o yi@s>ddlZddlmZmZddlmZdededefddZdS) N)Tensortensor)"_check_retrieval_functional_inputspredstargetreturncCs\t||\}}|std|jdS|tj|ddd}t|d}d|dd}|S)azComputes reciprocal rank (for information retrieval). See `Mean Reciprocal Rank`_ ``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 reciprocal rank (RR) of the predictions ``preds`` wrt the labels ``target``. Example: >>> from torchmetrics.functional import retrieval_reciprocal_rank >>> preds = torch.tensor([0.2, 0.3, 0.5]) >>> target = torch.tensor([False, True, False]) >>> retrieval_reciprocal_rank(preds, target) tensor(0.5000) g)deviceT)dim descendingg?r)rsumrrtorchargsortnonzeroview)rrpositionresre/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/retrieval/reciprocal_rank.pyretrieval_reciprocal_ranksr)r rrtorchmetrics.utilities.checksrrrrrrs