Auto-sklong

We’re excited to introduce **Auto-Sklong** `v0.0.4`! While this release is minor, it ensures that our dependencies remain up-to-date, particularly with the migration of **Scikit-Longitudinal** to version `0.0.7`. This upgrade brings compatibility improvements and prepare the foundation for better performance in longitudinal machine learning tasks.

Highlights

1. **Package Updates**
- Updated **Scikit-Longitudinal** to `v0.0.7` to incorporate compatibility adjustments for the recent migration to `uv`.

2. **Stability Improvements**
- Minor bug fixes and adjustments to ensure smoother workflows, especially with updated dependencies.

As always, thank you for your support and contributions! Let’s keep pushing forward together! 🚀

<details>
<summary>Previously in <code>v0.0.3</code></summary>

🎄 Migration to UV & Documentation Improvements

We are glad to present **Auto-Sklong** `v0.0.3`. This new release focusses on streamlining the development workflow by **migrating from PDM to [uv](https://github.com/astral-sh/uv)**, which speeds up installation and reduces complexity. Our documentation has also been updated, with clarifications to the Quick Start and new sections for Apple Silicon Mac customers. Most excitingly, our paper has been **accepted** to the **IEEE BIBM 2024** conference—stay tuned for the BibTeX citation and additional publication details when the proceedings are posted!

Highlights

1. **Migration from PDM to `uv`**
- Far simpler commands and fewer setup configurations for the community.
- Substantial speed improvements, as shown in this [benchmark comparison](https://github.com/astral-sh/uv/assets/1309177/03aa9163-1c79-4a87-a31d-7a9311ed9310#only-dark), pitting `uv` against poetry, PDM, and pip-sync.

2. **Documentation Enhancements**
- **Quick Start Fixes**: Thanks to anderdnavarro (in 4, 5, 6) for correcting parameter names in the Quick Start feature list examples.
- **Paper Acceptance**: Our paper on Auto-Sklong has been accepted to the [2024 IEEE BIBM Conference](https://ieeebibm.org/). We will add the BibTeX reference once the proceedings are finalised.
- **Apple Silicon Installation Guide**: The [Quick Start](https://simonprovost.github.io/Auto-Sklong/quick-start/#installing-auto-sklong-on-apple-silicon-macs) now includes a dedicated section for installing Auto-Sklong on Apple Silicon-based Macs, making setup for M1/M2 systems more transparent and accessible. Thanks once more to anderdnavarro for pointing that out!

Future Work

- **BibTeX Citation**: We will add a citation reference for our BIBM 2024 paper as soon as the proceedings are publicly available.
- **Documentation**: The experiments paper section will be simplified, and the redundant "Release History" tab in the documentation will be removed.
- **Examples**: We aim to launch a comprehensive Jupyter notebook tutorial to demonstrate how to use Auto-Sklong.

As always, thank you for your continued support. Let’s keep exploring the boundaries of longitudinal machine learning!

**Merry XMas!** 🎄

</details>

We are glad to present **Auto-Sklong** `v0.0.3`. This new release focusses on streamlining the development workflow by **migrating from PDM to [uv](https://github.com/astral-sh/uv)**, which speeds up installation and reduces complexity. Our documentation has also been updated, with clarifications to the Quick Start and new sections for Apple Silicon Mac customers. Most excitingly, our paper has been **accepted** to the **IEEE BIBM 2024** conference—stay tuned for the BibTeX citation and additional publication details when the proceedings are posted!

Highlights

1. **Migration from PDM to `uv`**
- Far simpler commands and fewer setup configurations for the community.
- Substantial speed improvements, as shown in this [benchmark comparison](https://github.com/astral-sh/uv/assets/1309177/03aa9163-1c79-4a87-a31d-7a9311ed9310#only-dark), pitting `uv` against poetry, PDM, and pip-sync.

2. **Documentation Enhancements**
- **Quick Start Fixes**: Thanks to anderdnavarro (in 4, 5, 6) for correcting parameter names in the Quick Start feature list examples.
- **Paper Acceptance**: Our paper on Auto-Sklong has been accepted to the [2024 IEEE BIBM Conference](https://ieeebibm.org/). We will add the BibTeX reference once the proceedings are finalised.
- **Apple Silicon Installation Guide**: The [Quick Start](https://simonprovost.github.io/Auto-Sklong/quick-start/#installing-auto-sklong-on-apple-silicon-macs) now includes a dedicated section for installing Auto-Sklong on Apple Silicon-based Macs, making setup for M1/M2 systems more transparent and accessible. Thanks once more to anderdnavarro for pointing that out!

Future Work

- **BibTeX Citation**: We will add a citation reference for our BIBM 2024 paper as soon as the proceedings are publicly available.
- **Documentation**: The experiments paper section will be simplified, and the redundant "Release History" tab in the documentation will be removed.
- **Examples**: We aim to launch a comprehensive Jupyter notebook tutorial to demonstrate how to use Auto-Sklong.

As always, thank you for your continued support. Let’s keep exploring the boundaries of longitudinal machine learning!

**Merry XMas!** 🎄

<details>
<summary>Previously in <code>v0.0.2</code></summary>

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)_. 🎉

About Auto-Sklong

Auto-Sklong is built on [PGijsbers’ General Automated Machine Learning (AutoML) Assistant (GAMA)](https://github.com/openml-labs/gama) framework—a flexible AutoML framework for experimenting with different search strategies and a customisable search space. We began improving GAMA locally for our own goals of tackling longitudinal machine learning tasks, resulting in Auto-Sklong. While it remains an AutoML system, it offers new features such as:
- A sequential search space via [ConfigSpace](https://automl.github.io/ConfigSpace/latest/).
- Bayesian optimisation using [SMAC3](https://github.com/automl/SMAC3).
- Additional built-in features inherited from GAMA.

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.