Speeding Up OPFython with Numba

• URL: http://arxiv.org/abs/2106.11828v1
• Date: Tue, 22 Jun 2021 14:39:32 GMT
• Title: Speeding Up OPFython with Numba
• Authors: Gustavo H. de Rosa, Jo\~ao Paulo Papa
• Abstract summary: Optimum-Path Forest (OPF) has proven to be a state-of-the-art algorithm comparable to Logistic Regressors, Support Vector Machines. Recently, its Python-based version, denoted as OPFython, has been proposed to provide a more friendly framework and a faster prototyping environment. This paper proposes 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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