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

from typing import Callable, Union

import torch

from ignite.metrics import EpochMetric


def median_absolute_error_compute_fn(y_pred: torch.Tensor, y: torch.Tensor) -> float:
    e = torch.abs(y.view_as(y_pred) - y_pred)
    return torch.median(e).item()


[docs]class MedianAbsoluteError(EpochMetric): r"""Calculates the Median Absolute Error. .. math:: \text{MdAE} = \text{MD}_{j=1,n} \left( |A_j - P_j| \right) 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)` and of type `float32`. .. warning:: Current implementation stores all input data (output and target) in as tensors before computing a metric. This can potentially lead to a memory error if the input data is larger than available RAM. __ https://arxiv.org/abs/1809.03006 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: optional device specification for internal storage. """ def __init__( self, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), ): super(MedianAbsoluteError, self).__init__( median_absolute_error_compute_fn, output_transform=output_transform, device=device, )

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