o .wi)&@sddlmZddlmZmZmZmZddlZddlmZddl m Z m Z ddl m Z ddlmZddlmZmZes=d gZ dd ejd ejd ejdejdejdejdejdejdedeejejejejejejejejffddZGddde ZdS))Sequence)AnyListOptionalUnionN)Tensor)_pearson_corrcoef_compute_pearson_corrcoef_update)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEPearsonCorrCoef.plot绽|=means_xmeans_y maxs_abs_x maxs_abs_yvars_xvars_ycorrs_xynbsepsreturnc % Cst|dkr |d|d|d|d|d|d|d|dfS|d} |d} |d} |d} |d} |d}|d}|d}tdt|D]}||}||}||}||}||}||}||}||}tt|||||}|| |||}|| |||}|||}|| }|| }| |||d} ||||d}!|||||}"t| |}#t| |}$|} |} |#} |$} | } |!}|"}|}qG|||#|$| |!|"|fS)aAggregate the statistics from multiple devices. Formula taken from here: `Parallel algorithm for calculating variance `_ We use `eps` to avoid division by zero when `n1` and `n2` are both zero. Generally, the value of `eps` should not matter, as if `n1` and `n2` are both zero, all the states will also be zero. r)lenrangetorchwhere logical_ormaximum)%rrrrrrrrrmx1my1max1may1vx1vy1cxy1n1imx2my2max2may2vx2vy2cxy2n2nbmean_xmean_yn12_bdelta_xdelta_yvar_xvar_ycorr_xy max_abs_dev_x max_abs_dev_yr>\/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/regression/pearson.py_final_aggregationsN 4   r@cseZdZUdZdZeed<dZeeed<dZ eed<dZ e ed<d Z e ed <e eed <e eed <eed <eed<eed<eed<eed<eed<eed<eed< d#dededdffdd Zd ed eddfddZdefddZ d$deeeeefd eedefd!d"ZZS)%PearsonCorrCoefaCompute `Pearson Correlation Coefficient`_. .. math:: P_{corr}(x,y) = \frac{cov(x,y)}{\sigma_x \sigma_y} Where :math:`y` is a tensor of target values, and :math:`x` is a tensor of predictions. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): either single output float tensor with shape ``(N,)`` or multioutput float tensor of shape ``(N,d)`` - ``target`` (:class:`~torch.Tensor`): either single output tensor with shape ``(N,)`` or multioutput tensor of shape ``(N,d)`` As output of ``forward`` and ``compute`` the metric returns the following output: - ``pearson`` (:class:`~torch.Tensor`): A tensor with the Pearson Correlation Coefficient 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.regression import PearsonCorrCoef >>> target = torch.tensor([3, -0.5, 2, 7]) >>> preds = torch.tensor([2.5, 0.0, 2, 8]) >>> pearson = PearsonCorrCoef() >>> pearson(preds, target) tensor(0.9849) Example (multi output regression): >>> from torchmetrics.regression import PearsonCorrCoef >>> target = torch.tensor([[3, -0.5], [2, 7]]) >>> preds = torch.tensor([[2.5, 0.0], [2, 8]]) >>> pearson = PearsonCorrCoef(num_outputs=2) >>> pearson(preds, target) tensor([1., 1.]) Tis_differentiableNhigher_is_betterfull_state_updategplot_lower_boundg?plot_upper_boundpredstargetr4r5r<r=r9r:r;n_totalr num_outputskwargsrc stjd i|t|ts|dkrtd||_|jdt|jdd|jdt|jdd|jdt|jdd|jdt|jdd|jdt|jdd|jd t|jdd|jd t|jdd|jd t|jdddS) NrzQExpected argument `num_outputs` to be an int larger than 0, but got {num_outputs}r4)defaultdist_reduce_fxr5r<r=r9r:r;rIr>) super__init__ isinstanceint ValueErrorrJ add_staterzeros)selfrJrK __class__r>r?rOszPearsonCorrCoef.__init__c CsTt|||j|j|j|j|j|j|j|j|j d \|_|_|_|_|_|_|_|_dS)z*Update state with predictions and targets.) rGrHr4r5r<r=r9r:r; num_priorrJN) r r4r5r<r=r9r:r;rIrJ)rUrGrHr>r>r?updates, zPearsonCorrCoef.updatec Cs|jdkr |jdks|jdkr4|jjdkr4t|j|j|j|j|j|j |j |j d\}}}}}}}}n|j}|j}|j}|j }|j }|j }t ||||||S)z3Compute pearson correlation coefficient over state.r)rrrrrrrr) rJr4numelndimr@r5r<r=r9r:r;rIr)rU_r<r=r9r:r;rIr>r>r?computes$. zPearsonCorrCoef.computevalaxcCs |||S)a Plot 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 PearsonCorrCoef >>> metric = PearsonCorrCoef() >>> 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 PearsonCorrCoef >>> metric = PearsonCorrCoef() >>> values = [] >>> for _ in range(10): ... values.append(metric(randn(10,), randn(10,))) >>> fig, ax = metric.plot(values) )_plot)rUr^r_r>r>r?plots (r)r)NN)__name__ __module__ __qualname____doc__rBbool__annotations__rCrrDrEfloatrFrrrQrrOrYr]rrr r ra __classcell__r>r>rVr?rAdsF  (     rA)r)collections.abcrtypingrrrrrr*torchmetrics.functional.regression.pearsonrr torchmetrics.metricr torchmetrics.utilities.importsr torchmetrics.utilities.plotr r __doctest_skip__rhtupler@rAr>r>r>r?sF       " G