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Source code for ignite.contrib.metrics.regression.fractional_absolute_error

import torch

from ignite.contrib.metrics.regression._base import _BaseRegression
from ignite.exceptions import NotComputableError


[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

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