o yi@sxddlmZmZddlZddlmZmZmZddlmZddl m Z  ddeded ee d e d eeeeff d d Z dS))OptionalTupleN)Tensorcumsumtensor)pad)"_check_retrieval_functional_inputsFpredstargetmax_k adaptive_kreturnc CsNt||\}}t|tstd|dur|jd}t|tr"|dks&td|rQ||jdkrQtjd|jdd|jd}t |d||jdfdt |jd}n tjd|d|jd}| sqtj ||jdtj ||jd|fS||j t||jddd d }tt |dtd|t|fdd dd }|| }||}|||fS) aComputes precision-recall pairs for different k (from 1 to `max_k`). In a ranked retrieval context, appropriate sets of retrieved documents are naturally given by the top k retrieved documents. Recall is the fraction of relevant documents retrieved among all the relevant documents. Precision is the fraction of relevant documents among all the retrieved documents. For each such set, precision and recall values can be plotted to give a recall-precision curve. ``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. max_k: Calculate recall and precision for all possible top k from 1 to max_k (default: `None`, which considers all possible top k) adaptive_k: adjust `max_k` to `min(max_k, number of documents)` for each query Returns: tensor with the precision values for each k (at ``k``) from 1 to `max_k` tensor with the recall values for each k (at ``k``) from 1 to `max_k` tensor with all possibles k Raises: ValueError: If ``max_k`` is not `None` or an integer larger than 0. ValueError: If ``adaptive_k`` is not boolean. Example: >>> from torchmetrics.functional import retrieval_precision_recall_curve >>> preds = tensor([0.2, 0.3, 0.5]) >>> target = tensor([True, False, True]) >>> precisions, recalls, top_k = retrieval_precision_recall_curve(preds, target, max_k=2) >>> precisions tensor([1.0000, 0.5000]) >>> recalls tensor([0.5000, 0.5000]) >>> top_k tensor([1, 2]) z `adaptive_k` has to be a booleanNrz,`max_k` has to be a positive integer or None)deviceconstant)dimg)r isinstancebool ValueErrorshapeinttorcharangerrfloatsumzerostopkminrmaxlen)r r r r rrelevantrecall precisionr$l/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/functional/retrieval/precision_recall_curve.py retrieval_precision_recall_curves$0  ("&(  r&)NF)typingrrrrrrtorch.nn.functionalrtorchmetrics.utilities.checksrrrr&r$r$r$r%s"