8000 FIX Ignore distutils warning in [scipy-dev] + fix numpy 1.22 support … · samronsin/scikit-learn@8f158d3 · GitHub
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thomasjpfanogrisel
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samronsin
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FIX Ignore distutils warning in [scipy-dev] + fix numpy 1.22 support in OneHotEncoder (scikit-learn#21517)
Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
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‎build_tools/azure/test_script.sh

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@@ -35,18 +35,21 @@ if [[ "$COVERAGE" == "true" ]]; then
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# report that otherwise hides the test failures and forces long scrolls in
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# the CI logs.
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export COVERAGE_PROCESS_START="$BUILD_SOURCESDIRECTORY/.coveragerc"
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TEST_CMD="$TEST_CMD --cov-config=$COVERAGE_PROCESS_START --cov sklearn --cov-report="
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TEST_CMD="$TEST_CMD --cov-config='$COVERAGE_PROCESS_START' --cov sklearn --cov-report="
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fi
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if [[ -n "$CHECK_WARNINGS" ]]; then
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# numpy's 1.19.0's tostring() deprecation is ignored until scipy and joblib removes its usage
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TEST_CMD="$TEST_CMD -Werror::DeprecationWarning -Werror::FutureWarning -Wignore:tostring:DeprecationWarning"
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# Python 3.10 deprecates disutils and is imported by numpy interally during import time
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TEST_CMD="$TEST_CMD -Wignore:The\ distutils:DeprecationWarning"
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fi
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if [[ "$PYTEST_XDIST_VERSION" != "none" ]]; then
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TEST_CMD="$TEST_CMD -n2"
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fi
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set -x
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$TEST_CMD --pyargs sklearn
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eval "$TEST_CMD --pyargs sklearn"
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set +x

‎doc/whats_new/v1.0.rst

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@@ -28,6 +28,12 @@ Changelog
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and :class:`decomposition.MiniBatchSparsePCA` to be convex and match the referenced
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article. :pr:`19210` by :user:`Jérémie du Boisberranger <jeremiedbb>`.
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:mod:`sklearn.preprocessing`
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............................
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- |Fix| Fixes compatibility bug with NumPy 1.22 in :class:`preprocessing.OneHotEncoder`.
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:pr:`21517` by `Thomas Fan`_.
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:mod:`sklearn.utils`
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....................
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‎sklearn/cluster/tests/test_dbscan.py

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@@ -180,7 +180,7 @@ def test_dbscan_metric_params():
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min_samples=min_samples,
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algorithm="ball_tree",
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).fit(X)
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assert not warns
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assert not warns, warns[0].message
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core_sample_1, labels_1 = db.core_sample_indices_, db.labels_
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# Test that sample labels are the same as passing Minkowski 'p' directly

‎sklearn/preprocessing/_encoders.py

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@@ -544,7 +544,7 @@ def transform(self, X):
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indptr = np.empty(n_samples + 1, dtype=int)
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indptr[0] = 0
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np.sum(X_mask, axis=1, out=indptr[1:])
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np.sum(X_mask, axis=1, out=indptr[1:], dtype=indptr.dtype)
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np.cumsum(indptr[1:], out=indptr[1:])
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data = np.ones(indptr[-1])
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