o yi, @shddlmZmZddlZddlmZddlmZmZmZdedefddZ d ed e defd d Z d!d edede dee ddf ddZ d ededeee ffddZ  d"d edede dee dedef ddZd ededeee ffddZ  d"d edede dee dedef ddZd ededeee ffddZ  d"d edede dee dedef dd ZdS)#)OptionalTupleN)Tensor)+_multilabel_confusion_matrix_arg_validation#_multilabel_confusion_matrix_format._multilabel_confusion_matrix_tensor_validationxreturncCsTttj|dddd\}}}Wdn1swY|jdd}||S)zRank data based on values.T)sortedreturn_inverse return_countsNrdim)torchno_graduniquecumsum)r_inversecountsranksrb/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/classification/ranking.py _rank_datas  rscore n_elementscCs||SNr)rrrrr_ranking_reduce#srpredstarget num_labels ignore_indexcCs*t|||||std|jdS)NzJExpected preds tensor to be floating point, but received input with dtype )ris_floating_point ValueErrordtype)rrr r!rrr%_multilabel_ranking_tensor_validation'sr%cCsnt|}|d||dk<||}|jddd}||dddfkjddtj}||fS)z$Accumulate state for coverage error. rr N)r zeros_likeminabssumtofloat32numel)rroffset preds_mod preds_mincoveragerrr!_multilabel_coverage_error_update/s $r3T validate_argscCP|rt|d|dt||||t|||d|dd\}}t||\}}t||S)aComputes multilabel coverage error [1]. The score measure how far we need to go through the ranked scores to cover all true labels. The best value is equal to the average number of labels in the target tensor per sample. 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. Args: preds: Tensor with predictions target: Tensor with true labels num_labels: Integer specifing the number of labels 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.functional.classification import multilabel_coverage_error >>> _ = torch.manual_seed(42) >>> preds = torch.rand(10, 5) >>> target = torch.randint(2, (10, 5)) >>> multilabel_coverage_error(preds, target, num_labels=5) tensor(3.9000) References: [1] Tsoumakas, G., Katakis, I., & Vlahavas, I. (2010). Mining multi-label data. In Data mining and knowledge discovery handbook (pp. 667-685). Springer US.  thresholdr!Fr8r!should_threshold)rr%rr3r)rrr r!r4r2totalrrrmultilabel_coverage_error9s)   r<c Cs| }tjd|jd}|j\}}t|D]5}||dk}t|||}t|dkrCt||krCt|||} || } nd} || 7}q||fS)z5Accumulate state for label ranking average precision.r6devicer'rg?) rtensorr>shaperangerfloatlenmean) rr neg_predsrn_predsn_labelsirelevantrankingrank score_idxrrr,_multilabel_ranking_average_precision_updatels    rMcCr5)aComputes label ranking average precision score for multilabel data [1]. The score is the average over each ground truth label assigned to each sample of the ratio of true vs. total labels with lower score. Best score is 1. 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. Args: preds: Tensor with predictions target: Tensor with true labels num_labels: Integer specifing the number of labels 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.functional.classification import multilabel_ranking_average_precision >>> _ = torch.manual_seed(42) >>> preds = torch.rand(10, 5) >>> target = torch.randint(2, (10, 5)) >>> multilabel_ranking_average_precision(preds, target, num_labels=5) tensor(0.7744) References: [1] Tsoumakas, G., Katakis, I., & Vlahavas, I. (2010). Mining multi-label data. In Data mining and knowledge discovery handbook (pp. 667-685). Springer US. r6r7Fr9)rr%rrMr)rrr r!r4rrrrr$multilabel_ranking_average_precision*   rNc Cs|j\}}|dk}|jdd}|dk||k@}||}||}||}t|dkr3tjd|jddfS|jddjdd}|||tj}d||d} |||} |jdd| | } | |fS)zAccumulate state for label ranking loss. Args: preds: tensor with predictions target: tensor with ground truth labels sample_weight: optional tensor with weight for each sample r'r rr6r=g?) r@r+rCrr?r>argsortr,r-) rrrFrGrI n_relevantmaskrper_label_loss correctiondenomlossrrr_multilabel_ranking_loss_updates     rWcCr5)aComputes the label ranking loss for multilabel data [1]. The score is corresponds to the average number of label pairs that are incorrectly ordered given some predictions weighted by the size of the label set and the number of labels not in the label set. The best score is 0. 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. Args: preds: Tensor with predictions target: Tensor with true labels num_labels: Integer specifing the number of labels 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.functional.classification import multilabel_ranking_loss >>> _ = torch.manual_seed(42) >>> preds = torch.rand(10, 5) >>> target = torch.randint(2, (10, 5)) >>> multilabel_ranking_loss(preds, target, num_labels=5) tensor(0.4167) References: [1] Tsoumakas, G., Katakis, I., & Vlahavas, I. (2010). Mining multi-label data. In Data mining and knowledge discovery handbook (pp. 667-685). Springer US. r6r7Fr9)rr%rrWr)rrr r!r4rVrrrrmultilabel_ranking_lossrOrXr)NT)typingrrrr7torchmetrics.functional.classification.confusion_matrixrrrrintrr%r3boolr<rMrNrWrXrrrrs     3 4"