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

from typing import Tuple

import torch

from ignite.contrib.metrics.regression._base import _BaseRegression
from ignite.exceptions import NotComputableError
from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce

[docs]class R2Score(_BaseRegression): r"""Calculates the R-Squared, the `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`. Parameters are inherited from ``Metric.__init__``. Args: output_transform: a callable that is used to transform the :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs. By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``. device: specifies which device updates are accumulated on. Setting the metric's device to be the same as your ``update`` arguments ensures the ``update`` method is non-blocking. By default, CPU. .. versionchanged:: 0.4.3 Works with DDP. """
[docs] @reinit__is_reduced def reset(self) -> None: self._num_examples = 0 self._sum_of_errors = torch.tensor(0.0, device=self._device) self._y_sq_sum = torch.tensor(0.0, device=self._device) self._y_sum = torch.tensor(0.0, device=self._device)
def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output self._num_examples += y.shape[0] self._sum_of_errors += torch.sum(torch.pow(y_pred - y, 2)).to(self._device) self._y_sum += torch.sum(y).to(self._device) self._y_sq_sum += torch.sum(torch.pow(y, 2)).to(self._device)
[docs] @sync_all_reduce("_num_examples", "_sum_of_errors", "_y_sq_sum", "_y_sum") def compute(self) -> float: 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.item() / (self._y_sq_sum.item() - (self._y_sum.item() ** 2) / self._num_examples)

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