Add ACS-housing fusion task (train panels, validation, test, features)

README.md

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+---
+license: cc0-1.0
+pretty_name: "Data Fusion: ACS Housing"
+language:
+- en
+tags:
+- data-fusion
+- statistical-matching
+- missing-data
+- imputation
+- tabular
+- census
+task_categories:
+- tabular-classification
+size_categories:
+- 10K<n<100K
+configs:
+- config_name: housing
+  default: true
+  data_files:
+  - split: train
+    path: housing/train.parquet
+  - split: validation
+    path: housing/validation.parquet
+  - split: test
+    path: housing/test.parquet
+---
+
+# Data Fusion: ACS Housing
+
+A small, public-domain dataset for **statistical data fusion**: two surveys share a block of covariates, each measures a different block of outcomes, and no respondent answers both. The task is to fill in, for each respondent, the block their survey did not ask.
+
+This is the ACS-housing task of [Cross-Block Conditioning in Deep Boltzmann Machines for Statistical Data Fusion](https://arxiv.org/abs/2609.14934). It is meant to be picked up in a few minutes: load it, see what fusion data look like, fit a baseline, and score it the way the paper does.
+
+## What fusion data look like
+
+| rows | $X$ (demographics) | $Y_A$ (labor and income) | $Y_B$ (housing) |
+|---|:---:|:---:|:---:|
+| panel A (train, `source == "A"`) | observed | observed | *missing* |
+| panel B (train, `source == "B"`) | observed | *missing* | observed |
+| validation, test | observed | observed | observed |
+
+The `train` split is 80,000 households divided at random into two panels, as if a labor-force survey and a housing survey had each interviewed half of them. **No training row observes both outcome blocks**; the block a panel did not collect is null. The `validation` and `test` splits keep every block, only so that predictions can be scored.
+
+Because $Y_A$ and $Y_B$ never appear together, any model of their joint distribution has to get the association between them from something other than paired examples, and any model that maps $X$ to outcomes has given up on it. That is what makes fusion different from ordinary supervised learning or from missing-value imputation with a few complete rows.
+
+## Quickstart
+
+```python
+import numpy as np
+from datasets import load_dataset
+from sklearn.linear_model import LogisticRegression
+
+ds = load_dataset("jniimi/datafusion-acs", "housing")
+train = ds["train"].to_pandas().head(2000)   # the first n rows are the paper's n_train = n
+test = ds["test"].to_pandas()
+
+X  = [c for c in train if c.startswith("x_")]    # common block: demographics (30)
+YA = [c for c in train if c.startswith("ya_")]   # outcome block A: labor and income (22)
+YB = [c for c in train if c.startswith("yb_")]   # outcome block B: housing (17)
+
+panel_a = train[train.source == "A"]   # observes X and Y_A; Y_B is null
+panel_b = train[train.source == "B"]   # observes X and Y_B; Y_A is null
+
+def fit_predict(panel, targets):
+    return {y: LogisticRegression(max_iter=1000).fit(panel[X], panel[y].astype(int))
+                                             .predict_proba(test[X])[:, 1] for y in targets}
+
+pred = {**fit_predict(panel_a, YA), **fit_predict(panel_b, YB)}
+acc = {y: np.mean((pred[y] > 0.5) == test[y]) for y in YA + YB}
+print(f"combined accuracy, X-only: {100 * np.mean(list(acc.values())):.2f}%")   # 83.10%
+```
+
+This is the simplest fusion baseline: each outcome block is predicted from $X$ alone, with a model fitted on the panel that observed it.
+
+## Scoring
+
+All variables are binary. The paper's headline metric is **combined accuracy**, the accuracy over all $22 + 17$ outcome columns at a threshold of 0.5 (equivalently, the block accuracies weighted by block width). Cross-entropy is a useful second metric.
+
+A model can be scored under two **conditioning sets**:
+
+- **$X$-only**: both outcome blocks are hidden and predicted from $X$. Every method can do this.
+- **cross-block**: $Y_A$ is predicted from $X$ and $Y_B$, and $Y_B$ from $X$ and $Y_A$. This is the operational task (a panel-B household has its housing answers and needs its labor answers filled in), but it asks for $p(y_A \mid x, y_B)$, which the training data never show directly.
+
+One rule keeps the evaluation honest: **choose hyperparameters and checkpoints on $X$-only validation accuracy only.** Real fusion data contain no row that observes both blocks, so whether cross-block conditioning helps can never be checked there. A method that tunes its cross-block predictions on complete validation rows is using information a practitioner would not have.
+
+## Reference results
+
+Test combined accuracy [%] on the released split (seed 0, all 30 $X$ columns), from the paper. Baselines are tuned on $X$-only validation; "+ cross" means the imputer is also given the other outcome block at test time.
+
+| method | $n$ = 500 | 2,000 | 10,000 |
+|---|---:|---:|---:|
+| predict zero everywhere | 79.89 | 79.89 | 79.89 |
+| $X$-logistic (tuned) | 82.10 | 83.09 | 83.44 |
+| $k$-NN | 81.77 | 82.53 | 82.91 |
+| MICE | 82.15 | 82.61 | 82.67 |
+| MICE + cross | 75.58 | 79.57 | 77.23 |
+| factor analysis | 81.63 | 82.32 | 82.39 |
+| MIWAE | 81.66 | 82.09 | 82.34 |
+| MIWAE + cross | 81.66 | 81.49 | 82.00 |
+| MLP ($X$ to outcomes) | 82.13 | 83.19 | 83.53 |
+| OBMP, $X$-only | 82.16 | 83.18 | 83.50 |
+| OBMP, cross-block | **82.44** | **83.66** | **84.02** |
+
+Two things stand out. Predicting from $X$ alone, a tuned logistic regression is hard to beat. And giving the imputers the other outcome block makes most of them *worse*: trained without a single paired row, they do not know how to use it. OBMP, a Deep Boltzmann Machine fine-tuned on the observed blocks, gains from it. The paper reports means over five seeds and the full grid of sample sizes and covariate widths; the pattern is the same there.
+
+There is room to do better. With paired rows (which fusion never has), a logistic regression gains about 1.3 points from the other block at $n = 2{,}000$; OBMP recovers about a third of that without them.
+
+## Files and columns
+
+- `housing/{train,validation,test}.parquet`
+  - `serialno`: the PUMS housing-unit serial number, so that other PUMS variables can be joined on.
+  - `source`: `"A"` or `"B"` in `train` (which panel the row belongs to), null in `validation` and `test`.
+  - `x_*`, `ya_*`, `yb_*`: binary indicators (nullable int), one-hot or quantile-binned from PUMS variables. A variable's reference level is the all-zero pattern.
+- `housing/features.parquet`: one row per indicator, with its block, source PUMS variable, level, a description and its prevalence. Read it with `pd.read_parquet("hf://datasets/jniimi/datafusion-acs/housing/features.parquet")`.
+
+**Blocks.** $X$ (30): age band, sex, race, Hispanic origin, marital status, education, citizenship, language at home, limited English, veteran status, household size, children present. $Y_A$ (22): employment status, class of worker, usual hours, weeks worked, wage tertiles, personal-income quartiles, and receipt of self-employment, retirement, Social Security and public-assistance income. $Y_B$ (17): tenure, household-income quartiles, SNAP receipt, vehicles, structure type, rent burden of at least 30%, no internet access.
+
+**Rows.** One row per occupied housing unit in California: the householder, aged 18 or over, joined to the housing record; group quarters are excluded. Keeping one row per household means housemates, who share every housing variable, cannot fall on both sides of a split. Survey weights are not used; the task is prediction on the sample.
+
+**Reproducing the paper.** The rows are the paper's seed-0 split, and `train` is ordered so that `train[:n]` is exactly the paper's training set for sample size $n$ with all 30 $X$ columns. For narrower common blocks the paper keeps the $k$ most prevalent $X$ columns (see `prevalence` in `features.parquet`).
+
+## Source and license
+
+Derived from the 2024 American Community Survey 1-year Public Use Microdata Sample for California ([US Census Bureau](https://www.census.gov/programs-surveys/acs/microdata.html)), a US federal work in the public domain. The derived files are released under CC0.
+
+This dataset uses Census Bureau data but is not endorsed or certified by the Census Bureau. PUMS records are already anonymized by the Census Bureau; do not attempt to identify individuals or households, or to link the records to other data for that purpose.
+
+## Citation
+
+```bibtex
+@article{niimi2026crossblock,
+  title   = {Cross-Block Conditioning in Deep {Boltzmann} Machines for Statistical Data Fusion},
+  author  = {Niimi, Junichiro},
+  journal = {arXiv preprint arXiv:2609.14934},
+  year    = {2026}
+}
+```

housing/features.parquet

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+size 7912

housing/test.parquet

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+oid sha256:ca8ce34afb98c481a5707f48cc7f44a4f6148a9cfddbbbb6048318a824bc5234
+size 132771

housing/train.parquet

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+size 1687964

housing/validation.parquet

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