o .wi@sddlmZddlmZmZmZddlZddlmZddlm Z ddl m Z m Z ddl mZddlmZdd lmZdd lmZmZesGd gZGd d d eZdS))Sequence)AnyOptionalUnionN)Tensor)Literal)_cramers_v_compute_cramers_v_update)_nominal_input_validation)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPE CramersV.plotc seZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <e ed <  d!d e dededdee deddf fdd Zde de ddfddZde fddZd"dee ee dfdeedefdd ZZS)#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_boundconfmatreplace num_classesbias_correction nan_strategy)rdropnan_replace_valuekwargsreturnNc sPtjdi|||_||_t||||_||_|jdt ||dddS)Nrsum)dist_reduce_fx) super__init__rrr rr add_statetorchzeros)selfrrrrr __class__r"Y/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/nominal/cramers.pyr$Zs zCramersV.__init__predstargetcCs(t|||j|j|j}|j|7_dS)z*Update state with predictions and targets.N)r rrrr)r(r,r-rr"r"r+updatelszCramersV.updatecCst|j|jS)zCompute Cramer's V statistic.)rrr)r(r"r"r+computeqszCramersV.computevalaxcCs |||S)a4Plot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> # Example plotting a single value >>> import torch >>> from torchmetrics.nominal import CramersV >>> metric = CramersV(num_classes=5) >>> metric.update(torch.randint(0, 4, (100,)), torch.randint(0, 4, (100,))) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.nominal import CramersV >>> metric = CramersV(num_classes=5) >>> values = [ ] >>> for _ in range(10): ... values.append(metric(torch.randint(0, 4, (100,)), torch.randint(0, 4, (100,)))) >>> fig_, ax_ = metric.plot(values) )_plot)r(r0r1r"r"r+plotus &r)Trr)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrintrrrr$r.r/rrr rr3 __classcell__r"r"r)r+rs6  3    2r)collections.abcrtypingrrrr&rtyping_extensionsr'torchmetrics.functional.nominal.cramersrr %torchmetrics.functional.nominal.utilsr torchmetrics.metricr torchmetrics.utilities.importsr torchmetrics.utilities.plotr r__doctest_skip__rr"r"r"r+s