F-Beta Score¶

Module Interface¶

FBetaScore¶

class torchmetrics.FBetaScore(task: Literal['binary', 'multiclass', 'multilabel'], beta: float = 1.0, 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 F-score metric:

$F_{\beta} = (1 + \beta^2) * \frac{\text{precision} * \text{recall}} {(\beta^2 * \text{precision}) + \text{recall}}$

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_fbeta_score(), multiclass_fbeta_score() and multilabel_fbeta_score() for the specific details of each argument influence and examples.

Legcy Example:

>>> import torch
>>> target = torch.tensor([0, 1, 2, 0, 1, 2])
>>> preds = torch.tensor([0, 2, 1, 0, 0, 1])
>>> f_beta = FBetaScore(task="multiclass", num_classes=3, beta=0.5)
>>> f_beta(preds, target)
tensor(0.3333)

BinaryFBetaScore¶

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

Computes F-score metric for binary tasks:

$F_{\beta} = (1 + \beta^2) * \frac{\text{precision} * \text{recall}} {(\beta^2 * \text{precision}) + \text{recall}}$

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

preds (Tensor): An int tensor 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. Addtionally, 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:

bfbs (Tensor): A tensor whose returned shape depends on the multidim_average argument:
- If multidim_average is set to global the output will be a scalar tensor
- If multidim_average is set to samplewise the output will be a tensor of shape (N,) consisting of a scalar value per sample.

Parameters

beta¶ (float) – Weighting between precision and recall in calculation. Setting to 1 corresponds to equal weight
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 torchmetrics.classification import BinaryFBetaScore
>>> target = torch.tensor([0, 1, 0, 1, 0, 1])
>>> preds = torch.tensor([0, 0, 1, 1, 0, 1])
>>> metric = BinaryFBetaScore(beta=2.0)
>>> metric(preds, target)
tensor(0.6667)

Example (preds is float tensor):

>>> from torchmetrics.classification import BinaryFBetaScore
>>> 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 = BinaryFBetaScore(beta=2.0)
>>> metric(preds, target)
tensor(0.6667)

Example (multidim tensors):

>>> from torchmetrics.classification import BinaryFBetaScore
>>> 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 = BinaryFBetaScore(beta=2.0, multidim_average='samplewise')
>>> metric(preds, target)
tensor([0.5882, 0.0000])

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

MulticlassFBetaScore¶

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

Computes F-score metric for multiclass tasks:

$F_{\beta} = (1 + \beta^2) * \frac{\text{precision} * \text{recall}} {(\beta^2 * \text{precision}) + \text{recall}}$

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:

mcfbs (Tensor): A tensor whose 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)

Parameters

beta¶ (float) – Weighting between precision and recall in calculation. Setting to 1 corresponds to equal weight
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.

Example (preds is int tensor):

>>> from torchmetrics.classification import MulticlassFBetaScore
>>> target = torch.tensor([2, 1, 0, 0])
>>> preds = torch.tensor([2, 1, 0, 1])
>>> metric = MulticlassFBetaScore(beta=2.0, num_classes=3)
>>> metric(preds, target)
tensor(0.7963)
>>> mcfbs = MulticlassFBetaScore(beta=2.0, num_classes=3, average=None)
>>> mcfbs(preds, target)
tensor([0.5556, 0.8333, 1.0000])

Example (preds is float tensor):

>>> from torchmetrics.classification import MulticlassFBetaScore
>>> 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 = MulticlassFBetaScore(beta=2.0, num_classes=3)
>>> metric(preds, target)
tensor(0.7963)
>>> mcfbs = MulticlassFBetaScore(beta=2.0, num_classes=3, average=None)
>>> mcfbs(preds, target)
tensor([0.5556, 0.8333, 1.0000])

Example (multidim tensors):

>>> from torchmetrics.classification import MulticlassFBetaScore
>>> 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 = MulticlassFBetaScore(beta=2.0, num_classes=3, multidim_average='samplewise')
>>> metric(preds, target)
tensor([0.4697, 0.2706])
>>> mcfbs = MulticlassFBetaScore(beta=2.0, num_classes=3, multidim_average='samplewise', average=None)
>>> mcfbs(preds, target)
tensor([[0.9091, 0.0000, 0.5000],
        [0.0000, 0.3571, 0.4545]])

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

MultilabelFBetaScore¶

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

Computes F-score metric for multilabel tasks:

$F_{\beta} = (1 + \beta^2) * \frac{\text{precision} * \text{recall}} {(\beta^2 * \text{precision}) + \text{recall}}$

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. Addtionally, 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:

mlfbs (Tensor): A tensor whose 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)

Parameters

beta¶ (float) – Weighting between precision and recall in calculation. Setting to 1 corresponds to equal weight
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.

Example (preds is int tensor):

>>> from torchmetrics.classification import MultilabelFBetaScore
>>> target = torch.tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = torch.tensor([[0, 0, 1], [1, 0, 1]])
>>> metric = MultilabelFBetaScore(beta=2.0, num_labels=3)
>>> metric(preds, target)
tensor(0.6111)
>>> mlfbs = MultilabelFBetaScore(beta=2.0, num_labels=3, average=None)
>>> mlfbs(preds, target)
tensor([1.0000, 0.0000, 0.8333])

Example (preds is float tensor):

>>> from torchmetrics.classification import MultilabelFBetaScore
>>> 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 = MultilabelFBetaScore(beta=2.0, num_labels=3)
>>> metric(preds, target)
tensor(0.6111)
>>> mlfbs = MultilabelFBetaScore(beta=2.0, num_labels=3, average=None)
>>> mlfbs(preds, target)
tensor([1.0000, 0.0000, 0.8333])

Example (multidim tensors):

>>> from torchmetrics.classification import MultilabelFBetaScore
>>> 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 = MultilabelFBetaScore(num_labels=3, beta=2.0, multidim_average='samplewise')
>>> metric(preds, target)
tensor([0.5556, 0.0000])
>>> mlfbs = MultilabelFBetaScore(num_labels=3, beta=2.0, multidim_average='samplewise', average=None)
>>> mlfbs(preds, target)
tensor([[0.8333, 0.8333, 0.0000],
        [0.0000, 0.0000, 0.0000]])

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

Functional Interface¶

fbeta_score¶

torchmetrics.functional.fbeta_score(preds, target, task, beta=1.0, 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 F-score metric:

$F_{\beta} = (1 + \beta^2) * \frac{\text{precision} * \text{recall}} {(\beta^2 * \text{precision}) + \text{recall}}$

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_fbeta_score(), multiclass_fbeta_score() and multilabel_fbeta_score() for the specific details of each argument influence and examples.

Legacy Example:

>>> target = torch.tensor([0, 1, 2, 0, 1, 2])
>>> preds = torch.tensor([0, 2, 1, 0, 0, 1])
>>> fbeta_score(preds, target, task="multiclass", num_classes=3, beta=0.5)
tensor(0.3333)

Return type: Tensor

binary_fbeta_score¶

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

Computes F-score metric for binary tasks:

$F_{\beta} = (1 + \beta^2) * \frac{\text{precision} * \text{recall}} {(\beta^2 * \text{precision}) + \text{recall}}$

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, ...)

Parameters

preds¶ (Tensor) – Tensor with predictions
target¶ (Tensor) – Tensor with true labels
beta¶ (float) – Weighting between precision and recall in calculation. Setting to 1 corresponds to equal weight
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_fbeta_score
>>> target = torch.tensor([0, 1, 0, 1, 0, 1])
>>> preds = torch.tensor([0, 0, 1, 1, 0, 1])
>>> binary_fbeta_score(preds, target, beta=2.0)
tensor(0.6667)

Example (preds is float tensor):

>>> from torchmetrics.functional.classification import binary_fbeta_score
>>> 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_fbeta_score(preds, target, beta=2.0)
tensor(0.6667)

Example (multidim tensors):

>>> from torchmetrics.functional.classification import binary_fbeta_score
>>> 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_fbeta_score(preds, target, beta=2.0, multidim_average='samplewise')
tensor([0.5882, 0.0000])

multiclass_fbeta_score¶

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

Computes F-score metric for multiclass tasks:

$F_{\beta} = (1 + \beta^2) * \frac{\text{precision} * \text{recall}} {(\beta^2 * \text{precision}) + \text{recall}}$

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
beta¶ (float) – Weighting between precision and recall in calculation. Setting to 1 corresponds to equal weight
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_fbeta_score
>>> target = torch.tensor([2, 1, 0, 0])
>>> preds = torch.tensor([2, 1, 0, 1])
>>> multiclass_fbeta_score(preds, target, beta=2.0, num_classes=3)
tensor(0.7963)
>>> multiclass_fbeta_score(preds, target, beta=2.0, num_classes=3, average=None)
tensor([0.5556, 0.8333, 1.0000])

Example (preds is float tensor):

>>> from torchmetrics.functional.classification import multiclass_fbeta_score
>>> 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_fbeta_score(preds, target, beta=2.0, num_classes=3)
tensor(0.7963)
>>> multiclass_fbeta_score(preds, target, beta=2.0, num_classes=3, average=None)
tensor([0.5556, 0.8333, 1.0000])

Example (multidim tensors):

>>> from torchmetrics.functional.classification import multiclass_fbeta_score
>>> 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_fbeta_score(preds, target, beta=2.0, num_classes=3, multidim_average='samplewise')
tensor([0.4697, 0.2706])
>>> multiclass_fbeta_score(preds, target, beta=2.0, num_classes=3, multidim_average='samplewise', average=None)
tensor([[0.9091, 0.0000, 0.5000],
        [0.0000, 0.3571, 0.4545]])

multilabel_fbeta_score¶

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

Computes F-score metric for multilabel tasks:

$F_{\beta} = (1 + \beta^2) * \frac{\text{precision} * \text{recall}} {(\beta^2 * \text{precision}) + \text{recall}}$

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, ...)

Parameters

preds¶ (Tensor) – Tensor with predictions
target¶ (Tensor) – Tensor with true labels
beta¶ (float) – Weighting between precision and recall in calculation. Setting to 1 corresponds to equal weight
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_fbeta_score
>>> target = torch.tensor([[0, 1, 0], [1, 0, 1]])
>>> preds = torch.tensor([[0, 0, 1], [1, 0, 1]])
>>> multilabel_fbeta_score(preds, target, beta=2.0, num_labels=3)
tensor(0.6111)
>>> multilabel_fbeta_score(preds, target, beta=2.0, num_labels=3, average=None)
tensor([1.0000, 0.0000, 0.8333])

Example (preds is float tensor):

>>> from torchmetrics.functional.classification import multilabel_fbeta_score
>>> 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_fbeta_score(preds, target, beta=2.0, num_labels=3)
tensor(0.6111)
>>> multilabel_fbeta_score(preds, target, beta=2.0, num_labels=3, average=None)
tensor([1.0000, 0.0000, 0.8333])

Example (multidim tensors):

>>> from torchmetrics.functional.classification import multilabel_fbeta_score
>>> 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_fbeta_score(preds, target, num_labels=3, beta=2.0, multidim_average='samplewise')
tensor([0.5556, 0.0000])
>>> multilabel_fbeta_score(preds, target, num_labels=3, beta=2.0, multidim_average='samplewise', average=None)
tensor([[0.8333, 0.8333, 0.0000],
        [0.0000, 0.0000, 0.0000]])