o yii@s>ddlZddlmZmZddlmZdededefddZdS) N)Tensortensor)"_check_retrieval_functional_inputspredstargetreturncCst||\}}|std|jdS|tj|ddd}tjdt|d|jtjd|dk}t tjt||jtjdd| }|S) aComputes average precision (for information retrieval), as explained in `IR Average precision`_. ``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. Return: a single-value tensor with the average precision (AP) of the predictions ``preds`` w.r.t. the labels ``target``. Example: >>> from torchmetrics.functional import retrieval_average_precision >>> preds = tensor([0.2, 0.3, 0.5]) >>> target = tensor([True, False, True]) >>> retrieval_average_precision(preds, target) tensor(0.8333) g)deviceT)dim descending)rdtyper) rsumrrtorchargsortarangelenfloat32divmean)rr positionsresrg/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/retrieval/average_precision.pyretrieval_average_precisions&(r)rrrtorchmetrics.utilities.checksrrrrrrs