a  dx@sddlmZmZmZmZddlZddlmZddlmZddl m Z m Z ddl m Z ddlmZddlmZdd lmZmZesd gZGd d d eZdS) )AnyOptionalSequenceUnionN)Tensor)Literal)_cramers_v_compute_cramers_v_update)_nominal_input_validation)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPE CramersV.plotcseZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <e ed <de eed eee e feddfdd Ze e ddddZe dddZdee ee dfeeedddZZS)CramersVa@ 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 specifing 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 torchmetrics.nominal import CramersV >>> _ = torch.manual_seed(42) >>> preds = torch.randint(0, 4, (100,)) >>> target = torch.round(preds + torch.randn(100)).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)rZdropN) num_classesbias_correction nan_strategynan_replace_valuekwargsreturnc sPtjfi|||_||_t||||_||_|jdt ||dddS)Nrsum)Zdist_reduce_fx) super__init__rrr rrZ add_statetorchzeros)selfrrrrr __class__e/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/nominal/cramers.pyr!Ys zCramersV.__init__)predstargetrcCs(t|||j|j|j}|j|7_dS)z*Update state with predictions and targets.N)r rrrr)r$r)r*rr'r'r(updatekszCramersV.update)rcCst|j|jS)zCompute Cramer's V statistic.)rrr)r$r'r'r(computepszCramersV.compute)valaxrcCs |||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) )Z_plot)r$r-r.r'r'r(plotts&r)Trr)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrintrrrrr!r+r,rr rr/ __classcell__r'r'r%r(rs* 3     r)typingrrrrr"rZtyping_extensionsrZ'torchmetrics.functional.nominal.cramersrr Z%torchmetrics.functional.nominal.utilsr Ztorchmetrics.metricr Ztorchmetrics.utilities.importsr Ztorchmetrics.utilities.plotr rZ__doctest_skip__rr'r'r'r(s