Ë ýÿæig ã ód—ddlmZddlmZmZddlmZddlmZ d dededee d ee d ef d „Z y) é)ÚOptional)ÚTensorÚtensor)Ú binary_auroc)Ú"_check_retrieval_functional_inputsNÚpredsÚtargetÚtop_kÚmax_fprÚreturncó‚—t||«\}}|xs|jd}t|t«r|dkDs t d«‚|j t ||jd«dd¬«d}||}d|vsd|vr"td|j|j¬«S||}t||j«|¬ «S) ažCompute area under the receiver operating characteristic curve (AUROC) for information retrieval. ``preds`` and ``target`` should be of the same shape and live on the same device. If no ``target`` is ``True``, ``0`` is returned. ``target`` must be either `bool` or `integers` and ``preds`` must be ``float``, otherwise an error is raised. Args: preds: estimated probabilities of each document to be relevant. target: ground truth about each document being relevant or not. top_k: consider only the top k elements (default: ``None``, which considers them all) max_fpr: If not ``None``, calculates standardized partial AUC over the range ``[0, max_fpr]``. Return: a single-value tensor with the auroc value of the predictions ``preds`` w.r.t. the labels ``target``. Raises: ValueError: If ``top_k`` is not ``None`` or an integer larger than 0. Example: >>> from torchmetrics.functional.retrieval import retrieval_auroc >>> preds = tensor([0.2, 0.3, 0.5]) >>> target = tensor([True, False, True]) >>> retrieval_auroc(preds, target) tensor(0.5000) éÿÿÿÿrz,`top_k` has to be a positive integer or NoneT)ÚsortedÚdimég)ÚdeviceÚdtype)r ) rÚshapeÚ isinstanceÚintÚ ValueErrorÚtopkÚminrrrr)rr r r Ú top_k_idxs ú|/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/torchmetrics/functional/retrieval/auroc.pyÚretrieval_aurocrs·€ô<7°u¸fÓEM€Eˆ6à Ò $U—[‘[ ‘_€EÜ uœcÔ " u¨q¢yÜÐGÓHÐHà— ‘ œ3˜u e§k¡k°"¡oÓ6¸tÈ ÓLÈQÑO€IØ IÑ €FØ ‰˜Q f™_Üc %§,¡,°e·k±kÔBÐBà )Ñ €EÜ ˜˜vŸz™z›|°WÔ =Ð=ó)NN) ÚtypingrÚtorchrrÚ,torchmetrics.functional.classification.aurocrÚtorchmetrics.utilities.checksrrÚfloatr©rrÚr$sPðõç åEÝLð\`ñ*>Ø ð*>Ø!ð*>Ø*2°3©-ð*>ØIQÐRWÉð*>à ô*>r