downstream/bootstrap/README.md

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

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 for the full column spec.

Uploaded with

tools/upload_leaderboard_bootstrap.py --track downstream in the code repo.