o .wi@sddlmZddlmZmZmZddlZddlmZddlm Z ddl m Z m Z ddl mZddlmZdd lmZdd lmZmZesGd gZGd d d eZdS))Sequence)AnyOptionalUnionN)Tensor)Literal)_theils_u_compute_theils_u_update)_nominal_input_validation)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPE TheilsU.plotc seZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <e ed < d d e deddee deddf fdd Zde de ddfddZde fddZd!dee ee dfdeedefddZZS)"TheilsUaCompute `Theil's U`_ statistic measuring the association between two categorical (nominal) data series. .. math:: U(X|Y) = \frac{H(X) - H(X|Y)}{H(X)} where :math:`H(X)` is entropy of variable :math:`X` while :math:`H(X|Y)` is the conditional entropy of :math:`X` given :math:`Y`. It is also know as the Uncertainty Coefficient. Theils's U is an asymmetric coefficient, i.e. :math:`TheilsU(preds, target) \neq TheilsU(target, preds)`, so the order of the inputs matters. The output values lies in [0, 1], where a 0 means y has no information about x while value 1 means y has complete information about x. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): Either 1D or 2D tensor of categorical (nominal) data from the first data series (called X in the above definition) with shape ``(batch_size,)`` or ``(batch_size, num_classes)``, respectively. - ``target`` (:class:`~torch.Tensor`): Either 1D or 2D tensor of categorical (nominal) data from the second data series (called Y in the above definition) with shape ``(batch_size,)`` or ``(batch_size, num_classes)``, respectively. As output of ``forward`` and ``compute`` the metric returns the following output: - ``theils_u`` (:class:`~torch.Tensor`): Scalar tensor containing the Theil's U statistic. Args: num_classes: Integer specifying the number of classes nan_strategy: Indication of whether to replace or drop ``NaN`` values nan_replace_value: Value to replace ``NaN``s when ``nan_strategy = 'replace'`` kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example:: >>> from torch import randint >>> from torchmetrics.nominal import TheilsU >>> preds = randint(10, (10,)) >>> target = randint(10, (10,)) >>> metric = TheilsU(num_classes=10) >>> metric(preds, target) tensor(0.8530) Ffull_state_updateis_differentiableThigher_is_betterplot_lower_boundg?plot_upper_boundconfmatreplace num_classes nan_strategy)rdropnan_replace_valuekwargsreturnNc sJtjdi|||_t||||_||_|jdt||dddS)Nrsum)dist_reduce_fx) super__init__rr rr add_statetorchzeros)selfrrrr __class__r!Z/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/nominal/theils_u.pyr#Ps  zTheilsU.__init__predstargetcCs(t|||j|j|j}|j|7_dS)z*Update state with predictions and targets.N)r rrrr)r'r+r,rr!r!r*update`szTheilsU.updatecCs t|jS)zCompute Theil's U statistic.)rr)r'r!r!r*computees zTheilsU.computevalaxcCs |||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 >>> # Example plotting a single value >>> import torch >>> from torchmetrics.nominal import TheilsU >>> metric = TheilsU(num_classes=10) >>> metric.update(torch.randint(10, (10,)), torch.randint(10, (10,))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.nominal import TheilsU >>> metric = TheilsU(num_classes=10) >>> values = [ ] >>> for _ in range(10): ... values.append(metric(torch.randint(10, (10,)), torch.randint(10, (10,)))) >>> fig_, ax_ = metric.plot(values) )_plot)r'r/r0r!r!r*plotis &r)rr)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrintrrrr#r-r.rrr rr2 __classcell__r!r!r(r*rs0  )    2r)collections.abcrtypingrrrr%rtyping_extensionsr(torchmetrics.functional.nominal.theils_urr %torchmetrics.functional.nominal.utilsr torchmetrics.metricr torchmetrics.utilities.importsr torchmetrics.utilities.plotr r__doctest_skip__rr!r!r!r*s