Symmetric Mean Absolute Percentage Error (SMAPE)¶
Module Interface¶
- class torchmetrics.SymmetricMeanAbsolutePercentageError(**kwargs)[source]
Computes symmetric mean absolute percentage error (SMAPE).
Where is a tensor of target values, and 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.
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
- 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)