o yi@sLddlmZmZddlmZmZddlmZddlm Z Gddde Z dS))AnyOptional)Tensortensor)retrieval_hit_rate)RetrievalMetricc seZdZUdZdZeed<dZeed<dZeed<   dd e d e e d e e d e d df fdd Z deded efddZZS)RetrievalHitRatea Computes `IR HitRate`. Works with binary target data. Accepts float predictions from a model output. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): A float tensor of shape ``(N, ...)`` - ``target`` (:class:`~torch.Tensor`): A long or bool tensor of shape ``(N, ...)`` - ``indexes`` (:class:`~torch.Tensor`): A long tensor of shape ``(N, ...)`` which indicate to which query a prediction belongs As output to ``forward`` and ``compute`` the metric returns the following output: - ``hr2`` (:class:`~torch.Tensor`): A single-value tensor with the hit rate (at ``k``) of the predictions ``preds`` w.r.t. the labels ``target`` All ``indexes``, ``preds`` and ``target`` must have the same dimension and will be flatten at the beginning, so that for example, a tensor of shape ``(N, M)`` is treated as ``(N * M, )``. Predictions will be first grouped by ``indexes`` and then will be computed as the mean of the metric over each query. Args: empty_target_action: Specify what to do with queries that do not have at least a positive ``target``. Choose from: - ``'neg'``: those queries count as ``0.0`` (default) - ``'pos'``: those queries count as ``1.0`` - ``'skip'``: skip those queries; if all queries are skipped, ``0.0`` is returned - ``'error'``: raise a ``ValueError`` ignore_index: Ignore predictions where the target is equal to this number. k: consider only the top k elements for each query (default: ``None``, which considers them all) kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: ValueError: If ``empty_target_action`` is not one of ``error``, ``skip``, ``neg`` or ``pos``. ValueError: If ``ignore_index`` is not `None` or an integer. ValueError: If ``k`` parameter is not `None` or an integer larger than 0. Example: >>> from torchmetrics import RetrievalHitRate >>> indexes = tensor([0, 0, 0, 1, 1, 1, 1]) >>> preds = tensor([0.2, 0.3, 0.5, 0.1, 0.3, 0.5, 0.2]) >>> target = tensor([True, False, False, False, True, False, True]) >>> hr2 = RetrievalHitRate(k=2) >>> hr2(preds, target, indexes=indexes) tensor(0.5000) Fis_differentiableThigher_is_betterfull_state_updatenegNempty_target_action ignore_indexkkwargsreturnc sDtjd||d||durt|tr|dkstd||_dS)N)r rrz(`k` has to be a positive integer or None)super__init__ isinstanceint ValueErrorr)selfr rrr __class__rS/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/retrieval/hit_rate.pyrPs zRetrievalHitRate.__init__predstargetcCst|||jdS)N)r)rr)rrrrrr_metricaszRetrievalHitRate._metric)r NN)__name__ __module__ __qualname____doc__r bool__annotations__r r strrrrrrr __classcell__rrrrrs(  5  rN) typingrrtorchrr*torchmetrics.functional.retrieval.hit_ratertorchmetrics.retrieval.baserrrrrrs