Optuna: A Next-generation Hyperparameter Optimization Framework
About
The purpose of this study is to introduce new design-criteria for next-generation hyperparameter optimization software. The criteria we propose include (1) define-by-run API that allows users to construct the parameter search space dynamically, (2) efficient implementation of both searching and pruning strategies, and (3) easy-to-setup, versatile architecture that can be deployed for various purposes, ranging from scalable distributed computing to light-weight experiment conducted via interactive interface. In order to prove our point, we will introduce Optuna, an optimization software which is a culmination of our effort in the development of a next generation optimization software. As an optimization software designed with define-by-run principle, Optuna is particularly the first of its kind. We will present the design-techniques that became necessary in the development of the software that meets the above criteria, and demonstrate the power of our new design through experimental results and real world applications. Our software is available under the MIT license (https://github.com/pfnet/optuna/).
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Code Generation | HumanEval | Pass@127.44 | 1043 | |
| Mathematical Reasoning | MATH | Accuracy4.74 | 535 | |
| Financial Strategy Generation | Crypto | ∆VaR9.1 | 34 | |
| Hyperparameter Optimization | California LGBM (test) | Final Simple Regret0.0188 | 22 | |
| Hyperparameter Optimization | Airlines LGBM (test) | Final Simple Regret0.013 | 22 | |
| Hyperparameter Optimization | Breast Cancer SVM (test) | Final Simple Regret0.0068 | 22 | |
| Hyperparameter Optimization | California XGB (test) | Final Simple Regret0.0282 | 22 | |
| Hyperparameter Optimization | Kin8nm XGB (test) | Final Simple Regret0.0089 | 22 | |
| Hyperparameter Optimization | Diabetes RF (test) | Final Simple Regret1.3137 | 22 | |
| Hyperparameter Optimization | Airlines XGB (test) | Final Simple Regret0.0253 | 22 |