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

from typing import Callable, Union

import torch

from ignite.metrics import EpochMetric

def median_absolute_percentage_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))
return 100.0 * torch.median(e).item()

[docs]class MedianAbsolutePercentageError(EpochMetric):
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

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, ...}.

.. testcode::

metric = MedianAbsolutePercentageError()
metric.attach(default_evaluator, 'mape')
y_true = torch.Tensor([1, 2, 3, 4, 5])
y_pred = y_true * 0.75
state = default_evaluator.run([[y_pred, y_true]])
print(state.metrics['mape'])

.. testoutput::

25.0...
"""

def __init__(
self, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu")
):
super(MedianAbsolutePercentageError, self).__init__(
median_absolute_percentage_error_compute_fn, output_transform=output_transform, device=device
)


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