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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)`. - `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

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