DPPy: Sampling DPPs with Python

TL;DR

DPPy is a Python library that provides exact and approximate algorithms for sampling from determinantal point processes, facilitating their use in various scientific and machine learning applications.

Contribution

The paper introduces DPPy, a comprehensive Python toolbox that consolidates known sampling algorithms for both finite and continuous DPPs, enhancing accessibility and usability.

Findings

Provides a unified Python interface for DPP sampling algorithms.

Supports both exact and approximate sampling methods.

Includes extensive documentation and is hosted on GitHub.

Abstract

Determinantal point processes (DPPs) are specific probability distributions over clouds of points that are used as models and computational tools across physics, probability, statistics, and more recently machine learning. Sampling from DPPs is a challenge and therefore we present DPPy, a Python toolbox that gathers known exact and approximate sampling algorithms for both finite and continuous DPPs. The project is hosted on GitHub and equipped with an extensive documentation.