imputation/bootstrap/README.md

# Imputation bootstrap reference

This subdir holds the **Phase-1 bootstrap reference** for the Track-2
(imputation) leaderboard recompute — the long-format per-draw frame that
the skill / rank / fairness CIs reduce from.

## Layout

```
imputation/bootstrap/
├── draws.parquet      # per-(method, scenario, channel, subgroup, draw) E/R/rank
└── draws.meta.json    # provenance: seed, n_boot, methods, scenarios, git commit
```

## What's it for

Each row of `draws.parquet` is one bootstrap draw of one task for one
method. Phase-2 aggregators reduce it three ways:

- **Skill score** `S = 1 − exp(mean_r log(R))` — paired against `locf`; per-(method, scope) mean / SE / percentile-CI across draws.
- **Average rank** — per-(method, scope) mean of the per-draw cross-method
  rank, mean / SE / CI across draws.
- **Fairness skill score** — per-attribute mean-absolute-pairwise-difference
  disparity ratio `D_method / D_baseline`, geomean-averaged across tasks,
  with BCa intervals.

The same draws frame backs all three; only the reducer changes.

## Provenance

Generated by `scripts/paper_results/imputation/bootstrap_imputation_draws.py`
in the code repo. The current snapshot was built with:

- `seed = 42`, `n_boot = 1000`
- `splits = ["test"]`
- All 6 imputation scenarios
- 16 methods (see `draws.meta.json:methods`)
- `age_bins = [18, 30, 40, 50, 60]`, `exclude_unknown = false`

See `draws.meta.json` for the exact git commit, method-dirs manifest, and
runtime metadata.

## Note: no pooled `per_user_errors.parquet`

The BCa LOO substrate (per-user errors pooled across all methods) is
**not** stored here — it is exactly the concatenation of the per-method
substrate files one level up:

```python
import glob, pandas as pd
pooled = pd.concat(
    [pd.read_parquet(p) for p in glob.glob("imputation/*.parquet")],
    ignore_index=True,
)
# 2,376,160 rows = 148,510 rows/method × 16 methods
```

The same provenance (seed, n_boot, method list, git commit) lives in
`draws.meta.json` here.

## Loading

```python
from huggingface_hub import hf_hub_download
import pandas as pd, json

draws_path = hf_hub_download(
    "MyHeartCounts/OpenMHC-leaderboard-data",
    "imputation/bootstrap/draws.parquet",
    repo_type="dataset",
)
meta_path = hf_hub_download(
    "MyHeartCounts/OpenMHC-leaderboard-data",
    "imputation/bootstrap/draws.meta.json",
    repo_type="dataset",
)
draws = pd.read_parquet(draws_path)
meta = json.loads(open(meta_path).read())
print(meta["seed"], meta["n_boot"], len(meta["methods"]))
```

See [`SCHEMA.md`](SCHEMA.md) for the full column spec.

## Sibling: 17-method variant

`imputation/bootstrap_with_dense_weekly/` holds the same reducer applied
to a 17-method pool that adds `lsm2_weekly` (dense 7-day LSM-2). Skill /
fairness numbers per method are identical to this canonical variant
(they're pairwise vs `locf`); only average-rank values shift because the
comparison pool grew. See that dir's README for when to use which.

## Uploaded with

`tools/upload_leaderboard_bootstrap.py` in the
[code repo](https://github.com/AshleyLab/myheartcounts-dataset).