Use typing_extensions.Self

pyproject.toml

@@ -58,7 +58,6 @@ ignore = [
     "PYI001",
     "PYI002",
     "PYI017",
-    "PYI019", # Request for more autofixes: https://github.com/astral-sh/ruff/issues/15798
     "PYI024",
     "PYI048",
     "PYI051", # Request for autofix: https://github.com/astral-sh/ruff/issues/14185

stubs/sklearn/base.pyi

@@ -5,7 +5,8 @@ import re
 import warnings
 from collections import defaultdict as defaultdict
 from collections.abc import Iterable
-from typing import Any, ClassVar, TypeVar
+from typing import Any, ClassVar
+from typing_extensions import Self
 
 import numpy as np
 from numpy import ndarray
@@ -18,16 +19,14 @@ from .utils._param_validation import validate_parameter_constraints as validate_
 from .utils._set_output import _SetOutputMixin
 from .utils.validation import check_array as check_array, check_is_fitted as check_is_fitted, check_X_y as check_X_y
 
-BaseEstimator_Self = TypeVar("BaseEstimator_Self", bound=BaseEstimator)
-
 # Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
 # License: BSD 3 clause
 
 def clone(estimator: BaseEstimator | Iterable[BaseEstimator], *, safe: bool = True) -> Any: ...
 
 class BaseEstimator:
     def get_params(self, deep: bool = True) -> dict: ...
-    def set_params(self: BaseEstimator_Self, **params) -> BaseEstimator_Self: ...
+    def set_params(self, **params) -> Self: ...
     def __repr__(self, N_CHAR_MAX: int = 700) -> str: ...
     def __getstate__(self): ...
     def __setstate__(self, state) -> None: ...

stubs/sklearn/calibration.pyi

@@ -4,7 +4,8 @@ from functools import partial as partial
 from inspect import signature as signature
 from math import log as log
 from numbers import Integral as Integral
-from typing import ClassVar, Literal, TypeVar
+from typing import ClassVar, Literal
+from typing_extensions import Self
 
 import numpy as np
 from matplotlib.artist import Artist
@@ -33,9 +34,6 @@ from .utils.multiclass import check_classification_targets as check_classificati
 from .utils.parallel import Parallel as Parallel, delayed as delayed
 from .utils.validation import check_consistent_length as check_consistent_length, check_is_fitted as check_is_fitted
 
-CalibratedClassifierCV_Self = TypeVar("CalibratedClassifierCV_Self", bound=CalibratedClassifierCV)
-_SigmoidCalibration_Self = TypeVar("_SigmoidCalibration_Self", bound=_SigmoidCalibration)
-
 class CalibratedClassifierCV(ClassifierMixin, MetaEstimatorMixin, BaseEstimator):
     calibrated_classifiers_: list = ...
     feature_names_in_: ndarray = ...
@@ -55,12 +53,12 @@ class CalibratedClassifierCV(ClassifierMixin, MetaEstimatorMixin, BaseEstimator)
         base_estimator: str | BaseEstimator = "deprecated",
     ) -> None: ...
     def fit(
-        self: CalibratedClassifierCV_Self,
+        self,
         X: MatrixLike,
         y: ArrayLike,
         sample_weight: None | ArrayLike = None,
         **fit_params,
-    ) -> CalibratedClassifierCV_Self: ...
+    ) -> Self: ...
     def predict_proba(self, X: MatrixLike) -> ndarray: ...
     def predict(self, X: MatrixLike) -> ndarray: ...
 
@@ -80,11 +78,11 @@ class _SigmoidCalibration(RegressorMixin, BaseEstimator):
     a_: float = ...
 
     def fit(
-        self: _SigmoidCalibration_Self,
+        self,
         X: ArrayLike,
         y: ArrayLike,
         sample_weight: None | ArrayLike = None,
-    ) -> _SigmoidCalibration_Self: ...
+    ) -> Self: ...
     def predict(self, T: ArrayLike) -> ndarray: ...
 
 def calibration_curve(

stubs/sklearn/cluster/_affinity_propagation.pyi

@@ -1,6 +1,7 @@
 import warnings
 from numbers import Integral as Integral, Real as Real
-from typing import Any, ClassVar, Literal, TypeVar
+from typing import Any, ClassVar, Literal
+from typing_extensions import Self
 
 import numpy as np
 from numpy import ndarray
@@ -15,8 +16,6 @@ from ..utils import as_float_array as as_float_array, check_random_state as chec
 from ..utils._param_validation import Interval as Interval, StrOptions as StrOptions
 from ..utils.validation import check_is_fitted as check_is_fitted
 
-AffinityPropagation_Self = TypeVar("AffinityPropagation_Self", bound=AffinityPropagation)
-
 ###############################################################################
 # Public API
 
@@ -56,6 +55,6 @@ class AffinityPropagation(ClusterMixin, BaseEstimator):
         verbose: bool = False,
         random_state: RandomState | None | Int = None,
     ) -> None: ...
-    def fit(self: AffinityPropagation_Self, X: MatrixLike, y: Any = None) -> AffinityPropagation_Self: ...
+    def fit(self, X: MatrixLike, y: Any = None) -> Self: ...
     def predict(self, X: MatrixLike | ArrayLike) -> ndarray: ...
     def fit_predict(self, X: MatrixLike, y: Any = None) -> ndarray: ...

stubs/sklearn/cluster/_agglomerative.pyi

@@ -1,7 +1,8 @@
 import warnings
 from heapq import heapify as heapify, heappop as heappop, heappush as heappush, heappushpop as heappushpop
 from numbers import Integral as Integral, Real as Real
-from typing import Any, Callable, ClassVar, Literal, TypeVar
+from typing import Any, Callable, ClassVar, Literal
+from typing_extensions import Self
 
 import numpy as np
 from joblib import Memory
@@ -21,9 +22,6 @@ from ..utils._param_validation import HasMethods as HasMethods, Hidden as Hidden
 from ..utils.validation import check_memory as check_memory
 from ._feature_agglomeration import AgglomerationTransform
 
-FeatureAgglomeration_Self = TypeVar("FeatureAgglomeration_Self", bound=FeatureAgglomeration)
-AgglomerativeClustering_Self = TypeVar("AgglomerativeClustering_Self", bound=AgglomerativeClustering)
-
 ###############################################################################
 # Hierarchical tree building functions
 
@@ -74,7 +72,7 @@ class AgglomerativeClustering(ClusterMixin, BaseEstimator):
         distance_threshold: None | Float = None,
         compute_distances: bool = False,
     ) -> None: ...
-    def fit(self: AgglomerativeClustering_Self, X: MatrixLike, y: Any = None) -> AgglomerativeClustering_Self: ...
+    def fit(self, X: MatrixLike, y: Any = None) -> Self: ...
     def fit_predict(self, X: MatrixLike, y: Any = None) -> ndarray: ...
 
 class FeatureAgglomeration(ClassNamePrefixFeaturesOutMixin, AgglomerativeClustering, AgglomerationTransform):
@@ -103,5 +101,5 @@ class FeatureAgglomeration(ClassNamePrefixFeaturesOutMixin, AgglomerativeCluster
         distance_threshold: None | Float = None,
         compute_distances: bool = False,
     ) -> None: ...
-    def fit(self: FeatureAgglomeration_Self, X: MatrixLike, y: Any = None) -> FeatureAgglomeration_Self: ...
+    def fit(self, X: MatrixLike, y: Any = None) -> Self: ...
     def fit_predict(self): ...

stubs/sklearn/cluster/_bicluster.pyi

@@ -1,6 +1,7 @@
 from abc import ABCMeta, abstractmethod
 from numbers import Integral as Integral
-from typing import Any, ClassVar, Literal, TypeVar
+from typing import Any, ClassVar, Literal
+from typing_extensions import Self
 
 import numpy as np
 from numpy import ndarray
@@ -21,8 +22,6 @@ from ..utils.extmath import (
 from ..utils.validation import assert_all_finite as assert_all_finite
 from . import KMeans as KMeans, MiniBatchKMeans as MiniBatchKMeans
 
-BaseSpectral_Self = TypeVar("BaseSpectral_Self", bound=BaseSpectral)
-
 __all__ = ["SpectralCoclustering", "SpectralBiclustering"]
 
 class BaseSpectral(BiclusterMixin, BaseEstimator, metaclass=ABCMeta):
@@ -39,7 +38,7 @@ class BaseSpectral(BiclusterMixin, BaseEstimator, metaclass=ABCMeta):
         n_init: int = 10,
         random_state: None | int = None,
     ) -> None: ...
-    def fit(self: BaseSpectral_Self, X: MatrixLike, y: Any = None) -> BaseSpectral_Self: ...
+    def fit(self, X: MatrixLike, y: Any = None) -> Self: ...
 
 class SpectralCoclustering(BaseSpectral):
     feature_names_in_: ndarray = ...

stubs/sklearn/cluster/_birch.pyi

@@ -1,7 +1,8 @@
 import warnings
 from math import sqrt as sqrt
 from numbers import Integral as Integral, Real as Real
-from typing import Any, ClassVar, TypeVar
+from typing import Any, ClassVar
+from typing_extensions import Self
 
 import numpy as np
 from numpy import ndarray
@@ -19,8 +20,6 @@ from ..utils.extmath import row_norms as row_norms
 from ..utils.validation import check_is_fitted as check_is_fitted
 from . import AgglomerativeClustering as AgglomerativeClustering
 
-Birch_Self = TypeVar("Birch_Self", bound=Birch)
-
 # Authors: Manoj Kumar <manojkumarsivaraj334@gmail.com>
 #          Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
 #          Joel Nothman <joel.nothman@gmail.com>
@@ -86,7 +85,7 @@ class Birch(ClassNamePrefixFeaturesOutMixin, ClusterMixin, TransformerMixin, Bas
         compute_labels: bool = True,
         copy: bool = True,
     ) -> None: ...
-    def fit(self: Birch_Self, X: MatrixLike | ArrayLike, y: Any = None) -> Birch_Self: ...
-    def partial_fit(self: Birch_Self, X: None | MatrixLike | ArrayLike = None, y: Any = None) -> Birch_Self: ...
+    def fit(self, X: MatrixLike | ArrayLike, y: Any = None) -> Self: ...
+    def partial_fit(self, X: None | MatrixLike | ArrayLike = None, y: Any = None) -> Self: ...
     def predict(self, X: MatrixLike | ArrayLike) -> ndarray: ...
     def transform(self, X: MatrixLike | ArrayLike) -> ndarray | spmatrix: ...

stubs/sklearn/cluster/_bisect_k_means.pyi

@@ -1,6 +1,7 @@
 import warnings
 from collections.abc import Iterator
-from typing import Any, Callable, ClassVar, Literal, TypeVar
+from typing import Any, Callable, ClassVar, Literal
+from typing_extensions import Self
 
 import numpy as np
 import scipy.sparse as sp
@@ -13,8 +14,6 @@ from ..utils.extmath import row_norms as row_norms
 from ..utils.validation import check_is_fitted as check_is_fitted, check_random_state as check_random_state
 from ._kmeans import _BaseKMeans
 
-BisectingKMeans_Self = TypeVar("BisectingKMeans_Self", bound=BisectingKMeans)
-
 # Author: Michal Krawczyk <mkrwczyk.1@gmail.com>
 
 class _BisectingTree:
@@ -47,9 +46,9 @@ class BisectingKMeans(_BaseKMeans):
         bisecting_strategy: Literal["biggest_inertia", "largest_cluster"] = "biggest_inertia",
     ) -> None: ...
     def fit(
-        self: BisectingKMeans_Self,
+        self,
         X: MatrixLike | ArrayLike,
         y: Any = None,
         sample_weight: None | ArrayLike = None,
-    ) -> BisectingKMeans_Self: ...
+    ) -> Self: ...
     def predict(self, X: MatrixLike | ArrayLike) -> ndarray: ...

stubs/sklearn/cluster/_dbscan.pyi

@@ -1,6 +1,7 @@
 import warnings
 from numbers import Integral as Integral, Real as Real
-from typing import Any, Callable, ClassVar, Literal, TypeVar
+from typing import Any, Callable, ClassVar, Literal
+from typing_extensions import Self
 
 import numpy as np
 from numpy import ndarray
@@ -12,8 +13,6 @@ from ..neighbors import NearestNeighbors as NearestNeighbors
 from ..utils._param_validation import Interval as Interval, StrOptions as StrOptions
 from ._dbscan_inner import dbscan_inner as dbscan_inner
 
-DBSCAN_Self = TypeVar("DBSCAN_Self", bound=DBSCAN)
-
 # Author: Robert Layton <robertlayton@gmail.com>
 #         Joel Nothman <joel.nothman@gmail.com>
 #         Lars Buitinck
@@ -56,9 +55,9 @@ class DBSCAN(ClusterMixin, BaseEstimator):
         n_jobs: None | Int = None,
     ) -> None: ...
     def fit(
-        self: DBSCAN_Self,
+        self,
         X: MatrixLike,
         y: Any = None,
         sample_weight: None | ArrayLike = None,
-    ) -> DBSCAN_Self: ...
+    ) -> Self: ...
     def fit_predict(self, X: MatrixLike, y: Any = None, sample_weight: None | ArrayLike = None) -> ndarray: ...

stubs/sklearn/cluster/_kmeans.pyi

@@ -1,7 +1,8 @@
 import warnings
 from abc import ABC, abstractmethod as abstractmethod
 from numbers import Integral as Integral, Real as Real
-from typing import Any, Callable, ClassVar, Literal, TypeVar
+from typing import Any, Callable, ClassVar, Literal
+from typing_extensions import Self
 
 import numpy as np
 import scipy.sparse as sp
@@ -36,9 +37,6 @@ from ._k_means_lloyd import (
     lloyd_iter_chunked_sparse as lloyd_iter_chunked_sparse,
 )
 
-KMeans_Self = TypeVar("KMeans_Self", bound=KMeans)
-MiniBatchKMeans_Self = TypeVar("MiniBatchKMeans_Self", bound=MiniBatchKMeans)
-
 ###############################################################################
 # Initialization heuristic
 
@@ -125,11 +123,11 @@ class KMeans(_BaseKMeans):
         algorithm: Literal["lloyd", "elkan", "auto", "full"] = "lloyd",
     ) -> None: ...
     def fit(
-        self: KMeans_Self,
+        self,
         X: MatrixLike | ArrayLike,
         y: Any = None,
         sample_weight: None | ArrayLike = None,
-    ) -> KMeans_Self: ...
+    ) -> Self: ...
 
 class MiniBatchKMeans(_BaseKMeans):
     feature_names_in_: ndarray = ...
@@ -159,14 +157,14 @@ class MiniBatchKMeans(_BaseKMeans):
         reassignment_ratio: Float = 0.01,
     ) -> None: ...
     def fit(
-        self: MiniBatchKMeans_Self,
+        self,
         X: MatrixLike | ArrayLike,
         y: Any = None,
         sample_weight: None | ArrayLike = None,
-    ) -> MiniBatchKMeans_Self: ...
+    ) -> Self: ...
     def partial_fit(
-        self: MiniBatchKMeans_Self,
+        self,
         X: MatrixLike | ArrayLike,
         y: Any = None,
         sample_weight: None | ArrayLike = None,
-    ) -> MiniBatchKMeans_Self: ...
+    ) -> Self: ...

stubs/sklearn/cluster/_mean_shift.pyi

@@ -1,7 +1,8 @@
 import warnings
 from collections import defaultdict as defaultdict
 from numbers import Integral as Integral, Real as Real
-from typing import Any, ClassVar, TypeVar
+from typing import Any, ClassVar
+from typing_extensions import Self
 
 import numpy as np
 from numpy import ndarray
@@ -17,8 +18,6 @@ from ..utils._param_validation import Interval as Interval
 from ..utils.parallel import Parallel as Parallel, delayed as delayed
 from ..utils.validation import check_is_fitted as check_is_fitted
 
-MeanShift_Self = TypeVar("MeanShift_Self", bound=MeanShift)
-
 # Authors: Conrad Lee <conradlee@gmail.com>
 #          Alexandre Gramfort <alexandre.gramfort@inria.fr>
 #          Gael Varoquaux <gael.varoquaux@normalesup.org>
@@ -65,5 +64,5 @@ class MeanShift(ClusterMixin, BaseEstimator):
         n_jobs: None | Int = None,
         max_iter: Int = 300,
     ) -> None: ...
-    def fit(self: MeanShift_Self, X: MatrixLike, y: Any = None) -> MeanShift_Self: ...
+    def fit(self, X: MatrixLike, y: Any = None) -> Self: ...
     def predict(self, X: MatrixLike) -> ndarray: ...

stubs/sklearn/cluster/_optics.pyi

@@ -1,6 +1,7 @@
 import warnings
 from numbers import Integral as Integral, Real as Real
-from typing import Any, Callable, ClassVar, Literal, TypeVar
+from typing import Any, Callable, ClassVar, Literal
+from typing_extensions import Self
 
 import numpy as np
 from joblib import Memory
@@ -17,8 +18,6 @@ from ..utils import gen_batches as gen_batches, get_chunk_n_rows as get_chunk_n_
 from ..utils._param_validation import HasMethods as HasMethods, Interval as Interval, StrOptions as StrOptions
 from ..utils.validation import check_memory as check_memory
 
-OPTICS_Self = TypeVar("OPTICS_Self", bound=OPTICS)
-
 class OPTICS(ClusterMixin, BaseEstimator):
     feature_names_in_: ndarray = ...
     n_features_in_: int = ...
@@ -49,7 +48,7 @@ class OPTICS(ClusterMixin, BaseEstimator):
         memory: None | Memory | str = None,
         n_jobs: None | Int = None,
     ) -> None: ...
-    def fit(self: OPTICS_Self, X: MatrixLike, y: Any = None) -> OPTICS_Self: ...
+    def fit(self, X: MatrixLike, y: Any = None) -> Self: ...
 
 def compute_optics_graph(
     X: MatrixLike,

stubs/sklearn/cluster/_spectral.pyi

@@ -1,7 +1,8 @@
 import warnings
 from collections.abc import Mapping
 from numbers import Integral as Integral, Real as Real
-from typing import Any, Callable, ClassVar, Literal, TypeVar
+from typing import Any, Callable, ClassVar, Literal
+from typing_extensions import Self
 
 import numpy as np
 from numpy import ndarray
@@ -19,8 +20,6 @@ from ..utils import as_float_array as as_float_array, check_random_state as chec
 from ..utils._param_validation import Interval as Interval, StrOptions as StrOptions
 from ._kmeans import k_means as k_means
 
-SpectralClustering_Self = TypeVar("SpectralClustering_Self", bound=SpectralClustering)
-
 def cluster_qr(vectors: MatrixLike) -> ndarray: ...
 def discretize(
     vectors: MatrixLike,
@@ -70,5 +69,5 @@ class SpectralClustering(ClusterMixin, BaseEstimator):
         n_jobs: None | Int = None,
         verbose: bool = False,
     ) -> None: ...
-    def fit(self: SpectralClustering_Self, X: MatrixLike, y: Any = None) -> SpectralClustering_Self: ...
+    def fit(self, X: MatrixLike, y: Any = None) -> Self: ...
     def fit_predict(self, X: MatrixLike, y: Any = None) -> ndarray: ...