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here is the log from the commit of package python-scikit-learn for
openSUSE:Factory checked in at 2022-10-27 13:53:08
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
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"
Thu Oct 27 13:53:08 2022 rev:19 rq:1030924 version:1.1.2
Changes:
--------
--- /work/SRC/openSUSE:Factory/python-scikit-learn/python-scikit-learn.changes
2022-09-17 20:08:13.760791552 +0200
+++
/work/SRC/openSUSE:Factory/.python-scikit-learn.new.2275/python-scikit-learn.changes
2022-10-27 13:53:23.116326617 +0200
@@ -1,0 +2,8 @@
+Tue Oct 11 13:10:22 UTC 2022 - Ben Greiner <[email protected]>
+
+- Update dependencies
+- Add sklearn-pr24283-gradient-segfault.patch
+ * gh#scikit-learn/scikit-learn#24283
+- Update test suite setup.
+
+-------------------------------------------------------------------
New:
----
sklearn-pr24283-gradient-segfault.patch
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Other differences:
------------------
++++++ python-scikit-learn.spec ++++++
--- /var/tmp/diff_new_pack.95fVps/_old 2022-10-27 13:53:23.736329780 +0200
+++ /var/tmp/diff_new_pack.95fVps/_new 2022-10-27 13:53:23.748329841 +0200
@@ -16,8 +16,6 @@
#
-%{?!python_module:%define python_module() python3-%{**}}
-%define skip_python2 1
%global flavor @BUILD_FLAVOR@%{nil}
%if "%{flavor}" == "test-py38"
%define psuffix -test-py38
@@ -42,6 +40,8 @@
%bcond_with test
%endif
%bcond_with extratest
+# enable pytest color output for local debugging: osc --with pytestcolor
+%bcond_with pytestcolor
Name: python-scikit-learn%{psuffix}
Version: 1.1.2
Release: 0
@@ -49,20 +49,22 @@
License: BSD-3-Clause
URL: https://scikit-learn.org/
Source0:
https://files.pythonhosted.org/packages/source/s/scikit-learn/scikit-learn-%{version}.tar.gz
-BuildRequires: %{python_module Cython >= 0.28.5}
-BuildRequires: %{python_module devel}
-BuildRequires: %{python_module joblib >= 0.11}
+# PATCH-FIX-UPSTREAM sklearn-pr24283-gradient-segfault.patch
gh#scikit-learn/scikit-learn#24283
+Patch0: sklearn-pr24283-gradient-segfault.patch
+BuildRequires: %{python_module Cython >= 0.29.24}
+BuildRequires: %{python_module devel >= 3.8}
+BuildRequires: %{python_module joblib >= 1.0.0}
BuildRequires: %{python_module numpy-devel >= 1.17.3}
BuildRequires: %{python_module scipy >= 1.3.2}
BuildRequires: %{python_module setuptools}
BuildRequires: %{python_module threadpoolctl >= 2.0.0}
-BuildRequires: %{python_module xml}
BuildRequires: fdupes
BuildRequires: gcc-c++
BuildRequires: gcc-fortran
BuildRequires: openblas-devel
BuildRequires: python-rpm-macros
-Requires: python-joblib >= 0.11
+# Check sklearn/_min_dependencies.py for dependencies
+Requires: python-joblib >= 1.0.0
Requires: python-numpy >= 1.17.3
Requires: python-scipy >= 1.3.2
Requires: python-threadpoolctl >= 2.0.0
@@ -70,18 +72,20 @@
Suggests: python-matplotlib
Suggests: python-pandas
Suggests: python-seaborn
-Provides: python-sklearn
+Provides: python-sklearn = %{version}-%{release}
%if "%{python_flavor}" == "python3" || "%{?python_provides}" == "python3"
-Provides: sklearn
+Provides: sklearn = %{version}-%{release}
%endif
# SECTION test requirements
%if %{with test}
-BuildRequires: %{python_module pytest >= 4.0}
-BuildRequires: %{python_module scikit-learn}
+BuildRequires: %{python_module pytest >= 5.0.1}
+BuildRequires: %{python_module pytest-rerunfailures}
+BuildRequires: %{python_module pytest-xdist}
+BuildRequires: %{python_module scikit-learn = %{version}}
%if %{with extratest}
BuildRequires: %{python_module matplotlib >= 3.1.2}
-BuildRequires: %{python_module pandas >= 0.25.0}
-BuildRequires: %{python_module scikit-image >= 0.13}
+BuildRequires: %{python_module pandas >= 1.0.5}
+BuildRequires: %{python_module scikit-image >= 0.16.2}
%endif
%endif
# /SECTION
@@ -93,8 +97,10 @@
%prep
%autosetup -p1 -n scikit-learn-%{version}
-
rm -rf sklearn/.pytest_cache
+%if !%{with pytestcolor}
+sed -i '/--color=yes/d' setup.cfg
+%endif
%build
%if !%{with test}
@@ -108,30 +114,20 @@
%endif
%if %{with test}
-# Precision-related errors on non-x86 platforms
-%ifarch %{ix86} x86_64
%check
+mkdir test_dir
+pushd test_dir
export SKLEARN_SKIP_NETWORK_TESTS=1
-NO_TESTS="test_feature_importance_regression or
test_minibatch_with_many_reassignments"
-NO_TESTS+=" or test_sparse_coder_parallel_mmap or test_explained_variances"
-# test_negative_sample_weights_mask_all_samples[weights-are-zero-NuSVC] Fatal
Python error: Aborted
-NO_TESTS+=" or test_negative_sample_weights_mask_all_samples"
-# Disable test_fetch_openml_verify_checksum for now, no clue why it fail.
-NO_TESTS+=" or test_fetch_openml_verify_checksum[True]"
-NO_TESTS+=" or test_fetch_openml_verify_checksum[False]"
-
-# Precision-related errors on 32 bit arch
+NO_TESTS="dummyprefix"
+%ifarch %{ix86} %{arm}
+# Precision-related errors on 32 bit
# https://github.com/scikit-learn/scikit-learn/issues/19230
-%ifarch i586 %{arm}
NO_TESTS+=" or test_convergence_dtype_consistency"
+NO_TESTS+=" or test_imputation_missing_value_in_test_array"
%endif
-
-mkdir test_dir
-pushd test_dir
-%pytest_arch -p no:cacheprovider -v -k "not ($NO_TESTS)"
%{$python_sitearch}/sklearn
+%pytest_arch -v --pyargs sklearn -n auto -k "not ($NO_TESTS)"
popd
%endif
-%endif
%if !%{with test}
%files %{python_files}
++++++ sklearn-pr24283-gradient-segfault.patch ++++++
>From dd7de2bfbf39222153f8c2deb203a0e1efef8640 Mon Sep 17 00:00:00 2001
From: "Thomas J. Fan" <[email protected]>
Date: Sat, 27 Aug 2022 10:28:03 -0400
Subject: FIX Treat negative categoricals as unknown during predict
PR: #24282
Fixes #24274
Index:
scikit-learn-1.1.2/sklearn/ensemble/_hist_gradient_boosting/_predictor.pyx
===================================================================
---
scikit-learn-1.1.2.orig/sklearn/ensemble/_hist_gradient_boosting/_predictor.pyx
+++ scikit-learn-1.1.2/sklearn/ensemble/_hist_gradient_boosting/_predictor.pyx
@@ -66,7 +66,10 @@ cdef inline Y_DTYPE_C _predict_one_from_
else:
node_idx = node.right
elif node.is_categorical:
- if in_bitset_2d_memoryview(
+ if data_val < 0:
+ # data_val is not in the accepted range, so it is treated as
missing value
+ node_idx = node.left if node.missing_go_to_left else node.right
+ elif in_bitset_2d_memoryview(
raw_left_cat_bitsets,
<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>`.
