:jddlmZddlmZmZmZddlZddlmZddlm Z m Z ddl m Z ddl mZddlmZmZesd gZGd d e Zy) )Sequence)AnyOptionalUnionN)Tensor)0_weighted_mean_absolute_percentage_error_compute/_weighted_mean_absolute_percentage_error_update)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEz(WeightedMeanAbsolutePercentageError.plotceZdZUdZdZeed<dZeed<dZeed<dZ e ed<e ed <e ed <d e d d ffd Z de de d d fdZd e fdZ ddeee ee fdeed efdZxZS)#WeightedMeanAbsolutePercentageErrorawCompute weighted mean absolute percentage error (`WMAPE`_). The output of WMAPE metric is a non-negative floating point, where the optimal value is 0. It is computes as: .. math:: \text{WMAPE} = \frac{\sum_{t=1}^n | y_t - \hat{y}_t | }{\sum_{t=1}^n |y_t| } Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions. As input to ``forward`` and ``update`` the metric accepts the following input: - ``preds`` (:class:`~torch.Tensor`): Predictions from model - ``target`` (:class:`~torch.Tensor`): Ground truth float tensor with shape ``(N,d)`` As output of ``forward`` and ``compute`` the metric returns the following output: - ``wmape`` (:class:`~torch.Tensor`): A tensor with non-negative floating point wmape value between 0 and 1 Args: kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Example: >>> from torch import randn >>> preds = randn(20,) >>> target = randn(20,) >>> wmape = WeightedMeanAbsolutePercentageError() >>> wmape(preds, target) tensor(1.3967) Tis_differentiableFhigher_is_betterfull_state_updateplot_lower_bound sum_abs_error sum_scalekwargsreturnNc t|di||jdtjdd|jdtjddy)Nrrsum)defaultdist_reduce_fxr)super__init__ add_statetorchtensor)selfr __class__s r/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchmetrics/regression/wmape.pyrz,WeightedMeanAbsolutePercentageError.__init__HsJ "6"  S0ARWX {ELL,=eTpredstargetcvt||\}}|xj|z c_|xj|z c_y)z*Update state with predictions and targets.N)r rr)r#r'r(rrs r%updatez*WeightedMeanAbsolutePercentageError.updateMs4#RSXZ`#a y m+ )#r&cBt|j|jS)z;Compute weighted mean absolute percentage error over state.)rrr)r#s r%computez+WeightedMeanAbsolutePercentageError.computeTs?@R@RTXTbTbccr&valaxc&|j||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 WeightedMeanAbsolutePercentageError >>> metric = WeightedMeanAbsolutePercentageError() >>> 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 WeightedMeanAbsolutePercentageError >>> metric = WeightedMeanAbsolutePercentageError() >>> values = [] >>> for _ in range(10): ... values.append(metric(randn(10,), randn(10,))) >>> fig, ax = metric.plot(values) )_plot)r#r-r.s r%plotz(WeightedMeanAbsolutePercentageError.plotXsPzz#r""r&)NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrrr*r,rrrr r r1 __classcell__)r$s@r%rr s>#t""d"#t#!e!UUU $F$F$t$dd _c(#E&(6*:":;<(#IQRZI[(# (#r&r)collections.abcrtypingrrrr!r(torchmetrics.functional.regression.wmaperr torchmetrics.metricr torchmetrics.utilities.importsr torchmetrics.utilities.plotr r __doctest_skip__rrr&r%rAs@%'' '@@BC`#&`#r&