o yiН@sddlmZmZmZmZmZddlZddlmZmZddl m Z ddl m Z ddlmZddlmZddlmZ  dCd ed ed eed edeeeeff ddZ dDdeeeeeefdeejdeefddZ  dDdeeeeeefdeeddfddZ dEd ed edeeddfddZ  dDd ed edeeeeeefdeedeeeeeff ddZd ed edeedeeeeefffddZ dFdeeeeeffdeed edeeeeffdd Z   !dGd ed edeeeeeefdeed"edeeeeff d#d$Z  dDd%edeeeeeefdeeddfd&d'Z dEd ed ed%edeeddf d(d)Z!  dDd ed ed%edeeeeeefdeedeeeeeff d*d+Z"d ed ed%edeedeeeeefff d,d-Z#deeeeeffd%edeedeeeeefeeeeeeefffd.d/Z$   !dGd ed ed%edeeeeeefdeed"edeeeeefeeeeeeefffd0d1Z%  dDd2edeeeeeefdeeddfd3d4Z& dEd ed ed2edeeddf d5d6Z'  dDd ed ed2edeeeeeefdeedeeeeeff d7d8Z(d ed ed2edeedeeeeefff d9d:Z) dEdeeeeeffd2edeedeedeeeeefeeeeeeefff d;d<Z*   !dGd ed ed2edeeeeeefdeed"edeeeeefeeeeeeefffd=d>Z+     !dHd ed ed?e d@deeeeeefd%eed2eedeed"edeeeeefeeeeeeefffdAdBZ,dS)I)ListOptionalSequenceTupleUnionN)Tensortensor) functional)Literal)_check_same_shape) _safe_divide) _bincountpredstargetsample_weights pos_labelreturnc CsDt|durt|tst||jtjd}|j|jkr%|dddf}tj|dd}||}||}|dur=||}nd}t |dd|ddd}t j |ddg| ddd }||k tj}tj||dd |}|durtjd||dd |} nd||} | |||fWdS1swYdS) aCalculates the tps and false positives for all unique thresholds in the preds tensor. Adapted from https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/metrics/_ranking.py. Args: preds: 1d tensor with predictions target: 1d tensor with true values sample_weights: a 1d tensor with a weight per sample pos_label: interger determining what the positive class in target tensor is Returns: fps: 1d tensor with false positives for different thresholds tps: 1d tensor with true positives for different thresholds thresholds: the unique thresholds use for calculating fps and tps N)devicedtyperT) descendingg?r)value)dim)torchno_grad isinstancerrrfloatndimargsortwhereFpadsizetolongcumsum) rrrrdesc_score_indicesweightdistinct_value_indicesthreshold_idxstpsfpsr-q/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/classification/precision_recall_curve.py_binary_clf_curves(   "  $r/ thresholdsrcCs8t|trtjdd||d}t|trtj||d}|S)zTUtility function for converting the threshold arg for list and int to tensor format.rrr)rintrlinspacelistr)r0rr-r-r._adjust_threshold_argRs  r5 ignore_indexcCs|durt|tttfstd|t|tr#|dkr#td|t|tr8tdd|Ds8td|t|trF|jdksFtd |durVt|tsXtd |dSdS) zValidate non tensor input. - ``threshold`` has to be None | a 1d tensor | a list of floats in the [0,1] range | an int - ``ignore_index`` has to be None or int NzdExpected argument `thresholds` to either be an integer, list of floats or tensor of floats, but got zQIf argument `thresholds` is an integer, expected it to be larger than 1, but got css2|]}t|tod|kodknVqdS)rrN)rr).0tr-r-r. ps0z@_binary_precision_recall_curve_arg_validation..zcIf argument `thresholds` is a list, expected all elements to be floats in the [0,1] range, but got rzCIf argument `thresholds` is an tensor, expected the tensor to be 1dzLExpected argument `ignore_index` to either be `None` or an integer, but got )rr4r2r ValueErrorallr)r0r6r-r-r.-_binary_precision_recall_curve_arg_validation]s* r=cCst|||rtd|j|std|jt|}|dur2t|dk|dk@}nt|dk|dk@||k@}|rYtd|d|durRddggn|gddS) zValidate tensor input. - tensors have to be of same shape - all values in target tensor that are not ignored have to be in {0, 1} - that the pred tensor is floating point zjExpected argument `target` to be an int or long tensor with ground truth labels but got tensor with dtype zlExpected argument `preds` to be an floating tensor with probability/logit scores, but got tensor with dtype Nrrz+Detected the following values in `target`: z( but expected only the following values .)r is_floating_pointr;rruniqueany RuntimeError)rrr6 unique_valuescheckr-r-r.0_binary_precision_recall_curve_tensor_validation|s0  rEcCsd|}|}|dur||k}||}||}td|k|dks'|}t||j}|||fS)zConvert all input to the right format. - flattens additional dimensions - Remove all datapoints that should be ignored - Applies sigmoid if pred tensor not in [0,1] range - Format thresholds arg to be a tensor Nrr)flattenrr<sigmoidr5r)rrr0r6idxr-r-r.%_binary_precision_recall_curve_formats   rIcCsx|dur||fSt|}|d|dk}|d|ddtj||jd}t|d|d}||ddSReturns the state to calculate the pr-curve with. If thresholds is `None` the direct preds and targets are used. If thresholds is not `None` we compute a multi threshold confusion matrix. Nrrr7r1 minlength) len unsqueezer%rarangerr rFreshape)rrr0len_tpreds_tunique_mappingbinsr-r-r.%_binary_precision_recall_curve_updates &rWstatec Cslt|trQ|ddddf}|ddddf}|ddddf}t|||}t|||}t|tjd|j|jdg}t|tjd|j|jdg}|||fSt |d|d|d\}}}|||}||d}t ||dkdd}t d| d} tt || tjd|j|jdg}tt || tjd|j|jdg}t || }|||fS)zComputes the final pr-curve. If state is a single tensor, then we calculate the pr-curve from a multi threshold confusion matrix. If state is original input, then we dynamically compute the binary classification curve. Nrrrr)rr)rrr rcatonesrrzerosr/r sliceitemreverseddetachclone) rXr0rr+r,fns precisionrecalllast_indslr-r-r.&_binary_precision_recall_curve_computes$    && rgT validate_argscCsD|r t||t|||t||||\}}}t|||}t||S)a Computes 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. Accepts the following input tensors: - ``preds`` (float tensor): ``(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`` (int tensor): ``(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. Additional dimension ``...`` will be flattened into the batch dimension. 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: preds: Tensor with predictions target: Tensor with true 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. validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. Returns: (tuple): a tuple of 3 tensors containing: - precision: an 1d tensor of size (n_thresholds+1, ) with precision values - recall: an 1d tensor of size (n_thresholds+1, ) with recall values - thresholds: an 1d tensor of size (n_thresholds, ) with increasing threshold values Example: >>> from torchmetrics.functional.classification import binary_precision_recall_curve >>> preds = torch.tensor([0, 0.5, 0.7, 0.8]) >>> target = torch.tensor([0, 1, 1, 0]) >>> binary_precision_recall_curve(preds, target, thresholds=None) # doctest: +NORMALIZE_WHITESPACE (tensor([0.6667, 0.5000, 0.0000, 1.0000]), tensor([1.0000, 0.5000, 0.0000, 0.0000]), tensor([0.5000, 0.7000, 0.8000])) >>> binary_precision_recall_curve(preds, target, thresholds=5) # 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])) )r=rErIrWrg)rrr0r6rhrXr-r-r.binary_precision_recall_curves ?    ri num_classescCs.t|tr |dkrtd|t||dS)zValidate non tensor input. - ``num_classes`` has to be an int larger than 1 - ``threshold`` has to be None | a 1d tensor | a list of floats in the [0,1] range | an int - ``ignore_index`` has to be None or int r7zHExpected argument `num_classes` to be an integer larger than 1, but got N)rr2r;r=)rjr0r6r-r-r.1_multiclass_precision_recall_curve_arg_validation:s rkcCs$|j|jdkstd|jd|j|r td|j|s,td|j|jd|krAtd|jdd|d|jd|jdksY|jd d |jdd kretd |jd|jtt|}|d uru||k}n||dk}|rtd |d ur|n|dd |dd S)a!Validate tensor input. - target should have one more dimension than preds and all dimensions except for preds.shape[1] should match exactly. preds.shape[1] should have size equal to number of classes - all values in target tensor that are not ignored have to be in {0, 1} rzBExpected `preds` to have one more dimension than `target` but got z and zRExpected argument `target` to be an int or long tensor, but got tensor with dtype z/Expected `preds` to be a float tensor, but got zGExpected `preds.shape[1]` to be equal to the number of classes but got r>rr7NziExpected the shape of `preds` should be (N, C, ...) and the shape of `target` should be (N, ...) but got zJDetected more unique values in `target` than `num_classes`. Expected only z but found z in `target`.) rr;r?rshaperOrr@rB)rrrjr6num_unique_valuesrDr-r-r.4_multiclass_precision_recall_curve_tensor_validationJsN  0  rncCst|dd|dj}|}|dur||k}||}||}td|k|dks/|d}t||j}|||fS)zConvert all input to the right format. - flattens additional dimensions - Remove all datapoints that should be ignored - Applies softmax if pred tensor not in [0,1] range - Format thresholds arg to be a tensor rrrN) transposerRTrFrr<softmaxr5r)rrrjr0r6rHr-r-r.)_multiclass_precision_recall_curve_formatus   rrc Cs|dur||fSt|}|d|ddk}tjjj||d}|d|d}|dtj||jddd7}|d|tj||jd7}t | d||d}| ||ddS) rKNrr)rjr7rLr1rM) rOrPr%rnnr one_hotrQrr rFrR) rrrjr0rSrTtarget_trUrVr-r-r.)_multiclass_precision_recall_curve_updates $rvc Cs4t|tr^|ddddddf}|ddddddf}|ddddddf}t|||}t|||}t|tjd||j|jdg}t|tjd||j|jdg}|j |j |fSggg}}}t |D]*}t |ddd|f|dgd|d} | | d| | d| | dqj|||fSzComputes the final pr-curve. If state is a single tensor, then we calculate the pr-curve from a multi threshold confusion matrix. If state is original input, then we dynamically compute the binary classification curve in an iterative way. NrrrY)r0rr7 rrr rrZr[rrr\rprangergappend) rXrjr0r+r,rbrcrdiresr-r-r.*_multiclass_precision_recall_curve_computes    & r}cCsN|rt|||t||||t|||||\}}}t||||}t|||S)azComputes 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. Accepts the following input tensors: - ``preds`` (float tensor): ``(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`` (int tensor): ``(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). Additional dimension ``...`` will be flattened into the batch dimension. 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: preds: Tensor with predictions target: Tensor with true labels num_classes: Integer specifing 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 an 1d `tensor` of floats, will use the indicated thresholds in the tensor as bins for the calculation. validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. Returns: (tuple): a tuple of either 3 tensors or 3 lists containing - precision: 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: 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: 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. Example: >>> from torchmetrics.functional.classification import multiclass_precision_recall_curve >>> 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]) >>> precision, recall, thresholds = multiclass_precision_recall_curve( ... preds, target, num_classes=5, thresholds=None ... ) >>> precision # doctest: +NORMALIZE_WHITESPACE [tensor([1., 1.]), tensor([1., 1.]), tensor([0.2500, 0.0000, 1.0000]), tensor([0.2500, 0.0000, 1.0000]), tensor([0., 1.])] >>> recall [tensor([1., 0.]), tensor([1., 0.]), tensor([1., 0., 0.]), tensor([1., 0., 0.]), tensor([nan, 0.])] >>> thresholds [tensor([0.7500]), tensor([0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500])] >>> multiclass_precision_recall_curve( ... preds, target, num_classes=5, thresholds=5 ... ) # 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])) )rkrnrrrvr})rrrjr0r6rhrXr-r-r.!multiclass_precision_recall_curves[    r~ num_labelscCst|||dS)zValidate non tensor input. - ``num_labels`` has to be an int larger than 1 - ``threshold`` has to be None | a 1d tensor | a list of floats in the [0,1] range | an int - ``ignore_index`` has to be None or int N)rk)rr0r6r-r-r.1_multilabel_precision_recall_curve_arg_validation,s rcCs8t||||jd|krtd|jdd|dS)zValidate tensor input. - tensors have to be of same shape - preds.shape[1] is equal to the number of labels - all values in target tensor that are not ignored have to be in {0, 1} - that the pred tensor is floating point rzaExpected both `target.shape[1]` and `preds.shape[1]` to be equal to the number of labels but got z and expected N)rErlr;)rrrr6r-r-r.4_multilabel_precision_recall_curve_tensor_validation:s rcCs|dd|dj}|dd|dj}td|k|dks%|}t||j}|dur_|dur_|}|}||k}d||durJt |nd||<d||durZt |nd||<|||fS)zConvert all input to the right format. - flattens additional dimensions - Mask all datapoints that should be ignored with negative values - Applies sigmoid if pred tensor not in [0,1] range - Format thresholds arg to be a tensor rrrN) rorRrprr<rGr5rrarO)rrrr0r6rHr-r-r.)_multilabel_precision_recall_curve_formatLs    rcCs|dur||fSt|}|d|ddk}|d|d}|dtj||jddd7}|d|tj||jd7}||dk}t|d||d}|||ddSrJ)rOrPr%rrQrr rR)rrrr0rSrTrUrVr-r-r.)_multilabel_precision_recall_curve_updateks $ rc Cslt|tr^|ddddddf}|ddddddf}|ddddddf}t|||}t|||}t|tjd||j|jdg}t|tjd||j|jdg}|j |j |fSggg}}}t |D]F} |ddd| f} |ddd| f} |dur| |k} | | } | | } t | | gddd} | | d| | d| | dqj|||fSrwrx)rXrr0r6r+r,rbrcrdr{rrrHr|r-r-r.*_multilabel_precision_recall_curve_computes,       rcCsP|rt|||t||||t|||||\}}}t||||}t||||S)aComputes 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. Accepts the following input tensors: - ``preds`` (float tensor): ``(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`` (int tensor): ``(N, C, ...)``. Target should be a tensor containing ground truth labels, and therefore only contain {0,1} values (except if `ignore_index` is specified). Additional dimension ``...`` will be flattened into the batch dimension. 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 specifing 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. validate_args: bool indicating if input arguments and tensors should be validated for correctness. Set to ``False`` for faster computations. Returns: (tuple): a tuple of either 3 tensors or 3 lists containing - precision: 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: 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: 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. Example: >>> from torchmetrics.functional.classification import multilabel_precision_recall_curve >>> 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]]) >>> precision, recall, thresholds = multilabel_precision_recall_curve( ... preds, target, num_labels=3, thresholds=None ... ) >>> precision # doctest: +NORMALIZE_WHITESPACE [tensor([0.5000, 0.5000, 1.0000, 1.0000]), tensor([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, 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([0.5500, 0.6500, 0.7500]), tensor([0.0500, 0.3500, 0.7500])] >>> multilabel_precision_recall_curve( ... preds, target, num_labels=3, thresholds=5 ... ) # 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])) )rrrrr)rrrr0r6rhrXr-r-r.!multilabel_precision_recall_curves\   rtask)binary multiclass multilabelcCsv|dkr t|||||S|dkr t|tsJt||||||S|dkr4t|ts+Jt||||||Std|)atComputes 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 :func:`binary_precision_recall_curve`, :func:`multiclass_precision_recall_curve` and :func:`multilabel_precision_recall_curve` for the specific details of each argument influence and examples. Legacy Example: >>> pred = torch.tensor([0.0, 1.0, 2.0, 3.0]) >>> target = torch.tensor([0, 1, 1, 0]) >>> precision, recall, thresholds = precision_recall_curve(pred, target, task='binary') >>> precision tensor([0.6667, 0.5000, 0.0000, 1.0000]) >>> recall tensor([1.0000, 0.5000, 0.0000, 0.0000]) >>> thresholds tensor([0.7311, 0.8808, 0.9526]) >>> 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]) >>> precision, recall, thresholds = precision_recall_curve(pred, target, task='multiclass', num_classes=5) >>> precision [tensor([1., 1.]), tensor([1., 1.]), tensor([0.2500, 0.0000, 1.0000]), tensor([0.2500, 0.0000, 1.0000]), tensor([0., 1.])] >>> recall [tensor([1., 0.]), tensor([1., 0.]), tensor([1., 0., 0.]), tensor([1., 0., 0.]), tensor([nan, 0.])] >>> thresholds [tensor([0.7500]), tensor([0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500])] rrrz[Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got )rirr2r~rr;)rrrr0rjrr6rhr-r-r.precision_recall_curve s+r)Nr)NN)N)r)NNT)NNNNT)-typingrrrrrrrrtorch.nnr r!typing_extensionsr torchmetrics.utilities.checksr torchmetrics.utilities.computer torchmetrics.utilities.datar r2r/rrr5r=rErIrWrgboolrirkrnrrrvr}r~rrrrrrrr-r-r-r.s&       8     '    (  I  /  * !* g    * (* j*