calc_uniqueness.py

def compare_bars(input_bar, artists_bars):
  '''
    input_bars are the fire bars our AI generates
    artists_bars are the original bars for the artist

    The lower the score the better! We want unique bars
  '''
  # Converts sentences to matrix of token counts
  avg_dist = 0
  total_counted = 0
  for bar in artists_bars:
    v = CountVectorizer()
    # Vectorize the sentences
    word_vector = v.fit_transform([input_bar, bar])
    # Compute the cosine distance between the sentence vectors
    cos_dist = 1-pdist(word_vector.toarray(), 'cosine')[0]
    if not math.isnan(cos_dist):
      avg_dist += 1-pdist(word_vector.toarray(), 'cosine')[0]
      total_counted += 1
  return avg_dist/total_counted