from abc import ABCMeta, abstractmethod from typing import Any, ClassVar from typing_extensions import Self from numpy import ndarray from numpy.random import RandomState from .._typing import ArrayLike, Int, MatrixLike from ..base import BaseEstimator, ClassifierMixin, RegressorMixin from ._base import BaseEnsemble # Author: Gilles Louppe # License: BSD 3 clause __all__ = ["BaggingClassifier", "BaggingRegressor"] MAX_INT = ... class BaseBagging(BaseEnsemble, metaclass=ABCMeta): _parameter_constraints: ClassVar[dict] = ... @abstractmethod def __init__( self, estimator=None, n_estimators: int = 10, *, max_samples: float = 1.0, max_features: float = 1.0, bootstrap: bool = True, bootstrap_features: bool = False, oob_score: bool = False, warm_start: bool = False, n_jobs=None, random_state=None, verbose: int = 0, base_estimator: str = "deprecated", ) -> None: ... def fit( self, X: MatrixLike | ArrayLike, y: ArrayLike, sample_weight: None | ArrayLike = None, ) -> BaggingRegressor | Self: ... def estimators_samples_(self): ... class BaggingClassifier(ClassifierMixin, BaseBagging): oob_decision_function_: ndarray = ... oob_score_: float = ... n_classes_: list | int = ... classes_: ndarray = ... estimators_features_: list[ArrayLike] = ... estimators_samples_: list[ArrayLike] = ... estimators_: list[BaseEstimator] = ... feature_names_in_: ndarray = ... n_features_in_: int = ... base_estimator_: BaseEstimator = ... estimator_: BaseEstimator = ... def __init__( self, estimator: Any = None, n_estimators: Int = 10, *, max_samples: float = 1.0, max_features: float = 1.0, bootstrap: bool = True, bootstrap_features: bool = False, oob_score: bool = False, warm_start: bool = False, n_jobs: None | Int = None, random_state: RandomState | None | Int = None, verbose: Int = 0, base_estimator: Any = "deprecated", ) -> None: ... def predict(self, X: MatrixLike | ArrayLike) -> ndarray: ... def predict_proba(self, X: MatrixLike | ArrayLike) -> ndarray: ... def predict_log_proba(self, X: MatrixLike | ArrayLike) -> ndarray: ... def decision_function(self, X: MatrixLike | ArrayLike) -> ndarray: ... class BaggingRegressor(RegressorMixin, BaseBagging): oob_prediction_: ndarray = ... oob_score_: float = ... estimators_features_: list[ArrayLike] = ... estimators_samples_: list[ArrayLike] = ... estimators_: list[BaseEstimator] = ... feature_names_in_: ndarray = ... n_features_in_: int = ... base_estimator_: BaseEstimator = ... estimator_: BaseEstimator = ... def __init__( self, estimator: Any = None, n_estimators: Int = 10, *, max_samples: float = 1.0, max_features: float = 1.0, bootstrap: bool = True, bootstrap_features: bool = False, oob_score: bool = False, warm_start: bool = False, n_jobs: None | Int = None, random_state: RandomState | None | Int = None, verbose: Int = 0, base_estimator: Any = "deprecated", ) -> None: ... def predict(self, X: MatrixLike | ArrayLike) -> ndarray: ...