95 lines
3.0 KiB
Python
95 lines
3.0 KiB
Python
from collections import defaultdict as defaultdict
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from typing import Any, ClassVar, Iterable, TypeVar
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from numpy import ndarray
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from ._config import get_config as get_config
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from ._typing import ArrayLike, Float, Int, MatrixLike
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from .metrics import accuracy_score as accuracy_score, r2_score as r2_score
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from .utils._estimator_html_repr import estimator_html_repr as estimator_html_repr
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from .utils._param_validation import validate_parameter_constraints as validate_parameter_constraints
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from .utils._set_output import _SetOutputMixin
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from .utils.validation import check_array as check_array, check_is_fitted as check_is_fitted, check_X_y as check_X_y
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BaseEstimator_Self = TypeVar("BaseEstimator_Self", bound="BaseEstimator")
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# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
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# License: BSD 3 clause
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import copy
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import inspect
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import platform
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import re
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import warnings
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import numpy as np
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def clone(estimator: BaseEstimator | Iterable[BaseEstimator], *, safe: bool = True) -> Any: ...
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class BaseEstimator:
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def get_params(self, deep: bool = True) -> dict: ...
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def set_params(self: BaseEstimator_Self, **params) -> BaseEstimator_Self: ...
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def __repr__(self, N_CHAR_MAX: int = 700) -> str: ...
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def __getstate__(self): ...
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def __setstate__(self, state) -> None: ...
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class ClassifierMixin:
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_estimator_type: ClassVar[str] = ...
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def score(
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self,
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X: MatrixLike,
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y: MatrixLike | ArrayLike,
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sample_weight: None | ArrayLike = None,
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) -> Float: ...
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class RegressorMixin:
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_estimator_type: ClassVar[str] = ...
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def score(
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self,
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X: MatrixLike,
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y: MatrixLike | ArrayLike,
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sample_weight: None | ArrayLike = None,
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) -> Float: ...
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class ClusterMixin:
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_estimator_type: ClassVar[str] = ...
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def fit_predict(self, X: MatrixLike, y: Any = None) -> ndarray: ...
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class BiclusterMixin:
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def biclusters_(self): ...
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def get_indices(self, i: Int) -> tuple[ndarray, ndarray]: ...
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def get_shape(self, i: Int) -> tuple[int, int]: ...
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def get_submatrix(self, i: Int, data: MatrixLike) -> ndarray: ...
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class TransformerMixin(_SetOutputMixin):
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def fit_transform(self, X: MatrixLike, y: None | MatrixLike | ArrayLike = None, **fit_params) -> ndarray: ...
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class OneToOneFeatureMixin:
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def get_feature_names_out(self, input_features: None | ArrayLike = None) -> ndarray: ...
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class ClassNamePrefixFeaturesOutMixin:
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def get_feature_names_out(self, input_features: None | ArrayLike = None) -> ndarray: ...
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class DensityMixin:
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_estimator_type: ClassVar[str] = ...
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def score(self, X: MatrixLike, y: Any = None) -> float: ...
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class OutlierMixin:
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_estimator_type: ClassVar[str] = ...
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def fit_predict(self, X: MatrixLike | ArrayLike, y: Any = None) -> ndarray: ...
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class MetaEstimatorMixin:
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_required_parameters: ClassVar[list] = ...
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class MultiOutputMixin: ...
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class _UnstableArchMixin: ...
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def is_classifier(estimator: Any) -> bool: ...
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def is_regressor(estimator: BaseEstimator) -> bool: ...
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def is_outlier_detector(estimator: BaseEstimator) -> bool: ...
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