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

from __future__ import division

import torch

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


[docs]class WaveHedgesDistance(_BaseRegression): r""" Calculates the Wave Hedges Distance. :math:`\text{WHD} = \sum_{j=1}^n\frac{|A_j - P_j|}{max(A_j, P_j)}`, 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)`. - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`. __ https://arxiv.org/abs/1809.03006 """ def reset(self): self._sum_of_errors = 0.0 def _update(self, output): y_pred, y = output errors = torch.abs(y.view_as(y_pred) - y_pred) / torch.max(y_pred, y.view_as(y_pred)) self._sum_of_errors += torch.sum(errors).item() def compute(self): return self._sum_of_errors

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