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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 max(\frac{|   y_i - \hat{y_i} |}{| y_i | + | \hat{y_i} |, \epsilon})

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

Parameters

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

Note

The epsilon value is taken from scikit-learn’s implementation of SMAPE.

Note

SMAPE output is a non-negative floating point between 0 and 1. Best result is 0.0 .

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.

compute()[source]

Computes mean absolute percentage error over state.

Return type

Tensor

update(preds, target)[source]

Update state with predictions and targets.

Parameters
  • preds (Tensor) – Predictions from model

  • target (Tensor) – Ground truth values

Return type

None

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)
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