Specificity¶

Module Interface¶

class torchmetrics.Specificity(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, **kwargs: Any)[source]

Computes Specificity.

$\text{Specificity} = \frac{\text{TN}}{\text{TN} + \text{FP}}$

Where $\text{TN}$ and $\text{FP}$ 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 BinarySpecificity, MulticlassSpecificity and MultilabelSpecificity for the specific details of each argument influence and examples.

Legacy Example:

>>> preds  = torch.tensor([2, 0, 2, 1])
>>> target = torch.tensor([1, 1, 2, 0])
>>> specificity = Specificity(task="multiclass", average='macro', num_classes=3)
>>> specificity(preds, target)
tensor(0.6111)
>>> specificity = Specificity(task="multiclass", average='micro', num_classes=3)
>>> specificity(preds, target)
tensor(0.6250)

BinarySpecificity¶

class torchmetrics.classification.BinarySpecificity(threshold=0.5, multidim_average='global', ignore_index=None, validate_args=True, **kwargs)[source]

Computes Specificity for binary tasks:

$\text{Specificity} = \frac{\text{TN}}{\text{TN} + \text{FP}}$

Where $\text{TN}$ and $\text{FP}$ 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. Addtionally, we convert to int tensor with thresholding using the value in threshold.
target (int tensor): (N, ...)

The influence of the additional dimension ... (if present) will be determined by the multidim_average argument.

Parameters

threshold¶ (float) – Threshold for transforming probability to binary {0,1} predictions
multidim_average¶ (Literal[‘global’, ‘samplewise’]) –
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¶ (Optional[int]) – Specifies a target value that is ignored and does not contribute to the metric calculation
validate_args¶ (bool) – 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 torchmetrics.classification import BinarySpecificity
>>> target = torch.tensor([0, 1, 0, 1, 0, 1])
>>> preds = torch.tensor([0, 0, 1, 1, 0, 1])
>>> metric = BinarySpecificity()
>>> metric(preds, target)
tensor(0.6667)

Example (preds is float tensor):

>>> from torchmetrics.classification import BinarySpecificity
>>> target = torch.tensor([0, 1, 0, 1, 0, 1])
>>> preds = torch.tensor([0.11, 0.22, 0.84, 0.73, 0.33, 0.92])
>>> metric = BinarySpecificity()
>>> metric(preds, target)
tensor(0.6667)

Example (multidim tensors):

>>> from torchmetrics.classification import BinarySpecificity
>>> target = torch.tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]])
>>> preds = torch.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]],
...     ]
... )
>>> metric = BinarySpecificity(multidim_average='samplewise')
>>> metric(preds, target)
tensor([0.0000, 0.3333])

Initializes internal Module state, shared by both nn.Module and ScriptModule.

compute()[source]

Computes the final statistics.

Return type

Tensor

Returns

The metric returns a tensor of shape (..., 5), where the last dimension corresponds to [tp, fp, tn, fn, sup] (sup stands for support and equals tp + fn). The shape depends on the multidim_average parameter:

If multidim_average is set to global, the shape will be (5,)
If multidim_average is set to samplewise, the shape will be (N, 5)

MulticlassSpecificity¶

class torchmetrics.classification.MulticlassSpecificity(num_classes, top_k=1, average='macro', multidim_average='global', ignore_index=None, validate_args=True, **kwargs)[source]

Computes Specificity for multiclass tasks:

$\text{Specificity} = \frac{\text{TN}}{\text{TN} + \text{FP}}$

Where $\text{TN}$ and $\text{FP}$ 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, ...)

The influence of the additional dimension ... (if present) will be determined by the multidim_average argument.

Parameters

num_classes¶ (int) – Integer specifing the number of classes
average¶ (Optional[Literal[‘micro’, ‘macro’, ‘weighted’, ‘none’]]) –
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¶ (int) – Number of highest probability or logit score predictions considered to find the correct label. Only works when preds contain probabilities/logits.
multidim_average¶ (Literal[‘global’, ‘samplewise’]) –
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¶ (Optional[int]) – Specifies a target value that is ignored and does not contribute to the metric calculation
validate_args¶ (bool) – 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:
- 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)

Return type

The returned shape depends on the average and multidim_average arguments

Example (preds is int tensor):

>>> from torchmetrics.classification import MulticlassSpecificity
>>> target = torch.tensor([2, 1, 0, 0])
>>> preds = torch.tensor([2, 1, 0, 1])
>>> metric = MulticlassSpecificity(num_classes=3)
>>> metric(preds, target)
tensor(0.8889)
>>> metric = MulticlassSpecificity(num_classes=3, average=None)
>>> metric(preds, target)
tensor([1.0000, 0.6667, 1.0000])

Example (preds is float tensor):

>>> from torchmetrics.classification import MulticlassSpecificity
>>> target = torch.tensor([2, 1, 0, 0])
>>> preds = torch.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],
... ])
>>> metric = MulticlassSpecificity(num_classes=3)
>>> metric(preds, target)
tensor(0.8889)
>>> metric = MulticlassSpecificity(num_classes=3, average=None)
>>> metric(preds, target)
tensor([1.0000, 0.6667, 1.0000])

Example (multidim tensors):

>>> from torchmetrics.classification import MulticlassSpecificity
>>> target = torch.tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]])
>>> preds = torch.tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]])
>>> metric = MulticlassSpecificity(num_classes=3, multidim_average='samplewise')
>>> metric(preds, target)
tensor([0.7500, 0.6556])
>>> metric = MulticlassSpecificity(num_classes=3, multidim_average='samplewise', average=None)
>>> metric(preds, target)
tensor([[0.7500, 0.7500, 0.7500],
        [0.8000, 0.6667, 0.5000]])

Initializes internal Module state, shared by both nn.Module and ScriptModule.

compute()[source]

Computes the final statistics.

Return type

Tensor

Returns

The metric returns a tensor of shape (..., 5), where the last dimension corresponds to [tp, fp, tn, fn, sup] (sup stands for support and equals tp + fn). The shape depends on average and multidim_average parameters:

If multidim_average is set to global
If average='micro'/'macro'/'weighted', the shape will be (5,)
If average=None/'none', the shape will be (C, 5)
If multidim_average is set to samplewise
If average='micro'/'macro'/'weighted', the shape will be (N, 5)
If average=None/'none', the shape will be (N, C, 5)

MultilabelSpecificity¶

class torchmetrics.classification.MultilabelSpecificity(num_labels, threshold=0.5, average='macro', multidim_average='global', ignore_index=None, validate_args=True, **kwargs)[source]

Computes Specificity for multilabel tasks.

$\text{Specificity} = \frac{\text{TN}}{\text{TN} + \text{FP}}$

Where $\text{TN}$ and $\text{FP}$ 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. Addtionally, we convert to int tensor with thresholding using the value in threshold.
target (int tensor): (N, C, ...)

The influence of the additional dimension ... (if present) will be determined by the multidim_average argument.

Parameters

num_labels¶ (int) – Integer specifing the number of labels
threshold¶ (float) – Threshold for transforming probability to binary (0,1) predictions
average¶ (Optional[Literal[‘micro’, ‘macro’, ‘weighted’, ‘none’]]) –
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¶ (Literal[‘global’, ‘samplewise’]) –
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¶ (Optional[int]) – Specifies a target value that is ignored and does not contribute to the metric calculation
validate_args¶ (bool) – 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 - 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)

Return type

The returned shape depends on the average and multidim_average arguments

Example (preds is int tensor):

>>> from torchmetrics.classification import MultilabelSpecificity
>>> target = torch.tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = torch.tensor([[0, 0, 1], [1, 0, 1]])
>>> metric = MultilabelSpecificity(num_labels=3)
>>> metric(preds, target)
tensor(0.6667)
>>> metric = MultilabelSpecificity(num_labels=3, average=None)
>>> metric(preds, target)
tensor([1., 1., 0.])

Example (preds is float tensor):

>>> from torchmetrics.classification import MultilabelSpecificity
>>> target = torch.tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = torch.tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]])
>>> metric = MultilabelSpecificity(num_labels=3)
>>> metric(preds, target)
tensor(0.6667)
>>> metric = MultilabelSpecificity(num_labels=3, average=None)
>>> metric(preds, target)
tensor([1., 1., 0.])

Example (multidim tensors):

>>> from torchmetrics.classification import MultilabelSpecificity
>>> target = torch.tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]])
>>> preds = torch.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]],
...     ]
... )
>>> metric = MultilabelSpecificity(num_labels=3, multidim_average='samplewise')
>>> metric(preds, target)
tensor([0.0000, 0.3333])
>>> metric = MultilabelSpecificity(num_labels=3, multidim_average='samplewise', average=None)
>>> metric(preds, target)
tensor([[0., 0., 0.],
        [0., 0., 1.]])

Initializes internal Module state, shared by both nn.Module and ScriptModule.

compute()[source]

Computes the final statistics.

Return type

Tensor

Returns

The metric returns a tensor of shape (..., 5), where the last dimension corresponds to [tp, fp, tn, fn, sup] (sup stands for support and equals tp + fn). The shape depends on average and multidim_average parameters:

If multidim_average is set to global
If average='micro'/'macro'/'weighted', the shape will be (5,)
If average=None/'none', the shape will be (C, 5)
If multidim_average is set to samplewise
If average='micro'/'macro'/'weighted', the shape will be (N, 5)
If average=None/'none', the shape will be (N, C, 5)

Functional Interface¶

torchmetrics.functional.specificity(preds, target, task, threshold=0.5, num_classes=None, num_labels=None, average='micro', multidim_average='global', top_k=1, ignore_index=None, validate_args=True)[source]

Computes Specificity.

$\text{Specificity} = \frac{\text{TN}}{\text{TN} + \text{FP}}$

Where $\text{TN}$ and $\text{FP}$ 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 binary_specificity(), multiclass_specificity() and multilabel_specificity() for the specific details of each argument influence and examples.

LegacyExample:

>>> preds  = torch.tensor([2, 0, 2, 1])
>>> target = torch.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)

Return type: Tensor

binary_specificity¶

torchmetrics.functional.classification.binary_specificity(preds, target, threshold=0.5, multidim_average='global', ignore_index=None, validate_args=True)[source]

Computes Specificity for binary tasks:

$\text{Specificity} = \frac{\text{TN}}{\text{TN} + \text{FP}}$

Where $\text{TN}$ and $\text{FP}$ 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. Addtionally, we convert to int tensor with thresholding using the value in threshold.
target (int tensor): (N, ...)

The influence of the additional dimension ... (if present) will be determined by the multidim_average argument.

Parameters

preds¶ (Tensor) – Tensor with predictions
target¶ (Tensor) – Tensor with true labels
threshold¶ (float) – Threshold for transforming probability to binary {0,1} predictions
multidim_average¶ (Literal[‘global’, ‘samplewise’]) –
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¶ (Optional[int]) – Specifies a target value that is ignored and does not contribute to the metric calculation
validate_args¶ (bool) – bool indicating if input arguments and tensors should be validated for correctness. Set to False for faster computations.

Return type

Tensor

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 torchmetrics.functional.classification import binary_specificity
>>> target = torch.tensor([0, 1, 0, 1, 0, 1])
>>> preds = torch.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 = torch.tensor([0, 1, 0, 1, 0, 1])
>>> preds = torch.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 = torch.tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]])
>>> preds = torch.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])

multiclass_specificity¶

torchmetrics.functional.classification.multiclass_specificity(preds, target, num_classes, average='macro', top_k=1, multidim_average='global', ignore_index=None, validate_args=True)[source]

Computes Specificity for multiclass tasks:

$\text{Specificity} = \frac{\text{TN}}{\text{TN} + \text{FP}}$

Where $\text{TN}$ and $\text{FP}$ 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, ...)

The influence of the additional dimension ... (if present) will be determined by the multidim_average argument.

Parameters

preds¶ (Tensor) – Tensor with predictions
target¶ (Tensor) – Tensor with true labels
num_classes¶ (int) – Integer specifing the number of classes
average¶ (Optional[Literal[‘micro’, ‘macro’, ‘weighted’, ‘none’]]) –
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¶ (int) – Number of highest probability or logit score predictions considered to find the correct label. Only works when preds contain probabilities/logits.
multidim_average¶ (Literal[‘global’, ‘samplewise’]) –
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¶ (Optional[int]) – Specifies a target value that is ignored and does not contribute to the metric calculation
validate_args¶ (bool) – 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:
- 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)

Return type

The returned shape depends on the average and multidim_average arguments

Example (preds is int tensor):

>>> from torchmetrics.functional.classification import multiclass_specificity
>>> target = torch.tensor([2, 1, 0, 0])
>>> preds = torch.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 = torch.tensor([2, 1, 0, 0])
>>> preds = torch.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 = torch.tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]])
>>> preds = torch.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]])

multilabel_specificity¶

torchmetrics.functional.classification.multilabel_specificity(preds, target, num_labels, threshold=0.5, average='macro', multidim_average='global', ignore_index=None, validate_args=True)[source]

Computes Specificity for multilabel tasks.

$\text{Specificity} = \frac{\text{TN}}{\text{TN} + \text{FP}}$

Where $\text{TN}$ and $\text{FP}$ 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. Addtionally, we convert to int tensor with thresholding using the value in threshold.
target (int tensor): (N, C, ...)

The influence of the additional dimension ... (if present) will be determined by the multidim_average argument.

Parameters

preds¶ (Tensor) – Tensor with predictions
target¶ (Tensor) – Tensor with true labels
num_labels¶ (int) – Integer specifing the number of labels
threshold¶ (float) – Threshold for transforming probability to binary (0,1) predictions
average¶ (Optional[Literal[‘micro’, ‘macro’, ‘weighted’, ‘none’]]) –
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¶ (Literal[‘global’, ‘samplewise’]) –
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¶ (Optional[int]) – Specifies a target value that is ignored and does not contribute to the metric calculation
validate_args¶ (bool) – 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:
- 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)

Return type

The returned shape depends on the average and multidim_average arguments

Example (preds is int tensor):

>>> from torchmetrics.functional.classification import multilabel_specificity
>>> target = torch.tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = torch.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 = torch.tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = torch.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 = torch.tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]])
>>> preds = torch.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.]])