o .wi=@sddlmZddlmZmZmZddlmZmZddl m Z m Z ddl m Z ddlmZddlmZmZes9dgZGd d d e Zd S) )Sequence)AnyOptionalUnion)Tensortensor)_r2_score_compute_r2_score_update)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPE R2Score.plotc seZdZUdZdZeed<dZeed<dZeed<dZ e ed<e ed <e ed <e ed <e ed < d de de deddffdd Zde de ddfddZde fddZ d!deee ee fdeedefddZZS)"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 :math:`-inf` and 1.0, with 1.0 indicating perfect prediction, 0 indicating constant prediction and negative values indicating worse than constant prediction. 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. .. warning:: Argument ``num_outputs`` in ``R2Score`` has been deprecated because it is no longer necessary and will be removed in v1.6.0 of TorchMetrics. The number of outputs is now automatically inferred from the shape of the input tensors. 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 (single output): >>> from torch import tensor >>> from torchmetrics.regression import R2Score >>> target = tensor([3, -0.5, 2, 7]) >>> preds = tensor([2.5, 0.0, 2, 8]) >>> r2score = R2Score() >>> r2score(preds, target) tensor(0.9486) Example (multioutput): >>> from torch import tensor >>> from torchmetrics.regression import R2Score >>> target = tensor([[0.5, 1], [-1, 1], [7, -6]]) >>> preds = tensor([[0, 2], [-1, 2], [8, -5]]) >>> r2score = R2Score(multioutput='raw_values') >>> r2score(preds, target) tensor([0.9654, 0.9082]) Tis_differentiablehigher_is_betterFfull_state_updateg?plot_upper_boundsum_squared_error sum_errorresidualtotalruniform_averageadjusted multioutputkwargsreturnNc stjd i||dkst|tstd||_d}||vr&td|||_|jdtddd|jd tddd|jd tddd|jd tddddS) Nrz?`adjusted` parameter should be an integer larger or equal to 0.) raw_valuesrvariance_weightedzFInvalid input to argument `multioutput`. Choose one of the following: rgsum)defaultdist_reduce_fxrrr) super__init__ isinstanceint ValueErrorrr add_stater)selfrrrallowed_multioutput __class__r"W/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/regression/r2.pyr$mszR2Score.__init__predstargetcCsFt||\}}}}|j||_|j||_|j||_|j||_dS)z*Update state with predictions and targets.N)r rrrr)r)r.r/rrrrr"r"r-updates    zR2Score.updatecCst|j|j|j|j|j|jS)z(Compute r2 score over the metric states.)rrrrrrr)r)r"r"r-computeszR2Score.computevalaxcCs |||S)aPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> from torch import randn >>> # Example plotting a single value >>> from torchmetrics.regression import R2Score >>> metric = R2Score() >>> metric.update(randn(10,), randn(10,)) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> from torch import randn >>> # Example plotting multiple values >>> from torchmetrics.regression import R2Score >>> metric = R2Score() >>> values = [] >>> for _ in range(10): ... values.append(metric(randn(10,), randn(10,))) >>> fig, ax = metric.plot(values) )_plot)r)r2r3r"r"r-plots (r)rr)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrr&strrr$r0r1rrrr r r5 __classcell__r"r"r+r-rs>  F    rN)collections.abcrtypingrrrtorchrr%torchmetrics.functional.regression.r2rr torchmetrics.metricr torchmetrics.utilities.importsr torchmetrics.utilities.plotr r __doctest_skip__rr"r"r"r-s