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# 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:

$\text{WMAPE} = \frac{\sum_{t=1}^n | y_t - \hat{y}_t | }{\sum_{t=1}^n |y_t| }$

Where $$y$$ is a tensor of target values, and $$\hat{y}$$ 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)

plot(val=None, ax=None)[source]

Plot a single or multiple values from the metric.

Parameters:
Return type:
Returns:

Figure and Axes object

Raises:

ModuleNotFoundError – If matplotlib is not installed

>>> 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()

>>> 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)


## 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:

$\text{WMAPE} = \frac{\sum_{t=1}^n | y_t - \hat{y}_t | }{\sum_{t=1}^n |y_t| }$

Where $$y$$ is a tensor of target values, and $$\hat{y}$$ 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)


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