---
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).

## Provenance

Generated by the OpenMHC evaluation harness. Baselines: `linear` (Track 1), `locf` (Track 2), `seasonal_naive` (Track 3).