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pyscipopt-ml

Python interface to automatically formulate Machine Learning models into Mixed-Integer Programs

https://github.com/opt-mucca/pyscipopt-ml

Science Score: 54.0%

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    Links to: arxiv.org
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    Low similarity (16.9%) to scientific vocabulary

Keywords

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: 4 months ago · JSON representation ·

Repository

Python interface to automatically formulate Machine Learning models into Mixed-Integer Programs

Basic Info
Statistics
  • Stars: 32
  • Watchers: 3
  • Forks: 2
  • Open Issues: 0
  • Releases: 0
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
Created about 2 years ago · Last pushed 7 months ago
Metadata Files
Readme License Citation Support

README.md

Python versions Black PyPI

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, PyTorch, Keras, and ONNX.

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 >= 5.4.1

The current version supports the following ML packages: - torch - keras - scikit-learn - XGBoost - LightGBM - 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 git@github.com: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 git@github.com: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:

@misc{turner2024pyscipoptmlembeddingtrainedmachine, title={PySCIPOpt-ML: Embedding Trained Machine Learning Models into Mixed-Integer Programs}, author={Mark Turner and Antonia Chmiela and Thorsten Koch and Michael Winkler}, year={2024}, eprint={2312.08074}, archivePrefix={arXiv}, primaryClass={math.OC}, url={https://arxiv.org/abs/2312.08074}, }

Acknowledgements

This code base was heavily inspired by Gurobi-MachineLearning. The API and general architecture was made to match, so that users could easily transfer between one to the other. If there is a feature missing here, or you are looking for alternatives, then give it a try!

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).

Owner

  • Name: Mark Turner
  • Login: Opt-Mucca
  • Kind: user
  • Location: Berlin
  • Company: Zuse Institute Berlin

Post-Doc at ZIB

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
title: PySCIPOpt-ML
url: https://github.com/Opt-Mucca/PySCIPOpt-ML
preferred-citation:
  type: article
  authors:
  - family-names: "Turner"
    given-names: "Mark"
    orcid: "https://orcid.org/0000-0001-7270-1496"
  - family-names: "Chmiela"
    given-names: "Antonia"
    orcid: "https://orcid.org/0000-0002-4809-2958"
  - family-names: "Koch"
    given-names: "Thorsten"
    orcid: "https://orcid.org/0000-0002-1967-0077"
  - family-names: "Winkler"
    given-names: "Michael"
  title: "PySCIPOpt-ML: Embedding Trained Machine Learning Models into Mixed-Integer Programs"
  doi: 10.48550/arXiv.2312.08074
  date-released: 2023-12-13
  url: "https://arxiv.org/abs/2312.08074"

GitHub Events

Total
  • Watch event: 8
  • Push event: 4
  • Fork event: 1
  • Create event: 1
Last Year
  • Watch event: 8
  • Push event: 4
  • Fork event: 1
  • Create event: 1

Committers

Last synced: about 1 year ago

All Time
  • Total Commits: 67
  • Total Committers: 2
  • Avg Commits per committer: 33.5
  • Development Distribution Score (DDS): 0.015
Past Year
  • Commits: 67
  • Committers: 2
  • Avg Commits per committer: 33.5
  • Development Distribution Score (DDS): 0.015
Top Committers
Name Email Commits
Mark Turner t****r@z****e 66
Mark Turner 6****a 1
Committer Domains (Top 20 + Academic)
zib.de: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time
  • Total issues: 0
  • Total pull requests: 8
  • Average time to close issues: N/A
  • Average time to close pull requests: about 2 hours
  • Total issue authors: 0
  • Total pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 8
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: about 19 hours
  • Issue authors: 0
  • Pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
Pull Request Authors
  • Opt-Mucca (13)
Top Labels
Issue Labels
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 176 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 11
  • Total maintainers: 1
pypi.org: pyscipopt-ml

automatically formulate and embed ML models into MIPs with SCIP

  • Versions: 11
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 176 Last month
Rankings
Dependent packages count: 10.1%
Forks count: 29.8%
Average: 36.5%
Stargazers count: 38.8%
Dependent repos count: 67.1%
Maintainers (1)
Opt-Mucca
Last synced: 4 months ago

Dependencies

docs/requirements.txt pypi
  • sphinx *
  • sphinx-rtd-theme *
  • sphinxcontrib-bibtex *
pyproject.toml pypi
  • numpy *
  • pyscipopt ==4.4.0