idddlmZddlmZmZmZddlmZddlm Z ddl m Z ddl m Z mZmZddlmZmZmZmZddlmZdd lmZdd lmZdd lmZmZesgd ZGd de ZGddeZ GddeZ!GddeZ"Gdde Z#Gdde!Z$Gdde Z%Gdde Z&y))Sequence)AnyOptionalUnion)Tensor)Literal)_ClassificationTaskWrapper)BinaryStatScoresMulticlassStatScoresMultilabelStatScores)"_binary_fbeta_score_arg_validation _fbeta_reduce&_multiclass_fbeta_score_arg_validation&_multilabel_fbeta_score_arg_validation)Metric)ClassificationTask)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPE)zBinaryFBetaScore.plotzMulticlassFBetaScore.plotzMultilabelFBetaScore.plotzBinaryF1Score.plotzMulticlassF1Score.plotzMultilabelF1Score.plotceZdZUdZdZeed<dZeeed<dZ eed<dZ e ed<d Z e ed < dd e d e de ddeedede dedd ffd ZdefdZ ddeeeeefdeedefdZxZS)BinaryFBetaScoreagCompute `F-score`_ metric for binary tasks. .. math:: F_{\beta} = (1 + \beta^2) * \frac{\text{precision} * \text{recall}} {(\beta^2 * \text{precision}) + \text{recall}} The metric is only proper defined when :math:`\text{TP} + \text{FP} \neq 0 \wedge \text{TP} + \text{FN} \neq 0` where :math:`\text{TP}`, :math:`\text{FP}` and :math:`\text{FN}` represent the number of true positives, false positives and false negatives respectively. If this case is encountered a score of `zero_division` (0 or 1, default is 0) is returned. 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. Additionally, 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. If ``multidim_average`` is set to ``samplewise`` we expect at least one additional dimension ``...`` to be present, which the reduction will then be applied over instead of the sample dimension ``N``. 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. zero_division: Should be `0` or `1`. The value returned when :math:`\text{TP} + \text{FP} = 0 \wedge \text{TP} + \text{FN} = 0`. Example (preds is int tensor): >>> from torch import tensor >>> from torchmetrics.classification import BinaryFBetaScore >>> target = tensor([0, 1, 0, 1, 0, 1]) >>> preds = 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 = tensor([0, 1, 0, 1, 0, 1]) >>> preds = 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 = 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]]]) >>> metric = BinaryFBetaScore(beta=2.0, multidim_average='samplewise') >>> metric(preds, target) tensor([0.5882, 0.0000]) Fis_differentiableThigher_is_betterfull_state_updateplot_lower_bound?plot_upper_boundNbeta thresholdmultidim_averageglobal samplewise ignore_index validate_args zero_divisionkwargsreturnc zt|d|||dd||rt|||||||_||_||_y)NF)r r!r%r&)super__init__r r&r'r) selfrr r!r%r&r'r( __class__s w/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/torchmetrics/classification/f_beta.pyr-zBinaryFBetaScore.__init__}s\  -%      .tY@PR^`m n** c |j\}}}}t|||||jd|j|jS)Compute metric.binaryaverager!r') _final_staterrr!r'r.tpfptnfns r0computezBinaryFBetaScore.computesM**,BB     II!22,,  r1valaxc&|j||S)aIPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure object and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import rand, randint >>> # Example plotting a single value >>> from torchmetrics.classification import BinaryFBetaScore >>> metric = BinaryFBetaScore(beta=2.0) >>> metric.update(rand(10), randint(2,(10,))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import rand, randint >>> # Example plotting multiple values >>> from torchmetrics.classification import BinaryFBetaScore >>> metric = BinaryFBetaScore(beta=2.0) >>> values = [ ] >>> for _ in range(10): ... values.append(metric(rand(10), randint(2,(10,)))) >>> fig_, ax_ = metric.plot(values) _plotr.r>r?s r0plotzBinaryFBetaScore.plotPzz#r""r1?r#NTrNN)__name__ __module__ __qualname____doc__rbool__annotations__rrrrfloatrrintrr-rr=rrrrrD __classcell__r/s@r0rr,sHT$t#'+htn+#t#!e!!e! >> from torch import tensor >>> from torchmetrics.classification import MulticlassFBetaScore >>> target = tensor([2, 1, 0, 0]) >>> preds = 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 = 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]]) >>> 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 = 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]]]) >>> 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]]) FrTrrrrrrClassplot_legend_nameNr num_classestop_kr6micromacroweightednoner!r"r%r&r'r(r)c t |d|||||dd| |rt|||||||||_||_||_y)NF)rWrXr6r!r%r&r+)r,r-rr&r'r) r.rrWrXr6r!r%r&r'r(r/s r0r-zMulticlassFBetaScore.__init__=sj  #-%     2k5'3C\S` +* r1c |j\}}}}t|||||j|j|j|j S)r3r5r7rrr6r!r'r8s r0r=zMulticlassFBetaScore.computeZsQ**,BB     IILL!22,,  r1r>r?c&|j||S)aPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure object and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import randint >>> # Example plotting a single value per class >>> from torchmetrics.classification import MulticlassFBetaScore >>> metric = MulticlassFBetaScore(num_classes=3, beta=2.0, average=None) >>> metric.update(randint(3, (20,)), randint(3, (20,))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import randint >>> # Example plotting a multiple values per class >>> from torchmetrics.classification import MulticlassFBetaScore >>> metric = MulticlassFBetaScore(num_classes=3, beta=2.0, average=None) >>> values = [] >>> for _ in range(20): ... values.append(metric(randint(3, (20,)), randint(3, (20,)))) >>> fig_, ax_ = metric.plot(values) rArCs r0rDzMulticlassFBetaScore.plothrEr1r[r#NTrrHrIrJrKrLrrMrNrrrrrOrrVstrrPrrr-rr=rrrrrDrQrRs@r0rTrTs,fP$t#'+htn+#t#!e!!e!#c# KR>> from torch import tensor >>> from torchmetrics.classification import MultilabelFBetaScore >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = 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 = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = 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 = 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]]]) >>> 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]]) FrTrrrrrrLabelrVNr num_labelsr r6rYr!r"r%r&r'r(r)c t |d|||||dd| |rt|||||||||_||_||_y)NF)rir r6r!r%r&r+)r,r-rr&r'r) r.rrir r6r!r%r&r'r(r/s r0r-zMultilabelFBetaScore.__init__sj  !-%     2j)W6F Vc +* r1c |j\}}}}t|||||j|j|jd|j  S)r3T)r6r! multilabelr'r`r8s r0r=zMultilabelFBetaScore.computesT**,BB     IILL!22,,  r1r>r?c&|j||S)aPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import rand, randint >>> # Example plotting a single value >>> from torchmetrics.classification import MultilabelFBetaScore >>> metric = MultilabelFBetaScore(num_labels=3, beta=2.0) >>> metric.update(randint(2, (20, 3)), randint(2, (20, 3))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import rand, randint >>> # Example plotting multiple values >>> from torchmetrics.classification import MultilabelFBetaScore >>> metric = MultilabelFBetaScore(num_labels=3, beta=2.0) >>> values = [ ] >>> for _ in range(10): ... values.append(metric(randint(2, (20, 3)), randint(2, (20, 3)))) >>> fig_, ax_ = metric.plot(values) rArCs r0rDzMultilabelFBetaScore.plot+rEr1rGr[r#NTrrHrdrRs@r0rgrgs,bH$t#'+htn+#t#!e!!e!#c# KR>> from torch import tensor >>> from torchmetrics.classification import BinaryF1Score >>> target = tensor([0, 1, 0, 1, 0, 1]) >>> preds = 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 = tensor([0, 1, 0, 1, 0, 1]) >>> preds = 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 = 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]]]) >>> metric = BinaryF1Score(multidim_average='samplewise') >>> metric(preds, target) tensor([0.5000, 0.0000]) FrTrrrrrrNr r!r"r%r&r'r(r)c 2t|dd|||||d|y)Nr)rr r!r%r&r'r+r,r-)r.r r!r%r&r'r(r/s r0r-zBinaryF1Score.__init__s3  -%''   r1r>r?c&|j||S)a-Plot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure object and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import rand, randint >>> # Example plotting a single value >>> from torchmetrics.classification import BinaryF1Score >>> metric = BinaryF1Score() >>> metric.update(rand(10), randint(2,(10,))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import rand, randint >>> # Example plotting multiple values >>> from torchmetrics.classification import BinaryF1Score >>> metric = BinaryF1Score() >>> values = [ ] >>> for _ in range(10): ... values.append(metric(rand(10), randint(2,(10,)))) >>> fig_, ax_ = metric.plot(values) rArCs r0rDzBinaryF1Score.plotrEr1rFrH)rIrJrKrLrrMrNrrrrrOrrrPrr-rrrrrrDrQrRs@r0rprpVsFP$t#'+htn+#t#!e!!e!>> from torch import tensor >>> from torchmetrics.classification import MulticlassF1Score >>> target = tensor([2, 1, 0, 0]) >>> preds = 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 = 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]]) >>> 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 = 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]]]) >>> 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]]) FrTrrrrrrrUrVNrWrXr6rYr!r"r%r&r'r(r)c 6t |dd|||||||d|y)Nr)rrWrXr6r!r%r&r'r+rr) r.rWrXr6r!r%r&r'r(r/s r0r-zMulticlassF1Score.__init__Rs9  #-%''  r1r>r?c&|j||S)aPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure object and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import randint >>> # Example plotting a single value per class >>> from torchmetrics.classification import MulticlassF1Score >>> metric = MulticlassF1Score(num_classes=3, average=None) >>> metric.update(randint(3, (20,)), randint(3, (20,))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import randint >>> # Example plotting a multiple values per class >>> from torchmetrics.classification import MulticlassF1Score >>> metric = MulticlassF1Score(num_classes=3, average=None) >>> values = [] >>> for _ in range(20): ... values.append(metric(randint(3, (20,)), randint(3, (20,)))) >>> fig_, ax_ = metric.plot(values) rArCs r0rDzMulticlassF1Score.plotirEr1rbrHrIrJrKrLrrMrNrrrrrOrrVrerPrrr-rrrrrrDrQrRs@r0ruruseN$t#'+htn+#t#!e!!e!#c# KR>> from torch import tensor >>> from torchmetrics.classification import MultilabelF1Score >>> target = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = 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 = tensor([[0, 1, 0], [1, 0, 1]]) >>> preds = 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 = 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]]]) >>> 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]]) FrTrrrrrrrhrVNrir r6rYr!r"r%r&r'r(r)c 6t |dd|||||||d|y)Nr)rrir r6r!r%r&r'r+rr) r.rir r6r!r%r&r'r(r/s r0r-zMultilabelF1Score.__init__s9  !-%''  r1r>r?c&|j||S)ajPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import rand, randint >>> # Example plotting a single value >>> from torchmetrics.classification import MultilabelF1Score >>> metric = MultilabelF1Score(num_labels=3) >>> metric.update(randint(2, (20, 3)), randint(2, (20, 3))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import rand, randint >>> # Example plotting multiple values >>> from torchmetrics.classification import MultilabelF1Score >>> metric = MultilabelF1Score(num_labels=3) >>> values = [ ] >>> for _ in range(10): ... values.append(metric(randint(2, (20, 3)), randint(2, (20, 3)))) >>> fig_, ax_ = metric.plot(values) rArCs r0rDzMultilabelF1Score.plotrEr1rnrHrxrRs@r0rzrzsaF$t#'+htn+#t#!e!!e!#c# KR>> from torch import tensor >>> target = tensor([0, 1, 2, 0, 1, 2]) >>> preds = tensor([0, 2, 1, 0, 0, 1]) >>> f_beta = FBetaScore(task="multiclass", num_classes=3, beta=0.5) >>> f_beta(preds, target) tensor(0.3333) Nclstaskr4 multiclassrlrr rWrir6rYr!r"rXr%r&r'r(r)c  6tj|}|J| j|| | | d|tjk(r t ||fi| S|tj k(r_t |tstdt|dt |tstdt|dt||||fi| S|tjk(r7t |tstdt|dt||||fi| Std|dzInitialize task metric.)r!r%r&r'z+`num_classes` is expected to be `int` but `z was passed.`z%`top_k` is expected to be `int` but `z*`num_labels` is expected to be `int` but `zTask z not supported!) rfrom_strupdateBINARYr MULTICLASS isinstancerP ValueErrortyperT MULTILABELrg) rrrr rWrir6r!rXr%r&r'r(s r0__new__zFBetaScore.__new___s1 "**40+++ 0(**   %,, ,#D)>v> > %00 0k3/ #NtT_O`Naan!oppeS) #He Ub!cdd'k5'TVT T %00 0j#. #MdS]N^M__l!mnn'j)WWPVW W5o677r1) rrGNNrZr#rcNTr rIrJrKrLrrrOrrPrMrrrr+r1r0r~r~As@%)$(KRFN &*" $8 , $8:;$8$8 $8 c] $8 SM $8'"FGH$8#7+A#BC$8}$8sm$8$8$8$8 $8r1r~ceZdZdZ ddeddeddedeedeed eed d eed d eedeede dede de fdZ y)F1ScoreaHCompute F-1 score. .. math:: F_{1} = 2\frac{\text{precision} * \text{recall}}{(\text{precision}) + \text{recall}} The metric is only proper defined when :math:`\text{TP} + \text{FP} \neq 0 \wedge \text{TP} + \text{FN} \neq 0` where :math:`\text{TP}`, :math:`\text{FP}` and :math:`\text{FN}` represent the number of true positives, false positives and false negatives respectively. If this case is encountered for any class/label, the metric for that class/label will be set to `zero_division` (0 or 1, default is 0) and the overall metric may therefore be affected in turn. This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of :class:`~torchmetrics.classification.BinaryF1Score`, :class:`~torchmetrics.classification.MulticlassF1Score` and :class:`~torchmetrics.classification.MultilabelF1Score` for the specific details of each argument influence and examples. Legacy Example: >>> from torch import tensor >>> target = tensor([0, 1, 2, 0, 1, 2]) >>> preds = tensor([0, 2, 1, 0, 0, 1]) >>> f1 = F1Score(task="multiclass", num_classes=3) >>> f1(preds, target) tensor(0.3333) Nrrrr rWrir6rYr!r"rXr%r&r'r(r)c  0tj|}|J| j||| | d|tjk(r t |fi| S|tj k(r^t |tstdt|dt |tstdt|dt|||fi| S|tjk(r6t |tstdt|dt|||fi| Std|dr) rrrrrprrrPrrrurrz) rrr rWrir6r!rXr%r&r'r(s r0rzF1Score.__new__s*"**40+++ 0(**   %,, , 5f5 5 %00 0k3/ #NtT_O`Naan!oppeS) #He Ub!cdd$[%KFK K %00 0j#. #MdS]N^M__l!mnn$ZGNvN N5o677r1) rGNNrZr#rcNTrrr+r1r0rrs<%)$(KRFN &*" #8 )_#8:;#8#8c] #8 SM #8 '"FGH #8#7+A#BC#8}#8sm#8#8#8#8 #8r1rN)'collections.abcrtypingrrrtorchrtyping_extensionsr torchmetrics.classification.baser 'torchmetrics.classification.stat_scoresr r r -torchmetrics.functional.classification.f_betar rrrtorchmetrics.metricrtorchmetrics.utilities.enumsrtorchmetrics.utilities.importsrtorchmetrics.utilities.plotrr__doctest_skip__rrTrgrprurzr~rr+r1r0rs%''%Gpp ';@@^#'^#BC#/C#L@#/@#FJ#$J#Zn#,n#bj#,j#ZB8+B8J?8(?8r1