Source code for ignite.contrib.metrics.regression.mean_absolute_relative_error
from __future__ import division
import torch
from ignite.exceptions import NotComputableError
from ignite.contrib.metrics.regression._base import _BaseRegression
[docs]class MeanAbsoluteRelativeError(_BaseRegression):
r"""
Calculate Mean Absolute Relative Error:
:math:`\text{MARE} = \frac{1}{n}\sum_{j=1}^n\frac{\left|A_j-P_j\right|}{\left|A_j\right|}`,
where :math:`A_j` is the ground truth and :math:`P_j` is the predicted value.
More details can be found in the reference `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/ftp/arxiv/papers/1809/1809.03006.pdf
"""
def reset(self):
self._sum_of_absolute_relative_errors = 0.0
self._num_samples = 0
def _update(self, output):
y_pred, y = output
if (y == 0).any():
raise NotComputableError('The ground truth has 0.')
absolute_error = torch.abs(y_pred - y.view_as(y_pred)) / torch.abs(y.view_as(y_pred))
self._sum_of_absolute_relative_errors += torch.sum(absolute_error).item()
self._num_samples += y.size()[0]
def compute(self):
if self._num_samples == 0:
raise NotComputableError('MeanAbsoluteRelativeError must have at least'
'one sample before it can be computed.')
return self._sum_of_absolute_relative_errors / self._num_samples