o yi @sddlmZmZddlZddlmZddlmZddlmZm Z m Z ddl m Z m Z mZmZddlmZGdd d eZGd d d e ZGd d d e ZGdddeZGdddeZGdddeZGdddZGdddZdS))AnyOptionalN)Tensor)Literal)BinaryStatScoresMulticlassStatScoresMultilabelStatScores)"_binary_fbeta_score_arg_validation _fbeta_reduce&_multiclass_fbeta_score_arg_validation&_multilabel_fbeta_score_arg_validation)MetriccseZdZUdZdZeed<dZeeed<dZ eed<   dd e d e d e d dee dede dd ffdd ZdefddZZS)BinaryFBetaScorea Computes `F-score`_ metric for binary tasks: .. math:: 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`` (:class:`~torch.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`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``. As output to ``forward`` and ``compute`` the metric returns the following output: - ``bfbs`` (:class:`~torch.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. Args: beta: Weighting between precision and recall in calculation. Setting to 1 corresponds to equal weight threshold: Threshold for transforming probability to binary {0,1} predictions multidim_average: Defines how additionally dimensions ``...`` should be handled. Should be one of the following: - ``global``: Additional dimensions are flatted along the batch dimension - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis. The statistics in this case are calculated over the additional dimensions. ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. 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]) Fis_differentiableThigher_is_betterfull_state_update?globalNbeta thresholdmultidim_averager samplewise ignore_index validate_argskwargsreturnc s>tjd|||dd||rt||||||_||_dS)NF)rrrr)super__init__r rr)selfrrrrrr __class__rV/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/classification/f_beta.pyres  zBinaryFBetaScore.__init__c Cs*|\}}}}t|||||jd|jdS)Nbinaryaverager) _final_stater rrr tpfptnfnrrr#computezszBinaryFBetaScore.computerrNT)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrintrrrr- __classcell__rrr!r#rs2  B rcseZdZUdZdZeed<dZeeed<dZ eed<   dd e d e d e dee dde ddee dede dd ffdd ZdefddZZS)MulticlassFBetaScorea Computes `F-score`_ metric for multiclass tasks: .. math:: 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`` (:class:`~torch.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`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``. As output to ``forward`` and ``compute`` the metric returns the following output: - ``mcfbs`` (:class:`~torch.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)`` Args: beta: Weighting between precision and recall in calculation. Setting to 1 corresponds to equal weight num_classes: Integer specifing the number of classes average: Defines the reduction that is applied over labels. Should be one of the following: - ``micro``: Sum statistics over all labels - ``macro``: Calculate statistics for each label and average them - ``weighted``: Calculates statistics for each label and computes weighted average using their support - ``"none"`` or ``None``: Calculates statistic for each label and applies no reduction top_k: Number of highest probability or logit score predictions considered to find the correct label. Only works when ``preds`` contain probabilities/logits. multidim_average: Defines how additionally dimensions ``...`` should be handled. Should be one of the following: - ``global``: Additional dimensions are flatted along the batch dimension - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis. The statistics in this case are calculated over the additional dimensions. ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. 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]]) FrTrrmacrorNr num_classestop_kr&micror:weightednonerrrrrrc Ftjd|||||dd||rt||||||||_||_dS)NF)r;r<r&rrrr)rrr rr) r rr;r<r&rrrrr!rr#r   zMulticlassFBetaScore.__init__c C,|\}}}}t|||||j|j|jdSNr%r'r rr&rr(rrr#r-zMulticlassFBetaScore.computer9r:rNTr/r0r1r2rr3r4rrrr5r6rrrrr-r7rrr!r#r8s<  ^    r8cseZdZUdZdZeed<dZeeed<dZ eed<   dd e d e d e dee dde ddee dede dd ffdd ZdefddZZS)MultilabelFBetaScoreaComputes `F-score`_ metric for multilabel tasks: .. math:: 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`` (:class:`~torch.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`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)``. As output to ``forward`` and ``compute`` the metric returns the following output: - ``mlfbs`` (:class:`~torch.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)`` Args: beta: Weighting between precision and recall in calculation. Setting to 1 corresponds to equal weight num_labels: Integer specifing the number of labels threshold: Threshold for transforming probability to binary (0,1) predictions average: Defines the reduction that is applied over labels. Should be one of the following: - ``micro``: Sum statistics over all labels - ``macro``: Calculate statistics for each label and average them - ``weighted``: Calculates statistics for each label and computes weighted average using their support - ``"none"`` or ``None``: Calculates statistic for each label and applies no reduction multidim_average: Defines how additionally dimensions ``...`` should be handled. Should be one of the following: - ``global``: Additional dimensions are flatted along the batch dimension - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis. The statistics in this case are calculated over the additional dimensions. ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. 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]]) FrTrrrr:rNr num_labelsrr&r=rrrrrrc rA)NF)rJrr&rrrr)rrr rr) r rrJrr&rrrrr!rr#rarBzMultilabelFBetaScore.__init__c CrCrDrEr(rrr#r-zrFzMultilabelFBetaScore.computerr:rNTrHrrr!r#rIs<  \    rIc sveZdZUdZdZeed<dZeeed<dZ eed<   dd e d e d d ee dede dd f fdd ZZS) BinaryF1Scorea Computes F-1 score for binary tasks: .. math:: F_{1} = 2\frac{\text{precision} * \text{recall}}{(\text{precision}) + \text{recall}} As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.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. Addtionally, we convert to int tensor with thresholding using the value in ``threshold``. - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)`` As output to ``forward`` and ``compute`` the metric returns the following output: - ``bf1s`` (:class:`~torch.Tensor`): A tensor whose returned shape depends on the ``multidim_average`` argument: - 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. Args: threshold: Threshold for transforming probability to binary {0,1} predictions multidim_average: Defines how additionally dimensions ``...`` should be handled. Should be one of the following: - ``global``: Additional dimensions are flatted along the batch dimension - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis. The statistics in this case are calculated over the additional dimensions. ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. Example (preds is int tensor): >>> from torchmetrics.classification import BinaryF1Score >>> target = torch.tensor([0, 1, 0, 1, 0, 1]) >>> preds = torch.tensor([0, 0, 1, 1, 0, 1]) >>> metric = BinaryF1Score() >>> metric(preds, target) tensor(0.6667) Example (preds is float tensor): >>> from torchmetrics.classification import BinaryF1Score >>> 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 = BinaryF1Score() >>> metric(preds, target) tensor(0.6667) Example (multidim tensors): >>> from torchmetrics.classification import BinaryF1Score >>> 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 = BinaryF1Score(multidim_average='samplewise') >>> metric(preds, target) tensor([0.5000, 0.0000]) FrTrrrrNrrrrrrrc s"tjdd||||d|dS)N?)rrrrrrrr)r rrrrrr!rr#rs zBinaryF1Score.__init__r.)r/r0r1r2rr3r4rrrr5rr6rrr7rrr!r#rLs,  @ rLcseZdZUdZdZeed<dZeeed<dZ eed<   dd e d e d ee dde ddee dede dd ffdd Z ZS)MulticlassF1ScoreadComputes F-1 score for multiclass tasks: .. math:: F_{1} = 2\frac{\text{precision} * \text{recall}}{(\text{precision}) + \text{recall}} As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.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`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)`` As output to ``forward`` and ``compute`` the metric returns the following output: - ``mcf1s`` (:class:`~torch.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)`` Args: preds: Tensor with predictions target: Tensor with true labels num_classes: Integer specifing the number of classes average: Defines the reduction that is applied over labels. Should be one of the following: - ``micro``: Sum statistics over all labels - ``macro``: Calculate statistics for each label and average them - ``weighted``: Calculates statistics for each label and computes weighted average using their support - ``"none"`` or ``None``: Calculates statistic for each label and applies no reduction top_k: Number of highest probability or logit score predictions considered to find the correct label. Only works when ``preds`` contain probabilities/logits. multidim_average: Defines how additionally dimensions ``...`` should be handled. Should be one of the following: - ``global``: Additional dimensions are flatted along the batch dimension - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis. The statistics in this case are calculated over the additional dimensions. ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. Example (preds is int tensor): >>> from torchmetrics.classification import MulticlassF1Score >>> target = torch.tensor([2, 1, 0, 0]) >>> preds = torch.tensor([2, 1, 0, 1]) >>> metric = MulticlassF1Score(num_classes=3) >>> metric(preds, target) tensor(0.7778) >>> mcf1s = MulticlassF1Score(num_classes=3, average=None) >>> mcf1s(preds, target) tensor([0.6667, 0.6667, 1.0000]) Example (preds is float tensor): >>> from torchmetrics.classification import MulticlassF1Score >>> 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 = MulticlassF1Score(num_classes=3) >>> metric(preds, target) tensor(0.7778) >>> mcf1s = MulticlassF1Score(num_classes=3, average=None) >>> mcf1s(preds, target) tensor([0.6667, 0.6667, 1.0000]) Example (multidim tensors): >>> from torchmetrics.classification import MulticlassF1Score >>> 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 = MulticlassF1Score(num_classes=3, multidim_average='samplewise') >>> metric(preds, target) tensor([0.4333, 0.2667]) >>> mcf1s = MulticlassF1Score(num_classes=3, multidim_average='samplewise', average=None) >>> mcf1s(preds, target) tensor([[0.8000, 0.0000, 0.5000], [0.0000, 0.4000, 0.4000]]) FrTrrr9r:rNr;r<r&r=rrrrrrc &tjdd||||||d|dS)NrM)rr;r<r&rrrrrN)r r;r<r&rrrrr!rr#r8  zMulticlassF1Score.__init__rG)r/r0r1r2rr3r4rrrr6rrrr7rrr!r#rOs6  ]   rOcseZdZUdZdZeed<dZeeed<dZ eed<   dd e d e d ee dde ddee dede dd ffdd ZZS)MultilabelF1ScoreaComputes F-1 score for multilabel tasks: .. math:: F_{1} = 2\frac{\text{precision} * \text{recall}}{(\text{precision}) + \text{recall}} As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.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`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)``. As output to ``forward`` and ``compute`` the metric returns the following output: - ``mlf1s`` (:class:`~torch.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)``` Args: num_labels: Integer specifing the number of labels threshold: Threshold for transforming probability to binary (0,1) predictions average: Defines the reduction that is applied over labels. Should be one of the following: - ``micro``: Sum statistics over all labels - ``macro``: Calculate statistics for each label and average them - ``weighted``: Calculates statistics for each label and computes weighted average using their support - ``"none"`` or ``None``: Calculates statistic for each label and applies no reduction multidim_average: Defines how additionally dimensions ``...`` should be handled. Should be one of the following: - ``global``: Additional dimensions are flatted along the batch dimension - ``samplewise``: Statistic will be calculated independently for each sample on the ``N`` axis. The statistics in this case are calculated over the additional dimensions. ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. Example (preds is int tensor): >>> from torchmetrics.classification import MultilabelF1Score >>> target = torch.tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = torch.tensor([[0, 0, 1], [1, 0, 1]]) >>> metric = MultilabelF1Score(num_labels=3) >>> metric(preds, target) tensor(0.5556) >>> mlf1s = MultilabelF1Score(num_labels=3, average=None) >>> mlf1s(preds, target) tensor([1.0000, 0.0000, 0.6667]) Example (preds is float tensor): >>> from torchmetrics.classification import MultilabelF1Score >>> 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 = MultilabelF1Score(num_labels=3) >>> metric(preds, target) tensor(0.5556) >>> mlf1s = MultilabelF1Score(num_labels=3, average=None) >>> mlf1s(preds, target) tensor([1.0000, 0.0000, 0.6667]) Example (multidim tensors): >>> from torchmetrics.classification import MultilabelF1Score >>> 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 = MultilabelF1Score(num_labels=3, multidim_average='samplewise') >>> metric(preds, target) tensor([0.4444, 0.0000]) >>> mlf1s = MultilabelF1Score(num_labels=3, multidim_average='samplewise', average=None) >>> mlf1s(preds, target) tensor([[0.6667, 0.6667, 0.0000], [0.0000, 0.0000, 0.0000]]) FrTrrrr:rNrJrr&r=rrrrrrc rP)NrM)rrJrr&rrrrrN)r rJrr&rrrrr!rr#rrQzMultilabelF1Score.__init__rK)r/r0r1r2rr3r4rrrr6r5rrrr7rrr!r#rRNs6  [   rRc@seZdZdZ         dd ed d ed ed eedeedeeddeeddeedeedede de fddZ dS) FBetaScoreaXComputes `F-score`_ metric: .. math:: 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 :func:`binary_fbeta_score`, :func:`multiclass_fbeta_score` and :func:`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) rMrNr>rr9Ttaskr$ multiclass multilabelrrr;rJr&r=rrr<rrrrc Ks|dusJ| t|| | d|dkrt||fi| S|dkr:t|ts(Jt|ts/Jt||||fi| S|dkrPt|tsEJt||||fi| Std|N)rrrr$rVrWz[Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got )updatedictr isinstancer6r8rI ValueError) clsrTrrr;rJr&rr<rrrrrr#__new__s zFBetaScore.__new__) rMrNNr>rr9NT r/r0r1r2rr5rr6r3rr r^rrrr#rSsH       rSc@s|eZdZdZ        dded d ed eed eed eeddeeddeedeedede de fddZ dS)F1ScoreaComputes F-1 score: .. math:: F_{1} = 2\frac{\text{precision} * \text{recall}}{(\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 :mod:`BinaryF1Score`, :mod:`MulticlassF1Score` and :mod:`MultilabelF1Score` for the specific details of each argument influence and examples. Legacy Example: >>> import torch >>> target = torch.tensor([0, 1, 2, 0, 1, 2]) >>> preds = torch.tensor([0, 2, 1, 0, 0, 1]) >>> f1 = F1Score(task="multiclass", num_classes=3) >>> f1(preds, target) tensor(0.3333) rNr>rr9TrTrUrr;rJr&r=rrr<rrrrc Ks|dusJ| t||| d|dkrt|fi| S|dkr8t|ts'Jt|ts.Jt|||fi| S|dkrMt|tsCJt|||fi| Std|rX)rYrZrLr[r6rOrRr\) r]rTrr;rJr&rr<rrrrrr#r^ s zF1Score.__new__)rNNr>rr9NTr_rrrr#r`sB      r`)typingrrtorchrtyping_extensionsr'torchmetrics.classification.stat_scoresrrr-torchmetrics.functional.classification.f_betar r r r torchmetrics.metricr rr8rIrLrOrRrSr`rrrr#s     aWxv3