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from typing import Callable, Union
import torch
from ignite.metrics.epoch_metric import EpochMetric
from ignite.metrics.regression._base import _torch_median
def median_relative_absolute_error_compute_fn(y_pred: torch.Tensor, y: torch.Tensor) -> float:
e = torch.abs(y.view_as(y_pred) - y_pred) / torch.abs(y.view_as(y_pred) - torch.mean(y))
return _torch_median(e)
class MedianRelativeAbsoluteError(EpochMetric):
r"""Calculates the Median Relative Absolute Error.
.. math::
\text{MdRAE} = \text{MD}_{j=1,n} \left( \frac{|A_j - P_j|}{|A_j - \bar{A}|} \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.
Examples:
To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine.
The output of the engine's ``process_function`` needs to be in format of
``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``.
.. include:: defaults.rst
:start-after: :orphan:
.. testcode::
metric = MedianRelativeAbsoluteError()
metric.attach(default_evaluator, 'mrae')
y_true = torch.tensor([0., 1., 2., 3., 4., 5.])
y_pred = y_true * 0.75
state = default_evaluator.run([[y_pred, y_true]])
print(state.metrics['mrae'])
.. testoutput::
0.5...
"""
def __init__(
self, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu")
):
super(MedianRelativeAbsoluteError, self).__init__(
median_relative_absolute_error_compute_fn, output_transform=output_transform, device=device
)
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