iddlmZddlmZmZmZmZddlmZddl m Z m Z ddl m Z ddlmZddlmZddlmZdd lmZmZesd gZGd d e Zy ))Sequence)AnyListOptionalUnion)Tensor)_spearman_corrcoef_compute_spearman_corrcoef_update)Metric)rank_zero_warn) dim_zero_cat)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEzSpearmanCorrCoef.plotceZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <e e ed <e e ed < dd ededdffd Zd e d e ddfdZde fdZ ddeee ee fdeedefdZxZS)SpearmanCorrCoefaCompute `spearmans rank correlation coefficient`_. .. math: r_s = = \frac{cov(rg_x, rg_y)}{\sigma_{rg_x} * \sigma_{rg_y}} where :math:`rg_x` and :math:`rg_y` are the rank associated to the variables :math:`x` and :math:`y`. Spearmans correlations coefficient corresponds to the standard pearsons correlation coefficient calculated on the rank variables. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): Predictions from model in float tensor with shape ``(N,d)`` - ``target`` (:class:`~torch.Tensor`): Ground truth values in float tensor with shape ``(N,d)`` As output of ``forward`` and ``compute`` the metric returns the following output: - ``spearman`` (:class:`~torch.Tensor`): A tensor with the spearman correlation(s) Args: num_outputs: Number of outputs in multioutput setting kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example (single output regression): >>> from torch import tensor >>> from torchmetrics.regression import SpearmanCorrCoef >>> target = tensor([3, -0.5, 2, 7]) >>> preds = tensor([2.5, 0.0, 2, 8]) >>> spearman = SpearmanCorrCoef() >>> spearman(preds, target) tensor(1.0000) Example (multi output regression): >>> from torchmetrics.regression import SpearmanCorrCoef >>> target = tensor([[3, -0.5], [2, 7]]) >>> preds = tensor([[2.5, 0.0], [2, 8]]) >>> spearman = SpearmanCorrCoef(num_outputs=2) >>> spearman(preds, target) tensor([1.0000, 1.0000]) Fis_differentiableThigher_is_betterfull_state_updategplot_lower_boundg?plot_upper_boundpredstarget num_outputskwargsreturnNc t|di|tdt|ts|dkrt d|||_|jdgd|jdgdy) NzMetric `SpearmanCorrcoef` will save all targets and predictions in the buffer. For large datasets, this may lead to large memory footprint.zDExpected argument `num_outputs` to be an int larger than 0, but got rcat)defaultdist_reduce_fxr)super__init__r isinstanceint ValueErrorr add_state)selfrr __class__s u/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/torchmetrics/regression/spearman.pyr$zSpearmanCorrCoef.__init__Qsv "6" L +s+ acdocpqr r& w5A xEBc t|||j\}}|jj|j |j |j j|j |j y)z*Update state with predictions and targets.)rN)r rrappendtodtyperr)rrs r+updatezSpearmanCorrCoef.updatebsX1%TM]M]^ v %((4::./ 699TZZ01r,cnt|j}t|j}t||S)z+Compute Spearman's correlation coefficient.)r rrr r1s r+computezSpearmanCorrCoef.computehs+TZZ(dkk*)%88r,valaxc&|j||S)aPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import randn >>> # Example plotting a single value >>> from torchmetrics.regression import SpearmanCorrCoef >>> metric = SpearmanCorrCoef() >>> metric.update(randn(10,), randn(10,)) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import randn >>> # Example plotting multiple values >>> from torchmetrics.regression import SpearmanCorrCoef >>> metric = SpearmanCorrCoef() >>> values = [] >>> for _ in range(10): ... values.append(metric(randn(10,), randn(10,))) >>> fig, ax = metric.plot(values) )_plot)r)r5r6s r+plotzSpearmanCorrCoef.plotnsPzz#r""r,)r)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrrr&rr$r2r4rrrrrr9 __classcell__)r*s@r+rrs'R$t#!d!#t#"e"!e! < LCCC  C"2F2F2t2 99_c(#E&(6*:":;<(#IQRZI[(# (#r,rN)collections.abcrtypingrrrrtorchr+torchmetrics.functional.regression.spearmanr r torchmetrics.metricr torchmetrics.utilitiesr torchmetrics.utilities.datar torchmetrics.utilities.importsrtorchmetrics.utilities.plotrr__doctest_skip__rr"r,r+rLs?%--m&14@@/0x#vx#r,