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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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