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https://github.com/opt-mucca/pyscipopt-ml
Python interface to automatically formulate Machine Learning models into Mixed-Integer Programs
https://github.com/opt-mucca/pyscipopt-ml
artificial-intelligence decision-trees keras lightgbm machine-learning mathematical-programming mixed-integer-optimization mixed-integer-programming neural-networks optimization python pytorch sklearn surrogate-models xgboost
Last synced: 3 months ago
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Python interface to automatically formulate Machine Learning models into Mixed-Integer Programs
- Host: GitHub
- URL: https://github.com/opt-mucca/pyscipopt-ml
- Owner: Opt-Mucca
- License: apache-2.0
- Created: 2023-12-10T20:28:22.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-06-21T08:32:09.000Z (8 months ago)
- Last Synced: 2024-07-31T15:20:55.888Z (6 months ago)
- Topics: artificial-intelligence, decision-trees, keras, lightgbm, machine-learning, mathematical-programming, mixed-integer-optimization, mixed-integer-programming, neural-networks, optimization, python, pytorch, sklearn, surrogate-models, xgboost
- Language: Python
- Homepage:
- Size: 2.12 MB
- Stars: 17
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
- Support: docs/supported.rst
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# 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](https://scikit-learn.org/stable/),
[XGBoost](https://xgboost.readthedocs.io/en/stable/), [LightGBM](https://lightgbm.readthedocs.io/en/stable/),
[PyTorch](https://pytorch.org/), [Keras](https://keras.io/), and [ONNX](https://onnx.ai/).
# Documentation
The latest user manual is available on [readthedocs](https://pyscipopt-ml.readthedocs.io/).
# Contact us
For reporting bugs, issues and feature requests please
[open an issue](https://github.com/Opt-Mucca/PySCIPOpt-ML/issues).
# Installation
## Dependencies
`pyscipopt-ml` requires the following:
- Python >= 3.8
- [`numpy`](https://pypi.org/project/numpy/) >= 1.23.0
- [`pyscipopt`](https://pypi.org/project/pyscipopt/) >= 4.4.0
The current version supports the following ML packages:
- [`torch`](https://pypi.org/project/torch/)
- [`keras`](https://pypi.org/project/tensorflow/)
- [`scikit-learn`](https://pypi.org/project/scikit-learn)
- [`XGBoost`](https://pypi.org/project/xgboost/)
- [`LightGBM`](https://pypi.org/project/lightgbm/)
- [`onnx`](https://pypi.org/project/onnx/)
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:
```shell
(venv) pip install pyscipopt-ml
```
This will also install the `numpy` and `pyscipopt` dependencies.
## 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:
```shell
git clone https://github.com/Opt-Mucca/PySCIPOpt-ML/
```
and SHH with
```shell
git clone [email protected]:Opt-Mucca/PySCIPOpt-ML.git
```
After cloning the repository entering the directory where it was cloned, one can run the command:
```shell
(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 ML frameworks:
```shell
pip install pytest
pip install scikit-learn
pip install torch
pip install tensorflow
pip install xgboost
pip install lightgbm
pip install onnx
pip install onnxruntime
pip install pre-commit
pre-commit install
```
## Source code
You can clone the latest sources with the command:
```shell
git clone [email protected]:Opt-Mucca/PySCIPOpt-ML.git
```
## Documentation
You can build the documentation locally with the command
```shell
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:
```shell
(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:
```shell
python -m venv venv
source venv/bin/activate
export PYTHONPATH="$(pwd):${PYTHONPATH}"
```
Then, you can install `pytest` and run a few basic tests:
```shell
(venv) pip install pytest
(venv) pytest
```
## How to cite this work
If this software was used for academic purposes, please cite our paper with the below information:
```
@article{turner2023pyscipopt,
title={PySCIPOpt-ML: Embedding Trained Machine Learning Models into Mixed-Integer Programs},
author={Turner, Mark and Chmiela, Antonia and Koch, Thorsten and Winkler, Michael},
journal={arXiv preprint arXiv:2312.08074},
year={2023}
}
```
## Funding Acknowledgements
The work for this article has been conducted in the Research Campus MODAL
funded by the German Federal Ministry of Education and Research (BMBF)
(fund numbers 05M14ZAM, 05M20ZBM).