o .wi @sddlmZddlmZmZmZmZddlmZddl m Z ddl m Z ddl mZddlmZddlmZmZes=d gZGd d d e Zd S) )Sequence)AnyListOptionalUnion)Tensor)fowlkes_mallows_index)Metric) dim_zero_cat)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEFowlkesMallowsIndex.plotcseZdZUdZdZeed<dZeeed<dZ eed<dZ e ed<d Z e ed <e eed <e eed <d eddffdd Zd ed eddfddZdefddZddeeeedfdeedefddZZS)FowlkesMallowsIndexaCompute `Fowlkes-Mallows Index`_. .. math:: FMI(U,V) = \frac{TP}{\sqrt{(TP + FP) * (TP + FN)}} Where :math:`TP` is the number of true positives, :math:`FP` is the number of false positives, and :math:`FN` is the number of false negatives. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): single integer tensor with shape ``(N,)`` with predicted cluster labels - ``target`` (:class:`~torch.Tensor`): single integer tensor with shape ``(N,)`` with ground truth cluster labels As output of ``forward`` and ``compute`` the metric returns the following output: - ``fmi`` (:class:`~torch.Tensor`): A tensor with the Fowlkes-Mallows index. Args: kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example:: >>> import torch >>> from torchmetrics.clustering import FowlkesMallowsIndex >>> preds = torch.tensor([2, 2, 0, 1, 0]) >>> target = torch.tensor([2, 2, 1, 1, 0]) >>> fmi = FowlkesMallowsIndex() >>> fmi(preds, target) tensor(0.5000) Tis_differentiablehigher_is_betterFfull_state_updategplot_lower_boundg?plot_upper_boundpredstargetkwargsreturnNc s6tjdi||jdgdd|jdgdddS)Nrcat)defaultdist_reduce_fxr)super__init__ add_state)selfr __class__rj/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/clustering/fowlkes_mallows_index.pyrEszFowlkesMallowsIndex.__init__cCs|j||j|dS)z*Update state with predictions and targets.N)rappendr)r rrrrr#updateKs zFowlkesMallowsIndex.updatecCstt|jt|jS)z)Compute Fowlkes-Mallows index over state.)rr rr)r rrr#computePszFowlkesMallowsIndex.computevalaxcCs |||S)aXPlot 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 >>> # Example plotting a single value >>> import torch >>> from torchmetrics.clustering import FowlkesMallowsIndex >>> metric = FowlkesMallowsIndex() >>> metric.update(torch.randint(0, 4, (10,)), torch.randint(0, 4, (10,))) >>> fig_, ax_ = metric.plot(metric.compute()) .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.clustering import FowlkesMallowsIndex >>> metric = FowlkesMallowsIndex() >>> values = [ ] >>> for _ in range(10): ... values.append(metric(torch.randint(0, 4, (10,)), torch.randint(0, 4, (10,)))) >>> fig_, ax_ = metric.plot(values) )_plot)r r'r(rrr#plotTs &r)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrrfloatrrrrrr%r&rrr r r* __classcell__rrr!r#rs       2rN)collections.abcrtypingrrrrtorchr"torchmetrics.functional.clusteringrtorchmetrics.metricr torchmetrics.utilities.datar torchmetrics.utilities.importsr torchmetrics.utilities.plotr r __doctest_skip__rrrrr#s