o yi @sPddlmZddlZddlmZddlmZmZddlmZGdddeZ dS))AnyN)Tensor)0_weighted_mean_absolute_percentage_error_compute/_weighted_mean_absolute_percentage_error_update)MetriccseZdZUdZdZeed<dZeed<dZeed<e ed<e ed<d e d d ffd d Z de de d d fddZ d e fddZ ZS)#WeightedMeanAbsolutePercentageErroraComputes 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_update sum_abs_error sum_scalekwargsreturnNc sBtjdi||jdtddd|jdtddddS)Nr gsum)defaultdist_reduce_fxr )super__init__ add_statetorchtensor)selfr  __class__rQ/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/regression/wmape.pyr>sz,WeightedMeanAbsolutePercentageError.__init__predstargetcCs.t||\}}|j|7_|j|7_dS)z*Update state with predictions and targets.N)rr r )rrrr r rrrupdateCsz*WeightedMeanAbsolutePercentageError.updatecCst|j|jS)zs