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

from __future__ import division

import torch

from ignite.exceptions import NotComputableError
from ignite.metrics.metric import Metric

[docs]class MeanNormalizedBias(Metric): r""" Calculates the Mean Normalized Bias: :math:`\text{MNB} = \frac{1}{n}\sum_{j=1}^n\frac{A_j - P_j}{A_j}`, 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)`. - `y` and `y_pred` must be of same shape. __ """
[docs] def reset(self): self._sum_of_errors = 0.0 self._num_examples = 0
[docs] def update(self, output): y_pred, y = output if (y == 0).any(): raise NotComputableError('The ground truth has 0.') errors = (y.view_as(y_pred) - y_pred) / y self._sum_of_errors += torch.sum(errors).item() self._num_examples += y.shape[0]
[docs] def compute(self): if self._num_examples == 0: raise NotComputableError('MeanNormalizedBias must have at least one example before it can be computed') return self._sum_of_errors / self._num_examples

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