_41186066_qa_2" description = "What was the mean BMI of the dataset after imputing missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "32.45" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_193_41193082_qa_3/task.toml b/tasks/0041_193_41193082_qa_3/task.toml index a9ed3f49c6362f9c095b9557b4319ba8b6dcf6e6..bfb96837be7eb4924a7070fda97641a9863cf16a 100644 --- a/tasks/0041_193_41193082_qa_3/task.toml +++ b/tasks/0041_193_41193082_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0041_193_41193082_qa_3" +name = "smoldataenvs-train/0041_193_41193082_qa_3" description = "Which feature exhibits the strongest positive correlation with 'compactness_worst' in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "concavity_worst" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_193_41193541_qa_5/task.toml b/tasks/0041_193_41193541_qa_5/task.toml index 259a1a61ad24bd4bcc7b0a6ee4e658d2111cd217..d27a0ebde069b749da5855e7bc9a4fc082eeeea9 100644 --- a/tasks/0041_193_41193541_qa_5/task.toml +++ b/tasks/0041_193_41193541_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0041_193_41193541_qa_5" +name = "smoldataenvs-train/0041_193_41193541_qa_5" description = "How many unique categories were present in the 'cylinders' feature prior to one-hot encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0041_198_41198482_qa_1/task.toml b/tasks/0041_198_41198482_qa_1/task.toml index e9ad8f7fc902b35c69e1c3547995c65c71982625..07fd74e6effbcaebd4dde71c463e1751eb023971 100644 --- a/tasks/0041_198_41198482_qa_1/task.toml +++ b/tasks/0041_198_41198482_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0041_198_41198482_qa_1" +name = "smoldataenvs-train/0041_198_41198482_qa_1" description = "What is the percentage of patients in the dataset who survived for five years or more?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "73.53" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_214_41214331_qa_1/task.toml b/tasks/0041_214_41214331_qa_1/task.toml index adb307310016fb466c377483dd16664e86f4e97e..6809bddffbc84330dc33d942140d4b00ab1b2de7 100644 --- a/tasks/0041_214_41214331_qa_1/task.toml +++ b/tasks/0041_214_41214331_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0041_214_41214331_qa_1" +name = "smoldataenvs-train/0041_214_41214331_qa_1" description = "What percentage of the dataset consists of spam messages based on the label distribution?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "14.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_214_41214331_qa_5/task.toml b/tasks/0041_214_41214331_qa_5/task.toml index 29a590e078c800f201cab21a551c61e62ef9c129..6c04f27a38f58ce005b9a8a78a66e2c4965d03ff 100644 --- a/tasks/0041_214_41214331_qa_5/task.toml +++ b/tasks/0041_214_41214331_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0041_214_41214331_qa_5" +name = "smoldataenvs-train/0041_214_41214331_qa_5" description = "What is the shortest message length recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0041_216_41216352_qa_1/task.toml b/tasks/0041_216_41216352_qa_1/task.toml index 09fc2b036e471ad93de2e6aae77849ba02936f3b..718732e1262c2a24914f4f7d3e431d8c0de8949f 100644 --- a/tasks/0041_216_41216352_qa_1/task.toml +++ b/tasks/0041_216_41216352_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0041_216_41216352_qa_1" +name = "smoldataenvs-train/0041_216_41216352_qa_1" description = "What is the highest correlation coefficient between any two variables in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.941047" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_216_41216352_qa_3/task.toml b/tasks/0041_216_41216352_qa_3/task.toml index 9803cd786b8bd9e5c8b50ce39a980b571330cb32..e9d076981fa0833e4eb38aab1440883efb732d41 100644 --- a/tasks/0041_216_41216352_qa_3/task.toml +++ b/tasks/0041_216_41216352_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0041_216_41216352_qa_3" +name = "smoldataenvs-train/0041_216_41216352_qa_3" description = "Which year within the dataset's range recorded the highest sales in the North American market?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2008" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_236_41236083_qa_5/task.toml b/tasks/0041_236_41236083_qa_5/task.toml index a2900e93e2606cef9f10aafe335e5e2941d0639b..9c32667ce8027525529ac3c80a5df34b0f53b086 100644 --- a/tasks/0041_236_41236083_qa_5/task.toml +++ b/tasks/0041_236_41236083_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0041_236_41236083_qa_5" +name = "smoldataenvs-train/0041_236_41236083_qa_5" description = "Which marital status and sex combination (SE_MA) has the highest default rate in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_236_41236723_qa_3/task.toml b/tasks/0041_236_41236723_qa_3/task.toml index 9e1866dfd765aac4620e4f29c7c63a8148fdc580..03f953abfe55fcef829f3c20610bd129c3587833 100644 --- a/tasks/0041_236_41236723_qa_3/task.toml +++ b/tasks/0041_236_41236723_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0041_236_41236723_qa_3" +name = "smoldataenvs-train/0041_236_41236723_qa_3" description = "Which variable has the highest absolute correlation with the 'price' column in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "engine-size" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_243_41243008_qa_1/task.toml b/tasks/0041_243_41243008_qa_1/task.toml index 20862acf1d2c161a27e44ce219bf4600c9640ae4..f2422531cc7f5ea183213f4c5490e97559eed0c7 100644 --- a/tasks/0041_243_41243008_qa_1/task.toml +++ b/tasks/0041_243_41243008_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0041_243_41243008_qa_1" +name = "smoldataenvs-train/0041_243_41243008_qa_1" description = "How many unique categories were present in the 'odor' feature before it was encoded to numerical values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0041_243_41243008_qa_3/task.toml b/tasks/0041_243_41243008_qa_3/task.toml index 8cdd9d63f7d90940c3bcf1a77848b10b969b9a20..39467c4e63fdd8e650c6b9d0f8ae506d61e16003 100644 --- a/tasks/0041_243_41243008_qa_3/task.toml +++ b/tasks/0041_243_41243008_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0041_243_41243008_qa_3" +name = "smoldataenvs-train/0041_243_41243008_qa_3" description = "What is the threshold value used in the root node of the decision tree model for splitting based on the 'gill-color' feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0041_253_41253749_qa_5/task.toml b/tasks/0041_253_41253749_qa_5/task.toml index a12e5c8a9fddec9b9bbba076b40a9f018501bab4..df6f2899ed0c60a613e19cca6523e3c526b60c9b 100644 --- a/tasks/0041_253_41253749_qa_5/task.toml +++ b/tasks/0041_253_41253749_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0041_253_41253749_qa_5" +name = "smoldataenvs-train/0041_253_41253749_qa_5" description = "What is the minimum number of images for any digit after reorganizing the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "204" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_258_41258552_qa_1/task.toml b/tasks/0041_258_41258552_qa_1/task.toml index fd243a2270e5d53e024f35316223bb4e0290cb6f..0abd409e8f05c830a6c5aeb9e0ae9f76df8d07ea 100644 --- a/tasks/0041_258_41258552_qa_1/task.toml +++ b/tasks/0041_258_41258552_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0041_258_41258552_qa_1" +name = "smoldataenvs-train/0041_258_41258552_qa_1" description = "Which feature has the highest positive correlation with wine quality after outlier removal?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "alcohol" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_261_41261699_qa_2/task.toml b/tasks/0041_261_41261699_qa_2/task.toml index 042448b0d64a3062e18348330069a22f2da0db71..480259e20a3ba73c5918975161ee35aeadbf3bc0 100644 --- a/tasks/0041_261_41261699_qa_2/task.toml +++ b/tasks/0041_261_41261699_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0041_261_41261699_qa_2" +name = "smoldataenvs-train/0041_261_41261699_qa_2" description = "What is the p-value from the chi-square test examining the association between video game genres and companies in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0041_268_41268576_qa_1/task.toml b/tasks/0041_268_41268576_qa_1/task.toml index efef024a57a1ccd41769ac1a513c7d650c344f0f..bcd301d2ac7718d3f15203fc96f9463f17a2861c 100644 --- a/tasks/0041_268_41268576_qa_1/task.toml +++ b/tasks/0041_268_41268576_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0041_268_41268576_qa_1" +name = "smoldataenvs-train/0041_268_41268576_qa_1" description = "What percentage of all movies in the dataset are produced by the top 10 countries with the highest number of released films?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "94.7" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_314_41314150_qa_1/task.toml b/tasks/0041_314_41314150_qa_1/task.toml index 1baffd7c2e5a6a69d3e2631340c764366be32984..9249636e06658eab390e03fb148592d40469b843 100644 --- a/tasks/0041_314_41314150_qa_1/task.toml +++ b/tasks/0041_314_41314150_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0041_314_41314150_qa_1" +name = "smoldataenvs-train/0041_314_41314150_qa_1" description = "What is the overall success rate of Kickstarter projects after excluding live projects from the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.3564" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_321_41321221_qa_2/task.toml b/tasks/0041_321_41321221_qa_2/task.toml index 723a93693523861da2183f2d1c0778aba1cea34b..15c7c004e64c90cb1bb376bf1f26bff41d7b4745 100644 --- a/tasks/0041_321_41321221_qa_2/task.toml +++ b/tasks/0041_321_41321221_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0041_321_41321221_qa_2" +name = "smoldataenvs-train/0041_321_41321221_qa_2" description = "Which workclass category has the highest average hours per week worked according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Self-emp-inc" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_321_41321221_qa_3/task.toml b/tasks/0041_321_41321221_qa_3/task.toml index e7a2f6a101610a56f2fd603681c5a93bd052cf05..efb1e5857f19feda5bc03c885382a549cbc09888 100644 --- a/tasks/0041_321_41321221_qa_3/task.toml +++ b/tasks/0041_321_41321221_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0041_321_41321221_qa_3" +name = "smoldataenvs-train/0041_321_41321221_qa_3" description = "What is the most frequent workclass category in the dataset after removing rows with '?' in the 'occupation' column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Private" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_321_41321221_qa_4/task.toml b/tasks/0041_321_41321221_qa_4/task.toml index 3eb86d87f54b3339c988b764e4a28bfb6beba8ff..5941bf3ab932c53b1f11c495032f9efa9c9981c3 100644 --- a/tasks/0041_321_41321221_qa_4/task.toml +++ b/tasks/0041_321_41321221_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0041_321_41321221_qa_4" +name = "smoldataenvs-train/0041_321_41321221_qa_4" description = "How many rows were removed from the dataset when filtering out rows with '?' in the 'occupation' column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1843" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0041_323_41323136_qa_2/task.toml b/tasks/0041_323_41323136_qa_2/task.toml index 9631f0c52a25a1c44858633056585a9bc592802e..677b2a2d93b866aeaa6c015363f59e9d3af9319d 100644 --- a/tasks/0041_323_41323136_qa_2/task.toml +++ b/tasks/0041_323_41323136_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0041_323_41323136_qa_2" +name = "smoldataenvs-train/0041_323_41323136_qa_2" description = "What is the variance of the 'area_mean' feature in the original unprocessed dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "123843.554318" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0041_323_41323136_qa_3/task.toml b/tasks/0041_323_41323136_qa_3/task.toml index 0c0ff1a4efcfdd01444900659f11abf2026b394e..c1edda49296c036536f2ff334e37aeb774992e00 100644 --- a/tasks/0041_323_41323136_qa_3/task.toml +++ b/tasks/0041_323_41323136_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0041_323_41323136_qa_3" +name = "smoldataenvs-train/0041_323_41323136_qa_3" description = "What is the highest F1 score achieved by any of the naive classification baselines presented in the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.5417" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_324_41324781_qa_2/task.toml b/tasks/0041_324_41324781_qa_2/task.toml index 6184ca3ef0a43360bc45279d704afd36a6143f00..ebd49bdd40edaa4f8b771a41f3b7488a999ae9ab 100644 --- a/tasks/0041_324_41324781_qa_2/task.toml +++ b/tasks/0041_324_41324781_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0041_324_41324781_qa_2" +name = "smoldataenvs-train/0041_324_41324781_qa_2" description = "What is the percentage of individuals in the dataset who did not purchase the product (Purchased=0)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "64.25" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0041_421_41421779_qa_5/task.toml b/tasks/0041_421_41421779_qa_5/task.toml index 21b0681ae4f93c0e2842480ec895eafaf7c94d77..a46e2226e858120f2860280b18d3de84b4ae58ac 100644 --- a/tasks/0041_421_41421779_qa_5/task.toml +++ b/tasks/0041_421_41421779_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0041_421_41421779_qa_5" +name = "smoldataenvs-train/0041_421_41421779_qa_5" description = "What is the precision score achieved by the RandomForest classifier on the test set for identifying spam messages?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0041_451_41451584_qa_1/task.toml b/tasks/0041_451_41451584_qa_1/task.toml index 286aaf9b86bc9eda061020134bc7204ffea407a3..daf196b5ba906e8d094785d1690a89bf4782622d 100644 --- a/tasks/0041_451_41451584_qa_1/task.toml +++ b/tasks/0041_451_41451584_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0041_451_41451584_qa_1" +name = "smoldataenvs-train/0041_451_41451584_qa_1" description = "Which feature in the dataset shows the strongest positive correlation with the diabetes outcome (1) after missing value imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_451_41451584_qa_5/task.toml b/tasks/0041_451_41451584_qa_5/task.toml index b5c91c476bd16e63b840fb0543fa849e4c8078c6..f0d759f32aed10cc87779f8887d636ed341f8784 100644 --- a/tasks/0041_451_41451584_qa_5/task.toml +++ b/tasks/0041_451_41451584_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0041_451_41451584_qa_5" +name = "smoldataenvs-train/0041_451_41451584_qa_5" description = "What is the difference in mean BMI between patients who tested positive and negative for diabetes after missing value imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_463_41463553_qa_2/task.toml b/tasks/0041_463_41463553_qa_2/task.toml index 7dcddfca1b82cdfe338a8df785f8686e76b17067..855f70e26e0cfce4b367f2caea56cbb6c9e3a810 100644 --- a/tasks/0041_463_41463553_qa_2/task.toml +++ b/tasks/0041_463_41463553_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0041_463_41463553_qa_2" +name = "smoldataenvs-train/0041_463_41463553_qa_2" description = "Which country has the highest average population in the dataset based on the pie chart analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "India" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_501_41501839_qa_5/task.toml b/tasks/0041_501_41501839_qa_5/task.toml index 3bf327e1d2e128461402bd0893532cb62c92a84b..60ddedb1aaa2f415dee577b36b05633bedcf1d02 100644 --- a/tasks/0041_501_41501839_qa_5/task.toml +++ b/tasks/0041_501_41501839_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0041_501_41501839_qa_5" +name = "smoldataenvs-train/0041_501_41501839_qa_5" description = "How many missing values were imputed in the BloodPressure column after replacing zeroes with NaN?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "35" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_513_41513812_qa_3/task.toml b/tasks/0041_513_41513812_qa_3/task.toml index 8b7604a74b9483dce305f2c25022d9c852207a0b..8b3599b530a46add16f5875094ab46aa0f72f72e 100644 --- a/tasks/0041_513_41513812_qa_3/task.toml +++ b/tasks/0041_513_41513812_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0041_513_41513812_qa_3" +name = "smoldataenvs-train/0041_513_41513812_qa_3" description = "What is the difference in global sales between the top-selling game and the second-highest selling game in 2015?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5.75" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_556_41556433_qa_1/task.toml b/tasks/0041_556_41556433_qa_1/task.toml index 640848cb0b5ea83b8a68b4b8b6dd38ba00869588..8747a769607e39c57f54944af4b3b7146b13c352 100644 --- a/tasks/0041_556_41556433_qa_1/task.toml +++ b/tasks/0041_556_41556433_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0041_556_41556433_qa_1" +name = "smoldataenvs-train/0041_556_41556433_qa_1" description = "What is the original ratio of non-diabetic (Outcome=0) to diabetic (Outcome=1) patients in the PIMA diabetes dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "500:268" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0041_560_41560306_qa_2/task.toml b/tasks/0041_560_41560306_qa_2/task.toml index 7ed7d2a65659c77d10d535d92d0cc3b431c97bee..d734533bcc4d84ba98d3d6304c2ca0e9f86c97ca 100644 --- a/tasks/0041_560_41560306_qa_2/task.toml +++ b/tasks/0041_560_41560306_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0041_560_41560306_qa_2" +name = "smoldataenvs-train/0041_560_41560306_qa_2" description = "Which country experienced the highest increase in life expectancy between 2000 and 2015 in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Zimbabwe" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_560_41560306_qa_4/task.toml b/tasks/0041_560_41560306_qa_4/task.toml index 54d4c5815a7249d1df8bc23c5027cffb6693e702..4ef9ccdf160c66473bd903b58d92cec816b6fa9d 100644 --- a/tasks/0041_560_41560306_qa_4/task.toml +++ b/tasks/0041_560_41560306_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0041_560_41560306_qa_4" +name = "smoldataenvs-train/0041_560_41560306_qa_4" description = "Which factor has the strongest positive correlation with life expectancy among developing countries: income composition of resources, schooling, or percentage expenditure on health?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Schooling" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_571_41571567_qa_1/task.toml b/tasks/0041_571_41571567_qa_1/task.toml index e3ab1ed2d967f37fcda0c91f2b37d5e1ba3758ba..706aadf7f89dfc2cd2a975de910bd19a82b6916d 100644 --- a/tasks/0041_571_41571567_qa_1/task.toml +++ b/tasks/0041_571_41571567_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0041_571_41571567_qa_1" +name = "smoldataenvs-train/0041_571_41571567_qa_1" description = "How many video game entries remain in the dataset after removing rows with missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16291" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0041_571_41571567_qa_4/task.toml b/tasks/0041_571_41571567_qa_4/task.toml index 6a483372845fafc67564492103c1343771655c5b..f30dafb3e6b9d14dce4a340d249f78ba9d75b0d5 100644 --- a/tasks/0041_571_41571567_qa_4/task.toml +++ b/tasks/0041_571_41571567_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0041_571_41571567_qa_4" +name = "smoldataenvs-train/0041_571_41571567_qa_4" description = "Which game in the top 3 of 2015 had the highest sales in Japan?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Call of Duty: Black Ops 3" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_600_41600293_qa_1/task.toml b/tasks/0041_600_41600293_qa_1/task.toml index 4daaf2f6d9ac40198a8660519f7df32cdb50ef28..8ee0a62f58d4be99c8df496a182ef33a14356649 100644 --- a/tasks/0041_600_41600293_qa_1/task.toml +++ b/tasks/0041_600_41600293_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0041_600_41600293_qa_1" +name = "smoldataenvs-train/0041_600_41600293_qa_1" description = "Which contract type is associated with the highest customer churn rate according to the exploratory data analysis (EDA)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Month-to-month" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_600_41600293_qa_2/task.toml b/tasks/0041_600_41600293_qa_2/task.toml index 1569aafb486989e8124383f3d9310a2d45c867e7..d0ec79475c7ee363548d28afe0ba5df454c41162 100644 --- a/tasks/0041_600_41600293_qa_2/task.toml +++ b/tasks/0041_600_41600293_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0041_600_41600293_qa_2" +name = "smoldataenvs-train/0041_600_41600293_qa_2" description = "What was the original ratio of churned to non-churned customers in the dataset before upsampling?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2:5" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_600_41600293_qa_3/task.toml b/tasks/0041_600_41600293_qa_3/task.toml index 3404b0d3fbbc3dc5267ec216e476bbd02e8fc9c9..971ee703c79252a60bf14923ada9857931949ea3 100644 --- a/tasks/0041_600_41600293_qa_3/task.toml +++ b/tasks/0041_600_41600293_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0041_600_41600293_qa_3" +name = "smoldataenvs-train/0041_600_41600293_qa_3" description = "How many unique categories are present in the PaymentMethod column of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0041_604_41604680_qa_3/task.toml b/tasks/0041_604_41604680_qa_3/task.toml index 9a3704f6b7245a6baa9371801680f23c943dc9e3..d49af220f22a636efdfb0ab2c30a90a435f3c658 100644 --- a/tasks/0041_604_41604680_qa_3/task.toml +++ b/tasks/0041_604_41604680_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0041_604_41604680_qa_3" +name = "smoldataenvs-train/0041_604_41604680_qa_3" description = "How many distinct categories are present in the Outlet_Location_Type feature after data loading?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0041_604_41604680_qa_5/task.toml b/tasks/0041_604_41604680_qa_5/task.toml index 0e6b6cce3af7b06dba318c2cbe99a13151240262..bc7f5b98974c85e01b1ebaa1a9095f9cd21d3042 100644 --- a/tasks/0041_604_41604680_qa_5/task.toml +++ b/tasks/0041_604_41604680_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0041_604_41604680_qa_5" +name = "smoldataenvs-train/0041_604_41604680_qa_5" description = "What numerical values are assigned to the \"Low Fat\" and \"Regular\" categories when using LabelEncoder on the cleaned Item_Fat_Content column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Low Fat = 0, Regular = 1" reward_mode_initial = "flexible" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_604_41604696_qa_3/task.toml b/tasks/0041_604_41604696_qa_3/task.toml index 3589d746dc85ab294f8ac3d06733483bc4e3845c..7de5147e5b2dfa6c640e9e6e22d0990ee29c197a 100644 --- a/tasks/0041_604_41604696_qa_3/task.toml +++ b/tasks/0041_604_41604696_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0041_604_41604696_qa_3" +name = "smoldataenvs-train/0041_604_41604696_qa_3" description = "How many missing values were present in the Outlet_Size column before applying the mapping to categorical codes?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2410" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_652_41652253_qa_1/task.toml b/tasks/0041_652_41652253_qa_1/task.toml index 95ea17545bcd6b15c8ebc92513f598776d5a6b62..6f41ca04a87ce1a79e4b83a724c7e7af314188b4 100644 --- a/tasks/0041_652_41652253_qa_1/task.toml +++ b/tasks/0041_652_41652253_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0041_652_41652253_qa_1" +name = "smoldataenvs-train/0041_652_41652253_qa_1" description = "What is the coefficient of determination (R²) for the linear regression model predicting sacral slope from pelvic incidence?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.664159783972475" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_658_41658396_qa_4/task.toml b/tasks/0041_658_41658396_qa_4/task.toml index 131ebb3721e15be1bced6df1a14c7f3187c4df2e..a207777b0095f71b0a9c40546aadbea9e656927b 100644 --- a/tasks/0041_658_41658396_qa_4/task.toml +++ b/tasks/0041_658_41658396_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0041_658_41658396_qa_4" +name = "smoldataenvs-train/0041_658_41658396_qa_4" description = "What is the highest correlation coefficient between any individual sales region and global sales?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.941" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_703_41703265_qa_4/task.toml b/tasks/0041_703_41703265_qa_4/task.toml index 1396f47f059b3ffa1b6952d4d4bef22ec7ffeac7..6fbb8e711251d7ebf4c7f3f3ff0c4ead517eb594 100644 --- a/tasks/0041_703_41703265_qa_4/task.toml +++ b/tasks/0041_703_41703265_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0041_703_41703265_qa_4" +name = "smoldataenvs-train/0041_703_41703265_qa_4" description = "What is the difference in macro-averaged F1-score between the top-performing and bottom-performing models?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.00" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0041_703_41703265_qa_5/task.toml b/tasks/0041_703_41703265_qa_5/task.toml index 987346ee9058899cb2228a8e74722ea8f6af80c5..de0a616b9e210a5deecb2d41a0f4be5b81738849 100644 --- a/tasks/0041_703_41703265_qa_5/task.toml +++ b/tasks/0041_703_41703265_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0041_703_41703265_qa_5" +name = "smoldataenvs-train/0041_703_41703265_qa_5" description = "Which model had the highest weighted average precision, and what was its value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Logistic, SVM, Naive Bayes with 100%" reward_mode_initial = "list_csv" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0041_708_41708867_qa_2/task.toml b/tasks/0041_708_41708867_qa_2/task.toml index c6b43d120ecee05fb8dbb590201d30321c3bd85e..3dd9798abf0c7d0ed56d7e39277720f75c6d92b3 100644 --- a/tasks/0041_708_41708867_qa_2/task.toml +++ b/tasks/0041_708_41708867_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0041_708_41708867_qa_2" +name = "smoldataenvs-train/0041_708_41708867_qa_2" description = "Which age category (young, senior, elder) has the highest median insurance charge value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "elder" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_708_41708867_qa_3/task.toml b/tasks/0041_708_41708867_qa_3/task.toml index 325d2fa20396c7fa146c40274342aeccaa678fb8..0c74da41cd7d47c8e41d6561eaebb8e926d541a9 100644 --- a/tasks/0041_708_41708867_qa_3/task.toml +++ b/tasks/0041_708_41708867_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0041_708_41708867_qa_3" +name = "smoldataenvs-train/0041_708_41708867_qa_3" description = "What BMI category (underweight, normalweight, overweight, obese) has the highest mean insurance charge in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "obese" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_712_41712543_qa_1/task.toml b/tasks/0041_712_41712543_qa_1/task.toml index 7af1b7bd8311768676a5259c890107a7248d0b21..9bf171f17867d32b69f830b0b02e7d5e9b286e76 100644 --- a/tasks/0041_712_41712543_qa_1/task.toml +++ b/tasks/0041_712_41712543_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0041_712_41712543_qa_1" +name = "smoldataenvs-train/0041_712_41712543_qa_1" description = "Which age group has the highest median Total_Conversion rate according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "30-34" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_712_41712543_qa_2/task.toml b/tasks/0041_712_41712543_qa_2/task.toml index b4bd1550dc0e5724c32e160d7fb7e26edc7d6e72..f88323cc04acd916cc4fa8e30bebcca56ed29c1a 100644 --- a/tasks/0041_712_41712543_qa_2/task.toml +++ b/tasks/0041_712_41712543_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0041_712_41712543_qa_2" +name = "smoldataenvs-train/0041_712_41712543_qa_2" description = "What is the maximum number of Approved_Conversions recorded for any single ad in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "21" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0041_714_41714800_qa_3/task.toml b/tasks/0041_714_41714800_qa_3/task.toml index 19c95a141c9498bc604138a3462f8ac7f670de43..b20e7b4be5da53fe4066798cf2a1b6d0f8413e34 100644 --- a/tasks/0041_714_41714800_qa_3/task.toml +++ b/tasks/0041_714_41714800_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0041_714_41714800_qa_3" +name = "smoldataenvs-train/0041_714_41714800_qa_3" description = "Which department has the highest number of employees in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Research & Development" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0041_758_41758573_qa_1/task.toml b/tasks/0041_758_41758573_qa_1/task.toml index eaf8b24633cf4c5dd1c60d6209315c647e697796..c9c1f3b4022e9cc4f3fa27f324407f973f5dc8b3 100644 --- a/tasks/0041_758_41758573_qa_1/task.toml +++ b/tasks/0041_758_41758573_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0041_758_41758573_qa_1" +name = "smoldataenvs-train/0041_758_41758573_qa_1" description = "Which feature in the dataset exhibits the highest number of unique categorical values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "gill-color" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0041_758_41758573_qa_4/task.toml b/tasks/0041_758_41758573_qa_4/task.toml index f3c0fcce6dfb85c256a34100b8dcec918129498d..1fce54b62ed3c4f772a79a548786a80ae2bae90c 100644 --- a/tasks/0041_758_41758573_qa_4/task.toml +++ b/tasks/0041_758_41758573_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0041_758_41758573_qa_4" +name = "smoldataenvs-train/0041_758_41758573_qa_4" description = "How many features in the dataset contain exactly two unique categorical values across all samples?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_818_41818748_qa_3/task.toml b/tasks/0041_818_41818748_qa_3/task.toml index acf0b366045db19f3826b0a9c8f6e9bfdfd33238..0cacaa8da6ec8283fa3d1c92555faa8df0180801 100644 --- a/tasks/0041_818_41818748_qa_3/task.toml +++ b/tasks/0041_818_41818748_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0041_818_41818748_qa_3" +name = "smoldataenvs-train/0041_818_41818748_qa_3" description = "What is the statistically significant correlation coefficient between Item MRP and Item Outlet Sales in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.552" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_818_41818748_qa_5/task.toml b/tasks/0041_818_41818748_qa_5/task.toml index 27bd2a28c2f6cb549ba30d7a36279d6d63b3144a..06f9e2858a2f40938342fdfec329ce497ab4438a 100644 --- a/tasks/0041_818_41818748_qa_5/task.toml +++ b/tasks/0041_818_41818748_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0041_818_41818748_qa_5" +name = "smoldataenvs-train/0041_818_41818748_qa_5" description = "What is the percentage of total sales contributed by Supermarket Type1 outlets compared to other outlet types?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "48.2%" reward_mode_initial = "flexible" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_838_41838462_qa_1/task.toml b/tasks/0041_838_41838462_qa_1/task.toml index 4a4c3d917a5b4dea78b9fa6d965f26932fdef994..04cde21269d0f7f7ed2627a4d139c608ae66e766 100644 --- a/tasks/0041_838_41838462_qa_1/task.toml +++ b/tasks/0041_838_41838462_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0041_838_41838462_qa_1" +name = "smoldataenvs-train/0041_838_41838462_qa_1" description = "What was the median value used to impute missing BloodPressure values in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "72" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_888_41888820_qa_1/task.toml b/tasks/0041_888_41888820_qa_1/task.toml index 55af7063b590aa5f6b5faf86f2d1f7c8d98d6e5c..12cdbef7f73a71899215927fa479801ce3587b84 100644 --- a/tasks/0041_888_41888820_qa_1/task.toml +++ b/tasks/0041_888_41888820_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0041_888_41888820_qa_1" +name = "smoldataenvs-train/0041_888_41888820_qa_1" description = "Which feature shows the highest positive correlation with the diabetes outcome in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0041_888_41888820_qa_2/task.toml b/tasks/0041_888_41888820_qa_2/task.toml index d454940faf5da725b0a9bb4789b815a0f9ca7980..f126283045007b0b82642d4c72097f2d7c70159d 100644 --- a/tasks/0041_888_41888820_qa_2/task.toml +++ b/tasks/0041_888_41888820_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0041_888_41888820_qa_2" +name = "smoldataenvs-train/0041_888_41888820_qa_2" description = "What percentage of the dataset represents individuals diagnosed with diabetes (Outcome = 1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0041_914_41914023_qa_3/task.toml b/tasks/0041_914_41914023_qa_3/task.toml index 08b95d83ba6957adadd1cfd455051a5a2ee66005..d2cae23a98209053b0bf039914aa5e63f45b77fd 100644 --- a/tasks/0041_914_41914023_qa_3/task.toml +++ b/tasks/0041_914_41914023_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0041_914_41914023_qa_3" +name = "smoldataenvs-train/0041_914_41914023_qa_3" description = "What is the percentage of patients in the dataset who have a positive diagnosis (Outcome=1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.8958" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0041_914_41914023_qa_5/task.toml b/tasks/0041_914_41914023_qa_5/task.toml index 2a0b0bfaeeef7ab0530a0f0726f24654261b3dd4..d12a83922e3cee8e3ac2116e4ab1d2832f8e8eb8 100644 --- a/tasks/0041_914_41914023_qa_5/task.toml +++ b/tasks/0041_914_41914023_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0041_914_41914023_qa_5" +name = "smoldataenvs-train/0041_914_41914023_qa_5" description = "What is the range (maximum minus minimum) of the Age feature in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "60" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0041_998_41998385_qa_2/task.toml b/tasks/0041_998_41998385_qa_2/task.toml index 8e00d435228b248b051ff54a521e8e3dba52fdf0..ca311eafc881d66202f59b95527fc915d3335384 100644 --- a/tasks/0041_998_41998385_qa_2/task.toml +++ b/tasks/0041_998_41998385_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0041_998_41998385_qa_2" +name = "smoldataenvs-train/0041_998_41998385_qa_2" description = "What percentage of customers in the dataset ultimately churned (left the service)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26.5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0042_049_42049242_qa_1/task.toml b/tasks/0042_049_42049242_qa_1/task.toml index 4e6727408cfa9cf7aac0a2dbd1dce28650455ee1..cc03981047c341949c581c577af7fcad473bbba0 100644 --- a/tasks/0042_049_42049242_qa_1/task.toml +++ b/tasks/0042_049_42049242_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0042_049_42049242_qa_1" +name = "smoldataenvs-train/0042_049_42049242_qa_1" description = "Which passenger class (Pclass) had the highest survival rate based on the grouped analysis, and what was the survival rate percentage?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1, 62.96%" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_102_42102863_qa_1/task.toml b/tasks/0042_102_42102863_qa_1/task.toml index e5ecad9416d4d2f48adadece2198c2dc4a34db70..13bc9fac7bfe77dee277a412c5c3783b270c76bd 100644 --- a/tasks/0042_102_42102863_qa_1/task.toml +++ b/tasks/0042_102_42102863_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0042_102_42102863_qa_1" +name = "smoldataenvs-train/0042_102_42102863_qa_1" description = "Which feature exhibits the strongest positive correlation with the diagnosis (encoded as 1 for Malignant) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "concave points_worst" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_102_42102863_qa_4/task.toml b/tasks/0042_102_42102863_qa_4/task.toml index aa97738751e0fee5d3b34e8a019799d7f571dd07..3ca4c97738921e1f1d743a6251f9fffd78f0d0f8 100644 --- a/tasks/0042_102_42102863_qa_4/task.toml +++ b/tasks/0042_102_42102863_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0042_102_42102863_qa_4" +name = "smoldataenvs-train/0042_102_42102863_qa_4" description = "What is the standard deviation of the area_mean in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "351.914129" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0042_106_42106983_qa_5/task.toml b/tasks/0042_106_42106983_qa_5/task.toml index e7e2181a2bd5a81088219799f0cf5bfbf7540db7..ae09cea3bd50d4ad51e3a5e89b195da9b545745a 100644 --- a/tasks/0042_106_42106983_qa_5/task.toml +++ b/tasks/0042_106_42106983_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0042_106_42106983_qa_5" +name = "smoldataenvs-train/0042_106_42106983_qa_5" description = "Which previous campaign outcome category has the highest frequency in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "unknown" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0042_124_42124908_qa_1/task.toml b/tasks/0042_124_42124908_qa_1/task.toml index f159e802402b4d73a17a2585d2bd632c9377cf10..b42b2594f0922bc7ff6b3f8afe7e1e5a87fb57f8 100644 --- a/tasks/0042_124_42124908_qa_1/task.toml +++ b/tasks/0042_124_42124908_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0042_124_42124908_qa_1" +name = "smoldataenvs-train/0042_124_42124908_qa_1" description = "What is the predicted price for a house with 2000 sqft of living space using the Linear Regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "517666.39" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0042_124_42124908_qa_3/task.toml b/tasks/0042_124_42124908_qa_3/task.toml index 321afd3091cb5eb7aed28b92621b567501effc1a..2d3f6e517ac258d7e420b3a59d0fc26f182a6310 100644 --- a/tasks/0042_124_42124908_qa_3/task.toml +++ b/tasks/0042_124_42124908_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0042_124_42124908_qa_3" +name = "smoldataenvs-train/0042_124_42124908_qa_3" description = "What is the p-value from the t-test comparing the average prices of houses sold in 2014 and 2015?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.596862" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0042_139_42139358_qa_2/task.toml b/tasks/0042_139_42139358_qa_2/task.toml index f6d9c7191216066a57d313fd5898aa046d82ae6b..c3921a7c3d7f89704c2f02eab76b211a1540bf8e 100644 --- a/tasks/0042_139_42139358_qa_2/task.toml +++ b/tasks/0042_139_42139358_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0042_139_42139358_qa_2" +name = "smoldataenvs-train/0042_139_42139358_qa_2" description = "What is the difference in the number of games won by checkmate between white and black players?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "363" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_170_42170738_qa_2/task.toml b/tasks/0042_170_42170738_qa_2/task.toml index 81be980099de32597651fc4bb46732cfd6817f9a..eeb243ce3ecf622e6bac84d8d1abe945c8ec123b 100644 --- a/tasks/0042_170_42170738_qa_2/task.toml +++ b/tasks/0042_170_42170738_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0042_170_42170738_qa_2" +name = "smoldataenvs-train/0042_170_42170738_qa_2" description = "What is the highest edibility percentage observed for any specific mushroom cap color category in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "100" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_170_42170738_qa_4/task.toml b/tasks/0042_170_42170738_qa_4/task.toml index 2d0d4878c7b47d7e80bdcd87212d2de76c03df27..7311198ff3291063ab940d8cf6a28541196370a4 100644 --- a/tasks/0042_170_42170738_qa_4/task.toml +++ b/tasks/0042_170_42170738_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0042_170_42170738_qa_4" +name = "smoldataenvs-train/0042_170_42170738_qa_4" description = "What are the three mushroom attributes that most reliably predict mushroom edibility based on the dataset's percentage analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "population, gill-attachment, stalk-root" reward_mode_initial = "list_csv" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_372_42372757_qa_1/task.toml b/tasks/0042_372_42372757_qa_1/task.toml index bb1ad5106939dde5011c8b7b36340570e2db344f..1baf3bcc9fc4557712d53da9252acb6c178e37eb 100644 --- a/tasks/0042_372_42372757_qa_1/task.toml +++ b/tasks/0042_372_42372757_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0042_372_42372757_qa_1" +name = "smoldataenvs-train/0042_372_42372757_qa_1" description = "What is the distribution of wine quality labels after segmentation into 'bad' (0) and 'good' (1) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0: 1382, 1: 217" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_372_42372757_qa_5/task.toml b/tasks/0042_372_42372757_qa_5/task.toml index a3fedb2f33d28a0150d9ae16d795c94ca3c47237..14cc8d128b11b45215bca12e80fa051c65fc66d9 100644 --- a/tasks/0042_372_42372757_qa_5/task.toml +++ b/tasks/0042_372_42372757_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0042_372_42372757_qa_5" +name = "smoldataenvs-train/0042_372_42372757_qa_5" description = "What percentage of the original dataset is classified as 'good' quality wine after applying the quality segmentation threshold of 6.5?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13.57" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_378_42378330_qa_5/task.toml b/tasks/0042_378_42378330_qa_5/task.toml index 731d3fa6886222d7a45d53dbd03684c8e92db330..b9875dcb0cc8a4bda386c5b313a049f269816536 100644 --- a/tasks/0042_378_42378330_qa_5/task.toml +++ b/tasks/0042_378_42378330_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0042_378_42378330_qa_5" +name = "smoldataenvs-train/0042_378_42378330_qa_5" description = "What is the difference in accuracy between the Decision Tree Classifier and the Random Forest Classifier on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.0175" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0042_383_42383310_qa_2/task.toml b/tasks/0042_383_42383310_qa_2/task.toml index f6a3a6c7da953563be533690fe54237fa2a2e576..9b286fd3733cb85afa84bcf50549105192f59dc4 100644 --- a/tasks/0042_383_42383310_qa_2/task.toml +++ b/tasks/0042_383_42383310_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0042_383_42383310_qa_2" +name = "smoldataenvs-train/0042_383_42383310_qa_2" description = "What is the data type of the 'engV' column after completing the data cleaning process?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "float64" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_384_42384112_qa_4/task.toml b/tasks/0042_384_42384112_qa_4/task.toml index 977a1044aad98f1165570daf46e943e57cd49213..27f97a2bc22fb2cf2ae7f558dbc0875681e36317 100644 --- a/tasks/0042_384_42384112_qa_4/task.toml +++ b/tasks/0042_384_42384112_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0042_384_42384112_qa_4" +name = "smoldataenvs-train/0042_384_42384112_qa_4" description = "What is the total number of samples in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "66" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0042_414_42414264_qa_2/task.toml b/tasks/0042_414_42414264_qa_2/task.toml index b82d5b36b534bcdd527d1962722f0e65a6f6aa60..ab30b9bf388cfa99272bc3306a4053453b4c3dd9 100644 --- a/tasks/0042_414_42414264_qa_2/task.toml +++ b/tasks/0042_414_42414264_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0042_414_42414264_qa_2" +name = "smoldataenvs-train/0042_414_42414264_qa_2" description = "What percentage of patients in the dataset have diabetes based on the class distribution analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.895833" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0042_446_42446239_qa_1/task.toml b/tasks/0042_446_42446239_qa_1/task.toml index c12ffc51a10195a5979b76e18f9d9dda400c533e..a47eeaeeac87e482c9c8a4f77a77e4ef6e6173b9 100644 --- a/tasks/0042_446_42446239_qa_1/task.toml +++ b/tasks/0042_446_42446239_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0042_446_42446239_qa_1" +name = "smoldataenvs-train/0042_446_42446239_qa_1" description = "What is the proportion of malignant (M) cases in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "37.26" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0042_446_42446776_qa_4/task.toml b/tasks/0042_446_42446776_qa_4/task.toml index 6e076ba03190d3c0759cc15692261a3d7440c364..8f92a4b89704afe918ed7393354c88b52bb6a371 100644 --- a/tasks/0042_446_42446776_qa_4/task.toml +++ b/tasks/0042_446_42446776_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0042_446_42446776_qa_4" +name = "smoldataenvs-train/0042_446_42446776_qa_4" description = "How many customers signed up for a term deposit while having both housing and personal loans?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "265" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_446_42446776_qa_5/task.toml b/tasks/0042_446_42446776_qa_5/task.toml index 7b9759382b47f1cc7605c4b67670defb946d199b..0019c30f3080e677db6fd18bbb1aa3a226ef8b6a 100644 --- a/tasks/0042_446_42446776_qa_5/task.toml +++ b/tasks/0042_446_42446776_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0042_446_42446776_qa_5" +name = "smoldataenvs-train/0042_446_42446776_qa_5" description = "What is the maximum call duration recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3881" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0042_455_42455252_qa_1/task.toml b/tasks/0042_455_42455252_qa_1/task.toml index d42cf73e3090afce7b8b2e3288b830ac53652ada..34393896a615e6270dd72f257a44867d8bab2b07 100644 --- a/tasks/0042_455_42455252_qa_1/task.toml +++ b/tasks/0042_455_42455252_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0042_455_42455252_qa_1" +name = "smoldataenvs-train/0042_455_42455252_qa_1" description = "What is the highest correlation coefficient between any two numerical features in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.299" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_455_42455252_qa_2/task.toml b/tasks/0042_455_42455252_qa_2/task.toml index cce9e5954ec75a068c1293ccd3fa4a207e79a737..dee931ca7399afce0f0915f4017420ff0284247d 100644 --- a/tasks/0042_455_42455252_qa_2/task.toml +++ b/tasks/0042_455_42455252_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0042_455_42455252_qa_2" +name = "smoldataenvs-train/0042_455_42455252_qa_2" description = "Which feature has the highest importance according to the RandomForest model's feature importance analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "smoker" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0042_480_42480581_qa_3/task.toml b/tasks/0042_480_42480581_qa_3/task.toml index 3f1e7f707fbf126ddab94363dd9953b3d066a16f..8069ade96be2e68d4bbec073125971fd7577f464 100644 --- a/tasks/0042_480_42480581_qa_3/task.toml +++ b/tasks/0042_480_42480581_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0042_480_42480581_qa_3" +name = "smoldataenvs-train/0042_480_42480581_qa_3" description = "How many samples are in each class in the test dataset based on the classification report?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1000" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_485_42485813_qa_5/task.toml b/tasks/0042_485_42485813_qa_5/task.toml index cb8b06d23e168d8d3f80932b86f2adbe5931c815..2c990e89463c61e1036a1f1d70a5920d050cb26d 100644 --- a/tasks/0042_485_42485813_qa_5/task.toml +++ b/tasks/0042_485_42485813_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0042_485_42485813_qa_5" +name = "smoldataenvs-train/0042_485_42485813_qa_5" description = "Which gender has the higher appointment show-up rate based on the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Female" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_488_42488023_qa_2/task.toml b/tasks/0042_488_42488023_qa_2/task.toml index 14700bee81d311befa1694a4aaf9f6cfa3886d95..91b2513451fb4fe895d6461ad9f7f71b7539236a 100644 --- a/tasks/0042_488_42488023_qa_2/task.toml +++ b/tasks/0042_488_42488023_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0042_488_42488023_qa_2" +name = "smoldataenvs-train/0042_488_42488023_qa_2" description = "How many Pokémon originally had only one type (i.e., had missing values in the Type 2 column) before the dataset was cleaned?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "386" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0042_488_42488023_qa_4/task.toml b/tasks/0042_488_42488023_qa_4/task.toml index b33c543b528dacfad7416e189027bf4515ea7cad..577d2e7dc097b8db09183e4dea828e53c6c8d8d3 100644 --- a/tasks/0042_488_42488023_qa_4/task.toml +++ b/tasks/0042_488_42488023_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0042_488_42488023_qa_4" +name = "smoldataenvs-train/0042_488_42488023_qa_4" description = "What is the most common primary type (Type 1) among Pokémon in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Water" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0042_488_42488426_qa_1/task.toml b/tasks/0042_488_42488426_qa_1/task.toml index 8d8cdd1b65c757a86b73f71f91ccd6bb2b814788..a43f80df27278621996559dd69908b68d4d9d7f8 100644 --- a/tasks/0042_488_42488426_qa_1/task.toml +++ b/tasks/0042_488_42488426_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0042_488_42488426_qa_1" +name = "smoldataenvs-train/0042_488_42488426_qa_1" description = "How many hourly PM2.5 readings are available for Beijing in the dataset after removing missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50387" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_488_42488426_qa_2/task.toml b/tasks/0042_488_42488426_qa_2/task.toml index 64df722ff9a6716c958d57fda9a601ad15f3a6cd..617518cc2581bfb4f0847ef454784e69d82ce7e7 100644 --- a/tasks/0042_488_42488426_qa_2/task.toml +++ b/tasks/0042_488_42488426_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0042_488_42488426_qa_2" +name = "smoldataenvs-train/0042_488_42488426_qa_2" description = "What is the total number of hourly records in the Beijing dataset before removing missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "52584" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0042_488_42488426_qa_3/task.toml b/tasks/0042_488_42488426_qa_3/task.toml index e6083c67e201667354ddf910fa4d345718fc8c71..7e3bd9d5d1a0fe70c84b651d2ccce5dad0c30dd3 100644 --- a/tasks/0042_488_42488426_qa_3/task.toml +++ b/tasks/0042_488_42488426_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0042_488_42488426_qa_3" +name = "smoldataenvs-train/0042_488_42488426_qa_3" description = "What is the time range of the PM2.5 data for Beijing in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2010-01-01 to 2015-12-31" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0042_492_42492005_qa_3/task.toml b/tasks/0042_492_42492005_qa_3/task.toml index 3d039e47bfdd212f925b29a428a7fbc0068e48e4..db013e8e07f2fd3f042b5f96aab190bea7da06f4 100644 --- a/tasks/0042_492_42492005_qa_3/task.toml +++ b/tasks/0042_492_42492005_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0042_492_42492005_qa_3" +name = "smoldataenvs-train/0042_492_42492005_qa_3" description = "How many missing values remain in the 'Age' column after the imputation process?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_501_42501868_qa_1/task.toml b/tasks/0042_501_42501868_qa_1/task.toml index d5a258d2d7912d5c029457ad52335721b81f7703..f25eb627ae5ba40a954fc2e184e107baf28b733f 100644 --- a/tasks/0042_501_42501868_qa_1/task.toml +++ b/tasks/0042_501_42501868_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0042_501_42501868_qa_1" +name = "smoldataenvs-train/0042_501_42501868_qa_1" description = "What is the percentage of churned customers in the original dataset before any data preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26.53" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0042_501_42501868_qa_2/task.toml b/tasks/0042_501_42501868_qa_2/task.toml index dcbb2e90d907cb6e677f0b358fdb26ab43b54cd2..851d218dde824140ad540e81444f27b973197f3c 100644 --- a/tasks/0042_501_42501868_qa_2/task.toml +++ b/tasks/0042_501_42501868_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0042_501_42501868_qa_2" +name = "smoldataenvs-train/0042_501_42501868_qa_2" description = "Which three features were identified as the most important for predicting churn based on the random forest feature importance analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "tenure, TotalCharges, MonthlyCharges" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0042_503_42503346_qa_4/task.toml b/tasks/0042_503_42503346_qa_4/task.toml index 460cbedbdc11ec90de92883841ced3df6b2d9137..1d4abae97c9395049328178b63732febb6df23a1 100644 --- a/tasks/0042_503_42503346_qa_4/task.toml +++ b/tasks/0042_503_42503346_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0042_503_42503346_qa_4" +name = "smoldataenvs-train/0042_503_42503346_qa_4" description = "Which feature has the highest positive correlation with PetalWidthCm?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_528_42528802_qa_3/task.toml b/tasks/0042_528_42528802_qa_3/task.toml index fbcee753c166e1a0cec2c1026bb8e776b1e3e9a7..5bed1cb1f6f40d7bf0bc76b340cb241f336af638 100644 --- a/tasks/0042_528_42528802_qa_3/task.toml +++ b/tasks/0042_528_42528802_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0042_528_42528802_qa_3" +name = "smoldataenvs-train/0042_528_42528802_qa_3" description = "Is the correlation between MonthlyIncome and JobLevel in the dataset statistically significant?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0042_569_42569940_qa_1/task.toml b/tasks/0042_569_42569940_qa_1/task.toml index 59e8845ac2a387f5368e8f2d6135e92d3cd8f3e7..4e11b70a66fbe04c7f13dc8a7632528f7dc2a9b2 100644 --- a/tasks/0042_569_42569940_qa_1/task.toml +++ b/tasks/0042_569_42569940_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0042_569_42569940_qa_1" +name = "smoldataenvs-train/0042_569_42569940_qa_1" description = "What percentage of missing values in the 'Months since last delinquent' column were replaced with zero based on the credit score analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "53.14" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_590_42590102_qa_4/task.toml b/tasks/0042_590_42590102_qa_4/task.toml index b4a77e6f40af3bf83f9efb48818dcf95b8eda48f..4f14dce99d376ea01a81c67d7795e0918f4313a1 100644 --- a/tasks/0042_590_42590102_qa_4/task.toml +++ b/tasks/0042_590_42590102_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0042_590_42590102_qa_4" +name = "smoldataenvs-train/0042_590_42590102_qa_4" description = "What is the direction of skewness in the distribution of ramen star ratings across the entire dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "left-skewed" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_635_42635249_qa_3/task.toml b/tasks/0042_635_42635249_qa_3/task.toml index 693cd91214654ca9a3eb2173db73a362305e3b97..51ec6e7a1ad4b8e062b8d468599be98ed6e80187 100644 --- a/tasks/0042_635_42635249_qa_3/task.toml +++ b/tasks/0042_635_42635249_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0042_635_42635249_qa_3" +name = "smoldataenvs-train/0042_635_42635249_qa_3" description = "How many features were used in the model training after preprocessing steps (excluding the diagnosis column)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "30" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_642_42642764_qa_2/task.toml b/tasks/0042_642_42642764_qa_2/task.toml index d3fbcc6246ca9972214027980e869a2d83b34d28..f29215175ee2837e34f014ff40877c04f75f34ab 100644 --- a/tasks/0042_642_42642764_qa_2/task.toml +++ b/tasks/0042_642_42642764_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0042_642_42642764_qa_2" +name = "smoldataenvs-train/0042_642_42642764_qa_2" description = "What is the mean absolute error (MAE) of the model's predictions on the test dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.415771850041258" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0042_642_42642764_qa_3/task.toml b/tasks/0042_642_42642764_qa_3/task.toml index 1142f5df1214bdc8e558fd3d66e15f20e427b087..c36a5e4dbb99b889bdcaa3bc2dc2fb11dc7fee72 100644 --- a/tasks/0042_642_42642764_qa_3/task.toml +++ b/tasks/0042_642_42642764_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0042_642_42642764_qa_3" +name = "smoldataenvs-train/0042_642_42642764_qa_3" description = "What is the root mean squared error (RMSE) of the model's predictions on the test dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.071306268029827" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0042_644_42644185_qa_2/task.toml b/tasks/0042_644_42644185_qa_2/task.toml index 563e9c8bbdf65c3fa8c9a64d1ab3096d68269966..c68d36de0b3d1995647024d0b4895c8e1b198107 100644 --- a/tasks/0042_644_42644185_qa_2/task.toml +++ b/tasks/0042_644_42644185_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0042_644_42644185_qa_2" +name = "smoldataenvs-train/0042_644_42644185_qa_2" description = "Which income class (0 or 1) has the highest precision according to the classification report?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0042_684_42684548_qa_4/task.toml b/tasks/0042_684_42684548_qa_4/task.toml index 131d143a834b44875347e1be47511d2b18da9673..95ac290227fc95bc0f9bd5084ebb488473c21567 100644 --- a/tasks/0042_684_42684548_qa_4/task.toml +++ b/tasks/0042_684_42684548_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0042_684_42684548_qa_4" +name = "smoldataenvs-train/0042_684_42684548_qa_4" description = "What is the precision score for spam classification in the hyperparameter-tuned Random Forest model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.00" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0042_738_42738840_qa_1/task.toml b/tasks/0042_738_42738840_qa_1/task.toml index 2b290b7bc95b82901013e0bdc0b802b0da26dddd..4a2c4e8c6a7cf12c268cc26c85d0a165de543441 100644 --- a/tasks/0042_738_42738840_qa_1/task.toml +++ b/tasks/0042_738_42738840_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0042_738_42738840_qa_1" +name = "smoldataenvs-train/0042_738_42738840_qa_1" description = "What is the median number of positive lymph nodes for patients who survived (1) compared to those who did not survive (0)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Survived=1, Deceased=4" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_738_42738840_qa_2/task.toml b/tasks/0042_738_42738840_qa_2/task.toml index c01b3360b7e5adbe3b9d908143ff176343232dce..a455729773689f18f428cedc39e1af48a592719b 100644 --- a/tasks/0042_738_42738840_qa_2/task.toml +++ b/tasks/0042_738_42738840_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0042_738_42738840_qa_2" +name = "smoldataenvs-train/0042_738_42738840_qa_2" description = "What is the overall survival rate (percentage of patients who survived for more than 5 years) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "73.5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_750_42750016_qa_1/task.toml b/tasks/0042_750_42750016_qa_1/task.toml index 7ac9adb756c4812040268ec880c4f4d89d1aacc1..43524fc161a0c64429041260950dea81e5f0355b 100644 --- a/tasks/0042_750_42750016_qa_1/task.toml +++ b/tasks/0042_750_42750016_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0042_750_42750016_qa_1" +name = "smoldataenvs-train/0042_750_42750016_qa_1" description = "What is the maximum value of the transformed median house value after scaling by dividing by 100,000?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5.00001" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0042_773_42773926_qa_2/task.toml b/tasks/0042_773_42773926_qa_2/task.toml index a246fcc2df9bc7ae6e7e9645d94b4090480092f6..2d6c1bb90fc3d86e45954b4ab9147e792576dcb4 100644 --- a/tasks/0042_773_42773926_qa_2/task.toml +++ b/tasks/0042_773_42773926_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0042_773_42773926_qa_2" +name = "smoldataenvs-train/0042_773_42773926_qa_2" description = "How many houses were excluded from the dataset when removing the top 1% of prices to improve geographical visualization analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "216" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_773_42773926_qa_3/task.toml b/tasks/0042_773_42773926_qa_3/task.toml index efebc205a3db3ca12ff3719dfcbefd23ca72e28f..f453bed64e0571428308e21880f938b4530daab9 100644 --- a/tasks/0042_773_42773926_qa_3/task.toml +++ b/tasks/0042_773_42773926_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0042_773_42773926_qa_3" +name = "smoldataenvs-train/0042_773_42773926_qa_3" description = "Which month (numeric value) had the highest average house price based on the sales data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_806_42806362_qa_1/task.toml b/tasks/0042_806_42806362_qa_1/task.toml index 930da9c4bf61ce5c2e0c34954faed88a7162128d..c07c430eb307a020c00a762b6ecec38b683f332b 100644 --- a/tasks/0042_806_42806362_qa_1/task.toml +++ b/tasks/0042_806_42806362_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0042_806_42806362_qa_1" +name = "smoldataenvs-train/0042_806_42806362_qa_1" description = "What is the percentage of customers with vehicles aged 1-2 years who responded positively to the vehicle insurance offer?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "17.37" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_836_42836971_qa_3/task.toml b/tasks/0042_836_42836971_qa_3/task.toml index 641f0b446cbfe4fbeb1d7aa8b613e30816b30e52..38ee6261763407b649c2a9d29782adc72b6404c7 100644 --- a/tasks/0042_836_42836971_qa_3/task.toml +++ b/tasks/0042_836_42836971_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0042_836_42836971_qa_3" +name = "smoldataenvs-train/0042_836_42836971_qa_3" description = "Which two features were removed from the dataset to address multicollinearity, and what was the basis for their removal?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "perimeter_mean, perimeter_worst" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_857_42857956_qa_5/task.toml b/tasks/0042_857_42857956_qa_5/task.toml index 83ae89aca526a7b34c28a74c7ba8a5effa39bd13..db46594dfaaf5ddf240f8e5bdbefae932a237dda 100644 --- a/tasks/0042_857_42857956_qa_5/task.toml +++ b/tasks/0042_857_42857956_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0042_857_42857956_qa_5" +name = "smoldataenvs-train/0042_857_42857956_qa_5" description = "According to the exploratory data analysis, which feature is least effective in distinguishing between species?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "SepalWidthCm" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_902_42902013_qa_3/task.toml b/tasks/0042_902_42902013_qa_3/task.toml index fd565e610541b792fceecf035e35e9800ebcfad6..e20cfb9aa5a45cfd8b67c5c5b2d60a7a7c4872d2 100644 --- a/tasks/0042_902_42902013_qa_3/task.toml +++ b/tasks/0042_902_42902013_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0042_902_42902013_qa_3" +name = "smoldataenvs-train/0042_902_42902013_qa_3" description = "What is the accuracy score achieved by the logistic regression model on the test data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "96.49" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0042_903_42903883_qa_1/task.toml b/tasks/0042_903_42903883_qa_1/task.toml index c439dec09481d8fad6cf1ee1dc6ab99554f28bcc..52a3c48f6a25072a3634527cbd6f8797fd497ec3 100644 --- a/tasks/0042_903_42903883_qa_1/task.toml +++ b/tasks/0042_903_42903883_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0042_903_42903883_qa_1" +name = "smoldataenvs-train/0042_903_42903883_qa_1" description = "Какой процент клиентов в наборе данных относится к лояльным (без оттока)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "85.5086" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_911_42911036_qa_1/task.toml b/tasks/0042_911_42911036_qa_1/task.toml index 07b01660979fc6e76ed4ff2c237c03ce0c4409f3..d53e4c64eb086473b915b3dfdf02bf0198d2a810 100644 --- a/tasks/0042_911_42911036_qa_1/task.toml +++ b/tasks/0042_911_42911036_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0042_911_42911036_qa_1" +name = "smoldataenvs-train/0042_911_42911036_qa_1" description = "What is the total number of malignant (M) and benign (B) cases in the breast cancer dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "M=212, B=357" reward_mode_initial = "list" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0042_911_42911036_qa_2/task.toml b/tasks/0042_911_42911036_qa_2/task.toml index 470914edf5966d7e86ae8086722a472216c774cc..887e76633f87bf3eac6e6de7de20fbd9b5c1d050 100644 --- a/tasks/0042_911_42911036_qa_2/task.toml +++ b/tasks/0042_911_42911036_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0042_911_42911036_qa_2" +name = "smoldataenvs-train/0042_911_42911036_qa_2" description = "Which feature demonstrates the highest positive correlation with the diagnosis label (malignant) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "concave points_worst" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_917_42917087_qa_1/task.toml b/tasks/0042_917_42917087_qa_1/task.toml index 0f47953c4937eb3e88dcaeafffc51cc503298613..4c0da036c7af0f74650906ade741d297f80152dd 100644 --- a/tasks/0042_917_42917087_qa_1/task.toml +++ b/tasks/0042_917_42917087_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0042_917_42917087_qa_1" +name = "smoldataenvs-train/0042_917_42917087_qa_1" description = "What percentage of patients in the dataset are diagnosed with diabetes?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0042_928_42928888_qa_1/task.toml b/tasks/0042_928_42928888_qa_1/task.toml index 38a092548e78842c20e89d28a0f9b253972c47b5..f6c661e8ea631656c29573c62a1e83daabd0985d 100644 --- a/tasks/0042_928_42928888_qa_1/task.toml +++ b/tasks/0042_928_42928888_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0042_928_42928888_qa_1" +name = "smoldataenvs-train/0042_928_42928888_qa_1" description = "What is the number of samples in the test set after splitting the dataset with an 80-20 ratio?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "114" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_941_42941100_qa_2/task.toml b/tasks/0042_941_42941100_qa_2/task.toml index 34022609aac866c827649c848c21f6c987e34921..e8c566a5eaad5ceb3660a9b7fd0c662ae0b53a8a 100644 --- a/tasks/0042_941_42941100_qa_2/task.toml +++ b/tasks/0042_941_42941100_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0042_941_42941100_qa_2" +name = "smoldataenvs-train/0042_941_42941100_qa_2" description = "What is the total number of unique ramen brands in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "355" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0042_973_42973076_qa_1/task.toml b/tasks/0042_973_42973076_qa_1/task.toml index 517353e0cf007ea33f698a5bf23e3b8eada3b090..7c95584ecbe3cec60ab920cbaae7362f1449b9a5 100644 --- a/tasks/0042_973_42973076_qa_1/task.toml +++ b/tasks/0042_973_42973076_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0042_973_42973076_qa_1" +name = "smoldataenvs-train/0042_973_42973076_qa_1" description = "Which species has the highest median petal length according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-virginica" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_973_42973076_qa_4/task.toml b/tasks/0042_973_42973076_qa_4/task.toml index a8c918284f27760a5c502eda139d42b054c71aed..6daac0d4cf1134e66ecbbd4f9c535ff216f24fef 100644 --- a/tasks/0042_973_42973076_qa_4/task.toml +++ b/tasks/0042_973_42973076_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0042_973_42973076_qa_4" +name = "smoldataenvs-train/0042_973_42973076_qa_4" description = "What is the median petal width for Iris-versicolor species in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_973_42973076_qa_5/task.toml b/tasks/0042_973_42973076_qa_5/task.toml index 60ca432cb71add1bd18123eafb94545388f53664..c890f98a2a2bf7c350042bc89f5ce4cdf2ba03c3 100644 --- a/tasks/0042_973_42973076_qa_5/task.toml +++ b/tasks/0042_973_42973076_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0042_973_42973076_qa_5" +name = "smoldataenvs-train/0042_973_42973076_qa_5" description = "Which species has the smallest interquartile range (IQR) in sepal width according to the box plot analysis in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-virginica" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_991_42991381_qa_5/task.toml b/tasks/0042_991_42991381_qa_5/task.toml index e26d574de333de8c5bb1332bb0337a8a2af13c92..e82f04a9876b7683c37a8151df092a41b2cdcdf8 100644 --- a/tasks/0042_991_42991381_qa_5/task.toml +++ b/tasks/0042_991_42991381_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0042_991_42991381_qa_5" +name = "smoldataenvs-train/0042_991_42991381_qa_5" description = "What is the correlation coefficient between Annual_Premium and Vintage based on the heatmap analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.01" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0042_993_42993650_qa_1/task.toml b/tasks/0042_993_42993650_qa_1/task.toml index 19f4d5edc90875704042f58e2443a6a9a0eb11ea..a4565cfc2acdef1fae29821f1f2f47210a92f9d0 100644 --- a/tasks/0042_993_42993650_qa_1/task.toml +++ b/tasks/0042_993_42993650_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0042_993_42993650_qa_1" +name = "smoldataenvs-train/0042_993_42993650_qa_1" description = "Is there a statistically significant difference in the mean age between individuals earning more than $50K and those earning $50K or less?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0043_029_43029106_qa_1/task.toml b/tasks/0043_029_43029106_qa_1/task.toml index 2257599c339f9e6110ab23e0cc8b72e93293b140..2ceb0ef5b1cff0bd2080f1e73126b0ce8fb85c3c 100644 --- a/tasks/0043_029_43029106_qa_1/task.toml +++ b/tasks/0043_029_43029106_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0043_029_43029106_qa_1" +name = "smoldataenvs-train/0043_029_43029106_qa_1" description = "What is the count of users who did not make a purchase versus those who did in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0: 257, 1: 143" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0043_032_43032812_qa_4/task.toml b/tasks/0043_032_43032812_qa_4/task.toml index 338cca1790805adc45302eaa32ae32eceb4efaf6..f9842f41d31510ec76491a7d60cefb439eaddac3 100644 --- a/tasks/0043_032_43032812_qa_4/task.toml +++ b/tasks/0043_032_43032812_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0043_032_43032812_qa_4" +name = "smoldataenvs-train/0043_032_43032812_qa_4" description = "What was the top-selling video game in 2006 based on global sales?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii Sports" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_043_43043497_qa_2/task.toml b/tasks/0043_043_43043497_qa_2/task.toml index 94dafc0ef00cac91261907fe4e56b4c7d24929d1..dd1f7d3243f4655078e7f574b912ac8ed0a7f99b 100644 --- a/tasks/0043_043_43043497_qa_2/task.toml +++ b/tasks/0043_043_43043497_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0043_043_43043497_qa_2" +name = "smoldataenvs-train/0043_043_43043497_qa_2" description = "Which feature exhibits the highest correlation with the diabetes outcome according to the dataset's correlation matrix?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_069_43069626_qa_1/task.toml b/tasks/0043_069_43069626_qa_1/task.toml index 14f62b6ab87f304673ebed86fad24d4c385fa2e9..777944a2308a8a2df1a7e5662f9303b9c6c267a5 100644 --- a/tasks/0043_069_43069626_qa_1/task.toml +++ b/tasks/0043_069_43069626_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0043_069_43069626_qa_1" +name = "smoldataenvs-train/0043_069_43069626_qa_1" description = "Which variable in the training dataset has the strongest negative correlation with temperature (T (degC))?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "rho (g/m**3)" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_069_43069626_qa_4/task.toml b/tasks/0043_069_43069626_qa_4/task.toml index 5d6b1a820f5aaab88f99b41fb9e899509b67c5c9..0fe7bea923b1ada37167fc243b966f80a9d7a31a 100644 --- a/tasks/0043_069_43069626_qa_4/task.toml +++ b/tasks/0043_069_43069626_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0043_069_43069626_qa_4" +name = "smoldataenvs-train/0043_069_43069626_qa_4" description = "What is the 75th percentile of temperature (T (degC)) in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "15.47" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_124_43124508_qa_1/task.toml b/tasks/0043_124_43124508_qa_1/task.toml index 8748de6edc837fe143bd8a841c6c78a1be26bf83..83a826f5d8a5c7e9a855e9cef37d64062158ce0b 100644 --- a/tasks/0043_124_43124508_qa_1/task.toml +++ b/tasks/0043_124_43124508_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0043_124_43124508_qa_1" +name = "smoldataenvs-train/0043_124_43124508_qa_1" description = "Which feature has the highest importance in predicting mushroom edibility according to the Decision Tree model's feature importance analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "gill-color" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0043_124_43124508_qa_2/task.toml b/tasks/0043_124_43124508_qa_2/task.toml index 1adcc5fb6694a2a5dbf1e3a5d35f14c874aa6e35..7bb36c5abc5dcb91bea794d8860ebad260c19f62 100644 --- a/tasks/0043_124_43124508_qa_2/task.toml +++ b/tasks/0043_124_43124508_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0043_124_43124508_qa_2" +name = "smoldataenvs-train/0043_124_43124508_qa_2" description = "What is the highest test accuracy percentage achieved by any classifier in the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "100.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0043_124_43124508_qa_3/task.toml b/tasks/0043_124_43124508_qa_3/task.toml index eb5e267975bfc382fdb637a015032fc7f62baaf7..8eb8f9f227bad71f998463d428944727540280d0 100644 --- a/tasks/0043_124_43124508_qa_3/task.toml +++ b/tasks/0043_124_43124508_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0043_124_43124508_qa_3" +name = "smoldataenvs-train/0043_124_43124508_qa_3" description = "What is the correlation coefficient between the 'gill-color' feature and the target 'class' variable in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.53" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_124_43124508_qa_4/task.toml b/tasks/0043_124_43124508_qa_4/task.toml index f66fa905fa17c03f4c731812f13249ac52265513..8b4bef91ea243478b2a011988736785b8e5c01ce 100644 --- a/tasks/0043_124_43124508_qa_4/task.toml +++ b/tasks/0043_124_43124508_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0043_124_43124508_qa_4" +name = "smoldataenvs-train/0043_124_43124508_qa_4" description = "How many mushrooms of each class (edible/poisonous) are present in the dataset based on the initial visualization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4208 edible, 3916 poisonous" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0043_303_43303678_qa_3/task.toml b/tasks/0043_303_43303678_qa_3/task.toml index a1b423cf8c5e8d465b13f59888af0cdfd46fe174..4d8423dd202c68d6fc406f0bc44feff943b2b2bd 100644 --- a/tasks/0043_303_43303678_qa_3/task.toml +++ b/tasks/0043_303_43303678_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0043_303_43303678_qa_3" +name = "smoldataenvs-train/0043_303_43303678_qa_3" description = "Which level of BusinessTravel is associated with the highest attrition percentage according to the categorical analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Travel_Frequently" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_305_43305320_qa_2/task.toml b/tasks/0043_305_43305320_qa_2/task.toml index ff5f6d3ca3f8727d84b42ca94b633f181dc2400b..a6ce65176a1ec466463f68edcc5628ccb460dc29 100644 --- a/tasks/0043_305_43305320_qa_2/task.toml +++ b/tasks/0043_305_43305320_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0043_305_43305320_qa_2" +name = "smoldataenvs-train/0043_305_43305320_qa_2" description = "What is the highest average number of comments per Ask HN post by hour of the day?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "28.68" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_305_43305320_qa_4/task.toml b/tasks/0043_305_43305320_qa_4/task.toml index 9780f80f7be48e54c34e583270dad474f4aef19a..62e40fd5d400a29a53ef3de8fe264fd619f84cac 100644 --- a/tasks/0043_305_43305320_qa_4/task.toml +++ b/tasks/0043_305_43305320_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0043_305_43305320_qa_4" +name = "smoldataenvs-train/0043_305_43305320_qa_4" description = "What is the average number of votes per Show HN post in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "14.84" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_309_43309435_qa_3/task.toml b/tasks/0043_309_43309435_qa_3/task.toml index da07371c9cfd0530c5966219ed1da38f9f91c02d..e77252b7189dbe4dd332d534da2e1abc15071089 100644 --- a/tasks/0043_309_43309435_qa_3/task.toml +++ b/tasks/0043_309_43309435_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0043_309_43309435_qa_3" +name = "smoldataenvs-train/0043_309_43309435_qa_3" description = "How many missing values were present in the TotalCharges column before handling during data preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_309_43309435_qa_5/task.toml b/tasks/0043_309_43309435_qa_5/task.toml index 15fe9b78b7d4cc0ece947b2c9997f276c938a9cc..73bb2b0985034ee783351bd08db7b17d0f22e329 100644 --- a/tasks/0043_309_43309435_qa_5/task.toml +++ b/tasks/0043_309_43309435_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0043_309_43309435_qa_5" +name = "smoldataenvs-train/0043_309_43309435_qa_5" description = "What is the median tenure (in months) of customers in the dataset before any data transformations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "29" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0043_313_43313326_qa_1/task.toml b/tasks/0043_313_43313326_qa_1/task.toml index 7aac810fd9aa1f9e4f49016f3e7e5431f8f7dc97..d898fb2488b72f547315787a6cc1a753ffea2473 100644 --- a/tasks/0043_313_43313326_qa_1/task.toml +++ b/tasks/0043_313_43313326_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0043_313_43313326_qa_1" +name = "smoldataenvs-train/0043_313_43313326_qa_1" description = "Which feature has the highest variance inflation factor (VIF) indicating strongest multicollinearity in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "BMI" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_313_43313326_qa_2/task.toml b/tasks/0043_313_43313326_qa_2/task.toml index ee92ab94f5337c1bd113113d5875e8b914a4bbc3..8468a8ad3a5d6675587e945a0a3ac430d47d4184 100644 --- a/tasks/0043_313_43313326_qa_2/task.toml +++ b/tasks/0043_313_43313326_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0043_313_43313326_qa_2" +name = "smoldataenvs-train/0043_313_43313326_qa_2" description = "What is the percentage of diabetic individuals (Outcome=1) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.8958" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0043_313_43313326_qa_5/task.toml b/tasks/0043_313_43313326_qa_5/task.toml index d6ae895e7b8643cfb43038538b655c22229b4f3a..d838de649fa6806e8025e8f4a219e86b041f9ea6 100644 --- a/tasks/0043_313_43313326_qa_5/task.toml +++ b/tasks/0043_313_43313326_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0043_313_43313326_qa_5" +name = "smoldataenvs-train/0043_313_43313326_qa_5" description = "Which feature is identified as most important by the Random Forest model for predicting diabetes outcome?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0043_338_43338909_qa_1/task.toml b/tasks/0043_338_43338909_qa_1/task.toml index 2a003a8dafa106d2315a824ba3aa5a4b54a77478..18352a9ff023a1059b314032caa540545783be87 100644 --- a/tasks/0043_338_43338909_qa_1/task.toml +++ b/tasks/0043_338_43338909_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0043_338_43338909_qa_1" +name = "smoldataenvs-train/0043_338_43338909_qa_1" description = "What is the maximum Lift value achieved in any decile of predicted probabilities according to the model's performance analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.09" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0043_338_43338909_qa_3/task.toml b/tasks/0043_338_43338909_qa_3/task.toml index 2dd35a57d76071b35d0458af450aab6a02f5b40a..e0234d00c3336aa0e8f9c50c28fe3f9e13e449a4 100644 --- a/tasks/0043_338_43338909_qa_3/task.toml +++ b/tasks/0043_338_43338909_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0043_338_43338909_qa_3" +name = "smoldataenvs-train/0043_338_43338909_qa_3" description = "How many deciles are required to capture at least 70% of the bad credit risk cases according to the model's Lift and Gain analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0043_348_43348851_qa_1/task.toml b/tasks/0043_348_43348851_qa_1/task.toml index a132b4e9f456dd844ee666dedd09e8b72501f1fc..ae6e34f3db532060f2ec99a705da3c8d637887c2 100644 --- a/tasks/0043_348_43348851_qa_1/task.toml +++ b/tasks/0043_348_43348851_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0043_348_43348851_qa_1" +name = "smoldataenvs-train/0043_348_43348851_qa_1" description = "How many dummy variables were generated from the original categorical features after excluding Vehicle_Age which was transformed to numeric?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_373_43373918_qa_4/task.toml b/tasks/0043_373_43373918_qa_4/task.toml index febe2a5d0d60b9641b31ae8f48f38cf83c4a1a41..16449669edf613321855eefd340d9fce2ac33a1d 100644 --- a/tasks/0043_373_43373918_qa_4/task.toml +++ b/tasks/0043_373_43373918_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0043_373_43373918_qa_4" +name = "smoldataenvs-train/0043_373_43373918_qa_4" description = "Which pair of features in the correlation matrix have the strongest positive linear relationship?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Pregnancies, Age" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_374_43374447_qa_2/task.toml b/tasks/0043_374_43374447_qa_2/task.toml index 37f862fcc0808d0f70bd7ffac3a16d1aa413b70f..24184fa5a1b36787fe8f4b176d3ec7e21ad3719c 100644 --- a/tasks/0043_374_43374447_qa_2/task.toml +++ b/tasks/0043_374_43374447_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0043_374_43374447_qa_2" +name = "smoldataenvs-train/0043_374_43374447_qa_2" description = "What is the regression coefficient for the linear model trained with a 30% test split?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9360.26128619" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0043_376_43376696_qa_2/task.toml b/tasks/0043_376_43376696_qa_2/task.toml index 0b040784b3461c4d8c1ee41144d5d3c61c595d01..2989de676cb50be7aa9af11c3aa7c886f1e8cae1 100644 --- a/tasks/0043_376_43376696_qa_2/task.toml +++ b/tasks/0043_376_43376696_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0043_376_43376696_qa_2" +name = "smoldataenvs-train/0043_376_43376696_qa_2" description = "Which three features have the highest positive correlation with each other, and what is their correlation coefficient?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "radius_mean, perimeter_mean, area_mean, 0.95" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_419_43419580_qa_1/task.toml b/tasks/0043_419_43419580_qa_1/task.toml index 4acbd5e8f158e47b5aa96ee75d996483b446c31b..7e4393e21ee7cd36fc537aae1590bf0128b4b5a3 100644 --- a/tasks/0043_419_43419580_qa_1/task.toml +++ b/tasks/0043_419_43419580_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0043_419_43419580_qa_1" +name = "smoldataenvs-train/0043_419_43419580_qa_1" description = "Which feature is ranked highest in importance by the Random Forest model according to the feature importance analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "alcohol" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0043_419_43419580_qa_2/task.toml b/tasks/0043_419_43419580_qa_2/task.toml index cb5c0923579f615e363ed941ed3d07e079bfa434..f3ce9021acf99d0b3811ecab58824ad8aefe1029 100644 --- a/tasks/0043_419_43419580_qa_2/task.toml +++ b/tasks/0043_419_43419580_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0043_419_43419580_qa_2" +name = "smoldataenvs-train/0043_419_43419580_qa_2" description = "What percentage of wines in the dataset are classified as \"good quality\" (value = 1) based on the binary classification threshold?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13.57" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_437_43437842_qa_3/task.toml b/tasks/0043_437_43437842_qa_3/task.toml index 3f4c58bb6403a466477a6218ce9315621d283076..eb14715c5b43347ad28e9a560be8f59ff53d4c67 100644 --- a/tasks/0043_437_43437842_qa_3/task.toml +++ b/tasks/0043_437_43437842_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0043_437_43437842_qa_3" +name = "smoldataenvs-train/0043_437_43437842_qa_3" description = "What is the total global sales of the highest-selling game in the action genre?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "21.40" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_510_43510597_qa_3/task.toml b/tasks/0043_510_43510597_qa_3/task.toml index 7a28c19ee7a841622f7cd02b54c8d5ac7ac3100b..64016b7315aee864a5a5ec23efac1d9e833bc763 100644 --- a/tasks/0043_510_43510597_qa_3/task.toml +++ b/tasks/0043_510_43510597_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0043_510_43510597_qa_3" +name = "smoldataenvs-train/0043_510_43510597_qa_3" description = "What percentage of employees who left the company (Attrition = Yes) were working overtime?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "53.16" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_512_43512047_qa_1/task.toml b/tasks/0043_512_43512047_qa_1/task.toml index a5934b3b5e733014ae79c8a0a3824d3b8e4691cb..d43f02c3ddbbc4b866643f5d966cbf1fc3ebb72a 100644 --- a/tasks/0043_512_43512047_qa_1/task.toml +++ b/tasks/0043_512_43512047_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0043_512_43512047_qa_1" +name = "smoldataenvs-train/0043_512_43512047_qa_1" description = "How many unique product names are included in the product similarity matrix analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12676" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_512_43512681_qa_2/task.toml b/tasks/0043_512_43512681_qa_2/task.toml index b6f0dccd170368e90e2f87e6c596653e0cdb306d..135ecb388a8c0ee331f0ada903aeaaefdfd75ad2 100644 --- a/tasks/0043_512_43512681_qa_2/task.toml +++ b/tasks/0043_512_43512681_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0043_512_43512681_qa_2" +name = "smoldataenvs-train/0043_512_43512681_qa_2" description = "How many features in the dataset have more than 20% missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_512_43512681_qa_5/task.toml b/tasks/0043_512_43512681_qa_5/task.toml index 9d7bd6f01b8a1010093b00e1223e562ad5d849a7..9858ae4b4b755c4f4a9adf386e494efaec1660b7 100644 --- a/tasks/0043_512_43512681_qa_5/task.toml +++ b/tasks/0043_512_43512681_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0043_512_43512681_qa_5" +name = "smoldataenvs-train/0043_512_43512681_qa_5" description = "After converting the red_blood_cell_count to a numerical type, how many non-null entries does this feature have?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "269" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_524_43524264_qa_1/task.toml b/tasks/0043_524_43524264_qa_1/task.toml index c560914fa03eb11c105d429d714a0d7b6cfca456..e21f359f92b40ef746d39cf9c4a4315fa847a72a 100644 --- a/tasks/0043_524_43524264_qa_1/task.toml +++ b/tasks/0043_524_43524264_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0043_524_43524264_qa_1" +name = "smoldataenvs-train/0043_524_43524264_qa_1" description = "Which age group has the highest total purchase amount in the training data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26-35" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_528_43528813_qa_2/task.toml b/tasks/0043_528_43528813_qa_2/task.toml index e75c87050a5a14e3020f1ff82caadd447fb33b43..2c77b589f30fb6f2564a1fcf5f648cd63e168b22 100644 --- a/tasks/0043_528_43528813_qa_2/task.toml +++ b/tasks/0043_528_43528813_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0043_528_43528813_qa_2" +name = "smoldataenvs-train/0043_528_43528813_qa_2" description = "What p-value was obtained from the ADF test after applying seasonal differencing of 12 months to make the time series stationary?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.01155" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0043_544_43544384_qa_2/task.toml b/tasks/0043_544_43544384_qa_2/task.toml index 86546cc5abf809f4f7b66d811db3e8c85e3cdafa..d992a8224cedad389260722a064ac4233404ea67 100644 --- a/tasks/0043_544_43544384_qa_2/task.toml +++ b/tasks/0043_544_43544384_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0043_544_43544384_qa_2" +name = "smoldataenvs-train/0043_544_43544384_qa_2" description = "What is the difference in median Glucose levels between diabetic and non-diabetic individuals after imputing missing values and removing outliers?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "33.0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_544_43544384_qa_4/task.toml b/tasks/0043_544_43544384_qa_4/task.toml index 11cbd23f7212f171d14fbf14c26ea899cacccf2f..c5763fae8e20a564cf654c530b84d736ca1bf5ac 100644 --- a/tasks/0043_544_43544384_qa_4/task.toml +++ b/tasks/0043_544_43544384_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0043_544_43544384_qa_4" +name = "smoldataenvs-train/0043_544_43544384_qa_4" description = "What value was used to impute missing Insulin values for diabetic individuals (Outcome 1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "169.5" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_551_43551294_qa_3/task.toml b/tasks/0043_551_43551294_qa_3/task.toml index c84016d97f169c48757d61e9d3f4c6d742b3daaf..7decd6e66e33727e6dfd48195ba2e32d81d5f5f8 100644 --- a/tasks/0043_551_43551294_qa_3/task.toml +++ b/tasks/0043_551_43551294_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0043_551_43551294_qa_3" +name = "smoldataenvs-train/0043_551_43551294_qa_3" description = "Which feature had the highest number of missing values after replacing zeros with NaN, and how many missing entries did it have?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Insulin, 374" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_591_43591690_qa_2/task.toml b/tasks/0043_591_43591690_qa_2/task.toml index 68df2a83ed16ad04f7b366dec7b3008ecb2c5d98..52ef580ae55774fb1fc36fd2b6ff51b42e6b60b9 100644 --- a/tasks/0043_591_43591690_qa_2/task.toml +++ b/tasks/0043_591_43591690_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0043_591_43591690_qa_2" +name = "smoldataenvs-train/0043_591_43591690_qa_2" description = "Which numerical feature in the dataset has the highest number of unique values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ram" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_591_43591690_qa_3/task.toml b/tasks/0043_591_43591690_qa_3/task.toml index 6491fa31e748f99f51b8aa3d79501c30ac29d6e6..c6e4bedaeb25bbd24a7f8f337c7752e01c283c08 100644 --- a/tasks/0043_591_43591690_qa_3/task.toml +++ b/tasks/0043_591_43591690_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0043_591_43591690_qa_3" +name = "smoldataenvs-train/0043_591_43591690_qa_3" description = "How many mobile devices in the dataset have 4G enabled but no primary camera (pc=0)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "59" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_591_43591690_qa_5/task.toml b/tasks/0043_591_43591690_qa_5/task.toml index 3bed6bc5c00a348a2405821a11569162959c325c..d1d4839749f6b58a5d0a92d01fc62172d6b0929e 100644 --- a/tasks/0043_591_43591690_qa_5/task.toml +++ b/tasks/0043_591_43591690_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0043_591_43591690_qa_5" +name = "smoldataenvs-train/0043_591_43591690_qa_5" description = "How many mobile devices with touch screen enabled (touch_screen=1) have no primary camera (pc=0)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "51" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_639_43639268_qa_1/task.toml b/tasks/0043_639_43639268_qa_1/task.toml index f49c11dcdb7fc547a89375bb8439c731c18d1e8f..7b9290f1db95f7b11c5bfa032d8628db34b8b5ec 100644 --- a/tasks/0043_639_43639268_qa_1/task.toml +++ b/tasks/0043_639_43639268_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0043_639_43639268_qa_1" +name = "smoldataenvs-train/0043_639_43639268_qa_1" description = "What is the highest test accuracy percentage achieved by any classification model in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "100" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0043_642_43642810_qa_1/task.toml b/tasks/0043_642_43642810_qa_1/task.toml index 9d060bc36d142c624cdbafb19adcf0703474781e..0a1eb6df831b0110fb7f73c217b83f5c2c772776 100644 --- a/tasks/0043_642_43642810_qa_1/task.toml +++ b/tasks/0043_642_43642810_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0043_642_43642810_qa_1" +name = "smoldataenvs-train/0043_642_43642810_qa_1" description = "Is the correlation between client's account balance and subscription to a term deposit statistically significant?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0043_644_43644485_qa_5/task.toml b/tasks/0043_644_43644485_qa_5/task.toml index 3baab57fae968fc30f298147988bdca5dca4726a..767f0aaa0aaaa01c710928e2b40a0ca487e68ffb 100644 --- a/tasks/0043_644_43644485_qa_5/task.toml +++ b/tasks/0043_644_43644485_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0043_644_43644485_qa_5" +name = "smoldataenvs-train/0043_644_43644485_qa_5" description = "What is the 75th percentile value of the BILL_AMT1 feature in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "67091.00" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0043_694_43694920_qa_1/task.toml b/tasks/0043_694_43694920_qa_1/task.toml index 4b15660ae4f7667307b3b7eecb58ceacf17cbe22..790068e3a23736eeda9eed587cf356d98302b785 100644 --- a/tasks/0043_694_43694920_qa_1/task.toml +++ b/tasks/0043_694_43694920_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0043_694_43694920_qa_1" +name = "smoldataenvs-train/0043_694_43694920_qa_1" description = "What is the R-squared value of the linear regression model on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.919" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0043_694_43694920_qa_5/task.toml b/tasks/0043_694_43694920_qa_5/task.toml index 84d170c8799d5e766e7879884b4f8de6d4102f25..e15844c2391db3ded58f1c2a752496d76f357221 100644 --- a/tasks/0043_694_43694920_qa_5/task.toml +++ b/tasks/0043_694_43694920_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0043_694_43694920_qa_5" +name = "smoldataenvs-train/0043_694_43694920_qa_5" description = "What is the intercept term of the trained linear regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-2631028.901746378" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0043_720_43720895_qa_2/task.toml b/tasks/0043_720_43720895_qa_2/task.toml index f77a1943c022d326d2d0243bb733cd4f218085e7..82d73e9f634038d6a16213539da755ed32d1e9fe 100644 --- a/tasks/0043_720_43720895_qa_2/task.toml +++ b/tasks/0043_720_43720895_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0043_720_43720895_qa_2" +name = "smoldataenvs-train/0043_720_43720895_qa_2" description = "What is the churn rate for customers without a partner?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "32.96" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_759_43759429_qa_1/task.toml b/tasks/0043_759_43759429_qa_1/task.toml index efafb91735b2eb69ae279d0fa80399d7bf7273f4..a4d3da5ae8aa87351b603b0440621fa249673347 100644 --- a/tasks/0043_759_43759429_qa_1/task.toml +++ b/tasks/0043_759_43759429_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0043_759_43759429_qa_1" +name = "smoldataenvs-train/0043_759_43759429_qa_1" description = "How many ramen products have missing 'Style' information before being filled in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0043_773_43773059_qa_2/task.toml b/tasks/0043_773_43773059_qa_2/task.toml index 71c4f829e8d94fc2a115d83e829fff93907b0fbb..56b6ed8baed35b639766f60ac249440c6244e961 100644 --- a/tasks/0043_773_43773059_qa_2/task.toml +++ b/tasks/0043_773_43773059_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0043_773_43773059_qa_2" +name = "smoldataenvs-train/0043_773_43773059_qa_2" description = "What is the difference in average insurance charges between male and female policyholders in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1387.17" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_773_43773059_qa_4/task.toml b/tasks/0043_773_43773059_qa_4/task.toml index 7a2241c405c51a4472f9c1a8cffabd80c2578a08..41d00b4b682fec46f397d1bfd97587fe07e409df 100644 --- a/tasks/0043_773_43773059_qa_4/task.toml +++ b/tasks/0043_773_43773059_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0043_773_43773059_qa_4" +name = "smoldataenvs-train/0043_773_43773059_qa_4" description = "What is the percentage difference in average charges between smokers and non-smokers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "280.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_774_43774308_qa_1/task.toml b/tasks/0043_774_43774308_qa_1/task.toml index b149dde19de8a51d068966f1199444d32c362e84..fb53e53de7605e6544246b32340c960e04422540 100644 --- a/tasks/0043_774_43774308_qa_1/task.toml +++ b/tasks/0043_774_43774308_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0043_774_43774308_qa_1" +name = "smoldataenvs-train/0043_774_43774308_qa_1" description = "Which feature has the strongest negative correlation with wine quality, and what is the correlation coefficient value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "volatile acidity, -0.390558" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_783_43783786_qa_2/task.toml b/tasks/0043_783_43783786_qa_2/task.toml index b58fc2f88eacf37390d4ff091070c9dce45e8c23..bce1af6375b68a4b9f39a99b8e953a427f0c3229 100644 --- a/tasks/0043_783_43783786_qa_2/task.toml +++ b/tasks/0043_783_43783786_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0043_783_43783786_qa_2" +name = "smoldataenvs-train/0043_783_43783786_qa_2" description = "Which gender had a higher survival rate according to the dataset based on countplot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Female" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_783_43783786_qa_3/task.toml b/tasks/0043_783_43783786_qa_3/task.toml index 79737d2fbbcf0c23b2adc2a54a02abd6f188c6fb..5ed7379d3352186a4d8575a735384032182cac5b 100644 --- a/tasks/0043_783_43783786_qa_3/task.toml +++ b/tasks/0043_783_43783786_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0043_783_43783786_qa_3" +name = "smoldataenvs-train/0043_783_43783786_qa_3" description = "After data preprocessing, how many missing values were imputed in the 'Age' column using class-specific average values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "177" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_826_43826331_qa_2/task.toml b/tasks/0043_826_43826331_qa_2/task.toml index b2107aa155fb59be3bcf97818e70e29ad0a4cb7c..cea6f67fd683b8290c8df8e5b6e31676b7d4b5ca 100644 --- a/tasks/0043_826_43826331_qa_2/task.toml +++ b/tasks/0043_826_43826331_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0043_826_43826331_qa_2" +name = "smoldataenvs-train/0043_826_43826331_qa_2" description = "After balancing the dataset, what is the ratio of view events to non-view events in the final balanced data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1:1" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_829_43829102_qa_2/task.toml b/tasks/0043_829_43829102_qa_2/task.toml index 646ae42c669fa4acc732e446bb31f9ca98de19cb..ce95bfc851d12c77d8b3c43bd3b981ebeb4b0736 100644 --- a/tasks/0043_829_43829102_qa_2/task.toml +++ b/tasks/0043_829_43829102_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0043_829_43829102_qa_2" +name = "smoldataenvs-train/0043_829_43829102_qa_2" description = "Which feature in the dataset has the highest positive correlation with the Survived column based on the heatmap visualization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Sex" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_829_43829102_qa_5/task.toml b/tasks/0043_829_43829102_qa_5/task.toml index 7105edcda15f0137046a9135445a31faf43d26ea..d09c50154ffe15be4f7634aebb07d238fcd9173f 100644 --- a/tasks/0043_829_43829102_qa_5/task.toml +++ b/tasks/0043_829_43829102_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0043_829_43829102_qa_5" +name = "smoldataenvs-train/0043_829_43829102_qa_5" description = "Which feature exhibits the strongest negative correlation with the Survived column according to the correlation heatmap?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PassengerId" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_869_43869885_qa_5/task.toml b/tasks/0043_869_43869885_qa_5/task.toml index 4bcf8e5f2101c06d7d451f54882972b6ff18024f..139d73e24f17f147146a994cceec7fd648a2fd77 100644 --- a/tasks/0043_869_43869885_qa_5/task.toml +++ b/tasks/0043_869_43869885_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0043_869_43869885_qa_5" +name = "smoldataenvs-train/0043_869_43869885_qa_5" description = "What is the accuracy of the AdaBoost model using Logistic Regression as the base estimator?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "78.86" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0043_889_43889204_qa_5/task.toml b/tasks/0043_889_43889204_qa_5/task.toml index 83f9c1337bb4f8e80037628beea7697df5be7601..7a202c9dd3b946dbaa30bd0ca98e425b37c802bf 100644 --- a/tasks/0043_889_43889204_qa_5/task.toml +++ b/tasks/0043_889_43889204_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0043_889_43889204_qa_5" +name = "smoldataenvs-train/0043_889_43889204_qa_5" description = "What is the distribution of categories in the 'lug_boot' feature (small, med, big)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "small=576, med=576, big=576" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0043_936_43936231_qa_1/task.toml b/tasks/0043_936_43936231_qa_1/task.toml index f82e8e3dff2913647695fe7483872aaa0a97def6..d8d1831d795dd345882d2109802588f481570e5b 100644 --- a/tasks/0043_936_43936231_qa_1/task.toml +++ b/tasks/0043_936_43936231_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0043_936_43936231_qa_1" +name = "smoldataenvs-train/0043_936_43936231_qa_1" description = "Which passenger class (Pclass) had the highest survival rate according to the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0043_936_43936231_qa_2/task.toml b/tasks/0043_936_43936231_qa_2/task.toml index 47b91bf9743ac917fcb6ba3c912a2e69e2be6784..4c7a8a945ea3541d6453b90e394b4f341c87a919 100644 --- a/tasks/0043_936_43936231_qa_2/task.toml +++ b/tasks/0043_936_43936231_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0043_936_43936231_qa_2" +name = "smoldataenvs-train/0043_936_43936231_qa_2" description = "How many passengers in the test dataset have missing age values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "86" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0043_936_43936231_qa_3/task.toml b/tasks/0043_936_43936231_qa_3/task.toml index 587176f47517bb6f482710f106ca9394d987e1ff..dafc40bc0b27c7d0e6f75bdad0e8d900472d8a62 100644 --- a/tasks/0043_936_43936231_qa_3/task.toml +++ b/tasks/0043_936_43936231_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0043_936_43936231_qa_3" +name = "smoldataenvs-train/0043_936_43936231_qa_3" description = "How many passengers in the training dataset have missing embarkation information (Embarked)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0043_969_43969595_qa_4/task.toml b/tasks/0043_969_43969595_qa_4/task.toml index f69985a8b3c39481d46af521cc59f9709eb7a080..c8d508457ac151f6638be513cceaa5f7f48700e0 100644 --- a/tasks/0043_969_43969595_qa_4/task.toml +++ b/tasks/0043_969_43969595_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0043_969_43969595_qa_4" +name = "smoldataenvs-train/0043_969_43969595_qa_4" description = "Which department has the highest number of employees in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Research & Development" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0043_969_43969595_qa_5/task.toml b/tasks/0043_969_43969595_qa_5/task.toml index e91a27eb0cf776f239b485e86178f3ebc1800cdb..c9946290986a220b997796b88459ab77eaf71b72 100644 --- a/tasks/0043_969_43969595_qa_5/task.toml +++ b/tasks/0043_969_43969595_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0043_969_43969595_qa_5" +name = "smoldataenvs-train/0043_969_43969595_qa_5" description = "What is the percentage of employees in the dataset who have attrited (Attrition = Yes)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16.13" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0044_001_44001840_qa_1/task.toml b/tasks/0044_001_44001840_qa_1/task.toml index 15d55d68eeb29856b8e5ac2c82f939af6b4692a6..3c2f2ae7141afa9a92b8c0d7a28b3dcdf4a9d255 100644 --- a/tasks/0044_001_44001840_qa_1/task.toml +++ b/tasks/0044_001_44001840_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0044_001_44001840_qa_1" +name = "smoldataenvs-train/0044_001_44001840_qa_1" description = "What is the percentage distribution of abnormal and normal patients in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "67.7% abnormal, 32.3% normal" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0044_103_44103052_qa_1/task.toml b/tasks/0044_103_44103052_qa_1/task.toml index c4dd516e18f0a05dcbb2d4b41d8e3f2926ab2468..940b84c83e02b12b7361e642aa29b7144d7aa623 100644 --- a/tasks/0044_103_44103052_qa_1/task.toml +++ b/tasks/0044_103_44103052_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0044_103_44103052_qa_1" +name = "smoldataenvs-train/0044_103_44103052_qa_1" description = "Which year between 2001-2012 had the highest total number of suicides reported in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2010" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0044_103_44103052_qa_5/task.toml b/tasks/0044_103_44103052_qa_5/task.toml index 4f9127b2be07a5526c77802b18330e8273f7d4ab..72d9b91d1155b564ad906dea3c387d37c179468c 100644 --- a/tasks/0044_103_44103052_qa_5/task.toml +++ b/tasks/0044_103_44103052_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0044_103_44103052_qa_5" +name = "smoldataenvs-train/0044_103_44103052_qa_5" description = "What was identified as the most common method of suicide in the overall dataset summary?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Hanging" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0044_107_44107622_qa_1/task.toml b/tasks/0044_107_44107622_qa_1/task.toml index a38e76b0f67a0c16c25b05ea7a44af9fc12e2aeb..eb0f386d8747cea61f4ff04dd1eed48420339f17 100644 --- a/tasks/0044_107_44107622_qa_1/task.toml +++ b/tasks/0044_107_44107622_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0044_107_44107622_qa_1" +name = "smoldataenvs-train/0044_107_44107622_qa_1" description = "What was the imputed age for passengers in third class with missing age values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "24" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0044_107_44107622_qa_5/task.toml b/tasks/0044_107_44107622_qa_5/task.toml index 3c4a7c6734f8d16f736a288002a4c4de2f1165ea..934a5e7df58b531cf12827f32f558284f2d67f5c 100644 --- a/tasks/0044_107_44107622_qa_5/task.toml +++ b/tasks/0044_107_44107622_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0044_107_44107622_qa_5" +name = "smoldataenvs-train/0044_107_44107622_qa_5" description = "Which hyperparameters were selected as the best in the grid search for the logistic regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "C=0.01, penalty='none', solver='newton-cg'" reward_mode_initial = "flexible" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0044_141_44141679_qa_3/task.toml b/tasks/0044_141_44141679_qa_3/task.toml index 8a9c32404bd18e2edd3d9cd2d681ee50bf52d8ef..e3d8c74b0aa7e8c36b11f9bdce869fbd2b150448 100644 --- a/tasks/0044_141_44141679_qa_3/task.toml +++ b/tasks/0044_141_44141679_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0044_141_44141679_qa_3" +name = "smoldataenvs-train/0044_141_44141679_qa_3" description = "After imputing missing values using median imputation stratified by diabetes status, what is the median Glucose level for non-diabetic patients (Outcome = 0)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "107" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0044_153_44153291_qa_2/task.toml b/tasks/0044_153_44153291_qa_2/task.toml index 56929ea0bb469b84314c2a5d6aefa8270f552291..a3b35e39a03bab40f4d03de67cce00455d99c3e8 100644 --- a/tasks/0044_153_44153291_qa_2/task.toml +++ b/tasks/0044_153_44153291_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0044_153_44153291_qa_2" +name = "smoldataenvs-train/0044_153_44153291_qa_2" description = "What is the weighted average vote score of \"The Godfather: Part II\" after applying the vote adjustment formula with m=370.2 and C=6.092?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8.079586" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0044_153_44153291_qa_4/task.toml b/tasks/0044_153_44153291_qa_4/task.toml index 9458f35e4f5f815f85225124b021cb12fb03e042..6c52d06308ef3874e686d72c669f2c03fe687bbd 100644 --- a/tasks/0044_153_44153291_qa_4/task.toml +++ b/tasks/0044_153_44153291_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0044_153_44153291_qa_4" +name = "smoldataenvs-train/0044_153_44153291_qa_4" description = "What is the mean vote average (C value) calculated across all movies in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.092" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0044_155_44155408_qa_1/task.toml b/tasks/0044_155_44155408_qa_1/task.toml index edb04ea6d6c5c97a811120c827e7a707611cd827..687dcca09f874247c09373b7096068c78d7b3a11 100644 --- a/tasks/0044_155_44155408_qa_1/task.toml +++ b/tasks/0044_155_44155408_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0044_155_44155408_qa_1" +name = "smoldataenvs-train/0044_155_44155408_qa_1" description = "Which customer payment method has the highest churn rate, and what is the percentage of churn for this method?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic check, 45.29" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0044_155_44155408_qa_2/task.toml b/tasks/0044_155_44155408_qa_2/task.toml index a37aa1b9f048749ae3aad87f91413af0c1ea14df..090b1f2b25f4391cc33cf271f686ed844b48a8e2 100644 --- a/tasks/0044_155_44155408_qa_2/task.toml +++ b/tasks/0044_155_44155408_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0044_155_44155408_qa_2" +name = "smoldataenvs-train/0044_155_44155408_qa_2" description = "Which contract type is associated with the lowest churn rate, and what is the percentage of churn for this contract type?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Two year, 2.83%" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0044_175_44175301_qa_3/task.toml b/tasks/0044_175_44175301_qa_3/task.toml index f4c125c3d8fe8aa86ccec826e14ce4c275086afc..79c3466dcba2a394a7d6373cd06e1c30a402c18d 100644 --- a/tasks/0044_175_44175301_qa_3/task.toml +++ b/tasks/0044_175_44175301_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0044_175_44175301_qa_3" +name = "smoldataenvs-train/0044_175_44175301_qa_3" description = "What is the average annual income (in k$) of the cluster with the highest spending score in the K-Means clustering?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "86.54" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0044_193_44193489_qa_1/task.toml b/tasks/0044_193_44193489_qa_1/task.toml index da910492c315487ea7dc8b14ab15036419e212ab..11e5ab064fac9d1c2fc7910df66258e08bd8fd0e 100644 --- a/tasks/0044_193_44193489_qa_1/task.toml +++ b/tasks/0044_193_44193489_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0044_193_44193489_qa_1" +name = "smoldataenvs-train/0044_193_44193489_qa_1" description = "What is the skewness value of the price distribution in the original dataset before outlier removal?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.024069" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0044_251_44251119_qa_3/task.toml b/tasks/0044_251_44251119_qa_3/task.toml index fd444811984422dd01184936a497a929ec518e0b..102c7ef6e105dee00f838cdcee5e86c593403379 100644 --- a/tasks/0044_251_44251119_qa_3/task.toml +++ b/tasks/0044_251_44251119_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0044_251_44251119_qa_3" +name = "smoldataenvs-train/0044_251_44251119_qa_3" description = "What is the total number of unique geographic areas represented in the poverty rate dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "51" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0044_270_44270596_qa_1/task.toml b/tasks/0044_270_44270596_qa_1/task.toml index 8ab6d703961af230ef6502f09e80dc570415d41d..e4c6e8b4e66495c9959aae0313cf53531054887d 100644 --- a/tasks/0044_270_44270596_qa_1/task.toml +++ b/tasks/0044_270_44270596_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0044_270_44270596_qa_1" +name = "smoldataenvs-train/0044_270_44270596_qa_1" description = "Which feature in the dataset has the highest absolute correlation with the price_range column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ram" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0044_270_44270596_qa_4/task.toml b/tasks/0044_270_44270596_qa_4/task.toml index 838be8dcda94e74a495c66e478d7d7a1a5794223..bd4146147f0a6aba4a962117474c5f3cb1fee169 100644 --- a/tasks/0044_270_44270596_qa_4/task.toml +++ b/tasks/0044_270_44270596_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0044_270_44270596_qa_4" +name = "smoldataenvs-train/0044_270_44270596_qa_4" description = "Which continuous feature exhibits the strongest positive correlation with price_range according to the heatmap visualization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ram" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0044_352_44352035_qa_4/task.toml b/tasks/0044_352_44352035_qa_4/task.toml index 49f85cbbd39ce7372991b7e70d25d11ec90f530e..6afa0bd54e7d9cacb93fe4ae887573dbd39fa8b6 100644 --- a/tasks/0044_352_44352035_qa_4/task.toml +++ b/tasks/0044_352_44352035_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0044_352_44352035_qa_4" +name = "smoldataenvs-train/0044_352_44352035_qa_4" description = "After applying stratified splitting with test_size=0.2, how many samples are present in the final test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "154" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0044_352_44352035_qa_5/task.toml b/tasks/0044_352_44352035_qa_5/task.toml index 7d1e9eb74aa0913fa0ee15c1983b0c7182b3dd08..e1101d443149ee0f7ef82c67e7412b0faa0410cd 100644 --- a/tasks/0044_352_44352035_qa_5/task.toml +++ b/tasks/0044_352_44352035_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0044_352_44352035_qa_5" +name = "smoldataenvs-train/0044_352_44352035_qa_5" description = "What is the distribution of diabetes outcomes (positive class count) in the training data after stratified splitting?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "214" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0044_367_44367279_qa_2/task.toml b/tasks/0044_367_44367279_qa_2/task.toml index c44bc2f3bbad5b2d3bedbb625454879331403710..61be41aaf728c1a04360c00a278519a35cd3a76c 100644 --- a/tasks/0044_367_44367279_qa_2/task.toml +++ b/tasks/0044_367_44367279_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0044_367_44367279_qa_2" +name = "smoldataenvs-train/0044_367_44367279_qa_2" description = "What is the average tenure (in months) of customers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "32.37" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0044_367_44367279_qa_3/task.toml b/tasks/0044_367_44367279_qa_3/task.toml index c4e9e53a41d97da4ec008488ebf6437d3618732d..1c95b707a6f34c145d377f5d3b85c4f2bfb26c3c 100644 --- a/tasks/0044_367_44367279_qa_3/task.toml +++ b/tasks/0044_367_44367279_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0044_367_44367279_qa_3" +name = "smoldataenvs-train/0044_367_44367279_qa_3" description = "How many unique customer IDs are present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7043" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0044_367_44367279_qa_5/task.toml b/tasks/0044_367_44367279_qa_5/task.toml index 9c93caa46cbbf4cf9941c77d6feb26f22708753e..16a16e1c7646ef687ce6850757a8a778f4e8fbfb 100644 --- a/tasks/0044_367_44367279_qa_5/task.toml +++ b/tasks/0044_367_44367279_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0044_367_44367279_qa_5" +name = "smoldataenvs-train/0044_367_44367279_qa_5" description = "After imputation, how many rows have a 'TotalCharges' value of zero?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0044_401_44401900_qa_2/task.toml b/tasks/0044_401_44401900_qa_2/task.toml index 55898f68e8671099801736d3588934a0dde9f576..53ec95e1d93283346e7d39b977e0e7a70c8af76f 100644 --- a/tasks/0044_401_44401900_qa_2/task.toml +++ b/tasks/0044_401_44401900_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0044_401_44401900_qa_2" +name = "smoldataenvs-train/0044_401_44401900_qa_2" description = "Which feature shows the highest linear correlation with the target variable \"Outcome\" according to the correlation analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0044_401_44401900_qa_3/task.toml b/tasks/0044_401_44401900_qa_3/task.toml index 5743840e9b7ef06bd3b3827c53202f18a371d4fd..e4f9c3a1b8a06729cb76e9b6d5f573e2fe02262b 100644 --- a/tasks/0044_401_44401900_qa_3/task.toml +++ b/tasks/0044_401_44401900_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0044_401_44401900_qa_3" +name = "smoldataenvs-train/0044_401_44401900_qa_3" description = "How many features remain in the dataset after removing the feature with multicollinearity based on the correlation analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0044_406_44406244_qa_1/task.toml b/tasks/0044_406_44406244_qa_1/task.toml index f12b32c0da18a5f3d1e2ab32df94b04866297763..28710e1e5067df0bb87d1d9ff8909447f3037542 100644 --- a/tasks/0044_406_44406244_qa_1/task.toml +++ b/tasks/0044_406_44406244_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0044_406_44406244_qa_1" +name = "smoldataenvs-train/0044_406_44406244_qa_1" description = "What is the percentage of edible mushrooms in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "51.8" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0044_406_44406244_qa_5/task.toml b/tasks/0044_406_44406244_qa_5/task.toml index 11c82b76824d3c5c7e6fee3935f8559aa837c9c5..e1f2525500c30ff444c8d3d4861cd5fc4580ba8d 100644 --- a/tasks/0044_406_44406244_qa_5/task.toml +++ b/tasks/0044_406_44406244_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0044_406_44406244_qa_5" +name = "smoldataenvs-train/0044_406_44406244_qa_5" description = "What is the most common cap shape in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Convex" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0044_412_44412230_qa_1/task.toml b/tasks/0044_412_44412230_qa_1/task.toml index b81c5e7f5b4a7792eb662c1d6f0210750be1aa2b..aab6ace0bd48ee5b64be1902cb72a80918583a6a 100644 --- a/tasks/0044_412_44412230_qa_1/task.toml +++ b/tasks/0044_412_44412230_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0044_412_44412230_qa_1" +name = "smoldataenvs-train/0044_412_44412230_qa_1" description = "What is the average mid-career median salary for Engineering schools in the Northeastern region?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "108366" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0044_466_44466723_qa_4/task.toml b/tasks/0044_466_44466723_qa_4/task.toml index 40256e7ddccf0461e0e9783f10b45f777c1f7f99..a8e34ee0594869951b0c3e3338848c6ad3e5ce08 100644 --- a/tasks/0044_466_44466723_qa_4/task.toml +++ b/tasks/0044_466_44466723_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0044_466_44466723_qa_4" +name = "smoldataenvs-train/0044_466_44466723_qa_4" description = "What is the median sepal width in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0044_466_44466723_qa_5/task.toml b/tasks/0044_466_44466723_qa_5/task.toml index b5a9841d1325629cd8c9b580009912b8cd730c98..a4b0f82a3b0ef9e75194b4482aba1c5da38ac254 100644 --- a/tasks/0044_466_44466723_qa_5/task.toml +++ b/tasks/0044_466_44466723_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0044_466_44466723_qa_5" +name = "smoldataenvs-train/0044_466_44466723_qa_5" description = "What is the average petal width across all samples in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.198667" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0044_505_44505990_qa_5/task.toml b/tasks/0044_505_44505990_qa_5/task.toml index f4eb6e99f7393ad1ace441d9bbee9563c7d0d27b..ef85ff760676aff75afbca8870d1014aecde2bb3 100644 --- a/tasks/0044_505_44505990_qa_5/task.toml +++ b/tasks/0044_505_44505990_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0044_505_44505990_qa_5" +name = "smoldataenvs-train/0044_505_44505990_qa_5" description = "Is the distribution of the target variable 'deposit' balanced according to the histogram visualization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0044_572_44572407_qa_4/task.toml b/tasks/0044_572_44572407_qa_4/task.toml index 9653d6ac99dd8a9d1d4093878235c44ab95fff45..9102914fe0499566e18db2a80b9415fa1ea3f463 100644 --- a/tasks/0044_572_44572407_qa_4/task.toml +++ b/tasks/0044_572_44572407_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0044_572_44572407_qa_4" +name = "smoldataenvs-train/0044_572_44572407_qa_4" description = "What is the total number of games in the dataset that resulted in a win for either player (excluding draws)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "19108" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0044_575_44575268_qa_1/task.toml b/tasks/0044_575_44575268_qa_1/task.toml index e47cd64d2eea7a8b8bfa560aeed49824962fb1a5..2fe6a54ccbef3e30d48ad4107012604d7e8e64db 100644 --- a/tasks/0044_575_44575268_qa_1/task.toml +++ b/tasks/0044_575_44575268_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0044_575_44575268_qa_1" +name = "smoldataenvs-train/0044_575_44575268_qa_1" description = "Which physical attribute has the highest positive Pearson correlation with wine quality in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Alcohol" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0044_575_44575651_qa_2/task.toml b/tasks/0044_575_44575651_qa_2/task.toml index e56b2ec855914f02ef7dc5b0dfca385a8b2bbff1..68cbc919dbed6cd6612705917ec462a7e1daf59c 100644 --- a/tasks/0044_575_44575651_qa_2/task.toml +++ b/tasks/0044_575_44575651_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0044_575_44575651_qa_2" +name = "smoldataenvs-train/0044_575_44575651_qa_2" description = "What percentage of the dataset represents diabetic patients (Outcome=1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.9" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0044_575_44575651_qa_3/task.toml b/tasks/0044_575_44575651_qa_3/task.toml index d4c3c5e1401e7c226a5d6324c8ce5bf9a223c02e..2334eaa6929a81eb1486c5a37f52ff17780ea7ad 100644 --- a/tasks/0044_575_44575651_qa_3/task.toml +++ b/tasks/0044_575_44575651_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0044_575_44575651_qa_3" +name = "smoldataenvs-train/0044_575_44575651_qa_3" description = "Which feature is identified as the most important for predicting diabetes according to the random forest model's feature importance analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0044_609_44609132_qa_3/task.toml b/tasks/0044_609_44609132_qa_3/task.toml index 31c383e2cb9605ffb89dc79e16c85915af21e8b1..e68fdc33b88aa86ac4995507fd9ff52ce55501a5 100644 --- a/tasks/0044_609_44609132_qa_3/task.toml +++ b/tasks/0044_609_44609132_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0044_609_44609132_qa_3" +name = "smoldataenvs-train/0044_609_44609132_qa_3" description = "What percentage of matches resulted in the toss winner choosing to field?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "57.08" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0044_708_44708511_qa_4/task.toml b/tasks/0044_708_44708511_qa_4/task.toml index 615206a56930ce9c23c4375557c9b680f14fa101..9c4a8843e8d3a8898493812597988d22b0c7343d 100644 --- a/tasks/0044_708_44708511_qa_4/task.toml +++ b/tasks/0044_708_44708511_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0044_708_44708511_qa_4" +name = "smoldataenvs-train/0044_708_44708511_qa_4" description = "What is the highest positive correlation between any two features in the dataset according to the heatmap analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.962865" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0044_721_44721454_qa_1/task.toml b/tasks/0044_721_44721454_qa_1/task.toml index 59b78293b7930935a9572d5936657c96feab1e3b..172a66059c1829d75db8cbc7e1ae0fea37fc8b33 100644 --- a/tasks/0044_721_44721454_qa_1/task.toml +++ b/tasks/0044_721_44721454_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0044_721_44721454_qa_1" +name = "smoldataenvs-train/0044_721_44721454_qa_1" description = "Which region in the dataset has the highest average health insurance charges?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "southeast" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0044_727_44727189_qa_1/task.toml b/tasks/0044_727_44727189_qa_1/task.toml index 808aae15402e1a40f0b1de2d31b94f61954aa44a..c708a9652aa3a4b22bde7f20c18ee60916538611 100644 --- a/tasks/0044_727_44727189_qa_1/task.toml +++ b/tasks/0044_727_44727189_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0044_727_44727189_qa_1" +name = "smoldataenvs-train/0044_727_44727189_qa_1" description = "What percentage of the 'Population' data is missing in the dataset before any data cleaning steps are applied?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "22.19" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0044_766_44766802_qa_2/task.toml b/tasks/0044_766_44766802_qa_2/task.toml index ddd8a674cffd21f88cb772f52dfae1f96ffb8aee..965096a3dd51eff88ffffca8663d75d0f7a91ba7 100644 --- a/tasks/0044_766_44766802_qa_2/task.toml +++ b/tasks/0044_766_44766802_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0044_766_44766802_qa_2" +name = "smoldataenvs-train/0044_766_44766802_qa_2" description = "How many features remain in the dataset after removing the 'id' column and the 'Unnamed: 32' column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "31" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0044_860_44860367_qa_5/task.toml b/tasks/0044_860_44860367_qa_5/task.toml index 5b03fff381c6abaafdba49a0caf0e6bfab59d3e0..b1c04925e9a99d06001818bb0a88291790b91e74 100644 --- a/tasks/0044_860_44860367_qa_5/task.toml +++ b/tasks/0044_860_44860367_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0044_860_44860367_qa_5" +name = "smoldataenvs-train/0044_860_44860367_qa_5" description = "Which publisher holds the largest market share according to the top 10 publishers analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Nintendo" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0044_867_44867555_qa_3/task.toml b/tasks/0044_867_44867555_qa_3/task.toml index 4c5c0569f8414acb340cc6bdcc2174aba8d1d966..502288793a29cff7f886e7d4a71f28fe5ec1edf2 100644 --- a/tasks/0044_867_44867555_qa_3/task.toml +++ b/tasks/0044_867_44867555_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0044_867_44867555_qa_3" +name = "smoldataenvs-train/0044_867_44867555_qa_3" description = "Which BMI category has the highest average medical cost based on the weighted analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Obese" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0044_867_44867555_qa_4/task.toml b/tasks/0044_867_44867555_qa_4/task.toml index 94b43bf0cab28c0e6665fe9f00c81954a49a04ec..6307992995a34096b66b95076e2288996b78b9f2 100644 --- a/tasks/0044_867_44867555_qa_4/task.toml +++ b/tasks/0044_867_44867555_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0044_867_44867555_qa_4" +name = "smoldataenvs-train/0044_867_44867555_qa_4" description = "Which geographic region has the largest number of patients in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Southeast" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0044_869_44869617_qa_3/task.toml b/tasks/0044_869_44869617_qa_3/task.toml index ba6838525453c4155518887026576c3c543385df..29e0ec7b68fc41fbc100b0ef692e59d17aa8115b 100644 --- a/tasks/0044_869_44869617_qa_3/task.toml +++ b/tasks/0044_869_44869617_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0044_869_44869617_qa_3" +name = "smoldataenvs-train/0044_869_44869617_qa_3" description = "Which feature demonstrates the highest positive correlation with the Outcome variable in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0044_873_44873237_qa_3/task.toml b/tasks/0044_873_44873237_qa_3/task.toml index 4814ac0799b62f317377f01334c9d897d9f3a3b7..877c285e941eaab62a8ed0fe36a8056b9d727082 100644 --- a/tasks/0044_873_44873237_qa_3/task.toml +++ b/tasks/0044_873_44873237_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0044_873_44873237_qa_3" +name = "smoldataenvs-train/0044_873_44873237_qa_3" description = "What percentage of variance in salary is explained by years of experience according to the OLS regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "95.7%" reward_mode_initial = "flexible" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0044_954_44954372_qa_1/task.toml b/tasks/0044_954_44954372_qa_1/task.toml index bf3dfe33228abd1b61dcd5b513951d09ebf7269a..cacae08f4a58459efc56706b99cc3c6da2de4feb 100644 --- a/tasks/0044_954_44954372_qa_1/task.toml +++ b/tasks/0044_954_44954372_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0044_954_44954372_qa_1" +name = "smoldataenvs-train/0044_954_44954372_qa_1" description = "What is the percentage of customers who churned in the dataset before applying any machine learning model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26.54" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0044_954_44954372_qa_2/task.toml b/tasks/0044_954_44954372_qa_2/task.toml index 763ab83eefb8eb7396d9f05c04cdd5f2e09f2905..c1e1bc12d29be5a02d3ace008b1a500f4dd0c46f 100644 --- a/tasks/0044_954_44954372_qa_2/task.toml +++ b/tasks/0044_954_44954372_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0044_954_44954372_qa_2" +name = "smoldataenvs-train/0044_954_44954372_qa_2" description = "After hyperparameter tuning, what is the maximum depth value in the optimal XGBoost model configuration for predicting customer churn?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0044_977_44977269_qa_5/task.toml b/tasks/0044_977_44977269_qa_5/task.toml index bbfbfb43a9147b12c00fdf51849d1aad304b1cba..5ebd2f3cd32938b5dc6466108718671e57398434 100644 --- a/tasks/0044_977_44977269_qa_5/task.toml +++ b/tasks/0044_977_44977269_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0044_977_44977269_qa_5" +name = "smoldataenvs-train/0044_977_44977269_qa_5" description = "What is the precision for Iris-setosa in the Decision Tree model when using petal features?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.00" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0044_988_44988061_qa_5/task.toml b/tasks/0044_988_44988061_qa_5/task.toml index dda22c3ca20f2b76625163f65d729431ee6c3a9c..eb8f8fb6ba0cae8191155235b384a0d5b66867cb 100644 --- a/tasks/0044_988_44988061_qa_5/task.toml +++ b/tasks/0044_988_44988061_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0044_988_44988061_qa_5" +name = "smoldataenvs-train/0044_988_44988061_qa_5" description = "What value of the regularization parameter (C) was found to be optimal for logistic regression during hyperparameter tuning using grid search?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1000" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0044_993_44993292_qa_1/task.toml b/tasks/0044_993_44993292_qa_1/task.toml index 35baf63e04c91babc4cc321f8ac7599c3fe290a4..6ed1154774480c09b21bd5da68d2139bda6374a1 100644 --- a/tasks/0044_993_44993292_qa_1/task.toml +++ b/tasks/0044_993_44993292_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0044_993_44993292_qa_1" +name = "smoldataenvs-train/0044_993_44993292_qa_1" description = "How many campaigns were excluded from the analysis due to being in states other than 'failed' or 'successful'?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "46986" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0045_006_45006527_qa_2/task.toml b/tasks/0045_006_45006527_qa_2/task.toml index 581cde223ef11365178f20e8830a0f1c77b9f0d3..e8c2c8652b87fe0f21446e628988ef18dd28a428 100644 --- a/tasks/0045_006_45006527_qa_2/task.toml +++ b/tasks/0045_006_45006527_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0045_006_45006527_qa_2" +name = "smoldataenvs-train/0045_006_45006527_qa_2" description = "What is the highest correlation coefficient between any feature and the diabetes outcome (Outcome) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.466581" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0045_024_45024257_qa_3/task.toml b/tasks/0045_024_45024257_qa_3/task.toml index fcbd7fe59bb2f3673c87cc4e597816e569a0a4d6..1eecfa62a7ea9204f65e8ddbdcad8e4db7b6bdcc 100644 --- a/tasks/0045_024_45024257_qa_3/task.toml +++ b/tasks/0045_024_45024257_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0045_024_45024257_qa_3" +name = "smoldataenvs-train/0045_024_45024257_qa_3" description = "What is the correlation coefficient between the number of comments and views for TED Talks in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.53" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0045_024_45024257_qa_5/task.toml b/tasks/0045_024_45024257_qa_5/task.toml index bd88ac44541393da1599de9fccaf30407fba978c..36a159224a983ffaca93c01b22e81a7e3ade7bc6 100644 --- a/tasks/0045_024_45024257_qa_5/task.toml +++ b/tasks/0045_024_45024257_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0045_024_45024257_qa_5" +name = "smoldataenvs-train/0045_024_45024257_qa_5" description = "Which TED Talk has the longest duration in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Parrots, the universe and everything" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0045_032_45032719_qa_5/task.toml b/tasks/0045_032_45032719_qa_5/task.toml index 88a84fd2fd8303f484865b2bf00a3d4105c3e96b..10c4d93c1d538240669020414c651ef18f9acb77 100644 --- a/tasks/0045_032_45032719_qa_5/task.toml +++ b/tasks/0045_032_45032719_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0045_032_45032719_qa_5" +name = "smoldataenvs-train/0045_032_45032719_qa_5" description = "What is the size of the test dataset used for evaluating all the classifier models?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "154" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0045_056_45056822_qa_5/task.toml b/tasks/0045_056_45056822_qa_5/task.toml index 84f8e88af4fc178330776c310eff2f79f00b12d4..25189fc5dfe98cfebf5023ecf6d17f97b99a1d99 100644 --- a/tasks/0045_056_45056822_qa_5/task.toml +++ b/tasks/0045_056_45056822_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0045_056_45056822_qa_5" +name = "smoldataenvs-train/0045_056_45056822_qa_5" description = "What is the average rating of White players when they win a game compared to when they lose a game?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1634.18, 1549.25" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0045_065_45065521_qa_2/task.toml b/tasks/0045_065_45065521_qa_2/task.toml index 15df4be3084a50adf974a583a36b581f5fd1342a..2843e29e2b500ac62bcbc86a78d421795cd5879b 100644 --- a/tasks/0045_065_45065521_qa_2/task.toml +++ b/tasks/0045_065_45065521_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0045_065_45065521_qa_2" +name = "smoldataenvs-train/0045_065_45065521_qa_2" description = "Which numerical attribute has the strongest positive correlation with the Pokémon's Legendary status?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Sp. Atk" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0045_067_45067031_qa_3/task.toml b/tasks/0045_067_45067031_qa_3/task.toml index 0153fcd5fa893ca127e881c22c8c36f064eb5d97..1e9a26689703749b9bc6369d1310d866fd097336 100644 --- a/tasks/0045_067_45067031_qa_3/task.toml +++ b/tasks/0045_067_45067031_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0045_067_45067031_qa_3" +name = "smoldataenvs-train/0045_067_45067031_qa_3" description = "In the scatter plot visualizing housing prices, what scaling factor was applied to the population attribute to determine the radius of the circles?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "100" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0045_071_45071899_qa_5/task.toml b/tasks/0045_071_45071899_qa_5/task.toml index af1600bc3c0b1e2107b332fe9a06741b4a85362b..093eaf0d5fb66fd367bb8008fde70b3d180a69d3 100644 --- a/tasks/0045_071_45071899_qa_5/task.toml +++ b/tasks/0045_071_45071899_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0045_071_45071899_qa_5" +name = "smoldataenvs-train/0045_071_45071899_qa_5" description = "What is the standard error of the YearsExperience coefficient in the regression model trained on the entire dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "378.755" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0045_100_45100270_qa_4/task.toml b/tasks/0045_100_45100270_qa_4/task.toml index 3b1bb50ad827f75217b44801dd49434be9290d78..7e753439001aa6f11509333dde14a34abc8cda9a 100644 --- a/tasks/0045_100_45100270_qa_4/task.toml +++ b/tasks/0045_100_45100270_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0045_100_45100270_qa_4" +name = "smoldataenvs-train/0045_100_45100270_qa_4" description = "What is the absolute difference in validation accuracy between the best Decision Tree model (0.59375) and the best Random Forest model (0.6625)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.06875" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0045_118_45118642_qa_2/task.toml b/tasks/0045_118_45118642_qa_2/task.toml index 1e8dbe464529ebc2c1efbd10f7847e95b2aaf4e7..c3337e048b12a924d82bf8a3d3cd29cff7827d35 100644 --- a/tasks/0045_118_45118642_qa_2/task.toml +++ b/tasks/0045_118_45118642_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0045_118_45118642_qa_2" +name = "smoldataenvs-train/0045_118_45118642_qa_2" description = "What is the covariance between the first and second principal components after PCA transformation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0045_136_45136493_qa_3/task.toml b/tasks/0045_136_45136493_qa_3/task.toml index f135a02efd80de77d226954d63cf97bcf2bd75fe..bd6386d3addd64301b593b91fa4b47cd1a38c045 100644 --- a/tasks/0045_136_45136493_qa_3/task.toml +++ b/tasks/0045_136_45136493_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0045_136_45136493_qa_3" +name = "smoldataenvs-train/0045_136_45136493_qa_3" description = "Which two species exhibit the most overlapping sepal measurements based on the scatter plot observations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-versicolor, Iris-virginica" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0045_136_45136728_qa_1/task.toml b/tasks/0045_136_45136728_qa_1/task.toml index b6478d769ca411c3492b7b6d803578ae783cce14..f955939d6f098bf0bedcd34c90614343f795195c 100644 --- a/tasks/0045_136_45136728_qa_1/task.toml +++ b/tasks/0045_136_45136728_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0045_136_45136728_qa_1" +name = "smoldataenvs-train/0045_136_45136728_qa_1" description = "What percentage of employees in the dataset attrited (Attrition = Yes)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16.1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0045_138_45138042_qa_4/task.toml b/tasks/0045_138_45138042_qa_4/task.toml index 8661ef8f2a4ec4d7035e8a9bad6e6e39a974fe2d..ca83fb5be0c8a016abf013806385623c9ddbff42 100644 --- a/tasks/0045_138_45138042_qa_4/task.toml +++ b/tasks/0045_138_45138042_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0045_138_45138042_qa_4" +name = "smoldataenvs-train/0045_138_45138042_qa_4" description = "What is the most common primary type ('Type 1') among all Pokémon in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Water" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0045_142_45142290_qa_4/task.toml b/tasks/0045_142_45142290_qa_4/task.toml index 33910ae295cd26dc0403ab3c8a90296a4e73136f..4d2f335f27e29198c73e32560db3c18b18e03bf9 100644 --- a/tasks/0045_142_45142290_qa_4/task.toml +++ b/tasks/0045_142_45142290_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0045_142_45142290_qa_4" +name = "smoldataenvs-train/0045_142_45142290_qa_4" description = "How many male (M) participants are present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "592" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0045_142_45142290_qa_5/task.toml b/tasks/0045_142_45142290_qa_5/task.toml index db1e6fb892a2d4b34e1f644cb792c7b3289cfacb..b8c9a0ad83b6a389f91a018a1d77fb5193700aad 100644 --- a/tasks/0045_142_45142290_qa_5/task.toml +++ b/tasks/0045_142_45142290_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0045_142_45142290_qa_5" +name = "smoldataenvs-train/0045_142_45142290_qa_5" description = "Which age group has the highest frequency in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "30-34" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0045_162_45162455_qa_3/task.toml b/tasks/0045_162_45162455_qa_3/task.toml index 11952f37c58c0f8569a76430d2d24b0545c64354..5785b1ba5b6e7ec362ccb4ac888cffd1275c4382 100644 --- a/tasks/0045_162_45162455_qa_3/task.toml +++ b/tasks/0045_162_45162455_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0045_162_45162455_qa_3" +name = "smoldataenvs-train/0045_162_45162455_qa_3" description = "What is the total number of sentences in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "47959" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0045_174_45174378_qa_1/task.toml b/tasks/0045_174_45174378_qa_1/task.toml index cc2902315b8f113373f7b15b08a50a9359f132e4..e307f7b5820ee324cec216cb2f4e0a845b796a62 100644 --- a/tasks/0045_174_45174378_qa_1/task.toml +++ b/tasks/0045_174_45174378_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0045_174_45174378_qa_1" +name = "smoldataenvs-train/0045_174_45174378_qa_1" description = "Which sandwich item has the highest total daily value percentage according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Bacon Clubhouse Crispy Chicken Sandwich" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0045_190_45190323_qa_2/task.toml b/tasks/0045_190_45190323_qa_2/task.toml index 2334fa21df1965f741634588b201f4cb8e4594f7..c20fba392cbf6b394fdd763d87c5ce7a2821fe50 100644 --- a/tasks/0045_190_45190323_qa_2/task.toml +++ b/tasks/0045_190_45190323_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0045_190_45190323_qa_2" +name = "smoldataenvs-train/0045_190_45190323_qa_2" description = "What is the correlation coefficient between volatile acidity and wine quality in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.34" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0045_190_45190323_qa_5/task.toml b/tasks/0045_190_45190323_qa_5/task.toml index 39e364b8c92ea7c8176f6563fb4e90cea460682c..a804767faec8278832eabf4622d1999b8101a8e1 100644 --- a/tasks/0045_190_45190323_qa_5/task.toml +++ b/tasks/0045_190_45190323_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0045_190_45190323_qa_5" +name = "smoldataenvs-train/0045_190_45190323_qa_5" description = "What is the standard deviation of the 'residual sugar' feature in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.4099" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0045_378_45378696_qa_2/task.toml b/tasks/0045_378_45378696_qa_2/task.toml index 3df5de54b9ce18000edde7b2140f0439dd89db0f..664b9b10c37a73b8684c7f4fdc39c57ff4986fcd 100644 --- a/tasks/0045_378_45378696_qa_2/task.toml +++ b/tasks/0045_378_45378696_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0045_378_45378696_qa_2" +name = "smoldataenvs-train/0045_378_45378696_qa_2" description = "Which Pokémon has the lowest Total value and what is that value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Sunkern, 180" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0045_378_45378696_qa_3/task.toml b/tasks/0045_378_45378696_qa_3/task.toml index c4fcad81ab3c4b60643712729560bd3703071bad..20e58fc9685a41f63533e545af3eba93645af35b 100644 --- a/tasks/0045_378_45378696_qa_3/task.toml +++ b/tasks/0045_378_45378696_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0045_378_45378696_qa_3" +name = "smoldataenvs-train/0045_378_45378696_qa_3" description = "Which Pokémon has the highest HP and what is that HP value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Blissey, 255" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0045_385_45385383_qa_4/task.toml b/tasks/0045_385_45385383_qa_4/task.toml index 205aec0798a7e183a47e35466c5eca817d674641..c55686382562cea02733818609c88942c4a4612f 100644 --- a/tasks/0045_385_45385383_qa_4/task.toml +++ b/tasks/0045_385_45385383_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0045_385_45385383_qa_4" +name = "smoldataenvs-train/0045_385_45385383_qa_4" description = "What is the interquartile range (IQR) of radius_mean for benign tumors?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.29" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0045_409_45409591_qa_1/task.toml b/tasks/0045_409_45409591_qa_1/task.toml index d8b1d830a4833e94462eec39c6ad66e0eee9f616..08aea62ba500ea9a215f009e4f05aeb2c2b480d8 100644 --- a/tasks/0045_409_45409591_qa_1/task.toml +++ b/tasks/0045_409_45409591_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0045_409_45409591_qa_1" +name = "smoldataenvs-train/0045_409_45409591_qa_1" description = "Which column was dropped due to containing the highest proportion of missing values in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "region_2" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0045_409_45409591_qa_2/task.toml b/tasks/0045_409_45409591_qa_2/task.toml index 691e764b225d84dcb7b778801c6ed92896bd04ea..338d0728bb1f2095baeb9dddf2a46a33b046a6fd 100644 --- a/tasks/0045_409_45409591_qa_2/task.toml +++ b/tasks/0045_409_45409591_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0045_409_45409591_qa_2" +name = "smoldataenvs-train/0045_409_45409591_qa_2" description = "How many rows contain a taster name but no associated Twitter handle after cleaning the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4969" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0045_409_45409591_qa_4/task.toml b/tasks/0045_409_45409591_qa_4/task.toml index 2311671d5b6e67a3873bf9cd15d2539857d42a3a..1487e087388cb598e657fc54a17eaa703316c8e3 100644 --- a/tasks/0045_409_45409591_qa_4/task.toml +++ b/tasks/0045_409_45409591_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0045_409_45409591_qa_4" +name = "smoldataenvs-train/0045_409_45409591_qa_4" description = "Does the 'price' column contain outliers based on the boxplot visualization in the outlier detection section?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] build_timeout_sec = 600.0 diff --git a/tasks/0045_433_45433575_qa_3/task.toml b/tasks/0045_433_45433575_qa_3/task.toml index 05acc471b78a3462cd2c57a98891db592710dfa3..d2504a801f89602d2f55fddf6db5cf135a813c4e 100644 --- a/tasks/0045_433_45433575_qa_3/task.toml +++ b/tasks/0045_433_45433575_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0045_433_45433575_qa_3" +name = "smoldataenvs-train/0045_433_45433575_qa_3" description = "What is the churn rate for customers on one-year contracts?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11.28" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0045_452_45452619_qa_3/task.toml b/tasks/0045_452_45452619_qa_3/task.toml index 7eca97719797795a16eab85b705c200fed0225dc..b61c0f20e2452fc2628e6161ca4822eaeac3b63d 100644 --- a/tasks/0045_452_45452619_qa_3/task.toml +++ b/tasks/0045_452_45452619_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0045_452_45452619_qa_3" +name = "smoldataenvs-train/0045_452_45452619_qa_3" description = "Is the \"veil-type\" feature statistically significant in predicting whether a mushroom is poisonous or edible?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0045_454_45454053_qa_3/task.toml b/tasks/0045_454_45454053_qa_3/task.toml index 7e079eae8d070c54cc30456c3b2777806d4d84d6..e7fc941b5f29f27e4ab121b6e0d0b53a9fc75504 100644 --- a/tasks/0045_454_45454053_qa_3/task.toml +++ b/tasks/0045_454_45454053_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0045_454_45454053_qa_3" +name = "smoldataenvs-train/0045_454_45454053_qa_3" description = "What is the percentage of variance retained by using 50 principal components after one-hot encoding all categorical variables?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "98.1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0045_466_45466628_qa_3/task.toml b/tasks/0045_466_45466628_qa_3/task.toml index c7ca7ca4a59dba7bb8bb4c36f39fb40008d22174..efa24205aced6590af923789c900136efc933660 100644 --- a/tasks/0045_466_45466628_qa_3/task.toml +++ b/tasks/0045_466_45466628_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0045_466_45466628_qa_3" +name = "smoldataenvs-train/0045_466_45466628_qa_3" description = "Which chemical component exhibits the highest standard deviation in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "total sulfur dioxide" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0045_476_45476147_qa_3/task.toml b/tasks/0045_476_45476147_qa_3/task.toml index bb65b1d6b1bb9d0704ba70cd870f17b8b80b8cef..5bd23e4b7a6add11d5cbb0f0183f7c3186168e76 100644 --- a/tasks/0045_476_45476147_qa_3/task.toml +++ b/tasks/0045_476_45476147_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0045_476_45476147_qa_3" +name = "smoldataenvs-train/0045_476_45476147_qa_3" description = "Which platform has the highest average global sales per title in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "GB" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0045_605_45605559_qa_1/task.toml b/tasks/0045_605_45605559_qa_1/task.toml index 3c7bc0a112fcef4d77a8be5e9a860f01953e1eec..e847d7cdfde0a10f6d4c33143fb27f5ba958ae1a 100644 --- a/tasks/0045_605_45605559_qa_1/task.toml +++ b/tasks/0045_605_45605559_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0045_605_45605559_qa_1" +name = "smoldataenvs-train/0045_605_45605559_qa_1" description = "What is the total number of samples remaining in the dataset after removing the 'Iris-virginica' class?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "100" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0045_605_45605559_qa_4/task.toml b/tasks/0045_605_45605559_qa_4/task.toml index c1ef70f9959b4f64ad64d272d81ef408d22d031a..df915ad74ddb0924ee5be9392cccab89f961a5f4 100644 --- a/tasks/0045_605_45605559_qa_4/task.toml +++ b/tasks/0045_605_45605559_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0045_605_45605559_qa_4" +name = "smoldataenvs-train/0045_605_45605559_qa_4" description = "What percentage accuracy does the TensorFlow-based neural network achieve on the test set after 200 epochs of training?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "100" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0045_668_45668013_qa_2/task.toml b/tasks/0045_668_45668013_qa_2/task.toml index bc0a8cb6ca410191a2e88724100464736444a387..b4bb8436661d4c31b9f8918e374db02a0b9b8d0a 100644 --- a/tasks/0045_668_45668013_qa_2/task.toml +++ b/tasks/0045_668_45668013_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0045_668_45668013_qa_2" +name = "smoldataenvs-train/0045_668_45668013_qa_2" description = "After data preprocessing, how many instances of missing values were present in the 'Insulin' column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "374" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0045_670_45670809_qa_3/task.toml b/tasks/0045_670_45670809_qa_3/task.toml index 99d632066e0ab5dfa9b4145742eb7de203d5d0ac..fed5e60825f02a7cb4e99d2d539521293a915b06 100644 --- a/tasks/0045_670_45670809_qa_3/task.toml +++ b/tasks/0045_670_45670809_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0045_670_45670809_qa_3" +name = "smoldataenvs-train/0045_670_45670809_qa_3" description = "What is the number of unique phone models in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8273" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0045_675_45675145_qa_5/task.toml b/tasks/0045_675_45675145_qa_5/task.toml index 561c8ad2ceb6dee85b388ea0b2efd13ea2b6ca18..9b9ed89400eff78141570b112dcfe0510327dd94 100644 --- a/tasks/0045_675_45675145_qa_5/task.toml +++ b/tasks/0045_675_45675145_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0045_675_45675145_qa_5" +name = "smoldataenvs-train/0045_675_45675145_qa_5" description = "Which department has the lowest attrition rate according to the dataset analysis conclusions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Research & Development" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0045_738_45738362_qa_2/task.toml b/tasks/0045_738_45738362_qa_2/task.toml index 856bcc604e6b0924c32740e467e5ed01dda7cdd5..21b7c571a323ee7f80e0ba337e678aa1317596e7 100644 --- a/tasks/0045_738_45738362_qa_2/task.toml +++ b/tasks/0045_738_45738362_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0045_738_45738362_qa_2" +name = "smoldataenvs-train/0045_738_45738362_qa_2" description = "What is the total number of unique years in the dataset before removing 2017 and 2020?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "39" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0045_738_45738362_qa_4/task.toml b/tasks/0045_738_45738362_qa_4/task.toml index 9c34b7ddeb1a894afafabbfb3bf33e6e8579e64d..8219da01e7d14174764a5ef5e127a82769df7f95 100644 --- a/tasks/0045_738_45738362_qa_4/task.toml +++ b/tasks/0045_738_45738362_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0045_738_45738362_qa_4" +name = "smoldataenvs-train/0045_738_45738362_qa_4" description = "How many publishers are included in the cumulative sales analysis for the top 30 publishers?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "30" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0045_754_45754674_qa_5/task.toml b/tasks/0045_754_45754674_qa_5/task.toml index d6f4f48860a98e5dbf4f1e3c382faaf43847b40f..9dc6fde775ac77bda55bf8e93c2a4bb1069b7bb6 100644 --- a/tasks/0045_754_45754674_qa_5/task.toml +++ b/tasks/0045_754_45754674_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0045_754_45754674_qa_5" +name = "smoldataenvs-train/0045_754_45754674_qa_5" description = "After replacing zeros with NaN, which feature had the highest number of missing values before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Insulin" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0045_758_45758482_qa_3/task.toml b/tasks/0045_758_45758482_qa_3/task.toml index ee770b096c865811ee0889c1fc852a67bd346e36..99b5e1e465848fc7e291cf13597c5d0ce8860153 100644 --- a/tasks/0045_758_45758482_qa_3/task.toml +++ b/tasks/0045_758_45758482_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0045_758_45758482_qa_3" +name = "smoldataenvs-train/0045_758_45758482_qa_3" description = "Which year showed the highest average percentage of funded goals (mean_goal_percent) for low-budget projects (goal < $1,000)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2011" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0045_818_45818362_qa_4/task.toml b/tasks/0045_818_45818362_qa_4/task.toml index 38b47d6f7e8bdb415afd5ffb16714d5bfefcc626..0ab2d8d2950b2ce74e91e5b50a52da9caca38b7e 100644 --- a/tasks/0045_818_45818362_qa_4/task.toml +++ b/tasks/0045_818_45818362_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0045_818_45818362_qa_4" +name = "smoldataenvs-train/0045_818_45818362_qa_4" description = "What is the percentage of missing values in any column of the Pima Indians Diabetes dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0045_821_45821165_qa_2/task.toml b/tasks/0045_821_45821165_qa_2/task.toml index 37f6367ad0dc9ddc799f76b7f9c6222580840bf4..3556eae64ed06af0ce53cb120859b543431e5f38 100644 --- a/tasks/0045_821_45821165_qa_2/task.toml +++ b/tasks/0045_821_45821165_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0045_821_45821165_qa_2" +name = "smoldataenvs-train/0045_821_45821165_qa_2" description = "What is the average house price in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "540088.1417665294" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0045_821_45821165_qa_3/task.toml b/tasks/0045_821_45821165_qa_3/task.toml index 955e43905e16ba6ee872cc9fe7138768370287f0..08345c0153a1f7eabe36f5bd30fb898c59b535ae 100644 --- a/tasks/0045_821_45821165_qa_3/task.toml +++ b/tasks/0045_821_45821165_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0045_821_45821165_qa_3" +name = "smoldataenvs-train/0045_821_45821165_qa_3" description = "What is the percentage of houses in the dataset that have a waterfront view?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.7541757209662947" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0045_821_45821165_qa_5/task.toml b/tasks/0045_821_45821165_qa_5/task.toml index 412b77a581a5d5496fd99a06cc5d8e7b391e276d..6aa5b37b1a10cd53e0524a96c43ed99d3fd66536 100644 --- a/tasks/0045_821_45821165_qa_5/task.toml +++ b/tasks/0045_821_45821165_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0045_821_45821165_qa_5" +name = "smoldataenvs-train/0045_821_45821165_qa_5" description = "What is the average house price for properties built in the year 2000 or later?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "612345.67" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0045_874_45874370_qa_5/task.toml b/tasks/0045_874_45874370_qa_5/task.toml index 631b6e78cc53d53ad2b0e0722206020c0ed9cc8a..a8a8caef8851dfdd396c98af953bab2036d92278 100644 --- a/tasks/0045_874_45874370_qa_5/task.toml +++ b/tasks/0045_874_45874370_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0045_874_45874370_qa_5" +name = "smoldataenvs-train/0045_874_45874370_qa_5" description = "What percentage of mobile phones in price range 0 are 3G compatible according to the pie chart visualization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "98.5%" reward_mode_initial = "flexible" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0045_880_45880804_qa_5/task.toml b/tasks/0045_880_45880804_qa_5/task.toml index 30fb10abef77d242eebac0b240fc09371a75d82f..3178798a4a2cff8e5b374d661b26cacafa851273 100644 --- a/tasks/0045_880_45880804_qa_5/task.toml +++ b/tasks/0045_880_45880804_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0045_880_45880804_qa_5" +name = "smoldataenvs-train/0045_880_45880804_qa_5" description = "Which numerical feature exhibits the highest standard deviation after outlier removal?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "area_worst" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0045_892_45892166_qa_5/task.toml b/tasks/0045_892_45892166_qa_5/task.toml index 4f13b68b24fb77bc7ff7473fc58e0101cbf5c7dd..0913e4126cf40523a42cfa681898812e7e0a215c 100644 --- a/tasks/0045_892_45892166_qa_5/task.toml +++ b/tasks/0045_892_45892166_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0045_892_45892166_qa_5" +name = "smoldataenvs-train/0045_892_45892166_qa_5" description = "What is the most similar movie to \"Star Wars: Episode III - Revenge of the Sith\" based on collaborative filtering cosine similarity scores?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10 Things I Hate About You" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0045_913_45913671_qa_3/task.toml b/tasks/0045_913_45913671_qa_3/task.toml index 9c3de8014e7f94674fdcf007f4c9822944213e18..7f7e08347d63f12a09991c1720fbc755341caeab 100644 --- a/tasks/0045_913_45913671_qa_3/task.toml +++ b/tasks/0045_913_45913671_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0045_913_45913671_qa_3" +name = "smoldataenvs-train/0045_913_45913671_qa_3" description = "What was the average unemployment rate in Alabama during 2016?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.77" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0045_943_45943372_qa_5/task.toml b/tasks/0045_943_45943372_qa_5/task.toml index 8a687256466a2cd4e566e222d9a96f52619ac04c..bb8cc8f57ac47b02975b60ab2e19e36b1f3157f4 100644 --- a/tasks/0045_943_45943372_qa_5/task.toml +++ b/tasks/0045_943_45943372_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0045_943_45943372_qa_5" +name = "smoldataenvs-train/0045_943_45943372_qa_5" description = "Which ocean proximity categories were retained as dummy variables after data preprocessing in the housing dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "<1H OCEAN, INLAND, NEAR BAY, NEAR OCEAN" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0045_969_45969290_qa_1/task.toml b/tasks/0045_969_45969290_qa_1/task.toml index 06c16e55a46a5ebad3ece9ed2b5e86a9625bbc66..a16b4cd9a1edc8f33cf142f43475b73d1c52edcb 100644 --- a/tasks/0045_969_45969290_qa_1/task.toml +++ b/tasks/0045_969_45969290_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0045_969_45969290_qa_1" +name = "smoldataenvs-train/0045_969_45969290_qa_1" description = "How many unique menu item categories are present in the dataset after label encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0045_969_45969290_qa_2/task.toml b/tasks/0045_969_45969290_qa_2/task.toml index ecacb74239c56cf857325906d16289d868b3746c..5ef12307b91dc8657e2cead213995d53071104b4 100644 --- a/tasks/0045_969_45969290_qa_2/task.toml +++ b/tasks/0045_969_45969290_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0045_969_45969290_qa_2" +name = "smoldataenvs-train/0045_969_45969290_qa_2" description = "What is the maximum sequence length determined for tokenized menu item names during text preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0046_035_46035466_qa_1/task.toml b/tasks/0046_035_46035466_qa_1/task.toml index 87be6a7f233af40cc27cf0154369cd294272c624..22dca3daeb0ac200950f4cf5e89b30244baad899 100644 --- a/tasks/0046_035_46035466_qa_1/task.toml +++ b/tasks/0046_035_46035466_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0046_035_46035466_qa_1" +name = "smoldataenvs-train/0046_035_46035466_qa_1" description = "What is the optimal number of clusters (k) determined by the elbow method in the K-means clustering analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0046_035_46035466_qa_2/task.toml b/tasks/0046_035_46035466_qa_2/task.toml index b7c0df18510db635c3e45343561b7d1d39eb6626..9a2212aadf028ba25a6a5efe2cd864ef555d2286 100644 --- a/tasks/0046_035_46035466_qa_2/task.toml +++ b/tasks/0046_035_46035466_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0046_035_46035466_qa_2" +name = "smoldataenvs-train/0046_035_46035466_qa_2" description = "How many data points are present in each species category of the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0046_103_46103508_qa_2/task.toml b/tasks/0046_103_46103508_qa_2/task.toml index 21fc99fdb52a8c28b51f516e4924737861e07bbc..6f5b2d0d1ddf2ab0a78551164e815ee80996d33c 100644 --- a/tasks/0046_103_46103508_qa_2/task.toml +++ b/tasks/0046_103_46103508_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0046_103_46103508_qa_2" +name = "smoldataenvs-train/0046_103_46103508_qa_2" description = "Which feature's removal results in the largest decrease in model accuracy?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ram" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0046_112_46112628_qa_3/task.toml b/tasks/0046_112_46112628_qa_3/task.toml index d74c884373b045a27179f46ed7c8b2c8eb74f02e..49a0a809d1eb2f79b3e31a600f0fbbc73cc77e41 100644 --- a/tasks/0046_112_46112628_qa_3/task.toml +++ b/tasks/0046_112_46112628_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0046_112_46112628_qa_3" +name = "smoldataenvs-train/0046_112_46112628_qa_3" description = "Which month of the year had the highest number of invoices recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "November" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0046_113_46113496_qa_1/task.toml b/tasks/0046_113_46113496_qa_1/task.toml index 9c0a8a15cae8fe08dd69fb3a03a9b3e49c4932d3..84f1fa711bd8bcd03e67c4eacfc04f33671d805e 100644 --- a/tasks/0046_113_46113496_qa_1/task.toml +++ b/tasks/0046_113_46113496_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0046_113_46113496_qa_1" +name = "smoldataenvs-train/0046_113_46113496_qa_1" description = "What is the ratio of ham to spam messages in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.46" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0046_113_46113496_qa_2/task.toml b/tasks/0046_113_46113496_qa_2/task.toml index 0253cea90c96ed55bc9694ecd8d6daab50cb4b82..42027e1bfdb4d3ec1289812a9abec1e6152eb348 100644 --- a/tasks/0046_113_46113496_qa_2/task.toml +++ b/tasks/0046_113_46113496_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0046_113_46113496_qa_2" +name = "smoldataenvs-train/0046_113_46113496_qa_2" description = "What is the median character count difference between spam and ham messages in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "97" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0046_115_46115058_qa_5/task.toml b/tasks/0046_115_46115058_qa_5/task.toml index 19b9c121bd4b65a0040f81246e5141163070981c..0e823f75d299d7c4350bca8c2722dbca2705507b 100644 --- a/tasks/0046_115_46115058_qa_5/task.toml +++ b/tasks/0046_115_46115058_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0046_115_46115058_qa_5" +name = "smoldataenvs-train/0046_115_46115058_qa_5" description = "What is the 95% confidence interval for the cross-validated accuracy of the final model using RFE feature selection?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.7264, 0.8075" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0046_178_46178687_qa_2/task.toml b/tasks/0046_178_46178687_qa_2/task.toml index 82596cf64ce20877e906d824bd2e4a8c8b086d23..fffdf11be48af8f9967537c4ba984ba95cdb7909 100644 --- a/tasks/0046_178_46178687_qa_2/task.toml +++ b/tasks/0046_178_46178687_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0046_178_46178687_qa_2" +name = "smoldataenvs-train/0046_178_46178687_qa_2" description = "What is the minimum scaled PetalWidthCm value after MinMaxScaler preprocessing in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0046_178_46178687_qa_4/task.toml b/tasks/0046_178_46178687_qa_4/task.toml index 4f13814b55a2037e5f51c2be970fc3b7ab6f14d1..860ba874439aac03e3cbb8fb1d1778166753fad0 100644 --- a/tasks/0046_178_46178687_qa_4/task.toml +++ b/tasks/0046_178_46178687_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0046_178_46178687_qa_4" +name = "smoldataenvs-train/0046_178_46178687_qa_4" description = "What is the maximum scaled PetalLengthCm value after MinMaxScaler preprocessing in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0046_263_46263849_qa_1/task.toml b/tasks/0046_263_46263849_qa_1/task.toml index 9712af827779d9661e38c01d5394c2b6a8c091f6..802881b5f4f933fe8daea9d26c06de82ae808122 100644 --- a/tasks/0046_263_46263849_qa_1/task.toml +++ b/tasks/0046_263_46263849_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0046_263_46263849_qa_1" +name = "smoldataenvs-train/0046_263_46263849_qa_1" description = "What is the predicted cluster label for the input sample [7.6, 3.9, 0.6, 1.9] using the K-means clustering model with 3 clusters?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0046_267_46267192_qa_3/task.toml b/tasks/0046_267_46267192_qa_3/task.toml index 7266d3f2fb0aa0d008479d7be103bc5becf42201..6d0626a60238b6273919b388f46f1bd19c97ace7 100644 --- a/tasks/0046_267_46267192_qa_3/task.toml +++ b/tasks/0046_267_46267192_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0046_267_46267192_qa_3" +name = "smoldataenvs-train/0046_267_46267192_qa_3" description = "What is the average total stat value of non-legendary Dragon-type Pokémon?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "476.85" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0046_282_46282379_qa_2/task.toml b/tasks/0046_282_46282379_qa_2/task.toml index 844e9683e3f8f6a36cdf8b1147961194a84b6f06..66bf0a3be7fb0f30c9c5522fc5275cfb1201fd18 100644 --- a/tasks/0046_282_46282379_qa_2/task.toml +++ b/tasks/0046_282_46282379_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0046_282_46282379_qa_2" +name = "smoldataenvs-train/0046_282_46282379_qa_2" description = "Which variable (wine price or wine point scores) has a higher standard deviation, and by what factor?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "price, 13.5x" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0046_305_46305988_qa_2/task.toml b/tasks/0046_305_46305988_qa_2/task.toml index 221e84d75233f8445dadd70c1b62589116b94307..91b0cd0e51dc62106885e0227f31abdbefaa4960 100644 --- a/tasks/0046_305_46305988_qa_2/task.toml +++ b/tasks/0046_305_46305988_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0046_305_46305988_qa_2" +name = "smoldataenvs-train/0046_305_46305988_qa_2" description = "What is the accuracy score of the K-Nearest Neighbors model when using 13 neighbors?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.932" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0046_333_46333553_qa_1/task.toml b/tasks/0046_333_46333553_qa_1/task.toml index 03c0432d42d163dead422fe62511ca59aed5e2f1..726df6c5461e37518a259c9b6abdeee0c32680b2 100644 --- a/tasks/0046_333_46333553_qa_1/task.toml +++ b/tasks/0046_333_46333553_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0046_333_46333553_qa_1" +name = "smoldataenvs-train/0046_333_46333553_qa_1" description = "Which feature has the highest positive correlation with the price_range in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "RAM" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0046_421_46421373_qa_5/task.toml b/tasks/0046_421_46421373_qa_5/task.toml index ffdb50471de8a95f81f62e94ff5dd6693cac47f1..5762ba6724f8913ae18dd0db69d9d273fe7513d0 100644 --- a/tasks/0046_421_46421373_qa_5/task.toml +++ b/tasks/0046_421_46421373_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0046_421_46421373_qa_5" +name = "smoldataenvs-train/0046_421_46421373_qa_5" description = "What was the mean Glucose level of all patients in the dataset before handling missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "120.894531" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0046_456_46456896_qa_3/task.toml b/tasks/0046_456_46456896_qa_3/task.toml index 35864ce129fad3c8335f70f66238949a89f9245c..630a8d8c638eb07a11b6cb022adfdad75559decb 100644 --- a/tasks/0046_456_46456896_qa_3/task.toml +++ b/tasks/0046_456_46456896_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0046_456_46456896_qa_3" +name = "smoldataenvs-train/0046_456_46456896_qa_3" description = "Which country had the highest percentage of drawn matches in the 2014/2015 season?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Italy" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0046_467_46467108_qa_2/task.toml b/tasks/0046_467_46467108_qa_2/task.toml index 69b0a6a859ca08d436401d5c8dbbf1c7bcee0268..a2c1c93d07fa336f0444d716a9a1bd25651af19b 100644 --- a/tasks/0046_467_46467108_qa_2/task.toml +++ b/tasks/0046_467_46467108_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0046_467_46467108_qa_2" +name = "smoldataenvs-train/0046_467_46467108_qa_2" description = "What is the correlation coefficient between Age and Attrition in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.159205" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0046_487_46487066_qa_1/task.toml b/tasks/0046_487_46487066_qa_1/task.toml index 42a1766cc5caf77daffa09c5e07a44cb95652635..b6b6e46b2df4c358d047c3493bc153698dfc682f 100644 --- a/tasks/0046_487_46487066_qa_1/task.toml +++ b/tasks/0046_487_46487066_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0046_487_46487066_qa_1" +name = "smoldataenvs-train/0046_487_46487066_qa_1" description = "Which number of clusters (between 2-7) has the highest silhouette score according to the cluster analysis in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0046_487_46487066_qa_3/task.toml b/tasks/0046_487_46487066_qa_3/task.toml index 98cbaa2a9b95c2b22afc09485072bdc5a56ea698..7c3fb23b5f5bbda821c30ce8f8e8db08f2c755f5 100644 --- a/tasks/0046_487_46487066_qa_3/task.toml +++ b/tasks/0046_487_46487066_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0046_487_46487066_qa_3" +name = "smoldataenvs-train/0046_487_46487066_qa_3" description = "How many missing values were present in the MINIMUM_PAYMENTS column before imputation in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "313" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0046_487_46487066_qa_4/task.toml b/tasks/0046_487_46487066_qa_4/task.toml index 818ef61f895cde4de2dc4daa1b50db75df26c93e..6f68d1475c9fa999c9ef2c58082fee90104cf477 100644 --- a/tasks/0046_487_46487066_qa_4/task.toml +++ b/tasks/0046_487_46487066_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0046_487_46487066_qa_4" +name = "smoldataenvs-train/0046_487_46487066_qa_4" description = "Which cluster (0, 1, or 2) has the highest average PURCHASES_FREQUENCY according to the cluster analysis description in the notebook?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0046_517_46517574_qa_1/task.toml b/tasks/0046_517_46517574_qa_1/task.toml index 7005e1dfd201482a46138def21c703435ea45495..12629d3b2acf26e59704d77090695782b9ed1730 100644 --- a/tasks/0046_517_46517574_qa_1/task.toml +++ b/tasks/0046_517_46517574_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0046_517_46517574_qa_1" +name = "smoldataenvs-train/0046_517_46517574_qa_1" description = "Which feature in the mushroom dataset contains the highest number of unique categorical values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "gill-color" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0046_528_46528108_qa_1/task.toml b/tasks/0046_528_46528108_qa_1/task.toml index 120eba5d0ce4aa82a3b3e84b1c6ff9d612056b6a..140558cfda6d880b30b3419e668dc27cc39fc386 100644 --- a/tasks/0046_528_46528108_qa_1/task.toml +++ b/tasks/0046_528_46528108_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0046_528_46528108_qa_1" +name = "smoldataenvs-train/0046_528_46528108_qa_1" description = "Which feature contributes the most to the predictive power of the Random Forest model according to the feature importance analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "odor" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0046_597_46597030_qa_1/task.toml b/tasks/0046_597_46597030_qa_1/task.toml index 80726f45446d34b7e13171b381dbf65504bd24ab..367023043e844e022151964a2ba8b35ca023a516 100644 --- a/tasks/0046_597_46597030_qa_1/task.toml +++ b/tasks/0046_597_46597030_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0046_597_46597030_qa_1" +name = "smoldataenvs-train/0046_597_46597030_qa_1" description = "Which passenger class (Pclass) had the highest survival rate based on the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0046_597_46597030_qa_4/task.toml b/tasks/0046_597_46597030_qa_4/task.toml index f70ee3d54a2d4412360950f3c965ee481d66807a..6dac49937c9ac1c603269327976d5ff11b6a500a 100644 --- a/tasks/0046_597_46597030_qa_4/task.toml +++ b/tasks/0046_597_46597030_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0046_597_46597030_qa_4" +name = "smoldataenvs-train/0046_597_46597030_qa_4" description = "What SibSp value had the highest survival rate according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0046_641_46641346_qa_1/task.toml b/tasks/0046_641_46641346_qa_1/task.toml index 2846d1885c1e2ae133b4b0f7539a14dc1cf5ddcf..5a88739d91dfdb9eb270b226cf8200402229b6d1 100644 --- a/tasks/0046_641_46641346_qa_1/task.toml +++ b/tasks/0046_641_46641346_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0046_641_46641346_qa_1" +name = "smoldataenvs-train/0046_641_46641346_qa_1" description = "What is the maximum age of patients who survived for 5 years or longer according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "77" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0046_651_46651682_qa_4/task.toml b/tasks/0046_651_46651682_qa_4/task.toml index f915dba96b1de65f4aef838c9bfcc3792eb01fcd..587501b626b2c40c7c215244837972b3431cb4c5 100644 --- a/tasks/0046_651_46651682_qa_4/task.toml +++ b/tasks/0046_651_46651682_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0046_651_46651682_qa_4" +name = "smoldataenvs-train/0046_651_46651682_qa_4" description = "Which two features show the most distinct separation between Iris-setosa and the other two species based on the pairplot visualizations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm, PetalWidthCm" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0046_658_46658315_qa_5/task.toml b/tasks/0046_658_46658315_qa_5/task.toml index 16e7e826e394f15226b195ee241442165fbc6536..a012ebb660408952aadc4f391163bdd300749b4e 100644 --- a/tasks/0046_658_46658315_qa_5/task.toml +++ b/tasks/0046_658_46658315_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0046_658_46658315_qa_5" +name = "smoldataenvs-train/0046_658_46658315_qa_5" description = "Which Platform Provider has the highest count of Sports games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Sony Playstation" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0046_660_46660345_qa_2/task.toml b/tasks/0046_660_46660345_qa_2/task.toml index 5193631fa8b3257f0cb70a85635c35a696abf80a..98f41410fbb4379f76a15879eec2d66f59e26369 100644 --- a/tasks/0046_660_46660345_qa_2/task.toml +++ b/tasks/0046_660_46660345_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0046_660_46660345_qa_2" +name = "smoldataenvs-train/0046_660_46660345_qa_2" description = "What is the highest accuracy score achieved by any classifier in the cross-validation evaluation using the training data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.999648" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0046_660_46660345_qa_5/task.toml b/tasks/0046_660_46660345_qa_5/task.toml index 84574d7a28dd6f60607fe47fb46594023ba0ca29..f012d74bab3c5f2563008b6accc64c3886da926e 100644 --- a/tasks/0046_660_46660345_qa_5/task.toml +++ b/tasks/0046_660_46660345_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0046_660_46660345_qa_5" +name = "smoldataenvs-train/0046_660_46660345_qa_5" description = "Which classifier was determined to have the best overall performance based on the highest accuracy, recall, and F1 score in the cross-validation evaluation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Random Forest Classifier" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0046_711_46711953_qa_2/task.toml b/tasks/0046_711_46711953_qa_2/task.toml index be48e8703a84aa5a8a2bc6f5a9677d04ee29cc76..1e9e71815072ba76cf0e4f55fb6a354079892fe7 100644 --- a/tasks/0046_711_46711953_qa_2/task.toml +++ b/tasks/0046_711_46711953_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0046_711_46711953_qa_2" +name = "smoldataenvs-train/0046_711_46711953_qa_2" description = "What is the average age of patients in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "37.09" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0046_788_46788472_qa_2/task.toml b/tasks/0046_788_46788472_qa_2/task.toml index 1df83033e8eed8b47dd37005a8f4631862358573..e2b430dda5dc8861b2e0c8f1cdeddd284ef82a29 100644 --- a/tasks/0046_788_46788472_qa_2/task.toml +++ b/tasks/0046_788_46788472_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0046_788_46788472_qa_2" +name = "smoldataenvs-train/0046_788_46788472_qa_2" description = "What is the range of years covered in the dataset (from the earliest to the latest game release year)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1980 to 2020" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0046_788_46788472_qa_3/task.toml b/tasks/0046_788_46788472_qa_3/task.toml index 579967dc08bcba3a5ccca6ae7f2ceb8fa4f79839..aff5229999ffcc9dbf89c85d3fd3b4de6e1cba1a 100644 --- a/tasks/0046_788_46788472_qa_3/task.toml +++ b/tasks/0046_788_46788472_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0046_788_46788472_qa_3" +name = "smoldataenvs-train/0046_788_46788472_qa_3" description = "What is the highest sales figure recorded for North America (NA_Sales) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "41.49" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0046_788_46788472_qa_4/task.toml b/tasks/0046_788_46788472_qa_4/task.toml index cea1a4df15ac83f75296b7501615050e9da2a80a..e2892156d0d3462c2de02da76597d603f963d561 100644 --- a/tasks/0046_788_46788472_qa_4/task.toml +++ b/tasks/0046_788_46788472_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0046_788_46788472_qa_4" +name = "smoldataenvs-train/0046_788_46788472_qa_4" description = "What is the average global sales value (Global_Sales) per game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.54" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0046_808_46808200_qa_4/task.toml b/tasks/0046_808_46808200_qa_4/task.toml index 949bb0c5c7d77fb22c2c5e267bad65b71f7268ce..b1630d2bb6d8fca466d600b44c3e27303099bef0 100644 --- a/tasks/0046_808_46808200_qa_4/task.toml +++ b/tasks/0046_808_46808200_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0046_808_46808200_qa_4" +name = "smoldataenvs-train/0046_808_46808200_qa_4" description = "How many missing values were present in the 'CREDIT_LIMIT' column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0046_857_46857117_qa_4/task.toml b/tasks/0046_857_46857117_qa_4/task.toml index 865bee10b0ce522cff971f2516676117857fc5c9..85c47da84959254d444ac6f846dada3dbf001699 100644 --- a/tasks/0046_857_46857117_qa_4/task.toml +++ b/tasks/0046_857_46857117_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0046_857_46857117_qa_4" +name = "smoldataenvs-train/0046_857_46857117_qa_4" description = "How many distinct simplified education attainment categories were created for the classification task?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0046_857_46857117_qa_5/task.toml b/tasks/0046_857_46857117_qa_5/task.toml index 7a0eb7771ff71ee2776aec45e4476f3637bdd184..bb8614239081807a2d83de197135c6482fdf9469 100644 --- a/tasks/0046_857_46857117_qa_5/task.toml +++ b/tasks/0046_857_46857117_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0046_857_46857117_qa_5" +name = "smoldataenvs-train/0046_857_46857117_qa_5" description = "What percentage of households fall into the lowest income category (Category 1) based on quartile distribution?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "25" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0046_875_46875284_qa_1/task.toml b/tasks/0046_875_46875284_qa_1/task.toml index 2a089520c8a840af3498014b481488521ea0b657..56745fd553e27cce4303043b5df66042703b5aaa 100644 --- a/tasks/0046_875_46875284_qa_1/task.toml +++ b/tasks/0046_875_46875284_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0046_875_46875284_qa_1" +name = "smoldataenvs-train/0046_875_46875284_qa_1" description = "How many Pokémon have both Defense greater than 200 and Attack greater than 100?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0046_875_46875284_qa_2/task.toml b/tasks/0046_875_46875284_qa_2/task.toml index 8183cb8a8e45a4210300f8df04d82d828d35c7b8..aa2afeb6821b183c212cecf11cbc16b2442d132b 100644 --- a/tasks/0046_875_46875284_qa_2/task.toml +++ b/tasks/0046_875_46875284_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0046_875_46875284_qa_2" +name = "smoldataenvs-train/0046_875_46875284_qa_2" description = "What is the highest Defense stat value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "230" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0046_905_46905660_qa_1/task.toml b/tasks/0046_905_46905660_qa_1/task.toml index f14e1a91c8548c164a973a1db6f982252c5b4442..53e1465094b72491e4e5014b9d7923bcadf932a1 100644 --- a/tasks/0046_905_46905660_qa_1/task.toml +++ b/tasks/0046_905_46905660_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0046_905_46905660_qa_1" +name = "smoldataenvs-train/0046_905_46905660_qa_1" description = "Which feature in the red wine dataset has the highest Pearson correlation coefficient with wine quality (target variable)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "alcohol" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0046_933_46933248_qa_5/task.toml b/tasks/0046_933_46933248_qa_5/task.toml index ad25121cca3921b50fc1779f7f9f236e49215645..d90e6407cb8537389335db06e07a3c8a6bdb8ee9 100644 --- a/tasks/0046_933_46933248_qa_5/task.toml +++ b/tasks/0046_933_46933248_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0046_933_46933248_qa_5" +name = "smoldataenvs-train/0046_933_46933248_qa_5" description = "What is the correlation coefficient between median income and median house value from the correlation matrix visualization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.687" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0046_955_46955725_qa_4/task.toml b/tasks/0046_955_46955725_qa_4/task.toml index 28171eb60a6ed795e5c25165bbd578f398d4f803..acc306d9f75ea926dd5e41f601285ff34966389a 100644 --- a/tasks/0046_955_46955725_qa_4/task.toml +++ b/tasks/0046_955_46955725_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0046_955_46955725_qa_4" +name = "smoldataenvs-train/0046_955_46955725_qa_4" description = "How many games in the dataset resulted in team 2 winning under the condition that all six early game advantages were secured?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1648" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0046_964_46964114_qa_1/task.toml b/tasks/0046_964_46964114_qa_1/task.toml index 69e2d6b9de105f41b089c8c3496321d223d2c095..d0919466f84289c38445a0269135fef8751da707 100644 --- a/tasks/0046_964_46964114_qa_1/task.toml +++ b/tasks/0046_964_46964114_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0046_964_46964114_qa_1" +name = "smoldataenvs-train/0046_964_46964114_qa_1" description = "Which department has the highest attrition rate based on the analysis of Department and MonthlyIncome impact on Attrition?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Sales" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0047_059_47059066_qa_2/task.toml b/tasks/0047_059_47059066_qa_2/task.toml index 8569671198a2ecd51a6fecba06292dfdda59d2b3..3db5e6c8cb47226e605712661b102ffad2ea0853 100644 --- a/tasks/0047_059_47059066_qa_2/task.toml +++ b/tasks/0047_059_47059066_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0047_059_47059066_qa_2" +name = "smoldataenvs-train/0047_059_47059066_qa_2" description = "What is the observed trend in average ratings as the number of genres associated with an anime increases?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "increases" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0047_266_47266614_qa_1/task.toml b/tasks/0047_266_47266614_qa_1/task.toml index 381419da4b7e1712c4035025d6aa9ca484ec2670..2b870f8678e95de45ebb0e51756c094c6634859f 100644 --- a/tasks/0047_266_47266614_qa_1/task.toml +++ b/tasks/0047_266_47266614_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0047_266_47266614_qa_1" +name = "smoldataenvs-train/0047_266_47266614_qa_1" description = "Which feature has the highest importance in predicting price_range according to the Random Forest model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ram" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0047_266_47266614_qa_3/task.toml b/tasks/0047_266_47266614_qa_3/task.toml index 1403a7d80cc9c8773976c34e3f366ff3a4c59cf5..8293a0c3f22ef162b7ac2b7b84cef4839b827e73 100644 --- a/tasks/0047_266_47266614_qa_3/task.toml +++ b/tasks/0047_266_47266614_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0047_266_47266614_qa_3" +name = "smoldataenvs-train/0047_266_47266614_qa_3" description = "How many features are required to capture 95% of the cumulative importance in predicting price_range using the Random Forest model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "14" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0047_271_47271508_qa_4/task.toml b/tasks/0047_271_47271508_qa_4/task.toml index cfd17454aeeb50117dcd9280f3c6f492d1eb0184..665b7356d07c0a7e828ae735c15d50098de32d49 100644 --- a/tasks/0047_271_47271508_qa_4/task.toml +++ b/tasks/0047_271_47271508_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0047_271_47271508_qa_4" +name = "smoldataenvs-train/0047_271_47271508_qa_4" description = "What is the accuracy of the model on the test dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.796" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0047_271_47271508_qa_5/task.toml b/tasks/0047_271_47271508_qa_5/task.toml index 532b42f7400cdedf7ec4b9667120389e29693382..0ee1355192060f42663d4e5d5221537ea7672ce4 100644 --- a/tasks/0047_271_47271508_qa_5/task.toml +++ b/tasks/0047_271_47271508_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0047_271_47271508_qa_5" +name = "smoldataenvs-train/0047_271_47271508_qa_5" description = "What is the average age of patients in the original dataset before feature engineering?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "37.088874" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0047_308_47308029_qa_1/task.toml b/tasks/0047_308_47308029_qa_1/task.toml index 14147de7847323394e3033c1047fcbd788adeffe..21ee412f084974f1d45681787a1906823536751b 100644 --- a/tasks/0047_308_47308029_qa_1/task.toml +++ b/tasks/0047_308_47308029_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0047_308_47308029_qa_1" +name = "smoldataenvs-train/0047_308_47308029_qa_1" description = "Which feature has the strongest absolute correlation with the median home value (MEDV) in the Boston housing dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "LSTAT" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0047_367_47367333_qa_4/task.toml b/tasks/0047_367_47367333_qa_4/task.toml index 5cb5120c10fe5ea7282e9da26cdd3836c7ca791b..98d2c28ac8054fc58241d9d3d2a4bd71bea55ae9 100644 --- a/tasks/0047_367_47367333_qa_4/task.toml +++ b/tasks/0047_367_47367333_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0047_367_47367333_qa_4" +name = "smoldataenvs-train/0047_367_47367333_qa_4" description = "Which mushroom characteristic had the highest number of unique categories after label encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "gill-color" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0047_398_47398075_qa_2/task.toml b/tasks/0047_398_47398075_qa_2/task.toml index e2ff2299a93bcb4d3353dd5e6ee5b685a7f94465..31971f471a3bfbadf5bc4534ea1a2cf07f7a1af8 100644 --- a/tasks/0047_398_47398075_qa_2/task.toml +++ b/tasks/0047_398_47398075_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0047_398_47398075_qa_2" +name = "smoldataenvs-train/0047_398_47398075_qa_2" description = "Which mental attribute shows the highest correlation with international appearances (IntCaps) for players with at least one cap?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Anticipation" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0047_398_47398075_qa_5/task.toml b/tasks/0047_398_47398075_qa_5/task.toml index 31e9389cef1d030aefb72679d8eb3943af1d9046..3744f807529a34ec1e89e5a8eff844b51b87793e 100644 --- a/tasks/0047_398_47398075_qa_5/task.toml +++ b/tasks/0047_398_47398075_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0047_398_47398075_qa_5" +name = "smoldataenvs-train/0047_398_47398075_qa_5" description = "What is the percentage of missing values in the 'PositionsDesc' column of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0047_417_47417997_qa_1/task.toml b/tasks/0047_417_47417997_qa_1/task.toml index 55c979331a2ebcd3fccc6cd36c4254603b04b3a2..a119ee82a7820acd95eeb1398e79b1408dc454e4 100644 --- a/tasks/0047_417_47417997_qa_1/task.toml +++ b/tasks/0047_417_47417997_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0047_417_47417997_qa_1" +name = "smoldataenvs-train/0047_417_47417997_qa_1" description = "Which feature was identified as the most important by the CatBoostClassifier according to the feature importance ranking?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0047_417_47417997_qa_3/task.toml b/tasks/0047_417_47417997_qa_3/task.toml index 056e9d0640cba23d18643e7d0334e513ac6b6629..14f3f0c1462ee33f6de8f401bfd34540a8a99cdb 100644 --- a/tasks/0047_417_47417997_qa_3/task.toml +++ b/tasks/0047_417_47417997_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0047_417_47417997_qa_3" +name = "smoldataenvs-train/0047_417_47417997_qa_3" description = "For class 1 (Outcome=1) in the validation set, what was the F1-score achieved by the model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.651" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0047_428_47428905_qa_2/task.toml b/tasks/0047_428_47428905_qa_2/task.toml index cdba5582798c552fc5321560b1479662dbd69c7a..17e69065855d45eb7e0ca45fa673bf7cc5332715 100644 --- a/tasks/0047_428_47428905_qa_2/task.toml +++ b/tasks/0047_428_47428905_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0047_428_47428905_qa_2" +name = "smoldataenvs-train/0047_428_47428905_qa_2" description = "After imputing missing values in the total_bedrooms column, what is the mean value of this variable?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "537.91" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0047_507_47507147_qa_5/task.toml b/tasks/0047_507_47507147_qa_5/task.toml index 3f5118cda7e38ccd600b722b2edd8db0260636f5..7df41d0ade4e36f71ae34a220d1558e83722b629 100644 --- a/tasks/0047_507_47507147_qa_5/task.toml +++ b/tasks/0047_507_47507147_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0047_507_47507147_qa_5" +name = "smoldataenvs-train/0047_507_47507147_qa_5" description = "Which payment method has the highest churn rate according to the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic check" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0047_560_47560034_qa_1/task.toml b/tasks/0047_560_47560034_qa_1/task.toml index 254509c2313f8e660f46432714bf67e8bf94c4da..88058f63a57ae477ebc1b64801b3a1c7d33801f9 100644 --- a/tasks/0047_560_47560034_qa_1/task.toml +++ b/tasks/0047_560_47560034_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0047_560_47560034_qa_1" +name = "smoldataenvs-train/0047_560_47560034_qa_1" description = "Which movie genre had the highest total count across all years from 2006 to 2016 in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Drama" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0047_560_47560034_qa_4/task.toml b/tasks/0047_560_47560034_qa_4/task.toml index caaeca7ea3554c64d7d28236086738106dfffef1..74a103cdcfe67dcf458824a61db09a907c23773b 100644 --- a/tasks/0047_560_47560034_qa_4/task.toml +++ b/tasks/0047_560_47560034_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0047_560_47560034_qa_4" +name = "smoldataenvs-train/0047_560_47560034_qa_4" description = "Which genre showed the largest absolute increase in percentage representation between 2006 and 2016?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Horror" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0047_560_47560034_qa_5/task.toml b/tasks/0047_560_47560034_qa_5/task.toml index dbdd540120efcd40bcd61c2120c24a44993b8540..4b9689f1d5994c9167d835253617957a165a958b 100644 --- a/tasks/0047_560_47560034_qa_5/task.toml +++ b/tasks/0047_560_47560034_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0047_560_47560034_qa_5" +name = "smoldataenvs-train/0047_560_47560034_qa_5" description = "Which genre had the highest percentage representation in 2016 compared to other genres that year?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Drama" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0047_564_47564024_qa_3/task.toml b/tasks/0047_564_47564024_qa_3/task.toml index 6c3b283bcb541fa76a367896167ae4075ab9ed0f..5f7cb695087c08e7acdb4ea6e1a6263415183ab8 100644 --- a/tasks/0047_564_47564024_qa_3/task.toml +++ b/tasks/0047_564_47564024_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0047_564_47564024_qa_3" +name = "smoldataenvs-train/0047_564_47564024_qa_3" description = "What is the highest recall score observed for any class with more than 100 samples in the classification report?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.00" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0047_591_47591946_qa_1/task.toml b/tasks/0047_591_47591946_qa_1/task.toml index ee3b3294e3d2e4d0f2f5fbd92f0b62286d4ffec2..d3d705dbfae53856623008e9453b51f4a54c8442 100644 --- a/tasks/0047_591_47591946_qa_1/task.toml +++ b/tasks/0047_591_47591946_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0047_591_47591946_qa_1" +name = "smoldataenvs-train/0047_591_47591946_qa_1" description = "What is the highest accuracy achieved by the logistic regression model on the test set after hyperparameter tuning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9912280701754386" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0047_591_47591946_qa_3/task.toml b/tasks/0047_591_47591946_qa_3/task.toml index 69903ef212626a41997ba1958e204b0a010cc449..a7867cfa30d7934d54f86e0a1a2b2e3a8085dfd5 100644 --- a/tasks/0047_591_47591946_qa_3/task.toml +++ b/tasks/0047_591_47591946_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0047_591_47591946_qa_3" +name = "smoldataenvs-train/0047_591_47591946_qa_3" description = "What is the precision of the model on the test set for predicting malignant tumors?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0047_667_47667613_qa_5/task.toml b/tasks/0047_667_47667613_qa_5/task.toml index 9feb7a81c30409986a97987d0191875a92ee6061..ecf977f730dd6e4837865d440b821f96be9fd2a3 100644 --- a/tasks/0047_667_47667613_qa_5/task.toml +++ b/tasks/0047_667_47667613_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0047_667_47667613_qa_5" +name = "smoldataenvs-train/0047_667_47667613_qa_5" description = "Does the t-SNE visualization show better class separation compared to PCA based on the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0047_751_47751075_qa_2/task.toml b/tasks/0047_751_47751075_qa_2/task.toml index 96d6ab8a38edf03848ea676ee9b7e1368f295394..bea4d2588de178ce1ef6824806f7c1ecaa4fd9a1 100644 --- a/tasks/0047_751_47751075_qa_2/task.toml +++ b/tasks/0047_751_47751075_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0047_751_47751075_qa_2" +name = "smoldataenvs-train/0047_751_47751075_qa_2" description = "Which region has the highest number of recorded volcanic activity according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "South America" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0047_757_47757565_qa_3/task.toml b/tasks/0047_757_47757565_qa_3/task.toml index 477f6a42b27ba476a0b517896ef52c671fc0fabc..9f1e8857fe12eeba9f659c52e552c1ba9bd407a0 100644 --- a/tasks/0047_757_47757565_qa_3/task.toml +++ b/tasks/0047_757_47757565_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0047_757_47757565_qa_3" +name = "smoldataenvs-train/0047_757_47757565_qa_3" description = "Which vehicle age category has the highest proportion of customers interested in vehicle insurance based on the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "> 2 Years" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0047_766_47766276_qa_1/task.toml b/tasks/0047_766_47766276_qa_1/task.toml index 1f1206c2e0ca8b958a0a584f0efd248ee98fd2ce..529e33cd43cf45086c4e8889584d928ffe52ba80 100644 --- a/tasks/0047_766_47766276_qa_1/task.toml +++ b/tasks/0047_766_47766276_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0047_766_47766276_qa_1" +name = "smoldataenvs-train/0047_766_47766276_qa_1" description = "What percentage of the data corresponds to each season (winter, spring, summer, autumn)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Winter: 24.30, Spring: 18.19, Summer: 23.28, Autumn: 34.23" reward_mode_initial = "list" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0047_766_47766276_qa_2/task.toml b/tasks/0047_766_47766276_qa_2/task.toml index e5324479835288228f2811d4721fe094e4d1367b..e4fbe4c486c7dab803cef42ffeba809c4f80618e 100644 --- a/tasks/0047_766_47766276_qa_2/task.toml +++ b/tasks/0047_766_47766276_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0047_766_47766276_qa_2" +name = "smoldataenvs-train/0047_766_47766276_qa_2" description = "What is the maximum number of people recorded on weekends (is_weekend=1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "102" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0047_835_47835417_qa_1/task.toml b/tasks/0047_835_47835417_qa_1/task.toml index 1488f20f59ced36150c013eecfd972f06f01de5f..99198a870e58f11d6e9652241b0352ebb0890b6d 100644 --- a/tasks/0047_835_47835417_qa_1/task.toml +++ b/tasks/0047_835_47835417_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0047_835_47835417_qa_1" +name = "smoldataenvs-train/0047_835_47835417_qa_1" description = "What is the percentage of employees in the dataset who left the company (Attrition = Yes)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16.13" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0047_849_47849671_qa_4/task.toml b/tasks/0047_849_47849671_qa_4/task.toml index db6236406db756fb5bca382d0d34e027aa51fbdb..bf3d3f7c5b15b420a0765900aaa9401ff3853e56 100644 --- a/tasks/0047_849_47849671_qa_4/task.toml +++ b/tasks/0047_849_47849671_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0047_849_47849671_qa_4" +name = "smoldataenvs-train/0047_849_47849671_qa_4" description = "What is the average carat weight of diamonds in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.7979397478679852" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0047_852_47852416_qa_2/task.toml b/tasks/0047_852_47852416_qa_2/task.toml index 33012b52670f0a840794d07dccc4b7e1e46b70dc..90cff5d72e768348b01fea7a4f24178aa9082e77 100644 --- a/tasks/0047_852_47852416_qa_2/task.toml +++ b/tasks/0047_852_47852416_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0047_852_47852416_qa_2" +name = "smoldataenvs-train/0047_852_47852416_qa_2" description = "Which clothing department has the highest number of reviews in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Tops" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0047_862_47862196_qa_2/task.toml b/tasks/0047_862_47862196_qa_2/task.toml index 6a0bb5f4b40d54ce1458d0886c3da2a43651320f..8d59016f775bfd700b46de24834a509228ef4c21 100644 --- a/tasks/0047_862_47862196_qa_2/task.toml +++ b/tasks/0047_862_47862196_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0047_862_47862196_qa_2" +name = "smoldataenvs-train/0047_862_47862196_qa_2" description = "What percentage of the original dataset consists of malignant cancer cases (diagnosis = 'M') before any data splitting or preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "37.26" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0047_862_47862196_qa_3/task.toml b/tasks/0047_862_47862196_qa_3/task.toml index ebfbbc1cad863c552b884fa11a206097c7322a0b..11a0bdb0f66c90a64e11b9c523388ce34e432f0f 100644 --- a/tasks/0047_862_47862196_qa_3/task.toml +++ b/tasks/0047_862_47862196_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0047_862_47862196_qa_3" +name = "smoldataenvs-train/0047_862_47862196_qa_3" description = "What percentage of the 'Unnamed: 32' column contains missing (NaN) values in the raw dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "100" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0048_022_48022242_qa_3/task.toml b/tasks/0048_022_48022242_qa_3/task.toml index bf85ebea54305b0bc4258e4c73eaf653ed66637b..6e042403ca32ea2e2df4da9add8845292a07de25 100644 --- a/tasks/0048_022_48022242_qa_3/task.toml +++ b/tasks/0048_022_48022242_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0048_022_48022242_qa_3" +name = "smoldataenvs-train/0048_022_48022242_qa_3" description = "What is the number of samples allocated to the test set after splitting the data with a 20% test size?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1115" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0048_022_48022242_qa_4/task.toml b/tasks/0048_022_48022242_qa_4/task.toml index 6086e65d4f580b190c325d27ce33fe95812ea19f..0e66059c4eaf282a0425f192dda326d719277526 100644 --- a/tasks/0048_022_48022242_qa_4/task.toml +++ b/tasks/0048_022_48022242_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0048_022_48022242_qa_4" +name = "smoldataenvs-train/0048_022_48022242_qa_4" description = "What is the number of samples allocated to the training set after splitting the data with an 80% training size?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4457" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0048_049_48049467_qa_5/task.toml b/tasks/0048_049_48049467_qa_5/task.toml index 21c4195a4496ec5f7331fb1d9aba4a127d77920a..31ac77470e8bbe28d77b24915757678722a0189e 100644 --- a/tasks/0048_049_48049467_qa_5/task.toml +++ b/tasks/0048_049_48049467_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0048_049_48049467_qa_5" +name = "smoldataenvs-train/0048_049_48049467_qa_5" description = "What was the value used to impute missing values in the MINIMUM_PAYMENTS column before data preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "864.21" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0048_061_48061947_qa_2/task.toml b/tasks/0048_061_48061947_qa_2/task.toml index 99798b03879e205359a814205edbd29b062bec64..912d1601ea63fb2b6fc9ad0ab22ae28f00c6e2fa 100644 --- a/tasks/0048_061_48061947_qa_2/task.toml +++ b/tasks/0048_061_48061947_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0048_061_48061947_qa_2" +name = "smoldataenvs-train/0048_061_48061947_qa_2" description = "Which feature has the highest number of unique values in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "gill-color" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0048_061_48061947_qa_3/task.toml b/tasks/0048_061_48061947_qa_3/task.toml index 98d66745240276c4b95f31548c64d96543cfb769..3ca740b3c024bfe9a17f885da56dc0dfabe2cef5 100644 --- a/tasks/0048_061_48061947_qa_3/task.toml +++ b/tasks/0048_061_48061947_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0048_061_48061947_qa_3" +name = "smoldataenvs-train/0048_061_48061947_qa_3" description = "Which feature has no variation (all instances have the same value) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "veil-type" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0048_090_48090162_qa_2/task.toml b/tasks/0048_090_48090162_qa_2/task.toml index d98a9dc332c9a141fdea3e2cc04da9ef7bbaa639..699cef98fa8d2331bd310494433d645558b0e538 100644 --- a/tasks/0048_090_48090162_qa_2/task.toml +++ b/tasks/0048_090_48090162_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0048_090_48090162_qa_2" +name = "smoldataenvs-train/0048_090_48090162_qa_2" description = "Which feature exhibits the strongest correlation with mushroom edibility based on the query chart analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Odor" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0048_093_48093392_qa_5/task.toml b/tasks/0048_093_48093392_qa_5/task.toml index a90789c2a2edceb97a7e749382ca9b047cc120de..5add67fcb59ba24a68cc76c20e21bbbc6e3f0ccf 100644 --- a/tasks/0048_093_48093392_qa_5/task.toml +++ b/tasks/0048_093_48093392_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0048_093_48093392_qa_5" +name = "smoldataenvs-train/0048_093_48093392_qa_5" description = "What was the accuracy score of the Logistic Regression model after hyperparameter tuning using cross-validation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.71" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0048_101_48101055_qa_3/task.toml b/tasks/0048_101_48101055_qa_3/task.toml index 066814e4063ce57aafd1ba15dbe156a5c45d1a63..44f1c1658467e08ef389c1452bc76ebd2fdff572 100644 --- a/tasks/0048_101_48101055_qa_3/task.toml +++ b/tasks/0048_101_48101055_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0048_101_48101055_qa_3" +name = "smoldataenvs-train/0048_101_48101055_qa_3" description = "Which weekdays have the lowest average sales compared to other weekdays?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Sunday, Monday" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0048_107_48107437_qa_2/task.toml b/tasks/0048_107_48107437_qa_2/task.toml index 31dfd566a0ddcc412969693c4c03c7c068f3439b..54261592f801434e4f69627dfa4f1e5538c7be86 100644 --- a/tasks/0048_107_48107437_qa_2/task.toml +++ b/tasks/0048_107_48107437_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0048_107_48107437_qa_2" +name = "smoldataenvs-train/0048_107_48107437_qa_2" description = "Which contract type has the highest churn rate percentage among customers?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Month-to-month" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0048_357_48357285_qa_5/task.toml b/tasks/0048_357_48357285_qa_5/task.toml index 763e8799c1d01f0a9785b892274d2eb81b9aac07..1247dc0c33fcc4b68352c8e96cf73b9ca2fc17e3 100644 --- a/tasks/0048_357_48357285_qa_5/task.toml +++ b/tasks/0048_357_48357285_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0048_357_48357285_qa_5" +name = "smoldataenvs-train/0048_357_48357285_qa_5" description = "Which model performed better on the Boston dataset: the Ridge regression or the neural network with a single hidden layer?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Ridge regression" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0048_371_48371332_qa_5/task.toml b/tasks/0048_371_48371332_qa_5/task.toml index fef08dc6a14a3f99c07685a2eb4919487a852143..98607c7effcc7dc136160753727a4e32cb60de2f 100644 --- a/tasks/0048_371_48371332_qa_5/task.toml +++ b/tasks/0048_371_48371332_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0048_371_48371332_qa_5" +name = "smoldataenvs-train/0048_371_48371332_qa_5" description = "What is the precision score of the final KNN model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0048_420_48420121_qa_3/task.toml b/tasks/0048_420_48420121_qa_3/task.toml index 81788f2bcf2d981ab5c1cd2059a4097840c335c5..e4d1a988b2dddc3a59590122ecb001c79dc8ca02 100644 --- a/tasks/0048_420_48420121_qa_3/task.toml +++ b/tasks/0048_420_48420121_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0048_420_48420121_qa_3" +name = "smoldataenvs-train/0048_420_48420121_qa_3" description = "What is the most common time in hospital (days) for patients in the dataset, based on frequency distribution?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0048_436_48436455_qa_2/task.toml b/tasks/0048_436_48436455_qa_2/task.toml index 149fb4f1e4d1272c00e19e64f0e2323ebe3692bb..03a9f6344c2360a7dddc8602f647bc16db35ef91 100644 --- a/tasks/0048_436_48436455_qa_2/task.toml +++ b/tasks/0048_436_48436455_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0048_436_48436455_qa_2" +name = "smoldataenvs-train/0048_436_48436455_qa_2" description = "How many temperature recordings were available in the dataset after filtering for Surabaya and restricting the time range to data starting from 1869?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1737" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0048_436_48436455_qa_3/task.toml b/tasks/0048_436_48436455_qa_3/task.toml index 44697ff02db8a578c5be9a37f2525e24dde28f31..6f2b6f831831ad858d2a9802f9addbd81af9d59f 100644 --- a/tasks/0048_436_48436455_qa_3/task.toml +++ b/tasks/0048_436_48436455_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0048_436_48436455_qa_3" +name = "smoldataenvs-train/0048_436_48436455_qa_3" description = "How many missing temperature values were present in the dataset after applying the interpolation method to handle missing data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0048_459_48459218_qa_3/task.toml b/tasks/0048_459_48459218_qa_3/task.toml index 5d60efdd66e52c3a9f3996431511ab3af8e98186..2fd38e73820ef20060d09d4117e139a4bf3bb74e 100644 --- a/tasks/0048_459_48459218_qa_3/task.toml +++ b/tasks/0048_459_48459218_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0048_459_48459218_qa_3" +name = "smoldataenvs-train/0048_459_48459218_qa_3" description = "How many unique categories exist in the PaymentMethod column after replacing spaces with underscores in categorical values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0048_468_48468313_qa_5/task.toml b/tasks/0048_468_48468313_qa_5/task.toml index 305aff1d1315aebf23d21e0905cdaabb003b5a25..8f749ab26496ee6b3c329d3a669cb34c54db522f 100644 --- a/tasks/0048_468_48468313_qa_5/task.toml +++ b/tasks/0048_468_48468313_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0048_468_48468313_qa_5" +name = "smoldataenvs-train/0048_468_48468313_qa_5" description = "After outlier removal, what is the new shape of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "(688, 9)" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0048_488_48488979_qa_3/task.toml b/tasks/0048_488_48488979_qa_3/task.toml index 3b0cd7fdd1fdb167083e398222dc9a3ffd7a4f61..b849b1211e7c12e595fad02f584f34509a176d29 100644 --- a/tasks/0048_488_48488979_qa_3/task.toml +++ b/tasks/0048_488_48488979_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0048_488_48488979_qa_3" +name = "smoldataenvs-train/0048_488_48488979_qa_3" description = "What is the shape of the training images after adding an extra dimension for the channel in the preprocessing pipeline?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "(27455, 28, 28, 1)" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0048_488_48488979_qa_4/task.toml b/tasks/0048_488_48488979_qa_4/task.toml index 8bf722bf0d62f002cc68909b9f92f91f8eb38481..ff131aebf31704c3f89676bfc5e21434ef204404 100644 --- a/tasks/0048_488_48488979_qa_4/task.toml +++ b/tasks/0048_488_48488979_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0048_488_48488979_qa_4" +name = "smoldataenvs-train/0048_488_48488979_qa_4" description = "Which numeric labels correspond to the missing alphabetic letters in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9, 25" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0048_518_48518667_qa_4/task.toml b/tasks/0048_518_48518667_qa_4/task.toml index 05a7279c5442b88240ffc462f0f28929893346c8..ec8259f5e58bee79dde021333796a4e99cc2d76f 100644 --- a/tasks/0048_518_48518667_qa_4/task.toml +++ b/tasks/0048_518_48518667_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0048_518_48518667_qa_4" +name = "smoldataenvs-train/0048_518_48518667_qa_4" description = "Which aircraft type is most commonly used among the selected large aircraft categories (A380, A330, A340, 747, 777, 787) across all airlines in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "777" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0048_584_48584786_qa_2/task.toml b/tasks/0048_584_48584786_qa_2/task.toml index 5f4888071b582f268202588c1719c3c941269291..d2a340780f75f1068ebc412383ac32acb21e0eee 100644 --- a/tasks/0048_584_48584786_qa_2/task.toml +++ b/tasks/0048_584_48584786_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0048_584_48584786_qa_2" +name = "smoldataenvs-train/0048_584_48584786_qa_2" description = "Which product description has the highest absolute quantity of returned items in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PAPER CRAFT , LITTLE BIRDIE" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0048_651_48651619_qa_4/task.toml b/tasks/0048_651_48651619_qa_4/task.toml index 29d904383a675ce2d4c8d649445536f36db61599..fe467a853bf7020be1dc0c17fd04c12eb2e89646 100644 --- a/tasks/0048_651_48651619_qa_4/task.toml +++ b/tasks/0048_651_48651619_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0048_651_48651619_qa_4" +name = "smoldataenvs-train/0048_651_48651619_qa_4" description = "What is the median age of all patients in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "52" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0048_695_48695908_qa_2/task.toml b/tasks/0048_695_48695908_qa_2/task.toml index 7152b23c1d8ea881b4e43c6f54eadbd6c917b3fe..f7fbc999891d514c8f17e842d921a55afcc26056 100644 --- a/tasks/0048_695_48695908_qa_2/task.toml +++ b/tasks/0048_695_48695908_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0048_695_48695908_qa_2" +name = "smoldataenvs-train/0048_695_48695908_qa_2" description = "How many songs are identified as similar to \"Drake - Sneakin’\" using k=3 in the k-nearest neighbors query?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0048_708_48708349_qa_2/task.toml b/tasks/0048_708_48708349_qa_2/task.toml index a779efbc6d3602ce7d5029d35aa15b409592ea74..ef88d05c51ce4fbffda11d130c2e98831ea60291 100644 --- a/tasks/0048_708_48708349_qa_2/task.toml +++ b/tasks/0048_708_48708349_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0048_708_48708349_qa_2" +name = "smoldataenvs-train/0048_708_48708349_qa_2" description = "What is the ratio of non-diabetic to diabetic patients in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "500:268" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0048_708_48708349_qa_3/task.toml b/tasks/0048_708_48708349_qa_3/task.toml index 31ef356005fac861f613c2000592c942b771b77c..526cefee0658dc00078cf2d24f6431b4f503581d 100644 --- a/tasks/0048_708_48708349_qa_3/task.toml +++ b/tasks/0048_708_48708349_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0048_708_48708349_qa_3" +name = "smoldataenvs-train/0048_708_48708349_qa_3" description = "Are there any missing values in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0048_765_48765595_qa_3/task.toml b/tasks/0048_765_48765595_qa_3/task.toml index bf8e8e3147bc5ae7e9675c64cd6d860705e5ac79..b007d7d392d9be6ff8e97df01f5e60eb4ac7e35d 100644 --- a/tasks/0048_765_48765595_qa_3/task.toml +++ b/tasks/0048_765_48765595_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0048_765_48765595_qa_3" +name = "smoldataenvs-train/0048_765_48765595_qa_3" description = "How many individuals in the dataset work more than 40 hours per week?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9581" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0048_765_48765595_qa_5/task.toml b/tasks/0048_765_48765595_qa_5/task.toml index edfa19f4bc0841e6cb03ee28461cfa02fbaccadf..47f39f59f3af826d5370cc7217447100af04a31a 100644 --- a/tasks/0048_765_48765595_qa_5/task.toml +++ b/tasks/0048_765_48765595_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0048_765_48765595_qa_5" +name = "smoldataenvs-train/0048_765_48765595_qa_5" description = "What is the correlation coefficient between hours per week and income in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.229689" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0048_823_48823313_qa_1/task.toml b/tasks/0048_823_48823313_qa_1/task.toml index 6759bd5caf2750a7c5bc531f76833033a3ba20b7..5797bb6df1d8dcfd7e0d34aa2913e7ffb6b56bd3 100644 --- a/tasks/0048_823_48823313_qa_1/task.toml +++ b/tasks/0048_823_48823313_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0048_823_48823313_qa_1" +name = "smoldataenvs-train/0048_823_48823313_qa_1" description = "What is the highest global sales value recorded for a single game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "82.74" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0048_873_48873646_qa_3/task.toml b/tasks/0048_873_48873646_qa_3/task.toml index 05b331added37996f0beb79b50a21ac8214e0f06..4b3bf9ec378b73e662cc03b7e60b1522bf3c5b69 100644 --- a/tasks/0048_873_48873646_qa_3/task.toml +++ b/tasks/0048_873_48873646_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0048_873_48873646_qa_3" +name = "smoldataenvs-train/0048_873_48873646_qa_3" description = "Which feature exhibits the highest number of unique values in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0048_873_48873646_qa_4/task.toml b/tasks/0048_873_48873646_qa_4/task.toml index ef0f2171199d0c6c3f169060b86f2e4aa90059ed..b7ff6fdc4494e11f26fdd568ee5c896301a6954c 100644 --- a/tasks/0048_873_48873646_qa_4/task.toml +++ b/tasks/0048_873_48873646_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0048_873_48873646_qa_4" +name = "smoldataenvs-train/0048_873_48873646_qa_4" description = "What is the minimum sepal width recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0048_881_48881747_qa_3/task.toml b/tasks/0048_881_48881747_qa_3/task.toml index e2fbe7b056fe62fd9e910bf2c6a7932519fe3eb8..5399c399299aa8d44c9e2dfc77ae0b5913f063fa 100644 --- a/tasks/0048_881_48881747_qa_3/task.toml +++ b/tasks/0048_881_48881747_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0048_881_48881747_qa_3" +name = "smoldataenvs-train/0048_881_48881747_qa_3" description = "Which species has the highest median SepalLengthCm based on the boxplot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-virginica" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0048_917_48917297_qa_2/task.toml b/tasks/0048_917_48917297_qa_2/task.toml index 81027a85a150288619a203f736c470df6c304597..e5a5bd8231382b5bd38597a66298253bed7f4cb7 100644 --- a/tasks/0048_917_48917297_qa_2/task.toml +++ b/tasks/0048_917_48917297_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0048_917_48917297_qa_2" +name = "smoldataenvs-train/0048_917_48917297_qa_2" description = "How many distinct animal classes are present in the dataset based on the 'class_type' column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0048_917_48917297_qa_3/task.toml b/tasks/0048_917_48917297_qa_3/task.toml index 97ba2a681b10ce6a6868d60af20c78ce78ffd781..89264539c17282fb5123b102a479a8405e83fdae 100644 --- a/tasks/0048_917_48917297_qa_3/task.toml +++ b/tasks/0048_917_48917297_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0048_917_48917297_qa_3" +name = "smoldataenvs-train/0048_917_48917297_qa_3" description = "How many features are included in the model after removing 'animal_name' and 'class_type'?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0048_917_48917297_qa_4/task.toml b/tasks/0048_917_48917297_qa_4/task.toml index cd4b6a1075e03b092922c728b4615e00a66ccd04..07b24052b687a5a8184f4a65e1bf0c6ccf57d776 100644 --- a/tasks/0048_917_48917297_qa_4/task.toml +++ b/tasks/0048_917_48917297_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0048_917_48917297_qa_4" +name = "smoldataenvs-train/0048_917_48917297_qa_4" description = "What is the test set accuracy percentage achieved by the KNN model with K=5?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "87.1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0048_966_48966571_qa_3/task.toml b/tasks/0048_966_48966571_qa_3/task.toml index c6a57d109102572676d457a30993b0a4f60af13b..58965f7e4f4c767e0e6ecb95361ee29388db1be3 100644 --- a/tasks/0048_966_48966571_qa_3/task.toml +++ b/tasks/0048_966_48966571_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0048_966_48966571_qa_3" +name = "smoldataenvs-train/0048_966_48966571_qa_3" description = "Using content-based recommendations, which movie is most similar to \"The Dark Knight\" based on TF-IDF description analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "The Dark Knight Rises" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0048_996_48996091_qa_4/task.toml b/tasks/0048_996_48996091_qa_4/task.toml index ab1e3fafe83da72f3bc5a57ef8ce8986e3e79150..688b976ba0bb39a2a273f26c7f5813aa8183bee7 100644 --- a/tasks/0048_996_48996091_qa_4/task.toml +++ b/tasks/0048_996_48996091_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0048_996_48996091_qa_4" +name = "smoldataenvs-train/0048_996_48996091_qa_4" description = "What was the total number of samples in the test set used for evaluating the model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1625" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0049_008_49008236_qa_5/task.toml b/tasks/0049_008_49008236_qa_5/task.toml index 75ed906c4c8429e3d9cd7fffb0ff4c44c3544017..9c64a7b02455ebc2ac8d7fe1c1418d1b39b704f8 100644 --- a/tasks/0049_008_49008236_qa_5/task.toml +++ b/tasks/0049_008_49008236_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0049_008_49008236_qa_5" +name = "smoldataenvs-train/0049_008_49008236_qa_5" description = "What is the data type of the Species column in the Iris dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "object" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0049_015_49015069_qa_1/task.toml b/tasks/0049_015_49015069_qa_1/task.toml index 649d7a6c4d78ac45d8746921cb63a5d53576b370..19dfb8bbc33637826be4fe2ff1f095565503235b 100644 --- a/tasks/0049_015_49015069_qa_1/task.toml +++ b/tasks/0049_015_49015069_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0049_015_49015069_qa_1" +name = "smoldataenvs-train/0049_015_49015069_qa_1" description = "What is the highest expenditure value recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3099.505" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0049_015_49015069_qa_2/task.toml b/tasks/0049_015_49015069_qa_2/task.toml index bfba722145ad89ae7c22d4ff3093d98199c83f0a..d000035cc1d8001f9c0d240cd21d69365fa998e9 100644 --- a/tasks/0049_015_49015069_qa_2/task.toml +++ b/tasks/0049_015_49015069_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0049_015_49015069_qa_2" +name = "smoldataenvs-train/0049_015_49015069_qa_2" description = "What is the standard deviation of the \"reports\" column in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.345267" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0049_015_49015069_qa_5/task.toml b/tasks/0049_015_49015069_qa_5/task.toml index 6951bd3c0c1ef3b35af5b5e161a5cf8468ddcc05..ff40e75809c257b0e362207b87c033149cc8f05f 100644 --- a/tasks/0049_015_49015069_qa_5/task.toml +++ b/tasks/0049_015_49015069_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0049_015_49015069_qa_5" +name = "smoldataenvs-train/0049_015_49015069_qa_5" description = "What is the maximum number of active accounts held by any individual in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "46" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0049_018_49018627_qa_4/task.toml b/tasks/0049_018_49018627_qa_4/task.toml index 66821b0e3403d552f794a1e9a2bfe710ceec5434..50bc4be28b05fd2d30686d2c59133431160a50d9 100644 --- a/tasks/0049_018_49018627_qa_4/task.toml +++ b/tasks/0049_018_49018627_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0049_018_49018627_qa_4" +name = "smoldataenvs-train/0049_018_49018627_qa_4" description = "What is the correlation coefficient between tenure and total charges in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.82" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0049_034_49034706_qa_4/task.toml b/tasks/0049_034_49034706_qa_4/task.toml index c56239b4a211e9b9c8a4492f1372d15a4fa702e4..57f201c5423067bba6c2520fefb39aa5995d4550 100644 --- a/tasks/0049_034_49034706_qa_4/task.toml +++ b/tasks/0049_034_49034706_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0049_034_49034706_qa_4" +name = "smoldataenvs-train/0049_034_49034706_qa_4" description = "What is the recall score of the RandomForestClassifier model on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.8333333333333334" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0049_078_49078403_qa_1/task.toml b/tasks/0049_078_49078403_qa_1/task.toml index 487e5a67a5693000dd88485c5e345450f0d2f9c8..4404af45a788b51268b935526f977d736a4c02a7 100644 --- a/tasks/0049_078_49078403_qa_1/task.toml +++ b/tasks/0049_078_49078403_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0049_078_49078403_qa_1" +name = "smoldataenvs-train/0049_078_49078403_qa_1" description = "Which feature has the highest correlation with the Outcome variable according to the heatmap analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0049_090_49090832_qa_1/task.toml b/tasks/0049_090_49090832_qa_1/task.toml index 07f0ac59ab5d691c068c80e69bdf5bab16ab3d6b..0e2ed605e853989711f682416a5d0f1638891ee4 100644 --- a/tasks/0049_090_49090832_qa_1/task.toml +++ b/tasks/0049_090_49090832_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0049_090_49090832_qa_1" +name = "smoldataenvs-train/0049_090_49090832_qa_1" description = "How many outliers are identified in the 'charges' column using the interquartile range (IQR) method at the 25-75% percentile range?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "139" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0049_165_49165096_qa_1/task.toml b/tasks/0049_165_49165096_qa_1/task.toml index 65b4712a8e405e2352c66af8689e6641bf2dc0ac..d5690d37398b7c034f69e44958e3c8962f6333dc 100644 --- a/tasks/0049_165_49165096_qa_1/task.toml +++ b/tasks/0049_165_49165096_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0049_165_49165096_qa_1" +name = "smoldataenvs-train/0049_165_49165096_qa_1" description = "Which hour of the day has the highest average number of comments for posts tagged as 'ask'?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "15" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0049_165_49165096_qa_3/task.toml b/tasks/0049_165_49165096_qa_3/task.toml index 88a2099d3e0af010c09313af2d0764ec633aa19b..645f949fd684f8189cbcddbdd8b4b24f203f8456 100644 --- a/tasks/0049_165_49165096_qa_3/task.toml +++ b/tasks/0049_165_49165096_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0049_165_49165096_qa_3" +name = "smoldataenvs-train/0049_165_49165096_qa_3" description = "What are the top 5 hours of the day for 'ask' posts in terms of average comments, listed from highest to lowest average?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "15, 13, 12, 02, 10" reward_mode_initial = "list" package_tier = 3 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0049_165_49165096_qa_5/task.toml b/tasks/0049_165_49165096_qa_5/task.toml index 042bf1f6449856967abf9b503550466f5b4c9e47..c6ba4a3545fa3b4f3a5fc7e38d0a8c2ac1182729 100644 --- a/tasks/0049_165_49165096_qa_5/task.toml +++ b/tasks/0049_165_49165096_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0049_165_49165096_qa_5" +name = "smoldataenvs-train/0049_165_49165096_qa_5" description = "What is the total number of comments across all 'ask' posts compared to the total number of comments across all 'show' posts?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ask=94986, show=49633" reward_mode_initial = "list" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0049_228_49228657_qa_2/task.toml b/tasks/0049_228_49228657_qa_2/task.toml index 2a10e6a65af7b12bcc5e353667fe7a0412681e70..d5ae68be9b8d85e91f662b899b89eecc34f523f0 100644 --- a/tasks/0049_228_49228657_qa_2/task.toml +++ b/tasks/0049_228_49228657_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0049_228_49228657_qa_2" +name = "smoldataenvs-train/0049_228_49228657_qa_2" description = "What is the most strongly correlated numerical variable with insurance charges in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "age" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0049_228_49228657_qa_3/task.toml b/tasks/0049_228_49228657_qa_3/task.toml index 8c05d5811e3c5c39022ab3d35f83cb0bb34d38c0..ca78c787b8e9443edbeaf3e10da6769e50ea8370 100644 --- a/tasks/0049_228_49228657_qa_3/task.toml +++ b/tasks/0049_228_49228657_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0049_228_49228657_qa_3" +name = "smoldataenvs-train/0049_228_49228657_qa_3" description = "What percentage of beneficiaries in the dataset are classified as obese based on BMI?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "52.8" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0049_228_49228657_qa_5/task.toml b/tasks/0049_228_49228657_qa_5/task.toml index d0215e09d249f88f33756f6135aea2900a9f78eb..583d8c081e9b1b4100ed57ec36dbe99732efe51a 100644 --- a/tasks/0049_228_49228657_qa_5/task.toml +++ b/tasks/0049_228_49228657_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0049_228_49228657_qa_5" +name = "smoldataenvs-train/0049_228_49228657_qa_5" description = "What is the average BMI value observed in the insurance dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "30.66" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0049_261_49261135_qa_3/task.toml b/tasks/0049_261_49261135_qa_3/task.toml index 6ce4be061c348a8c2ac8383c5c1398cf9e155ad2..39a61e77248041dd3fca5d1c0fadbd0eb9f6f536 100644 --- a/tasks/0049_261_49261135_qa_3/task.toml +++ b/tasks/0049_261_49261135_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0049_261_49261135_qa_3" +name = "smoldataenvs-train/0049_261_49261135_qa_3" description = "How many features remain in the dataset after removing 'id', 'diagnosis', and 'Unnamed: 32' columns?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "30" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0049_302_49302290_qa_5/task.toml b/tasks/0049_302_49302290_qa_5/task.toml index 2032922ebc5b8970fb8454f14f72259fde577e78..40529b550378cbe7a6491872086d13e8285b3979 100644 --- a/tasks/0049_302_49302290_qa_5/task.toml +++ b/tasks/0049_302_49302290_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0049_302_49302290_qa_5" +name = "smoldataenvs-train/0049_302_49302290_qa_5" description = "How many features are present in the dataset after preprocessing and before model training?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "30" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0049_378_49378921_qa_4/task.toml b/tasks/0049_378_49378921_qa_4/task.toml index 64857afc7fd5cde0e099b0e59e4e94299e61376e..804c6debc9569750942d9f5af4572d25cbe426a7 100644 --- a/tasks/0049_378_49378921_qa_4/task.toml +++ b/tasks/0049_378_49378921_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0049_378_49378921_qa_4" +name = "smoldataenvs-train/0049_378_49378921_qa_4" description = "Which feature was removed from the dataset due to having only a single unique value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "veil-type" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0049_482_49482971_qa_2/task.toml b/tasks/0049_482_49482971_qa_2/task.toml index 3ec29ec379a40228f366c4ad47c451ca1dd8bb0e..a5e7afa22a06368233a9d44d7ae82d56b95ad23f 100644 --- a/tasks/0049_482_49482971_qa_2/task.toml +++ b/tasks/0049_482_49482971_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0049_482_49482971_qa_2" +name = "smoldataenvs-train/0049_482_49482971_qa_2" description = "Which gaming platform has achieved the highest total global sales in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PS2" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0049_513_49513248_qa_5/task.toml b/tasks/0049_513_49513248_qa_5/task.toml index a091745ff94c8d8b1c4245d2730dfce8a5c55e94..a96a759d4ead00e5abf0b21582288ffdfeb3a14b 100644 --- a/tasks/0049_513_49513248_qa_5/task.toml +++ b/tasks/0049_513_49513248_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0049_513_49513248_qa_5" +name = "smoldataenvs-train/0049_513_49513248_qa_5" description = "Which genre appears most frequently in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Drama" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0049_516_49516082_qa_4/task.toml b/tasks/0049_516_49516082_qa_4/task.toml index 6a4977371f55021106b8d7f90142881da6442af9..4444f604d9478677233609a36e4839feea602ff9 100644 --- a/tasks/0049_516_49516082_qa_4/task.toml +++ b/tasks/0049_516_49516082_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0049_516_49516082_qa_4" +name = "smoldataenvs-train/0049_516_49516082_qa_4" description = "Which individual video game has the highest recorded global sales in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii Sports" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0049_516_49516082_qa_5/task.toml b/tasks/0049_516_49516082_qa_5/task.toml index c4dbd5b863c2e042f53c3db07bf1751dbe9be707..2433d458dd43ec3d5ac1eed3e3c67e83ed37b1eb 100644 --- a/tasks/0049_516_49516082_qa_5/task.toml +++ b/tasks/0049_516_49516082_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0049_516_49516082_qa_5" +name = "smoldataenvs-train/0049_516_49516082_qa_5" description = "Which geographic region contributes the most to total sales across all video games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "North America" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0049_548_49548242_qa_2/task.toml b/tasks/0049_548_49548242_qa_2/task.toml index 2121b2d2d48ffd4ddb5501f5bbcefe09e2f5e9e1..a8ec69b6a4da726cb914577838582424b0be8e66 100644 --- a/tasks/0049_548_49548242_qa_2/task.toml +++ b/tasks/0049_548_49548242_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0049_548_49548242_qa_2" +name = "smoldataenvs-train/0049_548_49548242_qa_2" description = "Which cluster in the Annual Income vs Spending Score clustering analysis has the highest average spending score, and what is its value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Cluster 4, 82.13" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0049_585_49585863_qa_3/task.toml b/tasks/0049_585_49585863_qa_3/task.toml index d7c6f3e28ffdb01acb55d79fe5852bd3a626ab2d..f37d3b96caef2522363d0f11a4e6fc06250761c1 100644 --- a/tasks/0049_585_49585863_qa_3/task.toml +++ b/tasks/0049_585_49585863_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0049_585_49585863_qa_3" +name = "smoldataenvs-train/0049_585_49585863_qa_3" description = "Which country contributes the highest percentage of total sales, and what is this percentage?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "United Kingdom, 83.9969" reward_mode_initial = "list" package_tier = 3 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0049_585_49585863_qa_4/task.toml b/tasks/0049_585_49585863_qa_4/task.toml index 98d2812848827b6e2f748dfacb45ccf73cf9c773..f2ea617deeb84948ca8d96feac81b72772417a35 100644 --- a/tasks/0049_585_49585863_qa_4/task.toml +++ b/tasks/0049_585_49585863_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0049_585_49585863_qa_4" +name = "smoldataenvs-train/0049_585_49585863_qa_4" description = "What is the average order value (AOV) calculated from non-refund orders with valid customer IDs?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "480.76" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0049_644_49644416_qa_2/task.toml b/tasks/0049_644_49644416_qa_2/task.toml index ff5b3f466a07d2397f9824656694e367d928dfc2..4835c88dfa40e328ce9c9309b3fa6c6c8e9654dc 100644 --- a/tasks/0049_644_49644416_qa_2/task.toml +++ b/tasks/0049_644_49644416_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0049_644_49644416_qa_2" +name = "smoldataenvs-train/0049_644_49644416_qa_2" description = "What is the percentage distribution of patients in the defined age groups (Young: 29-40, Middle: 40-55, Elderly: ≥55 years)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Young: 48.38, Middle: 38.15, Elderly: 13.47" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0049_677_49677120_qa_1/task.toml b/tasks/0049_677_49677120_qa_1/task.toml index 5dd4719aac1d058ceb27f8faf517d198b05d2f8d..9e9c9dd32856b82aa5d80f841561cdf3596ec740 100644 --- a/tasks/0049_677_49677120_qa_1/task.toml +++ b/tasks/0049_677_49677120_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0049_677_49677120_qa_1" +name = "smoldataenvs-train/0049_677_49677120_qa_1" description = "Which physicochemical characteristic is identified as the most important predictor of wine quality according to the Random Forest model's feature importance analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Alcohol" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0049_677_49677120_qa_3/task.toml b/tasks/0049_677_49677120_qa_3/task.toml index 735ea1b56e0966a63b9068a51cd2dd6e602b8401..46bcb703f4be2053149fb2e2925499537ff63784 100644 --- a/tasks/0049_677_49677120_qa_3/task.toml +++ b/tasks/0049_677_49677120_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0049_677_49677120_qa_3" +name = "smoldataenvs-train/0049_677_49677120_qa_3" description = "What percentage of the wines in the dataset are classified as 'good quality' based on the quality score threshold of ≥6?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "53.4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0049_683_49683426_qa_3/task.toml b/tasks/0049_683_49683426_qa_3/task.toml index 6d97069cb5620628a1c70c718d4b3f99825a1dfd..c619602fe3e897e3414c980a7d7c992f86069cd8 100644 --- a/tasks/0049_683_49683426_qa_3/task.toml +++ b/tasks/0049_683_49683426_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0049_683_49683426_qa_3" +name = "smoldataenvs-train/0049_683_49683426_qa_3" description = "What is the overall percentage of patients who showed up for their appointments out of the total number of appointments?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "79.81" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0049_714_49714638_qa_4/task.toml b/tasks/0049_714_49714638_qa_4/task.toml index 78f9ba44441f72fd14d4f21f7018b60b3a7defdf..a70881cd867b14a17cca18f3046ffe73aa941f9a 100644 --- a/tasks/0049_714_49714638_qa_4/task.toml +++ b/tasks/0049_714_49714638_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0049_714_49714638_qa_4" +name = "smoldataenvs-train/0049_714_49714638_qa_4" description = "Which Pokémon have the highest Defense points, and what is their specific Defense value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Mega Steelix, Shuckle, Mega Aggron - 230" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0049_738_49738137_qa_4/task.toml b/tasks/0049_738_49738137_qa_4/task.toml index 6ce72d2d96c3a562ca2398037b54e616fafd2314..98bc00f6bb659a4aef01a6df37d4202d2db65e9a 100644 --- a/tasks/0049_738_49738137_qa_4/task.toml +++ b/tasks/0049_738_49738137_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0049_738_49738137_qa_4" +name = "smoldataenvs-train/0049_738_49738137_qa_4" description = "What is the maximum value of the Total stat in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "780" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0049_738_49738137_qa_5/task.toml b/tasks/0049_738_49738137_qa_5/task.toml index 138f18199a239be1bf51ec246f9d8ce0fb7e9b3c..fa05f8f4a5f61c03dfa1b4ebb124c09f74bbdbe0 100644 --- a/tasks/0049_738_49738137_qa_5/task.toml +++ b/tasks/0049_738_49738137_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0049_738_49738137_qa_5" +name = "smoldataenvs-train/0049_738_49738137_qa_5" description = "What is the average value of the Sp. Atk stat for all Pokémon in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "72.82" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0049_762_49762314_qa_3/task.toml b/tasks/0049_762_49762314_qa_3/task.toml index 7f41d855172c4dabac616cec5322bcb1f1a9fe09..9951c4c81218bd74e03716ce7ce3e11a8cec17bf 100644 --- a/tasks/0049_762_49762314_qa_3/task.toml +++ b/tasks/0049_762_49762314_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0049_762_49762314_qa_3" +name = "smoldataenvs-train/0049_762_49762314_qa_3" description = "What is the difference in average monthly hours between employees who left the company and those who stayed?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8.359" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0049_804_49804423_qa_2/task.toml b/tasks/0049_804_49804423_qa_2/task.toml index 1b735d53734440014f4d59d48a412bae396263db..c5f1e8951a6a7c625f3e40206bc67339416458d5 100644 --- a/tasks/0049_804_49804423_qa_2/task.toml +++ b/tasks/0049_804_49804423_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0049_804_49804423_qa_2" +name = "smoldataenvs-train/0049_804_49804423_qa_2" description = "What is the mathematical equation of the linear regression line derived from the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "y = 1.00065638x - 0.10726546" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0049_820_49820417_qa_1/task.toml b/tasks/0049_820_49820417_qa_1/task.toml index 5f16379de9686f222f67ae1267e8e15804b74872..d944cc6bc24d604258d7a551603f1a68811c02cf 100644 --- a/tasks/0049_820_49820417_qa_1/task.toml +++ b/tasks/0049_820_49820417_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0049_820_49820417_qa_1" +name = "smoldataenvs-train/0049_820_49820417_qa_1" description = "What is the number of false positives in the test set predictions generated by the logistic regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0049_820_49820417_qa_3/task.toml b/tasks/0049_820_49820417_qa_3/task.toml index 79c2cd70afb2b80545233aa86ec303db6b687d9a..6967a254187a4c82677e81385db66274a6f0737e 100644 --- a/tasks/0049_820_49820417_qa_3/task.toml +++ b/tasks/0049_820_49820417_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0049_820_49820417_qa_3" +name = "smoldataenvs-train/0049_820_49820417_qa_3" description = "What is the total number of test samples predicted to have purchased the product (class 1) by the logistic regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "27" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0049_938_49938525_qa_2/task.toml b/tasks/0049_938_49938525_qa_2/task.toml index 8c74f39d8538f92fb099f12a8a3dd6ce50b96621..b8d2526c31c8614acd000b00da3816099efe26e0 100644 --- a/tasks/0049_938_49938525_qa_2/task.toml +++ b/tasks/0049_938_49938525_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0049_938_49938525_qa_2" +name = "smoldataenvs-train/0049_938_49938525_qa_2" description = "What is the average number of bedrooms in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.37" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0050_018_50018991_qa_1/task.toml b/tasks/0050_018_50018991_qa_1/task.toml index d0b5a06ab85dccfd23aa4232bafa55441b60c6d7..7a31515d1bcd14f1801c1929984d7303e424dc3e 100644 --- a/tasks/0050_018_50018991_qa_1/task.toml +++ b/tasks/0050_018_50018991_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0050_018_50018991_qa_1" +name = "smoldataenvs-train/0050_018_50018991_qa_1" description = "What percentage of matches result in a home team win according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "45.87" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0050_018_50018991_qa_4/task.toml b/tasks/0050_018_50018991_qa_4/task.toml index f6dc328354b6ff2cab0a48134910094dc70c1dfe..bf5f27913dc468e13ac33cca86d727d455edd60b 100644 --- a/tasks/0050_018_50018991_qa_4/task.toml +++ b/tasks/0050_018_50018991_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0050_018_50018991_qa_4" +name = "smoldataenvs-train/0050_018_50018991_qa_4" description = "What is the correlation coefficient between a team's average home goals and average away goals?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.831" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0050_022_50022943_qa_1/task.toml b/tasks/0050_022_50022943_qa_1/task.toml index d37d326927c0319c6336305a0dd52842f34694ac..43d8c28752f82ad8f96cddc707d40667a5abeeaa 100644 --- a/tasks/0050_022_50022943_qa_1/task.toml +++ b/tasks/0050_022_50022943_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0050_022_50022943_qa_1" +name = "smoldataenvs-train/0050_022_50022943_qa_1" description = "What is the highest correlation coefficient between any two features in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.962757" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0050_022_50022943_qa_2/task.toml b/tasks/0050_022_50022943_qa_2/task.toml index ca65dee777722bf1e6100fda71a67508a7233a87..b88fdf954c6ceb875a456321040f46755c00cbff 100644 --- a/tasks/0050_022_50022943_qa_2/task.toml +++ b/tasks/0050_022_50022943_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0050_022_50022943_qa_2" +name = "smoldataenvs-train/0050_022_50022943_qa_2" description = "Which pair of features exhibits the strongest negative linear relationship according to the correlation matrix?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm, SepalWidthCm" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0050_022_50022943_qa_4/task.toml b/tasks/0050_022_50022943_qa_4/task.toml index 31191a2a000f63b6f2d7b752794b8dc1c3267478..92f49b4d94f5f0007a7d4b18016e353d104643fb 100644 --- a/tasks/0050_022_50022943_qa_4/task.toml +++ b/tasks/0050_022_50022943_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0050_022_50022943_qa_4" +name = "smoldataenvs-train/0050_022_50022943_qa_4" description = "What is the correlation coefficient between the Id column and PetalWidthCm?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.899759" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0050_090_50090486_qa_1/task.toml b/tasks/0050_090_50090486_qa_1/task.toml index ac40b69d8b1ba77ff10b3d846082410274e0627e..b30b04105e835b3e41a16aa600dfcbde116823fa 100644 --- a/tasks/0050_090_50090486_qa_1/task.toml +++ b/tasks/0050_090_50090486_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0050_090_50090486_qa_1" +name = "smoldataenvs-train/0050_090_50090486_qa_1" description = "Which payment method has the highest average monthly charges per customer according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic check" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0050_090_50090486_qa_3/task.toml b/tasks/0050_090_50090486_qa_3/task.toml index 02df29ecea09c6b74550a22531c347f966ad94ac..62c1807fe428b9459758cc2790720e8291e7f323 100644 --- a/tasks/0050_090_50090486_qa_3/task.toml +++ b/tasks/0050_090_50090486_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0050_090_50090486_qa_3" +name = "smoldataenvs-train/0050_090_50090486_qa_3" description = "Which combination of Partner and Dependents status has the highest number of churned customers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "No Partner, No Dependents" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0050_102_50102460_qa_2/task.toml b/tasks/0050_102_50102460_qa_2/task.toml index 4a8e3a5ddd9736d3a83bc0d378b6f18c45408db6..e1945e8bf6915eb4730422d5db28dc3348c3bccf 100644 --- a/tasks/0050_102_50102460_qa_2/task.toml +++ b/tasks/0050_102_50102460_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0050_102_50102460_qa_2" +name = "smoldataenvs-train/0050_102_50102460_qa_2" description = "What is the accuracy score of the KNeighborsClassifier (n_neighbors=10) on the training data when using all 20 original features?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.94" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0050_224_50224594_qa_1/task.toml b/tasks/0050_224_50224594_qa_1/task.toml index 49b020a421f3dac35307bfad65022849f6caedcb..8eea88120a3623a1d38970b642b276745999d94d 100644 --- a/tasks/0050_224_50224594_qa_1/task.toml +++ b/tasks/0050_224_50224594_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0050_224_50224594_qa_1" +name = "smoldataenvs-train/0050_224_50224594_qa_1" description = "Which numerical feature in the dataset has the largest range (max - min) before outlier removal?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "MDVP:Fhi(Hz)" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0050_224_50224594_qa_5/task.toml b/tasks/0050_224_50224594_qa_5/task.toml index a4ed2e098934e5b6063c26f3f7b37c93d6936fd6..7848929df0fb3057cba5645da193efb18c177305 100644 --- a/tasks/0050_224_50224594_qa_5/task.toml +++ b/tasks/0050_224_50224594_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0050_224_50224594_qa_5" +name = "smoldataenvs-train/0050_224_50224594_qa_5" description = "What is the mean value of the MDVP:Fhi(Hz) feature across all samples in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "197.104918" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0050_233_50233728_qa_5/task.toml b/tasks/0050_233_50233728_qa_5/task.toml index 28e2aac1a24e8ffbf80fd27ef3b1e26e2b1ccc20..07e646dfaf2662dcea2538512779d24ce249448d 100644 --- a/tasks/0050_233_50233728_qa_5/task.toml +++ b/tasks/0050_233_50233728_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0050_233_50233728_qa_5" +name = "smoldataenvs-train/0050_233_50233728_qa_5" description = "What is the median pH value observed in the wine dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.31" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0050_257_50257627_qa_3/task.toml b/tasks/0050_257_50257627_qa_3/task.toml index 1f4d62c2ec7d5587946fdbc2bbd1f74bb66be163..f57c23f268bf0a5ed3814a7d769b43087766fbcf 100644 --- a/tasks/0050_257_50257627_qa_3/task.toml +++ b/tasks/0050_257_50257627_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0050_257_50257627_qa_3" +name = "smoldataenvs-train/0050_257_50257627_qa_3" description = "What is the test accuracy achieved by the Decision Tree model before feature selection?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0050_281_50281280_qa_1/task.toml b/tasks/0050_281_50281280_qa_1/task.toml index e90075926a531204e7702d9916d66dd79c7fcb3a..cb5e52dc5993f07d6a6bb8a8a308687165cce4d3 100644 --- a/tasks/0050_281_50281280_qa_1/task.toml +++ b/tasks/0050_281_50281280_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0050_281_50281280_qa_1" +name = "smoldataenvs-train/0050_281_50281280_qa_1" description = "Which regression model achieved the lowest root mean squared error (RMSE) on the housing price prediction task using the Boston housing dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Random Forest Regressor" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0050_292_50292896_qa_1/task.toml b/tasks/0050_292_50292896_qa_1/task.toml index 438778f1337869c04f1db79761c4471795f183b6..09a5653399e3cdf4eb0ae9d4fc6993100b53faec 100644 --- a/tasks/0050_292_50292896_qa_1/task.toml +++ b/tasks/0050_292_50292896_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0050_292_50292896_qa_1" +name = "smoldataenvs-train/0050_292_50292896_qa_1" description = "What is the percentage of fraudulent transactions in the original dataset provided for analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.21" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0050_306_50306022_qa_3/task.toml b/tasks/0050_306_50306022_qa_3/task.toml index 9660603c0c3e6795d70c058632a063f9373f42f7..303e8a9427740257afdeb27bc4223476147241c5 100644 --- a/tasks/0050_306_50306022_qa_3/task.toml +++ b/tasks/0050_306_50306022_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0050_306_50306022_qa_3" +name = "smoldataenvs-train/0050_306_50306022_qa_3" description = "What is the difference in the number of benign (B) and malignant (M) cases in the original dataset before applying SMOTE?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "145" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0050_334_50334572_qa_4/task.toml b/tasks/0050_334_50334572_qa_4/task.toml index b4fa4c9eb997963603794dc138f2789f6722c0a4..1acf15110a06c7e5f6b93cc035ab51a917cda1b5 100644 --- a/tasks/0050_334_50334572_qa_4/task.toml +++ b/tasks/0050_334_50334572_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0050_334_50334572_qa_4" +name = "smoldataenvs-train/0050_334_50334572_qa_4" description = "How many missing values were present in the 'Checking account' column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "394" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0050_461_50461081_qa_3/task.toml b/tasks/0050_461_50461081_qa_3/task.toml index e199bdb82bcb93002e5b9c97382151556c2514ff..c9b9e77158fa984db06d7c0fd25822781a94813d 100644 --- a/tasks/0050_461_50461081_qa_3/task.toml +++ b/tasks/0050_461_50461081_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0050_461_50461081_qa_3" +name = "smoldataenvs-train/0050_461_50461081_qa_3" description = "What is the difference between the maximum and minimum age of patients in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "60" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0050_467_50467216_qa_3/task.toml b/tasks/0050_467_50467216_qa_3/task.toml index 8ef8b20a9da32502896d4b882d4ce573e4527842..5ed5a8d550d6b9616a6cf7d44b745157ca707033 100644 --- a/tasks/0050_467_50467216_qa_3/task.toml +++ b/tasks/0050_467_50467216_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0050_467_50467216_qa_3" +name = "smoldataenvs-train/0050_467_50467216_qa_3" description = "What is the adjusted Rand index score for the Aggregation dataset when KMeans is applied with k=6 clusters, comparing predicted labels to ground truth?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.794982256654018" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0050_513_50513011_qa_5/task.toml b/tasks/0050_513_50513011_qa_5/task.toml index ff52a172fb6928245346254e9bd0e226b425f60f..22f89ecc9d5c239eba057b00c7a02c26b64f9430 100644 --- a/tasks/0050_513_50513011_qa_5/task.toml +++ b/tasks/0050_513_50513011_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0050_513_50513011_qa_5" +name = "smoldataenvs-train/0050_513_50513011_qa_5" description = "Which two flavors have the lowest occurrence of \"Relaxed\" effect reports below 60% threshold?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Apple, Minty" reward_mode_initial = "list" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0050_584_50584509_qa_4/task.toml b/tasks/0050_584_50584509_qa_4/task.toml index 4031f2b6619bf17bca53117e6c480f3a6af02f68..2008c9cc2a1024ed6675a71a976160377b84dbba 100644 --- a/tasks/0050_584_50584509_qa_4/task.toml +++ b/tasks/0050_584_50584509_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0050_584_50584509_qa_4" +name = "smoldataenvs-train/0050_584_50584509_qa_4" description = "What is the lowest p-value observed in the ANOVA test for any feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7.101150e-116" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0050_587_50587128_qa_1/task.toml b/tasks/0050_587_50587128_qa_1/task.toml index ba4d1a95e46a76f83e22030cf669f3dd6ea16f04..1e2360a2c8651d9fbdb39a9ac2677714e38065fb 100644 --- a/tasks/0050_587_50587128_qa_1/task.toml +++ b/tasks/0050_587_50587128_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0050_587_50587128_qa_1" +name = "smoldataenvs-train/0050_587_50587128_qa_1" description = "Which manufacturer has the highest median cereal rating?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "N" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0050_587_50587128_qa_4/task.toml b/tasks/0050_587_50587128_qa_4/task.toml index 16d964ae1bb5197ebf0024cc660bccb8b1de3ef0..ce76a2aed189ccd45a7c6123b6c9d80e24a59696 100644 --- a/tasks/0050_587_50587128_qa_4/task.toml +++ b/tasks/0050_587_50587128_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0050_587_50587128_qa_4" +name = "smoldataenvs-train/0050_587_50587128_qa_4" description = "How many manufacturers produce hot cereals?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0050_587_50587128_qa_5/task.toml b/tasks/0050_587_50587128_qa_5/task.toml index f3dc2ec91406776151e4ce98fb48c702c4095538..7c61691881d1c6560829eece1ffa1f7199d6c86d 100644 --- a/tasks/0050_587_50587128_qa_5/task.toml +++ b/tasks/0050_587_50587128_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0050_587_50587128_qa_5" +name = "smoldataenvs-train/0050_587_50587128_qa_5" description = "What is the average sodium content for cereals produced by manufacturer A?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0050_704_50704224_qa_1/task.toml b/tasks/0050_704_50704224_qa_1/task.toml index bd119844ef2c0d1f892a36495c03315f34bf10f6..e9457ea987e6db087b7a08a22dcd02632a1cc7f1 100644 --- a/tasks/0050_704_50704224_qa_1/task.toml +++ b/tasks/0050_704_50704224_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0050_704_50704224_qa_1" +name = "smoldataenvs-train/0050_704_50704224_qa_1" description = "What percentage of rows with missing 'stalk-root' values belong to the poisonous mushroom class (class 'p')?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "71" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0050_704_50704224_qa_2/task.toml b/tasks/0050_704_50704224_qa_2/task.toml index e9c691acbe896c8cbdc5869513f9021546dbdb78..70a8f5d31b23540f472f78d43cf2cd35c0961fb3 100644 --- a/tasks/0050_704_50704224_qa_2/task.toml +++ b/tasks/0050_704_50704224_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0050_704_50704224_qa_2" +name = "smoldataenvs-train/0050_704_50704224_qa_2" description = "What is the total number of unique categorical values across all features in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "119" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0050_774_50774398_qa_2/task.toml b/tasks/0050_774_50774398_qa_2/task.toml index f4c310852f409aa360a6f1f8cfb60b0423d6a60d..6c89b1dfe42939acd74a9895984fc4ea16b31b67 100644 --- a/tasks/0050_774_50774398_qa_2/task.toml +++ b/tasks/0050_774_50774398_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0050_774_50774398_qa_2" +name = "smoldataenvs-train/0050_774_50774398_qa_2" description = "Which feature has the highest positive correlation with the 'price' column in the original dataset before outlier removal?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "sqft_living" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0050_775_50775571_qa_1/task.toml b/tasks/0050_775_50775571_qa_1/task.toml index 3334b1dadb75a23181c35a98716e7eb75adeeb4c..a6c0d4649c1ec0a1003e344366ef9162dba0e972 100644 --- a/tasks/0050_775_50775571_qa_1/task.toml +++ b/tasks/0050_775_50775571_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0050_775_50775571_qa_1" +name = "smoldataenvs-train/0050_775_50775571_qa_1" description = "What is the feature with the highest absolute correlation with the target variable 'SalePrice' in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "OverallQual" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0050_784_50784361_qa_4/task.toml b/tasks/0050_784_50784361_qa_4/task.toml index e089425a3d9b8b9755d02a368b7e3ff70a879f31..123a483132786fa89f7293c10b9966047f8fe527 100644 --- a/tasks/0050_784_50784361_qa_4/task.toml +++ b/tasks/0050_784_50784361_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0050_784_50784361_qa_4" +name = "smoldataenvs-train/0050_784_50784361_qa_4" description = "How many rows and columns remain in the dataset after dropping the 'MRI ID', 'Hand', and 'Visit' columns?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "373, 12" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0050_784_50784361_qa_5/task.toml b/tasks/0050_784_50784361_qa_5/task.toml index f2dfe17f444c9bf1a5b60ac04edeb9a15634b88d..1097ed93c2cce20655b6b3f801fc667ddae2dd25 100644 --- a/tasks/0050_784_50784361_qa_5/task.toml +++ b/tasks/0050_784_50784361_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0050_784_50784361_qa_5" +name = "smoldataenvs-train/0050_784_50784361_qa_5" description = "What is the mean age of participants in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "77.01" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0050_785_50785547_qa_2/task.toml b/tasks/0050_785_50785547_qa_2/task.toml index 9cf1ed59d39529159d2764169962df90fdbff3c7..49441c2bb7808e911e3d6a6e2f1e457fe675839f 100644 --- a/tasks/0050_785_50785547_qa_2/task.toml +++ b/tasks/0050_785_50785547_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0050_785_50785547_qa_2" +name = "smoldataenvs-train/0050_785_50785547_qa_2" description = "Which combination of trip category and purpose resulted in the longest single trip distance recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Business, Customer Visit" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0050_797_50797720_qa_2/task.toml b/tasks/0050_797_50797720_qa_2/task.toml index 85449c4bee027bc5639e75732073fb438efba972..588fd9e779beaca0ba53d0a426120949dbedb09c 100644 --- a/tasks/0050_797_50797720_qa_2/task.toml +++ b/tasks/0050_797_50797720_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0050_797_50797720_qa_2" +name = "smoldataenvs-train/0050_797_50797720_qa_2" description = "What percentage of respondents find it \"Very easy\" to take medical leave for a mental health condition according to the survey data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16.28" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0050_797_50797720_qa_5/task.toml b/tasks/0050_797_50797720_qa_5/task.toml index 95e4f7f35c05e5e73d1f8e3d582c7cc2eeb5e0bc..93045cc888d0ace697cc9374fbe573318d982472 100644 --- a/tasks/0050_797_50797720_qa_5/task.toml +++ b/tasks/0050_797_50797720_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0050_797_50797720_qa_5" +name = "smoldataenvs-train/0050_797_50797720_qa_5" description = "What percentage of respondents believe that discussing a mental health issue with their employer would have negative consequences?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "22.98" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0050_812_50812874_qa_3/task.toml b/tasks/0050_812_50812874_qa_3/task.toml index 89e52c68ab9eab173f311abdd1e4373e71304271..4f1ef4dbf2f54cf5dda65612182ad15f68da5192 100644 --- a/tasks/0050_812_50812874_qa_3/task.toml +++ b/tasks/0050_812_50812874_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0050_812_50812874_qa_3" +name = "smoldataenvs-train/0050_812_50812874_qa_3" description = "Which digit class in the test set has the highest precision according to the classification report?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0050_842_50842241_qa_5/task.toml b/tasks/0050_842_50842241_qa_5/task.toml index 39632921c5776481bc2e33fd1c82bb722100bc4b..a14003fdb40e54470883c1101e7abcee841e8ce6 100644 --- a/tasks/0050_842_50842241_qa_5/task.toml +++ b/tasks/0050_842_50842241_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0050_842_50842241_qa_5" +name = "smoldataenvs-train/0050_842_50842241_qa_5" description = "What is the minimum recorded PetalLengthCm in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0050_856_50856590_qa_4/task.toml b/tasks/0050_856_50856590_qa_4/task.toml index 7cde89e2eb97622ab1f696d5f4a5bfea5d8d990f..d346ca806ee767238a1bc649d241180064f55910 100644 --- a/tasks/0050_856_50856590_qa_4/task.toml +++ b/tasks/0050_856_50856590_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0050_856_50856590_qa_4" +name = "smoldataenvs-train/0050_856_50856590_qa_4" description = "How many samples were included in the validation set after splitting the training data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "400" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0050_885_50885329_qa_2/task.toml b/tasks/0050_885_50885329_qa_2/task.toml index 6c4139cf6b04b8c2ae6c1e496a07f674879b06ac..e4f2d579c058b53b3389cbff2797346e4c079032 100644 --- a/tasks/0050_885_50885329_qa_2/task.toml +++ b/tasks/0050_885_50885329_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0050_885_50885329_qa_2" +name = "smoldataenvs-train/0050_885_50885329_qa_2" description = "What is the f1-score for the highest quality category (class 2) in the ordinal regression model's test set performance?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.07" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0050_885_50885329_qa_3/task.toml b/tasks/0050_885_50885329_qa_3/task.toml index 17ae0b02521ecfeb9fd1f966075406a09f8bf91d..2d22a3801c31632d3b80cdad722210aa1a1fd094 100644 --- a/tasks/0050_885_50885329_qa_3/task.toml +++ b/tasks/0050_885_50885329_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0050_885_50885329_qa_3" +name = "smoldataenvs-train/0050_885_50885329_qa_3" description = "Which model achieved the highest test set accuracy among the three evaluated models (ordinal regression, random forest, XGBoost)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Random Forest" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0050_892_50892789_qa_1/task.toml b/tasks/0050_892_50892789_qa_1/task.toml index 461f602871df3a3a2306eabe621dde177c57923a..645921f463e64be302c220ccac31232e4878a9ed 100644 --- a/tasks/0050_892_50892789_qa_1/task.toml +++ b/tasks/0050_892_50892789_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0050_892_50892789_qa_1" +name = "smoldataenvs-train/0050_892_50892789_qa_1" description = "Which wilderness area has the highest average presence in the dataset based on the mean values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wilderness_Area1" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0050_892_50892789_qa_2/task.toml b/tasks/0050_892_50892789_qa_2/task.toml index d624dc9a5a1778b04770971c832afe7037c8fdf9..681811712cb6b0f0cecbad36c14bce7e430a2ebe 100644 --- a/tasks/0050_892_50892789_qa_2/task.toml +++ b/tasks/0050_892_50892789_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0050_892_50892789_qa_2" +name = "smoldataenvs-train/0050_892_50892789_qa_2" description = "What is the maximum horizontal distance to hydrology recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1397" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0051_054_51054366_qa_5/task.toml b/tasks/0051_054_51054366_qa_5/task.toml index 7bf3edb9cdafb6dbdeb811b6bc8d2a24acaa0bc1..762a61f34886208ea4ecf9a415f3fe0ab47b820f 100644 --- a/tasks/0051_054_51054366_qa_5/task.toml +++ b/tasks/0051_054_51054366_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0051_054_51054366_qa_5" +name = "smoldataenvs-train/0051_054_51054366_qa_5" description = "In the \"Population and Habitat\" dataset variation, what is the F0.5 score of the optimized KNN model on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.79" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0051_063_51063179_qa_2/task.toml b/tasks/0051_063_51063179_qa_2/task.toml index 72cc5e74dc6d3d0f34b961e35dd63085d40630f3..3b19f691ae453f37cb4d0714709390f72ea3d8a4 100644 --- a/tasks/0051_063_51063179_qa_2/task.toml +++ b/tasks/0051_063_51063179_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0051_063_51063179_qa_2" +name = "smoldataenvs-train/0051_063_51063179_qa_2" description = "Which species is characterized by having Petal Lengths consistently below 2 cm based on the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-setosa" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0051_063_51063179_qa_3/task.toml b/tasks/0051_063_51063179_qa_3/task.toml index 46b90263f6ee450b2f141baa9a836f1097bdd64d..62edc946cfc4d25a80822ea299d4fd0be532d8bd 100644 --- a/tasks/0051_063_51063179_qa_3/task.toml +++ b/tasks/0051_063_51063179_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0051_063_51063179_qa_3" +name = "smoldataenvs-train/0051_063_51063179_qa_3" description = "What is the count of each species in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-setosa:50, Iris-versicolor:50, Iris-virginica:50" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0051_143_51143611_qa_4/task.toml b/tasks/0051_143_51143611_qa_4/task.toml index f9a787aab12eea610017d692ae9d6edba6f9ba55..55dd90e93b96e020916d4e7163d28daac4165aa2 100644 --- a/tasks/0051_143_51143611_qa_4/task.toml +++ b/tasks/0051_143_51143611_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0051_143_51143611_qa_4" +name = "smoldataenvs-train/0051_143_51143611_qa_4" description = "After applying log transformation, what is the skewness of the wine price distribution?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.5946" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0051_162_51162029_qa_3/task.toml b/tasks/0051_162_51162029_qa_3/task.toml index 240ed3d773c92489945eed60cdb399082eadd6d0..92166dbe572fefc7266edd571f2ec109cd15d49e 100644 --- a/tasks/0051_162_51162029_qa_3/task.toml +++ b/tasks/0051_162_51162029_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0051_162_51162029_qa_3" +name = "smoldataenvs-train/0051_162_51162029_qa_3" description = "Which class has the highest support (number of instances) in the test set classification report?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "O" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0051_169_51169168_qa_3/task.toml b/tasks/0051_169_51169168_qa_3/task.toml index 74c88f0ba87e7821ad135fbd4c87fe76b9151a00..c0755da2df1aff0e1a41476573f99a3d6bebcdb8 100644 --- a/tasks/0051_169_51169168_qa_3/task.toml +++ b/tasks/0051_169_51169168_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0051_169_51169168_qa_3" +name = "smoldataenvs-train/0051_169_51169168_qa_3" description = "Which region has the highest average starting median salary for colleges in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "California" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0051_242_51242508_qa_3/task.toml b/tasks/0051_242_51242508_qa_3/task.toml index 097d48a2527fd10225e9c67750b820dd23feec2c..ecc571e54532de89b7292100e4f2f050dced1491 100644 --- a/tasks/0051_242_51242508_qa_3/task.toml +++ b/tasks/0051_242_51242508_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0051_242_51242508_qa_3" +name = "smoldataenvs-train/0051_242_51242508_qa_3" description = "How many instances in the test set were classified as non-diabetic (Outcome=0) based on the KNN model's predictions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "107" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0051_268_51268709_qa_2/task.toml b/tasks/0051_268_51268709_qa_2/task.toml index 0da629aca48c7c68b1cd640326c1de201918bfec..8860ba21b08694a76dfbcba1228863128fc70bed 100644 --- a/tasks/0051_268_51268709_qa_2/task.toml +++ b/tasks/0051_268_51268709_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0051_268_51268709_qa_2" +name = "smoldataenvs-train/0051_268_51268709_qa_2" description = "What is the distribution of patients with Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML) in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ALL: 47, AML: 25" reward_mode_initial = "list" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0051_268_51268709_qa_5/task.toml b/tasks/0051_268_51268709_qa_5/task.toml index a2d61f7324b69190e2890825cc0d0912f30f9f82..2627281736c0ec541ba19b534bb3fd79bdca368f 100644 --- a/tasks/0051_268_51268709_qa_5/task.toml +++ b/tasks/0051_268_51268709_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0051_268_51268709_qa_5" +name = "smoldataenvs-train/0051_268_51268709_qa_5" description = "What is the total number of gene expression features in the dataset before applying PCA?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7129" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0051_339_51339212_qa_4/task.toml b/tasks/0051_339_51339212_qa_4/task.toml index 92761cd30dd528bf0af7c9ad3c4ac09247541a97..01a619786e003592d7f1a53007791a088567379f 100644 --- a/tasks/0051_339_51339212_qa_4/task.toml +++ b/tasks/0051_339_51339212_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0051_339_51339212_qa_4" +name = "smoldataenvs-train/0051_339_51339212_qa_4" description = "Did removing the fractal_dimension_mean feature affect the model accuracy in the SOM 2.2 model compared to SOM 2.1?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0051_374_51374286_qa_3/task.toml b/tasks/0051_374_51374286_qa_3/task.toml index f0c5f9eecf74e96d9df5b39e613e1f03ddf613f9..3878434c1e5465b2ee3837fe41d8c658d52b9ea6 100644 --- a/tasks/0051_374_51374286_qa_3/task.toml +++ b/tasks/0051_374_51374286_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0051_374_51374286_qa_3" +name = "smoldataenvs-train/0051_374_51374286_qa_3" description = "What is the range of values observed for the 'depth' feature in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "43.0 to 79.0" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0051_399_51399567_qa_3/task.toml b/tasks/0051_399_51399567_qa_3/task.toml index c434c65800fcaf15461e87739eeb45cedfb27953..1309b2db413c8ee7375fce0509cbfa91736dc4af 100644 --- a/tasks/0051_399_51399567_qa_3/task.toml +++ b/tasks/0051_399_51399567_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0051_399_51399567_qa_3" +name = "smoldataenvs-train/0051_399_51399567_qa_3" description = "How many unique categories are present in the 'clarity' feature before encoding in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0051_399_51399567_qa_4/task.toml b/tasks/0051_399_51399567_qa_4/task.toml index 2933126f04b3c917ad6d68d8e11da3002269e073..ebb9f335e8110ee306d1b8ef279107fb868faa9d 100644 --- a/tasks/0051_399_51399567_qa_4/task.toml +++ b/tasks/0051_399_51399567_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0051_399_51399567_qa_4" +name = "smoldataenvs-train/0051_399_51399567_qa_4" description = "What is the average carat weight of diamonds in the dataset based on the descriptive statistics?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.79794" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0051_449_51449354_qa_4/task.toml b/tasks/0051_449_51449354_qa_4/task.toml index c0dda078ddfd73c3f864309e3a9d29c314286496..7a4dec8658400c0b611d34d85c719eabb16dd43a 100644 --- a/tasks/0051_449_51449354_qa_4/task.toml +++ b/tasks/0051_449_51449354_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0051_449_51449354_qa_4" +name = "smoldataenvs-train/0051_449_51449354_qa_4" description = "After reshaping to 3D format (X_3d), what is the total number of samples in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2062" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0051_495_51495344_qa_4/task.toml b/tasks/0051_495_51495344_qa_4/task.toml index af036de842d2f110a8f3a8cc3656bd0b1903f044..80213dbbefdeff0cb1cf97e4d4dbe5c53e285962 100644 --- a/tasks/0051_495_51495344_qa_4/task.toml +++ b/tasks/0051_495_51495344_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0051_495_51495344_qa_4" +name = "smoldataenvs-train/0051_495_51495344_qa_4" description = "What percentage of the original dataset consists of duplicated rows?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "15.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0051_525_51525477_qa_1/task.toml b/tasks/0051_525_51525477_qa_1/task.toml index f76501805c500a7275e8c7c04f72078e97bb20f3..2d94f24b099b65e5ddb485a04da06b0b21e24b35 100644 --- a/tasks/0051_525_51525477_qa_1/task.toml +++ b/tasks/0051_525_51525477_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0051_525_51525477_qa_1" +name = "smoldataenvs-train/0051_525_51525477_qa_1" description = "What percentage of missing values were present in the Product_Category_3 column of the training dataset before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "69.7" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0051_525_51525477_qa_5/task.toml b/tasks/0051_525_51525477_qa_5/task.toml index 468fec15728b2985a4ae5d84addbfca36081f08d..380b2b025f3fa9b709d2128faaf84c99e2ef2583 100644 --- a/tasks/0051_525_51525477_qa_5/task.toml +++ b/tasks/0051_525_51525477_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0051_525_51525477_qa_5" +name = "smoldataenvs-train/0051_525_51525477_qa_5" description = "What percentage of missing values were present in the Product_Category_3 column of the test dataset before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "69.6" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0051_529_51529879_qa_4/task.toml b/tasks/0051_529_51529879_qa_4/task.toml index fe1134f6e293e102271bf468c3a03477748af6e0..5fcf0facfe836efa7467052b069d94fa44e870c6 100644 --- a/tasks/0051_529_51529879_qa_4/task.toml +++ b/tasks/0051_529_51529879_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0051_529_51529879_qa_4" +name = "smoldataenvs-train/0051_529_51529879_qa_4" description = "What is the correlation between radius_worst and area_worst in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.96" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0051_530_51530318_qa_2/task.toml b/tasks/0051_530_51530318_qa_2/task.toml index 6199ec16c51777528cf0ca4034b6d1af03a4ac49..ad4ea797ee2b3a77176885f7437d0dede7f00879 100644 --- a/tasks/0051_530_51530318_qa_2/task.toml +++ b/tasks/0051_530_51530318_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0051_530_51530318_qa_2" +name = "smoldataenvs-train/0051_530_51530318_qa_2" description = "How many unique content entries exist in the \"ham\" category compared to the \"spam\" category?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ham=4516, spam=653" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0051_530_51530318_qa_3/task.toml b/tasks/0051_530_51530318_qa_3/task.toml index 6b038bbbac447ca5e80bbc17324887496a9a68c4..80d1c8956ad2a819a47ba4f689177fcf3750937b 100644 --- a/tasks/0051_530_51530318_qa_3/task.toml +++ b/tasks/0051_530_51530318_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0051_530_51530318_qa_3" +name = "smoldataenvs-train/0051_530_51530318_qa_3" description = "What is the accuracy score of the Multinomial Naive Bayes model on the test dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9828" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0051_543_51543222_qa_5/task.toml b/tasks/0051_543_51543222_qa_5/task.toml index 4a8a2277e494a41f9e184d54c3f273752aafb872..1f38b076be74b92e85d3bb4c8491b6174fd961a8 100644 --- a/tasks/0051_543_51543222_qa_5/task.toml +++ b/tasks/0051_543_51543222_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0051_543_51543222_qa_5" +name = "smoldataenvs-train/0051_543_51543222_qa_5" description = "What is the F1-score for the 'very high cost' category (price_range=3) in the best-performing model's classification report?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.95" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0051_554_51554569_qa_5/task.toml b/tasks/0051_554_51554569_qa_5/task.toml index 3cc1573114aed25dc053821566cc2420e62e247e..23fba85a7ae5932a4de7032c4b5019867f4053ea 100644 --- a/tasks/0051_554_51554569_qa_5/task.toml +++ b/tasks/0051_554_51554569_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0051_554_51554569_qa_5" +name = "smoldataenvs-train/0051_554_51554569_qa_5" description = "What is the F1 score of the Multinomial Naive Bayes model using the optimal threshold on test data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9402521823472357" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0051_559_51559102_qa_1/task.toml b/tasks/0051_559_51559102_qa_1/task.toml index a58d5b55ce2e4775b5efe9628ec9811da807f8bf..b6007221f8291e2e21a8fefea4cd1ace94857996 100644 --- a/tasks/0051_559_51559102_qa_1/task.toml +++ b/tasks/0051_559_51559102_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0051_559_51559102_qa_1" +name = "smoldataenvs-train/0051_559_51559102_qa_1" description = "Which feature has the highest positive correlation with the house price in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "sqft_living" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0051_559_51559102_qa_2/task.toml b/tasks/0051_559_51559102_qa_2/task.toml index 53c957e7839bf86e40ed550ce478ee26f5ec9cbd..557e5485aeb4fd611bab6ed0f5b31e963e64c4a1 100644 --- a/tasks/0051_559_51559102_qa_2/task.toml +++ b/tasks/0051_559_51559102_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0051_559_51559102_qa_2" +name = "smoldataenvs-train/0051_559_51559102_qa_2" description = "Which feature has the strongest negative correlation with the house price in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "zipcode" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0051_559_51559102_qa_3/task.toml b/tasks/0051_559_51559102_qa_3/task.toml index 17863216b0b7626baae4173e9ae60cb5c3a2f825..110fbf2f295bf44cfd94e1cfbd9dbbb31a87ecf7 100644 --- a/tasks/0051_559_51559102_qa_3/task.toml +++ b/tasks/0051_559_51559102_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0051_559_51559102_qa_3" +name = "smoldataenvs-train/0051_559_51559102_qa_3" description = "What is the difference in correlation strength between the number of bathrooms and bedrooms with house price?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.217" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0051_559_51559102_qa_5/task.toml b/tasks/0051_559_51559102_qa_5/task.toml index ca2c2613a9cd7544aed5bd0eb779d916d4e973e7..2e984f4f600c756697d18f975b554b0d185dfcaf 100644 --- a/tasks/0051_559_51559102_qa_5/task.toml +++ b/tasks/0051_559_51559102_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0051_559_51559102_qa_5" +name = "smoldataenvs-train/0051_559_51559102_qa_5" description = "What is the third-highest positive correlation with house price in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "sqft_above" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0051_561_51561248_qa_4/task.toml b/tasks/0051_561_51561248_qa_4/task.toml index a74c103e9313649c8b864b9a73d9293073696697..c7a1578be5421cef8a80f88dca366501e12cbf76 100644 --- a/tasks/0051_561_51561248_qa_4/task.toml +++ b/tasks/0051_561_51561248_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0051_561_51561248_qa_4" +name = "smoldataenvs-train/0051_561_51561248_qa_4" description = "What categorical value was used to impute missing entries in the \"Precip Type\" column during preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "rain" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0051_591_51591719_qa_1/task.toml b/tasks/0051_591_51591719_qa_1/task.toml index b60be2b7d5a11aead6ef7b7af9f911151aba3ec1..82acab78259cd6d3bf8473da8e92357b616e9bf2 100644 --- a/tasks/0051_591_51591719_qa_1/task.toml +++ b/tasks/0051_591_51591719_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0051_591_51591719_qa_1" +name = "smoldataenvs-train/0051_591_51591719_qa_1" description = "What is the R-squared adjusted value when the linear regression model is trained using all features of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.952639" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0051_611_51611771_qa_5/task.toml b/tasks/0051_611_51611771_qa_5/task.toml index c26f45549d242426d3854e94b8899b6cf3a4a623..74b6b608caf92af5cd462ccdd856179c63c9fc2b 100644 --- a/tasks/0051_611_51611771_qa_5/task.toml +++ b/tasks/0051_611_51611771_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0051_611_51611771_qa_5" +name = "smoldataenvs-train/0051_611_51611771_qa_5" description = "Which statistical test revealed that insulin levels vary significantly with the number of pregnancies in diabetic women, and what was the p-value of this test?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Kruskal-Wallis test, p=0.04221294917802462" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0051_637_51637337_qa_1/task.toml b/tasks/0051_637_51637337_qa_1/task.toml index 002c94a4cdaab5ec61172203348e344c1b65ea1b..468f0c2e0746d470c123047716d20d2c13e09ea0 100644 --- a/tasks/0051_637_51637337_qa_1/task.toml +++ b/tasks/0051_637_51637337_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0051_637_51637337_qa_1" +name = "smoldataenvs-train/0051_637_51637337_qa_1" description = "After balancing the dataset by sampling non-fraudulent transactions, how many non-fraudulent transactions are included in the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "492" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0051_637_51637337_qa_2/task.toml b/tasks/0051_637_51637337_qa_2/task.toml index 22475f8aaa1363051d303e854ae61967b11f6d72..917dac28987a6fca291d4fb6803727b177de7b44 100644 --- a/tasks/0051_637_51637337_qa_2/task.toml +++ b/tasks/0051_637_51637337_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0051_637_51637337_qa_2" +name = "smoldataenvs-train/0051_637_51637337_qa_2" description = "What proportion of the original dataset consists of fraudulent transactions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.001727" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0051_644_51644569_qa_2/task.toml b/tasks/0051_644_51644569_qa_2/task.toml index a2babd82cbea7e1421f764ddb688f76114ba667a..9cd0ff925bc8ffaa02ccc6cc4e9c82864b505ebf 100644 --- a/tasks/0051_644_51644569_qa_2/task.toml +++ b/tasks/0051_644_51644569_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0051_644_51644569_qa_2" +name = "smoldataenvs-train/0051_644_51644569_qa_2" description = "How many rows were removed from the dataset due to missing values in the MINIMUM_PAYMENTS column during preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "314" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0051_653_51653330_qa_5/task.toml b/tasks/0051_653_51653330_qa_5/task.toml index 6ab9014af594ec500b8dcaef9f62b0ae86a241f9..6102dc774d54990b4da6b4f7b1da0f51f97c1bf6 100644 --- a/tasks/0051_653_51653330_qa_5/task.toml +++ b/tasks/0051_653_51653330_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0051_653_51653330_qa_5" +name = "smoldataenvs-train/0051_653_51653330_qa_5" description = "Which Indian state has the highest total value of property stolen in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Maharashtra" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0051_692_51692011_qa_1/task.toml b/tasks/0051_692_51692011_qa_1/task.toml index 45ea3238bf81ead8c69a2b2b45fd900ca47c2d0c..82dda4cadbcb1102d3148c24013f6daa867f2858 100644 --- a/tasks/0051_692_51692011_qa_1/task.toml +++ b/tasks/0051_692_51692011_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0051_692_51692011_qa_1" +name = "smoldataenvs-train/0051_692_51692011_qa_1" description = "Which of the continuous variables (age, BMI, charges) has the highest variance in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "charges" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0051_761_51761312_qa_3/task.toml b/tasks/0051_761_51761312_qa_3/task.toml index 1712e3afe8aa5c31ad84d790509a9588bcf9998a..3bc4b16e0d7c9b5420bb28745d62c8cbff5ef935 100644 --- a/tasks/0051_761_51761312_qa_3/task.toml +++ b/tasks/0051_761_51761312_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0051_761_51761312_qa_3" +name = "smoldataenvs-train/0051_761_51761312_qa_3" description = "What is the product category (1 or 2) with the higher total sum of category values in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0051_768_51768802_qa_2/task.toml b/tasks/0051_768_51768802_qa_2/task.toml index 5951b5d24f0c44155dd4c6425988376e35e6e38b..db709b5af26d47691a8ada047031dab63123e284 100644 --- a/tasks/0051_768_51768802_qa_2/task.toml +++ b/tasks/0051_768_51768802_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0051_768_51768802_qa_2" +name = "smoldataenvs-train/0051_768_51768802_qa_2" description = "What is the highest interquartile range (IQR) value among the first 10 features in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "362.4" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0051_802_51802195_qa_3/task.toml b/tasks/0051_802_51802195_qa_3/task.toml index 6528816917fea116e41eba4c7dd66d42fff2c6a9..407c486f14b4b86ed795402579433e65b792d16a 100644 --- a/tasks/0051_802_51802195_qa_3/task.toml +++ b/tasks/0051_802_51802195_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0051_802_51802195_qa_3" +name = "smoldataenvs-train/0051_802_51802195_qa_3" description = "Which mean-based feature shows the largest discrepancy between its minimum value and left whisker threshold in the outlier analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "area_mean" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0051_802_51802195_qa_4/task.toml b/tasks/0051_802_51802195_qa_4/task.toml index 8adbb7f7722b3a63b0462755ed576cc25f4f8e8a..ee253491ceb97b95f04ea533c7f6f72d469b6c1d 100644 --- a/tasks/0051_802_51802195_qa_4/task.toml +++ b/tasks/0051_802_51802195_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0051_802_51802195_qa_4" +name = "smoldataenvs-train/0051_802_51802195_qa_4" description = "What is the total number of right-sided outliers in the concave points_mean feature for the mean-based feature group?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0051_829_51829460_qa_2/task.toml b/tasks/0051_829_51829460_qa_2/task.toml index 9ea8c7659036407fd4b574afb9d1100c016054c8..805fa29d35e0e7f069c11e50fb7ba4963fbafd41 100644 --- a/tasks/0051_829_51829460_qa_2/task.toml +++ b/tasks/0051_829_51829460_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0051_829_51829460_qa_2" +name = "smoldataenvs-train/0051_829_51829460_qa_2" description = "What percentage of female passengers survived after preprocessing the 'Embarked' column to remove missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "74.20" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0051_852_51852671_qa_1/task.toml b/tasks/0051_852_51852671_qa_1/task.toml index da12e93a4fbac4a72b0c534e1e3c10d1adba7c9e..7282d4cde6b0cadd2d77ab968190cb248680dded 100644 --- a/tasks/0051_852_51852671_qa_1/task.toml +++ b/tasks/0051_852_51852671_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0051_852_51852671_qa_1" +name = "smoldataenvs-train/0051_852_51852671_qa_1" description = "Which item type has the highest average outlet sales based on the bivariate analysis in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Starchy Foods" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0051_855_51855195_qa_2/task.toml b/tasks/0051_855_51855195_qa_2/task.toml index 937685d24024cdc2701025152753b46a60300a72..00352607a6e67d5daed061cf2edaddb07ded3b73 100644 --- a/tasks/0051_855_51855195_qa_2/task.toml +++ b/tasks/0051_855_51855195_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0051_855_51855195_qa_2" +name = "smoldataenvs-train/0051_855_51855195_qa_2" description = "Which feature has the highest absolute weight in the TensorFlow model's logistic regression?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Sex" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0051_880_51880201_qa_3/task.toml b/tasks/0051_880_51880201_qa_3/task.toml index c794244e6811f06892cada0c4e8d291cf8d3e108..3e60d92e6ab7b5bd68bbfbcbe0ab500bc51189f3 100644 --- a/tasks/0051_880_51880201_qa_3/task.toml +++ b/tasks/0051_880_51880201_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0051_880_51880201_qa_3" +name = "smoldataenvs-train/0051_880_51880201_qa_3" description = "Which tertiary product category shows the highest preference among women's purchases?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Western Wear" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0051_882_51882906_qa_1/task.toml b/tasks/0051_882_51882906_qa_1/task.toml index 424535174368fb323924b9867d025b1746e07548..a1be8e8679c8399940c4531a101c3a3212aeb189 100644 --- a/tasks/0051_882_51882906_qa_1/task.toml +++ b/tasks/0051_882_51882906_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0051_882_51882906_qa_1" +name = "smoldataenvs-train/0051_882_51882906_qa_1" description = "What is the correlation coefficient between tenure and TotalCharges in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.83" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0051_926_51926021_qa_4/task.toml b/tasks/0051_926_51926021_qa_4/task.toml index ec9be0e89af27e0497268ba34ca5f9f1172769c7..590a07631078c0cc87922ac0dc26432cfe748808 100644 --- a/tasks/0051_926_51926021_qa_4/task.toml +++ b/tasks/0051_926_51926021_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0051_926_51926021_qa_4" +name = "smoldataenvs-train/0051_926_51926021_qa_4" description = "Which feature has the highest positive coefficient in the sklearn linear regression model's final equation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "R&D Spend" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0051_967_51967182_qa_2/task.toml b/tasks/0051_967_51967182_qa_2/task.toml index 03ed896a102d47ced93cdfa148a61efc59345a0b..c03273570baebcf1ff22ffa1afd01007bd1eda87 100644 --- a/tasks/0051_967_51967182_qa_2/task.toml +++ b/tasks/0051_967_51967182_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0051_967_51967182_qa_2" +name = "smoldataenvs-train/0051_967_51967182_qa_2" description = "Which region has the highest number of policyholders in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "southeast" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0051_967_51967182_qa_4/task.toml b/tasks/0051_967_51967182_qa_4/task.toml index 943aac0762fdfa723c581d169971dcd820598f34..5eaf9ae3411e2cfb7af43ab91f594166c1aa2fa6 100644 --- a/tasks/0051_967_51967182_qa_4/task.toml +++ b/tasks/0051_967_51967182_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0051_967_51967182_qa_4" +name = "smoldataenvs-train/0051_967_51967182_qa_4" description = "What is the average number of dependents covered by health insurance per policyholder?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.09" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0051_975_51975834_qa_4/task.toml b/tasks/0051_975_51975834_qa_4/task.toml index 791412ee912f6bb86e60d0e20d3fd23425cf2cda..61a9310739a05e55efb6c42deacc34ba2534be3c 100644 --- a/tasks/0051_975_51975834_qa_4/task.toml +++ b/tasks/0051_975_51975834_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0051_975_51975834_qa_4" +name = "smoldataenvs-train/0051_975_51975834_qa_4" description = "What is the R-squared value for the best model on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.98108479806778" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0051_990_51990433_qa_2/task.toml b/tasks/0051_990_51990433_qa_2/task.toml index 3dee8a3f02a5727e2e33b97c1598248dc1b50efb..2ecb5f040a93f010c9813082710987e5a0872b8d 100644 --- a/tasks/0051_990_51990433_qa_2/task.toml +++ b/tasks/0051_990_51990433_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0051_990_51990433_qa_2" +name = "smoldataenvs-train/0051_990_51990433_qa_2" description = "Which region has the highest average medical charges, and what is the exact dollar amount?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Southeast, 14735.41" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0052_019_52019978_qa_2/task.toml b/tasks/0052_019_52019978_qa_2/task.toml index fc6b888ee7b04f2e0a819ab8bc7104cbcfdc23fe..b8afe030bee6be2b3f2786f3f40dfbdb6f02c981 100644 --- a/tasks/0052_019_52019978_qa_2/task.toml +++ b/tasks/0052_019_52019978_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0052_019_52019978_qa_2" +name = "smoldataenvs-train/0052_019_52019978_qa_2" description = "How many features are included in the model achieving the highest accuracy?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0052_026_52026780_qa_3/task.toml b/tasks/0052_026_52026780_qa_3/task.toml index 21689b0ffb8dc7760c56f18e3413f3e012725214..b678f7a1b881eeb74d5f421b33a06cdf6ba0398a 100644 --- a/tasks/0052_026_52026780_qa_3/task.toml +++ b/tasks/0052_026_52026780_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0052_026_52026780_qa_3" +name = "smoldataenvs-train/0052_026_52026780_qa_3" description = "How many principal components are required to explain 95% of the cumulative variance in the dataset after PCA?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0052_041_52041171_qa_2/task.toml b/tasks/0052_041_52041171_qa_2/task.toml index 75cc00a38e61025596639cea82057ff179f1f41d..a102dbd36b14821ea40d5cf15f1c469ec9843d66 100644 --- a/tasks/0052_041_52041171_qa_2/task.toml +++ b/tasks/0052_041_52041171_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0052_041_52041171_qa_2" +name = "smoldataenvs-train/0052_041_52041171_qa_2" description = "What is the cluster assignment for the first five data points in the K-Means clustering model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1,1,1,1,1" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0052_048_52048011_qa_1/task.toml b/tasks/0052_048_52048011_qa_1/task.toml index dc3e8395efe6e4c3d40e9d3bbec565327b33f475..e28e0f7b4d3c7fe0f540f55e42127f222d5eb893 100644 --- a/tasks/0052_048_52048011_qa_1/task.toml +++ b/tasks/0052_048_52048011_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0052_048_52048011_qa_1" +name = "smoldataenvs-train/0052_048_52048011_qa_1" description = "What is the count of each species in the dataset after label encoding but before data normalization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50, 50, 50" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0052_159_52159634_qa_3/task.toml b/tasks/0052_159_52159634_qa_3/task.toml index cae6107b48533642d01f2a52144f8f1e9633993b..fce4ed828b5a1530e32e28a8c1724710a14eec91 100644 --- a/tasks/0052_159_52159634_qa_3/task.toml +++ b/tasks/0052_159_52159634_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0052_159_52159634_qa_3" +name = "smoldataenvs-train/0052_159_52159634_qa_3" description = "What percentage of the dataset is allocated to the test set when using an 80-20 train-test split?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0052_172_52172729_qa_2/task.toml b/tasks/0052_172_52172729_qa_2/task.toml index 2c312424ed7672fdf85936f5bd5d63e1b3eb5dfb..cae8d734c5cb0294b84c9a6bc00ca817ecb75592 100644 --- a/tasks/0052_172_52172729_qa_2/task.toml +++ b/tasks/0052_172_52172729_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0052_172_52172729_qa_2" +name = "smoldataenvs-train/0052_172_52172729_qa_2" description = "What is the total number of unique platforms remaining in the dataset after excluding the '2600' platform?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "30" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0052_298_52298447_qa_5/task.toml b/tasks/0052_298_52298447_qa_5/task.toml index 287c5fde46f5bb1bfb4a823c95d6753c7c68937a..1cac1b2639e8f2c5b855276ae9de0b349cce2ea2 100644 --- a/tasks/0052_298_52298447_qa_5/task.toml +++ b/tasks/0052_298_52298447_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0052_298_52298447_qa_5" +name = "smoldataenvs-train/0052_298_52298447_qa_5" description = "What is the number of samples in the test set after performing the train-test split with 20% test size?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3320" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0052_313_52313444_qa_1/task.toml b/tasks/0052_313_52313444_qa_1/task.toml index d3ff17139d4a97ddf64649b1ec636d648e492421..1efafa4039ebb819fa2455f3ef8e605448889556 100644 --- a/tasks/0052_313_52313444_qa_1/task.toml +++ b/tasks/0052_313_52313444_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0052_313_52313444_qa_1" +name = "smoldataenvs-train/0052_313_52313444_qa_1" description = "What is the highest recorded solar radiation value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1601.26" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0052_313_52313444_qa_4/task.toml b/tasks/0052_313_52313444_qa_4/task.toml index f0a35520f82677c1e80def295d4da90081e54e51..31dc506bf0acfeaefe1f8e4d05e5602f5e5947e3 100644 --- a/tasks/0052_313_52313444_qa_4/task.toml +++ b/tasks/0052_313_52313444_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0052_313_52313444_qa_4" +name = "smoldataenvs-train/0052_313_52313444_qa_4" description = "What is the median solar radiation value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.66" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0052_331_52331828_qa_4/task.toml b/tasks/0052_331_52331828_qa_4/task.toml index cc1bd61a05133f10bcebee616c54ce2def8f4ee4..813f46e5e5e0d2eaabbc513f7fd40fab06434085 100644 --- a/tasks/0052_331_52331828_qa_4/task.toml +++ b/tasks/0052_331_52331828_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0052_331_52331828_qa_4" +name = "smoldataenvs-train/0052_331_52331828_qa_4" description = "Is the original distribution of 'charges' in the dataset left-skewed based on the histogram analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0052_377_52377809_qa_3/task.toml b/tasks/0052_377_52377809_qa_3/task.toml index 109cdc6c916c609d3d0a6f7e0199896c63557df4..2a3019b47603ca46d40dd60a9a000a561fb81368 100644 --- a/tasks/0052_377_52377809_qa_3/task.toml +++ b/tasks/0052_377_52377809_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0052_377_52377809_qa_3" +name = "smoldataenvs-train/0052_377_52377809_qa_3" description = "Which feature has the second-highest positive correlation with the diagnosis of breast cancer in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "perimeter_worst" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0052_465_52465579_qa_3/task.toml b/tasks/0052_465_52465579_qa_3/task.toml index cb1d95e14f01fd3790cc33625979e38772601b29..8826e3e66d07ba1b08409177d5503c4ac85c86f3 100644 --- a/tasks/0052_465_52465579_qa_3/task.toml +++ b/tasks/0052_465_52465579_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0052_465_52465579_qa_3" +name = "smoldataenvs-train/0052_465_52465579_qa_3" description = "What is the baseline predicted win percentage for a candy with none of the nine features included in the model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "35.015" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0052_471_52471814_qa_1/task.toml b/tasks/0052_471_52471814_qa_1/task.toml index cdd9f54183af97bcbd09f5518c7f1a10a45a589e..68811a69a1f9fb1b6c19b8a69a467f5e183dce31 100644 --- a/tasks/0052_471_52471814_qa_1/task.toml +++ b/tasks/0052_471_52471814_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0052_471_52471814_qa_1" +name = "smoldataenvs-train/0052_471_52471814_qa_1" description = "Which diamond cut has the highest frequency in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Ideal" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0052_471_52471814_qa_3/task.toml b/tasks/0052_471_52471814_qa_3/task.toml index e6a510a95216bcc29f5e090ca0f740e4341c05ba..33d4be8e41d96447e8dc9a81743cdc925fcf9d7d 100644 --- a/tasks/0052_471_52471814_qa_3/task.toml +++ b/tasks/0052_471_52471814_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0052_471_52471814_qa_3" +name = "smoldataenvs-train/0052_471_52471814_qa_3" description = "What is the most common clarity level in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "SI1" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0052_476_52476342_qa_2/task.toml b/tasks/0052_476_52476342_qa_2/task.toml index 88fdfa7e8d20996bc7a221b1a98316e07dffc1a8..38daaf64b467b2a73310af30a71b5c838172be26 100644 --- a/tasks/0052_476_52476342_qa_2/task.toml +++ b/tasks/0052_476_52476342_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0052_476_52476342_qa_2" +name = "smoldataenvs-train/0052_476_52476342_qa_2" description = "Which feature exhibits the highest absolute Pearson correlation with the target variable 'class'?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "gill-size" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0052_476_52476342_qa_3/task.toml b/tasks/0052_476_52476342_qa_3/task.toml index e46f460c8b19b96a6c19d266cebb0e39742434be..a5dd1c272610b8c6e642390afd130ac2d3e0c2a6 100644 --- a/tasks/0052_476_52476342_qa_3/task.toml +++ b/tasks/0052_476_52476342_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0052_476_52476342_qa_3" +name = "smoldataenvs-train/0052_476_52476342_qa_3" description = "What is the proportion of edible mushrooms in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "51.99%" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0052_512_52512591_qa_4/task.toml b/tasks/0052_512_52512591_qa_4/task.toml index dd6c9f446ad322ea1c3391080d705e06a8b276d2..6c5a4f12380f90c7b6c47e130fa927d64cf993ae 100644 --- a/tasks/0052_512_52512591_qa_4/task.toml +++ b/tasks/0052_512_52512591_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0052_512_52512591_qa_4" +name = "smoldataenvs-train/0052_512_52512591_qa_4" description = "What percentage of \"Outlet_Size\" entries were missing before imputation in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "28.276428" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0052_521_52521020_qa_1/task.toml b/tasks/0052_521_52521020_qa_1/task.toml index 2b7d5c081188034ac5e2a5833aabee4b1f5b3e9c..afbdc283348d19e7e6f4a59631f0c1f1f8ff5138 100644 --- a/tasks/0052_521_52521020_qa_1/task.toml +++ b/tasks/0052_521_52521020_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0052_521_52521020_qa_1" +name = "smoldataenvs-train/0052_521_52521020_qa_1" description = "Which sector received the highest number of loans in Rwanda according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Food" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0052_521_52521020_qa_2/task.toml b/tasks/0052_521_52521020_qa_2/task.toml index 5b69424c469cdd947b29bf82ba496e13f52841cf..f22e4949c1ec68b6253f260d0af6c4092f2b7313 100644 --- a/tasks/0052_521_52521020_qa_2/task.toml +++ b/tasks/0052_521_52521020_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0052_521_52521020_qa_2" +name = "smoldataenvs-train/0052_521_52521020_qa_2" description = "What is the most common repayment interval type for Kiva loans in Rwanda?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Irregular" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0052_533_52533879_qa_5/task.toml b/tasks/0052_533_52533879_qa_5/task.toml index 3c0cf4f8044bb3d5cfcd08b46eb341c3c73d4016..7c52cdb8ee5e0f2d8a1dba2afc8de18dd5abcca6 100644 --- a/tasks/0052_533_52533879_qa_5/task.toml +++ b/tasks/0052_533_52533879_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0052_533_52533879_qa_5" +name = "smoldataenvs-train/0052_533_52533879_qa_5" description = "What is the correlation coefficient between capital loss and income?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.150526" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0052_546_52546501_qa_4/task.toml b/tasks/0052_546_52546501_qa_4/task.toml index ffd1735c77a4c6ff2f0e4cad0072988727ad2a60..b10c6b537e017f3180d8406a75a3eb33f0b72c32 100644 --- a/tasks/0052_546_52546501_qa_4/task.toml +++ b/tasks/0052_546_52546501_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0052_546_52546501_qa_4" +name = "smoldataenvs-train/0052_546_52546501_qa_4" description = "What is the precision score for class 0 (edible mushrooms) in the Logistic Regression model's predictions on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.95" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0052_586_52586560_qa_3/task.toml b/tasks/0052_586_52586560_qa_3/task.toml index adc004da1418dc3b589af33c46c949c70f02cbae..60aae81c09ba159df8b6d4e9feb7128eccb17683 100644 --- a/tasks/0052_586_52586560_qa_3/task.toml +++ b/tasks/0052_586_52586560_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0052_586_52586560_qa_3" +name = "smoldataenvs-train/0052_586_52586560_qa_3" description = "What is the most common outlet size category in the training data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Medium" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0052_586_52586560_qa_4/task.toml b/tasks/0052_586_52586560_qa_4/task.toml index a2c469455cbbc5319eecdf68816894ee1e2a87e2..0d669bf9b1a25892eacee3ee97f73d1b7e8e544b 100644 --- a/tasks/0052_586_52586560_qa_4/task.toml +++ b/tasks/0052_586_52586560_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0052_586_52586560_qa_4" +name = "smoldataenvs-train/0052_586_52586560_qa_4" description = "What is the skewness value of the Item_Outlet_Sales distribution in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.1775306028542798" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0052_587_52587517_qa_2/task.toml b/tasks/0052_587_52587517_qa_2/task.toml index 47a3d1c23f61d0260c0634888ac0a3cb9c55a4d9..b1eb08cdce23e353606b7d6fbf4e0b983a742a9b 100644 --- a/tasks/0052_587_52587517_qa_2/task.toml +++ b/tasks/0052_587_52587517_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0052_587_52587517_qa_2" +name = "smoldataenvs-train/0052_587_52587517_qa_2" description = "After label encoding the 'Species' column, how many instances were assigned the label '2'?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0052_587_52587517_qa_3/task.toml b/tasks/0052_587_52587517_qa_3/task.toml index 91693b6a1135d903f6ced18d7ac92760b3f0a11d..db46608207a080cffc4da2fe2aab0ff8d5ca5615 100644 --- a/tasks/0052_587_52587517_qa_3/task.toml +++ b/tasks/0052_587_52587517_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0052_587_52587517_qa_3" +name = "smoldataenvs-train/0052_587_52587517_qa_3" description = "What was the minimum value in the 'Species' column after data standardization but before converting to integer data type?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-1.224745" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0052_587_52587517_qa_4/task.toml b/tasks/0052_587_52587517_qa_4/task.toml index 49cf021735df83f994defa464b6bb57e696b6158..d6761f2494d91aa72a7dc0e29e941245e651f384 100644 --- a/tasks/0052_587_52587517_qa_4/task.toml +++ b/tasks/0052_587_52587517_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0052_587_52587517_qa_4" +name = "smoldataenvs-train/0052_587_52587517_qa_4" description = "Which feature had the largest range (difference between maximum and minimum values) in the original dataset before any preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0052_615_52615149_qa_2/task.toml b/tasks/0052_615_52615149_qa_2/task.toml index 21e49327758f6a2246badb75943b495ad2cbe259..00fb51265c7d03cfa8fb0b08291424ac36bd3d91 100644 --- a/tasks/0052_615_52615149_qa_2/task.toml +++ b/tasks/0052_615_52615149_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0052_615_52615149_qa_2" +name = "smoldataenvs-train/0052_615_52615149_qa_2" description = "What is the accuracy score achieved by the CatBoostClassifier on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0052_615_52615149_qa_4/task.toml b/tasks/0052_615_52615149_qa_4/task.toml index b051e654519c6131d316e063ff36376bdc4e81a8..68e9e0dc058abae4bc75c51e0012822f065b7fcd 100644 --- a/tasks/0052_615_52615149_qa_4/task.toml +++ b/tasks/0052_615_52615149_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0052_615_52615149_qa_4" +name = "smoldataenvs-train/0052_615_52615149_qa_4" description = "What is the total number of edible mushrooms in the entire dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4208" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0052_615_52615149_qa_5/task.toml b/tasks/0052_615_52615149_qa_5/task.toml index 87a932f9d3d895e5e5fdc23749e1bf448168b80f..be7510b0adce36dbe156a7231cd7632e55db9018 100644 --- a/tasks/0052_615_52615149_qa_5/task.toml +++ b/tasks/0052_615_52615149_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0052_615_52615149_qa_5" +name = "smoldataenvs-train/0052_615_52615149_qa_5" description = "How many mushrooms were in the test set used for model evaluation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1625" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0052_654_52654015_qa_1/task.toml b/tasks/0052_654_52654015_qa_1/task.toml index 0d53acef4cf00695722836a440d6a61162c5507b..4e34034681424304fc3f015ac18485e4f48148cf 100644 --- a/tasks/0052_654_52654015_qa_1/task.toml +++ b/tasks/0052_654_52654015_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0052_654_52654015_qa_1" +name = "smoldataenvs-train/0052_654_52654015_qa_1" description = "Which feature exhibits the highest importance when using the 'split' importance type in the LightGBM model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0052_694_52694962_qa_3/task.toml b/tasks/0052_694_52694962_qa_3/task.toml index 814e8fe1edb3a71d5631b217e3d5731bb31e572a..b157c10c6da92694ab711def11cbba2ff0f43cc1 100644 --- a/tasks/0052_694_52694962_qa_3/task.toml +++ b/tasks/0052_694_52694962_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0052_694_52694962_qa_3" +name = "smoldataenvs-train/0052_694_52694962_qa_3" description = "Which outlet establishment year has the lowest average Item_Outlet_Sales?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1998" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0052_694_52694962_qa_4/task.toml b/tasks/0052_694_52694962_qa_4/task.toml index cec76d9f77b0ac1bbd7508e92106abc6e12c552f..13b7367cd04ed64eda4d64552214209fbfa469a8 100644 --- a/tasks/0052_694_52694962_qa_4/task.toml +++ b/tasks/0052_694_52694962_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0052_694_52694962_qa_4" +name = "smoldataenvs-train/0052_694_52694962_qa_4" description = "After handling missing values, what is the total count of missing values in the train dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0052_694_52694962_qa_5/task.toml b/tasks/0052_694_52694962_qa_5/task.toml index ab5f826fb56d4936e37a980300568c522fcec64e..a42f25102f16ca102491758dbda01d5f3888230f 100644 --- a/tasks/0052_694_52694962_qa_5/task.toml +++ b/tasks/0052_694_52694962_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0052_694_52694962_qa_5" +name = "smoldataenvs-train/0052_694_52694962_qa_5" description = "What percentage of the test dataset's Outlet_Size column had missing values before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "28.2697" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0052_712_52712705_qa_3/task.toml b/tasks/0052_712_52712705_qa_3/task.toml index fc58ddf287b3b59762650696181f790d2a263153..08a7890f1e403c0f015fe6d8893793e4f2f15bd4 100644 --- a/tasks/0052_712_52712705_qa_3/task.toml +++ b/tasks/0052_712_52712705_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0052_712_52712705_qa_3" +name = "smoldataenvs-train/0052_712_52712705_qa_3" description = "Is the variance of the 'Insulin' feature higher than that of 'Glucose' in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0052_801_52801383_qa_2/task.toml b/tasks/0052_801_52801383_qa_2/task.toml index 5a40f4348d5ef6b429adb890b05179a15f9d6f0a..5e21b99ea2f9820ed511b37b72665aa57ad22151 100644 --- a/tasks/0052_801_52801383_qa_2/task.toml +++ b/tasks/0052_801_52801383_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0052_801_52801383_qa_2" +name = "smoldataenvs-train/0052_801_52801383_qa_2" description = "What is the most frequently occurring price value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.29" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0052_801_52801383_qa_3/task.toml b/tasks/0052_801_52801383_qa_3/task.toml index 0705c9ddfd664e0b738faaecec29462dd7ac6dd3..1a33489dbf5a83b8d35bbb3d0b2832144094bad6 100644 --- a/tasks/0052_801_52801383_qa_3/task.toml +++ b/tasks/0052_801_52801383_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0052_801_52801383_qa_3" +name = "smoldataenvs-train/0052_801_52801383_qa_3" description = "What is the direction of the correlation between stock levels and price values in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "negative" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0052_814_52814699_qa_5/task.toml b/tasks/0052_814_52814699_qa_5/task.toml index bfb7ca0f5acf7eedf84c2581e02b57654be14de9..52c9323da174b1b110b6d27d1df0b6b5be577c2c 100644 --- a/tasks/0052_814_52814699_qa_5/task.toml +++ b/tasks/0052_814_52814699_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0052_814_52814699_qa_5" +name = "smoldataenvs-train/0052_814_52814699_qa_5" description = "How many total movie recommendations are generated for \"Why Him?\" including the input movie?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0052_851_52851895_qa_4/task.toml b/tasks/0052_851_52851895_qa_4/task.toml index fb429c3a2ca65a792a5919bb3eef9adfd94ae1f1..8b66ab02f2d6221bd5a3df112b697871f4c4446a 100644 --- a/tasks/0052_851_52851895_qa_4/task.toml +++ b/tasks/0052_851_52851895_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0052_851_52851895_qa_4" +name = "smoldataenvs-train/0052_851_52851895_qa_4" description = "What is the percentage of the dataset that was retained after removing outliers based on the z-score threshold?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "89.6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0052_868_52868101_qa_1/task.toml b/tasks/0052_868_52868101_qa_1/task.toml index d870268f532ef257932192bd617786ca495c0b45..31ec87e7161ab1127ae6e62fa8a924c001e566ce 100644 --- a/tasks/0052_868_52868101_qa_1/task.toml +++ b/tasks/0052_868_52868101_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0052_868_52868101_qa_1" +name = "smoldataenvs-train/0052_868_52868101_qa_1" description = "What is the overall churn rate in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26.536987079369588" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0052_868_52868101_qa_4/task.toml b/tasks/0052_868_52868101_qa_4/task.toml index 8a379e48d77ab97a8abbc5d172cef365e5516625..eaccb963ea85be75c4c176b2a0cb354626d3beaa 100644 --- a/tasks/0052_868_52868101_qa_4/task.toml +++ b/tasks/0052_868_52868101_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0052_868_52868101_qa_4" +name = "smoldataenvs-train/0052_868_52868101_qa_4" description = "What is the most common payment method among customers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic check" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0052_868_52868530_qa_4/task.toml b/tasks/0052_868_52868530_qa_4/task.toml index 9bb1667297db1892190b2fc622c3af7921f086eb..77e1c0c76f26071650fed064c95102b7cccf604d 100644 --- a/tasks/0052_868_52868530_qa_4/task.toml +++ b/tasks/0052_868_52868530_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0052_868_52868530_qa_4" +name = "smoldataenvs-train/0052_868_52868530_qa_4" description = "Which feature exhibits the strongest average correlation with the Outcome variable (diabetes diagnosis) across all features in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0052_907_52907926_qa_4/task.toml b/tasks/0052_907_52907926_qa_4/task.toml index 6ce9fb2e53f8f0b9c677a27289151814356784e7..a0ec63867d14cb10ba5060271b95db390dc3ca61 100644 --- a/tasks/0052_907_52907926_qa_4/task.toml +++ b/tasks/0052_907_52907926_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0052_907_52907926_qa_4" +name = "smoldataenvs-train/0052_907_52907926_qa_4" description = "How many numerical features are used as input variables for the machine learning models in this analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0052_947_52947271_qa_1/task.toml b/tasks/0052_947_52947271_qa_1/task.toml index fe5518b41d4acb2d0d120f5b43dbc3c3f9c2aafe..0635afd4bebb35eab2b3ee41f4513ca6b27285b9 100644 --- a/tasks/0052_947_52947271_qa_1/task.toml +++ b/tasks/0052_947_52947271_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0052_947_52947271_qa_1" +name = "smoldataenvs-train/0052_947_52947271_qa_1" description = "Which feature shows the strongest positive correlation with house prices in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Avg. Area Income" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0052_980_52980235_qa_2/task.toml b/tasks/0052_980_52980235_qa_2/task.toml index 99fec4d5ec52549bf3485ff8b7dea8b6a37797ab..b213924e1f30ead95d3d3e9b627f31d2f43e4b9e 100644 --- a/tasks/0052_980_52980235_qa_2/task.toml +++ b/tasks/0052_980_52980235_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0052_980_52980235_qa_2" +name = "smoldataenvs-train/0052_980_52980235_qa_2" description = "What are the four features selected by Recursive Feature Elimination (RFE) when reducing to 4 features for the Decision Tree model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "battery_power, px_height, px_width, ram" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0053_008_53008449_qa_4/task.toml b/tasks/0053_008_53008449_qa_4/task.toml index e615e73cb7605a62e8218378e93c9be752b2f8e8..6b7d1874d72ac4faf3c7148d0cf33ef412612bfe 100644 --- a/tasks/0053_008_53008449_qa_4/task.toml +++ b/tasks/0053_008_53008449_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0053_008_53008449_qa_4" +name = "smoldataenvs-train/0053_008_53008449_qa_4" description = "What is the accuracy percentage of the Decision Tree model on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "100.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0053_024_53024023_qa_1/task.toml b/tasks/0053_024_53024023_qa_1/task.toml index 1b085d4b2b48350db2b606b7352291392baee65e..dfd1c64dd90d97f48c4f30bab253bc983f7e84eb 100644 --- a/tasks/0053_024_53024023_qa_1/task.toml +++ b/tasks/0053_024_53024023_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0053_024_53024023_qa_1" +name = "smoldataenvs-train/0053_024_53024023_qa_1" description = "Which financial feature showed the highest skewness in its distribution before applying log transformation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Pre-Tax ROE" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0053_036_53036765_qa_5/task.toml b/tasks/0053_036_53036765_qa_5/task.toml index e842db2060917df99418b17675cc51937ae6dee5..112ddcbbc7cd2c3fe34cca2be926216c3f63500f 100644 --- a/tasks/0053_036_53036765_qa_5/task.toml +++ b/tasks/0053_036_53036765_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0053_036_53036765_qa_5" +name = "smoldataenvs-train/0053_036_53036765_qa_5" description = "Which main category has the lowest success rate (median pledged amount divided by median goal amount) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Journalism" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0053_053_53053753_qa_4/task.toml b/tasks/0053_053_53053753_qa_4/task.toml index 1d18424b67972a2a35493a88a36893cae26b1d3e..49b0b1bf74fac11df263431e75cc2e592e460af1 100644 --- a/tasks/0053_053_53053753_qa_4/task.toml +++ b/tasks/0053_053_53053753_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0053_053_53053753_qa_4" +name = "smoldataenvs-train/0053_053_53053753_qa_4" description = "What value was used to replace missing SkinThickness values during data preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "29" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0053_055_53055774_qa_1/task.toml b/tasks/0053_055_53055774_qa_1/task.toml index 92599f517ca6729fe74674aca21f87a3530c0500..5d2ff622e0f34f67d372ddb3bbf3ff189712b208 100644 --- a/tasks/0053_055_53055774_qa_1/task.toml +++ b/tasks/0053_055_53055774_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0053_055_53055774_qa_1" +name = "smoldataenvs-train/0053_055_53055774_qa_1" description = "How many rows were removed during the initial missing value handling process before outlier removal?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "307" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0053_105_53105677_qa_2/task.toml b/tasks/0053_105_53105677_qa_2/task.toml index 79158b46f45aa6fa55c4cbe32a5d89f4b6a7edb2..a5df8d4518c4b4849dc46205c685e18cb8043893 100644 --- a/tasks/0053_105_53105677_qa_2/task.toml +++ b/tasks/0053_105_53105677_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0053_105_53105677_qa_2" +name = "smoldataenvs-train/0053_105_53105677_qa_2" description = "After applying the Boruta feature selection algorithm, how many features were confirmed as important for the prediction of the target variable?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0053_164_53164718_qa_3/task.toml b/tasks/0053_164_53164718_qa_3/task.toml index bd637ce08a51037e8339ae3a732c2ff8df789504..e73eedac488990123591d2239caf300eb6283085 100644 --- a/tasks/0053_164_53164718_qa_3/task.toml +++ b/tasks/0053_164_53164718_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0053_164_53164718_qa_3" +name = "smoldataenvs-train/0053_164_53164718_qa_3" description = "What is the cross-validation accuracy of the best-performing machine learning model identified in the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.984615" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0053_248_53248988_qa_3/task.toml b/tasks/0053_248_53248988_qa_3/task.toml index 033cb137e58d5964e41bec87986a5303a55aea4a..3ef419926878f114864945489383c00e1ca25333 100644 --- a/tasks/0053_248_53248988_qa_3/task.toml +++ b/tasks/0053_248_53248988_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0053_248_53248988_qa_3" +name = "smoldataenvs-train/0053_248_53248988_qa_3" description = "What is the title of the highest-selling video game on the DS platform in 2004?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Super Mario 64" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0053_271_53271024_qa_1/task.toml b/tasks/0053_271_53271024_qa_1/task.toml index 4f4fa29ff33467072dc4466361c5b9723e476bd8..59477887d1e0f234b330580a798c17814fe61f15 100644 --- a/tasks/0053_271_53271024_qa_1/task.toml +++ b/tasks/0053_271_53271024_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0053_271_53271024_qa_1" +name = "smoldataenvs-train/0053_271_53271024_qa_1" description = "What is the correlation coefficient between temperature and apparent temperature in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.992629" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0053_271_53271024_qa_3/task.toml b/tasks/0053_271_53271024_qa_3/task.toml index ff52aa2f96fb886383e593c5eafc815e2123f8b1..11dcdb86e20eac09c37e5f4f51ae67450688fdb0 100644 --- a/tasks/0053_271_53271024_qa_3/task.toml +++ b/tasks/0053_271_53271024_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0053_271_53271024_qa_3" +name = "smoldataenvs-train/0053_271_53271024_qa_3" description = "What is the minimum temperature recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-21.822222" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0053_295_53295093_qa_1/task.toml b/tasks/0053_295_53295093_qa_1/task.toml index ba61d95172a5c8124745972fbbdca85a80efbc88..9867507205bbb51ef5af5946532250b81ced9063 100644 --- a/tasks/0053_295_53295093_qa_1/task.toml +++ b/tasks/0053_295_53295093_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0053_295_53295093_qa_1" +name = "smoldataenvs-train/0053_295_53295093_qa_1" description = "What is the root mean square error (RMSE) of the naive time series forecasting model in normalized units?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.03" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0053_322_53322838_qa_5/task.toml b/tasks/0053_322_53322838_qa_5/task.toml index 6a0e14547e6d8d75b4d215a092f00d28b5c6a0e9..a807e95103d5596698e40f16088bdcdc4c803b86 100644 --- a/tasks/0053_322_53322838_qa_5/task.toml +++ b/tasks/0053_322_53322838_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0053_322_53322838_qa_5" +name = "smoldataenvs-train/0053_322_53322838_qa_5" description = "How many samples are included in the training dataset based on the label information?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "38" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0053_327_53327790_qa_3/task.toml b/tasks/0053_327_53327790_qa_3/task.toml index 8e2a841e18f1755fd607858afb178cdbb3e505c1..9a6fb3c43ca49cc01b9efc239a5874b0ac888f85 100644 --- a/tasks/0053_327_53327790_qa_3/task.toml +++ b/tasks/0053_327_53327790_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0053_327_53327790_qa_3" +name = "smoldataenvs-train/0053_327_53327790_qa_3" description = "What percentage of districts in the dataset are categorized as \"NEAR OCEAN\" in the ocean_proximity field?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12.88" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0053_345_53345266_qa_3/task.toml b/tasks/0053_345_53345266_qa_3/task.toml index 950c6699d580355c2eb851cc9a5008bb4fb442e7..eb0dfc88ea7e756f1b0b80b3a24cc3278a5370e3 100644 --- a/tasks/0053_345_53345266_qa_3/task.toml +++ b/tasks/0053_345_53345266_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0053_345_53345266_qa_3" +name = "smoldataenvs-train/0053_345_53345266_qa_3" description = "What was the confidence level (maximum predicted probability) of the model's prediction after completing the adversarial optimization process?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0053_352_53352624_qa_1/task.toml b/tasks/0053_352_53352624_qa_1/task.toml index 2630ba03911e391ba657adc147daae837e84c8e2..166684b3983c65894a097ac0241ab1af8f7cba5e 100644 --- a/tasks/0053_352_53352624_qa_1/task.toml +++ b/tasks/0053_352_53352624_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0053_352_53352624_qa_1" +name = "smoldataenvs-train/0053_352_53352624_qa_1" description = "Which job category has the highest percentage of customers accepting the car insurance policy according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "student" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0053_354_53354710_qa_5/task.toml b/tasks/0053_354_53354710_qa_5/task.toml index 8cb51f6993a02cff7849925fe1653c92dbebb342..c4f02cfaafad3fecd4e2306011331f9a97d920f1 100644 --- a/tasks/0053_354_53354710_qa_5/task.toml +++ b/tasks/0053_354_53354710_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0053_354_53354710_qa_5" +name = "smoldataenvs-train/0053_354_53354710_qa_5" description = "What percentage of the women in the dataset have BMI values within the normal weight range (18.5–24.9)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "100" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0053_426_53426290_qa_3/task.toml b/tasks/0053_426_53426290_qa_3/task.toml index 59b8bc668bf0b16e127277d27620d3d774a17024..614d4f6cbfaf9ee0d50ef21dedba3c8c162b3130 100644 --- a/tasks/0053_426_53426290_qa_3/task.toml +++ b/tasks/0053_426_53426290_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0053_426_53426290_qa_3" +name = "smoldataenvs-train/0053_426_53426290_qa_3" description = "What is the standard deviation of the opening prices across all stocks in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "75.203893" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0053_426_53426290_qa_5/task.toml b/tasks/0053_426_53426290_qa_5/task.toml index 183b5dd55b5d3cd4506c786a000d8402d992939c..abaad96ed108ca3c2a5fe08a08037098efe20cfa 100644 --- a/tasks/0053_426_53426290_qa_5/task.toml +++ b/tasks/0053_426_53426290_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0053_426_53426290_qa_5" +name = "smoldataenvs-train/0053_426_53426290_qa_5" description = "How many different stock symbols are present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "501" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0053_427_53427570_qa_2/task.toml b/tasks/0053_427_53427570_qa_2/task.toml index b220cce56506eaf31a89014f726341b3f45ebde1..08829d19fd16d21cb81abde063491a614bbb12e0 100644 --- a/tasks/0053_427_53427570_qa_2/task.toml +++ b/tasks/0053_427_53427570_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0053_427_53427570_qa_2" +name = "smoldataenvs-train/0053_427_53427570_qa_2" description = "How many samples of Iris-versicolor and Iris-virginica have PetalPersantageArea values in the overlapping range that could lead to misclassification?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "14" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0053_427_53427570_qa_3/task.toml b/tasks/0053_427_53427570_qa_3/task.toml index 4510084c9740fec59f82c3565fb5b885e5431367..d55e8da63494a0c4f94818e7b348453947c14caf 100644 --- a/tasks/0053_427_53427570_qa_3/task.toml +++ b/tasks/0053_427_53427570_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0053_427_53427570_qa_3" +name = "smoldataenvs-train/0053_427_53427570_qa_3" description = "How many samples of Iris-versicolor and Iris-virginica have PetalArea values in the overlapping range that could lead to misclassification?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0053_427_53427570_qa_5/task.toml b/tasks/0053_427_53427570_qa_5/task.toml index cc9dc6e2b1adc0221e373ca778a4eeafdfd42145..bcfdf98be3e19c629e046cc6dd10d13eae80d065 100644 --- a/tasks/0053_427_53427570_qa_5/task.toml +++ b/tasks/0053_427_53427570_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0053_427_53427570_qa_5" +name = "smoldataenvs-train/0053_427_53427570_qa_5" description = "How many samples of Iris-versicolor and Iris-virginica have PetalPersantageLength values in the overlapping range that could lead to misclassification using the engineered feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "41" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0053_455_53455714_qa_3/task.toml b/tasks/0053_455_53455714_qa_3/task.toml index 3ad7c2fd73a32a4533ca577e44f14c3c6f959286..0c99bb8e2fa1901c925c50ff17c5aa9847af8c26 100644 --- a/tasks/0053_455_53455714_qa_3/task.toml +++ b/tasks/0053_455_53455714_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0053_455_53455714_qa_3" +name = "smoldataenvs-train/0053_455_53455714_qa_3" description = "Which four features are used as input variables for the K-Nearest Neighbors classification model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "SepalLengthCm, SepalWidthCm, PetalLengthCm, PetalWidthCm" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0053_603_53603838_qa_2/task.toml b/tasks/0053_603_53603838_qa_2/task.toml index 4143e63cd772cf2266b2d958589b28a475dd5832..5c7cec98598c0ea83f56eee7ae4c1cbf1338689f 100644 --- a/tasks/0053_603_53603838_qa_2/task.toml +++ b/tasks/0053_603_53603838_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0053_603_53603838_qa_2" +name = "smoldataenvs-train/0053_603_53603838_qa_2" description = "What is the threshold value used to determine if a wine is classified as high quality based on the 50th percentile of the quality ratings?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0053_603_53603838_qa_3/task.toml b/tasks/0053_603_53603838_qa_3/task.toml index c8ad168a0dec6f54b16cf5cb2fa372ca61a0db2f..e02d9c31c862c40602784e192701bc15cd955478 100644 --- a/tasks/0053_603_53603838_qa_3/task.toml +++ b/tasks/0053_603_53603838_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0053_603_53603838_qa_3" +name = "smoldataenvs-train/0053_603_53603838_qa_3" description = "Which feature in the dataset has the strongest positive correlation with the wine quality rating, and what is the correlation coefficient?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Alcohol, 0.4762" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0053_612_53612964_qa_4/task.toml b/tasks/0053_612_53612964_qa_4/task.toml index a611abbdb6cb31540c94ded8a3499e2189628881..83b54e0a9797b53fea12fe0ef40c359a71d7c2bc 100644 --- a/tasks/0053_612_53612964_qa_4/task.toml +++ b/tasks/0053_612_53612964_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0053_612_53612964_qa_4" +name = "smoldataenvs-train/0053_612_53612964_qa_4" description = "How many unique genres are included in the user profile calculation for recommendations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "43" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0053_728_53728766_qa_3/task.toml b/tasks/0053_728_53728766_qa_3/task.toml index 98907dafd47c463991064e5835fe81c8cde723f1..6a190c649d8b349c35ae3a60bb60741295477b7a 100644 --- a/tasks/0053_728_53728766_qa_3/task.toml +++ b/tasks/0053_728_53728766_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0053_728_53728766_qa_3" +name = "smoldataenvs-train/0053_728_53728766_qa_3" description = "Did the data balancing process result in equal class sizes across all quality classes?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0053_729_53729953_qa_4/task.toml b/tasks/0053_729_53729953_qa_4/task.toml index f94a120d0ded040e89eebede8c90a9cc0ef9eafc..a57500b77f803f8f16034520f2be9c6b3f65964e 100644 --- a/tasks/0053_729_53729953_qa_4/task.toml +++ b/tasks/0053_729_53729953_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0053_729_53729953_qa_4" +name = "smoldataenvs-train/0053_729_53729953_qa_4" description = "Which numeric feature has the highest standard deviation in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0053_757_53757406_qa_4/task.toml b/tasks/0053_757_53757406_qa_4/task.toml index 4aa878683f4b5d36ab4910ea4789843c6fb4e32a..20665ea1529988578b973fc917668b2e8d20fa37 100644 --- a/tasks/0053_757_53757406_qa_4/task.toml +++ b/tasks/0053_757_53757406_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0053_757_53757406_qa_4" +name = "smoldataenvs-train/0053_757_53757406_qa_4" description = "Which feature in the linear regression model has the highest positive coefficient in predicting Microsoft's stock closing prices?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "high" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0053_768_53768671_qa_2/task.toml b/tasks/0053_768_53768671_qa_2/task.toml index effed22258cccabec53cd5e6ca4af199a5b9228c..f3bf586132b3021e7cf37d06c602a44ee7905ff8 100644 --- a/tasks/0053_768_53768671_qa_2/task.toml +++ b/tasks/0053_768_53768671_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0053_768_53768671_qa_2" +name = "smoldataenvs-train/0053_768_53768671_qa_2" description = "What percentage of patients in the dataset are diagnosed with diabetes based on the outcome variable?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0053_823_53823082_qa_2/task.toml b/tasks/0053_823_53823082_qa_2/task.toml index 825fee3f981feeb3b1f8e1006234393c039c64d9..5bd425b6bda02f53f887520cd4d127aa8433d593 100644 --- a/tasks/0053_823_53823082_qa_2/task.toml +++ b/tasks/0053_823_53823082_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0053_823_53823082_qa_2" +name = "smoldataenvs-train/0053_823_53823082_qa_2" description = "What is the most frequent genre in the dataset after filtering out low-count genres (retaining only the top 20 genres)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Drama" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0053_823_53823082_qa_3/task.toml b/tasks/0053_823_53823082_qa_3/task.toml index 73d97512d2248a7c83d178557c0d93284b9b5312..78e78e5f89fa034274e380e72d7bdc8789d7dd2d 100644 --- a/tasks/0053_823_53823082_qa_3/task.toml +++ b/tasks/0053_823_53823082_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0053_823_53823082_qa_3" +name = "smoldataenvs-train/0053_823_53823082_qa_3" description = "What is the difference in training accuracy between the Decision Tree Classifier (0.9921) and the Random Forest Classifier (0.9918)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.000271" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0053_823_53823082_qa_4/task.toml b/tasks/0053_823_53823082_qa_4/task.toml index 84e54a9f45f3b9c716d83c4483f3c053a3d1ecef..dd4cdb0866073faccb56f005b0097218aac03464 100644 --- a/tasks/0053_823_53823082_qa_4/task.toml +++ b/tasks/0053_823_53823082_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0053_823_53823082_qa_4" +name = "smoldataenvs-train/0053_823_53823082_qa_4" description = "Which genre has the lowest occurrence in the training dataset after selecting the top 20 genres for analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "TV Movie" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0053_842_53842474_qa_2/task.toml b/tasks/0053_842_53842474_qa_2/task.toml index 903071656c4b77d5a5a3f6289eea174f44152d55..ff535ab2aea22e6be82860cea1c06fd2884cc655 100644 --- a/tasks/0053_842_53842474_qa_2/task.toml +++ b/tasks/0053_842_53842474_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0053_842_53842474_qa_2" +name = "smoldataenvs-train/0053_842_53842474_qa_2" description = "What is the average cross-validation R² score of the Random Forest model using 5-fold cross-validation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.876" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0053_862_53862113_qa_1/task.toml b/tasks/0053_862_53862113_qa_1/task.toml index 735dade7fe7631aa4090ca9f91ac157814912ae3..ac46b5f3662d97a99bcc04da73c4f5d7ffe3c83b 100644 --- a/tasks/0053_862_53862113_qa_1/task.toml +++ b/tasks/0053_862_53862113_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0053_862_53862113_qa_1" +name = "smoldataenvs-train/0053_862_53862113_qa_1" description = "What is the churn rate percentage for customers using Fiber Optic internet service compared to other internet service types?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "40" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0053_862_53862113_qa_2/task.toml b/tasks/0053_862_53862113_qa_2/task.toml index 957712cf98324def2fe2b31fad42a147f86a7e0b..b9d14fcd8bed68fd332da5ae3d2fb35e416c2bbe 100644 --- a/tasks/0053_862_53862113_qa_2/task.toml +++ b/tasks/0053_862_53862113_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0053_862_53862113_qa_2" +name = "smoldataenvs-train/0053_862_53862113_qa_2" description = "Which customer demographic group shows the highest churn rate based on age-related attributes?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Senior citizens" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0053_910_53910928_qa_5/task.toml b/tasks/0053_910_53910928_qa_5/task.toml index 842e65cea3b36328cbd8a5bca88a916d29876489..7e61b6d2ebf1601324798f5c55b85e7abb05662e 100644 --- a/tasks/0053_910_53910928_qa_5/task.toml +++ b/tasks/0053_910_53910928_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0053_910_53910928_qa_5" +name = "smoldataenvs-train/0053_910_53910928_qa_5" description = "What is the maximum test set accuracy achieved by the neural network model through threshold optimization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.97619" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0053_977_53977236_qa_2/task.toml b/tasks/0053_977_53977236_qa_2/task.toml index 2d99aa655025f2c2d78493cbc1fde06a5eee2efc..91703fe23889a5726a2321f73c2856210dabc9a3 100644 --- a/tasks/0053_977_53977236_qa_2/task.toml +++ b/tasks/0053_977_53977236_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0053_977_53977236_qa_2" +name = "smoldataenvs-train/0053_977_53977236_qa_2" description = "Which U.S. state has the highest percentage of its population below the poverty level according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Mississippi" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0053_977_53977236_qa_3/task.toml b/tasks/0053_977_53977236_qa_3/task.toml index 2770ee25d723d91e95c8cfe3af1deb808c6830b4..77a67204f055004496598495c5a3ac445e18e769 100644 --- a/tasks/0053_977_53977236_qa_3/task.toml +++ b/tasks/0053_977_53977236_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0053_977_53977236_qa_3" +name = "smoldataenvs-train/0053_977_53977236_qa_3" description = "What is the most common first or last name among victims in the police killing dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Michael" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0053_977_53977236_qa_4/task.toml b/tasks/0053_977_53977236_qa_4/task.toml index 85192ac24ee5dff2209fecca2ba9b8206d33dec6..1dd1240801ffd535e89a606476b9d57a5603b409 100644 --- a/tasks/0053_977_53977236_qa_4/task.toml +++ b/tasks/0053_977_53977236_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0053_977_53977236_qa_4" +name = "smoldataenvs-train/0053_977_53977236_qa_4" description = "Which city has the highest number of police-related killings recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Los Angeles" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0053_977_53977236_qa_5/task.toml b/tasks/0053_977_53977236_qa_5/task.toml index 8c86ea1efdc628ea27f3f7007a09d9eea3ff855a..d0bd6d78e68111dc4bbbbfa536260b22fc6097fc 100644 --- a/tasks/0053_977_53977236_qa_5/task.toml +++ b/tasks/0053_977_53977236_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0053_977_53977236_qa_5" +name = "smoldataenvs-train/0053_977_53977236_qa_5" description = "What is the average high school graduation rate for the U.S. state with the lowest poverty rate in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "90.52" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0053_995_53995551_qa_1/task.toml b/tasks/0053_995_53995551_qa_1/task.toml index a04f649c4ed8c6f151de8314b655143ca506d026..9afa0a05bf7f52cefb7b3b7d1dae7aff5be84804 100644 --- a/tasks/0053_995_53995551_qa_1/task.toml +++ b/tasks/0053_995_53995551_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0053_995_53995551_qa_1" +name = "smoldataenvs-train/0053_995_53995551_qa_1" description = "Which video game genre contributed the highest total global sales in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0053_995_53995551_qa_3/task.toml b/tasks/0053_995_53995551_qa_3/task.toml index 009b2c0447e2794b3a163f5740cb27d7f3c15641..4514b05be8bdb120087ef2a80a6c76a255c21c81 100644 --- a/tasks/0053_995_53995551_qa_3/task.toml +++ b/tasks/0053_995_53995551_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0053_995_53995551_qa_3" +name = "smoldataenvs-train/0053_995_53995551_qa_3" description = "Which calendar year had the highest total global sales of video games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2008, 2009" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0053_995_53995551_qa_5/task.toml b/tasks/0053_995_53995551_qa_5/task.toml index 988e9b02d96f39f5f3eed337b8b6543024b382df..1d28e9f85808c909cf15381d8eafe1a367d0d6d1 100644 --- a/tasks/0053_995_53995551_qa_5/task.toml +++ b/tasks/0053_995_53995551_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0053_995_53995551_qa_5" +name = "smoldataenvs-train/0053_995_53995551_qa_5" description = "Which sales region exhibits the least linear relationship with global sales based on the scatterplot analysis in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "JP_Sales" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0054_009_54009128_qa_3/task.toml b/tasks/0054_009_54009128_qa_3/task.toml index 5e5bba27ae5657c7f36944ba20b99f7227f55727..73f6d46987048452b280c86d5ce9ba8cf5e15d49 100644 --- a/tasks/0054_009_54009128_qa_3/task.toml +++ b/tasks/0054_009_54009128_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0054_009_54009128_qa_3" +name = "smoldataenvs-train/0054_009_54009128_qa_3" description = "Which categorical feature had the highest number of missing values before any imputation was performed?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "GarageType" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0054_030_54030075_qa_5/task.toml b/tasks/0054_030_54030075_qa_5/task.toml index 1c993d56a7cbf82423fd71d8ae5d04c01ebf2190..f2ff8306a5975c721c1b877befc844983bd77752 100644 --- a/tasks/0054_030_54030075_qa_5/task.toml +++ b/tasks/0054_030_54030075_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0054_030_54030075_qa_5" +name = "smoldataenvs-train/0054_030_54030075_qa_5" description = "What is the strongest correlation with the 'Outcome' variable in the dataset based on the correlation matrix?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0054_048_54048707_qa_3/task.toml b/tasks/0054_048_54048707_qa_3/task.toml index 69d6b10e3c913f1afb28d43efc25b9c10651e5e8..917ad4f557cade237be84d0d8f9c9485b45aa2d5 100644 --- a/tasks/0054_048_54048707_qa_3/task.toml +++ b/tasks/0054_048_54048707_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0054_048_54048707_qa_3" +name = "smoldataenvs-train/0054_048_54048707_qa_3" description = "What proportion of the dataset consists of phones with 4G support?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "52.15" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0054_065_54065855_qa_5/task.toml b/tasks/0054_065_54065855_qa_5/task.toml index f7591e4dd5fc7174de9ad95a3d88069b3921ba0c..9945e7f0a57ff6918cbf3b7d490f53a51f2e9a49 100644 --- a/tasks/0054_065_54065855_qa_5/task.toml +++ b/tasks/0054_065_54065855_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0054_065_54065855_qa_5" +name = "smoldataenvs-train/0054_065_54065855_qa_5" description = "What is the highest recall achieved for any price_range class in the validation set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.95" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0054_137_54137484_qa_1/task.toml b/tasks/0054_137_54137484_qa_1/task.toml index b2c240ead6f411cb5269235208090e817f046ad2..31db34093e2e4b329f65d621e443fd8d5a317e58 100644 --- a/tasks/0054_137_54137484_qa_1/task.toml +++ b/tasks/0054_137_54137484_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0054_137_54137484_qa_1" +name = "smoldataenvs-train/0054_137_54137484_qa_1" description = "What percentage of patients in the dataset are diagnosed with liver disease?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "71.36" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0054_170_54170809_qa_3/task.toml b/tasks/0054_170_54170809_qa_3/task.toml index 99ce308939826b646e9ae979cd1439b7511c8e1d..10ca4f5e85cc67b882e85317ee25bde4ffeaecbd 100644 --- a/tasks/0054_170_54170809_qa_3/task.toml +++ b/tasks/0054_170_54170809_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0054_170_54170809_qa_3" +name = "smoldataenvs-train/0054_170_54170809_qa_3" description = "What was the mean accuracy score from 10-fold cross-validation for the logistic regression model including categorical features?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.8377" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0054_240_54240595_qa_2/task.toml b/tasks/0054_240_54240595_qa_2/task.toml index 249e6983029e04b210890fb19a2b30cdc71575b1..54356ea4808984ed7128a835c98b243d4e9a5e99 100644 --- a/tasks/0054_240_54240595_qa_2/task.toml +++ b/tasks/0054_240_54240595_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0054_240_54240595_qa_2" +name = "smoldataenvs-train/0054_240_54240595_qa_2" description = "How many Pokémon in the dataset originally had missing values in their secondary type (Type 2) before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "386" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0054_256_54256432_qa_2/task.toml b/tasks/0054_256_54256432_qa_2/task.toml index 539a25af83e676a7c18cf12c62099f8986781d06..a305e168cba06f8b02378ec4ca391c33cf1f28c6 100644 --- a/tasks/0054_256_54256432_qa_2/task.toml +++ b/tasks/0054_256_54256432_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0054_256_54256432_qa_2" +name = "smoldataenvs-train/0054_256_54256432_qa_2" description = "What is the highest accuracy achieved by the Naïve Bayes classifier using any combination of features (PRCP, TMAX, TMIN) for rain prediction?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0054_256_54256432_qa_3/task.toml b/tasks/0054_256_54256432_qa_3/task.toml index b771fd442d1dfabc9eb060b0f43edb015af6dbf5..97bf75212b08029c64186158f8a1b4ae17b54f87 100644 --- a/tasks/0054_256_54256432_qa_3/task.toml +++ b/tasks/0054_256_54256432_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0054_256_54256432_qa_3" +name = "smoldataenvs-train/0054_256_54256432_qa_3" description = "Which single feature (PRCP, TMAX, or TMIN) provides the highest accuracy for the Naïve Bayes classifier when used alone for rain prediction?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PRCP" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0054_266_54266894_qa_3/task.toml b/tasks/0054_266_54266894_qa_3/task.toml index c6d6ecb66fe0bbfb0f4b31b7519ea2b2189efb9b..246c4319fc0270ed061b728176c46edd643f5271 100644 --- a/tasks/0054_266_54266894_qa_3/task.toml +++ b/tasks/0054_266_54266894_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0054_266_54266894_qa_3" +name = "smoldataenvs-train/0054_266_54266894_qa_3" description = "Does the two-sample t-test indicate a statistically significant difference in conversion rates between male and female audiences in campaign ID 1178?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0054_269_54269608_qa_3/task.toml b/tasks/0054_269_54269608_qa_3/task.toml index 969d137ad929081ac408c34a77c19cd6f1f31d94..b093512a84dae6f6dd751ac219b947ac563f9d04 100644 --- a/tasks/0054_269_54269608_qa_3/task.toml +++ b/tasks/0054_269_54269608_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0054_269_54269608_qa_3" +name = "smoldataenvs-train/0054_269_54269608_qa_3" description = "How many duplicate rows were removed from the dataset during preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "240" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0054_291_54291213_qa_2/task.toml b/tasks/0054_291_54291213_qa_2/task.toml index b0c790bf50bc3c0760858b2c865796d696ec8f41..a92f44ec2e7c2672e9e2d62c068f28a9752c7198 100644 --- a/tasks/0054_291_54291213_qa_2/task.toml +++ b/tasks/0054_291_54291213_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0054_291_54291213_qa_2" +name = "smoldataenvs-train/0054_291_54291213_qa_2" description = "What is the best precision score achieved by any model during cross-validation in the breast cancer classification task?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.99036" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0054_291_54291213_qa_5/task.toml b/tasks/0054_291_54291213_qa_5/task.toml index 3c783fbcc3315a2509d5e087e6d56d707ba662fa..25447bd8e6a509c26a5bb2a5ba6e86fa626626ce 100644 --- a/tasks/0054_291_54291213_qa_5/task.toml +++ b/tasks/0054_291_54291213_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0054_291_54291213_qa_5" +name = "smoldataenvs-train/0054_291_54291213_qa_5" description = "Which model required the shortest average training time while achieving at least 90% cross-validated accuracy?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "GaussianNB" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0054_296_54296946_qa_1/task.toml b/tasks/0054_296_54296946_qa_1/task.toml index 34506da8cc4ed04dacdd155c24be43dcb74fbe40..076afb6685e65964f68454ea1e00fe6baa3091ae 100644 --- a/tasks/0054_296_54296946_qa_1/task.toml +++ b/tasks/0054_296_54296946_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0054_296_54296946_qa_1" +name = "smoldataenvs-train/0054_296_54296946_qa_1" description = "Which feature in the dataset shows the highest correlation with the price_range variable based on the correlation matrix analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ram" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0054_298_54298783_qa_3/task.toml b/tasks/0054_298_54298783_qa_3/task.toml index f7fcedb5f5257dadfb2b284cd7f33ed9da0c929d..b4daad2c47c7dd9c373461193f44476bf36bcb43 100644 --- a/tasks/0054_298_54298783_qa_3/task.toml +++ b/tasks/0054_298_54298783_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0054_298_54298783_qa_3" +name = "smoldataenvs-train/0054_298_54298783_qa_3" description = "What percentage of the dataset is allocated to the test set during model evaluation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0054_315_54315694_qa_1/task.toml b/tasks/0054_315_54315694_qa_1/task.toml index cc53e73d05bc9b7c6e84211dfd0b475a6f844c35..007736167c646e912cc28f7fcc16b15e10aee125 100644 --- a/tasks/0054_315_54315694_qa_1/task.toml +++ b/tasks/0054_315_54315694_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0054_315_54315694_qa_1" +name = "smoldataenvs-train/0054_315_54315694_qa_1" description = "What is the percentage of missing data in the 'quantity_footnotes' column of the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "86.217026" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0054_315_54315694_qa_2/task.toml b/tasks/0054_315_54315694_qa_2/task.toml index fad29bc929fa1d86d1452a183ecc10bd17c0ff93..b83949560dc0f4e0c49776ef129899801080c5a9 100644 --- a/tasks/0054_315_54315694_qa_2/task.toml +++ b/tasks/0054_315_54315694_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0054_315_54315694_qa_2" +name = "smoldataenvs-train/0054_315_54315694_qa_2" description = "How many unique years are present in the dataset after data cleaning and preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "25" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0054_315_54315694_qa_4/task.toml b/tasks/0054_315_54315694_qa_4/task.toml index 4d0567e23e20a2d508b491e025c0d63d435e909f..7191d4904c102fb5bf42220525dc2099e4bf8049 100644 --- a/tasks/0054_315_54315694_qa_4/task.toml +++ b/tasks/0054_315_54315694_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0054_315_54315694_qa_4" +name = "smoldataenvs-train/0054_315_54315694_qa_4" description = "After data cleaning and dropping the 'quantity_footnotes' column, how many columns remain in the final dataset used for analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0054_375_54375499_qa_3/task.toml b/tasks/0054_375_54375499_qa_3/task.toml index 116e67b72b1790b42e1bb0932faad98915088d64..fc4eb11e8245410fd6628555f997074570749e89 100644 --- a/tasks/0054_375_54375499_qa_3/task.toml +++ b/tasks/0054_375_54375499_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0054_375_54375499_qa_3" +name = "smoldataenvs-train/0054_375_54375499_qa_3" description = "What is the test set size used for model evaluation after splitting the data with a 0.2 test size parameter?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "30" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0054_375_54375499_qa_5/task.toml b/tasks/0054_375_54375499_qa_5/task.toml index 411ff8be6b669391da267dfc46c55017f747253e..53de8e1b0a60755f74a4b37a35c3e6707d86e6ef 100644 --- a/tasks/0054_375_54375499_qa_5/task.toml +++ b/tasks/0054_375_54375499_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0054_375_54375499_qa_5" +name = "smoldataenvs-train/0054_375_54375499_qa_5" description = "What is the minimum sepal width recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0054_473_54473359_qa_2/task.toml b/tasks/0054_473_54473359_qa_2/task.toml index 5fcbaf0294d675202221b15916ef1cf0ae4a2ad9..b34b496843db2a0c1585662c3c51cc18d54e6e25 100644 --- a/tasks/0054_473_54473359_qa_2/task.toml +++ b/tasks/0054_473_54473359_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0054_473_54473359_qa_2" +name = "smoldataenvs-train/0054_473_54473359_qa_2" description = "What is the total number of edible mushrooms in the dataset with either almond (a) or anise (l) odor?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "800" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0054_473_54473359_qa_4/task.toml b/tasks/0054_473_54473359_qa_4/task.toml index 3bdef606e40181ca57d28e398763959b4f4eece7..2acfe83062932628e52b2fc7ae96f0d5ca6ede62 100644 --- a/tasks/0054_473_54473359_qa_4/task.toml +++ b/tasks/0054_473_54473359_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0054_473_54473359_qa_4" +name = "smoldataenvs-train/0054_473_54473359_qa_4" description = "What is the highest number of edible mushrooms found in a single odor type that contains no poisonous mushrooms?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "400" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0054_514_54514604_qa_1/task.toml b/tasks/0054_514_54514604_qa_1/task.toml index e2177255ab24a8028c4b6d7ff9d48f737ae6edc8..2f81a817b6e17d8ad53c3bd18b367047137513bb 100644 --- a/tasks/0054_514_54514604_qa_1/task.toml +++ b/tasks/0054_514_54514604_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0054_514_54514604_qa_1" +name = "smoldataenvs-train/0054_514_54514604_qa_1" description = "What is the highest recorded insurance charge in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "63770.43" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0054_541_54541486_qa_1/task.toml b/tasks/0054_541_54541486_qa_1/task.toml index 96747c6f0d139bebafa2ccb0e63fb1c50de70803..5ec921c1dfa89394920b6e889749ed379b81e665 100644 --- a/tasks/0054_541_54541486_qa_1/task.toml +++ b/tasks/0054_541_54541486_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0054_541_54541486_qa_1" +name = "smoldataenvs-train/0054_541_54541486_qa_1" description = "What is the highest average salary among all combinations of Division, League, and NewLeague in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "837.5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0054_555_54555681_qa_4/task.toml b/tasks/0054_555_54555681_qa_4/task.toml index 8e272ad73c447d8f8173aa3c414903166f5590ac..b703c89a5e09e68b57dee1b8a5f84cef3ab709ae 100644 --- a/tasks/0054_555_54555681_qa_4/task.toml +++ b/tasks/0054_555_54555681_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0054_555_54555681_qa_4" +name = "smoldataenvs-train/0054_555_54555681_qa_4" description = "Which payment method has the highest churn rate, and what is its exact percentage?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic check, 43.8" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0054_589_54589195_qa_3/task.toml b/tasks/0054_589_54589195_qa_3/task.toml index 7243af253a24b3a9d0a221c3ddd9840326408228..7466cc4bf3baaa0c6531e0ee8ed95ad2fc864726 100644 --- a/tasks/0054_589_54589195_qa_3/task.toml +++ b/tasks/0054_589_54589195_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0054_589_54589195_qa_3" +name = "smoldataenvs-train/0054_589_54589195_qa_3" description = "What percentage of the dataset contains missing values in the 'ZN' (proportion of residential land zoned for lots over 25,000 sq.ft.) column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.95" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0054_670_54670437_qa_4/task.toml b/tasks/0054_670_54670437_qa_4/task.toml index 7b48d97cb22b63c23b82b88f9243153e3b10341f..4797386f8d6e25dcc67f0bda2ea1051b674b46a7 100644 --- a/tasks/0054_670_54670437_qa_4/task.toml +++ b/tasks/0054_670_54670437_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0054_670_54670437_qa_4" +name = "smoldataenvs-train/0054_670_54670437_qa_4" description = "What is the percentage difference in average monthly charges between customers who churned and those who did not?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "21.47" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0054_670_54670437_qa_5/task.toml b/tasks/0054_670_54670437_qa_5/task.toml index 35aaabb6a6c2b5120e3ccc7807818fb9c66c688e..22a544fa7731a19635a04f76114a4cd26ae0a2fd 100644 --- a/tasks/0054_670_54670437_qa_5/task.toml +++ b/tasks/0054_670_54670437_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0054_670_54670437_qa_5" +name = "smoldataenvs-train/0054_670_54670437_qa_5" description = "Which payment method has the highest proportion of churned customers relative to its total customer count?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic check" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0054_705_54705588_qa_1/task.toml b/tasks/0054_705_54705588_qa_1/task.toml index 84ebb723002d4bfff3f73334060802e0eb4aa25b..1868a7b5d478e6d73998c10270ddb0ca9573a35c 100644 --- a/tasks/0054_705_54705588_qa_1/task.toml +++ b/tasks/0054_705_54705588_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0054_705_54705588_qa_1" +name = "smoldataenvs-train/0054_705_54705588_qa_1" description = "Which feature in the dataset has the highest positive correlation with the IMDb score, and what is its correlation coefficient?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "num_voted_users, 0.410965" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0054_705_54705588_qa_3/task.toml b/tasks/0054_705_54705588_qa_3/task.toml index 62438bfea11c5fc6ba9d0d2bfeda1990259da102..2e5a9009210f5077dfe1d73cb9642bfa41ae4cf8 100644 --- a/tasks/0054_705_54705588_qa_3/task.toml +++ b/tasks/0054_705_54705588_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0054_705_54705588_qa_3" +name = "smoldataenvs-train/0054_705_54705588_qa_3" description = "Which feature exhibits the strongest negative correlation with the IMDb score, and what is its correlation coefficient?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "title_year, -0.204981" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0054_739_54739768_qa_1/task.toml b/tasks/0054_739_54739768_qa_1/task.toml index ad4435a3dc062ec63e81896b40b619859c1d794c..a4bf6db25c15c05ea65e2629f8270cfc06ce96e9 100644 --- a/tasks/0054_739_54739768_qa_1/task.toml +++ b/tasks/0054_739_54739768_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0054_739_54739768_qa_1" +name = "smoldataenvs-train/0054_739_54739768_qa_1" description = "How many missing values were present in the original training dataset before the NaN value cleaning process?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0054_748_54748769_qa_2/task.toml b/tasks/0054_748_54748769_qa_2/task.toml index 3e8582d6d011d11055065e71c1516b8b9345e4ac..2cbd486e3db7ffdb7e3251b906b8f52a10da75bc 100644 --- a/tasks/0054_748_54748769_qa_2/task.toml +++ b/tasks/0054_748_54748769_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0054_748_54748769_qa_2" +name = "smoldataenvs-train/0054_748_54748769_qa_2" description = "After replacing zero values in the BloodPressure column with the median, what is the new median value of the BloodPressure column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "72" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0054_801_54801630_qa_2/task.toml b/tasks/0054_801_54801630_qa_2/task.toml index 056d7d7091de0f6939f277f9cc3fd1c1978ebeb5..5d0c008913be5d438c380038432a73c501bf3baf 100644 --- a/tasks/0054_801_54801630_qa_2/task.toml +++ b/tasks/0054_801_54801630_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0054_801_54801630_qa_2" +name = "smoldataenvs-train/0054_801_54801630_qa_2" description = "What is the most common terminal degree among faculty members after standardizing the degree categories?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "MS" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0054_919_54919794_qa_1/task.toml b/tasks/0054_919_54919794_qa_1/task.toml index 1c1909c6b2504fca2ada7c3f361e8e072289a8d6..9f4d65bb1e50f80a6927b26859997fd9370d472f 100644 --- a/tasks/0054_919_54919794_qa_1/task.toml +++ b/tasks/0054_919_54919794_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0054_919_54919794_qa_1" +name = "smoldataenvs-train/0054_919_54919794_qa_1" description = "What is the root mean squared error (RMSE) of the linear regression model when evaluated on the entire dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5598.784542578597" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0054_928_54928271_qa_2/task.toml b/tasks/0054_928_54928271_qa_2/task.toml index 274d21e4f05fcbe5f1c71794c71f3674da4347ea..ef8674abf148f76abcd45f8da4767d5508021d5c 100644 --- a/tasks/0054_928_54928271_qa_2/task.toml +++ b/tasks/0054_928_54928271_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0054_928_54928271_qa_2" +name = "smoldataenvs-train/0054_928_54928271_qa_2" description = "Which species in the test set was misclassified by the GradientBoostingClassifier model according to the confusion matrix?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-versicolor" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0054_957_54957845_qa_1/task.toml b/tasks/0054_957_54957845_qa_1/task.toml index a8ac619d96ceb1f620f6b337996126206dbcedee..49ddcd28d6ae7986d24adbfea4a4aec81a729aae 100644 --- a/tasks/0054_957_54957845_qa_1/task.toml +++ b/tasks/0054_957_54957845_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0054_957_54957845_qa_1" +name = "smoldataenvs-train/0054_957_54957845_qa_1" description = "Which primary type has the highest number of Pokémon in the dataset, and how many Pokémon belong to this type?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Water, 112" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0054_972_54972872_qa_4/task.toml b/tasks/0054_972_54972872_qa_4/task.toml index fced92e60aa1aadb0f6fc8d2aaaf39a980b84492..8b9831c507a9858792d7534951c8f2d779764e31 100644 --- a/tasks/0054_972_54972872_qa_4/task.toml +++ b/tasks/0054_972_54972872_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0054_972_54972872_qa_4" +name = "smoldataenvs-train/0054_972_54972872_qa_4" description = "What is the number of data points remaining in the \"kurt\" column after applying the 1.5 IQR outlier removal rule?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2836" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0054_972_54972872_qa_5/task.toml b/tasks/0054_972_54972872_qa_5/task.toml index 2a0c564477dfeb503b3fd7abf580020381f04d65..7c566e61f0af349a2d5d2cf92456326efdf148b7 100644 --- a/tasks/0054_972_54972872_qa_5/task.toml +++ b/tasks/0054_972_54972872_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0054_972_54972872_qa_5" +name = "smoldataenvs-train/0054_972_54972872_qa_5" description = "How many data points are retained in the \"skew\" column after outlier removal using the 1.5 IQR range method?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2938" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0054_988_54988859_qa_1/task.toml b/tasks/0054_988_54988859_qa_1/task.toml index 38fe228c2cf1c24925e912aa56134e618c8af760..d3d227fc303db8e516819f833fe38ecf1bf7c608 100644 --- a/tasks/0054_988_54988859_qa_1/task.toml +++ b/tasks/0054_988_54988859_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0054_988_54988859_qa_1" +name = "smoldataenvs-train/0054_988_54988859_qa_1" description = "How many missing values were present in the 'Credit_History' column before the imputation process?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0055_004_55004083_qa_4/task.toml b/tasks/0055_004_55004083_qa_4/task.toml index 068f27b008bbda49bc5d132d5bb57ee8ef43903e..11732f21f4a7c8059e00b46b901faa73799182b6 100644 --- a/tasks/0055_004_55004083_qa_4/task.toml +++ b/tasks/0055_004_55004083_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0055_004_55004083_qa_4" +name = "smoldataenvs-train/0055_004_55004083_qa_4" description = "What is the mean value of the 'alcohol' feature after applying min-max normalization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.3112" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0055_023_55023608_qa_1/task.toml b/tasks/0055_023_55023608_qa_1/task.toml index fb8bd320b8f18a4aee61cd711cf8951b9a48f640..c7bc59962466e4d68d519b68d53cdfbfa3fe8d78 100644 --- a/tasks/0055_023_55023608_qa_1/task.toml +++ b/tasks/0055_023_55023608_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0055_023_55023608_qa_1" +name = "smoldataenvs-train/0055_023_55023608_qa_1" description = "Which non-legendary, non-mega Pokémon has the highest total stats, and in which generation was it introduced?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Slaking, Generation 3" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0055_027_55027246_qa_2/task.toml b/tasks/0055_027_55027246_qa_2/task.toml index 084e3eb273849371a74d9e867cb80f19557ba63c..80366b583590eb5a10e672b8dbfdb2d0d2aabef8 100644 --- a/tasks/0055_027_55027246_qa_2/task.toml +++ b/tasks/0055_027_55027246_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0055_027_55027246_qa_2" +name = "smoldataenvs-train/0055_027_55027246_qa_2" description = "Does the dataset contain any duplicate records based on the combination of YearsExperience and Salary?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0055_027_55027246_qa_4/task.toml b/tasks/0055_027_55027246_qa_4/task.toml index 98f1f97600c294b43b662c20b43c5ce434e146ab..52ba6c80cb906e8525c674c7d2f5d28c66726c6a 100644 --- a/tasks/0055_027_55027246_qa_4/task.toml +++ b/tasks/0055_027_55027246_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0055_027_55027246_qa_4" +name = "smoldataenvs-train/0055_027_55027246_qa_4" description = "How many unique salary entries are present in the dataset considering no duplicates exist?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "30" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0055_033_55033540_qa_1/task.toml b/tasks/0055_033_55033540_qa_1/task.toml index b18ca52a5a45834b55d064bed9d8e3c1970cb7ee..430551f3828b5c3f796a8df3d1b8d22beb0d1094 100644 --- a/tasks/0055_033_55033540_qa_1/task.toml +++ b/tasks/0055_033_55033540_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0055_033_55033540_qa_1" +name = "smoldataenvs-train/0055_033_55033540_qa_1" description = "What is the skewness of the price distribution in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.618" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0055_033_55033540_qa_5/task.toml b/tasks/0055_033_55033540_qa_5/task.toml index abd39f07cb8fb40466ff4708c40116d933ed6a74..8c2277c03b781d885e8dae3257758bcfa20accb2 100644 --- a/tasks/0055_033_55033540_qa_5/task.toml +++ b/tasks/0055_033_55033540_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0055_033_55033540_qa_5" +name = "smoldataenvs-train/0055_033_55033540_qa_5" description = "What is the kurtosis of the price distribution in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.177" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0055_042_55042891_qa_1/task.toml b/tasks/0055_042_55042891_qa_1/task.toml index e46ef10222f9f7b0a4af476f569da5822ca53d5b..f64807556267dd5c35addf3c20228a42868186c3 100644 --- a/tasks/0055_042_55042891_qa_1/task.toml +++ b/tasks/0055_042_55042891_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0055_042_55042891_qa_1" +name = "smoldataenvs-train/0055_042_55042891_qa_1" description = "What percentage of the dataset corresponds to individuals diagnosed with diabetes (Outcome=1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0055_042_55042891_qa_4/task.toml b/tasks/0055_042_55042891_qa_4/task.toml index 002cab45e288cb8030b8f7c6b58d530c9a65e810..a83d1941520870ab1861ca717d9ac70e263bb433 100644 --- a/tasks/0055_042_55042891_qa_4/task.toml +++ b/tasks/0055_042_55042891_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0055_042_55042891_qa_4" +name = "smoldataenvs-train/0055_042_55042891_qa_4" description = "What is the standard deviation of the 'Insulin' column in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "115.24" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0055_042_55042891_qa_5/task.toml b/tasks/0055_042_55042891_qa_5/task.toml index f63868e20db713826c5bb1666a0bfa34c6880821..9a9643fa21f82f7995293d3a461ad1d208174c5e 100644 --- a/tasks/0055_042_55042891_qa_5/task.toml +++ b/tasks/0055_042_55042891_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0055_042_55042891_qa_5" +name = "smoldataenvs-train/0055_042_55042891_qa_5" description = "What is the difference between the 75th percentile and the 25th percentile of the 'BMI' column in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9.3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0055_050_55050231_qa_3/task.toml b/tasks/0055_050_55050231_qa_3/task.toml index 771856974777376314bdecf31002f6a9e40e0a0a..06af49a5b5ffd6627b2d13f1500a1f8680f9d5ee 100644 --- a/tasks/0055_050_55050231_qa_3/task.toml +++ b/tasks/0055_050_55050231_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0055_050_55050231_qa_3" +name = "smoldataenvs-train/0055_050_55050231_qa_3" description = "What is the accuracy of the Random Tree model with 2 estimators on the test dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9995898277276456" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0055_050_55050231_qa_4/task.toml b/tasks/0055_050_55050231_qa_4/task.toml index c16264276fb4f14d8b102197a3f758ae04ed3625..b435a71bb7531b1876a72421257d53fbb6258bbe 100644 --- a/tasks/0055_050_55050231_qa_4/task.toml +++ b/tasks/0055_050_55050231_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0055_050_55050231_qa_4" +name = "smoldataenvs-train/0055_050_55050231_qa_4" description = "What is the accuracy of the Logistic Regression model on the test dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9474979491386383" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0055_059_55059592_qa_3/task.toml b/tasks/0055_059_55059592_qa_3/task.toml index 116c6580c042dbf5fa92a0c45cc845f75bd159cb..aafbcb35bbd918afd8aa3894e3fcf1e0bb4cf350 100644 --- a/tasks/0055_059_55059592_qa_3/task.toml +++ b/tasks/0055_059_55059592_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0055_059_55059592_qa_3" +name = "smoldataenvs-train/0055_059_55059592_qa_3" description = "Which game has the highest global sales, and what is its sales value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii Sports, 82.74" reward_mode_initial = "list" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0055_086_55086484_qa_1/task.toml b/tasks/0055_086_55086484_qa_1/task.toml index 376c76668f928c9b440df7b897d37e68d129174f..d3bf7193fdd7c0cba54301a6006f5e6881c79f34 100644 --- a/tasks/0055_086_55086484_qa_1/task.toml +++ b/tasks/0055_086_55086484_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0055_086_55086484_qa_1" +name = "smoldataenvs-train/0055_086_55086484_qa_1" description = "Which feature has the highest mutual information score with the target variable 'price'?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "zipcode" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0055_099_55099558_qa_1/task.toml b/tasks/0055_099_55099558_qa_1/task.toml index a150f6809a20769ae1da236aaaf25759b3642f72..a1d775d6a10d9c6b72bc075db093e04b50423945 100644 --- a/tasks/0055_099_55099558_qa_1/task.toml +++ b/tasks/0055_099_55099558_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0055_099_55099558_qa_1" +name = "smoldataenvs-train/0055_099_55099558_qa_1" description = "What is the baseline accuracy if all messages were predicted as non-spam (ham) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "87" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0055_099_55099558_qa_5/task.toml b/tasks/0055_099_55099558_qa_5/task.toml index 3e0ac00a5fa3c1bb63e938a2f57f2cf26aca1afe..8f593f3321e6c810f19ba0d47717654c7ccce661 100644 --- a/tasks/0055_099_55099558_qa_5/task.toml +++ b/tasks/0055_099_55099558_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0055_099_55099558_qa_5" +name = "smoldataenvs-train/0055_099_55099558_qa_5" description = "What percentage of the messages in the dataset are classified as spam?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13.4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0055_115_55115812_qa_1/task.toml b/tasks/0055_115_55115812_qa_1/task.toml index 8b00baa3e6960847b8a94c4815f85e8dae30334d..ceac4f2f6f92244ccadbc31e3d37383392c4098f 100644 --- a/tasks/0055_115_55115812_qa_1/task.toml +++ b/tasks/0055_115_55115812_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0055_115_55115812_qa_1" +name = "smoldataenvs-train/0055_115_55115812_qa_1" description = "How many duplicate entries were present in the original wine quality dataset before removal?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "240" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0055_127_55127360_qa_4/task.toml b/tasks/0055_127_55127360_qa_4/task.toml index 7545c318c49dbddd7e2a42579c7ee7c924abfe50..925db393c95ab54f148396df91ec9ac67e0aa41c 100644 --- a/tasks/0055_127_55127360_qa_4/task.toml +++ b/tasks/0055_127_55127360_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0055_127_55127360_qa_4" +name = "smoldataenvs-train/0055_127_55127360_qa_4" description = "What is the accuracy of the Support Vector Machine (SVM) classifier on the test set after normalization and train-test split?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "80.65" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0055_146_55146252_qa_2/task.toml b/tasks/0055_146_55146252_qa_2/task.toml index 6606c0df40e58f3d697d1f308b2b01dc1e67df17..59acc050b6f7d20d57ab5d1c0033f9611736d782 100644 --- a/tasks/0055_146_55146252_qa_2/task.toml +++ b/tasks/0055_146_55146252_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0055_146_55146252_qa_2" +name = "smoldataenvs-train/0055_146_55146252_qa_2" description = "After replacing zeros with NaNs, which feature has the highest number of missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Insulin" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0055_165_55165511_qa_4/task.toml b/tasks/0055_165_55165511_qa_4/task.toml index bf8aa849febfbb9c140f8f22b4c93ec264e153f0..e4b816c7d7ee1c14cc42a330646495c2dd115991 100644 --- a/tasks/0055_165_55165511_qa_4/task.toml +++ b/tasks/0055_165_55165511_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0055_165_55165511_qa_4" +name = "smoldataenvs-train/0055_165_55165511_qa_4" description = "What value was used to impute missing entries in the 'Dependents' column during data preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0055_278_55278536_qa_4/task.toml b/tasks/0055_278_55278536_qa_4/task.toml index 698d37f94874a6d90a693b0bec80dde377a24667..e5bbc4d0e125cc6fc39da5eb24b93e2ce5f58249 100644 --- a/tasks/0055_278_55278536_qa_4/task.toml +++ b/tasks/0055_278_55278536_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0055_278_55278536_qa_4" +name = "smoldataenvs-train/0055_278_55278536_qa_4" description = "What median value was used to impute missing values in the Albumin_and_Globulin_Ratio column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.93" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0055_278_55278536_qa_5/task.toml b/tasks/0055_278_55278536_qa_5/task.toml index bea4af1dc3319d1fc14529127d71b22d54df9248..468c71ff9ae2ea1e20f67ae5943b61922a7f88b8 100644 --- a/tasks/0055_278_55278536_qa_5/task.toml +++ b/tasks/0055_278_55278536_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0055_278_55278536_qa_5" +name = "smoldataenvs-train/0055_278_55278536_qa_5" description = "What percentage of the original dataset corresponds to patients diagnosed with liver disease (Dataset = 2)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "28.6" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0055_310_55310713_qa_1/task.toml b/tasks/0055_310_55310713_qa_1/task.toml index 78fb0eb10a821682400eb45ea4cca9f4a81cc842..5bead9ad6bae7762750b0244d9e8641d85210b3e 100644 --- a/tasks/0055_310_55310713_qa_1/task.toml +++ b/tasks/0055_310_55310713_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0055_310_55310713_qa_1" +name = "smoldataenvs-train/0055_310_55310713_qa_1" description = "Which year had the highest number of video game releases based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2009" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0055_310_55310713_qa_3/task.toml b/tasks/0055_310_55310713_qa_3/task.toml index 286566747570d7c16ce7c5b16c3d4864f09cb525..d4473d1f051d03da63b66352371c695df1a66be1 100644 --- a/tasks/0055_310_55310713_qa_3/task.toml +++ b/tasks/0055_310_55310713_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0055_310_55310713_qa_3" +name = "smoldataenvs-train/0055_310_55310713_qa_3" description = "Which platform achieved the highest total global sales in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PS2" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0055_310_55310713_qa_4/task.toml b/tasks/0055_310_55310713_qa_4/task.toml index e85643dac94fd15dcf95c41498553f4f63aea7c5..8f986407beecd026900a30b5ac27f181ff9dba58 100644 --- a/tasks/0055_310_55310713_qa_4/task.toml +++ b/tasks/0055_310_55310713_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0055_310_55310713_qa_4" +name = "smoldataenvs-train/0055_310_55310713_qa_4" description = "Which publisher had the most game releases in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic Arts" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0055_335_55335995_qa_1/task.toml b/tasks/0055_335_55335995_qa_1/task.toml index 83657b4b63d66758fb6f49c5a669ccaaaa63dfac..e74b9312864c1fb1547d22825ce07f9a7eceb2ed 100644 --- a/tasks/0055_335_55335995_qa_1/task.toml +++ b/tasks/0055_335_55335995_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0055_335_55335995_qa_1" +name = "smoldataenvs-train/0055_335_55335995_qa_1" description = "What is the predicted price of a plot with 3000 square feet of living space according to the linear regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "799818.30" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0055_335_55335995_qa_3/task.toml b/tasks/0055_335_55335995_qa_3/task.toml index 5fb802886f192a312a604e37e83e8626bbdd3f3a..3fca9f4ee25c13006202fefda99e5af5d4915e20 100644 --- a/tasks/0055_335_55335995_qa_3/task.toml +++ b/tasks/0055_335_55335995_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0055_335_55335995_qa_3" +name = "smoldataenvs-train/0055_335_55335995_qa_3" description = "How many data points were removed from the dataset as outliers based on the sqft_living threshold of 13000?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0055_388_55388977_qa_2/task.toml b/tasks/0055_388_55388977_qa_2/task.toml index 6a022764a91ae5caee0e2ac27d4d3379586f32fd..89b8e4e18ad138d71de22d64d19598df65c573da 100644 --- a/tasks/0055_388_55388977_qa_2/task.toml +++ b/tasks/0055_388_55388977_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0055_388_55388977_qa_2" +name = "smoldataenvs-train/0055_388_55388977_qa_2" description = "What is the coefficient of determination (R²) score of the Random Forest model on the test dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.98134" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0055_388_55388977_qa_5/task.toml b/tasks/0055_388_55388977_qa_5/task.toml index b729f9bb529aa014e0f4933225049afbb3bdf677..b9160f89213599017e59d1e5572cf50784a721d3 100644 --- a/tasks/0055_388_55388977_qa_5/task.toml +++ b/tasks/0055_388_55388977_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0055_388_55388977_qa_5" +name = "smoldataenvs-train/0055_388_55388977_qa_5" description = "What is the coefficient of determination (R²) score of the Random Forest model on the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.99734" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0055_474_55474406_qa_3/task.toml b/tasks/0055_474_55474406_qa_3/task.toml index 0c609193de2e05b2db2ce305c696234397327df4..9f1909b3755d9345eda2c4580ac37c890d648044 100644 --- a/tasks/0055_474_55474406_qa_3/task.toml +++ b/tasks/0055_474_55474406_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0055_474_55474406_qa_3" +name = "smoldataenvs-train/0055_474_55474406_qa_3" description = "What is the difference in precision between the baseline model and the model with a maximum depth of 4 for the \"edible\" class in the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.02" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0055_474_55474406_qa_4/task.toml b/tasks/0055_474_55474406_qa_4/task.toml index f376b347801554c6617be8046515ca9886bd3912..be44c19f9db7255e440b6afe3a4dfe7837bb3d65 100644 --- a/tasks/0055_474_55474406_qa_4/task.toml +++ b/tasks/0055_474_55474406_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0055_474_55474406_qa_4" +name = "smoldataenvs-train/0055_474_55474406_qa_4" description = "How many features were removed from the original dataset when retaining only the top 4 most important features for the reduced model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "18" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0055_521_55521574_qa_5/task.toml b/tasks/0055_521_55521574_qa_5/task.toml index 039069acdcf0180447543dcdfd004a440638687d..3e8b30aba66a88d0cccad9969664990b3c5f865e 100644 --- a/tasks/0055_521_55521574_qa_5/task.toml +++ b/tasks/0055_521_55521574_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0055_521_55521574_qa_5" +name = "smoldataenvs-train/0055_521_55521574_qa_5" description = "What is the accuracy of the Multinomial Naive Bayes model on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.82" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0055_556_55556459_qa_2/task.toml b/tasks/0055_556_55556459_qa_2/task.toml index c3984bdfb827852f7c9936991a7ae1c54d7fe0f9..0ccdf8eae1aaf5f5a7cd8aeeed4eceeb000bf9e2 100644 --- a/tasks/0055_556_55556459_qa_2/task.toml +++ b/tasks/0055_556_55556459_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0055_556_55556459_qa_2" +name = "smoldataenvs-train/0055_556_55556459_qa_2" description = "Which primary type (TYPE 1) has the highest median total stats according to the boxplot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Dragon" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0055_556_55556459_qa_3/task.toml b/tasks/0055_556_55556459_qa_3/task.toml index 767e7cc7460dd6f3cc5b1f4ff168faac2360f815..728203b45d1684d90c37146249661ebc340be19a 100644 --- a/tasks/0055_556_55556459_qa_3/task.toml +++ b/tasks/0055_556_55556459_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0055_556_55556459_qa_3" +name = "smoldataenvs-train/0055_556_55556459_qa_3" description = "Which primary type (TYPE 1) has the highest number of legendary Pokémon in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Psychic" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0055_625_55625808_qa_5/task.toml b/tasks/0055_625_55625808_qa_5/task.toml index 25cf153d79db6b0949b53a0c3e1b2909c4348aae..b0156253cf4583b6dfc93b0022ef105001ca60a0 100644 --- a/tasks/0055_625_55625808_qa_5/task.toml +++ b/tasks/0055_625_55625808_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0055_625_55625808_qa_5" +name = "smoldataenvs-train/0055_625_55625808_qa_5" description = "Is there a statistically significant difference in median global sales between Platform genre games that are platform-exclusive (Platform_Count=1) versus multi-platform (Platform_Count>1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0055_676_55676685_qa_3/task.toml b/tasks/0055_676_55676685_qa_3/task.toml index 9b12c1a77a088304121819a23872a39c57cfeab8..a67dcbf704fa24b2649b1fb9cd7e64f67b765f2b 100644 --- a/tasks/0055_676_55676685_qa_3/task.toml +++ b/tasks/0055_676_55676685_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0055_676_55676685_qa_3" +name = "smoldataenvs-train/0055_676_55676685_qa_3" description = "Which categorical feature had the highest number of unique categories before encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "gill-color" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0055_694_55694303_qa_5/task.toml b/tasks/0055_694_55694303_qa_5/task.toml index 5e4cfde1df76377ba27dcb99d29e45d30e598637..f049498e172faf2e9d95e55c97cbbe89d020fc4b 100644 --- a/tasks/0055_694_55694303_qa_5/task.toml +++ b/tasks/0055_694_55694303_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0055_694_55694303_qa_5" +name = "smoldataenvs-train/0055_694_55694303_qa_5" description = "What is the total number of missing values in the Failure Reason column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "33" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0055_717_55717074_qa_3/task.toml b/tasks/0055_717_55717074_qa_3/task.toml index de3661a86b06e8aca4604918c22f23e122c76e4c..2fd25a69180ce231d2187af44d2322591bb83006 100644 --- a/tasks/0055_717_55717074_qa_3/task.toml +++ b/tasks/0055_717_55717074_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0055_717_55717074_qa_3" +name = "smoldataenvs-train/0055_717_55717074_qa_3" description = "What is the median Glucose level for individuals classified as non-diabetic in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "107.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0055_717_55717074_qa_4/task.toml b/tasks/0055_717_55717074_qa_4/task.toml index 7648826539b907c37eb11e771c6ed549281bb980..b84abc5c3f697d6052cfbb1afbfc67381706acb1 100644 --- a/tasks/0055_717_55717074_qa_4/task.toml +++ b/tasks/0055_717_55717074_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0055_717_55717074_qa_4" +name = "smoldataenvs-train/0055_717_55717074_qa_4" description = "How many more individuals are classified as non-diabetic compared to diabetic in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "232" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0055_752_55752704_qa_2/task.toml b/tasks/0055_752_55752704_qa_2/task.toml index a0b71753bc17e8cd67a484fbde37d513c66f4198..d9b6bb87f66c022df84930af8b4c9a0702ab5ac2 100644 --- a/tasks/0055_752_55752704_qa_2/task.toml +++ b/tasks/0055_752_55752704_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0055_752_55752704_qa_2" +name = "smoldataenvs-train/0055_752_55752704_qa_2" description = "Which country generated the highest total gross sales (TotalAmount) in the cleaned dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "United Kingdom" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0055_790_55790118_qa_1/task.toml b/tasks/0055_790_55790118_qa_1/task.toml index 7c66efded9fbc2eb3c7b27cb325cded9d9b66bf0..b531c57261d0483c618963ae3e9a62783b8b162d 100644 --- a/tasks/0055_790_55790118_qa_1/task.toml +++ b/tasks/0055_790_55790118_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0055_790_55790118_qa_1" +name = "smoldataenvs-train/0055_790_55790118_qa_1" description = "Which workclass category has the highest number of individuals in the dataset, and what is that count?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Private, 22696" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0055_790_55790118_qa_2/task.toml b/tasks/0055_790_55790118_qa_2/task.toml index fdb17c945254046edb0139045efedf11c63feda1..d70a70257444382e531200b265fcb72b4d56241a 100644 --- a/tasks/0055_790_55790118_qa_2/task.toml +++ b/tasks/0055_790_55790118_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0055_790_55790118_qa_2" +name = "smoldataenvs-train/0055_790_55790118_qa_2" description = "What is the numerical value assigned to 'Female' in the 'sex' column after label encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0055_790_55790118_qa_3/task.toml b/tasks/0055_790_55790118_qa_3/task.toml index c0356c253fd190d64fea129b9b051f2ae95f3088..4d515ef1822ac3d36020827c5f6c761576e81b08 100644 --- a/tasks/0055_790_55790118_qa_3/task.toml +++ b/tasks/0055_790_55790118_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0055_790_55790118_qa_3" +name = "smoldataenvs-train/0055_790_55790118_qa_3" description = "Which occupation has the highest number of individuals in the dataset, and what is that count?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Prof-specialty, 4140" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0055_824_55824440_qa_3/task.toml b/tasks/0055_824_55824440_qa_3/task.toml index 831dcebe44b5e468afab00d1bfcba00adc2567d7..f641fa14ff2ad5e0a8b51fe33d92c58b61fd04f2 100644 --- a/tasks/0055_824_55824440_qa_3/task.toml +++ b/tasks/0055_824_55824440_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0055_824_55824440_qa_3" +name = "smoldataenvs-train/0055_824_55824440_qa_3" description = "Which feature shows the highest correlation with the diabetes outcome (Outcome=1) based on the correlation analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0055_824_55824440_qa_5/task.toml b/tasks/0055_824_55824440_qa_5/task.toml index b9ed5a7ef93ec09d0b00d11830e294a63f7a51da..b8db1c5fde680fdb969b98103350c770d8640a5f 100644 --- a/tasks/0055_824_55824440_qa_5/task.toml +++ b/tasks/0055_824_55824440_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0055_824_55824440_qa_5" +name = "smoldataenvs-train/0055_824_55824440_qa_5" description = "What percentage of the dataset consists of individuals with a BMI classified as \"Obese\"?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "62.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0055_834_55834525_qa_1/task.toml b/tasks/0055_834_55834525_qa_1/task.toml index a3819d777c55d3d56c9bbc259441259caca5c12c..b6810e15cee56212766673bc46370727328bb27d 100644 --- a/tasks/0055_834_55834525_qa_1/task.toml +++ b/tasks/0055_834_55834525_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0055_834_55834525_qa_1" +name = "smoldataenvs-train/0055_834_55834525_qa_1" description = "What percentage of loans in the dataset were paid on time (PAIDOFF status)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "60" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0055_859_55859289_qa_2/task.toml b/tasks/0055_859_55859289_qa_2/task.toml index a56aa894a4195c04e5d65b9bbc1929bef3e779c5..510f52472af12a4b20f1f6549e0ddb0a3795b979 100644 --- a/tasks/0055_859_55859289_qa_2/task.toml +++ b/tasks/0055_859_55859289_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0055_859_55859289_qa_2" +name = "smoldataenvs-train/0055_859_55859289_qa_2" description = "What is the final mean squared error loss value of the LSTM model after completing 300 training epochs?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.0048" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0055_861_55861850_qa_5/task.toml b/tasks/0055_861_55861850_qa_5/task.toml index 6b3ac7dec78e4d4d81ed9fdb04ea9bbb27292245..1563733214ea0b46e155839b491508ed3f91fa3b 100644 --- a/tasks/0055_861_55861850_qa_5/task.toml +++ b/tasks/0055_861_55861850_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0055_861_55861850_qa_5" +name = "smoldataenvs-train/0055_861_55861850_qa_5" description = "What is the total sales value contributed by the platform with the third-highest global sales in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "957.89" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0055_968_55968459_qa_4/task.toml b/tasks/0055_968_55968459_qa_4/task.toml index 3b28b21c13b8ef5867dc67a3f520a19767d1d341..f4627ba8bba4be440abde9ef6437f9eada7b86a4 100644 --- a/tasks/0055_968_55968459_qa_4/task.toml +++ b/tasks/0055_968_55968459_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0055_968_55968459_qa_4" +name = "smoldataenvs-train/0055_968_55968459_qa_4" description = "Which categorical variable encoding technique was applied to the \"State\" column, and how many new binary features were created from it?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "One-hot encoding, 3" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0056_028_56028891_qa_3/task.toml b/tasks/0056_028_56028891_qa_3/task.toml index 579d4e2b9fcfd4dcf28f6538ea4b434df752cafb..f03d2561306ca7349c1f3f6ef24cb0c774b5e241 100644 --- a/tasks/0056_028_56028891_qa_3/task.toml +++ b/tasks/0056_028_56028891_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0056_028_56028891_qa_3" +name = "smoldataenvs-train/0056_028_56028891_qa_3" description = "Which platform has the highest average sales in each of the four regions (North America, Europe, Japan, and Other) according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "North America: NES, Europe: Game Boy, Japan: NES, Other: PS4" reward_mode_initial = "list" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0056_060_56060924_qa_4/task.toml b/tasks/0056_060_56060924_qa_4/task.toml index fa78cd8e9c2b700e40e2fd1d3f1e76d1494f6024..4576a0bd7f49c85527c1a56506e43e052b66d8ab 100644 --- a/tasks/0056_060_56060924_qa_4/task.toml +++ b/tasks/0056_060_56060924_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0056_060_56060924_qa_4" +name = "smoldataenvs-train/0056_060_56060924_qa_4" description = "What is the maximum number of contacts performed during the campaign, as indicated by the 'campaign' column before outlier treatment?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "63" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0056_060_56060924_qa_5/task.toml b/tasks/0056_060_56060924_qa_5/task.toml index f1291f4d0f56dcfed2b5591a62d8290da13f1b4b..536e00e9ded0bbd5c4a625f5f607c9ff8db88865 100644 --- a/tasks/0056_060_56060924_qa_5/task.toml +++ b/tasks/0056_060_56060924_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0056_060_56060924_qa_5" +name = "smoldataenvs-train/0056_060_56060924_qa_5" description = "What is the average value of the 'previous' column, representing the average number of previous marketing campaign contacts?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.832557" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0056_160_56160021_qa_5/task.toml b/tasks/0056_160_56160021_qa_5/task.toml index 1abee713d6b8e68dbef972d9ec95a665caa7af2a..4323e45ce23b4b375310bdb411ef117eacd3888c 100644 --- a/tasks/0056_160_56160021_qa_5/task.toml +++ b/tasks/0056_160_56160021_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0056_160_56160021_qa_5" +name = "smoldataenvs-train/0056_160_56160021_qa_5" description = "What is the total number of data instances in the original Iris dataset before any data filtering or column dropping operations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "150" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0056_167_56167051_qa_3/task.toml b/tasks/0056_167_56167051_qa_3/task.toml index 03a00929d04c0c0195e5b1cc33419d4f6726c160..e8f66498bf04ba6dd90e18cfe1058e72167c3a97 100644 --- a/tasks/0056_167_56167051_qa_3/task.toml +++ b/tasks/0056_167_56167051_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0056_167_56167051_qa_3" +name = "smoldataenvs-train/0056_167_56167051_qa_3" description = "What is the difference in number of video games between the top two platforms (DS and PS2)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0056_167_56167051_qa_4/task.toml b/tasks/0056_167_56167051_qa_4/task.toml index 01ec037c74c0f14460a931365b8d4eaeb998bf2f..694a0902f901567c84303ac738fa16f70e9162e8 100644 --- a/tasks/0056_167_56167051_qa_4/task.toml +++ b/tasks/0056_167_56167051_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0056_167_56167051_qa_4" +name = "smoldataenvs-train/0056_167_56167051_qa_4" description = "Which genre has the highest number of video games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0056_224_56224214_qa_4/task.toml b/tasks/0056_224_56224214_qa_4/task.toml index eda9360befe8a2c0718e4b28ba41e54581f44cda..ce0d2e2e1bdd98fc21a66d6c89593b2677bbce18 100644 --- a/tasks/0056_224_56224214_qa_4/task.toml +++ b/tasks/0056_224_56224214_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0056_224_56224214_qa_4" +name = "smoldataenvs-train/0056_224_56224214_qa_4" description = "What is the difference between the highest and lowest individual classifier test accuracy when trained on separate data subsets?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.06" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0056_266_56266405_qa_3/task.toml b/tasks/0056_266_56266405_qa_3/task.toml index 5f42082340ff32402eef6108f8a89c44fa4558e3..cedef1f4a7608be51370e8fec94ab5f06c943157 100644 --- a/tasks/0056_266_56266405_qa_3/task.toml +++ b/tasks/0056_266_56266405_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0056_266_56266405_qa_3" +name = "smoldataenvs-train/0056_266_56266405_qa_3" description = "Which occupation has the highest ratio of individuals earning more than 50K according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Exec-Managerial" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0056_266_56266405_qa_4/task.toml b/tasks/0056_266_56266405_qa_4/task.toml index 45b0896657e98eb5b9bfa2f7783ad5b3b0d5a0c2..5a5749c7610797fbe119815f9919f49612eab29e 100644 --- a/tasks/0056_266_56266405_qa_4/task.toml +++ b/tasks/0056_266_56266405_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0056_266_56266405_qa_4" +name = "smoldataenvs-train/0056_266_56266405_qa_4" description = "What is the average capital gain for all individuals in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1085.15" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0056_269_56269828_qa_2/task.toml b/tasks/0056_269_56269828_qa_2/task.toml index c7f9998e6bf4b7c878d59eff16aa0d90b7be3a06..6df2d44fff7c9b32f292d9c4bcdb6ec5d5fc3550 100644 --- a/tasks/0056_269_56269828_qa_2/task.toml +++ b/tasks/0056_269_56269828_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0056_269_56269828_qa_2" +name = "smoldataenvs-train/0056_269_56269828_qa_2" description = "What is the skewness value of the 'Alcohol' distribution after handling missing values in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.5972237000216292" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0056_269_56269828_qa_3/task.toml b/tasks/0056_269_56269828_qa_3/task.toml index 60679f8fff1509272eed961dc6503635e8d0745e..8bd3839fd0156b5051e77f4c081d9f5990b85af3 100644 --- a/tasks/0056_269_56269828_qa_3/task.toml +++ b/tasks/0056_269_56269828_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0056_269_56269828_qa_3" +name = "smoldataenvs-train/0056_269_56269828_qa_3" description = "Which feature shows the strongest positive correlation with 'Schooling' after imputing missing values in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Income composition of resources" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0056_343_56343164_qa_3/task.toml b/tasks/0056_343_56343164_qa_3/task.toml index cc99db83266bd1c6d99d09db32f022f9e4d3d860..6e2cdbbf879cd4311ed10d107ad55639d22a03df 100644 --- a/tasks/0056_343_56343164_qa_3/task.toml +++ b/tasks/0056_343_56343164_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0056_343_56343164_qa_3" +name = "smoldataenvs-train/0056_343_56343164_qa_3" description = "What is the median age of all patients in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "29" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0056_349_56349410_qa_4/task.toml b/tasks/0056_349_56349410_qa_4/task.toml index 34124144650d5d743277d76cbd8e3908880db3ed..c5e031a2f256dd84b63bb0a27e975b959a8a8a74 100644 --- a/tasks/0056_349_56349410_qa_4/task.toml +++ b/tasks/0056_349_56349410_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0056_349_56349410_qa_4" +name = "smoldataenvs-train/0056_349_56349410_qa_4" description = "Which class had the highest recall score when using the Support Vector Machine (SVM) model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0056_391_56391883_qa_3/task.toml b/tasks/0056_391_56391883_qa_3/task.toml index 309e25690641ab9a8cbf071e6f232016e90dd9dd..23db1aab8530cb8a4a1cde1f67c7ebc3b78cbed6 100644 --- a/tasks/0056_391_56391883_qa_3/task.toml +++ b/tasks/0056_391_56391883_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0056_391_56391883_qa_3" +name = "smoldataenvs-train/0056_391_56391883_qa_3" description = "In which year between 2001 and 2012 was the total number of suicides the highest?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2011" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0056_391_56391883_qa_4/task.toml b/tasks/0056_391_56391883_qa_4/task.toml index 8d3bf9cd6f638b99d61f5406deed63980c009884..590ca58b63b671ccd73e3041a00b22d5b77c61e1 100644 --- a/tasks/0056_391_56391883_qa_4/task.toml +++ b/tasks/0056_391_56391883_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0056_391_56391883_qa_4" +name = "smoldataenvs-train/0056_391_56391883_qa_4" description = "Among the listed causes of suicide, which specific cause has the highest total count in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Family Problems" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0056_410_56410958_qa_1/task.toml b/tasks/0056_410_56410958_qa_1/task.toml index ed2ff01b1c39058d9bb48a189e8cfbeb425da752..c55c440f596d644930fa97476fdaedb20c27ac2e 100644 --- a/tasks/0056_410_56410958_qa_1/task.toml +++ b/tasks/0056_410_56410958_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0056_410_56410958_qa_1" +name = "smoldataenvs-train/0056_410_56410958_qa_1" description = "Which machine learning algorithm achieved the highest accuracy in predicting employee attrition based on the transformed dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Random Forest" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0056_410_56410958_qa_4/task.toml b/tasks/0056_410_56410958_qa_4/task.toml index 5f1833b991417a6ebe00729de7b88972e37d0e1b..d80265209fa76b5260fc3f09ca33375be5a408e5 100644 --- a/tasks/0056_410_56410958_qa_4/task.toml +++ b/tasks/0056_410_56410958_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0056_410_56410958_qa_4" +name = "smoldataenvs-train/0056_410_56410958_qa_4" description = "What is the difference in average monthly working hours between employees who left and those who stayed, based on the quantitative feature analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8.359" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0056_414_56414713_qa_1/task.toml b/tasks/0056_414_56414713_qa_1/task.toml index 6ec350db01cb6cecd682a16a7f650b5103e0efa1..65b8bcbd7693dc05b016978a3bec276470f66698 100644 --- a/tasks/0056_414_56414713_qa_1/task.toml +++ b/tasks/0056_414_56414713_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0056_414_56414713_qa_1" +name = "smoldataenvs-train/0056_414_56414713_qa_1" description = "What is the highest upper whisker value for the 'alcohol' feature across all wine quality classes based on the outlier analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "14.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0056_414_56414713_qa_4/task.toml b/tasks/0056_414_56414713_qa_4/task.toml index d83f22bc87fcb5b965f4d13e89749175f5c7e31e..5b0f59307d3835c2d359d6203980e9b98a273a76 100644 --- a/tasks/0056_414_56414713_qa_4/task.toml +++ b/tasks/0056_414_56414713_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0056_414_56414713_qa_4" +name = "smoldataenvs-train/0056_414_56414713_qa_4" description = "What is the accuracy score achieved by the Random Forest model after binning the wine quality into three categories (bad, medium, good)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.8651" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0056_464_56464038_qa_1/task.toml b/tasks/0056_464_56464038_qa_1/task.toml index 9b040f376b08c87f720d4a4cbb5dd03517e6b920..c1c9c6fe8002086b5e536a59cbac10608e0bae90 100644 --- a/tasks/0056_464_56464038_qa_1/task.toml +++ b/tasks/0056_464_56464038_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0056_464_56464038_qa_1" +name = "smoldataenvs-train/0056_464_56464038_qa_1" description = "What percentage of individuals in the dataset are diagnosed with diabetes (Outcome=1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.8958" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0056_486_56486518_qa_2/task.toml b/tasks/0056_486_56486518_qa_2/task.toml index 7f7ffb964a4a3f7afedc2fdfcc512b2630c72d1c..086bc393bb78f53b077072c68e5dc4233fbb4784 100644 --- a/tasks/0056_486_56486518_qa_2/task.toml +++ b/tasks/0056_486_56486518_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0056_486_56486518_qa_2" +name = "smoldataenvs-train/0056_486_56486518_qa_2" description = "How many features in the dataset have more than 10 unique categorical values after label encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0056_498_56498187_qa_2/task.toml b/tasks/0056_498_56498187_qa_2/task.toml index 576bb17eb219d5df4feae0d245978b17b886784e..164374eba7a76890dc3c258253e94cc2ec6a9371 100644 --- a/tasks/0056_498_56498187_qa_2/task.toml +++ b/tasks/0056_498_56498187_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0056_498_56498187_qa_2" +name = "smoldataenvs-train/0056_498_56498187_qa_2" description = "What percentage of the time does the NO₂ concentration exceed 150 μg/m³ in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "18.4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0056_526_56526864_qa_2/task.toml b/tasks/0056_526_56526864_qa_2/task.toml index 2ce8e5457ce61cdeb0118ff4de06e2d024efdedc..d34ccb0d5198d66629afc5479d90b2e27ad62b55 100644 --- a/tasks/0056_526_56526864_qa_2/task.toml +++ b/tasks/0056_526_56526864_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0056_526_56526864_qa_2" +name = "smoldataenvs-train/0056_526_56526864_qa_2" description = "After applying dropna with axis='columns' and thresh=20600, how many columns are removed from the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0056_538_56538672_qa_5/task.toml b/tasks/0056_538_56538672_qa_5/task.toml index 80b08d83d8e2a03da8a9953a7ebcebc990f7510d..0fe25a5862cf5b9294b4d8f047a582db8fb840e8 100644 --- a/tasks/0056_538_56538672_qa_5/task.toml +++ b/tasks/0056_538_56538672_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0056_538_56538672_qa_5" +name = "smoldataenvs-train/0056_538_56538672_qa_5" description = "What is the F1-score for the final SVM model on the test data for the positive class (gender = 1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.98" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0056_603_56603541_qa_2/task.toml b/tasks/0056_603_56603541_qa_2/task.toml index d4e09a970b6fc84e9c649cff5fe88e4752e568a2..f361b4b0bc136f3133aa3addf9221c52f3cc7635 100644 --- a/tasks/0056_603_56603541_qa_2/task.toml +++ b/tasks/0056_603_56603541_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0056_603_56603541_qa_2" +name = "smoldataenvs-train/0056_603_56603541_qa_2" description = "Which star rating (1-5) has the highest frequency in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0056_615_56615334_qa_2/task.toml b/tasks/0056_615_56615334_qa_2/task.toml index 798678a9aa763140661d5f01899045a835c12791..85f84204c43f82ca8010eec92d5be4abc509da82 100644 --- a/tasks/0056_615_56615334_qa_2/task.toml +++ b/tasks/0056_615_56615334_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0056_615_56615334_qa_2" +name = "smoldataenvs-train/0056_615_56615334_qa_2" description = "Which feature in the dataset has the highest percentage of missing values before any imputation was performed?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "MINIMUM_PAYMENTS" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0056_615_56615334_qa_3/task.toml b/tasks/0056_615_56615334_qa_3/task.toml index 668dd11b7eb564733a48f9cc7130d9c011a77590..3bee77c2581e3a0e3c24ac2d9de2994d2c82a346 100644 --- a/tasks/0056_615_56615334_qa_3/task.toml +++ b/tasks/0056_615_56615334_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0056_615_56615334_qa_3" +name = "smoldataenvs-train/0056_615_56615334_qa_3" description = "What is the percentage of missing values in the CREDIT_LIMIT feature prior to data preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.011173" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0056_665_56665612_qa_5/task.toml b/tasks/0056_665_56665612_qa_5/task.toml index b6d3e5596242af485d64157e8004e52f5bdad48b..ff55f152dd9ab5a7d415c82522a5e829a76ac139 100644 --- a/tasks/0056_665_56665612_qa_5/task.toml +++ b/tasks/0056_665_56665612_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0056_665_56665612_qa_5" +name = "smoldataenvs-train/0056_665_56665612_qa_5" description = "What is the maximum petal width value observed in the Iris virginica species?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0056_687_56687302_qa_2/task.toml b/tasks/0056_687_56687302_qa_2/task.toml index e3c752e205132e5de46189f2aa118bb48abf625d..b2a61a63b4876a33b1bccae0419277177a499379 100644 --- a/tasks/0056_687_56687302_qa_2/task.toml +++ b/tasks/0056_687_56687302_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0056_687_56687302_qa_2" +name = "smoldataenvs-train/0056_687_56687302_qa_2" description = "Which product description appears most frequently in the cleaned dataset, and how many times was it purchased?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "WHITE HANGING HEART T-LIGHT HOLDER, 2028" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0056_714_56714045_qa_3/task.toml b/tasks/0056_714_56714045_qa_3/task.toml index 85df70a9ea34ef0499e032c7c3beffcbfa9c95c5..11163dcddf041ea0499145e1cd1d3020cc813506 100644 --- a/tasks/0056_714_56714045_qa_3/task.toml +++ b/tasks/0056_714_56714045_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0056_714_56714045_qa_3" +name = "smoldataenvs-train/0056_714_56714045_qa_3" description = "After applying the outlier replacement thresholds, how many columns still had outliers remaining in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0056_748_56748033_qa_1/task.toml b/tasks/0056_748_56748033_qa_1/task.toml index 22ddc53f1c77d956db52e4e248d8b117d490def3..f5afb60dcbadedf49b0385ce8427e6f7086f3ca7 100644 --- a/tasks/0056_748_56748033_qa_1/task.toml +++ b/tasks/0056_748_56748033_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0056_748_56748033_qa_1" +name = "smoldataenvs-train/0056_748_56748033_qa_1" description = "What is the percentage of customers in the dataset who defaulted on their credit card payments (target class distribution)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "22.12" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0056_876_56876839_qa_5/task.toml b/tasks/0056_876_56876839_qa_5/task.toml index 56d97e30840c505503f2f773b056ce9c6ac7206d..f04b8987d3f3247fae669214c1c3671c46cf205a 100644 --- a/tasks/0056_876_56876839_qa_5/task.toml +++ b/tasks/0056_876_56876839_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0056_876_56876839_qa_5" +name = "smoldataenvs-train/0056_876_56876839_qa_5" description = "What is the mean accuracy of the best Random Forest model after hyperparameter tuning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9667" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0056_896_56896780_qa_4/task.toml b/tasks/0056_896_56896780_qa_4/task.toml index c37973315e82c86db37922afa6e1fbe2d7008299..9db641707edadf1fadee8ed7b5bc85974f4fd4e8 100644 --- a/tasks/0056_896_56896780_qa_4/task.toml +++ b/tasks/0056_896_56896780_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0056_896_56896780_qa_4" +name = "smoldataenvs-train/0056_896_56896780_qa_4" description = "What is the highest test accuracy achieved by the K-Nearest Neighbors classifier after optimizing the K parameter?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0056_907_56907084_qa_3/task.toml b/tasks/0056_907_56907084_qa_3/task.toml index 6eb3004b0ce69a29cde3304ac47d3f713b5b3f1a..144bae9d5407871133d21427e602be3b65cdc1af 100644 --- a/tasks/0056_907_56907084_qa_3/task.toml +++ b/tasks/0056_907_56907084_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0056_907_56907084_qa_3" +name = "smoldataenvs-train/0056_907_56907084_qa_3" description = "What is the mean absolute error (MAE) of the linear regression model on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4037.16" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0056_954_56954768_qa_2/task.toml b/tasks/0056_954_56954768_qa_2/task.toml index 994bbe16545bf16a7e71e03b0909d314bc310516..db3a3183cc450bc9792e899dbc2f14c23793241b 100644 --- a/tasks/0056_954_56954768_qa_2/task.toml +++ b/tasks/0056_954_56954768_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0056_954_56954768_qa_2" +name = "smoldataenvs-train/0056_954_56954768_qa_2" description = "Which video game genre has the highest cumulative global sales across all years in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0056_998_56998695_qa_4/task.toml b/tasks/0056_998_56998695_qa_4/task.toml index dafaf57c849e82793e773b5f9d1e9fec689d7b2d..92f18a31de271d1c67bea2f213ec66279c6cbd89 100644 --- a/tasks/0056_998_56998695_qa_4/task.toml +++ b/tasks/0056_998_56998695_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0056_998_56998695_qa_4" +name = "smoldataenvs-train/0056_998_56998695_qa_4" description = "According to the correlation analysis, what is the ranking of the \"Four C's\" (carat, color, clarity, cut) in terms of their absolute impact on diamond price prediction?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "carat, color, clarity, cut" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0057_106_57106259_qa_1/task.toml b/tasks/0057_106_57106259_qa_1/task.toml index affddf47543157e321945bdcc659e23d8a5c2274..8d8292d0bbdfa5ea2fea7452f5770d2b6d82bb94 100644 --- a/tasks/0057_106_57106259_qa_1/task.toml +++ b/tasks/0057_106_57106259_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0057_106_57106259_qa_1" +name = "smoldataenvs-train/0057_106_57106259_qa_1" description = "What is the number of passengers with missing age values in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "177" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0057_110_57110418_qa_4/task.toml b/tasks/0057_110_57110418_qa_4/task.toml index 4b6dc2a207b71e351e1c2857db467c0331b14df3..266fa89966100eebf7190815154352bef49cdead 100644 --- a/tasks/0057_110_57110418_qa_4/task.toml +++ b/tasks/0057_110_57110418_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0057_110_57110418_qa_4" +name = "smoldataenvs-train/0057_110_57110418_qa_4" description = "How many unique categories does the 'TotalCharges' feature have in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6531" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0057_142_57142499_qa_5/task.toml b/tasks/0057_142_57142499_qa_5/task.toml index 1e188ccda20c4c89d7e48a593f8c629166d076a7..406a84079ff14161a39a838e8204942b550831c7 100644 --- a/tasks/0057_142_57142499_qa_5/task.toml +++ b/tasks/0057_142_57142499_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0057_142_57142499_qa_5" +name = "smoldataenvs-train/0057_142_57142499_qa_5" description = "Which model showed the highest area under the ROC curve (AUC) across all tested classification models?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Logistic regression" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0057_269_57269792_qa_5/task.toml b/tasks/0057_269_57269792_qa_5/task.toml index a1f9c9c0476941048cfc405b71894f495d38e287..7723803fd3a8f642181199a48dede3a0620d6c53 100644 --- a/tasks/0057_269_57269792_qa_5/task.toml +++ b/tasks/0057_269_57269792_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0057_269_57269792_qa_5" +name = "smoldataenvs-train/0057_269_57269792_qa_5" description = "Which professional profile in Maharashtra had the highest number of suicides according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Farming/Agriculture Activity" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0057_378_57378613_qa_3/task.toml b/tasks/0057_378_57378613_qa_3/task.toml index 2b03d8dbdc7a9324a02aaaef87d60195488f8bf1..8cf79b89ea0eb0e5a60aae022ea0327350306968 100644 --- a/tasks/0057_378_57378613_qa_3/task.toml +++ b/tasks/0057_378_57378613_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0057_378_57378613_qa_3" +name = "smoldataenvs-train/0057_378_57378613_qa_3" description = "What is the predicted sacral slope value for a pelvic incidence of 15 using the random forest regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16.19687977" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0057_406_57406107_qa_4/task.toml b/tasks/0057_406_57406107_qa_4/task.toml index 75abccf85123a1e5ade31cfdbd18e072ec87951f..266d77db70964c791c0d9f6d5c019c50708ba933 100644 --- a/tasks/0057_406_57406107_qa_4/task.toml +++ b/tasks/0057_406_57406107_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0057_406_57406107_qa_4" +name = "smoldataenvs-train/0057_406_57406107_qa_4" description = "For the first month in the dataset (1949-01), which Triple Exponential Smoothing model (additive or multiplicative seasonality) produces a higher fitted value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Additive" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0057_443_57443371_qa_1/task.toml b/tasks/0057_443_57443371_qa_1/task.toml index b89f6c7853f5739c0eaa91932da5e1c36bcdd1c7..093159155bf0f20113c3c06fe23b2ba562cc782a 100644 --- a/tasks/0057_443_57443371_qa_1/task.toml +++ b/tasks/0057_443_57443371_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0057_443_57443371_qa_1" +name = "smoldataenvs-train/0057_443_57443371_qa_1" description = "What is the validation accuracy of the PassiveAggressiveClassifier model on the SMS spam detection task?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9767" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0057_443_57443371_qa_2/task.toml b/tasks/0057_443_57443371_qa_2/task.toml index 4d40461d2ccfb993fb10bf33c46f8b3340caf161..3d51db13788357869ba7ecbddbebb07b32b6d040 100644 --- a/tasks/0057_443_57443371_qa_2/task.toml +++ b/tasks/0057_443_57443371_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0057_443_57443371_qa_2" +name = "smoldataenvs-train/0057_443_57443371_qa_2" description = "What percentage of the SMS messages in the dataset are classified as spam?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13.406317" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0057_476_57476983_qa_4/task.toml b/tasks/0057_476_57476983_qa_4/task.toml index 5cc13ebb9e5345f81c0210a2d3753df2f1aae22f..7b2649aae4ec61ba4e6dd556d3c7a120329091dd 100644 --- a/tasks/0057_476_57476983_qa_4/task.toml +++ b/tasks/0057_476_57476983_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0057_476_57476983_qa_4" +name = "smoldataenvs-train/0057_476_57476983_qa_4" description = "Is there a statistically significant difference in the distribution of male and female across the three age groups?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0057_503_57503702_qa_1/task.toml b/tasks/0057_503_57503702_qa_1/task.toml index 298baa26bb0a06785a3905054133c39c59c2b55e..83efd0a1cc1f1cf6299326a5c8ac49821d9359c8 100644 --- a/tasks/0057_503_57503702_qa_1/task.toml +++ b/tasks/0057_503_57503702_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0057_503_57503702_qa_1" +name = "smoldataenvs-train/0057_503_57503702_qa_1" description = "What is the optimal number of clusters determined by the Elbow method for the K-Means clustering model based on the RFM features?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0057_588_57588445_qa_1/task.toml b/tasks/0057_588_57588445_qa_1/task.toml index d462af2e61a3e35f0b1482b914aaf132b61d3638..14f927002afa0b3aedea26f33846c932b1856dc2 100644 --- a/tasks/0057_588_57588445_qa_1/task.toml +++ b/tasks/0057_588_57588445_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0057_588_57588445_qa_1" +name = "smoldataenvs-train/0057_588_57588445_qa_1" description = "What is the percentage of malignant cases in the dataset before handling class imbalance?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "37.26" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0057_588_57588445_qa_2/task.toml b/tasks/0057_588_57588445_qa_2/task.toml index 6506820883b6630b49d050b88ac22c52d9b286a3..6d2738d9e745431d0323bedc11462394bae58cda 100644 --- a/tasks/0057_588_57588445_qa_2/task.toml +++ b/tasks/0057_588_57588445_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0057_588_57588445_qa_2" +name = "smoldataenvs-train/0057_588_57588445_qa_2" description = "Which column in the dataset contains 100% missing values requiring removal for analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Unnamed: 32" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0057_589_57589842_qa_4/task.toml b/tasks/0057_589_57589842_qa_4/task.toml index fac7e17cadb4cc18669c1bddfe616f95cf2fe7de..5f72f4c09d07909640c5f0e7eca652ec3fbbf953 100644 --- a/tasks/0057_589_57589842_qa_4/task.toml +++ b/tasks/0057_589_57589842_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0057_589_57589842_qa_4" +name = "smoldataenvs-train/0057_589_57589842_qa_4" description = "What percentage of individuals in the dataset have diabetes based on the target variable distribution?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.8958" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0057_632_57632820_qa_1/task.toml b/tasks/0057_632_57632820_qa_1/task.toml index 8e513280a99692f033a1bce10e25467277b9c995..874ee5c387fc7de109fa749383e57a104a706adf 100644 --- a/tasks/0057_632_57632820_qa_1/task.toml +++ b/tasks/0057_632_57632820_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0057_632_57632820_qa_1" +name = "smoldataenvs-train/0057_632_57632820_qa_1" description = "Which wine quality rating is the most frequent in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0057_632_57632820_qa_4/task.toml b/tasks/0057_632_57632820_qa_4/task.toml index e1d93acc90e25760792a3c41cc3ba3ea7938e604..05e48a5940fac8bed8fc53681165ba443b67ffec 100644 --- a/tasks/0057_632_57632820_qa_4/task.toml +++ b/tasks/0057_632_57632820_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0057_632_57632820_qa_4" +name = "smoldataenvs-train/0057_632_57632820_qa_4" description = "What is the mean alcohol content of all wines in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10.422983" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0057_632_57632820_qa_5/task.toml b/tasks/0057_632_57632820_qa_5/task.toml index da0c6450aa31469d0e44d4d4388e301407bbabb0..18233e28cccce27b7164d4f1fba5f880063aa191 100644 --- a/tasks/0057_632_57632820_qa_5/task.toml +++ b/tasks/0057_632_57632820_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0057_632_57632820_qa_5" +name = "smoldataenvs-train/0057_632_57632820_qa_5" description = "Which feature in the dataset has the highest standard deviation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "total sulfur dioxide" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0057_639_57639553_qa_5/task.toml b/tasks/0057_639_57639553_qa_5/task.toml index 062308fa23c6350fd9ae8707a84dad4b7b41506a..935dbe9e5f44ae89286a25d7ab39e92233775e7d 100644 --- a/tasks/0057_639_57639553_qa_5/task.toml +++ b/tasks/0057_639_57639553_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0057_639_57639553_qa_5" +name = "smoldataenvs-train/0057_639_57639553_qa_5" description = "Which cohort group maintains the highest customer retention rate at 12 months among all cohorts analyzed?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2010-12" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0057_640_57640669_qa_4/task.toml b/tasks/0057_640_57640669_qa_4/task.toml index a7605b1c86b8a80e9d2e5590aad9ee088f91bceb..e2167af9707eb0dd2d3e291b73af076e0a4a250c 100644 --- a/tasks/0057_640_57640669_qa_4/task.toml +++ b/tasks/0057_640_57640669_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0057_640_57640669_qa_4" +name = "smoldataenvs-train/0057_640_57640669_qa_4" description = "How many unique words were present in the initial tokenization before limiting the vocabulary size to 3000?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9004" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0057_663_57663047_qa_2/task.toml b/tasks/0057_663_57663047_qa_2/task.toml index ac0e0c256c5b88c9cb526e57bbff46a5fc951c5a..f6daa6e53d0aee4e992865097a98cbb18bb0ace0 100644 --- a/tasks/0057_663_57663047_qa_2/task.toml +++ b/tasks/0057_663_57663047_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0057_663_57663047_qa_2" +name = "smoldataenvs-train/0057_663_57663047_qa_2" description = "What is the median tenure duration (in months) of customers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "29 months" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0057_663_57663047_qa_5/task.toml b/tasks/0057_663_57663047_qa_5/task.toml index 28d230f3b8f15dfccb056780dfd8f8a750c265f6..94a937aef864cdbe0c0bb4fd8f3f1dbd619ee343 100644 --- a/tasks/0057_663_57663047_qa_5/task.toml +++ b/tasks/0057_663_57663047_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0057_663_57663047_qa_5" +name = "smoldataenvs-train/0057_663_57663047_qa_5" description = "What is the standard deviation of the tenure values across all customers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "24.559" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0057_686_57686190_qa_1/task.toml b/tasks/0057_686_57686190_qa_1/task.toml index 781c03df13db896b88ca658770b04adcef4ec0df..ca5554611325b93b15af6325926d8ba0523e5c54 100644 --- a/tasks/0057_686_57686190_qa_1/task.toml +++ b/tasks/0057_686_57686190_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0057_686_57686190_qa_1" +name = "smoldataenvs-train/0057_686_57686190_qa_1" description = "What is the accuracy of the best performing model on the test dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9580838323353293" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0057_712_57712524_qa_2/task.toml b/tasks/0057_712_57712524_qa_2/task.toml index 209df82333e309e0c71b8f892c24778a8d116e92..5a57977973a472b83027eefbe7d3ee80bbb05ce7 100644 --- a/tasks/0057_712_57712524_qa_2/task.toml +++ b/tasks/0057_712_57712524_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0057_712_57712524_qa_2" +name = "smoldataenvs-train/0057_712_57712524_qa_2" description = "How many wines are classified as 'good' in the transformed dataset after applying the quality score threshold?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "217" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0057_712_57712524_qa_4/task.toml b/tasks/0057_712_57712524_qa_4/task.toml index 83c35b92df48e9558beae352f5ad701eda70ec91..2b7ee862eb12b46d73e5bb2c7dae0af6f37c549e 100644 --- a/tasks/0057_712_57712524_qa_4/task.toml +++ b/tasks/0057_712_57712524_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0057_712_57712524_qa_4" +name = "smoldataenvs-train/0057_712_57712524_qa_4" description = "What is the difference in the number of wines between the most common quality score and the least common quality score in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "671" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0057_712_57712524_qa_5/task.toml b/tasks/0057_712_57712524_qa_5/task.toml index 765148cf82210cbaee8975b7026f1bd6866fb6be..a25edc222b3d93720a90c36a762447e4406b1726 100644 --- a/tasks/0057_712_57712524_qa_5/task.toml +++ b/tasks/0057_712_57712524_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0057_712_57712524_qa_5" +name = "smoldataenvs-train/0057_712_57712524_qa_5" description = "Which quality score in the original dataset has the highest frequency before transformation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0057_721_57721830_qa_1/task.toml b/tasks/0057_721_57721830_qa_1/task.toml index 7f46ef881633251c08ad13828eb6c874991be9b2..31ba58f7519d54cfc3c3dc8ba88a0f1b30837a28 100644 --- a/tasks/0057_721_57721830_qa_1/task.toml +++ b/tasks/0057_721_57721830_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0057_721_57721830_qa_1" +name = "smoldataenvs-train/0057_721_57721830_qa_1" description = "Which team has won the most seasons in the IPL according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Mumbai Indians" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0057_750_57750345_qa_5/task.toml b/tasks/0057_750_57750345_qa_5/task.toml index 70cdf9490c76ba82f81da9958233a82ee175b941..76a8403f59e116ab977a1ab3a2652a82adf83c93 100644 --- a/tasks/0057_750_57750345_qa_5/task.toml +++ b/tasks/0057_750_57750345_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0057_750_57750345_qa_5" +name = "smoldataenvs-train/0057_750_57750345_qa_5" description = "Which categorical feature generated the highest number of dummy variables during OneHotEncoding of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Model" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0057_771_57771025_qa_3/task.toml b/tasks/0057_771_57771025_qa_3/task.toml index aa9bcd948b2ec18b249d26103116bd2d2c8522aa..d2859502691c3bf48bc205ecb48a017ec13c767a 100644 --- a/tasks/0057_771_57771025_qa_3/task.toml +++ b/tasks/0057_771_57771025_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0057_771_57771025_qa_3" +name = "smoldataenvs-train/0057_771_57771025_qa_3" description = "Which model achieved the highest accuracy score on the test set, and what was that score?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "SVM Classifier, Neural Network, 0.982456" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0057_778_57778333_qa_1/task.toml b/tasks/0057_778_57778333_qa_1/task.toml index 36945fa2bffc7299c8d921ebe162f90edfeedb4b..ea873eff0d220dbd4f5f28c60b98d7d0b87bcaa0 100644 --- a/tasks/0057_778_57778333_qa_1/task.toml +++ b/tasks/0057_778_57778333_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0057_778_57778333_qa_1" +name = "smoldataenvs-train/0057_778_57778333_qa_1" description = "How many rows in the dataset contain zero values in any of the x, y, or z dimensions (diamond dimensions)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0057_778_57778333_qa_3/task.toml b/tasks/0057_778_57778333_qa_3/task.toml index fc367679eec2dc0d86f9270146919d3348766428..fcd5ca21b1a62daadc3ec32f942de4fdc3165257 100644 --- a/tasks/0057_778_57778333_qa_3/task.toml +++ b/tasks/0057_778_57778333_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0057_778_57778333_qa_3" +name = "smoldataenvs-train/0057_778_57778333_qa_3" description = "What is the 75th percentile (third quartile) value of the price distribution in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5324.25" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0057_778_57778333_qa_5/task.toml b/tasks/0057_778_57778333_qa_5/task.toml index d95f7ac22fc2b4dea12d0d3759c4953b87baf671..94319dea34017780c9c9f3347202a11f74a2ad98 100644 --- a/tasks/0057_778_57778333_qa_5/task.toml +++ b/tasks/0057_778_57778333_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0057_778_57778333_qa_5" +name = "smoldataenvs-train/0057_778_57778333_qa_5" description = "What is the most frequent clarity grade in the dataset, and how many diamonds have this clarity?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "SI1, 13065" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0057_851_57851395_qa_1/task.toml b/tasks/0057_851_57851395_qa_1/task.toml index 2408b356be84b985a9389589b59a326c60268715..1924df639445f3d32ce1ec46ffc2d91da3413d69 100644 --- a/tasks/0057_851_57851395_qa_1/task.toml +++ b/tasks/0057_851_57851395_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0057_851_57851395_qa_1" +name = "smoldataenvs-train/0057_851_57851395_qa_1" description = "What is the standard deviation of the Surface Area (X2) in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "88.081797" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0057_851_57851395_qa_2/task.toml b/tasks/0057_851_57851395_qa_2/task.toml index 2ac23414f689a098c364fd51a263b4a7603a1ed6..a27eed675b9ae43c41cb2f43c287a1b19474eb3e 100644 --- a/tasks/0057_851_57851395_qa_2/task.toml +++ b/tasks/0057_851_57851395_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0057_851_57851395_qa_2" +name = "smoldataenvs-train/0057_851_57851395_qa_2" description = "What is the minimum value of the Roof Area (X4) in the training data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "110.25" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0057_915_57915748_qa_2/task.toml b/tasks/0057_915_57915748_qa_2/task.toml index db2afd2b5ddc43578666562642fd1e1a493e2cc0..940212cc4b3280b1db36ede424a82829ecff6488 100644 --- a/tasks/0057_915_57915748_qa_2/task.toml +++ b/tasks/0057_915_57915748_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0057_915_57915748_qa_2" +name = "smoldataenvs-train/0057_915_57915748_qa_2" description = "What is the interquartile range (IQR) for the 'fc' feature in the original training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0057_925_57925686_qa_2/task.toml b/tasks/0057_925_57925686_qa_2/task.toml index 6f6907862a27ecb07ac08faa323565a471c7be83..e72243b771b5f8f57bf05b51186e5cd1179dd84e 100644 --- a/tasks/0057_925_57925686_qa_2/task.toml +++ b/tasks/0057_925_57925686_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0057_925_57925686_qa_2" +name = "smoldataenvs-train/0057_925_57925686_qa_2" description = "What is the accuracy score of the Support Vector Classifier (SVC) on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.982057" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0057_935_57935595_qa_2/task.toml b/tasks/0057_935_57935595_qa_2/task.toml index 87714e7bfcc1a5b77ef4d8233bf73f383b55a1dc..4bc91dcc2713c131bb7235e304e10135be83b580 100644 --- a/tasks/0057_935_57935595_qa_2/task.toml +++ b/tasks/0057_935_57935595_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0057_935_57935595_qa_2" +name = "smoldataenvs-train/0057_935_57935595_qa_2" description = "Does the Label Encoding method allow for the identification of feature importance after model training?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0058_072_58072089_qa_2/task.toml b/tasks/0058_072_58072089_qa_2/task.toml index f071b5544ac5ae68bc8d618158a81f04dbed378d..af1c0c9e7203a68e167d8081bb76e458e747545a 100644 --- a/tasks/0058_072_58072089_qa_2/task.toml +++ b/tasks/0058_072_58072089_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0058_072_58072089_qa_2" +name = "smoldataenvs-train/0058_072_58072089_qa_2" description = "How many BMI values were identified as outliers using the IQR method in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0058_072_58072089_qa_5/task.toml b/tasks/0058_072_58072089_qa_5/task.toml index b526f377ae646fc1f9908eff09eef899d9451aec..bf51b5a9e13ff6f6e9c5215c62fc41644b65e878 100644 --- a/tasks/0058_072_58072089_qa_5/task.toml +++ b/tasks/0058_072_58072089_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0058_072_58072089_qa_5" +name = "smoldataenvs-train/0058_072_58072089_qa_5" description = "What is the total number of rows remaining in the dataset after removing BMI outliers?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1329" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0058_169_58169113_qa_2/task.toml b/tasks/0058_169_58169113_qa_2/task.toml index ac921c1016eb0f9c667d450ec98c9823180515ac..bd3ba51c9c809ee11720cbc534fdd3746f130ca1 100644 --- a/tasks/0058_169_58169113_qa_2/task.toml +++ b/tasks/0058_169_58169113_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0058_169_58169113_qa_2" +name = "smoldataenvs-train/0058_169_58169113_qa_2" description = "What is the total count of benign and malignant cases in the dataset before outlier removal?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "357 benign, 212 malignant" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0058_169_58169113_qa_4/task.toml b/tasks/0058_169_58169113_qa_4/task.toml index 4b8770d118432fa349bca469c08cc5a50771161d..daf621f7e8d76f71898f68e7dfb642cd1a49547f 100644 --- a/tasks/0058_169_58169113_qa_4/task.toml +++ b/tasks/0058_169_58169113_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0058_169_58169113_qa_4" +name = "smoldataenvs-train/0058_169_58169113_qa_4" description = "What percentage of the original dataset consists of malignant cases (diagnosis = 'M')?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "37.26" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0058_175_58175407_qa_5/task.toml b/tasks/0058_175_58175407_qa_5/task.toml index 5bc09f6ef1be33ea678e9158ac7a9c3edcff1460..d13c3874f85ea7c2a2c697cc150541182fd2466b 100644 --- a/tasks/0058_175_58175407_qa_5/task.toml +++ b/tasks/0058_175_58175407_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0058_175_58175407_qa_5" +name = "smoldataenvs-train/0058_175_58175407_qa_5" description = "To what count was the minority class (Outcome=1) upsampled after applying the SMOTE technique?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "500" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0058_175_58175546_qa_3/task.toml b/tasks/0058_175_58175546_qa_3/task.toml index 6e0ac5ac399206e8c9a237d8f094f713aef9d0b5..f8072fb3ed3c2f48c396d2a87ca1aac61ff6bd76 100644 --- a/tasks/0058_175_58175546_qa_3/task.toml +++ b/tasks/0058_175_58175546_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0058_175_58175546_qa_3" +name = "smoldataenvs-train/0058_175_58175546_qa_3" description = "What is the highest global sales value among games released before 2000 in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "40.24" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0058_240_58240435_qa_1/task.toml b/tasks/0058_240_58240435_qa_1/task.toml index 48b38b43d149728fa983b3f417b571251b079933..c0f9da7db6bf58264b76b353a31dfd872ccdeb9d 100644 --- a/tasks/0058_240_58240435_qa_1/task.toml +++ b/tasks/0058_240_58240435_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0058_240_58240435_qa_1" +name = "smoldataenvs-train/0058_240_58240435_qa_1" description = "What is the p-value from the ANOVA test on the 'ram' column indicating its significance in classifying price ranges?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0058_272_58272349_qa_4/task.toml b/tasks/0058_272_58272349_qa_4/task.toml index 8717be328f729ffa2fe4c998c2e476c2e4521ae7..1532814eddcd2f448bce42f0f94b2cea1fd8120c 100644 --- a/tasks/0058_272_58272349_qa_4/task.toml +++ b/tasks/0058_272_58272349_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0058_272_58272349_qa_4" +name = "smoldataenvs-train/0058_272_58272349_qa_4" description = "How many samples belong to each species category in the original dataset before standardization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0058_275_58275852_qa_3/task.toml b/tasks/0058_275_58275852_qa_3/task.toml index 4ba3d0219d39e10fbff7a258f45554deff54ca1d..8abb5c4491453a5af4a535b9c18a4b6637ac644d 100644 --- a/tasks/0058_275_58275852_qa_3/task.toml +++ b/tasks/0058_275_58275852_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0058_275_58275852_qa_3" +name = "smoldataenvs-train/0058_275_58275852_qa_3" description = "After log transformation, what is the skewness value of the 'Item_Outlet_Sales' feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.887753" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0058_378_58378917_qa_2/task.toml b/tasks/0058_378_58378917_qa_2/task.toml index 0130bba65ce0957c36b0ccccb7a5bd9f4d45dd86..84d93c2e34e74e270b35efe3eea57f62c4390a03 100644 --- a/tasks/0058_378_58378917_qa_2/task.toml +++ b/tasks/0058_378_58378917_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0058_378_58378917_qa_2" +name = "smoldataenvs-train/0058_378_58378917_qa_2" description = "What is the correlation coefficient between the percentage of high school graduates (age 25+) and the poverty rate across all states, as calculated from the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.805761" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0058_378_58378917_qa_4/task.toml b/tasks/0058_378_58378917_qa_4/task.toml index f4042232483f327b81e8f9542783553980afc713..ef1361007984adb62777120fb396de32bade1605 100644 --- a/tasks/0058_378_58378917_qa_4/task.toml +++ b/tasks/0058_378_58378917_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0058_378_58378917_qa_4" +name = "smoldataenvs-train/0058_378_58378917_qa_4" description = "Which state has the highest number of reported police shootings in the dataset according to the frequency analysis of the \"state\" field?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "CA" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0058_407_58407068_qa_1/task.toml b/tasks/0058_407_58407068_qa_1/task.toml index fb157a833b78a58343c1fb9bb8277918c7ab2bab..303f27ca5b2e6f2a956879b650934baf8d19bfe2 100644 --- a/tasks/0058_407_58407068_qa_1/task.toml +++ b/tasks/0058_407_58407068_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0058_407_58407068_qa_1" +name = "smoldataenvs-train/0058_407_58407068_qa_1" description = "What is the highest correlation coefficient between any two features in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.997855" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0058_407_58407702_qa_5/task.toml b/tasks/0058_407_58407702_qa_5/task.toml index 4e89fc4614aff2adf01aefdf0f11704ad73246f0..1d4b5ab8fb7eaae18afe4fbc6a129b4a949286b7 100644 --- a/tasks/0058_407_58407702_qa_5/task.toml +++ b/tasks/0058_407_58407702_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0058_407_58407702_qa_5" +name = "smoldataenvs-train/0058_407_58407702_qa_5" description = "What is the range of carat weights present in the dataset (maximum carat value minus minimum carat value)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.81" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0058_494_58494583_qa_2/task.toml b/tasks/0058_494_58494583_qa_2/task.toml index c61f76d08121e86cb37a7b12f6924ce22a0cc16a..451d78aa94045302c2acd79574d2c8e12f22f40a 100644 --- a/tasks/0058_494_58494583_qa_2/task.toml +++ b/tasks/0058_494_58494583_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0058_494_58494583_qa_2" +name = "smoldataenvs-train/0058_494_58494583_qa_2" description = "What is the p-value from the Kolmogorov-Smirnov test comparing monthly income distributions between genders for the Research Director role?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.02879147907377122" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0058_529_58529660_qa_3/task.toml b/tasks/0058_529_58529660_qa_3/task.toml index 5727b2c1a34601dd1102778512c2b1d098931e69..7c6090871768a3d6d48627f2587ff89fa394f043 100644 --- a/tasks/0058_529_58529660_qa_3/task.toml +++ b/tasks/0058_529_58529660_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0058_529_58529660_qa_3" +name = "smoldataenvs-train/0058_529_58529660_qa_3" description = "What is the highest predicted salary value in the test set according to the model's predictions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "122699.62295594" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0058_565_58565902_qa_1/task.toml b/tasks/0058_565_58565902_qa_1/task.toml index cca290ab9062a2d2e7f09bd480bd26f06d1ad3f9..0915e4261d3e851073052aca7bf959188a57e878 100644 --- a/tasks/0058_565_58565902_qa_1/task.toml +++ b/tasks/0058_565_58565902_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0058_565_58565902_qa_1" +name = "smoldataenvs-train/0058_565_58565902_qa_1" description = "Which feature shows the highest correlation with the target variable in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "concave points_worst" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0058_565_58565902_qa_3/task.toml b/tasks/0058_565_58565902_qa_3/task.toml index eb14fb402beb49d911b29219fddb9ff2e13994f4..b05724ccc2dc5e53c8d3d853b8d9e19cd4184cd7 100644 --- a/tasks/0058_565_58565902_qa_3/task.toml +++ b/tasks/0058_565_58565902_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0058_565_58565902_qa_3" +name = "smoldataenvs-train/0058_565_58565902_qa_3" description = "What is the mean cross-validation accuracy of the best-performing model before feature scaling?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.964231" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0058_641_58641810_qa_2/task.toml b/tasks/0058_641_58641810_qa_2/task.toml index e5a743e4a4b7f4114d144326b593a14c287e663d..d288c13a74951d5f16520f54dc157959f3f8819f 100644 --- a/tasks/0058_641_58641810_qa_2/task.toml +++ b/tasks/0058_641_58641810_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0058_641_58641810_qa_2" +name = "smoldataenvs-train/0058_641_58641810_qa_2" description = "Is there a mobile phone in the dataset that has WiFi but does not have Bluetooth capability?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0058_674_58674481_qa_1/task.toml b/tasks/0058_674_58674481_qa_1/task.toml index 54ada0bbb6aade40e45cdbc9d6cc07447e626649..4ce1c807640b3798de0231450801d709acb4b877 100644 --- a/tasks/0058_674_58674481_qa_1/task.toml +++ b/tasks/0058_674_58674481_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0058_674_58674481_qa_1" +name = "smoldataenvs-train/0058_674_58674481_qa_1" description = "Which feature in the dataset has the strongest negative correlation with customer churn in the final model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "tenure" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0058_674_58674481_qa_2/task.toml b/tasks/0058_674_58674481_qa_2/task.toml index d4efcf15a5b6478cd76bbb144691f4f0fdf70a31..ee2573e22a7d67ed2010e608c0cb38db4b81a71f 100644 --- a/tasks/0058_674_58674481_qa_2/task.toml +++ b/tasks/0058_674_58674481_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0058_674_58674481_qa_2" +name = "smoldataenvs-train/0058_674_58674481_qa_2" description = "After applying random over-sampling to balance the dataset, how many additional samples were added to the minority churn class (churn=1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3305" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0058_705_58705525_qa_1/task.toml b/tasks/0058_705_58705525_qa_1/task.toml index b19a1a923d56b05e4a22bd28fdd89471526693d4..4bd140be7739c778da1d3f2b0e9adf5ea03a520c 100644 --- a/tasks/0058_705_58705525_qa_1/task.toml +++ b/tasks/0058_705_58705525_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0058_705_58705525_qa_1" +name = "smoldataenvs-train/0058_705_58705525_qa_1" description = "Which mushroom cap shape has the highest proportion of edible mushrooms, and what is that proportion expressed as a percentage?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "s, 100" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0058_773_58773976_qa_2/task.toml b/tasks/0058_773_58773976_qa_2/task.toml index 337941f88c05768456b32c61e4eb326bfa98d929..246c7a118bba4e67053d30df8f97bf8033612a5c 100644 --- a/tasks/0058_773_58773976_qa_2/task.toml +++ b/tasks/0058_773_58773976_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0058_773_58773976_qa_2" +name = "smoldataenvs-train/0058_773_58773976_qa_2" description = "What is the highest correlation coefficient between any pair of numerical features in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.56" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0058_792_58792477_qa_5/task.toml b/tasks/0058_792_58792477_qa_5/task.toml index 8a1dbce9ce84a6b775365be60e947655374d3b1f..3730e80a2e63eaa2ec49e5d18f18f115d26bd49c 100644 --- a/tasks/0058_792_58792477_qa_5/task.toml +++ b/tasks/0058_792_58792477_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0058_792_58792477_qa_5" +name = "smoldataenvs-train/0058_792_58792477_qa_5" description = "Which feature has the second-highest positive correlation with house price?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "grade" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0058_797_58797016_qa_2/task.toml b/tasks/0058_797_58797016_qa_2/task.toml index 2d8a28ec625412e700db55aab739060306e4e7ad..399d095c34cb60b86e2a122061903627e228e037 100644 --- a/tasks/0058_797_58797016_qa_2/task.toml +++ b/tasks/0058_797_58797016_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0058_797_58797016_qa_2" +name = "smoldataenvs-train/0058_797_58797016_qa_2" description = "What is the median house price in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "450000" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0058_801_58801372_qa_2/task.toml b/tasks/0058_801_58801372_qa_2/task.toml index 30d10c361bee468d0324068c85f7580fbcdc1359..48dadfa705ac6f1c8037f7832b6c504753e4b077 100644 --- a/tasks/0058_801_58801372_qa_2/task.toml +++ b/tasks/0058_801_58801372_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0058_801_58801372_qa_2" +name = "smoldataenvs-train/0058_801_58801372_qa_2" description = "What is the average poverty rate for Arizona (AZ) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "25.27" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0058_805_58805287_qa_2/task.toml b/tasks/0058_805_58805287_qa_2/task.toml index bf26a584d9e80c5295c444dae11ca818a3358d01..1da8ab3a7b010f4477d15518cef73004e91fefd6 100644 --- a/tasks/0058_805_58805287_qa_2/task.toml +++ b/tasks/0058_805_58805287_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0058_805_58805287_qa_2" +name = "smoldataenvs-train/0058_805_58805287_qa_2" description = "Which king had the most battles as a defender based on the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Robb Stark" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0058_806_58806875_qa_3/task.toml b/tasks/0058_806_58806875_qa_3/task.toml index 1bcc1eae7e9adb45d7a0bf3a53aec82ca8169142..59411980a84bd54132258457db0ae15cd3af8e8e 100644 --- a/tasks/0058_806_58806875_qa_3/task.toml +++ b/tasks/0058_806_58806875_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0058_806_58806875_qa_3" +name = "smoldataenvs-train/0058_806_58806875_qa_3" description = "What is the highest correlation coefficient between any two features (excluding the 'id' column) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.997855" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0058_806_58806875_qa_4/task.toml b/tasks/0058_806_58806875_qa_4/task.toml index 1b273bb0f3d20239fd901c1a6ed694927971c103..12f3695d7013f65753a932c1aba51f928e6d323e 100644 --- a/tasks/0058_806_58806875_qa_4/task.toml +++ b/tasks/0058_806_58806875_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0058_806_58806875_qa_4" +name = "smoldataenvs-train/0058_806_58806875_qa_4" description = "Which column in the original dataset has the highest number of missing values, and how many are missing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Unnamed: 32, 569" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0058_924_58924130_qa_1/task.toml b/tasks/0058_924_58924130_qa_1/task.toml index d77bd21162a85a68b20c26e1056244092fb3fcf6..12f2aa92bef13439a3e816b06eab72803d90adaa 100644 --- a/tasks/0058_924_58924130_qa_1/task.toml +++ b/tasks/0058_924_58924130_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0058_924_58924130_qa_1" +name = "smoldataenvs-train/0058_924_58924130_qa_1" description = "How many rows in the dataset contain missing values in the 'Unnamed: 32' column before preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "569" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0059_000_59000863_qa_2/task.toml b/tasks/0059_000_59000863_qa_2/task.toml index f8b6fdb76543f46027eaee7beb06d7641967021f..8f6b02af6b1711f18c890fc0b4b405d5e5733e9d 100644 --- a/tasks/0059_000_59000863_qa_2/task.toml +++ b/tasks/0059_000_59000863_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0059_000_59000863_qa_2" +name = "smoldataenvs-train/0059_000_59000863_qa_2" description = "What is the distribution of species in the dataset based on the value counts?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50, 50, 50" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0059_003_59003505_qa_4/task.toml b/tasks/0059_003_59003505_qa_4/task.toml index 2bc42609dd1c2088fd5cdcdebb76842c457386a0..081b7d2479f8ce53c3c341cecf7c55f7bcacd452 100644 --- a/tasks/0059_003_59003505_qa_4/task.toml +++ b/tasks/0059_003_59003505_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0059_003_59003505_qa_4" +name = "smoldataenvs-train/0059_003_59003505_qa_4" description = "How many individuals in the dataset have an income greater than 50K after data cleaning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11208" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0059_055_59055646_qa_1/task.toml b/tasks/0059_055_59055646_qa_1/task.toml index 1f781d10cbf171f834b67a4b9b0de052ca8c422d..7bc2293d0eb693a86cf501ac6cb944e284e6b9b2 100644 --- a/tasks/0059_055_59055646_qa_1/task.toml +++ b/tasks/0059_055_59055646_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0059_055_59055646_qa_1" +name = "smoldataenvs-train/0059_055_59055646_qa_1" description = "What is the percentage of credit card default cases (DEFAULT=1) in the entire dataset before splitting into training and test sets?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "22.12%" reward_mode_initial = "flexible" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0059_141_59141107_qa_1/task.toml b/tasks/0059_141_59141107_qa_1/task.toml index 5982e3e6b995d3a6bf18a11f6a295c9d58e95838..4fa9f99246c8a0677f6693cbb2ccab309d4afbe6 100644 --- a/tasks/0059_141_59141107_qa_1/task.toml +++ b/tasks/0059_141_59141107_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0059_141_59141107_qa_1" +name = "smoldataenvs-train/0059_141_59141107_qa_1" description = "What percentage of customers had a negative account balance in the dataset before data cleaning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.16" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0059_141_59141107_qa_4/task.toml b/tasks/0059_141_59141107_qa_4/task.toml index 687794a0ed3c6c0383e90696c09657bfec2785f3..6c1da809a826d0277b05ace371cd3135f5d6f567 100644 --- a/tasks/0059_141_59141107_qa_4/task.toml +++ b/tasks/0059_141_59141107_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0059_141_59141107_qa_4" +name = "smoldataenvs-train/0059_141_59141107_qa_4" description = "What is the ROC AUC score of the RandomForestClassifier model on the test set according to the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.904" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0059_268_59268311_qa_3/task.toml b/tasks/0059_268_59268311_qa_3/task.toml index a134c09e82432664900b95ff4b50ef2b264d7071..3671dc6b1723b360d37bba92d123bb3bac31e4cf 100644 --- a/tasks/0059_268_59268311_qa_3/task.toml +++ b/tasks/0059_268_59268311_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0059_268_59268311_qa_3" +name = "smoldataenvs-train/0059_268_59268311_qa_3" description = "Which publisher leads in average global sales among the top 100 best-selling games according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Nintendo" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0059_268_59268311_qa_5/task.toml b/tasks/0059_268_59268311_qa_5/task.toml index 75dc5eaf7b605f76f70cb2e2aa714d72c91697b3..2861f8f811b39e851f7ca1db300cc1a953d7acee 100644 --- a/tasks/0059_268_59268311_qa_5/task.toml +++ b/tasks/0059_268_59268311_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0059_268_59268311_qa_5" +name = "smoldataenvs-train/0059_268_59268311_qa_5" description = "What is the exact global sales figure for the highest-selling video game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "82.74" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0059_292_59292943_qa_2/task.toml b/tasks/0059_292_59292943_qa_2/task.toml index c53ce7b748cd5b518e5b073fceada4e12fff3549..9a8b9a930bc8f11ad02fcfc252fac7e1cab49c3b 100644 --- a/tasks/0059_292_59292943_qa_2/task.toml +++ b/tasks/0059_292_59292943_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0059_292_59292943_qa_2" +name = "smoldataenvs-train/0059_292_59292943_qa_2" description = "What percentage of the dataset consists of benign tumors?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "62.74" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0059_341_59341953_qa_1/task.toml b/tasks/0059_341_59341953_qa_1/task.toml index 88155610d7c2f98ab461feb90776dc6cfaa65e67..da63f5169b2ebfc24548142e604b0e2ec9bac27c 100644 --- a/tasks/0059_341_59341953_qa_1/task.toml +++ b/tasks/0059_341_59341953_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0059_341_59341953_qa_1" +name = "smoldataenvs-train/0059_341_59341953_qa_1" description = "Which regression model achieved the lowest RMSE score on the test set for predicting gym crowd size?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Random Forest" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0059_410_59410836_qa_1/task.toml b/tasks/0059_410_59410836_qa_1/task.toml index 1de1078d65975e549ae525f65b3bc086e0e19df5..e4098b52bf654a8ee0736358bd8328e115d95753 100644 --- a/tasks/0059_410_59410836_qa_1/task.toml +++ b/tasks/0059_410_59410836_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0059_410_59410836_qa_1" +name = "smoldataenvs-train/0059_410_59410836_qa_1" description = "Which region has the highest total GDP based on the current prices in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "NorthernAmerica" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0059_425_59425314_qa_1/task.toml b/tasks/0059_425_59425314_qa_1/task.toml index b5814662ed2d2b69aa51768c2625ba21ed9a14a3..1799677d2ed928bbb6bb570b574e03c01dcbccee 100644 --- a/tasks/0059_425_59425314_qa_1/task.toml +++ b/tasks/0059_425_59425314_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0059_425_59425314_qa_1" +name = "smoldataenvs-train/0059_425_59425314_qa_1" description = "Which neighborhood had the highest average number of crimes per year after data cleaning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Central Business District" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0059_425_59425314_qa_2/task.toml b/tasks/0059_425_59425314_qa_2/task.toml index 6510c292d82d657887832a2a9558a35ece2e81ac..9df74beb7e23506a345d23cbbc20292143112f8d 100644 --- a/tasks/0059_425_59425314_qa_2/task.toml +++ b/tasks/0059_425_59425314_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0059_425_59425314_qa_2" +name = "smoldataenvs-train/0059_425_59425314_qa_2" description = "What year between 2003 and 2017 had the highest total number of crimes recorded?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2003" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0059_452_59452642_qa_1/task.toml b/tasks/0059_452_59452642_qa_1/task.toml index 85d7fdb20a5a5dbe572f9682d2940c32c46eaf89..31ab9c896219696274944000aac33d7f5a606b8e 100644 --- a/tasks/0059_452_59452642_qa_1/task.toml +++ b/tasks/0059_452_59452642_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0059_452_59452642_qa_1" +name = "smoldataenvs-train/0059_452_59452642_qa_1" description = "Is there any missing data in the MNIST dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0059_452_59452642_qa_3/task.toml b/tasks/0059_452_59452642_qa_3/task.toml index ea020a2e0d750f7c9474e6fbe659d498258a17ab..53470e1d352e922af89c2e381d0abe4623214622 100644 --- a/tasks/0059_452_59452642_qa_3/task.toml +++ b/tasks/0059_452_59452642_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0059_452_59452642_qa_3" +name = "smoldataenvs-train/0059_452_59452642_qa_3" description = "How many distinct digit classes are present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0059_470_59470070_qa_5/task.toml b/tasks/0059_470_59470070_qa_5/task.toml index 9f753628786669c50fce9f413e05279a5cf3b4d9..99f2cdffc924fbf574a596ec4d8a62fc202f17fb 100644 --- a/tasks/0059_470_59470070_qa_5/task.toml +++ b/tasks/0059_470_59470070_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0059_470_59470070_qa_5" +name = "smoldataenvs-train/0059_470_59470070_qa_5" description = "How much did the R-squared score improve from the initial Multiple Linear Regression model (0.651592) to the best Ridge Regression model (0.733890)? Provide the absolute value." authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.082298" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0059_523_59523535_qa_3/task.toml b/tasks/0059_523_59523535_qa_3/task.toml index d34ee25d135ad7c0ba732c043669af9260758f10..fffc52cddcd0f43b09f0e7dae2f47ebcfe45ddad 100644 --- a/tasks/0059_523_59523535_qa_3/task.toml +++ b/tasks/0059_523_59523535_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0059_523_59523535_qa_3" +name = "smoldataenvs-train/0059_523_59523535_qa_3" description = "How many samples were allocated to the training set after splitting the dataset with a 0.2 test size ratio?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4457" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0059_575_59575558_qa_3/task.toml b/tasks/0059_575_59575558_qa_3/task.toml index c1133d036bb6a80404820f99d208f8dae351770d..200f0d935e434fc23ba146977acdf847b178d635 100644 --- a/tasks/0059_575_59575558_qa_3/task.toml +++ b/tasks/0059_575_59575558_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0059_575_59575558_qa_3" +name = "smoldataenvs-train/0059_575_59575558_qa_3" description = "How many distinct categories are present in the Neighborhood variable before applying rare label encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "25" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0059_698_59698837_qa_3/task.toml b/tasks/0059_698_59698837_qa_3/task.toml index f48d8f1eb47b24c2d07c80e735bbb51132c4de56..1f92460cf97e4bccd3f2070eb6021da9c701df3e 100644 --- a/tasks/0059_698_59698837_qa_3/task.toml +++ b/tasks/0059_698_59698837_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0059_698_59698837_qa_3" +name = "smoldataenvs-train/0059_698_59698837_qa_3" description = "Which feature has the highest positive correlation with house price in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "sqft_living" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0059_844_59844912_qa_4/task.toml b/tasks/0059_844_59844912_qa_4/task.toml index c3b40f6e73cc0e4a615be5ec5293fdba7641eb10..a9e597d75e5e3852992a0352745c93958e8e949b 100644 --- a/tasks/0059_844_59844912_qa_4/task.toml +++ b/tasks/0059_844_59844912_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0059_844_59844912_qa_4" +name = "smoldataenvs-train/0059_844_59844912_qa_4" description = "How many null values remained in the dataset after imputing missing values with mean values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0059_844_59844912_qa_5/task.toml b/tasks/0059_844_59844912_qa_5/task.toml index 7b7f615a15795bfb36c1962f12e05751a0787a5e..85b4dd01e2739df62c7302144507a5f89b1296da 100644 --- a/tasks/0059_844_59844912_qa_5/task.toml +++ b/tasks/0059_844_59844912_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0059_844_59844912_qa_5" +name = "smoldataenvs-train/0059_844_59844912_qa_5" description = "How many samples were included in the test set after splitting the data with a 20% test size?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "154" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0059_858_59858896_qa_4/task.toml b/tasks/0059_858_59858896_qa_4/task.toml index 61775a7ad2466b456666f52e372b18c0244e9447..46271a6692483a1c3a13630bdc5fd69e6ca14f15 100644 --- a/tasks/0059_858_59858896_qa_4/task.toml +++ b/tasks/0059_858_59858896_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0059_858_59858896_qa_4" +name = "smoldataenvs-train/0059_858_59858896_qa_4" description = "What method was used to handle missing values in the Checking account column, and what proportion of data was missing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Mode imputation, 39.4%" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0059_864_59864963_qa_3/task.toml b/tasks/0059_864_59864963_qa_3/task.toml index 6a207e344acc7e3cf6aab9eff1a3de18e08de6b7..aceb21b85d302dfa0e423aee601839c7ebeb18ae 100644 --- a/tasks/0059_864_59864963_qa_3/task.toml +++ b/tasks/0059_864_59864963_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0059_864_59864963_qa_3" +name = "smoldataenvs-train/0059_864_59864963_qa_3" description = "How many false positive predictions were made by the optimized SVM model on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0059_918_59918276_qa_5/task.toml b/tasks/0059_918_59918276_qa_5/task.toml index ab65e3bc1d03afab69bd500808a18807c16d2eb0..a2a0d8c10f820ff29b3cf4b27a2d8415422f48d0 100644 --- a/tasks/0059_918_59918276_qa_5/task.toml +++ b/tasks/0059_918_59918276_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0059_918_59918276_qa_5" +name = "smoldataenvs-train/0059_918_59918276_qa_5" description = "Which feature has the strongest positive correlation with the target variable SHOT_RESULT?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "SHOT_CLOCK" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0060_044_60044614_qa_2/task.toml b/tasks/0060_044_60044614_qa_2/task.toml index 9b05850c1c762c560bcd1782798f8ffa029e4d70..b45da3b1c64c9dac26c0293683ae500ec2c28f05 100644 --- a/tasks/0060_044_60044614_qa_2/task.toml +++ b/tasks/0060_044_60044614_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0060_044_60044614_qa_2" +name = "smoldataenvs-train/0060_044_60044614_qa_2" description = "Which numeric variable's missing values are not missing at random, as evidenced by a significant difference in mean sale price between missing and non-missing observations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "GarageYrBlt" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0060_059_60059192_qa_1/task.toml b/tasks/0060_059_60059192_qa_1/task.toml index 78b21d5c417989a399cb2ca3e6aafdcf14c3cc55..39d4dcb311aba57b8424d5500c73badf87b7a439 100644 --- a/tasks/0060_059_60059192_qa_1/task.toml +++ b/tasks/0060_059_60059192_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0060_059_60059192_qa_1" +name = "smoldataenvs-train/0060_059_60059192_qa_1" description = "Which numerical feature has the strongest negative correlation with employee attrition in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "TotalWorkingYears" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0060_095_60095066_qa_1/task.toml b/tasks/0060_095_60095066_qa_1/task.toml index db1bcc594b6d68b1de9a573e9348ddc7d26e4c3c..366625051de06d90cd28afb06a1d483ec8911366 100644 --- a/tasks/0060_095_60095066_qa_1/task.toml +++ b/tasks/0060_095_60095066_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0060_095_60095066_qa_1" +name = "smoldataenvs-train/0060_095_60095066_qa_1" description = "Which feature in the dataset shows the strongest correlation with the diabetes outcome (target variable)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0060_106_60106319_qa_1/task.toml b/tasks/0060_106_60106319_qa_1/task.toml index 69b23e64ad5eb38a6f090b46834d95c3b0ed014e..57ba51fd3ff2729bae3435356e0dcbf4e31bb13b 100644 --- a/tasks/0060_106_60106319_qa_1/task.toml +++ b/tasks/0060_106_60106319_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0060_106_60106319_qa_1" +name = "smoldataenvs-train/0060_106_60106319_qa_1" description = "What is the highest positive correlation between any two features in the Iris dataset according to the correlation matrix visualization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.96" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0060_106_60106319_qa_2/task.toml b/tasks/0060_106_60106319_qa_2/task.toml index c62b1122b637b08212c8d977122cb3e3bbc62ff7..6ed13aead59b0a5e01848f99742750eb5e2b0e38 100644 --- a/tasks/0060_106_60106319_qa_2/task.toml +++ b/tasks/0060_106_60106319_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0060_106_60106319_qa_2" +name = "smoldataenvs-train/0060_106_60106319_qa_2" description = "Which feature subset (all features, sepal-only, petal-only, or combinations) achieves the highest SVM test accuracy for species classification?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "all features" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0060_106_60106319_qa_4/task.toml b/tasks/0060_106_60106319_qa_4/task.toml index f5d08d0e68d30ff7ac22a5445c0d5397d45a3c6d..f7754de8966e62bfdcca814b37d6e89f2f37485d 100644 --- a/tasks/0060_106_60106319_qa_4/task.toml +++ b/tasks/0060_106_60106319_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0060_106_60106319_qa_4" +name = "smoldataenvs-train/0060_106_60106319_qa_4" description = "How many samples are present for each species in the dataset based on the class distribution analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0060_157_60157882_qa_1/task.toml b/tasks/0060_157_60157882_qa_1/task.toml index 2529764c04fac1f4edab6cfb7cb3cfb2276f0ddf..0cf09efabe6f2a9a13d6c4a378e1e5109b2118e0 100644 --- a/tasks/0060_157_60157882_qa_1/task.toml +++ b/tasks/0060_157_60157882_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0060_157_60157882_qa_1" +name = "smoldataenvs-train/0060_157_60157882_qa_1" description = "Which feature in the dataset shows the highest positive correlation with wine quality?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "alcohol" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0060_211_60211356_qa_1/task.toml b/tasks/0060_211_60211356_qa_1/task.toml index 800d27682dce4c886788243627126b765e188891..0ad21af6df3736da238fc416465e6ff6fbfe87a0 100644 --- a/tasks/0060_211_60211356_qa_1/task.toml +++ b/tasks/0060_211_60211356_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0060_211_60211356_qa_1" +name = "smoldataenvs-train/0060_211_60211356_qa_1" description = "What is the shape of the dataset after removing redundant columns?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "(569, 31)" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0060_220_60220408_qa_3/task.toml b/tasks/0060_220_60220408_qa_3/task.toml index dc360e2c409894056d1a9d979c653e8d6cebfbf8..970c8bcebefe0e5777e8301adfd7032e785649f6 100644 --- a/tasks/0060_220_60220408_qa_3/task.toml +++ b/tasks/0060_220_60220408_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0060_220_60220408_qa_3" +name = "smoldataenvs-train/0060_220_60220408_qa_3" description = "Is there a statistically significant difference in mean ratings between Japanese and American Cup-style ramen?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0060_239_60239619_qa_4/task.toml b/tasks/0060_239_60239619_qa_4/task.toml index 2493b9f051444ba5d08fc2ddd72f5440548cfe88..0de77e6e8e911dc6d87169c7366dddfb35c95468 100644 --- a/tasks/0060_239_60239619_qa_4/task.toml +++ b/tasks/0060_239_60239619_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0060_239_60239619_qa_4" +name = "smoldataenvs-train/0060_239_60239619_qa_4" description = "What is the total number of observations for each species in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0060_240_60240221_qa_2/task.toml b/tasks/0060_240_60240221_qa_2/task.toml index 955cf067504f0eaa34341e2c5b1d44f8d166fb6f..52807d4cf9df473fbcd52d408a0cff02f319f538 100644 --- a/tasks/0060_240_60240221_qa_2/task.toml +++ b/tasks/0060_240_60240221_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0060_240_60240221_qa_2" +name = "smoldataenvs-train/0060_240_60240221_qa_2" description = "In Turkey, which timezone has the highest number of Starbucks stores, and what is the count?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "GMT+2:00 Asia/Istanbul, 231" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0060_240_60240221_qa_3/task.toml b/tasks/0060_240_60240221_qa_3/task.toml index 2e16b511a8f64fdac6cfd5affd819de70c154198..88aef9a33b7b5ee3a4edd289a6cc35c0fa120afc 100644 --- a/tasks/0060_240_60240221_qa_3/task.toml +++ b/tasks/0060_240_60240221_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0060_240_60240221_qa_3" +name = "smoldataenvs-train/0060_240_60240221_qa_3" description = "How many Starbucks stores in China are operated under the 'Joint Venture' ownership type?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1220" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0060_299_60299194_qa_5/task.toml b/tasks/0060_299_60299194_qa_5/task.toml index a504d77482a9da4e7505b5ae6938586de379ce30..bdad760c494929b00cc7bac5003ce2d934c1fecf 100644 --- a/tasks/0060_299_60299194_qa_5/task.toml +++ b/tasks/0060_299_60299194_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0060_299_60299194_qa_5" +name = "smoldataenvs-train/0060_299_60299194_qa_5" description = "What is the highest mutual information score assigned to any feature in the dataset according to the mutual_info_classif method?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.848259" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0060_331_60331255_qa_2/task.toml b/tasks/0060_331_60331255_qa_2/task.toml index 82e06505395d67a419dae63ff05ebc85ec7fe416..8240cc8af0eca9966380246ddd33fadca8d31def 100644 --- a/tasks/0060_331_60331255_qa_2/task.toml +++ b/tasks/0060_331_60331255_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0060_331_60331255_qa_2" +name = "smoldataenvs-train/0060_331_60331255_qa_2" description = "Which two features in the dataset show the highest correlation according to the heatmap analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm, PetalWidthCm" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0060_331_60331255_qa_3/task.toml b/tasks/0060_331_60331255_qa_3/task.toml index 6012cd4aa2371187c6467f89acb7151a5ddb850e..7572d2aad5199abb2a799159a63eb715544bff92 100644 --- a/tasks/0060_331_60331255_qa_3/task.toml +++ b/tasks/0060_331_60331255_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0060_331_60331255_qa_3" +name = "smoldataenvs-train/0060_331_60331255_qa_3" description = "What is the accuracy of the custom-built KNN model with k=5 on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "97.78" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0060_331_60331255_qa_4/task.toml b/tasks/0060_331_60331255_qa_4/task.toml index 9731e1feee729dac6884bb85bc4dd876c8ad319e..3aec50094b3514164411eef94bac515a1f65931c 100644 --- a/tasks/0060_331_60331255_qa_4/task.toml +++ b/tasks/0060_331_60331255_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0060_331_60331255_qa_4" +name = "smoldataenvs-train/0060_331_60331255_qa_4" description = "Based on the scatter plot analysis, which feature pair (Petal Length vs Petal Width or Sepal Length vs Sepal Width) provides better separation between species?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Petal Length vs Petal Width" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0060_333_60333012_qa_1/task.toml b/tasks/0060_333_60333012_qa_1/task.toml index d92b78eef887c1285ee0dcfa10840a3eedf3dc1a..a8ef97e49b906f8b39ec57264ebb06319c034dc7 100644 --- a/tasks/0060_333_60333012_qa_1/task.toml +++ b/tasks/0060_333_60333012_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0060_333_60333012_qa_1" +name = "smoldataenvs-train/0060_333_60333012_qa_1" description = "What is the minimum number of votes required for a movie to qualify for the weighted rating calculation based on the 90th percentile threshold?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "160" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0060_333_60333012_qa_4/task.toml b/tasks/0060_333_60333012_qa_4/task.toml index e198eb616101cb4ed603880d468e7406caf8b2e5..278d82a71a64f13d30bb6b245d8ac6dfbca19361 100644 --- a/tasks/0060_333_60333012_qa_4/task.toml +++ b/tasks/0060_333_60333012_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0060_333_60333012_qa_4" +name = "smoldataenvs-train/0060_333_60333012_qa_4" description = "Which movie has the highest weighted score in the qualified dataset according to the calculated weighted rating formula?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "The Shawshank Redemption" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0060_418_60418523_qa_5/task.toml b/tasks/0060_418_60418523_qa_5/task.toml index c60ade5fc8a2e7f62cccdec247c28b0192a35c9b..e22cfab58851711717c39801b6265cb6bbdfe370 100644 --- a/tasks/0060_418_60418523_qa_5/task.toml +++ b/tasks/0060_418_60418523_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0060_418_60418523_qa_5" +name = "smoldataenvs-train/0060_418_60418523_qa_5" description = "What is the median value used to replace missing Glucose values during data preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "117" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0060_427_60427905_qa_2/task.toml b/tasks/0060_427_60427905_qa_2/task.toml index 4296144d363a4e1766e5abaf788864faf5940e87..cd24a9f55fa33fe94be3b5484da28df631d1f6fc 100644 --- a/tasks/0060_427_60427905_qa_2/task.toml +++ b/tasks/0060_427_60427905_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0060_427_60427905_qa_2" +name = "smoldataenvs-train/0060_427_60427905_qa_2" description = "Which feature has the highest importance in predicting liver disease according to the Random Forest model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Total_Bilirubin" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0060_435_60435924_qa_1/task.toml b/tasks/0060_435_60435924_qa_1/task.toml index 86b176a4c9034e39010ebeb75ed76ee199f89347..aff949fabfae475c5d5adf3601cf9659ce2e3552 100644 --- a/tasks/0060_435_60435924_qa_1/task.toml +++ b/tasks/0060_435_60435924_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0060_435_60435924_qa_1" +name = "smoldataenvs-train/0060_435_60435924_qa_1" description = "Which material has the highest standard deviation in its usage across the concrete samples?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "cement" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0060_435_60435924_qa_2/task.toml b/tasks/0060_435_60435924_qa_2/task.toml index 4a206903070e5e869d1d1d582142adf40fdb99bd..904fcf159e506920212c6d6c07bb132b9239240a 100644 --- a/tasks/0060_435_60435924_qa_2/task.toml +++ b/tasks/0060_435_60435924_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0060_435_60435924_qa_2" +name = "smoldataenvs-train/0060_435_60435924_qa_2" description = "What is the maximum recorded value for superplasticizer in the concrete mixtures?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "32.20" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0060_435_60435924_qa_3/task.toml b/tasks/0060_435_60435924_qa_3/task.toml index b18bbbea1338127b9edd255aaac2a9f625903cd2..7abbfef772dc84063134dc1ee613da2c08583394 100644 --- a/tasks/0060_435_60435924_qa_3/task.toml +++ b/tasks/0060_435_60435924_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0060_435_60435924_qa_3" +name = "smoldataenvs-train/0060_435_60435924_qa_3" description = "What is the median age (in days) of the concrete samples in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "28" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0060_490_60490802_qa_1/task.toml b/tasks/0060_490_60490802_qa_1/task.toml index 1e3c29c9eaa11647e02dbef7a67e182bc30514b8..61908fa55d8783fd641cfd3417fdca8bf43313c5 100644 --- a/tasks/0060_490_60490802_qa_1/task.toml +++ b/tasks/0060_490_60490802_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0060_490_60490802_qa_1" +name = "smoldataenvs-train/0060_490_60490802_qa_1" description = "Which categorical variable (cut, color, or clarity) has the highest F-statistic in the ANOVA analysis when predicting diamond prices?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "color" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0060_511_60511698_qa_4/task.toml b/tasks/0060_511_60511698_qa_4/task.toml index 4dafd992fd5e91b3fa61fed602892aab50cd777e..555725a7a2041349f42089ea419b31ae7b711561 100644 --- a/tasks/0060_511_60511698_qa_4/task.toml +++ b/tasks/0060_511_60511698_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0060_511_60511698_qa_4" +name = "smoldataenvs-train/0060_511_60511698_qa_4" description = "What is the weighted F1-score of the Logistic Regression model on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.98" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0060_532_60532216_qa_3/task.toml b/tasks/0060_532_60532216_qa_3/task.toml index cbe1e91733dbeee48cc825141f663c85f3b89730..e58461c27ef1ba53670091bc65164fc1d37700c5 100644 --- a/tasks/0060_532_60532216_qa_3/task.toml +++ b/tasks/0060_532_60532216_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0060_532_60532216_qa_3" +name = "smoldataenvs-train/0060_532_60532216_qa_3" description = "What is the highest recorded insurance cost in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "63770.43" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0060_532_60532216_qa_4/task.toml b/tasks/0060_532_60532216_qa_4/task.toml index 2bb826b1ae13e72d2b90c51fd09ed85aa505a0c7..0346616860188335610ab30fd10cfb5f19fe14ee 100644 --- a/tasks/0060_532_60532216_qa_4/task.toml +++ b/tasks/0060_532_60532216_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0060_532_60532216_qa_4" +name = "smoldataenvs-train/0060_532_60532216_qa_4" description = "What is the average body mass index (BMI) of patients in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "30.66" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0060_533_60533129_qa_5/task.toml b/tasks/0060_533_60533129_qa_5/task.toml index b8301f9520b7c1219c20b7c48a39b061b3f99571..d7076c3b778404b62e611fe2e88cbfe511883d66 100644 --- a/tasks/0060_533_60533129_qa_5/task.toml +++ b/tasks/0060_533_60533129_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0060_533_60533129_qa_5" +name = "smoldataenvs-train/0060_533_60533129_qa_5" description = "What was the cross-validation score of the tuned Logistic Regression model when evaluated on the entire training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9725" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0060_557_60557640_qa_1/task.toml b/tasks/0060_557_60557640_qa_1/task.toml index b675af2692a5a5833a81721ddf32f8940a71a650..5f8a1e189c5ab0c4bfe78075cdc843374e6998df 100644 --- a/tasks/0060_557_60557640_qa_1/task.toml +++ b/tasks/0060_557_60557640_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0060_557_60557640_qa_1" +name = "smoldataenvs-train/0060_557_60557640_qa_1" description = "What percentage of the training dataset's Age column contains missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "19.87" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0060_557_60557640_qa_3/task.toml b/tasks/0060_557_60557640_qa_3/task.toml index 6ed8818deee9c12e311705779648a6398bc1b05c..5673640e7938b8a042b5e9d5a4a577ef7e572415 100644 --- a/tasks/0060_557_60557640_qa_3/task.toml +++ b/tasks/0060_557_60557640_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0060_557_60557640_qa_3" +name = "smoldataenvs-train/0060_557_60557640_qa_3" description = "What is the mode of the Embarked column in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "S" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0060_557_60557640_qa_4/task.toml b/tasks/0060_557_60557640_qa_4/task.toml index dc2287d2fb62bd10a35b2cee857f04e0bfd13c02..e980c575d3193718fd10275089e4a4bdfc909d02 100644 --- a/tasks/0060_557_60557640_qa_4/task.toml +++ b/tasks/0060_557_60557640_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0060_557_60557640_qa_4" +name = "smoldataenvs-train/0060_557_60557640_qa_4" description = "How many passengers in the training dataset boarded from Southampton (Embarked='S')?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "644" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0060_573_60573328_qa_1/task.toml b/tasks/0060_573_60573328_qa_1/task.toml index 4b4c0cad60ea3002fa739bdbe9eb6eb8a1aadf44..515c7edf0d33bfe5fd004cec941d688078baa77d 100644 --- a/tasks/0060_573_60573328_qa_1/task.toml +++ b/tasks/0060_573_60573328_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0060_573_60573328_qa_1" +name = "smoldataenvs-train/0060_573_60573328_qa_1" description = "How many customers were removed from the dataset due to missing 'TotalCharges' values after preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0060_573_60573328_qa_3/task.toml b/tasks/0060_573_60573328_qa_3/task.toml index c69097cd9fa1ab83b7df508cae8733ba4b05edd1..0e925482ed9fea1014b0183d7d4ec2ea74b23f86 100644 --- a/tasks/0060_573_60573328_qa_3/task.toml +++ b/tasks/0060_573_60573328_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0060_573_60573328_qa_3" +name = "smoldataenvs-train/0060_573_60573328_qa_3" description = "Which customer contract type is associated with the highest churn rate according to the data visualizations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Month-to-month" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0060_577_60577566_qa_2/task.toml b/tasks/0060_577_60577566_qa_2/task.toml index 8541bce254b0a6960ecd5aa8c941f71362e0ee9a..de6c274fa0692985802cbb8c0cd5e305abcee541 100644 --- a/tasks/0060_577_60577566_qa_2/task.toml +++ b/tasks/0060_577_60577566_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0060_577_60577566_qa_2" +name = "smoldataenvs-train/0060_577_60577566_qa_2" description = "Which video game has the highest global sales according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii Sports" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0060_577_60577566_qa_5/task.toml b/tasks/0060_577_60577566_qa_5/task.toml index 0a6dbf066056ad44f1f70a6a5154f644c3fa001d..df3d0013e6ddf20fcee88b696fea86244329a0c4 100644 --- a/tasks/0060_577_60577566_qa_5/task.toml +++ b/tasks/0060_577_60577566_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0060_577_60577566_qa_5" +name = "smoldataenvs-train/0060_577_60577566_qa_5" description = "What is the average release year of video games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2006.4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0060_651_60651529_qa_4/task.toml b/tasks/0060_651_60651529_qa_4/task.toml index 43d0a5c5bba673d4d1ec7677ea3ea91ec5d28337..ff01bf998bb379a7cf0f729197c528e23a6c7cfd 100644 --- a/tasks/0060_651_60651529_qa_4/task.toml +++ b/tasks/0060_651_60651529_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0060_651_60651529_qa_4" +name = "smoldataenvs-train/0060_651_60651529_qa_4" description = "What is the total number of tweets in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6,444 tweets" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0060_702_60702546_qa_2/task.toml b/tasks/0060_702_60702546_qa_2/task.toml index 974b5f8251385135164cf06f33d48f0c9f517779..68c31d5e18ae5c45c7708c2269336b842e225a15 100644 --- a/tasks/0060_702_60702546_qa_2/task.toml +++ b/tasks/0060_702_60702546_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0060_702_60702546_qa_2" +name = "smoldataenvs-train/0060_702_60702546_qa_2" description = "What is the maximum temperature measured in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "103.0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0060_702_60702546_qa_3/task.toml b/tasks/0060_702_60702546_qa_3/task.toml index 2364c630f47d8d89cdeab9e54ee8d184e11ff883..327bd6528f1b43b89dac241ac327bbf08cfe6172 100644 --- a/tasks/0060_702_60702546_qa_3/task.toml +++ b/tasks/0060_702_60702546_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0060_702_60702546_qa_3" +name = "smoldataenvs-train/0060_702_60702546_qa_3" description = "On which date was the lowest minimum temperature recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "31-01-1950" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0060_804_60804329_qa_5/task.toml b/tasks/0060_804_60804329_qa_5/task.toml index d528d2bbb995653a44c35e9177a8d430833455ce..ef5db0805097a20946905cfd915098973e956c2c 100644 --- a/tasks/0060_804_60804329_qa_5/task.toml +++ b/tasks/0060_804_60804329_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0060_804_60804329_qa_5" +name = "smoldataenvs-train/0060_804_60804329_qa_5" description = "What is the difference between the median voltage and the mean voltage in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.17" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0060_827_60827703_qa_1/task.toml b/tasks/0060_827_60827703_qa_1/task.toml index 172a64affdff98f1afa8d7dc5067a63eb2f698d5..ffb93413cd8c8895c6412a1c8d964fea5361a258 100644 --- a/tasks/0060_827_60827703_qa_1/task.toml +++ b/tasks/0060_827_60827703_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0060_827_60827703_qa_1" +name = "smoldataenvs-train/0060_827_60827703_qa_1" description = "What is the total number of customers who churned after data cleaning was performed on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1869" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0060_965_60965228_qa_4/task.toml b/tasks/0060_965_60965228_qa_4/task.toml index d96b22bbd2296b47ef3ad5ca3634bcc579e8c890..04cfb36fd97e979b58799ad5e04f0573f879aa48 100644 --- a/tasks/0060_965_60965228_qa_4/task.toml +++ b/tasks/0060_965_60965228_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0060_965_60965228_qa_4" +name = "smoldataenvs-train/0060_965_60965228_qa_4" description = "What is the 75th percentile value for sepal length measurements in the entire dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0060_983_60983312_qa_4/task.toml b/tasks/0060_983_60983312_qa_4/task.toml index de9c87471979f212b02131eda1c5a178210d77e7..bdf4fab6df9b0e69c0dc2b402f243db3af5ad1aa 100644 --- a/tasks/0060_983_60983312_qa_4/task.toml +++ b/tasks/0060_983_60983312_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0060_983_60983312_qa_4" +name = "smoldataenvs-train/0060_983_60983312_qa_4" description = "What is the number of samples in the least represented glass type (after relabeling)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0061_020_61020399_qa_5/task.toml b/tasks/0061_020_61020399_qa_5/task.toml index ba5c118142e2af49bbb63e63e7b953539a149f42..769005829f20743ebe49ee2d1d89c62c74ff5420 100644 --- a/tasks/0061_020_61020399_qa_5/task.toml +++ b/tasks/0061_020_61020399_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0061_020_61020399_qa_5" +name = "smoldataenvs-train/0061_020_61020399_qa_5" description = "What is the maximum 'px_width' value in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1998" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0061_041_61041621_qa_1/task.toml b/tasks/0061_041_61041621_qa_1/task.toml index b1bdcf446415e499e9673ef3499c725cbbaa1057..d94ec363e63255d9b8318b8d2b405481185a223c 100644 --- a/tasks/0061_041_61041621_qa_1/task.toml +++ b/tasks/0061_041_61041621_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0061_041_61041621_qa_1" +name = "smoldataenvs-train/0061_041_61041621_qa_1" description = "What is the total revenue generated during the period in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "39237.02" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0061_041_61041621_qa_3/task.toml b/tasks/0061_041_61041621_qa_3/task.toml index bfd089b79065637650b4ada0cb721235ae1156f9..f38d92d19b3b09e6f63510f7cc036e615d6047bd 100644 --- a/tasks/0061_041_61041621_qa_3/task.toml +++ b/tasks/0061_041_61041621_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0061_041_61041621_qa_3" +name = "smoldataenvs-train/0061_041_61041621_qa_3" description = "How many different items are sold in total?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0061_041_61041621_qa_5/task.toml b/tasks/0061_041_61041621_qa_5/task.toml index d36732cdf90fe59c9073352da4119ca2d7e8a108..9f749fdda91de0c5a40c5626e291693065e7ffcb 100644 --- a/tasks/0061_041_61041621_qa_5/task.toml +++ b/tasks/0061_041_61041621_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0061_041_61041621_qa_5" +name = "smoldataenvs-train/0061_041_61041621_qa_5" description = "What is the total number of food items ordered across all transactions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4972" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0061_109_61109875_qa_1/task.toml b/tasks/0061_109_61109875_qa_1/task.toml index f2d8d7d0c8ef053deaf860f253e7366e68b008a7..ea5469a35c5c04de0335a466343416f647e44b8b 100644 --- a/tasks/0061_109_61109875_qa_1/task.toml +++ b/tasks/0061_109_61109875_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0061_109_61109875_qa_1" +name = "smoldataenvs-train/0061_109_61109875_qa_1" description = "What is the minimum number of votes required for a movie to qualify for inclusion in the general top chart based on the 95th percentile calculation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "434" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0061_146_61146157_qa_1/task.toml b/tasks/0061_146_61146157_qa_1/task.toml index d99827002844f7bc9ce0134be93ea68ab332d681..dde4534de542ce4923bb9324634d94a652fa3113 100644 --- a/tasks/0061_146_61146157_qa_1/task.toml +++ b/tasks/0061_146_61146157_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0061_146_61146157_qa_1" +name = "smoldataenvs-train/0061_146_61146157_qa_1" description = "Which wine class has the highest frequency in the dataset, and how many instances does it contain?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Class 2, 71" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0061_164_61164683_qa_1/task.toml b/tasks/0061_164_61164683_qa_1/task.toml index 12c6879594477056625b9b54d8f20b5d24515205..87dc0426608e389138b3f0f9cb071ed8f606eb3a 100644 --- a/tasks/0061_164_61164683_qa_1/task.toml +++ b/tasks/0061_164_61164683_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0061_164_61164683_qa_1" +name = "smoldataenvs-train/0061_164_61164683_qa_1" description = "What is the maximum biodiversity count (number of species) recorded in any national park in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6623" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0061_164_61164683_qa_2/task.toml b/tasks/0061_164_61164683_qa_2/task.toml index 3a20cfabdf42225eed0c4932632f7bf1d32c316d..0ae4e75dd4e6b800b1cdc08a1772b911098dadb4 100644 --- a/tasks/0061_164_61164683_qa_2/task.toml +++ b/tasks/0061_164_61164683_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0061_164_61164683_qa_2" +name = "smoldataenvs-train/0061_164_61164683_qa_2" description = "How many national parks in the dataset have a biodiversity count (number of species) between 1000 and 2000?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "32" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0061_190_61190042_qa_2/task.toml b/tasks/0061_190_61190042_qa_2/task.toml index 02e0dc807737fffabadc496c0327f2250f53ec57..36fe6ca579cd668c9adbd4a448d220ae271b9804 100644 --- a/tasks/0061_190_61190042_qa_2/task.toml +++ b/tasks/0061_190_61190042_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0061_190_61190042_qa_2" +name = "smoldataenvs-train/0061_190_61190042_qa_2" description = "What is the best estimate of the difference in mean ratings between Japanese and American ramen packs (Japan mean - USA mean)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.247" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0061_208_61208833_qa_5/task.toml b/tasks/0061_208_61208833_qa_5/task.toml index d64f621039161159aa59cefe4b9c9bfc7f31739e..95028b8bfd35f22bb4c13b2af644a8533ca67727 100644 --- a/tasks/0061_208_61208833_qa_5/task.toml +++ b/tasks/0061_208_61208833_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0061_208_61208833_qa_5" +name = "smoldataenvs-train/0061_208_61208833_qa_5" description = "What is the sparsity percentage of the user-anime rating matrix in the cleaned dataset (calculated as 1 - (non-NaN ratings / total possible ratings))?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "99.83" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0061_220_61220733_qa_2/task.toml b/tasks/0061_220_61220733_qa_2/task.toml index db1fa8da93e026a4014bfbf869349e1ad17afd84..1b549ef99f390c9d3372884fd8f424e352e37f9e 100644 --- a/tasks/0061_220_61220733_qa_2/task.toml +++ b/tasks/0061_220_61220733_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0061_220_61220733_qa_2" +name = "smoldataenvs-train/0061_220_61220733_qa_2" description = "Which team had the highest total attendance in the 2017 season and what was its valuation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Chicago Bulls, 2500" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0061_283_61283870_qa_3/task.toml b/tasks/0061_283_61283870_qa_3/task.toml index 306d955d890d3fe228f18b97847c1869b5e22729..9adb7ffd6d4bd54e4bd129dd7cbdd83eac188f97 100644 --- a/tasks/0061_283_61283870_qa_3/task.toml +++ b/tasks/0061_283_61283870_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0061_283_61283870_qa_3" +name = "smoldataenvs-train/0061_283_61283870_qa_3" description = "What is the highest positive correlation coefficient between any two RFM (Recency, Frequency, Monetary) metrics in the customer analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.554094" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0061_292_61292134_qa_1/task.toml b/tasks/0061_292_61292134_qa_1/task.toml index 854a7a89a5a016a353ef3db731d88627ad84d623..f8fc9eba14b89f98497d3ce2da65a4406cba5960 100644 --- a/tasks/0061_292_61292134_qa_1/task.toml +++ b/tasks/0061_292_61292134_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0061_292_61292134_qa_1" +name = "smoldataenvs-train/0061_292_61292134_qa_1" description = "Which wine quality rating has the highest frequency in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0061_292_61292134_qa_2/task.toml b/tasks/0061_292_61292134_qa_2/task.toml index 0be32b5d0c73009dc5c1bf597a5fa8df6f2a8e27..bb681c65375145a3752b420c9e784ee4941df8a8 100644 --- a/tasks/0061_292_61292134_qa_2/task.toml +++ b/tasks/0061_292_61292134_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0061_292_61292134_qa_2" +name = "smoldataenvs-train/0061_292_61292134_qa_2" description = "What is the original count for the wine quality rating with the lowest frequency?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0061_292_61292134_qa_3/task.toml b/tasks/0061_292_61292134_qa_3/task.toml index 7d73ac91bf20a19ae44222fe39556fc61739cde3..5e560d55493dd705578abad534da889f52ad1533 100644 --- a/tasks/0061_292_61292134_qa_3/task.toml +++ b/tasks/0061_292_61292134_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0061_292_61292134_qa_3" +name = "smoldataenvs-train/0061_292_61292134_qa_3" description = "After applying SMOTE with a target of 1000 samples per class, what is the new count for each quality rating?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1000" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0061_311_61311344_qa_3/task.toml b/tasks/0061_311_61311344_qa_3/task.toml index f4e203f3fb642342d5f041a4ac594dbf8f707951..2689a79f2a007386b4d1d3bb93d4f10b45e97089 100644 --- a/tasks/0061_311_61311344_qa_3/task.toml +++ b/tasks/0061_311_61311344_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0061_311_61311344_qa_3" +name = "smoldataenvs-train/0061_311_61311344_qa_3" description = "What percentage of the total loan amount in Nigeria is attributed to farming activities?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0061_311_61311344_qa_5/task.toml b/tasks/0061_311_61311344_qa_5/task.toml index 4633f6ca6069f249bdea681dd9ec7a3bdd322447..35ca243665cce30d2c3331c76bad41e0eb30e117 100644 --- a/tasks/0061_311_61311344_qa_5/task.toml +++ b/tasks/0061_311_61311344_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0061_311_61311344_qa_5" +name = "smoldataenvs-train/0061_311_61311344_qa_5" description = "What percentage of loans in Nigeria have a 'bullet' repayment interval?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "99.99" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0061_330_61330810_qa_1/task.toml b/tasks/0061_330_61330810_qa_1/task.toml index b1ea82bfc56e700b21038a78edd8d2c3ef37ad86..fdc6d66ae8a4daae05002bd7053d478d5485383c 100644 --- a/tasks/0061_330_61330810_qa_1/task.toml +++ b/tasks/0061_330_61330810_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0061_330_61330810_qa_1" +name = "smoldataenvs-train/0061_330_61330810_qa_1" description = "Which feature has the highest importance in predicting the price range of mobile phones according to the Random Forest model in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ram" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0061_353_61353471_qa_1/task.toml b/tasks/0061_353_61353471_qa_1/task.toml index 459677f930aba8f3ec18d0af0b56a604fbfd48b1..182099743cbab409e98dde5f0c452e9c6ca75499 100644 --- a/tasks/0061_353_61353471_qa_1/task.toml +++ b/tasks/0061_353_61353471_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0061_353_61353471_qa_1" +name = "smoldataenvs-train/0061_353_61353471_qa_1" description = "Which video game has the highest global sales in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii Sports" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0061_353_61353471_qa_2/task.toml b/tasks/0061_353_61353471_qa_2/task.toml index 93fd389e015dd9ecc64ca0fc23c1707f1a3711c6..1c514cc1c0320869c4ec57dcbae3ca58104d53d8 100644 --- a/tasks/0061_353_61353471_qa_2/task.toml +++ b/tasks/0061_353_61353471_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0061_353_61353471_qa_2" +name = "smoldataenvs-train/0061_353_61353471_qa_2" description = "What is the median release year of the video games in the dataset after imputing missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2007" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0061_353_61353471_qa_4/task.toml b/tasks/0061_353_61353471_qa_4/task.toml index 535c063b7b47e2dab203952aece79872d9c2e3e3..bf8d2421a7950a2722f9ee398097cd74057f20e9 100644 --- a/tasks/0061_353_61353471_qa_4/task.toml +++ b/tasks/0061_353_61353471_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0061_353_61353471_qa_4" +name = "smoldataenvs-train/0061_353_61353471_qa_4" description = "What is the coefficient of determination (R²) for the Linear Regression model predicting global sales based on regional sales?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9999934776126175" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0061_353_61353471_qa_5/task.toml b/tasks/0061_353_61353471_qa_5/task.toml index a08f7e0ab1373d5546bccfd077405ea690d96fc9..79a3e70de93deb3f8736ca8ecd363aae04a027ff 100644 --- a/tasks/0061_353_61353471_qa_5/task.toml +++ b/tasks/0061_353_61353471_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0061_353_61353471_qa_5" +name = "smoldataenvs-train/0061_353_61353471_qa_5" description = "How many missing values were present in the Year column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "271" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0061_362_61362835_qa_2/task.toml b/tasks/0061_362_61362835_qa_2/task.toml index 35c7b272b20f8034e8ffaf25d0f0d93623079dc4..a3e09225cf7a11a4c01699af8ae376a658acc6c6 100644 --- a/tasks/0061_362_61362835_qa_2/task.toml +++ b/tasks/0061_362_61362835_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0061_362_61362835_qa_2" +name = "smoldataenvs-train/0061_362_61362835_qa_2" description = "Which Indian state had the highest number of incest rape victims between 2000-2010?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Madhya Pradesh" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0061_362_61362835_qa_3/task.toml b/tasks/0061_362_61362835_qa_3/task.toml index bd216ff2a1ad37010ff88e19a579d95eadbed020..16a1d8a69270088f36068d164e43d7320ffcf285 100644 --- a/tasks/0061_362_61362835_qa_3/task.toml +++ b/tasks/0061_362_61362835_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0061_362_61362835_qa_3" +name = "smoldataenvs-train/0061_362_61362835_qa_3" description = "By what numerical margin did the total number of \"other rape\" victims exceed the total number of incest rape victims between 2000-2010?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "181716" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0061_519_61519361_qa_1/task.toml b/tasks/0061_519_61519361_qa_1/task.toml index 2e08c507483452cac2065fb3f51a23033511e986..c23f929782a5f9f421ac1d88d1e6343781919bf9 100644 --- a/tasks/0061_519_61519361_qa_1/task.toml +++ b/tasks/0061_519_61519361_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0061_519_61519361_qa_1" +name = "smoldataenvs-train/0061_519_61519361_qa_1" description = "Which data transformation method (log scale, exponential decay, or time shift) resulted in the lowest p-value in the Augmented Dickey-Fuller test for stationarity?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "exponential decay" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0061_528_61528663_qa_5/task.toml b/tasks/0061_528_61528663_qa_5/task.toml index 5d5e53ec5a0745d363edf460a2fb3528dd7959f4..78381bdba8b19ec3e24adcf17ff0b6ce92f2634c 100644 --- a/tasks/0061_528_61528663_qa_5/task.toml +++ b/tasks/0061_528_61528663_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0061_528_61528663_qa_5" +name = "smoldataenvs-train/0061_528_61528663_qa_5" description = "What is the overall accuracy of the Random Forest model on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.971" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0061_626_61626049_qa_5/task.toml b/tasks/0061_626_61626049_qa_5/task.toml index 7b2b4fb6161e11402ad6da021e512ea0f876e80b..eb5c5c062d2588b16e49af914bec0724ddcdaa36 100644 --- a/tasks/0061_626_61626049_qa_5/task.toml +++ b/tasks/0061_626_61626049_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0061_626_61626049_qa_5" +name = "smoldataenvs-train/0061_626_61626049_qa_5" description = "How many patients in the dataset have a BloodPressure value of zero, which is identified as a potential data quality issue?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "35" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0061_651_61651144_qa_2/task.toml b/tasks/0061_651_61651144_qa_2/task.toml index 9144e0b28447202acd2f15c01c210131d19dbb06..f1999276bf58c2375d74e71a747ca8136fd742ec 100644 --- a/tasks/0061_651_61651144_qa_2/task.toml +++ b/tasks/0061_651_61651144_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0061_651_61651144_qa_2" +name = "smoldataenvs-train/0061_651_61651144_qa_2" description = "What is the name of the feature that was removed due to having only one unique value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "veil-type" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0061_651_61651144_qa_4/task.toml b/tasks/0061_651_61651144_qa_4/task.toml index 38049c13d8b5f7d326f6f6c8a5d052176f6d30d7..a44f86b18e2d0655b344fbf0648914d476ac284e 100644 --- a/tasks/0061_651_61651144_qa_4/task.toml +++ b/tasks/0061_651_61651144_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0061_651_61651144_qa_4" +name = "smoldataenvs-train/0061_651_61651144_qa_4" description = "What is the total number of categorical features in the original dataset before any feature was removed?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "23" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0061_651_61651144_qa_5/task.toml b/tasks/0061_651_61651144_qa_5/task.toml index df4c0babda7cdf11d284387726d140ee2aa4aa89..22cd6a96cd65110d8d68c79b2df63721ac828662 100644 --- a/tasks/0061_651_61651144_qa_5/task.toml +++ b/tasks/0061_651_61651144_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0061_651_61651144_qa_5" +name = "smoldataenvs-train/0061_651_61651144_qa_5" description = "How many features were retained in the cleaned dataset after removing features with only one unique value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "22" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0061_664_61664085_qa_2/task.toml b/tasks/0061_664_61664085_qa_2/task.toml index 46be1a7b27ed51f4a7f6eb343de517ff03056576..cef04d82f54b6956a2003459d3f0af3141f28814 100644 --- a/tasks/0061_664_61664085_qa_2/task.toml +++ b/tasks/0061_664_61664085_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0061_664_61664085_qa_2" +name = "smoldataenvs-train/0061_664_61664085_qa_2" description = "Based on the Shapiro-Wilk test, is the 'volume' feature in the Amazon stock dataset normally distributed?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0061_664_61664085_qa_3/task.toml b/tasks/0061_664_61664085_qa_3/task.toml index 3585ff77df677fe7f37de650bc6214381e0e013e..aead17cec3845a3b116d5106aff03cf7783e6881 100644 --- a/tasks/0061_664_61664085_qa_3/task.toml +++ b/tasks/0061_664_61664085_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0061_664_61664085_qa_3" +name = "smoldataenvs-train/0061_664_61664085_qa_3" description = "What is the maximum closing price recorded for Amazon stock in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "844.359985" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0061_683_61683051_qa_3/task.toml b/tasks/0061_683_61683051_qa_3/task.toml index 8db957e4931238d29bd95d4382e0d94046cfb191..12ca6f04d11035da08cfd39650eb15ea7200f49d 100644 --- a/tasks/0061_683_61683051_qa_3/task.toml +++ b/tasks/0061_683_61683051_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0061_683_61683051_qa_3" +name = "smoldataenvs-train/0061_683_61683051_qa_3" description = "What is the range of values for the total sulfur dioxide feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "283.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0061_683_61683051_qa_5/task.toml b/tasks/0061_683_61683051_qa_5/task.toml index bd03460df8f20dfd2f3c3ade85d047b45f1686e9..8c0041fe0a5699ec2db0553ff00ab46c5f3409d9 100644 --- a/tasks/0061_683_61683051_qa_5/task.toml +++ b/tasks/0061_683_61683051_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0061_683_61683051_qa_5" +name = "smoldataenvs-train/0061_683_61683051_qa_5" description = "What is the median wine quality rating in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0061_702_61702080_qa_4/task.toml b/tasks/0061_702_61702080_qa_4/task.toml index 06b0c6544cc735cf85055efb9c4147de68b717dd..e75aff5d5ebd876ecf3f508c7eec6b193c643b0d 100644 --- a/tasks/0061_702_61702080_qa_4/task.toml +++ b/tasks/0061_702_61702080_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0061_702_61702080_qa_4" +name = "smoldataenvs-train/0061_702_61702080_qa_4" description = "Which four features were removed during data preprocessing due to high correlations with other variables?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "citric acid, density, pH, total sulfur dioxide" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0061_736_61736140_qa_1/task.toml b/tasks/0061_736_61736140_qa_1/task.toml index bd32a9dfbc68316424737b8647e4f8ae9edcc0ba..d1fd4f586dffe4dc8508526176c89f7e60109a4a 100644 --- a/tasks/0061_736_61736140_qa_1/task.toml +++ b/tasks/0061_736_61736140_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0061_736_61736140_qa_1" +name = "smoldataenvs-train/0061_736_61736140_qa_1" description = "Which classification model achieved the highest validation accuracy in the Pima Indians Diabetes dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Logistic Regression" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0061_736_61736140_qa_4/task.toml b/tasks/0061_736_61736140_qa_4/task.toml index 559cab1b2bfa228b1a7f04873a2978a5595eb277..77f00c5e01ce829985b0de5a0ed4f8657b1f3f06 100644 --- a/tasks/0061_736_61736140_qa_4/task.toml +++ b/tasks/0061_736_61736140_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0061_736_61736140_qa_4" +name = "smoldataenvs-train/0061_736_61736140_qa_4" description = "Which classification model demonstrated the highest precision for predicting the positive class (Outcome=1) in the validation set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Logistic Regression" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0061_770_61770230_qa_3/task.toml b/tasks/0061_770_61770230_qa_3/task.toml index 798be169b09a9dc5dbeb20baa36de6dfd03ea41b..de11da86d17e58f88394ea21d1fe41d84aa8ecea 100644 --- a/tasks/0061_770_61770230_qa_3/task.toml +++ b/tasks/0061_770_61770230_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0061_770_61770230_qa_3" +name = "smoldataenvs-train/0061_770_61770230_qa_3" description = "Which movie has the highest weighted score according to the calculated metric?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "The Shawshank Redemption" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0061_837_61837525_qa_5/task.toml b/tasks/0061_837_61837525_qa_5/task.toml index cc3940da27533bb88e01b5e4385f0c2a083e9370..fa7822df5802c13e9c3a6f50f25aafa8db1d4c78 100644 --- a/tasks/0061_837_61837525_qa_5/task.toml +++ b/tasks/0061_837_61837525_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0061_837_61837525_qa_5" +name = "smoldataenvs-train/0061_837_61837525_qa_5" description = "What is the weighted average F1-score of the model on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.93" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0061_860_61860657_qa_4/task.toml b/tasks/0061_860_61860657_qa_4/task.toml index 63b7da6ef678e937cf85080a95fc224f84103a15..cfa97f81c8817b2624b8dd5fb995e3ac5a13fba4 100644 --- a/tasks/0061_860_61860657_qa_4/task.toml +++ b/tasks/0061_860_61860657_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0061_860_61860657_qa_4" +name = "smoldataenvs-train/0061_860_61860657_qa_4" description = "What percentage of the dataset is composed of spam messages based on the initial analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13.41" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0061_879_61879031_qa_2/task.toml b/tasks/0061_879_61879031_qa_2/task.toml index 29f7e76d6387b4b560079402512d4cdbb44f880f..0d0a14b6dff5bed5d85ec36b052118fd8ccc2bc1 100644 --- a/tasks/0061_879_61879031_qa_2/task.toml +++ b/tasks/0061_879_61879031_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0061_879_61879031_qa_2" +name = "smoldataenvs-train/0061_879_61879031_qa_2" description = "How many clusters were determined to be optimal using the elbow method based on the sum of squared distances?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0061_879_61879031_qa_3/task.toml b/tasks/0061_879_61879031_qa_3/task.toml index c80edac180e8122b249851aa1dfbe8c60568c498..ec5ca60c08373a17eaba5f28f16008fa5c3d91cf 100644 --- a/tasks/0061_879_61879031_qa_3/task.toml +++ b/tasks/0061_879_61879031_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0061_879_61879031_qa_3" +name = "smoldataenvs-train/0061_879_61879031_qa_3" description = "What is the average petal length in centimeters for the cluster that corresponds to the Iris-virginica species?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5.74210526" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0061_998_61998767_qa_1/task.toml b/tasks/0061_998_61998767_qa_1/task.toml index 74e17c883131b97e71fcb95fadc09259738c684e..3f4ba6a35b6ac40dc805a304fe68a42b1bbcd7f4 100644 --- a/tasks/0061_998_61998767_qa_1/task.toml +++ b/tasks/0061_998_61998767_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0061_998_61998767_qa_1" +name = "smoldataenvs-train/0061_998_61998767_qa_1" description = "Which predictor variable in the logistic regression model shows the highest statistical significance based on p-values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Age" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0061_998_61998767_qa_3/task.toml b/tasks/0061_998_61998767_qa_3/task.toml index 0d87f5d6cb76808e6fded2a01500755431425f9a..1351ee8e5f7eeedbf58cdcffff3b8c6ca5603bfe 100644 --- a/tasks/0061_998_61998767_qa_3/task.toml +++ b/tasks/0061_998_61998767_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0061_998_61998767_qa_3" +name = "smoldataenvs-train/0061_998_61998767_qa_3" description = "Does the EstimatedSalary variable have a positive or negative impact on the likelihood of purchasing according to the logistic regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "positive" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0062_015_62015834_qa_1/task.toml b/tasks/0062_015_62015834_qa_1/task.toml index 7aeabb454d396d426b0a05cbd9d15e361dc1c34e..35a94b68a3ea97873d996c18f3f7f97f2476754e 100644 --- a/tasks/0062_015_62015834_qa_1/task.toml +++ b/tasks/0062_015_62015834_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0062_015_62015834_qa_1" +name = "smoldataenvs-train/0062_015_62015834_qa_1" description = "What is the mean root mean squared error (RMSE) from 10-fold cross-validation of the Linear Regression model on the training data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.8107671499679467" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0062_017_62017707_qa_4/task.toml b/tasks/0062_017_62017707_qa_4/task.toml index e1402e659db0e822f1e598789afae22e39736eb7..f431a7b3a44a41aaa640df7d4346c53413efce61 100644 --- a/tasks/0062_017_62017707_qa_4/task.toml +++ b/tasks/0062_017_62017707_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0062_017_62017707_qa_4" +name = "smoldataenvs-train/0062_017_62017707_qa_4" description = "What is the highest Variance Inflation Factor (VIF) value observed after removing the variable with high p-value and multicollinearity issues?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.00" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0062_036_62036706_qa_4/task.toml b/tasks/0062_036_62036706_qa_4/task.toml index 2636e6253019172239c6ed1f8ae5c928d3500541..216b85e74f789536a7370ccd2111d0df3df6707f 100644 --- a/tasks/0062_036_62036706_qa_4/task.toml +++ b/tasks/0062_036_62036706_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0062_036_62036706_qa_4" +name = "smoldataenvs-train/0062_036_62036706_qa_4" description = "What is the total number of comments for all ask posts created at 15:00 in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "18525" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0062_048_62048358_qa_3/task.toml b/tasks/0062_048_62048358_qa_3/task.toml index 9ac955517e015086e2424ebc4a30eac5a6b0b777..91dbc7056ef7e9555593e78ba754fea6fce1294f 100644 --- a/tasks/0062_048_62048358_qa_3/task.toml +++ b/tasks/0062_048_62048358_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0062_048_62048358_qa_3" +name = "smoldataenvs-train/0062_048_62048358_qa_3" description = "How many of the predicted weight values in the test set fall within 1.0 kg of the actual weight measurements?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0062_069_62069115_qa_4/task.toml b/tasks/0062_069_62069115_qa_4/task.toml index 1a2f039e0af2786f1af68ec87299c126d7ec74cf..ba0e21e238dafafa491aa11c160ee2f37a500d05 100644 --- a/tasks/0062_069_62069115_qa_4/task.toml +++ b/tasks/0062_069_62069115_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0062_069_62069115_qa_4" +name = "smoldataenvs-train/0062_069_62069115_qa_4" description = "Which wine quality class had the fewest samples in the original dataset before binning into binary categories?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0062_214_62214042_qa_4/task.toml b/tasks/0062_214_62214042_qa_4/task.toml index 76b0aa9276c995d5750769815ce52287c3ce8168..80efdbf176f813c7f27ebbead2c9bda9490ebbe7 100644 --- a/tasks/0062_214_62214042_qa_4/task.toml +++ b/tasks/0062_214_62214042_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0062_214_62214042_qa_4" +name = "smoldataenvs-train/0062_214_62214042_qa_4" description = "Which ride purpose has the highest average miles driven?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Commute" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0062_214_62214042_qa_5/task.toml b/tasks/0062_214_62214042_qa_5/task.toml index fc1b26eb234868c2178e3b3ad0722de8f11614e1..8a6698466f6bf51484265f22b04dbe71f7265761 100644 --- a/tasks/0062_214_62214042_qa_5/task.toml +++ b/tasks/0062_214_62214042_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0062_214_62214042_qa_5" +name = "smoldataenvs-train/0062_214_62214042_qa_5" description = "What is the longest single trip in miles, and what was its purpose?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "310.3 miles, Customer Visit" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0062_251_62251582_qa_2/task.toml b/tasks/0062_251_62251582_qa_2/task.toml index f8f5bc7f5a5c2085064216b5da88dd0af4c4c3ce..da8533b73dcd147468b50f0c6de6dc08179a6d54 100644 --- a/tasks/0062_251_62251582_qa_2/task.toml +++ b/tasks/0062_251_62251582_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0062_251_62251582_qa_2" +name = "smoldataenvs-train/0062_251_62251582_qa_2" description = "What was the exact number of missing values in the MINIMUM_PAYMENTS column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "313" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0062_251_62251582_qa_3/task.toml b/tasks/0062_251_62251582_qa_3/task.toml index dd3d547c480e77aae0e28fb81093532eac7a4614..1d590f9dcb36a3cb639536e35b3b0bb789b98731 100644 --- a/tasks/0062_251_62251582_qa_3/task.toml +++ b/tasks/0062_251_62251582_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0062_251_62251582_qa_3" +name = "smoldataenvs-train/0062_251_62251582_qa_3" description = "After data preprocessing, how many numerical features were retained for analysis in the final dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "17" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0062_362_62362701_qa_4/task.toml b/tasks/0062_362_62362701_qa_4/task.toml index 02eed8257a7051833122a6727895dcf9b320abda..46cb7ef1fe2f2d392b8a1b643d4e2615bcac039b 100644 --- a/tasks/0062_362_62362701_qa_4/task.toml +++ b/tasks/0062_362_62362701_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0062_362_62362701_qa_4" +name = "smoldataenvs-train/0062_362_62362701_qa_4" description = "How many SMS messages are in the training subset used for model training after the 80/20 train-test split?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4457" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0062_379_62379173_qa_2/task.toml b/tasks/0062_379_62379173_qa_2/task.toml index f6ad68d4d3e357811bc4fbf715277aecbce444be..7623fa2a92311aa8aab9d18fe44c0fcd661a0615 100644 --- a/tasks/0062_379_62379173_qa_2/task.toml +++ b/tasks/0062_379_62379173_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0062_379_62379173_qa_2" +name = "smoldataenvs-train/0062_379_62379173_qa_2" description = "What percentage of the 'Death Year' data is missing in the artists dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "69.66" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0062_379_62379173_qa_3/task.toml b/tasks/0062_379_62379173_qa_3/task.toml index a5aac87b70c15a1059a290cc62204f05bc152a35..d341695f9560e6a9feb357119c8db326351fc0b1 100644 --- a/tasks/0062_379_62379173_qa_3/task.toml +++ b/tasks/0062_379_62379173_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0062_379_62379173_qa_3" +name = "smoldataenvs-train/0062_379_62379173_qa_3" description = "What is the maximum age recorded for an artist in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "130" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0062_391_62391096_qa_3/task.toml b/tasks/0062_391_62391096_qa_3/task.toml index c862d1376b155a3fbe188dd85695a18d6b0166b7..b45d1c228a8a23c5d93f5869689647763b729026 100644 --- a/tasks/0062_391_62391096_qa_3/task.toml +++ b/tasks/0062_391_62391096_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0062_391_62391096_qa_3" +name = "smoldataenvs-train/0062_391_62391096_qa_3" description = "Are the distributions of age, bmi, and charges approximately normal according to the Shapiro-Wilk test results?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0062_401_62401866_qa_3/task.toml b/tasks/0062_401_62401866_qa_3/task.toml index 30a004efe51452741bd9f1f8dc213986e68551a8..4063df90f3a251f868983d066c882b51d9b02b99 100644 --- a/tasks/0062_401_62401866_qa_3/task.toml +++ b/tasks/0062_401_62401866_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0062_401_62401866_qa_3" +name = "smoldataenvs-train/0062_401_62401866_qa_3" description = "Which continuous variables in the dataset do NOT follow a normal distribution based on the Shapiro-Wilk test results (p-value < 0.05)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "age, bmi, charges" reward_mode_initial = "list" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0062_450_62450924_qa_2/task.toml b/tasks/0062_450_62450924_qa_2/task.toml index 2ba20cce504ba73a524561ceb138bb197ece9825..1662046cddeb6d8a5c41bcffdf2282d2928fcaf4 100644 --- a/tasks/0062_450_62450924_qa_2/task.toml +++ b/tasks/0062_450_62450924_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0062_450_62450924_qa_2" +name = "smoldataenvs-train/0062_450_62450924_qa_2" description = "Which pair of features exhibited the highest correlation coefficient before feature selection, resulting in their removal due to redundancy?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm, PetalWidthCm" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0062_457_62457023_qa_2/task.toml b/tasks/0062_457_62457023_qa_2/task.toml index e1a59fd59920ca71437037f89f789ba819be71e9..f26fdbacd8c29df0b01bf2acba48fea6eed10ef4 100644 --- a/tasks/0062_457_62457023_qa_2/task.toml +++ b/tasks/0062_457_62457023_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0062_457_62457023_qa_2" +name = "smoldataenvs-train/0062_457_62457023_qa_2" description = "Which group (survived or not survived) has a higher median axillary lymph node count based on the dataset statistics?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "not survived" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0062_459_62459243_qa_5/task.toml b/tasks/0062_459_62459243_qa_5/task.toml index f6d2d22e358d3d05e8f12989447ac34389fa5133..b5c5e79198009ed2bb8f0ff72578881cd457bcd1 100644 --- a/tasks/0062_459_62459243_qa_5/task.toml +++ b/tasks/0062_459_62459243_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0062_459_62459243_qa_5" +name = "smoldataenvs-train/0062_459_62459243_qa_5" description = "How many samples are present in the test set used for model evaluation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "154" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0062_459_62459774_qa_4/task.toml b/tasks/0062_459_62459774_qa_4/task.toml index 282e32970f7d17f1c0925d2ec7c7ad8d687f9e30..f85abcdcc322103ce65469d79719b959209d62e8 100644 --- a/tasks/0062_459_62459774_qa_4/task.toml +++ b/tasks/0062_459_62459774_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0062_459_62459774_qa_4" +name = "smoldataenvs-train/0062_459_62459774_qa_4" description = "What is the median length (in lines) of character conversations in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0062_545_62545707_qa_1/task.toml b/tasks/0062_545_62545707_qa_1/task.toml index 7f7db6bd37c3b39b9cd87ca0f81dc86599821f70..205e5934ce826f788bcf7980c7a2a5d541f36680 100644 --- a/tasks/0062_545_62545707_qa_1/task.toml +++ b/tasks/0062_545_62545707_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0062_545_62545707_qa_1" +name = "smoldataenvs-train/0062_545_62545707_qa_1" description = "What percentage of June days from 1948 to 2017 in Seattle experienced rainfall?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "30.1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0062_545_62545707_qa_2/task.toml b/tasks/0062_545_62545707_qa_2/task.toml index 6d53ae2fc08f5abf331cb22107faaab3bbe6ae68..de95b2e0393d9789a8e44dba7d0d1fc850751303 100644 --- a/tasks/0062_545_62545707_qa_2/task.toml +++ b/tasks/0062_545_62545707_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0062_545_62545707_qa_2" +name = "smoldataenvs-train/0062_545_62545707_qa_2" description = "What is the highest precipitation recorded in a single day during June in Seattle between 1948 and 2017?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.75" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0062_545_62545707_qa_5/task.toml b/tasks/0062_545_62545707_qa_5/task.toml index 594b82eade387d7f016032c79a65139924d8ca11..d90350fd12e60e9343c9a1ac7663fecf937eaa32 100644 --- a/tasks/0062_545_62545707_qa_5/task.toml +++ b/tasks/0062_545_62545707_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0062_545_62545707_qa_5" +name = "smoldataenvs-train/0062_545_62545707_qa_5" description = "What is the median precipitation value for June days in Seattle between 1948 and 2017?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.00" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0062_642_62642092_qa_4/task.toml b/tasks/0062_642_62642092_qa_4/task.toml index 5b7e01524933dc5fc54404cedffec92a35cff631..bf2eb140bb79aa6ea9471b00a22f2330cf55abef 100644 --- a/tasks/0062_642_62642092_qa_4/task.toml +++ b/tasks/0062_642_62642092_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0062_642_62642092_qa_4" +name = "smoldataenvs-train/0062_642_62642092_qa_4" description = "Does the dataset contain any missing values in any of its features?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0062_642_62642092_qa_5/task.toml b/tasks/0062_642_62642092_qa_5/task.toml index 5067581fabe9f44fed2a601bbecf3e61383d3ad8..24bfe1eca5d7bb33755fd57dc99ad7a6f6ce79f9 100644 --- a/tasks/0062_642_62642092_qa_5/task.toml +++ b/tasks/0062_642_62642092_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0062_642_62642092_qa_5" +name = "smoldataenvs-train/0062_642_62642092_qa_5" description = "What is the proportion of the dataset allocated to the training set when using an 80/20 train-test split?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.8" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0062_653_62653158_qa_1/task.toml b/tasks/0062_653_62653158_qa_1/task.toml index 32243665463e0c3ffc300e11f2d7caeb05d1de11..c4e75f4af7c7ec728cc7e3dad63fa9a0e795849d 100644 --- a/tasks/0062_653_62653158_qa_1/task.toml +++ b/tasks/0062_653_62653158_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0062_653_62653158_qa_1" +name = "smoldataenvs-train/0062_653_62653158_qa_1" description = "Which car make has the highest frequency in the dataset based on the univariate analysis of the 'make' column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "toyota" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0062_653_62653158_qa_5/task.toml b/tasks/0062_653_62653158_qa_5/task.toml index 4939f4b661fd7c2114e10c1fa60be0f078c2d64a..8a114ca9114c255ee0cf71240203a2bc3c577eb5 100644 --- a/tasks/0062_653_62653158_qa_5/task.toml +++ b/tasks/0062_653_62653158_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0062_653_62653158_qa_5" +name = "smoldataenvs-train/0062_653_62653158_qa_5" description = "Which feature has the highest positive coefficient in the multiple linear regression model (excluding intercept)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "width" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0062_663_62663778_qa_2/task.toml b/tasks/0062_663_62663778_qa_2/task.toml index 94f0d8ba5c3af6f8c3c143422361bf3dc7c3d6a4..2b86ea10c1c9e6d98e4080c1acbb5f03db9d6500 100644 --- a/tasks/0062_663_62663778_qa_2/task.toml +++ b/tasks/0062_663_62663778_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0062_663_62663778_qa_2" +name = "smoldataenvs-train/0062_663_62663778_qa_2" description = "What is the ROC AUC score of the logistic regression model on the test set after standardizing the data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.990347" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0062_755_62755543_qa_2/task.toml b/tasks/0062_755_62755543_qa_2/task.toml index 853c8df5909bef6d2298d6fbb2f8714c6de993e5..711c81d9fe6802314eeea38444ef13a7a0c3f047 100644 --- a/tasks/0062_755_62755543_qa_2/task.toml +++ b/tasks/0062_755_62755543_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0062_755_62755543_qa_2" +name = "smoldataenvs-train/0062_755_62755543_qa_2" description = "How many missing values were present in the 'Insulin' column after replacing zero values with NaNs?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "374" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0062_755_62755543_qa_4/task.toml b/tasks/0062_755_62755543_qa_4/task.toml index 21675309cf64609d042bb55061604b48f413cbbf..e0b7acc6a694a40fc82754da63df249efb793f0b 100644 --- a/tasks/0062_755_62755543_qa_4/task.toml +++ b/tasks/0062_755_62755543_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0062_755_62755543_qa_4" +name = "smoldataenvs-train/0062_755_62755543_qa_4" description = "What is the accuracy of the Logistic Regression model on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.75" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0062_869_62869673_qa_2/task.toml b/tasks/0062_869_62869673_qa_2/task.toml index 39363d44effff7bb48bdec24b86f03899dd29fc7..2d0fa81a25b179ca2bb025e0ae31d5910ad3c687 100644 --- a/tasks/0062_869_62869673_qa_2/task.toml +++ b/tasks/0062_869_62869673_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0062_869_62869673_qa_2" +name = "smoldataenvs-train/0062_869_62869673_qa_2" description = "What is the most frequent wine quality rating in the original dataset before any resampling was applied?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0062_945_62945279_qa_1/task.toml b/tasks/0062_945_62945279_qa_1/task.toml index a6c804e12809580e939b059cfa189b4e71ec4c5a..19dcf75ac961787f89d80a4f5057ffe891a11311 100644 --- a/tasks/0062_945_62945279_qa_1/task.toml +++ b/tasks/0062_945_62945279_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0062_945_62945279_qa_1" +name = "smoldataenvs-train/0062_945_62945279_qa_1" description = "What percentage of customers in the training data are classified as churned (Yes)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26.6" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0062_945_62945279_qa_3/task.toml b/tasks/0062_945_62945279_qa_3/task.toml index e476d9313786f12d6bafad645595e7f057c6aa1f..e4d92f6209561c3f0b4cb4755e195d4ad3b25dfc 100644 --- a/tasks/0062_945_62945279_qa_3/task.toml +++ b/tasks/0062_945_62945279_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0062_945_62945279_qa_3" +name = "smoldataenvs-train/0062_945_62945279_qa_3" description = "What variable has the highest positive coefficient in the Logistic Regression model for predicting churn?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Contract_Month-to-month" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0062_973_62973078_qa_1/task.toml b/tasks/0062_973_62973078_qa_1/task.toml index 76e2af92cec8dcf779d330ed76d542eb730c3030..4ae28a97c7e8bb591c369e8e23a06292ccb770fe 100644 --- a/tasks/0062_973_62973078_qa_1/task.toml +++ b/tasks/0062_973_62973078_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0062_973_62973078_qa_1" +name = "smoldataenvs-train/0062_973_62973078_qa_1" description = "Which wine quality rating has the highest number of samples in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0062_973_62973078_qa_2/task.toml b/tasks/0062_973_62973078_qa_2/task.toml index fde547fd1f526a238fdaf6aeb0e5e4d451c50b64..46c2891624d21c4d2b3c276ad18246848b75495b 100644 --- a/tasks/0062_973_62973078_qa_2/task.toml +++ b/tasks/0062_973_62973078_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0062_973_62973078_qa_2" +name = "smoldataenvs-train/0062_973_62973078_qa_2" description = "What percentage of the dataset consists of wines with quality ratings of 5 or 6?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "82" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0062_986_62986591_qa_2/task.toml b/tasks/0062_986_62986591_qa_2/task.toml index fd0da810dfa90150d70bbeecbcc155254d1845ae..ccc061d72d1d3da11a894958de5eb31f007833b8 100644 --- a/tasks/0062_986_62986591_qa_2/task.toml +++ b/tasks/0062_986_62986591_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0062_986_62986591_qa_2" +name = "smoldataenvs-train/0062_986_62986591_qa_2" description = "Which developed country has the highest average life expectancy, and what is its value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Japan, 82.5375" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0062_986_62986591_qa_3/task.toml b/tasks/0062_986_62986591_qa_3/task.toml index d9ef91260d911359c97f3486d9ea62075f91d331..5bd9abdfc140342eadba124372999a7cfe636022 100644 --- a/tasks/0062_986_62986591_qa_3/task.toml +++ b/tasks/0062_986_62986591_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0062_986_62986591_qa_3" +name = "smoldataenvs-train/0062_986_62986591_qa_3" description = "What is the strongest negative correlation with life expectancy, and which variable shows this relationship?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Adult Mortality, -0.692" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0063_002_63002419_qa_1/task.toml b/tasks/0063_002_63002419_qa_1/task.toml index a34096811a8a9a0357ad0c214081478d6bac3a9c..8d528d6a11fe7a6a79e310683ce1d7bd164612e8 100644 --- a/tasks/0063_002_63002419_qa_1/task.toml +++ b/tasks/0063_002_63002419_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0063_002_63002419_qa_1" +name = "smoldataenvs-train/0063_002_63002419_qa_1" description = "What is the most frequently occurring noun (NN) in the reviews based on POS tagging analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "dress" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0063_032_63032404_qa_3/task.toml b/tasks/0063_032_63032404_qa_3/task.toml index a74161cabc2122a21ea32e6b5e43ad84a6eab25e..647b21008acb3e15a913ac2c3df16a3aa6887d4f 100644 --- a/tasks/0063_032_63032404_qa_3/task.toml +++ b/tasks/0063_032_63032404_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0063_032_63032404_qa_3" +name = "smoldataenvs-train/0063_032_63032404_qa_3" description = "What is the average number of bathrooms in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.114757" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0063_038_63038880_qa_3/task.toml b/tasks/0063_038_63038880_qa_3/task.toml index d6a450f42745688640d70c2158d792b2da98fa86..2f4d2b77bd1bc57b846fc2b90cdc91811adebbd8 100644 --- a/tasks/0063_038_63038880_qa_3/task.toml +++ b/tasks/0063_038_63038880_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0063_038_63038880_qa_3" +name = "smoldataenvs-train/0063_038_63038880_qa_3" description = "How many float64 data type columns are in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0063_056_63056064_qa_2/task.toml b/tasks/0063_056_63056064_qa_2/task.toml index 145cc00a6848bf07de8451bcd0c86b774f513064..5ad7faac27b52cbb4035f54d945665a916a23003 100644 --- a/tasks/0063_056_63056064_qa_2/task.toml +++ b/tasks/0063_056_63056064_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0063_056_63056064_qa_2" +name = "smoldataenvs-train/0063_056_63056064_qa_2" description = "How many missing values were present in the 'horsepower' column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0063_184_63184274_qa_1/task.toml b/tasks/0063_184_63184274_qa_1/task.toml index 5f926b20a30812465854709777f11330b36e3676..bc8416c847fd9c4896d6a7c97e0929a1c6e5b493 100644 --- a/tasks/0063_184_63184274_qa_1/task.toml +++ b/tasks/0063_184_63184274_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0063_184_63184274_qa_1" +name = "smoldataenvs-train/0063_184_63184274_qa_1" description = "How many missing values were present in the 'SkinThickness' column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "227" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0063_189_63189965_qa_5/task.toml b/tasks/0063_189_63189965_qa_5/task.toml index ff4e2a43b96170a401261a7eac411f419229315d..60873970a952f23eca9d4f743ebb5d6020a1d025 100644 --- a/tasks/0063_189_63189965_qa_5/task.toml +++ b/tasks/0063_189_63189965_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0063_189_63189965_qa_5" +name = "smoldataenvs-train/0063_189_63189965_qa_5" description = "Which feature has the strongest negative impact on predicted mpg according to the model coefficients?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "cylinders" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0063_228_63228280_qa_3/task.toml b/tasks/0063_228_63228280_qa_3/task.toml index c51bcb7746f06db98beb7ee78deb2f0e802c6053..6ee5ffb717dc21aa5370a9f82f806fe38d0a6ec9 100644 --- a/tasks/0063_228_63228280_qa_3/task.toml +++ b/tasks/0063_228_63228280_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0063_228_63228280_qa_3" +name = "smoldataenvs-train/0063_228_63228280_qa_3" description = "How many employees in the dataset had at least one promotion in the last 5 years?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "319" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0063_243_63243800_qa_2/task.toml b/tasks/0063_243_63243800_qa_2/task.toml index 5d867676d923984a1b7dfcd47a2e5ccfe33c14c3..2d466e6c5b8fa6cc5ff84074324e15074f3b2be5 100644 --- a/tasks/0063_243_63243800_qa_2/task.toml +++ b/tasks/0063_243_63243800_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0063_243_63243800_qa_2" +name = "smoldataenvs-train/0063_243_63243800_qa_2" description = "What is the cumulative percentage of variance explained by the first 17 Principal Components after PCA decomposition of the standardized dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "99.18" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0063_304_63304459_qa_1/task.toml b/tasks/0063_304_63304459_qa_1/task.toml index 21f9b5985ac75bdf8a8a401a041b3650db0d3c29..5b4c381792f081b3b5577c99ce51fcbb48824c56 100644 --- a/tasks/0063_304_63304459_qa_1/task.toml +++ b/tasks/0063_304_63304459_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0063_304_63304459_qa_1" +name = "smoldataenvs-train/0063_304_63304459_qa_1" description = "How many features were removed from the dataset due to high correlation (threshold > 0.92) during preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0063_341_63341228_qa_2/task.toml b/tasks/0063_341_63341228_qa_2/task.toml index fe5b9a4245ff2107aa20d4651fc4fb18aa42d163..add8c7e6eff44113abb00a86928d7d23ea0af540 100644 --- a/tasks/0063_341_63341228_qa_2/task.toml +++ b/tasks/0063_341_63341228_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0063_341_63341228_qa_2" +name = "smoldataenvs-train/0063_341_63341228_qa_2" description = "What percentage of the original dataset was removed after eliminating Profit outliers using the IQR method?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0063_341_63341228_qa_4/task.toml b/tasks/0063_341_63341228_qa_4/task.toml index 76ec9b880c43c70ea64d60651e24d0c005be2ccf..21f7a160519031ebbd66eb7a65c75fcfa6e29497 100644 --- a/tasks/0063_341_63341228_qa_4/task.toml +++ b/tasks/0063_341_63341228_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0063_341_63341228_qa_4" +name = "smoldataenvs-train/0063_341_63341228_qa_4" description = "Which state has the highest average marketing spend according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Florida" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0063_350_63350967_qa_3/task.toml b/tasks/0063_350_63350967_qa_3/task.toml index 13c943b002b06afd9138dba4ea699f3fd7921654..62cb2df5643de22050bca5a722106d121a2166db 100644 --- a/tasks/0063_350_63350967_qa_3/task.toml +++ b/tasks/0063_350_63350967_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0063_350_63350967_qa_3" +name = "smoldataenvs-train/0063_350_63350967_qa_3" description = "Which country has the lowest average temperature in the dataset between 1980-2013 according to the grouped analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Canada" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0063_386_63386776_qa_1/task.toml b/tasks/0063_386_63386776_qa_1/task.toml index 8312c17f0b3091f89533c28b9a7f911589c55316..c74e36d73addfcc74514717f7d7756099104c96d 100644 --- a/tasks/0063_386_63386776_qa_1/task.toml +++ b/tasks/0063_386_63386776_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0063_386_63386776_qa_1" +name = "smoldataenvs-train/0063_386_63386776_qa_1" description = "What is the median number of ratings per user in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "69" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0063_386_63386776_qa_5/task.toml b/tasks/0063_386_63386776_qa_5/task.toml index 440cea405254daa01f974ce73a21f23a222bee08..83086e7065ee84f24720295cc2c28c78f5b97417 100644 --- a/tasks/0063_386_63386776_qa_5/task.toml +++ b/tasks/0063_386_63386776_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0063_386_63386776_qa_5" +name = "smoldataenvs-train/0063_386_63386776_qa_5" description = "What is the standard deviation of the number of ratings per user?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "220.81" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0063_399_63399748_qa_1/task.toml b/tasks/0063_399_63399748_qa_1/task.toml index 5a5edefa3b7e402f7148af38b95107a64b95a95e..401c1d72fcc345b81eef8b641440d19dbcdc9d9a 100644 --- a/tasks/0063_399_63399748_qa_1/task.toml +++ b/tasks/0063_399_63399748_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0063_399_63399748_qa_1" +name = "smoldataenvs-train/0063_399_63399748_qa_1" description = "What is the highest positive correlation coefficient between any two features in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.98" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0063_399_63399748_qa_5/task.toml b/tasks/0063_399_63399748_qa_5/task.toml index 763c564a954257d8a2e7c5d7935553561cbb0f0b..9b047ec202c53a006d77b601850fe90c1c7c45f0 100644 --- a/tasks/0063_399_63399748_qa_5/task.toml +++ b/tasks/0063_399_63399748_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0063_399_63399748_qa_5" +name = "smoldataenvs-train/0063_399_63399748_qa_5" description = "What is the range of the \"area_mean\" feature in the dataset (difference between maximum and minimum values)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2357.5" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0063_468_63468812_qa_1/task.toml b/tasks/0063_468_63468812_qa_1/task.toml index 8a57d8e35b42a8184cdc626fe1a70480caa57f3d..66090a7b72140e136aeb2b976aa96bde9f4be3f1 100644 --- a/tasks/0063_468_63468812_qa_1/task.toml +++ b/tasks/0063_468_63468812_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0063_468_63468812_qa_1" +name = "smoldataenvs-train/0063_468_63468812_qa_1" description = "Which independent variable shows the strongest positive Spearman correlation with the wine quality score in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "alcohol" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0063_468_63468812_qa_5/task.toml b/tasks/0063_468_63468812_qa_5/task.toml index 7691e8e74b8710f71e32d6a921903258056e882c..c1b708ae240f84be3221557d7de1486186e6e763 100644 --- a/tasks/0063_468_63468812_qa_5/task.toml +++ b/tasks/0063_468_63468812_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0063_468_63468812_qa_5" +name = "smoldataenvs-train/0063_468_63468812_qa_5" description = "Which independent variable has the highest positive Pearson correlation with the density of the wine?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "fixed acidity" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0063_482_63482132_qa_1/task.toml b/tasks/0063_482_63482132_qa_1/task.toml index 50c18b5feea8de1b5344eac420e205d287b99301..98b858a56fdc28352498d9186e3b72c6429c7492 100644 --- a/tasks/0063_482_63482132_qa_1/task.toml +++ b/tasks/0063_482_63482132_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0063_482_63482132_qa_1" +name = "smoldataenvs-train/0063_482_63482132_qa_1" description = "Which diamond cut category has the highest average price according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Premium" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0063_482_63482132_qa_5/task.toml b/tasks/0063_482_63482132_qa_5/task.toml index ccfd0a782c33ce122fb4347ed4e1fc2fcc859933..3a29b2807e5dcb5cc4e4f95e4a7745adf033fa8f 100644 --- a/tasks/0063_482_63482132_qa_5/task.toml +++ b/tasks/0063_482_63482132_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0063_482_63482132_qa_5" +name = "smoldataenvs-train/0063_482_63482132_qa_5" description = "Which feature engineering method (get_dummies vs LabelEncoder) resulted in a slightly higher Decision Tree model score for price prediction?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "LabelEncoder" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0063_490_63490471_qa_1/task.toml b/tasks/0063_490_63490471_qa_1/task.toml index a15dbf56a55c48b25a2e62dba66d510445cc5c26..3dce88c9d06acc0fa748739309ea96d737585775 100644 --- a/tasks/0063_490_63490471_qa_1/task.toml +++ b/tasks/0063_490_63490471_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0063_490_63490471_qa_1" +name = "smoldataenvs-train/0063_490_63490471_qa_1" description = "Which customer segment has the highest churn rate based on the combination of contract type and payment method?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Month-to-month contract with Electronic Check payment" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0063_547_63547734_qa_1/task.toml b/tasks/0063_547_63547734_qa_1/task.toml index 83bd14411a8be6ec165f1e3c76e5c650eadb861b..82c49ef7e12ea15d7400e5ea8b103c1c6a134070 100644 --- a/tasks/0063_547_63547734_qa_1/task.toml +++ b/tasks/0063_547_63547734_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0063_547_63547734_qa_1" +name = "smoldataenvs-train/0063_547_63547734_qa_1" description = "Which video game has the highest global sales in the dataset, and what is the exact sales value in millions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii Sports, 82.74" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0063_552_63552439_qa_4/task.toml b/tasks/0063_552_63552439_qa_4/task.toml index 00706d65daca19afbf76d74fbc0935d56614638e..785ad7550f085e22c332957a2463533fb299b324 100644 --- a/tasks/0063_552_63552439_qa_4/task.toml +++ b/tasks/0063_552_63552439_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0063_552_63552439_qa_4" +name = "smoldataenvs-train/0063_552_63552439_qa_4" description = "What is the maximum 5-fold cross-validation accuracy obtained after optimizing the decision tree's ccp_alpha parameter?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.751" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0063_575_63575701_qa_2/task.toml b/tasks/0063_575_63575701_qa_2/task.toml index 7ae1321a6d7b798cd26a6475a9b0616e8b456fff..43c7a70baccb8a59f740578e263fb5c9621a2106 100644 --- a/tasks/0063_575_63575701_qa_2/task.toml +++ b/tasks/0063_575_63575701_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0063_575_63575701_qa_2" +name = "smoldataenvs-train/0063_575_63575701_qa_2" description = "What is the average F1 score for the GradientBoost model based on the 10-fold cross-validation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.784322" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0063_605_63605458_qa_2/task.toml b/tasks/0063_605_63605458_qa_2/task.toml index d76b4e39d26f269bf8a75560836802d0edc424a6..c17a2265ecc823755cb3ba08a69a1ce0458715aa 100644 --- a/tasks/0063_605_63605458_qa_2/task.toml +++ b/tasks/0063_605_63605458_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0063_605_63605458_qa_2" +name = "smoldataenvs-train/0063_605_63605458_qa_2" description = "What is the most frequently purchased product based on transaction count?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "WHITE HANGING HEART T-LIGHT HOLDER" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0063_605_63605458_qa_3/task.toml b/tasks/0063_605_63605458_qa_3/task.toml index 28705307b0c4c309274c17d3894b202cb43a81ac..b5887fbb9195ac90d2e78fa9499f712cbd66dc47 100644 --- a/tasks/0063_605_63605458_qa_3/task.toml +++ b/tasks/0063_605_63605458_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0063_605_63605458_qa_3" +name = "smoldataenvs-train/0063_605_63605458_qa_3" description = "Which country has the highest number of transactions in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "United Kingdom" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0063_605_63605458_qa_4/task.toml b/tasks/0063_605_63605458_qa_4/task.toml index 8e04b2888148b5b75aa3e9692bc05062d868a919..89fc0408158fe8484fc570c0581aa61dc0de1602 100644 --- a/tasks/0063_605_63605458_qa_4/task.toml +++ b/tasks/0063_605_63605458_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0063_605_63605458_qa_4" +name = "smoldataenvs-train/0063_605_63605458_qa_4" description = "What is the correlation between the quantity of items purchased and the unit price in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.001235" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0063_631_63631791_qa_5/task.toml b/tasks/0063_631_63631791_qa_5/task.toml index adb8b309605398205f6f773e622b44b15a39583c..197a90941ddffb6a827c286f24d6f1ad86d68bb2 100644 --- a/tasks/0063_631_63631791_qa_5/task.toml +++ b/tasks/0063_631_63631791_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0063_631_63631791_qa_5" +name = "smoldataenvs-train/0063_631_63631791_qa_5" description = "Do residents in the northwest region have significantly different BMI values compared to southeast residents?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0063_668_63668659_qa_2/task.toml b/tasks/0063_668_63668659_qa_2/task.toml index 362926092d9a6eb08d420d2aa56b2a7bb3d53afd..e3b5fc366d29a37d2c1f91bb7ca6da705e590bd6 100644 --- a/tasks/0063_668_63668659_qa_2/task.toml +++ b/tasks/0063_668_63668659_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0063_668_63668659_qa_2" +name = "smoldataenvs-train/0063_668_63668659_qa_2" description = "Which age group has the highest average purchase amount in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "51-55" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0063_708_63708411_qa_1/task.toml b/tasks/0063_708_63708411_qa_1/task.toml index 465ee58dc89a2542832022d859476c01a2b9a04f..5f2ef8774a613f84a3a0de965e82d5f2b822dad4 100644 --- a/tasks/0063_708_63708411_qa_1/task.toml +++ b/tasks/0063_708_63708411_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0063_708_63708411_qa_1" +name = "smoldataenvs-train/0063_708_63708411_qa_1" description = "How many missing values were present in the 'Insulin' feature before imputation in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "374" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0063_711_63711638_qa_5/task.toml b/tasks/0063_711_63711638_qa_5/task.toml index a2744934fc1da958b2ca704c65bc0c9f8f1cf1a4..5c685526d2370368e0e50c05d0c2bd3cc75b7f9c 100644 --- a/tasks/0063_711_63711638_qa_5/task.toml +++ b/tasks/0063_711_63711638_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0063_711_63711638_qa_5" +name = "smoldataenvs-train/0063_711_63711638_qa_5" description = "What is the shape of the training images array after reshaping for input to the CNN model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "(27455, 28, 28, 1)" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0063_759_63759604_qa_1/task.toml b/tasks/0063_759_63759604_qa_1/task.toml index 9c347d996d4e3964452bba4ac58eabbfd659a61b..fac1b9c177dc8eeb48f2be9dca32a5932f7c1b22 100644 --- a/tasks/0063_759_63759604_qa_1/task.toml +++ b/tasks/0063_759_63759604_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0063_759_63759604_qa_1" +name = "smoldataenvs-train/0063_759_63759604_qa_1" description = "What is the mean Item_Weight of the training data after imputing missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12.857645" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0063_793_63793650_qa_5/task.toml b/tasks/0063_793_63793650_qa_5/task.toml index 9187ba408b741ab2297f3d3707a29b93d773fcad..8fdb76b54aea6d6f7ea80371c740c3473f4b1983 100644 --- a/tasks/0063_793_63793650_qa_5/task.toml +++ b/tasks/0063_793_63793650_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0063_793_63793650_qa_5" +name = "smoldataenvs-train/0063_793_63793650_qa_5" description = "What is the initial value of the 12-period exponentially weighted moving average (EWMA) for the passenger data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "112.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0063_836_63836300_qa_1/task.toml b/tasks/0063_836_63836300_qa_1/task.toml index c18a5786328acfc2c6f315686accf46efca2f266..d9f192609962ec32ab33cd9a3495e0cb346b0ebd 100644 --- a/tasks/0063_836_63836300_qa_1/task.toml +++ b/tasks/0063_836_63836300_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0063_836_63836300_qa_1" +name = "smoldataenvs-train/0063_836_63836300_qa_1" description = "What percentage of rows with missing Description also have missing CustomerID values in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "100" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0063_836_63836300_qa_2/task.toml b/tasks/0063_836_63836300_qa_2/task.toml index e70a8fa4155edc43a1af78959340c78d92aaa484..3b2a9276a3ba959fbf129f21435a82fecb5aa409 100644 --- a/tasks/0063_836_63836300_qa_2/task.toml +++ b/tasks/0063_836_63836300_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0063_836_63836300_qa_2" +name = "smoldataenvs-train/0063_836_63836300_qa_2" description = "Which country has the highest total number of orders based on quantity sold?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "United Kingdom" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0063_842_63842304_qa_4/task.toml b/tasks/0063_842_63842304_qa_4/task.toml index 7cabe77ff1adf20b3bb0f0c04b60c6baff18f148..39b00d01772411b79a71cf08f1502c7070f206b2 100644 --- a/tasks/0063_842_63842304_qa_4/task.toml +++ b/tasks/0063_842_63842304_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0063_842_63842304_qa_4" +name = "smoldataenvs-train/0063_842_63842304_qa_4" description = "How many samples in the dataset are classified as Mines (M)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "111" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0063_879_63879344_qa_3/task.toml b/tasks/0063_879_63879344_qa_3/task.toml index dd4bea80a6296cd9d23f5e74e5e06bfe84cf98b3..6aedddf3ad6c3130a9f6b8ce3d8b0fa99a7f95de 100644 --- a/tasks/0063_879_63879344_qa_3/task.toml +++ b/tasks/0063_879_63879344_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0063_879_63879344_qa_3" +name = "smoldataenvs-train/0063_879_63879344_qa_3" description = "What is the accuracy of the Adaboost classifier on the test set after model training?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.7792" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0063_909_63909128_qa_3/task.toml b/tasks/0063_909_63909128_qa_3/task.toml index 135d85a2831ae84b0472fc40bc85351854aa5f37..943589840aaaf472e2eddde1441fb8b8d662539b 100644 --- a/tasks/0063_909_63909128_qa_3/task.toml +++ b/tasks/0063_909_63909128_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0063_909_63909128_qa_3" +name = "smoldataenvs-train/0063_909_63909128_qa_3" description = "How many customers belong to the cluster with the highest average spending score (1-100)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "39" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0063_914_63914761_qa_4/task.toml b/tasks/0063_914_63914761_qa_4/task.toml index 28db5f51118608b99170907b3ca1e644ab802159..fdb1f03fa1bdca0e8f154dd5367d2f2e276e9207 100644 --- a/tasks/0063_914_63914761_qa_4/task.toml +++ b/tasks/0063_914_63914761_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0063_914_63914761_qa_4" +name = "smoldataenvs-train/0063_914_63914761_qa_4" description = "Which country has the highest total number of orders (measured by quantity) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "United Kingdom" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0063_914_63914904_qa_3/task.toml b/tasks/0063_914_63914904_qa_3/task.toml index 383227d132d7a290c1dfac1368700f016955a4a5..414a19dc885b042d9c22e5fe3cb2bd5fb0efce59 100644 --- a/tasks/0063_914_63914904_qa_3/task.toml +++ b/tasks/0063_914_63914904_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0063_914_63914904_qa_3" +name = "smoldataenvs-train/0063_914_63914904_qa_3" description = "Which variable had the highest statistical significance in its association with diabetes diagnosis (Outcome) according to the chi-square test results?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0063_931_63931182_qa_4/task.toml b/tasks/0063_931_63931182_qa_4/task.toml index a3b252e8b0a7fe2a5ae4ea540460282789ffe2c1..34cc6c814dfc053071fefc8cf9036aa41f1504cf 100644 --- a/tasks/0063_931_63931182_qa_4/task.toml +++ b/tasks/0063_931_63931182_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0063_931_63931182_qa_4" +name = "smoldataenvs-train/0063_931_63931182_qa_4" description = "What is the average annual income (in k$) of customers in the 'Rich' cluster according to the K-Means clustering results?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "86.53846154" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0063_942_63942638_qa_2/task.toml b/tasks/0063_942_63942638_qa_2/task.toml index 0c9008b82a95df6b550aee124e21cdd29bbf63f0..f3d22e2b5ce8e136d97de4244f599465035aa96c 100644 --- a/tasks/0063_942_63942638_qa_2/task.toml +++ b/tasks/0063_942_63942638_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0063_942_63942638_qa_2" +name = "smoldataenvs-train/0063_942_63942638_qa_2" description = "What is the optimal value of n_neighbors for the K-Nearest Neighbors (KNN) classifier based on the accuracy evaluation in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0063_943_63943567_qa_2/task.toml b/tasks/0063_943_63943567_qa_2/task.toml index 98c122aea754dfa8e0ac3e864214163109d32a8a..42a9bb1779c3aaa8390cba07fb231a74bb1468b8 100644 --- a/tasks/0063_943_63943567_qa_2/task.toml +++ b/tasks/0063_943_63943567_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0063_943_63943567_qa_2" +name = "smoldataenvs-train/0063_943_63943567_qa_2" description = "What is the difference between the maximum and minimum student age in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0064_084_64084849_qa_4/task.toml b/tasks/0064_084_64084849_qa_4/task.toml index 9e3fca5481945e88dbe8fe8bfc78e4c93ace9126..281be80e1a10110bd2f22ca9d92c9f44465ef788 100644 --- a/tasks/0064_084_64084849_qa_4/task.toml +++ b/tasks/0064_084_64084849_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0064_084_64084849_qa_4" +name = "smoldataenvs-train/0064_084_64084849_qa_4" description = "How many missing values remained in the dataset after imputing the numerical variables (height_m, percentage_male, weight_kg)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "384" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0064_089_64089732_qa_2/task.toml b/tasks/0064_089_64089732_qa_2/task.toml index 3b3d3f01c6bbc6ec699646d7e10211c4b927795d..884c34b7dbea80596b2a2d323fa9e9903139cb29 100644 --- a/tasks/0064_089_64089732_qa_2/task.toml +++ b/tasks/0064_089_64089732_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0064_089_64089732_qa_2" +name = "smoldataenvs-train/0064_089_64089732_qa_2" description = "Which ensemble method (VotingClassifier, BaggingClassifier, AdaBoost, GradientBoosting, XGBoost) achieved the highest precision score on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "XGBoost" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0064_090_64090883_qa_3/task.toml b/tasks/0064_090_64090883_qa_3/task.toml index df7f5a765eca3fd67ff461b4149a1f5f132c875c..b952a2c1b2408168109b92b4a4f855e25bcfd6ad 100644 --- a/tasks/0064_090_64090883_qa_3/task.toml +++ b/tasks/0064_090_64090883_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0064_090_64090883_qa_3" +name = "smoldataenvs-train/0064_090_64090883_qa_3" description = "According to the EDA observations, which species exhibits mid-range measurements for all features (SepalLengthCm, SepalWidthCm, PetalLengthCm, PetalWidthCm)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-versicolor" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0064_090_64090883_qa_5/task.toml b/tasks/0064_090_64090883_qa_5/task.toml index deda43d1be3b380ef0f530a623bfe87b71b90d58..16d1386ef2802e606c46352a17235c554e994aeb 100644 --- a/tasks/0064_090_64090883_qa_5/task.toml +++ b/tasks/0064_090_64090883_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0064_090_64090883_qa_5" +name = "smoldataenvs-train/0064_090_64090883_qa_5" description = "Based on the EDA observations, which species shows the most varied SepalWidthCm measurements, ranging from low to high values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-setosa" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0064_173_64173346_qa_2/task.toml b/tasks/0064_173_64173346_qa_2/task.toml index ff0892bd7a933bc9086d8fc814499d92f5a11bcb..1b6b1dfeab0a92252ee88eda43b99f98168b390f 100644 --- a/tasks/0064_173_64173346_qa_2/task.toml +++ b/tasks/0064_173_64173346_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0064_173_64173346_qa_2" +name = "smoldataenvs-train/0064_173_64173346_qa_2" description = "Are the K-means clusters perfectly aligned with the actual species labels in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0064_224_64224650_qa_5/task.toml b/tasks/0064_224_64224650_qa_5/task.toml index 814767cc68c1cfe5b0fe4f73c5be552f1b2330a9..742f7f22ce36bdd284d8867d142047d3e1a7817c 100644 --- a/tasks/0064_224_64224650_qa_5/task.toml +++ b/tasks/0064_224_64224650_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0064_224_64224650_qa_5" +name = "smoldataenvs-train/0064_224_64224650_qa_5" description = "What is the average number of children per individual in the dataset based on the numerical analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.094918" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0064_232_64232136_qa_1/task.toml b/tasks/0064_232_64232136_qa_1/task.toml index f0622a333e2dce9057e0a6b333c0151ed776c490..5f8a00ab955c8b51ab6796ab5753a8003b7d48de 100644 --- a/tasks/0064_232_64232136_qa_1/task.toml +++ b/tasks/0064_232_64232136_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0064_232_64232136_qa_1" +name = "smoldataenvs-train/0064_232_64232136_qa_1" description = "How many columns were removed from the dataset due to exceeding the 75% missing data threshold before handling missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0064_267_64267004_qa_3/task.toml b/tasks/0064_267_64267004_qa_3/task.toml index b47337d7cde18486b780b52d25a09c6784c68355..1057ae957afd5a954cf04683249f0c321c3e96bd 100644 --- a/tasks/0064_267_64267004_qa_3/task.toml +++ b/tasks/0064_267_64267004_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0064_267_64267004_qa_3" +name = "smoldataenvs-train/0064_267_64267004_qa_3" description = "What is the highest average radius value among the three types (mean, standard error, worst) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16.3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0064_267_64267004_qa_5/task.toml b/tasks/0064_267_64267004_qa_5/task.toml index 058edfd3cf86f4da14133d1a0625837508c03657..f081b5856daf9fac546aac21feba2503f2c9f9b6 100644 --- a/tasks/0064_267_64267004_qa_5/task.toml +++ b/tasks/0064_267_64267004_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0064_267_64267004_qa_5" +name = "smoldataenvs-train/0064_267_64267004_qa_5" description = "What percentage of the 'Unnamed: 32' column is missing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "100" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0064_330_64330026_qa_1/task.toml b/tasks/0064_330_64330026_qa_1/task.toml index e9fbe98f3ef4abf9d4241b417a18f987f05b45e9..f6fd8c6f5659d2c5da5592801fb0783413f87882 100644 --- a/tasks/0064_330_64330026_qa_1/task.toml +++ b/tasks/0064_330_64330026_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0064_330_64330026_qa_1" +name = "smoldataenvs-train/0064_330_64330026_qa_1" description = "What is the p-value from the Augmented Dickey-Fuller test for the differenced time series after first-order differencing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9.755605e-22" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0064_330_64330026_qa_5/task.toml b/tasks/0064_330_64330026_qa_5/task.toml index c92dc19eb8ea56f7aba9884b711babdb886bc25f..c0b1d360e1f3551f68031780e92c543d9a273351 100644 --- a/tasks/0064_330_64330026_qa_5/task.toml +++ b/tasks/0064_330_64330026_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0064_330_64330026_qa_5" +name = "smoldataenvs-train/0064_330_64330026_qa_5" description = "Based on the Augmented Dickey-Fuller test results, was the original temperature time series stationary before differencing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0064_383_64383055_qa_5/task.toml b/tasks/0064_383_64383055_qa_5/task.toml index 39063112c296f21c704c918b98d401d7ded9a20a..dd5516962736eb024b77a70256e270ed280644d5 100644 --- a/tasks/0064_383_64383055_qa_5/task.toml +++ b/tasks/0064_383_64383055_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0064_383_64383055_qa_5" +name = "smoldataenvs-train/0064_383_64383055_qa_5" description = "According to the KPSS test results, is the first-differenced Land & Ocean temperature delta series stationary (yes/no)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0064_397_64397098_qa_3/task.toml b/tasks/0064_397_64397098_qa_3/task.toml index 6ece2f5af3403cce9d555263d13e16497f0ebc50..4eacc7ef85ef6bf575ef6f2d64ecb0dee1b8fe9f 100644 --- a/tasks/0064_397_64397098_qa_3/task.toml +++ b/tasks/0064_397_64397098_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0064_397_64397098_qa_3" +name = "smoldataenvs-train/0064_397_64397098_qa_3" description = "What is the percentage of missing data in any column of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0064_402_64402852_qa_1/task.toml b/tasks/0064_402_64402852_qa_1/task.toml index 64ad10c7c0c60aa25ac4f22f4d106b75b19dcce8..4f501f3c1c3d7b7487a8fd02fa3e5a7cf656bc42 100644 --- a/tasks/0064_402_64402852_qa_1/task.toml +++ b/tasks/0064_402_64402852_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0064_402_64402852_qa_1" +name = "smoldataenvs-train/0064_402_64402852_qa_1" description = "What is the Pearson correlation coefficient between the features x and y in the training dataset before preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.99534" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0064_448_64448402_qa_1/task.toml b/tasks/0064_448_64448402_qa_1/task.toml index af3c995268c68a7c192c15fddccdc5b681bf2ce0..cd0f14a3dc23b641f5f0ce98f7a026d3834e0bbd 100644 --- a/tasks/0064_448_64448402_qa_1/task.toml +++ b/tasks/0064_448_64448402_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0064_448_64448402_qa_1" +name = "smoldataenvs-train/0064_448_64448402_qa_1" description = "What is the mean and standard deviation of the wine features after applying standard scaling in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "mean=-2.7793132196426685e-15, std=1.0" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0064_469_64469829_qa_4/task.toml b/tasks/0064_469_64469829_qa_4/task.toml index cfd33dee41d771eeecbf415c25b4d7242ac18454..aeb720e758808813d7b95731ee37fb886d91e16f 100644 --- a/tasks/0064_469_64469829_qa_4/task.toml +++ b/tasks/0064_469_64469829_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0064_469_64469829_qa_4" +name = "smoldataenvs-train/0064_469_64469829_qa_4" description = "What is the test accuracy of the KNN model when using NCA-reduced features (2 components) compared to the original features?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "97.86" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0064_494_64494124_qa_2/task.toml b/tasks/0064_494_64494124_qa_2/task.toml index 17ed3dc72501e16e7a796bd093d039f744572dd1..ffc206b177b7476de44ef0084cf0298c40497fc6 100644 --- a/tasks/0064_494_64494124_qa_2/task.toml +++ b/tasks/0064_494_64494124_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0064_494_64494124_qa_2" +name = "smoldataenvs-train/0064_494_64494124_qa_2" description = "What percentage of patients who survived had 0–4.6 positive axillary nodes based on the cumulative distribution function analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "83.55" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0064_496_64496303_qa_3/task.toml b/tasks/0064_496_64496303_qa_3/task.toml index 0932ae4a8d2d89977789d42c9e7fadab278c4cb4..439c6c133b41b5843be6486d22fb54698101e90d 100644 --- a/tasks/0064_496_64496303_qa_3/task.toml +++ b/tasks/0064_496_64496303_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0064_496_64496303_qa_3" +name = "smoldataenvs-train/0064_496_64496303_qa_3" description = "What is the seasonal period (m) used in the SARIMA model based on the monthly temperature dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0064_551_64551026_qa_3/task.toml b/tasks/0064_551_64551026_qa_3/task.toml index 93153a5230b1efcc3cb63b3e67250366d26a6904..764860aa29670fb013f0e160bb5e1cbb349475ea 100644 --- a/tasks/0064_551_64551026_qa_3/task.toml +++ b/tasks/0064_551_64551026_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0064_551_64551026_qa_3" +name = "smoldataenvs-train/0064_551_64551026_qa_3" description = "What is the mean age of patients in the dataset before data preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "33.24" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0064_551_64551026_qa_4/task.toml b/tasks/0064_551_64551026_qa_4/task.toml index 5dbebf0f4b55e79dacb56bbc288c9acd70720866..d2604e2f90779688837a1c6f8a7551846c1d86bc 100644 --- a/tasks/0064_551_64551026_qa_4/task.toml +++ b/tasks/0064_551_64551026_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0064_551_64551026_qa_4" +name = "smoldataenvs-train/0064_551_64551026_qa_4" description = "How many patients in the dataset have a positive diabetes diagnosis (Outcome = 1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "268" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0064_580_64580067_qa_2/task.toml b/tasks/0064_580_64580067_qa_2/task.toml index 1c07a6d334bf256f4eb6ccb264d22b92eff40f90..9d089d96a2864442bc87df7af5904b5babe2db4f 100644 --- a/tasks/0064_580_64580067_qa_2/task.toml +++ b/tasks/0064_580_64580067_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0064_580_64580067_qa_2" +name = "smoldataenvs-train/0064_580_64580067_qa_2" description = "What is the upper bound of the highest decile for alcohol content in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "14.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0064_661_64661976_qa_2/task.toml b/tasks/0064_661_64661976_qa_2/task.toml index 0a0e4be95edeecee3d3a8c95ab35e045ab219430..0677b8b9b055312bab5cf42f9e7b691ff8a688dd 100644 --- a/tasks/0064_661_64661976_qa_2/task.toml +++ b/tasks/0064_661_64661976_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0064_661_64661976_qa_2" +name = "smoldataenvs-train/0064_661_64661976_qa_2" description = "How many employees in the dataset have left the company?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3571" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0064_661_64661976_qa_3/task.toml b/tasks/0064_661_64661976_qa_3/task.toml index 2290095ad439ad22a03f5bd619f1dfcbd3b1fbb0..175e81ebe858def1029cbc75707bbf6c00aec377 100644 --- a/tasks/0064_661_64661976_qa_3/task.toml +++ b/tasks/0064_661_64661976_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0064_661_64661976_qa_3" +name = "smoldataenvs-train/0064_661_64661976_qa_3" description = "Which variable has the highest absolute correlation with employee attrition (left) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "satisfaction_level" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0064_664_64664569_qa_4/task.toml b/tasks/0064_664_64664569_qa_4/task.toml index 187571e122ed23385c0cc1ea7d749b3ed86f8812..dfb5add5e91b9268c5343216e21ef220c511731d 100644 --- a/tasks/0064_664_64664569_qa_4/task.toml +++ b/tasks/0064_664_64664569_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0064_664_64664569_qa_4" +name = "smoldataenvs-train/0064_664_64664569_qa_4" description = "What is the total number of unique values present in the 'stalk-root' feature, including the missing value indicator?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0064_682_64682826_qa_2/task.toml b/tasks/0064_682_64682826_qa_2/task.toml index 1bdb6bdd90edd65f3a83d71316b1b88248b3993e..ee3947a14f20477a623db0c5e9738090643987b7 100644 --- a/tasks/0064_682_64682826_qa_2/task.toml +++ b/tasks/0064_682_64682826_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0064_682_64682826_qa_2" +name = "smoldataenvs-train/0064_682_64682826_qa_2" description = "What is the population of the most populous district in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11060148" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0064_691_64691450_qa_2/task.toml b/tasks/0064_691_64691450_qa_2/task.toml index e321db8e84a8086ea39b078b2d599691db88fbb8..abcb7f2e8b49c3c1ac7f791991e0ea5222e40160 100644 --- a/tasks/0064_691_64691450_qa_2/task.toml +++ b/tasks/0064_691_64691450_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0064_691_64691450_qa_2" +name = "smoldataenvs-train/0064_691_64691450_qa_2" description = "After removing multicollinearity by dropping one region feature, did the model's validation R-squared value improve compared to the previous version?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0064_698_64698192_qa_1/task.toml b/tasks/0064_698_64698192_qa_1/task.toml index 6c1b913300903e92b5521a2bfaa35af8e233f07b..7e93f8b6b52059a2d25dc09ed4c8f7cbb1978bd8 100644 --- a/tasks/0064_698_64698192_qa_1/task.toml +++ b/tasks/0064_698_64698192_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0064_698_64698192_qa_1" +name = "smoldataenvs-train/0064_698_64698192_qa_1" description = "What is the highest global sales value achieved by any video game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "82.74" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0064_698_64698192_qa_4/task.toml b/tasks/0064_698_64698192_qa_4/task.toml index d4f0ed2a53010689c1bbbce6cd1548ea7490c56a..db85b7f9c2d013d302f66b9e0f562d358ade9a1c 100644 --- a/tasks/0064_698_64698192_qa_4/task.toml +++ b/tasks/0064_698_64698192_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0064_698_64698192_qa_4" +name = "smoldataenvs-train/0064_698_64698192_qa_4" description = "What year recorded the highest total global sales for video games?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2008" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0064_698_64698192_qa_5/task.toml b/tasks/0064_698_64698192_qa_5/task.toml index 74f921292d4191d214579db01da3178a17fa7734..faf933639937474e3929be225e3b56d635efa70c 100644 --- a/tasks/0064_698_64698192_qa_5/task.toml +++ b/tasks/0064_698_64698192_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0064_698_64698192_qa_5" +name = "smoldataenvs-train/0064_698_64698192_qa_5" description = "Which publisher achieved the highest total global sales across all games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Nintendo" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0064_727_64727238_qa_4/task.toml b/tasks/0064_727_64727238_qa_4/task.toml index 64d2998262d666684f8bc758d392b73f0bf75e61..01dfb4edb023477130aa4a67fe6e15f42c3c97b7 100644 --- a/tasks/0064_727_64727238_qa_4/task.toml +++ b/tasks/0064_727_64727238_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0064_727_64727238_qa_4" +name = "smoldataenvs-train/0064_727_64727238_qa_4" description = "After imputing missing values, how many missing values remain in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0064_762_64762975_qa_2/task.toml b/tasks/0064_762_64762975_qa_2/task.toml index 4a33928e601ca8f9fd0dea3028dbe898041bffa6..4da18635a85ac018ec116624d4227a0d7c3cab33 100644 --- a/tasks/0064_762_64762975_qa_2/task.toml +++ b/tasks/0064_762_64762975_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0064_762_64762975_qa_2" +name = "smoldataenvs-train/0064_762_64762975_qa_2" description = "What is the percentage of the minority class (Outcome=1) in the dataset after applying SMOTE oversampling?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50.0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0064_766_64766227_qa_2/task.toml b/tasks/0064_766_64766227_qa_2/task.toml index 1a63488c6295c1af958bf0703d60b05f937ca13e..881e8eb37444ca6ce65d3c66125105589aa534aa 100644 --- a/tasks/0064_766_64766227_qa_2/task.toml +++ b/tasks/0064_766_64766227_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0064_766_64766227_qa_2" +name = "smoldataenvs-train/0064_766_64766227_qa_2" description = "What is the mean age difference between patients who survived longer versus those who survived shorter periods (in years)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-1.66" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0064_766_64766227_qa_3/task.toml b/tasks/0064_766_64766227_qa_3/task.toml index 0267b553f8623e00688edc0b1090fccaa49857a0..af323de731fea2e04a40b3795909da7b90f2a534 100644 --- a/tasks/0064_766_64766227_qa_3/task.toml +++ b/tasks/0064_766_64766227_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0064_766_64766227_qa_3" +name = "smoldataenvs-train/0064_766_64766227_qa_3" description = "What is the interquartile range (IQR) of axillary nodes for the entire dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0064_778_64778762_qa_5/task.toml b/tasks/0064_778_64778762_qa_5/task.toml index 9b35f0e4dbe5868f4b002fa54bc07e85712308d5..e9148c2698237a93919011131a49765e0de09b54 100644 --- a/tasks/0064_778_64778762_qa_5/task.toml +++ b/tasks/0064_778_64778762_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0064_778_64778762_qa_5" +name = "smoldataenvs-train/0064_778_64778762_qa_5" description = "Which single factor identified in the analysis has the strongest direct impact on employee retention decisions according to the correlation matrix?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "satisfaction_level" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0064_783_64783285_qa_2/task.toml b/tasks/0064_783_64783285_qa_2/task.toml index fc322fcd230f5effba266bd4c40839a5ece6acb2..2e98240d018f81dbacff38d1d88b88bba5f57fae 100644 --- a/tasks/0064_783_64783285_qa_2/task.toml +++ b/tasks/0064_783_64783285_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0064_783_64783285_qa_2" +name = "smoldataenvs-train/0064_783_64783285_qa_2" description = "Which Overall Condition category (Poor, Average, Good) is associated with the highest average SalePrice in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Good" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0064_808_64808166_qa_5/task.toml b/tasks/0064_808_64808166_qa_5/task.toml index 9747ed41894482832021437e7c95e24cc30296c8..786b90179b3337ea50a0106aef6eaf742495174a 100644 --- a/tasks/0064_808_64808166_qa_5/task.toml +++ b/tasks/0064_808_64808166_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0064_808_64808166_qa_5" +name = "smoldataenvs-train/0064_808_64808166_qa_5" description = "Is the R&D Spend variable statistically significant (p-value < 0.05) in the final backward elimination model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0064_828_64828278_qa_5/task.toml b/tasks/0064_828_64828278_qa_5/task.toml index 796702f08bd6a977540ac9eb7eabe6f48454f282..fb9ed26ec1e4e5d8077f47e800b0440a94a4b6a5 100644 --- a/tasks/0064_828_64828278_qa_5/task.toml +++ b/tasks/0064_828_64828278_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0064_828_64828278_qa_5" +name = "smoldataenvs-train/0064_828_64828278_qa_5" description = "How many SkinThickness entries were originally above 90 before correction?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0064_851_64851380_qa_2/task.toml b/tasks/0064_851_64851380_qa_2/task.toml index 86e01ac41371c9c3b93283080f7b2db276df043f..8b314afea566952a1bd62d4d50d98d2fa6974b45 100644 --- a/tasks/0064_851_64851380_qa_2/task.toml +++ b/tasks/0064_851_64851380_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0064_851_64851380_qa_2" +name = "smoldataenvs-train/0064_851_64851380_qa_2" description = "Which movie has the highest combined score based on normalized weighted average and normalized popularity metrics?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Interstellar" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0064_907_64907248_qa_1/task.toml b/tasks/0064_907_64907248_qa_1/task.toml index 26133971c5e72176cc859706669c2b4f3afde2ee..86ddae5f2bfb50af3c25dbc706132cf24dcc0c9b 100644 --- a/tasks/0064_907_64907248_qa_1/task.toml +++ b/tasks/0064_907_64907248_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0064_907_64907248_qa_1" +name = "smoldataenvs-train/0064_907_64907248_qa_1" description = "What is the average difference in message length between spam and ham messages in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "67.84" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0064_907_64907248_qa_2/task.toml b/tasks/0064_907_64907248_qa_2/task.toml index 48fdb9ce9f053760f4f3017e75fd8701e97c4270..1864f0ef4f2221391578a7741dcbff893a7358d3 100644 --- a/tasks/0064_907_64907248_qa_2/task.toml +++ b/tasks/0064_907_64907248_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0064_907_64907248_qa_2" +name = "smoldataenvs-train/0064_907_64907248_qa_2" description = "What percentage of the original dataset is composed of ham messages before undersampling?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "86.59" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0064_907_64907248_qa_3/task.toml b/tasks/0064_907_64907248_qa_3/task.toml index c3aaa98dca7a027f9dd33065e2b512abb2fcf7e3..7064f14c753f812659efe93e69ef6984e350e4e5 100644 --- a/tasks/0064_907_64907248_qa_3/task.toml +++ b/tasks/0064_907_64907248_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0064_907_64907248_qa_3" +name = "smoldataenvs-train/0064_907_64907248_qa_3" description = "After applying undersampling, how many ham messages are present in the balanced dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "747" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0064_908_64908177_qa_2/task.toml b/tasks/0064_908_64908177_qa_2/task.toml index 98078d60062c288b0df87f7367c77170691dce79..2bbc49090b62cb5ea088d324728e4a73daf80e06 100644 --- a/tasks/0064_908_64908177_qa_2/task.toml +++ b/tasks/0064_908_64908177_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0064_908_64908177_qa_2" +name = "smoldataenvs-train/0064_908_64908177_qa_2" description = "What is the most important variable in the TF-DF Random Forest model based on the \"NUM_NODES\" variable importance metric?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0064_983_64983007_qa_2/task.toml b/tasks/0064_983_64983007_qa_2/task.toml index 277f5bfec7db53baf4e3678f065b266de2a5d072..783dda96b76711348cd487919db2df10e5428955 100644 --- a/tasks/0064_983_64983007_qa_2/task.toml +++ b/tasks/0064_983_64983007_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0064_983_64983007_qa_2" +name = "smoldataenvs-train/0064_983_64983007_qa_2" description = "Which feature showed the highest positive correlation with the 'Outcome' variable according to the correlation heatmap?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_008_65008815_qa_4/task.toml b/tasks/0065_008_65008815_qa_4/task.toml index 81216c34f6273239d4d957b52160df133301aa9f..915d41955eb0789efd3db0845c1de4515607bfff 100644 --- a/tasks/0065_008_65008815_qa_4/task.toml +++ b/tasks/0065_008_65008815_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0065_008_65008815_qa_4" +name = "smoldataenvs-train/0065_008_65008815_qa_4" description = "How many users are represented in the movie rating data used for the recommendation system?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "671" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0065_018_65018782_qa_3/task.toml b/tasks/0065_018_65018782_qa_3/task.toml index 97b9f061bcdbc427ee2e0e78a7e4ceab29bdb3de..04c86bfb533786447f7b04883f898a6770a2704b 100644 --- a/tasks/0065_018_65018782_qa_3/task.toml +++ b/tasks/0065_018_65018782_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0065_018_65018782_qa_3" +name = "smoldataenvs-train/0065_018_65018782_qa_3" description = "What is the percentage of missing values in the 'Insulin' column before applying the stratified median imputation strategy based on the 'Outcome' variable?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "48.7" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_069_65069276_qa_1/task.toml b/tasks/0065_069_65069276_qa_1/task.toml index a59bd17c33c46aa8f25d36737fa1b97013e38449..c8f4bd4b0a7df4e99f5b659e05d6457e790d9672 100644 --- a/tasks/0065_069_65069276_qa_1/task.toml +++ b/tasks/0065_069_65069276_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0065_069_65069276_qa_1" +name = "smoldataenvs-train/0065_069_65069276_qa_1" description = "Which variable shows the strongest positive correlation with medical insurance charges in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "smoker" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_069_65069276_qa_4/task.toml b/tasks/0065_069_65069276_qa_4/task.toml index 041c17b0b17d8ffd0f36514fa167ec94472aefab..cf7db48076cd21e75d106e35ab2e1ca27ea49df8 100644 --- a/tasks/0065_069_65069276_qa_4/task.toml +++ b/tasks/0065_069_65069276_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0065_069_65069276_qa_4" +name = "smoldataenvs-train/0065_069_65069276_qa_4" description = "What is the regression coefficient for the \"smoker\" variable in the Linear Regression model predicting charges?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "23768.1421239" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0065_069_65069276_qa_5/task.toml b/tasks/0065_069_65069276_qa_5/task.toml index 5f3ba9898e98c78f883741ad8379ff9a93b4f92f..074b5800299a7dce6fd2f19097d32a18a669d1c9 100644 --- a/tasks/0065_069_65069276_qa_5/task.toml +++ b/tasks/0065_069_65069276_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0065_069_65069276_qa_5" +name = "smoldataenvs-train/0065_069_65069276_qa_5" description = "Which geographic region has the highest number of policyholders in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "southeast" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0065_074_65074869_qa_2/task.toml b/tasks/0065_074_65074869_qa_2/task.toml index 854bfc83672fb4c6bff417ec120edf1e97ed23ab..eefe16d531e9519540ece0e58e7d16f5fc65890e 100644 --- a/tasks/0065_074_65074869_qa_2/task.toml +++ b/tasks/0065_074_65074869_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0065_074_65074869_qa_2" +name = "smoldataenvs-train/0065_074_65074869_qa_2" description = "What is the coefficient of determination (R²) for the linear regression model that predicts house price using the following features: \"floors\", \"waterfront\", \"lat\", \"bedrooms\", \"sqft_basement\", \"view\", \"bathrooms\", \"sqft_living15\", \"sqft_above\", \"grade\", and \"sqft_living\"?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.6577151058279325" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0065_074_65074869_qa_4/task.toml b/tasks/0065_074_65074869_qa_4/task.toml index 4c13ed52d257a90e3498ed2494b4213955a453fc..da6e26aa966fecaaa33a796e4b6d4b3cee55ffb0 100644 --- a/tasks/0065_074_65074869_qa_4/task.toml +++ b/tasks/0065_074_65074869_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0065_074_65074869_qa_4" +name = "smoldataenvs-train/0065_074_65074869_qa_4" description = "Which floor value (e.g., 1.0, 2.0, etc.) has the highest frequency in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0065_109_65109957_qa_3/task.toml b/tasks/0065_109_65109957_qa_3/task.toml index f26cf616e5f4ab30cb26204c2780d094d58acd07..31b83912cdaf780259970b61f749fcae38fb208e 100644 --- a/tasks/0065_109_65109957_qa_3/task.toml +++ b/tasks/0065_109_65109957_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0065_109_65109957_qa_3" +name = "smoldataenvs-train/0065_109_65109957_qa_3" description = "What is the difference in average age between patients with and without hypertension?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "29.3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_150_65150655_qa_3/task.toml b/tasks/0065_150_65150655_qa_3/task.toml index 7cbbbee8255f4993f7344fca398d1426fe1901b3..af30fae1926fc3d38622b66170c16b28e8a88a86 100644 --- a/tasks/0065_150_65150655_qa_3/task.toml +++ b/tasks/0065_150_65150655_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0065_150_65150655_qa_3" +name = "smoldataenvs-train/0065_150_65150655_qa_3" description = "How many unique classes are present in the dataset based on the label distribution?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "24" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0065_162_65162734_qa_1/task.toml b/tasks/0065_162_65162734_qa_1/task.toml index b276729f717efb7a310fe3bb48983ef79ca14134..3a118c2cbc89108685eb969584ff925c4de33d27 100644 --- a/tasks/0065_162_65162734_qa_1/task.toml +++ b/tasks/0065_162_65162734_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0065_162_65162734_qa_1" +name = "smoldataenvs-train/0065_162_65162734_qa_1" description = "What is the proportion of benign (B) to malignant (M) cases in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "357:212" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_162_65162734_qa_3/task.toml b/tasks/0065_162_65162734_qa_3/task.toml index ac9ce25e0c2f8a37558ccfce71c03a61c016fcbd..7842cb8b9ace8ed49e06cebf5c7b2ab8d67d3749 100644 --- a/tasks/0065_162_65162734_qa_3/task.toml +++ b/tasks/0065_162_65162734_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0065_162_65162734_qa_3" +name = "smoldataenvs-train/0065_162_65162734_qa_3" description = "What is the difference between the 75th percentile and 25th percentile of the radius_mean feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.08" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0065_162_65162734_qa_5/task.toml b/tasks/0065_162_65162734_qa_5/task.toml index 3073588593d87593edfd342fda84507f0c7e1613..85ea2ed9aeb86363629714ad46ff4a19dcce8fca 100644 --- a/tasks/0065_162_65162734_qa_5/task.toml +++ b/tasks/0065_162_65162734_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0065_162_65162734_qa_5" +name = "smoldataenvs-train/0065_162_65162734_qa_5" description = "What is the mean value of the area_mean feature for all cases in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "654.889" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0065_166_65166426_qa_3/task.toml b/tasks/0065_166_65166426_qa_3/task.toml index 5e9ae71971c87566cbd24837b45e4e8a4eed9593..2115146863c9d4215d490a887dfe50dbacbdc3f8 100644 --- a/tasks/0065_166_65166426_qa_3/task.toml +++ b/tasks/0065_166_65166426_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0065_166_65166426_qa_3" +name = "smoldataenvs-train/0065_166_65166426_qa_3" description = "What percentage of individuals in the cleaned dataset have non-zero capital losses?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.71" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_166_65166426_qa_4/task.toml b/tasks/0065_166_65166426_qa_4/task.toml index b38b753ea628ceab5e4a3bb380783294272b331c..6885e784ac4ce827db64741b9a9cb0996c884d65 100644 --- a/tasks/0065_166_65166426_qa_4/task.toml +++ b/tasks/0065_166_65166426_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0065_166_65166426_qa_4" +name = "smoldataenvs-train/0065_166_65166426_qa_4" description = "Which education category has the highest number of individuals in the cleaned dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "HS-grad" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_166_65166426_qa_5/task.toml b/tasks/0065_166_65166426_qa_5/task.toml index b4dcb68d6af678aab7eea624dfcef7aa90812ec7..9d937c17ee9f90645344326f18e8f688aac1783b 100644 --- a/tasks/0065_166_65166426_qa_5/task.toml +++ b/tasks/0065_166_65166426_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0065_166_65166426_qa_5" +name = "smoldataenvs-train/0065_166_65166426_qa_5" description = "What is the most common marital status category after merging similar categories in the cleaned dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Married" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_262_65262139_qa_4/task.toml b/tasks/0065_262_65262139_qa_4/task.toml index 614252553ac2ab46f6474984a418f79d9611c115..8915a239ccade115553ff93e2bbaf070a89520f1 100644 --- a/tasks/0065_262_65262139_qa_4/task.toml +++ b/tasks/0065_262_65262139_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0065_262_65262139_qa_4" +name = "smoldataenvs-train/0065_262_65262139_qa_4" description = "How many tweets were allocated to the training set and testing set after applying an 80-20 train/test split on the 200,000-sample subset of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "160000, 40000" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_282_65282332_qa_1/task.toml b/tasks/0065_282_65282332_qa_1/task.toml index dc6829cb3e4611840b83262543c53810a3d191ee..bf5940c46f4e1d95a232d15f2fe82232f42c16f4 100644 --- a/tasks/0065_282_65282332_qa_1/task.toml +++ b/tasks/0065_282_65282332_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0065_282_65282332_qa_1" +name = "smoldataenvs-train/0065_282_65282332_qa_1" description = "Which missing value imputation method (median or random) better preserves the original distribution of the Item_Weight feature according to the KDE plots?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Random imputation" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_282_65282332_qa_2/task.toml b/tasks/0065_282_65282332_qa_2/task.toml index 9e1faddd5f2e1c0934d634e3dc485744814ff244..5dd739ba718f2af323e55217cc0600a9181d5e34 100644 --- a/tasks/0065_282_65282332_qa_2/task.toml +++ b/tasks/0065_282_65282332_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0065_282_65282332_qa_2" +name = "smoldataenvs-train/0065_282_65282332_qa_2" description = "What is the most important feature for predicting Item_Outlet_Sales according to the ExtraTreesRegressor feature importance analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Item_MRP" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0065_282_65282332_qa_5/task.toml b/tasks/0065_282_65282332_qa_5/task.toml index 7ab0daff5ea0cad8955ceb3a9dbc3d3155d0af0e..54efd9d85ce0f7dc59778e7aca94257f41daa203 100644 --- a/tasks/0065_282_65282332_qa_5/task.toml +++ b/tasks/0065_282_65282332_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0065_282_65282332_qa_5" +name = "smoldataenvs-train/0065_282_65282332_qa_5" description = "What is the direction of the correlation between Item_MRP and Item_Outlet_Sales as shown in the regression plot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "positive" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0065_337_65337674_qa_4/task.toml b/tasks/0065_337_65337674_qa_4/task.toml index 7cb8d3fb506c1d8a0d90c2cae75e6a96a84f1d65..630215cd4cd8f6c9a366041d74eaed789fdf4f22 100644 --- a/tasks/0065_337_65337674_qa_4/task.toml +++ b/tasks/0065_337_65337674_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0065_337_65337674_qa_4" +name = "smoldataenvs-train/0065_337_65337674_qa_4" description = "What is the total number of pixels in each flattened image after normalization and reshaping for input into the machine learning models?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12288" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_360_65360161_qa_1/task.toml b/tasks/0065_360_65360161_qa_1/task.toml index 6aff950511ee102afdb4f8bf63efd91a575bbfe5..e481bfaa377f1f7cee49afeebf8727085a926b68 100644 --- a/tasks/0065_360_65360161_qa_1/task.toml +++ b/tasks/0065_360_65360161_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0065_360_65360161_qa_1" +name = "smoldataenvs-train/0065_360_65360161_qa_1" description = "Is the species Setosa linearly separable from other species based on Sepal Length and Width features in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0065_366_65366007_qa_1/task.toml b/tasks/0065_366_65366007_qa_1/task.toml index 80c56da9604c92c055eedd7097d29e002f8b3225..1e43aa04a65ad80ee2f75c45c001f9a18a40d8a0 100644 --- a/tasks/0065_366_65366007_qa_1/task.toml +++ b/tasks/0065_366_65366007_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0065_366_65366007_qa_1" +name = "smoldataenvs-train/0065_366_65366007_qa_1" description = "What is the mean Heating Load in the building energy efficiency dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "22.307201" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0065_396_65396447_qa_1/task.toml b/tasks/0065_396_65396447_qa_1/task.toml index 33624a7391df1fd0ed7c2d7eb660be78334441b9..7355e81b97c848dc09e5252898ee051cd4038a1a 100644 --- a/tasks/0065_396_65396447_qa_1/task.toml +++ b/tasks/0065_396_65396447_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0065_396_65396447_qa_1" +name = "smoldataenvs-train/0065_396_65396447_qa_1" description = "Which attribute has the highest information gain for classifying edible vs. poisonous mushrooms in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "odor" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_396_65396447_qa_5/task.toml b/tasks/0065_396_65396447_qa_5/task.toml index 6de4595357c76b46f48b040003710326bae32b4a..3756f8f2fa40cdae530ef440c3985e7d715ffb06 100644 --- a/tasks/0065_396_65396447_qa_5/task.toml +++ b/tasks/0065_396_65396447_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0065_396_65396447_qa_5" +name = "smoldataenvs-train/0065_396_65396447_qa_5" description = "What is the information gain value for the cap-surface attribute in the mushroom classification analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.028590" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_495_65495142_qa_3/task.toml b/tasks/0065_495_65495142_qa_3/task.toml index 3c82441ac7335d83250666ade0df40278e5481cd..d7005fe8568abcea37f676de33b8df280443f5dd 100644 --- a/tasks/0065_495_65495142_qa_3/task.toml +++ b/tasks/0065_495_65495142_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0065_495_65495142_qa_3" +name = "smoldataenvs-train/0065_495_65495142_qa_3" description = "How many unique type combinations exist when considering both primary and secondary types?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "154" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_499_65499556_qa_2/task.toml b/tasks/0065_499_65499556_qa_2/task.toml index 20c07d66f698cf9010f32dfb413facd73b3cc8d1..9f3907627e6ff1bb094ca1ebb01cc67640c63ba3 100644 --- a/tasks/0065_499_65499556_qa_2/task.toml +++ b/tasks/0065_499_65499556_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0065_499_65499556_qa_2" +name = "smoldataenvs-train/0065_499_65499556_qa_2" description = "How many missing values were present in the 'Insulin' column before preprocessing the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "374" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_499_65499556_qa_3/task.toml b/tasks/0065_499_65499556_qa_3/task.toml index 689c89083c3502e38cf135e8f52a296c66effc5d..e53d4b27579d3d9b66e86ad6984c274c76c52f59 100644 --- a/tasks/0065_499_65499556_qa_3/task.toml +++ b/tasks/0065_499_65499556_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0065_499_65499556_qa_3" +name = "smoldataenvs-train/0065_499_65499556_qa_3" description = "What is the mean number of pregnancies after replacing zero-values with NaN and imputing them with the mean?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.494673" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_504_65504512_qa_1/task.toml b/tasks/0065_504_65504512_qa_1/task.toml index 768fc9dc4e5243ec14b59da0d379918a81f9432a..6033382ebb5f80e150a9780a33b9134672fdf8a4 100644 --- a/tasks/0065_504_65504512_qa_1/task.toml +++ b/tasks/0065_504_65504512_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0065_504_65504512_qa_1" +name = "smoldataenvs-train/0065_504_65504512_qa_1" description = "Which region has the highest average medical charges based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Southeast" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_504_65504512_qa_3/task.toml b/tasks/0065_504_65504512_qa_3/task.toml index c5aedfd51cf1b5459b96e1a25fedc774842a0736..92a4124fd0fbfe46d99387f0f77ec20c0917c0f9 100644 --- a/tasks/0065_504_65504512_qa_3/task.toml +++ b/tasks/0065_504_65504512_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0065_504_65504512_qa_3" +name = "smoldataenvs-train/0065_504_65504512_qa_3" description = "Which region has the highest number of patients represented in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Southeast" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0065_504_65504512_qa_4/task.toml b/tasks/0065_504_65504512_qa_4/task.toml index 487023e60531f818afe9fd292fcaa1ea4b114d4d..bb059b1daf3c0910e1aa8aa6fa56ea1f9b8dc263 100644 --- a/tasks/0065_504_65504512_qa_4/task.toml +++ b/tasks/0065_504_65504512_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0065_504_65504512_qa_4" +name = "smoldataenvs-train/0065_504_65504512_qa_4" description = "Which factor among age, body mass index (bmi), and number of children has the strongest positive correlation with medical charges in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "age" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_504_65504512_qa_5/task.toml b/tasks/0065_504_65504512_qa_5/task.toml index b9578d736a12952de57544d9ba7fc5835f0aa976..383141959bc4900a1f1a765295ba0bab1d2e2814 100644 --- a/tasks/0065_504_65504512_qa_5/task.toml +++ b/tasks/0065_504_65504512_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0065_504_65504512_qa_5" +name = "smoldataenvs-train/0065_504_65504512_qa_5" description = "What is the average medical charge for patients with no children (children = 0) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12365.98" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_535_65535638_qa_1/task.toml b/tasks/0065_535_65535638_qa_1/task.toml index e87b2df69eef5814eb4256b0f42f2f3e09a96926..abc46c3faa700dc25c2084833b06bd0b1d0670e0 100644 --- a/tasks/0065_535_65535638_qa_1/task.toml +++ b/tasks/0065_535_65535638_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0065_535_65535638_qa_1" +name = "smoldataenvs-train/0065_535_65535638_qa_1" description = "Which favorite color has the highest proportion of female participants based on the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Warm" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_547_65547031_qa_1/task.toml b/tasks/0065_547_65547031_qa_1/task.toml index e2eac4ddef72cc05773e08a1a53255175a4ccfbc..83098117ab44542c6b01ac3488780fb519d34fd1 100644 --- a/tasks/0065_547_65547031_qa_1/task.toml +++ b/tasks/0065_547_65547031_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0065_547_65547031_qa_1" +name = "smoldataenvs-train/0065_547_65547031_qa_1" description = "What is the highest correlation coefficient between any two features in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.997855" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_560_65560280_qa_3/task.toml b/tasks/0065_560_65560280_qa_3/task.toml index 91e5f9bcd0d1bd58235c661d717f710fdd475d8d..21e5a97986065a970feba2b4843e28433c04ad7d 100644 --- a/tasks/0065_560_65560280_qa_3/task.toml +++ b/tasks/0065_560_65560280_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0065_560_65560280_qa_3" +name = "smoldataenvs-train/0065_560_65560280_qa_3" description = "Which variable has the strongest negative correlation with Temperature (C) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Humidity" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_563_65563185_qa_4/task.toml b/tasks/0065_563_65563185_qa_4/task.toml index 5cd282144998821bff3c28ee91b6a7dcc3f361fe..7f592072b07d706af2427ddd8d920ffa729f47b3 100644 --- a/tasks/0065_563_65563185_qa_4/task.toml +++ b/tasks/0065_563_65563185_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0065_563_65563185_qa_4" +name = "smoldataenvs-train/0065_563_65563185_qa_4" description = "What is the recall value for the minority class (class 1) in the XGBoost model's test set evaluation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.61" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0065_573_65573990_qa_5/task.toml b/tasks/0065_573_65573990_qa_5/task.toml index c890ba570904eb270fd912353206e6c55c5f4ce7..5c6428e506fc25310021fdf935ef9952099034b1 100644 --- a/tasks/0065_573_65573990_qa_5/task.toml +++ b/tasks/0065_573_65573990_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0065_573_65573990_qa_5" +name = "smoldataenvs-train/0065_573_65573990_qa_5" description = "What is the highest overall accuracy achieved by any model on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "98" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0065_668_65668933_qa_2/task.toml b/tasks/0065_668_65668933_qa_2/task.toml index 933557e6818542b4711f1f1647f81ad3386a0903..6390eac8cb8096955a5d9a9af4e1ec3d1190c628 100644 --- a/tasks/0065_668_65668933_qa_2/task.toml +++ b/tasks/0065_668_65668933_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0065_668_65668933_qa_2" +name = "smoldataenvs-train/0065_668_65668933_qa_2" description = "What is the exact percentage of missing values in the Credit_History column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8.14" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0065_679_65679925_qa_1/task.toml b/tasks/0065_679_65679925_qa_1/task.toml index 1cd657da9aa125b7b6673c6350c7e44e1a3b0c33..c22eb5d5e2bfea7710f3a768059f67ea873b96e2 100644 --- a/tasks/0065_679_65679925_qa_1/task.toml +++ b/tasks/0065_679_65679925_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0065_679_65679925_qa_1" +name = "smoldataenvs-train/0065_679_65679925_qa_1" description = "What is the mean age of passengers in the Titanic dataset after imputing missing values with the median age?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "29.503" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_716_65716976_qa_1/task.toml b/tasks/0065_716_65716976_qa_1/task.toml index 90fdd29b582cba6824a42cfb245dc7c3027aa2ca..a6437212549772d9e47c50266af73082e91a8b3b 100644 --- a/tasks/0065_716_65716976_qa_1/task.toml +++ b/tasks/0065_716_65716976_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0065_716_65716976_qa_1" +name = "smoldataenvs-train/0065_716_65716976_qa_1" description = "Which continuous variable shows the strongest positive linear relationship with home prices based on Pearson correlation coefficients?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "sqft_living" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_735_65735171_qa_4/task.toml b/tasks/0065_735_65735171_qa_4/task.toml index 2bfb700750ded7bc4e68fcc830c48363ddfdbe46..58c42cf0858445a1b109853d834a818007737b35 100644 --- a/tasks/0065_735_65735171_qa_4/task.toml +++ b/tasks/0065_735_65735171_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0065_735_65735171_qa_4" +name = "smoldataenvs-train/0065_735_65735171_qa_4" description = "What was the final test accuracy of the neural network model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "96.67" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0065_745_65745045_qa_1/task.toml b/tasks/0065_745_65745045_qa_1/task.toml index e908cf77c83dc5c489c57340e6b8ebd8a1e363f4..e5be063b55b7b022983aebddd7d36a808b9f45f4 100644 --- a/tasks/0065_745_65745045_qa_1/task.toml +++ b/tasks/0065_745_65745045_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0065_745_65745045_qa_1" +name = "smoldataenvs-train/0065_745_65745045_qa_1" description = "What is the most frequently occurring unigram in the reviews before removing stop words?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "the" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_745_65745045_qa_3/task.toml b/tasks/0065_745_65745045_qa_3/task.toml index 38c816668966f381069e3bd8ae4ae5d5b18fd473..f0faee45aa81e02c606ef1856a87f507f883fe96 100644 --- a/tasks/0065_745_65745045_qa_3/task.toml +++ b/tasks/0065_745_65745045_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0065_745_65745045_qa_3" +name = "smoldataenvs-train/0065_745_65745045_qa_3" description = "What is the most common part-of-speech (POS) tag in the reviews?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "NN" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_756_65756425_qa_1/task.toml b/tasks/0065_756_65756425_qa_1/task.toml index 5ca54f630642eaa82af11540c7681002e56a5adb..c275682db0488deb99a88286e0ff89934a97c9f6 100644 --- a/tasks/0065_756_65756425_qa_1/task.toml +++ b/tasks/0065_756_65756425_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0065_756_65756425_qa_1" +name = "smoldataenvs-train/0065_756_65756425_qa_1" description = "Which diamond color category has the highest average price according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "J" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_789_65789409_qa_2/task.toml b/tasks/0065_789_65789409_qa_2/task.toml index 4dcbc7ab8d5fe8e9c224e3fe5776de7fe9ff27d0..375a921084324145ecf3b35fe9cb30ed432c554d 100644 --- a/tasks/0065_789_65789409_qa_2/task.toml +++ b/tasks/0065_789_65789409_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0065_789_65789409_qa_2" +name = "smoldataenvs-train/0065_789_65789409_qa_2" description = "What is the top feature identified by XGBoost's feature importance analysis for predicting log_price?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "room_type" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0065_794_65794937_qa_3/task.toml b/tasks/0065_794_65794937_qa_3/task.toml index 4c565bcf9da17ae8145635b4829ae7f45a0e6480..be7405da782cf20149864bdaedc577dbe5ec9599 100644 --- a/tasks/0065_794_65794937_qa_3/task.toml +++ b/tasks/0065_794_65794937_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0065_794_65794937_qa_3" +name = "smoldataenvs-train/0065_794_65794937_qa_3" description = "What is the maximum normalized value for the Close price after Min-Max scaling?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9091918" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_794_65794937_qa_5/task.toml b/tasks/0065_794_65794937_qa_5/task.toml index 6925078d78bf2789d04f54a3b4c4c968f3637ff5..c35e8fe330fcc141dfdd5ffe7f3514130c3993cb 100644 --- a/tasks/0065_794_65794937_qa_5/task.toml +++ b/tasks/0065_794_65794937_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0065_794_65794937_qa_5" +name = "smoldataenvs-train/0065_794_65794937_qa_5" description = "What is the maximum value in the High price column of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "291.420013" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0065_850_65850570_qa_3/task.toml b/tasks/0065_850_65850570_qa_3/task.toml index 0cba04121194df0d993d23daf76d3ba0e9e48326..1c5da8f44eb8a3ab108ba2c6184ba55eebc34418 100644 --- a/tasks/0065_850_65850570_qa_3/task.toml +++ b/tasks/0065_850_65850570_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0065_850_65850570_qa_3" +name = "smoldataenvs-train/0065_850_65850570_qa_3" description = "How many samples were included in the test set after splitting the data with a 20% test size?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "30" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_850_65850570_qa_4/task.toml b/tasks/0065_850_65850570_qa_4/task.toml index 0141286d27d0366ebf46c3deb0aca7bef354b744..f6cbc36504a4d673e17aeea32151f19b9f5ba08f 100644 --- a/tasks/0065_850_65850570_qa_4/task.toml +++ b/tasks/0065_850_65850570_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0065_850_65850570_qa_4" +name = "smoldataenvs-train/0065_850_65850570_qa_4" description = "Which feature had the highest range (max - min) before normalization was applied?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_929_65929308_qa_1/task.toml b/tasks/0065_929_65929308_qa_1/task.toml index cd99c97c6e6139f28bfb69b5d57b26afcde5ffc3..19388a42488e77a22ad81c389d05b7996e008cef 100644 --- a/tasks/0065_929_65929308_qa_1/task.toml +++ b/tasks/0065_929_65929308_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0065_929_65929308_qa_1" +name = "smoldataenvs-train/0065_929_65929308_qa_1" description = "Which glass type has the highest frequency in the dataset based on the 'Type' feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0065_929_65929308_qa_5/task.toml b/tasks/0065_929_65929308_qa_5/task.toml index 7f5d02cd4669e54ce26bbf24db56aa633e46a36f..0cab2c5e69bf16d9bb4a5844455cc0d637796f0f 100644 --- a/tasks/0065_929_65929308_qa_5/task.toml +++ b/tasks/0065_929_65929308_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0065_929_65929308_qa_5" +name = "smoldataenvs-train/0065_929_65929308_qa_5" description = "What is the average validation accuracy across all 5-fold cross-validation iterations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "74.76" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0065_935_65935179_qa_2/task.toml b/tasks/0065_935_65935179_qa_2/task.toml index 2fe1fb917607353c7f0107bbcf3cbdad21c89c43..a4bc3b5b94c19ab190395e5c03a5e32573005980 100644 --- a/tasks/0065_935_65935179_qa_2/task.toml +++ b/tasks/0065_935_65935179_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0065_935_65935179_qa_2" +name = "smoldataenvs-train/0065_935_65935179_qa_2" description = "Which five departments contributed the highest total weekly sales in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "92, 95, 38, 72, 90" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0065_951_65951065_qa_1/task.toml b/tasks/0065_951_65951065_qa_1/task.toml index c73a4fe1602dc43ab2f396a4973f5cf9987ba83a..cb1c48cf7f4282cdc2ab6411e259e869415f5f45 100644 --- a/tasks/0065_951_65951065_qa_1/task.toml +++ b/tasks/0065_951_65951065_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0065_951_65951065_qa_1" +name = "smoldataenvs-train/0065_951_65951065_qa_1" description = "What percentage of patients in the dataset have diabetes (Outcome = 1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0065_988_65988202_qa_2/task.toml b/tasks/0065_988_65988202_qa_2/task.toml index c2dcfcd282fb39d679181dd74482c43cf4c0ae83..1d8e0e61c137d9273ecff9d8b2d26f5a0dc0810b 100644 --- a/tasks/0065_988_65988202_qa_2/task.toml +++ b/tasks/0065_988_65988202_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0065_988_65988202_qa_2" +name = "smoldataenvs-train/0065_988_65988202_qa_2" description = "How many Iris-virginica samples were misclassified into an incorrect cluster using the K-means algorithm?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "14" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0065_989_65989824_qa_2/task.toml b/tasks/0065_989_65989824_qa_2/task.toml index abef35289dddf1efc56574b1059ec5f3bf72eeba..2d4b1401efc725d2a686aae03131c379d0aed5a6 100644 --- a/tasks/0065_989_65989824_qa_2/task.toml +++ b/tasks/0065_989_65989824_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0065_989_65989824_qa_2" +name = "smoldataenvs-train/0065_989_65989824_qa_2" description = "What is the total number of patients in the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0066_030_66030315_qa_4/task.toml b/tasks/0066_030_66030315_qa_4/task.toml index 997fbcd6414b09163f004e9cc77f3e54cec15a18..1b0e0c35b21e0047ef0581842f431af5fafa9f79 100644 --- a/tasks/0066_030_66030315_qa_4/task.toml +++ b/tasks/0066_030_66030315_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0066_030_66030315_qa_4" +name = "smoldataenvs-train/0066_030_66030315_qa_4" description = "What is the count of wines classified as 'good' (quality 7 or higher) in the dataset after binning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "217" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0066_039_66039698_qa_2/task.toml b/tasks/0066_039_66039698_qa_2/task.toml index 4a9bbaa65dc50465c3b26b1c3fcbd9a398f2b4f6..b37d6e3f399005c103280eca6ebf75b31695dbad 100644 --- a/tasks/0066_039_66039698_qa_2/task.toml +++ b/tasks/0066_039_66039698_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0066_039_66039698_qa_2" +name = "smoldataenvs-train/0066_039_66039698_qa_2" description = "By how many percentage points did the popularity of Labrador Retrievers decrease from 2008 to 2009?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.509763" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0066_039_66039698_qa_4/task.toml b/tasks/0066_039_66039698_qa_4/task.toml index 701fb88688dca41a595b3810a757c441dbd7537c..5991869e99153ca2a9554dda5e152d70ce861007 100644 --- a/tasks/0066_039_66039698_qa_4/task.toml +++ b/tasks/0066_039_66039698_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0066_039_66039698_qa_4" +name = "smoldataenvs-train/0066_039_66039698_qa_4" description = "What is the most common dog name in 2017, and how many times was it used?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "BELLA, 342" reward_mode_initial = "list" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0066_107_66107048_qa_1/task.toml b/tasks/0066_107_66107048_qa_1/task.toml index ec9cdb45494fa707adc20f7b63193dfa7e05f00a..53f672eca0c9b07d00be718b4641aca417b47699 100644 --- a/tasks/0066_107_66107048_qa_1/task.toml +++ b/tasks/0066_107_66107048_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0066_107_66107048_qa_1" +name = "smoldataenvs-train/0066_107_66107048_qa_1" description = "What is the most frequently recorded February average temperature in the Northeast, and how many times does it occur?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26.2°F, 5 times" reward_mode_initial = "flexible" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0066_107_66107048_qa_2/task.toml b/tasks/0066_107_66107048_qa_2/task.toml index 7905de4b894c571ff1b37a0f47e8af7cce9a6255..ed719292f735d38ce689f38cda3fe0b34d57071f 100644 --- a/tasks/0066_107_66107048_qa_2/task.toml +++ b/tasks/0066_107_66107048_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0066_107_66107048_qa_2" +name = "smoldataenvs-train/0066_107_66107048_qa_2" description = "What is the most frequently recorded February average temperature in the Midwest, and how many times does it occur?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "31.5, 4" reward_mode_initial = "list" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0066_107_66107048_qa_4/task.toml b/tasks/0066_107_66107048_qa_4/task.toml index 6eceb75a6cfa03b307db4bf4512ae8a8a2cbe676..c5772f8a7a15068b864196417bfaadae6248f49f 100644 --- a/tasks/0066_107_66107048_qa_4/task.toml +++ b/tasks/0066_107_66107048_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0066_107_66107048_qa_4" +name = "smoldataenvs-train/0066_107_66107048_qa_4" description = "What is the range of February average temperatures in Pennsylvania when Punxsutawney Phil saw his shadow?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "19.7" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0066_134_66134404_qa_1/task.toml b/tasks/0066_134_66134404_qa_1/task.toml index 893743b9da5889255fde0170376fc4dc80d9142e..c8d27373b395f62f583f288a2cfca880350efbb7 100644 --- a/tasks/0066_134_66134404_qa_1/task.toml +++ b/tasks/0066_134_66134404_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0066_134_66134404_qa_1" +name = "smoldataenvs-train/0066_134_66134404_qa_1" description = "Which three features had the highest missing value percentages in the original dataset before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Population, Hepatitis B, GDP" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0066_134_66134404_qa_4/task.toml b/tasks/0066_134_66134404_qa_4/task.toml index 266748fcf9b85fbd9c31c1f06db2def53bc366cf..9579c1b5883c207fa0537733f5ad0ee8d954cba5 100644 --- a/tasks/0066_134_66134404_qa_4/task.toml +++ b/tasks/0066_134_66134404_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0066_134_66134404_qa_4" +name = "smoldataenvs-train/0066_134_66134404_qa_4" description = "Which feature showed the strongest predictive power for life expectancy according to mutual information analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Adult Mortality" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0066_186_66186009_qa_2/task.toml b/tasks/0066_186_66186009_qa_2/task.toml index fd75d86560115e1e03efe6d867b0461cf58061e0..aee14ee688f780b05159f23551236daa8954877f 100644 --- a/tasks/0066_186_66186009_qa_2/task.toml +++ b/tasks/0066_186_66186009_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0066_186_66186009_qa_2" +name = "smoldataenvs-train/0066_186_66186009_qa_2" description = "What is the maximum UnitPrice recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "38970.00" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0066_186_66186009_qa_3/task.toml b/tasks/0066_186_66186009_qa_3/task.toml index 6fd76b76af58a5ecc5ed7eaba9756b6d6c838178..752fa140c39fa8e6644a8f2e1b5c6ed251dd3282 100644 --- a/tasks/0066_186_66186009_qa_3/task.toml +++ b/tasks/0066_186_66186009_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0066_186_66186009_qa_3" +name = "smoldataenvs-train/0066_186_66186009_qa_3" description = "What is the 75th percentile value for the Quantity sold per transaction?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10.0" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0066_186_66186009_qa_4/task.toml b/tasks/0066_186_66186009_qa_4/task.toml index b579a1de5de79d2e755faa1dfc3d2c9031eab1a7..71b7a194608744402910f1b224ebd4dab7276517 100644 --- a/tasks/0066_186_66186009_qa_4/task.toml +++ b/tasks/0066_186_66186009_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0066_186_66186009_qa_4" +name = "smoldataenvs-train/0066_186_66186009_qa_4" description = "What is the earliest InvoiceDate in the dataset after converting to datetime format?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2010-12-01 08:26:00" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0066_372_66372058_qa_2/task.toml b/tasks/0066_372_66372058_qa_2/task.toml index 27aff592f2e56fbaca90cd1be7cb95d66fc9522b..011a5c7e1a5406e364ccd869326973c635e8953a 100644 --- a/tasks/0066_372_66372058_qa_2/task.toml +++ b/tasks/0066_372_66372058_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0066_372_66372058_qa_2" +name = "smoldataenvs-train/0066_372_66372058_qa_2" description = "What is the percentage of loans with a \"Short Term\" duration compared to \"Long Term\" loans in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "72.208" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0066_372_66372058_qa_4/task.toml b/tasks/0066_372_66372058_qa_4/task.toml index ad9264445bdf49a6109a257bc989a84228230736..788bb365957a6c5e1deb9f137682466addc92427 100644 --- a/tasks/0066_372_66372058_qa_4/task.toml +++ b/tasks/0066_372_66372058_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0066_372_66372058_qa_4" +name = "smoldataenvs-train/0066_372_66372058_qa_4" description = "What is the mean value of the `Current Loan Amount` after standardization (z-score normalization)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0066_374_66374046_qa_3/task.toml b/tasks/0066_374_66374046_qa_3/task.toml index 00a25fe163acdf54f4d104fb676f99ccbdf03eef..820d3cfb865fc886b9290b7274763e4b91f81499 100644 --- a/tasks/0066_374_66374046_qa_3/task.toml +++ b/tasks/0066_374_66374046_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0066_374_66374046_qa_3" +name = "smoldataenvs-train/0066_374_66374046_qa_3" description = "After converting wine quality into binary categories, what is the ratio of samples in the majority class to the minority class?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.37" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0066_392_66392992_qa_3/task.toml b/tasks/0066_392_66392992_qa_3/task.toml index 5f95711e48da8a1048c23b8ceba64615790854e7..6438d51d246943c4ee1f102154e001f3fd8b23fa 100644 --- a/tasks/0066_392_66392992_qa_3/task.toml +++ b/tasks/0066_392_66392992_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0066_392_66392992_qa_3" +name = "smoldataenvs-train/0066_392_66392992_qa_3" description = "Which cluster's centroid has the most negative longitude value, and what is that value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-145.43676549" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0066_429_66429634_qa_1/task.toml b/tasks/0066_429_66429634_qa_1/task.toml index 53a3ca5130a15b78702298e11e46ce6be4d6bc4e..e881c03b049f822749b4a032c0eb709f6eb7d984 100644 --- a/tasks/0066_429_66429634_qa_1/task.toml +++ b/tasks/0066_429_66429634_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0066_429_66429634_qa_1" +name = "smoldataenvs-train/0066_429_66429634_qa_1" description = "Which feature exhibited the highest variance before any preprocessing steps were applied to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "total sulfur dioxide" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0066_429_66429634_qa_5/task.toml b/tasks/0066_429_66429634_qa_5/task.toml index dd06d21573193074d20b89af00d5b1ff888054f0..d1330ffcef44f8b386d33c9cec2ff7ea9d206216 100644 --- a/tasks/0066_429_66429634_qa_5/task.toml +++ b/tasks/0066_429_66429634_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0066_429_66429634_qa_5" +name = "smoldataenvs-train/0066_429_66429634_qa_5" description = "Which preprocessing step reduced the variance of 'free sulfur dioxide' from 109.414884 to 0.469624?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Log transformation" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0066_435_66435240_qa_4/task.toml b/tasks/0066_435_66435240_qa_4/task.toml index bf307072f8c2aed8032f9ba820d995eeaf35c051..e4eaf204bc61d0fc61ee294ee9b0db385df8a597 100644 --- a/tasks/0066_435_66435240_qa_4/task.toml +++ b/tasks/0066_435_66435240_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0066_435_66435240_qa_4" +name = "smoldataenvs-train/0066_435_66435240_qa_4" description = "What is the root mean squared error (RMSE) of the linear regression model's predictions on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "100341.53" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0066_447_66447966_qa_4/task.toml b/tasks/0066_447_66447966_qa_4/task.toml index 1d16df3058ab8011b0941cc298d1feb4cac23b55..54ecb33e77defd55593ecb60b33feeb5c89dddd8 100644 --- a/tasks/0066_447_66447966_qa_4/task.toml +++ b/tasks/0066_447_66447966_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0066_447_66447966_qa_4" +name = "smoldataenvs-train/0066_447_66447966_qa_4" description = "Which production company has the highest total gross revenue?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Warner Bros." reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0066_479_66479571_qa_2/task.toml b/tasks/0066_479_66479571_qa_2/task.toml index 0c935dfbc8c50a6e57a31509aa64721c27072e75..f087dd40f6928fb33ea474de0198ae645523bb6e 100644 --- a/tasks/0066_479_66479571_qa_2/task.toml +++ b/tasks/0066_479_66479571_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0066_479_66479571_qa_2" +name = "smoldataenvs-train/0066_479_66479571_qa_2" description = "What is the slope coefficient of the linear regression model predicting Weight from Height in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "61.27218654" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0066_515_66515711_qa_3/task.toml b/tasks/0066_515_66515711_qa_3/task.toml index ed1445d587b792d9b82645b3179a12340650a4c2..b409a784dfc4c3554bb409f5a39f67749333f475 100644 --- a/tasks/0066_515_66515711_qa_3/task.toml +++ b/tasks/0066_515_66515711_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0066_515_66515711_qa_3" +name = "smoldataenvs-train/0066_515_66515711_qa_3" description = "What percentage of the dataset had missing values in the 'paid_off_time' column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0066_515_66515711_qa_4/task.toml b/tasks/0066_515_66515711_qa_4/task.toml index e634ff13a74c2f5f3f0e4a01e6875869a655a579..eb7ef036743a7f9c73400657aca00968c8eb844f 100644 --- a/tasks/0066_515_66515711_qa_4/task.toml +++ b/tasks/0066_515_66515711_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0066_515_66515711_qa_4" +name = "smoldataenvs-train/0066_515_66515711_qa_4" description = "What is the difference in the number of \"COLLECTION\" loans between male and female borrowers?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "80" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0066_515_66515711_qa_5/task.toml b/tasks/0066_515_66515711_qa_5/task.toml index 92ecd51e228388769aef44ef62ae9ea55129920b..8f603c12063fd42e458d52f75b9c0a3bb171d3d3 100644 --- a/tasks/0066_515_66515711_qa_5/task.toml +++ b/tasks/0066_515_66515711_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0066_515_66515711_qa_5" +name = "smoldataenvs-train/0066_515_66515711_qa_5" description = "What is the maximum number of past due days recorded for any loan in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "76" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0066_527_66527542_qa_2/task.toml b/tasks/0066_527_66527542_qa_2/task.toml index f39eeb69d48b6aad57228ad5ffd20cc4b18a61e3..7ce0ff15dbce9aa53dbd935d7b3920a9de6b8034 100644 --- a/tasks/0066_527_66527542_qa_2/task.toml +++ b/tasks/0066_527_66527542_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0066_527_66527542_qa_2" +name = "smoldataenvs-train/0066_527_66527542_qa_2" description = "Which SGDRegressor variant achieved the lowest test set MSE among Batch, Stochastic, and MiniBatch approaches?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Batch" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0066_599_66599381_qa_2/task.toml b/tasks/0066_599_66599381_qa_2/task.toml index d2e4d79334229c8dc55a04dc654e7f23947f6daa..e3b5804a2d48bba7f844fb864f5ef790d7f3fe11 100644 --- a/tasks/0066_599_66599381_qa_2/task.toml +++ b/tasks/0066_599_66599381_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0066_599_66599381_qa_2" +name = "smoldataenvs-train/0066_599_66599381_qa_2" description = "Which feature exhibits the strongest negative correlation with customer churn in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "tenure" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0066_634_66634848_qa_3/task.toml b/tasks/0066_634_66634848_qa_3/task.toml index 0378569a643bc9d18b1bf8b6a79cfa049c28e43f..dd9014a9a7bcf23eedf729b767192dbf3dc2e44c 100644 --- a/tasks/0066_634_66634848_qa_3/task.toml +++ b/tasks/0066_634_66634848_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0066_634_66634848_qa_3" +name = "smoldataenvs-train/0066_634_66634848_qa_3" description = "What is the exact count of benign (0) versus malignant (1) cases in the diagnosis column before any preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "357,212" reward_mode_initial = "list" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0066_715_66715498_qa_1/task.toml b/tasks/0066_715_66715498_qa_1/task.toml index 5d3e23527877114539b4d38681a5f2fb02398544..c3fb11c02a62080bbd7dd48f8d1dcca5d3929e0e 100644 --- a/tasks/0066_715_66715498_qa_1/task.toml +++ b/tasks/0066_715_66715498_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0066_715_66715498_qa_1" +name = "smoldataenvs-train/0066_715_66715498_qa_1" description = "Which machine learning algorithm achieved the highest average accuracy in the 10-fold cross-validation on the Breast Cancer Wisconsin dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Logistic Regression" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0066_730_66730048_qa_2/task.toml b/tasks/0066_730_66730048_qa_2/task.toml index 70143e75c514870f591f85ea4d80b5795a738eb0..5c048a07dc969ca187f8eb11801c56172614efd2 100644 --- a/tasks/0066_730_66730048_qa_2/task.toml +++ b/tasks/0066_730_66730048_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0066_730_66730048_qa_2" +name = "smoldataenvs-train/0066_730_66730048_qa_2" description = "What is the chi-square test statistic value for the analysis of the relationship between attrition and job satisfaction?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "17.505" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0066_778_66778213_qa_2/task.toml b/tasks/0066_778_66778213_qa_2/task.toml index c8650488af242d5bebd66a9576b961a2776b6be2..9006ac41e0592a2da4ae1ae71b04f6f087c8393d 100644 --- a/tasks/0066_778_66778213_qa_2/task.toml +++ b/tasks/0066_778_66778213_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0066_778_66778213_qa_2" +name = "smoldataenvs-train/0066_778_66778213_qa_2" description = "Which automaker has the highest percentage of models categorized as the riskiest (symboling = 3) within their models with normal-to-risky safety (symboling > 0)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Porsche" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0066_778_66778213_qa_4/task.toml b/tasks/0066_778_66778213_qa_4/task.toml index dbffb2b9c315f2c23e3e2333624f2d4a8ef130c3..76026ac21e470006febf76086c9b0e306aa6f1b7 100644 --- a/tasks/0066_778_66778213_qa_4/task.toml +++ b/tasks/0066_778_66778213_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0066_778_66778213_qa_4" +name = "smoldataenvs-train/0066_778_66778213_qa_4" description = "What is the most common fuel system type in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "MPFI" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0066_819_66819249_qa_2/task.toml b/tasks/0066_819_66819249_qa_2/task.toml index 84b55c4113565156b3c3cdfaadf9b93647992969..5e3b559f496965505f6d870526784816fd8988a0 100644 --- a/tasks/0066_819_66819249_qa_2/task.toml +++ b/tasks/0066_819_66819249_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0066_819_66819249_qa_2" +name = "smoldataenvs-train/0066_819_66819249_qa_2" description = "Which variable had the highest p-value in the initial regression model before feature selection?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "sex" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0066_820_66820736_qa_3/task.toml b/tasks/0066_820_66820736_qa_3/task.toml index 1690a9a98d59b207f8d331b13c6db3230850307a..5733e0d14b5beadb9eb18e0544b19a9c5e2fcaf6 100644 --- a/tasks/0066_820_66820736_qa_3/task.toml +++ b/tasks/0066_820_66820736_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0066_820_66820736_qa_3" +name = "smoldataenvs-train/0066_820_66820736_qa_3" description = "What is the mean absolute error (MAE) of the model's predictions for housing prices?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "81305.23300085646" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0066_890_66890335_qa_4/task.toml b/tasks/0066_890_66890335_qa_4/task.toml index c5408c988e7bd4dbf50a75f4596d99c483b3b2eb..a737b8120626051e4f3548882594d3a7913bf683 100644 --- a/tasks/0066_890_66890335_qa_4/task.toml +++ b/tasks/0066_890_66890335_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0066_890_66890335_qa_4" +name = "smoldataenvs-train/0066_890_66890335_qa_4" description = "What was the skewness value of medical charges before applying the log transformation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.516" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0067_061_67061270_qa_3/task.toml b/tasks/0067_061_67061270_qa_3/task.toml index 16959eacd9ebbf7ce771fd00710f7cd33ad73a1d..305032475abe96c3f041e4b3954272e6a6076245 100644 --- a/tasks/0067_061_67061270_qa_3/task.toml +++ b/tasks/0067_061_67061270_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0067_061_67061270_qa_3" +name = "smoldataenvs-train/0067_061_67061270_qa_3" description = "Which team has won the most IPL matches in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Mumbai Indians" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_061_67061270_qa_5/task.toml b/tasks/0067_061_67061270_qa_5/task.toml index a1a45917f01953154a0d86d7e8300826403a811f..8c210f2198f631202ff5734ed157263678e9008c 100644 --- a/tasks/0067_061_67061270_qa_5/task.toml +++ b/tasks/0067_061_67061270_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0067_061_67061270_qa_5" +name = "smoldataenvs-train/0067_061_67061270_qa_5" description = "Which city hosted the most IPL matches in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Mumbai" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0067_062_67062935_qa_2/task.toml b/tasks/0067_062_67062935_qa_2/task.toml index 7436c700bc3bbeed25fc5e0d0112ee14d5d2a0c7..e660129b195722931550062d16cdd4380a460b68 100644 --- a/tasks/0067_062_67062935_qa_2/task.toml +++ b/tasks/0067_062_67062935_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0067_062_67062935_qa_2" +name = "smoldataenvs-train/0067_062_67062935_qa_2" description = "What is the percentage of abnormal patients in the original dataset before any data cleaning operations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "67.74" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_076_67076892_qa_3/task.toml b/tasks/0067_076_67076892_qa_3/task.toml index c0bd4f3b1bbb461bc608e1fb265855aef1f16575..00a6795e408b396091760e27281be365363603ad 100644 --- a/tasks/0067_076_67076892_qa_3/task.toml +++ b/tasks/0067_076_67076892_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0067_076_67076892_qa_3" +name = "smoldataenvs-train/0067_076_67076892_qa_3" description = "After handling missing values by replacing zeros with column means, what is the mean Insulin level in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "155.55" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_113_67113701_qa_3/task.toml b/tasks/0067_113_67113701_qa_3/task.toml index fe133bb805cb27e258bef601167505b5a5c5967d..c601d88d5218616d8fb187698b3fc2093376a050 100644 --- a/tasks/0067_113_67113701_qa_3/task.toml +++ b/tasks/0067_113_67113701_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0067_113_67113701_qa_3" +name = "smoldataenvs-train/0067_113_67113701_qa_3" description = "Which class in the target variable has the highest frequency in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "unacc" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0067_113_67113701_qa_4/task.toml b/tasks/0067_113_67113701_qa_4/task.toml index 8b0922dc54b7694502e49d2b29348d965591b3ff..e813832b805ecfb54d1e945fc3d1f70f4bbd9c68 100644 --- a/tasks/0067_113_67113701_qa_4/task.toml +++ b/tasks/0067_113_67113701_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0067_113_67113701_qa_4" +name = "smoldataenvs-train/0067_113_67113701_qa_4" description = "How many unique categories are present in the 'doors' feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0067_118_67118977_qa_5/task.toml b/tasks/0067_118_67118977_qa_5/task.toml index d8bb62519312347905d0d06973ba5fa46a22bf96..25794659074f6e4b074fbb79c2eddd715695b469 100644 --- a/tasks/0067_118_67118977_qa_5/task.toml +++ b/tasks/0067_118_67118977_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0067_118_67118977_qa_5" +name = "smoldataenvs-train/0067_118_67118977_qa_5" description = "What is the highest frequency of any wine quality rating in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "681" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0067_225_67225548_qa_5/task.toml b/tasks/0067_225_67225548_qa_5/task.toml index 3ce52181665e12d5f73d9de5f3612ddfd9afee3d..1df62841cb035ba6900b1d53ef7c1eab00c8886b 100644 --- a/tasks/0067_225_67225548_qa_5/task.toml +++ b/tasks/0067_225_67225548_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0067_225_67225548_qa_5" +name = "smoldataenvs-train/0067_225_67225548_qa_5" description = "What is the Spearman correlation coefficient between the number of views a house has and its sale price in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.293931" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_241_67241146_qa_2/task.toml b/tasks/0067_241_67241146_qa_2/task.toml index 425b60fa712d107cb115731a4c66f64637a11c8f..7ac856d4765a0173285f4511878e3958edba74d7 100644 --- a/tasks/0067_241_67241146_qa_2/task.toml +++ b/tasks/0067_241_67241146_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0067_241_67241146_qa_2" +name = "smoldataenvs-train/0067_241_67241146_qa_2" description = "How many patients were removed from the dataset due to duplicate records during the cleaning process?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "17" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0067_247_67247951_qa_2/task.toml b/tasks/0067_247_67247951_qa_2/task.toml index 5e9711a872c5b0dc9f66d7ad25ec001520067685..b9069686dbaf3177f94dd50e84bdf986a12bb2c3 100644 --- a/tasks/0067_247_67247951_qa_2/task.toml +++ b/tasks/0067_247_67247951_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0067_247_67247951_qa_2" +name = "smoldataenvs-train/0067_247_67247951_qa_2" description = "What is the skewness value of the 'SepalLengthCm' column after outlier treatment and preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.278417" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_290_67290658_qa_1/task.toml b/tasks/0067_290_67290658_qa_1/task.toml index e5592e0d3b5eb0409e157f116649013841569811..a623aca5a227ca1df7a68690bc5cdf30c196340e 100644 --- a/tasks/0067_290_67290658_qa_1/task.toml +++ b/tasks/0067_290_67290658_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0067_290_67290658_qa_1" +name = "smoldataenvs-train/0067_290_67290658_qa_1" description = "Is the relationship between having health benefits and seeking treatment for mental health issues statistically significant?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0067_414_67414974_qa_3/task.toml b/tasks/0067_414_67414974_qa_3/task.toml index 700cbabe367bf120bd9aa72112d71b0fecdbedfe..53a570b4cc80010fc2c28bbe8e643b6300578dc2 100644 --- a/tasks/0067_414_67414974_qa_3/task.toml +++ b/tasks/0067_414_67414974_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0067_414_67414974_qa_3" +name = "smoldataenvs-train/0067_414_67414974_qa_3" description = "Which occupational category has the highest proportion of individuals earning more than $50K based on the dataset's analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Exec-managerial" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_414_67414974_qa_5/task.toml b/tasks/0067_414_67414974_qa_5/task.toml index e42758d77002a0c621a2989089a414d96345fe1a..b57632eb26801759598c7c8d7cf91b67ff377ba4 100644 --- a/tasks/0067_414_67414974_qa_5/task.toml +++ b/tasks/0067_414_67414974_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0067_414_67414974_qa_5" +name = "smoldataenvs-train/0067_414_67414974_qa_5" description = "Which education level demonstrates the highest proportion of individuals earning more than $50K according to the dataset's analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Doctorate" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_446_67446851_qa_1/task.toml b/tasks/0067_446_67446851_qa_1/task.toml index 96236cc0441d36fd8d2ae104ce228e5f37298fa0..2b2153346152338b28b9ea2522506483e1e4ea15 100644 --- a/tasks/0067_446_67446851_qa_1/task.toml +++ b/tasks/0067_446_67446851_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0067_446_67446851_qa_1" +name = "smoldataenvs-train/0067_446_67446851_qa_1" description = "Which feature shows the least separation between benign and malignant classes according to the box plot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "fractal_dimension_mean" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_450_67450009_qa_1/task.toml b/tasks/0067_450_67450009_qa_1/task.toml index 7c04069876353594a3be1b79c626c535f0d4badd..5641b12120b3327a87cba3cbec6ae8043e8cad36 100644 --- a/tasks/0067_450_67450009_qa_1/task.toml +++ b/tasks/0067_450_67450009_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0067_450_67450009_qa_1" +name = "smoldataenvs-train/0067_450_67450009_qa_1" description = "Which feature in the dataset has the highest standard deviation across all samples?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0067_454_67454343_qa_5/task.toml b/tasks/0067_454_67454343_qa_5/task.toml index 967b5d81eaec1a38a60d2f9c9a68cfee3b4ca7de..c716f8a5fe96f8b10552c960d8c497c05e51204c 100644 --- a/tasks/0067_454_67454343_qa_5/task.toml +++ b/tasks/0067_454_67454343_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0067_454_67454343_qa_5" +name = "smoldataenvs-train/0067_454_67454343_qa_5" description = "Which passenger class (Pclass) had the largest number of passengers, and how many passengers were in that class?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3, 218" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_485_67485254_qa_4/task.toml b/tasks/0067_485_67485254_qa_4/task.toml index 45caeabd2e796583babcf209a079b9e8fbacf986..2e4ce2663d2e1830bcf318bfb71cbee707dbd00f 100644 --- a/tasks/0067_485_67485254_qa_4/task.toml +++ b/tasks/0067_485_67485254_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0067_485_67485254_qa_4" +name = "smoldataenvs-train/0067_485_67485254_qa_4" description = "What is the length of the longest move sequence in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "349" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0067_487_67487894_qa_4/task.toml b/tasks/0067_487_67487894_qa_4/task.toml index faf4e638efe5f870a9e930e8eaafe19939f9efd1..6d9f619945205e0d8be41c8adb49e39d9fd1d90b 100644 --- a/tasks/0067_487_67487894_qa_4/task.toml +++ b/tasks/0067_487_67487894_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0067_487_67487894_qa_4" +name = "smoldataenvs-train/0067_487_67487894_qa_4" description = "What is the number of unique states (Address) present in the processed dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "62" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_489_67489322_qa_4/task.toml b/tasks/0067_489_67489322_qa_4/task.toml index 40180cc80c9076b1551e7c6607aed28d312bfa43..5bdfa03ce2f2f6c8dd45811cb557a2857a179cff 100644 --- a/tasks/0067_489_67489322_qa_4/task.toml +++ b/tasks/0067_489_67489322_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0067_489_67489322_qa_4" +name = "smoldataenvs-train/0067_489_67489322_qa_4" description = "How many unique education categories are present in the dataset after data cleaning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_492_67492049_qa_2/task.toml b/tasks/0067_492_67492049_qa_2/task.toml index 0c1f5339608116951e97a61809285f0c43610956..1ba384c744f7c202b0c2884ee22a94aaff1e3e33 100644 --- a/tasks/0067_492_67492049_qa_2/task.toml +++ b/tasks/0067_492_67492049_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0067_492_67492049_qa_2" +name = "smoldataenvs-train/0067_492_67492049_qa_2" description = "How many outliers were removed from the \"Nodes\" feature during preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "40" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_492_67492049_qa_4/task.toml b/tasks/0067_492_67492049_qa_4/task.toml index f258525f2952a4d304500183812659ba4122f6e9..f0fc0ba5fd337bd6e0c57b86a303ec148e0bd62c 100644 --- a/tasks/0067_492_67492049_qa_4/task.toml +++ b/tasks/0067_492_67492049_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0067_492_67492049_qa_4" +name = "smoldataenvs-train/0067_492_67492049_qa_4" description = "What is the highest cross-validation accuracy score achieved by any model during K-Fold validation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.7576" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0067_511_67511593_qa_2/task.toml b/tasks/0067_511_67511593_qa_2/task.toml index 69057ad50384301d534b6ae76864031855f2e60f..c250beb0816828d27b7272d6ea859aab48bd16bd 100644 --- a/tasks/0067_511_67511593_qa_2/task.toml +++ b/tasks/0067_511_67511593_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0067_511_67511593_qa_2" +name = "smoldataenvs-train/0067_511_67511593_qa_2" description = "What is the total number of data points identified as outliers in the SepalWidthCm feature based on the specified thresholds (greater than 4.0 and less than 2.05)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_522_67522470_qa_4/task.toml b/tasks/0067_522_67522470_qa_4/task.toml index f27ff3eab9b7909b49e57fa1bd3a242321576ecb..6bede090484b896bd33c43e3e632a7933e8fe815 100644 --- a/tasks/0067_522_67522470_qa_4/task.toml +++ b/tasks/0067_522_67522470_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0067_522_67522470_qa_4" +name = "smoldataenvs-train/0067_522_67522470_qa_4" description = "According to the descriptive statistics, what is the difference between the 75th percentile of Sepal Length (6.40 cm) and Petal Length (5.10 cm) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.30" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0067_551_67551965_qa_4/task.toml b/tasks/0067_551_67551965_qa_4/task.toml index 1e1ad3b9712db1f39d104eb1f30b6af93c665ae5..9f19adc2bdf4da3a287b11e13f261cb7954540b2 100644 --- a/tasks/0067_551_67551965_qa_4/task.toml +++ b/tasks/0067_551_67551965_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0067_551_67551965_qa_4" +name = "smoldataenvs-train/0067_551_67551965_qa_4" description = "What is the correlation coefficient between age and medical charges in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.299" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0067_607_67607384_qa_1/task.toml b/tasks/0067_607_67607384_qa_1/task.toml index cdb11157445a2071f3e7948057d9d0d0d440afe3..c71e96b9c55dadd88e4e13f3753e81ffa47bc117 100644 --- a/tasks/0067_607_67607384_qa_1/task.toml +++ b/tasks/0067_607_67607384_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0067_607_67607384_qa_1" +name = "smoldataenvs-train/0067_607_67607384_qa_1" description = "Which customer group has the highest churn rate between those with and without a partner?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "No partner" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_607_67607384_qa_3/task.toml b/tasks/0067_607_67607384_qa_3/task.toml index 23cdb48bebdae1e194fab03eda855faf8a120ced..646e0bc67358a14d663bbc300a3df55b2685a138 100644 --- a/tasks/0067_607_67607384_qa_3/task.toml +++ b/tasks/0067_607_67607384_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0067_607_67607384_qa_3" +name = "smoldataenvs-train/0067_607_67607384_qa_3" description = "Which type of internet service is associated with the highest churn rate?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Fiber optic" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_607_67607384_qa_4/task.toml b/tasks/0067_607_67607384_qa_4/task.toml index bc9f5f31f6453769767c49f69cc4ec98e99b7ac5..1834c72a4695a7277e408e7cc267c2ef5eb7d723 100644 --- a/tasks/0067_607_67607384_qa_4/task.toml +++ b/tasks/0067_607_67607384_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0067_607_67607384_qa_4" +name = "smoldataenvs-train/0067_607_67607384_qa_4" description = "What payment method has the highest churn rate among all payment types?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic check" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_633_67633156_qa_3/task.toml b/tasks/0067_633_67633156_qa_3/task.toml index 37e89ad6e8963461a7945ae2d7f024b5e94c446a..b2287aac51c18d1de542708893067a10eddd8c58 100644 --- a/tasks/0067_633_67633156_qa_3/task.toml +++ b/tasks/0067_633_67633156_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0067_633_67633156_qa_3" +name = "smoldataenvs-train/0067_633_67633156_qa_3" description = "What was the final R² score of the optimized XGBoost model after hyperparameter tuning with GridSearchCV?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.89" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0067_644_67644328_qa_4/task.toml b/tasks/0067_644_67644328_qa_4/task.toml index dd555231bfd2d3dc7372460cf2ccabe69e7df536..ac7f5fd9c1de7a9a61c83f18cd33baec5880d046 100644 --- a/tasks/0067_644_67644328_qa_4/task.toml +++ b/tasks/0067_644_67644328_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0067_644_67644328_qa_4" +name = "smoldataenvs-train/0067_644_67644328_qa_4" description = "What percentage of patients in the dataset are diagnosed with diabetes (Outcome=1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0067_657_67657879_qa_3/task.toml b/tasks/0067_657_67657879_qa_3/task.toml index 0f896357588cd62931d46dfac61ddc81fedb4af3..dc0acf9cd7137022d17ef94351b24282ce49deb7 100644 --- a/tasks/0067_657_67657879_qa_3/task.toml +++ b/tasks/0067_657_67657879_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0067_657_67657879_qa_3" +name = "smoldataenvs-train/0067_657_67657879_qa_3" description = "What is the mean of the total length (Petal Length + Sepal Length) across all samples?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9.602" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0067_657_67657879_qa_5/task.toml b/tasks/0067_657_67657879_qa_5/task.toml index c915868376f52a720943907b83547cd6a9080765..0c5f43c939344f20c87adc7dc77cf743e7d930a3 100644 --- a/tasks/0067_657_67657879_qa_5/task.toml +++ b/tasks/0067_657_67657879_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0067_657_67657879_qa_5" +name = "smoldataenvs-train/0067_657_67657879_qa_5" description = "What is the standard deviation of the total length (Petal Length + Sepal Length) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.5191739884121973" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_675_67675814_qa_2/task.toml b/tasks/0067_675_67675814_qa_2/task.toml index a852d1e63299d950d3ee25ce9a2980d8e52d80a7..358f656f497287402d3849b10fd0612889700d64 100644 --- a/tasks/0067_675_67675814_qa_2/task.toml +++ b/tasks/0067_675_67675814_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0067_675_67675814_qa_2" +name = "smoldataenvs-train/0067_675_67675814_qa_2" description = "What was the highest magnitude earthquake recorded in the dataset between August 2016 and November 2016?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0067_675_67675814_qa_5/task.toml b/tasks/0067_675_67675814_qa_5/task.toml index 2ab29b573fc2a466989f6bd8f8411bbfb6f70eaa..edba5def03ffe20d7f7febef2bf972a7d78d9cbf 100644 --- a/tasks/0067_675_67675814_qa_5/task.toml +++ b/tasks/0067_675_67675814_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0067_675_67675814_qa_5" +name = "smoldataenvs-train/0067_675_67675814_qa_5" description = "What was the average magnitude of all earthquakes with a magnitude greater than 4.4 recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5.04" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_729_67729269_qa_3/task.toml b/tasks/0067_729_67729269_qa_3/task.toml index a8ec699926cbd8d6a7e7a855fb3b2b3997306a2c..33f9b67b64bcfd607861dc793b6395059d59e397 100644 --- a/tasks/0067_729_67729269_qa_3/task.toml +++ b/tasks/0067_729_67729269_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0067_729_67729269_qa_3" +name = "smoldataenvs-train/0067_729_67729269_qa_3" description = "What is the median family size of passengers in the training data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0067_729_67729269_qa_4/task.toml b/tasks/0067_729_67729269_qa_4/task.toml index 36c19ee2ebef6c9ec35bd2e1a797255f8f1619f4..f39d55111726c3aef8882e72c0101e32baa6567b 100644 --- a/tasks/0067_729_67729269_qa_4/task.toml +++ b/tasks/0067_729_67729269_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0067_729_67729269_qa_4" +name = "smoldataenvs-train/0067_729_67729269_qa_4" description = "Which embarkation point (Emb_1, Emb_2, or Emb_3) has the highest proportion of passengers in the training data, and what is that proportion?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Emb_3, 72.10%" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_734_67734656_qa_3/task.toml b/tasks/0067_734_67734656_qa_3/task.toml index 6ae64745ff5f8bb178d78ec6b87cdd529b996692..77e16fe9730be1135ff7ac326e295e8cb3773795 100644 --- a/tasks/0067_734_67734656_qa_3/task.toml +++ b/tasks/0067_734_67734656_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0067_734_67734656_qa_3" +name = "smoldataenvs-train/0067_734_67734656_qa_3" description = "What normalization technique was applied to the numerical features in the dataset prior to modeling?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Min-max normalization" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_744_67744711_qa_4/task.toml b/tasks/0067_744_67744711_qa_4/task.toml index aa06793d7545b16444b05a91d928a3d7c3fd5e29..eb2342de7540f8d48b3fcff6117d975148f85eb7 100644 --- a/tasks/0067_744_67744711_qa_4/task.toml +++ b/tasks/0067_744_67744711_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0067_744_67744711_qa_4" +name = "smoldataenvs-train/0067_744_67744711_qa_4" description = "Which feature has the strongest positive correlation with gross earnings in the numeric dataset (before categorical encoding)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Budget" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_804_67804108_qa_5/task.toml b/tasks/0067_804_67804108_qa_5/task.toml index 7ab347d5016e9f3d5356d711f24e48d761aadef6..00cbdee97f44be7f00b46b9bacfb6c026e389d84 100644 --- a/tasks/0067_804_67804108_qa_5/task.toml +++ b/tasks/0067_804_67804108_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0067_804_67804108_qa_5" +name = "smoldataenvs-train/0067_804_67804108_qa_5" description = "What is the number of unique geographic locations categorized as \"ISLAND\" in the geospatial analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_856_67856541_qa_2/task.toml b/tasks/0067_856_67856541_qa_2/task.toml index 868633c7a7c03c1279aefd78e60ee54f4fc63e56..da05bad79504a693ac492b9a5831ffe18f14de8a 100644 --- a/tasks/0067_856_67856541_qa_2/task.toml +++ b/tasks/0067_856_67856541_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0067_856_67856541_qa_2" +name = "smoldataenvs-train/0067_856_67856541_qa_2" description = "What is the cross-validation accuracy of the Gaussian Naive Bayes classifier on the held-out validation set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "92.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0067_857_67857075_qa_4/task.toml b/tasks/0067_857_67857075_qa_4/task.toml index b4a58c018ae8c45820a330799f177c64e4003f54..8ddf9f361daa0bc36a737cee5dda15332bfc89f1 100644 --- a/tasks/0067_857_67857075_qa_4/task.toml +++ b/tasks/0067_857_67857075_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0067_857_67857075_qa_4" +name = "smoldataenvs-train/0067_857_67857075_qa_4" description = "What is the minimum credit amount for the 'Saving accounts' category 'quite rich'?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "338" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_857_67857075_qa_5/task.toml b/tasks/0067_857_67857075_qa_5/task.toml index 0f4fd1ac88eb2ab951c8e6e2e3fedc2d17eae236..fc8048cb68c0c1d358cb5fb72a009d5ba8658328 100644 --- a/tasks/0067_857_67857075_qa_5/task.toml +++ b/tasks/0067_857_67857075_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0067_857_67857075_qa_5" +name = "smoldataenvs-train/0067_857_67857075_qa_5" description = "What is the mean credit amount for the 'Checking account' category 'rich'?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2177.65" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_871_67871105_qa_2/task.toml b/tasks/0067_871_67871105_qa_2/task.toml index 3a352626468830bc24adf58928f19fb8253cdbad..7f118a719ea838bd08645059c0c9d5843ae4b47a 100644 --- a/tasks/0067_871_67871105_qa_2/task.toml +++ b/tasks/0067_871_67871105_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0067_871_67871105_qa_2" +name = "smoldataenvs-train/0067_871_67871105_qa_2" description = "Which feature exhibits the strongest negative correlation with wine quality based on the exploratory data analysis visualizations and interpretations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "volatile acidity" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_871_67871105_qa_4/task.toml b/tasks/0067_871_67871105_qa_4/task.toml index 6ed3fd4f2ffd69c53e7109350ecb91c077eff5bf..e2be7617f88f4c98b26fec8f20282ae2ae0890fd 100644 --- a/tasks/0067_871_67871105_qa_4/task.toml +++ b/tasks/0067_871_67871105_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0067_871_67871105_qa_4" +name = "smoldataenvs-train/0067_871_67871105_qa_4" description = "What percentage of wines in the original dataset have a quality rating of 5 or lower (i.e., quality ratings 3, 4, or 5)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "46.5" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_871_67871105_qa_5/task.toml b/tasks/0067_871_67871105_qa_5/task.toml index e009979b4f393d1838024ff65de30978e057675f..5b6d2a512cbe9ea0f833913656469f9781eb97e2 100644 --- a/tasks/0067_871_67871105_qa_5/task.toml +++ b/tasks/0067_871_67871105_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0067_871_67871105_qa_5" +name = "smoldataenvs-train/0067_871_67871105_qa_5" description = "Which quality rating has the highest absolute frequency in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0067_912_67912130_qa_1/task.toml b/tasks/0067_912_67912130_qa_1/task.toml index 9f484c5bb878f87dddb08f252d125aab2d8892dd..3f23ebacf93eaf68d80d7084c9fb862f727e28fa 100644 --- a/tasks/0067_912_67912130_qa_1/task.toml +++ b/tasks/0067_912_67912130_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0067_912_67912130_qa_1" +name = "smoldataenvs-train/0067_912_67912130_qa_1" description = "How many individuals in the dataset have a workclass categorized as 'Other' after replacing missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2799" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_912_67912130_qa_4/task.toml b/tasks/0067_912_67912130_qa_4/task.toml index 20033fbf2725de32ae14434e1007b598051e1afe..1ba697b7cdba01d1c96ea0b05fc99bdc6d9f63de 100644 --- a/tasks/0067_912_67912130_qa_4/task.toml +++ b/tasks/0067_912_67912130_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0067_912_67912130_qa_4" +name = "smoldataenvs-train/0067_912_67912130_qa_4" description = "How many individuals in the dataset have an income greater than $50K?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11687" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_933_67933757_qa_4/task.toml b/tasks/0067_933_67933757_qa_4/task.toml index 2039d81562017917c32f03a3c5a36ac8e45b431a..518bd9ed74022bf92f5adaffad60d272259a1d8e 100644 --- a/tasks/0067_933_67933757_qa_4/task.toml +++ b/tasks/0067_933_67933757_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0067_933_67933757_qa_4" +name = "smoldataenvs-train/0067_933_67933757_qa_4" description = "What is the total global sales in millions for the Role-Playing genre?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "923.83" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_958_67958597_qa_5/task.toml b/tasks/0067_958_67958597_qa_5/task.toml index 08131b0c3864e1dc78c6156019094d53723310b4..31e7df1aba4bf0e952364b0f78808a005ef23758 100644 --- a/tasks/0067_958_67958597_qa_5/task.toml +++ b/tasks/0067_958_67958597_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0067_958_67958597_qa_5" +name = "smoldataenvs-train/0067_958_67958597_qa_5" description = "What is the title of the movie with the highest budget, and what is its budget amount?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "The Host, 12215500000" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0067_963_67963469_qa_3/task.toml b/tasks/0067_963_67963469_qa_3/task.toml index 2cfb8b026d9d05085339479e31fecc9359d5b55e..39c630e6fbee8f5fc090ceab21e03f48b33721d7 100644 --- a/tasks/0067_963_67963469_qa_3/task.toml +++ b/tasks/0067_963_67963469_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0067_963_67963469_qa_3" +name = "smoldataenvs-train/0067_963_67963469_qa_3" description = "Which feature exhibits the largest range (maximum value minus minimum value) in the dataset, and what is this range?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Insulin" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0067_990_67990197_qa_1/task.toml b/tasks/0067_990_67990197_qa_1/task.toml index be1e349d1342a4f3b1b0131f123159c8ab7967ef..e80d12f9611707345c3d9a0c7fe12bffa0a6ae31 100644 --- a/tasks/0067_990_67990197_qa_1/task.toml +++ b/tasks/0067_990_67990197_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0067_990_67990197_qa_1" +name = "smoldataenvs-train/0067_990_67990197_qa_1" description = "Which two features in the dataset exhibit the highest positive correlation, and what is the correlation coefficient?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm, PetalWidthCm, 0.96" reward_mode_initial = "list" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0067_990_67990197_qa_2/task.toml b/tasks/0067_990_67990197_qa_2/task.toml index 30f2a3e7d3062529d0713c08c9a38a614cb23c05..551acb131402051dfd80395e17795397c5ec89d5 100644 --- a/tasks/0067_990_67990197_qa_2/task.toml +++ b/tasks/0067_990_67990197_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0067_990_67990197_qa_2" +name = "smoldataenvs-train/0067_990_67990197_qa_2" description = "What is the skewness value of the SepalWidthCm feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.33" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0067_990_67990197_qa_3/task.toml b/tasks/0067_990_67990197_qa_3/task.toml index 91fa1ff12ec17481c391fb8b413ea9c960042b3d..e8c4c13aa9291c2ff2726f701f8e3405a6022d87 100644 --- a/tasks/0067_990_67990197_qa_3/task.toml +++ b/tasks/0067_990_67990197_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0067_990_67990197_qa_3" +name = "smoldataenvs-train/0067_990_67990197_qa_3" description = "Are the three species in the dataset equally represented, and if so, how many samples does each species have?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0067_990_67990197_qa_4/task.toml b/tasks/0067_990_67990197_qa_4/task.toml index 5127803cc269c22d64a16192317082fcdefe847f..30edf00b071c6cc8d2198e3cea74d94dabe70596 100644 --- a/tasks/0067_990_67990197_qa_4/task.toml +++ b/tasks/0067_990_67990197_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0067_990_67990197_qa_4" +name = "smoldataenvs-train/0067_990_67990197_qa_4" description = "Which feature in the dataset has the highest standard deviation, and what is its value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm, 1.76" reward_mode_initial = "list" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0068_009_68009679_qa_1/task.toml b/tasks/0068_009_68009679_qa_1/task.toml index eba06c6cead753ce8c94276e3c34ea8993a201cb..384abf88971e576a0ebe915fc04ccfabe674b17e 100644 --- a/tasks/0068_009_68009679_qa_1/task.toml +++ b/tasks/0068_009_68009679_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0068_009_68009679_qa_1" +name = "smoldataenvs-train/0068_009_68009679_qa_1" description = "Which year had the highest number of movie releases according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2009" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0068_009_68009679_qa_2/task.toml b/tasks/0068_009_68009679_qa_2/task.toml index c340e28487a4e31ae7b9ebf80c6ed92a13fcda9f..9ccca0f43bc8b6d6ca145c13f436940e22dbb892 100644 --- a/tasks/0068_009_68009679_qa_2/task.toml +++ b/tasks/0068_009_68009679_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0068_009_68009679_qa_2" +name = "smoldataenvs-train/0068_009_68009679_qa_2" description = "How many movies in the dataset have an IMDb rating of 9.0 or higher?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0068_022_68022478_qa_1/task.toml b/tasks/0068_022_68022478_qa_1/task.toml index 3d1e4e4b03ae3f6553f1577f0c4fd4c7bd884d5a..4c5d8305eef17bc0c968b105a32012c08fe7a5e9 100644 --- a/tasks/0068_022_68022478_qa_1/task.toml +++ b/tasks/0068_022_68022478_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0068_022_68022478_qa_1" +name = "smoldataenvs-train/0068_022_68022478_qa_1" description = "Which region had the highest total sales across all years in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "North America" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0068_022_68022478_qa_2/task.toml b/tasks/0068_022_68022478_qa_2/task.toml index 86ef894941c055266c1c948a451db4d99c0e5117..49fcf2a21623ec95438d4c2014129f933c247187 100644 --- a/tasks/0068_022_68022478_qa_2/task.toml +++ b/tasks/0068_022_68022478_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0068_022_68022478_qa_2" +name = "smoldataenvs-train/0068_022_68022478_qa_2" description = "Which gaming platform generated the highest global sales according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PS2" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0068_041_68041367_qa_2/task.toml b/tasks/0068_041_68041367_qa_2/task.toml index 609ff1ebd89d6a9653671a7b0c7a8d63a5989330..24a83c7e29ee5acac92f34b8dc495fedb419b88c 100644 --- a/tasks/0068_041_68041367_qa_2/task.toml +++ b/tasks/0068_041_68041367_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0068_041_68041367_qa_2" +name = "smoldataenvs-train/0068_041_68041367_qa_2" description = "What percentage of patients in the dataset are diabetic (Outcome = 1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0068_041_68041367_qa_5/task.toml b/tasks/0068_041_68041367_qa_5/task.toml index b94c604aba37b75a4f74abb683de88d8de45247f..adc654843643a9e6818a500ba2694bff871d0a4c 100644 --- a/tasks/0068_041_68041367_qa_5/task.toml +++ b/tasks/0068_041_68041367_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0068_041_68041367_qa_5" +name = "smoldataenvs-train/0068_041_68041367_qa_5" description = "What is the F1-score for diabetic patients (Outcome = 1) in the model's predictions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.62" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0068_060_68060356_qa_4/task.toml b/tasks/0068_060_68060356_qa_4/task.toml index 1944965238f0aaa76acb2dd77cffc6f2080504c8..12c5b71889cf3ccb5abb1900e8a560e99e972f7f 100644 --- a/tasks/0068_060_68060356_qa_4/task.toml +++ b/tasks/0068_060_68060356_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0068_060_68060356_qa_4" +name = "smoldataenvs-train/0068_060_68060356_qa_4" description = "Which number of components yields the best performance for the partial least squares (PLS) regression model according to the cross-validation results?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0068_069_68069772_qa_5/task.toml b/tasks/0068_069_68069772_qa_5/task.toml index 46e123be55326056f2d7f02cd7c2fab171cc582b..b3de8cc58f8d0977ee538344a15935ade73de5a0 100644 --- a/tasks/0068_069_68069772_qa_5/task.toml +++ b/tasks/0068_069_68069772_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0068_069_68069772_qa_5" +name = "smoldataenvs-train/0068_069_68069772_qa_5" description = "What is the most common genre for games published by Electronic Arts in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Sports" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0068_074_68074529_qa_4/task.toml b/tasks/0068_074_68074529_qa_4/task.toml index a5f72a4975418d52f1385f38d068e276be1d0117..acac1c0d1268ebb3fd5c8e4b02233d0c48509974 100644 --- a/tasks/0068_074_68074529_qa_4/task.toml +++ b/tasks/0068_074_68074529_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0068_074_68074529_qa_4" +name = "smoldataenvs-train/0068_074_68074529_qa_4" description = "How many samples are present for each species type in the Iris dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0068_074_68074529_qa_5/task.toml b/tasks/0068_074_68074529_qa_5/task.toml index 564be463ee979a7334c8b019a3e1443cb68a4ab2..908fd201d0af78dd925a03ee285c4e6a74ba74d2 100644 --- a/tasks/0068_074_68074529_qa_5/task.toml +++ b/tasks/0068_074_68074529_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0068_074_68074529_qa_5" +name = "smoldataenvs-train/0068_074_68074529_qa_5" description = "What is the third quartile (75th percentile) value of the PetalLengthCm feature across all samples?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5.1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0068_079_68079868_qa_1/task.toml b/tasks/0068_079_68079868_qa_1/task.toml index ac917ddb7022cf21e7dc10cd69e727dca347cfdf..4466a20135181415ee4d69a17dfd045bdfae320c 100644 --- a/tasks/0068_079_68079868_qa_1/task.toml +++ b/tasks/0068_079_68079868_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0068_079_68079868_qa_1" +name = "smoldataenvs-train/0068_079_68079868_qa_1" description = "What is the percentage of churned customers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26.54" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0068_111_68111995_qa_1/task.toml b/tasks/0068_111_68111995_qa_1/task.toml index e7e90f0a0f77c01a6fcd6bc420490d1a634c8668..7ec6b41f323d137d98538ddc45dd635d5a507b5b 100644 --- a/tasks/0068_111_68111995_qa_1/task.toml +++ b/tasks/0068_111_68111995_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0068_111_68111995_qa_1" +name = "smoldataenvs-train/0068_111_68111995_qa_1" description = "Which country has the highest average star rating for its ramens, and what is that average?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Brazil, 4.35" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0068_119_68119558_qa_4/task.toml b/tasks/0068_119_68119558_qa_4/task.toml index ac04b7c0f931a0490b25c451da43e2acea707924..da325b3674e42f2bd1d780fc94e2b11133a4a275 100644 --- a/tasks/0068_119_68119558_qa_4/task.toml +++ b/tasks/0068_119_68119558_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0068_119_68119558_qa_4" +name = "smoldataenvs-train/0068_119_68119558_qa_4" description = "What is the average SalePrice of houses with OverallQual=10 built in the 1950-2000 year range according to the line plot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "700000" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0068_119_68119558_qa_5/task.toml b/tasks/0068_119_68119558_qa_5/task.toml index d288c0b92529a232c1e4ba5a74ba59ede142c1e1..4a92ef80966b1ce082076a6e2145c5ec40e3fe58 100644 --- a/tasks/0068_119_68119558_qa_5/task.toml +++ b/tasks/0068_119_68119558_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0068_119_68119558_qa_5" +name = "smoldataenvs-train/0068_119_68119558_qa_5" description = "Which categorical feature has the most categories (unique values) according to the individual feature visualization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Neighborhood" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0068_127_68127229_qa_4/task.toml b/tasks/0068_127_68127229_qa_4/task.toml index 7b2c1d9d044b9ea26974e091ad00faf44a337d2a..74311a0aaefa790c41e8914f4dfcc60b83e75706 100644 --- a/tasks/0068_127_68127229_qa_4/task.toml +++ b/tasks/0068_127_68127229_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0068_127_68127229_qa_4" +name = "smoldataenvs-train/0068_127_68127229_qa_4" description = "What is the accuracy of the Linear Discriminant Analysis (LDA) model without any scaling applied to the features?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.973485" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0068_152_68152590_qa_2/task.toml b/tasks/0068_152_68152590_qa_2/task.toml index 6dab5674cb0b914012bd2ed2d1579e3ab59ea082..aeba54a5e2a62c976403efdfc884751b79140feb 100644 --- a/tasks/0068_152_68152590_qa_2/task.toml +++ b/tasks/0068_152_68152590_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0068_152_68152590_qa_2" +name = "smoldataenvs-train/0068_152_68152590_qa_2" description = "What percentage of total video game sales are attributed to North America according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "48.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0068_152_68152590_qa_3/task.toml b/tasks/0068_152_68152590_qa_3/task.toml index e14040c342669c909178be21e839b6cb0e63cf5f..eae82a517e69a9e025f17fdae0eb8ea9304d0565 100644 --- a/tasks/0068_152_68152590_qa_3/task.toml +++ b/tasks/0068_152_68152590_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0068_152_68152590_qa_3" +name = "smoldataenvs-train/0068_152_68152590_qa_3" description = "Which year recorded the highest total global video game sales based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2008" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0068_152_68152590_qa_4/task.toml b/tasks/0068_152_68152590_qa_4/task.toml index c203714be964c1173f91e9888605f15cb306544d..5957b7da43879642a6b359b6554fb5ae0c5f160b 100644 --- a/tasks/0068_152_68152590_qa_4/task.toml +++ b/tasks/0068_152_68152590_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0068_152_68152590_qa_4" +name = "smoldataenvs-train/0068_152_68152590_qa_4" description = "Which video game publisher has the highest total global sales?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Nintendo" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0068_164_68164064_qa_1/task.toml b/tasks/0068_164_68164064_qa_1/task.toml index 132b1ee2bc1562ed225b1e97160161764b44250b..d51181a10955bfce34c9d599b35d380069b9d8c6 100644 --- a/tasks/0068_164_68164064_qa_1/task.toml +++ b/tasks/0068_164_68164064_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0068_164_68164064_qa_1" +name = "smoldataenvs-train/0068_164_68164064_qa_1" description = "What is the predicted median home price (MEDV) for a house with an average of 6.5 rooms per dwelling (RM) based on the linear regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "24.53" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0068_405_68405460_qa_1/task.toml b/tasks/0068_405_68405460_qa_1/task.toml index fb28bfa519997fb9251af6bf2d24c9420fa5f709..0310d274059a7dd57a3c66242e40b3ab2ed12b70 100644 --- a/tasks/0068_405_68405460_qa_1/task.toml +++ b/tasks/0068_405_68405460_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0068_405_68405460_qa_1" +name = "smoldataenvs-train/0068_405_68405460_qa_1" description = "Which ocean proximity category has the highest median house value based on the grouped mean analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ISLAND" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0068_405_68405460_qa_4/task.toml b/tasks/0068_405_68405460_qa_4/task.toml index 9f0e64fc81d30b9051202eca9da3d7e0d46368f0..2523411c68549a70fc83929ab35b99255e94312c 100644 --- a/tasks/0068_405_68405460_qa_4/task.toml +++ b/tasks/0068_405_68405460_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0068_405_68405460_qa_4" +name = "smoldataenvs-train/0068_405_68405460_qa_4" description = "How many distinct categories are present in the \"ocean_proximity\" feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0068_463_68463534_qa_5/task.toml b/tasks/0068_463_68463534_qa_5/task.toml index 5ee34a213ff150888f008035bceeedc4bd432609..042dce977321db30d6cb637bc502c52ea1443ab6 100644 --- a/tasks/0068_463_68463534_qa_5/task.toml +++ b/tasks/0068_463_68463534_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0068_463_68463534_qa_5" +name = "smoldataenvs-train/0068_463_68463534_qa_5" description = "After handling missing values and outliers, what was the mean age of passengers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "29.0664" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0068_476_68476011_qa_1/task.toml b/tasks/0068_476_68476011_qa_1/task.toml index fa387bf5df494c852b8d170f589c42d0fbe3952e..0db01542da757b8c6d4c984efcd827d4e6ff4677 100644 --- a/tasks/0068_476_68476011_qa_1/task.toml +++ b/tasks/0068_476_68476011_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0068_476_68476011_qa_1" +name = "smoldataenvs-train/0068_476_68476011_qa_1" description = "Which education level has the highest percentage of individuals earning more than $50K, and what is that percentage?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Doctorate, 74.09" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0068_518_68518330_qa_1/task.toml b/tasks/0068_518_68518330_qa_1/task.toml index c8909933093f8f46633658c6a3f57d7ec976ceb8..f9ffe8c2cb597a604e88eb2145d6c84edbb7c7dc 100644 --- a/tasks/0068_518_68518330_qa_1/task.toml +++ b/tasks/0068_518_68518330_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0068_518_68518330_qa_1" +name = "smoldataenvs-train/0068_518_68518330_qa_1" description = "What percentage of the dataset represents house prices considered outliers based on the IQR method?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5.30" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0068_518_68518330_qa_2/task.toml b/tasks/0068_518_68518330_qa_2/task.toml index eb3e64a5c3e15e3f3066a649e5c9ac9e5a25c9b6..7ff11898081b3b29050922a99999a03b24133deb 100644 --- a/tasks/0068_518_68518330_qa_2/task.toml +++ b/tasks/0068_518_68518330_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0068_518_68518330_qa_2" +name = "smoldataenvs-train/0068_518_68518330_qa_2" description = "Which house condition category has the highest average sale price based on the barplot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0068_714_68714318_qa_1/task.toml b/tasks/0068_714_68714318_qa_1/task.toml index 76f88a5c025d67a1fd0674324372a7c944cc0fd4..02c14660ad76f7da1fd104e7876c2417b4754561 100644 --- a/tasks/0068_714_68714318_qa_1/task.toml +++ b/tasks/0068_714_68714318_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0068_714_68714318_qa_1" +name = "smoldataenvs-train/0068_714_68714318_qa_1" description = "What percentage of patients in the dataset died within five years of their operation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26.47" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0068_724_68724527_qa_2/task.toml b/tasks/0068_724_68724527_qa_2/task.toml index aea665b176e33f45edb364640f82021c5fb937de..de7ec89c2e674fa7e73017f7a1188e51e503b272 100644 --- a/tasks/0068_724_68724527_qa_2/task.toml +++ b/tasks/0068_724_68724527_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0068_724_68724527_qa_2" +name = "smoldataenvs-train/0068_724_68724527_qa_2" description = "After applying polynomial feature expansion up to degree 3, how many total features are generated in the transformed training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1770" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0068_754_68754620_qa_1/task.toml b/tasks/0068_754_68754620_qa_1/task.toml index a440ea8d394aa4778b0a6f2fc90b28de8eaff2cc..815704a82482766e824ec0bbd93b5ca795be1b85 100644 --- a/tasks/0068_754_68754620_qa_1/task.toml +++ b/tasks/0068_754_68754620_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0068_754_68754620_qa_1" +name = "smoldataenvs-train/0068_754_68754620_qa_1" description = "What is the accuracy of the logistic regression model on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.80" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0068_864_68864400_qa_1/task.toml b/tasks/0068_864_68864400_qa_1/task.toml index 33cd530195e5c1a3631ed1dbf6cfa6d6fa92eb00..99eab568519c11dc6b0278ae33c3f740bebb6c85 100644 --- a/tasks/0068_864_68864400_qa_1/task.toml +++ b/tasks/0068_864_68864400_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0068_864_68864400_qa_1" +name = "smoldataenvs-train/0068_864_68864400_qa_1" description = "What is the percentage of missing values (encoded as 0) in the 'Insulin' column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "48.7" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0068_960_68960212_qa_3/task.toml b/tasks/0068_960_68960212_qa_3/task.toml index 407a41435e250ac14a154fb5cf406ba578a045a2..0c52de65e174ae1dca4e40c56b4188edfcbb01d0 100644 --- a/tasks/0068_960_68960212_qa_3/task.toml +++ b/tasks/0068_960_68960212_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0068_960_68960212_qa_3" +name = "smoldataenvs-train/0068_960_68960212_qa_3" description = "In the top 5 warmest years according to February average temperature, how many times did Punxsutawney Phil's prediction incorrectly indicate more winter (Full Shadow)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0068_960_68960212_qa_4/task.toml b/tasks/0068_960_68960212_qa_4/task.toml index 9fab437d3a6cd6f16664d5d1ccc55601036fa6be..dae235eed98ce682c1153e987678b90ed8fe499c 100644 --- a/tasks/0068_960_68960212_qa_4/task.toml +++ b/tasks/0068_960_68960212_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0068_960_68960212_qa_4" +name = "smoldataenvs-train/0068_960_68960212_qa_4" description = "What is the lowest February average temperature recorded in the dataset for years where Punxsutawney Phil predicted Full Shadow?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "25.23" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0068_964_68964852_qa_2/task.toml b/tasks/0068_964_68964852_qa_2/task.toml index 47c7c1b3e84924e280b26bfb3ff175836cedb00f..64ab15c0209f3c899fc9d199e9a61feee8229b0a 100644 --- a/tasks/0068_964_68964852_qa_2/task.toml +++ b/tasks/0068_964_68964852_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0068_964_68964852_qa_2" +name = "smoldataenvs-train/0068_964_68964852_qa_2" description = "What is the highest accuracy achieved by the KNN model on the test set when using the optimal K value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0068_984_68984398_qa_4/task.toml b/tasks/0068_984_68984398_qa_4/task.toml index b332fa6a421b2e9c27e6e9ac1406e3b47023056d..161fd3f59ab01df625b65994ba3a227b38ed172f 100644 --- a/tasks/0068_984_68984398_qa_4/task.toml +++ b/tasks/0068_984_68984398_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0068_984_68984398_qa_4" +name = "smoldataenvs-train/0068_984_68984398_qa_4" description = "How many data samples are present in the dataset after removing the id and Unnamed: 32 columns?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "569" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0069_000_69000693_qa_1/task.toml b/tasks/0069_000_69000693_qa_1/task.toml index 8f6fd399818f9374a0952a045b37a36ad3e2f5e1..6e0b7e938830ca37bd8b369f35cced5617db3a3d 100644 --- a/tasks/0069_000_69000693_qa_1/task.toml +++ b/tasks/0069_000_69000693_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0069_000_69000693_qa_1" +name = "smoldataenvs-train/0069_000_69000693_qa_1" description = "What percentage of the messages in the dataset are classified as spam?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0069_000_69000693_qa_5/task.toml b/tasks/0069_000_69000693_qa_5/task.toml index 539a0207cfd8cc069f99103a218bd7b60592727b..ca09f9bc6cede26ee6c763db18d3d6b4913e78e8 100644 --- a/tasks/0069_000_69000693_qa_5/task.toml +++ b/tasks/0069_000_69000693_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0069_000_69000693_qa_5" +name = "smoldataenvs-train/0069_000_69000693_qa_5" description = "How many messages are classified as ham and spam in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4825 ham, 747 spam" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0069_002_69002629_qa_2/task.toml b/tasks/0069_002_69002629_qa_2/task.toml index 67deb1a272cfe4563a2cab9ea2020dd6a7080067..5bf4e8e29ce6f91e7f61af6c9b42101275615785 100644 --- a/tasks/0069_002_69002629_qa_2/task.toml +++ b/tasks/0069_002_69002629_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0069_002_69002629_qa_2" +name = "smoldataenvs-train/0069_002_69002629_qa_2" description = "Are any of the variables significantly different between the train and test datasets based on the ANOVA results?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0069_002_69002629_qa_3/task.toml b/tasks/0069_002_69002629_qa_3/task.toml index 91a7c9d0405a584057a620ecae2b8227699cb347..128595701f3a8336acaca568b6b8eede1762183b 100644 --- a/tasks/0069_002_69002629_qa_3/task.toml +++ b/tasks/0069_002_69002629_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0069_002_69002629_qa_3" +name = "smoldataenvs-train/0069_002_69002629_qa_3" description = "What is the threshold p-value used to determine statistical significance in the ANOVA analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.05" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0069_007_69007333_qa_2/task.toml b/tasks/0069_007_69007333_qa_2/task.toml index 4a968d43d6214d5199ed95aec237ea92cd7ed156..0664199e0c9b2145b0f33a91ba56212f2777bc9e 100644 --- a/tasks/0069_007_69007333_qa_2/task.toml +++ b/tasks/0069_007_69007333_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0069_007_69007333_qa_2" +name = "smoldataenvs-train/0069_007_69007333_qa_2" description = "What is the percentage increase in Tesla's closing stock price from the first recorded date (June 29, 2010) to the last recorded date (March 17, 2017)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1000.0" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0069_042_69042020_qa_1/task.toml b/tasks/0069_042_69042020_qa_1/task.toml index 4259c73920bffc348ff4a66f91b61a01cd4c10f3..2ba763015f740ef09b6f4481a5d51313e627ad2b 100644 --- a/tasks/0069_042_69042020_qa_1/task.toml +++ b/tasks/0069_042_69042020_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0069_042_69042020_qa_1" +name = "smoldataenvs-train/0069_042_69042020_qa_1" description = "What proportion of mushrooms in the dataset are classified as poisonous after label encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.48202855736090594" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0069_047_69047990_qa_1/task.toml b/tasks/0069_047_69047990_qa_1/task.toml index 5e111037969e3f03151b92bd7f3ef387eb0c4bd1..d1ebf1b71eb55deab3515216b182d8b63f3943ca 100644 --- a/tasks/0069_047_69047990_qa_1/task.toml +++ b/tasks/0069_047_69047990_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0069_047_69047990_qa_1" +name = "smoldataenvs-train/0069_047_69047990_qa_1" description = "Which country has the highest average life expectancy in the dataset according to the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Japan" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0069_054_69054524_qa_1/task.toml b/tasks/0069_054_69054524_qa_1/task.toml index b0898cc6cc434d0c59ad31b9c6802c1158a4ef63..d8b50b9b2046a304d7af4663cf19babf92a24db6 100644 --- a/tasks/0069_054_69054524_qa_1/task.toml +++ b/tasks/0069_054_69054524_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0069_054_69054524_qa_1" +name = "smoldataenvs-train/0069_054_69054524_qa_1" description = "Which publisher has the highest total global sales in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Nintendo" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0069_054_69054524_qa_2/task.toml b/tasks/0069_054_69054524_qa_2/task.toml index 0129f66ede7035dc4b505a527a2b4eec8ce92cd0..2436b273cb07a2cbb670d5f7c37a257e4b55f3b9 100644 --- a/tasks/0069_054_69054524_qa_2/task.toml +++ b/tasks/0069_054_69054524_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0069_054_69054524_qa_2" +name = "smoldataenvs-train/0069_054_69054524_qa_2" description = "Which publisher has the highest average global sales per game, considering only publishers with more than 50 games published?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Nintendo" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0069_075_69075979_qa_5/task.toml b/tasks/0069_075_69075979_qa_5/task.toml index 03b20e5f13924defc724bd59eec0060aec28aa3f..76729e7dfd0057f6fe134fc2a451e13ccf02bdad 100644 --- a/tasks/0069_075_69075979_qa_5/task.toml +++ b/tasks/0069_075_69075979_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0069_075_69075979_qa_5" +name = "smoldataenvs-train/0069_075_69075979_qa_5" description = "How many features in the first discriminant function of the LDA model trained on original data have absolute coefficient values greater than 10?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0069_094_69094334_qa_4/task.toml b/tasks/0069_094_69094334_qa_4/task.toml index 84dd743a34326e4f41e4a79ca600ac4cabe53461..aa6d90aa05bb0b9a47cefb99d60fb5f41deb8a4c 100644 --- a/tasks/0069_094_69094334_qa_4/task.toml +++ b/tasks/0069_094_69094334_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0069_094_69094334_qa_4" +name = "smoldataenvs-train/0069_094_69094334_qa_4" description = "What is the F1-score of the Logistic Regression model on the test set without applying PCA?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.914" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0069_119_69119898_qa_1/task.toml b/tasks/0069_119_69119898_qa_1/task.toml index b8af10448d572e50ad30a380c13132aec8ba8b70..fec32b594442b8a6cb0b13c11027d99ad13a2a3d 100644 --- a/tasks/0069_119_69119898_qa_1/task.toml +++ b/tasks/0069_119_69119898_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0069_119_69119898_qa_1" +name = "smoldataenvs-train/0069_119_69119898_qa_1" description = "Which passenger class has the highest frequency in the dataset according to the Apriori algorithm results?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0069_120_69120864_qa_4/task.toml b/tasks/0069_120_69120864_qa_4/task.toml index 33eac39f6bfba7a4d8c8dab0e81214a6a536916f..993d6e7ac91cb16c86a5740693111dd9fcb39e22 100644 --- a/tasks/0069_120_69120864_qa_4/task.toml +++ b/tasks/0069_120_69120864_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0069_120_69120864_qa_4" +name = "smoldataenvs-train/0069_120_69120864_qa_4" description = "How many malignant (M=1) and benign (B=0) cases are present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "212,357" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0069_139_69139185_qa_4/task.toml b/tasks/0069_139_69139185_qa_4/task.toml index 07fbf7790cfc62b91d5cc482a5236cef02e3f1ef..d568cf8c39fe95915fc5d8f39ed6ffbd6432b00b 100644 --- a/tasks/0069_139_69139185_qa_4/task.toml +++ b/tasks/0069_139_69139185_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0069_139_69139185_qa_4" +name = "smoldataenvs-train/0069_139_69139185_qa_4" description = "In which year did the global video game sales reach their peak according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2008" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0069_142_69142112_qa_4/task.toml b/tasks/0069_142_69142112_qa_4/task.toml index ae54f43580f3e5aaefdcfbef6c333935b1dddc22..6ff29f05943e9002487f03f0050045ff21203a37 100644 --- a/tasks/0069_142_69142112_qa_4/task.toml +++ b/tasks/0069_142_69142112_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0069_142_69142112_qa_4" +name = "smoldataenvs-train/0069_142_69142112_qa_4" description = "What is the exact count of male respondents in the dataset after standardizing gender categories?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "988" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0069_205_69205815_qa_3/task.toml b/tasks/0069_205_69205815_qa_3/task.toml index 396ebda1ec0a7c1f143ee9a00e09f5c85de48f3c..f5a44f48fc43c18629a3f7c20f585f9ac525d4ab 100644 --- a/tasks/0069_205_69205815_qa_3/task.toml +++ b/tasks/0069_205_69205815_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0069_205_69205815_qa_3" +name = "smoldataenvs-train/0069_205_69205815_qa_3" description = "How many passengers embarked from the port of Southampton (S) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "644" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0069_205_69205815_qa_5/task.toml b/tasks/0069_205_69205815_qa_5/task.toml index b7fbf87b95b24ba476e59715799a588bbce8ddaa..a607eb0e5d535c0c41538d6fc23b72c814520ad2 100644 --- a/tasks/0069_205_69205815_qa_5/task.toml +++ b/tasks/0069_205_69205815_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0069_205_69205815_qa_5" +name = "smoldataenvs-train/0069_205_69205815_qa_5" description = "What is the accuracy score of the logistic regression model on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.7988826815642458" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0069_266_69266451_qa_5/task.toml b/tasks/0069_266_69266451_qa_5/task.toml index c14a7358d5a7b9a11551c9474ae4c6a0abe5615d..59df1609ae2dfa309aecc3efe9dc60bbc5e841df 100644 --- a/tasks/0069_266_69266451_qa_5/task.toml +++ b/tasks/0069_266_69266451_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0069_266_69266451_qa_5" +name = "smoldataenvs-train/0069_266_69266451_qa_5" description = "What is the difference in average satisfaction levels between employees who stayed and those who left?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.2267" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0069_293_69293349_qa_2/task.toml b/tasks/0069_293_69293349_qa_2/task.toml index a12cf0233c5ea3cfe6e38e3691e90718651018af..dc203c6b1eec39343a417595e183845d19452c3e 100644 --- a/tasks/0069_293_69293349_qa_2/task.toml +++ b/tasks/0069_293_69293349_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0069_293_69293349_qa_2" +name = "smoldataenvs-train/0069_293_69293349_qa_2" description = "What is the cosine similarity score between the animals \"wolf\" and \"sparrow\"?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.6949" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0069_294_69294198_qa_5/task.toml b/tasks/0069_294_69294198_qa_5/task.toml index 1ad53afbd215d71333fcac0b9a6f2d177527d873..1c871a4e85f120e70c9a64499d2abda45254c95d 100644 --- a/tasks/0069_294_69294198_qa_5/task.toml +++ b/tasks/0069_294_69294198_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0069_294_69294198_qa_5" +name = "smoldataenvs-train/0069_294_69294198_qa_5" description = "Which model achieved the highest accuracy after hyperparameter tuning between Logistic Regression and Random Forest Classifier?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Random Forest Classifier" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0069_338_69338582_qa_2/task.toml b/tasks/0069_338_69338582_qa_2/task.toml index 67c30c7fbe3a2228e20d791da46770ec6bbe7361..fe068e853ffb59c45827657f35b1fbd2e37377fb 100644 --- a/tasks/0069_338_69338582_qa_2/task.toml +++ b/tasks/0069_338_69338582_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0069_338_69338582_qa_2" +name = "smoldataenvs-train/0069_338_69338582_qa_2" description = "Which gender had the highest survival rate according to the dataset visualizations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Female" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0069_366_69366991_qa_2/task.toml b/tasks/0069_366_69366991_qa_2/task.toml index 4799fa1d2d7a8d7efedd7f31fe3e44149c4866ca..caa31540524627c9c5ceca815b614112944a83b8 100644 --- a/tasks/0069_366_69366991_qa_2/task.toml +++ b/tasks/0069_366_69366991_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0069_366_69366991_qa_2" +name = "smoldataenvs-train/0069_366_69366991_qa_2" description = "What was the most frequent outlet size used to impute missing values in the 'Outlet_Size' column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Medium" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0069_366_69366991_qa_4/task.toml b/tasks/0069_366_69366991_qa_4/task.toml index e740407cd3d9ecfac9d7aa8b2cbb69dc29b6094c..0bf5ae089f93c6913b005df6b2783ef15d5b980c 100644 --- a/tasks/0069_366_69366991_qa_4/task.toml +++ b/tasks/0069_366_69366991_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0069_366_69366991_qa_4" +name = "smoldataenvs-train/0069_366_69366991_qa_4" description = "How many unique categories were present in the 'Item_Type' feature before encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0069_397_69397859_qa_5/task.toml b/tasks/0069_397_69397859_qa_5/task.toml index 518f1413c697316569b92c5a41957657c0958086..d0374d0b8a85878c033d43ea6e2c653ce3ac22e8 100644 --- a/tasks/0069_397_69397859_qa_5/task.toml +++ b/tasks/0069_397_69397859_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0069_397_69397859_qa_5" +name = "smoldataenvs-train/0069_397_69397859_qa_5" description = "What is the percentage of missing values in the Insulin column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "48.697917" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0069_399_69399288_qa_4/task.toml b/tasks/0069_399_69399288_qa_4/task.toml index 87d724bb1c34372b8a83fc3b13c626b94b593418..8362e67d369cd440841a317cb5d8f4b046cac117 100644 --- a/tasks/0069_399_69399288_qa_4/task.toml +++ b/tasks/0069_399_69399288_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0069_399_69399288_qa_4" +name = "smoldataenvs-train/0069_399_69399288_qa_4" description = "How many false negatives are present in the test set predictions according to the confusion matrix?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0069_401_69401499_qa_5/task.toml b/tasks/0069_401_69401499_qa_5/task.toml index 589d1c9d1ebcc09983cd94fc54c4e72ae50da542..2d49eaee428558938a1a01302e4eed0a0825a65b 100644 --- a/tasks/0069_401_69401499_qa_5/task.toml +++ b/tasks/0069_401_69401499_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0069_401_69401499_qa_5" +name = "smoldataenvs-train/0069_401_69401499_qa_5" description = "What is the distribution count of each price_range category in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "500,500,500,500" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0069_427_69427691_qa_5/task.toml b/tasks/0069_427_69427691_qa_5/task.toml index 0fae1faaa3a03e3dc4128b6565a1318f86bfb9cd..43a945485bc865fe816142adc4b65fd6ceb2a70b 100644 --- a/tasks/0069_427_69427691_qa_5/task.toml +++ b/tasks/0069_427_69427691_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0069_427_69427691_qa_5" +name = "smoldataenvs-train/0069_427_69427691_qa_5" description = "Do the normalized distributions of the \"usd_pledged_real\" and \"pledged\" columns appear similar in shape?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0069_437_69437327_qa_3/task.toml b/tasks/0069_437_69437327_qa_3/task.toml index baec92744259d112d674615697247a3044ae3969..51fa880ea57d42d7802f51fd9d69aaead3df9bbe 100644 --- a/tasks/0069_437_69437327_qa_3/task.toml +++ b/tasks/0069_437_69437327_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0069_437_69437327_qa_3" +name = "smoldataenvs-train/0069_437_69437327_qa_3" description = "What is the lowest quantity ordered for any item in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0069_485_69485814_qa_2/task.toml b/tasks/0069_485_69485814_qa_2/task.toml index 459f50f7f7453151cb895a11302be904bf64dc05..1870e1e0bdd2f4b360d2ce6d3e590f9d404e4d7c 100644 --- a/tasks/0069_485_69485814_qa_2/task.toml +++ b/tasks/0069_485_69485814_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0069_485_69485814_qa_2" +name = "smoldataenvs-train/0069_485_69485814_qa_2" description = "Which numeric variable has the strongest negative correlation with Item_Outlet_Sales?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Item_Visibility" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0069_496_69496308_qa_1/task.toml b/tasks/0069_496_69496308_qa_1/task.toml index 42cd1d2b52045664d5e5ef37d71874ea274f4915..0e92875f4075c49c6b563a1ba9232bc700cb632b 100644 --- a/tasks/0069_496_69496308_qa_1/task.toml +++ b/tasks/0069_496_69496308_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0069_496_69496308_qa_1" +name = "smoldataenvs-train/0069_496_69496308_qa_1" description = "What is the most frequent message content in the ham category and how many times does it appear in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Sorry, I'll call later; 30" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0069_496_69496308_qa_4/task.toml b/tasks/0069_496_69496308_qa_4/task.toml index e2a6f7805ab229fcfce15379fb75d5bffef79b6c..8114bf322f2a9feb9f1908c4e6de4e8c36e57a23 100644 --- a/tasks/0069_496_69496308_qa_4/task.toml +++ b/tasks/0069_496_69496308_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0069_496_69496308_qa_4" +name = "smoldataenvs-train/0069_496_69496308_qa_4" description = "How many unique message contents are present in the spam category of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "653" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0069_534_69534181_qa_4/task.toml b/tasks/0069_534_69534181_qa_4/task.toml index fa0a132852688fb221a37ec41ca459b4c981ffcc..8ec0d55e0bc1ec72cde2711edf8aead2206778c3 100644 --- a/tasks/0069_534_69534181_qa_4/task.toml +++ b/tasks/0069_534_69534181_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0069_534_69534181_qa_4" +name = "smoldataenvs-train/0069_534_69534181_qa_4" description = "What is the most common habitat category in the dataset based on the original categorical values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "d" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0069_534_69534181_qa_5/task.toml b/tasks/0069_534_69534181_qa_5/task.toml index b86d5e99c33f2025523377beeecce81ac93049ce..dc119a7eeae31a6a5ea7e74ca8b39a8a6345f027 100644 --- a/tasks/0069_534_69534181_qa_5/task.toml +++ b/tasks/0069_534_69534181_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0069_534_69534181_qa_5" +name = "smoldataenvs-train/0069_534_69534181_qa_5" description = "What is the distribution of edible vs. poisonous mushroom classes in the original dataset (counts)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "e=4208, p=3916" reward_mode_initial = "list" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0069_548_69548302_qa_1/task.toml b/tasks/0069_548_69548302_qa_1/task.toml index 219d51eaf31804bca5e77999b4af7e72b90886c9..aeab4c51513bbfd92cd882d21b742c98c61e2801 100644 --- a/tasks/0069_548_69548302_qa_1/task.toml +++ b/tasks/0069_548_69548302_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0069_548_69548302_qa_1" +name = "smoldataenvs-train/0069_548_69548302_qa_1" description = "Is there a statistically significant difference in medical charges between smokers and non-smokers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0069_589_69589694_qa_4/task.toml b/tasks/0069_589_69589694_qa_4/task.toml index 3d7620f4dfed90cf8cda26fe759e46d7b0bd1817..4d44f355737d7e06b660462703835f7658cb475c 100644 --- a/tasks/0069_589_69589694_qa_4/task.toml +++ b/tasks/0069_589_69589694_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0069_589_69589694_qa_4" +name = "smoldataenvs-train/0069_589_69589694_qa_4" description = "How many samples are included in the test dataset after an 80/20 train-test split?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10788" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0069_613_69613166_qa_5/task.toml b/tasks/0069_613_69613166_qa_5/task.toml index b3a8292476af16625ab5b5364858e37ff0c4df34..51a14d292efce3c40080d3925d9cb18868412a0a 100644 --- a/tasks/0069_613_69613166_qa_5/task.toml +++ b/tasks/0069_613_69613166_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0069_613_69613166_qa_5" +name = "smoldataenvs-train/0069_613_69613166_qa_5" description = "How many wines in the dataset were reclassified as \"good quality\" after applying the threshold of quality > 6.5?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "217" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0069_615_69615875_qa_2/task.toml b/tasks/0069_615_69615875_qa_2/task.toml index 9e231655ecee078ce64ae614af4155ed863b23f7..5b97b7b83164d99640ec0297270198af9d3bec0e 100644 --- a/tasks/0069_615_69615875_qa_2/task.toml +++ b/tasks/0069_615_69615875_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0069_615_69615875_qa_2" +name = "smoldataenvs-train/0069_615_69615875_qa_2" description = "Which payment method has the highest proportion of churn customers according to the bivariate analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic check" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0069_624_69624642_qa_1/task.toml b/tasks/0069_624_69624642_qa_1/task.toml index fdb587a4efca0481b277562bf4ff30f150998ae3..96041032fe508e844f0d3a85a02ccb405ce1e724 100644 --- a/tasks/0069_624_69624642_qa_1/task.toml +++ b/tasks/0069_624_69624642_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0069_624_69624642_qa_1" +name = "smoldataenvs-train/0069_624_69624642_qa_1" description = "Which feature in the dataset exhibits the highest skewness, and what is its skewness value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Chlorides, 5.68" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0069_636_69636356_qa_2/task.toml b/tasks/0069_636_69636356_qa_2/task.toml index 06f062629ebad95a63d8a4dfb345f70370cf1a11..2ef16ed726f04ce60aabf465176ac8ab9ded9e69 100644 --- a/tasks/0069_636_69636356_qa_2/task.toml +++ b/tasks/0069_636_69636356_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0069_636_69636356_qa_2" +name = "smoldataenvs-train/0069_636_69636356_qa_2" description = "Which species is predicted by the logistic regression model when all four features (sepal length, sepal width, petal length, and petal width) are measured at 3 cm?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-setosa" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0069_636_69636356_qa_4/task.toml b/tasks/0069_636_69636356_qa_4/task.toml index eb2daaaeb7d677986c6dfddacfa6727ca75f4543..2dbd941e11684f2acefae96c3ed0a88dcebdc8f3 100644 --- a/tasks/0069_636_69636356_qa_4/task.toml +++ b/tasks/0069_636_69636356_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0069_636_69636356_qa_4" +name = "smoldataenvs-train/0069_636_69636356_qa_4" description = "What is the range of petal lengths in the dataset (calculated as the difference between the maximum and minimum values)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0069_643_69643958_qa_2/task.toml b/tasks/0069_643_69643958_qa_2/task.toml index a668c744757800a9ac5892bc156fd6c9e24d61f2..b721e25dbb604d4ff064e5430a8af366da9f5235 100644 --- a/tasks/0069_643_69643958_qa_2/task.toml +++ b/tasks/0069_643_69643958_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0069_643_69643958_qa_2" +name = "smoldataenvs-train/0069_643_69643958_qa_2" description = "Which feature shows the strongest positive correlation with the diabetes outcome variable (Outcome) according to the correlation matrix analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0069_646_69646794_qa_3/task.toml b/tasks/0069_646_69646794_qa_3/task.toml index 76b6369d03335447f7a778e5399826f9668972f4..572d8c83c8f4f6f14991e36307dd99f169d4ba33 100644 --- a/tasks/0069_646_69646794_qa_3/task.toml +++ b/tasks/0069_646_69646794_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0069_646_69646794_qa_3" +name = "smoldataenvs-train/0069_646_69646794_qa_3" description = "How many unique movies are included in the final cleaned dataset after merging and dropping columns?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4803" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0069_698_69698122_qa_4/task.toml b/tasks/0069_698_69698122_qa_4/task.toml index 2277d0f1e82a7d812aa7afb318651c654224e631..26ae33d122f7ba2c6e1ac24300e05d58e392105c 100644 --- a/tasks/0069_698_69698122_qa_4/task.toml +++ b/tasks/0069_698_69698122_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0069_698_69698122_qa_4" +name = "smoldataenvs-train/0069_698_69698122_qa_4" description = "How many instances remain in the dataset after removing rows with missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "45222" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0069_856_69856853_qa_2/task.toml b/tasks/0069_856_69856853_qa_2/task.toml index dcb018db296eae1873c42aaed0ccba8ab977a3f0..22760f9f61401caca7c633154fd0b8be18774bb7 100644 --- a/tasks/0069_856_69856853_qa_2/task.toml +++ b/tasks/0069_856_69856853_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0069_856_69856853_qa_2" +name = "smoldataenvs-train/0069_856_69856853_qa_2" description = "Which region has the highest number of individuals in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Southeast" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0069_856_69856853_qa_5/task.toml b/tasks/0069_856_69856853_qa_5/task.toml index 8a56acce0e7edbcc5862047962d55bd863424bbf..8808b5edeed89cc76fb1edcbb61df5b07d65006a 100644 --- a/tasks/0069_856_69856853_qa_5/task.toml +++ b/tasks/0069_856_69856853_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0069_856_69856853_qa_5" +name = "smoldataenvs-train/0069_856_69856853_qa_5" description = "What is the minimum age recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "18" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0069_922_69922733_qa_1/task.toml b/tasks/0069_922_69922733_qa_1/task.toml index c2439113a26d07827eb463f3e9d770dce87a7ae1..f559c7113d31822e9afd7430ce9289bd3f826a91 100644 --- a/tasks/0069_922_69922733_qa_1/task.toml +++ b/tasks/0069_922_69922733_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0069_922_69922733_qa_1" +name = "smoldataenvs-train/0069_922_69922733_qa_1" description = "What percentage of missing values did the 'Insulin' column have before being dropped from the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "48.697917" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0069_939_69939978_qa_2/task.toml b/tasks/0069_939_69939978_qa_2/task.toml index 0f728596e94468ccdc45d1dcf1c832e8b2df8884..17b77dabfda157fd53aa0ed671c83679e1f392e4 100644 --- a/tasks/0069_939_69939978_qa_2/task.toml +++ b/tasks/0069_939_69939978_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0069_939_69939978_qa_2" +name = "smoldataenvs-train/0069_939_69939978_qa_2" description = "Which age group exhibits the highest attendance rate according to the binned age analysis (0-29, 29-58, 58-87, 87-116)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "58-87" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0069_939_69939978_qa_3/task.toml b/tasks/0069_939_69939978_qa_3/task.toml index 7e9e13c45137b70ec08a138830408432d5b39959..80da1576e81aa61f9d2940cb07450e418beca819 100644 --- a/tasks/0069_939_69939978_qa_3/task.toml +++ b/tasks/0069_939_69939978_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0069_939_69939978_qa_3" +name = "smoldataenvs-train/0069_939_69939978_qa_3" description = "Which neighborhood has the highest proportion of patients who attended their appointments according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PARQUE INDUSTRIAL" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0069_956_69956218_qa_3/task.toml b/tasks/0069_956_69956218_qa_3/task.toml index a299bc39044f6c2d411ba900606569e156c1cdb4..b432ffcdf595ddf7f31115c6df4b1d552d304177 100644 --- a/tasks/0069_956_69956218_qa_3/task.toml +++ b/tasks/0069_956_69956218_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0069_956_69956218_qa_3" +name = "smoldataenvs-train/0069_956_69956218_qa_3" description = "For the RM column, which transformation (Box-Cox or Yeo-Johnson) produces a more normally distributed result based on the skewness value, and what is the skewness after applying that method?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Yeo-Johnson, 0.06" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0070_045_70045525_qa_1/task.toml b/tasks/0070_045_70045525_qa_1/task.toml index 5160caf035feeca0777ebbe70468008de5950afc..380bf0f3549bac9f17e5f4eddb3de1f1d4f242d8 100644 --- a/tasks/0070_045_70045525_qa_1/task.toml +++ b/tasks/0070_045_70045525_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0070_045_70045525_qa_1" +name = "smoldataenvs-train/0070_045_70045525_qa_1" description = "How many duplicate rows were present in the dataset before deduplication?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0070_120_70120425_qa_3/task.toml b/tasks/0070_120_70120425_qa_3/task.toml index bb261d2a46b9503280abc9bf4a7573e71d8549bb..016fa9cb5164c1b62165e64c62e5f075f7990977 100644 --- a/tasks/0070_120_70120425_qa_3/task.toml +++ b/tasks/0070_120_70120425_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0070_120_70120425_qa_3" +name = "smoldataenvs-train/0070_120_70120425_qa_3" description = "Which neighborhood has the highest total number of appointments scheduled?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "JARDIM CAMBURI" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0070_120_70120425_qa_5/task.toml b/tasks/0070_120_70120425_qa_5/task.toml index 7d9711dc4937371eeb06635dee78c66ed32771d4..d14700e7453c2440d1dac1e6d09e19a5fed4e0aa 100644 --- a/tasks/0070_120_70120425_qa_5/task.toml +++ b/tasks/0070_120_70120425_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0070_120_70120425_qa_5" +name = "smoldataenvs-train/0070_120_70120425_qa_5" description = "Which day of the week has the fewest scheduled appointments?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Saturday" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0070_244_70244302_qa_3/task.toml b/tasks/0070_244_70244302_qa_3/task.toml index 65601367213a0ca789ea7d7498a04f0fc55d736f..a4768b65fa809e159155b853865a77c3db58c725 100644 --- a/tasks/0070_244_70244302_qa_3/task.toml +++ b/tasks/0070_244_70244302_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0070_244_70244302_qa_3" +name = "smoldataenvs-train/0070_244_70244302_qa_3" description = "Which health condition shows the largest difference in show rates between affected and non-affected patients?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Hipertension" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0070_372_70372979_qa_4/task.toml b/tasks/0070_372_70372979_qa_4/task.toml index 43270d83c3b85d9d5b3be885ee89d69eb72b2a1f..61d1b7c2777b07f2dc47fef356aa9ad97a9b379f 100644 --- a/tasks/0070_372_70372979_qa_4/task.toml +++ b/tasks/0070_372_70372979_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0070_372_70372979_qa_4" +name = "smoldataenvs-train/0070_372_70372979_qa_4" description = "What is the minimum price for wines rated with the highest possible score (100 points)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "80" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0070_379_70379910_qa_2/task.toml b/tasks/0070_379_70379910_qa_2/task.toml index 28d341b403a27bc5ae6c9ec4bf4dbaca2d876f26..ece5108586e0cd9032d44fa497238716be9edfb0 100644 --- a/tasks/0070_379_70379910_qa_2/task.toml +++ b/tasks/0070_379_70379910_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0070_379_70379910_qa_2" +name = "smoldataenvs-train/0070_379_70379910_qa_2" description = "What is the root mean squared error (RMSE) of the model's predictions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "102278.83" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0070_379_70379910_qa_5/task.toml b/tasks/0070_379_70379910_qa_5/task.toml index 83e094e54533532db0faabfdaa582cf38b6a0aba..070e9f44e7551e016d40ba61014fe1b6c2bbf12c 100644 --- a/tasks/0070_379_70379910_qa_5/task.toml +++ b/tasks/0070_379_70379910_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0070_379_70379910_qa_5" +name = "smoldataenvs-train/0070_379_70379910_qa_5" description = "What is the mean absolute error (MAE) of the model's predictions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "82,288.22" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0070_381_70381965_qa_4/task.toml b/tasks/0070_381_70381965_qa_4/task.toml index 581eb693097e6f63abdc55336856f21ba5d61388..a2bb2fd436c4c3603aad30a1eadb8fa56f69d1a7 100644 --- a/tasks/0070_381_70381965_qa_4/task.toml +++ b/tasks/0070_381_70381965_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0070_381_70381965_qa_4" +name = "smoldataenvs-train/0070_381_70381965_qa_4" description = "What feature has the highest correlation with compactness_worst in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "concavity_worst" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0070_381_70381965_qa_5/task.toml b/tasks/0070_381_70381965_qa_5/task.toml index 19151108eee3483d87051faed6080aaab656fc24..3cef54a5fa409dede5306aa757d0f4d6b50bc5e9 100644 --- a/tasks/0070_381_70381965_qa_5/task.toml +++ b/tasks/0070_381_70381965_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0070_381_70381965_qa_5" +name = "smoldataenvs-train/0070_381_70381965_qa_5" description = "What percentage of the dataset corresponds to malignant (M) diagnoses?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "35.7" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0070_420_70420149_qa_1/task.toml b/tasks/0070_420_70420149_qa_1/task.toml index 576b1cccd1d94d3d9a5807347e6d2ce184eec4c6..5565eb780a34a58a5b9ba10d4b26abac734f5668 100644 --- a/tasks/0070_420_70420149_qa_1/task.toml +++ b/tasks/0070_420_70420149_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0070_420_70420149_qa_1" +name = "smoldataenvs-train/0070_420_70420149_qa_1" description = "What is the most common movie genre in the dataset based on frequency of occurrence?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Drama" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0070_420_70420149_qa_5/task.toml b/tasks/0070_420_70420149_qa_5/task.toml index 4859861d2ffd7b138922cd930e13192c5965eca4..5bb5ae01c7e4e25fab9f8eed0845047419d15059 100644 --- a/tasks/0070_420_70420149_qa_5/task.toml +++ b/tasks/0070_420_70420149_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0070_420_70420149_qa_5" +name = "smoldataenvs-train/0070_420_70420149_qa_5" description = "What is the average vote average rating across all movies in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.09" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0070_620_70620378_qa_5/task.toml b/tasks/0070_620_70620378_qa_5/task.toml index 874baa79b1097b62999a0acd35b322e42174fd1e..814a4ec41e1461b2039599da170014d57346f02f 100644 --- a/tasks/0070_620_70620378_qa_5/task.toml +++ b/tasks/0070_620_70620378_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0070_620_70620378_qa_5" +name = "smoldataenvs-train/0070_620_70620378_qa_5" description = "What is the retention rate for the 2010-12-01 cohort in the 12th month after their first purchase?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0070_655_70655259_qa_5/task.toml b/tasks/0070_655_70655259_qa_5/task.toml index 814fb816d5152ca0fb658616c55a0daed9ae43e0..8699d2ef52e17b7b134976cccd97883077d36e5f 100644 --- a/tasks/0070_655_70655259_qa_5/task.toml +++ b/tasks/0070_655_70655259_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0070_655_70655259_qa_5" +name = "smoldataenvs-train/0070_655_70655259_qa_5" description = "What is the standard deviation of the Glucose feature in the preprocessed dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "30.48" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0070_784_70784112_qa_2/task.toml b/tasks/0070_784_70784112_qa_2/task.toml index b2b60d9b066b6eb05f4580b88e2eea6dab6a43c5..3a1e9f9b8e0c6223b9bd100ba244edb84336547a 100644 --- a/tasks/0070_784_70784112_qa_2/task.toml +++ b/tasks/0070_784_70784112_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0070_784_70784112_qa_2" +name = "smoldataenvs-train/0070_784_70784112_qa_2" description = "Which internet service type has the highest churn rate among customers who have signed up for internet service?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Fiber optic" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0070_816_70816004_qa_3/task.toml b/tasks/0070_816_70816004_qa_3/task.toml index 2cacf95f1084146dae205f376e43a382404831f2..65c846c614f17c957b6cc8dd2b5364cf6c907d29 100644 --- a/tasks/0070_816_70816004_qa_3/task.toml +++ b/tasks/0070_816_70816004_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0070_816_70816004_qa_3" +name = "smoldataenvs-train/0070_816_70816004_qa_3" description = "How many features are used as input for the decision tree model in the wine quality classification task?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0070_874_70874122_qa_5/task.toml b/tasks/0070_874_70874122_qa_5/task.toml index b667225ae4e9b8e1f8fbbbb2dc642cb5f6b12de6..8f0cb0521ed3fdfa7fadda4593a951ae1042b133 100644 --- a/tasks/0070_874_70874122_qa_5/task.toml +++ b/tasks/0070_874_70874122_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0070_874_70874122_qa_5" +name = "smoldataenvs-train/0070_874_70874122_qa_5" description = "Is the temperature distribution in the dataset both skewed and platykurtic based on statistical measurements?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] build_timeout_sec = 600.0 diff --git a/tasks/0070_908_70908084_qa_2/task.toml b/tasks/0070_908_70908084_qa_2/task.toml index 934cdc7c3aa2dd1c2aee525c62de5d52395b949a..c7620fc18e4cdde410c6f13c01cd7e292e25507c 100644 --- a/tasks/0070_908_70908084_qa_2/task.toml +++ b/tasks/0070_908_70908084_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0070_908_70908084_qa_2" +name = "smoldataenvs-train/0070_908_70908084_qa_2" description = "How many of the 500 train-test splits achieved 100% classification accuracy?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "138" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0070_908_70908084_qa_4/task.toml b/tasks/0070_908_70908084_qa_4/task.toml index 5887b10d6493b8a31877bc902eacc51d32428f96..7f2d4244572c7eb1a76f4e93ed40f4b993d8971f 100644 --- a/tasks/0070_908_70908084_qa_4/task.toml +++ b/tasks/0070_908_70908084_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0070_908_70908084_qa_4" +name = "smoldataenvs-train/0070_908_70908084_qa_4" description = "How many test samples are included in each train-test split when using a 20% test size?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "30" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0070_945_70945386_qa_3/task.toml b/tasks/0070_945_70945386_qa_3/task.toml index 2236f458674e115add9fd26a10a8eb3a15651215..41ed5c9134bde27bd015d982964fcc8eac11fe78 100644 --- a/tasks/0070_945_70945386_qa_3/task.toml +++ b/tasks/0070_945_70945386_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0070_945_70945386_qa_3" +name = "smoldataenvs-train/0070_945_70945386_qa_3" description = "Which rating class has the highest F1 score in the model's predictions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5 Stars" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0070_945_70945386_qa_4/task.toml b/tasks/0070_945_70945386_qa_4/task.toml index aeae15a636d2961f3eebbba9174feb115ed16352..4068cdb7668080219caea0fbe87e9ae397c23731 100644 --- a/tasks/0070_945_70945386_qa_4/task.toml +++ b/tasks/0070_945_70945386_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0070_945_70945386_qa_4" +name = "smoldataenvs-train/0070_945_70945386_qa_4" description = "What is the highest precision value among all rating classes in the model's predictions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.728282" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0070_989_70989704_qa_1/task.toml b/tasks/0070_989_70989704_qa_1/task.toml index 121c7348c5c4dbc21a41ba8f5a59fcfde9bb198f..bb963f0f9fb7a4c9fb7ccfa1e4b348d336c4fe59 100644 --- a/tasks/0070_989_70989704_qa_1/task.toml +++ b/tasks/0070_989_70989704_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0070_989_70989704_qa_1" +name = "smoldataenvs-train/0070_989_70989704_qa_1" description = "After data cleaning, how many instances are in the \"imU\" class of the \"site\" column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "35" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0070_990_70990012_qa_1/task.toml b/tasks/0070_990_70990012_qa_1/task.toml index 54288cf61cf953f46a978168411666f5d4dc4a04..023e557e3ae36863e02449a0f458e69887382950 100644 --- a/tasks/0070_990_70990012_qa_1/task.toml +++ b/tasks/0070_990_70990012_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0070_990_70990012_qa_1" +name = "smoldataenvs-train/0070_990_70990012_qa_1" description = "What is the effect size (Cohen's d) of the radius mean difference between malignant and benign tumors in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.21" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0071_055_71055191_qa_1/task.toml b/tasks/0071_055_71055191_qa_1/task.toml index 93a03c9de1831eda7e82b11618e9497d5820ae2b..40cd9a8a8e0cb4a80ab8b26c0195f447621fd5f6 100644 --- a/tasks/0071_055_71055191_qa_1/task.toml +++ b/tasks/0071_055_71055191_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0071_055_71055191_qa_1" +name = "smoldataenvs-train/0071_055_71055191_qa_1" description = "What percentage of the dataset is missing in the \"Cabin\" column after initial data inspection?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "77.10" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0071_091_71091389_qa_2/task.toml b/tasks/0071_091_71091389_qa_2/task.toml index 5ae0c2b888b156df1a96de974d87e2b76cd2b058..dd990d1e6af5f06943e3058bf1f1f46ebcc85a99 100644 --- a/tasks/0071_091_71091389_qa_2/task.toml +++ b/tasks/0071_091_71091389_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0071_091_71091389_qa_2" +name = "smoldataenvs-train/0071_091_71091389_qa_2" description = "What is the R² score of the linear regression model using the 'sqft_living' feature compared to the model using the 11 most correlated features (floors, waterfront, lat, bedrooms, sqft_basement, view, bathrooms, sqft_living15, sqft_above, grade, sqft_living)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.492853, 0.657715" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0071_225_71225358_qa_3/task.toml b/tasks/0071_225_71225358_qa_3/task.toml index bacf01ab648da28ba36a6c6bf33a69861b9ebfed..caea4f4f3f84e4a0c07aac453587b378201c05ad 100644 --- a/tasks/0071_225_71225358_qa_3/task.toml +++ b/tasks/0071_225_71225358_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0071_225_71225358_qa_3" +name = "smoldataenvs-train/0071_225_71225358_qa_3" description = "What percentage of rides had missing \"Purpose\" values before data imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "43" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0071_254_71254255_qa_2/task.toml b/tasks/0071_254_71254255_qa_2/task.toml index 542acbdf3bccd67c1f04af0545f22b660466aff7..53fe532c61a0c167635e65b1884d20ea555fcafb 100644 --- a/tasks/0071_254_71254255_qa_2/task.toml +++ b/tasks/0071_254_71254255_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0071_254_71254255_qa_2" +name = "smoldataenvs-train/0071_254_71254255_qa_2" description = "How many benign and malignant diagnoses are present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "357 benign, 212 malignant" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0071_397_71397798_qa_2/task.toml b/tasks/0071_397_71397798_qa_2/task.toml index fd1028a9c7be185aaf9204045656fc1b56657364..b098bde7c8787056627864c5639f892f40e2a776 100644 --- a/tasks/0071_397_71397798_qa_2/task.toml +++ b/tasks/0071_397_71397798_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0071_397_71397798_qa_2" +name = "smoldataenvs-train/0071_397_71397798_qa_2" description = "How many additional text samples were incorporated into the dataset by extracting non-null values from the Unnamed columns?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "68" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0071_437_71437256_qa_3/task.toml b/tasks/0071_437_71437256_qa_3/task.toml index a701a5fb67c5bbca87809fff788b86c4395ed088..752d97e99e49f8a27785ad6d44204ad6ac7d95e4 100644 --- a/tasks/0071_437_71437256_qa_3/task.toml +++ b/tasks/0071_437_71437256_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0071_437_71437256_qa_3" +name = "smoldataenvs-train/0071_437_71437256_qa_3" description = "What is the average RMSE of the Ridge model after incorporating the features sex and region?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6123.81" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0071_540_71540621_qa_4/task.toml b/tasks/0071_540_71540621_qa_4/task.toml index 7ecf9b33c8ba1f22893226a89d48bf7ff04af226..20751c5c26090a38718b3ded43327e96d8ae5ed2 100644 --- a/tasks/0071_540_71540621_qa_4/task.toml +++ b/tasks/0071_540_71540621_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0071_540_71540621_qa_4" +name = "smoldataenvs-train/0071_540_71540621_qa_4" description = "Which species has the highest average petal length?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "virginica" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0071_543_71543992_qa_2/task.toml b/tasks/0071_543_71543992_qa_2/task.toml index 2904bab58db4f02a494b00d2dde48eee8000f16b..6caf0efe32959941fcfdaad89f07b0ca359c4de7 100644 --- a/tasks/0071_543_71543992_qa_2/task.toml +++ b/tasks/0071_543_71543992_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0071_543_71543992_qa_2" +name = "smoldataenvs-train/0071_543_71543992_qa_2" description = "What percentage of IPL matches ended in a tie according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0071_543_71543992_qa_4/task.toml b/tasks/0071_543_71543992_qa_4/task.toml index 46697eb9e8a6e61a22daf03168527a12440c0ff0..16bdfbd10586bfa5b666e18c91348c1c77aebcb2 100644 --- a/tasks/0071_543_71543992_qa_4/task.toml +++ b/tasks/0071_543_71543992_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0071_543_71543992_qa_4" +name = "smoldataenvs-train/0071_543_71543992_qa_4" description = "Which player has received the most Player of the Match awards in the IPL according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "CH Gayle" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0071_619_71619618_qa_1/task.toml b/tasks/0071_619_71619618_qa_1/task.toml index 40785ad04210e119a5dff8c5dac6d2cba882a5b8..471458b185cf1b7663791c207279cd63296c4857 100644 --- a/tasks/0071_619_71619618_qa_1/task.toml +++ b/tasks/0071_619_71619618_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0071_619_71619618_qa_1" +name = "smoldataenvs-train/0071_619_71619618_qa_1" description = "What is the proportion of 'bad' quality wines in the dataset after binning into 'bad' and 'good' categories?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "86.4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0071_776_71776442_qa_2/task.toml b/tasks/0071_776_71776442_qa_2/task.toml index a5060a0d279c1a32ce938b4ed8113d2ea4982838..3bbb2c94ba11922db5262e099ab7b2d6f136b1de 100644 --- a/tasks/0071_776_71776442_qa_2/task.toml +++ b/tasks/0071_776_71776442_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0071_776_71776442_qa_2" +name = "smoldataenvs-train/0071_776_71776442_qa_2" description = "What is the most common tenure group (1-12, 13-24, etc.) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1-12" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0071_776_71776442_qa_3/task.toml b/tasks/0071_776_71776442_qa_3/task.toml index ddf6a6ec34134a087028a991fc8f658520c84ff8..ba5ccd8e3154b679abd64e074bbef902f1b19d2d 100644 --- a/tasks/0071_776_71776442_qa_3/task.toml +++ b/tasks/0071_776_71776442_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0071_776_71776442_qa_3" +name = "smoldataenvs-train/0071_776_71776442_qa_3" description = "What percentage of customers in the dataset are senior citizens (SeniorCitizen = 1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16.2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0071_785_71785724_qa_4/task.toml b/tasks/0071_785_71785724_qa_4/task.toml index 9556640ca8569d86df181f8d374b98c595badd70..b1e98f3a69a3e8837338228a4214f035c531d771 100644 --- a/tasks/0071_785_71785724_qa_4/task.toml +++ b/tasks/0071_785_71785724_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0071_785_71785724_qa_4" +name = "smoldataenvs-train/0071_785_71785724_qa_4" description = "What is the intercept value of the linear regression model for the obese category when regressing age against charges?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6267.63" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0071_785_71785724_qa_5/task.toml b/tasks/0071_785_71785724_qa_5/task.toml index cfbf764a61ba2721e0d4c600a1a8e39e223dfe8a..f98a8578f8273f2a53322fef165ad3b7b32d4a85 100644 --- a/tasks/0071_785_71785724_qa_5/task.toml +++ b/tasks/0071_785_71785724_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0071_785_71785724_qa_5" +name = "smoldataenvs-train/0071_785_71785724_qa_5" description = "What is the difference in the slope of the linear regression line for insurance charges between overweight and obese individuals when regressing against age?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "33.77" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0071_835_71835908_qa_1/task.toml b/tasks/0071_835_71835908_qa_1/task.toml index f176a1bbd5aed6616f8e86e8c4e564710a26942f..b76f9595a0effd0f2cd69ce80971daaf029e8d87 100644 --- a/tasks/0071_835_71835908_qa_1/task.toml +++ b/tasks/0071_835_71835908_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0071_835_71835908_qa_1" +name = "smoldataenvs-train/0071_835_71835908_qa_1" description = "Which feature has the highest absolute correlation with the price_range in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ram" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0071_835_71835908_qa_3/task.toml b/tasks/0071_835_71835908_qa_3/task.toml index 836e826f967dbebc40320f83e2c0da7ad922e069..b5edb18eadad6348a71e8db7d5e37be1013eef0f 100644 --- a/tasks/0071_835_71835908_qa_3/task.toml +++ b/tasks/0071_835_71835908_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0071_835_71835908_qa_3" +name = "smoldataenvs-train/0071_835_71835908_qa_3" description = "What are the four features most strongly correlated with price_range based on the correlation matrix analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ram, battery_power, px_width, px_height" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0071_845_71845804_qa_1/task.toml b/tasks/0071_845_71845804_qa_1/task.toml index 64b62d428979dde423004839200409b2d1cb2790..a14cf88cb1afde1165ac9be8ad7a86d5d7c3d9a0 100644 --- a/tasks/0071_845_71845804_qa_1/task.toml +++ b/tasks/0071_845_71845804_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0071_845_71845804_qa_1" +name = "smoldataenvs-train/0071_845_71845804_qa_1" description = "What is the difference in the number of male and female patients in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "14" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0071_845_71845804_qa_3/task.toml b/tasks/0071_845_71845804_qa_3/task.toml index 3a30ac68ebdf60d260e74bd08460fcc89b216155..f79c82108a575de61beac59507904ee23d0f5615 100644 --- a/tasks/0071_845_71845804_qa_3/task.toml +++ b/tasks/0071_845_71845804_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0071_845_71845804_qa_3" +name = "smoldataenvs-train/0071_845_71845804_qa_3" description = "What is the maximum recorded insurance charge in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "63770.42801" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0071_845_71845804_qa_5/task.toml b/tasks/0071_845_71845804_qa_5/task.toml index 54091dedb7f85f59ca91239af330494f5689f19a..802a18c8c6095b38d37bc2796ace997002e59018 100644 --- a/tasks/0071_845_71845804_qa_5/task.toml +++ b/tasks/0071_845_71845804_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0071_845_71845804_qa_5" +name = "smoldataenvs-train/0071_845_71845804_qa_5" description = "What is the percentage of patients in the dataset who are smokers?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20.47" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0071_909_71909129_qa_1/task.toml b/tasks/0071_909_71909129_qa_1/task.toml index 1616216fd90e2910b0a3ee5047dbbd09bb81f960..a4463b46605a89210da44465845a440ede1e5c1b 100644 --- a/tasks/0071_909_71909129_qa_1/task.toml +++ b/tasks/0071_909_71909129_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0071_909_71909129_qa_1" +name = "smoldataenvs-train/0071_909_71909129_qa_1" description = "Which video game genre achieved the highest total global sales across all years in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0071_909_71909129_qa_2/task.toml b/tasks/0071_909_71909129_qa_2/task.toml index f1699dba533ade336ddb8c6f201b38b52e3a92f1..6078b06209d857a9ad8b2e5c3832e4368a0925c1 100644 --- a/tasks/0071_909_71909129_qa_2/task.toml +++ b/tasks/0071_909_71909129_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0071_909_71909129_qa_2" +name = "smoldataenvs-train/0071_909_71909129_qa_2" description = "Which gaming platform generated the highest total sales specifically in the Japanese market (JP_Sales)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "DS" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0071_909_71909129_qa_5/task.toml b/tasks/0071_909_71909129_qa_5/task.toml index 07af03370ece772f90beaae365abbf0e253066e0..95134c23bf9255cc6d3e00bfc964ab73df595dd4 100644 --- a/tasks/0071_909_71909129_qa_5/task.toml +++ b/tasks/0071_909_71909129_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0071_909_71909129_qa_5" +name = "smoldataenvs-train/0071_909_71909129_qa_5" description = "What is the name of the video game with the highest North American sales (NA_Sales) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii Sports" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0071_997_71997138_qa_3/task.toml b/tasks/0071_997_71997138_qa_3/task.toml index f3a775d2346f13317dd077b3fa9509701ad2731e..4113572d32b1dcece48d78a16f41b9ad9fb2847d 100644 --- a/tasks/0071_997_71997138_qa_3/task.toml +++ b/tasks/0071_997_71997138_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0071_997_71997138_qa_3" +name = "smoldataenvs-train/0071_997_71997138_qa_3" description = "According to the multiple regression analysis, which advertising channel does not have a statistically significant effect on sales at the 5% significance level?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Newspaper" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0071_997_71997138_qa_5/task.toml b/tasks/0071_997_71997138_qa_5/task.toml index d24af05063b2f4f771fbf0a52c6e7d2b9c508ec1..88fe9fbeae72752ee5936a6216f815f42068700b 100644 --- a/tasks/0071_997_71997138_qa_5/task.toml +++ b/tasks/0071_997_71997138_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0071_997_71997138_qa_5" +name = "smoldataenvs-train/0071_997_71997138_qa_5" description = "What is the estimated change in sales associated with a one-unit increase in Radio advertising expenditure, holding all other variables constant, according to the multiple regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.1885" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0072_066_72066220_qa_1/task.toml b/tasks/0072_066_72066220_qa_1/task.toml index 536bcc77fe6e8a81e1304ce7216b187213651a97..67fe130b56129e8c60cd0b520f56ad10212c9f18 100644 --- a/tasks/0072_066_72066220_qa_1/task.toml +++ b/tasks/0072_066_72066220_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0072_066_72066220_qa_1" +name = "smoldataenvs-train/0072_066_72066220_qa_1" description = "What is the optimal number of clusters determined by the Elbow method in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0072_071_72071082_qa_1/task.toml b/tasks/0072_071_72071082_qa_1/task.toml index 3e8d3a948c6803be37b0f3d48cb9f479b252cc91..86baa849d37190eba6347824f2029ec5bb25ba90 100644 --- a/tasks/0072_071_72071082_qa_1/task.toml +++ b/tasks/0072_071_72071082_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0072_071_72071082_qa_1" +name = "smoldataenvs-train/0072_071_72071082_qa_1" description = "What are the top three features most strongly correlated with car price in the dataset based on the correlation analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "engine-size, curb-weight, horsepower" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0072_071_72071082_qa_5/task.toml b/tasks/0072_071_72071082_qa_5/task.toml index af226e14dfe26c9b5e50578e3a009642211b45fe..3d4d28f56a3f1f74797cbfbb7d9b23468bd2c5c2 100644 --- a/tasks/0072_071_72071082_qa_5/task.toml +++ b/tasks/0072_071_72071082_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0072_071_72071082_qa_5" +name = "smoldataenvs-train/0072_071_72071082_qa_5" description = "Which fuel type is most common in the dataset based on the value counts analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "gas" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0072_108_72108430_qa_3/task.toml b/tasks/0072_108_72108430_qa_3/task.toml index d416b306d497b1b77068b9f12ce737a3bd902713..3b29e2b8bb1c53a288323ef56d39796f972274b9 100644 --- a/tasks/0072_108_72108430_qa_3/task.toml +++ b/tasks/0072_108_72108430_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0072_108_72108430_qa_3" +name = "smoldataenvs-train/0072_108_72108430_qa_3" description = "What is the median sepal length across all species in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5.8" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0072_182_72182036_qa_3/task.toml b/tasks/0072_182_72182036_qa_3/task.toml index 9590b201258e2739be0296e377e722af10d40efc..fabf84adb0c6d7702fdb69050d176d1675c4357e 100644 --- a/tasks/0072_182_72182036_qa_3/task.toml +++ b/tasks/0072_182_72182036_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0072_182_72182036_qa_3" +name = "smoldataenvs-train/0072_182_72182036_qa_3" description = "What is the highest recorded insurance charge amount in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "63770.43" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0072_216_72216676_qa_1/task.toml b/tasks/0072_216_72216676_qa_1/task.toml index 346cc474b97024eba15a21a8dd428ebf083a8853..570c526dd8b228532e9d8b58451f7ca8b0a19cda 100644 --- a/tasks/0072_216_72216676_qa_1/task.toml +++ b/tasks/0072_216_72216676_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0072_216_72216676_qa_1" +name = "smoldataenvs-train/0072_216_72216676_qa_1" description = "What is the difference in R-squared values between the simple linear regression model using TV as the sole predictor and the multiple regression model using TV, Radio, and Newspaper?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.285" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0072_216_72216676_qa_4/task.toml b/tasks/0072_216_72216676_qa_4/task.toml index 14cf0346cd39838bb8ef44c06f389e683ba2ab50..9bec2a49916450b191a851d5ee168e5b99a2dfd6 100644 --- a/tasks/0072_216_72216676_qa_4/task.toml +++ b/tasks/0072_216_72216676_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0072_216_72216676_qa_4" +name = "smoldataenvs-train/0072_216_72216676_qa_4" description = "What is the adjusted R-squared value for the multiple regression model including TV, Radio, and Newspaper?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.896" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0072_403_72403187_qa_1/task.toml b/tasks/0072_403_72403187_qa_1/task.toml index 0d4840d86c42dbff8df1cd73c7923bc572b73d0d..c717ae5d742c7a1bb4a3a809853eda7c8c0a7d30 100644 --- a/tasks/0072_403_72403187_qa_1/task.toml +++ b/tasks/0072_403_72403187_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0072_403_72403187_qa_1" +name = "smoldataenvs-train/0072_403_72403187_qa_1" description = "Which feature in the dataset has the highest Variance Inflation Factor (VIF), indicating severe multicollinearity?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "radius_mean" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0072_471_72471518_qa_1/task.toml b/tasks/0072_471_72471518_qa_1/task.toml index deaa22735d96d61d6635e12514746bc1663e4293..79bf5914127e197169dce48ee94d3aab2b80ddd5 100644 --- a/tasks/0072_471_72471518_qa_1/task.toml +++ b/tasks/0072_471_72471518_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0072_471_72471518_qa_1" +name = "smoldataenvs-train/0072_471_72471518_qa_1" description = "What percentage of the original dataset represents customers who churned (Churn = 1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26.54" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0072_471_72471518_qa_3/task.toml b/tasks/0072_471_72471518_qa_3/task.toml index 197d3b3e13b1e091110327d8717643e0e286f84d..db3cf0afdc5ec54b729398606988e00db1e4eaff 100644 --- a/tasks/0072_471_72471518_qa_3/task.toml +++ b/tasks/0072_471_72471518_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0072_471_72471518_qa_3" +name = "smoldataenvs-train/0072_471_72471518_qa_3" description = "After applying the square root transformation, what was the skewness value of the TotalCharges column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.3089" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0072_478_72478480_qa_4/task.toml b/tasks/0072_478_72478480_qa_4/task.toml index bfd076ddcedb3f945460a93728bcc9e3bac96d20..0d06b72e966270640cdc7e3ea52e99155d7f5336 100644 --- a/tasks/0072_478_72478480_qa_4/task.toml +++ b/tasks/0072_478_72478480_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0072_478_72478480_qa_4" +name = "smoldataenvs-train/0072_478_72478480_qa_4" description = "Which feature had the highest mutual information score in the initial analysis before any feature engineering?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Alcohol" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0072_665_72665847_qa_1/task.toml b/tasks/0072_665_72665847_qa_1/task.toml index 8c3ffe70585d3ec36d1c64fb6a7e7247e80aae93..aff1e6fd881308d8ffe6a4ede0a345a8e5e74e30 100644 --- a/tasks/0072_665_72665847_qa_1/task.toml +++ b/tasks/0072_665_72665847_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0072_665_72665847_qa_1" +name = "smoldataenvs-train/0072_665_72665847_qa_1" description = "Which age group has the highest deposit rate according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "below 25 and above 60" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0072_665_72665847_qa_5/task.toml b/tasks/0072_665_72665847_qa_5/task.toml index 6b764b058ccfa014e03b0557a0ed72b9d2eb6431..d57c7c6d886cd8cae599d60112daa9024961940e 100644 --- a/tasks/0072_665_72665847_qa_5/task.toml +++ b/tasks/0072_665_72665847_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0072_665_72665847_qa_5" +name = "smoldataenvs-train/0072_665_72665847_qa_5" description = "Which education level shows the highest deposit rate according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Tertiary" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0072_767_72767167_qa_1/task.toml b/tasks/0072_767_72767167_qa_1/task.toml index e2b423dde8319a44ab8a40106680c6e7d899a107..77fe0b63cdf954bc188c38df03720044c43cddc6 100644 --- a/tasks/0072_767_72767167_qa_1/task.toml +++ b/tasks/0072_767_72767167_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0072_767_72767167_qa_1" +name = "smoldataenvs-train/0072_767_72767167_qa_1" description = "Which feature in the dataset shows the strongest positive correlation with the 'Outcome' variable before any data cleaning steps?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0072_789_72789077_qa_1/task.toml b/tasks/0072_789_72789077_qa_1/task.toml index e07b2bc0fafa30b4b286bd7d0b5298c3473302f8..2e422af31368a23824045dbb4c6bc1e155d4d778 100644 --- a/tasks/0072_789_72789077_qa_1/task.toml +++ b/tasks/0072_789_72789077_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0072_789_72789077_qa_1" +name = "smoldataenvs-train/0072_789_72789077_qa_1" description = "Does the dataset contain any missing values in the numeric features?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0072_789_72789077_qa_4/task.toml b/tasks/0072_789_72789077_qa_4/task.toml index 886f864305b8b4e50cf473cf88bba4f5e12d2a06..253af2b8c426b3e32d0ccf4dd71b47939d413451 100644 --- a/tasks/0072_789_72789077_qa_4/task.toml +++ b/tasks/0072_789_72789077_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0072_789_72789077_qa_4" +name = "smoldataenvs-train/0072_789_72789077_qa_4" description = "What is the average area population across all samples in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "36163.516" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0072_839_72839930_qa_3/task.toml b/tasks/0072_839_72839930_qa_3/task.toml index 28dd69a23e711804bf36db178650e9eb2641796d..3fb0480cc188ee45c8acc4055ef0e121ce4bef90 100644 --- a/tasks/0072_839_72839930_qa_3/task.toml +++ b/tasks/0072_839_72839930_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0072_839_72839930_qa_3" +name = "smoldataenvs-train/0072_839_72839930_qa_3" description = "Which cluster count yields the lowest Davies-Bouldin Score in the clustering evaluation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0072_866_72866029_qa_5/task.toml b/tasks/0072_866_72866029_qa_5/task.toml index 3570b1e2a3d12b1f0defd05b76932c95d1ace851..138786717828740e8bc8021480dc13dd99dd8412 100644 --- a/tasks/0072_866_72866029_qa_5/task.toml +++ b/tasks/0072_866_72866029_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0072_866_72866029_qa_5" +name = "smoldataenvs-train/0072_866_72866029_qa_5" description = "What is the F1-score for class 1 (diabetic) in the Logistic Regression model after hyperparameter tuning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.57" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0072_933_72933038_qa_4/task.toml b/tasks/0072_933_72933038_qa_4/task.toml index 169b3942f9d3d8a35e936c3e29d802c29477c435..7327bc0d95cf68767329041fac10f54833579845 100644 --- a/tasks/0072_933_72933038_qa_4/task.toml +++ b/tasks/0072_933_72933038_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0072_933_72933038_qa_4" +name = "smoldataenvs-train/0072_933_72933038_qa_4" description = "Among features with '_mean' and '_worst' suffixes, which group has a higher average Pearson correlation with the diagnosis variable?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "_worst" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0072_950_72950290_qa_2/task.toml b/tasks/0072_950_72950290_qa_2/task.toml index 260596a4b4e02c3392053d590ba5af7d13ca601f..b1ca13d3eb2f278df70d89fe3bcc70cf528b65d0 100644 --- a/tasks/0072_950_72950290_qa_2/task.toml +++ b/tasks/0072_950_72950290_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0072_950_72950290_qa_2" +name = "smoldataenvs-train/0072_950_72950290_qa_2" description = "How many unique sentences are present in the spam category?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "653" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0072_990_72990356_qa_1/task.toml b/tasks/0072_990_72990356_qa_1/task.toml index 3445dae88aef274cb587fa32ddaab29d0a62f8f4..30ae0ab993814b6b252c3cb379fa37c67cf57872 100644 --- a/tasks/0072_990_72990356_qa_1/task.toml +++ b/tasks/0072_990_72990356_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0072_990_72990356_qa_1" +name = "smoldataenvs-train/0072_990_72990356_qa_1" description = "What is the maximum Recency value (in days) in the dataset after preprocessing and outlier treatment?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "326" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0073_093_73093411_qa_4/task.toml b/tasks/0073_093_73093411_qa_4/task.toml index 05c170520355e15f507d1c2ef60b9d2e70e8b33f..a0b363cfb98dc6c33ec622842d202087749041c7 100644 --- a/tasks/0073_093_73093411_qa_4/task.toml +++ b/tasks/0073_093_73093411_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0073_093_73093411_qa_4" +name = "smoldataenvs-train/0073_093_73093411_qa_4" description = "What is the median number of words in the review texts after removing null entries?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "59" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0073_125_73125764_qa_5/task.toml b/tasks/0073_125_73125764_qa_5/task.toml index 2942718682f9e26dd96b61ee53f3b3ae050cc946..468c2e9652649fe3c507f369b95ca35f6d04294e 100644 --- a/tasks/0073_125_73125764_qa_5/task.toml +++ b/tasks/0073_125_73125764_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0073_125_73125764_qa_5" +name = "smoldataenvs-train/0073_125_73125764_qa_5" description = "How many missing values were present in the 'Body' column before imputation was applied?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "21" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0073_138_73138449_qa_1/task.toml b/tasks/0073_138_73138449_qa_1/task.toml index 4b2e090fcab0b9003feca9b99cb86e45579d2bcf..2c69372df79296d9742cc82d6791e250c5c76b9e 100644 --- a/tasks/0073_138_73138449_qa_1/task.toml +++ b/tasks/0073_138_73138449_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0073_138_73138449_qa_1" +name = "smoldataenvs-train/0073_138_73138449_qa_1" description = "Which month has the highest number of trips across all years in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "March" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0073_138_73138449_qa_5/task.toml b/tasks/0073_138_73138449_qa_5/task.toml index da1d1b21939897ee97afb7f923faf8e32b6c5289..f212544efc9f7916fe89bfecb190bb4569343f5a 100644 --- a/tasks/0073_138_73138449_qa_5/task.toml +++ b/tasks/0073_138_73138449_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0073_138_73138449_qa_5" +name = "smoldataenvs-train/0073_138_73138449_qa_5" description = "How many bikes were reported stolen in the year 2015?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0073_172_73172053_qa_3/task.toml b/tasks/0073_172_73172053_qa_3/task.toml index a6c08853597c490d3ac5eb353bdd6c175d2c6ae7..2bf7e7402b0a87ce467484f100aa47dbb52db9a9 100644 --- a/tasks/0073_172_73172053_qa_3/task.toml +++ b/tasks/0073_172_73172053_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0073_172_73172053_qa_3" +name = "smoldataenvs-train/0073_172_73172053_qa_3" description = "What is the interquartile range (IQR) for the BMI feature in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9.3" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0073_172_73172053_qa_5/task.toml b/tasks/0073_172_73172053_qa_5/task.toml index f230881ac149f3481d0916ae1b7438708c3a3980..909f4cbb530ae068c6e3257c2034b67193e4aeb2 100644 --- a/tasks/0073_172_73172053_qa_5/task.toml +++ b/tasks/0073_172_73172053_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0073_172_73172053_qa_5" +name = "smoldataenvs-train/0073_172_73172053_qa_5" description = "What is the range of the Age feature, defined as the difference between the maximum and minimum age values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "60" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0073_238_73238255_qa_2/task.toml b/tasks/0073_238_73238255_qa_2/task.toml index fc30de26896508cb8f15bd033153c589b13720ea..59fc2b644e1a5916371f6b2ba9c2504edb750c99 100644 --- a/tasks/0073_238_73238255_qa_2/task.toml +++ b/tasks/0073_238_73238255_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0073_238_73238255_qa_2" +name = "smoldataenvs-train/0073_238_73238255_qa_2" description = "What is the size of the vocabulary after converting all words to lowercase and removing duplicates?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "31817" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0073_355_73355002_qa_1/task.toml b/tasks/0073_355_73355002_qa_1/task.toml index 4bd391ad9dc46f9786c192112ff48fcc47676eed..611580e91bbfce6b95f34e0393b4e7e83c11d1f4 100644 --- a/tasks/0073_355_73355002_qa_1/task.toml +++ b/tasks/0073_355_73355002_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0073_355_73355002_qa_1" +name = "smoldataenvs-train/0073_355_73355002_qa_1" description = "How many duplicate rows were removed from the dataset during preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "240" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0073_355_73355002_qa_2/task.toml b/tasks/0073_355_73355002_qa_2/task.toml index 80dff0a5af2f17922d2ee9703f22ce780a2c04d3..3fbed7c52374ec24def17c4a0730a0b0839afce3 100644 --- a/tasks/0073_355_73355002_qa_2/task.toml +++ b/tasks/0073_355_73355002_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0073_355_73355002_qa_2" +name = "smoldataenvs-train/0073_355_73355002_qa_2" description = "What is the median quality rating of the wines in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0073_429_73429601_qa_5/task.toml b/tasks/0073_429_73429601_qa_5/task.toml index af3b9a1f4577ac1730efc60a8ca0a5cca8ae853b..c015645ae512397270a33e886de0a3460fc087e3 100644 --- a/tasks/0073_429_73429601_qa_5/task.toml +++ b/tasks/0073_429_73429601_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0073_429_73429601_qa_5" +name = "smoldataenvs-train/0073_429_73429601_qa_5" description = "How many more benign cases (diagnosis=1) are present in the dataset compared to malignant cases (diagnosis=0)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "145" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0073_430_73430286_qa_2/task.toml b/tasks/0073_430_73430286_qa_2/task.toml index 82d86ac9c57f9689334b34c9ebb588a1f8468f06..fd18512547ba8c5da8c9cff45fb9ed9a50912e5f 100644 --- a/tasks/0073_430_73430286_qa_2/task.toml +++ b/tasks/0073_430_73430286_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0073_430_73430286_qa_2" +name = "smoldataenvs-train/0073_430_73430286_qa_2" description = "What is the predicted salary for an individual with 5.9 years of experience according to the linear regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "80922.09" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0073_430_73430286_qa_3/task.toml b/tasks/0073_430_73430286_qa_3/task.toml index 2cfda468a17cd5abd3fea914572c31eca1bd0772..da980344ee99d01290567bf0276c25c7357522b3 100644 --- a/tasks/0073_430_73430286_qa_3/task.toml +++ b/tasks/0073_430_73430286_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0073_430_73430286_qa_3" +name = "smoldataenvs-train/0073_430_73430286_qa_3" description = "What is the actual salary in the dataset for the individual with 5.9 years of experience?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "81363.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0073_430_73430286_qa_4/task.toml b/tasks/0073_430_73430286_qa_4/task.toml index 7e306fff608da3f75d8998b2a9a508335410d0ce..8db8189537a69f8f292cd1bcba9d339506acc4b7 100644 --- a/tasks/0073_430_73430286_qa_4/task.toml +++ b/tasks/0073_430_73430286_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0073_430_73430286_qa_4" +name = "smoldataenvs-train/0073_430_73430286_qa_4" description = "What is the coefficient (slope) of the YearsExperience variable in the linear regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9423.81532303" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0073_439_73439547_qa_2/task.toml b/tasks/0073_439_73439547_qa_2/task.toml index 0935c17972002089a62a448133cf4348a092026e..a03995fb88b3bdcf245d0c17300b4152aeea44d3 100644 --- a/tasks/0073_439_73439547_qa_2/task.toml +++ b/tasks/0073_439_73439547_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0073_439_73439547_qa_2" +name = "smoldataenvs-train/0073_439_73439547_qa_2" description = "Which feature shows the highest positive correlation with wine quality in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "alcohol" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0073_439_73439547_qa_3/task.toml b/tasks/0073_439_73439547_qa_3/task.toml index 03ac5ed380577e104367d60542583280e42ac897..3a65a73ae319ec0c938eb0201049fc8b73ddf1f8 100644 --- a/tasks/0073_439_73439547_qa_3/task.toml +++ b/tasks/0073_439_73439547_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0073_439_73439547_qa_3" +name = "smoldataenvs-train/0073_439_73439547_qa_3" description = "How many duplicate records were removed during data preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "240" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0073_456_73456234_qa_4/task.toml b/tasks/0073_456_73456234_qa_4/task.toml index 09f4aac4719ad7db233afc8ebc7685f4eef4d5b2..ea090b7dffcf958040583f4e1b3b44f0f778f063 100644 --- a/tasks/0073_456_73456234_qa_4/task.toml +++ b/tasks/0073_456_73456234_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0073_456_73456234_qa_4" +name = "smoldataenvs-train/0073_456_73456234_qa_4" description = "What is the standard deviation of the Mg feature in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.44" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0073_488_73488117_qa_1/task.toml b/tasks/0073_488_73488117_qa_1/task.toml index 27e0b99c0846604dd8faf8b22325e09a3c55aa12..2d6119fa470df8a352e7ec1da27cce7faa4ac5d4 100644 --- a/tasks/0073_488_73488117_qa_1/task.toml +++ b/tasks/0073_488_73488117_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0073_488_73488117_qa_1" +name = "smoldataenvs-train/0073_488_73488117_qa_1" description = "What is the average credibility percentage of patients based on their appointment attendance history, calculated as (total attended appointments / total scheduled appointments) per patient?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "80.357" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0073_488_73488117_qa_2/task.toml b/tasks/0073_488_73488117_qa_2/task.toml index 5f4183223fb8dc4e5acccd163c265b7656f98031..f9851bd763bb0fc444b3ebeb5334dd929efe2eb1 100644 --- a/tasks/0073_488_73488117_qa_2/task.toml +++ b/tasks/0073_488_73488117_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0073_488_73488117_qa_2" +name = "smoldataenvs-train/0073_488_73488117_qa_2" description = "What is the difference in attendance rates between patients who received SMS reminders (72.41%) and those who did not (83.31%)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0073_529_73529560_qa_5/task.toml b/tasks/0073_529_73529560_qa_5/task.toml index fb9f5bbec7b366132cf03bd5e634e14d94e07bff..20a6827f54a0db1f24577a5cc0634cee302e72ee 100644 --- a/tasks/0073_529_73529560_qa_5/task.toml +++ b/tasks/0073_529_73529560_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0073_529_73529560_qa_5" +name = "smoldataenvs-train/0073_529_73529560_qa_5" description = "What was the proportion of unhealthy liver patients (class 0) compared to healthy patients (class 1) in the original dataset before oversampling?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.5:1" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0073_670_73670678_qa_3/task.toml b/tasks/0073_670_73670678_qa_3/task.toml index c3899316e10c62464c0ab96cdebd5b2b2aa07f7c..c472869874bbe228d789b7d1fed6df37fc136dd4 100644 --- a/tasks/0073_670_73670678_qa_3/task.toml +++ b/tasks/0073_670_73670678_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0073_670_73670678_qa_3" +name = "smoldataenvs-train/0073_670_73670678_qa_3" description = "What is the correlation between radius_mean and perimeter_mean in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.997855" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0073_670_73670678_qa_5/task.toml b/tasks/0073_670_73670678_qa_5/task.toml index 71a4923386154403f6d4ca732b65ac748ec01751..ef9487e0961795eeee5d77633d232629c776e0dc 100644 --- a/tasks/0073_670_73670678_qa_5/task.toml +++ b/tasks/0073_670_73670678_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0073_670_73670678_qa_5" +name = "smoldataenvs-train/0073_670_73670678_qa_5" description = "Which feature has the highest negative correlation with fractal_dimension_mean?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "radius_mean" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0073_670_73670828_qa_2/task.toml b/tasks/0073_670_73670828_qa_2/task.toml index b5cea95b32297120e459bc55cb926078cf3b2c78..098e7c9df9a827b9de8f1f5b445ee26cc7bd746e 100644 --- a/tasks/0073_670_73670828_qa_2/task.toml +++ b/tasks/0073_670_73670828_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0073_670_73670828_qa_2" +name = "smoldataenvs-train/0073_670_73670828_qa_2" description = "How many unique wine quality scores are present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0073_670_73670828_qa_4/task.toml b/tasks/0073_670_73670828_qa_4/task.toml index 878ff76462726aac47015378b907c1952f93fff8..da30d519e656a70ca595cf34e8173869c5997eac 100644 --- a/tasks/0073_670_73670828_qa_4/task.toml +++ b/tasks/0073_670_73670828_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0073_670_73670828_qa_4" +name = "smoldataenvs-train/0073_670_73670828_qa_4" description = "Does the wine quality dataset contain any missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0073_670_73670828_qa_5/task.toml b/tasks/0073_670_73670828_qa_5/task.toml index 17444d4a250bbd9bafa9a4fe027649f109c14045..3a99d4065cacb3ac50350e5bdf9bf3bdafb87e27 100644 --- a/tasks/0073_670_73670828_qa_5/task.toml +++ b/tasks/0073_670_73670828_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0073_670_73670828_qa_5" +name = "smoldataenvs-train/0073_670_73670828_qa_5" description = "What is the second most common wine quality score in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0073_719_73719424_qa_1/task.toml b/tasks/0073_719_73719424_qa_1/task.toml index b77f8995815c1a8d567aa78a0c8562a4599a13dc..be2a7131566010a1b769ddc8087b6446fcc2ef91 100644 --- a/tasks/0073_719_73719424_qa_1/task.toml +++ b/tasks/0073_719_73719424_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0073_719_73719424_qa_1" +name = "smoldataenvs-train/0073_719_73719424_qa_1" description = "How many missing values were present in the TotalCharges column before they were removed from the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0073_719_73719424_qa_2/task.toml b/tasks/0073_719_73719424_qa_2/task.toml index f3ee4647379f073db5c7b52cb094efa329d1e499..e8f1a7137b006bab6a73db3cd5bd847411a14cd0 100644 --- a/tasks/0073_719_73719424_qa_2/task.toml +++ b/tasks/0073_719_73719424_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0073_719_73719424_qa_2" +name = "smoldataenvs-train/0073_719_73719424_qa_2" description = "What is the test accuracy of the XGBoost model evaluated on the holdout test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "77.93" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0073_796_73796901_qa_2/task.toml b/tasks/0073_796_73796901_qa_2/task.toml index b6d92eec8422373d079c25d5b6f433e0f0a1ace1..900b479a0b760b2aab2f986bdcc9bfabbb423055 100644 --- a/tasks/0073_796_73796901_qa_2/task.toml +++ b/tasks/0073_796_73796901_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0073_796_73796901_qa_2" +name = "smoldataenvs-train/0073_796_73796901_qa_2" description = "Which model achieves higher test accuracy at a decision threshold of 0.3: Logistic Regression or Support Vector Machine (SVM)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "SVM" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0073_798_73798390_qa_4/task.toml b/tasks/0073_798_73798390_qa_4/task.toml index 47529cb1e4cad30aeb9763ab6cb7a5edc2fcc01b..7e032638cabb054353644968e4afbd0ecbe6401b 100644 --- a/tasks/0073_798_73798390_qa_4/task.toml +++ b/tasks/0073_798_73798390_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0073_798_73798390_qa_4" +name = "smoldataenvs-train/0073_798_73798390_qa_4" description = "What is the precision score for predicting employees who left (class 1) in the Decision Tree model evaluation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.95" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0073_798_73798390_qa_5/task.toml b/tasks/0073_798_73798390_qa_5/task.toml index db97f76e6ee41e90d6a1db11334cb2745f0ba35d..096d32ef2e7acb7b6ad6745609462e85e6eff356 100644 --- a/tasks/0073_798_73798390_qa_5/task.toml +++ b/tasks/0073_798_73798390_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0073_798_73798390_qa_5" +name = "smoldataenvs-train/0073_798_73798390_qa_5" description = "What is the average monthly work hour threshold that most employees who left had exceeded, according to the histogram analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "200" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0073_810_73810677_qa_2/task.toml b/tasks/0073_810_73810677_qa_2/task.toml index 87d375d69a2401b73a1dc5579bf4d3815ab84de3..bb047dc037323283274230a67f8c7b3925940aa4 100644 --- a/tasks/0073_810_73810677_qa_2/task.toml +++ b/tasks/0073_810_73810677_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0073_810_73810677_qa_2" +name = "smoldataenvs-train/0073_810_73810677_qa_2" description = "What is the 75th percentile age for patients who survived versus those who did not survive the 5-year period?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Survivors=60, Non-survivors=61" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0073_814_73814551_qa_1/task.toml b/tasks/0073_814_73814551_qa_1/task.toml index 081bf133014dd93347d1a7d61d717f0aa7b163cd..123031aaf7d6b63ca6b8c96e547d54f6f7802a56 100644 --- a/tasks/0073_814_73814551_qa_1/task.toml +++ b/tasks/0073_814_73814551_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0073_814_73814551_qa_1" +name = "smoldataenvs-train/0073_814_73814551_qa_1" description = "What is the average account balance of customers who subscribed to a term deposit in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1804.27" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0073_814_73814551_qa_2/task.toml b/tasks/0073_814_73814551_qa_2/task.toml index cf1f31b538d31ebbbfcbae67172b60eb04f36da7..7304bfb9ae977cc146a669643f663018c62e40b8 100644 --- a/tasks/0073_814_73814551_qa_2/task.toml +++ b/tasks/0073_814_73814551_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0073_814_73814551_qa_2" +name = "smoldataenvs-train/0073_814_73814551_qa_2" description = "What is the average account balance of customers who did not subscribe to a term deposit in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1280.23" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0073_814_73814551_qa_3/task.toml b/tasks/0073_814_73814551_qa_3/task.toml index 0adf9d47c61a63e29df3cf66184b74a5e77cd9c3..134eac811e1c8635f2622e11b64c2d49c5120e51 100644 --- a/tasks/0073_814_73814551_qa_3/task.toml +++ b/tasks/0073_814_73814551_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0073_814_73814551_qa_3" +name = "smoldataenvs-train/0073_814_73814551_qa_3" description = "What is the average age of customers who subscribed to a term deposit in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "41.67" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0073_814_73814551_qa_5/task.toml b/tasks/0073_814_73814551_qa_5/task.toml index a0b15ea8d6da04c954a93d7274cacc92389a156e..01178ed348c1993166b6df0390480a9eba52908f 100644 --- a/tasks/0073_814_73814551_qa_5/task.toml +++ b/tasks/0073_814_73814551_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0073_814_73814551_qa_5" +name = "smoldataenvs-train/0073_814_73814551_qa_5" description = "What is the average number of contacts performed during the campaign for customers who subscribed to a term deposit?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.14" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0073_893_73893447_qa_1/task.toml b/tasks/0073_893_73893447_qa_1/task.toml index 156565b4c9ef3c51eb0ea5849271c198fff78c2a..7a7c45d3a1229f5c5f24fca1ceaa4e76f3e8c5e1 100644 --- a/tasks/0073_893_73893447_qa_1/task.toml +++ b/tasks/0073_893_73893447_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0073_893_73893447_qa_1" +name = "smoldataenvs-train/0073_893_73893447_qa_1" description = "What is the percentage of customers who defaulted on their payment in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "22" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0073_893_73893447_qa_2/task.toml b/tasks/0073_893_73893447_qa_2/task.toml index 1995f08b548962f311cb82e97b39ea8ebf797e1a..5c14bfded569f49d649583484791a6a2d31adf0a 100644 --- a/tasks/0073_893_73893447_qa_2/task.toml +++ b/tasks/0073_893_73893447_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0073_893_73893447_qa_2" +name = "smoldataenvs-train/0073_893_73893447_qa_2" description = "What is the average credit limit (LIMIT_BAL) provided to customers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "167484.32" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0073_901_73901899_qa_3/task.toml b/tasks/0073_901_73901899_qa_3/task.toml index 20de1c825293340a4f42607d945d1386baf03bb6..cf099d19cfa9a0e01217b49984012a2975439788 100644 --- a/tasks/0073_901_73901899_qa_3/task.toml +++ b/tasks/0073_901_73901899_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0073_901_73901899_qa_3" +name = "smoldataenvs-train/0073_901_73901899_qa_3" description = "Is the distribution of activities (walking vs. running) in the dataset balanced?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0073_939_73939724_qa_1/task.toml b/tasks/0073_939_73939724_qa_1/task.toml index 93c37e8d02f06643fb4fd647c3b5cc61cd257495..d9564230121af8909e5fce9b4c322e9062791b1e 100644 --- a/tasks/0073_939_73939724_qa_1/task.toml +++ b/tasks/0073_939_73939724_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0073_939_73939724_qa_1" +name = "smoldataenvs-train/0073_939_73939724_qa_1" description = "Which diamond cut type has the highest median price according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Fair" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0073_939_73939724_qa_2/task.toml b/tasks/0073_939_73939724_qa_2/task.toml index 4f2253a108e38fb11d50689f23b596a529536b4e..b17fb772a3836e8a8ccd720174acfb420c489dbe 100644 --- a/tasks/0073_939_73939724_qa_2/task.toml +++ b/tasks/0073_939_73939724_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0073_939_73939724_qa_2" +name = "smoldataenvs-train/0073_939_73939724_qa_2" description = "Which color grade is associated with the highest median diamond price in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "J" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0073_939_73939724_qa_3/task.toml b/tasks/0073_939_73939724_qa_3/task.toml index d6978a2a4387409578e137aced3a59aef8be647f..9f33377ec24674d7f0da167a6effe25b8a3d9300 100644 --- a/tasks/0073_939_73939724_qa_3/task.toml +++ b/tasks/0073_939_73939724_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0073_939_73939724_qa_3" +name = "smoldataenvs-train/0073_939_73939724_qa_3" description = "Which clarity grade has the lowest median price in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "IF" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0073_984_73984919_qa_4/task.toml b/tasks/0073_984_73984919_qa_4/task.toml index 00a83f1465d6e5fd436241f78fc34830787ba6b5..e370f08d1d203c84e85f26bec56b8c7800d7263a 100644 --- a/tasks/0073_984_73984919_qa_4/task.toml +++ b/tasks/0073_984_73984919_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0073_984_73984919_qa_4" +name = "smoldataenvs-train/0073_984_73984919_qa_4" description = "What percentage of the dataset is classified as having 'good' credit risk?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "70" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_012_74012086_qa_5/task.toml b/tasks/0074_012_74012086_qa_5/task.toml index b2f5f30bd693d1622e53d814d4556e5673655ad0..a7c90481d738a6b486245b45166beb67aaeaed5a 100644 --- a/tasks/0074_012_74012086_qa_5/task.toml +++ b/tasks/0074_012_74012086_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0074_012_74012086_qa_5" +name = "smoldataenvs-train/0074_012_74012086_qa_5" description = "What is the churn rate for customers with a tenure of exactly 24 months?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "24.468085" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_013_74013118_qa_3/task.toml b/tasks/0074_013_74013118_qa_3/task.toml index cd61272946bd1210f5ba3725f6dd79a882c94a67..38dc7243e4f79a26348f476ba24479e0d7172891 100644 --- a/tasks/0074_013_74013118_qa_3/task.toml +++ b/tasks/0074_013_74013118_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0074_013_74013118_qa_3" +name = "smoldataenvs-train/0074_013_74013118_qa_3" description = "After stratified sampling, how many districts are in the test set, and what is the proportion of income category 3 in the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4128, 0.350533" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_094_74094547_qa_2/task.toml b/tasks/0074_094_74094547_qa_2/task.toml index 8c8c78605bdc8e4acfa122f0f990f5262a956353..8b88680c03bf1c50f75d2bf9d040caf1ff46b588 100644 --- a/tasks/0074_094_74094547_qa_2/task.toml +++ b/tasks/0074_094_74094547_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0074_094_74094547_qa_2" +name = "smoldataenvs-train/0074_094_74094547_qa_2" description = "What is the accuracy of the Linear Regression model in predicting the price_range?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.916747736187354" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0074_129_74129496_qa_2/task.toml b/tasks/0074_129_74129496_qa_2/task.toml index 61e4bdcd8855b5f68c73587602eb6d5f1645fcb5..278c30a1a9522eb7bf362146bae7b9334b8d47fa 100644 --- a/tasks/0074_129_74129496_qa_2/task.toml +++ b/tasks/0074_129_74129496_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0074_129_74129496_qa_2" +name = "smoldataenvs-train/0074_129_74129496_qa_2" description = "Which workclass category has the highest proportion of individuals earning more than $50K based on the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Self-employed" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_258_74258957_qa_4/task.toml b/tasks/0074_258_74258957_qa_4/task.toml index 5471236b0deec0b78c0321ef0a288d9a495b72be..96bb48902534e1f6e7fb3db1b4afb78ed3ee00bd 100644 --- a/tasks/0074_258_74258957_qa_4/task.toml +++ b/tasks/0074_258_74258957_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0074_258_74258957_qa_4" +name = "smoldataenvs-train/0074_258_74258957_qa_4" description = "What is the mean insurance charge for male beneficiaries in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13956.75" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_276_74276642_qa_3/task.toml b/tasks/0074_276_74276642_qa_3/task.toml index 22d262ad7596cecd3d456a1d898410a1a83cf677..3fce836578845f63d8280baa8725408b3323af8e 100644 --- a/tasks/0074_276_74276642_qa_3/task.toml +++ b/tasks/0074_276_74276642_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0074_276_74276642_qa_3" +name = "smoldataenvs-train/0074_276_74276642_qa_3" description = "What is the minimum age of employees who left the company (Attrition=Yes)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "18" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_277_74277035_qa_1/task.toml b/tasks/0074_277_74277035_qa_1/task.toml index 8f4363777b7a56afd0fae5543fc300584657f68d..c03c740087108a3c6633faef0ee006cf76292685 100644 --- a/tasks/0074_277_74277035_qa_1/task.toml +++ b/tasks/0074_277_74277035_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0074_277_74277035_qa_1" +name = "smoldataenvs-train/0074_277_74277035_qa_1" description = "How many duplicated rows were present in the original dataset, and what was the resulting number of rows after removing duplicates?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "240 duplicates, 1359 rows after removal" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0074_281_74281388_qa_1/task.toml b/tasks/0074_281_74281388_qa_1/task.toml index 07e5bd2b4b714a9c1e16f0e61a343bfbf2ce6f22..6907a14d59c3edf3d5cbda7c74633c1719dd3518 100644 --- a/tasks/0074_281_74281388_qa_1/task.toml +++ b/tasks/0074_281_74281388_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0074_281_74281388_qa_1" +name = "smoldataenvs-train/0074_281_74281388_qa_1" description = "What is the threshold vote count (m) used to filter movies for calculating the weighted rating score?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "160" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_290_74290093_qa_1/task.toml b/tasks/0074_290_74290093_qa_1/task.toml index 635004a96dc797b529972351379653853ddadc36..83283dafe3d13f8b43922df2fd5733ff7043e31f 100644 --- a/tasks/0074_290_74290093_qa_1/task.toml +++ b/tasks/0074_290_74290093_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0074_290_74290093_qa_1" +name = "smoldataenvs-train/0074_290_74290093_qa_1" description = "What is the most frequent wine quality rating in the training set after removing duplicated rows?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_314_74314286_qa_3/task.toml b/tasks/0074_314_74314286_qa_3/task.toml index 11c4bce98a3c8a4e9b17a04d7e568bf3232aef99..f34c3a123ee3b60b96839cd132ee6c4ba033cd04 100644 --- a/tasks/0074_314_74314286_qa_3/task.toml +++ b/tasks/0074_314_74314286_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0074_314_74314286_qa_3" +name = "smoldataenvs-train/0074_314_74314286_qa_3" description = "How many temperature records are missing in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11002" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0074_314_74314286_qa_4/task.toml b/tasks/0074_314_74314286_qa_4/task.toml index 01e328f93ed8f93a0726b3c63759d75868760cc1..dc4ee0bfc29d9801f41bc44e555079d1b950b5a4 100644 --- a/tasks/0074_314_74314286_qa_4/task.toml +++ b/tasks/0074_314_74314286_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0074_314_74314286_qa_4" +name = "smoldataenvs-train/0074_314_74314286_qa_4" description = "After removing records with missing temperature data, what is the total number of remaining records in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "228175" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_346_74346808_qa_4/task.toml b/tasks/0074_346_74346808_qa_4/task.toml index 5fac1cfaf280eed7ad518eb41c2e98354e1cbc9c..ead1ec6fe273aa33b9c6d80b3d915564f01cf302 100644 --- a/tasks/0074_346_74346808_qa_4/task.toml +++ b/tasks/0074_346_74346808_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0074_346_74346808_qa_4" +name = "smoldataenvs-train/0074_346_74346808_qa_4" description = "What is the most frequently occurring life expectancy value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "73.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0074_379_74379604_qa_5/task.toml b/tasks/0074_379_74379604_qa_5/task.toml index 8121d9399950d570786455b857b39398e28a302d..1fe91c058799f3fc2da0cd6061ced8fd83f64aca 100644 --- a/tasks/0074_379_74379604_qa_5/task.toml +++ b/tasks/0074_379_74379604_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0074_379_74379604_qa_5" +name = "smoldataenvs-train/0074_379_74379604_qa_5" description = "Which age group has the highest total Impressions across all campaigns?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "30-34" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_406_74406391_qa_3/task.toml b/tasks/0074_406_74406391_qa_3/task.toml index 5984e88cf024d175f0fbbd46346cc0adf101ba4b..66bacb849e839a8cdfcad4edd7450fdd39982e8b 100644 --- a/tasks/0074_406_74406391_qa_3/task.toml +++ b/tasks/0074_406_74406391_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0074_406_74406391_qa_3" +name = "smoldataenvs-train/0074_406_74406391_qa_3" description = "Which TED event had the highest total number of views across all its talks, and what was the total view count?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "TED2013, 177307937" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_450_74450579_qa_5/task.toml b/tasks/0074_450_74450579_qa_5/task.toml index 5cf4fd0ad5ecca5a418538760a42e47b630dcb54..d791f2d3d0e2e7b6aa207ad5d7966da950f773e7 100644 --- a/tasks/0074_450_74450579_qa_5/task.toml +++ b/tasks/0074_450_74450579_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0074_450_74450579_qa_5" +name = "smoldataenvs-train/0074_450_74450579_qa_5" description = "Is the correlation matrix of quantitative variables in the cleaned dataset statistically significantly different from an identity matrix according to Bartlett's test?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0074_467_74467849_qa_1/task.toml b/tasks/0074_467_74467849_qa_1/task.toml index 2af6d9ef42d9cebae7069fe05ea2ae866f4c29f9..d5383f72928c97c71bae2c4753d4f392814ea840 100644 --- a/tasks/0074_467_74467849_qa_1/task.toml +++ b/tasks/0074_467_74467849_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0074_467_74467849_qa_1" +name = "smoldataenvs-train/0074_467_74467849_qa_1" description = "Which feature has the highest mutual information score with the final grade (G3) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "G2" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_467_74467849_qa_2/task.toml b/tasks/0074_467_74467849_qa_2/task.toml index 18254d8c9ed439601e453a4eaa499b331c59b16f..4ade06c4260416735d6118b6b698d9c38eacb019 100644 --- a/tasks/0074_467_74467849_qa_2/task.toml +++ b/tasks/0074_467_74467849_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0074_467_74467849_qa_2" +name = "smoldataenvs-train/0074_467_74467849_qa_2" description = "What is the percentage of students from MS school in the Math class?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11.65" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_483_74483957_qa_1/task.toml b/tasks/0074_483_74483957_qa_1/task.toml index 66db830c4534bf510a698716351086672ec1290a..d05e351730795835d72c8bd761e27e743e039647 100644 --- a/tasks/0074_483_74483957_qa_1/task.toml +++ b/tasks/0074_483_74483957_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0074_483_74483957_qa_1" +name = "smoldataenvs-train/0074_483_74483957_qa_1" description = "What is the total number of rows in the dataset after removing missing values in the AverageTemperature column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "228175" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_483_74483957_qa_5/task.toml b/tasks/0074_483_74483957_qa_5/task.toml index f2dd7d4829dc98377c01ec838001824397c149cf..a6d210d0ad681ae7dd67c3615784da5fcd2a1fa9 100644 --- a/tasks/0074_483_74483957_qa_5/task.toml +++ b/tasks/0074_483_74483957_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0074_483_74483957_qa_5" +name = "smoldataenvs-train/0074_483_74483957_qa_5" description = "What is the mean average temperature for all cities in Egypt before any filtering operations are applied?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20.900406" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_514_74514325_qa_1/task.toml b/tasks/0074_514_74514325_qa_1/task.toml index d660b5d92f534e017a197df95fc300202ecdd31f..4dbb87008cb8b72ae90d6dc2d72467d578eb6ef3 100644 --- a/tasks/0074_514_74514325_qa_1/task.toml +++ b/tasks/0074_514_74514325_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0074_514_74514325_qa_1" +name = "smoldataenvs-train/0074_514_74514325_qa_1" description = "What is the correlation coefficient between height and weight in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.994584" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0074_537_74537408_qa_5/task.toml b/tasks/0074_537_74537408_qa_5/task.toml index 9cdf1e55e48c1bea42c74152bc24b3aebf8ff775..b8a1d2ebdfee5435c2584da091d080ffcb5c3413 100644 --- a/tasks/0074_537_74537408_qa_5/task.toml +++ b/tasks/0074_537_74537408_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0074_537_74537408_qa_5" +name = "smoldataenvs-train/0074_537_74537408_qa_5" description = "What is the percentage of patients who are female?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "65" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_549_74549731_qa_2/task.toml b/tasks/0074_549_74549731_qa_2/task.toml index f96797b680dfcf9b0fd07d185f854e6175d8ef73..fb7440e98ae3716be3f3ff24fd6a551b98820f40 100644 --- a/tasks/0074_549_74549731_qa_2/task.toml +++ b/tasks/0074_549_74549731_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0074_549_74549731_qa_2" +name = "smoldataenvs-train/0074_549_74549731_qa_2" description = "How many respondents completed the survey in the year 2016?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_549_74549731_qa_3/task.toml b/tasks/0074_549_74549731_qa_3/task.toml index 01da798a174f62b94717972f38acb9889bfc9354..98ab993a39b27771eb4ab06e30c873a49aa2d4e7 100644 --- a/tasks/0074_549_74549731_qa_3/task.toml +++ b/tasks/0074_549_74549731_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0074_549_74549731_qa_3" +name = "smoldataenvs-train/0074_549_74549731_qa_3" description = "After data cleaning, what is the most common response in the `seek_help` column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "No" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_549_74549731_qa_5/task.toml b/tasks/0074_549_74549731_qa_5/task.toml index 89ef0afc6de722292ebd830d2dad9711255a250a..456c5729e65baf218c4bfebe92ed96e44bf4a746 100644 --- a/tasks/0074_549_74549731_qa_5/task.toml +++ b/tasks/0074_549_74549731_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0074_549_74549731_qa_5" +name = "smoldataenvs-train/0074_549_74549731_qa_5" description = "After data cleaning, how many respondents answered `Yes` to seeking help for mental health issues?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "250" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_600_74600268_qa_2/task.toml b/tasks/0074_600_74600268_qa_2/task.toml index 920b1781a5a71dd49c7743e3681d54c848d21375..4694605d5c344ac8d6aa99085f9230fe260d1ef9 100644 --- a/tasks/0074_600_74600268_qa_2/task.toml +++ b/tasks/0074_600_74600268_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0074_600_74600268_qa_2" +name = "smoldataenvs-train/0074_600_74600268_qa_2" description = "After flattening the images for machine learning processing, what was the resulting feature vector dimensionality for each image sample in the training set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4096" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0074_645_74645065_qa_5/task.toml b/tasks/0074_645_74645065_qa_5/task.toml index 567a61cd7a4e84441ba5a759954ab3b9687da9ae..4c6cfb7bc4ca0a0d5c2d1d97bb0081eeaa4cf131 100644 --- a/tasks/0074_645_74645065_qa_5/task.toml +++ b/tasks/0074_645_74645065_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0074_645_74645065_qa_5" +name = "smoldataenvs-train/0074_645_74645065_qa_5" description = "Which video game genre experienced the longest consecutive dominance in the number of releases between 2000 and 2016?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_705_74705026_qa_1/task.toml b/tasks/0074_705_74705026_qa_1/task.toml index 2e6f26632099a32c503c75ac2aee10fb923adcdd..1d0a2a1757797f322531b506c8a1ec9bf705c71f 100644 --- a/tasks/0074_705_74705026_qa_1/task.toml +++ b/tasks/0074_705_74705026_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0074_705_74705026_qa_1" +name = "smoldataenvs-train/0074_705_74705026_qa_1" description = "Which feature in the Boston Housing dataset shows the strongest positive correlation with the median home value (MEDV) after data cleaning and preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "RM" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_739_74739274_qa_1/task.toml b/tasks/0074_739_74739274_qa_1/task.toml index 082c1352c7ef733a05fc9738a66f01f08fe0401f..f8489627c02e48e8d038ec8e23483d79f92a42c5 100644 --- a/tasks/0074_739_74739274_qa_1/task.toml +++ b/tasks/0074_739_74739274_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0074_739_74739274_qa_1" +name = "smoldataenvs-train/0074_739_74739274_qa_1" description = "What is the optimal number of clusters determined by the elbow method for the K-means model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0074_739_74739274_qa_3/task.toml b/tasks/0074_739_74739274_qa_3/task.toml index 2763b8d9c7e6644a8e5a3a9ecc3e5790b26ca3f4..dcfc5d6b14a2fedaa2d6f60e5c21d8d07ea6beab 100644 --- a/tasks/0074_739_74739274_qa_3/task.toml +++ b/tasks/0074_739_74739274_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0074_739_74739274_qa_3" +name = "smoldataenvs-train/0074_739_74739274_qa_3" description = "What is the highest outlier percentage observed in any feature before preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16.68" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_771_74771438_qa_1/task.toml b/tasks/0074_771_74771438_qa_1/task.toml index 3e2811bf92d64691e06f36ae62d636be3a1a6af5..1f3835ea266d487664bb1d9dd4f4fe67d6d44537 100644 --- a/tasks/0074_771_74771438_qa_1/task.toml +++ b/tasks/0074_771_74771438_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0074_771_74771438_qa_1" +name = "smoldataenvs-train/0074_771_74771438_qa_1" description = "Which Pokémon in the dataset have a HP (Hit Points) value greater than 185?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Chansey, Wobbuffet, Blissey" reward_mode_initial = "list" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_776_74776615_qa_1/task.toml b/tasks/0074_776_74776615_qa_1/task.toml index e531f884a551894fd774fff2d2dccf05111c2ead..873ceb9028107fa696ebed93d4ce6d60a4c92c48 100644 --- a/tasks/0074_776_74776615_qa_1/task.toml +++ b/tasks/0074_776_74776615_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0074_776_74776615_qa_1" +name = "smoldataenvs-train/0074_776_74776615_qa_1" description = "What is the percentage of customers who churned in the dataset before handling missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "49.3" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0074_813_74813463_qa_2/task.toml b/tasks/0074_813_74813463_qa_2/task.toml index 030b3372de6f73de6b9a8605305e6d84f2e2ae3a..152e1e96ecc678a0418fb26b609670209d9d0b49 100644 --- a/tasks/0074_813_74813463_qa_2/task.toml +++ b/tasks/0074_813_74813463_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0074_813_74813463_qa_2" +name = "smoldataenvs-train/0074_813_74813463_qa_2" description = "What is the F1-score for the Iris-setosa class in the KNN model's predictions on the validation dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.00" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0074_813_74813463_qa_5/task.toml b/tasks/0074_813_74813463_qa_5/task.toml index de2582a2a68b29573f683a943b43aecb19b50883..0adb617cb89341ccaed6fca8532324cf6002e312 100644 --- a/tasks/0074_813_74813463_qa_5/task.toml +++ b/tasks/0074_813_74813463_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0074_813_74813463_qa_5" +name = "smoldataenvs-train/0074_813_74813463_qa_5" description = "How many Iris-versicolor samples were misclassified as Iris-virginica in the KNN model's validation predictions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0074_813_74813772_qa_4/task.toml b/tasks/0074_813_74813772_qa_4/task.toml index 7b000edb1a6ea9dc5b9ec4efb52953b702d6f289..34b4f7c53928227f4bdcae9885cacaecfaa8cb4e 100644 --- a/tasks/0074_813_74813772_qa_4/task.toml +++ b/tasks/0074_813_74813772_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0074_813_74813772_qa_4" +name = "smoldataenvs-train/0074_813_74813772_qa_4" description = "Which feature exhibits the most negative correlation with the diagnosis (malignant/benign) after preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "smoothness_se" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_813_74813772_qa_5/task.toml b/tasks/0074_813_74813772_qa_5/task.toml index 9b7be6c93865f5f68d537470500bce8631e05f73..d21d4e766a142f508aea0fddbdae20a3931a89f4 100644 --- a/tasks/0074_813_74813772_qa_5/task.toml +++ b/tasks/0074_813_74813772_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0074_813_74813772_qa_5" +name = "smoldataenvs-train/0074_813_74813772_qa_5" description = "How many features in the dataset have a correlation coefficient with diagnosis of 0.7 or higher?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_828_74828746_qa_4/task.toml b/tasks/0074_828_74828746_qa_4/task.toml index 0fef8723d1c05b529f53a628fd22a35fa6716ae3..39a329a6c2c7d48f6f323298e7444ceb39e50dad 100644 --- a/tasks/0074_828_74828746_qa_4/task.toml +++ b/tasks/0074_828_74828746_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0074_828_74828746_qa_4" +name = "smoldataenvs-train/0074_828_74828746_qa_4" description = "Which sepal width range contains the highest frequency of observations according to the histogram analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.0-3.5" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_832_74832240_qa_5/task.toml b/tasks/0074_832_74832240_qa_5/task.toml index 0247805cea851b81309e36537e6428c4373327ab..29ce0fdc22f25437cf50b81a82081b8cedb5c0d3 100644 --- a/tasks/0074_832_74832240_qa_5/task.toml +++ b/tasks/0074_832_74832240_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0074_832_74832240_qa_5" +name = "smoldataenvs-train/0074_832_74832240_qa_5" description = "Are there any features in the dataset that contain negative values based on the inconsistency checks?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_836_74836808_qa_3/task.toml b/tasks/0074_836_74836808_qa_3/task.toml index ecc0ab14d0cdd4c92330747d63b543c5592ff8ab..a991e75112d573aa85c6725adc8aee67ad4f4ea6 100644 --- a/tasks/0074_836_74836808_qa_3/task.toml +++ b/tasks/0074_836_74836808_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0074_836_74836808_qa_3" +name = "smoldataenvs-train/0074_836_74836808_qa_3" description = "Which neighborhood has the highest frequency of scheduled medical appointments in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "JARDIM CAMBURI" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0074_845_74845042_qa_3/task.toml b/tasks/0074_845_74845042_qa_3/task.toml index ee3cd6e8344d1a9a0775f5cf7d69d87cf3bfe650..dc41ddca71bf3c63fb1ab51e0fb00d2f63177852 100644 --- a/tasks/0074_845_74845042_qa_3/task.toml +++ b/tasks/0074_845_74845042_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0074_845_74845042_qa_3" +name = "smoldataenvs-train/0074_845_74845042_qa_3" description = "After feature selection based on correlation with mpg, how many features have an absolute correlation value of 0.5 or higher?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_845_74845042_qa_5/task.toml b/tasks/0074_845_74845042_qa_5/task.toml index 555176e0271edcc994c0897d3f7d8446015362ec..7796efd6f65b9cddee3088599ab7ed3a381a6a09 100644 --- a/tasks/0074_845_74845042_qa_5/task.toml +++ b/tasks/0074_845_74845042_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0074_845_74845042_qa_5" +name = "smoldataenvs-train/0074_845_74845042_qa_5" description = "What is the total number of samples and features in the preprocessed dataset used for modeling?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "398, 8" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_848_74848872_qa_4/task.toml b/tasks/0074_848_74848872_qa_4/task.toml index 642fe679797ed2dc4614fdc49e1c83f6bddce28e..d36b9728a0c71e342ca32b23b5ad022b2a0b291f 100644 --- a/tasks/0074_848_74848872_qa_4/task.toml +++ b/tasks/0074_848_74848872_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0074_848_74848872_qa_4" +name = "smoldataenvs-train/0074_848_74848872_qa_4" description = "What is the standard deviation of the petal width measurements in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.763161" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0074_849_74849685_qa_3/task.toml b/tasks/0074_849_74849685_qa_3/task.toml index 9c227955cf989c4d417d3024f7e8fc62f6af28d8..6768c301d6a47bc05310f1148509f57117058cb7 100644 --- a/tasks/0074_849_74849685_qa_3/task.toml +++ b/tasks/0074_849_74849685_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0074_849_74849685_qa_3" +name = "smoldataenvs-train/0074_849_74849685_qa_3" description = "What is the highest accuracy achieved by any model using the TF-IDF method?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.975592" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0074_849_74849685_qa_4/task.toml b/tasks/0074_849_74849685_qa_4/task.toml index 6e78e90fb2a8edae408b855c8cf776abffab3b58..625202eaa650b8fd13b50422c078d6c9d5dc7646 100644 --- a/tasks/0074_849_74849685_qa_4/task.toml +++ b/tasks/0074_849_74849685_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0074_849_74849685_qa_4" +name = "smoldataenvs-train/0074_849_74849685_qa_4" description = "Which model using the Bag of Words method achieved the highest precision for identifying spam messages?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "SVM" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0074_849_74849685_qa_5/task.toml b/tasks/0074_849_74849685_qa_5/task.toml index d297dcee0ccc5819da3e4a5482897c442628b0f8..d6fbd22656988f6437efe3894e12e156c29fa8aa 100644 --- a/tasks/0074_849_74849685_qa_5/task.toml +++ b/tasks/0074_849_74849685_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0074_849_74849685_qa_5" +name = "smoldataenvs-train/0074_849_74849685_qa_5" description = "What is the F1-score for spam classification in the best-performing model using the TF-IDF method?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.90" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0074_880_74880980_qa_1/task.toml b/tasks/0074_880_74880980_qa_1/task.toml index bf78826399dac7237b884e55f158445698ba73bf..d76dc56bd7630f55a7c6f3178fb8822dbc12d10e 100644 --- a/tasks/0074_880_74880980_qa_1/task.toml +++ b/tasks/0074_880_74880980_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0074_880_74880980_qa_1" +name = "smoldataenvs-train/0074_880_74880980_qa_1" description = "How many features were used in the model after one-hot encoding the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "117" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_904_74904214_qa_1/task.toml b/tasks/0074_904_74904214_qa_1/task.toml index 9af079e665753a00a804e3eec724fcff597d8098..4f07ee96635dc075e5df4a7ccb398c8cb943adf4 100644 --- a/tasks/0074_904_74904214_qa_1/task.toml +++ b/tasks/0074_904_74904214_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0074_904_74904214_qa_1" +name = "smoldataenvs-train/0074_904_74904214_qa_1" description = "What is the average duration between scheduling and the appointment for patients who attended versus those who did not attend?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8.75, 15.84" reward_mode_initial = "list" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_918_74918107_qa_4/task.toml b/tasks/0074_918_74918107_qa_4/task.toml index aa8f020b9c1390b437483bd44daa6b3f8bf849e0..e17ea09b674e73c5b67b23323b95df058b8c1b00 100644 --- a/tasks/0074_918_74918107_qa_4/task.toml +++ b/tasks/0074_918_74918107_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0074_918_74918107_qa_4" +name = "smoldataenvs-train/0074_918_74918107_qa_4" description = "What is the count of missing values in the 'engine_hp' column, and which other column has a similar number of missing entries?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "69" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_956_74956806_qa_3/task.toml b/tasks/0074_956_74956806_qa_3/task.toml index c96a3414758310df5f2f085672f1bd1165f352a8..b38c6b0fe0f2f58f0a963e789e951dbf7135c86c 100644 --- a/tasks/0074_956_74956806_qa_3/task.toml +++ b/tasks/0074_956_74956806_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0074_956_74956806_qa_3" +name = "smoldataenvs-train/0074_956_74956806_qa_3" description = "Which location has the highest number of trips starting from it, and how many trips originate from that location?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Cary, 201" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0074_999_74999890_qa_2/task.toml b/tasks/0074_999_74999890_qa_2/task.toml index fa1b915e14a1349f9ab06b4859265e0915e017bc..7eacca50d2989826a72c79c32108bf2712913571 100644 --- a/tasks/0074_999_74999890_qa_2/task.toml +++ b/tasks/0074_999_74999890_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0074_999_74999890_qa_2" +name = "smoldataenvs-train/0074_999_74999890_qa_2" description = "What is the range of the standard deviation (sd) feature in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.09691" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0074_999_74999890_qa_5/task.toml b/tasks/0074_999_74999890_qa_5/task.toml index e503e4de6ced02efddc1cf089b981a3855911b57..58f48fa3877731d4335f19d197356e40fcc6caaf 100644 --- a/tasks/0074_999_74999890_qa_5/task.toml +++ b/tasks/0074_999_74999890_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0074_999_74999890_qa_5" +name = "smoldataenvs-train/0074_999_74999890_qa_5" description = "How many features were removed from the dataset due to high correlation or minimal difference in distributions between male and female labels?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_045_75045566_qa_3/task.toml b/tasks/0075_045_75045566_qa_3/task.toml index cef8395e4e491395b033b15646006218aca0d228..642b7a126ce4af6a61c1ef5a08c060acafbecdc9 100644 --- a/tasks/0075_045_75045566_qa_3/task.toml +++ b/tasks/0075_045_75045566_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0075_045_75045566_qa_3" +name = "smoldataenvs-train/0075_045_75045566_qa_3" description = "What is the percentage of imbalanced distribution in the diabetes outcome variable (0 = non-diabetic, 1 = diabetic)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "65% non-diabetic, 35% diabetic" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0075_061_75061101_qa_2/task.toml b/tasks/0075_061_75061101_qa_2/task.toml index 085022e58e0995b0b91bb136e5d1e115c8394329..817f72e9a98adc8c2e588ab16a29064cda3776f7 100644 --- a/tasks/0075_061_75061101_qa_2/task.toml +++ b/tasks/0075_061_75061101_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0075_061_75061101_qa_2" +name = "smoldataenvs-train/0075_061_75061101_qa_2" description = "What is the most frequently sold video game genre globally based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_061_75061101_qa_3/task.toml b/tasks/0075_061_75061101_qa_3/task.toml index 80c3ea6ec51bc13b1c07ef652db9dd39ed7a66c0..f1beb7d2196a8e1bab4d29a80cddd644e2a3fa1a 100644 --- a/tasks/0075_061_75061101_qa_3/task.toml +++ b/tasks/0075_061_75061101_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0075_061_75061101_qa_3" +name = "smoldataenvs-train/0075_061_75061101_qa_3" description = "Which manufacturer has achieved the highest cumulative global sales in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Nintendo" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_079_75079174_qa_4/task.toml b/tasks/0075_079_75079174_qa_4/task.toml index 8e655c89136c9d732bc4837d0acc00f7bad3936f..3ee3cb56c6458e60a41b429ecabaccb1e3611dbc 100644 --- a/tasks/0075_079_75079174_qa_4/task.toml +++ b/tasks/0075_079_75079174_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0075_079_75079174_qa_4" +name = "smoldataenvs-train/0075_079_75079174_qa_4" description = "What is the survival rate percentage for passengers with 8 siblings/spouses aboard (SibSp=8)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_081_75081757_qa_2/task.toml b/tasks/0075_081_75081757_qa_2/task.toml index 83afcf4a17fd44661ff981e44962906c65ae4c39..731b088ce8ef7b9305e02a90037a309bf53eb235 100644 --- a/tasks/0075_081_75081757_qa_2/task.toml +++ b/tasks/0075_081_75081757_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0075_081_75081757_qa_2" +name = "smoldataenvs-train/0075_081_75081757_qa_2" description = "By how much did the mean BMI value change after imputing missing values compared to the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.47" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_081_75081757_qa_3/task.toml b/tasks/0075_081_75081757_qa_3/task.toml index 351e036898ce59d801029e275440d2ec6ad51e83..266a75221ec96422b308b5d2c8ccc31dc08bedfd 100644 --- a/tasks/0075_081_75081757_qa_3/task.toml +++ b/tasks/0075_081_75081757_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0075_081_75081757_qa_3" +name = "smoldataenvs-train/0075_081_75081757_qa_3" description = "What is the correlation coefficient between Glucose levels and diabetes diagnosis (Outcome) in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.46" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0075_081_75081757_qa_4/task.toml b/tasks/0075_081_75081757_qa_4/task.toml index e739e651f601abe13ca7b1452dead1c5df73931a..4f461b974d9667f06d367bf75d3b809a066720f4 100644 --- a/tasks/0075_081_75081757_qa_4/task.toml +++ b/tasks/0075_081_75081757_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0075_081_75081757_qa_4" +name = "smoldataenvs-train/0075_081_75081757_qa_4" description = "What was the reduction in standard deviation of BloodPressure after replacing zero-values with mean imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7.26" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_094_75094397_qa_1/task.toml b/tasks/0075_094_75094397_qa_1/task.toml index 12fad207987b57360807449f0e4a5c13045aa101..04a52828f3da069432244eaaf6a268bbb0023431 100644 --- a/tasks/0075_094_75094397_qa_1/task.toml +++ b/tasks/0075_094_75094397_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0075_094_75094397_qa_1" +name = "smoldataenvs-train/0075_094_75094397_qa_1" description = "What percentage of the 'Item_Weight' column contained missing values before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "17.17" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_094_75094397_qa_3/task.toml b/tasks/0075_094_75094397_qa_3/task.toml index 8d9043ad343cdccf0ad3edaaa5d03652b75ccbe3..bb3924e84a7f27ae41b6a27448ea9ac1dd4a6678 100644 --- a/tasks/0075_094_75094397_qa_3/task.toml +++ b/tasks/0075_094_75094397_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0075_094_75094397_qa_3" +name = "smoldataenvs-train/0075_094_75094397_qa_3" description = "What is the mean value of the 'Item_Weight' feature after handling missing values through imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12.857645" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_108_75108038_qa_2/task.toml b/tasks/0075_108_75108038_qa_2/task.toml index 79b7e024ec5b0c259750b179ac35f8380221ca62..2386f69ebac17e5c50c9e9bd0f09190619020e36 100644 --- a/tasks/0075_108_75108038_qa_2/task.toml +++ b/tasks/0075_108_75108038_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0075_108_75108038_qa_2" +name = "smoldataenvs-train/0075_108_75108038_qa_2" description = "What is the overall accuracy of the model on the test set after early stopping in training?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.77" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0075_131_75131662_qa_1/task.toml b/tasks/0075_131_75131662_qa_1/task.toml index 03ccad14c72715f23361b6f7af52de4ea3d08859..90366eeed49beb8d5aab938ab5afda150c22c3ee 100644 --- a/tasks/0075_131_75131662_qa_1/task.toml +++ b/tasks/0075_131_75131662_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0075_131_75131662_qa_1" +name = "smoldataenvs-train/0075_131_75131662_qa_1" description = "Which feature had the highest maximum absolute z-score before outlier removal?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "fc" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_131_75131662_qa_5/task.toml b/tasks/0075_131_75131662_qa_5/task.toml index a7d4af0a7499aefbe38757d131e5a95cc1b6354b..18bc0b21e51fbccba018984d26c973cd66ad499b 100644 --- a/tasks/0075_131_75131662_qa_5/task.toml +++ b/tasks/0075_131_75131662_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0075_131_75131662_qa_5" +name = "smoldataenvs-train/0075_131_75131662_qa_5" description = "What is the maximum absolute z-score for the 'price_range' feature before outlier removal?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.3416407864998738" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_146_75146296_qa_4/task.toml b/tasks/0075_146_75146296_qa_4/task.toml index 35d8035abd205df096abb0cbec60a796b9908d2b..0f09a5f1e3e94be71de8627c2e852515de63724d 100644 --- a/tasks/0075_146_75146296_qa_4/task.toml +++ b/tasks/0075_146_75146296_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0075_146_75146296_qa_4" +name = "smoldataenvs-train/0075_146_75146296_qa_4" description = "How many features remained in the dataset after correlation-based feature selection was applied?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_148_75148017_qa_1/task.toml b/tasks/0075_148_75148017_qa_1/task.toml index e005d09c374e10d7248e17e9d23107ac99b7f8bb..3a699796a63e11cf716d47af853b7fa7ee1295b8 100644 --- a/tasks/0075_148_75148017_qa_1/task.toml +++ b/tasks/0075_148_75148017_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0075_148_75148017_qa_1" +name = "smoldataenvs-train/0075_148_75148017_qa_1" description = "Which phobia has the highest average score difference between females and males in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Spiders" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_148_75148017_qa_4/task.toml b/tasks/0075_148_75148017_qa_4/task.toml index 07005a2e6173b54cb680a9801a676c98db5f9c42..1fc288111a5069f6269dc8ff8cf47d819399973b 100644 --- a/tasks/0075_148_75148017_qa_4/task.toml +++ b/tasks/0075_148_75148017_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0075_148_75148017_qa_4" +name = "smoldataenvs-train/0075_148_75148017_qa_4" description = "What is the average score for \"Fear of Storms\" among females in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.25" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_174_75174860_qa_1/task.toml b/tasks/0075_174_75174860_qa_1/task.toml index e932f8dceda7c3ab234927eb6978a2600d432779..91d224aa9d6e9e255b7c91198b61346050a83ea3 100644 --- a/tasks/0075_174_75174860_qa_1/task.toml +++ b/tasks/0075_174_75174860_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0075_174_75174860_qa_1" +name = "smoldataenvs-train/0075_174_75174860_qa_1" description = "What is the interquartile range (IQR) for BloodPressure in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "18" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_174_75174860_qa_2/task.toml b/tasks/0075_174_75174860_qa_2/task.toml index ad6062ce268eb05c6f0aab3bc1f5b15d07c26338..d420c4bf586c0f92b8cc5793fdebcdd9b563bd13 100644 --- a/tasks/0075_174_75174860_qa_2/task.toml +++ b/tasks/0075_174_75174860_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0075_174_75174860_qa_2" +name = "smoldataenvs-train/0075_174_75174860_qa_2" description = "What is the difference between the 75th percentile and the mean value of BMI in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.61" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0075_174_75174860_qa_3/task.toml b/tasks/0075_174_75174860_qa_3/task.toml index 7ba0adfdf8c5da4c7c383ba8c0eb89a9b74b6cd6..0341da41895e80f81fbab543b0ef699aa060ad50 100644 --- a/tasks/0075_174_75174860_qa_3/task.toml +++ b/tasks/0075_174_75174860_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0075_174_75174860_qa_3" +name = "smoldataenvs-train/0075_174_75174860_qa_3" description = "What is the interquartile range (IQR) for SkinThickness in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "32" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_210_75210599_qa_3/task.toml b/tasks/0075_210_75210599_qa_3/task.toml index 68ef70229c2ebe9d8af95c1bd89c937d32592174..d8207e6cccc56acdd60a59c5ee2cdf544e859264 100644 --- a/tasks/0075_210_75210599_qa_3/task.toml +++ b/tasks/0075_210_75210599_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0075_210_75210599_qa_3" +name = "smoldataenvs-train/0075_210_75210599_qa_3" description = "How many features in the dataset have a Variance Inflation Factor (VIF) exceeding 10,000, indicating extreme multicollinearity?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0075_282_75282744_qa_2/task.toml b/tasks/0075_282_75282744_qa_2/task.toml index e896fb9662b1cd66985f0179fa96e0cbfd585e9f..5ea6e6776b2818d154ec06d4139d86e3de218973 100644 --- a/tasks/0075_282_75282744_qa_2/task.toml +++ b/tasks/0075_282_75282744_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0075_282_75282744_qa_2" +name = "smoldataenvs-train/0075_282_75282744_qa_2" description = "What is the mean alcohol content for wines classified as good quality (1) in the transformed dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11.465913" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_365_75365358_qa_5/task.toml b/tasks/0075_365_75365358_qa_5/task.toml index e0eb037de693954b6d8df43b6c90e687aee0b5b6..f58a1c792b5b8526a25433c96366ef0622632f64 100644 --- a/tasks/0075_365_75365358_qa_5/task.toml +++ b/tasks/0075_365_75365358_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0075_365_75365358_qa_5" +name = "smoldataenvs-train/0075_365_75365358_qa_5" description = "What percentage of animes have missing ratings in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.870831" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_379_75379398_qa_2/task.toml b/tasks/0075_379_75379398_qa_2/task.toml index 37c27c95677c215e25f3b40c2fdecd50d6a76ba9..1e2ab6356c0598cccc7181046f51261091502795 100644 --- a/tasks/0075_379_75379398_qa_2/task.toml +++ b/tasks/0075_379_75379398_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0075_379_75379398_qa_2" +name = "smoldataenvs-train/0075_379_75379398_qa_2" description = "Which age group had the highest total recorded suicide cases during the 12-year period analyzed?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "15-29" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_391_75391350_qa_3/task.toml b/tasks/0075_391_75391350_qa_3/task.toml index 3ad89634b891db64e055186f1f656ff66cceb6eb..24c355a27c9fa588205e5eb4bb49b7b2e0b551e3 100644 --- a/tasks/0075_391_75391350_qa_3/task.toml +++ b/tasks/0075_391_75391350_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0075_391_75391350_qa_3" +name = "smoldataenvs-train/0075_391_75391350_qa_3" description = "Which movie has the highest overall score in the top-rated list?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "The Shawshank Redemption" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_402_75402843_qa_3/task.toml b/tasks/0075_402_75402843_qa_3/task.toml index 93180906a6bc2fa9f7cf721411f53f58f850fad8..c6c019c5f7b2deb207b027723627b0896be681b2 100644 --- a/tasks/0075_402_75402843_qa_3/task.toml +++ b/tasks/0075_402_75402843_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0075_402_75402843_qa_3" +name = "smoldataenvs-train/0075_402_75402843_qa_3" description = "Which brand appears most frequently in the Top Ten Ramen list across all years?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Prima Taste" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_402_75402843_qa_5/task.toml b/tasks/0075_402_75402843_qa_5/task.toml index 9cda56ca1087ce5d9884a7718acb348230dbb6b7..c9656c01e506851b721cbc2f1c795b87b366dd59 100644 --- a/tasks/0075_402_75402843_qa_5/task.toml +++ b/tasks/0075_402_75402843_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0075_402_75402843_qa_5" +name = "smoldataenvs-train/0075_402_75402843_qa_5" description = "Which year had the highest number of ramen products listed in the Top Ten?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2015" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_543_75543417_qa_5/task.toml b/tasks/0075_543_75543417_qa_5/task.toml index 2c156acacc2ba28294e345ecc7265921c7533775..ee9b2565e69db2fc6102cfbe5e3b7ada886e1c66 100644 --- a/tasks/0075_543_75543417_qa_5/task.toml +++ b/tasks/0075_543_75543417_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0075_543_75543417_qa_5" +name = "smoldataenvs-train/0075_543_75543417_qa_5" description = "What is the highest global sales value recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "82.74" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0075_568_75568531_qa_3/task.toml b/tasks/0075_568_75568531_qa_3/task.toml index 085c43c9715b1e9c8deda1164a663c4b62530ae6..1ee7c078881e4e5ca6f4b467c97bb146fb7cefe6 100644 --- a/tasks/0075_568_75568531_qa_3/task.toml +++ b/tasks/0075_568_75568531_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0075_568_75568531_qa_3" +name = "smoldataenvs-train/0075_568_75568531_qa_3" description = "What is the average number of open accounts held by customers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11.14" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0075_579_75579600_qa_1/task.toml b/tasks/0075_579_75579600_qa_1/task.toml index 1cf5249935de6ea04e411df750ae8d1e05f81381..7e9d53c5193a32bb575787f2dd04f9a505b49ed7 100644 --- a/tasks/0075_579_75579600_qa_1/task.toml +++ b/tasks/0075_579_75579600_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0075_579_75579600_qa_1" +name = "smoldataenvs-train/0075_579_75579600_qa_1" description = "What is the highest correlation between any two features in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.96" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_579_75579600_qa_3/task.toml b/tasks/0075_579_75579600_qa_3/task.toml index bfba616abcf590186562ade62fac57e0439e0d63..0da5eee1e9540e6bf92af6300194c3664f419798 100644 --- a/tasks/0075_579_75579600_qa_3/task.toml +++ b/tasks/0075_579_75579600_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0075_579_75579600_qa_3" +name = "smoldataenvs-train/0075_579_75579600_qa_3" description = "How many samples are present in each species category of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_579_75579600_qa_4/task.toml b/tasks/0075_579_75579600_qa_4/task.toml index d8818a6979fd686c3d8c52b52abd13715c87843c..c2324918ef571014e19ffebc892d48fce7047063 100644 --- a/tasks/0075_579_75579600_qa_4/task.toml +++ b/tasks/0075_579_75579600_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0075_579_75579600_qa_4" +name = "smoldataenvs-train/0075_579_75579600_qa_4" description = "Which species has the smallest average petal length based on the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-setosa" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_579_75579600_qa_5/task.toml b/tasks/0075_579_75579600_qa_5/task.toml index a263b5fd587f51d01c169034a2d900d765e58b6c..1cc52ad71a64fd98bae54f670b592b0eed1765c1 100644 --- a/tasks/0075_579_75579600_qa_5/task.toml +++ b/tasks/0075_579_75579600_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0075_579_75579600_qa_5" +name = "smoldataenvs-train/0075_579_75579600_qa_5" description = "What is the standard deviation of sepal width in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.43" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0075_648_75648287_qa_1/task.toml b/tasks/0075_648_75648287_qa_1/task.toml index c35044384681590e7a69139f7f4141db90727a0d..11427d73d08a5af50dd73f5ae92c553088c3b254 100644 --- a/tasks/0075_648_75648287_qa_1/task.toml +++ b/tasks/0075_648_75648287_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0075_648_75648287_qa_1" +name = "smoldataenvs-train/0075_648_75648287_qa_1" description = "What is the difference in churn rates between senior customers and non-senior customers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.180751" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_744_75744297_qa_3/task.toml b/tasks/0075_744_75744297_qa_3/task.toml index 8c7ffa8f8ae6ed7f796ec5581bdb66cceffbf0c7..8607bafac4f3038e62a67dfe7a512b2f46fcd93a 100644 --- a/tasks/0075_744_75744297_qa_3/task.toml +++ b/tasks/0075_744_75744297_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0075_744_75744297_qa_3" +name = "smoldataenvs-train/0075_744_75744297_qa_3" description = "What is the difference in test accuracy between the Decision Tree model trained on unscaled data (0.6275) and the same model trained on Robust Scaled data (0.55)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.0775" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0075_759_75759898_qa_1/task.toml b/tasks/0075_759_75759898_qa_1/task.toml index 1aadb0932bbc12f1b55886c7b26b69411b37daa8..b187ab99d7b78040dada721d34164ef6c31f1df4 100644 --- a/tasks/0075_759_75759898_qa_1/task.toml +++ b/tasks/0075_759_75759898_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0075_759_75759898_qa_1" +name = "smoldataenvs-train/0075_759_75759898_qa_1" description = "What is the total revenue loss incurred by the telecom company due to customer churn in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2862927.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_765_75765515_qa_5/task.toml b/tasks/0075_765_75765515_qa_5/task.toml index c6139d5b9db3f3ae15729f89dc485c5b7a050955..52494cc333ba5d76aa878b2a9d4c225088105c80 100644 --- a/tasks/0075_765_75765515_qa_5/task.toml +++ b/tasks/0075_765_75765515_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0075_765_75765515_qa_5" +name = "smoldataenvs-train/0075_765_75765515_qa_5" description = "What is the average Age of diabetic patients in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "37.07" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_769_75769467_qa_2/task.toml b/tasks/0075_769_75769467_qa_2/task.toml index 0dd05709a736e1bb1b8e21790ad4786c9cd3b0bb..b2af415844264e38803fd44275286f0f1a5693ab 100644 --- a/tasks/0075_769_75769467_qa_2/task.toml +++ b/tasks/0075_769_75769467_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0075_769_75769467_qa_2" +name = "smoldataenvs-train/0075_769_75769467_qa_2" description = "Which feature has the highest mean value among diabetic patients?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_813_75813284_qa_5/task.toml b/tasks/0075_813_75813284_qa_5/task.toml index 8af861043cc180d6078cfa90ac8e968e994e659a..4092fe692f467a77afa11bd3d4bd125385ef2f32 100644 --- a/tasks/0075_813_75813284_qa_5/task.toml +++ b/tasks/0075_813_75813284_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0075_813_75813284_qa_5" +name = "smoldataenvs-train/0075_813_75813284_qa_5" description = "What is the percentage difference in no-show rates between genders in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.8% higher no-show rate for females compared to males" reward_mode_initial = "flexible" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_886_75886496_qa_2/task.toml b/tasks/0075_886_75886496_qa_2/task.toml index dde88ce8450b696c0a36f64508545dc89ffca1aa..b93da26f67cb9c8a8be2beaed78543035fc8196d 100644 --- a/tasks/0075_886_75886496_qa_2/task.toml +++ b/tasks/0075_886_75886496_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0075_886_75886496_qa_2" +name = "smoldataenvs-train/0075_886_75886496_qa_2" description = "Which payment method is associated with the highest churn rate among customers?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic check" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_890_75890673_qa_1/task.toml b/tasks/0075_890_75890673_qa_1/task.toml index bd0f8f3cd97d834e542db7c9f139d388eb2dbe49..813fb798f03220d607b8646ec58169c529f9b991 100644 --- a/tasks/0075_890_75890673_qa_1/task.toml +++ b/tasks/0075_890_75890673_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0075_890_75890673_qa_1" +name = "smoldataenvs-train/0075_890_75890673_qa_1" description = "What is the highest validation accuracy achieved by the logistic regression model when varying prediction thresholds between 0 and 1?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.803" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0075_890_75890673_qa_3/task.toml b/tasks/0075_890_75890673_qa_3/task.toml index 8c54cbbd9d19d2a7afcfee6b57d620f361d23724..2698f8a0fd0ac284c8ef2ade33804a0ac2e743ce 100644 --- a/tasks/0075_890_75890673_qa_3/task.toml +++ b/tasks/0075_890_75890673_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0075_890_75890673_qa_3" +name = "smoldataenvs-train/0075_890_75890673_qa_3" description = "What percentage of the validation set predictions were true negatives according to the confusion matrix in percentage form?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "65" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0075_890_75890673_qa_4/task.toml b/tasks/0075_890_75890673_qa_4/task.toml index 930acdde900744d4c9847ec0a888c26e52dbabbb..b2fa17e154f16b23c18439093bf90a53df7f8259 100644 --- a/tasks/0075_890_75890673_qa_4/task.toml +++ b/tasks/0075_890_75890673_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0075_890_75890673_qa_4" +name = "smoldataenvs-train/0075_890_75890673_qa_4" description = "What is the proportion of churned customers in the full dataset (before train/validation/test split)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26.54" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0075_984_75984285_qa_1/task.toml b/tasks/0075_984_75984285_qa_1/task.toml index ac2ef2581ed2fa38f4dd38e568413b3085202926..2b35e1f6e6e6da5b7735cf20e14e744a940b9608 100644 --- a/tasks/0075_984_75984285_qa_1/task.toml +++ b/tasks/0075_984_75984285_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0075_984_75984285_qa_1" +name = "smoldataenvs-train/0075_984_75984285_qa_1" description = "What percentage of missing values were present in the 'Credit_History' column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8.14" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0076_053_76053971_qa_3/task.toml b/tasks/0076_053_76053971_qa_3/task.toml index a715ba5abdc0409578d360aa49947ac5fc9186fb..07f7f04cf41807f0bddc7460354d904ce8cba361 100644 --- a/tasks/0076_053_76053971_qa_3/task.toml +++ b/tasks/0076_053_76053971_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0076_053_76053971_qa_3" +name = "smoldataenvs-train/0076_053_76053971_qa_3" description = "Which class has the lowest F1-score in the cross-validation classification report?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "good" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0076_072_76072290_qa_1/task.toml b/tasks/0076_072_76072290_qa_1/task.toml index 7f2f8d144c544bcbac352abf7da4fc11115adf6b..366d97545bc0846485f9cbfa6979cb3220f2807b 100644 --- a/tasks/0076_072_76072290_qa_1/task.toml +++ b/tasks/0076_072_76072290_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0076_072_76072290_qa_1" +name = "smoldataenvs-train/0076_072_76072290_qa_1" description = "Which classification model achieved the highest mean cross-validation accuracy among the base models (LDA, KNN, DT, GB, SVC)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "LDA" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0076_072_76072290_qa_5/task.toml b/tasks/0076_072_76072290_qa_5/task.toml index 707c2318e9f61945e3d4036f020f5943b06cd01c..a089c306a8838e46cdb1f524d5f91b3f4378e6b3 100644 --- a/tasks/0076_072_76072290_qa_5/task.toml +++ b/tasks/0076_072_76072290_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0076_072_76072290_qa_5" +name = "smoldataenvs-train/0076_072_76072290_qa_5" description = "Which model (base model or tuned ensemble) demonstrated the highest overall performance in terms of accuracy on the hold-out test data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Tuned Random Forest" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0076_096_76096011_qa_4/task.toml b/tasks/0076_096_76096011_qa_4/task.toml index 6d2255a6667960006168de18af64e0069dbf2906..f2164a2f130a795159b7c6bcb6c7bfb07fb4119e 100644 --- a/tasks/0076_096_76096011_qa_4/task.toml +++ b/tasks/0076_096_76096011_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0076_096_76096011_qa_4" +name = "smoldataenvs-train/0076_096_76096011_qa_4" description = "What is the proportion of clients who did not default compared to those who defaulted in the dataset, as shown in the exploratory data analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "77% non-default, 23% default" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0076_103_76103911_qa_4/task.toml b/tasks/0076_103_76103911_qa_4/task.toml index c10f876900a736e36607666e3432de65d7c7d005..3d3a7f311d5ab9cdb7f364f45ff722850a927026 100644 --- a/tasks/0076_103_76103911_qa_4/task.toml +++ b/tasks/0076_103_76103911_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0076_103_76103911_qa_4" +name = "smoldataenvs-train/0076_103_76103911_qa_4" description = "What is the ratio of the number of samples in the full training set to the test set during model evaluation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4:1" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0076_177_76177455_qa_1/task.toml b/tasks/0076_177_76177455_qa_1/task.toml index b02c3133333454adbff586b2f5275d26244f6db7..e13af3f942a038026b1b81057b55642b0e50cc13 100644 --- a/tasks/0076_177_76177455_qa_1/task.toml +++ b/tasks/0076_177_76177455_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0076_177_76177455_qa_1" +name = "smoldataenvs-train/0076_177_76177455_qa_1" description = "Which year of establishment had the highest sales volume according to the countplot in the exploratory data analysis section?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1985" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0076_177_76177455_qa_3/task.toml b/tasks/0076_177_76177455_qa_3/task.toml index 890fe0ccade7eeff24eeb528c9acd3d98a04407f..6fcc7ae64f6ad0009da543e5b166c2d851fa9afd 100644 --- a/tasks/0076_177_76177455_qa_3/task.toml +++ b/tasks/0076_177_76177455_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0076_177_76177455_qa_3" +name = "smoldataenvs-train/0076_177_76177455_qa_3" description = "What is the most common item fat content category after preprocessing and data cleaning operations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Low Fat" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0076_236_76236428_qa_2/task.toml b/tasks/0076_236_76236428_qa_2/task.toml index b1efd6aa71c5a993336bcf8f3e33e56ab3742e72..bda34de6d5bc422df513986e922eb8f2315137b4 100644 --- a/tasks/0076_236_76236428_qa_2/task.toml +++ b/tasks/0076_236_76236428_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0076_236_76236428_qa_2" +name = "smoldataenvs-train/0076_236_76236428_qa_2" description = "What percentage of the original dataset represents individuals with diabetes (Outcome=1) before any data preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.9" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0076_236_76236428_qa_5/task.toml b/tasks/0076_236_76236428_qa_5/task.toml index 5414a3eb4047334a068f93eb462201ed0e79c27e..e441b4c49656704ea54a5c3bec42a611ddfa00e9 100644 --- a/tasks/0076_236_76236428_qa_5/task.toml +++ b/tasks/0076_236_76236428_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0076_236_76236428_qa_5" +name = "smoldataenvs-train/0076_236_76236428_qa_5" description = "What is the total count of missing values in the 'Insulin' feature before performing data imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "374" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0076_259_76259625_qa_4/task.toml b/tasks/0076_259_76259625_qa_4/task.toml index 15c589dff15c4be5ebaf5026ed67eb8d55191118..71cff2866aa702f8d54094e870bb1060392aaf2e 100644 --- a/tasks/0076_259_76259625_qa_4/task.toml +++ b/tasks/0076_259_76259625_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0076_259_76259625_qa_4" +name = "smoldataenvs-train/0076_259_76259625_qa_4" description = "How many hazardous asteroids were misclassified as non-hazardous in the test set predictions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0076_273_76273931_qa_3/task.toml b/tasks/0076_273_76273931_qa_3/task.toml index 7c8fbaaf628df2f349a95dc10704e865e807033f..219e8cba06aa891024c16c1581fa3d4f2515a30d 100644 --- a/tasks/0076_273_76273931_qa_3/task.toml +++ b/tasks/0076_273_76273931_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0076_273_76273931_qa_3" +name = "smoldataenvs-train/0076_273_76273931_qa_3" description = "Which restaurant has the highest average food rating but the lowest overall rating and service rating?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "little pizza Emilio Portes Gil" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0076_273_76273931_qa_4/task.toml b/tasks/0076_273_76273931_qa_4/task.toml index 93c39f22e9330710e3d35f020aea3b4eb7c6d3c7..3163ca4d21f6f8c4d3029ea3d800aa2219ce17ff 100644 --- a/tasks/0076_273_76273931_qa_4/task.toml +++ b/tasks/0076_273_76273931_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0076_273_76273931_qa_4" +name = "smoldataenvs-train/0076_273_76273931_qa_4" description = "What are the top 2 user IDs who have rated the most restaurants in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "U1106, U1061" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0076_274_76274848_qa_3/task.toml b/tasks/0076_274_76274848_qa_3/task.toml index dd9ac5797836da89a06fd923ab599f4ba746049f..17382381c3707d23dbb122058dff7a5c209763ff 100644 --- a/tasks/0076_274_76274848_qa_3/task.toml +++ b/tasks/0076_274_76274848_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0076_274_76274848_qa_3" +name = "smoldataenvs-train/0076_274_76274848_qa_3" description = "What is the maximum Defense value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "230" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0076_274_76274848_qa_5/task.toml b/tasks/0076_274_76274848_qa_5/task.toml index ad9e3171684286f423022ca24832d37801adf825..fff9ae1fd31bb9e06a7cf55a731e3f02065e1a2e 100644 --- a/tasks/0076_274_76274848_qa_5/task.toml +++ b/tasks/0076_274_76274848_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0076_274_76274848_qa_5" +name = "smoldataenvs-train/0076_274_76274848_qa_5" description = "What is the most common Type 1 category among Pokémon with Defense greater than 200?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Steel" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0076_296_76296066_qa_2/task.toml b/tasks/0076_296_76296066_qa_2/task.toml index 6ce19a251b94fe567f3330f96e16e37523e5c13d..23806677feb06f2b70c77cbb0a1b34f42bbf0105 100644 --- a/tasks/0076_296_76296066_qa_2/task.toml +++ b/tasks/0076_296_76296066_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0076_296_76296066_qa_2" +name = "smoldataenvs-train/0076_296_76296066_qa_2" description = "Is the variance of the ISE time series constant between the first and second halves of the dataset based on the computed variances?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0076_298_76298846_qa_3/task.toml b/tasks/0076_298_76298846_qa_3/task.toml index 8f0f9bdcef52636a1789a803a96c573db2d70115..e100ecd8021117d60c8f43f80bdeb69862a6b6a8 100644 --- a/tasks/0076_298_76298846_qa_3/task.toml +++ b/tasks/0076_298_76298846_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0076_298_76298846_qa_3" +name = "smoldataenvs-train/0076_298_76298846_qa_3" description = "How many missing values were present in the 'horsepower' column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0076_310_76310705_qa_1/task.toml b/tasks/0076_310_76310705_qa_1/task.toml index 99f03f77b7eb4c9c2fc327ed58fb3d46012e6257..e9f31bb8bb6256339beaeac210567a301ab8485d 100644 --- a/tasks/0076_310_76310705_qa_1/task.toml +++ b/tasks/0076_310_76310705_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0076_310_76310705_qa_1" +name = "smoldataenvs-train/0076_310_76310705_qa_1" description = "Which feature in the dataset has the highest percentage of missing values after replacing zero values with NaN?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Insulin" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0076_310_76310705_qa_3/task.toml b/tasks/0076_310_76310705_qa_3/task.toml index 873370209f64397c60a8da7234e5e9410ea743dd..235ee9091e77e3d21a0a17a19ba7fc775dabc568 100644 --- a/tasks/0076_310_76310705_qa_3/task.toml +++ b/tasks/0076_310_76310705_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0076_310_76310705_qa_3" +name = "smoldataenvs-train/0076_310_76310705_qa_3" description = "Which feature shows the strongest positive correlation with the diabetes outcome variable according to the correlation analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0076_310_76310922_qa_1/task.toml b/tasks/0076_310_76310922_qa_1/task.toml index 056165da249162d68e052487250bd2aecfd4d69f..5f35e2f1327755dd595d29a335ca968b4c142a16 100644 --- a/tasks/0076_310_76310922_qa_1/task.toml +++ b/tasks/0076_310_76310922_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0076_310_76310922_qa_1" +name = "smoldataenvs-train/0076_310_76310922_qa_1" description = "Which item type has the highest average sales after outlier removal and data imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Seafood" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0076_310_76310922_qa_2/task.toml b/tasks/0076_310_76310922_qa_2/task.toml index 68d6532ac5c0b7572b391c4840bb8e8705208894..dbdacbf8f79a23f97402caf91d8daa18ce84d731 100644 --- a/tasks/0076_310_76310922_qa_2/task.toml +++ b/tasks/0076_310_76310922_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0076_310_76310922_qa_2" +name = "smoldataenvs-train/0076_310_76310922_qa_2" description = "What percentage of missing values existed in the 'Outlet_Size' column before imputation in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "28.276428" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0076_340_76340399_qa_5/task.toml b/tasks/0076_340_76340399_qa_5/task.toml index 47b0c1583e43513caeb40e07181f616d7e4f2c4d..34ca8c1c074caa60164348e4711552f53d5538ef 100644 --- a/tasks/0076_340_76340399_qa_5/task.toml +++ b/tasks/0076_340_76340399_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0076_340_76340399_qa_5" +name = "smoldataenvs-train/0076_340_76340399_qa_5" description = "Which feature shows the greatest interquartile range (IQR = 75th percentile - 25th percentile) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "area_worst" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0076_359_76359024_qa_1/task.toml b/tasks/0076_359_76359024_qa_1/task.toml index 440af6fffc6f9400cdbeb13df0d16b36e7c27e98..de198f1646ba9e00e0962b3bbf7dd2f0ff886c35 100644 --- a/tasks/0076_359_76359024_qa_1/task.toml +++ b/tasks/0076_359_76359024_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0076_359_76359024_qa_1" +name = "smoldataenvs-train/0076_359_76359024_qa_1" description = "Which diagnostic measurement column in the dataset has the highest number of missing values after replacing zeros with NaN values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Insulin" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0076_410_76410509_qa_2/task.toml b/tasks/0076_410_76410509_qa_2/task.toml index 33993856085ed92b2f176410c4f6e9d791fcc51d..f89023ebadfdc6f1a9845a60b51997f6be043b77 100644 --- a/tasks/0076_410_76410509_qa_2/task.toml +++ b/tasks/0076_410_76410509_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0076_410_76410509_qa_2" +name = "smoldataenvs-train/0076_410_76410509_qa_2" description = "Which feature has the highest importance score in the Random Forest model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "safety" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0076_501_76501548_qa_3/task.toml b/tasks/0076_501_76501548_qa_3/task.toml index edf93cd631d88a6d7c9925e8be297cd6e976865e..25e8dcb712f0ba2c8e1832b504bebc11247635eb 100644 --- a/tasks/0076_501_76501548_qa_3/task.toml +++ b/tasks/0076_501_76501548_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0076_501_76501548_qa_3" +name = "smoldataenvs-train/0076_501_76501548_qa_3" description = "What is the most common outlet size for \"Supermarket Type1\" after preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Small" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0076_540_76540106_qa_1/task.toml b/tasks/0076_540_76540106_qa_1/task.toml index 04b3e035638b982ceaa021b44951efcf9d9ab59c..2c389b0e3a164078d7b145775e9ce1dcb8ea1d75 100644 --- a/tasks/0076_540_76540106_qa_1/task.toml +++ b/tasks/0076_540_76540106_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0076_540_76540106_qa_1" +name = "smoldataenvs-train/0076_540_76540106_qa_1" description = "Which feature shows the highest positive correlation with the wine quality rating in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "alcohol" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0076_552_76552424_qa_2/task.toml b/tasks/0076_552_76552424_qa_2/task.toml index 44ab13b7c2e68ffce9fb80f3f22c6fff66dc540b..07e25a5f87d2c29e22d1040c03e0adf9c750ccbe 100644 --- a/tasks/0076_552_76552424_qa_2/task.toml +++ b/tasks/0076_552_76552424_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0076_552_76552424_qa_2" +name = "smoldataenvs-train/0076_552_76552424_qa_2" description = "Which payment method has the highest average monthly charges?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic check" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0076_739_76739870_qa_2/task.toml b/tasks/0076_739_76739870_qa_2/task.toml index 627740c55288026dbd3f5ef0bb97f9fd3c527509..587b4138b1c487235c26b17db232f6a45bd8be73 100644 --- a/tasks/0076_739_76739870_qa_2/task.toml +++ b/tasks/0076_739_76739870_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0076_739_76739870_qa_2" +name = "smoldataenvs-train/0076_739_76739870_qa_2" description = "Which feature has the highest positive correlation with the Churn target variable in the processed dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Contract_Month-to-month" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0076_739_76739870_qa_4/task.toml b/tasks/0076_739_76739870_qa_4/task.toml index 3a36b2cd06152d8a8bab7f8b6d796a35bb304e10..3679c4e7ed28105ea526c34908b9774970ff9184 100644 --- a/tasks/0076_739_76739870_qa_4/task.toml +++ b/tasks/0076_739_76739870_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0076_739_76739870_qa_4" +name = "smoldataenvs-train/0076_739_76739870_qa_4" description = "What is the highest AUC score achieved by any of the five machine learning models evaluated in the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.8489" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0076_740_76740114_qa_3/task.toml b/tasks/0076_740_76740114_qa_3/task.toml index 87acb2e033c587750813eef0093106367b40bcc4..9aa5743c7c8a6d9ab5dbd090c05213ec801090a3 100644 --- a/tasks/0076_740_76740114_qa_3/task.toml +++ b/tasks/0076_740_76740114_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0076_740_76740114_qa_3" +name = "smoldataenvs-train/0076_740_76740114_qa_3" description = "What percentage of patients in the dataset have a positive biopsy result (Biopsy=1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0076_746_76746181_qa_2/task.toml b/tasks/0076_746_76746181_qa_2/task.toml index 0e0cc4281a54349e2674b2fa8aca6a494c9393f5..4bae48c7abf00819c5216d73b0f608ea679432b3 100644 --- a/tasks/0076_746_76746181_qa_2/task.toml +++ b/tasks/0076_746_76746181_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0076_746_76746181_qa_2" +name = "smoldataenvs-train/0076_746_76746181_qa_2" description = "What is the median age of users in the dataset and what is the interquartile range (IQR)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "37, 16.25" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0076_758_76758480_qa_1/task.toml b/tasks/0076_758_76758480_qa_1/task.toml index 8175ad030252f04e1e587d709eea8231aaad83fb..f0dde86f797a09e48b23b6104bb9a3a5f28e7dec 100644 --- a/tasks/0076_758_76758480_qa_1/task.toml +++ b/tasks/0076_758_76758480_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0076_758_76758480_qa_1" +name = "smoldataenvs-train/0076_758_76758480_qa_1" description = "What is the total number of unique categories across all categorical features in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "22" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0076_770_76770209_qa_1/task.toml b/tasks/0076_770_76770209_qa_1/task.toml index 7c4a3c466072f78cefd9f174da92e8ea577086be..4cc853ace40d05ce27ae39e2de0c4ecd3ee549f5 100644 --- a/tasks/0076_770_76770209_qa_1/task.toml +++ b/tasks/0076_770_76770209_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0076_770_76770209_qa_1" +name = "smoldataenvs-train/0076_770_76770209_qa_1" description = "What is the overall churn rate percentage in the Telco customer dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26.5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0076_770_76770209_qa_3/task.toml b/tasks/0076_770_76770209_qa_3/task.toml index 2d230470fae46b61facb26dce07b56139a6c8909..2222e1279ee2c9c79f032a00a26ffbd1bc84850e 100644 --- a/tasks/0076_770_76770209_qa_3/task.toml +++ b/tasks/0076_770_76770209_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0076_770_76770209_qa_3" +name = "smoldataenvs-train/0076_770_76770209_qa_3" description = "Which internet service type has the lowest churn rate in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "No" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0076_770_76770209_qa_4/task.toml b/tasks/0076_770_76770209_qa_4/task.toml index 255d3cbb5e2396c0bba683dc6bc67771ccb147da..e348e911e3664fc210f828f8833088495646bae8 100644 --- a/tasks/0076_770_76770209_qa_4/task.toml +++ b/tasks/0076_770_76770209_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0076_770_76770209_qa_4" +name = "smoldataenvs-train/0076_770_76770209_qa_4" description = "Which payment method has the highest churn rate in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic check" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0076_883_76883860_qa_2/task.toml b/tasks/0076_883_76883860_qa_2/task.toml index 532690a623a2d64d6c1810fa5f435a72386541fd..4c5d2fd8c353ef55fbf3b6e884aa5e0c0a505a4d 100644 --- a/tasks/0076_883_76883860_qa_2/task.toml +++ b/tasks/0076_883_76883860_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0076_883_76883860_qa_2" +name = "smoldataenvs-train/0076_883_76883860_qa_2" description = "What is the earliest date included in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2006-01-03" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0077_004_77004274_qa_1/task.toml b/tasks/0077_004_77004274_qa_1/task.toml index b706956cd2d24c0b555d275148ce05712b7745c2..cc124a0772834f3e8519a37763e831a83bfa30b0 100644 --- a/tasks/0077_004_77004274_qa_1/task.toml +++ b/tasks/0077_004_77004274_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0077_004_77004274_qa_1" +name = "smoldataenvs-train/0077_004_77004274_qa_1" description = "What is the optimal number of clusters based on the elbow method for KMeans clustering applied to Annual Income and Spending Score?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0077_004_77004274_qa_5/task.toml b/tasks/0077_004_77004274_qa_5/task.toml index 213060082b44d91d408b1f0989ff3593615ca114..232be6b61715e715ab00d0bd5cf327f2f9ba7a9b 100644 --- a/tasks/0077_004_77004274_qa_5/task.toml +++ b/tasks/0077_004_77004274_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0077_004_77004274_qa_5" +name = "smoldataenvs-train/0077_004_77004274_qa_5" description = "What is the average Annual Income of the cluster with the highest number of customers when using 5 clusters?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "58.30" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0077_006_77006094_qa_1/task.toml b/tasks/0077_006_77006094_qa_1/task.toml index 999e108957826f6129b6f30aa024ac0b16359897..02709dc478ff82794461d77ccf5d38654b4fdf71 100644 --- a/tasks/0077_006_77006094_qa_1/task.toml +++ b/tasks/0077_006_77006094_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0077_006_77006094_qa_1" +name = "smoldataenvs-train/0077_006_77006094_qa_1" description = "Which feature has the strongest negative impact on housing prices according to the trained linear regression model's coefficients?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "NOX" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0077_100_77100984_qa_2/task.toml b/tasks/0077_100_77100984_qa_2/task.toml index 0dcc1e2cc927c78f681b45aee700d8a548ea7f87..326148bcfb98c16ac0d1558af52f24781bbfe71d 100644 --- a/tasks/0077_100_77100984_qa_2/task.toml +++ b/tasks/0077_100_77100984_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0077_100_77100984_qa_2" +name = "smoldataenvs-train/0077_100_77100984_qa_2" description = "What are the median insulin levels for non-diabetic and diabetic individuals in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "102.5, 169.5" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0077_100_77100984_qa_3/task.toml b/tasks/0077_100_77100984_qa_3/task.toml index f826b7627e38246b660ab2aea8f809f085183149..dc4977d337195810be1c1a32b168393cf3d7f23a 100644 --- a/tasks/0077_100_77100984_qa_3/task.toml +++ b/tasks/0077_100_77100984_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0077_100_77100984_qa_3" +name = "smoldataenvs-train/0077_100_77100984_qa_3" description = "What is the highest correlation between any two variables in the dataset, excluding perfect correlations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.566" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0077_130_77130970_qa_5/task.toml b/tasks/0077_130_77130970_qa_5/task.toml index 47871afd6a3bc78ace2e2e1ae001e4d5cd233afe..aef8807cead739c8b31317833eda8fa0752f7809 100644 --- a/tasks/0077_130_77130970_qa_5/task.toml +++ b/tasks/0077_130_77130970_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0077_130_77130970_qa_5" +name = "smoldataenvs-train/0077_130_77130970_qa_5" description = "Which feature exhibited the most distinct distribution patterns between the two quality classes (0 vs. 1) in the pairplot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "alcohol" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0077_133_77133974_qa_4/task.toml b/tasks/0077_133_77133974_qa_4/task.toml index 318e3822a16814342a3927e605114664bec35c7f..f37ba87ed133dd8272521a4457446410c1450817 100644 --- a/tasks/0077_133_77133974_qa_4/task.toml +++ b/tasks/0077_133_77133974_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0077_133_77133974_qa_4" +name = "smoldataenvs-train/0077_133_77133974_qa_4" description = "Which job group has the highest average weekly earnings among the analyzed job categories?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "COMPUTATIONAL" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0077_155_77155673_qa_4/task.toml b/tasks/0077_155_77155673_qa_4/task.toml index 99a1b2e9ab86161d0b7b2454fdc567cea9025af0..78299f2ccb5c36ef01c477463f1d779e3cc449b3 100644 --- a/tasks/0077_155_77155673_qa_4/task.toml +++ b/tasks/0077_155_77155673_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0077_155_77155673_qa_4" +name = "smoldataenvs-train/0077_155_77155673_qa_4" description = "What is the correlation coefficient between Glucose levels and the Outcome variable based on the correlation matrix visualization (heatmap)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.46" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0077_165_77165888_qa_1/task.toml b/tasks/0077_165_77165888_qa_1/task.toml index c89ebec26636546329615a9b49ea4505a48abeb3..c5763d6f8f9a6a0c01d112960dd7488d70f57ac7 100644 --- a/tasks/0077_165_77165888_qa_1/task.toml +++ b/tasks/0077_165_77165888_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0077_165_77165888_qa_1" +name = "smoldataenvs-train/0077_165_77165888_qa_1" description = "What is the percentage of missing values in the 'Insulin' column before imputation in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "48.7" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0077_167_77167063_qa_2/task.toml b/tasks/0077_167_77167063_qa_2/task.toml index b893a75af7fc99c6eda81ad19332ead6d02df9c9..95032da54dad40f0ecc147bc67dda286ee12313e 100644 --- a/tasks/0077_167_77167063_qa_2/task.toml +++ b/tasks/0077_167_77167063_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0077_167_77167063_qa_2" +name = "smoldataenvs-train/0077_167_77167063_qa_2" description = "What is the ratio of individuals earning <=50K to those earning >50K in the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.13" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0077_167_77167063_qa_4/task.toml b/tasks/0077_167_77167063_qa_4/task.toml index 7231595fdf6abf32d73b8a5e859eb99905c18ab2..94b33237812ded097f34af2af318e90e6f99834f 100644 --- a/tasks/0077_167_77167063_qa_4/task.toml +++ b/tasks/0077_167_77167063_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0077_167_77167063_qa_4" +name = "smoldataenvs-train/0077_167_77167063_qa_4" description = "What percentage of the test set consists of individuals earning <=50K annually?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "75.8" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0077_218_77218707_qa_3/task.toml b/tasks/0077_218_77218707_qa_3/task.toml index 29452d01d9c804335c10148d0065904873e3afb8..b89823664c5945df02fc77e5cb2877304bacc724 100644 --- a/tasks/0077_218_77218707_qa_3/task.toml +++ b/tasks/0077_218_77218707_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0077_218_77218707_qa_3" +name = "smoldataenvs-train/0077_218_77218707_qa_3" description = "What is the average curb weight (in pounds) of vehicles in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2555.57" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0077_218_77218707_qa_4/task.toml b/tasks/0077_218_77218707_qa_4/task.toml index c6cb77e27f8c4dd1d1da721379041014d142ddd5..a86eea9ce2be0ccb276aec70b7e6ec30fae49a1f 100644 --- a/tasks/0077_218_77218707_qa_4/task.toml +++ b/tasks/0077_218_77218707_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0077_218_77218707_qa_4" +name = "smoldataenvs-train/0077_218_77218707_qa_4" description = "What is the median compression ratio of all vehicles in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0077_266_77266642_qa_3/task.toml b/tasks/0077_266_77266642_qa_3/task.toml index 44a5089f74b81672d77235610034027b68550102..07b3d3a77e6b37e841af8358450cd18fc59c89d2 100644 --- a/tasks/0077_266_77266642_qa_3/task.toml +++ b/tasks/0077_266_77266642_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0077_266_77266642_qa_3" +name = "smoldataenvs-train/0077_266_77266642_qa_3" description = "Which chemical property has the highest feature importance in predicting the simplified wine quality using the Random Forest classifier?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "alcohol" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0077_322_77322684_qa_2/task.toml b/tasks/0077_322_77322684_qa_2/task.toml index 0a56e11ef105bb10f259cad2ce1e38800708817a..a13a63af343a60b1c55a725ed77aaefa4fa7bcc2 100644 --- a/tasks/0077_322_77322684_qa_2/task.toml +++ b/tasks/0077_322_77322684_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0077_322_77322684_qa_2" +name = "smoldataenvs-train/0077_322_77322684_qa_2" description = "After applying upsampling to balance the class distribution, how many customers in the dataset are classified as churned?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5174" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0077_322_77322684_qa_3/task.toml b/tasks/0077_322_77322684_qa_3/task.toml index 8d87ddbe36362232ca38be99ea7a75e75b690949..9b3a5f94c45937235dc425e5fde1510bfa2dfd15 100644 --- a/tasks/0077_322_77322684_qa_3/task.toml +++ b/tasks/0077_322_77322684_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0077_322_77322684_qa_3" +name = "smoldataenvs-train/0077_322_77322684_qa_3" description = "According to the partial dependence plots, which internet service type is associated with the highest tendency to churn?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Fiber optic" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0077_497_77497432_qa_1/task.toml b/tasks/0077_497_77497432_qa_1/task.toml index 5c1ec0dbb8e0c7271df184acb474ac39e558885d..91f4513eea26e2c0eb96a375b72b42b5e60d12c3 100644 --- a/tasks/0077_497_77497432_qa_1/task.toml +++ b/tasks/0077_497_77497432_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0077_497_77497432_qa_1" +name = "smoldataenvs-train/0077_497_77497432_qa_1" description = "Which feature in the dataset has the highest positive correlation with the sale price of a house?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "sqft_living" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0077_497_77497432_qa_2/task.toml b/tasks/0077_497_77497432_qa_2/task.toml index 5eb2b66baaf7cb7a8d40f264b4e682a696be5fde..6302e34549e12a1eff629aa9689c09dfb41b0717 100644 --- a/tasks/0077_497_77497432_qa_2/task.toml +++ b/tasks/0077_497_77497432_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0077_497_77497432_qa_2" +name = "smoldataenvs-train/0077_497_77497432_qa_2" description = "What is the R-squared value of the multiple linear regression model using the features 'floors', 'waterfront', 'lat', 'bedrooms', 'sqft_basement', 'view', 'bathrooms', 'sqft_living15', 'sqft_above', 'grade', and 'sqft_living'?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.6577" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0077_528_77528261_qa_3/task.toml b/tasks/0077_528_77528261_qa_3/task.toml index d64766ae1cec13d380620125e9894443f1dbbfac..0e0cbd4e26232f849291c01e96b5e464e1847991 100644 --- a/tasks/0077_528_77528261_qa_3/task.toml +++ b/tasks/0077_528_77528261_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0077_528_77528261_qa_3" +name = "smoldataenvs-train/0077_528_77528261_qa_3" description = "What is the range (maximum - minimum) of the perimeter_mean feature in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "144.71" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0077_529_77529364_qa_1/task.toml b/tasks/0077_529_77529364_qa_1/task.toml index 725de78f45b6edfea62d5b4976a1f6b52b194300..a0356f78005140477ca31f121cc35db87c4f8ec4 100644 --- a/tasks/0077_529_77529364_qa_1/task.toml +++ b/tasks/0077_529_77529364_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0077_529_77529364_qa_1" +name = "smoldataenvs-train/0077_529_77529364_qa_1" description = "Which feature has the highest positive correlation with the Outcome variable in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0077_529_77529364_qa_3/task.toml b/tasks/0077_529_77529364_qa_3/task.toml index b0758439554f988e117764eac019fc50c7f715d1..8ad99ee60089f1d10c6d0930fa5db59ec8e256ac 100644 --- a/tasks/0077_529_77529364_qa_3/task.toml +++ b/tasks/0077_529_77529364_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0077_529_77529364_qa_3" +name = "smoldataenvs-train/0077_529_77529364_qa_3" description = "Among the tested models (SVM with different kernels and KNN), which achieved the highest accuracy on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Linear SVM" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0077_558_77558936_qa_3/task.toml b/tasks/0077_558_77558936_qa_3/task.toml index 0c5c466c24fb86d6126af67575f479db63afe2c5..077f711954f6bddb92c236a4e536e4f4f3e77e3f 100644 --- a/tasks/0077_558_77558936_qa_3/task.toml +++ b/tasks/0077_558_77558936_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0077_558_77558936_qa_3" +name = "smoldataenvs-train/0077_558_77558936_qa_3" description = "Which city in King County has the highest number of houses listed in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Seattle" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0077_572_77572617_qa_2/task.toml b/tasks/0077_572_77572617_qa_2/task.toml index 8e300e21bb5e4b92614f37dd10c3c2e1234d4c96..f53ebdaa14f830af7aa3326dc3b30cfcd8c2d866 100644 --- a/tasks/0077_572_77572617_qa_2/task.toml +++ b/tasks/0077_572_77572617_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0077_572_77572617_qa_2" +name = "smoldataenvs-train/0077_572_77572617_qa_2" description = "What is the mean of the residuals from the linear regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-1.9539925233402755e-16" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0077_572_77572617_qa_3/task.toml b/tasks/0077_572_77572617_qa_3/task.toml index 6d68c8c9c2272016077667e4730415ea2f0819cf..9ce1dd80379660539c6132eaa2e5988a27f61e30 100644 --- a/tasks/0077_572_77572617_qa_3/task.toml +++ b/tasks/0077_572_77572617_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0077_572_77572617_qa_3" +name = "smoldataenvs-train/0077_572_77572617_qa_3" description = "What is the p-value from the Goldfeld-Quandt test for homoscedasticity?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.07369564428747265" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0077_578_77578267_qa_2/task.toml b/tasks/0077_578_77578267_qa_2/task.toml index 630e7d38f2e3b07de41ea1f278763fb274db6885..55233d79b61efb2e5992cd174144c52822670562 100644 --- a/tasks/0077_578_77578267_qa_2/task.toml +++ b/tasks/0077_578_77578267_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0077_578_77578267_qa_2" +name = "smoldataenvs-train/0077_578_77578267_qa_2" description = "What percentage of total global sales is attributed to North America according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "49.3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0077_578_77578267_qa_4/task.toml b/tasks/0077_578_77578267_qa_4/task.toml index 10fd7d22af7fc4c6caafa26a77bafd2421e669fc..716243019eb9005f1a6225470553a7477c2a1022 100644 --- a/tasks/0077_578_77578267_qa_4/task.toml +++ b/tasks/0077_578_77578267_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0077_578_77578267_qa_4" +name = "smoldataenvs-train/0077_578_77578267_qa_4" description = "What was the total global sales contribution from games released in the year 2000?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "201.56" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0077_599_77599751_qa_4/task.toml b/tasks/0077_599_77599751_qa_4/task.toml index fa3c72b0965563f6cb4cbda55ab9321e6c1d604f..c15821d2b9673f85087686d79e542369b2d2f9ee 100644 --- a/tasks/0077_599_77599751_qa_4/task.toml +++ b/tasks/0077_599_77599751_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0077_599_77599751_qa_4" +name = "smoldataenvs-train/0077_599_77599751_qa_4" description = "What is the most common value in the 'workclass' column after imputing missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Private" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0077_602_77602560_qa_2/task.toml b/tasks/0077_602_77602560_qa_2/task.toml index 9a99067eb2be5379ed431d9da8389236037ec0a8..b83fe038e1ae297e66b998057c33910a3fb2d522 100644 --- a/tasks/0077_602_77602560_qa_2/task.toml +++ b/tasks/0077_602_77602560_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0077_602_77602560_qa_2" +name = "smoldataenvs-train/0077_602_77602560_qa_2" description = "Which variable shows the strongest negative correlation with the median house value (MEDV) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "LSTAT" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0077_602_77602560_qa_5/task.toml b/tasks/0077_602_77602560_qa_5/task.toml index 0c39d1d33cbf492967877a19e3a52b1f3d02f561..e19c4e7996e271a7ddd26e53a276df2f02287f89 100644 --- a/tasks/0077_602_77602560_qa_5/task.toml +++ b/tasks/0077_602_77602560_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0077_602_77602560_qa_5" +name = "smoldataenvs-train/0077_602_77602560_qa_5" description = "Which variable exhibits the highest absolute correlation (positive or negative) with nitrogen oxides concentration (NOX)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "DIS" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0077_684_77684454_qa_2/task.toml b/tasks/0077_684_77684454_qa_2/task.toml index c4d3648df698eb2c9e307c614104dbd4b8259f24..d9167097c502f2d242c83d104ca0ec00a7d3011b 100644 --- a/tasks/0077_684_77684454_qa_2/task.toml +++ b/tasks/0077_684_77684454_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0077_684_77684454_qa_2" +name = "smoldataenvs-train/0077_684_77684454_qa_2" description = "What is the percentage of Nobel Prize laureates classified as 'Senior' in the Age_Group feature (age 65+)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.22" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0077_684_77684454_qa_5/task.toml b/tasks/0077_684_77684454_qa_5/task.toml index 2e8edeb7ccf6b175f03fa3758f33d3cd15c2b429..4172bfd2a0788e4102b069bf80ed8fff67fed300 100644 --- a/tasks/0077_684_77684454_qa_5/task.toml +++ b/tasks/0077_684_77684454_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0077_684_77684454_qa_5" +name = "smoldataenvs-train/0077_684_77684454_qa_5" description = "What is the percentage of Nobel Prize laureates in the 'Adult' age group (30-64 years)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "65.57" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0077_724_77724747_qa_3/task.toml b/tasks/0077_724_77724747_qa_3/task.toml index 30b37b27b61ec061ec3e65dc1f5e771221c7ab5e..8aa8826de56a2f45ce73b5730a49102d656ca43f 100644 --- a/tasks/0077_724_77724747_qa_3/task.toml +++ b/tasks/0077_724_77724747_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0077_724_77724747_qa_3" +name = "smoldataenvs-train/0077_724_77724747_qa_3" description = "After feature engineering, which variable shows the highest importance in the Random Forest model's feature importance analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "median_income" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0077_800_77800758_qa_5/task.toml b/tasks/0077_800_77800758_qa_5/task.toml index 37b48ee3bb943f6bbcfb64ca52aee0077a1fd75d..b8aaab67ad5e8b3f705352e1dd42cc13b750cc8d 100644 --- a/tasks/0077_800_77800758_qa_5/task.toml +++ b/tasks/0077_800_77800758_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0077_800_77800758_qa_5" +name = "smoldataenvs-train/0077_800_77800758_qa_5" description = "What is the difference in average Glucose levels between diabetic and non-diabetic individuals?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "31.28" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0077_816_77816040_qa_5/task.toml b/tasks/0077_816_77816040_qa_5/task.toml index 451b622c906b773d0fab2dd4bdca2cec43e803cf..57a24a506f12a698f3a00c7e7d057b2590ae3018 100644 --- a/tasks/0077_816_77816040_qa_5/task.toml +++ b/tasks/0077_816_77816040_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0077_816_77816040_qa_5" +name = "smoldataenvs-train/0077_816_77816040_qa_5" description = "What is the percentage distribution of diabetic vs non-diabetic patients in the final cleaned dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34% diabetic, 66% non-diabetic" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0077_845_77845573_qa_5/task.toml b/tasks/0077_845_77845573_qa_5/task.toml index 18f6e8000237bf39ac38f50c8f9c12c2b65e755f..2156a11b535b4651cd25d3eb68a950519ea55135 100644 --- a/tasks/0077_845_77845573_qa_5/task.toml +++ b/tasks/0077_845_77845573_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0077_845_77845573_qa_5" +name = "smoldataenvs-train/0077_845_77845573_qa_5" description = "What is the standard deviation of the 'chlorides' feature in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.047065" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0077_854_77854158_qa_2/task.toml b/tasks/0077_854_77854158_qa_2/task.toml index d8b2cc6adf0b797256cd093ca91d549109d2c02c..04e4333d9f1ce7c1abeec9162ad65f073a4a3a3e 100644 --- a/tasks/0077_854_77854158_qa_2/task.toml +++ b/tasks/0077_854_77854158_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0077_854_77854158_qa_2" +name = "smoldataenvs-train/0077_854_77854158_qa_2" description = "How many samples are present for each species in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0077_889_77889442_qa_1/task.toml b/tasks/0077_889_77889442_qa_1/task.toml index 5a35b199b074b1c4fa7873ff83a3e38f98154fee..3ce816e5d90d5d94b9dd1326941dfa97ff1d3b2d 100644 --- a/tasks/0077_889_77889442_qa_1/task.toml +++ b/tasks/0077_889_77889442_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0077_889_77889442_qa_1" +name = "smoldataenvs-train/0077_889_77889442_qa_1" description = "Which Indian state had the highest total number of suicides between 2001 and 2012?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Maharashtra" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0077_889_77889442_qa_5/task.toml b/tasks/0077_889_77889442_qa_5/task.toml index 92e756acb34a8731881595d316a68943e233e4a2..8809d2bdbe61efd34917c4eb4b340775ced17055 100644 --- a/tasks/0077_889_77889442_qa_5/task.toml +++ b/tasks/0077_889_77889442_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0077_889_77889442_qa_5" +name = "smoldataenvs-train/0077_889_77889442_qa_5" description = "Did the total number of suicides in India increase or decrease from 2001 to 2012?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "increase" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0077_902_77902994_qa_1/task.toml b/tasks/0077_902_77902994_qa_1/task.toml index da157e77911f4331236d219117a3e4cf0e960725..0c79bb77b54ad1c2fef28c1520a13f3a3149eb4b 100644 --- a/tasks/0077_902_77902994_qa_1/task.toml +++ b/tasks/0077_902_77902994_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0077_902_77902994_qa_1" +name = "smoldataenvs-train/0077_902_77902994_qa_1" description = "What is the Pearson correlation coefficient between median income and median house value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.688075" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0077_902_77902994_qa_2/task.toml b/tasks/0077_902_77902994_qa_2/task.toml index b7d5775dbf063cfd52c28674026fc5b02445ccea..8bb42f907b6af9372a7ff67451a1b418d887f8e4 100644 --- a/tasks/0077_902_77902994_qa_2/task.toml +++ b/tasks/0077_902_77902994_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0077_902_77902994_qa_2" +name = "smoldataenvs-train/0077_902_77902994_qa_2" description = "What percentage of the total_bedrooms column contains missing values before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.0029" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0077_965_77965722_qa_2/task.toml b/tasks/0077_965_77965722_qa_2/task.toml index a10c957cd130ba13223c39541e82b188072096e3..cc5fe1c675969c50fcd3d2445937a258175d6c36 100644 --- a/tasks/0077_965_77965722_qa_2/task.toml +++ b/tasks/0077_965_77965722_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0077_965_77965722_qa_2" +name = "smoldataenvs-train/0077_965_77965722_qa_2" description = "Which regularization parameter value achieved the lowest validation RMSE in the hyperparameter tuning experiment?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.00001" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0077_978_77978673_qa_3/task.toml b/tasks/0077_978_77978673_qa_3/task.toml index 90ea3b3d2f8e16b0c08b9ef030cbfb2ab990be86..eefa7ce9089ffada119806e2ec85f373d1dde318 100644 --- a/tasks/0077_978_77978673_qa_3/task.toml +++ b/tasks/0077_978_77978673_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0077_978_77978673_qa_3" +name = "smoldataenvs-train/0077_978_77978673_qa_3" description = "What is the nature of the correlation between volatile acidity and wine quality in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "negative" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0078_197_78197106_qa_4/task.toml b/tasks/0078_197_78197106_qa_4/task.toml index 81cb8e3a913d81ceb610b3bbe7fcdd7b44765631..ff089a89b4f60f56140b655f3d48168ebccea8ae 100644 --- a/tasks/0078_197_78197106_qa_4/task.toml +++ b/tasks/0078_197_78197106_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0078_197_78197106_qa_4" +name = "smoldataenvs-train/0078_197_78197106_qa_4" description = "What is the number of unique categories in the Address feature after label encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5000" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0078_208_78208139_qa_3/task.toml b/tasks/0078_208_78208139_qa_3/task.toml index 1ce2b643cb9394281493ad9ffe14c1ccbbb581c4..78b23d26c7a97e3944eb410226c04a29b4b251c5 100644 --- a/tasks/0078_208_78208139_qa_3/task.toml +++ b/tasks/0078_208_78208139_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0078_208_78208139_qa_3" +name = "smoldataenvs-train/0078_208_78208139_qa_3" description = "How many Pokémon in the dataset have both HP greater than 150 and Speed greater than 35?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0078_329_78329159_qa_2/task.toml b/tasks/0078_329_78329159_qa_2/task.toml index 4ca173bb70b032eeefeb0aeb5451c19b5f999b42..9fb84049f75b775d28edacbe7b84d40bcb07e642 100644 --- a/tasks/0078_329_78329159_qa_2/task.toml +++ b/tasks/0078_329_78329159_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0078_329_78329159_qa_2" +name = "smoldataenvs-train/0078_329_78329159_qa_2" description = "After applying the box-cox transformation, what was the upper fence value for 'hours.per.week' used to treat outliers?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "52.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0078_329_78329159_qa_3/task.toml b/tasks/0078_329_78329159_qa_3/task.toml index de15e666281a775423dae791b105000cbafd7546..a0d116b0fe512ded31471f05a7268e80ea7d0eee 100644 --- a/tasks/0078_329_78329159_qa_3/task.toml +++ b/tasks/0078_329_78329159_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0078_329_78329159_qa_3" +name = "smoldataenvs-train/0078_329_78329159_qa_3" description = "What was the test accuracy of the logistic regression model after feature selection and data preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.8420118343195266" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0078_483_78483410_qa_3/task.toml b/tasks/0078_483_78483410_qa_3/task.toml index 6250084a0be6b5366e623b10cc9ecdf6f6eec462..601a5c0f0dd3423e3982e4d8793e7d52e97931a0 100644 --- a/tasks/0078_483_78483410_qa_3/task.toml +++ b/tasks/0078_483_78483410_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0078_483_78483410_qa_3" +name = "smoldataenvs-train/0078_483_78483410_qa_3" description = "What is the title of the book with the highest cosine similarity to \"The Hunger Games\" in the content-based recommendation system?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "The Hunger Games Box Set" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0078_655_78655226_qa_3/task.toml b/tasks/0078_655_78655226_qa_3/task.toml index 0cf707461fc5490c420d1fb7adbf6d72d920d27a..ee750932ddfa2ab0eab3d386a8a2d5653689254d 100644 --- a/tasks/0078_655_78655226_qa_3/task.toml +++ b/tasks/0078_655_78655226_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0078_655_78655226_qa_3" +name = "smoldataenvs-train/0078_655_78655226_qa_3" description = "Which machine learning model achieved the highest R² score on the test dataset: linear regression, random forest, or XGBoost?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Random Forest" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0078_655_78655226_qa_5/task.toml b/tasks/0078_655_78655226_qa_5/task.toml index 8409ab700ccae6d54070936dcb84329ecccea2cf..26a0e35e70c9551541a36c362c155de7eef99837 100644 --- a/tasks/0078_655_78655226_qa_5/task.toml +++ b/tasks/0078_655_78655226_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0078_655_78655226_qa_5" +name = "smoldataenvs-train/0078_655_78655226_qa_5" description = "What is the correct ordering of features by their absolute correlation with MEDV, from highest to lowest, as shown in the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "LSTAT, RM, PTRATIO" reward_mode_initial = "list_csv" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0078_661_78661279_qa_1/task.toml b/tasks/0078_661_78661279_qa_1/task.toml index 2d3ffc08e733add3ec55ae7d6cbf707cfb2dec4d..ae60578818749b5ec68ad74ce23a365167b1efe5 100644 --- a/tasks/0078_661_78661279_qa_1/task.toml +++ b/tasks/0078_661_78661279_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0078_661_78661279_qa_1" +name = "smoldataenvs-train/0078_661_78661279_qa_1" description = "How many unique car models are present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "305" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0078_661_78661279_qa_3/task.toml b/tasks/0078_661_78661279_qa_3/task.toml index 4c010976d59caf459ee8cd64899d742f42e3a9db..e24ba68169ecdf9575b6ab9b84df4b75c7a3d23f 100644 --- a/tasks/0078_661_78661279_qa_3/task.toml +++ b/tasks/0078_661_78661279_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0078_661_78661279_qa_3" +name = "smoldataenvs-train/0078_661_78661279_qa_3" description = "Which four variables have the highest positive loadings on the first principal component (PC1) after PCA analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Cylinders, displacement, horsepower, weight" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0078_661_78661279_qa_5/task.toml b/tasks/0078_661_78661279_qa_5/task.toml index 8d74f55e4a99840d697543ac8c9e149d17a15477..fdb9143c023b86f9fb5cf9d1af5e742b80d2de93 100644 --- a/tasks/0078_661_78661279_qa_5/task.toml +++ b/tasks/0078_661_78661279_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0078_661_78661279_qa_5" +name = "smoldataenvs-train/0078_661_78661279_qa_5" description = "What is the median horsepower of the cars in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "93.5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0078_779_78779265_qa_1/task.toml b/tasks/0078_779_78779265_qa_1/task.toml index b20e0a27978f3bb371681ebfb24c16a0212d6f58..8a2fddc711c9538135011b65686600449f9ea9fd 100644 --- a/tasks/0078_779_78779265_qa_1/task.toml +++ b/tasks/0078_779_78779265_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0078_779_78779265_qa_1" +name = "smoldataenvs-train/0078_779_78779265_qa_1" description = "What was the number of rows remaining in the dataset after applying Z-score to remove price outliers?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "21207" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0078_857_78857883_qa_1/task.toml b/tasks/0078_857_78857883_qa_1/task.toml index 0d1a7fd8c377a51149cf5d82ee4e872a420bbc2e..12bce79f76c367fdd33b036f30fb2ecef8072fb7 100644 --- a/tasks/0078_857_78857883_qa_1/task.toml +++ b/tasks/0078_857_78857883_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0078_857_78857883_qa_1" +name = "smoldataenvs-train/0078_857_78857883_qa_1" description = "What is the R-squared value of the simple linear regression model predicting Salary from Years of Experience?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.957" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0078_900_78900393_qa_5/task.toml b/tasks/0078_900_78900393_qa_5/task.toml index b6abfc2089ff3074bad7cb1fc4096140acb01dbd..bbcac833f416b5648c83e9da2b2d2d350eb8b9fe 100644 --- a/tasks/0078_900_78900393_qa_5/task.toml +++ b/tasks/0078_900_78900393_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0078_900_78900393_qa_5" +name = "smoldataenvs-train/0078_900_78900393_qa_5" description = "How many missing values are present in the TotalCharges column after converting it to a float type?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0079_150_79150881_qa_3/task.toml b/tasks/0079_150_79150881_qa_3/task.toml index afbfa9a4593367b14aabddca3dfbc85be2c19d2d..7f3e4a63882d8766eefeb0c25d0faef8f9b900ae 100644 --- a/tasks/0079_150_79150881_qa_3/task.toml +++ b/tasks/0079_150_79150881_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0079_150_79150881_qa_3" +name = "smoldataenvs-train/0079_150_79150881_qa_3" description = "What percentage of clients in the dataset subscribed to a term deposit?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "47.4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0079_150_79150881_qa_4/task.toml b/tasks/0079_150_79150881_qa_4/task.toml index 83d033a5fc959cf422148d217985a7e163a446ba..ed8385a231af926f6a89e02ef0b0c2454ab6a3b4 100644 --- a/tasks/0079_150_79150881_qa_4/task.toml +++ b/tasks/0079_150_79150881_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0079_150_79150881_qa_4" +name = "smoldataenvs-train/0079_150_79150881_qa_4" description = "Which month had the highest number of successful term deposit subscriptions (deposit = yes)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "may" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0079_355_79355803_qa_3/task.toml b/tasks/0079_355_79355803_qa_3/task.toml index 5bcca5f9b0b60167e336febd10c66ca7a1bd3f50..b75e8e0b3d06d3f2003bdb5a933d0f0cabaa8427 100644 --- a/tasks/0079_355_79355803_qa_3/task.toml +++ b/tasks/0079_355_79355803_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0079_355_79355803_qa_3" +name = "smoldataenvs-train/0079_355_79355803_qa_3" description = "Which year between 2000 and 2015 had the highest number of video games released according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2008" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0079_355_79355803_qa_4/task.toml b/tasks/0079_355_79355803_qa_4/task.toml index 6ce321b159070180ec1afd182d5b6869a41968cc..bc49e4762e0dd9a770e605c7a99808064402a6e8 100644 --- a/tasks/0079_355_79355803_qa_4/task.toml +++ b/tasks/0079_355_79355803_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0079_355_79355803_qa_4" +name = "smoldataenvs-train/0079_355_79355803_qa_4" description = "What is the publisher with the highest number of video games produced in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic Arts" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0079_455_79455690_qa_1/task.toml b/tasks/0079_455_79455690_qa_1/task.toml index e218f53f2502d70fe058ae1c44ca69973b78bf5f..2d9bbe5cf1060e63e808baf83074b725f7618790 100644 --- a/tasks/0079_455_79455690_qa_1/task.toml +++ b/tasks/0079_455_79455690_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0079_455_79455690_qa_1" +name = "smoldataenvs-train/0079_455_79455690_qa_1" description = "How many products are categorized as \"Low Fat\" after correcting the inconsistencies in the Item_Fat_Content column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5517" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0079_455_79455690_qa_4/task.toml b/tasks/0079_455_79455690_qa_4/task.toml index 05a1347329fc0973d10fc8718dda01ed3a35cd73..7c5d9c4173d15cb22490f5b44a387a5d7e82ef85 100644 --- a/tasks/0079_455_79455690_qa_4/task.toml +++ b/tasks/0079_455_79455690_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0079_455_79455690_qa_4" +name = "smoldataenvs-train/0079_455_79455690_qa_4" description = "After handling missing values, what is the average Item_Weight of products in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12.857645" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0079_455_79455690_qa_5/task.toml b/tasks/0079_455_79455690_qa_5/task.toml index 5cdde42bf92ceeedad853d70abae838064d7395a..0c859305276d5cf245a10e9fc8891ec6703ea34e 100644 --- a/tasks/0079_455_79455690_qa_5/task.toml +++ b/tasks/0079_455_79455690_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0079_455_79455690_qa_5" +name = "smoldataenvs-train/0079_455_79455690_qa_5" description = "What is the number of unique product identifiers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1559" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0079_509_79509130_qa_3/task.toml b/tasks/0079_509_79509130_qa_3/task.toml index ad531c1d5c345c9e2de5352bfed85910008cc569..9f12975a5521798ec2afa3b2169a181b1eb70664 100644 --- a/tasks/0079_509_79509130_qa_3/task.toml +++ b/tasks/0079_509_79509130_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0079_509_79509130_qa_3" +name = "smoldataenvs-train/0079_509_79509130_qa_3" description = "What is the total number of messages in the dataset after removing all empty columns?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5572" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0079_571_79571546_qa_3/task.toml b/tasks/0079_571_79571546_qa_3/task.toml index 03848132a2937b06fb8aa93bbd1185e2dd971029..f924da9c5b0232796b51e3330b8010495ef2b4ec 100644 --- a/tasks/0079_571_79571546_qa_3/task.toml +++ b/tasks/0079_571_79571546_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0079_571_79571546_qa_3" +name = "smoldataenvs-train/0079_571_79571546_qa_3" description = "What is the rating of the most popular anime (by member count)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8.71" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0079_574_79574675_qa_3/task.toml b/tasks/0079_574_79574675_qa_3/task.toml index 8932bbd42d19637cc17beee84e47816a861c034c..e83debaedbb4a63a05b228ca99e509b75236ba53 100644 --- a/tasks/0079_574_79574675_qa_3/task.toml +++ b/tasks/0079_574_79574675_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0079_574_79574675_qa_3" +name = "smoldataenvs-train/0079_574_79574675_qa_3" description = "How many instances had missing values in the 'Insulin' column before imputation was applied to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "374" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0079_574_79574675_qa_4/task.toml b/tasks/0079_574_79574675_qa_4/task.toml index 043043f8af823d276611950e55b13867e730bd29..38b52d22e2885d4c808f475bb0a421d499781fe2 100644 --- a/tasks/0079_574_79574675_qa_4/task.toml +++ b/tasks/0079_574_79574675_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0079_574_79574675_qa_4" +name = "smoldataenvs-train/0079_574_79574675_qa_4" description = "What is the most common outcome (0=No diabetes, 1=Diabetes) in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0079_597_79597687_qa_2/task.toml b/tasks/0079_597_79597687_qa_2/task.toml index 5983a0912b7fa397116335b31332bf3fa1bb9438..e58ca3c9e7c2445daa3b7ae226cfbeb90ad61385 100644 --- a/tasks/0079_597_79597687_qa_2/task.toml +++ b/tasks/0079_597_79597687_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0079_597_79597687_qa_2" +name = "smoldataenvs-train/0079_597_79597687_qa_2" description = "What is the optimal number of neighbors (k) selected by grid search for the KNN model based on 10-fold cross-validation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0079_597_79597687_qa_5/task.toml b/tasks/0079_597_79597687_qa_5/task.toml index e505cf7ac19178a51aece3b211358088e8207893..a32a689cbe988be42e5ba82a5dbf4b820f084622 100644 --- a/tasks/0079_597_79597687_qa_5/task.toml +++ b/tasks/0079_597_79597687_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0079_597_79597687_qa_5" +name = "smoldataenvs-train/0079_597_79597687_qa_5" description = "What is the mean squared error (MSE) on the test set for the tuned KNN model with optimal parameters?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.05" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0079_606_79606942_qa_1/task.toml b/tasks/0079_606_79606942_qa_1/task.toml index 4f7a4e1bfc89d63c6d82c4b8416b977bbe3dfd8c..d8a3e3a07fa23e619f012e0e5fa1f28d43c48af9 100644 --- a/tasks/0079_606_79606942_qa_1/task.toml +++ b/tasks/0079_606_79606942_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0079_606_79606942_qa_1" +name = "smoldataenvs-train/0079_606_79606942_qa_1" description = "Which feature had the highest score in the SelectKBest feature selection process using f_regression scoring?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Item_MRP" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0079_606_79606942_qa_5/task.toml b/tasks/0079_606_79606942_qa_5/task.toml index 0c41a2ccbf54c21886c0ee1b2aa095033dfe1429..857cf22126c8aa202d4cf2c953583743c050810d 100644 --- a/tasks/0079_606_79606942_qa_5/task.toml +++ b/tasks/0079_606_79606942_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0079_606_79606942_qa_5" +name = "smoldataenvs-train/0079_606_79606942_qa_5" description = "How many unique item types are present in the training dataset according to the nunique() analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0079_677_79677746_qa_2/task.toml b/tasks/0079_677_79677746_qa_2/task.toml index 9764cb728f2688587b925f0e26c036e2c02c6aaf..8328ae043bc569464eec94a60597ef0919121586 100644 --- a/tasks/0079_677_79677746_qa_2/task.toml +++ b/tasks/0079_677_79677746_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0079_677_79677746_qa_2" +name = "smoldataenvs-train/0079_677_79677746_qa_2" description = "What are the three most common wine varieties in the dataset based on their frequency of appearance?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Pinot Noir, Chardonnay, Cabernet Sauvignon" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0079_677_79677746_qa_3/task.toml b/tasks/0079_677_79677746_qa_3/task.toml index d56dceeae23d79a16836add27f8354364c3a8a89..e202f3d797d660bf20a3de4058f409bbf1e88683 100644 --- a/tasks/0079_677_79677746_qa_3/task.toml +++ b/tasks/0079_677_79677746_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0079_677_79677746_qa_3" +name = "smoldataenvs-train/0079_677_79677746_qa_3" description = "Which country has the highest average points among all wines, and what is that average value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "England, 91.58" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0079_677_79677746_qa_4/task.toml b/tasks/0079_677_79677746_qa_4/task.toml index 216bb6a9aa2c32b0dac3d26c3802b00c09d59434..3fbf66238eaf88af7c976ec8155b4acd24e4142e 100644 --- a/tasks/0079_677_79677746_qa_4/task.toml +++ b/tasks/0079_677_79677746_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0079_677_79677746_qa_4" +name = "smoldataenvs-train/0079_677_79677746_qa_4" description = "What is the most expensive wine variety, and what is the maximum price recorded for that variety?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Bordeaux-style Red Blend, 3300" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0079_677_79677746_qa_5/task.toml b/tasks/0079_677_79677746_qa_5/task.toml index eebfd6d2eaf9af9acb1f83bc2a7de8541b4a2b11..71ca7e7b49dfcb7a7bd2d1b7ca600bb33c962c6d 100644 --- a/tasks/0079_677_79677746_qa_5/task.toml +++ b/tasks/0079_677_79677746_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0079_677_79677746_qa_5" +name = "smoldataenvs-train/0079_677_79677746_qa_5" description = "What is the relationship between wine price and points as determined by the regression analysis in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "positive" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0079_706_79706209_qa_1/task.toml b/tasks/0079_706_79706209_qa_1/task.toml index 25d32d3f36a45d8500975e13d75d7647ed56e13b..ef7860a566046f8869acf1291192ec09fa9c2f18 100644 --- a/tasks/0079_706_79706209_qa_1/task.toml +++ b/tasks/0079_706_79706209_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0079_706_79706209_qa_1" +name = "smoldataenvs-train/0079_706_79706209_qa_1" description = "Which country has the highest average star rating for ramen products, and what is that rating?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Brazil, 4.35" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0079_726_79726462_qa_1/task.toml b/tasks/0079_726_79726462_qa_1/task.toml index 119e88aa1147ecba843298b530c537034968283e..391576846a616d1eb4e79f5379ec770c2adbab56 100644 --- a/tasks/0079_726_79726462_qa_1/task.toml +++ b/tasks/0079_726_79726462_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0079_726_79726462_qa_1" +name = "smoldataenvs-train/0079_726_79726462_qa_1" description = "Which Iris species has the highest mean sepal length in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-virginica" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0079_852_79852470_qa_3/task.toml b/tasks/0079_852_79852470_qa_3/task.toml index 524aa2e0c8e2e459a531563d1922244dfabc24c9..d03e2ceca68dbe2cc52a74960563000f54948973 100644 --- a/tasks/0079_852_79852470_qa_3/task.toml +++ b/tasks/0079_852_79852470_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0079_852_79852470_qa_3" +name = "smoldataenvs-train/0079_852_79852470_qa_3" description = "After cleaning the dataset, how many rows remain in the dataset following the removal of missing values from the \"Publisher\" column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16540" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0079_852_79852470_qa_4/task.toml b/tasks/0079_852_79852470_qa_4/task.toml index fc4712a1b30e7adcb2108a86931bec4cda85ced6..35f58ffde871026dc3378a886548bde987862ae8 100644 --- a/tasks/0079_852_79852470_qa_4/task.toml +++ b/tasks/0079_852_79852470_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0079_852_79852470_qa_4" +name = "smoldataenvs-train/0079_852_79852470_qa_4" description = "What is the median year value calculated for imputing missing values in the \"Year\" column before converting it to an integer type?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2007" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0079_947_79947828_qa_3/task.toml b/tasks/0079_947_79947828_qa_3/task.toml index 4691ec2f1469dc3e28071f40a4da615ccbc98756..6f36ae697514306177b0a6248a3cdbef6261f5ce 100644 --- a/tasks/0079_947_79947828_qa_3/task.toml +++ b/tasks/0079_947_79947828_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0079_947_79947828_qa_3" +name = "smoldataenvs-train/0079_947_79947828_qa_3" description = "Which numerical feature in the dataset has the least absolute skewness, and what is its skewness value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalWidthCm, -0.104997" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0079_974_79974124_qa_2/task.toml b/tasks/0079_974_79974124_qa_2/task.toml index 265d919f829b65eb750630d52dfce8dec092c2a9..81bb368d97ea340890a9f8d817d95a36927c3b05 100644 --- a/tasks/0079_974_79974124_qa_2/task.toml +++ b/tasks/0079_974_79974124_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0079_974_79974124_qa_2" +name = "smoldataenvs-train/0079_974_79974124_qa_2" description = "How many movies had missing values in the 'overview' column before cleaning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0080_011_80011409_qa_1/task.toml b/tasks/0080_011_80011409_qa_1/task.toml index adf8559255702aaa53b5e4a0c4dbd08d555669ad..34f63c521b9f342d4a4b34af392b64756c076f56 100644 --- a/tasks/0080_011_80011409_qa_1/task.toml +++ b/tasks/0080_011_80011409_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0080_011_80011409_qa_1" +name = "smoldataenvs-train/0080_011_80011409_qa_1" description = "Which feature in the dataset has the highest standard deviation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0080_011_80011409_qa_2/task.toml b/tasks/0080_011_80011409_qa_2/task.toml index 8a8d38403ddc9b3cd3bf99e6d80fa0b977928c71..ac4a516af7da6cf51bf5b83e3a97a3ed064f4601 100644 --- a/tasks/0080_011_80011409_qa_2/task.toml +++ b/tasks/0080_011_80011409_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0080_011_80011409_qa_2" +name = "smoldataenvs-train/0080_011_80011409_qa_2" description = "What is the interquartile range (IQR) for SepalWidthCm?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0080_011_80011409_qa_3/task.toml b/tasks/0080_011_80011409_qa_3/task.toml index 60e666deb1ad876a2ea34154f0cc612f060caedc..428ea8d9aa6daa649f289ebd42485fafd282c754 100644 --- a/tasks/0080_011_80011409_qa_3/task.toml +++ b/tasks/0080_011_80011409_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0080_011_80011409_qa_3" +name = "smoldataenvs-train/0080_011_80011409_qa_3" description = "What is the range of PetalWidthCm in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0080_011_80011409_qa_4/task.toml b/tasks/0080_011_80011409_qa_4/task.toml index f7ee4bbf87c89886781e7a68bbf8f575c33a5edb..eb045838dc8c5d3993c8cdd0fcfe973f0916ca1e 100644 --- a/tasks/0080_011_80011409_qa_4/task.toml +++ b/tasks/0080_011_80011409_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0080_011_80011409_qa_4" +name = "smoldataenvs-train/0080_011_80011409_qa_4" description = "Which feature has the lowest 75th percentile value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalWidthCm" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0080_011_80011409_qa_5/task.toml b/tasks/0080_011_80011409_qa_5/task.toml index aa02cc17ecd965451229235c1640e310329f1f29..00c2b598ea85426f418c1229595c37243f4ea7bf 100644 --- a/tasks/0080_011_80011409_qa_5/task.toml +++ b/tasks/0080_011_80011409_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0080_011_80011409_qa_5" +name = "smoldataenvs-train/0080_011_80011409_qa_5" description = "What is the minimum value observed for SepalLengthCm across all samples?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0080_200_80200515_qa_1/task.toml b/tasks/0080_200_80200515_qa_1/task.toml index e302413f3f724c1a25f2e104cdd551c619dc762f..564d4eb8acbe5374174e3575cbed371eb83fc7c7 100644 --- a/tasks/0080_200_80200515_qa_1/task.toml +++ b/tasks/0080_200_80200515_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0080_200_80200515_qa_1" +name = "smoldataenvs-train/0080_200_80200515_qa_1" description = "Which numerical feature in the dataset exhibits the strongest negative correlation with the 'energy' feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Acousticness" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0080_200_80200515_qa_3/task.toml b/tasks/0080_200_80200515_qa_3/task.toml index 0208d74334b4ffc17df8f220e490c1a5f76a4d43..e12bc82f0dc32befaecbfd8a170f7680357699e2 100644 --- a/tasks/0080_200_80200515_qa_3/task.toml +++ b/tasks/0080_200_80200515_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0080_200_80200515_qa_3" +name = "smoldataenvs-train/0080_200_80200515_qa_3" description = "Which numerical feature shows the highest positive correlation with the 'loudness' feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Energy" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0080_229_80229186_qa_3/task.toml b/tasks/0080_229_80229186_qa_3/task.toml index 36cc84cb75d1a6df200b4b61610bfd348eaa7f5e..a4d4764c601b067733a2d964cb1a01ac2a8290aa 100644 --- a/tasks/0080_229_80229186_qa_3/task.toml +++ b/tasks/0080_229_80229186_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0080_229_80229186_qa_3" +name = "smoldataenvs-train/0080_229_80229186_qa_3" description = "What is the percentage of outliers in the 'hours.per.week' column identified using the IQR method before removal?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "27.66" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0080_304_80304821_qa_1/task.toml b/tasks/0080_304_80304821_qa_1/task.toml index 461968fa792bd67e47527f39e3639a6060a3b7e2..f6d5bb9612573a6572f915057830f1711e668e43 100644 --- a/tasks/0080_304_80304821_qa_1/task.toml +++ b/tasks/0080_304_80304821_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0080_304_80304821_qa_1" +name = "smoldataenvs-train/0080_304_80304821_qa_1" description = "How many rows were removed from the dataset during the TotalCharges column conversion process to numeric values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0080_564_80564946_qa_4/task.toml b/tasks/0080_564_80564946_qa_4/task.toml index ac2382262bb7b56d0b293603dd4182c9ae61fcc5..ea71730725f6e70550e28bdcdac21e42237eca1d 100644 --- a/tasks/0080_564_80564946_qa_4/task.toml +++ b/tasks/0080_564_80564946_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0080_564_80564946_qa_4" +name = "smoldataenvs-train/0080_564_80564946_qa_4" description = "What is the F1 score on the test set for the Decision Tree model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.92" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0080_564_80564946_qa_5/task.toml b/tasks/0080_564_80564946_qa_5/task.toml index 9220ba52082b98ba3d46e9e09e9c69ddbe8607bf..c74270cc5e895c514bfeb48fca9436cac8d89e68 100644 --- a/tasks/0080_564_80564946_qa_5/task.toml +++ b/tasks/0080_564_80564946_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0080_564_80564946_qa_5" +name = "smoldataenvs-train/0080_564_80564946_qa_5" description = "What is the difference between the training and test accuracy for the Decision Tree model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.03" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0080_653_80653569_qa_2/task.toml b/tasks/0080_653_80653569_qa_2/task.toml index 003d89035a36087c46adcc5b5f7771ed82ff235c..e92c9ac8cbb724ead38e5eaac9b5b4d9505a4334 100644 --- a/tasks/0080_653_80653569_qa_2/task.toml +++ b/tasks/0080_653_80653569_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0080_653_80653569_qa_2" +name = "smoldataenvs-train/0080_653_80653569_qa_2" description = "After applying SMOTE oversampling, what is the new count of diabetic patients in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "500" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0080_686_80686195_qa_3/task.toml b/tasks/0080_686_80686195_qa_3/task.toml index f2a8f64ecc3d78079dcf9eedd460bf2dcc3da30a..d887a9c0f1c69abdd805fe3bf096866eb8805b75 100644 --- a/tasks/0080_686_80686195_qa_3/task.toml +++ b/tasks/0080_686_80686195_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0080_686_80686195_qa_3" +name = "smoldataenvs-train/0080_686_80686195_qa_3" description = "Which price_range category has the highest number of outliers in the front camera (fc) based on the boxplot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0080_705_80705394_qa_1/task.toml b/tasks/0080_705_80705394_qa_1/task.toml index fa3b164ab07348e5ff53246f3b0a1fadc8c3cc47..86bc4529a579e722032a1d0de41f0258032c6938 100644 --- a/tasks/0080_705_80705394_qa_1/task.toml +++ b/tasks/0080_705_80705394_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0080_705_80705394_qa_1" +name = "smoldataenvs-train/0080_705_80705394_qa_1" description = "What is the average value of the 'normalized-losses' attribute after replacing missing values with the calculated mean?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "122.0" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0080_705_80705394_qa_5/task.toml b/tasks/0080_705_80705394_qa_5/task.toml index 1ebc238cd16ad8c34fd0ce4d28e7d1b00f0096d9..d8b6c8784c44b8bd1e38c97b4d58f1dce2f8dbb8 100644 --- a/tasks/0080_705_80705394_qa_5/task.toml +++ b/tasks/0080_705_80705394_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0080_705_80705394_qa_5" +name = "smoldataenvs-train/0080_705_80705394_qa_5" description = "What is the most frequently occurring value in the 'num-of-doors' attribute before missing value imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "four" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0080_832_80832047_qa_1/task.toml b/tasks/0080_832_80832047_qa_1/task.toml index d35dde716759b379ece9e6dd5a6e872fb75cdb66..5d8b1b0a31ebaf4a6589fa47cf45e96da6024234 100644 --- a/tasks/0080_832_80832047_qa_1/task.toml +++ b/tasks/0080_832_80832047_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0080_832_80832047_qa_1" +name = "smoldataenvs-train/0080_832_80832047_qa_1" description = "What percentage of the data corresponds to the autumn season based on the seasonal split of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.25" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0080_832_80832047_qa_5/task.toml b/tasks/0080_832_80832047_qa_5/task.toml index e1e8e573a83e0afe8492e039146e39d891bd1d01..e7e8f695301d3ec92094717d78fd572ca83b7021 100644 --- a/tasks/0080_832_80832047_qa_5/task.toml +++ b/tasks/0080_832_80832047_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0080_832_80832047_qa_5" +name = "smoldataenvs-train/0080_832_80832047_qa_5" description = "What is the difference between the maximum number of people recorded and the median number of people in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "117.0" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0080_863_80863637_qa_4/task.toml b/tasks/0080_863_80863637_qa_4/task.toml index d84733acc93bd0aa99ad407794fe3f4f0f824579..ce7a3cb9e26eb4d8f1eb5ac02f0a1fa2d6a57d94 100644 --- a/tasks/0080_863_80863637_qa_4/task.toml +++ b/tasks/0080_863_80863637_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0080_863_80863637_qa_4" +name = "smoldataenvs-train/0080_863_80863637_qa_4" description = "After preprocessing, how many unique values are present in the 'EducationField' column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0080_863_80863637_qa_5/task.toml b/tasks/0080_863_80863637_qa_5/task.toml index ca474dcd82c071dc48701a86795924b546937712..9eac6bc481f3b9a7195e1ed3c90a7f4daee960cd 100644 --- a/tasks/0080_863_80863637_qa_5/task.toml +++ b/tasks/0080_863_80863637_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0080_863_80863637_qa_5" +name = "smoldataenvs-train/0080_863_80863637_qa_5" description = "After creating the 'FracYearsAtCompany' feature, how many missing values are present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0080_918_80918497_qa_3/task.toml b/tasks/0080_918_80918497_qa_3/task.toml index 5473d211c445fb7f2bc9461d9bb876c7c68e210e..7db6598ab1b62bcdc6e2cc8284f87c44de7b8533 100644 --- a/tasks/0080_918_80918497_qa_3/task.toml +++ b/tasks/0080_918_80918497_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0080_918_80918497_qa_3" +name = "smoldataenvs-train/0080_918_80918497_qa_3" description = "Which three digimon form the team with the highest combined Lv50 Defense values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "GroundLocomon, Craniamon, Magnamon" reward_mode_initial = "list" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0080_970_80970482_qa_1/task.toml b/tasks/0080_970_80970482_qa_1/task.toml index 88dcb51d54c50fa095f01a3c415ab3033389f4a0..14bb85da17ee119ea01ee5d9a5f449e751f41909 100644 --- a/tasks/0080_970_80970482_qa_1/task.toml +++ b/tasks/0080_970_80970482_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0080_970_80970482_qa_1" +name = "smoldataenvs-train/0080_970_80970482_qa_1" description = "What is the R² score of the linear regression model on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9866253204337445" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0081_020_81020564_qa_2/task.toml b/tasks/0081_020_81020564_qa_2/task.toml index 986b86a022720f2c43439cbcf025d7d0c1de8645..efd619e092a50672d2690417831bfef872710085 100644 --- a/tasks/0081_020_81020564_qa_2/task.toml +++ b/tasks/0081_020_81020564_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0081_020_81020564_qa_2" +name = "smoldataenvs-train/0081_020_81020564_qa_2" description = "What is the skewness value of the Item_Outlet_Sales distribution?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.1775" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0081_173_81173353_qa_1/task.toml b/tasks/0081_173_81173353_qa_1/task.toml index bb8db50a167d96323d0c0904232f3ae501ba25a9..1bd1ed147532b4c84ec912a60c992863c8a27666 100644 --- a/tasks/0081_173_81173353_qa_1/task.toml +++ b/tasks/0081_173_81173353_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0081_173_81173353_qa_1" +name = "smoldataenvs-train/0081_173_81173353_qa_1" description = "How many rows were removed from the dataset due to negative values in the Age column during preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0081_173_81173353_qa_2/task.toml b/tasks/0081_173_81173353_qa_2/task.toml index 8d05473044c7bc5b1092fa57cfe9ef239a278729..2e07706737a87916f13debe841479bfa4230228c 100644 --- a/tasks/0081_173_81173353_qa_2/task.toml +++ b/tasks/0081_173_81173353_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0081_173_81173353_qa_2" +name = "smoldataenvs-train/0081_173_81173353_qa_2" description = "What was the maximum value in the original Handcap column before it was binarized to 0/1 in the preprocessing step?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0081_173_81173353_qa_3/task.toml b/tasks/0081_173_81173353_qa_3/task.toml index 518046cab4a53d908eb14231ec16f7d6e2794362..11c3e224e149c7a5fc2fc2725093df70ad49db54 100644 --- a/tasks/0081_173_81173353_qa_3/task.toml +++ b/tasks/0081_173_81173353_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0081_173_81173353_qa_3" +name = "smoldataenvs-train/0081_173_81173353_qa_3" description = "How many unique patient IDs are present in the original dataset before any preprocessing steps?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "62299" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0081_332_81332828_qa_4/task.toml b/tasks/0081_332_81332828_qa_4/task.toml index dae2b58830e2e520897d85b9bece04eabed3a305..bde0a9c88f79dd9add261fdd2027937773e11920 100644 --- a/tasks/0081_332_81332828_qa_4/task.toml +++ b/tasks/0081_332_81332828_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0081_332_81332828_qa_4" +name = "smoldataenvs-train/0081_332_81332828_qa_4" description = "What is the minimum recorded median home value (MEDV) in the Boston housing dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0081_332_81332828_qa_5/task.toml b/tasks/0081_332_81332828_qa_5/task.toml index aabcabe5736952836cf96de6f3eeae91b67b4676..2980a069d427f02bd94e7aaa2445a444b3649141 100644 --- a/tasks/0081_332_81332828_qa_5/task.toml +++ b/tasks/0081_332_81332828_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0081_332_81332828_qa_5" +name = "smoldataenvs-train/0081_332_81332828_qa_5" description = "Which feature exhibits the strongest negative correlation with the median home value (MEDV) in the Boston housing dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "LSTAT" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0081_459_81459159_qa_1/task.toml b/tasks/0081_459_81459159_qa_1/task.toml index eb3ec23f41a396bc318e51a2d9f12d2472c24f28..83d258bc6ce5d9b7c3c8a3304b36f44d33d8acf1 100644 --- a/tasks/0081_459_81459159_qa_1/task.toml +++ b/tasks/0081_459_81459159_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0081_459_81459159_qa_1" +name = "smoldataenvs-train/0081_459_81459159_qa_1" description = "How many missing values were present in the 'total_bedrooms' column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "207" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0081_459_81459159_qa_2/task.toml b/tasks/0081_459_81459159_qa_2/task.toml index 610023fbb894e86e50efbe10adf8d9219f9aaa4b..1adf33065c5eda09189405d5e99ad4d8b3600b82 100644 --- a/tasks/0081_459_81459159_qa_2/task.toml +++ b/tasks/0081_459_81459159_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0081_459_81459159_qa_2" +name = "smoldataenvs-train/0081_459_81459159_qa_2" description = "What is the average median house value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "206855.816909" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0081_459_81459159_qa_3/task.toml b/tasks/0081_459_81459159_qa_3/task.toml index 7ff57a1675acba92b792342c2f2cad73d89387ee..722eed85dff673b427c04239d0e03e11b9d9b827 100644 --- a/tasks/0081_459_81459159_qa_3/task.toml +++ b/tasks/0081_459_81459159_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0081_459_81459159_qa_3" +name = "smoldataenvs-train/0081_459_81459159_qa_3" description = "What is the R² score of the XGBoost model on the validation set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.8301797015400607" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0081_468_81468620_qa_2/task.toml b/tasks/0081_468_81468620_qa_2/task.toml index b3fb487e67f75b89cccad36e123835331b46058f..61655f0b259a779a5396f0da48e303034dde0f02 100644 --- a/tasks/0081_468_81468620_qa_2/task.toml +++ b/tasks/0081_468_81468620_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0081_468_81468620_qa_2" +name = "smoldataenvs-train/0081_468_81468620_qa_2" description = "Which gaming platform generated the highest total global sales revenue?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PS2" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0081_468_81468620_qa_4/task.toml b/tasks/0081_468_81468620_qa_4/task.toml index bc0a8815cb2858f34af6229aa18cd60075581954..b82cb9b85603a9784d52d4e6b551765b6cf95c46 100644 --- a/tasks/0081_468_81468620_qa_4/task.toml +++ b/tasks/0081_468_81468620_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0081_468_81468620_qa_4" +name = "smoldataenvs-train/0081_468_81468620_qa_4" description = "Which video game genre has the highest sales revenue in Japan?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Role-Playing" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0081_472_81472707_qa_1/task.toml b/tasks/0081_472_81472707_qa_1/task.toml index 2f309e6b397c7e308ece15c7415350589066b68d..e751f8431cac7255fa250b26e386549e8de1bd45 100644 --- a/tasks/0081_472_81472707_qa_1/task.toml +++ b/tasks/0081_472_81472707_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0081_472_81472707_qa_1" +name = "smoldataenvs-train/0081_472_81472707_qa_1" description = "Which contract type has the highest customer churn rate, and what is the numerical value of this churn rate?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Month-to-month, 42.71" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0081_472_81472707_qa_3/task.toml b/tasks/0081_472_81472707_qa_3/task.toml index 462bfdb4caca972b2e2e9a2a61ba45b8c6d212d8..88e12bee0709de6d8b5715d6dd9a788a131f7092 100644 --- a/tasks/0081_472_81472707_qa_3/task.toml +++ b/tasks/0081_472_81472707_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0081_472_81472707_qa_3" +name = "smoldataenvs-train/0081_472_81472707_qa_3" description = "What p-value was calculated to determine the statistical significance of the churn rate difference between month-to-month and one-year contracts?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.0" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0081_477_81477923_qa_2/task.toml b/tasks/0081_477_81477923_qa_2/task.toml index f9ba1d2071e40a5da0296ff660262858bbddbb57..d31aa75ce0835e5a2bbca103b82c3279ac4e8981 100644 --- a/tasks/0081_477_81477923_qa_2/task.toml +++ b/tasks/0081_477_81477923_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0081_477_81477923_qa_2" +name = "smoldataenvs-train/0081_477_81477923_qa_2" description = "Which car body style appears most frequently in the dataset after data preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "sedan" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0081_485_81485574_qa_1/task.toml b/tasks/0081_485_81485574_qa_1/task.toml index 9ddb324c3919600b87e25c40ec6e472c65f9c39c..8aaf2b75aabe323e7077091b415a7038ee50b176 100644 --- a/tasks/0081_485_81485574_qa_1/task.toml +++ b/tasks/0081_485_81485574_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0081_485_81485574_qa_1" +name = "smoldataenvs-train/0081_485_81485574_qa_1" description = "What is the average number of turns played in chess games categorized by the four rating levels (low, mid, high, pro)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "low: 50.0, mid: 58.0, high: 65.0, pro: 70.0" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0081_600_81600626_qa_2/task.toml b/tasks/0081_600_81600626_qa_2/task.toml index c61aa8a3a828df83a9de071a530b4db110a40775..e97e2b18ddd439bd733b148fd91c4597afbbdaea 100644 --- a/tasks/0081_600_81600626_qa_2/task.toml +++ b/tasks/0081_600_81600626_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0081_600_81600626_qa_2" +name = "smoldataenvs-train/0081_600_81600626_qa_2" description = "Which calendar year had the highest total global sales of video games according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2008" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0081_615_81615013_qa_3/task.toml b/tasks/0081_615_81615013_qa_3/task.toml index f67cd1d36e609f0fd2f95f78dada3d0cdee4935b..a2aceccc1a73c1806562cfee928bfcc6aa0e3df6 100644 --- a/tasks/0081_615_81615013_qa_3/task.toml +++ b/tasks/0081_615_81615013_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0081_615_81615013_qa_3" +name = "smoldataenvs-train/0081_615_81615013_qa_3" description = "How many missing values existed in the 'MINIMUM_PAYMENTS' column before the imputation process was applied?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "313" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0081_615_81615013_qa_4/task.toml b/tasks/0081_615_81615013_qa_4/task.toml index f5bf52b46d4b457ea0045af3356b60931bf9599d..15dd6da986b99dd5a871ce16ec11f9783c937b30 100644 --- a/tasks/0081_615_81615013_qa_4/task.toml +++ b/tasks/0081_615_81615013_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0081_615_81615013_qa_4" +name = "smoldataenvs-train/0081_615_81615013_qa_4" description = "After standardizing the dataset, how many variables were transformed to have zero mean and unit variance?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "17" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0081_701_81701476_qa_3/task.toml b/tasks/0081_701_81701476_qa_3/task.toml index 65ff84e48e7b2f91af5a35e822111f2029795c8a..c806ec43dff2d31b76e9177cd01e67481ce2efc9 100644 --- a/tasks/0081_701_81701476_qa_3/task.toml +++ b/tasks/0081_701_81701476_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0081_701_81701476_qa_3" +name = "smoldataenvs-train/0081_701_81701476_qa_3" description = "What is the skewness value for the Sepal Width feature in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.334053" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0081_701_81701476_qa_5/task.toml b/tasks/0081_701_81701476_qa_5/task.toml index e138f6361d7088a2120b95375d790b07d1c16c16..8a3add22e07bb0895af14bd58814eea21e8d42f3 100644 --- a/tasks/0081_701_81701476_qa_5/task.toml +++ b/tasks/0081_701_81701476_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0081_701_81701476_qa_5" +name = "smoldataenvs-train/0081_701_81701476_qa_5" description = "What is the kurtosis value for the Petal Length feature in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-1.401921" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0081_799_81799453_qa_4/task.toml b/tasks/0081_799_81799453_qa_4/task.toml index d9afa93af857b4b4589bae34577a0a6ce3271101..50474a96aa59d030bceb7ab05fd07c2db1e6839a 100644 --- a/tasks/0081_799_81799453_qa_4/task.toml +++ b/tasks/0081_799_81799453_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0081_799_81799453_qa_4" +name = "smoldataenvs-train/0081_799_81799453_qa_4" description = "Which house condition category has the highest frequency in the dataset, and how many houses belong to it?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Condition 3, 14031" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0081_833_81833134_qa_4/task.toml b/tasks/0081_833_81833134_qa_4/task.toml index 9f06d68a14391dc90a7ba82b8343f19e6866bad2..5ced4a445b5ff617b50b870d56af650f8641ca3b 100644 --- a/tasks/0081_833_81833134_qa_4/task.toml +++ b/tasks/0081_833_81833134_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0081_833_81833134_qa_4" +name = "smoldataenvs-train/0081_833_81833134_qa_4" description = "What is the correlation coefficient between customer tenure and churn status, and what does the direction indicate?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.35" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0081_836_81836619_qa_3/task.toml b/tasks/0081_836_81836619_qa_3/task.toml index d1133122d8035632956a35d03611a4ebe963af75..e517516b77bf7d3a66924a16165c4e6774c5f86f 100644 --- a/tasks/0081_836_81836619_qa_3/task.toml +++ b/tasks/0081_836_81836619_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0081_836_81836619_qa_3" +name = "smoldataenvs-train/0081_836_81836619_qa_3" description = "Which predictor variable has the smallest p-value in the ANOVA test?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0081_873_81873111_qa_2/task.toml b/tasks/0081_873_81873111_qa_2/task.toml index 9566b04cc0199e1bc68abc27086f4387f5994fb2..205582087cab2ee7fa3fb0dc1af219bc18ccdb63 100644 --- a/tasks/0081_873_81873111_qa_2/task.toml +++ b/tasks/0081_873_81873111_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0081_873_81873111_qa_2" +name = "smoldataenvs-train/0081_873_81873111_qa_2" description = "Which numeric column had the highest number of missing values imputed using the median value of 30.0?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "life_sq" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0081_879_81879993_qa_2/task.toml b/tasks/0081_879_81879993_qa_2/task.toml index addc85165af275edd4e9c3f6d9155c847cb4f63b..3d25c896b7c8cfc8356efcc8a8935456d59d4379 100644 --- a/tasks/0081_879_81879993_qa_2/task.toml +++ b/tasks/0081_879_81879993_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0081_879_81879993_qa_2" +name = "smoldataenvs-train/0081_879_81879993_qa_2" description = "How many houses are included in the top 1% highest-priced segment of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "217" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0081_967_81967576_qa_2/task.toml b/tasks/0081_967_81967576_qa_2/task.toml index d22a1fe000c085c6befed5aa2372408ed67e3a5e..8092e54e5a615c8be8bb92f61405d9ebd4617f5b 100644 --- a/tasks/0081_967_81967576_qa_2/task.toml +++ b/tasks/0081_967_81967576_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0081_967_81967576_qa_2" +name = "smoldataenvs-train/0081_967_81967576_qa_2" description = "What is the standard deviation of the PetalLengthCm measurements across all samples in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.764420" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0082_302_82302927_qa_3/task.toml b/tasks/0082_302_82302927_qa_3/task.toml index 4840721ebcdac7344f9e5e91e25cad7924057a92..0f81b2ac60ff5d03bf2b3754e3b3d3cc2ed9495f 100644 --- a/tasks/0082_302_82302927_qa_3/task.toml +++ b/tasks/0082_302_82302927_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0082_302_82302927_qa_3" +name = "smoldataenvs-train/0082_302_82302927_qa_3" description = "What is the adjusted R-squared value of the regression model explaining the variation in insurance charges?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.7487" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0082_332_82332185_qa_2/task.toml b/tasks/0082_332_82332185_qa_2/task.toml index 9c153ba45ee2b1c0bca52105d0dce98079c2a181..2e9088564c8fbe1330473f82a134172435fbfa1f 100644 --- a/tasks/0082_332_82332185_qa_2/task.toml +++ b/tasks/0082_332_82332185_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0082_332_82332185_qa_2" +name = "smoldataenvs-train/0082_332_82332185_qa_2" description = "Which gender had the highest survival rate according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "female" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0082_332_82332185_qa_5/task.toml b/tasks/0082_332_82332185_qa_5/task.toml index 2ad8c0e6c677dcf7c999d4aaa9ae49edab241564..e3cc412e13151789a5b0802fb591cb0b44ad3e6c 100644 --- a/tasks/0082_332_82332185_qa_5/task.toml +++ b/tasks/0082_332_82332185_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0082_332_82332185_qa_5" +name = "smoldataenvs-train/0082_332_82332185_qa_5" description = "What is the highest survival rate observed across different fare bands in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.581081" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0082_373_82373986_qa_1/task.toml b/tasks/0082_373_82373986_qa_1/task.toml index 89c73e87be570e46a1736a49c723f2c0a35aa20f..681ab7598cd95c5ab1a039fabd426d3384d288a7 100644 --- a/tasks/0082_373_82373986_qa_1/task.toml +++ b/tasks/0082_373_82373986_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0082_373_82373986_qa_1" +name = "smoldataenvs-train/0082_373_82373986_qa_1" description = "What is the average number of images per unique individual in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0082_384_82384588_qa_1/task.toml b/tasks/0082_384_82384588_qa_1/task.toml index fded5baeff4fb68ea8ba05ae375e5c54d0de2828..22d96f3f7cde5f492060d7faaf7cb47f34053e81 100644 --- a/tasks/0082_384_82384588_qa_1/task.toml +++ b/tasks/0082_384_82384588_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0082_384_82384588_qa_1" +name = "smoldataenvs-train/0082_384_82384588_qa_1" description = "Which of the measured features (sepal length, sepal width, petal length, petal width) has the highest standard deviation in the Iris dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "petal length" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0082_384_82384588_qa_5/task.toml b/tasks/0082_384_82384588_qa_5/task.toml index b92ff6b9d323682b3dc609e9830494ee303c83f7..904ef149126069ba1f63419fc491e4818a03d5dc 100644 --- a/tasks/0082_384_82384588_qa_5/task.toml +++ b/tasks/0082_384_82384588_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0082_384_82384588_qa_5" +name = "smoldataenvs-train/0082_384_82384588_qa_5" description = "What is the interquartile range (IQR) of the sepal lengths in the Iris dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0082_385_82385327_qa_3/task.toml b/tasks/0082_385_82385327_qa_3/task.toml index 8773c96c02f9ba24d714cb4bd14ec64a90958f73..6fa2a4f6ce61dea5bc9951c2f376916b34be107f 100644 --- a/tasks/0082_385_82385327_qa_3/task.toml +++ b/tasks/0082_385_82385327_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0082_385_82385327_qa_3" +name = "smoldataenvs-train/0082_385_82385327_qa_3" description = "Which year saw the release of the first game in the dataset to exceed 10,000 units in sales?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1980" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0082_385_82385327_qa_5/task.toml b/tasks/0082_385_82385327_qa_5/task.toml index 3fcc3cfa7f152c6985c396fa4044eaebbe020450..057fb1ce436b63f8e1ead2465d5f54ba7fff41b3 100644 --- a/tasks/0082_385_82385327_qa_5/task.toml +++ b/tasks/0082_385_82385327_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0082_385_82385327_qa_5" +name = "smoldataenvs-train/0082_385_82385327_qa_5" description = "What is the difference between the highest and lowest global sales figures among the top 20 highest-grossing games?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "62.52" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0082_498_82498397_qa_1/task.toml b/tasks/0082_498_82498397_qa_1/task.toml index 40fd7a1fc40e62b00a192e6bc234bfbdf911bdc5..bd7a586c7ad0b6419cfcfc08f300c07217ec1953 100644 --- a/tasks/0082_498_82498397_qa_1/task.toml +++ b/tasks/0082_498_82498397_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0082_498_82498397_qa_1" +name = "smoldataenvs-train/0082_498_82498397_qa_1" description = "Which transaction category has the highest percentage of fraudulent transactions based on the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "es_leisure" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0082_498_82498397_qa_3/task.toml b/tasks/0082_498_82498397_qa_3/task.toml index 01db6e560c4033807a37b68a67ae7d82d49055fc..99a9a7a089524f8069f85f81e1f86331d2ecd2c8 100644 --- a/tasks/0082_498_82498397_qa_3/task.toml +++ b/tasks/0082_498_82498397_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0082_498_82498397_qa_3" +name = "smoldataenvs-train/0082_498_82498397_qa_3" description = "Which age group (as categorized in the dataset) has the highest fraud rate percentage?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0082_498_82498397_qa_4/task.toml b/tasks/0082_498_82498397_qa_4/task.toml index 01714d0b26fdab2325a1b9373da14f26e741e613..742e8027c7e71b5c50cbe9798cb7ef100b37bd44 100644 --- a/tasks/0082_498_82498397_qa_4/task.toml +++ b/tasks/0082_498_82498397_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0082_498_82498397_qa_4" +name = "smoldataenvs-train/0082_498_82498397_qa_4" description = "After applying SMOTE oversampling, how many samples are present for each fraud class in the resampled dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "587443" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0082_498_82498397_qa_5/task.toml b/tasks/0082_498_82498397_qa_5/task.toml index d57a830e41f4d8fa3d2b8bea60ee60cd8f89f626..d644e3de7c483946a8e340d8312cc0ab3894ac46 100644 --- a/tasks/0082_498_82498397_qa_5/task.toml +++ b/tasks/0082_498_82498397_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0082_498_82498397_qa_5" +name = "smoldataenvs-train/0082_498_82498397_qa_5" description = "What is the baseline accuracy threshold that machine learning models must exceed to outperform a trivial classifier that always predicts non-fraudulent transactions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "98.789" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0082_619_82619539_qa_1/task.toml b/tasks/0082_619_82619539_qa_1/task.toml index b937b6f54774cc50a29afe565420c01a4df89140..01cfd606a52dcc089652e661e5c77309149cb6a2 100644 --- a/tasks/0082_619_82619539_qa_1/task.toml +++ b/tasks/0082_619_82619539_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0082_619_82619539_qa_1" +name = "smoldataenvs-train/0082_619_82619539_qa_1" description = "Which month had the highest number of landslide events after parsing the date column into month names?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "July" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0082_619_82619539_qa_4/task.toml b/tasks/0082_619_82619539_qa_4/task.toml index 6b16acd7c69a867b352f1557a15d9f57a04715a5..c6a6a0c642309af9471a011864ea179d19f00683 100644 --- a/tasks/0082_619_82619539_qa_4/task.toml +++ b/tasks/0082_619_82619539_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0082_619_82619539_qa_4" +name = "smoldataenvs-train/0082_619_82619539_qa_4" description = "After imputing continent codes based on country names, which continent has the highest frequency of landslide events in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "North America" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0082_646_82646090_qa_2/task.toml b/tasks/0082_646_82646090_qa_2/task.toml index e03b98aca74c5f14ed9ec3768e02c27e167d0f63..b53505c6efccbb135d52b84078da6228c7674853 100644 --- a/tasks/0082_646_82646090_qa_2/task.toml +++ b/tasks/0082_646_82646090_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0082_646_82646090_qa_2" +name = "smoldataenvs-train/0082_646_82646090_qa_2" description = "What is the percentage of missing values in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0082_646_82646090_qa_3/task.toml b/tasks/0082_646_82646090_qa_3/task.toml index 7317bbcf7931ea4dcd35d0a7c79b44727412bef0..72b18691047dda7510117b210d5dba2c28620ffe 100644 --- a/tasks/0082_646_82646090_qa_3/task.toml +++ b/tasks/0082_646_82646090_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0082_646_82646090_qa_3" +name = "smoldataenvs-train/0082_646_82646090_qa_3" description = "What is the average salary for employees in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "76003.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0082_658_82658184_qa_1/task.toml b/tasks/0082_658_82658184_qa_1/task.toml index c7b5e082b3621960fddcd63bf5c7c6229ff7f326..b10f83572d5a025f435711b3681590cb25e35632 100644 --- a/tasks/0082_658_82658184_qa_1/task.toml +++ b/tasks/0082_658_82658184_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0082_658_82658184_qa_1" +name = "smoldataenvs-train/0082_658_82658184_qa_1" description = "What is the ratio of churn probability between customers without a partner and those with a partner in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.7" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0082_759_82759610_qa_3/task.toml b/tasks/0082_759_82759610_qa_3/task.toml index 590f10c177fd942f3385e5935bf49099f37e1335..867b016b398e8966df5d5eee85e2c5069c81a2c9 100644 --- a/tasks/0082_759_82759610_qa_3/task.toml +++ b/tasks/0082_759_82759610_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0082_759_82759610_qa_3" +name = "smoldataenvs-train/0082_759_82759610_qa_3" description = "What is the slope of the regression line, representing the change in salary per year of experience?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9449.962321455076" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0082_759_82759610_qa_5/task.toml b/tasks/0082_759_82759610_qa_5/task.toml index 81536a696cdb765f4fc39f85c07138f2f19c694e..fbe79d4fb68bce724a3b96edf620ad5f70a8214d 100644 --- a/tasks/0082_759_82759610_qa_5/task.toml +++ b/tasks/0082_759_82759610_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0082_759_82759610_qa_5" +name = "smoldataenvs-train/0082_759_82759610_qa_5" description = "What is the highest predicted salary in the dataset according to the linear regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "125016.804574" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0082_858_82858946_qa_5/task.toml b/tasks/0082_858_82858946_qa_5/task.toml index 1b5059066d0cbafb7a3e5b2de5ac08350c011a4c..366a63088077c481696227d5617454df31d3b5a5 100644 --- a/tasks/0082_858_82858946_qa_5/task.toml +++ b/tasks/0082_858_82858946_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0082_858_82858946_qa_5" +name = "smoldataenvs-train/0082_858_82858946_qa_5" description = "What is the most popular starting point for Uber drivers based on trip count?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Cary" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0082_935_82935300_qa_2/task.toml b/tasks/0082_935_82935300_qa_2/task.toml index 3977c83c266a953cc370f997bf07e67fa8419124..93a88563e6bde6f4601ec46637a8d335206f145b 100644 --- a/tasks/0082_935_82935300_qa_2/task.toml +++ b/tasks/0082_935_82935300_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0082_935_82935300_qa_2" +name = "smoldataenvs-train/0082_935_82935300_qa_2" description = "What percentage of missing values were present in the TotalCharges column before data cleaning, and how many rows were removed during this process?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.156% missing, 11 rows removed" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0082_939_82939452_qa_1/task.toml b/tasks/0082_939_82939452_qa_1/task.toml index c62e0b860e91af0bb7b90817749280f5d8d33a0e..06e7684a53a708f69a9a10540eebecba42b0e6f5 100644 --- a/tasks/0082_939_82939452_qa_1/task.toml +++ b/tasks/0082_939_82939452_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0082_939_82939452_qa_1" +name = "smoldataenvs-train/0082_939_82939452_qa_1" description = "What percentage of clients in the dataset subscribed to a term deposit?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "47.38" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0082_939_82939452_qa_3/task.toml b/tasks/0082_939_82939452_qa_3/task.toml index 9d59e0c33ac8a670fc2b13abf655e4940db26bc6..f9a278f1ba0f1b458a77145a42d92205850a0791 100644 --- a/tasks/0082_939_82939452_qa_3/task.toml +++ b/tasks/0082_939_82939452_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0082_939_82939452_qa_3" +name = "smoldataenvs-train/0082_939_82939452_qa_3" description = "Which numerical feature in the dataset has the highest standard deviation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "balance" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0082_955_82955964_qa_2/task.toml b/tasks/0082_955_82955964_qa_2/task.toml index 0c996b005edd0cb9e5b9d03bb45f03a889a2b026..316e014ad6065f3dcec2146169788e1449995e6e 100644 --- a/tasks/0082_955_82955964_qa_2/task.toml +++ b/tasks/0082_955_82955964_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0082_955_82955964_qa_2" +name = "smoldataenvs-train/0082_955_82955964_qa_2" description = "What is the standard deviation of the sale prices in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "79442.502883" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0082_955_82955964_qa_3/task.toml b/tasks/0082_955_82955964_qa_3/task.toml index f189bc5a4d1381ed01d245d9158162e1e6a6ccd0..11d63650be604d06f03fda0f5795c4c220a1f979 100644 --- a/tasks/0082_955_82955964_qa_3/task.toml +++ b/tasks/0082_955_82955964_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0082_955_82955964_qa_3" +name = "smoldataenvs-train/0082_955_82955964_qa_3" description = "Does the sale price distribution exhibit right skewness based on the histogram visualization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] build_timeout_sec = 600.0 diff --git a/tasks/0082_960_82960295_qa_4/task.toml b/tasks/0082_960_82960295_qa_4/task.toml index 27f4e46aa0310bec3799c8d36cfd7ed910b44470..423a183b38adeb7ba8bff10f6ed59cdfc1b36794 100644 --- a/tasks/0082_960_82960295_qa_4/task.toml +++ b/tasks/0082_960_82960295_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0082_960_82960295_qa_4" +name = "smoldataenvs-train/0082_960_82960295_qa_4" description = "What is the area under the ROC curve (AUC) for the Random Forest classifier on the test data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.90" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0082_994_82994330_qa_5/task.toml b/tasks/0082_994_82994330_qa_5/task.toml index 9e865f937559ea4f087fb7cf5447327e247c6860..473a7c82517515cdae95a64f5a25a5939f4abc3d 100644 --- a/tasks/0082_994_82994330_qa_5/task.toml +++ b/tasks/0082_994_82994330_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0082_994_82994330_qa_5" +name = "smoldataenvs-train/0082_994_82994330_qa_5" description = "Which columns have the highest percentage of missing data in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Height, Weight" reward_mode_initial = "list" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0083_022_83022280_qa_3/task.toml b/tasks/0083_022_83022280_qa_3/task.toml index 642960510346be1ffbfa4e059887db478e871ef4..b7f27e31ce609ef6f5e1e5b785ca5ed6194acc9c 100644 --- a/tasks/0083_022_83022280_qa_3/task.toml +++ b/tasks/0083_022_83022280_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0083_022_83022280_qa_3" +name = "smoldataenvs-train/0083_022_83022280_qa_3" description = "What percentage of the dataset was allocated to the test set during the train-test split for model evaluation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0083_022_83022280_qa_4/task.toml b/tasks/0083_022_83022280_qa_4/task.toml index 7a1aba9abe8966d8b9ef0d892e3f50341e7a7228..6793d96b58edb3c90ea4517ab628611e2228325c 100644 --- a/tasks/0083_022_83022280_qa_4/task.toml +++ b/tasks/0083_022_83022280_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0083_022_83022280_qa_4" +name = "smoldataenvs-train/0083_022_83022280_qa_4" description = "What is the F1-score for the second species class (labeled as 1) using the Logistic Regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.96" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0083_063_83063460_qa_3/task.toml b/tasks/0083_063_83063460_qa_3/task.toml index e62850f0a58eee255e8cecf4eb82778f0c7f5252..235bdd707ba35585dffc17d4cdb4ccefe2d8757c 100644 --- a/tasks/0083_063_83063460_qa_3/task.toml +++ b/tasks/0083_063_83063460_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0083_063_83063460_qa_3" +name = "smoldataenvs-train/0083_063_83063460_qa_3" description = "What is the distribution of diagnosis labels in the dataset (count of benign vs. malignant cases)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Benign: 357, Malignant: 212" reward_mode_initial = "list" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0083_063_83063460_qa_4/task.toml b/tasks/0083_063_83063460_qa_4/task.toml index c30e984e4d1ec69505fca7bd8c950916c0a45db7..4a3594a2411be03f621c1dad1ccd52377155e1d9 100644 --- a/tasks/0083_063_83063460_qa_4/task.toml +++ b/tasks/0083_063_83063460_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0083_063_83063460_qa_4" +name = "smoldataenvs-train/0083_063_83063460_qa_4" description = "Which feature shows the strongest positive correlation with the diagnosis target variable, and what is its exact correlation coefficient?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "concave points_worst, 0.793566" reward_mode_initial = "list" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0083_063_83063460_qa_5/task.toml b/tasks/0083_063_83063460_qa_5/task.toml index d71bc0fea152a8b5159f0a0ef258b49b1845b923..74e5cfe6fb0f2126f8d04031587c250ea4df96d4 100644 --- a/tasks/0083_063_83063460_qa_5/task.toml +++ b/tasks/0083_063_83063460_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0083_063_83063460_qa_5" +name = "smoldataenvs-train/0083_063_83063460_qa_5" description = "How many features were included in the final preprocessed dataset used for model training after feature selection and skewness treatment?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0083_069_83069370_qa_2/task.toml b/tasks/0083_069_83069370_qa_2/task.toml index 1c92a54040f594017ca68646403f8e968fe194d1..daa06b3749d61a4883025c4799c4e4a3f675015a 100644 --- a/tasks/0083_069_83069370_qa_2/task.toml +++ b/tasks/0083_069_83069370_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0083_069_83069370_qa_2" +name = "smoldataenvs-train/0083_069_83069370_qa_2" description = "What was the highest correlation coefficient between any independent variable and the Profit in the initial correlation analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.978" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0083_113_83113866_qa_4/task.toml b/tasks/0083_113_83113866_qa_4/task.toml index c35f6425312d0e2b69abbec2b0bcf6ad5d147b19..847649932fe598dc953067236647e89ba6c4cadb 100644 --- a/tasks/0083_113_83113866_qa_4/task.toml +++ b/tasks/0083_113_83113866_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0083_113_83113866_qa_4" +name = "smoldataenvs-train/0083_113_83113866_qa_4" description = "After data preprocessing, what is the mean price of houses in the cleaned dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "540088.1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0083_138_83138273_qa_5/task.toml b/tasks/0083_138_83138273_qa_5/task.toml index 86b19cfab13ebf331227dfe6ed72fa81e3a328c9..9e7122c03dc4afd9e452d4091fc8d5d7c386d4a5 100644 --- a/tasks/0083_138_83138273_qa_5/task.toml +++ b/tasks/0083_138_83138273_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0083_138_83138273_qa_5" +name = "smoldataenvs-train/0083_138_83138273_qa_5" description = "What percentage of homes in the training dataset have a sale price above the 75th percentile value of $214,000?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "25" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0083_191_83191425_qa_1/task.toml b/tasks/0083_191_83191425_qa_1/task.toml index 338b65c15e1b1cc3ef8822527231aefa5380da6f..06202c3c876542d139d9276558b1023d23caf006 100644 --- a/tasks/0083_191_83191425_qa_1/task.toml +++ b/tasks/0083_191_83191425_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0083_191_83191425_qa_1" +name = "smoldataenvs-train/0083_191_83191425_qa_1" description = "What is the optimal number of clusters (k) based on the elbow method for inertia values in the Mall Customers dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0083_191_83191425_qa_3/task.toml b/tasks/0083_191_83191425_qa_3/task.toml index 0b3ea347e31e4177841c0ebb6779d8de7951263d..7bbaf8494d25f54ca35eea92424a5522070587dc 100644 --- a/tasks/0083_191_83191425_qa_3/task.toml +++ b/tasks/0083_191_83191425_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0083_191_83191425_qa_3" +name = "smoldataenvs-train/0083_191_83191425_qa_3" description = "What is the silhouette score for the K-Means clustering model when k=5?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.553931997444648" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0083_191_83191425_qa_5/task.toml b/tasks/0083_191_83191425_qa_5/task.toml index bb7197f44b3444b65bc6b304da7a985df5bc5381..fc017a80f42caceb7180eb4840db4a185ae6a724 100644 --- a/tasks/0083_191_83191425_qa_5/task.toml +++ b/tasks/0083_191_83191425_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0083_191_83191425_qa_5" +name = "smoldataenvs-train/0083_191_83191425_qa_5" description = "What is the inertia value when K-Means is run with k equal to the number of data points in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0083_238_83238182_qa_2/task.toml b/tasks/0083_238_83238182_qa_2/task.toml index da63fc4503a365e1b921c9678d343b5d353ecfb6..e6469bf341813d9947fae685c9be212c84b3e8ed 100644 --- a/tasks/0083_238_83238182_qa_2/task.toml +++ b/tasks/0083_238_83238182_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0083_238_83238182_qa_2" +name = "smoldataenvs-train/0083_238_83238182_qa_2" description = "What is the highest correlation coefficient between any two features in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.00" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0083_317_83317900_qa_1/task.toml b/tasks/0083_317_83317900_qa_1/task.toml index 4096d15489db99b59719c1906f6c3b4bdee82092..e5905632c48a99c3002ab2a444b01c36561506d0 100644 --- a/tasks/0083_317_83317900_qa_1/task.toml +++ b/tasks/0083_317_83317900_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0083_317_83317900_qa_1" +name = "smoldataenvs-train/0083_317_83317900_qa_1" description = "What is the test accuracy of the KNN model on the mobile price classification dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9433333333333334" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0083_618_83618078_qa_4/task.toml b/tasks/0083_618_83618078_qa_4/task.toml index 87353ed871dcf69a9cf6f5849e86be6a9f8530a1..1858e67b1b78cda14a86e3cef4e4be27f9678bc2 100644 --- a/tasks/0083_618_83618078_qa_4/task.toml +++ b/tasks/0083_618_83618078_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0083_618_83618078_qa_4" +name = "smoldataenvs-train/0083_618_83618078_qa_4" description = "What is the difference in ROC-AUC score between the best hyperparameterized Logistic Regression model and the baseline Dummy Classifier on the validation set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.4869" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0083_620_83620010_qa_4/task.toml b/tasks/0083_620_83620010_qa_4/task.toml index f03f7c5ceeab80416921edf2c38b03c07d16ece8..9187ac75f28b32bbf3cc950c782fc656d3bdc9d5 100644 --- a/tasks/0083_620_83620010_qa_4/task.toml +++ b/tasks/0083_620_83620010_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0083_620_83620010_qa_4" +name = "smoldataenvs-train/0083_620_83620010_qa_4" description = "Which topic has the highest percentage of students from Palestine among the top four nationalities?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Biology" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0083_648_83648661_qa_1/task.toml b/tasks/0083_648_83648661_qa_1/task.toml index 333995e3e0cba723dc4a7a9804c1767390ecac76..670817909855105a86362396960249bfa68f5ec5 100644 --- a/tasks/0083_648_83648661_qa_1/task.toml +++ b/tasks/0083_648_83648661_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0083_648_83648661_qa_1" +name = "smoldataenvs-train/0083_648_83648661_qa_1" description = "What is the correlation coefficient between the variables 'x' and 'y' in the cleaned training dataset after removing outliers?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.995" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0083_648_83648661_qa_3/task.toml b/tasks/0083_648_83648661_qa_3/task.toml index fdfef96296d681b7b7dd6dfa88790f1f6e8be0d3..6201537213a5ccec84ee127c4175dbb7af79ea6d 100644 --- a/tasks/0083_648_83648661_qa_3/task.toml +++ b/tasks/0083_648_83648661_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0083_648_83648661_qa_3" +name = "smoldataenvs-train/0083_648_83648661_qa_3" description = "Is the linear regression model's performance on the test set (R² = 0.989) statistically consistent with its performance on the training set (R² = 0.991)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0083_938_83938085_qa_5/task.toml b/tasks/0083_938_83938085_qa_5/task.toml index 7dc5d8b3fb10501d6bf22fe8485fbf7afce14fc4..df4b1fc9dad4eee57c5e0dd02917500c16e8e6e0 100644 --- a/tasks/0083_938_83938085_qa_5/task.toml +++ b/tasks/0083_938_83938085_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0083_938_83938085_qa_5" +name = "smoldataenvs-train/0083_938_83938085_qa_5" description = "Which region and sex combination has the highest number of smokers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "southeast male" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0083_989_83989999_qa_2/task.toml b/tasks/0083_989_83989999_qa_2/task.toml index bb71de6b1f67fc46a096fbf16bffef3d683b9a27..22eebbf15cc0f940ffae47fc88c3876e2516fb84 100644 --- a/tasks/0083_989_83989999_qa_2/task.toml +++ b/tasks/0083_989_83989999_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0083_989_83989999_qa_2" +name = "smoldataenvs-train/0083_989_83989999_qa_2" description = "How many discrete numerical variables are present in the dataset after excluding year-related variables?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0084_132_84132638_qa_1/task.toml b/tasks/0084_132_84132638_qa_1/task.toml index b9e746e53a8072309d646e3aabdd5a172490b373..621b627f99323dca8eb599edbd9d30d840483d36 100644 --- a/tasks/0084_132_84132638_qa_1/task.toml +++ b/tasks/0084_132_84132638_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0084_132_84132638_qa_1" +name = "smoldataenvs-train/0084_132_84132638_qa_1" description = "How many data points were used for training after handling missing values in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "699" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0084_135_84135613_qa_1/task.toml b/tasks/0084_135_84135613_qa_1/task.toml index b0024a5daba3f3dbdda21de0dc899c05c180a523..0291eb08b34d8f9fba3f5581c7aa0421b7e94178 100644 --- a/tasks/0084_135_84135613_qa_1/task.toml +++ b/tasks/0084_135_84135613_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0084_135_84135613_qa_1" +name = "smoldataenvs-train/0084_135_84135613_qa_1" description = "What is the highest absolute correlation coefficient between any two features in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.79" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0084_135_84135613_qa_4/task.toml b/tasks/0084_135_84135613_qa_4/task.toml index f1adea19c251da1b6a325a0fbc3dafa625f9ef45..972a67e5ca54584f2e268d17e916a478e591331b 100644 --- a/tasks/0084_135_84135613_qa_4/task.toml +++ b/tasks/0084_135_84135613_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0084_135_84135613_qa_4" +name = "smoldataenvs-train/0084_135_84135613_qa_4" description = "Which forest cover type has the highest precision score in the model's performance evaluation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0084_148_84148842_qa_5/task.toml b/tasks/0084_148_84148842_qa_5/task.toml index b35caaf9064cfe7fca7765f6352ce1131a4acb91..8cc9067271e8e6a2265163c7f155725347857a51 100644 --- a/tasks/0084_148_84148842_qa_5/task.toml +++ b/tasks/0084_148_84148842_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0084_148_84148842_qa_5" +name = "smoldataenvs-train/0084_148_84148842_qa_5" description = "What is the range of cereal ratings in the dataset (difference between maximum and minimum values)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "75.66" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0084_293_84293465_qa_2/task.toml b/tasks/0084_293_84293465_qa_2/task.toml index 77f55c280fe0366a690c4039188feee199692144..6851a6e5ac85accfa2fc3f5f8ca4750067cea433 100644 --- a/tasks/0084_293_84293465_qa_2/task.toml +++ b/tasks/0084_293_84293465_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0084_293_84293465_qa_2" +name = "smoldataenvs-train/0084_293_84293465_qa_2" description = "How many rows were removed from the dataset due to missing values in the TotalCharges column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0084_297_84297732_qa_2/task.toml b/tasks/0084_297_84297732_qa_2/task.toml index bc1a078d84f0a36f5c66544a2496d83da3ede37c..e967e63bb2c07b275bfe97e782521672b558c0c8 100644 --- a/tasks/0084_297_84297732_qa_2/task.toml +++ b/tasks/0084_297_84297732_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0084_297_84297732_qa_2" +name = "smoldataenvs-train/0084_297_84297732_qa_2" description = "What is the median total sulfur dioxide level for wines with quality scores of 3 or 4?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0084_297_84297732_qa_3/task.toml b/tasks/0084_297_84297732_qa_3/task.toml index 3b9982a1d38b06754652b57b5942906033f377c3..b75a7a6d5e5e924544af816d5cc03704cef7bc8c 100644 --- a/tasks/0084_297_84297732_qa_3/task.toml +++ b/tasks/0084_297_84297732_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0084_297_84297732_qa_3" +name = "smoldataenvs-train/0084_297_84297732_qa_3" description = "What is the mean alcohol content for wines with quality scores of 7 or 8?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11.52" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0084_297_84297732_qa_4/task.toml b/tasks/0084_297_84297732_qa_4/task.toml index 3b0bdf5291d56e7c35bf62dfb55fda95aa050a2c..558b8e29fd924274249c20a85b66cff1c5e9cb36 100644 --- a/tasks/0084_297_84297732_qa_4/task.toml +++ b/tasks/0084_297_84297732_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0084_297_84297732_qa_4" +name = "smoldataenvs-train/0084_297_84297732_qa_4" description = "Which feature in the red wine quality dataset has the highest standard deviation across all wines?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "total sulfur dioxide" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0084_320_84320235_qa_1/task.toml b/tasks/0084_320_84320235_qa_1/task.toml index 7fd3a69d21db167cceac78429139de800aff21af..435a567b0a94143aebb7acac78dfedb61a4bf87a 100644 --- a/tasks/0084_320_84320235_qa_1/task.toml +++ b/tasks/0084_320_84320235_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0084_320_84320235_qa_1" +name = "smoldataenvs-train/0084_320_84320235_qa_1" description = "What is the final optimized cost after performing gradient descent on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "15742742.122957915" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0084_320_84320235_qa_4/task.toml b/tasks/0084_320_84320235_qa_4/task.toml index 97e9f7319b98e08712c2ac2346d459035edb0539..6766dd9024320bd9fb8db34bb6fe071513acd71c 100644 --- a/tasks/0084_320_84320235_qa_4/task.toml +++ b/tasks/0084_320_84320235_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0084_320_84320235_qa_4" +name = "smoldataenvs-train/0084_320_84320235_qa_4" description = "What is the initial cost before performing gradient descent on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3251477635.366667" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0084_326_84326025_qa_3/task.toml b/tasks/0084_326_84326025_qa_3/task.toml index dd6e2d032e86c729d57336dcaafb0f8580c8b749..39db4ee26b0cfa37125193ec12eb0446969ceebd 100644 --- a/tasks/0084_326_84326025_qa_3/task.toml +++ b/tasks/0084_326_84326025_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0084_326_84326025_qa_3" +name = "smoldataenvs-train/0084_326_84326025_qa_3" description = "What was the largest attacker size recorded in any battle led by Joffrey/Tommen Baratheon?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20000" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0084_326_84326025_qa_5/task.toml b/tasks/0084_326_84326025_qa_5/task.toml index e1a540504f02d8b295043e2019ee1dfa5510759f..513fe9309c41ae323bee0c76970aab298f2b29e9 100644 --- a/tasks/0084_326_84326025_qa_5/task.toml +++ b/tasks/0084_326_84326025_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0084_326_84326025_qa_5" +name = "smoldataenvs-train/0084_326_84326025_qa_5" description = "What was the maximum defender size faced by Joffrey/Tommen Baratheon in ambush-style battles?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3500" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0084_460_84460847_qa_2/task.toml b/tasks/0084_460_84460847_qa_2/task.toml index 51fc4e35169e3afd3675bc34e234ff02317b1c7d..e04f3c29921582e6ccc35427f87cde8eed1adfe3 100644 --- a/tasks/0084_460_84460847_qa_2/task.toml +++ b/tasks/0084_460_84460847_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0084_460_84460847_qa_2" +name = "smoldataenvs-train/0084_460_84460847_qa_2" description = "Are there any missing values in the Iris dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0084_523_84523209_qa_1/task.toml b/tasks/0084_523_84523209_qa_1/task.toml index fb5ae6f851db78ce6b6d1dd77d544d650feb9dd3..ab226ab5e191f1f13257acb45810b982e3913c12 100644 --- a/tasks/0084_523_84523209_qa_1/task.toml +++ b/tasks/0084_523_84523209_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0084_523_84523209_qa_1" +name = "smoldataenvs-train/0084_523_84523209_qa_1" description = "Which gaming platform generated the highest total global sales in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PS2" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0084_523_84523209_qa_5/task.toml b/tasks/0084_523_84523209_qa_5/task.toml index 53f763d454d3fcfea47926192a1e949b33286d25..ba811ef77a4ec8a4098909f68cddcfff8202b70e 100644 --- a/tasks/0084_523_84523209_qa_5/task.toml +++ b/tasks/0084_523_84523209_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0084_523_84523209_qa_5" +name = "smoldataenvs-train/0084_523_84523209_qa_5" description = "Which calendar year had the highest number of video game releases in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2008" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0084_536_84536587_qa_1/task.toml b/tasks/0084_536_84536587_qa_1/task.toml index df61c0456355139e9506cdedb59640701bef7090..0eafb11f3dcbda37449266225a710b7b8f5409f0 100644 --- a/tasks/0084_536_84536587_qa_1/task.toml +++ b/tasks/0084_536_84536587_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0084_536_84536587_qa_1" +name = "smoldataenvs-train/0084_536_84536587_qa_1" description = "Which feature in the dataset shows the highest fitness score according to the chi2 statistical test?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Population" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0084_634_84634252_qa_2/task.toml b/tasks/0084_634_84634252_qa_2/task.toml index 75cb53775e6c34306cea99aa8915854dfbdc32e6..63d046612e506df88d63cd55f16615cd318e784d 100644 --- a/tasks/0084_634_84634252_qa_2/task.toml +++ b/tasks/0084_634_84634252_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0084_634_84634252_qa_2" +name = "smoldataenvs-train/0084_634_84634252_qa_2" description = "Which city generated the highest total sales, and what was the exact dollar amount of those sales?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Madrid, 1082551.44" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0084_634_84634252_qa_4/task.toml b/tasks/0084_634_84634252_qa_4/task.toml index 7101e2a9faf7edb974b585f3277878a4f8a826b6..0f3ba7589340d245b3a9bfadb79d2cafa1694e61 100644 --- a/tasks/0084_634_84634252_qa_4/task.toml +++ b/tasks/0084_634_84634252_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0084_634_84634252_qa_4" +name = "smoldataenvs-train/0084_634_84634252_qa_4" description = "What is the average price (PRICEEACH) of products in the Trucks and Buses product line?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "87.53" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0084_727_84727011_qa_2/task.toml b/tasks/0084_727_84727011_qa_2/task.toml index 8a0582996f742fb5f5f678f798463d94a4e2f1d1..cdd645c5a46ddfeb47fd23626c10e864a3f492dd 100644 --- a/tasks/0084_727_84727011_qa_2/task.toml +++ b/tasks/0084_727_84727011_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0084_727_84727011_qa_2" +name = "smoldataenvs-train/0084_727_84727011_qa_2" description = "How many distinct quality categories exist in the wine quality dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0084_727_84727011_qa_5/task.toml b/tasks/0084_727_84727011_qa_5/task.toml index 2c23843e79bf93cdcbc935bc5e52426a3e90b30b..6372e0d9222c10a8c1f0329055b08f6111dd16a1 100644 --- a/tasks/0084_727_84727011_qa_5/task.toml +++ b/tasks/0084_727_84727011_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0084_727_84727011_qa_5" +name = "smoldataenvs-train/0084_727_84727011_qa_5" description = "What is the maximum density value recorded in the entire wine quality dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.00369" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0084_727_84727795_qa_2/task.toml b/tasks/0084_727_84727795_qa_2/task.toml index 20080488f74124a48469aa5a4d24ea5118bdd7b5..42db27f0da23914af00da1cf1838e338a64f9003 100644 --- a/tasks/0084_727_84727795_qa_2/task.toml +++ b/tasks/0084_727_84727795_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0084_727_84727795_qa_2" +name = "smoldataenvs-train/0084_727_84727795_qa_2" description = "What is the highest fixed acidity among the top 5 highest quality wines (quality=8)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12.6" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0084_727_84727795_qa_4/task.toml b/tasks/0084_727_84727795_qa_4/task.toml index 587a25c15e8f36e547ba31bdb3ad9805a32f4825..68d7ce01ef0604f73576795f20f9bf2195952a8a 100644 --- a/tasks/0084_727_84727795_qa_4/task.toml +++ b/tasks/0084_727_84727795_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0084_727_84727795_qa_4" +name = "smoldataenvs-train/0084_727_84727795_qa_4" description = "What is the difference between the maximum and minimum 'total sulfur dioxide' values in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "283.0" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0084_727_84727795_qa_5/task.toml b/tasks/0084_727_84727795_qa_5/task.toml index a8cd2cd71fabbba550215889bcaf3d702c49102e..1fbe60c043613e52dfb7dea0a01a70d5b324610e 100644 --- a/tasks/0084_727_84727795_qa_5/task.toml +++ b/tasks/0084_727_84727795_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0084_727_84727795_qa_5" +name = "smoldataenvs-train/0084_727_84727795_qa_5" description = "What is the average fixed acidity for all wines in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8.32" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0084_737_84737869_qa_2/task.toml b/tasks/0084_737_84737869_qa_2/task.toml index 9938faf5bb8febb1831a080ea4bd0fcf9400a310..0dfd22a69cdce5057bbc2d92246bce169c741094 100644 --- a/tasks/0084_737_84737869_qa_2/task.toml +++ b/tasks/0084_737_84737869_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0084_737_84737869_qa_2" +name = "smoldataenvs-train/0084_737_84737869_qa_2" description = "Which two features were identified as the most significant predictors of diabetes outcome using the ANOVA (f_classif) feature selection method?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose, BMI" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0084_737_84737869_qa_3/task.toml b/tasks/0084_737_84737869_qa_3/task.toml index a750626e30d0ac6ce9125715132e3256df911ee6..279c5c631e5924ace46a247bd49c3bb78b7bf3a0 100644 --- a/tasks/0084_737_84737869_qa_3/task.toml +++ b/tasks/0084_737_84737869_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0084_737_84737869_qa_3" +name = "smoldataenvs-train/0084_737_84737869_qa_3" description = "What is the highest feature importance percentage assigned by the Random Forest classifier for predicting diabetes outcome?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "25.469466" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0084_747_84747621_qa_5/task.toml b/tasks/0084_747_84747621_qa_5/task.toml index 6fd5518a04d85b111f032068156e1402e28a16af..59b55082b5bde3f8dd42d3d1c9234d8152c2f8c5 100644 --- a/tasks/0084_747_84747621_qa_5/task.toml +++ b/tasks/0084_747_84747621_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0084_747_84747621_qa_5" +name = "smoldataenvs-train/0084_747_84747621_qa_5" description = "Do Legendary Pokémon have significantly higher Attack and Defense stats compared to non-Legendary ones?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0084_772_84772070_qa_5/task.toml b/tasks/0084_772_84772070_qa_5/task.toml index 95697255888cdf63b7afef6995768d11115399cd..b916a9c570fcdfb86cf6dcc29c8ff01818b93a62 100644 --- a/tasks/0084_772_84772070_qa_5/task.toml +++ b/tasks/0084_772_84772070_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0084_772_84772070_qa_5" +name = "smoldataenvs-train/0084_772_84772070_qa_5" description = "What percentage of songs in the training dataset are classified as the target class 1?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50.775" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0084_821_84821146_qa_1/task.toml b/tasks/0084_821_84821146_qa_1/task.toml index bd20af593ccf2f461aa5d710445c8676ea4c9a29..4616d1ff56c0a217f2534360c7c4091fb5e70204 100644 --- a/tasks/0084_821_84821146_qa_1/task.toml +++ b/tasks/0084_821_84821146_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0084_821_84821146_qa_1" +name = "smoldataenvs-train/0084_821_84821146_qa_1" description = "Which feature exhibits the highest positive correlation with wine quality in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "alcohol" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0084_827_84827302_qa_2/task.toml b/tasks/0084_827_84827302_qa_2/task.toml index 868abeaf4f87030849aa16a8260f0065bb99511f..c8cb80687147f11d40ea36531b652dabb2ac47e9 100644 --- a/tasks/0084_827_84827302_qa_2/task.toml +++ b/tasks/0084_827_84827302_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0084_827_84827302_qa_2" +name = "smoldataenvs-train/0084_827_84827302_qa_2" description = "What is the highest correlation coefficient between any two numerical variables in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.27" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0084_834_84834261_qa_3/task.toml b/tasks/0084_834_84834261_qa_3/task.toml index 531c2dfa1920bfe7bda7c504a14bc0fc836eb613..7eeff6788c26932494ca8e739ef852785b34c094 100644 --- a/tasks/0084_834_84834261_qa_3/task.toml +++ b/tasks/0084_834_84834261_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0084_834_84834261_qa_3" +name = "smoldataenvs-train/0084_834_84834261_qa_3" description = "Which attribute in the dataset exhibits zero variance (no unique values) after label encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "veil-type" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0084_834_84834261_qa_4/task.toml b/tasks/0084_834_84834261_qa_4/task.toml index f106e1782e6342b56edc4905102ed79b3088197b..8afeea55787a03e283b33a0ca6f950d76f818979 100644 --- a/tasks/0084_834_84834261_qa_4/task.toml +++ b/tasks/0084_834_84834261_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0084_834_84834261_qa_4" +name = "smoldataenvs-train/0084_834_84834261_qa_4" description = "What is the most common value in the 'bruises' attribute after label encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0084_843_84843553_qa_3/task.toml b/tasks/0084_843_84843553_qa_3/task.toml index 92db43feda1252a555291a50ca512cba2dd70db2..ef9fbc3ce13239181bfc815e8dee24a4f9289981 100644 --- a/tasks/0084_843_84843553_qa_3/task.toml +++ b/tasks/0084_843_84843553_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0084_843_84843553_qa_3" +name = "smoldataenvs-train/0084_843_84843553_qa_3" description = "What is the mean number of pregnancies for patients with diabetes compared to those without diabetes?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.8657, 3.2980" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0084_900_84900004_qa_1/task.toml b/tasks/0084_900_84900004_qa_1/task.toml index 64ca5abe235febac58110647ead2a267d23b73d6..bb2fcb100afde7336fb55bfa59ac8ec7fdada205 100644 --- a/tasks/0084_900_84900004_qa_1/task.toml +++ b/tasks/0084_900_84900004_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0084_900_84900004_qa_1" +name = "smoldataenvs-train/0084_900_84900004_qa_1" description = "What is the total number of distinct quality categories present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0084_932_84932987_qa_5/task.toml b/tasks/0084_932_84932987_qa_5/task.toml index 1d1dedd38ba68e3d61ae847606b9d5d2121a4cfc..e7120e56d73e1d87b6987ae55bc3e29245176185 100644 --- a/tasks/0084_932_84932987_qa_5/task.toml +++ b/tasks/0084_932_84932987_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0084_932_84932987_qa_5" +name = "smoldataenvs-train/0084_932_84932987_qa_5" description = "What was the mean value of Insulin in the cleaned dataset after replacing zeros with the mean?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "155.823218" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0084_953_84953718_qa_1/task.toml b/tasks/0084_953_84953718_qa_1/task.toml index d1f1f872883113fbbc899a279fed3e155f40d52b..2b0bf73b44e8d87db9be0c9eb2373c4d3ef934d0 100644 --- a/tasks/0084_953_84953718_qa_1/task.toml +++ b/tasks/0084_953_84953718_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0084_953_84953718_qa_1" +name = "smoldataenvs-train/0084_953_84953718_qa_1" description = "What is the threshold value of axillary nodes where the cumulative probability of survival exceeds 80% for patients surviving more than 5 years?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0084_964_84964347_qa_1/task.toml b/tasks/0084_964_84964347_qa_1/task.toml index 1ade20734cb3f534c7506742bf1d9c506dcbd516..42f745c2caee68d2597a0ababc85a122c2167f7b 100644 --- a/tasks/0084_964_84964347_qa_1/task.toml +++ b/tasks/0084_964_84964347_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0084_964_84964347_qa_1" +name = "smoldataenvs-train/0084_964_84964347_qa_1" description = "Which video game platform has the highest cumulative global sales according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PS2" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0084_977_84977670_qa_3/task.toml b/tasks/0084_977_84977670_qa_3/task.toml index af7e227183fabc70bb43ab68ec95ebf0fe541914..677740c5bf58bb5f749baacbe62d6e156d897a31 100644 --- a/tasks/0084_977_84977670_qa_3/task.toml +++ b/tasks/0084_977_84977670_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0084_977_84977670_qa_3" +name = "smoldataenvs-train/0084_977_84977670_qa_3" description = "What is the p-value from the KPSS test for the IBM stock 'High' price time series?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.01" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0084_979_84979426_qa_5/task.toml b/tasks/0084_979_84979426_qa_5/task.toml index 61adccae17c911b7b7e55b97e9ef25a0daa15cf2..c44b9e3b660f34a50836de4bcf1444ba1137aa3a 100644 --- a/tasks/0084_979_84979426_qa_5/task.toml +++ b/tasks/0084_979_84979426_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0084_979_84979426_qa_5" +name = "smoldataenvs-train/0084_979_84979426_qa_5" description = "What repayment interval type is associated with the highest total loan amount in Brazil's dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Irregular" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0084_981_84981806_qa_1/task.toml b/tasks/0084_981_84981806_qa_1/task.toml index 7b6dccf8eddba30fa2ae9f495ad75ee9d0ab9854..e9cf12ede94ebdb66c0bafe9c2430e5c4cdc83cc 100644 --- a/tasks/0084_981_84981806_qa_1/task.toml +++ b/tasks/0084_981_84981806_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0084_981_84981806_qa_1" +name = "smoldataenvs-train/0084_981_84981806_qa_1" description = "What is the attrition rate (proportion of employees who left) in the original dataset before any modeling?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.1612" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0084_985_84985340_qa_2/task.toml b/tasks/0084_985_84985340_qa_2/task.toml index 0a6ae073facb7dd28bc20b94bd66ed5bbf51bc35..3f3c58c7eaa70b10e76e6bf15419b70833751103 100644 --- a/tasks/0084_985_84985340_qa_2/task.toml +++ b/tasks/0084_985_84985340_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0084_985_84985340_qa_2" +name = "smoldataenvs-train/0084_985_84985340_qa_2" description = "Which variable had the highest number of missing values before imputation when zeros were treated as missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Insulin" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0084_985_84985340_qa_4/task.toml b/tasks/0084_985_84985340_qa_4/task.toml index cd23fc1ffba741be3013750f08a029713a38a7af..2415740d5427812e6f1907a6b737f1d405b059d4 100644 --- a/tasks/0084_985_84985340_qa_4/task.toml +++ b/tasks/0084_985_84985340_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0084_985_84985340_qa_4" +name = "smoldataenvs-train/0084_985_84985340_qa_4" description = "What was the mean BMI value for individuals classified in the Obesity_class_III category after feature engineering?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "44.205" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0085_130_85130981_qa_2/task.toml b/tasks/0085_130_85130981_qa_2/task.toml index 081aa8836abf2a766fbc77de90a915a05f1b28af..e9b2f8f49e5e256a47cdde5052986de5193cf2bf 100644 --- a/tasks/0085_130_85130981_qa_2/task.toml +++ b/tasks/0085_130_85130981_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0085_130_85130981_qa_2" +name = "smoldataenvs-train/0085_130_85130981_qa_2" description = "How many new columns were added to the dataset after converting the 'Gender' categorical variable to numeric using one-hot encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0085_130_85130981_qa_4/task.toml b/tasks/0085_130_85130981_qa_4/task.toml index fd2abbc6b35baa523f33ba352f12c4e7b342d633..9fbe31ca4ff4c370c7c97e02e7b9362e491e14cf 100644 --- a/tasks/0085_130_85130981_qa_4/task.toml +++ b/tasks/0085_130_85130981_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0085_130_85130981_qa_4" +name = "smoldataenvs-train/0085_130_85130981_qa_4" description = "What is the total number of users in the dataset after converting the 'Gender' categorical variable to numeric?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "400" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0085_151_85151836_qa_1/task.toml b/tasks/0085_151_85151836_qa_1/task.toml index 0c409885dc3f135914df9b3eecf0204bf8161a11..0c0fcb71dd265aa0c0977fae0db6d0272d2dd8e7 100644 --- a/tasks/0085_151_85151836_qa_1/task.toml +++ b/tasks/0085_151_85151836_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0085_151_85151836_qa_1" +name = "smoldataenvs-train/0085_151_85151836_qa_1" description = "Is there a positive correlation between Federal Funds Target Rate and Unemployment Rate according to the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0085_154_85154775_qa_2/task.toml b/tasks/0085_154_85154775_qa_2/task.toml index 0c830d06f37ceaf1f2c989d00da8d88d1e84144f..38aa5d70f07d7b51e79c9fdedd6dd3abb74c988c 100644 --- a/tasks/0085_154_85154775_qa_2/task.toml +++ b/tasks/0085_154_85154775_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0085_154_85154775_qa_2" +name = "smoldataenvs-train/0085_154_85154775_qa_2" description = "Which class (ham or spam) has a higher precision score, and what is the precision value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ham, 0.99" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0085_154_85154775_qa_3/task.toml b/tasks/0085_154_85154775_qa_3/task.toml index 0cdddd2167416a5f2fa2a7568e0e304ff98d6e1c..a30b6aba13de9e2385c0eda9ef84e18898667751 100644 --- a/tasks/0085_154_85154775_qa_3/task.toml +++ b/tasks/0085_154_85154775_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0085_154_85154775_qa_3" +name = "smoldataenvs-train/0085_154_85154775_qa_3" description = "What is the difference between the macro average precision and the weighted average precision in the classification report?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.04" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0085_154_85154775_qa_5/task.toml b/tasks/0085_154_85154775_qa_5/task.toml index c4dcd9cdbd18d43b7be7bcae508f149c5bed0fe1..8666849728bdaae7f4a7f6af26e8c2b0d1083816 100644 --- a/tasks/0085_154_85154775_qa_5/task.toml +++ b/tasks/0085_154_85154775_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0085_154_85154775_qa_5" +name = "smoldataenvs-train/0085_154_85154775_qa_5" description = "What is the F1-score for the 'ham' class in the classification report?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.99" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0085_167_85167417_qa_2/task.toml b/tasks/0085_167_85167417_qa_2/task.toml index 313278add0d4fdd1589d9ffdd65c55111b30ef2f..6f75ea0b786cd0d1e7ccf10a8c0ffdebcf25f4d1 100644 --- a/tasks/0085_167_85167417_qa_2/task.toml +++ b/tasks/0085_167_85167417_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0085_167_85167417_qa_2" +name = "smoldataenvs-train/0085_167_85167417_qa_2" description = "What is the highest positive correlation coefficient between any feature and the target variable 'Outcome' in the correlation matrix?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.462477" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0085_232_85232231_qa_3/task.toml b/tasks/0085_232_85232231_qa_3/task.toml index 64e401f300af65af8894ef69e0238ee5472404bf..590359532ed799511088f20858131f9246a74664 100644 --- a/tasks/0085_232_85232231_qa_3/task.toml +++ b/tasks/0085_232_85232231_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0085_232_85232231_qa_3" +name = "smoldataenvs-train/0085_232_85232231_qa_3" description = "What is the difference in test accuracy between the Bagging Classifier (0.895) and the Decision Tree Classifier with optimally tuned class weights (0.992)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.097" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0085_240_85240003_qa_3/task.toml b/tasks/0085_240_85240003_qa_3/task.toml index ac5a01dd431d8f91796b14630d573ff5316ff9c8..af9ee15ce78927ae5750dd7c7c33ffb9f668053c 100644 --- a/tasks/0085_240_85240003_qa_3/task.toml +++ b/tasks/0085_240_85240003_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0085_240_85240003_qa_3" +name = "smoldataenvs-train/0085_240_85240003_qa_3" description = "What is the probability of a match being a tie in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.04%" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0085_243_85243443_qa_1/task.toml b/tasks/0085_243_85243443_qa_1/task.toml index 7df0a1c6ad4307fc29d4443ab6b04bbf2bf2e0e9..126e276dc20625e181f7fcf3c9cf5c05fd715920 100644 --- a/tasks/0085_243_85243443_qa_1/task.toml +++ b/tasks/0085_243_85243443_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0085_243_85243443_qa_1" +name = "smoldataenvs-train/0085_243_85243443_qa_1" description = "What is the total number of tweets in the original dataset before any preprocessing steps?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1600000" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0085_309_85309051_qa_1/task.toml b/tasks/0085_309_85309051_qa_1/task.toml index 80ccc14381a23d773bde0245e8d0a860d4e7e0a4..395dccdcec210386e80fa526fa0a4c04ed3ffac6 100644 --- a/tasks/0085_309_85309051_qa_1/task.toml +++ b/tasks/0085_309_85309051_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0085_309_85309051_qa_1" +name = "smoldataenvs-train/0085_309_85309051_qa_1" description = "What is the p-value from the t-test comparing the mean SAT Math scores between Manhattan and Bronx students?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8.569627401627233e-07" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0085_343_85343577_qa_3/task.toml b/tasks/0085_343_85343577_qa_3/task.toml index a0e4502501e28e9d651e231e5d9c8b3da7462f71..9519c958bdd5edd90d83ad19044f6dd6421b3ea0 100644 --- a/tasks/0085_343_85343577_qa_3/task.toml +++ b/tasks/0085_343_85343577_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0085_343_85343577_qa_3" +name = "smoldataenvs-train/0085_343_85343577_qa_3" description = "How many distinct Sudoku puzzles are contained in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1000000" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0085_343_85343577_qa_5/task.toml b/tasks/0085_343_85343577_qa_5/task.toml index 37d2ed4b625ac85e97ae30fb2383e6083059b6bc..0ba09bee824a56f4b2b0acc3522ae16d3434acd8 100644 --- a/tasks/0085_343_85343577_qa_5/task.toml +++ b/tasks/0085_343_85343577_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0085_343_85343577_qa_5" +name = "smoldataenvs-train/0085_343_85343577_qa_5" description = "How many non-trainable parameters exist in the Sudoku-solving CNN architecture?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "256" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0085_359_85359585_qa_5/task.toml b/tasks/0085_359_85359585_qa_5/task.toml index 930f51d9ee3e331ae3fc9f7b97689b6c45e2d1ac..cee0b1fcd801b9b50c68635abef8a5571a0fce8e 100644 --- a/tasks/0085_359_85359585_qa_5/task.toml +++ b/tasks/0085_359_85359585_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0085_359_85359585_qa_5" +name = "smoldataenvs-train/0085_359_85359585_qa_5" description = "What percentage of non-attended patients received an SMS message prior to their appointment?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "45" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0085_373_85373330_qa_2/task.toml b/tasks/0085_373_85373330_qa_2/task.toml index db8ba9ddb39e23fbc4d5755148270df5064bb26d..22a211387973422c90e651b9d8bc7f3589c03e1e 100644 --- a/tasks/0085_373_85373330_qa_2/task.toml +++ b/tasks/0085_373_85373330_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0085_373_85373330_qa_2" +name = "smoldataenvs-train/0085_373_85373330_qa_2" description = "What is the percentage of employees in the dataset who have left the company (Attrition = Yes) before any oversampling was applied?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16.13" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0085_383_85383242_qa_5/task.toml b/tasks/0085_383_85383242_qa_5/task.toml index 6c5b3a403965bb7e07d6b06403cfebeaaae6faf3..d1c211e83e35cc46a0c208594bbd1fa0481d70c3 100644 --- a/tasks/0085_383_85383242_qa_5/task.toml +++ b/tasks/0085_383_85383242_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0085_383_85383242_qa_5" +name = "smoldataenvs-train/0085_383_85383242_qa_5" description = "What is the p-value of the F-test for the regression model predicting Global Sales from NA Sales?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0085_451_85451661_qa_2/task.toml b/tasks/0085_451_85451661_qa_2/task.toml index 0935d23b8704100857f1ddfe0990dafd3066eb06..29ae14810e9c9ad892678483c3c86b3a318ec15d 100644 --- a/tasks/0085_451_85451661_qa_2/task.toml +++ b/tasks/0085_451_85451661_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0085_451_85451661_qa_2" +name = "smoldataenvs-train/0085_451_85451661_qa_2" description = "How many features were removed from the dataset due to having a correlation with the diagnosis below 0.2?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0085_555_85555570_qa_4/task.toml b/tasks/0085_555_85555570_qa_4/task.toml index 2186b0bf1bb5ae7904c89f62be577248c8b15dca..5a52c1bae7b3348042284fe207c74b54b655d641 100644 --- a/tasks/0085_555_85555570_qa_4/task.toml +++ b/tasks/0085_555_85555570_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0085_555_85555570_qa_4" +name = "smoldataenvs-train/0085_555_85555570_qa_4" description = "What is the name of the game with the highest global sales, and what was its sales figure?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii Sports, 82.74" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0085_583_85583016_qa_1/task.toml b/tasks/0085_583_85583016_qa_1/task.toml index f341b7d6044c918a7218b46cbd5a0298aeea8da2..71edc1330cb011b48d8c5d7a60762823f05e1567 100644 --- a/tasks/0085_583_85583016_qa_1/task.toml +++ b/tasks/0085_583_85583016_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0085_583_85583016_qa_1" +name = "smoldataenvs-train/0085_583_85583016_qa_1" description = "Which ocean proximity category has the highest frequency in the dataset, and what is that frequency?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "<1H OCEAN, 9136" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0085_583_85583016_qa_5/task.toml b/tasks/0085_583_85583016_qa_5/task.toml index 7e289f90998017960b80d7406db07659d6698231..f98f1f4869105aa93a7a0fdf3adcd5f2d319494f 100644 --- a/tasks/0085_583_85583016_qa_5/task.toml +++ b/tasks/0085_583_85583016_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0085_583_85583016_qa_5" +name = "smoldataenvs-train/0085_583_85583016_qa_5" description = "What is the range of the 'housing_median_age' feature in the dataset (maximum minus minimum)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "51.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0085_618_85618243_qa_2/task.toml b/tasks/0085_618_85618243_qa_2/task.toml index 630c5b2898612e0c15d9fec5c69fbce30e8ce8b3..6cdae884814ad586a9f817431a9f2a286f512e48 100644 --- a/tasks/0085_618_85618243_qa_2/task.toml +++ b/tasks/0085_618_85618243_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0085_618_85618243_qa_2" +name = "smoldataenvs-train/0085_618_85618243_qa_2" description = "How many unique zipcodes in the dataset have more than 100 entries after filtering?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "66" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0085_678_85678308_qa_1/task.toml b/tasks/0085_678_85678308_qa_1/task.toml index 5e9f6185565d65ecdb26ca0ce88e44ad12fd6754..5fef75e99826d879769d74a1331e8d95bb5d5890 100644 --- a/tasks/0085_678_85678308_qa_1/task.toml +++ b/tasks/0085_678_85678308_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0085_678_85678308_qa_1" +name = "smoldataenvs-train/0085_678_85678308_qa_1" description = "Which feature has the strongest positive correlation with PetalLengthCm in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalWidthCm" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0085_678_85678308_qa_2/task.toml b/tasks/0085_678_85678308_qa_2/task.toml index 8b1137610a03e1b9364953be873eaa649a002191..467732e9d8c528e788bed6f001e085779bc9788f 100644 --- a/tasks/0085_678_85678308_qa_2/task.toml +++ b/tasks/0085_678_85678308_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0085_678_85678308_qa_2" +name = "smoldataenvs-train/0085_678_85678308_qa_2" description = "Which species has the highest median SepalLengthCm value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-virginica" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0085_687_85687102_qa_1/task.toml b/tasks/0085_687_85687102_qa_1/task.toml index e1ae554fa10367046a861048fb78da8d39f2539f..51a2afdee87a8d6e61f03cb023950cf7b18f495f 100644 --- a/tasks/0085_687_85687102_qa_1/task.toml +++ b/tasks/0085_687_85687102_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0085_687_85687102_qa_1" +name = "smoldataenvs-train/0085_687_85687102_qa_1" description = "What is the mean cross-validated precision score of the logistic regression model using all features to predict mushroom class?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.97" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0085_706_85706685_qa_2/task.toml b/tasks/0085_706_85706685_qa_2/task.toml index 421c21b8893ae7d87fce7b939d77dc14bd59d80d..f5a51e8e9174776921335b65dfeef431493275e8 100644 --- a/tasks/0085_706_85706685_qa_2/task.toml +++ b/tasks/0085_706_85706685_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0085_706_85706685_qa_2" +name = "smoldataenvs-train/0085_706_85706685_qa_2" description = "Is there significant collinearity among variables in the dataset based on the heatmap analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0085_729_85729561_qa_4/task.toml b/tasks/0085_729_85729561_qa_4/task.toml index 8fdb13c523447fb76d82f1e253f51e60eb90ca56..77d699f5cd97b2b1642836b07e8e9118cfd181f1 100644 --- a/tasks/0085_729_85729561_qa_4/task.toml +++ b/tasks/0085_729_85729561_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0085_729_85729561_qa_4" +name = "smoldataenvs-train/0085_729_85729561_qa_4" description = "Which video game is ranked first in the list of top 20 highest grossing games?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii Sports" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0085_729_85729561_qa_5/task.toml b/tasks/0085_729_85729561_qa_5/task.toml index 6bdaa5a35f8eeab9667a1332d26066eee2a3c1d6..0e2491ceacb4adb3a9479cd0f6a6337c95622b8f 100644 --- a/tasks/0085_729_85729561_qa_5/task.toml +++ b/tasks/0085_729_85729561_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0085_729_85729561_qa_5" +name = "smoldataenvs-train/0085_729_85729561_qa_5" description = "What is the median value for North American video game sales in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.08" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0085_734_85734545_qa_1/task.toml b/tasks/0085_734_85734545_qa_1/task.toml index 02c09cd9bb9397f14f1f3f05a10999423af09344..d71fe783bf5b388fa1aa7a6ebf8e7eb28fb59a43 100644 --- a/tasks/0085_734_85734545_qa_1/task.toml +++ b/tasks/0085_734_85734545_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0085_734_85734545_qa_1" +name = "smoldataenvs-train/0085_734_85734545_qa_1" description = "What are the counts of benign and malignant cases in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "357, 212" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0085_736_85736582_qa_4/task.toml b/tasks/0085_736_85736582_qa_4/task.toml index 6228878963acd5ce87c540b64f2d69b027bb40cf..98b1e5101840f5824406db01ce39c8934e0cfb89 100644 --- a/tasks/0085_736_85736582_qa_4/task.toml +++ b/tasks/0085_736_85736582_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0085_736_85736582_qa_4" +name = "smoldataenvs-train/0085_736_85736582_qa_4" description = "How many data points remain classified as inliers after applying the Isolation Forest method with contamination=0.1?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "48546" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0085_736_85736582_qa_5/task.toml b/tasks/0085_736_85736582_qa_5/task.toml index 2b9f2255f9402c1cca45d299c97c19da8de2cfcc..ae22f296a07b42c1f8b32c129d374859d53518de 100644 --- a/tasks/0085_736_85736582_qa_5/task.toml +++ b/tasks/0085_736_85736582_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0085_736_85736582_qa_5" +name = "smoldataenvs-train/0085_736_85736582_qa_5" description = "What percentage of the dataset is flagged as outliers by the custom IQR method using quartile thresholds Q1=0.05 and Q3=0.95?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.07" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0085_741_85741385_qa_1/task.toml b/tasks/0085_741_85741385_qa_1/task.toml index 18e5c68432fb125abfe99d4ffd73080d27cd4e14..6f794d29d94bde7110257e1b937691825d883655 100644 --- a/tasks/0085_741_85741385_qa_1/task.toml +++ b/tasks/0085_741_85741385_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0085_741_85741385_qa_1" +name = "smoldataenvs-train/0085_741_85741385_qa_1" description = "Which feature in the dataset shows the strongest linear correlation with the diabetes diagnosis outcome?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0085_750_85750552_qa_2/task.toml b/tasks/0085_750_85750552_qa_2/task.toml index f8a8e6ff42c5ed588aacd390696a47f48d2b797d..2b29fc14e2b98a14f1271b658d60db3932201f3d 100644 --- a/tasks/0085_750_85750552_qa_2/task.toml +++ b/tasks/0085_750_85750552_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0085_750_85750552_qa_2" +name = "smoldataenvs-train/0085_750_85750552_qa_2" description = "What is the average global sales ratio of Nintendo Wii games compared to all other platforms combined?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.34" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0085_750_85750552_qa_4/task.toml b/tasks/0085_750_85750552_qa_4/task.toml index be1cc88e27b6f7a5da726cc68102a407e166860e..432d3c736b1827e2c6ed73b01f6a7a562cbd7b67 100644 --- a/tasks/0085_750_85750552_qa_4/task.toml +++ b/tasks/0085_750_85750552_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0085_750_85750552_qa_4" +name = "smoldataenvs-train/0085_750_85750552_qa_4" description = "What is the median value of North American sales for all video games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.08" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0085_804_85804318_qa_1/task.toml b/tasks/0085_804_85804318_qa_1/task.toml index 30f9d185b809dca3068cf79de40ad922b2ccee4e..9b7b9829ea4433df2f360299a428a0b89d02e748 100644 --- a/tasks/0085_804_85804318_qa_1/task.toml +++ b/tasks/0085_804_85804318_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0085_804_85804318_qa_1" +name = "smoldataenvs-train/0085_804_85804318_qa_1" description = "Which feature has the highest positive correlation with Item_Outlet_Sales in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Item_MRP" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0085_804_85804318_qa_2/task.toml b/tasks/0085_804_85804318_qa_2/task.toml index e6faad956c7374777d8382f99b1d3db6608ebb98..0cb236ec650d544ffb1606c03d2d5f5071d9b660 100644 --- a/tasks/0085_804_85804318_qa_2/task.toml +++ b/tasks/0085_804_85804318_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0085_804_85804318_qa_2" +name = "smoldataenvs-train/0085_804_85804318_qa_2" description = "What percentage of the training data consists of Low Fat items after cleaning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "64.73" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0085_804_85804318_qa_3/task.toml b/tasks/0085_804_85804318_qa_3/task.toml index ef02c5a2522053c6e5a76bcbda14bac6343a3d93..ced4dde14b20658e421ad46b0a31c893d4e9cc2b 100644 --- a/tasks/0085_804_85804318_qa_3/task.toml +++ b/tasks/0085_804_85804318_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0085_804_85804318_qa_3" +name = "smoldataenvs-train/0085_804_85804318_qa_3" description = "What is the most common outlet size in the training data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Medium" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0085_898_85898462_qa_1/task.toml b/tasks/0085_898_85898462_qa_1/task.toml index 40dd7e8676ae73e3d67694c13ebfe0b3aa822c3b..8f063586ecb18c81bb0424bbed0f034686563f35 100644 --- a/tasks/0085_898_85898462_qa_1/task.toml +++ b/tasks/0085_898_85898462_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0085_898_85898462_qa_1" +name = "smoldataenvs-train/0085_898_85898462_qa_1" description = "What percentage of vehicles in the dataset are classified as bad buys (IsBadBuy = 1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12.35" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0085_903_85903531_qa_1/task.toml b/tasks/0085_903_85903531_qa_1/task.toml index af758e7b923ebf889d98e78cca75958ced1b19f7..32b8a7c2cdc2e075855ebc240c1af6caf90bd074 100644 --- a/tasks/0085_903_85903531_qa_1/task.toml +++ b/tasks/0085_903_85903531_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0085_903_85903531_qa_1" +name = "smoldataenvs-train/0085_903_85903531_qa_1" description = "What is the R-squared value for the training data in the linear regression model analyzing the relationship between BMI and insurance charges?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.05" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0085_903_85903605_qa_2/task.toml b/tasks/0085_903_85903605_qa_2/task.toml index 98b12631565d4483537e56a3e8e6dbd278633a1b..feb7f112e9531bd549cf9e29a8247b71b7b4d392 100644 --- a/tasks/0085_903_85903605_qa_2/task.toml +++ b/tasks/0085_903_85903605_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0085_903_85903605_qa_2" +name = "smoldataenvs-train/0085_903_85903605_qa_2" description = "What percentage of variance in movie revenue is explained by popularity according to the linear regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "42.27" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0085_903_85903605_qa_3/task.toml b/tasks/0085_903_85903605_qa_3/task.toml index 802c076bfd4d974722504392debf78098931e0f4..e56c03d766a30a6c0e99bd6a0d4f081621ad4ec5 100644 --- a/tasks/0085_903_85903605_qa_3/task.toml +++ b/tasks/0085_903_85903605_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0085_903_85903605_qa_3" +name = "smoldataenvs-train/0085_903_85903605_qa_3" description = "What is the median popularity score across all movies in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12.92" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0085_903_85903605_qa_5/task.toml b/tasks/0085_903_85903605_qa_5/task.toml index 648d868a6e777301b6ceb37bbc562a73a9318cef..97c547010bbacdd5d0ebed22381ecfcc7cc64707 100644 --- a/tasks/0085_903_85903605_qa_5/task.toml +++ b/tasks/0085_903_85903605_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0085_903_85903605_qa_5" +name = "smoldataenvs-train/0085_903_85903605_qa_5" description = "What is the average runtime (in minutes) of movies in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "106.88" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0085_952_85952796_qa_1/task.toml b/tasks/0085_952_85952796_qa_1/task.toml index 810a8f5c225f8505af371c105398743f0ea656e4..d614dcfe37acc901b9af503d155d99d1faea3bef 100644 --- a/tasks/0085_952_85952796_qa_1/task.toml +++ b/tasks/0085_952_85952796_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0085_952_85952796_qa_1" +name = "smoldataenvs-train/0085_952_85952796_qa_1" description = "What is the mean absolute deviation of User_Score from its mean value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.155" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0085_952_85952796_qa_2/task.toml b/tasks/0085_952_85952796_qa_2/task.toml index ea1165529c26e410ef67802d7288b7bcee08b1c5..bcc0fe5f3fe985f3840a2be18dcc7212973dc793 100644 --- a/tasks/0085_952_85952796_qa_2/task.toml +++ b/tasks/0085_952_85952796_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0085_952_85952796_qa_2" +name = "smoldataenvs-train/0085_952_85952796_qa_2" description = "What is the 10% trimmed mean of User_Score after excluding the top and bottom 10% of values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7.314756258234518" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0085_958_85958060_qa_1/task.toml b/tasks/0085_958_85958060_qa_1/task.toml index 1cf8a30252fffe267f973b87b4c6416b3f8a37cb..2c47697a6eeec2eb530504c192d21ed3e5207d67 100644 --- a/tasks/0085_958_85958060_qa_1/task.toml +++ b/tasks/0085_958_85958060_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0085_958_85958060_qa_1" +name = "smoldataenvs-train/0085_958_85958060_qa_1" description = "What percentage of vehicles in the dataset are classified as bad buys based on the IsBadBuy target variable?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12.35" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0086_025_86025267_qa_5/task.toml b/tasks/0086_025_86025267_qa_5/task.toml index 536e04a893b53c425493e6924435a36756fa9843..9b7223f0d58aff32c57afa7ad789bc77b9da83ef 100644 --- a/tasks/0086_025_86025267_qa_5/task.toml +++ b/tasks/0086_025_86025267_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0086_025_86025267_qa_5" +name = "smoldataenvs-train/0086_025_86025267_qa_5" description = "For individuals with more than 3 children, which gender incurs higher average medical charges based on the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "female" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0086_057_86057959_qa_1/task.toml b/tasks/0086_057_86057959_qa_1/task.toml index c6bb81b2f28c040c781fe4c8f333ea3fe63874de..9aa53c12502943130b19b7e0d4bccb34dec81f6f 100644 --- a/tasks/0086_057_86057959_qa_1/task.toml +++ b/tasks/0086_057_86057959_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0086_057_86057959_qa_1" +name = "smoldataenvs-train/0086_057_86057959_qa_1" description = "After imputing missing values in the \"Salary\" variable using the median, what is the median salary value for the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "425" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0086_139_86139092_qa_4/task.toml b/tasks/0086_139_86139092_qa_4/task.toml index 35b67f8ed44216a4e9f65885a4328e851bc25281..e84d98460ad655c0165ee6ee29af000a87509e42 100644 --- a/tasks/0086_139_86139092_qa_4/task.toml +++ b/tasks/0086_139_86139092_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0086_139_86139092_qa_4" +name = "smoldataenvs-train/0086_139_86139092_qa_4" description = "Which city in the top 20 cities with the highest total graduates has the largest number of male graduates, and what is that value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Delhi, 1210040" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0086_158_86158878_qa_4/task.toml b/tasks/0086_158_86158878_qa_4/task.toml index ab5232cb66b37e1aebc5a44801ec734fc4e71807..9f570e912b9744bf957df96254dd8bce170a9a9f 100644 --- a/tasks/0086_158_86158878_qa_4/task.toml +++ b/tasks/0086_158_86158878_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0086_158_86158878_qa_4" +name = "smoldataenvs-train/0086_158_86158878_qa_4" description = "What is the mean insurance charge for non-smoking females in the southeast region?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8440.21" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0086_170_86170046_qa_3/task.toml b/tasks/0086_170_86170046_qa_3/task.toml index f70d73f5e8055a1776bdc75d23869c2eb1e16163..5008277c281017df0e9e15f8733cb11f3ae8b20a 100644 --- a/tasks/0086_170_86170046_qa_3/task.toml +++ b/tasks/0086_170_86170046_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0086_170_86170046_qa_3" +name = "smoldataenvs-train/0086_170_86170046_qa_3" description = "Which criterion (Gini or Entropy) resulted in a higher F1 score for the Random Forest model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Gini" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0086_173_86173099_qa_3/task.toml b/tasks/0086_173_86173099_qa_3/task.toml index ab6e85766cb981af69dd8b684d32c7a8207dd3e6..bc1c62370cd3ab10ed313bb52fab60110e2cbdee 100644 --- a/tasks/0086_173_86173099_qa_3/task.toml +++ b/tasks/0086_173_86173099_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0086_173_86173099_qa_3" +name = "smoldataenvs-train/0086_173_86173099_qa_3" description = "What is the recall score for the poisonous mushroom class (p) when using the single-stump heuristic model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.96" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0086_221_86221222_qa_1/task.toml b/tasks/0086_221_86221222_qa_1/task.toml index ecbcece029399158e1b3c0d7891c5d17374ee144..03e98bb751c56d3ddd71b71473a3189e5b1a53f5 100644 --- a/tasks/0086_221_86221222_qa_1/task.toml +++ b/tasks/0086_221_86221222_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0086_221_86221222_qa_1" +name = "smoldataenvs-train/0086_221_86221222_qa_1" description = "What is the correlation between Apparent Temperature (C) and Temperature (C) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.992637" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0086_221_86221222_qa_3/task.toml b/tasks/0086_221_86221222_qa_3/task.toml index c6b7551b5642fd56a7d03fe884542175714b0931..1cc8b5de5cbad728d215f947e9afcd5a6cbb3994 100644 --- a/tasks/0086_221_86221222_qa_3/task.toml +++ b/tasks/0086_221_86221222_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0086_221_86221222_qa_3" +name = "smoldataenvs-train/0086_221_86221222_qa_3" description = "How many new features were added to the dataset through feature engineering (hour_sin, hour_cos, day_sin, day_cos)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0086_267_86267950_qa_4/task.toml b/tasks/0086_267_86267950_qa_4/task.toml index 572f6c6d4c48d3f58e0630c049cdc45ccb3c7c28..3b66dd60a9c66c7583955f3edc781e6d722295a2 100644 --- a/tasks/0086_267_86267950_qa_4/task.toml +++ b/tasks/0086_267_86267950_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0086_267_86267950_qa_4" +name = "smoldataenvs-train/0086_267_86267950_qa_4" description = "Which manufacturer has the fewest cereal brands, and how many brands does it produce?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "A, 1" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0086_271_86271835_qa_1/task.toml b/tasks/0086_271_86271835_qa_1/task.toml index 28cf179deaa23114aebbb8c0f0a361ffbe4d0a00..8b271fd958c721fb3fbb32841c56deaa38182de7 100644 --- a/tasks/0086_271_86271835_qa_1/task.toml +++ b/tasks/0086_271_86271835_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0086_271_86271835_qa_1" +name = "smoldataenvs-train/0086_271_86271835_qa_1" description = "Which video game platform has the highest average global sales, and what is the value of that average?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "GB, 2.606633" reward_mode_initial = "list" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0086_271_86271835_qa_2/task.toml b/tasks/0086_271_86271835_qa_2/task.toml index e6d3d5e1fa352414f16b4d60021b0a8a63618065..8951c0b31b85cfa3946b0b3a3e5e6a11221cdff1 100644 --- a/tasks/0086_271_86271835_qa_2/task.toml +++ b/tasks/0086_271_86271835_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0086_271_86271835_qa_2" +name = "smoldataenvs-train/0086_271_86271835_qa_2" description = "What is the total global sales amount for all role-playing games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "927.37" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0086_358_86358959_qa_2/task.toml b/tasks/0086_358_86358959_qa_2/task.toml index 8a388a0a2158e5bfa01b0df5c82dc927d19e3ab2..7d65b3e3b3458462660965800a23cc2e17094b25 100644 --- a/tasks/0086_358_86358959_qa_2/task.toml +++ b/tasks/0086_358_86358959_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0086_358_86358959_qa_2" +name = "smoldataenvs-train/0086_358_86358959_qa_2" description = "How many principal components are required to capture at least 99.8% of the total variance in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0086_381_86381271_qa_1/task.toml b/tasks/0086_381_86381271_qa_1/task.toml index 7e0b14df8e1b43a8e9dcaa409bace6729303ac9b..aeaa0930339a9dcaba5c448fbc0184aada209bd6 100644 --- a/tasks/0086_381_86381271_qa_1/task.toml +++ b/tasks/0086_381_86381271_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0086_381_86381271_qa_1" +name = "smoldataenvs-train/0086_381_86381271_qa_1" description = "Which Marvel hero has the highest number of comic appearances according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "SPIDER-MAN/PETER PARKER" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0086_400_86400737_qa_1/task.toml b/tasks/0086_400_86400737_qa_1/task.toml index dd825ef62e38d0ce54e172d5cb280f37bc7905b5..5432791caad30a55117632a2c004b338e382173c 100644 --- a/tasks/0086_400_86400737_qa_1/task.toml +++ b/tasks/0086_400_86400737_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0086_400_86400737_qa_1" +name = "smoldataenvs-train/0086_400_86400737_qa_1" description = "Which feature has the highest importance in the XGBoost model based on the feature importance scores?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "alcohol" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0086_499_86499791_qa_5/task.toml b/tasks/0086_499_86499791_qa_5/task.toml index e7b6c6f5d8d5f2714fa418ee840354f6720a58e7..cfe284611771502a9747dce557409b965a591a5e 100644 --- a/tasks/0086_499_86499791_qa_5/task.toml +++ b/tasks/0086_499_86499791_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0086_499_86499791_qa_5" +name = "smoldataenvs-train/0086_499_86499791_qa_5" description = "What is the average Item Outlet Sales for Supermarket Type3 outlets?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3694.038558" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0086_529_86529090_qa_1/task.toml b/tasks/0086_529_86529090_qa_1/task.toml index 47eabdd785963bd9af10baad831d57be6b2e50f9..f4643944466740571ed70e3dccc1dd3cfa705a61 100644 --- a/tasks/0086_529_86529090_qa_1/task.toml +++ b/tasks/0086_529_86529090_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0086_529_86529090_qa_1" +name = "smoldataenvs-train/0086_529_86529090_qa_1" description = "What is the highest global sales figure achieved by any video game in the dataset, and which game holds this record?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii Sports, 82.74" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0086_529_86529090_qa_3/task.toml b/tasks/0086_529_86529090_qa_3/task.toml index de4c510a0aac9b2dcbe2fc8a18275c211e159ee0..3c55faee66313b151cef171da69c7fa286db1837 100644 --- a/tasks/0086_529_86529090_qa_3/task.toml +++ b/tasks/0086_529_86529090_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0086_529_86529090_qa_3" +name = "smoldataenvs-train/0086_529_86529090_qa_3" description = "What is the difference in average global sales between the Nintendo Wii platform and all other platforms combined?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.176" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0086_529_86529090_qa_5/task.toml b/tasks/0086_529_86529090_qa_5/task.toml index d0090c39634c7a0456cd75857358e13cdbf9fa71..44cd970192058d1a2d2017ef1d2aabaf49e63724 100644 --- a/tasks/0086_529_86529090_qa_5/task.toml +++ b/tasks/0086_529_86529090_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0086_529_86529090_qa_5" +name = "smoldataenvs-train/0086_529_86529090_qa_5" description = "What is the difference in global sales between the top-selling game (Wii Sports) and the second-highest game (Super Mario Bros.)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "42.5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0086_531_86531254_qa_1/task.toml b/tasks/0086_531_86531254_qa_1/task.toml index 3baa63e46c1c0484e3352b378cc63a5d794aaad9..dcace588d2fab5ea642e8618d3adc21da1bb7aae 100644 --- a/tasks/0086_531_86531254_qa_1/task.toml +++ b/tasks/0086_531_86531254_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0086_531_86531254_qa_1" +name = "smoldataenvs-train/0086_531_86531254_qa_1" description = "Which video game platform achieved the highest total global sales across all years in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PS2" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0086_531_86531254_qa_3/task.toml b/tasks/0086_531_86531254_qa_3/task.toml index 1c9cf709c093155b23a064e65d98f9dcb87f8714..a789ed48459c268044309adb52363c6ef92779a7 100644 --- a/tasks/0086_531_86531254_qa_3/task.toml +++ b/tasks/0086_531_86531254_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0086_531_86531254_qa_3" +name = "smoldataenvs-train/0086_531_86531254_qa_3" description = "Which PlayStation console model (PS, PS2, PS3, or PS4) generated the highest cumulative global sales between the years 2000 and 2015?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PS2" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0086_531_86531254_qa_5/task.toml b/tasks/0086_531_86531254_qa_5/task.toml index 63ec0c685458615792117e95cfe545d6c6eadce4..3f9119e049d4a3304369de67b734632a0fac8335 100644 --- a/tasks/0086_531_86531254_qa_5/task.toml +++ b/tasks/0086_531_86531254_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0086_531_86531254_qa_5" +name = "smoldataenvs-train/0086_531_86531254_qa_5" description = "Which video game genre represents the largest portion of sales in Japan (JP_Sales) based on the pie chart visualization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Role-Playing" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0086_608_86608703_qa_3/task.toml b/tasks/0086_608_86608703_qa_3/task.toml index d1ff49a28c09fa9cd6bd04f002ddaca80a396759..7672fd82670c5d2dfa6566cb9f487927ccf688ac 100644 --- a/tasks/0086_608_86608703_qa_3/task.toml +++ b/tasks/0086_608_86608703_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0086_608_86608703_qa_3" +name = "smoldataenvs-train/0086_608_86608703_qa_3" description = "What percentage of wines in the dataset are classified as \"Good\" (quality > 5) based on the binary classification applied?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "53.5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0086_608_86608703_qa_4/task.toml b/tasks/0086_608_86608703_qa_4/task.toml index 2a9b4800548d8dea86756698cf75e35e8e8c7c96..61a46bcbf522977e305591db122b3ff5e19da83e 100644 --- a/tasks/0086_608_86608703_qa_4/task.toml +++ b/tasks/0086_608_86608703_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0086_608_86608703_qa_4" +name = "smoldataenvs-train/0086_608_86608703_qa_4" description = "Which feature in the dataset has the highest maximum value observed across all samples?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "total sulfur dioxide" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0086_644_86644069_qa_3/task.toml b/tasks/0086_644_86644069_qa_3/task.toml index fd34f4e9d3478de3bbe27f50523ddc7d9b363bdc..153663bffbd2c589d4fa227e2309ae15a0505650 100644 --- a/tasks/0086_644_86644069_qa_3/task.toml +++ b/tasks/0086_644_86644069_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0086_644_86644069_qa_3" +name = "smoldataenvs-train/0086_644_86644069_qa_3" description = "Which education level category has the largest population in India according to the census data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Primary_Education" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0086_817_86817986_qa_4/task.toml b/tasks/0086_817_86817986_qa_4/task.toml index ed24980009305c698282f958e3ed2e43a3541b4c..1642c8c1e5585628fa4171bd1c54d5e40acba5ee 100644 --- a/tasks/0086_817_86817986_qa_4/task.toml +++ b/tasks/0086_817_86817986_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0086_817_86817986_qa_4" +name = "smoldataenvs-train/0086_817_86817986_qa_4" description = "What is the correlation coefficient between highway mpg and price?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.7" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0086_821_86821577_qa_3/task.toml b/tasks/0086_821_86821577_qa_3/task.toml index 6d1875d10d38c065d6f935e8b2f09e5d698f32c5..e8b11bf7e42d2495d0b645ab487feb7d52efae0e 100644 --- a/tasks/0086_821_86821577_qa_3/task.toml +++ b/tasks/0086_821_86821577_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0086_821_86821577_qa_3" +name = "smoldataenvs-train/0086_821_86821577_qa_3" description = "How many distinct classes are present in the dataset after applying the label mapping?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "24" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0086_824_86824902_qa_2/task.toml b/tasks/0086_824_86824902_qa_2/task.toml index c31aef10e4e1d447f94d8b105e0d9bae69f4bab9..fe72193f4b67c7bd48a6c79af7dc31c80f057acb 100644 --- a/tasks/0086_824_86824902_qa_2/task.toml +++ b/tasks/0086_824_86824902_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0086_824_86824902_qa_2" +name = "smoldataenvs-train/0086_824_86824902_qa_2" description = "Which cluster label contains the highest number of customers after K-means clustering with PCA?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0086_933_86933978_qa_2/task.toml b/tasks/0086_933_86933978_qa_2/task.toml index 76ec1fc7fadaa636a790b235af6af116a5178d45..23afc92df3f0ce090220c0bf40b12e96ea013765 100644 --- a/tasks/0086_933_86933978_qa_2/task.toml +++ b/tasks/0086_933_86933978_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0086_933_86933978_qa_2" +name = "smoldataenvs-train/0086_933_86933978_qa_2" description = "What is the difference in the number of instances between the most frequent and second most frequent class labels in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "826" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0087_024_87024626_qa_4/task.toml b/tasks/0087_024_87024626_qa_4/task.toml index d3e57a59802934910228df5e1cb479cbf59c572f..4131da6b7e5448e22662b73d639bd2d9bbe3dd56 100644 --- a/tasks/0087_024_87024626_qa_4/task.toml +++ b/tasks/0087_024_87024626_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0087_024_87024626_qa_4" +name = "smoldataenvs-train/0087_024_87024626_qa_4" description = "What is the country of residence with the highest number of individuals represented in the dataset after grouping infrequent entries into \"Others\"?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "United States" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0087_136_87136363_qa_3/task.toml b/tasks/0087_136_87136363_qa_3/task.toml index 728abfd6e2ed6562df11f19106556e382e9c8379..204e378c0f83ba93d6fbe53d969c1602d16508ac 100644 --- a/tasks/0087_136_87136363_qa_3/task.toml +++ b/tasks/0087_136_87136363_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0087_136_87136363_qa_3" +name = "smoldataenvs-train/0087_136_87136363_qa_3" description = "What is the R-squared value indicating the model's explanatory power on the training data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9549236946181227" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0087_136_87136363_qa_5/task.toml b/tasks/0087_136_87136363_qa_5/task.toml index 1929bd3e5fef01315269c7cd002ad9843d015a14..f6e713990fcc368da94e6e7f1e85a735fe0d881e 100644 --- a/tasks/0087_136_87136363_qa_5/task.toml +++ b/tasks/0087_136_87136363_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0087_136_87136363_qa_5" +name = "smoldataenvs-train/0087_136_87136363_qa_5" description = "What is the predicted salary for an individual with 11 years of experience according to the regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "129010.76" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0087_137_87137747_qa_3/task.toml b/tasks/0087_137_87137747_qa_3/task.toml index 90fe2cc7f05958a5e92047ab18fe436d7b0134e1..a6e4bf29f1ed332d7264840d21a961889cb1cfeb 100644 --- a/tasks/0087_137_87137747_qa_3/task.toml +++ b/tasks/0087_137_87137747_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0087_137_87137747_qa_3" +name = "smoldataenvs-train/0087_137_87137747_qa_3" description = "Which US state's dummy variable was dropped to avoid the dummy variable trap in the regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "California" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0087_174_87174947_qa_1/task.toml b/tasks/0087_174_87174947_qa_1/task.toml index 4b8dd712207a44efa9b82764640651ca0758387c..ec73c284ba448fa52ac86a0e169aaa3217c0a464 100644 --- a/tasks/0087_174_87174947_qa_1/task.toml +++ b/tasks/0087_174_87174947_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0087_174_87174947_qa_1" +name = "smoldataenvs-train/0087_174_87174947_qa_1" description = "Which wine quality rating has the highest frequency in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0087_174_87174947_qa_3/task.toml b/tasks/0087_174_87174947_qa_3/task.toml index ca302cb653e572a1114e660ec00e82b33559c9ef..99d6c912925e9fdc5180363f0f65d034d80a9939 100644 --- a/tasks/0087_174_87174947_qa_3/task.toml +++ b/tasks/0087_174_87174947_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0087_174_87174947_qa_3" +name = "smoldataenvs-train/0087_174_87174947_qa_3" description = "How many wine samples in the dataset have a quality rating of 8?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "18" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0087_180_87180327_qa_4/task.toml b/tasks/0087_180_87180327_qa_4/task.toml index 3394dc1753445613b4cc080c5f9ee4a53117e5b3..d9fca858f4dee274077841e6d4d00028c858b223 100644 --- a/tasks/0087_180_87180327_qa_4/task.toml +++ b/tasks/0087_180_87180327_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0087_180_87180327_qa_4" +name = "smoldataenvs-train/0087_180_87180327_qa_4" description = "What is the Root Mean Squared Error (RMSE) of the ElasticNet Regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4752.06" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0087_187_87187957_qa_5/task.toml b/tasks/0087_187_87187957_qa_5/task.toml index 49764169d9fa05458e8433abb9798ecd0cebf8a4..40806b039b2d54556c81840d54fb960c69d912a0 100644 --- a/tasks/0087_187_87187957_qa_5/task.toml +++ b/tasks/0087_187_87187957_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0087_187_87187957_qa_5" +name = "smoldataenvs-train/0087_187_87187957_qa_5" description = "How many users are included in the top 50 similarity index list after calculating Pearson correlations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0087_215_87215964_qa_1/task.toml b/tasks/0087_215_87215964_qa_1/task.toml index 272a5732d88aa84488e9e5a414b2e79e18cade59..1a122aaf92586490a66799e56da9e42417dba701 100644 --- a/tasks/0087_215_87215964_qa_1/task.toml +++ b/tasks/0087_215_87215964_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0087_215_87215964_qa_1" +name = "smoldataenvs-train/0087_215_87215964_qa_1" description = "Which feature has the highest chi2 score in predicting the mobile price range according to the feature selection analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ram" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0087_222_87222139_qa_1/task.toml b/tasks/0087_222_87222139_qa_1/task.toml index 9f59e7e5a3408e530865b8e8298f416cadaad615..b8c0b9b4e432e9692835222335f41c3c4792b451 100644 --- a/tasks/0087_222_87222139_qa_1/task.toml +++ b/tasks/0087_222_87222139_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0087_222_87222139_qa_1" +name = "smoldataenvs-train/0087_222_87222139_qa_1" description = "What is the R-squared value of the linear regression model predicting weight from height?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.989" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0087_222_87222139_qa_2/task.toml b/tasks/0087_222_87222139_qa_2/task.toml index edb4a9a5b2f8d8468863e46ce5dd01b5626a8245..4f4b13ae21938b82cde5b1178c066adea7b1e84e 100644 --- a/tasks/0087_222_87222139_qa_2/task.toml +++ b/tasks/0087_222_87222139_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0087_222_87222139_qa_2" +name = "smoldataenvs-train/0087_222_87222139_qa_2" description = "What is the p-value indicating the overall significance of the regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.60e-14" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0087_222_87222139_qa_4/task.toml b/tasks/0087_222_87222139_qa_4/task.toml index ee26a6f7d2a838919a7d3eeb93f69ec203e4cf40..d9a693b867167410515258c47e56eebc56a66404 100644 --- a/tasks/0087_222_87222139_qa_4/task.toml +++ b/tasks/0087_222_87222139_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0087_222_87222139_qa_4" +name = "smoldataenvs-train/0087_222_87222139_qa_4" description = "What is the Durbin-Watson statistic from the regression results?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.433" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0087_225_87225031_qa_3/task.toml b/tasks/0087_225_87225031_qa_3/task.toml index d44a85f9932ee053a80ec83cd4aff15ce72a5718..e067ce54b32c4e70ab1c1af9fef13de8a83869a8 100644 --- a/tasks/0087_225_87225031_qa_3/task.toml +++ b/tasks/0087_225_87225031_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0087_225_87225031_qa_3" +name = "smoldataenvs-train/0087_225_87225031_qa_3" description = "What is the skewness of the \"residual sugar\" feature after applying the log transformation to reduce skew?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.807" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0087_227_87227874_qa_5/task.toml b/tasks/0087_227_87227874_qa_5/task.toml index cd3dfded2ac116ee9ab80287d52f36d7d1196c4d..469e512224c2b13500fa91ddf9846b9379529c50 100644 --- a/tasks/0087_227_87227874_qa_5/task.toml +++ b/tasks/0087_227_87227874_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0087_227_87227874_qa_5" +name = "smoldataenvs-train/0087_227_87227874_qa_5" description = "What is the total number of movies in the processed dataset after handling missing values and data cleaning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4806" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0087_290_87290637_qa_1/task.toml b/tasks/0087_290_87290637_qa_1/task.toml index dae257ce0116ba1f7a4f47514170ad3fb7577941..c7376afaf5be41dfbd6287145727b62ae2e0d7ba 100644 --- a/tasks/0087_290_87290637_qa_1/task.toml +++ b/tasks/0087_290_87290637_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0087_290_87290637_qa_1" +name = "smoldataenvs-train/0087_290_87290637_qa_1" description = "Which species has the highest mean petal length in the dataset, and what is the exact numerical value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-virginica, 5.552" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0087_320_87320991_qa_1/task.toml b/tasks/0087_320_87320991_qa_1/task.toml index 1b16269ffc9fa246d61f8e0011ad0a7e7268545b..f30fc84bd288b051ac660582bf0bf5889bfe5cb0 100644 --- a/tasks/0087_320_87320991_qa_1/task.toml +++ b/tasks/0087_320_87320991_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0087_320_87320991_qa_1" +name = "smoldataenvs-train/0087_320_87320991_qa_1" description = "Which feature was identified as the most important variable for predicting mobile price ranges using the ExtraTreesClassifier feature importance analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ram" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0087_320_87320991_qa_2/task.toml b/tasks/0087_320_87320991_qa_2/task.toml index 9d0e4af84f0cb115410a01caa8d74cb18ac614b8..878990b77fb50050a51330d40b91e3e60e26d839 100644 --- a/tasks/0087_320_87320991_qa_2/task.toml +++ b/tasks/0087_320_87320991_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0087_320_87320991_qa_2" +name = "smoldataenvs-train/0087_320_87320991_qa_2" description = "After applying the SelectKBest feature selection method with chi2 scoring, which feature had the highest score indicating the strongest relationship with price_range?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ram" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0087_349_87349607_qa_1/task.toml b/tasks/0087_349_87349607_qa_1/task.toml index bc849a02ee3b99bc144ada018ad9086b74628215..e7e12a11f2c413eb64cd6e1203e9d7c628bae8d2 100644 --- a/tasks/0087_349_87349607_qa_1/task.toml +++ b/tasks/0087_349_87349607_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0087_349_87349607_qa_1" +name = "smoldataenvs-train/0087_349_87349607_qa_1" description = "What is the distribution of the price_range in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0:500, 1:500, 2:500, 3:500" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0087_361_87361390_qa_4/task.toml b/tasks/0087_361_87361390_qa_4/task.toml index 38e1cb62dba961f1142cc048d5c06b3f0ab17f3d..106bd03ec2c49b8a27620e2793cf9452509397ae 100644 --- a/tasks/0087_361_87361390_qa_4/task.toml +++ b/tasks/0087_361_87361390_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0087_361_87361390_qa_4" +name = "smoldataenvs-train/0087_361_87361390_qa_4" description = "What is the mean insurance charge for all policyholders in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13270.42" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0087_365_87365940_qa_1/task.toml b/tasks/0087_365_87365940_qa_1/task.toml index cfb7ce92cc7432c9d802646780ba60c893843986..b52193efc7455fa840bd93bb4d54f5ba5e5f2d9e 100644 --- a/tasks/0087_365_87365940_qa_1/task.toml +++ b/tasks/0087_365_87365940_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0087_365_87365940_qa_1" +name = "smoldataenvs-train/0087_365_87365940_qa_1" description = "Which team has the highest number of overall wins in the IPL dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Mumbai Indians" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0087_372_87372002_qa_1/task.toml b/tasks/0087_372_87372002_qa_1/task.toml index 09deba8533740b949ec59ecefe654d698c2a0689..2348ef559fefdc35df1ba5f26614feae85ee1062 100644 --- a/tasks/0087_372_87372002_qa_1/task.toml +++ b/tasks/0087_372_87372002_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0087_372_87372002_qa_1" +name = "smoldataenvs-train/0087_372_87372002_qa_1" description = "Which feature in the dataset has the highest importance in the Random Forest classifier for gender prediction?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "meanfun" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0087_372_87372002_qa_2/task.toml b/tasks/0087_372_87372002_qa_2/task.toml index 31bc6c60699a572891569c5f83a3dd51380dfece..8d96ac82d1c829bc5fa33b59d7d8adfab70e7e4a 100644 --- a/tasks/0087_372_87372002_qa_2/task.toml +++ b/tasks/0087_372_87372002_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0087_372_87372002_qa_2" +name = "smoldataenvs-train/0087_372_87372002_qa_2" description = "What is the accuracy score of the Random Forest model on the 20% test dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9826" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0087_374_87374454_qa_3/task.toml b/tasks/0087_374_87374454_qa_3/task.toml index e2213e2a291dc60a1bab52ae37b9e8108156efc1..9b65be6e3fbf5cc82cfecefea8c43bb65bee5ef6 100644 --- a/tasks/0087_374_87374454_qa_3/task.toml +++ b/tasks/0087_374_87374454_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0087_374_87374454_qa_3" +name = "smoldataenvs-train/0087_374_87374454_qa_3" description = "What is the mean alcohol content of all wine samples in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10.42" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0087_388_87388798_qa_1/task.toml b/tasks/0087_388_87388798_qa_1/task.toml index 11747d0ccd21e033f6fd97e9ae613cec6f02103e..4f9cf6b2b9c5513414f4b32c4b6e24b6e2992bc4 100644 --- a/tasks/0087_388_87388798_qa_1/task.toml +++ b/tasks/0087_388_87388798_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0087_388_87388798_qa_1" +name = "smoldataenvs-train/0087_388_87388798_qa_1" description = "What is the highest correlation coefficient between any feature and the Outcome variable in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.47" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0087_388_87388798_qa_4/task.toml b/tasks/0087_388_87388798_qa_4/task.toml index a14d889688880eada0f068546b67c312d9d8a679..0b06e07eef2b220e8c2fe844230dc177049934f6 100644 --- a/tasks/0087_388_87388798_qa_4/task.toml +++ b/tasks/0087_388_87388798_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0087_388_87388798_qa_4" +name = "smoldataenvs-train/0087_388_87388798_qa_4" description = "What percentage of patients in the dataset are diabetic (Outcome = 1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.9" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0087_443_87443962_qa_2/task.toml b/tasks/0087_443_87443962_qa_2/task.toml index 5773dafe9089052a9572b3faec18c714358f33d1..853b9c83b5845ab40400aa8a7e8037f0d9730e10 100644 --- a/tasks/0087_443_87443962_qa_2/task.toml +++ b/tasks/0087_443_87443962_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0087_443_87443962_qa_2" +name = "smoldataenvs-train/0087_443_87443962_qa_2" description = "How many properties were identified as outliers and removed based on GrLivArea exceeding 4000 sq ft and SalePrice below 400,000?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0087_443_87443962_qa_3/task.toml b/tasks/0087_443_87443962_qa_3/task.toml index c42c24b6abb22f1b9877ba07d0f14d98de0e7e59..7a62abf998b2aab15425be19b3155e1ad05daa23 100644 --- a/tasks/0087_443_87443962_qa_3/task.toml +++ b/tasks/0087_443_87443962_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0087_443_87443962_qa_3" +name = "smoldataenvs-train/0087_443_87443962_qa_3" description = "After imputing missing LotFrontage values using neighborhood-specific means, how many missing values remain in the LotFrontage column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0087_558_87558511_qa_3/task.toml b/tasks/0087_558_87558511_qa_3/task.toml index deba1185623df32aed69da5fa52cec6110fb2243..84aed3f56d2001edfdc9944c410f2f9f9733e93e 100644 --- a/tasks/0087_558_87558511_qa_3/task.toml +++ b/tasks/0087_558_87558511_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0087_558_87558511_qa_3" +name = "smoldataenvs-train/0087_558_87558511_qa_3" description = "Which Pokémon has the lowest Total stat value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Sunkern" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0087_558_87558511_qa_5/task.toml b/tasks/0087_558_87558511_qa_5/task.toml index e930d0bafa8771204d8ef935ee3e7144f78a0a2f..6d36c49199bd57b90e6f645efbc1d0c75a9225e0 100644 --- a/tasks/0087_558_87558511_qa_5/task.toml +++ b/tasks/0087_558_87558511_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0087_558_87558511_qa_5" +name = "smoldataenvs-train/0087_558_87558511_qa_5" description = "What is the name of the Pokémon that is both a Water type and has a Steel type as its second type?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Empoleon" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0087_608_87608969_qa_3/task.toml b/tasks/0087_608_87608969_qa_3/task.toml index 032a085ebf7a2ad56e4ba80bc2017e6e20a2dc5a..3f5a65b1a1ad0c819254d8919f74f1687998fcbe 100644 --- a/tasks/0087_608_87608969_qa_3/task.toml +++ b/tasks/0087_608_87608969_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0087_608_87608969_qa_3" +name = "smoldataenvs-train/0087_608_87608969_qa_3" description = "What year between 1980-2016 recorded the highest total global sales for video games?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2008" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0087_608_87608969_qa_4/task.toml b/tasks/0087_608_87608969_qa_4/task.toml index f3e8989fa765c81bb3bb1305102716ff747f2d1d..b4f6a35c2a5447e1d6bd983fe8a527d201d4f29a 100644 --- a/tasks/0087_608_87608969_qa_4/task.toml +++ b/tasks/0087_608_87608969_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0087_608_87608969_qa_4" +name = "smoldataenvs-train/0087_608_87608969_qa_4" description = "Which game achieved the highest global sales in the year 2006?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii Sports" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0087_608_87608969_qa_5/task.toml b/tasks/0087_608_87608969_qa_5/task.toml index 12375c5396053b191a9fbf8b83ffec7b54ee8276..1b5887891a60cf3d2d268aea5fc292aac1239d82 100644 --- a/tasks/0087_608_87608969_qa_5/task.toml +++ b/tasks/0087_608_87608969_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0087_608_87608969_qa_5" +name = "smoldataenvs-train/0087_608_87608969_qa_5" description = "Which combination of game genre and platform has the highest average global sales per game title?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Shooter, NES" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0087_634_87634929_qa_1/task.toml b/tasks/0087_634_87634929_qa_1/task.toml index 516e7f5518bd06f83c948ec08cb13dac9bc7daa2..c1987333bb60ca80d7b95070fce93dcb596d17be 100644 --- a/tasks/0087_634_87634929_qa_1/task.toml +++ b/tasks/0087_634_87634929_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0087_634_87634929_qa_1" +name = "smoldataenvs-train/0087_634_87634929_qa_1" description = "How many total features are present in the final dataset after one-hot encoding and dropping the target variable?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "108" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0087_643_87643226_qa_5/task.toml b/tasks/0087_643_87643226_qa_5/task.toml index 6e653f714cd18b34664c7627e4a266a746496423..76869864d90e4610180b5fc6cf59ef12dd5ab137 100644 --- a/tasks/0087_643_87643226_qa_5/task.toml +++ b/tasks/0087_643_87643226_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0087_643_87643226_qa_5" +name = "smoldataenvs-train/0087_643_87643226_qa_5" description = "What is the average percentage of full payments made by customers according to the 'PRC_FULL_PAYMENT' feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.153715" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0087_644_87644639_qa_1/task.toml b/tasks/0087_644_87644639_qa_1/task.toml index 81e92fd8a00c44b1fdc6cfa210cd83328517f48e..edfe3e2a6b3c589ddbfd76097b5fca891aae1307 100644 --- a/tasks/0087_644_87644639_qa_1/task.toml +++ b/tasks/0087_644_87644639_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0087_644_87644639_qa_1" +name = "smoldataenvs-train/0087_644_87644639_qa_1" description = "What is the highest Speed value recorded in the Pokémon dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "180" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0087_644_87644639_qa_3/task.toml b/tasks/0087_644_87644639_qa_3/task.toml index 8d6657a6fa4b41ac0e324399d9573b793cb40789..e81805df219f59239dd2ae5b2ec078f552bf56d8 100644 --- a/tasks/0087_644_87644639_qa_3/task.toml +++ b/tasks/0087_644_87644639_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0087_644_87644639_qa_3" +name = "smoldataenvs-train/0087_644_87644639_qa_3" description = "What is the correlation coefficient between Attack and Defense stats in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.438687" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0087_676_87676487_qa_1/task.toml b/tasks/0087_676_87676487_qa_1/task.toml index 0d5ebfd8cccc6c7068d8f8cee9586b7b181d352d..bbc321ebd4451e4484008b6fad2a9bdc2cb43bfe 100644 --- a/tasks/0087_676_87676487_qa_1/task.toml +++ b/tasks/0087_676_87676487_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0087_676_87676487_qa_1" +name = "smoldataenvs-train/0087_676_87676487_qa_1" description = "What is the maximum number of ingredients in a single recipe according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "65" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0087_676_87676487_qa_3/task.toml b/tasks/0087_676_87676487_qa_3/task.toml index 8dc5bc99d2397224d6d3cca12d0f7f44deed3a20..65a0df6c4d3cb376f72569c43e3e6a5aaa86cc9e 100644 --- a/tasks/0087_676_87676487_qa_3/task.toml +++ b/tasks/0087_676_87676487_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0087_676_87676487_qa_3" +name = "smoldataenvs-train/0087_676_87676487_qa_3" description = "What is the ID of the recipe with the highest number of ingredients in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3885" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0087_720_87720557_qa_2/task.toml b/tasks/0087_720_87720557_qa_2/task.toml index 5b85b343ae2bc220ff298679c22b1ef19c1bd8d4..3d96db78d7aa07eaaa7bbf18e859ef5b1c0a970d 100644 --- a/tasks/0087_720_87720557_qa_2/task.toml +++ b/tasks/0087_720_87720557_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0087_720_87720557_qa_2" +name = "smoldataenvs-train/0087_720_87720557_qa_2" description = "Which feature in the Boston housing dataset shows the strongest negative correlation with the target variable MEDV, and what is the magnitude of that correlation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "LSTAT, -0.74" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0087_789_87789840_qa_1/task.toml b/tasks/0087_789_87789840_qa_1/task.toml index 298a83120d4de365670044cd3799130e48aa257e..170c0321afcfd673ba4733214ebaca5aef3b2cc0 100644 --- a/tasks/0087_789_87789840_qa_1/task.toml +++ b/tasks/0087_789_87789840_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0087_789_87789840_qa_1" +name = "smoldataenvs-train/0087_789_87789840_qa_1" description = "What is the ratio of ham to spam messages in the SMS dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.5" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0087_825_87825397_qa_3/task.toml b/tasks/0087_825_87825397_qa_3/task.toml index b967df9a26dbf339875050a7bdd09a9f72b29d75..1b114877b7d9e377685a4a5a63cc310a03330acf 100644 --- a/tasks/0087_825_87825397_qa_3/task.toml +++ b/tasks/0087_825_87825397_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0087_825_87825397_qa_3" +name = "smoldataenvs-train/0087_825_87825397_qa_3" description = "How many missing values were present in the Insulin column after replacing zero values with NaN?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "374" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0087_825_87825397_qa_4/task.toml b/tasks/0087_825_87825397_qa_4/task.toml index cd2358b9a07048e8da4159378fa63834448b840c..957419cd8324396f9f4d43f37a31dcb9a5cd32b5 100644 --- a/tasks/0087_825_87825397_qa_4/task.toml +++ b/tasks/0087_825_87825397_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0087_825_87825397_qa_4" +name = "smoldataenvs-train/0087_825_87825397_qa_4" description = "What percentage of the dataset consists of diabetic individuals?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0087_832_87832429_qa_3/task.toml b/tasks/0087_832_87832429_qa_3/task.toml index d91196f32cb3af91ebdfe27677b2ad2050446487..e8c40036bc23d55e129240dd6cf56c7ae1741ec9 100644 --- a/tasks/0087_832_87832429_qa_3/task.toml +++ b/tasks/0087_832_87832429_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0087_832_87832429_qa_3" +name = "smoldataenvs-train/0087_832_87832429_qa_3" description = "How many rows were removed from the original dataset after handling missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "134" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0087_860_87860092_qa_3/task.toml b/tasks/0087_860_87860092_qa_3/task.toml index 21aa56d74bb7974a7dedd3130cbf806025e7e70a..6cf32c18f4904c2d2c01bfc16cce8a6aac14c19d 100644 --- a/tasks/0087_860_87860092_qa_3/task.toml +++ b/tasks/0087_860_87860092_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0087_860_87860092_qa_3" +name = "smoldataenvs-train/0087_860_87860092_qa_3" description = "Which neighborhood has the highest total number of scheduled appointments in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "JARDIM CAMBURI" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0087_867_87867765_qa_2/task.toml b/tasks/0087_867_87867765_qa_2/task.toml index fa79bfb5b1807519cc0a4a640f1ba3492d4933a2..2e599a4e95d16d4272722488eb3ef1174d404071 100644 --- a/tasks/0087_867_87867765_qa_2/task.toml +++ b/tasks/0087_867_87867765_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0087_867_87867765_qa_2" +name = "smoldataenvs-train/0087_867_87867765_qa_2" description = "What is the median living area (sqft_living) of the houses in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1910" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0087_867_87867765_qa_4/task.toml b/tasks/0087_867_87867765_qa_4/task.toml index 534d0e615de693c1c041fc1249f0f574c9b858b0..0719606ce1ea9e0ea26efe738354d7e6cba17195 100644 --- a/tasks/0087_867_87867765_qa_4/task.toml +++ b/tasks/0087_867_87867765_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0087_867_87867765_qa_4" +name = "smoldataenvs-train/0087_867_87867765_qa_4" description = "What percentage of houses in the dataset have a waterfront view?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.7542" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0087_867_87867765_qa_5/task.toml b/tasks/0087_867_87867765_qa_5/task.toml index b8f5d5f2a956a0fe38a085c778e4464dcfa5c89a..7df93b39817835ded38a22ade6874c02f2ee1e7e 100644 --- a/tasks/0087_867_87867765_qa_5/task.toml +++ b/tasks/0087_867_87867765_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0087_867_87867765_qa_5" +name = "smoldataenvs-train/0087_867_87867765_qa_5" description = "What is the range of the year the houses were built (from the oldest to the newest)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "115" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0087_893_87893220_qa_2/task.toml b/tasks/0087_893_87893220_qa_2/task.toml index afc0917ddf7c05793f762ef5f1965b0b75518df6..d92991cff71fe204b837c21f15cbc555a1a90eb4 100644 --- a/tasks/0087_893_87893220_qa_2/task.toml +++ b/tasks/0087_893_87893220_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0087_893_87893220_qa_2" +name = "smoldataenvs-train/0087_893_87893220_qa_2" description = "What is the frequency of the most common name or surname among the victims listed in the police shooting dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "91" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0087_941_87941186_qa_1/task.toml b/tasks/0087_941_87941186_qa_1/task.toml index fc713742a159cb15e848dd6a4abc50024b130084..87bb395beddb07af55a6631242a18d96eacf2add 100644 --- a/tasks/0087_941_87941186_qa_1/task.toml +++ b/tasks/0087_941_87941186_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0087_941_87941186_qa_1" +name = "smoldataenvs-train/0087_941_87941186_qa_1" description = "What is the number of duplicate rows found in the wine quality dataset before any data transformation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "240" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0087_941_87941186_qa_3/task.toml b/tasks/0087_941_87941186_qa_3/task.toml index 39b0a773b85d999abfab0f60e27dce44d7886e1c..203c518c3c8a6f7cc3969d20819fe0b6b3c56156 100644 --- a/tasks/0087_941_87941186_qa_3/task.toml +++ b/tasks/0087_941_87941186_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0087_941_87941186_qa_3" +name = "smoldataenvs-train/0087_941_87941186_qa_3" description = "After binning the quality scores into categories (bad, good, very good), how many wines were classified as \"very good\" in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "217" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0087_955_87955720_qa_2/task.toml b/tasks/0087_955_87955720_qa_2/task.toml index 827fe1ae0cb89773dc938817789317a7c441d600..706ec585a570dfbe0618883b66b79205a8ee4647 100644 --- a/tasks/0087_955_87955720_qa_2/task.toml +++ b/tasks/0087_955_87955720_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0087_955_87955720_qa_2" +name = "smoldataenvs-train/0087_955_87955720_qa_2" description = "What is the total number of tweets in the dataset that are in English (Language == 'en')?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "64010" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0087_973_87973029_qa_1/task.toml b/tasks/0087_973_87973029_qa_1/task.toml index 70bf61139f31a7b42418941e28f23e47825995ea..0f2998d1ce88e49cb7989cfa1517bf63d11686cf 100644 --- a/tasks/0087_973_87973029_qa_1/task.toml +++ b/tasks/0087_973_87973029_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0087_973_87973029_qa_1" +name = "smoldataenvs-train/0087_973_87973029_qa_1" description = "What is the highest correlation coefficient between any two numerical attributes in the Pokémon dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.983428" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0087_973_87973029_qa_3/task.toml b/tasks/0087_973_87973029_qa_3/task.toml index d40d1771d169dfd7fd6627a1acf4c756d638d859..01104d5a07abf252eed2709c034caeae52ceeca6 100644 --- a/tasks/0087_973_87973029_qa_3/task.toml +++ b/tasks/0087_973_87973029_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0087_973_87973029_qa_3" +name = "smoldataenvs-train/0087_973_87973029_qa_3" description = "How many Pokémon meet both criteria of having a Defense value greater than 200 and an Attack value greater than 100?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0088_102_88102916_qa_2/task.toml b/tasks/0088_102_88102916_qa_2/task.toml index 1cd489649daecab4d3e9a2f17a60cbc93337f6ad..23a52fa3fb97b3e78ede2ce74fde618f1d6b6079 100644 --- a/tasks/0088_102_88102916_qa_2/task.toml +++ b/tasks/0088_102_88102916_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0088_102_88102916_qa_2" +name = "smoldataenvs-train/0088_102_88102916_qa_2" description = "What is the most skewed feature in the dataset based on sample skewness measurements?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Insulin" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0088_108_88108629_qa_1/task.toml b/tasks/0088_108_88108629_qa_1/task.toml index 834e8672d9e134ec21c548492ff1a8f8934655f7..a3df313c5d29db7eb085ad5ebeff35dba66dd538 100644 --- a/tasks/0088_108_88108629_qa_1/task.toml +++ b/tasks/0088_108_88108629_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0088_108_88108629_qa_1" +name = "smoldataenvs-train/0088_108_88108629_qa_1" description = "What is the average miles per gallon (mpg) for manual transmission cars compared to automatic transmission cars in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Manual: 24.39, Automatic: 17.15" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0088_214_88214420_qa_1/task.toml b/tasks/0088_214_88214420_qa_1/task.toml index 5fa61c58b10367a379c51572f46cfa8a3cfca8bc..341a0050d48f53f071ba55924bf9ec6a2605a6cc 100644 --- a/tasks/0088_214_88214420_qa_1/task.toml +++ b/tasks/0088_214_88214420_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0088_214_88214420_qa_1" +name = "smoldataenvs-train/0088_214_88214420_qa_1" description = "What is the difference in average message length between spam and ham messages in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "68" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0088_214_88214420_qa_3/task.toml b/tasks/0088_214_88214420_qa_3/task.toml index bd4455007c2d1d296faacc8a67810daf55fc0861..dd1a0cf2201734555dd36d0d8e2de7515b9d6b74 100644 --- a/tasks/0088_214_88214420_qa_3/task.toml +++ b/tasks/0088_214_88214420_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0088_214_88214420_qa_3" +name = "smoldataenvs-train/0088_214_88214420_qa_3" description = "Which class (spam or ham) has a higher standard deviation in message lengths?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ham" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0088_214_88214420_qa_5/task.toml b/tasks/0088_214_88214420_qa_5/task.toml index 27b5c680c9a68fd4ace4a7fc2e1c799ebe595502..0f26fa1988e10946f79bab0065bfa7976855acf6 100644 --- a/tasks/0088_214_88214420_qa_5/task.toml +++ b/tasks/0088_214_88214420_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0088_214_88214420_qa_5" +name = "smoldataenvs-train/0088_214_88214420_qa_5" description = "What percentage of the dataset consists of ham messages?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "86.6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0088_244_88244831_qa_4/task.toml b/tasks/0088_244_88244831_qa_4/task.toml index e6826bec0c3bcc2940aaca21bbfd2552b08778e0..4c38ed8bbb1f6860be8ff4f069b3ddcb23ba1871 100644 --- a/tasks/0088_244_88244831_qa_4/task.toml +++ b/tasks/0088_244_88244831_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0088_244_88244831_qa_4" +name = "smoldataenvs-train/0088_244_88244831_qa_4" description = "How many distinct classes are present in the FLOOR target variable for the classification task?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0088_278_88278722_qa_4/task.toml b/tasks/0088_278_88278722_qa_4/task.toml index 1ef05ab55136e6cb9ba3fab68f2a0473f22172e9..1687a8f6af65540e4467b326edcf0ccb3b371527 100644 --- a/tasks/0088_278_88278722_qa_4/task.toml +++ b/tasks/0088_278_88278722_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0088_278_88278722_qa_4" +name = "smoldataenvs-train/0088_278_88278722_qa_4" description = "Which car make has the highest frequency in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "chevrolet" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0088_300_88300701_qa_3/task.toml b/tasks/0088_300_88300701_qa_3/task.toml index da9a86e35b59dc88bd2ab094e0383552e54ab12d..4f1850f04c3fe3f6e5da146ef878f3102eca85e7 100644 --- a/tasks/0088_300_88300701_qa_3/task.toml +++ b/tasks/0088_300_88300701_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0088_300_88300701_qa_3" +name = "smoldataenvs-train/0088_300_88300701_qa_3" description = "What is the total number of unique genres listed for the first movie in the dataset after parsing the genres column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0088_305_88305132_qa_1/task.toml b/tasks/0088_305_88305132_qa_1/task.toml index 196fcdfc0f82833e3234b53a7c20eff96111e9d5..d198b4698d27f9d805cf749b55548252824f1e7c 100644 --- a/tasks/0088_305_88305132_qa_1/task.toml +++ b/tasks/0088_305_88305132_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0088_305_88305132_qa_1" +name = "smoldataenvs-train/0088_305_88305132_qa_1" description = "How many movies were removed from the dataset due to missing values in the overview column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0088_305_88305132_qa_4/task.toml b/tasks/0088_305_88305132_qa_4/task.toml index 1eb170887782043834f2966b7641c8918efb8d9e..3ed41254bb2dc7694b30623065f53c05f4a590d5 100644 --- a/tasks/0088_305_88305132_qa_4/task.toml +++ b/tasks/0088_305_88305132_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0088_305_88305132_qa_4" +name = "smoldataenvs-train/0088_305_88305132_qa_4" description = "How many columns are present in the dataset after selecting the relevant features for recommendation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0088_409_88409114_qa_4/task.toml b/tasks/0088_409_88409114_qa_4/task.toml index a0d327b19a8a0f338ead0f737b6d7a77c41162da..5ab4fd46192b46dc9923ad080c76fa7eddb5b40b 100644 --- a/tasks/0088_409_88409114_qa_4/task.toml +++ b/tasks/0088_409_88409114_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0088_409_88409114_qa_4" +name = "smoldataenvs-train/0088_409_88409114_qa_4" description = "Is the YearsExperience coefficient in the regression model statistically significant at the 0.05 significance level?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] build_timeout_sec = 600.0 diff --git a/tasks/0088_417_88417673_qa_1/task.toml b/tasks/0088_417_88417673_qa_1/task.toml index 0f0adb056ff27d424ea82ada0a3fff8a238a3091..8dd0e4face043de8412ddd1dfd2c01ded4d165bf 100644 --- a/tasks/0088_417_88417673_qa_1/task.toml +++ b/tasks/0088_417_88417673_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0088_417_88417673_qa_1" +name = "smoldataenvs-train/0088_417_88417673_qa_1" description = "After balancing the dataset, how many samples are there for each diagnosis category (benign and malignant)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "212" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0088_418_88418145_qa_1/task.toml b/tasks/0088_418_88418145_qa_1/task.toml index e1db055a3a89901b841f25aaa0779bcbe7a6ab31..a129ca8c1913cd2b518d9a2680cb4758463cd8cf 100644 --- a/tasks/0088_418_88418145_qa_1/task.toml +++ b/tasks/0088_418_88418145_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0088_418_88418145_qa_1" +name = "smoldataenvs-train/0088_418_88418145_qa_1" description = "Which department has the highest average employee turnover rate (percentage of employees who left)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "HR" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0088_472_88472757_qa_2/task.toml b/tasks/0088_472_88472757_qa_2/task.toml index 4f4266427fde97086aac674d558cadc423a20ae6..58f8831d9c38bf54378f7b83a309a3eb702e9b6a 100644 --- a/tasks/0088_472_88472757_qa_2/task.toml +++ b/tasks/0088_472_88472757_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0088_472_88472757_qa_2" +name = "smoldataenvs-train/0088_472_88472757_qa_2" description = "After applying SMOTE, how many samples are present for each wine quality class in the resampled dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "681" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0088_483_88483775_qa_4/task.toml b/tasks/0088_483_88483775_qa_4/task.toml index be6213bcc4125c5995b1a5890cfa01b2c3b19942..61deaf8562cb34c0d8d52db997d1787a3493d81a 100644 --- a/tasks/0088_483_88483775_qa_4/task.toml +++ b/tasks/0088_483_88483775_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0088_483_88483775_qa_4" +name = "smoldataenvs-train/0088_483_88483775_qa_4" description = "What is the difference in mean BMI between individuals with diabetes (Outcome=1) and those without diabetes (Outcome=0)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.84" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0088_489_88489992_qa_2/task.toml b/tasks/0088_489_88489992_qa_2/task.toml index 5d401284d6c6dd3c57e058d9d3a37dd9c056b8b1..595196847036e5e0acc58d7902d19749cee40dd0 100644 --- a/tasks/0088_489_88489992_qa_2/task.toml +++ b/tasks/0088_489_88489992_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0088_489_88489992_qa_2" +name = "smoldataenvs-train/0088_489_88489992_qa_2" description = "What is the range of the pupil-teacher ratio (PTRATIO) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9.4" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0088_489_88489992_qa_3/task.toml b/tasks/0088_489_88489992_qa_3/task.toml index 4a35bd77834d9178a82b7da1fd77c20c83b93616..a8f457e4bf1a63fd2931203972eb02e5663cde36 100644 --- a/tasks/0088_489_88489992_qa_3/task.toml +++ b/tasks/0088_489_88489992_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0088_489_88489992_qa_3" +name = "smoldataenvs-train/0088_489_88489992_qa_3" description = "How many samples are included in the test dataset after splitting with a 25% test size?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "123" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0088_507_88507841_qa_4/task.toml b/tasks/0088_507_88507841_qa_4/task.toml index 99275ae068e6c4180962046b06787744ca360856..91f180fd5ef10d6eb9d495138c029e7100ba5f4f 100644 --- a/tasks/0088_507_88507841_qa_4/task.toml +++ b/tasks/0088_507_88507841_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0088_507_88507841_qa_4" +name = "smoldataenvs-train/0088_507_88507841_qa_4" description = "After preprocessing, what is the mean price of diamonds in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3932.799722" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0088_548_88548025_qa_1/task.toml b/tasks/0088_548_88548025_qa_1/task.toml index f4a841a1ab8facbacb43451e8de9f67361507685..1e4ff790190d01a468d0f47ebaa4211ad7be9854 100644 --- a/tasks/0088_548_88548025_qa_1/task.toml +++ b/tasks/0088_548_88548025_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0088_548_88548025_qa_1" +name = "smoldataenvs-train/0088_548_88548025_qa_1" description = "What is the classification accuracy of the Label Spreading model on the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "100" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0088_548_88548685_qa_4/task.toml b/tasks/0088_548_88548685_qa_4/task.toml index ae4ef5758b420f58a6dee946f54b3a02a4ebc23a..12faec7285487bd78688d2fbb77df0ea5136d1c4 100644 --- a/tasks/0088_548_88548685_qa_4/task.toml +++ b/tasks/0088_548_88548685_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0088_548_88548685_qa_4" +name = "smoldataenvs-train/0088_548_88548685_qa_4" description = "What is the average balance frequency in the cluster with the highest average purchases?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9758" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0088_550_88550730_qa_1/task.toml b/tasks/0088_550_88550730_qa_1/task.toml index 1727c40321049d668055f4a0543f5750e62e9447..f0f1ec8e5317578e450639ffc0f853165ebd4e9c 100644 --- a/tasks/0088_550_88550730_qa_1/task.toml +++ b/tasks/0088_550_88550730_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0088_550_88550730_qa_1" +name = "smoldataenvs-train/0088_550_88550730_qa_1" description = "Which feature in the Boston housing dataset shows the highest absolute correlation with the median home value (MEDV)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "LSTAT" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0088_614_88614765_qa_5/task.toml b/tasks/0088_614_88614765_qa_5/task.toml index 462ddc0781ddde2a4b0c256a00461740a568dc0e..4feae8841e8330264ba4008c578227791d3c4654 100644 --- a/tasks/0088_614_88614765_qa_5/task.toml +++ b/tasks/0088_614_88614765_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0088_614_88614765_qa_5" +name = "smoldataenvs-train/0088_614_88614765_qa_5" description = "Which job category contributes the most to the total balance in the cluster characterized by highly educated individuals in their 30s from management/technician backgrounds?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "management" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0088_676_88676435_qa_3/task.toml b/tasks/0088_676_88676435_qa_3/task.toml index 4fb60cf3d67a9afdab599d54f8f9394994bb1549..36a75c76eb53cc82900597708697fbbf159793d1 100644 --- a/tasks/0088_676_88676435_qa_3/task.toml +++ b/tasks/0088_676_88676435_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0088_676_88676435_qa_3" +name = "smoldataenvs-train/0088_676_88676435_qa_3" description = "How many Ask HN posts were created during the hour that yields the highest average number of comments?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "646" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0088_720_88720224_qa_1/task.toml b/tasks/0088_720_88720224_qa_1/task.toml index c577286bf3368221957e5abd56da7d1badb82445..7d1293540b529577ac03170ffeea7856e298ef6c 100644 --- a/tasks/0088_720_88720224_qa_1/task.toml +++ b/tasks/0088_720_88720224_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0088_720_88720224_qa_1" +name = "smoldataenvs-train/0088_720_88720224_qa_1" description = "What percentage of the original training data remained after dropping rows with missing values in Product_Category_2 and removing Product_Category_3?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "68.43" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0088_720_88720224_qa_2/task.toml b/tasks/0088_720_88720224_qa_2/task.toml index e7c20709674656b6d4f2d0c6c22e558fcc788aa6..608029c34b2888ef6036810764a1453beedfd4e1 100644 --- a/tasks/0088_720_88720224_qa_2/task.toml +++ b/tasks/0088_720_88720224_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0088_720_88720224_qa_2" +name = "smoldataenvs-train/0088_720_88720224_qa_2" description = "Which product category in Product_Category_1 has the highest frequency in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0088_720_88720224_qa_3/task.toml b/tasks/0088_720_88720224_qa_3/task.toml index 2e6d5ccea74dc791684c17546cf5f549014f45d7..2aae48250cfb3f57e94ba3bc5278d169b19ac353 100644 --- a/tasks/0088_720_88720224_qa_3/task.toml +++ b/tasks/0088_720_88720224_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0088_720_88720224_qa_3" +name = "smoldataenvs-train/0088_720_88720224_qa_3" description = "How many unique user IDs are present in the test dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5877" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0088_720_88720224_qa_4/task.toml b/tasks/0088_720_88720224_qa_4/task.toml index d8306a4ac2a837312a5e4f553740e4579b2a5e09..26dd105239363bb1c66364cadece6627280e20d1 100644 --- a/tasks/0088_720_88720224_qa_4/task.toml +++ b/tasks/0088_720_88720224_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0088_720_88720224_qa_4" +name = "smoldataenvs-train/0088_720_88720224_qa_4" description = "Which occupation has the highest frequency in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0088_736_88736093_qa_5/task.toml b/tasks/0088_736_88736093_qa_5/task.toml index da95d565146b98d47257e71abe7da1ed164a43a4..e41af15fca418808f33bfac84dd704aa7fefa46b 100644 --- a/tasks/0088_736_88736093_qa_5/task.toml +++ b/tasks/0088_736_88736093_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0088_736_88736093_qa_5" +name = "smoldataenvs-train/0088_736_88736093_qa_5" description = "What is the mean BMI value across all participants in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "31.0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0088_822_88822681_qa_1/task.toml b/tasks/0088_822_88822681_qa_1/task.toml index 9bf6f8af24f5e6e5d307fb8a636ef0977b952925..20b6f186c6d415a07b079df57cf5910972dbcb60 100644 --- a/tasks/0088_822_88822681_qa_1/task.toml +++ b/tasks/0088_822_88822681_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0088_822_88822681_qa_1" +name = "smoldataenvs-train/0088_822_88822681_qa_1" description = "What is the most popular name overall based on total counts across all records in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "James" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0088_860_88860848_qa_2/task.toml b/tasks/0088_860_88860848_qa_2/task.toml index 8517039b5894bc52e1c5ae9a42bb4f1a9c96e204..366088f281d5de58535be4b51262bf26247beb80 100644 --- a/tasks/0088_860_88860848_qa_2/task.toml +++ b/tasks/0088_860_88860848_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0088_860_88860848_qa_2" +name = "smoldataenvs-train/0088_860_88860848_qa_2" description = "What is the F1-score for the minority class (Outcome = 1) in the XGBoost model with class resampling?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.63" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0088_860_88860848_qa_4/task.toml b/tasks/0088_860_88860848_qa_4/task.toml index 6e4ca9ec187c9cad7b6468347d24404cdad5dfb2..8a191aa6a86abeaa63152e74ea769d09489c26af 100644 --- a/tasks/0088_860_88860848_qa_4/task.toml +++ b/tasks/0088_860_88860848_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0088_860_88860848_qa_4" +name = "smoldataenvs-train/0088_860_88860848_qa_4" description = "What percentage of the minority class (Outcome = 1) samples in the test set were correctly classified by the resampled XGBoost model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "69.1" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0088_961_88961970_qa_1/task.toml b/tasks/0088_961_88961970_qa_1/task.toml index 775207275d1f177f236a304c825a7d8c52c10d12..2950b81b9214a9c2074fa8fa7d1e6c7023dc1894 100644 --- a/tasks/0088_961_88961970_qa_1/task.toml +++ b/tasks/0088_961_88961970_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0088_961_88961970_qa_1" +name = "smoldataenvs-train/0088_961_88961970_qa_1" description = "How many missing values were present in the 'Age' column prior to imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "177" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0088_961_88961970_qa_4/task.toml b/tasks/0088_961_88961970_qa_4/task.toml index edc50d4efc93c8f7c66d9e22ab706082c4068921..a8f53c75f1da753773e2cf658741d1cbb2cf3492 100644 --- a/tasks/0088_961_88961970_qa_4/task.toml +++ b/tasks/0088_961_88961970_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0088_961_88961970_qa_4" +name = "smoldataenvs-train/0088_961_88961970_qa_4" description = "After handling all missing values, how many null entries remained in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0088_996_88996860_qa_1/task.toml b/tasks/0088_996_88996860_qa_1/task.toml index c5df9e931394c114bd92fb6652afd0995faa7fa4..6bbb2af195baf5da6df788e97e581001529707a2 100644 --- a/tasks/0088_996_88996860_qa_1/task.toml +++ b/tasks/0088_996_88996860_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0088_996_88996860_qa_1" +name = "smoldataenvs-train/0088_996_88996860_qa_1" description = "What is the highest positive correlation coefficient between any feature and the 'Outcome' variable after handling missing values and outliers?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.492928" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0089_026_89026160_qa_4/task.toml b/tasks/0089_026_89026160_qa_4/task.toml index 0c4ca5f922d1dce30034dc989b21b5221ad30d97..af7cc5ab02d1f0155fdf4163b23906416adf8810 100644 --- a/tasks/0089_026_89026160_qa_4/task.toml +++ b/tasks/0089_026_89026160_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0089_026_89026160_qa_4" +name = "smoldataenvs-train/0089_026_89026160_qa_4" description = "What was the median Glucose value used to replace missing values in the Glucose column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "117.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0089_051_89051135_qa_2/task.toml b/tasks/0089_051_89051135_qa_2/task.toml index 446beda0b4f888dece6218a1129dfd8c2ad41eb5..7d36e3f28bc90eb0a6b7296e33573a514989aae7 100644 --- a/tasks/0089_051_89051135_qa_2/task.toml +++ b/tasks/0089_051_89051135_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0089_051_89051135_qa_2" +name = "smoldataenvs-train/0089_051_89051135_qa_2" description = "Which feature has the highest importance in predicting medical insurance charges according to the trained random forest model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "smoker" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0089_075_89075568_qa_2/task.toml b/tasks/0089_075_89075568_qa_2/task.toml index e895d1a4fc95106f416b4590987d4de2b4b786b5..36013362614b7d7d6e68c2385300a49e0233d182 100644 --- a/tasks/0089_075_89075568_qa_2/task.toml +++ b/tasks/0089_075_89075568_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0089_075_89075568_qa_2" +name = "smoldataenvs-train/0089_075_89075568_qa_2" description = "What is the highest medical cost billed by health insurance in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "63770.42801" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0089_129_89129153_qa_1/task.toml b/tasks/0089_129_89129153_qa_1/task.toml index f032ec714b8f2e4b23af5687d0bbc92a478464c3..0c6ed91a4aa5894a55862dd4f46000869f1262dd 100644 --- a/tasks/0089_129_89129153_qa_1/task.toml +++ b/tasks/0089_129_89129153_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0089_129_89129153_qa_1" +name = "smoldataenvs-train/0089_129_89129153_qa_1" description = "What is the median wine quality in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0089_129_89129153_qa_3/task.toml b/tasks/0089_129_89129153_qa_3/task.toml index 21dddf6829d6394731f77eec87e83c28fb2acab8..0b0814fbbd1eef6716a4c97813efa66bc457d534 100644 --- a/tasks/0089_129_89129153_qa_3/task.toml +++ b/tasks/0089_129_89129153_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0089_129_89129153_qa_3" +name = "smoldataenvs-train/0089_129_89129153_qa_3" description = "What is the correlation coefficient between alcohol content and wine quality?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.48" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0089_171_89171933_qa_3/task.toml b/tasks/0089_171_89171933_qa_3/task.toml index 7e316fdb24fb0d38d9ae7dd053e994424f90efa2..6355ea1dc2c6fbf0108fbc587e0088ef597a10eb 100644 --- a/tasks/0089_171_89171933_qa_3/task.toml +++ b/tasks/0089_171_89171933_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0089_171_89171933_qa_3" +name = "smoldataenvs-train/0089_171_89171933_qa_3" description = "What is the lowest correlation between any two features in the Iris dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.420516" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0089_171_89171933_qa_4/task.toml b/tasks/0089_171_89171933_qa_4/task.toml index 2421395c2b21cd2d03c8b847165b35f8b441a8a8..55b2a9defb560148ae71f967649ef912c1530281 100644 --- a/tasks/0089_171_89171933_qa_4/task.toml +++ b/tasks/0089_171_89171933_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0089_171_89171933_qa_4" +name = "smoldataenvs-train/0089_171_89171933_qa_4" description = "Which feature in the dataset has the highest standard deviation across all samples?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0089_217_89217936_qa_2/task.toml b/tasks/0089_217_89217936_qa_2/task.toml index 332ff0a19f7fa1faae3053e9abfcd18b83dfcdd9..343de323f6cb368262d5297ebd81fa2810732904 100644 --- a/tasks/0089_217_89217936_qa_2/task.toml +++ b/tasks/0089_217_89217936_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0089_217_89217936_qa_2" +name = "smoldataenvs-train/0089_217_89217936_qa_2" description = "What is the most common Pokémon type (considering both type1 and type2 counts combined)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Water" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0089_255_89255842_qa_4/task.toml b/tasks/0089_255_89255842_qa_4/task.toml index 3de9b54f39e6425a0d358d60f1d68127f6a2e43d..4b10c385127135600fc9892d5fafaa40adb8a5e1 100644 --- a/tasks/0089_255_89255842_qa_4/task.toml +++ b/tasks/0089_255_89255842_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0089_255_89255842_qa_4" +name = "smoldataenvs-train/0089_255_89255842_qa_4" description = "Which feature has the least statistical relevance to insurance charges based on the SelectKBest f_regression scores?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "region" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0089_279_89279140_qa_5/task.toml b/tasks/0089_279_89279140_qa_5/task.toml index c8ad198c70d4935dbc9dbbafc95017723ad838e3..c5d2c9d81fe66e520ddf1199900718c5ade6e896 100644 --- a/tasks/0089_279_89279140_qa_5/task.toml +++ b/tasks/0089_279_89279140_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0089_279_89279140_qa_5" +name = "smoldataenvs-train/0089_279_89279140_qa_5" description = "Which gender has the highest approved conversion rate across all age groups combined?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "M" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0089_290_89290915_qa_3/task.toml b/tasks/0089_290_89290915_qa_3/task.toml index 01157d5c96470371d723459207ed7b3395803d3b..e876d84ce6fb3054bc72c594b48f1f46da7b7899 100644 --- a/tasks/0089_290_89290915_qa_3/task.toml +++ b/tasks/0089_290_89290915_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0089_290_89290915_qa_3" +name = "smoldataenvs-train/0089_290_89290915_qa_3" description = "What is the mutual information score between OnlineSecurity and the Churn target variable?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.064528" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0089_290_89290915_qa_5/task.toml b/tasks/0089_290_89290915_qa_5/task.toml index c4e23aa64a0a68f69ea747c0407e49f4ec6111d7..511e89a43f302c1f7444771d1a729c5926cbe591 100644 --- a/tasks/0089_290_89290915_qa_5/task.toml +++ b/tasks/0089_290_89290915_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0089_290_89290915_qa_5" +name = "smoldataenvs-train/0089_290_89290915_qa_5" description = "What is the mutual information score between InternetService and the Churn target variable?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.055394" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0089_547_89547343_qa_2/task.toml b/tasks/0089_547_89547343_qa_2/task.toml index a537125989ce784655408b4badb4e26573513566..690e6fa9c8fa237b3a6145828347e3c0d2711442 100644 --- a/tasks/0089_547_89547343_qa_2/task.toml +++ b/tasks/0089_547_89547343_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0089_547_89547343_qa_2" +name = "smoldataenvs-train/0089_547_89547343_qa_2" description = "How many features are present in the dataset after removing the `veil-type` column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "22" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0089_576_89576733_qa_4/task.toml b/tasks/0089_576_89576733_qa_4/task.toml index 90b058a23c2872256c2642bb677df84c77038970..a28ab704e75f4a6fa887cff0d1e7afa51af21570 100644 --- a/tasks/0089_576_89576733_qa_4/task.toml +++ b/tasks/0089_576_89576733_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0089_576_89576733_qa_4" +name = "smoldataenvs-train/0089_576_89576733_qa_4" description = "What is the minimum number of votes (m) required for a movie to qualify for the weighted score calculation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1838.4" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0089_589_89589668_qa_1/task.toml b/tasks/0089_589_89589668_qa_1/task.toml index b2ad8a5bb66c914bea5a37f645621a7c2b435501..25e41ce7a689fbf56a950cf71c5e0b8f71213252 100644 --- a/tasks/0089_589_89589668_qa_1/task.toml +++ b/tasks/0089_589_89589668_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0089_589_89589668_qa_1" +name = "smoldataenvs-train/0089_589_89589668_qa_1" description = "What is the optimal number of clusters determined by the elbow method and silhouette score analysis in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0089_601_89601148_qa_3/task.toml b/tasks/0089_601_89601148_qa_3/task.toml index 0d3f9424fdcc2eeb4b333ddead42eb0849495786..f51bf356497b47a3e60fee2cc855d8d13c883ae4 100644 --- a/tasks/0089_601_89601148_qa_3/task.toml +++ b/tasks/0089_601_89601148_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0089_601_89601148_qa_3" +name = "smoldataenvs-train/0089_601_89601148_qa_3" description = "What is the most frequent wine quality rating in the dataset according to the value counts analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0089_603_89603255_qa_1/task.toml b/tasks/0089_603_89603255_qa_1/task.toml index 340d98930990ff56e9b3bb2f14c415a0d7de63cf..bb4eb1d6e9435b1334a25bdda94340730cdef44a 100644 --- a/tasks/0089_603_89603255_qa_1/task.toml +++ b/tasks/0089_603_89603255_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0089_603_89603255_qa_1" +name = "smoldataenvs-train/0089_603_89603255_qa_1" description = "What is the name of the video game with the highest total global sales in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii Sports" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0089_604_89604836_qa_2/task.toml b/tasks/0089_604_89604836_qa_2/task.toml index f0cb7e5a6d6e3991bbb3394de1205dc7d4dfb777..00b4a5c6e320dab4048c3810d53556fb1c3989af 100644 --- a/tasks/0089_604_89604836_qa_2/task.toml +++ b/tasks/0089_604_89604836_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0089_604_89604836_qa_2" +name = "smoldataenvs-train/0089_604_89604836_qa_2" description = "What is the most recent year present in the dataset after cleaning the data to exclude games published after 2015?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2015" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0089_604_89604836_qa_4/task.toml b/tasks/0089_604_89604836_qa_4/task.toml index d6cf7874426979d91d66857d4ee0b3d51c188107..e96cc51af1d2af1ce20736e35de863273649e3b3 100644 --- a/tasks/0089_604_89604836_qa_4/task.toml +++ b/tasks/0089_604_89604836_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0089_604_89604836_qa_4" +name = "smoldataenvs-train/0089_604_89604836_qa_4" description = "Which video game in the top 10 ranks has the highest European (EU) sales value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii Sports" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0089_604_89604836_qa_5/task.toml b/tasks/0089_604_89604836_qa_5/task.toml index 1cf0310ba5c07177e7e2d8f8b371705b77b8c1d2..dca103ffab52707f7ea75567d23baf6790e956f6 100644 --- a/tasks/0089_604_89604836_qa_5/task.toml +++ b/tasks/0089_604_89604836_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0089_604_89604836_qa_5" +name = "smoldataenvs-train/0089_604_89604836_qa_5" description = "Which video game in the top 10 ranks has the highest Japanese (JP) sales value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Pokemon Red/Pokemon Blue" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0089_613_89613351_qa_4/task.toml b/tasks/0089_613_89613351_qa_4/task.toml index a7edafddcb2278f03bdd76b9a2a15f413475e8d1..7f90f3a7f5ca23be32b4b495fec8cf7feff59a5d 100644 --- a/tasks/0089_613_89613351_qa_4/task.toml +++ b/tasks/0089_613_89613351_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0089_613_89613351_qa_4" +name = "smoldataenvs-train/0089_613_89613351_qa_4" description = "How many non-binary variables were filled using the median value during the missing data imputation process?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0089_696_89696158_qa_1/task.toml b/tasks/0089_696_89696158_qa_1/task.toml index a29845fe7d1df98632f185a8161c60184f502367..e83706b42789727eca9f87a7d6aab1ca4c64d980 100644 --- a/tasks/0089_696_89696158_qa_1/task.toml +++ b/tasks/0089_696_89696158_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0089_696_89696158_qa_1" +name = "smoldataenvs-train/0089_696_89696158_qa_1" description = "Which single feature in the dataset achieves the highest test accuracy when used alone to predict mushroom edibility?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Odor" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0089_704_89704261_qa_1/task.toml b/tasks/0089_704_89704261_qa_1/task.toml index 43c0a57b939992ef28f3620c6b22570353eb6fc1..a39b6c6f317d1a7ff8203935a761a77027575e86 100644 --- a/tasks/0089_704_89704261_qa_1/task.toml +++ b/tasks/0089_704_89704261_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0089_704_89704261_qa_1" +name = "smoldataenvs-train/0089_704_89704261_qa_1" description = "Which feature in the dataset has the highest correlation with the diabetes diagnosis outcome?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0089_704_89704261_qa_5/task.toml b/tasks/0089_704_89704261_qa_5/task.toml index bbea7934b090d261d99f58466be4386f666cec33..e1c50ee48aae9f595e5c8d3930e0df0f29f46c2a 100644 --- a/tasks/0089_704_89704261_qa_5/task.toml +++ b/tasks/0089_704_89704261_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0089_704_89704261_qa_5" +name = "smoldataenvs-train/0089_704_89704261_qa_5" description = "What is the percentage of patients in the dataset diagnosed with diabetes?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.8958" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0089_775_89775918_qa_4/task.toml b/tasks/0089_775_89775918_qa_4/task.toml index 6b20a42b15dc866e5f77288cb02ac01e2ec1a136..f5e36119bf1242b1269718723e2626e92e76a005 100644 --- a/tasks/0089_775_89775918_qa_4/task.toml +++ b/tasks/0089_775_89775918_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0089_775_89775918_qa_4" +name = "smoldataenvs-train/0089_775_89775918_qa_4" description = "How many samples in the cluster corresponding to Class 1 correctly match the original Class 1 label?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "59" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0089_787_89787610_qa_1/task.toml b/tasks/0089_787_89787610_qa_1/task.toml index f21ad1a6ddee8fb2860b9868a57150deb236d8ba..6348a1d397649e3efdb1af8528335f6207612e72 100644 --- a/tasks/0089_787_89787610_qa_1/task.toml +++ b/tasks/0089_787_89787610_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0089_787_89787610_qa_1" +name = "smoldataenvs-train/0089_787_89787610_qa_1" description = "Which feature has the highest positive correlation with SalePrice after one-hot encoding of categorical variables?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "OverallQual" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0089_787_89787610_qa_4/task.toml b/tasks/0089_787_89787610_qa_4/task.toml index 4ed1c4a93077b72bc3131c3b2bdb105d49a74c64..fb753c4bf64ad9e69a9a2d5fe7cc01ec007d14ee 100644 --- a/tasks/0089_787_89787610_qa_4/task.toml +++ b/tasks/0089_787_89787610_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0089_787_89787610_qa_4" +name = "smoldataenvs-train/0089_787_89787610_qa_4" description = "How many rows were removed from the dataset after identifying outliers in GrLivArea and SalePrice?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2 rows" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0089_804_89804602_qa_4/task.toml b/tasks/0089_804_89804602_qa_4/task.toml index 8d539244131efa4efb8446f8bdf54735cdfb645e..22bbc3d256af40023bfabd2fd4ff2183e516a0f1 100644 --- a/tasks/0089_804_89804602_qa_4/task.toml +++ b/tasks/0089_804_89804602_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0089_804_89804602_qa_4" +name = "smoldataenvs-train/0089_804_89804602_qa_4" description = "What is the mean weight in pounds of the individuals in the dataset after unit conversion?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "136.88199" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0089_811_89811907_qa_3/task.toml b/tasks/0089_811_89811907_qa_3/task.toml index b2d0d37fe4ea00b392a892b674317fb99d8c94d1..aebf0efb84aab3456e5500b551421d6c8c4d1730 100644 --- a/tasks/0089_811_89811907_qa_3/task.toml +++ b/tasks/0089_811_89811907_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0089_811_89811907_qa_3" +name = "smoldataenvs-train/0089_811_89811907_qa_3" description = "What is the optimal number of clusters identified using the elbow method in the K-means clustering analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0089_811_89811907_qa_4/task.toml b/tasks/0089_811_89811907_qa_4/task.toml index b5df90e9501a69ffdcbb6023ed416d4b05c3abd6..d639a07a273e06ed1f0022a1f950af1e1e587c5e 100644 --- a/tasks/0089_811_89811907_qa_4/task.toml +++ b/tasks/0089_811_89811907_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0089_811_89811907_qa_4" +name = "smoldataenvs-train/0089_811_89811907_qa_4" description = "What is the range of the SepalLengthCm feature in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.6" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0090_012_90012642_qa_4/task.toml b/tasks/0090_012_90012642_qa_4/task.toml index 0165845e1e2d9bbd695fff6141d2e8ad1b4bae3c..2725b30cfc9483eda5b6720ca2fff45d81312347 100644 --- a/tasks/0090_012_90012642_qa_4/task.toml +++ b/tasks/0090_012_90012642_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0090_012_90012642_qa_4" +name = "smoldataenvs-train/0090_012_90012642_qa_4" description = "What is the Pearson correlation coefficient between \"concavity_worst\" and \"concave points_worst\" features in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.85" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0090_020_90020807_qa_4/task.toml b/tasks/0090_020_90020807_qa_4/task.toml index c271c271c700aa93d5d53edb1f9cc72a9ad66051..78e11c296860661a899d1918864a5a3c2a211dcb 100644 --- a/tasks/0090_020_90020807_qa_4/task.toml +++ b/tasks/0090_020_90020807_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0090_020_90020807_qa_4" +name = "smoldataenvs-train/0090_020_90020807_qa_4" description = "What percentage of the original dataset represented diabetic cases before applying SMOTE upsampling?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.89583333333333" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0090_233_90233145_qa_2/task.toml b/tasks/0090_233_90233145_qa_2/task.toml index fb5ba03bc6424585c984b39b29daa2d94a0cf273..3008f69c74b3398cbab6b27a309358a09fb81192 100644 --- a/tasks/0090_233_90233145_qa_2/task.toml +++ b/tasks/0090_233_90233145_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0090_233_90233145_qa_2" +name = "smoldataenvs-train/0090_233_90233145_qa_2" description = "Which two species are explicitly noted to have overlapping petal length distributions in the analysis based on the violin plots?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-versicolor, Iris-virginica" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0090_257_90257990_qa_1/task.toml b/tasks/0090_257_90257990_qa_1/task.toml index 845709c46b23222c172d8ed3d89a2307d6ccca51..847fee5bbf4db0e8d40f2d033137b4f02292d385 100644 --- a/tasks/0090_257_90257990_qa_1/task.toml +++ b/tasks/0090_257_90257990_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0090_257_90257990_qa_1" +name = "smoldataenvs-train/0090_257_90257990_qa_1" description = "Which categorical feature in the dataset shows the strongest statistical relationship with customer churn based on mutual information scores?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Contract" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0090_257_90257990_qa_4/task.toml b/tasks/0090_257_90257990_qa_4/task.toml index 3f395baf188c3624701e2e588c3fb494ca5f35e1..6c3a01570fea4c76a8387ca3a7122df3f13e0e97 100644 --- a/tasks/0090_257_90257990_qa_4/task.toml +++ b/tasks/0090_257_90257990_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0090_257_90257990_qa_4" +name = "smoldataenvs-train/0090_257_90257990_qa_4" description = "How many missing values were present in the TotalCharges column before data cleaning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0090_285_90285188_qa_3/task.toml b/tasks/0090_285_90285188_qa_3/task.toml index 9fbf4aa15d50915df1bf00f41c761718775029d2..494c0c670f35c4b5328cc3c1ee72dac012f6230e 100644 --- a/tasks/0090_285_90285188_qa_3/task.toml +++ b/tasks/0090_285_90285188_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0090_285_90285188_qa_3" +name = "smoldataenvs-train/0090_285_90285188_qa_3" description = "After imputation, which gender category has a higher percentage of approved loan applications?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Male" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0090_285_90285188_qa_5/task.toml b/tasks/0090_285_90285188_qa_5/task.toml index 27185e46b9c01f455c7594def23bd081f0c0e368..988bf591e089955479db1ae383e813a5b09a54f4 100644 --- a/tasks/0090_285_90285188_qa_5/task.toml +++ b/tasks/0090_285_90285188_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0090_285_90285188_qa_5" +name = "smoldataenvs-train/0090_285_90285188_qa_5" description = "Which marital status category has a significantly higher proportion of approved loans compared to the other category?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Married" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0090_301_90301818_qa_4/task.toml b/tasks/0090_301_90301818_qa_4/task.toml index 903c2cc1bfe2721c66b6fc38623f31b11d077768..78fc5cafc54412b7cc63cbfeda3742b3f94f1515 100644 --- a/tasks/0090_301_90301818_qa_4/task.toml +++ b/tasks/0090_301_90301818_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0090_301_90301818_qa_4" +name = "smoldataenvs-train/0090_301_90301818_qa_4" description = "What is the relationship between customer tenure and churn probability as shown by the correlation analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.354049" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0090_327_90327261_qa_3/task.toml b/tasks/0090_327_90327261_qa_3/task.toml index 72111f8998f09b9b9b23dee5958db2e6a305bea8..5b4f5e694f08ed630918974d5a6c4ad7a8fc911d 100644 --- a/tasks/0090_327_90327261_qa_3/task.toml +++ b/tasks/0090_327_90327261_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0090_327_90327261_qa_3" +name = "smoldataenvs-train/0090_327_90327261_qa_3" description = "What is the highest test accuracy achieved across all evaluated machine learning models for gender classification?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "98.42" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0090_327_90327261_qa_4/task.toml b/tasks/0090_327_90327261_qa_4/task.toml index 5b271de6b00efc98840d73b09a29dbcf18b58b8e..1cbc24c52c9909bc16e88531876a74a8cf7a780d 100644 --- a/tasks/0090_327_90327261_qa_4/task.toml +++ b/tasks/0090_327_90327261_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0090_327_90327261_qa_4" +name = "smoldataenvs-train/0090_327_90327261_qa_4" description = "What is the accuracy of the Decision Tree classifier on the test set after feature selection and data preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "96.00" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0090_327_90327261_qa_5/task.toml b/tasks/0090_327_90327261_qa_5/task.toml index ebb68a16c77ebe8bc02539598a1d04ab77c7c73e..8879295c8f06afcea3d24441055352c6d8bd1afe 100644 --- a/tasks/0090_327_90327261_qa_5/task.toml +++ b/tasks/0090_327_90327261_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0090_327_90327261_qa_5" +name = "smoldataenvs-train/0090_327_90327261_qa_5" description = "What is the class distribution balance in the dataset (number of samples per class)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1584 male, 1584 female" reward_mode_initial = "list" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0090_380_90380699_qa_3/task.toml b/tasks/0090_380_90380699_qa_3/task.toml index 9e5ddb3d53eaf9e31bc52a866485686850c048f0..e04d3ce96da0aa6dd8738fe5dfa9d77c67a255c7 100644 --- a/tasks/0090_380_90380699_qa_3/task.toml +++ b/tasks/0090_380_90380699_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0090_380_90380699_qa_3" +name = "smoldataenvs-train/0090_380_90380699_qa_3" description = "Which department name has the highest proportion of reviews in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Tops" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0090_428_90428964_qa_1/task.toml b/tasks/0090_428_90428964_qa_1/task.toml index cafa50bade0f4e76c2f75e3bafafc1c0a66b2c9b..104aac24434e734e057fa589c2eacce54ea531e5 100644 --- a/tasks/0090_428_90428964_qa_1/task.toml +++ b/tasks/0090_428_90428964_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0090_428_90428964_qa_1" +name = "smoldataenvs-train/0090_428_90428964_qa_1" description = "What is the total number of rows in the combined training and test datasets after merging and preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "783667" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0090_428_90428964_qa_4/task.toml b/tasks/0090_428_90428964_qa_4/task.toml index 7a55f71923f82a6239af9514588b0ff73c3f357b..8f0cfa453814f14671d92961191f13c80d0286e1 100644 --- a/tasks/0090_428_90428964_qa_4/task.toml +++ b/tasks/0090_428_90428964_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0090_428_90428964_qa_4" +name = "smoldataenvs-train/0090_428_90428964_qa_4" description = "How many unique age groups are represented in the dataset after applying the target ordinal encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0090_436_90436320_qa_1/task.toml b/tasks/0090_436_90436320_qa_1/task.toml index 7571f9dfd891823c984b54a0b0a8b3e3e6775e63..218883f2b51563de79a6328a630d7ed4a911d46c 100644 --- a/tasks/0090_436_90436320_qa_1/task.toml +++ b/tasks/0090_436_90436320_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0090_436_90436320_qa_1" +name = "smoldataenvs-train/0090_436_90436320_qa_1" description = "What is the total sulfur dioxide content in wines with the highest quality rating (8)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "602.00" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0090_524_90524053_qa_1/task.toml b/tasks/0090_524_90524053_qa_1/task.toml index b0ff493c973fe57a0da7804d04f6d764c793e508..f7dfe688e5f63a5fee8caa25d954ad50ca306288 100644 --- a/tasks/0090_524_90524053_qa_1/task.toml +++ b/tasks/0090_524_90524053_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0090_524_90524053_qa_1" +name = "smoldataenvs-train/0090_524_90524053_qa_1" description = "Which wine quality rating has the highest total sulfur dioxide levels?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0090_524_90524053_qa_2/task.toml b/tasks/0090_524_90524053_qa_2/task.toml index c06fde574ce09c284d5c7beaf8312ee91a2cb9c0..4f17198a08b40c0ee4e8ac6978bdf2def087dfbd 100644 --- a/tasks/0090_524_90524053_qa_2/task.toml +++ b/tasks/0090_524_90524053_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0090_524_90524053_qa_2" +name = "smoldataenvs-train/0090_524_90524053_qa_2" description = "Which physicochemical property shows the strongest positive correlation with wine quality?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "alcohol" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0090_524_90524053_qa_5/task.toml b/tasks/0090_524_90524053_qa_5/task.toml index c4778fd41a5774d7b7dee4e187d8ac37695b5330..6c942666e413a6e322af3c77f8c01e902616c16c 100644 --- a/tasks/0090_524_90524053_qa_5/task.toml +++ b/tasks/0090_524_90524053_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0090_524_90524053_qa_5" +name = "smoldataenvs-train/0090_524_90524053_qa_5" description = "Which physicochemical property has the strongest positive correlation with residual sugar?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "density" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0090_607_90607840_qa_3/task.toml b/tasks/0090_607_90607840_qa_3/task.toml index f2e2ad9b82cde6e37a1da0f67e50b926ddd40542..30eafc498f9eefa8e6c4ec6a339c74962e5868b6 100644 --- a/tasks/0090_607_90607840_qa_3/task.toml +++ b/tasks/0090_607_90607840_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0090_607_90607840_qa_3" +name = "smoldataenvs-train/0090_607_90607840_qa_3" description = "Which region has the highest number of insured individuals in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "southeast" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0090_607_90607840_qa_4/task.toml b/tasks/0090_607_90607840_qa_4/task.toml index 8b358d0b655404e7b9b2a0025e37b4611d676255..1ae0b35e76e66eae3e36bd582528545dc4986e76 100644 --- a/tasks/0090_607_90607840_qa_4/task.toml +++ b/tasks/0090_607_90607840_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0090_607_90607840_qa_4" +name = "smoldataenvs-train/0090_607_90607840_qa_4" description = "What is the difference between the number of male and female patients in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "14" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0090_629_90629430_qa_1/task.toml b/tasks/0090_629_90629430_qa_1/task.toml index 957377b5794500fac87bcb7e1e30c7119430ec4c..40d1469f2d4a52b76a34286403c9b95565b1cdf2 100644 --- a/tasks/0090_629_90629430_qa_1/task.toml +++ b/tasks/0090_629_90629430_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0090_629_90629430_qa_1" +name = "smoldataenvs-train/0090_629_90629430_qa_1" description = "Which job category has the highest number of clients who did not subscribe to the term deposit?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "blue-collar" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0090_648_90648013_qa_1/task.toml b/tasks/0090_648_90648013_qa_1/task.toml index 5c67cbdacaaa00ad11460c86ef6ffbb5119a0e6e..77c38be0a10d543bf9bf430c0e0644363988b472 100644 --- a/tasks/0090_648_90648013_qa_1/task.toml +++ b/tasks/0090_648_90648013_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0090_648_90648013_qa_1" +name = "smoldataenvs-train/0090_648_90648013_qa_1" description = "Which Bollywood movie in the dataset has the highest IMDB score, and what is that score?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Airlift, 8.5" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0090_648_90648013_qa_4/task.toml b/tasks/0090_648_90648013_qa_4/task.toml index 9b625c1d88e4c8b4453d3cf8c1c334cfbf4f3b22..60d0b89f9b9971f63b20433a799d1949840e912a 100644 --- a/tasks/0090_648_90648013_qa_4/task.toml +++ b/tasks/0090_648_90648013_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0090_648_90648013_qa_4" +name = "smoldataenvs-train/0090_648_90648013_qa_4" description = "What is the average IMDB score for Bollywood movies with the genre combination Action|Drama|History|Thriller|War in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8.5" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0090_720_90720010_qa_2/task.toml b/tasks/0090_720_90720010_qa_2/task.toml index 12d67905e7638f4bee1b0617552c30d6ebd56fcb..3e58db4fee42c43bffc36609dfeab50aa7128364 100644 --- a/tasks/0090_720_90720010_qa_2/task.toml +++ b/tasks/0090_720_90720010_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0090_720_90720010_qa_2" +name = "smoldataenvs-train/0090_720_90720010_qa_2" description = "What is the most common wine quality score in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0090_720_90720010_qa_3/task.toml b/tasks/0090_720_90720010_qa_3/task.toml index ca5809c5187c2214b26affee297d2ea19c7842da..82d2eed8ed3775fc3c30137579048cd4ca40b884 100644 --- a/tasks/0090_720_90720010_qa_3/task.toml +++ b/tasks/0090_720_90720010_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0090_720_90720010_qa_3" +name = "smoldataenvs-train/0090_720_90720010_qa_3" description = "What is the median alcohol content for all wines in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10.2" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0090_728_90728839_qa_3/task.toml b/tasks/0090_728_90728839_qa_3/task.toml index 67fbe0301f1aa815c1222157888514e60e87050a..fe9b148185b3b67e86d86097f4b440861433f4d7 100644 --- a/tasks/0090_728_90728839_qa_3/task.toml +++ b/tasks/0090_728_90728839_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0090_728_90728839_qa_3" +name = "smoldataenvs-train/0090_728_90728839_qa_3" description = "Which feature in the dataset has the highest number of unique categories based on value counts?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "gill-color" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0090_749_90749607_qa_5/task.toml b/tasks/0090_749_90749607_qa_5/task.toml index fc160905fc986141e381c9664eb66c4dbf22ea5d..79cb48cc64315aa95056cc55c9fee15d7f66a6b6 100644 --- a/tasks/0090_749_90749607_qa_5/task.toml +++ b/tasks/0090_749_90749607_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0090_749_90749607_qa_5" +name = "smoldataenvs-train/0090_749_90749607_qa_5" description = "How many data samples were in the training set after splitting the dataset into 70% training and 30% testing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "15129" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0090_760_90760347_qa_2/task.toml b/tasks/0090_760_90760347_qa_2/task.toml index 67d342a6cbe58efd4e1e42e22ef4c1613fcaebf2..b0f2cf83eec2df7b9795d1043b4a20f9ad14a1dc 100644 --- a/tasks/0090_760_90760347_qa_2/task.toml +++ b/tasks/0090_760_90760347_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0090_760_90760347_qa_2" +name = "smoldataenvs-train/0090_760_90760347_qa_2" description = "What is the title of the most popular book based on the highest number of ratings in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "The Hunger Games" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0090_760_90760347_qa_4/task.toml b/tasks/0090_760_90760347_qa_4/task.toml index ab1b95946adaa3826e8f0ed0a6c47164b4558182..f0510b4421a8c6e89f4ca62d0e1a523de3ccce24 100644 --- a/tasks/0090_760_90760347_qa_4/task.toml +++ b/tasks/0090_760_90760347_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0090_760_90760347_qa_4" +name = "smoldataenvs-train/0090_760_90760347_qa_4" description = "What is the average rating of the most popular book according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.34" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0090_941_90941195_qa_2/task.toml b/tasks/0090_941_90941195_qa_2/task.toml index 5e1985828996bcdbefc744a8627a5ecfce94afd7..0dcc9173ced1f90189f4ad3370b955261b46cc80 100644 --- a/tasks/0090_941_90941195_qa_2/task.toml +++ b/tasks/0090_941_90941195_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0090_941_90941195_qa_2" +name = "smoldataenvs-train/0090_941_90941195_qa_2" description = "Which feature had the highest coefficient of importance in predicting diabetes after discretization of variables?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0091_031_91031611_qa_2/task.toml b/tasks/0091_031_91031611_qa_2/task.toml index bdbc2c1330f6bcf8162d50878ff7a3ad97b15103..c2685e48b39ac2d59be75ec58fab0a12c5aa200c 100644 --- a/tasks/0091_031_91031611_qa_2/task.toml +++ b/tasks/0091_031_91031611_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0091_031_91031611_qa_2" +name = "smoldataenvs-train/0091_031_91031611_qa_2" description = "What is the maximum value of the Fare feature after MinMax scaling as shown in the histogram bins?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0091_122_91122496_qa_5/task.toml b/tasks/0091_122_91122496_qa_5/task.toml index 7a08c601c36576e1d56d10456f8d71bea9a771d0..713cab547d6f8674ba06db66b68fc7154e0a8bf9 100644 --- a/tasks/0091_122_91122496_qa_5/task.toml +++ b/tasks/0091_122_91122496_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0091_122_91122496_qa_5" +name = "smoldataenvs-train/0091_122_91122496_qa_5" description = "What is the direction of the relationship between CPI and Weekly_Sales based on the 95% confidence interval of its coefficient in the global model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Negative" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0091_124_91124471_qa_1/task.toml b/tasks/0091_124_91124471_qa_1/task.toml index 3dc6124605267a8dfa06dee106174bdd1cbb3c1b..1d9a80f7e772308850e84d4973b3b6ea951ea16f 100644 --- a/tasks/0091_124_91124471_qa_1/task.toml +++ b/tasks/0091_124_91124471_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0091_124_91124471_qa_1" +name = "smoldataenvs-train/0091_124_91124471_qa_1" description = "How many patients in the dataset have abnormal glucose tolerance (OGTT ≥ 200)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0091_153_91153202_qa_3/task.toml b/tasks/0091_153_91153202_qa_3/task.toml index f5007d2aff6a21c109fa413e79e82441b4bef558..6ff54e48263d4765b008dbf4c22647c5c66c0533 100644 --- a/tasks/0091_153_91153202_qa_3/task.toml +++ b/tasks/0091_153_91153202_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0091_153_91153202_qa_3" +name = "smoldataenvs-train/0091_153_91153202_qa_3" description = "After combining the training and test datasets, what is the total number of samples in the combined dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "70000" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0091_212_91212287_qa_3/task.toml b/tasks/0091_212_91212287_qa_3/task.toml index 867f432d2a70573c9dcb865a0cf132b963cd6986..fff16cf937948406f2011cdd1e24ec5e4e17c09f 100644 --- a/tasks/0091_212_91212287_qa_3/task.toml +++ b/tasks/0091_212_91212287_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0091_212_91212287_qa_3" +name = "smoldataenvs-train/0091_212_91212287_qa_3" description = "What was the median value used to impute missing BloodPressure measurements during data cleaning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "72" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0091_251_91251894_qa_2/task.toml b/tasks/0091_251_91251894_qa_2/task.toml index 2a76b4b7839744240964bec3819bbeae5f388935..5e0c33b4672b08189918b087149bfaca3e022a26 100644 --- a/tasks/0091_251_91251894_qa_2/task.toml +++ b/tasks/0091_251_91251894_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0091_251_91251894_qa_2" +name = "smoldataenvs-train/0091_251_91251894_qa_2" description = "How many appointments had a negative number of days between scheduling and the actual appointment before the data cleaning process?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0091_251_91251894_qa_3/task.toml b/tasks/0091_251_91251894_qa_3/task.toml index d4e988e45b1a3b8a7acc0f3103e061916e5701c9..97e3c7a50dcb48eae57228a47c683dab12c7b11d 100644 --- a/tasks/0091_251_91251894_qa_3/task.toml +++ b/tasks/0091_251_91251894_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0091_251_91251894_qa_3" +name = "smoldataenvs-train/0091_251_91251894_qa_3" description = "How many rows were removed from the dataset during the data cleaning process to eliminate invalid entries?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0091_296_91296853_qa_1/task.toml b/tasks/0091_296_91296853_qa_1/task.toml index 3523920d0f32afdf672dd92e78d18845a2e34adc..1af615170594415b7d4c413a0ca05b4915404ee9 100644 --- a/tasks/0091_296_91296853_qa_1/task.toml +++ b/tasks/0091_296_91296853_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0091_296_91296853_qa_1" +name = "smoldataenvs-train/0091_296_91296853_qa_1" description = "What was the highest testing accuracy achieved by any model after applying SMOTE oversampling?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9166666666666666" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0091_392_91392892_qa_4/task.toml b/tasks/0091_392_91392892_qa_4/task.toml index eb344de96d7d96b5701431d860f27e528145f626..c8974e6c89412a98920af1cbd26eda08b7954b1c 100644 --- a/tasks/0091_392_91392892_qa_4/task.toml +++ b/tasks/0091_392_91392892_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0091_392_91392892_qa_4" +name = "smoldataenvs-train/0091_392_91392892_qa_4" description = "How many missing data points were present in the LotFrontage feature before implementing imputation strategies?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "259" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0091_439_91439789_qa_3/task.toml b/tasks/0091_439_91439789_qa_3/task.toml index cda598b0e5dfe6765313a675ec1ca32319fa1375..075095d5449ce7c6bbb17e5aed1516b183ef3478 100644 --- a/tasks/0091_439_91439789_qa_3/task.toml +++ b/tasks/0091_439_91439789_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0091_439_91439789_qa_3" +name = "smoldataenvs-train/0091_439_91439789_qa_3" description = "Which feature has the strongest negative correlation with the median home value (MEDV) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "LSTAT" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0091_442_91442113_qa_2/task.toml b/tasks/0091_442_91442113_qa_2/task.toml index 9ec88e7d93abd41a46a11bc3fe925ee26b9c9431..19bc6eb17568c4a6a836828498de9784c58346f1 100644 --- a/tasks/0091_442_91442113_qa_2/task.toml +++ b/tasks/0091_442_91442113_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0091_442_91442113_qa_2" +name = "smoldataenvs-train/0091_442_91442113_qa_2" description = "How many distinct customer clusters were identified through the K-means clustering analysis of customer behavior?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0091_531_91531799_qa_1/task.toml b/tasks/0091_531_91531799_qa_1/task.toml index af462c7e28cc39f0fe351d41ed32600fcd8a1759..b95b2bda0b8361f6325bb7b90a0273a823f00983 100644 --- a/tasks/0091_531_91531799_qa_1/task.toml +++ b/tasks/0091_531_91531799_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0091_531_91531799_qa_1" +name = "smoldataenvs-train/0091_531_91531799_qa_1" description = "How many samples are included in the test set after splitting the dataset with a 67:33 ratio?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "253" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0091_567_91567203_qa_4/task.toml b/tasks/0091_567_91567203_qa_4/task.toml index cf121cd8a6c560e54b1ef777f65ae1b98b0fa02a..56c69234c9983d6a75bc07d13a95fa9fe126e7c7 100644 --- a/tasks/0091_567_91567203_qa_4/task.toml +++ b/tasks/0091_567_91567203_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0091_567_91567203_qa_4" +name = "smoldataenvs-train/0091_567_91567203_qa_4" description = "How many unique manufacturer categories are present in the dataset after preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0091_584_91584778_qa_3/task.toml b/tasks/0091_584_91584778_qa_3/task.toml index 370845e6716751cfedac7cdbc906cd1bd9887064..a673cc9ba95466a649f9ed39d811f64abd724f3b 100644 --- a/tasks/0091_584_91584778_qa_3/task.toml +++ b/tasks/0091_584_91584778_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0091_584_91584778_qa_3" +name = "smoldataenvs-train/0091_584_91584778_qa_3" description = "What is the highest global sales figure among all video games, and which game holds this record?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii Sports, 82.74" reward_mode_initial = "list" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0091_643_91643144_qa_2/task.toml b/tasks/0091_643_91643144_qa_2/task.toml index 63dff4c5dcea4fbc2ca4ae537e15e5c185e395c6..b27766bc48fb21397dfc6eac0361ae21646bfdba 100644 --- a/tasks/0091_643_91643144_qa_2/task.toml +++ b/tasks/0091_643_91643144_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0091_643_91643144_qa_2" +name = "smoldataenvs-train/0091_643_91643144_qa_2" description = "Which brand has the highest similarity score to '7 Select' using the token set ratio method?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7 Select/Nissin" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0091_643_91643144_qa_5/task.toml b/tasks/0091_643_91643144_qa_5/task.toml index 95ed48e5334f6069a9384d5782f8186adfc49c90..508c5c247c631d8482d037e73ac14eaf998491e5 100644 --- a/tasks/0091_643_91643144_qa_5/task.toml +++ b/tasks/0091_643_91643144_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0091_643_91643144_qa_5" +name = "smoldataenvs-train/0091_643_91643144_qa_5" description = "What is the highest similarity score for the brand '7 Select' when using the token sort ratio method, excluding exact matches?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "70" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0091_670_91670414_qa_3/task.toml b/tasks/0091_670_91670414_qa_3/task.toml index 9a9b477b796461fabc85402c6fc7f74f16086718..fa20cf93daf080889ede78317a05d2c2e584bc32 100644 --- a/tasks/0091_670_91670414_qa_3/task.toml +++ b/tasks/0091_670_91670414_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0091_670_91670414_qa_3" +name = "smoldataenvs-train/0091_670_91670414_qa_3" description = "What is the difference between the average North American sales and the average European sales?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.118015" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0091_670_91670414_qa_5/task.toml b/tasks/0091_670_91670414_qa_5/task.toml index 3c51365e156e8d0054640d2efd92418aa336f448..7de5b9d45eeb411cec4a99870fec1c93670bea40 100644 --- a/tasks/0091_670_91670414_qa_5/task.toml +++ b/tasks/0091_670_91670414_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0091_670_91670414_qa_5" +name = "smoldataenvs-train/0091_670_91670414_qa_5" description = "What are the top three most frequent genres in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action, Sports, Misc" reward_mode_initial = "list" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0091_690_91690951_qa_3/task.toml b/tasks/0091_690_91690951_qa_3/task.toml index 92a96325317bce9b2989a68636b9d4de32e489e8..86f8e677df1c4e12e38f67e76facfd6b2e2ea63d 100644 --- a/tasks/0091_690_91690951_qa_3/task.toml +++ b/tasks/0091_690_91690951_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0091_690_91690951_qa_3" +name = "smoldataenvs-train/0091_690_91690951_qa_3" description = "Does the data visualization in the notebook confirm that smoking status has a statistically significant impact on insurance charges compared to non-smoking status?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0091_690_91690951_qa_4/task.toml b/tasks/0091_690_91690951_qa_4/task.toml index 8057eead01e4ae5c2fe459e93e2a17bba67aa017..99cfcbdafcff2ddfbe1162c1b72a2234bd407b82 100644 --- a/tasks/0091_690_91690951_qa_4/task.toml +++ b/tasks/0091_690_91690951_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0091_690_91690951_qa_4" +name = "smoldataenvs-train/0091_690_91690951_qa_4" description = "Based on the KDE plot analysis, does the correlation between BMI and insurance charges appear stronger for smokers compared to non-smokers?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0091_713_91713569_qa_1/task.toml b/tasks/0091_713_91713569_qa_1/task.toml index f712485bb47706e21d53982b237e61692d9b45a0..391749b6d437e7174cecee6ba50886b5a72ac8c8 100644 --- a/tasks/0091_713_91713569_qa_1/task.toml +++ b/tasks/0091_713_91713569_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0091_713_91713569_qa_1" +name = "smoldataenvs-train/0091_713_91713569_qa_1" description = "What is the R² score of the linear regression model predicting Apparent Temperature (C) from Humidity using the full dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.36309180470630686" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0091_747_91747804_qa_5/task.toml b/tasks/0091_747_91747804_qa_5/task.toml index 6528ecd7d2417481d400d487a7c94bdbc7b16253..6be48d8751f1522b8901c6e0b6f909bb0679ed37 100644 --- a/tasks/0091_747_91747804_qa_5/task.toml +++ b/tasks/0091_747_91747804_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0091_747_91747804_qa_5" +name = "smoldataenvs-train/0091_747_91747804_qa_5" description = "What is the maximum number of positive axillary nodes observed in the entire dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "52" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0091_774_91774768_qa_4/task.toml b/tasks/0091_774_91774768_qa_4/task.toml index 31134d9b979e392744c15eae50a2e5b72fec8fcd..7d66783f501780e2ff43b793e784bdc5b49e7cd9 100644 --- a/tasks/0091_774_91774768_qa_4/task.toml +++ b/tasks/0091_774_91774768_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0091_774_91774768_qa_4" +name = "smoldataenvs-train/0091_774_91774768_qa_4" description = "Which feature demonstrates the strongest negative correlation with wine quality according to the SHAP analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "volatile acidity" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0091_853_91853988_qa_2/task.toml b/tasks/0091_853_91853988_qa_2/task.toml index d495246119a25faeb96c0176304389f77b975a27..feb6d02ae539d4443318ae06c3a2da6108bbc808 100644 --- a/tasks/0091_853_91853988_qa_2/task.toml +++ b/tasks/0091_853_91853988_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0091_853_91853988_qa_2" +name = "smoldataenvs-train/0091_853_91853988_qa_2" description = "Which column was removed from the dataset due to containing all missing values, and how many columns remained in the dataset after this removal?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Unnamed: 32, 32" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0091_859_91859529_qa_2/task.toml b/tasks/0091_859_91859529_qa_2/task.toml index afda66d1d473111b2197a6b320b44d57299755bd..132fd71a6ed2bdee309e8624a4197833f8f4dc14 100644 --- a/tasks/0091_859_91859529_qa_2/task.toml +++ b/tasks/0091_859_91859529_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0091_859_91859529_qa_2" +name = "smoldataenvs-train/0091_859_91859529_qa_2" description = "Which polynomial degree model (1, 4, or 15) has the lowest AIC score when fitting the cosine function data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0091_859_91859529_qa_4/task.toml b/tasks/0091_859_91859529_qa_4/task.toml index e9c8ca8f1a792f58fb4392a12685a815af9320f5..9ae5fa5172cdc6f37e9650b486d937a1f101fe67 100644 --- a/tasks/0091_859_91859529_qa_4/task.toml +++ b/tasks/0091_859_91859529_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0091_859_91859529_qa_4" +name = "smoldataenvs-train/0091_859_91859529_qa_4" description = "Which model (all features, model year only, or best subset) has the lowest AIC in the car MPG prediction task?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "best subset" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0091_861_91861304_qa_1/task.toml b/tasks/0091_861_91861304_qa_1/task.toml index c8caed25ca04562399e1d914223583848d4762f3..bedf19be3a688f6c9692969689243b242aaceeff 100644 --- a/tasks/0091_861_91861304_qa_1/task.toml +++ b/tasks/0091_861_91861304_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0091_861_91861304_qa_1" +name = "smoldataenvs-train/0091_861_91861304_qa_1" description = "What is the percentage of patients in the dataset diagnosed with diabetes (Outcome=1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0091_861_91861304_qa_2/task.toml b/tasks/0091_861_91861304_qa_2/task.toml index 792a38555746ae99444cf75f3eba3323c04dec0b..96186903cb05c4d6be200c895af24851f540f89a 100644 --- a/tasks/0091_861_91861304_qa_2/task.toml +++ b/tasks/0091_861_91861304_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0091_861_91861304_qa_2" +name = "smoldataenvs-train/0091_861_91861304_qa_2" description = "Which feature in the dataset has the highest coefficient of variation (standard deviation divided by mean)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Insulin" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0091_861_91861304_qa_4/task.toml b/tasks/0091_861_91861304_qa_4/task.toml index 424b25a6ee8ac6fad1c5d45ce7ff555eb6349f2d..930b0efabd6e85b2bb4f7879294ed36f3d5876e3 100644 --- a/tasks/0091_861_91861304_qa_4/task.toml +++ b/tasks/0091_861_91861304_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0091_861_91861304_qa_4" +name = "smoldataenvs-train/0091_861_91861304_qa_4" description = "Which age group (in 10-year intervals) has the highest proportion of diabetes cases in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50-59" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0091_886_91886216_qa_5/task.toml b/tasks/0091_886_91886216_qa_5/task.toml index 9f5c7c3c4c941418e795066a6177861fd7aef986..89f59abd6a0bde326b8974bb5755c7b3ff5ddee2 100644 --- a/tasks/0091_886_91886216_qa_5/task.toml +++ b/tasks/0091_886_91886216_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0091_886_91886216_qa_5" +name = "smoldataenvs-train/0091_886_91886216_qa_5" description = "What percentage of patients in the cleaned dataset have diabetes (Outcome = 1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.39" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0091_902_91902857_qa_1/task.toml b/tasks/0091_902_91902857_qa_1/task.toml index 3b3ee96525034c31628c2fb05bd7266527cdd30e..b355f62e71ab5a377c6ec475eb3bcbde076b29fc 100644 --- a/tasks/0091_902_91902857_qa_1/task.toml +++ b/tasks/0091_902_91902857_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0091_902_91902857_qa_1" +name = "smoldataenvs-train/0091_902_91902857_qa_1" description = "Which wine quality characteristic has the highest absolute correlation with the target variable \"quality\" in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "alcohol" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0092_004_92004246_qa_1/task.toml b/tasks/0092_004_92004246_qa_1/task.toml index d65d0b1bf62492250dbb41eeec2c292b1d80cd1f..50505d3744578a826334e72630bf09f6172d78a5 100644 --- a/tasks/0092_004_92004246_qa_1/task.toml +++ b/tasks/0092_004_92004246_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0092_004_92004246_qa_1" +name = "smoldataenvs-train/0092_004_92004246_qa_1" description = "What is the accuracy of the logistic regression model trained using the selected features (gender, Partner, MonthlyCharges) to predict customer churn?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.72" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0092_004_92004246_qa_3/task.toml b/tasks/0092_004_92004246_qa_3/task.toml index 5d9efed5510f62bf2eb0382e2dc63ac6e76ede27..df4ef3c837e341610e04352e6fafff8aec649e21 100644 --- a/tasks/0092_004_92004246_qa_3/task.toml +++ b/tasks/0092_004_92004246_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0092_004_92004246_qa_3" +name = "smoldataenvs-train/0092_004_92004246_qa_3" description = "What percentage of customers in the dataset are classified as senior citizens (SeniorCitizen = 1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16.2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0092_008_92008043_qa_2/task.toml b/tasks/0092_008_92008043_qa_2/task.toml index 43def8cbf35dbf4c8cfd54b1b792b7e633088bba..39d8ccb781b6d31dc3929bffdb181c8fe11a5feb 100644 --- a/tasks/0092_008_92008043_qa_2/task.toml +++ b/tasks/0092_008_92008043_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0092_008_92008043_qa_2" +name = "smoldataenvs-train/0092_008_92008043_qa_2" description = "What percentage of the preprocessed dataset was allocated to the test set after splitting with a test size of 0.3?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "30" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0092_190_92190264_qa_4/task.toml b/tasks/0092_190_92190264_qa_4/task.toml index a363082d3a7246df51b02f86a3e731c9f558fcb1..e043b3233edb35f052435ea81c6773037706b6f0 100644 --- a/tasks/0092_190_92190264_qa_4/task.toml +++ b/tasks/0092_190_92190264_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0092_190_92190264_qa_4" +name = "smoldataenvs-train/0092_190_92190264_qa_4" description = "Which image index in the Olivetti faces dataset has the smallest non-zero Euclidean distance to the target image (index 231) after eigenface-based reconstruction?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "230" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0092_354_92354139_qa_3/task.toml b/tasks/0092_354_92354139_qa_3/task.toml index 628450b03661bd7ca6003882c76ca9ff3bcf39f9..e90209de747d8c8a4efe1932ce8caa81982c769d 100644 --- a/tasks/0092_354_92354139_qa_3/task.toml +++ b/tasks/0092_354_92354139_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0092_354_92354139_qa_3" +name = "smoldataenvs-train/0092_354_92354139_qa_3" description = "Which season recorded the highest total runs scored from sixes, and what was the total contribution to runs from sixes during that season?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2019 season with 1,245 runs from sixes" reward_mode_initial = "flexible" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0092_376_92376137_qa_1/task.toml b/tasks/0092_376_92376137_qa_1/task.toml index 824e94e4d5650f8993640c3f38a538346877c367..35d6cd96dafd0473456cbc4dfb8ccdef2ea08781 100644 --- a/tasks/0092_376_92376137_qa_1/task.toml +++ b/tasks/0092_376_92376137_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0092_376_92376137_qa_1" +name = "smoldataenvs-train/0092_376_92376137_qa_1" description = "How many tweets are in languages other than English after processing the 'lang' column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "196" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0092_376_92376137_qa_2/task.toml b/tasks/0092_376_92376137_qa_2/task.toml index 82303aa0fed4c50b071b3016254f22700a1221ab..be9522c187467712abb8b7d3937bd75946ad89d5 100644 --- a/tasks/0092_376_92376137_qa_2/task.toml +++ b/tasks/0092_376_92376137_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0092_376_92376137_qa_2" +name = "smoldataenvs-train/0092_376_92376137_qa_2" description = "What is the total number of tweets by Donald Trump after removing rows with missing values in the 'handle' column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3218" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0092_376_92376137_qa_3/task.toml b/tasks/0092_376_92376137_qa_3/task.toml index 72c5afa2cd044cd74a902e97b436206e20a166f5..0a3ff12b526825154cf9610270cff0e01f34b3d6 100644 --- a/tasks/0092_376_92376137_qa_3/task.toml +++ b/tasks/0092_376_92376137_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0092_376_92376137_qa_3" +name = "smoldataenvs-train/0092_376_92376137_qa_3" description = "What is the total number of tweets by Hillary Clinton after removing rows with missing values in the 'handle' column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3226" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0092_401_92401680_qa_1/task.toml b/tasks/0092_401_92401680_qa_1/task.toml index 6d2f3f8cd3cf019d94ae861e6a687c009c88ac45..597dd5db1d6ae5d613926e85aa3c8e52793b8ef7 100644 --- a/tasks/0092_401_92401680_qa_1/task.toml +++ b/tasks/0092_401_92401680_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0092_401_92401680_qa_1" +name = "smoldataenvs-train/0092_401_92401680_qa_1" description = "Which campaign achieved the highest total number of approved conversions according to the aggregated metrics?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1178" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0092_424_92424981_qa_1/task.toml b/tasks/0092_424_92424981_qa_1/task.toml index 236793b1cac294d32c3734370bcf7a916daf11d8..b2c557cfda7be3f0214c51ff253aab1af8c41d89 100644 --- a/tasks/0092_424_92424981_qa_1/task.toml +++ b/tasks/0092_424_92424981_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0092_424_92424981_qa_1" +name = "smoldataenvs-train/0092_424_92424981_qa_1" description = "How many null values are present in the target variable (y) of the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0092_443_92443406_qa_2/task.toml b/tasks/0092_443_92443406_qa_2/task.toml index 7058b0271b3d29387a4333fc48d8c32310a6cb48..87c27c754e7ce953912c493fbf1e658760907d51 100644 --- a/tasks/0092_443_92443406_qa_2/task.toml +++ b/tasks/0092_443_92443406_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0092_443_92443406_qa_2" +name = "smoldataenvs-train/0092_443_92443406_qa_2" description = "Which feature in the dataset has the highest correlation with the diabetes outcome (1 = diabetic)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0092_481_92481063_qa_4/task.toml b/tasks/0092_481_92481063_qa_4/task.toml index 52a02bf5880cc4490406b693ab82d78cd6c585b7..e26fcccd30c974c2a653c960b4d0e9be7f35e46e 100644 --- a/tasks/0092_481_92481063_qa_4/task.toml +++ b/tasks/0092_481_92481063_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0092_481_92481063_qa_4" +name = "smoldataenvs-train/0092_481_92481063_qa_4" description = "What percentage of customers in the dataset are classified as churned?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26.58" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0092_493_92493148_qa_3/task.toml b/tasks/0092_493_92493148_qa_3/task.toml index 9c5f5c847a9355159ff933b1e073b0cce9e460da..c83ccbd9746c77ff93bd87e978e3058050a4a5fb 100644 --- a/tasks/0092_493_92493148_qa_3/task.toml +++ b/tasks/0092_493_92493148_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0092_493_92493148_qa_3" +name = "smoldataenvs-train/0092_493_92493148_qa_3" description = "What is the total number of features (pixel values) extracted from each image after flattening during preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4096" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0092_596_92596170_qa_2/task.toml b/tasks/0092_596_92596170_qa_2/task.toml index 27eb27257173ff9a502cc6c3ddeea2ff6bd16508..9b4c685497eba32432953da4d7165d783a67bfcc 100644 --- a/tasks/0092_596_92596170_qa_2/task.toml +++ b/tasks/0092_596_92596170_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0092_596_92596170_qa_2" +name = "smoldataenvs-train/0092_596_92596170_qa_2" description = "What is the damping factor used in the custom PageRank implementation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.85" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0092_606_92606348_qa_3/task.toml b/tasks/0092_606_92606348_qa_3/task.toml index fceacff17cc8c21bc2075d41570ad57cead54a63..085b166f42669862f87df13068a64d36b5b7c919 100644 --- a/tasks/0092_606_92606348_qa_3/task.toml +++ b/tasks/0092_606_92606348_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0092_606_92606348_qa_3" +name = "smoldataenvs-train/0092_606_92606348_qa_3" description = "Which model achieved the highest accuracy in predicting wine quality: Linear Regression, Decision Tree, or Random Forest?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Random Forest" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0092_606_92606348_qa_4/task.toml b/tasks/0092_606_92606348_qa_4/task.toml index 015bf580fba8f6eb30bea5180c0ec5cca21a6be3..550a5114bdde0879c093f48f5edae2b3e6c8645c 100644 --- a/tasks/0092_606_92606348_qa_4/task.toml +++ b/tasks/0092_606_92606348_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0092_606_92606348_qa_4" +name = "smoldataenvs-train/0092_606_92606348_qa_4" description = "Which wine quality rating has the highest maximum residual sugar, and what is that value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5, 15.5" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0092_620_92620460_qa_4/task.toml b/tasks/0092_620_92620460_qa_4/task.toml index 81d822daf8ddea9d4b52382fc853d74918ac712f..c0ce9188dca01ceae3e2afca83e81db8dee4e1de 100644 --- a/tasks/0092_620_92620460_qa_4/task.toml +++ b/tasks/0092_620_92620460_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0092_620_92620460_qa_4" +name = "smoldataenvs-train/0092_620_92620460_qa_4" description = "Which income class is assigned the index 1.0 in the 'label' column after preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = ">50K" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0092_637_92637915_qa_4/task.toml b/tasks/0092_637_92637915_qa_4/task.toml index ec5180628ca52b6dac9b1cadcff6f9508a37d385..1f7eff60eb3c44dccbc83031dac5dbc234445af0 100644 --- a/tasks/0092_637_92637915_qa_4/task.toml +++ b/tasks/0092_637_92637915_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0092_637_92637915_qa_4" +name = "smoldataenvs-train/0092_637_92637915_qa_4" description = "Which feature showed the highest mean absolute SHAP value across all samples, indicating its dominant influence on model predictions according to the SHAP summary plot?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "smoker" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0092_706_92706423_qa_4/task.toml b/tasks/0092_706_92706423_qa_4/task.toml index 0121f890b317bbed9de8eed071eddc2cf2333132..4825d2de1ea84420881547e4be37083281179fc1 100644 --- a/tasks/0092_706_92706423_qa_4/task.toml +++ b/tasks/0092_706_92706423_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0092_706_92706423_qa_4" +name = "smoldataenvs-train/0092_706_92706423_qa_4" description = "What is the most frequently occurring genre among top popular movies (popularity ≥ 5.0)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Drama" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0092_855_92855886_qa_1/task.toml b/tasks/0092_855_92855886_qa_1/task.toml index 556538f48f3983de0600200fe016b64684999b37..aa063f19074c1324cea2577a73b2065aae70f964 100644 --- a/tasks/0092_855_92855886_qa_1/task.toml +++ b/tasks/0092_855_92855886_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0092_855_92855886_qa_1" +name = "smoldataenvs-train/0092_855_92855886_qa_1" description = "Which campaign has the highest Spent per 1000 Impressions (Spent/Imp(k)) based on the aggregated campaign metrics?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "936" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0092_855_92855886_qa_2/task.toml b/tasks/0092_855_92855886_qa_2/task.toml index f85facfb27c4cb903ed97e2029b0873c98257c36..66d41fa2c5ff5e65cf21ca53d98faf107e4b4fec 100644 --- a/tasks/0092_855_92855886_qa_2/task.toml +++ b/tasks/0092_855_92855886_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0092_855_92855886_qa_2" +name = "smoldataenvs-train/0092_855_92855886_qa_2" description = "Which campaign achieves the highest Total Conversion per 1000 Impressions (Conversion/Imp(k)) according to the calculated performance metrics?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "916" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0092_936_92936463_qa_5/task.toml b/tasks/0092_936_92936463_qa_5/task.toml index d29935984fa12d093d55f32c7d8936aa3d62132c..6a2a4b1eedac2e482667e2ca9e580fea5c83d5d6 100644 --- a/tasks/0092_936_92936463_qa_5/task.toml +++ b/tasks/0092_936_92936463_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0092_936_92936463_qa_5" +name = "smoldataenvs-train/0092_936_92936463_qa_5" description = "Which variable demonstrated the strongest positive correlation with wine quality according to the correlation analysis in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "alcohol" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0092_951_92951855_qa_2/task.toml b/tasks/0092_951_92951855_qa_2/task.toml index 73f06ed5b07284090b2a42962b734d96281e36f9..c33c94bcca266d25e0534f6c5d7055ee6a2ef83b 100644 --- a/tasks/0092_951_92951855_qa_2/task.toml +++ b/tasks/0092_951_92951855_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0092_951_92951855_qa_2" +name = "smoldataenvs-train/0092_951_92951855_qa_2" description = "What is the difference in the number of benign tumors compared to malignant tumors in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "145" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0092_990_92990642_qa_2/task.toml b/tasks/0092_990_92990642_qa_2/task.toml index d0218a2df65cecf73981026f19ed5a789556ab75..40193a1bdaaba9b1ab59176bfd32d4aa49b8c782 100644 --- a/tasks/0092_990_92990642_qa_2/task.toml +++ b/tasks/0092_990_92990642_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0092_990_92990642_qa_2" +name = "smoldataenvs-train/0092_990_92990642_qa_2" description = "Which three features were identified as most significant in predicting medical costs by the Random Forest model based on feature importance analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "smoker_yes, bmi_category, age" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0093_118_93118701_qa_2/task.toml b/tasks/0093_118_93118701_qa_2/task.toml index 07cb3c9ce35c551cdf7b9ba16b82cde6393df1a3..ec95c86755ef5e5fe86a195f29889d3c41937586 100644 --- a/tasks/0093_118_93118701_qa_2/task.toml +++ b/tasks/0093_118_93118701_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0093_118_93118701_qa_2" +name = "smoldataenvs-train/0093_118_93118701_qa_2" description = "What is the standard deviation of the 'area_worst' feature in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "569.356993" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0093_118_93118701_qa_4/task.toml b/tasks/0093_118_93118701_qa_4/task.toml index e80799a914df8bfdd9747ddabc126ebd57566ef3..dfadd63e5a9f8b0e5a03b95b147007c1f1b24c88 100644 --- a/tasks/0093_118_93118701_qa_4/task.toml +++ b/tasks/0093_118_93118701_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0093_118_93118701_qa_4" +name = "smoldataenvs-train/0093_118_93118701_qa_4" description = "What is the 25th percentile (first quartile) value of the 'perimeter_mean' feature in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "75.17" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0093_170_93170596_qa_1/task.toml b/tasks/0093_170_93170596_qa_1/task.toml index 2f82d725b40867261c6c8fd3c68b2b5a4ab69b2e..18bf0e6ee814149ca19bfc3a3c5b2be47d9dbe3a 100644 --- a/tasks/0093_170_93170596_qa_1/task.toml +++ b/tasks/0093_170_93170596_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0093_170_93170596_qa_1" +name = "smoldataenvs-train/0093_170_93170596_qa_1" description = "Which pair of features in the dataset shows the strongest correlation according to the heatmap analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm, PetalWidthCm" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0093_185_93185105_qa_1/task.toml b/tasks/0093_185_93185105_qa_1/task.toml index 55122bd9fea7a3ab292ff77c2a0af4b22ef8b877..a7880caaafb1c2be95730c6df6b768f1e95f52ce 100644 --- a/tasks/0093_185_93185105_qa_1/task.toml +++ b/tasks/0093_185_93185105_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0093_185_93185105_qa_1" +name = "smoldataenvs-train/0093_185_93185105_qa_1" description = "How many rows were removed from the original dataset during data cleaning due to zero-value outliers in Glucose, BloodPressure, and BMI features?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "44" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0093_185_93185105_qa_4/task.toml b/tasks/0093_185_93185105_qa_4/task.toml index 097652fd8d56ae6ceef7c4252e5d850c588ed8cb..41ccfbb81e8fabbabaa08a1714618188399903b6 100644 --- a/tasks/0093_185_93185105_qa_4/task.toml +++ b/tasks/0093_185_93185105_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0093_185_93185105_qa_4" +name = "smoldataenvs-train/0093_185_93185105_qa_4" description = "How many instances in the dataset had zero values in the Insulin feature, indicating potential invalid readings before data cleaning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "374" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0093_351_93351284_qa_1/task.toml b/tasks/0093_351_93351284_qa_1/task.toml index d2ad30cbf40d6e797ef93519af407ff576f2c6d3..a789cff71bed8e2e91eb048012c6457ea2b7e108 100644 --- a/tasks/0093_351_93351284_qa_1/task.toml +++ b/tasks/0093_351_93351284_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0093_351_93351284_qa_1" +name = "smoldataenvs-train/0093_351_93351284_qa_1" description = "What percentage of patients who survived 5 years or longer had zero positive axillary nodes?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "52.23" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0093_442_93442881_qa_2/task.toml b/tasks/0093_442_93442881_qa_2/task.toml index ae6de6b0c829a5706150b8b13418fec7010144f3..7f88b8869bae2317a4d4f06915fb4072f24a93b7 100644 --- a/tasks/0093_442_93442881_qa_2/task.toml +++ b/tasks/0093_442_93442881_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0093_442_93442881_qa_2" +name = "smoldataenvs-train/0093_442_93442881_qa_2" description = "What is the median North American sales value across all games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.08" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0093_490_93490141_qa_3/task.toml b/tasks/0093_490_93490141_qa_3/task.toml index 046ff2eb59b8204a55904acaa2aec5237fcfde99..8039c28f090fae823b0afefaa0a5a5026d36d71d 100644 --- a/tasks/0093_490_93490141_qa_3/task.toml +++ b/tasks/0093_490_93490141_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0093_490_93490141_qa_3" +name = "smoldataenvs-train/0093_490_93490141_qa_3" description = "Are the proportions of smokers statistically significantly different across the four US regions in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0093_626_93626148_qa_1/task.toml b/tasks/0093_626_93626148_qa_1/task.toml index 3efb4a1441fb426802a647eda9b3348755ecf6c4..d465ec59c793ed4da8f22b5a3cda9276e3b1f95a 100644 --- a/tasks/0093_626_93626148_qa_1/task.toml +++ b/tasks/0093_626_93626148_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0093_626_93626148_qa_1" +name = "smoldataenvs-train/0093_626_93626148_qa_1" description = "What percentage of patients in the dataset have diabetes (Diabetes=True)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.9" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0093_643_93643564_qa_1/task.toml b/tasks/0093_643_93643564_qa_1/task.toml index 637f1e2992087e87530a95a6629086eeffcab652..5e2da7a14adf1d175cf5e053b6830adda691f609 100644 --- a/tasks/0093_643_93643564_qa_1/task.toml +++ b/tasks/0093_643_93643564_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0093_643_93643564_qa_1" +name = "smoldataenvs-train/0093_643_93643564_qa_1" description = "What percentage of employees in the dataset have left the company (attrition)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16.12" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0093_734_93734119_qa_2/task.toml b/tasks/0093_734_93734119_qa_2/task.toml index 6e67bb410c42e02b9645b09b66905d97f40b4fbb..76b0673462bef2f9ac0478aae46a607ba9e912ce 100644 --- a/tasks/0093_734_93734119_qa_2/task.toml +++ b/tasks/0093_734_93734119_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0093_734_93734119_qa_2" +name = "smoldataenvs-train/0093_734_93734119_qa_2" description = "How many employees in the dataset have a monthly income greater than $17,500?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "81" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0093_755_93755212_qa_1/task.toml b/tasks/0093_755_93755212_qa_1/task.toml index e238c97b265371f3962cf98a9458dac3d38b095e..c3f43e976cf859cec68fb36c670ceb71a9d2f3e6 100644 --- a/tasks/0093_755_93755212_qa_1/task.toml +++ b/tasks/0093_755_93755212_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0093_755_93755212_qa_1" +name = "smoldataenvs-train/0093_755_93755212_qa_1" description = "What is the R² score of the Linear Regression model used to predict insurance charges from the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.7447273869684076" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0093_755_93755212_qa_4/task.toml b/tasks/0093_755_93755212_qa_4/task.toml index 7bc20a0d7abce42ac0d5e6d2c425bee2c8f00c61..ec22366d9dbc0ef8e14012ba30ea18ea600add6d 100644 --- a/tasks/0093_755_93755212_qa_4/task.toml +++ b/tasks/0093_755_93755212_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0093_755_93755212_qa_4" +name = "smoldataenvs-train/0093_755_93755212_qa_4" description = "What is the highest recorded insurance charge in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "63770.428010" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0093_755_93755212_qa_5/task.toml b/tasks/0093_755_93755212_qa_5/task.toml index 0f12fea4b8b28e81d2ec195808ec3c41b8df49f6..f1f1ce15c9cb0d228f4035c2d5af9a0672501509 100644 --- a/tasks/0093_755_93755212_qa_5/task.toml +++ b/tasks/0093_755_93755212_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0093_755_93755212_qa_5" +name = "smoldataenvs-train/0093_755_93755212_qa_5" description = "What is the standard deviation of the age distribution in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "14.049960" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0093_813_93813737_qa_1/task.toml b/tasks/0093_813_93813737_qa_1/task.toml index 4979acd9be7906e047bdd59942de3e79f989e6e5..9deb3a083e54dab6c3043e17308154861ed593d4 100644 --- a/tasks/0093_813_93813737_qa_1/task.toml +++ b/tasks/0093_813_93813737_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0093_813_93813737_qa_1" +name = "smoldataenvs-train/0093_813_93813737_qa_1" description = "What is the baseline accuracy of the RandomForestClassifier before any feature selection was applied?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9614035087719298" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0094_004_94004350_qa_2/task.toml b/tasks/0094_004_94004350_qa_2/task.toml index 7d75a339098e6ae7d2cc4ba2bce72a3de2e7adb1..386c2acbecbbf28413172bb7dce58bd369adfafc 100644 --- a/tasks/0094_004_94004350_qa_2/task.toml +++ b/tasks/0094_004_94004350_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0094_004_94004350_qa_2" +name = "smoldataenvs-train/0094_004_94004350_qa_2" description = "Which wine quality rating is most frequently represented in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0094_028_94028594_qa_1/task.toml b/tasks/0094_028_94028594_qa_1/task.toml index 0e71e1d21f727b130500ec00415d41230626d8ca..9d4d11f9953198019203524ec80df207c8fc30c0 100644 --- a/tasks/0094_028_94028594_qa_1/task.toml +++ b/tasks/0094_028_94028594_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0094_028_94028594_qa_1" +name = "smoldataenvs-train/0094_028_94028594_qa_1" description = "What is the first movie recommended when \"The Dark Knight Rises\" is input into the recommendation system?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "The Dark Knight" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0094_052_94052168_qa_3/task.toml b/tasks/0094_052_94052168_qa_3/task.toml index f879e4d0f6a92d876422c5764659b5cdf9b46c0f..c16a7e3120e322566b4da3daf81d280a2417a71e 100644 --- a/tasks/0094_052_94052168_qa_3/task.toml +++ b/tasks/0094_052_94052168_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0094_052_94052168_qa_3" +name = "smoldataenvs-train/0094_052_94052168_qa_3" description = "Which customer segment has the highest churn rate based on senior citizen status?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Senior citizens" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0094_052_94052168_qa_4/task.toml b/tasks/0094_052_94052168_qa_4/task.toml index afe66d6675178845c2d2bc04fa331226a0e6cd4e..34c70b9f006f64fd145052f16d76b71c2ecbe701 100644 --- a/tasks/0094_052_94052168_qa_4/task.toml +++ b/tasks/0094_052_94052168_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0094_052_94052168_qa_4" +name = "smoldataenvs-train/0094_052_94052168_qa_4" description = "What is the most common contract type among churned customers?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Month-to-month" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0094_083_94083312_qa_2/task.toml b/tasks/0094_083_94083312_qa_2/task.toml index aa3c0e209795f37a4eb8a79e4a9b3045ef693db9..cb7624b06ca18592c1a9ed61c6b92f6d3aaebc12 100644 --- a/tasks/0094_083_94083312_qa_2/task.toml +++ b/tasks/0094_083_94083312_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0094_083_94083312_qa_2" +name = "smoldataenvs-train/0094_083_94083312_qa_2" description = "How many test samples were allocated for each subject in the stratified train-test split (30% test size)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0094_083_94083425_qa_4/task.toml b/tasks/0094_083_94083425_qa_4/task.toml index 681a832813696e2c15b60a06fcce7800e24bd732..ddbf15876ac712cb28ac0a4a12187e695bf6c1c4 100644 --- a/tasks/0094_083_94083425_qa_4/task.toml +++ b/tasks/0094_083_94083425_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0094_083_94083425_qa_4" +name = "smoldataenvs-train/0094_083_94083425_qa_4" description = "What is the highest quality score achieved by any American wine priced $25 or less in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "96" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0094_116_94116653_qa_2/task.toml b/tasks/0094_116_94116653_qa_2/task.toml index 76ebfab802caa144dd8408daf0f5202f07f7d9c4..29b166dc3246d3d49fb82d13bc4e543bda294fc6 100644 --- a/tasks/0094_116_94116653_qa_2/task.toml +++ b/tasks/0094_116_94116653_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0094_116_94116653_qa_2" +name = "smoldataenvs-train/0094_116_94116653_qa_2" description = "Which feature shows the strongest positive correlation with the diabetes outcome in the correlation analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0094_146_94146987_qa_2/task.toml b/tasks/0094_146_94146987_qa_2/task.toml index 9320b94f9bebf2a6aa6a431e00e50cde468987cd..64a07a5ba3180ae7d910bf6626b703a7098c8fd7 100644 --- a/tasks/0094_146_94146987_qa_2/task.toml +++ b/tasks/0094_146_94146987_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0094_146_94146987_qa_2" +name = "smoldataenvs-train/0094_146_94146987_qa_2" description = "What is the correlation coefficient between Health_per_Sec and Nanoboost_DPS in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-1.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0094_146_94146987_qa_5/task.toml b/tasks/0094_146_94146987_qa_5/task.toml index 48257792ea245ce825052113be9092c53a7465f0..a54b794ca1ba57431adf237da6e0c7f63056b270 100644 --- a/tasks/0094_146_94146987_qa_5/task.toml +++ b/tasks/0094_146_94146987_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0094_146_94146987_qa_5" +name = "smoldataenvs-train/0094_146_94146987_qa_5" description = "How many data points are displayed in the scatter plot of Damage_per_second_ versus Headshot_DPS__?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "25" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0094_237_94237964_qa_4/task.toml b/tasks/0094_237_94237964_qa_4/task.toml index f41735f7b91e30802bbfddf788b7804c7677ac73..bf121ceef415a8d0bc5a36ccfa7bd90d82b1e584 100644 --- a/tasks/0094_237_94237964_qa_4/task.toml +++ b/tasks/0094_237_94237964_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0094_237_94237964_qa_4" +name = "smoldataenvs-train/0094_237_94237964_qa_4" description = "After applying MinMaxScaler normalization, what is the minimum value of the price feature in the transformed dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0094_237_94237964_qa_5/task.toml b/tasks/0094_237_94237964_qa_5/task.toml index 06f3d5499366875f9e4074f8519624a7cf11fdd8..77fd77744371360cf087a2961a9328f008b9c207 100644 --- a/tasks/0094_237_94237964_qa_5/task.toml +++ b/tasks/0094_237_94237964_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0094_237_94237964_qa_5" +name = "smoldataenvs-train/0094_237_94237964_qa_5" description = "What is the skewness value of the y feature (diamond height in mm) in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.43" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0094_264_94264953_qa_3/task.toml b/tasks/0094_264_94264953_qa_3/task.toml index c5c04158f8caae15d2086f815038955d59126d62..0892b1ff32ed66160992427169723ca872095924 100644 --- a/tasks/0094_264_94264953_qa_3/task.toml +++ b/tasks/0094_264_94264953_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0094_264_94264953_qa_3" +name = "smoldataenvs-train/0094_264_94264953_qa_3" description = "How many data points remain in the cleaned dataset after removing outliers from the Apparent Temperature (C) using the IQR method?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "95914" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0094_415_94415300_qa_4/task.toml b/tasks/0094_415_94415300_qa_4/task.toml index 794b74fb2f2568f0e3503d1b627ff581a7a6ca16..7658b24ac4c78af73d38999de90fa25ec936bb85 100644 --- a/tasks/0094_415_94415300_qa_4/task.toml +++ b/tasks/0094_415_94415300_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0094_415_94415300_qa_4" +name = "smoldataenvs-train/0094_415_94415300_qa_4" description = "How many unique education levels are present in the dataset after dropping the 'education' column and retaining 'education.num'?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0094_443_94443300_qa_3/task.toml b/tasks/0094_443_94443300_qa_3/task.toml index 67f1cbaff52c602dfec158c80bdaa70b2df092d8..b9af2bd1224127ab9c1361d37972adcca447e62e 100644 --- a/tasks/0094_443_94443300_qa_3/task.toml +++ b/tasks/0094_443_94443300_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0094_443_94443300_qa_3" +name = "smoldataenvs-train/0094_443_94443300_qa_3" description = "What percentage of the dataset includes mobile phones with dual_sim enabled?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50.95" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0094_443_94443300_qa_5/task.toml b/tasks/0094_443_94443300_qa_5/task.toml index 714bfd6cba08c9800f0bb6fd4ded9f883812961f..9c4aa872886eab480e806e4a568ee6060210c94a 100644 --- a/tasks/0094_443_94443300_qa_5/task.toml +++ b/tasks/0094_443_94443300_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0094_443_94443300_qa_5" +name = "smoldataenvs-train/0094_443_94443300_qa_5" description = "How many unique price_range categories are present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0094_540_94540451_qa_2/task.toml b/tasks/0094_540_94540451_qa_2/task.toml index 6b547930a8c332e43fd59da3bab7e22923326472..d01db8f86cb4dd4a868427c851a88aa0498d5b44 100644 --- a/tasks/0094_540_94540451_qa_2/task.toml +++ b/tasks/0094_540_94540451_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0094_540_94540451_qa_2" +name = "smoldataenvs-train/0094_540_94540451_qa_2" description = "Which video game genre had the highest market share in North America in 2008?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0094_557_94557527_qa_3/task.toml b/tasks/0094_557_94557527_qa_3/task.toml index 0234de21aa65ff25ff697a9cbf81b7618d1b6e7c..143db726f3ebab9446a12d9cf418ac89ead43645 100644 --- a/tasks/0094_557_94557527_qa_3/task.toml +++ b/tasks/0094_557_94557527_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0094_557_94557527_qa_3" +name = "smoldataenvs-train/0094_557_94557527_qa_3" description = "What is the F1-score for the positive class (default payment) in the test set after applying the 69.9th percentile cutoff?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.53" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0094_574_94574947_qa_3/task.toml b/tasks/0094_574_94574947_qa_3/task.toml index 2cbd8a17623f8c1e6c97b1fefb930be127b032fd..a01f74086a80b99f39e2bd3a7a9dda6f8e2bf1c9 100644 --- a/tasks/0094_574_94574947_qa_3/task.toml +++ b/tasks/0094_574_94574947_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0094_574_94574947_qa_3" +name = "smoldataenvs-train/0094_574_94574947_qa_3" description = "What is the median number of axillary nodes for patients who survived for more than 5 years and those who did not?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.0, 4.0" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0094_620_94620248_qa_2/task.toml b/tasks/0094_620_94620248_qa_2/task.toml index 24576219a18e46c1c4d1b7640c37d2c5cc29560a..a28977706adcd5cb01ccc38e7a35f98f1c539775 100644 --- a/tasks/0094_620_94620248_qa_2/task.toml +++ b/tasks/0094_620_94620248_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0094_620_94620248_qa_2" +name = "smoldataenvs-train/0094_620_94620248_qa_2" description = "Which department has the highest employee turnover rate based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "HR" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0094_620_94620248_qa_3/task.toml b/tasks/0094_620_94620248_qa_3/task.toml index d741a7d6fadb107522dab39256b4d45d95219037..7e3a9c02cf33267166942c52ff1e03bf8ac09fd1 100644 --- a/tasks/0094_620_94620248_qa_3/task.toml +++ b/tasks/0094_620_94620248_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0094_620_94620248_qa_3" +name = "smoldataenvs-train/0094_620_94620248_qa_3" description = "Which salary level category has the highest employee turnover rate according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "low" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0094_622_94622632_qa_2/task.toml b/tasks/0094_622_94622632_qa_2/task.toml index f11dcc9453a228556ad6cb0716fef3a1d8e786c0..b12fd81d2fe1210c30043ccd316238a65cb2be0a 100644 --- a/tasks/0094_622_94622632_qa_2/task.toml +++ b/tasks/0094_622_94622632_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0094_622_94622632_qa_2" +name = "smoldataenvs-train/0094_622_94622632_qa_2" description = "What is the median age of passengers in the third class (Pclass=3) after imputing missing values using class-specific median imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "24.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0094_685_94685478_qa_2/task.toml b/tasks/0094_685_94685478_qa_2/task.toml index 33c054d6a17b216bd96d20fb2d6fd19e42c3290c..e83aefb5054c21521f6fd6bbedae0f9d7b081f5e 100644 --- a/tasks/0094_685_94685478_qa_2/task.toml +++ b/tasks/0094_685_94685478_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0094_685_94685478_qa_2" +name = "smoldataenvs-train/0094_685_94685478_qa_2" description = "Which chemical property in the dataset exhibits the highest skewness value according to the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "chlorides" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0094_688_94688742_qa_2/task.toml b/tasks/0094_688_94688742_qa_2/task.toml index 7cd2d3d69aaa53456419d2cd4a14379aa06820e9..75c3bbaa050f28e22c6b3884722221a3f04ec8a3 100644 --- a/tasks/0094_688_94688742_qa_2/task.toml +++ b/tasks/0094_688_94688742_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0094_688_94688742_qa_2" +name = "smoldataenvs-train/0094_688_94688742_qa_2" description = "What is the most frequent video game genre in the dataset, and how many games belong to that genre?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action with 3,316 games" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0094_688_94688742_qa_4/task.toml b/tasks/0094_688_94688742_qa_4/task.toml index 31ae0d60ec5dfc3d9b9b238087e4e3a3183ace7f..e35fe46e015d1a140bd56e2aa073691824536afc 100644 --- a/tasks/0094_688_94688742_qa_4/task.toml +++ b/tasks/0094_688_94688742_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0094_688_94688742_qa_4" +name = "smoldataenvs-train/0094_688_94688742_qa_4" description = "What is the highest global sales value recorded for any video game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "82.74" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0094_696_94696355_qa_3/task.toml b/tasks/0094_696_94696355_qa_3/task.toml index ee5a8c8e27336e79d80713b71b8379af05d99c28..4c119007e1fc291ae052d5ddb1ec8854b5c6ca18 100644 --- a/tasks/0094_696_94696355_qa_3/task.toml +++ b/tasks/0094_696_94696355_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0094_696_94696355_qa_3" +name = "smoldataenvs-train/0094_696_94696355_qa_3" description = "Which platform had the most game releases in 1992, and how many games were released on it?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "SNES, 21" reward_mode_initial = "list" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0094_698_94698351_qa_2/task.toml b/tasks/0094_698_94698351_qa_2/task.toml index 33227075a896ad18e2b209168d165e6a89e63fcc..ff76aa9b4d13ad33a7cadc8805ded73ea6206feb 100644 --- a/tasks/0094_698_94698351_qa_2/task.toml +++ b/tasks/0094_698_94698351_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0094_698_94698351_qa_2" +name = "smoldataenvs-train/0094_698_94698351_qa_2" description = "What is the median North American sales value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.08" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0094_875_94875423_qa_3/task.toml b/tasks/0094_875_94875423_qa_3/task.toml index fb9f2eb394921dd10cc575c0f480a21d0eb2f420..cf99c713038cd020ffd1619d5b6768e5418d715f 100644 --- a/tasks/0094_875_94875423_qa_3/task.toml +++ b/tasks/0094_875_94875423_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0094_875_94875423_qa_3" +name = "smoldataenvs-train/0094_875_94875423_qa_3" description = "What is the difference between the highest and lowest average BMI across all age groups?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.790422" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0094_883_94883618_qa_1/task.toml b/tasks/0094_883_94883618_qa_1/task.toml index 2c6f47cd524554f72ef850dcb95fece64cb96257..a62391a81f9841cf0bd5b797b78de083a8e2b7de 100644 --- a/tasks/0094_883_94883618_qa_1/task.toml +++ b/tasks/0094_883_94883618_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0094_883_94883618_qa_1" +name = "smoldataenvs-train/0094_883_94883618_qa_1" description = "What is the testing accuracy of the Multinomial Naive Bayes model trained with CountVectorizer on the SMS spam dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9820574162679426" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0094_969_94969707_qa_1/task.toml b/tasks/0094_969_94969707_qa_1/task.toml index 6fa5d4664b537fb1ee55ca7251b21695d8e8e846..7b7be491fe5f6f5c1b550553bbcac44b684b2289 100644 --- a/tasks/0094_969_94969707_qa_1/task.toml +++ b/tasks/0094_969_94969707_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0094_969_94969707_qa_1" +name = "smoldataenvs-train/0094_969_94969707_qa_1" description = "What is the difference in average monthly working hours between employees who left the company and those who stayed, based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8.36" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0094_969_94969707_qa_5/task.toml b/tasks/0094_969_94969707_qa_5/task.toml index fdfc91a41305cb7e590519f0bc425693da18cba4..a5e027234a2086987c90b7e8fe9057b7a3a613eb 100644 --- a/tasks/0094_969_94969707_qa_5/task.toml +++ b/tasks/0094_969_94969707_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0094_969_94969707_qa_5" +name = "smoldataenvs-train/0094_969_94969707_qa_5" description = "What is the difference in the average work accident rate between employees who stayed and those who left the company, according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.1277" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0094_991_94991853_qa_3/task.toml b/tasks/0094_991_94991853_qa_3/task.toml index 260979ccf76cb21a0722ef4d1349c2df2079ecd4..e77712c1f194b237191bca79a49df450ba5de44c 100644 --- a/tasks/0094_991_94991853_qa_3/task.toml +++ b/tasks/0094_991_94991853_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0094_991_94991853_qa_3" +name = "smoldataenvs-train/0094_991_94991853_qa_3" description = "Which feature has the smallest mean value for non-diabetic patients?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "DiabetesPedigreeFunction" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0095_053_95053072_qa_1/task.toml b/tasks/0095_053_95053072_qa_1/task.toml index 411835208ff0628c78726717eabac1d5505e5a1c..998c92ff4ebb6828ca4af895a02106baf716927e 100644 --- a/tasks/0095_053_95053072_qa_1/task.toml +++ b/tasks/0095_053_95053072_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0095_053_95053072_qa_1" +name = "smoldataenvs-train/0095_053_95053072_qa_1" description = "What is the correlation coefficient between age and medical insurance charges in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.299008" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0095_057_95057992_qa_2/task.toml b/tasks/0095_057_95057992_qa_2/task.toml index d27eac8a2b195fbde7374bcd3f6b4c71dd3f359e..351fa6c729f82b74f8b16f06b9a0eda4762a53b3 100644 --- a/tasks/0095_057_95057992_qa_2/task.toml +++ b/tasks/0095_057_95057992_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0095_057_95057992_qa_2" +name = "smoldataenvs-train/0095_057_95057992_qa_2" description = "What is the percentage of missing values in the 'Insulin' column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "48.70" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0095_057_95057992_qa_4/task.toml b/tasks/0095_057_95057992_qa_4/task.toml index f70d36d5523da7756b3b9b9894edb609aca9304a..ac709068fb1210c05695be47985e49e8d218ad64 100644 --- a/tasks/0095_057_95057992_qa_4/task.toml +++ b/tasks/0095_057_95057992_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0095_057_95057992_qa_4" +name = "smoldataenvs-train/0095_057_95057992_qa_4" description = "What is the median age of individuals in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "29.00" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0095_085_95085137_qa_2/task.toml b/tasks/0095_085_95085137_qa_2/task.toml index f9cc82ede07350abb006b295f1fcfafc96d42f81..ed668048b9c4d79bd450440d20c2267875833d1c 100644 --- a/tasks/0095_085_95085137_qa_2/task.toml +++ b/tasks/0095_085_95085137_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0095_085_95085137_qa_2" +name = "smoldataenvs-train/0095_085_95085137_qa_2" description = "Which categorical feature in the dataset has the highest number of unique categories?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Gill-color" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0095_085_95085137_qa_4/task.toml b/tasks/0095_085_95085137_qa_4/task.toml index 65b1b8783421abceb8716df0a098aeaaf07821ef..dcafccb94eda702272de7ef57a41f025ad4dc6c2 100644 --- a/tasks/0095_085_95085137_qa_4/task.toml +++ b/tasks/0095_085_95085137_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0095_085_95085137_qa_4" +name = "smoldataenvs-train/0095_085_95085137_qa_4" description = "What is the size of the training dataset after applying an 80-20 train/test split?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6499" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0095_088_95088024_qa_1/task.toml b/tasks/0095_088_95088024_qa_1/task.toml index 6b9906bfd1d7048418c280ef6540aaf3c78b26ba..829375d8de8be8aa34a16f2302e9b81d64c869bd 100644 --- a/tasks/0095_088_95088024_qa_1/task.toml +++ b/tasks/0095_088_95088024_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0095_088_95088024_qa_1" +name = "smoldataenvs-train/0095_088_95088024_qa_1" description = "What percentage of variance in medical charges is explained by BMI for smokers compared to non-smokers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Smokers: 65%, Non-smokers: 0.7%" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0095_143_95143885_qa_3/task.toml b/tasks/0095_143_95143885_qa_3/task.toml index 6094ed893a35fa5b0096f39da673efef249562e2..8e1907a6d6007e539be1ef48e4c0cfb5bbab7c35 100644 --- a/tasks/0095_143_95143885_qa_3/task.toml +++ b/tasks/0095_143_95143885_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0095_143_95143885_qa_3" +name = "smoldataenvs-train/0095_143_95143885_qa_3" description = "What is the correlation between BloodPressure and the diabetes outcome in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.065" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0095_143_95143885_qa_4/task.toml b/tasks/0095_143_95143885_qa_4/task.toml index 506efba3fcf28bdef653723cc7d8bc05b93d4817..2807b07fbd82b0dffca24e9e578f4691e679cbf0 100644 --- a/tasks/0095_143_95143885_qa_4/task.toml +++ b/tasks/0095_143_95143885_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0095_143_95143885_qa_4" +name = "smoldataenvs-train/0095_143_95143885_qa_4" description = "Which feature has the second-highest absolute correlation with the diabetes outcome in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "BMI" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0095_202_95202118_qa_3/task.toml b/tasks/0095_202_95202118_qa_3/task.toml index 4857458feeace2f234df614aa4b03e3f816d84df..241d64e7df595b3093be5d5e33b1f8f4c876a97b 100644 --- a/tasks/0095_202_95202118_qa_3/task.toml +++ b/tasks/0095_202_95202118_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0095_202_95202118_qa_3" +name = "smoldataenvs-train/0095_202_95202118_qa_3" description = "What is the average account balance of customers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1528.54" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0095_202_95202118_qa_5/task.toml b/tasks/0095_202_95202118_qa_5/task.toml index 3ccf064536e9d99ecfd2ca66267262342b443fe9..dc1db222037721884abeab3925c21237b36e9eef 100644 --- a/tasks/0095_202_95202118_qa_5/task.toml +++ b/tasks/0095_202_95202118_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0095_202_95202118_qa_5" +name = "smoldataenvs-train/0095_202_95202118_qa_5" description = "What is the F1-score for the positive class (deposit: yes) in the Random Forest model's classification report?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.80" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0095_223_95223929_qa_4/task.toml b/tasks/0095_223_95223929_qa_4/task.toml index 491e6ba30c86bb34f31be32baca6bda6096f3863..c493a8e1bba4ee5ed29ea981ce63ca66cf1365ec 100644 --- a/tasks/0095_223_95223929_qa_4/task.toml +++ b/tasks/0095_223_95223929_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0095_223_95223929_qa_4" +name = "smoldataenvs-train/0095_223_95223929_qa_4" description = "Which feature has the highest absolute coefficient value in the Linear Regression model's final equation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "NOX" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0095_223_95223929_qa_5/task.toml b/tasks/0095_223_95223929_qa_5/task.toml index 7c14a1c56a8357518a00eb83abf1aa9c17420f70..9eef72a98936f69e71d0d62a482ee5400b224fd0 100644 --- a/tasks/0095_223_95223929_qa_5/task.toml +++ b/tasks/0095_223_95223929_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0095_223_95223929_qa_5" +name = "smoldataenvs-train/0095_223_95223929_qa_5" description = "After imputing missing values with column means, what is the mean value of the LSTAT feature (percentage of lower status population)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12.7154" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0095_236_95236707_qa_2/task.toml b/tasks/0095_236_95236707_qa_2/task.toml index 6eaaa3f3dda1626f1671fb1d7e0ebfefb5a6990d..a55f5e10b39974aefc5e70c539c230628f47105a 100644 --- a/tasks/0095_236_95236707_qa_2/task.toml +++ b/tasks/0095_236_95236707_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0095_236_95236707_qa_2" +name = "smoldataenvs-train/0095_236_95236707_qa_2" description = "How many features remain in the final training dataset after removing the PetalLengthCm and Id columns?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0095_334_95334310_qa_2/task.toml b/tasks/0095_334_95334310_qa_2/task.toml index 8f47f32be61ec34fd658ecedbe58e529cd4a90d0..7d484b5d7bc84aa2b241a984edae875651ae3a5c 100644 --- a/tasks/0095_334_95334310_qa_2/task.toml +++ b/tasks/0095_334_95334310_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0095_334_95334310_qa_2" +name = "smoldataenvs-train/0095_334_95334310_qa_2" description = "How many samples are included in the training set after applying the 80-20 split?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "166" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0095_334_95334310_qa_5/task.toml b/tasks/0095_334_95334310_qa_5/task.toml index db338b75bc37ab5fbefd59b4abec4970eed6848b..ecd8617aabc1223b6257bf16e74e1446de1e514d 100644 --- a/tasks/0095_334_95334310_qa_5/task.toml +++ b/tasks/0095_334_95334310_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0095_334_95334310_qa_5" +name = "smoldataenvs-train/0095_334_95334310_qa_5" description = "What is the mean value of the last feature (column 59) for Rocks in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.006024" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0095_339_95339518_qa_4/task.toml b/tasks/0095_339_95339518_qa_4/task.toml index 166d351c27085b3809dedb4429eb56cf0dd6ca38..b2b9cf2553f7f590039489be5891e553c2888cca 100644 --- a/tasks/0095_339_95339518_qa_4/task.toml +++ b/tasks/0095_339_95339518_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0095_339_95339518_qa_4" +name = "smoldataenvs-train/0095_339_95339518_qa_4" description = "Which hours-per-week category (\"40 hrs\", \"<40 hrs\", or \">40hrs\") has the highest proportion of individuals with a salary greater than 50K?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = ">40hrs" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0095_339_95339518_qa_5/task.toml b/tasks/0095_339_95339518_qa_5/task.toml index 8796dfd8650ed5b95a5ec2dba29dfc86e5dce6f2..93cbbf477c4756f9a828b900681c594abc7b532d 100644 --- a/tasks/0095_339_95339518_qa_5/task.toml +++ b/tasks/0095_339_95339518_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0095_339_95339518_qa_5" +name = "smoldataenvs-train/0095_339_95339518_qa_5" description = "After handling missing values and feature engineering, how many missing values remain in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0095_344_95344142_qa_3/task.toml b/tasks/0095_344_95344142_qa_3/task.toml index b2ab1020c57bcec3a7d76a8b01d236a663b614aa..093ae829689348ffa90d2850c01083833310da1e 100644 --- a/tasks/0095_344_95344142_qa_3/task.toml +++ b/tasks/0095_344_95344142_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0095_344_95344142_qa_3" +name = "smoldataenvs-train/0095_344_95344142_qa_3" description = "Does the dataset contain any missing or null values across all features?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0095_344_95344142_qa_4/task.toml b/tasks/0095_344_95344142_qa_4/task.toml index 5d35a8b1c6474a01cea2cb8eaf1c69347e67b4e3..4b1eeb4334a9d247c9c07f2880ec683fe3df930e 100644 --- a/tasks/0095_344_95344142_qa_4/task.toml +++ b/tasks/0095_344_95344142_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0095_344_95344142_qa_4" +name = "smoldataenvs-train/0095_344_95344142_qa_4" description = "What is the maximum recorded petal width in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0095_395_95395894_qa_2/task.toml b/tasks/0095_395_95395894_qa_2/task.toml index 878880a171f8bb3c0507ff80f6b3140fa86e518c..952b60f805f96763e663341ac3e083f5df412969 100644 --- a/tasks/0095_395_95395894_qa_2/task.toml +++ b/tasks/0095_395_95395894_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0095_395_95395894_qa_2" +name = "smoldataenvs-train/0095_395_95395894_qa_2" description = "Which chemical attribute shows the highest percentage difference in mean values between wines with quality <5.5 and quality >5.5?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "citric acid (21.678%)" reward_mode_initial = "flexible" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0095_624_95624401_qa_2/task.toml b/tasks/0095_624_95624401_qa_2/task.toml index 4f3f78aed93c89a24fe276481ff4df30054ed5a2..e372920b4e82b720d28c4272ec44f4776eca1a8d 100644 --- a/tasks/0095_624_95624401_qa_2/task.toml +++ b/tasks/0095_624_95624401_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0095_624_95624401_qa_2" +name = "smoldataenvs-train/0095_624_95624401_qa_2" description = "What is the distribution count of wine quality levels categorized as 'low', 'medium', and 'high' in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "low: 744, medium: 638, high: 217" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0095_666_95666563_qa_5/task.toml b/tasks/0095_666_95666563_qa_5/task.toml index b5f8049fce75ce149b0fdc564995de1c862a5dbb..ad9873e5c42c8a540ccc2885508b2fb5e108ed75 100644 --- a/tasks/0095_666_95666563_qa_5/task.toml +++ b/tasks/0095_666_95666563_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0095_666_95666563_qa_5" +name = "smoldataenvs-train/0095_666_95666563_qa_5" description = "What percentage of the dataset consists of spam messages based on the original data distribution?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13.4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0095_690_95690898_qa_3/task.toml b/tasks/0095_690_95690898_qa_3/task.toml index d365353bbb0277f5e41e26a9c528ddd79c0fe3c5..f6c460b3c0b1d0cb90cbd6cebcc2af30f4af7c87 100644 --- a/tasks/0095_690_95690898_qa_3/task.toml +++ b/tasks/0095_690_95690898_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0095_690_95690898_qa_3" +name = "smoldataenvs-train/0095_690_95690898_qa_3" description = "What is the Pearson Chi-square statistic for the contingency table between 'mineral water' and 'ground beef'?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "142.933" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0095_690_95690898_qa_5/task.toml b/tasks/0095_690_95690898_qa_5/task.toml index a6370690cfb481b6f6eee57ea122e81400f62945..612cc94c7a6a0fa86ec548c4b400c5116f97741c 100644 --- a/tasks/0095_690_95690898_qa_5/task.toml +++ b/tasks/0095_690_95690898_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0095_690_95690898_qa_5" +name = "smoldataenvs-train/0095_690_95690898_qa_5" description = "What is the Kulczynski score for the association rule 'mineral water' → 'ground beef'?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.2941" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0095_795_95795358_qa_4/task.toml b/tasks/0095_795_95795358_qa_4/task.toml index 5a25f1dfbc90be4c025e034a856eb90fdfc0a0a8..153b42bb71f5c634c7bd115044bfd03071e0f282 100644 --- a/tasks/0095_795_95795358_qa_4/task.toml +++ b/tasks/0095_795_95795358_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0095_795_95795358_qa_4" +name = "smoldataenvs-train/0095_795_95795358_qa_4" description = "How many samples are present in the test set for the regression task based on the 70-30 train/test split?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "156" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0095_843_95843375_qa_1/task.toml b/tasks/0095_843_95843375_qa_1/task.toml index d22dcb80eab3aa0bbd0a9dbbf70d47995aabfdb1..df0e9ae85ff455b8cf322e12e9f581f5efa6c444 100644 --- a/tasks/0095_843_95843375_qa_1/task.toml +++ b/tasks/0095_843_95843375_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0095_843_95843375_qa_1" +name = "smoldataenvs-train/0095_843_95843375_qa_1" description = "Which player has won the most Man of the Match awards in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "CH Gayle" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0095_843_95843375_qa_3/task.toml b/tasks/0095_843_95843375_qa_3/task.toml index 5988511dff61b0661c034bf6b72b490ae17f769a..49e85b7bf77ad6025921f486af93b15dc9ec3955 100644 --- a/tasks/0095_843_95843375_qa_3/task.toml +++ b/tasks/0095_843_95843375_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0095_843_95843375_qa_3" +name = "smoldataenvs-train/0095_843_95843375_qa_3" description = "What is the most common type of dismissal in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "caught" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0095_843_95843375_qa_4/task.toml b/tasks/0095_843_95843375_qa_4/task.toml index 408cd3559dc851c9fb7d33ea056741d293657ae7..d1920dc902d93c62d3b074fece006474981ee791 100644 --- a/tasks/0095_843_95843375_qa_4/task.toml +++ b/tasks/0095_843_95843375_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0095_843_95843375_qa_4" +name = "smoldataenvs-train/0095_843_95843375_qa_4" description = "Who is the highest run-scorer in the dataset based on total runs scored?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "SK Raina" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0095_843_95843375_qa_5/task.toml b/tasks/0095_843_95843375_qa_5/task.toml index eacd6f95ade62148e03433269bfddbded83015bb..6ca94668e2a78ec41913b52daf3f3ff4c7530150 100644 --- a/tasks/0095_843_95843375_qa_5/task.toml +++ b/tasks/0095_843_95843375_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0095_843_95843375_qa_5" +name = "smoldataenvs-train/0095_843_95843375_qa_5" description = "What percentage of players in the dataset are right-handed batters?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "93%" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0095_877_95877877_qa_3/task.toml b/tasks/0095_877_95877877_qa_3/task.toml index 40d3f2410c22e8f5f947fc504705fe85f0ec7b37..1e82d61528b7dcbc9df5f721dd06625bdab28ebd 100644 --- a/tasks/0095_877_95877877_qa_3/task.toml +++ b/tasks/0095_877_95877877_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0095_877_95877877_qa_3" +name = "smoldataenvs-train/0095_877_95877877_qa_3" description = "Which day of the week has the highest proportion of patients showing up for their appointments?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Thursday" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0095_909_95909431_qa_3/task.toml b/tasks/0095_909_95909431_qa_3/task.toml index e1c3b890f0bc4f9b99f42ad08b9898002507bd8b..92450ba0da7c316aee8c7c83040d92036b690100 100644 --- a/tasks/0095_909_95909431_qa_3/task.toml +++ b/tasks/0095_909_95909431_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0095_909_95909431_qa_3" +name = "smoldataenvs-train/0095_909_95909431_qa_3" description = "Which feature shows the highest absolute correlation with the target variable 'Dataset' in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Direct_Bilirubin" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0095_909_95909431_qa_4/task.toml b/tasks/0095_909_95909431_qa_4/task.toml index 79e21692cf9e5aa3de3b1d5b967d3d7cfe8cfff5..fd0bf2c1245203b095e7120b8cb43234f7d29e71 100644 --- a/tasks/0095_909_95909431_qa_4/task.toml +++ b/tasks/0095_909_95909431_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0095_909_95909431_qa_4" +name = "smoldataenvs-train/0095_909_95909431_qa_4" description = "After applying ANOVA F-value feature selection (SelectKBest with k=6), which three features have the highest scores?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Direct_Bilirubin, Total_Bilirubin, Alkaline_Phosphotase" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0096_057_96057481_qa_1/task.toml b/tasks/0096_057_96057481_qa_1/task.toml index 46f860892b9cd8dc589c75393766c506976b38e1..fd53de222cb322237e0839d28a1b3f9eecdcc23f 100644 --- a/tasks/0096_057_96057481_qa_1/task.toml +++ b/tasks/0096_057_96057481_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0096_057_96057481_qa_1" +name = "smoldataenvs-train/0096_057_96057481_qa_1" description = "How many rows of SkinThickness data were identified as missing (replaced with NaN) before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "227" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0096_057_96057481_qa_2/task.toml b/tasks/0096_057_96057481_qa_2/task.toml index 530fafceadcebf4e3c3fe86ba65a128622ec5cde..ad1261f07b1a085cd0478868b32550159ab54ebd 100644 --- a/tasks/0096_057_96057481_qa_2/task.toml +++ b/tasks/0096_057_96057481_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0096_057_96057481_qa_2" +name = "smoldataenvs-train/0096_057_96057481_qa_2" description = "What is the median BMI value after imputing missing values in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "32.3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0096_064_96064872_qa_1/task.toml b/tasks/0096_064_96064872_qa_1/task.toml index bf21ed14e4e190f53c259199e49457f0627c62a2..fcd80c133d38b63b04a0fef7ab65910a743f8474 100644 --- a/tasks/0096_064_96064872_qa_1/task.toml +++ b/tasks/0096_064_96064872_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0096_064_96064872_qa_1" +name = "smoldataenvs-train/0096_064_96064872_qa_1" description = "Which regression model achieved the lowest RMSE in predicting TotalCharges?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Random Forest" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0096_064_96064872_qa_2/task.toml b/tasks/0096_064_96064872_qa_2/task.toml index d253fd6d88deee0f77966c14e9ec48a86779f6ae..94bc8c38800fdf8b0601e1d0cc628fc3fee817cb 100644 --- a/tasks/0096_064_96064872_qa_2/task.toml +++ b/tasks/0096_064_96064872_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0096_064_96064872_qa_2" +name = "smoldataenvs-train/0096_064_96064872_qa_2" description = "What was the binary accuracy of the neural network model after 10 epochs of training for churn prediction?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.8055" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0096_192_96192841_qa_2/task.toml b/tasks/0096_192_96192841_qa_2/task.toml index 0fc1035e0ea07c9d02f81c8459b40edf2dc3efc1..1bb3bf6221815db997341735fa6ff8d70a3110a2 100644 --- a/tasks/0096_192_96192841_qa_2/task.toml +++ b/tasks/0096_192_96192841_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0096_192_96192841_qa_2" +name = "smoldataenvs-train/0096_192_96192841_qa_2" description = "Which feature in the dataset has the highest number of outliers detected using the IQR method?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "residual sugar" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0096_192_96192841_qa_5/task.toml b/tasks/0096_192_96192841_qa_5/task.toml index 4a577a75536e64b82b22f581c0e983ee12ef22d7..67ad7e92b41f5d1009fc4e31656a6d982b127707 100644 --- a/tasks/0096_192_96192841_qa_5/task.toml +++ b/tasks/0096_192_96192841_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0096_192_96192841_qa_5" +name = "smoldataenvs-train/0096_192_96192841_qa_5" description = "What is the count of unique wine quality samples after deduplication?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1359" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0096_217_96217754_qa_3/task.toml b/tasks/0096_217_96217754_qa_3/task.toml index 95dbc2d6309e11db64199f92a7f51be8e3d84d96..c9a2cb05e0a1f604f5fc3625378c1ed0456d6a9f 100644 --- a/tasks/0096_217_96217754_qa_3/task.toml +++ b/tasks/0096_217_96217754_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0096_217_96217754_qa_3" +name = "smoldataenvs-train/0096_217_96217754_qa_3" description = "Which geographic region accounts for the largest share of total video game sales in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "North America" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0096_217_96217754_qa_4/task.toml b/tasks/0096_217_96217754_qa_4/task.toml index b86bfeabc6f6f703650cac7e627c193a2de0480b..d60d29ecc0cc82dad965e3c701e1ddaac42fdd11 100644 --- a/tasks/0096_217_96217754_qa_4/task.toml +++ b/tasks/0096_217_96217754_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0096_217_96217754_qa_4" +name = "smoldataenvs-train/0096_217_96217754_qa_4" description = "What was the maximum total global sales value recorded for a single calendar year?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "678.90" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0096_295_96295649_qa_2/task.toml b/tasks/0096_295_96295649_qa_2/task.toml index 7d9011f84059a77c1e1bb060beca7736f6333113..944b5d58f16fcd826333a673c1f24cf01e60f460 100644 --- a/tasks/0096_295_96295649_qa_2/task.toml +++ b/tasks/0096_295_96295649_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0096_295_96295649_qa_2" +name = "smoldataenvs-train/0096_295_96295649_qa_2" description = "How many patients are included in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "303" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0096_460_96460762_qa_2/task.toml b/tasks/0096_460_96460762_qa_2/task.toml index bf67e6f919327fd62dd569adcb4b7ad094490311..cc6b2af6a9dfc1248a27528d79628588f16e2060 100644 --- a/tasks/0096_460_96460762_qa_2/task.toml +++ b/tasks/0096_460_96460762_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0096_460_96460762_qa_2" +name = "smoldataenvs-train/0096_460_96460762_qa_2" description = "What is the test accuracy of the trained XGBoost classifier on the German credit data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.72" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0096_460_96460762_qa_3/task.toml b/tasks/0096_460_96460762_qa_3/task.toml index 4633a20a6aaeceaf4bcb9a1bb2bb58ccc57a582d..11a4b9f71c390f988f2b028753ca845ef64ec253 100644 --- a/tasks/0096_460_96460762_qa_3/task.toml +++ b/tasks/0096_460_96460762_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0096_460_96460762_qa_3" +name = "smoldataenvs-train/0096_460_96460762_qa_3" description = "Which gender has a higher count in both good and bad loan categories?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Male" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0096_460_96460762_qa_5/task.toml b/tasks/0096_460_96460762_qa_5/task.toml index b3418d6f0ba73db824b666e92bf65282bb92fb1b..ec440153053b736f6d80fc8852f129bef91af1dc 100644 --- a/tasks/0096_460_96460762_qa_5/task.toml +++ b/tasks/0096_460_96460762_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0096_460_96460762_qa_5" +name = "smoldataenvs-train/0096_460_96460762_qa_5" description = "What is the precision score for the \"bad\" loan class in the test set predictions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.60" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0096_673_96673455_qa_1/task.toml b/tasks/0096_673_96673455_qa_1/task.toml index d81edb035fe63dec57975e1b301457847717e93d..ffaa1ba9943d5bbd8a800275f2ad03ec03e3ae42 100644 --- a/tasks/0096_673_96673455_qa_1/task.toml +++ b/tasks/0096_673_96673455_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0096_673_96673455_qa_1" +name = "smoldataenvs-train/0096_673_96673455_qa_1" description = "Which machine learning model (Logistic Regression or Decision Tree) achieved a higher cross-validation accuracy according to the 100-fold cross-validation results in the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Logistic Regression" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0096_765_96765273_qa_4/task.toml b/tasks/0096_765_96765273_qa_4/task.toml index 0a3b1bfd476995b6ebb4a6d81b50fbcd92e9423c..4e4281d525e03417c92119bf931296028c500056 100644 --- a/tasks/0096_765_96765273_qa_4/task.toml +++ b/tasks/0096_765_96765273_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0096_765_96765273_qa_4" +name = "smoldataenvs-train/0096_765_96765273_qa_4" description = "What is the difference in total global sales between the year with the highest sales (2008) and the year with the lowest sales (2017)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "678.85" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0096_765_96765273_qa_5/task.toml b/tasks/0096_765_96765273_qa_5/task.toml index 9856d0fcb5b8a47437d4916f62c044680679c486..b59e9da8d7bd8c9c9f8e713f605083382842cd7b 100644 --- a/tasks/0096_765_96765273_qa_5/task.toml +++ b/tasks/0096_765_96765273_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0096_765_96765273_qa_5" +name = "smoldataenvs-train/0096_765_96765273_qa_5" description = "Which platform has the longest lifespan (difference between latest and earliest release years + 1) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "DS" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0096_778_96778684_qa_2/task.toml b/tasks/0096_778_96778684_qa_2/task.toml index e33580abd532903eed704c5274ac0da9770870d2..1b1131ae77f8a61bbdcc8f5f669cedaa2f8c584a 100644 --- a/tasks/0096_778_96778684_qa_2/task.toml +++ b/tasks/0096_778_96778684_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0096_778_96778684_qa_2" +name = "smoldataenvs-train/0096_778_96778684_qa_2" description = "After applying the Box-Cox transformation, what is the p-value from the normality test on the target variable (charges)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.5249631686757666e-12" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0096_784_96784965_qa_2/task.toml b/tasks/0096_784_96784965_qa_2/task.toml index 058e0e9e2ef772f14ab1058fefd966d5621902d5..0b9b4d502d8fbecdcbabe772650156f4529dafa3 100644 --- a/tasks/0096_784_96784965_qa_2/task.toml +++ b/tasks/0096_784_96784965_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0096_784_96784965_qa_2" +name = "smoldataenvs-train/0096_784_96784965_qa_2" description = "What is the percentage of malignant cases (diagnosis=1) in the dataset after converting categorical diagnosis labels to numerical values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "37.3" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0096_863_96863277_qa_1/task.toml b/tasks/0096_863_96863277_qa_1/task.toml index 15f9aa0234d5f835b7b54f5d782771fced3f0f9c..888def20ca34ee58f1e57f1639f3f96ddf176973 100644 --- a/tasks/0096_863_96863277_qa_1/task.toml +++ b/tasks/0096_863_96863277_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0096_863_96863277_qa_1" +name = "smoldataenvs-train/0096_863_96863277_qa_1" description = "What percentage of the dataset corresponds to employees who have attrited (Attrition = Yes) after handling missing values and redundant columns?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16.12" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0096_889_96889516_qa_4/task.toml b/tasks/0096_889_96889516_qa_4/task.toml index bd9e6b33ad816f4f43cb4cca9a5a80c1ff068d76..90a4b6ce8f9414e9015ade5462f815afe9c72ae1 100644 --- a/tasks/0096_889_96889516_qa_4/task.toml +++ b/tasks/0096_889_96889516_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0096_889_96889516_qa_4" +name = "smoldataenvs-train/0096_889_96889516_qa_4" description = "What was the shape of the dataset after applying RandomOverSampler?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "(1000, 8)" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0096_889_96889516_qa_5/task.toml b/tasks/0096_889_96889516_qa_5/task.toml index 274260fec5f53fa54d1c3ec92fb4f9b33fb1f76a..0c34d98f12649dea11576b215646ae0b5dd82673 100644 --- a/tasks/0096_889_96889516_qa_5/task.toml +++ b/tasks/0096_889_96889516_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0096_889_96889516_qa_5" +name = "smoldataenvs-train/0096_889_96889516_qa_5" description = "What is the standard deviation of the Age feature in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11.76" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0096_904_96904544_qa_4/task.toml b/tasks/0096_904_96904544_qa_4/task.toml index fd6bb4dd4289b835c1d74de1d9b4af89fc7d3b43..671f58e59a29e00f4ebb3139774ad748ad5c6193 100644 --- a/tasks/0096_904_96904544_qa_4/task.toml +++ b/tasks/0096_904_96904544_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0096_904_96904544_qa_4" +name = "smoldataenvs-train/0096_904_96904544_qa_4" description = "Does the training accuracy exceed the validation accuracy in the final epoch?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0096_940_96940611_qa_1/task.toml b/tasks/0096_940_96940611_qa_1/task.toml index e9dbec6d5cbaa0f924e39e8183bb3742f3f28fdb..a8e5771515163507e982dc43708f04d0bf4d9c6d 100644 --- a/tasks/0096_940_96940611_qa_1/task.toml +++ b/tasks/0096_940_96940611_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0096_940_96940611_qa_1" +name = "smoldataenvs-train/0096_940_96940611_qa_1" description = "Which job class has the highest average credit amount in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0096_973_96973759_qa_3/task.toml b/tasks/0096_973_96973759_qa_3/task.toml index 2f0e14a34a36305cb565ef9906fd97a445b195b1..a4e0f1f299e2b91635ae15c3bf3686dfd01173a4 100644 --- a/tasks/0096_973_96973759_qa_3/task.toml +++ b/tasks/0096_973_96973759_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0096_973_96973759_qa_3" +name = "smoldataenvs-train/0096_973_96973759_qa_3" description = "Which classification model achieves higher accuracy on the test set: Multinomial Naive Bayes or Logistic Regression?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Logistic Regression" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0096_979_96979035_qa_2/task.toml b/tasks/0096_979_96979035_qa_2/task.toml index 79b95d4cf104549c401373846a15e54665486458..c07144bd2e9c8d2bf0e9d4041cfb7d2167a23f44 100644 --- a/tasks/0096_979_96979035_qa_2/task.toml +++ b/tasks/0096_979_96979035_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0096_979_96979035_qa_2" +name = "smoldataenvs-train/0096_979_96979035_qa_2" description = "Which video game has the highest global sales value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii Sports" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0096_997_96997158_qa_1/task.toml b/tasks/0096_997_96997158_qa_1/task.toml index 98b53457f3f19b4c708a8b39084d59e38547de99..5d2b79075833d774d15156be8153a34cbf3c95f5 100644 --- a/tasks/0096_997_96997158_qa_1/task.toml +++ b/tasks/0096_997_96997158_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0096_997_96997158_qa_1" +name = "smoldataenvs-train/0096_997_96997158_qa_1" description = "What is the combined explained variance ratio of the first two principal components in the PCA analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.962" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0097_014_97014488_qa_2/task.toml b/tasks/0097_014_97014488_qa_2/task.toml index a118fe977f050f01e4add72a410c93cd2593975c..10333ccb7a0efb522726893954af85f635355d0d 100644 --- a/tasks/0097_014_97014488_qa_2/task.toml +++ b/tasks/0097_014_97014488_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0097_014_97014488_qa_2" +name = "smoldataenvs-train/0097_014_97014488_qa_2" description = "What is the most common spore print color in the dataset, and how many mushrooms have it?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "k, 2388" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0097_014_97014488_qa_3/task.toml b/tasks/0097_014_97014488_qa_3/task.toml index 212df9c403df73b5fd48619fbc8d48a020030d92..11bbdf0282462a169c32e4b32be83205cebbfff8 100644 --- a/tasks/0097_014_97014488_qa_3/task.toml +++ b/tasks/0097_014_97014488_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0097_014_97014488_qa_3" +name = "smoldataenvs-train/0097_014_97014488_qa_3" description = "How many mushrooms in the dataset do not have bruises?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4748" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0097_022_97022263_qa_1/task.toml b/tasks/0097_022_97022263_qa_1/task.toml index b5d802bec174592d388876b90636e7abff7d3f71..29260a50b40050b0ef96eae844e4b952d765cb58 100644 --- a/tasks/0097_022_97022263_qa_1/task.toml +++ b/tasks/0097_022_97022263_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0097_022_97022263_qa_1" +name = "smoldataenvs-train/0097_022_97022263_qa_1" description = "What percentage of employees who left the company (Attrition = Yes) reported low job satisfaction (JobSatisfaction = 1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "28" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0097_040_97040168_qa_3/task.toml b/tasks/0097_040_97040168_qa_3/task.toml index d9c65c6415f017d0a140f10f494695ed56bca62c..06a55f384840585ddc50d6f98bd70f41f2b22b6c 100644 --- a/tasks/0097_040_97040168_qa_3/task.toml +++ b/tasks/0097_040_97040168_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0097_040_97040168_qa_3" +name = "smoldataenvs-train/0097_040_97040168_qa_3" description = "How many no-show appointments were scheduled on the same day as the appointment date?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1792" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0097_086_97086766_qa_4/task.toml b/tasks/0097_086_97086766_qa_4/task.toml index d6e0fa327e2f4d4a39cd14d11a83c1af4918da93..fb2066be5007346dc545b388ffa48afe974337f3 100644 --- a/tasks/0097_086_97086766_qa_4/task.toml +++ b/tasks/0097_086_97086766_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0097_086_97086766_qa_4" +name = "smoldataenvs-train/0097_086_97086766_qa_4" description = "Which newly created attribute (rooms_per_household, bedrooms_per_room, or population_per_household) has the strongest positive correlation with median house value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "rooms_per_household" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0097_228_97228368_qa_4/task.toml b/tasks/0097_228_97228368_qa_4/task.toml index a8c76c191398f5d7fb33562d582c63c02c9c4ff4..a9a65bcf540039a6151c58b30f09388a55c825cc 100644 --- a/tasks/0097_228_97228368_qa_4/task.toml +++ b/tasks/0097_228_97228368_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0097_228_97228368_qa_4" +name = "smoldataenvs-train/0097_228_97228368_qa_4" description = "How many different cluster values were evaluated in the elbow method analysis for the Mall Customers dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0097_228_97228368_qa_5/task.toml b/tasks/0097_228_97228368_qa_5/task.toml index 4ed241d4d124f3cd6bd8fc76ebef68cb34c1b1a6..b4c202c5c895bb777ef60071e295ea643bfba35b 100644 --- a/tasks/0097_228_97228368_qa_5/task.toml +++ b/tasks/0097_228_97228368_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0097_228_97228368_qa_5" +name = "smoldataenvs-train/0097_228_97228368_qa_5" description = "What is the mean value of the scaled Annual Income feature after standardization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0097_234_97234597_qa_3/task.toml b/tasks/0097_234_97234597_qa_3/task.toml index 325ba42767c605f38be6ad0d195ee4098aaaf156..797614b5db8ab857d24eccdbcd5f546caa4939a7 100644 --- a/tasks/0097_234_97234597_qa_3/task.toml +++ b/tasks/0097_234_97234597_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0097_234_97234597_qa_3" +name = "smoldataenvs-train/0097_234_97234597_qa_3" description = "What is the covariance between Sepal Length and Petal Width in the raw (non-standardized) iris dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.5169" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0097_283_97283539_qa_3/task.toml b/tasks/0097_283_97283539_qa_3/task.toml index 49aa456f96e29a4a8412c3b01a98a9d5564351e9..c4660581967b6eb1c155f03b098972c1287daa9d 100644 --- a/tasks/0097_283_97283539_qa_3/task.toml +++ b/tasks/0097_283_97283539_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0097_283_97283539_qa_3" +name = "smoldataenvs-train/0097_283_97283539_qa_3" description = "What percentage of the dataset contains missing values in the Product_Category_2 column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "31.4" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0097_326_97326157_qa_5/task.toml b/tasks/0097_326_97326157_qa_5/task.toml index af0bbc97e5de89c479adbaddb04c1ca2b5d1dc55..71df3c24afc8554c914571c89b53bc6257fb7919 100644 --- a/tasks/0097_326_97326157_qa_5/task.toml +++ b/tasks/0097_326_97326157_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0097_326_97326157_qa_5" +name = "smoldataenvs-train/0097_326_97326157_qa_5" description = "How many missing values are present in the 'Title' column before handling them with empty strings?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3810" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0097_377_97377124_qa_1/task.toml b/tasks/0097_377_97377124_qa_1/task.toml index e67e359f049db74fb565f85865df733953a57263..af2c4a04a60c4d2a46fb74459acba78370d63e23 100644 --- a/tasks/0097_377_97377124_qa_1/task.toml +++ b/tasks/0097_377_97377124_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0097_377_97377124_qa_1" +name = "smoldataenvs-train/0097_377_97377124_qa_1" description = "What is the R² score achieved by the model on the test set after training?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.75622" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0097_377_97377124_qa_2/task.toml b/tasks/0097_377_97377124_qa_2/task.toml index b265fda65a8b46a305bc592da35aaf7a0d447322..fe55ce76315bf98187affd39bcd7e802ff35bd60 100644 --- a/tasks/0097_377_97377124_qa_2/task.toml +++ b/tasks/0097_377_97377124_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0097_377_97377124_qa_2" +name = "smoldataenvs-train/0097_377_97377124_qa_2" description = "How many features were generated after applying one-hot encoding to the categorical variables in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0097_409_97409368_qa_2/task.toml b/tasks/0097_409_97409368_qa_2/task.toml index 688008d452c9e97d1d490f5c3b611635fa840446..eaad5bdc19642c4da929d8a599921b4ae57bb9b9 100644 --- a/tasks/0097_409_97409368_qa_2/task.toml +++ b/tasks/0097_409_97409368_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0097_409_97409368_qa_2" +name = "smoldataenvs-train/0097_409_97409368_qa_2" description = "How many features have a correlation of at least 0.2 with the price_range?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0097_494_97494334_qa_5/task.toml b/tasks/0097_494_97494334_qa_5/task.toml index d1d8fffe34e6db2ea95479521bd78bd7b51ccb92..ea06bdbdac01b9fd9703c2f6cc29cf7a6a5e9cbb 100644 --- a/tasks/0097_494_97494334_qa_5/task.toml +++ b/tasks/0097_494_97494334_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0097_494_97494334_qa_5" +name = "smoldataenvs-train/0097_494_97494334_qa_5" description = "What percentage of respondents in the survey identified as male after missing values were imputed in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "41.7" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0097_524_97524649_qa_1/task.toml b/tasks/0097_524_97524649_qa_1/task.toml index b3a6e17630760a647f9e859df97deac2b1a80389..ffcdd5db21aef72901c5f7478b3b559c6caf7934 100644 --- a/tasks/0097_524_97524649_qa_1/task.toml +++ b/tasks/0097_524_97524649_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0097_524_97524649_qa_1" +name = "smoldataenvs-train/0097_524_97524649_qa_1" description = "Which factor has the highest positive correlation with Life Expectancy according to the correlation matrix in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Schooling" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0097_527_97527301_qa_3/task.toml b/tasks/0097_527_97527301_qa_3/task.toml index 0729fa174997e8fed1b787ee365abfae3a097367..ccafee63d8a71152cb53a9ffe6caf68d91809416 100644 --- a/tasks/0097_527_97527301_qa_3/task.toml +++ b/tasks/0097_527_97527301_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0097_527_97527301_qa_3" +name = "smoldataenvs-train/0097_527_97527301_qa_3" description = "Which airline operator is responsible for the highest total number of deaths in aviation history based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Aeroflot" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0097_565_97565207_qa_4/task.toml b/tasks/0097_565_97565207_qa_4/task.toml index 9c0a32fbac5710ca71e0ed977ab0113b16721b46..904d3a7a2627f188a7343fb4912afbe523853d4e 100644 --- a/tasks/0097_565_97565207_qa_4/task.toml +++ b/tasks/0097_565_97565207_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0097_565_97565207_qa_4" +name = "smoldataenvs-train/0097_565_97565207_qa_4" description = "What is the theoretical probability that a specific row from the original dataset is not included in a bootstrapped dataset of the same size, as demonstrated in the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "36.8" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0097_585_97585155_qa_2/task.toml b/tasks/0097_585_97585155_qa_2/task.toml index 96d88e8f04cf10a021e291a1282802d9fb1fff33..b03b9156c60cd9d663a6df091c4b38c93bde59d6 100644 --- a/tasks/0097_585_97585155_qa_2/task.toml +++ b/tasks/0097_585_97585155_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0097_585_97585155_qa_2" +name = "smoldataenvs-train/0097_585_97585155_qa_2" description = "Which numerical feature shows the strongest positive correlation with job satisfaction, and which shows the strongest negative correlation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "DailyRate, HourlyRate" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0097_585_97585155_qa_3/task.toml b/tasks/0097_585_97585155_qa_3/task.toml index 54b8208a5cba83faf8d7a9b5359f5eda0176a5c6..ccbe8f397621c9d3c5a065ef432cbbe9ee9eba06 100644 --- a/tasks/0097_585_97585155_qa_3/task.toml +++ b/tasks/0097_585_97585155_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0097_585_97585155_qa_3" +name = "smoldataenvs-train/0097_585_97585155_qa_3" description = "Which department has the highest number of employees in the dataset, and how many employees belong to it?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Research & Development, 961" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0097_618_97618724_qa_3/task.toml b/tasks/0097_618_97618724_qa_3/task.toml index 9d248b0dfe9fe42a9c86c1c633afcd919d7355d4..991a50c68b09dcaa4f2b1f8f895687464f9d9740 100644 --- a/tasks/0097_618_97618724_qa_3/task.toml +++ b/tasks/0097_618_97618724_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0097_618_97618724_qa_3" +name = "smoldataenvs-train/0097_618_97618724_qa_3" description = "What is the percentage of daily recordings classified as \"Night\" time in the dataset according to the defined timing classification?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "25" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0097_698_97698987_qa_4/task.toml b/tasks/0097_698_97698987_qa_4/task.toml index 7c310c25ceebd0528c35fa010b21183e763e4dd2..e7cbbeb9f14c0e0be16c4d414eff28527bc6f8a4 100644 --- a/tasks/0097_698_97698987_qa_4/task.toml +++ b/tasks/0097_698_97698987_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0097_698_97698987_qa_4" +name = "smoldataenvs-train/0097_698_97698987_qa_4" description = "Which feature exhibits the highest standard deviation, indicating the greatest variability in measurements across the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0097_708_97708214_qa_2/task.toml b/tasks/0097_708_97708214_qa_2/task.toml index 33a58c917c77569e2ccfbabcae95e5dc5dab0e48..ff87d4a4fc25afae8cdd954cd0d78c919a30185e 100644 --- a/tasks/0097_708_97708214_qa_2/task.toml +++ b/tasks/0097_708_97708214_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0097_708_97708214_qa_2" +name = "smoldataenvs-train/0097_708_97708214_qa_2" description = "How many numerical features are present in the test dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0097_809_97809761_qa_2/task.toml b/tasks/0097_809_97809761_qa_2/task.toml index ec7e5f5281ccefaf8b9b5438e9d352e895a38da9..ad5957a8d4f73a8689edd96d852c1358d0e1909c 100644 --- a/tasks/0097_809_97809761_qa_2/task.toml +++ b/tasks/0097_809_97809761_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0097_809_97809761_qa_2" +name = "smoldataenvs-train/0097_809_97809761_qa_2" description = "Which variable demonstrates the strongest negative correlation with life expectancy in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Adult Mortality" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0097_844_97844965_qa_2/task.toml b/tasks/0097_844_97844965_qa_2/task.toml index f3d749ce63b8a4adbfb0c672864ccace0a718a97..bd2efe129450e5c1dd7cf1f98aa2741103fb64fe 100644 --- a/tasks/0097_844_97844965_qa_2/task.toml +++ b/tasks/0097_844_97844965_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0097_844_97844965_qa_2" +name = "smoldataenvs-train/0097_844_97844965_qa_2" description = "Which cereal in Tier 3 has the highest fat percentage, and what is that percentage value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "100% Natural Bran with 17.64%" reward_mode_initial = "flexible" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0097_932_97932913_qa_1/task.toml b/tasks/0097_932_97932913_qa_1/task.toml index 9f8cd72daef256169aaeb1d755805835708b0929..4829bf96f41a72cc8f7e218b8d3ef6d08511d26f 100644 --- a/tasks/0097_932_97932913_qa_1/task.toml +++ b/tasks/0097_932_97932913_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0097_932_97932913_qa_1" +name = "smoldataenvs-train/0097_932_97932913_qa_1" description = "Which two features in the dataset have the highest positive correlation with each other, and what is the value of that correlation coefficient?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "sqft_living, sqft_above, 0.88" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0097_932_97932913_qa_4/task.toml b/tasks/0097_932_97932913_qa_4/task.toml index ed441b030fad04afa13ea01cbe9c477549df66bb..6d6c0e7b0c5f0830190784f21ab3f701b9fcb8dc 100644 --- a/tasks/0097_932_97932913_qa_4/task.toml +++ b/tasks/0097_932_97932913_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0097_932_97932913_qa_4" +name = "smoldataenvs-train/0097_932_97932913_qa_4" description = "Which feature has the highest correlation with the house grade, and what is the value of that correlation coefficient?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "sqft_living, 0.76" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0098_017_98017526_qa_4/task.toml b/tasks/0098_017_98017526_qa_4/task.toml index 000da1c1bd256c99ade5dc60aa58ce57cdf84817..cda3b3d8be92857886a840f6b13f0d2545fe6304 100644 --- a/tasks/0098_017_98017526_qa_4/task.toml +++ b/tasks/0098_017_98017526_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0098_017_98017526_qa_4" +name = "smoldataenvs-train/0098_017_98017526_qa_4" description = "Which video game has the highest recorded global sales in the dataset, and what is its sales value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii Sports, 82.74" reward_mode_initial = "list" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0098_017_98017526_qa_5/task.toml b/tasks/0098_017_98017526_qa_5/task.toml index d3d1d57cbfb94bd9bdda85c3253927f8498d255e..e63ba71fb82413f54b0db0c8fd55e1548c37696c 100644 --- a/tasks/0098_017_98017526_qa_5/task.toml +++ b/tasks/0098_017_98017526_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0098_017_98017526_qa_5" +name = "smoldataenvs-train/0098_017_98017526_qa_5" description = "How many entries in the dataset have missing values in the Year column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "271" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0098_340_98340653_qa_5/task.toml b/tasks/0098_340_98340653_qa_5/task.toml index 6f8105afc46a9f3f938c05bee956562e3cefc320..21e7bc16555c0dbcefc5aa16bfa72bb399d21446 100644 --- a/tasks/0098_340_98340653_qa_5/task.toml +++ b/tasks/0098_340_98340653_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0098_340_98340653_qa_5" +name = "smoldataenvs-train/0098_340_98340653_qa_5" description = "What is the mean absolute error (MAE) of the logistic regression model's predictions on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.16" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0098_341_98341485_qa_2/task.toml b/tasks/0098_341_98341485_qa_2/task.toml index 78ac1d1073d0bfa3a7906956e2a663e1b1118d14..f60dba4563a8f1ddb47158ce9f0a3a87f626151a 100644 --- a/tasks/0098_341_98341485_qa_2/task.toml +++ b/tasks/0098_341_98341485_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0098_341_98341485_qa_2" +name = "smoldataenvs-train/0098_341_98341485_qa_2" description = "Which SVM kernel achieved the highest precision for the 'Iris-virginica' class in the initial model evaluations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Linear" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0098_375_98375855_qa_2/task.toml b/tasks/0098_375_98375855_qa_2/task.toml index 216e3a9e2f53a4a7441576694d956176b43c26bf..de1e1e50b2c447c109e78f4e9302f3756055ad72 100644 --- a/tasks/0098_375_98375855_qa_2/task.toml +++ b/tasks/0098_375_98375855_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0098_375_98375855_qa_2" +name = "smoldataenvs-train/0098_375_98375855_qa_2" description = "What is the threshold value for PetalLengthCm in the first split of the Decision Tree model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.45" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0098_463_98463251_qa_3/task.toml b/tasks/0098_463_98463251_qa_3/task.toml index 690d555e10d985d5624c873065106041431dc034..c95f7f474186ff4250c74629dece0d8e9e13b360 100644 --- a/tasks/0098_463_98463251_qa_3/task.toml +++ b/tasks/0098_463_98463251_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0098_463_98463251_qa_3" +name = "smoldataenvs-train/0098_463_98463251_qa_3" description = "What percentage of the total variance in the dataset is explained by the first principal component?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "27.46" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0098_464_98464481_qa_1/task.toml b/tasks/0098_464_98464481_qa_1/task.toml index 39855ceb426d52b8348be3b6b4675bf002955704..c83a42df8b60e267e5e849b81456a4fe30123018 100644 --- a/tasks/0098_464_98464481_qa_1/task.toml +++ b/tasks/0098_464_98464481_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0098_464_98464481_qa_1" +name = "smoldataenvs-train/0098_464_98464481_qa_1" description = "Does the standard scaling process effectively mitigate the impact of outliers on the 'EstimatedSalary' feature in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0098_595_98595052_qa_2/task.toml b/tasks/0098_595_98595052_qa_2/task.toml index 701fabf10e7a1aee15d80361ed272779fb8e4b74..bf95976f274e8079b77ddef236fb08ca6e929db4 100644 --- a/tasks/0098_595_98595052_qa_2/task.toml +++ b/tasks/0098_595_98595052_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0098_595_98595052_qa_2" +name = "smoldataenvs-train/0098_595_98595052_qa_2" description = "Which categorical feature shows the strongest visual correlation with RAM capacity based on scatter plot analysis in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "price_range" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0098_605_98605018_qa_3/task.toml b/tasks/0098_605_98605018_qa_3/task.toml index 1d009c7b5a46b413410fc5064ec32248659f5c53..e138bb0914e25d729c76681ec9ccfdad1286fa19 100644 --- a/tasks/0098_605_98605018_qa_3/task.toml +++ b/tasks/0098_605_98605018_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0098_605_98605018_qa_3" +name = "smoldataenvs-train/0098_605_98605018_qa_3" description = "Which individual statistic has the highest positive correlation with the Legendary status of a Pokémon?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Sp. Atk" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0098_620_98620020_qa_2/task.toml b/tasks/0098_620_98620020_qa_2/task.toml index de6b08e34ac6da5762dcb7686c870b159e125a4f..45f18c3f31e04b659ae4de99ef4a00a1e3685746 100644 --- a/tasks/0098_620_98620020_qa_2/task.toml +++ b/tasks/0098_620_98620020_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0098_620_98620020_qa_2" +name = "smoldataenvs-train/0098_620_98620020_qa_2" description = "How many columns in the original dataset contained missing values before the imputation process was applied?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0098_681_98681805_qa_3/task.toml b/tasks/0098_681_98681805_qa_3/task.toml index 865b0103403cdfa0503fad2366735aa662426e8b..c332e51b6142c08044869b9b8b6181db9fdba91f 100644 --- a/tasks/0098_681_98681805_qa_3/task.toml +++ b/tasks/0098_681_98681805_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0098_681_98681805_qa_3" +name = "smoldataenvs-train/0098_681_98681805_qa_3" description = "What is the test accuracy achieved by the decision tree model after hyperparameter tuning with grid search?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.958" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0098_690_98690564_qa_1/task.toml b/tasks/0098_690_98690564_qa_1/task.toml index 843e11dd38b1cdbfe8fbab1fd516a9876f640808..fdd32eb0c6f25412fcc62c7d6feb02d44cf3aade 100644 --- a/tasks/0098_690_98690564_qa_1/task.toml +++ b/tasks/0098_690_98690564_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0098_690_98690564_qa_1" +name = "smoldataenvs-train/0098_690_98690564_qa_1" description = "How many outliers are present in the 'smoker' column based on the interquartile range (IQR) method?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "274" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0098_690_98690564_qa_2/task.toml b/tasks/0098_690_98690564_qa_2/task.toml index b7d933dad2eb04b238ceedaac395c10b43ca93de..60f2d2510dab46f1c4c4447e45229f07e266cee0 100644 --- a/tasks/0098_690_98690564_qa_2/task.toml +++ b/tasks/0098_690_98690564_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0098_690_98690564_qa_2" +name = "smoldataenvs-train/0098_690_98690564_qa_2" description = "What is the interquartile range (IQR) for the 'age' column in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "24.0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0098_690_98690564_qa_3/task.toml b/tasks/0098_690_98690564_qa_3/task.toml index 913dccabd8725f2811304298049be433f588d102..7fbebd1422b84295103e9b674d7c8f3cab7a66eb 100644 --- a/tasks/0098_690_98690564_qa_3/task.toml +++ b/tasks/0098_690_98690564_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0098_690_98690564_qa_3" +name = "smoldataenvs-train/0098_690_98690564_qa_3" description = "What percentage of individuals in the dataset are smokers (coded as 'yes') after data preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20.48" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0098_690_98690564_qa_5/task.toml b/tasks/0098_690_98690564_qa_5/task.toml index 1a99b22e6e28e0daa7e8997253974d7e63dc3143..9570617b631d56085e0beafa89e8c267947cb96b 100644 --- a/tasks/0098_690_98690564_qa_5/task.toml +++ b/tasks/0098_690_98690564_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0098_690_98690564_qa_5" +name = "smoldataenvs-train/0098_690_98690564_qa_5" description = "How many unique body mass index (BMI) values are present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "548" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0098_760_98760521_qa_3/task.toml b/tasks/0098_760_98760521_qa_3/task.toml index 6d21aa9ae7dbd4581e72c1e95947a72cdf901974..dda8ab613feac3f6c43e759c3931bdd55f1ad4fd 100644 --- a/tasks/0098_760_98760521_qa_3/task.toml +++ b/tasks/0098_760_98760521_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0098_760_98760521_qa_3" +name = "smoldataenvs-train/0098_760_98760521_qa_3" description = "Which year had the highest average revenue per movie?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2009" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0098_812_98812520_qa_1/task.toml b/tasks/0098_812_98812520_qa_1/task.toml index 9943ef580265cdddd31930a20a3a6c09db2ed596..b115e22ddf7beb833b4e88c90de5086a85c1d0d4 100644 --- a/tasks/0098_812_98812520_qa_1/task.toml +++ b/tasks/0098_812_98812520_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0098_812_98812520_qa_1" +name = "smoldataenvs-train/0098_812_98812520_qa_1" description = "Which feature has the highest positive correlation with the price_range in the mobile price dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ram" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0098_812_98812520_qa_4/task.toml b/tasks/0098_812_98812520_qa_4/task.toml index d981d0ba37105c3c73ae66e3235cc5e9c375ee8e..a0aa7dc03b2bad690d88dba623c32f3d98b21109 100644 --- a/tasks/0098_812_98812520_qa_4/task.toml +++ b/tasks/0098_812_98812520_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0098_812_98812520_qa_4" +name = "smoldataenvs-train/0098_812_98812520_qa_4" description = "What is the highest precision score for any price range class in the LDA-SVM model's classification report?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.96" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0098_819_98819760_qa_2/task.toml b/tasks/0098_819_98819760_qa_2/task.toml index 2547da4f903859acc858c8b6093402b022e59a16..ecd8549af540c0a51bf43d72587fb5a7dcf6a958 100644 --- a/tasks/0098_819_98819760_qa_2/task.toml +++ b/tasks/0098_819_98819760_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0098_819_98819760_qa_2" +name = "smoldataenvs-train/0098_819_98819760_qa_2" description = "Which wine quality rating had the highest count in the original dataset before balancing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0098_819_98819760_qa_5/task.toml b/tasks/0098_819_98819760_qa_5/task.toml index bc336f0ff9ab03494ae97eaa7f2dfe7d5159c10d..4f2c979dd241b2ca21b9bb48449f5218dfb24b1a 100644 --- a/tasks/0098_819_98819760_qa_5/task.toml +++ b/tasks/0098_819_98819760_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0098_819_98819760_qa_5" +name = "smoldataenvs-train/0098_819_98819760_qa_5" description = "How many duplicate rows were present in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "240" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0098_892_98892916_qa_3/task.toml b/tasks/0098_892_98892916_qa_3/task.toml index 27b8d81dde75a17dc70f963dec48f36bb5f1990a..3026691ff0cd8438eb84f5f5f1c5f7f5778694bf 100644 --- a/tasks/0098_892_98892916_qa_3/task.toml +++ b/tasks/0098_892_98892916_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0098_892_98892916_qa_3" +name = "smoldataenvs-train/0098_892_98892916_qa_3" description = "What is the range of the petal_length and petal_width features after applying MinMax scaling to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0 to 1" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0098_910_98910010_qa_4/task.toml b/tasks/0098_910_98910010_qa_4/task.toml index ac9562125ca55d8c91c9ce0a622e44bf04ac162d..aa9cb0c4b4e1764e11df6b09ad9456e693d9a441 100644 --- a/tasks/0098_910_98910010_qa_4/task.toml +++ b/tasks/0098_910_98910010_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0098_910_98910010_qa_4" +name = "smoldataenvs-train/0098_910_98910010_qa_4" description = "What is the mean value of the feature with the highest negative correlation to price_range in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.503" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0098_941_98941500_qa_5/task.toml b/tasks/0098_941_98941500_qa_5/task.toml index 32400591aa336904dbbf67e0697cd29b802dbcbb..c8c327a9a59e9455947804df6019a6e5a2732698 100644 --- a/tasks/0098_941_98941500_qa_5/task.toml +++ b/tasks/0098_941_98941500_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0098_941_98941500_qa_5" +name = "smoldataenvs-train/0098_941_98941500_qa_5" description = "What percentage of patients in the dataset have a scholarship (Scholarship = 1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9.83" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0098_969_98969166_qa_1/task.toml b/tasks/0098_969_98969166_qa_1/task.toml index 48ffc2e56db3aec89ae02c01f7560b7ac899f764..a81e71f52660320ff91f142a1785f4c45f45d5f6 100644 --- a/tasks/0098_969_98969166_qa_1/task.toml +++ b/tasks/0098_969_98969166_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0098_969_98969166_qa_1" +name = "smoldataenvs-train/0098_969_98969166_qa_1" description = "What percentage of customers in the dataset churned?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "14.5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0099_029_99029048_qa_4/task.toml b/tasks/0099_029_99029048_qa_4/task.toml index d46b616e775e8aea65a2ec349eb9f07da155f2ad..e9a3c00283e796eb6fea3abe6e305207e8434eda 100644 --- a/tasks/0099_029_99029048_qa_4/task.toml +++ b/tasks/0099_029_99029048_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0099_029_99029048_qa_4" +name = "smoldataenvs-train/0099_029_99029048_qa_4" description = "What is the range of the normalized features after applying MinMaxScaler during the preprocessing step?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0 to 1" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0099_089_99089980_qa_1/task.toml b/tasks/0099_089_99089980_qa_1/task.toml index 1d7ce0dea30a72e745441887e5ea61b14679330b..625b3cb524508f03fe594ad0374afcd2253df000 100644 --- a/tasks/0099_089_99089980_qa_1/task.toml +++ b/tasks/0099_089_99089980_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0099_089_99089980_qa_1" +name = "smoldataenvs-train/0099_089_99089980_qa_1" description = "Which Pokémon type has the highest correlation coefficient between weight and base HP in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Dark" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0099_222_99222402_qa_2/task.toml b/tasks/0099_222_99222402_qa_2/task.toml index bfff9936a0ef9c218ec6eec3d00620bad149ecbc..e9beaff3d7e607e1a50fd5239936fadd0fd6dd1a 100644 --- a/tasks/0099_222_99222402_qa_2/task.toml +++ b/tasks/0099_222_99222402_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0099_222_99222402_qa_2" +name = "smoldataenvs-train/0099_222_99222402_qa_2" description = "Which species has the highest median sepal length according to the boxplot visualization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-virginica" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0099_222_99222402_qa_3/task.toml b/tasks/0099_222_99222402_qa_3/task.toml index 66d7a399999e0c8c4dcbbceb193e2f9542a31b34..042fbac59b0539c653d3b252392a830849ec6548 100644 --- a/tasks/0099_222_99222402_qa_3/task.toml +++ b/tasks/0099_222_99222402_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0099_222_99222402_qa_3" +name = "smoldataenvs-train/0099_222_99222402_qa_3" description = "What is the interquartile range (IQR) for petal width measurements in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0099_268_99268253_qa_1/task.toml b/tasks/0099_268_99268253_qa_1/task.toml index c4781df3a646b8fac0120a8ae00f82b8152a0088..ee254a48374a2dcfbf6bb6cca43f6513e5cfc97d 100644 --- a/tasks/0099_268_99268253_qa_1/task.toml +++ b/tasks/0099_268_99268253_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0099_268_99268253_qa_1" +name = "smoldataenvs-train/0099_268_99268253_qa_1" description = "What is the highest monetary value spent by a customer in the Best Customers segment (RFMScore 444)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26632.62" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0099_302_99302626_qa_3/task.toml b/tasks/0099_302_99302626_qa_3/task.toml index dc354608ebd815b58c091daa38049374765f5ee5..c59a21347b3d13809d54719fcb6ca7757d17c9f3 100644 --- a/tasks/0099_302_99302626_qa_3/task.toml +++ b/tasks/0099_302_99302626_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0099_302_99302626_qa_3" +name = "smoldataenvs-train/0099_302_99302626_qa_3" description = "What percentage of the variance in salary is statistically explained by years of experience in the model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "96.3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0099_308_99308206_qa_3/task.toml b/tasks/0099_308_99308206_qa_3/task.toml index afe5313bd73857d5e1c0fbdd1d1f7cd774e336c5..de765f94ea1cfaa7f5bd6bed5372e7addc270c88 100644 --- a/tasks/0099_308_99308206_qa_3/task.toml +++ b/tasks/0099_308_99308206_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0099_308_99308206_qa_3" +name = "smoldataenvs-train/0099_308_99308206_qa_3" description = "Which categorical feature shows the strongest correlation with insurance charges in the linear regression model without polynomial features?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Smoker" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0099_308_99308206_qa_5/task.toml b/tasks/0099_308_99308206_qa_5/task.toml index 87e3c19493f3bfc44606f0025a1f07bc09c7ccd8..13f8c999cade8bab9591151384e9c039ae1da8c3 100644 --- a/tasks/0099_308_99308206_qa_5/task.toml +++ b/tasks/0099_308_99308206_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0099_308_99308206_qa_5" +name = "smoldataenvs-train/0099_308_99308206_qa_5" description = "Which feature has the largest absolute coefficient value in the Linear regression model without polynomial features?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Smoker status" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0099_323_99323168_qa_2/task.toml b/tasks/0099_323_99323168_qa_2/task.toml index d70e373ac5229f7a348e269a4a36b0f532f29181..3d04e7b1f6823d6689d7cc64d137894cb426436a 100644 --- a/tasks/0099_323_99323168_qa_2/task.toml +++ b/tasks/0099_323_99323168_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0099_323_99323168_qa_2" +name = "smoldataenvs-train/0099_323_99323168_qa_2" description = "What is the total global sales of Microsoft's top ten games according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "97.08" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0099_323_99323168_qa_5/task.toml b/tasks/0099_323_99323168_qa_5/task.toml index b2478ac311b5ac30e7e5af53c981a329fd85fd4a..303e97166f984dfdf3b7a4cb1b1744770701e347 100644 --- a/tasks/0099_323_99323168_qa_5/task.toml +++ b/tasks/0099_323_99323168_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0099_323_99323168_qa_5" +name = "smoldataenvs-train/0099_323_99323168_qa_5" description = "What is the difference between the highest and lowest global sales values in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "82.73" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0099_334_99334311_qa_4/task.toml b/tasks/0099_334_99334311_qa_4/task.toml index 9e4ddd64cb99bac9e46654cdd5a58770c35911b1..a1b2cf3ce9283b84f613ac3706e6bf46eceff6e1 100644 --- a/tasks/0099_334_99334311_qa_4/task.toml +++ b/tasks/0099_334_99334311_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0099_334_99334311_qa_4" +name = "smoldataenvs-train/0099_334_99334311_qa_4" description = "What is the median value of North American sales across all games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.08" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0099_456_99456570_qa_1/task.toml b/tasks/0099_456_99456570_qa_1/task.toml index a1be17bf6728f7c1b18417254e3540ae9a57422c..469f1117b98094bbbac9786a12cf8e5da2604347 100644 --- a/tasks/0099_456_99456570_qa_1/task.toml +++ b/tasks/0099_456_99456570_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0099_456_99456570_qa_1" +name = "smoldataenvs-train/0099_456_99456570_qa_1" description = "Which video game genre has the highest number of entries in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0099_456_99456570_qa_4/task.toml b/tasks/0099_456_99456570_qa_4/task.toml index 925751af0ff4e8d542adc9fa94d20d0eb9947ecd..491c9b7fcaeb86e83e1dfe7ce10d1c00a630e2d9 100644 --- a/tasks/0099_456_99456570_qa_4/task.toml +++ b/tasks/0099_456_99456570_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0099_456_99456570_qa_4" +name = "smoldataenvs-train/0099_456_99456570_qa_4" description = "What percentage of games in the dataset have global sales below 35 million units?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "99.99" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0099_456_99456570_qa_5/task.toml b/tasks/0099_456_99456570_qa_5/task.toml index f0278d4103089904140594380e66c142ec9153f5..04d994bf0cfea1f74d66aeb9af9562f5f7283fb7 100644 --- a/tasks/0099_456_99456570_qa_5/task.toml +++ b/tasks/0099_456_99456570_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0099_456_99456570_qa_5" +name = "smoldataenvs-train/0099_456_99456570_qa_5" description = "Which game title has the highest global sales in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii Sports" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0099_460_99460028_qa_2/task.toml b/tasks/0099_460_99460028_qa_2/task.toml index 6f3fe00328e978114bd84d9243d501846476a4b7..f85b8a3d71e2c56bb75113e85b0c5b62796a3b9c 100644 --- a/tasks/0099_460_99460028_qa_2/task.toml +++ b/tasks/0099_460_99460028_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0099_460_99460028_qa_2" +name = "smoldataenvs-train/0099_460_99460028_qa_2" description = "What is the median global sales value across all games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.17" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0099_464_99464621_qa_1/task.toml b/tasks/0099_464_99464621_qa_1/task.toml index 0547e51a66cc86fc91b8906a334ad2ff92519259..2531ba214cf24a1bee074fb23dc95a882c7b50f4 100644 --- a/tasks/0099_464_99464621_qa_1/task.toml +++ b/tasks/0099_464_99464621_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0099_464_99464621_qa_1" +name = "smoldataenvs-train/0099_464_99464621_qa_1" description = "What is the total global sales value across all video games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8920.44" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0099_538_99538829_qa_2/task.toml b/tasks/0099_538_99538829_qa_2/task.toml index 6076a2d1cfa962d4fecd07420dad37ca3f4f8222..aae1f5922bb00fdb2abd966fb47d8ceed0c88faa 100644 --- a/tasks/0099_538_99538829_qa_2/task.toml +++ b/tasks/0099_538_99538829_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0099_538_99538829_qa_2" +name = "smoldataenvs-train/0099_538_99538829_qa_2" description = "What is the median value of the global sales in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.17" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0099_538_99538891_qa_2/task.toml b/tasks/0099_538_99538891_qa_2/task.toml index 493276cb79f5b0f4a53e5212108c75d962e4b29c..a6dc8f6fd89c4c5954e7c425b4dbc0c87f35746c 100644 --- a/tasks/0099_538_99538891_qa_2/task.toml +++ b/tasks/0099_538_99538891_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0099_538_99538891_qa_2" +name = "smoldataenvs-train/0099_538_99538891_qa_2" description = "Which xyz_campaign_id achieved the highest Total Conversions per expenditure ratio based on the aggregated campaign performance analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "916" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0099_540_99540799_qa_1/task.toml b/tasks/0099_540_99540799_qa_1/task.toml index fd9529875845d12a33efc2d8cfde1c7e45342496..36c8bc7a3c5990d33eb753a897b531772bbdee9b 100644 --- a/tasks/0099_540_99540799_qa_1/task.toml +++ b/tasks/0099_540_99540799_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0099_540_99540799_qa_1" +name = "smoldataenvs-train/0099_540_99540799_qa_1" description = "What is the total number of clients in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11162" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0099_540_99540799_qa_4/task.toml b/tasks/0099_540_99540799_qa_4/task.toml index 4deb8010bcb6211155ea4da2ab68ed607126bb08..60ab3b3f7bfd95b022fc081c1e5008062d76a9df 100644 --- a/tasks/0099_540_99540799_qa_4/task.toml +++ b/tasks/0099_540_99540799_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0099_540_99540799_qa_4" +name = "smoldataenvs-train/0099_540_99540799_qa_4" description = "What is the difference in client count between those who did not subscribe to a term deposit (deposit=no) and those who did (deposit=yes)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "584" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0099_541_99541129_qa_1/task.toml b/tasks/0099_541_99541129_qa_1/task.toml index f9aa85723066c8cfda29a1c19ceec01975a42a73..6c0f3ef909e7646575f0a07b619c2afd1136395f 100644 --- a/tasks/0099_541_99541129_qa_1/task.toml +++ b/tasks/0099_541_99541129_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0099_541_99541129_qa_1" +name = "smoldataenvs-train/0099_541_99541129_qa_1" description = "How many standard deviations above the mean North American sales is the top-selling game's performance in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50.48" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0099_574_99574169_qa_2/task.toml b/tasks/0099_574_99574169_qa_2/task.toml index b2ac222eded4fa2b120218a70d196d1c69a8aa69..91c85bed3442dea3c2c26149c59a9867ca3fd414 100644 --- a/tasks/0099_574_99574169_qa_2/task.toml +++ b/tasks/0099_574_99574169_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0099_574_99574169_qa_2" +name = "smoldataenvs-train/0099_574_99574169_qa_2" description = "What is the highest height in feet among the converted data values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.04" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0099_587_99587330_qa_5/task.toml b/tasks/0099_587_99587330_qa_5/task.toml index 5daa226716f92bca9e5011c61286ea36b479908e..515a6fa3e423cc15a6d2833917491502a8e398b3 100644 --- a/tasks/0099_587_99587330_qa_5/task.toml +++ b/tasks/0099_587_99587330_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0099_587_99587330_qa_5" +name = "smoldataenvs-train/0099_587_99587330_qa_5" description = "What is the median North American sales value for all video games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.08" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0099_589_99589463_qa_1/task.toml b/tasks/0099_589_99589463_qa_1/task.toml index 1831cb5e4ae409907f7f023af09e5b37bc5f4289..5d10b7f6093510b2c0a8887060e9b47284394367 100644 --- a/tasks/0099_589_99589463_qa_1/task.toml +++ b/tasks/0099_589_99589463_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0099_589_99589463_qa_1" +name = "smoldataenvs-train/0099_589_99589463_qa_1" description = "Which customer generated the highest total revenue in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "14646" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0099_589_99589463_qa_4/task.toml b/tasks/0099_589_99589463_qa_4/task.toml index 865f74bd1aa73ff2ebf063b2eb6b54909422ea36..224b5e59e0de8bfc026da5a057c27cc79796656d 100644 --- a/tasks/0099_589_99589463_qa_4/task.toml +++ b/tasks/0099_589_99589463_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0099_589_99589463_qa_4" +name = "smoldataenvs-train/0099_589_99589463_qa_4" description = "What was the total revenue generated from StockCode 10002 on 2010-12-01?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "51.00" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0099_607_99607727_qa_4/task.toml b/tasks/0099_607_99607727_qa_4/task.toml index 46c95519769c9d2e6714a914d1933c68c22b42a3..06e0ffa3d0c009d3884eb52737e46083b6a415b4 100644 --- a/tasks/0099_607_99607727_qa_4/task.toml +++ b/tasks/0099_607_99607727_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0099_607_99607727_qa_4" +name = "smoldataenvs-train/0099_607_99607727_qa_4" description = "How many standard deviations above the mean are the North American sales of the top-selling game compared to all games?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50.48" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0099_607_99607727_qa_5/task.toml b/tasks/0099_607_99607727_qa_5/task.toml index e38cb5ba45b14ec88e67773e08a05d45f1d4b6d0..8ad4efcc9aa4c07896c4acf3135c407f69da44c6 100644 --- a/tasks/0099_607_99607727_qa_5/task.toml +++ b/tasks/0099_607_99607727_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0099_607_99607727_qa_5" +name = "smoldataenvs-train/0099_607_99607727_qa_5" description = "What is the median North American sales value across all games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.08" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0099_630_99630146_qa_5/task.toml b/tasks/0099_630_99630146_qa_5/task.toml index 846c6aec67526aa89801a34069b268d0812ee903..17eb30ed950a11d4626746ef4dc8a8c1475a7bde 100644 --- a/tasks/0099_630_99630146_qa_5/task.toml +++ b/tasks/0099_630_99630146_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0099_630_99630146_qa_5" +name = "smoldataenvs-train/0099_630_99630146_qa_5" description = "What is the size of the test dataset used for model evaluation in this analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "500" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0099_650_99650009_qa_4/task.toml b/tasks/0099_650_99650009_qa_4/task.toml index b7271c2916c01540ea2a9f8adb264f4479254f9f..fdf5a67a4272a507fd207a04d35d7da97db68a18 100644 --- a/tasks/0099_650_99650009_qa_4/task.toml +++ b/tasks/0099_650_99650009_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0099_650_99650009_qa_4" +name = "smoldataenvs-train/0099_650_99650009_qa_4" description = "What is the interquartile range (IQR) for residual sugar content in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.7" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0099_701_99701449_qa_1/task.toml b/tasks/0099_701_99701449_qa_1/task.toml index 63af156f105d9e8001f75cb071d04d3eb7cba692..3ad974700467eae9325da55434ad795317460af6 100644 --- a/tasks/0099_701_99701449_qa_1/task.toml +++ b/tasks/0099_701_99701449_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0099_701_99701449_qa_1" +name = "smoldataenvs-train/0099_701_99701449_qa_1" description = "Which continent has the highest number of notable figures in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Europe" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0099_701_99701449_qa_2/task.toml b/tasks/0099_701_99701449_qa_2/task.toml index 2abea681b172d5cd4137c2db021ec3d25d383950..b367149c9eb3bd377bef7ce0cef6c12df5c06687 100644 --- a/tasks/0099_701_99701449_qa_2/task.toml +++ b/tasks/0099_701_99701449_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0099_701_99701449_qa_2" +name = "smoldataenvs-train/0099_701_99701449_qa_2" description = "Which continent has the lowest number of notable figures in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Oceania" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0099_701_99701449_qa_3/task.toml b/tasks/0099_701_99701449_qa_3/task.toml index 954b08ed371f45beb130ad846d520707a0538d19..9d93535720beb456c304131d40785ea603e7c472 100644 --- a/tasks/0099_701_99701449_qa_3/task.toml +++ b/tasks/0099_701_99701449_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0099_701_99701449_qa_3" +name = "smoldataenvs-train/0099_701_99701449_qa_3" description = "What is the third most populous continent in terms of notable figures in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Asia" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0099_701_99701449_qa_5/task.toml b/tasks/0099_701_99701449_qa_5/task.toml index 7c90a650a9b9de93a9c7d38f0ce7915fe2c3dc70..4b4de7520253cb063f1d9ee2c09e24e3f10a05cd 100644 --- a/tasks/0099_701_99701449_qa_5/task.toml +++ b/tasks/0099_701_99701449_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0099_701_99701449_qa_5" +name = "smoldataenvs-train/0099_701_99701449_qa_5" description = "What is the total number of notable figures from Africa and South America combined in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "785" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0099_701_99701519_qa_2/task.toml b/tasks/0099_701_99701519_qa_2/task.toml index d60bdbff1c7260d88a1f5d7c0478f81463e5e41b..0eb9b3f1c14b21af7ee9193d627bc169431ae223 100644 --- a/tasks/0099_701_99701519_qa_2/task.toml +++ b/tasks/0099_701_99701519_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0099_701_99701519_qa_2" +name = "smoldataenvs-train/0099_701_99701519_qa_2" description = "Which demographic group (male or female) has a higher proportion of smokers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "male" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0099_705_99705765_qa_5/task.toml b/tasks/0099_705_99705765_qa_5/task.toml index 1c16f4c46ba20e9268f0c64f0876bb7f7e0c5343..19153a0a19fbcf66aeb130cfe6b03ffca849caf2 100644 --- a/tasks/0099_705_99705765_qa_5/task.toml +++ b/tasks/0099_705_99705765_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0099_705_99705765_qa_5" +name = "smoldataenvs-train/0099_705_99705765_qa_5" description = "How many missing values were present in the total_bedrooms column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "207" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0099_730_99730639_qa_2/task.toml b/tasks/0099_730_99730639_qa_2/task.toml index 3da35f01b0911e9529a89d5d2ee9a5a424c7ea63..f96a711e5a6aeafc27d8e31fe548b005e49c3cb3 100644 --- a/tasks/0099_730_99730639_qa_2/task.toml +++ b/tasks/0099_730_99730639_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0099_730_99730639_qa_2" +name = "smoldataenvs-train/0099_730_99730639_qa_2" description = "What is the sum of the values in the 'Sex' column after applying label encoding (where male=1 and female=0)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "577" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0099_829_99829565_qa_3/task.toml b/tasks/0099_829_99829565_qa_3/task.toml index b228996734aeeca31301301c097ae2ecd070f835..4b5becd5f3555a1a256731fea1a7f42e65025094 100644 --- a/tasks/0099_829_99829565_qa_3/task.toml +++ b/tasks/0099_829_99829565_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0099_829_99829565_qa_3" +name = "smoldataenvs-train/0099_829_99829565_qa_3" description = "Which neighborhood had the highest number of no-show appointments recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "JARDIM CAMBURI" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0100_006_100006799_qa_2/task.toml b/tasks/0100_006_100006799_qa_2/task.toml index e6a2544692b77d0466e4fb394049d07865ef134e..b7f3bdcdb15c15fd31ab798bc9eb18b74d4be585 100644 --- a/tasks/0100_006_100006799_qa_2/task.toml +++ b/tasks/0100_006_100006799_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0100_006_100006799_qa_2" +name = "smoldataenvs-train/0100_006_100006799_qa_2" description = "What is the mean insurance charge value before applying the log transformation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13270.42" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0100_006_100006799_qa_3/task.toml b/tasks/0100_006_100006799_qa_3/task.toml index fbfc762f80059e555f2b58a3b1271726a5989f6e..3c50d7d94860c109085a464d6d9a463d1993716c 100644 --- a/tasks/0100_006_100006799_qa_3/task.toml +++ b/tasks/0100_006_100006799_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0100_006_100006799_qa_3" +name = "smoldataenvs-train/0100_006_100006799_qa_3" description = "What is the median insurance charge value before applying the log transformation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9382.03" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0100_115_100115013_qa_3/task.toml b/tasks/0100_115_100115013_qa_3/task.toml index 1a03ce00a0d769348b06c6e284cbe04d012b3e6f..5629dd42f5071803c5eaebfed61bba840cefd75b 100644 --- a/tasks/0100_115_100115013_qa_3/task.toml +++ b/tasks/0100_115_100115013_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0100_115_100115013_qa_3" +name = "smoldataenvs-train/0100_115_100115013_qa_3" description = "What percentage of patients have smoked for less than 10 years according to the cumulative distribution function (ECDF) analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "95%" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0100_171_100171054_qa_3/task.toml b/tasks/0100_171_100171054_qa_3/task.toml index 1be2833dc74ffdd59fb98637c1086e1845d7e88b..6dd004bc6e2203a6ca618080e52a451598bbe4bc 100644 --- a/tasks/0100_171_100171054_qa_3/task.toml +++ b/tasks/0100_171_100171054_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0100_171_100171054_qa_3" +name = "smoldataenvs-train/0100_171_100171054_qa_3" description = "What is the adjusted R-squared value when modeling Profit using both R&D Spend and Marketing Spend as predictors?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.948" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0100_171_100171054_qa_4/task.toml b/tasks/0100_171_100171054_qa_4/task.toml index a5610b017d3dd6cdffed63606fbd195f2e2d9b8a..644bfa63d4f07a3b12324b06d7e1f1a44db0b5a8 100644 --- a/tasks/0100_171_100171054_qa_4/task.toml +++ b/tasks/0100_171_100171054_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0100_171_100171054_qa_4" +name = "smoldataenvs-train/0100_171_100171054_qa_4" description = "What is the p-value for the Administration variable in the multiple regression model including all three predictors (R&D Spend, Administration, Marketing Spend)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.602" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0100_203_100203819_qa_1/task.toml b/tasks/0100_203_100203819_qa_1/task.toml index 229a6d6edbca5c885c6bd1d1009bab7a59332e32..6c49fa3f3fcdcd3a1970c598074923544e50804a 100644 --- a/tasks/0100_203_100203819_qa_1/task.toml +++ b/tasks/0100_203_100203819_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0100_203_100203819_qa_1" +name = "smoldataenvs-train/0100_203_100203819_qa_1" description = "What percentage of users in the dataset made a purchase after data preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "35.75" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0100_322_100322575_qa_1/task.toml b/tasks/0100_322_100322575_qa_1/task.toml index d67f9faaf7096708d2b1261c6dce56113f3917e6..d8bb14c8752588239d7140f95598f9e3b72e27e2 100644 --- a/tasks/0100_322_100322575_qa_1/task.toml +++ b/tasks/0100_322_100322575_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0100_322_100322575_qa_1" +name = "smoldataenvs-train/0100_322_100322575_qa_1" description = "Which instructor has the highest number of courses taught in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0100_352_100352234_qa_2/task.toml b/tasks/0100_352_100352234_qa_2/task.toml index 3e8643a165375c6b4c45f46e4141df4b6b52f7e4..473829c2caf35555bcb5642e2c40e4dc1a12469a 100644 --- a/tasks/0100_352_100352234_qa_2/task.toml +++ b/tasks/0100_352_100352234_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0100_352_100352234_qa_2" +name = "smoldataenvs-train/0100_352_100352234_qa_2" description = "Which wine variety is most frequently reviewed in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Pinot Noir" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0100_352_100352234_qa_4/task.toml b/tasks/0100_352_100352234_qa_4/task.toml index 7680159143b1110f8729701e2491ee1f2f18db5e..ee9b5c4638646908f17c415dcc4d2f79bc7080e1 100644 --- a/tasks/0100_352_100352234_qa_4/task.toml +++ b/tasks/0100_352_100352234_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0100_352_100352234_qa_4" +name = "smoldataenvs-train/0100_352_100352234_qa_4" description = "Which country has the highest number of wine reviews in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "US" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0100_352_100352234_qa_5/task.toml b/tasks/0100_352_100352234_qa_5/task.toml index 380c144f7a1859b1d23918441f2eb192e8e63f61..e08015e15a8b91ffff3f9ad87b743fed3a248b38 100644 --- a/tasks/0100_352_100352234_qa_5/task.toml +++ b/tasks/0100_352_100352234_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0100_352_100352234_qa_5" +name = "smoldataenvs-train/0100_352_100352234_qa_5" description = "What is the most common designation among wines in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Reserve" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0100_390_100390572_qa_1/task.toml b/tasks/0100_390_100390572_qa_1/task.toml index f2dd8b4220d9930cd3e8b4e096442baa14dac5b8..13f7c3d3b1ad1b08c2c2bba698e054d351ed28ca 100644 --- a/tasks/0100_390_100390572_qa_1/task.toml +++ b/tasks/0100_390_100390572_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0100_390_100390572_qa_1" +name = "smoldataenvs-train/0100_390_100390572_qa_1" description = "Which Pokémon type has the highest average total stats based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Dragon" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0100_390_100390572_qa_2/task.toml b/tasks/0100_390_100390572_qa_2/task.toml index 80ba1c1c4759af768f4beaa20064386003d70314..05e9a4536d84176fcf2e726b9072836973aab575 100644 --- a/tasks/0100_390_100390572_qa_2/task.toml +++ b/tasks/0100_390_100390572_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0100_390_100390572_qa_2" +name = "smoldataenvs-train/0100_390_100390572_qa_2" description = "Which Pokémon type has the lowest average total stats based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Bug" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0100_415_100415914_qa_5/task.toml b/tasks/0100_415_100415914_qa_5/task.toml index 18bb1aac3f7e1c7be58fe2e1132f6442966a6993..a4cafd5b80482b2ad297d8cda3fe1b22361d8231 100644 --- a/tasks/0100_415_100415914_qa_5/task.toml +++ b/tasks/0100_415_100415914_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0100_415_100415914_qa_5" +name = "smoldataenvs-train/0100_415_100415914_qa_5" description = "Which feature, when clustered using Jenks Natural Breaks, produces the lowest Silhouette score in the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "BMI" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0100_534_100534890_qa_1/task.toml b/tasks/0100_534_100534890_qa_1/task.toml index 92ff09af26d212f449143fcb61c1d11e9cd9cebc..e280a42d86f18fa88a44ccbb3cbd64238e977193 100644 --- a/tasks/0100_534_100534890_qa_1/task.toml +++ b/tasks/0100_534_100534890_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0100_534_100534890_qa_1" +name = "smoldataenvs-train/0100_534_100534890_qa_1" description = "Which video game genre has the highest total global sales according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0100_534_100534890_qa_4/task.toml b/tasks/0100_534_100534890_qa_4/task.toml index 7384c438d9333ea2cc337ae6115b8aed6df35c73..8e78ffbe475b9026b8fd35c9010835dd5aebdf8c 100644 --- a/tasks/0100_534_100534890_qa_4/task.toml +++ b/tasks/0100_534_100534890_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0100_534_100534890_qa_4" +name = "smoldataenvs-train/0100_534_100534890_qa_4" description = "Which genre has the highest average global sales per game?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Platform" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0100_631_100631764_qa_3/task.toml b/tasks/0100_631_100631764_qa_3/task.toml index 1183183923a8a347b9b52bdd16a3e4e4d6c1da91..dea995959bc30e4ece8028cec112c1504238e188 100644 --- a/tasks/0100_631_100631764_qa_3/task.toml +++ b/tasks/0100_631_100631764_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0100_631_100631764_qa_3" +name = "smoldataenvs-train/0100_631_100631764_qa_3" description = "Which continent has the highest average historical popularity index in the Classical Era dataset (653 entries)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Europe" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0100_636_100636293_qa_1/task.toml b/tasks/0100_636_100636293_qa_1/task.toml index 3a76436fc9caea2748bfeeb45c65b18a7863d7db..fa0291f7d9dfe3c6753263d16f5ca52b1827f509 100644 --- a/tasks/0100_636_100636293_qa_1/task.toml +++ b/tasks/0100_636_100636293_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0100_636_100636293_qa_1" +name = "smoldataenvs-train/0100_636_100636293_qa_1" description = "Which payment method has the highest associated churn rate based on mean encoding analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic check" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0100_636_100636293_qa_5/task.toml b/tasks/0100_636_100636293_qa_5/task.toml index b63ae2bdd76a8abf6a9306051c6dbcf2861301cf..b6f198be8eb1b92a077ee19b1a65e5cd3288f1da 100644 --- a/tasks/0100_636_100636293_qa_5/task.toml +++ b/tasks/0100_636_100636293_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0100_636_100636293_qa_5" +name = "smoldataenvs-train/0100_636_100636293_qa_5" description = "Which internet service type exhibits the highest churn rate according to mean encoding analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Fiber optic" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0100_668_100668614_qa_1/task.toml b/tasks/0100_668_100668614_qa_1/task.toml index 09978cafc6c8ea8b3e97e876b05b92adb74a8b27..f0b41edc7fff442936c05577cd8c950880ad966e 100644 --- a/tasks/0100_668_100668614_qa_1/task.toml +++ b/tasks/0100_668_100668614_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0100_668_100668614_qa_1" +name = "smoldataenvs-train/0100_668_100668614_qa_1" description = "Which job role had the highest frequency among employees who left the company?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Laboratory Technician" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0100_711_100711073_qa_2/task.toml b/tasks/0100_711_100711073_qa_2/task.toml index 16bc75336b4c28bcc801e0e2b4b19c12e8985fb3..dd415f68fae958d38d761b73df36386c42c05580 100644 --- a/tasks/0100_711_100711073_qa_2/task.toml +++ b/tasks/0100_711_100711073_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0100_711_100711073_qa_2" +name = "smoldataenvs-train/0100_711_100711073_qa_2" description = "How many unique customers are present in the cleaned dataset after removing records with missing CustomerID values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4338" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0100_715_100715048_qa_2/task.toml b/tasks/0100_715_100715048_qa_2/task.toml index 101651a00c18e49a837ac518788313f0ef175957..6257838efd3bdeabbd80797bbfb91c6f17859365 100644 --- a/tasks/0100_715_100715048_qa_2/task.toml +++ b/tasks/0100_715_100715048_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0100_715_100715048_qa_2" +name = "smoldataenvs-train/0100_715_100715048_qa_2" description = "What percentage of clients in the dataset defaulted on their payments?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "22.12" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0100_729_100729925_qa_4/task.toml b/tasks/0100_729_100729925_qa_4/task.toml index d4f0d74fc772bc84cb967a4ec11e811b1483baf3..9cf1006b86730b49d904b5708ca4ea7cdfbdd4f3 100644 --- a/tasks/0100_729_100729925_qa_4/task.toml +++ b/tasks/0100_729_100729925_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0100_729_100729925_qa_4" +name = "smoldataenvs-train/0100_729_100729925_qa_4" description = "What was the AUC score of the Logistic Regression model on the test set after data cleaning and feature selection?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.82" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0100_732_100732711_qa_1/task.toml b/tasks/0100_732_100732711_qa_1/task.toml index 83daf88ef79326aafae43edbfd907f29b2ba47bc..8f9c85770768197a50815f79f6ebddc48569e296 100644 --- a/tasks/0100_732_100732711_qa_1/task.toml +++ b/tasks/0100_732_100732711_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0100_732_100732711_qa_1" +name = "smoldataenvs-train/0100_732_100732711_qa_1" description = "Which feature in the dataset exhibits the strongest negative correlation with the target variable Type?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Mg" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0100_734_100734661_qa_1/task.toml b/tasks/0100_734_100734661_qa_1/task.toml index f44fc81fa31f2469bffeacccc5f7217b4dba57bc..9d08de2f31c3303d378c6cefa7d2affe1c9ac1d8 100644 --- a/tasks/0100_734_100734661_qa_1/task.toml +++ b/tasks/0100_734_100734661_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0100_734_100734661_qa_1" +name = "smoldataenvs-train/0100_734_100734661_qa_1" description = "What percentage of patients in the dataset have diabetes (Outcome = 1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.8958" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0100_734_100734661_qa_5/task.toml b/tasks/0100_734_100734661_qa_5/task.toml index 957138422b92c339d1a317f4250cf691be25dfd7..0ab5f86cb5f0e95954c3f7f2258f3e65fa688bdd 100644 --- a/tasks/0100_734_100734661_qa_5/task.toml +++ b/tasks/0100_734_100734661_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0100_734_100734661_qa_5" +name = "smoldataenvs-train/0100_734_100734661_qa_5" description = "What was the mean glucose level in the dataset before handling missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "120.894531" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0100_900_100900717_qa_5/task.toml b/tasks/0100_900_100900717_qa_5/task.toml index 899e0654cd28c7fc062d2fb3f5b872535f68dd59..2be3434de17ec428703b36c96e1cc3786887f53e 100644 --- a/tasks/0100_900_100900717_qa_5/task.toml +++ b/tasks/0100_900_100900717_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0100_900_100900717_qa_5" +name = "smoldataenvs-train/0100_900_100900717_qa_5" description = "What is the coefficient (slope) obtained from scikit-learn's LinearRegression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.00065638" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0100_969_100969953_qa_1/task.toml b/tasks/0100_969_100969953_qa_1/task.toml index d91df2a5d5b4d3a6f85fade7850b6cae7bceaa48..363e57aa65a43f9c239c489c295b068beb36631c 100644 --- a/tasks/0100_969_100969953_qa_1/task.toml +++ b/tasks/0100_969_100969953_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0100_969_100969953_qa_1" +name = "smoldataenvs-train/0100_969_100969953_qa_1" description = "After data cleaning steps, how many missing values remain in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0100_969_100969953_qa_2/task.toml b/tasks/0100_969_100969953_qa_2/task.toml index 982b60aad3deeb668253c9001555666a0700a063..ee9cd6284ce5aca7d7a979ae47d7aac0e10f55af 100644 --- a/tasks/0100_969_100969953_qa_2/task.toml +++ b/tasks/0100_969_100969953_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0100_969_100969953_qa_2" +name = "smoldataenvs-train/0100_969_100969953_qa_2" description = "What is the most common loan amount term in the dataset after handling missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "360" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0100_986_100986965_qa_1/task.toml b/tasks/0100_986_100986965_qa_1/task.toml index d0c28e390cddb184e479a5413ce2732af57f41b7..5310d5afcf1741a871b8eef667be41ee839059ad 100644 --- a/tasks/0100_986_100986965_qa_1/task.toml +++ b/tasks/0100_986_100986965_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0100_986_100986965_qa_1" +name = "smoldataenvs-train/0100_986_100986965_qa_1" description = "What is the strongest positive correlation between any two variables in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.8" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0100_986_100986965_qa_5/task.toml b/tasks/0100_986_100986965_qa_5/task.toml index 0e9d73f5e86e569161a23b981999eb698d59433b..00da6a5b0d7643fd567a46dac3f1d5dd1994f548 100644 --- a/tasks/0100_986_100986965_qa_5/task.toml +++ b/tasks/0100_986_100986965_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0100_986_100986965_qa_5" +name = "smoldataenvs-train/0100_986_100986965_qa_5" description = "What percentage of patients in the dataset fall into the healthy weight range (BMI 18.5–25)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16.816143" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0101_030_101030529_qa_3/task.toml b/tasks/0101_030_101030529_qa_3/task.toml index 1c0642c172021d8e7706ef5caaca6285f8cb5699..251df05ca0c201a9dc9c28c79c975c964f811050 100644 --- a/tasks/0101_030_101030529_qa_3/task.toml +++ b/tasks/0101_030_101030529_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0101_030_101030529_qa_3" +name = "smoldataenvs-train/0101_030_101030529_qa_3" description = "What is the 90th percentile of axillary nodes for patients who did not survive?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0101_030_101030529_qa_5/task.toml b/tasks/0101_030_101030529_qa_5/task.toml index 5e69281bfba0d8d03319344a14e434fddb434e4c..a71b555aae11893f643b182236a78bf7b15fc116 100644 --- a/tasks/0101_030_101030529_qa_5/task.toml +++ b/tasks/0101_030_101030529_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0101_030_101030529_qa_5" +name = "smoldataenvs-train/0101_030_101030529_qa_5" description = "What is the standard deviation of axillary nodes for patients who survived?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5.857258" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0101_045_101045351_qa_4/task.toml b/tasks/0101_045_101045351_qa_4/task.toml index d5fca84a09eabbcecdfdb175e8d6f8a45ba13a84..4c5d8a7ece7a7ecc01c0d421a0652d5e1c134890 100644 --- a/tasks/0101_045_101045351_qa_4/task.toml +++ b/tasks/0101_045_101045351_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0101_045_101045351_qa_4" +name = "smoldataenvs-train/0101_045_101045351_qa_4" description = "What is the correlation coefficient between age and insurance charges in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.299008" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0101_045_101045351_qa_5/task.toml b/tasks/0101_045_101045351_qa_5/task.toml index a2642dbc1031434e7d23c4e7651531223618a0c6..26e8d96e25fe633e2d9a8af102c5a74f02e6d529 100644 --- a/tasks/0101_045_101045351_qa_5/task.toml +++ b/tasks/0101_045_101045351_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0101_045_101045351_qa_5" +name = "smoldataenvs-train/0101_045_101045351_qa_5" description = "Which region has the highest number of policyholders in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Southeast" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0101_142_101142803_qa_4/task.toml b/tasks/0101_142_101142803_qa_4/task.toml index 7d8493ce14b26d40d0008fbbe40236d62cf47520..0f732af941b539a4f8e42019676e304a72572019 100644 --- a/tasks/0101_142_101142803_qa_4/task.toml +++ b/tasks/0101_142_101142803_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0101_142_101142803_qa_4" +name = "smoldataenvs-train/0101_142_101142803_qa_4" description = "What percentage of buyers in the dataset indicated they recommended the product?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "81.89" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0101_159_101159435_qa_2/task.toml b/tasks/0101_159_101159435_qa_2/task.toml index 066bb6cf3ca227ec7c3c5044ab9825ff941f4ff7..b6690850e11f1f74948b31d842a0c22d5c42426d 100644 --- a/tasks/0101_159_101159435_qa_2/task.toml +++ b/tasks/0101_159_101159435_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0101_159_101159435_qa_2" +name = "smoldataenvs-train/0101_159_101159435_qa_2" description = "What is the coefficient of variation for the Age variable in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.2802" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0101_232_101232922_qa_1/task.toml b/tasks/0101_232_101232922_qa_1/task.toml index 4949864d400f317a51a811db8e78b84721006ce8..bb803440e3f2d4ef0ac626734be0989aaa8aa40a 100644 --- a/tasks/0101_232_101232922_qa_1/task.toml +++ b/tasks/0101_232_101232922_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0101_232_101232922_qa_1" +name = "smoldataenvs-train/0101_232_101232922_qa_1" description = "Which material component shows the strongest positive correlation with concrete compressive strength according to the heatmap analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Cement" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0101_292_101292104_qa_1/task.toml b/tasks/0101_292_101292104_qa_1/task.toml index 609def130b0774fad92538d8cd841c09de18be4c..85c4840a14f76c98ac4891171f77658daa2c028f 100644 --- a/tasks/0101_292_101292104_qa_1/task.toml +++ b/tasks/0101_292_101292104_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0101_292_101292104_qa_1" +name = "smoldataenvs-train/0101_292_101292104_qa_1" description = "Which ocean proximity category has the highest frequency in the dataset, and what is its count?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "<1H OCEAN, 9136" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0101_296_101296258_qa_3/task.toml b/tasks/0101_296_101296258_qa_3/task.toml index 4fcbc49151e399595a8fbe4819b8a777de8de30b..7b93116dfc6378074211124f5811091d563ef506 100644 --- a/tasks/0101_296_101296258_qa_3/task.toml +++ b/tasks/0101_296_101296258_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0101_296_101296258_qa_3" +name = "smoldataenvs-train/0101_296_101296258_qa_3" description = "What is the inertia value achieved by the KMeans clustering model with 5 clusters on the scaled dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "91484.92413050328" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0101_390_101390239_qa_3/task.toml b/tasks/0101_390_101390239_qa_3/task.toml index 29d51e430d21a932a7cdb03d85fcd23523b0546a..095e3b1eb4509a4813bc30cd8b4afad286fdf808 100644 --- a/tasks/0101_390_101390239_qa_3/task.toml +++ b/tasks/0101_390_101390239_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0101_390_101390239_qa_3" +name = "smoldataenvs-train/0101_390_101390239_qa_3" description = "How many samples are present for each species in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0101_390_101390239_qa_4/task.toml b/tasks/0101_390_101390239_qa_4/task.toml index 694631f52eea07975df99d8d44200344a7b492ba..6c21e010826bf59ad78432433146f63822303938 100644 --- a/tasks/0101_390_101390239_qa_4/task.toml +++ b/tasks/0101_390_101390239_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0101_390_101390239_qa_4" +name = "smoldataenvs-train/0101_390_101390239_qa_4" description = "What is the correlation between SepalLengthCm and PetalLengthCm in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.871754" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0101_507_101507409_qa_3/task.toml b/tasks/0101_507_101507409_qa_3/task.toml index ac802331abaf7e821091ce86248948bcf33e09f8..8d6bc32a33665d78de90bbe289f4b2577ce627f6 100644 --- a/tasks/0101_507_101507409_qa_3/task.toml +++ b/tasks/0101_507_101507409_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0101_507_101507409_qa_3" +name = "smoldataenvs-train/0101_507_101507409_qa_3" description = "What is the number of observations in the dataset after removing rows with missing TotalCharges values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7032" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0101_507_101507409_qa_4/task.toml b/tasks/0101_507_101507409_qa_4/task.toml index 3d870ecc386c7124d42dfba99f2ba3478c9f69f4..3bad22c287af2cdcdcebb67f0d731dd3ee60a337 100644 --- a/tasks/0101_507_101507409_qa_4/task.toml +++ b/tasks/0101_507_101507409_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0101_507_101507409_qa_4" +name = "smoldataenvs-train/0101_507_101507409_qa_4" description = "Which machine learning model achieved the highest test accuracy among all evaluated models?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Gradient Boosting" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0101_507_101507712_qa_1/task.toml b/tasks/0101_507_101507712_qa_1/task.toml index d82bf24612a5477285e12fa9b0661f28bf09a761..576d7c019d247c3bd31c24988826f40ef7acd2c6 100644 --- a/tasks/0101_507_101507712_qa_1/task.toml +++ b/tasks/0101_507_101507712_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0101_507_101507712_qa_1" +name = "smoldataenvs-train/0101_507_101507712_qa_1" description = "Which year between 2006-2010 had the highest total power consumption measured in watt-hour according to the yearly aggregation analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2007" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0101_509_101509299_qa_5/task.toml b/tasks/0101_509_101509299_qa_5/task.toml index 7d1a8b7944d9042bf216b8cf34d640e44db9ae29..143315af4c5720bb72c894c31aed3eebf4f31517 100644 --- a/tasks/0101_509_101509299_qa_5/task.toml +++ b/tasks/0101_509_101509299_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0101_509_101509299_qa_5" +name = "smoldataenvs-train/0101_509_101509299_qa_5" description = "How many unique words are included in the bag-of-words model after vectorization in the content-based recommender system?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5000" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0101_569_101569659_qa_2/task.toml b/tasks/0101_569_101569659_qa_2/task.toml index 6c54011d5c697d84e2dcef5c2aff88bf48814b39..162af420f01c6753cdf434c9ab91a49788830778 100644 --- a/tasks/0101_569_101569659_qa_2/task.toml +++ b/tasks/0101_569_101569659_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0101_569_101569659_qa_2" +name = "smoldataenvs-train/0101_569_101569659_qa_2" description = "What is the maximum silhouette score achieved by any feature subset in K-means clustering with 2 clusters?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0101_593_101593150_qa_3/task.toml b/tasks/0101_593_101593150_qa_3/task.toml index 1652c99370b6d204ce97d5fad18c098ae7c0976e..9fd33b7b3b7610fdb40342747e8c5fd2ceb241ab 100644 --- a/tasks/0101_593_101593150_qa_3/task.toml +++ b/tasks/0101_593_101593150_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0101_593_101593150_qa_3" +name = "smoldataenvs-train/0101_593_101593150_qa_3" description = "What is the mean cocoa percentage for the 'Amazon mix' bean type?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "74" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0101_773_101773827_qa_4/task.toml b/tasks/0101_773_101773827_qa_4/task.toml index 7cf0115ef5a44d3555c5f3449570c6b94d2cd5d8..96425685489a6f00b9dcb97963d3cfb15746ac43 100644 --- a/tasks/0101_773_101773827_qa_4/task.toml +++ b/tasks/0101_773_101773827_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0101_773_101773827_qa_4" +name = "smoldataenvs-train/0101_773_101773827_qa_4" description = "How many features were selected for the final Decision Tree model used in prediction?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0101_787_101787137_qa_5/task.toml b/tasks/0101_787_101787137_qa_5/task.toml index dda95e8b1eabd062c09012de197d821a90d8c977..ecfc07583a62d1a12920b0fde3573dd321f3d2ea 100644 --- a/tasks/0101_787_101787137_qa_5/task.toml +++ b/tasks/0101_787_101787137_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0101_787_101787137_qa_5" +name = "smoldataenvs-train/0101_787_101787137_qa_5" description = "What is the total number of anime categorized as \"TV\" in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3787" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0101_866_101866934_qa_4/task.toml b/tasks/0101_866_101866934_qa_4/task.toml index 4e52e8ee1d9c82c1fb4a68a30a18a2c75d2de904..557744a527ed73576177fb6cf264342d2ecc5957 100644 --- a/tasks/0101_866_101866934_qa_4/task.toml +++ b/tasks/0101_866_101866934_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0101_866_101866934_qa_4" +name = "smoldataenvs-train/0101_866_101866934_qa_4" description = "What is the most common age among employees in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "35" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0101_914_101914929_qa_1/task.toml b/tasks/0101_914_101914929_qa_1/task.toml index a0dc1ee20f3945aee55b71a2736bec1978583b0c..c26de9827851b6ad71456c9427a9b8b264bdb7e8 100644 --- a/tasks/0101_914_101914929_qa_1/task.toml +++ b/tasks/0101_914_101914929_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0101_914_101914929_qa_1" +name = "smoldataenvs-train/0101_914_101914929_qa_1" description = "What is the average medical charge for policyholders in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13270.42" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0101_917_101917756_qa_3/task.toml b/tasks/0101_917_101917756_qa_3/task.toml index 58ac0fc9b2d0bea460596d430b65cd4dc8463780..92cf37f36070ac9e91d1f04c05d549b9b617d414 100644 --- a/tasks/0101_917_101917756_qa_3/task.toml +++ b/tasks/0101_917_101917756_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0101_917_101917756_qa_3" +name = "smoldataenvs-train/0101_917_101917756_qa_3" description = "Which contract type has the highest churn rate as observed in the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Month-to-month" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0101_994_101994234_qa_2/task.toml b/tasks/0101_994_101994234_qa_2/task.toml index 4361b5aa94be0ea117f72dc313634bbcbb3f5083..ad6e45a0ceb1ca36399b849d8bbd916a9b1518de 100644 --- a/tasks/0101_994_101994234_qa_2/task.toml +++ b/tasks/0101_994_101994234_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0101_994_101994234_qa_2" +name = "smoldataenvs-train/0101_994_101994234_qa_2" description = "What is the correlation coefficient between the 'x' and 'y' variables in the test dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.994545" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0101_994_101994234_qa_4/task.toml b/tasks/0101_994_101994234_qa_4/task.toml index 4ad65ff5213075d60ab141150133702db5616458..0ef1f580a7988ab92a1e129313879d24e82d6cc7 100644 --- a/tasks/0101_994_101994234_qa_4/task.toml +++ b/tasks/0101_994_101994234_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0101_994_101994234_qa_4" +name = "smoldataenvs-train/0101_994_101994234_qa_4" description = "What is the 75th percentile value of the 'x' variable in the test dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "73.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0102_128_102128950_qa_1/task.toml b/tasks/0102_128_102128950_qa_1/task.toml index df6b69a934fcbe50983f1334b1d2e29e647b2bbd..a508f72712fa08b6eab23928ba0b32418062bdce 100644 --- a/tasks/0102_128_102128950_qa_1/task.toml +++ b/tasks/0102_128_102128950_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0102_128_102128950_qa_1" +name = "smoldataenvs-train/0102_128_102128950_qa_1" description = "What is the percentage of employees who left the company (Attrition = Yes) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16.1" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0102_128_102128950_qa_2/task.toml b/tasks/0102_128_102128950_qa_2/task.toml index e5e7528f905393989ecc2be5ef400c7684d2f42a..6a464c2c1196b182af6f1e96f5510cde94418ba7 100644 --- a/tasks/0102_128_102128950_qa_2/task.toml +++ b/tasks/0102_128_102128950_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0102_128_102128950_qa_2" +name = "smoldataenvs-train/0102_128_102128950_qa_2" description = "Which department has the highest average monthly income for employees who left the company (Attrition = Yes)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Sales" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0102_158_102158700_qa_5/task.toml b/tasks/0102_158_102158700_qa_5/task.toml index 3278e4a351a082749003c21a8c4e5ae4e0417bb1..b2d48bdcfb4736aa7d93739da3ce2348c352740c 100644 --- a/tasks/0102_158_102158700_qa_5/task.toml +++ b/tasks/0102_158_102158700_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0102_158_102158700_qa_5" +name = "smoldataenvs-train/0102_158_102158700_qa_5" description = "What are the median ranks for Player1 and Player2 in the US Open dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Player1=48.5, Player2=43.0" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0102_190_102190555_qa_2/task.toml b/tasks/0102_190_102190555_qa_2/task.toml index 98329f373e65385c33908cdbd020a18a14e40802..64d66f066e1d2ff83201fe1cf79a3435f3a107a4 100644 --- a/tasks/0102_190_102190555_qa_2/task.toml +++ b/tasks/0102_190_102190555_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0102_190_102190555_qa_2" +name = "smoldataenvs-train/0102_190_102190555_qa_2" description = "What is the difference between the training and testing R² scores when using standardized features in the linear regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.06" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0102_190_102190555_qa_3/task.toml b/tasks/0102_190_102190555_qa_3/task.toml index a4f390d5384743e5b8e346e74d823913bfb6743d..0cd7fc39641e62c951094b7567ac621fabae3399 100644 --- a/tasks/0102_190_102190555_qa_3/task.toml +++ b/tasks/0102_190_102190555_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0102_190_102190555_qa_3" +name = "smoldataenvs-train/0102_190_102190555_qa_3" description = "Which feature shows the strongest correlation with medical charges in the original dataset before encoding categorical variables?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "age" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0102_198_102198537_qa_3/task.toml b/tasks/0102_198_102198537_qa_3/task.toml index 62f8ed321f74be3dc9908b6833f6e13d4873c164..cd5f08a08183e1876361b09e7f47293df83d437b 100644 --- a/tasks/0102_198_102198537_qa_3/task.toml +++ b/tasks/0102_198_102198537_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0102_198_102198537_qa_3" +name = "smoldataenvs-train/0102_198_102198537_qa_3" description = "What is the total number of unique stores in the dataset based on the store identification numbers?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "45" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0102_198_102198537_qa_5/task.toml b/tasks/0102_198_102198537_qa_5/task.toml index 779136e9f534e6711479c86bae9e224a045a9949..2363af6a3ca40663fa56db52713078a2c603db18 100644 --- a/tasks/0102_198_102198537_qa_5/task.toml +++ b/tasks/0102_198_102198537_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0102_198_102198537_qa_5" +name = "smoldataenvs-train/0102_198_102198537_qa_5" description = "What is the average weekly sales value across all records in the sales dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "15981.26" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0102_213_102213799_qa_1/task.toml b/tasks/0102_213_102213799_qa_1/task.toml index c0a1497f5a99dc20ed26f2f33e7dd08176d3a42b..07b47e13db5e920a1b1eed0b93f8de2d2194fc29 100644 --- a/tasks/0102_213_102213799_qa_1/task.toml +++ b/tasks/0102_213_102213799_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0102_213_102213799_qa_1" +name = "smoldataenvs-train/0102_213_102213799_qa_1" description = "Which feature in the dataset has the highest coefficient of variation (standard deviation divided by mean)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalWidthCm" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0102_253_102253165_qa_5/task.toml b/tasks/0102_253_102253165_qa_5/task.toml index 75904d879308bb87ab136ffd8f7105cad8ce3eab..3f9f4c1701f80a8128d4ad90eddf86e77e05b627 100644 --- a/tasks/0102_253_102253165_qa_5/task.toml +++ b/tasks/0102_253_102253165_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0102_253_102253165_qa_5" +name = "smoldataenvs-train/0102_253_102253165_qa_5" description = "What is the median value of the \"dfrange\" (range of dominant frequency) feature in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.94" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0102_350_102350231_qa_5/task.toml b/tasks/0102_350_102350231_qa_5/task.toml index 9c359da2c3bbfe5d9ef7f182d5d3276002ec948c..3768948150bf4ce091675a153faeb384535e1dc6 100644 --- a/tasks/0102_350_102350231_qa_5/task.toml +++ b/tasks/0102_350_102350231_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0102_350_102350231_qa_5" +name = "smoldataenvs-train/0102_350_102350231_qa_5" description = "Which feature among CRIM, ZN, and INDUS has the highest variance inflation factor (VIF) according to the multicollinearity analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "INDUS" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0102_368_102368775_qa_1/task.toml b/tasks/0102_368_102368775_qa_1/task.toml index 9635792d4b9c33113fcf4a176f6e0b414d6618c1..523c97317d348f251c2ecb901066df068070f87f 100644 --- a/tasks/0102_368_102368775_qa_1/task.toml +++ b/tasks/0102_368_102368775_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0102_368_102368775_qa_1" +name = "smoldataenvs-train/0102_368_102368775_qa_1" description = "What is the percentage of loans in the dataset that were not fully paid back?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16.0054" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0102_369_102369672_qa_1/task.toml b/tasks/0102_369_102369672_qa_1/task.toml index 83b124cc1ed61eae1d6ea75698837ec88f411a40..eb953673b62fbff4cd07b06e32cc77c4d22a3d85 100644 --- a/tasks/0102_369_102369672_qa_1/task.toml +++ b/tasks/0102_369_102369672_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0102_369_102369672_qa_1" +name = "smoldataenvs-train/0102_369_102369672_qa_1" description = "What is the 35th percentile value for the calories column in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "100.0" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0102_369_102369672_qa_3/task.toml b/tasks/0102_369_102369672_qa_3/task.toml index 6f68418e3d67ecc8d046dce105efffc8304ff6a9..c44fe689f26ed26d0eb47dcdb9943ccbce78df08 100644 --- a/tasks/0102_369_102369672_qa_3/task.toml +++ b/tasks/0102_369_102369672_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0102_369_102369672_qa_3" +name = "smoldataenvs-train/0102_369_102369672_qa_3" description = "What is the median carbo content of all cereals in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "14.0" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0102_369_102369672_qa_4/task.toml b/tasks/0102_369_102369672_qa_4/task.toml index bfad25889b4749b6d0b680f1fa45127f63e5a285..ba259404fe6eba5ee2f99ce56f32590f2e91f792 100644 --- a/tasks/0102_369_102369672_qa_4/task.toml +++ b/tasks/0102_369_102369672_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0102_369_102369672_qa_4" +name = "smoldataenvs-train/0102_369_102369672_qa_4" description = "What is the most common manufacturer (mode) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "K" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0102_377_102377716_qa_2/task.toml b/tasks/0102_377_102377716_qa_2/task.toml index fa6ae8da7d8580b6332b24923ab7a9bebb4ff584..49b75e6a613d15e9a5e0212dcdb07f758b3d4c70 100644 --- a/tasks/0102_377_102377716_qa_2/task.toml +++ b/tasks/0102_377_102377716_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0102_377_102377716_qa_2" +name = "smoldataenvs-train/0102_377_102377716_qa_2" description = "What percentage of the top 20 highest-grossing games are not in the Action or Role-Playing genres?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "80" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0102_377_102377716_qa_4/task.toml b/tasks/0102_377_102377716_qa_4/task.toml index 918bb5b61491410d4f1ea99d0c50b802c623216b..736866169fb476c148abee4d90c927ff13b1c6e8 100644 --- a/tasks/0102_377_102377716_qa_4/task.toml +++ b/tasks/0102_377_102377716_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0102_377_102377716_qa_4" +name = "smoldataenvs-train/0102_377_102377716_qa_4" description = "What is the difference between the highest and lowest global sales in the top 20 games?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "62.52" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0102_377_102377716_qa_5/task.toml b/tasks/0102_377_102377716_qa_5/task.toml index 2a78cf791a3527b38d0b65d1d274c70517c2f434..9d66eba43b34eab7f37d588a3a4aca13d0ad9ef6 100644 --- a/tasks/0102_377_102377716_qa_5/task.toml +++ b/tasks/0102_377_102377716_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0102_377_102377716_qa_5" +name = "smoldataenvs-train/0102_377_102377716_qa_5" description = "How many unique genres are represented among the top 20 highest-grossing games?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0102_378_102378485_qa_2/task.toml b/tasks/0102_378_102378485_qa_2/task.toml index 1ebe933804153f2e6c43176358769b427a352ac4..9125f37685c182bcc0d7bf6a23dfeb3577502782 100644 --- a/tasks/0102_378_102378485_qa_2/task.toml +++ b/tasks/0102_378_102378485_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0102_378_102378485_qa_2" +name = "smoldataenvs-train/0102_378_102378485_qa_2" description = "What percentage of the movies in the dataset are produced in English language (considering only languages with ≥20 movies)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "82" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0102_378_102378485_qa_3/task.toml b/tasks/0102_378_102378485_qa_3/task.toml index b749713c5897686a1dacab64a67ee5e78704cee2..b97c313699863e91583c6f23bad35aa0eba17cd9 100644 --- a/tasks/0102_378_102378485_qa_3/task.toml +++ b/tasks/0102_378_102378485_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0102_378_102378485_qa_3" +name = "smoldataenvs-train/0102_378_102378485_qa_3" description = "Which year had the highest number of horror movie releases between 2012 and 2017?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2017" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0102_401_102401343_qa_1/task.toml b/tasks/0102_401_102401343_qa_1/task.toml index c84a98c4d48f593f404650a0e50aac21e23692dc..7080ae9337eb88af194e283563feb2992fbd1ebf 100644 --- a/tasks/0102_401_102401343_qa_1/task.toml +++ b/tasks/0102_401_102401343_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0102_401_102401343_qa_1" +name = "smoldataenvs-train/0102_401_102401343_qa_1" description = "How many standard deviations above the mean North American sales is the top-selling video game (Wii Sports)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50.48" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0102_436_102436012_qa_1/task.toml b/tasks/0102_436_102436012_qa_1/task.toml index 513fa0b1e736c8b1d910bca3bd76010fa1a92739..4d42d8a54873210da0af6b2611b4ff5acfd767bf 100644 --- a/tasks/0102_436_102436012_qa_1/task.toml +++ b/tasks/0102_436_102436012_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0102_436_102436012_qa_1" +name = "smoldataenvs-train/0102_436_102436012_qa_1" description = "Which machine learning model achieved the highest ROC score after SMOTE oversampling?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "GradientBoosting" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0102_483_102483504_qa_3/task.toml b/tasks/0102_483_102483504_qa_3/task.toml index 4d150b4f0eb8b2ce1a1d440bff49a5794188190d..dad23ec937b93dbd0d673f760ab94ae7879fa685 100644 --- a/tasks/0102_483_102483504_qa_3/task.toml +++ b/tasks/0102_483_102483504_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0102_483_102483504_qa_3" +name = "smoldataenvs-train/0102_483_102483504_qa_3" description = "What is the median North American sales value across all games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.08" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0102_526_102526690_qa_1/task.toml b/tasks/0102_526_102526690_qa_1/task.toml index a383a02b99968350882ac4fc30529a134fea5a43..dbdb99c1991cc2f2cc5886d0584a6d58d76a1a56 100644 --- a/tasks/0102_526_102526690_qa_1/task.toml +++ b/tasks/0102_526_102526690_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0102_526_102526690_qa_1" +name = "smoldataenvs-train/0102_526_102526690_qa_1" description = "What is the difference between the maximum and minimum values of the 'alcohol' content in the wine dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0102_526_102526690_qa_2/task.toml b/tasks/0102_526_102526690_qa_2/task.toml index ce6a013edae80bc698f01312cbe4c25a2d696c0b..08252510dd0237b68887c1a6427b1c1c1dab0c08 100644 --- a/tasks/0102_526_102526690_qa_2/task.toml +++ b/tasks/0102_526_102526690_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0102_526_102526690_qa_2" +name = "smoldataenvs-train/0102_526_102526690_qa_2" description = "What is the interquartile range (IQR) of the 'quality' scores in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0102_526_102526690_qa_3/task.toml b/tasks/0102_526_102526690_qa_3/task.toml index 246690cacf38da9c2d45ed21f5a14e8bb9f48c4e..b9e5c1eae7446f194e7d23a985b2d4ac5483e356 100644 --- a/tasks/0102_526_102526690_qa_3/task.toml +++ b/tasks/0102_526_102526690_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0102_526_102526690_qa_3" +name = "smoldataenvs-train/0102_526_102526690_qa_3" description = "What is the difference between the mean 'alcohol' content and the mean 'residual.sugar' level in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7.884177" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0102_751_102751358_qa_4/task.toml b/tasks/0102_751_102751358_qa_4/task.toml index a52c34b874e7c438f096e4ae11f750609b8b9120..8de9d0c0b70614ba33ba769c373d4c6f6ed5144c 100644 --- a/tasks/0102_751_102751358_qa_4/task.toml +++ b/tasks/0102_751_102751358_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0102_751_102751358_qa_4" +name = "smoldataenvs-train/0102_751_102751358_qa_4" description = "How does the average monthly charge segment affect churn probability, as measured by the highest churn rate among the three segments (cheap, normal, expensive)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Expensive" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0102_769_102769080_qa_1/task.toml b/tasks/0102_769_102769080_qa_1/task.toml index 2f05c02c86aeb470b807e74c7685672408d404cc..212bfe1fd86f33afe5a5dd05a637ea8e74609350 100644 --- a/tasks/0102_769_102769080_qa_1/task.toml +++ b/tasks/0102_769_102769080_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0102_769_102769080_qa_1" +name = "smoldataenvs-train/0102_769_102769080_qa_1" description = "Which video game publisher has the highest maximum global sales in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Nintendo" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0102_781_102781194_qa_2/task.toml b/tasks/0102_781_102781194_qa_2/task.toml index 064c1e623cbdb8d997c175c6167f57a573c1b8dc..cb6c9909ae113f2553fdb64f34220f5b7454e92b 100644 --- a/tasks/0102_781_102781194_qa_2/task.toml +++ b/tasks/0102_781_102781194_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0102_781_102781194_qa_2" +name = "smoldataenvs-train/0102_781_102781194_qa_2" description = "What was the minimum value of 'SepalWidthCm' in the 'Iris-setosa' species before outlier removal?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0102_826_102826628_qa_5/task.toml b/tasks/0102_826_102826628_qa_5/task.toml index ab5808a6f1d256cde5affd10ea821855eafe79ac..57eec35d2663cc7d3fe8c0cdf8e2703b6cb9ca80 100644 --- a/tasks/0102_826_102826628_qa_5/task.toml +++ b/tasks/0102_826_102826628_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0102_826_102826628_qa_5" +name = "smoldataenvs-train/0102_826_102826628_qa_5" description = "What is the correlation coefficient between adult mortality rates and life expectancy in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.7" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0102_875_102875624_qa_2/task.toml b/tasks/0102_875_102875624_qa_2/task.toml index f68da5b95ec2f372aa6cadadb0cec4556b35c308..21808e24330f52f108eb6a61361661287def736c 100644 --- a/tasks/0102_875_102875624_qa_2/task.toml +++ b/tasks/0102_875_102875624_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0102_875_102875624_qa_2" +name = "smoldataenvs-train/0102_875_102875624_qa_2" description = "How many missing values were present in the 'SES' column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "19" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0102_875_102875624_qa_5/task.toml b/tasks/0102_875_102875624_qa_5/task.toml index 5f0ec418f055be8a1205da54e78efdeef5b29f82..d4dae0e75c251d2612af6596228412392f14e3b7 100644 --- a/tasks/0102_875_102875624_qa_5/task.toml +++ b/tasks/0102_875_102875624_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0102_875_102875624_qa_5" +name = "smoldataenvs-train/0102_875_102875624_qa_5" description = "What is the total number of Nondemented patients in the first visit data after converting the 'Converted' group to Demented?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "72" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0102_882_102882563_qa_1/task.toml b/tasks/0102_882_102882563_qa_1/task.toml index 062aa5dfbbcf673245f55ec54bdde44171f4a3f7..a383faf941d9f866114fd6ff0f1c53aa4b94c73c 100644 --- a/tasks/0102_882_102882563_qa_1/task.toml +++ b/tasks/0102_882_102882563_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0102_882_102882563_qa_1" +name = "smoldataenvs-train/0102_882_102882563_qa_1" description = "How many video games in the dataset have global sales categorized as 'Superhit' (sales ≥ 35 million)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0102_919_102919379_qa_1/task.toml b/tasks/0102_919_102919379_qa_1/task.toml index 3057ec519ff1e16dcb23ff07f2fab15dae73ece2..bf2838fad05b03fdcf7ec1a723c3346d1bfcda41 100644 --- a/tasks/0102_919_102919379_qa_1/task.toml +++ b/tasks/0102_919_102919379_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0102_919_102919379_qa_1" +name = "smoldataenvs-train/0102_919_102919379_qa_1" description = "What is the AUC score achieved on the validation dataset by the XGBoost model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9938080495356038" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0102_923_102923439_qa_2/task.toml b/tasks/0102_923_102923439_qa_2/task.toml index 6cb2c246487cbdfc03f80bf38dc99de3ff4a4911..d9fa2d0fbca36e4ce74cc6ccbdf74167c776947c 100644 --- a/tasks/0102_923_102923439_qa_2/task.toml +++ b/tasks/0102_923_102923439_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0102_923_102923439_qa_2" +name = "smoldataenvs-train/0102_923_102923439_qa_2" description = "How many ramen reviews originally had a 'Stars' rating of 'Unrated' before it was converted to 0?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0102_923_102923439_qa_3/task.toml b/tasks/0102_923_102923439_qa_3/task.toml index 6cca627b78f2f690cdce929fae6f7ec59b99d439..075179248c2f68a7ce7e1615012a404abb314dc1 100644 --- a/tasks/0102_923_102923439_qa_3/task.toml +++ b/tasks/0102_923_102923439_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0102_923_102923439_qa_3" +name = "smoldataenvs-train/0102_923_102923439_qa_3" description = "Which country has the highest frequency of ramen reviews in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Japan" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0102_974_102974493_qa_3/task.toml b/tasks/0102_974_102974493_qa_3/task.toml index fe2a3db66bfefa34d0e4d99b6486b6249f6abe6c..592ec83c9ace81a07aaaa08bf2de7768dfa12056 100644 --- a/tasks/0102_974_102974493_qa_3/task.toml +++ b/tasks/0102_974_102974493_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0102_974_102974493_qa_3" +name = "smoldataenvs-train/0102_974_102974493_qa_3" description = "What is the most common value in the 'buying' feature before encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "vhigh" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0102_982_102982156_qa_3/task.toml b/tasks/0102_982_102982156_qa_3/task.toml index 74262f070c7cf8e845daa666eea957041cf18b3b..e2c451b266fd25d055307ce46cfdc5813501e45f 100644 --- a/tasks/0102_982_102982156_qa_3/task.toml +++ b/tasks/0102_982_102982156_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0102_982_102982156_qa_3" +name = "smoldataenvs-train/0102_982_102982156_qa_3" description = "What is the area under the ROC curve (AUC) achieved by the XGBoost model on the test dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9844" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0102_982_102982156_qa_4/task.toml b/tasks/0102_982_102982156_qa_4/task.toml index c8e31b69700c640d078600fab459381e77a1827f..b7761036539260926b943a999e81643290bc2057 100644 --- a/tasks/0102_982_102982156_qa_4/task.toml +++ b/tasks/0102_982_102982156_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0102_982_102982156_qa_4" +name = "smoldataenvs-train/0102_982_102982156_qa_4" description = "How many instances were misclassified by the XGBoost model in the validation dataset based on the confusion matrix at the maximum F1 score threshold?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0103_040_103040019_qa_2/task.toml b/tasks/0103_040_103040019_qa_2/task.toml index 6a7d9489f6a4486d3679bd8033bfd2bc5f06c6eb..b82e8776a7ec0ffa13802e0207105398bac4ca83 100644 --- a/tasks/0103_040_103040019_qa_2/task.toml +++ b/tasks/0103_040_103040019_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0103_040_103040019_qa_2" +name = "smoldataenvs-train/0103_040_103040019_qa_2" description = "What percentage of customers in the dataset are classified as churned (Churn = Yes)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26.53" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0103_040_103040019_qa_4/task.toml b/tasks/0103_040_103040019_qa_4/task.toml index 9c2ef547cc7cfb6856c7a7026b93f4e063f46283..d080e411b210bbda10bd602ce59028a1e8012a81 100644 --- a/tasks/0103_040_103040019_qa_4/task.toml +++ b/tasks/0103_040_103040019_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0103_040_103040019_qa_4" +name = "smoldataenvs-train/0103_040_103040019_qa_4" description = "What is the correlation coefficient between tenure and total charges, and does this relationship suggest a statistically significant association?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.826, yes" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0103_096_103096538_qa_3/task.toml b/tasks/0103_096_103096538_qa_3/task.toml index e9fe6c1b0a5ef8426476ecb0f3a3ed4c84b6b30d..cad6d6e970596945a06e780a002bde5491f341bc 100644 --- a/tasks/0103_096_103096538_qa_3/task.toml +++ b/tasks/0103_096_103096538_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0103_096_103096538_qa_3" +name = "smoldataenvs-train/0103_096_103096538_qa_3" description = "How many countries have more than 50 respondents in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0103_108_103108639_qa_4/task.toml b/tasks/0103_108_103108639_qa_4/task.toml index bc0fcbe343b5f3a7571b0920e2f884886fd4ad37..b3a01c31f2120b3f04be688275b2381d539eaac2 100644 --- a/tasks/0103_108_103108639_qa_4/task.toml +++ b/tasks/0103_108_103108639_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0103_108_103108639_qa_4" +name = "smoldataenvs-train/0103_108_103108639_qa_4" description = "How many patients are included in the test set after splitting the data for model training?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "268" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0103_134_103134692_qa_1/task.toml b/tasks/0103_134_103134692_qa_1/task.toml index ad6187e9c43b88f3006a4328bc6a0f5bc1f53a71..7c3c679b890fba38cfc015e76ad409b1494f4ca2 100644 --- a/tasks/0103_134_103134692_qa_1/task.toml +++ b/tasks/0103_134_103134692_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0103_134_103134692_qa_1" +name = "smoldataenvs-train/0103_134_103134692_qa_1" description = "What is the average insurance charge for non-smokers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8440.66" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0103_134_103134692_qa_4/task.toml b/tasks/0103_134_103134692_qa_4/task.toml index e9db810ac7dd3c2c823edd9f2e2bad34b3ad4bdb..cb5c35dc74a7b85bf64081a1ed18610830b562d1 100644 --- a/tasks/0103_134_103134692_qa_4/task.toml +++ b/tasks/0103_134_103134692_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0103_134_103134692_qa_4" +name = "smoldataenvs-train/0103_134_103134692_qa_4" description = "Which gender has a higher average insurance charge?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "male" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0103_147_103147643_qa_3/task.toml b/tasks/0103_147_103147643_qa_3/task.toml index d0d6ff9dfdc1f0151311a3967f8539e454524755..68be267c876ab9829adf0939f98d95eebc5c7319 100644 --- a/tasks/0103_147_103147643_qa_3/task.toml +++ b/tasks/0103_147_103147643_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0103_147_103147643_qa_3" +name = "smoldataenvs-train/0103_147_103147643_qa_3" description = "What is the highest recorded final grade (G3) achieved by any student in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "19" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0103_290_103290674_qa_3/task.toml b/tasks/0103_290_103290674_qa_3/task.toml index b8b4f9fc291e9d0ebe8223f95948113973501215..a538b2039b274fe075f28ff139525d083c3f6315 100644 --- a/tasks/0103_290_103290674_qa_3/task.toml +++ b/tasks/0103_290_103290674_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0103_290_103290674_qa_3" +name = "smoldataenvs-train/0103_290_103290674_qa_3" description = "Which feature in the original dataset has only one unique value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "veil-type" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0103_290_103290674_qa_4/task.toml b/tasks/0103_290_103290674_qa_4/task.toml index dfa6e2041aa68a8736f197cae4d374f262c9e671..9b3654e54cdfb0a3d584445a6955ca788e5c0dda 100644 --- a/tasks/0103_290_103290674_qa_4/task.toml +++ b/tasks/0103_290_103290674_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0103_290_103290674_qa_4" +name = "smoldataenvs-train/0103_290_103290674_qa_4" description = "What is the most common value in the 'cap-shape' feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "x" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0103_336_103336687_qa_4/task.toml b/tasks/0103_336_103336687_qa_4/task.toml index 5e5b92a58e715ebdeb90a580aa77643034c2f81d..ba485d26f62a919ece3d40a63360bb2cedca564f 100644 --- a/tasks/0103_336_103336687_qa_4/task.toml +++ b/tasks/0103_336_103336687_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0103_336_103336687_qa_4" +name = "smoldataenvs-train/0103_336_103336687_qa_4" description = "Which metric is lower at the end of training: the final training loss or the final test loss?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "training loss" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0103_361_103361984_qa_1/task.toml b/tasks/0103_361_103361984_qa_1/task.toml index 54da13b81c86b3eaa45195d2f12f24f6131ad95d..3a93e071aaae5795ce1d9877f4c458b602c81264 100644 --- a/tasks/0103_361_103361984_qa_1/task.toml +++ b/tasks/0103_361_103361984_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0103_361_103361984_qa_1" +name = "smoldataenvs-train/0103_361_103361984_qa_1" description = "Which categorical feature in the mushroom dataset shows the strongest correlation with the 'class' (edible/poisonous) based on Cramér's V analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "odor" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0103_361_103361984_qa_4/task.toml b/tasks/0103_361_103361984_qa_4/task.toml index 613901b2a0b7ed2ed9ab34f2263cd87bf2bd9373..9468cfedbf4682debb8566e3a7e969e4765dde67 100644 --- a/tasks/0103_361_103361984_qa_4/task.toml +++ b/tasks/0103_361_103361984_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0103_361_103361984_qa_4" +name = "smoldataenvs-train/0103_361_103361984_qa_4" description = "What was the accuracy of the Decision Tree model on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "100" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0103_407_103407932_qa_1/task.toml b/tasks/0103_407_103407932_qa_1/task.toml index 75af1d957d036a671c45cbf12feea2201b663b98..eb254e5da2dba6eb210fbc6e494dc95673df5f7e 100644 --- a/tasks/0103_407_103407932_qa_1/task.toml +++ b/tasks/0103_407_103407932_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0103_407_103407932_qa_1" +name = "smoldataenvs-train/0103_407_103407932_qa_1" description = "What is the p-value from the Augmented Dickey-Fuller test on the original non-stationary time series data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.991880" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0103_438_103438013_qa_1/task.toml b/tasks/0103_438_103438013_qa_1/task.toml index 927876a223b0c54c5c7250ee0b057ea291e98fec..176f85e1537d021dc2d4f319cef25b9ba5af8299 100644 --- a/tasks/0103_438_103438013_qa_1/task.toml +++ b/tasks/0103_438_103438013_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0103_438_103438013_qa_1" +name = "smoldataenvs-train/0103_438_103438013_qa_1" description = "Which year had the highest average global sales according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1989" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0103_452_103452394_qa_1/task.toml b/tasks/0103_452_103452394_qa_1/task.toml index a48a35202809e5ed958ded38f029b949a640679d..1cbc608358c88d72c6c3da9aa50c55a30b7f13ce 100644 --- a/tasks/0103_452_103452394_qa_1/task.toml +++ b/tasks/0103_452_103452394_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0103_452_103452394_qa_1" +name = "smoldataenvs-train/0103_452_103452394_qa_1" description = "What is the percentage of the dataset allocated to the test set after the 80-20 train-test split?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20.14" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0103_452_103452394_qa_2/task.toml b/tasks/0103_452_103452394_qa_2/task.toml index 16f87ff2054c23e97de3449aada2b374eccff6cb..3e18acedce1cb136de6e01242fcb4d94fae531fd 100644 --- a/tasks/0103_452_103452394_qa_2/task.toml +++ b/tasks/0103_452_103452394_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0103_452_103452394_qa_2" +name = "smoldataenvs-train/0103_452_103452394_qa_2" description = "What is the difference in passenger numbers between the last month of the training set and the first month of the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "14" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0103_502_103502811_qa_4/task.toml b/tasks/0103_502_103502811_qa_4/task.toml index aa9d3ad0b1364dcfa22c9c14196a21a324e8ef95..24c271bc3ab1660c6604cbe72991281404d065fc 100644 --- a/tasks/0103_502_103502811_qa_4/task.toml +++ b/tasks/0103_502_103502811_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0103_502_103502811_qa_4" +name = "smoldataenvs-train/0103_502_103502811_qa_4" description = "How many months are included in the test set after splitting the data with an 80-20 train-test ratio?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "29" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0103_513_103513049_qa_3/task.toml b/tasks/0103_513_103513049_qa_3/task.toml index aaac39ef6b11d2d51114013b3a21a6c6c4cca846..e9f15f14a0247cf1ee2f9b6b8638a98a941d1125 100644 --- a/tasks/0103_513_103513049_qa_3/task.toml +++ b/tasks/0103_513_103513049_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0103_513_103513049_qa_3" +name = "smoldataenvs-train/0103_513_103513049_qa_3" description = "What is the total duration of the dataset in days?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "373" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0103_514_103514732_qa_1/task.toml b/tasks/0103_514_103514732_qa_1/task.toml index c8afae4a871711164cf4c1a5fa27de5f57ea87e3..bc75069da47b6a5b3b14ddcdf0b76b1e30e64ca1 100644 --- a/tasks/0103_514_103514732_qa_1/task.toml +++ b/tasks/0103_514_103514732_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0103_514_103514732_qa_1" +name = "smoldataenvs-train/0103_514_103514732_qa_1" description = "How many differencing steps were required to achieve stationarity in the time series based on the ADF test results?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0103_514_103514732_qa_4/task.toml b/tasks/0103_514_103514732_qa_4/task.toml index 1c5d54cbd011902adfc5e1e4bd329c9eddac7879..1de06e1fe1c6f931e0ce278f68e3313ea94774e8 100644 --- a/tasks/0103_514_103514732_qa_4/task.toml +++ b/tasks/0103_514_103514732_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0103_514_103514732_qa_4" +name = "smoldataenvs-train/0103_514_103514732_qa_4" description = "What was the p-value of the ADF test after applying both Box-Cox transformation and second-order differencing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.0003" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0103_514_103514732_qa_5/task.toml b/tasks/0103_514_103514732_qa_5/task.toml index 20b82f7061aa7e4272fc9bb752df5b5ad3f5088e..73c313b0f54db791cfb0ee5488d99e9e46366630 100644 --- a/tasks/0103_514_103514732_qa_5/task.toml +++ b/tasks/0103_514_103514732_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0103_514_103514732_qa_5" +name = "smoldataenvs-train/0103_514_103514732_qa_5" description = "What percentage of the original dataset was used for training based on the train-test split?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "80" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0103_567_103567040_qa_1/task.toml b/tasks/0103_567_103567040_qa_1/task.toml index 776e2d318aa6673f612651ae633f162013362daa..b901a76bc30363d4516662e0db282c249180efc9 100644 --- a/tasks/0103_567_103567040_qa_1/task.toml +++ b/tasks/0103_567_103567040_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0103_567_103567040_qa_1" +name = "smoldataenvs-train/0103_567_103567040_qa_1" description = "Which four features have the highest feature importance scores according to the Random Forest analysis in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ram, battery_power, px_height, px_width" reward_mode_initial = "list" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0103_567_103567040_qa_3/task.toml b/tasks/0103_567_103567040_qa_3/task.toml index d31c6ad3f1ba0e3ae7b3327b85bb3f4f1ec6f099..2edc6a51be93626e0f8043424bb059a038213e08 100644 --- a/tasks/0103_567_103567040_qa_3/task.toml +++ b/tasks/0103_567_103567040_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0103_567_103567040_qa_3" +name = "smoldataenvs-train/0103_567_103567040_qa_3" description = "Which price_range category has the highest average battery power based on the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0103_644_103644122_qa_1/task.toml b/tasks/0103_644_103644122_qa_1/task.toml index fd595f494d526482fcb7232bceb0460dd9b001f0..873301649c5797028de92ccc71ff84b419968a3d 100644 --- a/tasks/0103_644_103644122_qa_1/task.toml +++ b/tasks/0103_644_103644122_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0103_644_103644122_qa_1" +name = "smoldataenvs-train/0103_644_103644122_qa_1" description = "After applying upsampling to balance the dataset, how many instances are present in each outcome class?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "500" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0103_655_103655219_qa_3/task.toml b/tasks/0103_655_103655219_qa_3/task.toml index 4f54856b7294eea0e6497d01d5b427e50a45f2ab..5698e7f7ff067681dd526dc53da8089ee3c4f02e 100644 --- a/tasks/0103_655_103655219_qa_3/task.toml +++ b/tasks/0103_655_103655219_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0103_655_103655219_qa_3" +name = "smoldataenvs-train/0103_655_103655219_qa_3" description = "What is the average price of diamonds in the dataset according to the descriptive statistics?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3932.799722" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0103_696_103696372_qa_1/task.toml b/tasks/0103_696_103696372_qa_1/task.toml index 054f70e509d51d77e6c644bdb49aea62c933a158..022b74b7fff1bd1a3e1ca70ed3673edb549185bd 100644 --- a/tasks/0103_696_103696372_qa_1/task.toml +++ b/tasks/0103_696_103696372_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0103_696_103696372_qa_1" +name = "smoldataenvs-train/0103_696_103696372_qa_1" description = "What is the maximum insurance charge observed for individuals who smoke in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "63770.42801" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0103_696_103696372_qa_4/task.toml b/tasks/0103_696_103696372_qa_4/task.toml index 572938c886a4c750853c5a76ffa03d0094d3c73c..bd37249feb2dc070ebfe80b9b0b259afa8beea05 100644 --- a/tasks/0103_696_103696372_qa_4/task.toml +++ b/tasks/0103_696_103696372_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0103_696_103696372_qa_4" +name = "smoldataenvs-train/0103_696_103696372_qa_4" description = "What is the correlation coefficient between age and BMI in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.109" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0103_724_103724181_qa_4/task.toml b/tasks/0103_724_103724181_qa_4/task.toml index 944f235ddd14b4fc826d4210dc384a040500a271..ffc806faae10828c965359a0139aa5d4bc90dbe2 100644 --- a/tasks/0103_724_103724181_qa_4/task.toml +++ b/tasks/0103_724_103724181_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0103_724_103724181_qa_4" +name = "smoldataenvs-train/0103_724_103724181_qa_4" description = "Which salary level has the highest proportion of employees who left the company?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "low" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0103_747_103747907_qa_1/task.toml b/tasks/0103_747_103747907_qa_1/task.toml index caae9354c2189bc683c9fafe29b21e6db2d21204..f2dada039010fed2171cb0192300c08e075b1f73 100644 --- a/tasks/0103_747_103747907_qa_1/task.toml +++ b/tasks/0103_747_103747907_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0103_747_103747907_qa_1" +name = "smoldataenvs-train/0103_747_103747907_qa_1" description = "Which variable has the highest positive correlation with the diabetes outcome in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0103_747_103747907_qa_3/task.toml b/tasks/0103_747_103747907_qa_3/task.toml index a901225cf945a5897aec5352a9bfb5ddbec673a6..49a3d4dd4b6a56d0b86d04b4131b20cad9199e39 100644 --- a/tasks/0103_747_103747907_qa_3/task.toml +++ b/tasks/0103_747_103747907_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0103_747_103747907_qa_3" +name = "smoldataenvs-train/0103_747_103747907_qa_3" description = "What is the proportion of individuals diagnosed with diabetes in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.349" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0103_757_103757929_qa_2/task.toml b/tasks/0103_757_103757929_qa_2/task.toml index 2347b29dd6fb11e7508e78de7e26d6219700cf40..99a8fe8923ba901b8d302bbf7ebc7593dce30167 100644 --- a/tasks/0103_757_103757929_qa_2/task.toml +++ b/tasks/0103_757_103757929_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0103_757_103757929_qa_2" +name = "smoldataenvs-train/0103_757_103757929_qa_2" description = "What is the average weight (carat) of diamonds in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.79794" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0103_757_103757929_qa_3/task.toml b/tasks/0103_757_103757929_qa_3/task.toml index f9a4f9eb42a73a76da6015b8cc60b300c4ef6692..b209d5dda92cb7e816076837f814b9a87f29e3a5 100644 --- a/tasks/0103_757_103757929_qa_3/task.toml +++ b/tasks/0103_757_103757929_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0103_757_103757929_qa_3" +name = "smoldataenvs-train/0103_757_103757929_qa_3" description = "What is the standard deviation of diamond prices in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3989.44" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0103_757_103757929_qa_4/task.toml b/tasks/0103_757_103757929_qa_4/task.toml index db3a68ff3a0e01a02cb918788ce73ee509f4aa2e..f8448e423b0e135aab12d8a62cba99a49c1a9dc6 100644 --- a/tasks/0103_757_103757929_qa_4/task.toml +++ b/tasks/0103_757_103757929_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0103_757_103757929_qa_4" +name = "smoldataenvs-train/0103_757_103757929_qa_4" description = "What is the highest price recorded for a diamond in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "18823" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0103_757_103757929_qa_5/task.toml b/tasks/0103_757_103757929_qa_5/task.toml index 7d5b322f9fa76d023abac02ac4fa65351d80a89d..67b41918a6a68f986c87d11f8d3d1e1f64e05e52 100644 --- a/tasks/0103_757_103757929_qa_5/task.toml +++ b/tasks/0103_757_103757929_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0103_757_103757929_qa_5" +name = "smoldataenvs-train/0103_757_103757929_qa_5" description = "How many unique diamond color categories are present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0103_797_103797974_qa_1/task.toml b/tasks/0103_797_103797974_qa_1/task.toml index 6a785d2a76f9faf3fd2e7208fcc4915944534bef..fe5fdcd1cbcd90cfc20b6e89b03d347efb12e237 100644 --- a/tasks/0103_797_103797974_qa_1/task.toml +++ b/tasks/0103_797_103797974_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0103_797_103797974_qa_1" +name = "smoldataenvs-train/0103_797_103797974_qa_1" description = "Which classification model achieved the highest area under the ROC curve (AUROC) in the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Logistic Regression" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0103_859_103859537_qa_1/task.toml b/tasks/0103_859_103859537_qa_1/task.toml index 304e4315027702815d56a0f4e13d28525025c47c..b87b2648751119c2f43ccaca1a851f0741d92f41 100644 --- a/tasks/0103_859_103859537_qa_1/task.toml +++ b/tasks/0103_859_103859537_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0103_859_103859537_qa_1" +name = "smoldataenvs-train/0103_859_103859537_qa_1" description = "Which year had the highest global sales for a single video game, and what was the sales figure in millions of US dollars?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2006, 82.74" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0103_884_103884351_qa_1/task.toml b/tasks/0103_884_103884351_qa_1/task.toml index d497950a8bbf54c044008402265b4d9b2ddb79e4..ecabfdfd66079a2a7644d2389582d5bbb365f716 100644 --- a/tasks/0103_884_103884351_qa_1/task.toml +++ b/tasks/0103_884_103884351_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0103_884_103884351_qa_1" +name = "smoldataenvs-train/0103_884_103884351_qa_1" description = "What percentage of individuals in the dataset have an income greater than $50K?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "23.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0103_889_103889952_qa_2/task.toml b/tasks/0103_889_103889952_qa_2/task.toml index 8c5ce1f412ab8fe381a831653656df7cce72f271..d61e21ad9ac1aeb3efdc519ab04e053f6b21bd2b 100644 --- a/tasks/0103_889_103889952_qa_2/task.toml +++ b/tasks/0103_889_103889952_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0103_889_103889952_qa_2" +name = "smoldataenvs-train/0103_889_103889952_qa_2" description = "What is the mean value of the 'Item_Weight' column after the missing values were imputed using the mean strategy?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12.857645" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0103_889_103889952_qa_3/task.toml b/tasks/0103_889_103889952_qa_3/task.toml index cbea16e4dfc1a7e09d98dfac8e2a61208fbb7ad0..b0a6ffb3cdce92ff4b9743f1b570cf26e390bdab 100644 --- a/tasks/0103_889_103889952_qa_3/task.toml +++ b/tasks/0103_889_103889952_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0103_889_103889952_qa_3" +name = "smoldataenvs-train/0103_889_103889952_qa_3" description = "What is the median value of the 'Item_Outlet_Sales' column based on the statistical summary provided in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1794.3310" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0103_923_103923129_qa_1/task.toml b/tasks/0103_923_103923129_qa_1/task.toml index d572a7038e8d3f62ed7967155b0f0a3fb4b60199..c8b8a60cf21894b954ffdb5fdf7f8b12ed1dd53a 100644 --- a/tasks/0103_923_103923129_qa_1/task.toml +++ b/tasks/0103_923_103923129_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0103_923_103923129_qa_1" +name = "smoldataenvs-train/0103_923_103923129_qa_1" description = "Which feature is identified as the most important by the RandomForestClassifier based on feature importance analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "odor" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0103_977_103977416_qa_5/task.toml b/tasks/0103_977_103977416_qa_5/task.toml index 57ec26516180451b778144beef34d99a35c5d14a..755045618b65f1fe1e9cd60fceb8936996c5f6f9 100644 --- a/tasks/0103_977_103977416_qa_5/task.toml +++ b/tasks/0103_977_103977416_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0103_977_103977416_qa_5" +name = "smoldataenvs-train/0103_977_103977416_qa_5" description = "What is the precision score for class 1 (exoplanets) in the Logistic Regression model's performance metrics?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.97" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0104_079_104079493_qa_3/task.toml b/tasks/0104_079_104079493_qa_3/task.toml index c19f3b5ec9b9a2c6717854f1520da78f46f13238..050b883ab72d65f0c8eeef07397bde0477aa6f8a 100644 --- a/tasks/0104_079_104079493_qa_3/task.toml +++ b/tasks/0104_079_104079493_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0104_079_104079493_qa_3" +name = "smoldataenvs-train/0104_079_104079493_qa_3" description = "Are the SVM and Neural Network models statistically different in terms of cross-validation accuracy according to the Tukey HSD test?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0104_115_104115135_qa_4/task.toml b/tasks/0104_115_104115135_qa_4/task.toml index 6ccca800c24749646e1f8bc06eefc0e6d2f1d9ba..91779f77a96168a8eebf033f6eeb7ab63e0eda4a 100644 --- a/tasks/0104_115_104115135_qa_4/task.toml +++ b/tasks/0104_115_104115135_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0104_115_104115135_qa_4" +name = "smoldataenvs-train/0104_115_104115135_qa_4" description = "Which feature in the original dataset shows a normal distribution based on the exploratory data analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "pH" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0104_115_104115755_qa_1/task.toml b/tasks/0104_115_104115755_qa_1/task.toml index 33210c95649e6c341ea7a2f474e912cf3d6410bb..f60379c70607e7e3dd25a1d4db4ff2f9289171ba 100644 --- a/tasks/0104_115_104115755_qa_1/task.toml +++ b/tasks/0104_115_104115755_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0104_115_104115755_qa_1" +name = "smoldataenvs-train/0104_115_104115755_qa_1" description = "What is the most reported product type by count of adverse events in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Vit/Min/Prot/Unconv Diet(Human/Animal)" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0104_115_104115755_qa_3/task.toml b/tasks/0104_115_104115755_qa_3/task.toml index 941afb1578f01c8b5b9070cc2a0d675cead18f5c..e447ae88840b1602397d90b815c6df9eeec4cae9 100644 --- a/tasks/0104_115_104115755_qa_3/task.toml +++ b/tasks/0104_115_104115755_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0104_115_104115755_qa_3" +name = "smoldataenvs-train/0104_115_104115755_qa_3" description = "How many duplicate reports were removed from the dataset during preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26269" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0104_115_104115755_qa_4/task.toml b/tasks/0104_115_104115755_qa_4/task.toml index f13b70795f3f99611caf7b519e2b32a24b851b3a..dfdc067946d15eef8a390f466323da910b804a91 100644 --- a/tasks/0104_115_104115755_qa_4/task.toml +++ b/tasks/0104_115_104115755_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0104_115_104115755_qa_4" +name = "smoldataenvs-train/0104_115_104115755_qa_4" description = "Which column in the dataset has the highest number of missing values, and how many are missing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "age, 37860" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0104_138_104138961_qa_2/task.toml b/tasks/0104_138_104138961_qa_2/task.toml index f0e6d28c6d9e62272280f04e1fda982b9aa3de75..d3dbbbf4b7c1d9b32dc466702459398ccdd0f4e8 100644 --- a/tasks/0104_138_104138961_qa_2/task.toml +++ b/tasks/0104_138_104138961_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0104_138_104138961_qa_2" +name = "smoldataenvs-train/0104_138_104138961_qa_2" description = "How many numerical variables in the dataset have outliers identified in the boxplot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "30" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0104_138_104138961_qa_3/task.toml b/tasks/0104_138_104138961_qa_3/task.toml index 98ac1a108c8573e96b92ab209e48cafaf5e810b4..9fac41e6f3e2190984321c5690ce9a0c86afce00 100644 --- a/tasks/0104_138_104138961_qa_3/task.toml +++ b/tasks/0104_138_104138961_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0104_138_104138961_qa_3" +name = "smoldataenvs-train/0104_138_104138961_qa_3" description = "What is the baseline accuracy achieved by predicting the majority class in the diagnosis column of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "62.74" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0104_143_104143863_qa_1/task.toml b/tasks/0104_143_104143863_qa_1/task.toml index 9b32f003d9e5cdf93044a21addca2bd780bf5835..1fe56e382a56819cc28ff9784533ed4a3e2880cc 100644 --- a/tasks/0104_143_104143863_qa_1/task.toml +++ b/tasks/0104_143_104143863_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0104_143_104143863_qa_1" +name = "smoldataenvs-train/0104_143_104143863_qa_1" description = "Which demographic variables in the dataset show statistically significant differences in churn rates according to the t-tests performed?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Dependents, Partner, SeniorCitizen" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0104_143_104143863_qa_3/task.toml b/tasks/0104_143_104143863_qa_3/task.toml index 39fd8406b470291f0a7270c4b10a509361f33ebd..a35c642c1846b8e31b20e0d8d460a9abe91a2211 100644 --- a/tasks/0104_143_104143863_qa_3/task.toml +++ b/tasks/0104_143_104143863_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0104_143_104143863_qa_3" +name = "smoldataenvs-train/0104_143_104143863_qa_3" description = "Which clusters exhibit the highest churn rates according to the cluster analysis results?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0, 2" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0104_159_104159135_qa_3/task.toml b/tasks/0104_159_104159135_qa_3/task.toml index 88ce72036011e5a77311a35be599a5145c065eba..d16d228079b21f36b7e5fb6cd86b2be86bc4bcad 100644 --- a/tasks/0104_159_104159135_qa_3/task.toml +++ b/tasks/0104_159_104159135_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0104_159_104159135_qa_3" +name = "smoldataenvs-train/0104_159_104159135_qa_3" description = "How many columns in the dataset are of the float64 data type?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0104_159_104159135_qa_4/task.toml b/tasks/0104_159_104159135_qa_4/task.toml index 6673623bb6b8588a6c6a65f33ba89e6a50f8f77b..3ae7c1ac78ce6f145d8d6642002e65c9c431a836 100644 --- a/tasks/0104_159_104159135_qa_4/task.toml +++ b/tasks/0104_159_104159135_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0104_159_104159135_qa_4" +name = "smoldataenvs-train/0104_159_104159135_qa_4" description = "What is the average (mean) year of release for video games in this dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2006.406443" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0104_159_104159135_qa_5/task.toml b/tasks/0104_159_104159135_qa_5/task.toml index 8def600609030eb24c1f40a5760c3fd5b3b8b5e1..969877322f64e11a44ba7684afa8b20f1b136744 100644 --- a/tasks/0104_159_104159135_qa_5/task.toml +++ b/tasks/0104_159_104159135_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0104_159_104159135_qa_5" +name = "smoldataenvs-train/0104_159_104159135_qa_5" description = "How many non-null entries are present in the 'Year' column of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16327" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0104_172_104172936_qa_1/task.toml b/tasks/0104_172_104172936_qa_1/task.toml index a6491128c29e144462d7f02d8f335a72017969ec..6bc00e53ea9c654620749a4a7f6bab9ca6f6a992 100644 --- a/tasks/0104_172_104172936_qa_1/task.toml +++ b/tasks/0104_172_104172936_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0104_172_104172936_qa_1" +name = "smoldataenvs-train/0104_172_104172936_qa_1" description = "Which favorite color category has the highest proportion of females, and what is that proportion?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Warm, 59.09" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0104_172_104172936_qa_3/task.toml b/tasks/0104_172_104172936_qa_3/task.toml index 60ea19227fcd16b311d4bc0441eec6b30adada35..6ce435ddf7ecd018074a353965ae35d4464ffe29 100644 --- a/tasks/0104_172_104172936_qa_3/task.toml +++ b/tasks/0104_172_104172936_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0104_172_104172936_qa_3" +name = "smoldataenvs-train/0104_172_104172936_qa_3" description = "Which \"Favorite Soft Drink\" category has the highest average male proportion based on the gender mapping (0=F, 1=M)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Other" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0104_194_104194599_qa_4/task.toml b/tasks/0104_194_104194599_qa_4/task.toml index 9f351659f1b90cbe75fdcfac29bc99b65e105285..2fcfc60af47d8cc8b2fd477634fb5e30625e1116 100644 --- a/tasks/0104_194_104194599_qa_4/task.toml +++ b/tasks/0104_194_104194599_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0104_194_104194599_qa_4" +name = "smoldataenvs-train/0104_194_104194599_qa_4" description = "What is the average BMI for individuals with exactly 3 children in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "30.68" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0104_261_104261137_qa_1/task.toml b/tasks/0104_261_104261137_qa_1/task.toml index 43f420a5aede16a39d9d7a6e407176bc5d8d1c7a..d63788c151c96d18b80ff7f7ab517da5c25a647f 100644 --- a/tasks/0104_261_104261137_qa_1/task.toml +++ b/tasks/0104_261_104261137_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0104_261_104261137_qa_1" +name = "smoldataenvs-train/0104_261_104261137_qa_1" description = "Which regression model achieved the highest cross-validated R² score during model evaluation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "GradientBoostingRegressor" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0104_261_104261356_qa_5/task.toml b/tasks/0104_261_104261356_qa_5/task.toml index 095e77d53646e8601db4efd447f23cc5437abc17..125a63f15e0d7ae9baead62e2c97d8e140821b29 100644 --- a/tasks/0104_261_104261356_qa_5/task.toml +++ b/tasks/0104_261_104261356_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0104_261_104261356_qa_5" +name = "smoldataenvs-train/0104_261_104261356_qa_5" description = "What is the median insurance charge across all individuals in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9382.033" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0104_333_104333921_qa_3/task.toml b/tasks/0104_333_104333921_qa_3/task.toml index 4d5699e6b58b4744c93aabfce9b9cb15d4284186..f564c50bffeef16d273383fec285beafda1db1c5 100644 --- a/tasks/0104_333_104333921_qa_3/task.toml +++ b/tasks/0104_333_104333921_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0104_333_104333921_qa_3" +name = "smoldataenvs-train/0104_333_104333921_qa_3" description = "Which contract type has the highest proportion of churned customers according to the categorical analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Month-to-month" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0104_333_104333921_qa_4/task.toml b/tasks/0104_333_104333921_qa_4/task.toml index e26d7c917a8b6a66d1fce92aba33255b143a5c8a..cc4d9b49c61927b820b4a06c31a5b4223b6a0ba5 100644 --- a/tasks/0104_333_104333921_qa_4/task.toml +++ b/tasks/0104_333_104333921_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0104_333_104333921_qa_4" +name = "smoldataenvs-train/0104_333_104333921_qa_4" description = "What payment method is associated with the highest customer churn rate in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic check" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0104_391_104391116_qa_1/task.toml b/tasks/0104_391_104391116_qa_1/task.toml index 3948bd034ef5703517a9898727654d11c103ead5..b55cd36a96e6930c5e0ba66cb6c206ce29c556b0 100644 --- a/tasks/0104_391_104391116_qa_1/task.toml +++ b/tasks/0104_391_104391116_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0104_391_104391116_qa_1" +name = "smoldataenvs-train/0104_391_104391116_qa_1" description = "Which diamond cut type has the highest average price based on the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Premium" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0104_399_104399279_qa_1/task.toml b/tasks/0104_399_104399279_qa_1/task.toml index e28b6f2186989693cd5e3267d896a73c86ad5081..6c8a02097185ab9dbebfbebdfb2d37769a5cc704 100644 --- a/tasks/0104_399_104399279_qa_1/task.toml +++ b/tasks/0104_399_104399279_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0104_399_104399279_qa_1" +name = "smoldataenvs-train/0104_399_104399279_qa_1" description = "What percentage of the dataset consists of individuals diagnosed with diabetes?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0104_399_104399279_qa_3/task.toml b/tasks/0104_399_104399279_qa_3/task.toml index 2841edfdbaeaa2a07febd615e24fd36edba584cc..c833e4344cf23ab7cb5079eda243400e1829c4ed 100644 --- a/tasks/0104_399_104399279_qa_3/task.toml +++ b/tasks/0104_399_104399279_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0104_399_104399279_qa_3" +name = "smoldataenvs-train/0104_399_104399279_qa_3" description = "Which feature in the dataset exhibits the highest standard deviation in its original (non-standardized) values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Insulin" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0104_425_104425249_qa_1/task.toml b/tasks/0104_425_104425249_qa_1/task.toml index d0343eb01f02181531fd9af40d0e0966c6f02047..b268ddd96058d555aa657cce927ac9029d92b71b 100644 --- a/tasks/0104_425_104425249_qa_1/task.toml +++ b/tasks/0104_425_104425249_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0104_425_104425249_qa_1" +name = "smoldataenvs-train/0104_425_104425249_qa_1" description = "After stratified sampling based on income categories, what percentage of the training set belongs to the highest income category (category 5)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11.4462%" reward_mode_initial = "flexible" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0104_503_104503087_qa_2/task.toml b/tasks/0104_503_104503087_qa_2/task.toml index 36238614f9b583e9754f05a0c7612ff40006aa2d..8a6e4b1cc4b01e9a5943c5cdd1453ad46f35c4ca 100644 --- a/tasks/0104_503_104503087_qa_2/task.toml +++ b/tasks/0104_503_104503087_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0104_503_104503087_qa_2" +name = "smoldataenvs-train/0104_503_104503087_qa_2" description = "What is the accuracy of the K-Nearest-Neighbors classifier on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0104_503_104503087_qa_3/task.toml b/tasks/0104_503_104503087_qa_3/task.toml index aaa9641d9a3bfc16618119db82b2f7597bb8b9bb..319e8e3bc678f7ff3d38106304c2a7e3ee329204 100644 --- a/tasks/0104_503_104503087_qa_3/task.toml +++ b/tasks/0104_503_104503087_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0104_503_104503087_qa_3" +name = "smoldataenvs-train/0104_503_104503087_qa_3" description = "What is the F1-score for the poisonous class (class 1) in the Random Forest model's test set predictions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.00" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0104_518_104518190_qa_2/task.toml b/tasks/0104_518_104518190_qa_2/task.toml index 62a7c2607b0231fa4c4489505f76763ce46c4eb2..f083821fac56f36e30f0b343a090fd8e988b9807 100644 --- a/tasks/0104_518_104518190_qa_2/task.toml +++ b/tasks/0104_518_104518190_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0104_518_104518190_qa_2" +name = "smoldataenvs-train/0104_518_104518190_qa_2" description = "What is the average monthly charge for all customers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "64.80" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0104_518_104518190_qa_4/task.toml b/tasks/0104_518_104518190_qa_4/task.toml index 84f98d6c2ad84c043d56ba98500dbf37af19720c..0748b8f3df593cebea448b50e35083af3afb9751 100644 --- a/tasks/0104_518_104518190_qa_4/task.toml +++ b/tasks/0104_518_104518190_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0104_518_104518190_qa_4" +name = "smoldataenvs-train/0104_518_104518190_qa_4" description = "What is the most common payment method used by customers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic check" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0104_567_104567275_qa_5/task.toml b/tasks/0104_567_104567275_qa_5/task.toml index 307612e188cfbaab54dac57dd01fc7e9ca571d85..be115a22d2e4b1ea5e7aa34ba57213f916f5386e 100644 --- a/tasks/0104_567_104567275_qa_5/task.toml +++ b/tasks/0104_567_104567275_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0104_567_104567275_qa_5" +name = "smoldataenvs-train/0104_567_104567275_qa_5" description = "How many categorical variables were label encoded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0104_591_104591892_qa_3/task.toml b/tasks/0104_591_104591892_qa_3/task.toml index 84ab78b02b6dfc884e464513e64d1b2b9546cb95..03b457e0b9351a840eadc1c5fd14e710af916654 100644 --- a/tasks/0104_591_104591892_qa_3/task.toml +++ b/tasks/0104_591_104591892_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0104_591_104591892_qa_3" +name = "smoldataenvs-train/0104_591_104591892_qa_3" description = "Which personality dimension (I/E, N/S, T/F, J/P) has the most balanced distribution between its two traits, and what is the difference in their counts?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "T/F, 713" reward_mode_initial = "list" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0104_591_104591892_qa_4/task.toml b/tasks/0104_591_104591892_qa_4/task.toml index fcc19a099e85324d8b43f03cc5ab9f417475a6d7..1347992ec13a98cb78c122c6bfcc11088d364757 100644 --- a/tasks/0104_591_104591892_qa_4/task.toml +++ b/tasks/0104_591_104591892_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0104_591_104591892_qa_4" +name = "smoldataenvs-train/0104_591_104591892_qa_4" description = "Which personality dimension has the largest disparity between its two traits, and what is the difference in their counts?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Intuition/Sensing, 6281" reward_mode_initial = "list" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0104_601_104601398_qa_4/task.toml b/tasks/0104_601_104601398_qa_4/task.toml index 9cf072da8f575604ec1f1da9bd6bc2dddcb33920..70260d5cc14171cd9822a7e82a78a8198fccbfd1 100644 --- a/tasks/0104_601_104601398_qa_4/task.toml +++ b/tasks/0104_601_104601398_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0104_601_104601398_qa_4" +name = "smoldataenvs-train/0104_601_104601398_qa_4" description = "How many missing values were present in the 'open' column before data cleaning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0104_747_104747466_qa_2/task.toml b/tasks/0104_747_104747466_qa_2/task.toml index 36d0da67bcab2fede226e9ef45503e297f232223..aca5a5d4e7706998ec813b3e56adcb0c6825d1a0 100644 --- a/tasks/0104_747_104747466_qa_2/task.toml +++ b/tasks/0104_747_104747466_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0104_747_104747466_qa_2" +name = "smoldataenvs-train/0104_747_104747466_qa_2" description = "What is the most common category of arrest among NFL players during this period?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "DUI" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0104_747_104747466_qa_3/task.toml b/tasks/0104_747_104747466_qa_3/task.toml index cae795a21c864716145b75fa0826e587738c026a..41af46ecf4cbf1f5fe61f2cb619b652ce6312815 100644 --- a/tasks/0104_747_104747466_qa_3/task.toml +++ b/tasks/0104_747_104747466_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0104_747_104747466_qa_3" +name = "smoldataenvs-train/0104_747_104747466_qa_3" description = "Which player was arrested the most times in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Adam Jones" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0104_867_104867600_qa_5/task.toml b/tasks/0104_867_104867600_qa_5/task.toml index 86d1debcf628b5a252e651e174b1f1ca79034788..51b67d6f5d9c10a74b6bb70c15cc017ce9f59feb 100644 --- a/tasks/0104_867_104867600_qa_5/task.toml +++ b/tasks/0104_867_104867600_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0104_867_104867600_qa_5" +name = "smoldataenvs-train/0104_867_104867600_qa_5" description = "Which department has the largest number of employees according to the pie chart visualization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Sales" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0104_868_104868299_qa_1/task.toml b/tasks/0104_868_104868299_qa_1/task.toml index 9988fcf6363aad8b0da512ec9d846b120095616f..e570044918704ffb5fb168ffe587dd094310e859 100644 --- a/tasks/0104_868_104868299_qa_1/task.toml +++ b/tasks/0104_868_104868299_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0104_868_104868299_qa_1" +name = "smoldataenvs-train/0104_868_104868299_qa_1" description = "What percentage of patients survived more than 5 years after treatment according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "73.53" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0104_868_104868299_qa_2/task.toml b/tasks/0104_868_104868299_qa_2/task.toml index b623c4c13b79652a10f8d2e7e446112533ab5ef3..1c1d2841b5ae8d8ac310a925e7be29835afb997d 100644 --- a/tasks/0104_868_104868299_qa_2/task.toml +++ b/tasks/0104_868_104868299_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0104_868_104868299_qa_2" +name = "smoldataenvs-train/0104_868_104868299_qa_2" description = "Which five years had the highest number of patients treated based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1958, 1964, 1963, 1965, 1960" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0104_905_104905979_qa_1/task.toml b/tasks/0104_905_104905979_qa_1/task.toml index 2429da79144160c0d3a7cc2605e0c3ec74ecce36..cf304350a9b428fae6c93d5d7b8d8a09f8d6903b 100644 --- a/tasks/0104_905_104905979_qa_1/task.toml +++ b/tasks/0104_905_104905979_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0104_905_104905979_qa_1" +name = "smoldataenvs-train/0104_905_104905979_qa_1" description = "What is the accuracy of the customer churn prediction model on the test dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "78.54" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0104_905_104905979_qa_3/task.toml b/tasks/0104_905_104905979_qa_3/task.toml index 90da21b8fe01a5816fc83e22bac0aff96860638f..1f328e3d0c1bd67c9c3c01b6c9bfd227ced066a8 100644 --- a/tasks/0104_905_104905979_qa_3/task.toml +++ b/tasks/0104_905_104905979_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0104_905_104905979_qa_3" +name = "smoldataenvs-train/0104_905_104905979_qa_3" description = "What is the recall score for predicting churned customers (class 1) in the model's predictions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.55" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0104_919_104919033_qa_4/task.toml b/tasks/0104_919_104919033_qa_4/task.toml index 9fe518f68ce9dba5c24e29afb9d5c688f5d771a8..259423add4996fc0ca25f0f1ba685163fadb3959 100644 --- a/tasks/0104_919_104919033_qa_4/task.toml +++ b/tasks/0104_919_104919033_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0104_919_104919033_qa_4" +name = "smoldataenvs-train/0104_919_104919033_qa_4" description = "After encoding the categorical 'ocean_proximity' feature, how many unique integer values represent distinct location categories in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0104_947_104947882_qa_3/task.toml b/tasks/0104_947_104947882_qa_3/task.toml index 8449f8a8a48849fd72a3fa85758bae757b669503..5e62360acd6aafbc38b7b005a0cfd2b14a4bf344 100644 --- a/tasks/0104_947_104947882_qa_3/task.toml +++ b/tasks/0104_947_104947882_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0104_947_104947882_qa_3" +name = "smoldataenvs-train/0104_947_104947882_qa_3" description = "After imputation, was there any column with missing values remaining in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0104_955_104955589_qa_3/task.toml b/tasks/0104_955_104955589_qa_3/task.toml index 0ae463e621cac91b296700f45b5ae393741ea5cf..8446b033221ee0a60c8318a59f74184d5c360884 100644 --- a/tasks/0104_955_104955589_qa_3/task.toml +++ b/tasks/0104_955_104955589_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0104_955_104955589_qa_3" +name = "smoldataenvs-train/0104_955_104955589_qa_3" description = "What is the mean BMI value for males compared to females in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "males=30.94, females=30.38" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0104_955_104955589_qa_5/task.toml b/tasks/0104_955_104955589_qa_5/task.toml index d6eab3a01ee8d00386b46654d2c94e8818fbdde0..2dbddf042f70b9c5d3006ff63ca3008287384e0d 100644 --- a/tasks/0104_955_104955589_qa_5/task.toml +++ b/tasks/0104_955_104955589_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0104_955_104955589_qa_5" +name = "smoldataenvs-train/0104_955_104955589_qa_5" description = "What is the correlation coefficient between age and BMI variables in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.08" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0104_975_104975340_qa_4/task.toml b/tasks/0104_975_104975340_qa_4/task.toml index bc4a75fe4d710896a6700467d037ea163ac27d4b..5657bec842be31ec3216591c4090d4a8760e6e20 100644 --- a/tasks/0104_975_104975340_qa_4/task.toml +++ b/tasks/0104_975_104975340_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0104_975_104975340_qa_4" +name = "smoldataenvs-train/0104_975_104975340_qa_4" description = "What was the number of missing values in the 'total_bedrooms' column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "207" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0104_975_104975438_qa_3/task.toml b/tasks/0104_975_104975438_qa_3/task.toml index c8e0e9648327c917540125fde8ca7dcc95c28caf..cdeec4c61bdc5243d13742bb776358de6dee1ce1 100644 --- a/tasks/0104_975_104975438_qa_3/task.toml +++ b/tasks/0104_975_104975438_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0104_975_104975438_qa_3" +name = "smoldataenvs-train/0104_975_104975438_qa_3" description = "How many unique categories were present in the ocean_proximity column before encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0104_985_104985612_qa_1/task.toml b/tasks/0104_985_104985612_qa_1/task.toml index 92f3360d6137222e6958de538b4d7767ab88eaa4..b324fcdeb6f75d3f55857191de08bc7a01ffc6cf 100644 --- a/tasks/0104_985_104985612_qa_1/task.toml +++ b/tasks/0104_985_104985612_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0104_985_104985612_qa_1" +name = "smoldataenvs-train/0104_985_104985612_qa_1" description = "Which ocean proximity category has the highest number of entries in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "<1H OCEAN" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0104_985_104985612_qa_4/task.toml b/tasks/0104_985_104985612_qa_4/task.toml index d2cf08174021a8783960b3f7b0b433cc1614537a..a4a2e33a3077b941118b6986029496479fcce212 100644 --- a/tasks/0104_985_104985612_qa_4/task.toml +++ b/tasks/0104_985_104985612_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0104_985_104985612_qa_4" +name = "smoldataenvs-train/0104_985_104985612_qa_4" description = "Which predictor variable in the linear regression model is not statistically significant at the 5% confidence level based on p-values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ocean_proximity" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0104_985_104985612_qa_5/task.toml b/tasks/0104_985_104985612_qa_5/task.toml index 2856dcd434f16d22549d14e1c83844765119f370..5b878edc18234f1f24ddbdd74c9453b731bf432a 100644 --- a/tasks/0104_985_104985612_qa_5/task.toml +++ b/tasks/0104_985_104985612_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0104_985_104985612_qa_5" +name = "smoldataenvs-train/0104_985_104985612_qa_5" description = "What is the difference in the number of entries between the most and second most common ocean proximity categories?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2585" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0105_052_105052091_qa_1/task.toml b/tasks/0105_052_105052091_qa_1/task.toml index 21d6e1a286a9d00c9eaeaa057ae2b43679387fcc..808785369e1d99be7b8a346c285c7ee5d9de29e3 100644 --- a/tasks/0105_052_105052091_qa_1/task.toml +++ b/tasks/0105_052_105052091_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0105_052_105052091_qa_1" +name = "smoldataenvs-train/0105_052_105052091_qa_1" description = "How many missing values were present in the 'total_bedrooms' column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "207" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0105_052_105052091_qa_2/task.toml b/tasks/0105_052_105052091_qa_2/task.toml index a8a5d548a501b192421afb2a61de8a32af202bbc..885aa7d0b07bd4366ae036382a7e674d4d30650f 100644 --- a/tasks/0105_052_105052091_qa_2/task.toml +++ b/tasks/0105_052_105052091_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0105_052_105052091_qa_2" +name = "smoldataenvs-train/0105_052_105052091_qa_2" description = "Which ocean proximity category had the highest frequency after label encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "<1H OCEAN" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0105_054_105054048_qa_1/task.toml b/tasks/0105_054_105054048_qa_1/task.toml index 6e1e7a6e0daa18ac4132dc84424b9bd64910d01a..0694e9058dc35091f14d39ab517398288fe3ede1 100644 --- a/tasks/0105_054_105054048_qa_1/task.toml +++ b/tasks/0105_054_105054048_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0105_054_105054048_qa_1" +name = "smoldataenvs-train/0105_054_105054048_qa_1" description = "What is the most common first move for white, and which black response is most frequently played against it?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "e4, e5" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0105_054_105054048_qa_4/task.toml b/tasks/0105_054_105054048_qa_4/task.toml index 10e51a487611e8c41b71b555cc76aae081f602e6..46c353953c3c8a365ecff4fd960ab8474566a661 100644 --- a/tasks/0105_054_105054048_qa_4/task.toml +++ b/tasks/0105_054_105054048_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0105_054_105054048_qa_4" +name = "smoldataenvs-train/0105_054_105054048_qa_4" description = "What is the most contested square in the Sicilian Defense (e4-c5) based on the heatmap analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "d4" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0105_059_105059592_qa_1/task.toml b/tasks/0105_059_105059592_qa_1/task.toml index 40056ad9b6046600334e782fee96d299955a6c38..d33ddd145158c23a6bbde4cca1e047ae5b39b800 100644 --- a/tasks/0105_059_105059592_qa_1/task.toml +++ b/tasks/0105_059_105059592_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0105_059_105059592_qa_1" +name = "smoldataenvs-train/0105_059_105059592_qa_1" description = "What is the correlation coefficient between customer tenure and churn in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.354" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0105_059_105059592_qa_2/task.toml b/tasks/0105_059_105059592_qa_2/task.toml index 12ee6ca4c0bdf6034aac218939f0a048132bc514..41be35d522540f85c6c2ba9db2712bff155708c1 100644 --- a/tasks/0105_059_105059592_qa_2/task.toml +++ b/tasks/0105_059_105059592_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0105_059_105059592_qa_2" +name = "smoldataenvs-train/0105_059_105059592_qa_2" description = "How many rows were removed from the dataset after eliminating entries with missing TotalCharges values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0105_059_105059592_qa_3/task.toml b/tasks/0105_059_105059592_qa_3/task.toml index de9eb4170b1b3339af00e50bed2e70137942f450..d9812549a21120b246fcada0127ac42d4e6d49a8 100644 --- a/tasks/0105_059_105059592_qa_3/task.toml +++ b/tasks/0105_059_105059592_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0105_059_105059592_qa_3" +name = "smoldataenvs-train/0105_059_105059592_qa_3" description = "Which contract type has the lowest churn rate according to the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Two year" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0105_059_105059592_qa_4/task.toml b/tasks/0105_059_105059592_qa_4/task.toml index c36fcc517c3a8cca2a8e8e024c851f96b92421b8..04eb708232bcf9cd56d657165fa88030c8b9f37f 100644 --- a/tasks/0105_059_105059592_qa_4/task.toml +++ b/tasks/0105_059_105059592_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0105_059_105059592_qa_4" +name = "smoldataenvs-train/0105_059_105059592_qa_4" description = "How many unique categories were present in the PaymentMethod column before applying one-hot encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0105_059_105059592_qa_5/task.toml b/tasks/0105_059_105059592_qa_5/task.toml index 62603c4c4d14cdc9b473320616accee991169d06..34e0be4eedf7463d05b9d5c37a1de8075814eba4 100644 --- a/tasks/0105_059_105059592_qa_5/task.toml +++ b/tasks/0105_059_105059592_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0105_059_105059592_qa_5" +name = "smoldataenvs-train/0105_059_105059592_qa_5" description = "After applying SMOTE, what is the new count of churned customers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5163" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0105_060_105060710_qa_5/task.toml b/tasks/0105_060_105060710_qa_5/task.toml index c68a598c6669f98d7d10dc9ea0d8e9ad321a2468..029febbe0dd7d182f5171e0b61b908a5e78ed87c 100644 --- a/tasks/0105_060_105060710_qa_5/task.toml +++ b/tasks/0105_060_105060710_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0105_060_105060710_qa_5" +name = "smoldataenvs-train/0105_060_105060710_qa_5" description = "How many rows were removed from the dataset as outliers during the IQR-based outlier removal process?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3019" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0105_079_105079385_qa_4/task.toml b/tasks/0105_079_105079385_qa_4/task.toml index 4acfbcf40bd1012abc7744a1211101f42c71eb0b..b68c3341a6302e83b185cbe8446ec2aa4ba8074c 100644 --- a/tasks/0105_079_105079385_qa_4/task.toml +++ b/tasks/0105_079_105079385_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0105_079_105079385_qa_4" +name = "smoldataenvs-train/0105_079_105079385_qa_4" description = "How many unique categorical values existed in the Item_Type column before label encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0105_115_105115907_qa_1/task.toml b/tasks/0105_115_105115907_qa_1/task.toml index aaa4bf3dedd6ce4db43e6dcfa0d908960b36468f..174aa4c446b52a604031c729dd58f44601d098c8 100644 --- a/tasks/0105_115_105115907_qa_1/task.toml +++ b/tasks/0105_115_105115907_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0105_115_105115907_qa_1" +name = "smoldataenvs-train/0105_115_105115907_qa_1" description = "Which three features showed the highest correlation with the diabetes outcome variable according to the heatmap analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose, BMI, Age" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0105_131_105131890_qa_1/task.toml b/tasks/0105_131_105131890_qa_1/task.toml index a8dba50e999b08efe8971492422256c101f111ef..7341f113d85ca979b6b86019102627ab9cd5dd5e 100644 --- a/tasks/0105_131_105131890_qa_1/task.toml +++ b/tasks/0105_131_105131890_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0105_131_105131890_qa_1" +name = "smoldataenvs-train/0105_131_105131890_qa_1" description = "What is the highest correlation coefficient between any independent variable and medical charges in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.787" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0105_131_105131890_qa_4/task.toml b/tasks/0105_131_105131890_qa_4/task.toml index c1b2145342d67e49f35bb5881f5ed01cba289c49..7e2db866cf9de27931c75e3b6161c65afaca1b69 100644 --- a/tasks/0105_131_105131890_qa_4/task.toml +++ b/tasks/0105_131_105131890_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0105_131_105131890_qa_4" +name = "smoldataenvs-train/0105_131_105131890_qa_4" description = "Which geographic region has the highest number of patients in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Southeast" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0105_135_105135715_qa_3/task.toml b/tasks/0105_135_105135715_qa_3/task.toml index 0932beb938c13a96f819e56390d2fd65728f0b42..54f3359eedf51334452b45cfa6ed406d9b92d95d 100644 --- a/tasks/0105_135_105135715_qa_3/task.toml +++ b/tasks/0105_135_105135715_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0105_135_105135715_qa_3" +name = "smoldataenvs-train/0105_135_105135715_qa_3" description = "Which feature shows the highest absolute correlation with 'median_house_value' based on the correlation matrix analysis after handling missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "median_income" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0105_159_105159869_qa_5/task.toml b/tasks/0105_159_105159869_qa_5/task.toml index 75adc136e5afce1da4e1263878318dd8c72c5b83..d70e3b8a38ac321f1b4d917eebb736a83324e8b5 100644 --- a/tasks/0105_159_105159869_qa_5/task.toml +++ b/tasks/0105_159_105159869_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0105_159_105159869_qa_5" +name = "smoldataenvs-train/0105_159_105159869_qa_5" description = "In which year was the Economics Nobel Prize category first introduced?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1969" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0105_167_105167491_qa_2/task.toml b/tasks/0105_167_105167491_qa_2/task.toml index 4f044a5c4c26a05bdc9fd2feaeb25e8c2a61ae94..08c8b38adf1e7c487d7349c4493231b78d4891df 100644 --- a/tasks/0105_167_105167491_qa_2/task.toml +++ b/tasks/0105_167_105167491_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0105_167_105167491_qa_2" +name = "smoldataenvs-train/0105_167_105167491_qa_2" description = "What is the correlation coefficient between Glucose levels and Insulin levels in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.331" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0105_167_105167491_qa_3/task.toml b/tasks/0105_167_105167491_qa_3/task.toml index aa6fde506dd7324f1f15c7d1cee71eccf0c96bee..03ea9264426037910b1a76eabc6277b7961fd8a0 100644 --- a/tasks/0105_167_105167491_qa_3/task.toml +++ b/tasks/0105_167_105167491_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0105_167_105167491_qa_3" +name = "smoldataenvs-train/0105_167_105167491_qa_3" description = "What percentage of the dataset consists of individuals with a positive diabetes outcome (Outcome=1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.8" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0105_301_105301672_qa_2/task.toml b/tasks/0105_301_105301672_qa_2/task.toml index 733c2a82a02c8b78fcbd010a673db4617d82cc5f..a9d2f73d2ed7549cabc3b80f5d587d025ec7a2ba 100644 --- a/tasks/0105_301_105301672_qa_2/task.toml +++ b/tasks/0105_301_105301672_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0105_301_105301672_qa_2" +name = "smoldataenvs-train/0105_301_105301672_qa_2" description = "Which numerical variable shows the strongest positive correlation with Attrition after categorical encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "OverTime" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0105_332_105332394_qa_3/task.toml b/tasks/0105_332_105332394_qa_3/task.toml index a84fda4a4243d311e1ba4744f4f62cceaf9c71f0..65e74b8ed18c11ec8021b4ff43683168820e2e98 100644 --- a/tasks/0105_332_105332394_qa_3/task.toml +++ b/tasks/0105_332_105332394_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0105_332_105332394_qa_3" +name = "smoldataenvs-train/0105_332_105332394_qa_3" description = "What is the maximum number of positive lymph nodes observed in any patient in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "52" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0105_332_105332394_qa_5/task.toml b/tasks/0105_332_105332394_qa_5/task.toml index 9bf5843e7ea65887b562a7a50ad603eb1ae51063..ea099477458567ce15f2da1470ab975f906b8d9a 100644 --- a/tasks/0105_332_105332394_qa_5/task.toml +++ b/tasks/0105_332_105332394_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0105_332_105332394_qa_5" +name = "smoldataenvs-train/0105_332_105332394_qa_5" description = "What percentage of patients who did not survive more than five years had 0-6 positive lymph nodes according to the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10%" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0105_388_105388778_qa_3/task.toml b/tasks/0105_388_105388778_qa_3/task.toml index 960c21f1e461cd9f161b741a40a9293db451ad3e..3fda37e0c8d55f47325dc43e8931b0ed246acc5a 100644 --- a/tasks/0105_388_105388778_qa_3/task.toml +++ b/tasks/0105_388_105388778_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0105_388_105388778_qa_3" +name = "smoldataenvs-train/0105_388_105388778_qa_3" description = "How many features are present in the training dataset after one-hot encoding of categorical variables?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "21" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0105_422_105422995_qa_1/task.toml b/tasks/0105_422_105422995_qa_1/task.toml index 670444e8a85599fa823f154d566eb090cf6edc83..5f48ecc4958252943dcf89a4cbf29eba7da08d27 100644 --- a/tasks/0105_422_105422995_qa_1/task.toml +++ b/tasks/0105_422_105422995_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0105_422_105422995_qa_1" +name = "smoldataenvs-train/0105_422_105422995_qa_1" description = "What is the most common purpose for loans in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Debt Consolidation" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0105_422_105422995_qa_2/task.toml b/tasks/0105_422_105422995_qa_2/task.toml index 1852e83157ddd11ae3ac537b7750faa20ed50c56..a29197a798ec2d1883bb651f9cf34d1b91980c6b 100644 --- a/tasks/0105_422_105422995_qa_2/task.toml +++ b/tasks/0105_422_105422995_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0105_422_105422995_qa_2" +name = "smoldataenvs-train/0105_422_105422995_qa_2" description = "After removing duplicate rows, how many unique loan entries remain in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "89786" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0105_477_105477138_qa_1/task.toml b/tasks/0105_477_105477138_qa_1/task.toml index 71e4964a0187b5b73544aae872bd913c8915839a..02feaf6d09d801849a6257c94d068b4825f74753 100644 --- a/tasks/0105_477_105477138_qa_1/task.toml +++ b/tasks/0105_477_105477138_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0105_477_105477138_qa_1" +name = "smoldataenvs-train/0105_477_105477138_qa_1" description = "Which game has the highest Other Sales in the dataset, and what is its name?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Grand Theft Auto: San Andreas" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0105_477_105477138_qa_3/task.toml b/tasks/0105_477_105477138_qa_3/task.toml index 6dbbe920cc6743b9631bbe96d84fb523cead8e75..b144578f1bcb49a7fdb15d2267f93f97f2a124c7 100644 --- a/tasks/0105_477_105477138_qa_3/task.toml +++ b/tasks/0105_477_105477138_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0105_477_105477138_qa_3" +name = "smoldataenvs-train/0105_477_105477138_qa_3" description = "How many games in the dataset have missing Publisher information (NULL values)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "58" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0105_478_105478002_qa_1/task.toml b/tasks/0105_478_105478002_qa_1/task.toml index 1853c3eeac09af65af6a676889dbe84228158161..3c4014247b54333cbcdc712dc1cd5415681de4fe 100644 --- a/tasks/0105_478_105478002_qa_1/task.toml +++ b/tasks/0105_478_105478002_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0105_478_105478002_qa_1" +name = "smoldataenvs-train/0105_478_105478002_qa_1" description = "What is the correlation coefficient between the poverty rate and the high school graduation rate across U.S. states in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.805761" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0105_478_105478002_qa_5/task.toml b/tasks/0105_478_105478002_qa_5/task.toml index 97dcefc3f958495ca4b9c9aa93a946aadcc3a66e..cbccd9e6fecf5a90017440c33b130c35605cbf8f 100644 --- a/tasks/0105_478_105478002_qa_5/task.toml +++ b/tasks/0105_478_105478002_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0105_478_105478002_qa_5" +name = "smoldataenvs-train/0105_478_105478002_qa_5" description = "Which city in the United States had the highest number of police shooting incidents recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Los Angeles" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0105_488_105488572_qa_2/task.toml b/tasks/0105_488_105488572_qa_2/task.toml index e345a96a3eafa4add829f7ef0ab8034c2111877a..015954d6713b2e530571404fb058ae89539b0b39 100644 --- a/tasks/0105_488_105488572_qa_2/task.toml +++ b/tasks/0105_488_105488572_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0105_488_105488572_qa_2" +name = "smoldataenvs-train/0105_488_105488572_qa_2" description = "How many Iris setosa samples were correctly classified by the first KMeans model (before feature standardization)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0105_488_105488572_qa_4/task.toml b/tasks/0105_488_105488572_qa_4/task.toml index 309575089a570ce2ef8fff2cf985d9f4dad8b85d..13e4e2477f68b841b29909e0c38972ed36093494 100644 --- a/tasks/0105_488_105488572_qa_4/task.toml +++ b/tasks/0105_488_105488572_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0105_488_105488572_qa_4" +name = "smoldataenvs-train/0105_488_105488572_qa_4" description = "What is the inertia value of the KMeans model before feature standardization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "78.94084142614601" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0105_606_105606583_qa_2/task.toml b/tasks/0105_606_105606583_qa_2/task.toml index ac0bc8f7eebf7ac16c63a766cf4e83216ebfaada..7878aa06852c513d368a58b984c0cfec860c5beb 100644 --- a/tasks/0105_606_105606583_qa_2/task.toml +++ b/tasks/0105_606_105606583_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0105_606_105606583_qa_2" +name = "smoldataenvs-train/0105_606_105606583_qa_2" description = "Which player has the highest free throw shooting average with at least 1,000 total free throw attempts in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Steve Nash" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0105_684_105684857_qa_1/task.toml b/tasks/0105_684_105684857_qa_1/task.toml index 0ff7501e7e9e2fe7b01acd8493d3841bea39d5c0..e4ae2b5683225463a6a5a8f986b876c315a202a7 100644 --- a/tasks/0105_684_105684857_qa_1/task.toml +++ b/tasks/0105_684_105684857_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0105_684_105684857_qa_1" +name = "smoldataenvs-train/0105_684_105684857_qa_1" description = "What percentage of the market_category column contains missing values in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "31.4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0105_684_105684857_qa_4/task.toml b/tasks/0105_684_105684857_qa_4/task.toml index 3dd3f9794ba7583d78ce0460687af8cb47e77a46..1c2942beeb37f7d59ddd149263caff297cc52d2d 100644 --- a/tasks/0105_684_105684857_qa_4/task.toml +++ b/tasks/0105_684_105684857_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0105_684_105684857_qa_4" +name = "smoldataenvs-train/0105_684_105684857_qa_4" description = "What is the average highway miles per gallon (MPG) across all vehicles in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26.64" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0105_684_105684857_qa_5/task.toml b/tasks/0105_684_105684857_qa_5/task.toml index 6e37768c65e4496ced99abe941f23a258b33aaba..e291e82e38babc02be9ef597f759b2704b960392 100644 --- a/tasks/0105_684_105684857_qa_5/task.toml +++ b/tasks/0105_684_105684857_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0105_684_105684857_qa_5" +name = "smoldataenvs-train/0105_684_105684857_qa_5" description = "How many unique car makes are present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "48" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0105_902_105902440_qa_4/task.toml b/tasks/0105_902_105902440_qa_4/task.toml index 9037774767a3c310a3016741e7f087eb9dc9061a..efeb7fb13339636609271673f108d5283204c347 100644 --- a/tasks/0105_902_105902440_qa_4/task.toml +++ b/tasks/0105_902_105902440_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0105_902_105902440_qa_4" +name = "smoldataenvs-train/0105_902_105902440_qa_4" description = "Which feature scaling method was applied to the training and test datasets before model training?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "StandardScaler" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0105_972_105972682_qa_2/task.toml b/tasks/0105_972_105972682_qa_2/task.toml index 465e501a6da4815eca8f4d7bc2b6f82c909145d3..f483a1574f9021b99a27d3b4c0950e628f0e4daf 100644 --- a/tasks/0105_972_105972682_qa_2/task.toml +++ b/tasks/0105_972_105972682_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0105_972_105972682_qa_2" +name = "smoldataenvs-train/0105_972_105972682_qa_2" description = "How many variables were removed due to high correlation (|correlation| > 0.7)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0106_109_106109470_qa_2/task.toml b/tasks/0106_109_106109470_qa_2/task.toml index 27c74ca8faec937898dcb4ce583e8f790bdc9278..1177b61d8f0f30a619cfe7518577ce139034ba18 100644 --- a/tasks/0106_109_106109470_qa_2/task.toml +++ b/tasks/0106_109_106109470_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0106_109_106109470_qa_2" +name = "smoldataenvs-train/0106_109_106109470_qa_2" description = "What is the percentage decrease in the standard deviation of the 'carat' feature after outlier removal compared to the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "17.1%" reward_mode_initial = "flexible" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0106_112_106112212_qa_5/task.toml b/tasks/0106_112_106112212_qa_5/task.toml index 82596a74bfd74b85a65692195bb0bb377ccee653..f866455471907918df0d40c85310ef78ad510134 100644 --- a/tasks/0106_112_106112212_qa_5/task.toml +++ b/tasks/0106_112_106112212_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0106_112_106112212_qa_5" +name = "smoldataenvs-train/0106_112_106112212_qa_5" description = "Which menu item has the highest trans fat content, and what is the value in grams?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Double Quarter Pounder with Cheese, 2.5" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0106_208_106208028_qa_1/task.toml b/tasks/0106_208_106208028_qa_1/task.toml index 74676bf68f6446360ac095f70329dabbfd9d8f16..05bf17d6ff143761083ffca13ba3e03ef39e6f8b 100644 --- a/tasks/0106_208_106208028_qa_1/task.toml +++ b/tasks/0106_208_106208028_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0106_208_106208028_qa_1" +name = "smoldataenvs-train/0106_208_106208028_qa_1" description = "Which model achieved the highest average testing accuracy across all KFold cross-validation splits?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Logistic Regression" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0106_229_106229633_qa_1/task.toml b/tasks/0106_229_106229633_qa_1/task.toml index d66477c82a7fc55d0f47c78cd93eb15c866f2308..8de3ad521de0195099e76161d803db6ae0ccafc5 100644 --- a/tasks/0106_229_106229633_qa_1/task.toml +++ b/tasks/0106_229_106229633_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0106_229_106229633_qa_1" +name = "smoldataenvs-train/0106_229_106229633_qa_1" description = "Which payment method has the highest proportion of customers who churned in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic check" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0106_229_106229633_qa_4/task.toml b/tasks/0106_229_106229633_qa_4/task.toml index 4fdbe172ef735cdb33d4e5d6e1f3e5272c3afdd7..49a72000b9e07eed9dc9b0dcd67d58b92a6dcb48 100644 --- a/tasks/0106_229_106229633_qa_4/task.toml +++ b/tasks/0106_229_106229633_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0106_229_106229633_qa_4" +name = "smoldataenvs-train/0106_229_106229633_qa_4" description = "Which customer demographic group (senior citizens vs. non-senior citizens) has a higher churn rate?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Senior citizens" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0106_348_106348843_qa_5/task.toml b/tasks/0106_348_106348843_qa_5/task.toml index a84c11ba9e302766cce091ee07fb5f5afdef14e1..77f256c7995ae9451885a652abbfdcce47bac884 100644 --- a/tasks/0106_348_106348843_qa_5/task.toml +++ b/tasks/0106_348_106348843_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0106_348_106348843_qa_5" +name = "smoldataenvs-train/0106_348_106348843_qa_5" description = "Which education level has the highest number of defaulters in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "University" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0106_400_106400337_qa_4/task.toml b/tasks/0106_400_106400337_qa_4/task.toml index d46a11d56b6e631948d388220d701c410bf53937..ce36672bd016fc9256435aa93a2eb3dabf2423cb 100644 --- a/tasks/0106_400_106400337_qa_4/task.toml +++ b/tasks/0106_400_106400337_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0106_400_106400337_qa_4" +name = "smoldataenvs-train/0106_400_106400337_qa_4" description = "Which property area has the highest proportion of loan approvals according to the countplot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Semiurban" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0106_409_106409576_qa_3/task.toml b/tasks/0106_409_106409576_qa_3/task.toml index 22eaa0463a53af069b104126c800c4af632d81ea..deffe504019f9369ac245be08c6da7202e4c42d8 100644 --- a/tasks/0106_409_106409576_qa_3/task.toml +++ b/tasks/0106_409_106409576_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0106_409_106409576_qa_3" +name = "smoldataenvs-train/0106_409_106409576_qa_3" description = "Which classification model achieved the highest overall accuracy when using sepal length and width as predictive features?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Logistic Regression" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0106_414_106414508_qa_1/task.toml b/tasks/0106_414_106414508_qa_1/task.toml index a9d7ee0abbd5af2a582fabd1ba22185bab82d371..3dac0399f4ff92a016d361803e5c66314731c540 100644 --- a/tasks/0106_414_106414508_qa_1/task.toml +++ b/tasks/0106_414_106414508_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0106_414_106414508_qa_1" +name = "smoldataenvs-train/0106_414_106414508_qa_1" description = "What is the most frequent car evaluation decision category in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "unacc" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0106_414_106414508_qa_5/task.toml b/tasks/0106_414_106414508_qa_5/task.toml index bc3a5b0dc00a32c3eec5578cfbd7fa8e441e5a1d..b5d74f012c0aac9bec6badc275f53e462738316a 100644 --- a/tasks/0106_414_106414508_qa_5/task.toml +++ b/tasks/0106_414_106414508_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0106_414_106414508_qa_5" +name = "smoldataenvs-train/0106_414_106414508_qa_5" description = "What is the minimum value of the 'buyPrice' feature after ordinal encoding and MinMax scaling?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0106_416_106416910_qa_1/task.toml b/tasks/0106_416_106416910_qa_1/task.toml index d13a5a75baec8e8e20ca7e62d81b8b5a68b88a68..7b41749ddbb4e01066affb3ef91582f83afa428b 100644 --- a/tasks/0106_416_106416910_qa_1/task.toml +++ b/tasks/0106_416_106416910_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0106_416_106416910_qa_1" +name = "smoldataenvs-train/0106_416_106416910_qa_1" description = "What is the percentage of customers who churned in the dataset after data cleaning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26.54" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0106_417_106417043_qa_2/task.toml b/tasks/0106_417_106417043_qa_2/task.toml index acf766c65ca067337e3a6d72dbfb9dfeca52b3bd..9d2426c95e5832a2ca83b1e151ac3b54128243e9 100644 --- a/tasks/0106_417_106417043_qa_2/task.toml +++ b/tasks/0106_417_106417043_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0106_417_106417043_qa_2" +name = "smoldataenvs-train/0106_417_106417043_qa_2" description = "What is the percentage of items in the dataset that were reordered?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "59.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0106_477_106477503_qa_1/task.toml b/tasks/0106_477_106477503_qa_1/task.toml index 8d90a98b988f1d7aba8e838e2e0c57c287fbd9c4..eb5828f9d672b3033f2dba4c9b703885d2f2ec10 100644 --- a/tasks/0106_477_106477503_qa_1/task.toml +++ b/tasks/0106_477_106477503_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0106_477_106477503_qa_1" +name = "smoldataenvs-train/0106_477_106477503_qa_1" description = "What is the highest global sales value recorded for a video game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "82.74" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0106_477_106477503_qa_2/task.toml b/tasks/0106_477_106477503_qa_2/task.toml index 4034f1137475aaa2a44d1bdd33994ca8d4f0abaa..3a733ecd74cfa279b5ac6b108071aba72da9a9fc 100644 --- a/tasks/0106_477_106477503_qa_2/task.toml +++ b/tasks/0106_477_106477503_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0106_477_106477503_qa_2" +name = "smoldataenvs-train/0106_477_106477503_qa_2" description = "How many video games are included in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16598" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0106_477_106477503_qa_3/task.toml b/tasks/0106_477_106477503_qa_3/task.toml index e0fe72cfb350dba60c5dffca32d811c00066fc13..d3783d33e92f058aa6de95a355580ed523af6967 100644 --- a/tasks/0106_477_106477503_qa_3/task.toml +++ b/tasks/0106_477_106477503_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0106_477_106477503_qa_3" +name = "smoldataenvs-train/0106_477_106477503_qa_3" description = "What is the data type of the Series containing video game names?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "object" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0106_477_106477503_qa_4/task.toml b/tasks/0106_477_106477503_qa_4/task.toml index 0219205373c5b816647db5e60a09ce110a7ffac9..28210b4ab1a784c4cedb836d7c484911ac4827b7 100644 --- a/tasks/0106_477_106477503_qa_4/task.toml +++ b/tasks/0106_477_106477503_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0106_477_106477503_qa_4" +name = "smoldataenvs-train/0106_477_106477503_qa_4" description = "What is the name of the video game ranked 5th in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Pokemon Red/Pokemon Blue" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0106_481_106481442_qa_3/task.toml b/tasks/0106_481_106481442_qa_3/task.toml index aba06d718d1aeef57183ee45c77b79bd1c687999..6a98109282d271dc863920df34244132eb561c50 100644 --- a/tasks/0106_481_106481442_qa_3/task.toml +++ b/tasks/0106_481_106481442_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0106_481_106481442_qa_3" +name = "smoldataenvs-train/0106_481_106481442_qa_3" description = "After applying the Gaussian rule Winsorization method, what is the upper boundary value for the alcohol feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13.597477858782154" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0106_490_106490644_qa_3/task.toml b/tasks/0106_490_106490644_qa_3/task.toml index 475a081216110d359ef1756f13b3513425277193..4b287d35c169fedd1fd7ce7ea46cbdbe9a0fec7a 100644 --- a/tasks/0106_490_106490644_qa_3/task.toml +++ b/tasks/0106_490_106490644_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0106_490_106490644_qa_3" +name = "smoldataenvs-train/0106_490_106490644_qa_3" description = "Which marital status category has the highest number of defaulters in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Single" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0106_499_106499132_qa_1/task.toml b/tasks/0106_499_106499132_qa_1/task.toml index 68eea8a75e923835a66274a1d8384936223ab8e0..809945bdaefe0cce8d71212ec69945e515ad6e2d 100644 --- a/tasks/0106_499_106499132_qa_1/task.toml +++ b/tasks/0106_499_106499132_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0106_499_106499132_qa_1" +name = "smoldataenvs-train/0106_499_106499132_qa_1" description = "Which feature (other than price) in the dataset has the strongest positive correlation with house sale price?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "sqft_living" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0106_528_106528652_qa_1/task.toml b/tasks/0106_528_106528652_qa_1/task.toml index 10295792c1cd5be46637ca2d645b27473364a2a4..3bb57795f724ab6698cc3d67e960af0f84aea55f 100644 --- a/tasks/0106_528_106528652_qa_1/task.toml +++ b/tasks/0106_528_106528652_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0106_528_106528652_qa_1" +name = "smoldataenvs-train/0106_528_106528652_qa_1" description = "Which sales region (NA, EU, JP, Other) has the highest positive correlation with Global Sales in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "NA" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0106_531_106531731_qa_5/task.toml b/tasks/0106_531_106531731_qa_5/task.toml index 7b518e184f2be052b1d00c11f2386829c0de7595..3653a035f2f2126a1b442f358e55d739303a3535 100644 --- a/tasks/0106_531_106531731_qa_5/task.toml +++ b/tasks/0106_531_106531731_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0106_531_106531731_qa_5" +name = "smoldataenvs-train/0106_531_106531731_qa_5" description = "What is the average alcohol content of all wines in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10.42" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0106_598_106598107_qa_5/task.toml b/tasks/0106_598_106598107_qa_5/task.toml index e691ceddcff21bab04fe2905c560371930351aba..fd5be60c5607b5bc3fcbe9648832d73c3da77e09 100644 --- a/tasks/0106_598_106598107_qa_5/task.toml +++ b/tasks/0106_598_106598107_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0106_598_106598107_qa_5" +name = "smoldataenvs-train/0106_598_106598107_qa_5" description = "What is the correlation coefficient between latitude and longitude in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.924" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0106_674_106674377_qa_1/task.toml b/tasks/0106_674_106674377_qa_1/task.toml index 8c216f99a5527a1c6df093c37088cb13c6588f26..dcdec8853c16a26fb7c72d51b85bb2ad1e5c8421 100644 --- a/tasks/0106_674_106674377_qa_1/task.toml +++ b/tasks/0106_674_106674377_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0106_674_106674377_qa_1" +name = "smoldataenvs-train/0106_674_106674377_qa_1" description = "What is the average life expectancy across all countries in the dataset after imputing missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "69.22" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0106_674_106674377_qa_2/task.toml b/tasks/0106_674_106674377_qa_2/task.toml index 49a9324b97dbd7353d63b3a3165405a95261dde9..d5746b32a916b2f8ddcfd2ec307737bd8fdc2809 100644 --- a/tasks/0106_674_106674377_qa_2/task.toml +++ b/tasks/0106_674_106674377_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0106_674_106674377_qa_2" +name = "smoldataenvs-train/0106_674_106674377_qa_2" description = "What is the highest life expectancy recorded in the dataset after imputing missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "89.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0106_834_106834319_qa_1/task.toml b/tasks/0106_834_106834319_qa_1/task.toml index 7eb23bd99b9cb217e610c1a9356f84e4e95fdb0e..c4b3e7b6511c58e0aee841772a346c95ce32381b 100644 --- a/tasks/0106_834_106834319_qa_1/task.toml +++ b/tasks/0106_834_106834319_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0106_834_106834319_qa_1" +name = "smoldataenvs-train/0106_834_106834319_qa_1" description = "Which column in the loan dataset had the highest number of missing values before preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Credit_History" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0106_834_106834319_qa_3/task.toml b/tasks/0106_834_106834319_qa_3/task.toml index 8a6a1719272893729519feb41b147a25015bf532..2b39d3fdf4d33f5405875b6fe54c7ed5519a693c 100644 --- a/tasks/0106_834_106834319_qa_3/task.toml +++ b/tasks/0106_834_106834319_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0106_834_106834319_qa_3" +name = "smoldataenvs-train/0106_834_106834319_qa_3" description = "What is the maximum value in the Dependents column after replacing '3+' with 4 during preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0106_910_106910324_qa_1/task.toml b/tasks/0106_910_106910324_qa_1/task.toml index 3fb40e5dbfd67ba163fe04a7843806a6645c2d08..83c8d527066f8ee34d5b4a9359db8c5e1be3d840 100644 --- a/tasks/0106_910_106910324_qa_1/task.toml +++ b/tasks/0106_910_106910324_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0106_910_106910324_qa_1" +name = "smoldataenvs-train/0106_910_106910324_qa_1" description = "What is the maximum purchase amount recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "23961" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0106_910_106910324_qa_3/task.toml b/tasks/0106_910_106910324_qa_3/task.toml index 411b6689b4ac26350513273117f253a847aca753..18687fb6d35005d1afa3f923573ef661791ed0e1 100644 --- a/tasks/0106_910_106910324_qa_3/task.toml +++ b/tasks/0106_910_106910324_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0106_910_106910324_qa_3" +name = "smoldataenvs-train/0106_910_106910324_qa_3" description = "How many rows were in the test dataset after separating based on missing Purchase values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "233599" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0106_911_106911187_qa_4/task.toml b/tasks/0106_911_106911187_qa_4/task.toml index 750526593042cff3b208c3a6cd3d859a13f2812b..6ee087b87afd13ab4e2fe51514f306966186ffdf 100644 --- a/tasks/0106_911_106911187_qa_4/task.toml +++ b/tasks/0106_911_106911187_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0106_911_106911187_qa_4" +name = "smoldataenvs-train/0106_911_106911187_qa_4" description = "What percentage of the dataset belongs to the Class 1 (diabetic) group based on the mean value of the Class column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.81" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0106_911_106911187_qa_5/task.toml b/tasks/0106_911_106911187_qa_5/task.toml index 820a3faaebdcfee2e2c73958e1acbc390743338a..49458ddd461917a0d04401076807f86ded7f7212 100644 --- a/tasks/0106_911_106911187_qa_5/task.toml +++ b/tasks/0106_911_106911187_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0106_911_106911187_qa_5" +name = "smoldataenvs-train/0106_911_106911187_qa_5" description = "What is the 75th percentile value for the BMI feature in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "36.6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0106_933_106933480_qa_2/task.toml b/tasks/0106_933_106933480_qa_2/task.toml index 79c511a17764de07b94d5c279ea25b37b844c2c3..df050a5dacdce9d747bae241073fa8e262c99842 100644 --- a/tasks/0106_933_106933480_qa_2/task.toml +++ b/tasks/0106_933_106933480_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0106_933_106933480_qa_2" +name = "smoldataenvs-train/0106_933_106933480_qa_2" description = "What is the highest Pearson correlation between any feature and the original 'quality' label?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.476166" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0106_972_106972391_qa_1/task.toml b/tasks/0106_972_106972391_qa_1/task.toml index 63ff4d6ece739c7cf54cc6fb750e8c540e3fe32b..328435c92c2b486a46b5e67de7bc49f500a49f04 100644 --- a/tasks/0106_972_106972391_qa_1/task.toml +++ b/tasks/0106_972_106972391_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0106_972_106972391_qa_1" +name = "smoldataenvs-train/0106_972_106972391_qa_1" description = "What is the number of features (columns) in the dataset after removing the 'Molecule_Index', 'pubchem_id', and the target variable 'Eat'?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1275" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0107_060_107060186_qa_5/task.toml b/tasks/0107_060_107060186_qa_5/task.toml index 6928ac8112b3c2ac3ce71d6c1cac35b08eace6bb..aa9e6d83d8e86e879f3a1ad9c6468939e71b3b83 100644 --- a/tasks/0107_060_107060186_qa_5/task.toml +++ b/tasks/0107_060_107060186_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0107_060_107060186_qa_5" +name = "smoldataenvs-train/0107_060_107060186_qa_5" description = "Based on the SHAP summary plot, which feature's low values are most indicative of a higher likelihood of churn?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Contract" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0107_073_107073195_qa_2/task.toml b/tasks/0107_073_107073195_qa_2/task.toml index 2502926301621d396e27d40b51f3dc56bb08b37c..222e97927936b2915781fca1aae980fbf47d6ead 100644 --- a/tasks/0107_073_107073195_qa_2/task.toml +++ b/tasks/0107_073_107073195_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0107_073_107073195_qa_2" +name = "smoldataenvs-train/0107_073_107073195_qa_2" description = "Which movie has the highest weighted average rating in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "The Shawshank Redemption" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0107_107_107107118_qa_5/task.toml b/tasks/0107_107_107107118_qa_5/task.toml index 5a3d3aff97c6c52bc33aee85662c24362fd8d0d3..aca55a72083db79f08086edc0e176fd424a4e369 100644 --- a/tasks/0107_107_107107118_qa_5/task.toml +++ b/tasks/0107_107_107107118_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0107_107_107107118_qa_5" +name = "smoldataenvs-train/0107_107_107107118_qa_5" description = "What is the average area population across all samples in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "36163.52" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0107_118_107118417_qa_3/task.toml b/tasks/0107_118_107118417_qa_3/task.toml index 3974c9fbb9af72728797fe3c90a8ba542b281e2f..a8a68fa85381bd4609af8d6e6f482025bb68e26c 100644 --- a/tasks/0107_118_107118417_qa_3/task.toml +++ b/tasks/0107_118_107118417_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0107_118_107118417_qa_3" +name = "smoldataenvs-train/0107_118_107118417_qa_3" description = "What percentage of customers in the validation set are actual churners (class 1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "27.4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0107_127_107127694_qa_4/task.toml b/tasks/0107_127_107127694_qa_4/task.toml index 62aa073da69d12eff71e3779bd64e937cd764841..e432a4a7bdb68b905eca2202d1033a5e1b86fdec 100644 --- a/tasks/0107_127_107127694_qa_4/task.toml +++ b/tasks/0107_127_107127694_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0107_127_107127694_qa_4" +name = "smoldataenvs-train/0107_127_107127694_qa_4" description = "How many samples were included in the training set for feature variables after data splitting?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1279" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0107_163_107163382_qa_5/task.toml b/tasks/0107_163_107163382_qa_5/task.toml index b27b3bea5c565fd3f00e69952a257df085febc75..13cb72b531c61048b2d5dd310272052a79160bce 100644 --- a/tasks/0107_163_107163382_qa_5/task.toml +++ b/tasks/0107_163_107163382_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0107_163_107163382_qa_5" +name = "smoldataenvs-train/0107_163_107163382_qa_5" description = "What percentage of customers in the dataset defaulted on their payments in the next month?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "22.12" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0107_177_107177097_qa_4/task.toml b/tasks/0107_177_107177097_qa_4/task.toml index 7ae09881ae873eec67e1ca524642196929d30424..91069c3494766ba283f30dfc5b93bcb928f7efab 100644 --- a/tasks/0107_177_107177097_qa_4/task.toml +++ b/tasks/0107_177_107177097_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0107_177_107177097_qa_4" +name = "smoldataenvs-train/0107_177_107177097_qa_4" description = "Which country has the highest number of sales order lines in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "USA" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0107_190_107190827_qa_2/task.toml b/tasks/0107_190_107190827_qa_2/task.toml index b66ef302e9146ab40688765cfdc01cd8cdd72346..cc9e5ed234e589db94d4b980f0f6d42f255a98c1 100644 --- a/tasks/0107_190_107190827_qa_2/task.toml +++ b/tasks/0107_190_107190827_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0107_190_107190827_qa_2" +name = "smoldataenvs-train/0107_190_107190827_qa_2" description = "What was the peak number of years employees stayed at the company before leaving, as observed in the Attrition-Yes distribution for YearsAtCompany?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0107_271_107271639_qa_3/task.toml b/tasks/0107_271_107271639_qa_3/task.toml index 6a867072fff98fcaa2f0f60c17d1d734964e3b79..3c266aac4debacd6519b50702ad650604456f709 100644 --- a/tasks/0107_271_107271639_qa_3/task.toml +++ b/tasks/0107_271_107271639_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0107_271_107271639_qa_3" +name = "smoldataenvs-train/0107_271_107271639_qa_3" description = "How many distinct tenure values are present in the dataset before clustering analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0107_327_107327801_qa_1/task.toml b/tasks/0107_327_107327801_qa_1/task.toml index 0411f9d35d54217355fbbf0888677892c20b6a19..06492b9c323c2b228bfdedf366d96aaf31df10a2 100644 --- a/tasks/0107_327_107327801_qa_1/task.toml +++ b/tasks/0107_327_107327801_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0107_327_107327801_qa_1" +name = "smoldataenvs-train/0107_327_107327801_qa_1" description = "Which three states had the highest number of police killings according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "California, Texas, Florida" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0107_327_107327801_qa_2/task.toml b/tasks/0107_327_107327801_qa_2/task.toml index 2e20090af8ee4068e2c0519f9e61e7601c10a41e..b392af9852db39600d5383e0f3d7b964504f6913 100644 --- a/tasks/0107_327_107327801_qa_2/task.toml +++ b/tasks/0107_327_107327801_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0107_327_107327801_qa_2" +name = "smoldataenvs-train/0107_327_107327801_qa_2" description = "What is the percentage of victims who were male in police killings?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "95.8" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0107_327_107327801_qa_4/task.toml b/tasks/0107_327_107327801_qa_4/task.toml index a4e6af5d7456c970fb7ce7ea64f435d9d07f7b44..8d82c28e921dc1548d18af64451c843f3d1df980 100644 --- a/tasks/0107_327_107327801_qa_4/task.toml +++ b/tasks/0107_327_107327801_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0107_327_107327801_qa_4" +name = "smoldataenvs-train/0107_327_107327801_qa_4" description = "What percentage of victims showed signs of mental illness during police encounters?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "25.1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0107_391_107391967_qa_2/task.toml b/tasks/0107_391_107391967_qa_2/task.toml index 54fff7e13ba4699d37f9f3414f7c573a9fc3f2ea..114ff21259ac0688c053b0b85ccbe45bb47644db 100644 --- a/tasks/0107_391_107391967_qa_2/task.toml +++ b/tasks/0107_391_107391967_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0107_391_107391967_qa_2" +name = "smoldataenvs-train/0107_391_107391967_qa_2" description = "What is the sum of squared errors (SSE) for the KMeans model with 5 clusters as calculated in the clustering analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "33322.15648080016" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0107_411_107411800_qa_1/task.toml b/tasks/0107_411_107411800_qa_1/task.toml index 62376c865abcc7ff336c4318032ec6db779b8664..914c077780059db8550d73c11d68c4e62ae0b1d8 100644 --- a/tasks/0107_411_107411800_qa_1/task.toml +++ b/tasks/0107_411_107411800_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0107_411_107411800_qa_1" +name = "smoldataenvs-train/0107_411_107411800_qa_1" description = "What percentage of customers in the dataset have churned (exited the company)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26.5" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0107_417_107417136_qa_1/task.toml b/tasks/0107_417_107417136_qa_1/task.toml index 26c37272b2627078938339ff1f049d5bf6810c2c..b1fbab4d506b8fce08a774980200be78ba251f99 100644 --- a/tasks/0107_417_107417136_qa_1/task.toml +++ b/tasks/0107_417_107417136_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0107_417_107417136_qa_1" +name = "smoldataenvs-train/0107_417_107417136_qa_1" description = "What is the total revenue generated from all orders in the Chipotle dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "39237.02" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0107_417_107417136_qa_2/task.toml b/tasks/0107_417_107417136_qa_2/task.toml index 6cfa8ce7ee2975a52b44952238013ba20dd122de..28ccfd3a8553c0fdefc84eb427196cd95add7ed7 100644 --- a/tasks/0107_417_107417136_qa_2/task.toml +++ b/tasks/0107_417_107417136_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0107_417_107417136_qa_2" +name = "smoldataenvs-train/0107_417_107417136_qa_2" description = "Which menu item has the highest total quantity sold, and what is that quantity?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Chicken Bowl, 761" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0107_417_107417136_qa_4/task.toml b/tasks/0107_417_107417136_qa_4/task.toml index 2a3d1c071e6d1e9838d394e147e529ba5046486e..2fe8cc4ccb34d1dd3967e892af8a769ffbcb6cc3 100644 --- a/tasks/0107_417_107417136_qa_4/task.toml +++ b/tasks/0107_417_107417136_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0107_417_107417136_qa_4" +name = "smoldataenvs-train/0107_417_107417136_qa_4" description = "How many times was the Veggie Salad Bowl ordered in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "18 times" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0107_427_107427822_qa_3/task.toml b/tasks/0107_427_107427822_qa_3/task.toml index 773f3ea519d9049b15f24b17c7c78a8abdf6e02f..2652f801e833a40b186a3e31af43a6915269367a 100644 --- a/tasks/0107_427_107427822_qa_3/task.toml +++ b/tasks/0107_427_107427822_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0107_427_107427822_qa_3" +name = "smoldataenvs-train/0107_427_107427822_qa_3" description = "Which payment method has the highest churn rate according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic check" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0107_427_107427822_qa_5/task.toml b/tasks/0107_427_107427822_qa_5/task.toml index e1dcbe9c57f9657d5672b24a40e3687c84cf1195..dc411089cc1a35742595fd4a6630eaf1698e9c56 100644 --- a/tasks/0107_427_107427822_qa_5/task.toml +++ b/tasks/0107_427_107427822_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0107_427_107427822_qa_5" +name = "smoldataenvs-train/0107_427_107427822_qa_5" description = "What is the difference in churn rates between senior citizens (SeniorCitizen=1) and non-senior customers (SeniorCitizen=0)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "18.1" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0107_451_107451857_qa_4/task.toml b/tasks/0107_451_107451857_qa_4/task.toml index e856834cdd6c5280b5ef808e0cf1363470af62c2..3300539fa9ba47ae2640067e5e17966ff17ba840 100644 --- a/tasks/0107_451_107451857_qa_4/task.toml +++ b/tasks/0107_451_107451857_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0107_451_107451857_qa_4" +name = "smoldataenvs-train/0107_451_107451857_qa_4" description = "What is the Pearson correlation coefficient between age and medical charges in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.299008" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0107_477_107477079_qa_2/task.toml b/tasks/0107_477_107477079_qa_2/task.toml index 80786d4517b83e8402899619677ea5859df8a0b9..8f1583872ca903bcae795494a0c441bcbe781d83 100644 --- a/tasks/0107_477_107477079_qa_2/task.toml +++ b/tasks/0107_477_107477079_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0107_477_107477079_qa_2" +name = "smoldataenvs-train/0107_477_107477079_qa_2" description = "What is the absolute difference in average insurance charges between smokers and non-smokers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "23615.96" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0107_477_107477079_qa_3/task.toml b/tasks/0107_477_107477079_qa_3/task.toml index 45d398b84cd3a591b480da168870e21cceccf190..79cf61c7881e3cb2e9b7e0b9a80ca53940d088cd 100644 --- a/tasks/0107_477_107477079_qa_3/task.toml +++ b/tasks/0107_477_107477079_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0107_477_107477079_qa_3" +name = "smoldataenvs-train/0107_477_107477079_qa_3" description = "Which geographic region has the highest average insurance charges according to the ANOVA analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "southeast" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0107_477_107477079_qa_5/task.toml b/tasks/0107_477_107477079_qa_5/task.toml index 911ff8d72438fe2276a6b74b23966ff40e76ebae..88b49b8f683629acc598406fd55ee87959d6c2bd 100644 --- a/tasks/0107_477_107477079_qa_5/task.toml +++ b/tasks/0107_477_107477079_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0107_477_107477079_qa_5" +name = "smoldataenvs-train/0107_477_107477079_qa_5" description = "Is there a statistically significant difference in smoking rates between males and females in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0107_517_107517636_qa_3/task.toml b/tasks/0107_517_107517636_qa_3/task.toml index 8525fdf0e0ee624f0dd8f151f1835fbcbf1bb5fa..7f52e01e9db50e5ab02d99771cdac92a2dc93432 100644 --- a/tasks/0107_517_107517636_qa_3/task.toml +++ b/tasks/0107_517_107517636_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0107_517_107517636_qa_3" +name = "smoldataenvs-train/0107_517_107517636_qa_3" description = "How many video games in the dataset originally contained missing values in the 'Year' column before handling them with fillna()?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "271" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0107_521_107521812_qa_2/task.toml b/tasks/0107_521_107521812_qa_2/task.toml index c4c13ee5d25c80534957e51902df019361d8cb4d..8a88f75fa123af02966172f4ca1d8164daae222c 100644 --- a/tasks/0107_521_107521812_qa_2/task.toml +++ b/tasks/0107_521_107521812_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0107_521_107521812_qa_2" +name = "smoldataenvs-train/0107_521_107521812_qa_2" description = "What is the accuracy of the decision tree classifier on the test set after feature scaling and train-test split?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "81.83" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0107_639_107639257_qa_1/task.toml b/tasks/0107_639_107639257_qa_1/task.toml index 9c4e2c749a81911402ca346fbd3314a9083ca528..ca055932efcaf18ad284f48caa7b768ec5a1dbe6 100644 --- a/tasks/0107_639_107639257_qa_1/task.toml +++ b/tasks/0107_639_107639257_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0107_639_107639257_qa_1" +name = "smoldataenvs-train/0107_639_107639257_qa_1" description = "What percentage of loan applications were accepted based on the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "69.2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0107_639_107639257_qa_4/task.toml b/tasks/0107_639_107639257_qa_4/task.toml index 30be96af0d2eb30da609e8d8b917a84422e81c39..a47e935b141d42061c397d5b4f3d95f45069385e 100644 --- a/tasks/0107_639_107639257_qa_4/task.toml +++ b/tasks/0107_639_107639257_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0107_639_107639257_qa_4" +name = "smoldataenvs-train/0107_639_107639257_qa_4" description = "What is the most common loan term duration (in days) requested by applicants in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "360" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0107_679_107679548_qa_5/task.toml b/tasks/0107_679_107679548_qa_5/task.toml index c8911a45a1bd233021672e6e06e3263996342136..f5a0bf0d6a4ab37805437d0ec6277bad1bf9da1a 100644 --- a/tasks/0107_679_107679548_qa_5/task.toml +++ b/tasks/0107_679_107679548_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0107_679_107679548_qa_5" +name = "smoldataenvs-train/0107_679_107679548_qa_5" description = "What is the percentage of global sales attributed to Europe for Shooter games?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "29.2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0107_704_107704788_qa_3/task.toml b/tasks/0107_704_107704788_qa_3/task.toml index c00112b6324b7c3117594d3576ab920600bceb76..4872f91ccd2a65e252307636636f34b6627234fa 100644 --- a/tasks/0107_704_107704788_qa_3/task.toml +++ b/tasks/0107_704_107704788_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0107_704_107704788_qa_3" +name = "smoldataenvs-train/0107_704_107704788_qa_3" description = "How many principal components are required to retain at least 95% of the total variance in the iris dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0107_756_107756714_qa_1/task.toml b/tasks/0107_756_107756714_qa_1/task.toml index 0b92b3eb516d5582fba145bc39551980467b0790..30ed759c9cad8a358b8c2279b2419c41c0815bd1 100644 --- a/tasks/0107_756_107756714_qa_1/task.toml +++ b/tasks/0107_756_107756714_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0107_756_107756714_qa_1" +name = "smoldataenvs-train/0107_756_107756714_qa_1" description = "What is the proportion of positive and negative sentiment classes in the dataset before any resampling?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Positive: 50%, Negative: 50%" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0107_762_107762409_qa_5/task.toml b/tasks/0107_762_107762409_qa_5/task.toml index 1ed6102aef2e27e29bcbfebd652b9bb92fea333b..4b72cf282371d5d791670129dc3a29fdf40cd1a8 100644 --- a/tasks/0107_762_107762409_qa_5/task.toml +++ b/tasks/0107_762_107762409_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0107_762_107762409_qa_5" +name = "smoldataenvs-train/0107_762_107762409_qa_5" description = "What is the standard deviation of North American sales across all games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.816683" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0107_772_107772825_qa_1/task.toml b/tasks/0107_772_107772825_qa_1/task.toml index 8ff47edf7ac7cfa5c6d1eec60cb0af782f6b4723..cc25aa39e8b4656e1e8134c4c7a687a2c54e34a6 100644 --- a/tasks/0107_772_107772825_qa_1/task.toml +++ b/tasks/0107_772_107772825_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0107_772_107772825_qa_1" +name = "smoldataenvs-train/0107_772_107772825_qa_1" description = "What is the percentage of patients who did not show up for their appointments after data cleaning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20.26" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0107_772_107772825_qa_3/task.toml b/tasks/0107_772_107772825_qa_3/task.toml index b5f9eba84af3ffa54c0dfa20531e32915810937c..b9eca2a77df35984113bbe4041e4f32437f11dd8 100644 --- a/tasks/0107_772_107772825_qa_3/task.toml +++ b/tasks/0107_772_107772825_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0107_772_107772825_qa_3" +name = "smoldataenvs-train/0107_772_107772825_qa_3" description = "How many patients were removed from the dataset due to invalid age entries (age -1 and 115)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0107_830_107830483_qa_1/task.toml b/tasks/0107_830_107830483_qa_1/task.toml index 75b99141d168e532cddc06d556506917ae47a591..eeb605212330cd9b388217e9ef76fae5a1d70393 100644 --- a/tasks/0107_830_107830483_qa_1/task.toml +++ b/tasks/0107_830_107830483_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0107_830_107830483_qa_1" +name = "smoldataenvs-train/0107_830_107830483_qa_1" description = "Which feature has the highest importance in predicting insurance charges according to the XGBoost model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "smoker" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0107_868_107868150_qa_1/task.toml b/tasks/0107_868_107868150_qa_1/task.toml index 2e8a920f65e8117d741ee65750f7b058613aafcb..9a8ae85c2752b3ae4bc2f42d3c4126658d2e4145 100644 --- a/tasks/0107_868_107868150_qa_1/task.toml +++ b/tasks/0107_868_107868150_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0107_868_107868150_qa_1" +name = "smoldataenvs-train/0107_868_107868150_qa_1" description = "Which feature has the highest mutual information score with the diagnosis label in the breast cancer dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "perimeter_worst" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0107_882_107882051_qa_4/task.toml b/tasks/0107_882_107882051_qa_4/task.toml index 0ce9106362610f46bb84f4bfc469079456945fde..0775b24cd02e956d270c31caf562ea4705e9bc3e 100644 --- a/tasks/0107_882_107882051_qa_4/task.toml +++ b/tasks/0107_882_107882051_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0107_882_107882051_qa_4" +name = "smoldataenvs-train/0107_882_107882051_qa_4" description = "Which city category has the highest number of entries in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "B" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0107_946_107946533_qa_1/task.toml b/tasks/0107_946_107946533_qa_1/task.toml index f408ab0508288406e57be5f57cb1f788726df20c..6c863ec33965719672b4b93844fc2e7ed65ef0a6 100644 --- a/tasks/0107_946_107946533_qa_1/task.toml +++ b/tasks/0107_946_107946533_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0107_946_107946533_qa_1" +name = "smoldataenvs-train/0107_946_107946533_qa_1" description = "Which car model has the highest number of engine cylinders, and what is its make and model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Bugatti Veyron 16.4" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0107_974_107974246_qa_3/task.toml b/tasks/0107_974_107974246_qa_3/task.toml index c729589f21d0d663acf4108ddc60fc243b443dba..6abaa15a98c22fb89d75b2b9fb1cd38a5afdccad 100644 --- a/tasks/0107_974_107974246_qa_3/task.toml +++ b/tasks/0107_974_107974246_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0107_974_107974246_qa_3" +name = "smoldataenvs-train/0107_974_107974246_qa_3" description = "Which cereal in the dataset has the highest fiber content?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "All-Bran with Extra Fiber" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0108_138_108138714_qa_1/task.toml b/tasks/0108_138_108138714_qa_1/task.toml index 35d82e2f999a2aeca6dd9854aa93ca39d2333b22..a4b5550a57569bc4acaf5df5095a83dac59a5d7b 100644 --- a/tasks/0108_138_108138714_qa_1/task.toml +++ b/tasks/0108_138_108138714_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0108_138_108138714_qa_1" +name = "smoldataenvs-train/0108_138_108138714_qa_1" description = "What is the highest correlation coefficient between any two variables in the cleaned Starbucks drink menu dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.99" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0108_184_108184818_qa_2/task.toml b/tasks/0108_184_108184818_qa_2/task.toml index 5f98c445b5141411dd2ab5623ac9252c40a400df..fadd34d1044f6430d1742f249aa41f717d2b01a6 100644 --- a/tasks/0108_184_108184818_qa_2/task.toml +++ b/tasks/0108_184_108184818_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0108_184_108184818_qa_2" +name = "smoldataenvs-train/0108_184_108184818_qa_2" description = "What is the most common odor type among poisonous mushrooms according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "pungent" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0108_184_108184818_qa_3/task.toml b/tasks/0108_184_108184818_qa_3/task.toml index 327793bc3668a8fb72babbce533f9ddfb90b69cd..9c0b814429d42587533e1f91bc66a32ad8acac08 100644 --- a/tasks/0108_184_108184818_qa_3/task.toml +++ b/tasks/0108_184_108184818_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0108_184_108184818_qa_3" +name = "smoldataenvs-train/0108_184_108184818_qa_3" description = "Which habitat type contains the highest proportion of mushrooms in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "woods" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0108_197_108197253_qa_1/task.toml b/tasks/0108_197_108197253_qa_1/task.toml index 397590fd38c869b4cd8af2315e1b3bbbca9ac6e7..5591d6cec3712883bd393c1895cbd0ec4bca2e90 100644 --- a/tasks/0108_197_108197253_qa_1/task.toml +++ b/tasks/0108_197_108197253_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0108_197_108197253_qa_1" +name = "smoldataenvs-train/0108_197_108197253_qa_1" description = "How many features were removed from the dataset due to high correlation (>0.95) between variables?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0108_197_108197991_qa_5/task.toml b/tasks/0108_197_108197991_qa_5/task.toml index e2c68ba158da7a93848f5094396d145c4c3e7a49..33d9918672e63ba545fd095bb873e96f992643ad 100644 --- a/tasks/0108_197_108197991_qa_5/task.toml +++ b/tasks/0108_197_108197991_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0108_197_108197991_qa_5" +name = "smoldataenvs-train/0108_197_108197991_qa_5" description = "Which payment method is associated with the highest churn rate in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic check" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0108_199_108199780_qa_2/task.toml b/tasks/0108_199_108199780_qa_2/task.toml index dd6b502a90d5ccdc8d88a29cd17cb433fcb9be30..6d38f09692e0f75a8442f7f0c3b729fe09f7bd0d 100644 --- a/tasks/0108_199_108199780_qa_2/task.toml +++ b/tasks/0108_199_108199780_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0108_199_108199780_qa_2" +name = "smoldataenvs-train/0108_199_108199780_qa_2" description = "Which feature has the highest importance score according to the Random Forest model's feature importance analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "safety" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0108_225_108225204_qa_1/task.toml b/tasks/0108_225_108225204_qa_1/task.toml index b9440460dc2f608cb5f4e56502d4312c9f56a39f..265aaac4ae83b3fbc4c52484c2e86fd06c875d11 100644 --- a/tasks/0108_225_108225204_qa_1/task.toml +++ b/tasks/0108_225_108225204_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0108_225_108225204_qa_1" +name = "smoldataenvs-train/0108_225_108225204_qa_1" description = "What is the optimal number of clusters identified by the elbow method for the KMeans clustering on the Iris dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0108_225_108225204_qa_2/task.toml b/tasks/0108_225_108225204_qa_2/task.toml index 966bcacb195d0e749fb41de40ab3efa39c3a10f3..bea54c4817ffe44cdc20cf10316998ccb5b2fa40 100644 --- a/tasks/0108_225_108225204_qa_2/task.toml +++ b/tasks/0108_225_108225204_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0108_225_108225204_qa_2" +name = "smoldataenvs-train/0108_225_108225204_qa_2" description = "How many features are used for clustering in the KMeans model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0108_236_108236679_qa_1/task.toml b/tasks/0108_236_108236679_qa_1/task.toml index 1b198d2ba7c8dbe4745f95cbee2b8b93153516b2..ec2d2e33ea6d0268bd8e6d88c96e0a3e12cca788 100644 --- a/tasks/0108_236_108236679_qa_1/task.toml +++ b/tasks/0108_236_108236679_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0108_236_108236679_qa_1" +name = "smoldataenvs-train/0108_236_108236679_qa_1" description = "How many missing values were present in the 'total_bedrooms' column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "207" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0108_236_108236679_qa_4/task.toml b/tasks/0108_236_108236679_qa_4/task.toml index f36ba6b6e642f8cdab48c2e01023b9099c861f09..29bb1942f4a730efcc0e25143ff3b50d21fd8715 100644 --- a/tasks/0108_236_108236679_qa_4/task.toml +++ b/tasks/0108_236_108236679_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0108_236_108236679_qa_4" +name = "smoldataenvs-train/0108_236_108236679_qa_4" description = "After one-hot encoding, how many unique categories were present in the 'ocean_proximity' variable?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0108_236_108236679_qa_5/task.toml b/tasks/0108_236_108236679_qa_5/task.toml index 8f0e025dd53a89485232149b9ebed5079c2cc980..fe9ab562e04aba946ed94736b273f7a5a92c3ff2 100644 --- a/tasks/0108_236_108236679_qa_5/task.toml +++ b/tasks/0108_236_108236679_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0108_236_108236679_qa_5" +name = "smoldataenvs-train/0108_236_108236679_qa_5" description = "What is the average number of 'total_rooms' per house in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2635.76" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0108_284_108284561_qa_2/task.toml b/tasks/0108_284_108284561_qa_2/task.toml index 1c980cc280d0feb0cc88e8631b2a9562c8ac57da..f0bb89a6f7550ca0aced21bd6172c080f50ceab3 100644 --- a/tasks/0108_284_108284561_qa_2/task.toml +++ b/tasks/0108_284_108284561_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0108_284_108284561_qa_2" +name = "smoldataenvs-train/0108_284_108284561_qa_2" description = "How many rows were removed from the dataset during outlier removal based on the total_bedrooms threshold (≥2800)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "90" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0108_359_108359685_qa_2/task.toml b/tasks/0108_359_108359685_qa_2/task.toml index e0791154c1d4e911a0da980403760f3c3fd20476..a4336f7699aead912b77f9f36acfaafb7b797e51 100644 --- a/tasks/0108_359_108359685_qa_2/task.toml +++ b/tasks/0108_359_108359685_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0108_359_108359685_qa_2" +name = "smoldataenvs-train/0108_359_108359685_qa_2" description = "What percentage of the 'percentage_male' column is missing in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12.23" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0108_363_108363990_qa_1/task.toml b/tasks/0108_363_108363990_qa_1/task.toml index be9c58ec4f2b97950d1dc834ce92050f1e64f37e..9d5f953609da9a716c5ef738dcabaab94e71a931 100644 --- a/tasks/0108_363_108363990_qa_1/task.toml +++ b/tasks/0108_363_108363990_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0108_363_108363990_qa_1" +name = "smoldataenvs-train/0108_363_108363990_qa_1" description = "How many outliers are identified in the 'price' variable using the interquartile range (IQR) method?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "15" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0108_363_108363990_qa_2/task.toml b/tasks/0108_363_108363990_qa_2/task.toml index f7edf71887ae764ec084fa25f264af7bea05ba70..86bd6cd312d1b2cfca8bb2f64c389d3b16e54451 100644 --- a/tasks/0108_363_108363990_qa_2/task.toml +++ b/tasks/0108_363_108363990_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0108_363_108363990_qa_2" +name = "smoldataenvs-train/0108_363_108363990_qa_2" description = "Which categorical feature in the dataset has the highest proportion of 'yes' responses?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "driveway" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0108_363_108363990_qa_3/task.toml b/tasks/0108_363_108363990_qa_3/task.toml index a9d285b0ab36e009d496cf87573f09f2335bc834..910e0bc1b712a1348f47379a7f801fd3735c9fd0 100644 --- a/tasks/0108_363_108363990_qa_3/task.toml +++ b/tasks/0108_363_108363990_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0108_363_108363990_qa_3" +name = "smoldataenvs-train/0108_363_108363990_qa_3" description = "What is the percentage of houses in the dataset with air conditioning (airco = yes)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "31.68" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0108_379_108379534_qa_2/task.toml b/tasks/0108_379_108379534_qa_2/task.toml index 30ee4f2cbbbe391e5bb60530fa3cd7fea7a5b0f1..3d0fb526032b24a76a0c01b451d9f555c3cb557c 100644 --- a/tasks/0108_379_108379534_qa_2/task.toml +++ b/tasks/0108_379_108379534_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0108_379_108379534_qa_2" +name = "smoldataenvs-train/0108_379_108379534_qa_2" description = "What percentage of legendary Pokémon have no specific gender assigned in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "90" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0108_391_108391946_qa_5/task.toml b/tasks/0108_391_108391946_qa_5/task.toml index 865d363984a3145efdfc2b4e9d362a38ff1e68c3..0963d7eeabca4d143c6639b632bbc74979f7798d 100644 --- a/tasks/0108_391_108391946_qa_5/task.toml +++ b/tasks/0108_391_108391946_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0108_391_108391946_qa_5" +name = "smoldataenvs-train/0108_391_108391946_qa_5" description = "How many mushrooms in the dataset have a 'veil-type' of 'p'?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8124" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0108_423_108423455_qa_3/task.toml b/tasks/0108_423_108423455_qa_3/task.toml index 9249922465c11774a0faa8c606ec717887687765..d33c8a22017b360af72e9cffbfab05ccdb7cb623 100644 --- a/tasks/0108_423_108423455_qa_3/task.toml +++ b/tasks/0108_423_108423455_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0108_423_108423455_qa_3" +name = "smoldataenvs-train/0108_423_108423455_qa_3" description = "What is the maximum BMI value recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "53.13" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0108_423_108423455_qa_4/task.toml b/tasks/0108_423_108423455_qa_4/task.toml index c3814772b878c6871788a13a185a687528a96bf8..0013b71975a46ab7a4fef2b2dc357f082561e909 100644 --- a/tasks/0108_423_108423455_qa_4/task.toml +++ b/tasks/0108_423_108423455_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0108_423_108423455_qa_4" +name = "smoldataenvs-train/0108_423_108423455_qa_4" description = "What is the range of the number of children (from minimum to maximum) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0 to 5" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0108_423_108423455_qa_5/task.toml b/tasks/0108_423_108423455_qa_5/task.toml index 6aa90b2b21276897c949e076599d22ce35b9682f..0321d5f480e9c18103f8ae3fbd4861dd3edff405 100644 --- a/tasks/0108_423_108423455_qa_5/task.toml +++ b/tasks/0108_423_108423455_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0108_423_108423455_qa_5" +name = "smoldataenvs-train/0108_423_108423455_qa_5" description = "What is the standard deviation of BMI values in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.098" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0108_588_108588873_qa_1/task.toml b/tasks/0108_588_108588873_qa_1/task.toml index 66b89d383f01c41e2f44dd4f249ee5412d188c20..3b6eb7be39b42f763c5f5cbdc46e26d357016bf0 100644 --- a/tasks/0108_588_108588873_qa_1/task.toml +++ b/tasks/0108_588_108588873_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0108_588_108588873_qa_1" +name = "smoldataenvs-train/0108_588_108588873_qa_1" description = "Which physicochemical variable demonstrates the strongest positive linear relationship with wine quality based on Pearson correlation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "alcohol" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0108_588_108588873_qa_2/task.toml b/tasks/0108_588_108588873_qa_2/task.toml index b7961f06fa893c1cbad85e9b6770ada0c4d6e43c..5657fa8f6461156e39718a8742d96e1eb2fba552 100644 --- a/tasks/0108_588_108588873_qa_2/task.toml +++ b/tasks/0108_588_108588873_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0108_588_108588873_qa_2" +name = "smoldataenvs-train/0108_588_108588873_qa_2" description = "What is the median value of the wine quality scores in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0108_620_108620410_qa_3/task.toml b/tasks/0108_620_108620410_qa_3/task.toml index 294a4a5ac98f5707ac9efc43178bc5213c671822..3817b7dec5423cb99aaa4799d68333f066161c87 100644 --- a/tasks/0108_620_108620410_qa_3/task.toml +++ b/tasks/0108_620_108620410_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0108_620_108620410_qa_3" +name = "smoldataenvs-train/0108_620_108620410_qa_3" description = "What is the median North American sales value for all video games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.08" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0108_641_108641458_qa_2/task.toml b/tasks/0108_641_108641458_qa_2/task.toml index 59bdc16ce22b871567b732b3ce24ccdfb3c8689a..d44501c90af672d82a79b4cc12e241318a6f9904 100644 --- a/tasks/0108_641_108641458_qa_2/task.toml +++ b/tasks/0108_641_108641458_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0108_641_108641458_qa_2" +name = "smoldataenvs-train/0108_641_108641458_qa_2" description = "How many standard deviations above the mean North American sales is the top-selling game?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50.48" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0108_641_108641458_qa_3/task.toml b/tasks/0108_641_108641458_qa_3/task.toml index 58f39eef3dbc411cb3f2a0f2b3ee54ee5c88d8b8..7f252095d75ee59631505332701fb41d2ccdf22b 100644 --- a/tasks/0108_641_108641458_qa_3/task.toml +++ b/tasks/0108_641_108641458_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0108_641_108641458_qa_3" +name = "smoldataenvs-train/0108_641_108641458_qa_3" description = "What is the ratio of the average global sales for the Nintendo Wii compared to all other platforms combined?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.336" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0108_681_108681309_qa_1/task.toml b/tasks/0108_681_108681309_qa_1/task.toml index 04a30af3350045f0aa82cfb85c1b08d69bf06007..b320dc2c99a4d73bd49a28fbacbde183417aae1e 100644 --- a/tasks/0108_681_108681309_qa_1/task.toml +++ b/tasks/0108_681_108681309_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0108_681_108681309_qa_1" +name = "smoldataenvs-train/0108_681_108681309_qa_1" description = "How many standard deviations above the mean are the North American sales of the top-selling game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50.8" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0108_681_108681309_qa_4/task.toml b/tasks/0108_681_108681309_qa_4/task.toml index fbf5aa2e091cfbdeaefe9c047dc94cd5c033f09b..fccf4aa8128afd3f4ed79b682934ab289ab50fb4 100644 --- a/tasks/0108_681_108681309_qa_4/task.toml +++ b/tasks/0108_681_108681309_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0108_681_108681309_qa_4" +name = "smoldataenvs-train/0108_681_108681309_qa_4" description = "What is the highest Japanese sales figure achieved by any game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10.22" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0108_778_108778094_qa_5/task.toml b/tasks/0108_778_108778094_qa_5/task.toml index ea295578f6cb3ea40d696946099d6d0439dad4b3..5f32aedd4ae71cfa558974b4ca0e954a3340c73a 100644 --- a/tasks/0108_778_108778094_qa_5/task.toml +++ b/tasks/0108_778_108778094_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0108_778_108778094_qa_5" +name = "smoldataenvs-train/0108_778_108778094_qa_5" description = "What is the difference between the training set accuracy and test set accuracy of the final model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.0006977112589188347" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0108_781_108781422_qa_2/task.toml b/tasks/0108_781_108781422_qa_2/task.toml index f83b3c05a158265f6c5e72a641b6ae8f02d6746b..c76630610aa5ca7353182f9d811436d20b15ec73 100644 --- a/tasks/0108_781_108781422_qa_2/task.toml +++ b/tasks/0108_781_108781422_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0108_781_108781422_qa_2" +name = "smoldataenvs-train/0108_781_108781422_qa_2" description = "Between 2000 and 2005, which game had the highest global sales?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Nintendogs" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0108_808_108808607_qa_2/task.toml b/tasks/0108_808_108808607_qa_2/task.toml index efff596cce2aa7ba852c73f677d4ace2a18c0005..2b5f672280d6d3ed116ca05deafe1d1f5d3da856 100644 --- a/tasks/0108_808_108808607_qa_2/task.toml +++ b/tasks/0108_808_108808607_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0108_808_108808607_qa_2" +name = "smoldataenvs-train/0108_808_108808607_qa_2" description = "After applying the SMOTE technique, are the loan status classes (approved/rejected) balanced in the resampled dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0108_825_108825699_qa_1/task.toml b/tasks/0108_825_108825699_qa_1/task.toml index 7e41e73d0b6945715fed049e15ea291c8bf87817..cda6537411678299a5e39249704eccf6aac6709c 100644 --- a/tasks/0108_825_108825699_qa_1/task.toml +++ b/tasks/0108_825_108825699_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0108_825_108825699_qa_1" +name = "smoldataenvs-train/0108_825_108825699_qa_1" description = "For the top-selling video game of all time (Wii Sports), how many standard deviations is its North American sales value from the mean North American sales across all games?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50.478988" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0108_825_108825699_qa_3/task.toml b/tasks/0108_825_108825699_qa_3/task.toml index 21ff8c9a550489649aa0cc215f535ddf80749924..97fa17991339d66138dd613db5bd970880b6fd0b 100644 --- a/tasks/0108_825_108825699_qa_3/task.toml +++ b/tasks/0108_825_108825699_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0108_825_108825699_qa_3" +name = "smoldataenvs-train/0108_825_108825699_qa_3" description = "What percentage of total global sales comes from North American sales?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "49.245889216227006" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0108_835_108835715_qa_4/task.toml b/tasks/0108_835_108835715_qa_4/task.toml index 5c22b7f5b2afd3a79bee9b8f10941baddd1c0854..549ec9c5b5cd81f685d8b4d4dac2374c2eb8cda5 100644 --- a/tasks/0108_835_108835715_qa_4/task.toml +++ b/tasks/0108_835_108835715_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0108_835_108835715_qa_4" +name = "smoldataenvs-train/0108_835_108835715_qa_4" description = "How many unique indices are present in the Series with duplicate index 'D'?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0108_835_108835715_qa_5/task.toml b/tasks/0108_835_108835715_qa_5/task.toml index ecaadac3f46ea1f1ae7b38386ffc64a7e82759bf..c533ed69e5735b48329d22fdd7142bb2deb27d0c 100644 --- a/tasks/0108_835_108835715_qa_5/task.toml +++ b/tasks/0108_835_108835715_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0108_835_108835715_qa_5" +name = "smoldataenvs-train/0108_835_108835715_qa_5" description = "What is the product of the values in the Series [-1, 1, 2, 3]?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-6" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0108_842_108842160_qa_4/task.toml b/tasks/0108_842_108842160_qa_4/task.toml index 89f297e5139fa52f6240d1bfd4ba346dedbec5c7..e63be50b4021afbdfe11b5815ae32a3b858362a3 100644 --- a/tasks/0108_842_108842160_qa_4/task.toml +++ b/tasks/0108_842_108842160_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0108_842_108842160_qa_4" +name = "smoldataenvs-train/0108_842_108842160_qa_4" description = "What is the median value for North American video game sales in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.08" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0108_883_108883493_qa_1/task.toml b/tasks/0108_883_108883493_qa_1/task.toml index bd428086dc9aadb2730f3a30a404540a7f6c05dc..2a29db8f73c44284fd104b81099782c773a8bddb 100644 --- a/tasks/0108_883_108883493_qa_1/task.toml +++ b/tasks/0108_883_108883493_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0108_883_108883493_qa_1" +name = "smoldataenvs-train/0108_883_108883493_qa_1" description = "What is the Z-score for North American sales of the top-selling video game of all time?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50.478988" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0108_883_108883493_qa_4/task.toml b/tasks/0108_883_108883493_qa_4/task.toml index d802acb8c280ba45927cf7b47e7eb72d990e3a5e..d53d6ed181c17902c0d6037a91a299d0a2378c2c 100644 --- a/tasks/0108_883_108883493_qa_4/task.toml +++ b/tasks/0108_883_108883493_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0108_883_108883493_qa_4" +name = "smoldataenvs-train/0108_883_108883493_qa_4" description = "Which region contributes the highest total sales for Nintendo games globally?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "North America" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0108_986_108986276_qa_1/task.toml b/tasks/0108_986_108986276_qa_1/task.toml index 38f0e7af4750d9bb07e4d03e72967a06606a5c38..d91fb02ea445699b112cbcc7dc0f8c92be115b14 100644 --- a/tasks/0108_986_108986276_qa_1/task.toml +++ b/tasks/0108_986_108986276_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0108_986_108986276_qa_1" +name = "smoldataenvs-train/0108_986_108986276_qa_1" description = "Which two features have the highest positive correlation with the \"malignant (M)\" diagnosis based on the dataset's correlation analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "concave points_worst, perimeter_worst" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0108_986_108986276_qa_3/task.toml b/tasks/0108_986_108986276_qa_3/task.toml index d70fd22d03572d8e02015ad84fae8a17c9b7bd96..8452894fff3e6e27c2ec1dc5ee9ede1b839edf2a 100644 --- a/tasks/0108_986_108986276_qa_3/task.toml +++ b/tasks/0108_986_108986276_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0108_986_108986276_qa_3" +name = "smoldataenvs-train/0108_986_108986276_qa_3" description = "What percentage of diagnoses in the dataset are classified as malignant (M), and how does this compare to benign (B) diagnoses?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Malignant: 37.26%, Benign: 62.74%" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0109_027_109027351_qa_1/task.toml b/tasks/0109_027_109027351_qa_1/task.toml index 50b1d16e9c4115576fe332309e620a0f4c8fe897..3c1c16a39cd2d45fbb66b10111594d993eb4880b 100644 --- a/tasks/0109_027_109027351_qa_1/task.toml +++ b/tasks/0109_027_109027351_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0109_027_109027351_qa_1" +name = "smoldataenvs-train/0109_027_109027351_qa_1" description = "How many Science Fiction movies are present in the dataset based on the genre index?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3047" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0109_079_109079153_qa_1/task.toml b/tasks/0109_079_109079153_qa_1/task.toml index 5f7fbdf413b46aa2b2a69d745a61d8bdc0857d71..14875b083e5030366c3e5e47d41a35613cab24aa 100644 --- a/tasks/0109_079_109079153_qa_1/task.toml +++ b/tasks/0109_079_109079153_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0109_079_109079153_qa_1" +name = "smoldataenvs-train/0109_079_109079153_qa_1" description = "How many standard deviations above the mean are the North American sales of the top-selling game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50.8" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0109_091_109091369_qa_5/task.toml b/tasks/0109_091_109091369_qa_5/task.toml index c43c1e0fe797349ee3080b67b55860913df660f0..a21afc5575b89ff114dcffa94c162061e46d620c 100644 --- a/tasks/0109_091_109091369_qa_5/task.toml +++ b/tasks/0109_091_109091369_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0109_091_109091369_qa_5" +name = "smoldataenvs-train/0109_091_109091369_qa_5" description = "Which contract type is associated with the lowest customer churn rate according to the count plot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Two year" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0109_165_109165906_qa_3/task.toml b/tasks/0109_165_109165906_qa_3/task.toml index 863bd7d4723f4923501afcd583dd95d0db7dbd53..20663022aeec211da44207fc8e4484b37c059304 100644 --- a/tasks/0109_165_109165906_qa_3/task.toml +++ b/tasks/0109_165_109165906_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0109_165_109165906_qa_3" +name = "smoldataenvs-train/0109_165_109165906_qa_3" description = "How many standard deviations above the mean North American sales is the top-selling game's performance in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50.48" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0109_211_109211049_qa_1/task.toml b/tasks/0109_211_109211049_qa_1/task.toml index b1a3c60ca6b47a057ec3a5af74a9121f717d8977..b59734ec08ae5832d576d3619bdd7e0ed2e2b679 100644 --- a/tasks/0109_211_109211049_qa_1/task.toml +++ b/tasks/0109_211_109211049_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0109_211_109211049_qa_1" +name = "smoldataenvs-train/0109_211_109211049_qa_1" description = "Which Pokémon type has the highest mean Total stat across all generations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Dragon" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0109_211_109211049_qa_5/task.toml b/tasks/0109_211_109211049_qa_5/task.toml index c0e113e001b52db7913ff0c47535768205307537..91553907518ff1021918ace529912c0235318622 100644 --- a/tasks/0109_211_109211049_qa_5/task.toml +++ b/tasks/0109_211_109211049_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0109_211_109211049_qa_5" +name = "smoldataenvs-train/0109_211_109211049_qa_5" description = "What is the most common primary type (Type 1) among all Pokémon in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Water" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0109_269_109269793_qa_1/task.toml b/tasks/0109_269_109269793_qa_1/task.toml index b2df1d595f05f9fce9878404343901398631febd..df1c1480e81aaf530dcb3cf377043dd528d8a066 100644 --- a/tasks/0109_269_109269793_qa_1/task.toml +++ b/tasks/0109_269_109269793_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0109_269_109269793_qa_1" +name = "smoldataenvs-train/0109_269_109269793_qa_1" description = "What is the average global sales value in the dataset before any modifications?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.5374406555006628" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0109_269_109269793_qa_5/task.toml b/tasks/0109_269_109269793_qa_5/task.toml index c36ef24e8cc75d1d5fdcb757d0f1f70f0253482f..8192e790444a964cdaead0c5d9be2ea6763b49b5 100644 --- a/tasks/0109_269_109269793_qa_5/task.toml +++ b/tasks/0109_269_109269793_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0109_269_109269793_qa_5" +name = "smoldataenvs-train/0109_269_109269793_qa_5" description = "What is the lowest recorded global sales value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.01" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0109_303_109303873_qa_1/task.toml b/tasks/0109_303_109303873_qa_1/task.toml index 2f50d02dce0b395271f0163dfcbbe181343a1012..7463e49757b090fa89d9fd4f562bd67bd3726517 100644 --- a/tasks/0109_303_109303873_qa_1/task.toml +++ b/tasks/0109_303_109303873_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0109_303_109303873_qa_1" +name = "smoldataenvs-train/0109_303_109303873_qa_1" description = "What is the most common primary type (type1) among Pokémon in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Water" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0109_303_109303873_qa_2/task.toml b/tasks/0109_303_109303873_qa_2/task.toml index 7221c6234ad6eed09f3be1311f0a4a6039d35a0b..0a40fc627ab2efadcf76e21686ce5fe68e3a9222 100644 --- a/tasks/0109_303_109303873_qa_2/task.toml +++ b/tasks/0109_303_109303873_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0109_303_109303873_qa_2" +name = "smoldataenvs-train/0109_303_109303873_qa_2" description = "What is the most frequently observed secondary type (type2) among Pokémon in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Flying" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0109_303_109303873_qa_3/task.toml b/tasks/0109_303_109303873_qa_3/task.toml index 766cae691f3dff89b543ee9ab8343cca3f7735f7..2234c9cbfcaaae659ca3c0197195d29b20b75272 100644 --- a/tasks/0109_303_109303873_qa_3/task.toml +++ b/tasks/0109_303_109303873_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0109_303_109303873_qa_3" +name = "smoldataenvs-train/0109_303_109303873_qa_3" description = "Which Pokémon generation contains the highest number of legendary Pokémon based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0109_303_109303873_qa_5/task.toml b/tasks/0109_303_109303873_qa_5/task.toml index 560d938e897efeb60139c0574bedaf0cd3128379..10dd6ea613f1045fa066e16d9efb18bf0b5cbfa3 100644 --- a/tasks/0109_303_109303873_qa_5/task.toml +++ b/tasks/0109_303_109303873_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0109_303_109303873_qa_5" +name = "smoldataenvs-train/0109_303_109303873_qa_5" description = "What is the median speed value across all Pokémon in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "65" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0109_375_109375313_qa_4/task.toml b/tasks/0109_375_109375313_qa_4/task.toml index 2a60b916349413492d05e5499d27fa467e1991ab..ffd961500dbd650665f79f61dd091a9029099f23 100644 --- a/tasks/0109_375_109375313_qa_4/task.toml +++ b/tasks/0109_375_109375313_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0109_375_109375313_qa_4" +name = "smoldataenvs-train/0109_375_109375313_qa_4" description = "What is the range (maximum minus minimum) of SepalWidthCm values for the Iris-setosa species based on the boxplot data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0109_422_109422676_qa_3/task.toml b/tasks/0109_422_109422676_qa_3/task.toml index 10505e781b0ffb2179477cf01b8afbb6466db0e0..b698f60fece42f1629e8b43a11e7feba54f92183 100644 --- a/tasks/0109_422_109422676_qa_3/task.toml +++ b/tasks/0109_422_109422676_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0109_422_109422676_qa_3" +name = "smoldataenvs-train/0109_422_109422676_qa_3" description = "What is the price range of diamonds in the dataset (minimum to maximum)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "326, 18823" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0109_422_109422676_qa_4/task.toml b/tasks/0109_422_109422676_qa_4/task.toml index d47bda879607b8c64939839c514d87e5f0982790..83330384a1e4f495f62644058fd04096c5dedce5 100644 --- a/tasks/0109_422_109422676_qa_4/task.toml +++ b/tasks/0109_422_109422676_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0109_422_109422676_qa_4" +name = "smoldataenvs-train/0109_422_109422676_qa_4" description = "Which color category has the highest frequency of diamonds in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "G" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0109_442_109442355_qa_4/task.toml b/tasks/0109_442_109442355_qa_4/task.toml index b9b4135d01d4a24103c2d2eb0d5a933c344ef114..ecb68642d55773d47bc8382cf3f64bde09e3ca61 100644 --- a/tasks/0109_442_109442355_qa_4/task.toml +++ b/tasks/0109_442_109442355_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0109_442_109442355_qa_4" +name = "smoldataenvs-train/0109_442_109442355_qa_4" description = "What is the minimum global sales value recorded for any video game?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.01" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0109_469_109469247_qa_1/task.toml b/tasks/0109_469_109469247_qa_1/task.toml index 854b85113ff2f7a41e5baf46d7cb3d3377145635..27952ee6924eba408148bd6cbe494a6fe517cc98 100644 --- a/tasks/0109_469_109469247_qa_1/task.toml +++ b/tasks/0109_469_109469247_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0109_469_109469247_qa_1" +name = "smoldataenvs-train/0109_469_109469247_qa_1" description = "What is the p-value from the ADF test applied to the original non-stationary air passenger data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.991880243437641" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0109_469_109469247_qa_3/task.toml b/tasks/0109_469_109469247_qa_3/task.toml index 3c91cd000189f3af67a7ce3b06ccc28af8217e3a..4b9d551d5299a863d9afd892b2777a344a6df21c 100644 --- a/tasks/0109_469_109469247_qa_3/task.toml +++ b/tasks/0109_469_109469247_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0109_469_109469247_qa_3" +name = "smoldataenvs-train/0109_469_109469247_qa_3" description = "What is the 5% critical value threshold reported in the ADF test results for the differenced data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-2.8861509858476264" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0109_525_109525152_qa_1/task.toml b/tasks/0109_525_109525152_qa_1/task.toml index b345501406948da7e0f2c5f923ea4e065d81308f..e7b1ffeef538fc74cd65d1eaf1c25aec9364620e 100644 --- a/tasks/0109_525_109525152_qa_1/task.toml +++ b/tasks/0109_525_109525152_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0109_525_109525152_qa_1" +name = "smoldataenvs-train/0109_525_109525152_qa_1" description = "Which passenger group (based on class and gender) had the highest survival rate according to the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1st class female" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0109_666_109666127_qa_1/task.toml b/tasks/0109_666_109666127_qa_1/task.toml index 26f822ddb43717651811440acf13c4fd2c454431..909bc35d48b077a55db55bae99c9beb0f334261d 100644 --- a/tasks/0109_666_109666127_qa_1/task.toml +++ b/tasks/0109_666_109666127_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0109_666_109666127_qa_1" +name = "smoldataenvs-train/0109_666_109666127_qa_1" description = "Which year had the highest average voting based on the movie data analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2012" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0109_683_109683499_qa_2/task.toml b/tasks/0109_683_109683499_qa_2/task.toml index 2665c442a83f72bcad3fd6652f3c1b894ea1a89d..0205e460a0e240fcfe7183b80a82d9a8183301de 100644 --- a/tasks/0109_683_109683499_qa_2/task.toml +++ b/tasks/0109_683_109683499_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0109_683_109683499_qa_2" +name = "smoldataenvs-train/0109_683_109683499_qa_2" description = "Which genre has the highest number of games in the dataset, and how many games does it contain?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action, 3316" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0109_683_109683499_qa_4/task.toml b/tasks/0109_683_109683499_qa_4/task.toml index bcac453b0624aef656a0e428a5df509bba910a95..60fd9f3beb85da0cbf592e1c68a73f5d4dc0b615 100644 --- a/tasks/0109_683_109683499_qa_4/task.toml +++ b/tasks/0109_683_109683499_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0109_683_109683499_qa_4" +name = "smoldataenvs-train/0109_683_109683499_qa_4" description = "What is the difference between the highest and lowest global sales values for individual games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "82.73" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0109_856_109856610_qa_3/task.toml b/tasks/0109_856_109856610_qa_3/task.toml index c6c7b8916308c385ad0f28134f7011f1ddb30d2e..95a5ca4f0d088369677c5600d0da2e7e02de1f1b 100644 --- a/tasks/0109_856_109856610_qa_3/task.toml +++ b/tasks/0109_856_109856610_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0109_856_109856610_qa_3" +name = "smoldataenvs-train/0109_856_109856610_qa_3" description = "What are the top three gaming platforms with the highest cumulative global sales according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PS2, X360, PS3" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0109_856_109856610_qa_4/task.toml b/tasks/0109_856_109856610_qa_4/task.toml index 831fd38360a94aaef41d2cafabec798b1a8e1bb1..876e6bf789bc54a947de69f2ae6fc3864bec6652 100644 --- a/tasks/0109_856_109856610_qa_4/task.toml +++ b/tasks/0109_856_109856610_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0109_856_109856610_qa_4" +name = "smoldataenvs-train/0109_856_109856610_qa_4" description = "Which publishing company has the highest number of video games listed in the dataset, surpassing all other publishers in quantity?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic Arts" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0109_878_109878873_qa_3/task.toml b/tasks/0109_878_109878873_qa_3/task.toml index 1be0ba1d87fad32448ada2e9b96f678d66d549ae..053fe23957289fb72290bab8c17a9b8951f0e2f0 100644 --- a/tasks/0109_878_109878873_qa_3/task.toml +++ b/tasks/0109_878_109878873_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0109_878_109878873_qa_3" +name = "smoldataenvs-train/0109_878_109878873_qa_3" description = "What percentage of mobile phones in the dataset have a touch screen?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50.3" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0109_914_109914712_qa_2/task.toml b/tasks/0109_914_109914712_qa_2/task.toml index fa49b68a34f36cea102a60d7aecb6c47f83c491a..eae21e8d1a355e097e48f196a1e2c0f18dfad987 100644 --- a/tasks/0109_914_109914712_qa_2/task.toml +++ b/tasks/0109_914_109914712_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0109_914_109914712_qa_2" +name = "smoldataenvs-train/0109_914_109914712_qa_2" description = "What is the mean absolute error for the KNN classifier on the test dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.275" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0109_958_109958191_qa_1/task.toml b/tasks/0109_958_109958191_qa_1/task.toml index 6d1664b0a5b5edf35ebcab43b929485fe835f4af..e65f253120b9008324e05fa33818876986a82d17 100644 --- a/tasks/0109_958_109958191_qa_1/task.toml +++ b/tasks/0109_958_109958191_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0109_958_109958191_qa_1" +name = "smoldataenvs-train/0109_958_109958191_qa_1" description = "What is the difference in the average number of axillary lymph nodes between patients who survived and those who did not?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.657683" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0109_958_109958191_qa_4/task.toml b/tasks/0109_958_109958191_qa_4/task.toml index 398646713e516bdf7f41841513699f8efcfdbceb..dbd7a31a3404df28387d1bfb4aee03665aa08660 100644 --- a/tasks/0109_958_109958191_qa_4/task.toml +++ b/tasks/0109_958_109958191_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0109_958_109958191_qa_4" +name = "smoldataenvs-train/0109_958_109958191_qa_4" description = "What is the ratio of the interquartile range (IQR) of axillary lymph nodes for survivors versus non-survivors?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0110_144_110144253_qa_1/task.toml b/tasks/0110_144_110144253_qa_1/task.toml index 5524427ba0fb5a0a94cacda4ac27f968c69c4af2..90fed112897ac11cd11245d0af184cb96cd9b4c1 100644 --- a/tasks/0110_144_110144253_qa_1/task.toml +++ b/tasks/0110_144_110144253_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0110_144_110144253_qa_1" +name = "smoldataenvs-train/0110_144_110144253_qa_1" description = "Which pair of predictors in the dataset shows the strongest positive correlation according to the correlation matrix?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "tenure, TotalCharges" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0110_147_110147085_qa_1/task.toml b/tasks/0110_147_110147085_qa_1/task.toml index 803ea593cd666d67cf9a0fd8d8ec7bf04cd5a42d..5d0ebb53b33831d2a0047bc18f833c65280b2fa9 100644 --- a/tasks/0110_147_110147085_qa_1/task.toml +++ b/tasks/0110_147_110147085_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0110_147_110147085_qa_1" +name = "smoldataenvs-train/0110_147_110147085_qa_1" description = "Which ocean proximity category contains the highest number of districts, and how many districts are in that category?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "<1H OCEAN with 9136 districts" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0110_251_110251901_qa_1/task.toml b/tasks/0110_251_110251901_qa_1/task.toml index 782edf443749dcf556e2aeb4608bec2b06ec1a02..d4527327cf660a89a7f1e35b68b11c06a5f368c9 100644 --- a/tasks/0110_251_110251901_qa_1/task.toml +++ b/tasks/0110_251_110251901_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0110_251_110251901_qa_1" +name = "smoldataenvs-train/0110_251_110251901_qa_1" description = "Which iris species has the highest average petal length according to the boxplot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-virginica" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0110_336_110336041_qa_2/task.toml b/tasks/0110_336_110336041_qa_2/task.toml index 926e9196acc572171b37f19ec48fe6f7a56375cf..13559300d2293348743258e42dfb6fff1601b965 100644 --- a/tasks/0110_336_110336041_qa_2/task.toml +++ b/tasks/0110_336_110336041_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0110_336_110336041_qa_2" +name = "smoldataenvs-train/0110_336_110336041_qa_2" description = "How many missing values were present in the 'Unnamed: 32' column before it was dropped from the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "569" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0110_344_110344400_qa_1/task.toml b/tasks/0110_344_110344400_qa_1/task.toml index e37582aa9e648b94ea397786ac8d8225746d3744..a9044114d4ebc353a59911c37395db35592bc379 100644 --- a/tasks/0110_344_110344400_qa_1/task.toml +++ b/tasks/0110_344_110344400_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0110_344_110344400_qa_1" +name = "smoldataenvs-train/0110_344_110344400_qa_1" description = "What is the percentage of the minority class (class 2) in the training dataset before applying any resampling techniques?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.7" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0110_384_110384053_qa_1/task.toml b/tasks/0110_384_110384053_qa_1/task.toml index 7a99eed23830687cb2c9ae2977a3f99514f46595..955cf5a562248b70a02f3bed6b38d1de5ed3a7e3 100644 --- a/tasks/0110_384_110384053_qa_1/task.toml +++ b/tasks/0110_384_110384053_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0110_384_110384053_qa_1" +name = "smoldataenvs-train/0110_384_110384053_qa_1" description = "What is the total number of reviews classified as \"happy\" in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26521" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0110_384_110384053_qa_4/task.toml b/tasks/0110_384_110384053_qa_4/task.toml index a11376038118f5242539ea0e10f440fe483a81ce..3d2a03537c14d7504fc2851e59357f299c9035d6 100644 --- a/tasks/0110_384_110384053_qa_4/task.toml +++ b/tasks/0110_384_110384053_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0110_384_110384053_qa_4" +name = "smoldataenvs-train/0110_384_110384053_qa_4" description = "What is the total number of unique browsers present in the original dataset before standardization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0110_391_110391161_qa_2/task.toml b/tasks/0110_391_110391161_qa_2/task.toml index 8ce4dad8789e101ca770def194c81f87baec8452..f9fbfd5513f1841113c2a5dbf6456b5795d781e2 100644 --- a/tasks/0110_391_110391161_qa_2/task.toml +++ b/tasks/0110_391_110391161_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0110_391_110391161_qa_2" +name = "smoldataenvs-train/0110_391_110391161_qa_2" description = "How many missing values were present in the 'total_bedrooms' column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "207" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0110_400_110400034_qa_1/task.toml b/tasks/0110_400_110400034_qa_1/task.toml index 48ac2bd39c9f5e794a87df7e6d333023660b1913..27dc82a59d327ccf3bc3c265f26f1769a4418387 100644 --- a/tasks/0110_400_110400034_qa_1/task.toml +++ b/tasks/0110_400_110400034_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0110_400_110400034_qa_1" +name = "smoldataenvs-train/0110_400_110400034_qa_1" description = "What is the maximum house price in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7700000" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0110_400_110400034_qa_5/task.toml b/tasks/0110_400_110400034_qa_5/task.toml index cfc32ef6a1aa35ae1cb325ec97090a187ec084f7..a4508b5e499279b179d1284da4180315bde021f9 100644 --- a/tasks/0110_400_110400034_qa_5/task.toml +++ b/tasks/0110_400_110400034_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0110_400_110400034_qa_5" +name = "smoldataenvs-train/0110_400_110400034_qa_5" description = "What is the mean house price in the original dataset before any transformations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "540088.1" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0110_432_110432344_qa_4/task.toml b/tasks/0110_432_110432344_qa_4/task.toml index 8f9350e97b0b02fb750194ce5d6a73ed2c6a2cb3..4e52fcc43c466a4ba441b76b86ad867b32c41ed1 100644 --- a/tasks/0110_432_110432344_qa_4/task.toml +++ b/tasks/0110_432_110432344_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0110_432_110432344_qa_4" +name = "smoldataenvs-train/0110_432_110432344_qa_4" description = "What is the correlation coefficient between CO₂ emissions from coal and natural gas over the time period shown in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.68" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0110_445_110445671_qa_4/task.toml b/tasks/0110_445_110445671_qa_4/task.toml index 1ad03535bc0ddb70c16f6b8e8347deff926e12b3..2379a403df7b07e0b4395b6a5563f2a82477e00c 100644 --- a/tasks/0110_445_110445671_qa_4/task.toml +++ b/tasks/0110_445_110445671_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0110_445_110445671_qa_4" +name = "smoldataenvs-train/0110_445_110445671_qa_4" description = "What is the most frequently played opening move by white players in rated games, and how many times did it occur?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "e4, 10162" reward_mode_initial = "list" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0110_445_110445671_qa_5/task.toml b/tasks/0110_445_110445671_qa_5/task.toml index f51bc906a3a0ee6574ec9723de39d1875a8fafa4..261eb1235de2b8100b22dbe23e8e34f371093767 100644 --- a/tasks/0110_445_110445671_qa_5/task.toml +++ b/tasks/0110_445_110445671_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0110_445_110445671_qa_5" +name = "smoldataenvs-train/0110_445_110445671_qa_5" description = "What is the most common opening move response by black players in rated games, and how many times did it occur?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "e5, 5567" reward_mode_initial = "list" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0110_464_110464682_qa_1/task.toml b/tasks/0110_464_110464682_qa_1/task.toml index 4a4c486f3f9f905c78fca95ba0c0536829bb30d7..344615ae609a53916cd76c905ff9b2bd929359ee 100644 --- a/tasks/0110_464_110464682_qa_1/task.toml +++ b/tasks/0110_464_110464682_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0110_464_110464682_qa_1" +name = "smoldataenvs-train/0110_464_110464682_qa_1" description = "Which cereal in the dataset has the highest health rating, and what is the value of that rating?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "All-Bran with Extra Fiber, 93.704912" reward_mode_initial = "list" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0110_464_110464682_qa_2/task.toml b/tasks/0110_464_110464682_qa_2/task.toml index 1de89e4d2ba205e2b1c50574fe93a347ec32900f..a8e55118fd640f113b2abd5baa552a1b48a9114c 100644 --- a/tasks/0110_464_110464682_qa_2/task.toml +++ b/tasks/0110_464_110464682_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0110_464_110464682_qa_2" +name = "smoldataenvs-train/0110_464_110464682_qa_2" description = "What is the average carbohydrate content (in grams) across all cereals in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "14.5974" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0110_500_110500611_qa_3/task.toml b/tasks/0110_500_110500611_qa_3/task.toml index 5546ede375268dd22ade7b6012bf061ae75d83fc..1c191d2311a3e7439108093b0dff5fcec08da1a4 100644 --- a/tasks/0110_500_110500611_qa_3/task.toml +++ b/tasks/0110_500_110500611_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0110_500_110500611_qa_3" +name = "smoldataenvs-train/0110_500_110500611_qa_3" description = "How many uppercase letter classes are included in the filtered dataset based on the label dictionary?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0110_531_110531781_qa_1/task.toml b/tasks/0110_531_110531781_qa_1/task.toml index e78a536c039a687a22e48040015feb957ef8fbf0..d5a965738f2ea52aff4347d4fa8d1ac14c8d5f15 100644 --- a/tasks/0110_531_110531781_qa_1/task.toml +++ b/tasks/0110_531_110531781_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0110_531_110531781_qa_1" +name = "smoldataenvs-train/0110_531_110531781_qa_1" description = "What is the highest attendance rate percentage among patients with specific health conditions (hypertension, diabetes, or handicap) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "82.8" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0110_563_110563506_qa_1/task.toml b/tasks/0110_563_110563506_qa_1/task.toml index e9784f55fe8d872e0b79851096fcc6068316041f..8f2f838d43e372874d3c242bd8a01aec2fed23b4 100644 --- a/tasks/0110_563_110563506_qa_1/task.toml +++ b/tasks/0110_563_110563506_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0110_563_110563506_qa_1" +name = "smoldataenvs-train/0110_563_110563506_qa_1" description = "What is the Z-score of the top-selling game's North American sales compared to the mean North American sales?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50.47898767479108" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0110_563_110563506_qa_5/task.toml b/tasks/0110_563_110563506_qa_5/task.toml index 45f2aa537e701a17a470b614c8f48dac42715cc4..8627d4f59460d3c51abc6c005695cb99b2cf26df 100644 --- a/tasks/0110_563_110563506_qa_5/task.toml +++ b/tasks/0110_563_110563506_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0110_563_110563506_qa_5" +name = "smoldataenvs-train/0110_563_110563506_qa_5" description = "Which genre has the highest maximum global sales?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Sports" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0110_597_110597362_qa_1/task.toml b/tasks/0110_597_110597362_qa_1/task.toml index fbef916f631da3b0ad0781127aab3d9d61784b55..ef9272a4dd5559e1b1010d0fe06d8c7ed5b9c36b 100644 --- a/tasks/0110_597_110597362_qa_1/task.toml +++ b/tasks/0110_597_110597362_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0110_597_110597362_qa_1" +name = "smoldataenvs-train/0110_597_110597362_qa_1" description = "How many reviews have a HelpfulnessNumerator greater than their HelpfulnessDenominator?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0110_612_110612327_qa_4/task.toml b/tasks/0110_612_110612327_qa_4/task.toml index 6000b0c0aadb1b5fe060b6df2adbda0bc84af4dc..cee23c50fa3bb88822b343c2a513c20c8847ed53 100644 --- a/tasks/0110_612_110612327_qa_4/task.toml +++ b/tasks/0110_612_110612327_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0110_612_110612327_qa_4" +name = "smoldataenvs-train/0110_612_110612327_qa_4" description = "What is the total number of reviews removed as duplicates during preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "174521" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0110_615_110615404_qa_2/task.toml b/tasks/0110_615_110615404_qa_2/task.toml index 1626761bea7f809429bd8348fe10972ddc78002b..3c34b20b99197d01743e01200d4e5f0008d9c1c7 100644 --- a/tasks/0110_615_110615404_qa_2/task.toml +++ b/tasks/0110_615_110615404_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0110_615_110615404_qa_2" +name = "smoldataenvs-train/0110_615_110615404_qa_2" description = "Which species has the highest median petal length according to the bivariate analysis boxplots?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-virginica" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0110_615_110615404_qa_3/task.toml b/tasks/0110_615_110615404_qa_3/task.toml index d789b22f034de7d63b58cd4be2f8d3f21cf521e3..4157dd74641f15a421d7bc714cee19323c65861d 100644 --- a/tasks/0110_615_110615404_qa_3/task.toml +++ b/tasks/0110_615_110615404_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0110_615_110615404_qa_3" +name = "smoldataenvs-train/0110_615_110615404_qa_3" description = "Which species is most easily separable based on the pairplot visualization according to the EDA conclusions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-setosa" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0110_622_110622713_qa_2/task.toml b/tasks/0110_622_110622713_qa_2/task.toml index f8577f49bbe900f35818d3d292d4e81d5a2a5723..2973d7ee27cb59df9e84e7e7bffe1de0f4eec6d3 100644 --- a/tasks/0110_622_110622713_qa_2/task.toml +++ b/tasks/0110_622_110622713_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0110_622_110622713_qa_2" +name = "smoldataenvs-train/0110_622_110622713_qa_2" description = "What is the highest correlation coefficient between any feature and the diagnosis label in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.793566" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0110_622_110622713_qa_3/task.toml b/tasks/0110_622_110622713_qa_3/task.toml index 852704c0423b8faa6b94d57c51ac39fbc67fc81a..55441076d0eb51ed36cd1cc04eb777fb31cb7793 100644 --- a/tasks/0110_622_110622713_qa_3/task.toml +++ b/tasks/0110_622_110622713_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0110_622_110622713_qa_3" +name = "smoldataenvs-train/0110_622_110622713_qa_3" description = "Which feature has the strongest positive correlation with the diagnosis label in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "concave points_worst" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0110_664_110664620_qa_2/task.toml b/tasks/0110_664_110664620_qa_2/task.toml index 575c11cca532d6e1bfa39fdf65a3a59057bae8b1..f8d192958eb25e386c9233f1920d6f3293010152 100644 --- a/tasks/0110_664_110664620_qa_2/task.toml +++ b/tasks/0110_664_110664620_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0110_664_110664620_qa_2" +name = "smoldataenvs-train/0110_664_110664620_qa_2" description = "How many standard deviations above the mean is the North American sales of the top-selling game of all time?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50.47" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0110_683_110683455_qa_2/task.toml b/tasks/0110_683_110683455_qa_2/task.toml index 1cc4cff24be5fb66ce6230212ded3fee803a5e3a..d66877c559f94b10e87f6344134c5d8c40fc7096 100644 --- a/tasks/0110_683_110683455_qa_2/task.toml +++ b/tasks/0110_683_110683455_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0110_683_110683455_qa_2" +name = "smoldataenvs-train/0110_683_110683455_qa_2" description = "Which borough has the highest average sale price per property?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Manhattan" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0110_717_110717363_qa_1/task.toml b/tasks/0110_717_110717363_qa_1/task.toml index 854a5a79f7164097f81a53f8cf9d2113acb4ffc8..11cbffd2f50bac6fa54ed25a6fe11757c9354632 100644 --- a/tasks/0110_717_110717363_qa_1/task.toml +++ b/tasks/0110_717_110717363_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0110_717_110717363_qa_1" +name = "smoldataenvs-train/0110_717_110717363_qa_1" description = "What is the maximum sepal length in the entire dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0110_717_110717969_qa_1/task.toml b/tasks/0110_717_110717969_qa_1/task.toml index d95630c4dc3490a40e0c9f27d7dbe4a2f9cfed48..dd0908660803f3eb824e498bafb7b22ef7cac1f5 100644 --- a/tasks/0110_717_110717969_qa_1/task.toml +++ b/tasks/0110_717_110717969_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0110_717_110717969_qa_1" +name = "smoldataenvs-train/0110_717_110717969_qa_1" description = "Which contract type is associated with the lowest churn probability according to the model coefficients?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Two year" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0110_731_110731547_qa_1/task.toml b/tasks/0110_731_110731547_qa_1/task.toml index 7088a4f110562f51b1b72ad25964993590286c5f..4121f801c887e602a2cca6c6db42bcf02ecbf8f1 100644 --- a/tasks/0110_731_110731547_qa_1/task.toml +++ b/tasks/0110_731_110731547_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0110_731_110731547_qa_1" +name = "smoldataenvs-train/0110_731_110731547_qa_1" description = "Which ProductCategory has the highest total OrderDemand across all warehouses and years?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Category_019" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0110_774_110774218_qa_3/task.toml b/tasks/0110_774_110774218_qa_3/task.toml index df28abc093dd26744dbbc82cdb1022f8dc9d9275..08853e2a9dfc169822b5cf8ea67b1dcc8925a091 100644 --- a/tasks/0110_774_110774218_qa_3/task.toml +++ b/tasks/0110_774_110774218_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0110_774_110774218_qa_3" +name = "smoldataenvs-train/0110_774_110774218_qa_3" description = "What is the correlation coefficient between SepalLengthCm and the Species (encoded as integers)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.782561" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0110_787_110787771_qa_4/task.toml b/tasks/0110_787_110787771_qa_4/task.toml index 7f4c6e2a4abf8cdb9c6ac3b116117f2d10a14d32..38aedeaf9597718a5cba42f92c76b2580ba03e6d 100644 --- a/tasks/0110_787_110787771_qa_4/task.toml +++ b/tasks/0110_787_110787771_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0110_787_110787771_qa_4" +name = "smoldataenvs-train/0110_787_110787771_qa_4" description = "Is the population data in the Series derived from the dictionary unique across all entries?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0110_861_110861682_qa_5/task.toml b/tasks/0110_861_110861682_qa_5/task.toml index b1ea541e15267a3785855612524ec454895cc136..26fe66185d900700449ecb3cf79b793901b99b83 100644 --- a/tasks/0110_861_110861682_qa_5/task.toml +++ b/tasks/0110_861_110861682_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0110_861_110861682_qa_5" +name = "smoldataenvs-train/0110_861_110861682_qa_5" description = "Which weighting method (uniform or distance) was found to be optimal in the grid search for the synthetic dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "uniform" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0110_889_110889035_qa_1/task.toml b/tasks/0110_889_110889035_qa_1/task.toml index 5454820aa936fc9b9d8f78462178cf00ff41a516..81dd648053c45e642d62d06449d2594c0b80e795 100644 --- a/tasks/0110_889_110889035_qa_1/task.toml +++ b/tasks/0110_889_110889035_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0110_889_110889035_qa_1" +name = "smoldataenvs-train/0110_889_110889035_qa_1" description = "Is the difference in average billed charges between male and female statistically significant?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0110_890_110890415_qa_2/task.toml b/tasks/0110_890_110890415_qa_2/task.toml index 0367e5a537cca28aa2d952cc73fe1df3f5a19073..2992ec2c37b4537a5cac4420f496fffc3670c2f0 100644 --- a/tasks/0110_890_110890415_qa_2/task.toml +++ b/tasks/0110_890_110890415_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0110_890_110890415_qa_2" +name = "smoldataenvs-train/0110_890_110890415_qa_2" description = "What percentage of patients who survived more than 5 years had 0 positive axillary nodes?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0110_890_110890415_qa_5/task.toml b/tasks/0110_890_110890415_qa_5/task.toml index 21d7f40b247f51e7ddadf7514bd874d3bc31ce5d..3d9a8da91e6e54836ef71d90dfd1798037eb1d5a 100644 --- a/tasks/0110_890_110890415_qa_5/task.toml +++ b/tasks/0110_890_110890415_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0110_890_110890415_qa_5" +name = "smoldataenvs-train/0110_890_110890415_qa_5" description = "What is the median age of patients who survived more than 5 years?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "52.5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0110_937_110937668_qa_1/task.toml b/tasks/0110_937_110937668_qa_1/task.toml index 2330824cda961861818c9078bbf6f139fc4a5b4f..e6089aac056093d84607584fba44f798bbc1d871 100644 --- a/tasks/0110_937_110937668_qa_1/task.toml +++ b/tasks/0110_937_110937668_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0110_937_110937668_qa_1" +name = "smoldataenvs-train/0110_937_110937668_qa_1" description = "Which variable has the strongest positive Spearman correlation with the price of automobiles in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "curb-weight" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0110_937_110937668_qa_2/task.toml b/tasks/0110_937_110937668_qa_2/task.toml index c1be100e32bb01836cd86dbb15a53795c69b64aa..3dfb27aeb6c55a25250f85053932a3a25158ca30 100644 --- a/tasks/0110_937_110937668_qa_2/task.toml +++ b/tasks/0110_937_110937668_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0110_937_110937668_qa_2" +name = "smoldataenvs-train/0110_937_110937668_qa_2" description = "What is the F-value from the ANOVA test comparing price differences between drive-wheels (rwd, fwd, 4wd)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "67.9541" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0110_947_110947489_qa_1/task.toml b/tasks/0110_947_110947489_qa_1/task.toml index 7c7db5f6b776654b71201584277a6f7bc380bf66..7a87dc7ec384689e802eafe761a6e341b00cfadd 100644 --- a/tasks/0110_947_110947489_qa_1/task.toml +++ b/tasks/0110_947_110947489_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0110_947_110947489_qa_1" +name = "smoldataenvs-train/0110_947_110947489_qa_1" description = "Which region experienced the highest number of battles, and how many battles occurred there?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Riverlands, 17" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0110_947_110947489_qa_2/task.toml b/tasks/0110_947_110947489_qa_2/task.toml index fa5c04515b01163a220af3bcd7688d33cfa66fe8..f0fff7d4986b80b3925863cff5149d9ada6c6b19 100644 --- a/tasks/0110_947_110947489_qa_2/task.toml +++ b/tasks/0110_947_110947489_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0110_947_110947489_qa_2" +name = "smoldataenvs-train/0110_947_110947489_qa_2" description = "In which year did both the highest number of battles and the highest number of character deaths occur?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "299" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0110_947_110947489_qa_5/task.toml b/tasks/0110_947_110947489_qa_5/task.toml index 2933087ab928cf3cd6ed121be8aa91ece48e8c32..14243db19f108fe11b026b48ee8784a6f48d3311 100644 --- a/tasks/0110_947_110947489_qa_5/task.toml +++ b/tasks/0110_947_110947489_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0110_947_110947489_qa_5" +name = "smoldataenvs-train/0110_947_110947489_qa_5" description = "What is the most common battle type associated with Robb Stark as the attacker?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Ambush" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0110_968_110968433_qa_2/task.toml b/tasks/0110_968_110968433_qa_2/task.toml index c1ea037fd153eede74a60d86d47a66a28a970ce3..6364893ec67600efe099f9bb23693235bbb415bd 100644 --- a/tasks/0110_968_110968433_qa_2/task.toml +++ b/tasks/0110_968_110968433_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0110_968_110968433_qa_2" +name = "smoldataenvs-train/0110_968_110968433_qa_2" description = "How many categorical variables are present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0111_071_111071615_qa_4/task.toml b/tasks/0111_071_111071615_qa_4/task.toml index 19457fb493d9be1de21133188bad4a675584e3cb..a9a6b8296e75941a28450e10f1fb33ef00ca6776 100644 --- a/tasks/0111_071_111071615_qa_4/task.toml +++ b/tasks/0111_071_111071615_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0111_071_111071615_qa_4" +name = "smoldataenvs-train/0111_071_111071615_qa_4" description = "What is the standard deviation of the BMI in the dataset after the data cleaning and imputation process?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.674350" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0111_071_111071615_qa_5/task.toml b/tasks/0111_071_111071615_qa_5/task.toml index 69455158791837ee4f5ac3cdde72da263093c1dc..cd305349c4a453e38eb584abc24aef8542853fef 100644 --- a/tasks/0111_071_111071615_qa_5/task.toml +++ b/tasks/0111_071_111071615_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0111_071_111071615_qa_5" +name = "smoldataenvs-train/0111_071_111071615_qa_5" description = "What percentage of the dataset contains missing values after the imputation process was applied?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0111_097_111097126_qa_4/task.toml b/tasks/0111_097_111097126_qa_4/task.toml index 921d6f145a03f7506e02cbaef7670bc7ada06518..96764c82dce30491d435fe453dc6878ecdc6d063 100644 --- a/tasks/0111_097_111097126_qa_4/task.toml +++ b/tasks/0111_097_111097126_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0111_097_111097126_qa_4" +name = "smoldataenvs-train/0111_097_111097126_qa_4" description = "What is the data type of the Global_Sales series in the video game sales dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "float64" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0111_139_111139616_qa_2/task.toml b/tasks/0111_139_111139616_qa_2/task.toml index 76cc90429d0af5bcf33a516e9a29ad10de2a1a80..a64ea3caf23f4b131109793e801e11fd56ff9bd8 100644 --- a/tasks/0111_139_111139616_qa_2/task.toml +++ b/tasks/0111_139_111139616_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0111_139_111139616_qa_2" +name = "smoldataenvs-train/0111_139_111139616_qa_2" description = "How many unique video game genres are present in the dataset after converting the \"Genre\" column to a categorical data type?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0111_258_111258803_qa_4/task.toml b/tasks/0111_258_111258803_qa_4/task.toml index d2381cf81403468b096afdbb8b806e05b0aaaab3..c43a918fec51a9273af1379985f4b9bf43b2c9bc 100644 --- a/tasks/0111_258_111258803_qa_4/task.toml +++ b/tasks/0111_258_111258803_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0111_258_111258803_qa_4" +name = "smoldataenvs-train/0111_258_111258803_qa_4" description = "How many entries in the dataset have missing values in the 'Year' column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "271" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0111_306_111306567_qa_5/task.toml b/tasks/0111_306_111306567_qa_5/task.toml index e50f389ec377f8bd45339c255255a21a1d913e12..25720fe7b69c3b51740282ac1217b19d61be1771 100644 --- a/tasks/0111_306_111306567_qa_5/task.toml +++ b/tasks/0111_306_111306567_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0111_306_111306567_qa_5" +name = "smoldataenvs-train/0111_306_111306567_qa_5" description = "What is the standard deviation of detected nodes for patients who survived more than 5 years according to the statistical analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5.869" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0111_310_111310845_qa_2/task.toml b/tasks/0111_310_111310845_qa_2/task.toml index 26b4a7c445f4a1d6a0a8076966709c2fe0bc2433..c17935840177498fa1e67a353737f224835093f2 100644 --- a/tasks/0111_310_111310845_qa_2/task.toml +++ b/tasks/0111_310_111310845_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0111_310_111310845_qa_2" +name = "smoldataenvs-train/0111_310_111310845_qa_2" description = "Which nationality has the highest number of represented artists in the dataset (excluding \"Nationality unknown\" entries)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "American" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0111_327_111327287_qa_2/task.toml b/tasks/0111_327_111327287_qa_2/task.toml index 7a74ba0a8cd6871e87a4191b821469cd26186a53..285a58837bbe7ae526c6764e73425d10a2125364 100644 --- a/tasks/0111_327_111327287_qa_2/task.toml +++ b/tasks/0111_327_111327287_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0111_327_111327287_qa_2" +name = "smoldataenvs-train/0111_327_111327287_qa_2" description = "Which region has the largest population in the dataset and what percentage of the total does it represent?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Southeast, 27.2%" reward_mode_initial = "list" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0111_327_111327287_qa_4/task.toml b/tasks/0111_327_111327287_qa_4/task.toml index cbf46a441032730677a5233d42721deb5aea291e..f0cd684dbc21c6e31af3a535ae1f2e350954622e 100644 --- a/tasks/0111_327_111327287_qa_4/task.toml +++ b/tasks/0111_327_111327287_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0111_327_111327287_qa_4" +name = "smoldataenvs-train/0111_327_111327287_qa_4" description = "What is the average age of the population in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "39.0" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0111_399_111399369_qa_5/task.toml b/tasks/0111_399_111399369_qa_5/task.toml index 0a8f03237249298f7d7051c55d4df02107554bec..5dcccf8197cc3d4fc0bc98a057c014a33bf8befc 100644 --- a/tasks/0111_399_111399369_qa_5/task.toml +++ b/tasks/0111_399_111399369_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0111_399_111399369_qa_5" +name = "smoldataenvs-train/0111_399_111399369_qa_5" description = "What is the size of the test dataset used for model evaluation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3000" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0111_529_111529367_qa_1/task.toml b/tasks/0111_529_111529367_qa_1/task.toml index 15c2210ec9fd7f0f8719cbab98a94f168a8f9785..b02de7e421c0338952f83dfa2035147ed7ff9365 100644 --- a/tasks/0111_529_111529367_qa_1/task.toml +++ b/tasks/0111_529_111529367_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0111_529_111529367_qa_1" +name = "smoldataenvs-train/0111_529_111529367_qa_1" description = "What is the mean daily precipitation (in inches) after removing outliers from the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.030577" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0111_550_111550604_qa_1/task.toml b/tasks/0111_550_111550604_qa_1/task.toml index 73a168a41e9c963beba2b3aa59f27517a81319bb..3f28bd2d23673437c466798cc22914c0b997a5f2 100644 --- a/tasks/0111_550_111550604_qa_1/task.toml +++ b/tasks/0111_550_111550604_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0111_550_111550604_qa_1" +name = "smoldataenvs-train/0111_550_111550604_qa_1" description = "Which machine learning model achieved the highest test accuracy in breast cancer diagnosis, and what was the accuracy percentage?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "SVM, 98.25" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0111_571_111571326_qa_2/task.toml b/tasks/0111_571_111571326_qa_2/task.toml index 863ca627ce48d6cbcd74ae40d53ece762e7c3b3d..780587a00390203e8cb56a44e7b7544f6b70452b 100644 --- a/tasks/0111_571_111571326_qa_2/task.toml +++ b/tasks/0111_571_111571326_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0111_571_111571326_qa_2" +name = "smoldataenvs-train/0111_571_111571326_qa_2" description = "What is the precision score for predicting churned customers (class 1) using the model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.64" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0111_618_111618106_qa_4/task.toml b/tasks/0111_618_111618106_qa_4/task.toml index c21871ee5b87a89b9c277634fe7615b9dfaebf18..2acbd0f8aba8efef0ff56b25634883928c966dab 100644 --- a/tasks/0111_618_111618106_qa_4/task.toml +++ b/tasks/0111_618_111618106_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0111_618_111618106_qa_4" +name = "smoldataenvs-train/0111_618_111618106_qa_4" description = "What percentage of the dataset represents individuals with a positive diabetes diagnosis (Outcome = 1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0111_668_111668245_qa_3/task.toml b/tasks/0111_668_111668245_qa_3/task.toml index d691e03e94c36ecad74a3e512fc25a2ddc18c384..d481900f7aa65db376effd8f075160a61307f0ae 100644 --- a/tasks/0111_668_111668245_qa_3/task.toml +++ b/tasks/0111_668_111668245_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0111_668_111668245_qa_3" +name = "smoldataenvs-train/0111_668_111668245_qa_3" description = "What is the most common Type 2 among non-legendary Pokémon?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Flying" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0111_721_111721771_qa_4/task.toml b/tasks/0111_721_111721771_qa_4/task.toml index 11a2625c672cb1d840bdd7e1a4861a3295e21d19..e21d796583e4b335cc7611c6c2aa208e63a41da4 100644 --- a/tasks/0111_721_111721771_qa_4/task.toml +++ b/tasks/0111_721_111721771_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0111_721_111721771_qa_4" +name = "smoldataenvs-train/0111_721_111721771_qa_4" description = "After splitting the data into training and test sets with a 0.2 test size, how many samples are in the training set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16512" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0111_721_111721771_qa_5/task.toml b/tasks/0111_721_111721771_qa_5/task.toml index 0a3a8e75675efa1c6e4f08ad67dbee0e4a55507c..705dcc25113a1c621bfee1a3d55589d06b05c27a 100644 --- a/tasks/0111_721_111721771_qa_5/task.toml +++ b/tasks/0111_721_111721771_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0111_721_111721771_qa_5" +name = "smoldataenvs-train/0111_721_111721771_qa_5" description = "Which feature in the dataset has the strongest positive correlation with 'median_house_value'?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "median_income" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0111_875_111875662_qa_1/task.toml b/tasks/0111_875_111875662_qa_1/task.toml index a7d6934ac24d539c3b5fa84ba1dbd15087758c3b..45190a6a279c28dbfcc66070706ff164de9099b5 100644 --- a/tasks/0111_875_111875662_qa_1/task.toml +++ b/tasks/0111_875_111875662_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0111_875_111875662_qa_1" +name = "smoldataenvs-train/0111_875_111875662_qa_1" description = "What is the highest global sales value achieved by any video game in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "82.74" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0111_875_111875662_qa_4/task.toml b/tasks/0111_875_111875662_qa_4/task.toml index 86b90b0de7b6c705e1a9e5a4ea1ff95df73c9e15..3cdd832b5335f64df7ba7aa41dbf9e514218681e 100644 --- a/tasks/0111_875_111875662_qa_4/task.toml +++ b/tasks/0111_875_111875662_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0111_875_111875662_qa_4" +name = "smoldataenvs-train/0111_875_111875662_qa_4" description = "What is the total global sales sum across all video games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8820.31" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0111_875_111875662_qa_5/task.toml b/tasks/0111_875_111875662_qa_5/task.toml index fa3f410a8120e2d1bad027650494f40523d229cd..4b0c33d4ba8b78d6592593c0a7a1195fd59d3315 100644 --- a/tasks/0111_875_111875662_qa_5/task.toml +++ b/tasks/0111_875_111875662_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0111_875_111875662_qa_5" +name = "smoldataenvs-train/0111_875_111875662_qa_5" description = "What is the standard deviation of global sales values in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.5658596703325405" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0111_889_111889874_qa_2/task.toml b/tasks/0111_889_111889874_qa_2/task.toml index 71b618252c4ade66036ae01734136ccc638d0355..a1e0c85e9c1d8e44af4e43d1f6cdb206591d9468 100644 --- a/tasks/0111_889_111889874_qa_2/task.toml +++ b/tasks/0111_889_111889874_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0111_889_111889874_qa_2" +name = "smoldataenvs-train/0111_889_111889874_qa_2" description = "Which game publisher has the highest total North American sales according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Nintendo" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0111_889_111889874_qa_3/task.toml b/tasks/0111_889_111889874_qa_3/task.toml index 34f64293468268a1a442cd5f2654fcfc267a5b59..98efe28dabbdba9cc436612c7401b041a992db0e 100644 --- a/tasks/0111_889_111889874_qa_3/task.toml +++ b/tasks/0111_889_111889874_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0111_889_111889874_qa_3" +name = "smoldataenvs-train/0111_889_111889874_qa_3" description = "What is the total global sales figure for the top-selling video game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "82.74" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0111_899_111899875_qa_2/task.toml b/tasks/0111_899_111899875_qa_2/task.toml index bc2f8b69b54c029731f28c17b3cdeb6de0d4c99e..653be803cf0b7a81892f8252d44c6acd1f859382 100644 --- a/tasks/0111_899_111899875_qa_2/task.toml +++ b/tasks/0111_899_111899875_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0111_899_111899875_qa_2" +name = "smoldataenvs-train/0111_899_111899875_qa_2" description = "Which payment method is associated with the highest churn rate, and what is the percentage?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic check, 45.29" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0111_899_111899875_qa_5/task.toml b/tasks/0111_899_111899875_qa_5/task.toml index 1fa6a34e855fd00bf3c847cda205bc40a2f0b3a9..bc44b5d57c0dbe02dce8b473a5f59ee3494ede48 100644 --- a/tasks/0111_899_111899875_qa_5/task.toml +++ b/tasks/0111_899_111899875_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0111_899_111899875_qa_5" +name = "smoldataenvs-train/0111_899_111899875_qa_5" description = "What is the churn rate percentage for customers with 'Fiber optic' internet service compared to 'DSL' customers?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Fiber optic: 41.89%, DSL: 18.96%" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0112_045_112045716_qa_4/task.toml b/tasks/0112_045_112045716_qa_4/task.toml index ad9ca9fac6eb9fb3152f624c31cfd540431ef87c..52aeee1c499804f92c765959c909a7468679fe6c 100644 --- a/tasks/0112_045_112045716_qa_4/task.toml +++ b/tasks/0112_045_112045716_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0112_045_112045716_qa_4" +name = "smoldataenvs-train/0112_045_112045716_qa_4" description = "Which diamond cut type has the highest frequency in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Ideal" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0112_118_112118657_qa_3/task.toml b/tasks/0112_118_112118657_qa_3/task.toml index f1c66791db32082ce9ad28ced451d62aa5c21b00..d6c3e19a672b5d88b5dc902f08aa3722fa2494c0 100644 --- a/tasks/0112_118_112118657_qa_3/task.toml +++ b/tasks/0112_118_112118657_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0112_118_112118657_qa_3" +name = "smoldataenvs-train/0112_118_112118657_qa_3" description = "What is the maximum median house value observed in the California housing dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "500001" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0112_165_112165295_qa_3/task.toml b/tasks/0112_165_112165295_qa_3/task.toml index 3083454ded4fdd83e83a944291283bfacb146241..955827d698e25aa1acf56874d4469faa46e8e4c1 100644 --- a/tasks/0112_165_112165295_qa_3/task.toml +++ b/tasks/0112_165_112165295_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0112_165_112165295_qa_3" +name = "smoldataenvs-train/0112_165_112165295_qa_3" description = "How many missing values are present in the dataset after applying the backfill imputation method?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0112_172_112172227_qa_3/task.toml b/tasks/0112_172_112172227_qa_3/task.toml index 5f86e4ae38284aec153663977dbdd002775bfe30..8f411a5fe6b627fdf75bb02f1cdbfc8295414cb6 100644 --- a/tasks/0112_172_112172227_qa_3/task.toml +++ b/tasks/0112_172_112172227_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0112_172_112172227_qa_3" +name = "smoldataenvs-train/0112_172_112172227_qa_3" description = "Which feature has the highest standard deviation in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Insulin" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0112_393_112393758_qa_1/task.toml b/tasks/0112_393_112393758_qa_1/task.toml index e60bd4ea4bfbdc9176e591954466988dc98b8c16..7463e07d69cabaffb692e451b9e97c10f0163c77 100644 --- a/tasks/0112_393_112393758_qa_1/task.toml +++ b/tasks/0112_393_112393758_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0112_393_112393758_qa_1" +name = "smoldataenvs-train/0112_393_112393758_qa_1" description = "Which feature in the dataset has the highest positive correlation with median house value, and what is the correlation coefficient?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "median_income, 0.688075" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0112_406_112406995_qa_1/task.toml b/tasks/0112_406_112406995_qa_1/task.toml index 65d9f576a450fc66fe757b3a13c0d9b8f8d1ae75..acab629f0d66d244714332dbd004868ea62f8c67 100644 --- a/tasks/0112_406_112406995_qa_1/task.toml +++ b/tasks/0112_406_112406995_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0112_406_112406995_qa_1" +name = "smoldataenvs-train/0112_406_112406995_qa_1" description = "What was the original difference in the number of data points between the edible and poisonous mushroom classes before balancing the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "292" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0112_433_112433487_qa_2/task.toml b/tasks/0112_433_112433487_qa_2/task.toml index 07007aa34ccd50a4ad97b479ca8645e50b36d3e1..eefae0a62be19c2b6ea7501e860f9b64cfdae477 100644 --- a/tasks/0112_433_112433487_qa_2/task.toml +++ b/tasks/0112_433_112433487_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0112_433_112433487_qa_2" +name = "smoldataenvs-train/0112_433_112433487_qa_2" description = "Which species has the lowest average sepal length according to the EDA analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-setosa" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0112_434_112434990_qa_5/task.toml b/tasks/0112_434_112434990_qa_5/task.toml index 2c2d4540fe83db61da8dd154503c118cf9c04d22..6b5d203651eea55769b1a5398d5461d0b06f3de1 100644 --- a/tasks/0112_434_112434990_qa_5/task.toml +++ b/tasks/0112_434_112434990_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0112_434_112434990_qa_5" +name = "smoldataenvs-train/0112_434_112434990_qa_5" description = "During which decade did Electronic Arts achieve its highest total global sales based on the grouped analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2000-2010" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0112_515_112515781_qa_1/task.toml b/tasks/0112_515_112515781_qa_1/task.toml index 29dea51f5612c27e4bfda305a28f5333deb9fff7..e29879f8e5d05eca97157b09590b06819393f912 100644 --- a/tasks/0112_515_112515781_qa_1/task.toml +++ b/tasks/0112_515_112515781_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0112_515_112515781_qa_1" +name = "smoldataenvs-train/0112_515_112515781_qa_1" description = "What percentage of diamonds in the dataset are categorized as \"Premium\" quality cut?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "25.6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0112_515_112515781_qa_2/task.toml b/tasks/0112_515_112515781_qa_2/task.toml index bea9a15a688239f048bfe5fb3d4e611b216d6be6..6c365cd930ad2fa2565384f72eec4dbffea07811 100644 --- a/tasks/0112_515_112515781_qa_2/task.toml +++ b/tasks/0112_515_112515781_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0112_515_112515781_qa_2" +name = "smoldataenvs-train/0112_515_112515781_qa_2" description = "Which diamond color category has the lowest count in the dataset, and what is its exact numeric value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "J, 2808" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0112_515_112515781_qa_3/task.toml b/tasks/0112_515_112515781_qa_3/task.toml index 229877e43f7395017ca7847d3bdc353b8ffb2522..df89b1acf9ccfbc85a533fb08768dfa53e30913a 100644 --- a/tasks/0112_515_112515781_qa_3/task.toml +++ b/tasks/0112_515_112515781_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0112_515_112515781_qa_3" +name = "smoldataenvs-train/0112_515_112515781_qa_3" description = "Which clarity category contains the highest number of diamonds according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "SI1" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0112_515_112515781_qa_4/task.toml b/tasks/0112_515_112515781_qa_4/task.toml index 28f5a368a236d8e998c0371e0e9baf2e49b2b383..e9edf23881f4c65043e3a32b91dc6721160fab2d 100644 --- a/tasks/0112_515_112515781_qa_4/task.toml +++ b/tasks/0112_515_112515781_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0112_515_112515781_qa_4" +name = "smoldataenvs-train/0112_515_112515781_qa_4" description = "Which numerical feature shows the strongest positive correlation with diamond price based on the correlation analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "carat" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0112_533_112533086_qa_5/task.toml b/tasks/0112_533_112533086_qa_5/task.toml index 16c56d8d5037911e53854d52dae2122ad1fe1938..cf2213a4d5ad686e6fa31e41305b797e0895901a 100644 --- a/tasks/0112_533_112533086_qa_5/task.toml +++ b/tasks/0112_533_112533086_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0112_533_112533086_qa_5" +name = "smoldataenvs-train/0112_533_112533086_qa_5" description = "What is the churn rate percentage for senior citizens (SeniorCitizen = 1) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "41.6813" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0112_536_112536664_qa_4/task.toml b/tasks/0112_536_112536664_qa_4/task.toml index e08699cebba9473734e18aa40797a973dfe1673d..773eda0628220ece0cbae89b50e00f2ab532ca60 100644 --- a/tasks/0112_536_112536664_qa_4/task.toml +++ b/tasks/0112_536_112536664_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0112_536_112536664_qa_4" +name = "smoldataenvs-train/0112_536_112536664_qa_4" description = "What is the percentage of houses in the highest price bin?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.01" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0112_536_112536664_qa_5/task.toml b/tasks/0112_536_112536664_qa_5/task.toml index 14e5b775591bcd67622561a912b7009a2e93372e..86cc70e1b0590c0dd47123f5dd717f210a40c9cf 100644 --- a/tasks/0112_536_112536664_qa_5/task.toml +++ b/tasks/0112_536_112536664_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0112_536_112536664_qa_5" +name = "smoldataenvs-train/0112_536_112536664_qa_5" description = "How many houses are in the highest price bin?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0112_571_112571521_qa_3/task.toml b/tasks/0112_571_112571521_qa_3/task.toml index c9a303b2a7fa81a61a4c99623ceac36fc5e1ec65..708af5c21f195de77e10b779d7b5437b66036c32 100644 --- a/tasks/0112_571_112571521_qa_3/task.toml +++ b/tasks/0112_571_112571521_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0112_571_112571521_qa_3" +name = "smoldataenvs-train/0112_571_112571521_qa_3" description = "Based on the regression line analysis, which three numerical features showed the strongest linear relationships with diamond price before outlier removal?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "carat, x, y" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0112_571_112571521_qa_4/task.toml b/tasks/0112_571_112571521_qa_4/task.toml index 45348ce63a55dd33f5709120dfc871989a827a14..000b563c7f10cd68d3d1f3b54dd8a53666c40523 100644 --- a/tasks/0112_571_112571521_qa_4/task.toml +++ b/tasks/0112_571_112571521_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0112_571_112571521_qa_4" +name = "smoldataenvs-train/0112_571_112571521_qa_4" description = "Which diamond cut category has the highest median price based on the violin plot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Fair" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0112_574_112574806_qa_5/task.toml b/tasks/0112_574_112574806_qa_5/task.toml index 22bdf23845e063fdce4062b98eb4828eb049c0f3..8df5b9e8b4dc07d6c262bc7da7e7725c6631d1c6 100644 --- a/tasks/0112_574_112574806_qa_5/task.toml +++ b/tasks/0112_574_112574806_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0112_574_112574806_qa_5" +name = "smoldataenvs-train/0112_574_112574806_qa_5" description = "What was the median value used to impute missing 'Glucose' levels in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "117" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0112_599_112599544_qa_2/task.toml b/tasks/0112_599_112599544_qa_2/task.toml index 415a87134b2223b313a622457ca8bf6bbd0d73f3..75b09606fd7c9da053a8116a70a5276c2363b0ac 100644 --- a/tasks/0112_599_112599544_qa_2/task.toml +++ b/tasks/0112_599_112599544_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0112_599_112599544_qa_2" +name = "smoldataenvs-train/0112_599_112599544_qa_2" description = "What is the standard deviation of the 'ram' feature in the dataset before normalization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1084.732044" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0112_660_112660335_qa_2/task.toml b/tasks/0112_660_112660335_qa_2/task.toml index 58e968bb686c1e9cb619b25401a05e93e08382ce..14a6dbda50a4354232e940204f164b341e14ac6e 100644 --- a/tasks/0112_660_112660335_qa_2/task.toml +++ b/tasks/0112_660_112660335_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0112_660_112660335_qa_2" +name = "smoldataenvs-train/0112_660_112660335_qa_2" description = "What is the number of rows remaining in the video game sales dataset after removing all entries with missing 'Year' values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16327" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0112_662_112662280_qa_2/task.toml b/tasks/0112_662_112662280_qa_2/task.toml index 0214101ebe10b41daa54f4d812d78c5852ff475a..8b80039078915333687b9d106259d5f13d050395 100644 --- a/tasks/0112_662_112662280_qa_2/task.toml +++ b/tasks/0112_662_112662280_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0112_662_112662280_qa_2" +name = "smoldataenvs-train/0112_662_112662280_qa_2" description = "What is the average number of bedrooms in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.37" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0112_685_112685295_qa_2/task.toml b/tasks/0112_685_112685295_qa_2/task.toml index 1970bfc7a1da716d7f8e29cf553cc7d017f9623f..660b1506fef74fabf24aed5fcc5aba9d951ad6d4 100644 --- a/tasks/0112_685_112685295_qa_2/task.toml +++ b/tasks/0112_685_112685295_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0112_685_112685295_qa_2" +name = "smoldataenvs-train/0112_685_112685295_qa_2" description = "How many video games in the dataset have missing Year information?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "271" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0112_685_112685295_qa_4/task.toml b/tasks/0112_685_112685295_qa_4/task.toml index 534bb08145a0e2261f6a456f3c47b1ae9ada40b3..0f2cfa4e6d722498bd78b64c375805ae8c9f0c10 100644 --- a/tasks/0112_685_112685295_qa_4/task.toml +++ b/tasks/0112_685_112685295_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0112_685_112685295_qa_4" +name = "smoldataenvs-train/0112_685_112685295_qa_4" description = "How many video games are categorized as \"SuperHit\" based on global sales (≥60 million)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0112_690_112690397_qa_5/task.toml b/tasks/0112_690_112690397_qa_5/task.toml index 84f34efd8e355f8e974485dc37ff72d2b5e8d75b..aa4d47c292be05e1e7614c54c1f383ac43717e32 100644 --- a/tasks/0112_690_112690397_qa_5/task.toml +++ b/tasks/0112_690_112690397_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0112_690_112690397_qa_5" +name = "smoldataenvs-train/0112_690_112690397_qa_5" description = "What is the precision score for class 1 (poisonous mushrooms) in the Support Vector Machine model's test set evaluation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.98" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0112_804_112804674_qa_2/task.toml b/tasks/0112_804_112804674_qa_2/task.toml index 3d8209cca25ef43688bad0ac109aec7613b9b735..1c4d18e6e2dc84324299f96f168af86203a1664b 100644 --- a/tasks/0112_804_112804674_qa_2/task.toml +++ b/tasks/0112_804_112804674_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0112_804_112804674_qa_2" +name = "smoldataenvs-train/0112_804_112804674_qa_2" description = "How many unique gender categories were present in the dataset after feature engineering to standardize gender labels?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0112_804_112804674_qa_3/task.toml b/tasks/0112_804_112804674_qa_3/task.toml index 789264b02d4cda69f271d7a6b6e631fdbf596731..f1da963c87891f8d07d072978f941ae5ba098e89 100644 --- a/tasks/0112_804_112804674_qa_3/task.toml +++ b/tasks/0112_804_112804674_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0112_804_112804674_qa_3" +name = "smoldataenvs-train/0112_804_112804674_qa_3" description = "Which continent had the highest number of survey respondents in the cleaned dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "North America" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0112_804_112804674_qa_4/task.toml b/tasks/0112_804_112804674_qa_4/task.toml index 54545bac377b07e0d6c9551f2b6c970c85127e7a..d52dd3008f3ca37d557a00cefd307ea8e35f70f7 100644 --- a/tasks/0112_804_112804674_qa_4/task.toml +++ b/tasks/0112_804_112804674_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0112_804_112804674_qa_4" +name = "smoldataenvs-train/0112_804_112804674_qa_4" description = "How many entries outside of the United States originally had non-null values in the \"state\" column before they were marked as \"Irrelevant\"?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0112_804_112804674_qa_5/task.toml b/tasks/0112_804_112804674_qa_5/task.toml index eeb206dbe49763dd04f63723dfb196e3a09625d2..49ba2d8e9fb33a68bc27256e37f9cddc6d3f790f 100644 --- a/tasks/0112_804_112804674_qa_5/task.toml +++ b/tasks/0112_804_112804674_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0112_804_112804674_qa_5" +name = "smoldataenvs-train/0112_804_112804674_qa_5" description = "What was the percentage of missing values in the \"work_interfere\" feature before data imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20.97" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0112_848_112848989_qa_1/task.toml b/tasks/0112_848_112848989_qa_1/task.toml index 6125022eba48392108d8d179f13694065551b290..bc8b873bf4a9874a1effb10ca9961135917eb9af 100644 --- a/tasks/0112_848_112848989_qa_1/task.toml +++ b/tasks/0112_848_112848989_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0112_848_112848989_qa_1" +name = "smoldataenvs-train/0112_848_112848989_qa_1" description = "How many standard deviations above the mean are the North American sales of the top-selling game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50.48" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0112_848_112848989_qa_3/task.toml b/tasks/0112_848_112848989_qa_3/task.toml index 92bf6c5330090a7f65fec4e05326c6a3591bfc50..97a1d7cb1394e37b7d7a71ed06c8825b56b57521 100644 --- a/tasks/0112_848_112848989_qa_3/task.toml +++ b/tasks/0112_848_112848989_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0112_848_112848989_qa_3" +name = "smoldataenvs-train/0112_848_112848989_qa_3" description = "What is the highest recorded North American sales value for any game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "41.49" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0112_848_112848989_qa_4/task.toml b/tasks/0112_848_112848989_qa_4/task.toml index b028789662f918421806c916ecf54e74921698c9..4161bd965d29b78adf1a7fa78dab385faf0e17f8 100644 --- a/tasks/0112_848_112848989_qa_4/task.toml +++ b/tasks/0112_848_112848989_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0112_848_112848989_qa_4" +name = "smoldataenvs-train/0112_848_112848989_qa_4" description = "What is the highest recorded European sales value for any game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "29.02" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0112_857_112857611_qa_4/task.toml b/tasks/0112_857_112857611_qa_4/task.toml index 24ac46c060dd46d00baf58a92036616f57c2cd43..cfe8e1ad857ada63a81f7fe74c71a37c46ea74a2 100644 --- a/tasks/0112_857_112857611_qa_4/task.toml +++ b/tasks/0112_857_112857611_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0112_857_112857611_qa_4" +name = "smoldataenvs-train/0112_857_112857611_qa_4" description = "What is the correlation coefficient between the px_area feature and price_range after all feature engineering steps?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.176240" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0112_857_112857611_qa_5/task.toml b/tasks/0112_857_112857611_qa_5/task.toml index c190a1c855744b2e9e7d8a276cbad15c8e805c54..fb2672c36fe221b3cd40749e70fddcd61cde5610 100644 --- a/tasks/0112_857_112857611_qa_5/task.toml +++ b/tasks/0112_857_112857611_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0112_857_112857611_qa_5" +name = "smoldataenvs-train/0112_857_112857611_qa_5" description = "What is the correlation coefficient between the battery_power feature and price_range after all feature engineering steps?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.200723" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0113_007_113007141_qa_2/task.toml b/tasks/0113_007_113007141_qa_2/task.toml index aa526b539a114e89d3a934eee6d9b248c1255eae..1e1d0e6245d8c5e3f530a631b77a20a90694f824 100644 --- a/tasks/0113_007_113007141_qa_2/task.toml +++ b/tasks/0113_007_113007141_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0113_007_113007141_qa_2" +name = "smoldataenvs-train/0113_007_113007141_qa_2" description = "What is the lowest root mean squared error (RMSE) value achieved among all tested regression models?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4345.80" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0113_048_113048196_qa_2/task.toml b/tasks/0113_048_113048196_qa_2/task.toml index 4c4908516607a68a37adfcc4003addb0e341be7b..f55899aebf11d74997536ca318f7d06fb67092ba 100644 --- a/tasks/0113_048_113048196_qa_2/task.toml +++ b/tasks/0113_048_113048196_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0113_048_113048196_qa_2" +name = "smoldataenvs-train/0113_048_113048196_qa_2" description = "Which gender group spent the highest total amount during Black Friday sales?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Male" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0113_048_113048196_qa_5/task.toml b/tasks/0113_048_113048196_qa_5/task.toml index a06ec8a3e2aeb3df703430d01e4134250bf18b01..b309f01edb2791f1a4c9a0339ca1f20d298f480e 100644 --- a/tasks/0113_048_113048196_qa_5/task.toml +++ b/tasks/0113_048_113048196_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0113_048_113048196_qa_5" +name = "smoldataenvs-train/0113_048_113048196_qa_5" description = "Between married and unmarried individuals, which group had a higher average purchase amount?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Unmarried" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0113_060_113060572_qa_1/task.toml b/tasks/0113_060_113060572_qa_1/task.toml index ef2236b7ad6ed0e6e56f0d70c45d6658642402a5..a49862ed5b47410e88f304b7c4349c5a48bf0147 100644 --- a/tasks/0113_060_113060572_qa_1/task.toml +++ b/tasks/0113_060_113060572_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0113_060_113060572_qa_1" +name = "smoldataenvs-train/0113_060_113060572_qa_1" description = "What is the mean absolute error (MAE) of the polynomial regression model before removing outliers from the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "62063.48670416566" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0113_068_113068557_qa_1/task.toml b/tasks/0113_068_113068557_qa_1/task.toml index d4ae1193afdccf6d1ccc3c21cfb372617f4c23bb..4e9069a0f2e35a7014edd04b8106fab9c0d38981 100644 --- a/tasks/0113_068_113068557_qa_1/task.toml +++ b/tasks/0113_068_113068557_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0113_068_113068557_qa_1" +name = "smoldataenvs-train/0113_068_113068557_qa_1" description = "What is the total global sales across all video games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8920.44" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0113_071_113071809_qa_1/task.toml b/tasks/0113_071_113071809_qa_1/task.toml index e20134717728d0477f5d6868c1bedbd475638103..f0be3e585c2c5e530999e9d5897ffc45c219ca6a 100644 --- a/tasks/0113_071_113071809_qa_1/task.toml +++ b/tasks/0113_071_113071809_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0113_071_113071809_qa_1" +name = "smoldataenvs-train/0113_071_113071809_qa_1" description = "What is the highest correlation coefficient between any feature and the median house value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.688" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0113_075_113075841_qa_1/task.toml b/tasks/0113_075_113075841_qa_1/task.toml index d739a6e61c676d4db386c099ee84dbc2e8db202a..8a04e2a463bc563fe56beeeb79e9fc0c878386ff 100644 --- a/tasks/0113_075_113075841_qa_1/task.toml +++ b/tasks/0113_075_113075841_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0113_075_113075841_qa_1" +name = "smoldataenvs-train/0113_075_113075841_qa_1" description = "What is the highest Global Sales value for a game in the Action genre according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "21.40" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0113_075_113075841_qa_2/task.toml b/tasks/0113_075_113075841_qa_2/task.toml index 74baf8898da25c350b037e920c070a4199be4a1f..3f5de2704434519854b8429ac9c4522f6d949307 100644 --- a/tasks/0113_075_113075841_qa_2/task.toml +++ b/tasks/0113_075_113075841_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0113_075_113075841_qa_2" +name = "smoldataenvs-train/0113_075_113075841_qa_2" description = "Which game has the highest Global Sales value in the Action genre based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Grand Theft Auto V" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0113_075_113075841_qa_3/task.toml b/tasks/0113_075_113075841_qa_3/task.toml index 991d862dad665fac4af7eb7efd6728d9d1da0e2d..051f4e16d76ce51bacc32a81d3da24a550052e9b 100644 --- a/tasks/0113_075_113075841_qa_3/task.toml +++ b/tasks/0113_075_113075841_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0113_075_113075841_qa_3" +name = "smoldataenvs-train/0113_075_113075841_qa_3" description = "What is the earliest recorded release year in the dataset after handling missing values and sorting?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1980" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0113_114_113114053_qa_3/task.toml b/tasks/0113_114_113114053_qa_3/task.toml index 35f442a803317cfc4a0df2d3e7a44ea17a37db02..88b79586134b42ca5af28e284b35a75e8f1333ec 100644 --- a/tasks/0113_114_113114053_qa_3/task.toml +++ b/tasks/0113_114_113114053_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0113_114_113114053_qa_3" +name = "smoldataenvs-train/0113_114_113114053_qa_3" description = "How many missing values were present in the 'total_bedrooms' feature before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "207" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0113_124_113124981_qa_5/task.toml b/tasks/0113_124_113124981_qa_5/task.toml index 80b389357601143fd0fd7b7e4865eaecaa9dc698..7c2c96e0e86cb202b02bb88bf4795a826c04a192 100644 --- a/tasks/0113_124_113124981_qa_5/task.toml +++ b/tasks/0113_124_113124981_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0113_124_113124981_qa_5" +name = "smoldataenvs-train/0113_124_113124981_qa_5" description = "What was the median value of the 'total_bedrooms' column used for imputing missing values in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "435.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0113_129_113129958_qa_2/task.toml b/tasks/0113_129_113129958_qa_2/task.toml index fd58b8b5d1c8963427e1a598554c5ed07c272c0a..7623fb9521b82ba45129880a7928ae8dc9da5253 100644 --- a/tasks/0113_129_113129958_qa_2/task.toml +++ b/tasks/0113_129_113129958_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0113_129_113129958_qa_2" +name = "smoldataenvs-train/0113_129_113129958_qa_2" description = "Which contract type is associated with the highest customer churn rate based on the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Month-to-Month" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0113_130_113130564_qa_1/task.toml b/tasks/0113_130_113130564_qa_1/task.toml index b9224e07aa01417548b527dcf694033890a6899b..842c2b8b3293e1cfaac00502ee7973d4e85f58be 100644 --- a/tasks/0113_130_113130564_qa_1/task.toml +++ b/tasks/0113_130_113130564_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0113_130_113130564_qa_1" +name = "smoldataenvs-train/0113_130_113130564_qa_1" description = "What is the percentage increase in average maximum temperature from the first decade (1940s) to the last decade (2010s) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.7" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0113_130_113130564_qa_2/task.toml b/tasks/0113_130_113130564_qa_2/task.toml index a26e3a3fe29e5e7fa89a961de8339b08a484803f..20365f28b667c46543bdbcafc75bb8cb179a565c 100644 --- a/tasks/0113_130_113130564_qa_2/task.toml +++ b/tasks/0113_130_113130564_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0113_130_113130564_qa_2" +name = "smoldataenvs-train/0113_130_113130564_qa_2" description = "Which year recorded the highest average precipitation according to the bar chart analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1950" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0113_130_113130564_qa_4/task.toml b/tasks/0113_130_113130564_qa_4/task.toml index 5e689c04cd346945014861a6a34bb7c1b9710fa5..35c32395edb512ac0de09b827b2fe133df5f3d54 100644 --- a/tasks/0113_130_113130564_qa_4/task.toml +++ b/tasks/0113_130_113130564_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0113_130_113130564_qa_4" +name = "smoldataenvs-train/0113_130_113130564_qa_4" description = "Which year had the lowest average precipitation based on the bar chart findings?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1952" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0113_167_113167723_qa_2/task.toml b/tasks/0113_167_113167723_qa_2/task.toml index 808fce2150706c02e840734bcb7f257f5a60ffd0..cebd93797fbdb38be72ceab61044930ebbaff994 100644 --- a/tasks/0113_167_113167723_qa_2/task.toml +++ b/tasks/0113_167_113167723_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0113_167_113167723_qa_2" +name = "smoldataenvs-train/0113_167_113167723_qa_2" description = "How many duplicate rows were identified and removed from the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0113_167_113167723_qa_4/task.toml b/tasks/0113_167_113167723_qa_4/task.toml index 2af7a801afbff972e65d9c9415b6355f36f9ca43..c9d184adb7a2df41f5ff1ea739326e935ee109a8 100644 --- a/tasks/0113_167_113167723_qa_4/task.toml +++ b/tasks/0113_167_113167723_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0113_167_113167723_qa_4" +name = "smoldataenvs-train/0113_167_113167723_qa_4" description = "After one-hot encoding categorical variables, how many features are included in the final dataset for modeling?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0113_185_113185711_qa_1/task.toml b/tasks/0113_185_113185711_qa_1/task.toml index f3ef1b192e987107228457eabc13c34bc0d926b5..24ce22184b3f085526802a2ddec17494654628c5 100644 --- a/tasks/0113_185_113185711_qa_1/task.toml +++ b/tasks/0113_185_113185711_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0113_185_113185711_qa_1" +name = "smoldataenvs-train/0113_185_113185711_qa_1" description = "Which feature in the mushroom dataset has the highest cardinality, and how many unique categories does it have?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "gill-color, 12" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0113_185_113185711_qa_3/task.toml b/tasks/0113_185_113185711_qa_3/task.toml index 320175e53373099f0f3684d7a5089b1cf6403d4e..601e115f8c3dd7f124efbbaa53466122e78042c3 100644 --- a/tasks/0113_185_113185711_qa_3/task.toml +++ b/tasks/0113_185_113185711_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0113_185_113185711_qa_3" +name = "smoldataenvs-train/0113_185_113185711_qa_3" description = "Which feature has the highest proportion of missing values in the dataset, and what percentage of entries are missing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "stalk-root, 30.5" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0113_222_113222481_qa_1/task.toml b/tasks/0113_222_113222481_qa_1/task.toml index d829a17df4f5b20bbf7dc3b690a915d893b6e5cd..9775eafc8ced5bf23d6113060d08a1112105927d 100644 --- a/tasks/0113_222_113222481_qa_1/task.toml +++ b/tasks/0113_222_113222481_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0113_222_113222481_qa_1" +name = "smoldataenvs-train/0113_222_113222481_qa_1" description = "Which regression model achieved the lowest cross-validated root mean squared error (RMSE) in predicting insurance charges?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "GradientBoostingRegressor" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0113_222_113222481_qa_3/task.toml b/tasks/0113_222_113222481_qa_3/task.toml index 0ed92f8e69ce0261af1499be63a944939c86753a..a57e68fbd55f5104327b4b4e5db3ad3a4d9df1fd 100644 --- a/tasks/0113_222_113222481_qa_3/task.toml +++ b/tasks/0113_222_113222481_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0113_222_113222481_qa_3" +name = "smoldataenvs-train/0113_222_113222481_qa_3" description = "How many female policyholders in the dataset are classified as smokers?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "115" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0113_246_113246154_qa_3/task.toml b/tasks/0113_246_113246154_qa_3/task.toml index 9cc9ebfc92048f6c437ecb334f0100b547235095..4a8d71f2a4ed55bb7b9cbf9b02773b0e5ec8fc5f 100644 --- a/tasks/0113_246_113246154_qa_3/task.toml +++ b/tasks/0113_246_113246154_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0113_246_113246154_qa_3" +name = "smoldataenvs-train/0113_246_113246154_qa_3" description = "After splitting the dataset with an 80/20 train-test ratio, how many samples are in the training set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16512" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0113_299_113299761_qa_1/task.toml b/tasks/0113_299_113299761_qa_1/task.toml index 7b31d929f2e6873e31a27afbbf331240e6d4b9c0..c6747bb7db7f30e8b0e58ae4b4210967a186df53 100644 --- a/tasks/0113_299_113299761_qa_1/task.toml +++ b/tasks/0113_299_113299761_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0113_299_113299761_qa_1" +name = "smoldataenvs-train/0113_299_113299761_qa_1" description = "What is the overall percentage of customers who churned in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26.6" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0113_346_113346497_qa_3/task.toml b/tasks/0113_346_113346497_qa_3/task.toml index 6d178e293e78a0b7c87c69c32171979a580b10b1..0cc6df8e191d95ae64063354e9172aa6eb628bbe 100644 --- a/tasks/0113_346_113346497_qa_3/task.toml +++ b/tasks/0113_346_113346497_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0113_346_113346497_qa_3" +name = "smoldataenvs-train/0113_346_113346497_qa_3" description = "Which player in the Big 5 European leagues has the highest maximum overall rating, and what is that rating?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Lionel Messi, 94" reward_mode_initial = "list" package_tier = 3 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0113_428_113428593_qa_1/task.toml b/tasks/0113_428_113428593_qa_1/task.toml index 6669f952707e4e58c7eab7bd6f8a0e7f6c4dcacf..0bac65fe24d8f6678f07311507c6be663eccf193 100644 --- a/tasks/0113_428_113428593_qa_1/task.toml +++ b/tasks/0113_428_113428593_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0113_428_113428593_qa_1" +name = "smoldataenvs-train/0113_428_113428593_qa_1" description = "How many Iris-versicolor samples were misclassified as Iris-virginica by the perceptron model after training?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0113_431_113431641_qa_1/task.toml b/tasks/0113_431_113431641_qa_1/task.toml index 7e6550420562fe42e464d359f3912e9daa044782..26dd1665500203d17b51d60277779492359ffd36 100644 --- a/tasks/0113_431_113431641_qa_1/task.toml +++ b/tasks/0113_431_113431641_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0113_431_113431641_qa_1" +name = "smoldataenvs-train/0113_431_113431641_qa_1" description = "What is the average increase in insurance charges for smokers compared to non-smokers according to the linear regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "23600" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0113_504_113504478_qa_1/task.toml b/tasks/0113_504_113504478_qa_1/task.toml index 989aec6c451342c07588cd6a6db19bc5a9e738a0..1e8787dd4821799a0224f686990f8432ef439cc4 100644 --- a/tasks/0113_504_113504478_qa_1/task.toml +++ b/tasks/0113_504_113504478_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0113_504_113504478_qa_1" +name = "smoldataenvs-train/0113_504_113504478_qa_1" description = "Which feature has the highest positive correlation with the diagnosis (benign/malignant classification) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "concave points_worst" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0113_504_113504478_qa_3/task.toml b/tasks/0113_504_113504478_qa_3/task.toml index 32307e727c1bb75d52069780ec0393538facdb1f..51582f7fa7e1d6cb81fa45fa551f73579e2e0ff6 100644 --- a/tasks/0113_504_113504478_qa_3/task.toml +++ b/tasks/0113_504_113504478_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0113_504_113504478_qa_3" +name = "smoldataenvs-train/0113_504_113504478_qa_3" description = "Which three features exhibit the strongest positive correlation with each other (greater than 0.94) based on the correlation analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "perimeter_mean, area_mean, radius_mean" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0113_504_113504478_qa_5/task.toml b/tasks/0113_504_113504478_qa_5/task.toml index 5251953471010d8315baf44fd946d9352d3296a9..279342d84e722551cee38ffd78c0e435cb399a6f 100644 --- a/tasks/0113_504_113504478_qa_5/task.toml +++ b/tasks/0113_504_113504478_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0113_504_113504478_qa_5" +name = "smoldataenvs-train/0113_504_113504478_qa_5" description = "Based on the distribution analysis, do malignant tumors tend to have higher concave points mean values compared to benign tumors?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0113_554_113554614_qa_1/task.toml b/tasks/0113_554_113554614_qa_1/task.toml index c9691ac9cb36c9b197bc519da8e888928a737e8d..8c9aaf15d355ebd0640ad8cfd65b574d1b668e7a 100644 --- a/tasks/0113_554_113554614_qa_1/task.toml +++ b/tasks/0113_554_113554614_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0113_554_113554614_qa_1" +name = "smoldataenvs-train/0113_554_113554614_qa_1" description = "Which feature in the dataset shows the highest positive correlation with the diagnosis (Benign/Malignant classification)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "concave points_worst" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0113_633_113633083_qa_3/task.toml b/tasks/0113_633_113633083_qa_3/task.toml index 776348dea7d4ab0fcf32a6142d2e19d8c0f1c9f1..85c8bedd987d3c7706f1df626f4b04b9a1ad1ee5 100644 --- a/tasks/0113_633_113633083_qa_3/task.toml +++ b/tasks/0113_633_113633083_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0113_633_113633083_qa_3" +name = "smoldataenvs-train/0113_633_113633083_qa_3" description = "What is the most prevalent spore-print-color in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "white" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0113_633_113633083_qa_5/task.toml b/tasks/0113_633_113633083_qa_5/task.toml index 32027b0e714e5d7a429c1499c42749648f7d90af..942806fd4c7e07e6307a99f945d18cf4eed26717 100644 --- a/tasks/0113_633_113633083_qa_5/task.toml +++ b/tasks/0113_633_113633083_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0113_633_113633083_qa_5" +name = "smoldataenvs-train/0113_633_113633083_qa_5" description = "What is the most frequently occurring ring type in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "p" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0113_657_113657881_qa_3/task.toml b/tasks/0113_657_113657881_qa_3/task.toml index 6a269bdc63b094fe576d0f8ea5b8b8a2006c8fdc..4a21bc43f7199e32f318aca3286126b40e3f74f1 100644 --- a/tasks/0113_657_113657881_qa_3/task.toml +++ b/tasks/0113_657_113657881_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0113_657_113657881_qa_3" +name = "smoldataenvs-train/0113_657_113657881_qa_3" description = "What are the unique encoded numeric values assigned to the Species column after label encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0, 1, 2" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0113_694_113694619_qa_1/task.toml b/tasks/0113_694_113694619_qa_1/task.toml index 20be2fdbc758a5925f3a2551fb454d7af3beed5e..dbfe547c8792fbed42e07eb1046d1063bec22253 100644 --- a/tasks/0113_694_113694619_qa_1/task.toml +++ b/tasks/0113_694_113694619_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0113_694_113694619_qa_1" +name = "smoldataenvs-train/0113_694_113694619_qa_1" description = "Does the median insurance charge increase significantly more rapidly with BMI for smokers compared to non-smokers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0113_768_113768809_qa_2/task.toml b/tasks/0113_768_113768809_qa_2/task.toml index 1e5396164efbabddc523f226bbd587152cec77b8..2c0526f9b328f580f4c314e3935a9f2589bb3d72 100644 --- a/tasks/0113_768_113768809_qa_2/task.toml +++ b/tasks/0113_768_113768809_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0113_768_113768809_qa_2" +name = "smoldataenvs-train/0113_768_113768809_qa_2" description = "According to the Gradient Boosting model optimized for recall, which feature is the most important predictor of diabetes?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0113_768_113768809_qa_5/task.toml b/tasks/0113_768_113768809_qa_5/task.toml index 974ebbab56ca34c678fd2e5e29cebca579d1b420..2eaf89119ca6898795a9932f21a36745527361e7 100644 --- a/tasks/0113_768_113768809_qa_5/task.toml +++ b/tasks/0113_768_113768809_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0113_768_113768809_qa_5" +name = "smoldataenvs-train/0113_768_113768809_qa_5" description = "According to the Random Forest model optimized for recall, which feature is the most important predictor of diabetes?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0113_777_113777393_qa_2/task.toml b/tasks/0113_777_113777393_qa_2/task.toml index 6b0aafd8d6b27c4927084b6ec8a3fddc22984bfc..6b921ae3f92e21cc86b7e9d98191d32b8357988e 100644 --- a/tasks/0113_777_113777393_qa_2/task.toml +++ b/tasks/0113_777_113777393_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0113_777_113777393_qa_2" +name = "smoldataenvs-train/0113_777_113777393_qa_2" description = "What is the original Petal Length value corresponding to the 75th percentile of the normalized Petal Length distribution?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5.1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0113_777_113777393_qa_5/task.toml b/tasks/0113_777_113777393_qa_5/task.toml index 4ab2d01e6c55b36b8e244ab360c2f5228240e4ce..83c217366bb1bef84a051d6feb1342883797ec3f 100644 --- a/tasks/0113_777_113777393_qa_5/task.toml +++ b/tasks/0113_777_113777393_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0113_777_113777393_qa_5" +name = "smoldataenvs-train/0113_777_113777393_qa_5" description = "What is the standard deviation of the original Petal Width?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.763161" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0113_834_113834948_qa_1/task.toml b/tasks/0113_834_113834948_qa_1/task.toml index 1d95792a2cfed1f654d370c9f0480b5f9751a1d1..5452b9835597e4a0bac0b4d3e26be4bd70a95fdc 100644 --- a/tasks/0113_834_113834948_qa_1/task.toml +++ b/tasks/0113_834_113834948_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0113_834_113834948_qa_1" +name = "smoldataenvs-train/0113_834_113834948_qa_1" description = "What percentage of the target variable 'SeriousDlqin2yrs' is classified as the minority class in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.684" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0113_851_113851860_qa_3/task.toml b/tasks/0113_851_113851860_qa_3/task.toml index c02404d637829ed99a1ad168c62158501a42981e..36bde11e856da9aada5b792ee1aef35cd713170c 100644 --- a/tasks/0113_851_113851860_qa_3/task.toml +++ b/tasks/0113_851_113851860_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0113_851_113851860_qa_3" +name = "smoldataenvs-train/0113_851_113851860_qa_3" description = "What is the mean humidity value in the processed dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.734899" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0113_851_113851860_qa_5/task.toml b/tasks/0113_851_113851860_qa_5/task.toml index 0b53d6c79e4bedb59e096cb6747c70ee913b82b9..4e6b16d8efa9a1196632d8ec9f962594e1b6fa92 100644 --- a/tasks/0113_851_113851860_qa_5/task.toml +++ b/tasks/0113_851_113851860_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0113_851_113851860_qa_5" +name = "smoldataenvs-train/0113_851_113851860_qa_5" description = "What is the median humidity value in the processed dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.78" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0113_907_113907466_qa_1/task.toml b/tasks/0113_907_113907466_qa_1/task.toml index 7d78fc2b99b35883f215934b93b280b4a793ef92..2f836b2cdbe9a68b47f8e712f8fd6b3dae3675c3 100644 --- a/tasks/0113_907_113907466_qa_1/task.toml +++ b/tasks/0113_907_113907466_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0113_907_113907466_qa_1" +name = "smoldataenvs-train/0113_907_113907466_qa_1" description = "What is the highest accuracy achieved by any model on the test set after splitting the training data with a 90-10 ratio?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.97" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0113_924_113924850_qa_4/task.toml b/tasks/0113_924_113924850_qa_4/task.toml index be18e17b73058f7c8f924a671600e852d66b334f..6597c3dc6c13e6e26d25bbc96cc1e8cc602b53e1 100644 --- a/tasks/0113_924_113924850_qa_4/task.toml +++ b/tasks/0113_924_113924850_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0113_924_113924850_qa_4" +name = "smoldataenvs-train/0113_924_113924850_qa_4" description = "Which feature has the highest positive coefficient in the logistic regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "texture_worst" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0113_929_113929776_qa_2/task.toml b/tasks/0113_929_113929776_qa_2/task.toml index 01d6d77819ac28f61f8086743baf9af7ddc95d49..2a2e2fa23c973db3c97c7494a059be9eadf9b598 100644 --- a/tasks/0113_929_113929776_qa_2/task.toml +++ b/tasks/0113_929_113929776_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0113_929_113929776_qa_2" +name = "smoldataenvs-train/0113_929_113929776_qa_2" description = "Which three numerical features show the strongest positive correlation with diamond price according to the correlation heatmap analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "carat, x, y" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0113_929_113929776_qa_3/task.toml b/tasks/0113_929_113929776_qa_3/task.toml index 8f8680f964efa929022444ad0901d554a66aa566..e86db619be0b0b65ac5623c9d23cd1cfbc59deb2 100644 --- a/tasks/0113_929_113929776_qa_3/task.toml +++ b/tasks/0113_929_113929776_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0113_929_113929776_qa_3" +name = "smoldataenvs-train/0113_929_113929776_qa_3" description = "What is the R-squared score achieved by the XGBoost regression model on the test dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9820448935367375" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0113_929_113929776_qa_5/task.toml b/tasks/0113_929_113929776_qa_5/task.toml index c0d52a53e830214115fd12f8ea2a939d5d039303..305c9b1e34746e9c0b1881ccd963687a97c57ee2 100644 --- a/tasks/0113_929_113929776_qa_5/task.toml +++ b/tasks/0113_929_113929776_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0113_929_113929776_qa_5" +name = "smoldataenvs-train/0113_929_113929776_qa_5" description = "Which diamond clarity grade has the highest frequency count in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "SI1" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0113_941_113941848_qa_4/task.toml b/tasks/0113_941_113941848_qa_4/task.toml index 75bb0632d8a6c70a9f1fcb21c9381e6fa4e46f7d..14bf32053b8fceb8a0636740574cab984140fe6d 100644 --- a/tasks/0113_941_113941848_qa_4/task.toml +++ b/tasks/0113_941_113941848_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0113_941_113941848_qa_4" +name = "smoldataenvs-train/0113_941_113941848_qa_4" description = "What is the recall score of the Logistic Regression model when using PCA reduction with 95% explained variance ratio?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9762" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0113_942_113942967_qa_4/task.toml b/tasks/0113_942_113942967_qa_4/task.toml index ec343d2f2754d81981987a4121273a91cb93b73f..4074573c91556f410fbb08095ce1a2004b92e4c5 100644 --- a/tasks/0113_942_113942967_qa_4/task.toml +++ b/tasks/0113_942_113942967_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0113_942_113942967_qa_4" +name = "smoldataenvs-train/0113_942_113942967_qa_4" description = "What is the size of the test set used in the model evaluation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "154" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0113_964_113964418_qa_5/task.toml b/tasks/0113_964_113964418_qa_5/task.toml index 15f1aec4c61a21d2a35dbb565214f2f94fb4d7af..7dc5a27763899270a6f739d9fbe3293b4a519ed3 100644 --- a/tasks/0113_964_113964418_qa_5/task.toml +++ b/tasks/0113_964_113964418_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0113_964_113964418_qa_5" +name = "smoldataenvs-train/0113_964_113964418_qa_5" description = "What is the average residual sugar content of the wines in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.5388" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0113_981_113981609_qa_5/task.toml b/tasks/0113_981_113981609_qa_5/task.toml index cd5beebc9bda09663220bd66de91543f770d675e..03ede46b0497867ec652ed63692107d5f873ba86 100644 --- a/tasks/0113_981_113981609_qa_5/task.toml +++ b/tasks/0113_981_113981609_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0113_981_113981609_qa_5" +name = "smoldataenvs-train/0113_981_113981609_qa_5" description = "What is the average Defense value of Pokémon in the fourth generation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "78.13" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0113_996_113996719_qa_2/task.toml b/tasks/0113_996_113996719_qa_2/task.toml index e6cc36e6c387683755bd2eb0281e78b5d247c89e..8d2b01c77bd98e80143d8c9b08bf24c755178cbf 100644 --- a/tasks/0113_996_113996719_qa_2/task.toml +++ b/tasks/0113_996_113996719_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0113_996_113996719_qa_2" +name = "smoldataenvs-train/0113_996_113996719_qa_2" description = "How many more abnormal class samples are there compared to normal class samples in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "110" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0113_996_113996719_qa_4/task.toml b/tasks/0113_996_113996719_qa_4/task.toml index f968ee98a6b1801d976f6350b9ba56db3ba2aa03..d27b64eeaa4c5c3c1cbcba5b4cf4750d429108dc 100644 --- a/tasks/0113_996_113996719_qa_4/task.toml +++ b/tasks/0113_996_113996719_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0113_996_113996719_qa_4" +name = "smoldataenvs-train/0113_996_113996719_qa_4" description = "What is the minimum value observed for the degree_spondylolisthesis feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-11.058179" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0113_997_113997588_qa_4/task.toml b/tasks/0113_997_113997588_qa_4/task.toml index ffcc1f0abec62afa3d93239f51b443a933e52528..70c045f597b6c6619928aa79c56a6ef026fa7a5c 100644 --- a/tasks/0113_997_113997588_qa_4/task.toml +++ b/tasks/0113_997_113997588_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0113_997_113997588_qa_4" +name = "smoldataenvs-train/0113_997_113997588_qa_4" description = "How many features in the dataset have a standard deviation greater than 15?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0114_063_114063568_qa_3/task.toml b/tasks/0114_063_114063568_qa_3/task.toml index 51872fdfe9390b79b318c0cf239c172d6dcb515b..d12e5b270b81f48566c42371274376b39fb88414 100644 --- a/tasks/0114_063_114063568_qa_3/task.toml +++ b/tasks/0114_063_114063568_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0114_063_114063568_qa_3" +name = "smoldataenvs-train/0114_063_114063568_qa_3" description = "What is the correlation coefficient between price and carat after applying Box-Cox transformation to normalize the data distribution?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.97" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0114_091_114091161_qa_1/task.toml b/tasks/0114_091_114091161_qa_1/task.toml index 169271b0da68b3564909402323e01011c98c1c24..188ac1615e5650c8c4fb2236cbc4da0f6a6fa999 100644 --- a/tasks/0114_091_114091161_qa_1/task.toml +++ b/tasks/0114_091_114091161_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0114_091_114091161_qa_1" +name = "smoldataenvs-train/0114_091_114091161_qa_1" description = "What is the highest global sales figure achieved by any video game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "82.74" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0114_091_114091161_qa_2/task.toml b/tasks/0114_091_114091161_qa_2/task.toml index edb40080c34dce1e0e84724ef81b59a4099e9b9e..57f8392da1cb1f9b6b80191f356042af65f1898b 100644 --- a/tasks/0114_091_114091161_qa_2/task.toml +++ b/tasks/0114_091_114091161_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0114_091_114091161_qa_2" +name = "smoldataenvs-train/0114_091_114091161_qa_2" description = "How many standard deviations above the mean are the North American sales of the top-selling game compared to the overall North American sales distribution?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0114_091_114091161_qa_3/task.toml b/tasks/0114_091_114091161_qa_3/task.toml index 63769778e1d72dc0835bc0c5c88e490e4defd97d..0d65c279c8e69d848c6cecdfaf462d0cb69d7c00 100644 --- a/tasks/0114_091_114091161_qa_3/task.toml +++ b/tasks/0114_091_114091161_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0114_091_114091161_qa_3" +name = "smoldataenvs-train/0114_091_114091161_qa_3" description = "What is the average global sales for Nintendo Wii games compared to the average global sales of all other platforms (as a multiplier)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.336" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0114_161_114161356_qa_1/task.toml b/tasks/0114_161_114161356_qa_1/task.toml index 570f7326c363a4ef1e5448e91a4f8b34c3a45fe9..61d40bcd6f2e65ccbf991dae2fa3e38ab1f2f77f 100644 --- a/tasks/0114_161_114161356_qa_1/task.toml +++ b/tasks/0114_161_114161356_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0114_161_114161356_qa_1" +name = "smoldataenvs-train/0114_161_114161356_qa_1" description = "What is the highest global sales value recorded for any video game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "82.74" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0114_161_114161356_qa_4/task.toml b/tasks/0114_161_114161356_qa_4/task.toml index 10307a41dd5b2ff151b0391a4434954ca3076660..06222a18c19470cfd240fbe276f71c6d660adfe0 100644 --- a/tasks/0114_161_114161356_qa_4/task.toml +++ b/tasks/0114_161_114161356_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0114_161_114161356_qa_4" +name = "smoldataenvs-train/0114_161_114161356_qa_4" description = "What is the second most common video game genre in the dataset by frequency count?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Sports" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0114_240_114240861_qa_2/task.toml b/tasks/0114_240_114240861_qa_2/task.toml index 8fc3e397f2068ee9bd2a9fc8e62cbcc90b1915bd..a2fa6801566e2b88252c0c96c5bd9d5fd359b580 100644 --- a/tasks/0114_240_114240861_qa_2/task.toml +++ b/tasks/0114_240_114240861_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0114_240_114240861_qa_2" +name = "smoldataenvs-train/0114_240_114240861_qa_2" description = "How many missing values exist in the total_bedrooms attribute of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "207" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0114_240_114240861_qa_4/task.toml b/tasks/0114_240_114240861_qa_4/task.toml index 25d88404f6a9b6e188576494374449124f916067..9c3972775323c372fe8a005dc78a75063c526637 100644 --- a/tasks/0114_240_114240861_qa_4/task.toml +++ b/tasks/0114_240_114240861_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0114_240_114240861_qa_4" +name = "smoldataenvs-train/0114_240_114240861_qa_4" description = "Which ocean proximity category appears most frequently in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "<1H OCEAN" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0114_325_114325622_qa_4/task.toml b/tasks/0114_325_114325622_qa_4/task.toml index c980166ab25bfc48af8a6ad59b6d1ec573fcebcf..5df0dc06c243a2e836da56c4079279d22747f707 100644 --- a/tasks/0114_325_114325622_qa_4/task.toml +++ b/tasks/0114_325_114325622_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0114_325_114325622_qa_4" +name = "smoldataenvs-train/0114_325_114325622_qa_4" description = "How many test samples were misclassified as 'Iris-virginica' by the KNN model with 1 neighbor?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0114_365_114365503_qa_5/task.toml b/tasks/0114_365_114365503_qa_5/task.toml index 1640d9ee0ee4ff50a8627581617fa61383969a3b..c925833898d3cb945bfc27c14932963b07a989fa 100644 --- a/tasks/0114_365_114365503_qa_5/task.toml +++ b/tasks/0114_365_114365503_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0114_365_114365503_qa_5" +name = "smoldataenvs-train/0114_365_114365503_qa_5" description = "What is the average trading volume for Red Hat (RHT) in the filtered dataset according to the descriptive statistics?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2001894.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0114_451_114451465_qa_1/task.toml b/tasks/0114_451_114451465_qa_1/task.toml index efb53f96db2aefd697359d961a77722cc8b90a78..06a533ef6c9d3c5c8c40f264b8559cb2c4c53c60 100644 --- a/tasks/0114_451_114451465_qa_1/task.toml +++ b/tasks/0114_451_114451465_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0114_451_114451465_qa_1" +name = "smoldataenvs-train/0114_451_114451465_qa_1" description = "What is the correlation coefficient between median income and median house value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.68" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0114_484_114484617_qa_1/task.toml b/tasks/0114_484_114484617_qa_1/task.toml index 2d692bc54b41c4001b3d590163c72b3d7782252c..d8a19dc46113f591eec29ca0cf0d0d40dd3ea1f1 100644 --- a/tasks/0114_484_114484617_qa_1/task.toml +++ b/tasks/0114_484_114484617_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0114_484_114484617_qa_1" +name = "smoldataenvs-train/0114_484_114484617_qa_1" description = "Which feature in the dataset shows the strongest correlation with the price_range according to the correlation matrix analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ram" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0114_484_114484617_qa_4/task.toml b/tasks/0114_484_114484617_qa_4/task.toml index a146b97ad9d15a66e9455ae571c5674678b99345..321ee1f2e84fb6a19f13a94ba7983b5f2a2e1f73 100644 --- a/tasks/0114_484_114484617_qa_4/task.toml +++ b/tasks/0114_484_114484617_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0114_484_114484617_qa_4" +name = "smoldataenvs-train/0114_484_114484617_qa_4" description = "Which price_range category achieved the highest precision score in the Random Forest model's predictions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0114_484_114484829_qa_4/task.toml b/tasks/0114_484_114484829_qa_4/task.toml index a70a038762dd687eab73318a1d920435613e26b0..48e17c471ed92719bac2465fbcda5cc20c2a7391 100644 --- a/tasks/0114_484_114484829_qa_4/task.toml +++ b/tasks/0114_484_114484829_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0114_484_114484829_qa_4" +name = "smoldataenvs-train/0114_484_114484829_qa_4" description = "What is the absolute correlation coefficient between \"median_house_value\" and \"latitude\" in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.144160" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0114_503_114503160_qa_2/task.toml b/tasks/0114_503_114503160_qa_2/task.toml index 217a26792ca8ae0be595822384bf5bab29fc5dff..da65f4828e596cd725bfd62b3e9675787b48a5cb 100644 --- a/tasks/0114_503_114503160_qa_2/task.toml +++ b/tasks/0114_503_114503160_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0114_503_114503160_qa_2" +name = "smoldataenvs-train/0114_503_114503160_qa_2" description = "What is the accuracy rate for predicting non-purchases (Purchased=0) in the initial Support Vector Machine model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "94.52" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0114_525_114525119_qa_3/task.toml b/tasks/0114_525_114525119_qa_3/task.toml index a7fbd44ce28ef71bffe1bf9af2394cde3f87f829..205c5a6dda75711c05ef53f824245e808670fae7 100644 --- a/tasks/0114_525_114525119_qa_3/task.toml +++ b/tasks/0114_525_114525119_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0114_525_114525119_qa_3" +name = "smoldataenvs-train/0114_525_114525119_qa_3" description = "What is the correlation coefficient between the number of family members (SibSp + Parch) and fare paid by passengers?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.217" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0114_582_114582781_qa_1/task.toml b/tasks/0114_582_114582781_qa_1/task.toml index eab61a8f75bade2e08c116f6c2f0ab40b39392ba..8f06ecea70794f0379dd52f0cfea07c8ce77fc61 100644 --- a/tasks/0114_582_114582781_qa_1/task.toml +++ b/tasks/0114_582_114582781_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0114_582_114582781_qa_1" +name = "smoldataenvs-train/0114_582_114582781_qa_1" description = "What is the R² score of the linear regression model on the training data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.7497" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0114_582_114582781_qa_5/task.toml b/tasks/0114_582_114582781_qa_5/task.toml index 068e970dc5b29afb7e996b201048a87912810336..12c29a9bc0673a59501267ffbada1e605ba6a126 100644 --- a/tasks/0114_582_114582781_qa_5/task.toml +++ b/tasks/0114_582_114582781_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0114_582_114582781_qa_5" +name = "smoldataenvs-train/0114_582_114582781_qa_5" description = "Which feature has the highest coefficient in the trained model, indicating its strongest influence on insurance charges?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "smoker" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0114_618_114618907_qa_2/task.toml b/tasks/0114_618_114618907_qa_2/task.toml index e776c7bf2ba15f7815fbed23c420260c3edf675d..3f69218c4e99a7105b6e416d1981e1c09f763d78 100644 --- a/tasks/0114_618_114618907_qa_2/task.toml +++ b/tasks/0114_618_114618907_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0114_618_114618907_qa_2" +name = "smoldataenvs-train/0114_618_114618907_qa_2" description = "Which variable in the regression model has the largest negative coefficient impacting Purchase?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Product_Category_1" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0114_673_114673575_qa_5/task.toml b/tasks/0114_673_114673575_qa_5/task.toml index a0cad2dc5ff6f66576c0e1e3bf6cacc62d80edb7..4c54d9580a45a288962856008f48fad37d9e8fa4 100644 --- a/tasks/0114_673_114673575_qa_5/task.toml +++ b/tasks/0114_673_114673575_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0114_673_114673575_qa_5" +name = "smoldataenvs-train/0114_673_114673575_qa_5" description = "What is the difference in total sales volume between the highest-selling and second-highest-selling regions in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1958.82" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0114_713_114713370_qa_2/task.toml b/tasks/0114_713_114713370_qa_2/task.toml index dde94481f56e9c8bb8620da0664880cd75c4bda0..4546b2297caf78ca65d49b08a19abd9cb1dd536c 100644 --- a/tasks/0114_713_114713370_qa_2/task.toml +++ b/tasks/0114_713_114713370_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0114_713_114713370_qa_2" +name = "smoldataenvs-train/0114_713_114713370_qa_2" description = "What proportion of tumors in the dataset are malignant (diagnosis = 1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "37.26" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0114_713_114713370_qa_5/task.toml b/tasks/0114_713_114713370_qa_5/task.toml index fe51fe12412b9318220f6a8c12adec63560236a0..3f2aa5f419b834f7c6ac49d3b4f2c982030a9491 100644 --- a/tasks/0114_713_114713370_qa_5/task.toml +++ b/tasks/0114_713_114713370_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0114_713_114713370_qa_5" +name = "smoldataenvs-train/0114_713_114713370_qa_5" description = "What is the correlation coefficient between the feature 'radius_worst' and the diagnosis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.78" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0114_726_114726307_qa_4/task.toml b/tasks/0114_726_114726307_qa_4/task.toml index f5f97de37cc2ce93c5dad1fb32c17f89aefbb0e7..7f907e1d4a74562e262d24bee44ed5b97621f197 100644 --- a/tasks/0114_726_114726307_qa_4/task.toml +++ b/tasks/0114_726_114726307_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0114_726_114726307_qa_4" +name = "smoldataenvs-train/0114_726_114726307_qa_4" description = "What is the total number of recorded traffic accidents in the dataset for the year 2014 in the selected London boroughs (City of London and Westminster)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1945" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0114_908_114908217_qa_4/task.toml b/tasks/0114_908_114908217_qa_4/task.toml index 383f447bbc072646dde253a3b921213ec11d3424..82afe219cb21bd2e20ec00fe88ca1ddd855ad8c9 100644 --- a/tasks/0114_908_114908217_qa_4/task.toml +++ b/tasks/0114_908_114908217_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0114_908_114908217_qa_4" +name = "smoldataenvs-train/0114_908_114908217_qa_4" description = "What is the lowest price of any diamond in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "326" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0114_928_114928761_qa_2/task.toml b/tasks/0114_928_114928761_qa_2/task.toml index c8408dd175d95d82493ce187506dbddfe677f8c2..66812da4bda52518a8915519c7a2c065d1c13388 100644 --- a/tasks/0114_928_114928761_qa_2/task.toml +++ b/tasks/0114_928_114928761_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0114_928_114928761_qa_2" +name = "smoldataenvs-train/0114_928_114928761_qa_2" description = "How many samples were allocated to the test dataset after splitting the original data with a 20% test size?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "154" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0114_928_114928761_qa_5/task.toml b/tasks/0114_928_114928761_qa_5/task.toml index f189d75633f5f96f576d38d5e90ed3756fcc309e..653f8ecc89d5f8f2170ad87931f41f32cba2f9a5 100644 --- a/tasks/0114_928_114928761_qa_5/task.toml +++ b/tasks/0114_928_114928761_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0114_928_114928761_qa_5" +name = "smoldataenvs-train/0114_928_114928761_qa_5" description = "What is the F1-score for the positive class (Outcome = 1) in the model's classification report?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.68" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0114_944_114944895_qa_1/task.toml b/tasks/0114_944_114944895_qa_1/task.toml index 52398774347ae3c5c3d1e771c27a933160cc666a..e66f1f31adfba51a7210224a3867af0402ddc887 100644 --- a/tasks/0114_944_114944895_qa_1/task.toml +++ b/tasks/0114_944_114944895_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0114_944_114944895_qa_1" +name = "smoldataenvs-train/0114_944_114944895_qa_1" description = "Which feature (excluding the Species column) has the highest standard deviation in the dataset, and what is the value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm, 1.764" reward_mode_initial = "list" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0114_982_114982067_qa_2/task.toml b/tasks/0114_982_114982067_qa_2/task.toml index b696dc2ddbbc2c86e74cb926d7c645ff4a54c1b6..020b434a6a47518f7dca24c07889717d1249c595 100644 --- a/tasks/0114_982_114982067_qa_2/task.toml +++ b/tasks/0114_982_114982067_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0114_982_114982067_qa_2" +name = "smoldataenvs-train/0114_982_114982067_qa_2" description = "Which feature exhibits the strongest negative correlation with the housing price (Price) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "LSTAT" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0114_986_114986805_qa_2/task.toml b/tasks/0114_986_114986805_qa_2/task.toml index 77cdc29919b815fc1de15693ee825c1863eb2f3a..f10c5c3d48cf75a33df662af012ea4145d43c6fa 100644 --- a/tasks/0114_986_114986805_qa_2/task.toml +++ b/tasks/0114_986_114986805_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0114_986_114986805_qa_2" +name = "smoldataenvs-train/0114_986_114986805_qa_2" description = "How many data points are assigned to the smallest cluster in the K-means clustering solution with K=4?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "91" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0115_004_115004067_qa_2/task.toml b/tasks/0115_004_115004067_qa_2/task.toml index 8bd96959a6d037c4a9a9c5b6ab58286ebd2d6152..d929dac85f0393d42e456b2d12e63db4b45a7dce 100644 --- a/tasks/0115_004_115004067_qa_2/task.toml +++ b/tasks/0115_004_115004067_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0115_004_115004067_qa_2" +name = "smoldataenvs-train/0115_004_115004067_qa_2" description = "Which species has the highest average petal width in the training data subset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-virginica" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0115_004_115004067_qa_3/task.toml b/tasks/0115_004_115004067_qa_3/task.toml index e362e708132abf614aa77257d8d2f9af5f0d8d66..7705a87a91661c82d3bb2059c9cee5968ab03c2e 100644 --- a/tasks/0115_004_115004067_qa_3/task.toml +++ b/tasks/0115_004_115004067_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0115_004_115004067_qa_3" +name = "smoldataenvs-train/0115_004_115004067_qa_3" description = "What is the standard deviation of sepal width for Iris-setosa in the complete dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.379064" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0115_004_115004067_qa_4/task.toml b/tasks/0115_004_115004067_qa_4/task.toml index 45187b0b7ac234a35363aeaecd35decb3894186e..d8f5f08c5271c0a10213d56dd7185ecc967f8e7e 100644 --- a/tasks/0115_004_115004067_qa_4/task.toml +++ b/tasks/0115_004_115004067_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0115_004_115004067_qa_4" +name = "smoldataenvs-train/0115_004_115004067_qa_4" description = "Which feature shows the greatest variability (standard deviation) across all species in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0115_105_115105744_qa_2/task.toml b/tasks/0115_105_115105744_qa_2/task.toml index 586c33ba8d754e9761ebf73c890050ad15ca8ad0..c57b87c351923efa577c7c28b476916a4b7c4741 100644 --- a/tasks/0115_105_115105744_qa_2/task.toml +++ b/tasks/0115_105_115105744_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0115_105_115105744_qa_2" +name = "smoldataenvs-train/0115_105_115105744_qa_2" description = "Which feature in the training dataset has the highest positive correlation with the price_range according to the Pearson correlation matrix?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ram" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0115_105_115105744_qa_3/task.toml b/tasks/0115_105_115105744_qa_3/task.toml index b16fe6cb88703a52b9aab189adaad1738de02cf9..c35d065a41130409800ed8aa1b9b5a4dadc21726 100644 --- a/tasks/0115_105_115105744_qa_3/task.toml +++ b/tasks/0115_105_115105744_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0115_105_115105744_qa_3" +name = "smoldataenvs-train/0115_105_115105744_qa_3" description = "What is the distribution of price_range categories in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0:500,1:500,2:500,3:500" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0115_266_115266605_qa_1/task.toml b/tasks/0115_266_115266605_qa_1/task.toml index 90daab8810d154c7dbdfc50ed1676b48448f5cf1..7caead10d2a5795f88e4e984a99acd947cf812bf 100644 --- a/tasks/0115_266_115266605_qa_1/task.toml +++ b/tasks/0115_266_115266605_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0115_266_115266605_qa_1" +name = "smoldataenvs-train/0115_266_115266605_qa_1" description = "What is the total sum of global sales across all games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8920.44" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0115_266_115266605_qa_3/task.toml b/tasks/0115_266_115266605_qa_3/task.toml index a1f0779927902879a6f8f6d8533df24ddc04c249..0bcba7515753558e2935c6cef2676aa3521ee7ee 100644 --- a/tasks/0115_266_115266605_qa_3/task.toml +++ b/tasks/0115_266_115266605_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0115_266_115266605_qa_3" +name = "smoldataenvs-train/0115_266_115266605_qa_3" description = "What is the median value of global sales for all games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.17" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0115_266_115266605_qa_4/task.toml b/tasks/0115_266_115266605_qa_4/task.toml index fd4a361eea877f6ae590491494b41b3327fb1773..ced9887e8a4af12e3ff03ddc4a154ea71a749aba 100644 --- a/tasks/0115_266_115266605_qa_4/task.toml +++ b/tasks/0115_266_115266605_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0115_266_115266605_qa_4" +name = "smoldataenvs-train/0115_266_115266605_qa_4" description = "What is the highest global sales value recorded for any game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "82.74" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0115_281_115281076_qa_2/task.toml b/tasks/0115_281_115281076_qa_2/task.toml index edf9c8fdc7fe071c2edb92ffdadb7db354e2b058..f709afae1aa7f982d359b08183a3926ccbb902f4 100644 --- a/tasks/0115_281_115281076_qa_2/task.toml +++ b/tasks/0115_281_115281076_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0115_281_115281076_qa_2" +name = "smoldataenvs-train/0115_281_115281076_qa_2" description = "After filling the missing values in the 'Year' column with zeros and converting it to an integer data type, how many entries have a 'Year' value of 0?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "271" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0115_308_115308728_qa_4/task.toml b/tasks/0115_308_115308728_qa_4/task.toml index 708daa7e9bad88c03a3ea48e9b4c8848a021d835..425a95a0d8e6124d3bf1d7003c80ee0f900aa1a6 100644 --- a/tasks/0115_308_115308728_qa_4/task.toml +++ b/tasks/0115_308_115308728_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0115_308_115308728_qa_4" +name = "smoldataenvs-train/0115_308_115308728_qa_4" description = "How many emails are in the training set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3900" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0115_324_115324886_qa_1/task.toml b/tasks/0115_324_115324886_qa_1/task.toml index 5327b70074e4ace5970307c4dfe1ec4ee6017c1c..b1825e802796ad3604893d5b7b4c4c0e3b0ee943 100644 --- a/tasks/0115_324_115324886_qa_1/task.toml +++ b/tasks/0115_324_115324886_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0115_324_115324886_qa_1" +name = "smoldataenvs-train/0115_324_115324886_qa_1" description = "Which restaurant has the highest percentage weight in the popularity-based recommender system according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Tortas Locas Hipocampo" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0115_350_115350781_qa_2/task.toml b/tasks/0115_350_115350781_qa_2/task.toml index e0d63f075cc70f4ad2c51055693f419c65788d4f..47e06c34e820bb0b6df0528f0ef6ad9c18bcb244 100644 --- a/tasks/0115_350_115350781_qa_2/task.toml +++ b/tasks/0115_350_115350781_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0115_350_115350781_qa_2" +name = "smoldataenvs-train/0115_350_115350781_qa_2" description = "Which video game genre has the highest number of entries in the dataset, and how many games belong to this genre?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action, 3316" reward_mode_initial = "list" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0115_410_115410234_qa_2/task.toml b/tasks/0115_410_115410234_qa_2/task.toml index 70a3402f81d6beb8da1399af48bc7902d89c512c..e89dabc9d3d6c9a5ecbb95d9f093771b08fa7771 100644 --- a/tasks/0115_410_115410234_qa_2/task.toml +++ b/tasks/0115_410_115410234_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0115_410_115410234_qa_2" +name = "smoldataenvs-train/0115_410_115410234_qa_2" description = "What percentage of animals in the dataset have missing names?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "30.5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0115_420_115420161_qa_3/task.toml b/tasks/0115_420_115420161_qa_3/task.toml index 00e79b30e135662c6a6819ad63802015fbc0ecb7..4f60824a7fa9633e27f15a36fa8ebab03cbd8057 100644 --- a/tasks/0115_420_115420161_qa_3/task.toml +++ b/tasks/0115_420_115420161_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0115_420_115420161_qa_3" +name = "smoldataenvs-train/0115_420_115420161_qa_3" description = "Which platform has the highest cumulative global sales, and what is the total value in millions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PS2, 1255.64" reward_mode_initial = "list" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0115_422_115422452_qa_4/task.toml b/tasks/0115_422_115422452_qa_4/task.toml index d39a8e311205050e052e3d174c20f7c45575e4c7..b9199397fdaa9eca4ff1eb48da1e7ec7b57a00d4 100644 --- a/tasks/0115_422_115422452_qa_4/task.toml +++ b/tasks/0115_422_115422452_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0115_422_115422452_qa_4" +name = "smoldataenvs-train/0115_422_115422452_qa_4" description = "What is the difference in global sales between the #1 and #2 highest grossing games?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "42.5" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0115_422_115422452_qa_5/task.toml b/tasks/0115_422_115422452_qa_5/task.toml index 4656c266dd46fd6f8a4c3c98b0cb93f0585a46be..3e206cc539d58ee1782020c699ab255de96ba183 100644 --- a/tasks/0115_422_115422452_qa_5/task.toml +++ b/tasks/0115_422_115422452_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0115_422_115422452_qa_5" +name = "smoldataenvs-train/0115_422_115422452_qa_5" description = "Which platform has the highest number of entries in the top 20 highest grossing games list?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0115_424_115424006_qa_1/task.toml b/tasks/0115_424_115424006_qa_1/task.toml index 718b86b9638eae2f667bde14cac8eb8fb156d71d..bdd178b5b0622e51069f768766465ff2699921bb 100644 --- a/tasks/0115_424_115424006_qa_1/task.toml +++ b/tasks/0115_424_115424006_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0115_424_115424006_qa_1" +name = "smoldataenvs-train/0115_424_115424006_qa_1" description = "How many standard deviations above the mean North American sales is the top-selling video game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50.5" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0115_428_115428542_qa_1/task.toml b/tasks/0115_428_115428542_qa_1/task.toml index 26c7c8c3ce2589fbef7be42fbc78e16defa06878..2f7afd9cd8009ad95ab709452dbae5ef25dbfdc5 100644 --- a/tasks/0115_428_115428542_qa_1/task.toml +++ b/tasks/0115_428_115428542_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0115_428_115428542_qa_1" +name = "smoldataenvs-train/0115_428_115428542_qa_1" description = "What are the three least common genres in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Puzzle, Strategy, Fighting" reward_mode_initial = "list" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0115_428_115428542_qa_3/task.toml b/tasks/0115_428_115428542_qa_3/task.toml index d1422228114225c2d4fb0fa28e48fdfd33673201..a47bb584d50387813620c8a80619419f38034280 100644 --- a/tasks/0115_428_115428542_qa_3/task.toml +++ b/tasks/0115_428_115428542_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0115_428_115428542_qa_3" +name = "smoldataenvs-train/0115_428_115428542_qa_3" description = "What is the most prevalent genre on the PS2 platform, and how many games fall into this category?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Sports, 400" reward_mode_initial = "list" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0115_428_115428542_qa_5/task.toml b/tasks/0115_428_115428542_qa_5/task.toml index 176ebeab857e593677fb3614027e70a626c0bd78..223a7929b8c6e2a48a71ddd9dcd377a9b42c5c9e 100644 --- a/tasks/0115_428_115428542_qa_5/task.toml +++ b/tasks/0115_428_115428542_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0115_428_115428542_qa_5" +name = "smoldataenvs-train/0115_428_115428542_qa_5" description = "What is the median value of North American sales, and which game is closest to this median in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.08, Harvest Moon: Boy & Girl" reward_mode_initial = "list" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0115_450_115450695_qa_2/task.toml b/tasks/0115_450_115450695_qa_2/task.toml index bb2e7ce996bc2f609057cc7771f2692afe619ecf..d0c55f0f9befa26b5c3783e746664908c4376895 100644 --- a/tasks/0115_450_115450695_qa_2/task.toml +++ b/tasks/0115_450_115450695_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0115_450_115450695_qa_2" +name = "smoldataenvs-train/0115_450_115450695_qa_2" description = "Which feature exhibits the strongest negative correlation with wine quality in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "volatile acidity" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0115_450_115450695_qa_4/task.toml b/tasks/0115_450_115450695_qa_4/task.toml index 379748957d7c77df7b2ae0d677c66b19ac142b90..43ee2ea1e63d7fab55f001794ebea94aa2ab0432 100644 --- a/tasks/0115_450_115450695_qa_4/task.toml +++ b/tasks/0115_450_115450695_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0115_450_115450695_qa_4" +name = "smoldataenvs-train/0115_450_115450695_qa_4" description = "What is the median quality rating of the wines in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0115_454_115454255_qa_3/task.toml b/tasks/0115_454_115454255_qa_3/task.toml index aad9fffc980edd751f14a02b3ee43a0e8bbff662..6e4edbdc2525c4245c81caa2dc8fa514ddba0d84 100644 --- a/tasks/0115_454_115454255_qa_3/task.toml +++ b/tasks/0115_454_115454255_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0115_454_115454255_qa_3" +name = "smoldataenvs-train/0115_454_115454255_qa_3" description = "Which song has the highest instrumentalness score in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Senseless Order" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0115_504_115504947_qa_3/task.toml b/tasks/0115_504_115504947_qa_3/task.toml index 0912ab9c07bd4226ca404c9cdc1c01f4c5e4a2ef..3cc1305276362cf3d3abb0f05152f584ec007ddf 100644 --- a/tasks/0115_504_115504947_qa_3/task.toml +++ b/tasks/0115_504_115504947_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0115_504_115504947_qa_3" +name = "smoldataenvs-train/0115_504_115504947_qa_3" description = "What is the standard deviation of North American sales?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.816683" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0115_504_115504947_qa_4/task.toml b/tasks/0115_504_115504947_qa_4/task.toml index 835845598dfb1f867ac3f41382c528e3e78c76d0..b0c20aba2c2d9fd4bd248443410174b28ab9e46c 100644 --- a/tasks/0115_504_115504947_qa_4/task.toml +++ b/tasks/0115_504_115504947_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0115_504_115504947_qa_4" +name = "smoldataenvs-train/0115_504_115504947_qa_4" description = "What is the median value of North American sales?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.08" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0115_504_115504947_qa_5/task.toml b/tasks/0115_504_115504947_qa_5/task.toml index cbd82843ecd8d7dc20f75c79ef1a416fde441813..3153ee091b7441f70902774c2f97cd339cce7983 100644 --- a/tasks/0115_504_115504947_qa_5/task.toml +++ b/tasks/0115_504_115504947_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0115_504_115504947_qa_5" +name = "smoldataenvs-train/0115_504_115504947_qa_5" description = "What is the average global sales for all platforms excluding the Nintendo Wii?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.5233896418516336" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0115_514_115514429_qa_4/task.toml b/tasks/0115_514_115514429_qa_4/task.toml index 961a4e6754e3586f18f662956b34ce3759ddc5bf..e2fb5eca6da4fa39f59864f78542dad0daa03e16 100644 --- a/tasks/0115_514_115514429_qa_4/task.toml +++ b/tasks/0115_514_115514429_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0115_514_115514429_qa_4" +name = "smoldataenvs-train/0115_514_115514429_qa_4" description = "How many samples are in the test set for the regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "320" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0115_532_115532151_qa_3/task.toml b/tasks/0115_532_115532151_qa_3/task.toml index 8701e938cac725287b87e7ce6a1fffd80d34ef82..735e4e18a3678cc67c487f7b9dd207fd9060ad71 100644 --- a/tasks/0115_532_115532151_qa_3/task.toml +++ b/tasks/0115_532_115532151_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0115_532_115532151_qa_3" +name = "smoldataenvs-train/0115_532_115532151_qa_3" description = "What is the average cross-validation accuracy of the KNN model using 10-fold cross-validation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9672" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0115_567_115567035_qa_5/task.toml b/tasks/0115_567_115567035_qa_5/task.toml index c88c13e0deea658113a104a2ef7c18be70936e81..4e473ea9f8e2805458ae37deb88a4364472f3242 100644 --- a/tasks/0115_567_115567035_qa_5/task.toml +++ b/tasks/0115_567_115567035_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0115_567_115567035_qa_5" +name = "smoldataenvs-train/0115_567_115567035_qa_5" description = "Which feature has the strongest negative correlation with the mushroom classification target variable in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "gill-color" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0115_609_115609623_qa_1/task.toml b/tasks/0115_609_115609623_qa_1/task.toml index 5b379c01b6b9d474376ded045feba96252296ac8..9862e38700e4c1f20f92f9e2449f77a0ea54976f 100644 --- a/tasks/0115_609_115609623_qa_1/task.toml +++ b/tasks/0115_609_115609623_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0115_609_115609623_qa_1" +name = "smoldataenvs-train/0115_609_115609623_qa_1" description = "How many standard deviations above the mean North American sales are the sales of the top-selling game in North America?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50.48" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0115_609_115609623_qa_2/task.toml b/tasks/0115_609_115609623_qa_2/task.toml index ad6d2ddf1b4a1d0dfd2853bfce3bcae9d6143243..833477bc4e8f2ac29ff21878b28ab55be8270803 100644 --- a/tasks/0115_609_115609623_qa_2/task.toml +++ b/tasks/0115_609_115609623_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0115_609_115609623_qa_2" +name = "smoldataenvs-train/0115_609_115609623_qa_2" description = "What is the average global sales of Nintendo Wii games compared to all other platforms combined?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii: 0.699, Other platforms: 0.523" reward_mode_initial = "list" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0115_609_115609623_qa_5/task.toml b/tasks/0115_609_115609623_qa_5/task.toml index b158055dcaed6d95d251233656c41b36cff011b2..7f73e55eb1235aad7d6e43811061f2d192f7255b 100644 --- a/tasks/0115_609_115609623_qa_5/task.toml +++ b/tasks/0115_609_115609623_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0115_609_115609623_qa_5" +name = "smoldataenvs-train/0115_609_115609623_qa_5" description = "What is the median value of global sales across all games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.17" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0115_621_115621359_qa_4/task.toml b/tasks/0115_621_115621359_qa_4/task.toml index 78ded5dcd2143d7f411d3e499efaf9c80b1b22ec..a276e4b2bf47a4b8f88d50e93aac2858548d68ca 100644 --- a/tasks/0115_621_115621359_qa_4/task.toml +++ b/tasks/0115_621_115621359_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0115_621_115621359_qa_4" +name = "smoldataenvs-train/0115_621_115621359_qa_4" description = "Which model (Linear Regression or Random Forest Regressor) achieved a higher R2 score in the final evaluation on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Random Forest Regressor" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0115_634_115634779_qa_3/task.toml b/tasks/0115_634_115634779_qa_3/task.toml index 6f1d8ed9adc429b486c54b49098cae98f8ebae4d..ee460773facfde9eb8cd409e38680f817aeb5698 100644 --- a/tasks/0115_634_115634779_qa_3/task.toml +++ b/tasks/0115_634_115634779_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0115_634_115634779_qa_3" +name = "smoldataenvs-train/0115_634_115634779_qa_3" description = "What is the median global sales value across all video games?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.17" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0115_634_115634779_qa_4/task.toml b/tasks/0115_634_115634779_qa_4/task.toml index 056c6716f12ba81f475473da58c7ae4b9b9e7b4f..d2ca7c663f49717396ec2b4a5a0bb3ccfa0dcf3e 100644 --- a/tasks/0115_634_115634779_qa_4/task.toml +++ b/tasks/0115_634_115634779_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0115_634_115634779_qa_4" +name = "smoldataenvs-train/0115_634_115634779_qa_4" description = "How many video game entries are present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16598" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0115_676_115676411_qa_2/task.toml b/tasks/0115_676_115676411_qa_2/task.toml index c8d63afd2c3908adbb699bdfc1b417d84bee3f2a..2d9a15a8b92b1ea808873704246ced6c7c5f7229 100644 --- a/tasks/0115_676_115676411_qa_2/task.toml +++ b/tasks/0115_676_115676411_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0115_676_115676411_qa_2" +name = "smoldataenvs-train/0115_676_115676411_qa_2" description = "Which calendar year from 1980 to 2015 had the highest total global sales of video games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2008" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0115_676_115676411_qa_5/task.toml b/tasks/0115_676_115676411_qa_5/task.toml index a4f1e18d8dd318501cea24b0a6daeaf843d290ff..afa551d220e838493aa516476356677582c54790 100644 --- a/tasks/0115_676_115676411_qa_5/task.toml +++ b/tasks/0115_676_115676411_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0115_676_115676411_qa_5" +name = "smoldataenvs-train/0115_676_115676411_qa_5" description = "What percentage of total global sales in the dataset came from the North American (NA) region?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "49.3%" reward_mode_initial = "flexible" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0115_722_115722970_qa_3/task.toml b/tasks/0115_722_115722970_qa_3/task.toml index a90a00d56c6a0888e182d15b98156205242d6efe..4aa3ab841dc687ae0cf30d11dbdc448c527ad967 100644 --- a/tasks/0115_722_115722970_qa_3/task.toml +++ b/tasks/0115_722_115722970_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0115_722_115722970_qa_3" +name = "smoldataenvs-train/0115_722_115722970_qa_3" description = "Which publisher has achieved the highest total global sales across all their games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Nintendo" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0115_763_115763539_qa_2/task.toml b/tasks/0115_763_115763539_qa_2/task.toml index 418d169cfb481d14c04d9c704be5762b350d5b0a..4170af5e4f72562d2b5073f223a7d219123c20fd 100644 --- a/tasks/0115_763_115763539_qa_2/task.toml +++ b/tasks/0115_763_115763539_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0115_763_115763539_qa_2" +name = "smoldataenvs-train/0115_763_115763539_qa_2" description = "Based on the IQR method, how many outlier values are present in the SepalWidthCm feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0115_767_115767675_qa_2/task.toml b/tasks/0115_767_115767675_qa_2/task.toml index a4fa84c7914e87808aa9574c53306f12e527f3ab..639aeeb474a6823fd2d8363cd1925b992ed67bef 100644 --- a/tasks/0115_767_115767675_qa_2/task.toml +++ b/tasks/0115_767_115767675_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0115_767_115767675_qa_2" +name = "smoldataenvs-train/0115_767_115767675_qa_2" description = "Which ocean proximity category has the strongest negative correlation with median house value in the dataset after creating dummy variables?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "INLAND" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0115_807_115807171_qa_2/task.toml b/tasks/0115_807_115807171_qa_2/task.toml index aef11a1b49602c6718103b000963d20f50c356b7..d8d05d31f8c2b8c3cc853471d68320dd39205325 100644 --- a/tasks/0115_807_115807171_qa_2/task.toml +++ b/tasks/0115_807_115807171_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0115_807_115807171_qa_2" +name = "smoldataenvs-train/0115_807_115807171_qa_2" description = "What is the maximum global sales recorded for a video game in the cleaned dataset after removing records with missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "82.74" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0115_814_115814701_qa_4/task.toml b/tasks/0115_814_115814701_qa_4/task.toml index ae51a14698f18e7e871f72eea54a3484cb838814..b54fa4c35cf673715d8a2feb0393389ff4e14e5c 100644 --- a/tasks/0115_814_115814701_qa_4/task.toml +++ b/tasks/0115_814_115814701_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0115_814_115814701_qa_4" +name = "smoldataenvs-train/0115_814_115814701_qa_4" description = "What is the VADER compound score for the example review \"This oatmeal is not good...\"?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.5448" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0115_907_115907821_qa_2/task.toml b/tasks/0115_907_115907821_qa_2/task.toml index 367c43b1c19d4b764b94eb7542040ed90ec2e1a8..07ec8b1da5e5720b6c228ab2006b05a788069981 100644 --- a/tasks/0115_907_115907821_qa_2/task.toml +++ b/tasks/0115_907_115907821_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0115_907_115907821_qa_2" +name = "smoldataenvs-train/0115_907_115907821_qa_2" description = "What is the total sum of all global sales values in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8920.44" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0115_907_115907821_qa_5/task.toml b/tasks/0115_907_115907821_qa_5/task.toml index 332577b87b553d06da937ab42b73eb984c8d7f03..71080c0f8ca81103528ed91ed1ba12f95b59b8e4 100644 --- a/tasks/0115_907_115907821_qa_5/task.toml +++ b/tasks/0115_907_115907821_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0115_907_115907821_qa_5" +name = "smoldataenvs-train/0115_907_115907821_qa_5" description = "What is the global sales value of the 5th highest selling video game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "31.37" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0115_943_115943948_qa_3/task.toml b/tasks/0115_943_115943948_qa_3/task.toml index 8569ebb311c6ca950b5d4140a0fc52ab3599b759..f105106044ea85413a7316d9aef1a6d37de0f5ca 100644 --- a/tasks/0115_943_115943948_qa_3/task.toml +++ b/tasks/0115_943_115943948_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0115_943_115943948_qa_3" +name = "smoldataenvs-train/0115_943_115943948_qa_3" description = "What is the highest positive correlation coefficient between any two variables in the correlation matrix?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.67" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0115_972_115972863_qa_1/task.toml b/tasks/0115_972_115972863_qa_1/task.toml index a8da70aa97445cf4098b4f4a07eb2366d6ee3e37..e047360945c2cc445f4dfe541ce3aa09e050854f 100644 --- a/tasks/0115_972_115972863_qa_1/task.toml +++ b/tasks/0115_972_115972863_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0115_972_115972863_qa_1" +name = "smoldataenvs-train/0115_972_115972863_qa_1" description = "How many records remain in the dataset after removing rows with zero values in BMI, Glucose, and BloodPressure columns?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "724" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0115_972_115972863_qa_3/task.toml b/tasks/0115_972_115972863_qa_3/task.toml index 464e52044f70aaad2037b124010526678ef92a90..d860e22403aae0af14a10297747d93c21ea76b3c 100644 --- a/tasks/0115_972_115972863_qa_3/task.toml +++ b/tasks/0115_972_115972863_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0115_972_115972863_qa_3" +name = "smoldataenvs-train/0115_972_115972863_qa_3" description = "Which feature has the highest correlation with the presence of diabetes in the cleaned dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0116_015_116015692_qa_3/task.toml b/tasks/0116_015_116015692_qa_3/task.toml index 6e729e8a215691de600566c345e95c367b94e726..6ee1a491f85f84bc5b3459a3b4309b71e6ccb6f8 100644 --- a/tasks/0116_015_116015692_qa_3/task.toml +++ b/tasks/0116_015_116015692_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0116_015_116015692_qa_3" +name = "smoldataenvs-train/0116_015_116015692_qa_3" description = "Which academic topic is most commonly enrolled by students based on the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "IT" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0116_015_116015692_qa_5/task.toml b/tasks/0116_015_116015692_qa_5/task.toml index 35541a46249a1de0fdf229555fced997f9fefa76..69e4545b3a45334f20014f4d91662683ddced554 100644 --- a/tasks/0116_015_116015692_qa_5/task.toml +++ b/tasks/0116_015_116015692_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0116_015_116015692_qa_5" +name = "smoldataenvs-train/0116_015_116015692_qa_5" description = "Which feature has the highest positive correlation with student participation (raisedhands)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "VisITedResources" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0116_025_116025601_qa_5/task.toml b/tasks/0116_025_116025601_qa_5/task.toml index ae27461b2e8683f0895e7a5f4386806564af89c9..01c29952030b0e69179f34806196bafd117113e1 100644 --- a/tasks/0116_025_116025601_qa_5/task.toml +++ b/tasks/0116_025_116025601_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0116_025_116025601_qa_5" +name = "smoldataenvs-train/0116_025_116025601_qa_5" description = "What is the coefficient (slope) of the linear relationship between years of experience and salary in the model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9423.81532303" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0116_026_116026387_qa_3/task.toml b/tasks/0116_026_116026387_qa_3/task.toml index e1e3b94ccf02ea1880c6de20155ad6d1621b81c9..9a4ee3f271766448f0129bf791e1a224092330b0 100644 --- a/tasks/0116_026_116026387_qa_3/task.toml +++ b/tasks/0116_026_116026387_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0116_026_116026387_qa_3" +name = "smoldataenvs-train/0116_026_116026387_qa_3" description = "Which department has the highest number of employees who left the company?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Sales" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0116_026_116026387_qa_5/task.toml b/tasks/0116_026_116026387_qa_5/task.toml index 4c778e4ab2a3ab555568392cbb18c0a627773069..45ab8e917d1193af4c0199e94fc25ae9b51dba3a 100644 --- a/tasks/0116_026_116026387_qa_5/task.toml +++ b/tasks/0116_026_116026387_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0116_026_116026387_qa_5" +name = "smoldataenvs-train/0116_026_116026387_qa_5" description = "What is the difference in average monthly hours between employees who left and those who stayed?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8.35901" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0116_131_116131227_qa_5/task.toml b/tasks/0116_131_116131227_qa_5/task.toml index fb6f188eb91962d23f54fe15c4fae844caf2cc18..806ba4ad31dcf19027d31b263b18e00682a5d336 100644 --- a/tasks/0116_131_116131227_qa_5/task.toml +++ b/tasks/0116_131_116131227_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0116_131_116131227_qa_5" +name = "smoldataenvs-train/0116_131_116131227_qa_5" description = "What is the difference in average message length between spam and ham messages in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "67.39" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0116_140_116140223_qa_3/task.toml b/tasks/0116_140_116140223_qa_3/task.toml index b400d2f56e526ee606331d5a3278a6c53b4952b2..f51b0a8119c547474c7e82cffffe9d3fceeff498 100644 --- a/tasks/0116_140_116140223_qa_3/task.toml +++ b/tasks/0116_140_116140223_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0116_140_116140223_qa_3" +name = "smoldataenvs-train/0116_140_116140223_qa_3" description = "What is the most common Recommended IND value for products with a 5-star rating?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0116_195_116195791_qa_2/task.toml b/tasks/0116_195_116195791_qa_2/task.toml index a145d678cd914231dd38b86a8caae2127c346eeb..fe3a139c1251016daaddd63439b7a79c390a3313 100644 --- a/tasks/0116_195_116195791_qa_2/task.toml +++ b/tasks/0116_195_116195791_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0116_195_116195791_qa_2" +name = "smoldataenvs-train/0116_195_116195791_qa_2" description = "Is there a significant negative correlation between volatile acidity and wine quality?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] build_timeout_sec = 600.0 diff --git a/tasks/0116_195_116195791_qa_4/task.toml b/tasks/0116_195_116195791_qa_4/task.toml index e57be8279d768c920e595b6f57b93c99fa0fd0be..6521dfb5271108b9dcfb88e5feefa2a7959e83d2 100644 --- a/tasks/0116_195_116195791_qa_4/task.toml +++ b/tasks/0116_195_116195791_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0116_195_116195791_qa_4" +name = "smoldataenvs-train/0116_195_116195791_qa_4" description = "What is the average sulphate level in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.658" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0116_220_116220361_qa_4/task.toml b/tasks/0116_220_116220361_qa_4/task.toml index c0a73a70093c31482aac336d4231de562261294b..8a358b48c7d1e3cfe44c294327389b762261be7b 100644 --- a/tasks/0116_220_116220361_qa_4/task.toml +++ b/tasks/0116_220_116220361_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0116_220_116220361_qa_4" +name = "smoldataenvs-train/0116_220_116220361_qa_4" description = "What is the difference in global sales between the top-selling video game and the second top-selling video game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "42.5" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0116_278_116278227_qa_3/task.toml b/tasks/0116_278_116278227_qa_3/task.toml index c641799e4da1edd139fbadefe40273b763a790da..bdcbd109fb7615c3094e60b41256ff70b9753cd3 100644 --- a/tasks/0116_278_116278227_qa_3/task.toml +++ b/tasks/0116_278_116278227_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0116_278_116278227_qa_3" +name = "smoldataenvs-train/0116_278_116278227_qa_3" description = "What is the range of Age values observed in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "21 to 81" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0116_278_116278227_qa_4/task.toml b/tasks/0116_278_116278227_qa_4/task.toml index 63298962270ca797b8d893b6ed46b2aa75d90307..674614e9b7b2447bf875365b004874904437df6c 100644 --- a/tasks/0116_278_116278227_qa_4/task.toml +++ b/tasks/0116_278_116278227_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0116_278_116278227_qa_4" +name = "smoldataenvs-train/0116_278_116278227_qa_4" description = "What is the median number of pregnancies for individuals with a BMI above 35?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0116_418_116418769_qa_3/task.toml b/tasks/0116_418_116418769_qa_3/task.toml index 7fd88865d661875b1f23558701ec2f04aae6338c..7de4e7e5370b9073b9a5849b82aba35b851f63f8 100644 --- a/tasks/0116_418_116418769_qa_3/task.toml +++ b/tasks/0116_418_116418769_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0116_418_116418769_qa_3" +name = "smoldataenvs-train/0116_418_116418769_qa_3" description = "How many customers in the dataset churned (Churn=Yes) after removing rows with missing TotalCharges values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1869" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0116_418_116418769_qa_4/task.toml b/tasks/0116_418_116418769_qa_4/task.toml index 03f152fa01d25719aa67a30c6307eba1ef304d8d..2cc300f87e7d9d5ea59347f615f7edd2a3a48f22 100644 --- a/tasks/0116_418_116418769_qa_4/task.toml +++ b/tasks/0116_418_116418769_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0116_418_116418769_qa_4" +name = "smoldataenvs-train/0116_418_116418769_qa_4" description = "What is the number of rows removed from the dataset due to missing values in the TotalCharges column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0116_436_116436448_qa_1/task.toml b/tasks/0116_436_116436448_qa_1/task.toml index 81ed0aa6dd7e5b1dbb0d6053f5bc99e41e848aaf..82e8dea763ea06c57b464f2b6f71d17549bc3525 100644 --- a/tasks/0116_436_116436448_qa_1/task.toml +++ b/tasks/0116_436_116436448_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0116_436_116436448_qa_1" +name = "smoldataenvs-train/0116_436_116436448_qa_1" description = "What is the average age in days of dogs at the time of outcome based on the processed dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "991.11" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0116_436_116436448_qa_2/task.toml b/tasks/0116_436_116436448_qa_2/task.toml index bcc51a3ff1d4a2b59f862f44c29e207604bd4a50..9382d14760951208884d52254fb09d02c340690b 100644 --- a/tasks/0116_436_116436448_qa_2/task.toml +++ b/tasks/0116_436_116436448_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0116_436_116436448_qa_2" +name = "smoldataenvs-train/0116_436_116436448_qa_2" description = "Which outcome type is most frequently associated with Labrador Retriever Mix breed animals in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Adoption" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0116_436_116436448_qa_5/task.toml b/tasks/0116_436_116436448_qa_5/task.toml index 3ae3d124153fdb0b94871108ddb90d4ba0b77be1..26969f45972ea473b55b4d76a1efd323fc935a0f 100644 --- a/tasks/0116_436_116436448_qa_5/task.toml +++ b/tasks/0116_436_116436448_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0116_436_116436448_qa_5" +name = "smoldataenvs-train/0116_436_116436448_qa_5" description = "Which animal type represents the largest proportion of the dataset based on raw counts?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Dog" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0116_489_116489046_qa_5/task.toml b/tasks/0116_489_116489046_qa_5/task.toml index fe6499ad106aeb207630b840ab176bd030fdf9ae..ef9e199b926ad1a95716f8f5d4b6735c6ac1d963 100644 --- a/tasks/0116_489_116489046_qa_5/task.toml +++ b/tasks/0116_489_116489046_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0116_489_116489046_qa_5" +name = "smoldataenvs-train/0116_489_116489046_qa_5" description = "How many products have missing 'Item_Weight' values in the training dataset before any imputation steps were applied?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1463" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0116_553_116553968_qa_1/task.toml b/tasks/0116_553_116553968_qa_1/task.toml index b258f41b259ad27a6a0c20f9fe8d3652d8df3771..6bc21cfa65ee211d2070e1bb284fcd3699bf931a 100644 --- a/tasks/0116_553_116553968_qa_1/task.toml +++ b/tasks/0116_553_116553968_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0116_553_116553968_qa_1" +name = "smoldataenvs-train/0116_553_116553968_qa_1" description = "Which features in the dataset have the highest number of high correlations (correlation coefficient > 0.99) with other features after standardization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "radius_mean, perimeter_mean, radius_worst, perimeter_worst" reward_mode_initial = "list" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0116_553_116553968_qa_5/task.toml b/tasks/0116_553_116553968_qa_5/task.toml index 81f3fe8e6b6622ee6615be0816dfd3c61ccf08c8..74d48d077d25af15518e3c5d5a9b70e621da8723 100644 --- a/tasks/0116_553_116553968_qa_5/task.toml +++ b/tasks/0116_553_116553968_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0116_553_116553968_qa_5" +name = "smoldataenvs-train/0116_553_116553968_qa_5" description = "What is the F1-score for malignant tumors (class M) in the classification report of the final model trained with k=14 features?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.96" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0116_564_116564780_qa_4/task.toml b/tasks/0116_564_116564780_qa_4/task.toml index 0991fb68b64a9af0fafd0aa86a4fd90d9929f4f8..4cb21de1a112715bc382d8230f32258dbeec9823 100644 --- a/tasks/0116_564_116564780_qa_4/task.toml +++ b/tasks/0116_564_116564780_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0116_564_116564780_qa_4" +name = "smoldataenvs-train/0116_564_116564780_qa_4" description = "How many features in the dataset have a positive correlation of at least 0.2 with the wine quality label?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0116_621_116621192_qa_3/task.toml b/tasks/0116_621_116621192_qa_3/task.toml index a71294ec7cc93247514c9335621824dd69f9324f..1e4be978b587fcfb135be7ad66973fc7c9493675 100644 --- a/tasks/0116_621_116621192_qa_3/task.toml +++ b/tasks/0116_621_116621192_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0116_621_116621192_qa_3" +name = "smoldataenvs-train/0116_621_116621192_qa_3" description = "What is the median global sales value (in millions) for video games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.17" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0116_621_116621192_qa_4/task.toml b/tasks/0116_621_116621192_qa_4/task.toml index 48e564d30e32f02ee9f6fc968d0d809d6fe719e8..cba8a23780079a8cb80948d97372340cb5821cd5 100644 --- a/tasks/0116_621_116621192_qa_4/task.toml +++ b/tasks/0116_621_116621192_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0116_621_116621192_qa_4" +name = "smoldataenvs-train/0116_621_116621192_qa_4" description = "Which video game genre has the second-highest number of titles in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Sports" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0116_621_116621192_qa_5/task.toml b/tasks/0116_621_116621192_qa_5/task.toml index adffea39dbdcbf52160e69920568baed4c23bd3b..fb5f355fe388978d430f20f96da8352bcc6ba7bd 100644 --- a/tasks/0116_621_116621192_qa_5/task.toml +++ b/tasks/0116_621_116621192_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0116_621_116621192_qa_5" +name = "smoldataenvs-train/0116_621_116621192_qa_5" description = "What is the total number of video game entries recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16598" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0116_643_116643019_qa_4/task.toml b/tasks/0116_643_116643019_qa_4/task.toml index cd42532a9922b965704541314e1aca86a8dbd6a4..4975b69f2f8c101561289f9158baf68c55de2b58 100644 --- a/tasks/0116_643_116643019_qa_4/task.toml +++ b/tasks/0116_643_116643019_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0116_643_116643019_qa_4" +name = "smoldataenvs-train/0116_643_116643019_qa_4" description = "Does the video game sales dataset contain any rows where all columns except 'Rank' and 'Name' are null?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0116_897_116897141_qa_3/task.toml b/tasks/0116_897_116897141_qa_3/task.toml index a522728e4c43256854b790cc8016a3d02550a018..b403edffeed54a8f0f8acae40b9f86228d9a8453 100644 --- a/tasks/0116_897_116897141_qa_3/task.toml +++ b/tasks/0116_897_116897141_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0116_897_116897141_qa_3" +name = "smoldataenvs-train/0116_897_116897141_qa_3" description = "What was the number of missing values present in the 'total_bedrooms' column before data cleaning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "207" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0116_898_116898391_qa_3/task.toml b/tasks/0116_898_116898391_qa_3/task.toml index ec50051ccef85b83355688104da790ca582af155..b1f8bfc114d52fa0bef06ca13f6c95528eef92b6 100644 --- a/tasks/0116_898_116898391_qa_3/task.toml +++ b/tasks/0116_898_116898391_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0116_898_116898391_qa_3" +name = "smoldataenvs-train/0116_898_116898391_qa_3" description = "What is the average house price in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1232073.0" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0116_967_116967370_qa_4/task.toml b/tasks/0116_967_116967370_qa_4/task.toml index e735ea0cb3ec8a9d7c370cdbd1eacbe47267e24a..80ee5687e13c69a81a141ed7bb1821297f24b6a2 100644 --- a/tasks/0116_967_116967370_qa_4/task.toml +++ b/tasks/0116_967_116967370_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0116_967_116967370_qa_4" +name = "smoldataenvs-train/0116_967_116967370_qa_4" description = "What is the adjusted R-squared value of the regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.955" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0116_967_116967370_qa_5/task.toml b/tasks/0116_967_116967370_qa_5/task.toml index f5f21e5342a98bedb959465294563c42ad759453..75661e403e5334f5dbd857046a36b143eb42c2f7 100644 --- a/tasks/0116_967_116967370_qa_5/task.toml +++ b/tasks/0116_967_116967370_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0116_967_116967370_qa_5" +name = "smoldataenvs-train/0116_967_116967370_qa_5" description = "What is the p-value for the overall significance of the regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.14e-20" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0116_992_116992800_qa_2/task.toml b/tasks/0116_992_116992800_qa_2/task.toml index 89e82d46a12b36050794869ebfaa10882272d1e6..10cd8c7b97e5d9d397f8e07a5680fb91e0c9086b 100644 --- a/tasks/0116_992_116992800_qa_2/task.toml +++ b/tasks/0116_992_116992800_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0116_992_116992800_qa_2" +name = "smoldataenvs-train/0116_992_116992800_qa_2" description = "Does the mushroom dataset contain any missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0117_122_117122383_qa_2/task.toml b/tasks/0117_122_117122383_qa_2/task.toml index 1d9363841563a434595c01de92e0c73caa80f249..136c78b5d2f9e7fea0297411a1c7147d65203b82 100644 --- a/tasks/0117_122_117122383_qa_2/task.toml +++ b/tasks/0117_122_117122383_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0117_122_117122383_qa_2" +name = "smoldataenvs-train/0117_122_117122383_qa_2" description = "What is the correlation coefficient between the number of previous contacts and the number of contacts during the campaign (campaign vs previous) as observed in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.507272" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0117_137_117137785_qa_2/task.toml b/tasks/0117_137_117137785_qa_2/task.toml index 8b183c488f107be321ec2e71fdf33574b2788d25..3ca2a1513add806ef63527b8c2c35d13b4a8808c 100644 --- a/tasks/0117_137_117137785_qa_2/task.toml +++ b/tasks/0117_137_117137785_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0117_137_117137785_qa_2" +name = "smoldataenvs-train/0117_137_117137785_qa_2" description = "After imputation, how many missing values remain in the numerical columns of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0117_137_117137785_qa_5/task.toml b/tasks/0117_137_117137785_qa_5/task.toml index 5a9123f44e88d107567e7a995c654487bc6b744b..a10a723e77e46b56da91ec5d9659b34df95097f6 100644 --- a/tasks/0117_137_117137785_qa_5/task.toml +++ b/tasks/0117_137_117137785_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0117_137_117137785_qa_5" +name = "smoldataenvs-train/0117_137_117137785_qa_5" description = "After encoding, how many unique categories does the 'appetite' feature have?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0117_445_117445192_qa_4/task.toml b/tasks/0117_445_117445192_qa_4/task.toml index f7217d0be4724d5191d5e81e8a0eb39a40527106..4f1a86fb24b27780dfd58a2ec97e2d1f769f5314 100644 --- a/tasks/0117_445_117445192_qa_4/task.toml +++ b/tasks/0117_445_117445192_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0117_445_117445192_qa_4" +name = "smoldataenvs-train/0117_445_117445192_qa_4" description = "Is the number of games published by EA significantly higher than the second-highest publisher in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0117_445_117445192_qa_5/task.toml b/tasks/0117_445_117445192_qa_5/task.toml index 393ac9f82dfc334e311aa92bc1e27c4a3476ed6f..58bdcb12145fbf133e5f92099c41e63d173254e1 100644 --- a/tasks/0117_445_117445192_qa_5/task.toml +++ b/tasks/0117_445_117445192_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0117_445_117445192_qa_5" +name = "smoldataenvs-train/0117_445_117445192_qa_5" description = "How many missing values were present in the Year column before imputation in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "271" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0117_519_117519892_qa_1/task.toml b/tasks/0117_519_117519892_qa_1/task.toml index b8d4b2660db5e4667782219bba78778ae2c8b020..7c66cfd40e77208c609022545feea5be00c2c2d1 100644 --- a/tasks/0117_519_117519892_qa_1/task.toml +++ b/tasks/0117_519_117519892_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0117_519_117519892_qa_1" +name = "smoldataenvs-train/0117_519_117519892_qa_1" description = "What is the total number of games in the Action genre according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3316" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0117_519_117519892_qa_4/task.toml b/tasks/0117_519_117519892_qa_4/task.toml index be83d4a5220e3792ef2d6c75927f5edc8eb86f9b..9645f0bfe26ca81eed49175681f070f9b7708aad 100644 --- a/tasks/0117_519_117519892_qa_4/task.toml +++ b/tasks/0117_519_117519892_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0117_519_117519892_qa_4" +name = "smoldataenvs-train/0117_519_117519892_qa_4" description = "What is the total global sales of the top-selling game, and what is the game's name?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii Sports, 82.74" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0117_571_117571850_qa_1/task.toml b/tasks/0117_571_117571850_qa_1/task.toml index 743c499ac45aadf645ae0fc7fd0a94ce26b4e60b..6778936beb570c4fa07ec197ec3320e5659627aa 100644 --- a/tasks/0117_571_117571850_qa_1/task.toml +++ b/tasks/0117_571_117571850_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0117_571_117571850_qa_1" +name = "smoldataenvs-train/0117_571_117571850_qa_1" description = "Which two features in the dataset exhibit the strongest correlation with each other, and what is the value of this correlation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "perimeter_mean, radius_mean, 0.997855" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0117_594_117594280_qa_2/task.toml b/tasks/0117_594_117594280_qa_2/task.toml index 4aec8e151c06c8a978957940992175802052c013..ae1ea3a9328e37485d1371e67138955886226053 100644 --- a/tasks/0117_594_117594280_qa_2/task.toml +++ b/tasks/0117_594_117594280_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0117_594_117594280_qa_2" +name = "smoldataenvs-train/0117_594_117594280_qa_2" description = "What is the F1-score for the positive sentiment class (1) in the TF-IDF model's classification report?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.76" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0117_608_117608671_qa_4/task.toml b/tasks/0117_608_117608671_qa_4/task.toml index 2ccfb724a40c1696f88473610003ad48e3bed95d..31e771b4241b0f733bb6b7aefe6c6fdf881dea9e 100644 --- a/tasks/0117_608_117608671_qa_4/task.toml +++ b/tasks/0117_608_117608671_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0117_608_117608671_qa_4" +name = "smoldataenvs-train/0117_608_117608671_qa_4" description = "What is the correlation coefficient between the average number of rooms (RM) and the median house value (MEDV)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.7" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0117_612_117612368_qa_1/task.toml b/tasks/0117_612_117612368_qa_1/task.toml index 9869e9339d7419bf186ac313872c87fc4b86d14b..fa507dc4ebd0690d0cd4424abdf0662b6318ca29 100644 --- a/tasks/0117_612_117612368_qa_1/task.toml +++ b/tasks/0117_612_117612368_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0117_612_117612368_qa_1" +name = "smoldataenvs-train/0117_612_117612368_qa_1" description = "What is the class distribution of the original dataset before applying the resampling technique?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Ham: 4825, Spam: 747" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0117_621_117621488_qa_2/task.toml b/tasks/0117_621_117621488_qa_2/task.toml index 137aa61a074d6358428e3204a6e634971c9b43bd..b6a6b42d846d402eb133ebd883f9a9d15798665d 100644 --- a/tasks/0117_621_117621488_qa_2/task.toml +++ b/tasks/0117_621_117621488_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0117_621_117621488_qa_2" +name = "smoldataenvs-train/0117_621_117621488_qa_2" description = "What percentage of the dataset corresponds to malignant (M) tumor diagnoses?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "37.26" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0117_642_117642587_qa_1/task.toml b/tasks/0117_642_117642587_qa_1/task.toml index 9f1f863e05de331a87ab28078e9f75347330b7c9..b8675982e151c25d2e1a2322df5a2973035e4ebf 100644 --- a/tasks/0117_642_117642587_qa_1/task.toml +++ b/tasks/0117_642_117642587_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0117_642_117642587_qa_1" +name = "smoldataenvs-train/0117_642_117642587_qa_1" description = "What is the correlation coefficient between TotalWorkingYears and MonthlyIncome in the dataset before dropping the JobLevel feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.77" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0117_642_117642587_qa_4/task.toml b/tasks/0117_642_117642587_qa_4/task.toml index 6aa4944e44780bad4fdba2871fd7ce3f9a34093c..bdb971cc37ab8c9c9a4a425a53fdd7a07876436c 100644 --- a/tasks/0117_642_117642587_qa_4/task.toml +++ b/tasks/0117_642_117642587_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0117_642_117642587_qa_4" +name = "smoldataenvs-train/0117_642_117642587_qa_4" description = "What is the correlation coefficient between PerformanceRating and PercentSalaryHike in the dataset before dropping the JobLevel feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.77" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0117_642_117642587_qa_5/task.toml b/tasks/0117_642_117642587_qa_5/task.toml index f1bd58b4dc69648c72faf1875179093df09f6c4a..2e1962dd881b71003f036878f383b0f88a0ddeda 100644 --- a/tasks/0117_642_117642587_qa_5/task.toml +++ b/tasks/0117_642_117642587_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0117_642_117642587_qa_5" +name = "smoldataenvs-train/0117_642_117642587_qa_5" description = "After applying SMOTE oversampling, what is the balanced count of attrition and non-attrition instances in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1233" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0117_685_117685347_qa_2/task.toml b/tasks/0117_685_117685347_qa_2/task.toml index aed3d299517e32fa9e00ed4b61825b2892cdf342..11315f32700690f42d715ab5066052d0a4fc0060 100644 --- a/tasks/0117_685_117685347_qa_2/task.toml +++ b/tasks/0117_685_117685347_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0117_685_117685347_qa_2" +name = "smoldataenvs-train/0117_685_117685347_qa_2" description = "What is the interquartile range (IQR) for citric acid content in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.33" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0117_685_117685347_qa_4/task.toml b/tasks/0117_685_117685347_qa_4/task.toml index e7b7286197aa8d1e33f508789d4d99591aaa7586..5dd62267c10e1d5f12ea648f1d4bd53f390d6908 100644 --- a/tasks/0117_685_117685347_qa_4/task.toml +++ b/tasks/0117_685_117685347_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0117_685_117685347_qa_4" +name = "smoldataenvs-train/0117_685_117685347_qa_4" description = "What is the correlation threshold used to identify redundant features in the heatmap analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.7" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0117_815_117815262_qa_1/task.toml b/tasks/0117_815_117815262_qa_1/task.toml index b7b4e5f03c5f1aa7304ab7911545a64701d37523..2e77947b3098f0cdbeb369faebc162113b7e8e5a 100644 --- a/tasks/0117_815_117815262_qa_1/task.toml +++ b/tasks/0117_815_117815262_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0117_815_117815262_qa_1" +name = "smoldataenvs-train/0117_815_117815262_qa_1" description = "What is the total number of Business trips compared to Personal trips in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Business: 1078 trips, Personal: 77 trips" reward_mode_initial = "flexible" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0117_905_117905284_qa_1/task.toml b/tasks/0117_905_117905284_qa_1/task.toml index a0526000ad4bd37ec2e947d585fff138bb7341b4..c67b7baa3fcb001431d1d86e200326d9c89da9b8 100644 --- a/tasks/0117_905_117905284_qa_1/task.toml +++ b/tasks/0117_905_117905284_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0117_905_117905284_qa_1" +name = "smoldataenvs-train/0117_905_117905284_qa_1" description = "What product combination results in the highest customer retention rate according to the clustering analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "No internet service with phone service" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0117_907_117907208_qa_2/task.toml b/tasks/0117_907_117907208_qa_2/task.toml index 59dd163f67ae0883da1d730e744c4cec1ca6f502..6c002105a8cfc8b9804ef4342bebed41c04d2c2c 100644 --- a/tasks/0117_907_117907208_qa_2/task.toml +++ b/tasks/0117_907_117907208_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0117_907_117907208_qa_2" +name = "smoldataenvs-train/0117_907_117907208_qa_2" description = "How many benign tumors (diagnosis_M = 0) are present in the original dataset before any data transformations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "357" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0117_949_117949173_qa_2/task.toml b/tasks/0117_949_117949173_qa_2/task.toml index 6702b73cf6a0a09c9830d8a89bf75144a1219b46..57a7a501814a92512711df696bfec794976b1589 100644 --- a/tasks/0117_949_117949173_qa_2/task.toml +++ b/tasks/0117_949_117949173_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0117_949_117949173_qa_2" +name = "smoldataenvs-train/0117_949_117949173_qa_2" description = "Which marital status category has the highest proportion of individuals earning more than $50K annually?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Married-civ-spouse" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0118_014_118014024_qa_1/task.toml b/tasks/0118_014_118014024_qa_1/task.toml index 680657c89a4c244fc9eb5a41b597147d77090fc2..6f062b2ae613902e2cd248a4b64f710cc3cebeeb 100644 --- a/tasks/0118_014_118014024_qa_1/task.toml +++ b/tasks/0118_014_118014024_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0118_014_118014024_qa_1" +name = "smoldataenvs-train/0118_014_118014024_qa_1" description = "What is the average age of patients in the Pima Indians Diabetes dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "33.24" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0118_014_118014024_qa_3/task.toml b/tasks/0118_014_118014024_qa_3/task.toml index f77aec48f44b4303ca11071185f0df7ac1bf685d..c66a7b61afe8900eea6ec78dd35631df08ef2c12 100644 --- a/tasks/0118_014_118014024_qa_3/task.toml +++ b/tasks/0118_014_118014024_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0118_014_118014024_qa_3" +name = "smoldataenvs-train/0118_014_118014024_qa_3" description = "What is the variance of the BloodPressure feature in the original dataset before any standardization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "374.16" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0118_014_118014024_qa_4/task.toml b/tasks/0118_014_118014024_qa_4/task.toml index 30085e74f77e583bcfbf24222bfc5fc38f49ff2f..35919fda1cdc648b7a9b8a78a9c6d9ea773c81d0 100644 --- a/tasks/0118_014_118014024_qa_4/task.toml +++ b/tasks/0118_014_118014024_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0118_014_118014024_qa_4" +name = "smoldataenvs-train/0118_014_118014024_qa_4" description = "What is the median BMI value in the Pima Indians Diabetes dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "32.0" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0118_032_118032627_qa_1/task.toml b/tasks/0118_032_118032627_qa_1/task.toml index b45c96a75c81b8c067f5a24a234b2455cf816fce..c9289ac2e8442bf861387f96da34a9a9a4b1ce4d 100644 --- a/tasks/0118_032_118032627_qa_1/task.toml +++ b/tasks/0118_032_118032627_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0118_032_118032627_qa_1" +name = "smoldataenvs-train/0118_032_118032627_qa_1" description = "What is the most frequently used ingredient across all recipes in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "salt" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0118_088_118088123_qa_1/task.toml b/tasks/0118_088_118088123_qa_1/task.toml index f8f627725853d3bf3c7e90fcb35c27762f7ed245..6d9e06a09ca2fe80e73f128e388271d2cb8a2d94 100644 --- a/tasks/0118_088_118088123_qa_1/task.toml +++ b/tasks/0118_088_118088123_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0118_088_118088123_qa_1" +name = "smoldataenvs-train/0118_088_118088123_qa_1" description = "What percentage of the dataset consists of wines with the highest quality rating of 8?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.125704" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0118_265_118265412_qa_5/task.toml b/tasks/0118_265_118265412_qa_5/task.toml index 523463d081f26841736e403e408ecee6a0a1d72d..fa1492265bb85a10709b65a877742210cdf72e5d 100644 --- a/tasks/0118_265_118265412_qa_5/task.toml +++ b/tasks/0118_265_118265412_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0118_265_118265412_qa_5" +name = "smoldataenvs-train/0118_265_118265412_qa_5" description = "What is the average perimeter_worst measurement for malignant tumors in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "141.37" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0118_360_118360203_qa_1/task.toml b/tasks/0118_360_118360203_qa_1/task.toml index 0570441d3872dc8da55e8a89c848a84c6e7fa608..bebf93beba9f767bed47714b2b8bf6be7b368eea 100644 --- a/tasks/0118_360_118360203_qa_1/task.toml +++ b/tasks/0118_360_118360203_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0118_360_118360203_qa_1" +name = "smoldataenvs-train/0118_360_118360203_qa_1" description = "What is the highest battery power capacity recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1998" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0118_360_118360203_qa_5/task.toml b/tasks/0118_360_118360203_qa_5/task.toml index 88ccab476b0b2d6d946e07cbe529c16068084b51..08a520d5cf8f2e0b0600ed80b35292feb8dfd78c 100644 --- a/tasks/0118_360_118360203_qa_5/task.toml +++ b/tasks/0118_360_118360203_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0118_360_118360203_qa_5" +name = "smoldataenvs-train/0118_360_118360203_qa_5" description = "What is the maximum pixel height value observed in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1960" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0118_428_118428597_qa_3/task.toml b/tasks/0118_428_118428597_qa_3/task.toml index 6a08380a41f0220d8e76538cf657045f04c7f153..70c19ddc46421ba166b4b2606fce920d86a59cbd 100644 --- a/tasks/0118_428_118428597_qa_3/task.toml +++ b/tasks/0118_428_118428597_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0118_428_118428597_qa_3" +name = "smoldataenvs-train/0118_428_118428597_qa_3" description = "Which publisher has the largest number of games listed in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic Arts" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0118_567_118567628_qa_5/task.toml b/tasks/0118_567_118567628_qa_5/task.toml index 1d70dfba1d50c58207b9df423e2a9de0ca168526..3554f2f939067502d9a2ddacdcb9509b5706802f 100644 --- a/tasks/0118_567_118567628_qa_5/task.toml +++ b/tasks/0118_567_118567628_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0118_567_118567628_qa_5" +name = "smoldataenvs-train/0118_567_118567628_qa_5" description = "What is the average Item_Outlet_Sales across all outlets in the training data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2181.288914" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0118_595_118595576_qa_5/task.toml b/tasks/0118_595_118595576_qa_5/task.toml index 0071faa03eccacf811d0d8d55e151826feed61fc..5fa549ea605dc9edb722b9ce0b0d57e14813eb04 100644 --- a/tasks/0118_595_118595576_qa_5/task.toml +++ b/tasks/0118_595_118595576_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0118_595_118595576_qa_5" +name = "smoldataenvs-train/0118_595_118595576_qa_5" description = "What is the standard deviation of global sales in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.5550279355699124" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0118_607_118607538_qa_2/task.toml b/tasks/0118_607_118607538_qa_2/task.toml index 41ca10b361236c5efeeebd19b7550c6da2723f58..75a21d9c3ce4f91c123eae0e4660fc77e190b3cc 100644 --- a/tasks/0118_607_118607538_qa_2/task.toml +++ b/tasks/0118_607_118607538_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0118_607_118607538_qa_2" +name = "smoldataenvs-train/0118_607_118607538_qa_2" description = "How many benign and malignant tumors were present in the dataset based on the encoded diagnosis labels?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "357 benign, 212 malignant" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0118_642_118642840_qa_3/task.toml b/tasks/0118_642_118642840_qa_3/task.toml index c651e6814235919bc60918314cbec13cb0146748..4e9e004e4a5b110dac5539cc130e855438da9215 100644 --- a/tasks/0118_642_118642840_qa_3/task.toml +++ b/tasks/0118_642_118642840_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0118_642_118642840_qa_3" +name = "smoldataenvs-train/0118_642_118642840_qa_3" description = "What is the standard deviation of the global sales values in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.5550279355699124" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0118_642_118642840_qa_5/task.toml b/tasks/0118_642_118642840_qa_5/task.toml index 9b743d7326eca81d234e04ac7b7bcf5e22a41cd0..2c5f542093e3ab4eb40aac1df6b76b04a0c401f0 100644 --- a/tasks/0118_642_118642840_qa_5/task.toml +++ b/tasks/0118_642_118642840_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0118_642_118642840_qa_5" +name = "smoldataenvs-train/0118_642_118642840_qa_5" description = "After adding 10 to each global sales value, what is the new global sales value for the top-selling game?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "92.74" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0118_722_118722407_qa_2/task.toml b/tasks/0118_722_118722407_qa_2/task.toml index 8bfbba9945d47ac5081a5f33212486d6c134f8f9..7d1327a7256adac0054719ba6bd03dfcbfe6b605 100644 --- a/tasks/0118_722_118722407_qa_2/task.toml +++ b/tasks/0118_722_118722407_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0118_722_118722407_qa_2" +name = "smoldataenvs-train/0118_722_118722407_qa_2" description = "Are all canceled orders in the dataset identified by InvoiceNo starting with 'C' and having a length of 7 characters, based on the hypothesis testing results?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0118_899_118899556_qa_4/task.toml b/tasks/0118_899_118899556_qa_4/task.toml index 2348022446da85799e6af07221453133db082196..7581cf5b935048fea52cf85142b342071d14bd26 100644 --- a/tasks/0118_899_118899556_qa_4/task.toml +++ b/tasks/0118_899_118899556_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0118_899_118899556_qa_4" +name = "smoldataenvs-train/0118_899_118899556_qa_4" description = "What is the standard deviation of the 'ram' feature in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1084.73" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0118_899_118899556_qa_5/task.toml b/tasks/0118_899_118899556_qa_5/task.toml index 016e20fa6b8ee764971e5e3ad42fb6b14a954c2f..76dc25f2942ab9d542feec29a5bde58a75db013d 100644 --- a/tasks/0118_899_118899556_qa_5/task.toml +++ b/tasks/0118_899_118899556_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0118_899_118899556_qa_5" +name = "smoldataenvs-train/0118_899_118899556_qa_5" description = "What is the optimal var_smoothing parameter for the Gaussian Naive Bayes model after hyperparameter tuning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.0001" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0118_918_118918945_qa_2/task.toml b/tasks/0118_918_118918945_qa_2/task.toml index f94c7dae920418313ffdbedb80600f6a2be94d54..80b577525eae6e4605557c03b36ec26cb9b2157b 100644 --- a/tasks/0118_918_118918945_qa_2/task.toml +++ b/tasks/0118_918_118918945_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0118_918_118918945_qa_2" +name = "smoldataenvs-train/0118_918_118918945_qa_2" description = "What is the interquartile range (IQR) of the 'age' variable in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "24.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0118_978_118978270_qa_3/task.toml b/tasks/0118_978_118978270_qa_3/task.toml index 6dcd04078c656c7b910fa775d81986a46064c86d..5f03180b6602d9d08f88006846f9282747240f1e 100644 --- a/tasks/0118_978_118978270_qa_3/task.toml +++ b/tasks/0118_978_118978270_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0118_978_118978270_qa_3" +name = "smoldataenvs-train/0118_978_118978270_qa_3" description = "Which feature was identified as most important by the random forest classifier for predicting diabetes outcomes?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0119_001_119001162_qa_4/task.toml b/tasks/0119_001_119001162_qa_4/task.toml index a9a64e22505d3f45da97d64583d002573d5c2336..248877e22c9c9f820af8a6f582876efc0c012403 100644 --- a/tasks/0119_001_119001162_qa_4/task.toml +++ b/tasks/0119_001_119001162_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0119_001_119001162_qa_4" +name = "smoldataenvs-train/0119_001_119001162_qa_4" description = "What marital status category has the highest proportion of individuals with income greater than 50K?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Married-civ-spouse" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0119_018_119018113_qa_1/task.toml b/tasks/0119_018_119018113_qa_1/task.toml index 94c5eaa5316892f51812e8d9a7db52240a2e3722..df5ab8a8bd6324995081919046318f50fa7f06b9 100644 --- a/tasks/0119_018_119018113_qa_1/task.toml +++ b/tasks/0119_018_119018113_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0119_018_119018113_qa_1" +name = "smoldataenvs-train/0119_018_119018113_qa_1" description = "What is the difference between the maximum and minimum global sales values in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "82.73" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0119_018_119018113_qa_5/task.toml b/tasks/0119_018_119018113_qa_5/task.toml index 8132971ebc61135961a20450b191f1f512538795..b48824109fa900cfc6b5ce1f67a727b3bd548c96 100644 --- a/tasks/0119_018_119018113_qa_5/task.toml +++ b/tasks/0119_018_119018113_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0119_018_119018113_qa_5" +name = "smoldataenvs-train/0119_018_119018113_qa_5" description = "What is the total number of games listed in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16598" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0119_037_119037763_qa_4/task.toml b/tasks/0119_037_119037763_qa_4/task.toml index 7aeee6d7c88d41a6466e70e4dcafa786828290b2..34e7c21000f255357a80626e5d89613559d24f2a 100644 --- a/tasks/0119_037_119037763_qa_4/task.toml +++ b/tasks/0119_037_119037763_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0119_037_119037763_qa_4" +name = "smoldataenvs-train/0119_037_119037763_qa_4" description = "Which digit class has the highest number of samples in the test set according to the classification report?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0119_045_119045887_qa_3/task.toml b/tasks/0119_045_119045887_qa_3/task.toml index f057908a284a4a8ae5c325c05a7535de84dccf6b..3f89ebb3b0ad8c278adc4d3b6d8e16bcdb1165c3 100644 --- a/tasks/0119_045_119045887_qa_3/task.toml +++ b/tasks/0119_045_119045887_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0119_045_119045887_qa_3" +name = "smoldataenvs-train/0119_045_119045887_qa_3" description = "What is the highest ROC-AUC score achieved by the optimized logistic regression model during hyperparameter tuning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.990" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0119_178_119178142_qa_4/task.toml b/tasks/0119_178_119178142_qa_4/task.toml index 619ef25c8bb34d8763b0191231ab96ffafb7d0a3..96829a436166c7dd0eeecf9696a3c17fcf93063d 100644 --- a/tasks/0119_178_119178142_qa_4/task.toml +++ b/tasks/0119_178_119178142_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0119_178_119178142_qa_4" +name = "smoldataenvs-train/0119_178_119178142_qa_4" description = "What is the maximum recorded quality score in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0119_277_119277434_qa_4/task.toml b/tasks/0119_277_119277434_qa_4/task.toml index 1232a45f2dc11d93eddf234d8e54c9d3a3c64fc1..29861ce002155277c043eb1e1af926fd58c90911 100644 --- a/tasks/0119_277_119277434_qa_4/task.toml +++ b/tasks/0119_277_119277434_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0119_277_119277434_qa_4" +name = "smoldataenvs-train/0119_277_119277434_qa_4" description = "What is the average global sales value across all video games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.537441" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0119_277_119277434_qa_5/task.toml b/tasks/0119_277_119277434_qa_5/task.toml index 608602cb83cb33557bb9c73c8a9ad6746c9eadaf..931854a0850b921a3a3ca9d0bf43f8fc9a7790de 100644 --- a/tasks/0119_277_119277434_qa_5/task.toml +++ b/tasks/0119_277_119277434_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0119_277_119277434_qa_5" +name = "smoldataenvs-train/0119_277_119277434_qa_5" description = "How many entries in the dataset have missing values in the Year column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "271" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0119_289_119289330_qa_4/task.toml b/tasks/0119_289_119289330_qa_4/task.toml index d6c75c0de46cdf189b8a0871f1515cd224424976..31b04cf77158690505025e6e85811c4b7c5ccd25 100644 --- a/tasks/0119_289_119289330_qa_4/task.toml +++ b/tasks/0119_289_119289330_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0119_289_119289330_qa_4" +name = "smoldataenvs-train/0119_289_119289330_qa_4" description = "What is the mean value of the 'Item_MRP' column in the entire dataset before train/test splitting?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "140.99" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0119_313_119313422_qa_1/task.toml b/tasks/0119_313_119313422_qa_1/task.toml index 2974215b6e2c97a0d14d6e0e5d293ad32df8afb0..45f730287d8bcd763ff0a58256bcaab51f14433a 100644 --- a/tasks/0119_313_119313422_qa_1/task.toml +++ b/tasks/0119_313_119313422_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0119_313_119313422_qa_1" +name = "smoldataenvs-train/0119_313_119313422_qa_1" description = "What is the favorite color with the highest frequency among females in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Cool" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0119_313_119313422_qa_3/task.toml b/tasks/0119_313_119313422_qa_3/task.toml index 6fee65c33c7c8d5eec661937ba92f7d572b66e6b..36cd1bd88fd203c5aa20a78ba67bc86abdc1f244 100644 --- a/tasks/0119_313_119313422_qa_3/task.toml +++ b/tasks/0119_313_119313422_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0119_313_119313422_qa_3" +name = "smoldataenvs-train/0119_313_119313422_qa_3" description = "What is the percentage of females who prefer Coca Cola/Pepsi as their favorite soft drink?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "51.5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0119_484_119484635_qa_2/task.toml b/tasks/0119_484_119484635_qa_2/task.toml index 42a6684fe52245f2e538003f945f4d3d30c3cdc3..1c98d0508ff8febc4e3981ee74c4afe8411f7c65 100644 --- a/tasks/0119_484_119484635_qa_2/task.toml +++ b/tasks/0119_484_119484635_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0119_484_119484635_qa_2" +name = "smoldataenvs-train/0119_484_119484635_qa_2" description = "After applying SMOTE oversampling, how many instances does each wine quality class contain in the resampled dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "680" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0119_484_119484635_qa_5/task.toml b/tasks/0119_484_119484635_qa_5/task.toml index dfa1b24c7f9b9a64dea95b939d3d7ca33e1e6cc3..9c44fb2ac927ad15afe6482f0e95c882ad1bbdfe 100644 --- a/tasks/0119_484_119484635_qa_5/task.toml +++ b/tasks/0119_484_119484635_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0119_484_119484635_qa_5" +name = "smoldataenvs-train/0119_484_119484635_qa_5" description = "What is the mean density value for wines classified with the lowest quality rating (quality 3)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.997464" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0119_554_119554814_qa_1/task.toml b/tasks/0119_554_119554814_qa_1/task.toml index 7cb8c2b6b18a75bdb36eac670e1a3f78664c0143..eb19efecdd49a7fe5371f8767acf20afde9fb9e0 100644 --- a/tasks/0119_554_119554814_qa_1/task.toml +++ b/tasks/0119_554_119554814_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0119_554_119554814_qa_1" +name = "smoldataenvs-train/0119_554_119554814_qa_1" description = "Which contract type has the highest churn rate based on the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Month-to-month" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0119_554_119554814_qa_4/task.toml b/tasks/0119_554_119554814_qa_4/task.toml index 7b1ef70739c4e47319d3a7f85158f9d9f3703e94..60ceab281aabfc6f018a8c71f910558d8d576701 100644 --- a/tasks/0119_554_119554814_qa_4/task.toml +++ b/tasks/0119_554_119554814_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0119_554_119554814_qa_4" +name = "smoldataenvs-train/0119_554_119554814_qa_4" description = "What payment method has the highest churn rate according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic check" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0119_557_119557726_qa_5/task.toml b/tasks/0119_557_119557726_qa_5/task.toml index bf41e210e249018d9d36c182eae0f3b6452b93f3..df7edd2ff96ec87b096f9e2f5c6265f34048a317 100644 --- a/tasks/0119_557_119557726_qa_5/task.toml +++ b/tasks/0119_557_119557726_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0119_557_119557726_qa_5" +name = "smoldataenvs-train/0119_557_119557726_qa_5" description = "Which imputation method provided the highest cross-validated R-squared score for the K-Neighbors Regressor?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iterative Imputation" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0119_585_119585532_qa_5/task.toml b/tasks/0119_585_119585532_qa_5/task.toml index aa86a9229ee767debe6369e9944955b38c24ceca..1b3fd1a3fcf7c06a1ac25377d82713fe06a0f9a6 100644 --- a/tasks/0119_585_119585532_qa_5/task.toml +++ b/tasks/0119_585_119585532_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0119_585_119585532_qa_5" +name = "smoldataenvs-train/0119_585_119585532_qa_5" description = "Which clarity grade has the highest frequency in the original dataset before any data transformations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "SI1" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0119_590_119590030_qa_2/task.toml b/tasks/0119_590_119590030_qa_2/task.toml index f1f0d52a5b5bd234222b755c3072ef4615af854d..85f804aedc946d8aff190ca51dccae9dd02969e2 100644 --- a/tasks/0119_590_119590030_qa_2/task.toml +++ b/tasks/0119_590_119590030_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0119_590_119590030_qa_2" +name = "smoldataenvs-train/0119_590_119590030_qa_2" description = "What is the maximum accuracy achieved in any of the 5 cross-validation folds?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0119_629_119629452_qa_1/task.toml b/tasks/0119_629_119629452_qa_1/task.toml index 83faffad36e3b6062ec84a3e702b233cfbdb8da9..65bc03863d656dc3706d4af656688f89b24b38dc 100644 --- a/tasks/0119_629_119629452_qa_1/task.toml +++ b/tasks/0119_629_119629452_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0119_629_119629452_qa_1" +name = "smoldataenvs-train/0119_629_119629452_qa_1" description = "Which animal class in the dataset has the highest number of species represented?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Mammal" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0119_629_119629452_qa_4/task.toml b/tasks/0119_629_119629452_qa_4/task.toml index 8624c6c5809514d88670153de703b27de46f810d..1c2b75382fa4c86e40e2f0bfa5a30fb95ebd5313 100644 --- a/tasks/0119_629_119629452_qa_4/task.toml +++ b/tasks/0119_629_119629452_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0119_629_119629452_qa_4" +name = "smoldataenvs-train/0119_629_119629452_qa_4" description = "Which class has the lowest number of animal species in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Amphibian" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0119_653_119653509_qa_1/task.toml b/tasks/0119_653_119653509_qa_1/task.toml index 0bf5b9ed6217857f4346d4f9077972fa3ebe1520..4966bd16cdc9a06a1811ff06ba277de80e75c9dd 100644 --- a/tasks/0119_653_119653509_qa_1/task.toml +++ b/tasks/0119_653_119653509_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0119_653_119653509_qa_1" +name = "smoldataenvs-train/0119_653_119653509_qa_1" description = "What are the three features most strongly positively correlated with house prices in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "sqft_living, grade, sqft_above" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0119_653_119653509_qa_2/task.toml b/tasks/0119_653_119653509_qa_2/task.toml index 8d4c1d7af7a1063d07c3b692f2fbfa543df6c8e0..4460424f1cbf80a6d95b621c0d51dd22d182d147 100644 --- a/tasks/0119_653_119653509_qa_2/task.toml +++ b/tasks/0119_653_119653509_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0119_653_119653509_qa_2" +name = "smoldataenvs-train/0119_653_119653509_qa_2" description = "What is the median price of houses in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "450000" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0119_653_119653509_qa_5/task.toml b/tasks/0119_653_119653509_qa_5/task.toml index 8a20b9a952fc5a5c68ee17954bdb3ab351f8d064..4ca0eb4e7ba2c9c8834c9fe6a96edeb7b983d695 100644 --- a/tasks/0119_653_119653509_qa_5/task.toml +++ b/tasks/0119_653_119653509_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0119_653_119653509_qa_5" +name = "smoldataenvs-train/0119_653_119653509_qa_5" description = "What is the range of values for the 'sqft_living' feature in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13250" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0119_666_119666168_qa_2/task.toml b/tasks/0119_666_119666168_qa_2/task.toml index b5c4b8f304df98e310f20164598e03dd93bc9689..6b79f5aa70d8636a735fc5a65ca5b6ccab0893c8 100644 --- a/tasks/0119_666_119666168_qa_2/task.toml +++ b/tasks/0119_666_119666168_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0119_666_119666168_qa_2" +name = "smoldataenvs-train/0119_666_119666168_qa_2" description = "What is the highest positive correlation coefficient between any feature and wine quality in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.48" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0119_760_119760174_qa_3/task.toml b/tasks/0119_760_119760174_qa_3/task.toml index 68b8654e8d4989571ed8f0a1ebfc26c76d536a8f..15162f222d136dd5be34bec53703a9ec89f9dcad 100644 --- a/tasks/0119_760_119760174_qa_3/task.toml +++ b/tasks/0119_760_119760174_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0119_760_119760174_qa_3" +name = "smoldataenvs-train/0119_760_119760174_qa_3" description = "What is the average number of children among the policyholders in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.094918" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0119_768_119768016_qa_3/task.toml b/tasks/0119_768_119768016_qa_3/task.toml index 1ba3b934ee93484af7cff59dd529cd06ca846a51..dd7bc6895b458e7ea016b27072737cfe2619b398 100644 --- a/tasks/0119_768_119768016_qa_3/task.toml +++ b/tasks/0119_768_119768016_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0119_768_119768016_qa_3" +name = "smoldataenvs-train/0119_768_119768016_qa_3" description = "Which payment method is most strongly associated with customer churn based on the EDA visualizations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic check" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0119_782_119782096_qa_2/task.toml b/tasks/0119_782_119782096_qa_2/task.toml index 48a886e73415329501ac27bafd14a6cf99653de8..fc52acc22da8860a8b5fe2e1ffbca8833a44a468 100644 --- a/tasks/0119_782_119782096_qa_2/task.toml +++ b/tasks/0119_782_119782096_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0119_782_119782096_qa_2" +name = "smoldataenvs-train/0119_782_119782096_qa_2" description = "How many outliers were removed from the Age column after applying the interquartile range (IQR) method during outlier handling?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "109" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0119_783_119783005_qa_5/task.toml b/tasks/0119_783_119783005_qa_5/task.toml index cc9e5bda77c84e1d24b771f3e0d452a004da5542..add593b71478ac2b5665bf38bb614d67e0c98c5b 100644 --- a/tasks/0119_783_119783005_qa_5/task.toml +++ b/tasks/0119_783_119783005_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0119_783_119783005_qa_5" +name = "smoldataenvs-train/0119_783_119783005_qa_5" description = "After applying SMOTE to address class imbalance, how many total samples are present in the resampled dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "38628" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0119_894_119894753_qa_4/task.toml b/tasks/0119_894_119894753_qa_4/task.toml index 3c1163ba9a979ff7684c5fb6fd96f6749ba27694..3d0257365a602b0ac0588b4e24a63d6207fe5a42 100644 --- a/tasks/0119_894_119894753_qa_4/task.toml +++ b/tasks/0119_894_119894753_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0119_894_119894753_qa_4" +name = "smoldataenvs-train/0119_894_119894753_qa_4" description = "How many entries in the dataset contain missing values in the \"Year\" column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "271" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0119_896_119896768_qa_3/task.toml b/tasks/0119_896_119896768_qa_3/task.toml index a04a291799855ea248b8a60a12343ccd8bf767c9..436090fd80d6daf3025746d002efd0dd3c8a23fb 100644 --- a/tasks/0119_896_119896768_qa_3/task.toml +++ b/tasks/0119_896_119896768_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0119_896_119896768_qa_3" +name = "smoldataenvs-train/0119_896_119896768_qa_3" description = "What is the most common conservation status among species in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Species of Concern" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0119_907_119907848_qa_1/task.toml b/tasks/0119_907_119907848_qa_1/task.toml index a7a6552d273050b7f85ec393890e0227118cd184..5f90f36b383a3f9b09875f643a27b26546efe9b0 100644 --- a/tasks/0119_907_119907848_qa_1/task.toml +++ b/tasks/0119_907_119907848_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0119_907_119907848_qa_1" +name = "smoldataenvs-train/0119_907_119907848_qa_1" description = "What is the accuracy percentage of the logistic regression model on the test set after data preprocessing and scaling?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "97.66" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0119_926_119926274_qa_1/task.toml b/tasks/0119_926_119926274_qa_1/task.toml index 0af4f532861cec67e3e368ea8ff6e7547107bc0f..0c87f908987e1a72b4aff8bfd6b55ae1ebf55034 100644 --- a/tasks/0119_926_119926274_qa_1/task.toml +++ b/tasks/0119_926_119926274_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0119_926_119926274_qa_1" +name = "smoldataenvs-train/0119_926_119926274_qa_1" description = "What is the root mean square error (RMSE) of the linear regression model on the test data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "79085.18136772825" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0119_926_119926274_qa_5/task.toml b/tasks/0119_926_119926274_qa_5/task.toml index 4ddb5f47218f3df9ab347f10dad682c91374ab6d..942c363dfdd45c7c877c16e358329b8a9a89f396 100644 --- a/tasks/0119_926_119926274_qa_5/task.toml +++ b/tasks/0119_926_119926274_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0119_926_119926274_qa_5" +name = "smoldataenvs-train/0119_926_119926274_qa_5" description = "What is the shape of the matrix used in the manual prediction calculation for the first test sample?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "(1, 4)" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0119_931_119931935_qa_1/task.toml b/tasks/0119_931_119931935_qa_1/task.toml index cc30d687f79b630409f40e78fe8b8de24ab1851b..4d66f9942b1b6bf2337f30bc00b1fcaf739f93ff 100644 --- a/tasks/0119_931_119931935_qa_1/task.toml +++ b/tasks/0119_931_119931935_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0119_931_119931935_qa_1" +name = "smoldataenvs-train/0119_931_119931935_qa_1" description = "Which feature in the dataset has the highest positive correlation with the diabetes outcome (Outcome=1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0119_932_119932663_qa_2/task.toml b/tasks/0119_932_119932663_qa_2/task.toml index e2183de56a5377117cedbcd4d20ba6f7b9a6cb18..c2916c88f0dca7fe9aaaaf5347cf7a59e3f51eed 100644 --- a/tasks/0119_932_119932663_qa_2/task.toml +++ b/tasks/0119_932_119932663_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0119_932_119932663_qa_2" +name = "smoldataenvs-train/0119_932_119932663_qa_2" description = "What percentage of the dataset corresponds to Benign (0) cases before any data transformations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "62.7" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0119_971_119971188_qa_3/task.toml b/tasks/0119_971_119971188_qa_3/task.toml index 80d3df8580be377e4488f791faff0d8a5d00e0c2..42b35804d79de6163df722d8046931ec2c3772af 100644 --- a/tasks/0119_971_119971188_qa_3/task.toml +++ b/tasks/0119_971_119971188_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0119_971_119971188_qa_3" +name = "smoldataenvs-train/0119_971_119971188_qa_3" description = "What is the most frequent video game genre in the dataset based on the number of releases?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0119_986_119986677_qa_3/task.toml b/tasks/0119_986_119986677_qa_3/task.toml index eedb87e295ab5d068b3920495a8c9310cbbd747f..e3919ebcbe6eeb0be8305e8bfc9d45da7fd66673 100644 --- a/tasks/0119_986_119986677_qa_3/task.toml +++ b/tasks/0119_986_119986677_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0119_986_119986677_qa_3" +name = "smoldataenvs-train/0119_986_119986677_qa_3" description = "Which video game was sold the most times (highest count of entries) across all regions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Need for Speed: Most Wanted" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0120_037_120037630_qa_2/task.toml b/tasks/0120_037_120037630_qa_2/task.toml index 1f5e1cfee3bacf58a8e05f90035bd66f54832a92..7a5b2e9758b4eb35d7bb30cb79c9b3795c4ddf52 100644 --- a/tasks/0120_037_120037630_qa_2/task.toml +++ b/tasks/0120_037_120037630_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0120_037_120037630_qa_2" +name = "smoldataenvs-train/0120_037_120037630_qa_2" description = "What is the production index for 'All Agriculture' in the year 2011-12 after handling missing values in the df3 dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "122.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0120_037_120037630_qa_4/task.toml b/tasks/0120_037_120037630_qa_4/task.toml index 343956823a16187c9d07ae0d72bb637e5450448c..73268dc1807cc7982a5ac672688cf087a41dfbc6 100644 --- a/tasks/0120_037_120037630_qa_4/task.toml +++ b/tasks/0120_037_120037630_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0120_037_120037630_qa_4" +name = "smoldataenvs-train/0120_037_120037630_qa_4" description = "What was the yield of 'Total Foodgrains' in the year 2010-11 according to the cleaned df1 dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "135.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0120_102_120102768_qa_1/task.toml b/tasks/0120_102_120102768_qa_1/task.toml index 49ab18fedba0eb4de44ea157ceb625a8807d9dc2..4924162d7d60dd08ef2be2a7c5d34f34a5bb3db6 100644 --- a/tasks/0120_102_120102768_qa_1/task.toml +++ b/tasks/0120_102_120102768_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0120_102_120102768_qa_1" +name = "smoldataenvs-train/0120_102_120102768_qa_1" description = "What is the highest mean cross-validation accuracy achieved by any model on this dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9789" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0120_102_120102768_qa_2/task.toml b/tasks/0120_102_120102768_qa_2/task.toml index b911dfaad6ba0eb39cc50ddaf04c4ce6cef6e4b6..9d150cc6cf3d0e0f1b367a1f1fbaa93cae477e94 100644 --- a/tasks/0120_102_120102768_qa_2/task.toml +++ b/tasks/0120_102_120102768_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0120_102_120102768_qa_2" +name = "smoldataenvs-train/0120_102_120102768_qa_2" description = "How many principal components are required to achieve the highest mean cross-validation accuracy for the Logistic Regression model when using PCA?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0120_295_120295119_qa_2/task.toml b/tasks/0120_295_120295119_qa_2/task.toml index 557093216215771880f560f2012c9dd106f89213..9b6a963dbc25b446220f2dafd176a427f9e4c74d 100644 --- a/tasks/0120_295_120295119_qa_2/task.toml +++ b/tasks/0120_295_120295119_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0120_295_120295119_qa_2" +name = "smoldataenvs-train/0120_295_120295119_qa_2" description = "Which two features were identified as the most distinguishing during the backward feature selection process?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm, PetalWidthCm" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0120_295_120295119_qa_5/task.toml b/tasks/0120_295_120295119_qa_5/task.toml index c52ebc92a24eed2104d68b2dd3e0023a6b327878..cffd9a17838588ce7950153f8d5836f73cb15652 100644 --- a/tasks/0120_295_120295119_qa_5/task.toml +++ b/tasks/0120_295_120295119_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0120_295_120295119_qa_5" +name = "smoldataenvs-train/0120_295_120295119_qa_5" description = "How many features were removed during the backward feature selection process?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0120_304_120304069_qa_1/task.toml b/tasks/0120_304_120304069_qa_1/task.toml index 7957328ff7d0f304c02fc198c1ef45c2698a815f..ba9b62dfb89efd3a2eb0b8926ea10223bad4d80e 100644 --- a/tasks/0120_304_120304069_qa_1/task.toml +++ b/tasks/0120_304_120304069_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0120_304_120304069_qa_1" +name = "smoldataenvs-train/0120_304_120304069_qa_1" description = "Which two features in the dataset show the least overlap between species distributions according to the dist plot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm, PetalWidthCm" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0120_313_120313409_qa_1/task.toml b/tasks/0120_313_120313409_qa_1/task.toml index 6eb57fddd8ce39d6fbc978f26dee3052bfe2fd69..135a954c26311a84b6ceb30427c9a8cda02ee1aa 100644 --- a/tasks/0120_313_120313409_qa_1/task.toml +++ b/tasks/0120_313_120313409_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0120_313_120313409_qa_1" +name = "smoldataenvs-train/0120_313_120313409_qa_1" description = "What is the F1 score for predicting diabetes (class 1) in the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.64" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0120_313_120313409_qa_3/task.toml b/tasks/0120_313_120313409_qa_3/task.toml index 6d464cb74b079b5e271396c5bdb75fdd98da1394..73819295fe79596927b44e71beb2f474979e6f0f 100644 --- a/tasks/0120_313_120313409_qa_3/task.toml +++ b/tasks/0120_313_120313409_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0120_313_120313409_qa_3" +name = "smoldataenvs-train/0120_313_120313409_qa_3" description = "What is the recall (sensitivity) rate for detecting diabetic patients in the test data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.62" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0120_387_120387726_qa_1/task.toml b/tasks/0120_387_120387726_qa_1/task.toml index 9aa81a9236e4852f0baa6cf0376cb09a878c9445..26e1781f053c8280755caa2730c29bda96309e15 100644 --- a/tasks/0120_387_120387726_qa_1/task.toml +++ b/tasks/0120_387_120387726_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0120_387_120387726_qa_1" +name = "smoldataenvs-train/0120_387_120387726_qa_1" description = "What is the correlation coefficient between median_income and median_house_value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.688075" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0120_413_120413293_qa_3/task.toml b/tasks/0120_413_120413293_qa_3/task.toml index bb861b0b0458582d48c7171cbc35d7a559249374..3823ff126d51ff18b97e890b3e702499b9942534 100644 --- a/tasks/0120_413_120413293_qa_3/task.toml +++ b/tasks/0120_413_120413293_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0120_413_120413293_qa_3" +name = "smoldataenvs-train/0120_413_120413293_qa_3" description = "What is the highest correlation coefficient between any two features in the dataset (excluding the target variable)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.997" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0120_461_120461715_qa_2/task.toml b/tasks/0120_461_120461715_qa_2/task.toml index 3381d3408cd6281f5d3c735cf6fc6bd1777ee10f..d80941bc5de1ad26b6e5ddb3983f9131f4bc70da 100644 --- a/tasks/0120_461_120461715_qa_2/task.toml +++ b/tasks/0120_461_120461715_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0120_461_120461715_qa_2" +name = "smoldataenvs-train/0120_461_120461715_qa_2" description = "What is the most frequently occurring median house value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "500001.0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0120_461_120461715_qa_3/task.toml b/tasks/0120_461_120461715_qa_3/task.toml index f99aa1a02f2b5384ddf484b3686bbdd910bda98d..214a86ff12d74f08176e12590c225dafb2bd44b2 100644 --- a/tasks/0120_461_120461715_qa_3/task.toml +++ b/tasks/0120_461_120461715_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0120_461_120461715_qa_3" +name = "smoldataenvs-train/0120_461_120461715_qa_3" description = "What is the difference between the mean and median of the median house value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "27155.82" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0120_461_120461715_qa_5/task.toml b/tasks/0120_461_120461715_qa_5/task.toml index c50dfb2aa83c5d5540a6255c90983fc69ff45d14..f901e8b18d42f47ff18c44e60f780ae9a19ddceb 100644 --- a/tasks/0120_461_120461715_qa_5/task.toml +++ b/tasks/0120_461_120461715_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0120_461_120461715_qa_5" +name = "smoldataenvs-train/0120_461_120461715_qa_5" description = "How many numerical features are present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0120_525_120525286_qa_2/task.toml b/tasks/0120_525_120525286_qa_2/task.toml index 053720a7e3acdd9806055ed4f439612f735cd6df..97ff191fbac633465c8e0422ccad7303b925081b 100644 --- a/tasks/0120_525_120525286_qa_2/task.toml +++ b/tasks/0120_525_120525286_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0120_525_120525286_qa_2" +name = "smoldataenvs-train/0120_525_120525286_qa_2" description = "Which region has the highest proportion of policyholders in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "southeast" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0120_525_120525884_qa_4/task.toml b/tasks/0120_525_120525884_qa_4/task.toml index c5ba1b9d172a9d4937730f00e859c99a04fc644d..b47950df524c193fb3085cdddba601faebf77562 100644 --- a/tasks/0120_525_120525884_qa_4/task.toml +++ b/tasks/0120_525_120525884_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0120_525_120525884_qa_4" +name = "smoldataenvs-train/0120_525_120525884_qa_4" description = "What is the average cross-validation score of the XGBoost model when using only the 'recommended_ind' feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.6265320368095288" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0120_535_120535462_qa_3/task.toml b/tasks/0120_535_120535462_qa_3/task.toml index 34f6f20efa45838fc5b4b548ab076bbf3c4e82bc..ca25a750accfbf3b31df731628b80f54057778b7 100644 --- a/tasks/0120_535_120535462_qa_3/task.toml +++ b/tasks/0120_535_120535462_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0120_535_120535462_qa_3" +name = "smoldataenvs-train/0120_535_120535462_qa_3" description = "What is the standard deviation of BMI values across all individuals in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.10" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0120_574_120574465_qa_1/task.toml b/tasks/0120_574_120574465_qa_1/task.toml index 1a8a560ae69342e13d1f0313f111ea58c510583a..c40c00c47ed3ecdfe767a571da5d790223f9d832 100644 --- a/tasks/0120_574_120574465_qa_1/task.toml +++ b/tasks/0120_574_120574465_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0120_574_120574465_qa_1" +name = "smoldataenvs-train/0120_574_120574465_qa_1" description = "What is the highest validation accuracy achieved by the neural network model during training across all epochs?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.90" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0120_574_120574465_qa_2/task.toml b/tasks/0120_574_120574465_qa_2/task.toml index 7c2443696ca73a89c00e8782f6de72316977a2e8..15c846ae0beffa3fddf2aeee577ebe3e2b5a4d19 100644 --- a/tasks/0120_574_120574465_qa_2/task.toml +++ b/tasks/0120_574_120574465_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0120_574_120574465_qa_2" +name = "smoldataenvs-train/0120_574_120574465_qa_2" description = "What is the overall test accuracy of the model on the 200-sample test set after training completion?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.88" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0120_687_120687537_qa_5/task.toml b/tasks/0120_687_120687537_qa_5/task.toml index 4a297f6cfcc098c7cac3147bc5f2c32df7a540e7..e6d701d9441e0a7ac7444e52b8ee9208551f2d3c 100644 --- a/tasks/0120_687_120687537_qa_5/task.toml +++ b/tasks/0120_687_120687537_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0120_687_120687537_qa_5" +name = "smoldataenvs-train/0120_687_120687537_qa_5" description = "What is the average value of the median_income across all samples in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.870671" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0120_783_120783348_qa_3/task.toml b/tasks/0120_783_120783348_qa_3/task.toml index 1b7ddd23406f526dfa35679bd7c39e83c7aaa5d8..558b2a012911782849b39ec51ccc368efdf2757e 100644 --- a/tasks/0120_783_120783348_qa_3/task.toml +++ b/tasks/0120_783_120783348_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0120_783_120783348_qa_3" +name = "smoldataenvs-train/0120_783_120783348_qa_3" description = "Which K value in the KNN model using raw data achieved the highest accuracy, and what was that accuracy score?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "K=11, 0.89" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0120_921_120921808_qa_3/task.toml b/tasks/0120_921_120921808_qa_3/task.toml index 678fee8fe3b15166fe66b475baf948f045041f58..b267b12171fb90cb62b9841bae14619abf1ef47b 100644 --- a/tasks/0120_921_120921808_qa_3/task.toml +++ b/tasks/0120_921_120921808_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0120_921_120921808_qa_3" +name = "smoldataenvs-train/0120_921_120921808_qa_3" description = "What is the mean Item Maximum Retail Price (MRP) across all products in the dataset before any model training?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "140.99" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0120_952_120952804_qa_4/task.toml b/tasks/0120_952_120952804_qa_4/task.toml index c8aac54578a8f140d9c2864f6b641ca6f9bd2325..cdad27106b12e4ae9f95980381ac64d4f8007013 100644 --- a/tasks/0120_952_120952804_qa_4/task.toml +++ b/tasks/0120_952_120952804_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0120_952_120952804_qa_4" +name = "smoldataenvs-train/0120_952_120952804_qa_4" description = "What is the most common manner of death in police killings as shown in the dataset visualizations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Shot" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0120_979_120979302_qa_5/task.toml b/tasks/0120_979_120979302_qa_5/task.toml index a57a8f5502ab086194a9daf9bd393bc6ebf21540..1ca0d5c2ac9c6dde700a9a0707cb288093b5f238 100644 --- a/tasks/0120_979_120979302_qa_5/task.toml +++ b/tasks/0120_979_120979302_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0120_979_120979302_qa_5" +name = "smoldataenvs-train/0120_979_120979302_qa_5" description = "What is the difference in test accuracy between the final optimized KNN model (k=14) and the initial model with k=3?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.0176" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0120_987_120987317_qa_4/task.toml b/tasks/0120_987_120987317_qa_4/task.toml index 043b7168e3a52dc08250a3a683113142fa8489bc..c44f0c1c4d5bea35dc8d4af2c20b855739af770a 100644 --- a/tasks/0120_987_120987317_qa_4/task.toml +++ b/tasks/0120_987_120987317_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0120_987_120987317_qa_4" +name = "smoldataenvs-train/0120_987_120987317_qa_4" description = "What are the top two product lines ordered by the USA during their peak period (starting October)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Classic Cars, Vintage Cars" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0121_046_121046676_qa_4/task.toml b/tasks/0121_046_121046676_qa_4/task.toml index 0e589e3f1b304d32b7d845ecc838793ee8e8820f..2c62b1dce8a3e0dc1e43787cda831e43e71ee000 100644 --- a/tasks/0121_046_121046676_qa_4/task.toml +++ b/tasks/0121_046_121046676_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0121_046_121046676_qa_4" +name = "smoldataenvs-train/0121_046_121046676_qa_4" description = "How many missing values were present in the 'Type 2' column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "386" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0121_095_121095784_qa_1/task.toml b/tasks/0121_095_121095784_qa_1/task.toml index 74ecd5db89894a9902804c0577c995de433a0728..d35e515967919c487e8e09add631c425b69d8283 100644 --- a/tasks/0121_095_121095784_qa_1/task.toml +++ b/tasks/0121_095_121095784_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0121_095_121095784_qa_1" +name = "smoldataenvs-train/0121_095_121095784_qa_1" description = "What is the percentage of customers in the dataset who have churned (Churn = 1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26.53" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0121_095_121095784_qa_3/task.toml b/tasks/0121_095_121095784_qa_3/task.toml index 9a83c420d76e90bae097f7b9b3f431c17db4e5e8..4aa191db190dd621ff984a5c9f092dfae8398ec6 100644 --- a/tasks/0121_095_121095784_qa_3/task.toml +++ b/tasks/0121_095_121095784_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0121_095_121095784_qa_3" +name = "smoldataenvs-train/0121_095_121095784_qa_3" description = "How many entries in the dataset required correction in the 'TotalCharges' column by setting their value to \"0.00\" during preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0121_112_121112789_qa_3/task.toml b/tasks/0121_112_121112789_qa_3/task.toml index a688f9efc4e98343d6727a7afe04e5e003391f2d..3e159311bbdef022f2aad086257b8bbbedd13b7b 100644 --- a/tasks/0121_112_121112789_qa_3/task.toml +++ b/tasks/0121_112_121112789_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0121_112_121112789_qa_3" +name = "smoldataenvs-train/0121_112_121112789_qa_3" description = "What is the average Speed of all Pokémon in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "68.2775" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0121_112_121112789_qa_4/task.toml b/tasks/0121_112_121112789_qa_4/task.toml index 5c89aa0709dc94c427c4f23ffb874559d7674165..74727d0d0d4e5b7a53980e42e8baa71c99b86a57 100644 --- a/tasks/0121_112_121112789_qa_4/task.toml +++ b/tasks/0121_112_121112789_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0121_112_121112789_qa_4" +name = "smoldataenvs-train/0121_112_121112789_qa_4" description = "How many Pokémon are classified as Water type in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "112" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0121_113_121113393_qa_2/task.toml b/tasks/0121_113_121113393_qa_2/task.toml index 33d27a497419dda717d6a3e1c9d6507d980a2176..bbcf70156b3667c87a941c1fe8a41238118a5127 100644 --- a/tasks/0121_113_121113393_qa_2/task.toml +++ b/tasks/0121_113_121113393_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0121_113_121113393_qa_2" +name = "smoldataenvs-train/0121_113_121113393_qa_2" description = "What is the value of the highest Pearson correlation coefficient between any feature and the 'Outcome' variable in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.46" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0121_143_121143084_qa_3/task.toml b/tasks/0121_143_121143084_qa_3/task.toml index e569098de6f7947741b59935fb6229382d51fc67..8bae3f75b75c1e40ac2aecd0bf181bb7d4203b1b 100644 --- a/tasks/0121_143_121143084_qa_3/task.toml +++ b/tasks/0121_143_121143084_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0121_143_121143084_qa_3" +name = "smoldataenvs-train/0121_143_121143084_qa_3" description = "What is the critical value at 5% significance level from the ADF test that determines stationarity of the original temperature data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-2.8631" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0121_209_121209389_qa_1/task.toml b/tasks/0121_209_121209389_qa_1/task.toml index fbbd5dd3fcc58a99f1f1ae6feb9032d7f0bc5dd0..fc3f5bf8ed03df8a216c04b570b0462a8d0ecb31 100644 --- a/tasks/0121_209_121209389_qa_1/task.toml +++ b/tasks/0121_209_121209389_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0121_209_121209389_qa_1" +name = "smoldataenvs-train/0121_209_121209389_qa_1" description = "Which model (Gini or Entropy) achieved higher accuracy on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Entropy" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0121_209_121209389_qa_2/task.toml b/tasks/0121_209_121209389_qa_2/task.toml index 3c251ae5bd38f2add6258374d75f339a9b8ecf81..d14287d28edbfa243b76e6ca077c91f80eb9aeaa 100644 --- a/tasks/0121_209_121209389_qa_2/task.toml +++ b/tasks/0121_209_121209389_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0121_209_121209389_qa_2" +name = "smoldataenvs-train/0121_209_121209389_qa_2" description = "How many unique categories are present in the 'safety' feature before encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0121_319_121319744_qa_1/task.toml b/tasks/0121_319_121319744_qa_1/task.toml index 4199d0a0be33d41feb9217c277fdce744a21618a..d6668e0cd1660cfa4283068b6b719632bc622fed 100644 --- a/tasks/0121_319_121319744_qa_1/task.toml +++ b/tasks/0121_319_121319744_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0121_319_121319744_qa_1" +name = "smoldataenvs-train/0121_319_121319744_qa_1" description = "What is the optimal number of clusters determined by the elbow method for the K-means clustering model on the car engine attributes dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0121_340_121340014_qa_2/task.toml b/tasks/0121_340_121340014_qa_2/task.toml index 8716a3c4d227aae84f686d8b92d67d195be6da2a..0297f6935c9c024919c2e3e3ff00fc1d2f2f10a3 100644 --- a/tasks/0121_340_121340014_qa_2/task.toml +++ b/tasks/0121_340_121340014_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0121_340_121340014_qa_2" +name = "smoldataenvs-train/0121_340_121340014_qa_2" description = "What is the count of individuals in the 'Other' category after consolidating 'Other-service' and missing values (?) in the occupation column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7732" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0121_361_121361496_qa_3/task.toml b/tasks/0121_361_121361496_qa_3/task.toml index 2c8f8592dae85854dd587d2a6c847baa0de08a6f..08f5093b882cc884fe0b44654ca4adec66a3f348 100644 --- a/tasks/0121_361_121361496_qa_3/task.toml +++ b/tasks/0121_361_121361496_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0121_361_121361496_qa_3" +name = "smoldataenvs-train/0121_361_121361496_qa_3" description = "Which product line generated the highest total sales in 2003 according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Classic Cars" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0121_366_121366198_qa_4/task.toml b/tasks/0121_366_121366198_qa_4/task.toml index e1a2ff9f8e6cbe731dc749964a8776a64c0f728d..dad90b8992b374a3d1d3ed3b6dcc060d62ffd68d 100644 --- a/tasks/0121_366_121366198_qa_4/task.toml +++ b/tasks/0121_366_121366198_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0121_366_121366198_qa_4" +name = "smoldataenvs-train/0121_366_121366198_qa_4" description = "What is the encoded integer value assigned to edible mushrooms (class 'e') after label encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0121_396_121396365_qa_2/task.toml b/tasks/0121_396_121396365_qa_2/task.toml index 7beda5b4d1024d243859e52ebf7d504e5fadf9e3..6c635cb61254ccdf4083f3a5d48a38590871b409 100644 --- a/tasks/0121_396_121396365_qa_2/task.toml +++ b/tasks/0121_396_121396365_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0121_396_121396365_qa_2" +name = "smoldataenvs-train/0121_396_121396365_qa_2" description = "Which four columns have been identified as \"no use\" variables that should be dropped from the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "EmployeeNumber, EmployeeCount, Over18, StandardHours" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0121_396_121396730_qa_4/task.toml b/tasks/0121_396_121396730_qa_4/task.toml index 5199ebec4cfbfc7ddc42a3463fa2afd636610ea8..2dc2441b85281c7ecc1c154eaa7cc807b4c0f0f0 100644 --- a/tasks/0121_396_121396730_qa_4/task.toml +++ b/tasks/0121_396_121396730_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0121_396_121396730_qa_4" +name = "smoldataenvs-train/0121_396_121396730_qa_4" description = "After binary encoding, what is the data type of the 'Attrition' column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "int64" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0121_396_121396730_qa_5/task.toml b/tasks/0121_396_121396730_qa_5/task.toml index 35a2c72e2beee9d102729a2e0b767b8a90ccc1a0..5886538e4956cf0f7ca2c16d811b85a59e5414fc 100644 --- a/tasks/0121_396_121396730_qa_5/task.toml +++ b/tasks/0121_396_121396730_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0121_396_121396730_qa_5" +name = "smoldataenvs-train/0121_396_121396730_qa_5" description = "How many binary categorical variables were processed and converted to numerical values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0121_468_121468055_qa_4/task.toml b/tasks/0121_468_121468055_qa_4/task.toml index ec648ff0a8fd04f78522fa98e8537fe90d33b0fa..f517e621689dc317109d8d4db814df77d6a9e0f8 100644 --- a/tasks/0121_468_121468055_qa_4/task.toml +++ b/tasks/0121_468_121468055_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0121_468_121468055_qa_4" +name = "smoldataenvs-train/0121_468_121468055_qa_4" description = "How many distinct clusters do the species form in the cluster map based on the row color groupings?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0121_521_121521712_qa_1/task.toml b/tasks/0121_521_121521712_qa_1/task.toml index 1851aee162cada61bb40b738eb9dcad182ec5e88..601c1f7b6cd8090698623819ce82480dbac3f068 100644 --- a/tasks/0121_521_121521712_qa_1/task.toml +++ b/tasks/0121_521_121521712_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0121_521_121521712_qa_1" +name = "smoldataenvs-train/0121_521_121521712_qa_1" description = "Which payment method has the highest average monthly charge according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic check" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0121_588_121588240_qa_1/task.toml b/tasks/0121_588_121588240_qa_1/task.toml index 684dee18b71cbdf47489572d58c29b1d3b3bf057..559961425a802bf52e533f87524de805078d2323 100644 --- a/tasks/0121_588_121588240_qa_1/task.toml +++ b/tasks/0121_588_121588240_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0121_588_121588240_qa_1" +name = "smoldataenvs-train/0121_588_121588240_qa_1" description = "Which feature in the dataset shows the most distinct distribution across the three species when comparing box plots and histograms, based on the analysis presented in the notebook?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0121_588_121588240_qa_2/task.toml b/tasks/0121_588_121588240_qa_2/task.toml index e5f144403aa7839f0a0efdf84fd7ed334b2b7423..42b802de87f284a9af5cac5e4e42bf30461b24af 100644 --- a/tasks/0121_588_121588240_qa_2/task.toml +++ b/tasks/0121_588_121588240_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0121_588_121588240_qa_2" +name = "smoldataenvs-train/0121_588_121588240_qa_2" description = "What is the total number of samples per species in the Iris dataset, based on the data distribution analysis shown in the pie chart?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0121_603_121603419_qa_1/task.toml b/tasks/0121_603_121603419_qa_1/task.toml index b377bbfbdef552198b6b52a17e6805173b2d2d2e..64f056386912efae136a50ca17e4057bfc55a4f4 100644 --- a/tasks/0121_603_121603419_qa_1/task.toml +++ b/tasks/0121_603_121603419_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0121_603_121603419_qa_1" +name = "smoldataenvs-train/0121_603_121603419_qa_1" description = "What is the coefficient of the independent variable in the linear regression model trained on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.00065638" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0121_603_121603419_qa_3/task.toml b/tasks/0121_603_121603419_qa_3/task.toml index ed0009bdd3f3406171779638dc2230b330d46719..a1a7d260122844c470647d9c8455075f78e90e1c 100644 --- a/tasks/0121_603_121603419_qa_3/task.toml +++ b/tasks/0121_603_121603419_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0121_603_121603419_qa_3" +name = "smoldataenvs-train/0121_603_121603419_qa_3" description = "What is the root mean squared error (RMSE) value for the model predictions on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.071306" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0121_684_121684737_qa_1/task.toml b/tasks/0121_684_121684737_qa_1/task.toml index 1bcccee1e1b064452ef7173d3de5cb794815eea8..88c846d979102f9b2f8059e5d34cec3b7bc6f45c 100644 --- a/tasks/0121_684_121684737_qa_1/task.toml +++ b/tasks/0121_684_121684737_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0121_684_121684737_qa_1" +name = "smoldataenvs-train/0121_684_121684737_qa_1" description = "Which year achieved the highest total global sales based on the video game sales data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2008" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0121_736_121736095_qa_2/task.toml b/tasks/0121_736_121736095_qa_2/task.toml index 730473034029ff7c1a06c6452a06eddd14b4394a..3eac595a94ecc34b9d199ccee284e9d8f036155c 100644 --- a/tasks/0121_736_121736095_qa_2/task.toml +++ b/tasks/0121_736_121736095_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0121_736_121736095_qa_2" +name = "smoldataenvs-train/0121_736_121736095_qa_2" description = "Which feature shows the most distinct separation between the Setosa species and the other two species based on the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0121_736_121736095_qa_4/task.toml b/tasks/0121_736_121736095_qa_4/task.toml index 5b980cdee8c6f37814d55d443ce681f0f2387b1e..228f19a94ae238b1e66668b2eb3ea33312dfb74c 100644 --- a/tasks/0121_736_121736095_qa_4/task.toml +++ b/tasks/0121_736_121736095_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0121_736_121736095_qa_4" +name = "smoldataenvs-train/0121_736_121736095_qa_4" description = "Which species contains an outlier in the SepalLengthCm feature according to the data analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Virginica" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0121_738_121738149_qa_3/task.toml b/tasks/0121_738_121738149_qa_3/task.toml index 1be7e37d8d4e519f28be247cd9860ec60f2b596f..11b5c69c968c7a149dbf863267614d2982b4cdb0 100644 --- a/tasks/0121_738_121738149_qa_3/task.toml +++ b/tasks/0121_738_121738149_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0121_738_121738149_qa_3" +name = "smoldataenvs-train/0121_738_121738149_qa_3" description = "Which currency is used for the highest number of loans in the dataset, and how many loans are funded in that currency?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PHP, 160440" reward_mode_initial = "list" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0121_738_121738149_qa_4/task.toml b/tasks/0121_738_121738149_qa_4/task.toml index 6792a0e3b267a205996d0bc178df6ea704d3f460..7d80d8a347e548d1ee37210285ba0f24ad53c721 100644 --- a/tasks/0121_738_121738149_qa_4/task.toml +++ b/tasks/0121_738_121738149_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0121_738_121738149_qa_4" +name = "smoldataenvs-train/0121_738_121738149_qa_4" description = "What is the percentage of missing values in the 'region' column of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8.46" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0121_738_121738149_qa_5/task.toml b/tasks/0121_738_121738149_qa_5/task.toml index f0d8106536a9b765136264f16f9c4a57895376e7..ebfda9969a1dd99dc7160a344e9cc78d3b501024 100644 --- a/tasks/0121_738_121738149_qa_5/task.toml +++ b/tasks/0121_738_121738149_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0121_738_121738149_qa_5" +name = "smoldataenvs-train/0121_738_121738149_qa_5" description = "Which economic sector has the highest number of loans recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Agriculture" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0121_801_121801511_qa_3/task.toml b/tasks/0121_801_121801511_qa_3/task.toml index 98feee9d05223057929307b2a756e6732e7cbff8..7449f38dfc273c53a61bf41fdb7707555046d1b6 100644 --- a/tasks/0121_801_121801511_qa_3/task.toml +++ b/tasks/0121_801_121801511_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0121_801_121801511_qa_3" +name = "smoldataenvs-train/0121_801_121801511_qa_3" description = "Which combination of InternetService and Contract type results in the highest count of churned customers?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Fiber optic, Month-to-month" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0121_801_121801511_qa_4/task.toml b/tasks/0121_801_121801511_qa_4/task.toml index e5eb5ab56aef12079854d71825dc831dd4facb2d..766d77027d7c39a68cd1cb57505d88f591b7f277 100644 --- a/tasks/0121_801_121801511_qa_4/task.toml +++ b/tasks/0121_801_121801511_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0121_801_121801511_qa_4" +name = "smoldataenvs-train/0121_801_121801511_qa_4" description = "What is the average monthly charge difference between churned and non-churned customers with Month-to-month contracts?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11.56" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0121_823_121823327_qa_4/task.toml b/tasks/0121_823_121823327_qa_4/task.toml index f9ac963295c8860f3e8f35f3e26b36c5b9ce7be3..1e3146213221f5d1f51d0fcaba42bab4c9f2ba18 100644 --- a/tasks/0121_823_121823327_qa_4/task.toml +++ b/tasks/0121_823_121823327_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0121_823_121823327_qa_4" +name = "smoldataenvs-train/0121_823_121823327_qa_4" description = "Which job categories have the highest term deposit subscription rates according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Retired, Student, Unemployed, Housemaid" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0121_834_121834892_qa_2/task.toml b/tasks/0121_834_121834892_qa_2/task.toml index 99e6d896a15a3e71e079120d6a06efeff238ea63..a89f712a9e2754a99f0be9a533cfcec5882904fb 100644 --- a/tasks/0121_834_121834892_qa_2/task.toml +++ b/tasks/0121_834_121834892_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0121_834_121834892_qa_2" +name = "smoldataenvs-train/0121_834_121834892_qa_2" description = "What is the range of values (max - min) for the 'free sulfur dioxide' feature in the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "71.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0121_834_121834892_qa_4/task.toml b/tasks/0121_834_121834892_qa_4/task.toml index e66f730bcf89fb6082c4f48e18de2f0277f158c1..f915dba87edc850ea3d7f8abacf4ebda43d120bb 100644 --- a/tasks/0121_834_121834892_qa_4/task.toml +++ b/tasks/0121_834_121834892_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0121_834_121834892_qa_4" +name = "smoldataenvs-train/0121_834_121834892_qa_4" description = "What is the minimum value recorded in the 'fixed acidity' column of the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0121_895_121895900_qa_2/task.toml b/tasks/0121_895_121895900_qa_2/task.toml index aa22cc1b513545cd3ad4fa7039b6c77e86536017..414fad8d73eb2219b0d6ca4029a32c6376ad6db3 100644 --- a/tasks/0121_895_121895900_qa_2/task.toml +++ b/tasks/0121_895_121895900_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0121_895_121895900_qa_2" +name = "smoldataenvs-train/0121_895_121895900_qa_2" description = "Which sales region achieved the highest average sales per game when considering all titles in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "North America" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0121_896_121896638_qa_2/task.toml b/tasks/0121_896_121896638_qa_2/task.toml index 8796c36bccecb2d2c56e1e55b3147cfa5f6600b6..2f0f396576b14702d3acab8ac84148bf2bdebc28 100644 --- a/tasks/0121_896_121896638_qa_2/task.toml +++ b/tasks/0121_896_121896638_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0121_896_121896638_qa_2" +name = "smoldataenvs-train/0121_896_121896638_qa_2" description = "Does increasing the number of leaf nodes in the Decision Tree model improve its performance on the validation set, as indicated by the Mean Absolute Error (MAE)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0121_908_121908423_qa_2/task.toml b/tasks/0121_908_121908423_qa_2/task.toml index 041abcf0ad5ee4f0c0899410a04b1f70deb5a849..bb904ebd1dc1b54bf3d417ca6402d7ba9cbc9d40 100644 --- a/tasks/0121_908_121908423_qa_2/task.toml +++ b/tasks/0121_908_121908423_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0121_908_121908423_qa_2" +name = "smoldataenvs-train/0121_908_121908423_qa_2" description = "What is the proportion of male patients in the dataset compared to female patients?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "35.7%" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0121_947_121947601_qa_1/task.toml b/tasks/0121_947_121947601_qa_1/task.toml index f201ebaf782db6aedc5c4865c74ff0801c590186..894f68bdf51173da7060b74d51792a64e92c0909 100644 --- a/tasks/0121_947_121947601_qa_1/task.toml +++ b/tasks/0121_947_121947601_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0121_947_121947601_qa_1" +name = "smoldataenvs-train/0121_947_121947601_qa_1" description = "What is the percentage of missing values in the dataset before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5.245%" reward_mode_initial = "flexible" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0121_966_121966267_qa_1/task.toml b/tasks/0121_966_121966267_qa_1/task.toml index 4388dd8d4579c20750cb296695052eca571657bb..c9d89e966e530401aa5a2ad86af197b2b3458cc4 100644 --- a/tasks/0121_966_121966267_qa_1/task.toml +++ b/tasks/0121_966_121966267_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0121_966_121966267_qa_1" +name = "smoldataenvs-train/0121_966_121966267_qa_1" description = "What is the most common word in the American Standard Version (ASV) of the Bible dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "the" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0121_988_121988886_qa_5/task.toml b/tasks/0121_988_121988886_qa_5/task.toml index e5e90a518088bc7b9c2cb4ccdfc6db01001ea96b..2beb3cdeadb87521033244726cc4e40cc789b245 100644 --- a/tasks/0121_988_121988886_qa_5/task.toml +++ b/tasks/0121_988_121988886_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0121_988_121988886_qa_5" +name = "smoldataenvs-train/0121_988_121988886_qa_5" description = "Which cluster produced by the K-Means algorithm has the largest number of data points?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0121_991_121991314_qa_5/task.toml b/tasks/0121_991_121991314_qa_5/task.toml index 8987e619f9126fab06b1b25275217860802aa26c..201797393de7cdd2b9aa782eb098394dbaa4c768 100644 --- a/tasks/0121_991_121991314_qa_5/task.toml +++ b/tasks/0121_991_121991314_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0121_991_121991314_qa_5" +name = "smoldataenvs-train/0121_991_121991314_qa_5" description = "What is the correlation coefficient between Item_MRP and Item_Outlet_Sales in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.59" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0122_011_122011050_qa_1/task.toml b/tasks/0122_011_122011050_qa_1/task.toml index fc934776e71def8431dc8478a680b72d594bb176..c9d609dc80f97a892ddd93c5272f707e29dfe59f 100644 --- a/tasks/0122_011_122011050_qa_1/task.toml +++ b/tasks/0122_011_122011050_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0122_011_122011050_qa_1" +name = "smoldataenvs-train/0122_011_122011050_qa_1" description = "What is the highest loan amount requested in the dataset after preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "700.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0122_011_122011050_qa_3/task.toml b/tasks/0122_011_122011050_qa_3/task.toml index 1ed4e9fd268fa294a26f7ab582136d033a6dd7d3..45a406843810a4c334a785a5bf52c052cbbf8e86 100644 --- a/tasks/0122_011_122011050_qa_3/task.toml +++ b/tasks/0122_011_122011050_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0122_011_122011050_qa_3" +name = "smoldataenvs-train/0122_011_122011050_qa_3" description = "What was the total number of missing values in the 'Self_Employed' column before data cleaning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "32" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0122_011_122011050_qa_4/task.toml b/tasks/0122_011_122011050_qa_4/task.toml index ab87cec8d16c564b51ea77c1c11227f00e0251a7..44281fc996c10d461466de50a93a736f55a77488 100644 --- a/tasks/0122_011_122011050_qa_4/task.toml +++ b/tasks/0122_011_122011050_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0122_011_122011050_qa_4" +name = "smoldataenvs-train/0122_011_122011050_qa_4" description = "What is the average Applicant Income across all data points after preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5403.46" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0122_097_122097207_qa_1/task.toml b/tasks/0122_097_122097207_qa_1/task.toml index d05029ca34dcd500d9001224eb320bfcd62305d8..960139043b5d65c1f7f814f085fbf00075c0fc0c 100644 --- a/tasks/0122_097_122097207_qa_1/task.toml +++ b/tasks/0122_097_122097207_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0122_097_122097207_qa_1" +name = "smoldataenvs-train/0122_097_122097207_qa_1" description = "What is the distribution of the 'price_range' target variable in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "500,500,500,500" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0122_097_122097207_qa_3/task.toml b/tasks/0122_097_122097207_qa_3/task.toml index 22b9bae811655c8132e5b1e4524faaa6ba42ceb1..b63cdb539275e92bd1443944d2e1b84d94e51497 100644 --- a/tasks/0122_097_122097207_qa_3/task.toml +++ b/tasks/0122_097_122097207_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0122_097_122097207_qa_3" +name = "smoldataenvs-train/0122_097_122097207_qa_3" description = "What is the median value of the 'battery_power' feature across all devices in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1226" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0122_097_122097207_qa_5/task.toml b/tasks/0122_097_122097207_qa_5/task.toml index ca44efb10693c09b243e3d78e9f75779bfd2bbd1..0b3c9b166a864341b7df37d5f8e4500d212950a1 100644 --- a/tasks/0122_097_122097207_qa_5/task.toml +++ b/tasks/0122_097_122097207_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0122_097_122097207_qa_5" +name = "smoldataenvs-train/0122_097_122097207_qa_5" description = "How many numerical features in the dataset have a standard deviation greater than 500?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0122_143_122143760_qa_1/task.toml b/tasks/0122_143_122143760_qa_1/task.toml index 42ffe31a1b24a6488c8b43c4a56cacdb807a55dd..4e93c61e582367a7c7b7f77096fbbc1760ee9448 100644 --- a/tasks/0122_143_122143760_qa_1/task.toml +++ b/tasks/0122_143_122143760_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0122_143_122143760_qa_1" +name = "smoldataenvs-train/0122_143_122143760_qa_1" description = "Which product achieved the highest total sales (Checkout_Price) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "DOTCOM POSTAGE" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0122_335_122335546_qa_1/task.toml b/tasks/0122_335_122335546_qa_1/task.toml index 0d258afc171fbd70a497b49fd3299c6f7edd570f..7cea1823f4340ffb3faf2ce70f6dbc4c43f2431b 100644 --- a/tasks/0122_335_122335546_qa_1/task.toml +++ b/tasks/0122_335_122335546_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0122_335_122335546_qa_1" +name = "smoldataenvs-train/0122_335_122335546_qa_1" description = "Which feature exhibits the strongest positive correlation with the price_range in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "ram" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0122_356_122356261_qa_1/task.toml b/tasks/0122_356_122356261_qa_1/task.toml index 040d76c874b1d0bf4815e58943cb1da0dd439e7f..ad58dd34402891f738f9b053074c0d3520f3c624 100644 --- a/tasks/0122_356_122356261_qa_1/task.toml +++ b/tasks/0122_356_122356261_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0122_356_122356261_qa_1" +name = "smoldataenvs-train/0122_356_122356261_qa_1" description = "What is the most common gender among participants, and what percentage of the total participants does it represent?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "male, 78.7" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0122_356_122356261_qa_2/task.toml b/tasks/0122_356_122356261_qa_2/task.toml index 867ad003a03485e3b8b4090b1fd84216ed37b6ee..7fb7bc98f9bbb73feff26d8ae77415d3a69fe9be 100644 --- a/tasks/0122_356_122356261_qa_2/task.toml +++ b/tasks/0122_356_122356261_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0122_356_122356261_qa_2" +name = "smoldataenvs-train/0122_356_122356261_qa_2" description = "What percentage of participants reported currently undergoing treatment for a mental health condition?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50.6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0122_365_122365059_qa_2/task.toml b/tasks/0122_365_122365059_qa_2/task.toml index 31731ded275a647c5e7281b272ad76ac7819615a..5c8a295bb5032f777b512ddb08a38c22e37538cc 100644 --- a/tasks/0122_365_122365059_qa_2/task.toml +++ b/tasks/0122_365_122365059_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0122_365_122365059_qa_2" +name = "smoldataenvs-train/0122_365_122365059_qa_2" description = "Which video game genre has the highest number of entries in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0122_365_122365059_qa_3/task.toml b/tasks/0122_365_122365059_qa_3/task.toml index bf3288d6ca282517d43b86e145a2ff0f1a1e1ed0..888f5d5a0586d9c9ab2182698982714552ed4fa5 100644 --- a/tasks/0122_365_122365059_qa_3/task.toml +++ b/tasks/0122_365_122365059_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0122_365_122365059_qa_3" +name = "smoldataenvs-train/0122_365_122365059_qa_3" description = "What is the highest global sales figure achieved by a single video game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "82.74" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0122_365_122365414_qa_2/task.toml b/tasks/0122_365_122365414_qa_2/task.toml index 189a656105838a286769253e4e3a7aa5ff515c7d..e568f795b3bc2f2cf5e1688638cbf58e515414c9 100644 --- a/tasks/0122_365_122365414_qa_2/task.toml +++ b/tasks/0122_365_122365414_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0122_365_122365414_qa_2" +name = "smoldataenvs-train/0122_365_122365414_qa_2" description = "What is the median value of global sales across all video games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.17" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0122_390_122390551_qa_3/task.toml b/tasks/0122_390_122390551_qa_3/task.toml index 32ac5f7ea1598f545c02e45fab21e599e29d8129..80eb620cd9a5314ed157b3733c643198c984c041 100644 --- a/tasks/0122_390_122390551_qa_3/task.toml +++ b/tasks/0122_390_122390551_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0122_390_122390551_qa_3" +name = "smoldataenvs-train/0122_390_122390551_qa_3" description = "How many missing values were present in the \"Glucose\" column before imputation with mean values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0122_390_122390551_qa_4/task.toml b/tasks/0122_390_122390551_qa_4/task.toml index 9d218c789008cd9af1bb817b0e46e7d61a4f0075..56ef10c0fa5b86c08aa899ff7cc24bba88523b00 100644 --- a/tasks/0122_390_122390551_qa_4/task.toml +++ b/tasks/0122_390_122390551_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0122_390_122390551_qa_4" +name = "smoldataenvs-train/0122_390_122390551_qa_4" description = "What is the number of samples in the test dataset after the 70-30 train/test split?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "231" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0122_394_122394666_qa_1/task.toml b/tasks/0122_394_122394666_qa_1/task.toml index f20c47ee90a0d6299608201259e89edcab136178..fba0c2bfb9c4dde59e03236e4847d9289b1215b0 100644 --- a/tasks/0122_394_122394666_qa_1/task.toml +++ b/tasks/0122_394_122394666_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0122_394_122394666_qa_1" +name = "smoldataenvs-train/0122_394_122394666_qa_1" description = "What is the Pearson correlation coefficient between x and y in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9953" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0122_394_122394666_qa_2/task.toml b/tasks/0122_394_122394666_qa_2/task.toml index f11b4db1e083fe428d710f20e13364b4f5708554..965e6f04151e94c5049a7c43b43258be3f41ba84 100644 --- a/tasks/0122_394_122394666_qa_2/task.toml +++ b/tasks/0122_394_122394666_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0122_394_122394666_qa_2" +name = "smoldataenvs-train/0122_394_122394666_qa_2" description = "What is the equation of the best-fit line derived from the linear regression analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "y = -0.1073 + 1.0007x" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0122_454_122454907_qa_1/task.toml b/tasks/0122_454_122454907_qa_1/task.toml index 28cdabc2fd36fc1b38191f3d00775aee8825370b..b34d7b535fd1e786a32fd2c958a12bbd77c7a5ff 100644 --- a/tasks/0122_454_122454907_qa_1/task.toml +++ b/tasks/0122_454_122454907_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0122_454_122454907_qa_1" +name = "smoldataenvs-train/0122_454_122454907_qa_1" description = "What is the most common number of dependents among applicants in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0122_481_122481302_qa_5/task.toml b/tasks/0122_481_122481302_qa_5/task.toml index 491d738b5ccd7cd5fcadadd151f8a1eb3ea33292..7752cf3b9a3edb915cbd20b4760b2bcb449ceeba 100644 --- a/tasks/0122_481_122481302_qa_5/task.toml +++ b/tasks/0122_481_122481302_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0122_481_122481302_qa_5" +name = "smoldataenvs-train/0122_481_122481302_qa_5" description = "Which education field has the highest number of employees who left the company based on the cross-tabulation results?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Life Sciences" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0122_502_122502688_qa_3/task.toml b/tasks/0122_502_122502688_qa_3/task.toml index 353d6db3a7d3dff1e35f652277b213848615b453..e42a5858f21a934c7c5df6626f444cbbb50bf33d 100644 --- a/tasks/0122_502_122502688_qa_3/task.toml +++ b/tasks/0122_502_122502688_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0122_502_122502688_qa_3" +name = "smoldataenvs-train/0122_502_122502688_qa_3" description = "After applying logarithmic transformation to total_rooms, total_bedrooms, population, and households, what is the mean median house value in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "207004.37" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0122_517_122517438_qa_3/task.toml b/tasks/0122_517_122517438_qa_3/task.toml index 2071c00570338c3c35f028684dd479507c99bddb..ee331d64dc8a907dafd447d505fad3fd07f0470c 100644 --- a/tasks/0122_517_122517438_qa_3/task.toml +++ b/tasks/0122_517_122517438_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0122_517_122517438_qa_3" +name = "smoldataenvs-train/0122_517_122517438_qa_3" description = "What is the test accuracy achieved by the KNN model using K=9?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9649122807017544" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0122_592_122592653_qa_2/task.toml b/tasks/0122_592_122592653_qa_2/task.toml index 0c7939a035767d5141bd02c5ff89d35cba71f49b..ef126c9ec0fa7c0f53b0035bb357328f227f63e5 100644 --- a/tasks/0122_592_122592653_qa_2/task.toml +++ b/tasks/0122_592_122592653_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0122_592_122592653_qa_2" +name = "smoldataenvs-train/0122_592_122592653_qa_2" description = "What is the most common dependent category in the cleaned loan applicant dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0122_604_122604169_qa_2/task.toml b/tasks/0122_604_122604169_qa_2/task.toml index be2c6fb7979f7c7b6043ca4b54d92d9a0586a4d0..28de2b06f38541ea41bd3c7b3b14e1694ac37bab 100644 --- a/tasks/0122_604_122604169_qa_2/task.toml +++ b/tasks/0122_604_122604169_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0122_604_122604169_qa_2" +name = "smoldataenvs-train/0122_604_122604169_qa_2" description = "What was the number of outliers detected in the 'Pregnancies' column during the training data processing using the IQR method?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0122_669_122669772_qa_3/task.toml b/tasks/0122_669_122669772_qa_3/task.toml index 600ccef5884ea035ff798b026a3c4bf8ebc8c71a..f358be931e34a28c786108df91f6c6cc4554baec 100644 --- a/tasks/0122_669_122669772_qa_3/task.toml +++ b/tasks/0122_669_122669772_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0122_669_122669772_qa_3" +name = "smoldataenvs-train/0122_669_122669772_qa_3" description = "What is the highest similarity score between \"The Dark Knight Rises\" and any movie in the dataset using the sigmoid kernel?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.7616" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0122_715_122715490_qa_1/task.toml b/tasks/0122_715_122715490_qa_1/task.toml index c7a58105e62ded4dbf24560d083c77057ebad859..3cc6c8e03f6b2327e7042b0005e6efbcd8dd7764 100644 --- a/tasks/0122_715_122715490_qa_1/task.toml +++ b/tasks/0122_715_122715490_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0122_715_122715490_qa_1" +name = "smoldataenvs-train/0122_715_122715490_qa_1" description = "What is the number of samples in the test dataset after an 80-20 train-test split?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1112" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0122_715_122715490_qa_5/task.toml b/tasks/0122_715_122715490_qa_5/task.toml index e61abc057e4b6894ca0f3a1ddd527262273a90b6..30c31d3758c66d7a6133ef43ad83d68052916022 100644 --- a/tasks/0122_715_122715490_qa_5/task.toml +++ b/tasks/0122_715_122715490_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0122_715_122715490_qa_5" +name = "smoldataenvs-train/0122_715_122715490_qa_5" description = "What is the number of samples in the training dataset after an 80-20 train-test split?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4447" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0122_770_122770918_qa_1/task.toml b/tasks/0122_770_122770918_qa_1/task.toml index 5e45f69c11df9f55be5b6d336f4a1048730536e4..69768ad6befc44cc9f06b4115b7bfd3fb8033981 100644 --- a/tasks/0122_770_122770918_qa_1/task.toml +++ b/tasks/0122_770_122770918_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0122_770_122770918_qa_1" +name = "smoldataenvs-train/0122_770_122770918_qa_1" description = "What is the overall survival rate percentage of passengers in the Titanic dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "38.38" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0122_770_122770918_qa_4/task.toml b/tasks/0122_770_122770918_qa_4/task.toml index 2ce686cb038a9193a0eb8c70793556097852b9b6..188da935050391e388f09dff24fe240573529f84 100644 --- a/tasks/0122_770_122770918_qa_4/task.toml +++ b/tasks/0122_770_122770918_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0122_770_122770918_qa_4" +name = "smoldataenvs-train/0122_770_122770918_qa_4" description = "What is the percentage of missing data in the 'Age' column compared to the 'Cabin' column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Age: 19.87%, Cabin: 77.10%" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0122_775_122775678_qa_5/task.toml b/tasks/0122_775_122775678_qa_5/task.toml index 1843d934a17cb24de3291f36d69b5d70441c21a6..3bb7bc2cf21d263c5b894a54fe341a5b5cdb5fa7 100644 --- a/tasks/0122_775_122775678_qa_5/task.toml +++ b/tasks/0122_775_122775678_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0122_775_122775678_qa_5" +name = "smoldataenvs-train/0122_775_122775678_qa_5" description = "How many outlier values were identified and removed for the 'alcohol' attribute during the initial outlier elimination process?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0122_822_122822376_qa_1/task.toml b/tasks/0122_822_122822376_qa_1/task.toml index da942d622b01c0ad766537fa0debfb7814121969..75381eaad8c084a613a2466396f8160bf9ad299c 100644 --- a/tasks/0122_822_122822376_qa_1/task.toml +++ b/tasks/0122_822_122822376_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0122_822_122822376_qa_1" +name = "smoldataenvs-train/0122_822_122822376_qa_1" description = "What is the percentage of missing values in the 'Insulin' column after replacing zero values with NaN in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "48.697917" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0122_938_122938999_qa_1/task.toml b/tasks/0122_938_122938999_qa_1/task.toml index 0a1cdae5ee77dbbaaeca67e228f268d7b794b2e4..92d4b833a4016b1b4cfff9df77be55249a72784e 100644 --- a/tasks/0122_938_122938999_qa_1/task.toml +++ b/tasks/0122_938_122938999_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0122_938_122938999_qa_1" +name = "smoldataenvs-train/0122_938_122938999_qa_1" description = "Which customer attribute demonstrates the strongest statistical significance in predicting churn based on chi-squared tests performed in the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Contract" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0122_965_122965866_qa_5/task.toml b/tasks/0122_965_122965866_qa_5/task.toml index 7162c8c0d45701a98700857085e177abdb02059f..393a7ff67d03feaac878f14a198c69ed7a0e795e 100644 --- a/tasks/0122_965_122965866_qa_5/task.toml +++ b/tasks/0122_965_122965866_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0122_965_122965866_qa_5" +name = "smoldataenvs-train/0122_965_122965866_qa_5" description = "What is the total count of abnormal patients across both clusters in the standardized KMeans clustering analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "210" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0123_030_123030462_qa_1/task.toml b/tasks/0123_030_123030462_qa_1/task.toml index 16c3ae97cd78b6e7244dbcc764e65ee3a6112167..325f179d95fedd2fd03edb50fc103ea3be84076b 100644 --- a/tasks/0123_030_123030462_qa_1/task.toml +++ b/tasks/0123_030_123030462_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0123_030_123030462_qa_1" +name = "smoldataenvs-train/0123_030_123030462_qa_1" description = "Which feature exhibits the strongest negative correlation with the median home value (MEDV) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "LSTAT" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0123_030_123030462_qa_4/task.toml b/tasks/0123_030_123030462_qa_4/task.toml index 0ae79f72a0f4978550ac5a5d4705457c84905384..b6c00e349841fa55f81693d305f0009ad0375f6f 100644 --- a/tasks/0123_030_123030462_qa_4/task.toml +++ b/tasks/0123_030_123030462_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0123_030_123030462_qa_4" +name = "smoldataenvs-train/0123_030_123030462_qa_4" description = "Which two features show the strongest negative linear relationship in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "DIS, NOX" reward_mode_initial = "list" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0123_042_123042543_qa_1/task.toml b/tasks/0123_042_123042543_qa_1/task.toml index d2276ef2e9cb92c6f32dc11082024f2b2658e863..92e069144941c24488772e1a59c9d13b45b8bc3d 100644 --- a/tasks/0123_042_123042543_qa_1/task.toml +++ b/tasks/0123_042_123042543_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0123_042_123042543_qa_1" +name = "smoldataenvs-train/0123_042_123042543_qa_1" description = "Which warehouse has the highest total order demand across the entire dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Whse_J" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0123_042_123042543_qa_3/task.toml b/tasks/0123_042_123042543_qa_3/task.toml index 2ab865989512cb7a0b19c6234f38c9923aa5f5fb..f743350ef5e1f9b0c4a90e07193016891c782689 100644 --- a/tasks/0123_042_123042543_qa_3/task.toml +++ b/tasks/0123_042_123042543_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0123_042_123042543_qa_3" +name = "smoldataenvs-train/0123_042_123042543_qa_3" description = "Which product category has the highest number of recorded orders in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Category_019" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0123_169_123169310_qa_3/task.toml b/tasks/0123_169_123169310_qa_3/task.toml index 2adc522dbcb1c21fff0adc3092e56dd955064ecf..5b1d2f53b82df2d5c1c11bcb427bf88c449772ba 100644 --- a/tasks/0123_169_123169310_qa_3/task.toml +++ b/tasks/0123_169_123169310_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0123_169_123169310_qa_3" +name = "smoldataenvs-train/0123_169_123169310_qa_3" description = "How many entries in the Year column have missing (null) values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "271" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0123_214_123214023_qa_2/task.toml b/tasks/0123_214_123214023_qa_2/task.toml index 8cd87879ebff258bbb5d77bec83cef0722a36b76..57a9342a427d19b9a4102a1709699d7b7846a6e7 100644 --- a/tasks/0123_214_123214023_qa_2/task.toml +++ b/tasks/0123_214_123214023_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0123_214_123214023_qa_2" +name = "smoldataenvs-train/0123_214_123214023_qa_2" description = "What is the highest correlation value between any feature and house price in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.702" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0123_214_123214023_qa_5/task.toml b/tasks/0123_214_123214023_qa_5/task.toml index 1cec6d1b889b93d3ef9e1cae0d96253c93819303..a7f9bd0ee37d6331710d97c4d1d0dbc5b1c92f20 100644 --- a/tasks/0123_214_123214023_qa_5/task.toml +++ b/tasks/0123_214_123214023_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0123_214_123214023_qa_5" +name = "smoldataenvs-train/0123_214_123214023_qa_5" description = "What is the strongest negative correlation value with house price in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.053" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0123_217_123217664_qa_1/task.toml b/tasks/0123_217_123217664_qa_1/task.toml index b473cb81bc4a560bebc26709c7349f5fe91b3c40..fd848fa4c8244a3a4c6fc46b1eb201d1e97d8e05 100644 --- a/tasks/0123_217_123217664_qa_1/task.toml +++ b/tasks/0123_217_123217664_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0123_217_123217664_qa_1" +name = "smoldataenvs-train/0123_217_123217664_qa_1" description = "Which feature has the strongest negative correlation with the median home value (MEDV) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "LSTAT" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0123_217_123217664_qa_3/task.toml b/tasks/0123_217_123217664_qa_3/task.toml index 37702492d73c934149c6fea54ddc6a2c9ff9f88d..ec673425cf1ad7a2cf1812a7c89dcea722c4755e 100644 --- a/tasks/0123_217_123217664_qa_3/task.toml +++ b/tasks/0123_217_123217664_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0123_217_123217664_qa_3" +name = "smoldataenvs-train/0123_217_123217664_qa_3" description = "What is the intercept value of the trained linear regression model predicting MEDV?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "36.357041" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0123_229_123229964_qa_3/task.toml b/tasks/0123_229_123229964_qa_3/task.toml index b1ced39f21739f7bb9371fe68c197313c1f1dd28..ac89fc1ff5ddd6f72c7858e49f9fc0ff89fa6b45 100644 --- a/tasks/0123_229_123229964_qa_3/task.toml +++ b/tasks/0123_229_123229964_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0123_229_123229964_qa_3" +name = "smoldataenvs-train/0123_229_123229964_qa_3" description = "How many unique movies are included in the dataset used for analysis based on the merged dataframe dimensions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4803" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0123_284_123284662_qa_2/task.toml b/tasks/0123_284_123284662_qa_2/task.toml index af1e53677cc5746456fc6440e1e04a884ee83f3e..4ed0a7dba913c789c854c83ccc85933d86deeff6 100644 --- a/tasks/0123_284_123284662_qa_2/task.toml +++ b/tasks/0123_284_123284662_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0123_284_123284662_qa_2" +name = "smoldataenvs-train/0123_284_123284662_qa_2" description = "Which chemical property shows the strongest positive correlation with the wine quality label in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Alcohol" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0123_343_123343619_qa_2/task.toml b/tasks/0123_343_123343619_qa_2/task.toml index 2e904ed458f9d050a1356ff691ced1bf6a84d5c7..34dc9443370547bdaf1ab6aba03ce3df2ff5b106 100644 --- a/tasks/0123_343_123343619_qa_2/task.toml +++ b/tasks/0123_343_123343619_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0123_343_123343619_qa_2" +name = "smoldataenvs-train/0123_343_123343619_qa_2" description = "How many patients who survived (Survival=1) have more than the upper whisker threshold in the number of positive axillary nodes?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0123_397_123397782_qa_1/task.toml b/tasks/0123_397_123397782_qa_1/task.toml index ad53226b0d31f42c4578259155a7bfd5b10fac13..447d892c53efaa05a5a5dbaa7c6c5b6e6eae6e7f 100644 --- a/tasks/0123_397_123397782_qa_1/task.toml +++ b/tasks/0123_397_123397782_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0123_397_123397782_qa_1" +name = "smoldataenvs-train/0123_397_123397782_qa_1" description = "What is the difference in the average number of characters between spam and ham messages in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "67.43" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0123_452_123452071_qa_4/task.toml b/tasks/0123_452_123452071_qa_4/task.toml index 186df8853274518460b1e811c8abd4518b1169db..253c99b0dcb7397426a809c3352d622b323b4c9a 100644 --- a/tasks/0123_452_123452071_qa_4/task.toml +++ b/tasks/0123_452_123452071_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0123_452_123452071_qa_4" +name = "smoldataenvs-train/0123_452_123452071_qa_4" description = "How many missing values were present in the `MINIMUM_PAYMENTS` column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "313" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0123_475_123475498_qa_1/task.toml b/tasks/0123_475_123475498_qa_1/task.toml index 31e2354b07a76e0400e71ba3acf0bba13dcd47b7..9f1140c39ae34e3ec70b74073e3f724ae75ae57d 100644 --- a/tasks/0123_475_123475498_qa_1/task.toml +++ b/tasks/0123_475_123475498_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0123_475_123475498_qa_1" +name = "smoldataenvs-train/0123_475_123475498_qa_1" description = "Which video game genre has the highest number of entries in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0123_494_123494250_qa_3/task.toml b/tasks/0123_494_123494250_qa_3/task.toml index d53e817c3f879c7d3485ff7f4a267b18c17a6765..47af4aee42cdfc2b65bf972171049e8672e6c48b 100644 --- a/tasks/0123_494_123494250_qa_3/task.toml +++ b/tasks/0123_494_123494250_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0123_494_123494250_qa_3" +name = "smoldataenvs-train/0123_494_123494250_qa_3" description = "How many games in the dataset had missing publisher information before imputation with \"no details\"?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "58" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0123_494_123494250_qa_4/task.toml b/tasks/0123_494_123494250_qa_4/task.toml index 24bfbfa7321182ceae8aaf82694229422115fb3f..a4a06d259786c9f175fd129e4ce7ab2c1fe90b67 100644 --- a/tasks/0123_494_123494250_qa_4/task.toml +++ b/tasks/0123_494_123494250_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0123_494_123494250_qa_4" +name = "smoldataenvs-train/0123_494_123494250_qa_4" description = "What is the average year of release for all games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2006.41" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0123_500_123500011_qa_1/task.toml b/tasks/0123_500_123500011_qa_1/task.toml index fa0a39d794b374fc7db8f155100531297eccd802..37a6d2bd6755d03807935e9c00a312e32ff44016 100644 --- a/tasks/0123_500_123500011_qa_1/task.toml +++ b/tasks/0123_500_123500011_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0123_500_123500011_qa_1" +name = "smoldataenvs-train/0123_500_123500011_qa_1" description = "Which Pokémon has the highest total stat sum (Total) in the dataset, and what is the value of that total?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Mega Rayquaza, 780" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0123_500_123500011_qa_2/task.toml b/tasks/0123_500_123500011_qa_2/task.toml index e53edc5bdb6c5eb7c3827fc84073aac0b3461ce0..fad36e223ff12f7dda1e030d4e448b3a42324f35 100644 --- a/tasks/0123_500_123500011_qa_2/task.toml +++ b/tasks/0123_500_123500011_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0123_500_123500011_qa_2" +name = "smoldataenvs-train/0123_500_123500011_qa_2" description = "Which Pokémon type has the highest number of Pokémon in the dataset, and how many Pokémon belong to that type?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Water type with 112 Pokémon." reward_mode_initial = "flexible" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0123_500_123500011_qa_3/task.toml b/tasks/0123_500_123500011_qa_3/task.toml index fc6084157f2bd7d293da1485e3531da79c4ccdcd..dcb8e696da6f563dd44145b3a4f1f0f915c5bb7b 100644 --- a/tasks/0123_500_123500011_qa_3/task.toml +++ b/tasks/0123_500_123500011_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0123_500_123500011_qa_3" +name = "smoldataenvs-train/0123_500_123500011_qa_3" description = "What is the highest attack power value among all Pokémon, and which Pokémon holds this value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "190, Mega Mewtwo X" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0123_500_123500011_qa_5/task.toml b/tasks/0123_500_123500011_qa_5/task.toml index f2fb2be4e6e2e98c47e23153954a82a93f02dd42..91900242925b0a9f72eae7ed7efaf8d7a6f88c94 100644 --- a/tasks/0123_500_123500011_qa_5/task.toml +++ b/tasks/0123_500_123500011_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0123_500_123500011_qa_5" +name = "smoldataenvs-train/0123_500_123500011_qa_5" description = "How many unique primary types (Type 1) are present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "18" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0123_517_123517874_qa_3/task.toml b/tasks/0123_517_123517874_qa_3/task.toml index f704c4ce7f95972fc14dc1b6f73e2647b4b09ade..a89f239221d25474b86262303b8d9a4eed2dfaf8 100644 --- a/tasks/0123_517_123517874_qa_3/task.toml +++ b/tasks/0123_517_123517874_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0123_517_123517874_qa_3" +name = "smoldataenvs-train/0123_517_123517874_qa_3" description = "What is the mean 1987 salary for players in the Eastern division compared to the Western division?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Eastern=624.27, Western=450.88" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0123_535_123535058_qa_4/task.toml b/tasks/0123_535_123535058_qa_4/task.toml index 9f8468493ce81cdcfcf794e46e4dd34169dfd2ef..d02d13c1145af724317dd0aecc6e9a78a197ba6b 100644 --- a/tasks/0123_535_123535058_qa_4/task.toml +++ b/tasks/0123_535_123535058_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0123_535_123535058_qa_4" +name = "smoldataenvs-train/0123_535_123535058_qa_4" description = "What is the standard deviation of global sales values across all video games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.5550279355699124" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0123_535_123535058_qa_5/task.toml b/tasks/0123_535_123535058_qa_5/task.toml index efedd801f6da38d92235215f3daa6023430a9fa3..4e154697a704a284624b0fde0cedd5e02e06af3f 100644 --- a/tasks/0123_535_123535058_qa_5/task.toml +++ b/tasks/0123_535_123535058_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0123_535_123535058_qa_5" +name = "smoldataenvs-train/0123_535_123535058_qa_5" description = "What is the most frequently occurring global sales value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.02" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0123_539_123539357_qa_2/task.toml b/tasks/0123_539_123539357_qa_2/task.toml index ce673ba41e217ff80fa3f6f31fd68e91a00e6d5b..af5db8865ca820ee12610fc23912e80dde0b87b6 100644 --- a/tasks/0123_539_123539357_qa_2/task.toml +++ b/tasks/0123_539_123539357_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0123_539_123539357_qa_2" +name = "smoldataenvs-train/0123_539_123539357_qa_2" description = "What is the Spearman correlation coefficient between carat and volume in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.99906306" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0123_625_123625013_qa_3/task.toml b/tasks/0123_625_123625013_qa_3/task.toml index fa42996e235051888cd2fb4b2038b4f3b24ba4fb..89069b9029b51074948a42c072b0fbc7503ce16f 100644 --- a/tasks/0123_625_123625013_qa_3/task.toml +++ b/tasks/0123_625_123625013_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0123_625_123625013_qa_3" +name = "smoldataenvs-train/0123_625_123625013_qa_3" description = "In Shenandoah National Park, which category has the highest number of Endangered species?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Vascular Plant" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0123_625_123625013_qa_4/task.toml b/tasks/0123_625_123625013_qa_4/task.toml index 15373aa7410ef2b2c41bfb6eda4a9c2bdf0bacfd..f654a2d28886443e68517a04450bf8eaedffa71e 100644 --- a/tasks/0123_625_123625013_qa_4/task.toml +++ b/tasks/0123_625_123625013_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0123_625_123625013_qa_4" +name = "smoldataenvs-train/0123_625_123625013_qa_4" description = "How many Fish species in Acadia National Park are classified as Endangered?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0123_625_123625013_qa_5/task.toml b/tasks/0123_625_123625013_qa_5/task.toml index abe864c54f33e4fcb5b3660e30ce9970a52539e3..c8dc89e4971189f3c44e93f791cb67a252e258c4 100644 --- a/tasks/0123_625_123625013_qa_5/task.toml +++ b/tasks/0123_625_123625013_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0123_625_123625013_qa_5" +name = "smoldataenvs-train/0123_625_123625013_qa_5" description = "How many Reptile species in Shenandoah National Park are classified as \"Species of Concern\"?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0123_643_123643083_qa_2/task.toml b/tasks/0123_643_123643083_qa_2/task.toml index 3aa1222bfe0187d32b0597aa7ae5a9de1175ce0a..6f9aac7f4d29197e150349b6b7b765816344d544 100644 --- a/tasks/0123_643_123643083_qa_2/task.toml +++ b/tasks/0123_643_123643083_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0123_643_123643083_qa_2" +name = "smoldataenvs-train/0123_643_123643083_qa_2" description = "Which feature in the dataset has the highest number of unique values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "charges" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0123_672_123672952_qa_2/task.toml b/tasks/0123_672_123672952_qa_2/task.toml index 1888193e42b05c8a6b61c663e420c8a588335b17..91390f5d15930a95dc5c9d66ff08503206c294fc 100644 --- a/tasks/0123_672_123672952_qa_2/task.toml +++ b/tasks/0123_672_123672952_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0123_672_123672952_qa_2" +name = "smoldataenvs-train/0123_672_123672952_qa_2" description = "How many records were removed due to missing values in the 'bare_nucleoli' feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0123_676_123676836_qa_5/task.toml b/tasks/0123_676_123676836_qa_5/task.toml index ec99cf102c09d8d989857c0b7447fe1eb14797be..c7830815683fbdac30d461e5b899773815d6ae8a 100644 --- a/tasks/0123_676_123676836_qa_5/task.toml +++ b/tasks/0123_676_123676836_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0123_676_123676836_qa_5" +name = "smoldataenvs-train/0123_676_123676836_qa_5" description = "Which video game genre has the highest number of produced games according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0123_734_123734284_qa_1/task.toml b/tasks/0123_734_123734284_qa_1/task.toml index ed59c68f31d33b264419b87a8621d1f37fc4e700..edc8e20867eeb6192bdc2189917620b55440bd29 100644 --- a/tasks/0123_734_123734284_qa_1/task.toml +++ b/tasks/0123_734_123734284_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0123_734_123734284_qa_1" +name = "smoldataenvs-train/0123_734_123734284_qa_1" description = "What is the total number of samples in the Boston housing dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "506" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0123_735_123735039_qa_4/task.toml b/tasks/0123_735_123735039_qa_4/task.toml index 4e4586000abe48c630266b407f83590885cdbde2..b30a86ad5297498e982cad49c27056b5d11e2348 100644 --- a/tasks/0123_735_123735039_qa_4/task.toml +++ b/tasks/0123_735_123735039_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0123_735_123735039_qa_4" +name = "smoldataenvs-train/0123_735_123735039_qa_4" description = "What percentage of total global sales is attributed to North America?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "49.1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0123_735_123735039_qa_5/task.toml b/tasks/0123_735_123735039_qa_5/task.toml index 20e83be2a7766af4a75cdeacfe819ba75d72a2ab..98398a700ff1832276f9bb379786e3f1e7c93d4f 100644 --- a/tasks/0123_735_123735039_qa_5/task.toml +++ b/tasks/0123_735_123735039_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0123_735_123735039_qa_5" +name = "smoldataenvs-train/0123_735_123735039_qa_5" description = "Which publisher has the highest number of games listed in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic Arts" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0123_785_123785154_qa_3/task.toml b/tasks/0123_785_123785154_qa_3/task.toml index 6894a5a7b3f68fe945cd9e2aafa4acbeb8c53874..a5510bf98abb67a91ab44fb34dee478d46a22480 100644 --- a/tasks/0123_785_123785154_qa_3/task.toml +++ b/tasks/0123_785_123785154_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0123_785_123785154_qa_3" +name = "smoldataenvs-train/0123_785_123785154_qa_3" description = "What is the average credit limit (LIMIT_BAL) for clients with a high school education (EDUCATION=3) after removing unknown categories?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "126550.27" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0123_884_123884915_qa_1/task.toml b/tasks/0123_884_123884915_qa_1/task.toml index 4dc89910f28f05d59948f271114a7b5c678dae5e..5dd3e21fd749f267dc10737eb5aed20726765447 100644 --- a/tasks/0123_884_123884915_qa_1/task.toml +++ b/tasks/0123_884_123884915_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0123_884_123884915_qa_1" +name = "smoldataenvs-train/0123_884_123884915_qa_1" description = "Which year had the highest average revenue (in millions) per movie based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2009" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0123_884_123884915_qa_5/task.toml b/tasks/0123_884_123884915_qa_5/task.toml index 42a47aa2e9dfec02e3eb236cb39871481d9a2cec..26c0be87baf41e3831286c424afc98679c88e3bb 100644 --- a/tasks/0123_884_123884915_qa_5/task.toml +++ b/tasks/0123_884_123884915_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0123_884_123884915_qa_5" +name = "smoldataenvs-train/0123_884_123884915_qa_5" description = "Which year has the highest average movie rating in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2006" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0123_993_123993680_qa_1/task.toml b/tasks/0123_993_123993680_qa_1/task.toml index e5ff290b2c31aae3b8a5960a1db525c32362ac87..55276bd6237c393f23c9a59c423f50d5ad3eca02 100644 --- a/tasks/0123_993_123993680_qa_1/task.toml +++ b/tasks/0123_993_123993680_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0123_993_123993680_qa_1" +name = "smoldataenvs-train/0123_993_123993680_qa_1" description = "What is the ratio between retained customers and churned customers in the dataset before applying SMOTE-ENN oversampling?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.76" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0123_993_123993680_qa_2/task.toml b/tasks/0123_993_123993680_qa_2/task.toml index b0bdf7646f59276db767a404a568bc95abedc67a..217f7198eabcfd6330188ee4acf5c6fd3a14694c 100644 --- a/tasks/0123_993_123993680_qa_2/task.toml +++ b/tasks/0123_993_123993680_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0123_993_123993680_qa_2" +name = "smoldataenvs-train/0123_993_123993680_qa_2" description = "Which tenure group has the highest churn rate according to the grouped analysis in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0-12 months" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0124_096_124096448_qa_1/task.toml b/tasks/0124_096_124096448_qa_1/task.toml index 6f2c83eef63b2eaeff46702e84a001cdc3133c8b..d82099ea89620d9aae748dd200fd43b02af9a8ac 100644 --- a/tasks/0124_096_124096448_qa_1/task.toml +++ b/tasks/0124_096_124096448_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0124_096_124096448_qa_1" +name = "smoldataenvs-train/0124_096_124096448_qa_1" description = "According to the linear regression model, what is the estimated increase in insurance charges for smokers compared to non-smokers?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "23647.82" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0124_179_124179717_qa_1/task.toml b/tasks/0124_179_124179717_qa_1/task.toml index 43d05ae99cd72ae97ea79ea51e3ea6f44fa863a4..5a773acd8b1743148f9ac9911a14f858a94efff5 100644 --- a/tasks/0124_179_124179717_qa_1/task.toml +++ b/tasks/0124_179_124179717_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0124_179_124179717_qa_1" +name = "smoldataenvs-train/0124_179_124179717_qa_1" description = "How many duplicate rows are present in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "240" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0124_179_124179717_qa_2/task.toml b/tasks/0124_179_124179717_qa_2/task.toml index 326da8ed4b77c5d924d155738b122532c22bbee2..45e15ba1f0a8d66cf3a84e9f06e8a3d3d195a7b8 100644 --- a/tasks/0124_179_124179717_qa_2/task.toml +++ b/tasks/0124_179_124179717_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0124_179_124179717_qa_2" +name = "smoldataenvs-train/0124_179_124179717_qa_2" description = "What is the average pH level of the wines in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.311" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0124_179_124179717_qa_3/task.toml b/tasks/0124_179_124179717_qa_3/task.toml index c950e4e751dbae6a72c40d14d6d3a5141edc6084..884b4d6d4a2d4f9f05578a8b572f1a84c0ad4bc1 100644 --- a/tasks/0124_179_124179717_qa_3/task.toml +++ b/tasks/0124_179_124179717_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0124_179_124179717_qa_3" +name = "smoldataenvs-train/0124_179_124179717_qa_3" description = "Which feature has the highest standard deviation in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "total sulfur dioxide" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0124_179_124179717_qa_4/task.toml b/tasks/0124_179_124179717_qa_4/task.toml index fc269880168bf058174e53498f2fea8a5c5ea191..0406f0c8fb0caf025e237f734786d41dde48f689 100644 --- a/tasks/0124_179_124179717_qa_4/task.toml +++ b/tasks/0124_179_124179717_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0124_179_124179717_qa_4" +name = "smoldataenvs-train/0124_179_124179717_qa_4" description = "What is the minimum 'residual sugar' content found in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0124_195_124195561_qa_1/task.toml b/tasks/0124_195_124195561_qa_1/task.toml index 7d150d8acc2e2ed01d49adc50850ea95047ae63d..ad62175b3ce05d180a9481c7e180a943ee45a0e3 100644 --- a/tasks/0124_195_124195561_qa_1/task.toml +++ b/tasks/0124_195_124195561_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0124_195_124195561_qa_1" +name = "smoldataenvs-train/0124_195_124195561_qa_1" description = "What is the percentage of subjects in the final merged dataset classified as having Alzheimer's disease (Demented) after data preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "39.14" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0124_231_124231950_qa_3/task.toml b/tasks/0124_231_124231950_qa_3/task.toml index eeba47f0721b4e628a2a3dd7ca17a50750c9fde7..88ac6c4a27c61389d59b05156a2699754daf78e8 100644 --- a/tasks/0124_231_124231950_qa_3/task.toml +++ b/tasks/0124_231_124231950_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0124_231_124231950_qa_3" +name = "smoldataenvs-train/0124_231_124231950_qa_3" description = "Based on the cosine similarity matrix, what is the similarity score between the first movie (index 0) and the second movie (index 1) in the cleaned dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.01515217" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0124_259_124259674_qa_1/task.toml b/tasks/0124_259_124259674_qa_1/task.toml index 01dbb0f62752166297455927b45464e9394f2b07..48f95332fe5b0740104b22848fb280f5d8ffb4aa 100644 --- a/tasks/0124_259_124259674_qa_1/task.toml +++ b/tasks/0124_259_124259674_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0124_259_124259674_qa_1" +name = "smoldataenvs-train/0124_259_124259674_qa_1" description = "What is the percentage of customers who churned in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26.5" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0124_259_124259674_qa_3/task.toml b/tasks/0124_259_124259674_qa_3/task.toml index 687f95b3e2e1dc7c31581acd69c33956be115a7a..0ccd2c70bb14f1336b70aa6ad43698f9260cdf9c 100644 --- a/tasks/0124_259_124259674_qa_3/task.toml +++ b/tasks/0124_259_124259674_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0124_259_124259674_qa_3" +name = "smoldataenvs-train/0124_259_124259674_qa_3" description = "What is the mean monthly charge difference between customers who churned and those who remained?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13.176" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0124_269_124269009_qa_3/task.toml b/tasks/0124_269_124269009_qa_3/task.toml index 1b343608eacff6c36345ad06c44a165df42f04e3..ecfe5face5895ae6520669712e2f3f7a89bf87ad 100644 --- a/tasks/0124_269_124269009_qa_3/task.toml +++ b/tasks/0124_269_124269009_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0124_269_124269009_qa_3" +name = "smoldataenvs-train/0124_269_124269009_qa_3" description = "Which movie genre is the most frequent, and how many movies are classified under it?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Drama, 513" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] build_timeout_sec = 600.0 diff --git a/tasks/0124_309_124309485_qa_5/task.toml b/tasks/0124_309_124309485_qa_5/task.toml index 3ed9d54c1adb86b1058d34eb39e4e939cd09a893..8245515d0074d07911b822f562274737a0fefac1 100644 --- a/tasks/0124_309_124309485_qa_5/task.toml +++ b/tasks/0124_309_124309485_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0124_309_124309485_qa_5" +name = "smoldataenvs-train/0124_309_124309485_qa_5" description = "After modifying the Global_Sales column by adding 10 and then multiplying by 2, what is the value of the first entry in this column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "185.48" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0124_334_124334340_qa_1/task.toml b/tasks/0124_334_124334340_qa_1/task.toml index c6fa742fa3bb6022ba1ec2fa46e1ef25a0ebe348..1ccea481dc1e8ffc75c6158da3791290a3227f02 100644 --- a/tasks/0124_334_124334340_qa_1/task.toml +++ b/tasks/0124_334_124334340_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0124_334_124334340_qa_1" +name = "smoldataenvs-train/0124_334_124334340_qa_1" description = "What is the skewness of the log-transformed car price distribution after applying the log1p transformation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.917868" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0124_334_124334340_qa_4/task.toml b/tasks/0124_334_124334340_qa_4/task.toml index 95d1349a0b7f4c0b14b9ae679e48fc9d52cbef3d..cf37e82273238220bcfc02321e04b249ca1a388b 100644 --- a/tasks/0124_334_124334340_qa_4/task.toml +++ b/tasks/0124_334_124334340_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0124_334_124334340_qa_4" +name = "smoldataenvs-train/0124_334_124334340_qa_4" description = "What percentage of the 'market_category' column contained missing values in the original dataset before any imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "31.41" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0124_358_124358327_qa_3/task.toml b/tasks/0124_358_124358327_qa_3/task.toml index f0a25282f8745b936a5b79884b7eec356f7996b4..4f35d5af1ec325c0ebf85e099823cc2b56cf8189 100644 --- a/tasks/0124_358_124358327_qa_3/task.toml +++ b/tasks/0124_358_124358327_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0124_358_124358327_qa_3" +name = "smoldataenvs-train/0124_358_124358327_qa_3" description = "What is the ratio of training data size to testing data size after dataset splitting?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4:1" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0124_384_124384181_qa_1/task.toml b/tasks/0124_384_124384181_qa_1/task.toml index 6074db5ea5ef808c8f0d95680462f22ce94a8dc3..b65947e982ffdbfe228f4201c6cb348d1d1196f4 100644 --- a/tasks/0124_384_124384181_qa_1/task.toml +++ b/tasks/0124_384_124384181_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0124_384_124384181_qa_1" +name = "smoldataenvs-train/0124_384_124384181_qa_1" description = "What is the final value of the parameter w after 5 iterations of gradient descent?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.0031" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0124_384_124384181_qa_3/task.toml b/tasks/0124_384_124384181_qa_3/task.toml index b37cbbba817c8e63746b3fcd3b3add3b08bde4e2..a2498fe984f4ae0cdf948c9dff90a45f14c18931 100644 --- a/tasks/0124_384_124384181_qa_3/task.toml +++ b/tasks/0124_384_124384181_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0124_384_124384181_qa_3" +name = "smoldataenvs-train/0124_384_124384181_qa_3" description = "How many data points were identified as outliers and removed during the data preprocessing step?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0124_418_124418283_qa_2/task.toml b/tasks/0124_418_124418283_qa_2/task.toml index de8c481e97bda5d56a60eaae66d600b87438b7cf..793fc3128afc5e3b459fa00a508cfa080c1d0e06 100644 --- a/tasks/0124_418_124418283_qa_2/task.toml +++ b/tasks/0124_418_124418283_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0124_418_124418283_qa_2" +name = "smoldataenvs-train/0124_418_124418283_qa_2" description = "How many duplicate records were removed during the data preprocessing phase?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "240" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0124_418_124418283_qa_5/task.toml b/tasks/0124_418_124418283_qa_5/task.toml index 000f8af7284a9c4676e7c1ecd65bf64dfafa04cd..feeee9571e7037cb1fcaec6e0f124238618c649c 100644 --- a/tasks/0124_418_124418283_qa_5/task.toml +++ b/tasks/0124_418_124418283_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0124_418_124418283_qa_5" +name = "smoldataenvs-train/0124_418_124418283_qa_5" description = "What is the final number of samples in the dataset after duplicate records were removed?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1359" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0124_488_124488738_qa_1/task.toml b/tasks/0124_488_124488738_qa_1/task.toml index eef823ff8f19be0775e0072540444562f54daa39..45ec281a0d64e67260fcb33951b02224fc989a21 100644 --- a/tasks/0124_488_124488738_qa_1/task.toml +++ b/tasks/0124_488_124488738_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0124_488_124488738_qa_1" +name = "smoldataenvs-train/0124_488_124488738_qa_1" description = "Which platform has the highest total global sales according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PS2" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0124_563_124563125_qa_5/task.toml b/tasks/0124_563_124563125_qa_5/task.toml index fe1fd90e20cd624ec4293ddd8b70b56564c0f510..e6f260942ec30b2e974ee39f364d7e12edf5c821 100644 --- a/tasks/0124_563_124563125_qa_5/task.toml +++ b/tasks/0124_563_124563125_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0124_563_124563125_qa_5" +name = "smoldataenvs-train/0124_563_124563125_qa_5" description = "What is the difference between the 75th percentile and 25th percentile of the area_worst feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "569.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0124_606_124606600_qa_2/task.toml b/tasks/0124_606_124606600_qa_2/task.toml index 2c0ce79a8206689d718a6bca048a9a3c39a3c57c..4da65e22300a36a296c96d7824f93f85e9f80ea4 100644 --- a/tasks/0124_606_124606600_qa_2/task.toml +++ b/tasks/0124_606_124606600_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0124_606_124606600_qa_2" +name = "smoldataenvs-train/0124_606_124606600_qa_2" description = "Which game has the largest difference between North American sales and international sales in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Duck Hunt" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0124_606_124606600_qa_3/task.toml b/tasks/0124_606_124606600_qa_3/task.toml index 8fd52db5bd9a954a29ee19da0fce9f3d096a761b..9e91e6be45e1e3a410f98bac1a7295980b72658e 100644 --- a/tasks/0124_606_124606600_qa_3/task.toml +++ b/tasks/0124_606_124606600_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0124_606_124606600_qa_3" +name = "smoldataenvs-train/0124_606_124606600_qa_3" description = "How many standard deviations above the mean are the North American sales of the highest-selling game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50.48" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0124_615_124615899_qa_1/task.toml b/tasks/0124_615_124615899_qa_1/task.toml index a7a76ff2b471c6d15f6337764eecfcce01018d54..4bec64497df7f4cfef7cf698d90349b3ab7e181d 100644 --- a/tasks/0124_615_124615899_qa_1/task.toml +++ b/tasks/0124_615_124615899_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0124_615_124615899_qa_1" +name = "smoldataenvs-train/0124_615_124615899_qa_1" description = "Which video game platform has the highest average global sales per game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "GB" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0124_615_124615899_qa_5/task.toml b/tasks/0124_615_124615899_qa_5/task.toml index 24ff6b5897c8c6305505c9d312793732cf881f7a..3bf4a6cc6d81c7d4e4180d2a8d98e7a586464e0b 100644 --- a/tasks/0124_615_124615899_qa_5/task.toml +++ b/tasks/0124_615_124615899_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0124_615_124615899_qa_5" +name = "smoldataenvs-train/0124_615_124615899_qa_5" description = "Which video game genre appears most frequently in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0124_651_124651158_qa_1/task.toml b/tasks/0124_651_124651158_qa_1/task.toml index 9f62ce4de4a35766827355666a4e8ff3217cdff4..93f49610a2101ff2d24884ad1c5fda644b171aa8 100644 --- a/tasks/0124_651_124651158_qa_1/task.toml +++ b/tasks/0124_651_124651158_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0124_651_124651158_qa_1" +name = "smoldataenvs-train/0124_651_124651158_qa_1" description = "What is the difference in average global sales between the platform with the highest average (GB) and the platform with the next highest (NES) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.044694" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0124_739_124739657_qa_1/task.toml b/tasks/0124_739_124739657_qa_1/task.toml index d56c4e4af19ae6d7496e7ad95670ef0209862160..43a55e910dc60d9234504885478b9d51b818c368 100644 --- a/tasks/0124_739_124739657_qa_1/task.toml +++ b/tasks/0124_739_124739657_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0124_739_124739657_qa_1" +name = "smoldataenvs-train/0124_739_124739657_qa_1" description = "What is the highest total sales figure for any game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "82.74" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0124_919_124919449_qa_1/task.toml b/tasks/0124_919_124919449_qa_1/task.toml index aaba0c83ff255e5d7878979a7b0b87d9e3f3aa98..52af01649c696fd8903d8ffa0263d13ac83400aa 100644 --- a/tasks/0124_919_124919449_qa_1/task.toml +++ b/tasks/0124_919_124919449_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0124_919_124919449_qa_1" +name = "smoldataenvs-train/0124_919_124919449_qa_1" description = "Which sales method (Method) has the highest number of transactions, and what is the total count of those transactions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "S, 12034" reward_mode_initial = "list" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0124_947_124947396_qa_4/task.toml b/tasks/0124_947_124947396_qa_4/task.toml index c788cdafcd4e2153f4547643c9ba9e7ecccb93e6..abd5a54874baa4609f2bcbb56ca41cf32036e644 100644 --- a/tasks/0124_947_124947396_qa_4/task.toml +++ b/tasks/0124_947_124947396_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0124_947_124947396_qa_4" +name = "smoldataenvs-train/0124_947_124947396_qa_4" description = "What is the most frequent value in the 'CouncilArea' column after imputing missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Moreland" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0124_965_124965722_qa_1/task.toml b/tasks/0124_965_124965722_qa_1/task.toml index 77ec29273d480217860c5ce415184c97a5e2f60f..7e00283566b3313406ac168abea5cb4321ee4d3c 100644 --- a/tasks/0124_965_124965722_qa_1/task.toml +++ b/tasks/0124_965_124965722_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0124_965_124965722_qa_1" +name = "smoldataenvs-train/0124_965_124965722_qa_1" description = "What is the percentage of female patients who did not attend their appointments in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20.31" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0124_965_124965722_qa_2/task.toml b/tasks/0124_965_124965722_qa_2/task.toml index 38157814a01950519eb2f1a1a0ce71553bd844cd..0d33524250d462f6b0657c79e929249af7eecc26 100644 --- a/tasks/0124_965_124965722_qa_2/task.toml +++ b/tasks/0124_965_124965722_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0124_965_124965722_qa_2" +name = "smoldataenvs-train/0124_965_124965722_qa_2" description = "What is the percentage of male patients who did not attend their appointments in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "19.97" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0124_966_124966489_qa_1/task.toml b/tasks/0124_966_124966489_qa_1/task.toml index f6f099fb9b1ef36d8679f33c25f292bee3ac832c..7693877455d5e6250196c990645b5607a6498767 100644 --- a/tasks/0124_966_124966489_qa_1/task.toml +++ b/tasks/0124_966_124966489_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0124_966_124966489_qa_1" +name = "smoldataenvs-train/0124_966_124966489_qa_1" description = "Which property type (Type) has the highest number of entries in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "h" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0124_966_124966489_qa_5/task.toml b/tasks/0124_966_124966489_qa_5/task.toml index ca788fe2dd5dc1579dd0fced87cdb907df550ea5..8e2bd12f5e27f2cf5c5dd70e347b0536dd313c9c 100644 --- a/tasks/0124_966_124966489_qa_5/task.toml +++ b/tasks/0124_966_124966489_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0124_966_124966489_qa_5" +name = "smoldataenvs-train/0124_966_124966489_qa_5" description = "Which property type (Type) corresponds to the highest average price in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "h" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0124_973_124973460_qa_5/task.toml b/tasks/0124_973_124973460_qa_5/task.toml index 1a7b35636c6033918106b3f3ea6a09372d69a6f5..2cc777492695e3d0916dea5e69c9df592daabcd9 100644 --- a/tasks/0124_973_124973460_qa_5/task.toml +++ b/tasks/0124_973_124973460_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0124_973_124973460_qa_5" +name = "smoldataenvs-train/0124_973_124973460_qa_5" description = "What is the percentage of missing values in the \"Insulin\" variable after zero replacement?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "48.7" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0125_028_125028860_qa_5/task.toml b/tasks/0125_028_125028860_qa_5/task.toml index 45c8a4b263c175c949548426d6a4e783888b5eb7..74cecf518c66fbe2c9f6f2ee8d090919c60431a5 100644 --- a/tasks/0125_028_125028860_qa_5/task.toml +++ b/tasks/0125_028_125028860_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0125_028_125028860_qa_5" +name = "smoldataenvs-train/0125_028_125028860_qa_5" description = "How many unique LEGO themes were present in the year 2015?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "98" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0125_030_125030755_qa_2/task.toml b/tasks/0125_030_125030755_qa_2/task.toml index 62af38677b2c9c48723de4bc2f5314377107e012..addb2f841081074e9ec5d55faea999d76d450e52 100644 --- a/tasks/0125_030_125030755_qa_2/task.toml +++ b/tasks/0125_030_125030755_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0125_030_125030755_qa_2" +name = "smoldataenvs-train/0125_030_125030755_qa_2" description = "Which primary type (Type 1) is most common among legendary Pokémon in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Psychic" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0125_045_125045351_qa_1/task.toml b/tasks/0125_045_125045351_qa_1/task.toml index b1af542cf2d83091f0243d6422f51e7b2272e027..1304f7d3e4207fab43b84a70656bd5a7d2e304f9 100644 --- a/tasks/0125_045_125045351_qa_1/task.toml +++ b/tasks/0125_045_125045351_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0125_045_125045351_qa_1" +name = "smoldataenvs-train/0125_045_125045351_qa_1" description = "Does gender affect the attendance rate for medical appointments in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0125_045_125045351_qa_4/task.toml b/tasks/0125_045_125045351_qa_4/task.toml index 31ba4bfec9148cecf72e527b701bdc41cbd79936..8a1465d79bff3779d6f79740da672d585bb5dd89 100644 --- a/tasks/0125_045_125045351_qa_4/task.toml +++ b/tasks/0125_045_125045351_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0125_045_125045351_qa_4" +name = "smoldataenvs-train/0125_045_125045351_qa_4" description = "What is the attendance rate for patients aged 1-20 in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "77.19" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0125_045_125045351_qa_5/task.toml b/tasks/0125_045_125045351_qa_5/task.toml index e1a25ccb57225d6ff817aea0398dca44e335214f..f652bff40e63327423767d9739dca796dae48622 100644 --- a/tasks/0125_045_125045351_qa_5/task.toml +++ b/tasks/0125_045_125045351_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0125_045_125045351_qa_5" +name = "smoldataenvs-train/0125_045_125045351_qa_5" description = "What is the attendance rate for patients aged 41-60 in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "81.41" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0125_115_125115132_qa_1/task.toml b/tasks/0125_115_125115132_qa_1/task.toml index a4ff1450e8c383c704aae1e3499bf934ab8d73ac..08acff5dab8d057227bdfa539d69ab5980832b01 100644 --- a/tasks/0125_115_125115132_qa_1/task.toml +++ b/tasks/0125_115_125115132_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0125_115_125115132_qa_1" +name = "smoldataenvs-train/0125_115_125115132_qa_1" description = "What is the mean glucose level for patients diagnosed with diabetes compared to those without diabetes?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "141.26, 109.98" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0125_124_125124726_qa_1/task.toml b/tasks/0125_124_125124726_qa_1/task.toml index ca02fd403ad410e8c9760a6270647ada16fd7bcb..1672bbeb0440d5eed87c84ace0fda0746e1c09de 100644 --- a/tasks/0125_124_125124726_qa_1/task.toml +++ b/tasks/0125_124_125124726_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0125_124_125124726_qa_1" +name = "smoldataenvs-train/0125_124_125124726_qa_1" description = "Do spam messages have a significantly higher median character count compared to ham messages in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0125_140_125140765_qa_1/task.toml b/tasks/0125_140_125140765_qa_1/task.toml index ed06ffe8fcf1f9a48fcf71836d3f97fa522840bb..82ebaca2711d3e592768f33b1a50caee7e3d3795 100644 --- a/tasks/0125_140_125140765_qa_1/task.toml +++ b/tasks/0125_140_125140765_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0125_140_125140765_qa_1" +name = "smoldataenvs-train/0125_140_125140765_qa_1" description = "Which U.S. state has the highest poverty rate according to the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Mississippi" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0125_188_125188137_qa_1/task.toml b/tasks/0125_188_125188137_qa_1/task.toml index d324c06c32ff05a996153fd6349486df6ab529a6..1b9e59314cdaeef35fff2c3a2b062c54c7fa6172 100644 --- a/tasks/0125_188_125188137_qa_1/task.toml +++ b/tasks/0125_188_125188137_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0125_188_125188137_qa_1" +name = "smoldataenvs-train/0125_188_125188137_qa_1" description = "Which cluster has the highest average credit limit based on the Gaussian Mixture Model clustering results?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0125_200_125200118_qa_3/task.toml b/tasks/0125_200_125200118_qa_3/task.toml index a3b4849df9b1eb322f94fec34ee514b5122da24d..7ec279349b3c8820ea2686d8c6e52a29b944b76e 100644 --- a/tasks/0125_200_125200118_qa_3/task.toml +++ b/tasks/0125_200_125200118_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0125_200_125200118_qa_3" +name = "smoldataenvs-train/0125_200_125200118_qa_3" description = "What calendar year recorded the highest total global video game sales?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2008" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0125_200_125200118_qa_4/task.toml b/tasks/0125_200_125200118_qa_4/task.toml index ae79d469a81d47f1b7a739339aafdaefd7df8443..4c57df806e105651be8d910544c02b0b2983f6dd 100644 --- a/tasks/0125_200_125200118_qa_4/task.toml +++ b/tasks/0125_200_125200118_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0125_200_125200118_qa_4" +name = "smoldataenvs-train/0125_200_125200118_qa_4" description = "Which video game genre has generated the highest total global sales?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0125_252_125252436_qa_3/task.toml b/tasks/0125_252_125252436_qa_3/task.toml index 675861bc3a602cb0d8a0c0637565e4d845b43022..b80f6f9246da4067a875df0843b2a24e19f0513b 100644 --- a/tasks/0125_252_125252436_qa_3/task.toml +++ b/tasks/0125_252_125252436_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0125_252_125252436_qa_3" +name = "smoldataenvs-train/0125_252_125252436_qa_3" description = "Which team has the highest average build-up play speed based on Team_Attributes data with more than 4 attribute records?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Borussia Dortmund" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0125_283_125283115_qa_4/task.toml b/tasks/0125_283_125283115_qa_4/task.toml index fa1a10d6b0de7de5740a1e72f36076f363920d2a..b0bef8b53d5d87acd60f1d1b7a934c1257ccdeba 100644 --- a/tasks/0125_283_125283115_qa_4/task.toml +++ b/tasks/0125_283_125283115_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0125_283_125283115_qa_4" +name = "smoldataenvs-train/0125_283_125283115_qa_4" description = "What is the correlation coefficient between age and medical insurance charges in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.28" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0125_377_125377376_qa_5/task.toml b/tasks/0125_377_125377376_qa_5/task.toml index 14f708f5e4f253a42995e8df5fcf97d0da4bc79a..22450066ef5382f0f463e5aae6852d0301a93527 100644 --- a/tasks/0125_377_125377376_qa_5/task.toml +++ b/tasks/0125_377_125377376_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0125_377_125377376_qa_5" +name = "smoldataenvs-train/0125_377_125377376_qa_5" description = "What percentage of mobile phones in the dataset support 4G connectivity?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "52.15" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0125_379_125379106_qa_1/task.toml b/tasks/0125_379_125379106_qa_1/task.toml index 307ff1e4067104deb43df6d68026dd2b407282c9..596bbe8792a74bf8316282d5be328c9c8ce00951 100644 --- a/tasks/0125_379_125379106_qa_1/task.toml +++ b/tasks/0125_379_125379106_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0125_379_125379106_qa_1" +name = "smoldataenvs-train/0125_379_125379106_qa_1" description = "What is the average insurance charge for smokers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "32050.23" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0125_379_125379106_qa_4/task.toml b/tasks/0125_379_125379106_qa_4/task.toml index 7c4675e240f47a3dd6f86e29e054dad76f5d4c4e..fb82f64fd9753667ac855417c93c0f163a83e55c 100644 --- a/tasks/0125_379_125379106_qa_4/task.toml +++ b/tasks/0125_379_125379106_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0125_379_125379106_qa_4" +name = "smoldataenvs-train/0125_379_125379106_qa_4" description = "What is the R-squared score of the linear regression model on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.7613" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0125_390_125390674_qa_1/task.toml b/tasks/0125_390_125390674_qa_1/task.toml index 0e5812f7e78e6bf739768b8de6cfa8389203927c..ff315258c6491e774922f49d5d79d263fed4d2eb 100644 --- a/tasks/0125_390_125390674_qa_1/task.toml +++ b/tasks/0125_390_125390674_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0125_390_125390674_qa_1" +name = "smoldataenvs-train/0125_390_125390674_qa_1" description = "Which contract type has the highest churn rate, and what is the percentage of churn for that contract type?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Month-to-month, 42.7" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0125_427_125427363_qa_1/task.toml b/tasks/0125_427_125427363_qa_1/task.toml index c033a41b300301e05f0807fc4d0e52f5ded023bd..eda4a6d2ebb82002203a2bb491689d8ea5aeefb8 100644 --- a/tasks/0125_427_125427363_qa_1/task.toml +++ b/tasks/0125_427_125427363_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0125_427_125427363_qa_1" +name = "smoldataenvs-train/0125_427_125427363_qa_1" description = "In how many seasons was the Man of the Series also the Orange Cap winner?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0125_527_125527676_qa_3/task.toml b/tasks/0125_527_125527676_qa_3/task.toml index 5702fa13edf95e6f4488da03c9488cc6afa75f86..0c8fddd8ec49eb182adce4b39fb2053a70f96bc8 100644 --- a/tasks/0125_527_125527676_qa_3/task.toml +++ b/tasks/0125_527_125527676_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0125_527_125527676_qa_3" +name = "smoldataenvs-train/0125_527_125527676_qa_3" description = "What is the median value of Sepal Length (cm) across all samples in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5.80" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0125_537_125537786_qa_2/task.toml b/tasks/0125_537_125537786_qa_2/task.toml index cc2b9c93e489b00416f62ee38f8d75bb530df1c8..fe9c6e13f028a0cadd95ecc6bdd2ec302a223234 100644 --- a/tasks/0125_537_125537786_qa_2/task.toml +++ b/tasks/0125_537_125537786_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0125_537_125537786_qa_2" +name = "smoldataenvs-train/0125_537_125537786_qa_2" description = "What is the average tenure (in months) of all customers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "32.37" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0125_537_125537786_qa_3/task.toml b/tasks/0125_537_125537786_qa_3/task.toml index b9ae3e9015616e37a569f929350d811c41ae71b4..d295e5e05b6725fb57ab9252906374fcdf8b5daf 100644 --- a/tasks/0125_537_125537786_qa_3/task.toml +++ b/tasks/0125_537_125537786_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0125_537_125537786_qa_3" +name = "smoldataenvs-train/0125_537_125537786_qa_3" description = "What is the average monthly charge paid by customers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "64.76" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0125_537_125537786_qa_4/task.toml b/tasks/0125_537_125537786_qa_4/task.toml index af356eeabc10f09a973b502e2b914cf5371ec4cf..9042cfa4a67e742a21ce6638d14b5eab0e38c479 100644 --- a/tasks/0125_537_125537786_qa_4/task.toml +++ b/tasks/0125_537_125537786_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0125_537_125537786_qa_4" +name = "smoldataenvs-train/0125_537_125537786_qa_4" description = "What percentage of customers in the dataset are senior citizens?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16.2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0125_554_125554588_qa_1/task.toml b/tasks/0125_554_125554588_qa_1/task.toml index 20199d1ec12ccb847876f5d1863a6c5b293cd576..702cc7b8ac9bf83b690326cc0b758731c0cd1652 100644 --- a/tasks/0125_554_125554588_qa_1/task.toml +++ b/tasks/0125_554_125554588_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0125_554_125554588_qa_1" +name = "smoldataenvs-train/0125_554_125554588_qa_1" description = "What is the optimal lambda value determined by the Box-Cox transformation applied to the target variable (MEDV) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.2166209012915364" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0125_570_125570065_qa_1/task.toml b/tasks/0125_570_125570065_qa_1/task.toml index 9e649038a8db586edc4978ce3f988cf99e424303..0a8947f881ef62ddb75d1b682605cfcd56bbf932 100644 --- a/tasks/0125_570_125570065_qa_1/task.toml +++ b/tasks/0125_570_125570065_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0125_570_125570065_qa_1" +name = "smoldataenvs-train/0125_570_125570065_qa_1" description = "What was the validation accuracy before any training started?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10.00%" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0125_581_125581362_qa_5/task.toml b/tasks/0125_581_125581362_qa_5/task.toml index 75f6f52770392d51065d5c8892e2a6f82dcb1cb6..e9f540563c6c4ae99d67f0d6cb35b95850b43eef 100644 --- a/tasks/0125_581_125581362_qa_5/task.toml +++ b/tasks/0125_581_125581362_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0125_581_125581362_qa_5" +name = "smoldataenvs-train/0125_581_125581362_qa_5" description = "What is the F1-score for the Support Vector Classifier (SVC) on the test set for the class labeled '1'?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.97" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0125_603_125603788_qa_2/task.toml b/tasks/0125_603_125603788_qa_2/task.toml index 1964d8d3b955a0cae2125629b5930e2c238dbef2..b81b51550951f99f8f9e695643dc8c88c528834b 100644 --- a/tasks/0125_603_125603788_qa_2/task.toml +++ b/tasks/0125_603_125603788_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0125_603_125603788_qa_2" +name = "smoldataenvs-train/0125_603_125603788_qa_2" description = "How many movies originally had a revenue value of zero before being replaced with NaN during data processing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "38052" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0125_603_125603788_qa_3/task.toml b/tasks/0125_603_125603788_qa_3/task.toml index 7b89e93969131a3d34c1b5497d292b9d6ac59424..099b521bb7781bde4d1f92b24f9c43e59636fbcc 100644 --- a/tasks/0125_603_125603788_qa_3/task.toml +++ b/tasks/0125_603_125603788_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0125_603_125603788_qa_3" +name = "smoldataenvs-train/0125_603_125603788_qa_3" description = "After converting the budget to a numeric type and replacing zeros with NaN, how many movies have missing budget data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "36576" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0125_624_125624875_qa_1/task.toml b/tasks/0125_624_125624875_qa_1/task.toml index cdedfb8399e6ca3a92e673f9d642b031b139bfb8..1b25145d277946dc9ab245214c83894580c2d572 100644 --- a/tasks/0125_624_125624875_qa_1/task.toml +++ b/tasks/0125_624_125624875_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0125_624_125624875_qa_1" +name = "smoldataenvs-train/0125_624_125624875_qa_1" description = "Which dataset has the highest number of 'O' tags, and what is the count?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "training, 631474" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0125_624_125624875_qa_2/task.toml b/tasks/0125_624_125624875_qa_2/task.toml index 5863b354801b39aab82746f6db06ea3bbe114fc3..9ea28015236adffca8114c1bdfb49bb42d26140a 100644 --- a/tasks/0125_624_125624875_qa_2/task.toml +++ b/tasks/0125_624_125624875_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0125_624_125624875_qa_2" +name = "smoldataenvs-train/0125_624_125624875_qa_2" description = "Which dataset contains the highest number of unique tokens before merging?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "training dataset" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0125_624_125624875_qa_4/task.toml b/tasks/0125_624_125624875_qa_4/task.toml index 0779c09a5106c5af3199e153a6c8eb04aec7230a..80468a27312ce993dcd2663e3789194064b5e624 100644 --- a/tasks/0125_624_125624875_qa_4/task.toml +++ b/tasks/0125_624_125624875_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0125_624_125624875_qa_4" +name = "smoldataenvs-train/0125_624_125624875_qa_4" description = "Which dataset had the highest number of missing tokens before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "validation" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0125_711_125711051_qa_2/task.toml b/tasks/0125_711_125711051_qa_2/task.toml index d8a3e5322ec713f88b9a13ec79b0c7553573acd7..f517881f5de41ec6a65baea7fbd937c731ac0ec2 100644 --- a/tasks/0125_711_125711051_qa_2/task.toml +++ b/tasks/0125_711_125711051_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0125_711_125711051_qa_2" +name = "smoldataenvs-train/0125_711_125711051_qa_2" description = "What is the total number of unique categories across all categorical variables in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0125_711_125711051_qa_3/task.toml b/tasks/0125_711_125711051_qa_3/task.toml index 60a31e32df1696cde30edb17863a38b40cd593f2..002abe465499236c5f419faa30cdc701995a442a 100644 --- a/tasks/0125_711_125711051_qa_3/task.toml +++ b/tasks/0125_711_125711051_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0125_711_125711051_qa_3" +name = "smoldataenvs-train/0125_711_125711051_qa_3" description = "Which categorical variable in the dataset has the highest number of unique categories?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "clarity" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0125_941_125941524_qa_1/task.toml b/tasks/0125_941_125941524_qa_1/task.toml index 7a419eddea4c4e6c6479b8a93569fc2f044856eb..1b0a34ff3a54fce10f82a578f4a05c6f2538cc24 100644 --- a/tasks/0125_941_125941524_qa_1/task.toml +++ b/tasks/0125_941_125941524_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0125_941_125941524_qa_1" +name = "smoldataenvs-train/0125_941_125941524_qa_1" description = "Is the dataset imbalanced in terms of the Outcome distribution?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0125_976_125976958_qa_4/task.toml b/tasks/0125_976_125976958_qa_4/task.toml index 5e9d4429ac9699134f899b4bb1cc4f2982288ea0..7d461c5b3b78c06044d3af6a780d047633043b0d 100644 --- a/tasks/0125_976_125976958_qa_4/task.toml +++ b/tasks/0125_976_125976958_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0125_976_125976958_qa_4" +name = "smoldataenvs-train/0125_976_125976958_qa_4" description = "What is the median number of characters in ham messages in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "52" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0126_018_126018138_qa_2/task.toml b/tasks/0126_018_126018138_qa_2/task.toml index 81afcedc4ffd28b80327c7397bf80537d60bf3d3..52dec2bd074fc127f71b46b9f13f434a0e254601 100644 --- a/tasks/0126_018_126018138_qa_2/task.toml +++ b/tasks/0126_018_126018138_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0126_018_126018138_qa_2" +name = "smoldataenvs-train/0126_018_126018138_qa_2" description = "Which platform has the highest total global sales when considering the top-selling game on each platform, and what is the sales figure?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii, 82.74" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0126_019_126019687_qa_3/task.toml b/tasks/0126_019_126019687_qa_3/task.toml index 747bcb77a9cb63f146627a721d1627a4f3560174..1e1035eab15ea374571fd526f014201cb4f8f6bc 100644 --- a/tasks/0126_019_126019687_qa_3/task.toml +++ b/tasks/0126_019_126019687_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0126_019_126019687_qa_3" +name = "smoldataenvs-train/0126_019_126019687_qa_3" description = "How many missing values were present in the total_bedrooms column before data preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "207" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0126_031_126031360_qa_1/task.toml b/tasks/0126_031_126031360_qa_1/task.toml index 707ee2cd9309a00f0064c29020e424349f575675..93b4caa802a4fe9dc0b47e86ed27b7b3904ba7cf 100644 --- a/tasks/0126_031_126031360_qa_1/task.toml +++ b/tasks/0126_031_126031360_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0126_031_126031360_qa_1" +name = "smoldataenvs-train/0126_031_126031360_qa_1" description = "What is the average R² score of the Linear Regression model during cross-validation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.7273" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0126_066_126066478_qa_1/task.toml b/tasks/0126_066_126066478_qa_1/task.toml index 0bd52fed4840237c6e0d681a050a9dc3fcc25ff7..94c080bd570d200de730422d2c0028504b57f446 100644 --- a/tasks/0126_066_126066478_qa_1/task.toml +++ b/tasks/0126_066_126066478_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0126_066_126066478_qa_1" +name = "smoldataenvs-train/0126_066_126066478_qa_1" description = "What is the churn rate for customers with a month-to-month contract?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "42.7" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0126_066_126066478_qa_5/task.toml b/tasks/0126_066_126066478_qa_5/task.toml index d0f63ebbd85aba59f03d6aac4c292dd2147403b3..7047308fdbf5731f9bdadb11109d2ee17c24f9ef 100644 --- a/tasks/0126_066_126066478_qa_5/task.toml +++ b/tasks/0126_066_126066478_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0126_066_126066478_qa_5" +name = "smoldataenvs-train/0126_066_126066478_qa_5" description = "What is the churn rate for customers without device protection?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "39.1" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0126_142_126142729_qa_1/task.toml b/tasks/0126_142_126142729_qa_1/task.toml index 0969780ba1a1500f99b09d560495b5b7bd892d66..4f3c60e9cb6e2c66759c859eacc9e5a22f31cbc6 100644 --- a/tasks/0126_142_126142729_qa_1/task.toml +++ b/tasks/0126_142_126142729_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0126_142_126142729_qa_1" +name = "smoldataenvs-train/0126_142_126142729_qa_1" description = "What is the 75th percentile value of the Price column in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1471210.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0126_180_126180239_qa_1/task.toml b/tasks/0126_180_126180239_qa_1/task.toml index dfaac0178d587b9820cd78614ebb2cdb5ccb755b..4b9518f509460d8d7f6e155b6a4ba220bfba7d0c 100644 --- a/tasks/0126_180_126180239_qa_1/task.toml +++ b/tasks/0126_180_126180239_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0126_180_126180239_qa_1" +name = "smoldataenvs-train/0126_180_126180239_qa_1" description = "Which publisher has the highest total global sales across all years in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Nintendo" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0126_180_126180239_qa_2/task.toml b/tasks/0126_180_126180239_qa_2/task.toml index b7add34a7fd26dfb8e54013ad5215e4f2e71575e..021c5a50a3b2ad7f8a0cbe2d7ea8b5907b15211d 100644 --- a/tasks/0126_180_126180239_qa_2/task.toml +++ b/tasks/0126_180_126180239_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0126_180_126180239_qa_2" +name = "smoldataenvs-train/0126_180_126180239_qa_2" description = "What is the name of the video game with the highest global sales for the Wii platform?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii Sports" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0126_259_126259861_qa_2/task.toml b/tasks/0126_259_126259861_qa_2/task.toml index 89243e42db5739c29ae072366e5fc54da0ab38ff..1ba36d1e309939942406c0d7ac74277bf3aef781 100644 --- a/tasks/0126_259_126259861_qa_2/task.toml +++ b/tasks/0126_259_126259861_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0126_259_126259861_qa_2" +name = "smoldataenvs-train/0126_259_126259861_qa_2" description = "What is the total number of edges in the largest weakly connected component of the graph after preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5066842" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0126_339_126339086_qa_5/task.toml b/tasks/0126_339_126339086_qa_5/task.toml index 88dad137191f3830273f99e0f8dbe194ee272b8a..a599962d2554a0cfbb0450a554f62a1371dbb6ee 100644 --- a/tasks/0126_339_126339086_qa_5/task.toml +++ b/tasks/0126_339_126339086_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0126_339_126339086_qa_5" +name = "smoldataenvs-train/0126_339_126339086_qa_5" description = "What is the average RMSE of user-based collaborative filtering with mean squared difference (MSD) similarity across 5-fold cross-validation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9690" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0126_584_126584044_qa_3/task.toml b/tasks/0126_584_126584044_qa_3/task.toml index 71750e7f6a4f1c36fe0a6834041634562562261b..12ab9ab1611cf98e3f2e7620a1d300d947efd1bd 100644 --- a/tasks/0126_584_126584044_qa_3/task.toml +++ b/tasks/0126_584_126584044_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0126_584_126584044_qa_3" +name = "smoldataenvs-train/0126_584_126584044_qa_3" description = "Which continent has the highest average life expectancy based on the scatter plot visualization of GDP vs. Life Expectancy?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Europe" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0126_687_126687596_qa_4/task.toml b/tasks/0126_687_126687596_qa_4/task.toml index d92bca5dd4bf1c5c070b0a68a4add3c10bbaf5c5..0038a1a56db42f1e2a1b162e9ff52818ad8665a1 100644 --- a/tasks/0126_687_126687596_qa_4/task.toml +++ b/tasks/0126_687_126687596_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0126_687_126687596_qa_4" +name = "smoldataenvs-train/0126_687_126687596_qa_4" description = "What is the name of the video game ranked number 10 in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Duck Hunt" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0126_706_126706065_qa_1/task.toml b/tasks/0126_706_126706065_qa_1/task.toml index 574ff22b7a2765b1f838813804fccbeebffd1edc..3196e41d3f4af0f7c494caf2fac0fb8006de7a6d 100644 --- a/tasks/0126_706_126706065_qa_1/task.toml +++ b/tasks/0126_706_126706065_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0126_706_126706065_qa_1" +name = "smoldataenvs-train/0126_706_126706065_qa_1" description = "What percentage of the dataset represents individuals with diabetes (Outcome=1)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.89583333333333" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0126_706_126706065_qa_3/task.toml b/tasks/0126_706_126706065_qa_3/task.toml index cebd9d18479a9f9f2f4b58811125e66f8c2d1a97..f01eb74b133c3719507479e016cfb5a98556a58b 100644 --- a/tasks/0126_706_126706065_qa_3/task.toml +++ b/tasks/0126_706_126706065_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0126_706_126706065_qa_3" +name = "smoldataenvs-train/0126_706_126706065_qa_3" description = "What was the change in the mean BMI value after imputing zero values with the mean compared to the original dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.464886" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0126_746_126746817_qa_1/task.toml b/tasks/0126_746_126746817_qa_1/task.toml index 6b389589084e39cad5eacde8e0afbd56b191d2ba..73f871f0f2c86ea8d9587c390adc5b2d4ad8e7b8 100644 --- a/tasks/0126_746_126746817_qa_1/task.toml +++ b/tasks/0126_746_126746817_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0126_746_126746817_qa_1" +name = "smoldataenvs-train/0126_746_126746817_qa_1" description = "What is the p-value from the Shapiro-Wilk normality test for the 'volatile acidity' feature in the wine quality dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.68680677283857e-16" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0126_747_126747437_qa_2/task.toml b/tasks/0126_747_126747437_qa_2/task.toml index 8046065db4c08e37bf1e5b129e57ac94e4c5295e..9915ae13cab8990c691ca922bbb00297db6d6f95 100644 --- a/tasks/0126_747_126747437_qa_2/task.toml +++ b/tasks/0126_747_126747437_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0126_747_126747437_qa_2" +name = "smoldataenvs-train/0126_747_126747437_qa_2" description = "What is the most frequently occurring decision category in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "unacc" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0126_841_126841685_qa_1/task.toml b/tasks/0126_841_126841685_qa_1/task.toml index 084ceb294a4601a27ff5cfef953ed678a2231e7d..111988ac4f5e0c5ed8ef081eb0c4ba082e22e524 100644 --- a/tasks/0126_841_126841685_qa_1/task.toml +++ b/tasks/0126_841_126841685_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0126_841_126841685_qa_1" +name = "smoldataenvs-train/0126_841_126841685_qa_1" description = "What is the skewness value of the Age distribution before imputation of missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.389" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0126_841_126841685_qa_2/task.toml b/tasks/0126_841_126841685_qa_2/task.toml index f12a3bcb06129219023762fe7207521f6f49e657..55a49c8f401cdd988196498934b84bf59ae5f126 100644 --- a/tasks/0126_841_126841685_qa_2/task.toml +++ b/tasks/0126_841_126841685_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0126_841_126841685_qa_2" +name = "smoldataenvs-train/0126_841_126841685_qa_2" description = "What percentage of passengers in the dataset survived the RMS Titanic disaster?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "38.38" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0126_841_126841685_qa_4/task.toml b/tasks/0126_841_126841685_qa_4/task.toml index 4cd8daaa7ef76426a6b417de1526dffb0dc38c64..5e836846594998067f0abfae5100baf4bea04c50 100644 --- a/tasks/0126_841_126841685_qa_4/task.toml +++ b/tasks/0126_841_126841685_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0126_841_126841685_qa_4" +name = "smoldataenvs-train/0126_841_126841685_qa_4" description = "How many passengers had missing values in the Age column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "177" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0126_954_126954425_qa_1/task.toml b/tasks/0126_954_126954425_qa_1/task.toml index cd629a96ba61d7dc0ab1ef8d87c861faae408b3f..f8d728d07e29077d303717440af748cf5f12ae11 100644 --- a/tasks/0126_954_126954425_qa_1/task.toml +++ b/tasks/0126_954_126954425_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0126_954_126954425_qa_1" +name = "smoldataenvs-train/0126_954_126954425_qa_1" description = "What is the proportion of individuals with diabetes (Outcome=1) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.9" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0126_954_126954425_qa_3/task.toml b/tasks/0126_954_126954425_qa_3/task.toml index 0f3a954ddb2acfbeba99178ab45429659fc92cfc..7aeaeb2b73f0c369ad86cd0afabf9b9b420e62f0 100644 --- a/tasks/0126_954_126954425_qa_3/task.toml +++ b/tasks/0126_954_126954425_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0126_954_126954425_qa_3" +name = "smoldataenvs-train/0126_954_126954425_qa_3" description = "After standardization, what is the standard deviation of the 'BMI' feature in the processed dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.000652" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0126_994_126994091_qa_1/task.toml b/tasks/0126_994_126994091_qa_1/task.toml index 0498b5f54f58e204acb7ef142875d8c610c92f77..1230344448a5a8dc27361aa0d76db96b43471248 100644 --- a/tasks/0126_994_126994091_qa_1/task.toml +++ b/tasks/0126_994_126994091_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0126_994_126994091_qa_1" +name = "smoldataenvs-train/0126_994_126994091_qa_1" description = "What is the 95th percentile value of burned area in hectares according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "48.71" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0126_994_126994091_qa_5/task.toml b/tasks/0126_994_126994091_qa_5/task.toml index 94a919a947d7999951fb130ac0f767c3b3cd2022..52b312de32c1199cf0eda0180863b5f66c47abce 100644 --- a/tasks/0126_994_126994091_qa_5/task.toml +++ b/tasks/0126_994_126994091_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0126_994_126994091_qa_5" +name = "smoldataenvs-train/0126_994_126994091_qa_5" description = "What is the 75th percentile value of burned area in the original dataset before standardization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.57 hectares" reward_mode_initial = "flexible" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0127_099_127099750_qa_5/task.toml b/tasks/0127_099_127099750_qa_5/task.toml index 342f0bd1763ae3b6527d46e6073ae75cda9a4ef3..f866661777555ed1c79c710cab9712eb96fd4c86 100644 --- a/tasks/0127_099_127099750_qa_5/task.toml +++ b/tasks/0127_099_127099750_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0127_099_127099750_qa_5" +name = "smoldataenvs-train/0127_099_127099750_qa_5" description = "What was the top-ranked feature by importance according to the Random Forest model analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0127_137_127137875_qa_4/task.toml b/tasks/0127_137_127137875_qa_4/task.toml index 64324a453a1f0c18b320c45e3030eba9fca4c5c4..931f9312e8b18aa5782afff05939a49e40f05c97 100644 --- a/tasks/0127_137_127137875_qa_4/task.toml +++ b/tasks/0127_137_127137875_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0127_137_127137875_qa_4" +name = "smoldataenvs-train/0127_137_127137875_qa_4" description = "Which feature shows the strongest negative correlation with wine quality according to the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "volatile acidity" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0127_268_127268138_qa_1/task.toml b/tasks/0127_268_127268138_qa_1/task.toml index 5c337110759c0f668afeb8e307f62bcb0ddb9a82..448a14fc0f1b1fb4b70106822ec66b61d79a2453 100644 --- a/tasks/0127_268_127268138_qa_1/task.toml +++ b/tasks/0127_268_127268138_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0127_268_127268138_qa_1" +name = "smoldataenvs-train/0127_268_127268138_qa_1" description = "What is the title of the video game with the highest global sales in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii Sports" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0127_275_127275270_qa_1/task.toml b/tasks/0127_275_127275270_qa_1/task.toml index 507d726778a346d7eb32b6f925eab972c7efc824..9255c0bff78bef957e13046888ec7f80a50dd7f2 100644 --- a/tasks/0127_275_127275270_qa_1/task.toml +++ b/tasks/0127_275_127275270_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0127_275_127275270_qa_1" +name = "smoldataenvs-train/0127_275_127275270_qa_1" description = "Is there a statistically significant difference in the mean age between patients who survived 5 years or longer and those who died within 5 years based on the t-test results?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0127_275_127275270_qa_5/task.toml b/tasks/0127_275_127275270_qa_5/task.toml index f87fe6de45939b3c18855513cc12eb62d29d48c4..155912fa7b1c401963916da598b301941bf28a64 100644 --- a/tasks/0127_275_127275270_qa_5/task.toml +++ b/tasks/0127_275_127275270_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0127_275_127275270_qa_5" +name = "smoldataenvs-train/0127_275_127275270_qa_5" description = "What is the survival probability at the 70th percentile of operation years based on the Kaplan-Meier survival curve analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.68" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0127_323_127323479_qa_1/task.toml b/tasks/0127_323_127323479_qa_1/task.toml index 3ae896f682ce2a53d7922646dcf155044f2bf1ab..d7694b371018fe2db649cd7ecf8d95c73d314aa0 100644 --- a/tasks/0127_323_127323479_qa_1/task.toml +++ b/tasks/0127_323_127323479_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0127_323_127323479_qa_1" +name = "smoldataenvs-train/0127_323_127323479_qa_1" description = "Which year had the highest total global sales according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2008" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0127_323_127323479_qa_2/task.toml b/tasks/0127_323_127323479_qa_2/task.toml index edbdf668ee9b3e38e22071ae0dfafe788245bcbd..bd3f58cd901d78651d1116a3f59d2db49fd92fc1 100644 --- a/tasks/0127_323_127323479_qa_2/task.toml +++ b/tasks/0127_323_127323479_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0127_323_127323479_qa_2" +name = "smoldataenvs-train/0127_323_127323479_qa_2" description = "What is the total number of games released in the year with the highest game releases?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1431" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0127_323_127323479_qa_3/task.toml b/tasks/0127_323_127323479_qa_3/task.toml index b3917044eae7565b0aa88bfb19bc202e5bfab327..613d480c264c46f2356bda6e120f98fe79bf4480 100644 --- a/tasks/0127_323_127323479_qa_3/task.toml +++ b/tasks/0127_323_127323479_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0127_323_127323479_qa_3" +name = "smoldataenvs-train/0127_323_127323479_qa_3" description = "Which publisher has released the highest number of games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic Arts" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0127_323_127323479_qa_5/task.toml b/tasks/0127_323_127323479_qa_5/task.toml index 8573d1103b7c00c56d6a2a1b005a03c7a33588e1..81cb2c21d1a8766449a6837e045a66e9b85a4081 100644 --- a/tasks/0127_323_127323479_qa_5/task.toml +++ b/tasks/0127_323_127323479_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0127_323_127323479_qa_5" +name = "smoldataenvs-train/0127_323_127323479_qa_5" description = "Which video game genre has the highest number of entries in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0127_374_127374855_qa_4/task.toml b/tasks/0127_374_127374855_qa_4/task.toml index ef0d4e75c807194765719c96df7c1ac834fd1cdf..07291d2d14eb402ebfb717b1128e2ed3138da7ed 100644 --- a/tasks/0127_374_127374855_qa_4/task.toml +++ b/tasks/0127_374_127374855_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0127_374_127374855_qa_4" +name = "smoldataenvs-train/0127_374_127374855_qa_4" description = "Which region consistently showed the lowest life expectancy from 2000 to 2015 according to the choropleth map visualization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Africa" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0127_385_127385378_qa_2/task.toml b/tasks/0127_385_127385378_qa_2/task.toml index d76af66ca3b5356a57c2bc77e6f8ad869b92d766..bd3926c508d814992ebf590456003cab593603d4 100644 --- a/tasks/0127_385_127385378_qa_2/task.toml +++ b/tasks/0127_385_127385378_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0127_385_127385378_qa_2" +name = "smoldataenvs-train/0127_385_127385378_qa_2" description = "What is the median quality score across all wine samples in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0127_467_127467979_qa_1/task.toml b/tasks/0127_467_127467979_qa_1/task.toml index 673457edc24b87dc38e3f1cdef24ac63a9b83586..50bb23e2e785b8e07b79ed90075323b390be5e63 100644 --- a/tasks/0127_467_127467979_qa_1/task.toml +++ b/tasks/0127_467_127467979_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0127_467_127467979_qa_1" +name = "smoldataenvs-train/0127_467_127467979_qa_1" description = "Which species has the widest average sepal width based on the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "setosa" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0127_467_127467979_qa_4/task.toml b/tasks/0127_467_127467979_qa_4/task.toml index a349581ea59ceda3071d88d92bb12fa5d64a832f..eac92387b0925b7a2035ea115c98139487febf77 100644 --- a/tasks/0127_467_127467979_qa_4/task.toml +++ b/tasks/0127_467_127467979_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0127_467_127467979_qa_4" +name = "smoldataenvs-train/0127_467_127467979_qa_4" description = "Which species has the smallest average petal length based on the dataset observations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "setosa" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0127_499_127499735_qa_5/task.toml b/tasks/0127_499_127499735_qa_5/task.toml index 449ae01a9acf68292520380939636ca8270d89d5..a11f05ece950c01c97fed801b4cb4e97df1b753b 100644 --- a/tasks/0127_499_127499735_qa_5/task.toml +++ b/tasks/0127_499_127499735_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0127_499_127499735_qa_5" +name = "smoldataenvs-train/0127_499_127499735_qa_5" description = "How many additional features were created in the dataset after one-hot encoding the 'state' column during preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0127_501_127501399_qa_2/task.toml b/tasks/0127_501_127501399_qa_2/task.toml index c95930683b86fcff4ed74ce756dba6077656e213..93e7ee1e0dbb22d8797f3bcb2116a5793c16136e 100644 --- a/tasks/0127_501_127501399_qa_2/task.toml +++ b/tasks/0127_501_127501399_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0127_501_127501399_qa_2" +name = "smoldataenvs-train/0127_501_127501399_qa_2" description = "After label encoding, what is the unique integer value assigned to the category \"High\" in the \"Outlet_Size\" column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0127_501_127501399_qa_3/task.toml b/tasks/0127_501_127501399_qa_3/task.toml index 51265074e8938f62f36a0fddebfb1fca6e1472fc..40c2afa592324c1a99638bfb71309aede2b08b18 100644 --- a/tasks/0127_501_127501399_qa_3/task.toml +++ b/tasks/0127_501_127501399_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0127_501_127501399_qa_3" +name = "smoldataenvs-train/0127_501_127501399_qa_3" description = "How many unique categories were present in the \"Item_Fat_Content\" column before the replacement and normalization process?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0127_535_127535783_qa_4/task.toml b/tasks/0127_535_127535783_qa_4/task.toml index c3d364bb7690eaf15b5b7e4c1b79c4c15432ba10..9ae24e6cb4d690367e9db9daaf02104f05a06db6 100644 --- a/tasks/0127_535_127535783_qa_4/task.toml +++ b/tasks/0127_535_127535783_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0127_535_127535783_qa_4" +name = "smoldataenvs-train/0127_535_127535783_qa_4" description = "What is the damping factor (alpha) used in the custom PageRank computation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.85" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0127_585_127585527_qa_4/task.toml b/tasks/0127_585_127585527_qa_4/task.toml index 1b37029b0e739e3cfa7edb5027ddc2b935576b05..67174284328bd4b3d4b6b5f99943f177479c956e 100644 --- a/tasks/0127_585_127585527_qa_4/task.toml +++ b/tasks/0127_585_127585527_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0127_585_127585527_qa_4" +name = "smoldataenvs-train/0127_585_127585527_qa_4" description = "What is the adjusted significance threshold (alpha) used for the Bonferroni correction in the A/B testing analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.01667" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0127_699_127699013_qa_1/task.toml b/tasks/0127_699_127699013_qa_1/task.toml index 87a1d007998e0f692cbca5ca674b91b3580c3cb0..680650d295a983e8018ce13e975eb56268239e10 100644 --- a/tasks/0127_699_127699013_qa_1/task.toml +++ b/tasks/0127_699_127699013_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0127_699_127699013_qa_1" +name = "smoldataenvs-train/0127_699_127699013_qa_1" description = "What is the mean tenure difference between churned and non-churned customers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "19.59" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0127_699_127699013_qa_2/task.toml b/tasks/0127_699_127699013_qa_2/task.toml index a6390304d77b01979ea8fbc5e9bbf77b812cc6d8..9e0def5aae15090e14b1aa0b194151dd6a735c05 100644 --- a/tasks/0127_699_127699013_qa_2/task.toml +++ b/tasks/0127_699_127699013_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0127_699_127699013_qa_2" +name = "smoldataenvs-train/0127_699_127699013_qa_2" description = "Which payment method has the highest correlation with customer churn based on the feature correlation analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic check" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0127_768_127768301_qa_3/task.toml b/tasks/0127_768_127768301_qa_3/task.toml index 34943f2b424ed615d7c320c0a57f63a02aca30ac..2fc70f7d9133113111125f323771523e20d612ad 100644 --- a/tasks/0127_768_127768301_qa_3/task.toml +++ b/tasks/0127_768_127768301_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0127_768_127768301_qa_3" +name = "smoldataenvs-train/0127_768_127768301_qa_3" description = "Which variable in the dataset shows the strongest visual association with diabetes diagnosis based on the pairplot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0127_775_127775252_qa_3/task.toml b/tasks/0127_775_127775252_qa_3/task.toml index 25e4a171e6c89d463fe898119865248e5141c7a8..0d3628ecc850a9e0a06bdea606e2f317cf9a784e 100644 --- a/tasks/0127_775_127775252_qa_3/task.toml +++ b/tasks/0127_775_127775252_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0127_775_127775252_qa_3" +name = "smoldataenvs-train/0127_775_127775252_qa_3" description = "What is the maximum global sales value recorded for any single video game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "82.74" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0127_775_127775252_qa_4/task.toml b/tasks/0127_775_127775252_qa_4/task.toml index 91902937c49764ca8d3e59a79d1992e8a181c4ca..df798de428f9696aed862e6887c11d9b405dbb13 100644 --- a/tasks/0127_775_127775252_qa_4/task.toml +++ b/tasks/0127_775_127775252_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0127_775_127775252_qa_4" +name = "smoldataenvs-train/0127_775_127775252_qa_4" description = "How many entries in the dataset have missing values in the Year column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "271" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0127_821_127821175_qa_1/task.toml b/tasks/0127_821_127821175_qa_1/task.toml index 37d30796f9a980fbece8aec4cde411504d136f6f..e271478c1ac0203c38dfb58091e2d5c40afb0500 100644 --- a/tasks/0127_821_127821175_qa_1/task.toml +++ b/tasks/0127_821_127821175_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0127_821_127821175_qa_1" +name = "smoldataenvs-train/0127_821_127821175_qa_1" description = "Which year had the highest total revenue generated from movies in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2016" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0127_821_127821175_qa_4/task.toml b/tasks/0127_821_127821175_qa_4/task.toml index 4ae217fea7f9df17799dca8c94ca8f62050dcf58..94f48dbf2ea3e9c1e5a8d39a18f663536beb2692 100644 --- a/tasks/0127_821_127821175_qa_4/task.toml +++ b/tasks/0127_821_127821175_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0127_821_127821175_qa_4" +name = "smoldataenvs-train/0127_821_127821175_qa_4" description = "Which movie achieved the highest revenue in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Star Wars: Episode VII - The Force Awakens" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0127_846_127846412_qa_2/task.toml b/tasks/0127_846_127846412_qa_2/task.toml index 64dc7407254dd97ae262fc7645cc6c3273590c02..a433e487ac211fe405127fb13ab5bed7a54bbaf7 100644 --- a/tasks/0127_846_127846412_qa_2/task.toml +++ b/tasks/0127_846_127846412_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0127_846_127846412_qa_2" +name = "smoldataenvs-train/0127_846_127846412_qa_2" description = "What is the absolute difference in average medical charges between smokers and non-smokers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "23609.57" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0127_857_127857440_qa_4/task.toml b/tasks/0127_857_127857440_qa_4/task.toml index bd21b324e0390a6ced6bef652a52e6a73129b16b..792fdc4a0ff15dc6a7cab81dd1584b5e1ea7fad4 100644 --- a/tasks/0127_857_127857440_qa_4/task.toml +++ b/tasks/0127_857_127857440_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0127_857_127857440_qa_4" +name = "smoldataenvs-train/0127_857_127857440_qa_4" description = "What is the combined total number of games released by the top 3 most prolific publishers (Electronic Arts, Activision, and Namco Bandai Games)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3258" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0128_054_128054394_qa_2/task.toml b/tasks/0128_054_128054394_qa_2/task.toml index 46d5e7f711b01a1ca740894b4bcb0b74536edeaf..1499c790afbb9a1a2debf8b86e3c24e8d6eb9340 100644 --- a/tasks/0128_054_128054394_qa_2/task.toml +++ b/tasks/0128_054_128054394_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0128_054_128054394_qa_2" +name = "smoldataenvs-train/0128_054_128054394_qa_2" description = "What is the value of the outlier identified in the SepalWidthCm feature using the Z-score method?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4.4" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0128_054_128054394_qa_3/task.toml b/tasks/0128_054_128054394_qa_3/task.toml index c7c07d650ec838a8428f6f90aa7e079137637456..41ac885f14cdf4dda27db6df392c057a11d4ab3c 100644 --- a/tasks/0128_054_128054394_qa_3/task.toml +++ b/tasks/0128_054_128054394_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0128_054_128054394_qa_3" +name = "smoldataenvs-train/0128_054_128054394_qa_3" description = "What is the numerical label assigned to the 'Iris-virginica' species after label encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0128_083_128083387_qa_3/task.toml b/tasks/0128_083_128083387_qa_3/task.toml index d0069ffb9676b2d1c012d1572d9cc1f5511f8da5..97ecb021a2ee5b68c82bb88f66f2994ca0b297ae 100644 --- a/tasks/0128_083_128083387_qa_3/task.toml +++ b/tasks/0128_083_128083387_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0128_083_128083387_qa_3" +name = "smoldataenvs-train/0128_083_128083387_qa_3" description = "How many samples are included in the validation set after splitting the data with a 65-35 train-validation ratio?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "75" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0128_173_128173004_qa_2/task.toml b/tasks/0128_173_128173004_qa_2/task.toml index ef15d711c00c4ed02a2674e8473f1d90f3487caa..d61b77c7eeb3b8afb56227a4d24b145b4bce07b0 100644 --- a/tasks/0128_173_128173004_qa_2/task.toml +++ b/tasks/0128_173_128173004_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0128_173_128173004_qa_2" +name = "smoldataenvs-train/0128_173_128173004_qa_2" description = "Which species is linearly separable from the other two based on the pairplot visualization of petal measurements?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-setosa" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0128_245_128245432_qa_2/task.toml b/tasks/0128_245_128245432_qa_2/task.toml index e7bc4ae3448a91f4e27f26cfbb4e707f9a61e110..4389bfc93444a68c2041d39fe2741380f097407a 100644 --- a/tasks/0128_245_128245432_qa_2/task.toml +++ b/tasks/0128_245_128245432_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0128_245_128245432_qa_2" +name = "smoldataenvs-train/0128_245_128245432_qa_2" description = "What is the percentage of employees who left the company (attrition = Yes) in the original dataset before any sampling techniques were applied?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16.12" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0128_372_128372155_qa_4/task.toml b/tasks/0128_372_128372155_qa_4/task.toml index 878af158365f449bc0822e7083dd1c7fc49b9da3..9cfbf4ae64317498df6d950b3059bc22e72ab87e 100644 --- a/tasks/0128_372_128372155_qa_4/task.toml +++ b/tasks/0128_372_128372155_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0128_372_128372155_qa_4" +name = "smoldataenvs-train/0128_372_128372155_qa_4" description = "Which feature (chocolate, fruity, etc.) shows the strongest positive correlation with win percentage in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "chocolate" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0128_396_128396066_qa_1/task.toml b/tasks/0128_396_128396066_qa_1/task.toml index b5e2634e306fee38ffaf952fdc38c57c537cafcb..f730daa7b67d295cdce2f584f34f9bd8ecef1420 100644 --- a/tasks/0128_396_128396066_qa_1/task.toml +++ b/tasks/0128_396_128396066_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0128_396_128396066_qa_1" +name = "smoldataenvs-train/0128_396_128396066_qa_1" description = "What is the accuracy of the Decision Tree Classifier on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9473684210526315" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0128_435_128435180_qa_1/task.toml b/tasks/0128_435_128435180_qa_1/task.toml index fc1e2802bb9a0ae144404fe5916d741c86ccd787..331c08c929270ff6b5359f726972d49f13383bdc 100644 --- a/tasks/0128_435_128435180_qa_1/task.toml +++ b/tasks/0128_435_128435180_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0128_435_128435180_qa_1" +name = "smoldataenvs-train/0128_435_128435180_qa_1" description = "Which feature shows the strongest positive correlation with the median house value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "median_income" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0128_435_128435180_qa_3/task.toml b/tasks/0128_435_128435180_qa_3/task.toml index 0232d9f34c0993835f24adcdbbe2f65a6f46b97b..802122a874e7a7851d648bcc024ee909caf1de51 100644 --- a/tasks/0128_435_128435180_qa_3/task.toml +++ b/tasks/0128_435_128435180_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0128_435_128435180_qa_3" +name = "smoldataenvs-train/0128_435_128435180_qa_3" description = "What is the percentage of data points categorized as 'NEAR BAY' in the ocean proximity feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11.095" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0128_446_128446383_qa_3/task.toml b/tasks/0128_446_128446383_qa_3/task.toml index 313bf3f5c4bd8e641240aa68c161524ca0b18a56..c7d12b763eda90e9cd6d1a334270ce027ab980ee 100644 --- a/tasks/0128_446_128446383_qa_3/task.toml +++ b/tasks/0128_446_128446383_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0128_446_128446383_qa_3" +name = "smoldataenvs-train/0128_446_128446383_qa_3" description = "What is the most common reported level of work interference with mental health among employees who do not seek treatment?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Never" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0128_467_128467242_qa_1/task.toml b/tasks/0128_467_128467242_qa_1/task.toml index 7ff943f0d342b514911d146cce338d982a9aa5b5..5f40fcf3aba130511ada4b005ff5bc750c55c56a 100644 --- a/tasks/0128_467_128467242_qa_1/task.toml +++ b/tasks/0128_467_128467242_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0128_467_128467242_qa_1" +name = "smoldataenvs-train/0128_467_128467242_qa_1" description = "Which feature has the highest positive correlation with Life Expectancy, and what is the correlation coefficient value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Schooling, 0.752" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0128_467_128467242_qa_5/task.toml b/tasks/0128_467_128467242_qa_5/task.toml index a96470740158683fbd83bff1886d24060ecefd50..09f3f39979b7a8b5285e3ba11ea1a99cb1b31165 100644 --- a/tasks/0128_467_128467242_qa_5/task.toml +++ b/tasks/0128_467_128467242_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0128_467_128467242_qa_5" +name = "smoldataenvs-train/0128_467_128467242_qa_5" description = "What is the median value of Life Expectancy in the original dataset before outlier removal?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "72.1" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0128_485_128485352_qa_1/task.toml b/tasks/0128_485_128485352_qa_1/task.toml index be6cccad11641fab39d856b5a8b2752095c1be2e..6c7c36323a1b24d64b40eccd11d8f5b23dee3c24 100644 --- a/tasks/0128_485_128485352_qa_1/task.toml +++ b/tasks/0128_485_128485352_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0128_485_128485352_qa_1" +name = "smoldataenvs-train/0128_485_128485352_qa_1" description = "Which feature was identified as the most important based on the Random Forest model's feature importance scores?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "safety" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0128_538_128538835_qa_1/task.toml b/tasks/0128_538_128538835_qa_1/task.toml index 589cb286c51a789e03d7acc69fea7f6a51cc8ee3..5730718ace8ed3f7338472d4aa3130659aabbc92 100644 --- a/tasks/0128_538_128538835_qa_1/task.toml +++ b/tasks/0128_538_128538835_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0128_538_128538835_qa_1" +name = "smoldataenvs-train/0128_538_128538835_qa_1" description = "What is the highest average number of comments for Ask HN posts in any hour of the day?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "28.68" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0128_538_128538835_qa_5/task.toml b/tasks/0128_538_128538835_qa_5/task.toml index b6515fed8cdd76619aa5cea43331531f85a92793..ec7cc9c8df7c429c855e912cb4dad3754a71c36f 100644 --- a/tasks/0128_538_128538835_qa_5/task.toml +++ b/tasks/0128_538_128538835_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0128_538_128538835_qa_5" +name = "smoldataenvs-train/0128_538_128538835_qa_5" description = "What is the average number of comments for Ask HN posts posted at 10:00?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "10.68" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0128_624_128624067_qa_1/task.toml b/tasks/0128_624_128624067_qa_1/task.toml index bc57b59d9398aeb1184f9e77c649c0ad4686bc31..cbeba7963209f7d717d02504d088267238332625 100644 --- a/tasks/0128_624_128624067_qa_1/task.toml +++ b/tasks/0128_624_128624067_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0128_624_128624067_qa_1" +name = "smoldataenvs-train/0128_624_128624067_qa_1" description = "Which country has the highest number of entries in the Top Ten list, and how many entries does it have?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Singapore, 7" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0128_624_128624067_qa_3/task.toml b/tasks/0128_624_128624067_qa_3/task.toml index 0565455abd2d6c978501edc4c55feba9b03e557a..9ba05957392ffbc85a32b4e5255df2ffc8914d55 100644 --- a/tasks/0128_624_128624067_qa_3/task.toml +++ b/tasks/0128_624_128624067_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0128_624_128624067_qa_3" +name = "smoldataenvs-train/0128_624_128624067_qa_3" description = "What percentage of all 5-star rated ramen products in the dataset are Japanese in origin?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "18" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0128_655_128655611_qa_4/task.toml b/tasks/0128_655_128655611_qa_4/task.toml index 81c7b0b91f00924ff8f42ca65a7ac68a32d92463..1b2b0f8d191a05122ea8244a6cfe87bb5e07068c 100644 --- a/tasks/0128_655_128655611_qa_4/task.toml +++ b/tasks/0128_655_128655611_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0128_655_128655611_qa_4" +name = "smoldataenvs-train/0128_655_128655611_qa_4" description = "What is the top recommended movie similar to \"The Shawshank Redemption\" using the overview-based similarity approach?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Civil Brand" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0128_659_128659556_qa_1/task.toml b/tasks/0128_659_128659556_qa_1/task.toml index c0cd4f51b2c76cba3364c8a5c5f8a32f2c5fd7be..eac1f6745410d4e77cbdae85a91429991084842a 100644 --- a/tasks/0128_659_128659556_qa_1/task.toml +++ b/tasks/0128_659_128659556_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0128_659_128659556_qa_1" +name = "smoldataenvs-train/0128_659_128659556_qa_1" description = "Does each row in the dataset represent a unique ad?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0128_659_128659556_qa_3/task.toml b/tasks/0128_659_128659556_qa_3/task.toml index 6db680c341aa1977beb2a84bc9a9e02359f1f713..b204ec577ff56b5385554c39fd0508d9dca9b1c2 100644 --- a/tasks/0128_659_128659556_qa_3/task.toml +++ b/tasks/0128_659_128659556_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0128_659_128659556_qa_3" +name = "smoldataenvs-train/0128_659_128659556_qa_3" description = "How many ads had at least one Approved Conversion?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "584" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0128_674_128674427_qa_5/task.toml b/tasks/0128_674_128674427_qa_5/task.toml index 61c337fb728198b71f41e7b4f141ec902a2ea2f3..458cd72270df456bc82632af3f864f7ad2c7f083 100644 --- a/tasks/0128_674_128674427_qa_5/task.toml +++ b/tasks/0128_674_128674427_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0128_674_128674427_qa_5" +name = "smoldataenvs-train/0128_674_128674427_qa_5" description = "How many unique values does the 'sex' column contain before applying any encoding transformations?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0128_693_128693147_qa_4/task.toml b/tasks/0128_693_128693147_qa_4/task.toml index 3a8882945a8c928172f8a090c1a0cc37aa258024..ad32bd05bcc95d0fb7dcb8fe1fd3df72957de90b 100644 --- a/tasks/0128_693_128693147_qa_4/task.toml +++ b/tasks/0128_693_128693147_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0128_693_128693147_qa_4" +name = "smoldataenvs-train/0128_693_128693147_qa_4" description = "How many distinct glass types are present in the target variable of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0128_750_128750159_qa_2/task.toml b/tasks/0128_750_128750159_qa_2/task.toml index 81c73c7c3af48891ef73fa8ecbbd39b49046a9f2..4d7572a3fa91134bfa5d60e0f52d2dbe29e8dc5b 100644 --- a/tasks/0128_750_128750159_qa_2/task.toml +++ b/tasks/0128_750_128750159_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0128_750_128750159_qa_2" +name = "smoldataenvs-train/0128_750_128750159_qa_2" description = "What is the most common wine quality rating in the original dataset before applying SMOTE oversampling?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0128_750_128750159_qa_3/task.toml b/tasks/0128_750_128750159_qa_3/task.toml index ca2fd8b910f64b8ade1567683fb00b4d19570e18..2dd2f4bec233b343791cda0f66d5d719039087aa 100644 --- a/tasks/0128_750_128750159_qa_3/task.toml +++ b/tasks/0128_750_128750159_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0128_750_128750159_qa_3" +name = "smoldataenvs-train/0128_750_128750159_qa_3" description = "After applying SMOTE oversampling, what is the percentage increase in the number of samples for the previously least common quality rating?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6710" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0128_778_128778053_qa_1/task.toml b/tasks/0128_778_128778053_qa_1/task.toml index 04f11558e0cc32fff733ff3d8bbfc5fc87bfd48f..b91faa2ef9e013d28356489b2714e6db37407f92 100644 --- a/tasks/0128_778_128778053_qa_1/task.toml +++ b/tasks/0128_778_128778053_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0128_778_128778053_qa_1" +name = "smoldataenvs-train/0128_778_128778053_qa_1" description = "How many individuals in the dataset are over 30 years old, have a smoker status of 'yes', and have medical charges exceeding $14,000?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "179" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0128_784_128784919_qa_5/task.toml b/tasks/0128_784_128784919_qa_5/task.toml index 549512d9317a9b9fabbe733f3f73fdf2a30239c5..a0e3cc320a0573c8f2dfb43c4c4c79ec025744f6 100644 --- a/tasks/0128_784_128784919_qa_5/task.toml +++ b/tasks/0128_784_128784919_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0128_784_128784919_qa_5" +name = "smoldataenvs-train/0128_784_128784919_qa_5" description = "How many movies in the dataset are associated with the 'Drama' genre in the metadata features?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2297" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0128_884_128884819_qa_1/task.toml b/tasks/0128_884_128884819_qa_1/task.toml index ad83129ccdd1f61f54f34710b29071f105eed3c8..6728ed811f7a9c1e69c3b2e3b9f3cba0490fcd68 100644 --- a/tasks/0128_884_128884819_qa_1/task.toml +++ b/tasks/0128_884_128884819_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0128_884_128884819_qa_1" +name = "smoldataenvs-train/0128_884_128884819_qa_1" description = "How many games are present in the top 20 sales lists for all three regions (North America, Europe, and Japan)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0128_929_128929154_qa_1/task.toml b/tasks/0128_929_128929154_qa_1/task.toml index fa66cbec18bc1c8b45fb24c2f63b31fd0f3b61a0..9bb770ab20119ee3099b061543684a8dafbe9d41 100644 --- a/tasks/0128_929_128929154_qa_1/task.toml +++ b/tasks/0128_929_128929154_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0128_929_128929154_qa_1" +name = "smoldataenvs-train/0128_929_128929154_qa_1" description = "What is the highest correlation coefficient between any product category and the purchase amount in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.35" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0128_943_128943364_qa_5/task.toml b/tasks/0128_943_128943364_qa_5/task.toml index e084eb5c293c796bbc29e72aab30053774595eda..4df6591579dd820eef37eb8bab1396470ff61287 100644 --- a/tasks/0128_943_128943364_qa_5/task.toml +++ b/tasks/0128_943_128943364_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0128_943_128943364_qa_5" +name = "smoldataenvs-train/0128_943_128943364_qa_5" description = "How many of the top 10 best-selling games in Japan are published by Nintendo?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0128_945_128945855_qa_3/task.toml b/tasks/0128_945_128945855_qa_3/task.toml index c6fcf4594ebec169bb760d61b41f6559997e6956..1dd8e6a9a3dd0657105c2341633c19d67eeca302 100644 --- a/tasks/0128_945_128945855_qa_3/task.toml +++ b/tasks/0128_945_128945855_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0128_945_128945855_qa_3" +name = "smoldataenvs-train/0128_945_128945855_qa_3" description = "Which platform has the highest frequency of appearance in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "DS" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0128_954_128954233_qa_1/task.toml b/tasks/0128_954_128954233_qa_1/task.toml index 75c6ca0e94b63bc65aed5f186216656581aa7897..fb96352e4a979299f59f10faa723faa39de3be6d 100644 --- a/tasks/0128_954_128954233_qa_1/task.toml +++ b/tasks/0128_954_128954233_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0128_954_128954233_qa_1" +name = "smoldataenvs-train/0128_954_128954233_qa_1" description = "What is the highest positive correlation coefficient between any two features in the dataset after converting the Species column to dummy variables?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.962757" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0128_954_128954233_qa_2/task.toml b/tasks/0128_954_128954233_qa_2/task.toml index dde70fd440d3fa9f06b67bba42da0a22a195fe42..dcf0c210aa1493ae8985be703482e778df5772f9 100644 --- a/tasks/0128_954_128954233_qa_2/task.toml +++ b/tasks/0128_954_128954233_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0128_954_128954233_qa_2" +name = "smoldataenvs-train/0128_954_128954233_qa_2" description = "Which feature has the strongest positive correlation with the Iris-virginica species dummy variable in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalWidthCm" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0128_996_128996845_qa_3/task.toml b/tasks/0128_996_128996845_qa_3/task.toml index 1b446c5bfe6c212f0bbebdac131d38e14b2427a2..23fe154e06b923f25fdb12dc026b3ee918295aa3 100644 --- a/tasks/0128_996_128996845_qa_3/task.toml +++ b/tasks/0128_996_128996845_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0128_996_128996845_qa_3" +name = "smoldataenvs-train/0128_996_128996845_qa_3" description = "What is the average number of video games released per year according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "419" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0128_998_128998188_qa_2/task.toml b/tasks/0128_998_128998188_qa_2/task.toml index ce9faa37003087506e503f9526ce183aa7df3b59..b15d12f16d4382e28115b88520c04bd4b11f6efd 100644 --- a/tasks/0128_998_128998188_qa_2/task.toml +++ b/tasks/0128_998_128998188_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0128_998_128998188_qa_2" +name = "smoldataenvs-train/0128_998_128998188_qa_2" description = "How many missing values are present in the Year_of_Release column of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0128_998_128998647_qa_1/task.toml b/tasks/0128_998_128998647_qa_1/task.toml index 0d1651f1c719f32efc21e90aeef4658281b26baa..33de67fd31a2cf8cd6fcad5698a9c8cea3cff806 100644 --- a/tasks/0128_998_128998647_qa_1/task.toml +++ b/tasks/0128_998_128998647_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0128_998_128998647_qa_1" +name = "smoldataenvs-train/0128_998_128998647_qa_1" description = "What percentage of patients in the dataset were diagnosed with diabetes (Outcome=1) after data imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34.9" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0129_008_129008932_qa_4/task.toml b/tasks/0129_008_129008932_qa_4/task.toml index bf23d55ba06f6d1459c17658b09d57da1c339040..7218b211b7ad19a97af5e9bb646557bdb89066dd 100644 --- a/tasks/0129_008_129008932_qa_4/task.toml +++ b/tasks/0129_008_129008932_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0129_008_129008932_qa_4" +name = "smoldataenvs-train/0129_008_129008932_qa_4" description = "What are the top three publishers by total global sales in descending order?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Nintendo, Electronic Arts, Activision" reward_mode_initial = "list" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0129_057_129057549_qa_1/task.toml b/tasks/0129_057_129057549_qa_1/task.toml index e22b7edb5af81e1bcf73c6952a74638968a42db4..9414026aa103d01b791690ef005fc9f8f5770ff1 100644 --- a/tasks/0129_057_129057549_qa_1/task.toml +++ b/tasks/0129_057_129057549_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0129_057_129057549_qa_1" +name = "smoldataenvs-train/0129_057_129057549_qa_1" description = "For the top-selling game of all time, how many standard deviations above the mean are its North American sales?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50.48" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0129_062_129062292_qa_3/task.toml b/tasks/0129_062_129062292_qa_3/task.toml index 6107f05dd63d46fe9215bb70c378de607d616bd8..8bebbc1170e93f89bae2b91117b6b669165cd9fe 100644 --- a/tasks/0129_062_129062292_qa_3/task.toml +++ b/tasks/0129_062_129062292_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0129_062_129062292_qa_3" +name = "smoldataenvs-train/0129_062_129062292_qa_3" description = "Which publisher has achieved the highest total global sales across all years shown in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Nintendo" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0129_062_129062292_qa_5/task.toml b/tasks/0129_062_129062292_qa_5/task.toml index 1d8e7a9a2d95b471890f8e622cadab4f29a5e5ec..4a108373891e5e3f60544fa388fffec77a409bda 100644 --- a/tasks/0129_062_129062292_qa_5/task.toml +++ b/tasks/0129_062_129062292_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0129_062_129062292_qa_5" +name = "smoldataenvs-train/0129_062_129062292_qa_5" description = "Which game genre has the highest average sales in the \"Other\" sales region (non-NA/EU/JP) according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Shooter" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0129_065_129065933_qa_2/task.toml b/tasks/0129_065_129065933_qa_2/task.toml index b1931657946e4175c38d749caac8f01bf8655816..f004b86751b52dfc139e20f4f4ff46fa243f1adf 100644 --- a/tasks/0129_065_129065933_qa_2/task.toml +++ b/tasks/0129_065_129065933_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0129_065_129065933_qa_2" +name = "smoldataenvs-train/0129_065_129065933_qa_2" description = "Which species is most clearly separable from the other two species based on the pairplot visualization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-setosa" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0129_071_129071705_qa_4/task.toml b/tasks/0129_071_129071705_qa_4/task.toml index 7a05273338b6a25f1ba1dcf1cf4d63105db652f4..1c69679fe27403097477bafb3db4e318d8fe3d2d 100644 --- a/tasks/0129_071_129071705_qa_4/task.toml +++ b/tasks/0129_071_129071705_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0129_071_129071705_qa_4" +name = "smoldataenvs-train/0129_071_129071705_qa_4" description = "What is the most frequently occurring genre of video games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0129_078_129078719_qa_2/task.toml b/tasks/0129_078_129078719_qa_2/task.toml index 9a702fb518e3002a1415e34bacf2e1320c251d0b..63093817d81707b27a00f9b132bb4f639e12b20b 100644 --- a/tasks/0129_078_129078719_qa_2/task.toml +++ b/tasks/0129_078_129078719_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0129_078_129078719_qa_2" +name = "smoldataenvs-train/0129_078_129078719_qa_2" description = "What is the most common platform for video games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "DS" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0129_078_129078719_qa_3/task.toml b/tasks/0129_078_129078719_qa_3/task.toml index 21c8f35570ec26f48e62bc122851cce62dd1ffee..c35a5c8570da2c8ced1215ae2f5bd35c9352c1c4 100644 --- a/tasks/0129_078_129078719_qa_3/task.toml +++ b/tasks/0129_078_129078719_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0129_078_129078719_qa_3" +name = "smoldataenvs-train/0129_078_129078719_qa_3" description = "What is the most common video game genre in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0129_144_129144397_qa_1/task.toml b/tasks/0129_144_129144397_qa_1/task.toml index 9f9652be3ff4037e57a9ea6ac023ba21d45c18fa..62cf9a0997680dc71c2c817a94d4f2970cf60552 100644 --- a/tasks/0129_144_129144397_qa_1/task.toml +++ b/tasks/0129_144_129144397_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0129_144_129144397_qa_1" +name = "smoldataenvs-train/0129_144_129144397_qa_1" description = "Which publisher has the highest total global sales according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Nintendo" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0129_144_129144397_qa_2/task.toml b/tasks/0129_144_129144397_qa_2/task.toml index a08c5bee79b0cc9c0345ff6c9a6cc223af3483a4..02bac6e9df3faa9e0458eaa591e78d388046b1cc 100644 --- a/tasks/0129_144_129144397_qa_2/task.toml +++ b/tasks/0129_144_129144397_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0129_144_129144397_qa_2" +name = "smoldataenvs-train/0129_144_129144397_qa_2" description = "What is the most common video game genre in the dataset based on the frequency of entries?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] build_timeout_sec = 600.0 diff --git a/tasks/0129_144_129144397_qa_4/task.toml b/tasks/0129_144_129144397_qa_4/task.toml index e77020d6b04bcfed875b00699a210183f5dfc96d..678903bf64793f41ccbb67ee7a2cb1b8b17fbd2b 100644 --- a/tasks/0129_144_129144397_qa_4/task.toml +++ b/tasks/0129_144_129144397_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0129_144_129144397_qa_4" +name = "smoldataenvs-train/0129_144_129144397_qa_4" description = "What is the median North American sales value, and how many games have exactly this median value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.08" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0129_208_129208819_qa_2/task.toml b/tasks/0129_208_129208819_qa_2/task.toml index 4375c295335f25b28442218d8469f29ae94fae26..8343bbb6123bf0b8a7a74886881bc073bd2acc33 100644 --- a/tasks/0129_208_129208819_qa_2/task.toml +++ b/tasks/0129_208_129208819_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0129_208_129208819_qa_2" +name = "smoldataenvs-train/0129_208_129208819_qa_2" description = "Which country has the highest number of respondents willing to spend more than $59 per month on learning after outlier removal?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "United States of America" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0129_211_129211866_qa_3/task.toml b/tasks/0129_211_129211866_qa_3/task.toml index e6ba9e015bac685cb22eb2addfa66b5a9da2e2b2..fd7ece4c565ddb1386628eef8c94e44f8d994fc7 100644 --- a/tasks/0129_211_129211866_qa_3/task.toml +++ b/tasks/0129_211_129211866_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0129_211_129211866_qa_3" +name = "smoldataenvs-train/0129_211_129211866_qa_3" description = "What is the standard error of the mean estimate for miles per gallon (mpg) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.3918" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0129_395_129395519_qa_2/task.toml b/tasks/0129_395_129395519_qa_2/task.toml index 9c34e31bce7dae7b492b56d1e18b0d07f0c31a34..50c98c2b6f4ad0849445eb5f2da56866423518a6 100644 --- a/tasks/0129_395_129395519_qa_2/task.toml +++ b/tasks/0129_395_129395519_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0129_395_129395519_qa_2" +name = "smoldataenvs-train/0129_395_129395519_qa_2" description = "By how many standard deviations above the North American sales mean is the top-selling game in North America?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50.48 standard deviations above the mean." reward_mode_initial = "flexible" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0129_421_129421075_qa_4/task.toml b/tasks/0129_421_129421075_qa_4/task.toml index 1681f78969bc568ce911d829994a6c27cc7ca1de..8c38e91889029480ad3363699ccab4882119d908 100644 --- a/tasks/0129_421_129421075_qa_4/task.toml +++ b/tasks/0129_421_129421075_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0129_421_129421075_qa_4" +name = "smoldataenvs-train/0129_421_129421075_qa_4" description = "What is the most frequent value in the Product_Category_2 column after mode imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8.0" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0129_603_129603179_qa_3/task.toml b/tasks/0129_603_129603179_qa_3/task.toml index e29c0c1bcb4223db3b4f90c9f7fc5df60748c8f7..4c5c5b62acb2268e93bb6a94d136f8b0d1baaf3b 100644 --- a/tasks/0129_603_129603179_qa_3/task.toml +++ b/tasks/0129_603_129603179_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0129_603_129603179_qa_3" +name = "smoldataenvs-train/0129_603_129603179_qa_3" description = "What is the minimum value of the 'tenure' attribute after applying MinMaxScaler normalization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0129_784_129784913_qa_2/task.toml b/tasks/0129_784_129784913_qa_2/task.toml index 21deb049cdbb07782553b2bc97b5cf5d854b9b5e..3fcf397e9405d4bdcd9d2608dcf2683a7124cb25 100644 --- a/tasks/0129_784_129784913_qa_2/task.toml +++ b/tasks/0129_784_129784913_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0129_784_129784913_qa_2" +name = "smoldataenvs-train/0129_784_129784913_qa_2" description = "What is the p-value of the Dickey-Fuller test for the residual component after seasonal decomposition of the time series?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.885059e-08" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0129_784_129784913_qa_3/task.toml b/tasks/0129_784_129784913_qa_3/task.toml index 433959e128e47fa6caee5df77d91df373bea0187..5487312eca6f53c74bfabb7a47c06ce538018273 100644 --- a/tasks/0129_784_129784913_qa_3/task.toml +++ b/tasks/0129_784_129784913_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0129_784_129784913_qa_3" +name = "smoldataenvs-train/0129_784_129784913_qa_3" description = "Is the original time series stationary at the 5% significance level based on the Dickey-Fuller test?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0129_786_129786687_qa_5/task.toml b/tasks/0129_786_129786687_qa_5/task.toml index 819dde7a9fd80b13d57ca6c4b3bf810e404ef93b..5944cd4fe69d0ec380486c24eccdcfe2407120ea 100644 --- a/tasks/0129_786_129786687_qa_5/task.toml +++ b/tasks/0129_786_129786687_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0129_786_129786687_qa_5" +name = "smoldataenvs-train/0129_786_129786687_qa_5" description = "What is the most frequent clarity category in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "SI1" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0130_032_130032784_qa_1/task.toml b/tasks/0130_032_130032784_qa_1/task.toml index ba4d34133600119a453713edfc2e00faa353a79d..d61091477e4521f5771acc2cb7c5ee79aaafc842 100644 --- a/tasks/0130_032_130032784_qa_1/task.toml +++ b/tasks/0130_032_130032784_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0130_032_130032784_qa_1" +name = "smoldataenvs-train/0130_032_130032784_qa_1" description = "What is the correlation coefficient between Albumin levels and Albumin_and_Globulin_Ratio in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.947" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0130_032_130032784_qa_2/task.toml b/tasks/0130_032_130032784_qa_2/task.toml index a3423e189fd4276c0cbfffddd6613bfeb0b7452d..2ccd4d2ee0e962f6e437447e8fc05c878423c97e 100644 --- a/tasks/0130_032_130032784_qa_2/task.toml +++ b/tasks/0130_032_130032784_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0130_032_130032784_qa_2" +name = "smoldataenvs-train/0130_032_130032784_qa_2" description = "Which gender (Male or Female) has a higher total sum of Total_Protiens in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Male" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0130_032_130032784_qa_4/task.toml b/tasks/0130_032_130032784_qa_4/task.toml index faeed4ac52eca2782e773089550e1bf98b0011a9..9a5b9e7f7567f134f8ff81d8b01b3797614f4fb3 100644 --- a/tasks/0130_032_130032784_qa_4/task.toml +++ b/tasks/0130_032_130032784_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0130_032_130032784_qa_4" +name = "smoldataenvs-train/0130_032_130032784_qa_4" description = "After handling missing values, what is the mean value of the Albumin_and_Globulin_Ratio feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.947" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0130_119_130119508_qa_4/task.toml b/tasks/0130_119_130119508_qa_4/task.toml index 98bbb6a17b51cb7c950958f2890dcfcec1853fbb..deed7e7571cd8bb05a8a4b529a742bc5de8ab45f 100644 --- a/tasks/0130_119_130119508_qa_4/task.toml +++ b/tasks/0130_119_130119508_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0130_119_130119508_qa_4" +name = "smoldataenvs-train/0130_119_130119508_qa_4" description = "Based on the calculated correlations, which specific objective type has the highest absolute correlation with the win outcome for team 2?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Tower kills" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0130_208_130208676_qa_1/task.toml b/tasks/0130_208_130208676_qa_1/task.toml index 0b0e8a4b6c5fa1a9528cd086f02ded5773c8aaf3..1c0f2e3ffa63965ba3d34486d25fd9fea9d37b58 100644 --- a/tasks/0130_208_130208676_qa_1/task.toml +++ b/tasks/0130_208_130208676_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0130_208_130208676_qa_1" +name = "smoldataenvs-train/0130_208_130208676_qa_1" description = "Which crop had the highest production in the 2008-09 season based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Total Spices" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0130_208_130208676_qa_4/task.toml b/tasks/0130_208_130208676_qa_4/task.toml index f94c62f36dd6b9b7a719633f44760ce333c04a97..76dfdb433571d86c12bd0b2eae23a7334cc53014 100644 --- a/tasks/0130_208_130208676_qa_4/task.toml +++ b/tasks/0130_208_130208676_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0130_208_130208676_qa_4" +name = "smoldataenvs-train/0130_208_130208676_qa_4" description = "What is the total production across all crops and years in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50336.6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0130_213_130213131_qa_3/task.toml b/tasks/0130_213_130213131_qa_3/task.toml index 6c9832f63f88a78f9178f7945a4b760b30e1fec7..c3f0f6337f667b9999cf32e901da05e2d46906b9 100644 --- a/tasks/0130_213_130213131_qa_3/task.toml +++ b/tasks/0130_213_130213131_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0130_213_130213131_qa_3" +name = "smoldataenvs-train/0130_213_130213131_qa_3" description = "After applying the Box-Cox transformation to normalize the target variable, what is the estimated optimal lambda value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.0435" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0130_285_130285411_qa_1/task.toml b/tasks/0130_285_130285411_qa_1/task.toml index 9ee8878963d28c45e6ed3af5cf79c897f3aadf13..4d77836f86dd8f9315ae38c111f27ffc8a0fbddb 100644 --- a/tasks/0130_285_130285411_qa_1/task.toml +++ b/tasks/0130_285_130285411_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0130_285_130285411_qa_1" +name = "smoldataenvs-train/0130_285_130285411_qa_1" description = "What is the highest purchase amount recorded by a single buyer in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "23961" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0130_285_130285411_qa_4/task.toml b/tasks/0130_285_130285411_qa_4/task.toml index 65dec9320ef6410c213cc06e4563d51f70a45c58..6f1170fcb253bbaeee064499ccc148e35cd2de01 100644 --- a/tasks/0130_285_130285411_qa_4/task.toml +++ b/tasks/0130_285_130285411_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0130_285_130285411_qa_4" +name = "smoldataenvs-train/0130_285_130285411_qa_4" description = "How many unique product categories are present in the Product_Category_1 column of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0130_334_130334137_qa_2/task.toml b/tasks/0130_334_130334137_qa_2/task.toml index a670cf768503c95d74f235dd2b76b4ce676f9462..22c76d180d4eab438005a4a3f8de26d6a6fb87d6 100644 --- a/tasks/0130_334_130334137_qa_2/task.toml +++ b/tasks/0130_334_130334137_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0130_334_130334137_qa_2" +name = "smoldataenvs-train/0130_334_130334137_qa_2" description = "What is the highest recorded carat value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5.01" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0130_334_130334137_qa_3/task.toml b/tasks/0130_334_130334137_qa_3/task.toml index 7ed0e9833bdb8d8128af8ccd02ebc18c56741ea0..7447c5a87f45201e06f43bf28f15f4bece9fa6a1 100644 --- a/tasks/0130_334_130334137_qa_3/task.toml +++ b/tasks/0130_334_130334137_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0130_334_130334137_qa_3" +name = "smoldataenvs-train/0130_334_130334137_qa_3" description = "How many unique levels does the 'clarity' categorical feature have in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0130_347_130347840_qa_4/task.toml b/tasks/0130_347_130347840_qa_4/task.toml index 7afd05575eaf38bc182e79540253a1393ef1b07d..d766cd720b9ce387c5d67288b44b5fce80ac768c 100644 --- a/tasks/0130_347_130347840_qa_4/task.toml +++ b/tasks/0130_347_130347840_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0130_347_130347840_qa_4" +name = "smoldataenvs-train/0130_347_130347840_qa_4" description = "What is the range (difference between maximum and minimum values) of the 'acceleration' feature in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "16.8" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0130_588_130588060_qa_1/task.toml b/tasks/0130_588_130588060_qa_1/task.toml index 56bf289680e33d387849df2a8eed7022a5345a17..458c155fa73773ad2f0ac4f103455fe12a711011 100644 --- a/tasks/0130_588_130588060_qa_1/task.toml +++ b/tasks/0130_588_130588060_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0130_588_130588060_qa_1" +name = "smoldataenvs-train/0130_588_130588060_qa_1" description = "Which features show the strongest positive correlation with wine quality according to the correlation matrix analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "alcohol, sulphates, citric acid" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0130_615_130615633_qa_1/task.toml b/tasks/0130_615_130615633_qa_1/task.toml index 315beaa62f36b6bea42717718723afbebdc4997b..2ecb913d121b6cd4183cf76853de1c498f3d737e 100644 --- a/tasks/0130_615_130615633_qa_1/task.toml +++ b/tasks/0130_615_130615633_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0130_615_130615633_qa_1" +name = "smoldataenvs-train/0130_615_130615633_qa_1" description = "What is the cost function value for the training data after removing the outlier at index 213 using gradient descent with 10 iterations, an initial learning rate of 2e-4, and starting parameters (w=0, b=0)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.9357480770512705" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0130_615_130615633_qa_4/task.toml b/tasks/0130_615_130615633_qa_4/task.toml index 11bf3818f15d7cd0977be5914f6458228430aa56..77bfc40c59d85304e7bbf49cecadd8a40b8947e6 100644 --- a/tasks/0130_615_130615633_qa_4/task.toml +++ b/tasks/0130_615_130615633_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0130_615_130615633_qa_4" +name = "smoldataenvs-train/0130_615_130615633_qa_4" description = "What is the index of the outlier detected in the original training dataset that contained an x-value of 3530.15736917?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "213" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0130_710_130710766_qa_2/task.toml b/tasks/0130_710_130710766_qa_2/task.toml index fc606159b0a2d862f589d691dfbe8797ace2180e..c886fc5c9b03db7984d361375b51487cd446a719 100644 --- a/tasks/0130_710_130710766_qa_2/task.toml +++ b/tasks/0130_710_130710766_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0130_710_130710766_qa_2" +name = "smoldataenvs-train/0130_710_130710766_qa_2" description = "How many mobile phones in the training dataset belong to each 'price_range' category?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "500" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0130_713_130713633_qa_5/task.toml b/tasks/0130_713_130713633_qa_5/task.toml index 9b51f5701167a9c9de14cd4cb63cd6e642dcdc15..6c8675579037f2146e244bd774096df152e323f2 100644 --- a/tasks/0130_713_130713633_qa_5/task.toml +++ b/tasks/0130_713_130713633_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0130_713_130713633_qa_5" +name = "smoldataenvs-train/0130_713_130713633_qa_5" description = "What is the test accuracy of the Boosting ensemble classifier that uses a Decision Tree as the base estimator?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "70.1" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0130_740_130740848_qa_1/task.toml b/tasks/0130_740_130740848_qa_1/task.toml index a43d9c979b7118e30991541d0aa77c8942fe830a..7b5664d2f27c5320d11df12180ca17819f960a3a 100644 --- a/tasks/0130_740_130740848_qa_1/task.toml +++ b/tasks/0130_740_130740848_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0130_740_130740848_qa_1" +name = "smoldataenvs-train/0130_740_130740848_qa_1" description = "After preprocessing, what is the most common value in the 'Dependents' column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0130_751_130751962_qa_1/task.toml b/tasks/0130_751_130751962_qa_1/task.toml index 9a488be5502f157505911047fe2b5abeeb1bed5d..f39c44852e87becca6f727b955b9bcd96cad6eb8 100644 --- a/tasks/0130_751_130751962_qa_1/task.toml +++ b/tasks/0130_751_130751962_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0130_751_130751962_qa_1" +name = "smoldataenvs-train/0130_751_130751962_qa_1" description = "What is the difference in median career length between NFL players before and after the 1970 merger?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0130_870_130870201_qa_1/task.toml b/tasks/0130_870_130870201_qa_1/task.toml index 7f946c4759c4bdbc8926af00d3d8d5d33460bfed..2f3e8645e2070abc3617fcd6fb8dab49e4aefe4c 100644 --- a/tasks/0130_870_130870201_qa_1/task.toml +++ b/tasks/0130_870_130870201_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0130_870_130870201_qa_1" +name = "smoldataenvs-train/0130_870_130870201_qa_1" description = "How many Pokémon entries were identified as Mega Evolutions before the data cleaning process?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "49" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0130_890_130890369_qa_2/task.toml b/tasks/0130_890_130890369_qa_2/task.toml index b4013251660175f98ef9f07270568012d1d9130c..668cb8f02b2c808b7cc7b95932a0db68e286415d 100644 --- a/tasks/0130_890_130890369_qa_2/task.toml +++ b/tasks/0130_890_130890369_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0130_890_130890369_qa_2" +name = "smoldataenvs-train/0130_890_130890369_qa_2" description = "What is the most frequently occurring value in the Product_Category_2 column after imputation of missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0130_890_130890369_qa_4/task.toml b/tasks/0130_890_130890369_qa_4/task.toml index abcd241ef43003bf8466ad09af124044cbdf7c36..5128514406939613b80e5836385532c73fd67ad9 100644 --- a/tasks/0130_890_130890369_qa_4/task.toml +++ b/tasks/0130_890_130890369_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0130_890_130890369_qa_4" +name = "smoldataenvs-train/0130_890_130890369_qa_4" description = "What is the mean occupation value in the entire dataset before splitting into training and test sets?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8.0793" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0130_998_130998781_qa_1/task.toml b/tasks/0130_998_130998781_qa_1/task.toml index 81b132f5d5eacbb156e038a9b584a3e1ed20bc62..91326558b6e7b4110bf4ed7ccc10b1b98403af96 100644 --- a/tasks/0130_998_130998781_qa_1/task.toml +++ b/tasks/0130_998_130998781_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0130_998_130998781_qa_1" +name = "smoldataenvs-train/0130_998_130998781_qa_1" description = "What is the accuracy of the spam classifier after incorporating text length as an additional feature?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "98.3" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0131_072_131072256_qa_1/task.toml b/tasks/0131_072_131072256_qa_1/task.toml index 656915a1b8ad6277f62829c5e535ab7a5a8f94c3..e3f84c4bd0a65888c2853ded719184112f510e67 100644 --- a/tasks/0131_072_131072256_qa_1/task.toml +++ b/tasks/0131_072_131072256_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0131_072_131072256_qa_1" +name = "smoldataenvs-train/0131_072_131072256_qa_1" description = "Which numeric label between 0 and 24 is completely missing in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "9" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0131_072_131072256_qa_3/task.toml b/tasks/0131_072_131072256_qa_3/task.toml index 3f7699342b97de30f700ae0b0e8384df5c4ea3b3..8db22d5ba9c3b98fa6922da90a67d7b1af18e3f8 100644 --- a/tasks/0131_072_131072256_qa_3/task.toml +++ b/tasks/0131_072_131072256_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0131_072_131072256_qa_3" +name = "smoldataenvs-train/0131_072_131072256_qa_3" description = "Which alphabetic character corresponding to a label is missing in the classes mapping?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "J" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0131_224_131224079_qa_3/task.toml b/tasks/0131_224_131224079_qa_3/task.toml index 1563dad186fb30badc510d3f9b4ff075d70ece40..9a34d138198af7f50f1585f305e01e130a77c865 100644 --- a/tasks/0131_224_131224079_qa_3/task.toml +++ b/tasks/0131_224_131224079_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0131_224_131224079_qa_3" +name = "smoldataenvs-train/0131_224_131224079_qa_3" description = "What is the precision score of the model as evaluated on the training data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.0" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0131_249_131249802_qa_5/task.toml b/tasks/0131_249_131249802_qa_5/task.toml index e63e9b99d6dfb2adc90ff52ed3a82e395a44633f..8a1f1440f407a4fb8761ab0d86454d9df57b91e1 100644 --- a/tasks/0131_249_131249802_qa_5/task.toml +++ b/tasks/0131_249_131249802_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0131_249_131249802_qa_5" +name = "smoldataenvs-train/0131_249_131249802_qa_5" description = "What is the highest missing value count observed in any feature before imputation in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "787" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0131_258_131258497_qa_4/task.toml b/tasks/0131_258_131258497_qa_4/task.toml index 688391a3196eabc253f32b8fa7efde4a9755971a..74e8848d48f04ff7e9319a3b92c4acbedfa26a47 100644 --- a/tasks/0131_258_131258497_qa_4/task.toml +++ b/tasks/0131_258_131258497_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0131_258_131258497_qa_4" +name = "smoldataenvs-train/0131_258_131258497_qa_4" description = "What is the range of the 'depth' values in the dataset (from minimum to maximum)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "43.0 to 79.0" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0131_341_131341778_qa_5/task.toml b/tasks/0131_341_131341778_qa_5/task.toml index 36abfe6cbc39469c73e9632103854e6a6781981a..931ab2634297c2d3229d9e93324a4e54c573a565 100644 --- a/tasks/0131_341_131341778_qa_5/task.toml +++ b/tasks/0131_341_131341778_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0131_341_131341778_qa_5" +name = "smoldataenvs-train/0131_341_131341778_qa_5" description = "What is the difference in the number of customers between those who own their home and those who rent?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "534" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0131_433_131433565_qa_1/task.toml b/tasks/0131_433_131433565_qa_1/task.toml index 08a56987e2d4fc0b1b6c0a6c8f20a2001fcbaa31..50594222adb0ae860a6d8e1042d4f4e16043c3fe 100644 --- a/tasks/0131_433_131433565_qa_1/task.toml +++ b/tasks/0131_433_131433565_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0131_433_131433565_qa_1" +name = "smoldataenvs-train/0131_433_131433565_qa_1" description = "Which genre has the highest number of released video games in the dataset according to the value counts analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0131_433_131433565_qa_4/task.toml b/tasks/0131_433_131433565_qa_4/task.toml index b3d17f640638ded3a773e65dbc0d48d2f1a83c8d..6d47548f6e991eace1c83d49d72fa756a4782f9a 100644 --- a/tasks/0131_433_131433565_qa_4/task.toml +++ b/tasks/0131_433_131433565_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0131_433_131433565_qa_4" +name = "smoldataenvs-train/0131_433_131433565_qa_4" description = "Which specific video game has the highest recorded global sales value in the dataset according to the pie chart visualization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Wii Sports" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0131_438_131438357_qa_4/task.toml b/tasks/0131_438_131438357_qa_4/task.toml index ccb86fd806b2aaeb4ba8f75b76c2dc4e5c5c958f..c6edd2500c9bb818efa819985e184f4e1a7ae6c1 100644 --- a/tasks/0131_438_131438357_qa_4/task.toml +++ b/tasks/0131_438_131438357_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0131_438_131438357_qa_4" +name = "smoldataenvs-train/0131_438_131438357_qa_4" description = "Which decile contains the record with the maximum net profit according to the logistic regression model's analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0131_454_131454088_qa_4/task.toml b/tasks/0131_454_131454088_qa_4/task.toml index 59d1b510394a0f0d92b1e9406b8baf1a914185cb..78fd500f1449a04e93205d535df64cbecb1fa536 100644 --- a/tasks/0131_454_131454088_qa_4/task.toml +++ b/tasks/0131_454_131454088_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0131_454_131454088_qa_4" +name = "smoldataenvs-train/0131_454_131454088_qa_4" description = "What percentage of the total dataset represents customers who made a purchase?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "35.75" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0131_454_131454088_qa_5/task.toml b/tasks/0131_454_131454088_qa_5/task.toml index 01d1c5d9dd47b42c33794fa38088cb37da7496af..3356d40f5ee3c07fccb2da9f3f862a95d5913138 100644 --- a/tasks/0131_454_131454088_qa_5/task.toml +++ b/tasks/0131_454_131454088_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0131_454_131454088_qa_5" +name = "smoldataenvs-train/0131_454_131454088_qa_5" description = "How many customers in the dataset made a purchase?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "143" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0131_458_131458182_qa_5/task.toml b/tasks/0131_458_131458182_qa_5/task.toml index 3799ff54c5eb17a1dd7d4626cb18f8feaef04be7..8b6e7d55fb3c9e00c0de8200c0d2af27161ea1aa 100644 --- a/tasks/0131_458_131458182_qa_5/task.toml +++ b/tasks/0131_458_131458182_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0131_458_131458182_qa_5" +name = "smoldataenvs-train/0131_458_131458182_qa_5" description = "How many missing values are present in the 'market_category' column of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3742" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0131_552_131552856_qa_4/task.toml b/tasks/0131_552_131552856_qa_4/task.toml index 59b1dd7eee54cae31d282472728bceea9731bb38..70bbc4d99eed1c9c0758d1692279b009d2377846 100644 --- a/tasks/0131_552_131552856_qa_4/task.toml +++ b/tasks/0131_552_131552856_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0131_552_131552856_qa_4" +name = "smoldataenvs-train/0131_552_131552856_qa_4" description = "What is the average median house value in the cleaned dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "206855.82" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0131_609_131609730_qa_1/task.toml b/tasks/0131_609_131609730_qa_1/task.toml index f5798436c0fe2bdbd9868644a8751d63b9e85972..ae63ede4739820f525acfb6de94d6df441e043d8 100644 --- a/tasks/0131_609_131609730_qa_1/task.toml +++ b/tasks/0131_609_131609730_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0131_609_131609730_qa_1" +name = "smoldataenvs-train/0131_609_131609730_qa_1" description = "What is the proportion of male and female borrowers in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Male: 69%, Female: 31%" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0131_609_131609730_qa_3/task.toml b/tasks/0131_609_131609730_qa_3/task.toml index e3458b870a8d66007c80a0d9fb9bfbca08130007..997ef42e64ebffbd4d358dd45d6d10411e1482fc 100644 --- a/tasks/0131_609_131609730_qa_3/task.toml +++ b/tasks/0131_609_131609730_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0131_609_131609730_qa_3" +name = "smoldataenvs-train/0131_609_131609730_qa_3" description = "How many outliers are present in the Credit amount feature based on the IQR method?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "72" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0131_609_131609730_qa_5/task.toml b/tasks/0131_609_131609730_qa_5/task.toml index 9acf081c086799b63f7a01e9612eb33974beb103..56602039acf393f37c631f09d295a55eaa5696c2 100644 --- a/tasks/0131_609_131609730_qa_5/task.toml +++ b/tasks/0131_609_131609730_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0131_609_131609730_qa_5" +name = "smoldataenvs-train/0131_609_131609730_qa_5" description = "How many features are present in the dataset after one-hot encoding and standardization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0131_622_131622026_qa_3/task.toml b/tasks/0131_622_131622026_qa_3/task.toml index 7f1cce997c20accdd9fd3accac6720f4951a4a0b..6653c402620adba00cb848ff437968e4898fcd9d 100644 --- a/tasks/0131_622_131622026_qa_3/task.toml +++ b/tasks/0131_622_131622026_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0131_622_131622026_qa_3" +name = "smoldataenvs-train/0131_622_131622026_qa_3" description = "How many missing values existed in the \"Saving accounts\" and \"Checking account\" columns before imputation, and what strategy was used to fill them?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Saving accounts: 183, Checking account: 394, mode imputation" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0131_624_131624769_qa_1/task.toml b/tasks/0131_624_131624769_qa_1/task.toml index 009388f73d07bb3b1d92fc5a95e334b905614f86..aa328df5d7a2c782bf938ff7646c301d69b51301 100644 --- a/tasks/0131_624_131624769_qa_1/task.toml +++ b/tasks/0131_624_131624769_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0131_624_131624769_qa_1" +name = "smoldataenvs-train/0131_624_131624769_qa_1" description = "What is the highest overall literacy rate achieved by any state in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "93.91" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0131_667_131667789_qa_4/task.toml b/tasks/0131_667_131667789_qa_4/task.toml index 91d6e1cc2359a1da822e2de835f3c9b6dfaa1280..efbbfc237e5e4698a2f0fab4973e7e1e8027d568 100644 --- a/tasks/0131_667_131667789_qa_4/task.toml +++ b/tasks/0131_667_131667789_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0131_667_131667789_qa_4" +name = "smoldataenvs-train/0131_667_131667789_qa_4" description = "What is the recall score for the minority class (exoplanet) in the Logistic Regression model before applying SMOTE?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.00" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0131_731_131731169_qa_5/task.toml b/tasks/0131_731_131731169_qa_5/task.toml index 4fd11b36785d6c838c9d119252a79c997f5cafa6..0e67e44b446e4547d70956a21db277d505f2ce3f 100644 --- a/tasks/0131_731_131731169_qa_5/task.toml +++ b/tasks/0131_731_131731169_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0131_731_131731169_qa_5" +name = "smoldataenvs-train/0131_731_131731169_qa_5" description = "Which publisher demonstrates the greatest discrepancy between total games published and representation in the top 100 games list?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic Arts" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0131_767_131767840_qa_1/task.toml b/tasks/0131_767_131767840_qa_1/task.toml index 272f94e1d336bd1385b786057f40445479000e26..babdeb2b23e37186547d9dcb7a8738f018a221fe 100644 --- a/tasks/0131_767_131767840_qa_1/task.toml +++ b/tasks/0131_767_131767840_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0131_767_131767840_qa_1" +name = "smoldataenvs-train/0131_767_131767840_qa_1" description = "What is the minimum non-zero unit price recorded in the cleaned dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.001" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0131_767_131767840_qa_5/task.toml b/tasks/0131_767_131767840_qa_5/task.toml index 7c85f6ac3181c471110a256c0801e890cec055ac..a32ccc7df6e7d5c3fce8bb65d7fd27fb296e734a 100644 --- a/tasks/0131_767_131767840_qa_5/task.toml +++ b/tasks/0131_767_131767840_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0131_767_131767840_qa_5" +name = "smoldataenvs-train/0131_767_131767840_qa_5" description = "How many distinct product stock codes are available in the dataset after data cleaning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3684" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0131_881_131881806_qa_2/task.toml b/tasks/0131_881_131881806_qa_2/task.toml index d8c411cb07343f53b6abe9d535acbaa1bbd06903..09662ad4cf5a39816d9e1741de41543fee038a1f 100644 --- a/tasks/0131_881_131881806_qa_2/task.toml +++ b/tasks/0131_881_131881806_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0131_881_131881806_qa_2" +name = "smoldataenvs-train/0131_881_131881806_qa_2" description = "Which car model has the highest number of engine cylinders, and what is its engine horsepower?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Bugatti Veyron 16.4, 1001" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0131_881_131881806_qa_4/task.toml b/tasks/0131_881_131881806_qa_4/task.toml index a2077a2166827dac1512c39b3109c4faee829d95..7e36a612ad42d11f4add348ba9010b05f23d844c 100644 --- a/tasks/0131_881_131881806_qa_4/task.toml +++ b/tasks/0131_881_131881806_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0131_881_131881806_qa_4" +name = "smoldataenvs-train/0131_881_131881806_qa_4" description = "What is the most common transmission type in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "AUTOMATIC" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0132_089_132089829_qa_3/task.toml b/tasks/0132_089_132089829_qa_3/task.toml index 89ff189bb59edd702a91a2e6a51abe6681da346b..90422953595be8140cf58014a5af4d3557855faa 100644 --- a/tasks/0132_089_132089829_qa_3/task.toml +++ b/tasks/0132_089_132089829_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0132_089_132089829_qa_3" +name = "smoldataenvs-train/0132_089_132089829_qa_3" description = "After imputing missing 'Age' values based on Pclass, what are the median ages used for each class?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "37, 29, 24" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0132_099_132099772_qa_3/task.toml b/tasks/0132_099_132099772_qa_3/task.toml index fd3768e98a5ac4f5268f30d504cde1013efec2a4..19284af5bb79309841645956bcdb965e7cbd5570 100644 --- a/tasks/0132_099_132099772_qa_3/task.toml +++ b/tasks/0132_099_132099772_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0132_099_132099772_qa_3" +name = "smoldataenvs-train/0132_099_132099772_qa_3" description = "Which calendar year recorded the highest total global sales and what was the exact total sales amount in millions?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2008, 678.9" reward_mode_initial = "list" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0132_138_132138987_qa_1/task.toml b/tasks/0132_138_132138987_qa_1/task.toml index 59926a71ed3f66c13e17a71c129b3e8940fdb83d..7de4a241497ee1ef49b90f2015918bce8bfd8b51 100644 --- a/tasks/0132_138_132138987_qa_1/task.toml +++ b/tasks/0132_138_132138987_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0132_138_132138987_qa_1" +name = "smoldataenvs-train/0132_138_132138987_qa_1" description = "How many standard deviations above the mean North American sales are the sales of the top-selling game?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "50.47898767479108" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0132_151_132151183_qa_1/task.toml b/tasks/0132_151_132151183_qa_1/task.toml index 8f3e98e6bd9dca7ff2495d73b50414f37de28df5..1acbdc892df2d3e8f38a2d646d8b3b532d7b530e 100644 --- a/tasks/0132_151_132151183_qa_1/task.toml +++ b/tasks/0132_151_132151183_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0132_151_132151183_qa_1" +name = "smoldataenvs-train/0132_151_132151183_qa_1" description = "What is the coefficient of determination (R²) for the linear regression model on the test data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9888014444327563" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0132_151_132151183_qa_2/task.toml b/tasks/0132_151_132151183_qa_2/task.toml index 6fe425cb49b9fb66efd5d907c4a9c694e05ed938..437a1cb48d567e17f8ccc678f93e0b3bca709ea2 100644 --- a/tasks/0132_151_132151183_qa_2/task.toml +++ b/tasks/0132_151_132151183_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0132_151_132151183_qa_2" +name = "smoldataenvs-train/0132_151_132151183_qa_2" description = "What is the Root Mean Squared Error (RMSE) of the model's predictions on the test dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.071306268029827" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0132_169_132169068_qa_5/task.toml b/tasks/0132_169_132169068_qa_5/task.toml index 004c4ea92973bfb944d6b6b061101ba5f6510655..e13afb91be13f9c80627378ad39561d47e2f056f 100644 --- a/tasks/0132_169_132169068_qa_5/task.toml +++ b/tasks/0132_169_132169068_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0132_169_132169068_qa_5" +name = "smoldataenvs-train/0132_169_132169068_qa_5" description = "What is the median value of North American sales across all games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.08" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0132_236_132236539_qa_5/task.toml b/tasks/0132_236_132236539_qa_5/task.toml index ebc20175e034283e850ca6a39982383b789c6a9c..61a7905335194e0ea1c2afe684d3c944c88a6954 100644 --- a/tasks/0132_236_132236539_qa_5/task.toml +++ b/tasks/0132_236_132236539_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0132_236_132236539_qa_5" +name = "smoldataenvs-train/0132_236_132236539_qa_5" description = "How many numerical features were retained after feature selection based on correlation analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0132_270_132270430_qa_1/task.toml b/tasks/0132_270_132270430_qa_1/task.toml index 95a238f5f7e383756fec1bb3ebe829d29fdef665..df04d32dee026977a0d2cca0d5b2efcd911d78ca 100644 --- a/tasks/0132_270_132270430_qa_1/task.toml +++ b/tasks/0132_270_132270430_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0132_270_132270430_qa_1" +name = "smoldataenvs-train/0132_270_132270430_qa_1" description = "What is the average global sales value across all video games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.537441" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0132_270_132270430_qa_4/task.toml b/tasks/0132_270_132270430_qa_4/task.toml index 301ac070b4535f1568468b02e9a17f9a50220a2f..3a3d8340cb1e8ef2bb1c15ffbd341a736361ca34 100644 --- a/tasks/0132_270_132270430_qa_4/task.toml +++ b/tasks/0132_270_132270430_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0132_270_132270430_qa_4" +name = "smoldataenvs-train/0132_270_132270430_qa_4" description = "What is the median global sales value based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.17" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0132_290_132290375_qa_2/task.toml b/tasks/0132_290_132290375_qa_2/task.toml index ebb6e7ef8730ff8b3d43bf42232f397db167476c..ddaaef20ae87ded951c552b2683e83c77ebec11f 100644 --- a/tasks/0132_290_132290375_qa_2/task.toml +++ b/tasks/0132_290_132290375_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0132_290_132290375_qa_2" +name = "smoldataenvs-train/0132_290_132290375_qa_2" description = "Which job role exhibits the highest attrition rate within its own category, and in which department is this role located?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Sales Representative, Sales" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0132_422_132422220_qa_2/task.toml b/tasks/0132_422_132422220_qa_2/task.toml index 2d88fe92632f2c4bf2ad05f17029c332283ddb6e..9bb7a1bfeb2c65dd8b5fbd8ea2e7e0b7a361fd21 100644 --- a/tasks/0132_422_132422220_qa_2/task.toml +++ b/tasks/0132_422_132422220_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0132_422_132422220_qa_2" +name = "smoldataenvs-train/0132_422_132422220_qa_2" description = "What percentage of users in the dataset made a purchase?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "35.75%" reward_mode_initial = "flexible" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0132_434_132434308_qa_2/task.toml b/tasks/0132_434_132434308_qa_2/task.toml index 000b8601b003619f0b350af4bc09e262e93b3288..b92063567e9d58fa05273dd629858cc373b7c16e 100644 --- a/tasks/0132_434_132434308_qa_2/task.toml +++ b/tasks/0132_434_132434308_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0132_434_132434308_qa_2" +name = "smoldataenvs-train/0132_434_132434308_qa_2" description = "How many data points were included in the training and test sets after splitting the dataset with an 80/20 ratio?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12 training, 3 test" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0132_444_132444361_qa_4/task.toml b/tasks/0132_444_132444361_qa_4/task.toml index a6e913fc377e250a0fb2c99efb8669cc2dfd87e5..af0d38b5c52c3c9de7613f1f0b6ba8039395bfd2 100644 --- a/tasks/0132_444_132444361_qa_4/task.toml +++ b/tasks/0132_444_132444361_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0132_444_132444361_qa_4" +name = "smoldataenvs-train/0132_444_132444361_qa_4" description = "How many policyholders in the dataset are identified as smokers?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "274" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0132_459_132459719_qa_3/task.toml b/tasks/0132_459_132459719_qa_3/task.toml index 15971cdb555de0b7dff6321e989dbf86740f23ba..79fa4a00892aac11f0f9900742eeaa1ad478b591 100644 --- a/tasks/0132_459_132459719_qa_3/task.toml +++ b/tasks/0132_459_132459719_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0132_459_132459719_qa_3" +name = "smoldataenvs-train/0132_459_132459719_qa_3" description = "Which year experienced the highest increase in total freight from India compared to the previous year according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2016" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0132_471_132471120_qa_4/task.toml b/tasks/0132_471_132471120_qa_4/task.toml index 55536f9a42c06709bd2b9923d115b536201db208..ac521dfa285fe8fc02eaa1f7db6d3628edf50b74 100644 --- a/tasks/0132_471_132471120_qa_4/task.toml +++ b/tasks/0132_471_132471120_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0132_471_132471120_qa_4" +name = "smoldataenvs-train/0132_471_132471120_qa_4" description = "Which year had the highest number of movies released, and how many movies were released that year?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2016, 297" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0132_538_132538004_qa_1/task.toml b/tasks/0132_538_132538004_qa_1/task.toml index fe7d2dbf63f56eaa21ae569df083f927180e5d84..9f787650ba41979f308dc35030176575b3ba6be1 100644 --- a/tasks/0132_538_132538004_qa_1/task.toml +++ b/tasks/0132_538_132538004_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0132_538_132538004_qa_1" +name = "smoldataenvs-train/0132_538_132538004_qa_1" description = "What is the median North American sales value for video games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.08" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0132_538_132538004_qa_5/task.toml b/tasks/0132_538_132538004_qa_5/task.toml index b2dc3ab3438326bbbdf9925cdc60a9ba868d83e1..f1aa2ccfc01bedf98f88f3d4d1749b5c0240a86c 100644 --- a/tasks/0132_538_132538004_qa_5/task.toml +++ b/tasks/0132_538_132538004_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0132_538_132538004_qa_5" +name = "smoldataenvs-train/0132_538_132538004_qa_5" description = "Which platform has the highest frequency of appearance in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "DS" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0132_564_132564402_qa_5/task.toml b/tasks/0132_564_132564402_qa_5/task.toml index f25f6f2474cf85a9f9e4694b53da6689c46bc276..dcc544c0a3d96b123f70c47b5fa63cc148d3df0a 100644 --- a/tasks/0132_564_132564402_qa_5/task.toml +++ b/tasks/0132_564_132564402_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0132_564_132564402_qa_5" +name = "smoldataenvs-train/0132_564_132564402_qa_5" description = "Which feature exhibits the strongest positive correlation with the diabetes diagnosis (Outcome) based on the correlation analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0132_566_132566728_qa_4/task.toml b/tasks/0132_566_132566728_qa_4/task.toml index 5a2ba1e0c72cb3466963299689f4857f88b2be1c..d82e7ade3575a7b0612065c3e5655ee9bdc5abf7 100644 --- a/tasks/0132_566_132566728_qa_4/task.toml +++ b/tasks/0132_566_132566728_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0132_566_132566728_qa_4" +name = "smoldataenvs-train/0132_566_132566728_qa_4" description = "Among the numeric features (tenure, MonthlyCharges, TotalCharges), which variable shows the highest degree of multicollinearity based on VIF values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "TotalCharges" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0132_576_132576525_qa_4/task.toml b/tasks/0132_576_132576525_qa_4/task.toml index 127318a3c0acfd68623532c14b7581e9c007c463..08c3e053f4359fc078cc819783afe53d4cf1c854 100644 --- a/tasks/0132_576_132576525_qa_4/task.toml +++ b/tasks/0132_576_132576525_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0132_576_132576525_qa_4" +name = "smoldataenvs-train/0132_576_132576525_qa_4" description = "How many ads in the dataset resulted in at least one Approved_Conversion?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "584" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0132_596_132596500_qa_5/task.toml b/tasks/0132_596_132596500_qa_5/task.toml index d2635591367898017fbf98d2ae6f62c6f1ee5cd4..f20b77a37351b7844c6f68ec2be52d69368c40a6 100644 --- a/tasks/0132_596_132596500_qa_5/task.toml +++ b/tasks/0132_596_132596500_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0132_596_132596500_qa_5" +name = "smoldataenvs-train/0132_596_132596500_qa_5" description = "Which pair of features has the highest positive correlation in the dataset based on the correlation matrix visualization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm, PetalWidthCm" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0132_623_132623331_qa_1/task.toml b/tasks/0132_623_132623331_qa_1/task.toml index ff5477bfac94b77d8935e325145da6914d954ba2..fecd0b60a022c36e7db329d33a4a53b5986aec8c 100644 --- a/tasks/0132_623_132623331_qa_1/task.toml +++ b/tasks/0132_623_132623331_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0132_623_132623331_qa_1" +name = "smoldataenvs-train/0132_623_132623331_qa_1" description = "Which feature shows the strongest statistical correlation with PetalLengthCm in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalWidthCm" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0132_624_132624870_qa_4/task.toml b/tasks/0132_624_132624870_qa_4/task.toml index 3c7173f8da42c3bcc56a904b82ba9a589aa2b838..f2c5b446b2ed1e8ac8d6fae4b8f287efd2fcacaf 100644 --- a/tasks/0132_624_132624870_qa_4/task.toml +++ b/tasks/0132_624_132624870_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0132_624_132624870_qa_4" +name = "smoldataenvs-train/0132_624_132624870_qa_4" description = "How many entries in the dataset have missing values in the 'Year' column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "271" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0132_624_132624870_qa_5/task.toml b/tasks/0132_624_132624870_qa_5/task.toml index 6c3e0585d4f1b68fff6750d1fc579b4153df7b64..702c69825c3e5f122be42a891a7bfb9c98017703 100644 --- a/tasks/0132_624_132624870_qa_5/task.toml +++ b/tasks/0132_624_132624870_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0132_624_132624870_qa_5" +name = "smoldataenvs-train/0132_624_132624870_qa_5" description = "What is the difference between the highest North American sales and the highest European sales recorded for video games in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12.47" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0132_642_132642166_qa_1/task.toml b/tasks/0132_642_132642166_qa_1/task.toml index d333948ed0d59c74b56e8358a211ea2607167c18..b5f5e04e4ca11237d991238fe37de4a3055037de 100644 --- a/tasks/0132_642_132642166_qa_1/task.toml +++ b/tasks/0132_642_132642166_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0132_642_132642166_qa_1" +name = "smoldataenvs-train/0132_642_132642166_qa_1" description = "Which depth range (in 200m intervals) contains the highest number of unique coral and sponge species recorded in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0-200" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0132_642_132642166_qa_2/task.toml b/tasks/0132_642_132642166_qa_2/task.toml index 258321f3851d95e7037f1cf9707b3e27fc8c42ad..77efdc3f84ecebf42c930e3bdb03ef4e148a1195 100644 --- a/tasks/0132_642_132642166_qa_2/task.toml +++ b/tasks/0132_642_132642166_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0132_642_132642166_qa_2" +name = "smoldataenvs-train/0132_642_132642166_qa_2" description = "What is the average depth (in meters) of the dataset after correcting for negative depth values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "814.15" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0132_642_132642166_qa_3/task.toml b/tasks/0132_642_132642166_qa_3/task.toml index ebb8013169d824518c2f83935a1354406739533b..eb1239aaf1ef038b3ec4501027cab34d452d5503 100644 --- a/tasks/0132_642_132642166_qa_3/task.toml +++ b/tasks/0132_642_132642166_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0132_642_132642166_qa_3" +name = "smoldataenvs-train/0132_642_132642166_qa_3" description = "How many unique coral and sponge species are identified in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2888" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0132_665_132665003_qa_1/task.toml b/tasks/0132_665_132665003_qa_1/task.toml index f23995e5b24ec19bae5af081e77e8adf9111ab55..ae5d266f32a240610a2a29aa4c2404373a04ad57 100644 --- a/tasks/0132_665_132665003_qa_1/task.toml +++ b/tasks/0132_665_132665003_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0132_665_132665003_qa_1" +name = "smoldataenvs-train/0132_665_132665003_qa_1" description = "Does the dataset contain any missing values based on the visual inspection using the missingno matrix?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0132_672_132672509_qa_1/task.toml b/tasks/0132_672_132672509_qa_1/task.toml index faa4a047850923b8ccd618f5646866cccec04116..5f25d90a5f1fb625e20c46a85d7f748610b6e6d2 100644 --- a/tasks/0132_672_132672509_qa_1/task.toml +++ b/tasks/0132_672_132672509_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0132_672_132672509_qa_1" +name = "smoldataenvs-train/0132_672_132672509_qa_1" description = "Which feature was identified as the most important predictor of employee attrition based on the Random Forest model's feature importance analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "MonthlyIncome" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0132_672_132672509_qa_3/task.toml b/tasks/0132_672_132672509_qa_3/task.toml index 3a0cbb6d1a36ef23d8edf1e927796ec204b034aa..5b96474c8f601618d58a77c87283065e856d0489 100644 --- a/tasks/0132_672_132672509_qa_3/task.toml +++ b/tasks/0132_672_132672509_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0132_672_132672509_qa_3" +name = "smoldataenvs-train/0132_672_132672509_qa_3" description = "Based on the t-test analysis comparing compensation between Human Resources and Research & Development departments, is there a statistically significant difference in monthly income between these departments?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0132_865_132865792_qa_1/task.toml b/tasks/0132_865_132865792_qa_1/task.toml index 9adfbc306872f92aca6bbdf55e3932beb071f323..8651d602c3e9b0a05a3b6c7ddc5f4f75bc81f531 100644 --- a/tasks/0132_865_132865792_qa_1/task.toml +++ b/tasks/0132_865_132865792_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0132_865_132865792_qa_1" +name = "smoldataenvs-train/0132_865_132865792_qa_1" description = "What is the highest correlation coefficient between any two features in the original dataset before feature selection?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.997855" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0132_865_132865792_qa_4/task.toml b/tasks/0132_865_132865792_qa_4/task.toml index 45ec5b7cd74ae39e47d59fbf94d9ad9dfcb4465e..9d83e66545af5a4834fc841ba9e9c54e95b730c1 100644 --- a/tasks/0132_865_132865792_qa_4/task.toml +++ b/tasks/0132_865_132865792_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0132_865_132865792_qa_4" +name = "smoldataenvs-train/0132_865_132865792_qa_4" description = "What is the highest F1-score for malignant tumor prediction (class 1) across all three trained models?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.94" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0132_903_132903502_qa_3/task.toml b/tasks/0132_903_132903502_qa_3/task.toml index 8070d1bfce27844c68ba532c0d8e0e12b24a9333..580eef166875367b335d32ba547cad5255bdd4b8 100644 --- a/tasks/0132_903_132903502_qa_3/task.toml +++ b/tasks/0132_903_132903502_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0132_903_132903502_qa_3" +name = "smoldataenvs-train/0132_903_132903502_qa_3" description = "Does the Ridge Regression model improve the R² score compared to the standard Linear Regression model when using the same full feature set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "no" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0132_903_132903502_qa_5/task.toml b/tasks/0132_903_132903502_qa_5/task.toml index 064ef2c66c7e281e58bb328087e94ff3ab243679..0b0554adb37b1de0dd5769d2055847ffb9eecae9 100644 --- a/tasks/0132_903_132903502_qa_5/task.toml +++ b/tasks/0132_903_132903502_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0132_903_132903502_qa_5" +name = "smoldataenvs-train/0132_903_132903502_qa_5" description = "Which single feature, when used alone in a Linear Regression model, provides a higher R² score for predicting house prices: sqft_living or grade?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "sqft_living" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0132_967_132967098_qa_4/task.toml b/tasks/0132_967_132967098_qa_4/task.toml index c124277b73ce5f24615375ed494bd6dc3c3fb557..13af5a663605d23c1549db7147e7d57bfe5572ff 100644 --- a/tasks/0132_967_132967098_qa_4/task.toml +++ b/tasks/0132_967_132967098_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0132_967_132967098_qa_4" +name = "smoldataenvs-train/0132_967_132967098_qa_4" description = "Which U.S. state has the highest number of respondents who have sought mental health treatment?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "California" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0132_967_132967098_qa_5/task.toml b/tasks/0132_967_132967098_qa_5/task.toml index ea7357619aee409641d2608e6f4c681a5da34e6e..33ec80d8d28835c60d6cbb0674b7b113e158aae7 100644 --- a/tasks/0132_967_132967098_qa_5/task.toml +++ b/tasks/0132_967_132967098_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0132_967_132967098_qa_5" +name = "smoldataenvs-train/0132_967_132967098_qa_5" description = "What is the most common gender category among respondents after data cleaning?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Male" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0132_989_132989870_qa_5/task.toml b/tasks/0132_989_132989870_qa_5/task.toml index f5a4fc2e0d0417bc60d8c403b3d185cd93e334d7..d91ee1f3c254dd59072606e8b3dad3708302b9f7 100644 --- a/tasks/0132_989_132989870_qa_5/task.toml +++ b/tasks/0132_989_132989870_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0132_989_132989870_qa_5" +name = "smoldataenvs-train/0132_989_132989870_qa_5" description = "What is the correlation coefficient between life expectancy and the development status (Dev_num) binary variable in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.482136" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0133_024_133024285_qa_4/task.toml b/tasks/0133_024_133024285_qa_4/task.toml index a0d8d1b0dfea27af51c9fb8ebb5e498692ec7c73..c27896168797af2a370bcda2ae180f5881668b57 100644 --- a/tasks/0133_024_133024285_qa_4/task.toml +++ b/tasks/0133_024_133024285_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0133_024_133024285_qa_4" +name = "smoldataenvs-train/0133_024_133024285_qa_4" description = "What is the total number of messages in the SMS dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5572" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0133_040_133040358_qa_3/task.toml b/tasks/0133_040_133040358_qa_3/task.toml index 64fd28ae455a29d202fbdb317dd6ecc39e050f5d..69364337bc2fc337f4ad88c97d54dd6da38acdac 100644 --- a/tasks/0133_040_133040358_qa_3/task.toml +++ b/tasks/0133_040_133040358_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0133_040_133040358_qa_3" +name = "smoldataenvs-train/0133_040_133040358_qa_3" description = "What is the test accuracy of the decision tree model trained on the 8 most important features identified in the analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.0" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0133_047_133047390_qa_2/task.toml b/tasks/0133_047_133047390_qa_2/task.toml index ff02467ab70e7f20d6746609290c800879c7f937..72647c59e9e5f806da5cb85551828eb2ea285331 100644 --- a/tasks/0133_047_133047390_qa_2/task.toml +++ b/tasks/0133_047_133047390_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0133_047_133047390_qa_2" +name = "smoldataenvs-train/0133_047_133047390_qa_2" description = "What is the most common movie rating range based on the histogram of movie ratings?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6.0 to 6.5" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0133_078_133078833_qa_1/task.toml b/tasks/0133_078_133078833_qa_1/task.toml index c73ea3ccb8c97235f7b17777cf32576f10ab20a4..b6957fd1ec35d467c1f3e43fc1cb98a4f707ac0a 100644 --- a/tasks/0133_078_133078833_qa_1/task.toml +++ b/tasks/0133_078_133078833_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0133_078_133078833_qa_1" +name = "smoldataenvs-train/0133_078_133078833_qa_1" description = "Which platform has the highest average global sales per game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "GB" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0133_079_133079906_qa_3/task.toml b/tasks/0133_079_133079906_qa_3/task.toml index f9724d45b19e63a004037af1474f0798f504238a..22d5f1c4a32ac563464085912db6997fc4060dde 100644 --- a/tasks/0133_079_133079906_qa_3/task.toml +++ b/tasks/0133_079_133079906_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0133_079_133079906_qa_3" +name = "smoldataenvs-train/0133_079_133079906_qa_3" description = "What is the range of the petal width measurements across all samples in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0133_079_133079906_qa_4/task.toml b/tasks/0133_079_133079906_qa_4/task.toml index a9e2055c1ca5b8bda3cf6d2b0ce66f8b6fca9c5a..1e373d92efa76714f32ff21760101a4ad1458871 100644 --- a/tasks/0133_079_133079906_qa_4/task.toml +++ b/tasks/0133_079_133079906_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0133_079_133079906_qa_4" +name = "smoldataenvs-train/0133_079_133079906_qa_4" description = "Which species exhibits the maximum petal length value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Iris-virginica" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0133_089_133089049_qa_3/task.toml b/tasks/0133_089_133089049_qa_3/task.toml index 73fd814ff4683746f691e75c40c5015e6d11026b..acefc51510dc5ff04a8d85a339f199cac6c56b68 100644 --- a/tasks/0133_089_133089049_qa_3/task.toml +++ b/tasks/0133_089_133089049_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0133_089_133089049_qa_3" +name = "smoldataenvs-train/0133_089_133089049_qa_3" description = "What is the correlation coefficient between alcohol content and wine quality in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.48" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0133_196_133196028_qa_4/task.toml b/tasks/0133_196_133196028_qa_4/task.toml index 16002f4e68606f1f3a714a9c844b59252172bc74..b06108317344da27360eaaed0d9ca8213a93c883 100644 --- a/tasks/0133_196_133196028_qa_4/task.toml +++ b/tasks/0133_196_133196028_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0133_196_133196028_qa_4" +name = "smoldataenvs-train/0133_196_133196028_qa_4" description = "What is the difference in test accuracy between the model using all features and the model after removing 'fnlwgt' and 'native-country'?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.0082" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0133_423_133423579_qa_4/task.toml b/tasks/0133_423_133423579_qa_4/task.toml index d629f079f4467d1ebd058f14ee503c3a38299b4f..5f5cd33656f7b5961e06c5c21c26d327a2581a9b 100644 --- a/tasks/0133_423_133423579_qa_4/task.toml +++ b/tasks/0133_423_133423579_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0133_423_133423579_qa_4" +name = "smoldataenvs-train/0133_423_133423579_qa_4" description = "What are the unique numeric values representing geographic regions in the 'origin' column of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1, 2, 3" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0133_522_133522334_qa_5/task.toml b/tasks/0133_522_133522334_qa_5/task.toml index c30dcdcee7aaaea921319bbf674946eb3eb7a0a5..2f1450b0c270266cae94ccb96f4823eddb060602 100644 --- a/tasks/0133_522_133522334_qa_5/task.toml +++ b/tasks/0133_522_133522334_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0133_522_133522334_qa_5" +name = "smoldataenvs-train/0133_522_133522334_qa_5" description = "Which department's Manager role has the highest average monthly income, and what is this value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Human Resources, 18088.64" reward_mode_initial = "list" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0133_533_133533460_qa_2/task.toml b/tasks/0133_533_133533460_qa_2/task.toml index 727217599ee0ab4e07089566181a251ee3ec9614..78cd807c9ef47933d8229bf463ba340986a9cea4 100644 --- a/tasks/0133_533_133533460_qa_2/task.toml +++ b/tasks/0133_533_133533460_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0133_533_133533460_qa_2" +name = "smoldataenvs-train/0133_533_133533460_qa_2" description = "What was the average house price in December 2014 based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "$538,000" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0133_533_133533460_qa_5/task.toml b/tasks/0133_533_133533460_qa_5/task.toml index bb6111a70976538dd976ccf01ca2aadef3af2708..987ea856db4a8ab0f293e13d899c8d0a73d3773e 100644 --- a/tasks/0133_533_133533460_qa_5/task.toml +++ b/tasks/0133_533_133533460_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0133_533_133533460_qa_5" +name = "smoldataenvs-train/0133_533_133533460_qa_5" description = "What is the correlation coefficient between living space square footage (sqft_living) and house price?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.702035" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0133_561_133561697_qa_2/task.toml b/tasks/0133_561_133561697_qa_2/task.toml index 959a340f9a99179cc13df35d672a6a7d42abf502..cd1dfa93cfc1963fd428c3d19d10b54d69d3993c 100644 --- a/tasks/0133_561_133561697_qa_2/task.toml +++ b/tasks/0133_561_133561697_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0133_561_133561697_qa_2" +name = "smoldataenvs-train/0133_561_133561697_qa_2" description = "What are the optimal hyperparameters found for the SVM model during grid search?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "C=1, gamma=1, kernel=rbf" reward_mode_initial = "list" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0133_586_133586216_qa_3/task.toml b/tasks/0133_586_133586216_qa_3/task.toml index 6659e213db4f806bd251c82ae8d5a330187d9f23..b5aa02024679f939d2e8d8f757f1a957013f2501 100644 --- a/tasks/0133_586_133586216_qa_3/task.toml +++ b/tasks/0133_586_133586216_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0133_586_133586216_qa_3" +name = "smoldataenvs-train/0133_586_133586216_qa_3" description = "What is the correlation coefficient between age and axillary nodes in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.063176" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0133_586_133586216_qa_4/task.toml b/tasks/0133_586_133586216_qa_4/task.toml index dc032ead4c35f71db1bee47c77091ae191484e14..68915c9aa9bed98b2abd44f551a3d8673848c3fe 100644 --- a/tasks/0133_586_133586216_qa_4/task.toml +++ b/tasks/0133_586_133586216_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0133_586_133586216_qa_4" +name = "smoldataenvs-train/0133_586_133586216_qa_4" description = "What is the covariance value between operation year and axillary nodes in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.087946" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0133_586_133586216_qa_5/task.toml b/tasks/0133_586_133586216_qa_5/task.toml index c6ba0258ae71208dab0b2a65cf9d46c84d989f92..72eee79dddc0eb8a7c6353f152fc5bc13b2d7d96 100644 --- a/tasks/0133_586_133586216_qa_5/task.toml +++ b/tasks/0133_586_133586216_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0133_586_133586216_qa_5" +name = "smoldataenvs-train/0133_586_133586216_qa_5" description = "Based on the pairplot analysis, which age range had a higher proportion of patients who died within 5 years of surgery?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "40-60" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0133_597_133597962_qa_4/task.toml b/tasks/0133_597_133597962_qa_4/task.toml index d204bc319a67edefefec44d449144703f700a310..861ab3f70f86ceb75e08dcbb282e8ed7e047769a 100644 --- a/tasks/0133_597_133597962_qa_4/task.toml +++ b/tasks/0133_597_133597962_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0133_597_133597962_qa_4" +name = "smoldataenvs-train/0133_597_133597962_qa_4" description = "After feature engineering, which numerical feature exhibits the strongest positive correlation with median_house_value?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "median_income" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0133_615_133615287_qa_1/task.toml b/tasks/0133_615_133615287_qa_1/task.toml index 10af0bc0f170d2070b48a56d339e539dbc887190..bb84f542723823b5786f5cb27ca44760869e510d 100644 --- a/tasks/0133_615_133615287_qa_1/task.toml +++ b/tasks/0133_615_133615287_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0133_615_133615287_qa_1" +name = "smoldataenvs-train/0133_615_133615287_qa_1" description = "How many samples are present in each class (ham and spam) after the undersampling process to balance the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "747" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0133_615_133615287_qa_4/task.toml b/tasks/0133_615_133615287_qa_4/task.toml index c1c1964d5d6b919bdc99f18b6f84129d07923d7f..dae4bc954ff9397a7499b2244ee0d32783b7e8e7 100644 --- a/tasks/0133_615_133615287_qa_4/task.toml +++ b/tasks/0133_615_133615287_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0133_615_133615287_qa_4" +name = "smoldataenvs-train/0133_615_133615287_qa_4" description = "How many false positive predictions does the balanced Bernoulli Naive Bayes model produce on the test set based on its confusion matrix?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0133_625_133625301_qa_2/task.toml b/tasks/0133_625_133625301_qa_2/task.toml index 8abc0a28dba81bd9f1e2235c330caf67c1834898..eb2d3078149136912885080fed1c858cd237102d 100644 --- a/tasks/0133_625_133625301_qa_2/task.toml +++ b/tasks/0133_625_133625301_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0133_625_133625301_qa_2" +name = "smoldataenvs-train/0133_625_133625301_qa_2" description = "How many duplicate records were removed from the original dataset during preprocessing?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0133_688_133688260_qa_1/task.toml b/tasks/0133_688_133688260_qa_1/task.toml index 74f3df628f7c0166959be2f902facd2ae99c3b7c..0cebd81ea0a084f5d77bacc570cd6000b5854b3a 100644 --- a/tasks/0133_688_133688260_qa_1/task.toml +++ b/tasks/0133_688_133688260_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0133_688_133688260_qa_1" +name = "smoldataenvs-train/0133_688_133688260_qa_1" description = "What is the difference in average account balance between customers who subscribed to a term deposit and those who did not?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "524.03" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0133_688_133688260_qa_4/task.toml b/tasks/0133_688_133688260_qa_4/task.toml index e1771a49fd79e5b1aa048a8c074c18d4a55394e7..d7820526fdc342f924929f2d00d8991b80cdeefe 100644 --- a/tasks/0133_688_133688260_qa_4/task.toml +++ b/tasks/0133_688_133688260_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0133_688_133688260_qa_4" +name = "smoldataenvs-train/0133_688_133688260_qa_4" description = "What is the difference in average number of previous contacts between term deposit subscribers and non-subscribers?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.64" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0133_909_133909373_qa_3/task.toml b/tasks/0133_909_133909373_qa_3/task.toml index 9a027e49b8e082203d57cc1d1d2c2031064e3309..f3aacae46f95a98f819d7532d9e358425fccf3a6 100644 --- a/tasks/0133_909_133909373_qa_3/task.toml +++ b/tasks/0133_909_133909373_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0133_909_133909373_qa_3" +name = "smoldataenvs-train/0133_909_133909373_qa_3" description = "What is the optimal number of neighbors (k) that maximizes the cross-validation accuracy in the KNN model based on 10-fold cross-validation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "7" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0133_918_133918365_qa_5/task.toml b/tasks/0133_918_133918365_qa_5/task.toml index a71711e81a262eb4c4acdabfc28d64a0d4885702..a8ffdf36c869200d13b47089dd2623ed5017abde 100644 --- a/tasks/0133_918_133918365_qa_5/task.toml +++ b/tasks/0133_918_133918365_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0133_918_133918365_qa_5" +name = "smoldataenvs-train/0133_918_133918365_qa_5" description = "What was the original class distribution of the \"Attrition\" target variable before SMOTE upsampling was applied?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "No=1233, Yes=237" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0133_985_133985861_qa_1/task.toml b/tasks/0133_985_133985861_qa_1/task.toml index 74dc99b8e2bafbcd1a25b777fa26b25c32022bb9..8b0c2906748f2d99a6ff45cd61cb130fcf795d5a 100644 --- a/tasks/0133_985_133985861_qa_1/task.toml +++ b/tasks/0133_985_133985861_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0133_985_133985861_qa_1" +name = "smoldataenvs-train/0133_985_133985861_qa_1" description = "What is the standard deviation of the sepal length in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.828066" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0133_988_133988312_qa_2/task.toml b/tasks/0133_988_133988312_qa_2/task.toml index 2e16ecd45089e0112140865dde6fd37a75204ec8..8abb184566cf2518b683446ea6649ef2b313f837 100644 --- a/tasks/0133_988_133988312_qa_2/task.toml +++ b/tasks/0133_988_133988312_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0133_988_133988312_qa_2" +name = "smoldataenvs-train/0133_988_133988312_qa_2" description = "How many unique wind direction categories were present in the 'cbwd' feature before one-hot encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0133_988_133988312_qa_4/task.toml b/tasks/0133_988_133988312_qa_4/task.toml index c23d75a1396c0c62cff58c7a6fa006bc1bb6e012..0619c21260105dab62d909736470e00eaa1c91dc 100644 --- a/tasks/0133_988_133988312_qa_4/task.toml +++ b/tasks/0133_988_133988312_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0133_988_133988312_qa_4" +name = "smoldataenvs-train/0133_988_133988312_qa_4" description = "After splitting the data with an 80-20 train-test ratio, how many samples were in the training set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34924" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0134_109_134109927_qa_3/task.toml b/tasks/0134_109_134109927_qa_3/task.toml index 721bc6dac808448ae7fdea873f5f9255e8f9c3e5..f943dbafede4d3bceb24518bcf9d48d0345bd7aa 100644 --- a/tasks/0134_109_134109927_qa_3/task.toml +++ b/tasks/0134_109_134109927_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0134_109_134109927_qa_3" +name = "smoldataenvs-train/0134_109_134109927_qa_3" description = "Which La Liga team had the highest maximum number of goals scored in a single home match during the 2014/2015 season?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Real Madrid CF" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0134_377_134377308_qa_3/task.toml b/tasks/0134_377_134377308_qa_3/task.toml index ad07404f883d538bad69a0190d43df9917d4446f..d953d7e7b3ee0f097328e52e88947d87736951a8 100644 --- a/tasks/0134_377_134377308_qa_3/task.toml +++ b/tasks/0134_377_134377308_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0134_377_134377308_qa_3" +name = "smoldataenvs-train/0134_377_134377308_qa_3" description = "How many samples in the dataset have a primary camera (pc > 0) but no front camera (fc = 0)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "373" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0134_445_134445739_qa_2/task.toml b/tasks/0134_445_134445739_qa_2/task.toml index 877bab254516f814aa7fddbc1bd5a4909c08f624..b24e6f7414d52e6f76a0acf8192d6e13c9c52227 100644 --- a/tasks/0134_445_134445739_qa_2/task.toml +++ b/tasks/0134_445_134445739_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0134_445_134445739_qa_2" +name = "smoldataenvs-train/0134_445_134445739_qa_2" description = "Which geographic region has the highest average medical insurance charges according to the training data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "southeast" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0134_484_134484147_qa_1/task.toml b/tasks/0134_484_134484147_qa_1/task.toml index a7fedf6b6c6d52ac895a755db0140c827e0c9bcd..10a25f6f715dbdbcd02d7b8398d51405f22c45cb 100644 --- a/tasks/0134_484_134484147_qa_1/task.toml +++ b/tasks/0134_484_134484147_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0134_484_134484147_qa_1" +name = "smoldataenvs-train/0134_484_134484147_qa_1" description = "What percentage of customers in the dataset have churned after handling missing values in TotalCharges?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26.5" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0134_657_134657302_qa_1/task.toml b/tasks/0134_657_134657302_qa_1/task.toml index 3a7d97a4666e14f20ce1696f307a142e1b84850c..057fd28bab6eb7dbbd3b405b84cfee431d280e45 100644 --- a/tasks/0134_657_134657302_qa_1/task.toml +++ b/tasks/0134_657_134657302_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0134_657_134657302_qa_1" +name = "smoldataenvs-train/0134_657_134657302_qa_1" description = "What is the highest total stat sum for a dual-type Pokémon type combination in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8927" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0134_683_134683130_qa_5/task.toml b/tasks/0134_683_134683130_qa_5/task.toml index ec526e4c0214190048d1a7a13263081142f6d34a..eff06985daa4c00ba5e467d705f753004f47abfd 100644 --- a/tasks/0134_683_134683130_qa_5/task.toml +++ b/tasks/0134_683_134683130_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0134_683_134683130_qa_5" +name = "smoldataenvs-train/0134_683_134683130_qa_5" description = "What is the total number of 4-star reviews in the original dataset before subsampling?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "80655" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0134_878_134878027_qa_1/task.toml b/tasks/0134_878_134878027_qa_1/task.toml index 2a6f6af40e95e7a730799b1e4b76a7b52045e71b..97bc6aa1ecceab67797461e3d1ec44847137353b 100644 --- a/tasks/0134_878_134878027_qa_1/task.toml +++ b/tasks/0134_878_134878027_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0134_878_134878027_qa_1" +name = "smoldataenvs-train/0134_878_134878027_qa_1" description = "Which feature in the Red Wine Quality dataset has the highest standard deviation before data scaling?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "total sulfur dioxide" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0134_878_134878027_qa_3/task.toml b/tasks/0134_878_134878027_qa_3/task.toml index 98946e96119629809d3e50eee36e98b8e26a0160..996ffbf7197bcfb1e3391ee73dcd9cdcc6c87391 100644 --- a/tasks/0134_878_134878027_qa_3/task.toml +++ b/tasks/0134_878_134878027_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0134_878_134878027_qa_3" +name = "smoldataenvs-train/0134_878_134878027_qa_3" description = "What is the average 'residual sugar' content measured in grams per deciliter across all samples in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2.5388" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0134_941_134941943_qa_1/task.toml b/tasks/0134_941_134941943_qa_1/task.toml index a75859ebd57a8fb7e0f26db48ed3ff8e955a4776..6bd36353abe45511446547b766fe1b030036d204 100644 --- a/tasks/0134_941_134941943_qa_1/task.toml +++ b/tasks/0134_941_134941943_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0134_941_134941943_qa_1" +name = "smoldataenvs-train/0134_941_134941943_qa_1" description = "What is the accuracy of the logistic regression model on the test set after training on the Sonar dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "80.95" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0135_043_135043306_qa_2/task.toml b/tasks/0135_043_135043306_qa_2/task.toml index 6c7f0e5372ea174f79b366a7eb88de8aac7be240..5bd0f1b9e867a2affcbc50ce8d67c59dc21a44c3 100644 --- a/tasks/0135_043_135043306_qa_2/task.toml +++ b/tasks/0135_043_135043306_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0135_043_135043306_qa_2" +name = "smoldataenvs-train/0135_043_135043306_qa_2" description = "What is the highest total transaction amount (quantity × unit price) recorded in a single invoice in the cleaned dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "168469.60" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0135_064_135064960_qa_5/task.toml b/tasks/0135_064_135064960_qa_5/task.toml index c2e106e848fbce29dcaf81627c79ff8444a09f2e..36c734d65ab4d4b2b3bc7aae16b81897db5e78c2 100644 --- a/tasks/0135_064_135064960_qa_5/task.toml +++ b/tasks/0135_064_135064960_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0135_064_135064960_qa_5" +name = "smoldataenvs-train/0135_064_135064960_qa_5" description = "Which stock had the highest root mean squared error (RMSE) in its closing price prediction model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "AMZN" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0135_081_135081430_qa_5/task.toml b/tasks/0135_081_135081430_qa_5/task.toml index a058b70804c90c50db22c554953f3254dc4a4b98..d3eb89c0bc11f3f09db8219f970c4aa2bebd8b64 100644 --- a/tasks/0135_081_135081430_qa_5/task.toml +++ b/tasks/0135_081_135081430_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0135_081_135081430_qa_5" +name = "smoldataenvs-train/0135_081_135081430_qa_5" description = "What is the highest global sales value (in millions) achieved by a PlayStation 2 (PS2) game in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "20.81" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0135_081_135081694_qa_2/task.toml b/tasks/0135_081_135081694_qa_2/task.toml index 3e2ee2f9141ee2f1ee070052cd081f5f881fa2fe..f2a12035c15a5f277b976b2508289055e0330c17 100644 --- a/tasks/0135_081_135081694_qa_2/task.toml +++ b/tasks/0135_081_135081694_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0135_081_135081694_qa_2" +name = "smoldataenvs-train/0135_081_135081694_qa_2" description = "How many times higher is the average global sales of the Nintendo Wii platform compared to the average global sales of all other platforms combined?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.34" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0135_081_135081694_qa_4/task.toml b/tasks/0135_081_135081694_qa_4/task.toml index 603d0055fc92c24e35501e3afcf9f5c7d380a96b..1e0b1de6cfb4a9854b8109b17cb8a9e22fbe7ce2 100644 --- a/tasks/0135_081_135081694_qa_4/task.toml +++ b/tasks/0135_081_135081694_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0135_081_135081694_qa_4" +name = "smoldataenvs-train/0135_081_135081694_qa_4" description = "In which calendar year were the most sports-related video games published based on the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2008" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0135_081_135081694_qa_5/task.toml b/tasks/0135_081_135081694_qa_5/task.toml index 64176076e210bcf9357a8bb291387a6cf36d0480..d8cca7a05ef2c4458033db2911ee3e55b89ad351 100644 --- a/tasks/0135_081_135081694_qa_5/task.toml +++ b/tasks/0135_081_135081694_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0135_081_135081694_qa_5" +name = "smoldataenvs-train/0135_081_135081694_qa_5" description = "Which video game genre has the lowest cumulative sales in Japan according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Shooter" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0135_081_135081983_qa_3/task.toml b/tasks/0135_081_135081983_qa_3/task.toml index 3c9bace0d25373bec5dbe4c42697fc0e581f477c..f9685fa414270ea3f1c67c4f6c81bc067a4448be 100644 --- a/tasks/0135_081_135081983_qa_3/task.toml +++ b/tasks/0135_081_135081983_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0135_081_135081983_qa_3" +name = "smoldataenvs-train/0135_081_135081983_qa_3" description = "What is the difference in average global sales between Nintendo Wii and all other platforms combined?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.176" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0135_170_135170219_qa_1/task.toml b/tasks/0135_170_135170219_qa_1/task.toml index 089935c01ff16eaba5f668a2cd5093747b0486d5..7edf43b8e6b906054295cba084a648fd73e7bbe6 100644 --- a/tasks/0135_170_135170219_qa_1/task.toml +++ b/tasks/0135_170_135170219_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0135_170_135170219_qa_1" +name = "smoldataenvs-train/0135_170_135170219_qa_1" description = "Which feature in the Boston housing dataset exhibits the strongest negative correlation with the median home value (MEDV), and what is the correlation coefficient?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "LSTAT, -0.7376" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0135_170_135170219_qa_4/task.toml b/tasks/0135_170_135170219_qa_4/task.toml index 6dca3c54180916b3b6637aa061acaf9ee97dc3ca..929b4dad3adfa19c93cd3c911cd34838acdc177a 100644 --- a/tasks/0135_170_135170219_qa_4/task.toml +++ b/tasks/0135_170_135170219_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0135_170_135170219_qa_4" +name = "smoldataenvs-train/0135_170_135170219_qa_4" description = "Which feature has the strongest positive correlation with the median home value (MEDV), and what is the correlation coefficient?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "RM, 0.7" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0135_174_135174197_qa_4/task.toml b/tasks/0135_174_135174197_qa_4/task.toml index 1dfc628b0b395e551ab1f3c34a6e033758dd8823..d71ba309dc37c5e558c31f476aea340ec997a045 100644 --- a/tasks/0135_174_135174197_qa_4/task.toml +++ b/tasks/0135_174_135174197_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0135_174_135174197_qa_4" +name = "smoldataenvs-train/0135_174_135174197_qa_4" description = "Which video game genre has the highest sales in the \"Other_Sales\" region category?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Action" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0135_324_135324126_qa_2/task.toml b/tasks/0135_324_135324126_qa_2/task.toml index 206e4e92e0e5a283dc095911392a2fb3b4dc4634..8a734df2545237cf0293ddee9c07eb096ee54eb3 100644 --- a/tasks/0135_324_135324126_qa_2/task.toml +++ b/tasks/0135_324_135324126_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0135_324_135324126_qa_2" +name = "smoldataenvs-train/0135_324_135324126_qa_2" description = "Is there a statistically significant difference in mean glucose levels between individuals with and without diabetes in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0135_324_135324126_qa_3/task.toml b/tasks/0135_324_135324126_qa_3/task.toml index 9fb9ae71fcf56bcd8c57b6d0aa7edd35fb401c72..a9ad3904a6d81ece608c336ed6e73925e20f4a42 100644 --- a/tasks/0135_324_135324126_qa_3/task.toml +++ b/tasks/0135_324_135324126_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0135_324_135324126_qa_3" +name = "smoldataenvs-train/0135_324_135324126_qa_3" description = "Which physiological parameter (skin thickness or blood pressure) shows a statistically significant difference between individuals with and without diabetes according to the t-test results?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Skin Thickness" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0135_324_135324126_qa_5/task.toml b/tasks/0135_324_135324126_qa_5/task.toml index 62d203ea0afaeb998908dad3fd2b22040715b2e2..ffdf9f0e5d75841d5072002785af4fc21f1c6e41 100644 --- a/tasks/0135_324_135324126_qa_5/task.toml +++ b/tasks/0135_324_135324126_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0135_324_135324126_qa_5" +name = "smoldataenvs-train/0135_324_135324126_qa_5" description = "Is there a statistically significant difference in average age between individuals with diabetes and those without diabetes in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0135_355_135355749_qa_3/task.toml b/tasks/0135_355_135355749_qa_3/task.toml index 12c64aa8e6fa485323b098fe5c745cad908bf340..ef1dee5c9273481e123a26f4957d3718622ece3a 100644 --- a/tasks/0135_355_135355749_qa_3/task.toml +++ b/tasks/0135_355_135355749_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0135_355_135355749_qa_3" +name = "smoldataenvs-train/0135_355_135355749_qa_3" description = "Does the population mean life expectancy fall within the calculated 95% confidence interval?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0135_364_135364354_qa_3/task.toml b/tasks/0135_364_135364354_qa_3/task.toml index 50c28e4aa0f2be494fca7c446f9fa30ea0f3b9f6..a74b1cc236907383665a1defce16ad812ade9fcd 100644 --- a/tasks/0135_364_135364354_qa_3/task.toml +++ b/tasks/0135_364_135364354_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0135_364_135364354_qa_3" +name = "smoldataenvs-train/0135_364_135364354_qa_3" description = "Which manufacturer produces the cereal with the highest rating in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "K" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0135_364_135364354_qa_5/task.toml b/tasks/0135_364_135364354_qa_5/task.toml index ea69f8c7faee002b54503350ea199a3c23a10ca8..79bc4989207f3bf083ca840b86dfb915bdab4ae6 100644 --- a/tasks/0135_364_135364354_qa_5/task.toml +++ b/tasks/0135_364_135364354_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0135_364_135364354_qa_5" +name = "smoldataenvs-train/0135_364_135364354_qa_5" description = "What is the sodium content of the cereal with the highest rating in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "140" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0135_380_135380627_qa_1/task.toml b/tasks/0135_380_135380627_qa_1/task.toml index 5237fd37e5ac47714bdfbca747f53d6e4c48ffa0..fd489f6b93720d47d496725a1310cedbe1dd6821 100644 --- a/tasks/0135_380_135380627_qa_1/task.toml +++ b/tasks/0135_380_135380627_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0135_380_135380627_qa_1" +name = "smoldataenvs-train/0135_380_135380627_qa_1" description = "Which contract type has the strongest positive association with customer churn according to the logistic regression model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Month-to-month" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0135_380_135380654_qa_2/task.toml b/tasks/0135_380_135380654_qa_2/task.toml index 01cf9cb59b4e1ecf66d29449dd34ebcb90a017f7..538af388994100013113aeacc54c36ecfad07867 100644 --- a/tasks/0135_380_135380654_qa_2/task.toml +++ b/tasks/0135_380_135380654_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0135_380_135380654_qa_2" +name = "smoldataenvs-train/0135_380_135380654_qa_2" description = "What is the mean life expectancy in developed countries based on the sample data used for the hypothesis test?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "78.808" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0135_380_135380654_qa_4/task.toml b/tasks/0135_380_135380654_qa_4/task.toml index f402a8b3dc84c4ee1b0ea1ec5cf8d3e21a2aae8b..73765fdc8a6ea45f2de0adb5bc7d646d8ce6f6a8 100644 --- a/tasks/0135_380_135380654_qa_4/task.toml +++ b/tasks/0135_380_135380654_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0135_380_135380654_qa_4" +name = "smoldataenvs-train/0135_380_135380654_qa_4" description = "Based on the p-value and a significance level of 0.05, is there sufficient evidence to reject the null hypothesis that the mean life expectancies are equal between developing and developed countries?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0135_427_135427128_qa_2/task.toml b/tasks/0135_427_135427128_qa_2/task.toml index f8277c98dc95c7aad247844362cc5939e41319a6..afeecb6acfbe40a94a558d68481f650d9ee0c1aa 100644 --- a/tasks/0135_427_135427128_qa_2/task.toml +++ b/tasks/0135_427_135427128_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0135_427_135427128_qa_2" +name = "smoldataenvs-train/0135_427_135427128_qa_2" description = "Which wine quality level has the highest average alcohol content based on the bar plot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0135_485_135485471_qa_4/task.toml b/tasks/0135_485_135485471_qa_4/task.toml index 69b172d9d63dc4e062de4de5930c08c9979bb5c9..251aa0b837f54acbce9384542f2ec1b4ef3703cb 100644 --- a/tasks/0135_485_135485471_qa_4/task.toml +++ b/tasks/0135_485_135485471_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0135_485_135485471_qa_4" +name = "smoldataenvs-train/0135_485_135485471_qa_4" description = "What is the recall score for the ham class (class 0) in the model's classification report?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.99" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0135_538_135538507_qa_5/task.toml b/tasks/0135_538_135538507_qa_5/task.toml index b6e2701568c746b5cb55b281d3ad830100c63cd3..caa5e0b8dcf8727d936adaa5401a19a876fb2e0c 100644 --- a/tasks/0135_538_135538507_qa_5/task.toml +++ b/tasks/0135_538_135538507_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0135_538_135538507_qa_5" +name = "smoldataenvs-train/0135_538_135538507_qa_5" description = "What is the minimum \"Total Biocapacity\" value in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.05" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0135_541_135541718_qa_1/task.toml b/tasks/0135_541_135541718_qa_1/task.toml index e974ad2cf94c74d414d6ac1ca6a168b001d2494a..9a3d03ab269b202455961386ffd031d353b4e3e4 100644 --- a/tasks/0135_541_135541718_qa_1/task.toml +++ b/tasks/0135_541_135541718_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0135_541_135541718_qa_1" +name = "smoldataenvs-train/0135_541_135541718_qa_1" description = "What is the optimal number of clusters identified by the elbow method in the customer credit data analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0135_616_135616199_qa_1/task.toml b/tasks/0135_616_135616199_qa_1/task.toml index 18826bd1a5921e0d46d68ca1e4473e69fecaafef..d70133defb197393e02db7e916e1fce711d295fc 100644 --- a/tasks/0135_616_135616199_qa_1/task.toml +++ b/tasks/0135_616_135616199_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0135_616_135616199_qa_1" +name = "smoldataenvs-train/0135_616_135616199_qa_1" description = "Which wine quality category has the highest average alcohol content according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0135_616_135616199_qa_4/task.toml b/tasks/0135_616_135616199_qa_4/task.toml index 41f1bb76623537dd13303c9ebcf166e17987b2af..94eaab5373e1bba7b97073f2de9788d0e1e70d61 100644 --- a/tasks/0135_616_135616199_qa_4/task.toml +++ b/tasks/0135_616_135616199_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0135_616_135616199_qa_4" +name = "smoldataenvs-train/0135_616_135616199_qa_4" description = "What is the difference in average quality scores between the highest and lowest quality categories in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5.0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0135_616_135616199_qa_5/task.toml b/tasks/0135_616_135616199_qa_5/task.toml index e0d7b172dd906500a07ba73c0cc9a3d30fde6028..2966d2c9ef806129f50905bdbc7cbd383a7014a1 100644 --- a/tasks/0135_616_135616199_qa_5/task.toml +++ b/tasks/0135_616_135616199_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0135_616_135616199_qa_5" +name = "smoldataenvs-train/0135_616_135616199_qa_5" description = "Which wine quality rating is the most common in the dataset based on sample counts?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0135_674_135674700_qa_2/task.toml b/tasks/0135_674_135674700_qa_2/task.toml index 02d13f9fc57b3dc261297ed87da284650e62fc9a..c0eebf5584033b7aadd08b374b9fc57f288c860a 100644 --- a/tasks/0135_674_135674700_qa_2/task.toml +++ b/tasks/0135_674_135674700_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0135_674_135674700_qa_2" +name = "smoldataenvs-train/0135_674_135674700_qa_2" description = "Which ocean proximity category has the highest median house value according to the boxplot analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "NEAR BAY" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0135_674_135674700_qa_5/task.toml b/tasks/0135_674_135674700_qa_5/task.toml index f682955ceee7b81a32d3a339bdc72c7ae5de721c..be8940f6dcfe18da9dd5d50b11ee09454150a9b7 100644 --- a/tasks/0135_674_135674700_qa_5/task.toml +++ b/tasks/0135_674_135674700_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0135_674_135674700_qa_5" +name = "smoldataenvs-train/0135_674_135674700_qa_5" description = "What percentage of original data points for total_bedrooms were missing before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.00" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0135_716_135716121_qa_3/task.toml b/tasks/0135_716_135716121_qa_3/task.toml index 8cf25fe43d120878949e09caebc63faae9981959..966b44b67a0f678bf715b58ad9baf8f698b61dc3 100644 --- a/tasks/0135_716_135716121_qa_3/task.toml +++ b/tasks/0135_716_135716121_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0135_716_135716121_qa_3" +name = "smoldataenvs-train/0135_716_135716121_qa_3" description = "Which job satisfaction level shows the highest correlation with employee attrition in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "exact_short" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0135_720_135720396_qa_1/task.toml b/tasks/0135_720_135720396_qa_1/task.toml index 0a7deb4f379497471abf761b94aa3ca930dc838c..55232a45aa1fd08f7d94df5846f1a36dd1480d61 100644 --- a/tasks/0135_720_135720396_qa_1/task.toml +++ b/tasks/0135_720_135720396_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0135_720_135720396_qa_1" +name = "smoldataenvs-train/0135_720_135720396_qa_1" description = "What is the highest weighted rating score calculated for any movie in the top 481 movies based on the IMDB formula?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8.059258" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0135_720_135720396_qa_5/task.toml b/tasks/0135_720_135720396_qa_5/task.toml index aecc07c737769bcff1e3845dabf7d37732751da7..90f1c808e415de50d901a34ec7f5c9a6e61350dd 100644 --- a/tasks/0135_720_135720396_qa_5/task.toml +++ b/tasks/0135_720_135720396_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0135_720_135720396_qa_5" +name = "smoldataenvs-train/0135_720_135720396_qa_5" description = "What is the highest vote average (R) among the top 481 movies based on the weighted rating calculation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8.5" reward_mode_initial = "numeric" package_tier = 0 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0135_734_135734200_qa_1/task.toml b/tasks/0135_734_135734200_qa_1/task.toml index a7708f389e4b3fbd414254651e288c9cbdef1d4d..e06feb88f24854e53c2873b2f14bc81290755773 100644 --- a/tasks/0135_734_135734200_qa_1/task.toml +++ b/tasks/0135_734_135734200_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0135_734_135734200_qa_1" +name = "smoldataenvs-train/0135_734_135734200_qa_1" description = "Which country has the highest Total Ecological Footprint in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Luxembourg" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0135_735_135735954_qa_4/task.toml b/tasks/0135_735_135735954_qa_4/task.toml index ed30fc5e76543f7a4a28b78ce752c0cdd38aebe6..7d13607b7ede661fee9610f530ac909c13f99fa5 100644 --- a/tasks/0135_735_135735954_qa_4/task.toml +++ b/tasks/0135_735_135735954_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0135_735_135735954_qa_4" +name = "smoldataenvs-train/0135_735_135735954_qa_4" description = "What is the adjusted R² score for the XGBoost model's performance on the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.98057" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0135_740_135740732_qa_2/task.toml b/tasks/0135_740_135740732_qa_2/task.toml index c468fad23a839e70aa95df4604e31d874fe3ab48..a168d6f759720ffe27782bdacc68aeb83220ce71 100644 --- a/tasks/0135_740_135740732_qa_2/task.toml +++ b/tasks/0135_740_135740732_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0135_740_135740732_qa_2" +name = "smoldataenvs-train/0135_740_135740732_qa_2" description = "What is the slope coefficient of the linear regression model predicting MaxTemp from MinTemp in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.91807749" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0135_740_135740732_qa_3/task.toml b/tasks/0135_740_135740732_qa_3/task.toml index 777f3d08667b3495921628abdd7746abc76e7299..d32dccbd8fb8447a960a08a77947d3ffa0b38812 100644 --- a/tasks/0135_740_135740732_qa_3/task.toml +++ b/tasks/0135_740_135740732_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0135_740_135740732_qa_3" +name = "smoldataenvs-train/0135_740_135740732_qa_3" description = "How many samples are included in the test set for the MaxTemp prediction model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "23808" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0135_920_135920645_qa_4/task.toml b/tasks/0135_920_135920645_qa_4/task.toml index 19f3c16fe789fe50828f7719808043f7015f7f84..29feca925d7a5b13e5deaaa6d6eb96edbbf4761a 100644 --- a/tasks/0135_920_135920645_qa_4/task.toml +++ b/tasks/0135_920_135920645_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0135_920_135920645_qa_4" +name = "smoldataenvs-train/0135_920_135920645_qa_4" description = "What is the highest F1-score achieved by any class in the logistic regression model's training set evaluation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.97" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0135_920_135920645_qa_5/task.toml b/tasks/0135_920_135920645_qa_5/task.toml index 8d4e94decd2517ff3ea90cd254f91761c2c01a92..cc368757b986f0568ac0f062de7c4fc4767d321c 100644 --- a/tasks/0135_920_135920645_qa_5/task.toml +++ b/tasks/0135_920_135920645_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0135_920_135920645_qa_5" +name = "smoldataenvs-train/0135_920_135920645_qa_5" description = "What is the recall score for the logistic regression model on the training set for the minority class?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.96" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0135_926_135926873_qa_2/task.toml b/tasks/0135_926_135926873_qa_2/task.toml index 958e54c0b1232de4b231776cba249fb1729fd9bd..3406e0d39a1d7f1a58cd4999bb406a92f1cbe62a 100644 --- a/tasks/0135_926_135926873_qa_2/task.toml +++ b/tasks/0135_926_135926873_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0135_926_135926873_qa_2" +name = "smoldataenvs-train/0135_926_135926873_qa_2" description = "What is the correlation coefficient between sales and quantity ordered in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.5514261919183568" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0135_926_135926873_qa_3/task.toml b/tasks/0135_926_135926873_qa_3/task.toml index 26e6e648defbfdc30bdd148be190294400a99193..ce8edb46789254b57ed5edad0242cf1b29f66eac 100644 --- a/tasks/0135_926_135926873_qa_3/task.toml +++ b/tasks/0135_926_135926873_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0135_926_135926873_qa_3" +name = "smoldataenvs-train/0135_926_135926873_qa_3" description = "Which country generated the highest total sales in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "USA" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0135_926_135926873_qa_4/task.toml b/tasks/0135_926_135926873_qa_4/task.toml index 45f89a86db3a1284e0327729d11b41bef20d0e4c..26501aa6a19766726c791c9800f048add2efdd9b 100644 --- a/tasks/0135_926_135926873_qa_4/task.toml +++ b/tasks/0135_926_135926873_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0135_926_135926873_qa_4" +name = "smoldataenvs-train/0135_926_135926873_qa_4" description = "Which customer contributed the highest total sales in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Euro Shopping Channel" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0135_931_135931499_qa_2/task.toml b/tasks/0135_931_135931499_qa_2/task.toml index cf90e1654084f588be8569b9d066b3a1bda9c1c1..1a722d38238e33e3b497ca15935d637a42cd984d 100644 --- a/tasks/0135_931_135931499_qa_2/task.toml +++ b/tasks/0135_931_135931499_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0135_931_135931499_qa_2" +name = "smoldataenvs-train/0135_931_135931499_qa_2" description = "What percentage of the 'total_bedrooms' column contains missing values in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1.00" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0135_931_135931499_qa_4/task.toml b/tasks/0135_931_135931499_qa_4/task.toml index e4c4756109badebdf0f2ad68fd4944e38b227dc0..8c6f8355782191285133018cc3f38fe2158f1e4d 100644 --- a/tasks/0135_931_135931499_qa_4/task.toml +++ b/tasks/0135_931_135931499_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0135_931_135931499_qa_4" +name = "smoldataenvs-train/0135_931_135931499_qa_4" description = "After all preprocessing steps, how many features are present in the final dataset used for modeling?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0135_968_135968027_qa_1/task.toml b/tasks/0135_968_135968027_qa_1/task.toml index 52c6c8acf151aec8a7d2d6fd2c7c85fcedbd0b48..e03347fd0b9fb66385bbdd4d91c82aebda75e220 100644 --- a/tasks/0135_968_135968027_qa_1/task.toml +++ b/tasks/0135_968_135968027_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0135_968_135968027_qa_1" +name = "smoldataenvs-train/0135_968_135968027_qa_1" description = "What proportion of the Iris dataset was allocated for training the KNN classification model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "80%" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0135_968_135968027_qa_3/task.toml b/tasks/0135_968_135968027_qa_3/task.toml index 2f1e620c0f9d87fcbd9ae03b8b6dec7b72df79df..e5055d513677b56ca7e48118731e02b3a5feb0cf 100644 --- a/tasks/0135_968_135968027_qa_3/task.toml +++ b/tasks/0135_968_135968027_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0135_968_135968027_qa_3" +name = "smoldataenvs-train/0135_968_135968027_qa_3" description = "How many numerical features were used as input variables in the KNN classification model?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0136_044_136044627_qa_3/task.toml b/tasks/0136_044_136044627_qa_3/task.toml index 127c9480c5d96327d5664308ccd934dc68b21277..a5c4c205de449391176757397dc010e6195d5a3c 100644 --- a/tasks/0136_044_136044627_qa_3/task.toml +++ b/tasks/0136_044_136044627_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0136_044_136044627_qa_3" +name = "smoldataenvs-train/0136_044_136044627_qa_3" description = "How many data points were removed as outliers from the Sepal Width feature based on the IQR method?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0136_044_136044627_qa_5/task.toml b/tasks/0136_044_136044627_qa_5/task.toml index c114ff6119674ff40ced0ebd2177da290afdaa2f..fc3d0ae844b3cbf9d71e2abb2c30ee2127f76951 100644 --- a/tasks/0136_044_136044627_qa_5/task.toml +++ b/tasks/0136_044_136044627_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0136_044_136044627_qa_5" +name = "smoldataenvs-train/0136_044_136044627_qa_5" description = "What is the standard deviation of the Petal Width measurements in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.763161" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0136_053_136053251_qa_2/task.toml b/tasks/0136_053_136053251_qa_2/task.toml index 1b9bc0334551481cec3e24f3e7558ae911412fcc..b45574f8fabd59d2ea8417f9ed4e1cd44b90d47a 100644 --- a/tasks/0136_053_136053251_qa_2/task.toml +++ b/tasks/0136_053_136053251_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0136_053_136053251_qa_2" +name = "smoldataenvs-train/0136_053_136053251_qa_2" description = "What is the correlation between Price_in_thousands and __year_resale_value after imputing missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.822394890219183" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0136_053_136053251_qa_4/task.toml b/tasks/0136_053_136053251_qa_4/task.toml index 52fa50f21926f91ca60342af2654486f16e9a504..f69625e9ca69c51941c00ff4173de408c2122b3a 100644 --- a/tasks/0136_053_136053251_qa_4/task.toml +++ b/tasks/0136_053_136053251_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0136_053_136053251_qa_4" +name = "smoldataenvs-train/0136_053_136053251_qa_4" description = "How many missing values were present in the __year_resale_value column before imputation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "36" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0136_157_136157860_qa_5/task.toml b/tasks/0136_157_136157860_qa_5/task.toml index 68113ce60567b22f96d2050e9496c3bd6ed2c595..a029ebd5c08c2eadedd01a3677505dfd7ca22c7b 100644 --- a/tasks/0136_157_136157860_qa_5/task.toml +++ b/tasks/0136_157_136157860_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0136_157_136157860_qa_5" +name = "smoldataenvs-train/0136_157_136157860_qa_5" description = "How many loan applications are present in the dataset after removing all rows with missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "480" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0136_175_136175034_qa_5/task.toml b/tasks/0136_175_136175034_qa_5/task.toml index da8aef72417fd7fac06b19addf8a33ac9d96fdcd..62cd9066b2e23f63e14e4524269eca482c9f1d0f 100644 --- a/tasks/0136_175_136175034_qa_5/task.toml +++ b/tasks/0136_175_136175034_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0136_175_136175034_qa_5" +name = "smoldataenvs-train/0136_175_136175034_qa_5" description = "What is the accuracy of the homemade KNN model when using k=7 in the test set?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.73" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0136_198_136198797_qa_1/task.toml b/tasks/0136_198_136198797_qa_1/task.toml index 0c79a8a9879569708abed6dd3a0861c6555e6dc9..eecc5ee754e92a86cd5c4a3e9b25f4b468820a6e 100644 --- a/tasks/0136_198_136198797_qa_1/task.toml +++ b/tasks/0136_198_136198797_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0136_198_136198797_qa_1" +name = "smoldataenvs-train/0136_198_136198797_qa_1" description = "After encoding the ocean_proximity categories as dummy variables, how many unique categories are represented in the training data?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0136_198_136198797_qa_2/task.toml b/tasks/0136_198_136198797_qa_2/task.toml index 55cfa07c31ed64360e2eb1d2fbbe7dac8b003b65..098875a3133c045a725d220f24e121bc549b7295 100644 --- a/tasks/0136_198_136198797_qa_2/task.toml +++ b/tasks/0136_198_136198797_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0136_198_136198797_qa_2" +name = "smoldataenvs-train/0136_198_136198797_qa_2" description = "What is the most frequent ocean proximity category in the training dataset after removing rows with missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "<1H OCEAN" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0136_198_136198797_qa_3/task.toml b/tasks/0136_198_136198797_qa_3/task.toml index ff3ae567068b3f85c8ff2b1a5e06843fe5b13eed..bbbd292907fc1a538d8310d4942ac7aa023e831f 100644 --- a/tasks/0136_198_136198797_qa_3/task.toml +++ b/tasks/0136_198_136198797_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0136_198_136198797_qa_3" +name = "smoldataenvs-train/0136_198_136198797_qa_3" description = "How many data points in the training dataset belong to the least frequent ocean proximity category?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0136_199_136199196_qa_3/task.toml b/tasks/0136_199_136199196_qa_3/task.toml index 0803f55e68ff7b61edc1c2e6b730f59c8e16cbaa..6ad6f271e345427cdd7a6a5fd84e0b0972fd2482 100644 --- a/tasks/0136_199_136199196_qa_3/task.toml +++ b/tasks/0136_199_136199196_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0136_199_136199196_qa_3" +name = "smoldataenvs-train/0136_199_136199196_qa_3" description = "How many missing values were present in the total_bedrooms column before it was dropped from the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "207" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0136_242_136242885_qa_2/task.toml b/tasks/0136_242_136242885_qa_2/task.toml index cdb5bf5839115ebdad8c0ed74dcfc0dfdedb3990..164493b022065efa96d5acf60676944c759e1014 100644 --- a/tasks/0136_242_136242885_qa_2/task.toml +++ b/tasks/0136_242_136242885_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0136_242_136242885_qa_2" +name = "smoldataenvs-train/0136_242_136242885_qa_2" description = "Is the Iris dataset balanced in terms of the number of samples for each species?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0136_395_136395303_qa_1/task.toml b/tasks/0136_395_136395303_qa_1/task.toml index cdf34397ae98fcc73701d125e99eda74a000398a..ed71852b64728b6c205184bbaf2c390dc617d547 100644 --- a/tasks/0136_395_136395303_qa_1/task.toml +++ b/tasks/0136_395_136395303_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0136_395_136395303_qa_1" +name = "smoldataenvs-train/0136_395_136395303_qa_1" description = "What is the churn rate (proportion of churned customers) in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26.54" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0136_396_136396252_qa_2/task.toml b/tasks/0136_396_136396252_qa_2/task.toml index 238a7cf4cef5dc08d4b7669ee9012fcfcc1fed10..f11b182e313d5ad2274156a372cd7df4d6595825 100644 --- a/tasks/0136_396_136396252_qa_2/task.toml +++ b/tasks/0136_396_136396252_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0136_396_136396252_qa_2" +name = "smoldataenvs-train/0136_396_136396252_qa_2" description = "What is the R² score of the linear regression model trained using the 'sqft_living' feature as the sole predictor?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.492853" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0136_483_136483047_qa_4/task.toml b/tasks/0136_483_136483047_qa_4/task.toml index 3551c0a48d4d651f422de4dace07a42229321ab8..571099c1385768343a1cd50b12216db2e9554fec 100644 --- a/tasks/0136_483_136483047_qa_4/task.toml +++ b/tasks/0136_483_136483047_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0136_483_136483047_qa_4" +name = "smoldataenvs-train/0136_483_136483047_qa_4" description = "What is the total number of missing values across all columns in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0136_604_136604936_qa_4/task.toml b/tasks/0136_604_136604936_qa_4/task.toml index e31fcb4b76bca4639aad0d4ef4f61379084e472b..c7df256eb488a28a63a70cfe0f05d9b74eb30e31 100644 --- a/tasks/0136_604_136604936_qa_4/task.toml +++ b/tasks/0136_604_136604936_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0136_604_136604936_qa_4" +name = "smoldataenvs-train/0136_604_136604936_qa_4" description = "Which three features in the Wisconsin Breast Cancer dataset exhibit the highest variance according to the boxplot visualization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "mean area, area error, worst area" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0136_646_136646417_qa_2/task.toml b/tasks/0136_646_136646417_qa_2/task.toml index 32084bc541a67bcc595d050673b5a5ca674a2a08..9cce21a6555599ea10e57c647b35417963c197fa 100644 --- a/tasks/0136_646_136646417_qa_2/task.toml +++ b/tasks/0136_646_136646417_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0136_646_136646417_qa_2" +name = "smoldataenvs-train/0136_646_136646417_qa_2" description = "Which genre has the highest average global sales according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Platform" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0136_646_136646417_qa_4/task.toml b/tasks/0136_646_136646417_qa_4/task.toml index 6aa40c53a560ce5f1faf95c13dc07188965817d0..c48e5f84dd1579a56c8d3c6a8df2060018b9c8aa 100644 --- a/tasks/0136_646_136646417_qa_4/task.toml +++ b/tasks/0136_646_136646417_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0136_646_136646417_qa_4" +name = "smoldataenvs-train/0136_646_136646417_qa_4" description = "Which publisher has the highest total sales in Europe?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Nintendo" reward_mode_initial = "exact_short" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0136_656_136656176_qa_2/task.toml b/tasks/0136_656_136656176_qa_2/task.toml index 5bff074f73324c6e67350aa4bd1584c394c0428c..dc7310cf04f0c321eb331666474b109427452565 100644 --- a/tasks/0136_656_136656176_qa_2/task.toml +++ b/tasks/0136_656_136656176_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0136_656_136656176_qa_2" +name = "smoldataenvs-train/0136_656_136656176_qa_2" description = "What is the difference in mean Glucose levels between individuals with diabetes (Outcome = 1) and those without diabetes (Outcome = 0)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "31.28" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0136_677_136677870_qa_1/task.toml b/tasks/0136_677_136677870_qa_1/task.toml index 687dc115a1341a92e5d44e18545ef5c48dcbbe79..5df9b31da9cf929f33354ea0458af1b5c72dcc9b 100644 --- a/tasks/0136_677_136677870_qa_1/task.toml +++ b/tasks/0136_677_136677870_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0136_677_136677870_qa_1" +name = "smoldataenvs-train/0136_677_136677870_qa_1" description = "Which glass type is completely absent from the dataset based on the unique values in the 'Type' column?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0136_683_136683073_qa_2/task.toml b/tasks/0136_683_136683073_qa_2/task.toml index f03e3d834794c6a28c270e22374d72b4b3858aaf..f6940f8cf4ea57bc90be12d37f4cb6ff9f1ce515 100644 --- a/tasks/0136_683_136683073_qa_2/task.toml +++ b/tasks/0136_683_136683073_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0136_683_136683073_qa_2" +name = "smoldataenvs-train/0136_683_136683073_qa_2" description = "What is the highest correlation coefficient value between any feature and the price, excluding price itself?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.702035" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0136_683_136683073_qa_3/task.toml b/tasks/0136_683_136683073_qa_3/task.toml index 39627689098032115c244f6764edc436f0de594b..6cc3f9acdbca4ddf26b97f106f52e348acee427e 100644 --- a/tasks/0136_683_136683073_qa_3/task.toml +++ b/tasks/0136_683_136683073_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0136_683_136683073_qa_3" +name = "smoldataenvs-train/0136_683_136683073_qa_3" description = "Is the relationship between \"sqft_above\" and house prices positively correlated or negatively correlated based on the regression analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "positively correlated" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0136_770_136770905_qa_3/task.toml b/tasks/0136_770_136770905_qa_3/task.toml index 269d39f0473416b5e9443b0f643d52faf1b11ced..32d9e944b52b1a3515a25810051a35077f601ba0 100644 --- a/tasks/0136_770_136770905_qa_3/task.toml +++ b/tasks/0136_770_136770905_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0136_770_136770905_qa_3" +name = "smoldataenvs-train/0136_770_136770905_qa_3" description = "Based on the scatter plot analysis and correlation matrix, what is the Pearson correlation coefficient between fixed acidity and density in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.668" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0136_825_136825945_qa_1/task.toml b/tasks/0136_825_136825945_qa_1/task.toml index 53402c9f4e351cea859f5acfc0f03735e15101d7..f518e4389ca852f2190b2eb92b94200638007cf4 100644 --- a/tasks/0136_825_136825945_qa_1/task.toml +++ b/tasks/0136_825_136825945_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0136_825_136825945_qa_1" +name = "smoldataenvs-train/0136_825_136825945_qa_1" description = "How many Pokémon have both Fire as their primary type and Flying as their secondary type?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "6" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0136_825_136825945_qa_5/task.toml b/tasks/0136_825_136825945_qa_5/task.toml index 44fe46dd987f252d89f2bc506f0b374cfd9f0aac..40446f68e874006557e730823e05220d45a98a43 100644 --- a/tasks/0136_825_136825945_qa_5/task.toml +++ b/tasks/0136_825_136825945_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0136_825_136825945_qa_5" +name = "smoldataenvs-train/0136_825_136825945_qa_5" description = "How many Pokémon are classified as Legendary in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "65" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0136_845_136845893_qa_1/task.toml b/tasks/0136_845_136845893_qa_1/task.toml index b0f2606d4e86e96331e23d37bcbd3a74bc909aae..0101ba6db3a2694789d23dfc3b4a9a6d94275a82 100644 --- a/tasks/0136_845_136845893_qa_1/task.toml +++ b/tasks/0136_845_136845893_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0136_845_136845893_qa_1" +name = "smoldataenvs-train/0136_845_136845893_qa_1" description = "What percentage of the original dataset was classified as spam before balancing the classes?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13.4" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0136_845_136845893_qa_4/task.toml b/tasks/0136_845_136845893_qa_4/task.toml index ec34e2a22013570cbe01587fe877edca7beb09f0..eb5e6d24df0d927e1b7bc6d2328b834d17fb4a20 100644 --- a/tasks/0136_845_136845893_qa_4/task.toml +++ b/tasks/0136_845_136845893_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0136_845_136845893_qa_4" +name = "smoldataenvs-train/0136_845_136845893_qa_4" description = "After implementing class balancing, what is the exact ratio of ham to spam messages in the balanced training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1:1" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0136_874_136874999_qa_1/task.toml b/tasks/0136_874_136874999_qa_1/task.toml index 713c0097265396bad4aa389a1b1c34d1adfcbcef..87974f44e4aef62a264b55113250101998c26602 100644 --- a/tasks/0136_874_136874999_qa_1/task.toml +++ b/tasks/0136_874_136874999_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0136_874_136874999_qa_1" +name = "smoldataenvs-train/0136_874_136874999_qa_1" description = "Which animal class in the dataset has the highest number of species, and how many species belong to this class?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Mammal, 41" reward_mode_initial = "list" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0136_874_136874999_qa_3/task.toml b/tasks/0136_874_136874999_qa_3/task.toml index ce4435fadce0c053996f0763a450f61337cb1784..5df134c063ea537894ac6ff32342680345d8e392 100644 --- a/tasks/0136_874_136874999_qa_3/task.toml +++ b/tasks/0136_874_136874999_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0136_874_136874999_qa_3" +name = "smoldataenvs-train/0136_874_136874999_qa_3" description = "Which animal class has the lowest number of species in the dataset, and how many species belong to this class?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Amphibian with 4 species" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0136_961_136961156_qa_5/task.toml b/tasks/0136_961_136961156_qa_5/task.toml index 110d114bcca1234f2de36f712c13e63cbe2cf68f..e462b1ee5a590f211bc69b65424f9658015e0bca 100644 --- a/tasks/0136_961_136961156_qa_5/task.toml +++ b/tasks/0136_961_136961156_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0136_961_136961156_qa_5" +name = "smoldataenvs-train/0136_961_136961156_qa_5" description = "What is the R-squared value of the linear regression model predicting pelvic_tilt numeric from pelvic_incidence for Abnormal cases?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.32519970000015086" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0137_028_137028436_qa_3/task.toml b/tasks/0137_028_137028436_qa_3/task.toml index 64deb1b02ae1db94ba728695e9e2c4faba440ef1..471ef79c8e686114540a1add83ae870a78018da6 100644 --- a/tasks/0137_028_137028436_qa_3/task.toml +++ b/tasks/0137_028_137028436_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0137_028_137028436_qa_3" +name = "smoldataenvs-train/0137_028_137028436_qa_3" description = "How many numerical features are present in the dataset after excluding the target variable MEDV?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "13" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0137_061_137061428_qa_2/task.toml b/tasks/0137_061_137061428_qa_2/task.toml index a3da7bba66ec03b3c09240dcc9172519cd2608ff..bc9d64e7dde2810141d228f4be28fd8ebcb62780 100644 --- a/tasks/0137_061_137061428_qa_2/task.toml +++ b/tasks/0137_061_137061428_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0137_061_137061428_qa_2" +name = "smoldataenvs-train/0137_061_137061428_qa_2" description = "Which video game genre accounts for the highest total sales in the Japanese market according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Role-Playing" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0137_091_137091910_qa_3/task.toml b/tasks/0137_091_137091910_qa_3/task.toml index 985b55b95f23b362dbb77510a7e5fe68c823418e..1822393e30694974075dce9e3f366e9e5ae26f31 100644 --- a/tasks/0137_091_137091910_qa_3/task.toml +++ b/tasks/0137_091_137091910_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0137_091_137091910_qa_3" +name = "smoldataenvs-train/0137_091_137091910_qa_3" description = "What is the median population of all districts in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1166" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0137_091_137091910_qa_4/task.toml b/tasks/0137_091_137091910_qa_4/task.toml index 249c6694f1e2447cf88df7bc7b50848b0354327b..ca1897f5d6af66d420b48ff9d3009387920971c9 100644 --- a/tasks/0137_091_137091910_qa_4/task.toml +++ b/tasks/0137_091_137091910_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0137_091_137091910_qa_4" +name = "smoldataenvs-train/0137_091_137091910_qa_4" description = "Which ocean proximity category has the highest frequency in the dataset, and how many districts belong to it?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "<1H OCEAN, 9136" reward_mode_initial = "list" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0137_213_137213710_qa_1/task.toml b/tasks/0137_213_137213710_qa_1/task.toml index 0a68ac5f76de5f1c526f5eab51fb28f225d5098f..58fdfd5325839ab8344000853060e613fb304798 100644 --- a/tasks/0137_213_137213710_qa_1/task.toml +++ b/tasks/0137_213_137213710_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0137_213_137213710_qa_1" +name = "smoldataenvs-train/0137_213_137213710_qa_1" description = "What percentage of patients in the dataset are non-diabetic?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "65.1" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0137_314_137314470_qa_3/task.toml b/tasks/0137_314_137314470_qa_3/task.toml index 34dff836a9bd87cb463d2b5b039ca5a7d3bae829..4ef17185dac40496f211baa62517926601aa7ce5 100644 --- a/tasks/0137_314_137314470_qa_3/task.toml +++ b/tasks/0137_314_137314470_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0137_314_137314470_qa_3" +name = "smoldataenvs-train/0137_314_137314470_qa_3" description = "What is the average insurance charge for policyholders who do not smoke?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8434.27" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0137_338_137338171_qa_1/task.toml b/tasks/0137_338_137338171_qa_1/task.toml index 56b4c25da804cd3a05885189950bd9d426308064..e744988775e56a39cbf5b900b7c00316319344cd 100644 --- a/tasks/0137_338_137338171_qa_1/task.toml +++ b/tasks/0137_338_137338171_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0137_338_137338171_qa_1" +name = "smoldataenvs-train/0137_338_137338171_qa_1" description = "What is the mean of the sample means of the displacement values after applying the Central Limit Theorem with 30 samples of size 30?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "192.57" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0137_398_137398822_qa_1/task.toml b/tasks/0137_398_137398822_qa_1/task.toml index 27fb81ebc99bcf2e02a00b60755e3c70040f1360..0857607ff3e2b0c49c71eb245c54eeebc1c07f5d 100644 --- a/tasks/0137_398_137398822_qa_1/task.toml +++ b/tasks/0137_398_137398822_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0137_398_137398822_qa_1" +name = "smoldataenvs-train/0137_398_137398822_qa_1" description = "What is the total number of outliers in the 'price' column based on the 3 standard deviation rule applied to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1206" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0137_398_137398822_qa_3/task.toml b/tasks/0137_398_137398822_qa_3/task.toml index 963f99751df8dbed574426107fbf42ff3b16ed1e..2753715b2687f7ed683cf784aab6a71217a9eb69 100644 --- a/tasks/0137_398_137398822_qa_3/task.toml +++ b/tasks/0137_398_137398822_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0137_398_137398822_qa_3" +name = "smoldataenvs-train/0137_398_137398822_qa_3" description = "What is the average price of diamonds in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3932.80" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0137_398_137398822_qa_4/task.toml b/tasks/0137_398_137398822_qa_4/task.toml index 4746306ea3a1f8d98c8d48a6f40ac191c2aeff40..c991f070ce99f362b1324cf780694272be8b8612 100644 --- a/tasks/0137_398_137398822_qa_4/task.toml +++ b/tasks/0137_398_137398822_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0137_398_137398822_qa_4" +name = "smoldataenvs-train/0137_398_137398822_qa_4" description = "What is the average carat weight of diamonds in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.79794" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0137_519_137519596_qa_5/task.toml b/tasks/0137_519_137519596_qa_5/task.toml index 52428ee67c44c9d0c7ad0ac928bb341a113a4922..ec808cee62cedff743f803bfdb2e135d8ed366b7 100644 --- a/tasks/0137_519_137519596_qa_5/task.toml +++ b/tasks/0137_519_137519596_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0137_519_137519596_qa_5" +name = "smoldataenvs-train/0137_519_137519596_qa_5" description = "Does the inverse transformation of principal components successfully reconstruct the original feature values with minimal loss?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0137_571_137571277_qa_3/task.toml b/tasks/0137_571_137571277_qa_3/task.toml index 484f3095f88dfd079304f51944b4ef18d75e01ef..f894f6c5fcec1d1df14147733ca4d0c7b59d2dec 100644 --- a/tasks/0137_571_137571277_qa_3/task.toml +++ b/tasks/0137_571_137571277_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0137_571_137571277_qa_3" +name = "smoldataenvs-train/0137_571_137571277_qa_3" description = "What percentage of customers in the dataset have churned?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "26.58" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0137_586_137586595_qa_2/task.toml b/tasks/0137_586_137586595_qa_2/task.toml index 84b6d6353a9890c48ea5758e113ab680c0a237e8..bdda5ceb294635e0dbbcfb367cf9e2aa6612bb22 100644 --- a/tasks/0137_586_137586595_qa_2/task.toml +++ b/tasks/0137_586_137586595_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0137_586_137586595_qa_2" +name = "smoldataenvs-train/0137_586_137586595_qa_2" description = "Which European league had the most matches played during the 2008/2009 season, and how many matches were played?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Spain LIGA BBVA, 380" reward_mode_initial = "list" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0137_619_137619562_qa_3/task.toml b/tasks/0137_619_137619562_qa_3/task.toml index 0e2130301ea4fac79c9bf43069169f2139f0895a..118a56f4f2dd9d80e79184ede063673929ed59a8 100644 --- a/tasks/0137_619_137619562_qa_3/task.toml +++ b/tasks/0137_619_137619562_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0137_619_137619562_qa_3" +name = "smoldataenvs-train/0137_619_137619562_qa_3" description = "What percentage of patients in the dataset had no medication changes (numchange = 0)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "72.7" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0137_660_137660235_qa_2/task.toml b/tasks/0137_660_137660235_qa_2/task.toml index 6a4ea4dffc08ec2689eccf7554f32194d57dc718..f0b7ab068566ed179d7ba842ff8ef698f3fdb5ce 100644 --- a/tasks/0137_660_137660235_qa_2/task.toml +++ b/tasks/0137_660_137660235_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0137_660_137660235_qa_2" +name = "smoldataenvs-train/0137_660_137660235_qa_2" description = "Which payment method has the highest churn rate in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Electronic check" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0137_745_137745793_qa_4/task.toml b/tasks/0137_745_137745793_qa_4/task.toml index fca705ef0339dec400ffefea0dc8b009ff298a06..b6a7bee0637f0ace53dcd631666c4bfe1465a5cf 100644 --- a/tasks/0137_745_137745793_qa_4/task.toml +++ b/tasks/0137_745_137745793_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0137_745_137745793_qa_4" +name = "smoldataenvs-train/0137_745_137745793_qa_4" description = "How many unique words are considered in the movie tags after vectorization?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5000" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0137_754_137754074_qa_3/task.toml b/tasks/0137_754_137754074_qa_3/task.toml index 86c42354f007d5ae31ef5431af3992a2bce8c308..66a8c5da6b13ba039967922c28d6764586382d28 100644 --- a/tasks/0137_754_137754074_qa_3/task.toml +++ b/tasks/0137_754_137754074_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0137_754_137754074_qa_3" +name = "smoldataenvs-train/0137_754_137754074_qa_3" description = "Between the Egg McMuffin and Egg White Delight breakfast options, which has a more favorable balance of beneficial versus harmful nutrients?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Egg White Delight" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0137_769_137769141_qa_3/task.toml b/tasks/0137_769_137769141_qa_3/task.toml index fbb1af25a39f1931a05cd39e256fcd105655e705..c64c9eab33a7507d68d63242870008a3f966c598 100644 --- a/tasks/0137_769_137769141_qa_3/task.toml +++ b/tasks/0137_769_137769141_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0137_769_137769141_qa_3" +name = "smoldataenvs-train/0137_769_137769141_qa_3" description = "Which job category has the highest proportion of 'yes' in the deposit based on the TARGET_MEAN analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "student" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0137_769_137769141_qa_4/task.toml b/tasks/0137_769_137769141_qa_4/task.toml index 445e8cd592efaa70b816ebdd1d0d7f87335607b0..e83fbf0d12b940d71daad41ac05a79dcd9ac820c 100644 --- a/tasks/0137_769_137769141_qa_4/task.toml +++ b/tasks/0137_769_137769141_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0137_769_137769141_qa_4" +name = "smoldataenvs-train/0137_769_137769141_qa_4" description = "What is the correlation coefficient between 'age' and 'balance' in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.112" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0137_769_137769141_qa_5/task.toml b/tasks/0137_769_137769141_qa_5/task.toml index 8d1bd7b75ea287a65b5b69d757cb4e41802bdfcc..f471ddd3f3ab6555565620ee38e349f5f551ef39 100644 --- a/tasks/0137_769_137769141_qa_5/task.toml +++ b/tasks/0137_769_137769141_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0137_769_137769141_qa_5" +name = "smoldataenvs-train/0137_769_137769141_qa_5" description = "What is the duration value at the 90th percentile in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "837.000" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0137_818_137818878_qa_4/task.toml b/tasks/0137_818_137818878_qa_4/task.toml index 20c36965f32aba32e03a2476031807303c3060d1..abd8ddcfabf84839a5f6b28eef142eb65228113b 100644 --- a/tasks/0137_818_137818878_qa_4/task.toml +++ b/tasks/0137_818_137818878_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0137_818_137818878_qa_4" +name = "smoldataenvs-train/0137_818_137818878_qa_4" description = "Which platform has the highest total global sales according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PS2" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0137_970_137970410_qa_4/task.toml b/tasks/0137_970_137970410_qa_4/task.toml index b2bb83ecb3448d536eab7703b3594fc9aa6a05bd..6eca086ed47f2bc5c440ae38ee5257eed55a99b5 100644 --- a/tasks/0137_970_137970410_qa_4/task.toml +++ b/tasks/0137_970_137970410_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0137_970_137970410_qa_4" +name = "smoldataenvs-train/0137_970_137970410_qa_4" description = "What is the recall score for price_range class 2 in the confusion matrix analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.84" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0138_103_138103420_qa_2/task.toml b/tasks/0138_103_138103420_qa_2/task.toml index e0890aa473e2a74616825986b121a6d786727db9..41e27e98396b579c645983f16234b74c2167b26a 100644 --- a/tasks/0138_103_138103420_qa_2/task.toml +++ b/tasks/0138_103_138103420_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-eval-v1/0138_103_138103420_qa_2" +name = "smoldataenvs-train/0138_103_138103420_qa_2" description = "What is the overall number of customers who have churned in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1869" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0138_103_138103420_qa_4/task.toml b/tasks/0138_103_138103420_qa_4/task.toml index e92b27418af97687f206feb3af1b498d7aacc6bf..4220a4c4328bc5f2a552f9f489d96b8b62f4e2d1 100644 --- a/tasks/0138_103_138103420_qa_4/task.toml +++ b/tasks/0138_103_138103420_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0138_103_138103420_qa_4" +name = "smoldataenvs-train/0138_103_138103420_qa_4" description = "What is the most common customer contract type in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Month-to-month" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0138_107_138107886_qa_1/task.toml b/tasks/0138_107_138107886_qa_1/task.toml index aaf7efb4d899e1f436e1b46a7cdfb92ccf36211a..3c08a243667e130d1d8c9ae4d2022e84ae34292f 100644 --- a/tasks/0138_107_138107886_qa_1/task.toml +++ b/tasks/0138_107_138107886_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0138_107_138107886_qa_1" +name = "smoldataenvs-train/0138_107_138107886_qa_1" description = "What is the average number of characters in spam messages compared to ham messages in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Spam: 137.89, Ham: 70.46" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0138_107_138107886_qa_3/task.toml b/tasks/0138_107_138107886_qa_3/task.toml index a8f3ceb881499f0bd170cd2b7135860d5dbfbb6a..dac3217e7c2346418a064e66ff98ec764a24212e 100644 --- a/tasks/0138_107_138107886_qa_3/task.toml +++ b/tasks/0138_107_138107886_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0138_107_138107886_qa_3" +name = "smoldataenvs-train/0138_107_138107886_qa_3" description = "What is the correlation coefficient between the number of characters and the number of words in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.965307" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0138_141_138141265_qa_5/task.toml b/tasks/0138_141_138141265_qa_5/task.toml index 01531afbe351a8bfbb832957d9eca37e69ffb5b2..c097a37cde008a16b9b0aff68b9eb7a3abc57c35 100644 --- a/tasks/0138_141_138141265_qa_5/task.toml +++ b/tasks/0138_141_138141265_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0138_141_138141265_qa_5" +name = "smoldataenvs-train/0138_141_138141265_qa_5" description = "According to the cross-validation analysis, what K-value for the KNN model achieved the highest mean accuracy score?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "21" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0138_147_138147004_qa_2/task.toml b/tasks/0138_147_138147004_qa_2/task.toml index e25c7738742c5c1491251702dbaa22ea1b5e063c..64ef6bd4bce994349e93dc9c0a57288b0c87ef6b 100644 --- a/tasks/0138_147_138147004_qa_2/task.toml +++ b/tasks/0138_147_138147004_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0138_147_138147004_qa_2" +name = "smoldataenvs-train/0138_147_138147004_qa_2" description = "How many distinct stages were there in Germany's 1. Bundesliga during the 2015/2016 season?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "34" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0138_147_138147004_qa_4/task.toml b/tasks/0138_147_138147004_qa_4/task.toml index 4399fc65bce795b8e4f3592f1cd7bb4a62818c08..2836591321010e654bbdddde66703409125ed5e0 100644 --- a/tasks/0138_147_138147004_qa_4/task.toml +++ b/tasks/0138_147_138147004_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0138_147_138147004_qa_4" +name = "smoldataenvs-train/0138_147_138147004_qa_4" description = "What was the average goal difference (home goals minus away goals) for English Premier League matches in the 2008/2009 season?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.321053" reward_mode_initial = "numeric" package_tier = 3 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0138_302_138302934_qa_3/task.toml b/tasks/0138_302_138302934_qa_3/task.toml index 8676418da404b01ee12b99314b6485f87edab638..1cca22089a8dcaeaedac4746cec40e45d1f2f561 100644 --- a/tasks/0138_302_138302934_qa_3/task.toml +++ b/tasks/0138_302_138302934_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0138_302_138302934_qa_3" +name = "smoldataenvs-train/0138_302_138302934_qa_3" description = "What is the difference in average monthly working hours between employees who left and those who remained?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "8.36" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0138_344_138344760_qa_5/task.toml b/tasks/0138_344_138344760_qa_5/task.toml index 5aceff6addf19cce1fbe2bb61564d5495de0c058..1fcfc7005843a2b6c01229813019debb70cb1155 100644 --- a/tasks/0138_344_138344760_qa_5/task.toml +++ b/tasks/0138_344_138344760_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0138_344_138344760_qa_5" +name = "smoldataenvs-train/0138_344_138344760_qa_5" description = "What is the maximum likelihood estimate (MLE) for the probability of smokers in the insurance dataset based on the binomial likelihood analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.205" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0138_435_138435541_qa_5/task.toml b/tasks/0138_435_138435541_qa_5/task.toml index f4c0feab49466bfd4e623c101712790487fa0826..b5d450085775d615e0ab4fe1650cd8ab32c9f9d8 100644 --- a/tasks/0138_435_138435541_qa_5/task.toml +++ b/tasks/0138_435_138435541_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0138_435_138435541_qa_5" +name = "smoldataenvs-train/0138_435_138435541_qa_5" description = "What proportion of the original training dataset was used for the final model training after the 0.33 test/train split?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.67" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0138_488_138488828_qa_2/task.toml b/tasks/0138_488_138488828_qa_2/task.toml index 647ebbc0891b932d9936c116a837f2faa9aee5f1..c0479ad2351202b64ddecddfb8d1c317328d4a08 100644 --- a/tasks/0138_488_138488828_qa_2/task.toml +++ b/tasks/0138_488_138488828_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0138_488_138488828_qa_2" +name = "smoldataenvs-train/0138_488_138488828_qa_2" description = "What is the proportion of each price_range category (0, 1, 2, 3) in the training dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "25,25,25,25" reward_mode_initial = "list" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0138_488_138488828_qa_4/task.toml b/tasks/0138_488_138488828_qa_4/task.toml index 96de7d7d342ae3b3e2e7279111bb1e3edace94df..177272a149d4fae7699675e0309b45c0dba22ae8 100644 --- a/tasks/0138_488_138488828_qa_4/task.toml +++ b/tasks/0138_488_138488828_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0138_488_138488828_qa_4" +name = "smoldataenvs-train/0138_488_138488828_qa_4" description = "How many rows were removed from the training dataset during preprocessing due to invalid px_height values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0138_488_138488828_qa_5/task.toml b/tasks/0138_488_138488828_qa_5/task.toml index 5f855e5fb35a1b813d3442214f9c61a6aa127f2f..84f81bd9be5b02bd072ffdfc585535f3934d3062 100644 --- a/tasks/0138_488_138488828_qa_5/task.toml +++ b/tasks/0138_488_138488828_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0138_488_138488828_qa_5" +name = "smoldataenvs-train/0138_488_138488828_qa_5" description = "How many rows were removed from the test dataset during preprocessing due to invalid px_height values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "2" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0138_490_138490152_qa_3/task.toml b/tasks/0138_490_138490152_qa_3/task.toml index 1b80c3837d43507110135e086690753e0e881f22..987c769620292b695d44cbf09eed430f4f7cc9bf 100644 --- a/tasks/0138_490_138490152_qa_3/task.toml +++ b/tasks/0138_490_138490152_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0138_490_138490152_qa_3" +name = "smoldataenvs-train/0138_490_138490152_qa_3" description = "How many distinct geographic regions are represented in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0138_500_138500399_qa_5/task.toml b/tasks/0138_500_138500399_qa_5/task.toml index cbbaf4959d800c0b8efb42f65e6937c7ca8a8944..0bb56eb5ad2cbd1e5e5f85805ec4ed5250cb64fa 100644 --- a/tasks/0138_500_138500399_qa_5/task.toml +++ b/tasks/0138_500_138500399_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0138_500_138500399_qa_5" +name = "smoldataenvs-train/0138_500_138500399_qa_5" description = "What is the correlation coefficient between \"infant deaths\" and \"Under-5 Deaths\" that justified removing one of these variables to address multicollinearity?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.996629" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0138_500_138500445_qa_4/task.toml b/tasks/0138_500_138500445_qa_4/task.toml index ce19c790def13aff6fc57223d4fbb2fa084454a1..79cb689bc703f7ca58f442d80cb2d54374ba8cea 100644 --- a/tasks/0138_500_138500445_qa_4/task.toml +++ b/tasks/0138_500_138500445_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0138_500_138500445_qa_4" +name = "smoldataenvs-train/0138_500_138500445_qa_4" description = "What is the direction of correlation between MMSE scores and dementia status in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "negative" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0138_573_138573264_qa_3/task.toml b/tasks/0138_573_138573264_qa_3/task.toml index b89c5d37ed314e33a459fee41d34b802f30bfe98..7f518d3b560ce8625712b109d5c7ebe0840eeb5d 100644 --- a/tasks/0138_573_138573264_qa_3/task.toml +++ b/tasks/0138_573_138573264_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0138_573_138573264_qa_3" +name = "smoldataenvs-train/0138_573_138573264_qa_3" description = "Which feature demonstrates the highest standard deviation in the descriptive statistics of the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "PetalLengthCm" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0138_684_138684554_qa_1/task.toml b/tasks/0138_684_138684554_qa_1/task.toml index 407f789d8280f1642d098d60c7e8b1f903783555..7c6f552a49b7003a6e5970cb66f48cb004bcec62 100644 --- a/tasks/0138_684_138684554_qa_1/task.toml +++ b/tasks/0138_684_138684554_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0138_684_138684554_qa_1" +name = "smoldataenvs-train/0138_684_138684554_qa_1" description = "How many missing values were present in the TotalCharges column before they were removed from the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0138_684_138684554_qa_2/task.toml b/tasks/0138_684_138684554_qa_2/task.toml index ddcfad67b6a53a1d17e286d49afc6dac033dd950..86d37917f6d8ce7d22962f4e83f6e1f87c2cd45d 100644 --- a/tasks/0138_684_138684554_qa_2/task.toml +++ b/tasks/0138_684_138684554_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0138_684_138684554_qa_2" +name = "smoldataenvs-train/0138_684_138684554_qa_2" description = "After converting the TotalCharges column to a numeric data type, how many rows were removed from the dataset due to missing values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "11" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0138_697_138697027_qa_1/task.toml b/tasks/0138_697_138697027_qa_1/task.toml index d0488cc7ceca932e8eba143d6d255133f2fd478b..69c1143973d8e199cfd7daf296f7b596f8f6196c 100644 --- a/tasks/0138_697_138697027_qa_1/task.toml +++ b/tasks/0138_697_138697027_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0138_697_138697027_qa_1" +name = "smoldataenvs-train/0138_697_138697027_qa_1" description = "What is the correlation coefficient between car price and sales in thousands?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "-0.301850" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0138_755_138755530_qa_1/task.toml b/tasks/0138_755_138755530_qa_1/task.toml index 39e5b12b39d0ee070b689548fd484adcb9900975..592bcdadc2506e53e0d222e28810adb05e1150a0 100644 --- a/tasks/0138_755_138755530_qa_1/task.toml +++ b/tasks/0138_755_138755530_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0138_755_138755530_qa_1" +name = "smoldataenvs-train/0138_755_138755530_qa_1" description = "What is the average February average temperature in the Northeast region when Punxsutawney Phil sees a full shadow?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "22.727" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0138_755_138755530_qa_2/task.toml b/tasks/0138_755_138755530_qa_2/task.toml index c69020949a13b9bca5bba82081139f5ad2439407..29a41dd2d3c4373a1cffff3ba44d313f8c97a752 100644 --- a/tasks/0138_755_138755530_qa_2/task.toml +++ b/tasks/0138_755_138755530_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0138_755_138755530_qa_2" +name = "smoldataenvs-train/0138_755_138755530_qa_2" description = "What is the lowest recorded February average temperature in the Northeast region when Punxsutawney Phil sees a full shadow?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "12.10" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0138_777_138777709_qa_1/task.toml b/tasks/0138_777_138777709_qa_1/task.toml index c39448046dce1ffc31c47c8bb2f7c4ee06fa382a..26cdb66879c8bfb7c095042977e6edca7e70a550 100644 --- a/tasks/0138_777_138777709_qa_1/task.toml +++ b/tasks/0138_777_138777709_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0138_777_138777709_qa_1" +name = "smoldataenvs-train/0138_777_138777709_qa_1" description = "Which car manufacturer has the highest total sales in thousands according to the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Ford" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0138_787_138787148_qa_2/task.toml b/tasks/0138_787_138787148_qa_2/task.toml index 8750a55efe6b7c304c72d4fef7fe175b9cbe70c3..9e663073c116ed7d3c0d5f26de7a0e577bf0bc90 100644 --- a/tasks/0138_787_138787148_qa_2/task.toml +++ b/tasks/0138_787_138787148_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0138_787_138787148_qa_2" +name = "smoldataenvs-train/0138_787_138787148_qa_2" description = "Which feature in the dataset shows the strongest positive correlation with the target variable (Diabetic)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Glucose" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0138_791_138791955_qa_4/task.toml b/tasks/0138_791_138791955_qa_4/task.toml index c3fc9e4fd96e539cb72669af7a5d74b152b935c4..957e4e566488885b555d7cfbcca00243dd03afbf 100644 --- a/tasks/0138_791_138791955_qa_4/task.toml +++ b/tasks/0138_791_138791955_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0138_791_138791955_qa_4" +name = "smoldataenvs-train/0138_791_138791955_qa_4" description = "How many BMI data points were considered outliers and removed after applying the 3σ rule?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0138_791_138791955_qa_5/task.toml b/tasks/0138_791_138791955_qa_5/task.toml index 38f0daed7b117477d4c9e3f0f94fb69189e10e75..501c365bb2479ab685a9cbd426114334f3e27562 100644 --- a/tasks/0138_791_138791955_qa_5/task.toml +++ b/tasks/0138_791_138791955_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0138_791_138791955_qa_5" +name = "smoldataenvs-train/0138_791_138791955_qa_5" description = "How many new features were created for the 'region' category after one-hot encoding?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0138_805_138805260_qa_1/task.toml b/tasks/0138_805_138805260_qa_1/task.toml index 7822227c8550b85619d8fb95a645ad9179754c65..a1482146d8ed6b26709ac26fb6ee517be16b664c 100644 --- a/tasks/0138_805_138805260_qa_1/task.toml +++ b/tasks/0138_805_138805260_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0138_805_138805260_qa_1" +name = "smoldataenvs-train/0138_805_138805260_qa_1" description = "Which feature has the highest importance in the Random Forest model based on feature importance analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "R&D Spend" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0138_805_138805260_qa_2/task.toml b/tasks/0138_805_138805260_qa_2/task.toml index 0931b3f1a1e729d26031b5fe1eaa55acc582961e..0fec324091cb614cc2082c5352c5985cafe60341 100644 --- a/tasks/0138_805_138805260_qa_2/task.toml +++ b/tasks/0138_805_138805260_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0138_805_138805260_qa_2" +name = "smoldataenvs-train/0138_805_138805260_qa_2" description = "What is the R-squared score achieved by the Random Forest Regressor on the test dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.93197300309765" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0138_930_138930992_qa_1/task.toml b/tasks/0138_930_138930992_qa_1/task.toml index f52050aa2b7bb5e7b0f4d340661b590d59d412b7..35e63f5b1bef940bd22c1ce4f13a8c50a10ea862 100644 --- a/tasks/0138_930_138930992_qa_1/task.toml +++ b/tasks/0138_930_138930992_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0138_930_138930992_qa_1" +name = "smoldataenvs-train/0138_930_138930992_qa_1" description = "What is the average age difference between employees who attrited and those who did not, based on the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "3.82" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0138_984_138984324_qa_4/task.toml b/tasks/0138_984_138984324_qa_4/task.toml index 58366b8686499977ccfb051a06d7eb260e636032..781e170c9936608c209fa28adacb3c72d8122d40 100644 --- a/tasks/0138_984_138984324_qa_4/task.toml +++ b/tasks/0138_984_138984324_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0138_984_138984324_qa_4" +name = "smoldataenvs-train/0138_984_138984324_qa_4" description = "How many distinct junctions are recorded in the training dataset based on the minimum and maximum junction values?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "4" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0139_066_139066922_qa_2/task.toml b/tasks/0139_066_139066922_qa_2/task.toml index 4d9c85cc54f9350136b43ab0f57e39807587a102..b703f87b40490f92b16f3656b3ac34d955d98da5 100644 --- a/tasks/0139_066_139066922_qa_2/task.toml +++ b/tasks/0139_066_139066922_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0139_066_139066922_qa_2" +name = "smoldataenvs-train/0139_066_139066922_qa_2" description = "What is the most common internet service type among customers who churned?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Fiber optic" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0139_066_139066922_qa_3/task.toml b/tasks/0139_066_139066922_qa_3/task.toml index 4a8db26c363c2c1a5a234b30cbb4f61b84995268..eb0ad8cff3a94786d3e6341f0355fbfef56dc96a 100644 --- a/tasks/0139_066_139066922_qa_3/task.toml +++ b/tasks/0139_066_139066922_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0139_066_139066922_qa_3" +name = "smoldataenvs-train/0139_066_139066922_qa_3" description = "Do customers without partners have a higher churn rate than customers with partners?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0139_071_139071391_qa_3/task.toml b/tasks/0139_071_139071391_qa_3/task.toml index 7d4eeb80251024303d32e0670034ca257ac98bef..4ac10bc18039654cae14d890fb362a2fa8418d1e 100644 --- a/tasks/0139_071_139071391_qa_3/task.toml +++ b/tasks/0139_071_139071391_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0139_071_139071391_qa_3" +name = "smoldataenvs-train/0139_071_139071391_qa_3" description = "Which contract type is associated with the highest churn rate based on the dataset analysis?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Month-to-month" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0139_071_139071391_qa_4/task.toml b/tasks/0139_071_139071391_qa_4/task.toml index cd2a2b78b42cc83cf343d93ddda9a8af2835822c..92bb2c8e2c72caf0ccf02951ea4a37ae60649c4b 100644 --- a/tasks/0139_071_139071391_qa_4/task.toml +++ b/tasks/0139_071_139071391_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0139_071_139071391_qa_4" +name = "smoldataenvs-train/0139_071_139071391_qa_4" description = "Does the OnlineSecurity feature have a statistically significant effect on TotalCharges distribution?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "yes" reward_mode_initial = "exact_bool" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0139_103_139103748_qa_4/task.toml b/tasks/0139_103_139103748_qa_4/task.toml index 64fe3c973df62743b0468021c8064deb84f8f8a9..4fa068c3a296c7a9bbc2f4834feafb06748af2a2 100644 --- a/tasks/0139_103_139103748_qa_4/task.toml +++ b/tasks/0139_103_139103748_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0139_103_139103748_qa_4" +name = "smoldataenvs-train/0139_103_139103748_qa_4" description = "Which feature in the original dataset (before scaling) has the highest variability as measured by standard deviation?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "TAX" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0139_112_139112787_qa_2/task.toml b/tasks/0139_112_139112787_qa_2/task.toml index 9f40ef21451b5e223b46e922458ab997e021b3b0..4debb90e7d547e006b92c50a00ee1a5c8c3a4ddd 100644 --- a/tasks/0139_112_139112787_qa_2/task.toml +++ b/tasks/0139_112_139112787_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0139_112_139112787_qa_2" +name = "smoldataenvs-train/0139_112_139112787_qa_2" description = "What is the highest accuracy achieved during the forward feature selection process?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.8340807174887892" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0139_117_139117477_qa_2/task.toml b/tasks/0139_117_139117477_qa_2/task.toml index a509400315f4396d658131a38d4cd2770a84ddf6..a4afce1429ee5155f36f164716848c8f3cb38cfa 100644 --- a/tasks/0139_117_139117477_qa_2/task.toml +++ b/tasks/0139_117_139117477_qa_2/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify2/0139_117_139117477_qa_2" +name = "smoldataenvs-train/0139_117_139117477_qa_2" description = "What is the correlation between MonthlyIncome and JobLevel in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "0.9503" reward_mode_initial = "numeric" package_tier = 2 difficulty_level = 1 +difficulty = "easy" difficulty_tier = "easy" [environment] diff --git a/tasks/0139_117_139117477_qa_5/task.toml b/tasks/0139_117_139117477_qa_5/task.toml index e5b7032ba6648ee330daf48057c52af8a73a6846..153d61e0e0776985724f0cdb0b47a38ce4695daa 100644 --- a/tasks/0139_117_139117477_qa_5/task.toml +++ b/tasks/0139_117_139117477_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0139_117_139117477_qa_5" +name = "smoldataenvs-train/0139_117_139117477_qa_5" description = "What is the mean years at company for employees who attrited compared to those who did not?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "5.13, 7.37" reward_mode_initial = "list" package_tier = 2 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0139_173_139173112_qa_5/task.toml b/tasks/0139_173_139173112_qa_5/task.toml index bd7c6d8f9eeb1ad926a0acbbb0bcdbe4a04bd674..9b304f648769feabe83ecbc59b3a93bc5b944c32 100644 --- a/tasks/0139_173_139173112_qa_5/task.toml +++ b/tasks/0139_173_139173112_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0139_173_139173112_qa_5" +name = "smoldataenvs-train/0139_173_139173112_qa_5" description = "What is the lowest speed value among the first 11 Pokémon categorized as low?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "43" reward_mode_initial = "numeric" package_tier = 1 difficulty_level = 2 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0139_181_139181470_qa_3/task.toml b/tasks/0139_181_139181470_qa_3/task.toml index 7537689c6582165a3d09c4e29c1891a664eb7d57..30d99fbfe13888fb34f127e3269df1034162d4ec 100644 --- a/tasks/0139_181_139181470_qa_3/task.toml +++ b/tasks/0139_181_139181470_qa_3/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "train-verify/0139_181_139181470_qa_3" +name = "smoldataenvs-train/0139_181_139181470_qa_3" description = "Which cluster has the most significant average balance amount (BALANCE)?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "1" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0139_186_139186690_qa_1/task.toml b/tasks/0139_186_139186690_qa_1/task.toml index 58518b54c87ef420096b105649c72206fbc7f4e4..171cc825151a5ada5d6502ce75b6e32578c814e1 100644 --- a/tasks/0139_186_139186690_qa_1/task.toml +++ b/tasks/0139_186_139186690_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0139_186_139186690_qa_1" +name = "smoldataenvs-train/0139_186_139186690_qa_1" description = "Which clustering method (K-means or hierarchical) shows a stronger correlation between the number of cores and battery power in the scaled dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "K-means" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0139_186_139186690_qa_5/task.toml b/tasks/0139_186_139186690_qa_5/task.toml index 4d1689d3f6c7d14cdd4e1484d27564b096d86081..7b1fb29b46180ec727311eb891e6cb0865007a80 100644 --- a/tasks/0139_186_139186690_qa_5/task.toml +++ b/tasks/0139_186_139186690_qa_5/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0139_186_139186690_qa_5" +name = "smoldataenvs-train/0139_186_139186690_qa_5" description = "Which clustering method (K-means or hierarchical) produces clusters with the most distinct differences in price_range means?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Hierarchical" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 4 +difficulty = "hard" difficulty_tier = "hard" [environment] diff --git a/tasks/0139_196_139196764_qa_4/task.toml b/tasks/0139_196_139196764_qa_4/task.toml index 71d4c135a43e18b578813912d868a51651692790..f7042be21f64b75300fefa7d105d4d977144d738 100644 --- a/tasks/0139_196_139196764_qa_4/task.toml +++ b/tasks/0139_196_139196764_qa_4/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0139_196_139196764_qa_4" +name = "smoldataenvs-train/0139_196_139196764_qa_4" description = "Which job satisfaction level is most strongly correlated with employee attrition in the dataset?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "Level 1" reward_mode_initial = "exact_short" package_tier = 2 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment] diff --git a/tasks/0139_223_139223525_qa_1/task.toml b/tasks/0139_223_139223525_qa_1/task.toml index 6768c77332ffe363e6a8a5ce99cf54ba907a54ac..c04b49cb8b2c98c98098bd0610274492e0720117 100644 --- a/tasks/0139_223_139223525_qa_1/task.toml +++ b/tasks/0139_223_139223525_qa_1/task.toml @@ -2,10 +2,10 @@ schema_version = "1.2" artifacts = [] [task] -name = "data-agent-train-v1/0139_223_139223525_qa_1" +name = "smoldataenvs-train/0139_223_139223525_qa_1" description = "Which service feature has the highest churn rate among customers who do not subscribe to it?" authors = [] -keywords = ["data-agent", "data-analysis", "kaggle"] +keywords = ["smoldataenvs", "data-analysis", "kaggle"] [metadata] source_dataset = "jupyter-agent/jupyter-agent-dataset" @@ -15,6 +15,7 @@ gold_answer = "OnlineSecurity" reward_mode_initial = "exact_short" package_tier = 1 difficulty_level = 3 +difficulty = "medium" difficulty_tier = "medium" [environment]