def get_feature_columns()

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

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

def get_feature_columns(metadata_path, embeddings_dim):
  def _get_num_bucket():
    with tf.io.gfile.GFile(metadata_path, 'rb') as f:
      metadata = pickle.load(f)
    return metadata['N']
    
  categorical_col = tf.feature_column.categorical_column_with_identity(
      key='movie_ids', num_buckets=_get_num_bucket())
  feature_columns = [
    # movie_ids
    tf.feature_column.embedding_column(
      categorical_column=categorical_col, dimension=embeddings_dim,
      combiner='mean')
  ]
  return feature_columns