torchscalers is a Python package providing a set of feature scalers for PyTorch pipelines. Unlike scikit-learn scalers, every scaler in torchscalers is a torch.nn.Module subclass. Fitted statistics are stored as module buffers, which means they travel with the model to any device, are saved and restored with every torch.save / torch.load checkpoint, and plug directly into nn.Sequential pipelines.
Available Scalers
| Scaler | Description |
|---|---|
ZScoreScaler |
Standardise to zero mean and unit variance. |
MinMaxScaler |
Scale to [0, 1] using per-feature min/max. |
MaxAbsScaler |
Scale to [-1, 1] by dividing by the maximum absolute value. |
RobustScaler |
Scale using median and IQR — robust to outliers. |
ShiftScaleScaler |
Apply a user-specified (x + shift) * scale transformation. |
LogScaler |
Apply a log transformation: log(x + eps). |
PerDomainScaler |
Apply a separate scaler instance per string domain ID. |
MixedDomainScaler |
Apply a different scaler type per string domain ID. |