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# Source code for ignite.contrib.metrics.regression.median_absolute_percentage_error

import torch

from ignite.contrib.metrics.regression._base import _BaseRegressionEpoch

def median_absolute_percentage_error_compute_fn(y_pred, y):
e = torch.abs(y.view_as(y_pred) - y_pred) / torch.abs(y.view_as(y_pred))
return 100.0 * torch.median(e).item()

[docs]class MedianAbsolutePercentageError(_BaseRegressionEpoch):
r"""
Calculates the Median Absolute Percentage Error:

:math:\text{MdAPE} = 100 \cdot \text{MD}_{j=1,n} \left( \frac{|A_j - P_j|}{|A_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

"""

def __init__(self, output_transform=lambda x: x):
super(MedianAbsolutePercentageError, self).__init__(
median_absolute_percentage_error_compute_fn, output_transform
)


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