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:

preds (Tensor): Predictions from model
target (Tensor): Ground truth float tensor with shape (N,d)

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

>>> from torch import randn
>>> preds = randn(20,)
>>> target = 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:

val¶ (Union[Tensor, Sequence[Tensor], None]) – Either a single result from calling metric.forward or metric.compute or a list of these results. If no value is provided, will automatically call metric.compute and plot that result.
ax¶ (Optional[Axes]) – An matplotlib axis object. If provided will add plot to that axis

Return type:

Tuple[Figure, Union[Axes, ndarray]]

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()

../_images/weighted_mean_absolute_percentage_error-1.png

>>> 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)

../_images/weighted_mean_absolute_percentage_error-2.png

Functional Interface¶

torchmetrics.functional.weighted_mean_absolute_percentage_error(preds, target)[source]¶