8000 FIX array api support for `clip` param of `MinMaxScaler` by StefanieSenger · Pull Request #29615 · scikit-learn/scikit-learn · GitHub
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FIX array api support for clip param of MinMaxScaler #29615

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FIX array api support for clip param of MinMaxScaler #29615

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55a406c
fix array api support for clip param
StefanieSenger Aug 2, 2024
8e29418
for debugging failing cuda test
StefanieSenger Aug 5, 2024
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for debugging cuda tests
StefanieSenger Aug 5, 2024
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for debugging cuda tests [ci skip]
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StefanieSenger Aug 5, 2024
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StefanieSenger Aug 5, 2024
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StefanieSenger Aug 5, 2024
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StefanieSenger Aug 5, 2024
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StefanieSenger Aug 7, 2024
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StefanieSenger Aug 7, 2024
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2 changes: 1 addition & 1 deletion sklearn/preprocessing/_data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -537,7 +537,7 @@ def transform(self, X):
X *= self.scale_
X += self.min_
if self.clip:
xp.clip(X, self.feature_range[0], self.feature_range[1], out=X)
X = xp.clip(X, self.feature_range[0], self.feature_range[1])
return X

def inverse_transform(self, X):
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22 changes: 14 additions & 8 deletions sklearn/preprocessing/tests/test_data.py
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Expand Up @@ -9,7 +9,7 @@
import pytest
from scipy import sparse, stats

from sklearn import datasets
from sklearn import config_context, datasets
from sklearn.base import clone
from sklearn.exceptions import NotFittedError
from sklearn.metrics.pairwise import linear_kernel
Expand Down Expand Up @@ -41,6 +41,7 @@
yield_namespace_device_dtype_combinations,
)
from sklearn.utils._testing import (
_array_api_for_tests,
_convert_container,
assert_allclose,
assert_allclose_dense_sparse,
Expand Down Expand Up @@ -2475,14 +2476,19 @@ def test_standard_scaler_sparse_partial_fit_finite_variance(X_2):
assert np.isfinite(scaler.var_[0])


@pytest.mark.parametrize("feature_range", [(0, 1), (-10, 10)])
def test_minmax_scaler_clip(feature_range):
@pytest.mark.parametrize(
"array_namespace, device, _", yield_namespace_device_dtype_combinations()
)
@pytest.mark.parametrize("feature_range", [(0.0, 1.1), (-10.0, 10.0)])
def test_minmax_scaler_clip(feature_range, array_namespace, device, _):
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Let's put array_api in the test name so that it can be picked up by the CUDA CI that only runs tests with "array_api" in their name to avoid spending GPU time running non-array API tests.

Suggested change
def test_minmax_scaler_clip(feature_range, array_namespace, device, _):
def test_minmax_scaler_clip_array_api(feature_range, array_namespace, device, _):

# test behaviour of the parameter 'clip' in MinMaxScaler
X = iris.data
scaler = MinMaxScaler(feature_range=feature_range, clip=True).fit(X)
X_min, X_max = np.min(X, axis=0), np.max(X, axis=0)
X_test = [np.r_[X_min[:2] - 10, X_max[2:] + 10]]
X_transformed = scaler.transform(X_test)
xp = _array_api_for_tests(array_namespace, device)
X = xp.asarray(iris.data)
with config_context(array_api_dispatch=True):
scaler = MinMaxScaler(feature_range=feature_range, clip=True).fit(X)
X_min, X_max = xp.min(X, axis=0), xp.max(X, axis=0)
X_test = xp.asarray([np.r_[X_min[:2] - 10, X_max[2:] + 10]])
X_transformed = scaler.transform(X_test)
assert_allclose(
X_transformed,
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assert_allclose in not array API compliant. We should therefore always explicitly convert the arrays to compare to a numpy counterpart.

Suggested change
X_transformed,
_convert_to_numpy(X_transformed),

_convert_to_numpy is imported from sklearn.utils._array_api.

[[feature_range[0], feature_range[0], feature_range[1], feature_range[1]]],
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4 changes: 2 additions & 2 deletions sklearn/utils/tests/test_array_api.py
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Expand Up @@ -351,8 +351,8 @@ def __init__(self, device_name):
assert array1.device == device(array1, array1, array2)


# TODO: add cupy and cupy.array_api to the list of libraries once the
# the following upstream issue has been fixed:
# TODO: add cupy and cupy.array_api to the list of libraries now that the
# the following upstream issue had been fixed:
# https://github.com/cupy/cupy/issues/8180
@skip_if_array_api_compat_not_configured
@pytest.mark.parametrize("library", ["numpy", "array_api_strict", "torch"])
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