o .wi@s8ddlmZmZmZmZddlZddlmZddlmZddl m Z ddl m Z ddl mZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZddlmZdd l m!Z!dd l"m#Z#dd l$m%Z%dd l&m'Z'dd l(m)Z)m*Z*m+Z+e'szgdZ,GdddeZ-GdddeZ.GdddeZ/Gddde Z0dS))AnyListOptionalUnionN)Tensor)Literal)_ClassificationTaskWrapper) _reduce_auroc)_adjust_threshold_arg-_binary_precision_recall_curve_arg_validation&_binary_precision_recall_curve_compute%_binary_precision_recall_curve_format0_binary_precision_recall_curve_tensor_validation%_binary_precision_recall_curve_update1_multiclass_precision_recall_curve_arg_validation*_multiclass_precision_recall_curve_compute)_multiclass_precision_recall_curve_format4_multiclass_precision_recall_curve_tensor_validation)_multiclass_precision_recall_curve_update1_multilabel_precision_recall_curve_arg_validation*_multilabel_precision_recall_curve_compute)_multilabel_precision_recall_curve_format4_multilabel_precision_recall_curve_tensor_validation)_multilabel_precision_recall_curve_update)Metric)_auc_compute_without_check) dim_zero_cat)ClassificationTask)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPE plot_curve)BinaryPrecisionRecallCurve.plot#MulticlassPrecisionRecallCurve.plot#MultilabelPrecisionRecallCurve.plotc s eZdZUdZdZeed<dZeeed<dZ eed<e e ed<e e ed<e ed <   dd ee e eee fd ee d ededdf fdd Zde de ddfddZdee e e ffddZ   ddeee e e fdee e efdeedefddZZS)BinaryPrecisionRecallCurveaCompute the precision-recall curve for binary tasks. The curve consist of multiple pairs of precision and recall values evaluated at different thresholds, such that the tradeoff between the two values can been seen. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, ...)``. Preds should be a tensor containing probabilities or logits for each observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per element. - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``. Target should be a tensor containing ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified). The value 1 always encodes the positive class. .. tip:: Additional dimension ``...`` will be flattened into the batch dimension. As output to ``forward`` and ``compute`` the metric returns the following output: - ``precision`` (:class:`~torch.Tensor`): if `thresholds=None` a list for each class is returned with an 1d tensor of size ``(n_thresholds+1, )`` with precision values (length may differ between classes). If `thresholds` is set to something else, then a single 2d tensor of size ``(n_classes, n_thresholds+1)`` with precision values is returned. - ``recall`` (:class:`~torch.Tensor`): if `thresholds=None` a list for each class is returned with an 1d tensor of size ``(n_thresholds+1, )`` with recall values (length may differ between classes). If `thresholds` is set to something else, then a single 2d tensor of size ``(n_classes, n_thresholds+1)`` with recall values is returned. - ``thresholds`` (:class:`~torch.Tensor`): if `thresholds=None` a list for each class is returned with an 1d tensor of size ``(n_thresholds, )`` with increasing threshold values (length may differ between classes). If `threshold` is set to something else, then a single 1d tensor of size ``(n_thresholds, )`` is returned with shared threshold values for all classes. .. note:: The implementation both supports calculating the metric in a non-binned but accurate version and a binned version that is less accurate but more memory efficient. Setting the `thresholds` argument to `None` will activate the non-binned version that uses memory of size :math:`\mathcal{O}(n_{samples})` whereas setting the `thresholds` argument to either an integer, list or a 1d tensor will use a binned version that uses memory of size :math:`\mathcal{O}(n_{thresholds})` (constant memory). Args: thresholds: Can be one of: - If set to `None`, will use a non-binned approach where thresholds are dynamically calculated from all the data. Most accurate but also most memory consuming approach. - If set to an `int` (larger than 1), will use that number of thresholds linearly spaced from 0 to 1 as bins for the calculation. - If set to an `list` of floats, will use the indicated thresholds in the list as bins for the calculation - If set to an 1d `tensor` of floats, will use the indicated thresholds in the tensor as bins for the calculation. 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. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example: >>> from torchmetrics.classification import BinaryPrecisionRecallCurve >>> preds = torch.tensor([0, 0.5, 0.7, 0.8]) >>> target = torch.tensor([0, 1, 1, 0]) >>> bprc = BinaryPrecisionRecallCurve(thresholds=None) >>> bprc(preds, target) # doctest: +NORMALIZE_WHITESPACE (tensor([0.5000, 0.6667, 0.5000, 0.0000, 1.0000]), tensor([1.0000, 1.0000, 0.5000, 0.0000, 0.0000]), tensor([0.0000, 0.5000, 0.7000, 0.8000])) >>> bprc = BinaryPrecisionRecallCurve(thresholds=5) >>> bprc(preds, target) # doctest: +NORMALIZE_WHITESPACE (tensor([0.5000, 0.6667, 0.6667, 0.0000, 0.0000, 1.0000]), tensor([1., 1., 1., 0., 0., 0.]), tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000])) Fis_differentiableNhigher_is_betterfull_state_updatepredstargetconfmatT thresholds ignore_index validate_argskwargsreturnc stjd i||rt||||_||_t|}|dur3||_|jdgdd|jdgdddS|jd|dd|jdt j t |d d t j d d ddS Nr)cat)defaultdist_reduce_fxr*r,F) persistentr+)dtypesum) super__init__r r-r.r r, add_stateregister_buffertorchzeroslenlong)selfr,r-r.r/ __class__r9o/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/classification/precision_recall_curve.pyr;s  z#BinaryPrecisionRecallCurve.__init__cCsz|jr t|||jt|||j|j\}}}t|||j}t|tr+|j|7_dS|j |d|j |ddSzUpdate metric states.rN) r.rr-r r,r isinstancerr+r)appendr*rBr)r*_stater9r9rEupdates z!BinaryPrecisionRecallCurve.updatecCs0|jdurt|jt|jfn|j}t||jSzCompute metric.N)r,rr)r*r+r rBrLr9r9rEcomputes$ z"BinaryPrecisionRecallCurve.computecurvescoreaxcCs^|p|}|d|d|df}|s"|dur"t|d|dddnd}t|||d|jjd S) aPlot a single curve from the metric. Args: curve: the output of either `metric.compute` or `metric.forward`. If no value is provided, will automatically call `metric.compute` and plot that result. score: Provide a area-under-the-curve score to be displayed on the plot. If `True` and no curve is provided, will automatically compute the score. The score is computed by using the trapezoidal rule to compute the area under the curve. 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 >>> from torchmetrics.classification import BinaryPrecisionRecallCurve >>> preds = rand(20) >>> target = randint(2, (20,)) >>> metric = BinaryPrecisionRecallCurve() >>> metric.update(preds, target) >>> fig_, ax_ = metric.plot(score=True) rGrr6T) directionNRecall PrecisionrRrS label_namesname)rPrr!rD__name__rBrQrRrScurve_computedr9r9rEplots #r"NNTNNN)r\ __module__ __qualname____doc__r&bool__annotations__r'rr(rrrintlistfloatrr;rMtuplerPrr r_ __classcell__r9r9rCrEr%7sH  I    r%csZeZdZUdZdZeed<dZeeed<dZ eed<e e ed<e e ed<e ed <    dd e d ee e eee fd eeddee dededdffdd Zde de ddfddZde ee e e fee e e e e e fffddZ   ddee ee e e fee e e e e e ffdee e efdeedefddZZS) MulticlassPrecisionRecallCurveaCompute the precision-recall curve for multiclass tasks. The curve consist of multiple pairs of precision and recall values evaluated at different thresholds, such that the tradeoff between the two values can been seen. For multiclass the metric is calculated by iteratively treating each class as the positive class and all other classes as the negative, which is referred to as the one-vs-rest approach. One-vs-one is currently not supported by this metric. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, C, ...)``. Preds should be a tensor containing probabilities or logits for each observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply softmax per sample. - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``. Target should be a tensor containing ground truth labels, and therefore only contain values in the [0, n_classes-1] range (except if `ignore_index` is specified). .. tip:: Additional dimension ``...`` will be flattened into the batch dimension. As output to ``forward`` and ``compute`` the metric returns the following output: - ``precision`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_thresholds+1, )`` with precision values - ``recall`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_thresholds+1, )`` with recall values - ``thresholds`` (:class:`~torch.Tensor`): A 1d tensor of size ``(n_thresholds, )`` with increasing threshold values .. note:: The implementation both supports calculating the metric in a non-binned but accurate version and a binned version that is less accurate but more memory efficient. Setting the `thresholds` argument to `None` will activate the non-binned version that uses memory of size :math:`\mathcal{O}(n_{samples})` whereas setting the `thresholds` argument to either an integer, list or a 1d tensor will use a binned version that uses memory of size :math:`\mathcal{O}(n_{thresholds} \times n_{classes})` (constant memory). Args: num_classes: Integer specifying the number of classes thresholds: Can be one of: - If set to `None`, will use a non-binned approach where thresholds are dynamically calculated from all the data. Most accurate but also most memory consuming approach. - If set to an `int` (larger than 1), will use that number of thresholds linearly spaced from 0 to 1 as bins for the calculation. - If set to an `list` of floats, will use the indicated thresholds in the list as bins for the calculation - If set to a 1D `tensor` of floats, will use the indicated thresholds in the tensor as bins for the calculation. average: If aggregation of curves should be applied. By default, the curves are not aggregated and a curve for each class is returned. If `average` is set to ``"micro"``, the metric will aggregate the curves by one hot encoding the targets and flattening the predictions, considering all classes jointly as a binary problem. If `average` is set to ``"macro"``, the metric will aggregate the curves by first interpolating the curves from each class at a combined set of thresholds and then average over the classwise interpolated curves. See `averaging curve objects`_ for more info on the different averaging methods. 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. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example: >>> from torchmetrics.classification import MulticlassPrecisionRecallCurve >>> preds = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05], ... [0.05, 0.75, 0.05, 0.05, 0.05], ... [0.05, 0.05, 0.75, 0.05, 0.05], ... [0.05, 0.05, 0.05, 0.75, 0.05]]) >>> target = torch.tensor([0, 1, 3, 2]) >>> mcprc = MulticlassPrecisionRecallCurve(num_classes=5, thresholds=None) >>> precision, recall, thresholds = mcprc(preds, target) >>> precision # doctest: +NORMALIZE_WHITESPACE [tensor([0.2500, 1.0000, 1.0000]), tensor([0.2500, 1.0000, 1.0000]), tensor([0.2500, 0.0000, 1.0000]), tensor([0.2500, 0.0000, 1.0000]), tensor([0., 1.])] >>> recall [tensor([1., 1., 0.]), tensor([1., 1., 0.]), tensor([1., 0., 0.]), tensor([1., 0., 0.]), tensor([nan, 0.])] >>> thresholds [tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor(0.0500)] >>> mcprc = MulticlassPrecisionRecallCurve(num_classes=5, thresholds=5) >>> mcprc(preds, target) # doctest: +NORMALIZE_WHITESPACE (tensor([[0.2500, 1.0000, 1.0000, 1.0000, 0.0000, 1.0000], [0.2500, 1.0000, 1.0000, 1.0000, 0.0000, 1.0000], [0.2500, 0.0000, 0.0000, 0.0000, 0.0000, 1.0000], [0.2500, 0.0000, 0.0000, 0.0000, 0.0000, 1.0000], [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 1.0000]]), tensor([[1., 1., 1., 1., 0., 0.], [1., 1., 1., 1., 0., 0.], [1., 0., 0., 0., 0., 0.], [1., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0.]]), tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000])) Fr&Nr'r(r)r*r+T num_classesr,average)micromacror-r.r/r0c stjd i||rt||||||_||_||_||_t|}|dur;||_|j dgdd|j dgdddS|j d|dd|j dt j t ||d d t jd d ddSr1)r:r;rrmrnr-r.r r,r<r=r>r?r@rA)rBrmr,rnr-r.r/rCr9rEr;Js$  z'MulticlassPrecisionRecallCurve.__init__cCs|jr t|||j|jt|||j|j|j|j\}}}t|||j|j|j}t|t r5|j |7_ dS|j |d|j |ddSrF)r.rrmr-rr,rnrrHrr+r)rIr*rJr9r9rErMis  z%MulticlassPrecisionRecallCurve.updatecC8|jdurt|jt|jfn|j}t||j|j|jSrN)r,rr)r*r+rrmrnrOr9r9rErPy$z&MulticlassPrecisionRecallCurve.computerQrRrScC`|p|}|d|d|df}|s#|dur#t|d|ddddnd}t|||d|jjd S) aPlot a single or multiple values from the metric. Args: curve: the output of either `metric.compute` or `metric.forward`. If no value is provided, will automatically call `metric.compute` and plot that result. score: Provide a area-under-the-curve score to be displayed on the plot. If `True` and no curve is provided, will automatically compute the score. The score is computed by using the trapezoidal rule to compute the area under the curve. 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 randn, randint >>> from torchmetrics.classification import MulticlassPrecisionRecallCurve >>> preds = randn(20, 3).softmax(dim=-1) >>> target = randint(3, (20,)) >>> metric = MulticlassPrecisionRecallCurve(num_classes=3) >>> metric.update(preds, target) >>> fig_, ax_ = metric.plot(score=True) rGrr6TNrTrnrUrVrYrPr r!rDr\r]r9r9rEr_~ #r#)NNNTra)r\rbrcrdr&rerfr'rr(rrrgrrhrirrr;rMrjrPrr r_rkr9r9rCrErlsR  ]    6.rlc sLeZdZUdZdZeed<dZeeed<dZ eed<e e ed<e e ed<e ed <   dd e d ee e eee fd ee dededdf fdd Zde de ddfddZde ee e e fee e e e e e fffddZ   ddee ee e e fee e e e e e ffdee e efdeedefddZZS)MultilabelPrecisionRecallCurveahCompute the precision-recall curve for multilabel tasks. The curve consist of multiple pairs of precision and recall values evaluated at different thresholds, such that the tradeoff between the two values can been seen. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, C, ...)``. Preds should be a tensor containing probabilities or logits for each observation. If preds has values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per element. - ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, C, ...)``. Target should be a tensor containing ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified). .. tip:: Additional dimension ``...`` will be flattened into the batch dimension. As output to ``forward`` and ``compute`` the metric returns the following a tuple of either 3 tensors or 3 lists containing: - ``precision`` (:class:`~torch.Tensor` or :class:`~List`): if `thresholds=None` a list for each label is returned with an 1d tensor of size ``(n_thresholds+1, )`` with precision values (length may differ between labels). If `thresholds` is set to something else, then a single 2d tensor of size ``(n_labels, n_thresholds+1)`` with precision values is returned. - ``recall`` (:class:`~torch.Tensor` or :class:`~List`): if `thresholds=None` a list for each label is returned with an 1d tensor of size ``(n_thresholds+1, )`` with recall values (length may differ between labels). If `thresholds` is set to something else, then a single 2d tensor of size ``(n_labels, n_thresholds+1)`` with recall values is returned. - ``thresholds`` (:class:`~torch.Tensor` or :class:`~List`): if `thresholds=None` a list for each label is returned with an 1d tensor of size ``(n_thresholds, )`` with increasing threshold values (length may differ between labels). If `threshold` is set to something else, then a single 1d tensor of size ``(n_thresholds, )`` is returned with shared threshold values for all labels. .. note:: The implementation both supports calculating the metric in a non-binned but accurate version and a binned version that is less accurate but more memory efficient. Setting the `thresholds` argument to `None` will activate the non-binned version that uses memory of size :math:`\mathcal{O}(n_{samples})` whereas setting the `thresholds` argument to either an integer, list or a 1d tensor will use a binned version that uses memory of size :math:`\mathcal{O}(n_{thresholds} \times n_{labels})` (constant memory). Args: preds: Tensor with predictions target: Tensor with true labels num_labels: Integer specifying the number of labels thresholds: Can be one of: - If set to `None`, will use a non-binned approach where thresholds are dynamically calculated from all the data. Most accurate but also most memory consuming approach. - If set to an `int` (larger than 1), will use that number of thresholds linearly spaced from 0 to 1 as bins for the calculation. - If set to an `list` of floats, will use the indicated thresholds in the list as bins for the calculation - If set to an 1d `tensor` of floats, will use the indicated thresholds in the tensor as bins for the calculation. ignore_index: Specifies a target value that is ignored and does not contribute to the metric calculation validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. Example: >>> from torchmetrics.classification import MultilabelPrecisionRecallCurve >>> preds = torch.tensor([[0.75, 0.05, 0.35], ... [0.45, 0.75, 0.05], ... [0.05, 0.55, 0.75], ... [0.05, 0.65, 0.05]]) >>> target = torch.tensor([[1, 0, 1], ... [0, 0, 0], ... [0, 1, 1], ... [1, 1, 1]]) >>> mlprc = MultilabelPrecisionRecallCurve(num_labels=3, thresholds=None) >>> precision, recall, thresholds = mlprc(preds, target) >>> precision # doctest: +NORMALIZE_WHITESPACE [tensor([0.5000, 0.5000, 1.0000, 1.0000]), tensor([0.5000, 0.6667, 0.5000, 0.0000, 1.0000]), tensor([0.7500, 1.0000, 1.0000, 1.0000])] >>> recall # doctest: +NORMALIZE_WHITESPACE [tensor([1.0000, 0.5000, 0.5000, 0.0000]), tensor([1.0000, 1.0000, 0.5000, 0.0000, 0.0000]), tensor([1.0000, 0.6667, 0.3333, 0.0000])] >>> thresholds # doctest: +NORMALIZE_WHITESPACE [tensor([0.0500, 0.4500, 0.7500]), tensor([0.0500, 0.5500, 0.6500, 0.7500]), tensor([0.0500, 0.3500, 0.7500])] >>> mlprc = MultilabelPrecisionRecallCurve(num_labels=3, thresholds=5) >>> mlprc(preds, target) # doctest: +NORMALIZE_WHITESPACE (tensor([[0.5000, 0.5000, 1.0000, 1.0000, 0.0000, 1.0000], [0.5000, 0.6667, 0.6667, 0.0000, 0.0000, 1.0000], [0.7500, 1.0000, 1.0000, 1.0000, 0.0000, 1.0000]]), tensor([[1.0000, 0.5000, 0.5000, 0.5000, 0.0000, 0.0000], [1.0000, 1.0000, 1.0000, 0.0000, 0.0000, 0.0000], [1.0000, 0.6667, 0.3333, 0.3333, 0.0000, 0.0000]]), tensor([0.0000, 0.2500, 0.5000, 0.7500, 1.0000])) Fr&Nr'r(r)r*r+T num_labelsr,r-r.r/r0c stjd i||rt|||||_||_||_t|}|dur7||_|jdgdd|jdgdddS|j d|dd|jdt j t ||d d t j d d ddSr1)r:r;rrxr-r.r r,r<r=r>r?r@rA)rBrxr,r-r.r/rCr9rEr;s"  z'MultilabelPrecisionRecallCurve.__init__cCs|jr t|||j|jt|||j|j|j\}}}t|||j|j}t|tr1|j |7_ dS|j |d|j |ddSrF) r.rrxr-rr,rrHrr+r)rIr*rJr9r9rErM/s  z%MultilabelPrecisionRecallCurve.updatecCrqrN)r,rr)r*r+rrxr-rOr9r9rErP=rrz&MultilabelPrecisionRecallCurve.computerQrRrScCrs) aPlot a single or multiple values from the metric. Args: curve: the output of either `metric.compute` or `metric.forward`. If no value is provided, will automatically call `metric.compute` and plot that result. score: Provide a area-under-the-curve score to be displayed on the plot. If `True` and no curve is provided, will automatically compute the score. The score is computed by using the trapezoidal rule to compute the area under the curve. 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 >>> from torchmetrics.classification import MultilabelPrecisionRecallCurve >>> preds = rand(20, 3) >>> target = randint(2, (20,3)) >>> metric = MultilabelPrecisionRecallCurve(num_labels=3) >>> metric.update(preds, target) >>> fig_, ax_ = metric.plot(score=True) rGrr6TNrTrtrVrYrur]r9r9rEr_Brvr$r`ra)r\rbrcrdr&rerfr'rr(rrrgrrhrirr;rMrjrPrr r_rkr9r9rCrErwsL  [   6.rwc@speZdZdZ     ddeddeddeeee e e fdeed eed eed e d e d efddZdS)PrecisionRecallCurveaeCompute the precision-recall curve. The curve consist of multiple pairs of precision and recall values evaluated at different thresholds, such that the tradeoff between the two values can been seen. 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.BinaryPrecisionRecallCurve`, :class:`~torchmetrics.classification.MulticlassPrecisionRecallCurve` and :class:`~torchmetrics.classification.MultilabelPrecisionRecallCurve` for the specific details of each argument influence and examples. Legacy Example: >>> pred = torch.tensor([0, 0.1, 0.8, 0.4]) >>> target = torch.tensor([0, 1, 1, 0]) >>> pr_curve = PrecisionRecallCurve(task="binary") >>> precision, recall, thresholds = pr_curve(pred, target) >>> precision tensor([0.5000, 0.6667, 0.5000, 1.0000, 1.0000]) >>> recall tensor([1.0000, 1.0000, 0.5000, 0.5000, 0.0000]) >>> thresholds tensor([0.0000, 0.1000, 0.4000, 0.8000]) >>> pred = torch.tensor([[0.75, 0.05, 0.05, 0.05, 0.05], ... [0.05, 0.75, 0.05, 0.05, 0.05], ... [0.05, 0.05, 0.75, 0.05, 0.05], ... [0.05, 0.05, 0.05, 0.75, 0.05]]) >>> target = torch.tensor([0, 1, 3, 2]) >>> pr_curve = PrecisionRecallCurve(task="multiclass", num_classes=5) >>> precision, recall, thresholds = pr_curve(pred, target) >>> precision [tensor([0.2500, 1.0000, 1.0000]), tensor([0.2500, 1.0000, 1.0000]), tensor([0.2500, 0.0000, 1.0000]), tensor([0.2500, 0.0000, 1.0000]), tensor([0., 1.])] >>> recall [tensor([1., 1., 0.]), tensor([1., 1., 0.]), tensor([1., 0., 0.]), tensor([1., 0., 0.]), tensor([nan, 0.])] >>> thresholds [tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor(0.0500)] NTclstask)binary multiclass multilabelr,rmrxr-r.r/r0cKst|}||||d|tjkrtdi|S|tjkr6t|ts.tdt |dt |fi|S|tj krRt|tsJtdt |dt |fi|Std|d) zInitialize task metric.)r,r-r.z+`num_classes` is expected to be `int` but `z was passed.`z*`num_labels` is expected to be `int` but `zTask z not supported!Nr9) rfrom_strrMBINARYr% MULTICLASSrHrg ValueErrortyperl MULTILABELrw)rzr{r,rmrxr-r.r/r9r9rE__new__s      zPrecisionRecallCurve.__new__)NNNNT)r\rbrcrdrrrrrgrhrirrerrrr9r9r9rEryrs4- ry)1typingrrrrr>rtyping_extensionsr torchmetrics.classification.baser,torchmetrics.functional.classification.aurocr =torchmetrics.functional.classification.precision_recall_curver r r r rrrrrrrrrrrrtorchmetrics.metricrtorchmetrics.utilities.computertorchmetrics.utilities.datartorchmetrics.utilities.enumsrtorchmetrics.utilities.importsrtorchmetrics.utilities.plotrr r!__doctest_skip__r%rlrwryr9r9r9rEs,     H     .KE