🎉 First Public Release Github & PyPi

We are pleased to announce that Auto-Sklong is now available in its first public release under the tag 0.0.2, despite numerous Pypi misadventures (lesson learned, Pypi-Tests). 🎉

📽️ Auto-Sklong is built on @PGijsbers' General Automated Machine Learning (AutoML) Assistant (GAMA) framework. A flexible AutoML framework for experimenting with different search strategies and a customisable search space, among other cool features. We began using and improving locally GAMA for our own goals of tackling the Longitudinal machine learning tasks via AutoML, then created Auto-Sklong, which, while an AutoML system, differs from the very goal of GAMA; however, the improvements made to GAMA by doing Auto-Sklong were "generalised" for the GAMA goal, and we submitted three pull requests (see further in our readme).

💡 Auto-Sklong introduces a completely new search space by leveraging ConfigSpace, a sequential search space. Introduces a new search method, bayesian optimisation, via SMAC3. It also includes all of GAMA's built-in features, such as different search methods and other cool stuff. Read the Auto-Sklong and GAMA documentation. In order to achieve the end goal: Auto-Sklong is now capable of solving both the (1) Longitudinal Machine Learning task problem by understanding the temporal dependency in the dataset – leveraging Sklong – and the (2) Combined Algorithm Selection and Hyperparameter Optimisation (CASH Optimisation).

Paper has been submitted to a conference. Will be updated if accepted.

🫵
https://pypi.org/project/Auto-Sklong/0.0.2

[v0.0.2] - 2024-07-12 - First Public Release

Added

• New Search Space: ConfigSpace supported search space via GAMA. Pull request ongoing on the original repository.
• New Search Method: Bayesian Optimization via SMAC3 is now feasible. Pull request ongoing on the GAMA original repository.
• Documentation: Comprehensive new documentation with Material for MKDocs. This includes a detailed tutorial on understanding vectors of waves in longitudinal datasets, a contribution guide, an FAQ section, and complete API references which use a lot of Sklong and GAMA documentation to guide the users.
• PyPI Availability: Auto-Sklong is now available on PyPI.
• Continuous Integration: Integrated unit testing, documentation, and PyPI publishing within the CI pipeline.

To-Do

• Finalize PRs on GAMA: Ongoing pull requests on GAMA would facilitate the alignment between Auto-Sklong and GAMA's latest version. They need to be worked on and published so that we can make compatibility adjustments between both libraries for the sake of Auto-Sklong's long-term goals (being able to benefit from future GAMA features if any).
• Future Enhancements: Ongoing improvements and new features as they are identified.
• Documentation Examples: Add examples to the documentation to help users understand how to use the library with Jupyter notebooks.

Note, no tag 0.0.1 will ever be available.