Source code for ignite.contrib.metrics.regression.maximum_absolute_error
import torch
from ignite.exceptions import NotComputableError
from ignite.contrib.metrics.regression._base import _BaseRegression
[docs]class MaximumAbsoluteError(_BaseRegression):
r"""
Calculates the Maximum Absolute Error:
:math:`\text{MaxAE} = \max_{j=1,n} \left( \lvert A_j-P_j \rvert \right)`,
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._max_of_absolute_errors = -1
def _update(self, output):
y_pred, y = output
mae = torch.abs(y_pred - y.view_as(y_pred)).max().item()
if self._max_of_absolute_errors < mae:
self._max_of_absolute_errors = mae
def compute(self):
if self._max_of_absolute_errors < 0:
raise NotComputableError('MaximumAbsoluteError must have at least one example before it can be computed.')
return self._max_of_absolute_errors