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

from __future__ import division

import torch

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

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

:math:\text{GMAE} = \exp(\frac{1}{n}\sum_{j=1}^n\ln(|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).
- 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 = torch.log(torch.abs(y.view_as(y_pred) - y_pred))
self._sum_of_errors += torch.sum(errors)
self._num_examples += y.shape[0]

def compute(self):
if self._num_examples == 0:
raise NotComputableError('GeometricMeanAbsoluteError must have at '
'least one example before it can be computed.')
return torch.exp(self._sum_of_errors / self._num_examples).item()


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