forecasting/SCHEMA.md

Forecasting submission substrate — schema

This is the schema of the file a submitter uploads for the Track-3 (forecasting) leaderboard: one per-method, per-user metric parquet plus a small display sidecar.

forecasting/<method>.parquet      # per-user substrate (this file)
forecasting/<method>.meta.json    # display sidecar (below)

The parquet is produced by the evaluation run — pass both output_dir= and method_name="<method>" to openmhc.evaluate_forecasting and it writes per_user_errors.parquet with the model column set to <method>. Upload that file verbatim (renamed to <method>.parquet); do not hand-author it. The maintainers concatenate every forecasting/*.parquet and recompute paired skill, fair skill, and average rank against the seasonal_naive baseline during ingestion, so the columns and dtypes below must match exactly.

method_name is required and must equal your <method> stem. It defaults to "custom", and ingestion groups rows by the model column — so a submission left at the default collides with every other default submission and is scored under the wrong identity. This is the method_name argument to evaluate_forecasting (which sets the parquet column), not the cosmetic to_submission_yaml(method_name=...) display name. It must differ from the baseline name seasonal_naive.

For the separate bootstrap reference frame (per-draw CIs, not the submission file), see bootstrap/SCHEMA.md.

<method>.parquet

One row per (model, group, metric, channel_idx, channel_name, user_id). Single Parquet, dictionary-encoded string columns, float64 metric value, zstd compression. Unlike Track 2 (which stores the error E_per_user), the forecasting substrate stores the raw per-user metric value, so one file serves all three reducers — skill and fairness convert it to an error on load, rank uses it directly.

column	type	description
model	string (dict)	your method identifier; must equal the <method> filename stem — set it via evaluate_forecasting(method_name="<method>") (defaults to "custom")
group	string (dict)	continuous (ch 0–6, scored on mae) or binary (ch 7–18, scored on auroc)
metric	string (dict)	scored metric: mae (continuous) or auroc (binary)
channel_idx	int16	sensor channel index (0–18)
channel_name	string (dict)	human-readable channel name
user_id	string (dict)	participant id from the canonical split
metric_value	float64	RAW per-user metric, micro-pooled over the user's forecast windows (Σcell / Σcount)
n_values	int64	finite horizon-cell count behind metric_value

Value semantics

• metric_value — the raw per-user metric, before any cross-user reduction and before any error conversion. Continuous channels: per-user MAE. Binary channels: per-user AUROC. Stored at float64 for byte-exact reproduction of the published aggregates.
• On load the maintainers convert to an error: continuous error = metric_value (MAE, lower is better); binary error = max(1 − auroc, 0.005) (the 0.005 floor keeps perfect-AUROC users finite). Rank uses metric_value directly. Skill is the paired geomean of clipped per-user error ratios vs the baseline.
• Structurally-absent cells are omitted, not NaN-filled — a (channel, metric, user) with no finite horizon cells simply has no row; the grid is not a full cartesian product. The shipped Seasonal-Naive baseline (src/openmhc/data/baselines/forecasting_seasonal_naive_per_user_errors.parquet) is the canonical shape your submission should mirror.

No subgroup rows — fairness is joined server-side

Unlike Track 2, the forecasting substrate is keyed by user_id only and carries no subgroup_attr / subgroup_value columns. The fairness skill score is computed maintainer-side by joining demographics (age_group, sex) onto user_id from the private label tables, so submitters do not ship subgroup rows — evaluate_forecasting emits exactly the columns above.

<method>.meta.json

Tiny display sidecar the leaderboard reads to render the row:

{
  "display_name": "My Forecaster",   // shown in the leaderboard
  "type": "Deep Learning",           // category label
  "submitter": "Stanford CS",        // lab / team
  "subtrack": "other",               // Track 3 has no single-day/long-context split
  "fallback_rate": 0.0               // Seasonal-Naive substitution fraction (see below)
}

fallback_rate

Scalar in [0, 1]. The run's overall_fallback_rate (mirrors openmhc._results.ForecastingResults.overall_fallback_rate): the fraction of forecast cells the model emitted as non-finite (NaN), which the harness then substituted with the Seasonal-Naive baseline before scoring.

• 0.0 — every forecast cell was finite; the harness never substituted.
• >0 — the model returned NaN at that fraction of forecast cells and the harness filled them with Seasonal-Naive. Treat headline scores cautiously when fallback_rate > ~5%, since the metric is inflated with baseline performance on the substituted positions.

The field is added to the sidecar by tools/upload_leaderboard_substrate.py in one of two ways:

1. Explicit: --fallback-rate FLOAT
2. Auto: --results-json PATH — the tool reads the run's results.json and extracts the top-level overall_fallback_rate.

Existing sidecar fields are preserved on update (the tool fetches the current sidecar from HF and merges only the provided fields).

Conventions

• Evaluated against the canonical split sharable_users_seed42_2026 (test).
• Track-3 baseline for skill / fairness: seasonal_naive (server-side; do not submit it).
• Scored set: mae (continuous channels 0–6) + auroc (binary channels 7–18); ratio clip [0.01, 100]; within-user aggregation micro; unit user.
• Fairness disparity primitive: mean absolute pairwise difference (MAPD).
• Must use the standard evaluation protocol — canonical dataset version, split file, sample-index, and horizon (24 h).

Uploaded with

Submitters open a PR on the dataset with HfApi().upload_folder(..., create_pr=True) (see the repo README, "Submit to the Leaderboard"). Maintainers can use tools/upload_leaderboard_substrate.py in the code repo.