Source code for ignite.contrib.metrics.regression.geometric_mean_relative_absolute_error
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)`` or ``{'y_pred': y_pred, 'y': 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."
)
return torch.exp(torch.mean(self._sum_of_errors / self._num_examples)).item()