o yi @sTddlmZddlZddlmZmZddlmZd dededeedefd d ZdS) )OptionalN)Tensortensor)"_check_retrieval_functional_inputspredstargetkreturncCst||\}}|dur|jd}t|tr|dkstd|s(td|jdS|tj |dddd| }||S) aComputes the recall metric (for information retrieval). Recall is the fraction of relevant documents retrieved among all the relevant 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 Recall@K, ``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. k: consider only the top k elements (default: `None`, which considers them all) Returns: a single-value tensor with the recall (at ``k``) of the predictions ``preds`` w.r.t. the labels ``target``. Raises: ValueError: If ``k`` parameter is not `None` or an integer larger than 0 Example: >>> from torchmetrics.functional import retrieval_recall >>> preds = tensor([0.2, 0.3, 0.5]) >>> target = tensor([True, False, True]) >>> retrieval_recall(preds, target, k=2) tensor(0.5000) Nrz(`k` has to be a positive integer or Noneg)deviceT)dim descending) rshape isinstanceint ValueErrorsumrr torchargsortfloat)rrrrelevantr\/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/retrieval/recall.pyretrieval_recalls $ r)N) typingrrrrtorchmetrics.utilities.checksrrrrrrrs  $