o .wi@sddlmZddlmZmZmZmZddlmZddl m Z ddl m Z ddl mZddlmZddlmZmZes=d gZGd d d eZd S) )Sequence)AnyCallableOptionalUnion)Tensor)Literal)retrieval_precision)RetrievalMetric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPERetrievalPrecision.plotcseZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <  d de de ede ededeedefdedd ffdd ZdededefddZ d!de eeeefde edefddZZS)"RetrievalPrecisiona Compute `IR 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: - ``p@k`` (:class:`~torch.Tensor`): A single-value tensor with the precision (at ``top_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. top_k: Consider only the top k elements for each query (default: ``None``, which considers them all) adaptive_k: Adjust ``top_k`` to ``min(k, number of documents)`` for each query 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. ValueError: If ``top_k`` is not ``None`` or not an integer greater than 0. ValueError: If ``adaptive_k`` is not boolean. Example: >>> from torch import tensor >>> from torchmetrics.retrieval import RetrievalPrecision >>> 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 = RetrievalPrecision(top_k=2) >>> p2(preds, target, indexes=indexes) tensor(0.5000) Fis_differentiableThigher_is_betterfull_state_updategplot_lower_boundg?plot_upper_boundnegNmeanempty_target_action ignore_indextop_k adaptive_k aggregation)rmedianminmaxkwargsreturnc s^tjd|||d||durt|tr|dkstdt|ts'td||_||_dS)N)rrrrz,`top_k` has to be a positive integer or Nonez `adaptive_k` has to be a boolean)super__init__ isinstanceint ValueErrorboolrr)selfrrrrrr __class__r!]/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/retrieval/precision.pyr#es   zRetrievalPrecision.__init__predstargetcCst|||j|jdS)N)rr)r rr)r(r,r-r!r!r+_metric|szRetrievalPrecision._metricvalaxcCs |||S)acPlot 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 RetrievalPrecision >>> # Example plotting a single value >>> metric = RetrievalPrecision() >>> 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 RetrievalPrecision >>> # Example plotting multiple values >>> metric = RetrievalPrecision() >>> 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)r(r/r0r!r!r+plots (r)rNNFr)NN)__name__ __module__ __qualname____doc__rr'__annotations__rrrfloatrstrrr%rrrrr#rr.rr r r2 __classcell__r!r!r)r+rsH  A    rN)collections.abcrtypingrrrrtorchrtyping_extensionsr+torchmetrics.functional.retrieval.precisionr torchmetrics.retrieval.baser torchmetrics.utilities.importsr torchmetrics.utilities.plotr r __doctest_skip__rr!r!r!r+s