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cohen_kappa.py
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cohen_kappa.py
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# Copyright The PyTorch 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 typing import Any, Optional
import torch
from torch import Tensor
from torchmetrics.functional.classification.cohen_kappa import _cohen_kappa_compute, _cohen_kappa_update
from torchmetrics.metric import Metric
class CohenKappa(Metric):
r"""
Calculates `Cohen's kappa score <https://en.wikipedia.org/wiki/Cohen%27s_kappa>`_ that measures
inter-annotator agreement. It is defined as
.. 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.
Works with binary, multiclass, and multilabel data. Accepts probabilities from a model output or
integer class values in prediction. Works with multi-dimensional preds and target.
Forward accepts
- ``preds`` (float or long tensor): ``(N, ...)`` or ``(N, C, ...)`` where C is the number of classes
- ``target`` (long tensor): ``(N, ...)``
If preds and target are the same shape and preds is a float tensor, we use the ``self.threshold`` argument
to convert into integer labels. This is the case for binary and multi-label probabilities.
If preds has an extra dimension as in the case of multi-class scores we perform an argmax on ``dim=1``.
Args:
num_classes: Number of classes in the dataset.
weights: Weighting type to calculate the score. Choose from
- ``None`` or ``'none'``: no weighting
- ``'linear'``: linear weighting
- ``'quadratic'``: quadratic weighting
threshold:
Threshold value for binary or multi-label probabilites. default: 0.5
compute_on_step:
Forward only calls ``update()`` and return None if this is set to False. default: True
dist_sync_on_step:
Synchronize metric state across processes at each ``forward()``
before returning the value at the step. default: False
process_group:
Specify the process group on which synchronization is called. default: None (which selects the entire world)
Example:
>>> from torchmetrics import CohenKappa
>>> target = torch.tensor([1, 1, 0, 0])
>>> preds = torch.tensor([0, 1, 0, 0])
>>> cohenkappa = CohenKappa(num_classes=2)
>>> cohenkappa(preds, target)
tensor(0.5000)
"""
def __init__(
self,
num_classes: int,
weights: Optional[str] = None,
threshold: float = 0.5,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
):
super().__init__(
compute_on_step=compute_on_step,
dist_sync_on_step=dist_sync_on_step,
process_group=process_group,
)
self.num_classes = num_classes
self.weights = weights
self.threshold = threshold
allowed_weights = ('linear', 'quadratic', 'none', None)
assert self.weights in allowed_weights, \
f"Argument weights needs to one of the following: {allowed_weights}"
self.add_state("confmat", default=torch.zeros(num_classes, num_classes), dist_reduce_fx="sum")
def update(self, preds: Tensor, target: Tensor):
"""
Update state with predictions and targets.
Args:
preds: Predictions from model
target: Ground truth values
"""
confmat = _cohen_kappa_update(preds, target, self.num_classes, self.threshold)
self.confmat += confmat
def compute(self) -> Tensor:
"""
Computes cohen kappa score
"""
return _cohen_kappa_compute(self.confmat, self.weights)
@property
def is_differentiable(self):
"""
cohen kappa is not differentiable since the implementation
is based on calculating the confusion matrix which in general
is not differentiable
"""
return False