scikit-learn · jnothman · Apr 23, 2019 · Apr 14, 2019 · Apr 15, 2019 · Apr 15, 2019
diff --git a/doc/whats_new/v0.21.rst b/doc/whats_new/v0.21.rst
@@ -27,6 +27,9 @@ random sampling procedures.
 - :class:`linear_model.LogisticRegression` and
   :class:`linear_model.LogisticRegressionCV` with 'saga' solver. |Fix|
 - :class:`ensemble.GradientBoostingClassifier` |Fix|
+- :class:`sklearn.feature_extraction.text.HashingVectorizer`,
+  :class:`sklearn.feature_extraction.text.TfidfVectorizer`, and
+  :class:`sklearn.feature_extraction.text.CountVectorizer` |API|
 - :class:`neural_network.MLPClassifier` |Fix|
 - :func:`svm.SVC.decision_function` and
   :func:`multiclass.OneVsOneClassifier.decision_function`. |Fix|
@@ -265,6 +268,17 @@ Support for Python 3.4 and below has been officially dropped.
 - |API| Deprecated :mod:`externals.six` since we have dropped support for
   Python 2.7. :issue:`12916` by :user:`Hanmin Qin <qinhanmin2014>`.
 
+:mod:`sklearn.feature_extraction`
+.................................
+
+- |API| If ``input='file'`` or ``input='filename'``, and a callable is given
+  as the ``analyzer``, :class:`sklearn.feature_extraction.text.HashingVectorizer`,
+  :class:`sklearn.feature_extraction.text.TfidfVectorizer`, and
+  :class:`sklearn.feature_extraction.text.CountVectorizer` now read the data
+  from the file(s) and then pass it to the given ``analyzer``, instead of
+  passing the file name(s) or the file object(s) to the analyzer.
+  :issue:`13641` by `Adrin Jalali`_.
+
 :mod:`sklearn.impute`
 .....................
 

diff --git a/sklearn/feature_extraction/tests/test_text.py b/sklearn/feature_extraction/tests/test_text.py
@@ -29,6 +29,7 @@
 from numpy.testing import assert_array_almost_equal
 from numpy.testing import assert_array_equal
 from sklearn.utils import IS_PYPY
+from sklearn.exceptions import ChangedBehaviorWarning
 from sklearn.utils.testing import (assert_equal, assert_not_equal,
                                    assert_almost_equal, assert_in,
                                    assert_less, assert_greater,
@@ -1196,3 +1197,47 @@ def build_preprocessor(self):
                                             .findall(doc),
                     stop_words=['and'])
     assert _check_stop_words_consistency(vec) is True
+
+
+@pytest.mark.parametrize('Estimator',
+                         [CountVectorizer, TfidfVectorizer, HashingVectorizer])
+@pytest.mark.parametrize(
+    'input_type, err_type, err_msg',
+    [('filename', FileNotFoundError, ''),
+     ('file', AttributeError, "'str' object has no attribute 'read'")]
+)
+def test_callable_analyzer_error(Estimator, input_type, err_type, err_msg):
+    data = ['this is text, not file or filename']
+    with pytest.raises(err_type, match=err_msg):
+        Estimator(analyzer=lambda x: x.split(),
+                  input=input_type).fit_transform(data)
+
+
+@pytest.mark.parametrize('Estimator',
+                         [CountVectorizer, TfidfVectorizer, HashingVectorizer])
+@pytest.mark.parametrize(
+    'analyzer', [lambda doc: open(doc, 'r'), lambda doc: doc.read()]
+)
+@pytest.mark.parametrize('input_type', ['file', 'filename'])
+def test_callable_analyzer_change_behavior(Estimator, analyzer, input_type):
+    data = ['this is text, not file or filename']
+    warn_msg = 'Since v0.21, vectorizer'
+    with pytest.raises((FileNotFoundError, AttributeError)):
+        with pytest.warns(ChangedBehaviorWarning, match=warn_msg) as records:
+            Estimator(analyzer=analyzer, input=input_type).fit_transform(data)
+    assert len(records) == 1
+    assert warn_msg in str(records[0])
+
+
+@pytest.mark.parametrize('Estimator',
+                         [CountVectorizer, TfidfVectorizer, HashingVectorizer])
+def test_callable_analyzer_reraise_error(tmpdir, Estimator):
+    # check if a custom exception from the analyzer is shown to the user
+    def analyzer(doc):
+        raise Exception("testing")
+
+    f = tmpdir.join("file.txt")
+    f.write("sample content\n")
+
+    with pytest.raises(Exception, match="testing"):
+        Estimator(analyzer=analyzer, input='file').fit_transform([f])
diff --git a/sklearn/feature_extraction/text.py b/sklearn/feature_extraction/text.py
@@ -31,6 +31,7 @@
 from ..utils.validation import check_is_fitted, check_array, FLOAT_DTYPES
 from ..utils import _IS_32BIT
 from ..utils.fixes import _astype_copy_false
+from ..exceptions import ChangedBehaviorWarning
 
 
 __all__ = ['HashingVectorizer',
@@ -304,10 +305,34 @@ def _check_stop_words_consistency(self, stop_words, preprocess, tokenize):
             self._stop_words_id = id(self.stop_words)
             return 'error'
 
+    def _validate_custom_analyzer(self):
+        # This is to check if the given custom analyzer expects file or a
+        # filename instead of data.
+        # Behavior changed in v0.21, function could be removed in v0.23
+        import tempfile
+        with tempfile.NamedTemporaryFile() as f:
+            fname = f.name
+        # now we're sure fname doesn't exist
+
+        msg = ("Since v0.21, vectorizers pass the data to the custom analyzer "
+               "and not the file names or the file objects. This warning "
+               "will be removed in v0.23.")
+        try:
+            self.analyzer(fname)
+        except FileNotFoundError:
+            warnings.warn(msg, ChangedBehaviorWarning)
+        except AttributeError as e:
+            if str(e) == "'str' object has no attribute 'read'":
+                warnings.warn(msg, ChangedBehaviorWarning)
+        except Exception:
+            pass
+
     def build_analyzer(self):
         """Return a callable that handles preprocessing and tokenization"""
         if callable(self.analyzer):
-            return self.analyzer
+            if self.input in ['file', 'filename']:
+                self._validate_custom_analyzer()
+            return lambda doc: self.analyzer(self.decode(doc))
 
         preprocess = self.build_preprocessor()
 
@@ -490,6 +515,11 @@ class HashingVectorizer(BaseEstimator, VectorizerMixin, TransformerMixin):
         If a callable is passed it is used to extract the sequence of features
         out of the raw, unprocessed input.
 
+        .. versionchanged:: 0.21
+        Since v0.21, if ``input`` is ``filename`` or ``file``, the data is
+        first read from the file and then passed to the given callable
+        analyzer.
+
     n_features : integer, default=(2 ** 20)
         The number of features (columns) in the output matrices. Small numbers
         of features are likely to cause hash collisions, but large numbers
@@ -745,6 +775,11 @@ class CountVectorizer(BaseEstimator, VectorizerMixin):
         If a callable is passed it is used to extract the sequence of features
         out of the raw, unprocessed input.
 
+        .. versionchanged:: 0.21
+        Since v0.21, if ``input`` is ``filename`` or ``file``, the data is
+        first read from the file and then passed to the given callable
+        analyzer.
+
     max_df : float in range [0.0, 1.0] or int, default=1.0
         When building the vocabulary ignore terms that have a document
         frequency strictly higher than the given threshold (corpus-specific
@@ -1369,6 +1404,11 @@ class TfidfVectorizer(CountVectorizer):
         If a callable is passed it is used to extract the sequence of features
         out of the raw, unprocessed input.
 
+        .. versionchanged:: 0.21
+        Since v0.21, if ``input`` is ``filename`` or ``file``, the data is
+        first read from the file and then passed to the given callable
+        analyzer.
+
     stop_words : string {'english'}, list, or None (default=None)
         If a string, it is passed to _check_stop_list and the appropriate stop
         list is returned. 'english' is currently the only supported string