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# Source code for ignite.contrib.metrics.regression.r2_score

import torch

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

[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)


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