o yik@slddlmZmZddlZddlmZddlmZmZddlm Z ddl m Z ddl m Z Gdd d e ZdS) )AnyListN)Tensor)_spearman_corrcoef_compute_spearman_corrcoef_update)Metric)rank_zero_warn) dim_zero_catcseZdZUdZdZeed<dZeed<dZeed<e e ed<e e ed< dd e d e d d ffdd Z de de d d fddZd e fddZZS)SpearmanCorrCoefa`Computes `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 torchmetrics import SpearmanCorrCoef >>> target = torch.tensor([3, -0.5, 2, 7]) >>> preds = torch.tensor([2.5, 0.0, 2, 8]) >>> spearman = SpearmanCorrCoef() >>> spearman(preds, target) tensor(1.0000) Example (multi output regression): >>> from torchmetrics import SpearmanCorrCoef >>> target = torch.tensor([[3, -0.5], [2, 7]]) >>> preds = torch.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_updatepredstarget num_outputskwargsreturnNc s^tjdi|tdt|ts|dkrtd||_|jdgdd|jdgdddS) NzMetric `SpearmanCorrcoef` will save all targets and predictions in the buffer. For large datasets, this may lead to large memory footprint.rzQExpected argument `num_outputs` to be an int larger than 0, but got {num_outputs}rcat)defaultdist_reduce_fxr)super__init__r isinstanceint ValueErrorr add_state)selfrr __class__rT/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/regression/spearman.pyrFszSpearmanCorrCoef.__init__cCs0t|||jd\}}|j||j|dS)z*Update state with predictions and targets.)rN)rrrappendrrrrrrr!updateWs zSpearmanCorrCoef.updatecCst|j}t|j}t||S)z,Computes Spearman's correlation coefficient.)r rrrr#rrr!compute]s   zSpearmanCorrCoef.compute)r)__name__ __module__ __qualname____doc__r bool__annotations__r r rrrrrr$r% __classcell__rrrr!r s"  &    r )typingrrtorchr+torchmetrics.functional.regression.spearmanrrtorchmetrics.metricrtorchmetrics.utilitiesrtorchmetrics.utilities.datar r rrrr!s