# 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.

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.

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 = default_evaluator.run([[y_pred, y_true]])
print(state.metrics['ck'])

0.4285...


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]