iddlmZddlmZmZmZddlZddlmZddlm Z ddl m Z m Z ddl mZddlmZdd lmZdd lmZmZesd gZGd d eZy))Sequence)AnyOptionalUnionN)Tensor)Literal))_pearsons_contingency_coefficient_compute(_pearsons_contingency_coefficient_update)_nominal_input_validation)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEz#PearsonsContingencyCoefficient.plotc eZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <e ed < dd e d eddee deddf fd Zde de ddfdZde fdZddee ee dfdeedefdZxZS)PearsonsContingencyCoefficienta Compute `Pearson's Contingency Coefficient`_ statistic. This metric measures the association between two categorical (nominal) data series. .. math:: Pearson = \sqrt{\frac{\chi^2 / n}{1 + \chi^2 / n}} 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. Pearson's Contingency Coefficient is a symmetric coefficient, i.e. :math:`Pearson(preds, target) = Pearson(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: - ``pearsons_cc`` (:class:`~torch.Tensor`): Scalar tensor containing the Pearsons Contingency Coefficient 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. 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 PearsonsContingencyCoefficient >>> preds = randint(0, 4, (100,)) >>> target = (preds + randn(100)).round().clamp(0, 4) >>> pearsons_contingency_coefficient = PearsonsContingencyCoefficient(num_classes=5) >>> pearsons_contingency_coefficient(preds, target) tensor(0.6948) Ffull_state_updateis_differentiableThigher_is_betterplot_lower_boundg?plot_upper_boundconfmat num_classes nan_strategy)replacedropnan_replace_valuekwargsreturnNc t|di|||_t||||_||_|j dtj||dy)Nrsum)dist_reduce_fx) super__init__rr rr add_statetorchzeros)selfrrrr __class__s q/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/torchmetrics/nominal/pearson.pyr%z'PearsonsContingencyCoefficient.__init__^sV "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+updatez%PearsonsContingencyCoefficient.updatens9: 64++T->->@V@V   r,c,t|jS)z4Compute Pearson's Contingency Coefficient statistic.)r r)r)s r+computez&PearsonsContingencyCoefficient.computeus8FFr,valaxc&|j||S)aPlot 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 PearsonsContingencyCoefficient >>> metric = PearsonsContingencyCoefficient(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 PearsonsContingencyCoefficient >>> metric = PearsonsContingencyCoefficient(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)r3r4s r+plotz#PearsonsContingencyCoefficient.plotysLzz#r""r,)rr)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrintrrrr%r0r2rrrrr7 __classcell__)r*s@r+rr"s2h$t##t#!d!!e!!e! O 4=-0 __/0_$E? _  _  _  F F t GG&#fhv&6<=&#(S[J\&#hv&#r,r)collections.abcrtypingrrrr'rtyping_extensionsr'torchmetrics.functional.nominal.pearsonr r %torchmetrics.functional.nominal.utilsr torchmetrics.metricr torchmetrics.utilities.importsr torchmetrics.utilities.plotrr__doctest_skip__rr#r,r+rJsG%'' %L&@@=>}#V}#r,