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matthews_corrcoef.py
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matthews_corrcoef.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.
import torch
from torch import Tensor
from torchmetrics.functional.classification.confusion_matrix import _confusion_matrix_update
_matthews_corrcoef_update = _confusion_matrix_update
def _matthews_corrcoef_compute(confmat: Tensor) -> Tensor:
"""Computes Matthews correlation coefficient.
Args:
confmat: Confusion matrix
Example:
>>> target = torch.tensor([1, 1, 0, 0])
>>> preds = torch.tensor([0, 1, 0, 0])
>>> confmat = _matthews_corrcoef_update(preds, target, num_classes=2)
>>> _matthews_corrcoef_compute(confmat)
tensor(0.5774)
"""
tk = confmat.sum(dim=1).float()
pk = confmat.sum(dim=0).float()
c = torch.trace(confmat).float()
s = confmat.sum().float()
return (c * s - sum(tk * pk)) / (torch.sqrt(s ** 2 - sum(pk * pk)) * torch.sqrt(s ** 2 - sum(tk * tk)))
def matthews_corrcoef(
preds: Tensor,
target: Tensor,
num_classes: int,
threshold: float = 0.5,
) -> Tensor:
r"""
Calculates `Matthews correlation coefficient`_ that measures
the general correlation or quality of a classification. In the binary case it
is defined as:
.. math::
MCC = \frac{TP*TN - FP*FN}{\sqrt{(TP+FP)*(TP+FN)*(TN+FP)*(TN+FN)}}
where TP, TN, FP and FN are respectively the true postitives, true negatives,
false positives and false negatives. Also works in the case of multi-label or
multi-class input.
Args:
preds: (float or long tensor), Either a ``(N, ...)`` tensor with labels or
``(N, C, ...)`` where C is the number of classes, tensor with labels/probabilities
target: ``target`` (long tensor), tensor with shape ``(N, ...)`` with ground true labels
num_classes: Number of classes in the dataset.
threshold:
Threshold value for binary or multi-label probabilities.
Example:
>>> from torchmetrics.functional import matthews_corrcoef
>>> target = torch.tensor([1, 1, 0, 0])
>>> preds = torch.tensor([0, 1, 0, 0])
>>> matthews_corrcoef(preds, target, num_classes=2)
tensor(0.5774)
"""
confmat = _matthews_corrcoef_update(preds, target, num_classes, threshold)
return _matthews_corrcoef_compute(confmat)