coco_pipe.fm.cbramod

Foundation model pipelines for CoCo Pipe.

This module hosts pipelines built on top of the CBRAMod foundation model.

Submodules

Classes

CBRAModRegressionPipeline

Regression pipeline that routes features through the CBRAMod foundation

FoundationRegressor

Thin sklearn-compatible wrapper around a foundation model embedder.

Package Contents

class coco_pipe.fm.cbramod.CBRAModRegressionPipeline(X: pandas.DataFrame | numpy.ndarray, y: pandas.Series | numpy.ndarray, embed_fn: Callable[[Any], numpy.ndarray], metrics: str | Sequence[str] | None = None, base_regressor: sklearn.base.BaseEstimator | None = None, hp_search_params: Dict[str, Sequence[Any]] | None = None, use_scaler: bool = False, random_state: int = 42, n_jobs: int = -1, cv_kwargs: Dict[str, Any] | None = None, groups: pandas.Series | numpy.ndarray | None = None, verbose: bool = False)

Bases: coco_pipe.ml.base.BasePipeline

Regression pipeline that routes features through the CBRAMod foundation model before fitting a lightweight regressor.

The interface mirrors RegressionPipeline for analysis_type dispatch but exposes a required embed_fn to obtain embeddings from the foundation model.

embed_fn
verbose = False
model_name = 'CBRAMod'
run(analysis_type: str = 'baseline', n_features: int | None = None, direction: str = 'forward', search_type: str = 'grid', n_iter: int = 50, scoring: str | None = None) → Dict[str, Any]
class coco_pipe.fm.cbramod.FoundationRegressor(embed_fn: Callable[[Any], numpy.ndarray], base_regressor: sklearn.base.BaseEstimator | None = None, multioutput: bool = False)

Bases: sklearn.base.BaseEstimator, sklearn.base.RegressorMixin

Thin sklearn-compatible wrapper around a foundation model embedder.

Parameters:

  • embed_fn (callable) – Callable that maps X to a 2D numpy array of embeddings. Either a __call__ or transform method will be used.

  • base_regressor (BaseEstimator, optional) – Downstream regressor trained on the embeddings. Defaults to Ridge.

  • multioutput (bool, optional) – If True, wraps the base regressor in MultiOutputRegressor for multi-target regression.

embed_fn
base_regressor
multioutput = False
_embed(X: Any) → numpy.ndarray
fit(X: Any, y: Any)
predict(X: Any) → numpy.ndarray