Source code for ignite.metrics.cohen_kappa

from typing import Callable, Optional, Union

import torch

from ignite.metrics.epoch_metric import EpochMetric

[docs]class CohenKappa(EpochMetric): """Compute different types of Cohen's Kappa: Non-Wieghted, Linear, Quadratic. Accumulating predictions and the ground-truth during an epoch and applying `sklearn.metrics.cohen_kappa_score < generated/sklearn.metrics.cohen_kappa_score.html>`_ . 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. weights: a string is used to define the type of Cohen's Kappa whether Non-Weighted or Linear or Quadratic. Default, None. check_compute_fn: Default False. If True, `cohen_kappa_score <>`_ is run on the first batch of data to ensure there are no issues. User will be warned in case there are any issues computing the function. device: optional device specification for internal storage. 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 the format of ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. If not, ``output_tranform`` can be added to the metric to transform the output into the form expected by the metric. .. include:: defaults.rst :start-after: :orphan: .. testcode:: metric = CohenKappa() metric.attach(default_evaluator, 'ck') y_true = torch.tensor([2, 0, 2, 2, 0, 1]) y_pred = torch.tensor([0, 0, 2, 2, 0, 2]) state =[[y_pred, y_true]]) print(state.metrics['ck']) .. testoutput:: 0.4285... """ def __init__( self, output_transform: Callable = lambda x: x, weights: Optional[str] = None, check_compute_fn: bool = False, device: Union[str, torch.device] = torch.device("cpu"), ): try: from sklearn.metrics import cohen_kappa_score # noqa: F401 except ImportError: raise ModuleNotFoundError("This contrib module requires scikit-learn to be installed.") if weights not in (None, "linear", "quadratic"): raise ValueError("Kappa Weighting type must be None or linear or quadratic.") # initalize weights self.weights = weights self.cohen_kappa_compute = self.get_cohen_kappa_fn() super(CohenKappa, self).__init__( self.cohen_kappa_compute, output_transform=output_transform, check_compute_fn=check_compute_fn, device=device, )
[docs] def get_cohen_kappa_fn(self) -> Callable[[torch.Tensor, torch.Tensor], float]: """Return a function computing Cohen Kappa from scikit-learn.""" from sklearn.metrics import cohen_kappa_score def wrapper(y_targets: torch.Tensor, y_preds: torch.Tensor) -> float: y_true = y_targets.cpu().numpy() y_pred = y_preds.cpu().numpy() return cohen_kappa_score(y_true, y_pred, weights=self.weights) return wrapper

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