def load_movielens_data()

in recommended-item-search/data_preparation.py [0:0]

• 16 lines of code
• 1 McCabe index (conditional complexity)

def load_movielens_data():
  # Download MovieLens dataset
  urllib.request.urlretrieve(
      url=os.path.join(FLAGS.base_url, FLAGS.filename),
      filename=FLAGS.filename)
  
  # Extract MovieLens dataset from zipfile
  zipfile.ZipFile(FLAGS.filename, 'r').extractall()

  # Load MovieLens dataset
  tmp_dir = FLAGS.filename.split('.')[0]
  movies = pandas.read_csv(
      filepath_or_buffer=os.path.join(tmp_dir, 'movies.csv'),
      names=['movie_id', 'title', 'genres'], header=0)
  ratings = pandas.read_csv(
      filepath_or_buffer=os.path.join(tmp_dir, 'ratings.csv'),
      names=['user_id', 'movie_id', 'rating', 'unix_timestamp'],
      header=0)
  
  # Remove unnecessary files.
  tf.gfile.DeleteRecursively(tmp_dir)
  tf.gfile.Remove(FLAGS.filename)
  return movies, ratings