def compute_average_precision()

in detic/evaluation/oideval.py [0:0]

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

def compute_average_precision(precision, recall):
  """Compute Average Precision according to the definition in VOCdevkit.
  Precision is modified to ensure that it does not decrease as recall
  decrease.
  Args:
    precision: A float [N, 1] numpy array of precisions
    recall: A float [N, 1] numpy array of recalls
  Raises:
    ValueError: if the input is not of the correct format
  Returns:
    average_precison: The area under the precision recall curve. NaN if
      precision and recall are None.
  """
  if precision is None:
    if recall is not None:
      raise ValueError("If precision is None, recall must also be None")
    return np.NAN

  if not isinstance(precision, np.ndarray) or not isinstance(
      recall, np.ndarray):
    raise ValueError("precision and recall must be numpy array")
  if precision.dtype != np.float or recall.dtype != np.float:
    raise ValueError("input must be float numpy array.")
  if len(precision) != len(recall):
    raise ValueError("precision and recall must be of the same size.")
  if not precision.size:
    return 0.0
  if np.amin(precision) < 0 or np.amax(precision) > 1:
    raise ValueError("Precision must be in the range of [0, 1].")
  if np.amin(recall) < 0 or np.amax(recall) > 1:
    raise ValueError("recall must be in the range of [0, 1].")
  if not all(recall[i] <= recall[i + 1] for i in range(len(recall) - 1)):
    raise ValueError("recall must be a non-decreasing array")

  recall = np.concatenate([[0], recall, [1]])
  precision = np.concatenate([[0], precision, [0]])

  for i in range(len(precision) - 2, -1, -1):
    precision[i] = np.maximum(precision[i], precision[i + 1])
  indices = np.where(recall[1:] != recall[:-1])[0] + 1
  average_precision = np.sum(
      (recall[indices] - recall[indices - 1]) * precision[indices])
  return average_precision