refactor(versions): add v0.0.4 Scikit-Longitudinal changelog [cd buil…

…d] [cd tests]

CHANGELOG.MD

@@ -5,13 +5,39 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [v0.0.4] - 2024-07-04 - First Public Release and Major Enhancements
+
+### Added
+
+- **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 for all estimators, preprocessors, data preparations, and the pipeline manager.
+- **Docker Installation**: Added new Docker installation process.
+- **Windows Support**: Windows is now supported via Docker.
+- **New Classifiers/Regressors**: Introduced Lexico Deep Forest, Lexico Gradient Boosting, and Lexico Decision Tree Regressor.
+- **PyPI Availability**: Scikit-Longitudinal is now available on PyPI.
+- **Continuous Integration**: Integrated unit testing, documentation, and PyPI publishing within the CI pipeline.
+
+### Improved
+
+- **PDM Setup and Installation**: Enhanced setup and installation processes using PDM.
+- **Testing Coverage**: Improved testing coverage, ensuring that nearly 90% of the library is tested.
+- **Scikit-Lexicographical-Trees**: Extracted the lexicographical scikit-learn tree node splitting function into its own repository and published it to PyPI as Scikit-Lexicographical-Trees. This is now leveraged by our lexico-based estimators.
+- **.env Management**: Improved management of environment variables.
+- **Lexicographical Enhancements**: Integrated lexicographical enhancements of the waves vector within the variant of scikit-learn, scikit-lexicographical-trees, improving memory and time efficiency by handling algorithmic temporality directly in C++.
+
+### To-Do
+
+- **Docstrings Alignment**: Ensure that docstrings in the codebase align with the official documentation to avoid confusion.
+- **Native Windows Compatibility**: Achieve Windows compatibility without relying on Docker (requires access to a Windows machine).
+- **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.
+
 ## [v0.0.3] - 2023-10-31 - Usability, Maintainability, and Compliance Enhancements
 
 ### Added
 
 - Features Group Missing Waves Handling: Introduced mechanisms for gracefully handling missing waves in features groups.
 - Readiness Descriptions: New readiness indicators provide detailed descriptions of temporal data management across the library.
-- AutoLD Compliance: The library is now compliant with AutoLD standards.
+- Auto-Sklong Compliance: The library is now compliant with Auto-Sklong standards.
 - Package Management Transition: Switched from Poetry to PDM for improved package and dependency management.
 - Docker Support: Linux-based Docker environment setup for streamlined installation and deployment.
 - Platform Testing: Library is tested on both Mac and Linux, with Windows support nearing completion.
@@ -21,7 +47,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Removed
 
-- Irrelevant Scripts: Removed scripts related to visualisations not core to the library's functionality.
+- Irrelevant Scripts: Removed scripts related to visualizations not core to the library's functionality.
 - Experiments Branch: Moved all experiment-related codes to a dedicated `Experiments` branch.
 
 ## [v0.0.2] - 2023-05-17 - Enhanced Longitudinal Analysis and Parallelization Features
@@ -47,7 +73,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - CFS per Group for Longitudinal Data: Python implementation with parallelism for better performance.
 
 [Unreleased]: https://github.com/simonprovost/scikit-longitudinal/compare/v0.0.3...HEAD
+[v0.0.4]: https://github.com/simonprovost/scikit-longitudinal/releases/tag/v0.0.4
 [v0.0.3]: https://github.com/simonprovost/scikit-longitudinal/releases/tag/v0.0.3
 [v0.0.2]: https://github.com/simonprovost/scikit-longitudinal/releases/tag/v0.0.2
-[v0.0.1]: https://github.com/simonprovost/scikit-longitudinal/releases/tag/v0.0.1
-
+[v0.0.1]: https://github.com/simonprovost/scikit-longitudinal/releases/tag/v0.0.1

docs/examples/index.md

@@ -6,7 +6,12 @@ hide:
 # :construction:Coming Soon!
 # :construction: Coming Soon! 
 
-We're currently working on creating a comprehensive examples page for you. This page will feature `Jupyter notebooks` that demonstrate how to use `Scikit-Longitudinal` effectively. 
+We're currently working on creating a comprehensive examples page for you. 
+This page will feature `Jupyter notebooks` that demonstrate how to use `Scikit-Longitudinal` effectively. 
+
+!!! note
+    In the meantime, throughout each estimator, preprocessors, data preparations, and the pipeline manager,
+    you can find some starting-point examples in the end of each documentation-based page.
 
 <iframe 
     src="https://giphy.com/embed/l3vQYe7l1TInypnYA" 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "Scikit-longitudinal"
-version = "0.0.2"
+version = "0.0.4"
 description = "Scikit-longitudinal, an open-source Python lib for longitudinal data analysis, builds on Scikit-learn's foundation. It offers specialized tools to tackle challenges of repeated measures data, ideal for researchers, data scientists, & analysts."
 authors = [
     {name = "Provost Simon", email = "simon.gilbert.provost@gmail.com"},
@@ -29,7 +29,6 @@ dependencies = [
 ]
 requires-python = ">=3.9,<3.10"
 readme = "README.md"
-license = { file = "LICENSE" }
 
 [project.urls]
 Homepage = "https://github.com/simonprovost/scikit-longitudinal"

setup.py

@@ -2,7 +2,7 @@
 
 setup(  # pragma: no cover
     name="Scikit-longitudinal",
-    version="0.0.22",
+    version="0.0.4",
     long_description=open('README.md').read(),
     long_description_content_type='text/markdown',
     url="https://github.com/simonprovost/scikit-longitudinal",