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Remove deprecated sample_weight in naive bayes/sample_weights in hinge_loss #8221

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Merged
rapids-bot merged 2 commits into from
Jun 4, 2026
Merged

Remove deprecated sample_weight in naive bayes/sample_weights in hinge_loss #8221

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c715202
Remove deprecated `sample_weights` in `hinge_loss`
jcrist Jun 3, 2026
6b38f08
Remove deprecated `sample_weight` in discrete naive bayes
jcrist Jun 3, 2026
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24 changes: 0 additions & 24 deletions python/cuml/cuml/metrics/hinge_loss.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,8 +2,6 @@
# SPDX-FileCopyrightText: Copyright (c) 2021-2026, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
#
import warnings

import cupy as cp
import numpy as np

Expand All @@ -20,8 +18,6 @@ def hinge_loss(
pred_decision,
labels=None,
sample_weight=None,
*,
sample_weights="deprecated",
) -> float:
"""
Calculates non-regularized hinge loss. Adapted from scikit-learn hinge loss.
Expand All @@ -42,31 +38,11 @@ def hinge_loss(
sample_weight : array-like of shape (n_samples,), default=None
Sample weights to be used for computing the average.

sample_weights : array-like, default="deprecated"
Deprecated alias for ``sample_weight``.

.. deprecated:: 26.06
``sample_weights`` was renamed to ``sample_weight`` and will be
removed in 26.08.

Returns
-------
loss : float
The average hinge loss.
"""
# Handle the deprecated `sample_weights` alias.
if not (
isinstance(sample_weights, str) and sample_weights == "deprecated"
):
warnings.warn(
"`sample_weights` was renamed to `sample_weight` in 26.06 and "
"will be removed in 26.08.",
FutureWarning,
stacklevel=2,
)
if sample_weight is None:
sample_weight = sample_weights

pred_decision = check_array(
pred_decision,
ensure_2d=False,
Expand Down
49 changes: 4 additions & 45 deletions python/cuml/cuml/naive_bayes/naive_bayes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,8 +2,6 @@
# SPDX-FileCopyrightText: Copyright (c) 2020-2026, NVIDIA CORPORATION.
# SPDX-License-Identifier: Apache-2.0
#
import warnings

import cupy as cp
import cupyx
import numpy as np
Expand Down Expand Up @@ -624,7 +622,6 @@ def partial_fit(
X,
y,
classes=None,
sample_weight="deprecated",
) -> "_BaseDiscreteNB":
"""
Incremental fit on a batch of samples.
Expand All @@ -651,23 +648,12 @@ def partial_fit(
List of all the classes that can possibly appear in the y vector.
Must be provided at the first call to partial_fit, can be omitted
in subsequent calls.
sample_weight : array-like of shape (n_samples)

.. deprecated:: 26.06

The `sample_weight` argument was deprecated in version 26.06
and will be removed in version 26.08. `sample_weight` was
previously silently ignored; we're opting to remove the
parameter for now rather than having a parameter that's not
supported.

Returns
-------
self : object
"""
return self._partial_fit(
X, y, sample_weight=sample_weight, classes=classes
)
return self._partial_fit(X, y, classes=classes)
Comment thread
divyegala marked this conversation as resolved.

@nvtx.annotate(
message="naive_bayes._BaseDiscreteNB._partial_fit",
Expand All @@ -677,40 +663,22 @@ def _partial_fit(
self,
X,
y,
sample_weight="deprecated",
classes=None,
reset=False,
convert_dtype=True,
) -> "_BaseDiscreteNB":
if isinstance(sample_weight, str) and sample_weight == "deprecated":
sample_weight = None
else:
warnings.warn(
"`sample_weight` was deprecated in version 26.06 and will be removed "
"in version 26.08. Passing sample weights was previously silently "
"ignored; we're opting to remove the parameter for now rather "
"than continue having a parameter that's not supported.",
FutureWarning,
)

if self.alpha < 0:
raise ValueError(f"Expected alpha >= 0, got {self.alpha}")

classes, reset = self._check_classes(classes, reset)
X, y, classes, sample_weight = self._check_fit(
X, y, classes, _ = self._check_fit(
X,
y,
classes=classes,
sample_weight=sample_weight,
reset=reset,
convert_dtype=convert_dtype,
)

if sample_weight is not None:
raise NotImplementedError(
"sample_weight support is not implemented"
)

if reset:
self.classes_ = classes
self.n_classes_ = self.classes_.shape[0]
Expand All @@ -728,7 +696,7 @@ def _partial_fit(
return self

@run_in_internal_context
def fit(self, X, y, sample_weight="deprecated") -> "_BaseDiscreteNB":
def fit(self, X, y) -> "_BaseDiscreteNB":
"""
Fit Naive Bayes classifier according to X, y

Expand All @@ -739,17 +707,8 @@ def fit(self, X, y, sample_weight="deprecated") -> "_BaseDiscreteNB":
n_features is the number of features.
y : array-like shape (n_samples)
Target values.
sample_weight : array-like of shape (n_samples)

.. deprecated:: 26.06

The `sample_weight` argument was deprecated in version 26.06
and will be removed in version 26.08. `sample_weight` was
previously silently ignored; we're opting to remove the
parameter for now rather than having a parameter that's not
supported.
"""
return self._partial_fit(X, y, reset=True, sample_weight=sample_weight)
return self._partial_fit(X, y, reset=True)

def _init_counters(self, n_effective_classes, n_features, dtype):
self.class_count_ = cp.zeros(
Expand Down
10 changes: 0 additions & 10 deletions python/cuml/tests/test_metrics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1959,16 +1959,6 @@ def test_hinge_loss_multiclass_missing_labels():
cuml_hinge(np.array([0, 1, 2]), rng.randn(3, 2))


def test_hinge_loss_sample_weights_deprecated():
y_true = np.array([-1, 1, 1, -1])
pred_decision = np.array([-2.18, 2.36, 0.09, -1.0])
sw = np.array([1.0, 2.0, 1.0, 1.0])
expected = cuml_hinge(y_true, pred_decision, sample_weight=sw)
with pytest.warns(FutureWarning, match="sample_weights"):
result = cuml_hinge(y_true, pred_decision, sample_weights=sw)
np.testing.assert_allclose(result, expected)


@pytest.mark.parametrize(
"nfeatures",
[
Expand Down
26 changes: 0 additions & 26 deletions python/cuml/tests/test_naive_bayes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -610,29 +610,3 @@ def test_categorical_parameters(

assert_allclose(y_log_prob, y_log_prob_sk, rtol=1e-4, atol=1e-10)
assert_array_equal(y_hat, y_hat_sk)


@pytest.mark.parametrize(
"cls", [BernoulliNB, CategoricalNB, ComplementNB, MultinomialNB]
)
def test_sample_weight_deprecated(cls):
X = np.array([[1, 2], [3, 4], [5, 6]])
y = np.array([0, 1, 1])
classes = np.array([0, 1])
sample_weight = np.full(3, 0.5, dtype="float32")

# Passing _anything_ leads to a warning, even if it's supported
with pytest.warns(FutureWarning, match="sample_weight"):
cls().fit(X, y, sample_weight=None)
with pytest.warns(FutureWarning, match="sample_weight"):
cls().partial_fit(X, y, classes=classes, sample_weight=None)

# Passing an actual array also errors
with pytest.raises(NotImplementedError, match="sample_weight"):
with pytest.warns(FutureWarning, match="sample_weight"):
cls().fit(X, y, sample_weight=sample_weight)
with pytest.raises(NotImplementedError, match="sample_weight"):
with pytest.warns(FutureWarning, match="sample_weight"):
cls().partial_fit(
X, y, classes=classes, sample_weight=sample_weight
)
32 changes: 0 additions & 32 deletions python/cuml/tests/test_sklearn_compatibility.py
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Original file line number Diff line number Diff line change
Expand Up @@ -333,50 +333,18 @@ def _all_cuml_estimators():
BernoulliNB: {
"check_estimator_tags_renamed": "No support for modern tags infrastructure",
"check_classifier_data_not_an_array": "bug in reflection prevents this",
"check_sample_weights_pandas_series": "sample_weight not implemented",
"check_sample_weights_not_an_array": "sample_weight not implemented",
"check_sample_weights_shape": "sample_weight not implemented",
"check_sample_weight_equivalence_on_dense_data": "sample_weight not implemented",
"check_sample_weights_list": "sample_weight not implemented",
"check_sample_weights_not_overwritten": "sample_weight not implemented",
"check_sample_weight_equivalence_on_sparse_data": "sample_weight not implemented",
"check_classifiers_one_label_sample_weights": "sample_weight not implemented",
},
ComplementNB: {
"check_estimator_tags_renamed": "No support for modern tags infrastructure",
"check_classifier_data_not_an_array": "bug in reflection prevents this",
"check_sample_weights_pandas_series": "sample_weight not implemented",
"check_sample_weights_not_an_array": "sample_weight not implemented",
"check_sample_weights_shape": "sample_weight not implemented",
"check_sample_weight_equivalence_on_dense_data": "sample_weight not implemented",
"check_sample_weights_list": "sample_weight not implemented",
"check_sample_weights_not_overwritten": "sample_weight not implemented",
"check_sample_weight_equivalence_on_sparse_data": "sample_weight not implemented",
"check_classifiers_one_label_sample_weights": "sample_weight not implemented",
},
CategoricalNB: {
"check_estimator_tags_renamed": "No support for modern tags infrastructure",
"check_classifier_data_not_an_array": "bug in reflection prevents this",
"check_sample_weights_pandas_series": "sample_weight not implemented",
"check_sample_weights_not_an_array": "sample_weight not implemented",
"check_sample_weights_shape": "sample_weight not implemented",
"check_sample_weight_equivalence_on_dense_data": "sample_weight not implemented",
"check_sample_weights_list": "sample_weight not implemented",
"check_sample_weights_not_overwritten": "sample_weight not implemented",
"check_sample_weight_equivalence_on_sparse_data": "sample_weight not implemented",
"check_classifiers_one_label_sample_weights": "sample_weight not implemented",
},
MultinomialNB: {
"check_estimator_tags_renamed": "No support for modern tags infrastructure",
"check_classifier_data_not_an_array": "bug in reflection prevents this",
"check_sample_weights_pandas_series": "sample_weight not implemented",
"check_sample_weights_not_an_array": "sample_weight not implemented",
"check_sample_weights_shape": "sample_weight not implemented",
"check_sample_weight_equivalence_on_dense_data": "sample_weight not implemented",
"check_sample_weights_list": "sample_weight not implemented",
"check_sample_weights_not_overwritten": "sample_weight not implemented",
"check_sample_weight_equivalence_on_sparse_data": "sample_weight not implemented",
"check_classifiers_one_label_sample_weights": "sample_weight not implemented",
},
}

Expand Down
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