Source code for ignite.contrib.metrics.regression.r2_score
from __future__ import division
import torch
from ignite.exceptions import NotComputableError
from ignite.contrib.metrics.regression._base import _BaseRegression
[docs]class R2Score(_BaseRegression):
r"""
Calculates the R-Squared, the
`coefficient of determination <https://en.wikipedia.org/wiki/Coefficient_of_determination>`_:
:math:`R^2 = 1 - \frac{\sum_{j=1}^n(A_j - P_j)^2}{\sum_{j=1}^n(A_j - \bar{A})^2}`,
where :math:`A_j` is the ground truth, :math:`P_j` is the predicted value and
:math:`\bar{A}` is the mean of the ground truth.
- `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`.
"""
def reset(self):
self._num_examples = 0
self._sum_of_errors = 0
self._y_sq_sum = 0
self._y_sum = 0
def _update(self, output):
y_pred, y = output
self._num_examples += y.shape[0]
self._sum_of_errors += torch.sum(torch.pow(y_pred - y, 2)).item()
self._y_sum += torch.sum(y).item()
self._y_sq_sum += torch.sum(torch.pow(y, 2)).item()
def compute(self):
if self._num_examples == 0:
raise NotComputableError('R2Score must have at least one example before it can be computed.')
return 1 - self._sum_of_errors / (self._y_sq_sum - (self._y_sum ** 2) / self._num_examples)