Source code for pipelinex.extras.ops.ignite.metrics.cohen_kappa_score

from typing import Sequence, Union

import torch
from ignite.exceptions import NotComputableError
from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce
from sklearn.metrics import cohen_kappa_score

__all__ = ["CohenKappaScore"]

[docs]class CohenKappaScore(Metric): """ Calculates the cohen kappa score. - `update` must receive output of the form `(y_pred, y)` or `{'y_pred': y_pred, 'y': y}`. """
[docs] def __init__(self, *args, **kwargs): self.args = args self.kwargs = kwargs super(CohenKappaScore, self).__init__(device="cpu")
[docs] @reinit__is_reduced def reset(self) -> None: self._sum_of_cohen_kappa = 0.0 self._num_examples = 0
[docs] @reinit__is_reduced def update(self, output: Sequence[torch.Tensor]) -> None: y_pred, y = output assert y_pred.shape == y.shape y_pred_arr = y_pred.numpy().flatten().astype(int) y_arr = y.numpy().flatten().astype(int) cohen_kappa = cohen_kappa_score(y_pred_arr, y_arr, *self.args, **self.kwargs) self._sum_of_cohen_kappa += cohen_kappa self._num_examples += y.shape[0]
[docs] @sync_all_reduce("_sum_of_squared_errors", "_num_examples") def compute(self) -> Union[float, torch.Tensor]: if self._num_examples == 0: raise NotComputableError( "MeanSquaredError must have at least one example before it can be computed." ) return self._sum_of_cohen_kappa / self._num_examples