# 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.

compute()[source]

Computes weighted mean absolute percentage error over state.

Return type

Tensor

update(preds, target)[source]

Update state with predictions and targets.

Parameters
Return type

None

## 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

Tensor

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)