Datasets:

c3aiia3c
/

read-bench-results

license: cc-by-nc-sa-4.0
pretty_name: READ-Bench — Leaderboard Submissions
tags:
  - leaderboard
  - time-series
  - retrieval
  - anomaly-detection
  - benchmark

READ-Bench — Leaderboard Submissions

This repository collects community submissions to the READ-Bench leaderboard for historical-instance retrieval in time-series diagnosis (arXiv:2609.32123).

Data lives in c3aiia3c/read-bench.
Submissions (code + results) live here, one folder per method.
The live board is the READ-Bench Leaderboard Space, which renders every accepted submission.

Submissions are reproducible by design: you include runnable code, and the official harness (eval_submission.py, in this repo) runs your method against the hidden relevance labels to produce the score — so numbers can't be faked and anyone can rerun them.

What a submission contains

Add one folder, submissions/<your-slug>/, with:

file	required	what
`method.py`	✅	exposes `rank(query, corpus) -> list[int]` (see contract below)
`metadata.yaml`	✅	method name, family, supervision, authors, links
`requirements.txt`	✅	any deps your `method.py` imports
`results.json`	✅	produced by the harness (do not hand-edit)

Copy example_submission/ as your starting point — it's a working Euclidean-distance baseline.

The method contract

def rank(query: dict, corpus: list[dict]) -> list[int]:
    """Return corpus INDICES ordered most- to least-relevant to `query`.

    Each entry has: ts_values ([T][C] floats), channels, label, class_label,
    rca_target, rca_affected, dataset, entry_id, native_split, extras.

    Rules:
      * NEVER read query["class_label"] — it is the hidden target (and is
        masked out before your function is called).
      * You may read corpus class_labels ONLY if you declare your method
        `supervision: supervised` or `neighbour-labels` in metadata.yaml.
    """

How to submit (all on Hugging Face — no GitHub needed)

# 1. clone this repo (git-backed, like GitHub)
git clone https://huggingface.co/datasets/c3aiia3c/read-bench-results
cd read-bench-results

# 2. create your submission from the template
cp -r example_submission submissions/my-method
# ... edit submissions/my-method/method.py + metadata.yaml + requirements.txt

# 3. validate structure, then score with the official harness
pip install -r submissions/my-method/requirements.txt
python validate_submission.py --submission submissions/my-method --run
#   -> writes submissions/my-method/results.json

# 4. open a Pull Request on this repo (Community tab) with your folder.

To open the PR: push your branch, or use the "Community" → "New pull request" button on this repo's page and upload your submissions/<slug>/ folder. A maintainer re-runs the harness to confirm your results.json, then merges — and the Leaderboard Space picks it up automatically.

Scoring

Primary metric: NDCG@10, macro-averaged over the 12 datasets, at pollution levels ρ = 0 / 10 / 20% (fraction of the corpus that is Normal).
Also reported: P@{1,3,5,10,20}, HR@{...}, NDCG@{1,3,5,20}, macro-F1.
Relevance is binary & multi-target: a corpus item is relevant to a query iff they share the same non-normal class_label.
The split is reconstructed deterministically (seed 42): corpus = train-split anomalies + Normals to the pollution fraction; queries = test-split anomalies.

See eval_submission.py for the exact, self-contained implementation (only datasets + numpy).

Citation

@article{readbench2026,
  title  = {READ-Bench: Benchmarking Historical Instance Retrieval for Time-Series Diagnosis},
  author = {Pastrana, Gerardo and Li, Haojun and Mehta, Dhruv and Vyas, Anoushka and
            Khoshfetrat Pakazad, Sina and Ohlsson, Henrik and Paparrizos, John},
  journal = {arXiv preprint arXiv:2609.32123},
  year    = {2026}
}