def compute()

in metrics/auroc.py [0:0]

• 47 lines of code
• 5 McCabe index (conditional complexity)

  def compute(self) -> torch.Tensor:
    """
    Compute and return the accumulated AUROC.
    """
    weights = dim_zero_cat(self.weights)
    predictions = dim_zero_cat(self.predictions)
    target = dim_zero_cat(self.target).type_as(predictions)

    negative_mask = target <= self.label_threshold
    positive_mask = torch.logical_not(negative_mask)

    if not negative_mask.any():
      msg = "Negative class missing. AUROC returned will be meaningless."
      if self.raise_missing_class:
        raise ValueError(msg)
      else:
        logging.warn(msg)
    if not positive_mask.any():
      msg = "Positive class missing. AUROC returned will be meaningless."
      if self.raise_missing_class:
        raise ValueError(msg)
      else:
        logging.warn(msg)

    weighted_actual_negative_sum = torch.sum(
      torch.where(negative_mask, weights, torch.zeros_like(weights))
    )

    weighted_actual_positive_sum = torch.sum(
      torch.where(positive_mask, weights, torch.zeros_like(weights))
    )

    max_positive_negative_weighted_sum = torch.max(
      weighted_actual_negative_sum, weighted_actual_positive_sum
    )

    min_positive_negative_weighted_sum = torch.min(
      weighted_actual_negative_sum, weighted_actual_positive_sum
    )

    # Compute auroc with the weight set to 1 when positive & negative have identical scores.
    auroc_le = _compute_helper(
      target=target,
      weights=weights,
      predictions=predictions,
      min_positive_negative_weighted_sum=min_positive_negative_weighted_sum,
      max_positive_negative_weighted_sum=max_positive_negative_weighted_sum,
      equal_predictions_as_incorrect=False,
    )

    # Compute auroc with the weight set to 0 when positive & negative have identical scores.
    auroc_lt = _compute_helper(
      target=target,
      weights=weights,
      predictions=predictions,
      min_positive_negative_weighted_sum=min_positive_negative_weighted_sum,
      max_positive_negative_weighted_sum=max_positive_negative_weighted_sum,
      equal_predictions_as_incorrect=True,
    )

    # Compute auroc with the weight set to 1/2 when positive & negative have identical scores.
    return auroc_le - (auroc_le - auroc_lt) / 2.0