Weighted MAPE¶
Module Interface¶
- class torchmetrics.WeightedMeanAbsolutePercentageError(**kwargs)[source]
Computes 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:
Where is a tensor of target values, and is a tensor of predictions.
- Parameters
kwargs¶ (
Any
) – Additional keyword arguments, see Advanced metric settings for more info.
Example
>>> import torch >>> _ = torch.manual_seed(42) >>> preds = torch.randn(20,) >>> target = torch.randn(20,) >>> metric = WeightedMeanAbsolutePercentageError() >>> metric(preds, target) tensor(1.3967)
Initializes internal Module state, shared by both nn.Module and ScriptModule.
Functional Interface¶
- torchmetrics.functional.weighted_mean_absolute_percentage_error(preds, target)[source]
Computes 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:
Where is a tensor of target values, and is a tensor of predictions.
- Parameters
- Return type
- Returns
Tensor with WMAPE.
Example
>>> import torch >>> _ = torch.manual_seed(42) >>> preds = torch.randn(20,) >>> target = torch.randn(20,) >>> weighted_mean_absolute_percentage_error(preds, target) tensor(1.3967)