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

from typing import Tuple

import torch

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


[docs]class CanberraMetric(_BaseRegression): r"""Calculates the Canberra Metric. .. math:: \text{CM} = \sum_{j=1}^n\frac{|A_j - P_j|}{|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`_ or `scikit-learn distance metrics`_ - ``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)`. .. _scikit-learn distance metrics: https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.DistanceMetric.html 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. .. _`Botchkarev 2018`: https://arxiv.org/ftp/arxiv/papers/1809/1809.03006.pdf .. versionchanged:: 0.4.3 - Fixed implementation: ``abs`` in denominator. - Works with DDP. """
[docs] @reinit__is_reduced def reset(self) -> None: self._sum_of_errors = torch.tensor(0.0, device=self._device)
def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() errors = torch.abs(y - y_pred) / (torch.abs(y_pred) + torch.abs(y) + 1e-15) self._sum_of_errors += torch.sum(errors).to(self._device)
[docs] @sync_all_reduce("_sum_of_errors") def compute(self) -> float: return self._sum_of_errors.item()

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