Symmetric Mean Absolute Percentage Error (SMAPE)¶

Module Interface¶

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

Computes 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 1

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

Example

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

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Functional Interface¶

torchmetrics.functional.symmetric_mean_absolute_percentage_error(preds, target)[source]

Computes 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 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)