a  þdCã@srddlmZmZmZddlmZddlmZddlm Z ddl m Z ddl m Z mZe s^dgZGdd „d e ƒZd S) é)ÚOptionalÚSequenceÚUnion)ÚTensor©Úretrieval_r_precision)ÚRetrievalMetric)Ú_MATPLOTLIB_AVAILABLE)Ú_AX_TYPEÚ_PLOT_OUT_TYPEúRetrievalRPrecision.plotc@sˆeZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <e e e d œd d „Z deee ee feeedœdd„ZdS)ÚRetrievalRPrecisionaÛCompute `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. 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) FÚis_differentiableTÚhigher_is_betterÚfull_state_updategÚplot_lower_boundgð?Úplot_upper_bound)ÚpredsÚtargetÚreturncCs t||ƒS)Nr)Úselfrr©rúk/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/retrieval/r_precision.pyÚ_metricTszRetrievalRPrecision._metricN)ÚvalÚaxrcCs | ||¡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) )Z_plot)rrrrrrÚplotWs(r )NN)Ú__name__Ú __module__Ú __qualname__Ú__doc__rÚboolÚ__annotations__rrrÚfloatrrrrrrr r rrrrrr s 2     ÿþr N)ÚtypingrrrZtorchrZ-torchmetrics.functional.retrieval.r_precisionrZtorchmetrics.retrieval.baserZtorchmetrics.utilities.importsr Ztorchmetrics.utilities.plotr r Z__doctest_skip__r rrrrÚs