o yiD@sxddlmZmZmZmZmZddlmZddlm Z ddl m Z m Z m Z mZddlmZddlmZGdddeZd S) )AnyListOptionalTupleUnion)Tensor)Literal)_kendall_corrcoef_compute_kendall_corrcoef_update_MetricVariant_TestAlternative)Metric) dim_zero_catc seZdZUdZdZdZdZeee d<eee d<    dd e d d e d e e dde def fdd ZdededdfddZdeeeeefffddZZS)KendallRankCorrCoefa Computes `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): >>> import torch >>> from torchmetrics.regression import KendallRankCorrCoef >>> preds = torch.tensor([2.5, 0.0, 2, 8]) >>> target = torch.tensor([3, -0.5, 2, 1]) >>> kendall = KendallRankCorrCoef() >>> kendall(preds, target) tensor(0.3333) Example (multi output regression): >>> import torch >>> from torchmetrics.regression import KendallRankCorrCoef >>> preds = torch.tensor([[2.5, 0.0], [2, 8]]) >>> target = torch.tensor([[3, -0.5], [2, 1]]) >>> kendall = KendallRankCorrCoef(num_outputs=2) >>> kendall(preds, target) tensor([1., 1.]) Example (single output regression with t-test): >>> import torch >>> from torchmetrics.regression import KendallRankCorrCoef >>> preds = torch.tensor([2.5, 0.0, 2, 8]) >>> target = torch.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): >>> import torch >>> from torchmetrics.regression import KendallRankCorrCoef >>> preds = torch.tensor([[2.5, 0.0], [2, 8]]) >>> target = torch.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])) FNTpredstargetb two-sidedvariant)arct_test alternative)rlessgreater num_outputskwargsc stjdi|t|tstdt|d|r"|dur"tdt||_|r1|r1t |nd|_ ||_ |j dgdd|j dgdddS) 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_strrr rr add_state)selfrrrrr __class__r!S/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/regression/kendall.pyr#ws   zKendallRankCorrCoef.__init__returncCs$t|||j|j|jd\|_|_dS)zJUpdate variables required to compute Kendall rank correlation coefficient.)rN)r rrr)r*rrr!r!r-updateszKendallRankCorrCoef.updatecCs>t|j}t|j}t|||j|j\}}|dur||fS|S)zoCompute Kendall rank correlation coefficient, and optionally p-value of corresponding statistical test.N)rrrr rr)r*rrtaup_valuer!r!r-computes  zKendallRankCorrCoef.compute)rFrr)__name__ __module__ __qualname____doc__is_differentiablehigher_is_betterfull_state_updaterr__annotations__rr%rintrr#r/rrr2 __classcell__r!r!r+r-rs0 R   & rN)typingrrrrrtorchrtyping_extensionsr*torchmetrics.functional.regression.kendallr r r r torchmetrics.metricr torchmetrics.utilities.datarrr!r!r!r-s