Installation Guide

This guide provides detailed instructions on how to set up the Ensemble Genetic Algorithm project. You can choose between a manual installation or using the provided setup script.

Prerequisites

Before you begin, ensure you have the following installed on your system:

Python: Version 3.12 or higher (required for pyproject.toml compatibility).
Git: For cloning the repository.
(Optional) NVIDIA GPU with CUDA: If you plan to use GPU-accelerated computations, ensure you have a compatible NVIDIA GPU and the CUDA Toolkit installed.

Manual Installation

If you prefer to set up the environment manually, follow these steps. This project uses pyproject.toml to manage its dependencies.

Clone the repository:

git clone https://github.com/SamoraHunter/ensemble_genetic_algorithm.git
cd ensemble_genetic_algorithm

Create and activate a virtual environment: It is highly recommended to use a virtual environment to manage project dependencies.
```
python3 -m venv .venv
source .venv/bin/activate  # On Windows, use `.venv\Scripts\activate`
```
Install dependencies: Install the project and its core dependencies using pip. The dependencies are defined in pyproject.toml.
```
pip install .
```
To install with optional dependencies for development (e.g., testing tools, documentation tools), use:
```
pip install .[dev]
```
If you need GPU support and have the necessary CUDA setup, you would typically install torch with CUDA support manually or via the setup.sh --gpu option. The pyproject.toml currently lists torch==2.0.1 as a core dependency, which might default to a CPU version depending on your pip configuration. The package requires PyTorch, and for GPU support, it is highly recommended that you first install PyTorch manually by following the official instructions at pytorch.org to ensure the correct version for your CUDA toolkit is installed.

Using the Setup Script (Alternative)

The project includes a comprehensive setup.sh script that automates the creation of a dedicated Python virtual environment and installs all necessary dependencies, including specific PyTorch versions for CPU or GPU.

Clone the repository:

git clone https://github.com/SamoraHunter/ensemble_genetic_algorithm.git
cd ensemble_genetic_algorithm

Run the setup script: Make the script executable and then run it.
```
chmod +x setup.sh
./setup.sh
```
This will create a virtual environment named ga_env, install the default dependencies from pyproject.toml, and set up a Jupyter kernel. The environment will be activated for your current terminal session.

Installation Options with `setup.sh`

The setup.sh script supports different installation profiles. You can specify one using command-line flags:

./setup.sh --cpu: Installs the CPU-only version of PyTorch. Ideal for systems without a dedicated GPU.
./setup.sh --gpu: Installs dependencies with GPU support (requires a compatible NVIDIA GPU and CUDA toolkit). This option will attempt to install the CUDA-enabled PyTorch.
./setup.sh --dev: Installs all development dependencies, including tools for testing, linting, and documentation.
./setup.sh --all: Installs everything, combining GPU and development dependencies.
./setup.sh --force: Forces the recreation of the virtual environment if it already exists.

To see all available options, run:

./setup.sh --help

Activating the Environment

The setup.sh script activates the ga_env environment for your current terminal session. For future sessions, or if you installed manually into .venv, you can activate it manually:

source ga_env/bin/activate # If using setup.sh
source .venv/bin/activate   # If using manual installation with .venv