Source code for ignite.contrib.metrics.regression.fractional_absolute_error
from __future__ import division
import torch
from ignite.exceptions import NotComputableError
from ignite.contrib.metrics.regression._base import _BaseRegression
[docs]class FractionalAbsoluteError(_BaseRegression):
r"""
Calculates the Fractional Absolute Error.
:math:`\text{FAE} = \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)` or `{'y_pred': y_pred, 'y': 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 * torch.abs(y.view_as(y_pred) - y_pred) / (torch.abs(y_pred) + torch.abs(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('FractionalAbsoluteError must have at least '
'one example before it can be computed.')
return self._sum_of_errors / self._num_examples