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

