Metadata-Version: 2.1
Name: dppy
Version: 0.3.1
Summary: DPPy is a Python library for exact and approximate sampling of Determinantal Point Processes.
Home-page: https://github.com/guilgautier/DPPy
Author: Guillaume Gautier
Author-email: guillaume.gga@gmail.com
License: MIT
Project-URL: Companion paper, https://github.com/guilgautier/DPPy_paper
Project-URL: arXiv, https://arxiv.org/abs/1809.07258
Project-URL: Coverage, https://coveralls.io/github/guilgautier/DPPy?branch=master
Project-URL: Travis, https://travis-ci.com/guilgautier/DPPy
Project-URL: Documentation, https://dppy.readthedocs.io/en/latest/
Project-URL: Source code, https://github.com/guilgautier/DPPy
Description: DPPy: Sampling Determinantal Point Processes with Python
        ========================================================
        
        |Documentation Status| |Build Status| |Coverage Status| |PyPI package|
        
        .. |Documentation Status| image:: https://readthedocs.org/projects/dppy/badge/?version=latest
          :target: https://dppy.readthedocs.io/en/latest/?badge=latest
        
        .. |Build Status| image:: https://travis-ci.com/guilgautier/DPPy.svg?branch=master
          :target: https://travis-ci.com/guilgautier/DPPy
        
        .. |Coverage Status| image:: https://coveralls.io/repos/github/guilgautier/DPPy/badge.svg?branch=master
          :target: https://coveralls.io/github/guilgautier/DPPy?branch=master
        
        .. |PyPI package| image:: https://img.shields.io/pypi/v/dppy?color=blue
          :target: https://pypi.org/project/dppy/
        
        .. |Google Colab| image:: https://badgen.net/badge/Launch/on%20Google%20Colab/blue?icon=terminal
          :target: https://colab.research.google.com/github/guilgautier/DPPy/blob/master/notebooks/Tuto_DPPy.ipynb
        
        *"Anything that can go wrong, will go wrong"* − `Murphy's Law <http://phdcomics.com/comics/archive.php?comicid=1867>`_
        
        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 <https://www.python.org/>`__ library that puts together all
        exact and approximate sampling algorithms for DPPs.
        
        Installation
        ------------
        
        DPPy works with `Python 3.6+ <http://docs.python.org/3/>`__.
        
        Dependencies
        ~~~~~~~~~~~~
        
        This project depends on the following libraries, which are automatically downloaded during installation:
        
        -  `NumPy <http://www.numpy.org>`__
        -  `SciPy <http://www.scipy.org/>`__
        -  `Matplotlib <http://matplotlib.org/>`__
        
        The following dependencies are optional, and unlock extra functionality if installed:
        
        -  `Networkx <http://networkx.github.io/>`__ to play with `uniform
           spanning
           trees <https://dppy.readthedocs.io/en/latest/exotic_dpps/index.html#uniform-spanning-trees>`__ .
        -  `CVXOPT <http://cvxopt.org>`__ to use the ``zono_sampling`` MCMC
           sampler for finite DPPs. **CVXOPT itself requires**
           `GCC <http://gcc.gnu.org>`__,
        
           -  On MAC it comes with
              `Xcode <https://developer.apple.com/xcode/>`__
           -  On UNIX, use your package manager (``apt``, ``yum`` etc)
        
              .. code:: bash
        
                  sudo apt install -qq gcc g++
        
        -  `Sphinx <http://www.sphinx-doc.org/en/master/>`__ to modify and rebuild the documentation
        
        Installation instructions
        ~~~~~~~~~~~~~~~~~~~~~~~~~
        
        DPPy is now available on `PyPI <https://pypi.org/project/dppy/>`__ |PyPI package|
        
        .. code:: bash
        
          pip install dppy
        
        For a full installation, including extra dependencies and dependencies necessary to build the documentation (see above), use :code:`pip install dppy['zonotope','trees','docs']`.
        
        Note that only stable DPPy releases are available on PyPI, and recently included improvements might only be available on the git master branch. If you want to work with the latest version of DPPy
        
        1. If you have a GitHub account please consider forking DPPy and use git to clone your copy of the repo
        
           .. code:: bash
        
               cd <directory_of_your_choice>
               git clone https://github.com/<username>/DPPy.git
        
        2. If you only use git, clone this repository
        
           .. code:: bash
        
               cd <directory_of_your_choice>
               git clone https://github.com/guilgautier/DPPy.git
        
        3. Otherwise simply download the project
        
        4. In any case, install the project with
        
           .. code:: bash
        
               cd DPPy
               pip install .
        
        Use :code:`pip install .['zonotope','trees','docs']` to perform a full install from a local source tree.
        
        How to use it
        -------------
        
        The main DPPy documentation is available online at `http://dppy.readthedocs.io <http://dppy.readthedocs.io>`_.
        There are also some interactive tutorials using Jupyter available at https://github.com/guilgautier/DPPy/blob/master/notebooks/.
        For more details, check below.
        
        Tutorials in `Jupyter notebooks <https://github.com/guilgautier/DPPy/blob/master/notebooks/>`_
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        You can read and work on these interactive tutorial `Notebooks <https://github.com/guilgautier/DPPy/blob/master/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!
        
        Building the documentation
        ~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        The
        `documentation <http://dppy.readthedocs.io/>`__
        is generated locally with
        `Sphinx <http://www.sphinx-doc.org/en/master/>`__ and then built online
        by `ReadTheDocs <https://readthedocs.org/projects/dppy/>`__.
        If you wish to contribute to the documentation or just play with it
        locally, you can install the necessary dependencies and then:
        
        -  Generate the docs locally
        
           .. code:: bash
        
               cd DPPy/docs
               make html
        
        -  Open the local HTML version of the documentation located at
           ``DPPy/docs/_build/html/index.html``
        
           .. code:: bash
        
               open _build/html/index.html
        
        How to cite this work?
        ~~~~~~~~~~~~~~~~~~~~~~
        
        `We wrote a companion paper to DPPy which got accepted for publication in the Machine Learning Open Source Software track of JMLR <http://jmlr.org/papers/v20/19-179.html>`__.
        
        If you use the DPPy toolbox, please consider citing it with this piece of
        BibTeX:
        
        .. code:: bibtex
        
          @article{GPBV19,
            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)},
            title = {{DPPy: DPP Sampling with Python}},
            keywords = {Computer Science - Machine Learning, Computer Science - Mathematical Software, Statistics - Machine Learning},
            url = {http://jmlr.org/papers/v20/19-179.html},
            year = {2019},
            archivePrefix = {arXiv},
            arxivId = {1809.07258},
            note = {Code at http://github.com/guilgautier/DPPy/ Documentation at http://dppy.readthedocs.io/}
          }
        
        Many of the algorithms implemented in DPPy also have associated literature that you should consider citing.
        Please refer to the `bibliography on the documentation page <https://dppy.readthedocs.io/en/latest/bibliography/>`__ and docstrings of each specific DPP sampler for details.
        
        Reproducibility
        ---------------
        
        We would like to thank `Guillermo Polito <https://guillep.github.io/>`__
        for leading our reproducible research
        `workgroup <https://github.com/CRIStAL-PADR/reproducible-research-SE-notes>`__,
        this project owes him a lot.
        
        Take a look at the corresponding
        `booklet <https://github.com/CRIStAL-PADR/reproducible-research-SE-notes>`__
        to learn more on how to make your research reproducible!
        
Keywords: Determinantal Point Processes,sampling schemes,random matrices
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Operating System :: Unix
Provides-Extra: trees
Provides-Extra: docs
Provides-Extra: zonotope
