:jddlmZddlmZmZmZddlZddlmZddlm Z ddl m Z m Z ddl mZddlmZdd lmZdd lmZmZesd gZGd d eZy))Sequence)AnyOptionalUnionN)Tensor)Literal)_cramers_v_compute_cramers_v_update)_nominal_input_validation)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEz CramersV.plotc eZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <e ed < dd e d ededdee deddf fd Zde de ddfdZde fdZddee ee dfdeedefdZxZS)CramersVa9 Compute `Cramer's V`_ statistic measuring the association between two categorical (nominal) data series. .. math:: V = \sqrt{\frac{\chi^2 / n}{\min(r - 1, k - 1)}} where .. math:: \chi^2 = \sum_{i,j} \ frac{\left(n_{ij} - \frac{n_{i.} n_{.j}}{n}\right)^2}{\frac{n_{i.} n_{.j}}{n}} where :math:`n_{ij}` denotes the number of times the values :math:`(A_i, B_j)` are observed with :math:`A_i, B_j` represent frequencies of values in ``preds`` and ``target``, respectively. Cramer's V is a symmetric coefficient, i.e. :math:`V(preds, target) = V(target, preds)`, so order of input arguments does not matter. The output values lies in [0, 1] with 1 meaning the perfect association. 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 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 with shape ``(batch_size,)`` or ``(batch_size, num_classes)``, respectively. As output of ``forward`` and ``compute`` the metric returns the following output: - ``cramers_v`` (:class:`~torch.Tensor`): Scalar tensor containing the Cramer's V statistic. Args: num_classes: Integer specifying the number of classes bias_correction: Indication of whether to use bias correction. 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. Raises: ValueError: If `nan_strategy` is not one of `'replace'` and `'drop'` ValueError: If `nan_strategy` is equal to `'replace'` and `nan_replace_value` is not an `int` or `float` Example:: >>> from torch import randint, randn >>> from torchmetrics.nominal import CramersV >>> preds = randint(0, 4, (100,)) >>> target = (preds + randn(100)).round().clamp(0, 4) >>> cramers_v = CramersV(num_classes=5) >>> cramers_v(preds, target) tensor(0.5284) Ffull_state_updateis_differentiableThigher_is_betterplot_lower_boundg?plot_upper_boundconfmat num_classesbias_correction nan_strategy)replacedropnan_replace_valuekwargsreturnNc t|di|||_||_t ||||_||_|jdtj||dy)Nrsum)dist_reduce_fx) super__init__rrr rr add_statetorchzeros)selfrrrrr __class__s q/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchmetrics/nominal/cramers.pyr&zCramersV.__init__Zs^ "6"&.!,0AB(!2 y%++k;"GX]^predstargetct|||j|j|j}|xj|z c_y)z*Update state with predictions and targets.N)r rrrr)r*r.r/rs r,updatezCramersV.updatels5#E643C3CTEVEVX\XnXno  r-cBt|j|jS)zCompute Cramer's V statistic.)r rr)r*s r,computezCramersV.computeqs!$,,0D0DEEr-valaxc&|j||S)a4Plot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> # Example plotting a single value >>> import torch >>> from torchmetrics.nominal import CramersV >>> metric = CramersV(num_classes=5) >>> metric.update(torch.randint(0, 4, (100,)), torch.randint(0, 4, (100,))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.nominal import CramersV >>> metric = CramersV(num_classes=5) >>> values = [ ] >>> for _ in range(10): ... values.append(metric(torch.randint(0, 4, (100,)), torch.randint(0, 4, (100,)))) >>> fig_, ax_ = metric.plot(values) )_plot)r*r4r5s r,plotz CramersV.plotusLzz#r""r-)Trr)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrintrrrr&r1r3rrrrr8 __classcell__)r+s@r,rrs1f$t##t#!d!!e!!e! O !%3<-0 ___/0 _ $E? _  _ _$ F F t FF&#fhv&6<=&#(S[J\&#hv&#r-r)collections.abcrtypingrrrr(rtyping_extensionsr'torchmetrics.functional.nominal.cramersr r %torchmetrics.functional.nominal.utilsr torchmetrics.metricr torchmetrics.utilities.importsr torchmetrics.utilities.plotrr__doctest_skip__rr$r-r,rKsB%'' %YK&@@'(|#v|#r-