iddlmZddlmZmZmZmZddlZddlmZm Z ddl m Z ddl m Z ddlmZmZddlmZmZdd lmZdd lmZmZesd gZGd d eZy))Sequence)AnyCallableOptionalUnionN)Tensortensor)Literal)retrieval_fall_out)RetrievalMetric_retrieval_aggregate)_flexible_bincount dim_zero_cat)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEzRetrievalFallOut.plotc eZdZUdZdZeed<dZeed<dZeed<dZ e ed<dZ e ed < dd e d e ed e edeedefdedd f fd ZdefdZdededefdZ dde eeeefde edefdZxZS)RetrievalFallOuta Compute `Fall-out`_. 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: - ``fallout@k`` (:class:`~torch.Tensor`): A tensor with the computed metric 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 negative ``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) 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. Example: >>> from torchmetrics.retrieval import RetrievalFallOut >>> 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]) >>> rfo = RetrievalFallOut(top_k=2) >>> rfo(preds, target, indexes=indexes) tensor(0.5000) Fis_differentiablehigher_is_betterfull_state_updateplot_lower_bound?plot_upper_boundNempty_target_action ignore_indextop_k aggregation)meanmedianminmaxkwargsreturnc ~t|d|||d|| t|tr|dkDs t d||_y)N)rrrrz,`top_k` has to be a positive integer or None)super__init__ isinstanceint ValueErrorr)selfrrrrr$ __class__s t/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/torchmetrics/retrieval/fall_out.pyr)zRetrievalFallOut.__init__bsT  3%#     j&<KL L c t|j}t|j}t|j}t j |\}}||}||}t |jjj}g}tt j||dt j||dD]\}}d|z jso|jdk(r td|jdk(r|jt!d]|jdk(sm|jt!d |j|j#|||rGt%t j&|D cgc]} | j)|c} |j* St!d j)|Scc} w) aFirst concat state ``indexes``, ``preds`` and ``target`` since they were stored as lists. After that, compute list of groups that will help in keeping together predictions about the same query. Finally, for each group compute the `_metric` if the number of negative targets is at least 1, otherwise behave as specified by `self.empty_target_action`. r)dimerrorzC`compute` method was provided with a query with no negative target.posrnegr)r)rindexespredstargettorchsortrdetachcputolistzipsplitsumrr,appendr _metricr stacktor) r-r7r8r9indices split_sizesres mini_preds mini_targetxs r/computezRetrievalFallOut.computeust||,TZZ(dkk* ::g.g(188:>>@GGI '* KK{ 2EKK YZ4[( #J  O((*++w6$%jkk++u4JJvc{+--6JJvc{+ 4<< K@A(  !3-G3aadd5k3-G!HVZVfVf g & -GsG r8r9c2t|||jS)N)r)r r)r-r8r9s r/rCzRetrievalFallOut._metrics!%tzzBBr0valaxc&|j||S)a[Plot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> import torch >>> from torchmetrics.retrieval import RetrievalFallOut >>> # Example plotting a single value >>> metric = RetrievalFallOut() >>> metric.update(torch.rand(10,), torch.randint(2, (10,)), indexes=torch.randint(2,(10,))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> import torch >>> from torchmetrics.retrieval import RetrievalFallOut >>> # Example plotting multiple values >>> metric = RetrievalFallOut() >>> values = [] >>> for _ in range(10): ... values.append(metric(torch.rand(10,), torch.randint(2, (10,)), indexes=torch.randint(2,(10,)))) >>> fig, ax = metric.plot(values) )_plot)r-rNrOs r/plotzRetrievalFallOut.plotsPzz#r""r0)r5NNr )NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrstrrr+rr rrr)rrLrCrrrrR __classcell__)r.s@r/rrs:x$t#"d"#t#!e!!e!$)&*#PV  sm}  7#ABHLM    &% % NCVCVCC_c(#E&(6*:":;<(#IQRZI[(# (#r0r)collections.abcrtypingrrrrr:rr typing_extensionsr *torchmetrics.functional.retrieval.fall_outr torchmetrics.retrieval.baser r torchmetrics.utilities.datarrtorchmetrics.utilities.importsrtorchmetrics.utilities.plotrr__doctest_skip__rr'r0r/resB%11 %IMH@@/0h#h#r0