:j^ddlmZddlmZmZmZddlZddlmZmZddl m Z ddl m Z ddl mZddlmZdd lmZmZesd gZGd d eZy) )Sequence)AnyOptionalUnionN)Tensortensor)_r2_score_update)_relative_squared_error_compute)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEzRelativeSquaredError.plotc eZdZUdZdZdZdZeed<eed<eed<eed< dde d e d e d d ffd Z deded d fdZ d efdZ ddeeeeefdeed efdZxZS)RelativeSquaredErroraComputes the relative squared error (RSE). .. math:: \text{RSE} = \frac{\sum_i^N(y_i - \hat{y_i})^2}{\sum_i^N(y_i - \overline{y})^2} Where :math:`y` is a tensor of target values with mean :math:`\overline{y}`, and :math:`\hat{y}` is a tensor of predictions. If num_outputs > 1, the returned value is averaged over all the outputs. 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: - ``rse`` (:class:`~torch.Tensor`): A tensor with the RSE score(s) Args: num_outputs: Number of outputs in multioutput setting squared: If True returns RSE value, if False returns RRSE value. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example: >>> from torchmetrics.regression import RelativeSquaredError >>> target = torch.tensor([3, -0.5, 2, 7]) >>> preds = torch.tensor([2.5, 0.0, 2, 8]) >>> relative_squared_error = RelativeSquaredError() >>> relative_squared_error(preds, target) tensor(0.0514) TFsum_squared_error sum_errorresidualtotal num_outputssquaredkwargsreturnNc t|di|||_|jdt j |jd|jdt j |jd|jdt j |jd|jdt dd||_y) Nrsum)defaultdist_reduce_fxrrrr)super__init__r add_statetorchzerosrr)selfrrr __class__s p/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchmetrics/regression/rse.pyrzRelativeSquaredError.__init__Js "6"& *EKK@P@P4Qbgh {EKK8H8H,IZ_` z5;;t7G7G+HY^_ wq %H predstargetct||\}}}}|xj|z c_|xj|z c_|xj|z c_|xj|z c_y)z*Update state with predictions and targets.N)r rrrr)r#r'r(rrrrs r%updatezRelativeSquaredError.updateZsU8HPV8W59h "33 )# !  e r&ct|j|j|j|j|j S)z+Computes relative squared error over state.)r)r rrrrr)r#s r%computezRelativeSquaredError.computecs3.  " "DNNDMM4::W[WcWc  r&valaxc&|j||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 RelativeSquaredError >>> metric = RelativeSquaredError() >>> 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 RelativeSquaredError >>> metric = RelativeSquaredError() >>> values = [] >>> for _ in range(10): ... values.append(metric(randn(10,), randn(10,))) >>> fig, ax = metric.plot(values) )_plot)r#r-r.s r%plotzRelativeSquaredError.plotisPzz#r""r&)T)NN)__name__ __module__ __qualname____doc__is_differentiablehigher_is_betterfull_state_updater__annotations__intboolrrr*r,rrrr rr1 __classcell__)r$s@r%rrs!F M    FFt  _c(#E&(6*:":;<(#IQRZI[(# (#r&r)collections.abcrtypingrrrr!rr%torchmetrics.functional.regression.r2r &torchmetrics.functional.regression.rser torchmetrics.metricr torchmetrics.utilities.importsr torchmetrics.utilities.plotr r__doctest_skip__rrr&r%rFs?%'' BR&@@34s#6s#r&