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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
from torch import Tensor
from typing_extensions import Literal
from torchmetrics.functional.classification.stat_scores import (
_binary_stat_scores_arg_validation,
_binary_stat_scores_format,
_binary_stat_scores_tensor_validation,
_binary_stat_scores_update,
_multiclass_stat_scores_arg_validation,
_multiclass_stat_scores_format,
_multiclass_stat_scores_tensor_validation,
_multiclass_stat_scores_update,
_multilabel_stat_scores_arg_validation,
_multilabel_stat_scores_format,
_multilabel_stat_scores_tensor_validation,
_multilabel_stat_scores_update,
)
from torchmetrics.utilities.compute import _adjust_weights_safe_divide, _safe_divide
from torchmetrics.utilities.enums import ClassificationTask
def _specificity_reduce(
tp: Tensor,
fp: Tensor,
tn: Tensor,
fn: Tensor,
average: Optional[Literal["binary", "micro", "macro", "weighted", "none"]],
multidim_average: Literal["global", "samplewise"] = "global",
multilabel: bool = False,
) -> Tensor:
if average == "binary":
return _safe_divide(tn, tn + fp)
if average == "micro":
tn = tn.sum(dim=0 if multidim_average == "global" else 1)
fp = fp.sum(dim=0 if multidim_average == "global" else 1)
return _safe_divide(tn, tn + fp)
specificity_score = _safe_divide(tn, tn + fp)
return _adjust_weights_safe_divide(specificity_score, average, multilabel, tp, fp, fn)
def binary_specificity(
preds: Tensor,
target: Tensor,
threshold: float = 0.5,
multidim_average: Literal["global", "samplewise"] = "global",
ignore_index: Optional[int] = None,
validate_args: bool = True,
) -> Tensor:
r"""Compute `Specificity`_ for binary tasks.
.. math:: \text{Specificity} = \frac{\text{TN}}{\text{TN} + \text{FP}}
Where :math:`\text{TN}` and :math:`\text{FP}` represent the number of true negatives and
false positives respecitively.
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. Additionally,
we convert to int tensor with thresholding using the value in ``threshold``.
- ``target`` (int tensor): ``(N, ...)``
Args:
preds: Tensor with predictions
target: Tensor with true labels
threshold: Threshold for transforming probability to binary {0,1} predictions
multidim_average:
Defines how additionally dimensions ``...`` should be handled. Should be one of the following:
- ``global``: Additional dimensions are flatted along the batch dimension
- ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis.
The statistics in this case are calculated over the additional dimensions.
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.
Returns:
If ``multidim_average`` is set to ``global``, the metric returns a scalar value. If ``multidim_average``
is set to ``samplewise``, the metric returns ``(N,)`` vector consisting of a scalar value per sample.
Example (preds is int tensor):
>>> from torch import tensor
>>> from torchmetrics.functional.classification import binary_specificity
>>> target = tensor([0, 1, 0, 1, 0, 1])
>>> preds = tensor([0, 0, 1, 1, 0, 1])
>>> binary_specificity(preds, target)
tensor(0.6667)
Example (preds is float tensor):
>>> from torchmetrics.functional.classification import binary_specificity
>>> target = tensor([0, 1, 0, 1, 0, 1])
>>> preds = tensor([0.11, 0.22, 0.84, 0.73, 0.33, 0.92])
>>> binary_specificity(preds, target)
tensor(0.6667)
Example (multidim tensors):
>>> from torchmetrics.functional.classification import binary_specificity
>>> target = tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]])
>>> preds = tensor([[[0.59, 0.91], [0.91, 0.99], [0.63, 0.04]],
... [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]]])
>>> binary_specificity(preds, target, multidim_average='samplewise')
tensor([0.0000, 0.3333])
"""
if validate_args:
_binary_stat_scores_arg_validation(threshold, multidim_average, ignore_index)
_binary_stat_scores_tensor_validation(preds, target, multidim_average, ignore_index)
preds, target = _binary_stat_scores_format(preds, target, threshold, ignore_index)
tp, fp, tn, fn = _binary_stat_scores_update(preds, target, multidim_average)
return _specificity_reduce(tp, fp, tn, fn, average="binary", multidim_average=multidim_average)
def multiclass_specificity(
preds: Tensor,
target: Tensor,
num_classes: int,
average: Optional[Literal["micro", "macro", "weighted", "none"]] = "macro",
top_k: int = 1,
multidim_average: Literal["global", "samplewise"] = "global",
ignore_index: Optional[int] = None,
validate_args: bool = True,
) -> Tensor:
r"""Compute `Specificity`_ for multiclass tasks.
.. math:: \text{Specificity} = \frac{\text{TN}}{\text{TN} + \text{FP}}
Where :math:`\text{TN}` and :math:`\text{FP}` represent the number of true negatives and
false positives respecitively.
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, ...)``
Args:
preds: Tensor with predictions
target: Tensor with true labels
num_classes: Integer specifying the number of classes
average:
Defines the reduction that is applied over labels. Should be one of the following:
- ``micro``: Sum statistics over all labels
- ``macro``: Calculate statistics for each label and average them
- ``weighted``: calculates statistics for each label and computes weighted average using their support
- ``"none"`` or ``None``: calculates statistic for each label and applies no reduction
top_k:
Number of highest probability or logit score predictions considered to find the correct label.
Only works when ``preds`` contain probabilities/logits.
multidim_average:
Defines how additionally dimensions ``...`` should be handled. Should be one of the following:
- ``global``: Additional dimensions are flatted along the batch dimension
- ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis.
The statistics in this case are calculated over the additional dimensions.
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.
Returns:
The returned shape depends on the ``average`` and ``multidim_average`` arguments:
- If ``multidim_average`` is set to ``global``:
- If ``average='micro'/'macro'/'weighted'``, the output will be a scalar tensor
- If ``average=None/'none'``, the shape will be ``(C,)``
- If ``multidim_average`` is set to ``samplewise``:
- If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
- If ``average=None/'none'``, the shape will be ``(N, C)``
Example (preds is int tensor):
>>> from torch import tensor
>>> from torchmetrics.functional.classification import multiclass_specificity
>>> target = tensor([2, 1, 0, 0])
>>> preds = tensor([2, 1, 0, 1])
>>> multiclass_specificity(preds, target, num_classes=3)
tensor(0.8889)
>>> multiclass_specificity(preds, target, num_classes=3, average=None)
tensor([1.0000, 0.6667, 1.0000])
Example (preds is float tensor):
>>> from torchmetrics.functional.classification import multiclass_specificity
>>> 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_specificity(preds, target, num_classes=3)
tensor(0.8889)
>>> multiclass_specificity(preds, target, num_classes=3, average=None)
tensor([1.0000, 0.6667, 1.0000])
Example (multidim tensors):
>>> from torchmetrics.functional.classification import multiclass_specificity
>>> target = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]])
>>> preds = tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]])
>>> multiclass_specificity(preds, target, num_classes=3, multidim_average='samplewise')
tensor([0.7500, 0.6556])
>>> multiclass_specificity(preds, target, num_classes=3, multidim_average='samplewise', average=None)
tensor([[0.7500, 0.7500, 0.7500],
[0.8000, 0.6667, 0.5000]])
"""
if validate_args:
_multiclass_stat_scores_arg_validation(num_classes, top_k, average, multidim_average, ignore_index)
_multiclass_stat_scores_tensor_validation(preds, target, num_classes, multidim_average, ignore_index)
preds, target = _multiclass_stat_scores_format(preds, target, top_k)
tp, fp, tn, fn = _multiclass_stat_scores_update(
preds, target, num_classes, top_k, average, multidim_average, ignore_index
)
return _specificity_reduce(tp, fp, tn, fn, average=average, multidim_average=multidim_average)
def multilabel_specificity(
preds: Tensor,
target: Tensor,
num_labels: int,
threshold: float = 0.5,
average: Optional[Literal["micro", "macro", "weighted", "none"]] = "macro",
multidim_average: Literal["global", "samplewise"] = "global",
ignore_index: Optional[int] = None,
validate_args: bool = True,
) -> Tensor:
r"""Compute `Specificity`_ for multilabel tasks.
.. math:: \text{Specificity} = \frac{\text{TN}}{\text{TN} + \text{FP}}
Where :math:`\text{TN}` and :math:`\text{FP}` represent the number of true negatives and
false positives respecitively.
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. Additionally,
we convert to int tensor with thresholding using the value in ``threshold``.
- ``target`` (int tensor): ``(N, C, ...)``
Args:
preds: Tensor with predictions
target: Tensor with true labels
num_labels: Integer specifying the number of labels
threshold: Threshold for transforming probability to binary (0,1) predictions
average:
Defines the reduction that is applied over labels. Should be one of the following:
- ``micro``: Sum statistics over all labels
- ``macro``: Calculate statistics for each label and average them
- ``weighted``: calculates statistics for each label and computes weighted average using their support
- ``"none"`` or ``None``: calculates statistic for each label and applies no reduction
multidim_average:
Defines how additionally dimensions ``...`` should be handled. Should be one of the following:
- ``global``: Additional dimensions are flatted along the batch dimension
- ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis.
The statistics in this case are calculated over the additional dimensions.
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.
Returns:
The returned shape depends on the ``average`` and ``multidim_average`` arguments:
- If ``multidim_average`` is set to ``global``:
- If ``average='micro'/'macro'/'weighted'``, the output will be a scalar tensor
- If ``average=None/'none'``, the shape will be ``(C,)``
- If ``multidim_average`` is set to ``samplewise``:
- If ``average='micro'/'macro'/'weighted'``, the shape will be ``(N,)``
- If ``average=None/'none'``, the shape will be ``(N, C)``
Example (preds is int tensor):
>>> from torch import tensor
>>> from torchmetrics.functional.classification import multilabel_specificity
>>> target = tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = tensor([[0, 0, 1], [1, 0, 1]])
>>> multilabel_specificity(preds, target, num_labels=3)
tensor(0.6667)
>>> multilabel_specificity(preds, target, num_labels=3, average=None)
tensor([1., 1., 0.])
Example (preds is float tensor):
>>> from torchmetrics.functional.classification import multilabel_specificity
>>> target = tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]])
>>> multilabel_specificity(preds, target, num_labels=3)
tensor(0.6667)
>>> multilabel_specificity(preds, target, num_labels=3, average=None)
tensor([1., 1., 0.])
Example (multidim tensors):
>>> from torchmetrics.functional.classification import multilabel_specificity
>>> target = tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]])
>>> preds = tensor([[[0.59, 0.91], [0.91, 0.99], [0.63, 0.04]],
... [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]]])
>>> multilabel_specificity(preds, target, num_labels=3, multidim_average='samplewise')
tensor([0.0000, 0.3333])
>>> multilabel_specificity(preds, target, num_labels=3, multidim_average='samplewise', average=None)
tensor([[0., 0., 0.],
[0., 0., 1.]])
"""
if validate_args:
_multilabel_stat_scores_arg_validation(num_labels, threshold, average, multidim_average, ignore_index)
_multilabel_stat_scores_tensor_validation(preds, target, num_labels, multidim_average, ignore_index)
preds, target = _multilabel_stat_scores_format(preds, target, num_labels, threshold, ignore_index)
tp, fp, tn, fn = _multilabel_stat_scores_update(preds, target, multidim_average)
return _specificity_reduce(tp, fp, tn, fn, average=average, multidim_average=multidim_average, multilabel=True)
def specificity(
preds: Tensor,
target: Tensor,
task: Literal["binary", "multiclass", "multilabel"],
threshold: float = 0.5,
num_classes: Optional[int] = None,
num_labels: Optional[int] = None,
average: Optional[Literal["micro", "macro", "weighted", "none"]] = "micro",
multidim_average: Optional[Literal["global", "samplewise"]] = "global",
top_k: Optional[int] = 1,
ignore_index: Optional[int] = None,
validate_args: bool = True,
) -> Tensor:
r"""Compute `Specificity`_.
.. math:: \text{Specificity} = \frac{\text{TN}}{\text{TN} + \text{FP}}
Where :math:`\text{TN}` and :math:`\text{FP}` represent the number of true negatives and
false positives respecitively.
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_specificity`,
:func:`~torchmetrics.functional.classification.multiclass_specificity` and
:func:`~torchmetrics.functional.classification.multilabel_specificity` for the specific
details of each argument influence and examples.
LegacyExample:
>>> from torch import tensor
>>> preds = tensor([2, 0, 2, 1])
>>> target = tensor([1, 1, 2, 0])
>>> specificity(preds, target, task="multiclass", average='macro', num_classes=3)
tensor(0.6111)
>>> specificity(preds, target, task="multiclass", average='micro', num_classes=3)
tensor(0.6250)
"""
task = ClassificationTask.from_str(task)
assert multidim_average is not None # noqa: S101 # needed for mypy
if task == ClassificationTask.BINARY:
return binary_specificity(preds, target, threshold, multidim_average, 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.`")
if not isinstance(top_k, int):
raise ValueError(f"`top_k` is expected to be `int` but `{type(top_k)} was passed.`")
return multiclass_specificity(
preds, target, num_classes, average, top_k, multidim_average, 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_specificity(
preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
)
raise ValueError(f"Not handled value: {task}")
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