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

from typing import Tuple

import torch

from ignite.contrib.metrics.regression._base import _BaseRegression
from ignite.exceptions import NotComputableError
from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce


[docs]class MeanAbsoluteRelativeError(_BaseRegression): r"""Calculate Mean Absolute Relative Error. .. math:: \text{MARE} = \frac{1}{n}\sum_{j=1}^n\frac{\left|A_j-P_j\right|}{\left|A_j\right|} where :math:`A_j` is the ground truth and :math:`P_j` is the predicted value. More details can be found in the reference `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/ftp/arxiv/papers/1809/1809.03006.pdf 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. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = MeanAbsoluteRelativeError() metric.attach(default_evaluator, 'mare') y_true = torch.Tensor([1, 2, 3, 4, 5]) y_pred = y_true * 0.75 state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['mare']) .. testoutput:: 0.25... .. versionchanged:: 0.4.5 - Works with DDP. """
[docs] @reinit__is_reduced def reset(self) -> None: self._sum_of_absolute_relative_errors = torch.tensor(0.0, device=self._device) self._num_samples = 0
def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() if (y == 0).any(): raise NotComputableError("The ground truth has 0.") absolute_error = torch.abs(y_pred - y.view_as(y_pred)) / torch.abs(y.view_as(y_pred)) self._sum_of_absolute_relative_errors += torch.sum(absolute_error).to(self._device) self._num_samples += y.size()[0]
[docs] @sync_all_reduce("_sum_of_absolute_relative_errors", "_num_samples") def compute(self) -> float: if self._num_samples == 0: raise NotComputableError( "MeanAbsoluteRelativeError must have at least one sample before it can be computed." ) return self._sum_of_absolute_relative_errors.item() / self._num_samples

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