o y“©iÅ ã@s<ddlmZmZddlmZddlmZGdd„deƒZdS)é)ÚTensorÚtensor©Úretrieval_r_precision)ÚRetrievalMetricc@sLeZdZUdZdZeed<dZeed<dZeed<de de d e fd d „Z d S) ÚRetrievalRPrecisiona¸Computes `IR R-Precision`_. 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: - ``p2`` (:class:`~torch.Tensor`): A single-value tensor with the r-precision 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. 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. Example: >>> from torchmetrics import RetrievalRPrecision >>> 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([False, False, True, False, True, False, True]) >>> p2 = RetrievalRPrecision() >>> p2(preds, target, indexes=indexes) tensor(0.7500) FÚis_differentiableTÚhigher_is_betterÚfull_state_updateÚpredsÚtargetÚreturncCs t||ƒS)Nr)Úselfr r ©rúV/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/retrieval/r_precision.pyÚ_metricJs zRetrievalRPrecision._metricN) Ú__name__Ú __module__Ú __qualname__Ú__doc__rÚboolÚ__annotations__r r rrrrrrrs  1  rN)ÚtorchrrÚ-torchmetrics.functional.retrieval.r_precisionrÚtorchmetrics.retrieval.baserrrrrrÚs