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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)

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