a  d@sddlmZddlmZddlmZddlmZmZm Z m Z m Z m Z m Z mZmZmZmZmZddlmZmZddlmZd8eeeeeeed ed eed d d Zd9eeed eeddddZd:eeeeed eeeedddZd;eeeeeded eeddddZdeeeeeeeded eeeed# d$d%Z!d?eeeed eeeed&d'd(Z"d@eeeeedeed eeeed) d*d+Z#dAeeeeeeded eeeed, d-d.Z$dBeeed1eeeeeeeedeed eeeeeed2 d3d4Z%dCeeed1eeeeeeedeed eeeeeed5 d6d7Z&dS)D)Optional)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)_adjust_weights_safe_divide _safe_divide)ClassificationTaskglobalF)binarymicromacroweightednone)rZ samplewise) tpfptnfnbetaaveragemultidim_average multilabelreturnc Cs|d}|dkr6td||d|||||S|dkr|j|dkrNdndd}|j|dkrfdndd}|j|dkr~dndd}td||d|||||Std||d|||||} t| |||||S)Nrrrr)Zdim)rsumr) rrrrrrr r!Zbeta2 fbeta_scorer'v/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/functional/classification/f_beta.py _fbeta_reduce%s &&&r)?N)r thresholdr ignore_indexr"cCs2t|tr|dks"td|dt|||dSNrz>Expected argument `beta` to be a float larger than 0, but got .) isinstancefloat ValueErrorr)rr+r r,r'r'r("_binary_fbeta_score_arg_validation<sr2T)predstargetrr+r r, validate_argsr"c Cs\|r t||||t||||t||||\}}t|||\}}} } t||| | |d|dS)a` Compute `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 torch import tensor >>> from torchmetrics.functional.classification import binary_fbeta_score >>> target = tensor([0, 1, 0, 1, 0, 1]) >>> preds = 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 = tensor([0, 1, 0, 1, 0, 1]) >>> preds = 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 = tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]]) >>> preds = tensor([[[0.59, 0.91], [0.91, 0.99], [0.63, 0.04]], ... [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]]]) >>> binary_fbeta_score(preds, target, beta=2.0, multidim_average='samplewise') tensor([0.5882, 0.0000]) rrr )r2rrrr)) r3r4rr+r r,r5rrrrr'r'r(binary_fbeta_scoreGs Cr7r$r)rrrr)r num_classestop_krr r,r"cCs6t|tr|dks"td|dt|||||dSr-)r/r0r1r )rr8r9rr r,r'r'r(&_multiclass_fbeta_score_arg_validationsr:) r3r4rr8rr9r r,r5r"c Csh|r&t||||||t|||||t|||\}}t|||||||\} } } } t| | | | |||dS)aCompute `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 torch import tensor >>> from torchmetrics.functional.classification import multiclass_fbeta_score >>> target = tensor([2, 1, 0, 0]) >>> preds = 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 = tensor([2, 1, 0, 0]) >>> preds = tensor([[0.16, 0.26, 0.58], ... [0.22, 0.61, 0.17], ... [0.71, 0.09, 0.20], ... [0.05, 0.82, 0.13]]) >>> multiclass_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 = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]]) >>> preds = tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]]) >>> multiclass_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]]) r6)r:r r r r)) r3r4rr8rr9r r,r5rrrrr'r'r(multiclass_fbeta_scoresa r;)r num_labelsr+rr r,r"cCs6t|tr|dks"td|dt|||||dSr-)r/r0r1r )rr<r+rr r,r'r'r(&_multilabel_fbeta_score_arg_validation sr=) r3r4rr<r+rr r,r5r"c Csf|r&t||||||t|||||t|||||\}}t|||\} } } } t| | | | |||ddS)aCompute `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 torch import tensor >>> from torchmetrics.functional.classification import multilabel_fbeta_score >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = 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 = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = 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 = tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]]) >>> preds = tensor([[[0.59, 0.91], [0.91, 0.99], [0.63, 0.04]], ... [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]]]) >>> multilabel_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]]) T)rr r!)r=rrrr)) r3r4rr<r+rr r,r5rrrrr'r'r(multilabel_fbeta_scores ^r>)r3r4r+r r,r5r"c Cst||d||||dS)a Compute 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 torch import tensor >>> from torchmetrics.functional.classification import binary_f1_score >>> target = tensor([0, 1, 0, 1, 0, 1]) >>> preds = 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 = tensor([0, 1, 0, 1, 0, 1]) >>> preds = 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 = tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]]) >>> preds = tensor([[[0.59, 0.91], [0.91, 0.99], [0.63, 0.04]], ... [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]]]) >>> binary_f1_score(preds, target, multidim_average='samplewise') tensor([0.5000, 0.0000]) ?)r3r4rr+r r,r5)r7)r3r4r+r r,r5r'r'r(binary_f1_score}s@r@) r3r4r8rr9r r,r5r"c Cst||d||||||d S)aCompute 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 torch import tensor >>> from torchmetrics.functional.classification import multiclass_f1_score >>> target = tensor([2, 1, 0, 0]) >>> preds = 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 = tensor([2, 1, 0, 0]) >>> preds = tensor([[0.16, 0.26, 0.58], ... [0.22, 0.61, 0.17], ... [0.71, 0.09, 0.20], ... [0.05, 0.82, 0.13]]) >>> multiclass_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 = tensor([[[0, 1], [2, 1], [0, 2]], [[1, 1], [2, 0], [1, 2]]]) >>> preds = tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]]) >>> multiclass_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]]) r?) r3r4rr8rr9r r,r5)r;)r3r4r8rr9r r,r5r'r'r(multiclass_f1_scores^rA) r3r4r<r+rr r,r5r"c Cst||d||||||d S)aCompute 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 torch import tensor >>> from torchmetrics.functional.classification import multilabel_f1_score >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = 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 = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = 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 = tensor([[[0, 1], [1, 0], [0, 1]], [[1, 1], [0, 0], [1, 0]]]) >>> preds = tensor([[[0.59, 0.91], [0.91, 0.99], [0.63, 0.04]], ... [[0.38, 0.04], [0.86, 0.780], [0.45, 0.37]]]) >>> multilabel_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]]) r?) r3r4rr<r+rr r,r5)r>)r3r4r<r+rr r,r5r'r'r(multilabel_f1_score3s[rBr?r)rZ multiclassr!) r3r4taskrr+r8r<rr r9r,r5r"c Cst|}|dusJ|tjkr4t|||||| | S|tjkrt|ts\tdt|dt| tsztdt| dt |||||| || | S|tj krt|tstdt|dt |||||||| | Std|ddS)aCompute `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:`~torchmetrics.functional.classification.binary_fbeta_score`, :func:`~torchmetrics.functional.classification.multiclass_fbeta_score` and :func:`~torchmetrics.functional.classification.multilabel_fbeta_score` for the specific details of each argument influence and examples. Legacy Example: >>> from torch import tensor >>> target = tensor([0, 1, 2, 0, 1, 2]) >>> preds = tensor([0, 2, 1, 0, 0, 1]) >>> fbeta_score(preds, target, task="multiclass", num_classes=3, beta=0.5) tensor(0.3333) N+`num_classes` is expected to be `int` but ` was passed.`%`top_k` is expected to be `int` but `*`num_labels` is expected to be `int` but `Unsupported task ` ` passed.) rfrom_strBINARYr7 MULTICLASSr/intr1typer; MULTILABELr>) r3r4rCrr+r8r<rr r9r,r5r'r'r(r&s&#        r&) r3r4rCr+r8r<rr r9r,r5r"c Cst|}|dusJ|tjkr2t||||| | S|tjkrt|tsZtdt|dt|tsxtdt|dt ||||||| | S|tj krt|tstdt|dt ||||||| | Std|ddS)azCompute 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:`~torchmetrics.functional.classification.binary_f1_score`, :func:`~torchmetrics.functional.classification.multiclass_f1_score` and :func:`~torchmetrics.functional.classification.multilabel_f1_score` for the specific details of each argument influence and examples. Legacy Example: >>> from torch import tensor >>> target = tensor([0, 1, 2, 0, 1, 2]) >>> preds = tensor([0, 2, 1, 0, 0, 1]) >>> f1_score(preds, target, task="multiclass", num_classes=3) tensor(0.3333) NrDrErFrGrHrI) rrJrKr@rLr/rMr1rNrArOrB) r3r4rCr+r8r<rr r9r,r5r'r'r(f1_scores&!        rP)rF)r*rN)r*rNT)r$rrN)rr$rNT)r*rrN)r*rrNT)r*rNT)rr$rNT)r*rrNT) r?r*NNrrr$NT)r*NNrrr$NT)'typingrZtorchrZtyping_extensionsrZ2torchmetrics.functional.classification.stat_scoresrrrrr r r r r rrrZtorchmetrics.utilities.computerrZtorchmetrics.utilities.enumsrr0boolr)rMr2r7r:r;r=r>r@rArBr&rPr'r'r'r(s   8     N    n    i O  o  l   <