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[MRG+1] Test to make sure deletion of stop_words_ does not affect transformation. #4037

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Merged
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Feb 1, 2015
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[MRG+1] Test to make sure deletion of stop_words_ does not affect transformation. #4037

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23 changes: 23 additions & 0 deletions sklearn/feature_extraction/tests/test_text.py
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Original file line number Diff line number Diff line change
Expand Up @@ -859,6 +859,29 @@ def test_pickling_vectorizer():
orig.fit_transform(JUNK_FOOD_DOCS).toarray())


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)
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22 changes: 21 additions & 1 deletion sklearn/feature_extraction/text.py
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Expand Up @@ -628,6 +628,12 @@ class CountVectorizer(BaseEstimator, VectorizerMixin):
See also
--------
HashingVectorizer, TfidfVectorizer

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.
"""

def __init__(self, input='content', encoding='utf-8',
Expand Down Expand Up @@ -1158,6 +1164,15 @@ class TfidfVectorizer(CountVectorizer):
The learned idf vector (global term weights)
when ``use_idf`` is set to True, None otherwise.

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`).

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@jnothman Should I revert this to Notes?

This is only available if no vocabulary was given.

See also
--------
CountVectorizer
Expand All @@ -1167,7 +1182,12 @@ class TfidfVectorizer(CountVectorizer):
TfidfTransformer
Apply Term Frequency Inverse Document Frequency normalization to a
sparse matrix of occurrence 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.
"""

def __init__(self, input='content', encoding='utf-8',
Expand Down