Submodlib: A Submodular Optimization Library

TL;DR

Submodlib is an open-source Python library that provides efficient, scalable algorithms for submodular optimization, facilitating tasks like data summarization, subset selection, and hyperparameter tuning.

Contribution

It introduces a user-friendly, high-performance library with a C++ engine for submodular optimization, enabling practical applications in data science and machine learning.

Findings

Efficient implementation of submodular optimization algorithms

Supports diverse applications like summarization and data subset selection

Open-source availability encourages widespread adoption

Abstract

Submodular functions are a special class of set functions which naturally model the notion of representativeness, diversity, coverage etc. and have been shown to be computationally very efficient. A lot of past work has applied submodular optimization to find optimal subsets in various contexts. Some examples include data summarization for efficient human consumption, finding effective smaller subsets of training data to reduce the model development time (training, hyper parameter tuning), finding effective subsets of unlabeled data to reduce the labeling costs, etc. A recent work has also leveraged submodular functions to propose submodular information measures which have been found to be very useful in solving the problems of guided subset selection and guided summarization. In this work, we present Submodlib which is an open-source, easy-to-use, efficient and scalable Python library for submodular optimization with a C++ optimization engine. Submodlib finds its application in summarization, data subset selection, hyper parameter tuning, efficient training and more. Through a rich API, it offers a great deal of flexibility in the way it can be used. Source of Submodlib is available at https://github.com/decile-team/submodlib.