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from typing import Tuple
import torch
from ignite.exceptions import NotComputableError
from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce
from ignite.metrics.regression._base import _BaseRegression
class MeanAbsoluteRelativeError(_BaseRegression):
r"""Calculate Mean Absolute Relative Error (MARE), also known as Mean Absolute Percentage Error (MAPE).
.. math::
\text{MARE} = \frac{1}{n}\sum_{j=1}^n\frac{\left|A_j-P_j\right|}{\left|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 the reference `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)`.
__ https://arxiv.org/ftp/arxiv/papers/1809/1809.03006.pdf
Parameters are inherited from ``Metric.__init__``.
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: specifies which device updates are accumulated on. Setting the
metric's device to be the same as your ``update`` arguments ensures the ``update`` method is
non-blocking. By default, CPU.
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 = MeanAbsoluteRelativeError()
metric.attach(default_evaluator, 'mare')
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['mare'])
.. testoutput::
0.25...
.. versionchanged:: 0.4.5
- Works with DDP.
"""
_state_dict_all_req_keys = ("_sum_of_absolute_relative_errors", "_num_samples")
@reinit__is_reduced
def reset(self) -> None:
self._sum_of_absolute_relative_errors = torch.tensor(0.0, device=self._device)
self._num_samples = 0
def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None:
y_pred, y = output[0].detach(), output[1].detach()
if (y == 0).any():
raise NotComputableError("The ground truth has 0.")
absolute_error = torch.abs(y_pred - y.view_as(y_pred)) / torch.abs(y.view_as(y_pred))
self._sum_of_absolute_relative_errors += torch.sum(absolute_error).to(self._device)
self._num_samples += y.size()[0]
@sync_all_reduce("_sum_of_absolute_relative_errors", "_num_samples")
def compute(self) -> float:
if self._num_samples == 0:
raise NotComputableError(
"MeanAbsoluteRelativeError must have at least one sample before it can be computed."
)
return self._sum_of_absolute_relative_errors.item() / self._num_samples
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