o .wig @sbddlmZddlmZmZddlmZddlmZ d dededee d ee d ef d d Z dS))Optional)Tensortensor) binary_auroc)"_check_retrieval_functional_inputsNpredstargettop_kmax_fprreturncCst||\}}|p |jd}t|tr|dkstd|jt||jddddd}||}d|vs7d|vr@td|j|j dS||}t |||d S) aCompute 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) rz,`top_k` has to be a positive integer or NoneT)sorteddimg)devicedtype)r ) rshape isinstanceint ValueErrortopkminrrrr)rrr r top_k_idxrd/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/functional/retrieval/auroc.pyretrieval_aurocs r)NN) typingrtorchrr,torchmetrics.functional.classification.aurocrtorchmetrics.utilities.checksrrfloatrrrrrs