scikit-learn
diff --git a/‎sklearn/feature_extraction/tests/test_text.py
+24 b/‎sklearn/feature_extraction/tests/test_text.py
+24
diff --git a/‎sklearn/feature_extraction/text.py
+14 b/‎sklearn/feature_extraction/text.py
+14
@@ -35,6 +35,7 @@
 import pickle
 from io import StringIO
 
+import pytest
 
 JUNK_FOOD_DOCS = (
     "the pizza pizza beer copyright",
@@ -995,3 +996,26 @@ def test_vectorizer_string_object_as_input():
             ValueError, message, vec.fit, "hello world!")
         assert_raise_message(
             ValueError, message, vec.transform, "hello world!")
+
+
+@pytest.mark.parametrize("vec", [
+        HashingVectorizer(ngram_range=(2, 1)),
+        CountVectorizer(ngram_range=(2, 1)),
+        TfidfVectorizer(ngram_range=(2, 1))
+    ])
+def test_vectorizers_invalid_ngram_range(vec):
+    # vectorizers could be initialized with invalid ngram range
+    # test for raising error message
+    invalid_range = vec.ngram_range
+    message = ("Invalid value for ngram_range=%s "
+               "lower boundary larger than the upper boundary."
+               % str(invalid_range))
+
+    assert_raise_message(
+        ValueError, message, vec.fit, ["good news everyone"])
+    assert_raise_message(
+        ValueError, message, vec.fit_transform, ["good news everyone"])
+
+    if isinstance(vec, HashingVectorizer):
+        assert_raise_message(
+            ValueError, message, vec.transform, ["good news everyone"])
@@ -306,6 +306,15 @@ def _check_vocabulary(self):
         if len(self.vocabulary_) == 0:
             raise ValueError("Vocabulary is empty")
 
+    def _validate_params(self):
+        """Check validity of ngram_range parameter"""
+        min_n, max_m = self.ngram_range
+        if min_n > max_m:
+            raise ValueError(
+                "Invalid value for ngram_range=%s "
+                "lower boundary larger than the upper boundary."
+                % str(self.ngram_range))
+
 
 class HashingVectorizer(BaseEstimator, VectorizerMixin, TransformerMixin):
     """Convert a collection of text documents to a matrix of token occurrences
@@ -497,6 +506,8 @@ def fit(self, X, y=None):
                 "Iterable over raw text documents expected, "
                 "string object received.")
 
+        self._validate_params()
+
         self._get_hasher().fit(X, y
B9AF
=y)
         return self
 
@@ -520,6 +531,8 @@ def transform(self, X):
                 "Iterable over raw text documents expected, "
                 "string object received.")
 
+        self._validate_params()
+
         analyzer = self.build_analyzer()
         X = self._get_hasher().transform(analyzer(doc) for doc in X)
         if self.binary:
@@ -882,6 +895,7 @@ def fit_transform(self, raw_documents, y=None):
                 "Iterable over raw text documents expected, "
                 "string object received.")
 
+        self._validate_params()
         self._validate_vocabulary()
         max_df = self.max_df
         min_df = self.min_df