# Weighted MAPE¶

## Module Interface¶

class torchmetrics.WeightedMeanAbsolutePercentageError(**kwargs)[source]

Compute 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.

As input to forward and update the metric accepts the following input:

As output of forward and compute the metric returns the following output:

• wmape (Tensor): A tensor with non-negative floating point wmape value between 0 and 1

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,)
>>> wmape = WeightedMeanAbsolutePercentageError()
>>> wmape(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]

Compute 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)