a  d@sddlmZmZmZmZddlZddlmZmZddlm Z m Z ddl m Z ddl mZddlmZmZesrdgZGd d d e ZdS) )AnyOptionalSequenceUnionN)Tensortensor)_r2_score_compute_r2_score_update)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPE R2Score.plotcseZdZUdZdZeed<dZeed<dZeed<dZ e ed<d Z e ed <e ed <e ed <e ed <e ed<de e eeddfdd Ze e ddddZe dddZd eee ee feeedddZZS)!R2Scorea Compute r2 score also known as `R2 Score_Coefficient Determination`_. .. math:: R^2 = 1 - \frac{SS_{res}}{SS_{tot}} where :math:`SS_{res}=\sum_i (y_i - f(x_i))^2` is the sum of residual squares, and :math:`SS_{tot}=\sum_i (y_i - \bar{y})^2` is total sum of squares. Can also calculate adjusted r2 score given by .. math:: R^2_{adj} = 1 - \frac{(1-R^2)(n-1)}{n-k-1} where the parameter :math:`k` (the number of independent regressors) should be provided as the `adjusted` argument. The score is only proper defined when :math:`SS_{tot}\neq 0`, which can happen for near constant targets. In this case a score of 0 is returned. By definition the score is bounded between 0 and 1, where 1 corresponds to the predictions exactly matching the targets. 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,)`` or ``(N, M)`` (multioutput) - ``target`` (:class:`~torch.Tensor`): Ground truth values in float tensor with shape ``(N,)`` or ``(N, M)`` (multioutput) As output of ``forward`` and ``compute`` the metric returns the following output: - ``r2score`` (:class:`~torch.Tensor`): A tensor with the r2 score(s) In the case of multioutput, as default the variances will be uniformly averaged over the additional dimensions. Please see argument ``multioutput`` for changing this behavior. Args: num_outputs: Number of outputs in multioutput setting adjusted: number of independent regressors for calculating adjusted r2 score. multioutput: Defines aggregation in the case of multiple output scores. Can be one of the following strings: * ``'raw_values'`` returns full set of scores * ``'uniform_average'`` scores are uniformly averaged * ``'variance_weighted'`` scores are weighted by their individual variances kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: ValueError: If ``adjusted`` parameter is not an integer larger or equal to 0. ValueError: If ``multioutput`` is not one of ``"raw_values"``, ``"uniform_average"`` or ``"variance_weighted"``. Example: >>> from torchmetrics.regression import R2Score >>> target = torch.tensor([3, -0.5, 2, 7]) >>> preds = torch.tensor([2.5, 0.0, 2, 8]) >>> r2score = R2Score() >>> r2score(preds, target) tensor(0.9486) >>> target = torch.tensor([[0.5, 1], [-1, 1], [7, -6]]) >>> preds = torch.tensor([[0, 2], [-1, 2], [8, -5]]) >>> r2score = R2Score(num_outputs=2, multioutput='raw_values') >>> r2score(preds, target) tensor([0.9654, 0.9082]) Tis_differentiablehigher_is_betterFfull_state_updategplot_lower_boundg?plot_upper_boundsum_squared_error sum_errorresidualtotalruniform_averageN) num_outputsadjusted multioutputkwargsreturnc stjfi|||_|dks*t|ts2td||_d}||vrRtd|||_|jdt |jdd|jdt |jdd|jd t |jdd|jd t ddddS) Nrz?`adjusted` parameter should be an integer larger or equal to 0.)Z raw_valuesrZvariance_weightedzFInvalid input to argument `multioutput`. Choose one of the following: rsum)defaultZdist_reduce_fxrrr) super__init__r isinstanceint ValueErrorrrZ add_statetorchzerosr)selfrrrrZallowed_multioutput __class__c/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/regression/r2.pyr#dszR2Score.__init__)predstargetrcCsNt||\}}}}|j|7_|j|7_|j|7_|j|7_dS)z*Update state with predictions and targets.N)r rrrr)r)r.r/rrrrr,r,r-updates zR2Score.update)rcCst|j|j|j|j|j|jS)z(Compute r2 score over the metric states.)rrrrrrr)r)r,r,r-computeszR2Score.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 R2Score >>> metric = R2Score() >>> 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 R2Score >>> metric = R2Score() >>> values = [] >>> for _ in range(10): ... values.append(metric(randn(10,), randn(10,))) >>> fig, ax = metric.plot(values) )Z_plot)r)r2r3r,r,r-plots(r)rrr)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrr%strrr#r0r1rrrr r r4 __classcell__r,r,r*r-rs6 <      r)typingrrrrr'rrZ%torchmetrics.functional.regression.r2rr Ztorchmetrics.metricr Ztorchmetrics.utilities.importsr Ztorchmetrics.utilities.plotr r Z__doctest_skip__rr,r,r,r-s