Specificity¶

Module Interface¶

class torchmetrics.Specificity(**kwargs)[source]

Compute 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 respectively. The metric is only proper defined when $$\text{TN} + \text{FP} \neq 0$$. If this case is encountered for any class/label, the metric for that class/label will be set to 0 and the overall metric may therefore be affected in turn.

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:
>>> from torch import tensor
>>> preds  = tensor([2, 0, 2, 1])
>>> target = 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)

static __new__(cls, task, threshold=0.5, num_classes=None, num_labels=None, average='micro', multidim_average='global', top_k=1, ignore_index=None, validate_args=True, **kwargs)[source]

Return type:

Metric

BinarySpecificity¶

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

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

Where $$\text{TN}$$ and $$\text{FP}$$ represent the number of true negatives and false positives respectively. The metric is only proper defined when $$\text{TN} + \text{FP} \neq 0$$. If this case is encountered a score of 0 is returned.

As input to forward and update the metric accepts the following input:

• preds (Tensor): An int or float tensor of shape (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 (Tensor): An int tensor of shape (N, ...)

As output to forward and compute the metric returns the following output:

• bs (Tensor): 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.

If multidim_average is set to samplewise we expect at least one additional dimension ... to be present, which the reduction will then be applied over instead of the sample dimension N.

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.

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

plot(val=None, ax=None)[source]

Plot a single or multiple values from the metric.

Parameters:
Return type:
Returns:

Figure object and Axes object

Raises:

ModuleNotFoundError – If matplotlib is not installed

>>> from torch import rand, randint
>>> # Example plotting a single value
>>> from torchmetrics.classification import BinarySpecificity
>>> metric = BinarySpecificity()
>>> metric.update(rand(10), randint(2,(10,)))
>>> fig_, ax_ = metric.plot()

>>> from torch import rand, randint
>>> # Example plotting multiple values
>>> from torchmetrics.classification import BinarySpecificity
>>> metric = BinarySpecificity()
>>> values = [ ]
>>> for _ in range(10):
...     values.append(metric(rand(10), randint(2,(10,))))
>>> fig_, ax_ = metric.plot(values)


MulticlassSpecificity¶

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

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

Where $$\text{TN}$$ and $$\text{FP}$$ represent the number of true negatives and false positives respectively. The metric is only proper defined when $$\text{TN} + \text{FP} \neq 0$$. If this case is encountered for any class, the metric for that class will be set to 0 and the overall metric may therefore be affected in turn.

As input to forward and update the metric accepts the following input:

• preds (Tensor): An int tensor of shape (N, ...) or float tensor of shape (N, C, ..). If preds is a floating point we apply torch.argmax along the C dimension to automatically convert probabilities/logits into an int tensor.

• target (Tensor): An int tensor of shape (N, ...)

As output to forward and compute the metric returns the following output:

• mcs (Tensor): 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)

If multidim_average is set to samplewise we expect at least one additional dimension ... to be present, which the reduction will then be applied over instead of the sample dimension N.

Parameters:
• num_classes (int) – Integer specifying 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.

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

Example (preds is float tensor):
>>> from torchmetrics.classification import MulticlassSpecificity
>>> 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]])
>>> metric = MulticlassSpecificity(num_classes=3)
>>> metric(preds, target)
tensor(0.8889)
>>> mcs = MulticlassSpecificity(num_classes=3, average=None)
>>> mcs(preds, target)
tensor([1.0000, 0.6667, 1.0000])

Example (multidim tensors):
>>> from torchmetrics.classification import MulticlassSpecificity
>>> 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]]])
>>> metric = MulticlassSpecificity(num_classes=3, multidim_average='samplewise')
>>> metric(preds, target)
tensor([0.7500, 0.6556])
>>> mcs = MulticlassSpecificity(num_classes=3, multidim_average='samplewise', average=None)
>>> mcs(preds, target)
tensor([[0.7500, 0.7500, 0.7500],
[0.8000, 0.6667, 0.5000]])

plot(val=None, ax=None)[source]

Plot a single or multiple values from the metric.

Parameters:
Return type:
Returns:

Figure object and Axes object

Raises:

ModuleNotFoundError – If matplotlib is not installed

>>> from torch import randint
>>> # Example plotting a single value per class
>>> from torchmetrics.classification import MulticlassSpecificity
>>> metric = MulticlassSpecificity(num_classes=3, average=None)
>>> metric.update(randint(3, (20,)), randint(3, (20,)))
>>> fig_, ax_ = metric.plot()

>>> from torch import randint
>>> # Example plotting a multiple values per class
>>> from torchmetrics.classification import MulticlassSpecificity
>>> metric = MulticlassSpecificity(num_classes=3, average=None)
>>> values = []
>>> for _ in range(20):
...     values.append(metric(randint(3, (20,)), randint(3, (20,))))
>>> fig_, ax_ = metric.plot(values)


MultilabelSpecificity¶

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

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

Where $$\text{TN}$$ and $$\text{FP}$$ represent the number of true negatives and false positives respectively. The metric is only proper defined when $$\text{TN} + \text{FP} \neq 0$$. If this case is encountered for any label, the metric for that label will be set to 0 and the overall metric may therefore be affected in turn.

As input to forward and update the metric accepts the following input:

• preds (Tensor): An int or float tensor of shape (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 (Tensor): An int tensor of shape (N, C, ...)

As output to forward and compute the metric returns the following output:

• mls (Tensor): 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)

If multidim_average is set to samplewise we expect at least one additional dimension ... to be present, which the reduction will then be applied over instead of the sample dimension N.

Parameters:
• num_labels (int) – Integer specifying 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.

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

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

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

plot(val=None, ax=None)[source]

Plot a single or multiple values from the metric.

Parameters:
Return type:
Returns:

Figure object and Axes object

Raises:

ModuleNotFoundError – If matplotlib is not installed

>>> from torch import rand, randint
>>> # Example plotting a single value
>>> from torchmetrics.classification import MultilabelSpecificity
>>> metric = MultilabelSpecificity(num_labels=3)
>>> metric.update(randint(2, (20, 3)), randint(2, (20, 3)))
>>> fig_, ax_ = metric.plot()

>>> from torch import rand, randint
>>> # Example plotting multiple values
>>> from torchmetrics.classification import MultilabelSpecificity
>>> metric = MultilabelSpecificity(num_labels=3)
>>> values = [ ]
>>> for _ in range(10):
...     values.append(metric(randint(2, (20, 3)), randint(2, (20, 3))))
>>> fig_, ax_ = metric.plot(values)


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]

Compute Specificity. :rtype: Tensor

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

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:
>>> 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)


binary_specificity¶

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

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

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. Additionally, we convert to int tensor with thresholding using the value in threshold.

• target (int tensor): (N, ...)

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 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])


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]

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

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, ...)

Parameters:
• preds (Tensor) – Tensor with predictions

• target (Tensor) – Tensor with true labels

• num_classes (int) – Integer specifying 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 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]])


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]

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

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. Additionally, we convert to int tensor with thresholding using the value in threshold.

• target (int tensor): (N, C, ...)

Parameters:
• preds (Tensor) – Tensor with predictions

• target (Tensor) – Tensor with true labels

• num_labels (int) – Integer specifying 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 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.]])