- class ignite.contrib.metrics.CohenKappa(output_transform=<function CohenKappa.<lambda>>, weights=None, check_compute_fn=False, device=device(type='cpu'))#
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 .
output_transform (Callable) – a callable that is used to transform the
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 (Optional[str]) – a string is used to define the type of Cohen’s Kappa whether Non-Weighted or Linear or Quadratic. Default, None.
check_compute_fn (bool) – 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 (Union[str, torch.device]) – optional device specification for internal storage.
def activated_output_transform(output): y_pred, y = output return y_pred, y weights = None or linear or quadratic cohen_kappa = CohenKappa(activated_output_transform, weights)
Return a function computing Cohen Kappa from scikit-learn.