Datasets:
license: openrail
pretty_name: OpenMHC Leaderboard Data
tags:
- wearables
- benchmark
- myheartcounts
- time-series
OpenMHC Leaderboard Data
Per-user substrate behind the OpenMHC 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 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
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.
Provenance
Generated by the OpenMHC evaluation harness. Baselines: linear (Track 1), locf (Track 2), seasonal_naive (Track 3).