Speeding Up OPFython with Numba

TL;DR

This paper enhances the Python implementation of the OPF classifier by integrating Numba to significantly improve its computational speed, especially for large datasets.

Contribution

It introduces a Numba-based acceleration method for OPFython, significantly boosting its performance over naive Python implementations.

Findings

Achieved faster distance calculations with Numba integration.

Outperformed naive Python OPF in speed and efficiency.

Demonstrated improved scalability for large datasets.

Abstract

A graph-inspired classifier, known as Optimum-Path Forest (OPF), has proven to be a state-of-the-art algorithm comparable to Logistic Regressors, Support Vector Machines in a wide variety of tasks. Recently, its Python-based version, denoted as OPFython, has been proposed to provide a more friendly framework and a faster prototyping environment. Nevertheless, Python-based algorithms are slower than their counterpart C-based algorithms, impacting their performance when confronted with large amounts of data. Therefore, this paper proposed a simple yet highly efficient speed up using the Numba package, which accelerates Numpy-based calculations and attempts to increase the algorithm's overall performance. Experimental results showed that the proposed approach achieved better results than the na\"ive Python-based OPF and speeded up its distance measurement calculation.