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


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.
    """

    @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)

    @sync_all_reduce("_sum_of_errors")
    def compute(self) -> float:
        return self._sum_of_errors.item()