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license: apache-2.0
tags:
- cgm
- time-series
- glucose-forecasting
- ridge-regression
- metabonet
library_name: transformers
pipeline_tag: time-series-forecasting
---
# Ridge multi-horizon CGM forecaster (MetaboNet)
A `sklearn`-trained Ridge regressor (with `StandardScaler`) re-packaged as a
`transformers`-compatible Hub model. One repo holds four feature ablations
selectable at load time:
- `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).
History length is 24 (= 2 hours at 5-minute sampling). Output is 12 future
CGM values (5–60 min horizons).
## Files
- `config.json` — `auto_map` wiring + per-ablation feature lists.
- `model.py` — `RidgeMultiHorizonConfig` / `RidgeMultiHorizonModel`
(`trust_remote_code=True`).
- `model_<ablation>.safetensors` — one per ablation, holding `scaler_mean`,
`scaler_scale`, `coef` (12 × F), `intercept` (12).
## Usage
```python
from transformers import AutoConfig, AutoModel
cfg = AutoConfig.from_pretrained(
"anonymous-4FAD/Ridge", trust_remote_code=True, ablation="cgm"
)
model = AutoModel.from_pretrained(
"anonymous-4FAD/Ridge", 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/ridge.py`](https://github.com/njeffrie/MetaboNet-Bench/blob/main/models/ridge.py)
exposes the same API used by `benchmark.py`.
## Feature convention
`CGM_t<i>` denotes the i-th sample within the last `history_length` steps,
ordered oldest -> newest (`CGM_t0` is the oldest of the 24, `CGM_t23` is the
newest). The same convention applies to `Insulin_t<i>` and `Carbs_t<i>`.
`hour_sin` / `hour_cos` are derived from the most recent input timestamp.
## Provenance
Trained via
[`other_models/results/train_ridge.py`](https://github.com/njeffrie/MetaboNet-Bench/blob/main/other_models/results/train_ridge.py)
on the public MetaboNet train split. The `safetensors` checkpoints are produced
by [`scripts/build_other_models_hub.py`](https://github.com/njeffrie/MetaboNet-Bench/blob/main/scripts/build_other_models_hub.py)
from the original sklearn pickles.
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