a  dw@sddlmZmZmZmZmZddlmZddlm Z m Z ddl m Z ddl mZddlmZddlmZddlmZmZesd gZGd d d e Zd S) )AnyListOptionalSequenceUnion)Tensor)_spearman_corrcoef_compute_spearman_corrcoef_update)Metric)rank_zero_warn) dim_zero_cat)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPESpearmanCorrCoef.plotcseZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <e e ed <e e ed <deeddfdd Ze e ddddZe dddZdeee ee feeedddZZS)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_boundpredstargetN) num_outputskwargsreturnc s^tjfi|tdt|ts4|dkr4td||_|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)defaultZdist_reduce_fxr)super__init__r isinstanceint ValueErrorrZ add_state)selfrr __class__i/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/regression/spearman.pyr OszSpearmanCorrCoef.__init__)rrrcCs0t|||jd\}}|j||j|dS)z*Update state with predictions and targets.)rN)r rrappendrr$rrr'r'r(update`s zSpearmanCorrCoef.update)rcCst|j}t|j}t||S)z+Compute Spearman's correlation coefficient.)r rrrr*r'r'r(computefs  zSpearmanCorrCoef.compute)valaxrcCs |||S)aPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import randn >>> # Example plotting a single value >>> from torchmetrics.regression import SpearmanCorrCoef >>> metric = SpearmanCorrCoef() >>> metric.update(randn(10,), randn(10,)) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import randn >>> # Example plotting multiple values >>> from torchmetrics.regression import SpearmanCorrCoef >>> metric = SpearmanCorrCoef() >>> values = [] >>> for _ in range(10): ... values.append(metric(randn(10,), randn(10,))) >>> fig, ax = metric.plot(values) )Z_plot)r$r-r.r'r'r(plotls(r)r)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrrr"rr r+r,rrrrrr/ __classcell__r'r'r%r(rs* (       rN)typingrrrrrZtorchrZ+torchmetrics.functional.regression.spearmanrr Ztorchmetrics.metricr Ztorchmetrics.utilitiesr Ztorchmetrics.utilities.datar Ztorchmetrics.utilities.importsr Ztorchmetrics.utilities.plotrrZ__doctest_skip__rr'r'r'r(s