Disallow blanket ignores and remove unused noqa

pyproject.toml

@@ -56,10 +56,13 @@ target-version = "py39"
 extend-select = [
     "FA", # flake8-future-annotations
     "I", # isort
+    "PGH", # pygrep-hooks
+    "PGH", # pygrep-hooks and blanket-noqa
+    "PIE790", # unnecessary-placeholder
     "PYI", # flake8-pyi
+    "RUF", # Ruff-specific and unused-noqa
     "UP", # pyupgrade
     "W", # pycodestyle Warning
-    "PIE790", # unnecessary-placeholder
 ]
 ignore = [
     ###
@@ -82,6 +85,11 @@ ignore = [
 
 [tool.ruff.lint.per-file-ignores]
 "*.pyi" = [
+    # Ruff 0.8.0 added sorting of __all__ and __slots_.
+    # There is no consensus in typeshed on whether they want to apply this to stubs, so keeping the status quo.
+    # See https://github.com/python/typeshed/pull/13108
+    "RUF022", # `__all__` is not sorted
+    "RUF023", # `{}.__slots__` is not sorted
     ###
     # Rules that are out of the control of stub authors:
     ###

stubs/matplotlib/backends/backend_qt.pyi

@@ -47,8 +47,8 @@ class FigureCanvasQT(QtWidgets.QWidget, FigureCanvasBase):
     def keyPressEvent(self, event) -> None: ...
     def keyReleaseEvent(self, event) -> None: ...
     def resizeEvent(self, event) -> None: ...
-    def sizeHint(self) -> QtCore.QSize: ...  # type: ignore
-    def minumumSizeHint(self) -> QtCore.QSize: ...  # type: ignore
+    def sizeHint(self) -> QtCore.QSize: ...  # type: ignore[name-defined]
+    def minumumSizeHint(self) -> QtCore.QSize: ...  # type: ignore[name-defined]
     def flush_events(self) -> None: ...
     def start_event_loop(self, timeout=...) -> None: ...
     def stop_event_loop(self, event=...) -> None: ...
@@ -58,7 +58,7 @@ class FigureCanvasQT(QtWidgets.QWidget, FigureCanvasBase):
     def drawRectangle(self, rect) -> None: ...
 
 class MainWindow(QtWidgets.QMainWindow):
-    closing: QtCore.Signal = ...  # type: ignore
+    closing: QtCore.Signal = ...  # type: ignore[name-defined]
     def closeEvent(self, event) -> None: ...
 
 class FigureManagerQT(FigureManagerBase):
@@ -71,7 +71,7 @@ class FigureManagerQT(FigureManagerBase):
     def set_window_title(self, title) -> None: ...
 
 class NavigationToolbar2QT(NavigationToolbar2, QtWidgets.QToolBar):
-    message: QtCore.Signal = ...  # type: ignore
+    message: QtCore.Signal = ...  # type: ignore[name-defined]
     toolitems: list = ...
     def __init__(self, canvas, parent=..., coordinates=...) -> None: ...
     def edit_parameters(self) -> None: ...

stubs/networkx/algorithms/approximation/kcomponents.pyi

@@ -1,12 +1,7 @@
-import itertools
-from collections import defaultdict
 from collections.abc import Mapping
 from functools import cached_property
 
 from ...classes.graph import Graph
-from ...exception import NetworkXError
-from ...utils import not_implemented_for
-from . import local_node_connectivity
 
 __all__ = ["k_components"]
 
@@ -17,7 +12,7 @@ class _AntiGraph(Graph):
 
     def single_edge_dict(self): ...
 
-    edge_attr_dict_factory = single_edge_dict  # type: ignore
+    edge_attr_dict_factory = single_edge_dict
 
     def __getitem__(self, n) -> Mapping: ...
     def neighbors(self, n): ...

stubs/networkx/classes/digraph.pyi

@@ -1,12 +1,7 @@
-from copy import deepcopy
 from functools import cached_property
 
-import networkx.convert as convert
-
-from ..classes.coreviews import AdjacencyView
 from ..classes.graph import Graph
-from ..classes.reportviews import DiDegreeView, InDegreeView, InEdgeView, OutDegreeView, OutEdgeView
-from ..exception import NetworkXError
+from ..classes.reportviews import DiDegreeView, InEdgeView, OutEdgeView
 
 __all__ = ["DiGraph"]
 
@@ -18,8 +13,8 @@ class _CachedPropertyResetterPred:
 
 class DiGraph(Graph):
     graph = ...
-    _adj = ...  # type: ignore
-    _succ = ...  # type: ignore
+    _adj = ...
+    _succ = ...
     _pred = ...
 
     def __init__(self, incoming_graph_data=None, **attr): ...

stubs/networkx/utils/decorators.pyi

@@ -1,19 +1,6 @@
-import bz2
-import collections
-import gzip
-import inspect
-import itertools
-import re
-from collections import defaultdict
 from collections.abc import Sequence
-from contextlib import contextmanager
-from os.path import splitext
-from pathlib import Path
 from typing import Callable
 
-from ..classes.graph import Graph
-from ..utils import create_py_random_state, create_random_state
-
 __all__ = [
     "not_implemented_for",
     "open_file",
@@ -30,7 +17,7 @@ def not_implemented_for(*graph_types): ...
 # To handle new extensions, define a function accepting a `path` and `mode`.
 # Then add the extension to _dispatch_dict.
 fopeners: dict = ...
-_dispatch_dict = ...  # type: ignore
+_dispatch_dict = ...
 
 def open_file(path_arg: str | int, mode: str = "r"): ...
 def nodes_or_number(which_args: str | int | Sequence[str]): ...

stubs/skimage/__init__.pyi

@@ -1,5 +1,5 @@
 from ._shared import lazy as lazy
-from ._shared.tester import PytestTester as PytestTester  # noqa
+from ._shared.tester import PytestTester as PytestTester
 from ._shared.version_requirements import ensure_python_version as ensure_python_version
 
 __version__: str = ...

stubs/skimage/data/_registry.pyi

@@ -1,5 +1,3 @@
-# flake8: noqa
-
 # This minimal dataset was available as part of
 # scikit-image 0.15 and will be retained until
 # further notice.

stubs/sklearn/_build_utils/openmp_helpers.pyi

@@ -1,5 +1,2 @@
-# This code is adapted for a large part from the astropy openmp helpers, which
-# can be found at: https://github.com/astropy/extension-helpers/blob/master/extension_helpers/_openmp_helpers.py  # noqa
-
 def get_openmp_flag(compiler): ...
 def check_openmp_support(): ...

stubs/sklearn/decomposition/_dict_learning.pyi

@@ -6,14 +6,7 @@ from numpy.random import RandomState
 
 from .._typing import ArrayLike, Float, Int, MatrixLike
 from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, TransformerMixin
-from ..utils import (
-    deprecated,
-)
 
-# Author: Vlad Niculae, Gael Varoquaux, Alexandre Gramfort
-# License: BSD 3 clause
-
-# XXX : could be moved to the linear_model module
 def sparse_encode(
     X: ArrayLike,
     dictionary: MatrixLike,
@@ -180,15 +173,6 @@ class MiniBatchDictionaryLearning(_BaseSparseCoding, BaseEstimator):
         tol: Float = 1e-3,
         max_no_improvement: Int = 10,
     ) -> None: ...
-    @deprecated("The attribute `iter_offset_` is deprecated in 1.1 and will be removed in 1.3.")  # type: ignore
-    @property
-    def iter_offset_(self) -> int: ...
-    @deprecated("The attribute `random_state_` is deprecated in 1.1 and will be removed in 1.3.")  # type: ignore
-    @property
-    def random_state_(self) -> RandomState: ...
-    @deprecated("The attribute `inner_stats_` is deprecated in 1.1 and will be removed in 1.3.")  # type: ignore
-    @property
-    def inner_stats_(self) -> tuple[ndarray, ndarray]: ...
     def fit(self, X: MatrixLike, y: Any = None) -> Self: ...
     def partial_fit(
         self,

stubs/sklearn/decomposition/_pca.pyi

@@ -5,7 +5,6 @@ from numpy import ndarray
 from numpy.random import RandomState
 
 from .._typing import Float, Int, MatrixLike
-from ..utils.deprecation import deprecated
 from ._base import _BasePCA
 
 class PCA(_BasePCA):
@@ -35,14 +34,6 @@ class PCA(_BasePCA):
         power_iteration_normalizer: Literal["auto", "QR", "LU", "none"] = "auto",
         random_state: RandomState | None | Int = None,
     ) -> None: ...
-
-    # TODO(1.4): remove in 1.4
-    # mypy error: Decorated property not supported
-    @deprecated(  # type: ignore
-        "Attribute `n_features_` was deprecated in version 1.2 and will be removed in 1.4. Use `n_features_in_` instead."
-    )
-    @property
-    def n_features_(self) -> int: ...
     def fit(self, X: MatrixLike, y: Any = None) -> Self: ...
     def fit_transform(self, X: MatrixLike, y: Any = None) -> ndarray: ...
     def score_samples(self, X: MatrixLike) -> ndarray: ...

stubs/sklearn/ensemble/_base.pyi

@@ -5,7 +5,7 @@ from typing_extensions import Self
 
 from .._typing import Int
 from ..base import BaseEstimator, MetaEstimatorMixin
-from ..utils import Bunch, deprecated
+from ..utils import Bunch
 from ..utils.metaestimators import _BaseComposition
 
 class BaseEnsemble(MetaEstimatorMixin, BaseEstimator, metaclass=ABCMeta):
@@ -23,18 +23,6 @@ class BaseEnsemble(MetaEstimatorMixin, BaseEstimator, metaclass=ABCMeta):
         estimator_params: Sequence[str] = ...,
         base_estimator: Any = "deprecated",
     ) -> None: ...
-
-    # TODO(1.4): remove
-    # mypy error: Decorated property not supported
-    @deprecated(  # type: ignore
-        "Attribute `base_estimator_` was deprecated in version 1.2 and will be removed in 1.4. Use `estimator_` instead."
-    )
-    @property
-    def base_estimator_(self) -> BaseEstimator: ...
-
-    # TODO(1.4): remove
-    @property
-    def estimator_(self) -> BaseEstimator: ...
     def __len__(self) -> int: ...
     def __getitem__(self, index): ...
     def __iter__(self): ...

stubs/sklearn/ensemble/_gb.pyi

@@ -8,7 +8,6 @@ from numpy.random import RandomState
 
 from .._typing import ArrayLike, Float, Int, MatrixLike
 from ..base import BaseEstimator, ClassifierMixin, RegressorMixin
-from ..utils import deprecated
 from ._base import BaseEnsemble
 from ._gb_losses import LossFunction
 
@@ -57,11 +56,6 @@ class BaseGradientBoosting(BaseEnsemble, metaclass=ABCMeta):
     def feature_importances_(self) -> ndarray: ...
     def apply(self, X: MatrixLike | ArrayLike) -> ndarray: ...
 
-    # TODO(1.3): Remove
-    # mypy error: Decorated property not supported
-    @deprecated("Attribute `loss_` was deprecated in version 1.1 and will be removed in 1.3.")  # type: ignore
-    def loss_(self): ...
-
 class GradientBoostingClassifier(ClassifierMixin, BaseGradientBoosting):
     max_features_: int = ...
     n_classes_: int = ...

stubs/sklearn/linear_model/_glm/glm.pyi

@@ -8,7 +8,6 @@ from ..._loss.loss import (
 )
 from ..._typing import ArrayLike, Float, Int, MatrixLike
 from ...base import BaseEstimator, RegressorMixin
-from ...utils import deprecated
 
 class _GeneralizedLinearRegressor(RegressorMixin, BaseEstimator):
     _base_loss: BaseLoss = ...
@@ -46,10 +45,6 @@ class _GeneralizedLinearRegressor(RegressorMixin, BaseEstimator):
         sample_weight: None | ArrayLike = None,
     ) -> Float: ...
 
-    # TODO(1.3): remove
-    @deprecated("Attribute `family` was deprecated in version 1.1 and will be removed in 1.3.")  # type: ignore
-    def family(self): ...
-
 class PoissonRegressor(_GeneralizedLinearRegressor):
     n_iter_: int = ...
     feature_names_in_: ndarray = ...

stubs/sklearn/svm/_classes.pyi

@@ -8,7 +8,6 @@ from numpy.random import RandomState
 from .._typing import ArrayLike, Float, Int, MatrixLike
 from ..base import BaseEstimator, OutlierMixin, RegressorMixin
 from ..linear_model._base import LinearClassifierMixin, LinearModel, SparseCoefMixin
-from ..utils import deprecated
 from ._base import BaseLibSVM, BaseSVC
 
 class LinearSVC(LinearClassifierMixin, SparseCoefMixin, BaseEstimator):
@@ -187,11 +186,6 @@ class SVR(RegressorMixin, BaseLibSVM):
         max_iter: Int = ...,
     ) -> None: ...
 
-    # TODO(1.4): Remove
-    @deprecated("Attribute `class_weight_` was deprecated in version 1.2 and will be removed in 1.4.")  # type: ignore
-    @property
-    def class_weight_(self) -> ndarray: ...
-
 class NuSVR(RegressorMixin, BaseLibSVM):
     support_vectors_: ndarray = ...
     support_: ndarray = ...
@@ -225,11 +219,6 @@ class NuSVR(RegressorMixin, BaseLibSVM):
         max_iter: Int = ...,
     ) -> None: ...
 
-    # TODO(1.4): Remove
-    @deprecated("Attribute `class_weight_` was deprecated in version 1.2 and will be removed in 1.4.")  # type: ignore
-    @property
-    def class_weight_(self) -> ndarray: ...
-
 class OneClassSVM(OutlierMixin, BaseLibSVM):
     support_vectors_: ndarray = ...
     support_: ndarray = ...
@@ -262,11 +251,6 @@ class OneClassSVM(OutlierMixin, BaseLibSVM):
         verbose: bool = False,
         max_iter: Int = ...,
     ) -> None: ...
-
-    # TODO(1.4): Remove
-    @deprecated("Attribute `class_weight_` was deprecated in version 1.2 and will be removed in 1.4.")  # type: ignore
-    @property
-    def class_weight_(self) -> ndarray: ...
     def fit(
         self,
         X: MatrixLike | ArrayLike,

stubs/sklearn/utils/_estimator_html_repr.pyi

@@ -21,6 +21,6 @@ class _VisualBlock:
         dash_wrapped: bool = True,
     ) -> None: ...
 
-_STYLE = ...  # noqa
+_STYLE = ...
 
 def estimator_html_repr(estimator: BaseEstimator) -> str: ...

stubs/sklearn/utils/_pprint.pyi

@@ -79,8 +79,4 @@ class _EstimatorPrettyPrinter(pprint.PrettyPrinter):
     ) -> None: ...
     def format(self, object, context: dict[Any, Any] | dict[int, int], maxlevels, level: int) -> tuple[str, bool, bool]: ...
 
-    # Note: need to copy _dispatch to prevent instances of the builtin
-    # PrettyPrinter class to call methods of _EstimatorPrettyPrinter (see issue
-    # 12906)
-    # mypy error: "Type[PrettyPrinter]" has no attribute "_dispatch"
-    _dispatch = ...  # type: ignore
+    _dispatch = ...

stubs/sympy-stubs/assumptions/assume.pyi

@@ -24,7 +24,7 @@ class AppliedPredicate(Boolean):
     def binary_symbols(self) -> set[Basic] | set[Any]: ...
 
 class PredicateMeta(type):
-    def __new__(cls, clsname, bases, dct) -> Self:  # type: ignore
+    def __new__(cls, clsname, bases, dct) -> Self:  # pyright: ignore[reportGeneralTypeIssues]
         ...
     @property
     def __doc__(cls): ...

stubs/sympy-stubs/core/function.pyi

@@ -72,7 +72,7 @@ class UndefSageHelper:
 _undef_sage_helper = ...
 
 class UndefinedFunction(FunctionClass):
-    def __new__(cls, name, bases=..., __dict__=..., **kwargs) -> Self:  # type: ignore
+    def __new__(cls, name, bases=..., __dict__=..., **kwargs) -> Self:  # pyright: ignore[reportGeneralTypeIssues]
         ...
     def __instancecheck__(cls, instance) -> bool: ...
 

stubs/sympy-stubs/core/kind.pyi

@@ -4,7 +4,7 @@ from typing_extensions import LiteralString, Self
 from sympy.core.cache import cacheit
 
 class KindMeta(type):
-    def __new__(cls, clsname, bases, dct) -> Self:  # type: ignore
+    def __new__(cls, clsname, bases, dct) -> Self:  # pyright: ignore[reportGeneralTypeIssues]
         ...
 
 class Kind(metaclass=KindMeta):

stubs/sympy-stubs/polys/agca/extensions.pyi

@@ -11,7 +11,7 @@ if sys.version_info >= (3, 10):
 else:
     NotImplementedType: TypeAlias = Any
 
-class ExtensionElement(DomainElement, DefaultPrinting):  # type: ignore
+class ExtensionElement(DomainElement, DefaultPrinting):
     __slots__ = ...
     def __init__(self, rep, ext) -> None: ...
     def parent(self) -> Any: ...
@@ -47,7 +47,7 @@ class ExtensionElement(DomainElement, DefaultPrinting):  # type: ignore
 
 ExtElem = ExtensionElement
 
-class MonogenicFiniteExtension(Domain):  # type: ignore
+class MonogenicFiniteExtension(Domain):
     is_FiniteExtension = ...
     dtype = ExtensionElement
     def __init__(self, mod) -> None: ...

stubs/sympy-stubs/polys/domains/finitefield.pyi

@@ -5,7 +5,7 @@ from sympy.core.numbers import Integer
 from sympy.polys.domains.field import Field
 from sympy.polys.domains.simpledomain import SimpleDomain
 
-class FiniteField(Field, SimpleDomain):  # type: ignore
+class FiniteField(Field, SimpleDomain):
     rep = ...
     alias = ...
     is_FF = ...

stubs/sympy-stubs/polys/domains/gaussiandomains.pyi

@@ -12,7 +12,7 @@ if sys.version_info >= (3, 10):
 else:
     NotImplementedType: TypeAlias = Any
 
-class GaussianElement(DomainElement):  # type: ignore
+class GaussianElement(DomainElement):
     base: Domain
     _parent: Domain
     __slots__ = ...