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,
)