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from typing import Callable, Optional, Union
import torch
from ignite.metrics.epoch_metric import EpochMetric
class CohenKappa(EpochMetric):
"""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 <https://scikit-learn.org/stable/modules/
generated/sklearn.metrics.cohen_kappa_score.html>`_ .
Args:
output_transform: a callable that is used to transform the
:class:`~ignite.engine.engine.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: a string is used to define the type of Cohen's Kappa whether Non-Weighted or Linear
or Quadratic. Default, None.
check_compute_fn: Default False. If True, `cohen_kappa_score
<https://scikit-learn.org/stable/modules/generated/sklearn.metrics.cohen_kappa_score.html>`_
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: optional device specification for internal storage.
skip_unrolling: 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.
.. include:: defaults.rst
:start-after: :orphan:
.. testcode::
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'])
.. testoutput::
0.4285...
.. versionchanged:: 0.5.1
``skip_unrolling`` argument is added.
"""
def __init__(
self,
output_transform: Callable = lambda x: x,
weights: Optional[str] = None,
check_compute_fn: bool = False,
device: Union[str, torch.device] = torch.device("cpu"),
skip_unrolling: bool = False,
):
try:
from sklearn.metrics import cohen_kappa_score # noqa: F401
except ImportError:
raise ModuleNotFoundError("This contrib module requires scikit-learn to be installed.")
if weights not in (None, "linear", "quadratic"):
raise ValueError("Kappa Weighting type must be None or linear or quadratic.")
# initalize weights
self.weights = weights
self.cohen_kappa_compute = self.get_cohen_kappa_fn()
super(CohenKappa, self).__init__(
self.cohen_kappa_compute,
output_transform=output_transform,
check_compute_fn=check_compute_fn,
device=device,
skip_unrolling=skip_unrolling,
)
def get_cohen_kappa_fn(self) -> Callable[[torch.Tensor, torch.Tensor], float]:
"""Return a function computing Cohen Kappa from scikit-learn."""
from sklearn.metrics import cohen_kappa_score
def wrapper(y_targets: torch.Tensor, y_preds: torch.Tensor) -> float:
y_true = y_targets.cpu().numpy()
y_pred = y_preds.cpu().numpy()
return cohen_kappa_score(y_true, y_pred, weights=self.weights)
return wrapper
|
