a  d@sddlmZmZmZmZddlZddlmZddlmZm Z ddl m Z ddl m Z ddlmZmZe sndgZGd d d e ZdS) )AnyOptionalSequenceUnionN)Tensor)0_weighted_mean_absolute_percentage_error_compute/_weighted_mean_absolute_percentage_error_update)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPE(WeightedMeanAbsolutePercentageError.plotcseZdZUdZdZeed<dZeed<dZeed<dZ e ed<e ed <e ed <e d d fd d Z e e d dddZe dddZdeee ee feeedddZZS)#WeightedMeanAbsolutePercentageErroraCompute 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: >>> import torch >>> _ = torch.manual_seed(42) >>> preds = torch.randn(20,) >>> target = torch.randn(20,) >>> wmape = WeightedMeanAbsolutePercentageError() >>> wmape(preds, target) tensor(1.3967) Tis_differentiableFhigher_is_betterfull_state_updateplot_lower_bound sum_abs_error sum_scaleN)kwargsreturnc sBtjfi||jdtddd|jdtddddS)Nrrsum)defaultZdist_reduce_fxr)super__init__Z add_statetorchZtensor)selfr __class__f/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/regression/wmape.pyrGsz,WeightedMeanAbsolutePercentageError.__init__)predstargetrcCs.t||\}}|j|7_|j|7_dS)z*Update state with predictions and targets.N)rrr)rr"r#rrr r r!updateLsz*WeightedMeanAbsolutePercentageError.update)rcCst|j|jS)z;Compute weighted mean absolute percentage error over state.)rrr)rr r r!computeSsz+WeightedMeanAbsolutePercentageError.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 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) )Z_plot)rr&r'r r r!plotWs(r )NN)__name__ __module__ __qualname____doc__rbool__annotations__rrrfloatrrrr$r%rrrr r r( __classcell__r r rr!rs     r)typingrrrrrrZ(torchmetrics.functional.regression.wmaperrZtorchmetrics.metricr Ztorchmetrics.utilities.importsr Ztorchmetrics.utilities.plotr r Z__doctest_skip__rr r r r!s