o .wii @sPddlmZddlZddlmZddlmZd dededeedefd d ZdS) )OptionalN)Tensor)"_check_retrieval_functional_inputspredstargettop_kreturncCsft||\}}|dur|jd}t|tr|dkstd|tj|dddd|}|dkS)aCompute the hit rate for information retrieval. The hit rate is 1.0 if there is at least one relevant document among all the top `k` retrieved documents. ``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. If you want to measure HitRate@K, ``top_k`` must be a positive integer. 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) Returns: A single-value tensor with the hit rate (at ``top_k``) of the predictions ``preds`` w.r.t. the labels ``target``. Raises: ValueError: If ``top_k`` parameter is not `None` or an integer larger than 0 Example: >>> from torch import tensor >>> preds = tensor([0.2, 0.3, 0.5]) >>> target = tensor([True, False, True]) >>> retrieval_hit_rate(preds, target, top_k=2) tensor(1.) Nrz,`top_k` has to be a positive integer or NoneT)dim descending) rshape isinstanceint ValueErrortorchargsortsumfloat)rrrrelevantrg/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/functional/retrieval/hit_rate.pyretrieval_hit_rates   r)N)typingrrrtorchmetrics.utilities.checksrrrrrrrs   $