o .wi\@sddlmZmZmZddlZddlmZmZddlmZddl m Z m Z m Z m Z mZmZmZmZmZmZmZmZddlmZmZmZddlmZmZddlmZdd lm Z dd l!m"Z"  d9d eeeefdeeeefdeeddeede#def ddZ$   d:dee#deee%e&e#efdee%ddfddZ'  d;deee(eeffdeedee#de%def ddZ)    ddeee(eeffd&e%deeddeedef d)d*Z-   d?d!ed"ed&e%deeddeee%e&e#efdee%d#e*defd+d,Z.  d@d-e%deed.deee%e&e#efdee%ddf d/d0Z/ dAdeee(eeffd-e%deed.deedee%def d1d2Z0   d?d!ed"ed-e%deed.deee%e&e#efdee%d#e*defd3d4Z1      dBd!ed"ed5ed6deee%e&e#efd&ee%d-ee%deeddee#dee%d#e*deefd7d8Z2dS)C)ListOptionalUnionN)Tensortensor)Literal) -_binary_precision_recall_curve_arg_validation%_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_format4_multiclass_precision_recall_curve_tensor_validation)_multiclass_precision_recall_curve_update1_multilabel_precision_recall_curve_arg_validation)_multilabel_precision_recall_curve_format4_multilabel_precision_recall_curve_tensor_validation)_multilabel_precision_recall_curve_update)_binary_roc_compute_multiclass_roc_compute_multilabel_roc_compute)_auc_compute_without_check _safe_divide) _bincount)ClassificationTask)rank_zero_warnmacro?fprtpraverage)rweightednoneweights directionreturncst|trt|trt||dd}ntfddt||D}|dus*|dkr,|St|r8sz!_reduce_auroc..Nr"zUAverage precision score for one or more classes was `nan`. Ignoring these classes in z-averagerr!zBReceived an incompatible combinations of inputs to make reduction.) isinstancerrtorchstackzipisnananyr UserWarningmeanrsum ValueError)rrr r#r$residxr,r(r- _reduce_auroc-s"   r;max_fpr thresholds ignore_indexcCsPt|||dur"t|ts$d|krdkr&ndStd|dSdSdS)Nrr&z@Arguments `max_fpr` should be a float in range (0, 1], but got: )rr/floatr8)r<r=r>r,r,r-_binary_auroc_arg_validationIs , r@r&state pos_labelcCs$t|||\}}}|dus|dks|dks|dkr#t||dSt|tr+|jn|dj}t||d}tj||ddd} ||| d|| || d} t || d|| | } t |d| | dg}t |d| | dg}t||d} d|d} dd| | || S) Nr&rr)deviceT) out_int32rightg?) rr7rr/rrCrr0 bucketizelerpcatview)rAr=r<rBrr__devicemax_areastopweight interp_tpr partial_aucmin_arear,r,r-_binary_auroc_computeSs(  $  rSTpredstarget validate_argscCsH|rt|||t|||t||||\}}}t|||}t|||S)a Compute Area Under the Receiver Operating Characteristic Curve (`ROC AUC`_) for binary tasks. The AUROC score summarizes the ROC curve into an single number that describes the performance of a model for multiple thresholds at the same time. Notably, an AUROC score of 1 is a perfect score and an AUROC score of 0.5 corresponds to random guessing. 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 max_fpr: If not ``None``, calculates standardized partial AUC over the range ``[0, max_fpr]``. 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. Returns: A single scalar with the auroc score Example: >>> from torchmetrics.functional.classification import binary_auroc >>> preds = torch.tensor([0, 0.5, 0.7, 0.8]) >>> target = torch.tensor([0, 1, 1, 0]) >>> binary_auroc(preds, target, thresholds=None) tensor(0.5000) >>> binary_auroc(preds, target, thresholds=5) tensor(0.5000) )r@r r r rS)rTrUr<r=r>rVrAr,r,r- binary_aurocns ?    rW num_classescC0t|||d}||vrtd|d|dS)N)rr!r"N)Expected argument `average` to be one of but got )r r8)rXr r=r>allowed_averager,r,r- _multiclass_auroc_arg_validation r]c Cs^t|||\}}}t||||durt|d|ddS|ddddddfddS)Nr&) minlengthrr#)rr;rr?r7)rArXr r=rrrKr,r,r-_multiclass_auroc_computesrbcCsR|rt||||t||||t|||||\}}}t||||}t||||S)aCompute Area Under the Receiver Operating Characteristic Curve (`ROC AUC`_) for multiclass tasks. The AUROC score summarizes the ROC curve into an single number that describes the performance of a model for multiple thresholds at the same time. Notably, an AUROC score of 1 is a perfect score and an AUROC score of 0.5 corresponds to random guessing. 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 specifying the number of classes average: Defines the reduction that is applied over classes. Should be one of the following: - ``macro``: Calculate score for each class and average them - ``weighted``: calculates score for each class and computes weighted average using their support - ``"none"`` or ``None``: calculates score for each class and applies no reduction 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. Returns: If `average=None|"none"` then a 1d tensor of shape (n_classes, ) will be returned with auroc score per class. If `average="macro"|"weighted"` then a single scalar is returned. Example: >>> from torchmetrics.functional.classification import multiclass_auroc >>> 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]) >>> multiclass_auroc(preds, target, num_classes=5, average="macro", thresholds=None) tensor(0.5333) >>> multiclass_auroc(preds, target, num_classes=5, average=None, thresholds=None) tensor([1.0000, 1.0000, 0.3333, 0.3333, 0.0000]) >>> multiclass_auroc(preds, target, num_classes=5, average="macro", thresholds=5) tensor(0.5333) >>> multiclass_auroc(preds, target, num_classes=5, average=None, thresholds=5) tensor([1.0000, 1.0000, 0.3333, 0.3333, 0.0000]) )r]rr rrb)rTrUrXr r=r>rVrAr,r,r-multiclass_aurocsN  rc num_labels)microrr!r"cCrY)N)rerr!r"NrZr[)rr8)rdr r=r>r\r,r,r- _multilabel_auroc_arg_validation(r^rfc Cs|dkr>t|tr|durt|d|ddS|d}|d}|dur5||k}||}||}t||f|ddSt||||\}} } t|| ||dur^|ddkjdddS|ddddddfddS)Nrer&)r<r)dimr`ra)r/rrSr7flattenrr;r?) rArdr r=r>rTrUr:rrrKr,r,r-_multilabel_auroc_compute4s&    ricCsT|rt||||t||||t|||||\}}}t||||}t|||||S)alCompute Area Under the Receiver Operating Characteristic Curve (`ROC AUC`_) for multilabel tasks. The AUROC score summarizes the ROC curve into an single number that describes the performance of a model for multiple thresholds at the same time. Notably, an AUROC score of 1 is a perfect score and an AUROC score of 0.5 corresponds to random guessing. 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 specifying the number of labels average: Defines the reduction that is applied over labels. Should be one of the following: - ``micro``: Sum score over all labels - ``macro``: Calculate score for each label and average them - ``weighted``: calculates score for each label and computes weighted average using their support - ``"none"`` or ``None``: calculates score for each label and applies no reduction 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. Returns: If `average=None|"none"` then a 1d tensor of shape (n_classes, ) will be returned with auroc score per class. If `average="micro|macro"|"weighted"` then a single scalar is returned. Example: >>> from torchmetrics.functional.classification import multilabel_auroc >>> 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]]) >>> multilabel_auroc(preds, target, num_labels=3, average="macro", thresholds=None) tensor(0.6528) >>> multilabel_auroc(preds, target, num_labels=3, average=None, thresholds=None) tensor([0.6250, 0.5000, 0.8333]) >>> multilabel_auroc(preds, target, num_labels=3, average="macro", thresholds=5) tensor(0.6528) >>> multilabel_auroc(preds, target, num_labels=3, average=None, thresholds=5) tensor([0.6250, 0.5000, 0.8333]) )rfrrrri)rTrUrdr r=r>rVrAr,r,r-multilabel_aurocPsR  rjtask)binary multiclass multilabelc Cst|}|tjkrt|||||| S|tjkr1t|ts'tdt|dt ||||||| S|tj krOt|tsEtdt|dt ||||||| SdS)aCompute Area Under the Receiver Operating Characteristic Curve (`ROC AUC`_). The AUROC score summarizes the ROC curve into an single number that describes the performance of a model for multiple thresholds at the same time. Notably, an AUROC score of 1 is a perfect score and an AUROC score of 0.5 corresponds to random guessing. This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``'multilabel'``. See the documentation of :func:`~torchmetrics.functional.classification.binary_auroc`, :func:`~torchmetrics.functional.classification.multiclass_auroc` and :func:`~torchmetrics.functional.classification.multilabel_auroc` for the specific details of each argument influence and examples. Legacy Example: >>> preds = torch.tensor([0.13, 0.26, 0.08, 0.19, 0.34]) >>> target = torch.tensor([0, 0, 1, 1, 1]) >>> auroc(preds, target, task='binary') tensor(0.5000) >>> preds = torch.tensor([[0.90, 0.05, 0.05], ... [0.05, 0.90, 0.05], ... [0.05, 0.05, 0.90], ... [0.85, 0.05, 0.10], ... [0.10, 0.10, 0.80]]) >>> target = torch.tensor([0, 1, 1, 2, 2]) >>> auroc(preds, target, task='multiclass', num_classes=3) tensor(0.7778) z+`num_classes` is expected to be `int` but `z was passed.`z*`num_labels` is expected to be `int` but `N) rfrom_strBINARYrW MULTICLASSr/intr8typerc MULTILABELrj) rTrUrkr=rXrdr r<r>rVr,r,r-aurocs )     ru)rNr)NNN)Nr&)NNNT)rNN)rN)rNNT)NN)N)NNNrNNT)3typingrrrr0rrtyping_extensionsr=torchmetrics.functional.classification.precision_recall_curverr r r r r rrrrrr*torchmetrics.functional.classification.rocrrrtorchmetrics.utilities.computerrtorchmetrics.utilities.datartorchmetrics.utilities.enumsrtorchmetrics.utilities.printsrr?r;rrlistr@tuplerSboolrWr]rbrcrfrirjrur,r,r,r-s  8         I      [       `