Add common_terms parameter to sklearn_api.PhrasesTransformer

gensim/sklearn_api/phrases.py

@@ -30,6 +30,7 @@
 from sklearn.exceptions import NotFittedError
 
 from gensim import models
+from gensim.models.phrases import Phraser
 
 
 class PhrasesTransformer(TransformerMixin, BaseEstimator):
@@ -41,7 +42,7 @@ class PhrasesTransformer(TransformerMixin, BaseEstimator):
 
     """
     def __init__(self, min_count=5, threshold=10.0, max_vocab_size=40000000,
-                 delimiter=b'_', progress_per=10000, scoring='default'):
+                 delimiter=b'_', progress_per=10000, scoring='default', common_terms=frozenset()):
         """
 
         Parameters
@@ -84,15 +85,20 @@ def __init__(self, min_count=5, threshold=10.0, max_vocab_size=40000000,
 
             A scoring function without any of these parameters (even if the parameters are not used) will
             raise a ValueError on initialization of the Phrases class. The scoring function must be pickleable.
+        common_terms : set of str, optional
+            List of "stop words" that won't affect frequency count of expressions containing them.
+            Allow to detect expressions like "bank_of_america" or "eye_of_the_beholder".
 
         """
         self.gensim_model = None
+        self.phraser = None
         self.min_count = min_count
         self.threshold = threshold
         self.max_vocab_size = max_vocab_size
         self.delimiter = delimiter
         self.progress_per = progress_per
         self.scoring = scoring
+        self.common_terms = common_terms
 
     def fit(self, X, y=None):
         """Fit the model according to the given training data.
@@ -111,8 +117,9 @@ def fit(self, X, y=None):
         self.gensim_model = models.Phrases(
             sentences=X, min_count=self.min_count, threshold=self.threshold,
             max_vocab_size=self.max_vocab_size, delimiter=self.delimiter,
-            progress_per=self.progress_per, scoring=self.scoring
+            progress_per=self.progress_per, scoring=self.scoring, common_terms=self.common_terms
         )
+        self.phraser = Phraser(self.gensim_model)
         return self
 
     def transform(self, docs):
@@ -131,15 +138,16 @@ def transform(self, docs):
             Phrase representation for each of the input sentences.
 
         """
-        if self.gensim_model is None:
+        if self.gensim_model is None or self.phraser is None:
             raise NotFittedError(
                 "This model has not been fitted yet. Call 'fit' with appropriate arguments before using this method."
             )
 
         # input as python lists
         if isinstance(docs[0], string_types):
             docs = [docs]
-        return [self.gensim_model[doc] for doc in docs]
+
+        return [self.phraser[doc] for doc in docs]
 
     def partial_fit(self, X):
         """Train model over a potentially incomplete set of sentences.
@@ -163,8 +171,9 @@ def partial_fit(self, X):
             self.gensim_model = models.Phrases(
                 sentences=X, min_count=self.min_count, threshold=self.threshold,
                 max_vocab_size=self.max_vocab_size, delimiter=self.delimiter,
-                progress_per=self.progress_per, scoring=self.scoring
+                progress_per=self.progress_per, scoring=self.scoring, common_terms=self.common_terms
             )
 
         self.gensim_model.add_vocab(X)
+        self.phraser = Phraser(self.gensim_model)
         return self

gensim/test/test_sklearn_api.py

@@ -286,6 +286,14 @@
     ['graph', 'minors', 'survey', 'human', 'interface']
 ]
 
+common_terms = ["of", "the", "was", "are"]
+phrases_w_common_terms = [
+    [u'the', u'mayor', u'of', u'new', u'york', u'was', u'there'],
+    [u'the', u'mayor', u'of', u'new', u'orleans', u'was', u'there'],
+    [u'the', u'bank', u'of', u'america', u'offices', u'are', u'open'],
+    [u'the', u'bank', u'of', u'america', u'offices', u'are', u'closed']
+]
+
 
 class TestLdaWrapper(unittest.TestCase):
     def setUp(self):
@@ -1151,6 +1159,58 @@ def testModelNotFitted(self):
         self.assertRaises(NotFittedError, phrases_transformer.transform, phrases_sentences[0])
 
 
+class TestPhrasesTransformerCommonTerms(unittest.TestCase):
+    def setUp(self):
+        numpy.random.seed(0)
+        self.model = PhrasesTransformer(min_count=1, threshold=1, common_terms=common_terms)
+        self.expected_transformations = [
+            [u'the', u'mayor_of_new', u'york', u'was', u'there'],
+            [u'the', u'mayor_of_new', u'orleans', u'was', u'there'],
+            [u'the', u'bank_of_america', u'offices', u'are', u'open'],
+            [u'the', u'bank_of_america', u'offices', u'are', u'closed']
+        ]
+
+    def testFitAndTransform(self):
+        self.model.fit(phrases_w_common_terms)
+
+        transformed = self.model.transform(phrases_w_common_terms)
+        self.assertEqual(transformed, self.expected_transformations)
+
+    def testFitTransform(self):
+        transformed = self.model.fit_transform(phrases_w_common_terms)
+        self.assertEqual(transformed, self.expected_transformations)
+
+    def testPartialFit(self):
+        # fit half of the sentences
+        self.model.fit(phrases_w_common_terms[:2])
+
+        expected_transformations_0 = [
+            [u'the', u'mayor_of_new', u'york', u'was', u'there'],
+            [u'the', u'mayor_of_new', u'orleans', u'was', u'there'],
+            [u'the', u'bank', u'of', u'america', u'offices', u'are', u'open'],
+            [u'the', u'bank', u'of', u'america', u'offices', u'are', u'closed']
+        ]
+        # transform all sentences, second half should be same as original
+        transformed_0 = self.model.transform(phrases_w_common_terms)
+        self.assertEqual(transformed_0, expected_transformations_0)
+
+        # fit remaining sentences, result should be the same as in the other tests
+        self.model.partial_fit(phrases_w_common_terms[2:])
+        transformed_1 = self.model.fit_transform(phrases_w_common_terms)
+        self.assertEqual(transformed_1, self.expected_transformations)
+
+        new_phrases = [[u'offices', u'are', u'open'], [u'offices', u'are', u'closed']]
+        self.model.partial_fit(new_phrases)
+        expected_transformations_2 = [
+            [u'the', u'mayor_of_new', u'york', u'was', u'there'],
+            [u'the', u'mayor_of_new', u'orleans', u'was', u'there'],
+            [u'the', u'bank_of_america', u'offices_are_open'],
+            [u'the', u'bank_of_america', u'offices_are_closed']
+        ]
+        transformed_2 = self.model.transform(phrases_w_common_terms)
+        self.assertEqual(transformed_2, expected_transformations_2)
+
+
 # specifically test pluggable scoring in Phrases, because possible pickling issues with function parameter
 
 # this is intentionally in main rather than a class method to support pickling