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Source code for ignite.contrib.metrics.regression.geometric_mean_absolute_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 GeometricMeanAbsoluteError(_BaseRegression): r"""Calculates the Geometric Mean Absolute Error. .. math:: \text{GMAE} = \exp(\frac{1}{n}\sum_{j=1}^n\ln(|A_j - P_j|)) 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. 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, ...}``. .. testcode:: metric = GeometricMeanAbsoluteError() metric.attach(default_evaluator, 'gmae') y_pred = torch.Tensor([[3.8], [9.9], [-5.4], [2.1]]) y_true = y_pred * 1.5 state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['gmae']) .. testoutput:: 2.2723... .. versionchanged:: 0.4.5 - Works with DDP. """
[docs] @reinit__is_reduced def reset(self) -> None: self._sum_of_errors = torch.tensor(0.0, device=self._device) self._num_examples = 0
def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() errors = torch.log(torch.abs(y.view_as(y_pred) - y_pred)) self._sum_of_errors += torch.sum(errors).to(self._device) self._num_examples += y.shape[0]
[docs] @sync_all_reduce("_sum_of_errors", "_num_examples") def compute(self) -> float: if self._num_examples == 0: raise NotComputableError( "GeometricMeanAbsoluteError must have at least one example before it can be computed." ) return torch.exp((self._sum_of_errors) / self._num_examples).item()

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