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[sklearn]: use npt.NDArray instead of np.ndarray #388

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[sklearn]: use npt.NDArray instead of np.ndarray #388

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5 changes: 4 additions & 1 deletion stubs/sklearn/_typing.pyi
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Original file line number Diff line number Diff line change
Expand Up @@ -5,8 +5,10 @@
import io
import typing_extensions
from decimal import Decimal as Decimal
from typing import Any

import numpy as np
import numpy.typing as npt
import pandas as pd
from numpy.typing import ArrayLike as ArrayLike
from scipy.sparse import spmatrix
Expand All @@ -15,7 +17,8 @@ from .base import BaseEstimator, ClassifierMixin, RegressorMixin

PythonScalar: typing_extensions.TypeAlias = str | int | float | bool

MatrixLike: typing_extensions.TypeAlias = np.ndarray | pd.DataFrame | spmatrix
NDArray: typing_extensions.TypeAlias = npt.NDArray[Any]
MatrixLike: typing_extensions.TypeAlias = NDArray | pd.DataFrame | spmatrix
FileLike = io.IOBase
PathLike = str
Int: typing_extensions.TypeAlias = int | np.int8 | np.int16 | np.int32 | np.int64
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17 changes: 8 additions & 9 deletions stubs/sklearn/linear_model/_base.pyi
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Expand Up @@ -2,10 +2,9 @@ from abc import ABCMeta, abstractmethod
from typing import ClassVar
from typing_extensions import Self

from numpy import ndarray
from numpy.random.mtrand import RandomState

from .._typing import ArrayLike, Int, MatrixLike
from .._typing import ArrayLike, Int, MatrixLike, NDArray
from ..base import BaseEstimator as BaseEstimator, ClassifierMixin, MultiOutputMixin, RegressorMixin
from ..utils._seq_dataset import ArrayDataset64, CSRDataset64
from ._stochastic_gradient import SGDClassifier
Expand All @@ -22,23 +21,23 @@ def make_dataset(
class LinearModel(BaseEstimator, metaclass=ABCMeta):
@abstractmethod
def fit(self, X, y): ...
def predict(self, X: MatrixLike) -> ndarray: ...
def predict(self, X: MatrixLike) -> NDArray: ...

class LinearClassifierMixin(ClassifierMixin):
def decision_function(self, X: MatrixLike | ArrayLike) -> ndarray: ...
def predict(self, X: MatrixLike | ArrayLike) -> ndarray: ...
def decision_function(self, X: MatrixLike | ArrayLike) -> NDArray: ...
def predict(self, X: MatrixLike | ArrayLike) -> NDArray: ...

class SparseCoefMixin:
def densify(self) -> Self: ...
def sparsify(self) -> SGDClassifier | Self: ...

class LinearRegression(MultiOutputMixin, RegressorMixin, LinearModel):
feature_names_in_: ndarray = ...
feature_names_in_: NDArray = ...
n_features_in_: int = ...
intercept_: float | ndarray = ...
singular_: ndarray = ...
intercept_: float | NDArray = ...
singular_: NDArray = ...
rank_: int = ...
coef_: ndarray = ...
coef_: NDArray = ...

_parameter_constraints: ClassVar[dict] = ...

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