From 03baf0f459a82dd5d9cd71abb09c58c9ef3bf412 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Tue, 20 May 2025 11:50:43 +0200 Subject: [PATCH 001/103] MNT Update array-api-compat to 1.12 (#31388) --- maint_tools/vendor_array_api_compat.sh | 2 +- .../externals/array_api_compat/__init__.py | 2 +- .../externals/array_api_compat/_internal.py | 25 +- .../array_api_compat/common/__init__.py | 2 +- .../array_api_compat/common/_aliases.py | 508 +++++++++++------- .../externals/array_api_compat/common/_fft.py | 144 ++--- .../array_api_compat/common/_helpers.py | 487 ++++++++++------- .../array_api_compat/common/_linalg.py | 156 ++++-- .../array_api_compat/common/_typing.py | 192 ++++++- .../array_api_compat/cupy/__init__.py | 3 - .../array_api_compat/cupy/_aliases.py | 77 ++- .../externals/array_api_compat/cupy/_info.py | 20 +- .../array_api_compat/cupy/_typing.py | 63 +-- .../array_api_compat/dask/array/__init__.py | 9 +- .../array_api_compat/dask/array/_aliases.py | 205 +++---- .../array_api_compat/dask/array/_info.py | 137 +++-- .../array_api_compat/dask/array/fft.py | 13 +- .../array_api_compat/dask/array/linalg.py | 43 +- .../array_api_compat/numpy/__init__.py | 28 +- .../array_api_compat/numpy/_aliases.py | 136 +++-- .../externals/array_api_compat/numpy/_info.py | 50 +- .../array_api_compat/numpy/_typing.py | 70 +-- .../externals/array_api_compat/numpy/fft.py | 16 +- .../array_api_compat/numpy/linalg.py | 97 +++- sklearn/externals/array_api_compat/py.typed | 0 .../array_api_compat/torch/__init__.py | 6 +- .../array_api_compat/torch/_aliases.py | 324 ++++++----- .../externals/array_api_compat/torch/_info.py | 41 +- .../array_api_compat/torch/_typing.py | 3 + .../externals/array_api_compat/torch/fft.py | 35 +- .../array_api_compat/torch/linalg.py | 32 +- 31 files changed, 1823 insertions(+), 1103 deletions(-) create mode 100644 sklearn/externals/array_api_compat/py.typed create mode 100644 sklearn/externals/array_api_compat/torch/_typing.py diff --git a/maint_tools/vendor_array_api_compat.sh b/maint_tools/vendor_array_api_compat.sh index 52fa4c570a534..51056ce477cbb 100755 --- a/maint_tools/vendor_array_api_compat.sh +++ b/maint_tools/vendor_array_api_compat.sh @@ -6,7 +6,7 @@ set -o nounset set -o errexit URL="https://github.com/data-apis/array-api-compat.git" -VERSION="1.11.2" +VERSION="1.12" ROOT_DIR=sklearn/externals/array_api_compat diff --git a/sklearn/externals/array_api_compat/__init__.py b/sklearn/externals/array_api_compat/__init__.py index 96b061e721808..653cb40a37607 100644 --- a/sklearn/externals/array_api_compat/__init__.py +++ b/sklearn/externals/array_api_compat/__init__.py @@ -17,6 +17,6 @@ this implementation for the default when working with NumPy arrays. """ -__version__ = '1.11.2' +__version__ = '1.12.0' from .common import * # noqa: F401, F403 diff --git a/sklearn/externals/array_api_compat/_internal.py b/sklearn/externals/array_api_compat/_internal.py index 170a1ff9e6459..cd8d939f36de2 100644 --- a/sklearn/externals/array_api_compat/_internal.py +++ b/sklearn/externals/array_api_compat/_internal.py @@ -2,10 +2,16 @@ Internal helpers """ +from collections.abc import Callable from functools import wraps from inspect import signature +from types import ModuleType +from typing import TypeVar -def get_xp(xp): +_T = TypeVar("_T") + + +def get_xp(xp: ModuleType) -> Callable[[Callable[..., _T]], Callable[..., _T]]: """ Decorator to automatically replace xp with the corresponding array module. @@ -22,14 +28,14 @@ def func(x, /, xp, kwarg=None): """ - def inner(f): + def inner(f: Callable[..., _T], /) -> Callable[..., _T]: @wraps(f) - def wrapped_f(*args, **kwargs): + def wrapped_f(*args: object, **kwargs: object) -> object: return f(*args, xp=xp, **kwargs) sig = signature(f) new_sig = sig.replace( - parameters=[sig.parameters[i] for i in sig.parameters if i != "xp"] + parameters=[par for i, par in sig.parameters.items() if i != "xp"] ) if wrapped_f.__doc__ is None: @@ -40,7 +46,14 @@ def wrapped_f(*args, **kwargs): specification for more details. """ - wrapped_f.__signature__ = new_sig - return wrapped_f + wrapped_f.__signature__ = new_sig # pyright: ignore[reportAttributeAccessIssue] + return wrapped_f # pyright: ignore[reportReturnType] return inner + + +__all__ = ["get_xp"] + + +def __dir__() -> list[str]: + return __all__ diff --git a/sklearn/externals/array_api_compat/common/__init__.py b/sklearn/externals/array_api_compat/common/__init__.py index 91ab1c405e1d7..8236080738175 100644 --- a/sklearn/externals/array_api_compat/common/__init__.py +++ b/sklearn/externals/array_api_compat/common/__init__.py @@ -1 +1 @@ -from ._helpers import * # noqa: F403 +from ._helpers import * # noqa: F403 diff --git a/sklearn/externals/array_api_compat/common/_aliases.py b/sklearn/externals/array_api_compat/common/_aliases.py index 35262d3a93538..8ea9162a9edc8 100644 --- a/sklearn/externals/array_api_compat/common/_aliases.py +++ b/sklearn/externals/array_api_compat/common/_aliases.py @@ -4,142 +4,171 @@ from __future__ import annotations -from typing import TYPE_CHECKING -if TYPE_CHECKING: - from typing import Optional, Sequence, Tuple, Union - from ._typing import ndarray, Device, Dtype - -from typing import NamedTuple import inspect +from typing import TYPE_CHECKING, Any, NamedTuple, Optional, Sequence, cast -from ._helpers import array_namespace, _check_device, device, is_cupy_namespace +from ._helpers import _check_device, array_namespace +from ._helpers import device as _get_device +from ._helpers import is_cupy_namespace as _is_cupy_namespace +from ._typing import Array, Device, DType, Namespace + +if TYPE_CHECKING: + # TODO: import from typing (requires Python >=3.13) + from typing_extensions import TypeIs # These functions are modified from the NumPy versions. -# Creation functions add the device keyword (which does nothing for NumPy) +# Creation functions add the device keyword (which does nothing for NumPy and Dask) + def arange( - start: Union[int, float], + start: float, /, - stop: Optional[Union[int, float]] = None, - step: Union[int, float] = 1, + stop: float | None = None, + step: float = 1, *, - xp, - dtype: Optional[Dtype] = None, - device: Optional[Device] = None, - **kwargs -) -> ndarray: + xp: Namespace, + dtype: DType | None = None, + device: Device | None = None, + **kwargs: object, +) -> Array: _check_device(xp, device) return xp.arange(start, stop=stop, step=step, dtype=dtype, **kwargs) + def empty( - shape: Union[int, Tuple[int, ...]], - xp, + shape: int | tuple[int, ...], + xp: Namespace, *, - dtype: Optional[Dtype] = None, - device: Optional[Device] = None, - **kwargs -) -> ndarray: + dtype: DType | None = None, + device: Device | None = None, + **kwargs: object, +) -> Array: _check_device(xp, device) return xp.empty(shape, dtype=dtype, **kwargs) + def empty_like( - x: ndarray, /, xp, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None, - **kwargs -) -> ndarray: + x: Array, + /, + xp: Namespace, + *, + dtype: DType | None = None, + device: Device | None = None, + **kwargs: object, +) -> Array: _check_device(xp, device) return xp.empty_like(x, dtype=dtype, **kwargs) + def eye( n_rows: int, - n_cols: Optional[int] = None, + n_cols: int | None = None, /, *, - xp, + xp: Namespace, k: int = 0, - dtype: Optional[Dtype] = None, - device: Optional[Device] = None, - **kwargs, -) -> ndarray: + dtype: DType | None = None, + device: Device | None = None, + **kwargs: object, +) -> Array: _check_device(xp, device) return xp.eye(n_rows, M=n_cols, k=k, dtype=dtype, **kwargs) + def full( - shape: Union[int, Tuple[int, ...]], - fill_value: Union[int, float], - xp, + shape: int | tuple[int, ...], + fill_value: complex, + xp: Namespace, *, - dtype: Optional[Dtype] = None, - device: Optional[Device] = None, - **kwargs, -) -> ndarray: + dtype: DType | None = None, + device: Device | None = None, + **kwargs: object, +) -> Array: _check_device(xp, device) return xp.full(shape, fill_value, dtype=dtype, **kwargs) + def full_like( - x: ndarray, + x: Array, /, - fill_value: Union[int, float], + fill_value: complex, *, - xp, - dtype: Optional[Dtype] = None, - device: Optional[Device] = None, - **kwargs, -) -> ndarray: + xp: Namespace, + dtype: DType | None = None, + device: Device | None = None, + **kwargs: object, +) -> Array: _check_device(xp, device) return xp.full_like(x, fill_value, dtype=dtype, **kwargs) + def linspace( - start: Union[int, float], - stop: Union[int, float], + start: float, + stop: float, /, num: int, *, - xp, - dtype: Optional[Dtype] = None, - device: Optional[Device] = None, + xp: Namespace, + dtype: DType | None = None, + device: Device | None = None, endpoint: bool = True, - **kwargs, -) -> ndarray: + **kwargs: object, +) -> Array: _check_device(xp, device) return xp.linspace(start, stop, num, dtype=dtype, endpoint=endpoint, **kwargs) + def ones( - shape: Union[int, Tuple[int, ...]], - xp, + shape: int | tuple[int, ...], + xp: Namespace, *, - dtype: Optional[Dtype] = None, - device: Optional[Device] = None, - **kwargs, -) -> ndarray: + dtype: DType | None = None, + device: Device | None = None, + **kwargs: object, +) -> Array: _check_device(xp, device) return xp.ones(shape, dtype=dtype, **kwargs) + def ones_like( - x: ndarray, /, xp, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None, - **kwargs, -) -> ndarray: + x: Array, + /, + xp: Namespace, + *, + dtype: DType | None = None, + device: Device | None = None, + **kwargs: object, +) -> Array: _check_device(xp, device) return xp.ones_like(x, dtype=dtype, **kwargs) + def zeros( - shape: Union[int, Tuple[int, ...]], - xp, + shape: int | tuple[int, ...], + xp: Namespace, *, - dtype: Optional[Dtype] = None, - device: Optional[Device] = None, - **kwargs, -) -> ndarray: + dtype: DType | None = None, + device: Device | None = None, + **kwargs: object, +) -> Array: _check_device(xp, device) return xp.zeros(shape, dtype=dtype, **kwargs) + def zeros_like( - x: ndarray, /, xp, *, dtype: Optional[Dtype] = None, device: Optional[Device] = None, - **kwargs, -) -> ndarray: + x: Array, + /, + xp: Namespace, + *, + dtype: DType | None = None, + device: Device | None = None, + **kwargs: object, +) -> Array: _check_device(xp, device) return xp.zeros_like(x, dtype=dtype, **kwargs) + # np.unique() is split into four functions in the array API: # unique_all, unique_counts, unique_inverse, and unique_values (this is done # to remove polymorphic return types). @@ -147,35 +176,37 @@ def zeros_like( # The functions here return namedtuples (np.unique() returns a normal # tuple). + # Note that these named tuples aren't actually part of the standard namespace, # but I don't see any issue with exporting the names here regardless. class UniqueAllResult(NamedTuple): - values: ndarray - indices: ndarray - inverse_indices: ndarray - counts: ndarray + values: Array + indices: Array + inverse_indices: Array + counts: Array class UniqueCountsResult(NamedTuple): - values: ndarray - counts: ndarray + values: Array + counts: Array class UniqueInverseResult(NamedTuple): - values: ndarray - inverse_indices: ndarray + values: Array + inverse_indices: Array -def _unique_kwargs(xp): +def _unique_kwargs(xp: Namespace) -> dict[str, bool]: # Older versions of NumPy and CuPy do not have equal_nan. Rather than # trying to parse version numbers, just check if equal_nan is in the # signature. s = inspect.signature(xp.unique) - if 'equal_nan' in s.parameters: - return {'equal_nan': False} + if "equal_nan" in s.parameters: + return {"equal_nan": False} return {} -def unique_all(x: ndarray, /, xp) -> UniqueAllResult: + +def unique_all(x: Array, /, xp: Namespace) -> UniqueAllResult: kwargs = _unique_kwargs(xp) values, indices, inverse_indices, counts = xp.unique( x, @@ -195,20 +226,16 @@ def unique_all(x: ndarray, /, xp) -> UniqueAllResult: ) -def unique_counts(x: ndarray, /, xp) -> UniqueCountsResult: +def unique_counts(x: Array, /, xp: Namespace) -> UniqueCountsResult: kwargs = _unique_kwargs(xp) res = xp.unique( - x, - return_counts=True, - return_index=False, - return_inverse=False, - **kwargs + x, return_counts=True, return_index=False, return_inverse=False, **kwargs ) return UniqueCountsResult(*res) -def unique_inverse(x: ndarray, /, xp) -> UniqueInverseResult: +def unique_inverse(x: Array, /, xp: Namespace) -> UniqueInverseResult: kwargs = _unique_kwargs(xp) values, inverse_indices = xp.unique( x, @@ -223,7 +250,7 @@ def unique_inverse(x: ndarray, /, xp) -> UniqueInverseResult: return UniqueInverseResult(values, inverse_indices) -def unique_values(x: ndarray, /, xp) -> ndarray: +def unique_values(x: Array, /, xp: Namespace) -> Array: kwargs = _unique_kwargs(xp) return xp.unique( x, @@ -233,51 +260,58 @@ def unique_values(x: ndarray, /, xp) -> ndarray: **kwargs, ) + # These functions have different keyword argument names + def std( - x: ndarray, + x: Array, /, - xp, + xp: Namespace, *, - axis: Optional[Union[int, Tuple[int, ...]]] = None, - correction: Union[int, float] = 0.0, # correction instead of ddof + axis: int | tuple[int, ...] | None = None, + correction: float = 0.0, # correction instead of ddof keepdims: bool = False, - **kwargs, -) -> ndarray: + **kwargs: object, +) -> Array: return xp.std(x, axis=axis, ddof=correction, keepdims=keepdims, **kwargs) + def var( - x: ndarray, + x: Array, /, - xp, + xp: Namespace, *, - axis: Optional[Union[int, Tuple[int, ...]]] = None, - correction: Union[int, float] = 0.0, # correction instead of ddof + axis: int | tuple[int, ...] | None = None, + correction: float = 0.0, # correction instead of ddof keepdims: bool = False, - **kwargs, -) -> ndarray: + **kwargs: object, +) -> Array: return xp.var(x, axis=axis, ddof=correction, keepdims=keepdims, **kwargs) + # cumulative_sum is renamed from cumsum, and adds the include_initial keyword # argument + def cumulative_sum( - x: ndarray, + x: Array, /, - xp, + xp: Namespace, *, - axis: Optional[int] = None, - dtype: Optional[Dtype] = None, + axis: int | None = None, + dtype: DType | None = None, include_initial: bool = False, - **kwargs -) -> ndarray: + **kwargs: object, +) -> Array: wrapped_xp = array_namespace(x) # TODO: The standard is not clear about what should happen when x.ndim == 0. if axis is None: if x.ndim > 1: - raise ValueError("axis must be specified in cumulative_sum for more than one dimension") + raise ValueError( + "axis must be specified in cumulative_sum for more than one dimension" + ) axis = 0 res = xp.cumsum(x, axis=axis, dtype=dtype, **kwargs) @@ -287,27 +321,34 @@ def cumulative_sum( initial_shape = list(x.shape) initial_shape[axis] = 1 res = xp.concatenate( - [wrapped_xp.zeros(shape=initial_shape, dtype=res.dtype, device=device(res)), res], + [ + wrapped_xp.zeros( + shape=initial_shape, dtype=res.dtype, device=_get_device(res) + ), + res, + ], axis=axis, ) return res def cumulative_prod( - x: ndarray, + x: Array, /, - xp, + xp: Namespace, *, - axis: Optional[int] = None, - dtype: Optional[Dtype] = None, + axis: int | None = None, + dtype: DType | None = None, include_initial: bool = False, - **kwargs -) -> ndarray: + **kwargs: object, +) -> Array: wrapped_xp = array_namespace(x) if axis is None: if x.ndim > 1: - raise ValueError("axis must be specified in cumulative_prod for more than one dimension") + raise ValueError( + "axis must be specified in cumulative_prod for more than one dimension" + ) axis = 0 res = xp.cumprod(x, axis=axis, dtype=dtype, **kwargs) @@ -317,25 +358,32 @@ def cumulative_prod( initial_shape = list(x.shape) initial_shape[axis] = 1 res = xp.concatenate( - [wrapped_xp.ones(shape=initial_shape, dtype=res.dtype, device=device(res)), res], + [ + wrapped_xp.ones( + shape=initial_shape, dtype=res.dtype, device=_get_device(res) + ), + res, + ], axis=axis, ) return res + # The min and max argument names in clip are different and not optional in numpy, and type # promotion behavior is different. def clip( - x: ndarray, + x: Array, /, - min: Optional[Union[int, float, ndarray]] = None, - max: Optional[Union[int, float, ndarray]] = None, + min: float | Array | None = None, + max: float | Array | None = None, *, - xp, + xp: Namespace, # TODO: np.clip has other ufunc kwargs - out: Optional[ndarray] = None, -) -> ndarray: - def _isscalar(a): + out: Array | None = None, +) -> Array: + def _isscalar(a: object) -> TypeIs[int | float | None]: return isinstance(a, (int, float, type(None))) + min_shape = () if _isscalar(min) else min.shape max_shape = () if _isscalar(max) else max.shape @@ -360,7 +408,6 @@ def _isscalar(a): # but an answer of 0 might be preferred. See # https://github.com/numpy/numpy/issues/24976 for more discussion on this issue. - # At least handle the case of Python integers correctly (see # https://github.com/numpy/numpy/pull/26892). if wrapped_xp.isdtype(x.dtype, "integral"): @@ -369,9 +416,10 @@ def _isscalar(a): if type(max) is int and max >= wrapped_xp.iinfo(x.dtype).max: max = None - dev = device(x) + dev = _get_device(x) if out is None: out = wrapped_xp.empty(result_shape, dtype=x.dtype, device=dev) + assert out is not None # workaround for a type-narrowing issue in pyright out[()] = x if min is not None: @@ -389,16 +437,22 @@ def _isscalar(a): # Return a scalar for 0-D return out[()] + # Unlike transpose(), the axes argument to permute_dims() is required. -def permute_dims(x: ndarray, /, axes: Tuple[int, ...], xp) -> ndarray: +def permute_dims(x: Array, /, axes: tuple[int, ...], xp: Namespace) -> Array: return xp.transpose(x, axes) + # np.reshape calls the keyword argument 'newshape' instead of 'shape' -def reshape(x: ndarray, - /, - shape: Tuple[int, ...], - xp, copy: Optional[bool] = None, - **kwargs) -> ndarray: +def reshape( + x: Array, + /, + shape: tuple[int, ...], + xp: Namespace, + *, + copy: Optional[bool] = None, + **kwargs: object, +) -> Array: if copy is True: x = x.copy() elif copy is False: @@ -407,17 +461,24 @@ def reshape(x: ndarray, return y return xp.reshape(x, shape, **kwargs) + # The descending keyword is new in sort and argsort, and 'kind' replaced with # 'stable' def argsort( - x: ndarray, /, xp, *, axis: int = -1, descending: bool = False, stable: bool = True, - **kwargs, -) -> ndarray: + x: Array, + /, + xp: Namespace, + *, + axis: int = -1, + descending: bool = False, + stable: bool = True, + **kwargs: object, +) -> Array: # Note: this keyword argument is different, and the default is different. # We set it in kwargs like this because numpy.sort uses kind='quicksort' # as the default whereas cupy.sort uses kind=None. if stable: - kwargs['kind'] = "stable" + kwargs["kind"] = "stable" if not descending: res = xp.argsort(x, axis=axis, **kwargs) else: @@ -434,69 +495,87 @@ def argsort( res = max_i - res return res + def sort( - x: ndarray, /, xp, *, axis: int = -1, descending: bool = False, stable: bool = True, - **kwargs, -) -> ndarray: + x: Array, + /, + xp: Namespace, + *, + axis: int = -1, + descending: bool = False, + stable: bool = True, + **kwargs: object, +) -> Array: # Note: this keyword argument is different, and the default is different. # We set it in kwargs like this because numpy.sort uses kind='quicksort' # as the default whereas cupy.sort uses kind=None. if stable: - kwargs['kind'] = "stable" + kwargs["kind"] = "stable" res = xp.sort(x, axis=axis, **kwargs) if descending: res = xp.flip(res, axis=axis) return res + # nonzero should error for zero-dimensional arrays -def nonzero(x: ndarray, /, xp, **kwargs) -> Tuple[ndarray, ...]: +def nonzero(x: Array, /, xp: Namespace, **kwargs: object) -> tuple[Array, ...]: if x.ndim == 0: raise ValueError("nonzero() does not support zero-dimensional arrays") return xp.nonzero(x, **kwargs) + # ceil, floor, and trunc return integers for integer inputs -def ceil(x: ndarray, /, xp, **kwargs) -> ndarray: + +def ceil(x: Array, /, xp: Namespace, **kwargs: object) -> Array: if xp.issubdtype(x.dtype, xp.integer): return x return xp.ceil(x, **kwargs) -def floor(x: ndarray, /, xp, **kwargs) -> ndarray: + +def floor(x: Array, /, xp: Namespace, **kwargs: object) -> Array: if xp.issubdtype(x.dtype, xp.integer): return x return xp.floor(x, **kwargs) -def trunc(x: ndarray, /, xp, **kwargs) -> ndarray: + +def trunc(x: Array, /, xp: Namespace, **kwargs: object) -> Array: if xp.issubdtype(x.dtype, xp.integer): return x return xp.trunc(x, **kwargs) + # linear algebra functions -def matmul(x1: ndarray, x2: ndarray, /, xp, **kwargs) -> ndarray: + +def matmul(x1: Array, x2: Array, /, xp: Namespace, **kwargs: object) -> Array: return xp.matmul(x1, x2, **kwargs) + # Unlike transpose, matrix_transpose only transposes the last two axes. -def matrix_transpose(x: ndarray, /, xp) -> ndarray: +def matrix_transpose(x: Array, /, xp: Namespace) -> Array: if x.ndim < 2: raise ValueError("x must be at least 2-dimensional for matrix_transpose") return xp.swapaxes(x, -1, -2) -def tensordot(x1: ndarray, - x2: ndarray, - /, - xp, - *, - axes: Union[int, Tuple[Sequence[int], Sequence[int]]] = 2, - **kwargs, -) -> ndarray: + +def tensordot( + x1: Array, + x2: Array, + /, + xp: Namespace, + *, + axes: int | tuple[Sequence[int], Sequence[int]] = 2, + **kwargs: object, +) -> Array: return xp.tensordot(x1, x2, axes=axes, **kwargs) -def vecdot(x1: ndarray, x2: ndarray, /, xp, *, axis: int = -1) -> ndarray: + +def vecdot(x1: Array, x2: Array, /, xp: Namespace, *, axis: int = -1) -> Array: if x1.shape[axis] != x2.shape[axis]: raise ValueError("x1 and x2 must have the same size along the given axis") - if hasattr(xp, 'broadcast_tensors'): + if hasattr(xp, "broadcast_tensors"): _broadcast = xp.broadcast_tensors else: _broadcast = xp.broadcast_arrays @@ -508,11 +587,16 @@ def vecdot(x1: ndarray, x2: ndarray, /, xp, *, axis: int = -1) -> ndarray: res = xp.conj(x1_[..., None, :]) @ x2_[..., None] return res[..., 0, 0] + # isdtype is a new function in the 2022.12 array API specification. + def isdtype( - dtype: Dtype, kind: Union[Dtype, str, Tuple[Union[Dtype, str], ...]], xp, - *, _tuple=True, # Disallow nested tuples + dtype: DType, + kind: DType | str | tuple[DType | str, ...], + xp: Namespace, + *, + _tuple: bool = True, # Disallow nested tuples ) -> bool: """ Returns a boolean indicating whether a provided dtype is of a specified data type ``kind``. @@ -525,21 +609,24 @@ def isdtype( for more details """ if isinstance(kind, tuple) and _tuple: - return any(isdtype(dtype, k, xp, _tuple=False) for k in kind) + return any( + isdtype(dtype, k, xp, _tuple=False) + for k in cast("tuple[DType | str, ...]", kind) + ) elif isinstance(kind, str): - if kind == 'bool': + if kind == "bool": return dtype == xp.bool_ - elif kind == 'signed integer': + elif kind == "signed integer": return xp.issubdtype(dtype, xp.signedinteger) - elif kind == 'unsigned integer': + elif kind == "unsigned integer": return xp.issubdtype(dtype, xp.unsignedinteger) - elif kind == 'integral': + elif kind == "integral": return xp.issubdtype(dtype, xp.integer) - elif kind == 'real floating': + elif kind == "real floating": return xp.issubdtype(dtype, xp.floating) - elif kind == 'complex floating': + elif kind == "complex floating": return xp.issubdtype(dtype, xp.complexfloating) - elif kind == 'numeric': + elif kind == "numeric": return xp.issubdtype(dtype, xp.number) else: raise ValueError(f"Unrecognized data type kind: {kind!r}") @@ -550,32 +637,91 @@ def isdtype( # array_api_strict implementation will be very strict. return dtype == kind + # unstack is a new function in the 2023.12 array API standard -def unstack(x: ndarray, /, xp, *, axis: int = 0) -> Tuple[ndarray, ...]: +def unstack(x: Array, /, xp: Namespace, *, axis: int = 0) -> tuple[Array, ...]: if x.ndim == 0: raise ValueError("Input array must be at least 1-d.") return tuple(xp.moveaxis(x, axis, 0)) + # numpy 1.26 does not use the standard definition for sign on complex numbers -def sign(x: ndarray, /, xp, **kwargs) -> ndarray: - if isdtype(x.dtype, 'complex floating', xp=xp): - out = (x/xp.abs(x, **kwargs))[...] + +def sign(x: Array, /, xp: Namespace, **kwargs: object) -> Array: + if isdtype(x.dtype, "complex floating", xp=xp): + out = (x / xp.abs(x, **kwargs))[...] # sign(0) = 0 but the above formula would give nan - out[x == 0+0j] = 0+0j + out[x == 0j] = 0j else: out = xp.sign(x, **kwargs) # CuPy sign() does not propagate nans. See # https://github.com/data-apis/array-api-compat/issues/136 - if is_cupy_namespace(xp) and isdtype(x.dtype, 'real floating', xp=xp): + if _is_cupy_namespace(xp) and isdtype(x.dtype, "real floating", xp=xp): out[xp.isnan(x)] = xp.nan return out[()] -__all__ = ['arange', 'empty', 'empty_like', 'eye', 'full', 'full_like', - 'linspace', 'ones', 'ones_like', 'zeros', 'zeros_like', - 'UniqueAllResult', 'UniqueCountsResult', 'UniqueInverseResult', - 'unique_all', 'unique_counts', 'unique_inverse', 'unique_values', - 'std', 'var', 'cumulative_sum', 'cumulative_prod','clip', 'permute_dims', - 'reshape', 'argsort', 'sort', 'nonzero', 'ceil', 'floor', 'trunc', - 'matmul', 'matrix_transpose', 'tensordot', 'vecdot', 'isdtype', - 'unstack', 'sign'] + +def finfo(type_: DType | Array, /, xp: Namespace) -> Any: + # It is surprisingly difficult to recognize a dtype apart from an array. + # np.int64 is not the same as np.asarray(1).dtype! + try: + return xp.finfo(type_) + except (ValueError, TypeError): + return xp.finfo(type_.dtype) + + +def iinfo(type_: DType | Array, /, xp: Namespace) -> Any: + try: + return xp.iinfo(type_) + except (ValueError, TypeError): + return xp.iinfo(type_.dtype) + + +__all__ = [ + "arange", + "empty", + "empty_like", + "eye", + "full", + "full_like", + "linspace", + "ones", + "ones_like", + "zeros", + "zeros_like", + "UniqueAllResult", + "UniqueCountsResult", + "UniqueInverseResult", + "unique_all", + "unique_counts", + "unique_inverse", + "unique_values", + "std", + "var", + "cumulative_sum", + "cumulative_prod", + "clip", + "permute_dims", + "reshape", + "argsort", + "sort", + "nonzero", + "ceil", + "floor", + "trunc", + "matmul", + "matrix_transpose", + "tensordot", + "vecdot", + "isdtype", + "unstack", + "sign", + "finfo", + "iinfo", +] +_all_ignore = ["inspect", "array_namespace", "NamedTuple"] + + +def __dir__() -> list[str]: + return __all__ diff --git a/sklearn/externals/array_api_compat/common/_fft.py b/sklearn/externals/array_api_compat/common/_fft.py index e5caebef732c1..18839d37f8494 100644 --- a/sklearn/externals/array_api_compat/common/_fft.py +++ b/sklearn/externals/array_api_compat/common/_fft.py @@ -1,149 +1,150 @@ from __future__ import annotations -from typing import TYPE_CHECKING, Union, Optional, Literal +from collections.abc import Sequence +from typing import Literal, TypeAlias -if TYPE_CHECKING: - from ._typing import Device, ndarray, DType - from collections.abc import Sequence +from ._typing import Array, Device, DType, Namespace + +_Norm: TypeAlias = Literal["backward", "ortho", "forward"] # Note: NumPy fft functions improperly upcast float32 and complex64 to # complex128, which is why we require wrapping them all here. def fft( - x: ndarray, + x: Array, /, - xp, + xp: Namespace, *, - n: Optional[int] = None, + n: int | None = None, axis: int = -1, - norm: Literal["backward", "ortho", "forward"] = "backward", -) -> ndarray: + norm: _Norm = "backward", +) -> Array: res = xp.fft.fft(x, n=n, axis=axis, norm=norm) if x.dtype in [xp.float32, xp.complex64]: return res.astype(xp.complex64) return res def ifft( - x: ndarray, + x: Array, /, - xp, + xp: Namespace, *, - n: Optional[int] = None, + n: int | None = None, axis: int = -1, - norm: Literal["backward", "ortho", "forward"] = "backward", -) -> ndarray: + norm: _Norm = "backward", +) -> Array: res = xp.fft.ifft(x, n=n, axis=axis, norm=norm) if x.dtype in [xp.float32, xp.complex64]: return res.astype(xp.complex64) return res def fftn( - x: ndarray, + x: Array, /, - xp, + xp: Namespace, *, - s: Sequence[int] = None, - axes: Sequence[int] = None, - norm: Literal["backward", "ortho", "forward"] = "backward", -) -> ndarray: + s: Sequence[int] | None = None, + axes: Sequence[int] | None = None, + norm: _Norm = "backward", +) -> Array: res = xp.fft.fftn(x, s=s, axes=axes, norm=norm) if x.dtype in [xp.float32, xp.complex64]: return res.astype(xp.complex64) return res def ifftn( - x: ndarray, + x: Array, /, - xp, + xp: Namespace, *, - s: Sequence[int] = None, - axes: Sequence[int] = None, - norm: Literal["backward", "ortho", "forward"] = "backward", -) -> ndarray: + s: Sequence[int] | None = None, + axes: Sequence[int] | None = None, + norm: _Norm = "backward", +) -> Array: res = xp.fft.ifftn(x, s=s, axes=axes, norm=norm) if x.dtype in [xp.float32, xp.complex64]: return res.astype(xp.complex64) return res def rfft( - x: ndarray, + x: Array, /, - xp, + xp: Namespace, *, - n: Optional[int] = None, + n: int | None = None, axis: int = -1, - norm: Literal["backward", "ortho", "forward"] = "backward", -) -> ndarray: + norm: _Norm = "backward", +) -> Array: res = xp.fft.rfft(x, n=n, axis=axis, norm=norm) if x.dtype == xp.float32: return res.astype(xp.complex64) return res def irfft( - x: ndarray, + x: Array, /, - xp, + xp: Namespace, *, - n: Optional[int] = None, + n: int | None = None, axis: int = -1, - norm: Literal["backward", "ortho", "forward"] = "backward", -) -> ndarray: + norm: _Norm = "backward", +) -> Array: res = xp.fft.irfft(x, n=n, axis=axis, norm=norm) if x.dtype == xp.complex64: return res.astype(xp.float32) return res def rfftn( - x: ndarray, + x: Array, /, - xp, + xp: Namespace, *, - s: Sequence[int] = None, - axes: Sequence[int] = None, - norm: Literal["backward", "ortho", "forward"] = "backward", -) -> ndarray: + s: Sequence[int] | None = None, + axes: Sequence[int] | None = None, + norm: _Norm = "backward", +) -> Array: res = xp.fft.rfftn(x, s=s, axes=axes, norm=norm) if x.dtype == xp.float32: return res.astype(xp.complex64) return res def irfftn( - x: ndarray, + x: Array, /, - xp, + xp: Namespace, *, - s: Sequence[int] = None, - axes: Sequence[int] = None, - norm: Literal["backward", "ortho", "forward"] = "backward", -) -> ndarray: + s: Sequence[int] | None = None, + axes: Sequence[int] | None = None, + norm: _Norm = "backward", +) -> Array: res = xp.fft.irfftn(x, s=s, axes=axes, norm=norm) if x.dtype == xp.complex64: return res.astype(xp.float32) return res def hfft( - x: ndarray, + x: Array, /, - xp, + xp: Namespace, *, - n: Optional[int] = None, + n: int | None = None, axis: int = -1, - norm: Literal["backward", "ortho", "forward"] = "backward", -) -> ndarray: + norm: _Norm = "backward", +) -> Array: res = xp.fft.hfft(x, n=n, axis=axis, norm=norm) if x.dtype in [xp.float32, xp.complex64]: return res.astype(xp.float32) return res def ihfft( - x: ndarray, + x: Array, /, - xp, + xp: Namespace, *, - n: Optional[int] = None, + n: int | None = None, axis: int = -1, - norm: Literal["backward", "ortho", "forward"] = "backward", -) -> ndarray: + norm: _Norm = "backward", +) -> Array: res = xp.fft.ihfft(x, n=n, axis=axis, norm=norm) if x.dtype in [xp.float32, xp.complex64]: return res.astype(xp.complex64) @@ -152,12 +153,12 @@ def ihfft( def fftfreq( n: int, /, - xp, + xp: Namespace, *, d: float = 1.0, - dtype: Optional[DType] = None, - device: Optional[Device] = None -) -> ndarray: + dtype: DType | None = None, + device: Device | None = None, +) -> Array: if device not in ["cpu", None]: raise ValueError(f"Unsupported device {device!r}") res = xp.fft.fftfreq(n, d=d) @@ -168,12 +169,12 @@ def fftfreq( def rfftfreq( n: int, /, - xp, + xp: Namespace, *, d: float = 1.0, - dtype: Optional[DType] = None, - device: Optional[Device] = None -) -> ndarray: + dtype: DType | None = None, + device: Device | None = None, +) -> Array: if device not in ["cpu", None]: raise ValueError(f"Unsupported device {device!r}") res = xp.fft.rfftfreq(n, d=d) @@ -181,10 +182,14 @@ def rfftfreq( return res.astype(dtype) return res -def fftshift(x: ndarray, /, xp, *, axes: Union[int, Sequence[int]] = None) -> ndarray: +def fftshift( + x: Array, /, xp: Namespace, *, axes: int | Sequence[int] | None = None +) -> Array: return xp.fft.fftshift(x, axes=axes) -def ifftshift(x: ndarray, /, xp, *, axes: Union[int, Sequence[int]] = None) -> ndarray: +def ifftshift( + x: Array, /, xp: Namespace, *, axes: int | Sequence[int] | None = None +) -> Array: return xp.fft.ifftshift(x, axes=axes) __all__ = [ @@ -203,3 +208,6 @@ def ifftshift(x: ndarray, /, xp, *, axes: Union[int, Sequence[int]] = None) -> n "fftshift", "ifftshift", ] + +def __dir__() -> list[str]: + return __all__ diff --git a/sklearn/externals/array_api_compat/common/_helpers.py b/sklearn/externals/array_api_compat/common/_helpers.py index 970450e8ff2e9..77175d0d1e974 100644 --- a/sklearn/externals/array_api_compat/common/_helpers.py +++ b/sklearn/externals/array_api_compat/common/_helpers.py @@ -5,35 +5,97 @@ that are in __all__ are intended as additional helper functions for use by end users of the compat library. """ + from __future__ import annotations -from typing import TYPE_CHECKING +import inspect +import math +import sys +import warnings +from collections.abc import Collection, Hashable +from functools import lru_cache +from typing import ( + TYPE_CHECKING, + Any, + Final, + Literal, + SupportsIndex, + TypeAlias, + TypeGuard, + TypeVar, + cast, + overload, +) + +from ._typing import Array, Device, HasShape, Namespace, SupportsArrayNamespace if TYPE_CHECKING: - from typing import Optional, Union, Any - from ._typing import Array, Device, Namespace -import sys -import math -import inspect -import warnings + import dask.array as da + import jax + import ndonnx as ndx + import numpy as np + import numpy.typing as npt + import sparse # pyright: ignore[reportMissingTypeStubs] + import torch + + # TODO: import from typing (requires Python >=3.13) + from typing_extensions import TypeIs, TypeVar + + _SizeT = TypeVar("_SizeT", bound = int | None) -def _is_jax_zero_gradient_array(x: object) -> bool: + _ZeroGradientArray: TypeAlias = npt.NDArray[np.void] + _CupyArray: TypeAlias = Any # cupy has no py.typed + + _ArrayApiObj: TypeAlias = ( + npt.NDArray[Any] + | da.Array + | jax.Array + | ndx.Array + | sparse.SparseArray + | torch.Tensor + | SupportsArrayNamespace[Any] + | _CupyArray + ) + +_API_VERSIONS_OLD: Final = frozenset({"2021.12", "2022.12", "2023.12"}) +_API_VERSIONS: Final = _API_VERSIONS_OLD | frozenset({"2024.12"}) + + +@lru_cache(100) +def _issubclass_fast(cls: type, modname: str, clsname: str) -> bool: + try: + mod = sys.modules[modname] + except KeyError: + return False + parent_cls = getattr(mod, clsname) + return issubclass(cls, parent_cls) + + +def _is_jax_zero_gradient_array(x: object) -> TypeGuard[_ZeroGradientArray]: """Return True if `x` is a zero-gradient array. These arrays are a design quirk of Jax that may one day be removed. See https://github.com/google/jax/issues/20620. """ - if 'numpy' not in sys.modules or 'jax' not in sys.modules: + # Fast exit + try: + dtype = x.dtype # type: ignore[attr-defined] + except AttributeError: + return False + cls = cast(Hashable, type(dtype)) + if not _issubclass_fast(cls, "numpy.dtypes", "VoidDType"): return False - import numpy as np - import jax + if "jax" not in sys.modules: + return False - return isinstance(x, np.ndarray) and x.dtype == jax.float0 + import jax + # jax.float0 is a np.dtype([('float0', 'V')]) + return dtype == jax.float0 -def is_numpy_array(x: object) -> bool: +def is_numpy_array(x: object) -> TypeGuard[npt.NDArray[Any]]: """ Return True if `x` is a NumPy array. @@ -54,15 +116,12 @@ def is_numpy_array(x: object) -> bool: is_jax_array is_pydata_sparse_array """ - # Avoid importing NumPy if it isn't already - if 'numpy' not in sys.modules: - return False - - import numpy as np - # TODO: Should we reject ndarray subclasses? - return (isinstance(x, (np.ndarray, np.generic)) - and not _is_jax_zero_gradient_array(x)) + cls = cast(Hashable, type(x)) + return ( + _issubclass_fast(cls, "numpy", "ndarray") + or _issubclass_fast(cls, "numpy", "generic") + ) and not _is_jax_zero_gradient_array(x) def is_cupy_array(x: object) -> bool: @@ -86,17 +145,11 @@ def is_cupy_array(x: object) -> bool: is_jax_array is_pydata_sparse_array """ - # Avoid importing CuPy if it isn't already - if 'cupy' not in sys.modules: - return False - - import cupy as cp - - # TODO: Should we reject ndarray subclasses? - return isinstance(x, cp.ndarray) + cls = cast(Hashable, type(x)) + return _issubclass_fast(cls, "cupy", "ndarray") -def is_torch_array(x: object) -> bool: +def is_torch_array(x: object) -> TypeIs[torch.Tensor]: """ Return True if `x` is a PyTorch tensor. @@ -114,17 +167,11 @@ def is_torch_array(x: object) -> bool: is_jax_array is_pydata_sparse_array """ - # Avoid importing torch if it isn't already - if 'torch' not in sys.modules: - return False - - import torch - - # TODO: Should we reject ndarray subclasses? - return isinstance(x, torch.Tensor) + cls = cast(Hashable, type(x)) + return _issubclass_fast(cls, "torch", "Tensor") -def is_ndonnx_array(x: object) -> bool: +def is_ndonnx_array(x: object) -> TypeIs[ndx.Array]: """ Return True if `x` is a ndonnx Array. @@ -143,16 +190,11 @@ def is_ndonnx_array(x: object) -> bool: is_jax_array is_pydata_sparse_array """ - # Avoid importing torch if it isn't already - if 'ndonnx' not in sys.modules: - return False - - import ndonnx as ndx - - return isinstance(x, ndx.Array) + cls = cast(Hashable, type(x)) + return _issubclass_fast(cls, "ndonnx", "Array") -def is_dask_array(x: object) -> bool: +def is_dask_array(x: object) -> TypeIs[da.Array]: """ Return True if `x` is a dask.array Array. @@ -171,16 +213,11 @@ def is_dask_array(x: object) -> bool: is_jax_array is_pydata_sparse_array """ - # Avoid importing dask if it isn't already - if 'dask.array' not in sys.modules: - return False - - import dask.array - - return isinstance(x, dask.array.Array) + cls = cast(Hashable, type(x)) + return _issubclass_fast(cls, "dask.array", "Array") -def is_jax_array(x: object) -> bool: +def is_jax_array(x: object) -> TypeIs[jax.Array]: """ Return True if `x` is a JAX array. @@ -200,16 +237,11 @@ def is_jax_array(x: object) -> bool: is_dask_array is_pydata_sparse_array """ - # Avoid importing jax if it isn't already - if 'jax' not in sys.modules: - return False - - import jax - - return isinstance(x, jax.Array) or _is_jax_zero_gradient_array(x) + cls = cast(Hashable, type(x)) + return _issubclass_fast(cls, "jax", "Array") or _is_jax_zero_gradient_array(x) -def is_pydata_sparse_array(x) -> bool: +def is_pydata_sparse_array(x: object) -> TypeIs[sparse.SparseArray]: """ Return True if `x` is an array from the `sparse` package. @@ -229,17 +261,12 @@ def is_pydata_sparse_array(x) -> bool: is_dask_array is_jax_array """ - # Avoid importing jax if it isn't already - if 'sparse' not in sys.modules: - return False - - import sparse - # TODO: Account for other backends. - return isinstance(x, sparse.SparseArray) + cls = cast(Hashable, type(x)) + return _issubclass_fast(cls, "sparse", "SparseArray") -def is_array_api_obj(x: object) -> bool: +def is_array_api_obj(x: object) -> TypeIs[_ArrayApiObj]: # pyright: ignore[reportUnknownParameterType] """ Return True if `x` is an array API compatible array object. @@ -254,21 +281,34 @@ def is_array_api_obj(x: object) -> bool: is_dask_array is_jax_array """ - return is_numpy_array(x) \ - or is_cupy_array(x) \ - or is_torch_array(x) \ - or is_dask_array(x) \ - or is_jax_array(x) \ - or is_pydata_sparse_array(x) \ - or hasattr(x, '__array_namespace__') + return ( + hasattr(x, '__array_namespace__') + or _is_array_api_cls(cast(Hashable, type(x))) + ) + + +@lru_cache(100) +def _is_array_api_cls(cls: type) -> bool: + return ( + # TODO: drop support for numpy<2 which didn't have __array_namespace__ + _issubclass_fast(cls, "numpy", "ndarray") + or _issubclass_fast(cls, "numpy", "generic") + or _issubclass_fast(cls, "cupy", "ndarray") + or _issubclass_fast(cls, "torch", "Tensor") + or _issubclass_fast(cls, "dask.array", "Array") + or _issubclass_fast(cls, "sparse", "SparseArray") + # TODO: drop support for jax<0.4.32 which didn't have __array_namespace__ + or _issubclass_fast(cls, "jax", "Array") + ) def _compat_module_name() -> str: - assert __name__.endswith('.common._helpers') - return __name__.removesuffix('.common._helpers') + assert __name__.endswith(".common._helpers") + return __name__.removesuffix(".common._helpers") -def is_numpy_namespace(xp) -> bool: +@lru_cache(100) +def is_numpy_namespace(xp: Namespace) -> bool: """ Returns True if `xp` is a NumPy namespace. @@ -286,10 +326,11 @@ def is_numpy_namespace(xp) -> bool: is_pydata_sparse_namespace is_array_api_strict_namespace """ - return xp.__name__ in {'numpy', _compat_module_name() + '.numpy'} + return xp.__name__ in {"numpy", _compat_module_name() + ".numpy"} -def is_cupy_namespace(xp) -> bool: +@lru_cache(100) +def is_cupy_namespace(xp: Namespace) -> bool: """ Returns True if `xp` is a CuPy namespace. @@ -307,10 +348,11 @@ def is_cupy_namespace(xp) -> bool: is_pydata_sparse_namespace is_array_api_strict_namespace """ - return xp.__name__ in {'cupy', _compat_module_name() + '.cupy'} + return xp.__name__ in {"cupy", _compat_module_name() + ".cupy"} -def is_torch_namespace(xp) -> bool: +@lru_cache(100) +def is_torch_namespace(xp: Namespace) -> bool: """ Returns True if `xp` is a PyTorch namespace. @@ -328,10 +370,10 @@ def is_torch_namespace(xp) -> bool: is_pydata_sparse_namespace is_array_api_strict_namespace """ - return xp.__name__ in {'torch', _compat_module_name() + '.torch'} + return xp.__name__ in {"torch", _compat_module_name() + ".torch"} -def is_ndonnx_namespace(xp) -> bool: +def is_ndonnx_namespace(xp: Namespace) -> bool: """ Returns True if `xp` is an NDONNX namespace. @@ -347,10 +389,11 @@ def is_ndonnx_namespace(xp) -> bool: is_pydata_sparse_namespace is_array_api_strict_namespace """ - return xp.__name__ == 'ndonnx' + return xp.__name__ == "ndonnx" -def is_dask_namespace(xp) -> bool: +@lru_cache(100) +def is_dask_namespace(xp: Namespace) -> bool: """ Returns True if `xp` is a Dask namespace. @@ -368,10 +411,10 @@ def is_dask_namespace(xp) -> bool: is_pydata_sparse_namespace is_array_api_strict_namespace """ - return xp.__name__ in {'dask.array', _compat_module_name() + '.dask.array'} + return xp.__name__ in {"dask.array", _compat_module_name() + ".dask.array"} -def is_jax_namespace(xp) -> bool: +def is_jax_namespace(xp: Namespace) -> bool: """ Returns True if `xp` is a JAX namespace. @@ -390,10 +433,10 @@ def is_jax_namespace(xp) -> bool: is_pydata_sparse_namespace is_array_api_strict_namespace """ - return xp.__name__ in {'jax.numpy', 'jax.experimental.array_api'} + return xp.__name__ in {"jax.numpy", "jax.experimental.array_api"} -def is_pydata_sparse_namespace(xp) -> bool: +def is_pydata_sparse_namespace(xp: Namespace) -> bool: """ Returns True if `xp` is a pydata/sparse namespace. @@ -409,10 +452,10 @@ def is_pydata_sparse_namespace(xp) -> bool: is_jax_namespace is_array_api_strict_namespace """ - return xp.__name__ == 'sparse' + return xp.__name__ == "sparse" -def is_array_api_strict_namespace(xp) -> bool: +def is_array_api_strict_namespace(xp: Namespace) -> bool: """ Returns True if `xp` is an array-api-strict namespace. @@ -428,18 +471,25 @@ def is_array_api_strict_namespace(xp) -> bool: is_jax_namespace is_pydata_sparse_namespace """ - return xp.__name__ == 'array_api_strict' + return xp.__name__ == "array_api_strict" -def _check_api_version(api_version: str) -> None: - if api_version in ['2021.12', '2022.12', '2023.12']: - warnings.warn(f"The {api_version} version of the array API specification was requested but the returned namespace is actually version 2024.12") - elif api_version is not None and api_version not in ['2021.12', '2022.12', - '2023.12', '2024.12']: - raise ValueError("Only the 2024.12 version of the array API specification is currently supported") +def _check_api_version(api_version: str | None) -> None: + if api_version in _API_VERSIONS_OLD: + warnings.warn( + f"The {api_version} version of the array API specification was requested but the returned namespace is actually version 2024.12" + ) + elif api_version is not None and api_version not in _API_VERSIONS: + raise ValueError( + "Only the 2024.12 version of the array API specification is currently supported" + ) -def array_namespace(*xs, api_version=None, use_compat=None) -> Namespace: +def array_namespace( + *xs: Array | complex | None, + api_version: str | None = None, + use_compat: bool | None = None, +) -> Namespace: """ Get the array API compatible namespace for the arrays `xs`. @@ -508,11 +558,13 @@ def your_function(x, y): _use_compat = use_compat in [None, True] - namespaces = set() + namespaces: set[Namespace] = set() for x in xs: if is_numpy_array(x): - from .. import numpy as numpy_namespace import numpy as np + + from .. import numpy as numpy_namespace + if use_compat is True: _check_api_version(api_version) namespaces.add(numpy_namespace) @@ -526,25 +578,31 @@ def your_function(x, y): if _use_compat: _check_api_version(api_version) from .. import cupy as cupy_namespace + namespaces.add(cupy_namespace) else: - import cupy as cp + import cupy as cp # pyright: ignore[reportMissingTypeStubs] + namespaces.add(cp) elif is_torch_array(x): if _use_compat: _check_api_version(api_version) from .. import torch as torch_namespace + namespaces.add(torch_namespace) else: import torch + namespaces.add(torch) elif is_dask_array(x): if _use_compat: _check_api_version(api_version) from ..dask import array as dask_namespace + namespaces.add(dask_namespace) else: import dask.array as da + namespaces.add(da) elif is_jax_array(x): if use_compat is True: @@ -556,23 +614,27 @@ def your_function(x, y): # JAX v0.4.32 and newer implements the array API directly in jax.numpy. # For older JAX versions, it is available via jax.experimental.array_api. import jax.numpy + if hasattr(jax.numpy, "__array_api_version__"): jnp = jax.numpy else: - import jax.experimental.array_api as jnp + import jax.experimental.array_api as jnp # pyright: ignore[reportMissingImports] namespaces.add(jnp) elif is_pydata_sparse_array(x): if use_compat is True: _check_api_version(api_version) raise ValueError("`sparse` does not have an array-api-compat wrapper") else: - import sparse + import sparse # pyright: ignore[reportMissingTypeStubs] # `sparse` is already an array namespace. We do not have a wrapper # submodule for it. namespaces.add(sparse) - elif hasattr(x, '__array_namespace__'): + elif hasattr(x, "__array_namespace__"): if use_compat is True: - raise ValueError("The given array does not have an array-api-compat wrapper") + raise ValueError( + "The given array does not have an array-api-compat wrapper" + ) + x = cast("SupportsArrayNamespace[Any]", x) namespaces.add(x.__array_namespace__(api_version=api_version)) elif isinstance(x, (bool, int, float, complex, type(None))): continue @@ -586,34 +648,55 @@ def your_function(x, y): if len(namespaces) != 1: raise TypeError(f"Multiple namespaces for array inputs: {namespaces}") - xp, = namespaces + (xp,) = namespaces return xp + # backwards compatibility alias get_namespace = array_namespace -def _check_device(xp, device): - if xp == sys.modules.get('numpy'): - if device not in ["cpu", None]: + +def _check_device(bare_xp: Namespace, device: Device) -> None: # pyright: ignore[reportUnusedFunction] + """ + Validate dummy device on device-less array backends. + + Notes + ----- + This function is also invoked by CuPy, which does have multiple devices + if there are multiple GPUs available. + However, CuPy multi-device support is currently impossible + without using the global device or a context manager: + + https://github.com/data-apis/array-api-compat/pull/293 + """ + if bare_xp is sys.modules.get("numpy"): + if device not in ("cpu", None): raise ValueError(f"Unsupported device for NumPy: {device!r}") + elif bare_xp is sys.modules.get("dask.array"): + if device not in ("cpu", _DASK_DEVICE, None): + raise ValueError(f"Unsupported device for Dask: {device!r}") + + # Placeholder object to represent the dask device # when the array backend is not the CPU. # (since it is not easy to tell which device a dask array is on) class _dask_device: - def __repr__(self): + def __repr__(self) -> Literal["DASK_DEVICE"]: return "DASK_DEVICE" + _DASK_DEVICE = _dask_device() + # device() is not on numpy.ndarray or dask.array and to_device() is not on numpy.ndarray # or cupy.ndarray. They are not included in array objects of this library # because this library just reuses the respective ndarray classes without # wrapping or subclassing them. These helper functions can be used instead of # the wrapper functions for libraries that need to support both NumPy/CuPy and # other libraries that use devices. -def device(x: Array, /) -> Device: +def device(x: _ArrayApiObj, /) -> Device: """ Hardware device the array data resides on. @@ -649,7 +732,7 @@ def device(x: Array, /) -> Device: return "cpu" elif is_dask_array(x): # Peek at the metadata of the Dask array to determine type - if is_numpy_array(x._meta): + if is_numpy_array(x._meta): # pyright: ignore # Must be on CPU since backed by numpy return "cpu" return _DASK_DEVICE @@ -659,7 +742,7 @@ def device(x: Array, /) -> Device: # Return None in this case. Note that this workaround breaks # the standard and will result in new arrays being created on the # default device instead of the same device as the input array(s). - x_device = getattr(x, 'device', None) + x_device = getattr(x, "device", None) # Older JAX releases had .device() as a method, which has been replaced # with a property in accordance with the standard. if inspect.ismethod(x_device): @@ -668,66 +751,66 @@ def device(x: Array, /) -> Device: return x_device elif is_pydata_sparse_array(x): # `sparse` will gain `.device`, so check for this first. - x_device = getattr(x, 'device', None) + x_device = getattr(x, "device", None) if x_device is not None: return x_device # Everything but DOK has this attr. try: - inner = x.data + inner = x.data # pyright: ignore except AttributeError: return "cpu" # Return the device of the constituent array - return device(inner) - return x.device + return device(inner) # pyright: ignore + return x.device # pyright: ignore + # Prevent shadowing, used below _device = device + # Based on cupy.array_api.Array.to_device -def _cupy_to_device(x, device, /, stream=None): +def _cupy_to_device( + x: _CupyArray, + device: Device, + /, + stream: int | Any | None = None, +) -> _CupyArray: import cupy as cp - from cupy.cuda import Device as _Device - from cupy.cuda import stream as stream_module - from cupy_backends.cuda.api import runtime - if device == x.device: - return x - elif device == "cpu": + if device == "cpu": # allowing us to use `to_device(x, "cpu")` # is useful for portable test swapping between # host and device backends return x.get() - elif not isinstance(device, _Device): - raise ValueError(f"Unsupported device {device!r}") - else: - # see cupy/cupy#5985 for the reason how we handle device/stream here - prev_device = runtime.getDevice() - prev_stream: stream_module.Stream = None - if stream is not None: - prev_stream = stream_module.get_current_stream() - # stream can be an int as specified in __dlpack__, or a CuPy stream - if isinstance(stream, int): - stream = cp.cuda.ExternalStream(stream) - elif isinstance(stream, cp.cuda.Stream): - pass - else: - raise ValueError('the input stream is not recognized') - stream.use() - try: - runtime.setDevice(device.id) - arr = x.copy() - finally: - runtime.setDevice(prev_device) - if stream is not None: - prev_stream.use() - return arr - -def _torch_to_device(x, device, /, stream=None): + if not isinstance(device, cp.cuda.Device): + raise TypeError(f"Unsupported device type {device!r}") + + if stream is None: + with device: + return cp.asarray(x) + + # stream can be an int as specified in __dlpack__, or a CuPy stream + if isinstance(stream, int): + stream = cp.cuda.ExternalStream(stream) + elif not isinstance(stream, cp.cuda.Stream): + raise TypeError(f"Unsupported stream type {stream!r}") + + with device, stream: + return cp.asarray(x) + + +def _torch_to_device( + x: torch.Tensor, + device: torch.device | str | int, + /, + stream: None = None, +) -> torch.Tensor: if stream is not None: raise NotImplementedError return x.to(device) -def to_device(x: Array, device: Device, /, *, stream: Optional[Union[int, Any]] = None) -> Array: + +def to_device(x: Array, device: Device, /, *, stream: int | Any | None = None) -> Array: """ Copy the array from the device on which it currently resides to the specified ``device``. @@ -747,7 +830,7 @@ def to_device(x: Array, device: Device, /, *, stream: Optional[Union[int, Any]] a ``device`` object (see the `Device Support `__ section of the array API specification). - stream: Optional[Union[int, Any]] + stream: int | Any | None stream object to use during copy. In addition to the types supported in ``array.__dlpack__``, implementations may choose to support any library-specific stream object with the caveat that any code using @@ -779,25 +862,26 @@ def to_device(x: Array, device: Device, /, *, stream: Optional[Union[int, Any]] if is_numpy_array(x): if stream is not None: raise ValueError("The stream argument to to_device() is not supported") - if device == 'cpu': + if device == "cpu": return x raise ValueError(f"Unsupported device {device!r}") elif is_cupy_array(x): # cupy does not yet have to_device return _cupy_to_device(x, device, stream=stream) elif is_torch_array(x): - return _torch_to_device(x, device, stream=stream) + return _torch_to_device(x, device, stream=stream) # pyright: ignore[reportArgumentType] elif is_dask_array(x): if stream is not None: raise ValueError("The stream argument to to_device() is not supported") # TODO: What if our array is on the GPU already? - if device == 'cpu': + if device == "cpu": return x raise ValueError(f"Unsupported device {device!r}") elif is_jax_array(x): if not hasattr(x, "__array_namespace__"): # In JAX v0.4.31 and older, this import adds to_device method to x... - import jax.experimental.array_api # noqa: F401 + import jax.experimental.array_api # noqa: F401 # pyright: ignore + # ... but only on eager JAX. It won't work inside jax.jit. if not hasattr(x, "to_device"): return x @@ -806,10 +890,16 @@ def to_device(x: Array, device: Device, /, *, stream: Optional[Union[int, Any]] # Perform trivial check to return the same array if # device is same instead of err-ing. return x - return x.to_device(device, stream=stream) + return x.to_device(device, stream=stream) # pyright: ignore -def size(x: Array) -> int | None: +@overload +def size(x: HasShape[Collection[SupportsIndex]]) -> int: ... +@overload +def size(x: HasShape[Collection[None]]) -> None: ... +@overload +def size(x: HasShape[Collection[SupportsIndex | None]]) -> int | None: ... +def size(x: HasShape[Collection[SupportsIndex | None]]) -> int | None: """ Return the total number of elements of x. @@ -824,11 +914,24 @@ def size(x: Array) -> int | None: # Lazy API compliant arrays, such as ndonnx, can contain None in their shape if None in x.shape: return None - out = math.prod(x.shape) + out = math.prod(cast("Collection[SupportsIndex]", x.shape)) # dask.array.Array.shape can contain NaN return None if math.isnan(out) else out +@lru_cache(100) +def _is_writeable_cls(cls: type) -> bool | None: + if ( + _issubclass_fast(cls, "numpy", "generic") + or _issubclass_fast(cls, "jax", "Array") + or _issubclass_fast(cls, "sparse", "SparseArray") + ): + return False + if _is_array_api_cls(cls): + return True + return None + + def is_writeable_array(x: object) -> bool: """ Return False if ``x.__setitem__`` is expected to raise; True otherwise. @@ -839,11 +942,32 @@ def is_writeable_array(x: object) -> bool: As there is no standard way to check if an array is writeable without actually writing to it, this function blindly returns True for all unknown array types. """ - if is_numpy_array(x): - return x.flags.writeable - if is_jax_array(x) or is_pydata_sparse_array(x): + cls = cast(Hashable, type(x)) + if _issubclass_fast(cls, "numpy", "ndarray"): + return cast("npt.NDArray", x).flags.writeable + res = _is_writeable_cls(cls) + if res is not None: + return res + return hasattr(x, '__array_namespace__') + + +@lru_cache(100) +def _is_lazy_cls(cls: type) -> bool | None: + if ( + _issubclass_fast(cls, "numpy", "ndarray") + or _issubclass_fast(cls, "numpy", "generic") + or _issubclass_fast(cls, "cupy", "ndarray") + or _issubclass_fast(cls, "torch", "Tensor") + or _issubclass_fast(cls, "sparse", "SparseArray") + ): return False - return is_array_api_obj(x) + if ( + _issubclass_fast(cls, "jax", "Array") + or _issubclass_fast(cls, "dask.array", "Array") + or _issubclass_fast(cls, "ndonnx", "Array") + ): + return True + return None def is_lazy_array(x: object) -> bool: @@ -859,14 +983,6 @@ def is_lazy_array(x: object) -> bool: This function errs on the side of caution for array types that may or may not be lazy, e.g. JAX arrays, by always returning True for them. """ - if ( - is_numpy_array(x) - or is_cupy_array(x) - or is_torch_array(x) - or is_pydata_sparse_array(x) - ): - return False - # **JAX note:** while it is possible to determine if you're inside or outside # jax.jit by testing the subclass of a jax.Array object, as well as testing bool() # as we do below for unknown arrays, this is not recommended by JAX best practices. @@ -876,10 +992,14 @@ def is_lazy_array(x: object) -> bool: # compatibility, is highly detrimental to performance as the whole graph will end # up being computed multiple times. - if is_jax_array(x) or is_dask_array(x) or is_ndonnx_array(x): - return True + # Note: skipping reclassification of JAX zero gradient arrays, as one will + # exclusively get them once they leave a jax.grad JIT context. + cls = cast(Hashable, type(x)) + res = _is_lazy_cls(cls) + if res is not None: + return res - if not is_array_api_obj(x): + if not hasattr(x, "__array_namespace__"): return False # Unknown Array API compatible object. Note that this test may have dire consequences @@ -887,7 +1007,7 @@ def is_lazy_array(x: object) -> bool: # on __bool__ (dask is one such example, which however is special-cased above). # Select a single point of the array - s = size(x) + s = size(cast("HasShape[Collection[SupportsIndex | None]]", x)) if s is None: return True xp = array_namespace(x) @@ -899,7 +1019,7 @@ def is_lazy_array(x: object) -> bool: try: bool(x) return False - # The Array API standard dictates that __bool__ should raise TypeError if the + # The Array API standard dictactes that __bool__ should raise TypeError if the # output cannot be defined. # Here we allow for it to raise arbitrary exceptions, e.g. like Dask does. except Exception: @@ -932,4 +1052,7 @@ def is_lazy_array(x: object) -> bool: "to_device", ] -_all_ignore = ['sys', 'math', 'inspect', 'warnings'] +_all_ignore = ['lru_cache', 'sys', 'math', 'inspect', 'warnings'] + +def __dir__() -> list[str]: + return __all__ diff --git a/sklearn/externals/array_api_compat/common/_linalg.py b/sklearn/externals/array_api_compat/common/_linalg.py index bfa1f1b937fdd..7ad87a1be9105 100644 --- a/sklearn/externals/array_api_compat/common/_linalg.py +++ b/sklearn/externals/array_api_compat/common/_linalg.py @@ -1,85 +1,114 @@ from __future__ import annotations -from typing import TYPE_CHECKING, NamedTuple -if TYPE_CHECKING: - from typing import Literal, Optional, Tuple, Union - from ._typing import ndarray - import math +from typing import Literal, NamedTuple, cast import numpy as np + if np.__version__[0] == "2": from numpy.lib.array_utils import normalize_axis_tuple else: from numpy.core.numeric import normalize_axis_tuple -from ._aliases import matmul, matrix_transpose, tensordot, vecdot, isdtype from .._internal import get_xp +from ._aliases import isdtype, matmul, matrix_transpose, tensordot, vecdot +from ._typing import Array, DType, JustFloat, JustInt, Namespace + # These are in the main NumPy namespace but not in numpy.linalg -def cross(x1: ndarray, x2: ndarray, /, xp, *, axis: int = -1, **kwargs) -> ndarray: +def cross( + x1: Array, + x2: Array, + /, + xp: Namespace, + *, + axis: int = -1, + **kwargs: object, +) -> Array: return xp.cross(x1, x2, axis=axis, **kwargs) -def outer(x1: ndarray, x2: ndarray, /, xp, **kwargs) -> ndarray: +def outer(x1: Array, x2: Array, /, xp: Namespace, **kwargs: object) -> Array: return xp.outer(x1, x2, **kwargs) class EighResult(NamedTuple): - eigenvalues: ndarray - eigenvectors: ndarray + eigenvalues: Array + eigenvectors: Array class QRResult(NamedTuple): - Q: ndarray - R: ndarray + Q: Array + R: Array class SlogdetResult(NamedTuple): - sign: ndarray - logabsdet: ndarray + sign: Array + logabsdet: Array class SVDResult(NamedTuple): - U: ndarray - S: ndarray - Vh: ndarray + U: Array + S: Array + Vh: Array # These functions are the same as their NumPy counterparts except they return # a namedtuple. -def eigh(x: ndarray, /, xp, **kwargs) -> EighResult: +def eigh(x: Array, /, xp: Namespace, **kwargs: object) -> EighResult: return EighResult(*xp.linalg.eigh(x, **kwargs)) -def qr(x: ndarray, /, xp, *, mode: Literal['reduced', 'complete'] = 'reduced', - **kwargs) -> QRResult: +def qr( + x: Array, + /, + xp: Namespace, + *, + mode: Literal["reduced", "complete"] = "reduced", + **kwargs: object, +) -> QRResult: return QRResult(*xp.linalg.qr(x, mode=mode, **kwargs)) -def slogdet(x: ndarray, /, xp, **kwargs) -> SlogdetResult: +def slogdet(x: Array, /, xp: Namespace, **kwargs: object) -> SlogdetResult: return SlogdetResult(*xp.linalg.slogdet(x, **kwargs)) -def svd(x: ndarray, /, xp, *, full_matrices: bool = True, **kwargs) -> SVDResult: +def svd( + x: Array, + /, + xp: Namespace, + *, + full_matrices: bool = True, + **kwargs: object, +) -> SVDResult: return SVDResult(*xp.linalg.svd(x, full_matrices=full_matrices, **kwargs)) # These functions have additional keyword arguments # The upper keyword argument is new from NumPy -def cholesky(x: ndarray, /, xp, *, upper: bool = False, **kwargs) -> ndarray: +def cholesky( + x: Array, + /, + xp: Namespace, + *, + upper: bool = False, + **kwargs: object, +) -> Array: L = xp.linalg.cholesky(x, **kwargs) if upper: U = get_xp(xp)(matrix_transpose)(L) if get_xp(xp)(isdtype)(U.dtype, 'complex floating'): - U = xp.conj(U) + U = xp.conj(U) # pyright: ignore[reportConstantRedefinition] return U return L # The rtol keyword argument of matrix_rank() and pinv() is new from NumPy. # Note that it has a different semantic meaning from tol and rcond. -def matrix_rank(x: ndarray, - /, - xp, - *, - rtol: Optional[Union[float, ndarray]] = None, - **kwargs) -> ndarray: +def matrix_rank( + x: Array, + /, + xp: Namespace, + *, + rtol: float | Array | None = None, + **kwargs: object, +) -> Array: # this is different from xp.linalg.matrix_rank, which supports 1 # dimensional arrays. if x.ndim < 2: raise xp.linalg.LinAlgError("1-dimensional array given. Array must be at least two-dimensional") - S = get_xp(xp)(svdvals)(x, **kwargs) + S: Array = get_xp(xp)(svdvals)(x, **kwargs) if rtol is None: tol = S.max(axis=-1, keepdims=True) * max(x.shape[-2:]) * xp.finfo(S.dtype).eps else: @@ -88,7 +117,14 @@ def matrix_rank(x: ndarray, tol = S.max(axis=-1, keepdims=True)*xp.asarray(rtol)[..., xp.newaxis] return xp.count_nonzero(S > tol, axis=-1) -def pinv(x: ndarray, /, xp, *, rtol: Optional[Union[float, ndarray]] = None, **kwargs) -> ndarray: +def pinv( + x: Array, + /, + xp: Namespace, + *, + rtol: float | Array | None = None, + **kwargs: object, +) -> Array: # this is different from xp.linalg.pinv, which does not multiply the # default tolerance by max(M, N). if rtol is None: @@ -97,15 +133,30 @@ def pinv(x: ndarray, /, xp, *, rtol: Optional[Union[float, ndarray]] = None, **k # These functions are new in the array API spec -def matrix_norm(x: ndarray, /, xp, *, keepdims: bool = False, ord: Optional[Union[int, float, Literal['fro', 'nuc']]] = 'fro') -> ndarray: +def matrix_norm( + x: Array, + /, + xp: Namespace, + *, + keepdims: bool = False, + ord: Literal[1, 2, -1, -2] | JustFloat | Literal["fro", "nuc"] | None = "fro", +) -> Array: return xp.linalg.norm(x, axis=(-2, -1), keepdims=keepdims, ord=ord) # svdvals is not in NumPy (but it is in SciPy). It is equivalent to # xp.linalg.svd(compute_uv=False). -def svdvals(x: ndarray, /, xp) -> Union[ndarray, Tuple[ndarray, ...]]: +def svdvals(x: Array, /, xp: Namespace) -> Array | tuple[Array, ...]: return xp.linalg.svd(x, compute_uv=False) -def vector_norm(x: ndarray, /, xp, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False, ord: Optional[Union[int, float]] = 2) -> ndarray: +def vector_norm( + x: Array, + /, + xp: Namespace, + *, + axis: int | tuple[int, ...] | None = None, + keepdims: bool = False, + ord: JustInt | JustFloat = 2, +) -> Array: # xp.linalg.norm tries to do a matrix norm whenever axis is a 2-tuple or # when axis=None and the input is 2-D, so to force a vector norm, we make # it so the input is 1-D (for axis=None), or reshape so that norm is done @@ -117,7 +168,10 @@ def vector_norm(x: ndarray, /, xp, *, axis: Optional[Union[int, Tuple[int, ...]] elif isinstance(axis, tuple): # Note: The axis argument supports any number of axes, whereas # xp.linalg.norm() only supports a single axis for vector norm. - normalized_axis = normalize_axis_tuple(axis, x.ndim) + normalized_axis = cast( + "tuple[int, ...]", + normalize_axis_tuple(axis, x.ndim), # pyright: ignore[reportCallIssue] + ) rest = tuple(i for i in range(x.ndim) if i not in normalized_axis) newshape = axis + rest _x = xp.transpose(x, newshape).reshape( @@ -133,7 +187,13 @@ def vector_norm(x: ndarray, /, xp, *, axis: Optional[Union[int, Tuple[int, ...]] # We can't reuse xp.linalg.norm(keepdims) because of the reshape hacks # above to avoid matrix norm logic. shape = list(x.shape) - _axis = normalize_axis_tuple(range(x.ndim) if axis is None else axis, x.ndim) + _axis = cast( + "tuple[int, ...]", + normalize_axis_tuple( # pyright: ignore[reportCallIssue] + range(x.ndim) if axis is None else axis, + x.ndim, + ), + ) for i in _axis: shape[i] = 1 res = xp.reshape(res, tuple(shape)) @@ -143,14 +203,30 @@ def vector_norm(x: ndarray, /, xp, *, axis: Optional[Union[int, Tuple[int, ...]] # xp.diagonal and xp.trace operate on the first two axes whereas these # operates on the last two -def diagonal(x: ndarray, /, xp, *, offset: int = 0, **kwargs) -> ndarray: +def diagonal(x: Array, /, xp: Namespace, *, offset: int = 0, **kwargs: object) -> Array: return xp.diagonal(x, offset=offset, axis1=-2, axis2=-1, **kwargs) -def trace(x: ndarray, /, xp, *, offset: int = 0, dtype=None, **kwargs) -> ndarray: - return xp.asarray(xp.trace(x, offset=offset, dtype=dtype, axis1=-2, axis2=-1, **kwargs)) +def trace( + x: Array, + /, + xp: Namespace, + *, + offset: int = 0, + dtype: DType | None = None, + **kwargs: object, +) -> Array: + return xp.asarray( + xp.trace(x, offset=offset, dtype=dtype, axis1=-2, axis2=-1, **kwargs) + ) __all__ = ['cross', 'matmul', 'outer', 'tensordot', 'EighResult', 'QRResult', 'SlogdetResult', 'SVDResult', 'eigh', 'qr', 'slogdet', 'svd', 'cholesky', 'matrix_rank', 'pinv', 'matrix_norm', 'matrix_transpose', 'svdvals', 'vecdot', 'vector_norm', 'diagonal', 'trace'] + +_all_ignore = ['math', 'normalize_axis_tuple', 'get_xp', 'np', 'isdtype'] + + +def __dir__() -> list[str]: + return __all__ diff --git a/sklearn/externals/array_api_compat/common/_typing.py b/sklearn/externals/array_api_compat/common/_typing.py index d8acdef7060d9..cd26feeba4dff 100644 --- a/sklearn/externals/array_api_compat/common/_typing.py +++ b/sklearn/externals/array_api_compat/common/_typing.py @@ -1,26 +1,192 @@ from __future__ import annotations -__all__ = [ - "NestedSequence", - "SupportsBufferProtocol", -] - -from types import ModuleType +from collections.abc import Mapping +from types import ModuleType as Namespace from typing import ( - Any, - TypeVar, + TYPE_CHECKING, + Literal, Protocol, + TypeAlias, + TypedDict, + TypeVar, + final, ) +if TYPE_CHECKING: + from _typeshed import Incomplete + + SupportsBufferProtocol: TypeAlias = Incomplete + Array: TypeAlias = Incomplete + Device: TypeAlias = Incomplete + DType: TypeAlias = Incomplete +else: + SupportsBufferProtocol = object + Array = object + Device = object + DType = object + + _T_co = TypeVar("_T_co", covariant=True) + +# These "Just" types are equivalent to the `Just` type from the `optype` library, +# apart from them not being `@runtime_checkable`. +# - docs: https://github.com/jorenham/optype/blob/master/README.md#just +# - code: https://github.com/jorenham/optype/blob/master/optype/_core/_just.py +@final +class JustInt(Protocol): + @property + def __class__(self, /) -> type[int]: ... + @__class__.setter + def __class__(self, value: type[int], /) -> None: ... # pyright: ignore[reportIncompatibleMethodOverride] + + +@final +class JustFloat(Protocol): + @property + def __class__(self, /) -> type[float]: ... + @__class__.setter + def __class__(self, value: type[float], /) -> None: ... # pyright: ignore[reportIncompatibleMethodOverride] + + +@final +class JustComplex(Protocol): + @property + def __class__(self, /) -> type[complex]: ... + @__class__.setter + def __class__(self, value: type[complex], /) -> None: ... # pyright: ignore[reportIncompatibleMethodOverride] + + +# + + class NestedSequence(Protocol[_T_co]): def __getitem__(self, key: int, /) -> _T_co | NestedSequence[_T_co]: ... def __len__(self, /) -> int: ... -SupportsBufferProtocol = Any -Array = Any -Device = Any -DType = Any -Namespace = ModuleType +class SupportsArrayNamespace(Protocol[_T_co]): + def __array_namespace__(self, /, *, api_version: str | None) -> _T_co: ... + + +class HasShape(Protocol[_T_co]): + @property + def shape(self, /) -> _T_co: ... + + +# Return type of `__array_namespace_info__.default_dtypes` +Capabilities = TypedDict( + "Capabilities", + { + "boolean indexing": bool, + "data-dependent shapes": bool, + "max dimensions": int, + }, +) + +# Return type of `__array_namespace_info__.default_dtypes` +DefaultDTypes = TypedDict( + "DefaultDTypes", + { + "real floating": DType, + "complex floating": DType, + "integral": DType, + "indexing": DType, + }, +) + + +_DTypeKind: TypeAlias = Literal[ + "bool", + "signed integer", + "unsigned integer", + "integral", + "real floating", + "complex floating", + "numeric", +] +# Type of the `kind` parameter in `__array_namespace_info__.dtypes` +DTypeKind: TypeAlias = _DTypeKind | tuple[_DTypeKind, ...] + + +# `__array_namespace_info__.dtypes(kind="bool")` +class DTypesBool(TypedDict): + bool: DType + + +# `__array_namespace_info__.dtypes(kind="signed integer")` +class DTypesSigned(TypedDict): + int8: DType + int16: DType + int32: DType + int64: DType + + +# `__array_namespace_info__.dtypes(kind="unsigned integer")` +class DTypesUnsigned(TypedDict): + uint8: DType + uint16: DType + uint32: DType + uint64: DType + + +# `__array_namespace_info__.dtypes(kind="integral")` +class DTypesIntegral(DTypesSigned, DTypesUnsigned): + pass + + +# `__array_namespace_info__.dtypes(kind="real floating")` +class DTypesReal(TypedDict): + float32: DType + float64: DType + + +# `__array_namespace_info__.dtypes(kind="complex floating")` +class DTypesComplex(TypedDict): + complex64: DType + complex128: DType + + +# `__array_namespace_info__.dtypes(kind="numeric")` +class DTypesNumeric(DTypesIntegral, DTypesReal, DTypesComplex): + pass + + +# `__array_namespace_info__.dtypes(kind=None)` (default) +class DTypesAll(DTypesBool, DTypesNumeric): + pass + + +# `__array_namespace_info__.dtypes(kind=?)` (fallback) +DTypesAny: TypeAlias = Mapping[str, DType] + + +__all__ = [ + "Array", + "Capabilities", + "DType", + "DTypeKind", + "DTypesAny", + "DTypesAll", + "DTypesBool", + "DTypesNumeric", + "DTypesIntegral", + "DTypesSigned", + "DTypesUnsigned", + "DTypesReal", + "DTypesComplex", + "DefaultDTypes", + "Device", + "HasShape", + "Namespace", + "JustInt", + "JustFloat", + "JustComplex", + "NestedSequence", + "SupportsArrayNamespace", + "SupportsBufferProtocol", +] + + +def __dir__() -> list[str]: + return __all__ diff --git a/sklearn/externals/array_api_compat/cupy/__init__.py b/sklearn/externals/array_api_compat/cupy/__init__.py index 59e010582c6ed..9a30f95ddf12c 100644 --- a/sklearn/externals/array_api_compat/cupy/__init__.py +++ b/sklearn/externals/array_api_compat/cupy/__init__.py @@ -8,9 +8,6 @@ # See the comment in the numpy __init__.py __import__(__package__ + '.linalg') - __import__(__package__ + '.fft') -from ..common._helpers import * # noqa: F401,F403 - __array_api_version__ = '2024.12' diff --git a/sklearn/externals/array_api_compat/cupy/_aliases.py b/sklearn/externals/array_api_compat/cupy/_aliases.py index 30d9fe48cb451..90b48f059bafa 100644 --- a/sklearn/externals/array_api_compat/cupy/_aliases.py +++ b/sklearn/externals/array_api_compat/cupy/_aliases.py @@ -1,16 +1,14 @@ from __future__ import annotations +from typing import Optional + import cupy as cp from ..common import _aliases, _helpers +from ..common._typing import NestedSequence, SupportsBufferProtocol from .._internal import get_xp - from ._info import __array_namespace_info__ - -from typing import TYPE_CHECKING -if TYPE_CHECKING: - from typing import Optional, Union - from ._typing import ndarray, Device, Dtype, NestedSequence, SupportsBufferProtocol +from ._typing import Array, Device, DType bool = cp.bool_ @@ -63,26 +61,25 @@ matrix_transpose = get_xp(cp)(_aliases.matrix_transpose) tensordot = get_xp(cp)(_aliases.tensordot) sign = get_xp(cp)(_aliases.sign) +finfo = get_xp(cp)(_aliases.finfo) +iinfo = get_xp(cp)(_aliases.iinfo) -_copy_default = object() # asarray also adds the copy keyword, which is not present in numpy 1.0. def asarray( - obj: Union[ - ndarray, - bool, - int, - float, - NestedSequence[bool | int | float], - SupportsBufferProtocol, - ], + obj: ( + Array + | bool | int | float | complex + | NestedSequence[bool | int | float | complex] + | SupportsBufferProtocol + ), /, *, - dtype: Optional[Dtype] = None, + dtype: Optional[DType] = None, device: Optional[Device] = None, - copy: Optional[bool] = _copy_default, + copy: Optional[bool] = None, **kwargs, -) -> ndarray: +) -> Array: """ Array API compatibility wrapper for asarray(). @@ -90,35 +87,23 @@ def asarray( specification for more details. """ with cp.cuda.Device(device): - # cupy is like NumPy 1.26 (except without _CopyMode). See the comments - # in asarray in numpy/_aliases.py. - if copy is not _copy_default: - # A future version of CuPy will change the meaning of copy=False - # to mean no-copy. We don't know for certain what version it will - # be yet, so to avoid breaking that version, we use a different - # default value for copy so asarray(obj) with no copy kwarg will - # always do the copy-if-needed behavior. - - # This will still need to be updated to remove the - # NotImplementedError for copy=False, but at least this won't - # break the default or existing behavior. - if copy is None: - copy = False - elif copy is False: - raise NotImplementedError("asarray(copy=False) is not yet supported in cupy") - kwargs['copy'] = copy - - return cp.array(obj, dtype=dtype, **kwargs) + if copy is None: + return cp.asarray(obj, dtype=dtype, **kwargs) + else: + res = cp.array(obj, dtype=dtype, copy=copy, **kwargs) + if not copy and res is not obj: + raise ValueError("Unable to avoid copy while creating an array as requested") + return res def astype( - x: ndarray, - dtype: Dtype, + x: Array, + dtype: DType, /, *, copy: bool = True, device: Optional[Device] = None, -) -> ndarray: +) -> Array: if device is None: return x.astype(dtype=dtype, copy=copy) out = _helpers.to_device(x.astype(dtype=dtype, copy=False), device) @@ -127,10 +112,10 @@ def astype( # cupy.count_nonzero does not have keepdims def count_nonzero( - x: ndarray, + x: Array, axis=None, keepdims=False -) -> ndarray: +) -> Array: result = cp.count_nonzero(x, axis) if keepdims: if axis is None: @@ -139,6 +124,11 @@ def count_nonzero( return result +# take_along_axis: axis defaults to -1 but in cupy (and numpy) axis is a required arg +def take_along_axis(x: Array, indices: Array, /, *, axis: int = -1): + return cp.take_along_axis(x, indices, axis=axis) + + # These functions are completely new here. If the library already has them # (i.e., numpy 2.0), use the library version instead of our wrapper. if hasattr(cp, 'vecdot'): @@ -160,6 +150,7 @@ def count_nonzero( 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atan2', 'atanh', 'bitwise_left_shift', 'bitwise_invert', 'bitwise_right_shift', - 'bool', 'concat', 'count_nonzero', 'pow', 'sign'] + 'bool', 'concat', 'count_nonzero', 'pow', 'sign', + 'take_along_axis'] _all_ignore = ['cp', 'get_xp'] diff --git a/sklearn/externals/array_api_compat/cupy/_info.py b/sklearn/externals/array_api_compat/cupy/_info.py index 790621e4f7c36..78e48a3358cf5 100644 --- a/sklearn/externals/array_api_compat/cupy/_info.py +++ b/sklearn/externals/array_api_compat/cupy/_info.py @@ -26,6 +26,7 @@ complex128, ) + class __array_namespace_info__: """ Get the array API inspection namespace for CuPy. @@ -49,7 +50,7 @@ class __array_namespace_info__: Examples -------- - >>> info = np.__array_namespace_info__() + >>> info = xp.__array_namespace_info__() >>> info.default_dtypes() {'real floating': cupy.float64, 'complex floating': cupy.complex128, @@ -94,13 +95,13 @@ def capabilities(self): >>> info = xp.__array_namespace_info__() >>> info.capabilities() {'boolean indexing': True, - 'data-dependent shapes': True} + 'data-dependent shapes': True, + 'max dimensions': 64} """ return { "boolean indexing": True, "data-dependent shapes": True, - # 'max rank' will be part of the 2024.12 standard "max dimensions": 64, } @@ -117,7 +118,7 @@ def default_device(self): Returns ------- - device : str + device : Device The default device used for new CuPy arrays. Examples @@ -126,6 +127,15 @@ def default_device(self): >>> info.default_device() Device(0) + Notes + ----- + This method returns the static default device when CuPy is initialized. + However, the *current* device used by creation functions (``empty`` etc.) + can be changed globally or with a context manager. + + See Also + -------- + https://github.com/data-apis/array-api/issues/835 """ return cuda.Device(0) @@ -312,7 +322,7 @@ def devices(self): Returns ------- - devices : list of str + devices : list[Device] The devices supported by CuPy. See Also diff --git a/sklearn/externals/array_api_compat/cupy/_typing.py b/sklearn/externals/array_api_compat/cupy/_typing.py index f3d9aab67e52f..d8e49ca773dc5 100644 --- a/sklearn/externals/array_api_compat/cupy/_typing.py +++ b/sklearn/externals/array_api_compat/cupy/_typing.py @@ -1,46 +1,31 @@ from __future__ import annotations -__all__ = [ - "ndarray", - "Device", - "Dtype", -] +__all__ = ["Array", "DType", "Device"] +_all_ignore = ["cp"] -import sys -from typing import ( - Union, - TYPE_CHECKING, -) - -from cupy import ( - ndarray, - dtype, - int8, - int16, - int32, - int64, - uint8, - uint16, - uint32, - uint64, - float32, - float64, -) +from typing import TYPE_CHECKING +import cupy as cp +from cupy import ndarray as Array from cupy.cuda.device import Device -if TYPE_CHECKING or sys.version_info >= (3, 9): - Dtype = dtype[Union[ - int8, - int16, - int32, - int64, - uint8, - uint16, - uint32, - uint64, - float32, - float64, - ]] +if TYPE_CHECKING: + # NumPy 1.x on Python 3.10 fails to parse np.dtype[] + DType = cp.dtype[ + cp.intp + | cp.int8 + | cp.int16 + | cp.int32 + | cp.int64 + | cp.uint8 + | cp.uint16 + | cp.uint32 + | cp.uint64 + | cp.float32 + | cp.float64 + | cp.complex64 + | cp.complex128 + | cp.bool_ + ] else: - Dtype = dtype + DType = cp.dtype diff --git a/sklearn/externals/array_api_compat/dask/array/__init__.py b/sklearn/externals/array_api_compat/dask/array/__init__.py index a6e69ad382e4b..1e47b9606b774 100644 --- a/sklearn/externals/array_api_compat/dask/array/__init__.py +++ b/sklearn/externals/array_api_compat/dask/array/__init__.py @@ -1,9 +1,12 @@ -from dask.array import * # noqa: F403 +from typing import Final + +from dask.array import * # noqa: F403 # These imports may overwrite names from the import * above. -from ._aliases import * # noqa: F403 +from ._aliases import * # noqa: F403 -__array_api_version__ = '2024.12' +__array_api_version__: Final = "2024.12" +# See the comment in the numpy __init__.py __import__(__package__ + '.linalg') __import__(__package__ + '.fft') diff --git a/sklearn/externals/array_api_compat/dask/array/_aliases.py b/sklearn/externals/array_api_compat/dask/array/_aliases.py index 80d66281912ca..d43881ab18f1c 100644 --- a/sklearn/externals/array_api_compat/dask/array/_aliases.py +++ b/sklearn/externals/array_api_compat/dask/array/_aliases.py @@ -1,49 +1,47 @@ -from __future__ import annotations - -from typing import Callable +# pyright: reportPrivateUsage=false +# pyright: reportUnknownArgumentType=false +# pyright: reportUnknownMemberType=false +# pyright: reportUnknownVariableType=false -from ...common import _aliases, array_namespace +from __future__ import annotations -from ..._internal import get_xp +from builtins import bool as py_bool +from collections.abc import Callable +from typing import TYPE_CHECKING, Any -from ._info import __array_namespace_info__ +if TYPE_CHECKING: + from typing_extensions import TypeIs +import dask.array as da import numpy as np +from numpy import bool_ as bool from numpy import ( - # Dtypes - iinfo, - finfo, - bool_ as bool, + can_cast, + complex64, + complex128, float32, float64, int8, int16, int32, int64, + result_type, uint8, uint16, uint32, uint64, - complex64, - complex128, - can_cast, - result_type, ) -from typing import TYPE_CHECKING - -if TYPE_CHECKING: - from typing import Optional, Union - - from ...common._typing import ( - Device, - Dtype, - Array, - NestedSequence, - SupportsBufferProtocol, - ) - -import dask.array as da +from ..._internal import get_xp +from ...common import _aliases, _helpers, array_namespace +from ...common._typing import ( + Array, + Device, + DType, + NestedSequence, + SupportsBufferProtocol, +) +from ._info import __array_namespace_info__ isdtype = get_xp(np)(_aliases.isdtype) unstack = get_xp(da)(_aliases.unstack) @@ -52,11 +50,11 @@ # da.astype doesn't respect copy=True def astype( x: Array, - dtype: Dtype, + dtype: DType, /, *, - copy: bool = True, - device: Optional[Device] = None, + copy: py_bool = True, + device: Device | None = None, ) -> Array: """ Array API compatibility wrapper for astype(). @@ -65,6 +63,7 @@ def astype( specification for more details. """ # TODO: respect device keyword? + _helpers._check_device(da, device) if not copy and dtype == x.dtype: return x @@ -79,14 +78,14 @@ def astype( # not pass stop/step as keyword arguments, which will cause # an error with dask def arange( - start: Union[int, float], + start: float, /, - stop: Optional[Union[int, float]] = None, - step: Union[int, float] = 1, + stop: float | None = None, + step: float = 1, *, - dtype: Optional[Dtype] = None, - device: Optional[Device] = None, - **kwargs, + dtype: DType | None = None, + device: Device | None = None, + **kwargs: object, ) -> Array: """ Array API compatibility wrapper for arange(). @@ -95,8 +94,9 @@ def arange( specification for more details. """ # TODO: respect device keyword? + _helpers._check_device(da, device) - args = [start] + args: list[Any] = [start] if stop is not None: args.append(stop) else: @@ -140,24 +140,19 @@ def arange( matmul = get_xp(np)(_aliases.matmul) tensordot = get_xp(np)(_aliases.tensordot) sign = get_xp(np)(_aliases.sign) +finfo = get_xp(np)(_aliases.finfo) +iinfo = get_xp(np)(_aliases.iinfo) # asarray also adds the copy keyword, which is not present in numpy 1.0. def asarray( - obj: Union[ - Array, - bool, - int, - float, - NestedSequence[bool | int | float], - SupportsBufferProtocol, - ], + obj: complex | NestedSequence[complex] | Array | SupportsBufferProtocol, /, *, - dtype: Optional[Dtype] = None, - device: Optional[Device] = None, - copy: Optional[Union[bool, np._CopyMode]] = None, - **kwargs, + dtype: DType | None = None, + device: Device | None = None, + copy: py_bool | None = None, + **kwargs: object, ) -> Array: """ Array API compatibility wrapper for asarray(). @@ -166,16 +161,17 @@ def asarray( specification for more details. """ # TODO: respect device keyword? + _helpers._check_device(da, device) if isinstance(obj, da.Array): if dtype is not None and dtype != obj.dtype: if copy is False: raise ValueError("Unable to avoid copy when changing dtype") obj = obj.astype(dtype) - return obj.copy() if copy else obj + return obj.copy() if copy else obj # pyright: ignore[reportAttributeAccessIssue] if copy is False: - raise NotImplementedError( + raise ValueError( "Unable to avoid copy when converting a non-dask object to dask" ) @@ -185,22 +181,21 @@ def asarray( return da.from_array(obj) -from dask.array import ( - # Element wise aliases - arccos as acos, - arccosh as acosh, - arcsin as asin, - arcsinh as asinh, - arctan as atan, - arctan2 as atan2, - arctanh as atanh, - left_shift as bitwise_left_shift, - right_shift as bitwise_right_shift, - invert as bitwise_invert, - power as pow, - # Other - concatenate as concat, -) +# Element wise aliases +from dask.array import arccos as acos +from dask.array import arccosh as acosh +from dask.array import arcsin as asin +from dask.array import arcsinh as asinh +from dask.array import arctan as atan +from dask.array import arctan2 as atan2 +from dask.array import arctanh as atanh + +# Other +from dask.array import concatenate as concat +from dask.array import invert as bitwise_invert +from dask.array import left_shift as bitwise_left_shift +from dask.array import power as pow +from dask.array import right_shift as bitwise_right_shift # dask.array.clip does not work unless all three arguments are provided. @@ -210,8 +205,8 @@ def asarray( def clip( x: Array, /, - min: Optional[Union[int, float, Array]] = None, - max: Optional[Union[int, float, Array]] = None, + min: float | Array | None = None, + max: float | Array | None = None, ) -> Array: """ Array API compatibility wrapper for clip(). @@ -220,8 +215,8 @@ def clip( specification for more details. """ - def _isscalar(a): - return isinstance(a, (int, float, type(None))) + def _isscalar(a: float | Array | None, /) -> TypeIs[float | None]: + return a is None or isinstance(a, (int, float)) min_shape = () if _isscalar(min) else min.shape max_shape = () if _isscalar(max) else max.shape @@ -274,7 +269,12 @@ def _ensure_single_chunk(x: Array, axis: int) -> tuple[Array, Callable[[Array], def sort( - x: Array, /, *, axis: int = -1, descending: bool = False, stable: bool = True + x: Array, + /, + *, + axis: int = -1, + descending: py_bool = False, + stable: py_bool = True, ) -> Array: """ Array API compatibility layer around the lack of sort() in Dask. @@ -304,7 +304,12 @@ def sort( def argsort( - x: Array, /, *, axis: int = -1, descending: bool = False, stable: bool = True + x: Array, + /, + *, + axis: int = -1, + descending: py_bool = False, + stable: py_bool = True, ) -> Array: """ Array API compatibility layer around the lack of argsort() in Dask. @@ -338,26 +343,34 @@ def argsort( # dask.array.count_nonzero does not have keepdims def count_nonzero( x: Array, - axis=None, - keepdims=False + axis: int | None = None, + keepdims: py_bool = False, ) -> Array: - result = da.count_nonzero(x, axis) - if keepdims: - if axis is None: - return da.reshape(result, [1]*x.ndim) - return da.expand_dims(result, axis) - return result - - - -__all__ = _aliases.__all__ + [ - '__array_namespace_info__', 'asarray', 'astype', 'acos', - 'acosh', 'asin', 'asinh', 'atan', 'atan2', - 'atanh', 'bitwise_left_shift', 'bitwise_invert', - 'bitwise_right_shift', 'concat', 'pow', 'iinfo', 'finfo', 'can_cast', - 'result_type', 'bool', 'float32', 'float64', 'int8', 'int16', 'int32', 'int64', - 'uint8', 'uint16', 'uint32', 'uint64', - 'complex64', 'complex128', 'iinfo', 'finfo', - 'can_cast', 'count_nonzero', 'result_type'] - -_all_ignore = ["Callable", "array_namespace", "get_xp", "da", "np"] + result = da.count_nonzero(x, axis) + if keepdims: + if axis is None: + return da.reshape(result, [1] * x.ndim) + return da.expand_dims(result, axis) + return result + + +__all__ = [ + "__array_namespace_info__", + "count_nonzero", + "bool", + "int8", "int16", "int32", "int64", + "uint8", "uint16", "uint32", "uint64", + "float32", "float64", + "complex64", "complex128", + "asarray", "astype", "can_cast", "result_type", + "pow", + "concat", + "acos", "acosh", "asin", "asinh", "atan", "atan2", "atanh", + "bitwise_left_shift", "bitwise_right_shift", "bitwise_invert", +] # fmt: skip +__all__ += _aliases.__all__ +_all_ignore = ["array_namespace", "get_xp", "da", "np"] + + +def __dir__() -> list[str]: + return __all__ diff --git a/sklearn/externals/array_api_compat/dask/array/_info.py b/sklearn/externals/array_api_compat/dask/array/_info.py index e15a69f4629ab..9e4d736f99657 100644 --- a/sklearn/externals/array_api_compat/dask/array/_info.py +++ b/sklearn/externals/array_api_compat/dask/array/_info.py @@ -7,25 +7,51 @@ more details. """ + +# pyright: reportPrivateUsage=false + +from __future__ import annotations + +from typing import Literal as L +from typing import TypeAlias, overload + +from numpy import bool_ as bool from numpy import ( + complex64, + complex128, dtype, - bool_ as bool, - intp, + float32, + float64, int8, int16, int32, int64, + intp, uint8, uint16, uint32, uint64, - float32, - float64, - complex64, - complex128, ) -from ...common._helpers import _DASK_DEVICE +from ...common._helpers import _DASK_DEVICE, _dask_device +from ...common._typing import ( + Capabilities, + DefaultDTypes, + DType, + DTypeKind, + DTypesAll, + DTypesAny, + DTypesBool, + DTypesComplex, + DTypesIntegral, + DTypesNumeric, + DTypesReal, + DTypesSigned, + DTypesUnsigned, +) + +_Device: TypeAlias = L["cpu"] | _dask_device + class __array_namespace_info__: """ @@ -50,7 +76,7 @@ class __array_namespace_info__: Examples -------- - >>> info = np.__array_namespace_info__() + >>> info = xp.__array_namespace_info__() >>> info.default_dtypes() {'real floating': dask.float64, 'complex floating': dask.complex128, @@ -59,20 +85,31 @@ class __array_namespace_info__: """ - __module__ = 'dask.array' + __module__ = "dask.array" - def capabilities(self): + def capabilities(self) -> Capabilities: """ Return a dictionary of array API library capabilities. The resulting dictionary has the following keys: - **"boolean indexing"**: boolean indicating whether an array library - supports boolean indexing. Always ``False`` for Dask. + supports boolean indexing. + + Dask support boolean indexing as long as both the index + and the indexed arrays have known shapes. + Note however that the output .shape and .size properties + will contain a non-compliant math.nan instead of None. - **"data-dependent shapes"**: boolean indicating whether an array - library supports data-dependent output shapes. Always ``False`` for - Dask. + library supports data-dependent output shapes. + + Dask implements unique_values et.al. + Note however that the output .shape and .size properties + will contain a non-compliant math.nan instead of None. + + - **"max dimensions"**: integer indicating the maximum number of + dimensions supported by the array library. See https://data-apis.org/array-api/latest/API_specification/generated/array_api.info.capabilities.html @@ -92,20 +129,20 @@ def capabilities(self): Examples -------- - >>> info = np.__array_namespace_info__() + >>> info = xp.__array_namespace_info__() >>> info.capabilities() {'boolean indexing': True, - 'data-dependent shapes': True} + 'data-dependent shapes': True, + 'max dimensions': 64} """ return { - "boolean indexing": False, - "data-dependent shapes": False, - # 'max rank' will be part of the 2024.12 standard + "boolean indexing": True, + "data-dependent shapes": True, "max dimensions": 64, } - def default_device(self): + def default_device(self) -> L["cpu"]: """ The default device used for new Dask arrays. @@ -120,19 +157,19 @@ def default_device(self): Returns ------- - device : str + device : Device The default device used for new Dask arrays. Examples -------- - >>> info = np.__array_namespace_info__() + >>> info = xp.__array_namespace_info__() >>> info.default_device() 'cpu' """ return "cpu" - def default_dtypes(self, *, device=None): + def default_dtypes(self, /, *, device: _Device | None = None) -> DefaultDTypes: """ The default data types used for new Dask arrays. @@ -163,7 +200,7 @@ def default_dtypes(self, *, device=None): Examples -------- - >>> info = np.__array_namespace_info__() + >>> info = xp.__array_namespace_info__() >>> info.default_dtypes() {'real floating': dask.float64, 'complex floating': dask.complex128, @@ -173,8 +210,8 @@ def default_dtypes(self, *, device=None): """ if device not in ["cpu", _DASK_DEVICE, None]: raise ValueError( - 'Device not understood. Only "cpu" or _DASK_DEVICE is allowed, but received:' - f' {device}' + f'Device not understood. Only "cpu" or _DASK_DEVICE is allowed, ' + f"but received: {device!r}" ) return { "real floating": dtype(float64), @@ -183,7 +220,41 @@ def default_dtypes(self, *, device=None): "indexing": dtype(intp), } - def dtypes(self, *, device=None, kind=None): + @overload + def dtypes( + self, /, *, device: _Device | None = None, kind: None = None + ) -> DTypesAll: ... + @overload + def dtypes( + self, /, *, device: _Device | None = None, kind: L["bool"] + ) -> DTypesBool: ... + @overload + def dtypes( + self, /, *, device: _Device | None = None, kind: L["signed integer"] + ) -> DTypesSigned: ... + @overload + def dtypes( + self, /, *, device: _Device | None = None, kind: L["unsigned integer"] + ) -> DTypesUnsigned: ... + @overload + def dtypes( + self, /, *, device: _Device | None = None, kind: L["integral"] + ) -> DTypesIntegral: ... + @overload + def dtypes( + self, /, *, device: _Device | None = None, kind: L["real floating"] + ) -> DTypesReal: ... + @overload + def dtypes( + self, /, *, device: _Device | None = None, kind: L["complex floating"] + ) -> DTypesComplex: ... + @overload + def dtypes( + self, /, *, device: _Device | None = None, kind: L["numeric"] + ) -> DTypesNumeric: ... + def dtypes( + self, /, *, device: _Device | None = None, kind: DTypeKind | None = None + ) -> DTypesAny: """ The array API data types supported by Dask. @@ -229,7 +300,7 @@ def dtypes(self, *, device=None, kind=None): Examples -------- - >>> info = np.__array_namespace_info__() + >>> info = xp.__array_namespace_info__() >>> info.dtypes(kind='signed integer') {'int8': dask.int8, 'int16': dask.int16, @@ -240,7 +311,7 @@ def dtypes(self, *, device=None, kind=None): if device not in ["cpu", _DASK_DEVICE, None]: raise ValueError( 'Device not understood. Only "cpu" or _DASK_DEVICE is allowed, but received:' - f' {device}' + f" {device}" ) if kind is None: return { @@ -310,14 +381,14 @@ def dtypes(self, *, device=None, kind=None): "complex64": dtype(complex64), "complex128": dtype(complex128), } - if isinstance(kind, tuple): - res = {} + if isinstance(kind, tuple): # type: ignore[reportUnnecessaryIsinstanceCall] + res: dict[str, DType] = {} for k in kind: res.update(self.dtypes(kind=k)) return res raise ValueError(f"unsupported kind: {kind!r}") - def devices(self): + def devices(self) -> list[_Device]: """ The devices supported by Dask. @@ -325,7 +396,7 @@ def devices(self): Returns ------- - devices : list of str + devices : list[Device] The devices supported by Dask. See Also @@ -337,7 +408,7 @@ def devices(self): Examples -------- - >>> info = np.__array_namespace_info__() + >>> info = xp.__array_namespace_info__() >>> info.devices() ['cpu', DASK_DEVICE] diff --git a/sklearn/externals/array_api_compat/dask/array/fft.py b/sklearn/externals/array_api_compat/dask/array/fft.py index aebd86f7b201d..3f40dffe7abd5 100644 --- a/sklearn/externals/array_api_compat/dask/array/fft.py +++ b/sklearn/externals/array_api_compat/dask/array/fft.py @@ -4,9 +4,10 @@ # from dask.array.fft import __all__ as linalg_all _n = {} exec('from dask.array.fft import *', _n) -del _n['__builtins__'] +for k in ("__builtins__", "Sequence", "annotations", "warnings"): + _n.pop(k, None) fft_all = list(_n) -del _n +del _n, k from ...common import _fft from ..._internal import get_xp @@ -16,9 +17,5 @@ fftfreq = get_xp(da)(_fft.fftfreq) rfftfreq = get_xp(da)(_fft.rfftfreq) -__all__ = [elem for elem in fft_all if elem != "annotations"] + ["fftfreq", "rfftfreq"] - -del get_xp -del da -del fft_all -del _fft +__all__ = fft_all + ["fftfreq", "rfftfreq"] +_all_ignore = ["da", "fft_all", "get_xp", "warnings"] diff --git a/sklearn/externals/array_api_compat/dask/array/linalg.py b/sklearn/externals/array_api_compat/dask/array/linalg.py index 49c26d8b819f8..0825386ed5dc3 100644 --- a/sklearn/externals/array_api_compat/dask/array/linalg.py +++ b/sklearn/externals/array_api_compat/dask/array/linalg.py @@ -1,33 +1,29 @@ from __future__ import annotations -from ...common import _linalg -from ..._internal import get_xp +from typing import Literal -# Exports -from dask.array.linalg import * # noqa: F403 -from dask.array import outer +import dask.array as da -# These functions are in both the main and linalg namespaces -from dask.array import matmul, tensordot -from ._aliases import matrix_transpose, vecdot +# The `matmul` and `tensordot` functions are in both the main and linalg namespaces +from dask.array import matmul, outer, tensordot -import dask.array as da +# Exports +from dask.array.linalg import * # noqa: F403 -from typing import TYPE_CHECKING -if TYPE_CHECKING: - from ...common._typing import Array - from typing import Literal +from ..._internal import get_xp +from ...common import _linalg +from ...common._typing import Array as _Array +from ._aliases import matrix_transpose, vecdot # dask.array.linalg doesn't have __all__. If it is added, replace this with # # from dask.array.linalg import __all__ as linalg_all _n = {} exec('from dask.array.linalg import *', _n) -del _n['__builtins__'] -if 'annotations' in _n: - del _n['annotations'] +for k in ('__builtins__', 'annotations', 'operator', 'warnings', 'Array'): + _n.pop(k, None) linalg_all = list(_n) -del _n +del _n, k EighResult = _linalg.EighResult QRResult = _linalg.QRResult @@ -37,8 +33,11 @@ # supports the mode keyword on QR # https://github.com/dask/dask/issues/10388 #qr = get_xp(da)(_linalg.qr) -def qr(x: Array, mode: Literal['reduced', 'complete'] = 'reduced', - **kwargs) -> QRResult: +def qr( + x: _Array, + mode: Literal["reduced", "complete"] = "reduced", + **kwargs: object, +) -> QRResult: if mode != "reduced": raise ValueError("dask arrays only support using mode='reduced'") return QRResult(*da.linalg.qr(x, **kwargs)) @@ -51,12 +50,12 @@ def qr(x: Array, mode: Literal['reduced', 'complete'] = 'reduced', # Wrap the svd functions to not pass full_matrices to dask # when full_matrices=False (as that is the default behavior for dask), # and dask doesn't have the full_matrices keyword -def svd(x: Array, full_matrices: bool = True, **kwargs) -> SVDResult: +def svd(x: _Array, full_matrices: bool = True, **kwargs) -> SVDResult: if full_matrices: raise ValueError("full_matrics=True is not supported by dask.") return da.linalg.svd(x, coerce_signs=False, **kwargs) -def svdvals(x: Array) -> Array: +def svdvals(x: _Array) -> _Array: # TODO: can't avoid computing U or V for dask _, s, _ = svd(x) return s @@ -70,4 +69,4 @@ def svdvals(x: Array) -> Array: "cholesky", "matrix_rank", "matrix_norm", "svdvals", "vector_norm", "diagonal"] -_all_ignore = ['get_xp', 'da', 'linalg_all'] +_all_ignore = ['get_xp', 'da', 'linalg_all', 'warnings'] diff --git a/sklearn/externals/array_api_compat/numpy/__init__.py b/sklearn/externals/array_api_compat/numpy/__init__.py index 02c55d28a01e8..3e138f53db006 100644 --- a/sklearn/externals/array_api_compat/numpy/__init__.py +++ b/sklearn/externals/array_api_compat/numpy/__init__.py @@ -1,10 +1,16 @@ -from numpy import * # noqa: F403 +# ruff: noqa: PLC0414 +from typing import Final + +from numpy import * # noqa: F403 # pyright: ignore[reportWildcardImportFromLibrary] # from numpy import * doesn't overwrite these builtin names -from numpy import abs, max, min, round # noqa: F401 +from numpy import abs as abs +from numpy import max as max +from numpy import min as min +from numpy import round as round # These imports may overwrite names from the import * above. -from ._aliases import * # noqa: F403 +from ._aliases import * # noqa: F403 # Don't know why, but we have to do an absolute import to import linalg. If we # instead do @@ -13,18 +19,10 @@ # # It doesn't overwrite np.linalg from above. The import is generated # dynamically so that the library can be vendored. -__import__(__package__ + '.linalg') - -__import__(__package__ + '.fft') - -from .linalg import matrix_transpose, vecdot # noqa: F401 +__import__(__package__ + ".linalg") -from ..common._helpers import * # noqa: F403 +__import__(__package__ + ".fft") -try: - # Used in asarray(). Not present in older versions. - from numpy import _CopyMode # noqa: F401 -except ImportError: - pass +from .linalg import matrix_transpose, vecdot # type: ignore[no-redef] # noqa: F401 -__array_api_version__ = '2024.12' +__array_api_version__: Final = "2024.12" diff --git a/sklearn/externals/array_api_compat/numpy/_aliases.py b/sklearn/externals/array_api_compat/numpy/_aliases.py index a47f712146e4a..a1aee5c0df796 100644 --- a/sklearn/externals/array_api_compat/numpy/_aliases.py +++ b/sklearn/externals/array_api_compat/numpy/_aliases.py @@ -1,17 +1,24 @@ +# pyright: reportPrivateUsage=false from __future__ import annotations -from ..common import _aliases +from builtins import bool as py_bool +from typing import TYPE_CHECKING, Any, Literal, TypeAlias, cast -from .._internal import get_xp +import numpy as np +from .._internal import get_xp +from ..common import _aliases, _helpers +from ..common._typing import NestedSequence, SupportsBufferProtocol from ._info import __array_namespace_info__ +from ._typing import Array, Device, DType -from typing import TYPE_CHECKING if TYPE_CHECKING: - from typing import Optional, Union - from ._typing import ndarray, Device, Dtype, NestedSequence, SupportsBufferProtocol + from typing_extensions import Buffer, TypeIs + +# The values of the `_CopyMode` enum can be either `False`, `True`, or `2`: +# https://github.com/numpy/numpy/blob/5a8a6a79d9c2fff8f07dcab5d41e14f8508d673f/numpy/_globals.pyi#L7-L10 +_Copy: TypeAlias = py_bool | Literal[2] | np._CopyMode -import numpy as np bool = np.bool_ # Basic renames @@ -63,104 +70,121 @@ matrix_transpose = get_xp(np)(_aliases.matrix_transpose) tensordot = get_xp(np)(_aliases.tensordot) sign = get_xp(np)(_aliases.sign) +finfo = get_xp(np)(_aliases.finfo) +iinfo = get_xp(np)(_aliases.iinfo) -def _supports_buffer_protocol(obj): + +def _supports_buffer_protocol(obj: object) -> TypeIs[Buffer]: # pyright: ignore[reportUnusedFunction] try: - memoryview(obj) + memoryview(obj) # pyright: ignore[reportArgumentType] except TypeError: return False return True + # asarray also adds the copy keyword, which is not present in numpy 1.0. # asarray() is different enough between numpy, cupy, and dask, the logic # complicated enough that it's easier to define it separately for each module # rather than trying to combine everything into one function in common/ def asarray( - obj: Union[ - ndarray, - bool, - int, - float, - NestedSequence[bool | int | float], - SupportsBufferProtocol, - ], + obj: Array | complex | NestedSequence[complex] | SupportsBufferProtocol, /, *, - dtype: Optional[Dtype] = None, - device: Optional[Device] = None, - copy: "Optional[Union[bool, np._CopyMode]]" = None, - **kwargs, -) -> ndarray: + dtype: DType | None = None, + device: Device | None = None, + copy: _Copy | None = None, + **kwargs: Any, +) -> Array: """ Array API compatibility wrapper for asarray(). See the corresponding documentation in the array library and/or the array API specification for more details. """ - if device not in ["cpu", None]: - raise ValueError(f"Unsupported device for NumPy: {device!r}") + _helpers._check_device(np, device) - if hasattr(np, '_CopyMode'): - if copy is None: - copy = np._CopyMode.IF_NEEDED - elif copy is False: - copy = np._CopyMode.NEVER - elif copy is True: - copy = np._CopyMode.ALWAYS - else: - # Not present in older NumPys. In this case, we cannot really support - # copy=False. - if copy is False: - raise NotImplementedError("asarray(copy=False) requires a newer version of NumPy.") + if copy is None: + copy = np._CopyMode.IF_NEEDED + elif copy is False: + copy = np._CopyMode.NEVER + elif copy is True: + copy = np._CopyMode.ALWAYS - return np.array(obj, copy=copy, dtype=dtype, **kwargs) + return np.array(obj, copy=copy, dtype=dtype, **kwargs) # pyright: ignore def astype( - x: ndarray, - dtype: Dtype, + x: Array, + dtype: DType, /, *, - copy: bool = True, - device: Optional[Device] = None, -) -> ndarray: + copy: py_bool = True, + device: Device | None = None, +) -> Array: + _helpers._check_device(np, device) return x.astype(dtype=dtype, copy=copy) # count_nonzero returns a python int for axis=None and keepdims=False # https://github.com/numpy/numpy/issues/17562 def count_nonzero( - x : ndarray, - axis=None, - keepdims=False -) -> ndarray: - result = np.count_nonzero(x, axis=axis, keepdims=keepdims) + x: Array, + axis: int | tuple[int, ...] | None = None, + keepdims: py_bool = False, +) -> Array: + # NOTE: this is currently incorrectly typed in numpy, but will be fixed in + # numpy 2.2.5 and 2.3.0: https://github.com/numpy/numpy/pull/28750 + result = cast("Any", np.count_nonzero(x, axis=axis, keepdims=keepdims)) # pyright: ignore[reportArgumentType, reportCallIssue] if axis is None and not keepdims: return np.asarray(result) return result +# take_along_axis: axis defaults to -1 but in numpy axis is a required arg +def take_along_axis(x: Array, indices: Array, /, *, axis: int = -1): + return np.take_along_axis(x, indices, axis=axis) + + # These functions are completely new here. If the library already has them # (i.e., numpy 2.0), use the library version instead of our wrapper. -if hasattr(np, 'vecdot'): +if hasattr(np, "vecdot"): vecdot = np.vecdot else: vecdot = get_xp(np)(_aliases.vecdot) -if hasattr(np, 'isdtype'): +if hasattr(np, "isdtype"): isdtype = np.isdtype else: isdtype = get_xp(np)(_aliases.isdtype) -if hasattr(np, 'unstack'): +if hasattr(np, "unstack"): unstack = np.unstack else: unstack = get_xp(np)(_aliases.unstack) -__all__ = _aliases.__all__ + ['__array_namespace_info__', 'asarray', 'astype', - 'acos', 'acosh', 'asin', 'asinh', 'atan', - 'atan2', 'atanh', 'bitwise_left_shift', - 'bitwise_invert', 'bitwise_right_shift', - 'bool', 'concat', 'count_nonzero', 'pow'] - -_all_ignore = ['np', 'get_xp'] +__all__ = [ + "__array_namespace_info__", + "asarray", + "astype", + "acos", + "acosh", + "asin", + "asinh", + "atan", + "atan2", + "atanh", + "bitwise_left_shift", + "bitwise_invert", + "bitwise_right_shift", + "bool", + "concat", + "count_nonzero", + "pow", + "take_along_axis" +] +__all__ += _aliases.__all__ +_all_ignore = ["np", "get_xp"] + + +def __dir__() -> list[str]: + return __all__ diff --git a/sklearn/externals/array_api_compat/numpy/_info.py b/sklearn/externals/array_api_compat/numpy/_info.py index e706d1188bf14..f307f62c5d5d5 100644 --- a/sklearn/externals/array_api_compat/numpy/_info.py +++ b/sklearn/externals/array_api_compat/numpy/_info.py @@ -7,24 +7,28 @@ more details. """ +from __future__ import annotations + +from numpy import bool_ as bool from numpy import ( + complex64, + complex128, dtype, - bool_ as bool, - intp, + float32, + float64, int8, int16, int32, int64, + intp, uint8, uint16, uint32, uint64, - float32, - float64, - complex64, - complex128, ) +from ._typing import Device, DType + class __array_namespace_info__: """ @@ -94,13 +98,13 @@ def capabilities(self): >>> info = np.__array_namespace_info__() >>> info.capabilities() {'boolean indexing': True, - 'data-dependent shapes': True} + 'data-dependent shapes': True, + 'max dimensions': 64} """ return { "boolean indexing": True, "data-dependent shapes": True, - # 'max rank' will be part of the 2024.12 standard "max dimensions": 64, } @@ -119,7 +123,7 @@ def default_device(self): Returns ------- - device : str + device : Device The default device used for new NumPy arrays. Examples @@ -131,7 +135,11 @@ def default_device(self): """ return "cpu" - def default_dtypes(self, *, device=None): + def default_dtypes( + self, + *, + device: Device | None = None, + ) -> dict[str, dtype[intp | float64 | complex128]]: """ The default data types used for new NumPy arrays. @@ -183,7 +191,12 @@ def default_dtypes(self, *, device=None): "indexing": dtype(intp), } - def dtypes(self, *, device=None, kind=None): + def dtypes( + self, + *, + device: Device | None = None, + kind: str | tuple[str, ...] | None = None, + ) -> dict[str, DType]: """ The array API data types supported by NumPy. @@ -260,7 +273,7 @@ def dtypes(self, *, device=None, kind=None): "complex128": dtype(complex128), } if kind == "bool": - return {"bool": bool} + return {"bool": dtype(bool)} if kind == "signed integer": return { "int8": dtype(int8), @@ -312,13 +325,13 @@ def dtypes(self, *, device=None, kind=None): "complex128": dtype(complex128), } if isinstance(kind, tuple): - res = {} + res: dict[str, DType] = {} for k in kind: res.update(self.dtypes(kind=k)) return res raise ValueError(f"unsupported kind: {kind!r}") - def devices(self): + def devices(self) -> list[Device]: """ The devices supported by NumPy. @@ -326,7 +339,7 @@ def devices(self): Returns ------- - devices : list of str + devices : list[Device] The devices supported by NumPy. See Also @@ -344,3 +357,10 @@ def devices(self): """ return ["cpu"] + + +__all__ = ["__array_namespace_info__"] + + +def __dir__() -> list[str]: + return __all__ diff --git a/sklearn/externals/array_api_compat/numpy/_typing.py b/sklearn/externals/array_api_compat/numpy/_typing.py index c5ebb5abb9875..e771c788bbcab 100644 --- a/sklearn/externals/array_api_compat/numpy/_typing.py +++ b/sklearn/externals/array_api_compat/numpy/_typing.py @@ -1,46 +1,30 @@ from __future__ import annotations -__all__ = [ - "ndarray", - "Device", - "Dtype", -] - -import sys -from typing import ( - Literal, - Union, - TYPE_CHECKING, -) - -from numpy import ( - ndarray, - dtype, - int8, - int16, - int32, - int64, - uint8, - uint16, - uint32, - uint64, - float32, - float64, -) - -Device = Literal["cpu"] -if TYPE_CHECKING or sys.version_info >= (3, 9): - Dtype = dtype[Union[ - int8, - int16, - int32, - int64, - uint8, - uint16, - uint32, - uint64, - float32, - float64, - ]] +from typing import TYPE_CHECKING, Any, Literal, TypeAlias + +import numpy as np + +Device: TypeAlias = Literal["cpu"] + +if TYPE_CHECKING: + + # NumPy 1.x on Python 3.10 fails to parse np.dtype[] + DType: TypeAlias = np.dtype[ + np.bool_ + | np.integer[Any] + | np.float32 + | np.float64 + | np.complex64 + | np.complex128 + ] + Array: TypeAlias = np.ndarray[Any, DType] else: - Dtype = dtype + DType: TypeAlias = np.dtype + Array: TypeAlias = np.ndarray + +__all__ = ["Array", "DType", "Device"] +_all_ignore = ["np"] + + +def __dir__() -> list[str]: + return __all__ diff --git a/sklearn/externals/array_api_compat/numpy/fft.py b/sklearn/externals/array_api_compat/numpy/fft.py index 286675946e0fb..06875f00b4312 100644 --- a/sklearn/externals/array_api_compat/numpy/fft.py +++ b/sklearn/externals/array_api_compat/numpy/fft.py @@ -1,10 +1,9 @@ -from numpy.fft import * # noqa: F403 +import numpy as np from numpy.fft import __all__ as fft_all +from numpy.fft import fft2, ifft2, irfft2, rfft2 -from ..common import _fft from .._internal import get_xp - -import numpy as np +from ..common import _fft fft = get_xp(np)(_fft.fft) ifft = get_xp(np)(_fft.ifft) @@ -21,7 +20,14 @@ fftshift = get_xp(np)(_fft.fftshift) ifftshift = get_xp(np)(_fft.ifftshift) -__all__ = fft_all + _fft.__all__ + +__all__ = ["rfft2", "irfft2", "fft2", "ifft2"] +__all__ += _fft.__all__ + + +def __dir__() -> list[str]: + return __all__ + del get_xp del np diff --git a/sklearn/externals/array_api_compat/numpy/linalg.py b/sklearn/externals/array_api_compat/numpy/linalg.py index 8f01593bd0ae6..2d3e731da3fc0 100644 --- a/sklearn/externals/array_api_compat/numpy/linalg.py +++ b/sklearn/externals/array_api_compat/numpy/linalg.py @@ -1,14 +1,35 @@ -from numpy.linalg import * # noqa: F403 -from numpy.linalg import __all__ as linalg_all -import numpy as _np +# pyright: reportAttributeAccessIssue=false +# pyright: reportUnknownArgumentType=false +# pyright: reportUnknownMemberType=false +# pyright: reportUnknownVariableType=false + +from __future__ import annotations + +import numpy as np + +# intersection of `np.linalg.__all__` on numpy 1.22 and 2.2, minus `_linalg.__all__` +from numpy.linalg import ( + LinAlgError, + cond, + det, + eig, + eigvals, + eigvalsh, + inv, + lstsq, + matrix_power, + multi_dot, + norm, + tensorinv, + tensorsolve, +) -from ..common import _linalg from .._internal import get_xp +from ..common import _linalg # These functions are in both the main and linalg namespaces -from ._aliases import matmul, matrix_transpose, tensordot, vecdot # noqa: F401 - -import numpy as np +from ._aliases import matmul, matrix_transpose, tensordot, vecdot # noqa: F401 +from ._typing import Array cross = get_xp(np)(_linalg.cross) outer = get_xp(np)(_linalg.outer) @@ -38,19 +59,28 @@ # To workaround this, the below is the code from np.linalg.solve except # only calling solve1 in the exactly 1D case. + # This code is here instead of in common because it is numpy specific. Also # note that CuPy's solve() does not currently support broadcasting (see # https://github.com/cupy/cupy/blob/main/cupy/cublas.py#L43). -def solve(x1: _np.ndarray, x2: _np.ndarray, /) -> _np.ndarray: +def solve(x1: Array, x2: Array, /) -> Array: try: from numpy.linalg._linalg import ( - _makearray, _assert_stacked_2d, _assert_stacked_square, - _commonType, isComplexType, _raise_linalgerror_singular + _assert_stacked_2d, + _assert_stacked_square, + _commonType, + _makearray, + _raise_linalgerror_singular, + isComplexType, ) except ImportError: from numpy.linalg.linalg import ( - _makearray, _assert_stacked_2d, _assert_stacked_square, - _commonType, isComplexType, _raise_linalgerror_singular + _assert_stacked_2d, + _assert_stacked_square, + _commonType, + _makearray, + _raise_linalgerror_singular, + isComplexType, ) from numpy.linalg import _umath_linalg @@ -61,6 +91,7 @@ def solve(x1: _np.ndarray, x2: _np.ndarray, /) -> _np.ndarray: t, result_t = _commonType(x1, x2) # This part is different from np.linalg.solve + gufunc: np.ufunc if x2.ndim == 1: gufunc = _umath_linalg.solve1 else: @@ -68,23 +99,45 @@ def solve(x1: _np.ndarray, x2: _np.ndarray, /) -> _np.ndarray: # This does nothing currently but is left in because it will be relevant # when complex dtype support is added to the spec in 2022. - signature = 'DD->D' if isComplexType(t) else 'dd->d' - with _np.errstate(call=_raise_linalgerror_singular, invalid='call', - over='ignore', divide='ignore', under='ignore'): - r = gufunc(x1, x2, signature=signature) + signature = "DD->D" if isComplexType(t) else "dd->d" + with np.errstate( + call=_raise_linalgerror_singular, + invalid="call", + over="ignore", + divide="ignore", + under="ignore", + ): + r: Array = gufunc(x1, x2, signature=signature) return wrap(r.astype(result_t, copy=False)) + # These functions are completely new here. If the library already has them # (i.e., numpy 2.0), use the library version instead of our wrapper. -if hasattr(np.linalg, 'vector_norm'): +if hasattr(np.linalg, "vector_norm"): vector_norm = np.linalg.vector_norm else: vector_norm = get_xp(np)(_linalg.vector_norm) -__all__ = linalg_all + _linalg.__all__ + ['solve'] -del get_xp -del np -del linalg_all -del _linalg +__all__ = [ + "LinAlgError", + "cond", + "det", + "eig", + "eigvals", + "eigvalsh", + "inv", + "lstsq", + "matrix_power", + "multi_dot", + "norm", + "tensorinv", + "tensorsolve", +] +__all__ += _linalg.__all__ +__all__ += ["solve", "vector_norm"] + + +def __dir__() -> list[str]: + return __all__ diff --git a/sklearn/externals/array_api_compat/py.typed b/sklearn/externals/array_api_compat/py.typed new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sklearn/externals/array_api_compat/torch/__init__.py b/sklearn/externals/array_api_compat/torch/__init__.py index a985986e649c3..69fd19ce83a56 100644 --- a/sklearn/externals/array_api_compat/torch/__init__.py +++ b/sklearn/externals/array_api_compat/torch/__init__.py @@ -9,16 +9,14 @@ or 'cpu' in n or 'backward' in n): continue - exec(n + ' = torch.' + n) + exec(f"{n} = torch.{n}") +del n # These imports may overwrite names from the import * above. from ._aliases import * # noqa: F403 # See the comment in the numpy __init__.py __import__(__package__ + '.linalg') - __import__(__package__ + '.fft') -from ..common._helpers import * # noqa: F403 - __array_api_version__ = '2024.12' diff --git a/sklearn/externals/array_api_compat/torch/_aliases.py b/sklearn/externals/array_api_compat/torch/_aliases.py index 4b727f1c22ba8..de5d1a5d40eb5 100644 --- a/sklearn/externals/array_api_compat/torch/_aliases.py +++ b/sklearn/externals/array_api_compat/torch/_aliases.py @@ -2,21 +2,15 @@ from functools import reduce as _reduce, wraps as _wraps from builtins import all as _builtin_all, any as _builtin_any - -from ..common import _aliases -from .._internal import get_xp - -from ._info import __array_namespace_info__ +from typing import Any, List, Optional, Sequence, Tuple, Union, Literal import torch -from typing import TYPE_CHECKING -if TYPE_CHECKING: - from typing import List, Optional, Sequence, Tuple, Union - from ..common._typing import Device - from torch import dtype as Dtype - - array = torch.Tensor +from .._internal import get_xp +from ..common import _aliases +from ..common._typing import NestedSequence, SupportsBufferProtocol +from ._info import __array_namespace_info__ +from ._typing import Array, Device, DType _int_dtypes = { torch.uint8, @@ -41,47 +35,23 @@ torch.complex128, } -_promotion_table = { - # bool - (torch.bool, torch.bool): torch.bool, +_promotion_table = { # ints - (torch.int8, torch.int8): torch.int8, (torch.int8, torch.int16): torch.int16, (torch.int8, torch.int32): torch.int32, (torch.int8, torch.int64): torch.int64, - (torch.int16, torch.int8): torch.int16, - (torch.int16, torch.int16): torch.int16, (torch.int16, torch.int32): torch.int32, (torch.int16, torch.int64): torch.int64, - (torch.int32, torch.int8): torch.int32, - (torch.int32, torch.int16): torch.int32, - (torch.int32, torch.int32): torch.int32, (torch.int32, torch.int64): torch.int64, - (torch.int64, torch.int8): torch.int64, - (torch.int64, torch.int16): torch.int64, - (torch.int64, torch.int32): torch.int64, - (torch.int64, torch.int64): torch.int64, - # uints - (torch.uint8, torch.uint8): torch.uint8, # ints and uints (mixed sign) - (torch.int8, torch.uint8): torch.int16, - (torch.int16, torch.uint8): torch.int16, - (torch.int32, torch.uint8): torch.int32, - (torch.int64, torch.uint8): torch.int64, (torch.uint8, torch.int8): torch.int16, (torch.uint8, torch.int16): torch.int16, (torch.uint8, torch.int32): torch.int32, (torch.uint8, torch.int64): torch.int64, # floats - (torch.float32, torch.float32): torch.float32, (torch.float32, torch.float64): torch.float64, - (torch.float64, torch.float32): torch.float64, - (torch.float64, torch.float64): torch.float64, # complexes - (torch.complex64, torch.complex64): torch.complex64, (torch.complex64, torch.complex128): torch.complex128, - (torch.complex128, torch.complex64): torch.complex128, - (torch.complex128, torch.complex128): torch.complex128, # Mixed float and complex (torch.float32, torch.complex64): torch.complex64, (torch.float32, torch.complex128): torch.complex128, @@ -89,6 +59,9 @@ (torch.float64, torch.complex128): torch.complex128, } +_promotion_table.update({(b, a): c for (a, b), c in _promotion_table.items()}) +_promotion_table.update({(a, a): a for a in _array_api_dtypes}) + def _two_arg(f): @_wraps(f) @@ -123,7 +96,9 @@ def _fix_promotion(x1, x2, only_scalar=True): _py_scalars = (bool, int, float, complex) -def result_type(*arrays_and_dtypes: Union[array, Dtype, bool, int, float, complex]) -> Dtype: +def result_type( + *arrays_and_dtypes: Array | DType | bool | int | float | complex +) -> DType: num = len(arrays_and_dtypes) if num == 0: @@ -154,13 +129,18 @@ def result_type(*arrays_and_dtypes: Union[array, Dtype, bool, int, float, comple return _reduce(_result_type, others + scalars) -def _result_type(x, y): +def _result_type( + x: Array | DType | bool | int | float | complex, + y: Array | DType | bool | int | float | complex, +) -> DType: if not (isinstance(x, _py_scalars) or isinstance(y, _py_scalars)): - xdt = x.dtype if not isinstance(x, torch.dtype) else x - ydt = y.dtype if not isinstance(y, torch.dtype) else y + xdt = x if isinstance(x, torch.dtype) else x.dtype + ydt = y if isinstance(y, torch.dtype) else y.dtype - if (xdt, ydt) in _promotion_table: + try: return _promotion_table[xdt, ydt] + except KeyError: + pass # This doesn't result_type(dtype, dtype) for non-array API dtypes # because torch.result_type only accepts tensors. This does however, allow @@ -170,7 +150,7 @@ def _result_type(x, y): return torch.result_type(x, y) -def can_cast(from_: Union[Dtype, array], to: Dtype, /) -> bool: +def can_cast(from_: Union[DType, Array], to: DType, /) -> bool: if not isinstance(from_, torch.dtype): from_ = from_.dtype return torch.can_cast(from_, to) @@ -212,17 +192,39 @@ def can_cast(from_: Union[Dtype, array], to: Dtype, /) -> bool: remainder = _two_arg(torch.remainder) subtract = _two_arg(torch.subtract) + +def asarray( + obj: ( + Array + | bool | int | float | complex + | NestedSequence[bool | int | float | complex] + | SupportsBufferProtocol + ), + /, + *, + dtype: DType | None = None, + device: Device | None = None, + copy: bool | None = None, + **kwargs: Any, +) -> Array: + # torch.asarray does not respect input->output device propagation + # https://github.com/pytorch/pytorch/issues/150199 + if device is None and isinstance(obj, torch.Tensor): + device = obj.device + return torch.asarray(obj, dtype=dtype, device=device, copy=copy, **kwargs) + + # These wrappers are mostly based on the fact that pytorch uses 'dim' instead # of 'axis'. # torch.min and torch.max return a tuple and don't support multiple axes https://github.com/pytorch/pytorch/issues/58745 -def max(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array: +def max(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> Array: # https://github.com/pytorch/pytorch/issues/29137 if axis == (): return torch.clone(x) return torch.amax(x, axis, keepdims=keepdims) -def min(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> array: +def min(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False) -> Array: # https://github.com/pytorch/pytorch/issues/29137 if axis == (): return torch.clone(x) @@ -232,10 +234,13 @@ def min(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keep unstack = get_xp(torch)(_aliases.unstack) cumulative_sum = get_xp(torch)(_aliases.cumulative_sum) cumulative_prod = get_xp(torch)(_aliases.cumulative_prod) +finfo = get_xp(torch)(_aliases.finfo) +iinfo = get_xp(torch)(_aliases.iinfo) + # torch.sort also returns a tuple # https://github.com/pytorch/pytorch/issues/70921 -def sort(x: array, /, *, axis: int = -1, descending: bool = False, stable: bool = True, **kwargs) -> array: +def sort(x: Array, /, *, axis: int = -1, descending: bool = False, stable: bool = True, **kwargs) -> Array: return torch.sort(x, dim=axis, descending=descending, stable=stable, **kwargs).values def _normalize_axes(axis, ndim): @@ -280,28 +285,35 @@ def _reduce_multiple_axes(f, x, axis, keepdims=False, **kwargs): out = torch.unsqueeze(out, a) return out -def prod(x: array, + +def _sum_prod_no_axis(x: Array, dtype: DType | None) -> Array: + """ + Implements `sum(..., axis=())` and `prod(..., axis=())`. + + Works around https://github.com/pytorch/pytorch/issues/29137 + """ + if dtype is not None: + return x.clone() if dtype == x.dtype else x.to(dtype) + + # We can't upcast uint8 according to the spec because there is no + # torch.uint64, so at least upcast to int64 which is what prod does + # when axis=None. + if x.dtype in (torch.uint8, torch.int8, torch.int16, torch.int32): + return x.to(torch.int64) + + return x.clone() + + +def prod(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, - dtype: Optional[Dtype] = None, + dtype: Optional[DType] = None, keepdims: bool = False, - **kwargs) -> array: - x = torch.asarray(x) - ndim = x.ndim + **kwargs) -> Array: - # https://github.com/pytorch/pytorch/issues/29137. Separate from the logic - # below because it still needs to upcast. if axis == (): - if dtype is None: - # We can't upcast uint8 according to the spec because there is no - # torch.uint64, so at least upcast to int64 which is what sum does - # when axis=None. - if x.dtype in [torch.int8, torch.int16, torch.int32, torch.uint8]: - return x.to(torch.int64) - return x.clone() - return x.to(dtype) - + return _sum_prod_no_axis(x, dtype) # torch.prod doesn't support multiple axes # (https://github.com/pytorch/pytorch/issues/56586). if isinstance(axis, tuple): @@ -310,51 +322,38 @@ def prod(x: array, # torch doesn't support keepdims with axis=None # (https://github.com/pytorch/pytorch/issues/71209) res = torch.prod(x, dtype=dtype, **kwargs) - res = _axis_none_keepdims(res, ndim, keepdims) + res = _axis_none_keepdims(res, x.ndim, keepdims) return res return torch.prod(x, axis, dtype=dtype, keepdims=keepdims, **kwargs) -def sum(x: array, +def sum(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, - dtype: Optional[Dtype] = None, + dtype: Optional[DType] = None, keepdims: bool = False, - **kwargs) -> array: - x = torch.asarray(x) - ndim = x.ndim + **kwargs) -> Array: - # https://github.com/pytorch/pytorch/issues/29137. - # Make sure it upcasts. if axis == (): - if dtype is None: - # We can't upcast uint8 according to the spec because there is no - # torch.uint64, so at least upcast to int64 which is what sum does - # when axis=None. - if x.dtype in [torch.int8, torch.int16, torch.int32, torch.uint8]: - return x.to(torch.int64) - return x.clone() - return x.to(dtype) - + return _sum_prod_no_axis(x, dtype) if axis is None: # torch doesn't support keepdims with axis=None # (https://github.com/pytorch/pytorch/issues/71209) res = torch.sum(x, dtype=dtype, **kwargs) - res = _axis_none_keepdims(res, ndim, keepdims) + res = _axis_none_keepdims(res, x.ndim, keepdims) return res return torch.sum(x, axis, dtype=dtype, keepdims=keepdims, **kwargs) -def any(x: array, +def any(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False, - **kwargs) -> array: - x = torch.asarray(x) - ndim = x.ndim + **kwargs) -> Array: + if axis == (): return x.to(torch.bool) # torch.any doesn't support multiple axes @@ -366,20 +365,19 @@ def any(x: array, # torch doesn't support keepdims with axis=None # (https://github.com/pytorch/pytorch/issues/71209) res = torch.any(x, **kwargs) - res = _axis_none_keepdims(res, ndim, keepdims) + res = _axis_none_keepdims(res, x.ndim, keepdims) return res.to(torch.bool) # torch.any doesn't return bool for uint8 return torch.any(x, axis, keepdims=keepdims).to(torch.bool) -def all(x: array, +def all(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False, - **kwargs) -> array: - x = torch.asarray(x) - ndim = x.ndim + **kwargs) -> Array: + if axis == (): return x.to(torch.bool) # torch.all doesn't support multiple axes @@ -391,18 +389,18 @@ def all(x: array, # torch doesn't support keepdims with axis=None # (https://github.com/pytorch/pytorch/issues/71209) res = torch.all(x, **kwargs) - res = _axis_none_keepdims(res, ndim, keepdims) + res = _axis_none_keepdims(res, x.ndim, keepdims) return res.to(torch.bool) # torch.all doesn't return bool for uint8 return torch.all(x, axis, keepdims=keepdims).to(torch.bool) -def mean(x: array, +def mean(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False, - **kwargs) -> array: + **kwargs) -> Array: # https://github.com/pytorch/pytorch/issues/29137 if axis == (): return torch.clone(x) @@ -414,13 +412,13 @@ def mean(x: array, return res return torch.mean(x, axis, keepdims=keepdims, **kwargs) -def std(x: array, +def std(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, correction: Union[int, float] = 0.0, keepdims: bool = False, - **kwargs) -> array: + **kwargs) -> Array: # Note, float correction is not supported # https://github.com/pytorch/pytorch/issues/61492. We don't try to # implement it here for now. @@ -445,13 +443,13 @@ def std(x: array, return res return torch.std(x, axis, correction=_correction, keepdims=keepdims, **kwargs) -def var(x: array, +def var(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, correction: Union[int, float] = 0.0, keepdims: bool = False, - **kwargs) -> array: + **kwargs) -> Array: # Note, float correction is not supported # https://github.com/pytorch/pytorch/issues/61492. We don't try to # implement it here for now. @@ -474,11 +472,11 @@ def var(x: array, # torch.concat doesn't support dim=None # https://github.com/pytorch/pytorch/issues/70925 -def concat(arrays: Union[Tuple[array, ...], List[array]], +def concat(arrays: Union[Tuple[Array, ...], List[Array]], /, *, axis: Optional[int] = 0, - **kwargs) -> array: + **kwargs) -> Array: if axis is None: arrays = tuple(ar.flatten() for ar in arrays) axis = 0 @@ -487,7 +485,7 @@ def concat(arrays: Union[Tuple[array, ...], List[array]], # torch.squeeze only accepts int dim and doesn't require it # https://github.com/pytorch/pytorch/issues/70924. Support for tuple dim was # added at https://github.com/pytorch/pytorch/pull/89017. -def squeeze(x: array, /, axis: Union[int, Tuple[int, ...]]) -> array: +def squeeze(x: Array, /, axis: Union[int, Tuple[int, ...]]) -> Array: if isinstance(axis, int): axis = (axis,) for a in axis: @@ -501,27 +499,27 @@ def squeeze(x: array, /, axis: Union[int, Tuple[int, ...]]) -> array: return x # torch.broadcast_to uses size instead of shape -def broadcast_to(x: array, /, shape: Tuple[int, ...], **kwargs) -> array: +def broadcast_to(x: Array, /, shape: Tuple[int, ...], **kwargs) -> Array: return torch.broadcast_to(x, shape, **kwargs) # torch.permute uses dims instead of axes -def permute_dims(x: array, /, axes: Tuple[int, ...]) -> array: +def permute_dims(x: Array, /, axes: Tuple[int, ...]) -> Array: return torch.permute(x, axes) # The axis parameter doesn't work for flip() and roll() # https://github.com/pytorch/pytorch/issues/71210. Also torch.flip() doesn't # accept axis=None -def flip(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, **kwargs) -> array: +def flip(x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, **kwargs) -> Array: if axis is None: axis = tuple(range(x.ndim)) # torch.flip doesn't accept dim as an int but the method does # https://github.com/pytorch/pytorch/issues/18095 return x.flip(axis, **kwargs) -def roll(x: array, /, shift: Union[int, Tuple[int, ...]], *, axis: Optional[Union[int, Tuple[int, ...]]] = None, **kwargs) -> array: +def roll(x: Array, /, shift: Union[int, Tuple[int, ...]], *, axis: Optional[Union[int, Tuple[int, ...]]] = None, **kwargs) -> Array: return torch.roll(x, shift, axis, **kwargs) -def nonzero(x: array, /, **kwargs) -> Tuple[array, ...]: +def nonzero(x: Array, /, **kwargs) -> Tuple[Array, ...]: if x.ndim == 0: raise ValueError("nonzero() does not support zero-dimensional arrays") return torch.nonzero(x, as_tuple=True, **kwargs) @@ -529,45 +527,60 @@ def nonzero(x: array, /, **kwargs) -> Tuple[array, ...]: # torch uses `dim` instead of `axis` def diff( - x: array, + x: Array, /, *, axis: int = -1, n: int = 1, - prepend: Optional[array] = None, - append: Optional[array] = None, -) -> array: + prepend: Optional[Array] = None, + append: Optional[Array] = None, +) -> Array: return torch.diff(x, dim=axis, n=n, prepend=prepend, append=append) # torch uses `dim` instead of `axis`, does not have keepdims def count_nonzero( - x: array, + x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False, -) -> array: +) -> Array: result = torch.count_nonzero(x, dim=axis) if keepdims: - if axis is not None: + if isinstance(axis, int): return result.unsqueeze(axis) + elif isinstance(axis, tuple): + n_axis = [x.ndim + ax if ax < 0 else ax for ax in axis] + sh = [1 if i in n_axis else x.shape[i] for i in range(x.ndim)] + return torch.reshape(result, sh) return _axis_none_keepdims(result, x.ndim, keepdims) else: return result +# "repeat" is torch.repeat_interleave; also the dim argument +def repeat(x: Array, repeats: int | Array, /, *, axis: int | None = None) -> Array: + return torch.repeat_interleave(x, repeats, axis) -def where(condition: array, x1: array, x2: array, /) -> array: + +def where( + condition: Array, + x1: Array | bool | int | float | complex, + x2: Array | bool | int | float | complex, + /, +) -> Array: x1, x2 = _fix_promotion(x1, x2) return torch.where(condition, x1, x2) + # torch.reshape doesn't have the copy keyword -def reshape(x: array, +def reshape(x: Array, /, shape: Tuple[int, ...], + *, copy: Optional[bool] = None, - **kwargs) -> array: + **kwargs) -> Array: if copy is not None: raise NotImplementedError("torch.reshape doesn't yet support the copy keyword") return torch.reshape(x, shape, **kwargs) @@ -581,9 +594,9 @@ def arange(start: Union[int, float], stop: Optional[Union[int, float]] = None, step: Union[int, float] = 1, *, - dtype: Optional[Dtype] = None, + dtype: Optional[DType] = None, device: Optional[Device] = None, - **kwargs) -> array: + **kwargs) -> Array: if stop is None: start, stop = 0, start if step > 0 and stop <= start or step < 0 and stop >= start: @@ -602,9 +615,9 @@ def eye(n_rows: int, /, *, k: int = 0, - dtype: Optional[Dtype] = None, + dtype: Optional[DType] = None, device: Optional[Device] = None, - **kwargs) -> array: + **kwargs) -> Array: if n_cols is None: n_cols = n_rows z = torch.zeros(n_rows, n_cols, dtype=dtype, device=device, **kwargs) @@ -618,10 +631,10 @@ def linspace(start: Union[int, float], /, num: int, *, - dtype: Optional[Dtype] = None, + dtype: Optional[DType] = None, device: Optional[Device] = None, endpoint: bool = True, - **kwargs) -> array: + **kwargs) -> Array: if not endpoint: return torch.linspace(start, stop, num+1, dtype=dtype, device=device, **kwargs)[:-1] return torch.linspace(start, stop, num, dtype=dtype, device=device, **kwargs) @@ -629,11 +642,11 @@ def linspace(start: Union[int, float], # torch.full does not accept an int size # https://github.com/pytorch/pytorch/issues/70906 def full(shape: Union[int, Tuple[int, ...]], - fill_value: Union[bool, int, float, complex], + fill_value: bool | int | float | complex, *, - dtype: Optional[Dtype] = None, + dtype: Optional[DType] = None, device: Optional[Device] = None, - **kwargs) -> array: + **kwargs) -> Array: if isinstance(shape, int): shape = (shape,) @@ -642,52 +655,52 @@ def full(shape: Union[int, Tuple[int, ...]], # ones, zeros, and empty do not accept shape as a keyword argument def ones(shape: Union[int, Tuple[int, ...]], *, - dtype: Optional[Dtype] = None, + dtype: Optional[DType] = None, device: Optional[Device] = None, - **kwargs) -> array: + **kwargs) -> Array: return torch.ones(shape, dtype=dtype, device=device, **kwargs) def zeros(shape: Union[int, Tuple[int, ...]], *, - dtype: Optional[Dtype] = None, + dtype: Optional[DType] = None, device: Optional[Device] = None, - **kwargs) -> array: + **kwargs) -> Array: return torch.zeros(shape, dtype=dtype, device=device, **kwargs) def empty(shape: Union[int, Tuple[int, ...]], *, - dtype: Optional[Dtype] = None, + dtype: Optional[DType] = None, device: Optional[Device] = None, - **kwargs) -> array: + **kwargs) -> Array: return torch.empty(shape, dtype=dtype, device=device, **kwargs) # tril and triu do not call the keyword argument k -def tril(x: array, /, *, k: int = 0) -> array: +def tril(x: Array, /, *, k: int = 0) -> Array: return torch.tril(x, k) -def triu(x: array, /, *, k: int = 0) -> array: +def triu(x: Array, /, *, k: int = 0) -> Array: return torch.triu(x, k) # Functions that aren't in torch https://github.com/pytorch/pytorch/issues/58742 -def expand_dims(x: array, /, *, axis: int = 0) -> array: +def expand_dims(x: Array, /, *, axis: int = 0) -> Array: return torch.unsqueeze(x, axis) def astype( - x: array, - dtype: Dtype, + x: Array, + dtype: DType, /, *, copy: bool = True, device: Optional[Device] = None, -) -> array: +) -> Array: if device is not None: return x.to(device, dtype=dtype, copy=copy) return x.to(dtype=dtype, copy=copy) -def broadcast_arrays(*arrays: array) -> List[array]: +def broadcast_arrays(*arrays: Array) -> List[Array]: shape = torch.broadcast_shapes(*[a.shape for a in arrays]) return [torch.broadcast_to(a, shape) for a in arrays] @@ -697,7 +710,7 @@ def broadcast_arrays(*arrays: array) -> List[array]: UniqueInverseResult) # https://github.com/pytorch/pytorch/issues/70920 -def unique_all(x: array) -> UniqueAllResult: +def unique_all(x: Array) -> UniqueAllResult: # torch.unique doesn't support returning indices. # https://github.com/pytorch/pytorch/issues/36748. The workaround # suggested in that issue doesn't actually function correctly (it relies @@ -710,7 +723,7 @@ def unique_all(x: array) -> UniqueAllResult: # counts[torch.isnan(values)] = 1 # return UniqueAllResult(values, indices, inverse_indices, counts) -def unique_counts(x: array) -> UniqueCountsResult: +def unique_counts(x: Array) -> UniqueCountsResult: values, counts = torch.unique(x, return_counts=True) # torch.unique incorrectly gives a 0 count for nan values. @@ -718,14 +731,14 @@ def unique_counts(x: array) -> UniqueCountsResult: counts[torch.isnan(values)] = 1 return UniqueCountsResult(values, counts) -def unique_inverse(x: array) -> UniqueInverseResult: +def unique_inverse(x: Array) -> UniqueInverseResult: values, inverse = torch.unique(x, return_inverse=True) return UniqueInverseResult(values, inverse) -def unique_values(x: array) -> array: +def unique_values(x: Array) -> Array: return torch.unique(x) -def matmul(x1: array, x2: array, /, **kwargs) -> array: +def matmul(x1: Array, x2: Array, /, **kwargs) -> Array: # torch.matmul doesn't type promote (but differently from _fix_promotion) x1, x2 = _fix_promotion(x1, x2, only_scalar=False) return torch.matmul(x1, x2, **kwargs) @@ -733,12 +746,19 @@ def matmul(x1: array, x2: array, /, **kwargs) -> array: matrix_transpose = get_xp(torch)(_aliases.matrix_transpose) _vecdot = get_xp(torch)(_aliases.vecdot) -def vecdot(x1: array, x2: array, /, *, axis: int = -1) -> array: +def vecdot(x1: Array, x2: Array, /, *, axis: int = -1) -> Array: x1, x2 = _fix_promotion(x1, x2, only_scalar=False) return _vecdot(x1, x2, axis=axis) # torch.tensordot uses dims instead of axes -def tensordot(x1: array, x2: array, /, *, axes: Union[int, Tuple[Sequence[int], Sequence[int]]] = 2, **kwargs) -> array: +def tensordot( + x1: Array, + x2: Array, + /, + *, + axes: Union[int, Tuple[Sequence[int], Sequence[int]]] = 2, + **kwargs, +) -> Array: # Note: torch.tensordot fails with integer dtypes when there is only 1 # element in the axis (https://github.com/pytorch/pytorch/issues/84530). x1, x2 = _fix_promotion(x1, x2, only_scalar=False) @@ -746,7 +766,7 @@ def tensordot(x1: array, x2: array, /, *, axes: Union[int, Tuple[Sequence[int], def isdtype( - dtype: Dtype, kind: Union[Dtype, str, Tuple[Union[Dtype, str], ...]], + dtype: DType, kind: Union[DType, str, Tuple[Union[DType, str], ...]], *, _tuple=True, # Disallow nested tuples ) -> bool: """ @@ -781,7 +801,7 @@ def isdtype( else: return dtype == kind -def take(x: array, indices: array, /, *, axis: Optional[int] = None, **kwargs) -> array: +def take(x: Array, indices: Array, /, *, axis: Optional[int] = None, **kwargs) -> Array: if axis is None: if x.ndim != 1: raise ValueError("axis must be specified when ndim > 1") @@ -789,11 +809,11 @@ def take(x: array, indices: array, /, *, axis: Optional[int] = None, **kwargs) - return torch.index_select(x, axis, indices, **kwargs) -def take_along_axis(x: array, indices: array, /, *, axis: int = -1) -> array: +def take_along_axis(x: Array, indices: Array, /, *, axis: int = -1) -> Array: return torch.take_along_dim(x, indices, dim=axis) -def sign(x: array, /) -> array: +def sign(x: Array, /) -> Array: # torch sign() does not support complex numbers and does not propagate # nans. See https://github.com/data-apis/array-api-compat/issues/136 if x.dtype.is_complex: @@ -808,7 +828,13 @@ def sign(x: array, /) -> array: return out -__all__ = ['__array_namespace_info__', 'result_type', 'can_cast', +def meshgrid(*arrays: Array, indexing: Literal['xy', 'ij'] = 'xy') -> List[Array]: + # enforce the default of 'xy' + # TODO: is the return type a list or a tuple + return list(torch.meshgrid(*arrays, indexing='xy')) + + +__all__ = ['__array_namespace_info__', 'asarray', 'result_type', 'can_cast', 'permute_dims', 'bitwise_invert', 'newaxis', 'conj', 'add', 'atan2', 'bitwise_and', 'bitwise_left_shift', 'bitwise_or', 'bitwise_right_shift', 'bitwise_xor', 'copysign', 'count_nonzero', @@ -824,6 +850,6 @@ def sign(x: array, /) -> array: 'UniqueAllResult', 'UniqueCountsResult', 'UniqueInverseResult', 'unique_all', 'unique_counts', 'unique_inverse', 'unique_values', 'matmul', 'matrix_transpose', 'vecdot', 'tensordot', 'isdtype', - 'take', 'take_along_axis', 'sign'] + 'take', 'take_along_axis', 'sign', 'finfo', 'iinfo', 'repeat', 'meshgrid'] _all_ignore = ['torch', 'get_xp'] diff --git a/sklearn/externals/array_api_compat/torch/_info.py b/sklearn/externals/array_api_compat/torch/_info.py index 34fbcb21aa53f..818e5d3702e38 100644 --- a/sklearn/externals/array_api_compat/torch/_info.py +++ b/sklearn/externals/array_api_compat/torch/_info.py @@ -34,7 +34,7 @@ class __array_namespace_info__: Examples -------- - >>> info = np.__array_namespace_info__() + >>> info = xp.__array_namespace_info__() >>> info.default_dtypes() {'real floating': numpy.float64, 'complex floating': numpy.complex128, @@ -76,16 +76,16 @@ def capabilities(self): Examples -------- - >>> info = np.__array_namespace_info__() + >>> info = xp.__array_namespace_info__() >>> info.capabilities() {'boolean indexing': True, - 'data-dependent shapes': True} + 'data-dependent shapes': True, + 'max dimensions': 64} """ return { "boolean indexing": True, "data-dependent shapes": True, - # 'max rank' will be part of the 2024.12 standard "max dimensions": 64, } @@ -102,15 +102,24 @@ def default_device(self): Returns ------- - device : str + device : Device The default device used for new PyTorch arrays. Examples -------- - >>> info = np.__array_namespace_info__() + >>> info = xp.__array_namespace_info__() >>> info.default_device() - 'cpu' + device(type='cpu') + Notes + ----- + This method returns the static default device when PyTorch is initialized. + However, the *current* device used by creation functions (``empty`` etc.) + can be changed at runtime. + + See Also + -------- + https://github.com/data-apis/array-api/issues/835 """ return torch.device("cpu") @@ -120,9 +129,9 @@ def default_dtypes(self, *, device=None): Parameters ---------- - device : str, optional - The device to get the default data types for. For PyTorch, only - ``'cpu'`` is allowed. + device : Device, optional + The device to get the default data types for. + Unused for PyTorch, as all devices use the same default dtypes. Returns ------- @@ -139,7 +148,7 @@ def default_dtypes(self, *, device=None): Examples -------- - >>> info = np.__array_namespace_info__() + >>> info = xp.__array_namespace_info__() >>> info.default_dtypes() {'real floating': torch.float32, 'complex floating': torch.complex64, @@ -250,8 +259,9 @@ def dtypes(self, *, device=None, kind=None): Parameters ---------- - device : str, optional + device : Device, optional The device to get the data types for. + Unused for PyTorch, as all devices use the same dtypes. kind : str or tuple of str, optional The kind of data types to return. If ``None``, all data types are returned. If a string, only data types of that kind are returned. @@ -287,7 +297,7 @@ def dtypes(self, *, device=None, kind=None): Examples -------- - >>> info = np.__array_namespace_info__() + >>> info = xp.__array_namespace_info__() >>> info.dtypes(kind='signed integer') {'int8': numpy.int8, 'int16': numpy.int16, @@ -310,7 +320,7 @@ def devices(self): Returns ------- - devices : list of str + devices : list[Device] The devices supported by PyTorch. See Also @@ -322,7 +332,7 @@ def devices(self): Examples -------- - >>> info = np.__array_namespace_info__() + >>> info = xp.__array_namespace_info__() >>> info.devices() [device(type='cpu'), device(type='mps', index=0), device(type='meta')] @@ -333,6 +343,7 @@ def devices(self): # device: try: torch.device('notadevice') + raise AssertionError("unreachable") # pragma: nocover except RuntimeError as e: # The error message is something like: # "Expected one of cpu, cuda, ipu, xpu, mkldnn, opengl, opencl, ideep, hip, ve, fpga, ort, xla, lazy, vulkan, mps, meta, hpu, mtia, privateuseone device type at start of device string: notadevice" diff --git a/sklearn/externals/array_api_compat/torch/_typing.py b/sklearn/externals/array_api_compat/torch/_typing.py new file mode 100644 index 0000000000000..5267087156371 --- /dev/null +++ b/sklearn/externals/array_api_compat/torch/_typing.py @@ -0,0 +1,3 @@ +__all__ = ["Array", "Device", "DType"] + +from torch import device as Device, dtype as DType, Tensor as Array diff --git a/sklearn/externals/array_api_compat/torch/fft.py b/sklearn/externals/array_api_compat/torch/fft.py index 3c9117ee57d35..50e6a0d0a3968 100644 --- a/sklearn/externals/array_api_compat/torch/fft.py +++ b/sklearn/externals/array_api_compat/torch/fft.py @@ -1,76 +1,75 @@ from __future__ import annotations -from typing import TYPE_CHECKING -if TYPE_CHECKING: - import torch - array = torch.Tensor - from typing import Union, Sequence, Literal +from typing import Union, Sequence, Literal -from torch.fft import * # noqa: F403 +import torch import torch.fft +from torch.fft import * # noqa: F403 + +from ._typing import Array # Several torch fft functions do not map axes to dim def fftn( - x: array, + x: Array, /, *, s: Sequence[int] = None, axes: Sequence[int] = None, norm: Literal["backward", "ortho", "forward"] = "backward", **kwargs, -) -> array: +) -> Array: return torch.fft.fftn(x, s=s, dim=axes, norm=norm, **kwargs) def ifftn( - x: array, + x: Array, /, *, s: Sequence[int] = None, axes: Sequence[int] = None, norm: Literal["backward", "ortho", "forward"] = "backward", **kwargs, -) -> array: +) -> Array: return torch.fft.ifftn(x, s=s, dim=axes, norm=norm, **kwargs) def rfftn( - x: array, + x: Array, /, *, s: Sequence[int] = None, axes: Sequence[int] = None, norm: Literal["backward", "ortho", "forward"] = "backward", **kwargs, -) -> array: +) -> Array: return torch.fft.rfftn(x, s=s, dim=axes, norm=norm, **kwargs) def irfftn( - x: array, + x: Array, /, *, s: Sequence[int] = None, axes: Sequence[int] = None, norm: Literal["backward", "ortho", "forward"] = "backward", **kwargs, -) -> array: +) -> Array: return torch.fft.irfftn(x, s=s, dim=axes, norm=norm, **kwargs) def fftshift( - x: array, + x: Array, /, *, axes: Union[int, Sequence[int]] = None, **kwargs, -) -> array: +) -> Array: return torch.fft.fftshift(x, dim=axes, **kwargs) def ifftshift( - x: array, + x: Array, /, *, axes: Union[int, Sequence[int]] = None, **kwargs, -) -> array: +) -> Array: return torch.fft.ifftshift(x, dim=axes, **kwargs) diff --git a/sklearn/externals/array_api_compat/torch/linalg.py b/sklearn/externals/array_api_compat/torch/linalg.py index e26198b9b562e..70d7240500ce4 100644 --- a/sklearn/externals/array_api_compat/torch/linalg.py +++ b/sklearn/externals/array_api_compat/torch/linalg.py @@ -1,14 +1,7 @@ from __future__ import annotations -from typing import TYPE_CHECKING -if TYPE_CHECKING: - import torch - array = torch.Tensor - from torch import dtype as Dtype - from typing import Optional, Union, Tuple, Literal - inf = float('inf') - -from ._aliases import _fix_promotion, sum +import torch +from typing import Optional, Union, Tuple from torch.linalg import * # noqa: F403 @@ -19,15 +12,18 @@ # outer is implemented in torch but aren't in the linalg namespace from torch import outer +from ._aliases import _fix_promotion, sum # These functions are in both the main and linalg namespaces from ._aliases import matmul, matrix_transpose, tensordot +from ._typing import Array, DType +from ..common._typing import JustInt, JustFloat # Note: torch.linalg.cross does not default to axis=-1 (it defaults to the # first axis with size 3), see https://github.com/pytorch/pytorch/issues/58743 # torch.cross also does not support broadcasting when it would add new # dimensions https://github.com/pytorch/pytorch/issues/39656 -def cross(x1: array, x2: array, /, *, axis: int = -1) -> array: +def cross(x1: Array, x2: Array, /, *, axis: int = -1) -> Array: x1, x2 = _fix_promotion(x1, x2, only_scalar=False) if not (-min(x1.ndim, x2.ndim) <= axis < max(x1.ndim, x2.ndim)): raise ValueError(f"axis {axis} out of bounds for cross product of arrays with shapes {x1.shape} and {x2.shape}") @@ -36,7 +32,7 @@ def cross(x1: array, x2: array, /, *, axis: int = -1) -> array: x1, x2 = torch.broadcast_tensors(x1, x2) return torch_linalg.cross(x1, x2, dim=axis) -def vecdot(x1: array, x2: array, /, *, axis: int = -1, **kwargs) -> array: +def vecdot(x1: Array, x2: Array, /, *, axis: int = -1, **kwargs) -> Array: from ._aliases import isdtype x1, x2 = _fix_promotion(x1, x2, only_scalar=False) @@ -58,7 +54,7 @@ def vecdot(x1: array, x2: array, /, *, axis: int = -1, **kwargs) -> array: return res[..., 0, 0] return torch.linalg.vecdot(x1, x2, dim=axis, **kwargs) -def solve(x1: array, x2: array, /, **kwargs) -> array: +def solve(x1: Array, x2: Array, /, **kwargs) -> Array: x1, x2 = _fix_promotion(x1, x2, only_scalar=False) # Torch tries to emulate NumPy 1 solve behavior by using batched 1-D solve # whenever @@ -79,19 +75,20 @@ def solve(x1: array, x2: array, /, **kwargs) -> array: return torch.linalg.solve(x1, x2, **kwargs) # torch.trace doesn't support the offset argument and doesn't support stacking -def trace(x: array, /, *, offset: int = 0, dtype: Optional[Dtype] = None) -> array: +def trace(x: Array, /, *, offset: int = 0, dtype: Optional[DType] = None) -> Array: # Use our wrapped sum to make sure it does upcasting correctly return sum(torch.diagonal(x, offset=offset, dim1=-2, dim2=-1), axis=-1, dtype=dtype) def vector_norm( - x: array, + x: Array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None, keepdims: bool = False, - ord: Union[int, float, Literal[inf, -inf]] = 2, + # JustFloat stands for inf | -inf, which are not valid for Literal + ord: JustInt | JustFloat = 2, **kwargs, -) -> array: +) -> Array: # torch.vector_norm incorrectly treats axis=() the same as axis=None if axis == (): out = kwargs.get('out') @@ -119,3 +116,6 @@ def vector_norm( _all_ignore = ['torch_linalg', 'sum'] del linalg_all + +def __dir__() -> list[str]: + return __all__ From 40220143999acf9279838526e950676b7f876007 Mon Sep 17 00:00:00 2001 From: "Christine P. Chai" Date: Sat, 31 May 2025 20:58:45 -0700 Subject: [PATCH 002/103] DOC: Correct a typo: this examples -> this example (#31458) --- examples/frozen/plot_frozen_examples.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/frozen/plot_frozen_examples.py b/examples/frozen/plot_frozen_examples.py index 373e47ff2d68c..7237003090d13 100644 --- a/examples/frozen/plot_frozen_examples.py +++ b/examples/frozen/plot_frozen_examples.py @@ -3,7 +3,7 @@ Examples of Using `FrozenEstimator` =================================== -This examples showcases some use cases of :class:`~sklearn.frozen.FrozenEstimator`. +This example showcases some use cases of :class:`~sklearn.frozen.FrozenEstimator`. :class:`~sklearn.frozen.FrozenEstimator` is a utility class that allows to freeze a fitted estimator. This is useful, for instance, when we want to pass a fitted estimator From 27835fa716ea0e285636262dabf6e96d6c0d4c91 Mon Sep 17 00:00:00 2001 From: Lucy Liu Date: Mon, 2 Jun 2025 17:25:59 +1000 Subject: [PATCH 003/103] DOC Use `from_cv_results` in `plot_roc_crossval.py` (#31455) --- examples/model_selection/plot_roc_crossval.py | 56 +++++++++++-------- 1 file changed, 33 insertions(+), 23 deletions(-) diff --git a/examples/model_selection/plot_roc_crossval.py b/examples/model_selection/plot_roc_crossval.py index 868454626451c..3c5c3fc9119b7 100644 --- a/examples/model_selection/plot_roc_crossval.py +++ b/examples/model_selection/plot_roc_crossval.py @@ -62,8 +62,9 @@ # Classification and ROC analysis # ------------------------------- # -# Here we run a :class:`~sklearn.svm.SVC` classifier with cross-validation and -# plot the ROC curves fold-wise. Notice that the baseline to define the chance +# Here we run :func:`~sklearn.model_selection.cross_validate` on a +# :class:`~sklearn.svm.SVC` classifier, then use the computed cross-validation results +# to plot the ROC curves fold-wise. Notice that the baseline to define the chance # level (dashed ROC curve) is a classifier that would always predict the most # frequent class. @@ -71,37 +72,46 @@ from sklearn import svm from sklearn.metrics import RocCurveDisplay, auc -from sklearn.model_selection import StratifiedKFold +from sklearn.model_selection import StratifiedKFold, cross_validate n_splits = 6 cv = StratifiedKFold(n_splits=n_splits) classifier = svm.SVC(kernel="linear", probability=True, random_state=random_state) +cv_results = cross_validate( + classifier, X, y, cv=cv, return_estimator=True, return_indices=True +) + +prop_cycle = plt.rcParams["axes.prop_cycle"] +colors = prop_cycle.by_key()["color"] +curve_kwargs_list = [ + dict(alpha=0.3, lw=1, color=colors[fold % len(colors)]) for fold in range(n_splits) +] +names = [f"ROC fold {idx}" for idx in range(n_splits)] -tprs = [] -aucs = [] mean_fpr = np.linspace(0, 1, 100) +interp_tprs = [] + +_, ax = plt.subplots(figsize=(6, 6)) +viz = RocCurveDisplay.from_cv_results( + cv_results, + X, + y, + ax=ax, + name=names, + curve_kwargs=curve_kwargs_list, + plot_chance_level=True, +) -fig, ax = plt.subplots(figsize=(6, 6)) -for fold, (train, test) in enumerate(cv.split(X, y)): - classifier.fit(X[train], y[train]) - viz = RocCurveDisplay.from_estimator( - classifier, - X[test], - y[test], - name=f"ROC fold {fold}", - curve_kwargs=dict(alpha=0.3, lw=1), - ax=ax, - plot_chance_level=(fold == n_splits - 1), - ) - interp_tpr = np.interp(mean_fpr, viz.fpr, viz.tpr) +for idx in range(n_splits): + interp_tpr = np.interp(mean_fpr, viz.fpr[idx], viz.tpr[idx]) interp_tpr[0] = 0.0 - tprs.append(interp_tpr) - aucs.append(viz.roc_auc) + interp_tprs.append(interp_tpr) -mean_tpr = np.mean(tprs, axis=0) +mean_tpr = np.mean(interp_tprs, axis=0) mean_tpr[-1] = 1.0 mean_auc = auc(mean_fpr, mean_tpr) -std_auc = np.std(aucs) +std_auc = np.std(viz.roc_auc) + ax.plot( mean_fpr, mean_tpr, @@ -111,7 +121,7 @@ alpha=0.8, ) -std_tpr = np.std(tprs, axis=0) +std_tpr = np.std(interp_tprs, axis=0) tprs_upper = np.minimum(mean_tpr + std_tpr, 1) tprs_lower = np.maximum(mean_tpr - std_tpr, 0) ax.fill_between( From 67749c37f7667aaba960e734d4b4f0cbdae25eb2 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 2 Jun 2025 17:48:10 +0200 Subject: [PATCH 004/103] :lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#31466) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Lock file bot Co-authored-by: Loïc Estève --- ...latest_conda_forge_mkl_linux-64_conda.lock | 64 +++++++++---------- ...pylatest_conda_forge_mkl_osx-64_conda.lock | 26 ++++---- ...st_pip_openblas_pandas_linux-64_conda.lock | 10 +-- .../pymin_conda_forge_mkl_win-64_conda.lock | 12 ++-- ...nblas_min_dependencies_linux-64_conda.lock | 20 +++--- ...e_openblas_ubuntu_2204_linux-64_conda.lock | 8 +-- build_tools/azure/ubuntu_atlas_lock.txt | 2 +- build_tools/circle/doc_linux-64_conda.lock | 30 ++++----- .../doc_min_dependencies_linux-64_conda.lock | 22 +++---- ...n_conda_forge_arm_linux-aarch64_conda.lock | 16 ++--- sklearn/model_selection/_search.py | 2 +- 11 files changed, 106 insertions(+), 106 deletions(-) diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index b53cd9ad6a1a7..e99219a40736d 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock @@ -6,7 +6,7 @@ https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6 https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2#4d59c254e01d9cde7957100457e2d5fb https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda#49023d73832ef61042f6a237cb2687e7 -https://conda.anaconda.org/conda-forge/linux-64/libopentelemetry-cpp-headers-1.20.0-ha770c72_0.conda#96806e6c31dc89253daff2134aeb58f3 +https://conda.anaconda.org/conda-forge/linux-64/libopentelemetry-cpp-headers-1.21.0-ha770c72_0.conda#11b1bed92c943d3b741e8a1e1a815ed1 https://conda.anaconda.org/conda-forge/linux-64/mkl-include-2024.2.2-ha957f24_16.conda#42b0d14354b5910a9f41e29289914f6b https://conda.anaconda.org/conda-forge/linux-64/nlohmann_json-3.12.0-h3f2d84a_0.conda#d76872d096d063e226482c99337209dc https://conda.anaconda.org/conda-forge/noarch/python_abi-3.13-7_cp313.conda#e84b44e6300f1703cb25d29120c5b1d8 @@ -15,7 +15,7 @@ https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.4.26-hbd8a1cb https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766549260d6815b0c52253f1fb1bb29 https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_4.conda#01f8d123c96816249efd255a31ad7712 https://conda.anaconda.org/conda-forge/linux-64/libglvnd-1.7.0-ha4b6fd6_2.conda#434ca7e50e40f4918ab701e3facd59a0 -https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-20.1.5-h024ca30_0.conda#86f58be65a51d62ccc06cacfd83ff987 +https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-20.1.6-h024ca30_0.conda#e4ece7ed81e43ae97a3b58ac4230c3c5 https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-3_kmp_llvm.conda#ee5c2118262e30b972bc0b4db8ef0ba5 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab https://conda.anaconda.org/conda-forge/linux-64/libegl-1.7.0-ha4b6fd6_2.conda#c151d5eb730e9b7480e6d48c0fc44048 @@ -33,10 +33,11 @@ https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.1.0-hcea5267_2.c https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h4ce23a2_1.conda#e796ff8ddc598affdf7c173d6145f087 https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.1.0-hb9d3cd8_0.conda#9fa334557db9f63da6c9285fd2a48638 https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_1.conda#a76fd702c93cd2dfd89eff30a5fd45a8 +https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-hb9d3cd8_0.conda#c7e925f37e3b40d893459e625f6a53f1 https://conda.anaconda.org/conda-forge/linux-64/libntlm-1.8-hb9d3cd8_0.conda#7c7927b404672409d9917d49bff5f2d6 https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_2.conda#1cb1c67961f6dd257eae9e9691b341aa -https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.10.0-h4c51ac1_0.conda#aeccfff2806ae38430638ffbb4be9610 -https://conda.anaconda.org/conda-forge/linux-64/libuv-1.50.0-hb9d3cd8_0.conda#771ee65e13bc599b0b62af5359d80169 +https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.10.0-h202a827_0.conda#0f98f3e95272d118f7931b6bef69bfe5 +https://conda.anaconda.org/conda-forge/linux-64/libuv-1.51.0-hb9d3cd8_0.conda#1349c022c92c5efd3fd705a79a5804d8 https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.5.0-h851e524_0.conda#63f790534398730f59e1b899c3644d4a https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 @@ -45,9 +46,9 @@ https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-hb9d3cd8_1002. https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.2-hb9d3cd8_0.conda#fb901ff28063514abb6046c9ec2c4a45 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-hb9d3cd8_0.conda#f6ebe2cb3f82ba6c057dde5d9debe4f7 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https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2 # pip cython @ https://files.pythonhosted.org/packages/ca/90/9fe8b93fa239b4871252274892c232415f53d5af0859c4a6ac9b1cbf9950/cython-3.1.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=7da069ca769903c5dee56c5f7ab47b2b7b91030eee48912630db5f4f3ec5954a # pip docutils @ https://files.pythonhosted.org/packages/8f/d7/9322c609343d929e75e7e5e6255e614fcc67572cfd083959cdef3b7aad79/docutils-0.21.2-py3-none-any.whl#sha256=dafca5b9e384f0e419294eb4d2ff9fa826435bf15f15b7bd45723e8ad76811b2 # pip execnet @ https://files.pythonhosted.org/packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl#sha256=26dee51f1b80cebd6d0ca8e74dd8745419761d3bef34163928cbebbdc4749fdc -# pip fonttools @ https://files.pythonhosted.org/packages/60/49/aaecb1b3cea2b9b9c7cea6240d6bc8090feb5489a6fbf93cb68003be979b/fonttools-4.58.0-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=2ceef6f6ab58061a811967e3e32e630747fcb823dcc33a9a2c80e2d0d17cb292 +# pip fonttools @ https://files.pythonhosted.org/packages/c1/46/8b46469c6edac393de1c380c7ec61922d5440f25605dfca7849e5ffff295/fonttools-4.58.1-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=9b8860f8cd48b345bd1df1d7be650f600f69ee971ffe338c5bd5bcb6bdb3b92c # pip idna @ https://files.pythonhosted.org/packages/76/c6/c88e154df9c4e1a2a66ccf0005a88dfb2650c1dffb6f5ce603dfbd452ce3/idna-3.10-py3-none-any.whl#sha256=946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3 # pip imagesize @ https://files.pythonhosted.org/packages/ff/62/85c4c919272577931d407be5ba5d71c20f0b616d31a0befe0ae45bb79abd/imagesize-1.4.1-py2.py3-none-any.whl#sha256=0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b # pip iniconfig @ https://files.pythonhosted.org/packages/2c/e1/e6716421ea10d38022b952c159d5161ca1193197fb744506875fbb87ea7b/iniconfig-2.1.0-py3-none-any.whl#sha256=9deba5723312380e77435581c6bf4935c94cbfab9b1ed33ef8d238ea168eb760 @@ -45,7 +45,7 @@ https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2 # pip kiwisolver @ https://files.pythonhosted.org/packages/8f/e9/6a7d025d8da8c4931522922cd706105aa32b3291d1add8c5427cdcd66e63/kiwisolver-1.4.8-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=a5ce1e481a74b44dd5e92ff03ea0cb371ae7a0268318e202be06c8f04f4f1246 # pip markupsafe @ https://files.pythonhosted.org/packages/0c/91/96cf928db8236f1bfab6ce15ad070dfdd02ed88261c2afafd4b43575e9e9/MarkupSafe-3.0.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=15ab75ef81add55874e7ab7055e9c397312385bd9ced94920f2802310c930396 # pip meson @ https://files.pythonhosted.org/packages/46/77/726b14be352aa6911e206ca7c4d95c5be49660604dfee0bfed0fc75823e5/meson-1.8.1-py3-none-any.whl#sha256=374bbf71247e629475fc10b0bd2ef66fc418c2d8f4890572f74de0f97d0d42da -# pip networkx @ https://files.pythonhosted.org/packages/b9/54/dd730b32ea14ea797530a4479b2ed46a6fb250f682a9cfb997e968bf0261/networkx-3.4.2-py3-none-any.whl#sha256=df5d4365b724cf81b8c6a7312509d0c22386097011ad1abe274afd5e9d3bbc5f +# pip networkx @ https://files.pythonhosted.org/packages/eb/8d/776adee7bbf76365fdd7f2552710282c79a4ead5d2a46408c9043a2b70ba/networkx-3.5-py3-none-any.whl#sha256=0030d386a9a06dee3565298b4a734b68589749a544acbb6c412dc9e2489ec6ec # pip ninja @ https://files.pythonhosted.org/packages/eb/7a/455d2877fe6cf99886849c7f9755d897df32eaf3a0fba47b56e615f880f7/ninja-1.11.1.4-py3-none-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=096487995473320de7f65d622c3f1d16c3ad174797602218ca8c967f51ec38a0 # pip numpy @ https://files.pythonhosted.org/packages/19/49/4df9123aafa7b539317bf6d342cb6d227e49f7a35b99c287a6109b13dd93/numpy-2.2.6-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=1bc23a79bfabc5d056d106f9befb8d50c31ced2fbc70eedb8155aec74a45798f # pip packaging @ https://files.pythonhosted.org/packages/20/12/38679034af332785aac8774540895e234f4d07f7545804097de4b666afd8/packaging-25.0-py3-none-any.whl#sha256=29572ef2b1f17581046b3a2227d5c611fb25ec70ca1ba8554b24b0e69331a484 @@ -77,15 +77,15 @@ https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2 # pip python-dateutil @ https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl#sha256=a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 # pip requests @ https://files.pythonhosted.org/packages/f9/9b/335f9764261e915ed497fcdeb11df5dfd6f7bf257d4a6a2a686d80da4d54/requests-2.32.3-py3-none-any.whl#sha256=70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6 # pip scipy @ https://files.pythonhosted.org/packages/b5/09/c5b6734a50ad4882432b6bb7c02baf757f5b2f256041da5df242e2d7e6b6/scipy-1.15.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=c9deabd6d547aee2c9a81dee6cc96c6d7e9a9b1953f74850c179f91fdc729cb7 -# pip tifffile @ https://files.pythonhosted.org/packages/f5/34/59df6d47b6a8203e1db8f9a0585faf4cd68918e4a860f3bcb57909b6099f/tifffile-2025.5.24-py3-none-any.whl#sha256=2d913e41356425a2eeb4dc06dcc7286193cfa65ae1dedb5f51f04f60cf06713d +# pip tifffile @ https://files.pythonhosted.org/packages/4d/77/7f7dfcf2d847c1c1c63a2d4157c480eb4c74e4aa56e844008795ff01f86d/tifffile-2025.6.1-py3-none-any.whl#sha256=ff7163f1aaea519b769a2ac77c43be69e7d83e5b5d5d6a676497399de50535e5 # pip lightgbm @ https://files.pythonhosted.org/packages/42/86/dabda8fbcb1b00bcfb0003c3776e8ade1aa7b413dff0a2c08f457dace22f/lightgbm-4.6.0-py3-none-manylinux_2_28_x86_64.whl#sha256=cb19b5afea55b5b61cbb2131095f50538bd608a00655f23ad5d25ae3e3bf1c8d # pip matplotlib @ https://files.pythonhosted.org/packages/f5/64/41c4367bcaecbc03ef0d2a3ecee58a7065d0a36ae1aa817fe573a2da66d4/matplotlib-3.10.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=a80fcccbef63302c0efd78042ea3c2436104c5b1a4d3ae20f864593696364ac7 # pip meson-python @ https://files.pythonhosted.org/packages/28/58/66db620a8a7ccb32633de9f403fe49f1b63c68ca94e5c340ec5cceeb9821/meson_python-0.18.0-py3-none-any.whl#sha256=3b0fe051551cc238f5febb873247c0949cd60ded556efa130aa57021804868e2 # pip pandas @ https://files.pythonhosted.org/packages/e8/31/aa8da88ca0eadbabd0a639788a6da13bb2ff6edbbb9f29aa786450a30a91/pandas-2.2.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=f3a255b2c19987fbbe62a9dfd6cff7ff2aa9ccab3fc75218fd4b7530f01efa24 # pip pyamg @ https://files.pythonhosted.org/packages/cd/a7/0df731cbfb09e73979a1a032fc7bc5be0eba617d798b998a0f887afe8ade/pyamg-5.2.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=6999b351ab969c79faacb81faa74c0fa9682feeff3954979212872a3ee40c298 # pip pytest-cov @ https://files.pythonhosted.org/packages/28/d0/def53b4a790cfb21483016430ed828f64830dd981ebe1089971cd10cab25/pytest_cov-6.1.1-py3-none-any.whl#sha256=bddf29ed2d0ab6f4df17b4c55b0a657287db8684af9c42ea546b21b1041b3dde -# pip pytest-xdist @ https://files.pythonhosted.org/packages/6d/82/1d96bf03ee4c0fdc3c0cbe61470070e659ca78dc0086fb88b66c185e2449/pytest_xdist-3.6.1-py3-none-any.whl#sha256=9ed4adfb68a016610848639bb7e02c9352d5d9f03d04809919e2dafc3be4cca7 +# pip pytest-xdist @ https://files.pythonhosted.org/packages/0d/b2/0e802fde6f1c5b2f7ae7e9ad42b83fd4ecebac18a8a8c2f2f14e39dce6e1/pytest_xdist-3.7.0-py3-none-any.whl#sha256=7d3fbd255998265052435eb9daa4e99b62e6fb9cfb6efd1f858d4d8c0c7f0ca0 # pip scikit-image @ https://files.pythonhosted.org/packages/cd/9b/c3da56a145f52cd61a68b8465d6a29d9503bc45bc993bb45e84371c97d94/scikit_image-0.25.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=b8abd3c805ce6944b941cfed0406d88faeb19bab3ed3d4b50187af55cf24d147 -# pip scipy-doctest @ https://files.pythonhosted.org/packages/76/eb/668949f884d5fe8a0d231dcba42c02e7b84626b35ca9072d6283c3aae773/scipy_doctest-1.7.1-py3-none-any.whl#sha256=dece106ec5ac8c595cc6372480d724e68c684450124dd0ddeb6be487ad62b365 +# pip scipy-doctest @ https://files.pythonhosted.org/packages/c9/13/cd25d1875f3804b73fd4a4ae00e2c76e274e1e0608d79148cac251b644b1/scipy_doctest-1.8.0-py3-none-any.whl#sha256=5863208368c35486e143ce3283ab2f517a0d6b0c63d0d5f19f38a823fc82016f # pip sphinx @ https://files.pythonhosted.org/packages/31/53/136e9eca6e0b9dc0e1962e2c908fbea2e5ac000c2a2fbd9a35797958c48b/sphinx-8.2.3-py3-none-any.whl#sha256=4405915165f13521d875a8c29c8970800a0141c14cc5416a38feca4ea5d9b9c3 # pip numpydoc @ https://files.pythonhosted.org/packages/6c/45/56d99ba9366476cd8548527667f01869279cedb9e66b28eb4dfb27701679/numpydoc-1.8.0-py3-none-any.whl#sha256=72024c7fd5e17375dec3608a27c03303e8ad00c81292667955c6fea7a3ccf541 diff --git a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock index 98aa76361cb56..9e7e414a90156 100644 --- a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock @@ -31,14 +31,14 @@ https://conda.anaconda.org/conda-forge/win-64/libffi-3.4.6-h537db12_1.conda#85d8 https://conda.anaconda.org/conda-forge/win-64/libiconv-1.18-h135ad9c_1.conda#21fc5dba2cbcd8e5e26ff976a312122c https://conda.anaconda.org/conda-forge/win-64/libjpeg-turbo-3.1.0-h2466b09_0.conda#7c51d27540389de84852daa1cdb9c63c https://conda.anaconda.org/conda-forge/win-64/liblzma-5.8.1-h2466b09_1.conda#14a1042c163181e143a7522dfb8ad6ab -https://conda.anaconda.org/conda-forge/win-64/libsqlite-3.49.2-h67fdade_0.conda#a3900c97ba9e03332e9a911fe63f7d64 +https://conda.anaconda.org/conda-forge/win-64/libsqlite-3.50.0-h67fdade_0.conda#92b11b0b2120d563caa1629928122cee https://conda.anaconda.org/conda-forge/win-64/libwebp-base-1.5.0-h3b0e114_0.conda#33f7313967072c6e6d8f865f5493c7ae https://conda.anaconda.org/conda-forge/win-64/libzlib-1.3.1-h2466b09_2.conda#41fbfac52c601159df6c01f875de31b9 https://conda.anaconda.org/conda-forge/win-64/ninja-1.12.1-hc790b64_1.conda#3974c522f3248d4a93e6940c463d2de7 https://conda.anaconda.org/conda-forge/win-64/openssl-3.5.0-ha4e3fda_1.conda#72c07e46b6766bb057018a9a74861b89 https://conda.anaconda.org/conda-forge/win-64/pixman-0.46.0-had0cd8c_0.conda#01617534ef71b5385ebba940a6d6150d https://conda.anaconda.org/conda-forge/win-64/qhull-2020.2-hc790b64_5.conda#854fbdff64b572b5c0b470f334d34c11 -https://conda.anaconda.org/conda-forge/win-64/tk-8.6.13-h5226925_1.conda#fc048363eb8f03cd1737600a5d08aafe +https://conda.anaconda.org/conda-forge/win-64/tk-8.6.13-h2c6b04d_2.conda#ebd0e761de9aa879a51d22cc721bd095 https://conda.anaconda.org/conda-forge/win-64/krb5-1.21.3-hdf4eb48_0.conda#31aec030344e962fbd7dbbbbd68e60a9 https://conda.anaconda.org/conda-forge/win-64/libbrotlidec-1.1.0-h2466b09_2.conda#9bae75ce723fa34e98e239d21d752a7e https://conda.anaconda.org/conda-forge/win-64/libbrotlienc-1.1.0-h2466b09_2.conda#85741a24d97954a991e55e34bc55990b @@ -56,7 +56,7 @@ https://conda.anaconda.org/conda-forge/win-64/cython-3.1.1-py310h6bd2d47_1.conda https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_1.conda#a71efeae2c160f6789900ba2631a2c90 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https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda#5aa797f8787fe7a17d1b0821485b5adc https://conda.anaconda.org/conda-forge/linux-64/ninja-1.12.1-hff21bea_1.conda#2322531904f27501ee19847b87ba7c64 https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8c095d6_2.conda#283b96675859b20a825f8fa30f311446 -https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h4845f30_101.conda#d453b98d9c83e71da0741bb0ff4d76bc +https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_hd72426e_102.conda#a0116df4f4ed05c303811a837d5b39d8 https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb8e6e7a_2.conda#6432cb5d4ac0046c3ac0a8a0f95842f9 https://conda.anaconda.org/conda-forge/linux-64/libfreetype6-2.13.3-h48d6fc4_1.conda#3c255be50a506c50765a93a6644f32fe https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-15.1.0-h69a702a_2.conda#a483a87b71e974bb75d1b9413d4436dd @@ -72,7 +72,7 @@ https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.1-pyhd8ed1ab_0.conda https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha55dd90_7.conda#461219d1a5bd61342293efa2c0c90eac https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2025.2-pyhd8ed1ab_0.conda#88476ae6ebd24f39261e0854ac244f33 https://conda.anaconda.org/conda-forge/noarch/pytz-2025.2-pyhd8ed1ab_0.conda#bc8e3267d44011051f2eb14d22fb0960 -https://conda.anaconda.org/conda-forge/noarch/setuptools-80.8.0-pyhff2d567_0.conda#ea075e94dc0106c7212128b6a25bbc4c +https://conda.anaconda.org/conda-forge/noarch/setuptools-80.9.0-pyhff2d567_0.conda#4de79c071274a53dcaf2a8c749d1499e https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhd8ed1ab_0.conda#a451d576819089b0d672f18768be0f65 https://conda.anaconda.org/conda-forge/noarch/snowballstemmer-3.0.1-pyhd8ed1ab_0.conda#755cf22df8693aa0d1aec1c123fa5863 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-jsmath-1.0.1-pyhd8ed1ab_1.conda#fa839b5ff59e192f411ccc7dae6588bb @@ -101,7 +101,7 @@ https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.5-pyhd8ed1ab_0.conda#c3 https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py310ha75aee5_2.conda#f9254b5b0193982416b91edcb4b2676f https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-31_h1ea3ea9_openblas.conda#ba652ee0576396d4765e567f043c57f9 https://conda.anaconda.org/conda-forge/linux-64/pandas-2.2.3-py310h5eaa309_3.conda#07697a584fab513ce895c4511f7a2403 -https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.6.1-pyhd8ed1ab_1.conda#59aad4fb37cabc0bacc73cf344612ddd +https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.7.0-pyhd8ed1ab_0.conda#15353a2a0ea6dfefaa52fc5ab5b98f41 https://conda.anaconda.org/conda-forge/linux-64/scipy-1.15.2-py310h1d65ade_0.conda#8c29cd33b64b2eb78597fa28b5595c8d https://conda.anaconda.org/conda-forge/noarch/urllib3-2.4.0-pyhd8ed1ab_0.conda#c1e349028e0052c4eea844e94f773065 https://conda.anaconda.org/conda-forge/linux-64/blas-2.131-openblas.conda#38b2ec894c69bb4be0e66d2ef7fc60bf diff --git a/build_tools/azure/ubuntu_atlas_lock.txt b/build_tools/azure/ubuntu_atlas_lock.txt index ab86e683f6f09..9c1faa23ab962 100644 --- a/build_tools/azure/ubuntu_atlas_lock.txt +++ b/build_tools/azure/ubuntu_atlas_lock.txt @@ -33,7 +33,7 @@ pytest==8.3.5 # via # -r build_tools/azure/ubuntu_atlas_requirements.txt # pytest-xdist -pytest-xdist==3.6.1 +pytest-xdist==3.7.0 # via -r build_tools/azure/ubuntu_atlas_requirements.txt threadpoolctl==3.1.0 # via -r build_tools/azure/ubuntu_atlas_requirements.txt diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock index db2d896dc6ddc..d19f830684796 100644 --- a/build_tools/circle/doc_linux-64_conda.lock +++ b/build_tools/circle/doc_linux-64_conda.lock @@ -63,7 +63,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hd590300_0.conda#30 https://conda.anaconda.org/conda-forge/linux-64/libpciaccess-0.18-hd590300_0.conda#48f4330bfcd959c3cfb704d424903c82 https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.47-h943b412_0.conda#55199e2ae2c3651f6f9b2a447b47bdc9 https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-13.3.0-he8ea267_2.conda#2b6cdf7bb95d3d10ef4e38ce0bc95dba -https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.49.2-hee588c1_0.conda#93048463501053a00739215ea3f36324 +https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.50.0-hee588c1_0.conda#71888e92098d0f8c41b09a671ad289bc https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.1.0-h4852527_2.conda#9d2072af184b5caa29492bf2344597bb https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda#40b61aab5c7ba9ff276c41cfffe6b80b https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.17.0-h8a09558_0.conda#92ed62436b625154323d40d5f2f11dd7 @@ -74,7 +74,7 @@ https://conda.anaconda.org/conda-forge/linux-64/pixman-0.46.0-h29eaf8c_0.conda#d https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8c095d6_2.conda#283b96675859b20a825f8fa30f311446 https://conda.anaconda.org/conda-forge/linux-64/snappy-1.2.1-h8bd8927_1.conda#3b3e64af585eadfb52bb90b553db5edf https://conda.anaconda.org/conda-forge/linux-64/svt-av1-3.0.2-h5888daf_0.conda#0096882bd623e6cc09e8bf920fc8fb47 -https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h4845f30_101.conda#d453b98d9c83e71da0741bb0ff4d76bc +https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_hd72426e_102.conda#a0116df4f4ed05c303811a837d5b39d8 https://conda.anaconda.org/conda-forge/linux-64/wayland-1.23.1-h3e06ad9_1.conda#a37843723437ba75f42c9270ffe800b1 https://conda.anaconda.org/conda-forge/linux-64/zfp-1.0.1-h5888daf_2.conda#e0409515c467b87176b070bff5d9442e https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.2.4-h7955e40_0.conda#c8a816dbf59eb8ba6346a8f10014b302 @@ -138,7 +138,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.13.8-h4bc477f_0.conda# https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.2-py310h89163eb_1.conda#8ce3f0332fd6de0d737e2911d329523f https://conda.anaconda.org/conda-forge/noarch/meson-1.8.1-pyhe01879c_0.conda#f3cccd9a6ce5331ae33f69ade5529162 https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2#2ba8498c1018c1e9c61eb99b973dfe19 -https://conda.anaconda.org/conda-forge/noarch/narwhals-1.40.0-pyhe01879c_0.conda#578a7350e50600779eac971be60cafdf +https://conda.anaconda.org/conda-forge/noarch/narwhals-1.41.0-pyhe01879c_0.conda#580a340cc0f5eab2b18adb1b55e032e5 https://conda.anaconda.org/conda-forge/noarch/networkx-3.4.2-pyh267e887_2.conda#fd40bf7f7f4bc4b647dc8512053d9873 https://conda.anaconda.org/conda-forge/linux-64/openblas-0.3.29-pthreads_h6ec200e_0.conda#7e4d48870b3258bea920d51b7f495a81 https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.3-h5fbd93e_0.conda#9e5816bc95d285c115a3ebc2f8563564 @@ -152,7 +152,7 @@ https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.2.3-pyhd8ed1ab_1.conda https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha55dd90_7.conda#461219d1a5bd61342293efa2c0c90eac https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2025.2-pyhd8ed1ab_0.conda#88476ae6ebd24f39261e0854ac244f33 https://conda.anaconda.org/conda-forge/noarch/pytz-2025.2-pyhd8ed1ab_0.conda#bc8e3267d44011051f2eb14d22fb0960 -https://conda.anaconda.org/conda-forge/noarch/setuptools-80.8.0-pyhff2d567_0.conda#ea075e94dc0106c7212128b6a25bbc4c +https://conda.anaconda.org/conda-forge/noarch/setuptools-80.9.0-pyhff2d567_0.conda#4de79c071274a53dcaf2a8c749d1499e https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhd8ed1ab_0.conda#a451d576819089b0d672f18768be0f65 https://conda.anaconda.org/conda-forge/noarch/snowballstemmer-3.0.1-pyhd8ed1ab_0.conda#755cf22df8693aa0d1aec1c123fa5863 https://conda.anaconda.org/conda-forge/noarch/soupsieve-2.7-pyhd8ed1ab_0.conda#fb32097c717486aa34b38a9db57eb49e @@ -169,7 +169,7 @@ https://conda.anaconda.org/conda-forge/linux-64/xkeyboard-config-2.44-hb9d3cd8_0 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxext-1.3.6-hb9d3cd8_0.conda#febbab7d15033c913d53c7a2c102309d https://conda.anaconda.org/conda-forge/linux-64/xorg-libxfixes-6.0.1-hb9d3cd8_0.conda#4bdb303603e9821baf5fe5fdff1dc8f8 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrender-0.9.12-hb9d3cd8_0.conda#96d57aba173e878a2089d5638016dc5e -https://conda.anaconda.org/conda-forge/noarch/zipp-3.21.0-pyhd8ed1ab_1.conda#0c3cc595284c5e8f0f9900a9b228a332 +https://conda.anaconda.org/conda-forge/noarch/zipp-3.22.0-pyhd8ed1ab_0.conda#234be740b00b8e41567e5b0ed95aaba9 https://conda.anaconda.org/conda-forge/noarch/accessible-pygments-0.0.5-pyhd8ed1ab_1.conda#74ac5069774cdbc53910ec4d631a3999 https://conda.anaconda.org/conda-forge/noarch/babel-2.17.0-pyhd8ed1ab_0.conda#0a01c169f0ab0f91b26e77a3301fbfe4 https://conda.anaconda.org/conda-forge/linux-64/brunsli-0.1-h9c3ff4c_0.tar.bz2#c1ac6229d0bfd14f8354ff9ad2a26cad @@ -177,7 +177,7 @@ https://conda.anaconda.org/conda-forge/linux-64/c-compiler-1.9.0-h2b85faf_0.cond https://conda.anaconda.org/conda-forge/linux-64/cffi-1.17.1-py310h8deb56e_0.conda#1fc24a3196ad5ede2a68148be61894f4 https://conda.anaconda.org/conda-forge/linux-64/dbus-1.16.2-h3c4dab8_0.conda#679616eb5ad4e521c83da4650860aba7 https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda#72e42d28960d875c7654614f8b50939a -https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.58.0-py310h89163eb_0.conda#4532df8a45ab4e37b2cc71186304dd5a +https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.58.1-py310h89163eb_0.conda#f4f46207c6defa5ea17b0299298ba849 https://conda.anaconda.org/conda-forge/linux-64/freetype-2.13.3-ha770c72_1.conda#9ccd736d31e0c6e41f54e704e5312811 https://conda.anaconda.org/conda-forge/linux-64/gfortran-13.3.0-h9576a4e_2.conda#19e6d3c9cde10a0a9a170a684082588e https://conda.anaconda.org/conda-forge/linux-64/gfortran_linux-64-13.3.0-hb919d3a_10.conda#7ce070e3329cd10bf79dbed562a21bd4 @@ -191,14 +191,14 @@ https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.1-pyhd8ed1ab_0.conda#fb https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-31_he106b2a_openblas.conda#abb32c727da370c481a1c206f5159ce9 https://conda.anaconda.org/conda-forge/linux-64/libgl-1.7.0-ha4b6fd6_2.conda#928b8be80851f5d8ffb016f9c81dae7a https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-31_h7ac8fdf_openblas.conda#452b98eafe050ecff932f0ec832dd03f -https://conda.anaconda.org/conda-forge/linux-64/libllvm20-20.1.5-he9d0ab4_0.conda#8d2f5a2f019bd76ccba5eb771852d411 +https://conda.anaconda.org/conda-forge/linux-64/libllvm20-20.1.6-he9d0ab4_0.conda#bf8ccdd2c1c1a54a3fa25bb61f26460e https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.10.0-h65c71a3_0.conda#fedf6bfe5d21d21d2b1785ec00a8889a https://conda.anaconda.org/conda-forge/linux-64/libxslt-1.1.39-h76b75d6_0.conda#e71f31f8cfb0a91439f2086fc8aa0461 https://conda.anaconda.org/conda-forge/noarch/memory_profiler-0.61.0-pyhd8ed1ab_1.conda#71abbefb6f3b95e1668cd5e0af3affb9 https://conda.anaconda.org/conda-forge/linux-64/openldap-2.6.10-he970967_0.conda#2e5bf4f1da39c0b32778561c3c4e5878 https://conda.anaconda.org/conda-forge/linux-64/pillow-11.2.1-py310h7e6dc6c_0.conda#5645a243d90adb50909b9edc209d84fe https://conda.anaconda.org/conda-forge/noarch/pip-25.1.1-pyh8b19718_0.conda#32d0781ace05105cc99af55d36cbec7c -https://conda.anaconda.org/conda-forge/noarch/plotly-6.1.1-pyhd8ed1ab_0.conda#b18343b5d779bf6d9205822c2cf76d6e +https://conda.anaconda.org/conda-forge/noarch/plotly-6.1.2-pyhd8ed1ab_0.conda#f547ee092ef42452ddaffdfa59ff4987 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.9.1-pyhd8ed1ab_0.conda#22ae7c6ea81e0c8661ef32168dda929b https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhff2d567_1.conda#5ba79d7c71f03c678c8ead841f347d6e https://conda.anaconda.org/conda-forge/noarch/python-gil-3.10.17-hd8ed1ab_0.conda#c856adbd93a57004e21cd26564f4f724 @@ -217,8 +217,8 @@ https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.15.0-h7e30c49_1.con https://conda.anaconda.org/conda-forge/linux-64/fortran-compiler-1.9.0-h36df796_0.conda#cc0cf942201f9d3b0e9654ea02e12486 https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.5.2-pyhd8ed1ab_0.conda#e376ea42e9ae40f3278b0f79c9bf9826 https://conda.anaconda.org/conda-forge/noarch/lazy-loader-0.4-pyhd8ed1ab_2.conda#d10d9393680734a8febc4b362a4c94f2 -https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp20.1-20.1.5-default_h1df26ce_1.conda#330b1dadfa7c3205a01fa9599fabe808 -https://conda.anaconda.org/conda-forge/linux-64/libclang13-20.1.5-default_he06ed0a_1.conda#12117145218e7e1a528c8396ed803058 +https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp20.1-20.1.6-default_h1df26ce_0.conda#99ead3b974685e44df8b1e3953503cfc +https://conda.anaconda.org/conda-forge/linux-64/libclang13-20.1.6-default_he06ed0a_0.conda#cc6c469d9d7fc0ac106cef5f45d973a9 https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-31_he2f377e_openblas.conda#7e5fff7d0db69be3a266f7e79a3bb0e2 https://conda.anaconda.org/conda-forge/linux-64/libpq-17.5-h27ae623_0.conda#6458be24f09e1b034902ab44fe9de908 https://conda.anaconda.org/conda-forge/noarch/meson-python-0.18.0-pyh70fd9c4_0.conda#576c04b9d9f8e45285fb4d9452c26133 @@ -230,12 +230,12 @@ https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-31_h1ea3ea9_ope https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.4-h3394656_0.conda#09262e66b19567aff4f592fb53b28760 https://conda.anaconda.org/conda-forge/linux-64/compilers-1.9.0-ha770c72_0.conda#5859096e397aba423340d0bbbb11ec64 https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.2-py310h3788b33_0.conda#b6420d29123c7c823de168f49ccdfe6a -https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2024.12.30-py310h481ba9f_1.conda#1a0a3b184dec933b4922a2721b3150a0 +https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2025.3.30-py310h481ba9f_0.conda#453c8da1b70f7b76b3884e18015bc568 https://conda.anaconda.org/conda-forge/noarch/imageio-2.37.0-pyhfb79c49_0.conda#b5577bc2212219566578fd5af9993af6 https://conda.anaconda.org/conda-forge/linux-64/pandas-2.2.3-py310h5eaa309_3.conda#07697a584fab513ce895c4511f7a2403 https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.1-pyhd8ed1ab_1.conda#ee23fabfd0a8c6b8d6f3729b47b2859d https://conda.anaconda.org/conda-forge/linux-64/polars-default-1.30.0-py39hfac2b71_0.conda#cd33cf1e631b4d766858c90e333b4832 -https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.6.1-pyhd8ed1ab_1.conda#59aad4fb37cabc0bacc73cf344612ddd +https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.7.0-pyhd8ed1ab_0.conda#15353a2a0ea6dfefaa52fc5ab5b98f41 https://conda.anaconda.org/conda-forge/linux-64/pywavelets-1.8.0-py310hf462985_0.conda#4c441eff2be2e65bd67765c5642051c5 https://conda.anaconda.org/conda-forge/linux-64/scipy-1.15.2-py310h1d65ade_0.conda#8c29cd33b64b2eb78597fa28b5595c8d https://conda.anaconda.org/conda-forge/noarch/towncrier-24.8.0-pyhd8ed1ab_1.conda#820b6a1ddf590fba253f8204f7200d82 @@ -300,7 +300,7 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1 # pip argon2-cffi-bindings @ https://files.pythonhosted.org/packages/ec/f7/378254e6dd7ae6f31fe40c8649eea7d4832a42243acaf0f1fff9083b2bed/argon2_cffi_bindings-21.2.0-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=b746dba803a79238e925d9046a63aa26bf86ab2a2fe74ce6b009a1c3f5c8f2ae # pip arrow @ https://files.pythonhosted.org/packages/f8/ed/e97229a566617f2ae958a6b13e7cc0f585470eac730a73e9e82c32a3cdd2/arrow-1.3.0-py3-none-any.whl#sha256=c728b120ebc00eb84e01882a6f5e7927a53960aa990ce7dd2b10f39005a67f80 # pip doit @ https://files.pythonhosted.org/packages/44/83/a2960d2c975836daa629a73995134fd86520c101412578c57da3d2aa71ee/doit-0.36.0-py3-none-any.whl#sha256=ebc285f6666871b5300091c26eafdff3de968a6bd60ea35dd1e3fc6f2e32479a -# pip jupyter-core @ https://files.pythonhosted.org/packages/c9/fb/108ecd1fe961941959ad0ee4e12ee7b8b1477247f30b1fdfd83ceaf017f0/jupyter_core-5.7.2-py3-none-any.whl#sha256=4f7315d2f6b4bcf2e3e7cb6e46772eba760ae459cd1f59d29eb57b0a01bd7409 +# pip jupyter-core @ https://files.pythonhosted.org/packages/2f/57/6bffd4b20b88da3800c5d691e0337761576ee688eb01299eae865689d2df/jupyter_core-5.8.1-py3-none-any.whl#sha256=c28d268fc90fb53f1338ded2eb410704c5449a358406e8a948b75706e24863d0 # pip markdown-it-py @ https://files.pythonhosted.org/packages/42/d7/1ec15b46af6af88f19b8e5ffea08fa375d433c998b8a7639e76935c14f1f/markdown_it_py-3.0.0-py3-none-any.whl#sha256=355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1 # pip mistune @ https://files.pythonhosted.org/packages/01/4d/23c4e4f09da849e127e9f123241946c23c1e30f45a88366879e064211815/mistune-3.1.3-py3-none-any.whl#sha256=1a32314113cff28aa6432e99e522677c8587fd83e3d51c29b82a52409c842bd9 # pip pyzmq @ https://files.pythonhosted.org/packages/c1/3e/2de5928cdadc2105e7c8f890cc5f404136b41ce5b6eae5902167f1d5641c/pyzmq-26.4.0-cp310-cp310-manylinux_2_28_x86_64.whl#sha256=7dacb06a9c83b007cc01e8e5277f94c95c453c5851aac5e83efe93e72226353f @@ -317,11 +317,11 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1 # pip jupyter-server-terminals @ https://files.pythonhosted.org/packages/07/2d/2b32cdbe8d2a602f697a649798554e4f072115438e92249624e532e8aca6/jupyter_server_terminals-0.5.3-py3-none-any.whl#sha256=41ee0d7dc0ebf2809c668e0fc726dfaf258fcd3e769568996ca731b6194ae9aa # pip jupyterlite-core @ https://files.pythonhosted.org/packages/46/15/1d9160819d1e6e018d15de0e98b9297d0a09cfcfdc73add6e24ee3b2b83c/jupyterlite_core-0.5.1-py3-none-any.whl#sha256=76381619a632f06bf67fb47e5464af762ad8836df5ffe3d7e7ee0e316c1407ee # pip mdit-py-plugins @ 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https://files.pythonhosted.org/packages/e2/48/577993f1f99c552f18a0428731a755e06171f9902fa118c379eb7c04ea22/jupyter_events-0.12.0-py3-none-any.whl#sha256=6464b2fa5ad10451c3d35fabc75eab39556ae1e2853ad0c0cc31b656731a97fb # pip nbformat @ https://files.pythonhosted.org/packages/a9/82/0340caa499416c78e5d8f5f05947ae4bc3cba53c9f038ab6e9ed964e22f1/nbformat-5.10.4-py3-none-any.whl#sha256=3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b -# pip jupytext @ https://files.pythonhosted.org/packages/12/b7/e7e3d34c8095c19228874b1babedfb5d901374e40d51ae66f2a90203be53/jupytext-1.17.1-py3-none-any.whl#sha256=99145b1e1fa96520c21ba157de7d354ffa4904724dcebdcd70b8413688a312de +# pip jupytext @ https://files.pythonhosted.org/packages/ed/f1/82ea8e783433707cafd9790099a2d19f113c22f32a31c8bb5abdc7a61dbb/jupytext-1.17.2-py3-none-any.whl#sha256=4f85dc43bb6a24b75491c5c434001ad5ef563932f68f15dd3e1c8ce12a4a426b # pip nbclient @ https://files.pythonhosted.org/packages/34/6d/e7fa07f03a4a7b221d94b4d586edb754a9b0dc3c9e2c93353e9fa4e0d117/nbclient-0.10.2-py3-none-any.whl#sha256=4ffee11e788b4a27fabeb7955547e4318a5298f34342a4bfd01f2e1faaeadc3d # pip nbconvert @ https://files.pythonhosted.org/packages/cc/9a/cd673b2f773a12c992f41309ef81b99da1690426bd2f96957a7ade0d3ed7/nbconvert-7.16.6-py3-none-any.whl#sha256=1375a7b67e0c2883678c48e506dc320febb57685e5ee67faa51b18a90f3a712b # pip jupyter-server @ https://files.pythonhosted.org/packages/46/1f/5ebbced977171d09a7b0c08a285ff9a20aafb9c51bde07e52349ff1ddd71/jupyter_server-2.16.0-py3-none-any.whl#sha256=3d8db5be3bc64403b1c65b400a1d7f4647a5ce743f3b20dbdefe8ddb7b55af9e diff --git a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock index cf6232d4ba950..89d34a59f3b7b 100644 --- a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock +++ b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock @@ -74,7 +74,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hd590300_0.conda#30 https://conda.anaconda.org/conda-forge/linux-64/libpciaccess-0.18-hd590300_0.conda#48f4330bfcd959c3cfb704d424903c82 https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.47-h943b412_0.conda#55199e2ae2c3651f6f9b2a447b47bdc9 https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-13.3.0-he8ea267_2.conda#2b6cdf7bb95d3d10ef4e38ce0bc95dba -https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.49.2-hee588c1_0.conda#93048463501053a00739215ea3f36324 +https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.50.0-hee588c1_0.conda#71888e92098d0f8c41b09a671ad289bc https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.1.0-h4852527_2.conda#9d2072af184b5caa29492bf2344597bb https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda#40b61aab5c7ba9ff276c41cfffe6b80b https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.17.0-h8a09558_0.conda#92ed62436b625154323d40d5f2f11dd7 @@ -87,7 +87,7 @@ https://conda.anaconda.org/conda-forge/linux-64/pixman-0.46.0-h29eaf8c_0.conda#d https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8c095d6_2.conda#283b96675859b20a825f8fa30f311446 https://conda.anaconda.org/conda-forge/linux-64/snappy-1.2.1-h8bd8927_1.conda#3b3e64af585eadfb52bb90b553db5edf https://conda.anaconda.org/conda-forge/linux-64/svt-av1-3.0.2-h5888daf_0.conda#0096882bd623e6cc09e8bf920fc8fb47 -https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h4845f30_101.conda#d453b98d9c83e71da0741bb0ff4d76bc +https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_hd72426e_102.conda#a0116df4f4ed05c303811a837d5b39d8 https://conda.anaconda.org/conda-forge/linux-64/yaml-0.2.5-h7f98852_2.tar.bz2#4cb3ad778ec2d5a7acbdf254eb1c42ae https://conda.anaconda.org/conda-forge/linux-64/zfp-1.0.1-h5888daf_2.conda#e0409515c467b87176b070bff5d9442e https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.2.4-h7955e40_0.conda#c8a816dbf59eb8ba6346a8f10014b302 @@ -113,7 +113,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libjxl-0.11.1-h7b0646d_1.conda#9 https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.7.0-hf01ce69_5.conda#e79a094918988bb1807462cd42c83962 https://conda.anaconda.org/conda-forge/linux-64/libvorbis-1.3.7-h9c3ff4c_0.tar.bz2#309dec04b70a3cc0f1e84a4013683bc0 https://conda.anaconda.org/conda-forge/linux-64/libzopfli-1.0.3-h9c3ff4c_0.tar.bz2#c66fe2d123249af7651ebde8984c51c2 -https://conda.anaconda.org/conda-forge/linux-64/nss-3.111-h159eef7_0.conda#311e8370c9db254611ec87250f6370a0 +https://conda.anaconda.org/conda-forge/linux-64/nss-3.112-h159eef7_0.conda#688a8bc02e57e6b741a040c84e931a7d https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.45-hc749103_0.conda#b90bece58b4c2bf25969b70f3be42d25 https://conda.anaconda.org/conda-forge/linux-64/python-3.10.17-hd6af730_0_cpython.conda#7bb89638dae9ce1b8e051d0b721e83c2 https://conda.anaconda.org/conda-forge/linux-64/xcb-util-0.4.1-hb711507_2.conda#8637c3e5821654d0edf97e2b0404b443 @@ -174,7 +174,7 @@ https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.2.3-pyhd8ed1ab_1.conda https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha55dd90_7.conda#461219d1a5bd61342293efa2c0c90eac https://conda.anaconda.org/conda-forge/noarch/pytz-2025.2-pyhd8ed1ab_0.conda#bc8e3267d44011051f2eb14d22fb0960 https://conda.anaconda.org/conda-forge/linux-64/pyyaml-6.0.2-py310h89163eb_2.conda#fd343408e64cf1e273ab7c710da374db -https://conda.anaconda.org/conda-forge/noarch/setuptools-80.8.0-pyhff2d567_0.conda#ea075e94dc0106c7212128b6a25bbc4c +https://conda.anaconda.org/conda-forge/noarch/setuptools-80.9.0-pyhff2d567_0.conda#4de79c071274a53dcaf2a8c749d1499e 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https://conda.anaconda.org/conda-forge/linux-aarch64/blas-devel-3.9.0-31_h9678261_openblas.conda#a2cc143d7e25e52a915cb320e5b0d592 https://conda.anaconda.org/conda-forge/linux-aarch64/cairo-1.18.4-h83712da_0.conda#cd55953a67ec727db5dc32b167201aa6 https://conda.anaconda.org/conda-forge/linux-aarch64/contourpy-1.3.2-py310hf54e67a_0.conda#779694434d1f0a67c5260db76b7b7907 -https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.6.1-pyhd8ed1ab_1.conda#59aad4fb37cabc0bacc73cf344612ddd +https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.7.0-pyhd8ed1ab_0.conda#15353a2a0ea6dfefaa52fc5ab5b98f41 https://conda.anaconda.org/conda-forge/linux-aarch64/scipy-1.15.2-py310hf37559f_0.conda#5c9b72f10d2118d943a5eaaf2f396891 https://conda.anaconda.org/conda-forge/linux-aarch64/blas-2.131-openblas.conda#51c5f346e1ebee750f76066490059df9 https://conda.anaconda.org/conda-forge/linux-aarch64/harfbuzz-11.2.1-h405b6a2_0.conda#b55680fc90e9747dc858e7ceb0abc2b2 diff --git a/sklearn/model_selection/_search.py b/sklearn/model_selection/_search.py index b6b537a68d401..5bd3f81195631 100644 --- a/sklearn/model_selection/_search.py +++ b/sklearn/model_selection/_search.py @@ -1946,7 +1946,7 @@ class RandomizedSearchCV(BaseSearchCV): >>> clf = RandomizedSearchCV(logistic, distributions, random_state=0) >>> search = clf.fit(iris.data, iris.target) >>> search.best_params_ - {'C': np.float64(2.195), 'penalty': 'l1'} + {'C': np.float64(2.195...), 'penalty': 'l1'} """ _parameter_constraints: dict = { From 1b1df698af78ff23140c4a64aacc3d1d42af6160 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Miro=20Hron=C4=8Dok?= Date: Tue, 3 Jun 2025 08:38:49 +0200 Subject: [PATCH 005/103] MNT Use tmp_path fixture for test_check_memory (#31453) --- sklearn/utils/tests/test_validation.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/sklearn/utils/tests/test_validation.py b/sklearn/utils/tests/test_validation.py index 1aaf7c346b1d3..99db6cdfb16aa 100644 --- a/sklearn/utils/tests/test_validation.py +++ b/sklearn/utils/tests/test_validation.py @@ -1161,9 +1161,10 @@ class WrongDummyMemory: pass -def test_check_memory(): - memory = check_memory("cache_directory") - assert memory.location == "cache_directory" +def test_check_memory(tmp_path): + cache_directory = str(tmp_path / "cache_directory") + memory = check_memory(cache_directory) + assert memory.location == cache_directory memory = check_memory(None) assert memory.location is None From b7051392c4ac629a524bff8acdd1999bf2295179 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Tue, 3 Jun 2025 09:46:36 +0200 Subject: [PATCH 006/103] :lock: :robot: CI Update lock files for array-api CI build(s) :lock: :robot: (#31465) Co-authored-by: Lock file bot --- ...a_forge_cuda_array-api_linux-64_conda.lock | 26 +++++++++---------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/build_tools/github/pylatest_conda_forge_cuda_array-api_linux-64_conda.lock b/build_tools/github/pylatest_conda_forge_cuda_array-api_linux-64_conda.lock index 78846708d0a03..315164f96c77c 100644 --- a/build_tools/github/pylatest_conda_forge_cuda_array-api_linux-64_conda.lock +++ b/build_tools/github/pylatest_conda_forge_cuda_array-api_linux-64_conda.lock @@ -16,7 +16,7 @@ https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.4.26-hbd8a1cb https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766549260d6815b0c52253f1fb1bb29 https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_4.conda#01f8d123c96816249efd255a31ad7712 https://conda.anaconda.org/conda-forge/linux-64/libglvnd-1.7.0-ha4b6fd6_2.conda#434ca7e50e40f4918ab701e3facd59a0 -https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-20.1.5-h024ca30_0.conda#86f58be65a51d62ccc06cacfd83ff987 +https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-20.1.6-h024ca30_0.conda#e4ece7ed81e43ae97a3b58ac4230c3c5 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https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 @@ -63,10 +64,9 @@ https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20250104-pl5321h7949 https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda#172bf1cd1ff8629f2b1179945ed45055 https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda#a1cfcc585f0c42bf8d5546bb1dfb668d https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.1.0-h69a702a_2.conda#f92e6e0a3c0c0c85561ef61aa59d555d -https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-h4bc722e_0.conda#aeb98fdeb2e8f25d43ef71fbacbeec80 https://conda.anaconda.org/conda-forge/linux-64/libpciaccess-0.18-hd590300_0.conda#48f4330bfcd959c3cfb704d424903c82 https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.47-h943b412_0.conda#55199e2ae2c3651f6f9b2a447b47bdc9 -https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.49.2-hee588c1_0.conda#93048463501053a00739215ea3f36324 +https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.50.0-hee588c1_0.conda#71888e92098d0f8c41b09a671ad289bc https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.11.1-hcf80075_0.conda#eecce068c7e4eddeb169591baac20ac4 https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.1.0-h4852527_2.conda#9d2072af184b5caa29492bf2344597bb https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda#40b61aab5c7ba9ff276c41cfffe6b80b @@ -78,7 +78,7 @@ https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8c095d6_2.conda#28 https://conda.anaconda.org/conda-forge/linux-64/s2n-1.5.14-h6c98b2b_0.conda#efab4ad81ba5731b2fefa0ab4359e884 https://conda.anaconda.org/conda-forge/linux-64/sleef-3.8-h1b44611_0.conda#aec4dba5d4c2924730088753f6fa164b 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https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2025.2-pyhd8ed1ab_0.conda#88476ae6ebd24f39261e0854ac244f33 https://conda.anaconda.org/conda-forge/noarch/pytz-2025.2-pyhd8ed1ab_0.conda#bc8e3267d44011051f2eb14d22fb0960 https://conda.anaconda.org/conda-forge/linux-64/re2-2024.07.02-h9925aae_2.conda#e84ddf12bde691e8ec894b00ea829ddf -https://conda.anaconda.org/conda-forge/noarch/setuptools-80.8.0-pyhff2d567_0.conda#ea075e94dc0106c7212128b6a25bbc4c +https://conda.anaconda.org/conda-forge/noarch/setuptools-80.9.0-pyhff2d567_0.conda#4de79c071274a53dcaf2a8c749d1499e https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhd8ed1ab_0.conda#a451d576819089b0d672f18768be0f65 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda#9d64911b31d57ca443e9f1e36b04385f https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_1.conda#b0dd904de08b7db706167240bf37b164 @@ -169,7 +169,7 @@ 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https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.1-pyhd8ed1ab_0.conda#fb1c14694de51a476ce8636d92b6f42c @@ -179,7 +179,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libgl-1.7.0-ha4b6fd6_2.conda#928 https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.67.1-h25350d4_2.conda#bfcedaf5f9b003029cc6abe9431f66bf https://conda.anaconda.org/conda-forge/linux-64/libhwloc-2.11.2-default_h0d58e46_1001.conda#804ca9e91bcaea0824a341d55b1684f2 https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-31_h7ac8fdf_openblas.conda#452b98eafe050ecff932f0ec832dd03f -https://conda.anaconda.org/conda-forge/linux-64/libllvm20-20.1.5-he9d0ab4_0.conda#8d2f5a2f019bd76ccba5eb771852d411 +https://conda.anaconda.org/conda-forge/linux-64/libllvm20-20.1.6-he9d0ab4_0.conda#bf8ccdd2c1c1a54a3fa25bb61f26460e https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.10.0-h65c71a3_0.conda#fedf6bfe5d21d21d2b1785ec00a8889a https://conda.anaconda.org/conda-forge/linux-64/libxslt-1.1.39-h76b75d6_0.conda#e71f31f8cfb0a91439f2086fc8aa0461 https://conda.anaconda.org/conda-forge/linux-64/mpc-1.3.1-h24ddda3_1.conda#aa14b9a5196a6d8dd364164b7ce56acf @@ -203,8 +203,8 @@ https://conda.anaconda.org/conda-forge/linux-64/azure-storage-common-cpp-12.8.0- https://conda.anaconda.org/conda-forge/linux-64/cudnn-9.10.1.4-haad7af6_0.conda#8382d957333e0d3280dcbf5691516dc1 https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.15.0-h7e30c49_1.conda#8f5b0b297b59e1ac160ad4beec99dbee https://conda.anaconda.org/conda-forge/linux-64/gmpy2-2.2.1-py313h11186cd_0.conda#54d020e0eaacf1e99bfb2410b9aa2e5e -https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp20.1-20.1.5-default_h1df26ce_1.conda#330b1dadfa7c3205a01fa9599fabe808 -https://conda.anaconda.org/conda-forge/linux-64/libclang13-20.1.5-default_he06ed0a_1.conda#12117145218e7e1a528c8396ed803058 +https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp20.1-20.1.6-default_h1df26ce_0.conda#99ead3b974685e44df8b1e3953503cfc +https://conda.anaconda.org/conda-forge/linux-64/libclang13-20.1.6-default_he06ed0a_0.conda#cc6c469d9d7fc0ac106cef5f45d973a9 https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.36.0-h2b5623c_0.conda#c96ca58ad3352a964bfcb85de6cd1496 https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-31_he2f377e_openblas.conda#7e5fff7d0db69be3a266f7e79a3bb0e2 https://conda.anaconda.org/conda-forge/linux-64/libmagma-2.9.0-h45b15fe_0.conda#703a1ab01e36111d8bb40bc7517e900b @@ -228,7 +228,7 @@ https://conda.anaconda.org/conda-forge/linux-64/mkl-2024.2.2-ha957f24_16.conda#1 https://conda.anaconda.org/conda-forge/linux-64/pandas-2.2.3-py313ha87cce1_3.conda#6248b529e537b1d4cb5ab3ef7f537795 https://conda.anaconda.org/conda-forge/linux-64/polars-default-1.30.0-py39hfac2b71_0.conda#cd33cf1e631b4d766858c90e333b4832 https://conda.anaconda.org/conda-forge/noarch/pytest-cov-6.1.1-pyhd8ed1ab_0.conda#1e35d8f975bc0e984a19819aa91c440a -https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.6.1-pyhd8ed1ab_1.conda#59aad4fb37cabc0bacc73cf344612ddd +https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.7.0-pyhd8ed1ab_0.conda#15353a2a0ea6dfefaa52fc5ab5b98f41 https://conda.anaconda.org/conda-forge/linux-64/scipy-1.15.2-py313h86fcf2b_0.conda#ca68acd9febc86448eeed68d0c6c8643 https://conda.anaconda.org/conda-forge/noarch/sympy-1.14.0-pyh2585a3b_105.conda#8c09fac3785696e1c477156192d64b91 https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.510-h37a5c72_3.conda#beb8577571033140c6897d257acc7724 From 9e3a206f479789a92f434712c94111b1377c53f0 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Tue, 3 Jun 2025 09:49:08 +0200 Subject: [PATCH 007/103] :lock: :robot: CI Update lock files for scipy-dev CI build(s) :lock: :robot: (#31463) Co-authored-by: Lock file bot --- build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock index 4aa3536528c84..a8fac4ea35b6c 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock @@ -65,6 +65,6 @@ https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2 # pip meson-python @ https://files.pythonhosted.org/packages/28/58/66db620a8a7ccb32633de9f403fe49f1b63c68ca94e5c340ec5cceeb9821/meson_python-0.18.0-py3-none-any.whl#sha256=3b0fe051551cc238f5febb873247c0949cd60ded556efa130aa57021804868e2 # pip pooch @ https://files.pythonhosted.org/packages/a8/87/77cc11c7a9ea9fd05503def69e3d18605852cd0d4b0d3b8f15bbeb3ef1d1/pooch-1.8.2-py3-none-any.whl#sha256=3529a57096f7198778a5ceefd5ac3ef0e4d06a6ddaf9fc2d609b806f25302c47 # pip pytest-cov @ https://files.pythonhosted.org/packages/28/d0/def53b4a790cfb21483016430ed828f64830dd981ebe1089971cd10cab25/pytest_cov-6.1.1-py3-none-any.whl#sha256=bddf29ed2d0ab6f4df17b4c55b0a657287db8684af9c42ea546b21b1041b3dde -# pip pytest-xdist @ https://files.pythonhosted.org/packages/6d/82/1d96bf03ee4c0fdc3c0cbe61470070e659ca78dc0086fb88b66c185e2449/pytest_xdist-3.6.1-py3-none-any.whl#sha256=9ed4adfb68a016610848639bb7e02c9352d5d9f03d04809919e2dafc3be4cca7 +# pip pytest-xdist @ https://files.pythonhosted.org/packages/0d/b2/0e802fde6f1c5b2f7ae7e9ad42b83fd4ecebac18a8a8c2f2f14e39dce6e1/pytest_xdist-3.7.0-py3-none-any.whl#sha256=7d3fbd255998265052435eb9daa4e99b62e6fb9cfb6efd1f858d4d8c0c7f0ca0 # pip sphinx @ https://files.pythonhosted.org/packages/31/53/136e9eca6e0b9dc0e1962e2c908fbea2e5ac000c2a2fbd9a35797958c48b/sphinx-8.2.3-py3-none-any.whl#sha256=4405915165f13521d875a8c29c8970800a0141c14cc5416a38feca4ea5d9b9c3 # pip numpydoc @ https://files.pythonhosted.org/packages/6c/45/56d99ba9366476cd8548527667f01869279cedb9e66b28eb4dfb27701679/numpydoc-1.8.0-py3-none-any.whl#sha256=72024c7fd5e17375dec3608a27c03303e8ad00c81292667955c6fea7a3ccf541 From 5301945f839fd3bae17e2288c863405253e4d6b6 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Tue, 3 Jun 2025 09:49:52 +0200 Subject: [PATCH 008/103] :lock: :robot: CI Update lock files for free-threaded CI build(s) :lock: :robot: (#31464) Co-authored-by: Lock file bot --- .../azure/pylatest_free_threaded_linux-64_conda.lock | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock b/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock index a75f20be093c2..40254398d3bb7 100644 --- a/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock +++ b/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock @@ -15,19 +15,19 @@ https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.6-h2dba641_1.conda#ed https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.1.0-h69a702a_2.conda#ddca86c7040dd0e73b2b69bd7833d225 https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.1.0-hcea5267_2.conda#01de444988ed960031dbe84cf4f9b1fc https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_1.conda#a76fd702c93cd2dfd89eff30a5fd45a8 +https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-hb9d3cd8_0.conda#c7e925f37e3b40d893459e625f6a53f1 https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_2.conda#1cb1c67961f6dd257eae9e9691b341aa https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.0-h7b32b05_1.conda#de356753cfdbffcde5bb1e86e3aa6cd0 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62ee74e96c5ebb0af99386de58cf9553 https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.1.0-h69a702a_2.conda#f92e6e0a3c0c0c85561ef61aa59d555d -https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-h4bc722e_0.conda#aeb98fdeb2e8f25d43ef71fbacbeec80 -https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.49.2-hee588c1_0.conda#93048463501053a00739215ea3f36324 +https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.50.0-hee588c1_0.conda#71888e92098d0f8c41b09a671ad289bc https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.1.0-h4852527_2.conda#9d2072af184b5caa29492bf2344597bb https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda#40b61aab5c7ba9ff276c41cfffe6b80b https://conda.anaconda.org/conda-forge/linux-64/ninja-1.12.1-hff21bea_1.conda#2322531904f27501ee19847b87ba7c64 https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8c095d6_2.conda#283b96675859b20a825f8fa30f311446 -https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h4845f30_101.conda#d453b98d9c83e71da0741bb0ff4d76bc +https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_hd72426e_102.conda#a0116df4f4ed05c303811a837d5b39d8 https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb8e6e7a_2.conda#6432cb5d4ac0046c3ac0a8a0f95842f9 https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-15.1.0-h69a702a_2.conda#a483a87b71e974bb75d1b9413d4436dd https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.29-pthreads_h94d23a6_0.conda#0a4d0252248ef9a0f88f2ba8b8a08e12 @@ -43,7 +43,7 @@ https://conda.anaconda.org/conda-forge/noarch/meson-1.8.1-pyhe01879c_0.conda#f3c https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda#58335b26c38bf4a20f399384c33cbcf9 https://conda.anaconda.org/conda-forge/noarch/pip-25.1.1-pyh145f28c_0.conda#01384ff1639c6330a0924791413b8714 https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda#7da7ccd349dbf6487a7778579d2bb971 -https://conda.anaconda.org/conda-forge/noarch/setuptools-80.8.0-pyhff2d567_0.conda#ea075e94dc0106c7212128b6a25bbc4c +https://conda.anaconda.org/conda-forge/noarch/setuptools-80.9.0-pyhff2d567_0.conda#4de79c071274a53dcaf2a8c749d1499e https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda#9d64911b31d57ca443e9f1e36b04385f https://conda.anaconda.org/conda-forge/noarch/tomli-2.2.1-pyhd8ed1ab_1.conda#ac944244f1fed2eb49bae07193ae8215 https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.13.2-pyh29332c3_0.conda#83fc6ae00127671e301c9f44254c31b8 @@ -57,5 +57,5 @@ https://conda.anaconda.org/conda-forge/noarch/python-freethreading-3.13.3-h92d6c https://conda.anaconda.org/conda-forge/noarch/meson-python-0.18.0-pyh70fd9c4_0.conda#576c04b9d9f8e45285fb4d9452c26133 https://conda.anaconda.org/conda-forge/linux-64/numpy-2.2.6-py313h103f029_0.conda#7ae0a483b2cbbdf15d8429eb38f74a9e https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.5-pyhd8ed1ab_0.conda#c3c9316209dec74a705a36797970c6be -https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.6.1-pyhd8ed1ab_1.conda#59aad4fb37cabc0bacc73cf344612ddd +https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.7.0-pyhd8ed1ab_0.conda#15353a2a0ea6dfefaa52fc5ab5b98f41 https://conda.anaconda.org/conda-forge/linux-64/scipy-1.15.2-py313h7f7b39c_0.conda#65f0c403e4324062633e648933f20a2e From d714d4789c9463fa8544a327ea7be3b7babe8ad9 Mon Sep 17 00:00:00 2001 From: Shivam <112275066+shivamchhuneja@users.noreply.github.com> Date: Wed, 4 Jun 2025 20:46:44 +0530 Subject: [PATCH 009/103] DOC Add link to plot_monotonic_constraints.py in ensemble examples (#31471) Co-authored-by: Stefanie Senger <91849487+StefanieSenger@users.noreply.github.com> --- doc/modules/ensemble.rst | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/doc/modules/ensemble.rst b/doc/modules/ensemble.rst index 6b0fc93e437ff..31ca150df372e 100644 --- a/doc/modules/ensemble.rst +++ b/doc/modules/ensemble.rst @@ -369,13 +369,17 @@ following modelling constraint: Also, monotonic constraints are not supported for multiclass classification. +For a practical implementation of monotonic constraints with the histogram-based +gradient boosting, including how they can improve generalization when domain knowledge +is available, see +:ref:`sphx_glr_auto_examples_ensemble_plot_monotonic_constraints.py`. + .. note:: Since categories are unordered quantities, it is not possible to enforce monotonic constraints on categorical features. .. rubric:: Examples -* :ref:`sphx_glr_auto_examples_ensemble_plot_monotonic_constraints.py` * :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py` .. _interaction_cst_hgbt: From 22b23f49336bcdf9533ddfc6246a57d820c0aff6 Mon Sep 17 00:00:00 2001 From: TJ Norred Date: Thu, 5 Jun 2025 14:32:37 +0000 Subject: [PATCH 010/103] DOC Add link for prediction latency plot example in SGD Regression#30621 (#31477) --- doc/modules/sgd.rst | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/doc/modules/sgd.rst b/doc/modules/sgd.rst index 4f34b7f50e072..84812a0cccf12 100644 --- a/doc/modules/sgd.rst +++ b/doc/modules/sgd.rst @@ -231,6 +231,10 @@ For regression with a squared loss and a :math:`L_2` penalty, another variant of SGD with an averaging strategy is available with Stochastic Average Gradient (SAG) algorithm, available as a solver in :class:`Ridge`. +.. rubric:: Examples + +- :ref:`sphx_glr_auto_examples_applications_plot_prediction_latency.py` + .. _sgd_online_one_class_svm: Online One-Class SVM From d4087a37b5cc2dbfc6d49318f8a60abfeeaae635 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 9 Jun 2025 10:23:51 +0200 Subject: [PATCH 011/103] :lock: :robot: CI Update lock files for scipy-dev CI build(s) :lock: :robot: (#31505) Co-authored-by: Lock file bot --- .../azure/pylatest_pip_scipy_dev_linux-64_conda.lock | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock index a8fac4ea35b6c..9edd5d56f86a8 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock @@ -18,13 +18,19 @@ https://repo.anaconda.com/pkgs/main/linux-64/libmpdec-4.0.0-h5eee18b_0.conda#feb https://repo.anaconda.com/pkgs/main/linux-64/libuuid-1.41.5-h5eee18b_0.conda#4a6a2354414c9080327274aa514e5299 https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda#5558eec6e2191741a92f832ea826251c https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.16-h5eee18b_0.conda#5875526739afa058cfa84da1fa7a2ef4 +https://repo.anaconda.com/pkgs/main/linux-64/pthread-stubs-0.3-h0ce48e5_1.conda#973a642312d2a28927aaf5b477c67250 +https://repo.anaconda.com/pkgs/main/linux-64/xorg-libxau-1.0.12-h9b100fa_0.conda#a8005a9f6eb903e113cd5363e8a11459 +https://repo.anaconda.com/pkgs/main/linux-64/xorg-libxdmcp-1.1.5-h9b100fa_0.conda#c284a09ddfba81d9c4e740110f09ea06 +https://repo.anaconda.com/pkgs/main/linux-64/xorg-xorgproto-2024.1-h5eee18b_1.conda#412a0d97a7a51d23326e57226189da92 https://repo.anaconda.com/pkgs/main/linux-64/xz-5.6.4-h5eee18b_1.conda#3581505fa450962d631bd82b8616350e https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_1.conda#92e42d8310108b0a440fb2e60b2b2a25 https://repo.anaconda.com/pkgs/main/linux-64/ccache-3.7.9-hfe4627d_0.conda#bef6fc681c273bb7bd0c67d1a591365e +https://repo.anaconda.com/pkgs/main/linux-64/libxcb-1.17.0-h9b100fa_0.conda#fdf0d380fa3809a301e2dbc0d5183883 https://repo.anaconda.com/pkgs/main/linux-64/readline-8.2-h5eee18b_0.conda#be42180685cce6e6b0329201d9f48efb -https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.14-h39e8969_0.conda#78dbc5e3c69143ebc037fc5d5b22e597 https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.45.3-h5eee18b_0.conda#acf93d6aceb74d6110e20b44cc45939e -https://repo.anaconda.com/pkgs/main/linux-64/python-3.13.2-hf623796_100_cp313.conda#bf836f30ac4c16fd3d71c1aaa25da08c +https://repo.anaconda.com/pkgs/main/linux-64/xorg-libx11-1.8.12-h9b100fa_1.conda#6298b27afae6f49f03765b2a03df2fcb +https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.14-h993c535_1.conda#bfe656b29fc64afe5d4bd46dbd5fd240 +https://repo.anaconda.com/pkgs/main/linux-64/python-3.13.4-h4612cfd_100_cp313.conda#f8f9a0c1eff2663e73ef296d5303c3f8 https://repo.anaconda.com/pkgs/main/linux-64/setuptools-78.1.1-py313h06a4308_0.conda#8f8e1c1e3af9d2d371aaa0ee8316ae7c https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.45.1-py313h06a4308_0.conda#29057e876eedce0e37c2388c138a19f9 https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2a700153fefe0e69438b18e1 @@ -59,7 +65,7 @@ https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2 # pip urllib3 @ https://files.pythonhosted.org/packages/6b/11/cc635220681e93a0183390e26485430ca2c7b5f9d33b15c74c2861cb8091/urllib3-2.4.0-py3-none-any.whl#sha256=4e16665048960a0900c702d4a66415956a584919c03361cac9f1df5c5dd7e813 # pip jinja2 @ https://files.pythonhosted.org/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl#sha256=85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67 # pip pyproject-metadata @ https://files.pythonhosted.org/packages/7e/b1/8e63033b259e0a4e40dd1ec4a9fee17718016845048b43a36ec67d62e6fe/pyproject_metadata-0.9.1-py3-none-any.whl#sha256=ee5efde548c3ed9b75a354fc319d5afd25e9585fa918a34f62f904cc731973ad -# pip pytest @ https://files.pythonhosted.org/packages/30/3d/64ad57c803f1fa1e963a7946b6e0fea4a70df53c1a7fed304586539c2bac/pytest-8.3.5-py3-none-any.whl#sha256=c69214aa47deac29fad6c2a4f590b9c4a9fdb16a403176fe154b79c0b4d4d820 +# pip pytest @ https://files.pythonhosted.org/packages/2f/de/afa024cbe022b1b318a3d224125aa24939e99b4ff6f22e0ba639a2eaee47/pytest-8.4.0-py3-none-any.whl#sha256=f40f825768ad76c0977cbacdf1fd37c6f7a468e460ea6a0636078f8972d4517e # pip python-dateutil @ https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl#sha256=a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 # pip requests @ https://files.pythonhosted.org/packages/f9/9b/335f9764261e915ed497fcdeb11df5dfd6f7bf257d4a6a2a686d80da4d54/requests-2.32.3-py3-none-any.whl#sha256=70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6 # pip meson-python @ https://files.pythonhosted.org/packages/28/58/66db620a8a7ccb32633de9f403fe49f1b63c68ca94e5c340ec5cceeb9821/meson_python-0.18.0-py3-none-any.whl#sha256=3b0fe051551cc238f5febb873247c0949cd60ded556efa130aa57021804868e2 From 113b23143a86a4d05db7e7143da4343d92b69e0d Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 9 Jun 2025 10:24:27 +0200 Subject: [PATCH 012/103] :lock: :robot: CI Update lock files for free-threaded CI build(s) :lock: :robot: (#31506) Co-authored-by: Lock file bot --- .../azure/pylatest_free_threaded_linux-64_conda.lock | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock b/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock index 40254398d3bb7..58cd11edc75fb 100644 --- a/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock +++ b/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock @@ -14,7 +14,7 @@ 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https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda#58335b26c38bf4a20f399384c33cbcf9 https://conda.anaconda.org/conda-forge/noarch/pip-25.1.1-pyh145f28c_0.conda#01384ff1639c6330a0924791413b8714 https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda#7da7ccd349dbf6487a7778579d2bb971 +https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.1-pyhd8ed1ab_0.conda#232fb4577b6687b2d503ef8e254270c9 https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.2.3-pyhd8ed1ab_1.conda#513d3c262ee49b54a8fec85c5bc99764 https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2025.2-pyhd8ed1ab_0.conda#88476ae6ebd24f39261e0854ac244f33 https://conda.anaconda.org/conda-forge/noarch/pytz-2025.2-pyhd8ed1ab_0.conda#bc8e3267d44011051f2eb14d22fb0960 @@ -156,9 +157,9 @@ https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.c https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_1.conda#b0dd904de08b7db706167240bf37b164 https://conda.anaconda.org/conda-forge/noarch/tomli-2.2.1-pyhd8ed1ab_1.conda#ac944244f1fed2eb49bae07193ae8215 https://conda.anaconda.org/conda-forge/linux-64/tornado-6.5.1-py313h536fd9c_0.conda#e9434a5155db25c38ade26f71a2f5a48 -https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.13.2-pyh29332c3_0.conda#83fc6ae00127671e301c9f44254c31b8 +https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.14.0-pyhe01879c_0.conda#2adcd9bb86f656d3d43bf84af59a1faf https://conda.anaconda.org/conda-forge/linux-64/xcb-util-image-0.4.0-hb711507_2.conda#a0901183f08b6c7107aab109733a3c91 -https://conda.anaconda.org/conda-forge/linux-64/xkeyboard-config-2.44-hb9d3cd8_0.conda#7c91bfc90672888259675ad2ad28af9c +https://conda.anaconda.org/conda-forge/linux-64/xkeyboard-config-2.45-hb9d3cd8_0.conda#397a013c2dc5145a70737871aaa87e98 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxext-1.3.6-hb9d3cd8_0.conda#febbab7d15033c913d53c7a2c102309d https://conda.anaconda.org/conda-forge/linux-64/xorg-libxfixes-6.0.1-hb9d3cd8_0.conda#4bdb303603e9821baf5fe5fdff1dc8f8 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrender-0.9.12-hb9d3cd8_0.conda#96d57aba173e878a2089d5638016dc5e @@ -169,7 +170,7 @@ https://conda.anaconda.org/conda-forge/linux-64/ccache-4.11.3-h80c52d3_0.conda#e https://conda.anaconda.org/conda-forge/linux-64/coverage-7.8.2-py313h8060acc_0.conda#b278629953bd3424060870fca744de4a https://conda.anaconda.org/conda-forge/linux-64/dbus-1.16.2-h3c4dab8_0.conda#679616eb5ad4e521c83da4650860aba7 https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda#72e42d28960d875c7654614f8b50939a -https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.58.1-py313h8060acc_0.conda#f03a1dc39346922cb5cf2ee190ac9b95 +https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.58.2-py313h8060acc_0.conda#e651d100ab0c032d68923868653fe00a https://conda.anaconda.org/conda-forge/linux-64/freetype-2.13.3-ha770c72_1.conda#9ccd736d31e0c6e41f54e704e5312811 https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.6-pyhd8ed1ab_0.conda#446bd6c8cb26050d528881df495ce646 https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.1-pyhd8ed1ab_0.conda#fb1c14694de51a476ce8636d92b6f42c @@ -211,8 +212,8 @@ https://conda.anaconda.org/conda-forge/linux-64/libmagma-2.9.0-h45b15fe_0.conda# https://conda.anaconda.org/conda-forge/linux-64/libopentelemetry-cpp-1.18.0-hfcad708_1.conda#1f5a5d66e77a39dc5bd639ec953705cf https://conda.anaconda.org/conda-forge/linux-64/libpq-17.5-h27ae623_0.conda#6458be24f09e1b034902ab44fe9de908 https://conda.anaconda.org/conda-forge/noarch/meson-python-0.18.0-pyh70fd9c4_0.conda#576c04b9d9f8e45285fb4d9452c26133 -https://conda.anaconda.org/conda-forge/linux-64/numpy-2.2.6-py313h17eae1a_0.conda#7a2d2f9adecd86ed5c29c2115354f615 -https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.5-pyhd8ed1ab_0.conda#c3c9316209dec74a705a36797970c6be +https://conda.anaconda.org/conda-forge/linux-64/numpy-2.3.0-py313h17eae1a_0.conda#db18a34466bef0863e9301b518a75e8f +https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.0-pyhd8ed1ab_0.conda#516d31f063ce7e49ced17f105b63a1f1 https://conda.anaconda.org/conda-forge/linux-64/tbb-2021.13.0-hceb3a55_1.conda#ba7726b8df7b9d34ea80e82b097a4893 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxtst-1.2.5-hb9d3cd8_3.conda#7bbe9a0cc0df0ac5f5a8ad6d6a11af2f https://conda.anaconda.org/conda-forge/noarch/array-api-strict-2.3.1-pyhd8ed1ab_0.conda#11107d0aeb8c590a34fee0894909816b @@ -225,7 +226,7 @@ https://conda.anaconda.org/conda-forge/linux-64/cupy-core-13.4.1-py313hc2a895b_0 https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.36.0-h0121fbd_0.conda#fc5efe1833a4d709953964037985bb72 https://conda.anaconda.org/conda-forge/linux-64/libmagma_sparse-2.9.0-h45b15fe_0.conda#beac0a5bbe0af75db6b16d3d8fd24f7e https://conda.anaconda.org/conda-forge/linux-64/mkl-2024.2.2-ha957f24_16.conda#1459379c79dda834673426504d52b319 -https://conda.anaconda.org/conda-forge/linux-64/pandas-2.2.3-py313ha87cce1_3.conda#6248b529e537b1d4cb5ab3ef7f537795 +https://conda.anaconda.org/conda-forge/linux-64/pandas-2.3.0-py313ha87cce1_0.conda#8664b4fa9b5b23b0d1cdc55c7195fcfe https://conda.anaconda.org/conda-forge/linux-64/polars-default-1.30.0-py39hfac2b71_0.conda#cd33cf1e631b4d766858c90e333b4832 https://conda.anaconda.org/conda-forge/noarch/pytest-cov-6.1.1-pyhd8ed1ab_0.conda#1e35d8f975bc0e984a19819aa91c440a https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.7.0-pyhd8ed1ab_0.conda#15353a2a0ea6dfefaa52fc5ab5b98f41 @@ -242,11 +243,11 @@ https://conda.anaconda.org/conda-forge/linux-64/polars-1.30.0-default_h1443d73_0 https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.2.1-py313hf0ab243_1.conda#4c769bf3858f424cb2ecf952175ec600 https://conda.anaconda.org/conda-forge/linux-64/libarrow-19.0.1-hc7b3859_3_cpu.conda#9ed3ded6da29dec8417f2e1db68798f2 https://conda.anaconda.org/conda-forge/linux-64/pytorch-2.4.1-cuda118_mkl_py313_h909c4c2_306.conda#de6e45613bbdb51127e9ff483c31bf41 -https://conda.anaconda.org/conda-forge/linux-64/qt6-main-6.9.0-h0384650_3.conda#8aa69e15597a205fd6f81781fe62c232 +https://conda.anaconda.org/conda-forge/linux-64/qt6-main-6.9.1-h0384650_0.conda#e1f80d7fca560024b107368dd77d96be https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-19.0.1-hcb10f89_3_cpu.conda#8f8dc214d89e06933f1bc1dcd2310b9c https://conda.anaconda.org/conda-forge/linux-64/libparquet-19.0.1-h081d1f1_3_cpu.conda#1d04307cdb1d8aeb5f55b047d5d403ea https://conda.anaconda.org/conda-forge/linux-64/pyarrow-core-19.0.1-py313he5f92c8_0_cpu.conda#7d8649531c807b24295c8f9a0a396a78 -https://conda.anaconda.org/conda-forge/linux-64/pyside6-6.9.0-py313h5f61773_0.conda#f51f25ec8fcbf777f8b186bb5deeed40 +https://conda.anaconda.org/conda-forge/linux-64/pyside6-6.9.1-py313h7dabd7a_0.conda#42a24d0f4fe3a2e8307de3838e162452 https://conda.anaconda.org/conda-forge/linux-64/pytorch-gpu-2.4.1-cuda118_mkl_hf8a3b2d_306.conda#b1802a39f1ca7ebed5f8c35755bffec1 https://conda.anaconda.org/conda-forge/linux-64/libarrow-dataset-19.0.1-hcb10f89_3_cpu.conda#a28f04b6e68a1c76de76783108ad729d https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.10.3-py313h78bf25f_0.conda#cc9324e614a297fdf23439d887d3513d From 9742b1c4bc040c6bd41b9fd6f880f91254952a65 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 9 Jun 2025 10:26:49 +0200 Subject: [PATCH 014/103] :lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#31508) Co-authored-by: Lock file bot --- build_tools/azure/debian_32bit_lock.txt | 4 +- ...latest_conda_forge_mkl_linux-64_conda.lock | 65 ++++++++++--------- ...pylatest_conda_forge_mkl_osx-64_conda.lock | 25 +++---- ...test_conda_mkl_no_openmp_osx-64_conda.lock | 6 +- ...st_pip_openblas_pandas_linux-64_conda.lock | 18 +++-- .../pymin_conda_forge_mkl_win-64_conda.lock | 27 ++++---- ...nblas_min_dependencies_linux-64_conda.lock | 31 ++++----- ...e_openblas_ubuntu_2204_linux-64_conda.lock | 14 ++-- build_tools/azure/ubuntu_atlas_lock.txt | 6 +- build_tools/circle/doc_linux-64_conda.lock | 60 ++++++++--------- .../doc_min_dependencies_linux-64_conda.lock | 44 ++++++------- ...n_conda_forge_arm_linux-aarch64_conda.lock | 29 +++++---- 12 files changed, 172 insertions(+), 157 deletions(-) diff --git a/build_tools/azure/debian_32bit_lock.txt b/build_tools/azure/debian_32bit_lock.txt index c7b8cbceccacb..c36a03e098d7f 100644 --- a/build_tools/azure/debian_32bit_lock.txt +++ b/build_tools/azure/debian_32bit_lock.txt @@ -25,9 +25,11 @@ packaging==25.0 # pytest pluggy==1.6.0 # via pytest +pygments==2.19.1 + # via pytest pyproject-metadata==0.9.1 # via meson-python -pytest==8.3.5 +pytest==8.4.0 # via # -r build_tools/azure/debian_32bit_requirements.txt # pytest-cov diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index e99219a40736d..b2e57e38963aa 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock @@ -24,7 +24,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_2.conda#e https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.14-hb9d3cd8_0.conda#76df83c2a9035c54df5d04ff81bcc02d https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.12.3-hb9d3cd8_0.conda#8448031a22c697fac3ed98d69e8a9160 https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.5-hb9d3cd8_0.conda#f7f0d6cc2dc986d42ac2689ec88192be -https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hb9d3cd8_2.conda#41b599ed2b02abcfdd84302bff174b23 +https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hb9d3cd8_3.conda#cb98af5db26e3f482bebb80ce9d947d3 https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.24-h86f0d12_0.conda#64f0c503da58ec25ebd359e4d990afa8 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.0-h5888daf_0.conda#db0bfbe7dd197b68ad5f30333bae6ce0 https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.6-h2dba641_1.conda#ede4673863426c0883c0063d853bbd85 @@ -32,7 +32,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.1.0-h69a702a_2.cond https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.1.0-hcea5267_2.conda#01de444988ed960031dbe84cf4f9b1fc https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h4ce23a2_1.conda#e796ff8ddc598affdf7c173d6145f087 https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.1.0-hb9d3cd8_0.conda#9fa334557db9f63da6c9285fd2a48638 -https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_1.conda#a76fd702c93cd2dfd89eff30a5fd45a8 +https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_2.conda#1a580f7796c7bf6393fddb8bbbde58dc https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-hb9d3cd8_0.conda#c7e925f37e3b40d893459e625f6a53f1 https://conda.anaconda.org/conda-forge/linux-64/libntlm-1.8-hb9d3cd8_0.conda#7c7927b404672409d9917d49bff5f2d6 https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_2.conda#1cb1c67961f6dd257eae9e9691b341aa @@ -46,7 +46,7 @@ https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-hb9d3cd8_1002. https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.2-hb9d3cd8_0.conda#fb901ff28063514abb6046c9ec2c4a45 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-hb9d3cd8_0.conda#f6ebe2cb3f82ba6c057dde5d9debe4f7 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.5-hb9d3cd8_0.conda#8035c64cb77ed555e3f150b7b3972480 -https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.9.1-h5e3027f_0.conda#da0b556585013ad26b3c052b61205f74 +https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.9.2-h5e3027f_0.conda#0ead3ab65460d51efb27e5186f50f8e4 https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.3.1-hafb2847_5.conda#e96cc668c0f9478f5771b37d57f90386 https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.2.4-hafb2847_0.conda#65853df44b7e4029d978c50be888ed89 https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.2.7-hafb2847_1.conda#6d28d50637fac4f081a0903b4b33d56d @@ -56,15 +56,15 @@ 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a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock +++ b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock @@ -26,14 +26,14 @@ https://repo.anaconda.com/pkgs/main/osx-64/ninja-base-1.12.1-h1962661_0.conda#9c https://repo.anaconda.com/pkgs/main/osx-64/openssl-3.0.16-h184c1cd_0.conda#8e3c130ef85c3260d535153b4d0fd63a https://repo.anaconda.com/pkgs/main/osx-64/readline-8.2-hca72f7f_0.conda#971667436260e523f6f7355fdfa238bf https://repo.anaconda.com/pkgs/main/osx-64/tbb-2021.8.0-ha357a0b_0.conda#fb48530a3eea681c11dafb95b3387c0f -https://repo.anaconda.com/pkgs/main/osx-64/tk-8.6.14-h4d00af3_0.conda#a2c03940c2ae54614301ec82e6a98d75 +https://repo.anaconda.com/pkgs/main/osx-64/tk-8.6.14-h0a12a5f_1.conda#b5c23bac899d2e153b438a2b638c2c9b https://repo.anaconda.com/pkgs/main/osx-64/freetype-2.13.3-h02243ff_0.conda#acf5e48106235eb200eecb79119c7ffc https://repo.anaconda.com/pkgs/main/osx-64/libgfortran-5.0.0-11_3_0_hecd8cb5_28.conda#2eb13b680803f1064e53873ae0aaafb3 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-https://repo.anaconda.com/pkgs/main/linux-64/python-3.13.2-hf623796_100_cp313.conda#bf836f30ac4c16fd3d71c1aaa25da08c +https://repo.anaconda.com/pkgs/main/linux-64/xorg-libx11-1.8.12-h9b100fa_1.conda#6298b27afae6f49f03765b2a03df2fcb +https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.14-h993c535_1.conda#bfe656b29fc64afe5d4bd46dbd5fd240 +https://repo.anaconda.com/pkgs/main/linux-64/python-3.13.4-h4612cfd_100_cp313.conda#f8f9a0c1eff2663e73ef296d5303c3f8 https://repo.anaconda.com/pkgs/main/linux-64/setuptools-78.1.1-py313h06a4308_0.conda#8f8e1c1e3af9d2d371aaa0ee8316ae7c https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.45.1-py313h06a4308_0.conda#29057e876eedce0e37c2388c138a19f9 https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2a700153fefe0e69438b18e1 @@ -37,7 +43,7 @@ https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2 # pip cython @ https://files.pythonhosted.org/packages/ca/90/9fe8b93fa239b4871252274892c232415f53d5af0859c4a6ac9b1cbf9950/cython-3.1.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=7da069ca769903c5dee56c5f7ab47b2b7b91030eee48912630db5f4f3ec5954a # pip docutils @ https://files.pythonhosted.org/packages/8f/d7/9322c609343d929e75e7e5e6255e614fcc67572cfd083959cdef3b7aad79/docutils-0.21.2-py3-none-any.whl#sha256=dafca5b9e384f0e419294eb4d2ff9fa826435bf15f15b7bd45723e8ad76811b2 # pip execnet @ https://files.pythonhosted.org/packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl#sha256=26dee51f1b80cebd6d0ca8e74dd8745419761d3bef34163928cbebbdc4749fdc -# pip fonttools @ 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https://files.pythonhosted.org/packages/ff/62/85c4c919272577931d407be5ba5d71c20f0b616d31a0befe0ae45bb79abd/imagesize-1.4.1-py2.py3-none-any.whl#sha256=0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b # pip iniconfig @ https://files.pythonhosted.org/packages/2c/e1/e6716421ea10d38022b952c159d5161ca1193197fb744506875fbb87ea7b/iniconfig-2.1.0-py3-none-any.whl#sha256=9deba5723312380e77435581c6bf4935c94cbfab9b1ed33ef8d238ea168eb760 @@ -47,7 +53,7 @@ https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2 # pip meson @ https://files.pythonhosted.org/packages/46/77/726b14be352aa6911e206ca7c4d95c5be49660604dfee0bfed0fc75823e5/meson-1.8.1-py3-none-any.whl#sha256=374bbf71247e629475fc10b0bd2ef66fc418c2d8f4890572f74de0f97d0d42da # pip networkx @ https://files.pythonhosted.org/packages/eb/8d/776adee7bbf76365fdd7f2552710282c79a4ead5d2a46408c9043a2b70ba/networkx-3.5-py3-none-any.whl#sha256=0030d386a9a06dee3565298b4a734b68589749a544acbb6c412dc9e2489ec6ec # pip ninja @ https://files.pythonhosted.org/packages/eb/7a/455d2877fe6cf99886849c7f9755d897df32eaf3a0fba47b56e615f880f7/ninja-1.11.1.4-py3-none-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=096487995473320de7f65d622c3f1d16c3ad174797602218ca8c967f51ec38a0 -# pip numpy @ https://files.pythonhosted.org/packages/19/49/4df9123aafa7b539317bf6d342cb6d227e49f7a35b99c287a6109b13dd93/numpy-2.2.6-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=1bc23a79bfabc5d056d106f9befb8d50c31ced2fbc70eedb8155aec74a45798f +# pip numpy @ https://files.pythonhosted.org/packages/1c/12/734dce1087eed1875f2297f687e671cfe53a091b6f2f55f0c7241aad041b/numpy-2.3.0-cp313-cp313-manylinux_2_28_x86_64.whl#sha256=87717eb24d4a8a64683b7a4e91ace04e2f5c7c77872f823f02a94feee186168f # pip packaging @ https://files.pythonhosted.org/packages/20/12/38679034af332785aac8774540895e234f4d07f7545804097de4b666afd8/packaging-25.0-py3-none-any.whl#sha256=29572ef2b1f17581046b3a2227d5c611fb25ec70ca1ba8554b24b0e69331a484 # pip pillow @ https://files.pythonhosted.org/packages/13/eb/2552ecebc0b887f539111c2cd241f538b8ff5891b8903dfe672e997529be/pillow-11.2.1-cp313-cp313-manylinux_2_28_x86_64.whl#sha256=ad275964d52e2243430472fc5d2c2334b4fc3ff9c16cb0a19254e25efa03a155 # pip pluggy @ https://files.pythonhosted.org/packages/54/20/4d324d65cc6d9205fabedc306948156824eb9f0ee1633355a8f7ec5c66bf/pluggy-1.6.0-py3-none-any.whl#sha256=e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746 @@ -73,7 +79,7 @@ https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2 # pip jinja2 @ https://files.pythonhosted.org/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl#sha256=85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67 # pip lazy-loader @ https://files.pythonhosted.org/packages/83/60/d497a310bde3f01cb805196ac61b7ad6dc5dcf8dce66634dc34364b20b4f/lazy_loader-0.4-py3-none-any.whl#sha256=342aa8e14d543a154047afb4ba8ef17f5563baad3fc610d7b15b213b0f119efc # pip pyproject-metadata @ https://files.pythonhosted.org/packages/7e/b1/8e63033b259e0a4e40dd1ec4a9fee17718016845048b43a36ec67d62e6fe/pyproject_metadata-0.9.1-py3-none-any.whl#sha256=ee5efde548c3ed9b75a354fc319d5afd25e9585fa918a34f62f904cc731973ad -# pip pytest @ https://files.pythonhosted.org/packages/30/3d/64ad57c803f1fa1e963a7946b6e0fea4a70df53c1a7fed304586539c2bac/pytest-8.3.5-py3-none-any.whl#sha256=c69214aa47deac29fad6c2a4f590b9c4a9fdb16a403176fe154b79c0b4d4d820 +# pip pytest @ https://files.pythonhosted.org/packages/2f/de/afa024cbe022b1b318a3d224125aa24939e99b4ff6f22e0ba639a2eaee47/pytest-8.4.0-py3-none-any.whl#sha256=f40f825768ad76c0977cbacdf1fd37c6f7a468e460ea6a0636078f8972d4517e # pip python-dateutil @ https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl#sha256=a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 # pip requests @ https://files.pythonhosted.org/packages/f9/9b/335f9764261e915ed497fcdeb11df5dfd6f7bf257d4a6a2a686d80da4d54/requests-2.32.3-py3-none-any.whl#sha256=70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6 # pip scipy @ https://files.pythonhosted.org/packages/b5/09/c5b6734a50ad4882432b6bb7c02baf757f5b2f256041da5df242e2d7e6b6/scipy-1.15.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=c9deabd6d547aee2c9a81dee6cc96c6d7e9a9b1953f74850c179f91fdc729cb7 @@ -81,7 +87,7 @@ https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2 # pip lightgbm @ https://files.pythonhosted.org/packages/42/86/dabda8fbcb1b00bcfb0003c3776e8ade1aa7b413dff0a2c08f457dace22f/lightgbm-4.6.0-py3-none-manylinux_2_28_x86_64.whl#sha256=cb19b5afea55b5b61cbb2131095f50538bd608a00655f23ad5d25ae3e3bf1c8d # pip matplotlib @ https://files.pythonhosted.org/packages/f5/64/41c4367bcaecbc03ef0d2a3ecee58a7065d0a36ae1aa817fe573a2da66d4/matplotlib-3.10.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=a80fcccbef63302c0efd78042ea3c2436104c5b1a4d3ae20f864593696364ac7 # pip meson-python @ https://files.pythonhosted.org/packages/28/58/66db620a8a7ccb32633de9f403fe49f1b63c68ca94e5c340ec5cceeb9821/meson_python-0.18.0-py3-none-any.whl#sha256=3b0fe051551cc238f5febb873247c0949cd60ded556efa130aa57021804868e2 -# pip pandas @ https://files.pythonhosted.org/packages/e8/31/aa8da88ca0eadbabd0a639788a6da13bb2ff6edbbb9f29aa786450a30a91/pandas-2.2.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=f3a255b2c19987fbbe62a9dfd6cff7ff2aa9ccab3fc75218fd4b7530f01efa24 +# pip pandas @ https://files.pythonhosted.org/packages/2a/b3/463bfe819ed60fb7e7ddffb4ae2ee04b887b3444feee6c19437b8f834837/pandas-2.3.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=213cd63c43263dbb522c1f8a7c9d072e25900f6975596f883f4bebd77295d4f3 # pip pyamg @ https://files.pythonhosted.org/packages/cd/a7/0df731cbfb09e73979a1a032fc7bc5be0eba617d798b998a0f887afe8ade/pyamg-5.2.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=6999b351ab969c79faacb81faa74c0fa9682feeff3954979212872a3ee40c298 # pip pytest-cov @ https://files.pythonhosted.org/packages/28/d0/def53b4a790cfb21483016430ed828f64830dd981ebe1089971cd10cab25/pytest_cov-6.1.1-py3-none-any.whl#sha256=bddf29ed2d0ab6f4df17b4c55b0a657287db8684af9c42ea546b21b1041b3dde # pip pytest-xdist @ https://files.pythonhosted.org/packages/0d/b2/0e802fde6f1c5b2f7ae7e9ad42b83fd4ecebac18a8a8c2f2f14e39dce6e1/pytest_xdist-3.7.0-py3-none-any.whl#sha256=7d3fbd255998265052435eb9daa4e99b62e6fb9cfb6efd1f858d4d8c0c7f0ca0 diff --git a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock index 9e7e414a90156..2ae01d9250434 100644 --- a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock @@ -24,14 +24,14 @@ https://conda.anaconda.org/conda-forge/win-64/double-conversion-3.3.1-he0c23c2_0 https://conda.anaconda.org/conda-forge/win-64/graphite2-1.3.13-h63175ca_1003.conda#3194499ee7d1a67404a87d0eefdd92c6 https://conda.anaconda.org/conda-forge/win-64/icu-75.1-he0c23c2_0.conda#8579b6bb8d18be7c0b27fb08adeeeb40 https://conda.anaconda.org/conda-forge/win-64/lerc-4.0.0-h6470a55_1.conda#c1b81da6d29a14b542da14a36c9fbf3f -https://conda.anaconda.org/conda-forge/win-64/libbrotlicommon-1.1.0-h2466b09_2.conda#f7dc9a8f21d74eab46456df301da2972 +https://conda.anaconda.org/conda-forge/win-64/libbrotlicommon-1.1.0-h2466b09_3.conda#cf20c8b8b48ab5252ec64b9c66bfe0a4 https://conda.anaconda.org/conda-forge/win-64/libdeflate-1.24-h76ddb4d_0.conda#08d988e266c6ae77e03d164b83786dc4 https://conda.anaconda.org/conda-forge/win-64/libexpat-2.7.0-he0c23c2_0.conda#b6f5352fdb525662f4169a0431d2dd7a https://conda.anaconda.org/conda-forge/win-64/libffi-3.4.6-h537db12_1.conda#85d8fa5e55ed8f93f874b3b23ed54ec6 https://conda.anaconda.org/conda-forge/win-64/libiconv-1.18-h135ad9c_1.conda#21fc5dba2cbcd8e5e26ff976a312122c https://conda.anaconda.org/conda-forge/win-64/libjpeg-turbo-3.1.0-h2466b09_0.conda#7c51d27540389de84852daa1cdb9c63c -https://conda.anaconda.org/conda-forge/win-64/liblzma-5.8.1-h2466b09_1.conda#14a1042c163181e143a7522dfb8ad6ab -https://conda.anaconda.org/conda-forge/win-64/libsqlite-3.50.0-h67fdade_0.conda#92b11b0b2120d563caa1629928122cee +https://conda.anaconda.org/conda-forge/win-64/liblzma-5.8.1-h2466b09_2.conda#c15148b2e18da456f5108ccb5e411446 +https://conda.anaconda.org/conda-forge/win-64/libsqlite-3.50.1-h67fdade_0.conda#0e11a893eeeb46510520fd3fdd9c346a https://conda.anaconda.org/conda-forge/win-64/libwebp-base-1.5.0-h3b0e114_0.conda#33f7313967072c6e6d8f865f5493c7ae https://conda.anaconda.org/conda-forge/win-64/libzlib-1.3.1-h2466b09_2.conda#41fbfac52c601159df6c01f875de31b9 https://conda.anaconda.org/conda-forge/win-64/ninja-1.12.1-hc790b64_1.conda#3974c522f3248d4a93e6940c463d2de7 @@ -40,16 +40,16 @@ https://conda.anaconda.org/conda-forge/win-64/pixman-0.46.0-had0cd8c_0.conda#016 https://conda.anaconda.org/conda-forge/win-64/qhull-2020.2-hc790b64_5.conda#854fbdff64b572b5c0b470f334d34c11 https://conda.anaconda.org/conda-forge/win-64/tk-8.6.13-h2c6b04d_2.conda#ebd0e761de9aa879a51d22cc721bd095 https://conda.anaconda.org/conda-forge/win-64/krb5-1.21.3-hdf4eb48_0.conda#31aec030344e962fbd7dbbbbd68e60a9 -https://conda.anaconda.org/conda-forge/win-64/libbrotlidec-1.1.0-h2466b09_2.conda#9bae75ce723fa34e98e239d21d752a7e -https://conda.anaconda.org/conda-forge/win-64/libbrotlienc-1.1.0-h2466b09_2.conda#85741a24d97954a991e55e34bc55990b +https://conda.anaconda.org/conda-forge/win-64/libbrotlidec-1.1.0-h2466b09_3.conda#a342933dbc6d814541234c7c81cb5205 +https://conda.anaconda.org/conda-forge/win-64/libbrotlienc-1.1.0-h2466b09_3.conda#7ef0af55d70cbd9de324bb88b7f9d81e https://conda.anaconda.org/conda-forge/win-64/libgcc-15.1.0-h1383e82_2.conda#9bedb24480136bfeb81ebc81d4285e70 https://conda.anaconda.org/conda-forge/win-64/libintl-0.22.5-h5728263_3.conda#2cf0cf76cc15d360dfa2f17fd6cf9772 https://conda.anaconda.org/conda-forge/win-64/libpng-1.6.47-h7a4582a_0.conda#ad620e92b82d2948bc019e029c574ebb https://conda.anaconda.org/conda-forge/win-64/libxml2-2.13.8-h442d1da_0.conda#833c2dbc1a5020007b520b044c713ed3 https://conda.anaconda.org/conda-forge/win-64/pcre2-10.45-h99c9b8b_0.conda#f4c483274001678e129f5cbaf3a8d765 -https://conda.anaconda.org/conda-forge/win-64/python-3.10.17-h8c5b53a_0_cpython.conda#0c59918f056ab2e9c7bb45970d32b2ea +https://conda.anaconda.org/conda-forge/win-64/python-3.10.18-h8c5b53a_0_cpython.conda#f1775dab55c8a073ebd024bfb2f689c1 https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-hbeecb71_2.conda#21f56217d6125fb30c3c3f10c786d751 -https://conda.anaconda.org/conda-forge/win-64/brotli-bin-1.1.0-h2466b09_2.conda#d22534a9be5771fc58eb7564947f669d +https://conda.anaconda.org/conda-forge/win-64/brotli-bin-1.1.0-h2466b09_3.conda#c7c345559c1ac25eede6dccb7b931202 https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda#962b9857ee8e7018c22f2776ffa0b2d7 https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_1.conda#44600c4667a319d67dbe0681fc0bc833 https://conda.anaconda.org/conda-forge/win-64/cython-3.1.1-py310h6bd2d47_1.conda#165131d296d24f798fa76a26694d4565 @@ -67,6 +67,7 @@ https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2 https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda#58335b26c38bf4a20f399384c33cbcf9 https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda#7da7ccd349dbf6487a7778579d2bb971 https://conda.anaconda.org/conda-forge/win-64/pthread-stubs-0.4-h0e40799_1002.conda#3c8f2573569bb816483e5cf57efbbe29 +https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.1-pyhd8ed1ab_0.conda#232fb4577b6687b2d503ef8e254270c9 https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.2.3-pyhd8ed1ab_1.conda#513d3c262ee49b54a8fec85c5bc99764 https://conda.anaconda.org/conda-forge/noarch/setuptools-80.9.0-pyhff2d567_0.conda#4de79c071274a53dcaf2a8c749d1499e https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhd8ed1ab_0.conda#a451d576819089b0d672f18768be0f65 @@ -74,12 +75,12 @@ https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.c https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_1.conda#b0dd904de08b7db706167240bf37b164 https://conda.anaconda.org/conda-forge/noarch/tomli-2.2.1-pyhd8ed1ab_1.conda#ac944244f1fed2eb49bae07193ae8215 https://conda.anaconda.org/conda-forge/win-64/tornado-6.5.1-py310ha8f682b_0.conda#4c8f599990e386f3a0aba3f3bd8608da -https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.13.2-pyh29332c3_0.conda#83fc6ae00127671e301c9f44254c31b8 +https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.14.0-pyhe01879c_0.conda#2adcd9bb86f656d3d43bf84af59a1faf https://conda.anaconda.org/conda-forge/win-64/unicodedata2-16.0.0-py310ha8f682b_0.conda#b28aead44c6e19a1fbba7752aa242b34 https://conda.anaconda.org/conda-forge/noarch/wheel-0.45.1-pyhd8ed1ab_1.conda#75cb7132eb58d97896e173ef12ac9986 https://conda.anaconda.org/conda-forge/win-64/xorg-libxau-1.0.12-h0e40799_0.conda#2ffbfae4548098297c033228256eb96e https://conda.anaconda.org/conda-forge/win-64/xorg-libxdmcp-1.1.5-h0e40799_0.conda#8393c0f7e7870b4eb45553326f81f0ff -https://conda.anaconda.org/conda-forge/win-64/brotli-1.1.0-h2466b09_2.conda#378f1c9421775dfe644731cb121c8979 +https://conda.anaconda.org/conda-forge/win-64/brotli-1.1.0-h2466b09_3.conda#c2a23d8a8986c72148c63bdf855ac99a https://conda.anaconda.org/conda-forge/win-64/coverage-7.8.2-py310h38315fa_0.conda#5e09090744ab0b70b2882bc415c0d5ad https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda#72e42d28960d875c7654614f8b50939a https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.1-pyhd8ed1ab_0.conda#fb1c14694de51a476ce8636d92b6f42c @@ -91,12 +92,12 @@ https://conda.anaconda.org/conda-forge/noarch/pip-25.1.1-pyh8b19718_0.conda#32d0 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.9.1-pyhd8ed1ab_0.conda#22ae7c6ea81e0c8661ef32168dda929b https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhff2d567_1.conda#5ba79d7c71f03c678c8ead841f347d6e https://conda.anaconda.org/conda-forge/win-64/tbb-2021.13.0-h62715c5_1.conda#9190dd0a23d925f7602f9628b3aed511 -https://conda.anaconda.org/conda-forge/win-64/fonttools-4.58.1-py310h38315fa_0.conda#76a9c04ac1c23cee8b00733eb942f8e5 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https://conda.anaconda.org/conda-forge/linux-64/libpq-17.5-h27ae623_0.conda#6458be24f09e1b034902ab44fe9de908 https://conda.anaconda.org/conda-forge/linux-64/libsndfile-1.2.2-hc60ed4a_1.conda#ef1910918dd895516a769ed36b5b3a4e https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.conda#e16f0dbf502da873be9f9adb0dc52547 -https://conda.anaconda.org/conda-forge/linux-64/pyqt5-sip-12.17.0-py310hf71b8c6_0.conda#012465861673a67a30bc8ca6284074f3 -https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.5-pyhd8ed1ab_0.conda#c3c9316209dec74a705a36797970c6be +https://conda.anaconda.org/conda-forge/linux-64/pyqt5-sip-12.17.0-py310hf71b8c6_1.conda#696c7414297907d7647a5176031c8c69 +https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.0-pyhd8ed1ab_0.conda#516d31f063ce7e49ced17f105b63a1f1 https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.4-h3394656_0.conda#09262e66b19567aff4f592fb53b28760 https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.24.11-hc37bda9_0.conda#056d86cacf2b48c79c6a562a2486eb8c https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-20_linux64_openblas.conda#05c5862c7dc25e65ba6c471d96429dae @@ -185,5 +186,5 @@ https://conda.anaconda.org/conda-forge/linux-64/scipy-1.8.0-py310hea5193d_1.tar. https://conda.anaconda.org/conda-forge/linux-64/blas-2.120-openblas.conda#c8f6916a81a340650078171b1d852574 https://conda.anaconda.org/conda-forge/linux-64/pyamg-4.2.1-py310h7c3ba0c_0.tar.bz2#89f5a48e1f23b5cf3163a6094903d181 https://conda.anaconda.org/conda-forge/linux-64/qt-main-5.15.15-hea1682b_4.conda#c054d7f22cc719e12c72d454b2328d6c -https://conda.anaconda.org/conda-forge/linux-64/pyqt-5.15.11-py310hf392a12_0.conda#65924d3e57be25342c76530d23d75f0f +https://conda.anaconda.org/conda-forge/linux-64/pyqt-5.15.11-py310hf392a12_1.conda#e07b23661b711fb46d25b14206e0db47 https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.5.0-py310hff52083_0.tar.bz2#1b2f3b135d5d9c594b5e0e6150c03b7b diff --git a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock index 08a8597ed4fae..695e8e8037662 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock @@ -16,7 +16,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.6-h2dba641_1.conda#ed https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.1.0-h69a702a_2.conda#ddca86c7040dd0e73b2b69bd7833d225 https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.1.0-hcea5267_2.conda#01de444988ed960031dbe84cf4f9b1fc https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.1.0-hb9d3cd8_0.conda#9fa334557db9f63da6c9285fd2a48638 -https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_1.conda#a76fd702c93cd2dfd89eff30a5fd45a8 +https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_2.conda#1a580f7796c7bf6393fddb8bbbde58dc https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_2.conda#1cb1c67961f6dd257eae9e9691b341aa https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.5.0-h851e524_0.conda#63f790534398730f59e1b899c3644d4a https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 @@ -30,7 +30,7 @@ https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h0aef613_1.conda#9344 https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.1.0-h69a702a_2.conda#f92e6e0a3c0c0c85561ef61aa59d555d https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hd590300_0.conda#30fd6e37fe21f86f4bd26d6ee73eeec7 https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.47-h943b412_0.conda#55199e2ae2c3651f6f9b2a447b47bdc9 -https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.50.0-hee588c1_0.conda#71888e92098d0f8c41b09a671ad289bc +https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.50.1-hee588c1_0.conda#96a7e36bff29f1d0ddf5b771e0da373a https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.1.0-h4852527_2.conda#9d2072af184b5caa29492bf2344597bb https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda#40b61aab5c7ba9ff276c41cfffe6b80b https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.17.0-h8a09558_0.conda#92ed62436b625154323d40d5f2f11dd7 @@ -43,9 +43,9 @@ https://conda.anaconda.org/conda-forge/linux-64/libfreetype6-2.13.3-h48d6fc4_1.c https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-15.1.0-h69a702a_2.conda#a483a87b71e974bb75d1b9413d4436dd https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.29-pthreads_h94d23a6_0.conda#0a4d0252248ef9a0f88f2ba8b8a08e12 https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.7.0-hf01ce69_5.conda#e79a094918988bb1807462cd42c83962 -https://conda.anaconda.org/conda-forge/linux-64/python-3.10.17-hd6af730_0_cpython.conda#7bb89638dae9ce1b8e051d0b721e83c2 +https://conda.anaconda.org/conda-forge/linux-64/python-3.10.18-hd6af730_0_cpython.conda#4ea0c77cdcb0b81813a0436b162d7316 https://conda.anaconda.org/conda-forge/noarch/alabaster-1.0.0-pyhd8ed1ab_1.conda#1fd9696649f65fd6611fcdb4ffec738a -https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py310hf71b8c6_2.conda#bf502c169c71e3c6ac0d6175addfacc2 +https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py310hf71b8c6_3.conda#63d24a5dd21c738d706f91569dbd1892 https://conda.anaconda.org/conda-forge/noarch/certifi-2025.4.26-pyhd8ed1ab_0.conda#c33eeaaa33f45031be34cda513df39b6 https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.2-pyhd8ed1ab_0.conda#40fe4284b8b5835a9073a645139f35af https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda#962b9857ee8e7018c22f2776ffa0b2d7 @@ -79,7 +79,7 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-jsmath-1.0.1-pyhd8ed https://conda.anaconda.org/conda-forge/noarch/tabulate-0.9.0-pyhd8ed1ab_2.conda#959484a66b4b76befcddc4fa97c95567 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda#9d64911b31d57ca443e9f1e36b04385f https://conda.anaconda.org/conda-forge/noarch/tomli-2.2.1-pyhd8ed1ab_1.conda#ac944244f1fed2eb49bae07193ae8215 -https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.13.2-pyh29332c3_0.conda#83fc6ae00127671e301c9f44254c31b8 +https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.14.0-pyhe01879c_0.conda#2adcd9bb86f656d3d43bf84af59a1faf https://conda.anaconda.org/conda-forge/noarch/wheel-0.45.1-pyhd8ed1ab_1.conda#75cb7132eb58d97896e173ef12ac9986 https://conda.anaconda.org/conda-forge/noarch/babel-2.17.0-pyhd8ed1ab_0.conda#0a01c169f0ab0f91b26e77a3301fbfe4 https://conda.anaconda.org/conda-forge/linux-64/ccache-4.11.3-h80c52d3_0.conda#eb517c6a2b960c3ccb6f1db1005f063a @@ -97,10 +97,10 @@ https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhff2 https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-31_he2f377e_openblas.conda#7e5fff7d0db69be3a266f7e79a3bb0e2 https://conda.anaconda.org/conda-forge/noarch/meson-python-0.18.0-pyh70fd9c4_0.conda#576c04b9d9f8e45285fb4d9452c26133 https://conda.anaconda.org/conda-forge/linux-64/numpy-2.2.6-py310hefbff90_0.conda#b0cea2c364bf65cd19e023040eeab05d -https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.5-pyhd8ed1ab_0.conda#c3c9316209dec74a705a36797970c6be +https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.0-pyhd8ed1ab_0.conda#516d31f063ce7e49ced17f105b63a1f1 https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py310ha75aee5_2.conda#f9254b5b0193982416b91edcb4b2676f https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-31_h1ea3ea9_openblas.conda#ba652ee0576396d4765e567f043c57f9 -https://conda.anaconda.org/conda-forge/linux-64/pandas-2.2.3-py310h5eaa309_3.conda#07697a584fab513ce895c4511f7a2403 +https://conda.anaconda.org/conda-forge/linux-64/pandas-2.3.0-py310h5eaa309_0.conda#379844614e3a24e59e59d8c69c6e9403 https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.7.0-pyhd8ed1ab_0.conda#15353a2a0ea6dfefaa52fc5ab5b98f41 https://conda.anaconda.org/conda-forge/linux-64/scipy-1.15.2-py310h1d65ade_0.conda#8c29cd33b64b2eb78597fa28b5595c8d https://conda.anaconda.org/conda-forge/noarch/urllib3-2.4.0-pyhd8ed1ab_0.conda#c1e349028e0052c4eea844e94f773065 diff --git a/build_tools/azure/ubuntu_atlas_lock.txt b/build_tools/azure/ubuntu_atlas_lock.txt index 9c1faa23ab962..24b6b67120de8 100644 --- a/build_tools/azure/ubuntu_atlas_lock.txt +++ b/build_tools/azure/ubuntu_atlas_lock.txt @@ -27,9 +27,11 @@ packaging==25.0 # pytest pluggy==1.6.0 # via pytest +pygments==2.19.1 + # via pytest pyproject-metadata==0.9.1 # via meson-python -pytest==8.3.5 +pytest==8.4.0 # via # -r build_tools/azure/ubuntu_atlas_requirements.txt # pytest-xdist @@ -41,5 +43,5 @@ tomli==2.2.1 # via # meson-python # pytest -typing-extensions==4.13.2 +typing-extensions==4.14.0 # via exceptiongroup diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock index d19f830684796..0752850efab8c 100644 --- a/build_tools/circle/doc_linux-64_conda.lock +++ b/build_tools/circle/doc_linux-64_conda.lock @@ -27,7 +27,7 @@ https://conda.anaconda.org/conda-forge/linux-64/binutils-2.43-h4852527_4.conda#2 https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.43-h4852527_4.conda#c87e146f5b685672d4aa6b527c6d3b5e https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_2.conda#ea8ac52380885ed41c1baa8f1d6d2b93 https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.14-hb9d3cd8_0.conda#76df83c2a9035c54df5d04ff81bcc02d -https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hb9d3cd8_2.conda#41b599ed2b02abcfdd84302bff174b23 +https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hb9d3cd8_3.conda#cb98af5db26e3f482bebb80ce9d947d3 https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.24-h86f0d12_0.conda#64f0c503da58ec25ebd359e4d990afa8 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.0-h5888daf_0.conda#db0bfbe7dd197b68ad5f30333bae6ce0 https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.6-h2dba641_1.conda#ede4673863426c0883c0063d853bbd85 @@ -35,7 +35,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.1.0-h69a702a_2.cond https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.1.0-hcea5267_2.conda#01de444988ed960031dbe84cf4f9b1fc https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h4ce23a2_1.conda#e796ff8ddc598affdf7c173d6145f087 https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.1.0-hb9d3cd8_0.conda#9fa334557db9f63da6c9285fd2a48638 -https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_1.conda#a76fd702c93cd2dfd89eff30a5fd45a8 +https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_2.conda#1a580f7796c7bf6393fddb8bbbde58dc https://conda.anaconda.org/conda-forge/linux-64/libntlm-1.8-hb9d3cd8_0.conda#7c7927b404672409d9917d49bff5f2d6 https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_2.conda#1cb1c67961f6dd257eae9e9691b341aa https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.5.0-h851e524_0.conda#63f790534398730f59e1b899c3644d4a @@ 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https://conda.anaconda.org/conda-forge/noarch/imageio-2.37.0-pyhfb79c49_0.conda#b5577bc2212219566578fd5af9993af6 -https://conda.anaconda.org/conda-forge/linux-64/pandas-2.2.3-py310h5eaa309_3.conda#07697a584fab513ce895c4511f7a2403 +https://conda.anaconda.org/conda-forge/linux-64/pandas-2.3.0-py310h5eaa309_0.conda#379844614e3a24e59e59d8c69c6e9403 https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.1-pyhd8ed1ab_1.conda#ee23fabfd0a8c6b8d6f3729b47b2859d https://conda.anaconda.org/conda-forge/linux-64/polars-default-1.30.0-py39hfac2b71_0.conda#cd33cf1e631b4d766858c90e333b4832 https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.7.0-pyhd8ed1ab_0.conda#15353a2a0ea6dfefaa52fc5ab5b98f41 @@ -249,10 +249,10 @@ https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_1.conda https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.14.4-py310hf462985_0.conda#636d3c500d8a851e377360e88ec95372 https://conda.anaconda.org/conda-forge/noarch/tifffile-2025.5.10-pyhd8ed1ab_0.conda#1fdb801f28bf4987294c49aaa314bf5e https://conda.anaconda.org/conda-forge/noarch/pooch-1.8.2-pyhd8ed1ab_1.conda#b3e783e8e8ed7577cf0b6dee37d1fbac -https://conda.anaconda.org/conda-forge/linux-64/qt6-main-6.9.0-h0384650_3.conda#8aa69e15597a205fd6f81781fe62c232 +https://conda.anaconda.org/conda-forge/linux-64/qt6-main-6.9.1-h0384650_0.conda#e1f80d7fca560024b107368dd77d96be https://conda.anaconda.org/conda-forge/linux-64/scikit-image-0.25.2-py310h5eaa309_1.conda#ed21ab72d049ecdb60f829f04b4dca1c https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.13.2-pyhd8ed1ab_3.conda#fd96da444e81f9e6fcaac38590f3dd42 -https://conda.anaconda.org/conda-forge/linux-64/pyside6-6.9.0-py310hfd10a26_0.conda#1610ccfe262ee519716bb69bd4395572 +https://conda.anaconda.org/conda-forge/linux-64/pyside6-6.9.1-py310h21765ff_0.conda#a64f8b57dd1b84d5d4f02f565a3cb630 https://conda.anaconda.org/conda-forge/noarch/seaborn-0.13.2-hd8ed1ab_3.conda#62afb877ca2c2b4b6f9ecb37320085b6 https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.10.3-py310hff52083_0.conda#4162a00ddf1d805557aff34ddf113f46 https://conda.anaconda.org/conda-forge/noarch/numpydoc-1.8.0-pyhd8ed1ab_1.conda#5af206d64d18d6c8dfb3122b4d9e643b @@ -282,7 +282,7 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1 # pip overrides @ https://files.pythonhosted.org/packages/2c/ab/fc8290c6a4c722e5514d80f62b2dc4c4df1a68a41d1364e625c35990fcf3/overrides-7.7.0-py3-none-any.whl#sha256=c7ed9d062f78b8e4c1a7b70bd8796b35ead4d9f510227ef9c5dc7626c60d7e49 # pip pandocfilters @ https://files.pythonhosted.org/packages/ef/af/4fbc8cab944db5d21b7e2a5b8e9211a03a79852b1157e2c102fcc61ac440/pandocfilters-1.5.1-py2.py3-none-any.whl#sha256=93be382804a9cdb0a7267585f157e5d1731bbe5545a85b268d6f5fe6232de2bc # pip pkginfo @ 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https://files.pythonhosted.org/packages/08/20/0f2523b9e50a8052bc6a8b732dfc8568abbdc42010aef03a2d750bdab3b2/python_json_logger-3.3.0-py3-none-any.whl#sha256=dd980fae8cffb24c13caf6e158d3d61c0d6d22342f932cb6e9deedab3d35eec7 # pip pyyaml @ https://files.pythonhosted.org/packages/6b/4e/1523cb902fd98355e2e9ea5e5eb237cbc5f3ad5f3075fa65087aa0ecb669/PyYAML-6.0.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=ec031d5d2feb36d1d1a24380e4db6d43695f3748343d99434e6f5f9156aaa2ed @@ -309,16 +309,16 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1 # pip sphinxcontrib-sass @ https://files.pythonhosted.org/packages/3f/ec/194f2dbe55b3fe0941b43286c21abb49064d9d023abfb99305c79ad77cad/sphinxcontrib_sass-0.3.5-py2.py3-none-any.whl#sha256=850c83a36ed2d2059562504ccf496ca626c9c0bb89ec642a2d9c42105704bef6 # pip terminado @ 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https://files.pythonhosted.org/packages/fc/55/96142937f66150805c25c4d0f31ee4132fd33497753400734f9dfdcbdc66/bleach-6.2.0-py3-none-any.whl#sha256=117d9c6097a7c3d22fd578fcd8d35ff1e125df6736f554da4e432fdd63f31e5e # pip isoduration @ https://files.pythonhosted.org/packages/7b/55/e5326141505c5d5e34c5e0935d2908a74e4561eca44108fbfb9c13d2911a/isoduration-20.11.0-py3-none-any.whl#sha256=b2904c2a4228c3d44f409c8ae8e2370eb21a26f7ac2ec5446df141dde3452042 # pip jsonschema-specifications @ https://files.pythonhosted.org/packages/01/0e/b27cdbaccf30b890c40ed1da9fd4a3593a5cf94dae54fb34f8a4b74fcd3f/jsonschema_specifications-2025.4.1-py3-none-any.whl#sha256=4653bffbd6584f7de83a67e0d620ef16900b390ddc7939d56684d6c81e33f1af # pip jupyter-client @ https://files.pythonhosted.org/packages/11/85/b0394e0b6fcccd2c1eeefc230978a6f8cb0c5df1e4cd3e7625735a0d7d1e/jupyter_client-8.6.3-py3-none-any.whl#sha256=e8a19cc986cc45905ac3362915f410f3af85424b4c0905e94fa5f2cb08e8f23f # pip jupyter-server-terminals @ https://files.pythonhosted.org/packages/07/2d/2b32cdbe8d2a602f697a649798554e4f072115438e92249624e532e8aca6/jupyter_server_terminals-0.5.3-py3-none-any.whl#sha256=41ee0d7dc0ebf2809c668e0fc726dfaf258fcd3e769568996ca731b6194ae9aa -# pip jupyterlite-core @ https://files.pythonhosted.org/packages/46/15/1d9160819d1e6e018d15de0e98b9297d0a09cfcfdc73add6e24ee3b2b83c/jupyterlite_core-0.5.1-py3-none-any.whl#sha256=76381619a632f06bf67fb47e5464af762ad8836df5ffe3d7e7ee0e316c1407ee +# pip jupyterlite-core @ https://files.pythonhosted.org/packages/48/3a/7a230e176440220de3ed72b9d72be99ce9ca6d9a958cec95c4e28ccc0254/jupyterlite_core-0.6.1-py3-none-any.whl#sha256=d23db96ede9cfe6edcb0242730d6d2068b47e340daf2effefa9892fa3c091357 # pip mdit-py-plugins @ https://files.pythonhosted.org/packages/a7/f7/7782a043553ee469c1ff49cfa1cdace2d6bf99a1f333cf38676b3ddf30da/mdit_py_plugins-0.4.2-py3-none-any.whl#sha256=0c673c3f889399a33b95e88d2f0d111b4447bdfea7f237dab2d488f459835636 # pip jsonschema @ https://files.pythonhosted.org/packages/a2/3d/023389198f69c722d039351050738d6755376c8fd343e91dc493ea485905/jsonschema-4.24.0-py3-none-any.whl#sha256=a462455f19f5faf404a7902952b6f0e3ce868f3ee09a359b05eca6673bd8412d -# pip jupyterlite-pyodide-kernel @ https://files.pythonhosted.org/packages/1b/b5/959a03ca011d1031abac03c18af9e767c18d6a9beb443eb106dda609748c/jupyterlite_pyodide_kernel-0.5.2-py3-none-any.whl#sha256=63ba6ce28d32f2cd19f636c40c153e171369a24189e11e2235457bd7000c5907 +# pip jupyterlite-pyodide-kernel @ https://files.pythonhosted.org/packages/92/a4/bf3270357175d410d98edd00e42c1826cb26e33742c1ee5421d00d4cf97d/jupyterlite_pyodide_kernel-0.6.1-py3-none-any.whl#sha256=d16f2e44dedd60d7a5578cd901a4de1ac34d30c80671abba7ec1ac70a65e2972 # pip jupyter-events @ https://files.pythonhosted.org/packages/e2/48/577993f1f99c552f18a0428731a755e06171f9902fa118c379eb7c04ea22/jupyter_events-0.12.0-py3-none-any.whl#sha256=6464b2fa5ad10451c3d35fabc75eab39556ae1e2853ad0c0cc31b656731a97fb # pip nbformat @ https://files.pythonhosted.org/packages/a9/82/0340caa499416c78e5d8f5f05947ae4bc3cba53c9f038ab6e9ed964e22f1/nbformat-5.10.4-py3-none-any.whl#sha256=3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b # pip jupytext @ https://files.pythonhosted.org/packages/ed/f1/82ea8e783433707cafd9790099a2d19f113c22f32a31c8bb5abdc7a61dbb/jupytext-1.17.2-py3-none-any.whl#sha256=4f85dc43bb6a24b75491c5c434001ad5ef563932f68f15dd3e1c8ce12a4a426b @@ -326,4 +326,4 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1 # pip nbconvert @ https://files.pythonhosted.org/packages/cc/9a/cd673b2f773a12c992f41309ef81b99da1690426bd2f96957a7ade0d3ed7/nbconvert-7.16.6-py3-none-any.whl#sha256=1375a7b67e0c2883678c48e506dc320febb57685e5ee67faa51b18a90f3a712b # pip jupyter-server @ https://files.pythonhosted.org/packages/46/1f/5ebbced977171d09a7b0c08a285ff9a20aafb9c51bde07e52349ff1ddd71/jupyter_server-2.16.0-py3-none-any.whl#sha256=3d8db5be3bc64403b1c65b400a1d7f4647a5ce743f3b20dbdefe8ddb7b55af9e # pip jupyterlab-server @ https://files.pythonhosted.org/packages/54/09/2032e7d15c544a0e3cd831c51d77a8ca57f7555b2e1b2922142eddb02a84/jupyterlab_server-2.27.3-py3-none-any.whl#sha256=e697488f66c3db49df675158a77b3b017520d772c6e1548c7d9bcc5df7944ee4 -# pip jupyterlite-sphinx @ https://files.pythonhosted.org/packages/b8/68/d35f70a5ae17b30da996c48138c2d655232c2ee839c881ef44587d75d0d3/jupyterlite_sphinx-0.20.1-py3-none-any.whl#sha256=6f477879e9793813b5ed554f08d87b2d949b68595ec5b7570332aa2d0fe0a8c1 +# pip jupyterlite-sphinx @ https://files.pythonhosted.org/packages/fd/0d/1df67bfb12568fea71c1aa597f91c1fbd5335c05e68fa97302c0ff008ca4/jupyterlite_sphinx-0.20.2-py3-none-any.whl#sha256=6607a2df506fdca7bc2de374f26759bb26baf007847511f63f2c876441730503 diff --git 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https://conda.anaconda.org/conda-forge/linux-aarch64/xcb-util-image-0.4.0-h5c728e9_2.conda#b82e5c78dbbfa931980e8bfe83bce913 -https://conda.anaconda.org/conda-forge/linux-aarch64/xkeyboard-config-2.44-h86ecc28_0.conda#4d91bf5ccb5b31be8e070fda2ed13c50 +https://conda.anaconda.org/conda-forge/linux-aarch64/xkeyboard-config-2.45-h86ecc28_0.conda#01251d1503a253e39be4fa9bcf447d63 https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxext-1.3.6-h57736b2_0.conda#bd1e86dd8aa3afd78a4bfdb4ef918165 https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxfixes-6.0.1-h57736b2_0.conda#78f8715c002cc66991d7c11e3cf66039 https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxrender-0.9.12-h86ecc28_0.conda#ae2c2dd0e2d38d249887727db2af960e https://conda.anaconda.org/conda-forge/linux-aarch64/ccache-4.11.3-h4889ad1_0.conda#e0b9e519da2bf0fb8c48381daf87a194 https://conda.anaconda.org/conda-forge/linux-aarch64/dbus-1.16.2-heda779d_0.conda#9203b74bb1f3fa0d6f308094b3b44c1e https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda#72e42d28960d875c7654614f8b50939a -https://conda.anaconda.org/conda-forge/linux-aarch64/fonttools-4.58.1-py310heeae437_0.conda#837e7673572a3d0ecd6cf5a31dee2f35 +https://conda.anaconda.org/conda-forge/linux-aarch64/fonttools-4.58.2-py310heeae437_0.conda#7922ae42fe405708861274a76e255e6e https://conda.anaconda.org/conda-forge/linux-aarch64/freetype-2.13.3-h8af1aa0_1.conda#71c4cbe1b384a8e7b56993394a435343 https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.1-pyhd8ed1ab_0.conda#fb1c14694de51a476ce8636d92b6f42c https://conda.anaconda.org/conda-forge/linux-aarch64/libcblas-3.9.0-31_hab92f65_openblas.conda#6b81dbae56a519f1ec2f25e0ee2f4334 @@ -145,7 +146,7 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/liblapacke-3.9.0-31_hc659ca https://conda.anaconda.org/conda-forge/linux-aarch64/libpq-17.5-hf590da8_0.conda#b5a01e5aa04651ccf5865c2d029affa3 https://conda.anaconda.org/conda-forge/noarch/meson-python-0.18.0-pyh70fd9c4_0.conda#576c04b9d9f8e45285fb4d9452c26133 https://conda.anaconda.org/conda-forge/linux-aarch64/numpy-2.2.6-py310h6e5608f_0.conda#9e9f1f279eb02c41bda162a42861adc0 -https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.5-pyhd8ed1ab_0.conda#c3c9316209dec74a705a36797970c6be +https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.0-pyhd8ed1ab_0.conda#516d31f063ce7e49ced17f105b63a1f1 https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxtst-1.2.5-h57736b2_3.conda#c05698071b5c8e0da82a282085845860 https://conda.anaconda.org/conda-forge/linux-aarch64/blas-devel-3.9.0-31_h9678261_openblas.conda#a2cc143d7e25e52a915cb320e5b0d592 https://conda.anaconda.org/conda-forge/linux-aarch64/cairo-1.18.4-h83712da_0.conda#cd55953a67ec727db5dc32b167201aa6 @@ -155,6 +156,6 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/scipy-1.15.2-py310hf37559f_ https://conda.anaconda.org/conda-forge/linux-aarch64/blas-2.131-openblas.conda#51c5f346e1ebee750f76066490059df9 https://conda.anaconda.org/conda-forge/linux-aarch64/harfbuzz-11.2.1-h405b6a2_0.conda#b55680fc90e9747dc858e7ceb0abc2b2 https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-base-3.10.3-py310h2cc5e2d_0.conda#e29f4329f4f76cf14f74ed86dcc59bac -https://conda.anaconda.org/conda-forge/linux-aarch64/qt6-main-6.9.0-h13135bf_3.conda#f3d24ce6f388642e76f4917b5069c2e9 -https://conda.anaconda.org/conda-forge/linux-aarch64/pyside6-6.9.0-py310hee8ad4f_0.conda#68f556281ac23f1780381f00de99d66d +https://conda.anaconda.org/conda-forge/linux-aarch64/qt6-main-6.9.1-h13135bf_0.conda#6e8335a319b6b1988d6959f895116c74 +https://conda.anaconda.org/conda-forge/linux-aarch64/pyside6-6.9.1-py310hd3bda28_0.conda#1a105dc54d3cd250526c9d52379133c9 https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-3.10.3-py310hbbe02a8_0.conda#08982f6ac753e962d59160b08839221b From 0475caeaa90874a76afe13eace8c96d71320105c Mon Sep 17 00:00:00 2001 From: Shaurya Bisht <87357655+ShauryaDusht@users.noreply.github.com> Date: Tue, 10 Jun 2025 14:15:00 +0530 Subject: [PATCH 015/103] DOC Add additional donation options to About page (#31379) --- doc/about.rst | 17 ++++++++++++++--- 1 file changed, 14 insertions(+), 3 deletions(-) diff --git a/doc/about.rst b/doc/about.rst index 4db39f9709e73..876e792a179f4 100644 --- a/doc/about.rst +++ b/doc/about.rst @@ -594,17 +594,28 @@ Donating to the project ======================= If you are interested in donating to the project or to one of our code-sprints, -please donate via the `NumFOCUS Donations Page -`_. +you have several options: .. raw:: html

- Help us, donate! + Donate via NumFOCUS + + + Donate via GitHub Sponsors

+**Donation Options:** + +* **NumFOCUS**: Donate via the `NumFOCUS Donations Page + `_, scikit-learn's fiscal sponsor. + +* **GitHub Sponsors**: Support the project directly through `GitHub Sponsors + `_. + + All donations will be handled by `NumFOCUS `_, a non-profit organization which is managed by a board of `Scipy community members `_. NumFOCUS's mission is to foster scientific From ba9fa2bf36b94eb4f6afa98b3c211461aff90a75 Mon Sep 17 00:00:00 2001 From: SIKAI ZHANG <34108862+MatthewSZhang@users.noreply.github.com> Date: Tue, 10 Jun 2025 18:02:30 +0800 Subject: [PATCH 016/103] MNT Add free-threaded wheel for Linux arm (#31513) --- .github/workflows/wheels.yml | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/.github/workflows/wheels.yml b/.github/workflows/wheels.yml index 33e8897c147f7..2f285b4edf60b 100644 --- a/.github/workflows/wheels.yml +++ b/.github/workflows/wheels.yml @@ -117,6 +117,11 @@ jobs: python: 313 platform_id: manylinux_aarch64 manylinux_image: manylinux2014 + - os: ubuntu-24.04-arm + python: 313t + platform_id: manylinux_aarch64 + manylinux_image: manylinux2014 + free_threaded_support: True # MacOS x86_64 - os: macos-13 From 5ca79f22bd0c8acd44339069233334d56820b343 Mon Sep 17 00:00:00 2001 From: Somdutta Banerjee Date: Tue, 10 Jun 2025 08:16:01 -0700 Subject: [PATCH 017/103] DOC Added example comparing L1-based models to ARD user guide (#31425) --- doc/modules/linear_model.rst | 8 +++----- sklearn/linear_model/_bayes.py | 11 ++++++----- sklearn/linear_model/_coordinate_descent.py | 9 +++++++++ 3 files changed, 18 insertions(+), 10 deletions(-) diff --git a/doc/modules/linear_model.rst b/doc/modules/linear_model.rst index 9edd90321bd02..83451406ffa54 100644 --- a/doc/modules/linear_model.rst +++ b/doc/modules/linear_model.rst @@ -837,13 +837,11 @@ prior over all :math:`\lambda_i` is chosen to be the same gamma distribution given by the hyperparameters :math:`\lambda_1` and :math:`\lambda_2`. ARD is also known in the literature as *Sparse Bayesian Learning* and *Relevance -Vector Machine* [3]_ [4]_. For a worked-out comparison between ARD and `Bayesian -Ridge Regression`_, see the example below. +Vector Machine* [3]_ [4]_. -.. rubric:: Examples - -* :ref:`sphx_glr_auto_examples_linear_model_plot_ard.py` +See :ref:`sphx_glr_auto_examples_linear_model_plot_ard.py` for a worked-out comparison between ARD and `Bayesian Ridge Regression`_. +See :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_and_elasticnet.py` for a comparison between various methods - Lasso, ARD and ElasticNet - on correlated data. .. rubric:: References diff --git a/sklearn/linear_model/_bayes.py b/sklearn/linear_model/_bayes.py index adf515d44d1d9..e519660323d80 100644 --- a/sklearn/linear_model/_bayes.py +++ b/sklearn/linear_model/_bayes.py @@ -568,11 +568,6 @@ class ARDRegression(RegressorMixin, LinearModel): -------- BayesianRidge : Bayesian ridge regression. - Notes - ----- - For an example, see :ref:`examples/linear_model/plot_ard.py - `. - References ---------- D. J. C. MacKay, Bayesian nonlinear modeling for the prediction @@ -594,6 +589,12 @@ class ARDRegression(RegressorMixin, LinearModel): ARDRegression() >>> clf.predict([[1, 1]]) array([1.]) + + - :ref:`sphx_glr_auto_examples_linear_model_plot_ard.py` demonstrates ARD + Regression. + - :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_and_elasticnet.py` + showcases ARD Regression alongside Lasso and Elastic-Net for sparse, + correlated signals, in the presence of noise. """ _parameter_constraints: dict = { diff --git a/sklearn/linear_model/_coordinate_descent.py b/sklearn/linear_model/_coordinate_descent.py index 62096133ada2f..940ae6f5e3a30 100644 --- a/sklearn/linear_model/_coordinate_descent.py +++ b/sklearn/linear_model/_coordinate_descent.py @@ -875,6 +875,10 @@ class ElasticNet(MultiOutputMixin, RegressorMixin, LinearModel): 1.451 >>> print(regr.predict([[0, 0]])) [1.451] + + - :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_and_elasticnet.py` + showcases ElasticNet alongside Lasso and ARD Regression for sparse + signal recovery in the presence of noise and feature correlation. """ # "check_input" is used for optimisation and isn't something to be passed @@ -1304,6 +1308,11 @@ class Lasso(ElasticNet): [0.85 0. ] >>> print(clf.intercept_) 0.15 + + - :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_and_elasticnet.py` + compares Lasso with other L1-based regression models (ElasticNet and ARD + Regression) for sparse signal recovery in the presence of noise and + feature correlation. """ _parameter_constraints: dict = { From ba9e063bf31d0ae44c6d146ea266c773c6d89056 Mon Sep 17 00:00:00 2001 From: KALLA GANASEKHAR Date: Wed, 11 Jun 2025 17:42:10 +0530 Subject: [PATCH 018/103] DOC: Add link to plot_ensemble_oob example (#31457) --- sklearn/ensemble/_forest.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/sklearn/ensemble/_forest.py b/sklearn/ensemble/_forest.py index 5def6ac60816b..5b27e789b1d13 100644 --- a/sklearn/ensemble/_forest.py +++ b/sklearn/ensemble/_forest.py @@ -1298,6 +1298,9 @@ class RandomForestClassifier(ForestClassifier): Provide a callable with signature `metric(y_true, y_pred)` to use a custom metric. Only available if `bootstrap=True`. + For an illustration of out-of-bag (OOB) error estimation, see the example + :ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py`. + n_jobs : int, default=None The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`, :meth:`decision_path` and :meth:`apply` are all parallelized over the @@ -1709,6 +1712,9 @@ class RandomForestRegressor(ForestRegressor): Provide a callable with signature `metric(y_true, y_pred)` to use a custom metric. Only available if `bootstrap=True`. + For an illustration of out-of-bag (OOB) error estimation, see the example + :ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py`. + n_jobs : int, default=None The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`, :meth:`decision_path` and :meth:`apply` are all parallelized over the @@ -2054,6 +2060,9 @@ class ExtraTreesClassifier(ForestClassifier): Provide a callable with signature `metric(y_true, y_pred)` to use a custom metric. Only available if `bootstrap=True`. + For an illustration of out-of-bag (OOB) error estimation, see the example + :ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py`. + n_jobs : int, default=None The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`, :meth:`decision_path` and :meth:`apply` are all parallelized over the @@ -2449,6 +2458,9 @@ class ExtraTreesRegressor(ForestRegressor): Provide a callable with signature `metric(y_true, y_pred)` to use a custom metric. Only available if `bootstrap=True`. + For an illustration of out-of-bag (OOB) error estimation, see the example + :ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py`. + n_jobs : int, default=None The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`, :meth:`decision_path` and :meth:`apply` are all parallelized over the From 7fef2fde2316245b7cb4cfd3fbc97fd42b6bf307 Mon Sep 17 00:00:00 2001 From: Christian Veenhuis <124370897+ChVeen@users.noreply.github.com> Date: Thu, 12 Jun 2025 02:32:13 +0200 Subject: [PATCH 019/103] FIX: fix wrongly used `plt.show()` in examples (#31524) --- examples/bicluster/plot_spectral_biclustering.py | 6 +++--- examples/svm/plot_svm_kernels.py | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/bicluster/plot_spectral_biclustering.py b/examples/bicluster/plot_spectral_biclustering.py index 86245325ae493..b3eb1017b6217 100644 --- a/examples/bicluster/plot_spectral_biclustering.py +++ b/examples/bicluster/plot_spectral_biclustering.py @@ -43,7 +43,7 @@ plt.matshow(data, cmap=plt.cm.Blues) plt.title("Original dataset") -_ = plt.show() +plt.show() # %% # We shuffle the data and the goal is to reconstruct it afterwards using @@ -62,7 +62,7 @@ plt.matshow(data, cmap=plt.cm.Blues) plt.title("Shuffled dataset") -_ = plt.show() +plt.show() # %% # Fitting `SpectralBiclustering` @@ -102,7 +102,7 @@ plt.matshow(reordered_data, cmap=plt.cm.Blues) plt.title("After biclustering; rearranged to show biclusters") -_ = plt.show() +plt.show() # %% # As a last step, we want to demonstrate the relationships between the row diff --git a/examples/svm/plot_svm_kernels.py b/examples/svm/plot_svm_kernels.py index d01f049dbe0b4..6173a2e7642be 100644 --- a/examples/svm/plot_svm_kernels.py +++ b/examples/svm/plot_svm_kernels.py @@ -79,7 +79,7 @@ scatter = ax.scatter(X[:, 0], X[:, 1], s=150, c=y, label=y, edgecolors="k") ax.legend(*scatter.legend_elements(), loc="upper right", title="Classes") ax.set_title("Samples in two-dimensional feature space") -_ = plt.show() +plt.show() # %% # We can see that the samples are not clearly separable by a straight line. From be757113341ab9d573728f99e102e0583dfbbe69 Mon Sep 17 00:00:00 2001 From: Adrin Jalali Date: Thu, 12 Jun 2025 07:36:09 +0200 Subject: [PATCH 020/103] DOC add reference to higher level functions in estimator_checks_generator (#31480) Co-authored-by: Virgil Chan --- sklearn/utils/estimator_checks.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py index 6347692842615..ef92835f88cc9 100644 --- a/sklearn/utils/estimator_checks.py +++ b/sklearn/utils/estimator_checks.py @@ -506,6 +506,13 @@ def estimator_checks_generator( ): """Iteratively yield all check callables for an estimator. + This function is used by + :func:`~sklearn.utils.estimator_checks.parametrize_with_checks` and + :func:`~sklearn.utils.estimator_checks.check_estimator` to yield all check callables + for an estimator. In most cases, these functions should be used instead. When + implementing a custom equivalent, please refer to their source code to + understand how `estimator_checks_generator` is intended to be used. + .. versionadded:: 1.6 Parameters From 210cdca29c53f9373b5733bd51d246be175424a2 Mon Sep 17 00:00:00 2001 From: Ayrat Date: Thu, 12 Jun 2025 11:11:12 +0200 Subject: [PATCH 021/103] DOC Scale data before using k-neighbours regression (#31201) Co-authored-by: Olivier Grisel Co-authored-by: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com> Co-authored-by: Virgil Chan Co-authored-by: Tim Head --- ...t_iterative_imputer_variants_comparison.py | 85 +++++---- examples/impute/plot_missing_values.py | 178 +++++++----------- 2 files changed, 121 insertions(+), 142 deletions(-) diff --git a/examples/impute/plot_iterative_imputer_variants_comparison.py b/examples/impute/plot_iterative_imputer_variants_comparison.py index d2a68d351ce8a..854d443b229d0 100644 --- a/examples/impute/plot_iterative_imputer_variants_comparison.py +++ b/examples/impute/plot_iterative_imputer_variants_comparison.py @@ -13,7 +13,7 @@ imputation with :class:`~impute.IterativeImputer`: * :class:`~linear_model.BayesianRidge`: regularized linear regression -* :class:`~ensemble.RandomForestRegressor`: Forests of randomized trees regression +* :class:`~ensemble.RandomForestRegressor`: forests of randomized trees regression * :func:`~pipeline.make_pipeline` (:class:`~kernel_approximation.Nystroem`, :class:`~linear_model.Ridge`): a pipeline with the expansion of a degree 2 polynomial kernel and regularized linear regression @@ -62,11 +62,10 @@ from sklearn.model_selection import cross_val_score from sklearn.neighbors import KNeighborsRegressor from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import RobustScaler N_SPLITS = 5 -rng = np.random.RandomState(0) - X_full, y_full = fetch_california_housing(return_X_y=True) # ~2k samples is enough for the purpose of the example. # Remove the following two lines for a slower run with different error bars. @@ -74,16 +73,28 @@ y_full = y_full[::10] n_samples, n_features = X_full.shape + +def compute_score_for(X, y, imputer=None): + # We scale data before imputation and training a target estimator, + # because our target estimator and some of the imputers assume + # that the features have similar scales. + if imputer is None: + estimator = make_pipeline(RobustScaler(), BayesianRidge()) + else: + estimator = make_pipeline(RobustScaler(), imputer, BayesianRidge()) + return cross_val_score( + estimator, X, y, scoring="neg_mean_squared_error", cv=N_SPLITS + ) + + # Estimate the score on the entire dataset, with no missing values -br_estimator = BayesianRidge() score_full_data = pd.DataFrame( - cross_val_score( - br_estimator, X_full, y_full, scoring="neg_mean_squared_error", cv=N_SPLITS - ), + compute_score_for(X_full, y_full), columns=["Full Data"], ) # Add a single missing value to each row +rng = np.random.RandomState(0) X_missing = X_full.copy() y_missing = y_full missing_samples = np.arange(n_samples) @@ -93,48 +104,52 @@ # Estimate the score after imputation (mean and median strategies) score_simple_imputer = pd.DataFrame() for strategy in ("mean", "median"): - estimator = make_pipeline( - SimpleImputer(missing_values=np.nan, strategy=strategy), br_estimator - ) - score_simple_imputer[strategy] = cross_val_score( - estimator, X_missing, y_missing, scoring="neg_mean_squared_error", cv=N_SPLITS + score_simple_imputer[strategy] = compute_score_for( + X_missing, y_missing, SimpleImputer(strategy=strategy) ) # Estimate the score after iterative imputation of the missing values # with different estimators -estimators = [ - BayesianRidge(), - RandomForestRegressor( - # We tuned the hyperparameters of the RandomForestRegressor to get a good - # enough predictive performance for a restricted execution time. - n_estimators=4, - max_depth=10, - bootstrap=True, - max_samples=0.5, - n_jobs=2, - random_state=0, +named_estimators = [ + ("Bayesian Ridge", BayesianRidge()), + ( + "Random Forest", + RandomForestRegressor( + # We tuned the hyperparameters of the RandomForestRegressor to get a good + # enough predictive performance for a restricted execution time. + n_estimators=5, + max_depth=10, + bootstrap=True, + max_samples=0.5, + n_jobs=2, + random_state=0, + ), ), - make_pipeline( - Nystroem(kernel="polynomial", degree=2, random_state=0), Ridge(alpha=1e3) + ( + "Nystroem + Ridge", + make_pipeline( + Nystroem(kernel="polynomial", degree=2, random_state=0), Ridge(alpha=1e4) + ), + ), + ( + "k-NN", + KNeighborsRegressor(n_neighbors=10), ), - KNeighborsRegressor(n_neighbors=15), ] score_iterative_imputer = pd.DataFrame() -# iterative imputer is sensible to the tolerance and +# Iterative imputer is sensitive to the tolerance and # dependent on the estimator used internally. -# we tuned the tolerance to keep this example run with limited computational +# We tuned the tolerance to keep this example run with limited computational # resources while not changing the results too much compared to keeping the # stricter default value for the tolerance parameter. tolerances = (1e-3, 1e-1, 1e-1, 1e-2) -for impute_estimator, tol in zip(estimators, tolerances): - estimator = make_pipeline( +for (name, impute_estimator), tol in zip(named_estimators, tolerances): + score_iterative_imputer[name] = compute_score_for( + X_missing, + y_missing, IterativeImputer( - random_state=0, estimator=impute_estimator, max_iter=25, tol=tol + random_state=0, estimator=impute_estimator, max_iter=40, tol=tol ), - br_estimator, - ) - score_iterative_imputer[impute_estimator.__class__.__name__] = cross_val_score( - estimator, X_missing, y_missing, scoring="neg_mean_squared_error", cv=N_SPLITS ) scores = pd.concat( diff --git a/examples/impute/plot_missing_values.py b/examples/impute/plot_missing_values.py index 851bfd419453b..c7474eb338357 100644 --- a/examples/impute/plot_missing_values.py +++ b/examples/impute/plot_missing_values.py @@ -9,14 +9,15 @@ In this example we will investigate different imputation techniques: - imputation by the constant value 0 -- imputation by the mean value of each feature combined with a missing-ness - indicator auxiliary variable +- imputation by the mean value of each feature - k nearest neighbor imputation - iterative imputation +In all the cases, for each feature, we add a new feature indicating the missingness. + We will use two datasets: Diabetes dataset which consists of 10 feature variables collected from diabetes patients with an aim to predict disease -progression and California Housing dataset for which the target is the median +progression and California housing dataset for which the target is the median house value for California districts. As neither of these datasets have missing values, we will remove some @@ -36,9 +37,9 @@ # ############################################## # # First we download the two datasets. Diabetes dataset is shipped with -# scikit-learn. It has 442 entries, each with 10 features. California Housing +# scikit-learn. It has 442 entries, each with 10 features. California housing # dataset is much larger with 20640 entries and 8 features. It needs to be -# downloaded. We will only use the first 400 entries for the sake of speeding +# downloaded. We will only use the first 300 entries for the sake of speeding # up the calculations but feel free to use the whole dataset. # @@ -46,17 +47,16 @@ from sklearn.datasets import fetch_california_housing, load_diabetes -rng = np.random.RandomState(42) - X_diabetes, y_diabetes = load_diabetes(return_X_y=True) X_california, y_california = fetch_california_housing(return_X_y=True) -X_california = X_california[:300] -y_california = y_california[:300] + X_diabetes = X_diabetes[:300] y_diabetes = y_diabetes[:300] +X_california = X_california[:300] +y_california = y_california[:300] -def add_missing_values(X_full, y_full): +def add_missing_values(X_full, y_full, rng): n_samples, n_features = X_full.shape # Add missing values in 75% of the lines @@ -75,20 +75,22 @@ def add_missing_values(X_full, y_full): return X_missing, y_missing -X_miss_california, y_miss_california = add_missing_values(X_california, y_california) - -X_miss_diabetes, y_miss_diabetes = add_missing_values(X_diabetes, y_diabetes) +rng = np.random.RandomState(42) +X_miss_diabetes, y_miss_diabetes = add_missing_values(X_diabetes, y_diabetes, rng) +X_miss_california, y_miss_california = add_missing_values( + X_california, y_california, rng +) # %% # Impute the missing data and score # ################################# # Now we will write a function which will score the results on the differently -# imputed data. Let's look at each imputer separately: +# imputed data, including the case of no imputation for full data. +# We will use :class:`~sklearn.ensemble.RandomForestRegressor` for the target +# regression. # -rng = np.random.RandomState(0) - from sklearn.ensemble import RandomForestRegressor # To use the experimental IterativeImputer, we need to explicitly ask for it: @@ -96,33 +98,29 @@ def add_missing_values(X_full, y_full): from sklearn.impute import IterativeImputer, KNNImputer, SimpleImputer from sklearn.model_selection import cross_val_score from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import RobustScaler N_SPLITS = 4 -regressor = RandomForestRegressor(random_state=0) - -# %% -# Missing information -# ------------------- -# In addition to imputing the missing values, the imputers have an -# `add_indicator` parameter that marks the values that were missing, which -# might carry some information. -# -def get_scores_for_imputer(imputer, X_missing, y_missing): - estimator = make_pipeline(imputer, regressor) - impute_scores = cross_val_score( - estimator, X_missing, y_missing, scoring="neg_mean_squared_error", cv=N_SPLITS +def get_score(X, y, imputer=None): + regressor = RandomForestRegressor(random_state=0) + if imputer is not None: + estimator = make_pipeline(imputer, regressor) + else: + estimator = regressor + scores = cross_val_score( + estimator, X, y, scoring="neg_mean_squared_error", cv=N_SPLITS ) - return impute_scores + return scores.mean(), scores.std() x_labels = [] -mses_california = np.zeros(5) -stds_california = np.zeros(5) mses_diabetes = np.zeros(5) stds_diabetes = np.zeros(5) +mses_california = np.zeros(5) +stds_california = np.zeros(5) # %% # Estimate the score @@ -131,16 +129,9 @@ def get_scores_for_imputer(imputer, X_missing, y_missing): # -def get_full_score(X_full, y_full): - full_scores = cross_val_score( - regressor, X_full, y_full, scoring="neg_mean_squared_error", cv=N_SPLITS - ) - return full_scores.mean(), full_scores.std() - - -mses_california[0], stds_california[0] = get_full_score(X_california, y_california) -mses_diabetes[0], stds_diabetes[0] = get_full_score(X_diabetes, y_diabetes) -x_labels.append("Full data") +mses_diabetes[0], stds_diabetes[0] = get_score(X_diabetes, y_diabetes) +mses_california[0], stds_california[0] = get_score(X_california, y_california) +x_labels.append("Full Data") # %% @@ -151,22 +142,28 @@ def get_full_score(X_full, y_full): # replaced by 0: # +imputer = SimpleImputer(strategy="constant", fill_value=0, add_indicator=True) +mses_diabetes[1], stds_diabetes[1] = get_score( + X_miss_diabetes, y_miss_diabetes, imputer +) +mses_california[1], stds_california[1] = get_score( + X_miss_california, y_miss_california, imputer +) +x_labels.append("Zero Imputation") -def get_impute_zero_score(X_missing, y_missing): - imputer = SimpleImputer( - missing_values=np.nan, add_indicator=True, strategy="constant", fill_value=0 - ) - zero_impute_scores = get_scores_for_imputer(imputer, X_missing, y_missing) - return zero_impute_scores.mean(), zero_impute_scores.std() - +# %% +# Impute missing values with mean +# ------------------------------- +# -mses_california[1], stds_california[1] = get_impute_zero_score( - X_miss_california, y_miss_california +imputer = SimpleImputer(strategy="mean", add_indicator=True) +mses_diabetes[2], stds_diabetes[2] = get_score( + X_miss_diabetes, y_miss_diabetes, imputer ) -mses_diabetes[1], stds_diabetes[1] = get_impute_zero_score( - X_miss_diabetes, y_miss_diabetes +mses_california[2], stds_california[2] = get_score( + X_miss_california, y_miss_california, imputer ) -x_labels.append("Zero imputation") +x_labels.append("Mean Imputation") # %% @@ -174,74 +171,41 @@ def get_impute_zero_score(X_missing, y_missing): # ------------------------------------ # # :class:`~sklearn.impute.KNNImputer` imputes missing values using the weighted -# or unweighted mean of the desired number of nearest neighbors. - - -def get_impute_knn_score(X_missing, y_missing): - imputer = KNNImputer(missing_values=np.nan, add_indicator=True) - knn_impute_scores = get_scores_for_imputer(imputer, X_missing, y_missing) - return knn_impute_scores.mean(), knn_impute_scores.std() - +# or unweighted mean of the desired number of nearest neighbors. If your features +# have vastly different scales (as in the California housing dataset), +# consider re-scaling them to potentially improve performance. +# -mses_california[2], stds_california[2] = get_impute_knn_score( - X_miss_california, y_miss_california +imputer = KNNImputer(add_indicator=True) +mses_diabetes[3], stds_diabetes[3] = get_score( + X_miss_diabetes, y_miss_diabetes, imputer ) -mses_diabetes[2], stds_diabetes[2] = get_impute_knn_score( - X_miss_diabetes, y_miss_diabetes +mses_california[3], stds_california[3] = get_score( + X_miss_california, y_miss_california, make_pipeline(RobustScaler(), imputer) ) x_labels.append("KNN Imputation") -# %% -# Impute missing values with mean -# ------------------------------- -# - - -def get_impute_mean(X_missing, y_missing): - imputer = SimpleImputer(missing_values=np.nan, strategy="mean", add_indicator=True) - mean_impute_scores = get_scores_for_imputer(imputer, X_missing, y_missing) - return mean_impute_scores.mean(), mean_impute_scores.std() - - -mses_california[3], stds_california[3] = get_impute_mean( - X_miss_california, y_miss_california -) -mses_diabetes[3], stds_diabetes[3] = get_impute_mean(X_miss_diabetes, y_miss_diabetes) -x_labels.append("Mean Imputation") - - # %% # Iterative imputation of the missing values # ------------------------------------------ # # Another option is the :class:`~sklearn.impute.IterativeImputer`. This uses -# round-robin linear regression, modeling each feature with missing values as a -# function of other features, in turn. -# The version implemented assumes Gaussian (output) variables. If your features -# are obviously non-normal, consider transforming them to look more normal -# to potentially improve performance. +# round-robin regression, modeling each feature with missing values as a +# function of other features, in turn. We use the class's default choice +# of the regressor model (:class:`~sklearn.linear_model.BayesianRidge`) +# to predict missing feature values. The performance of the predictor +# may be negatively affected by vastly different scales of the features, +# so we re-scale the features in the California housing dataset. # +imputer = IterativeImputer(add_indicator=True) -def get_impute_iterative(X_missing, y_missing): - imputer = IterativeImputer( - missing_values=np.nan, - add_indicator=True, - random_state=0, - n_nearest_features=3, - max_iter=1, - sample_posterior=True, - ) - iterative_impute_scores = get_scores_for_imputer(imputer, X_missing, y_missing) - return iterative_impute_scores.mean(), iterative_impute_scores.std() - - -mses_california[4], stds_california[4] = get_impute_iterative( - X_miss_california, y_miss_california +mses_diabetes[4], stds_diabetes[4] = get_score( + X_miss_diabetes, y_miss_diabetes, imputer ) -mses_diabetes[4], stds_diabetes[4] = get_impute_iterative( - X_miss_diabetes, y_miss_diabetes +mses_california[4], stds_california[4] = get_score( + X_miss_california, y_miss_california, make_pipeline(RobustScaler(), imputer) ) x_labels.append("Iterative Imputation") From 2b2a5c4c3c0e47f870cae1632df54bb7c238087f Mon Sep 17 00:00:00 2001 From: Evgeni Burovski Date: Thu, 12 Jun 2025 13:12:31 +0200 Subject: [PATCH 022/103] Preemptively fix incompatibilities with an upcoming array-api-strict release (#31517) --- sklearn/metrics/tests/test_common.py | 7 ++++--- sklearn/utils/_array_api.py | 2 +- sklearn/utils/estimator_checks.py | 7 ++++--- 3 files changed, 9 insertions(+), 7 deletions(-) diff --git a/sklearn/metrics/tests/test_common.py b/sklearn/metrics/tests/test_common.py index bad71e29573b8..39522876e8f24 100644 --- a/sklearn/metrics/tests/test_common.py +++ b/sklearn/metrics/tests/test_common.py @@ -1893,10 +1893,11 @@ def check_array_api_metric( np.asarray(a_xp) np.asarray(b_xp) numpy_as_array_works = True - except (TypeError, RuntimeError): + except (TypeError, RuntimeError, ValueError): # PyTorch with CUDA device and CuPy raise TypeError consistently. - # array-api-strict chose to raise RuntimeError instead. Exception type - # may need to be updated in the future for other libraries. + # array-api-strict chose to raise RuntimeError instead. NumPy raises + # a ValueError if the `__array__` dunder does not return an array. + # Exception type may need to be updated in the future for other libraries. numpy_as_array_works = False if numpy_as_array_works: diff --git a/sklearn/utils/_array_api.py b/sklearn/utils/_array_api.py index a9f35516f17b6..6a59249d99bd7 100644 --- a/sklearn/utils/_array_api.py +++ b/sklearn/utils/_array_api.py @@ -638,7 +638,7 @@ def _average(a, axis=None, weights=None, normalize=True, xp=None): # If weights are 1D, add singleton dimensions for broadcasting shape = [1] * a.ndim shape[axis] = a.shape[axis] - weights = xp.reshape(weights, shape) + weights = xp.reshape(weights, tuple(shape)) if xp.isdtype(a.dtype, "complex floating"): raise NotImplementedError( diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py index ef92835f88cc9..a78ef93a86324 100644 --- a/sklearn/utils/estimator_checks.py +++ b/sklearn/utils/estimator_checks.py @@ -1130,10 +1130,11 @@ def check_array_api_input( # now since array-api-strict seems a bit too strict ... numpy_asarray_works = xp.__name__ != "array_api_strict" - except (TypeError, RuntimeError): + except (TypeError, RuntimeError, ValueError): # PyTorch with CUDA device and CuPy raise TypeError consistently. - # array-api-strict chose to raise RuntimeError instead. Exception type - # may need to be updated in the future for other libraries. + # array-api-strict chose to raise RuntimeError instead. NumPy emits + # a ValueError if `__array__` dunder does not return an array. + # Exception type may need to be updated in the future for other libraries. numpy_asarray_works = False if numpy_asarray_works: From 073714ca1eb66d5f4c3212b7f0d10663b345cf7c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Thu, 12 Jun 2025 17:23:09 +0200 Subject: [PATCH 023/103] CI Fix wheels build after cibuildwheel 3.0 release (#31532) --- .github/workflows/wheels.yml | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/.github/workflows/wheels.yml b/.github/workflows/wheels.yml index 2f285b4edf60b..37096eab184b1 100644 --- a/.github/workflows/wheels.yml +++ b/.github/workflows/wheels.yml @@ -75,7 +75,7 @@ jobs: - os: windows-latest python: 313t platform_id: win_amd64 - free_threaded_support: True + cibw_enable: cpython-freethreading # Linux 64 bit manylinux2014 - os: ubuntu-latest @@ -98,7 +98,7 @@ jobs: python: 313t platform_id: manylinux_x86_64 manylinux_image: manylinux2014 - free_threaded_support: True + cibw_enable: cpython-freethreading # # Linux 64 bit manylinux2014 - os: ubuntu-24.04-arm @@ -121,7 +121,7 @@ jobs: python: 313t platform_id: manylinux_aarch64 manylinux_image: manylinux2014 - free_threaded_support: True + cibw_enable: cpython-freethreading # MacOS x86_64 - os: macos-13 @@ -139,7 +139,7 @@ jobs: - os: macos-13 python: 313t platform_id: macosx_x86_64 - free_threaded_support: True + cibw_enable: cpython-freethreading # MacOS arm64 - os: macos-14 @@ -157,7 +157,7 @@ jobs: - os: macos-14 python: 313t platform_id: macosx_arm64 - free_threaded_support: True + cibw_enable: cpython-freethreading steps: - name: Checkout scikit-learn @@ -173,8 +173,7 @@ jobs: - name: Build and test wheels env: - CIBW_PRERELEASE_PYTHONS: ${{ matrix.prerelease_pythons }} - CIBW_FREE_THREADED_SUPPORT: ${{ matrix.free_threaded_support }} + CIBW_ENABLE: ${{ matrix.cibw_enable }} CIBW_ENVIRONMENT: SKLEARN_SKIP_NETWORK_TESTS=1 CIBW_BUILD: cp${{ matrix.python }}-${{ matrix.platform_id }} CIBW_ARCHS: all From 44e12aed5f6902b3cb3f2b700ad287bb38dc71d2 Mon Sep 17 00:00:00 2001 From: Stefanie Senger <91849487+StefanieSenger@users.noreply.github.com> Date: Fri, 13 Jun 2025 08:16:30 +0200 Subject: [PATCH 024/103] DOC Clarify metadata routing docs from `_metadata_requests.py` module (#31419) Co-authored-by: Lucy Liu Co-authored-by: Adrin Jalali --- pyproject.toml | 1 + sklearn/tests/test_metadata_routing.py | 2 +- sklearn/utils/_metadata_requests.py | 262 ++++++++++++++----------- 3 files changed, 145 insertions(+), 120 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index c068fed72eda1..ce8bcfb8bf4f2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -194,6 +194,7 @@ notice-rgx = "\\#\\ Authors:\\ The\\ scikit\\-learn\\ developers\\\r?\\\n\\#\\ S "examples/svm/plot_rbf_parameters.py"=["CPY001"] # __all__ has un-imported names "sklearn/__init__.py"=["F822"] +"sklearn/utils/_metadata_requests.py"=["CPY001"] [tool.mypy] ignore_missing_imports = true diff --git a/sklearn/tests/test_metadata_routing.py b/sklearn/tests/test_metadata_routing.py index 46391e9d82bfd..d936fc1c4f3c0 100644 --- a/sklearn/tests/test_metadata_routing.py +++ b/sklearn/tests/test_metadata_routing.py @@ -425,7 +425,7 @@ def test_nested_routing_conflict(): "In WeightedMetaRegressor, there is a conflict on sample_weight between" " what is requested for this estimator and what is requested by its" " children. You can resolve this conflict by using an alias for the" - " child estimator(s) requested metadata." + " child estimators' requested metadata." ) ), ): diff --git a/sklearn/utils/_metadata_requests.py b/sklearn/utils/_metadata_requests.py index 2c7e650b133d6..a58d8197feed7 100644 --- a/sklearn/utils/_metadata_requests.py +++ b/sklearn/utils/_metadata_requests.py @@ -9,43 +9,51 @@ developers and users who implement custom meta-estimators, need to deal with the objects implemented in this file. -All estimators (should) implement a ``get_metadata_routing`` method, returning -the routing requests set for the estimator. This method is automatically -implemented via ``BaseEstimator`` for all simple estimators, but needs a custom -implementation for meta-estimators. - -In non-routing consumers, i.e. the simplest case, e.g. ``SVM``, -``get_metadata_routing`` returns a ``MetadataRequest`` object. - -In routers, e.g. meta-estimators and a multi metric scorer, -``get_metadata_routing`` returns a ``MetadataRouter`` object. - -An object which is both a router and a consumer, e.g. a meta-estimator which -consumes ``sample_weight`` and routes ``sample_weight`` to its sub-estimators, -routing information includes both information about the object itself (added -via ``MetadataRouter.add_self_request``), as well as the routing information -for its sub-estimators. - -A ``MetadataRequest`` instance includes one ``MethodMetadataRequest`` per -method in ``METHODS``, which includes ``fit``, ``score``, etc. - -Request values are added to the routing mechanism by adding them to -``MethodMetadataRequest`` instances, e.g. -``metadatarequest.fit.add(param="sample_weight", alias="my_weights")``. This is -used in ``set_{method}_request`` which are automatically generated, so users -and developers almost never need to directly call methods on a -``MethodMetadataRequest``. - -The ``alias`` above in the ``add`` method has to be either a string (an alias), -or a {True (requested), False (unrequested), None (error if passed)}``. There +The routing is coordinated by building ``MetadataRequest`` objects +for objects that consume metadata, and ``MetadataRouter`` objects for objects that +can route metadata, which are then aligned during a call to `process_routing()`. This +function returns a Bunch object (dictionary-like) with all the information on the +consumers and which metadata they had requested and the actual metadata values. A +routing method (such as `fit` in a meta-estimator) can now provide the metadata to the +relevant consuming method (such as `fit` in a sub-estimator). + +The ``MetadataRequest`` and ``MetadataRouter`` objects are constructed via a +``get_metadata_routing`` method, which all scikit-learn estimators provide. +This method is automatically implemented via ``BaseEstimator`` for all simple +estimators, but needs a custom implementation for meta-estimators. + +MetadataRequest +~~~~~~~~~~~~~~~ + +In non-routing consumers, the simplest case, e.g. ``SVM``, ``get_metadata_routing`` +returns a ``MetadataRequest`` object which is assigned to the consumer's +`_metadata_request` attribute. It stores which metadata is required by each method of +the consumer by including one ``MethodMetadataRequest`` per method in ``METHODS`` +(e. g. ``fit``, ``score``, etc). + +Users and developers almost never need to directly add a new ``MethodMetadataRequest``, +to the consumer's `_metadata_request` attribute, since these are generated +automatically. This attribute is modified while running `set_{method}_request` methods +(such as `set_fit_request()`), which adds the request via +`method_metadata_request.add_request(param=prop, alias=alias)`. + +The ``alias`` in the ``add_request`` method has to be either a string (an alias), +or one of ``[True (requested), False (unrequested), None (error if passed)]``. There are some other special values such as ``UNUSED`` and ``WARN`` which are used for purposes such as warning of removing a metadata in a child class, but not used by the end users. -``MetadataRouter`` includes information about sub-objects' routing and how -methods are mapped together. For instance, the information about which methods -of a sub-estimator are called in which methods of the meta-estimator are all -stored here. Conceptually, this information looks like: +MetadataRouter +~~~~~~~~~~~~~~ + +In routers (such as meta-estimators or multi metric scorers), ``get_metadata_routing`` +returns a ``MetadataRouter`` object. It provides information about which method, from +the router object, calls which method in a consumer's object, and also, which metadata +had been requested by the consumer's methods, thus specifying how metadata is to be +passed. If a sub-estimator is a router as well, their routing information is also stored +in the meta-estimators router. + +Conceptually, this information looks like: ``` { @@ -57,17 +65,30 @@ } ``` +The `MetadataRouter` objects are never stored and are always recreated anew whenever +the object's `get_metadata_routing` method is called. + +An object that is both a router and a consumer, e.g. a meta-estimator which +consumes ``sample_weight`` and routes ``sample_weight`` to its sub-estimators +also returns a ``MetadataRouter`` object. Its routing information includes both +information about what metadata is required by the object itself (added via +``MetadataRouter.add_self_request``), as well as the routing information for its +sub-estimators (added via ``MetadataRouter.add``). + +Implementation Details +~~~~~~~~~~~~~~~~~~~~~~ + To give the above representation some structure, we use the following objects: -- ``(caller=..., callee=...)`` is a namedtuple called ``MethodPair`` +- ``(caller=..., callee=...)`` is a namedtuple called ``MethodPair``. - The list of ``MethodPair`` stored in the ``mapping`` field of a `RouterMappingPair` is - a ``MethodMapping`` object + a ``MethodMapping`` object. -- ``(mapping=..., router=...)`` is a namedtuple called ``RouterMappingPair`` +- ``(mapping=..., router=...)`` is a namedtuple called ``RouterMappingPair``. The ``set_{method}_request`` methods are dynamically generated for estimators -which inherit from the ``BaseEstimator``. This is done by attaching instances +which inherit from ``BaseEstimator``. This is done by attaching instances of the ``RequestMethod`` descriptor to classes, which is done in the ``_MetadataRequester`` class, and ``BaseEstimator`` inherits from this mixin. This mixin also implements the ``get_metadata_routing``, which meta-estimators @@ -242,7 +263,7 @@ def get_metadata_routing(self): def request_is_alias(item): - """Check if an item is a valid alias. + """Check if an item is a valid string alias for a metadata. Values in ``VALID_REQUEST_VALUES`` are not considered aliases in this context. Only a string which is a valid identifier is. @@ -250,7 +271,7 @@ def request_is_alias(item): Parameters ---------- item : object - The given item to be checked if it can be an alias. + The given item to be checked if it can be an alias for the metadata. Returns ------- @@ -287,9 +308,10 @@ def request_is_valid(item): class MethodMetadataRequest: - """A prescription of how metadata is to be passed to a single method. + """Container for metadata requests associated with a single method. - Refer to :class:`MetadataRequest` for how this class is used. + Instances of this class get used within a :class:`MetadataRequest` - one per each + public method (`fit`, `transform`, ...) that its owning consumer has. .. versionadded:: 1.3 @@ -326,13 +348,14 @@ def add_request( Parameters ---------- param : str - The property for which a request is set. + The metadata for which a request is set. alias : str, or {True, False, None} - Specifies which metadata should be routed to `param` + Specifies which metadata should be routed to the method that owns this + `MethodMetadataRequest`. - str: the name (or alias) of metadata given to a meta-estimator that - should be routed to this parameter. + should be routed to the method that owns this `MethodMetadataRequest`. - True: requested @@ -378,7 +401,7 @@ def _get_param_names(self, return_alias): Returns ------- names : set of str - A set of strings with the names of all parameters. + A set of strings with the names of all metadata. """ return set( alias if return_alias and not request_is_valid(alias) else prop @@ -413,10 +436,10 @@ def _check_warnings(self, *, params): ) def _route_params(self, params, parent, caller): - """Prepare the given parameters to be passed to the method. + """Prepare the given metadata to be passed to the method. The output of this method can be used directly as the input to the - corresponding method as extra props. + corresponding method as **kwargs. Parameters ---------- @@ -432,8 +455,8 @@ def _route_params(self, params, parent, caller): Returns ------- params : Bunch - A :class:`~sklearn.utils.Bunch` of {prop: value} which can be given to the - corresponding method. + A :class:`~sklearn.utils.Bunch` of {metadata: value} which can be + passed to the corresponding method. """ self._check_warnings(params=params) unrequested = dict() @@ -478,7 +501,7 @@ def _route_params(self, params, parent, caller): return res def _consumes(self, params): - """Check whether the given parameters are consumed by this method. + """Check whether the given metadata are consumed by this method. Parameters ---------- @@ -548,7 +571,7 @@ def __init__(self, owner): ) def consumes(self, method, params): - """Check whether the given parameters are consumed by the given method. + """Check whether the given metadata are consumed by the given method. .. versionadded:: 1.4 @@ -619,7 +642,7 @@ def _get_param_names(self, method, return_alias, ignore_self_request=None): Returns ------- names : set of str - A set of strings with the names of all parameters. + A set of strings with the names of all metadata. """ return getattr(self, method)._get_param_names(return_alias=return_alias) @@ -647,8 +670,8 @@ def _route_params(self, *, params, method, parent, caller): Returns ------- params : Bunch - A :class:`~sklearn.utils.Bunch` of {prop: value} which can be given to the - corresponding method. + A :class:`~sklearn.utils.Bunch` of {metadata: value} which can be given to + the corresponding method. """ return getattr(self, method)._route_params( params=params, parent=parent, caller=caller @@ -708,7 +731,7 @@ def __str__(self): class MethodMapping: - """Stores the mapping between caller and callee methods for a router. + """Stores the mapping between caller and callee methods for a :term:`router`. This class is primarily used in a ``get_metadata_routing()`` of a router object when defining the mapping between the router's methods and a sub-object (a @@ -777,14 +800,15 @@ def __str__(self): class MetadataRouter: - """Stores and handles metadata routing for a router object. + """Coordinates metadata routing for a :term:`router` object. - This class is used by router objects to store and handle metadata routing. - Routing information is stored as a dictionary of the form ``{"object_name": - RouteMappingPair(method_mapping, routing_info)}``, where ``method_mapping`` + This class is used by :term:`meta-estimators` or functions that can route metadata, + to handle their metadata routing. Routing information is stored in a + dictionary-like structure of the form ``{"object_name": + RouterMappingPair(mapping, router)}``, where ``mapping`` is an instance of :class:`~sklearn.utils.metadata_routing.MethodMapping` and - ``routing_info`` is either a - :class:`~sklearn.utils.metadata_routing.MetadataRequest` or a + ``router`` is either a + :class:`~sklearn.utils.metadata_routing.MetadataRequest` or another :class:`~sklearn.utils.metadata_routing.MetadataRouter` instance. .. versionadded:: 1.3 @@ -804,21 +828,21 @@ def __init__(self, owner): self._route_mappings = dict() # `_self_request` is used if the router is also a consumer. # _self_request, (added using `add_self_request()`) is treated - # differently from the other objects which are stored in + # differently from the other consumer objects which are stored in # _route_mappings. self._self_request = None self.owner = owner def add_self_request(self, obj): - """Add `self` (as a consumer) to the routing. + """Add `self` (as a :term:`consumer`) to the `MetadataRouter`. - This method is used if the router is also a consumer, and hence the - router itself needs to be included in the routing. The passed object + This method is used if the :term:`router` is also a :term:`consumer`, and hence + the router itself needs to be included in the routing. The passed object can be an estimator or a :class:`~sklearn.utils.metadata_routing.MetadataRequest`. A router should add itself using this method instead of `add` since it - should be treated differently than the other objects to which metadata + should be treated differently than the other consumer objects to which metadata is routed by the router. Parameters @@ -846,15 +870,20 @@ def add_self_request(self, obj): return self def add(self, *, method_mapping, **objs): - """Add named objects with their corresponding method mapping. + """Add :term:`consumers ` to the `MetadataRouter`. + + The estimators that consume metadata are passed as named objects along with a + method mapping, that defines how their methods relate to those of the + :term:`router`. Parameters ---------- method_mapping : MethodMapping - The mapping between the child and the parent's methods. + The mapping between the child (:term:`consumer`) and the parent's + (:term:`router`'s) methods. **objs : dict - A dictionary of objects from which metadata is extracted by calling + A dictionary of objects, whose requests are extracted by calling :func:`~sklearn.utils.metadata_routing.get_routing_for_object` on them. Returns @@ -871,7 +900,7 @@ def add(self, *, method_mapping, **objs): return self def consumes(self, method, params): - """Check whether the given parameters are consumed by the given method. + """Check whether the given metadata is consumed by the given method. .. versionadded:: 1.4 @@ -925,7 +954,7 @@ def _get_param_names(self, *, method, return_alias, ignore_self_request): Returns ------- names : set of str - A set of strings with the names of all parameters. + A set of strings with the names of all metadata. """ res = set() if self._self_request and not ignore_self_request: @@ -946,14 +975,14 @@ def _get_param_names(self, *, method, return_alias, ignore_self_request): return res def _route_params(self, *, params, method, parent, caller): - """Prepare the given parameters to be passed to the method. + """Prepare the given metadata to be passed to the method. This is used when a router is used as a child object of another router. The parent router then passes all parameters understood by the child object to it and delegates their validation to the child. The output of this method can be used directly as the input to the - corresponding method as extra props. + corresponding method as **kwargs. Parameters ---------- @@ -961,8 +990,7 @@ def _route_params(self, *, params, method, parent, caller): A dictionary of provided metadata. method : str - The name of the method for which the parameters are requested and - routed. + The name of the method for which the metadata is requested and routed. parent : object Parent class object, that routes the metadata. @@ -973,8 +1001,8 @@ def _route_params(self, *, params, method, parent, caller): Returns ------- params : Bunch - A :class:`~sklearn.utils.Bunch` of {prop: value} which can be given to the - corresponding method. + A :class:`~sklearn.utils.Bunch` of {metadata: value} which can be given to + the corresponding method. """ res = Bunch() if self._self_request: @@ -1001,28 +1029,28 @@ def _route_params(self, *, params, method, parent, caller): f"In {self.owner}, there is a conflict on {key} between what is" " requested for this estimator and what is requested by its" " children. You can resolve this conflict by using an alias for" - " the child estimator(s) requested metadata." + " the child estimators' requested metadata." ) res.update(child_params) return res def route_params(self, *, caller, params): - """Return the input parameters requested by child objects. + """Get the values of metadata requested by :term:`consumers `. - The output of this method is a :class:`~sklearn.utils.Bunch`, which includes the - metadata for all methods of each child object that is used in the router's - `caller` method. + Returns a :class:`~sklearn.utils.Bunch` containing the metadata that this + :term:`router`'s `caller` method needs to route, organized by each + :term:`consumer` and their corresponding methods. - If the router is also a consumer, it also checks for warnings of - `self`'s/consumer's requested metadata. + This can be used to pass the required metadata to corresponding methods in + consumers. Parameters ---------- caller : str - The name of the method for which the parameters are requested and - routed. If called inside the :term:`fit` method of a router, it - would be `"fit"`. + The name of the :term:`router`'s method through which the metadata is + routed. For example, if called inside the :term:`fit` method of a router, + this would be `"fit"`. params : dict A dictionary of provided metadata. @@ -1031,9 +1059,7 @@ def route_params(self, *, caller, params): ------- params : Bunch A :class:`~sklearn.utils.Bunch` of the form - ``{"object_name": {"method_name": {params: value}}}`` which can be - used to pass the required metadata to corresponding methods or - corresponding child objects. + ``{"object_name": {"method_name": {metadata: value}}}``. """ if self._self_request: self._self_request._check_warnings(params=params, method=caller) @@ -1062,9 +1088,9 @@ def validate_metadata(self, *, method, params): Parameters ---------- method : str - The name of the method for which the parameters are requested and - routed. If called inside the :term:`fit` method of a router, it - would be `"fit"`. + The name of the :term:`router`'s method through which the metadata is + routed. For example, if called inside the :term:`fit` method of a router, + this would be `"fit"`. params : dict A dictionary of provided metadata. @@ -1149,8 +1175,8 @@ def get_routing_for_object(obj=None): Returns ------- - obj : MetadataRequest or MetadataRouting - A ``MetadataRequest`` or a ``MetadataRouting`` taken or created from + obj : MetadataRequest or MetadataRouter + A ``MetadataRequest`` or a ``MetadataRouter`` taken or created from the given object. """ # doing this instead of a try/except since an AttributeError could be raised @@ -1166,16 +1192,18 @@ def get_routing_for_object(obj=None): # Request method # ============== -# This section includes what's needed for the request method descriptor and -# their dynamic generation in a meta class. - -# These strings are used to dynamically generate the docstrings for -# set_{method}_request methods. -REQUESTER_DOC = """ Request metadata passed to the ``{method}`` method. - - Note that this method is only relevant if - ``enable_metadata_routing=True`` (see :func:`sklearn.set_config`). - Please see :ref:`User Guide ` on how the routing +# This section includes what's needed for the `RequestMethod` descriptor and +# the dynamic generation of `set_{method}_request` methods in the `_MetadataRequester` +# mixin class. + +# These strings are used to dynamically generate the docstrings for the methods. +REQUESTER_DOC = """ +Configure whether metadata should be requested to be passed to the ``{method}`` method. + + Note that this method is only relevant when this estimator is used as a + sub-estimator within a :term:`meta-estimator` and metadata routing is enabled + with ``enable_metadata_routing=True`` (see :func:`sklearn.set_config`). + Please check the :ref:`User Guide ` on how the routing mechanism works. The options for each parameter are: @@ -1199,11 +1227,6 @@ def get_routing_for_object(obj=None): .. versionadded:: 1.3 - .. note:: - This method is only relevant if this estimator is used as a - sub-estimator of a meta-estimator, e.g. used inside a - :class:`~sklearn.pipeline.Pipeline`. Otherwise it has no effect. - Parameters ---------- """ @@ -1221,7 +1244,7 @@ def get_routing_for_object(obj=None): class RequestMethod: """ - A descriptor for request methods. + Descriptor for defining `set_{method}_request` methods in estimators. .. versionadded:: 1.3 @@ -1260,10 +1283,11 @@ def __init__(self, name, keys, validate_keys=True): def __get__(self, instance, owner): # we would want to have a method which accepts only the expected args def func(*args, **kw): - """Updates the request for provided parameters + """Updates the `_metadata_request` attribute of the consumer (`instance`) + for the parameters provided as `**kw`. This docstring is overwritten below. - See REQUESTER_DOC for expected functionality + See REQUESTER_DOC for expected functionality. """ if not _routing_enabled(): raise RuntimeError( @@ -1488,7 +1512,7 @@ class attributes, as well as determining request keys from method return requests def _get_metadata_request(self): - """Get requested data properties. + """Get requested metadata for the instance. Please check :ref:`User Guide ` on how the routing mechanism works. @@ -1531,9 +1555,9 @@ def get_metadata_routing(self): # prefix to reduce the chances of name collisions with the passed metadata, and # since they're positional only, users will never type those underscores. def process_routing(_obj, _method, /, **kwargs): - """Validate and route input parameters. + """Validate and route metadata. - This function is used inside a router's method, e.g. :term:`fit`, + This function is used inside a :term:`router`'s method, e.g. :term:`fit`, to validate the metadata and handle the routing. Assuming this signature of a router's fit method: @@ -1551,7 +1575,7 @@ def process_routing(_obj, _method, /, **kwargs): ---------- _obj : object An object implementing ``get_metadata_routing``. Typically a - meta-estimator. + :term:`meta-estimator`. _method : str The name of the router's method in which this function is called. @@ -1563,9 +1587,9 @@ def process_routing(_obj, _method, /, **kwargs): ------- routed_params : Bunch A :class:`~utils.Bunch` of the form ``{"object_name": {"method_name": - {params: value}}}`` which can be used to pass the required metadata to + {metadata: value}}}`` which can be used to pass the required metadata to A :class:`~sklearn.utils.Bunch` of the form ``{"object_name": {"method_name": - {params: value}}}`` which can be used to pass the required metadata to + {metadata: value}}}`` which can be used to pass the required metadata to corresponding methods or corresponding child objects. The object names are those defined in `obj.get_metadata_routing()`. """ From 87ced4d81ed2cbc7025e1f8ba63d769e3f6e32e6 Mon Sep 17 00:00:00 2001 From: Reshama Shaikh Date: Fri, 13 Jun 2025 09:29:16 -0400 Subject: [PATCH 025/103] DOC Update About Us page (#31519) --- doc/about.rst | 95 +++++++++++++++++++++++++----------- doc/maintainers_emeritus.rst | 4 +- 2 files changed, 68 insertions(+), 31 deletions(-) diff --git a/doc/about.rst b/doc/about.rst index 876e792a179f4..b64a1eee6aee7 100644 --- a/doc/about.rst +++ b/doc/about.rst @@ -32,10 +32,10 @@ The decision making process and governance structure of scikit-learn, like roles The people behind scikit-learn ============================== -Scikit-learn is a community project, developed by a large group of +scikit-learn is a community project, developed by a large group of people, all across the world. A few core contributor teams, listed below, have central roles, however a more complete list of contributors can be found `on -github +GitHub `__. Active Core Contributors @@ -158,12 +158,13 @@ Bibtex entry:: pages = {108--122}, } -Artwork -======= +Branding & Logos +================ -High quality PNG and SVG logos are available in the `doc/logos/ +High quality PNG and SVG logos are available in the `doc/logos `_ -source directory. +source directory. The color palette is available in the +`Branding Guide `_. .. image:: images/scikit-learn-logo-notext.png :align: center @@ -580,8 +581,43 @@ the past: |hf| + +Donations in Kind +----------------- +The following organizations provide non-financial contributions to the +scikit-learn project. + +.. raw:: html + + + + + + + + + + + + + + + + + + + + + + + + + + +
CompanyContribution
Anaconda IncStorage for our staging and nightly builds
CircleCICPU time on their Continuous Integration servers
GitHubTeams account
Microsoft AzureCPU time on their Continuous Integration servers
+ Coding Sprints -============== +-------------- The scikit-learn project has a long history of `open source coding sprints `_ with over 50 sprint @@ -593,8 +629,9 @@ list of events. Donating to the project ======================= -If you are interested in donating to the project or to one of our code-sprints, -you have several options: +If you have found scikit-learn to be useful in your work, research, or company, +please consider making a donation to the project commensurate with your resources. +There are several options for making donations: .. raw:: html @@ -605,6 +642,9 @@ you have several options: Donate via GitHub Sponsors + + Donate via Benevity +

**Donation Options:** @@ -615,29 +655,26 @@ you have several options: * **GitHub Sponsors**: Support the project directly through `GitHub Sponsors `_. +* **Benevity**: If your company uses scikit-learn, you can also support the + project through Benevity, a platform to manage employee donations. It is + widely used by hundreds of Fortune 1000 companies to streamline and scale + their social impact initiatives. If your company uses Benevity, you are + able to make a donation with a company match as high as 100%. Our project + ID is `433725 `_. -All donations will be handled by `NumFOCUS `_, a non-profit -organization which is managed by a board of `Scipy community members -`_. NumFOCUS's mission is to foster scientific -computing software, in particular in Python. As a fiscal home of scikit-learn, it -ensures that money is available when needed to keep the project funded and available -while in compliance with tax regulations. - -The received donations for the scikit-learn project mostly will go towards covering -travel-expenses for code sprints, as well as towards the organization budget of the -project [#f1]_. +All donations are managed by `NumFOCUS `_, a 501(c)(3) +non-profit organization based in Austin, Texas, USA. The NumFOCUS board +consists of `SciPy community members `_. +Contributions are tax-deductible to the extent allowed by law. .. rubric:: Notes -.. [#f1] Regarding the organization budget, in particular, we might use some of - the donated funds to pay for other project expenses such as DNS, - hosting or continuous integration services. - +Contributions support the maintenance of the project, including development, +documentation, infrastructure and coding sprints. -Infrastructure support -====================== -We would also like to thank `Microsoft Azure `_, -`CircleCl `_ for free CPU -time on their Continuous Integration servers, and `Anaconda Inc. `_ -for the storage they provide for our staging and nightly builds. +scikit-learn Swag +----------------- +Official scikit-learn swag is available for purchase at the `NumFOCUS online store +`_. +A portion of the proceeds from each sale goes to support the scikit-learn project. diff --git a/doc/maintainers_emeritus.rst b/doc/maintainers_emeritus.rst index f5640ab2caf31..9df0488d2d3b6 100644 --- a/doc/maintainers_emeritus.rst +++ b/doc/maintainers_emeritus.rst @@ -17,7 +17,7 @@ - Arnaud Joly - Thouis (Ray) Jones - Kyle Kastner -- manoj kumar +- Manoj Kumar - Robert Layton - Wei Li - Paolo Losi @@ -39,4 +39,4 @@ - Nelle Varoquaux - David Warde-Farley - Ron Weiss -- Roman Yurchak +- Roman Yurchak \ No newline at end of file From 46b2ebdcb2ea8f41a6f176f191a4f634455e3cc7 Mon Sep 17 00:00:00 2001 From: "ayoub.agouzoul" <34219939+TheAyos@users.noreply.github.com> Date: Fri, 13 Jun 2025 15:39:55 +0200 Subject: [PATCH 026/103] TST use global_random_seed in sklearn/feature_extraction/tests/test_image.py (#31310) --- .../feature_extraction/tests/test_image.py | 26 +++++++++++-------- 1 file changed, 15 insertions(+), 11 deletions(-) diff --git a/sklearn/feature_extraction/tests/test_image.py b/sklearn/feature_extraction/tests/test_image.py index 2edf1a22d676a..cb490fcd576ee 100644 --- a/sklearn/feature_extraction/tests/test_image.py +++ b/sklearn/feature_extraction/tests/test_image.py @@ -223,13 +223,15 @@ def test_reconstruct_patches_perfect_color(orange_face): np.testing.assert_array_almost_equal(face, face_reconstructed) -def test_patch_extractor_fit(downsampled_face_collection): +def test_patch_extractor_fit(downsampled_face_collection, global_random_seed): faces = downsampled_face_collection - extr = PatchExtractor(patch_size=(8, 8), max_patches=100, random_state=0) + extr = PatchExtractor( + patch_size=(8, 8), max_patches=100, random_state=global_random_seed + ) assert extr == extr.fit(faces) -def test_patch_extractor_max_patches(downsampled_face_collection): +def test_patch_extractor_max_patches(downsampled_face_collection, global_random_seed): faces = downsampled_face_collection i_h, i_w = faces.shape[1:3] p_h, p_w = 8, 8 @@ -237,7 +239,7 @@ def test_patch_extractor_max_patches(downsampled_face_collection): max_patches = 100 expected_n_patches = len(faces) * max_patches extr = PatchExtractor( - patch_size=(p_h, p_w), max_patches=max_patches, random_state=0 + patch_size=(p_h, p_w), max_patches=max_patches, random_state=global_random_seed ) patches = extr.transform(faces) assert patches.shape == (expected_n_patches, p_h, p_w) @@ -247,35 +249,37 @@ def test_patch_extractor_max_patches(downsampled_face_collection): (i_h - p_h + 1) * (i_w - p_w + 1) * max_patches ) extr = PatchExtractor( - patch_size=(p_h, p_w), max_patches=max_patches, random_state=0 + patch_size=(p_h, p_w), max_patches=max_patches, random_state=global_random_seed ) patches = extr.transform(faces) assert patches.shape == (expected_n_patches, p_h, p_w) -def test_patch_extractor_max_patches_default(downsampled_face_collection): +def test_patch_extractor_max_patches_default( + downsampled_face_collection, global_random_seed +): faces = downsampled_face_collection - extr = PatchExtractor(max_patches=100, random_state=0) + extr = PatchExtractor(max_patches=100, random_state=global_random_seed) patches = extr.transform(faces) assert patches.shape == (len(faces) * 100, 19, 25) -def test_patch_extractor_all_patches(downsampled_face_collection): +def test_patch_extractor_all_patches(downsampled_face_collection, global_random_seed): faces = downsampled_face_collection i_h, i_w = faces.shape[1:3] p_h, p_w = 8, 8 expected_n_patches = len(faces) * (i_h - p_h + 1) * (i_w - p_w + 1) - extr = PatchExtractor(patch_size=(p_h, p_w), random_state=0) + extr = PatchExtractor(patch_size=(p_h, p_w), random_state=global_random_seed) patches = extr.transform(faces) assert patches.shape == (expected_n_patches, p_h, p_w) -def test_patch_extractor_color(orange_face): +def test_patch_extractor_color(orange_face, global_random_seed): faces = _make_images(orange_face) i_h, i_w = faces.shape[1:3] p_h, p_w = 8, 8 expected_n_patches = len(faces) * (i_h - p_h + 1) * (i_w - p_w + 1) - extr = PatchExtractor(patch_size=(p_h, p_w), random_state=0) + extr = PatchExtractor(patch_size=(p_h, p_w), random_state=global_random_seed) patches = extr.transform(faces) assert patches.shape == (expected_n_patches, p_h, p_w, 3) From 9c1b7292d57fc84ea53819c389bb5c38472930a7 Mon Sep 17 00:00:00 2001 From: Peter Holzer Date: Tue, 17 Jun 2025 23:22:06 +0200 Subject: [PATCH 027/103] DOC link kernel_approximation example to Nystrom and RBFSampler in User Guide (#31562) --- doc/modules/kernel_approximation.rst | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/doc/modules/kernel_approximation.rst b/doc/modules/kernel_approximation.rst index 0bbd19d05de33..334aecc9ac7f6 100644 --- a/doc/modules/kernel_approximation.rst +++ b/doc/modules/kernel_approximation.rst @@ -94,6 +94,8 @@ also the dimensionality of the features computed - is given by the parameter :ref:`sphx_glr_auto_examples_applications_plot_cyclical_feature_engineering.py`, that shows an efficient machine learning pipeline that uses a :class:`Nystroem` kernel. +* See :ref:`sphx_glr_auto_examples_miscellaneous_plot_kernel_approximation.py` + for a comparison of :class:`Nystroem` kernel with :class:`RBFSampler`. .. _rbf_kernel_approx: @@ -145,7 +147,9 @@ use of larger feature spaces more efficient. .. rubric:: Examples -* :ref:`sphx_glr_auto_examples_miscellaneous_plot_kernel_approximation.py` +* See :ref:`sphx_glr_auto_examples_miscellaneous_plot_kernel_approximation.py` for a + comparison of :class:`Nystroem` kernel with :class:`RBFSampler`. + .. _additive_chi_kernel_approx: From 60e4b194a203d7f74a6517a04cb0cb8d040cebb0 Mon Sep 17 00:00:00 2001 From: Lucy Liu Date: Wed, 18 Jun 2025 17:03:12 +1000 Subject: [PATCH 028/103] DOC Fix `RocCurveDisplay` docstring and parameter order (#31578) --- sklearn/metrics/_plot/roc_curve.py | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/sklearn/metrics/_plot/roc_curve.py b/sklearn/metrics/_plot/roc_curve.py index 439c6cfcdd996..383f14e688859 100644 --- a/sklearn/metrics/_plot/roc_curve.py +++ b/sklearn/metrics/_plot/roc_curve.py @@ -59,15 +59,14 @@ class RocCurveDisplay(_BinaryClassifierCurveDisplayMixin): Now accepts a list for plotting multiple curves. name : str or list of str, default=None - Name for labeling legend entries. The number of legend entries - is determined by the `curve_kwargs` passed to `plot`. + Name for labeling legend entries. The number of legend entries is determined + by the `curve_kwargs` passed to `plot`, and is not affected by `name`. To label each curve, provide a list of strings. To avoid labeling individual curves that have the same appearance, this cannot be used in conjunction with `curve_kwargs` being a dictionary or None. If a string is provided, it will be used to either label the single legend entry or if there are multiple legend entries, label each individual curve with - the same name. If `None`, set to `name` provided at `RocCurveDisplay` - initialization. If still `None`, no name is shown in the legend. + the same name. If still `None`, no name is shown in the legend. .. versionadded:: 1.7 @@ -185,7 +184,7 @@ def plot( name : str or list of str, default=None Name for labeling legend entries. The number of legend entries - is determined by `curve_kwargs`. + is determined by `curve_kwargs`, and is not affected by `name`. To label each curve, provide a list of strings. To avoid labeling individual curves that have the same appearance, this cannot be used in conjunction with `curve_kwargs` being a dictionary or None. If a @@ -441,9 +440,9 @@ def from_estimator( y_score=y_score, sample_weight=sample_weight, drop_intermediate=drop_intermediate, + pos_label=pos_label, name=name, ax=ax, - pos_label=pos_label, curve_kwargs=curve_kwargs, plot_chance_level=plot_chance_level, chance_level_kw=chance_level_kw, @@ -687,7 +686,7 @@ def from_cv_results( name : str or list of str, default=None Name for labeling legend entries. The number of legend entries - is determined by `curve_kwargs`. + is determined by `curve_kwargs`, and is not affected by `name`. To label each curve, provide a list of strings. To avoid labeling individual curves that have the same appearance, this cannot be used in conjunction with `curve_kwargs` being a dictionary or None. If a @@ -783,8 +782,8 @@ def from_cv_results( viz = cls( fpr=fpr_folds, tpr=tpr_folds, - name=name, roc_auc=auc_folds, + name=name, pos_label=pos_label_, ) return viz.plot( From 59349e4baedaba7baca7f946e12c04fa4c3fa0db Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Wed, 18 Jun 2025 11:31:23 +0200 Subject: [PATCH 029/103] :lock: :robot: CI Update lock files for scipy-dev CI build(s) :lock: :robot: (#31549) Co-authored-by: Lock file bot --- .../azure/pylatest_pip_scipy_dev_linux-64_conda.lock | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock index 9edd5d56f86a8..d51e606a390ca 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock @@ -30,22 +30,22 @@ https://repo.anaconda.com/pkgs/main/linux-64/readline-8.2-h5eee18b_0.conda#be421 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Wed, 18 Jun 2025 11:32:35 +0200 Subject: [PATCH 030/103] :lock: :robot: CI Update lock files for array-api CI build(s) :lock: :robot: (#31551) Co-authored-by: Lock file bot --- ...a_forge_cuda_array-api_linux-64_conda.lock | 44 +++++++++---------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/build_tools/github/pylatest_conda_forge_cuda_array-api_linux-64_conda.lock b/build_tools/github/pylatest_conda_forge_cuda_array-api_linux-64_conda.lock index 3b0d5b0d86361..8c279235eba38 100644 --- a/build_tools/github/pylatest_conda_forge_cuda_array-api_linux-64_conda.lock +++ b/build_tools/github/pylatest_conda_forge_cuda_array-api_linux-64_conda.lock @@ -12,11 +12,11 @@ https://conda.anaconda.org/conda-forge/linux-64/libopentelemetry-cpp-headers-1.1 https://conda.anaconda.org/conda-forge/linux-64/nlohmann_json-3.12.0-h3f2d84a_0.conda#d76872d096d063e226482c99337209dc https://conda.anaconda.org/conda-forge/noarch/python_abi-3.13-7_cp313.conda#e84b44e6300f1703cb25d29120c5b1d8 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https://conda.anaconda.org/conda-forge/linux-64/xcb-util-cursor-0.1.5-hb9d3cd8_0.conda#eb44b3b6deb1cab08d72cb61686fe64c https://conda.anaconda.org/conda-forge/linux-64/xorg-libxcomposite-0.4.6-hb9d3cd8_2.conda#d3c295b50f092ab525ffe3c2aa4b7413 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxcursor-1.2.3-hb9d3cd8_0.conda#2ccd714aa2242315acaf0a67faea780b @@ -204,15 +204,15 @@ https://conda.anaconda.org/conda-forge/linux-64/azure-storage-common-cpp-12.8.0- https://conda.anaconda.org/conda-forge/linux-64/cudnn-9.10.1.4-haad7af6_0.conda#8382d957333e0d3280dcbf5691516dc1 https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.15.0-h7e30c49_1.conda#8f5b0b297b59e1ac160ad4beec99dbee https://conda.anaconda.org/conda-forge/linux-64/gmpy2-2.2.1-py313h11186cd_0.conda#54d020e0eaacf1e99bfb2410b9aa2e5e -https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp20.1-20.1.6-default_h1df26ce_0.conda#99ead3b974685e44df8b1e3953503cfc -https://conda.anaconda.org/conda-forge/linux-64/libclang13-20.1.6-default_he06ed0a_0.conda#cc6c469d9d7fc0ac106cef5f45d973a9 +https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp20.1-20.1.7-default_h1df26ce_0.conda#f9ef7bce54a7673cdbc2fadd8bca1956 +https://conda.anaconda.org/conda-forge/linux-64/libclang13-20.1.7-default_he06ed0a_0.conda#846875a174de6b6ff19e205a7d90eb74 https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.36.0-h2b5623c_0.conda#c96ca58ad3352a964bfcb85de6cd1496 https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-31_he2f377e_openblas.conda#7e5fff7d0db69be3a266f7e79a3bb0e2 https://conda.anaconda.org/conda-forge/linux-64/libmagma-2.9.0-h45b15fe_0.conda#703a1ab01e36111d8bb40bc7517e900b https://conda.anaconda.org/conda-forge/linux-64/libopentelemetry-cpp-1.18.0-hfcad708_1.conda#1f5a5d66e77a39dc5bd639ec953705cf https://conda.anaconda.org/conda-forge/linux-64/libpq-17.5-h27ae623_0.conda#6458be24f09e1b034902ab44fe9de908 https://conda.anaconda.org/conda-forge/noarch/meson-python-0.18.0-pyh70fd9c4_0.conda#576c04b9d9f8e45285fb4d9452c26133 -https://conda.anaconda.org/conda-forge/linux-64/numpy-2.3.0-py313h17eae1a_0.conda#db18a34466bef0863e9301b518a75e8f +https://conda.anaconda.org/conda-forge/linux-64/numpy-2.2.6-py313h17eae1a_0.conda#7a2d2f9adecd86ed5c29c2115354f615 https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.0-pyhd8ed1ab_0.conda#516d31f063ce7e49ced17f105b63a1f1 https://conda.anaconda.org/conda-forge/linux-64/tbb-2021.13.0-hceb3a55_1.conda#ba7726b8df7b9d34ea80e82b097a4893 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxtst-1.2.5-hb9d3cd8_3.conda#7bbe9a0cc0df0ac5f5a8ad6d6a11af2f @@ -222,20 +222,20 @@ https://conda.anaconda.org/conda-forge/linux-64/azure-storage-blobs-cpp-12.13.0- https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-31_h1ea3ea9_openblas.conda#ba652ee0576396d4765e567f043c57f9 https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.4-h3394656_0.conda#09262e66b19567aff4f592fb53b28760 https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.2-py313h33d0bda_0.conda#5dc81fffe102f63045225007a33d6199 -https://conda.anaconda.org/conda-forge/linux-64/cupy-core-13.4.1-py313hc2a895b_0.conda#46dd595e816b278b178e3bef8a6acf71 +https://conda.anaconda.org/conda-forge/linux-64/cupy-core-13.4.1-py313hc2a895b_1.conda#48458b46f4aaf023c876bddba25343db https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.36.0-h0121fbd_0.conda#fc5efe1833a4d709953964037985bb72 https://conda.anaconda.org/conda-forge/linux-64/libmagma_sparse-2.9.0-h45b15fe_0.conda#beac0a5bbe0af75db6b16d3d8fd24f7e https://conda.anaconda.org/conda-forge/linux-64/mkl-2024.2.2-ha957f24_16.conda#1459379c79dda834673426504d52b319 https://conda.anaconda.org/conda-forge/linux-64/pandas-2.3.0-py313ha87cce1_0.conda#8664b4fa9b5b23b0d1cdc55c7195fcfe https://conda.anaconda.org/conda-forge/linux-64/polars-default-1.30.0-py39hfac2b71_0.conda#cd33cf1e631b4d766858c90e333b4832 -https://conda.anaconda.org/conda-forge/noarch/pytest-cov-6.1.1-pyhd8ed1ab_0.conda#1e35d8f975bc0e984a19819aa91c440a +https://conda.anaconda.org/conda-forge/noarch/pytest-cov-6.2.1-pyhd8ed1ab_0.conda#ce978e1b9ed8b8d49164e90a5cdc94cd https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.7.0-pyhd8ed1ab_0.conda#15353a2a0ea6dfefaa52fc5ab5b98f41 https://conda.anaconda.org/conda-forge/linux-64/scipy-1.15.2-py313h86fcf2b_0.conda#ca68acd9febc86448eeed68d0c6c8643 https://conda.anaconda.org/conda-forge/noarch/sympy-1.14.0-pyh2585a3b_105.conda#8c09fac3785696e1c477156192d64b91 https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.510-h37a5c72_3.conda#beb8577571033140c6897d257acc7724 https://conda.anaconda.org/conda-forge/linux-64/azure-storage-files-datalake-cpp-12.12.0-ha633028_1.conda#7c1980f89dd41b097549782121a73490 https://conda.anaconda.org/conda-forge/linux-64/blas-2.131-openblas.conda#38b2ec894c69bb4be0e66d2ef7fc60bf -https://conda.anaconda.org/conda-forge/linux-64/cupy-13.4.1-py313h66a2ee2_0.conda#784d6bd149ef2b5d9c733ea3dd4d15ad +https://conda.anaconda.org/conda-forge/linux-64/cupy-13.4.1-py313h66a2ee2_1.conda#6019a63d505256ad144a011b51e9b8f3 https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-11.2.1-h3beb420_0.conda#0e6e192d4b3d95708ad192d957cf3163 https://conda.anaconda.org/conda-forge/linux-64/libtorch-2.4.1-cuda118_mkl_hee7131c_306.conda#28b3b3da11973494ed0100aa50f47328 https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.10.3-py313h129903b_0.conda#4f8816d006b1c155ec416bcf7ff6cee2 From 4d6eef6eab5948f01ec1e06f4c7bebdbd06717e9 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Wed, 18 Jun 2025 12:46:56 +0200 Subject: [PATCH 031/103] :lock: :robot: CI Update lock files for free-threaded CI build(s) :lock: :robot: (#31550) Co-authored-by: Lock file bot --- .../pylatest_free_threaded_linux-64_conda.lock | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock b/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock index 58cd11edc75fb..b90aab167e247 100644 --- a/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock +++ b/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock @@ -5,8 +5,8 @@ https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/noarch/python_abi-3.13-7_cp313t.conda#df81edcc11a1176315e8226acab83eec https://conda.anaconda.org/conda-forge/noarch/tzdata-2025b-h78e105d_0.conda#4222072737ccff51314b5ece9c7d6f5a -https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.4.26-hbd8a1cb_0.conda#95db94f75ba080a22eb623590993167b -https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_4.conda#01f8d123c96816249efd255a31ad7712 +https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.6.15-hbd8a1cb_0.conda#72525f07d72806e3b639ad4504c30ce5 +https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h1423503_5.conda#6dc9e1305e7d3129af4ad0dabda30e56 https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.1.0-h767d61c_2.conda#fbe7d535ff9d3a168c148e07358cd5b1 https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2#73aaf86a425cc6e73fcf236a5a46396d https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_2.conda#ea8ac52380885ed41c1baa8f1d6d2b93 @@ -31,15 +31,15 @@ https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_hd72426e_102.con https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb8e6e7a_2.conda#6432cb5d4ac0046c3ac0a8a0f95842f9 https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-15.1.0-h69a702a_2.conda#a483a87b71e974bb75d1b9413d4436dd https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.29-pthreads_h94d23a6_0.conda#0a4d0252248ef9a0f88f2ba8b8a08e12 -https://conda.anaconda.org/conda-forge/linux-64/python-3.13.3-h4724d56_1_cp313t.conda#8193603fe48ace3d8801cfb246f44491 +https://conda.anaconda.org/conda-forge/linux-64/python-3.13.5-h4724d56_1_cp313t.conda#98969f9d8c567eb275f9ebf72276d7ef https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda#962b9857ee8e7018c22f2776ffa0b2d7 -https://conda.anaconda.org/conda-forge/noarch/cpython-3.13.3-py313hd8ed1ab_1.conda#6ba9ba47b91b7758cb963d0f0eaf3422 -https://conda.anaconda.org/conda-forge/noarch/cython-3.1.1-pyh2c78169_101.conda#f085516359786c4fea51bf05227ef3d2 +https://conda.anaconda.org/conda-forge/noarch/cpython-3.13.5-py313hd8ed1ab_1.conda#207261fe0d91ff40a65587e07f6566a5 +https://conda.anaconda.org/conda-forge/noarch/cython-3.1.2-pyh2c78169_102.conda#e250288041263e65630a5802c72fa76b https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_1.conda#a71efeae2c160f6789900ba2631a2c90 https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_1.conda#6837f3eff7dcea42ecd714ce1ac2b108 https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-31_h59b9bed_openblas.conda#728dbebd0f7a20337218beacffd37916 https://conda.anaconda.org/conda-forge/linux-64/libhiredis-1.0.2-h2cc385e_0.tar.bz2#b34907d3a81a3cd8095ee83d174c074a -https://conda.anaconda.org/conda-forge/noarch/meson-1.8.1-pyhe01879c_0.conda#f3cccd9a6ce5331ae33f69ade5529162 +https://conda.anaconda.org/conda-forge/noarch/meson-1.8.2-pyhe01879c_0.conda#f0e001c8de8d959926d98edf0458cb2d https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda#58335b26c38bf4a20f399384c33cbcf9 https://conda.anaconda.org/conda-forge/noarch/pip-25.1.1-pyh145f28c_0.conda#01384ff1639c6330a0924791413b8714 https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda#7da7ccd349dbf6487a7778579d2bb971 @@ -54,7 +54,7 @@ https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.1-pyhd8ed1ab_0.conda#fb https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-31_he106b2a_openblas.conda#abb32c727da370c481a1c206f5159ce9 https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-31_h7ac8fdf_openblas.conda#452b98eafe050ecff932f0ec832dd03f https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.9.1-pyhd8ed1ab_0.conda#22ae7c6ea81e0c8661ef32168dda929b -https://conda.anaconda.org/conda-forge/noarch/python-freethreading-3.13.3-h92d6c8b_1.conda#4fa25290aec662a01642ba4b3c0ff5c1 +https://conda.anaconda.org/conda-forge/noarch/python-freethreading-3.13.5-h92d6c8b_1.conda#1ab75b4ca3339ba51226ae20a72e2b6f https://conda.anaconda.org/conda-forge/noarch/meson-python-0.18.0-pyh70fd9c4_0.conda#576c04b9d9f8e45285fb4d9452c26133 https://conda.anaconda.org/conda-forge/linux-64/numpy-2.3.0-py313h103f029_0.conda#d24d95f39ffa3c70827df0183b01df04 https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.0-pyhd8ed1ab_0.conda#516d31f063ce7e49ced17f105b63a1f1 From 0424ae905affc7ba680261541af0cc5b0ccae896 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Wed, 18 Jun 2025 12:47:48 +0200 Subject: [PATCH 032/103] :lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#31552) Co-authored-by: Lock file bot --- build_tools/azure/debian_32bit_lock.txt | 12 ++-- ...latest_conda_forge_mkl_linux-64_conda.lock | 62 +++++++++---------- ...pylatest_conda_forge_mkl_osx-64_conda.lock | 23 ++++--- ...test_conda_mkl_no_openmp_osx-64_conda.lock | 4 +- ...st_pip_openblas_pandas_linux-64_conda.lock | 18 +++--- .../pymin_conda_forge_mkl_win-64_conda.lock | 18 +++--- ...nblas_min_dependencies_linux-64_conda.lock | 32 +++++----- ...e_openblas_ubuntu_2204_linux-64_conda.lock | 12 ++-- build_tools/azure/ubuntu_atlas_lock.txt | 2 +- build_tools/circle/doc_linux-64_conda.lock | 48 +++++++------- .../doc_min_dependencies_linux-64_conda.lock | 40 ++++++------ ...n_conda_forge_arm_linux-aarch64_conda.lock | 26 ++++---- 12 files changed, 149 insertions(+), 148 deletions(-) diff --git a/build_tools/azure/debian_32bit_lock.txt b/build_tools/azure/debian_32bit_lock.txt index c36a03e098d7f..bb5a373786f0f 100644 --- a/build_tools/azure/debian_32bit_lock.txt +++ b/build_tools/azure/debian_32bit_lock.txt @@ -4,15 +4,15 @@ # # pip-compile --output-file=build_tools/azure/debian_32bit_lock.txt build_tools/azure/debian_32bit_requirements.txt # -coverage[toml]==7.8.2 +coverage[toml]==7.9.1 # via pytest-cov -cython==3.1.1 +cython==3.1.2 # via -r build_tools/azure/debian_32bit_requirements.txt iniconfig==2.1.0 # via pytest joblib==1.5.1 # via -r build_tools/azure/debian_32bit_requirements.txt -meson==1.8.1 +meson==1.8.2 # via meson-python meson-python==0.18.0 # via -r build_tools/azure/debian_32bit_requirements.txt @@ -24,7 +24,9 @@ packaging==25.0 # pyproject-metadata # pytest pluggy==1.6.0 - # via pytest + # via + # pytest + # pytest-cov pygments==2.19.1 # via pytest pyproject-metadata==0.9.1 @@ -33,7 +35,7 @@ pytest==8.4.0 # via # -r build_tools/azure/debian_32bit_requirements.txt # pytest-cov -pytest-cov==6.1.1 +pytest-cov==6.2.1 # via -r build_tools/azure/debian_32bit_requirements.txt threadpoolctl==3.6.0 # via -r build_tools/azure/debian_32bit_requirements.txt diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index b2e57e38963aa..c7dd0f634b9da 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock @@ -11,11 +11,11 @@ https://conda.anaconda.org/conda-forge/linux-64/mkl-include-2024.2.2-ha957f24_16 https://conda.anaconda.org/conda-forge/linux-64/nlohmann_json-3.12.0-h3f2d84a_0.conda#d76872d096d063e226482c99337209dc https://conda.anaconda.org/conda-forge/noarch/python_abi-3.13-7_cp313.conda#e84b44e6300f1703cb25d29120c5b1d8 https://conda.anaconda.org/conda-forge/noarch/tzdata-2025b-h78e105d_0.conda#4222072737ccff51314b5ece9c7d6f5a -https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.4.26-hbd8a1cb_0.conda#95db94f75ba080a22eb623590993167b +https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.6.15-hbd8a1cb_0.conda#72525f07d72806e3b639ad4504c30ce5 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766549260d6815b0c52253f1fb1bb29 -https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_4.conda#01f8d123c96816249efd255a31ad7712 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https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_2.conda#1a580f7796c7bf6393fddb8bbbde58dc https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-hb9d3cd8_0.conda#c7e925f37e3b40d893459e625f6a53f1 https://conda.anaconda.org/conda-forge/linux-64/libntlm-1.8-hb9d3cd8_0.conda#7c7927b404672409d9917d49bff5f2d6 +https://conda.anaconda.org/conda-forge/linux-64/libpciaccess-0.18-hb9d3cd8_0.conda#70e3400cbbfa03e96dcde7fc13e38c7b https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_2.conda#1cb1c67961f6dd257eae9e9691b341aa https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.10.0-h202a827_0.conda#0f98f3e95272d118f7931b6bef69bfe5 https://conda.anaconda.org/conda-forge/linux-64/libuv-1.51.0-hb9d3cd8_0.conda#1349c022c92c5efd3fd705a79a5804d8 @@ -58,11 +59,11 @@ https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h0aef613_1.conda#9344 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https://conda.anaconda.org/conda-forge/osx-64/xorg-libxau-1.0.12-h6e16a3a_0.conda#4cf40e60b444d56512a64f39d12c20bd @@ -36,7 +35,7 @@ https://conda.anaconda.org/conda-forge/osx-64/libgfortran5-14.2.0-h58528f3_105.c https://conda.anaconda.org/conda-forge/osx-64/libpng-1.6.47-h3c4a55f_0.conda#8461ab86d2cdb76d6e971aab225be73f https://conda.anaconda.org/conda-forge/osx-64/libsqlite-3.50.1-hdb6dae5_0.conda#00116248e7b4025ae01632472b300d29 https://conda.anaconda.org/conda-forge/osx-64/libxcb-1.17.0-hf1f96e2_0.conda#bbeca862892e2898bdb45792a61c4afc -https://conda.anaconda.org/conda-forge/osx-64/libxml2-2.14.3-h8c082e5_0.conda#f886f309637a6ff2ff858b38b7395aa1 +https://conda.anaconda.org/conda-forge/osx-64/libxml2-2.14.3-h060b8bb_0.conda#6698f8e240c5a7aa87754f3cf29043ea https://conda.anaconda.org/conda-forge/osx-64/mkl-2023.2.0-h54c2260_50500.conda#0a342ccdc79e4fcd359245ac51941e7b https://conda.anaconda.org/conda-forge/osx-64/ninja-1.12.1-hd6aca1a_1.conda#1cf196736676270fa876001901e4e1db https://conda.anaconda.org/conda-forge/osx-64/openssl-3.5.0-hc426f3f_1.conda#919faa07b9647beb99a0e7404596a465 @@ -54,12 +53,12 @@ https://conda.anaconda.org/conda-forge/osx-64/libllvm18-18.1.8-default_h3571c67_ https://conda.anaconda.org/conda-forge/osx-64/libtiff-4.7.0-h1167cee_5.conda#fc84af14a09e779f1d37ab1d16d5c4e2 https://conda.anaconda.org/conda-forge/osx-64/mkl-devel-2023.2.0-h694c41f_50500.conda#1b4d0235ef253a1e19459351badf4f9f https://conda.anaconda.org/conda-forge/osx-64/mpfr-4.2.1-haed47dc_3.conda#d511e58aaaabfc23136880d9956fa7a6 -https://conda.anaconda.org/conda-forge/osx-64/python-3.13.3-h534c281_101_cp313.conda#ebcc7c42561d8d8b01477020b63218c0 +https://conda.anaconda.org/conda-forge/osx-64/python-3.13.5-h534c281_101_cp313.conda#abd2cb74090d7ae4f1d33ed1eefa0f2f https://conda.anaconda.org/conda-forge/osx-64/sigtool-0.1.3-h88f4db0_0.tar.bz2#fbfb84b9de9a6939cb165c02c69b1865 https://conda.anaconda.org/conda-forge/osx-64/brotli-1.1.0-h6e16a3a_3.conda#44903b29bc866576c42d5c0a25e76569 https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda#962b9857ee8e7018c22f2776ffa0b2d7 https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_1.conda#44600c4667a319d67dbe0681fc0bc833 -https://conda.anaconda.org/conda-forge/osx-64/cython-3.1.1-py313h9efc8c2_1.conda#b94bca8fec5fbaa69375656928e05c1d +https://conda.anaconda.org/conda-forge/osx-64/cython-3.1.2-py313h9efc8c2_2.conda#c37814cffeee2c9184595d522b381b95 https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_1.conda#a71efeae2c160f6789900ba2631a2c90 https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_1.conda#6837f3eff7dcea42ecd714ce1ac2b108 https://conda.anaconda.org/conda-forge/osx-64/kiwisolver-1.4.7-py313h0c4e38b_0.conda#c37fceab459e104e77bb5456e219fc37 @@ -71,9 +70,9 @@ https://conda.anaconda.org/conda-forge/osx-64/libfreetype-2.13.3-h694c41f_1.cond https://conda.anaconda.org/conda-forge/osx-64/libhiredis-1.0.2-h2beb688_0.tar.bz2#524282b2c46c9dedf051b3bc2ae05494 https://conda.anaconda.org/conda-forge/osx-64/liblapack-3.9.0-20_osx64_mkl.conda#58f08e12ad487fac4a08f90ff0b87aec https://conda.anaconda.org/conda-forge/osx-64/llvm-tools-18-18.1.8-default_h3571c67_5.conda#4391981e855468ced32ca1940b3d7613 -https://conda.anaconda.org/conda-forge/noarch/meson-1.8.1-pyhe01879c_0.conda#f3cccd9a6ce5331ae33f69ade5529162 +https://conda.anaconda.org/conda-forge/noarch/meson-1.8.2-pyhe01879c_0.conda#f0e001c8de8d959926d98edf0458cb2d https://conda.anaconda.org/conda-forge/osx-64/mpc-1.3.1-h9d8efa1_1.conda#0520855aaae268ea413d6bc913f1384c -https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2#2ba8498c1018c1e9c61eb99b973dfe19 +https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyhd8ed1ab_1.conda#37293a85a0f4f77bbd9cf7aaefc62609 https://conda.anaconda.org/conda-forge/osx-64/openjpeg-2.5.3-h7fd6d84_0.conda#025c711177fc3309228ca1a32374458d https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda#58335b26c38bf4a20f399384c33cbcf9 https://conda.anaconda.org/conda-forge/noarch/pip-25.1.1-pyh145f28c_0.conda#01384ff1639c6330a0924791413b8714 @@ -91,9 +90,9 @@ https://conda.anaconda.org/conda-forge/osx-64/tornado-6.5.1-py313h63b0ddb_0.cond https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.14.0-pyhe01879c_0.conda#2adcd9bb86f656d3d43bf84af59a1faf https://conda.anaconda.org/conda-forge/osx-64/ccache-4.11.3-h33566b8_0.conda#b65cad834bd6c1f660c101cca09430bf https://conda.anaconda.org/conda-forge/osx-64/clang-18-18.1.8-default_h3571c67_10.conda#62e1cd0882dad47d6a6878ad037f7b9d -https://conda.anaconda.org/conda-forge/osx-64/coverage-7.8.2-py313h717bdf5_0.conda#73eb83ea3d00f06bf78e242cca5e8e44 +https://conda.anaconda.org/conda-forge/osx-64/coverage-7.9.1-py313h717bdf5_0.conda#dc9348f206ef595c238e426ba1a61503 https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda#72e42d28960d875c7654614f8b50939a -https://conda.anaconda.org/conda-forge/osx-64/fonttools-4.58.2-py313h717bdf5_0.conda#fab72be60803ac1f776e4df5fb21962b +https://conda.anaconda.org/conda-forge/osx-64/fonttools-4.58.4-py313h717bdf5_0.conda#4bd6c0129d25eb2661fa7b744de75a21 https://conda.anaconda.org/conda-forge/osx-64/freetype-2.13.3-h694c41f_1.conda#126dba1baf5030cb6f34533718924577 https://conda.anaconda.org/conda-forge/osx-64/gfortran_impl_osx-64-13.3.0-hbf5bf67_105.conda#f56a107c8d1253346d01785ecece7977 https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.1-pyhd8ed1ab_0.conda#fb1c14694de51a476ce8636d92b6f42c @@ -117,7 +116,7 @@ https://conda.anaconda.org/conda-forge/osx-64/cctools-1010.6-ha66f10e_6.conda#a1 https://conda.anaconda.org/conda-forge/osx-64/clangxx-18.1.8-default_heb2e8d1_10.conda#c39251c90faf5ba495d9f9ef88d7563e https://conda.anaconda.org/conda-forge/osx-64/matplotlib-base-3.10.3-py313he981572_0.conda#91c22969c0974f2f23470d517774d457 https://conda.anaconda.org/conda-forge/osx-64/pyamg-5.2.1-py313h0322a6a_1.conda#4bda5182eeaef3d2017a2ec625802e1a -https://conda.anaconda.org/conda-forge/noarch/pytest-cov-6.1.1-pyhd8ed1ab_0.conda#1e35d8f975bc0e984a19819aa91c440a +https://conda.anaconda.org/conda-forge/noarch/pytest-cov-6.2.1-pyhd8ed1ab_0.conda#ce978e1b9ed8b8d49164e90a5cdc94cd https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.7.0-pyhd8ed1ab_0.conda#15353a2a0ea6dfefaa52fc5ab5b98f41 https://conda.anaconda.org/conda-forge/noarch/compiler-rt_osx-64-18.1.8-hf2b8a54_1.conda#76f906e6bdc58976c5593f650290ae20 https://conda.anaconda.org/conda-forge/osx-64/matplotlib-3.10.3-py313habf4b1d_0.conda#c1043254f405998ece984e5f66a10943 diff --git a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock index 974502b01d648..238e88d201aeb 100644 --- a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock +++ b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock @@ -75,8 +75,8 @@ https://repo.anaconda.com/pkgs/main/osx-64/numexpr-2.8.7-py312hac873b0_0.conda#6 https://repo.anaconda.com/pkgs/main/osx-64/scipy-1.11.4-py312h81688c2_0.conda#7d57b4c21a9261f97fa511e0940c5d93 https://repo.anaconda.com/pkgs/main/osx-64/pandas-2.2.3-py312h6d0c2b6_0.conda#84ce5b8ec4a986d13a5df17811f556a2 https://repo.anaconda.com/pkgs/main/osx-64/pyamg-5.2.1-py312h1962661_0.conda#58881950d4ce74c9302b56961f97a43c -# pip cython @ https://files.pythonhosted.org/packages/78/06/83ff82381319ff68ae46f9dd3024b1d5101997e81a8e955811525b6f934b/cython-3.1.1-cp312-cp312-macosx_10_13_x86_64.whl#sha256=9d7dc0e4d0cd491fac679a61e9ede348c64ca449f99a284f9a01851aa1dbc7f6 -# pip meson @ https://files.pythonhosted.org/packages/46/77/726b14be352aa6911e206ca7c4d95c5be49660604dfee0bfed0fc75823e5/meson-1.8.1-py3-none-any.whl#sha256=374bbf71247e629475fc10b0bd2ef66fc418c2d8f4890572f74de0f97d0d42da +# pip cython @ https://files.pythonhosted.org/packages/22/86/9393ab7204d5bb65f415dd271b658c18f57b9345d06002cae069376a5a7a/cython-3.1.2-cp312-cp312-macosx_10_13_x86_64.whl#sha256=9c2c4b6f9a941c857b40168b3f3c81d514e509d985c2dcd12e1a4fea9734192e +# pip meson @ https://files.pythonhosted.org/packages/8e/6e/b9dfeac98dd508f88bcaff134ee0bf5e602caf3ccb5a12b5dd9466206df1/meson-1.8.2-py3-none-any.whl#sha256=274b49dbe26e00c9a591442dd30f4ae9da8ce11ce53d0f4682cd10a45d50f6fd # pip threadpoolctl @ https://files.pythonhosted.org/packages/32/d5/f9a850d79b0851d1d4ef6456097579a9005b31fea68726a4ae5f2d82ddd9/threadpoolctl-3.6.0-py3-none-any.whl#sha256=43a0b8fd5a2928500110039e43a5eed8480b918967083ea48dc3ab9f13c4a7fb # pip pyproject-metadata @ https://files.pythonhosted.org/packages/7e/b1/8e63033b259e0a4e40dd1ec4a9fee17718016845048b43a36ec67d62e6fe/pyproject_metadata-0.9.1-py3-none-any.whl#sha256=ee5efde548c3ed9b75a354fc319d5afd25e9585fa918a34f62f904cc731973ad # pip meson-python @ https://files.pythonhosted.org/packages/28/58/66db620a8a7ccb32633de9f403fe49f1b63c68ca94e5c340ec5cceeb9821/meson_python-0.18.0-py3-none-any.whl#sha256=3b0fe051551cc238f5febb873247c0949cd60ded556efa130aa57021804868e2 diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock index 52e6d8241b20c..de1e1ef5447bd 100644 --- a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock @@ -30,27 +30,27 @@ https://repo.anaconda.com/pkgs/main/linux-64/readline-8.2-h5eee18b_0.conda#be421 https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.45.3-h5eee18b_0.conda#acf93d6aceb74d6110e20b44cc45939e https://repo.anaconda.com/pkgs/main/linux-64/xorg-libx11-1.8.12-h9b100fa_1.conda#6298b27afae6f49f03765b2a03df2fcb https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.14-h993c535_1.conda#bfe656b29fc64afe5d4bd46dbd5fd240 -https://repo.anaconda.com/pkgs/main/linux-64/python-3.13.4-h4612cfd_100_cp313.conda#f8f9a0c1eff2663e73ef296d5303c3f8 +https://repo.anaconda.com/pkgs/main/linux-64/python-3.13.5-h4612cfd_100_cp313.conda#1adf42b71c42a4a540eae2c0026f02c3 https://repo.anaconda.com/pkgs/main/linux-64/setuptools-78.1.1-py313h06a4308_0.conda#8f8e1c1e3af9d2d371aaa0ee8316ae7c https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.45.1-py313h06a4308_0.conda#29057e876eedce0e37c2388c138a19f9 https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2a700153fefe0e69438b18e1 # pip alabaster @ https://files.pythonhosted.org/packages/7e/b3/6b4067be973ae96ba0d615946e314c5ae35f9f993eca561b356540bb0c2b/alabaster-1.0.0-py3-none-any.whl#sha256=fc6786402dc3fcb2de3cabd5fe455a2db534b371124f1f21de8731783dec828b # pip babel @ https://files.pythonhosted.org/packages/b7/b8/3fe70c75fe32afc4bb507f75563d39bc5642255d1d94f1f23604725780bf/babel-2.17.0-py3-none-any.whl#sha256=4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2 -# pip certifi @ https://files.pythonhosted.org/packages/4a/7e/3db2bd1b1f9e95f7cddca6d6e75e2f2bd9f51b1246e546d88addca0106bd/certifi-2025.4.26-py3-none-any.whl#sha256=30350364dfe371162649852c63336a15c70c6510c2ad5015b21c2345311805f3 +# pip certifi @ https://files.pythonhosted.org/packages/84/ae/320161bd181fc06471eed047ecce67b693fd7515b16d495d8932db763426/certifi-2025.6.15-py3-none-any.whl#sha256=2e0c7ce7cb5d8f8634ca55d2ba7e6ec2689a2fd6537d8dec1296a477a4910057 # pip charset-normalizer @ 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https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl#sha256=85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30 -# pip cython @ https://files.pythonhosted.org/packages/ca/90/9fe8b93fa239b4871252274892c232415f53d5af0859c4a6ac9b1cbf9950/cython-3.1.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=7da069ca769903c5dee56c5f7ab47b2b7b91030eee48912630db5f4f3ec5954a +# pip cython @ https://files.pythonhosted.org/packages/b3/9b/20a8a12d1454416141479380f7722f2ad298d2b41d0d7833fc409894715d/cython-3.1.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=80d0ce057672ca50728153757d022842d5dcec536b50c79615a22dda2a874ea0 # pip docutils @ https://files.pythonhosted.org/packages/8f/d7/9322c609343d929e75e7e5e6255e614fcc67572cfd083959cdef3b7aad79/docutils-0.21.2-py3-none-any.whl#sha256=dafca5b9e384f0e419294eb4d2ff9fa826435bf15f15b7bd45723e8ad76811b2 # pip execnet @ https://files.pythonhosted.org/packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl#sha256=26dee51f1b80cebd6d0ca8e74dd8745419761d3bef34163928cbebbdc4749fdc -# pip fonttools @ https://files.pythonhosted.org/packages/2a/34/345f153a24c1340daa62340c3be2d1e5ee6c1ee57e13f6d15613209e688b/fonttools-4.58.2-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=27d74b9f6970cefbcda33609a3bee1618e5e57176c8b972134c4e22461b9c791 +# pip fonttools @ https://files.pythonhosted.org/packages/b2/11/c9972e46a6abd752a40a46960e431c795ad1f306775fc1f9e8c3081a1274/fonttools-4.58.4-cp313-cp313-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl#sha256=fe5807fc64e4ba5130f1974c045a6e8d795f3b7fb6debfa511d1773290dbb76b # pip idna @ https://files.pythonhosted.org/packages/76/c6/c88e154df9c4e1a2a66ccf0005a88dfb2650c1dffb6f5ce603dfbd452ce3/idna-3.10-py3-none-any.whl#sha256=946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3 # pip imagesize @ https://files.pythonhosted.org/packages/ff/62/85c4c919272577931d407be5ba5d71c20f0b616d31a0befe0ae45bb79abd/imagesize-1.4.1-py2.py3-none-any.whl#sha256=0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b # pip iniconfig @ https://files.pythonhosted.org/packages/2c/e1/e6716421ea10d38022b952c159d5161ca1193197fb744506875fbb87ea7b/iniconfig-2.1.0-py3-none-any.whl#sha256=9deba5723312380e77435581c6bf4935c94cbfab9b1ed33ef8d238ea168eb760 # pip joblib @ https://files.pythonhosted.org/packages/7d/4f/1195bbac8e0c2acc5f740661631d8d750dc38d4a32b23ee5df3cde6f4e0d/joblib-1.5.1-py3-none-any.whl#sha256=4719a31f054c7d766948dcd83e9613686b27114f190f717cec7eaa2084f8a74a # pip kiwisolver @ https://files.pythonhosted.org/packages/8f/e9/6a7d025d8da8c4931522922cd706105aa32b3291d1add8c5427cdcd66e63/kiwisolver-1.4.8-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=a5ce1e481a74b44dd5e92ff03ea0cb371ae7a0268318e202be06c8f04f4f1246 # pip markupsafe @ https://files.pythonhosted.org/packages/0c/91/96cf928db8236f1bfab6ce15ad070dfdd02ed88261c2afafd4b43575e9e9/MarkupSafe-3.0.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=15ab75ef81add55874e7ab7055e9c397312385bd9ced94920f2802310c930396 -# pip meson @ https://files.pythonhosted.org/packages/46/77/726b14be352aa6911e206ca7c4d95c5be49660604dfee0bfed0fc75823e5/meson-1.8.1-py3-none-any.whl#sha256=374bbf71247e629475fc10b0bd2ef66fc418c2d8f4890572f74de0f97d0d42da +# pip meson @ https://files.pythonhosted.org/packages/8e/6e/b9dfeac98dd508f88bcaff134ee0bf5e602caf3ccb5a12b5dd9466206df1/meson-1.8.2-py3-none-any.whl#sha256=274b49dbe26e00c9a591442dd30f4ae9da8ce11ce53d0f4682cd10a45d50f6fd # pip networkx @ https://files.pythonhosted.org/packages/eb/8d/776adee7bbf76365fdd7f2552710282c79a4ead5d2a46408c9043a2b70ba/networkx-3.5-py3-none-any.whl#sha256=0030d386a9a06dee3565298b4a734b68589749a544acbb6c412dc9e2489ec6ec # pip ninja @ https://files.pythonhosted.org/packages/eb/7a/455d2877fe6cf99886849c7f9755d897df32eaf3a0fba47b56e615f880f7/ninja-1.11.1.4-py3-none-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=096487995473320de7f65d622c3f1d16c3ad174797602218ca8c967f51ec38a0 # pip numpy @ https://files.pythonhosted.org/packages/1c/12/734dce1087eed1875f2297f687e671cfe53a091b6f2f55f0c7241aad041b/numpy-2.3.0-cp313-cp313-manylinux_2_28_x86_64.whl#sha256=87717eb24d4a8a64683b7a4e91ace04e2f5c7c77872f823f02a94feee186168f @@ -81,15 +81,15 @@ https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2 # pip pyproject-metadata @ https://files.pythonhosted.org/packages/7e/b1/8e63033b259e0a4e40dd1ec4a9fee17718016845048b43a36ec67d62e6fe/pyproject_metadata-0.9.1-py3-none-any.whl#sha256=ee5efde548c3ed9b75a354fc319d5afd25e9585fa918a34f62f904cc731973ad # pip pytest @ https://files.pythonhosted.org/packages/2f/de/afa024cbe022b1b318a3d224125aa24939e99b4ff6f22e0ba639a2eaee47/pytest-8.4.0-py3-none-any.whl#sha256=f40f825768ad76c0977cbacdf1fd37c6f7a468e460ea6a0636078f8972d4517e # pip python-dateutil @ https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl#sha256=a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 -# pip requests @ https://files.pythonhosted.org/packages/f9/9b/335f9764261e915ed497fcdeb11df5dfd6f7bf257d4a6a2a686d80da4d54/requests-2.32.3-py3-none-any.whl#sha256=70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6 +# pip requests @ https://files.pythonhosted.org/packages/7c/e4/56027c4a6b4ae70ca9de302488c5ca95ad4a39e190093d6c1a8ace08341b/requests-2.32.4-py3-none-any.whl#sha256=27babd3cda2a6d50b30443204ee89830707d396671944c998b5975b031ac2b2c # pip scipy @ https://files.pythonhosted.org/packages/b5/09/c5b6734a50ad4882432b6bb7c02baf757f5b2f256041da5df242e2d7e6b6/scipy-1.15.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=c9deabd6d547aee2c9a81dee6cc96c6d7e9a9b1953f74850c179f91fdc729cb7 -# pip tifffile @ https://files.pythonhosted.org/packages/4d/77/7f7dfcf2d847c1c1c63a2d4157c480eb4c74e4aa56e844008795ff01f86d/tifffile-2025.6.1-py3-none-any.whl#sha256=ff7163f1aaea519b769a2ac77c43be69e7d83e5b5d5d6a676497399de50535e5 +# pip tifffile @ https://files.pythonhosted.org/packages/3a/d8/1ba8f32bfc9cb69e37edeca93738e883f478fbe84ae401f72c0d8d507841/tifffile-2025.6.11-py3-none-any.whl#sha256=32effb78b10b3a283eb92d4ebf844ae7e93e151458b0412f38518b4e6d2d7542 # pip lightgbm @ https://files.pythonhosted.org/packages/42/86/dabda8fbcb1b00bcfb0003c3776e8ade1aa7b413dff0a2c08f457dace22f/lightgbm-4.6.0-py3-none-manylinux_2_28_x86_64.whl#sha256=cb19b5afea55b5b61cbb2131095f50538bd608a00655f23ad5d25ae3e3bf1c8d # pip matplotlib @ https://files.pythonhosted.org/packages/f5/64/41c4367bcaecbc03ef0d2a3ecee58a7065d0a36ae1aa817fe573a2da66d4/matplotlib-3.10.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=a80fcccbef63302c0efd78042ea3c2436104c5b1a4d3ae20f864593696364ac7 # pip meson-python @ https://files.pythonhosted.org/packages/28/58/66db620a8a7ccb32633de9f403fe49f1b63c68ca94e5c340ec5cceeb9821/meson_python-0.18.0-py3-none-any.whl#sha256=3b0fe051551cc238f5febb873247c0949cd60ded556efa130aa57021804868e2 # pip pandas @ https://files.pythonhosted.org/packages/2a/b3/463bfe819ed60fb7e7ddffb4ae2ee04b887b3444feee6c19437b8f834837/pandas-2.3.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=213cd63c43263dbb522c1f8a7c9d072e25900f6975596f883f4bebd77295d4f3 # pip pyamg @ https://files.pythonhosted.org/packages/cd/a7/0df731cbfb09e73979a1a032fc7bc5be0eba617d798b998a0f887afe8ade/pyamg-5.2.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=6999b351ab969c79faacb81faa74c0fa9682feeff3954979212872a3ee40c298 -# pip pytest-cov @ https://files.pythonhosted.org/packages/28/d0/def53b4a790cfb21483016430ed828f64830dd981ebe1089971cd10cab25/pytest_cov-6.1.1-py3-none-any.whl#sha256=bddf29ed2d0ab6f4df17b4c55b0a657287db8684af9c42ea546b21b1041b3dde +# pip pytest-cov @ https://files.pythonhosted.org/packages/bc/16/4ea354101abb1287856baa4af2732be351c7bee728065aed451b678153fd/pytest_cov-6.2.1-py3-none-any.whl#sha256=f5bc4c23f42f1cdd23c70b1dab1bbaef4fc505ba950d53e0081d0730dd7e86d5 # pip pytest-xdist @ https://files.pythonhosted.org/packages/0d/b2/0e802fde6f1c5b2f7ae7e9ad42b83fd4ecebac18a8a8c2f2f14e39dce6e1/pytest_xdist-3.7.0-py3-none-any.whl#sha256=7d3fbd255998265052435eb9daa4e99b62e6fb9cfb6efd1f858d4d8c0c7f0ca0 # pip scikit-image @ https://files.pythonhosted.org/packages/cd/9b/c3da56a145f52cd61a68b8465d6a29d9503bc45bc993bb45e84371c97d94/scikit_image-0.25.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=b8abd3c805ce6944b941cfed0406d88faeb19bab3ed3d4b50187af55cf24d147 # pip scipy-doctest @ https://files.pythonhosted.org/packages/c9/13/cd25d1875f3804b73fd4a4ae00e2c76e274e1e0608d79148cac251b644b1/scipy_doctest-1.8.0-py3-none-any.whl#sha256=5863208368c35486e143ce3283ab2f517a0d6b0c63d0d5f19f38a823fc82016f diff --git a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock index 2ae01d9250434..193123a87434f 100644 --- a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock @@ -11,7 +11,7 @@ https://conda.anaconda.org/conda-forge/win-64/mkl-include-2024.2.2-h66d3029_15.c https://conda.anaconda.org/conda-forge/noarch/python_abi-3.10-7_cp310.conda#44e871cba2b162368476a84b8d040b6c https://conda.anaconda.org/conda-forge/noarch/tzdata-2025b-h78e105d_0.conda#4222072737ccff51314b5ece9c7d6f5a https://conda.anaconda.org/conda-forge/win-64/ucrt-10.0.22621.0-h57928b3_1.conda#6797b005cd0f439c4c5c9ac565783700 -https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.4.26-h4c7d964_0.conda#23c7fd5062b48d8294fc7f61bf157fba +https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.6.15-h4c7d964_0.conda#b01649832f7bc7ff94f8df8bd2ee6457 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766549260d6815b0c52253f1fb1bb29 https://conda.anaconda.org/conda-forge/win-64/libwinpthread-12.0.0.r4.gg4f2fc60ca-h57928b3_9.conda#08bfa5da6e242025304b206d152479ef https://conda.anaconda.org/conda-forge/win-64/vc14_runtime-14.42.34438-hfd919c2_26.conda#91651a36d31aa20c7ba36299fb7068f4 @@ -36,7 +36,7 @@ https://conda.anaconda.org/conda-forge/win-64/libwebp-base-1.5.0-h3b0e114_0.cond https://conda.anaconda.org/conda-forge/win-64/libzlib-1.3.1-h2466b09_2.conda#41fbfac52c601159df6c01f875de31b9 https://conda.anaconda.org/conda-forge/win-64/ninja-1.12.1-hc790b64_1.conda#3974c522f3248d4a93e6940c463d2de7 https://conda.anaconda.org/conda-forge/win-64/openssl-3.5.0-ha4e3fda_1.conda#72c07e46b6766bb057018a9a74861b89 -https://conda.anaconda.org/conda-forge/win-64/pixman-0.46.0-had0cd8c_0.conda#01617534ef71b5385ebba940a6d6150d +https://conda.anaconda.org/conda-forge/win-64/pixman-0.46.2-had0cd8c_0.conda#2566a45fb15e2f540eff14261f1242af https://conda.anaconda.org/conda-forge/win-64/qhull-2020.2-hc790b64_5.conda#854fbdff64b572b5c0b470f334d34c11 https://conda.anaconda.org/conda-forge/win-64/tk-8.6.13-h2c6b04d_2.conda#ebd0e761de9aa879a51d22cc721bd095 https://conda.anaconda.org/conda-forge/win-64/krb5-1.21.3-hdf4eb48_0.conda#31aec030344e962fbd7dbbbbd68e60a9 @@ -52,18 +52,18 @@ https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-hbeecb71_2.conda#21f562 https://conda.anaconda.org/conda-forge/win-64/brotli-bin-1.1.0-h2466b09_3.conda#c7c345559c1ac25eede6dccb7b931202 https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda#962b9857ee8e7018c22f2776ffa0b2d7 https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_1.conda#44600c4667a319d67dbe0681fc0bc833 -https://conda.anaconda.org/conda-forge/win-64/cython-3.1.1-py310h6bd2d47_1.conda#165131d296d24f798fa76a26694d4565 +https://conda.anaconda.org/conda-forge/win-64/cython-3.1.2-py310h6bd2d47_2.conda#4cc20be3a890b2e640504478b2aa7d56 https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_1.conda#a71efeae2c160f6789900ba2631a2c90 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https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.11-h651a532_0.conda#d8d8894f8ced2c9be76dc9ad1ae531ce diff --git a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock index 695e8e8037662..0c7c5ac749057 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock @@ -5,8 +5,8 @@ https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/noarch/python_abi-3.10-7_cp310.conda#44e871cba2b162368476a84b8d040b6c https://conda.anaconda.org/conda-forge/noarch/tzdata-2025b-h78e105d_0.conda#4222072737ccff51314b5ece9c7d6f5a -https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.4.26-hbd8a1cb_0.conda#95db94f75ba080a22eb623590993167b 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https://conda.anaconda.org/conda-forge/noarch/alabaster-1.0.0-pyhd8ed1ab_1.conda#1fd9696649f65fd6611fcdb4ffec738a https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py310hf71b8c6_3.conda#63d24a5dd21c738d706f91569dbd1892 -https://conda.anaconda.org/conda-forge/noarch/certifi-2025.4.26-pyhd8ed1ab_0.conda#c33eeaaa33f45031be34cda513df39b6 +https://conda.anaconda.org/conda-forge/noarch/certifi-2025.6.15-pyhd8ed1ab_0.conda#781d068df0cc2407d4db0ecfbb29225b https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.2-pyhd8ed1ab_0.conda#40fe4284b8b5835a9073a645139f35af https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda#962b9857ee8e7018c22f2776ffa0b2d7 -https://conda.anaconda.org/conda-forge/linux-64/cython-3.1.1-py310had8cdd9_1.conda#4904cb1ba6e72940ff22a5235554532d +https://conda.anaconda.org/conda-forge/linux-64/cython-3.1.2-py310had8cdd9_2.conda#be416b1d5ffef48c394cbbb04bc864ae https://conda.anaconda.org/conda-forge/noarch/docutils-0.21.2-pyhd8ed1ab_1.conda#24c1ca34138ee57de72a943237cde4cc https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_1.conda#a71efeae2c160f6789900ba2631a2c90 https://conda.anaconda.org/conda-forge/noarch/hpack-4.1.0-pyhd8ed1ab_0.conda#0a802cb9888dd14eeefc611f05c40b6e @@ -62,7 +62,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-31_h59b9bed_openbl https://conda.anaconda.org/conda-forge/linux-64/libfreetype-2.13.3-ha770c72_1.conda#51f5be229d83ecd401fb369ab96ae669 https://conda.anaconda.org/conda-forge/linux-64/libhiredis-1.0.2-h2cc385e_0.tar.bz2#b34907d3a81a3cd8095ee83d174c074a https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.2-py310h89163eb_1.conda#8ce3f0332fd6de0d737e2911d329523f -https://conda.anaconda.org/conda-forge/noarch/meson-1.8.1-pyhe01879c_0.conda#f3cccd9a6ce5331ae33f69ade5529162 +https://conda.anaconda.org/conda-forge/noarch/meson-1.8.2-pyhe01879c_0.conda#f0e001c8de8d959926d98edf0458cb2d https://conda.anaconda.org/conda-forge/linux-64/openblas-0.3.29-pthreads_h6ec200e_0.conda#7e4d48870b3258bea920d51b7f495a81 https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.3-h5fbd93e_0.conda#9e5816bc95d285c115a3ebc2f8563564 https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda#58335b26c38bf4a20f399384c33cbcf9 @@ -106,7 +106,7 @@ https://conda.anaconda.org/conda-forge/linux-64/scipy-1.15.2-py310h1d65ade_0.con https://conda.anaconda.org/conda-forge/noarch/urllib3-2.4.0-pyhd8ed1ab_0.conda#c1e349028e0052c4eea844e94f773065 https://conda.anaconda.org/conda-forge/linux-64/blas-2.131-openblas.conda#38b2ec894c69bb4be0e66d2ef7fc60bf https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.2.1-py310ha2bacc8_1.conda#817d32861729e14f474249f1036291c4 -https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_1.conda#a9b9368f3701a417eac9edbcae7cb737 +https://conda.anaconda.org/conda-forge/noarch/requests-2.32.4-pyhd8ed1ab_0.conda#f6082eae112814f1447b56a5e1f6ed05 https://conda.anaconda.org/conda-forge/noarch/numpydoc-1.8.0-pyhd8ed1ab_1.conda#5af206d64d18d6c8dfb3122b4d9e643b https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-applehelp-2.0.0-pyhd8ed1ab_1.conda#16e3f039c0aa6446513e94ab18a8784b https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-2.0.0-pyhd8ed1ab_1.conda#910f28a05c178feba832f842155cbfff diff --git a/build_tools/azure/ubuntu_atlas_lock.txt b/build_tools/azure/ubuntu_atlas_lock.txt index 24b6b67120de8..ddbe7a200dba1 100644 --- a/build_tools/azure/ubuntu_atlas_lock.txt +++ b/build_tools/azure/ubuntu_atlas_lock.txt @@ -14,7 +14,7 @@ iniconfig==2.1.0 # via pytest joblib==1.2.0 # via -r build_tools/azure/ubuntu_atlas_requirements.txt -meson==1.8.1 +meson==1.8.2 # via meson-python meson-python==0.18.0 # via -r build_tools/azure/ubuntu_atlas_requirements.txt diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock index 0752850efab8c..14a5b8303d947 100644 --- a/build_tools/circle/doc_linux-64_conda.lock +++ b/build_tools/circle/doc_linux-64_conda.lock @@ -10,21 +10,21 @@ https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.co https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-3.10.0-he073ed8_18.conda#ad8527bf134a90e1c9ed35fa0b64318c https://conda.anaconda.org/conda-forge/noarch/python_abi-3.10-7_cp310.conda#44e871cba2b162368476a84b8d040b6c https://conda.anaconda.org/conda-forge/noarch/tzdata-2025b-h78e105d_0.conda#4222072737ccff51314b5ece9c7d6f5a -https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.4.26-hbd8a1cb_0.conda#95db94f75ba080a22eb623590993167b 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https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-31_h1ea3ea9_ope https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.4-h3394656_0.conda#09262e66b19567aff4f592fb53b28760 https://conda.anaconda.org/conda-forge/linux-64/compilers-1.9.0-ha770c72_0.conda#5859096e397aba423340d0bbbb11ec64 https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.2-py310h3788b33_0.conda#b6420d29123c7c823de168f49ccdfe6a -https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2025.3.30-py310h481ba9f_0.conda#453c8da1b70f7b76b3884e18015bc568 +https://conda.anaconda.org/conda-forge/linux-64/imagecodecs-2025.3.30-py310ha75bb41_1.conda#3ffa2ba4ede9da257dc0c1f9ab14f11d https://conda.anaconda.org/conda-forge/noarch/imageio-2.37.0-pyhfb79c49_0.conda#b5577bc2212219566578fd5af9993af6 https://conda.anaconda.org/conda-forge/linux-64/pandas-2.3.0-py310h5eaa309_0.conda#379844614e3a24e59e59d8c69c6e9403 https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.1-pyhd8ed1ab_1.conda#ee23fabfd0a8c6b8d6f3729b47b2859d @@ -245,7 +245,7 @@ https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-11.2.1-h3beb420_0.conda https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.10.3-py310h68603db_0.conda#50084ca38bf28440e2762966bac143fc https://conda.anaconda.org/conda-forge/linux-64/polars-1.30.0-default_h1443d73_0.conda#19698b29e8544d2dd615699826037039 https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.2.1-py310ha2bacc8_1.conda#817d32861729e14f474249f1036291c4 -https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_1.conda#a9b9368f3701a417eac9edbcae7cb737 +https://conda.anaconda.org/conda-forge/noarch/requests-2.32.4-pyhd8ed1ab_0.conda#f6082eae112814f1447b56a5e1f6ed05 https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.14.4-py310hf462985_0.conda#636d3c500d8a851e377360e88ec95372 https://conda.anaconda.org/conda-forge/noarch/tifffile-2025.5.10-pyhd8ed1ab_0.conda#1fdb801f28bf4987294c49aaa314bf5e https://conda.anaconda.org/conda-forge/noarch/pooch-1.8.2-pyhd8ed1ab_1.conda#b3e783e8e8ed7577cf0b6dee37d1fbac @@ -268,7 +268,7 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.1.0-pyhd8 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-2.0.0-pyhd8ed1ab_1.conda#00534ebcc0375929b45c3039b5ba7636 https://conda.anaconda.org/conda-forge/noarch/sphinx-8.1.3-pyhd8ed1ab_1.conda#1a3281a0dc355c02b5506d87db2d78ac https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.10-pyhd8ed1ab_1.conda#3bc61f7161d28137797e038263c04c54 -https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1ab_1.conda#79f5d05ad914baf152fb7f75073fe36d +https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.10.0-pyhd8ed1ab_0.conda#c9446c05bf81e5b613bdafa3bc15becf # pip attrs @ https://files.pythonhosted.org/packages/77/06/bb80f5f86020c4551da315d78b3ab75e8228f89f0162f2c3a819e407941a/attrs-25.3.0-py3-none-any.whl#sha256=427318ce031701fea540783410126f03899a97ffc6f61596ad581ac2e40e3bc3 # pip cloudpickle @ https://files.pythonhosted.org/packages/7e/e8/64c37fadfc2816a7701fa8a6ed8d87327c7d54eacfbfb6edab14a2f2be75/cloudpickle-3.1.1-py3-none-any.whl#sha256=c8c5a44295039331ee9dad40ba100a9c7297b6f988e50e87ccdf3765a668350e # pip defusedxml @ https://files.pythonhosted.org/packages/07/6c/aa3f2f849e01cb6a001cd8554a88d4c77c5c1a31c95bdf1cf9301e6d9ef4/defusedxml-0.7.1-py2.py3-none-any.whl#sha256=a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61 @@ -303,7 +303,7 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1 # pip jupyter-core @ https://files.pythonhosted.org/packages/2f/57/6bffd4b20b88da3800c5d691e0337761576ee688eb01299eae865689d2df/jupyter_core-5.8.1-py3-none-any.whl#sha256=c28d268fc90fb53f1338ded2eb410704c5449a358406e8a948b75706e24863d0 # pip markdown-it-py @ https://files.pythonhosted.org/packages/42/d7/1ec15b46af6af88f19b8e5ffea08fa375d433c998b8a7639e76935c14f1f/markdown_it_py-3.0.0-py3-none-any.whl#sha256=355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1 # pip mistune @ https://files.pythonhosted.org/packages/01/4d/23c4e4f09da849e127e9f123241946c23c1e30f45a88366879e064211815/mistune-3.1.3-py3-none-any.whl#sha256=1a32314113cff28aa6432e99e522677c8587fd83e3d51c29b82a52409c842bd9 -# pip pyzmq @ https://files.pythonhosted.org/packages/c1/3e/2de5928cdadc2105e7c8f890cc5f404136b41ce5b6eae5902167f1d5641c/pyzmq-26.4.0-cp310-cp310-manylinux_2_28_x86_64.whl#sha256=7dacb06a9c83b007cc01e8e5277f94c95c453c5851aac5e83efe93e72226353f +# pip pyzmq @ https://files.pythonhosted.org/packages/a5/fe/fc7b9c1a50981928e25635a926653cb755364316db59ccd6e79cfb9a0b4f/pyzmq-27.0.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl#sha256=cf209a6dc4b420ed32a7093642843cbf8703ed0a7d86c16c0b98af46762ebefb # pip referencing @ https://files.pythonhosted.org/packages/c1/b1/3baf80dc6d2b7bc27a95a67752d0208e410351e3feb4eb78de5f77454d8d/referencing-0.36.2-py3-none-any.whl#sha256=e8699adbbf8b5c7de96d8ffa0eb5c158b3beafce084968e2ea8bb08c6794dcd0 # pip rfc3339-validator @ https://files.pythonhosted.org/packages/7b/44/4e421b96b67b2daff264473f7465db72fbdf36a07e05494f50300cc7b0c6/rfc3339_validator-0.1.4-py2.py3-none-any.whl#sha256=24f6ec1eda14ef823da9e36ec7113124b39c04d50a4d3d3a3c2859577e7791fa # pip sphinxcontrib-sass @ https://files.pythonhosted.org/packages/3f/ec/194f2dbe55b3fe0941b43286c21abb49064d9d023abfb99305c79ad77cad/sphinxcontrib_sass-0.3.5-py2.py3-none-any.whl#sha256=850c83a36ed2d2059562504ccf496ca626c9c0bb89ec642a2d9c42105704bef6 diff --git 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https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766549260d6815b0c52253f1fb1bb29 -https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_4.conda#01f8d123c96816249efd255a31ad7712 +https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_5.conda#acd9213a63cb62521290e581ef82de80 https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-13.3.0-hc03c837_102.conda#4c1d6961a6a54f602ae510d9bf31fa60 https://conda.anaconda.org/conda-forge/linux-64/libglvnd-1.7.0-ha4b6fd6_2.conda#434ca7e50e40f4918ab701e3facd59a0 https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.1.0-h767d61c_2.conda#fbe7d535ff9d3a168c148e07358cd5b1 https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-13.3.0-hc03c837_102.conda#aa38de2738c5f4a72a880e3d31ffe8b4 https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.17-h0157908_18.conda#460eba7851277ec1fd80a1a24080787a https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2#73aaf86a425cc6e73fcf236a5a46396d -https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.43-h4bf12b8_4.conda#ef67db625ad0d2dce398837102f875ed +https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.43-h4bf12b8_5.conda#18852d82df8e5737e320a8731ace51b9 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab https://conda.anaconda.org/conda-forge/linux-64/libegl-1.7.0-ha4b6fd6_2.conda#c151d5eb730e9b7480e6d48c0fc44048 https://conda.anaconda.org/conda-forge/linux-64/libopengl-1.7.0-ha4b6fd6_2.conda#7df50d44d4a14d6c31a2c54f2cd92157 -https://conda.anaconda.org/conda-forge/linux-64/binutils-2.43-h4852527_4.conda#29782348a527eda3ecfc673109d28e93 -https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.43-h4852527_4.conda#c87e146f5b685672d4aa6b527c6d3b5e +https://conda.anaconda.org/conda-forge/linux-64/binutils-2.43-h4852527_5.conda#4846404183ea94fd6652e9fb6ac5e16f +https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.43-h4852527_5.conda#327ef163ac88b57833c1c1a20a9e7e0d https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_2.conda#ea8ac52380885ed41c1baa8f1d6d2b93 https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.14-hb9d3cd8_0.conda#76df83c2a9035c54df5d04ff81bcc02d https://conda.anaconda.org/conda-forge/linux-64/gettext-tools-0.24.1-h5888daf_0.conda#d54305672f0361c2f3886750e7165b5f @@ -41,6 +41,7 @@ https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_2.conda#1 https://conda.anaconda.org/conda-forge/linux-64/libntlm-1.8-hb9d3cd8_0.conda#7c7927b404672409d9917d49bff5f2d6 https://conda.anaconda.org/conda-forge/linux-64/libogg-1.3.5-hd0c01bc_1.conda#68e52064ed3897463c0e958ab5c8f91b https://conda.anaconda.org/conda-forge/linux-64/libopus-1.5.2-hd0c01bc_0.conda#b64523fb87ac6f87f0790f324ad43046 +https://conda.anaconda.org/conda-forge/linux-64/libpciaccess-0.18-hb9d3cd8_0.conda#70e3400cbbfa03e96dcde7fc13e38c7b https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_2.conda#1cb1c67961f6dd257eae9e9691b341aa https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.5.0-h851e524_0.conda#63f790534398730f59e1b899c3644d4a https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 @@ -61,9 +62,11 @@ https://conda.anaconda.org/conda-forge/linux-64/jxrlib-1.1-hd590300_3.conda#5aea https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2#30186d27e2c9fa62b45fb1476b7200e3 https://conda.anaconda.org/conda-forge/linux-64/lame-3.100-h166bdaf_1003.tar.bz2#a8832b479f93521a9e7b5b743803be51 https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h0aef613_1.conda#9344155d33912347b37f0ae6c410a835 +https://conda.anaconda.org/conda-forge/linux-64/libaec-1.1.4-h3f801dc_0.conda#01ba04e414e47f95c03d6ddd81fd37be https://conda.anaconda.org/conda-forge/linux-64/libasprintf-0.24.1-h8e693c7_0.conda#57566a81dd1e5aa3d98ac7582e8bfe03 https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.1.0-hb9d3cd8_3.conda#1c6eecffad553bde44c5238770cfb7da https://conda.anaconda.org/conda-forge/linux-64/libbrotlienc-1.1.0-hb9d3cd8_3.conda#3facafe58f3858eb95527c7d3a3fc578 +https://conda.anaconda.org/conda-forge/linux-64/libdrm-2.4.125-hb9d3cd8_0.conda#4c0ab57463117fbb8df85268415082f5 https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20250104-pl5321h7949ede_0.conda#c277e0a4d549b03ac1e9d6cbbe3d017b https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda#a1cfcc585f0c42bf8d5546bb1dfb668d 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https://conda.anaconda.org/conda-forge/noarch/meson-python-0.18.0-pyh70fd9c4_0.conda#576c04b9d9f8e45285fb4d9452c26133 From c88a15bdcaeeb0201cdcf7d5fd89d690963917e3 Mon Sep 17 00:00:00 2001 From: Natalia Mokeeva <91160475+natmokval@users.noreply.github.com> Date: Wed, 18 Jun 2025 13:48:21 +0200 Subject: [PATCH 033/103] DOC: add link to the plot_mahalanobis_distances example (#31485) Co-authored-by: Adrin Jalali --- sklearn/covariance/_empirical_covariance.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/sklearn/covariance/_empirical_covariance.py b/sklearn/covariance/_empirical_covariance.py index 7c4db63b4e363..c8ee198cc4772 100644 --- a/sklearn/covariance/_empirical_covariance.py +++ b/sklearn/covariance/_empirical_covariance.py @@ -135,7 +135,7 @@ class EmpiricalCovariance(BaseEstimator): Estimated location, i.e. the estimated mean. covariance_ : ndarray of shape (n_features, n_features) - Estimated covariance matrix + Estimated covariance matrix. precision_ : ndarray of shape (n_features, n_features) Estimated pseudo-inverse matrix. @@ -343,6 +343,9 @@ def error_norm(self, comp_cov, norm="frobenius", scaling=True, squared=True): def mahalanobis(self, X): """Compute the squared Mahalanobis distances of given observations. + For a detailed example of how outliers affects the Mahalanobis distance, + see :ref:`sphx_glr_auto_examples_covariance_plot_mahalanobis_distances.py`. + Parameters ---------- X : array-like of shape (n_samples, n_features) From b6746e8e36f2e13cae149c686168b1e6f78725e5 Mon Sep 17 00:00:00 2001 From: Natalia Mokeeva <91160475+natmokval@users.noreply.github.com> Date: Wed, 18 Jun 2025 13:50:34 +0200 Subject: [PATCH 034/103] Doc add link plot robust vs empirical covariance examples (#31511) Co-authored-by: Stefanie Senger <91849487+StefanieSenger@users.noreply.github.com> --- sklearn/covariance/_robust_covariance.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/sklearn/covariance/_robust_covariance.py b/sklearn/covariance/_robust_covariance.py index f386879e693fb..81fc194c6e410 100644 --- a/sklearn/covariance/_robust_covariance.py +++ b/sklearn/covariance/_robust_covariance.py @@ -633,6 +633,10 @@ class MinCovDet(EmpiricalCovariance): location_ : ndarray of shape (n_features,) Estimated robust location. + For an example of comparing raw robust estimates with + the true location and covariance, refer to + :ref:`sphx_glr_auto_examples_covariance_plot_robust_vs_empirical_covariance.py`. + covariance_ : ndarray of shape (n_features, n_features) Estimated robust covariance matrix. From 063fd288b4c11f1a682a6299c5f0dc9f699a3489 Mon Sep 17 00:00:00 2001 From: sisird864 <137139127+sisird864@users.noreply.github.com> Date: Wed, 18 Jun 2025 04:54:22 -0700 Subject: [PATCH 035/103] DOC add link to plot_cv_predict example in cross_val_predict doc (#31504) Co-authored-by: Virgil Chan --- sklearn/model_selection/_validation.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/sklearn/model_selection/_validation.py b/sklearn/model_selection/_validation.py index 8b70bf42603ef..c5a1406e6c2a5 100644 --- a/sklearn/model_selection/_validation.py +++ b/sklearn/model_selection/_validation.py @@ -1162,6 +1162,10 @@ def cross_val_predict( >>> y = diabetes.target[:150] >>> lasso = linear_model.Lasso() >>> y_pred = cross_val_predict(lasso, X, y, cv=3) + + For a detailed example of using ``cross_val_predict`` to visualize + prediction errors, please see + :ref:`sphx_glr_auto_examples_model_selection_plot_cv_predict.py`. """ _check_groups_routing_disabled(groups) X, y = indexable(X, y) From 2e7bac12f03499193026b6abb47be48259e8b274 Mon Sep 17 00:00:00 2001 From: Virgil Chan Date: Wed, 18 Jun 2025 05:10:22 -0700 Subject: [PATCH 036/103] MNT replace `fetch_california_housing` with `make_regression` in `getting_started.rst` and `compose.rst` (#31579) --- doc/getting_started.rst | 10 +++++++--- doc/modules/compose.rst | 25 +++++++++++++++---------- 2 files changed, 22 insertions(+), 13 deletions(-) diff --git a/doc/getting_started.rst b/doc/getting_started.rst index 14e0178f0826b..ec0ff9858f8ff 100644 --- a/doc/getting_started.rst +++ b/doc/getting_started.rst @@ -167,13 +167,17 @@ a :class:`~sklearn.ensemble.RandomForestRegressor` that has been fitted with the best set of parameters. Read more in the :ref:`User Guide `:: - >>> from sklearn.datasets import fetch_california_housing + >>> from sklearn.datasets import make_regression >>> from sklearn.ensemble import RandomForestRegressor >>> from sklearn.model_selection import RandomizedSearchCV >>> from sklearn.model_selection import train_test_split >>> from scipy.stats import randint ... - >>> X, y = fetch_california_housing(return_X_y=True) + >>> # create a synthetic dataset + >>> X, y = make_regression(n_samples=20640, + ... n_features=8, + ... noise=0.1, + ... random_state=0) >>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) ... >>> # define the parameter space that will be searched over @@ -196,7 +200,7 @@ the best set of parameters. Read more in the :ref:`User Guide >>> # the search object now acts like a normal random forest estimator >>> # with max_depth=9 and n_estimators=4 >>> search.score(X_test, y_test) - 0.73... + 0.84... .. note:: diff --git a/doc/modules/compose.rst b/doc/modules/compose.rst index 3ef0d94236aa6..86e95c12f0940 100644 --- a/doc/modules/compose.rst +++ b/doc/modules/compose.rst @@ -286,12 +286,17 @@ the regressor that will be used for prediction, and the transformer that will be applied to the target variable:: >>> import numpy as np - >>> from sklearn.datasets import fetch_california_housing + >>> from sklearn.datasets import make_regression >>> from sklearn.compose import TransformedTargetRegressor >>> from sklearn.preprocessing import QuantileTransformer >>> from sklearn.linear_model import LinearRegression >>> from sklearn.model_selection import train_test_split - >>> X, y = fetch_california_housing(return_X_y=True) + >>> # create a synthetic dataset + >>> X, y = make_regression(n_samples=20640, + ... n_features=8, + ... noise=100.0, + ... random_state=0) + >>> y = np.exp( 1 + (y - y.min()) * (4 / (y.max() - y.min()))) >>> X, y = X[:2000, :], y[:2000] # select a subset of data >>> transformer = QuantileTransformer(output_distribution='normal') >>> regressor = LinearRegression() @@ -300,11 +305,11 @@ be applied to the target variable:: >>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) >>> regr.fit(X_train, y_train) TransformedTargetRegressor(...) - >>> print('R2 score: {0:.2f}'.format(regr.score(X_test, y_test))) - R2 score: 0.61 + >>> print(f"R2 score: {regr.score(X_test, y_test):.2f}") + R2 score: 0.67 >>> raw_target_regr = LinearRegression().fit(X_train, y_train) - >>> print('R2 score: {0:.2f}'.format(raw_target_regr.score(X_test, y_test))) - R2 score: 0.59 + >>> print(f"R2 score: {raw_target_regr.score(X_test, y_test):.2f}") + R2 score: 0.64 For simple transformations, instead of a Transformer object, a pair of functions can be passed, defining the transformation and its inverse mapping:: @@ -321,8 +326,8 @@ Subsequently, the object is created as:: ... inverse_func=inverse_func) >>> regr.fit(X_train, y_train) TransformedTargetRegressor(...) - >>> print('R2 score: {0:.2f}'.format(regr.score(X_test, y_test))) - R2 score: 0.51 + >>> print(f"R2 score: {regr.score(X_test, y_test):.2f}") + R2 score: 0.67 By default, the provided functions are checked at each fit to be the inverse of each other. However, it is possible to bypass this checking by setting @@ -336,8 +341,8 @@ each other. However, it is possible to bypass this checking by setting ... check_inverse=False) >>> regr.fit(X_train, y_train) TransformedTargetRegressor(...) - >>> print('R2 score: {0:.2f}'.format(regr.score(X_test, y_test))) - R2 score: -1.57 + >>> print(f"R2 score: {regr.score(X_test, y_test):.2f}") + R2 score: -3.02 .. note:: From 2b0c017d2a39d7f2ed5f413c7aeaab2f73432bce Mon Sep 17 00:00:00 2001 From: VirenPassi <143885194+VirenPassi@users.noreply.github.com> Date: Wed, 18 Jun 2025 18:06:42 +0530 Subject: [PATCH 037/103] DOC:Add inline example link to RFECV class docstring (#30621) (#31476) Co-authored-by: adrinjalali --- sklearn/feature_selection/_rfe.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/sklearn/feature_selection/_rfe.py b/sklearn/feature_selection/_rfe.py index d2bd78e225a54..d647ad0ca19b1 100644 --- a/sklearn/feature_selection/_rfe.py +++ b/sklearn/feature_selection/_rfe.py @@ -564,6 +564,7 @@ class RFECV(RFE): different numbers of selected features and aggregated together. Finally, the scores are averaged across folds and the number of features selected is set to the number of features that maximize the cross-validation score. + See glossary entry for :term:`cross-validation estimator`. Read more in the :ref:`User Guide `. @@ -755,6 +756,10 @@ class RFECV(RFE): False]) >>> selector.ranking_ array([1, 1, 1, 1, 1, 6, 4, 3, 2, 5]) + + For a detailed example of using RFECV to select features when training a + :class:`~sklearn.linear_model.LogisticRegression`, see + :ref:`sphx_glr_auto_examples_feature_selection_plot_rfe_with_cross_validation.py`. """ _parameter_constraints: dict = { From 77f6889c993710835cd554cded0c7e160732ab6e Mon Sep 17 00:00:00 2001 From: Mayank Raj <88675779+mayankraj25@users.noreply.github.com> Date: Wed, 18 Jun 2025 18:09:22 +0530 Subject: [PATCH 038/103] DOC change 'relation' to 'relationship' in classification_threshold (#31570) --- doc/modules/classification_threshold.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/modules/classification_threshold.rst b/doc/modules/classification_threshold.rst index ee7028f469b5f..94a5e0a30b716 100644 --- a/doc/modules/classification_threshold.rst +++ b/doc/modules/classification_threshold.rst @@ -28,7 +28,7 @@ cut-off rules: a positive class is predicted when the conditional probability :math:`P(y|X)` is greater than 0.5 (obtained with :term:`predict_proba`) or if the decision score is greater than 0 (obtained with :term:`decision_function`). -Here, we show an example that illustrates the relation between conditional +Here, we show an example that illustrates the relatonship between conditional probability estimates :math:`P(y|X)` and class labels:: >>> from sklearn.datasets import make_classification From 48245087dd5ae1ddd28f749e220360bc2f208d13 Mon Sep 17 00:00:00 2001 From: Eugen-Bleck Date: Wed, 18 Jun 2025 05:42:06 -0700 Subject: [PATCH 039/103] DOC implement responsive multi-column layout for emeritus contributors to reduce whitespace (#31565) --- doc/about.rst | 1 + doc/scss/custom.scss | 11 +++++++++++ 2 files changed, 12 insertions(+) diff --git a/doc/about.rst b/doc/about.rst index b64a1eee6aee7..ba265e21889df 100644 --- a/doc/about.rst +++ b/doc/about.rst @@ -93,6 +93,7 @@ Emeritus Maintainers Team The following people have been active contributors in the past, but are no longer active in the project: +.. rst-class:: grid-list-three-columns .. include:: maintainers_emeritus.rst Emeritus Communication Team diff --git a/doc/scss/custom.scss b/doc/scss/custom.scss index cac81b03e7ce2..ed95c15276e1f 100644 --- a/doc/scss/custom.scss +++ b/doc/scss/custom.scss @@ -251,3 +251,14 @@ div.sk-text-image-grid-small { div.sk-text-image-grid-large { @include sk-text-image-grid(100px); } + +/* Responsive three-column grid list */ +.grid-list-three-columns { + display: grid; + grid-template-columns: repeat(3, 1fr); + gap: 1rem; + + @media screen and (max-width: 500px) { + grid-template-columns: 1fr; + } +} From 5148b0bea359ab79dfabf6531a951ac5f5af3589 Mon Sep 17 00:00:00 2001 From: Virgil Chan Date: Wed, 18 Jun 2025 05:46:10 -0700 Subject: [PATCH 040/103] DOC fix formatting of `intercept_scaling` parameter in `LogisticRegression`-related docs (#31577) --- sklearn/linear_model/_logistic.py | 83 +++++++++++++++++-------------- 1 file changed, 46 insertions(+), 37 deletions(-) diff --git a/sklearn/linear_model/_logistic.py b/sklearn/linear_model/_logistic.py index f0c97268c612d..b85c01ee69f9e 100644 --- a/sklearn/linear_model/_logistic.py +++ b/sklearn/linear_model/_logistic.py @@ -194,17 +194,19 @@ def _logistic_regression_path( only supported by the 'saga' solver. intercept_scaling : float, default=1. - Useful only when the solver 'liblinear' is used - and self.fit_intercept is set to True. In this case, x becomes - [x, self.intercept_scaling], + Useful only when the solver `liblinear` is used + and `self.fit_intercept` is set to `True`. In this case, `x` becomes + `[x, self.intercept_scaling]`, i.e. a "synthetic" feature with constant value equal to - intercept_scaling is appended to the instance vector. - The intercept becomes ``intercept_scaling * synthetic_feature_weight``. + `intercept_scaling` is appended to the instance vector. + The intercept becomes + ``intercept_scaling * synthetic_feature_weight``. - Note! the synthetic feature weight is subject to l1/l2 regularization - as all other features. - To lessen the effect of regularization on synthetic feature weight - (and therefore on the intercept) intercept_scaling has to be increased. + .. note:: + The synthetic feature weight is subject to L1 or L2 + regularization as all other features. + To lessen the effect of regularization on synthetic feature weight + (and therefore on the intercept) `intercept_scaling` has to be increased. multi_class : {'ovr', 'multinomial', 'auto'}, default='auto' If the option chosen is 'ovr', then a binary problem is fit for each @@ -692,16 +694,19 @@ def _log_reg_scoring_path( n_samples > n_features. intercept_scaling : float - Useful only when the solver 'liblinear' is used - and self.fit_intercept is set to True. In this case, x becomes - [x, self.intercept_scaling], - i.e. a "synthetic" feature with constant value equals to - intercept_scaling is appended to the instance vector. - The intercept becomes intercept_scaling * synthetic feature weight - Note! the synthetic feature weight is subject to l1/l2 regularization - as all other features. - To lessen the effect of regularization on synthetic feature weight - (and therefore on the intercept) intercept_scaling has to be increased. + Useful only when the solver `liblinear` is used + and `self.fit_intercept` is set to `True`. In this case, `x` becomes + `[x, self.intercept_scaling]`, + i.e. a "synthetic" feature with constant value equal to + `intercept_scaling` is appended to the instance vector. + The intercept becomes + ``intercept_scaling * synthetic_feature_weight``. + + .. note:: + The synthetic feature weight is subject to L1 or L2 + regularization as all other features. + To lessen the effect of regularization on synthetic feature weight + (and therefore on the intercept) `intercept_scaling` has to be increased. multi_class : {'auto', 'ovr', 'multinomial'} If the option chosen is 'ovr', then a binary problem is fit for each @@ -881,17 +886,19 @@ class LogisticRegression(LinearClassifierMixin, SparseCoefMixin, BaseEstimator): added to the decision function. intercept_scaling : float, default=1 - Useful only when the solver 'liblinear' is used - and self.fit_intercept is set to True. In this case, x becomes - [x, self.intercept_scaling], + Useful only when the solver `liblinear` is used + and `self.fit_intercept` is set to `True`. In this case, `x` becomes + `[x, self.intercept_scaling]`, i.e. a "synthetic" feature with constant value equal to - intercept_scaling is appended to the instance vector. - The intercept becomes ``intercept_scaling * synthetic_feature_weight``. + `intercept_scaling` is appended to the instance vector. + The intercept becomes + ``intercept_scaling * synthetic_feature_weight``. - Note! the synthetic feature weight is subject to l1/l2 regularization - as all other features. - To lessen the effect of regularization on synthetic feature weight - (and therefore on the intercept) intercept_scaling has to be increased. + .. note:: + The synthetic feature weight is subject to L1 or L2 + regularization as all other features. + To lessen the effect of regularization on synthetic feature weight + (and therefore on the intercept) `intercept_scaling` has to be increased. class_weight : dict or 'balanced', default=None Weights associated with classes in the form ``{class_label: weight}``. @@ -1643,17 +1650,19 @@ class LogisticRegressionCV(LogisticRegression, LinearClassifierMixin, BaseEstima best scores across folds are averaged. intercept_scaling : float, default=1 - Useful only when the solver 'liblinear' is used - and self.fit_intercept is set to True. In this case, x becomes - [x, self.intercept_scaling], + Useful only when the solver `liblinear` is used + and `self.fit_intercept` is set to `True`. In this case, `x` becomes + `[x, self.intercept_scaling]`, i.e. a "synthetic" feature with constant value equal to - intercept_scaling is appended to the instance vector. - The intercept becomes ``intercept_scaling * synthetic_feature_weight``. + `intercept_scaling` is appended to the instance vector. + The intercept becomes + ``intercept_scaling * synthetic_feature_weight``. - Note! the synthetic feature weight is subject to l1/l2 regularization - as all other features. - To lessen the effect of regularization on synthetic feature weight - (and therefore on the intercept) intercept_scaling has to be increased. + .. note:: + The synthetic feature weight is subject to L1 or L2 + regularization as all other features. + To lessen the effect of regularization on synthetic feature weight + (and therefore on the intercept) `intercept_scaling` has to be increased. multi_class : {'auto, 'ovr', 'multinomial'}, default='auto' If the option chosen is 'ovr', then a binary problem is fit for each From e62bcc21e780036df49a0789defe2f70a974758b Mon Sep 17 00:00:00 2001 From: Richard Harris Date: Wed, 18 Jun 2025 14:48:39 +0200 Subject: [PATCH 041/103] BLD: use more modern way to specify license metadata (#31560) --- ...in_conda_forge_openblas_min_dependencies_environment.yml | 2 +- ...onda_forge_openblas_min_dependencies_linux-64_conda.lock | 2 +- pyproject.toml | 6 +++--- sklearn/_min_dependencies.py | 2 +- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_environment.yml b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_environment.yml index a179c55fed993..0a9b524ddc62f 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_environment.yml +++ b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_environment.yml @@ -19,7 +19,7 @@ dependencies: - pillow - pip - ninja - - meson-python=0.16.0 # min + - meson-python=0.17.1 # min - pytest-cov - coverage - ccache diff --git a/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock index a82583f3d2974..286d79241390f 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock @@ -166,7 +166,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libclang13-20.1.7-default_he06ed https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-20_linux64_openblas.conda#6fabc51f5e647d09cc010c40061557e0 https://conda.anaconda.org/conda-forge/linux-64/libpq-17.5-h27ae623_0.conda#6458be24f09e1b034902ab44fe9de908 https://conda.anaconda.org/conda-forge/linux-64/libsndfile-1.2.2-hc60ed4a_1.conda#ef1910918dd895516a769ed36b5b3a4e -https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.conda#e16f0dbf502da873be9f9adb0dc52547 +https://conda.anaconda.org/conda-forge/noarch/meson-python-0.17.1-pyh70fd9c4_0.conda#722b649da38842068d83b6e6770f11a1 https://conda.anaconda.org/conda-forge/linux-64/pyqt5-sip-12.17.0-py310hf71b8c6_1.conda#696c7414297907d7647a5176031c8c69 https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.0-pyhd8ed1ab_0.conda#516d31f063ce7e49ced17f105b63a1f1 https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.4-h3394656_0.conda#09262e66b19567aff4f592fb53b28760 diff --git a/pyproject.toml b/pyproject.toml index ce8bcfb8bf4f2..dc22a51a06081 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -13,11 +13,11 @@ dependencies = [ "threadpoolctl>=3.1.0", ] requires-python = ">=3.10" -license = {file = "COPYING"} +license = "BSD-3-Clause" +license-files = ["COPYING"] classifiers=[ "Intended Audience :: Science/Research", "Intended Audience :: Developers", - "License :: OSI Approved :: BSD License", "Programming Language :: C", "Programming Language :: Python", "Topic :: Software Development", @@ -43,7 +43,7 @@ tracker = "https://github.com/scikit-learn/scikit-learn/issues" "release notes" = "https://scikit-learn.org/stable/whats_new" [project.optional-dependencies] -build = ["numpy>=1.22.0", "scipy>=1.8.0", "cython>=3.0.10", "meson-python>=0.16.0"] +build = ["numpy>=1.22.0", "scipy>=1.8.0", "cython>=3.0.10", "meson-python>=0.17.1"] install = ["numpy>=1.22.0", "scipy>=1.8.0", "joblib>=1.2.0", "threadpoolctl>=3.1.0"] benchmark = ["matplotlib>=3.5.0", "pandas>=1.4.0", "memory_profiler>=0.57.0"] docs = [ diff --git a/sklearn/_min_dependencies.py b/sklearn/_min_dependencies.py index 8d39075630437..ac58820686914 100644 --- a/sklearn/_min_dependencies.py +++ b/sklearn/_min_dependencies.py @@ -24,7 +24,7 @@ "joblib": (JOBLIB_MIN_VERSION, "install"), "threadpoolctl": (THREADPOOLCTL_MIN_VERSION, "install"), "cython": (CYTHON_MIN_VERSION, "build"), - "meson-python": ("0.16.0", "build"), + "meson-python": ("0.17.1", "build"), "matplotlib": ("3.5.0", "benchmark, docs, examples, tests"), "scikit-image": ("0.19.0", "docs, examples, tests"), "pandas": ("1.4.0", "benchmark, docs, examples, tests"), From 2b55c3a2312bba1cc155f896fdd686ca1dd7b906 Mon Sep 17 00:00:00 2001 From: Dhyey Findoriya <131277481+dhyeyinf@users.noreply.github.com> Date: Wed, 18 Jun 2025 18:29:17 +0530 Subject: [PATCH 042/103] =?UTF-8?q?DOC:=20Improve=20Ridge=20regression=20e?= =?UTF-8?q?xample=20=E2=80=94=20fix=20typo,=20clarify=20title,=20add=20leg?= =?UTF-8?q?end=20(#31539)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Dhyey Findoriya --- examples/linear_model/plot_ridge_path.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/examples/linear_model/plot_ridge_path.py b/examples/linear_model/plot_ridge_path.py index d3c19acd9e18c..eca65bb509c7b 100644 --- a/examples/linear_model/plot_ridge_path.py +++ b/examples/linear_model/plot_ridge_path.py @@ -22,7 +22,7 @@ squared loss function and the coefficients tend to zero. At the end of the path, as alpha tends toward zero and the solution tends towards the ordinary least squares, coefficients -exhibit big oscillations. In practise it is necessary to tune alpha +exhibit big oscillations. In practice it is necessary to tune alpha in such a way that a balance is maintained between both. """ @@ -63,6 +63,9 @@ ax.set_xlim(ax.get_xlim()[::-1]) # reverse axis plt.xlabel("alpha") plt.ylabel("weights") -plt.title("Ridge coefficients as a function of the regularization") +plt.title("Ridge Coefficients vs Regularization Strength (alpha)") plt.axis("tight") +plt.legend( + [f"Feature {i + 1}" for i in range(X.shape[1])], loc="best", fontsize="small" +) plt.show() From c0e1490170b06a1f96a622275a82996d19570fb0 Mon Sep 17 00:00:00 2001 From: Olivier Grisel Date: Wed, 18 Jun 2025 15:03:16 +0200 Subject: [PATCH 043/103] FIX set `CategoricalNB().__sklearn_tags__.input_tags.categorical` to `True` (#31556) --- .../upcoming_changes/sklearn.naive_bayes/31556.fix.rst | 3 +++ sklearn/naive_bayes.py | 1 + sklearn/tests/test_naive_bayes.py | 9 +++++++++ sklearn/utils/_test_common/instance_generator.py | 10 ---------- sklearn/utils/estimator_checks.py | 5 ++++- 5 files changed, 17 insertions(+), 11 deletions(-) create mode 100644 doc/whats_new/upcoming_changes/sklearn.naive_bayes/31556.fix.rst diff --git a/doc/whats_new/upcoming_changes/sklearn.naive_bayes/31556.fix.rst b/doc/whats_new/upcoming_changes/sklearn.naive_bayes/31556.fix.rst new file mode 100644 index 0000000000000..0f5b969bd9e6f --- /dev/null +++ b/doc/whats_new/upcoming_changes/sklearn.naive_bayes/31556.fix.rst @@ -0,0 +1,3 @@ +- :class:`naive_bayes.CategoricalNB` now correctly declares that it accepts + categorical features in the tags returned by its `__sklearn_tags__` method. + By :user:`Olivier Grisel ` diff --git a/sklearn/naive_bayes.py b/sklearn/naive_bayes.py index e5b03abbb903a..31a1b87af2916 100644 --- a/sklearn/naive_bayes.py +++ b/sklearn/naive_bayes.py @@ -1433,6 +1433,7 @@ def partial_fit(self, X, y, classes=None, sample_weight=None): def __sklearn_tags__(self): tags = super().__sklearn_tags__() + tags.input_tags.categorical = True tags.input_tags.sparse = False tags.input_tags.positive_only = True return tags diff --git a/sklearn/tests/test_naive_bayes.py b/sklearn/tests/test_naive_bayes.py index 99cfe030a940f..f5638e7384e86 100644 --- a/sklearn/tests/test_naive_bayes.py +++ b/sklearn/tests/test_naive_bayes.py @@ -968,3 +968,12 @@ def test_predict_joint_proba(Estimator, global_random_seed): log_prob_x = logsumexp(jll, axis=1) log_prob_x_y = jll - np.atleast_2d(log_prob_x).T assert_allclose(est.predict_log_proba(X2), log_prob_x_y) + + +@pytest.mark.parametrize("Estimator", ALL_NAIVE_BAYES_CLASSES) +def test_categorical_input_tag(Estimator): + tags = Estimator().__sklearn_tags__() + if Estimator is CategoricalNB: + assert tags.input_tags.categorical + else: + assert not tags.input_tags.categorical diff --git a/sklearn/utils/_test_common/instance_generator.py b/sklearn/utils/_test_common/instance_generator.py index 221236f8bc998..8d88ad23eb5e9 100644 --- a/sklearn/utils/_test_common/instance_generator.py +++ b/sklearn/utils/_test_common/instance_generator.py @@ -144,7 +144,6 @@ MultiOutputRegressor, RegressorChain, ) -from sklearn.naive_bayes import CategoricalNB from sklearn.neighbors import ( KernelDensity, KNeighborsClassifier, @@ -898,15 +897,6 @@ def _yield_instances_for_check(check, estimator_orig): "sample_weight is not equivalent to removing/repeating samples." ), }, - CategoricalNB: { - # TODO: fix sample_weight handling of this estimator, see meta-issue #16298 - "check_sample_weight_equivalence_on_dense_data": ( - "sample_weight is not equivalent to removing/repeating samples." - ), - "check_sample_weight_equivalence_on_sparse_data": ( - "sample_weight is not equivalent to removing/repeating samples." - ), - }, ColumnTransformer: { "check_estimators_empty_data_messages": "FIXME", "check_estimators_nan_inf": "FIXME", diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py index a78ef93a86324..349c1ae0712da 100644 --- a/sklearn/utils/estimator_checks.py +++ b/sklearn/utils/estimator_checks.py @@ -3997,7 +3997,10 @@ def check_positive_only_tag_during_fit(name, estimator_orig): y = _enforce_estimator_tags_y(estimator, y) set_random_state(estimator, 0) X = _enforce_estimator_tags_X(estimator, X) - X -= X.mean() + # Make sure that the dtype of X stays unchanged: for instance estimator + # that expect categorical inputs typically expected integer-based encoded + # categories. + X -= X.mean().astype(X.dtype) if tags.input_tags.positive_only: with raises(ValueError, match="Negative values in data"): From 38ef3ce016d8edfc20e2ed5d866eb6d1e7135902 Mon Sep 17 00:00:00 2001 From: Steffen Rehberg Date: Wed, 18 Jun 2025 17:50:20 +0200 Subject: [PATCH 044/103] DOC Fix example Recursive feature elimination with cross-validation (#31516) --- examples/feature_selection/plot_rfe_with_cross_validation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/feature_selection/plot_rfe_with_cross_validation.py b/examples/feature_selection/plot_rfe_with_cross_validation.py index 951b82bffa46d..307707c5aa069 100644 --- a/examples/feature_selection/plot_rfe_with_cross_validation.py +++ b/examples/feature_selection/plot_rfe_with_cross_validation.py @@ -105,7 +105,7 @@ for i in range(cv.n_splits): mask = rfecv.cv_results_[f"split{i}_support"][ - rfecv.n_features_ + rfecv.n_features_ - 1 ] # mask of features selected by the RFE features_selected = np.ma.compressed(np.ma.masked_array(feat_names, mask=1 - mask)) print(f"Features selected in fold {i}: {features_selected}") From 69bdef0c28d0546dc68fb821b8bdf40a4528401f Mon Sep 17 00:00:00 2001 From: Marija Vlajic Date: Wed, 18 Jun 2025 19:02:55 +0200 Subject: [PATCH 045/103] DOC Add examples of make_scorer usage to fbeta_score docstring (#28755) Co-authored-by: Olivier Grisel Co-authored-by: Guillaume Lemaitre Co-authored-by: adrinjalali --- sklearn/metrics/_classification.py | 29 +++++++++++++++++++++++++++-- 1 file changed, 27 insertions(+), 2 deletions(-) diff --git a/sklearn/metrics/_classification.py b/sklearn/metrics/_classification.py index 2e31320ddb1f4..361e8825f3601 100644 --- a/sklearn/metrics/_classification.py +++ b/sklearn/metrics/_classification.py @@ -1627,6 +1627,11 @@ def fbeta_score( returns 0.0 and raises ``UndefinedMetricWarning``. This behavior can be modified by setting ``zero_division``. + F-beta score is not implemented as a named scorer that can be passed to + the `scoring` parameter of cross-validation tools directly: it requires to be + wrapped with :func:`make_scorer` so as to specify the value of `beta`. See + examples for details. + References ---------- .. [1] R. Baeza-Yates and B. Ribeiro-Neto (2011). @@ -1650,9 +1655,29 @@ def fbeta_score( >>> fbeta_score(y_true, y_pred, average=None, beta=0.5) array([0.71, 0. , 0. ]) >>> y_pred_empty = [0, 0, 0, 0, 0, 0] - >>> fbeta_score(y_true, y_pred_empty, - ... average="macro", zero_division=np.nan, beta=0.5) + >>> fbeta_score( + ... y_true, + ... y_pred_empty, + ... average="macro", + ... zero_division=np.nan, + ... beta=0.5, + ... ) 0.128 + + In order to use :func:`fbeta_scorer` as a scorer, a callable + scorer objects needs to be created first with :func:`make_scorer`, + passing the value for the `beta` parameter. + + >>> from sklearn.metrics import fbeta_score, make_scorer + >>> ftwo_scorer = make_scorer(fbeta_score, beta=2) + >>> from sklearn.model_selection import GridSearchCV + >>> from sklearn.svm import LinearSVC + >>> grid = GridSearchCV( + ... LinearSVC(dual="auto"), + ... param_grid={'C': [1, 10]}, + ... scoring=ftwo_scorer, + ... cv=5 + ... ) """ _, _, f, _ = precision_recall_fscore_support( From 1a19e3c69f8e4ffc4281f812736d536802e75f6d Mon Sep 17 00:00:00 2001 From: Tingwei Zhu <852445892@qq.com> Date: Thu, 19 Jun 2025 16:40:25 +0800 Subject: [PATCH 046/103] DOC Improve older whats_new doc entries (#31589) --- doc/whats_new/v1.3.rst | 2 +- doc/whats_new/v1.4.rst | 2 +- doc/whats_new/v1.5.rst | 15 ++++++++------- 3 files changed, 10 insertions(+), 9 deletions(-) diff --git a/doc/whats_new/v1.3.rst b/doc/whats_new/v1.3.rst index f523c02e14447..e581f451fc741 100644 --- a/doc/whats_new/v1.3.rst +++ b/doc/whats_new/v1.3.rst @@ -770,7 +770,7 @@ Changelog :func:`model_selection.validation_curve`. :pr:`25120` by :user:`Guillaume Lemaitre `. -- |API| The parameter `log_scale` in the class +- |API| The parameter `log_scale` in the method `plot` of the class :class:`model_selection.LearningCurveDisplay` has been deprecated in 1.3 and will be removed in 1.5. The default scale can be overridden by setting it directly on the `ax` object and will be set automatically from the spacing diff --git a/doc/whats_new/v1.4.rst b/doc/whats_new/v1.4.rst index 3dfcde90c9e81..c90ffc5865af7 100644 --- a/doc/whats_new/v1.4.rst +++ b/doc/whats_new/v1.4.rst @@ -989,7 +989,7 @@ Changelog when the input is a Series instead of a DataFrame. :pr:`28090` by :user:`Stan Furrer ` and :user:`Yao Xiao `. -- |API| :func:`sklearn.extmath.log_logistic` is deprecated and will be removed in 1.6. +- |API| :func:`sklearn.utils.extmath.log_logistic` is deprecated and will be removed in 1.6. Use `-np.logaddexp(0, -x)` instead. :pr:`27544` by :user:`Christian Lorentzen `. diff --git a/doc/whats_new/v1.5.rst b/doc/whats_new/v1.5.rst index 411a1b6b5dd95..2117de11b3b3d 100644 --- a/doc/whats_new/v1.5.rst +++ b/doc/whats_new/v1.5.rst @@ -347,12 +347,13 @@ Changelog the previous predicted values were not affected by this bug. :pr:`28612` by :user:`Guillaume Lemaitre `. -- |API| Deprecates `Y` in favor of `y` in the methods fit, transform and - inverse_transform of: - :class:`cross_decomposition.PLSRegression`. - :class:`cross_decomposition.PLSCanonical`, - :class:`cross_decomposition.CCA`, - and :class:`cross_decomposition.PLSSVD`. +- |API| Deprecates `Y` in favor of `y` in the methods `fit`, `transform` and + `inverse_transform` of: + :class:`cross_decomposition.PLSRegression`, + :class:`cross_decomposition.PLSCanonical`, + and :class:`cross_decomposition.CCA`, + and methods `fit` and `transform` of: + :class:`cross_decomposition.PLSSVD`. `Y` will be removed in version 1.7. :pr:`28604` by :user:`David Leon `. @@ -508,7 +509,7 @@ Changelog `OneVsRestClassifier(LogisticRegression(..))`. :pr:`28703` by :user:`Christian Lorentzen `. -- |API| `store_cv_values` and `cv_values_` are deprecated in favor of +- |API| Parameters `store_cv_values` and `cv_values_` are deprecated in favor of `store_cv_results` and `cv_results_` in `~linear_model.RidgeCV` and `~linear_model.RidgeClassifierCV`. :pr:`28915` by :user:`Lucy Liu `. From b2aaea044ea5a6746528af20313689a2836be658 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Dea=20Mar=C3=ADa=20L=C3=A9on?= Date: Thu, 19 Jun 2025 11:12:12 +0200 Subject: [PATCH 047/103] FIX fix comparison between array-like parameters when detecting non-default params for HTML representation (#31528) --- .../sklearn.base/31528.fix.rst | 3 + sklearn/base.py | 6 +- sklearn/tests/test_base.py | 81 ++++++++++++++++++- 3 files changed, 87 insertions(+), 3 deletions(-) create mode 100644 doc/whats_new/upcoming_changes/sklearn.base/31528.fix.rst diff --git a/doc/whats_new/upcoming_changes/sklearn.base/31528.fix.rst b/doc/whats_new/upcoming_changes/sklearn.base/31528.fix.rst new file mode 100644 index 0000000000000..312c8318eadcd --- /dev/null +++ b/doc/whats_new/upcoming_changes/sklearn.base/31528.fix.rst @@ -0,0 +1,3 @@ +- Fix regression in HTML representation when detecting the non-default parameters + that where of array-like types. + By :user:`Dea María Léon ` diff --git a/sklearn/base.py b/sklearn/base.py index 309b482357e12..e9308d8f1376f 100644 --- a/sklearn/base.py +++ b/sklearn/base.py @@ -292,12 +292,14 @@ def is_non_default(param_name, param_value): init_default_params[param_name] ): return True - - if param_value != init_default_params[param_name] and not ( + if not np.array_equal( + param_value, init_default_params[param_name] + ) and not ( is_scalar_nan(init_default_params[param_name]) and is_scalar_nan(param_value) ): return True + return False # reorder the parameters from `self.get_params` using the `__init__` diff --git a/sklearn/tests/test_base.py b/sklearn/tests/test_base.py index e57d36351f0d4..0842cf0c82b48 100644 --- a/sklearn/tests/test_base.py +++ b/sklearn/tests/test_base.py @@ -26,7 +26,8 @@ from sklearn.decomposition import PCA from sklearn.ensemble import IsolationForest from sklearn.exceptions import InconsistentVersionWarning -from sklearn.model_selection import GridSearchCV +from sklearn.metrics import get_scorer +from sklearn.model_selection import GridSearchCV, KFold from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler from sklearn.svm import SVC, SVR @@ -1000,3 +1001,81 @@ def test_get_params_html(): assert est._get_params_html() == {"l1": 0, "empty": "test"} assert est._get_params_html().non_default == ("empty",) + + +def make_estimator_with_param(default_value): + class DynamicEstimator(BaseEstimator): + def __init__(self, param=default_value): + self.param = param + + return DynamicEstimator + + +@pytest.mark.parametrize( + "default_value, test_value", + [ + ((), (1,)), + ((), [1]), + ((), np.array([1])), + ((1, 2), (3, 4)), + ((1, 2), [3, 4]), + ((1, 2), np.array([3, 4])), + (None, 1), + (None, []), + (None, lambda x: x), + (np.nan, 1.0), + (np.nan, np.array([np.nan])), + ("abc", "def"), + ("abc", ["abc"]), + (True, False), + (1, 2), + (1, [1]), + (1, np.array([1])), + (1.0, 2.0), + (1.0, [1.0]), + (1.0, np.array([1.0])), + ([1, 2], [3]), + (np.array([1]), [2, 3]), + (None, KFold()), + (None, get_scorer("accuracy")), + ], +) +def test_param_is_non_default(default_value, test_value): + """Check that we detect non-default parameters with various types. + + Non-regression test for: + https://github.com/scikit-learn/scikit-learn/issues/31525 + """ + estimator = make_estimator_with_param(default_value)(param=test_value) + non_default = estimator._get_params_html().non_default + assert "param" in non_default + + +@pytest.mark.parametrize( + "default_value, test_value", + [ + (None, None), + ((), ()), + ((), []), + ((), np.array([])), + ((1, 2, 3), (1, 2, 3)), + ((1, 2, 3), [1, 2, 3]), + ((1, 2, 3), np.array([1, 2, 3])), + (np.nan, np.nan), + ("abc", "abc"), + (True, True), + (1, 1), + (1.0, 1.0), + (2, 2.0), + ], +) +def test_param_is_default(default_value, test_value): + """Check that we detect the default parameters and values in an array-like will + be reported as default as well. + + Non-regression test for: + https://github.com/scikit-learn/scikit-learn/issues/31525 + """ + estimator = make_estimator_with_param(default_value)(param=test_value) + non_default = estimator._get_params_html().non_default + assert "param" not in non_default From 194dd9ab4e8341fb9ee1bc85c9354bdaa502f79e Mon Sep 17 00:00:00 2001 From: "Christine P. Chai" Date: Fri, 20 Jun 2025 02:13:02 -0700 Subject: [PATCH 048/103] DOC Revise the math formatting for eta_0 (#31598) --- doc/modules/sgd.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/modules/sgd.rst b/doc/modules/sgd.rst index 84812a0cccf12..95b16224fc18e 100644 --- a/doc/modules/sgd.rst +++ b/doc/modules/sgd.rst @@ -514,9 +514,9 @@ For regression the default learning rate schedule is inverse scaling .. math:: - \eta^{(t)} = \frac{eta_0}{t^{power\_t}} + \eta^{(t)} = \frac{\eta_0}{t^{power\_t}} -where :math:`eta_0` and :math:`power\_t` are hyperparameters chosen by the +where :math:`\eta_0` and :math:`power\_t` are hyperparameters chosen by the user via ``eta0`` and ``power_t``, respectively. For a constant learning rate use ``learning_rate='constant'`` and use ``eta0`` From 441bcea6e20b4600ddfc80490f9735bc84f98c28 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Mon, 23 Jun 2025 10:35:57 +0200 Subject: [PATCH 049/103] MNT Simplify inefficient regex (#31603) --- sklearn/tests/test_min_dependencies_readme.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/sklearn/tests/test_min_dependencies_readme.py b/sklearn/tests/test_min_dependencies_readme.py index cc986bd17aeae..6afcd3e57ca04 100644 --- a/sklearn/tests/test_min_dependencies_readme.py +++ b/sklearn/tests/test_min_dependencies_readme.py @@ -32,9 +32,9 @@ def test_min_dependencies_readme(): # sklearn/_min_dependencies.py pattern = re.compile( - r"(\.\. \|)" - r"(([A-Za-z]+\-?)+)" - r"(MinVersion\| replace::)" + r"\.\. \|" + r"([A-Za-z-]+)" + r"MinVersion\| replace::" r"( [0-9]+\.[0-9]+(\.[0-9]+)?)" ) @@ -53,7 +53,7 @@ def test_min_dependencies_readme(): if not matched: continue - package, version = matched.group(2), matched.group(5) + package, version = matched.group(0), matched.group(1) package = package.lower() if package in dependent_packages: From c4b7489bc83fba5e089de645e2ffc0997a814dda Mon Sep 17 00:00:00 2001 From: Christian Lorentzen Date: Mon, 23 Jun 2025 20:27:35 +0200 Subject: [PATCH 050/103] GOV more pragmatic decision making process for small PRs (#31430) --- doc/governance.rst | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/doc/governance.rst b/doc/governance.rst index 5601f80573651..cbe35c0ebe0a4 100644 --- a/doc/governance.rst +++ b/doc/governance.rst @@ -146,20 +146,22 @@ decision making process**". Decisions (in addition to adding core contributors and TC membership as above) are made according to the following rules: -* **Minor Documentation changes**, such as typo fixes, or addition / correction +* **Minor code and documentation changes**, such as small maintenance changes without + modification of code logic, typo fixes, or addition / correction of a sentence, but no change of the ``scikit-learn.org`` landing page or the - “about” page: Requires +1 by a maintainer, no -1 by a maintainer (lazy - consensus), happens on the issue or pull request page. Maintainers are - expected to give “reasonable time” to others to give their opinion on the + “about” page: Requires +1 by a core contributor, no -1 by a core contributor + (lazy consensus), happens on the issue or pull request page. Core contributors + are expected to give “reasonable time” to others to give their opinion on the pull request if they're not confident others would agree. * **Code changes and major documentation changes** - require +1 by two maintainers, no -1 by a maintainer (lazy + require +1 by two core contributors, no -1 by a core contributor (lazy consensus), happens on the issue of pull-request page. * **Changes to the API principles and changes to dependencies or supported - versions** happen via :ref:`slep` and follows the decision-making process - outlined above. + versions** follow the decision-making process outlined above. In particular + changes to API principles are backed via a :ref:`slep`. Smaller decisions + like supported versions can happen on a GitHub issue or pull request. * **Changes to the governance model** follow the process outlined in `SLEP020 `__. From 92ae8db0184169f7c41fd99860e9149e5155b4d1 Mon Sep 17 00:00:00 2001 From: GaetandeCast <115986055+GaetandeCast@users.noreply.github.com> Date: Tue, 24 Jun 2025 15:42:05 +0200 Subject: [PATCH 051/103] DOC Fix misleading statement about model refitting in permutation importance docs (#31596) --- examples/ensemble/plot_forest_importances.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/ensemble/plot_forest_importances.py b/examples/ensemble/plot_forest_importances.py index b77e30c327fb4..5fb8f21364450 100644 --- a/examples/ensemble/plot_forest_importances.py +++ b/examples/ensemble/plot_forest_importances.py @@ -102,10 +102,10 @@ forest_importances = pd.Series(result.importances_mean, index=feature_names) # %% -# The computation for full permutation importance is more costly. Features are -# shuffled n times and the model refitted to estimate the importance of it. -# Please see :ref:`permutation_importance` for more details. We can now plot -# the importance ranking. +# The computation for full permutation importance is more costly. Each feature is +# shuffled n times and the model is used to make predictions on the permuted data to see +# the drop in performance. Please see :ref:`permutation_importance` for more details. +# We can now plot the importance ranking. fig, ax = plt.subplots() forest_importances.plot.bar(yerr=result.importances_std, ax=ax) From 2b0d2aa672328339b3368da6f06e9385ac9f1765 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Thu, 26 Jun 2025 09:52:31 +0200 Subject: [PATCH 052/103] CI Avoid Windows timeout by switching to OpenBLAS (#31641) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Loïc Estève --- azure-pipelines.yml | 4 +- ...ymin_conda_forge_openblas_environment.yml} | 2 +- ...in_conda_forge_openblas_win-64_conda.lock} | 50 +++++++++---------- build_tools/azure/windows.yml | 16 ++++++ .../update_environments_and_lock_files.py | 4 +- 5 files changed, 44 insertions(+), 32 deletions(-) rename build_tools/azure/{pymin_conda_forge_mkl_environment.yml => pymin_conda_forge_openblas_environment.yml} (94%) rename build_tools/azure/{pymin_conda_forge_mkl_win-64_conda.lock => pymin_conda_forge_openblas_win-64_conda.lock} (82%) diff --git a/azure-pipelines.yml b/azure-pipelines.yml index a36daf39b50db..5226308afe48b 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -253,9 +253,9 @@ jobs: not(contains(dependencies['git_commit']['outputs']['commit.message'], '[ci skip]')) ) matrix: - pymin_conda_forge_mkl: + pymin_conda_forge_openblas: DISTRIB: 'conda' - LOCK_FILE: ./build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock + LOCK_FILE: ./build_tools/azure/pymin_conda_forge_openblas_win-64_conda.lock SKLEARN_WARNINGS_AS_ERRORS: '1' # The Azure Windows runner is typically much slower than other CI # runners due to the lack of compiler cache. Running the tests with diff --git a/build_tools/azure/pymin_conda_forge_mkl_environment.yml b/build_tools/azure/pymin_conda_forge_openblas_environment.yml similarity index 94% rename from build_tools/azure/pymin_conda_forge_mkl_environment.yml rename to build_tools/azure/pymin_conda_forge_openblas_environment.yml index fe6ce91950e4a..7fce5776e930a 100644 --- a/build_tools/azure/pymin_conda_forge_mkl_environment.yml +++ b/build_tools/azure/pymin_conda_forge_openblas_environment.yml @@ -6,7 +6,7 @@ channels: dependencies: - python=3.10 - numpy - - blas[build=mkl] + - blas[build=openblas] - scipy - cython - joblib diff --git a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_win-64_conda.lock similarity index 82% rename from build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock rename to build_tools/azure/pymin_conda_forge_openblas_win-64_conda.lock index 193123a87434f..ba4245727766f 100644 --- a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_openblas_win-64_conda.lock @@ -1,27 +1,25 @@ # Generated by conda-lock. # platform: win-64 -# input_hash: cc5e2a711eb32773dc46fe159e1c3fe14f4fd07565fc8d3dedf2d748d4f2f694 +# input_hash: 4ff41dadb8a7a77d0b784bfc6b32126b8e1a41c8b9a87375b48c18c9aee4ea2a @EXPLICIT https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45 https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6 https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2#4d59c254e01d9cde7957100457e2d5fb https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda#49023d73832ef61042f6a237cb2687e7 -https://conda.anaconda.org/conda-forge/win-64/intel-openmp-2024.2.1-h57928b3_1083.conda#2d89243bfb53652c182a7c73182cce4f -https://conda.anaconda.org/conda-forge/win-64/mkl-include-2024.2.2-h66d3029_15.conda#e2f516189b44b6e042199d13e7015361 https://conda.anaconda.org/conda-forge/noarch/python_abi-3.10-7_cp310.conda#44e871cba2b162368476a84b8d040b6c https://conda.anaconda.org/conda-forge/noarch/tzdata-2025b-h78e105d_0.conda#4222072737ccff51314b5ece9c7d6f5a https://conda.anaconda.org/conda-forge/win-64/ucrt-10.0.22621.0-h57928b3_1.conda#6797b005cd0f439c4c5c9ac565783700 https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.6.15-h4c7d964_0.conda#b01649832f7bc7ff94f8df8bd2ee6457 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766549260d6815b0c52253f1fb1bb29 https://conda.anaconda.org/conda-forge/win-64/libwinpthread-12.0.0.r4.gg4f2fc60ca-h57928b3_9.conda#08bfa5da6e242025304b206d152479ef -https://conda.anaconda.org/conda-forge/win-64/vc14_runtime-14.42.34438-hfd919c2_26.conda#91651a36d31aa20c7ba36299fb7068f4 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https://conda.anaconda.org/conda-forge/win-64/double-conversion-3.3.1-he0c23c2_0.conda#e9a1402439c18a4e3c7a52e4246e9e1c -https://conda.anaconda.org/conda-forge/win-64/graphite2-1.3.13-h63175ca_1003.conda#3194499ee7d1a67404a87d0eefdd92c6 +https://conda.anaconda.org/conda-forge/win-64/graphite2-1.3.14-he0c23c2_0.conda#692bc31c646f7e221af07ccc924e1ae4 https://conda.anaconda.org/conda-forge/win-64/icu-75.1-he0c23c2_0.conda#8579b6bb8d18be7c0b27fb08adeeeb40 https://conda.anaconda.org/conda-forge/win-64/lerc-4.0.0-h6470a55_1.conda#c1b81da6d29a14b542da14a36c9fbf3f https://conda.anaconda.org/conda-forge/win-64/libbrotlicommon-1.1.0-h2466b09_3.conda#cf20c8b8b48ab5252ec64b9c66bfe0a4 @@ -31,21 +29,24 @@ https://conda.anaconda.org/conda-forge/win-64/libffi-3.4.6-h537db12_1.conda#85d8 https://conda.anaconda.org/conda-forge/win-64/libiconv-1.18-h135ad9c_1.conda#21fc5dba2cbcd8e5e26ff976a312122c 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https://conda.anaconda.org/conda-forge/win-64/pcre2-10.45-h99c9b8b_0.conda#f4c483274001678e129f5cbaf3a8d765 https://conda.anaconda.org/conda-forge/win-64/python-3.10.18-h8c5b53a_0_cpython.conda#f1775dab55c8a073ebd024bfb2f689c1 https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-hbeecb71_2.conda#21f56217d6125fb30c3c3f10c786d751 @@ -56,10 +57,11 @@ https://conda.anaconda.org/conda-forge/win-64/cython-3.1.2-py310h6bd2d47_2.conda https://conda.anaconda.org/conda-forge/noarch/execnet-2.1.1-pyhd8ed1ab_1.conda#a71efeae2c160f6789900ba2631a2c90 https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_1.conda#6837f3eff7dcea42ecd714ce1ac2b108 https://conda.anaconda.org/conda-forge/win-64/kiwisolver-1.4.7-py310hc19bc0b_0.conda#50d96539497fc7493cbe469fbb6b8b6e +https://conda.anaconda.org/conda-forge/win-64/libcblas-3.9.0-32_h9bd4c3b_openblas.conda#69e8e83a9ed37d070b0c5ed4996648a8 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-https://conda.anaconda.org/conda-forge/win-64/liblapacke-3.9.0-31_h845c4fa_mkl.conda#003a2041cb07a7cf698f48dd26301273 -https://conda.anaconda.org/conda-forge/win-64/numpy-2.2.6-py310h4987827_0.conda#d2596785ac2cf5bab04e2ee9e5d04041 -https://conda.anaconda.org/conda-forge/win-64/blas-devel-3.9.0-31_hfb1a452_mkl.conda#0deeb3d9d6f0e56393c55ef382899010 -https://conda.anaconda.org/conda-forge/win-64/contourpy-1.3.2-py310hc19bc0b_0.conda#039416813b5290e7d100a05bb4326110 https://conda.anaconda.org/conda-forge/win-64/qt6-main-6.9.1-h02ddd7d_0.conda#feaaaae25a51188fb0544aca8b26ef4d -https://conda.anaconda.org/conda-forge/win-64/scipy-1.15.2-py310h15c175c_0.conda#81798168111d1021e3d815217c444418 -https://conda.anaconda.org/conda-forge/win-64/blas-2.131-mkl.conda#1842bfaa4e349875c47bde1d9871bda6 -https://conda.anaconda.org/conda-forge/win-64/matplotlib-base-3.10.3-py310h37e0a56_0.conda#de9ddae6f97b78860c256de480ea1a84 https://conda.anaconda.org/conda-forge/win-64/pyside6-6.9.1-py310h2d19612_0.conda#01b830c0fd6ca7ab03c85a008a6f4a2d https://conda.anaconda.org/conda-forge/win-64/matplotlib-3.10.3-py310h5588dad_0.conda#103adee33db124a0263d0b4551e232e3 diff --git a/build_tools/azure/windows.yml b/build_tools/azure/windows.yml index b3fcf130f9350..9f4416823dd50 100644 --- a/build_tools/azure/windows.yml +++ b/build_tools/azure/windows.yml @@ -27,6 +27,22 @@ jobs: - bash: python build_tools/azure/get_selected_tests.py displayName: Check selected tests for all random seeds condition: eq(variables['Build.Reason'], 'PullRequest') + - task: PowerShell@2 + displayName: 'Get CPU Information' + inputs: + targetType: 'inline' + script: | + Write-Host "=== CPU Information ===" + $cpu = Get-WmiObject -Class Win32_Processor + Write-Host "CPU Model: $($cpu.Name)" + Write-Host "Architecture: $($cpu.Architecture)" + Write-Host "Physical Cores: $($cpu.NumberOfCores)" + Write-Host "Logical Processors: $($cpu.NumberOfLogicalProcessors)" + Write-Host "Max Clock Speed: $($cpu.MaxClockSpeed) MHz" + Write-Host "Current Clock Speed: $($cpu.CurrentClockSpeed) MHz" + Write-Host "L2 Cache Size: $($cpu.L2CacheSize) KB" + Write-Host "L3 Cache Size: $($cpu.L3CacheSize) KB" + Write-Host "===========================" - bash: echo "##vso[task.prependpath]$CONDA/Scripts" displayName: Add conda to PATH condition: startsWith(variables['DISTRIB'], 'conda') diff --git a/build_tools/update_environments_and_lock_files.py b/build_tools/update_environments_and_lock_files.py index 5efd7f12cffd7..f487e1cfbd2b3 100644 --- a/build_tools/update_environments_and_lock_files.py +++ b/build_tools/update_environments_and_lock_files.py @@ -284,7 +284,7 @@ def remove_from(alist, to_remove): ], }, { - "name": "pymin_conda_forge_mkl", + "name": "pymin_conda_forge_openblas", "type": "conda", "tag": "main-ci", "folder": "build_tools/azure", @@ -297,7 +297,7 @@ def remove_from(alist, to_remove): ], "package_constraints": { "python": "3.10", - "blas": "[build=mkl]", + "blas": "[build=openblas]", }, }, { From 3c3e4c121c431b03f485f381b2be3386e6d614f0 Mon Sep 17 00:00:00 2001 From: Stefanie Senger <91849487+StefanieSenger@users.noreply.github.com> Date: Thu, 26 Jun 2025 11:06:40 +0200 Subject: [PATCH 053/103] FIX use pyarrow types in pyarrow.filter() for older pyarrow versions (#31605) Co-authored-by: Christian Lorentzen --- ..._openblas_min_dependencies_environment.yml | 1 + ...nblas_min_dependencies_linux-64_conda.lock | 83 ++++++++++++++----- .../update_environments_and_lock_files.py | 3 +- sklearn/utils/_indexing.py | 12 +++ sklearn/utils/fixes.py | 12 +++ 5 files changed, 89 insertions(+), 22 deletions(-) diff --git a/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_environment.yml b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_environment.yml index 0a9b524ddc62f..1e7c36708ee30 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_environment.yml +++ b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_environment.yml @@ -24,3 +24,4 @@ dependencies: - coverage - ccache - polars=0.20.30 # min + - pyarrow=12.0.0 # min diff --git a/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock index 286d79241390f..9bbafc5b603d5 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 41111e5656d9d33f83f1160f643ec4ab314aa8e179923dbe1350c87b0ac2f400 +# input_hash: 0f062944edccd8efd48c86d9c76c5f9ea5bde5a64b16e6076bca3d84b06da831 @EXPLICIT https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45 https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6 @@ -19,8 +19,8 @@ https://conda.anaconda.org/conda-forge/linux-64/libegl-1.7.0-ha4b6fd6_2.conda#c1 https://conda.anaconda.org/conda-forge/linux-64/libopengl-1.7.0-ha4b6fd6_2.conda#7df50d44d4a14d6c31a2c54f2cd92157 https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_2.conda#ea8ac52380885ed41c1baa8f1d6d2b93 https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.14-hb9d3cd8_0.conda#76df83c2a9035c54df5d04ff81bcc02d +https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.5-hb9d3cd8_0.conda#f7f0d6cc2dc986d42ac2689ec88192be https://conda.anaconda.org/conda-forge/linux-64/gettext-tools-0.24.1-h5888daf_0.conda#d54305672f0361c2f3886750e7165b5f -https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hb9d3cd8_3.conda#cb98af5db26e3f482bebb80ce9d947d3 https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.24-h86f0d12_0.conda#64f0c503da58ec25ebd359e4d990afa8 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.0-h5888daf_0.conda#db0bfbe7dd197b68ad5f30333bae6ce0 https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.6-h2dba641_1.conda#ede4673863426c0883c0063d853bbd85 @@ -30,11 +30,14 @@ https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.1.0-hcea5267_2.c https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h4ce23a2_1.conda#e796ff8ddc598affdf7c173d6145f087 https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.1.0-hb9d3cd8_0.conda#9fa334557db9f63da6c9285fd2a48638 https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_2.conda#1a580f7796c7bf6393fddb8bbbde58dc +https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hb9d3cd8_1.conda#d864d34357c3b65a4b731f78c0801dc4 https://conda.anaconda.org/conda-forge/linux-64/libntlm-1.8-hb9d3cd8_0.conda#7c7927b404672409d9917d49bff5f2d6 +https://conda.anaconda.org/conda-forge/linux-64/libnuma-2.0.18-hb9d3cd8_3.conda#20ab6b90150325f1af7ca96bffafde63 https://conda.anaconda.org/conda-forge/linux-64/libogg-1.3.5-hd0c01bc_1.conda#68e52064ed3897463c0e958ab5c8f91b https://conda.anaconda.org/conda-forge/linux-64/libopus-1.5.2-hd0c01bc_0.conda#b64523fb87ac6f87f0790f324ad43046 https://conda.anaconda.org/conda-forge/linux-64/libpciaccess-0.18-hb9d3cd8_0.conda#70e3400cbbfa03e96dcde7fc13e38c7b https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_2.conda#1cb1c67961f6dd257eae9e9691b341aa +https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.8.0-hf23e847_1.conda#b1aa0faa95017bca11369bd080487ec4 https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.5.0-h851e524_0.conda#63f790534398730f59e1b899c3644d4a https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 @@ -45,59 +48,82 @@ https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-hb9d3cd8_0.co https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.5-hb9d3cd8_0.conda#8035c64cb77ed555e3f150b7b3972480 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxshmfence-1.3.3-hb9d3cd8_0.conda#9a809ce9f65460195777f2f2116bae02 https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.1-h166bdaf_1.tar.bz2#d9c69a24ad678ffce24c6543a0176b00 +https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.8.23-hd590300_0.conda#cc4f06f7eedb1523f3b83fd0fb3942ff https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62ee74e96c5ebb0af99386de58cf9553 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+https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.1-pyhd8ed1ab_0.conda#a49c2283f24696a7b30367b7346a0144 +https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.3.12-he2a37c1_2.conda#44876aca9aa47da1e5e2d3f9906169ba https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.4-h3394656_0.conda#09262e66b19567aff4f592fb53b28760 https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.24.11-hc37bda9_0.conda#056d86cacf2b48c79c6a562a2486eb8c https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-20_linux64_openblas.conda#05c5862c7dc25e65ba6c471d96429dae https://conda.anaconda.org/conda-forge/linux-64/numpy-1.22.0-py310h454958d_1.tar.bz2#607c66f0cce2986515a8fe9e136b2b57 -https://conda.anaconda.org/conda-forge/linux-64/pulseaudio-client-17.0-hac146a9_1.conda#66b1fa9608d8836e25f9919159adc9c6 +https://conda.anaconda.org/conda-forge/linux-64/pulseaudio-client-17.0-hb77b528_0.conda#07f45f1be1c25345faddb8db0de8039b https://conda.anaconda.org/conda-forge/noarch/pytest-cov-6.2.1-pyhd8ed1ab_0.conda#ce978e1b9ed8b8d49164e90a5cdc94cd https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.7.0-pyhd8ed1ab_0.conda#15353a2a0ea6dfefaa52fc5ab5b98f41 +https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.20.2-h2a5cb19_18.conda#7313674073496cec938f73b71163bc31 https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-20_linux64_openblas.conda#9932a1d4e9ecf2d35fb19475446e361e https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.11-h651a532_0.conda#d8d8894f8ced2c9be76dc9ad1ae531ce https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-11.2.1-h3beb420_0.conda#0e6e192d4b3d95708ad192d957cf3163 @@ -183,8 +221,11 @@ https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.5.0-py310h23f4 https://conda.anaconda.org/conda-forge/linux-64/pandas-1.4.0-py310hb5077e9_0.tar.bz2#43e920bc9856daa7d8d18fcbfb244c4e https://conda.anaconda.org/conda-forge/linux-64/polars-0.20.30-py310h031f9ce_0.conda#0743f5db9f978b6df92d412935ff8371 https://conda.anaconda.org/conda-forge/linux-64/scipy-1.8.0-py310hea5193d_1.tar.bz2#664d80ddeb51241629b3ada5ea926e4d +https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.10.57-h7b9373a_16.conda#54db1af780a69493a2e0675113a027f9 https://conda.anaconda.org/conda-forge/linux-64/blas-2.120-openblas.conda#c8f6916a81a340650078171b1d852574 https://conda.anaconda.org/conda-forge/linux-64/pyamg-4.2.1-py310h7c3ba0c_0.tar.bz2#89f5a48e1f23b5cf3163a6094903d181 https://conda.anaconda.org/conda-forge/linux-64/qt-main-5.15.15-hea1682b_4.conda#c054d7f22cc719e12c72d454b2328d6c +https://conda.anaconda.org/conda-forge/linux-64/libarrow-12.0.0-hc410076_9_cpu.conda#3dcb50139596ef80908e2dd9a931d84c https://conda.anaconda.org/conda-forge/linux-64/pyqt-5.15.11-py310hf392a12_1.conda#e07b23661b711fb46d25b14206e0db47 https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.5.0-py310hff52083_0.tar.bz2#1b2f3b135d5d9c594b5e0e6150c03b7b +https://conda.anaconda.org/conda-forge/linux-64/pyarrow-12.0.0-py310h0576679_9_cpu.conda#b2d6ee1cff5acc5509633f8eac7108f7 diff --git a/build_tools/update_environments_and_lock_files.py b/build_tools/update_environments_and_lock_files.py index f487e1cfbd2b3..8bec9d266b82c 100644 --- a/build_tools/update_environments_and_lock_files.py +++ b/build_tools/update_environments_and_lock_files.py @@ -175,7 +175,7 @@ def remove_from(alist, to_remove): "folder": "build_tools/azure", "platform": "linux-64", "channels": ["conda-forge"], - "conda_dependencies": common_dependencies + ["ccache", "polars"], + "conda_dependencies": common_dependencies + ["ccache", "polars", "pyarrow"], "package_constraints": { "python": "3.10", "blas": "[build=openblas]", @@ -189,6 +189,7 @@ def remove_from(alist, to_remove): "pandas": "min", "polars": "min", "pyamg": "min", + "pyarrow": "min", }, }, { diff --git a/sklearn/utils/_indexing.py b/sklearn/utils/_indexing.py index 09427376a4059..ec83cf6660b25 100644 --- a/sklearn/utils/_indexing.py +++ b/sklearn/utils/_indexing.py @@ -10,6 +10,8 @@ import numpy as np from scipy.sparse import issparse +from sklearn.utils.fixes import PYARROW_VERSION_BELOW_17 + from ._array_api import _is_numpy_namespace, get_namespace from ._param_validation import Interval, validate_params from .extmath import _approximate_mode @@ -131,7 +133,17 @@ def _pyarrow_indexing(X, key, key_dtype, axis): key = np.asarray(key) if key_dtype == "bool": + # TODO(pyarrow): remove version checking and following if-branch when + # pyarrow==17.0.0 is the minimal version, see pyarrow issue + # https://github.com/apache/arrow/issues/42013 for more info + if PYARROW_VERSION_BELOW_17: + import pyarrow + + if not isinstance(key, pyarrow.BooleanArray): + key = pyarrow.array(key, type=pyarrow.bool_()) + X_indexed = X.filter(key) + else: X_indexed = X.take(key) diff --git a/sklearn/utils/fixes.py b/sklearn/utils/fixes.py index d85ef82680bbb..624938d6f0a82 100644 --- a/sklearn/utils/fixes.py +++ b/sklearn/utils/fixes.py @@ -392,3 +392,15 @@ def _in_unstable_openblas_configuration(): # See discussions in https://github.com/numpy/numpy/issues/19411 return True # pragma: no cover return False + + +# TODO(pyarrow): Remove when minimum pyarrow version is 17.0.0 +PYARROW_VERSION_BELOW_17 = False +try: + import pyarrow + + pyarrow_version = parse_version(pyarrow.__version__) + if pyarrow_version < parse_version("17.0.0"): + PYARROW_VERSION_BELOW_17 = True +except ModuleNotFoundError: # pragma: no cover + pass From 77b2afdf67d01eb7d38645f32e7a1bc4f05f220b Mon Sep 17 00:00:00 2001 From: Olivier Grisel Date: Thu, 26 Jun 2025 12:03:49 +0200 Subject: [PATCH 054/103] Fix `make_swiss_roll` docstring to resolve a copyright ambiguity (#31646) --- sklearn/datasets/_samples_generator.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/sklearn/datasets/_samples_generator.py b/sklearn/datasets/_samples_generator.py index e2d80422e7df7..c3b4622d6a91b 100644 --- a/sklearn/datasets/_samples_generator.py +++ b/sklearn/datasets/_samples_generator.py @@ -1864,6 +1864,8 @@ def make_swiss_roll(n_samples=100, *, noise=0.0, random_state=None, hole=False): Read more in the :ref:`User Guide `. + Adapted with permission from Stephen Marsland's code [1]. + Parameters ---------- n_samples : int, default=100 From 4c4cb5a5645fab143520ff81b110a684292a3f03 Mon Sep 17 00:00:00 2001 From: Lucy Liu Date: Fri, 27 Jun 2025 00:15:36 +1000 Subject: [PATCH 055/103] DOC Clarify how mixed array input types handled in array api (#31452) --- doc/modules/array_api.rst | 82 +++++++++++++++++++++++++++++++++------ 1 file changed, 71 insertions(+), 11 deletions(-) diff --git a/doc/modules/array_api.rst b/doc/modules/array_api.rst index e1a499c97506b..eb4a3f7be7ffa 100644 --- a/doc/modules/array_api.rst +++ b/doc/modules/array_api.rst @@ -178,29 +178,89 @@ Tools Coverage is expected to grow over time. Please follow the dedicated `meta-issue on GitHub `_ to track progress. -Type of return values and fitted attributes -------------------------------------------- +Input and output array type handling +==================================== -When calling functions or methods with Array API compatible inputs, the -convention is to return array values of the same array container type and +Estimators and scoring functions are able to accept input arrays +from different array libraries and/or devices. When a mixed set of input arrays is +passed, scikit-learn converts arrays as needed to make them all consistent. + +For estimators, the rule is **"everything follows `X`"** - mixed array inputs are +converted so that they all match the array library and device of `X`. +For scoring functions the rule is **"everything follows `y_pred`"** - mixed array +inputs are converted so that they all match the array library and device of `y_pred`. + +When a function or method has been called with array API compatible inputs, the +convention is to return arrays from the same array library and on the same device as the input data. -Similarly, when an estimator is fitted with Array API compatible inputs, the -fitted attributes will be arrays from the same library as the input and stored -on the same device. The `predict` and `transform` method subsequently expect +Estimators +---------- + +When an estimator is fitted with an array API compatible `X`, all other +array inputs, including constructor arguments, (e.g., `y`, `sample_weight`) +will be converted to match the array library and device of `X`, if they do not already. +This behaviour enables switching from processing on the CPU to processing +on the GPU at any point within a pipeline. + +This allows estimators to accept mixed input types, enabling `X` to be moved +to a different device within a pipeline, without explicitly moving `y`. +Note that scikit-learn pipelines do not allow transformation of `y` (to avoid +:ref:`leakage `). + +Take for example a pipeline where `X` and `y` both start on CPU, and go through +the following three steps: + +* :class:`~sklearn.preprocessing.TargetEncoder`, which will transform categorial + `X` but also requires `y`, meaning both `X` and `y` need to be on CPU. +* :class:`FunctionTransformer(func=partial(torch.asarray, device="cuda")) `, + which moves `X` to GPU, to improve performance in the next step. +* :class:`~sklearn.linear_model.Ridge`, whose performance can be improved when + passed arrays on a GPU, as they can handle large matrix operations very efficiently. + +`X` initially contains categorical string data (thus needs to be on CPU), which is +target encoded to numerical values in :class:`~sklearn.preprocessing.TargetEncoder`. +`X` is then explicitly moved to GPU to improve the performance of +:class:`~sklearn.linear_model.Ridge`. `y` cannot be transformed by the pipeline +(recall scikit-learn pipelines do not allow transformation of `y`) but as +:class:`~sklearn.linear_model.Ridge` is able to accept mixed input types, +this is not a problem and the pipeline is able to be run. + +The fitted attributes of an estimator fitted with an array API compatible `X`, will +be arrays from the same library as the input and stored on the same device. +The `predict` and `transform` method subsequently expect inputs from the same array library and device as the data passed to the `fit` method. -Note however that scoring functions that return scalar values return Python -scalars (typically a `float` instance) instead of an array scalar value. +Scoring functions +----------------- + +When an array API compatible `y_pred` is passed to a scoring function, +all other array inputs (e.g., `y_true`, `sample_weight`) will be converted +to match the array library and device of `y_pred`, if they do not already. +This allows scoring functions to accept mixed input types, enabling them to be +used within a :term:`meta-estimator` (or function that accepts estimators), with a +pipeline that moves input arrays between devices (e.g., CPU to GPU). + +For example, to be able to use the pipeline described above within e.g., +:func:`~sklearn.model_selection.cross_validate` or +:class:`~sklearn.model_selection.GridSearchCV`, the scoring function internally +called needs to be able to accept mixed input types. + +The output type of scoring functions depends on the number of output values. +When a scoring function returns a scalar value, it will return a Python +scalar (typically a `float` instance) instead of an array scalar value. +For scoring functions that support :term:`multiclass` or :term:`multioutput`, +an array from the same array library and device as `y_pred` will be returned when +multiple values need to be output. Common estimator checks ======================= Add the `array_api_support` tag to an estimator's set of tags to indicate that -it supports the Array API. This will enable dedicated checks as part of the +it supports the array API. This will enable dedicated checks as part of the common tests to verify that the estimators' results are the same when using -vanilla NumPy and Array API inputs. +vanilla NumPy and array API inputs. To run these checks you need to install `array-api-strict `_ in your From 77d38fa2bf4e5ea5ebccb3f0063f4afc390ae628 Mon Sep 17 00:00:00 2001 From: Michael Burkhart Date: Sun, 29 Jun 2025 12:08:18 -0400 Subject: [PATCH 056/103] DOC Fixed typo (#31667) --- sklearn/metrics/_classification.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/metrics/_classification.py b/sklearn/metrics/_classification.py index 361e8825f3601..168cb025a5779 100644 --- a/sklearn/metrics/_classification.py +++ b/sklearn/metrics/_classification.py @@ -3539,7 +3539,7 @@ def brier_score_loss( When True, scale the Brier score by 1/2 to lie in the [0, 1] range instead of the [0, 2] range. The default "auto" option implements the rescaling to [0, 1] only for binary classification (as customary) but keeps the - original [0, 2] range for multiclasss classification. + original [0, 2] range for multiclass classification. .. versionadded:: 1.7 From dac8ed91f5700934fe1d5b86ab445bcce5ec2b69 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Sun, 29 Jun 2025 18:34:08 +0200 Subject: [PATCH 057/103] MNT Remove deprecated `iprint` and `disp` usage in scipy 1.15 LBFGS (#31642) Co-authored-by: Tim Head --- sklearn/linear_model/_glm/_newton_solver.py | 3 ++- sklearn/linear_model/_glm/glm.py | 3 ++- sklearn/linear_model/_huber.py | 7 ++++++- sklearn/linear_model/_logistic.py | 3 ++- sklearn/neighbors/_nca.py | 6 +++++- sklearn/neural_network/_multilayer_perceptron.py | 3 ++- sklearn/utils/fixes.py | 10 ++++++++++ 7 files changed, 29 insertions(+), 6 deletions(-) diff --git a/sklearn/linear_model/_glm/_newton_solver.py b/sklearn/linear_model/_glm/_newton_solver.py index c5c940bed6c39..24085f903882f 100644 --- a/sklearn/linear_model/_glm/_newton_solver.py +++ b/sklearn/linear_model/_glm/_newton_solver.py @@ -14,6 +14,7 @@ from ..._loss.loss import HalfSquaredError from ...exceptions import ConvergenceWarning +from ...utils.fixes import _get_additional_lbfgs_options_dict from ...utils.optimize import _check_optimize_result from .._linear_loss import LinearModelLoss @@ -187,9 +188,9 @@ def fallback_lbfgs_solve(self, X, y, sample_weight): options={ "maxiter": max_iter, "maxls": 50, # default is 20 - "iprint": self.verbose - 1, "gtol": self.tol, "ftol": 64 * np.finfo(np.float64).eps, + **_get_additional_lbfgs_options_dict("iprint", self.verbose - 1), }, args=(X, y, sample_weight, self.l2_reg_strength, self.n_threads), ) diff --git a/sklearn/linear_model/_glm/glm.py b/sklearn/linear_model/_glm/glm.py index 7f138f420dc36..8ba24878b95b2 100644 --- a/sklearn/linear_model/_glm/glm.py +++ b/sklearn/linear_model/_glm/glm.py @@ -21,6 +21,7 @@ from ...utils import check_array from ...utils._openmp_helpers import _openmp_effective_n_threads from ...utils._param_validation import Hidden, Interval, StrOptions +from ...utils.fixes import _get_additional_lbfgs_options_dict from ...utils.optimize import _check_optimize_result from ...utils.validation import _check_sample_weight, check_is_fitted, validate_data from .._linear_loss import LinearModelLoss @@ -273,12 +274,12 @@ def fit(self, X, y, sample_weight=None): options={ "maxiter": self.max_iter, "maxls": 50, # default is 20 - "iprint": self.verbose - 1, "gtol": self.tol, # The constant 64 was found empirically to pass the test suite. # The point is that ftol is very small, but a bit larger than # machine precision for float64, which is the dtype used by lbfgs. "ftol": 64 * np.finfo(float).eps, + **_get_additional_lbfgs_options_dict("iprint", self.verbose - 1), }, args=(X, y, sample_weight, l2_reg_strength, n_threads), ) diff --git a/sklearn/linear_model/_huber.py b/sklearn/linear_model/_huber.py index 51f24035a3c83..87e735ec998db 100644 --- a/sklearn/linear_model/_huber.py +++ b/sklearn/linear_model/_huber.py @@ -10,6 +10,7 @@ from ..utils._mask import axis0_safe_slice from ..utils._param_validation import Interval from ..utils.extmath import safe_sparse_dot +from ..utils.fixes import _get_additional_lbfgs_options_dict from ..utils.optimize import _check_optimize_result from ..utils.validation import _check_sample_weight, validate_data from ._base import LinearModel @@ -329,7 +330,11 @@ def fit(self, X, y, sample_weight=None): method="L-BFGS-B", jac=True, args=(X, y, self.epsilon, self.alpha, sample_weight), - options={"maxiter": self.max_iter, "gtol": self.tol, "iprint": -1}, + options={ + "maxiter": self.max_iter, + "gtol": self.tol, + **_get_additional_lbfgs_options_dict("iprint", -1), + }, bounds=bounds, ) diff --git a/sklearn/linear_model/_logistic.py b/sklearn/linear_model/_logistic.py index b85c01ee69f9e..2c564bb1a8b5a 100644 --- a/sklearn/linear_model/_logistic.py +++ b/sklearn/linear_model/_logistic.py @@ -30,6 +30,7 @@ ) from ..utils._param_validation import Hidden, Interval, StrOptions from ..utils.extmath import row_norms, softmax +from ..utils.fixes import _get_additional_lbfgs_options_dict from ..utils.metadata_routing import ( MetadataRouter, MethodMapping, @@ -464,9 +465,9 @@ def _logistic_regression_path( options={ "maxiter": max_iter, "maxls": 50, # default is 20 - "iprint": iprint, "gtol": tol, "ftol": 64 * np.finfo(float).eps, + **_get_additional_lbfgs_options_dict("iprint", iprint), }, ) n_iter_i = _check_optimize_result( diff --git a/sklearn/neighbors/_nca.py b/sklearn/neighbors/_nca.py index a4ef3c303b851..8383f95338932 100644 --- a/sklearn/neighbors/_nca.py +++ b/sklearn/neighbors/_nca.py @@ -25,6 +25,7 @@ from ..preprocessing import LabelEncoder from ..utils._param_validation import Interval, StrOptions from ..utils.extmath import softmax +from ..utils.fixes import _get_additional_lbfgs_options_dict from ..utils.multiclass import check_classification_targets from ..utils.random import check_random_state from ..utils.validation import check_array, check_is_fitted, validate_data @@ -312,7 +313,10 @@ def fit(self, X, y): "jac": True, "x0": transformation, "tol": self.tol, - "options": dict(maxiter=self.max_iter, disp=disp), + "options": dict( + maxiter=self.max_iter, + **_get_additional_lbfgs_options_dict("disp", disp), + ), "callback": self._callback, } diff --git a/sklearn/neural_network/_multilayer_perceptron.py b/sklearn/neural_network/_multilayer_perceptron.py index a8a00fe3b4ac5..e8260164202e6 100644 --- a/sklearn/neural_network/_multilayer_perceptron.py +++ b/sklearn/neural_network/_multilayer_perceptron.py @@ -31,6 +31,7 @@ ) from ..utils._param_validation import Interval, Options, StrOptions from ..utils.extmath import safe_sparse_dot +from ..utils.fixes import _get_additional_lbfgs_options_dict from ..utils.metaestimators import available_if from ..utils.multiclass import ( _check_partial_fit_first_call, @@ -585,8 +586,8 @@ def _fit_lbfgs( options={ "maxfun": self.max_fun, "maxiter": self.max_iter, - "iprint": iprint, "gtol": self.tol, + **_get_additional_lbfgs_options_dict("iprint", iprint), }, args=( X, diff --git a/sklearn/utils/fixes.py b/sklearn/utils/fixes.py index 624938d6f0a82..5ceb9930b993b 100644 --- a/sklearn/utils/fixes.py +++ b/sklearn/utils/fixes.py @@ -394,6 +394,16 @@ def _in_unstable_openblas_configuration(): return False +# TODO: Remove when Scipy 1.15 is the minimum supported version. In scipy 1.15, +# the internal info details (via 'iprint' and 'disp' options) were dropped, +# following the LBFGS rewrite from Fortran to C, see +# https://github.com/scipy/scipy/issues/23186#issuecomment-2987801035. For +# scipy 1.15, 'iprint' and 'disp' have no effect and for scipy >= 1.16 a +# DeprecationWarning is emitted. +def _get_additional_lbfgs_options_dict(key, value): + return {} if sp_version >= parse_version("1.15") else {key: value} + + # TODO(pyarrow): Remove when minimum pyarrow version is 17.0.0 PYARROW_VERSION_BELOW_17 = False try: From 86da0f65b48de46d8cc4a94ef334b4f0f1dd9633 Mon Sep 17 00:00:00 2001 From: Sylvain Combettes <48064216+sylvaincom@users.noreply.github.com> Date: Sun, 29 Jun 2025 19:09:10 +0200 Subject: [PATCH 058/103] DOC fix minor typo in `TimeSeriesSplit` docstrings (#31640) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Jérémie du Boisberranger --- sklearn/model_selection/_split.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/sklearn/model_selection/_split.py b/sklearn/model_selection/_split.py index ee85af7fe39e6..640b7f6eee2f0 100644 --- a/sklearn/model_selection/_split.py +++ b/sklearn/model_selection/_split.py @@ -1110,10 +1110,10 @@ class TimeSeriesSplit(_BaseKFold): while the train set size accumulates data from previous splits. This cross-validation object is a variation of :class:`KFold`. - In the kth split, it returns first k folds as train set and the - (k+1)th fold as test set. + In the k-th split, it returns the first k folds as the train set and the + (k+1)-th fold as the test set. - Note that unlike standard cross-validation methods, successive + Note that, unlike standard cross-validation methods, successive training sets are supersets of those that come before them. Read more in the :ref:`User Guide `. From a9daa12bed1cdb9111dfe35e6247e79711294a2d Mon Sep 17 00:00:00 2001 From: "Christine P. Chai" Date: Sun, 29 Jun 2025 10:13:20 -0700 Subject: [PATCH 059/103] DOC Fix typo math formating (#31622) --- doc/modules/model_evaluation.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/modules/model_evaluation.rst b/doc/modules/model_evaluation.rst index c304966fccdb2..cca1ec88c23cd 100644 --- a/doc/modules/model_evaluation.rst +++ b/doc/modules/model_evaluation.rst @@ -1950,7 +1950,7 @@ achieves the best score only when the estimated probabilities equal the true ones. Note that in the binary case, the Brier score is usually divided by two and -ranges between :math:`[0,1]`. For binary targets :math:`y_i \in {0, 1}` and +ranges between :math:`[0,1]`. For binary targets :math:`y_i \in \{0, 1\}` and probability estimates :math:`\hat{p}_i \approx \operatorname{Pr}(y_i = 1)` for the positive class, the Brier score is then equal to: From df66d126ff09bd90754b5bc86e81f8cb9e1e2492 Mon Sep 17 00:00:00 2001 From: Shaurya Bisht <87357655+ShauryaDusht@users.noreply.github.com> Date: Sun, 29 Jun 2025 23:45:21 +0530 Subject: [PATCH 060/103] DOC: Remove FIXME tags from glossary (#31677) --- doc/glossary.rst | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/doc/glossary.rst b/doc/glossary.rst index caf6b952553c4..ae6ea4dd46324 100644 --- a/doc/glossary.rst +++ b/doc/glossary.rst @@ -940,8 +940,11 @@ Class APIs and Estimator Types :class:`ensemble.BaggingClassifier`. In a meta-estimator's :term:`fit` method, any contained estimators - should be :term:`cloned` before they are fit (although FIXME: Pipeline - and FeatureUnion do not do this currently). An exception to this is + should be :term:`cloned` before they are fit. + + .. FIXME: Pipeline and FeatureUnion do not do this currently + + An exception to this is that an estimator may explicitly document that it accepts a pre-fitted estimator (e.g. using ``prefit=True`` in :class:`feature_selection.SelectFromModel`). One known issue with this @@ -1590,8 +1593,7 @@ functions or non-estimator constructors. estimators: some, but not all, use it to mean a single epoch (i.e. a pass over every sample in the data). - FIXME perhaps we should have some common tests about the relationship - between ConvergenceWarning and max_iter. + .. FIXME: perhaps we should have some common tests about the relationship between ConvergenceWarning and max_iter. ``memory`` Some estimators make use of :class:`joblib.Memory` to @@ -1859,12 +1861,11 @@ See concept :term:`sample property`. the weight. Weights may be specified as floats, so that sample weights are usually equivalent up to a constant positive scaling factor. - FIXME Is this interpretation always the case in practice? We have no - common tests. + .. FIXME: Is this interpretation always the case in practice? We have no common tests. Some estimators, such as decision trees, support negative weights. - FIXME: This feature or its absence may not be tested or documented in - many estimators. + + .. FIXME: This feature or its absence may not be tested or documented in many estimators. This is not entirely the case where other parameters of the model consider the number of samples in a region, as with ``min_samples`` in From 8bae7eb8df7154dcda8c61f40a16d7c4acef3d96 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 30 Jun 2025 10:22:53 +0200 Subject: [PATCH 061/103] :lock: :robot: CI Update lock files for free-threaded CI build(s) :lock: :robot: (#31629) Co-authored-by: Lock file bot --- ...pylatest_free_threaded_linux-64_conda.lock | 40 +++++++++---------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock b/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock index b90aab167e247..88727be760190 100644 --- a/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock +++ b/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock @@ -7,43 +7,43 @@ https://conda.anaconda.org/conda-forge/noarch/python_abi-3.13-7_cp313t.conda#df8 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https://conda.anaconda.org/conda-forge/linux-64/libllvm20-20.1.7-he9d0ab4_0.conda#63f1accca4913e6b66a2d546c30ff4db https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.10.0-h65c71a3_0.conda#fedf6bfe5d21d21d2b1785ec00a8889a https://conda.anaconda.org/conda-forge/linux-64/libxslt-1.1.39-h76b75d6_0.conda#e71f31f8cfb0a91439f2086fc8aa0461 @@ -188,8 +189,8 @@ https://conda.anaconda.org/conda-forge/linux-64/openldap-2.6.10-he970967_0.conda https://conda.anaconda.org/conda-forge/linux-64/pillow-11.2.1-py313h8db990d_0.conda#91b00afee98d72d29dc3d1c1ab0008d7 https://conda.anaconda.org/conda-forge/linux-64/prometheus-cpp-1.3.0-ha5d0236_0.conda#a83f6a2fdc079e643237887a37460668 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.9.1-pyhd8ed1ab_0.conda#22ae7c6ea81e0c8661ef32168dda929b -https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhff2d567_1.conda#5ba79d7c71f03c678c8ead841f347d6e -https://conda.anaconda.org/conda-forge/noarch/python-gil-3.13.5-h4df99d1_101.conda#5e543cf41c3f66e53a5f47a07d88d10c +https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda#5b8d21249ff20967101ffa321cab24e8 +https://conda.anaconda.org/conda-forge/noarch/python-gil-3.13.5-h4df99d1_102.conda#2eabcede0db21acee23c181db58b4128 https://conda.anaconda.org/conda-forge/linux-64/xcb-util-cursor-0.1.5-hb9d3cd8_0.conda#eb44b3b6deb1cab08d72cb61686fe64c https://conda.anaconda.org/conda-forge/linux-64/xorg-libxcomposite-0.4.6-hb9d3cd8_2.conda#d3c295b50f092ab525ffe3c2aa4b7413 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxcursor-1.2.3-hb9d3cd8_0.conda#2ccd714aa2242315acaf0a67faea780b @@ -207,19 +208,19 @@ https://conda.anaconda.org/conda-forge/linux-64/gmpy2-2.2.1-py313h11186cd_0.cond https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp20.1-20.1.7-default_h1df26ce_0.conda#f9ef7bce54a7673cdbc2fadd8bca1956 https://conda.anaconda.org/conda-forge/linux-64/libclang13-20.1.7-default_he06ed0a_0.conda#846875a174de6b6ff19e205a7d90eb74 https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.36.0-h2b5623c_0.conda#c96ca58ad3352a964bfcb85de6cd1496 -https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-31_he2f377e_openblas.conda#7e5fff7d0db69be3a266f7e79a3bb0e2 +https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-32_he2f377e_openblas.conda#54e7f7896d0dbf56665bcb0078bfa9d2 https://conda.anaconda.org/conda-forge/linux-64/libmagma-2.9.0-h45b15fe_0.conda#703a1ab01e36111d8bb40bc7517e900b https://conda.anaconda.org/conda-forge/linux-64/libopentelemetry-cpp-1.18.0-hfcad708_1.conda#1f5a5d66e77a39dc5bd639ec953705cf https://conda.anaconda.org/conda-forge/linux-64/libpq-17.5-h27ae623_0.conda#6458be24f09e1b034902ab44fe9de908 https://conda.anaconda.org/conda-forge/noarch/meson-python-0.18.0-pyh70fd9c4_0.conda#576c04b9d9f8e45285fb4d9452c26133 https://conda.anaconda.org/conda-forge/linux-64/numpy-2.2.6-py313h17eae1a_0.conda#7a2d2f9adecd86ed5c29c2115354f615 -https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.0-pyhd8ed1ab_0.conda#516d31f063ce7e49ced17f105b63a1f1 +https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.1-pyhd8ed1ab_0.conda#a49c2283f24696a7b30367b7346a0144 https://conda.anaconda.org/conda-forge/linux-64/tbb-2021.13.0-hceb3a55_1.conda#ba7726b8df7b9d34ea80e82b097a4893 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxtst-1.2.5-hb9d3cd8_3.conda#7bbe9a0cc0df0ac5f5a8ad6d6a11af2f -https://conda.anaconda.org/conda-forge/noarch/array-api-strict-2.3.1-pyhd8ed1ab_0.conda#11107d0aeb8c590a34fee0894909816b +https://conda.anaconda.org/conda-forge/noarch/array-api-strict-2.4-pyhe01879c_1.conda#61d4f8b95dac300a1b7f665bcc79653a https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.31.0-h55f77e1_4.conda#0627af705ed70681f5bede31e72348e5 https://conda.anaconda.org/conda-forge/linux-64/azure-storage-blobs-cpp-12.13.0-h3cf044e_1.conda#7eb66060455c7a47d9dcdbfa9f46579b -https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-31_h1ea3ea9_openblas.conda#ba652ee0576396d4765e567f043c57f9 +https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-32_h1ea3ea9_openblas.conda#34cb4b6753b38a62ae25f3a73efd16b0 https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.4-h3394656_0.conda#09262e66b19567aff4f592fb53b28760 https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.2-py313h33d0bda_0.conda#5dc81fffe102f63045225007a33d6199 https://conda.anaconda.org/conda-forge/linux-64/cupy-core-13.4.1-py313hc2a895b_1.conda#48458b46f4aaf023c876bddba25343db @@ -227,23 +228,23 @@ https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.36.0-h https://conda.anaconda.org/conda-forge/linux-64/libmagma_sparse-2.9.0-h45b15fe_0.conda#beac0a5bbe0af75db6b16d3d8fd24f7e https://conda.anaconda.org/conda-forge/linux-64/mkl-2024.2.2-ha957f24_16.conda#1459379c79dda834673426504d52b319 https://conda.anaconda.org/conda-forge/linux-64/pandas-2.3.0-py313ha87cce1_0.conda#8664b4fa9b5b23b0d1cdc55c7195fcfe -https://conda.anaconda.org/conda-forge/linux-64/polars-default-1.30.0-py39hfac2b71_0.conda#cd33cf1e631b4d766858c90e333b4832 +https://conda.anaconda.org/conda-forge/linux-64/polars-default-1.31.0-py39hfac2b71_0.conda#412f48979db22009a89706d57384756e https://conda.anaconda.org/conda-forge/noarch/pytest-cov-6.2.1-pyhd8ed1ab_0.conda#ce978e1b9ed8b8d49164e90a5cdc94cd https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.7.0-pyhd8ed1ab_0.conda#15353a2a0ea6dfefaa52fc5ab5b98f41 -https://conda.anaconda.org/conda-forge/linux-64/scipy-1.15.2-py313h86fcf2b_0.conda#ca68acd9febc86448eeed68d0c6c8643 +https://conda.anaconda.org/conda-forge/linux-64/scipy-1.16.0-py313h86fcf2b_0.conda#8c60fe574a5abab59cd365d32e279872 https://conda.anaconda.org/conda-forge/noarch/sympy-1.14.0-pyh2585a3b_105.conda#8c09fac3785696e1c477156192d64b91 https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.510-h37a5c72_3.conda#beb8577571033140c6897d257acc7724 https://conda.anaconda.org/conda-forge/linux-64/azure-storage-files-datalake-cpp-12.12.0-ha633028_1.conda#7c1980f89dd41b097549782121a73490 -https://conda.anaconda.org/conda-forge/linux-64/blas-2.131-openblas.conda#38b2ec894c69bb4be0e66d2ef7fc60bf +https://conda.anaconda.org/conda-forge/linux-64/blas-2.132-openblas.conda#9c4a27ab2463f9b1d9019e0a798a5b81 https://conda.anaconda.org/conda-forge/linux-64/cupy-13.4.1-py313h66a2ee2_1.conda#6019a63d505256ad144a011b51e9b8f3 https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-11.2.1-h3beb420_0.conda#0e6e192d4b3d95708ad192d957cf3163 https://conda.anaconda.org/conda-forge/linux-64/libtorch-2.4.1-cuda118_mkl_hee7131c_306.conda#28b3b3da11973494ed0100aa50f47328 https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.10.3-py313h129903b_0.conda#4f8816d006b1c155ec416bcf7ff6cee2 -https://conda.anaconda.org/conda-forge/linux-64/polars-1.30.0-default_h1443d73_0.conda#19698b29e8544d2dd615699826037039 +https://conda.anaconda.org/conda-forge/linux-64/polars-1.31.0-default_h1650462_0.conda#2372c82ef3c85bc1cc94025b9bf4d329 https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.2.1-py313hf0ab243_1.conda#4c769bf3858f424cb2ecf952175ec600 https://conda.anaconda.org/conda-forge/linux-64/libarrow-19.0.1-hc7b3859_3_cpu.conda#9ed3ded6da29dec8417f2e1db68798f2 https://conda.anaconda.org/conda-forge/linux-64/pytorch-2.4.1-cuda118_mkl_py313_h909c4c2_306.conda#de6e45613bbdb51127e9ff483c31bf41 -https://conda.anaconda.org/conda-forge/linux-64/qt6-main-6.9.1-h0384650_0.conda#e1f80d7fca560024b107368dd77d96be +https://conda.anaconda.org/conda-forge/linux-64/qt6-main-6.9.1-h0384650_1.conda#3610aa92d2de36047886f30e99342f21 https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-19.0.1-hcb10f89_3_cpu.conda#8f8dc214d89e06933f1bc1dcd2310b9c https://conda.anaconda.org/conda-forge/linux-64/libparquet-19.0.1-h081d1f1_3_cpu.conda#1d04307cdb1d8aeb5f55b047d5d403ea https://conda.anaconda.org/conda-forge/linux-64/pyarrow-core-19.0.1-py313he5f92c8_0_cpu.conda#7d8649531c807b24295c8f9a0a396a78 From 87648a69767b96da4cec41178ac030e988bfd98b Mon Sep 17 00:00:00 2001 From: Adrin Jalali Date: Mon, 30 Jun 2025 13:27:42 +0200 Subject: [PATCH 063/103] BLD fix missing license file (#31594) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Jérémie du Boisberranger --- build_tools/wheels/check_license.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/build_tools/wheels/check_license.py b/build_tools/wheels/check_license.py index 00fe4169be65d..bad33ae3cbc37 100644 --- a/build_tools/wheels/check_license.py +++ b/build_tools/wheels/check_license.py @@ -20,7 +20,7 @@ except StopIteration as e: raise RuntimeError("Unable to find scikit-learn's dist-info") from e -license_text = (distinfo_path / "COPYING").read_text() +license_text = (distinfo_path / "licenses" / "COPYING").read_text() assert "Copyright (c)" in license_text From 8e1c6909bd16ae525b579dab0a1b2205004e0440 Mon Sep 17 00:00:00 2001 From: Evgeni Burovski Date: Mon, 30 Jun 2025 14:57:18 +0200 Subject: [PATCH 064/103] MAINT: make scipy-doctest usage forward-compatible (#31609) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Jérémie du Boisberranger --- build_tools/azure/test_docs.sh | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/build_tools/azure/test_docs.sh b/build_tools/azure/test_docs.sh index f3f824d5806b0..f41072bf23a8b 100755 --- a/build_tools/azure/test_docs.sh +++ b/build_tools/azure/test_docs.sh @@ -14,8 +14,6 @@ if [[ "$scipy_doctest_installed" == "True" ]]; then # conda with putting conda in the PATH and source activate, rather than # source /etc/profile.d/conda.sh + conda activate. cd $TEST_DIR - # with scipy-doctest, --doctest-modules only runs doctests (in contrary to - # vanilla pytest where it runs doctests on top of normal tests) - python -m pytest --doctest-modules --pyargs sklearn + python -m pytest --doctest-modules --doctest-only-doctests=true --pyargs sklearn python -m pytest --doctest-modules $doc_rst_files fi From 0eaf1c3854089d34a4d8d5b2def5b4dd656b6af8 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Wed, 2 Jul 2025 11:10:02 +0200 Subject: [PATCH 065/103] :lock: :robot: CI Update lock files for scipy-dev CI build(s) :lock: :robot: (#31630) Co-authored-by: Lock file bot Co-authored-by: Olivier Grisel --- .../pylatest_pip_scipy_dev_environment.yml | 2 +- .../pylatest_pip_scipy_dev_linux-64_conda.lock | 17 +++++++++++------ .../update_environments_and_lock_files.py | 6 +++++- 3 files changed, 17 insertions(+), 8 deletions(-) diff --git a/build_tools/azure/pylatest_pip_scipy_dev_environment.yml b/build_tools/azure/pylatest_pip_scipy_dev_environment.yml index 01709b79e3720..4cfae9d333631 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_environment.yml +++ b/build_tools/azure/pylatest_pip_scipy_dev_environment.yml @@ -18,5 +18,5 @@ dependencies: - coverage - pooch - sphinx - - numpydoc + - numpydoc<1.9.0 - python-dateutil diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock index d51e606a390ca..fad69044932e5 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 7555819e95d879c5a5147e6431581e17ffc5d77e8a43b19c8a911821378d2521 +# input_hash: 1610c503ca7a3d6d0938907d0ff877bdd8a888e7be4c73fbe31e38633420a783 @EXPLICIT https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9 https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2025.2.25-h06a4308_0.conda#495015d24da8ad929e3ae2d18571016d @@ -13,20 +13,25 @@ https://repo.anaconda.com/pkgs/main/linux-64/_openmp_mutex-5.1-1_gnu.conda#71d28 https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-11.2.0-h1234567_1.conda#a87728dabf3151fb9cfa990bd2eb0464 https://repo.anaconda.com/pkgs/main/linux-64/bzip2-1.0.8-h5eee18b_6.conda#f21a3ff51c1b271977f53ce956a69297 https://repo.anaconda.com/pkgs/main/linux-64/expat-2.7.1-h6a678d5_0.conda#269942a9f3f943e2e5d8a2516a861f7c +https://repo.anaconda.com/pkgs/main/linux-64/fmt-9.1.0-hdb19cb5_1.conda#4f12930203ff2d84df5d287af9b29858 https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_1.conda#70646cc713f0c43926cfdcfe9b695fe0 +https://repo.anaconda.com/pkgs/main/linux-64/libhiredis-1.3.0-h6a678d5_0.conda#68b0289d6a3024e06b032f56dd7e46cf https://repo.anaconda.com/pkgs/main/linux-64/libmpdec-4.0.0-h5eee18b_0.conda#feb10f42b1a7b523acbf85461be41a3e https://repo.anaconda.com/pkgs/main/linux-64/libuuid-1.41.5-h5eee18b_0.conda#4a6a2354414c9080327274aa514e5299 +https://repo.anaconda.com/pkgs/main/linux-64/lz4-c-1.9.4-h6a678d5_1.conda#2ee58861f2b92b868ce761abb831819d https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda#5558eec6e2191741a92f832ea826251c https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.16-h5eee18b_0.conda#5875526739afa058cfa84da1fa7a2ef4 https://repo.anaconda.com/pkgs/main/linux-64/pthread-stubs-0.3-h0ce48e5_1.conda#973a642312d2a28927aaf5b477c67250 https://repo.anaconda.com/pkgs/main/linux-64/xorg-libxau-1.0.12-h9b100fa_0.conda#a8005a9f6eb903e113cd5363e8a11459 https://repo.anaconda.com/pkgs/main/linux-64/xorg-libxdmcp-1.1.5-h9b100fa_0.conda#c284a09ddfba81d9c4e740110f09ea06 https://repo.anaconda.com/pkgs/main/linux-64/xorg-xorgproto-2024.1-h5eee18b_1.conda#412a0d97a7a51d23326e57226189da92 +https://repo.anaconda.com/pkgs/main/linux-64/xxhash-0.8.0-h7f8727e_3.conda#196b013514e82fd8476558de622c0d46 https://repo.anaconda.com/pkgs/main/linux-64/xz-5.6.4-h5eee18b_1.conda#3581505fa450962d631bd82b8616350e https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_1.conda#92e42d8310108b0a440fb2e60b2b2a25 -https://repo.anaconda.com/pkgs/main/linux-64/ccache-3.7.9-hfe4627d_0.conda#bef6fc681c273bb7bd0c67d1a591365e https://repo.anaconda.com/pkgs/main/linux-64/libxcb-1.17.0-h9b100fa_0.conda#fdf0d380fa3809a301e2dbc0d5183883 https://repo.anaconda.com/pkgs/main/linux-64/readline-8.2-h5eee18b_0.conda#be42180685cce6e6b0329201d9f48efb +https://repo.anaconda.com/pkgs/main/linux-64/zstd-1.5.6-hc292b87_0.conda#78ae7abd3020b41f827b35085845e1b8 +https://repo.anaconda.com/pkgs/main/linux-64/ccache-4.11.3-hc6a6a4f_0.conda#3e660215a7953958c1eb910dde81eb52 https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.45.3-h5eee18b_0.conda#acf93d6aceb74d6110e20b44cc45939e https://repo.anaconda.com/pkgs/main/linux-64/xorg-libx11-1.8.12-h9b100fa_1.conda#6298b27afae6f49f03765b2a03df2fcb https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.14-h993c535_1.conda#bfe656b29fc64afe5d4bd46dbd5fd240 @@ -50,7 +55,7 @@ https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2 # pip packaging @ https://files.pythonhosted.org/packages/20/12/38679034af332785aac8774540895e234f4d07f7545804097de4b666afd8/packaging-25.0-py3-none-any.whl#sha256=29572ef2b1f17581046b3a2227d5c611fb25ec70ca1ba8554b24b0e69331a484 # pip platformdirs @ https://files.pythonhosted.org/packages/fe/39/979e8e21520d4e47a0bbe349e2713c0aac6f3d853d0e5b34d76206c439aa/platformdirs-4.3.8-py3-none-any.whl#sha256=ff7059bb7eb1179e2685604f4aaf157cfd9535242bd23742eadc3c13542139b4 # pip pluggy @ https://files.pythonhosted.org/packages/54/20/4d324d65cc6d9205fabedc306948156824eb9f0ee1633355a8f7ec5c66bf/pluggy-1.6.0-py3-none-any.whl#sha256=e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746 -# pip pygments @ https://files.pythonhosted.org/packages/8a/0b/9fcc47d19c48b59121088dd6da2488a49d5f72dacf8262e2790a1d2c7d15/pygments-2.19.1-py3-none-any.whl#sha256=9ea1544ad55cecf4b8242fab6dd35a93bbce657034b0611ee383099054ab6d8c +# pip pygments @ https://files.pythonhosted.org/packages/c7/21/705964c7812476f378728bdf590ca4b771ec72385c533964653c68e86bdc/pygments-2.19.2-py3-none-any.whl#sha256=86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b # pip roman-numerals-py @ https://files.pythonhosted.org/packages/53/97/d2cbbaa10c9b826af0e10fdf836e1bf344d9f0abb873ebc34d1f49642d3f/roman_numerals_py-3.1.0-py3-none-any.whl#sha256=9da2ad2fb670bcf24e81070ceb3be72f6c11c440d73bd579fbeca1e9f330954c # pip six @ https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl#sha256=4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274 # pip snowballstemmer @ https://files.pythonhosted.org/packages/c8/78/3565d011c61f5a43488987ee32b6f3f656e7f107ac2782dd57bdd7d91d9a/snowballstemmer-3.0.1-py3-none-any.whl#sha256=6cd7b3897da8d6c9ffb968a6781fa6532dce9c3618a4b127d920dab764a19064 @@ -62,15 +67,15 @@ https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2 # pip sphinxcontrib-serializinghtml @ https://files.pythonhosted.org/packages/52/a7/d2782e4e3f77c8450f727ba74a8f12756d5ba823d81b941f1b04da9d033a/sphinxcontrib_serializinghtml-2.0.0-py3-none-any.whl#sha256=6e2cb0eef194e10c27ec0023bfeb25badbbb5868244cf5bc5bdc04e4464bf331 # pip tabulate @ https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl#sha256=024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f # pip threadpoolctl @ https://files.pythonhosted.org/packages/32/d5/f9a850d79b0851d1d4ef6456097579a9005b31fea68726a4ae5f2d82ddd9/threadpoolctl-3.6.0-py3-none-any.whl#sha256=43a0b8fd5a2928500110039e43a5eed8480b918967083ea48dc3ab9f13c4a7fb -# pip urllib3 @ https://files.pythonhosted.org/packages/6b/11/cc635220681e93a0183390e26485430ca2c7b5f9d33b15c74c2861cb8091/urllib3-2.4.0-py3-none-any.whl#sha256=4e16665048960a0900c702d4a66415956a584919c03361cac9f1df5c5dd7e813 +# pip urllib3 @ https://files.pythonhosted.org/packages/a7/c2/fe1e52489ae3122415c51f387e221dd0773709bad6c6cdaa599e8a2c5185/urllib3-2.5.0-py3-none-any.whl#sha256=e6b01673c0fa6a13e374b50871808eb3bf7046c4b125b216f6bf1cc604cff0dc # pip jinja2 @ https://files.pythonhosted.org/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl#sha256=85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67 # pip pyproject-metadata @ https://files.pythonhosted.org/packages/7e/b1/8e63033b259e0a4e40dd1ec4a9fee17718016845048b43a36ec67d62e6fe/pyproject_metadata-0.9.1-py3-none-any.whl#sha256=ee5efde548c3ed9b75a354fc319d5afd25e9585fa918a34f62f904cc731973ad -# pip pytest @ https://files.pythonhosted.org/packages/2f/de/afa024cbe022b1b318a3d224125aa24939e99b4ff6f22e0ba639a2eaee47/pytest-8.4.0-py3-none-any.whl#sha256=f40f825768ad76c0977cbacdf1fd37c6f7a468e460ea6a0636078f8972d4517e +# pip pytest @ https://files.pythonhosted.org/packages/29/16/c8a903f4c4dffe7a12843191437d7cd8e32751d5de349d45d3fe69544e87/pytest-8.4.1-py3-none-any.whl#sha256=539c70ba6fcead8e78eebbf1115e8b589e7565830d7d006a8723f19ac8a0afb7 # pip python-dateutil @ https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl#sha256=a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 # pip requests @ https://files.pythonhosted.org/packages/7c/e4/56027c4a6b4ae70ca9de302488c5ca95ad4a39e190093d6c1a8ace08341b/requests-2.32.4-py3-none-any.whl#sha256=27babd3cda2a6d50b30443204ee89830707d396671944c998b5975b031ac2b2c # pip meson-python @ https://files.pythonhosted.org/packages/28/58/66db620a8a7ccb32633de9f403fe49f1b63c68ca94e5c340ec5cceeb9821/meson_python-0.18.0-py3-none-any.whl#sha256=3b0fe051551cc238f5febb873247c0949cd60ded556efa130aa57021804868e2 # pip pooch @ https://files.pythonhosted.org/packages/a8/87/77cc11c7a9ea9fd05503def69e3d18605852cd0d4b0d3b8f15bbeb3ef1d1/pooch-1.8.2-py3-none-any.whl#sha256=3529a57096f7198778a5ceefd5ac3ef0e4d06a6ddaf9fc2d609b806f25302c47 # pip pytest-cov @ https://files.pythonhosted.org/packages/bc/16/4ea354101abb1287856baa4af2732be351c7bee728065aed451b678153fd/pytest_cov-6.2.1-py3-none-any.whl#sha256=f5bc4c23f42f1cdd23c70b1dab1bbaef4fc505ba950d53e0081d0730dd7e86d5 -# pip pytest-xdist @ https://files.pythonhosted.org/packages/0d/b2/0e802fde6f1c5b2f7ae7e9ad42b83fd4ecebac18a8a8c2f2f14e39dce6e1/pytest_xdist-3.7.0-py3-none-any.whl#sha256=7d3fbd255998265052435eb9daa4e99b62e6fb9cfb6efd1f858d4d8c0c7f0ca0 +# pip pytest-xdist @ https://files.pythonhosted.org/packages/ca/31/d4e37e9e550c2b92a9cbc2e4d0b7420a27224968580b5a447f420847c975/pytest_xdist-3.8.0-py3-none-any.whl#sha256=202ca578cfeb7370784a8c33d6d05bc6e13b4f25b5053c30a152269fd10f0b88 # pip sphinx @ https://files.pythonhosted.org/packages/31/53/136e9eca6e0b9dc0e1962e2c908fbea2e5ac000c2a2fbd9a35797958c48b/sphinx-8.2.3-py3-none-any.whl#sha256=4405915165f13521d875a8c29c8970800a0141c14cc5416a38feca4ea5d9b9c3 # pip numpydoc @ https://files.pythonhosted.org/packages/6c/45/56d99ba9366476cd8548527667f01869279cedb9e66b28eb4dfb27701679/numpydoc-1.8.0-py3-none-any.whl#sha256=72024c7fd5e17375dec3608a27c03303e8ad00c81292667955c6fea7a3ccf541 diff --git a/build_tools/update_environments_and_lock_files.py b/build_tools/update_environments_and_lock_files.py index 8bec9d266b82c..5cfb51431360a 100644 --- a/build_tools/update_environments_and_lock_files.py +++ b/build_tools/update_environments_and_lock_files.py @@ -83,7 +83,11 @@ docstring_test_dependencies = ["sphinx", "numpydoc"] -default_package_constraints = {} +default_package_constraints = { + # TODO: remove once https://github.com/numpy/numpydoc/issues/638 is fixed + # and released. + "numpydoc": "<1.9.0", +} def remove_from(alist, to_remove): From 1ae703c02077e1b7e386cdaf3b164f3ae9cbb874 Mon Sep 17 00:00:00 2001 From: Olivier Grisel Date: Wed, 2 Jul 2025 11:26:40 +0200 Subject: [PATCH 066/103] MAINT upgrade CI to cibuildwheel 3.0 (#31688) Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/cuda-ci.yml | 2 +- .github/workflows/emscripten.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/cuda-ci.yml b/.github/workflows/cuda-ci.yml index 028ff06903e8a..49bdaed720b5d 100644 --- a/.github/workflows/cuda-ci.yml +++ b/.github/workflows/cuda-ci.yml @@ -18,7 +18,7 @@ jobs: - uses: actions/checkout@v4 - name: Build wheels - uses: pypa/cibuildwheel@faf86a6ed7efa889faf6996aa23820831055001a + uses: pypa/cibuildwheel@5f22145df44122af0f5a201f93cf0207171beca7 env: CIBW_BUILD: cp313-manylinux_x86_64 CIBW_MANYLINUX_X86_64_IMAGE: manylinux2014 diff --git a/.github/workflows/emscripten.yml b/.github/workflows/emscripten.yml index 6ed68496de8b2..dbd2439e9b32d 100644 --- a/.github/workflows/emscripten.yml +++ b/.github/workflows/emscripten.yml @@ -67,7 +67,7 @@ jobs: with: persist-credentials: false - - uses: pypa/cibuildwheel@faf86a6ed7efa889faf6996aa23820831055001a + - uses: pypa/cibuildwheel@5f22145df44122af0f5a201f93cf0207171beca7 env: CIBW_PLATFORM: pyodide SKLEARN_SKIP_OPENMP_TEST: "true" From 9c729452e9637d32f47ff4f2d033a86e0e07b463 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Wed, 2 Jul 2025 12:56:46 +0200 Subject: [PATCH 067/103] :lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#31632) Co-authored-by: Lock file bot Co-authored-by: Olivier Grisel --- build_tools/azure/debian_32bit_lock.txt | 4 +- ...latest_conda_forge_mkl_linux-64_conda.lock | 103 +++++++++--------- ...pylatest_conda_forge_mkl_osx-64_conda.lock | 35 +++--- ...test_conda_mkl_no_openmp_osx-64_conda.lock | 12 +- ...latest_pip_openblas_pandas_environment.yml | 2 +- ...st_pip_openblas_pandas_linux-64_conda.lock | 25 +++-- ...nblas_min_dependencies_linux-64_conda.lock | 22 ++-- ...forge_openblas_ubuntu_2204_environment.yml | 2 +- ...e_openblas_ubuntu_2204_linux-64_conda.lock | 52 ++++----- ...min_conda_forge_openblas_win-64_conda.lock | 8 +- build_tools/azure/ubuntu_atlas_lock.txt | 6 +- build_tools/circle/doc_environment.yml | 2 +- build_tools/circle/doc_linux-64_conda.lock | 84 +++++++------- .../doc_min_dependencies_linux-64_conda.lock | 64 +++++------ ...n_conda_forge_arm_linux-aarch64_conda.lock | 56 +++++----- sklearn/linear_model/_glm/tests/test_glm.py | 10 +- 16 files changed, 249 insertions(+), 238 deletions(-) diff --git a/build_tools/azure/debian_32bit_lock.txt b/build_tools/azure/debian_32bit_lock.txt index bb5a373786f0f..3439458550ccd 100644 --- a/build_tools/azure/debian_32bit_lock.txt +++ b/build_tools/azure/debian_32bit_lock.txt @@ -27,11 +27,11 @@ pluggy==1.6.0 # via # pytest # pytest-cov -pygments==2.19.1 +pygments==2.19.2 # via pytest pyproject-metadata==0.9.1 # via meson-python -pytest==8.4.0 +pytest==8.4.1 # via # -r build_tools/azure/debian_32bit_requirements.txt # pytest-cov diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index c7dd0f634b9da..aa3ea81d106df 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock @@ -20,7 +20,7 @@ https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-3_kmp_llvm.con https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab https://conda.anaconda.org/conda-forge/linux-64/libegl-1.7.0-ha4b6fd6_2.conda#c151d5eb730e9b7480e6d48c0fc44048 https://conda.anaconda.org/conda-forge/linux-64/libopengl-1.7.0-ha4b6fd6_2.conda#7df50d44d4a14d6c31a2c54f2cd92157 -https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_2.conda#ea8ac52380885ed41c1baa8f1d6d2b93 +https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_3.conda#9e60c55e725c20d23125a5f0dd69af5d 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https://repo.anaconda.com/pkgs/main/osx-64/python-3.12.11-he8d2d4c_0.conda#9783e45825df3d441392b7fa66759899 https://repo.anaconda.com/pkgs/main/osx-64/brotli-python-1.0.9-py312h6d0c2b6_9.conda#425936421fe402074163ac3ffe33a060 @@ -47,6 +50,7 @@ https://repo.anaconda.com/pkgs/main/osx-64/ninja-1.12.1-hecd8cb5_0.conda#ee3b660 https://repo.anaconda.com/pkgs/main/osx-64/openjpeg-2.5.2-h2d09ccc_1.conda#0f2e221843154b436b5982c695df627b https://repo.anaconda.com/pkgs/main/osx-64/packaging-24.2-py312hecd8cb5_0.conda#76512e47c9c37443444ef0624769f620 https://repo.anaconda.com/pkgs/main/osx-64/pluggy-1.5.0-py312hecd8cb5_0.conda#ca381e438f1dbd7986ac0fa0da70c9d8 +https://repo.anaconda.com/pkgs/main/osx-64/pygments-2.19.1-py312hecd8cb5_0.conda#ca4be8769d62deee6127c0bf3703b0f6 https://repo.anaconda.com/pkgs/main/osx-64/pyparsing-3.2.0-py312hecd8cb5_0.conda#e4086daaaed13f68cc8d5b9da7db73cc https://repo.anaconda.com/pkgs/main/noarch/python-tzdata-2025.2-pyhd3eb1b0_0.conda#5ac858f05dbf9d3cdb04d53516901247 https://repo.anaconda.com/pkgs/main/osx-64/pytz-2024.1-py312hecd8cb5_0.conda#2b28ec0e0d07f5c0c701f75200b1e8b6 @@ -60,7 +64,7 @@ https://repo.anaconda.com/pkgs/main/osx-64/fonttools-4.55.3-py312h46256e1_0.cond https://repo.anaconda.com/pkgs/main/osx-64/numpy-base-1.26.4-py312h6f81483_0.conda#87f73efbf26ab2e2ea7c32481a71bd47 https://repo.anaconda.com/pkgs/main/osx-64/pillow-11.1.0-py312h935ef2f_1.conda#c2f7a3f027cc93a3626d50b765b75dc5 https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2a700153fefe0e69438b18e1 -https://repo.anaconda.com/pkgs/main/osx-64/pytest-8.3.4-py312hecd8cb5_0.conda#b15ee02022967632dfa1672669228bee +https://repo.anaconda.com/pkgs/main/osx-64/pytest-8.4.1-py312hecd8cb5_0.conda#438421697d4806567af06bd006b26db0 https://repo.anaconda.com/pkgs/main/osx-64/python-dateutil-2.9.0post0-py312hecd8cb5_2.conda#1047dde28f78127dd9f6121e882926dd https://repo.anaconda.com/pkgs/main/osx-64/pytest-cov-6.0.0-py312hecd8cb5_0.conda#db697e319a4d1145363246a51eef0352 https://repo.anaconda.com/pkgs/main/osx-64/pytest-xdist-3.6.1-py312hecd8cb5_0.conda#38df9520774ee82bf143218f1271f936 diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml b/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml index 6c3da4bb863b4..ba17d37ff1555 100644 --- a/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml +++ b/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml @@ -24,7 +24,7 @@ dependencies: - pytest-cov - coverage - sphinx - - numpydoc + - numpydoc<1.9.0 - lightgbm - scikit-image - array-api-strict diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock index de1e1ef5447bd..8ae0316c42fad 100644 --- a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 50f16a0198b6eb575a737fee25051b52a644d72f5fca26bd661651a85fcb6a07 +# input_hash: 692a667e331896943137778007c0834c42c3aa297986d4f8eda8b51a7f158d98 @EXPLICIT https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9 https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2025.2.25-h06a4308_0.conda#495015d24da8ad929e3ae2d18571016d @@ -13,20 +13,25 @@ https://repo.anaconda.com/pkgs/main/linux-64/_openmp_mutex-5.1-1_gnu.conda#71d28 https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-11.2.0-h1234567_1.conda#a87728dabf3151fb9cfa990bd2eb0464 https://repo.anaconda.com/pkgs/main/linux-64/bzip2-1.0.8-h5eee18b_6.conda#f21a3ff51c1b271977f53ce956a69297 https://repo.anaconda.com/pkgs/main/linux-64/expat-2.7.1-h6a678d5_0.conda#269942a9f3f943e2e5d8a2516a861f7c +https://repo.anaconda.com/pkgs/main/linux-64/fmt-9.1.0-hdb19cb5_1.conda#4f12930203ff2d84df5d287af9b29858 https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_1.conda#70646cc713f0c43926cfdcfe9b695fe0 +https://repo.anaconda.com/pkgs/main/linux-64/libhiredis-1.3.0-h6a678d5_0.conda#68b0289d6a3024e06b032f56dd7e46cf https://repo.anaconda.com/pkgs/main/linux-64/libmpdec-4.0.0-h5eee18b_0.conda#feb10f42b1a7b523acbf85461be41a3e https://repo.anaconda.com/pkgs/main/linux-64/libuuid-1.41.5-h5eee18b_0.conda#4a6a2354414c9080327274aa514e5299 +https://repo.anaconda.com/pkgs/main/linux-64/lz4-c-1.9.4-h6a678d5_1.conda#2ee58861f2b92b868ce761abb831819d https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda#5558eec6e2191741a92f832ea826251c https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.16-h5eee18b_0.conda#5875526739afa058cfa84da1fa7a2ef4 https://repo.anaconda.com/pkgs/main/linux-64/pthread-stubs-0.3-h0ce48e5_1.conda#973a642312d2a28927aaf5b477c67250 https://repo.anaconda.com/pkgs/main/linux-64/xorg-libxau-1.0.12-h9b100fa_0.conda#a8005a9f6eb903e113cd5363e8a11459 https://repo.anaconda.com/pkgs/main/linux-64/xorg-libxdmcp-1.1.5-h9b100fa_0.conda#c284a09ddfba81d9c4e740110f09ea06 https://repo.anaconda.com/pkgs/main/linux-64/xorg-xorgproto-2024.1-h5eee18b_1.conda#412a0d97a7a51d23326e57226189da92 +https://repo.anaconda.com/pkgs/main/linux-64/xxhash-0.8.0-h7f8727e_3.conda#196b013514e82fd8476558de622c0d46 https://repo.anaconda.com/pkgs/main/linux-64/xz-5.6.4-h5eee18b_1.conda#3581505fa450962d631bd82b8616350e https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_1.conda#92e42d8310108b0a440fb2e60b2b2a25 -https://repo.anaconda.com/pkgs/main/linux-64/ccache-3.7.9-hfe4627d_0.conda#bef6fc681c273bb7bd0c67d1a591365e https://repo.anaconda.com/pkgs/main/linux-64/libxcb-1.17.0-h9b100fa_0.conda#fdf0d380fa3809a301e2dbc0d5183883 https://repo.anaconda.com/pkgs/main/linux-64/readline-8.2-h5eee18b_0.conda#be42180685cce6e6b0329201d9f48efb +https://repo.anaconda.com/pkgs/main/linux-64/zstd-1.5.6-hc292b87_0.conda#78ae7abd3020b41f827b35085845e1b8 +https://repo.anaconda.com/pkgs/main/linux-64/ccache-4.11.3-hc6a6a4f_0.conda#3e660215a7953958c1eb910dde81eb52 https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.45.3-h5eee18b_0.conda#acf93d6aceb74d6110e20b44cc45939e https://repo.anaconda.com/pkgs/main/linux-64/xorg-libx11-1.8.12-h9b100fa_1.conda#6298b27afae6f49f03765b2a03df2fcb https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.14-h993c535_1.conda#bfe656b29fc64afe5d4bd46dbd5fd240 @@ -53,11 +58,11 @@ https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2 # pip meson @ https://files.pythonhosted.org/packages/8e/6e/b9dfeac98dd508f88bcaff134ee0bf5e602caf3ccb5a12b5dd9466206df1/meson-1.8.2-py3-none-any.whl#sha256=274b49dbe26e00c9a591442dd30f4ae9da8ce11ce53d0f4682cd10a45d50f6fd # pip networkx @ https://files.pythonhosted.org/packages/eb/8d/776adee7bbf76365fdd7f2552710282c79a4ead5d2a46408c9043a2b70ba/networkx-3.5-py3-none-any.whl#sha256=0030d386a9a06dee3565298b4a734b68589749a544acbb6c412dc9e2489ec6ec # pip ninja @ https://files.pythonhosted.org/packages/eb/7a/455d2877fe6cf99886849c7f9755d897df32eaf3a0fba47b56e615f880f7/ninja-1.11.1.4-py3-none-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=096487995473320de7f65d622c3f1d16c3ad174797602218ca8c967f51ec38a0 -# pip numpy @ https://files.pythonhosted.org/packages/1c/12/734dce1087eed1875f2297f687e671cfe53a091b6f2f55f0c7241aad041b/numpy-2.3.0-cp313-cp313-manylinux_2_28_x86_64.whl#sha256=87717eb24d4a8a64683b7a4e91ace04e2f5c7c77872f823f02a94feee186168f +# pip numpy @ https://files.pythonhosted.org/packages/50/30/af1b277b443f2fb08acf1c55ce9d68ee540043f158630d62cef012750f9f/numpy-2.3.1-cp313-cp313-manylinux_2_28_x86_64.whl#sha256=5902660491bd7a48b2ec16c23ccb9124b8abfd9583c5fdfa123fe6b421e03de1 # pip packaging @ https://files.pythonhosted.org/packages/20/12/38679034af332785aac8774540895e234f4d07f7545804097de4b666afd8/packaging-25.0-py3-none-any.whl#sha256=29572ef2b1f17581046b3a2227d5c611fb25ec70ca1ba8554b24b0e69331a484 -# pip pillow @ https://files.pythonhosted.org/packages/13/eb/2552ecebc0b887f539111c2cd241f538b8ff5891b8903dfe672e997529be/pillow-11.2.1-cp313-cp313-manylinux_2_28_x86_64.whl#sha256=ad275964d52e2243430472fc5d2c2334b4fc3ff9c16cb0a19254e25efa03a155 +# pip pillow @ https://files.pythonhosted.org/packages/d5/1c/a2a29649c0b1983d3ef57ee87a66487fdeb45132df66ab30dd37f7dbe162/pillow-11.3.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl#sha256=13f87d581e71d9189ab21fe0efb5a23e9f28552d5be6979e84001d3b8505abe8 # pip pluggy @ https://files.pythonhosted.org/packages/54/20/4d324d65cc6d9205fabedc306948156824eb9f0ee1633355a8f7ec5c66bf/pluggy-1.6.0-py3-none-any.whl#sha256=e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746 -# pip pygments @ https://files.pythonhosted.org/packages/8a/0b/9fcc47d19c48b59121088dd6da2488a49d5f72dacf8262e2790a1d2c7d15/pygments-2.19.1-py3-none-any.whl#sha256=9ea1544ad55cecf4b8242fab6dd35a93bbce657034b0611ee383099054ab6d8c +# pip pygments @ https://files.pythonhosted.org/packages/c7/21/705964c7812476f378728bdf590ca4b771ec72385c533964653c68e86bdc/pygments-2.19.2-py3-none-any.whl#sha256=86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b # pip pyparsing @ https://files.pythonhosted.org/packages/05/e7/df2285f3d08fee213f2d041540fa4fc9ca6c2d44cf36d3a035bf2a8d2bcc/pyparsing-3.2.3-py3-none-any.whl#sha256=a749938e02d6fd0b59b356ca504a24982314bb090c383e3cf201c95ef7e2bfcf # pip pytz @ https://files.pythonhosted.org/packages/81/c4/34e93fe5f5429d7570ec1fa436f1986fb1f00c3e0f43a589fe2bbcd22c3f/pytz-2025.2-py2.py3-none-any.whl#sha256=5ddf76296dd8c44c26eb8f4b6f35488f3ccbf6fbbd7adee0b7262d43f0ec2f00 # pip roman-numerals-py @ https://files.pythonhosted.org/packages/53/97/d2cbbaa10c9b826af0e10fdf836e1bf344d9f0abb873ebc34d1f49642d3f/roman_numerals_py-3.1.0-py3-none-any.whl#sha256=9da2ad2fb670bcf24e81070ceb3be72f6c11c440d73bd579fbeca1e9f330954c @@ -72,17 +77,17 @@ https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2 # pip tabulate @ https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl#sha256=024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f # pip threadpoolctl @ https://files.pythonhosted.org/packages/32/d5/f9a850d79b0851d1d4ef6456097579a9005b31fea68726a4ae5f2d82ddd9/threadpoolctl-3.6.0-py3-none-any.whl#sha256=43a0b8fd5a2928500110039e43a5eed8480b918967083ea48dc3ab9f13c4a7fb # pip tzdata @ https://files.pythonhosted.org/packages/5c/23/c7abc0ca0a1526a0774eca151daeb8de62ec457e77262b66b359c3c7679e/tzdata-2025.2-py2.py3-none-any.whl#sha256=1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8 -# pip urllib3 @ https://files.pythonhosted.org/packages/6b/11/cc635220681e93a0183390e26485430ca2c7b5f9d33b15c74c2861cb8091/urllib3-2.4.0-py3-none-any.whl#sha256=4e16665048960a0900c702d4a66415956a584919c03361cac9f1df5c5dd7e813 -# pip array-api-strict @ https://files.pythonhosted.org/packages/fe/c7/a97e26083985b49a7a54006364348cf1c26e5523850b8522a39b02b19715/array_api_strict-2.3.1-py3-none-any.whl#sha256=0ca6988be1c82d2f05b6cd44bc7e14cb390555d1455deb50f431d6d0cf468ded +# pip urllib3 @ https://files.pythonhosted.org/packages/a7/c2/fe1e52489ae3122415c51f387e221dd0773709bad6c6cdaa599e8a2c5185/urllib3-2.5.0-py3-none-any.whl#sha256=e6b01673c0fa6a13e374b50871808eb3bf7046c4b125b216f6bf1cc604cff0dc +# pip array-api-strict @ https://files.pythonhosted.org/packages/e5/33/cede42b7b866db4b77432889314fc652ecc5cb6988f831ef08881a767089/array_api_strict-2.4-py3-none-any.whl#sha256=1cb20acd008f171ad8cce49589cc59897d8a242d1acf8ce6a61c3d57b61ecd14 # pip contourpy @ https://files.pythonhosted.org/packages/c8/65/5245ce8c548a8422236c13ffcdcdada6a2a812c361e9e0c70548bb40b661/contourpy-1.3.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=434f0adf84911c924519d2b08fc10491dd282b20bdd3fa8f60fd816ea0b48841 # pip imageio @ https://files.pythonhosted.org/packages/cb/bd/b394387b598ed84d8d0fa90611a90bee0adc2021820ad5729f7ced74a8e2/imageio-2.37.0-py3-none-any.whl#sha256=11efa15b87bc7871b61590326b2d635439acc321cf7f8ce996f812543ce10eed # pip jinja2 @ https://files.pythonhosted.org/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl#sha256=85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67 # pip lazy-loader @ https://files.pythonhosted.org/packages/83/60/d497a310bde3f01cb805196ac61b7ad6dc5dcf8dce66634dc34364b20b4f/lazy_loader-0.4-py3-none-any.whl#sha256=342aa8e14d543a154047afb4ba8ef17f5563baad3fc610d7b15b213b0f119efc # pip pyproject-metadata @ https://files.pythonhosted.org/packages/7e/b1/8e63033b259e0a4e40dd1ec4a9fee17718016845048b43a36ec67d62e6fe/pyproject_metadata-0.9.1-py3-none-any.whl#sha256=ee5efde548c3ed9b75a354fc319d5afd25e9585fa918a34f62f904cc731973ad -# pip pytest @ https://files.pythonhosted.org/packages/2f/de/afa024cbe022b1b318a3d224125aa24939e99b4ff6f22e0ba639a2eaee47/pytest-8.4.0-py3-none-any.whl#sha256=f40f825768ad76c0977cbacdf1fd37c6f7a468e460ea6a0636078f8972d4517e +# pip pytest @ https://files.pythonhosted.org/packages/29/16/c8a903f4c4dffe7a12843191437d7cd8e32751d5de349d45d3fe69544e87/pytest-8.4.1-py3-none-any.whl#sha256=539c70ba6fcead8e78eebbf1115e8b589e7565830d7d006a8723f19ac8a0afb7 # pip python-dateutil @ https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl#sha256=a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 # pip requests @ https://files.pythonhosted.org/packages/7c/e4/56027c4a6b4ae70ca9de302488c5ca95ad4a39e190093d6c1a8ace08341b/requests-2.32.4-py3-none-any.whl#sha256=27babd3cda2a6d50b30443204ee89830707d396671944c998b5975b031ac2b2c -# pip scipy @ https://files.pythonhosted.org/packages/b5/09/c5b6734a50ad4882432b6bb7c02baf757f5b2f256041da5df242e2d7e6b6/scipy-1.15.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=c9deabd6d547aee2c9a81dee6cc96c6d7e9a9b1953f74850c179f91fdc729cb7 +# pip scipy @ https://files.pythonhosted.org/packages/11/6b/3443abcd0707d52e48eb315e33cc669a95e29fc102229919646f5a501171/scipy-1.16.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl#sha256=1d8747f7736accd39289943f7fe53a8333be7f15a82eea08e4afe47d79568c32 # pip tifffile @ https://files.pythonhosted.org/packages/3a/d8/1ba8f32bfc9cb69e37edeca93738e883f478fbe84ae401f72c0d8d507841/tifffile-2025.6.11-py3-none-any.whl#sha256=32effb78b10b3a283eb92d4ebf844ae7e93e151458b0412f38518b4e6d2d7542 # pip lightgbm @ https://files.pythonhosted.org/packages/42/86/dabda8fbcb1b00bcfb0003c3776e8ade1aa7b413dff0a2c08f457dace22f/lightgbm-4.6.0-py3-none-manylinux_2_28_x86_64.whl#sha256=cb19b5afea55b5b61cbb2131095f50538bd608a00655f23ad5d25ae3e3bf1c8d # pip matplotlib @ https://files.pythonhosted.org/packages/f5/64/41c4367bcaecbc03ef0d2a3ecee58a7065d0a36ae1aa817fe573a2da66d4/matplotlib-3.10.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=a80fcccbef63302c0efd78042ea3c2436104c5b1a4d3ae20f864593696364ac7 @@ -90,7 +95,7 @@ https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2 # pip pandas @ 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pytest-xdist @ https://files.pythonhosted.org/packages/ca/31/d4e37e9e550c2b92a9cbc2e4d0b7420a27224968580b5a447f420847c975/pytest_xdist-3.8.0-py3-none-any.whl#sha256=202ca578cfeb7370784a8c33d6d05bc6e13b4f25b5053c30a152269fd10f0b88 # pip scikit-image @ https://files.pythonhosted.org/packages/cd/9b/c3da56a145f52cd61a68b8465d6a29d9503bc45bc993bb45e84371c97d94/scikit_image-0.25.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=b8abd3c805ce6944b941cfed0406d88faeb19bab3ed3d4b50187af55cf24d147 # pip scipy-doctest @ https://files.pythonhosted.org/packages/c9/13/cd25d1875f3804b73fd4a4ae00e2c76e274e1e0608d79148cac251b644b1/scipy_doctest-1.8.0-py3-none-any.whl#sha256=5863208368c35486e143ce3283ab2f517a0d6b0c63d0d5f19f38a823fc82016f # pip sphinx @ https://files.pythonhosted.org/packages/31/53/136e9eca6e0b9dc0e1962e2c908fbea2e5ac000c2a2fbd9a35797958c48b/sphinx-8.2.3-py3-none-any.whl#sha256=4405915165f13521d875a8c29c8970800a0141c14cc5416a38feca4ea5d9b9c3 diff --git 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+https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_3.conda#9e60c55e725c20d23125a5f0dd69af5d https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.14-hb9d3cd8_0.conda#76df83c2a9035c54df5d04ff81bcc02d https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.5-hb9d3cd8_0.conda#f7f0d6cc2dc986d42ac2689ec88192be https://conda.anaconda.org/conda-forge/linux-64/gettext-tools-0.24.1-h5888daf_0.conda#d54305672f0361c2f3886750e7165b5f https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.24-h86f0d12_0.conda#64f0c503da58ec25ebd359e4d990afa8 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.0-h5888daf_0.conda#db0bfbe7dd197b68ad5f30333bae6ce0 https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.6-h2dba641_1.conda#ede4673863426c0883c0063d853bbd85 -https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.1.0-h69a702a_2.conda#ddca86c7040dd0e73b2b69bd7833d225 +https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.1.0-h69a702a_3.conda#e66f2b8ad787e7beb0f846e4bd7e8493 https://conda.anaconda.org/conda-forge/linux-64/libgettextpo-0.24.1-h5888daf_0.conda#2ee6d71b72f75d50581f2f68e965efdb -https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.1.0-hcea5267_2.conda#01de444988ed960031dbe84cf4f9b1fc +https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.1.0-hcea5267_3.conda#530566b68c3b8ce7eec4cd047eae19fe https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h4ce23a2_1.conda#e796ff8ddc598affdf7c173d6145f087 https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.1.0-hb9d3cd8_0.conda#9fa334557db9f63da6c9285fd2a48638 https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_2.conda#1a580f7796c7bf6393fddb8bbbde58dc @@ -36,12 +36,12 @@ https://conda.anaconda.org/conda-forge/linux-64/libnuma-2.0.18-hb9d3cd8_3.conda# 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https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.0-h7b32b05_1.conda#de356753cfdbffcde5bb1e86e3aa6cd0 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.1-h7b32b05_0.conda#c87df2ab1448ba69169652ab9547082d https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-hb9d3cd8_1002.conda#b3c17d95b5a10c6e64a21fa17573e70e https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.2-hb9d3cd8_0.conda#fb901ff28063514abb6046c9ec2c4a45 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-hb9d3cd8_0.conda#f6ebe2cb3f82ba6c057dde5d9debe4f7 @@ -62,13 +62,13 @@ https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20250104-pl5321h7949 https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda#172bf1cd1ff8629f2b1179945ed45055 https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda#a1cfcc585f0c42bf8d5546bb1dfb668d https://conda.anaconda.org/conda-forge/linux-64/libgettextpo-devel-0.24.1-h5888daf_0.conda#8f04c7aae6a46503bc36d1ed5abc8c7c -https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.1.0-h69a702a_2.conda#f92e6e0a3c0c0c85561ef61aa59d555d +https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.1.0-h69a702a_3.conda#bfbca721fd33188ef923dfe9ba172f29 https://conda.anaconda.org/conda-forge/linux-64/libgpg-error-1.55-h3f2d84a_0.conda#2bd47db5807daade8500ed7ca4c512a4 https://conda.anaconda.org/conda-forge/linux-64/liblzma-devel-5.8.1-hb9d3cd8_2.conda#f61edadbb301530bd65a32646bd81552 https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.49-h943b412_0.conda#37511c874cf3b8d0034c8d24e73c0884 -https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.50.1-hee588c1_4.conda#c79ba4d93602695bc60c6960ee59d2b1 +https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.50.2-h6cd9bfd_0.conda#b04c7eda6d7dab1e6503135e7fad4d25 https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.11.1-hcf80075_0.conda#eecce068c7e4eddeb169591baac20ac4 -https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.1.0-h4852527_2.conda#9d2072af184b5caa29492bf2344597bb +https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.1.0-h4852527_3.conda#57541755b5a51691955012b8e197c06c https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda#40b61aab5c7ba9ff276c41cfffe6b80b https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.17.0-h8a09558_0.conda#92ed62436b625154323d40d5f2f11dd7 https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda#5aa797f8787fe7a17d1b0821485b5adc @@ -98,7 +98,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libcap-2.71-h39aace5_0.conda#dd1 https://conda.anaconda.org/conda-forge/linux-64/libcrc32c-1.1.2-h9c3ff4c_0.tar.bz2#c965a5aa0d5c1c37ffc62dff36e28400 https://conda.anaconda.org/conda-forge/linux-64/libfreetype6-2.13.3-h48d6fc4_1.conda#3c255be50a506c50765a93a6644f32fe https://conda.anaconda.org/conda-forge/linux-64/libgcrypt-lib-1.11.1-hb9d3cd8_0.conda#8504a291085c9fb809b66cabd5834307 -https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-15.1.0-h69a702a_2.conda#a483a87b71e974bb75d1b9413d4436dd +https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-15.1.0-h69a702a_3.conda#6e5d0574e57a38c36e674e9a18eee2b4 https://conda.anaconda.org/conda-forge/linux-64/libnghttp2-1.64.0-h161d5f1_0.conda#19e57602824042dfd0446292ef90488b https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-3.21.12-hfc55251_2.conda#e3a7d4ba09b8dc939b98fef55f539220 https://conda.anaconda.org/conda-forge/linux-64/libthrift-0.18.1-h8fd135c_2.conda#bbf65f7688512872f063810623b755dc @@ -147,7 +147,7 @@ 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https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdamage-1.1.6-hb9d3cd8_0.conda#b5fcc7172d22516e1f965490e65e33a4 diff --git a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_environment.yml b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_environment.yml index 267c149fd1c35..30466d12a3f20 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_environment.yml +++ b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_environment.yml @@ -20,5 +20,5 @@ dependencies: - ninja - meson-python - sphinx - - numpydoc + - numpydoc<1.9.0 - ccache diff --git a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock index 0c7c5ac749057..6ad5e47e38591 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock @@ -1,47 +1,47 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 26bb2530999c20f24bbab0f7b6e3545ad84d059a25027cb624997210afc23693 +# input_hash: 4abfb998e26e3beaa198409ac1ebc1278024921c4b3c6fc8de5c93be1b6193ba @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/noarch/python_abi-3.10-7_cp310.conda#44e871cba2b162368476a84b8d040b6c https://conda.anaconda.org/conda-forge/noarch/tzdata-2025b-h78e105d_0.conda#4222072737ccff51314b5ece9c7d6f5a https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.6.15-hbd8a1cb_0.conda#72525f07d72806e3b639ad4504c30ce5 https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h1423503_5.conda#6dc9e1305e7d3129af4ad0dabda30e56 -https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.1.0-h767d61c_2.conda#fbe7d535ff9d3a168c148e07358cd5b1 +https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.1.0-h767d61c_3.conda#3cd1a7238a0dd3d0860fdefc496cc854 https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2#73aaf86a425cc6e73fcf236a5a46396d -https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_2.conda#ea8ac52380885ed41c1baa8f1d6d2b93 +https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_3.conda#9e60c55e725c20d23125a5f0dd69af5d https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.24-h86f0d12_0.conda#64f0c503da58ec25ebd359e4d990afa8 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.0-h5888daf_0.conda#db0bfbe7dd197b68ad5f30333bae6ce0 https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.6-h2dba641_1.conda#ede4673863426c0883c0063d853bbd85 -https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.1.0-h69a702a_2.conda#ddca86c7040dd0e73b2b69bd7833d225 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https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8c095d6_2.conda#283b96675859b20a825f8fa30f311446 https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_hd72426e_102.conda#a0116df4f4ed05c303811a837d5b39d8 https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb8e6e7a_2.conda#6432cb5d4ac0046c3ac0a8a0f95842f9 https://conda.anaconda.org/conda-forge/linux-64/libfreetype6-2.13.3-h48d6fc4_1.conda#3c255be50a506c50765a93a6644f32fe -https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-15.1.0-h69a702a_2.conda#a483a87b71e974bb75d1b9413d4436dd -https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.29-pthreads_h94d23a6_0.conda#0a4d0252248ef9a0f88f2ba8b8a08e12 +https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-15.1.0-h69a702a_3.conda#6e5d0574e57a38c36e674e9a18eee2b4 +https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.30-pthreads_h94d23a6_0.conda#323dc8f259224d13078aaf7ce96c3efe 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https://conda.anaconda.org/conda-forge/linux-64/numpy-2.2.6-py310hefbff90_0.conda#b0cea2c364bf65cd19e023040eeab05d -https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.0-pyhd8ed1ab_0.conda#516d31f063ce7e49ced17f105b63a1f1 +https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.1-pyhd8ed1ab_0.conda#a49c2283f24696a7b30367b7346a0144 https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py310ha75aee5_2.conda#f9254b5b0193982416b91edcb4b2676f -https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-31_h1ea3ea9_openblas.conda#ba652ee0576396d4765e567f043c57f9 +https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-32_h1ea3ea9_openblas.conda#34cb4b6753b38a62ae25f3a73efd16b0 https://conda.anaconda.org/conda-forge/linux-64/pandas-2.3.0-py310h5eaa309_0.conda#379844614e3a24e59e59d8c69c6e9403 https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.7.0-pyhd8ed1ab_0.conda#15353a2a0ea6dfefaa52fc5ab5b98f41 https://conda.anaconda.org/conda-forge/linux-64/scipy-1.15.2-py310h1d65ade_0.conda#8c29cd33b64b2eb78597fa28b5595c8d -https://conda.anaconda.org/conda-forge/noarch/urllib3-2.4.0-pyhd8ed1ab_0.conda#c1e349028e0052c4eea844e94f773065 -https://conda.anaconda.org/conda-forge/linux-64/blas-2.131-openblas.conda#38b2ec894c69bb4be0e66d2ef7fc60bf +https://conda.anaconda.org/conda-forge/noarch/urllib3-2.5.0-pyhd8ed1ab_0.conda#436c165519e140cb08d246a4472a9d6a +https://conda.anaconda.org/conda-forge/linux-64/blas-2.132-openblas.conda#9c4a27ab2463f9b1d9019e0a798a5b81 https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.2.1-py310ha2bacc8_1.conda#817d32861729e14f474249f1036291c4 https://conda.anaconda.org/conda-forge/noarch/requests-2.32.4-pyhd8ed1ab_0.conda#f6082eae112814f1447b56a5e1f6ed05 https://conda.anaconda.org/conda-forge/noarch/numpydoc-1.8.0-pyhd8ed1ab_1.conda#5af206d64d18d6c8dfb3122b4d9e643b diff --git a/build_tools/azure/pymin_conda_forge_openblas_win-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_win-64_conda.lock index ba4245727766f..c6e2cb99c3f5b 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_win-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_openblas_win-64_conda.lock @@ -30,11 +30,11 @@ https://conda.anaconda.org/conda-forge/win-64/libiconv-1.18-h135ad9c_1.conda#21f https://conda.anaconda.org/conda-forge/win-64/libjpeg-turbo-3.1.0-h2466b09_0.conda#7c51d27540389de84852daa1cdb9c63c https://conda.anaconda.org/conda-forge/win-64/liblzma-5.8.1-h2466b09_2.conda#c15148b2e18da456f5108ccb5e411446 https://conda.anaconda.org/conda-forge/win-64/libopenblas-0.3.30-pthreads_ha4fe6b2_0.conda#c09864590782cb17fee135db4796bdcb -https://conda.anaconda.org/conda-forge/win-64/libsqlite-3.50.1-hf5d6505_6.conda#c01fd2d0873bdc8d35bfa3c6eb2f54e5 +https://conda.anaconda.org/conda-forge/win-64/libsqlite-3.50.2-hf5d6505_0.conda#e1e6cac409e95538acdc3d33a0f34d6a https://conda.anaconda.org/conda-forge/win-64/libwebp-base-1.5.0-h3b0e114_0.conda#33f7313967072c6e6d8f865f5493c7ae https://conda.anaconda.org/conda-forge/win-64/libzlib-1.3.1-h2466b09_2.conda#41fbfac52c601159df6c01f875de31b9 https://conda.anaconda.org/conda-forge/win-64/ninja-1.13.0-h79cd779_0.conda#fb5cb20bc807076f05ac18a628322fd7 -https://conda.anaconda.org/conda-forge/win-64/openssl-3.5.0-ha4e3fda_1.conda#72c07e46b6766bb057018a9a74861b89 +https://conda.anaconda.org/conda-forge/win-64/openssl-3.5.1-h725018a_0.conda#d124fc2fd7070177b5e2450627f8fc1a https://conda.anaconda.org/conda-forge/win-64/pixman-0.46.2-had0cd8c_0.conda#2566a45fb15e2f540eff14261f1242af https://conda.anaconda.org/conda-forge/win-64/qhull-2020.2-hc790b64_5.conda#854fbdff64b572b5c0b470f334d34c11 https://conda.anaconda.org/conda-forge/win-64/tk-8.6.13-h2c6b04d_2.conda#ebd0e761de9aa879a51d22cc721bd095 @@ -94,7 +94,7 @@ https://conda.anaconda.org/conda-forge/win-64/numpy-2.2.6-py310h4987827_0.conda# https://conda.anaconda.org/conda-forge/win-64/openjpeg-2.5.3-h4d64b90_0.conda#fc050366dd0b8313eb797ed1ffef3a29 https://conda.anaconda.org/conda-forge/noarch/pip-25.1.1-pyh8b19718_0.conda#32d0781ace05105cc99af55d36cbec7c https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.9.1-pyhd8ed1ab_0.conda#22ae7c6ea81e0c8661ef32168dda929b -https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhff2d567_1.conda#5ba79d7c71f03c678c8ead841f347d6e +https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda#5b8d21249ff20967101ffa321cab24e8 https://conda.anaconda.org/conda-forge/win-64/blas-devel-3.9.0-32_hc0f8095_openblas.conda#c07c54d62ee5a9886933051e10ad4b1e https://conda.anaconda.org/conda-forge/win-64/contourpy-1.3.2-py310hc19bc0b_0.conda#039416813b5290e7d100a05bb4326110 https://conda.anaconda.org/conda-forge/win-64/fonttools-4.58.4-py310h38315fa_0.conda#f7a8769f5923bebdc10acbbb41d28628 @@ -110,6 +110,6 @@ https://conda.anaconda.org/conda-forge/noarch/pytest-cov-6.2.1-pyhd8ed1ab_0.cond https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.7.0-pyhd8ed1ab_0.conda#15353a2a0ea6dfefaa52fc5ab5b98f41 https://conda.anaconda.org/conda-forge/win-64/cairo-1.18.4-h5782bbf_0.conda#20e32ced54300292aff690a69c5e7b97 https://conda.anaconda.org/conda-forge/win-64/harfbuzz-11.2.1-h8796e6f_0.conda#bccea58fbf7910ce868b084f27ffe8bd -https://conda.anaconda.org/conda-forge/win-64/qt6-main-6.9.1-h02ddd7d_0.conda#feaaaae25a51188fb0544aca8b26ef4d +https://conda.anaconda.org/conda-forge/win-64/qt6-main-6.9.1-h02ddd7d_1.conda#fc796cf6c16db38d44c2efefbe6afcea https://conda.anaconda.org/conda-forge/win-64/pyside6-6.9.1-py310h2d19612_0.conda#01b830c0fd6ca7ab03c85a008a6f4a2d https://conda.anaconda.org/conda-forge/win-64/matplotlib-3.10.3-py310h5588dad_0.conda#103adee33db124a0263d0b4551e232e3 diff --git a/build_tools/azure/ubuntu_atlas_lock.txt b/build_tools/azure/ubuntu_atlas_lock.txt index ddbe7a200dba1..569cbbb2b5344 100644 --- a/build_tools/azure/ubuntu_atlas_lock.txt +++ b/build_tools/azure/ubuntu_atlas_lock.txt @@ -27,15 +27,15 @@ packaging==25.0 # pytest pluggy==1.6.0 # via pytest -pygments==2.19.1 +pygments==2.19.2 # via pytest pyproject-metadata==0.9.1 # via meson-python -pytest==8.4.0 +pytest==8.4.1 # via # -r build_tools/azure/ubuntu_atlas_requirements.txt # pytest-xdist -pytest-xdist==3.7.0 +pytest-xdist==3.8.0 # via -r build_tools/azure/ubuntu_atlas_requirements.txt threadpoolctl==3.1.0 # via -r build_tools/azure/ubuntu_atlas_requirements.txt diff --git a/build_tools/circle/doc_environment.yml b/build_tools/circle/doc_environment.yml index bc36e178de058..360be7b52b9a9 100644 --- a/build_tools/circle/doc_environment.yml +++ b/build_tools/circle/doc_environment.yml @@ -27,7 +27,7 @@ dependencies: - sphinx - sphinx-gallery - sphinx-copybutton - - numpydoc + - numpydoc<1.9.0 - sphinx-prompt - plotly - polars diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock index 14a5b8303d947..4d948db6e5db5 100644 --- a/build_tools/circle/doc_linux-64_conda.lock +++ b/build_tools/circle/doc_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 93cb6f7aa17dce662512650f1419e87eae56ed49163348847bf965697cd268bb +# input_hash: f8748904ea3a3b4e57ef03e9ef12f4ec17e4998ed6cbe6d15bc058d26bd37454 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45 @@ -15,7 +15,7 @@ https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766 https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_5.conda#acd9213a63cb62521290e581ef82de80 https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-13.3.0-hc03c837_102.conda#4c1d6961a6a54f602ae510d9bf31fa60 https://conda.anaconda.org/conda-forge/linux-64/libglvnd-1.7.0-ha4b6fd6_2.conda#434ca7e50e40f4918ab701e3facd59a0 -https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.1.0-h767d61c_2.conda#fbe7d535ff9d3a168c148e07358cd5b1 +https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.1.0-h767d61c_3.conda#3cd1a7238a0dd3d0860fdefc496cc854 https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-13.3.0-hc03c837_102.conda#aa38de2738c5f4a72a880e3d31ffe8b4 https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.17-h0157908_18.conda#460eba7851277ec1fd80a1a24080787a https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2#73aaf86a425cc6e73fcf236a5a46396d @@ -25,24 +25,25 @@ https://conda.anaconda.org/conda-forge/linux-64/libegl-1.7.0-ha4b6fd6_2.conda#c1 https://conda.anaconda.org/conda-forge/linux-64/libopengl-1.7.0-ha4b6fd6_2.conda#7df50d44d4a14d6c31a2c54f2cd92157 https://conda.anaconda.org/conda-forge/linux-64/binutils-2.43-h4852527_5.conda#4846404183ea94fd6652e9fb6ac5e16f https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.43-h4852527_5.conda#327ef163ac88b57833c1c1a20a9e7e0d -https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_2.conda#ea8ac52380885ed41c1baa8f1d6d2b93 +https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_3.conda#9e60c55e725c20d23125a5f0dd69af5d https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.14-hb9d3cd8_0.conda#76df83c2a9035c54df5d04ff81bcc02d https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hb9d3cd8_3.conda#cb98af5db26e3f482bebb80ce9d947d3 https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.24-h86f0d12_0.conda#64f0c503da58ec25ebd359e4d990afa8 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.0-h5888daf_0.conda#db0bfbe7dd197b68ad5f30333bae6ce0 https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.6-h2dba641_1.conda#ede4673863426c0883c0063d853bbd85 -https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.1.0-h69a702a_2.conda#ddca86c7040dd0e73b2b69bd7833d225 -https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.1.0-hcea5267_2.conda#01de444988ed960031dbe84cf4f9b1fc +https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.1.0-h69a702a_3.conda#e66f2b8ad787e7beb0f846e4bd7e8493 +https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.1.0-hcea5267_3.conda#530566b68c3b8ce7eec4cd047eae19fe https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h4ce23a2_1.conda#e796ff8ddc598affdf7c173d6145f087 https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.1.0-hb9d3cd8_0.conda#9fa334557db9f63da6c9285fd2a48638 https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_2.conda#1a580f7796c7bf6393fddb8bbbde58dc +https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hb9d3cd8_1.conda#d864d34357c3b65a4b731f78c0801dc4 https://conda.anaconda.org/conda-forge/linux-64/libntlm-1.8-hb9d3cd8_0.conda#7c7927b404672409d9917d49bff5f2d6 https://conda.anaconda.org/conda-forge/linux-64/libpciaccess-0.18-hb9d3cd8_0.conda#70e3400cbbfa03e96dcde7fc13e38c7b -https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_2.conda#1cb1c67961f6dd257eae9e9691b341aa +https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_3.conda#6d11a5edae89fe413c0569f16d308f5a https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.5.0-h851e524_0.conda#63f790534398730f59e1b899c3644d4a https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.0-h7b32b05_1.conda#de356753cfdbffcde5bb1e86e3aa6cd0 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.1-h7b32b05_0.conda#c87df2ab1448ba69169652ab9547082d https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-hb9d3cd8_1002.conda#b3c17d95b5a10c6e64a21fa17573e70e https://conda.anaconda.org/conda-forge/linux-64/rav1e-0.7.1-h8fae777_3.conda#2c42649888aac645608191ffdc80d13a https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.2-hb9d3cd8_0.conda#fb901ff28063514abb6046c9ec2c4a45 @@ -52,6 +53,7 @@ https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62e https://conda.anaconda.org/conda-forge/linux-64/dav1d-1.2.1-hd590300_0.conda#418c6ca5929a611cbd69204907a83995 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https://conda.anaconda.org/conda-forge/noarch/tifffile-2025.5.10-pyhd8ed1ab_0.conda#1fdb801f28bf4987294c49aaa314bf5e https://conda.anaconda.org/conda-forge/noarch/pooch-1.8.2-pyhd8ed1ab_1.conda#b3e783e8e8ed7577cf0b6dee37d1fbac -https://conda.anaconda.org/conda-forge/linux-64/qt6-main-6.9.1-h0384650_0.conda#e1f80d7fca560024b107368dd77d96be +https://conda.anaconda.org/conda-forge/linux-64/qt6-main-6.9.1-h0384650_1.conda#3610aa92d2de36047886f30e99342f21 https://conda.anaconda.org/conda-forge/linux-64/scikit-image-0.25.2-py310h5eaa309_1.conda#ed21ab72d049ecdb60f829f04b4dca1c https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.13.2-pyhd8ed1ab_3.conda#fd96da444e81f9e6fcaac38590f3dd42 https://conda.anaconda.org/conda-forge/linux-64/pyside6-6.9.1-py310h21765ff_0.conda#a64f8b57dd1b84d5d4f02f565a3cb630 @@ -287,7 +287,7 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.10.0-pyhd8ed # pip python-json-logger @ https://files.pythonhosted.org/packages/08/20/0f2523b9e50a8052bc6a8b732dfc8568abbdc42010aef03a2d750bdab3b2/python_json_logger-3.3.0-py3-none-any.whl#sha256=dd980fae8cffb24c13caf6e158d3d61c0d6d22342f932cb6e9deedab3d35eec7 # pip pyyaml @ https://files.pythonhosted.org/packages/6b/4e/1523cb902fd98355e2e9ea5e5eb237cbc5f3ad5f3075fa65087aa0ecb669/PyYAML-6.0.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=ec031d5d2feb36d1d1a24380e4db6d43695f3748343d99434e6f5f9156aaa2ed # pip rfc3986-validator @ https://files.pythonhosted.org/packages/9e/51/17023c0f8f1869d8806b979a2bffa3f861f26a3f1a66b094288323fba52f/rfc3986_validator-0.1.1-py2.py3-none-any.whl#sha256=2f235c432ef459970b4306369336b9d5dbdda31b510ca1e327636e01f528bfa9 -# pip rpds-py @ https://files.pythonhosted.org/packages/eb/76/66b523ffc84cf47db56efe13ae7cf368dee2bacdec9d89b9baca5e2e6301/rpds_py-0.25.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=0701942049095741a8aeb298a31b203e735d1c61f4423511d2b1a41dcd8a16da +# pip rpds-py @ https://files.pythonhosted.org/packages/15/93/fde36cd6e4685df2cd08508f6c45a841e82f5bb98c8d5ecf05649522acb5/rpds_py-0.26.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=c70d9ec912802ecfd6cd390dadb34a9578b04f9bcb8e863d0a7598ba5e9e7ccc # pip send2trash @ https://files.pythonhosted.org/packages/40/b0/4562db6223154aa4e22f939003cb92514c79f3d4dccca3444253fd17f902/Send2Trash-1.8.3-py3-none-any.whl#sha256=0c31227e0bd08961c7665474a3d1ef7193929fedda4233843689baa056be46c9 # pip sniffio @ https://files.pythonhosted.org/packages/e9/44/75a9c9421471a6c4805dbf2356f7c181a29c1879239abab1ea2cc8f38b40/sniffio-1.3.1-py3-none-any.whl#sha256=2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2 # pip traitlets @ https://files.pythonhosted.org/packages/00/c0/8f5d070730d7836adc9c9b6408dec68c6ced86b304a9b26a14df072a6e8c/traitlets-5.14.3-py3-none-any.whl#sha256=b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f @@ -315,7 +315,7 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.10.0-pyhd8ed # pip jsonschema-specifications @ https://files.pythonhosted.org/packages/01/0e/b27cdbaccf30b890c40ed1da9fd4a3593a5cf94dae54fb34f8a4b74fcd3f/jsonschema_specifications-2025.4.1-py3-none-any.whl#sha256=4653bffbd6584f7de83a67e0d620ef16900b390ddc7939d56684d6c81e33f1af # pip jupyter-client @ https://files.pythonhosted.org/packages/11/85/b0394e0b6fcccd2c1eeefc230978a6f8cb0c5df1e4cd3e7625735a0d7d1e/jupyter_client-8.6.3-py3-none-any.whl#sha256=e8a19cc986cc45905ac3362915f410f3af85424b4c0905e94fa5f2cb08e8f23f # pip jupyter-server-terminals @ https://files.pythonhosted.org/packages/07/2d/2b32cdbe8d2a602f697a649798554e4f072115438e92249624e532e8aca6/jupyter_server_terminals-0.5.3-py3-none-any.whl#sha256=41ee0d7dc0ebf2809c668e0fc726dfaf258fcd3e769568996ca731b6194ae9aa -# pip jupyterlite-core @ https://files.pythonhosted.org/packages/48/3a/7a230e176440220de3ed72b9d72be99ce9ca6d9a958cec95c4e28ccc0254/jupyterlite_core-0.6.1-py3-none-any.whl#sha256=d23db96ede9cfe6edcb0242730d6d2068b47e340daf2effefa9892fa3c091357 +# pip jupyterlite-core @ https://files.pythonhosted.org/packages/65/df/be4e5c0400f7e7dc0f289e73fadfd20156ed2b4aaadc7a7142592385f9eb/jupyterlite_core-0.6.3-py3-none-any.whl#sha256=46cfd804ede5bc5adfeae596863fbd3a3278bfe0f725d62ce7876a62434ed9b4 # pip mdit-py-plugins @ https://files.pythonhosted.org/packages/a7/f7/7782a043553ee469c1ff49cfa1cdace2d6bf99a1f333cf38676b3ddf30da/mdit_py_plugins-0.4.2-py3-none-any.whl#sha256=0c673c3f889399a33b95e88d2f0d111b4447bdfea7f237dab2d488f459835636 # pip jsonschema @ https://files.pythonhosted.org/packages/a2/3d/023389198f69c722d039351050738d6755376c8fd343e91dc493ea485905/jsonschema-4.24.0-py3-none-any.whl#sha256=a462455f19f5faf404a7902952b6f0e3ce868f3ee09a359b05eca6673bd8412d # pip jupyterlite-pyodide-kernel @ https://files.pythonhosted.org/packages/92/a4/bf3270357175d410d98edd00e42c1826cb26e33742c1ee5421d00d4cf97d/jupyterlite_pyodide_kernel-0.6.1-py3-none-any.whl#sha256=d16f2e44dedd60d7a5578cd901a4de1ac34d30c80671abba7ec1ac70a65e2972 diff --git a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock index 1a92eceb7c026..9aad6026a9888 100644 --- a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock +++ b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock @@ -15,7 +15,7 @@ https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766 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-https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.0-pyhd8ed1ab_0.conda#516d31f063ce7e49ced17f105b63a1f1 +https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.1-pyhd8ed1ab_0.conda#a49c2283f24696a7b30367b7346a0144 https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxtst-1.2.5-h57736b2_3.conda#c05698071b5c8e0da82a282085845860 -https://conda.anaconda.org/conda-forge/linux-aarch64/blas-devel-3.9.0-31_h9678261_openblas.conda#a2cc143d7e25e52a915cb320e5b0d592 +https://conda.anaconda.org/conda-forge/linux-aarch64/blas-devel-3.9.0-32_h9678261_openblas.conda#9c18808e64a8557732e664eac92df74d https://conda.anaconda.org/conda-forge/linux-aarch64/cairo-1.18.4-h83712da_0.conda#cd55953a67ec727db5dc32b167201aa6 https://conda.anaconda.org/conda-forge/linux-aarch64/contourpy-1.3.2-py310hf54e67a_0.conda#779694434d1f0a67c5260db76b7b7907 https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.7.0-pyhd8ed1ab_0.conda#15353a2a0ea6dfefaa52fc5ab5b98f41 https://conda.anaconda.org/conda-forge/linux-aarch64/scipy-1.15.2-py310hf37559f_0.conda#5c9b72f10d2118d943a5eaaf2f396891 -https://conda.anaconda.org/conda-forge/linux-aarch64/blas-2.131-openblas.conda#51c5f346e1ebee750f76066490059df9 +https://conda.anaconda.org/conda-forge/linux-aarch64/blas-2.132-openblas.conda#2c1e3662c8c5e7b92a49fd6372bb659f https://conda.anaconda.org/conda-forge/linux-aarch64/harfbuzz-11.2.1-h405b6a2_0.conda#b55680fc90e9747dc858e7ceb0abc2b2 https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-base-3.10.3-py310h2cc5e2d_0.conda#e29f4329f4f76cf14f74ed86dcc59bac -https://conda.anaconda.org/conda-forge/linux-aarch64/qt6-main-6.9.1-h13135bf_0.conda#6e8335a319b6b1988d6959f895116c74 +https://conda.anaconda.org/conda-forge/linux-aarch64/qt6-main-6.9.1-h13135bf_1.conda#def3ca3fcfa60a6c954bdd8f5bb00cd2 https://conda.anaconda.org/conda-forge/linux-aarch64/pyside6-6.9.1-py310hd3bda28_0.conda#1a105dc54d3cd250526c9d52379133c9 https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-3.10.3-py310hbbe02a8_0.conda#08982f6ac753e962d59160b08839221b diff --git a/sklearn/linear_model/_glm/tests/test_glm.py b/sklearn/linear_model/_glm/tests/test_glm.py index fbcc4d61a8e1c..e2e1e09d76401 100644 --- a/sklearn/linear_model/_glm/tests/test_glm.py +++ b/sklearn/linear_model/_glm/tests/test_glm.py @@ -656,6 +656,7 @@ def test_glm_sample_weight_consistency(fit_intercept, alpha, GLMEstimator): X = rng.rand(n_samples, n_features) y = rng.rand(n_samples) glm_params = dict(alpha=alpha, fit_intercept=fit_intercept) + tols = dict(rtol=1e-12, atol=1e-14) glm = GLMEstimator(**glm_params).fit(X, y) coef = glm.coef_.copy() @@ -663,12 +664,12 @@ def test_glm_sample_weight_consistency(fit_intercept, alpha, GLMEstimator): # sample_weight=np.ones(..) should be equivalent to sample_weight=None sample_weight = np.ones(y.shape) glm.fit(X, y, sample_weight=sample_weight) - assert_allclose(glm.coef_, coef, rtol=1e-12) + assert_allclose(glm.coef_, coef, **tols) # sample_weight are normalized to 1 so, scaling them has no effect sample_weight = 2 * np.ones(y.shape) glm.fit(X, y, sample_weight=sample_weight) - assert_allclose(glm.coef_, coef, rtol=1e-12) + assert_allclose(glm.coef_, coef, **tols) # setting one element of sample_weight to 0 is equivalent to removing # the corresponding sample @@ -677,7 +678,7 @@ def test_glm_sample_weight_consistency(fit_intercept, alpha, GLMEstimator): glm.fit(X, y, sample_weight=sample_weight) coef1 = glm.coef_.copy() glm.fit(X[:-1], y[:-1]) - assert_allclose(glm.coef_, coef1, rtol=1e-12) + assert_allclose(glm.coef_, coef1, **tols) # check that multiplying sample_weight by 2 is equivalent # to repeating corresponding samples twice @@ -687,9 +688,8 @@ def test_glm_sample_weight_consistency(fit_intercept, alpha, GLMEstimator): sample_weight_1[: n_samples // 2] = 2 glm1 = GLMEstimator(**glm_params).fit(X, y, sample_weight=sample_weight_1) - glm2 = GLMEstimator(**glm_params).fit(X2, y2, sample_weight=None) - assert_allclose(glm1.coef_, glm2.coef_) + assert_allclose(glm1.coef_, glm2.coef_, rtol=1e-10, atol=1e-14) @pytest.mark.parametrize("solver", SOLVERS) From dd5b9eeca3e1f6941a8345838bf343c066b03f5b Mon Sep 17 00:00:00 2001 From: SiyuJin-1 Date: Wed, 2 Jul 2025 06:05:19 -0700 Subject: [PATCH 068/103] DOC Add Links to plot_lof_novelty_detection example (#31405) Co-authored-by: Jsy Co-authored-by: Stefanie Senger <91849487+StefanieSenger@users.noreply.github.com> --- doc/modules/outlier_detection.rst | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/doc/modules/outlier_detection.rst b/doc/modules/outlier_detection.rst index 7de2da4f1818e..bdb6b1aeacdbf 100644 --- a/doc/modules/outlier_detection.rst +++ b/doc/modules/outlier_detection.rst @@ -411,7 +411,8 @@ Note that ``fit_predict`` is not available in this case to avoid inconsistencies The scores of abnormality of the training samples are always accessible through the ``negative_outlier_factor_`` attribute. -Novelty detection with Local Outlier Factor is illustrated below. +Novelty detection with :class:`neighbors.LocalOutlierFactor` is illustrated below +(see :ref:`sphx_glr_auto_examples_neighbors_plot_lof_novelty_detection.py`). .. figure:: ../auto_examples/neighbors/images/sphx_glr_plot_lof_novelty_detection_001.png :target: ../auto_examples/neighbors/plot_lof_novelty_detection.html From 382dac726b692855292c0c136f33cda8da1dbb16 Mon Sep 17 00:00:00 2001 From: Stefanie Senger <91849487+StefanieSenger@users.noreply.github.com> Date: Wed, 2 Jul 2025 15:13:10 +0200 Subject: [PATCH 069/103] DOC Improve docstrings for scikit-learn configuration functions (#31486) Co-authored-by: Adrin Jalali --- sklearn/_config.py | 67 +++++++++++++++++++++++++++++++++------------- 1 file changed, 49 insertions(+), 18 deletions(-) diff --git a/sklearn/_config.py b/sklearn/_config.py index 05549c88a9ddc..66d119e02d1a3 100644 --- a/sklearn/_config.py +++ b/sklearn/_config.py @@ -33,7 +33,10 @@ def _get_threadlocal_config(): def get_config(): - """Retrieve current values for configuration set by :func:`set_config`. + """Retrieve the current scikit-learn configuration. + + This reflects the effective global configurations as established by default upon + library import, or modified via :func:`set_config` or :func:`config_context`. Returns ------- @@ -71,6 +74,15 @@ def set_config( ): """Set global scikit-learn configuration. + These settings control the behaviour of scikit-learn functions during a library + usage session. Global configuration defaults (as described in the parameter list + below) take effect when scikit-learn is imported. + + This function can be used to modify the global scikit-learn configuration at + runtime. Passing `None` as an argument (the default) leaves the corresponding + setting unchanged. This allows users to selectively update the global configuration + values without affecting the others. + .. versionadded:: 0.19 Parameters @@ -79,7 +91,7 @@ def set_config( If True, validation for finiteness will be skipped, saving time, but leading to potential crashes. If False, validation for finiteness will be performed, - avoiding error. Global default: False. + avoiding error. Global default: False. .. versionadded:: 0.19 @@ -96,20 +108,22 @@ def set_config( values will be printed when printing an estimator. For example, ``print(SVC())`` while True will only print 'SVC()' while the default behaviour would be to print 'SVC(C=1.0, cache_size=200, ...)' with - all the non-changed parameters. + all the non-changed parameters. Global default: True. .. versionadded:: 0.21 + .. versionchanged:: 0.23 + Global default configuration changed from False to True. display : {'text', 'diagram'}, default=None If 'diagram', estimators will be displayed as a diagram in a Jupyter lab or notebook context. If 'text', estimators will be displayed as - text. Default is 'diagram'. + text. Global default: 'diagram'. .. versionadded:: 0.23 pairwise_dist_chunk_size : int, default=None The number of row vectors per chunk for the accelerated pairwise- - distances reduction backend. Default is 256 (suitable for most of + distances reduction backend. Global default: 256 (suitable for most of modern laptops' caches and architectures). Intended for easier benchmarking and testing of scikit-learn internals. @@ -130,7 +144,7 @@ def set_config( array_api_dispatch : bool, default=None Use Array API dispatching when inputs follow the Array API standard. - Default is False. + Global default: False. See the :ref:`User Guide ` for more details. @@ -147,6 +161,8 @@ def set_config( - `"polars"`: Polars output - `None`: Transform configuration is unchanged + Global default: "default". + .. versionadded:: 1.2 .. versionadded:: 1.4 `"polars"` option was added. @@ -161,6 +177,8 @@ def set_config( - `False`: Metadata routing is disabled, use the old syntax. - `None`: Configuration is unchanged + Global default: False. + .. versionadded:: 1.3 skip_parameter_validation : bool, default=None @@ -168,6 +186,7 @@ def set_config( the fit method of estimators and for arguments passed to public helper functions. It can save time in some situations but can lead to low level crashes and exceptions with confusing error messages. + Global default: False. Note that for data parameters, such as `X` and `y`, only type validation is skipped but validation with `check_array` will continue to run. @@ -225,7 +244,14 @@ def config_context( enable_metadata_routing=None, skip_parameter_validation=None, ): - """Context manager for global scikit-learn configuration. + """Context manager to temporarily change the global scikit-learn configuration. + + This context manager can be used to apply scikit-learn configuration changes within + the scope of the with statement. Once the context exits, the global configuration is + restored again. + + The default global configurations (which take effect when scikit-learn is imported) + are defined below in the parameter list. Parameters ---------- @@ -233,38 +259,38 @@ def config_context( If True, validation for finiteness will be skipped, saving time, but leading to potential crashes. If False, validation for finiteness will be performed, - avoiding error. If None, the existing value won't change. - The default value is False. + avoiding error. If None, the existing configuration won't change. + Global default: False. working_memory : int, default=None If set, scikit-learn will attempt to limit the size of temporary arrays to this number of MiB (per job when parallelised), often saving both computation time and memory on expensive operations that can be - performed in chunks. If None, the existing value won't change. - The default value is 1024. + performed in chunks. If None, the existing configuration won't change. + Global default: 1024. print_changed_only : bool, default=None If True, only the parameters that were set to non-default values will be printed when printing an estimator. For example, ``print(SVC())`` while True will only print 'SVC()', but would print 'SVC(C=1.0, cache_size=200, ...)' with all the non-changed parameters - when False. If None, the existing value won't change. - The default value is True. + when False. If None, the existing configuration won't change. + Global default: True. .. versionchanged:: 0.23 - Default changed from False to True. + Global default configuration changed from False to True. display : {'text', 'diagram'}, default=None If 'diagram', estimators will be displayed as a diagram in a Jupyter lab or notebook context. If 'text', estimators will be displayed as - text. If None, the existing value won't change. - The default value is 'diagram'. + text. If None, the existing configuration won't change. + Global default: 'diagram'. .. versionadded:: 0.23 pairwise_dist_chunk_size : int, default=None The number of row vectors per chunk for the accelerated pairwise- - distances reduction backend. Default is 256 (suitable for most of + distances reduction backend. Global default: 256 (suitable for most of modern laptops' caches and architectures). Intended for easier benchmarking and testing of scikit-learn internals. @@ -285,7 +311,7 @@ def config_context( array_api_dispatch : bool, default=None Use Array API dispatching when inputs follow the Array API standard. - Default is False. + Global default: False. See the :ref:`User Guide ` for more details. @@ -302,6 +328,8 @@ def config_context( - `"polars"`: Polars output - `None`: Transform configuration is unchanged + Global default: "default". + .. versionadded:: 1.2 .. versionadded:: 1.4 `"polars"` option was added. @@ -316,6 +344,8 @@ def config_context( - `False`: Metadata routing is disabled, use the old syntax. - `None`: Configuration is unchanged + Global default: False. + .. versionadded:: 1.3 skip_parameter_validation : bool, default=None @@ -323,6 +353,7 @@ def config_context( the fit method of estimators and for arguments passed to public helper functions. It can save time in some situations but can lead to low level crashes and exceptions with confusing error messages. + Global default: False. Note that for data parameters, such as `X` and `y`, only type validation is skipped but validation with `check_array` will continue to run. From 7fdbe27e5a0bc37c2d101484cc49f9fbe1d2450b Mon Sep 17 00:00:00 2001 From: Lucy Liu Date: Thu, 3 Jul 2025 01:31:39 +1000 Subject: [PATCH 070/103] DOC Recommend setting `array_api_dispatch` globally in array API docs (#31687) --- doc/modules/array_api.rst | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/doc/modules/array_api.rst b/doc/modules/array_api.rst index eb4a3f7be7ffa..7af1e4ea7a43a 100644 --- a/doc/modules/array_api.rst +++ b/doc/modules/array_api.rst @@ -30,7 +30,7 @@ data structures and automatically dispatch operations to the underlying namespac instead of relying on NumPy. At this stage, this support is **considered experimental** and must be enabled -explicitly as explained in the following. +explicitly by the `array_api_dispatch` configuration. See below for details. .. note:: Currently, only `array-api-strict`, `cupy`, and `PyTorch` are known to work @@ -45,7 +45,13 @@ and how it facilitates interoperability between array libraries: Example usage ============= -Here is an example code snippet to demonstrate how to use `CuPy +The configuration `array_api_dispatch=True` needs to be set to `True` to enable array +API support. We recommend setting this configuration globally to ensure consistent +behaviour and prevent accidental mixing of array namespaces. +Note that we set it with :func:`config_context` below to avoid having to call +:func:`set_config(array_api_dispatch=False)` at the end of every code snippet +that uses the array API. +The example code snippet below demonstrates how to use `CuPy `_ to run :class:`~discriminant_analysis.LinearDiscriminantAnalysis` on a GPU:: @@ -82,8 +88,7 @@ transfers an estimator attributes from Array API to a ndarray:: PyTorch Support --------------- -PyTorch Tensors are supported by setting `array_api_dispatch=True` and passing in -the tensors directly:: +PyTorch Tensors can also be passed directly:: >>> import torch >>> X_torch = torch.asarray(X_np, device="cuda", dtype=torch.float32) From 66da5a4584c360763c4890b891d90871a817645a Mon Sep 17 00:00:00 2001 From: Olivier Grisel Date: Wed, 2 Jul 2025 18:05:36 +0200 Subject: [PATCH 071/103] MAINT CI cleanups and checks (#31690) --- .github/workflows/cuda-ci.yml | 2 +- build_tools/wheels/build_wheels.sh | 7 ------- sklearn/svm/tests/test_sparse.py | 9 ++++++--- 3 files changed, 7 insertions(+), 11 deletions(-) diff --git a/.github/workflows/cuda-ci.yml b/.github/workflows/cuda-ci.yml index 49bdaed720b5d..a8e82b4488229 100644 --- a/.github/workflows/cuda-ci.yml +++ b/.github/workflows/cuda-ci.yml @@ -21,7 +21,7 @@ jobs: uses: pypa/cibuildwheel@5f22145df44122af0f5a201f93cf0207171beca7 env: CIBW_BUILD: cp313-manylinux_x86_64 - CIBW_MANYLINUX_X86_64_IMAGE: manylinux2014 + CIBW_MANYLINUX_X86_64_IMAGE: manylinux_2_28 CIBW_BUILD_VERBOSITY: 1 CIBW_ARCHS: x86_64 diff --git a/build_tools/wheels/build_wheels.sh b/build_tools/wheels/build_wheels.sh index 02b05bc8a2795..f29747cdc509d 100755 --- a/build_tools/wheels/build_wheels.sh +++ b/build_tools/wheels/build_wheels.sh @@ -49,13 +49,6 @@ if [[ $(uname) == "Darwin" ]]; then export LDFLAGS="$LDFLAGS -Wl,-rpath,$PREFIX/lib -L$PREFIX/lib -lomp" fi -if [[ "$CIBW_FREE_THREADED_SUPPORT" =~ [tT]rue ]]; then - # Numpy, scipy, Cython only have free-threaded wheels on scientific-python-nightly-wheels - # TODO: remove this after CPython 3.13 is released (scheduled October 2024) - # and our dependencies have free-threaded wheels on PyPI - export CIBW_BUILD_FRONTEND='pip; args: --pre --extra-index-url "https://pypi.anaconda.org/scientific-python-nightly-wheels/simple" --only-binary :all:' -fi - # The version of the built dependencies are specified # in the pyproject.toml file, while the tests are run # against the most recent version of the dependencies diff --git a/sklearn/svm/tests/test_sparse.py b/sklearn/svm/tests/test_sparse.py index 59fede29f359c..4e22c86a66cd8 100644 --- a/sklearn/svm/tests/test_sparse.py +++ b/sklearn/svm/tests/test_sparse.py @@ -125,6 +125,7 @@ def test_unsorted_indices(csr_container): X, y = load_digits(return_X_y=True) X_test = csr_container(X[50:100]) X, y = X[:50], y[:50] + tols = dict(rtol=1e-12, atol=1e-14) X_sparse = csr_container(X) coef_dense = ( @@ -135,7 +136,7 @@ def test_unsorted_indices(csr_container): ) coef_sorted = sparse_svc.coef_ # make sure dense and sparse SVM give the same result - assert_allclose(coef_dense, coef_sorted.toarray()) + assert_allclose(coef_dense, coef_sorted.toarray(), **tols) # reverse each row's indices def scramble_indices(X): @@ -158,9 +159,11 @@ def scramble_indices(X): ) coef_unsorted = unsorted_svc.coef_ # make sure unsorted indices give same result - assert_allclose(coef_unsorted.toarray(), coef_sorted.toarray()) + assert_allclose(coef_unsorted.toarray(), coef_sorted.toarray(), **tols) assert_allclose( - sparse_svc.predict_proba(X_test_unsorted), sparse_svc.predict_proba(X_test) + sparse_svc.predict_proba(X_test_unsorted), + sparse_svc.predict_proba(X_test), + **tols, ) From 8d238303ca45ac735ce513b6821aee9232f25545 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adriano=20Le=C3=A3o?= <146663648+AdrianoCLeao@users.noreply.github.com> Date: Wed, 2 Jul 2025 13:18:12 -0300 Subject: [PATCH 072/103] FIX wrong >= in error message in `_locally_linear_embedding` (#29716) --- sklearn/manifold/_locally_linear.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/manifold/_locally_linear.py b/sklearn/manifold/_locally_linear.py index e6967446274ad..7e3f456f7ca57 100644 --- a/sklearn/manifold/_locally_linear.py +++ b/sklearn/manifold/_locally_linear.py @@ -224,7 +224,7 @@ def _locally_linear_embedding( ) if n_neighbors >= N: raise ValueError( - "Expected n_neighbors <= n_samples, but n_samples = %d, n_neighbors = %d" + "Expected n_neighbors < n_samples, but n_samples = %d, n_neighbors = %d" % (N, n_neighbors) ) From 50e0107193ef388813fb810789a7c5fff67af429 Mon Sep 17 00:00:00 2001 From: Adrin Jalali Date: Thu, 3 Jul 2025 11:24:52 +0200 Subject: [PATCH 073/103] Revert ":lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#31632)" (#31691) --- build_tools/azure/debian_32bit_lock.txt | 4 +- ...latest_conda_forge_mkl_linux-64_conda.lock | 103 +++++++++--------- ...pylatest_conda_forge_mkl_osx-64_conda.lock | 35 +++--- ...test_conda_mkl_no_openmp_osx-64_conda.lock | 12 +- ...latest_pip_openblas_pandas_environment.yml | 2 +- ...st_pip_openblas_pandas_linux-64_conda.lock | 25 ++--- ...nblas_min_dependencies_linux-64_conda.lock | 22 ++-- ...forge_openblas_ubuntu_2204_environment.yml | 2 +- ...e_openblas_ubuntu_2204_linux-64_conda.lock | 52 ++++----- ...min_conda_forge_openblas_win-64_conda.lock | 8 +- build_tools/azure/ubuntu_atlas_lock.txt | 6 +- build_tools/circle/doc_environment.yml | 2 +- build_tools/circle/doc_linux-64_conda.lock | 84 +++++++------- .../doc_min_dependencies_linux-64_conda.lock | 64 +++++------ ...n_conda_forge_arm_linux-aarch64_conda.lock | 56 +++++----- sklearn/linear_model/_glm/tests/test_glm.py | 10 +- 16 files changed, 238 insertions(+), 249 deletions(-) diff --git a/build_tools/azure/debian_32bit_lock.txt b/build_tools/azure/debian_32bit_lock.txt index 3439458550ccd..bb5a373786f0f 100644 --- a/build_tools/azure/debian_32bit_lock.txt +++ b/build_tools/azure/debian_32bit_lock.txt @@ -27,11 +27,11 @@ pluggy==1.6.0 # via # pytest # pytest-cov -pygments==2.19.2 +pygments==2.19.1 # via pytest pyproject-metadata==0.9.1 # via meson-python -pytest==8.4.1 +pytest==8.4.0 # via # -r build_tools/azure/debian_32bit_requirements.txt # pytest-cov diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index aa3ea81d106df..c7dd0f634b9da 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock @@ -20,7 +20,7 @@ https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-3_kmp_llvm.con https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab https://conda.anaconda.org/conda-forge/linux-64/libegl-1.7.0-ha4b6fd6_2.conda#c151d5eb730e9b7480e6d48c0fc44048 https://conda.anaconda.org/conda-forge/linux-64/libopengl-1.7.0-ha4b6fd6_2.conda#7df50d44d4a14d6c31a2c54f2cd92157 -https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_3.conda#9e60c55e725c20d23125a5f0dd69af5d +https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_2.conda#ea8ac52380885ed41c1baa8f1d6d2b93 https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.14-hb9d3cd8_0.conda#76df83c2a9035c54df5d04ff81bcc02d https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.12.3-hb9d3cd8_0.conda#8448031a22c697fac3ed98d69e8a9160 https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.5-hb9d3cd8_0.conda#f7f0d6cc2dc986d42ac2689ec88192be @@ -28,21 +28,21 @@ https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hb9d3cd8_3 https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.24-h86f0d12_0.conda#64f0c503da58ec25ebd359e4d990afa8 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.0-h5888daf_0.conda#db0bfbe7dd197b68ad5f30333bae6ce0 https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.6-h2dba641_1.conda#ede4673863426c0883c0063d853bbd85 -https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.1.0-h69a702a_3.conda#e66f2b8ad787e7beb0f846e4bd7e8493 -https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.1.0-hcea5267_3.conda#530566b68c3b8ce7eec4cd047eae19fe +https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.1.0-h69a702a_2.conda#ddca86c7040dd0e73b2b69bd7833d225 +https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.1.0-hcea5267_2.conda#01de444988ed960031dbe84cf4f9b1fc https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h4ce23a2_1.conda#e796ff8ddc598affdf7c173d6145f087 https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.1.0-hb9d3cd8_0.conda#9fa334557db9f63da6c9285fd2a48638 https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_2.conda#1a580f7796c7bf6393fddb8bbbde58dc https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-hb9d3cd8_0.conda#c7e925f37e3b40d893459e625f6a53f1 https://conda.anaconda.org/conda-forge/linux-64/libntlm-1.8-hb9d3cd8_0.conda#7c7927b404672409d9917d49bff5f2d6 https://conda.anaconda.org/conda-forge/linux-64/libpciaccess-0.18-hb9d3cd8_0.conda#70e3400cbbfa03e96dcde7fc13e38c7b -https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_3.conda#6d11a5edae89fe413c0569f16d308f5a +https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_2.conda#1cb1c67961f6dd257eae9e9691b341aa https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.10.0-h202a827_0.conda#0f98f3e95272d118f7931b6bef69bfe5 https://conda.anaconda.org/conda-forge/linux-64/libuv-1.51.0-hb9d3cd8_0.conda#1349c022c92c5efd3fd705a79a5804d8 https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.5.0-h851e524_0.conda#63f790534398730f59e1b899c3644d4a https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.1-h7b32b05_0.conda#c87df2ab1448ba69169652ab9547082d +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.0-h7b32b05_1.conda#de356753cfdbffcde5bb1e86e3aa6cd0 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-hb9d3cd8_1002.conda#b3c17d95b5a10c6e64a21fa17573e70e https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.2-hb9d3cd8_0.conda#fb901ff28063514abb6046c9ec2c4a45 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https://conda.anaconda.org/conda-forge/linux-64/libabseil-20250127.1-cxx17_hbbce691_0.conda#00290e549c5c8a32cc271020acc9ec6b @@ -64,16 +63,15 @@ https://conda.anaconda.org/conda-forge/linux-64/libdrm-2.4.125-hb9d3cd8_0.conda# https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20250104-pl5321h7949ede_0.conda#c277e0a4d549b03ac1e9d6cbbe3d017b https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda#172bf1cd1ff8629f2b1179945ed45055 https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda#a1cfcc585f0c42bf8d5546bb1dfb668d -https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.1.0-h69a702a_3.conda#bfbca721fd33188ef923dfe9ba172f29 -https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.49-h943b412_0.conda#37511c874cf3b8d0034c8d24e73c0884 -https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.50.2-h6cd9bfd_0.conda#b04c7eda6d7dab1e6503135e7fad4d25 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https://repo.anaconda.com/pkgs/main/osx-64/libwebp-base-1.3.2-h46256e1_1.conda#399c11b50e6e7a6969aca9a84ea416b7 -https://repo.anaconda.com/pkgs/main/osx-64/llvm-openmp-17.0.6-hdd4a2e0_0.conda#0871f60a4c389ef44c343aa33b5a3acd +https://repo.anaconda.com/pkgs/main/osx-64/llvm-openmp-14.0.6-h0dcd299_0.conda#b5804d32b87dc61ca94561ade33d5f2d https://repo.anaconda.com/pkgs/main/osx-64/ncurses-6.4-hcec6c5f_0.conda#0214d1ee980e217fabc695f1e40662aa https://repo.anaconda.com/pkgs/main/noarch/tzdata-2025b-h04d1e81_0.conda#1d027393db3427ab22a02aa44a56f143 -https://repo.anaconda.com/pkgs/main/osx-64/xxhash-0.8.0-h9ed2024_3.conda#79507f6b51082e0dc409046ee1471e8b https://repo.anaconda.com/pkgs/main/osx-64/xz-5.6.4-h46256e1_1.conda#ce989a528575ad332a650bb7c7f7e5d5 https://repo.anaconda.com/pkgs/main/osx-64/zlib-1.2.13-h4b97444_1.conda#38e35f7c817fac0973034bfce6706ec2 +https://repo.anaconda.com/pkgs/main/osx-64/ccache-3.7.9-hf120daa_0.conda#a01515a32e721c51d631283f991bc8ea https://repo.anaconda.com/pkgs/main/osx-64/expat-2.7.1-h6d0c2b6_0.conda#6cdc93776b7551083854e7f106a62720 -https://repo.anaconda.com/pkgs/main/osx-64/fmt-9.1.0-ha357a0b_1.conda#3cdbe6929571bdef216641b8a3eac194 https://repo.anaconda.com/pkgs/main/osx-64/intel-openmp-2023.1.0-ha357a0b_43548.conda#ba8a89ffe593eb88e4c01334753c40c3 https://repo.anaconda.com/pkgs/main/osx-64/lerc-4.0.0-h6d0c2b6_0.conda#824f87854c58df1525557c8639ce7f93 https://repo.anaconda.com/pkgs/main/osx-64/libgfortran5-11.3.0-h9dfd629_28.conda#1fa1a27ee100b1918c3021dbfa3895a3 -https://repo.anaconda.com/pkgs/main/osx-64/libhiredis-1.3.0-h6d0c2b6_0.conda#fa6c45039d776b9d70f865eab152dd30 https://repo.anaconda.com/pkgs/main/osx-64/libpng-1.6.39-h6c40b1e_0.conda#a3c824835f53ad27aeb86d2b55e47804 https://repo.anaconda.com/pkgs/main/osx-64/lz4-c-1.9.4-hcec6c5f_1.conda#aee0efbb45220e1985533dbff48551f8 https://repo.anaconda.com/pkgs/main/osx-64/ninja-base-1.12.1-h1962661_0.conda#9c0a94a811e88f182519d9309cf5f634 @@ -34,7 +32,6 @@ https://repo.anaconda.com/pkgs/main/osx-64/libgfortran-5.0.0-11_3_0_hecd8cb5_28. https://repo.anaconda.com/pkgs/main/osx-64/mkl-2023.1.0-h8e150cf_43560.conda#85d0f3431dd5c6ae44f8725fdd3d3e59 https://repo.anaconda.com/pkgs/main/osx-64/sqlite-3.45.3-h6c40b1e_0.conda#2edf909b937b3aad48322c9cb2e8f1a0 https://repo.anaconda.com/pkgs/main/osx-64/zstd-1.5.6-h138b38a_0.conda#f4d15d7d0054d39e6a24fe8d7d1e37c5 -https://repo.anaconda.com/pkgs/main/osx-64/ccache-4.11.3-h451b914_0.conda#5e4db702c976c28fbf50bdbaea47d3fa https://repo.anaconda.com/pkgs/main/osx-64/libtiff-4.7.0-h2dfa3ea_0.conda#82a118ce0139e2bf6f7a99c4cfbd4749 https://repo.anaconda.com/pkgs/main/osx-64/python-3.12.11-he8d2d4c_0.conda#9783e45825df3d441392b7fa66759899 https://repo.anaconda.com/pkgs/main/osx-64/brotli-python-1.0.9-py312h6d0c2b6_9.conda#425936421fe402074163ac3ffe33a060 @@ -50,7 +47,6 @@ https://repo.anaconda.com/pkgs/main/osx-64/ninja-1.12.1-hecd8cb5_0.conda#ee3b660 https://repo.anaconda.com/pkgs/main/osx-64/openjpeg-2.5.2-h2d09ccc_1.conda#0f2e221843154b436b5982c695df627b https://repo.anaconda.com/pkgs/main/osx-64/packaging-24.2-py312hecd8cb5_0.conda#76512e47c9c37443444ef0624769f620 https://repo.anaconda.com/pkgs/main/osx-64/pluggy-1.5.0-py312hecd8cb5_0.conda#ca381e438f1dbd7986ac0fa0da70c9d8 -https://repo.anaconda.com/pkgs/main/osx-64/pygments-2.19.1-py312hecd8cb5_0.conda#ca4be8769d62deee6127c0bf3703b0f6 https://repo.anaconda.com/pkgs/main/osx-64/pyparsing-3.2.0-py312hecd8cb5_0.conda#e4086daaaed13f68cc8d5b9da7db73cc https://repo.anaconda.com/pkgs/main/noarch/python-tzdata-2025.2-pyhd3eb1b0_0.conda#5ac858f05dbf9d3cdb04d53516901247 https://repo.anaconda.com/pkgs/main/osx-64/pytz-2024.1-py312hecd8cb5_0.conda#2b28ec0e0d07f5c0c701f75200b1e8b6 @@ -64,7 +60,7 @@ https://repo.anaconda.com/pkgs/main/osx-64/fonttools-4.55.3-py312h46256e1_0.cond https://repo.anaconda.com/pkgs/main/osx-64/numpy-base-1.26.4-py312h6f81483_0.conda#87f73efbf26ab2e2ea7c32481a71bd47 https://repo.anaconda.com/pkgs/main/osx-64/pillow-11.1.0-py312h935ef2f_1.conda#c2f7a3f027cc93a3626d50b765b75dc5 https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2a700153fefe0e69438b18e1 -https://repo.anaconda.com/pkgs/main/osx-64/pytest-8.4.1-py312hecd8cb5_0.conda#438421697d4806567af06bd006b26db0 +https://repo.anaconda.com/pkgs/main/osx-64/pytest-8.3.4-py312hecd8cb5_0.conda#b15ee02022967632dfa1672669228bee https://repo.anaconda.com/pkgs/main/osx-64/python-dateutil-2.9.0post0-py312hecd8cb5_2.conda#1047dde28f78127dd9f6121e882926dd https://repo.anaconda.com/pkgs/main/osx-64/pytest-cov-6.0.0-py312hecd8cb5_0.conda#db697e319a4d1145363246a51eef0352 https://repo.anaconda.com/pkgs/main/osx-64/pytest-xdist-3.6.1-py312hecd8cb5_0.conda#38df9520774ee82bf143218f1271f936 diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml b/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml index ba17d37ff1555..6c3da4bb863b4 100644 --- a/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml +++ b/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml @@ -24,7 +24,7 @@ dependencies: - pytest-cov - coverage - sphinx - - numpydoc<1.9.0 + - numpydoc - lightgbm - scikit-image - array-api-strict diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock index 8ae0316c42fad..de1e1ef5447bd 100644 --- a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 692a667e331896943137778007c0834c42c3aa297986d4f8eda8b51a7f158d98 +# input_hash: 50f16a0198b6eb575a737fee25051b52a644d72f5fca26bd661651a85fcb6a07 @EXPLICIT https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9 https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2025.2.25-h06a4308_0.conda#495015d24da8ad929e3ae2d18571016d @@ -13,25 +13,20 @@ https://repo.anaconda.com/pkgs/main/linux-64/_openmp_mutex-5.1-1_gnu.conda#71d28 https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-11.2.0-h1234567_1.conda#a87728dabf3151fb9cfa990bd2eb0464 https://repo.anaconda.com/pkgs/main/linux-64/bzip2-1.0.8-h5eee18b_6.conda#f21a3ff51c1b271977f53ce956a69297 https://repo.anaconda.com/pkgs/main/linux-64/expat-2.7.1-h6a678d5_0.conda#269942a9f3f943e2e5d8a2516a861f7c -https://repo.anaconda.com/pkgs/main/linux-64/fmt-9.1.0-hdb19cb5_1.conda#4f12930203ff2d84df5d287af9b29858 https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_1.conda#70646cc713f0c43926cfdcfe9b695fe0 -https://repo.anaconda.com/pkgs/main/linux-64/libhiredis-1.3.0-h6a678d5_0.conda#68b0289d6a3024e06b032f56dd7e46cf https://repo.anaconda.com/pkgs/main/linux-64/libmpdec-4.0.0-h5eee18b_0.conda#feb10f42b1a7b523acbf85461be41a3e https://repo.anaconda.com/pkgs/main/linux-64/libuuid-1.41.5-h5eee18b_0.conda#4a6a2354414c9080327274aa514e5299 -https://repo.anaconda.com/pkgs/main/linux-64/lz4-c-1.9.4-h6a678d5_1.conda#2ee58861f2b92b868ce761abb831819d https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda#5558eec6e2191741a92f832ea826251c https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.16-h5eee18b_0.conda#5875526739afa058cfa84da1fa7a2ef4 https://repo.anaconda.com/pkgs/main/linux-64/pthread-stubs-0.3-h0ce48e5_1.conda#973a642312d2a28927aaf5b477c67250 https://repo.anaconda.com/pkgs/main/linux-64/xorg-libxau-1.0.12-h9b100fa_0.conda#a8005a9f6eb903e113cd5363e8a11459 https://repo.anaconda.com/pkgs/main/linux-64/xorg-libxdmcp-1.1.5-h9b100fa_0.conda#c284a09ddfba81d9c4e740110f09ea06 https://repo.anaconda.com/pkgs/main/linux-64/xorg-xorgproto-2024.1-h5eee18b_1.conda#412a0d97a7a51d23326e57226189da92 -https://repo.anaconda.com/pkgs/main/linux-64/xxhash-0.8.0-h7f8727e_3.conda#196b013514e82fd8476558de622c0d46 https://repo.anaconda.com/pkgs/main/linux-64/xz-5.6.4-h5eee18b_1.conda#3581505fa450962d631bd82b8616350e https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_1.conda#92e42d8310108b0a440fb2e60b2b2a25 +https://repo.anaconda.com/pkgs/main/linux-64/ccache-3.7.9-hfe4627d_0.conda#bef6fc681c273bb7bd0c67d1a591365e https://repo.anaconda.com/pkgs/main/linux-64/libxcb-1.17.0-h9b100fa_0.conda#fdf0d380fa3809a301e2dbc0d5183883 https://repo.anaconda.com/pkgs/main/linux-64/readline-8.2-h5eee18b_0.conda#be42180685cce6e6b0329201d9f48efb -https://repo.anaconda.com/pkgs/main/linux-64/zstd-1.5.6-hc292b87_0.conda#78ae7abd3020b41f827b35085845e1b8 -https://repo.anaconda.com/pkgs/main/linux-64/ccache-4.11.3-hc6a6a4f_0.conda#3e660215a7953958c1eb910dde81eb52 https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.45.3-h5eee18b_0.conda#acf93d6aceb74d6110e20b44cc45939e https://repo.anaconda.com/pkgs/main/linux-64/xorg-libx11-1.8.12-h9b100fa_1.conda#6298b27afae6f49f03765b2a03df2fcb https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.14-h993c535_1.conda#bfe656b29fc64afe5d4bd46dbd5fd240 @@ -58,11 +53,11 @@ https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2 # pip meson @ https://files.pythonhosted.org/packages/8e/6e/b9dfeac98dd508f88bcaff134ee0bf5e602caf3ccb5a12b5dd9466206df1/meson-1.8.2-py3-none-any.whl#sha256=274b49dbe26e00c9a591442dd30f4ae9da8ce11ce53d0f4682cd10a45d50f6fd # pip networkx @ https://files.pythonhosted.org/packages/eb/8d/776adee7bbf76365fdd7f2552710282c79a4ead5d2a46408c9043a2b70ba/networkx-3.5-py3-none-any.whl#sha256=0030d386a9a06dee3565298b4a734b68589749a544acbb6c412dc9e2489ec6ec # pip ninja @ https://files.pythonhosted.org/packages/eb/7a/455d2877fe6cf99886849c7f9755d897df32eaf3a0fba47b56e615f880f7/ninja-1.11.1.4-py3-none-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=096487995473320de7f65d622c3f1d16c3ad174797602218ca8c967f51ec38a0 -# pip numpy @ https://files.pythonhosted.org/packages/50/30/af1b277b443f2fb08acf1c55ce9d68ee540043f158630d62cef012750f9f/numpy-2.3.1-cp313-cp313-manylinux_2_28_x86_64.whl#sha256=5902660491bd7a48b2ec16c23ccb9124b8abfd9583c5fdfa123fe6b421e03de1 +# pip numpy @ https://files.pythonhosted.org/packages/1c/12/734dce1087eed1875f2297f687e671cfe53a091b6f2f55f0c7241aad041b/numpy-2.3.0-cp313-cp313-manylinux_2_28_x86_64.whl#sha256=87717eb24d4a8a64683b7a4e91ace04e2f5c7c77872f823f02a94feee186168f # pip packaging @ https://files.pythonhosted.org/packages/20/12/38679034af332785aac8774540895e234f4d07f7545804097de4b666afd8/packaging-25.0-py3-none-any.whl#sha256=29572ef2b1f17581046b3a2227d5c611fb25ec70ca1ba8554b24b0e69331a484 -# pip pillow @ https://files.pythonhosted.org/packages/d5/1c/a2a29649c0b1983d3ef57ee87a66487fdeb45132df66ab30dd37f7dbe162/pillow-11.3.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl#sha256=13f87d581e71d9189ab21fe0efb5a23e9f28552d5be6979e84001d3b8505abe8 +# pip pillow @ https://files.pythonhosted.org/packages/13/eb/2552ecebc0b887f539111c2cd241f538b8ff5891b8903dfe672e997529be/pillow-11.2.1-cp313-cp313-manylinux_2_28_x86_64.whl#sha256=ad275964d52e2243430472fc5d2c2334b4fc3ff9c16cb0a19254e25efa03a155 # pip pluggy @ https://files.pythonhosted.org/packages/54/20/4d324d65cc6d9205fabedc306948156824eb9f0ee1633355a8f7ec5c66bf/pluggy-1.6.0-py3-none-any.whl#sha256=e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746 -# pip pygments @ https://files.pythonhosted.org/packages/c7/21/705964c7812476f378728bdf590ca4b771ec72385c533964653c68e86bdc/pygments-2.19.2-py3-none-any.whl#sha256=86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b +# pip pygments @ https://files.pythonhosted.org/packages/8a/0b/9fcc47d19c48b59121088dd6da2488a49d5f72dacf8262e2790a1d2c7d15/pygments-2.19.1-py3-none-any.whl#sha256=9ea1544ad55cecf4b8242fab6dd35a93bbce657034b0611ee383099054ab6d8c # pip pyparsing @ https://files.pythonhosted.org/packages/05/e7/df2285f3d08fee213f2d041540fa4fc9ca6c2d44cf36d3a035bf2a8d2bcc/pyparsing-3.2.3-py3-none-any.whl#sha256=a749938e02d6fd0b59b356ca504a24982314bb090c383e3cf201c95ef7e2bfcf # pip pytz @ https://files.pythonhosted.org/packages/81/c4/34e93fe5f5429d7570ec1fa436f1986fb1f00c3e0f43a589fe2bbcd22c3f/pytz-2025.2-py2.py3-none-any.whl#sha256=5ddf76296dd8c44c26eb8f4b6f35488f3ccbf6fbbd7adee0b7262d43f0ec2f00 # pip roman-numerals-py @ https://files.pythonhosted.org/packages/53/97/d2cbbaa10c9b826af0e10fdf836e1bf344d9f0abb873ebc34d1f49642d3f/roman_numerals_py-3.1.0-py3-none-any.whl#sha256=9da2ad2fb670bcf24e81070ceb3be72f6c11c440d73bd579fbeca1e9f330954c @@ -77,17 +72,17 @@ https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2 # pip tabulate @ https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl#sha256=024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f # pip threadpoolctl @ https://files.pythonhosted.org/packages/32/d5/f9a850d79b0851d1d4ef6456097579a9005b31fea68726a4ae5f2d82ddd9/threadpoolctl-3.6.0-py3-none-any.whl#sha256=43a0b8fd5a2928500110039e43a5eed8480b918967083ea48dc3ab9f13c4a7fb # pip tzdata @ https://files.pythonhosted.org/packages/5c/23/c7abc0ca0a1526a0774eca151daeb8de62ec457e77262b66b359c3c7679e/tzdata-2025.2-py2.py3-none-any.whl#sha256=1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8 -# pip urllib3 @ https://files.pythonhosted.org/packages/a7/c2/fe1e52489ae3122415c51f387e221dd0773709bad6c6cdaa599e8a2c5185/urllib3-2.5.0-py3-none-any.whl#sha256=e6b01673c0fa6a13e374b50871808eb3bf7046c4b125b216f6bf1cc604cff0dc -# pip array-api-strict @ https://files.pythonhosted.org/packages/e5/33/cede42b7b866db4b77432889314fc652ecc5cb6988f831ef08881a767089/array_api_strict-2.4-py3-none-any.whl#sha256=1cb20acd008f171ad8cce49589cc59897d8a242d1acf8ce6a61c3d57b61ecd14 +# pip urllib3 @ https://files.pythonhosted.org/packages/6b/11/cc635220681e93a0183390e26485430ca2c7b5f9d33b15c74c2861cb8091/urllib3-2.4.0-py3-none-any.whl#sha256=4e16665048960a0900c702d4a66415956a584919c03361cac9f1df5c5dd7e813 +# pip array-api-strict @ https://files.pythonhosted.org/packages/fe/c7/a97e26083985b49a7a54006364348cf1c26e5523850b8522a39b02b19715/array_api_strict-2.3.1-py3-none-any.whl#sha256=0ca6988be1c82d2f05b6cd44bc7e14cb390555d1455deb50f431d6d0cf468ded # pip contourpy @ https://files.pythonhosted.org/packages/c8/65/5245ce8c548a8422236c13ffcdcdada6a2a812c361e9e0c70548bb40b661/contourpy-1.3.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=434f0adf84911c924519d2b08fc10491dd282b20bdd3fa8f60fd816ea0b48841 # pip imageio @ https://files.pythonhosted.org/packages/cb/bd/b394387b598ed84d8d0fa90611a90bee0adc2021820ad5729f7ced74a8e2/imageio-2.37.0-py3-none-any.whl#sha256=11efa15b87bc7871b61590326b2d635439acc321cf7f8ce996f812543ce10eed # pip jinja2 @ https://files.pythonhosted.org/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl#sha256=85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67 # pip lazy-loader @ https://files.pythonhosted.org/packages/83/60/d497a310bde3f01cb805196ac61b7ad6dc5dcf8dce66634dc34364b20b4f/lazy_loader-0.4-py3-none-any.whl#sha256=342aa8e14d543a154047afb4ba8ef17f5563baad3fc610d7b15b213b0f119efc # pip pyproject-metadata @ https://files.pythonhosted.org/packages/7e/b1/8e63033b259e0a4e40dd1ec4a9fee17718016845048b43a36ec67d62e6fe/pyproject_metadata-0.9.1-py3-none-any.whl#sha256=ee5efde548c3ed9b75a354fc319d5afd25e9585fa918a34f62f904cc731973ad -# pip pytest @ https://files.pythonhosted.org/packages/29/16/c8a903f4c4dffe7a12843191437d7cd8e32751d5de349d45d3fe69544e87/pytest-8.4.1-py3-none-any.whl#sha256=539c70ba6fcead8e78eebbf1115e8b589e7565830d7d006a8723f19ac8a0afb7 +# pip pytest @ https://files.pythonhosted.org/packages/2f/de/afa024cbe022b1b318a3d224125aa24939e99b4ff6f22e0ba639a2eaee47/pytest-8.4.0-py3-none-any.whl#sha256=f40f825768ad76c0977cbacdf1fd37c6f7a468e460ea6a0636078f8972d4517e # pip python-dateutil @ https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl#sha256=a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 # pip requests @ https://files.pythonhosted.org/packages/7c/e4/56027c4a6b4ae70ca9de302488c5ca95ad4a39e190093d6c1a8ace08341b/requests-2.32.4-py3-none-any.whl#sha256=27babd3cda2a6d50b30443204ee89830707d396671944c998b5975b031ac2b2c -# pip scipy @ https://files.pythonhosted.org/packages/11/6b/3443abcd0707d52e48eb315e33cc669a95e29fc102229919646f5a501171/scipy-1.16.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl#sha256=1d8747f7736accd39289943f7fe53a8333be7f15a82eea08e4afe47d79568c32 +# pip scipy @ https://files.pythonhosted.org/packages/b5/09/c5b6734a50ad4882432b6bb7c02baf757f5b2f256041da5df242e2d7e6b6/scipy-1.15.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=c9deabd6d547aee2c9a81dee6cc96c6d7e9a9b1953f74850c179f91fdc729cb7 # pip tifffile @ https://files.pythonhosted.org/packages/3a/d8/1ba8f32bfc9cb69e37edeca93738e883f478fbe84ae401f72c0d8d507841/tifffile-2025.6.11-py3-none-any.whl#sha256=32effb78b10b3a283eb92d4ebf844ae7e93e151458b0412f38518b4e6d2d7542 # pip lightgbm @ https://files.pythonhosted.org/packages/42/86/dabda8fbcb1b00bcfb0003c3776e8ade1aa7b413dff0a2c08f457dace22f/lightgbm-4.6.0-py3-none-manylinux_2_28_x86_64.whl#sha256=cb19b5afea55b5b61cbb2131095f50538bd608a00655f23ad5d25ae3e3bf1c8d # pip matplotlib @ https://files.pythonhosted.org/packages/f5/64/41c4367bcaecbc03ef0d2a3ecee58a7065d0a36ae1aa817fe573a2da66d4/matplotlib-3.10.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=a80fcccbef63302c0efd78042ea3c2436104c5b1a4d3ae20f864593696364ac7 @@ -95,7 +90,7 @@ https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2 # pip pandas @ https://files.pythonhosted.org/packages/2a/b3/463bfe819ed60fb7e7ddffb4ae2ee04b887b3444feee6c19437b8f834837/pandas-2.3.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=213cd63c43263dbb522c1f8a7c9d072e25900f6975596f883f4bebd77295d4f3 # pip pyamg @ https://files.pythonhosted.org/packages/cd/a7/0df731cbfb09e73979a1a032fc7bc5be0eba617d798b998a0f887afe8ade/pyamg-5.2.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=6999b351ab969c79faacb81faa74c0fa9682feeff3954979212872a3ee40c298 # pip pytest-cov @ https://files.pythonhosted.org/packages/bc/16/4ea354101abb1287856baa4af2732be351c7bee728065aed451b678153fd/pytest_cov-6.2.1-py3-none-any.whl#sha256=f5bc4c23f42f1cdd23c70b1dab1bbaef4fc505ba950d53e0081d0730dd7e86d5 -# pip pytest-xdist @ https://files.pythonhosted.org/packages/ca/31/d4e37e9e550c2b92a9cbc2e4d0b7420a27224968580b5a447f420847c975/pytest_xdist-3.8.0-py3-none-any.whl#sha256=202ca578cfeb7370784a8c33d6d05bc6e13b4f25b5053c30a152269fd10f0b88 +# pip pytest-xdist @ https://files.pythonhosted.org/packages/0d/b2/0e802fde6f1c5b2f7ae7e9ad42b83fd4ecebac18a8a8c2f2f14e39dce6e1/pytest_xdist-3.7.0-py3-none-any.whl#sha256=7d3fbd255998265052435eb9daa4e99b62e6fb9cfb6efd1f858d4d8c0c7f0ca0 # pip scikit-image @ https://files.pythonhosted.org/packages/cd/9b/c3da56a145f52cd61a68b8465d6a29d9503bc45bc993bb45e84371c97d94/scikit_image-0.25.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=b8abd3c805ce6944b941cfed0406d88faeb19bab3ed3d4b50187af55cf24d147 # pip scipy-doctest @ https://files.pythonhosted.org/packages/c9/13/cd25d1875f3804b73fd4a4ae00e2c76e274e1e0608d79148cac251b644b1/scipy_doctest-1.8.0-py3-none-any.whl#sha256=5863208368c35486e143ce3283ab2f517a0d6b0c63d0d5f19f38a823fc82016f # pip sphinx @ https://files.pythonhosted.org/packages/31/53/136e9eca6e0b9dc0e1962e2c908fbea2e5ac000c2a2fbd9a35797958c48b/sphinx-8.2.3-py3-none-any.whl#sha256=4405915165f13521d875a8c29c8970800a0141c14cc5416a38feca4ea5d9b9c3 diff --git a/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock index e276c738d9915..9bbafc5b603d5 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_openblas_min_dependencies_linux-64_conda.lock @@ -17,16 +17,16 @@ https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-3_kmp_llvm.con https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab https://conda.anaconda.org/conda-forge/linux-64/libegl-1.7.0-ha4b6fd6_2.conda#c151d5eb730e9b7480e6d48c0fc44048 https://conda.anaconda.org/conda-forge/linux-64/libopengl-1.7.0-ha4b6fd6_2.conda#7df50d44d4a14d6c31a2c54f2cd92157 -https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_3.conda#9e60c55e725c20d23125a5f0dd69af5d +https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_2.conda#ea8ac52380885ed41c1baa8f1d6d2b93 https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.14-hb9d3cd8_0.conda#76df83c2a9035c54df5d04ff81bcc02d https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.5-hb9d3cd8_0.conda#f7f0d6cc2dc986d42ac2689ec88192be https://conda.anaconda.org/conda-forge/linux-64/gettext-tools-0.24.1-h5888daf_0.conda#d54305672f0361c2f3886750e7165b5f https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.24-h86f0d12_0.conda#64f0c503da58ec25ebd359e4d990afa8 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.0-h5888daf_0.conda#db0bfbe7dd197b68ad5f30333bae6ce0 https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.6-h2dba641_1.conda#ede4673863426c0883c0063d853bbd85 -https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.1.0-h69a702a_3.conda#e66f2b8ad787e7beb0f846e4bd7e8493 +https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.1.0-h69a702a_2.conda#ddca86c7040dd0e73b2b69bd7833d225 https://conda.anaconda.org/conda-forge/linux-64/libgettextpo-0.24.1-h5888daf_0.conda#2ee6d71b72f75d50581f2f68e965efdb -https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.1.0-hcea5267_3.conda#530566b68c3b8ce7eec4cd047eae19fe +https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.1.0-hcea5267_2.conda#01de444988ed960031dbe84cf4f9b1fc https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h4ce23a2_1.conda#e796ff8ddc598affdf7c173d6145f087 https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.1.0-hb9d3cd8_0.conda#9fa334557db9f63da6c9285fd2a48638 https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_2.conda#1a580f7796c7bf6393fddb8bbbde58dc @@ -36,12 +36,12 @@ https://conda.anaconda.org/conda-forge/linux-64/libnuma-2.0.18-hb9d3cd8_3.conda# https://conda.anaconda.org/conda-forge/linux-64/libogg-1.3.5-hd0c01bc_1.conda#68e52064ed3897463c0e958ab5c8f91b https://conda.anaconda.org/conda-forge/linux-64/libopus-1.5.2-hd0c01bc_0.conda#b64523fb87ac6f87f0790f324ad43046 https://conda.anaconda.org/conda-forge/linux-64/libpciaccess-0.18-hb9d3cd8_0.conda#70e3400cbbfa03e96dcde7fc13e38c7b -https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_3.conda#6d11a5edae89fe413c0569f16d308f5a +https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_2.conda#1cb1c67961f6dd257eae9e9691b341aa https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.8.0-hf23e847_1.conda#b1aa0faa95017bca11369bd080487ec4 https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.5.0-h851e524_0.conda#63f790534398730f59e1b899c3644d4a https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.1-h7b32b05_0.conda#c87df2ab1448ba69169652ab9547082d +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.0-h7b32b05_1.conda#de356753cfdbffcde5bb1e86e3aa6cd0 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-hb9d3cd8_1002.conda#b3c17d95b5a10c6e64a21fa17573e70e https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.2-hb9d3cd8_0.conda#fb901ff28063514abb6046c9ec2c4a45 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-hb9d3cd8_0.conda#f6ebe2cb3f82ba6c057dde5d9debe4f7 @@ -62,13 +62,13 @@ https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20250104-pl5321h7949 https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda#172bf1cd1ff8629f2b1179945ed45055 https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda#a1cfcc585f0c42bf8d5546bb1dfb668d https://conda.anaconda.org/conda-forge/linux-64/libgettextpo-devel-0.24.1-h5888daf_0.conda#8f04c7aae6a46503bc36d1ed5abc8c7c -https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.1.0-h69a702a_3.conda#bfbca721fd33188ef923dfe9ba172f29 +https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.1.0-h69a702a_2.conda#f92e6e0a3c0c0c85561ef61aa59d555d https://conda.anaconda.org/conda-forge/linux-64/libgpg-error-1.55-h3f2d84a_0.conda#2bd47db5807daade8500ed7ca4c512a4 https://conda.anaconda.org/conda-forge/linux-64/liblzma-devel-5.8.1-hb9d3cd8_2.conda#f61edadbb301530bd65a32646bd81552 https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.49-h943b412_0.conda#37511c874cf3b8d0034c8d24e73c0884 -https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.50.2-h6cd9bfd_0.conda#b04c7eda6d7dab1e6503135e7fad4d25 +https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.50.1-hee588c1_4.conda#c79ba4d93602695bc60c6960ee59d2b1 https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.11.1-hcf80075_0.conda#eecce068c7e4eddeb169591baac20ac4 -https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.1.0-h4852527_3.conda#57541755b5a51691955012b8e197c06c +https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.1.0-h4852527_2.conda#9d2072af184b5caa29492bf2344597bb https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda#40b61aab5c7ba9ff276c41cfffe6b80b https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.17.0-h8a09558_0.conda#92ed62436b625154323d40d5f2f11dd7 https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda#5aa797f8787fe7a17d1b0821485b5adc @@ -98,7 +98,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libcap-2.71-h39aace5_0.conda#dd1 https://conda.anaconda.org/conda-forge/linux-64/libcrc32c-1.1.2-h9c3ff4c_0.tar.bz2#c965a5aa0d5c1c37ffc62dff36e28400 https://conda.anaconda.org/conda-forge/linux-64/libfreetype6-2.13.3-h48d6fc4_1.conda#3c255be50a506c50765a93a6644f32fe https://conda.anaconda.org/conda-forge/linux-64/libgcrypt-lib-1.11.1-hb9d3cd8_0.conda#8504a291085c9fb809b66cabd5834307 -https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-15.1.0-h69a702a_3.conda#6e5d0574e57a38c36e674e9a18eee2b4 +https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-15.1.0-h69a702a_2.conda#a483a87b71e974bb75d1b9413d4436dd https://conda.anaconda.org/conda-forge/linux-64/libnghttp2-1.64.0-h161d5f1_0.conda#19e57602824042dfd0446292ef90488b https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-3.21.12-hfc55251_2.conda#e3a7d4ba09b8dc939b98fef55f539220 https://conda.anaconda.org/conda-forge/linux-64/libthrift-0.18.1-h8fd135c_2.conda#bbf65f7688512872f063810623b755dc @@ -147,7 +147,7 @@ https://conda.anaconda.org/conda-forge/linux-64/orc-1.8.4-h2f23424_0.conda#4bb92 https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda#58335b26c38bf4a20f399384c33cbcf9 https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda#7da7ccd349dbf6487a7778579d2bb971 https://conda.anaconda.org/conda-forge/noarch/ply-3.11-pyhd8ed1ab_3.conda#fd5062942bfa1b0bd5e0d2a4397b099e -https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda#6b6ece66ebcae2d5f326c77ef2c5a066 +https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.1-pyhd8ed1ab_0.conda#232fb4577b6687b2d503ef8e254270c9 https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.2.3-pyhd8ed1ab_1.conda#513d3c262ee49b54a8fec85c5bc99764 https://conda.anaconda.org/conda-forge/noarch/pytz-2025.2-pyhd8ed1ab_0.conda#bc8e3267d44011051f2eb14d22fb0960 https://conda.anaconda.org/conda-forge/noarch/setuptools-80.9.0-pyhff2d567_0.conda#4de79c071274a53dcaf2a8c749d1499e @@ -186,7 +186,7 @@ https://conda.anaconda.org/conda-forge/linux-64/openldap-2.6.10-he970967_0.conda https://conda.anaconda.org/conda-forge/linux-64/pillow-11.2.1-py310h7e6dc6c_0.conda#5645a243d90adb50909b9edc209d84fe https://conda.anaconda.org/conda-forge/noarch/pip-25.1.1-pyh8b19718_0.conda#32d0781ace05105cc99af55d36cbec7c https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.9.1-pyhd8ed1ab_0.conda#22ae7c6ea81e0c8661ef32168dda929b -https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda#5b8d21249ff20967101ffa321cab24e8 +https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhff2d567_1.conda#5ba79d7c71f03c678c8ead841f347d6e https://conda.anaconda.org/conda-forge/linux-64/sip-6.10.0-py310hf71b8c6_0.conda#2d7e4445be227e8210140b75725689ad https://conda.anaconda.org/conda-forge/linux-64/xorg-libxcomposite-0.4.6-hb9d3cd8_2.conda#d3c295b50f092ab525ffe3c2aa4b7413 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdamage-1.1.6-hb9d3cd8_0.conda#b5fcc7172d22516e1f965490e65e33a4 diff --git a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_environment.yml b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_environment.yml index 30466d12a3f20..267c149fd1c35 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_environment.yml +++ b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_environment.yml @@ -20,5 +20,5 @@ dependencies: - ninja - meson-python - sphinx - - numpydoc<1.9.0 + - numpydoc - ccache diff --git a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock index 6ad5e47e38591..0c7c5ac749057 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock @@ -1,47 +1,47 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 4abfb998e26e3beaa198409ac1ebc1278024921c4b3c6fc8de5c93be1b6193ba +# input_hash: 26bb2530999c20f24bbab0f7b6e3545ad84d059a25027cb624997210afc23693 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/noarch/python_abi-3.10-7_cp310.conda#44e871cba2b162368476a84b8d040b6c https://conda.anaconda.org/conda-forge/noarch/tzdata-2025b-h78e105d_0.conda#4222072737ccff51314b5ece9c7d6f5a https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.6.15-hbd8a1cb_0.conda#72525f07d72806e3b639ad4504c30ce5 https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h1423503_5.conda#6dc9e1305e7d3129af4ad0dabda30e56 -https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.1.0-h767d61c_3.conda#3cd1a7238a0dd3d0860fdefc496cc854 +https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.1.0-h767d61c_2.conda#fbe7d535ff9d3a168c148e07358cd5b1 https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2#73aaf86a425cc6e73fcf236a5a46396d -https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_3.conda#9e60c55e725c20d23125a5f0dd69af5d +https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_2.conda#ea8ac52380885ed41c1baa8f1d6d2b93 https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.24-h86f0d12_0.conda#64f0c503da58ec25ebd359e4d990afa8 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.0-h5888daf_0.conda#db0bfbe7dd197b68ad5f30333bae6ce0 https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.6-h2dba641_1.conda#ede4673863426c0883c0063d853bbd85 -https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.1.0-h69a702a_3.conda#e66f2b8ad787e7beb0f846e4bd7e8493 -https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.1.0-hcea5267_3.conda#530566b68c3b8ce7eec4cd047eae19fe +https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.1.0-h69a702a_2.conda#ddca86c7040dd0e73b2b69bd7833d225 +https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.1.0-hcea5267_2.conda#01de444988ed960031dbe84cf4f9b1fc https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.1.0-hb9d3cd8_0.conda#9fa334557db9f63da6c9285fd2a48638 https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_2.conda#1a580f7796c7bf6393fddb8bbbde58dc -https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hb9d3cd8_1.conda#d864d34357c3b65a4b731f78c0801dc4 -https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_3.conda#6d11a5edae89fe413c0569f16d308f5a +https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_2.conda#1cb1c67961f6dd257eae9e9691b341aa https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.5.0-h851e524_0.conda#63f790534398730f59e1b899c3644d4a https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.1-h7b32b05_0.conda#c87df2ab1448ba69169652ab9547082d +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.0-h7b32b05_1.conda#de356753cfdbffcde5bb1e86e3aa6cd0 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-hb9d3cd8_1002.conda#b3c17d95b5a10c6e64a21fa17573e70e https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-hb9d3cd8_0.conda#f6ebe2cb3f82ba6c057dde5d9debe4f7 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.5-hb9d3cd8_0.conda#8035c64cb77ed555e3f150b7b3972480 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62ee74e96c5ebb0af99386de58cf9553 https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h0aef613_1.conda#9344155d33912347b37f0ae6c410a835 -https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.1.0-h69a702a_3.conda#bfbca721fd33188ef923dfe9ba172f29 -https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.49-h943b412_0.conda#37511c874cf3b8d0034c8d24e73c0884 -https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.50.2-h6cd9bfd_0.conda#b04c7eda6d7dab1e6503135e7fad4d25 -https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.1.0-h4852527_3.conda#57541755b5a51691955012b8e197c06c +https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.1.0-h69a702a_2.conda#f92e6e0a3c0c0c85561ef61aa59d555d +https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hd590300_0.conda#30fd6e37fe21f86f4bd26d6ee73eeec7 +https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.47-h943b412_0.conda#55199e2ae2c3651f6f9b2a447b47bdc9 +https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.50.1-hee588c1_0.conda#96a7e36bff29f1d0ddf5b771e0da373a +https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.1.0-h4852527_2.conda#9d2072af184b5caa29492bf2344597bb https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda#40b61aab5c7ba9ff276c41cfffe6b80b https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.17.0-h8a09558_0.conda#92ed62436b625154323d40d5f2f11dd7 https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda#5aa797f8787fe7a17d1b0821485b5adc -https://conda.anaconda.org/conda-forge/linux-64/ninja-1.13.0-h7aa8ee6_0.conda#2f67cb5c5ec172faeba94348ae8af444 +https://conda.anaconda.org/conda-forge/linux-64/ninja-1.12.1-hff21bea_1.conda#2322531904f27501ee19847b87ba7c64 https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8c095d6_2.conda#283b96675859b20a825f8fa30f311446 https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_hd72426e_102.conda#a0116df4f4ed05c303811a837d5b39d8 https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb8e6e7a_2.conda#6432cb5d4ac0046c3ac0a8a0f95842f9 https://conda.anaconda.org/conda-forge/linux-64/libfreetype6-2.13.3-h48d6fc4_1.conda#3c255be50a506c50765a93a6644f32fe -https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-15.1.0-h69a702a_3.conda#6e5d0574e57a38c36e674e9a18eee2b4 -https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.30-pthreads_h94d23a6_0.conda#323dc8f259224d13078aaf7ce96c3efe +https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-15.1.0-h69a702a_2.conda#a483a87b71e974bb75d1b9413d4436dd +https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.29-pthreads_h94d23a6_0.conda#0a4d0252248ef9a0f88f2ba8b8a08e12 https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.7.0-hf01ce69_5.conda#e79a094918988bb1807462cd42c83962 https://conda.anaconda.org/conda-forge/linux-64/python-3.10.18-hd6af730_0_cpython.conda#4ea0c77cdcb0b81813a0436b162d7316 https://conda.anaconda.org/conda-forge/noarch/alabaster-1.0.0-pyhd8ed1ab_1.conda#1fd9696649f65fd6611fcdb4ffec738a @@ -58,17 +58,17 @@ https://conda.anaconda.org/conda-forge/noarch/idna-3.10-pyhd8ed1ab_1.conda#39a4f https://conda.anaconda.org/conda-forge/noarch/imagesize-1.4.1-pyhd8ed1ab_0.tar.bz2#7de5386c8fea29e76b303f37dde4c352 https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_1.conda#6837f3eff7dcea42ecd714ce1ac2b108 https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.17-h717163a_0.conda#000e85703f0fd9594c81710dd5066471 -https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-32_h59b9bed_openblas.conda#2af9f3d5c2e39f417ce040f5a35c40c6 +https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-31_h59b9bed_openblas.conda#728dbebd0f7a20337218beacffd37916 https://conda.anaconda.org/conda-forge/linux-64/libfreetype-2.13.3-ha770c72_1.conda#51f5be229d83ecd401fb369ab96ae669 https://conda.anaconda.org/conda-forge/linux-64/libhiredis-1.0.2-h2cc385e_0.tar.bz2#b34907d3a81a3cd8095ee83d174c074a https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.2-py310h89163eb_1.conda#8ce3f0332fd6de0d737e2911d329523f https://conda.anaconda.org/conda-forge/noarch/meson-1.8.2-pyhe01879c_0.conda#f0e001c8de8d959926d98edf0458cb2d -https://conda.anaconda.org/conda-forge/linux-64/openblas-0.3.30-pthreads_h6ec200e_0.conda#15fa8c1f683e68ff08ef0ea106012add +https://conda.anaconda.org/conda-forge/linux-64/openblas-0.3.29-pthreads_h6ec200e_0.conda#7e4d48870b3258bea920d51b7f495a81 https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.3-h5fbd93e_0.conda#9e5816bc95d285c115a3ebc2f8563564 https://conda.anaconda.org/conda-forge/noarch/packaging-25.0-pyh29332c3_1.conda#58335b26c38bf4a20f399384c33cbcf9 https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda#7da7ccd349dbf6487a7778579d2bb971 https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyh29332c3_1.conda#12c566707c80111f9799308d9e265aef -https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda#6b6ece66ebcae2d5f326c77ef2c5a066 +https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.1-pyhd8ed1ab_0.conda#232fb4577b6687b2d503ef8e254270c9 https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha55dd90_7.conda#461219d1a5bd61342293efa2c0c90eac https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2025.2-pyhd8ed1ab_0.conda#88476ae6ebd24f39261e0854ac244f33 https://conda.anaconda.org/conda-forge/noarch/pytz-2025.2-pyhd8ed1ab_0.conda#bc8e3267d44011051f2eb14d22fb0960 @@ -88,23 +88,23 @@ https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0. https://conda.anaconda.org/conda-forge/noarch/h2-4.2.0-pyhd8ed1ab_0.conda#b4754fb1bdcb70c8fd54f918301582c6 https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.6-pyhd8ed1ab_0.conda#446bd6c8cb26050d528881df495ce646 https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.1-pyhd8ed1ab_0.conda#fb1c14694de51a476ce8636d92b6f42c -https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-32_he106b2a_openblas.conda#3d3f9355e52f269cd8bc2c440d8a5263 -https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-32_h7ac8fdf_openblas.conda#6c3f04ccb6c578138e9f9899da0bd714 +https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-31_he106b2a_openblas.conda#abb32c727da370c481a1c206f5159ce9 +https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-31_h7ac8fdf_openblas.conda#452b98eafe050ecff932f0ec832dd03f https://conda.anaconda.org/conda-forge/linux-64/pillow-11.2.1-py310h7e6dc6c_0.conda#5645a243d90adb50909b9edc209d84fe https://conda.anaconda.org/conda-forge/noarch/pip-25.1.1-pyh8b19718_0.conda#32d0781ace05105cc99af55d36cbec7c https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.9.1-pyhd8ed1ab_0.conda#22ae7c6ea81e0c8661ef32168dda929b -https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda#5b8d21249ff20967101ffa321cab24e8 -https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-32_he2f377e_openblas.conda#54e7f7896d0dbf56665bcb0078bfa9d2 +https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhff2d567_1.conda#5ba79d7c71f03c678c8ead841f347d6e +https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-31_he2f377e_openblas.conda#7e5fff7d0db69be3a266f7e79a3bb0e2 https://conda.anaconda.org/conda-forge/noarch/meson-python-0.18.0-pyh70fd9c4_0.conda#576c04b9d9f8e45285fb4d9452c26133 https://conda.anaconda.org/conda-forge/linux-64/numpy-2.2.6-py310hefbff90_0.conda#b0cea2c364bf65cd19e023040eeab05d -https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.1-pyhd8ed1ab_0.conda#a49c2283f24696a7b30367b7346a0144 +https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.0-pyhd8ed1ab_0.conda#516d31f063ce7e49ced17f105b63a1f1 https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py310ha75aee5_2.conda#f9254b5b0193982416b91edcb4b2676f -https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-32_h1ea3ea9_openblas.conda#34cb4b6753b38a62ae25f3a73efd16b0 +https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-31_h1ea3ea9_openblas.conda#ba652ee0576396d4765e567f043c57f9 https://conda.anaconda.org/conda-forge/linux-64/pandas-2.3.0-py310h5eaa309_0.conda#379844614e3a24e59e59d8c69c6e9403 https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.7.0-pyhd8ed1ab_0.conda#15353a2a0ea6dfefaa52fc5ab5b98f41 https://conda.anaconda.org/conda-forge/linux-64/scipy-1.15.2-py310h1d65ade_0.conda#8c29cd33b64b2eb78597fa28b5595c8d -https://conda.anaconda.org/conda-forge/noarch/urllib3-2.5.0-pyhd8ed1ab_0.conda#436c165519e140cb08d246a4472a9d6a -https://conda.anaconda.org/conda-forge/linux-64/blas-2.132-openblas.conda#9c4a27ab2463f9b1d9019e0a798a5b81 +https://conda.anaconda.org/conda-forge/noarch/urllib3-2.4.0-pyhd8ed1ab_0.conda#c1e349028e0052c4eea844e94f773065 +https://conda.anaconda.org/conda-forge/linux-64/blas-2.131-openblas.conda#38b2ec894c69bb4be0e66d2ef7fc60bf https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.2.1-py310ha2bacc8_1.conda#817d32861729e14f474249f1036291c4 https://conda.anaconda.org/conda-forge/noarch/requests-2.32.4-pyhd8ed1ab_0.conda#f6082eae112814f1447b56a5e1f6ed05 https://conda.anaconda.org/conda-forge/noarch/numpydoc-1.8.0-pyhd8ed1ab_1.conda#5af206d64d18d6c8dfb3122b4d9e643b diff --git a/build_tools/azure/pymin_conda_forge_openblas_win-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_win-64_conda.lock index c6e2cb99c3f5b..ba4245727766f 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_win-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_openblas_win-64_conda.lock @@ -30,11 +30,11 @@ https://conda.anaconda.org/conda-forge/win-64/libiconv-1.18-h135ad9c_1.conda#21f https://conda.anaconda.org/conda-forge/win-64/libjpeg-turbo-3.1.0-h2466b09_0.conda#7c51d27540389de84852daa1cdb9c63c https://conda.anaconda.org/conda-forge/win-64/liblzma-5.8.1-h2466b09_2.conda#c15148b2e18da456f5108ccb5e411446 https://conda.anaconda.org/conda-forge/win-64/libopenblas-0.3.30-pthreads_ha4fe6b2_0.conda#c09864590782cb17fee135db4796bdcb -https://conda.anaconda.org/conda-forge/win-64/libsqlite-3.50.2-hf5d6505_0.conda#e1e6cac409e95538acdc3d33a0f34d6a +https://conda.anaconda.org/conda-forge/win-64/libsqlite-3.50.1-hf5d6505_6.conda#c01fd2d0873bdc8d35bfa3c6eb2f54e5 https://conda.anaconda.org/conda-forge/win-64/libwebp-base-1.5.0-h3b0e114_0.conda#33f7313967072c6e6d8f865f5493c7ae https://conda.anaconda.org/conda-forge/win-64/libzlib-1.3.1-h2466b09_2.conda#41fbfac52c601159df6c01f875de31b9 https://conda.anaconda.org/conda-forge/win-64/ninja-1.13.0-h79cd779_0.conda#fb5cb20bc807076f05ac18a628322fd7 -https://conda.anaconda.org/conda-forge/win-64/openssl-3.5.1-h725018a_0.conda#d124fc2fd7070177b5e2450627f8fc1a +https://conda.anaconda.org/conda-forge/win-64/openssl-3.5.0-ha4e3fda_1.conda#72c07e46b6766bb057018a9a74861b89 https://conda.anaconda.org/conda-forge/win-64/pixman-0.46.2-had0cd8c_0.conda#2566a45fb15e2f540eff14261f1242af https://conda.anaconda.org/conda-forge/win-64/qhull-2020.2-hc790b64_5.conda#854fbdff64b572b5c0b470f334d34c11 https://conda.anaconda.org/conda-forge/win-64/tk-8.6.13-h2c6b04d_2.conda#ebd0e761de9aa879a51d22cc721bd095 @@ -94,7 +94,7 @@ https://conda.anaconda.org/conda-forge/win-64/numpy-2.2.6-py310h4987827_0.conda# https://conda.anaconda.org/conda-forge/win-64/openjpeg-2.5.3-h4d64b90_0.conda#fc050366dd0b8313eb797ed1ffef3a29 https://conda.anaconda.org/conda-forge/noarch/pip-25.1.1-pyh8b19718_0.conda#32d0781ace05105cc99af55d36cbec7c https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.9.1-pyhd8ed1ab_0.conda#22ae7c6ea81e0c8661ef32168dda929b -https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda#5b8d21249ff20967101ffa321cab24e8 +https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhff2d567_1.conda#5ba79d7c71f03c678c8ead841f347d6e https://conda.anaconda.org/conda-forge/win-64/blas-devel-3.9.0-32_hc0f8095_openblas.conda#c07c54d62ee5a9886933051e10ad4b1e https://conda.anaconda.org/conda-forge/win-64/contourpy-1.3.2-py310hc19bc0b_0.conda#039416813b5290e7d100a05bb4326110 https://conda.anaconda.org/conda-forge/win-64/fonttools-4.58.4-py310h38315fa_0.conda#f7a8769f5923bebdc10acbbb41d28628 @@ -110,6 +110,6 @@ https://conda.anaconda.org/conda-forge/noarch/pytest-cov-6.2.1-pyhd8ed1ab_0.cond https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.7.0-pyhd8ed1ab_0.conda#15353a2a0ea6dfefaa52fc5ab5b98f41 https://conda.anaconda.org/conda-forge/win-64/cairo-1.18.4-h5782bbf_0.conda#20e32ced54300292aff690a69c5e7b97 https://conda.anaconda.org/conda-forge/win-64/harfbuzz-11.2.1-h8796e6f_0.conda#bccea58fbf7910ce868b084f27ffe8bd -https://conda.anaconda.org/conda-forge/win-64/qt6-main-6.9.1-h02ddd7d_1.conda#fc796cf6c16db38d44c2efefbe6afcea +https://conda.anaconda.org/conda-forge/win-64/qt6-main-6.9.1-h02ddd7d_0.conda#feaaaae25a51188fb0544aca8b26ef4d https://conda.anaconda.org/conda-forge/win-64/pyside6-6.9.1-py310h2d19612_0.conda#01b830c0fd6ca7ab03c85a008a6f4a2d https://conda.anaconda.org/conda-forge/win-64/matplotlib-3.10.3-py310h5588dad_0.conda#103adee33db124a0263d0b4551e232e3 diff --git a/build_tools/azure/ubuntu_atlas_lock.txt b/build_tools/azure/ubuntu_atlas_lock.txt index 569cbbb2b5344..ddbe7a200dba1 100644 --- a/build_tools/azure/ubuntu_atlas_lock.txt +++ b/build_tools/azure/ubuntu_atlas_lock.txt @@ -27,15 +27,15 @@ packaging==25.0 # pytest pluggy==1.6.0 # via pytest -pygments==2.19.2 +pygments==2.19.1 # via pytest pyproject-metadata==0.9.1 # via meson-python -pytest==8.4.1 +pytest==8.4.0 # via # -r build_tools/azure/ubuntu_atlas_requirements.txt # pytest-xdist -pytest-xdist==3.8.0 +pytest-xdist==3.7.0 # via -r build_tools/azure/ubuntu_atlas_requirements.txt threadpoolctl==3.1.0 # via -r build_tools/azure/ubuntu_atlas_requirements.txt diff --git a/build_tools/circle/doc_environment.yml b/build_tools/circle/doc_environment.yml index 360be7b52b9a9..bc36e178de058 100644 --- a/build_tools/circle/doc_environment.yml +++ b/build_tools/circle/doc_environment.yml @@ -27,7 +27,7 @@ dependencies: - sphinx - sphinx-gallery - sphinx-copybutton - - numpydoc<1.9.0 + - numpydoc - sphinx-prompt - plotly - polars diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock index 4d948db6e5db5..14a5b8303d947 100644 --- a/build_tools/circle/doc_linux-64_conda.lock +++ b/build_tools/circle/doc_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: f8748904ea3a3b4e57ef03e9ef12f4ec17e4998ed6cbe6d15bc058d26bd37454 +# input_hash: 93cb6f7aa17dce662512650f1419e87eae56ed49163348847bf965697cd268bb @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45 @@ -15,7 +15,7 @@ https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766 https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_5.conda#acd9213a63cb62521290e581ef82de80 https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-13.3.0-hc03c837_102.conda#4c1d6961a6a54f602ae510d9bf31fa60 https://conda.anaconda.org/conda-forge/linux-64/libglvnd-1.7.0-ha4b6fd6_2.conda#434ca7e50e40f4918ab701e3facd59a0 -https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.1.0-h767d61c_3.conda#3cd1a7238a0dd3d0860fdefc496cc854 +https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.1.0-h767d61c_2.conda#fbe7d535ff9d3a168c148e07358cd5b1 https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-13.3.0-hc03c837_102.conda#aa38de2738c5f4a72a880e3d31ffe8b4 https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.17-h0157908_18.conda#460eba7851277ec1fd80a1a24080787a https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2#73aaf86a425cc6e73fcf236a5a46396d @@ -25,25 +25,24 @@ https://conda.anaconda.org/conda-forge/linux-64/libegl-1.7.0-ha4b6fd6_2.conda#c1 https://conda.anaconda.org/conda-forge/linux-64/libopengl-1.7.0-ha4b6fd6_2.conda#7df50d44d4a14d6c31a2c54f2cd92157 https://conda.anaconda.org/conda-forge/linux-64/binutils-2.43-h4852527_5.conda#4846404183ea94fd6652e9fb6ac5e16f https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.43-h4852527_5.conda#327ef163ac88b57833c1c1a20a9e7e0d -https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_3.conda#9e60c55e725c20d23125a5f0dd69af5d +https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_2.conda#ea8ac52380885ed41c1baa8f1d6d2b93 https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.14-hb9d3cd8_0.conda#76df83c2a9035c54df5d04ff81bcc02d https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hb9d3cd8_3.conda#cb98af5db26e3f482bebb80ce9d947d3 https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.24-h86f0d12_0.conda#64f0c503da58ec25ebd359e4d990afa8 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.0-h5888daf_0.conda#db0bfbe7dd197b68ad5f30333bae6ce0 https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.6-h2dba641_1.conda#ede4673863426c0883c0063d853bbd85 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+https://conda.anaconda.org/conda-forge/linux-aarch64/liblapack-3.9.0-31_h411afd4_openblas.conda#41dbff5eb805a75c120a7b7a1c744dc2 https://conda.anaconda.org/conda-forge/linux-aarch64/libllvm20-20.1.7-h07bd352_0.conda#391cbb3bd5206abf6601efc793ee429e https://conda.anaconda.org/conda-forge/linux-aarch64/libxkbcommon-1.10.0-hbab7b08_0.conda#36cd1db31e923c6068b7e0e6fce2cd7b https://conda.anaconda.org/conda-forge/linux-aarch64/libxslt-1.1.39-h1cc9640_0.conda#13e1d3f9188e85c6d59a98651aced002 @@ -131,7 +131,7 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/openldap-2.6.10-h30c48ee_0. https://conda.anaconda.org/conda-forge/linux-aarch64/pillow-11.2.1-py310h34c99de_0.conda#116816e9f034fcaeafcd878ef8b1e323 https://conda.anaconda.org/conda-forge/noarch/pip-25.1.1-pyh8b19718_0.conda#32d0781ace05105cc99af55d36cbec7c https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.9.1-pyhd8ed1ab_0.conda#22ae7c6ea81e0c8661ef32168dda929b -https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda#5b8d21249ff20967101ffa321cab24e8 +https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhff2d567_1.conda#5ba79d7c71f03c678c8ead841f347d6e https://conda.anaconda.org/conda-forge/linux-aarch64/xcb-util-cursor-0.1.5-h86ecc28_0.conda#d6bb2038d26fa118d5cbc2761116f3e5 https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxcomposite-0.4.6-h86ecc28_2.conda#86051eee0766c3542be24844a9c3cf36 https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxcursor-1.2.3-h86ecc28_0.conda#f2054759c2203d12d0007005e1f1296d @@ -142,20 +142,20 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxxf86vm-1.1.6-h86ec https://conda.anaconda.org/conda-forge/linux-aarch64/fontconfig-2.15.0-h8dda3cd_1.conda#112b71b6af28b47c624bcbeefeea685b https://conda.anaconda.org/conda-forge/linux-aarch64/libclang-cpp20.1-20.1.7-default_h7d4303a_0.conda#b698f9517041dcf9b54cdb95f08860e3 https://conda.anaconda.org/conda-forge/linux-aarch64/libclang13-20.1.7-default_h9e36cb9_0.conda#bd57f9ace2cde6f3ecbacc3e2d70bcdc -https://conda.anaconda.org/conda-forge/linux-aarch64/liblapacke-3.9.0-32_hc659ca5_openblas.conda#1cd2cbdb80386aae8c584ab9f1175ca6 +https://conda.anaconda.org/conda-forge/linux-aarch64/liblapacke-3.9.0-31_hc659ca5_openblas.conda#256bb281d78e5b8927ff13a1cde9f6f5 https://conda.anaconda.org/conda-forge/linux-aarch64/libpq-17.5-hf590da8_0.conda#b5a01e5aa04651ccf5865c2d029affa3 https://conda.anaconda.org/conda-forge/noarch/meson-python-0.18.0-pyh70fd9c4_0.conda#576c04b9d9f8e45285fb4d9452c26133 https://conda.anaconda.org/conda-forge/linux-aarch64/numpy-2.2.6-py310h6e5608f_0.conda#9e9f1f279eb02c41bda162a42861adc0 -https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.1-pyhd8ed1ab_0.conda#a49c2283f24696a7b30367b7346a0144 +https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.0-pyhd8ed1ab_0.conda#516d31f063ce7e49ced17f105b63a1f1 https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxtst-1.2.5-h57736b2_3.conda#c05698071b5c8e0da82a282085845860 -https://conda.anaconda.org/conda-forge/linux-aarch64/blas-devel-3.9.0-32_h9678261_openblas.conda#9c18808e64a8557732e664eac92df74d +https://conda.anaconda.org/conda-forge/linux-aarch64/blas-devel-3.9.0-31_h9678261_openblas.conda#a2cc143d7e25e52a915cb320e5b0d592 https://conda.anaconda.org/conda-forge/linux-aarch64/cairo-1.18.4-h83712da_0.conda#cd55953a67ec727db5dc32b167201aa6 https://conda.anaconda.org/conda-forge/linux-aarch64/contourpy-1.3.2-py310hf54e67a_0.conda#779694434d1f0a67c5260db76b7b7907 https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.7.0-pyhd8ed1ab_0.conda#15353a2a0ea6dfefaa52fc5ab5b98f41 https://conda.anaconda.org/conda-forge/linux-aarch64/scipy-1.15.2-py310hf37559f_0.conda#5c9b72f10d2118d943a5eaaf2f396891 -https://conda.anaconda.org/conda-forge/linux-aarch64/blas-2.132-openblas.conda#2c1e3662c8c5e7b92a49fd6372bb659f +https://conda.anaconda.org/conda-forge/linux-aarch64/blas-2.131-openblas.conda#51c5f346e1ebee750f76066490059df9 https://conda.anaconda.org/conda-forge/linux-aarch64/harfbuzz-11.2.1-h405b6a2_0.conda#b55680fc90e9747dc858e7ceb0abc2b2 https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-base-3.10.3-py310h2cc5e2d_0.conda#e29f4329f4f76cf14f74ed86dcc59bac -https://conda.anaconda.org/conda-forge/linux-aarch64/qt6-main-6.9.1-h13135bf_1.conda#def3ca3fcfa60a6c954bdd8f5bb00cd2 +https://conda.anaconda.org/conda-forge/linux-aarch64/qt6-main-6.9.1-h13135bf_0.conda#6e8335a319b6b1988d6959f895116c74 https://conda.anaconda.org/conda-forge/linux-aarch64/pyside6-6.9.1-py310hd3bda28_0.conda#1a105dc54d3cd250526c9d52379133c9 https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-3.10.3-py310hbbe02a8_0.conda#08982f6ac753e962d59160b08839221b diff --git a/sklearn/linear_model/_glm/tests/test_glm.py b/sklearn/linear_model/_glm/tests/test_glm.py index e2e1e09d76401..fbcc4d61a8e1c 100644 --- a/sklearn/linear_model/_glm/tests/test_glm.py +++ b/sklearn/linear_model/_glm/tests/test_glm.py @@ -656,7 +656,6 @@ def test_glm_sample_weight_consistency(fit_intercept, alpha, GLMEstimator): X = rng.rand(n_samples, n_features) y = rng.rand(n_samples) glm_params = dict(alpha=alpha, fit_intercept=fit_intercept) - tols = dict(rtol=1e-12, atol=1e-14) glm = GLMEstimator(**glm_params).fit(X, y) coef = glm.coef_.copy() @@ -664,12 +663,12 @@ def test_glm_sample_weight_consistency(fit_intercept, alpha, GLMEstimator): # sample_weight=np.ones(..) should be equivalent to sample_weight=None sample_weight = np.ones(y.shape) glm.fit(X, y, sample_weight=sample_weight) - assert_allclose(glm.coef_, coef, **tols) + assert_allclose(glm.coef_, coef, rtol=1e-12) # sample_weight are normalized to 1 so, scaling them has no effect sample_weight = 2 * np.ones(y.shape) glm.fit(X, y, sample_weight=sample_weight) - assert_allclose(glm.coef_, coef, **tols) + assert_allclose(glm.coef_, coef, rtol=1e-12) # setting one element of sample_weight to 0 is equivalent to removing # the corresponding sample @@ -678,7 +677,7 @@ def test_glm_sample_weight_consistency(fit_intercept, alpha, GLMEstimator): glm.fit(X, y, sample_weight=sample_weight) coef1 = glm.coef_.copy() glm.fit(X[:-1], y[:-1]) - assert_allclose(glm.coef_, coef1, **tols) + assert_allclose(glm.coef_, coef1, rtol=1e-12) # check that multiplying sample_weight by 2 is equivalent # to repeating corresponding samples twice @@ -688,8 +687,9 @@ def test_glm_sample_weight_consistency(fit_intercept, alpha, GLMEstimator): sample_weight_1[: n_samples // 2] = 2 glm1 = GLMEstimator(**glm_params).fit(X, y, sample_weight=sample_weight_1) + glm2 = GLMEstimator(**glm_params).fit(X2, y2, sample_weight=None) - assert_allclose(glm1.coef_, glm2.coef_, rtol=1e-10, atol=1e-14) + assert_allclose(glm1.coef_, glm2.coef_) @pytest.mark.parametrize("solver", SOLVERS) From 0baaca33eed56c5d63f772586a5acd8a2231e793 Mon Sep 17 00:00:00 2001 From: Lucy Liu Date: Thu, 3 Jul 2025 20:50:38 +1000 Subject: [PATCH 074/103] DOC Make references to scipy modules/functions links in `pairwise.py` (#31694) --- sklearn/metrics/pairwise.py | 29 ++++++++++++++--------------- 1 file changed, 14 insertions(+), 15 deletions(-) diff --git a/sklearn/metrics/pairwise.py b/sklearn/metrics/pairwise.py index f0e6cee65bc28..050b58866c8ef 100644 --- a/sklearn/metrics/pairwise.py +++ b/sklearn/metrics/pairwise.py @@ -299,7 +299,7 @@ def euclidean_distances( However, this is not the most precise way of doing this computation, because this equation potentially suffers from "catastrophic cancellation". Also, the distance matrix returned by this function may not be exactly - symmetric as required by, e.g., ``scipy.spatial.distance`` functions. + symmetric as required by, e.g., :mod:`scipy.spatial.distance` functions. Read more in the :ref:`User Guide `. @@ -751,7 +751,7 @@ def pairwise_distances_argmin_min( metric : str or callable, default='euclidean' Metric to use for distance computation. Any metric from scikit-learn - or scipy.spatial.distance can be used. + or :mod:`scipy.spatial.distance` can be used. If metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable @@ -766,13 +766,13 @@ def pairwise_distances_argmin_min( - from scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2', 'manhattan', 'nan_euclidean'] - - from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev', + - from :mod:`scipy.spatial.distance`: ['braycurtis', 'canberra', 'chebyshev', 'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule'] - See the documentation for scipy.spatial.distance for details on these + See the documentation for :mod:`scipy.spatial.distance` for details on these metrics. .. note:: @@ -899,7 +899,7 @@ def pairwise_distances_argmin(X, Y, *, axis=1, metric="euclidean", metric_kwargs metric : str or callable, default="euclidean" Metric to use for distance computation. Any metric from scikit-learn - or scipy.spatial.distance can be used. + or :mod:`scipy.spatial.distance` can be used. If metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable @@ -914,13 +914,13 @@ def pairwise_distances_argmin(X, Y, *, axis=1, metric="euclidean", metric_kwargs - from scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2', 'manhattan', 'nan_euclidean'] - - from scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev', + - from :mod:`scipy.spatial.distance`: ['braycurtis', 'canberra', 'chebyshev', 'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule'] - See the documentation for scipy.spatial.distance for details on these + See the documentation for :mod:`scipy.spatial.distance` for details on these metrics. .. note:: @@ -2124,7 +2124,7 @@ def pairwise_distances_chunked( metric : str or callable, default='euclidean' The metric to use when calculating distance between instances in a feature array. If metric is a string, it must be one of the options - allowed by scipy.spatial.distance.pdist for its metric parameter, + allowed by :func:`scipy.spatial.distance.pdist` for its metric parameter, or a metric listed in pairwise.PAIRWISE_DISTANCE_FUNCTIONS. If metric is "precomputed", X is assumed to be a distance matrix. Alternatively, if metric is a callable function, it is called on @@ -2148,7 +2148,7 @@ def pairwise_distances_chunked( **kwds : optional keyword parameters Any further parameters are passed directly to the distance function. - If using a scipy.spatial.distance metric, the parameters are still + If using a :mod:`scipy.spatial.distance` metric, the parameters are still metric dependent. See the scipy docs for usage examples. Yields @@ -2304,12 +2304,11 @@ def pairwise_distances( 'manhattan', 'nan_euclidean']. All metrics support sparse matrix inputs except 'nan_euclidean'. - - From scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev', + - From :mod:`scipy.spatial.distance`: ['braycurtis', 'canberra', 'chebyshev', 'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', - 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule'] - See the documentation for scipy.spatial.distance for details on these - metrics. These metrics do not support sparse matrix inputs. + 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule']. + These metrics do not support sparse matrix inputs. .. note:: `'kulsinski'` is deprecated from SciPy 1.9 and will be removed in SciPy 1.11. @@ -2318,7 +2317,7 @@ def pairwise_distances( `'matching'` has been removed in SciPy 1.9 (use `'hamming'` instead). Note that in the case of 'cityblock', 'cosine' and 'euclidean' (which are - valid scipy.spatial.distance metrics), the scikit-learn implementation + valid :mod:`scipy.spatial.distance` metrics), the scikit-learn implementation will be used, which is faster and has support for sparse matrices (except for 'cityblock'). For a verbose description of the metrics from scikit-learn, see :func:`sklearn.metrics.pairwise.distance_metrics` @@ -2341,7 +2340,7 @@ def pairwise_distances( metric : str or callable, default='euclidean' The metric to use when calculating distance between instances in a feature array. If metric is a string, it must be one of the options - allowed by scipy.spatial.distance.pdist for its metric parameter, or + allowed by :func:`scipy.spatial.distance.pdist` for its metric parameter, or a metric listed in ``pairwise.PAIRWISE_DISTANCE_FUNCTIONS``. If metric is "precomputed", X is assumed to be a distance matrix. Alternatively, if metric is a callable function, it is called on each From 8db769a002b86716b67b3af7f55c44b04a86dcdb Mon Sep 17 00:00:00 2001 From: "Christine P. Chai" Date: Sun, 6 Jul 2025 19:43:54 -0700 Subject: [PATCH 075/103] DOC: Replace the tag XXX with Note in Glossary (#31710) --- doc/glossary.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/doc/glossary.rst b/doc/glossary.rst index ae6ea4dd46324..f522073f25e7e 100644 --- a/doc/glossary.rst +++ b/doc/glossary.rst @@ -1191,7 +1191,7 @@ Target Types :term:`multiclass` targets, horizontally stacked into an array of shape ``(n_samples, n_outputs)``. - XXX: For simplicity, we may not always support string class labels + Note: For simplicity, we may not always support string class labels for multiclass multioutput, and integer class labels should be used. :mod:`~sklearn.multioutput` provides estimators which estimate multi-output @@ -1384,7 +1384,7 @@ Methods To clear the model, a new estimator should be constructed, for instance with :func:`base.clone`. - NOTE: Using ``partial_fit`` after ``fit`` results in undefined behavior. + Note: Using ``partial_fit`` after ``fit`` results in undefined behavior. ``predict`` Makes a prediction for each sample, usually only taking :term:`X` as @@ -1613,7 +1613,7 @@ functions or non-estimator constructors. for some algorithms, an improper distance metric (one that does not obey the triangle inequality, such as Cosine Distance) may be used. - XXX: hierarchical clustering uses ``affinity`` with this meaning. + Note: Hierarchical clustering uses ``affinity`` with this meaning. We also use *metric* to refer to :term:`evaluation metrics`, but avoid using this sense as a parameter name. From 01d52cc462debcb02fb9f9df46ab3e2d1a6cbe77 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 7 Jul 2025 09:46:58 +0200 Subject: [PATCH 076/103] :lock: :robot: CI Update lock files for scipy-dev CI build(s) :lock: :robot: (#31713) Co-authored-by: Lock file bot --- build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock index fad69044932e5..534fb9be5b52b 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock @@ -43,7 +43,7 @@ https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2 # pip babel @ 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a/doc/modules/ensemble.rst b/doc/modules/ensemble.rst index 31ca150df372e..e48d3772fff06 100644 --- a/doc/modules/ensemble.rst +++ b/doc/modules/ensemble.rst @@ -965,7 +965,7 @@ from a sample drawn with replacement (i.e., a bootstrap sample) from the training set. Furthermore, when splitting each node during the construction of a tree, the -best split is found through an exhaustive search of the features values of +best split is found through an exhaustive search of the feature values of either all input features or a random subset of size ``max_features``. (See the :ref:`parameter tuning guidelines ` for more details.) From 06032cbb71b08b7226c1c233afa8b6243b4e9e70 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 7 Jul 2025 10:35:59 +0200 Subject: [PATCH 079/103] :lock: :robot: CI Update lock files for free-threaded CI build(s) :lock: :robot: (#31714) Co-authored-by: Lock file bot --- .../azure/pylatest_free_threaded_linux-64_conda.lock | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock b/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock index 88727be760190..68c45067fd01e 100644 --- a/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock +++ b/build_tools/azure/pylatest_free_threaded_linux-64_conda.lock @@ -6,7 +6,7 @@ https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.ta https://conda.anaconda.org/conda-forge/noarch/python_abi-3.13-7_cp313t.conda#df81edcc11a1176315e8226acab83eec https://conda.anaconda.org/conda-forge/noarch/tzdata-2025b-h78e105d_0.conda#4222072737ccff51314b5ece9c7d6f5a https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.6.15-hbd8a1cb_0.conda#72525f07d72806e3b639ad4504c30ce5 -https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h1423503_5.conda#6dc9e1305e7d3129af4ad0dabda30e56 +https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.44-h1423503_0.conda#e31316a586cac398b1fcdb10ace786b9 https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.1.0-h767d61c_3.conda#3cd1a7238a0dd3d0860fdefc496cc854 https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2#73aaf86a425cc6e73fcf236a5a46396d https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_3.conda#9e60c55e725c20d23125a5f0dd69af5d @@ -19,7 +19,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-hb9d3cd8_0.conda# https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_3.conda#6d11a5edae89fe413c0569f16d308f5a https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.0-h7b32b05_1.conda#de356753cfdbffcde5bb1e86e3aa6cd0 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.1-h7b32b05_0.conda#c87df2ab1448ba69169652ab9547082d https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62ee74e96c5ebb0af99386de58cf9553 https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.1.0-h69a702a_3.conda#bfbca721fd33188ef923dfe9ba172f29 https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.50.2-h6cd9bfd_0.conda#b04c7eda6d7dab1e6503135e7fad4d25 @@ -47,7 +47,7 @@ https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.2-pyhd8ed1ab_0.conda https://conda.anaconda.org/conda-forge/noarch/setuptools-80.9.0-pyhff2d567_0.conda#4de79c071274a53dcaf2a8c749d1499e https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda#9d64911b31d57ca443e9f1e36b04385f https://conda.anaconda.org/conda-forge/noarch/tomli-2.2.1-pyhd8ed1ab_1.conda#ac944244f1fed2eb49bae07193ae8215 -https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.14.0-pyhe01879c_0.conda#2adcd9bb86f656d3d43bf84af59a1faf +https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.14.1-pyhe01879c_0.conda#e523f4f1e980ed7a4240d7e27e9ec81f https://conda.anaconda.org/conda-forge/linux-64/ccache-4.11.3-h80c52d3_0.conda#eb517c6a2b960c3ccb6f1db1005f063a https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda#72e42d28960d875c7654614f8b50939a https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.1-pyhd8ed1ab_0.conda#fb1c14694de51a476ce8636d92b6f42c @@ -56,7 +56,7 @@ https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-32_h7ac8fdf_open https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.9.1-pyhd8ed1ab_0.conda#22ae7c6ea81e0c8661ef32168dda929b https://conda.anaconda.org/conda-forge/noarch/python-freethreading-3.13.5-h92d6c8b_2.conda#32180e39991faf3fd42b4d74ef01daa0 https://conda.anaconda.org/conda-forge/noarch/meson-python-0.18.0-pyh70fd9c4_0.conda#576c04b9d9f8e45285fb4d9452c26133 -https://conda.anaconda.org/conda-forge/linux-64/numpy-2.3.0-py313h103f029_0.conda#d24d95f39ffa3c70827df0183b01df04 +https://conda.anaconda.org/conda-forge/linux-64/numpy-2.3.1-py313h103f029_0.conda#c583d7057dfbd9e0e076062f3667b38c https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.1-pyhd8ed1ab_0.conda#a49c2283f24696a7b30367b7346a0144 -https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.7.0-pyhd8ed1ab_0.conda#15353a2a0ea6dfefaa52fc5ab5b98f41 +https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.8.0-pyhd8ed1ab_0.conda#8375cfbda7c57fbceeda18229be10417 https://conda.anaconda.org/conda-forge/linux-64/scipy-1.16.0-py313h7f7b39c_0.conda#efa6724dab9395e1307c65a589d35459 From 31a22863fc6b3b1669a66b153912449c7c88f776 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9r=C3=A9mie=20du=20Boisberranger?= Date: Mon, 7 Jul 2025 11:10:42 +0200 Subject: [PATCH 080/103] FIX Revert tarfile_extractall clean-up (#31685) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Loïc Estève --- .../upcoming_changes/sklearn.datasets/31685.fix.rst | 5 +++++ sklearn/datasets/_lfw.py | 6 ++---- sklearn/datasets/_twenty_newsgroups.py | 6 ++---- sklearn/utils/fixes.py | 11 +++++++++++ 4 files changed, 20 insertions(+), 8 deletions(-) create mode 100644 doc/whats_new/upcoming_changes/sklearn.datasets/31685.fix.rst diff --git a/doc/whats_new/upcoming_changes/sklearn.datasets/31685.fix.rst b/doc/whats_new/upcoming_changes/sklearn.datasets/31685.fix.rst new file mode 100644 index 0000000000000..5d954e538d707 --- /dev/null +++ b/doc/whats_new/upcoming_changes/sklearn.datasets/31685.fix.rst @@ -0,0 +1,5 @@ +- Fixed a regression preventing to extract the downloaded dataset in + :func:`datasets.fetch_20newsgroups`, :func:`datasets.fetch_20newsgroups_vectorized`, + :func:`datasets.fetch_lfw_people` and :func:`datasets.fetch_lfw_pairs`. This + only affects Python versions `>=3.10.0,<=3.10.11` and `>=3.11.0,<=3.11.3`. + By :user:`Jérémie du Boisberranger `. diff --git a/sklearn/datasets/_lfw.py b/sklearn/datasets/_lfw.py index 06420c41ed246..4f725b9250cc5 100644 --- a/sklearn/datasets/_lfw.py +++ b/sklearn/datasets/_lfw.py @@ -19,6 +19,7 @@ from ..utils import Bunch from ..utils._param_validation import Hidden, Interval, StrOptions, validate_params +from ..utils.fixes import tarfile_extractall from ._base import ( RemoteFileMetadata, _fetch_remote, @@ -117,10 +118,7 @@ def _check_fetch_lfw( logger.debug("Decompressing the data archive to %s", data_folder_path) with tarfile.open(archive_path, "r:gz") as fp: - # Use filter="data" to prevent the most dangerous security issues. - # For more details, see - # https://docs.python.org/3.9/library/tarfile.html#tarfile.TarFile.extractall - fp.extractall(path=lfw_home, filter="data") + tarfile_extractall(fp, path=lfw_home) remove(archive_path) diff --git a/sklearn/datasets/_twenty_newsgroups.py b/sklearn/datasets/_twenty_newsgroups.py index 62db8c5cbdc8e..1dc5fb6244f1b 100644 --- a/sklearn/datasets/_twenty_newsgroups.py +++ b/sklearn/datasets/_twenty_newsgroups.py @@ -43,6 +43,7 @@ from ..feature_extraction.text import CountVectorizer from ..utils import Bunch, check_random_state from ..utils._param_validation import Interval, StrOptions, validate_params +from ..utils.fixes import tarfile_extractall from . import get_data_home, load_files from ._base import ( RemoteFileMetadata, @@ -81,10 +82,7 @@ def _download_20newsgroups(target_dir, cache_path, n_retries, delay): logger.debug("Decompressing %s", archive_path) with tarfile.open(archive_path, "r:gz") as fp: - # Use filter="data" to prevent the most dangerous security issues. - # For more details, see - # https://docs.python.org/3.9/library/tarfile.html#tarfile.TarFile.extractall - fp.extractall(path=target_dir, filter="data") + tarfile_extractall(fp, path=target_dir) with suppress(FileNotFoundError): os.remove(archive_path) diff --git a/sklearn/utils/fixes.py b/sklearn/utils/fixes.py index 5ceb9930b993b..29c847d3aa34c 100644 --- a/sklearn/utils/fixes.py +++ b/sklearn/utils/fixes.py @@ -361,6 +361,17 @@ def _smallest_admissible_index_dtype(arrays=(), maxval=None, check_contents=Fals ) +# TODO: Remove when Python min version >= 3.12. +def tarfile_extractall(tarfile, path): + try: + # Use filter="data" to prevent the most dangerous security issues. + # For more details, see + # https://docs.python.org/3/library/tarfile.html#tarfile.TarFile.extractall + tarfile.extractall(path, filter="data") + except TypeError: + tarfile.extractall(path) + + def _in_unstable_openblas_configuration(): """Return True if in an unstable configuration for OpenBLAS""" From 08af3e9a4cf2ee8d387a2bebe461b341b000927e Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 7 Jul 2025 11:15:55 +0200 Subject: [PATCH 081/103] :lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#31716) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Lock file bot Co-authored-by: Jérémie du Boisberranger --- build_tools/azure/debian_32bit_lock.txt | 6 +- ...latest_conda_forge_mkl_linux-64_conda.lock | 121 +++++++++--------- ...pylatest_conda_forge_mkl_osx-64_conda.lock | 47 +++---- ...test_conda_mkl_no_openmp_osx-64_conda.lock | 12 +- ...latest_pip_openblas_pandas_environment.yml | 2 +- ...st_pip_openblas_pandas_linux-64_conda.lock | 29 +++-- ...nblas_min_dependencies_linux-64_conda.lock | 50 ++++---- ...forge_openblas_ubuntu_2204_environment.yml | 2 +- ...e_openblas_ubuntu_2204_linux-64_conda.lock | 60 ++++----- ...min_conda_forge_openblas_win-64_conda.lock | 22 ++-- build_tools/azure/ubuntu_atlas_lock.txt | 8 +- build_tools/circle/doc_environment.yml | 2 +- build_tools/circle/doc_linux-64_conda.lock | 106 +++++++-------- .../doc_min_dependencies_linux-64_conda.lock | 96 +++++++------- ...n_conda_forge_arm_linux-aarch64_conda.lock | 70 +++++----- sklearn/linear_model/_glm/tests/test_glm.py | 14 +- 16 files changed, 329 insertions(+), 318 deletions(-) diff --git a/build_tools/azure/debian_32bit_lock.txt b/build_tools/azure/debian_32bit_lock.txt index bb5a373786f0f..c9526638fdfbc 100644 --- a/build_tools/azure/debian_32bit_lock.txt +++ b/build_tools/azure/debian_32bit_lock.txt @@ -4,7 +4,7 @@ # # pip-compile --output-file=build_tools/azure/debian_32bit_lock.txt build_tools/azure/debian_32bit_requirements.txt # -coverage[toml]==7.9.1 +coverage[toml]==7.9.2 # via pytest-cov cython==3.1.2 # via -r build_tools/azure/debian_32bit_requirements.txt @@ -27,11 +27,11 @@ pluggy==1.6.0 # via # pytest # pytest-cov -pygments==2.19.1 +pygments==2.19.2 # via pytest pyproject-metadata==0.9.1 # via meson-python -pytest==8.4.0 +pytest==8.4.1 # via # -r build_tools/azure/debian_32bit_requirements.txt # pytest-cov diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index c7dd0f634b9da..81b6230365cb7 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock @@ -13,14 +13,14 @@ https://conda.anaconda.org/conda-forge/noarch/python_abi-3.13-7_cp313.conda#e84b https://conda.anaconda.org/conda-forge/noarch/tzdata-2025b-h78e105d_0.conda#4222072737ccff51314b5ece9c7d6f5a https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.6.15-hbd8a1cb_0.conda#72525f07d72806e3b639ad4504c30ce5 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766549260d6815b0c52253f1fb1bb29 -https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h1423503_5.conda#6dc9e1305e7d3129af4ad0dabda30e56 +https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.44-h1423503_0.conda#e31316a586cac398b1fcdb10ace786b9 https://conda.anaconda.org/conda-forge/linux-64/libglvnd-1.7.0-ha4b6fd6_2.conda#434ca7e50e40f4918ab701e3facd59a0 https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-20.1.7-h024ca30_0.conda#b9c9b2f494533250a9eb7ece830f4422 https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-3_kmp_llvm.conda#ee5c2118262e30b972bc0b4db8ef0ba5 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab https://conda.anaconda.org/conda-forge/linux-64/libegl-1.7.0-ha4b6fd6_2.conda#c151d5eb730e9b7480e6d48c0fc44048 https://conda.anaconda.org/conda-forge/linux-64/libopengl-1.7.0-ha4b6fd6_2.conda#7df50d44d4a14d6c31a2c54f2cd92157 -https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_2.conda#ea8ac52380885ed41c1baa8f1d6d2b93 +https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_3.conda#9e60c55e725c20d23125a5f0dd69af5d https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.14-hb9d3cd8_0.conda#76df83c2a9035c54df5d04ff81bcc02d https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.12.3-hb9d3cd8_0.conda#8448031a22c697fac3ed98d69e8a9160 https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.5-hb9d3cd8_0.conda#f7f0d6cc2dc986d42ac2689ec88192be @@ -28,21 +28,21 @@ https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hb9d3cd8_3 https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.24-h86f0d12_0.conda#64f0c503da58ec25ebd359e4d990afa8 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.0-h5888daf_0.conda#db0bfbe7dd197b68ad5f30333bae6ce0 https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.6-h2dba641_1.conda#ede4673863426c0883c0063d853bbd85 -https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.1.0-h69a702a_2.conda#ddca86c7040dd0e73b2b69bd7833d225 -https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.1.0-hcea5267_2.conda#01de444988ed960031dbe84cf4f9b1fc +https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.1.0-h69a702a_3.conda#e66f2b8ad787e7beb0f846e4bd7e8493 +https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.1.0-hcea5267_3.conda#530566b68c3b8ce7eec4cd047eae19fe https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h4ce23a2_1.conda#e796ff8ddc598affdf7c173d6145f087 https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.1.0-hb9d3cd8_0.conda#9fa334557db9f63da6c9285fd2a48638 https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_2.conda#1a580f7796c7bf6393fddb8bbbde58dc https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-hb9d3cd8_0.conda#c7e925f37e3b40d893459e625f6a53f1 https://conda.anaconda.org/conda-forge/linux-64/libntlm-1.8-hb9d3cd8_0.conda#7c7927b404672409d9917d49bff5f2d6 https://conda.anaconda.org/conda-forge/linux-64/libpciaccess-0.18-hb9d3cd8_0.conda#70e3400cbbfa03e96dcde7fc13e38c7b -https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_2.conda#1cb1c67961f6dd257eae9e9691b341aa +https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_3.conda#6d11a5edae89fe413c0569f16d308f5a https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.10.0-h202a827_0.conda#0f98f3e95272d118f7931b6bef69bfe5 https://conda.anaconda.org/conda-forge/linux-64/libuv-1.51.0-hb9d3cd8_0.conda#1349c022c92c5efd3fd705a79a5804d8 https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.5.0-h851e524_0.conda#63f790534398730f59e1b899c3644d4a https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 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+https://conda.anaconda.org/conda-forge/osx-64/cxx-compiler-1.10.0-h20888b2_0.conda#b3a935ade707c54ebbea5f8a7c6f4549 +https://conda.anaconda.org/conda-forge/osx-64/fortran-compiler-1.10.0-h02557f8_0.conda#aa3288408631f87b70295594cd4daba8 +https://conda.anaconda.org/conda-forge/osx-64/compilers-1.10.0-h694c41f_0.conda#d43a090863429d66e0986c84de7a7906 diff --git a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock index 238e88d201aeb..d3fca9974ae2e 100644 --- a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock +++ b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock @@ -6,20 +6,22 @@ https://repo.anaconda.com/pkgs/main/osx-64/blas-1.0-mkl.conda#cb2c87e85ac8e0ceae https://repo.anaconda.com/pkgs/main/osx-64/bzip2-1.0.8-h6c40b1e_6.conda#96224786021d0765ce05818fa3c59bdb https://repo.anaconda.com/pkgs/main/osx-64/ca-certificates-2025.2.25-hecd8cb5_0.conda#12ab77db61795036e15a5b14929ad4a1 https://repo.anaconda.com/pkgs/main/osx-64/jpeg-9e-h46256e1_3.conda#b1d9769eac428e11f5f922531a1da2e0 -https://repo.anaconda.com/pkgs/main/osx-64/libcxx-14.0.6-h9765a3e_0.conda#387757bb354ae9042370452cd0fb5627 +https://repo.anaconda.com/pkgs/main/osx-64/libcxx-17.0.6-hf547dac_4.conda#9f8b90f30742eab3e6800f46fdd89936 https://repo.anaconda.com/pkgs/main/osx-64/libdeflate-1.22-h46256e1_0.conda#7612fb79e5e76fcd16655c7d026f4a66 https://repo.anaconda.com/pkgs/main/osx-64/libffi-3.4.4-hecd8cb5_1.conda#eb7f09ada4d95f1a26f483f1009d9286 https://repo.anaconda.com/pkgs/main/osx-64/libwebp-base-1.3.2-h46256e1_1.conda#399c11b50e6e7a6969aca9a84ea416b7 -https://repo.anaconda.com/pkgs/main/osx-64/llvm-openmp-14.0.6-h0dcd299_0.conda#b5804d32b87dc61ca94561ade33d5f2d +https://repo.anaconda.com/pkgs/main/osx-64/llvm-openmp-17.0.6-hdd4a2e0_0.conda#0871f60a4c389ef44c343aa33b5a3acd https://repo.anaconda.com/pkgs/main/osx-64/ncurses-6.4-hcec6c5f_0.conda#0214d1ee980e217fabc695f1e40662aa https://repo.anaconda.com/pkgs/main/noarch/tzdata-2025b-h04d1e81_0.conda#1d027393db3427ab22a02aa44a56f143 +https://repo.anaconda.com/pkgs/main/osx-64/xxhash-0.8.0-h9ed2024_3.conda#79507f6b51082e0dc409046ee1471e8b https://repo.anaconda.com/pkgs/main/osx-64/xz-5.6.4-h46256e1_1.conda#ce989a528575ad332a650bb7c7f7e5d5 https://repo.anaconda.com/pkgs/main/osx-64/zlib-1.2.13-h4b97444_1.conda#38e35f7c817fac0973034bfce6706ec2 -https://repo.anaconda.com/pkgs/main/osx-64/ccache-3.7.9-hf120daa_0.conda#a01515a32e721c51d631283f991bc8ea https://repo.anaconda.com/pkgs/main/osx-64/expat-2.7.1-h6d0c2b6_0.conda#6cdc93776b7551083854e7f106a62720 +https://repo.anaconda.com/pkgs/main/osx-64/fmt-9.1.0-ha357a0b_1.conda#3cdbe6929571bdef216641b8a3eac194 https://repo.anaconda.com/pkgs/main/osx-64/intel-openmp-2023.1.0-ha357a0b_43548.conda#ba8a89ffe593eb88e4c01334753c40c3 https://repo.anaconda.com/pkgs/main/osx-64/lerc-4.0.0-h6d0c2b6_0.conda#824f87854c58df1525557c8639ce7f93 https://repo.anaconda.com/pkgs/main/osx-64/libgfortran5-11.3.0-h9dfd629_28.conda#1fa1a27ee100b1918c3021dbfa3895a3 +https://repo.anaconda.com/pkgs/main/osx-64/libhiredis-1.3.0-h6d0c2b6_0.conda#fa6c45039d776b9d70f865eab152dd30 https://repo.anaconda.com/pkgs/main/osx-64/libpng-1.6.39-h6c40b1e_0.conda#a3c824835f53ad27aeb86d2b55e47804 https://repo.anaconda.com/pkgs/main/osx-64/lz4-c-1.9.4-hcec6c5f_1.conda#aee0efbb45220e1985533dbff48551f8 https://repo.anaconda.com/pkgs/main/osx-64/ninja-base-1.12.1-h1962661_0.conda#9c0a94a811e88f182519d9309cf5f634 @@ -32,6 +34,7 @@ https://repo.anaconda.com/pkgs/main/osx-64/libgfortran-5.0.0-11_3_0_hecd8cb5_28. https://repo.anaconda.com/pkgs/main/osx-64/mkl-2023.1.0-h8e150cf_43560.conda#85d0f3431dd5c6ae44f8725fdd3d3e59 https://repo.anaconda.com/pkgs/main/osx-64/sqlite-3.45.3-h6c40b1e_0.conda#2edf909b937b3aad48322c9cb2e8f1a0 https://repo.anaconda.com/pkgs/main/osx-64/zstd-1.5.6-h138b38a_0.conda#f4d15d7d0054d39e6a24fe8d7d1e37c5 +https://repo.anaconda.com/pkgs/main/osx-64/ccache-4.11.3-h451b914_0.conda#5e4db702c976c28fbf50bdbaea47d3fa https://repo.anaconda.com/pkgs/main/osx-64/libtiff-4.7.0-h2dfa3ea_0.conda#82a118ce0139e2bf6f7a99c4cfbd4749 https://repo.anaconda.com/pkgs/main/osx-64/python-3.12.11-he8d2d4c_0.conda#9783e45825df3d441392b7fa66759899 https://repo.anaconda.com/pkgs/main/osx-64/brotli-python-1.0.9-py312h6d0c2b6_9.conda#425936421fe402074163ac3ffe33a060 @@ -47,6 +50,7 @@ https://repo.anaconda.com/pkgs/main/osx-64/ninja-1.12.1-hecd8cb5_0.conda#ee3b660 https://repo.anaconda.com/pkgs/main/osx-64/openjpeg-2.5.2-h2d09ccc_1.conda#0f2e221843154b436b5982c695df627b https://repo.anaconda.com/pkgs/main/osx-64/packaging-24.2-py312hecd8cb5_0.conda#76512e47c9c37443444ef0624769f620 https://repo.anaconda.com/pkgs/main/osx-64/pluggy-1.5.0-py312hecd8cb5_0.conda#ca381e438f1dbd7986ac0fa0da70c9d8 +https://repo.anaconda.com/pkgs/main/osx-64/pygments-2.19.1-py312hecd8cb5_0.conda#ca4be8769d62deee6127c0bf3703b0f6 https://repo.anaconda.com/pkgs/main/osx-64/pyparsing-3.2.0-py312hecd8cb5_0.conda#e4086daaaed13f68cc8d5b9da7db73cc https://repo.anaconda.com/pkgs/main/noarch/python-tzdata-2025.2-pyhd3eb1b0_0.conda#5ac858f05dbf9d3cdb04d53516901247 https://repo.anaconda.com/pkgs/main/osx-64/pytz-2024.1-py312hecd8cb5_0.conda#2b28ec0e0d07f5c0c701f75200b1e8b6 @@ -60,7 +64,7 @@ https://repo.anaconda.com/pkgs/main/osx-64/fonttools-4.55.3-py312h46256e1_0.cond https://repo.anaconda.com/pkgs/main/osx-64/numpy-base-1.26.4-py312h6f81483_0.conda#87f73efbf26ab2e2ea7c32481a71bd47 https://repo.anaconda.com/pkgs/main/osx-64/pillow-11.1.0-py312h935ef2f_1.conda#c2f7a3f027cc93a3626d50b765b75dc5 https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2a700153fefe0e69438b18e1 -https://repo.anaconda.com/pkgs/main/osx-64/pytest-8.3.4-py312hecd8cb5_0.conda#b15ee02022967632dfa1672669228bee +https://repo.anaconda.com/pkgs/main/osx-64/pytest-8.4.1-py312hecd8cb5_0.conda#438421697d4806567af06bd006b26db0 https://repo.anaconda.com/pkgs/main/osx-64/python-dateutil-2.9.0post0-py312hecd8cb5_2.conda#1047dde28f78127dd9f6121e882926dd https://repo.anaconda.com/pkgs/main/osx-64/pytest-cov-6.0.0-py312hecd8cb5_0.conda#db697e319a4d1145363246a51eef0352 https://repo.anaconda.com/pkgs/main/osx-64/pytest-xdist-3.6.1-py312hecd8cb5_0.conda#38df9520774ee82bf143218f1271f936 diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml b/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml index 6c3da4bb863b4..ba17d37ff1555 100644 --- a/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml +++ b/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml @@ -24,7 +24,7 @@ dependencies: - pytest-cov - coverage - sphinx - - numpydoc + - numpydoc<1.9.0 - lightgbm - scikit-image - array-api-strict diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock index de1e1ef5447bd..5eb0f04ee24b6 100644 --- a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 50f16a0198b6eb575a737fee25051b52a644d72f5fca26bd661651a85fcb6a07 +# input_hash: 692a667e331896943137778007c0834c42c3aa297986d4f8eda8b51a7f158d98 @EXPLICIT https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9 https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2025.2.25-h06a4308_0.conda#495015d24da8ad929e3ae2d18571016d @@ -13,20 +13,25 @@ https://repo.anaconda.com/pkgs/main/linux-64/_openmp_mutex-5.1-1_gnu.conda#71d28 https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-11.2.0-h1234567_1.conda#a87728dabf3151fb9cfa990bd2eb0464 https://repo.anaconda.com/pkgs/main/linux-64/bzip2-1.0.8-h5eee18b_6.conda#f21a3ff51c1b271977f53ce956a69297 https://repo.anaconda.com/pkgs/main/linux-64/expat-2.7.1-h6a678d5_0.conda#269942a9f3f943e2e5d8a2516a861f7c +https://repo.anaconda.com/pkgs/main/linux-64/fmt-9.1.0-hdb19cb5_1.conda#4f12930203ff2d84df5d287af9b29858 https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_1.conda#70646cc713f0c43926cfdcfe9b695fe0 +https://repo.anaconda.com/pkgs/main/linux-64/libhiredis-1.3.0-h6a678d5_0.conda#68b0289d6a3024e06b032f56dd7e46cf https://repo.anaconda.com/pkgs/main/linux-64/libmpdec-4.0.0-h5eee18b_0.conda#feb10f42b1a7b523acbf85461be41a3e https://repo.anaconda.com/pkgs/main/linux-64/libuuid-1.41.5-h5eee18b_0.conda#4a6a2354414c9080327274aa514e5299 +https://repo.anaconda.com/pkgs/main/linux-64/lz4-c-1.9.4-h6a678d5_1.conda#2ee58861f2b92b868ce761abb831819d https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda#5558eec6e2191741a92f832ea826251c https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.16-h5eee18b_0.conda#5875526739afa058cfa84da1fa7a2ef4 https://repo.anaconda.com/pkgs/main/linux-64/pthread-stubs-0.3-h0ce48e5_1.conda#973a642312d2a28927aaf5b477c67250 https://repo.anaconda.com/pkgs/main/linux-64/xorg-libxau-1.0.12-h9b100fa_0.conda#a8005a9f6eb903e113cd5363e8a11459 https://repo.anaconda.com/pkgs/main/linux-64/xorg-libxdmcp-1.1.5-h9b100fa_0.conda#c284a09ddfba81d9c4e740110f09ea06 https://repo.anaconda.com/pkgs/main/linux-64/xorg-xorgproto-2024.1-h5eee18b_1.conda#412a0d97a7a51d23326e57226189da92 +https://repo.anaconda.com/pkgs/main/linux-64/xxhash-0.8.0-h7f8727e_3.conda#196b013514e82fd8476558de622c0d46 https://repo.anaconda.com/pkgs/main/linux-64/xz-5.6.4-h5eee18b_1.conda#3581505fa450962d631bd82b8616350e https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_1.conda#92e42d8310108b0a440fb2e60b2b2a25 -https://repo.anaconda.com/pkgs/main/linux-64/ccache-3.7.9-hfe4627d_0.conda#bef6fc681c273bb7bd0c67d1a591365e https://repo.anaconda.com/pkgs/main/linux-64/libxcb-1.17.0-h9b100fa_0.conda#fdf0d380fa3809a301e2dbc0d5183883 https://repo.anaconda.com/pkgs/main/linux-64/readline-8.2-h5eee18b_0.conda#be42180685cce6e6b0329201d9f48efb +https://repo.anaconda.com/pkgs/main/linux-64/zstd-1.5.6-hc292b87_0.conda#78ae7abd3020b41f827b35085845e1b8 +https://repo.anaconda.com/pkgs/main/linux-64/ccache-4.11.3-hc6a6a4f_0.conda#3e660215a7953958c1eb910dde81eb52 https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.45.3-h5eee18b_0.conda#acf93d6aceb74d6110e20b44cc45939e https://repo.anaconda.com/pkgs/main/linux-64/xorg-libx11-1.8.12-h9b100fa_1.conda#6298b27afae6f49f03765b2a03df2fcb https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.14-h993c535_1.conda#bfe656b29fc64afe5d4bd46dbd5fd240 @@ -38,12 +43,12 @@ https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2 # pip babel @ https://files.pythonhosted.org/packages/b7/b8/3fe70c75fe32afc4bb507f75563d39bc5642255d1d94f1f23604725780bf/babel-2.17.0-py3-none-any.whl#sha256=4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2 # pip certifi @ https://files.pythonhosted.org/packages/84/ae/320161bd181fc06471eed047ecce67b693fd7515b16d495d8932db763426/certifi-2025.6.15-py3-none-any.whl#sha256=2e0c7ce7cb5d8f8634ca55d2ba7e6ec2689a2fd6537d8dec1296a477a4910057 # pip charset-normalizer @ https://files.pythonhosted.org/packages/e2/28/ffc026b26f441fc67bd21ab7f03b313ab3fe46714a14b516f931abe1a2d8/charset_normalizer-3.4.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=6c9379d65defcab82d07b2a9dfbfc2e95bc8fe0ebb1b176a3190230a3ef0e07c -# pip coverage @ https://files.pythonhosted.org/packages/f5/e8/eed18aa5583b0423ab7f04e34659e51101135c41cd1dcb33ac1d7013a6d6/coverage-7.9.1-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=34ed2186fe52fcc24d4561041979a0dec69adae7bce2ae8d1c49eace13e55c43 +# pip coverage @ https://files.pythonhosted.org/packages/49/d9/4616b787d9f597d6443f5588619c1c9f659e1f5fc9eebf63699eb6d34b78/coverage-7.9.2-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=256ea87cb2a1ed992bcdfc349d8042dcea1b80436f4ddf6e246d6bee4b5d73b6 # pip cycler @ https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl#sha256=85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30 # pip cython @ https://files.pythonhosted.org/packages/b3/9b/20a8a12d1454416141479380f7722f2ad298d2b41d0d7833fc409894715d/cython-3.1.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=80d0ce057672ca50728153757d022842d5dcec536b50c79615a22dda2a874ea0 # pip docutils @ https://files.pythonhosted.org/packages/8f/d7/9322c609343d929e75e7e5e6255e614fcc67572cfd083959cdef3b7aad79/docutils-0.21.2-py3-none-any.whl#sha256=dafca5b9e384f0e419294eb4d2ff9fa826435bf15f15b7bd45723e8ad76811b2 # pip execnet @ https://files.pythonhosted.org/packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl#sha256=26dee51f1b80cebd6d0ca8e74dd8745419761d3bef34163928cbebbdc4749fdc -# pip fonttools @ https://files.pythonhosted.org/packages/b2/11/c9972e46a6abd752a40a46960e431c795ad1f306775fc1f9e8c3081a1274/fonttools-4.58.4-cp313-cp313-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl#sha256=fe5807fc64e4ba5130f1974c045a6e8d795f3b7fb6debfa511d1773290dbb76b +# pip fonttools @ https://files.pythonhosted.org/packages/ab/47/f92b135864fa777e11ad68420bf89446c91a572fe2782745586f8e6aac0c/fonttools-4.58.5-cp313-cp313-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl#sha256=a6d7709fcf4577b0f294ee6327088884ca95046e1eccde87c53bbba4d5008541 # pip idna @ https://files.pythonhosted.org/packages/76/c6/c88e154df9c4e1a2a66ccf0005a88dfb2650c1dffb6f5ce603dfbd452ce3/idna-3.10-py3-none-any.whl#sha256=946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3 # pip imagesize @ https://files.pythonhosted.org/packages/ff/62/85c4c919272577931d407be5ba5d71c20f0b616d31a0befe0ae45bb79abd/imagesize-1.4.1-py2.py3-none-any.whl#sha256=0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b # pip iniconfig @ https://files.pythonhosted.org/packages/2c/e1/e6716421ea10d38022b952c159d5161ca1193197fb744506875fbb87ea7b/iniconfig-2.1.0-py3-none-any.whl#sha256=9deba5723312380e77435581c6bf4935c94cbfab9b1ed33ef8d238ea168eb760 @@ -53,11 +58,11 @@ https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2 # pip meson @ https://files.pythonhosted.org/packages/8e/6e/b9dfeac98dd508f88bcaff134ee0bf5e602caf3ccb5a12b5dd9466206df1/meson-1.8.2-py3-none-any.whl#sha256=274b49dbe26e00c9a591442dd30f4ae9da8ce11ce53d0f4682cd10a45d50f6fd # pip networkx @ https://files.pythonhosted.org/packages/eb/8d/776adee7bbf76365fdd7f2552710282c79a4ead5d2a46408c9043a2b70ba/networkx-3.5-py3-none-any.whl#sha256=0030d386a9a06dee3565298b4a734b68589749a544acbb6c412dc9e2489ec6ec # pip ninja @ https://files.pythonhosted.org/packages/eb/7a/455d2877fe6cf99886849c7f9755d897df32eaf3a0fba47b56e615f880f7/ninja-1.11.1.4-py3-none-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=096487995473320de7f65d622c3f1d16c3ad174797602218ca8c967f51ec38a0 -# pip numpy @ https://files.pythonhosted.org/packages/1c/12/734dce1087eed1875f2297f687e671cfe53a091b6f2f55f0c7241aad041b/numpy-2.3.0-cp313-cp313-manylinux_2_28_x86_64.whl#sha256=87717eb24d4a8a64683b7a4e91ace04e2f5c7c77872f823f02a94feee186168f +# pip numpy @ https://files.pythonhosted.org/packages/50/30/af1b277b443f2fb08acf1c55ce9d68ee540043f158630d62cef012750f9f/numpy-2.3.1-cp313-cp313-manylinux_2_28_x86_64.whl#sha256=5902660491bd7a48b2ec16c23ccb9124b8abfd9583c5fdfa123fe6b421e03de1 # pip packaging @ https://files.pythonhosted.org/packages/20/12/38679034af332785aac8774540895e234f4d07f7545804097de4b666afd8/packaging-25.0-py3-none-any.whl#sha256=29572ef2b1f17581046b3a2227d5c611fb25ec70ca1ba8554b24b0e69331a484 -# pip pillow @ https://files.pythonhosted.org/packages/13/eb/2552ecebc0b887f539111c2cd241f538b8ff5891b8903dfe672e997529be/pillow-11.2.1-cp313-cp313-manylinux_2_28_x86_64.whl#sha256=ad275964d52e2243430472fc5d2c2334b4fc3ff9c16cb0a19254e25efa03a155 +# pip pillow @ https://files.pythonhosted.org/packages/d5/1c/a2a29649c0b1983d3ef57ee87a66487fdeb45132df66ab30dd37f7dbe162/pillow-11.3.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl#sha256=13f87d581e71d9189ab21fe0efb5a23e9f28552d5be6979e84001d3b8505abe8 # pip pluggy @ https://files.pythonhosted.org/packages/54/20/4d324d65cc6d9205fabedc306948156824eb9f0ee1633355a8f7ec5c66bf/pluggy-1.6.0-py3-none-any.whl#sha256=e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746 -# pip pygments @ https://files.pythonhosted.org/packages/8a/0b/9fcc47d19c48b59121088dd6da2488a49d5f72dacf8262e2790a1d2c7d15/pygments-2.19.1-py3-none-any.whl#sha256=9ea1544ad55cecf4b8242fab6dd35a93bbce657034b0611ee383099054ab6d8c +# pip pygments @ https://files.pythonhosted.org/packages/c7/21/705964c7812476f378728bdf590ca4b771ec72385c533964653c68e86bdc/pygments-2.19.2-py3-none-any.whl#sha256=86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b # pip pyparsing @ https://files.pythonhosted.org/packages/05/e7/df2285f3d08fee213f2d041540fa4fc9ca6c2d44cf36d3a035bf2a8d2bcc/pyparsing-3.2.3-py3-none-any.whl#sha256=a749938e02d6fd0b59b356ca504a24982314bb090c383e3cf201c95ef7e2bfcf # pip pytz @ https://files.pythonhosted.org/packages/81/c4/34e93fe5f5429d7570ec1fa436f1986fb1f00c3e0f43a589fe2bbcd22c3f/pytz-2025.2-py2.py3-none-any.whl#sha256=5ddf76296dd8c44c26eb8f4b6f35488f3ccbf6fbbd7adee0b7262d43f0ec2f00 # pip roman-numerals-py @ https://files.pythonhosted.org/packages/53/97/d2cbbaa10c9b826af0e10fdf836e1bf344d9f0abb873ebc34d1f49642d3f/roman_numerals_py-3.1.0-py3-none-any.whl#sha256=9da2ad2fb670bcf24e81070ceb3be72f6c11c440d73bd579fbeca1e9f330954c @@ -72,17 +77,17 @@ https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2 # pip tabulate @ https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl#sha256=024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f # pip threadpoolctl @ https://files.pythonhosted.org/packages/32/d5/f9a850d79b0851d1d4ef6456097579a9005b31fea68726a4ae5f2d82ddd9/threadpoolctl-3.6.0-py3-none-any.whl#sha256=43a0b8fd5a2928500110039e43a5eed8480b918967083ea48dc3ab9f13c4a7fb # pip tzdata @ https://files.pythonhosted.org/packages/5c/23/c7abc0ca0a1526a0774eca151daeb8de62ec457e77262b66b359c3c7679e/tzdata-2025.2-py2.py3-none-any.whl#sha256=1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8 -# pip urllib3 @ https://files.pythonhosted.org/packages/6b/11/cc635220681e93a0183390e26485430ca2c7b5f9d33b15c74c2861cb8091/urllib3-2.4.0-py3-none-any.whl#sha256=4e16665048960a0900c702d4a66415956a584919c03361cac9f1df5c5dd7e813 -# pip array-api-strict @ https://files.pythonhosted.org/packages/fe/c7/a97e26083985b49a7a54006364348cf1c26e5523850b8522a39b02b19715/array_api_strict-2.3.1-py3-none-any.whl#sha256=0ca6988be1c82d2f05b6cd44bc7e14cb390555d1455deb50f431d6d0cf468ded +# pip urllib3 @ https://files.pythonhosted.org/packages/a7/c2/fe1e52489ae3122415c51f387e221dd0773709bad6c6cdaa599e8a2c5185/urllib3-2.5.0-py3-none-any.whl#sha256=e6b01673c0fa6a13e374b50871808eb3bf7046c4b125b216f6bf1cc604cff0dc +# pip array-api-strict @ https://files.pythonhosted.org/packages/e5/33/cede42b7b866db4b77432889314fc652ecc5cb6988f831ef08881a767089/array_api_strict-2.4-py3-none-any.whl#sha256=1cb20acd008f171ad8cce49589cc59897d8a242d1acf8ce6a61c3d57b61ecd14 # pip contourpy @ https://files.pythonhosted.org/packages/c8/65/5245ce8c548a8422236c13ffcdcdada6a2a812c361e9e0c70548bb40b661/contourpy-1.3.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=434f0adf84911c924519d2b08fc10491dd282b20bdd3fa8f60fd816ea0b48841 # pip imageio @ https://files.pythonhosted.org/packages/cb/bd/b394387b598ed84d8d0fa90611a90bee0adc2021820ad5729f7ced74a8e2/imageio-2.37.0-py3-none-any.whl#sha256=11efa15b87bc7871b61590326b2d635439acc321cf7f8ce996f812543ce10eed # pip jinja2 @ https://files.pythonhosted.org/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl#sha256=85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67 # pip lazy-loader @ https://files.pythonhosted.org/packages/83/60/d497a310bde3f01cb805196ac61b7ad6dc5dcf8dce66634dc34364b20b4f/lazy_loader-0.4-py3-none-any.whl#sha256=342aa8e14d543a154047afb4ba8ef17f5563baad3fc610d7b15b213b0f119efc # pip pyproject-metadata @ https://files.pythonhosted.org/packages/7e/b1/8e63033b259e0a4e40dd1ec4a9fee17718016845048b43a36ec67d62e6fe/pyproject_metadata-0.9.1-py3-none-any.whl#sha256=ee5efde548c3ed9b75a354fc319d5afd25e9585fa918a34f62f904cc731973ad -# pip pytest @ https://files.pythonhosted.org/packages/2f/de/afa024cbe022b1b318a3d224125aa24939e99b4ff6f22e0ba639a2eaee47/pytest-8.4.0-py3-none-any.whl#sha256=f40f825768ad76c0977cbacdf1fd37c6f7a468e460ea6a0636078f8972d4517e +# pip pytest @ https://files.pythonhosted.org/packages/29/16/c8a903f4c4dffe7a12843191437d7cd8e32751d5de349d45d3fe69544e87/pytest-8.4.1-py3-none-any.whl#sha256=539c70ba6fcead8e78eebbf1115e8b589e7565830d7d006a8723f19ac8a0afb7 # pip python-dateutil @ https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl#sha256=a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 # pip requests @ https://files.pythonhosted.org/packages/7c/e4/56027c4a6b4ae70ca9de302488c5ca95ad4a39e190093d6c1a8ace08341b/requests-2.32.4-py3-none-any.whl#sha256=27babd3cda2a6d50b30443204ee89830707d396671944c998b5975b031ac2b2c -# pip scipy @ https://files.pythonhosted.org/packages/b5/09/c5b6734a50ad4882432b6bb7c02baf757f5b2f256041da5df242e2d7e6b6/scipy-1.15.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=c9deabd6d547aee2c9a81dee6cc96c6d7e9a9b1953f74850c179f91fdc729cb7 +# pip scipy @ https://files.pythonhosted.org/packages/11/6b/3443abcd0707d52e48eb315e33cc669a95e29fc102229919646f5a501171/scipy-1.16.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl#sha256=1d8747f7736accd39289943f7fe53a8333be7f15a82eea08e4afe47d79568c32 # pip tifffile @ https://files.pythonhosted.org/packages/3a/d8/1ba8f32bfc9cb69e37edeca93738e883f478fbe84ae401f72c0d8d507841/tifffile-2025.6.11-py3-none-any.whl#sha256=32effb78b10b3a283eb92d4ebf844ae7e93e151458b0412f38518b4e6d2d7542 # pip lightgbm @ https://files.pythonhosted.org/packages/42/86/dabda8fbcb1b00bcfb0003c3776e8ade1aa7b413dff0a2c08f457dace22f/lightgbm-4.6.0-py3-none-manylinux_2_28_x86_64.whl#sha256=cb19b5afea55b5b61cbb2131095f50538bd608a00655f23ad5d25ae3e3bf1c8d # pip matplotlib @ https://files.pythonhosted.org/packages/f5/64/41c4367bcaecbc03ef0d2a3ecee58a7065d0a36ae1aa817fe573a2da66d4/matplotlib-3.10.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=a80fcccbef63302c0efd78042ea3c2436104c5b1a4d3ae20f864593696364ac7 @@ -90,7 +95,7 @@ https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2 # pip pandas @ https://files.pythonhosted.org/packages/2a/b3/463bfe819ed60fb7e7ddffb4ae2ee04b887b3444feee6c19437b8f834837/pandas-2.3.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=213cd63c43263dbb522c1f8a7c9d072e25900f6975596f883f4bebd77295d4f3 # pip pyamg @ https://files.pythonhosted.org/packages/cd/a7/0df731cbfb09e73979a1a032fc7bc5be0eba617d798b998a0f887afe8ade/pyamg-5.2.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=6999b351ab969c79faacb81faa74c0fa9682feeff3954979212872a3ee40c298 # pip pytest-cov @ https://files.pythonhosted.org/packages/bc/16/4ea354101abb1287856baa4af2732be351c7bee728065aed451b678153fd/pytest_cov-6.2.1-py3-none-any.whl#sha256=f5bc4c23f42f1cdd23c70b1dab1bbaef4fc505ba950d53e0081d0730dd7e86d5 -# pip pytest-xdist @ https://files.pythonhosted.org/packages/0d/b2/0e802fde6f1c5b2f7ae7e9ad42b83fd4ecebac18a8a8c2f2f14e39dce6e1/pytest_xdist-3.7.0-py3-none-any.whl#sha256=7d3fbd255998265052435eb9daa4e99b62e6fb9cfb6efd1f858d4d8c0c7f0ca0 +# pip pytest-xdist @ https://files.pythonhosted.org/packages/ca/31/d4e37e9e550c2b92a9cbc2e4d0b7420a27224968580b5a447f420847c975/pytest_xdist-3.8.0-py3-none-any.whl#sha256=202ca578cfeb7370784a8c33d6d05bc6e13b4f25b5053c30a152269fd10f0b88 # pip scikit-image @ 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https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.7.10-h9ab9c9b_2.conda#cf49873da2e59f876a2ad4794b05801b https://conda.anaconda.org/conda-forge/linux-64/brotli-1.0.9-h166bdaf_9.conda#4601544b4982ba1861fa9b9c607b2c06 https://conda.anaconda.org/conda-forge/linux-64/ccache-4.11.3-h80c52d3_0.conda#eb517c6a2b960c3ccb6f1db1005f063a -https://conda.anaconda.org/conda-forge/linux-64/coverage-7.9.1-py310h89163eb_0.conda#0acae6de150b85b7f3119ec88558d22a +https://conda.anaconda.org/conda-forge/linux-64/coverage-7.9.2-py310h89163eb_0.conda#f02d32dc5b0547e137f871a33e032842 https://conda.anaconda.org/conda-forge/linux-64/dbus-1.16.2-h3c4dab8_0.conda#679616eb5ad4e521c83da4650860aba7 https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda#72e42d28960d875c7654614f8b50939a https://conda.anaconda.org/conda-forge/linux-64/freetype-2.13.3-ha770c72_1.conda#9ccd736d31e0c6e41f54e704e5312811 @@ -183,10 +183,10 @@ https://conda.anaconda.org/conda-forge/linux-64/libllvm20-20.1.7-he9d0ab4_0.cond https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.10.0-h65c71a3_0.conda#fedf6bfe5d21d21d2b1785ec00a8889a https://conda.anaconda.org/conda-forge/linux-64/openblas-0.3.25-pthreads_h7a3da1a_0.conda#87661673941b5e702275fdf0fc095ad0 https://conda.anaconda.org/conda-forge/linux-64/openldap-2.6.10-he970967_0.conda#2e5bf4f1da39c0b32778561c3c4e5878 -https://conda.anaconda.org/conda-forge/linux-64/pillow-11.2.1-py310h7e6dc6c_0.conda#5645a243d90adb50909b9edc209d84fe +https://conda.anaconda.org/conda-forge/linux-64/pillow-11.3.0-py310h7e6dc6c_0.conda#e609995f031bc848be8ea159865e8afc https://conda.anaconda.org/conda-forge/noarch/pip-25.1.1-pyh8b19718_0.conda#32d0781ace05105cc99af55d36cbec7c https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.9.1-pyhd8ed1ab_0.conda#22ae7c6ea81e0c8661ef32168dda929b -https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhff2d567_1.conda#5ba79d7c71f03c678c8ead841f347d6e +https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda#5b8d21249ff20967101ffa321cab24e8 https://conda.anaconda.org/conda-forge/linux-64/sip-6.10.0-py310hf71b8c6_0.conda#2d7e4445be227e8210140b75725689ad https://conda.anaconda.org/conda-forge/linux-64/xorg-libxcomposite-0.4.6-hb9d3cd8_2.conda#d3c295b50f092ab525ffe3c2aa4b7413 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdamage-1.1.6-hb9d3cd8_0.conda#b5fcc7172d22516e1f965490e65e33a4 @@ -194,7 +194,7 @@ https://conda.anaconda.org/conda-forge/linux-64/xorg-libxxf86vm-1.1.6-hb9d3cd8_0 https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.7.0-h435f46f_0.conda#c7726f96aab024855ede05e0ca6e94a0 https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.8.13-hd4f18eb_5.conda#860fb8c0efec64a4a678eb2ea066ff65 https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.15.0-h7e30c49_1.conda#8f5b0b297b59e1ac160ad4beec99dbee -https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.58.4-py310h89163eb_0.conda#723a77ff55b436601008d28acc982547 +https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.58.5-py310h89163eb_0.conda#f84b125a5ba0e319936be9aba48276ff https://conda.anaconda.org/conda-forge/linux-64/glib-2.84.2-h6287aef_0.conda#704648df3a01d4d24bc2c0466b718d63 https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-20_linux64_openblas.conda#36d486d72ab64ffea932329a1d3729a3 https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp20.1-20.1.7-default_h1df26ce_0.conda#f9ef7bce54a7673cdbc2fadd8bca1956 @@ -212,7 +212,7 @@ https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-20_linux64_open https://conda.anaconda.org/conda-forge/linux-64/numpy-1.22.0-py310h454958d_1.tar.bz2#607c66f0cce2986515a8fe9e136b2b57 https://conda.anaconda.org/conda-forge/linux-64/pulseaudio-client-17.0-hb77b528_0.conda#07f45f1be1c25345faddb8db0de8039b https://conda.anaconda.org/conda-forge/noarch/pytest-cov-6.2.1-pyhd8ed1ab_0.conda#ce978e1b9ed8b8d49164e90a5cdc94cd -https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.7.0-pyhd8ed1ab_0.conda#15353a2a0ea6dfefaa52fc5ab5b98f41 +https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.8.0-pyhd8ed1ab_0.conda#8375cfbda7c57fbceeda18229be10417 https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.20.2-h2a5cb19_18.conda#7313674073496cec938f73b71163bc31 https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-20_linux64_openblas.conda#9932a1d4e9ecf2d35fb19475446e361e https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.24.11-h651a532_0.conda#d8d8894f8ced2c9be76dc9ad1ae531ce diff --git a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_environment.yml b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_environment.yml index 267c149fd1c35..30466d12a3f20 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_environment.yml +++ b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_environment.yml @@ -20,5 +20,5 @@ dependencies: - ninja - meson-python - sphinx - - numpydoc + - numpydoc<1.9.0 - ccache diff --git a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock index 0c7c5ac749057..9d928e2a64783 100644 --- a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock +++ b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock @@ -1,47 +1,47 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 26bb2530999c20f24bbab0f7b6e3545ad84d059a25027cb624997210afc23693 +# input_hash: 4abfb998e26e3beaa198409ac1ebc1278024921c4b3c6fc8de5c93be1b6193ba @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/noarch/python_abi-3.10-7_cp310.conda#44e871cba2b162368476a84b8d040b6c https://conda.anaconda.org/conda-forge/noarch/tzdata-2025b-h78e105d_0.conda#4222072737ccff51314b5ece9c7d6f5a https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.6.15-hbd8a1cb_0.conda#72525f07d72806e3b639ad4504c30ce5 -https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h1423503_5.conda#6dc9e1305e7d3129af4ad0dabda30e56 -https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.1.0-h767d61c_2.conda#fbe7d535ff9d3a168c148e07358cd5b1 +https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.44-h1423503_0.conda#e31316a586cac398b1fcdb10ace786b9 +https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.1.0-h767d61c_3.conda#3cd1a7238a0dd3d0860fdefc496cc854 https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2#73aaf86a425cc6e73fcf236a5a46396d -https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_2.conda#ea8ac52380885ed41c1baa8f1d6d2b93 +https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_3.conda#9e60c55e725c20d23125a5f0dd69af5d https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.24-h86f0d12_0.conda#64f0c503da58ec25ebd359e4d990afa8 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.0-h5888daf_0.conda#db0bfbe7dd197b68ad5f30333bae6ce0 https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.6-h2dba641_1.conda#ede4673863426c0883c0063d853bbd85 -https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.1.0-h69a702a_2.conda#ddca86c7040dd0e73b2b69bd7833d225 -https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.1.0-hcea5267_2.conda#01de444988ed960031dbe84cf4f9b1fc +https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.1.0-h69a702a_3.conda#e66f2b8ad787e7beb0f846e4bd7e8493 +https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.1.0-hcea5267_3.conda#530566b68c3b8ce7eec4cd047eae19fe https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.1.0-hb9d3cd8_0.conda#9fa334557db9f63da6c9285fd2a48638 https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_2.conda#1a580f7796c7bf6393fddb8bbbde58dc -https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_2.conda#1cb1c67961f6dd257eae9e9691b341aa +https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hb9d3cd8_1.conda#d864d34357c3b65a4b731f78c0801dc4 +https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_3.conda#6d11a5edae89fe413c0569f16d308f5a https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.5.0-h851e524_0.conda#63f790534398730f59e1b899c3644d4a https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.0-h7b32b05_1.conda#de356753cfdbffcde5bb1e86e3aa6cd0 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.1-h7b32b05_0.conda#c87df2ab1448ba69169652ab9547082d https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-hb9d3cd8_1002.conda#b3c17d95b5a10c6e64a21fa17573e70e https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-hb9d3cd8_0.conda#f6ebe2cb3f82ba6c057dde5d9debe4f7 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.5-hb9d3cd8_0.conda#8035c64cb77ed555e3f150b7b3972480 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62ee74e96c5ebb0af99386de58cf9553 https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h0aef613_1.conda#9344155d33912347b37f0ae6c410a835 -https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.1.0-h69a702a_2.conda#f92e6e0a3c0c0c85561ef61aa59d555d -https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hd590300_0.conda#30fd6e37fe21f86f4bd26d6ee73eeec7 -https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.47-h943b412_0.conda#55199e2ae2c3651f6f9b2a447b47bdc9 -https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.50.1-hee588c1_0.conda#96a7e36bff29f1d0ddf5b771e0da373a -https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.1.0-h4852527_2.conda#9d2072af184b5caa29492bf2344597bb +https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.1.0-h69a702a_3.conda#bfbca721fd33188ef923dfe9ba172f29 +https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.50-h943b412_0.conda#51de14db340a848869e69c632b43cca7 +https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.50.2-h6cd9bfd_0.conda#b04c7eda6d7dab1e6503135e7fad4d25 +https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.1.0-h4852527_3.conda#57541755b5a51691955012b8e197c06c https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda#40b61aab5c7ba9ff276c41cfffe6b80b https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.17.0-h8a09558_0.conda#92ed62436b625154323d40d5f2f11dd7 https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda#5aa797f8787fe7a17d1b0821485b5adc -https://conda.anaconda.org/conda-forge/linux-64/ninja-1.12.1-hff21bea_1.conda#2322531904f27501ee19847b87ba7c64 +https://conda.anaconda.org/conda-forge/linux-64/ninja-1.13.0-h7aa8ee6_0.conda#2f67cb5c5ec172faeba94348ae8af444 https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8c095d6_2.conda#283b96675859b20a825f8fa30f311446 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https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda#72e42d28960d875c7654614f8b50939a https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.1-pyhd8ed1ab_0.conda#fb1c14694de51a476ce8636d92b6f42c https://conda.anaconda.org/conda-forge/win-64/lcms2-2.17-hbcf6048_0.conda#3538827f77b82a837fa681a4579e37a1 @@ -94,22 +94,22 @@ https://conda.anaconda.org/conda-forge/win-64/numpy-2.2.6-py310h4987827_0.conda# https://conda.anaconda.org/conda-forge/win-64/openjpeg-2.5.3-h4d64b90_0.conda#fc050366dd0b8313eb797ed1ffef3a29 https://conda.anaconda.org/conda-forge/noarch/pip-25.1.1-pyh8b19718_0.conda#32d0781ace05105cc99af55d36cbec7c https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.9.1-pyhd8ed1ab_0.conda#22ae7c6ea81e0c8661ef32168dda929b -https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhff2d567_1.conda#5ba79d7c71f03c678c8ead841f347d6e +https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda#5b8d21249ff20967101ffa321cab24e8 https://conda.anaconda.org/conda-forge/win-64/blas-devel-3.9.0-32_hc0f8095_openblas.conda#c07c54d62ee5a9886933051e10ad4b1e https://conda.anaconda.org/conda-forge/win-64/contourpy-1.3.2-py310hc19bc0b_0.conda#039416813b5290e7d100a05bb4326110 -https://conda.anaconda.org/conda-forge/win-64/fonttools-4.58.4-py310h38315fa_0.conda#f7a8769f5923bebdc10acbbb41d28628 +https://conda.anaconda.org/conda-forge/win-64/fonttools-4.58.5-py310hdb0e946_0.conda#4838fda5927aa6d029d5951efd350c8e https://conda.anaconda.org/conda-forge/win-64/freetype-2.13.3-h57928b3_1.conda#633504fe3f96031192e40e3e6c18ef06 https://conda.anaconda.org/conda-forge/noarch/meson-python-0.18.0-pyh70fd9c4_0.conda#576c04b9d9f8e45285fb4d9452c26133 -https://conda.anaconda.org/conda-forge/win-64/pillow-11.2.1-py310h9595edc_0.conda#33d0663d469cc146b5fc68587348f450 +https://conda.anaconda.org/conda-forge/win-64/pillow-11.3.0-py310h6d647b9_0.conda#246b33a0eb812754b529065262aeb1c5 https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.1-pyhd8ed1ab_0.conda#a49c2283f24696a7b30367b7346a0144 https://conda.anaconda.org/conda-forge/win-64/scipy-1.15.2-py310h15c175c_0.conda#81798168111d1021e3d815217c444418 https://conda.anaconda.org/conda-forge/win-64/blas-2.132-openblas.conda#b59780f3fbd2bf992d3702e59d8d1653 https://conda.anaconda.org/conda-forge/win-64/fontconfig-2.15.0-h765892d_1.conda#9bb0026a2131b09404c59c4290c697cd https://conda.anaconda.org/conda-forge/win-64/matplotlib-base-3.10.3-py310h37e0a56_0.conda#de9ddae6f97b78860c256de480ea1a84 https://conda.anaconda.org/conda-forge/noarch/pytest-cov-6.2.1-pyhd8ed1ab_0.conda#ce978e1b9ed8b8d49164e90a5cdc94cd -https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.7.0-pyhd8ed1ab_0.conda#15353a2a0ea6dfefaa52fc5ab5b98f41 +https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.8.0-pyhd8ed1ab_0.conda#8375cfbda7c57fbceeda18229be10417 https://conda.anaconda.org/conda-forge/win-64/cairo-1.18.4-h5782bbf_0.conda#20e32ced54300292aff690a69c5e7b97 https://conda.anaconda.org/conda-forge/win-64/harfbuzz-11.2.1-h8796e6f_0.conda#bccea58fbf7910ce868b084f27ffe8bd -https://conda.anaconda.org/conda-forge/win-64/qt6-main-6.9.1-h02ddd7d_0.conda#feaaaae25a51188fb0544aca8b26ef4d +https://conda.anaconda.org/conda-forge/win-64/qt6-main-6.9.1-h02ddd7d_1.conda#fc796cf6c16db38d44c2efefbe6afcea https://conda.anaconda.org/conda-forge/win-64/pyside6-6.9.1-py310h2d19612_0.conda#01b830c0fd6ca7ab03c85a008a6f4a2d https://conda.anaconda.org/conda-forge/win-64/matplotlib-3.10.3-py310h5588dad_0.conda#103adee33db124a0263d0b4551e232e3 diff --git a/build_tools/azure/ubuntu_atlas_lock.txt b/build_tools/azure/ubuntu_atlas_lock.txt index ddbe7a200dba1..12f0cadf784e6 100644 --- a/build_tools/azure/ubuntu_atlas_lock.txt +++ b/build_tools/azure/ubuntu_atlas_lock.txt @@ -27,15 +27,15 @@ packaging==25.0 # pytest pluggy==1.6.0 # via pytest -pygments==2.19.1 +pygments==2.19.2 # via pytest pyproject-metadata==0.9.1 # via meson-python -pytest==8.4.0 +pytest==8.4.1 # via # -r build_tools/azure/ubuntu_atlas_requirements.txt # pytest-xdist -pytest-xdist==3.7.0 +pytest-xdist==3.8.0 # via -r build_tools/azure/ubuntu_atlas_requirements.txt threadpoolctl==3.1.0 # via -r build_tools/azure/ubuntu_atlas_requirements.txt @@ -43,5 +43,5 @@ tomli==2.2.1 # via # meson-python # pytest -typing-extensions==4.14.0 +typing-extensions==4.14.1 # via exceptiongroup diff --git a/build_tools/circle/doc_environment.yml b/build_tools/circle/doc_environment.yml index bc36e178de058..360be7b52b9a9 100644 --- a/build_tools/circle/doc_environment.yml +++ b/build_tools/circle/doc_environment.yml @@ -27,7 +27,7 @@ dependencies: - sphinx - sphinx-gallery - sphinx-copybutton - - numpydoc + - numpydoc<1.9.0 - sphinx-prompt - plotly - polars diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock index 14a5b8303d947..637d089d51881 100644 --- a/build_tools/circle/doc_linux-64_conda.lock +++ b/build_tools/circle/doc_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 93cb6f7aa17dce662512650f1419e87eae56ed49163348847bf965697cd268bb +# input_hash: f8748904ea3a3b4e57ef03e9ef12f4ec17e4998ed6cbe6d15bc058d26bd37454 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45 @@ -12,37 +12,38 @@ https://conda.anaconda.org/conda-forge/noarch/python_abi-3.10-7_cp310.conda#44e8 https://conda.anaconda.org/conda-forge/noarch/tzdata-2025b-h78e105d_0.conda#4222072737ccff51314b5ece9c7d6f5a https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.6.15-hbd8a1cb_0.conda#72525f07d72806e3b639ad4504c30ce5 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766549260d6815b0c52253f1fb1bb29 -https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_5.conda#acd9213a63cb62521290e581ef82de80 +https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.44-h1423503_0.conda#e31316a586cac398b1fcdb10ace786b9 https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-13.3.0-hc03c837_102.conda#4c1d6961a6a54f602ae510d9bf31fa60 https://conda.anaconda.org/conda-forge/linux-64/libglvnd-1.7.0-ha4b6fd6_2.conda#434ca7e50e40f4918ab701e3facd59a0 -https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.1.0-h767d61c_2.conda#fbe7d535ff9d3a168c148e07358cd5b1 +https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.1.0-h767d61c_3.conda#3cd1a7238a0dd3d0860fdefc496cc854 https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-13.3.0-hc03c837_102.conda#aa38de2738c5f4a72a880e3d31ffe8b4 https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.17-h0157908_18.conda#460eba7851277ec1fd80a1a24080787a https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2#73aaf86a425cc6e73fcf236a5a46396d -https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.43-h4bf12b8_5.conda#18852d82df8e5737e320a8731ace51b9 +https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.44-h4bf12b8_0.conda#7a1b5c3fbc0419961eaed361eedc90d4 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab https://conda.anaconda.org/conda-forge/linux-64/libegl-1.7.0-ha4b6fd6_2.conda#c151d5eb730e9b7480e6d48c0fc44048 https://conda.anaconda.org/conda-forge/linux-64/libopengl-1.7.0-ha4b6fd6_2.conda#7df50d44d4a14d6c31a2c54f2cd92157 -https://conda.anaconda.org/conda-forge/linux-64/binutils-2.43-h4852527_5.conda#4846404183ea94fd6652e9fb6ac5e16f -https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.43-h4852527_5.conda#327ef163ac88b57833c1c1a20a9e7e0d -https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_2.conda#ea8ac52380885ed41c1baa8f1d6d2b93 +https://conda.anaconda.org/conda-forge/linux-64/binutils-2.44-h4852527_0.conda#878f293b0a7163e5036d25f1fa9480ec +https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.44-h4852527_0.conda#9f88de9963795dcfab936e092eac3424 +https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_3.conda#9e60c55e725c20d23125a5f0dd69af5d https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.14-hb9d3cd8_0.conda#76df83c2a9035c54df5d04ff81bcc02d https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hb9d3cd8_3.conda#cb98af5db26e3f482bebb80ce9d947d3 https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.24-h86f0d12_0.conda#64f0c503da58ec25ebd359e4d990afa8 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.0-h5888daf_0.conda#db0bfbe7dd197b68ad5f30333bae6ce0 https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.6-h2dba641_1.conda#ede4673863426c0883c0063d853bbd85 -https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.1.0-h69a702a_2.conda#ddca86c7040dd0e73b2b69bd7833d225 -https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.1.0-hcea5267_2.conda#01de444988ed960031dbe84cf4f9b1fc +https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.1.0-h69a702a_3.conda#e66f2b8ad787e7beb0f846e4bd7e8493 +https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.1.0-hcea5267_3.conda#530566b68c3b8ce7eec4cd047eae19fe https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h4ce23a2_1.conda#e796ff8ddc598affdf7c173d6145f087 https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.1.0-hb9d3cd8_0.conda#9fa334557db9f63da6c9285fd2a48638 https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_2.conda#1a580f7796c7bf6393fddb8bbbde58dc +https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hb9d3cd8_1.conda#d864d34357c3b65a4b731f78c0801dc4 https://conda.anaconda.org/conda-forge/linux-64/libntlm-1.8-hb9d3cd8_0.conda#7c7927b404672409d9917d49bff5f2d6 https://conda.anaconda.org/conda-forge/linux-64/libpciaccess-0.18-hb9d3cd8_0.conda#70e3400cbbfa03e96dcde7fc13e38c7b -https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_2.conda#1cb1c67961f6dd257eae9e9691b341aa +https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_3.conda#6d11a5edae89fe413c0569f16d308f5a https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.5.0-h851e524_0.conda#63f790534398730f59e1b899c3644d4a https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.0-h7b32b05_1.conda#de356753cfdbffcde5bb1e86e3aa6cd0 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.1-h7b32b05_0.conda#c87df2ab1448ba69169652ab9547082d https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-hb9d3cd8_1002.conda#b3c17d95b5a10c6e64a21fa17573e70e https://conda.anaconda.org/conda-forge/linux-64/rav1e-0.7.1-h8fae777_3.conda#2c42649888aac645608191ffdc80d13a https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.2-hb9d3cd8_0.conda#fb901ff28063514abb6046c9ec2c4a45 @@ -52,6 +53,7 @@ https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62e https://conda.anaconda.org/conda-forge/linux-64/dav1d-1.2.1-hd590300_0.conda#418c6ca5929a611cbd69204907a83995 https://conda.anaconda.org/conda-forge/linux-64/double-conversion-3.3.1-h5888daf_0.conda#bfd56492d8346d669010eccafe0ba058 https://conda.anaconda.org/conda-forge/linux-64/giflib-5.2.2-hd590300_0.conda#3bf7b9fd5a7136126e0234db4b87c8b6 +https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.14-h5888daf_0.conda#951ff8d9e5536896408e89d63230b8d5 https://conda.anaconda.org/conda-forge/linux-64/jxrlib-1.1-hd590300_3.conda#5aeabe88534ea4169d4c49998f293d6c https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2#30186d27e2c9fa62b45fb1476b7200e3 https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h0aef613_1.conda#9344155d33912347b37f0ae6c410a835 @@ -60,45 +62,43 @@ https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.1.0-hb9d3cd8_3.co https://conda.anaconda.org/conda-forge/linux-64/libbrotlienc-1.1.0-hb9d3cd8_3.conda#3facafe58f3858eb95527c7d3a3fc578 https://conda.anaconda.org/conda-forge/linux-64/libdrm-2.4.125-hb9d3cd8_0.conda#4c0ab57463117fbb8df85268415082f5 https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20250104-pl5321h7949ede_0.conda#c277e0a4d549b03ac1e9d6cbbe3d017b -https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.1.0-h69a702a_2.conda#f92e6e0a3c0c0c85561ef61aa59d555d +https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.1.0-h69a702a_3.conda#bfbca721fd33188ef923dfe9ba172f29 https://conda.anaconda.org/conda-forge/linux-64/libhwy-1.2.0-hf40a0c7_0.conda#2f433d593a66044c3f163cb25f0a09de -https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hd590300_0.conda#30fd6e37fe21f86f4bd26d6ee73eeec7 -https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.47-h943b412_0.conda#55199e2ae2c3651f6f9b2a447b47bdc9 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https://conda.anaconda.org/conda-forge/noarch/pooch-1.8.2-pyhd8ed1ab_1.conda#b3e783e8e8ed7577cf0b6dee37d1fbac -https://conda.anaconda.org/conda-forge/linux-64/qt6-main-6.9.1-h0384650_0.conda#e1f80d7fca560024b107368dd77d96be +https://conda.anaconda.org/conda-forge/linux-64/qt6-main-6.9.1-h0384650_1.conda#3610aa92d2de36047886f30e99342f21 https://conda.anaconda.org/conda-forge/linux-64/scikit-image-0.25.2-py310h5eaa309_1.conda#ed21ab72d049ecdb60f829f04b4dca1c https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.13.2-pyhd8ed1ab_3.conda#fd96da444e81f9e6fcaac38590f3dd42 https://conda.anaconda.org/conda-forge/linux-64/pyside6-6.9.1-py310h21765ff_0.conda#a64f8b57dd1b84d5d4f02f565a3cb630 @@ -287,7 +287,7 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.10.0-pyhd8ed # pip python-json-logger @ https://files.pythonhosted.org/packages/08/20/0f2523b9e50a8052bc6a8b732dfc8568abbdc42010aef03a2d750bdab3b2/python_json_logger-3.3.0-py3-none-any.whl#sha256=dd980fae8cffb24c13caf6e158d3d61c0d6d22342f932cb6e9deedab3d35eec7 # pip pyyaml @ https://files.pythonhosted.org/packages/6b/4e/1523cb902fd98355e2e9ea5e5eb237cbc5f3ad5f3075fa65087aa0ecb669/PyYAML-6.0.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=ec031d5d2feb36d1d1a24380e4db6d43695f3748343d99434e6f5f9156aaa2ed # pip rfc3986-validator @ https://files.pythonhosted.org/packages/9e/51/17023c0f8f1869d8806b979a2bffa3f861f26a3f1a66b094288323fba52f/rfc3986_validator-0.1.1-py2.py3-none-any.whl#sha256=2f235c432ef459970b4306369336b9d5dbdda31b510ca1e327636e01f528bfa9 -# pip rpds-py @ https://files.pythonhosted.org/packages/eb/76/66b523ffc84cf47db56efe13ae7cf368dee2bacdec9d89b9baca5e2e6301/rpds_py-0.25.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=0701942049095741a8aeb298a31b203e735d1c61f4423511d2b1a41dcd8a16da +# pip rpds-py @ https://files.pythonhosted.org/packages/15/93/fde36cd6e4685df2cd08508f6c45a841e82f5bb98c8d5ecf05649522acb5/rpds_py-0.26.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=c70d9ec912802ecfd6cd390dadb34a9578b04f9bcb8e863d0a7598ba5e9e7ccc # pip send2trash @ https://files.pythonhosted.org/packages/40/b0/4562db6223154aa4e22f939003cb92514c79f3d4dccca3444253fd17f902/Send2Trash-1.8.3-py3-none-any.whl#sha256=0c31227e0bd08961c7665474a3d1ef7193929fedda4233843689baa056be46c9 # pip sniffio @ https://files.pythonhosted.org/packages/e9/44/75a9c9421471a6c4805dbf2356f7c181a29c1879239abab1ea2cc8f38b40/sniffio-1.3.1-py3-none-any.whl#sha256=2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2 # pip traitlets @ 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+https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.8.0-pyhd8ed1ab_0.conda#8375cfbda7c57fbceeda18229be10417 https://conda.anaconda.org/conda-forge/linux-aarch64/scipy-1.15.2-py310hf37559f_0.conda#5c9b72f10d2118d943a5eaaf2f396891 -https://conda.anaconda.org/conda-forge/linux-aarch64/blas-2.131-openblas.conda#51c5f346e1ebee750f76066490059df9 +https://conda.anaconda.org/conda-forge/linux-aarch64/blas-2.132-openblas.conda#2c1e3662c8c5e7b92a49fd6372bb659f https://conda.anaconda.org/conda-forge/linux-aarch64/harfbuzz-11.2.1-h405b6a2_0.conda#b55680fc90e9747dc858e7ceb0abc2b2 https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-base-3.10.3-py310h2cc5e2d_0.conda#e29f4329f4f76cf14f74ed86dcc59bac -https://conda.anaconda.org/conda-forge/linux-aarch64/qt6-main-6.9.1-h13135bf_0.conda#6e8335a319b6b1988d6959f895116c74 +https://conda.anaconda.org/conda-forge/linux-aarch64/qt6-main-6.9.1-h13135bf_1.conda#def3ca3fcfa60a6c954bdd8f5bb00cd2 https://conda.anaconda.org/conda-forge/linux-aarch64/pyside6-6.9.1-py310hd3bda28_0.conda#1a105dc54d3cd250526c9d52379133c9 https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-3.10.3-py310hbbe02a8_0.conda#08982f6ac753e962d59160b08839221b diff --git a/sklearn/linear_model/_glm/tests/test_glm.py b/sklearn/linear_model/_glm/tests/test_glm.py index fbcc4d61a8e1c..535651f3242f5 100644 --- a/sklearn/linear_model/_glm/tests/test_glm.py +++ b/sklearn/linear_model/_glm/tests/test_glm.py @@ -8,7 +8,6 @@ import numpy as np import pytest import scipy -from numpy.testing import assert_allclose from scipy import linalg from scipy.optimize import minimize, root @@ -28,6 +27,7 @@ from sklearn.linear_model._linear_loss import LinearModelLoss from sklearn.metrics import d2_tweedie_score, mean_poisson_deviance from sklearn.model_selection import train_test_split +from sklearn.utils._testing import assert_allclose SOLVERS = ["lbfgs", "newton-cholesky"] @@ -636,11 +636,11 @@ def test_glm_identity_regression(fit_intercept): ) if fit_intercept: glm.fit(X[:, 1:], y) - assert_allclose(glm.coef_, coef[1:], rtol=1e-10) - assert_allclose(glm.intercept_, coef[0], rtol=1e-10) + assert_allclose(glm.coef_, coef[1:]) + assert_allclose(glm.intercept_, coef[0]) else: glm.fit(X, y) - assert_allclose(glm.coef_, coef, rtol=1e-12) + assert_allclose(glm.coef_, coef) @pytest.mark.parametrize("fit_intercept", [False, True]) @@ -663,12 +663,12 @@ def test_glm_sample_weight_consistency(fit_intercept, alpha, GLMEstimator): # sample_weight=np.ones(..) should be equivalent to sample_weight=None sample_weight = np.ones(y.shape) glm.fit(X, y, sample_weight=sample_weight) - assert_allclose(glm.coef_, coef, rtol=1e-12) + assert_allclose(glm.coef_, coef) # sample_weight are normalized to 1 so, scaling them has no effect sample_weight = 2 * np.ones(y.shape) glm.fit(X, y, sample_weight=sample_weight) - assert_allclose(glm.coef_, coef, rtol=1e-12) + assert_allclose(glm.coef_, coef) # setting one element of sample_weight to 0 is equivalent to removing # the corresponding sample @@ -677,7 +677,7 @@ def test_glm_sample_weight_consistency(fit_intercept, alpha, GLMEstimator): glm.fit(X, y, sample_weight=sample_weight) coef1 = glm.coef_.copy() glm.fit(X[:-1], y[:-1]) - assert_allclose(glm.coef_, coef1, rtol=1e-12) + assert_allclose(glm.coef_, coef1) # check that multiplying sample_weight by 2 is equivalent # to repeating corresponding samples twice From 9ab893a24199ae86eb94a1cf3dc8a31d8f1f92e5 Mon Sep 17 00:00:00 2001 From: "Thomas J. Fan" Date: Mon, 7 Jul 2025 05:56:02 -0400 Subject: [PATCH 082/103] BLD Use Cython's shared memoryview utility to reduce wheel size (#31151) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Loïc Estève --- sklearn/meson.build | 23 +++++++++++++++++++++-- 1 file changed, 21 insertions(+), 2 deletions(-) diff --git a/sklearn/meson.build b/sklearn/meson.build index 30feb944029d3..966da14c1338b 100644 --- a/sklearn/meson.build +++ b/sklearn/meson.build @@ -180,6 +180,8 @@ else: check: true ).stdout().strip() +cython_program = find_program(cython.cmd_array()[0]) + scikit_learn_cython_args = [ '-X language_level=3', '-X boundscheck=' + boundscheck, '-X wraparound=False', '-X initializedcheck=False', '-X nonecheck=False', '-X cdivision=True', @@ -190,7 +192,25 @@ scikit_learn_cython_args = [ ] cython_args += scikit_learn_cython_args -cython_program = find_program(cython.cmd_array()[0]) +if cython.version().version_compare('>=3.1.0') + cython_shared_src = custom_target( + install: false, + output: '_cyutility.c', + command: [ + cython_program, '-3', '--fast-fail', + '--generate-shared=' + meson.current_build_dir()/'_cyutility.c' + ], + ) + + py.extension_module('_cyutility', + cython_shared_src, + subdir: 'sklearn', + cython_args: cython_args, + install: true, + ) + + cython_args += ['--shared=sklearn._cyutility'] +endif cython_gen = generator(cython_program, arguments : cython_args + ['@INPUT@', '--output-file', '@OUTPUT@'], @@ -202,7 +222,6 @@ cython_gen_cpp = generator(cython_program, output : '@BASENAME@.cpp', ) - # Write file in Meson build dir to be able to figure out from Python code # whether scikit-learn was built with Meson. Adapted from pandas # _version_meson.py. From 34a10d59f2d0d492efb754864ff17fe5f70ca806 Mon Sep 17 00:00:00 2001 From: Reshama Shaikh Date: Mon, 7 Jul 2025 06:04:21 -0400 Subject: [PATCH 083/103] DOC add link for Discussions in the footer (#31704) --- doc/templates/index.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/templates/index.html b/doc/templates/index.html index ff71b52ebd59c..93c63742ac518 100644 --- a/doc/templates/index.html +++ b/doc/templates/index.html @@ -222,7 +222,7 @@

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    • From 9ef6593b8367a09baece145ca323a4717bb9b827 Mon Sep 17 00:00:00 2001 From: Guillaume Lemaitre Date: Tue, 8 Jul 2025 14:46:58 +0200 Subject: [PATCH 084/103] FIX short deprecation cycle for private module (#31500) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Jérémie du Boisberranger --- sklearn/tests/test_common.py | 4 +++ sklearn/utils/_estimator_html_repr.py | 34 +++++++++++++++++++ .../utils/tests/test_estimator_html_repr.py | 21 ++++++++++++ 3 files changed, 59 insertions(+) create mode 100644 sklearn/utils/_estimator_html_repr.py create mode 100644 sklearn/utils/tests/test_estimator_html_repr.py diff --git a/sklearn/tests/test_common.py b/sklearn/tests/test_common.py index de5003687ca95..0ada8c5ef0a30 100644 --- a/sklearn/tests/test_common.py +++ b/sklearn/tests/test_common.py @@ -136,6 +136,10 @@ def test_check_estimator_generate_only_deprecation(): "ignore:Since version 1.0, it is not needed to import " "enable_hist_gradient_boosting anymore" ) +# TODO(1.8): remove this filter +@pytest.mark.filterwarnings( + "ignore:Importing from sklearn.utils._estimator_html_repr is deprecated." +) def test_import_all_consistency(): sklearn_path = [os.path.dirname(sklearn.__file__)] # Smoke test to check that any name in a __all__ list is actually defined diff --git a/sklearn/utils/_estimator_html_repr.py b/sklearn/utils/_estimator_html_repr.py new file mode 100644 index 0000000000000..f7898ae5e76cc --- /dev/null +++ b/sklearn/utils/_estimator_html_repr.py @@ -0,0 +1,34 @@ +# Authors: The scikit-learn developers +# SPDX-License-Identifier: BSD-3-Clause + +import warnings + +from ._repr_html.base import _HTMLDocumentationLinkMixin +from ._repr_html.estimator import ( + _get_visual_block, + _IDCounter, + _VisualBlock, + _write_estimator_html, + _write_label_html, + estimator_html_repr, +) + +__all__ = [ + "_HTMLDocumentationLinkMixin", + "_IDCounter", + "_VisualBlock", + "_get_visual_block", + "_write_estimator_html", + "_write_label_html", + "estimator_html_repr", +] + +# TODO(1.8): Remove the entire module +warnings.warn( + "Importing from sklearn.utils._estimator_html_repr is deprecated. The tools have " + "been moved to sklearn.utils._repr_html. Be aware that this module is private and " + "may be subject to change in the future. The module _estimator_html_repr will be " + "removed in 1.8.0.", + FutureWarning, + stacklevel=2, +) diff --git a/sklearn/utils/tests/test_estimator_html_repr.py b/sklearn/utils/tests/test_estimator_html_repr.py new file mode 100644 index 0000000000000..d24e357b74426 --- /dev/null +++ b/sklearn/utils/tests/test_estimator_html_repr.py @@ -0,0 +1,21 @@ +# Authors: The scikit-learn developers +# SPDX-License-Identifier: BSD-3-Clause + +import importlib +import sys + +import pytest + + +# TODO(1.8): Remove the entire file +def test_estimator_html_repr_warning(): + with pytest.warns(FutureWarning): + # Make sure that we check for the warning when loading the module (reloading it + # if needed). + module_name = "sklearn.utils._estimator_html_repr" + if module_name in sys.modules: + importlib.reload(sys.modules[module_name]) + else: + importlib.import_module(module_name) + + assert sys.modules[module_name] is not None From 84d31fced250cf4d30e752057ee1fdb9ce9b5e86 Mon Sep 17 00:00:00 2001 From: Stefanie Senger <91849487+StefanieSenger@users.noreply.github.com> Date: Tue, 8 Jul 2025 15:03:13 +0200 Subject: [PATCH 085/103] CI Move some pip_dependencies to conda_dependencies (#31623) --- ...latest_conda_mkl_no_openmp_environment.yml | 9 +- ...test_conda_mkl_no_openmp_osx-64_conda.lock | 12 +- build_tools/circle/doc_environment.yml | 4 +- build_tools/circle/doc_linux-64_conda.lock | 127 ++++++++++-------- .../doc_min_dependencies_environment.yml | 2 +- .../doc_min_dependencies_linux-64_conda.lock | 4 +- .../update_environments_and_lock_files.py | 14 +- 7 files changed, 86 insertions(+), 86 deletions(-) diff --git a/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml b/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml index 0c2eec344c26b..21c4934f004e5 100644 --- a/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml +++ b/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml @@ -8,7 +8,9 @@ dependencies: - numpy - blas[build=mkl] - scipy<1.12 + - cython - joblib + - threadpoolctl - matplotlib - pandas - pyamg @@ -17,12 +19,7 @@ dependencies: - pillow - pip - ninja + - meson-python - pytest-cov - coverage - ccache - - pip - - pip: - - cython - - threadpoolctl - - meson-python - - meson diff --git a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock index d3fca9974ae2e..e22d3e87ccf24 100644 --- a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock +++ b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: osx-64 -# input_hash: cc639ea0beeaceb46e2ad729ba559d5d5e746b8f6ff522bc718109af6265069c +# input_hash: 5adafa63ab1109c544ec1b12e91e8b32a70341ed5a2ae7c9eeea50ecb2907ebd @EXPLICIT https://repo.anaconda.com/pkgs/main/osx-64/blas-1.0-mkl.conda#cb2c87e85ac8e0ceae776d26d4214c8a https://repo.anaconda.com/pkgs/main/osx-64/bzip2-1.0.8-h6c40b1e_6.conda#96224786021d0765ce05818fa3c59bdb @@ -40,6 +40,7 @@ https://repo.anaconda.com/pkgs/main/osx-64/python-3.12.11-he8d2d4c_0.conda#9783e https://repo.anaconda.com/pkgs/main/osx-64/brotli-python-1.0.9-py312h6d0c2b6_9.conda#425936421fe402074163ac3ffe33a060 https://repo.anaconda.com/pkgs/main/osx-64/coverage-7.6.9-py312h46256e1_0.conda#f8c1547bbf522a600ee795901240a7b0 https://repo.anaconda.com/pkgs/main/noarch/cycler-0.11.0-pyhd3eb1b0_0.conda#f5e365d2cdb66d547eb8c3ab93843aab +https://repo.anaconda.com/pkgs/main/osx-64/cython-3.0.11-py312h46256e1_1.conda#44443579c3f4ae02940aeefb77e6115e https://repo.anaconda.com/pkgs/main/noarch/execnet-2.1.1-pyhd3eb1b0_0.conda#b3cb797432ee4657d5907b91a5dc65ad https://repo.anaconda.com/pkgs/main/noarch/iniconfig-1.1.1-pyhd3eb1b0_0.tar.bz2#e40edff2c5708f342cef43c7f280c507 https://repo.anaconda.com/pkgs/main/osx-64/joblib-1.4.2-py312hecd8cb5_0.conda#8ab03dfa447b4e0bfa0bd3d25930f3b6 @@ -56,16 +57,20 @@ https://repo.anaconda.com/pkgs/main/noarch/python-tzdata-2025.2-pyhd3eb1b0_0.con https://repo.anaconda.com/pkgs/main/osx-64/pytz-2024.1-py312hecd8cb5_0.conda#2b28ec0e0d07f5c0c701f75200b1e8b6 https://repo.anaconda.com/pkgs/main/osx-64/setuptools-78.1.1-py312hecd8cb5_0.conda#76b66b96a1564cb76011408c1eb8df3e https://repo.anaconda.com/pkgs/main/osx-64/six-1.17.0-py312hecd8cb5_0.conda#aadd782bc06426887ae0835eedd98ceb +https://repo.anaconda.com/pkgs/main/noarch/threadpoolctl-2.2.0-pyh0d69192_0.conda#bbfdbae4934150b902f97daaf287efe2 https://repo.anaconda.com/pkgs/main/noarch/toml-0.10.2-pyhd3eb1b0_0.conda#cda05f5f6d8509529d1a2743288d197a https://repo.anaconda.com/pkgs/main/osx-64/tornado-6.5.1-py312h46256e1_0.conda#8ce574315c742b52790459087e273fb4 https://repo.anaconda.com/pkgs/main/osx-64/unicodedata2-15.1.0-py312h46256e1_1.conda#4a7fd1dec7277c8ab71aa11aa08df86b https://repo.anaconda.com/pkgs/main/osx-64/wheel-0.45.1-py312hecd8cb5_0.conda#fafb8687668467d8624d2ddd0909bce9 https://repo.anaconda.com/pkgs/main/osx-64/fonttools-4.55.3-py312h46256e1_0.conda#f7680dd6b8b1c2f8aab17cf6630c6deb +https://repo.anaconda.com/pkgs/main/osx-64/meson-1.6.0-py312hecd8cb5_0.conda#7fda9195b93d66b3799a47d643782467 https://repo.anaconda.com/pkgs/main/osx-64/numpy-base-1.26.4-py312h6f81483_0.conda#87f73efbf26ab2e2ea7c32481a71bd47 https://repo.anaconda.com/pkgs/main/osx-64/pillow-11.1.0-py312h935ef2f_1.conda#c2f7a3f027cc93a3626d50b765b75dc5 https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2a700153fefe0e69438b18e1 +https://repo.anaconda.com/pkgs/main/osx-64/pyproject-metadata-0.9.0-py312hecd8cb5_0.conda#d249fcd6371bb45263d32a3f74087116 https://repo.anaconda.com/pkgs/main/osx-64/pytest-8.4.1-py312hecd8cb5_0.conda#438421697d4806567af06bd006b26db0 https://repo.anaconda.com/pkgs/main/osx-64/python-dateutil-2.9.0post0-py312hecd8cb5_2.conda#1047dde28f78127dd9f6121e882926dd +https://repo.anaconda.com/pkgs/main/osx-64/meson-python-0.17.1-py312h46256e1_0.conda#8ec02421632bd391150e12f6924f6172 https://repo.anaconda.com/pkgs/main/osx-64/pytest-cov-6.0.0-py312hecd8cb5_0.conda#db697e319a4d1145363246a51eef0352 https://repo.anaconda.com/pkgs/main/osx-64/pytest-xdist-3.6.1-py312hecd8cb5_0.conda#38df9520774ee82bf143218f1271f936 https://repo.anaconda.com/pkgs/main/osx-64/bottleneck-1.4.2-py312ha2b695f_0.conda#7efb63b6a5b33829a3b2c7a3efcf53ce @@ -79,8 +84,3 @@ https://repo.anaconda.com/pkgs/main/osx-64/numexpr-2.8.7-py312hac873b0_0.conda#6 https://repo.anaconda.com/pkgs/main/osx-64/scipy-1.11.4-py312h81688c2_0.conda#7d57b4c21a9261f97fa511e0940c5d93 https://repo.anaconda.com/pkgs/main/osx-64/pandas-2.2.3-py312h6d0c2b6_0.conda#84ce5b8ec4a986d13a5df17811f556a2 https://repo.anaconda.com/pkgs/main/osx-64/pyamg-5.2.1-py312h1962661_0.conda#58881950d4ce74c9302b56961f97a43c -# pip cython @ https://files.pythonhosted.org/packages/22/86/9393ab7204d5bb65f415dd271b658c18f57b9345d06002cae069376a5a7a/cython-3.1.2-cp312-cp312-macosx_10_13_x86_64.whl#sha256=9c2c4b6f9a941c857b40168b3f3c81d514e509d985c2dcd12e1a4fea9734192e -# pip meson @ https://files.pythonhosted.org/packages/8e/6e/b9dfeac98dd508f88bcaff134ee0bf5e602caf3ccb5a12b5dd9466206df1/meson-1.8.2-py3-none-any.whl#sha256=274b49dbe26e00c9a591442dd30f4ae9da8ce11ce53d0f4682cd10a45d50f6fd -# pip threadpoolctl @ https://files.pythonhosted.org/packages/32/d5/f9a850d79b0851d1d4ef6456097579a9005b31fea68726a4ae5f2d82ddd9/threadpoolctl-3.6.0-py3-none-any.whl#sha256=43a0b8fd5a2928500110039e43a5eed8480b918967083ea48dc3ab9f13c4a7fb -# pip pyproject-metadata @ https://files.pythonhosted.org/packages/7e/b1/8e63033b259e0a4e40dd1ec4a9fee17718016845048b43a36ec67d62e6fe/pyproject_metadata-0.9.1-py3-none-any.whl#sha256=ee5efde548c3ed9b75a354fc319d5afd25e9585fa918a34f62f904cc731973ad -# pip meson-python @ https://files.pythonhosted.org/packages/28/58/66db620a8a7ccb32633de9f403fe49f1b63c68ca94e5c340ec5cceeb9821/meson_python-0.18.0-py3-none-any.whl#sha256=3b0fe051551cc238f5febb873247c0949cd60ded556efa130aa57021804868e2 diff --git a/build_tools/circle/doc_environment.yml b/build_tools/circle/doc_environment.yml index 360be7b52b9a9..dcf3f0b0db699 100644 --- a/build_tools/circle/doc_environment.yml +++ b/build_tools/circle/doc_environment.yml @@ -37,8 +37,8 @@ dependencies: - sphinx-design - pydata-sphinx-theme - towncrier + - jupyterlite-sphinx + - jupyterlite-pyodide-kernel - pip - pip: - - jupyterlite-sphinx - - jupyterlite-pyodide-kernel - sphinxcontrib-sass diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock index 637d089d51881..a655496d4c993 100644 --- a/build_tools/circle/doc_linux-64_conda.lock +++ b/build_tools/circle/doc_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: f8748904ea3a3b4e57ef03e9ef12f4ec17e4998ed6cbe6d15bc058d26bd37454 +# input_hash: 207a7209ba4771c5fc039939c36a47d93b9e5478fbdf6fe01c4ac5837581d49a @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45 @@ -66,6 +66,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.1.0-h69a702a_3.co https://conda.anaconda.org/conda-forge/linux-64/libhwy-1.2.0-hf40a0c7_0.conda#2f433d593a66044c3f163cb25f0a09de https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.50-h943b412_0.conda#51de14db340a848869e69c632b43cca7 https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-13.3.0-he8ea267_2.conda#2b6cdf7bb95d3d10ef4e38ce0bc95dba +https://conda.anaconda.org/conda-forge/linux-64/libsodium-1.0.20-h4ab18f5_0.conda#a587892d3c13b6621a6091be690dbca2 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b/build_tools/circle/doc_min_dependencies_environment.yml @@ -32,11 +32,11 @@ dependencies: - plotly=5.14.0 # min - polars=0.20.30 # min - pooch=1.6.0 # min + - sphinxext-opengraph=0.9.1 # min - sphinx-remove-toctrees=1.0.0.post1 # min - sphinx-design=0.6.0 # min - pydata-sphinx-theme=0.15.3 # min - towncrier=24.8.0 # min - pip - pip: - - sphinxext-opengraph==0.9.1 # min - sphinxcontrib-sass==0.3.4 # min diff --git a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock index 5916e10bc57a6..c7314fbedd286 100644 --- a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock +++ b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: cf86af2534e8e281654ed19bc893b468656b355b2b200b12321dbc61cce562db +# input_hash: e32b19b18fba3e64af830b6f9b7d9e826f7c625fc3ed7a3a5d16edad94228ad6 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45 @@ -291,6 +291,6 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.1.0-pyhd8 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-2.0.0-pyhd8ed1ab_1.conda#00534ebcc0375929b45c3039b5ba7636 https://conda.anaconda.org/conda-forge/noarch/sphinx-7.3.7-pyhd8ed1ab_0.conda#7b1465205e28d75d2c0e1a868ee00a67 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.10-pyhd8ed1ab_1.conda#3bc61f7161d28137797e038263c04c54 +https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1ab_1.conda#79f5d05ad914baf152fb7f75073fe36d # pip libsass @ https://files.pythonhosted.org/packages/fd/5a/eb5b62641df0459a3291fc206cf5bd669c0feed7814dded8edef4ade8512/libsass-0.23.0-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.whl#sha256=4a218406d605f325d234e4678bd57126a66a88841cb95bee2caeafdc6f138306 # pip sphinxcontrib-sass @ https://files.pythonhosted.org/packages/2e/87/7c2eb08e3ca1d6baae32c0a5e005330fe1cec93a36aa085e714c3b3a3c7d/sphinxcontrib_sass-0.3.4-py2.py3-none-any.whl#sha256=a0c79a44ae8b8935c02dc340ebe40c9e002c839331201c899dc93708970c355a -# pip sphinxext-opengraph @ https://files.pythonhosted.org/packages/92/0a/970b80b4fa1feeb6deb6f2e22d4cb14e388b27b315a1afdb9db930ff91a4/sphinxext_opengraph-0.9.1-py3-none-any.whl#sha256=b3b230cc6a5b5189139df937f0d9c7b23c7c204493b22646273687969dcb760e diff --git a/build_tools/update_environments_and_lock_files.py b/build_tools/update_environments_and_lock_files.py index 5cfb51431360a..d95289f04a903 100644 --- a/build_tools/update_environments_and_lock_files.py +++ b/build_tools/update_environments_and_lock_files.py @@ -157,10 +157,7 @@ def remove_from(alist, to_remove): "folder": "build_tools/azure", "platform": "osx-64", "channels": ["defaults"], - "conda_dependencies": remove_from( - common_dependencies, ["cython", "threadpoolctl", "meson-python"] - ) - + ["ccache"], + "conda_dependencies": common_dependencies + ["ccache"], "package_constraints": { "blas": "[build=mkl]", # scipy 1.12.x crashes on this platform (https://github.com/scipy/scipy/pull/20086) @@ -168,9 +165,6 @@ def remove_from(alist, to_remove): # channel. "scipy": "<1.12", }, - # TODO: put cython, threadpoolctl and meson-python back to conda - # dependencies when required version is available on the main channel - "pip_dependencies": ["cython", "threadpoolctl", "meson-python", "meson"], }, { "name": "pymin_conda_forge_openblas_min_dependencies", @@ -326,13 +320,13 @@ def remove_from(alist, to_remove): "plotly", "polars", "pooch", + "sphinxext-opengraph", "sphinx-remove-toctrees", "sphinx-design", "pydata-sphinx-theme", "towncrier", ], "pip_dependencies": [ - "sphinxext-opengraph", "sphinxcontrib-sass", ], "package_constraints": { @@ -386,10 +380,10 @@ def remove_from(alist, to_remove): "sphinx-design", "pydata-sphinx-theme", "towncrier", - ], - "pip_dependencies": [ "jupyterlite-sphinx", "jupyterlite-pyodide-kernel", + ], + "pip_dependencies": [ "sphinxcontrib-sass", ], "package_constraints": { From 7132d02ec2548a1588df786b79e8f0219c185066 Mon Sep 17 00:00:00 2001 From: Stefanie Senger <91849487+StefanieSenger@users.noreply.github.com> Date: Thu, 10 Jul 2025 03:02:27 +0200 Subject: [PATCH 086/103] DOC Fix broken formatting of `cohen_kappa_score` docstring (#31732) --- sklearn/metrics/_classification.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/sklearn/metrics/_classification.py b/sklearn/metrics/_classification.py index 168cb025a5779..06503046790be 100644 --- a/sklearn/metrics/_classification.py +++ b/sklearn/metrics/_classification.py @@ -831,8 +831,8 @@ class labels [2]_. List of labels to index the matrix. This may be used to select a subset of labels. If `None`, all labels that appear at least once in ``y1`` or ``y2`` are used. Note that at least one label in `labels` must be - present in `y1`, even though this function is otherwise agnostic to the order - of `y1` and `y2`. + present in `y1`, even though this function is otherwise agnostic to the order + of `y1` and `y2`. weights : {'linear', 'quadratic'}, default=None Weighting type to calculate the score. `None` means not weighted; From 1c398897d6e0c9f8a608d799e3fbb4bf9851ee2c Mon Sep 17 00:00:00 2001 From: "Christine P. Chai" Date: Thu, 10 Jul 2025 06:14:37 -0700 Subject: [PATCH 087/103] DOC: Correct several math typos in the User Guide (#31736) --- doc/modules/density.rst | 2 +- doc/modules/gaussian_process.rst | 2 +- doc/modules/linear_model.rst | 2 +- doc/modules/neural_networks_supervised.rst | 4 ++-- doc/modules/sgd.rst | 2 +- 5 files changed, 6 insertions(+), 6 deletions(-) diff --git a/doc/modules/density.rst b/doc/modules/density.rst index 16c73bd5349a2..b629857827c74 100644 --- a/doc/modules/density.rst +++ b/doc/modules/density.rst @@ -90,7 +90,7 @@ Here we have used ``kernel='gaussian'``, as seen above. Mathematically, a kernel is a positive function :math:`K(x;h)` which is controlled by the bandwidth parameter :math:`h`. Given this kernel form, the density estimate at a point :math:`y` within -a group of points :math:`x_i; i=1\cdots N` is given by: +a group of points :math:`x_i; i=1, \cdots, N` is given by: .. math:: \rho_K(y) = \sum_{i=1}^{N} K(y - x_i; h) diff --git a/doc/modules/gaussian_process.rst b/doc/modules/gaussian_process.rst index 46d04ac35d832..b8b3fd62709d6 100644 --- a/doc/modules/gaussian_process.rst +++ b/doc/modules/gaussian_process.rst @@ -337,7 +337,7 @@ of a :class:`Sum` kernel, where it modifies the mean of the Gaussian process. It depends on a parameter :math:`constant\_value`. It is defined as: .. math:: - k(x_i, x_j) = constant\_value \;\forall\; x_1, x_2 + k(x_i, x_j) = constant\_value \;\forall\; x_i, x_j The main use-case of the :class:`WhiteKernel` kernel is as part of a sum-kernel where it explains the noise-component of the signal. Tuning its diff --git a/doc/modules/linear_model.rst b/doc/modules/linear_model.rst index 83451406ffa54..48acba45fec17 100644 --- a/doc/modules/linear_model.rst +++ b/doc/modules/linear_model.rst @@ -383,7 +383,7 @@ scikit-learn. For a linear Gaussian model, the maximum log-likelihood is defined as: .. math:: - \log(\hat{L}) = - \frac{n}{2} \log(2 \pi) - \frac{n}{2} \ln(\sigma^2) - \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{2\sigma^2} + \log(\hat{L}) = - \frac{n}{2} \log(2 \pi) - \frac{n}{2} \log(\sigma^2) - \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{2\sigma^2} where :math:`\sigma^2` is an estimate of the noise variance, :math:`y_i` and :math:`\hat{y}_i` are respectively the true and predicted diff --git a/doc/modules/neural_networks_supervised.rst b/doc/modules/neural_networks_supervised.rst index 13611b7f52775..155d987baed13 100644 --- a/doc/modules/neural_networks_supervised.rst +++ b/doc/modules/neural_networks_supervised.rst @@ -22,7 +22,7 @@ Multi-layer Perceptron **Multi-layer Perceptron (MLP)** is a supervised learning algorithm that learns a function :math:`f: R^m \rightarrow R^o` by training on a dataset, where :math:`m` is the number of dimensions for input and :math:`o` is the -number of dimensions for output. Given a set of features :math:`X = {x_1, x_2, ..., x_m}` +number of dimensions for output. Given a set of features :math:`X = \{x_1, x_2, ..., x_m\}` and a target :math:`y`, it can learn a non-linear function approximator for either classification or regression. It is different from logistic regression, in that between the input and the output layer, there can be one or more non-linear @@ -233,7 +233,7 @@ training. .. dropdown:: Mathematical formulation - Given a set of training examples :math:`(x_1, y_1), (x_2, y_2), \ldots, (x_n, y_n)` + Given a set of training examples :math:`\{(x_1, y_1), (x_2, y_2), \ldots, (x_n, y_n)\}` where :math:`x_i \in \mathbf{R}^n` and :math:`y_i \in \{0, 1\}`, a one hidden layer one hidden neuron MLP learns the function :math:`f(x) = W_2 g(W_1^T x + b_1) + b_2` where :math:`W_1 \in \mathbf{R}^m` and :math:`W_2, b_1, b_2 \in \mathbf{R}` are diff --git a/doc/modules/sgd.rst b/doc/modules/sgd.rst index 95b16224fc18e..360ba2f11c994 100644 --- a/doc/modules/sgd.rst +++ b/doc/modules/sgd.rst @@ -405,7 +405,7 @@ Mathematical formulation We describe here the mathematical details of the SGD procedure. A good overview with convergence rates can be found in [#6]_. -Given a set of training examples :math:`(x_1, y_1), \ldots, (x_n, y_n)` where +Given a set of training examples :math:`\{(x_1, y_1), \ldots, (x_n, y_n)\}` where :math:`x_i \in \mathbf{R}^m` and :math:`y_i \in \mathbf{R}` (:math:`y_i \in \{-1, 1\}` for classification), our goal is to learn a linear scoring function From a8e4f1af7759d02842bcbb6bc61ff1199cfa22bd Mon Sep 17 00:00:00 2001 From: Stefanie Senger <91849487+StefanieSenger@users.noreply.github.com> Date: Thu, 10 Jul 2025 17:00:11 +0200 Subject: [PATCH 088/103] CI Remove constraints for scipy version in `pylatest_conda_mkl_no_openmp` job (#31729) --- .../azure/pylatest_conda_mkl_no_openmp_environment.yml | 2 +- .../azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock | 5 +++-- build_tools/update_environments_and_lock_files.py | 4 ---- 3 files changed, 4 insertions(+), 7 deletions(-) diff --git a/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml b/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml index 21c4934f004e5..faf9f7e981666 100644 --- a/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml +++ b/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml @@ -7,7 +7,7 @@ dependencies: - python - numpy - blas[build=mkl] - - scipy<1.12 + - scipy - cython - joblib - threadpoolctl diff --git a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock index e22d3e87ccf24..b4e9c64e0dbb1 100644 --- a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock +++ b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: osx-64 -# input_hash: 5adafa63ab1109c544ec1b12e91e8b32a70341ed5a2ae7c9eeea50ecb2907ebd +# input_hash: 272bc18497f5ac80413d90a152efd3e60065cca52254829eb4ec33cec3001534 @EXPLICIT https://repo.anaconda.com/pkgs/main/osx-64/blas-1.0-mkl.conda#cb2c87e85ac8e0ceae776d26d4214c8a https://repo.anaconda.com/pkgs/main/osx-64/bzip2-1.0.8-h6c40b1e_6.conda#96224786021d0765ce05818fa3c59bdb @@ -12,6 +12,7 @@ https://repo.anaconda.com/pkgs/main/osx-64/libffi-3.4.4-hecd8cb5_1.conda#eb7f09a https://repo.anaconda.com/pkgs/main/osx-64/libwebp-base-1.3.2-h46256e1_1.conda#399c11b50e6e7a6969aca9a84ea416b7 https://repo.anaconda.com/pkgs/main/osx-64/llvm-openmp-17.0.6-hdd4a2e0_0.conda#0871f60a4c389ef44c343aa33b5a3acd https://repo.anaconda.com/pkgs/main/osx-64/ncurses-6.4-hcec6c5f_0.conda#0214d1ee980e217fabc695f1e40662aa +https://repo.anaconda.com/pkgs/main/noarch/pybind11-abi-5-hd3eb1b0_0.conda#7f0df6639fdf60ccd3045ee6faedd32f https://repo.anaconda.com/pkgs/main/noarch/tzdata-2025b-h04d1e81_0.conda#1d027393db3427ab22a02aa44a56f143 https://repo.anaconda.com/pkgs/main/osx-64/xxhash-0.8.0-h9ed2024_3.conda#79507f6b51082e0dc409046ee1471e8b https://repo.anaconda.com/pkgs/main/osx-64/xz-5.6.4-h46256e1_1.conda#ce989a528575ad332a650bb7c7f7e5d5 @@ -81,6 +82,6 @@ https://repo.anaconda.com/pkgs/main/osx-64/mkl_fft-1.3.8-py312h6c40b1e_0.conda#d https://repo.anaconda.com/pkgs/main/osx-64/mkl_random-1.2.4-py312ha357a0b_0.conda#c1ea9c8eee79a5af3399f3c31be0e9c6 https://repo.anaconda.com/pkgs/main/osx-64/numpy-1.26.4-py312hac873b0_0.conda#3150bac1e382156f82a153229e1ebd06 https://repo.anaconda.com/pkgs/main/osx-64/numexpr-2.8.7-py312hac873b0_0.conda#6303ba071636ef57fddf69eb6f440ec1 -https://repo.anaconda.com/pkgs/main/osx-64/scipy-1.11.4-py312h81688c2_0.conda#7d57b4c21a9261f97fa511e0940c5d93 +https://repo.anaconda.com/pkgs/main/osx-64/scipy-1.13.0-py312h81688c2_0.conda#b7431aa846b36c7fa2db35fe32c9c123 https://repo.anaconda.com/pkgs/main/osx-64/pandas-2.2.3-py312h6d0c2b6_0.conda#84ce5b8ec4a986d13a5df17811f556a2 https://repo.anaconda.com/pkgs/main/osx-64/pyamg-5.2.1-py312h1962661_0.conda#58881950d4ce74c9302b56961f97a43c diff --git a/build_tools/update_environments_and_lock_files.py b/build_tools/update_environments_and_lock_files.py index d95289f04a903..b619ab22f0a7e 100644 --- a/build_tools/update_environments_and_lock_files.py +++ b/build_tools/update_environments_and_lock_files.py @@ -160,10 +160,6 @@ def remove_from(alist, to_remove): "conda_dependencies": common_dependencies + ["ccache"], "package_constraints": { "blas": "[build=mkl]", - # scipy 1.12.x crashes on this platform (https://github.com/scipy/scipy/pull/20086) - # TODO: release scipy constraint when 1.13 is available in the "default" - # channel. - "scipy": "<1.12", }, }, { From 729f6a36297d0ad6d92fd5996dcc9b6ff46fd1ac Mon Sep 17 00:00:00 2001 From: Dimitri Papadopoulos Orfanos <3234522+DimitriPapadopoulos@users.noreply.github.com> Date: Fri, 11 Jul 2025 11:09:48 +0200 Subject: [PATCH 089/103] MNT Update pre-commit ruff legacy alias (#31740) --- .pre-commit-config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 48871d2a4abed..d02000a24581a 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -7,9 +7,9 @@ repos: - id: end-of-file-fixer - id: trailing-whitespace - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.11.7 + rev: v0.12.2 hooks: - - id: ruff + - id: ruff-check args: ["--fix", "--output-format=full"] - id: ruff-format - repo: https://github.com/pre-commit/mirrors-mypy From f54a11fb5dfbebb25bbb34eeb770ab394da9dfe6 Mon Sep 17 00:00:00 2001 From: "Christine P. Chai" Date: Fri, 11 Jul 2025 02:29:26 -0700 Subject: [PATCH 090/103] DOC: Update a link to a research paper (#31739) --- examples/calibration/plot_compare_calibration.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/calibration/plot_compare_calibration.py b/examples/calibration/plot_compare_calibration.py index aa60de1032765..fb41527eb0d44 100644 --- a/examples/calibration/plot_compare_calibration.py +++ b/examples/calibration/plot_compare_calibration.py @@ -271,12 +271,12 @@ def predict_proba(self, X): # Niculescu-Mizil & R. Caruana, ICML 2005 # # .. [2] `Beyond independence: Conditions for the optimality of the simple -# bayesian classifier +# Bayesian classifier # `_ # Domingos, P., & Pazzani, M., Proc. 13th Intl. Conf. Machine Learning. # 1996. # # .. [3] `Obtaining calibrated probability estimates from decision trees and # naive Bayesian classifiers -# `_ +# `_ # Zadrozny, Bianca, and Charles Elkan. Icml. Vol. 1. 2001. From 30c033f0219ea4c4eb8422a0cfc32b2d0d1f4337 Mon Sep 17 00:00:00 2001 From: Nicolas Bolle Date: Fri, 11 Jul 2025 12:12:40 -0400 Subject: [PATCH 091/103] Fix `PandasAdapter` causes crash or misattributed features (#31079) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Jérémie du Boisberranger --- .../sklearn.compose/31079.fix.rst | 3 +++ .../compose/tests/test_column_transformer.py | 24 +++++++++++++++++++ sklearn/utils/_set_output.py | 2 +- sklearn/utils/tests/test_set_output.py | 9 ++++++- 4 files changed, 36 insertions(+), 2 deletions(-) create mode 100644 doc/whats_new/upcoming_changes/sklearn.compose/31079.fix.rst diff --git a/doc/whats_new/upcoming_changes/sklearn.compose/31079.fix.rst b/doc/whats_new/upcoming_changes/sklearn.compose/31079.fix.rst new file mode 100644 index 0000000000000..b7ecaf67292b9 --- /dev/null +++ b/doc/whats_new/upcoming_changes/sklearn.compose/31079.fix.rst @@ -0,0 +1,3 @@ +- :class:`compose.ColumnTransformer` now correctly preserves non-default index + when mixing pandas Series and Dataframes. + By :user:`Nicolas Bolle `. diff --git a/sklearn/compose/tests/test_column_transformer.py b/sklearn/compose/tests/test_column_transformer.py index daa4111c9393d..819cef01e44d6 100644 --- a/sklearn/compose/tests/test_column_transformer.py +++ b/sklearn/compose/tests/test_column_transformer.py @@ -20,6 +20,7 @@ make_column_transformer, ) from sklearn.exceptions import NotFittedError +from sklearn.feature_extraction import DictVectorizer from sklearn.feature_selection import VarianceThreshold from sklearn.preprocessing import ( FunctionTransformer, @@ -2619,6 +2620,29 @@ def test_column_transformer_auto_memmap(): assert_allclose(Xt, StandardScaler().fit_transform(X[:, [0]])) +def test_column_transformer_non_default_index(): + """Check index handling when both pd.Series and pd.DataFrame slices are used in + ColumnTransformer. + + Non-regression test for issue #31546. + """ + pd = pytest.importorskip("pandas") + df = pd.DataFrame( + { + "dict_col": [{"foo": 1, "bar": 2}, {"foo": 3, "baz": 1}], + "dummy_col": [1, 2], + }, + index=[1, 2], + ) + t = make_column_transformer( + (DictVectorizer(sparse=False), "dict_col"), + (FunctionTransformer(), ["dummy_col"]), + ) + t.set_output(transform="pandas") + X = t.fit_transform(df) + assert list(X.index) == [1, 2] + + # Metadata Routing Tests # ====================== diff --git a/sklearn/utils/_set_output.py b/sklearn/utils/_set_output.py index e6a6fd0c4c305..6219b2f172a27 100644 --- a/sklearn/utils/_set_output.py +++ b/sklearn/utils/_set_output.py @@ -124,7 +124,7 @@ def create_container(self, X_output, X_original, columns, inplace=True): # because `list` exposes an `index` attribute. if isinstance(X_output, pd.DataFrame): index = X_output.index - elif isinstance(X_original, pd.DataFrame): + elif isinstance(X_original, (pd.DataFrame, pd.Series)): index = X_original.index else: index = None diff --git a/sklearn/utils/tests/test_set_output.py b/sklearn/utils/tests/test_set_output.py index 2b756ada64a6d..146f0a6c28592 100644 --- a/sklearn/utils/tests/test_set_output.py +++ b/sklearn/utils/tests/test_set_output.py @@ -25,8 +25,9 @@ def test_pandas_adapter(): pd = pytest.importorskip("pandas") X_np = np.asarray([[1, 0, 3], [0, 0, 1]]) columns = np.asarray(["f0", "f1", "f2"], dtype=object) - index = np.asarray([0, 1]) + index = np.asarray([1, 2]) X_df_orig = pd.DataFrame([[1, 2], [1, 3]], index=index) + X_ser_orig = pd.Series([2, 3], index=index) adapter = ADAPTERS_MANAGER.adapters["pandas"] X_container = adapter.create_container(X_np, X_df_orig, columns=lambda: columns) @@ -34,6 +35,12 @@ def test_pandas_adapter(): assert_array_equal(X_container.columns, columns) assert_array_equal(X_container.index, index) + # use original index when the original is a series + X_container = adapter.create_container(X_np, X_ser_orig, columns=lambda: columns) + assert isinstance(X_container, pd.DataFrame) + assert_array_equal(X_container.columns, columns) + assert_array_equal(X_container.index, index) + # Input dataframe's index does not change new_columns = np.asarray(["f0", "f1"], dtype=object) X_df = pd.DataFrame([[1, 2], [1, 3]], index=[10, 12]) From 1480c04896fa05dc7b7f2ae9a795fb5071ed0104 Mon Sep 17 00:00:00 2001 From: saskra Date: Mon, 14 Jul 2025 03:21:58 +0200 Subject: [PATCH 092/103] Fix spurious warning from type_of_target when called on estimator.classes_ (#31584) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Jérémie du Boisberranger Co-authored-by: Lucy Liu --- .../sklearn.utils/31584.fix.rst | 4 ++++ sklearn/utils/multiclass.py | 2 +- sklearn/utils/tests/test_multiclass.py | 14 ++++++++++- sklearn/utils/tests/test_response.py | 23 +++++++++++++++++++ 4 files changed, 41 insertions(+), 2 deletions(-) create mode 100644 doc/whats_new/upcoming_changes/sklearn.utils/31584.fix.rst diff --git a/doc/whats_new/upcoming_changes/sklearn.utils/31584.fix.rst b/doc/whats_new/upcoming_changes/sklearn.utils/31584.fix.rst new file mode 100644 index 0000000000000..5417dd80df975 --- /dev/null +++ b/doc/whats_new/upcoming_changes/sklearn.utils/31584.fix.rst @@ -0,0 +1,4 @@ +- Fixed a spurious warning (about the number of unique classes being + greater than 50% of the number of samples) that could occur when + passing `classes` :func:`utils.multiclass.type_of_target`. + By :user:`Sascha D. Krauss `. diff --git a/sklearn/utils/multiclass.py b/sklearn/utils/multiclass.py index 3a81e2b9eb6fe..d7c81a6f51624 100644 --- a/sklearn/utils/multiclass.py +++ b/sklearn/utils/multiclass.py @@ -414,7 +414,7 @@ def _raise_or_return(): if issparse(first_row_or_val): first_row_or_val = first_row_or_val.data classes = cached_unique(y) - if y.shape[0] > 20 and classes.shape[0] > round(0.5 * y.shape[0]): + if y.shape[0] > 20 and y.shape[0] > classes.shape[0] > round(0.5 * y.shape[0]): # Only raise the warning when we have at least 20 samples. warnings.warn( "The number of unique classes is greater than 50% of the number " diff --git a/sklearn/utils/tests/test_multiclass.py b/sklearn/utils/tests/test_multiclass.py index 433e8118923fb..a686b721f2393 100644 --- a/sklearn/utils/tests/test_multiclass.py +++ b/sklearn/utils/tests/test_multiclass.py @@ -302,7 +302,11 @@ def test_type_of_target_too_many_unique_classes(): We need to check that we don't raise if we have less than 20 samples. """ - y = np.arange(25) + # Create array of unique labels, except '0', which appears twice. + # This does raise a warning. + # Note warning would not be raised if we passed only unique + # labels, which happens when `type_of_target` is passed `classes_`. + y = np.hstack((np.arange(20), [0])) msg = r"The number of unique classes is greater than 50% of the number of samples." with pytest.warns(UserWarning, match=msg): type_of_target(y) @@ -313,6 +317,14 @@ def test_type_of_target_too_many_unique_classes(): warnings.simplefilter("error") type_of_target(y) + # More than 20 samples but only unique classes, simulating passing + # `classes_` to `type_of_target` (when number of classes is large). + # No warning should be raised + y = np.arange(25) + with warnings.catch_warnings(): + warnings.simplefilter("ignore", UserWarning) + type_of_target(y) + def test_unique_labels_non_specific(): # Test unique_labels with a variety of collected examples diff --git a/sklearn/utils/tests/test_response.py b/sklearn/utils/tests/test_response.py index 858c16cca4df1..5f791b59dfaa3 100644 --- a/sklearn/utils/tests/test_response.py +++ b/sklearn/utils/tests/test_response.py @@ -1,3 +1,5 @@ +import warnings + import numpy as np import pytest @@ -369,3 +371,24 @@ def test_get_response_values_multilabel_indicator(response_method): assert (y_pred > 1).sum() > 0 else: # response_method == "predict" assert np.logical_or(y_pred == 0, y_pred == 1).all() + + +def test_response_values_type_of_target_on_classes_no_warning(): + """ + Ensure `_get_response_values` doesn't raise spurious warning. + + "The number of unique classes is greater than > 50% of samples" + warning should not be raised when calling `type_of_target(classes_)`. + + Non-regression test for issue #31583. + """ + X = np.random.RandomState(0).randn(120, 3) + # 30 classes, less than 50% of number of samples + y = np.repeat(np.arange(30), 4) + + clf = LogisticRegression().fit(X, y) + + with warnings.catch_warnings(): + warnings.simplefilter("error", UserWarning) + + _get_response_values(clf, X, response_method="predict_proba") From dbcfbf9b30b0ed9da83b9f258fa81ac0a98bbde2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Dea=20Mar=C3=ADa=20L=C3=A9on?= Date: Mon, 14 Jul 2025 15:02:41 +0200 Subject: [PATCH 093/103] FIX Avoid fitting a pipeline without steps (#31723) --- sklearn/pipeline.py | 3 ++- sklearn/tests/test_pipeline.py | 10 ++++++++++ 2 files changed, 12 insertions(+), 1 deletion(-) diff --git a/sklearn/pipeline.py b/sklearn/pipeline.py index b291d970b1c79..dddf33668903b 100644 --- a/sklearn/pipeline.py +++ b/sklearn/pipeline.py @@ -320,6 +320,8 @@ def set_params(self, **kwargs): return self def _validate_steps(self): + if not self.steps: + raise ValueError("The pipeline is empty. Please add steps.") names, estimators = zip(*self.steps) # validate names @@ -1289,7 +1291,6 @@ def __sklearn_is_fitted__(self): An empty pipeline is considered fitted. """ - # First find the last step that is not 'passthrough' last_step = None for _, estimator in reversed(self.steps): diff --git a/sklearn/tests/test_pipeline.py b/sklearn/tests/test_pipeline.py index ad00ffb67a616..3815f264a8e7f 100644 --- a/sklearn/tests/test_pipeline.py +++ b/sklearn/tests/test_pipeline.py @@ -282,6 +282,16 @@ def test_pipeline_invalid_parameters(): assert params == params2 +def test_empty_pipeline(): + X = iris.data + y = iris.target + + pipe = Pipeline([]) + msg = "The pipeline is empty. Please add steps." + with pytest.raises(ValueError, match=msg): + pipe.fit(X, y) + + def test_pipeline_init_tuple(): # Pipeline accepts steps as tuple X = np.array([[1, 2]]) From 63ca649b4ca8648abd1ca834c2987454bdd6f5c0 Mon Sep 17 00:00:00 2001 From: Lucy Liu Date: Tue, 15 Jul 2025 02:29:59 +1000 Subject: [PATCH 094/103] Mention possibility of regression targets in warning about unique classes >50% of n_samples (#31689) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Jérémie du Boisberranger --- sklearn/utils/multiclass.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/sklearn/utils/multiclass.py b/sklearn/utils/multiclass.py index d7c81a6f51624..b3b8611341805 100644 --- a/sklearn/utils/multiclass.py +++ b/sklearn/utils/multiclass.py @@ -418,7 +418,8 @@ def _raise_or_return(): # Only raise the warning when we have at least 20 samples. warnings.warn( "The number of unique classes is greater than 50% of the number " - "of samples.", + "of samples. `y` could represent a regression problem, not a " + "classification problem.", UserWarning, stacklevel=2, ) From a73deeebd45457bed42ee0ff1ba67272c18f823a Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Tue, 15 Jul 2025 14:36:40 +0200 Subject: [PATCH 095/103] :lock: :robot: CI Update lock files for main CI build(s) :lock: :robot: (#31758) Co-authored-by: Lock file bot --- ...latest_conda_forge_mkl_linux-64_conda.lock | 150 +++++++-------- ...pylatest_conda_forge_mkl_osx-64_conda.lock | 12 +- ...test_conda_mkl_no_openmp_osx-64_conda.lock | 4 +- ...st_pip_openblas_pandas_linux-64_conda.lock | 4 +- ...nblas_min_dependencies_linux-64_conda.lock | 106 +++++------ ...e_openblas_ubuntu_2204_linux-64_conda.lock | 47 ++--- ...min_conda_forge_openblas_win-64_conda.lock | 10 +- build_tools/circle/doc_linux-64_conda.lock | 178 +++++++++--------- .../doc_min_dependencies_linux-64_conda.lock | 162 ++++++++-------- ...n_conda_forge_arm_linux-aarch64_conda.lock | 78 ++++---- 10 files changed, 376 insertions(+), 375 deletions(-) diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index 81b6230365cb7..293b6cef62d3c 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock @@ -11,9 +11,9 @@ https://conda.anaconda.org/conda-forge/linux-64/mkl-include-2024.2.2-ha957f24_16 https://conda.anaconda.org/conda-forge/linux-64/nlohmann_json-3.12.0-h3f2d84a_0.conda#d76872d096d063e226482c99337209dc https://conda.anaconda.org/conda-forge/noarch/python_abi-3.13-7_cp313.conda#e84b44e6300f1703cb25d29120c5b1d8 https://conda.anaconda.org/conda-forge/noarch/tzdata-2025b-h78e105d_0.conda#4222072737ccff51314b5ece9c7d6f5a -https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.6.15-hbd8a1cb_0.conda#72525f07d72806e3b639ad4504c30ce5 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https://conda.anaconda.org/conda-forge/linux-64/pyarrow-20.0.0-py313h78bf25f_0.conda#6b8d388845ce750fe2ad8436669182f3 diff --git a/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock index ca63d8be87142..4be3e09e6bf37 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock @@ -8,10 +8,10 @@ https://conda.anaconda.org/conda-forge/noarch/python_abi-3.13-7_cp313.conda#e84b https://conda.anaconda.org/conda-forge/osx-64/tbb-2021.10.0-h1c7c39f_2.conda#73434bcf87082942e938352afae9b0fa https://conda.anaconda.org/conda-forge/noarch/tzdata-2025b-h78e105d_0.conda#4222072737ccff51314b5ece9c7d6f5a https://conda.anaconda.org/conda-forge/osx-64/bzip2-1.0.8-hfdf4475_7.conda#7ed4301d437b59045be7e051a0308211 -https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.6.15-hbd8a1cb_0.conda#72525f07d72806e3b639ad4504c30ce5 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https://conda.anaconda.org/conda-forge/osx-64/libjpeg-turbo-3.1.0-h6e16a3a_0.conda#87537967e6de2f885a9fcebd42b7cb10 https://conda.anaconda.org/conda-forge/osx-64/liblzma-5.8.1-hd471939_2.conda#8468beea04b9065b9807fc8b9cdc5894 https://conda.anaconda.org/conda-forge/osx-64/libmpdec-4.0.0-h6e16a3a_0.conda#18b81186a6adb43f000ad19ed7b70381 -https://conda.anaconda.org/conda-forge/osx-64/libwebp-base-1.5.0-h6cf52b4_0.conda#5e0cefc99a231ac46ba21e27ae44689f +https://conda.anaconda.org/conda-forge/osx-64/libwebp-base-1.6.0-hb807250_0.conda#7bb6608cf1f83578587297a158a6630b https://conda.anaconda.org/conda-forge/osx-64/libzlib-1.3.1-hd23fc13_2.conda#003a54a4e32b02f7355b50a837e699da https://conda.anaconda.org/conda-forge/osx-64/llvm-openmp-20.1.7-ha54dae1_0.conda#e240159643214102dc88395c4ecee9cf https://conda.anaconda.org/conda-forge/osx-64/ncurses-6.5-h0622a9a_3.conda#ced34dd9929f491ca6dab6a2927aff25 @@ -34,11 +34,11 @@ 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b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock @@ -41,7 +41,7 @@ https://repo.anaconda.com/pkgs/main/osx-64/python-3.12.11-he8d2d4c_0.conda#9783e https://repo.anaconda.com/pkgs/main/osx-64/brotli-python-1.0.9-py312h6d0c2b6_9.conda#425936421fe402074163ac3ffe33a060 https://repo.anaconda.com/pkgs/main/osx-64/coverage-7.6.9-py312h46256e1_0.conda#f8c1547bbf522a600ee795901240a7b0 https://repo.anaconda.com/pkgs/main/noarch/cycler-0.11.0-pyhd3eb1b0_0.conda#f5e365d2cdb66d547eb8c3ab93843aab -https://repo.anaconda.com/pkgs/main/osx-64/cython-3.0.11-py312h46256e1_1.conda#44443579c3f4ae02940aeefb77e6115e +https://repo.anaconda.com/pkgs/main/osx-64/cython-3.0.12-py312h46256e1_0.conda#fa2e1e199a4bef20f43c572f361083c7 https://repo.anaconda.com/pkgs/main/noarch/execnet-2.1.1-pyhd3eb1b0_0.conda#b3cb797432ee4657d5907b91a5dc65ad https://repo.anaconda.com/pkgs/main/noarch/iniconfig-1.1.1-pyhd3eb1b0_0.tar.bz2#e40edff2c5708f342cef43c7f280c507 https://repo.anaconda.com/pkgs/main/osx-64/joblib-1.4.2-py312hecd8cb5_0.conda#8ab03dfa447b4e0bfa0bd3d25930f3b6 @@ -55,7 +55,7 @@ https://repo.anaconda.com/pkgs/main/osx-64/pluggy-1.5.0-py312hecd8cb5_0.conda#ca https://repo.anaconda.com/pkgs/main/osx-64/pygments-2.19.1-py312hecd8cb5_0.conda#ca4be8769d62deee6127c0bf3703b0f6 https://repo.anaconda.com/pkgs/main/osx-64/pyparsing-3.2.0-py312hecd8cb5_0.conda#e4086daaaed13f68cc8d5b9da7db73cc https://repo.anaconda.com/pkgs/main/noarch/python-tzdata-2025.2-pyhd3eb1b0_0.conda#5ac858f05dbf9d3cdb04d53516901247 -https://repo.anaconda.com/pkgs/main/osx-64/pytz-2024.1-py312hecd8cb5_0.conda#2b28ec0e0d07f5c0c701f75200b1e8b6 +https://repo.anaconda.com/pkgs/main/osx-64/pytz-2025.2-py312hecd8cb5_0.conda#37f5d42a57b8fe2932b20a243e867680 https://repo.anaconda.com/pkgs/main/osx-64/setuptools-78.1.1-py312hecd8cb5_0.conda#76b66b96a1564cb76011408c1eb8df3e https://repo.anaconda.com/pkgs/main/osx-64/six-1.17.0-py312hecd8cb5_0.conda#aadd782bc06426887ae0835eedd98ceb https://repo.anaconda.com/pkgs/main/noarch/threadpoolctl-2.2.0-pyh0d69192_0.conda#bbfdbae4934150b902f97daaf287efe2 diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock index 5eb0f04ee24b6..57486b815a530 100644 --- a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock @@ -41,7 +41,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.45.1-py313h06a4308_0.conda# https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2a700153fefe0e69438b18e1 # pip alabaster @ https://files.pythonhosted.org/packages/7e/b3/6b4067be973ae96ba0d615946e314c5ae35f9f993eca561b356540bb0c2b/alabaster-1.0.0-py3-none-any.whl#sha256=fc6786402dc3fcb2de3cabd5fe455a2db534b371124f1f21de8731783dec828b # pip babel @ https://files.pythonhosted.org/packages/b7/b8/3fe70c75fe32afc4bb507f75563d39bc5642255d1d94f1f23604725780bf/babel-2.17.0-py3-none-any.whl#sha256=4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2 -# pip certifi @ https://files.pythonhosted.org/packages/84/ae/320161bd181fc06471eed047ecce67b693fd7515b16d495d8932db763426/certifi-2025.6.15-py3-none-any.whl#sha256=2e0c7ce7cb5d8f8634ca55d2ba7e6ec2689a2fd6537d8dec1296a477a4910057 +# pip certifi @ https://files.pythonhosted.org/packages/4f/52/34c6cf5bb9285074dc3531c437b3919e825d976fde097a7a73f79e726d03/certifi-2025.7.14-py3-none-any.whl#sha256=6b31f564a415d79ee77df69d757bb49a5bb53bd9f756cbbe24394ffd6fc1f4b2 # pip charset-normalizer @ https://files.pythonhosted.org/packages/e2/28/ffc026b26f441fc67bd21ab7f03b313ab3fe46714a14b516f931abe1a2d8/charset_normalizer-3.4.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=6c9379d65defcab82d07b2a9dfbfc2e95bc8fe0ebb1b176a3190230a3ef0e07c # pip coverage @ https://files.pythonhosted.org/packages/49/d9/4616b787d9f597d6443f5588619c1c9f659e1f5fc9eebf63699eb6d34b78/coverage-7.9.2-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=256ea87cb2a1ed992bcdfc349d8042dcea1b80436f4ddf6e246d6bee4b5d73b6 # pip cycler @ https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl#sha256=85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30 @@ -92,7 +92,7 @@ https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2 # pip lightgbm @ https://files.pythonhosted.org/packages/42/86/dabda8fbcb1b00bcfb0003c3776e8ade1aa7b413dff0a2c08f457dace22f/lightgbm-4.6.0-py3-none-manylinux_2_28_x86_64.whl#sha256=cb19b5afea55b5b61cbb2131095f50538bd608a00655f23ad5d25ae3e3bf1c8d # pip matplotlib @ https://files.pythonhosted.org/packages/f5/64/41c4367bcaecbc03ef0d2a3ecee58a7065d0a36ae1aa817fe573a2da66d4/matplotlib-3.10.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=a80fcccbef63302c0efd78042ea3c2436104c5b1a4d3ae20f864593696364ac7 # pip meson-python @ https://files.pythonhosted.org/packages/28/58/66db620a8a7ccb32633de9f403fe49f1b63c68ca94e5c340ec5cceeb9821/meson_python-0.18.0-py3-none-any.whl#sha256=3b0fe051551cc238f5febb873247c0949cd60ded556efa130aa57021804868e2 -# pip pandas @ https://files.pythonhosted.org/packages/2a/b3/463bfe819ed60fb7e7ddffb4ae2ee04b887b3444feee6c19437b8f834837/pandas-2.3.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=213cd63c43263dbb522c1f8a7c9d072e25900f6975596f883f4bebd77295d4f3 +# pip pandas @ https://files.pythonhosted.org/packages/e9/e2/20a317688435470872885e7fc8f95109ae9683dec7c50be29b56911515a5/pandas-2.3.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=2ba6aff74075311fc88504b1db890187a3cd0f887a5b10f5525f8e2ef55bfdb9 # pip pyamg @ https://files.pythonhosted.org/packages/cd/a7/0df731cbfb09e73979a1a032fc7bc5be0eba617d798b998a0f887afe8ade/pyamg-5.2.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=6999b351ab969c79faacb81faa74c0fa9682feeff3954979212872a3ee40c298 # pip pytest-cov @ https://files.pythonhosted.org/packages/bc/16/4ea354101abb1287856baa4af2732be351c7bee728065aed451b678153fd/pytest_cov-6.2.1-py3-none-any.whl#sha256=f5bc4c23f42f1cdd23c70b1dab1bbaef4fc505ba950d53e0081d0730dd7e86d5 # pip pytest-xdist @ https://files.pythonhosted.org/packages/ca/31/d4e37e9e550c2b92a9cbc2e4d0b7420a27224968580b5a447f420847c975/pytest_xdist-3.8.0-py3-none-any.whl#sha256=202ca578cfeb7370784a8c33d6d05bc6e13b4f25b5053c30a152269fd10f0b88 diff --git 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https://conda.anaconda.org/conda-forge/linux-64/libpciaccess-0.18-hb9d3cd8_0.conda#70e3400cbbfa03e96dcde7fc13e38c7b https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_3.conda#6d11a5edae89fe413c0569f16d308f5a https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.8.0-hf23e847_1.conda#b1aa0faa95017bca11369bd080487ec4 -https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.5.0-h851e524_0.conda#63f790534398730f59e1b899c3644d4a +https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.6.0-hd42ef1d_0.conda#aea31d2e5b1091feca96fcfe945c3cf9 https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.1-h7b32b05_0.conda#c87df2ab1448ba69169652ab9547082d @@ -66,14 +66,13 @@ 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-https://conda.anaconda.org/conda-forge/linux-64/numpy-2.3.1-py313h103f029_0.conda#c583d7057dfbd9e0e076062f3667b38c https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.1-pyhd8ed1ab_0.conda#a49c2283f24696a7b30367b7346a0144 https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.8.0-pyhd8ed1ab_0.conda#8375cfbda7c57fbceeda18229be10417 -https://conda.anaconda.org/conda-forge/linux-64/scipy-1.16.0-py313h7f7b39c_0.conda#efa6724dab9395e1307c65a589d35459 From 6b2b4b4e2a98470d9b795e92723107beaaee0df9 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Tue, 15 Jul 2025 14:38:34 +0200 Subject: [PATCH 098/103] :lock: :robot: CI Update lock files for scipy-dev CI build(s) :lock: :robot: (#31755) Co-authored-by: Lock file bot --- build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock index 534fb9be5b52b..6d75cdaddf813 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock @@ -41,7 +41,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.45.1-py313h06a4308_0.conda# https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2a700153fefe0e69438b18e1 # pip alabaster @ https://files.pythonhosted.org/packages/7e/b3/6b4067be973ae96ba0d615946e314c5ae35f9f993eca561b356540bb0c2b/alabaster-1.0.0-py3-none-any.whl#sha256=fc6786402dc3fcb2de3cabd5fe455a2db534b371124f1f21de8731783dec828b # pip babel @ https://files.pythonhosted.org/packages/b7/b8/3fe70c75fe32afc4bb507f75563d39bc5642255d1d94f1f23604725780bf/babel-2.17.0-py3-none-any.whl#sha256=4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2 -# pip certifi @ https://files.pythonhosted.org/packages/84/ae/320161bd181fc06471eed047ecce67b693fd7515b16d495d8932db763426/certifi-2025.6.15-py3-none-any.whl#sha256=2e0c7ce7cb5d8f8634ca55d2ba7e6ec2689a2fd6537d8dec1296a477a4910057 +# pip certifi @ https://files.pythonhosted.org/packages/4f/52/34c6cf5bb9285074dc3531c437b3919e825d976fde097a7a73f79e726d03/certifi-2025.7.14-py3-none-any.whl#sha256=6b31f564a415d79ee77df69d757bb49a5bb53bd9f756cbbe24394ffd6fc1f4b2 # pip charset-normalizer @ https://files.pythonhosted.org/packages/e2/28/ffc026b26f441fc67bd21ab7f03b313ab3fe46714a14b516f931abe1a2d8/charset_normalizer-3.4.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=6c9379d65defcab82d07b2a9dfbfc2e95bc8fe0ebb1b176a3190230a3ef0e07c # pip coverage @ https://files.pythonhosted.org/packages/49/d9/4616b787d9f597d6443f5588619c1c9f659e1f5fc9eebf63699eb6d34b78/coverage-7.9.2-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=256ea87cb2a1ed992bcdfc349d8042dcea1b80436f4ddf6e246d6bee4b5d73b6 # pip docutils @ https://files.pythonhosted.org/packages/8f/d7/9322c609343d929e75e7e5e6255e614fcc67572cfd083959cdef3b7aad79/docutils-0.21.2-py3-none-any.whl#sha256=dafca5b9e384f0e419294eb4d2ff9fa826435bf15f15b7bd45723e8ad76811b2 From 6cba1d8022368d56fed3c61e10dc21f125306f34 Mon Sep 17 00:00:00 2001 From: jshn9515 Date: Tue, 15 Jul 2025 20:53:31 +0800 Subject: [PATCH 099/103] FIX: Regression in DecisionBoundaryDisplay.from_estimator with colors and plot_method='contour' (#31553) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Jérémie du Boisberranger --- .../sklearn.inspection/31553.fix.rst | 7 ++ sklearn/inspection/_plot/decision_boundary.py | 82 ++++++++++--------- .../tests/test_boundary_decision_display.py | 59 +++++++++---- 3 files changed, 93 insertions(+), 55 deletions(-) create mode 100644 doc/whats_new/upcoming_changes/sklearn.inspection/31553.fix.rst diff --git a/doc/whats_new/upcoming_changes/sklearn.inspection/31553.fix.rst b/doc/whats_new/upcoming_changes/sklearn.inspection/31553.fix.rst new file mode 100644 index 0000000000000..bd9bb339bb68c --- /dev/null +++ b/doc/whats_new/upcoming_changes/sklearn.inspection/31553.fix.rst @@ -0,0 +1,7 @@ +- Fix multiple issues in the multiclass setting of :class:`inspection.DecisionBoundaryDisplay`: + + - `contour` plotting now correctly shows the decision boundary. + - `cmap` and `colors` are now properly ignored in favor of `multiclass_colors`. + - Linear segmented colormaps are now fully supported. + + By :user:`Yunjie Lin ` diff --git a/sklearn/inspection/_plot/decision_boundary.py b/sklearn/inspection/_plot/decision_boundary.py index bc28708d7c488..2ef8538058393 100644 --- a/sklearn/inspection/_plot/decision_boundary.py +++ b/sklearn/inspection/_plot/decision_boundary.py @@ -221,17 +221,22 @@ def plot(self, plot_method="contourf", ax=None, xlabel=None, ylabel=None, **kwar self.surface_ = plot_func(self.xx0, self.xx1, self.response, **kwargs) else: # self.response.ndim == 3 n_responses = self.response.shape[-1] - if ( - isinstance(self.multiclass_colors, str) - or self.multiclass_colors is None + for kwarg in ("cmap", "colors"): + if kwarg in kwargs: + warnings.warn( + f"'{kwarg}' is ignored in favor of 'multiclass_colors' " + "in the multiclass case when the response method is " + "'decision_function' or 'predict_proba'." + ) + del kwargs[kwarg] + + if self.multiclass_colors is None or isinstance( + self.multiclass_colors, str ): - if isinstance(self.multiclass_colors, str): - cmap = self.multiclass_colors + if self.multiclass_colors is None: + cmap = "tab10" if n_responses <= 10 else "gist_rainbow" else: - if n_responses <= 10: - cmap = "tab10" - else: - cmap = "gist_rainbow" + cmap = self.multiclass_colors # Special case for the tab10 and tab20 colormaps that encode a # discrete set of colors that are easily distinguishable @@ -241,40 +246,41 @@ def plot(self, plot_method="contourf", ax=None, xlabel=None, ylabel=None, **kwar elif cmap == "tab20" and n_responses <= 20: colors = plt.get_cmap("tab20", 20).colors[:n_responses] else: - colors = plt.get_cmap(cmap, n_responses).colors - elif isinstance(self.multiclass_colors, str): - colors = colors = plt.get_cmap( - self.multiclass_colors, n_responses - ).colors - else: + cmap = plt.get_cmap(cmap, n_responses) + if not hasattr(cmap, "colors"): + # For LinearSegmentedColormap + colors = cmap(np.linspace(0, 1, n_responses)) + else: + colors = cmap.colors + elif isinstance(self.multiclass_colors, list): colors = [mpl.colors.to_rgba(color) for color in self.multiclass_colors] + else: + raise ValueError("'multiclass_colors' must be a list or a str.") self.multiclass_colors_ = colors - multiclass_cmaps = [ - mpl.colors.LinearSegmentedColormap.from_list( - f"colormap_{class_idx}", [(1.0, 1.0, 1.0, 1.0), (r, g, b, 1.0)] - ) - for class_idx, (r, g, b, _) in enumerate(colors) - ] - - self.surface_ = [] - for class_idx, cmap in enumerate(multiclass_cmaps): - response = np.ma.array( - self.response[:, :, class_idx], - mask=~(self.response.argmax(axis=2) == class_idx), + if plot_method == "contour": + # Plot only argmax map for contour + class_map = self.response.argmax(axis=2) + self.surface_ = plot_func( + self.xx0, self.xx1, class_map, colors=colors, **kwargs ) - # `cmap` should not be in kwargs - safe_kwargs = kwargs.copy() - if "cmap" in safe_kwargs: - del safe_kwargs["cmap"] - warnings.warn( - "Plotting max class of multiclass 'decision_function' or " - "'predict_proba', thus 'multiclass_colors' used and " - "'cmap' kwarg ignored." + else: + multiclass_cmaps = [ + mpl.colors.LinearSegmentedColormap.from_list( + f"colormap_{class_idx}", [(1.0, 1.0, 1.0, 1.0), (r, g, b, 1.0)] + ) + for class_idx, (r, g, b, _) in enumerate(colors) + ] + + self.surface_ = [] + for class_idx, cmap in enumerate(multiclass_cmaps): + response = np.ma.array( + self.response[:, :, class_idx], + mask=~(self.response.argmax(axis=2) == class_idx), + ) + self.surface_.append( + plot_func(self.xx0, self.xx1, response, cmap=cmap, **kwargs) ) - self.surface_.append( - plot_func(self.xx0, self.xx1, response, cmap=cmap, **safe_kwargs) - ) if xlabel is not None or not ax.get_xlabel(): xlabel = self.xlabel if xlabel is None else xlabel diff --git a/sklearn/inspection/_plot/tests/test_boundary_decision_display.py b/sklearn/inspection/_plot/tests/test_boundary_decision_display.py index 3284f42241fa5..f409a50ab58c0 100644 --- a/sklearn/inspection/_plot/tests/test_boundary_decision_display.py +++ b/sklearn/inspection/_plot/tests/test_boundary_decision_display.py @@ -169,6 +169,10 @@ def test_input_validation_errors(pyplot, kwargs, error_msg, fitted_clf): @pytest.mark.parametrize( "kwargs, error_msg", [ + ( + {"multiclass_colors": {"dict": "not_list"}}, + "'multiclass_colors' must be a list or a str.", + ), ({"multiclass_colors": "not_cmap"}, "it must be a valid Matplotlib colormap"), ({"multiclass_colors": ["red", "green"]}, "it must be of the same length"), ( @@ -617,6 +621,7 @@ def test_multiclass_plot_max_class(pyplot, response_method): "multiclass_colors", [ "plasma", + "Blues", ["red", "green", "blue"], ], ) @@ -642,31 +647,51 @@ def test_multiclass_colors_cmap(pyplot, plot_method, multiclass_colors): if multiclass_colors == "plasma": colors = mpl.pyplot.get_cmap(multiclass_colors, len(clf.classes_)).colors + elif multiclass_colors == "Blues": + cmap = mpl.pyplot.get_cmap(multiclass_colors, len(clf.classes_)) + colors = cmap(np.linspace(0, 1, len(clf.classes_))) else: colors = [mpl.colors.to_rgba(color) for color in multiclass_colors] - cmaps = [ - mpl.colors.LinearSegmentedColormap.from_list( - f"colormap_{class_idx}", [(1.0, 1.0, 1.0, 1.0), (r, g, b, 1.0)] - ) - for class_idx, (r, g, b, _) in enumerate(colors) - ] - - for idx, quad in enumerate(disp.surface_): - assert quad.cmap == cmaps[idx] + if plot_method != "contour": + cmaps = [ + mpl.colors.LinearSegmentedColormap.from_list( + f"colormap_{class_idx}", [(1.0, 1.0, 1.0, 1.0), (r, g, b, 1.0)] + ) + for class_idx, (r, g, b, _) in enumerate(colors) + ] + for idx, quad in enumerate(disp.surface_): + assert quad.cmap == cmaps[idx] + else: + assert_allclose(disp.surface_.colors, colors) -def test_multiclass_plot_max_class_cmap_kwarg(pyplot): - """Check `cmap` kwarg ignored when using plotting max multiclass class.""" +def test_cmap_and_colors_logic(pyplot): + """Check the handling logic for `cmap` and `colors`.""" X, y = load_iris_2d_scaled() clf = LogisticRegression().fit(X, y) - msg = ( - "Plotting max class of multiclass 'decision_function' or 'predict_proba', " - "thus 'multiclass_colors' used and 'cmap' kwarg ignored." - ) - with pytest.warns(UserWarning, match=msg): - DecisionBoundaryDisplay.from_estimator(clf, X, cmap="viridis") + with pytest.warns( + UserWarning, + match="'cmap' is ignored in favor of 'multiclass_colors'", + ): + DecisionBoundaryDisplay.from_estimator( + clf, + X, + multiclass_colors="plasma", + cmap="Blues", + ) + + with pytest.warns( + UserWarning, + match="'colors' is ignored in favor of 'multiclass_colors'", + ): + DecisionBoundaryDisplay.from_estimator( + clf, + X, + multiclass_colors="plasma", + colors="blue", + ) def test_subclass_named_constructors_return_type_is_subclass(pyplot): From f97fe77af5a6b3c6d01ecc6d748d58415d380cad Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Wed, 16 Jul 2025 17:13:36 +0200 Subject: [PATCH 100/103] CI Avoid miniconda CondaToSNonInteractiveError and stop using the default channel (#31771) --- azure-pipelines.yml | 4 +- build_tools/azure/install_setup_conda.sh | 34 ++++-- ...conda_forge_mkl_no_openmp_environment.yml} | 2 +- ...onda_forge_mkl_no_openmp_osx-64_conda.lock | 102 ++++++++++++++++++ ...test_conda_mkl_no_openmp_osx-64_conda.lock | 87 --------------- ...latest_pip_openblas_pandas_environment.yml | 2 +- ...st_pip_openblas_pandas_linux-64_conda.lock | 73 ++++++------- .../pylatest_pip_scipy_dev_environment.yml | 2 +- ...pylatest_pip_scipy_dev_linux-64_conda.lock | 71 ++++++------ build_tools/azure/windows.yml | 4 +- .../update_environments_and_lock_files.py | 8 +- 11 files changed, 203 insertions(+), 186 deletions(-) rename build_tools/azure/{pylatest_conda_mkl_no_openmp_environment.yml => pylatest_conda_forge_mkl_no_openmp_environment.yml} (96%) create mode 100644 build_tools/azure/pylatest_conda_forge_mkl_no_openmp_osx-64_conda.lock delete mode 100644 build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock diff --git a/azure-pipelines.yml b/azure-pipelines.yml index 5226308afe48b..4d3248f2d0729 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -234,9 +234,9 @@ jobs: LOCK_FILE: './build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock' SKLEARN_TESTS_GLOBAL_RANDOM_SEED: '5' # non-default seed SCIPY_ARRAY_API: '1' - pylatest_conda_mkl_no_openmp: + pylatest_conda_forge_mkl_no_openmp: DISTRIB: 'conda' - LOCK_FILE: './build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock' + LOCK_FILE: './build_tools/azure/pylatest_conda_forge_mkl_no_openmp_osx-64_conda.lock' SKLEARN_TEST_NO_OPENMP: 'true' SKLEARN_SKIP_OPENMP_TEST: 'true' SKLEARN_TESTS_GLOBAL_RANDOM_SEED: '6' # non-default seed diff --git a/build_tools/azure/install_setup_conda.sh b/build_tools/azure/install_setup_conda.sh index d09a02cda5a9f..e57d7dbe155be 100755 --- a/build_tools/azure/install_setup_conda.sh +++ b/build_tools/azure/install_setup_conda.sh @@ -3,22 +3,34 @@ set -e set -x -if [[ -z "${CONDA}" ]]; then - # In some runners (macOS-13 and macOS-14 in October 2024) conda is not - # installed so we install it ourselves - MINIFORGE_URL="https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh" - wget ${MINIFORGE_URL} -O miniforge.sh - bash miniforge.sh -b -u -p $HOME/miniforge3 - CONDA="$HOME/miniforge3" +PLATFORM=$(uname) +if [[ "$PLATFORM" =~ MINGW|MSYS ]]; then + PLATFORM=Windows +fi +if [[ "$PLATFORM" == "Windows" ]]; then + EXTENSION="exe" +else + EXTENSION="sh" +fi +INSTALLER="miniforge.$EXTENSION" +MINIFORGE_URL="https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$PLATFORM-$(uname -m).$EXTENSION" +curl -L ${MINIFORGE_URL} -o "$INSTALLER" + +MINIFORGE_DIR="$HOME/miniforge3" +if [[ "$PLATFORM" == "Windows" ]]; then + WIN_MINIFORGE_DIR=$(cygpath -w "$MINIFORGE_DIR") + cmd "/C $INSTALLER /InstallationType=JustMe /RegisterPython=0 /S /D=$WIN_MINIFORGE_DIR" else - # In most runners (in October 2024) conda is installed, - # but in a system folder and we want it user writable - sudo chown -R $USER $CONDA + bash "$INSTALLER" -b -u -p $MINIFORGE_DIR fi # Add conda to the PATH so that it can be used in further Azure CI steps. # Need set +x for ##vso Azure magic otherwise it may add a quote in the PATH. # For more details, see https://github.com/microsoft/azure-pipelines-tasks/issues/10331 set +x -echo "##vso[task.prependpath]$CONDA/bin" +if [[ "$PLATFORM" == "Windows" ]]; then + echo "##vso[task.prependpath]$MINIFORGE_DIR/Scripts" +else + echo "##vso[task.prependpath]$MINIFORGE_DIR/bin" +fi set -x diff --git a/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml b/build_tools/azure/pylatest_conda_forge_mkl_no_openmp_environment.yml similarity index 96% rename from build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml rename to build_tools/azure/pylatest_conda_forge_mkl_no_openmp_environment.yml index faf9f7e981666..8d8fe676698e6 100644 --- a/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml +++ b/build_tools/azure/pylatest_conda_forge_mkl_no_openmp_environment.yml @@ -2,7 +2,7 @@ # following script to centralize the configuration for CI builds: # build_tools/update_environments_and_lock_files.py channels: - - defaults + - conda-forge dependencies: - python - numpy diff --git a/build_tools/azure/pylatest_conda_forge_mkl_no_openmp_osx-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_no_openmp_osx-64_conda.lock new file mode 100644 index 0000000000000..7b1ee01c0f5b7 --- /dev/null +++ b/build_tools/azure/pylatest_conda_forge_mkl_no_openmp_osx-64_conda.lock @@ -0,0 +1,102 @@ +# Generated by conda-lock. +# platform: osx-64 +# input_hash: 12e3e511a3041fa8d542ec769028e21d8276a3aacad33a6e0125494942ec565e +@EXPLICIT 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-https://repo.anaconda.com/pkgs/main/osx-64/mkl_random-1.2.4-py312ha357a0b_0.conda#c1ea9c8eee79a5af3399f3c31be0e9c6 -https://repo.anaconda.com/pkgs/main/osx-64/numpy-1.26.4-py312hac873b0_0.conda#3150bac1e382156f82a153229e1ebd06 -https://repo.anaconda.com/pkgs/main/osx-64/numexpr-2.8.7-py312hac873b0_0.conda#6303ba071636ef57fddf69eb6f440ec1 -https://repo.anaconda.com/pkgs/main/osx-64/scipy-1.13.0-py312h81688c2_0.conda#b7431aa846b36c7fa2db35fe32c9c123 -https://repo.anaconda.com/pkgs/main/osx-64/pandas-2.2.3-py312h6d0c2b6_0.conda#84ce5b8ec4a986d13a5df17811f556a2 -https://repo.anaconda.com/pkgs/main/osx-64/pyamg-5.2.1-py312h1962661_0.conda#58881950d4ce74c9302b56961f97a43c diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml b/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml index ba17d37ff1555..64baefb3e816d 100644 --- a/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml +++ b/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml @@ -2,7 +2,7 @@ # following script to centralize the configuration for CI builds: # build_tools/update_environments_and_lock_files.py channels: - - defaults + - conda-forge dependencies: - python - ccache diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock index 57486b815a530..cf3091466d2ea 100644 --- a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock @@ -1,44 +1,39 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 692a667e331896943137778007c0834c42c3aa297986d4f8eda8b51a7f158d98 +# input_hash: 0668d85ecef342f1056dfe3d1fd8d677c967d4037f6f95fff49c097fec0cd624 @EXPLICIT -https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9 -https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2025.2.25-h06a4308_0.conda#495015d24da8ad929e3ae2d18571016d -https://repo.anaconda.com/pkgs/main/linux-64/ld_impl_linux-64-2.40-h12ee557_0.conda#ee672b5f635340734f58d618b7bca024 -https://repo.anaconda.com/pkgs/main/linux-64/python_abi-3.13-0_cp313.conda#d4009c49dd2b54ffded7f1365b5f6505 -https://repo.anaconda.com/pkgs/main/noarch/tzdata-2025b-h04d1e81_0.conda#1d027393db3427ab22a02aa44a56f143 -https://repo.anaconda.com/pkgs/main/linux-64/libgomp-11.2.0-h1234567_1.conda#b372c0eea9b60732fdae4b817a63c8cd -https://repo.anaconda.com/pkgs/main/linux-64/libstdcxx-ng-11.2.0-h1234567_1.conda#57623d10a70e09e1d048c2b2b6f4e2dd -https://repo.anaconda.com/pkgs/main/linux-64/_openmp_mutex-5.1-1_gnu.conda#71d281e9c2192cb3fa425655a8defb85 -https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-11.2.0-h1234567_1.conda#a87728dabf3151fb9cfa990bd2eb0464 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-https://repo.anaconda.com/pkgs/main/linux-64/setuptools-78.1.1-py313h06a4308_0.conda#8f8e1c1e3af9d2d371aaa0ee8316ae7c -https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.45.1-py313h06a4308_0.conda#29057e876eedce0e37c2388c138a19f9 -https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2a700153fefe0e69438b18e1 +https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 +https://conda.anaconda.org/conda-forge/noarch/python_abi-3.13-7_cp313.conda#e84b44e6300f1703cb25d29120c5b1d8 +https://conda.anaconda.org/conda-forge/noarch/tzdata-2025b-h78e105d_0.conda#4222072737ccff51314b5ece9c7d6f5a +https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.7.14-hbd8a1cb_0.conda#d16c90324aef024877d8713c0b7fea5b +https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.44-h1423503_1.conda#0be7c6e070c19105f966d3758448d018 +https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.1.0-h767d61c_3.conda#3cd1a7238a0dd3d0860fdefc496cc854 +https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2#73aaf86a425cc6e73fcf236a5a46396d +https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_3.conda#9e60c55e725c20d23125a5f0dd69af5d +https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.0-h5888daf_0.conda#db0bfbe7dd197b68ad5f30333bae6ce0 +https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.6-h2dba641_1.conda#ede4673863426c0883c0063d853bbd85 +https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.1.0-h69a702a_3.conda#e66f2b8ad787e7beb0f846e4bd7e8493 +https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.1.0-hcea5267_3.conda#530566b68c3b8ce7eec4cd047eae19fe +https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_2.conda#1a580f7796c7bf6393fddb8bbbde58dc +https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-hb9d3cd8_0.conda#c7e925f37e3b40d893459e625f6a53f1 +https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_3.conda#6d11a5edae89fe413c0569f16d308f5a +https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 +https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.1-h7b32b05_0.conda#c87df2ab1448ba69169652ab9547082d +https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62ee74e96c5ebb0af99386de58cf9553 +https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.1.0-h69a702a_3.conda#bfbca721fd33188ef923dfe9ba172f29 +https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.1.0-h4852527_3.conda#57541755b5a51691955012b8e197c06c +https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda#40b61aab5c7ba9ff276c41cfffe6b80b +https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8c095d6_2.conda#283b96675859b20a825f8fa30f311446 +https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_hd72426e_102.conda#a0116df4f4ed05c303811a837d5b39d8 +https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb8e6e7a_2.conda#6432cb5d4ac0046c3ac0a8a0f95842f9 +https://conda.anaconda.org/conda-forge/linux-64/icu-75.1-he02047a_0.conda#8b189310083baabfb622af68fd9d3ae3 +https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-15.1.0-h69a702a_3.conda#6e5d0574e57a38c36e674e9a18eee2b4 +https://conda.anaconda.org/conda-forge/linux-64/libhiredis-1.0.2-h2cc385e_0.tar.bz2#b34907d3a81a3cd8095ee83d174c074a +https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.50.2-hee844dc_2.conda#be96b9fdd7b579159df77ece9bb80e48 +https://conda.anaconda.org/conda-forge/linux-64/ccache-4.11.3-h80c52d3_0.conda#eb517c6a2b960c3ccb6f1db1005f063a +https://conda.anaconda.org/conda-forge/linux-64/python-3.13.5-hec9711d_102_cp313.conda#89e07d92cf50743886f41638d58c4328 +https://conda.anaconda.org/conda-forge/noarch/pip-25.1.1-pyh145f28c_0.conda#01384ff1639c6330a0924791413b8714 # pip alabaster @ https://files.pythonhosted.org/packages/7e/b3/6b4067be973ae96ba0d615946e314c5ae35f9f993eca561b356540bb0c2b/alabaster-1.0.0-py3-none-any.whl#sha256=fc6786402dc3fcb2de3cabd5fe455a2db534b371124f1f21de8731783dec828b # pip babel @ https://files.pythonhosted.org/packages/b7/b8/3fe70c75fe32afc4bb507f75563d39bc5642255d1d94f1f23604725780bf/babel-2.17.0-py3-none-any.whl#sha256=4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2 # pip certifi @ https://files.pythonhosted.org/packages/4f/52/34c6cf5bb9285074dc3531c437b3919e825d976fde097a7a73f79e726d03/certifi-2025.7.14-py3-none-any.whl#sha256=6b31f564a415d79ee77df69d757bb49a5bb53bd9f756cbbe24394ffd6fc1f4b2 @@ -48,7 +43,7 @@ https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2 # pip cython @ https://files.pythonhosted.org/packages/b3/9b/20a8a12d1454416141479380f7722f2ad298d2b41d0d7833fc409894715d/cython-3.1.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=80d0ce057672ca50728153757d022842d5dcec536b50c79615a22dda2a874ea0 # pip docutils @ https://files.pythonhosted.org/packages/8f/d7/9322c609343d929e75e7e5e6255e614fcc67572cfd083959cdef3b7aad79/docutils-0.21.2-py3-none-any.whl#sha256=dafca5b9e384f0e419294eb4d2ff9fa826435bf15f15b7bd45723e8ad76811b2 # pip execnet @ https://files.pythonhosted.org/packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl#sha256=26dee51f1b80cebd6d0ca8e74dd8745419761d3bef34163928cbebbdc4749fdc -# pip fonttools @ https://files.pythonhosted.org/packages/ab/47/f92b135864fa777e11ad68420bf89446c91a572fe2782745586f8e6aac0c/fonttools-4.58.5-cp313-cp313-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl#sha256=a6d7709fcf4577b0f294ee6327088884ca95046e1eccde87c53bbba4d5008541 +# pip fonttools @ https://files.pythonhosted.org/packages/75/b4/b96bb66f6f8cc4669de44a158099b249c8159231d254ab6b092909388be5/fonttools-4.59.0-cp313-cp313-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl#sha256=efd7e6660674e234e29937bc1481dceb7e0336bfae75b856b4fb272b5093c5d4 # pip idna @ https://files.pythonhosted.org/packages/76/c6/c88e154df9c4e1a2a66ccf0005a88dfb2650c1dffb6f5ce603dfbd452ce3/idna-3.10-py3-none-any.whl#sha256=946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3 # pip imagesize @ https://files.pythonhosted.org/packages/ff/62/85c4c919272577931d407be5ba5d71c20f0b616d31a0befe0ae45bb79abd/imagesize-1.4.1-py2.py3-none-any.whl#sha256=0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b # pip iniconfig @ https://files.pythonhosted.org/packages/2c/e1/e6716421ea10d38022b952c159d5161ca1193197fb744506875fbb87ea7b/iniconfig-2.1.0-py3-none-any.whl#sha256=9deba5723312380e77435581c6bf4935c94cbfab9b1ed33ef8d238ea168eb760 diff --git a/build_tools/azure/pylatest_pip_scipy_dev_environment.yml b/build_tools/azure/pylatest_pip_scipy_dev_environment.yml index 4cfae9d333631..a4bf229b5f0fa 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_environment.yml +++ b/build_tools/azure/pylatest_pip_scipy_dev_environment.yml @@ -2,7 +2,7 @@ # following script to centralize the configuration for CI builds: # build_tools/update_environments_and_lock_files.py channels: - - defaults + - conda-forge dependencies: - python - ccache diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock index 6d75cdaddf813..8667dd977f242 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock @@ -1,44 +1,39 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 1610c503ca7a3d6d0938907d0ff877bdd8a888e7be4c73fbe31e38633420a783 +# input_hash: 66c01323547a35e8550a7303dac1f0cb19e0af6173e62d689006d7ca8f1cd385 @EXPLICIT -https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9 -https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2025.2.25-h06a4308_0.conda#495015d24da8ad929e3ae2d18571016d -https://repo.anaconda.com/pkgs/main/linux-64/ld_impl_linux-64-2.40-h12ee557_0.conda#ee672b5f635340734f58d618b7bca024 -https://repo.anaconda.com/pkgs/main/linux-64/python_abi-3.13-0_cp313.conda#d4009c49dd2b54ffded7f1365b5f6505 -https://repo.anaconda.com/pkgs/main/noarch/tzdata-2025b-h04d1e81_0.conda#1d027393db3427ab22a02aa44a56f143 -https://repo.anaconda.com/pkgs/main/linux-64/libgomp-11.2.0-h1234567_1.conda#b372c0eea9b60732fdae4b817a63c8cd -https://repo.anaconda.com/pkgs/main/linux-64/libstdcxx-ng-11.2.0-h1234567_1.conda#57623d10a70e09e1d048c2b2b6f4e2dd -https://repo.anaconda.com/pkgs/main/linux-64/_openmp_mutex-5.1-1_gnu.conda#71d281e9c2192cb3fa425655a8defb85 -https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-11.2.0-h1234567_1.conda#a87728dabf3151fb9cfa990bd2eb0464 -https://repo.anaconda.com/pkgs/main/linux-64/bzip2-1.0.8-h5eee18b_6.conda#f21a3ff51c1b271977f53ce956a69297 -https://repo.anaconda.com/pkgs/main/linux-64/expat-2.7.1-h6a678d5_0.conda#269942a9f3f943e2e5d8a2516a861f7c -https://repo.anaconda.com/pkgs/main/linux-64/fmt-9.1.0-hdb19cb5_1.conda#4f12930203ff2d84df5d287af9b29858 -https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_1.conda#70646cc713f0c43926cfdcfe9b695fe0 -https://repo.anaconda.com/pkgs/main/linux-64/libhiredis-1.3.0-h6a678d5_0.conda#68b0289d6a3024e06b032f56dd7e46cf -https://repo.anaconda.com/pkgs/main/linux-64/libmpdec-4.0.0-h5eee18b_0.conda#feb10f42b1a7b523acbf85461be41a3e -https://repo.anaconda.com/pkgs/main/linux-64/libuuid-1.41.5-h5eee18b_0.conda#4a6a2354414c9080327274aa514e5299 -https://repo.anaconda.com/pkgs/main/linux-64/lz4-c-1.9.4-h6a678d5_1.conda#2ee58861f2b92b868ce761abb831819d -https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda#5558eec6e2191741a92f832ea826251c -https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.16-h5eee18b_0.conda#5875526739afa058cfa84da1fa7a2ef4 -https://repo.anaconda.com/pkgs/main/linux-64/pthread-stubs-0.3-h0ce48e5_1.conda#973a642312d2a28927aaf5b477c67250 -https://repo.anaconda.com/pkgs/main/linux-64/xorg-libxau-1.0.12-h9b100fa_0.conda#a8005a9f6eb903e113cd5363e8a11459 -https://repo.anaconda.com/pkgs/main/linux-64/xorg-libxdmcp-1.1.5-h9b100fa_0.conda#c284a09ddfba81d9c4e740110f09ea06 -https://repo.anaconda.com/pkgs/main/linux-64/xorg-xorgproto-2024.1-h5eee18b_1.conda#412a0d97a7a51d23326e57226189da92 -https://repo.anaconda.com/pkgs/main/linux-64/xxhash-0.8.0-h7f8727e_3.conda#196b013514e82fd8476558de622c0d46 -https://repo.anaconda.com/pkgs/main/linux-64/xz-5.6.4-h5eee18b_1.conda#3581505fa450962d631bd82b8616350e -https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_1.conda#92e42d8310108b0a440fb2e60b2b2a25 -https://repo.anaconda.com/pkgs/main/linux-64/libxcb-1.17.0-h9b100fa_0.conda#fdf0d380fa3809a301e2dbc0d5183883 -https://repo.anaconda.com/pkgs/main/linux-64/readline-8.2-h5eee18b_0.conda#be42180685cce6e6b0329201d9f48efb -https://repo.anaconda.com/pkgs/main/linux-64/zstd-1.5.6-hc292b87_0.conda#78ae7abd3020b41f827b35085845e1b8 -https://repo.anaconda.com/pkgs/main/linux-64/ccache-4.11.3-hc6a6a4f_0.conda#3e660215a7953958c1eb910dde81eb52 -https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.45.3-h5eee18b_0.conda#acf93d6aceb74d6110e20b44cc45939e -https://repo.anaconda.com/pkgs/main/linux-64/xorg-libx11-1.8.12-h9b100fa_1.conda#6298b27afae6f49f03765b2a03df2fcb -https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.14-h993c535_1.conda#bfe656b29fc64afe5d4bd46dbd5fd240 -https://repo.anaconda.com/pkgs/main/linux-64/python-3.13.5-h4612cfd_100_cp313.conda#1adf42b71c42a4a540eae2c0026f02c3 -https://repo.anaconda.com/pkgs/main/linux-64/setuptools-78.1.1-py313h06a4308_0.conda#8f8e1c1e3af9d2d371aaa0ee8316ae7c -https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.45.1-py313h06a4308_0.conda#29057e876eedce0e37c2388c138a19f9 -https://repo.anaconda.com/pkgs/main/noarch/pip-25.1-pyhc872135_2.conda#2778327d2a700153fefe0e69438b18e1 +https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 +https://conda.anaconda.org/conda-forge/noarch/python_abi-3.13-7_cp313.conda#e84b44e6300f1703cb25d29120c5b1d8 +https://conda.anaconda.org/conda-forge/noarch/tzdata-2025b-h78e105d_0.conda#4222072737ccff51314b5ece9c7d6f5a +https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.7.14-hbd8a1cb_0.conda#d16c90324aef024877d8713c0b7fea5b +https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.44-h1423503_1.conda#0be7c6e070c19105f966d3758448d018 +https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.1.0-h767d61c_3.conda#3cd1a7238a0dd3d0860fdefc496cc854 +https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2#73aaf86a425cc6e73fcf236a5a46396d +https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_3.conda#9e60c55e725c20d23125a5f0dd69af5d +https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.0-h5888daf_0.conda#db0bfbe7dd197b68ad5f30333bae6ce0 +https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.6-h2dba641_1.conda#ede4673863426c0883c0063d853bbd85 +https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.1.0-h69a702a_3.conda#e66f2b8ad787e7beb0f846e4bd7e8493 +https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.1.0-hcea5267_3.conda#530566b68c3b8ce7eec4cd047eae19fe +https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_2.conda#1a580f7796c7bf6393fddb8bbbde58dc +https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-hb9d3cd8_0.conda#c7e925f37e3b40d893459e625f6a53f1 +https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_3.conda#6d11a5edae89fe413c0569f16d308f5a +https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda#edb0dca6bc32e4f4789199455a1dbeb8 +https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda#47e340acb35de30501a76c7c799c41d7 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.1-h7b32b05_0.conda#c87df2ab1448ba69169652ab9547082d +https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62ee74e96c5ebb0af99386de58cf9553 +https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.1.0-h69a702a_3.conda#bfbca721fd33188ef923dfe9ba172f29 +https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.1.0-h4852527_3.conda#57541755b5a51691955012b8e197c06c +https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda#40b61aab5c7ba9ff276c41cfffe6b80b +https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8c095d6_2.conda#283b96675859b20a825f8fa30f311446 +https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_hd72426e_102.conda#a0116df4f4ed05c303811a837d5b39d8 +https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb8e6e7a_2.conda#6432cb5d4ac0046c3ac0a8a0f95842f9 +https://conda.anaconda.org/conda-forge/linux-64/icu-75.1-he02047a_0.conda#8b189310083baabfb622af68fd9d3ae3 +https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-15.1.0-h69a702a_3.conda#6e5d0574e57a38c36e674e9a18eee2b4 +https://conda.anaconda.org/conda-forge/linux-64/libhiredis-1.0.2-h2cc385e_0.tar.bz2#b34907d3a81a3cd8095ee83d174c074a +https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.50.2-hee844dc_2.conda#be96b9fdd7b579159df77ece9bb80e48 +https://conda.anaconda.org/conda-forge/linux-64/ccache-4.11.3-h80c52d3_0.conda#eb517c6a2b960c3ccb6f1db1005f063a +https://conda.anaconda.org/conda-forge/linux-64/python-3.13.5-hec9711d_102_cp313.conda#89e07d92cf50743886f41638d58c4328 +https://conda.anaconda.org/conda-forge/noarch/pip-25.1.1-pyh145f28c_0.conda#01384ff1639c6330a0924791413b8714 # pip alabaster @ https://files.pythonhosted.org/packages/7e/b3/6b4067be973ae96ba0d615946e314c5ae35f9f993eca561b356540bb0c2b/alabaster-1.0.0-py3-none-any.whl#sha256=fc6786402dc3fcb2de3cabd5fe455a2db534b371124f1f21de8731783dec828b # pip babel @ https://files.pythonhosted.org/packages/b7/b8/3fe70c75fe32afc4bb507f75563d39bc5642255d1d94f1f23604725780bf/babel-2.17.0-py3-none-any.whl#sha256=4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2 # pip certifi @ https://files.pythonhosted.org/packages/4f/52/34c6cf5bb9285074dc3531c437b3919e825d976fde097a7a73f79e726d03/certifi-2025.7.14-py3-none-any.whl#sha256=6b31f564a415d79ee77df69d757bb49a5bb53bd9f756cbbe24394ffd6fc1f4b2 diff --git a/build_tools/azure/windows.yml b/build_tools/azure/windows.yml index 9f4416823dd50..b49273d40a16d 100644 --- a/build_tools/azure/windows.yml +++ b/build_tools/azure/windows.yml @@ -43,8 +43,8 @@ jobs: Write-Host "L2 Cache Size: $($cpu.L2CacheSize) KB" Write-Host "L3 Cache Size: $($cpu.L3CacheSize) KB" Write-Host "===========================" - - bash: echo "##vso[task.prependpath]$CONDA/Scripts" - displayName: Add conda to PATH + - bash: build_tools/azure/install_setup_conda.sh + displayName: Install conda if necessary and set it up condition: startsWith(variables['DISTRIB'], 'conda') - task: UsePythonVersion@0 inputs: diff --git a/build_tools/update_environments_and_lock_files.py b/build_tools/update_environments_and_lock_files.py index b619ab22f0a7e..b99e0e8f8d416 100644 --- a/build_tools/update_environments_and_lock_files.py +++ b/build_tools/update_environments_and_lock_files.py @@ -151,12 +151,12 @@ def remove_from(alist, to_remove): }, }, { - "name": "pylatest_conda_mkl_no_openmp", + "name": "pylatest_conda_forge_mkl_no_openmp", "type": "conda", "tag": "main-ci", "folder": "build_tools/azure", "platform": "osx-64", - "channels": ["defaults"], + "channels": ["conda-forge"], "conda_dependencies": common_dependencies + ["ccache"], "package_constraints": { "blas": "[build=mkl]", @@ -209,7 +209,7 @@ def remove_from(alist, to_remove): "tag": "main-ci", "folder": "build_tools/azure", "platform": "linux-64", - "channels": ["defaults"], + "channels": ["conda-forge"], "conda_dependencies": ["python", "ccache"], "pip_dependencies": ( remove_from(common_dependencies, ["python", "blas", "pip"]) @@ -228,7 +228,7 @@ def remove_from(alist, to_remove): "tag": "scipy-dev", "folder": "build_tools/azure", "platform": "linux-64", - "channels": ["defaults"], + "channels": ["conda-forge"], "conda_dependencies": ["python", "ccache"], "pip_dependencies": ( remove_from( From 33893bd00a620243b3af90aa886f1a4b611b1312 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9r=C3=A9mie=20du=20Boisberranger?= Date: Wed, 16 Jul 2025 21:57:27 +0200 Subject: [PATCH 101/103] set version --- sklearn/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sklearn/__init__.py b/sklearn/__init__.py index 1ee488e44d713..c14c38b872d47 100644 --- a/sklearn/__init__.py +++ b/sklearn/__init__.py @@ -42,7 +42,7 @@ # Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer. # 'X.Y.dev0' is the canonical version of 'X.Y.dev' # -__version__ = "1.7.0" +__version__ = "1.7.1" # On OSX, we can get a runtime error due to multiple OpenMP libraries loaded From 35afe8df998681ac072ad2f960f9382e90988179 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9r=C3=A9mie=20du=20Boisberranger?= Date: Wed, 16 Jul 2025 22:03:14 +0200 Subject: [PATCH 102/103] generate changelog 1.7.1 --- .../sklearn.base/31528.fix.rst | 3 - .../sklearn.compose/31079.fix.rst | 3 - .../sklearn.datasets/31685.fix.rst | 5 - .../sklearn.inspection/31553.fix.rst | 7 -- .../sklearn.naive_bayes/31556.fix.rst | 3 - .../sklearn.utils/31584.fix.rst | 4 - doc/whats_new/v1.7.rst | 119 +++++++++++++----- 7 files changed, 90 insertions(+), 54 deletions(-) delete mode 100644 doc/whats_new/upcoming_changes/sklearn.base/31528.fix.rst delete mode 100644 doc/whats_new/upcoming_changes/sklearn.compose/31079.fix.rst delete mode 100644 doc/whats_new/upcoming_changes/sklearn.datasets/31685.fix.rst delete mode 100644 doc/whats_new/upcoming_changes/sklearn.inspection/31553.fix.rst delete mode 100644 doc/whats_new/upcoming_changes/sklearn.naive_bayes/31556.fix.rst delete mode 100644 doc/whats_new/upcoming_changes/sklearn.utils/31584.fix.rst diff --git a/doc/whats_new/upcoming_changes/sklearn.base/31528.fix.rst b/doc/whats_new/upcoming_changes/sklearn.base/31528.fix.rst deleted file mode 100644 index 312c8318eadcd..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.base/31528.fix.rst +++ /dev/null @@ -1,3 +0,0 @@ -- Fix regression in HTML representation when detecting the non-default parameters - that where of array-like types. - By :user:`Dea María Léon ` diff --git a/doc/whats_new/upcoming_changes/sklearn.compose/31079.fix.rst b/doc/whats_new/upcoming_changes/sklearn.compose/31079.fix.rst deleted file mode 100644 index b7ecaf67292b9..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.compose/31079.fix.rst +++ /dev/null @@ -1,3 +0,0 @@ -- :class:`compose.ColumnTransformer` now correctly preserves non-default index - when mixing pandas Series and Dataframes. - By :user:`Nicolas Bolle `. diff --git a/doc/whats_new/upcoming_changes/sklearn.datasets/31685.fix.rst b/doc/whats_new/upcoming_changes/sklearn.datasets/31685.fix.rst deleted file mode 100644 index 5d954e538d707..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.datasets/31685.fix.rst +++ /dev/null @@ -1,5 +0,0 @@ -- Fixed a regression preventing to extract the downloaded dataset in - :func:`datasets.fetch_20newsgroups`, :func:`datasets.fetch_20newsgroups_vectorized`, - :func:`datasets.fetch_lfw_people` and :func:`datasets.fetch_lfw_pairs`. This - only affects Python versions `>=3.10.0,<=3.10.11` and `>=3.11.0,<=3.11.3`. - By :user:`Jérémie du Boisberranger `. diff --git a/doc/whats_new/upcoming_changes/sklearn.inspection/31553.fix.rst b/doc/whats_new/upcoming_changes/sklearn.inspection/31553.fix.rst deleted file mode 100644 index bd9bb339bb68c..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.inspection/31553.fix.rst +++ /dev/null @@ -1,7 +0,0 @@ -- Fix multiple issues in the multiclass setting of :class:`inspection.DecisionBoundaryDisplay`: - - - `contour` plotting now correctly shows the decision boundary. - - `cmap` and `colors` are now properly ignored in favor of `multiclass_colors`. - - Linear segmented colormaps are now fully supported. - - By :user:`Yunjie Lin ` diff --git a/doc/whats_new/upcoming_changes/sklearn.naive_bayes/31556.fix.rst b/doc/whats_new/upcoming_changes/sklearn.naive_bayes/31556.fix.rst deleted file mode 100644 index 0f5b969bd9e6f..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.naive_bayes/31556.fix.rst +++ /dev/null @@ -1,3 +0,0 @@ -- :class:`naive_bayes.CategoricalNB` now correctly declares that it accepts - categorical features in the tags returned by its `__sklearn_tags__` method. - By :user:`Olivier Grisel ` diff --git a/doc/whats_new/upcoming_changes/sklearn.utils/31584.fix.rst b/doc/whats_new/upcoming_changes/sklearn.utils/31584.fix.rst deleted file mode 100644 index 5417dd80df975..0000000000000 --- a/doc/whats_new/upcoming_changes/sklearn.utils/31584.fix.rst +++ /dev/null @@ -1,4 +0,0 @@ -- Fixed a spurious warning (about the number of unique classes being - greater than 50% of the number of samples) that could occur when - passing `classes` :func:`utils.multiclass.type_of_target`. - By :user:`Sascha D. Krauss `. diff --git a/doc/whats_new/v1.7.rst b/doc/whats_new/v1.7.rst index ab022414982ff..462bd5d64a8f6 100644 --- a/doc/whats_new/v1.7.rst +++ b/doc/whats_new/v1.7.rst @@ -15,6 +15,62 @@ For a short description of the main highlights of the release, please refer to .. towncrier release notes start +.. _changes_1_7_1: + +Version 1.7.1 +============= + +**July 2025** + +:mod:`sklearn.base` +------------------- + +- |Fix| Fix regression in HTML representation when detecting the non-default parameters + that where of array-like types. + By :user:`Dea María Léon ` :pr:`31528` + +:mod:`sklearn.compose` +---------------------- + +- |Fix| :class:`compose.ColumnTransformer` now correctly preserves non-default index + when mixing pandas Series and Dataframes. + By :user:`Nicolas Bolle `. :pr:`31079` + +:mod:`sklearn.datasets` +----------------------- + +- |Fix| Fixed a regression preventing to extract the downloaded dataset in + :func:`datasets.fetch_20newsgroups`, :func:`datasets.fetch_20newsgroups_vectorized`, + :func:`datasets.fetch_lfw_people` and :func:`datasets.fetch_lfw_pairs`. This + only affects Python versions `>=3.10.0,<=3.10.11` and `>=3.11.0,<=3.11.3`. + By :user:`Jérémie du Boisberranger `. :pr:`31685` + +:mod:`sklearn.inspection` +------------------------- + +- |Fix| Fix multiple issues in the multiclass setting of :class:`inspection.DecisionBoundaryDisplay`: + + - `contour` plotting now correctly shows the decision boundary. + - `cmap` and `colors` are now properly ignored in favor of `multiclass_colors`. + - Linear segmented colormaps are now fully supported. + + By :user:`Yunjie Lin ` :pr:`31553` + +:mod:`sklearn.naive_bayes` +-------------------------- + +- |Fix| :class:`naive_bayes.CategoricalNB` now correctly declares that it accepts + categorical features in the tags returned by its `__sklearn_tags__` method. + By :user:`Olivier Grisel ` :pr:`31556` + +:mod:`sklearn.utils` +-------------------- + +- |Fix| Fixed a spurious warning (about the number of unique classes being + greater than 50% of the number of samples) that could occur when + passing `classes` :func:`utils.multiclass.type_of_target`. + By :user:`Sascha D. Krauss `. :pr:`31584` + .. _changes_1_7_0: Version 1.7.0 @@ -483,32 +539,37 @@ more details. Thanks to everyone who has contributed to the maintenance and improvement of the project since version 1.6, including: -4hm3d, Aaron Schumacher, Abhijeetsingh Meena, Acciaro Gennaro Daniele, -Achraf Tasfaout, Adrien Linares, Adrin Jalali, Agriya Khetarpal, Aiden Frank, -Aitsaid Azzedine Idir, ajay-sentry, Akanksha Mhadolkar, Alfredo Saucedo, -Anderson Chaves, Andres Guzman-Ballen, Aniruddha Saha, antoinebaker, Antony -Lee, Arjun S, ArthurDbrn, Arturo, Arturo Amor, ash, Ashton Powell, -ayoub.agouzoul, Bagus Tris Atmaja, Benjamin Danek, Boney Patel, Camille -Troillard, Chems Ben, Christian Lorentzen, Christian Veenhuis, Christine P. -Chai, claudio, Code_Blooded, Colas, Colin Coe, Connor Lane, Corey Farwell, -Daniel Agyapong, Dan Schult, Dea María Léon, Deepak Saldanha, -dependabot[bot], Dimitri Papadopoulos Orfanos, Dmitry Kobak, Domenico, Elham -Babaei, emelia-hdz, EmilyXinyi, Emma Carballal, Eric Larson, fabianhenning, -Gael Varoquaux, Gil Ramot, Gordon Grey, Goutam, G Sreeja, Guillaume Lemaitre, -Haesun Park, Hanjun Kim, Helder Geovane Gomes de Lima, Henri Bonamy, Hleb -Levitski, Hugo Boulenger, IlyaSolomatin, Irene, Jérémie du Boisberranger, -Jérôme Dockès, JoaoRodriguesIST, Joel Nothman, Josh, Kevin Klein, Loic -Esteve, Lucas Colley, Luc Rocher, Lucy Liu, Luis M. B. Varona, lunovian, Mamduh -Zabidi, Marc Bresson, Marco Edward Gorelli, Marco Maggi, Maren Westermann, -Marie Sacksick, Martin Jurča, Miguel González Duque, Mihir Waknis, Mohamed -Ali SRIR, Mohamed DHIFALLAH, mohammed benyamna, Mohit Singh Thakur, Mounir -Lbath, myenugula, Natalia Mokeeva, Olivier Grisel, omahs, Omar Salman, Pedro -Lopes, Pedro Olivares, Preyas Shah, Radovenchyk, Rahil Parikh, Rémi Flamary, -Reshama Shaikh, Rishab Saini, rolandrmgservices, SanchitD, Santiago Castro, -Santiago Víquez, scikit-learn-bot, Scott Huberty, Shruti Nath, Siddharth -Bansal, Simarjot Sidhu, Sortofamudkip, sotagg, Sourabh Kumar, Stefan, Stefanie -Senger, Stefano Gaspari, Stephen Pardy, Success Moses, Sylvain Combettes, Tahar -Allouche, Thomas J. Fan, Thomas Li, ThorbenMaa, Tim Head, Umberto Fasci, UV, -Vasco Pereira, Vassilis Margonis, Velislav Babatchev, Victoria Shevchenko, -viktor765, Vipsa Kamani, Virgil Chan, vpz, Xiao Yuan, Yaich Mohamed, Yair -Shimony, Yao Xiao, Yaroslav Halchenko, Yulia Vilensky, Yuvi Panda +4hm3d, Aaron Schumacher, Abhijeetsingh Meena, Acciaro Gennaro Daniele, +Achraf Tasfaout, Adriano Leão, Adrien Linares, Adrin Jalali, Agriya Khetarpal, +Aiden Frank, Aitsaid Azzedine Idir, ajay-sentry, Akanksha Mhadolkar, Alfredo +Saucedo, Anderson Chaves, Andres Guzman-Ballen, Aniruddha Saha, antoinebaker, +Antony Lee, Arjun S, ArthurDbrn, Arturo, Arturo Amor, ash, Ashton Powell, +ayoub.agouzoul, Ayrat, Bagus Tris Atmaja, Benjamin Danek, Boney Patel, Camille +Troillard, Chems Ben, Christian Lorentzen, Christian Veenhuis, Christine P. +Chai, claudio, Code_Blooded, Colas, Colin Coe, Connor Lane, Corey Farwell, +Daniel Agyapong, Dan Schult, Dea María Léon, Deepak Saldanha, +dependabot[bot], Dhyey Findoriya, Dimitri Papadopoulos Orfanos, Dmitry Kobak, +Domenico, Elham Babaei, emelia-hdz, EmilyXinyi, Emma Carballal, Eric Larson, +Eugen-Bleck, Evgeni Burovski, fabianhenning, Gael Varoquaux, GaetandeCast, Gil +Ramot, Gordon Grey, Goutam, G Sreeja, Guillaume Lemaitre, Haesun Park, Hanjun +Kim, Helder Geovane Gomes de Lima, Henri Bonamy, Hleb Levitski, Hugo Boulenger, +IlyaSolomatin, Irene, Jérémie du Boisberranger, Jérôme Dockès, +JoaoRodriguesIST, Joel Nothman, Josh, jshn9515, KALLA GANASEKHAR, Kevin Klein, +Loic Esteve, Lucas Colley, Luc Rocher, Lucy Liu, Luis M. B. Varona, lunovian, +Mamduh Zabidi, Marc Bresson, Marco Edward Gorelli, Marco Maggi, Maren +Westermann, Marie Sacksick, Marija Vlajic, Martin Jurča, Mayank Raj, Michael +Burkhart, Miguel González Duque, Mihir Waknis, Miro Hrončok, Mohamed Ali +SRIR, Mohamed DHIFALLAH, mohammed benyamna, Mohit Singh Thakur, Mounir Lbath, +myenugula, Natalia Mokeeva, Nicolas Bolle, Olivier Grisel, omahs, Omar Salman, +Pedro Lopes, Pedro Olivares, Peter Holzer, Preyas Shah, Radovenchyk, Rahil +Parikh, Rémi Flamary, Reshama Shaikh, Richard Harris, Rishab Saini, +rolandrmgservices, SanchitD, Santiago Castro, Santiago Víquez, saskra, +scikit-learn-bot, Scott Huberty, Shaurya Bisht, Shivam, Shruti Nath, Siddharth +Bansal, SIKAI ZHANG, Simarjot Sidhu, sisird864, SiyuJin-1, Somdutta Banerjee, +Sortofamudkip, sotagg, Sourabh Kumar, Stefan, Stefanie Senger, Stefano Gaspari, +Steffen Rehberg, Stephen Pardy, Success Moses, Sylvain Combettes, Tahar +Allouche, Thomas J. Fan, Thomas Li, ThorbenMaa, Tim Head, Tingwei Zhu, TJ +Norred, Umberto Fasci, UV, Vasco Pereira, Vassilis Margonis, Velislav +Babatchev, Victoria Shevchenko, viktor765, Vipsa Kamani, VirenPassi, Virgil +Chan, vpz, Xiao Yuan, Yaich Mohamed, Yair Shimony, Yao Xiao, Yaroslav +Halchenko, Yulia Vilensky, Yuvi Panda From 672a2dd6842860747704d7b3ba3dd0323bf85470 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9r=C3=A9mie=20du=20Boisberranger?= Date: Wed, 16 Jul 2025 22:03:34 +0200 Subject: [PATCH 103/103] [cd build] Trigger wheel builder workflow