CohenKappa

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

Compute different types of Cohen’s Kappa: Non-Weighted, Linear, Quadratic.

When num_classes is provided, accumulates a running confusion matrix via ConfusionMatrix (memory-efficient, no raw tensor buffering). When num_classes is None (default), buffers predictions and targets via EpochMetric and infers the number of classes from data.

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 (Literal['linear', 'quadratic'] | None) – 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, the compute function 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 (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-output as (y_pred_a, y_pred_b) Alternatively, output_transform can be used to handle this.

• num_classes (int | None) – number of classes. If provided, uses a running confusion matrix (memory-efficient). If None, infers from data at compute time (backward-compatible default).

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_transform 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.fairness import *
from ignite.metrics.rec_sys 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.

Changed in version 0.6.0: Replaced scikit-learn dependency with a native PyTorch implementation. Added num_classes argument; routes to a running-confusion-matrix backend when provided.

Methods

compute	Computes the metric based on its accumulated state.
reset	Resets the metric to its initial state.
update	Updates the metric's state using the passed batch output.

compute()

Computes the metric based on its accumulated state.

By default, this is called at the end of each epoch.

Returns:

the actual quantity of interest. However, if a Mapping is returned, it will be (shallow) flattened into engine.state.metrics when completed() is called.

Return type:

Any

Raises:

NotComputableError – raised when the metric cannot be computed.

reset()

Resets the metric to its initial state.

By default, this is called at the start of each epoch.

Return type:

None

update(output)

Updates the metric’s state using the passed batch output.

By default, this is called once for each batch.

Parameters:

output (tuple[torch.Tensor, torch.Tensor]) – the is the output from the engine’s process function.

Return type:

None