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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.
from typing import Any, Callable, Optional
import torch
from torch import Tensor
from torchmetrics.functional.classification.matthews_corrcoef import (
_matthews_corrcoef_compute,
_matthews_corrcoef_update,
)
from torchmetrics.metric import Metric
class MatthewsCorrcoef(Metric):
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.
Note:
This metric produces a multi-dimensional output, so it can not be directly logged.
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.
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)
dist_sync_fn:
Callback that performs the allgather operation on the metric state. When ``None``, DDP
will be used to perform the allgather
Example:
>>> from torchmetrics import MatthewsCorrcoef
>>> target = torch.tensor([1, 1, 0, 0])
>>> preds = torch.tensor([0, 1, 0, 0])
>>> matthews_corrcoef = MatthewsCorrcoef(num_classes=2)
>>> matthews_corrcoef(preds, target)
tensor(0.5774)
"""
is_differentiable = False
higher_is_better = True
confmat: Tensor
def __init__(
self,
num_classes: int,
threshold: float = 0.5,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
dist_sync_fn: Callable = None,
) -> None:
super().__init__(
compute_on_step=compute_on_step,
dist_sync_on_step=dist_sync_on_step,
process_group=process_group,
dist_sync_fn=dist_sync_fn,
)
self.num_classes = num_classes
self.threshold = threshold
self.add_state("confmat", default=torch.zeros(num_classes, num_classes), dist_reduce_fx="sum")
def update(self, preds: Tensor, target: Tensor) -> None: # type: ignore
"""Update state with predictions and targets.
Args:
preds: Predictions from model
target: Ground truth values
"""
confmat = _matthews_corrcoef_update(preds, target, self.num_classes, self.threshold)
self.confmat += confmat
def compute(self) -> Tensor:
"""Computes matthews correlation coefficient."""
return _matthews_corrcoef_compute(self.confmat)