!42 Fix CVE-2024-5206

From: @starlet-dx 
Reviewed-by: @cherry530 
Signed-off-by: @cherry530

backport-CVE-2024-5206.patch

@@ -0,0 +1,207 @@
+From 70ca21f106b603b611da73012c9ade7cd8e438b8 Mon Sep 17 00:00:00 2001
+From: Olivier Grisel <olivier.grisel@ensta.org>
+Date: Mon, 22 Apr 2024 15:10:46 +0200
+Subject: [PATCH] FIX remove the computed stop_words_ attribute of text
+ vectorizer (#28823)
+
+Origin:
+https://github.com/scikit-learn/scikit-learn/commit/70ca21f106b603b611da73012c9ade7cd8e438b8
+---
+ sklearn/feature_extraction/tests/test_text.py | 42 -------------------
+ sklearn/feature_extraction/text.py            | 36 +---------------
+ 2 files changed, 2 insertions(+), 76 deletions(-)
+
+diff --git a/sklearn/feature_extraction/tests/test_text.py b/sklearn/feature_extraction/tests/test_text.py
+index b46958c..ac55021 100644
+--- a/sklearn/feature_extraction/tests/test_text.py
++++ b/sklearn/feature_extraction/tests/test_text.py
+@@ -764,21 +764,11 @@ def test_feature_names(get_names):
+ @pytest.mark.parametrize("Vectorizer", (CountVectorizer, TfidfVectorizer))
+ def test_vectorizer_max_features(Vectorizer):
+     expected_vocabulary = {"burger", "beer", "salad", "pizza"}
+-    expected_stop_words = {
+-        "celeri",
+-        "tomato",
+-        "copyright",
+-        "coke",
+-        "sparkling",
+-        "water",
+-        "the",
+-    }
+ 
+     # test bounded number of extracted features
+     vectorizer = Vectorizer(max_df=0.6, max_features=4)
+     vectorizer.fit(ALL_FOOD_DOCS)
+     assert set(vectorizer.vocabulary_) == expected_vocabulary
+-    assert vectorizer.stop_words_ == expected_stop_words
+ 
+ 
+ # TODO: Remove in 1.2 when get_feature_names is removed.
+@@ -816,21 +806,16 @@ def test_vectorizer_max_df():
+     vect.fit(test_data)
+     assert "a" in vect.vocabulary_.keys()
+     assert len(vect.vocabulary_.keys()) == 6
+-    assert len(vect.stop_words_) == 0
+ 
+     vect.max_df = 0.5  # 0.5 * 3 documents -> max_doc_count == 1.5
+     vect.fit(test_data)
+     assert "a" not in vect.vocabulary_.keys()  # {ae} ignored
+     assert len(vect.vocabulary_.keys()) == 4  # {bcdt} remain
+-    assert "a" in vect.stop_words_
+-    assert len(vect.stop_words_) == 2
+ 
+     vect.max_df = 1
+     vect.fit(test_data)
+     assert "a" not in vect.vocabulary_.keys()  # {ae} ignored
+     assert len(vect.vocabulary_.keys()) == 4  # {bcdt} remain
+-    assert "a" in vect.stop_words_
+-    assert len(vect.stop_words_) == 2
+ 
+ 
+ def test_vectorizer_min_df():
+@@ -839,21 +824,16 @@ def test_vectorizer_min_df():
+     vect.fit(test_data)
+     assert "a" in vect.vocabulary_.keys()
+     assert len(vect.vocabulary_.keys()) == 6
+-    assert len(vect.stop_words_) == 0
+ 
+     vect.min_df = 2
+     vect.fit(test_data)
+     assert "c" not in vect.vocabulary_.keys()  # {bcdt} ignored
+     assert len(vect.vocabulary_.keys()) == 2  # {ae} remain
+-    assert "c" in vect.stop_words_
+-    assert len(vect.stop_words_) == 4
+ 
+     vect.min_df = 0.8  # 0.8 * 3 documents -> min_doc_count == 2.4
+     vect.fit(test_data)
+     assert "c" not in vect.vocabulary_.keys()  # {bcdet} ignored
+     assert len(vect.vocabulary_.keys()) == 1  # {a} remains
+-    assert "c" in vect.stop_words_
+-    assert len(vect.stop_words_) == 5
+ 
+ 
+ @pytest.mark.parametrize(
+@@ -1195,28 +1175,6 @@ def test_countvectorizer_vocab_dicts_when_pickling(get_names):
+         assert_array_equal(getattr(cv, get_names)(), getattr(unpickled_cv, get_names)())
+ 
+ 
+-def test_stop_words_removal():
+-    # Ensure that deleting the stop_words_ attribute doesn't affect transform
+-
+-    fitted_vectorizers = (
+-        TfidfVectorizer().fit(JUNK_FOOD_DOCS),
+-        CountVectorizer(preprocessor=strip_tags).fit(JUNK_FOOD_DOCS),
+-        CountVectorizer(strip_accents=strip_eacute).fit(JUNK_FOOD_DOCS),
+-    )
+-
+-    for vect in fitted_vectorizers:
+-        vect_transform = vect.transform(JUNK_FOOD_DOCS).toarray()
+-
+-        vect.stop_words_ = None
+-        stop_None_transform = vect.transform(JUNK_FOOD_DOCS).toarray()
+-
+-        delattr(vect, "stop_words_")
+-        stop_del_transform = vect.transform(JUNK_FOOD_DOCS).toarray()
+-
+-        assert_array_equal(stop_None_transform, vect_transform)
+-        assert_array_equal(stop_del_transform, vect_transform)
+-
+-
+ def test_pickling_transformer():
+     X = CountVectorizer().fit_transform(JUNK_FOOD_DOCS)
+     orig = TfidfTransformer().fit(X)
+diff --git a/sklearn/feature_extraction/text.py b/sklearn/feature_extraction/text.py
+index b565aea..2735ded 100644
+--- a/sklearn/feature_extraction/text.py
++++ b/sklearn/feature_extraction/text.py
+@@ -1040,15 +1040,6 @@ class CountVectorizer(_VectorizerMixin, BaseEstimator):
+         True if a fixed vocabulary of term to indices mapping
+         is provided by the user.
+ 
+-    stop_words_ : set
+-        Terms that were ignored because they either:
+-
+-          - occurred in too many documents (`max_df`)
+-          - occurred in too few documents (`min_df`)
+-          - were cut off by feature selection (`max_features`).
+-
+-        This is only available if no vocabulary was given.
+-
+     See Also
+     --------
+     HashingVectorizer : Convert a collection of text documents to a
+@@ -1057,12 +1048,6 @@ class CountVectorizer(_VectorizerMixin, BaseEstimator):
+     TfidfVectorizer : Convert a collection of raw documents to a matrix
+         of TF-IDF features.
+ 
+-    Notes
+-    -----
+-    The ``stop_words_`` attribute can get large and increase the model size
+-    when pickling. This attribute is provided only for introspection and can
+-    be safely removed using delattr or set to None before pickling.
+-
+     Examples
+     --------
+     >>> from sklearn.feature_extraction.text import CountVectorizer
+@@ -1175,19 +1160,17 @@ class CountVectorizer(_VectorizerMixin, BaseEstimator):
+             mask = new_mask
+ 
+         new_indices = np.cumsum(mask) - 1  # maps old indices to new
+-        removed_terms = set()
+         for term, old_index in list(vocabulary.items()):
+             if mask[old_index]:
+                 vocabulary[term] = new_indices[old_index]
+             else:
+                 del vocabulary[term]
+-                removed_terms.add(term)
+         kept_indices = np.where(mask)[0]
+         if len(kept_indices) == 0:
+             raise ValueError(
+                 "After pruning, no terms remain. Try a lower min_df or a higher max_df."
+             )
+-        return X[:, kept_indices], removed_terms
++        return X[:, kept_indices]
+ 
+     def _count_vocab(self, raw_documents, fixed_vocab):
+         """Create sparse feature matrix, and vocabulary where fixed_vocab=False"""
+@@ -1352,7 +1335,7 @@ class CountVectorizer(_VectorizerMixin, BaseEstimator):
+                 raise ValueError("max_df corresponds to < documents than min_df")
+             if max_features is not None:
+                 X = self._sort_features(X, vocabulary)
+-            X, self.stop_words_ = self._limit_features(
++            X = self._limit_features(
+                 X, vocabulary, max_doc_count, min_doc_count, max_features
+             )
+             if max_features is None:
+@@ -1882,15 +1865,6 @@ class TfidfVectorizer(CountVectorizer):
+         The inverse document frequency (IDF) vector; only defined
+         if ``use_idf`` is True.
+ 
+-    stop_words_ : set
+-        Terms that were ignored because they either:
+-
+-          - occurred in too many documents (`max_df`)
+-          - occurred in too few documents (`min_df`)
+-          - were cut off by feature selection (`max_features`).
+-
+-        This is only available if no vocabulary was given.
+-
+     See Also
+     --------
+     CountVectorizer : Transforms text into a sparse matrix of n-gram counts.
+@@ -1898,12 +1872,6 @@ class TfidfVectorizer(CountVectorizer):
+     TfidfTransformer : Performs the TF-IDF transformation from a provided
+         matrix of counts.
+ 
+-    Notes
+-    -----
+-    The ``stop_words_`` attribute can get large and increase the model size
+-    when pickling. This attribute is provided only for introspection and can
+-    be safely removed using delattr or set to None before pickling.
+-
+     Examples
+     --------
+     >>> from sklearn.feature_extraction.text import TfidfVectorizer
+-- 
+2.33.0
+

python-scikit-learn.spec

@@ -2,11 +2,12 @@
 
 Name:           python-scikit-learn
 Summary:        A Python module for machine learning built on top of SciPy
-Version:        0.24.0
+Version:        1.1.1
 Release:        2
 License:        BSD
 URL:            https://scikit-learn.org/stable/
 Source0:        https://github.com/scikit-learn/scikit-learn/archive/%{version}/scikit-learn-%{version}.tar.gz
+Patch3000:      backport-CVE-2024-5206.patch
 
 %global _description\
 scikit-learn is a Python module for machine learning built on top of SciPy\
@@ -16,15 +17,17 @@ and is distributed under the 3-Clause BSD license.\
 
 %package -n python3-scikit-learn
 Summary:        %summary
+%{?python_provide:%python_provide python3-scikit-learn}
+%{?python_provide:%python_provide python3-sklearn}
 
-BuildRequires:  git python3-devel python3-numpy python3-Cython python3-pytest python3-scipy
+BuildRequires:  python3-devel python3-numpy python3-Cython python3-pytest python3-scipy g++
 Requires:       python3 >= 3.5 python3-numpy >= 1.11.0
 Requires:       python3-scipy >= 0.17.0 python3-joblib >=  0.11
 
 %description -n python3-scikit-learn %_description
 
 %prep
-%autosetup -n scikit-learn-%{version} -p1 -Sgit
+%autosetup -n scikit-learn-%{version} -p1
 
 %build
 CFLAGS="$RPM_OPT_FLAGS -s"
@@ -40,13 +43,25 @@ CFLAGS="$RPM_OPT_FLAGS -s"
 %{python3_sitearch}/scikit_learn-*.egg-info
 
 %changelog
+* Mon Jun 17 2024 yaoxin <yao_xin001@hoperun.com> - 1.1.1-2
+- Fix CVE-2024-5206
+
+* Tue Oct 25 2022 xu_ping <xuping33@h-partners.com> - 1.1.1-1
+- Upgrade to version 1.1.1
+
+* Fri Jul 30 2021 chenyanpanHW <chenyanpan@huawei.com> - 0.24.0-4
+- DESC: delete -Sgit from %autosetup, and delete BuildRequires git
+
+* Wed Jun 23 2021 liudabo <liudabo1@huawei.com> - 0.24.0-3
+- Add g++ build dependcy
+
 * Sat Mar 20 2021 shixuantong <shixuantong@huawei> - 0.24.0-2
 - strip binary files
 
 * Wed Jan 13 2021 SimpleUpdate Robot <tc@openeuler.org> - 0.24.0-1
 - Upgrade to version 0.24.0
 
-* Fri Oct 1 2020 Zhipeng Xie <xiezhipeng1@huawei.com> - 0.23.1-2
+* Thu Oct 1 2020 Zhipeng Xie <xiezhipeng1@huawei.com> - 0.23.1-2
 - upgrade to 0.23.2
 
 * Fri Jul 31 2020 Zhipeng Xie <xiezhipeng1@huawei.com> - 0.23.1-1