o yi8@sddlmZddlZddlmZddlmZddlmZmZm Z m Z m Z m Z m Z mZmZmZmZmZddlmZ d;ded ed ed ed ed eeddeddefddZ   dd ed!ed"ed eed#deddeeddfd$d%Z    d?deded ed!ed eed#d"ededdeededefd&d'Z   d@d ed(eded eed#deddeeddfd)d*Z    dAdeded ed(eded eed#deddeededefd+d,Z    d=dededededdeededefd-d.Z    d?deded!ed eed#d"ededdeededefd/d0Z    dAdeded(eded eed#deddeededefd1d2Z! 3    4    dBdeded5ed6d eded!eed(eed eed#deedd"eedeededefd7d8Z"    4    dCdeded5ed6ded!eed(eed eed#deedd"eedeededefd9d:Z#dS)D)OptionalN)Tensor)Literal) "_binary_stat_scores_arg_validation_binary_stat_scores_format%_binary_stat_scores_tensor_validation_binary_stat_scores_update&_multiclass_stat_scores_arg_validation_multiclass_stat_scores_format)_multiclass_stat_scores_tensor_validation_multiclass_stat_scores_update&_multilabel_stat_scores_arg_validation_multilabel_stat_scores_format)_multilabel_stat_scores_tensor_validation_multilabel_stat_scores_update) _safe_divideglobaltpfptnfnbetaaverage)binarymicromacroweightednonemultidim_average)r samplewisereturnc Cs |d}|dkrtd||d|||||S|dkrV|j|dkr'dndd}|j|dkr3dndd}|j|dkr?dndd}td||d|||||Std||d|||||}|dusq|dkrs|S|d kr|||} nt|} t| || jd d d d S) Nrrrr)dimrrT)keepdim)rsumtorch ones_like) rrrrrrrbeta2 fbeta_scoreweightsr,a/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/classification/f_beta.py _fbeta_reduce%s &&&  r.? threshold ignore_indexcCs2t|tr |dkstd|dt|||dSNrz>Expected argument `beta` to be a float larger than 0, but got .) isinstancefloat ValueErrorr)rr0rr1r,r,r-"_binary_fbeta_score_arg_validationAsr7Tpredstarget validate_argsc Cs\|rt||||t||||t||||\}}t|||\}}} } t||| | |d|dS)a Computes `F-score`_ metric for binary tasks: .. math:: 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, ...)`` Args: preds: Tensor with predictions target: Tensor with true labels 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. 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]) rrr)r7rrrr.) r8r9rr0rr1r:rrrrr,r,r-binary_fbeta_scoreLs Er<r"r num_classestop_k)rrrrcC6t|tr |dkstd|dt|||||dSr2)r4r5r6r )rr=r>rrr1r,r,r-&_multiclass_fbeta_score_arg_validationr@c Csh|rt||||||t|||||t|||\}}t|||||||\} } } } t| | | | |||dS)aComputes `F-score`_ metric for multiclass tasks: .. math:: 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, ...)`` Args: preds: Tensor with predictions target: Tensor with true labels 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. Returns: 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)`` 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]]) r;)r@r r r r.) r8r9rr=rr>rr1r:rrrrr,r,r-multiclass_fbeta_scoresa rB num_labelscCr?r2)r4r5r6r )rrCr0rrr1r,r,r-&_multilabel_fbeta_score_arg_validationrArDc Csd|rt||||||t|||||t|||||\}}t|||\} } } } t| | | | |||dS)aComputes `F-score`_ metric for multilabel tasks: .. math:: 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, ...)`` Args: preds: Tensor with predictions target: Tensor with true labels 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. Returns: 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)`` 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]]) r;)rDrrrr.) r8r9rrCr0rrr1r:rrrrr,r,r-multilabel_fbeta_scores `rEc Cst||d||||dS)a Computes F-1 score for binary tasks: .. math:: F_{1} = 2\frac{\text{precision} * \text{recall}}{(\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, ...)`` Args: preds: Tensor with predictions target: Tensor with true labels 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. 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_f1_score >>> target = torch.tensor([0, 1, 0, 1, 0, 1]) >>> preds = torch.tensor([0, 0, 1, 1, 0, 1]) >>> binary_f1_score(preds, target) tensor(0.6667) Example (preds is float tensor): >>> from torchmetrics.functional.classification import binary_f1_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_f1_score(preds, target) tensor(0.6667) Example (multidim tensors): >>> from torchmetrics.functional.classification import binary_f1_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_f1_score(preds, target, multidim_average='samplewise') tensor([0.5000, 0.0000]) ?)r8r9rr0rr1r:)r<)r8r9r0rr1r:r,r,r-binary_f1_scoresBrGc Ct||d||||||d S)aComputes F-1 score for multiclass tasks: .. math:: F_{1} = 2\frac{\text{precision} * \text{recall}}{(\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, ...)`` 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. Returns: 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)`` Example (preds is int tensor): >>> from torchmetrics.functional.classification import multiclass_f1_score >>> target = torch.tensor([2, 1, 0, 0]) >>> preds = torch.tensor([2, 1, 0, 1]) >>> multiclass_f1_score(preds, target, num_classes=3) tensor(0.7778) >>> multiclass_f1_score(preds, target, num_classes=3, average=None) tensor([0.6667, 0.6667, 1.0000]) Example (preds is float tensor): >>> from torchmetrics.functional.classification import multiclass_f1_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_f1_score(preds, target, num_classes=3) tensor(0.7778) >>> multiclass_f1_score(preds, target, num_classes=3, average=None) tensor([0.6667, 0.6667, 1.0000]) Example (multidim tensors): >>> from torchmetrics.functional.classification import multiclass_f1_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_f1_score(preds, target, num_classes=3, multidim_average='samplewise') tensor([0.4333, 0.2667]) >>> multiclass_f1_score(preds, target, num_classes=3, multidim_average='samplewise', average=None) tensor([[0.8000, 0.0000, 0.5000], [0.0000, 0.4000, 0.4000]]) rF) r8r9rr=rr>rr1r:)rB)r8r9r=rr>rr1r:r,r,r-multiclass_f1_scores^rIc CrH)aComputes F-1 score for multilabel tasks: .. math:: F_{1} = 2\frac{\text{precision} * \text{recall}}{(\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, ...)`` Args: preds: Tensor with predictions target: Tensor with true labels 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. Returns: 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)`` Example (preds is int tensor): >>> from torchmetrics.functional.classification import multilabel_f1_score >>> target = torch.tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = torch.tensor([[0, 0, 1], [1, 0, 1]]) >>> multilabel_f1_score(preds, target, num_labels=3) tensor(0.5556) >>> multilabel_f1_score(preds, target, num_labels=3, average=None) tensor([1.0000, 0.0000, 0.6667]) Example (preds is float tensor): >>> from torchmetrics.functional.classification import multilabel_f1_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_f1_score(preds, target, num_labels=3) tensor(0.5556) >>> multilabel_f1_score(preds, target, num_labels=3, average=None) tensor([1.0000, 0.0000, 0.6667]) Example (multidim tensors): >>> from torchmetrics.functional.classification import multilabel_f1_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_f1_score(preds, target, num_labels=3, multidim_average='samplewise') tensor([0.4444, 0.0000]) >>> multilabel_f1_score(preds, target, num_labels=3, multidim_average='samplewise', average=None) tensor([[0.6667, 0.6667, 0.0000], [0.0000, 0.0000, 0.0000]]) rF) r8r9rrCr0rrr1r:)rE)r8r9rCr0rrr1r:r,r,r-multilabel_f1_score>s]rJrFrtask)r multiclass multilabelc Cs|dusJ|dkrt|||||| | S|dkr2t|tsJt| ts&Jt|||||| || | S|dkrIt|ts=Jt|||||||| | Std|)a%Computes `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. 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) NrrLrM[Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got )r<r4intrBrEr6) r8r9rKrr0r=rCrrr>r1r:r,r,r-r*s" r*c Cs|dusJ|dkrt||||| | S|dkr0t|tsJt|ts%Jt||||||| | S|dkrFt|ts;Jt||||||| | Std|)aComputes 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 :func:`binary_f1_score`, :func:`multiclass_f1_score` and :func:`multilabel_f1_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]) >>> f1_score(preds, target, task="multiclass", num_classes=3) tensor(0.3333) NrrLrMrN)rGr4rOrIrJr6) r8r9rKr0r=rCrrr>r1r:r,r,r-f1_scores" rP)r)r/rN)r/rNT)r"rrN)rr"rNT)r/rrN)r/rrNT) rFr/NNrrr"NT)r/NNrrr"NT)$typingrr'rtyping_extensionsr2torchmetrics.functional.classification.stat_scoresrrrrr r r r r rrrtorchmetrics.utilities.computerr5r.rOr7boolr<r@rBrDrErGrIrJr*rPr,r,r,r-s:   8      P     n     k Q  o  n      6