Symmetric Mean Absolute Percentage Error (SMAPE)

Module Interface

class torchmetrics.SymmetricMeanAbsolutePercentageError(**kwargs)

Compute symmetric mean absolute percentage error (SMAPE).

\[\text{SMAPE} = \frac{2}{n}\sum_1^n\frac{| y_i - \hat{y_i} |}{\max(| y_i | + | \hat{y_i} |, \epsilon)}\]

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 values

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

• smape (Tensor): A tensor with non-negative floating point smape value between 0 and 2

Parameters:

kwargs (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Example

>>> from torchmetrics.regression import SymmetricMeanAbsolutePercentageError
>>> target = tensor([1, 10, 1e6])
>>> preds = tensor([0.9, 15, 1.2e6])
>>> smape = SymmetricMeanAbsolutePercentageError()
>>> smape(preds, target)
tensor(0.2290)

plot(val=None, ax=None)

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 SymmetricMeanAbsolutePercentageError
>>> metric = SymmetricMeanAbsolutePercentageError()
>>> metric.update(randn(10,), randn(10,))
>>> fig_, ax_ = metric.plot()

>>> from torch import randn
>>> # Example plotting multiple values
>>> from torchmetrics.regression import SymmetricMeanAbsolutePercentageError
>>> metric = SymmetricMeanAbsolutePercentageError()
>>> values = []
>>> for _ in range(10):
...     values.append(metric(randn(10,), randn(10,)))
>>> fig, ax = metric.plot(values)

Functional Interface

torchmetrics.functional.symmetric_mean_absolute_percentage_error(preds, target)

Compute symmetric mean absolute percentage error (SMAPE).

\[\text{SMAPE} = \frac{2}{n}\sum_1^n\frac{| y_i - \hat{y_i} |}{max(| y_i | + | \hat{y_i} |, \epsilon)}\]

Where \(y\) is a tensor of target values, and \(\hat{y}\) is a tensor of predictions.

Parameters:

• preds (Tensor) – estimated labels

• target (Tensor) – ground truth labels

Return type:

Tensor

Returns:

Tensor with SMAPE.

Example

>>> from torchmetrics.functional.regression import symmetric_mean_absolute_percentage_error
>>> target = torch.tensor([1, 10, 1e6])
>>> preds = torch.tensor([0.9, 15, 1.2e6])
>>> symmetric_mean_absolute_percentage_error(preds, target)
tensor(0.2290)