PySCIPOpt-ML 0.0.1

automatically formulate and embed ML models into MIPs with SCIP

PySCIPOpt-ML

PySCIPOpt-ML is a python interface to automatically formulate Machine Learning (ML) models into Mixed-Integer Programs (MIPs). PySCIPOPT-ML allows users to easily optimise MIPs with embedded ML constraints.

The package currently supports various ML objects from Scikit-Learn, XGBoost, LightGBM, and PyTorch

Documentation

The latest user manual is available on readthedocs.

Contact us

For reporting bugs, issues and feature requests please open an issue.

Installation

Dependencies

pyscipopt-ml requires the following:

• Python >= 3.8
• numpy >= 1.23.0
• pyscipopt >= 4.4.0

The current version supports the following ML packages:

• torch
• scikit-learn
• XGBoost
• LightGBM

Installing these packages is only required if the predictor you want to insert uses them (i.e. to insert a XGBoost based predictor you need to have xgboost installed).

Pip installation

The easiest way to install PySCIPOpt-ML is using pip. It is recommended to always install packages in a virtual environment:

(venv) pip install pyscipopt-ml

This will also install the numpy, pyscipopt dependencies.

Please note that gurobipy is commercial software and requires a license. When installed via pip or conda, gurobipy ships with a free license which is only for testing and can only solve models of limited size.

Installation from source

An alternative way to install PySCIPOpt-ML is from source. First this repository needs to be cloned. This can be achieved via HTTPS with:

git clone https://github.com/Opt-Mucca/PySCIPOpt-ML/

and SHH with

git clone [e-mail]:Opt-Mucca/PySCIPOpt-ML.git

After cloning the repository entering the directory where it was cloned, one can run the command:

(venv) python -m pip install .

Development

This project is completely open to any contributions. Feel free to implement your own functionalities.

Before committing anything, please install pytest, pre-commit, and all MLframeworks:

pip install pytest
pip install scikit-learn
pip install torch
pip install xgboost
pip install lightgbm
pip install pre-commit
pre-commit install

Source code

You can clone the latest sources with the command:

git clone [e-mail]:Opt-Mucca/PySCIPOpt-ML.git

Documentation

You can build the documentation locally with the command

pip install -r docs/requirements.txt
sphinx-build docs docs/_build

Às the documentation requires additional python packages, one should run the following command before building the documentation for the first time:

(venv) pip install -r docs/requirements.txt

Testing

After cloning the project, you can run the tests by invoking pytest. For this, you will need to create a virtual environment and activate it. Please also make sure to append your python path:

python -m venv venv
source venv/bin/activate
export PYTHONPATH="$(pwd):${PYTHONPATH}"

Then, you can install pytest and run a few basic tests:

(venv) pip install pytest
(venv) pytest