a  d @sddlmZmZmZmZmZmZddlZddlmZddl m Z m Z ddl m Z ddlmZddlmZmZesvdgZeeeeeeeeeeeeefd d d ZGd d d e ZdS))AnyListOptionalSequenceTupleUnionN)Tensor)_pearson_corrcoef_compute_pearson_corrcoef_update)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEPearsonCorrCoef.plot)means_xmeans_yvars_xvars_ycorrs_xynbsreturncCs>t|dkr4|d|d|d|d|d|dfS|d|d|d|d|d|df\}}}} } } tdt|D]} || || || || || || f\} }}}}}| |}| ||| |}| ||||}| d|| |}|||||||d7}|d||| }||| ||||d7}||}| d|| |}| ||||||d7} |d|||}|||||||d7}| |}| ||||||||7} ||| ||||||7}| |}||||||f\}}}} } } qv||||||fS)zAggregate the statistics from multiple devices. Formula taken from here: `Aggregate the statistics from multiple devices`_ r)lenrange)rrrrrrZmx1Zmy1Zvx1Zvy1Zcxy1Zn1iZmx2Zmy2Zvx2Zvy2Zcxy2Zn2nbmean_xmean_yZ element_x1Z element_x2var_xZ element_y1Z element_y2var_ycorr_xyr"h/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/regression/pearson.py_final_aggregations. (44    $$r$cseZdZUdZdZeed<dZeeed<dZ eed<dZ e ed<d Z e ed <e eed <e eed <eed <eed<eed<eed<eed<eed<d eeddfdd ZeeddddZedddZd!eeeeefeeedddZZS)"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_boundpredstargetrrrr r!n_totalr) num_outputskwargsrc stjfi|t|ts,|dkr,td||_|jdt|jdd|jdt|jdd|jdt|jdd|jdt|jdd|jdt|jdd|jd t|jdddS) NrzQExpected argument `num_outputs` to be an int larger than 0, but got {num_outputs}r)defaultZdist_reduce_fxrrr r!r-) super__init__ isinstanceint ValueErrorr.Z add_statetorchzeros)selfr.r/ __class__r"r#r2szPearsonCorrCoef.__init__)r+r,rc CsBt|||j|j|j|j|j|j|j \|_|_|_|_|_|_dS)z*Update state with predictions and targets.N)r rrrr r!r-r.)r8r+r,r"r"r#updateszPearsonCorrCoef.update)rcCs|jdkr|jdks.|jdkrZ|jjdkrZt|j|j|j|j|j|j \}}}}}}n|j}|j}|j}|j }t ||||S)z3Compute pearson correlation coefficient over state.r) r.rZnumelndimr$rrr r!r-r )r8_rr r!r-r"r"r#computes.zPearsonCorrCoef.compute)valaxrcCs |||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) )Z_plot)r8r?r@r"r"r#plots(r)r)NN)__name__ __module__ __qualname____doc__r&bool__annotations__r'rr(r)floatr*rrr4rr2r;r>rrr rrA __classcell__r"r"r9r#r%Is6 '      r%)typingrrrrrrr6rZ*torchmetrics.functional.regression.pearsonr r Ztorchmetrics.metricr Ztorchmetrics.utilities.importsr Ztorchmetrics.utilities.plotr rZ__doctest_skip__r$r%r"r"r"r#s"     -