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Source code for ignite.metrics.regression.pearson_correlation

from typing import Callable, Tuple, Union

import torch

from ignite.exceptions import NotComputableError
from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce

from ignite.metrics.regression._base import _BaseRegression


[docs]class PearsonCorrelation(_BaseRegression): r"""Calculates the `Pearson correlation coefficient <https://en.wikipedia.org/wiki/Pearson_correlation_coefficient>`_. .. math:: r = \frac{\sum_{j=1}^n (P_j-\bar{P})(A_j-\bar{A})} {\max (\sqrt{\sum_{j=1}^n (P_j-\bar{P})^2 \sum_{j=1}^n (A_j-\bar{A})^2}, \epsilon)}, \quad \bar{P}=\frac{1}{n}\sum_{j=1}^n P_j, \quad \bar{A}=\frac{1}{n}\sum_{j=1}^n A_j where :math:`A_j` is the ground truth and :math:`P_j` is the predicted value. - ``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)`. Parameters are inherited from ``Metric.__init__``. Args: eps: a small value to avoid division by zero. Default: 1e-8 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. Examples: To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine. The output of the engine's ``process_function`` needs to be in format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = PearsonCorrelation() metric.attach(default_evaluator, 'corr') y_true = torch.tensor([0., 1., 2., 3., 4., 5.]) y_pred = torch.tensor([0.5, 1.3, 1.9, 2.8, 4.1, 6.0]) state = default_evaluator.run([[y_pred, y_true]]) print(state.metrics['corr']) .. testoutput:: 0.9768688678741455 """ def __init__( self, eps: float = 1e-8, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"), ): super().__init__(output_transform, device) self.eps = eps _state_dict_all_req_keys = ( "_sum_of_y_preds", "_sum_of_ys", "_sum_of_y_pred_squares", "_sum_of_y_squares", "_sum_of_products", "_num_examples", )
[docs] @reinit__is_reduced def reset(self) -> None: self._sum_of_y_preds = torch.tensor(0.0, device=self._device) self._sum_of_ys = torch.tensor(0.0, device=self._device) self._sum_of_y_pred_squares = torch.tensor(0.0, device=self._device) self._sum_of_y_squares = torch.tensor(0.0, device=self._device) self._sum_of_products = torch.tensor(0.0, device=self._device) self._num_examples = 0
def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None: y_pred, y = output[0].detach(), output[1].detach() self._sum_of_y_preds += y_pred.sum().to(self._device) self._sum_of_ys += y.sum().to(self._device) self._sum_of_y_pred_squares += y_pred.square().sum().to(self._device) self._sum_of_y_squares += y.square().sum().to(self._device) self._sum_of_products += (y_pred * y).sum().to(self._device) self._num_examples += y.shape[0]
[docs] @sync_all_reduce( "_sum_of_y_preds", "_sum_of_ys", "_sum_of_y_pred_squares", "_sum_of_y_squares", "_sum_of_products", "_num_examples", ) def compute(self) -> float: n = self._num_examples if n == 0: raise NotComputableError("PearsonCorrelation must have at least one example before it can be computed.") # cov = E[xy] - E[x]*E[y] cov = self._sum_of_products / n - self._sum_of_y_preds * self._sum_of_ys / (n * n) # var = E[x^2] - E[x]^2 y_pred_mean = self._sum_of_y_preds / n y_pred_var = self._sum_of_y_pred_squares / n - y_pred_mean * y_pred_mean y_pred_var = torch.clamp(y_pred_var, min=0.0) y_mean = self._sum_of_ys / n y_var = self._sum_of_y_squares / n - y_mean * y_mean y_var = torch.clamp(y_var, min=0.0) r = cov / torch.clamp(torch.sqrt(y_pred_var * y_var), min=self.eps) return float(r.item())

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