Source code for ignite.metrics.mean_absolute_error

from typing import Sequence, Union

import torch

from ignite.exceptions import NotComputableError
from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce

__all__ = ["MeanAbsoluteError"]


class MeanAbsoluteError(Metric):
    r"""Calculates `the mean absolute error <https://en.wikipedia.org/wiki/Mean_absolute_error>`_.

    .. math:: \text{MAE} = \frac{1}{N} \sum_{i=1}^N \lvert y_{i} - x_{i} \rvert

    where :math:`y_{i}` is the prediction tensor and :math:`x_{i}` is ground true tensor.

    - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
    """

    @reinit__is_reduced
    def reset(self) -> None:
        self._sum_of_absolute_errors = torch.tensor(0.0, device=self._device)
        self._num_examples = 0

    @reinit__is_reduced
    def update(self, output: Sequence[torch.Tensor]) -> None:
        y_pred, y = output[0].detach(), output[1].detach()
        absolute_errors = torch.abs(y_pred - y.view_as(y_pred))
        self._sum_of_absolute_errors += torch.sum(absolute_errors).to(self._device)
        self._num_examples += y.shape[0]

    @sync_all_reduce("_sum_of_absolute_errors", "_num_examples")
    def compute(self) -> Union[float, torch.Tensor]:
        if self._num_examples == 0:
            raise NotComputableError("MeanAbsoluteError must have at least one example before it can be computed.")
        return self._sum_of_absolute_errors.item() / self._num_examples