Source code for ignite.contrib.metrics.regression.mean_normalized_bias
import torch
from ignite.contrib.metrics.regression._base import _BaseRegression
from ignite.exceptions import NotComputableError
[docs]class MeanNormalizedBias(_BaseRegression):
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)`` or ``{'y_pred': y_pred, 'y': 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
self._num_examples = 0
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]
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