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FIX Add support for scikit-learn 1.8 #360

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FIX Add support for scikit-learn 1.8 #360

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802074c
Handle sklearn force_all_finite rename via local wrappers
dherrera1911 Mar 10, 2026
6775954
Simplify wrappers
dherrera1911 Mar 10, 2026
c691bc0
Change EOL to CRLF
dherrera1911 Mar 14, 2026
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90 changes: 57 additions & 33 deletions metric_learn/_util.py
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Original file line number Diff line number Diff line change
@@ -1,25 +1,49 @@
import numpy as np
from numpy.linalg import LinAlgError
from sklearn.datasets import make_spd_matrix
from sklearn.decomposition import PCA
from sklearn.utils import check_array
from sklearn.utils.validation import check_X_y, check_random_state
from .exceptions import PreprocessorError, NonPSDError
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from scipy.linalg import pinvh, eigh
import sys
import time
import warnings

# hack around lack of axis kwarg in older numpy versions
try:
np.linalg.norm([[4]], axis=1)
except TypeError:
def vector_norm(X):
return np.apply_along_axis(np.linalg.norm, 1, X)
else:
def vector_norm(X):
return np.linalg.norm(X, axis=1)
import numpy as np
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@perimosocordiae perimosocordiae Mar 13, 2026

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I'm confused why this line is showing up as part of the diff. Maybe a line-ending related change?

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@dherrera1911 dherrera1911 Mar 14, 2026

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You are right. The file uses CRLF and I had accidentally changed it to LF. I converted the file to CRLF to remove this from the diff.

from numpy.linalg import LinAlgError
from inspect import signature
from sklearn.datasets import make_spd_matrix
from sklearn.decomposition import PCA
from sklearn.utils import check_array
from sklearn.utils.validation import check_X_y, check_random_state
from .exceptions import PreprocessorError, NonPSDError
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from scipy.linalg import pinvh, eigh
import sys
import time
import warnings

# hack around lack of axis kwarg in older numpy versions
try:
np.linalg.norm([[4]], axis=1)
except TypeError:
def vector_norm(X):
return np.apply_along_axis(np.linalg.norm, 1, X)
else:
def vector_norm(X):
return np.linalg.norm(X, axis=1)


_CHECK_ARRAY_SUPPORTS_FORCE_ALL_FINITE = (
'force_all_finite' in signature(check_array).parameters)
_CHECK_X_Y_SUPPORTS_FORCE_ALL_FINITE = (
'force_all_finite' in signature(check_X_y).parameters)


def _check_array(*args, **kwargs):
"""Local wrapper around `sklearn.utils.check_array` to deal with the change
from `force_all_finite` to `ensure_all_finite` in scikit-learn."""
if not _CHECK_ARRAY_SUPPORTS_FORCE_ALL_FINITE and "force_all_finite" in kwargs:
kwargs = kwargs.copy()
kwargs["ensure_all_finite"] = kwargs.pop("force_all_finite")
return check_array(*args, **kwargs)

def _check_X_y(*args, **kwargs):
"""Local wrapper around `sklearn.utils.check_X_y` to deal with the change
from `force_all_finite` to `ensure_all_finite` in scikit-learn."""
if not _CHECK_X_Y_SUPPORTS_FORCE_ALL_FINITE and "force_all_finite" in kwargs:
kwargs = kwargs.copy()
kwargs["ensure_all_finite"] = kwargs.pop("force_all_finite")
return check_X_y(*args, **kwargs)


def check_input(input_data, y=None, preprocessor=None,
Expand Down Expand Up @@ -115,14 +139,14 @@ def check_input(input_data, y=None, preprocessor=None,

# We need to convert input_data into a numpy.ndarray if possible, before
# any further checks or conversions, and deal with y if needed. Therefore
# we use check_array/check_X_y with fixed permissive arguments.
# we use the wrappers _check_array/_check_X_y with fixed permissive arguments.
if y is None:
input_data = check_array(input_data, ensure_2d=False, allow_nd=True,
input_data = _check_array(input_data, ensure_2d=False, allow_nd=True,
copy=False, force_all_finite=False,
accept_sparse=True, dtype=None,
ensure_min_features=0, ensure_min_samples=0)
else:
input_data, y = check_X_y(input_data, y, ensure_2d=False, allow_nd=True,
input_data, y = _check_X_y(input_data, y, ensure_2d=False, allow_nd=True,
copy=False, force_all_finite=False,
accept_sparse=True, dtype=None,
ensure_min_features=0, ensure_min_samples=0,
Expand Down Expand Up @@ -165,9 +189,9 @@ def check_input_tuples(input_data, context, preprocessor, args_for_sk_checks,
make_error_input(420, input_data, context)
else:
make_error_input(200, input_data, context)
input_data = check_array(input_data, allow_nd=True, ensure_2d=False,
input_data = _check_array(input_data, allow_nd=True, ensure_2d=False,
**args_for_sk_checks)
# we need to check num_features because check_array does not check it
# we need to check num_features because _check_array does not check it
# for 3D inputs:
if args_for_sk_checks['ensure_min_features'] > 0:
n_features = input_data.shape[2]
Expand All @@ -180,7 +204,7 @@ def check_input_tuples(input_data, context, preprocessor, args_for_sk_checks,
# normally we don't need to check_tuple_size too because tuple_size
# shouldn't be able to be modified by any preprocessor
if input_data.ndim != 3:
# we have to ensure this because check_array above does not
# we have to ensure this because _check_array above does not
if preprocessor_has_been_applied:
make_error_input(211, input_data, context)
else:
Expand All @@ -205,10 +229,10 @@ def check_input_classic(input_data, context, preprocessor, args_for_sk_checks):
else:
make_error_input(100, input_data, context)

input_data = check_array(input_data, allow_nd=True, ensure_2d=False,
input_data = _check_array(input_data, allow_nd=True, ensure_2d=False,
**args_for_sk_checks)
if input_data.ndim != 2:
# we have to ensure this because check_array above does not
# we have to ensure this because _check_array above does not
if preprocessor_has_been_applied:
make_error_input(111, input_data, context)
else:
Expand Down Expand Up @@ -317,7 +341,7 @@ def __init__(self, X):
# format with arguments in check_input, and only this latter function
# should return the appropriate errors). We do this only to have a numpy
# array object which can be indexed by another numpy array object.
X = check_array(X,
X = _check_array(X,
accept_sparse=True, dtype=None,
force_all_finite=False,
ensure_2d=False, allow_nd=True,
Expand Down Expand Up @@ -514,7 +538,7 @@ def _initialize_components(n_components, input, y=None, init='auto',
if isinstance(init, np.ndarray):
# we copy the array, so that if we update the metric, we don't want to
# update the init
init = check_array(init, copy=True)
init = _check_array(init, copy=True)

# Assert that init.shape[1] = X.shape[1]
if init.shape[1] != n_features:
Expand Down Expand Up @@ -656,7 +680,7 @@ def _initialize_metric_mahalanobis(input, init='identity', random_state=None,
if isinstance(init, np.ndarray):
# we copy the array, so that if we update the metric, we don't want to
# update the init
init = check_array(init, copy=True)
init = _check_array(init, copy=True)

# Assert that init.shape[1] = n_features
if init.shape != (n_features,) * 2:
Expand Down
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