:jV ddlmZddlmZmZmZmZddlmZddl m Z ddl m Z m Z mZmZddlmZddlmZddlmZdd lmZmZesd gZGd d eZy ))Sequence)AnyListOptionalUnion)Tensor)Literal)_kendall_corrcoef_compute_kendall_corrcoef_update_MetricVariant_TestAlternative)Metric) dim_zero_cat)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEzKendallRankCorrCoef.plotc eZdZUdZdZdZdZdZee d<dZ ee d<e e e d <e e e d < dd e d d edee ddededdf fd Zd e d e ddfdZdee ee e fffdZ ddeee ee fdeedefdZxZS)KendallRankCorrCoefa Compute `Kendall Rank Correlation Coefficient`_. .. math:: tau_a = \frac{C - D}{C + D} where :math:`C` represents concordant pairs, :math:`D` stands for discordant pairs. .. math:: tau_b = \frac{C - D}{\sqrt{(C + D + T_{preds}) * (C + D + T_{target})}} where :math:`C` represents concordant pairs, :math:`D` stands for discordant pairs and :math:`T` represents a total number of ties. .. math:: tau_c = 2 * \frac{C - D}{n^2 * \frac{m - 1}{m}} where :math:`C` represents concordant pairs, :math:`D` stands for discordant pairs, :math:`n` is a total number of observations and :math:`m` is a ``min`` of unique values in ``preds`` and ``target`` sequence. Definitions according to Definition according to `The Treatment of Ties in Ranking Problems`_. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): Sequence of data in float tensor of either shape ``(N,)`` or ``(N,d)`` - ``target`` (:class:`~torch.Tensor`): Sequence of data in float tensor of either shape ``(N,)`` or ``(N,d)`` As output of ``forward`` and ``compute`` the metric returns the following output: - ``kendall`` (:class:`~torch.Tensor`): A tensor with the correlation tau statistic, and if it is not None, the p-value of corresponding statistical test. Args: variant: Indication of which variant of Kendall's tau to be used t_test: Indication whether to run t-test alternative: Alternative hypothesis for t-test. Possible values: - 'two-sided': the rank correlation is nonzero - 'less': the rank correlation is negative (less than zero) - 'greater': the rank correlation is positive (greater than zero) num_outputs: Number of outputs in multioutput setting kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: ValueError: If ``t_test`` is not of a type bool ValueError: If ``t_test=True`` and ``alternative=None`` Example (single output regression): >>> from torch import tensor >>> from torchmetrics.regression import KendallRankCorrCoef >>> preds = tensor([2.5, 0.0, 2, 8]) >>> target = tensor([3, -0.5, 2, 1]) >>> kendall = KendallRankCorrCoef() >>> kendall(preds, target) tensor(0.3333) Example (multi output regression): >>> from torchmetrics.regression import KendallRankCorrCoef >>> preds = tensor([[2.5, 0.0], [2, 8]]) >>> target = tensor([[3, -0.5], [2, 1]]) >>> kendall = KendallRankCorrCoef(num_outputs=2) >>> kendall(preds, target) tensor([1., 1.]) Example (single output regression with t-test): >>> from torchmetrics.regression import KendallRankCorrCoef >>> preds = tensor([2.5, 0.0, 2, 8]) >>> target = tensor([3, -0.5, 2, 1]) >>> kendall = KendallRankCorrCoef(t_test=True, alternative='two-sided') >>> kendall(preds, target) (tensor(0.3333), tensor(0.4969)) Example (multi output regression with t-test): >>> from torchmetrics.regression import KendallRankCorrCoef >>> preds = tensor([[2.5, 0.0], [2, 8]]) >>> target = tensor([[3, -0.5], [2, 1]]) >>> kendall = KendallRankCorrCoef(t_test=True, alternative='two-sided', num_outputs=2) >>> kendall(preds, target) (tensor([1., 1.]), tensor([nan, nan])) FNTgplot_lower_boundg?plot_upper_boundpredstargetvariant)abct_test alternative) two-sidedlessgreater num_outputskwargsreturnc t|di|t|tst dt |d|r | t dt jt||_ |rtjt|nd|_ ||_ |jdgd|jdgdy) Nz>Argument `t_test` is expected to be of a type `bool`, but got .zCArgument `alternative` is required if `t_test=True` but got `None`.rcat)dist_reduce_fxr)super__init__ isinstancebool ValueErrortyper from_strstrrr rr" add_state)selfrrrr"r# __class__s t/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchmetrics/regression/kendall.pyr+zKendallRankCorrCoef.__init__~s "6"&$']^bci^j]kklmn n k)bc c%..s7|< JP+44S5EFVZ& w59 xE:czt|||j|j|j\|_|_y)zJUpdate variables required to compute Kendall rank correlation coefficient.)r"N)r rrr")r3rrs r5updatezKendallRankCorrCoef.updates2":   JJ KK(( #  DKr6ct|j}t|j}t|||j|j \}}|||fS|S)zgCompute Kendall rank correlation coefficient, and optionally p-value of corresponding statistical test.)rrrr rr)r3rrtaup_values r5computezKendallRankCorrCoef.computesYTZZ(dkk*0   LL     W  <  r6valaxc&|j||S)aPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import randn >>> # Example plotting a single value >>> from torchmetrics.regression import KendallRankCorrCoef >>> metric = KendallRankCorrCoef() >>> metric.update(randn(10,), randn(10,)) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import randn >>> # Example plotting multiple values >>> from torchmetrics.regression import KendallRankCorrCoef >>> metric = KendallRankCorrCoef() >>> values = [] >>> for _ in range(10): ... values.append(metric(randn(10,), randn(10,))) >>> fig, ax = metric.plot(values) )_plot)r3r=r>s r5plotzKendallRankCorrCoef.plotsPzz#r""r6)rFr)NN)__name__ __module__ __qualname____doc__is_differentiablehigher_is_betterfull_state_updaterfloat__annotations__rrrr r-rintrr+r8rtupler<rrrrA __classcell__)r4s@r5rr$sN`!e!!e! < L+.IT ;';;g&DEF ;  ;  ; ;* F F t  vuVV^'<<=  _c(#E&(6*:":;<(#IQRZI[(# (#r6rN)collections.abcrtypingrrrrtorchrtyping_extensionsr *torchmetrics.functional.regression.kendallr r r r torchmetrics.metricrtorchmetrics.utilities.datartorchmetrics.utilities.importsrtorchmetrics.utilities.plotrr__doctest_skip__rr)r6r5rYsH%--% '4@@23p#&p#r6