DPPy: Sampling DPPs with Python
This provides a practical tool for researchers and practitioners in fields like machine learning and physics who need to sample from DPPs, but it is incremental as it gathers existing algorithms into a unified package.
The authors tackled the challenge of sampling from determinantal point processes (DPPs) by developing DPPy, a Python toolbox that provides exact and approximate algorithms for finite and continuous DPPs, making it accessible for use in physics, statistics, and machine learning.
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.