fix flake8 warnings in docstrings

piskvorky · Oct 10, 2020 · 2ea2634 · 2ea2634
1 parent 1acb47c
commit 2ea2634
Showing 1 changed file with 6 additions and 6 deletions.
diff --git a/gensim/models/phrases.py b/gensim/models/phrases.py
@@ -21,7 +21,7 @@
 
     >>> from gensim.test.utils import datapath
     >>> from gensim.models.word2vec import Text8Corpus
-    >>> from gensim.models.phrases import Phrases, FrozenPhrases
+    >>> from gensim.models.phrases import Phrases
     >>>
     >>> # Create training corpus. Must be a sequence of sentences (e.g. an iterable or a generator).
     >>> sentences = Text8Corpus(datapath('testcorpus.txt'))
@@ -475,7 +475,7 @@ def __init__(
 
             >>> from gensim.test.utils import datapath
             >>> from gensim.models.word2vec import Text8Corpus
-            >>> from gensim.models.phrases import Phrases, FrozenPhrases
+            >>> from gensim.models.phrases import Phrases
             >>>
             >>> # Load corpus and train a model.
             >>> sentences = Text8Corpus(datapath('testcorpus.txt'))
@@ -732,22 +732,22 @@ def __init__(self, phrases_model):
 
         Notes
         -----
-        After the one-time initialization, a :class:`~gensim.models.phrases.FrozenPhrases` will be much smaller and
-        faster than using the full :class:`~gensim.models.phrases.Phrases` model.
+        After the one-time initialization, a :class:`~gensim.models.phrases.FrozenPhrases` will be much
+        smaller and faster than using the full :class:`~gensim.models.phrases.Phrases` model.
 
         Examples
         ----------
         .. sourcecode:: pycon
 
             >>> from gensim.test.utils import datapath
             >>> from gensim.models.word2vec import Text8Corpus
-            >>> from gensim.models.phrases import Phrases, FrozenPhrases
+            >>> from gensim.models.phrases import Phrases
             >>>
             >>> # Load corpus and train a model.
             >>> sentences = Text8Corpus(datapath('testcorpus.txt'))
             >>> phrases = Phrases(sentences, min_count=1, threshold=1)
             >>>
-            >>> # Export FrozenPhrases that is more efficient but doesn't allow any more training.
+            >>> # Export a FrozenPhrases object that is more efficient but doesn't allow further training.
             >>> frozen_phrases = phrases.freeze()
             >>> print(frozen_phrases[sent])
             [u'trees_graph', u'minors']