eval_metrics.py 文件源码

def label_ranking_reciprocal_rank(label,  # [sent_num]
                                  preds): # [sent_num]
  """ Calcualting the reciprocal rank according to definition,
  """
  rank = np.argsort(preds)[::-1]

  #pos_rank = np.take(rank, np.where(label == 1)[0])
  #return np.mean(1.0 / pos_rank)

  if_find = False 
  pos = 0
  for r in rank:
      pos += 1
      if label[r] == 1:
          first_pos_r = pos
          if_find = True
          break

  assert(if_find)

  return 1.0 / first_pos_r