iddlmZddlmZmZddlmZddlmZddl m Z ddl m Z ddl mZmZe sdgZGd d e Zy ) )Sequence)OptionalUnion)Tensorretrieval_r_precision)RetrievalMetric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEzRetrievalRPrecision.plotceZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <d e d e d e fdZ ddeee ee fdeed efdZy)RetrievalRPrecisionax Compute `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: - ``rp`` (: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. aggregation: Specify how to aggregate over indexes. Can either a custom callable function that takes in a single tensor and returns a scalar value or one of the following strings: - ``'mean'``: average value is returned - ``'median'``: median value is returned - ``'max'``: max value is returned - ``'min'``: min value is returned 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 torch import tensor >>> from torchmetrics.retrieval 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) Fis_differentiableThigher_is_betterfull_state_updategplot_lower_boundg?plot_upper_boundpredstargetreturnct||S)Nr)selfrrs w/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/torchmetrics/retrieval/r_precision.py_metriczRetrievalRPrecision._metric^s$UF33Nvalaxc&|j||S)agPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> import torch >>> from torchmetrics.retrieval import RetrievalRPrecision >>> # Example plotting a single value >>> metric = RetrievalRPrecision() >>> metric.update(torch.rand(10,), torch.randint(2, (10,)), indexes=torch.randint(2,(10,))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> import torch >>> from torchmetrics.retrieval import RetrievalRPrecision >>> # Example plotting multiple values >>> metric = RetrievalRPrecision() >>> values = [] >>> for _ in range(10): ... values.append(metric(torch.rand(10,), torch.randint(2, (10,)), indexes=torch.randint(2,(10,)))) >>> fig, ax = metric.plot(values) )_plot)rrrs rplotzRetrievalRPrecision.plotasPzz#r""r)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrrrrrr r r rrrrs9v$t#!d!#t#!e!!e!4V4V44_c(#E&(6*:":;<(#IQRZI[(# (#rrN)collections.abcrtypingrrtorchr-torchmetrics.functional.retrieval.r_precisionrtorchmetrics.retrieval.baser torchmetrics.utilities.importsr torchmetrics.utilities.plotr r __doctest_skip__rr(rrr1s6%"O7@@23m#/m#r