o .wi@s4ddlmZddlmZmZmZddlmZddlm Z ddl m Z ddl m Z mZmZddlmZmZmZmZddlmZdd lmZdd lmZdd lmZmZesXgd ZGd dde ZGdddeZ GdddeZ!GdddeZ"Gddde Z#Gddde!Z$Gddde Z%Gddde Z&dS))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)BinaryFBetaScore.plotMulticlassFBetaScore.plotMultilabelFBetaScore.plotBinaryF1Score.plotMulticlassF1Score.plotMultilabelF1Score.plotcseZdZUdZdZeed<dZeeed<dZ eed<dZ e ed<d Z e ed <  d de de de ddeedede dedd ffdd ZdefddZ d!deeeeefdeedefddZZS)"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_bound?globalNrbeta thresholdmultidim_averager$ samplewise ignore_index validate_args zero_divisionkwargsreturnc sFtjd|||dd||rt|||||||_||_||_dS)NF)r&r'r*r+)super__init__r r+r,r%)selfr%r&r'r*r+r,r- __class__r/_/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/classification/f_beta.pyr1}s  zBinaryFBetaScore.__init__c Cs.|\}}}}t|||||jd|j|jdS)Compute metric.binaryaverager'r,) _final_stater r%r'r,r2tpfptnfnr/r/r5computeszBinaryFBetaScore.computevalaxcC |||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) _plotr2rArBr/r/r5plot (rr#r$NTrNN)__name__ __module__ __qualname____doc__rbool__annotations__rrrr floatr"rintrr1rr@rrrrrG __classcell__r/r/r3r5r,sL  J    rcseZdZUdZdZeed<dZeeed<dZ eed<dZ e ed<d Z e ed <d Z eed <     d&de dededeeddeddeedede deddffdd Zdefd d!Z d'd"eeeeefd#eedefd$d%ZZS)(MulticlassFBetaScoreaPCompute `F-score`_ metric for multiclass 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 for any class, the metric for that class will be set to `zero_division` (0 or 1, default is 0) and the overall metric may therefore be affected in turn. 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)`` 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 num_classes: Integer specifying 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. 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 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]]) FrTrrrr r!r"Classplot_legend_namemacror$Nrr% num_classestop_kr9microrXweightednoner'r(r*r+r,r-r.c Ntjd|||||dd| |rt|||||||||_||_||_dS)NF)rYrZr9r'r*r+r/)r0r1rr+r,r%) r2r%rYrZr9r'r*r+r,r-r3r/r5r1="   zMulticlassFBetaScore.__init__c Cs0|\}}}}t|||||j|j|j|jdS)r6r8r:r r%r9r'r,r;r/r/r5r@ZszMulticlassFBetaScore.computerArBcCrC)aPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure object and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import randint >>> # Example plotting a single value per class >>> from torchmetrics.classification import MulticlassFBetaScore >>> metric = MulticlassFBetaScore(num_classes=3, beta=2.0, average=None) >>> metric.update(randint(3, (20,)), randint(3, (20,))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import randint >>> # Example plotting a multiple values per class >>> from torchmetrics.classification import MulticlassFBetaScore >>> metric = MulticlassFBetaScore(num_classes=3, beta=2.0, average=None) >>> values = [] >>> for _ in range(20): ... values.append(metric(randint(3, (20,)), randint(3, (20,)))) >>> fig_, ax_ = metric.plot(values) rDrFr/r/r5rGhrHrrWrXr$NTrrJrKrLrMrNrrOrPrrrr rQr"rVstrrRrrr1rr@rrrrrGrSr/r/r3r5rTsX  h        rTcseZdZUdZdZeed<dZeeed<dZ eed<dZ e ed<d Z e ed <d Z eed <     d&de dede deeddeddeedede deddffdd Zdefd d!Z d'd"eeeeefd#eedefd$d%ZZS)(MultilabelFBetaScoreaCompute `F-score`_ metric for multilabel 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 for any label, the metric for that label will be set to `zero_division` (0 or 1, default is 0) and the overall metric may therefore be affected in turn. 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. Additionally, 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)`` 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 num_labels: Integer specifying 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. 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 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]]) FrTrrrr r!r"LabelrVr#rXr$Nrr% num_labelsr&r9r[r'r(r*r+r,r-r.c r_)NF)rgr&r9r'r*r+r/)r0r1rr+r,r%) r2r%rgr&r9r'r*r+r,r-r3r/r5r1r`zMultilabelFBetaScore.__init__c Cs2|\}}}}t|||||j|j|jd|jd S)r6T)r9r' multilabelr,rar;r/r/r5r@szMultilabelFBetaScore.computerArBcCrC)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) rDrFr/r/r5rG+rHrr#rXr$NTrrJrcr/r/r3r5resX  d        recseZdZUdZdZeed<dZeeed<dZ eed<dZ e ed<d Z e ed <  dde de ddeedede dedd ffdd Z ddeeeeefdeedefddZZS) BinaryF1ScoreaCompute F-1 score for binary tasks. .. 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 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 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: - ``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. 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: 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 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]) FrTrrrr r!r"r#r$Nrr&r'r(r*r+r,r-r.c s$tjdd|||||d|dS)Nr!)r%r&r'r*r+r,r/r0r1)r2r&r'r*r+r,r-r3r/r5r1s  zBinaryF1Score.__init__rArBcCrC)a-Plot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure object and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import rand, randint >>> # Example plotting a single value >>> from torchmetrics.classification import BinaryF1Score >>> metric = BinaryF1Score() >>> metric.update(rand(10), randint(2,(10,))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import rand, randint >>> # Example plotting multiple values >>> from torchmetrics.classification import BinaryF1Score >>> metric = BinaryF1Score() >>> values = [ ] >>> for _ in range(10): ... values.append(metric(rand(10), randint(2,(10,)))) >>> fig_, ax_ = metric.plot(values) rDrFr/r/r5rGrHrrIrJ)rKrLrMrNrrOrPrrrr rQr"rrRrr1rrrrrrGrSr/r/r3r5rjVsF  H   rjcseZdZUdZdZeed<dZeeed<dZ eed<dZ e ed<d Z e ed <d Z eed <     d#dededeeddeddeedede deddffdd Z d$deeeeefd eedefd!d"ZZS)%MulticlassF1ScoreaCompute F-1 score for multiclass tasks. .. 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, the metric for that class will be set to `zero_division` (0 or 1, default is 0) and the overall metric may therefore be affected in turn. 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)`` 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: preds: Tensor with predictions target: Tensor with true labels num_classes: Integer specifying 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. 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 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]]) FrTrrrr r!r"rUrVrWrXr$NrrYrZr9r[r'r(r*r+r,r-r.c (tjdd|||||||d|dS)Nr!)r%rYrZr9r'r*r+r,r/rk) r2rYrZr9r'r*r+r,r-r3r/r5r1R  zMulticlassF1Score.__init__rArBcCrC)aPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure object and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import randint >>> # Example plotting a single value per class >>> from torchmetrics.classification import MulticlassF1Score >>> metric = MulticlassF1Score(num_classes=3, average=None) >>> metric.update(randint(3, (20,)), randint(3, (20,))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import randint >>> # Example plotting a multiple values per class >>> from torchmetrics.classification import MulticlassF1Score >>> metric = MulticlassF1Score(num_classes=3, average=None) >>> values = [] >>> for _ in range(20): ... values.append(metric(randint(3, (20,)), randint(3, (20,)))) >>> fig_, ax_ = metric.plot(values) rDrFr/r/r5rGirHrrbrJrKrLrMrNrrOrPrrrr rQr"rVrdrRrrr1rrrrrrGrSr/r/r3r5rlsR  g       rlcseZdZUdZdZeed<dZeeed<dZ eed<dZ e ed<d Z e ed <d Z eed <     d#dede deeddeddeedede deddffdd Z d$deeeeefd eedefd!d"ZZS)%MultilabelF1ScoreaCompute F-1 score for multilabel tasks. .. 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 label, the metric for that label will be set to `zero_division` (0 or 1, default is 0) and the overall metric may therefore be affected in turn. 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. Additionally, 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)``` 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: num_labels: Integer specifying 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. 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 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]]) FrTrrrr r!r"rfrVr#rXr$Nrrgr&r9r[r'r(r*r+r,r-r.c rm)Nr!)r%rgr&r9r'r*r+r,r/rk) r2rgr&r9r'r*r+r,r-r3r/r5r1rnzMultilabelF1Score.__init__rArBcCrC)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) rDrFr/r/r5rGrHrrirJror/r/r3r5rpsR  c       rpc@seZdZdZ          dd edd ed d ededeedeedeeddeeddeedeede dede de fddZ dS) FBetaScoreaCompute `F-score`_ metric. .. 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 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.BinaryFBetaScore`, :class:`~torchmetrics.classification.MulticlassFBetaScore` and :class:`~torchmetrics.classification.MultilabelFBetaScore` for the specific details of each argument influence and examples. Legcy Example: >>> 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) r!r#Nr\r$rWTrclstaskr7 multiclassrhr%r&rYrgr9r[r'r(rZr*r+r,r-r.c Kst|}|dus J| || | | d|tjkr#t||fi| S|tjkrQt|ts7tdt |dt|tsFtdt |dt ||||fi| S|tj krpt|tsetdt |dt ||||fi| Std|d zInitialize task metric.N)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 isinstancerR ValueErrortyperT MULTILABELre) rrrsr%r&rYrgr9r'rZr*r+r,r-r/r/r5__new___s*        zFBetaScore.__new__) r!r#NNr\r$rWNTr rKrLrMrNr}rrQrrRrOrrrr/r/r/r5rqAsR        rqc@seZdZdZ         dd edd ed d ed eedeedeeddeeddeedeede dede de fddZ dS)F1ScoreaHCompute F-1 score. .. math:: F_{1} = 2\frac{\text{precision} * \text{recall}}{(\text{precision}) + \text{recall}} The metric is only proper defined when :math:`\text{TP} + \text{FP} \neq 0 \wedge \text{TP} + \text{FN} \neq 0` where :math:`\text{TP}`, :math:`\text{FP}` and :math:`\text{FN}` represent the number of true positives, false positives and false negatives respectively. If this case is encountered for any class/label, the metric for that class/label will be set to `zero_division` (0 or 1, default is 0) and the overall metric may therefore be affected in turn. This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of :class:`~torchmetrics.classification.BinaryF1Score`, :class:`~torchmetrics.classification.MulticlassF1Score` and :class:`~torchmetrics.classification.MultilabelF1Score` for the specific details of each argument influence and examples. Legacy Example: >>> from torch import tensor >>> target = tensor([0, 1, 2, 0, 1, 2]) >>> preds = tensor([0, 2, 1, 0, 0, 1]) >>> f1 = F1Score(task="multiclass", num_classes=3) >>> f1(preds, target) tensor(0.3333) r#Nr\r$rWTrrrrsrtr&rYrgr9r[r'r(rZr*r+r,r-r.c Kst|}|dus J| ||| | d|tjkr"t|fi| S|tjkrOt|ts6tdt |dt|tsEtdt |dt |||fi| S|tj krmt|tsctdt |dt |||fi| Std|drv) rrwrxryrjrzr{rRr|r}rlr~rp) rrrsr&rYrgr9r'rZr*r+r,r-r/r/r5rs*        zF1Score.__new__) r#NNr\r$rWNTrrr/r/r/r5rsL       rN)'collections.abcrtypingrrrtorchrtyping_extensionsr torchmetrics.classification.baser'torchmetrics.classification.stat_scoresr r r -torchmetrics.functional.classification.f_betar r rrtorchmetrics.metricrtorchmetrics.utilities.enumsrtorchmetrics.utilities.importsrtorchmetrics.utilities.plotrr__doctest_skip__rrTrerjrlrprqrr/r/r/r5s6        "GD2.E