autoprognosis.plugins.ensemble.regression module

class BaseRegressionEnsemble

Bases: object

Abstract ensemble interface

enable_explainer(explainer_plugins: list = [], explanations_nepoch: int = 10000) → None

abstract explain(X: DataFrame, *args: Any) → DataFrame

abstract fit(X: DataFrame, Y: DataFrame) → BaseRegressionEnsemble

abstract is_fitted() → bool

abstract classmethod load(buff: bytes) → BaseRegressionEnsemble

abstract name() → str

abstract predict(X: DataFrame, *args: Any) → DataFrame

abstract save() → bytes

class WeightedRegressionEnsemble(models: List[PipelineMeta], weights: List[float], explainer_plugins: list = [], explainers: Dict | None = None, explanations_nepoch: int = 10000)

Bases: BaseRegressionEnsemble

Weighted ensemble

Parameters:

• models – list. List of base models.

• weights – list. The weights for each base model.

• explainer_plugins – list. List of explainers attached to the ensemble.

enable_explainer(explainer_plugins: list = [], explanations_nepoch: int = 10000) → None

explain(X: DataFrame, *args: Any) → DataFrame

fit(X: DataFrame, Y: DataFrame) → WeightedRegressionEnsemble

is_fitted() → bool

classmethod load(buff: bytes) → WeightedRegressionEnsemble

name() → str

predict(X: DataFrame, *args: Any) → DataFrame

save() → bytes