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# Copyright The Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections.abc import Sequence
from typing import Any, Optional, Union
from torch import Tensor
from typing_extensions import Literal
from torchmetrics.classification.base import _ClassificationTaskWrapper
from torchmetrics.classification.confusion_matrix import BinaryConfusionMatrix, MulticlassConfusionMatrix
from torchmetrics.functional.classification.cohen_kappa import (
_binary_cohen_kappa_arg_validation,
_cohen_kappa_reduce,
_multiclass_cohen_kappa_arg_validation,
)
from torchmetrics.metric import Metric
from torchmetrics.utilities.enums import ClassificationTaskNoMultilabel
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["BinaryCohenKappa.plot", "MulticlassCohenKappa.plot"]
class BinaryCohenKappa(BinaryConfusionMatrix):
r"""Calculate `Cohen's kappa score`_ that measures inter-annotator agreement for binary tasks.
.. math::
\kappa = (p_o - p_e) / (1 - p_e)
where :math:`p_o` is the empirical probability of agreement and :math:`p_e` is
the expected agreement when both annotators assign labels randomly. Note that
:math:`p_e` is estimated using a per-annotator empirical prior over the
class labels.
As input to ``forward`` and ``update`` the metric accepts the following input:
- ``preds`` (:class:`~torch.Tensor`): A int or float tensor of shape ``(N, ...)``. If preds is a floating point
tensor with values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per element.
Additionally, we convert to int tensor with thresholding using the value in ``threshold``.
- ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``.
.. tip::
Additional dimension ``...`` will be flattened into the batch dimension.
As output to ``forward`` and ``compute`` the metric returns the following output:
- ``bc_kappa`` (:class:`~torch.Tensor`): A tensor containing cohen kappa score
Args:
threshold: Threshold for transforming probability to binary (0,1) predictions
ignore_index:
Specifies a target value that is ignored and does not contribute to the metric calculation
weights: Weighting type to calculate the score. Choose from:
- ``None`` or ``'none'``: no weighting
- ``'linear'``: linear weighting
- ``'quadratic'``: quadratic weighting
validate_args: bool indicating if input arguments and tensors should be validated for correctness.
Set to ``False`` for faster computations.
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Example (preds is int tensor):
>>> from torch import tensor
>>> from torchmetrics.classification import BinaryCohenKappa
>>> target = tensor([1, 1, 0, 0])
>>> preds = tensor([0, 1, 0, 0])
>>> metric = BinaryCohenKappa()
>>> metric(preds, target)
tensor(0.5000)
Example (preds is float tensor):
>>> from torchmetrics.classification import BinaryCohenKappa
>>> target = tensor([1, 1, 0, 0])
>>> preds = tensor([0.35, 0.85, 0.48, 0.01])
>>> metric = BinaryCohenKappa()
>>> metric(preds, target)
tensor(0.5000)
"""
is_differentiable: bool = False
higher_is_better: bool = True
full_state_update: bool = False
plot_lower_bound: float = 0.0
plot_upper_bound: float = 1.0
def __init__(
self,
threshold: float = 0.5,
ignore_index: Optional[int] = None,
weights: Optional[Literal["linear", "quadratic", "none"]] = None,
validate_args: bool = True,
**kwargs: Any,
) -> None:
super().__init__(threshold, ignore_index, normalize=None, validate_args=False, **kwargs)
if validate_args:
_binary_cohen_kappa_arg_validation(threshold, ignore_index, weights)
self.weights = weights
self.validate_args = validate_args
def compute(self) -> Tensor:
"""Compute metric."""
return _cohen_kappa_reduce(self.confmat, self.weights)
def plot( # type: ignore[override]
self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
) -> _PLOT_OUT_TYPE:
"""Plot a single or multiple values from the metric.
Args:
val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
If no value is provided, will automatically call `metric.compute` and plot that result.
ax: An matplotlib axis object. If provided will add plot to that axis
Returns:
Figure object and Axes object
Raises:
ModuleNotFoundError:
If `matplotlib` is not installed
.. plot::
:scale: 75
>>> from torch import rand, randint
>>> # Example plotting a single value
>>> from torchmetrics.classification import BinaryCohenKappa
>>> metric = BinaryCohenKappa()
>>> metric.update(rand(10), randint(2,(10,)))
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> from torch import rand, randint
>>> # Example plotting multiple values
>>> from torchmetrics.classification import BinaryCohenKappa
>>> metric = BinaryCohenKappa()
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(rand(10), randint(2,(10,))))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)
class MulticlassCohenKappa(MulticlassConfusionMatrix):
r"""Calculate `Cohen's kappa score`_ that measures inter-annotator agreement for multiclass tasks.
.. math::
\kappa = (p_o - p_e) / (1 - p_e)
where :math:`p_o` is the empirical probability of agreement and :math:`p_e` is
the expected agreement when both annotators assign labels randomly. Note that
:math:`p_e` is estimated using a per-annotator empirical prior over the
class labels.
As input to ``forward`` and ``update`` the metric accepts the following input:
- ``preds`` (:class:`~torch.Tensor`): Either an int tensor of shape ``(N, ...)` or float tensor of shape
``(N, C, ..)``. If preds is a floating point we apply ``torch.argmax`` along the ``C`` dimension to automatically
convert probabilities/logits into an int tensor.
- ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``.
.. tip::
Additional dimension ``...`` will be flattened into the batch dimension.
As output to ``forward`` and ``compute`` the metric returns the following output:
- ``mcck`` (:class:`~torch.Tensor`): A tensor containing cohen kappa score
Args:
num_classes: Integer specifying the number of classes
ignore_index:
Specifies a target value that is ignored and does not contribute to the metric calculation
weights: Weighting type to calculate the score. Choose from:
- ``None`` or ``'none'``: no weighting
- ``'linear'``: linear weighting
- ``'quadratic'``: quadratic weighting
validate_args: bool indicating if input arguments and tensors should be validated for correctness.
Set to ``False`` for faster computations.
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Example (pred is integer tensor):
>>> from torch import tensor
>>> from torchmetrics.classification import MulticlassCohenKappa
>>> target = tensor([2, 1, 0, 0])
>>> preds = tensor([2, 1, 0, 1])
>>> metric = MulticlassCohenKappa(num_classes=3)
>>> metric(preds, target)
tensor(0.6364)
Example (pred is float tensor):
>>> from torchmetrics.classification import MulticlassCohenKappa
>>> target = tensor([2, 1, 0, 0])
>>> preds = tensor([[0.16, 0.26, 0.58],
... [0.22, 0.61, 0.17],
... [0.71, 0.09, 0.20],
... [0.05, 0.82, 0.13]])
>>> metric = MulticlassCohenKappa(num_classes=3)
>>> metric(preds, target)
tensor(0.6364)
"""
is_differentiable: bool = False
higher_is_better: bool = True
full_state_update: bool = False
plot_lower_bound: float = 0.0
plot_upper_bound: float = 1.0
plot_legend_name: str = "Class"
def __init__(
self,
num_classes: int,
ignore_index: Optional[int] = None,
weights: Optional[Literal["linear", "quadratic", "none"]] = None,
validate_args: bool = True,
**kwargs: Any,
) -> None:
super().__init__(num_classes, ignore_index, normalize=None, validate_args=False, **kwargs)
if validate_args:
_multiclass_cohen_kappa_arg_validation(num_classes, ignore_index, weights)
self.weights = weights
self.validate_args = validate_args
def compute(self) -> Tensor:
"""Compute metric."""
return _cohen_kappa_reduce(self.confmat, self.weights)
def plot( # type: ignore[override]
self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
) -> _PLOT_OUT_TYPE:
"""Plot a single or multiple values from the metric.
Args:
val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
If no value is provided, will automatically call `metric.compute` and plot that result.
ax: An matplotlib axis object. If provided will add plot to that axis
Returns:
Figure object and Axes object
Raises:
ModuleNotFoundError:
If `matplotlib` is not installed
.. plot::
:scale: 75
>>> from torch import randn, randint
>>> # Example plotting a single value
>>> from torchmetrics.classification import MulticlassCohenKappa
>>> metric = MulticlassCohenKappa(num_classes=3)
>>> metric.update(randn(20,3).softmax(dim=-1), randint(3, (20,)))
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> from torch import randn, randint
>>> # Example plotting a multiple values
>>> from torchmetrics.classification import MulticlassCohenKappa
>>> metric = MulticlassCohenKappa(num_classes=3)
>>> values = []
>>> for _ in range(20):
... values.append(metric(randn(20,3).softmax(dim=-1), randint(3, (20,))))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)
class CohenKappa(_ClassificationTaskWrapper):
r"""Calculate `Cohen's kappa score`_ that measures inter-annotator agreement.
.. math::
\kappa = (p_o - p_e) / (1 - p_e)
where :math:`p_o` is the empirical probability of agreement and :math:`p_e` is
the expected agreement when both annotators assign labels randomly. Note that
:math:`p_e` is estimated using a per-annotator empirical prior over the
class labels.
This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
``task`` argument to either ``'binary'`` or ``'multiclass'``. See the documentation of
:class:`~torchmetrics.classification.BinaryCohenKappa` and
:class:`~torchmetrics.classification.MulticlassCohenKappa` for the specific details of each argument influence and
examples.
Legacy Example:
>>> from torch import tensor
>>> target = tensor([1, 1, 0, 0])
>>> preds = tensor([0, 1, 0, 0])
>>> cohenkappa = CohenKappa(task="multiclass", num_classes=2)
>>> cohenkappa(preds, target)
tensor(0.5000)
"""
def __new__( # type: ignore[misc]
cls: type["CohenKappa"],
task: Literal["binary", "multiclass"],
threshold: float = 0.5,
num_classes: Optional[int] = None,
weights: Optional[Literal["linear", "quadratic", "none"]] = None,
ignore_index: Optional[int] = None,
validate_args: bool = True,
**kwargs: Any,
) -> Metric:
"""Initialize task metric."""
task = ClassificationTaskNoMultilabel.from_str(task)
kwargs.update({"weights": weights, "ignore_index": ignore_index, "validate_args": validate_args})
if task == ClassificationTaskNoMultilabel.BINARY:
return BinaryCohenKappa(threshold, **kwargs)
if task == ClassificationTaskNoMultilabel.MULTICLASS:
if not isinstance(num_classes, int):
raise ValueError(f"`num_classes` is expected to be `int` but `{type(num_classes)} was passed.`")
return MulticlassCohenKappa(num_classes, **kwargs)
raise ValueError(f"Task {task} not supported!")
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