eval_util.py 文件源码

def calculate_precision_at_equal_recall_rate(predictions, actuals):
  """Performs a local (numpy) calculation of the PERR.

  Args:
    predictions: Matrix containing the outputs of the model.
      Dimensions are 'batch' x 'num_classes'.
    actuals: Matrix containing the ground truth labels.
      Dimensions are 'batch' x 'num_classes'.

  Returns:
    float: The average precision at equal recall rate across the entire batch.
  """
  aggregated_precision = 0.0
  num_videos = actuals.shape[0]
  for row in numpy.arange(num_videos):
    num_labels = int(numpy.sum(actuals[row]))
    top_indices = numpy.argpartition(predictions[row],
                                     -num_labels)[-num_labels:]
    item_precision = 0.0
    for label_index in top_indices:
      if predictions[row][label_index] > 0:
        item_precision += actuals[row][label_index]
    item_precision /= top_indices.size
    aggregated_precision += item_precision
  aggregated_precision /= num_videos
  return aggregated_precision