# CohenKappa#

class ignite.contrib.metrics.CohenKappa(output_transform=<function CohenKappa.<lambda>>, weights=None, check_compute_fn=False, device=device(type='cpu'))[source]#

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 .

Parameters
• output_transform (Callable) – a callable that is used to transform the 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 (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)


Methods

 get_cohen_kappa_fn Return a function computing Cohen Kappa from scikit-learn.
get_cohen_kappa_fn()[source]#

Return a function computing Cohen Kappa from scikit-learn.

Return type

Callable[[torch.Tensor, torch.Tensor], float]