CohenKappa

class ignite.metrics.CohenKappa(output_transform=<function CohenKappa.<lambda>>, weights=None, check_compute_fn=False, device=device(type='cpu'), skip_unrolling=False)

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, device]) – optional device specification for internal storage.

• skip_unrolling (bool) – specifies whether output should be unrolled before being fed to update method. Should be true for multi-output model, for example, if y_pred contains multi-ouput as (y_pred_a, y_pred_b) Alternatively, output_transform can be used to handle this.

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.

from collections import OrderedDict

import torch
from torch import nn, optim

from ignite.engine import *
from ignite.handlers import *
from ignite.metrics import *
from ignite.metrics.clustering import *
from ignite.metrics.regression import *
from ignite.utils import *

# create default evaluator for doctests

def eval_step(engine, batch):
    return batch

default_evaluator = Engine(eval_step)

# create default optimizer for doctests

param_tensor = torch.zeros([1], requires_grad=True)
default_optimizer = torch.optim.SGD([param_tensor], lr=0.1)

# create default trainer for doctests
# as handlers could be attached to the trainer,
# each test must define his own trainer using `.. testsetup:`

def get_default_trainer():

    def train_step(engine, batch):
        return batch

    return Engine(train_step)

# create default model for doctests

default_model = nn.Sequential(OrderedDict([
    ('base', nn.Linear(4, 2)),
    ('fc', nn.Linear(2, 1))
]))

manual_seed(666)

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...

Changed in version 0.5.1: skip_unrolling argument is added.

Methods