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

from typing import Tuple

import torch

from ignite.contrib.metrics.regression._base import _BaseRegression
from ignite.exceptions import NotComputableError


[docs]class MeanError(_BaseRegression): r"""Calculates the Mean Error. .. math:: \text{ME} = \frac{1}{n}\sum_{j=1}^n (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 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/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. """
[docs] def reset(self) -> None: self._sum_of_errors = 0.0 self._num_examples = 0
def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output errors = y.view_as(y_pred) - y_pred self._sum_of_errors += torch.sum(errors).item() self._num_examples += y.shape[0]
[docs] def compute(self) -> float: if self._num_examples == 0: raise NotComputableError("MeanError must have at least one example before it can be computed.") return self._sum_of_errors / self._num_examples

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