# Speeding Up OPFython with Numba

arXiv: https://arxiv.org/abs/2106.11828 · HTML: https://mlsanity.com/paper/2106.11828/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 22 Jun 2021.

## Builds on (most cited first, 2 of 2)

- [Distance and Similarity Measures Effect on the Performance of K-Nearest Neighbor Classifier -- A Review](https://mlsanity.com/paper/1708.04321/) (2017)
- [OPFython: A Python-Inspired Optimum-Path Forest Classifier](https://mlsanity.com/paper/2001.10420/) (2020)

## Abstract (arXiv)

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.