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

from typing import Callable, Union

import torch

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

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)


[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, ...}``. .. include:: defaults.rst :start-after: :orphan: .. 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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