Fix lint issues after pull.

Change: 151154030

1 files changed, 19 insertions, 15 deletions

diff --git a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py
index f54a7c37a1..13e5717b0d 100644
--- a/tensorflow/examples/tutorials/word2vec/word2vec_basic.py
+++ b/tensorflow/examples/tutorials/word2vec/word2vec_basic.py
@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
+"""Basic word2vec example."""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -50,21 +51,22 @@ filename = maybe_download('text8.zip', 31344016)
 
 # Read the data into a list of strings.
 def read_data(filename):
-  """Extract the first file enclosed in a zip file as a list of words"""
+  """Extract the first file enclosed in a zip file as a list of words."""
   with zipfile.ZipFile(filename) as f:
     data = tf.compat.as_str(f.read(f.namelist()[0])).split()
   return data
 
-words = read_data(filename)
-print('Data size', len(words))
+vocabulary = read_data(filename)
+print('Data size', len(vocabulary))
 
 # Step 2: Build the dictionary and replace rare words with UNK token.
 vocabulary_size = 50000
 
 
-def build_dataset(words, vocabulary_size):
+def build_dataset(words, n_words):
+  """Process raw inputs into a dataset."""
   count = [['UNK', -1]]
-  count.extend(collections.Counter(words).most_common(vocabulary_size - 1))
+  count.extend(collections.Counter(words).most_common(n_words - 1))
   dictionary = dict()
   for word, _ in count:
     dictionary[word] = len(dictionary)
@@ -78,11 +80,12 @@ def build_dataset(words, vocabulary_size):
       unk_count += 1
     data.append(index)
   count[0][1] = unk_count
-  reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys()))
-  return data, count, dictionary, reverse_dictionary
+  reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys()))
+  return data, count, dictionary, reversed_dictionary
 
-data, count, dictionary, reverse_dictionary = build_dataset(words, vocabulary_size)
-del words  # Hint to reduce memory.
+data, count, dictionary, reverse_dictionary = build_dataset(vocabulary,
+                                                            vocabulary_size)
+del vocabulary  # Hint to reduce memory.
 print('Most common words (+UNK)', count[:5])
 print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])
 
@@ -189,7 +192,7 @@ num_steps = 100001
 with tf.Session(graph=graph) as session:
   # We must initialize all variables before we use them.
   init.run()
-  print("Initialized")
+  print('Initialized')
 
   average_loss = 0
   for step in xrange(num_steps):
@@ -206,7 +209,7 @@ with tf.Session(graph=graph) as session:
       if step > 0:
         average_loss /= 2000
       # The average loss is an estimate of the loss over the last 2000 batches.
-      print("Average loss at step ", step, ": ", average_loss)
+      print('Average loss at step ', step, ': ', average_loss)
       average_loss = 0
 
     # Note that this is expensive (~20% slowdown if computed every 500 steps)
@@ -216,10 +219,10 @@ with tf.Session(graph=graph) as session:
         valid_word = reverse_dictionary[valid_examples[i]]
         top_k = 8  # number of nearest neighbors
         nearest = (-sim[i, :]).argsort()[1:top_k + 1]
-        log_str = "Nearest to %s:" % valid_word
+        log_str = 'Nearest to %s:' % valid_word
         for k in xrange(top_k):
           close_word = reverse_dictionary[nearest[k]]
-          log_str = "%s %s," % (log_str, close_word)
+          log_str = '%s %s,' % (log_str, close_word)
         print(log_str)
   final_embeddings = normalized_embeddings.eval()
 
@@ -227,7 +230,7 @@ with tf.Session(graph=graph) as session:
 
 
 def plot_with_labels(low_dim_embs, labels, filename='tsne.png'):
-  assert low_dim_embs.shape[0] >= len(labels), "More labels than embeddings"
+  assert low_dim_embs.shape[0] >= len(labels), 'More labels than embeddings'
   plt.figure(figsize=(18, 18))  # in inches
   for i, label in enumerate(labels):
     x, y = low_dim_embs[i, :]
@@ -242,6 +245,7 @@ def plot_with_labels(low_dim_embs, labels, filename='tsne.png'):
   plt.savefig(filename)
 
 try:
+  # pylint: disable=g-import-not-at-top
   from sklearn.manifold import TSNE
   import matplotlib.pyplot as plt
 
@@ -252,4 +256,4 @@ try:
   plot_with_labels(low_dim_embs, labels)
 
 except ImportError:
-  print("Please install sklearn, matplotlib, and scipy to visualize embeddings.")
+  print('Please install sklearn, matplotlib, and scipy to show embeddings.')