Rapid Experimentation with Python Considering Optional and Hierarchical Inputs

TL;DR

This paper introduces raxpy, a Python package that automates the design of space-filling experiments with optional and hierarchical inputs, simplifying complex experimental setups in modeling and simulation.

Contribution

The paper presents novel algorithms for space-filling experimental design tailored to optional and hierarchical input spaces, integrated into an accessible Python package.

Findings

Algorithms produce improved experiment designs

Supports parallel and distributed execution

Effective handling of complex input spaces

Abstract

Space-filling experimental design techniques are commonly used in many computer modeling and simulation studies to explore the effects of inputs on outputs. This research presents raxpy, a Python package that leverages expressive annotation of Python functions and classes to simplify space-filling experimentation. It incorporates code introspection to derive a Python function's input space and novel algorithms to automate the design of space-filling experiments for spaces with optional and hierarchical input dimensions. In this paper, we review the criteria for design evaluation given these types of dimensions and compare the proposed algorithms with numerical experiments. The results demonstrate the ability of the proposed algorithms to create improved space-filling experiment designs. The package includes support for parallelism and distributed execution. raxpy is available as free and open-source software under a MIT license.