githubmirror/scikit-learn

sklearn/utils/fixes.py
394 строки14 KB
omahs
DOC Fix various typos in documentation and comments (#31404)
05 июн 2025, 03:32
05 июн 2025, 03:326c6ef28
"""Compatibility fixes for older version of python, numpy and scipy

If you add content to this file, please give the version of the package
at which the fix is no longer needed.
"""

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

import platform
import struct

import numpy as np
import scipy
import scipy.sparse.linalg
import scipy.stats
from scipy import optimize

try:
    import pandas as pd
except ImportError:
    pd = None

from ..externals._packaging.version import parse as parse_version
from .parallel import _get_threadpool_controller

_IS_32BIT = 8 * struct.calcsize("P") == 32
_IS_WASM = platform.machine() in ["wasm32", "wasm64"]

np_version = parse_version(np.__version__)
np_base_version = parse_version(np_version.base_version)
sp_version = parse_version(scipy.__version__)
sp_base_version = parse_version(sp_version.base_version)

# TODO: We can consider removing the containers and importing
# directly from SciPy when sparse matrices will be deprecated.
CSR_CONTAINERS = [scipy.sparse.csr_matrix, scipy.sparse.csr_array]
CSC_CONTAINERS = [scipy.sparse.csc_matrix, scipy.sparse.csc_array]
COO_CONTAINERS = [scipy.sparse.coo_matrix, scipy.sparse.coo_array]
LIL_CONTAINERS = [scipy.sparse.lil_matrix, scipy.sparse.lil_array]
DOK_CONTAINERS = [scipy.sparse.dok_matrix, scipy.sparse.dok_array]
BSR_CONTAINERS = [scipy.sparse.bsr_matrix, scipy.sparse.bsr_array]
DIA_CONTAINERS = [scipy.sparse.dia_matrix, scipy.sparse.dia_array]

# Remove when minimum scipy version is 1.11.0
try:
    from scipy.sparse import sparray  # noqa: F401

    SPARRAY_PRESENT = True
except ImportError:
    SPARRAY_PRESENT = False


def _object_dtype_isnan(X):
    return X != X


# TODO: Remove when SciPy 1.11 is the minimum supported version
def _mode(a, axis=0):
    if sp_version >= parse_version("1.9.0"):
        mode = scipy.stats.mode(a, axis=axis, keepdims=True)
        if sp_version >= parse_version("1.10.999"):
            # scipy.stats.mode has changed returned array shape with axis=None
            # and keepdims=True, see https://github.com/scipy/scipy/pull/17561
            if axis is None:
                mode = np.ravel(mode)
        return mode
    return scipy.stats.mode(a, axis=axis)


# TODO: Remove when Scipy 1.12 is the minimum supported version
if sp_base_version >= parse_version("1.12.0"):
    _sparse_linalg_cg = scipy.sparse.linalg.cg
else:

    def _sparse_linalg_cg(A, b, **kwargs):
        if "rtol" in kwargs:
            kwargs["tol"] = kwargs.pop("rtol")
        if "atol" not in kwargs:
            kwargs["atol"] = "legacy"
        return scipy.sparse.linalg.cg(A, b, **kwargs)


# TODO : remove this when required minimum version of scipy >= 1.9.0
def _yeojohnson_lambda(_neg_log_likelihood, x):
    """Estimate the optimal Yeo-Johnson transformation parameter (lambda).

    This function provides a compatibility workaround for versions of SciPy
    older than 1.9.0, where `scipy.stats.yeojohnson` did not return
    the estimated lambda directly.

    Parameters
    ----------
    _neg_log_likelihood : callable
        A function that computes the negative log-likelihood of the Yeo-Johnson
        transformation for a given lambda. Used only for SciPy versions < 1.9.0.

    x : array-like
        Input data to estimate the Yeo-Johnson transformation parameter.

    Returns
    -------
    lmbda : float
        The estimated lambda parameter for the Yeo-Johnson transformation.
    """
    min_scipy_version = "1.9.0"

    if sp_version < parse_version(min_scipy_version):
        # choosing bracket -2, 2 like for boxcox
        return optimize.brent(_neg_log_likelihood, brack=(-2, 2))

    _, lmbda = scipy.stats.yeojohnson(x, lmbda=None)
    return lmbda


# TODO: Fuse the modern implementations of _sparse_min_max and _sparse_nan_min_max
# into the public min_max_axis function when Scipy 1.11 is the minimum supported
# version and delete the backport in the else branch below.
if sp_base_version >= parse_version("1.11.0"):

    def _sparse_min_max(X, axis):
        the_min = X.min(axis=axis)
        the_max = X.max(axis=axis)

        if axis is not None:
            the_min = the_min.toarray().ravel()
            the_max = the_max.toarray().ravel()

        return the_min, the_max

    def _sparse_nan_min_max(X, axis):
        the_min = X.nanmin(axis=axis)
        the_max = X.nanmax(axis=axis)

        if axis is not None:
            the_min = the_min.toarray().ravel()
            the_max = the_max.toarray().ravel()

        return the_min, the_max

else:
    # This code is mostly taken from scipy 0.14 and extended to handle nans, see
    # https://github.com/scikit-learn/scikit-learn/pull/11196
    def _minor_reduce(X, ufunc):
        major_index = np.flatnonzero(np.diff(X.indptr))

        # reduceat tries casts X.indptr to intp, which errors
        # if it is int64 on a 32 bit system.
        # Reinitializing prevents this where possible, see #13737
        X = type(X)((X.data, X.indices, X.indptr), shape=X.shape)
        value = ufunc.reduceat(X.data, X.indptr[major_index])
        return major_index, value

    def _min_or_max_axis(X, axis, min_or_max):
        N = X.shape[axis]
        if N == 0:
            raise ValueError("zero-size array to reduction operation")
        M = X.shape[1 - axis]
        mat = X.tocsc() if axis == 0 else X.tocsr()
        mat.sum_duplicates()
        major_index, value = _minor_reduce(mat, min_or_max)
        not_full = np.diff(mat.indptr)[major_index] < N
        value[not_full] = min_or_max(value[not_full], 0)
        mask = value != 0
        major_index = np.compress(mask, major_index)
        value = np.compress(mask, value)

        if axis == 0:
            res = scipy.sparse.coo_matrix(
                (value, (np.zeros(len(value)), major_index)),
                dtype=X.dtype,
                shape=(1, M),
            )
        else:
            res = scipy.sparse.coo_matrix(
                (value, (major_index, np.zeros(len(value)))),
                dtype=X.dtype,
                shape=(M, 1),
            )
        return res.toarray().ravel()

    def _sparse_min_or_max(X, axis, min_or_max):
        if axis is None:
            if 0 in X.shape:
                raise ValueError("zero-size array to reduction operation")
            zero = X.dtype.type(0)
            if X.nnz == 0:
                return zero
            m = min_or_max.reduce(X.data.ravel())
            if X.nnz != np.prod(X.shape):
                m = min_or_max(zero, m)
            return m
        if axis < 0:
            axis += 2
        if (axis == 0) or (axis == 1):
            return _min_or_max_axis(X, axis, min_or_max)
        else:
            raise ValueError("invalid axis, use 0 for rows, or 1 for columns")

    def _sparse_min_max(X, axis):
        return (
            _sparse_min_or_max(X, axis, np.minimum),
            _sparse_min_or_max(X, axis, np.maximum),
        )

    def _sparse_nan_min_max(X, axis):
        return (
            _sparse_min_or_max(X, axis, np.fmin),
            _sparse_min_or_max(X, axis, np.fmax),
        )


# For +1.25 NumPy versions exceptions and warnings are being moved
# to a dedicated submodule.
if np_version >= parse_version("1.25.0"):
    from numpy.exceptions import ComplexWarning, VisibleDeprecationWarning
else:
    from numpy import (  # noqa: F401
        ComplexWarning,
        VisibleDeprecationWarning,
    )


# TODO: Adapt when Pandas > 2.2 is the minimum supported version
def pd_fillna(pd, frame):
    pd_version = parse_version(pd.__version__).base_version
    if parse_version(pd_version) < parse_version("2.2"):
        frame = frame.fillna(value=np.nan)
    else:
        infer_objects_kwargs = (
            {} if parse_version(pd_version) >= parse_version("3") else {"copy": False}
        )
        with pd.option_context("future.no_silent_downcasting", True):
            frame = frame.fillna(value=np.nan).infer_objects(**infer_objects_kwargs)
    return frame


# TODO: remove when SciPy 1.12 is the minimum supported version
def _preserve_dia_indices_dtype(
    sparse_container, original_container_format, requested_sparse_format
):
    """Preserve indices dtype for SciPy < 1.12 when converting from DIA to CSR/CSC.

    For SciPy < 1.12, DIA arrays indices are upcasted to `np.int64` that is
    inconsistent with DIA matrices. We downcast the indices dtype to `np.int32` to
    be consistent with DIA matrices.

    The converted indices arrays are affected back inplace to the sparse container.

    Parameters
    ----------
    sparse_container : sparse container
        Sparse container to be checked.
    requested_sparse_format : str or bool
        The type of format of `sparse_container`.

    Notes
    -----
    See https://github.com/scipy/scipy/issues/19245 for more details.
    """
    if original_container_format == "dia_array" and requested_sparse_format in (
        "csr",
        "coo",
    ):
        if requested_sparse_format == "csr":
            index_dtype = _smallest_admissible_index_dtype(
                arrays=(sparse_container.indptr, sparse_container.indices),
                maxval=max(sparse_container.nnz, sparse_container.shape[1]),
                check_contents=True,
            )
            sparse_container.indices = sparse_container.indices.astype(
                index_dtype, copy=False
            )
            sparse_container.indptr = sparse_container.indptr.astype(
                index_dtype, copy=False
            )
        else:  # requested_sparse_format == "coo"
            index_dtype = _smallest_admissible_index_dtype(
                maxval=max(sparse_container.shape)
            )
            sparse_container.row = sparse_container.row.astype(index_dtype, copy=False)
            sparse_container.col = sparse_container.col.astype(index_dtype, copy=False)


# TODO: remove when SciPy 1.12 is the minimum supported version
def _smallest_admissible_index_dtype(arrays=(), maxval=None, check_contents=False):
    """Based on input (integer) arrays `a`, determine a suitable index data
    type that can hold the data in the arrays.

    This function returns `np.int64` if it either required by `maxval` or based on the
    largest precision of the dtype of the arrays passed as argument, or by their
    contents (when `check_contents is True`). If none of the condition requires
    `np.int64` then this function returns `np.int32`.

    Parameters
    ----------
    arrays : ndarray or tuple of ndarrays, default=()
        Input arrays whose types/contents to check.

    maxval : float, default=None
        Maximum value needed.

    check_contents : bool, default=False
        Whether to check the values in the arrays and not just their types.
        By default, check only the types.

    Returns
    -------
    dtype : {np.int32, np.int64}
        Suitable index data type (int32 or int64).
    """

    int32min = np.int32(np.iinfo(np.int32).min)
    int32max = np.int32(np.iinfo(np.int32).max)

    if maxval is not None:
        if maxval > np.iinfo(np.int64).max:
            raise ValueError(
                f"maxval={maxval} is to large to be represented as np.int64."
            )
        if maxval > int32max:
            return np.int64

    if isinstance(arrays, np.ndarray):
        arrays = (arrays,)

    for arr in arrays:
        if not isinstance(arr, np.ndarray):
            raise TypeError(
                f"Arrays should be of type np.ndarray, got {type(arr)} instead."
            )
        if not np.issubdtype(arr.dtype, np.integer):
            raise ValueError(
                f"Array dtype {arr.dtype} is not supported for index dtype. We expect "
                "integral values."
            )
        if not np.can_cast(arr.dtype, np.int32):
            if not check_contents:
                # when `check_contents` is False, we stay on the safe side and return
                # np.int64.
                return np.int64
            if arr.size == 0:
                # a bigger type not needed yet, let's look at the next array
                continue
            else:
                maxval = arr.max()
                minval = arr.min()
                if minval < int32min or maxval > int32max:
                    # a big index type is actually needed
                    return np.int64

    return np.int32


# TODO: Remove when Scipy 1.12 is the minimum supported version
if sp_version < parse_version("1.12"):
    from ..externals._scipy.sparse.csgraph import laplacian
else:
    from scipy.sparse.csgraph import (
        laplacian,  # noqa: F401  # pragma: no cover
    )


def _in_unstable_openblas_configuration():
    """Return True if in an unstable configuration for OpenBLAS"""

    # Import libraries which might load OpenBLAS.
    import numpy  # noqa: F401
    import scipy  # noqa: F401

    modules_info = _get_threadpool_controller().info()

    open_blas_used = any(info["internal_api"] == "openblas" for info in modules_info)
    if not open_blas_used:
        return False

    # OpenBLAS 0.3.16 fixed instability for arm64, see:
    # https://github.com/xianyi/OpenBLAS/blob/1b6db3dbba672b4f8af935bd43a1ff6cff4d20b7/Changelog.txt#L56-L58
    openblas_arm64_stable_version = parse_version("0.3.16")
    for info in modules_info:
        if info["internal_api"] != "openblas":
            continue
        openblas_version = info.get("version")
        openblas_architecture = info.get("architecture")
        if openblas_version is None or openblas_architecture is None:
            # Cannot be sure that OpenBLAS is good enough. Assume unstable:
            return True  # pragma: no cover
        if (
            openblas_architecture == "neoversen1"
            and parse_version(openblas_version) < openblas_arm64_stable_version
        ):
            # See discussions in https://github.com/numpy/numpy/issues/19411
            return True  # pragma: no cover
    return False