Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance

TL;DR

This paper analyzes the components of the Tree-Structured Parzen Estimator (TPE) algorithm, clarifies their roles, and demonstrates how specific parameter settings can enhance its empirical performance in hyperparameter tuning.

Contribution

It provides a detailed ablation study of TPE's control parameters, revealing their impacts and proposing optimized settings for better tuning results.

Findings

Identified key roles of TPE control parameters

Optimized parameter settings improve TPE performance

Enhanced understanding of TPE's algorithm components

Abstract

Recent scientific advances require complex experiment design, necessitating the meticulous tuning of many experiment parameters. Tree-structured Parzen estimator (TPE) is a widely used Bayesian optimization method in recent parameter tuning frameworks such as Hyperopt and Optuna. Despite its popularity, the roles of each control parameter in TPE and the algorithm intuition have not been discussed so far. The goal of this paper is to identify the roles of each control parameter and their impacts on parameter tuning based on the ablation studies using diverse benchmark datasets. The recommended setting concluded from the ablation studies is demonstrated to improve the performance of TPE. Our TPE implementation used in this paper is available at https://github.com/nabenabe0928/tpe/tree/single-opt.