# Downstream bootstrap reference

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

## Layout

```
downstream/bootstrap/
├── draws.parquet      # per-(method, task, subgroup, draw) error E = 1 − metric
└── draws.meta.json    # provenance: seed, n_boot, methods, n_tasks, fairness attrs
```

## What's it for

Each row of `draws.parquet` is one bootstrap draw of one task for one method,
carrying the per-draw error `E = 1 − metric` (binary AUPRC, ordinal Spearman,
regression Pearson). Phase-2 reduces the same frame three ways, all paired vs the
`linear` baseline:

- **Skill score** `S = 1 − geomean_task(E_method / E_linear)` — domain-balanced
  macro; per-(method, scope) mean / SE / percentile-CI across draws.
- **Average rank** — per-(method, scope) mean of the per-draw cross-method rank
  (lower `E` → rank 1).
- **Fairness skill score** — per-attribute disparity ratio over the `age_group` /
  `sex` subgroup rows, with BCa intervals.

The per-method `downstream/<method>.parquet` substrate (the raw user pairs, one level
up) is the *input* the draws were bootstrapped from; this frame is the precomputed
result so consumers need not re-run the 1000-draw paired bootstrap.

## Provenance

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

- `seed = 42`, `n_boot = 1000`
- `split = test`, canonical `sharable_users_seed42_2026`
- 8 methods (see `draws.meta.json:methods`), 32 tasks, baseline `linear`
- fairness attributes `age_group`, `sex`

Parity of this frame against the uploaded per-method substrate is enforced by
`scripts/paper_results/downstream/parity/parity_substrate.py` (a substrate-driven
bootstrap must reproduce these draws).

## Loading

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

draws_path = hf_hub_download(
    "MyHeartCounts/OpenMHC-leaderboard-data",
    "downstream/bootstrap/draws.parquet",
    repo_type="dataset",
)
meta_path = hf_hub_download(
    "MyHeartCounts/OpenMHC-leaderboard-data",
    "downstream/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.

## Uploaded with

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