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matthews_corrcoef.py
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matthews_corrcoef.py
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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 typing import Optional
import torch
from torch import Tensor
from typing_extensions import Literal
from torchmetrics.functional.classification.confusion_matrix import (
_binary_confusion_matrix_arg_validation,
_binary_confusion_matrix_format,
_binary_confusion_matrix_tensor_validation,
_binary_confusion_matrix_update,
_multiclass_confusion_matrix_arg_validation,
_multiclass_confusion_matrix_format,
_multiclass_confusion_matrix_tensor_validation,
_multiclass_confusion_matrix_update,
_multilabel_confusion_matrix_arg_validation,
_multilabel_confusion_matrix_format,
_multilabel_confusion_matrix_tensor_validation,
_multilabel_confusion_matrix_update,
)
from torchmetrics.utilities.enums import ClassificationTask
def _matthews_corrcoef_reduce(confmat: Tensor) -> Tensor:
"""Reduce an un-normalized confusion matrix of shape (n_classes, n_classes) into the matthews corrcoef score.
See: https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-019-6413-7 for more info.
"""
# convert multilabel into binary
confmat = confmat.sum(0) if confmat.ndim == 3 else confmat
if confmat.numel() == 4: # binary case
tn, fp, fn, tp = confmat.reshape(-1)
if tp + tn != 0 and fp + fn == 0:
return torch.tensor(1.0, dtype=confmat.dtype, device=confmat.device)
if tp + tn == 0 and fp + fn != 0:
return torch.tensor(-1.0, dtype=confmat.dtype, device=confmat.device)
tk = confmat.sum(dim=-1).float()
pk = confmat.sum(dim=-2).float()
c = torch.trace(confmat).float()
s = confmat.sum().float()
cov_ytyp = c * s - sum(tk * pk)
cov_ypyp = s**2 - sum(pk * pk)
cov_ytyt = s**2 - sum(tk * tk)
numerator = cov_ytyp
denom = cov_ypyp * cov_ytyt
if denom == 0 and confmat.numel() == 4:
if tp == 0 or tn == 0:
a = tp + tn
if fp == 0 or fn == 0:
b = fp + fn
eps = torch.tensor(torch.finfo(torch.float32).eps, dtype=torch.float32, device=confmat.device)
numerator = torch.sqrt(eps) * (a - b)
denom = (tp + fp + eps) * (tp + fn + eps) * (tn + fp + eps) * (tn + fn + eps)
elif denom == 0:
return torch.tensor(0, dtype=confmat.dtype, device=confmat.device)
return numerator / torch.sqrt(denom)
def binary_matthews_corrcoef(
preds: Tensor,
target: Tensor,
threshold: float = 0.5,
ignore_index: Optional[int] = None,
validate_args: bool = True,
) -> Tensor:
r"""Calculate `Matthews correlation coefficient`_ for binary tasks.
This metric measures the general correlation or quality of a classification.
Accepts the following input tensors:
- ``preds`` (int or float tensor): ``(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. Addtionally,
we convert to int tensor with thresholding using the value in ``threshold``.
- ``target`` (int tensor): ``(N, ...)``
Additional dimension ``...`` will be flattened into the batch dimension.
Args:
preds: Tensor with predictions
target: Tensor with true labels
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
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.functional.classification import binary_matthews_corrcoef
>>> target = tensor([1, 1, 0, 0])
>>> preds = tensor([0, 1, 0, 0])
>>> binary_matthews_corrcoef(preds, target)
tensor(0.5774)
Example (preds is float tensor):
>>> from torchmetrics.functional.classification import binary_matthews_corrcoef
>>> target = tensor([1, 1, 0, 0])
>>> preds = tensor([0.35, 0.85, 0.48, 0.01])
>>> binary_matthews_corrcoef(preds, target)
tensor(0.5774)
"""
if validate_args:
_binary_confusion_matrix_arg_validation(threshold, ignore_index, normalize=None)
_binary_confusion_matrix_tensor_validation(preds, target, ignore_index)
preds, target = _binary_confusion_matrix_format(preds, target, threshold, ignore_index)
confmat = _binary_confusion_matrix_update(preds, target)
return _matthews_corrcoef_reduce(confmat)
def multiclass_matthews_corrcoef(
preds: Tensor,
target: Tensor,
num_classes: int,
ignore_index: Optional[int] = None,
validate_args: bool = True,
) -> Tensor:
r"""Calculate `Matthews correlation coefficient`_ for multiclass tasks.
This metric measures the general correlation or quality of a classification.
Accepts the following input tensors:
- ``preds``: ``(N, ...)`` (int tensor) or ``(N, C, ..)`` (float tensor). If preds is a floating point
we apply ``torch.argmax`` along the ``C`` dimension to automatically convert probabilities/logits into
an int tensor.
- ``target`` (int tensor): ``(N, ...)``
Additional dimension ``...`` will be flattened into the batch dimension.
Args:
preds: Tensor with predictions
target: Tensor with true labels
num_classes: Integer specifing the number of classes
ignore_index:
Specifies a target value that is ignored and does not contribute to the metric calculation
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.functional.classification import multiclass_matthews_corrcoef
>>> target = tensor([2, 1, 0, 0])
>>> preds = tensor([2, 1, 0, 1])
>>> multiclass_matthews_corrcoef(preds, target, num_classes=3)
tensor(0.7000)
Example (pred is float tensor):
>>> from torchmetrics.functional.classification import multiclass_matthews_corrcoef
>>> 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]])
>>> multiclass_matthews_corrcoef(preds, target, num_classes=3)
tensor(0.7000)
"""
if validate_args:
_multiclass_confusion_matrix_arg_validation(num_classes, ignore_index, normalize=None)
_multiclass_confusion_matrix_tensor_validation(preds, target, num_classes, ignore_index)
preds, target = _multiclass_confusion_matrix_format(preds, target, ignore_index)
confmat = _multiclass_confusion_matrix_update(preds, target, num_classes)
return _matthews_corrcoef_reduce(confmat)
def multilabel_matthews_corrcoef(
preds: Tensor,
target: Tensor,
num_labels: int,
threshold: float = 0.5,
ignore_index: Optional[int] = None,
validate_args: bool = True,
) -> Tensor:
r"""Calculate `Matthews correlation coefficient`_ for multilabel tasks.
This metric measures the general correlation or quality of a classification.
Accepts the following input tensors:
- ``preds`` (int or float tensor): ``(N, C, ...)``. 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. Addtionally,
we convert to int tensor with thresholding using the value in ``threshold``.
- ``target`` (int tensor): ``(N, C, ...)``
Additional dimension ``...`` will be flattened into the batch dimension.
Args:
preds: Tensor with predictions
target: Tensor with true labels
num_labels: Integer specifing the number of labels
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
validate_args: bool indicating if input arguments and tensors should be validated for correctness.
Set to ``False`` for faster computations.
Example (preds is int tensor):
>>> from torch import tensor
>>> from torchmetrics.functional.classification import multilabel_matthews_corrcoef
>>> target = tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = tensor([[0, 0, 1], [1, 0, 1]])
>>> multilabel_matthews_corrcoef(preds, target, num_labels=3)
tensor(0.3333)
Example (preds is float tensor):
>>> from torchmetrics.functional.classification import multilabel_matthews_corrcoef
>>> target = tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]])
>>> multilabel_matthews_corrcoef(preds, target, num_labels=3)
tensor(0.3333)
"""
if validate_args:
_multilabel_confusion_matrix_arg_validation(num_labels, threshold, ignore_index, normalize=None)
_multilabel_confusion_matrix_tensor_validation(preds, target, num_labels, ignore_index)
preds, target = _multilabel_confusion_matrix_format(preds, target, num_labels, threshold, ignore_index)
confmat = _multilabel_confusion_matrix_update(preds, target, num_labels)
return _matthews_corrcoef_reduce(confmat)
def matthews_corrcoef(
preds: Tensor,
target: Tensor,
task: Literal["binary", "multiclass", "multilabel"],
threshold: float = 0.5,
num_classes: Optional[int] = None,
num_labels: Optional[int] = None,
ignore_index: Optional[int] = None,
validate_args: bool = True,
) -> Tensor:
r"""Calculate `Matthews correlation coefficient`_ .
This metric measures the general correlation or quality of a classification.
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'``, ``'multiclass'`` or ``multilabel``. See the documentation of
:func:`~torchmetrics.functional.classification.binary_matthews_corrcoef`,
:func:`~torchmetrics.functional.classification.multiclass_matthews_corrcoef` and
:func:`~torchmetrics.functional.classification.multilabel_matthews_corrcoef` 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])
>>> matthews_corrcoef(preds, target, task="multiclass", num_classes=2)
tensor(0.5774)
"""
task = ClassificationTask.from_str(task)
if task == ClassificationTask.BINARY:
return binary_matthews_corrcoef(preds, target, threshold, ignore_index, validate_args)
if task == ClassificationTask.MULTICLASS:
if not isinstance(num_classes, int):
raise ValueError(f"`num_classes` is expected to be `int` but `{type(num_classes)} was passed.`")
return multiclass_matthews_corrcoef(preds, target, num_classes, ignore_index, validate_args)
if task == ClassificationTask.MULTILABEL:
if not isinstance(num_labels, int):
raise ValueError(f"`num_labels` is expected to be `int` but `{type(num_labels)} was passed.`")
return multilabel_matthews_corrcoef(preds, target, num_labels, threshold, ignore_index, validate_args)
raise ValueError(f"Not handled value: {task}")