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Source code for ignite.contrib.metrics.regression.maximum_absolute_error

import torch

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


[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

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