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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]])
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