text: from __future__ import division
  
  import torch
  
  from ignite.exceptions import NotComputableError
  from ignite.contrib.metrics.regression._base import _BaseRegression
  
  
  class FractionalBias(_BaseRegression):
      r"""
      Calculates the Fractional Bias:
  
      :math:`\text{FB} = \frac{1}{n}\sum_{j=1}^n\frac{2 (A_j - P_j)}{A_j + P_j}`,
  
      where :math:`A_j` is the ground truth and :math:`P_j` is the predicted value.
  
      More details can be found in `Botchkarev 2018`__.
  
      - `update` must receive output of the form `(y_pred, y)`.
      - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`.
  
      __ https://arxiv.org/abs/1809.03006
  
      """
      def reset(self):
          self._sum_of_errors = 0.0
          self._num_examples = 0
  
      def _update(self, output):
          y_pred, y = output
          errors = 2 * (y.view_as(y_pred) - y_pred) / (y_pred + y.view_as(y_pred))
          self._sum_of_errors += torch.sum(errors).item()
          self._num_examples += y.shape[0]
  
      def compute(self):
          if self._num_examples == 0:
              raise NotComputableError('FractionalBias must have at least one example before it can be computed.')
          return self._sum_of_errors / self._num_examples
  