Discretized Interpretable Multi Layer Perceptron (DIMLP) and related algorithms

pip install dimlpfidex

To get the latest source code, install git and clone the repository:

$ git clone https://github.com/HES-XPLAIN/dimlpfidex.git

To download the required dependencies on your system, run:

$ git submodule init
$ git submodule update

Install with your package manager:

• a C++ compiler (gcc/g++)
• the ninja build system (ninja or ninja-build)
• cmake

• Install Visual Studio Community with the "Desktop development with C++" component:

winget install Microsoft.VisualStudio.2022.Community

The MSVC compiler is necessary for proper packaging of the bindings.

• Install CMake:

winget install Kitware.CMake

Ensure cmake.exe is accessible in the $PATH environment variable.

cmake.exe --version

Install Python, version 3.9 or newer (3.11 is recommended):

• Linux, macOS, Windows/WSL: Use your package manager to install python3 and python3-dev
• Windows: winget install Python.Python.3.11

python -m venv .venv
source .venv/bin/activate
pip install .

To activate the virtualenv, use the standard methods:

• Unix: source .venv/bin/activate
• Windows: .venv\Scripts\activate

To leave the virtualenv, use deactivate.

To add new dependencies to the project, add them to the pyproject.toml file. To add them to the virtualenv, use:

pip install .

Git hooks are used to ensure quality checks are run by all developers every time before a commit.

Install with pip install pre-commit.

To enable pre-commit:

pre-commit install

Pre-commit hooks can be run manually with:

pre-commit run --all-files

Compile:

mkdir build && cd build
cmake ..
cmake --build .

Create archives for distribution, from the root of the project:

python -m build

Install Doxygen:

• Linux, macOS, Windows/WSL: Use your package manager to install doxygen
• Windows: winget install DimitriVanHeesch.Doxygen

Create the documentation:

mkdir build && cd build
cmake -DBUILD_DOCUMENTATION=ON ..
cmake --build .

The generated HTML documentation will be found in build/docs/sphinx.

To publish the package on PyPI, refer to RELEASE.

Our test suite is using Obesity or CVD risk dataset from AravindPCoder (under CC BY-SA 4.0 license)