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

from typing import Callable, Tuple, Union

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


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`.

    .. versionchanged:: 0.4.3
        Works with DDP.
    """

    def __init__(
        self, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"),
    ):
        super(R2Score, self).__init__(output_transform, device)

    @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)

    @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)