Fix issues of flake8==3.7.1

gensim/models/deprecated/fasttext.py

@@ -601,9 +601,9 @@ def train(self, sentences, total_examples=None, total_words=None,
         """
         self.neg_labels = []
         if self.negative > 0:
-                # precompute negative labels optimization for pure-python training
-                self.neg_labels = zeros(self.negative + 1)
-                self.neg_labels[0] = 1.
+            # precompute negative labels optimization for pure-python training
+            self.neg_labels = zeros(self.negative + 1)
+            self.neg_labels[0] = 1.
 
         Word2Vec.train(
             self, sentences, total_examples=self.corpus_count, epochs=self.iter,

gensim/models/ldaseqmodel.py

@@ -1635,6 +1635,7 @@ def df_obs(x, *args):
     if model == "DTM":
         deriv = sslm.compute_obs_deriv(word, word_counts, totals, mean_deriv_mtx, deriv)
     elif model == "DIM":
-        deriv = sslm.compute_obs_deriv_fixed(p.word, p.word_counts, p.totals, p.sslm, p.mean_deriv_mtx, deriv)  # noqa:F821
+        deriv = sslm.compute_obs_deriv_fixed(
+            p.word, p.word_counts, p.totals, p.sslm, p.mean_deriv_mtx, deriv)  # noqa:F821
 
     return np.negative(deriv)

gensim/models/utils_any2vec.py

@@ -247,48 +247,48 @@ def ft_ngram_hashes(word, minn, maxn, num_buckets, fb_compatible=True):
 
 
 def _save_word2vec_format(fname, vocab, vectors, fvocab=None, binary=False, total_vec=None):
-        """Store the input-hidden weight matrix in the same format used by the original
-        C word2vec-tool, for compatibility.
-
-        Parameters
-        ----------
-        fname : str
-            The file path used to save the vectors in.
-        vocab : dict
-            The vocabulary of words.
-        vectors : numpy.array
-            The vectors to be stored.
-        fvocab : str, optional
-            File path used to save the vocabulary.
-        binary : bool, optional
-            If True, the data wil be saved in binary word2vec format, else it will be saved in plain text.
-        total_vec : int, optional
-            Explicitly specify total number of vectors
-            (in case word vectors are appended with document vectors afterwards).
-
-        """
-        if not (vocab or vectors):
-            raise RuntimeError("no input")
-        if total_vec is None:
-            total_vec = len(vocab)
-        vector_size = vectors.shape[1]
-        if fvocab is not None:
-            logger.info("storing vocabulary in %s", fvocab)
-            with utils.smart_open(fvocab, 'wb') as vout:
-                for word, vocab_ in sorted(iteritems(vocab), key=lambda item: -item[1].count):
-                    vout.write(utils.to_utf8("%s %s\n" % (word, vocab_.count)))
-        logger.info("storing %sx%s projection weights into %s", total_vec, vector_size, fname)
-        assert (len(vocab), vector_size) == vectors.shape
-        with utils.smart_open(fname, 'wb') as fout:
-            fout.write(utils.to_utf8("%s %s\n" % (total_vec, vector_size)))
-            # store in sorted order: most frequent words at the top
+    """Store the input-hidden weight matrix in the same format used by the original
+    C word2vec-tool, for compatibility.
+
+    Parameters
+    ----------
+    fname : str
+        The file path used to save the vectors in.
+    vocab : dict
+        The vocabulary of words.
+    vectors : numpy.array
+        The vectors to be stored.
+    fvocab : str, optional
+        File path used to save the vocabulary.
+    binary : bool, optional
+        If True, the data wil be saved in binary word2vec format, else it will be saved in plain text.
+    total_vec : int, optional
+        Explicitly specify total number of vectors
+        (in case word vectors are appended with document vectors afterwards).
+
+    """
+    if not (vocab or vectors):
+        raise RuntimeError("no input")
+    if total_vec is None:
+        total_vec = len(vocab)
+    vector_size = vectors.shape[1]
+    if fvocab is not None:
+        logger.info("storing vocabulary in %s", fvocab)
+        with utils.smart_open(fvocab, 'wb') as vout:
             for word, vocab_ in sorted(iteritems(vocab), key=lambda item: -item[1].count):
-                row = vectors[vocab_.index]
-                if binary:
-                    row = row.astype(REAL)
-                    fout.write(utils.to_utf8(word) + b" " + row.tostring())
-                else:
-                    fout.write(utils.to_utf8("%s %s\n" % (word, ' '.join(repr(val) for val in row))))
+                vout.write(utils.to_utf8("%s %s\n" % (word, vocab_.count)))
+    logger.info("storing %sx%s projection weights into %s", total_vec, vector_size, fname)
+    assert (len(vocab), vector_size) == vectors.shape
+    with utils.smart_open(fname, 'wb') as fout:
+        fout.write(utils.to_utf8("%s %s\n" % (total_vec, vector_size)))
+        # store in sorted order: most frequent words at the top
+        for word, vocab_ in sorted(iteritems(vocab), key=lambda item: -item[1].count):
+            row = vectors[vocab_.index]
+            if binary:
+                row = row.astype(REAL)
+                fout.write(utils.to_utf8(word) + b" " + row.tostring())
+            else:
+                fout.write(utils.to_utf8("%s %s\n" % (word, ' '.join(repr(val) for val in row))))
 
 
 def _load_word2vec_format(cls, fname, fvocab=None, binary=False, encoding='utf8', unicode_errors='strict',

gensim/similarities/__init__.py

@@ -3,6 +3,15 @@
 """
 
 # bring classes directly into package namespace, to save some typing
-from .docsim import Similarity, MatrixSimilarity, SparseMatrixSimilarity, SoftCosineSimilarity, WmdSimilarity  # noqa:F401
-from .termsim import TermSimilarityIndex, UniformTermSimilarityIndex, SparseTermSimilarityMatrix  # noqa:F401
+
+from .docsim import (  # noqa:F401
+    Similarity,
+    MatrixSimilarity,
+    SparseMatrixSimilarity,
+    SoftCosineSimilarity,
+    WmdSimilarity)
+from .termsim import (  # noqa:F401
+    TermSimilarityIndex,
+    UniformTermSimilarityIndex,
+    SparseTermSimilarityMatrix)
 from .levenshtein import LevenshteinSimilarityIndex  # noqa:F401

gensim/test/test_ldaseqmodel.py

@@ -13,7 +13,7 @@
 
 
 class TestLdaSeq(unittest.TestCase):
-        # we are setting up a DTM model and fitting it, and checking topic-word and doc-topic results.
+    # we are setting up a DTM model and fitting it, and checking topic-word and doc-topic results.
     def setUp(self):
         texts = [
             [u'senior', u'studios', u'studios', u'studios', u'creators', u'award', u'mobile', u'currently',