Merge pull request #45 from simonprovost/feature/v0.0.4

First Release Scikit-Longitudinal Github and PyPi

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

README.md

@@ -3,7 +3,7 @@
    <p align="center">
    <h1 align="center">
       <br>
-      <a href="https://raw.githubusercontent.com/simonprovost/scikit-longitudinal/main/logo.png?token=GHSAT0AAAAAACIVGTU4VMW5XBIL6ITAYXJSZUFEX2Q"><img src="https://raw.githubusercontent.com/simonprovost/scikit-longitudinal/main/logo.png?token=GHSAT0AAAAAACIVGTU4VMW5XBIL6ITAYXJSZUFEX2Q" alt="Scikit-longitudinal" width="200"></a>
+      <a href="https://i.imgur.com/jCtPpTF.png"><img src="https://i.imgur.com/jCtPpTF.png" alt="Scikit-longitudinal" width="200"></a>
       <br>
       Scikit-longitudinal
       <br>

dockerfile

@@ -1,106 +1,37 @@
-ARG CONDA_VER=2023.09-0
-ARG OS_TYPE
+ARG PYTHON_BASE=3.9-slim
+FROM python:$PYTHON_BASE AS builder
 
-FROM python:3.9.8 AS builder
+RUN pip install -U pdm
+ENV PDM_CHECK_UPDATE=false
 
-# ==========================
-# Dockerfile for Scikit Longitudinal Project
-# System: Linux under Python 3.9.8 lightweight image
-# Python: 3.9.8
-# ==========================
-
-RUN echo "==========================\nStage 1: The Build Process\n=========================="
-
-# -----------------------------------
-# 🛠 System-level Setup and Libraries 🛠
-# -----------------------------------
-RUN apt-get update && apt-get install -y libomp-dev
-
-# ------------------------
-# 🛠 Compiler Configurations 🛠
-# ------------------------
+RUN apt-get update && apt-get install -y build-essential libomp-dev libc-dev && apt-get clean && rm -rf /var/lib/apt/lists/*
 ENV CC=gcc
 ENV CXX=g++
 ENV CPPFLAGS="-I/usr/local/include"
 ENV CFLAGS="-Wall"
 ENV CXXFLAGS="-Wall"
 ENV LDFLAGS="-L/usr/local/lib"
 
-# -------------------
-# 🛠 Python Utilities 🛠
-# -------------------
-RUN echo "🛠 Python Utilities 🛠"
 RUN pip install -U pip setuptools wheel
-RUN pip install pdm
 
-# ---------------------------
-# 📦 Python Dependency Setup 📦
-# ---------------------------
 COPY pyproject.toml pdm.lock /scikit_longitudinal/
-WORKDIR /scikit_longitudinal
-RUN mkdir __pypackages__
-
-FROM python:3.9
-RUN echo "==========================\nStage 2: The Run-Time Setup\n=========================="
-
-# -----------------------------------
-# 🛠 System-level Setup and Libraries 🛠
-# -----------------------------------
-RUN echo "🛠 System-level Setup and Libraries 🛠"
-RUN apt-get update && apt-get install -y libomp-dev build-essential wget curl libc-dev && \
-    apt-get clean && \
-    rm -rf /var/lib/apt/lists/*
-
-# -------------------------
-# 🐍 Anaconda Installation 🐍
-# -------------------------
-RUN echo "🐍 Anaconda Installation 🐍"
-ARG CONDA_VER
-ARG OS_TYPE
-RUN if [ -z "${OS_TYPE}" ]; then echo "OS_TYPE argument not provided"; exit 1; fi
-RUN wget -q "https://repo.anaconda.com/archive/Anaconda3-${CONDA_VER}-Linux-${OS_TYPE}.sh" -O ~/Anaconda.sh
-RUN dpkg --add-architecture arm64
-RUN apt update -y
-RUN apt install -y libc6:arm64
-RUN bash ~/Anaconda.sh -b -p /anaconda
-RUN rm ~/Anaconda.sh
-ENV PATH=/anaconda/bin:${PATH}
-RUN conda update --quiet -y conda
+COPY scikit_longitudinal/ /scikit_longitudinal/scikit_longitudinal/
+COPY data/ /scikit_longitudinal/data/
+COPY scripts/ /scikit_longitudinal/scripts/
+COPY .env README.md .coveragerc /scikit_longitudinal/
 
-# ------------------------
-# 🛠 Compiler Configurations 🛠
-# ------------------------
-RUN echo "🛠 Compiler Configurations 🛠"
-ENV CC=gcc
-ENV CXX=g++
-ENV CPPFLAGS="-I/usr/local/include"
-ENV CFLAGS="-Wall"
-ENV CXXFLAGS="-Wall"
-ENV LDFLAGS="-L/usr/local/lib"
+WORKDIR /scikit_longitudinal
+RUN pdm install --check --with :all --no-editable
 
-# ---------------------------
-# 🐍 Python Environment Setup 🐍
-# ---------------------------
-RUN echo "🐍 Python Environment Setup 🐍"
-ENV PYTHONPATH=/scikit_longitudinal/pkgs
+FROM python:$PYTHON_BASE
 
-# ----------------------
-# 📦 Project File Setup 📦
-# ----------------------
-RUN echo "📦 Project File Setup 📦"
+COPY --from=builder /scikit_longitudinal/.venv/ /scikit_longitudinal/.venv
+ENV PATH="/scikit_longitudinal/.venv/bin:$PATH"
 COPY pyproject.toml pdm.lock /scikit_longitudinal/
 COPY scikit_longitudinal/ /scikit_longitudinal/scikit_longitudinal/
-COPY scikit-learn/ /scikit_longitudinal/scikit-learn/
 COPY data/ /scikit_longitudinal/data/
 COPY scripts/ /scikit_longitudinal/scripts/
-COPY .env README.md .coveragerc /scripts/linux/docker_scikit_longitudinal_installs.sh /scripts/linux/docker_start_pdm_env.sh /scikit_longitudinal/
+COPY .env README.md .coveragerc /scikit_longitudinal/
 
-# -------------------------------
-# 🚀 Scikit Longitudinal Installation 🚀
-# -------------------------------
-RUN echo "🚀 Scikit Longitudinal Installation 🚀"
 WORKDIR /scikit_longitudinal
-RUN pip install pdm
-ENV PDM_IN_ENV=in-project
-RUN chmod +x /scikit_longitudinal/scripts/linux/docker_scikit_longitudinal_installs.sh /scikit_longitudinal/scripts/linux/docker_start_pdm_env.sh
-RUN /scikit_longitudinal/scripts/linux/docker_scikit_longitudinal_installs.sh
+CMD ["/bin/bash"]

docs/contribution.md

@@ -69,7 +69,13 @@ Please follow the instructions below for setting up your development environment
 !!! warning "Fully-working environment setup is not guaranteed on Windows. We recommend using a Unix-based system for
 development. Such as MacOS or Linux. On Windows, Docker is recommended having been tested on Windows 10 & 11."
 
-To manually configure your environment, please adhere to the following procedure meticulously:
+Prior-all, you need to open the `.env` file at the root and set:
+```bash
+SKLONG_PYTHON_VERSION=<your_python_version> # e.g. 3.9.8
+SKLONG_PYTHON_PATH=<your_python_path> # e.g. /usr/bin/python3.9
+```
+
+Next, to manually configure your environment, please adhere to the following procedure meticulously:
 
 1. **Setting up the package manager:**
     - Initialise the package manager with Conda as the backend for virtual environments:
@@ -208,7 +214,38 @@ feel free to open an issue on the GitHub repository for additional support.
         ```bash
         git config --global core.autocrlf true
         ```
-
+
+!!! tip "Docker and Jetbrains"
+    If you are using JetBrains, you should be able to leverage the `.run/` configurations at the root of the folder.
+    They should be automatically detected by your Jetbrains IDE (e.g PyCharm) and you can run the tests from there.
+    Make sure to edit the configuration to adapt to your use-case.
+
+    Configs available:
+    - `Scikit_longitudinal_ARM_architecture.run.xml`: If you are on an ARM architecture. Such as Macbook with Apple Silicon chips.
+    - `Scikit_longitudinal_Intel_architecture.run.xml`: If you are on an Intel architecture. Such as most of the Windows and Linux machines or Macbook with Intel chips. 
+
+!!! warning "Docker with Apple Silicon"
+    If you are on an Apple Silicon chip, the current library is `x86_64` based. Therefore, you should configure Docker
+    so that it runs on such architecture.  Be at the root of the project and run the following commands: 
+
+    1. **Prepare [QUS](https://github.com/dbhi/qus) for Docker:**
+        ```bash
+        docker run --rm --privileged aptman/qus -- -r
+        docker run --rm --privileged aptman/qus -s -- -p x86_64
+        ```
+    2. **Build the Docker Image:**
+        ```bash
+        docker buildx create --use
+        docker buildx build --platform linux/amd64 -t scikit_longitudinal:latest .
+        ```
+    3. **Run the Docker Container:**
+        ```bash
+        docker run -it scikit_longitudinal:latest /bin/bash
+        ```
+    4. **Run the tests:**
+        ```bash
+        pytest scikit_longitudinal/ --cov=./ --cov-report=html --cov-config=.coveragerc --cov-report=html:htmlcov/scikit_longitudinal -s -vv --capture=no
+        ```
 ## ⚙️ How To Build The Distribution Packages
 
 To build the distribution packages for the project, follow these steps:

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"},
@@ -25,11 +25,10 @@ dependencies = [
     "joblib>=0.11",
     "deep-forest>=0.1.7",
     "starboost==0.0.2",
-    "scikit-lexicographical-trees>=0.0.2",
+    "scikit-lexicographical-trees==0.0.2",
 ]
 requires-python = ">=3.9,<3.10"
 readme = "README.md"
-license = { file = "LICENSE" }
 
 [project.urls]
 Homepage = "https://github.com/simonprovost/scikit-longitudinal"
@@ -75,12 +74,12 @@ doc = [
 _set_pdm_use_venv = { cmd = "pdm use --venv ${PDM_IN_ENV}" }
 _use_python39.shell = "pdm use \"${SKLONG_PYTHON_PATH}\""
 _use_python39.env_file = ".env"
-_create_env.shell = "pdm venv create --with-pip ${SKLONG_PYTHON_VERSION} --force"
+_create_env.shell = "pdm venv create --with-pip ${SKLONG_PYTHON_VERSION}"
 _create_env.env_file = ".env"
 _check_sklong_vars = {shell = "echo 'Make sure to set the SKLONG_PYTHON_PATH and SKLONG_PYTHON_VERSION environment variables'"}
 _set_env_var = {shell = "echo 'could you please run `export PDM_IN_ENV=in-project`'"}
 _activate_env = {cmd = "echo 'could you please activate the environment via `eval $(pdm venv activate (dollar-sign)PDM_IN_ENV)`'"}
-setup_sklong = {composite = ["_check_sklong_vars", "_use_python39", "_create_env", "_set_env_var", "_activate_env"]}
+setup_sklong = {composite = ["_check_sklong_vars", "_create_env", "_set_env_var", "_activate_env"]}
 remove_env = { cmd = "pdm venv remove ${PDM_IN_ENV}" }
 _clean_project = {cmd = "echo could you please deactivate the environment via `conda deactivate` then run `pdm run remove_env`"}
 clean = {composite = ["_check_pdm_vars", "_clean_project"] }

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",