Source code for ignite.contrib.metrics.regression.median_relative_absolute_error
import torch
from ignite.contrib.metrics.regression._base import _BaseRegressionEpoch
def median_relative_absolute_error_compute_fn(y_pred, y):
e = torch.abs(y.view_as(y_pred) - y_pred) / torch.abs(y.view_as(y_pred) - torch.mean(y))
return torch.median(e).item()
[docs]class MedianRelativeAbsoluteError(_BaseRegressionEpoch):
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
"""
def __init__(self, output_transform=lambda x: x):
super(MedianRelativeAbsoluteError, self).__init__(median_relative_absolute_error_compute_fn, output_transform)