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here is the log from the commit of package python-scikit-learn for
openSUSE:Factory checked in at 2022-10-29 20:16:04
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Comparing /work/SRC/openSUSE:Factory/python-scikit-learn (Old)
and /work/SRC/openSUSE:Factory/.python-scikit-learn.new.2275 (New)
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Package is "python-scikit-learn"
Sat Oct 29 20:16:04 2022 rev:20 rq:1032043 version:1.1.3
Changes:
--------
--- /work/SRC/openSUSE:Factory/python-scikit-learn/python-scikit-learn.changes
2022-10-27 13:53:23.116326617 +0200
+++
/work/SRC/openSUSE:Factory/.python-scikit-learn.new.2275/python-scikit-learn.changes
2022-10-29 20:17:06.050208776 +0200
@@ -1,0 +2,8 @@
+Thu Oct 27 18:40:17 UTC 2022 - Ben Greiner <c...@bnavigator.de>
+
+- Update to version 1.1.3
+ * This bugfix release only includes fixes for compatibility with
+ the latest SciPy release >= 1.9.2.
+- Update sklearn-pr24283-gradient-segfault.patch
+
+-------------------------------------------------------------------
Old:
----
scikit-learn-1.1.2.tar.gz
New:
----
scikit-learn-1.1.3.tar.gz
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Other differences:
------------------
++++++ python-scikit-learn.spec ++++++
--- /var/tmp/diff_new_pack.I25QIz/_old 2022-10-29 20:17:06.838212975 +0200
+++ /var/tmp/diff_new_pack.I25QIz/_new 2022-10-29 20:17:06.846213017 +0200
@@ -43,7 +43,7 @@
# enable pytest color output for local debugging: osc --with pytestcolor
%bcond_with pytestcolor
Name: python-scikit-learn%{psuffix}
-Version: 1.1.2
+Version: 1.1.3
Release: 0
Summary: Python modules for machine learning and data mining
License: BSD-3-Clause
@@ -104,6 +104,7 @@
%build
%if !%{with test}
+export CFLAGS="%{optflags}"
%python_build
%endif
++++++ scikit-learn-1.1.2.tar.gz -> scikit-learn-1.1.3.tar.gz ++++++
/work/SRC/openSUSE:Factory/python-scikit-learn/scikit-learn-1.1.2.tar.gz
/work/SRC/openSUSE:Factory/.python-scikit-learn.new.2275/scikit-learn-1.1.3.tar.gz
differ: char 5, line 1
++++++ sklearn-pr24283-gradient-segfault.patch ++++++
--- /var/tmp/diff_new_pack.I25QIz/_old 2022-10-29 20:17:06.930213465 +0200
+++ /var/tmp/diff_new_pack.I25QIz/_new 2022-10-29 20:17:06.934213486 +0200
@@ -23,58 +23,4 @@
<X_BINNED_DTYPE_C>data_val,
node.bitset_idx):
Index:
scikit-learn-1.1.2/sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py
-===================================================================
----
scikit-learn-1.1.2.orig/sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py
-+++
scikit-learn-1.1.2/sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py
-@@ -1159,3 +1159,28 @@ def test_no_user_warning_with_scoring():
- with warnings.catch_warnings():
- warnings.simplefilter("error", UserWarning)
- est.fit(X_df, y)
-+
-+
-+def test_unknown_category_that_are_negative():
-+ """Check that unknown categories that are negative does not error.
-+
-+ Non-regression test for #24274.
-+ """
-+ rng = np.random.RandomState(42)
-+ n_samples = 1000
-+ X = np.c_[rng.rand(n_samples), rng.randint(4, size=n_samples)]
-+ y = np.zeros(shape=n_samples)
-+ y[X[:, 1] % 2 == 0] = 1
-+
-+ hist = HistGradientBoostingRegressor(
-+ random_state=0,
-+ categorical_features=[False, True],
-+ max_iter=10,
-+ ).fit(X, y)
-+
-+ # Check that negative values from the second column are treated like a
-+ # missing category
-+ X_test_neg = np.asarray([[1, -2], [3, -4]])
-+ X_test_nan = np.asarray([[1, np.nan], [3, np.nan]])
-+
-+ assert_allclose(hist.predict(X_test_neg), hist.predict(X_test_nan))
-Index:
scikit-learn-1.1.2/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py
-===================================================================
----
scikit-learn-1.1.2.orig/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py
-+++
scikit-learn-1.1.2/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py
-@@ -1186,6 +1186,8 @@ class HistGradientBoostingRegressor(Regr
-
- For each categorical feature, there must be at most `max_bins` unique
- categories, and each categorical value must be in [0, max_bins -1].
-+ During prediction, categories encoded as a negative value are treated
as
-+ missing values.
-
- Read more in the :ref:`User Guide <categorical_support_gbdt>`.
-
-@@ -1515,6 +1517,8 @@ class HistGradientBoostingClassifier(Cla
-
- For each categorical feature, there must be at most `max_bins` unique
- categories, and each categorical value must be in [0, max_bins -1].
-+ During prediction, categories encoded as a negative value are treated
as
-+ missing values.
-
- Read more in the :ref:`User Guide <categorical_support_gbdt>`.
-
