DAL Toolbox Deep Python

daltoolboxdp extends daltoolbox with Python-backed components, with emphasis on deep learning and Python-native modeling. In the package name, dp stands for Deep Python.

It currently focuses on:

• Deep learning models backed by torch
• Scikit-learn classifiers exposed through the daltoolbox API
• Time-series forecasting models backed by Python
• Integration of Python model objects into the daltoolbox architecture

These capabilities rely on the reticulate bridge, so the package can keep the object and workflow conventions of daltoolbox while delegating training, encoding, and prediction to Python libraries such as torch and scikit-learn.

The architecture is inspired by the Experiment Lines approach, which promotes modularity, extensibility, and interoperability across tools.
More information on Experiment Lines is available in Ogasawara et al. (2009).

Examples

The example set is organized by topic and generated from the source files under Rmd/. If you are exploring the package for the first time, start from the rendered indexes under examples/.

The current topics are organized around these questions:

• Which Python-backed autoencoder should I use to compress time-series windows?
• Which scikit-learn classifier wrappers are available in the daltoolbox architecture?
• Which Python-backed regression wrappers are available for numeric prediction?
• How do the time-series examples cover both representation learning and direct forecasting?

Rendered examples are available at:

• Autoencoders - Autoencoders for time-series windows: simple, convolutional, denoising, LSTM, stacked, and variational variants, in both encode and encode-decode forms.
• Classification - Classification wrappers backed by Python libraries, including scikit-learn and PyTorch neural models.
• Regression - Regression wrappers backed by Python libraries, currently including the PyTorch MLP regressor.
• Time Series - Time-series examples for encoding, reconstruction, and direct forecasting with PyTorch models.

Installation

You can install the latest stable version from CRAN:

install.packages("daltoolboxdp")

To install the development version from GitHub:

library(devtools)
devtools::install_github("cefet-rj-dal/daltoolboxdp", force = TRUE, dependencies = FALSE, upgrade = "never")

Bug reports and feature requests

Please report issues or suggest new features via:

• GitHub Issues