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# Source code for ignite.contrib.metrics.regression.geometric_mean_relative_absolute_error

from __future__ import division

import torch

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

[docs]class GeometricMeanRelativeAbsoluteError(_BaseRegression):
r"""
Calculates the Geometric Mean Relative Absolute Error:

:math:\text{GMRAE} = \exp(\frac{1}{n}\sum_{j=1}^n \ln\frac{|A_j - P_j|}{|A_j - \bar{A}|})

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_y = 0.0
self._num_examples = 0
self._sum_of_errors = 0.0

def _update(self, output):
y_pred, y = output
self._sum_y += y.sum()
self._num_examples += y.shape[0]
y_mean = self._sum_y / self._num_examples
numerator = torch.abs(y.view_as(y_pred) - y_pred)
denominator = torch.abs(y.view_as(y_pred) - y_mean)
self._sum_of_errors += torch.log(numerator / denominator).sum()

def compute(self):
if self._num_examples == 0:
raise NotComputableError('GeometricMeanRelativeAbsoluteError must have at least '
'one example before it can be computed.')