AutoOED: Automated Optimal Experiment Design Platform
Paper | Website | Documentation | Contact
AutoOED is an optimal experiment design platform powered with automated machine learning to accelerate the discovery of optimal solutions. Our platform solves multi-objective optimization problems and automatically guides the design of experiment to be evaluated.
AutoOED is developed by Yunsheng Tian, Mina Konaković Luković, Timothy Erps, Michael Foshey and Wojciech Matusik from Computational Design & Fabrication Group at MIT Computer Science and Artificial Intelligence Laboratory. See our motivation behind this project in this article covered by MIT CSAIL Alliances Newsletter: New MIT CSAIL Open-Source Project Automates Experimental Design Optimization.
Overview
AutoOED is a powerful and easy-to-use tool written in Python for design parameter optimization with multiple objectives, which can be used for any kind of experiment settings (chemistry, material, physics, engineering, computer science…). AutoOED aims at improving the sample efficiency of optimization problems, i.e., using less number of samples to achieve the best performance, by applying state-of-the-art machine learning approaches, which is most powerful when the performance evaluation of your problem is expensive (for example, in terms of time or money).
One of the most important features of AutoOED is an intuitive graphical user interface (GUI), which is provided to visualize and guide the experiments for users with little or no experience with coding, machine learning, or optimization. Furthermore, a distributed system is integrated to enable parallelized experimental evaluations either by multiple processes on a single computer or by independent technicians in remote locations.
Installation
AutoOED can be installed either directly from the links to the executable files, or from source code. Source code is the most up-to-date version, while executable files are relatively stable. AutoOED generally works across all Windows/MacOS/Linux operating systems. After installation, there are some extra steps to take if you want to link your own evaluation programs to AutoOED for fully automatic experimentation.
Executable files
Please see the instructions in our documentation for directly installing executable files.
Source code
Step 1: General (Required)
Install by conda with pip:
conda env create -f environment.yml
conda activate autooed
pip install -r requirements_extra.txt
Or install purely by pip:
pip install -r requirements.txt
pip install -r requirements_extra.txt
Note: We recommend to install with Python 3.7, because we have not tested on other versions. If you cannot properly run the programs after installation, please check if the version of these packages match our specifications.
Step 2: Team Version using Windows (Optional)
If you plan to use team version of the software on windows, please run the following commands to replace the mysql connector package, otherwise the optimization will throw some error:
pip uninstall mysql-connector-python
pip install mysql-connector==2.2.9
And if you are using MySQL >= 8.0, you also need to change the authentication method to Legacy Authentication Method in MySQL installer.
Step 3: Custom Evaluation Programs (Optional)
There is some more work to do if you want to link your own evaluation programs to AutoOED to achieve fully automated experimentation, please see our documentation for more details.
Getting Started
After installing from source code, please run the following commands to start AutoOED for different versions respectively.
Personal version
The personal version of AutoOED has all the supported features except distributed collaboration. This version is cleaner and easier to work with, especially when the optimization and evaluation can be done on a single computer.
python run_personal.py
Team version
The team version enables distributed collaboration around the globe by leveraging a centralized MySQL database that can be connected through the Internet. Using this version, the scientist can focus on controlling the optimization and data analysis, while the technicians can evaluate in a distributed fashion and synchronize the evaluated results with other members of the team in real-time, through our provided simple and intuitive user interface. This version provides different software for different roles of a team (manager, scientist, and technician) with proper privilege control implemented. Below are the scripts for different roles respectively, but before running these scripts, please make sure that you have MySQL database management system pre-installed on your computer.
python run_team_manager.py
python run_team_scientist.py
python run_team_technician.py
For more detailed usage and information of AutoOED, please checkout our documentation.
Citation
If you find our work helpful to your research, please consider citing our paper.
@misc{tian2021autooed,
title={AutoOED: Automated Optimal Experiment Design Platform},
author={Yunsheng Tian and Mina Konaković Luković and Timothy Erps and Michael Foshey and Wojciech Matusik},
year={2021},
eprint={2104.05959},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
Contributing
We highly welcome all kinds of contributions, including but not limited to bug fixes, new feature suggestions, more intuitive error messages, and so on.
Especially, the algorithmic part of our code repository is written in a clean and modular way, facilitating extensions and tailoring the code, serving as a testbed for machine learning researchers to easily develop and evaluate their own multi-objective Bayesian optimization algorithms. We are looking forward to supporting more powerful optimization algorithms on our platform.
Contact
If you experience any issues during installing or using the software, or if you want to contribute to AutoOED, please feel free to reach out to us either by creating issues on GitHub or sending emails to [email protected].
65 Dec 8, 2022
4k Jan 9, 2023
419 Jan 1, 2023
1 Nov 21, 2021
152 Jan 7, 2023
1 Jan 11, 2022
8.9k Jan 9, 2023
6.7k Jan 7, 2023
1.4k Jan 6, 2023
465 Jan 2, 2023
2.4k Jan 2, 2023
148 Jul 5, 2021
42 Dec 23, 2022
216 Dec 23, 2022
10 May 15, 2022
47 Dec 17, 2022
2.7k Jan 5, 2023
16 Sep 23, 2022