Source code for ignite.contrib.metrics.regression.geometric_mean_relative_absolute_error
from typing import Tuple, Union, cast
import torch
from ignite.contrib.metrics.regression._base import _BaseRegression
from ignite.exceptions import NotComputableError
[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
Parameters are inherited from ``Metric.__init__``.
Args:
output_transform: a callable that is used to transform the
:class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
form expected by the metric. This can be useful if, for example, you have a multi-output model and
you want to compute the metric with respect to one of the outputs.
By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
device: specifies which device updates are accumulated on. Setting the
metric's device to be the same as your ``update`` arguments ensures the ``update`` method is
non-blocking. By default, CPU.
"""
[docs] def reset(self) -> None:
self._sum_y = 0.0 # type: Union[float, torch.Tensor]
self._num_examples = 0
self._sum_of_errors = 0.0 # type: Union[float, torch.Tensor]
def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None:
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()
[docs] def compute(self) -> float:
if self._num_examples == 0:
raise NotComputableError(
"GeometricMeanRelativeAbsoluteError must have at least one example before it can be computed."
)
return torch.exp(torch.mean(cast(torch.Tensor, self._sum_of_errors) / self._num_examples)).item()