o .wi@sddlmZmZmZmZddlZddlmZddlmZddl m Z ddl m Z ddl mZmZddlmZmZes=d gZesCd d gZGd d d e ZdS) )AnyOptionalSequenceUnionN)Tensor)multiclass_confusion_matrix)_cluster_accuracy_compute)Metric)_MATPLOTLIB_AVAILABLE"_TORCH_LINEAR_ASSIGNMENT_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEClusterAccuracy.plotClusterAccuracycseZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <e ed <d e d eddffdd Zde de ddfddZde fddZddee ee dfdeedefddZZS)raCompute `Cluster Accuracy`_ between predicted and target clusters. .. math:: \text{Cluster Accuracy} = \max_g \frac{1}{N} \sum_{n=1}^N \mathbb{1}_{g(p_n) = t_n} Where :math:`g` is a function that maps predicted clusters :math:`p` to target clusters :math:`t`, :math:`N` is the number of samples, :math:`p_n` is the predicted cluster for sample :math:`n`, :math:`t_n` is the target cluster for sample :math:`n`, and :math:`\mathbb{1}` is the indicator function. The function :math:`g` is determined by solving the linear sum assignment problem. This clustering metric is an extrinsic measure, because it requires ground truth clustering labels, which may not be available in practice since clustering in generally is used for unsupervised learning. 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: - ``acc_score`` (:class:`~torch.Tensor`): A tensor with the Cluster Accuracy score Args: num_classes: number of classes kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: RuntimeError: If ``torch_linear_assignment`` is not installed. To install, run ``pip install torchmetrics[clustering]``. ValueError If ``num_classes`` is not a positive integer Example:: >>> import torch >>> from torchmetrics.clustering import ClusterAccuracy >>> preds = torch.tensor([0, 0, 1, 1]) >>> target = torch.tensor([1, 1, 0, 0]) >>> metric = ClusterAccuracy(num_classes=2) >>> metric(preds, target) tensor(1.) Fis_differentiableThigher_is_betterfull_state_updategplot_lower_boundg?plot_upper_boundconfmat num_classeskwargsreturnNc sbtjdi|tstdt|tr|dkrtd|jdtj ||ftj ddd||_ dS) NzaMissing `torch_linear_assignment`. Please install it with `pip install torchmetrics[clustering]`.rz3Argument `num_classes` should be a positive integerr)dtypesum)defaultdist_reduce_fx) super__init__r RuntimeError isinstanceint ValueError add_statetorchzerosint64r)selfrr __class__re/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/clustering/cluster_accuracy.pyrWs zClusterAccuracy.__init__predstargetcCs|jt|||jd7_dS)zAUpdate the confusion matrix with the new predictions and targets.)rN)rrr)r(r,r-rrr+updateeszClusterAccuracy.updatecCs t|jS)z!Computes the clustering accuracy.)rr)r(rrr+computeis zClusterAccuracy.computevalaxcCs |||S)avPlot 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 ClusterAccuracy >>> metric = ClusterAccuracy(num_classes=4) >>> 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 ClusterAccuracy >>> metric = ClusterAccuracy(num_classes=4) >>> 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(r0r1rrr+plotms 'r)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrr"rrr.r/rrrr r r3 __classcell__rrr)r+r#s  ,    2)typingrrrrr%r&torchmetrics.functional.classificationr3torchmetrics.functional.clustering.cluster_accuracyrtorchmetrics.metricr torchmetrics.utilities.importsr r torchmetrics.utilities.plotr r __doctest_skip__rrrrr+s