Source code for ignite.contrib.metrics.regression.median_absolute_percentage_error
from __future__ import division
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)