Fix CVE-2024-5206
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starlet-dx
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backport-CVE-2024-5206.patch
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203
backport-CVE-2024-5206.patch
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From 70ca21f106b603b611da73012c9ade7cd8e438b8 Mon Sep 17 00:00:00 2001
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From: Olivier Grisel <olivier.grisel@ensta.org>
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Date: Mon, 22 Apr 2024 15:10:46 +0200
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Subject: [PATCH] FIX remove the computed stop_words_ attribute of text
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vectorizer (#28823)
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Origin:
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https://github.com/scikit-learn/scikit-learn/commit/70ca21f106b603b611da73012c9ade7cd8e438b8
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---
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sklearn/feature_extraction/tests/test_text.py | 35 ----------------
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sklearn/feature_extraction/text.py | 40 +++----------------
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2 files changed, 5 insertions(+), 70 deletions(-)
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diff --git a/sklearn/feature_extraction/tests/test_text.py b/sklearn/feature_extraction/tests/test_text.py
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index 79c5486..9c4a017 100644
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--- a/sklearn/feature_extraction/tests/test_text.py
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+++ b/sklearn/feature_extraction/tests/test_text.py
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@@ -584,14 +584,11 @@ def test_feature_names():
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@pytest.mark.parametrize('Vectorizer', (CountVectorizer, TfidfVectorizer))
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def test_vectorizer_max_features(Vectorizer):
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expected_vocabulary = set(['burger', 'beer', 'salad', 'pizza'])
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- expected_stop_words = set([u'celeri', u'tomato', u'copyright', u'coke',
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- u'sparkling', u'water', u'the'])
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# test bounded number of extracted features
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vectorizer = Vectorizer(max_df=0.6, max_features=4)
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vectorizer.fit(ALL_FOOD_DOCS)
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assert_equal(set(vectorizer.vocabulary_), expected_vocabulary)
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- assert_equal(vectorizer.stop_words_, expected_stop_words)
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def test_count_vectorizer_max_features():
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@@ -626,21 +623,16 @@ def test_vectorizer_max_df():
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vect.fit(test_data)
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assert 'a' in vect.vocabulary_.keys()
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assert_equal(len(vect.vocabulary_.keys()), 6)
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- assert_equal(len(vect.stop_words_), 0)
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vect.max_df = 0.5 # 0.5 * 3 documents -> max_doc_count == 1.5
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vect.fit(test_data)
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assert 'a' not in vect.vocabulary_.keys() # {ae} ignored
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assert_equal(len(vect.vocabulary_.keys()), 4) # {bcdt} remain
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- assert 'a' in vect.stop_words_
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- assert_equal(len(vect.stop_words_), 2)
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vect.max_df = 1
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vect.fit(test_data)
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assert 'a' not in vect.vocabulary_.keys() # {ae} ignored
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assert_equal(len(vect.vocabulary_.keys()), 4) # {bcdt} remain
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- assert 'a' in vect.stop_words_
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- assert_equal(len(vect.stop_words_), 2)
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def test_vectorizer_min_df():
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@@ -649,21 +641,16 @@ def test_vectorizer_min_df():
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vect.fit(test_data)
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assert 'a' in vect.vocabulary_.keys()
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assert_equal(len(vect.vocabulary_.keys()), 6)
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- assert_equal(len(vect.stop_words_), 0)
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vect.min_df = 2
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vect.fit(test_data)
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assert 'c' not in vect.vocabulary_.keys() # {bcdt} ignored
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assert_equal(len(vect.vocabulary_.keys()), 2) # {ae} remain
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- assert 'c' in vect.stop_words_
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- assert_equal(len(vect.stop_words_), 4)
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vect.min_df = 0.8 # 0.8 * 3 documents -> min_doc_count == 2.4
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vect.fit(test_data)
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assert 'c' not in vect.vocabulary_.keys() # {bcdet} ignored
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assert_equal(len(vect.vocabulary_.keys()), 1) # {a} remains
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- assert 'c' in vect.stop_words_
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- assert_equal(len(vect.stop_words_), 5)
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def test_count_binary_occurrences():
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@@ -936,28 +923,6 @@ def test_countvectorizer_vocab_dicts_when_pickling():
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assert_equal(cv.get_feature_names(), unpickled_cv.get_feature_names())
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-def test_stop_words_removal():
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- # Ensure that deleting the stop_words_ attribute doesn't affect transform
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-
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- fitted_vectorizers = (
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- TfidfVectorizer().fit(JUNK_FOOD_DOCS),
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- CountVectorizer(preprocessor=strip_tags).fit(JUNK_FOOD_DOCS),
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- CountVectorizer(strip_accents=strip_eacute).fit(JUNK_FOOD_DOCS)
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- )
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-
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- for vect in fitted_vectorizers:
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- vect_transform = vect.transform(JUNK_FOOD_DOCS).toarray()
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-
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- vect.stop_words_ = None
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- stop_None_transform = vect.transform(JUNK_FOOD_DOCS).toarray()
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-
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- delattr(vect, 'stop_words_')
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- stop_del_transform = vect.transform(JUNK_FOOD_DOCS).toarray()
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-
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- assert_array_equal(stop_None_transform, vect_transform)
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- assert_array_equal(stop_del_transform, vect_transform)
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-
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-
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def test_pickling_transformer():
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X = CountVectorizer().fit_transform(JUNK_FOOD_DOCS)
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orig = TfidfTransformer().fit(X)
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diff --git a/sklearn/feature_extraction/text.py b/sklearn/feature_extraction/text.py
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index 19d5c7f..bd9da0b 100644
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--- a/sklearn/feature_extraction/text.py
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+++ b/sklearn/feature_extraction/text.py
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@@ -795,15 +795,6 @@ class CountVectorizer(BaseEstimator, VectorizerMixin):
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vocabulary_ : dict
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A mapping of terms to feature indices.
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- stop_words_ : set
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- Terms that were ignored because they either:
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-
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- - occurred in too many documents (`max_df`)
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- - occurred in too few documents (`min_df`)
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- - were cut off by feature selection (`max_features`).
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-
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- This is only available if no vocabulary was given.
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-
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Examples
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--------
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>>> from sklearn.feature_extraction.text import CountVectorizer
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@@ -827,11 +818,6 @@ class CountVectorizer(BaseEstimator, VectorizerMixin):
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--------
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HashingVectorizer, TfidfVectorizer
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- Notes
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- -----
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- The ``stop_words_`` attribute can get large and increase the model size
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- when pickling. This attribute is provided only for introspection and can
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- be safely removed using delattr or set to None before pickling.
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"""
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def __init__(self, input='content', encoding='utf-8',
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@@ -909,18 +895,16 @@ class CountVectorizer(BaseEstimator, VectorizerMixin):
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mask = new_mask
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new_indices = np.cumsum(mask) - 1 # maps old indices to new
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- removed_terms = set()
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for term, old_index in list(six.iteritems(vocabulary)):
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if mask[old_index]:
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vocabulary[term] = new_indices[old_index]
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else:
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del vocabulary[term]
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- removed_terms.add(term)
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kept_indices = np.where(mask)[0]
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if len(kept_indices) == 0:
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raise ValueError("After pruning, no terms remain. Try a lower"
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" min_df or a higher max_df.")
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- return X[:, kept_indices], removed_terms
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+ return X[:, kept_indices]
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def _count_vocab(self, raw_documents, fixed_vocab):
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"""Create sparse feature matrix, and vocabulary where fixed_vocab=False
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@@ -1046,10 +1030,10 @@ class CountVectorizer(BaseEstimator, VectorizerMixin):
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if max_doc_count < min_doc_count:
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raise ValueError(
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"max_df corresponds to < documents than min_df")
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- X, self.stop_words_ = self._limit_features(X, vocabulary,
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- max_doc_count,
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- min_doc_count,
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- max_features)
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+ X = self._limit_features(X, vocabulary,
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+ max_doc_count,
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+ min_doc_count,
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+ max_features)
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self.vocabulary_ = vocabulary
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@@ -1459,15 +1443,6 @@ class TfidfVectorizer(CountVectorizer):
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The inverse document frequency (IDF) vector; only defined
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if ``use_idf`` is True.
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- stop_words_ : set
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- Terms that were ignored because they either:
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-
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- - occurred in too many documents (`max_df`)
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- - occurred in too few documents (`min_df`)
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- - were cut off by feature selection (`max_features`).
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-
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- This is only available if no vocabulary was given.
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-
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Examples
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--------
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>>> from sklearn.feature_extraction.text import TfidfVectorizer
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@@ -1491,11 +1466,6 @@ class TfidfVectorizer(CountVectorizer):
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TfidfTransformer : Performs the TF-IDF transformation from a provided
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matrix of counts.
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- Notes
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- -----
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- The ``stop_words_`` attribute can get large and increase the model size
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- when pickling. This attribute is provided only for introspection and can
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- be safely removed using delattr or set to None before pickling.
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"""
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def __init__(self, input='content', encoding='utf-8',
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--
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2.33.0
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@ -3,10 +3,11 @@
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Name: python-scikit-learn
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Summary: A Python module for machine learning built on top of SciPy
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Version: 0.20.4
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Release: 4
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Release: 5
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License: BSD
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URL: https://scikit-learn.org/stable/
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Source0: https://github.com/scikit-learn/scikit-learn/archive/%{version}/scikit-learn-%{version}.tar.gz
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Patch3000: backport-CVE-2024-5206.patch
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%global _description\
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scikit-learn is a Python module for machine learning built on top of SciPy\
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@ -19,14 +20,14 @@ Summary: %summary
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%{?python_provide:%python_provide python3-scikit-learn}
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%{?python_provide:%python_provide python3-sklearn}
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BuildRequires: git python3-devel python3-numpy python3-Cython python3-pytest
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BuildRequires: python3-devel python3-numpy python3-Cython python3-pytest
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Requires: python3 >= 3.5 python3-numpy >= 1.11.0
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Requires: python3-scipy >= 0.17.0 python3-joblib >= 0.11
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%description -n python3-scikit-learn %_description
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%prep
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%autosetup -n scikit-learn-%{version} -p1 -Sgit
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%autosetup -n scikit-learn-%{version} -p1
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%build
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%py3_build
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@ -41,6 +42,9 @@ Requires: python3-scipy >= 0.17.0 python3-joblib >= 0.11
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%{python3_sitearch}/scikit_learn-*.egg-info
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%changelog
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* Mon Jun 17 2024 yaoxin <yao_xin001@hoperun.com> - 0.20.4-5
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- Fix CVE-2024-5206
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* Mon Sep 27 2021 lingsheng <lingsheng@huawei.com> - 0.20.4-4
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- Provides python-scikit-learn and python-sklearn for compatibility
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