LightGBM

LightGBM multi-horizon CGM forecaster (MetaboNet)

A MultiOutputRegressor(LGBMRegressor) trained on the MetaboNet tabular split and re-packaged as a transformers-compatible Hub model. One repo holds four feature ablations, each with 12 boosters (one per 5-minute horizon up to 60 min):

• cgm — 24 CGM lags + hour_sin/hour_cos (26 features).
• insulin — cgm features + 24 Insulin lags (50 features).
• carbs — cgm features + 24 Carbs lags (50 features).
• all — cgm features + 24 Insulin lags + 24 Carbs lags (74 features).

Files

• config.json — auto_map wiring + per-ablation feature lists.
• model.py — LightGBMMultiHorizonConfig / LightGBMMultiHorizonModel (trust_remote_code=True).
• boosters/<ablation>/horizon_<NN>.txt — Booster.save_model text dumps (4 ablations x 12 horizons = 48 files).

Usage

from transformers import AutoConfig, AutoModel

cfg = AutoConfig.from_pretrained(
    "anonymous-4FAD/LightGBM", trust_remote_code=True, ablation="cgm"
)
model = AutoModel.from_pretrained(
    "anonymous-4FAD/LightGBM", trust_remote_code=True, config=cfg
)

# Inputs match the MetaboNet benchmark.py contract:
#   timestamps: int64 ns, shape (B, T_in)
#   cgm/insulin/carbs: float, shape (B, T_in); only the last 24 steps are used
preds = model.predict(timestamps, cgm, insulin, carbs)  # -> (B, 12)

The thin local wrapper in models/lightgbm.py exposes the same API used by benchmark.py.

lightgbm>=4.0 must be installed locally (boosters are loaded via lightgbm.Booster(model_file=...)); inference is CPU-only.

Feature convention

CGM_t<i> denotes the i-th sample within the last history_length=24 steps, ordered oldest -> newest. Same for Insulin_t<i> / Carbs_t<i>. hour_sin and hour_cos come from the most recent input timestamp. The original boosters were trained on numpy arrays so the feature names embedded in the boosters are anonymized (Column_0..); the explicit names listed in config.json come from the matched Ridge artifacts (same preprocessing schema, same column order).

Provenance

Trained via other_models/results/train_lightgbm.py on the public MetaboNet train split. The Hub repo is staged by scripts/build_other_models_hub.py which copies the booster text files verbatim and writes config.json.