| --- |
| license: openrail |
| pretty_name: OpenMHC Leaderboard Data |
| tags: |
| - wearables |
| - benchmark |
| - myheartcounts |
| - time-series |
| --- |
| |
| # OpenMHC Leaderboard Data |
|
|
| Per-user **substrate** behind the [OpenMHC](https://myheartcounts.stanford.edu/benchmark) wearable-health benchmark leaderboard. Each file is one method's reduced per-user, per-task values for one track; the leaderboard recompute consumes these to produce paired **skill scores**, cross-method **ranks**, and **fairness skill scores**. |
|
|
| This repo holds *reduced metrics / predictions keyed by pseudonymous participant id* — **not** raw sensor data. |
|
|
| ## Layout |
|
|
| ``` |
| <track>/<method>.parquet e.g. downstream/xgboost.parquet, imputation/locf.parquet, forecasting/seasonal_naive.parquet |
| <track>/bootstrap/draws.parquet per-draw bootstrap reference for the CIs |
| ``` |
|
|
| Tracks: `downstream` (Track 1), `imputation` (Track 2), `forecasting` (Track 3) — all live. |
|
|
| ## Schema |
|
|
| See [`SCHEMA.md`](SCHEMA.md) for the full column spec per track (and each `<track>/SCHEMA.md` for track-specific submission details). In brief: each row is a per-user value for one task cell, evaluated on the canonical `sharable_users_seed42_2026` test split. The tracks differ in what each row holds: |
|
|
| - **Track 1 (downstream / predictive tasks)** stores the raw per-user prediction **pairs** (`y_true`, `y_pred`, `y_proba`) — its cohort-level metrics (AUPRC / Spearman / Pearson) don't decompose into a per-user error. |
| - **Track 2 (imputation)** stores the per-user error `E_per_user` (MAE / `1 − AUC`). |
| - **Track 3 (forecasting)** stores the **raw** per-user `metric_value` (so one file serves skill, rank, and fairness — each reducer converts/uses it on load). |
|
|
| ## Why per-user (not aggregate) |
|
|
| The skill score is a *paired per-user* statistic against the track baseline (`linear` for downstream, `locf` for imputation, `seasonal_naive` for forecasting), and the rank is a per-user rank across **all** methods — so both require each method's per-user values, paired on `user_id`, not per-task aggregates. |
|
|
| ## Usage |
|
|
| ```python |
| import glob, os |
| import pandas as pd |
| from huggingface_hub import snapshot_download |
| |
| root = snapshot_download("MyHeartCounts/OpenMHC-leaderboard-data", repo_type="dataset") |
| # one track at a time (schemas differ per track) |
| frames = [pd.read_parquet(p) for p in glob.glob(os.path.join(root, "downstream", "*.parquet"))] |
| df = pd.concat(frames, ignore_index=True) |
| ``` |
|
|
| Methods are uploaded with `tools/upload_leaderboard_substrate.py`; the per-track bootstrap references with `tools/upload_leaderboard_bootstrap.py`, both in the [code repo](https://github.com/AshleyLab/myheartcounts-dataset). |
|
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| ## Provenance |
|
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| Generated by the OpenMHC evaluation harness. Baselines: `linear` (Track 1), `locf` (Track 2), `seasonal_naive` (Track 3). |
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|
