from abc import ABCMeta, abstractmethod from collections.abc import Mapping, Sequence from typing import Any, ClassVar, Literal from typing_extensions import Self from numpy import ndarray from numpy.random import RandomState from scipy.sparse import spmatrix from .._typing import ArrayLike, Float, Int, MatrixLike from ..base import ClassifierMixin, MultiOutputMixin, RegressorMixin, TransformerMixin from ..preprocessing import OneHotEncoder from ..tree import ( DecisionTreeClassifier, DecisionTreeRegressor, ExtraTreeRegressor, ) from ._base import BaseEnsemble __all__ = [ "RandomForestClassifier", "RandomForestRegressor", "ExtraTreesClassifier", "ExtraTreesRegressor", "RandomTreesEmbedding", ] MAX_INT = ... class BaseForest(MultiOutputMixin, BaseEnsemble, metaclass=ABCMeta): _parameter_constraints: ClassVar[dict] = ... @abstractmethod def __init__( self, estimator, n_estimators: int = 100, *, estimator_params=..., bootstrap: bool = False, oob_score: bool = False, n_jobs=None, random_state=None, verbose: int = 0, warm_start: bool = False, class_weight=None, max_samples=None, base_estimator: str = "deprecated", ) -> None: ... def apply(self, X: MatrixLike | ArrayLike) -> ndarray: ... def decision_path(self, X: MatrixLike | ArrayLike) -> tuple[spmatrix, ndarray]: ... def fit( self, X: MatrixLike | ArrayLike, y: MatrixLike | ArrayLike, sample_weight: None | ArrayLike = None, ) -> Self: ... @property def feature_importances_(self) -> ndarray: ... class ForestClassifier(ClassifierMixin, BaseForest, metaclass=ABCMeta): @abstractmethod def __init__( self, estimator, n_estimators: int = 100, *, estimator_params=..., bootstrap: bool = False, oob_score: bool = False, n_jobs=None, random_state=None, verbose: int = 0, warm_start: bool = False, class_weight=None, max_samples=None, base_estimator: str = "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: ... class ForestRegressor(RegressorMixin, BaseForest, metaclass=ABCMeta): @abstractmethod def __init__( self, estimator, n_estimators: int = 100, *, estimator_params=..., bootstrap: bool = False, oob_score: bool = False, n_jobs=None, random_state=None, verbose: int = 0, warm_start: bool = False, max_samples=None, base_estimator: str = "deprecated", ) -> None: ... def predict(self, X: MatrixLike | ArrayLike) -> ndarray: ... class RandomForestClassifier(ForestClassifier): oob_decision_function_: ndarray = ... oob_score_: float = ... feature_importances_: ndarray = ... n_outputs_: int = ... feature_names_in_: ndarray = ... n_features_in_: int = ... n_classes_: list | int = ... classes_: ndarray = ... estimators_: list[DecisionTreeClassifier] = ... base_estimator_: DecisionTreeClassifier = ... estimator_: DecisionTreeClassifier = ... _parameter_constraints: ClassVar[dict] = ... def __init__( self, n_estimators: Int = 100, *, criterion: Literal["gini", "entropy", "log_loss"] = "gini", max_depth: None | Int = None, min_samples_split: float = 2, min_samples_leaf: float = 1, min_weight_fraction_leaf: Float = 0.0, max_features: float | Literal["sqrt", "log2"] = "sqrt", max_leaf_nodes: None | Int = None, min_impurity_decrease: Float = 0.0, bootstrap: bool = True, oob_score: bool = False, n_jobs: None | Int = None, random_state: RandomState | None | Int = None, verbose: Int = 0, warm_start: bool = False, class_weight: Literal["balanced", "balanced_subsample"] | None | Mapping | Sequence[Mapping] = None, ccp_alpha: float = 0.0, max_samples: float | None = None, ) -> None: ... class RandomForestRegressor(ForestRegressor): oob_prediction_: ndarray = ... oob_score_: float = ... n_outputs_: int = ... feature_names_in_: ndarray = ... n_features_in_: int = ... feature_importances_: ndarray = ... estimators_: list[DecisionTreeRegressor] = ... base_estimator_: DecisionTreeRegressor = ... estimator_: DecisionTreeRegressor = ... _parameter_constraints: ClassVar[dict] = ... def __init__( self, n_estimators: Int = 100, *, criterion: Literal["squared_error", "absolute_error", "friedman_mse", "poisson"] = "squared_error", max_depth: None | Int = None, min_samples_split: float = 2, min_samples_leaf: float = 1, min_weight_fraction_leaf: Float = 0.0, max_features: float | Literal["sqrt", "log2"] = 1.0, max_leaf_nodes: None | Int = None, min_impurity_decrease: Float = 0.0, bootstrap: bool = True, oob_score: bool = False, n_jobs: None | Int = None, random_state: RandomState | None | Int = None, verbose: Int = 0, warm_start: bool = False, ccp_alpha: float = 0.0, max_samples: float | None = None, ) -> None: ... class ExtraTreesClassifier(ForestClassifier): oob_decision_function_: ndarray = ... oob_score_: float = ... n_outputs_: int = ... feature_names_in_: ndarray = ... n_features_in_: int = ... feature_importances_: ndarray = ... n_classes_: list | int = ... classes_: ndarray = ... estimators_: list[DecisionTreeClassifier] = ... base_estimator_: ExtraTreesClassifier = ... estimator_: ExtraTreesClassifier = ... _parameter_constraints: ClassVar[dict] = ... def __init__( self, n_estimators: Int = 100, *, criterion: Literal["gini", "entropy", "log_loss"] = "gini", max_depth: None | Int = None, min_samples_split: float = 2, min_samples_leaf: float = 1, min_weight_fraction_leaf: Float = 0.0, max_features: float | Literal["sqrt", "log2"] = "sqrt", max_leaf_nodes: None | Int = None, min_impurity_decrease: Float = 0.0, bootstrap: bool = False, oob_score: bool = False, n_jobs: None | Int = None, random_state: RandomState | None | Int = None, verbose: Int = 0, warm_start: bool = False, class_weight: Literal["balanced", "balanced_subsample"] | None | Mapping | Sequence[Mapping] = None, ccp_alpha: float = 0.0, max_samples: float | None = None, ) -> None: ... class ExtraTreesRegressor(ForestRegressor): oob_prediction_: ndarray = ... oob_score_: float = ... n_outputs_: int = ... feature_names_in_: ndarray = ... n_features_in_: int = ... feature_importances_: ndarray = ... estimators_: list[DecisionTreeRegressor] = ... base_estimator_: ExtraTreeRegressor = ... estimator_: ExtraTreeRegressor = ... _parameter_constraints: ClassVar[dict] = ... def __init__( self, n_estimators: Int = 100, *, criterion: Literal["squared_error", "absolute_error", "friedman_mse", "poisson"] = "squared_error", max_depth: None | Int = None, min_samples_split: float = 2, min_samples_leaf: float = 1, min_weight_fraction_leaf: Float = 0.0, max_features: float | Literal["sqrt", "log2"] = 1.0, max_leaf_nodes: None | Int = None, min_impurity_decrease: Float = 0.0, bootstrap: bool = False, oob_score: bool = False, n_jobs: None | Int = None, random_state: RandomState | None | Int = None, verbose: Int = 0, warm_start: bool = False, ccp_alpha: float = 0.0, max_samples: float | None = None, ) -> None: ... class RandomTreesEmbedding(TransformerMixin, BaseForest): one_hot_encoder_: OneHotEncoder = ... n_outputs_: int = ... feature_names_in_: ndarray = ... n_features_in_: int = ... feature_importances_: ndarray = ... estimators_: list[ExtraTreeRegressor] = ... base_estimator_: ExtraTreeRegressor = ... estimator_: ExtraTreeRegressor = ... _parameter_constraints: ClassVar[dict] = ... criterion: ClassVar[str] = ... max_features: ClassVar[int] = ... def __init__( self, n_estimators: Int = 100, *, max_depth: Int = 5, min_samples_split: float = 2, min_samples_leaf: float = 1, min_weight_fraction_leaf: Float = 0.0, max_leaf_nodes: None | Int = None, min_impurity_decrease: Float = 0.0, sparse_output: bool = True, n_jobs: None | Int = None, random_state: RandomState | None | Int = None, verbose: Int = 0, warm_start: bool = False, ) -> None: ... def fit( self, X: MatrixLike | ArrayLike, y: Any = None, sample_weight: None | ArrayLike = None, ) -> Self: ... def fit_transform( self, X: MatrixLike | ArrayLike, y: Any = None, sample_weight: None | ArrayLike = None, ) -> spmatrix: ... def get_feature_names_out(self, input_features: None | ArrayLike = None) -> ndarray: ... def transform(self, X: MatrixLike | ArrayLike) -> spmatrix: ...