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DPPy is a Python library for exact and approximate sampling of Determinantal Point Processes.

Project description

Documentation Status Build Status Coverage Status PyPI package

“Anything that can go wrong, will go wrong”Murphy’s Law

Introduction

Determinantal point processes (DPPs) are specific probability distributions over clouds of points that have been popular as models or computational tools across physics, probability, statistics, and more recently of booming interest in machine learning. Sampling from DPPs is a nontrivial matter, and many approaches have been proposed. DPPy is a Python library that puts together all exact and approximate sampling algorithms for DPPs.

Installation

DPPy works with Python 3.4+.

Dependencies

This project depends on the following libraries, which are automatically downloaded during installation:

Download instructions

DPPy is now available on PyPI PyPI package

pip install dppy

However you may not work with the latest version, so

  1. If you have a GitHub account

    • Please consider forking DPPy

    • Use git to clone your copy of the repo

      cd <directory_of_your_choice>
      git clone https://github.com/<username>/DPPy.git
      
  2. If you only use git, clone this repository

    cd <directory_of_your_choice>
    git clone https://github.com/guilgautier/DPPy.git
    
  3. Otherwise simply dowload the project

  4. In any case, install the project with

    cd DPPy
    pip install .
    

How to use it

To avoid duplicating the docs, the entire DPPy documentation is in read the docs in the following link http://dppy.readthedocs.io/. There are also some interactive tutorials using Jupyter in here https://github.com/guilgautier/DPPy/blob/master/notebooks/. For more details, check below.

Tutorials in Jupyter notebooks

You can read and work on these interactive tutorial Notebooks, directly from your web browser, without having to download or install Python or anything. Just click, wait a little bit, and play with the notebook!

Documentation

The documentation is generated locally with Sphinx and then built online by ReadTheDocs.

If you wish to contribute to the documentation or just play with it locally, you can:

  • Install Sphinx

    pip install -U sphinx
    
  • Generate the docs locally

    cd DPPy/docs
    make html
    
  • Open the local HTML version of the documentation located at DPPy/docs/_build/html/index.html

    open _build/html/index.html
    

How to cite this work?

We wrote a companion paper to DPPy which got accepted for publication in the MLOSS track of JMLR.

The companion paper is available on

If you use this package, please consider citing it with this piece of BibTeX:

@article{GPBV19,
  archivePrefix = {arXiv},
  arxivId = {1809.07258},
  author = {Gautier, Guillaume and Polito, Guillermo and Bardenet, R{\'{e}}mi and Valko, Michal},
  journal = {Journal of Machine Learning Research - Machine Learning Open Source Software (JMLR-MLOSS), in press},
  title = {{DPPy: DPP Sampling with Python}},
  keywords = {Computer Science - Machine Learning, Computer Science - Mathematical Software, Statistics - Machine Learning},
  url = {http://arxiv.org/abs/1809.07258},
  year = {2019},
  note = {Code at http://github.com/guilgautier/DPPy/ Documentation at http://dppy.readthedocs.io/}
}

Reproducibility

We would like to thank Guillermo Polito for leading our reproducible research workgroup, this project owes him a lot.

Take a look at the corresponding booklet to learn more on how to make your research reproducible!

Project details


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