apsis - Framework for Automated Optimization of Machine Learning Hyper Parameters

TL;DR

The paper introduces apsis, a flexible Python toolkit for hyperparameter optimization that supports random and Bayesian methods, easily integrates with popular ML frameworks, and encourages community contributions.

Contribution

It presents a versatile, open-source framework for hyperparameter tuning compatible with various Python machine learning tools.

Findings

Supports both random and Bayesian optimization methods

Easily integrates with scikit-learn and other Python ML frameworks

Open-source with community engagement on GitHub

Abstract

The apsis toolkit presented in this paper provides a flexible framework for hyperparameter optimization and includes both random search and a bayesian optimizer. It is implemented in Python and its architecture features adaptability to any desired machine learning code. It can easily be used with common Python ML frameworks such as scikit-learn. Published under the MIT License other researchers are heavily encouraged to check out the code, contribute or raise any suggestions. The code can be found at github.com/FrederikDiehl/apsis.