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# Symmetric Mean Absolute Percentage Error (SMAPE)¶

## Module Interface¶

class torchmetrics.SymmetricMeanAbsolutePercentageError(compute_on_step=None, **kwargs)[source]

Computes symmetric mean absolute percentage error (SMAPE).

Where is a tensor of target values, and is a tensor of predictions.

Parameters

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
Return type

None

## Functional Interface¶

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

Computes symmetric mean absolute percentage error (SMAPE):

Where is a tensor of target values, and is a tensor of predictions.

Parameters
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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