openbox 0.7.9

Efficient and generalized blackbox optimization (BBO) system

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OpenBox Doc | 简体中文

OpenBox: Generalized and Efficient Blackbox Optimization System.

OpenBox is an efficient and generalized blackbox optimization (BBO) system, which owns the following characteristics:

1. Basic BBO algorithms.
2. BBO with constraints.
3. BBO with multiple objectives.
4. BBO with transfer learning.
5. BBO with distributed parallelization.
6. BBO with multi-fidelity acceleration.
7. BBO with early stops.

Deployment Artifacts

Standalone Python package.

Users can install the released package and use it using Python.

Distributed BBO service.

We adopt the "BBO as a service" paradigm and implement OpenBox as a managed general service for black-box optimization. Users can access this service via REST API conveniently, and do not need to worry about other issues such as environment setup, software maintenance, programming, and optimization of the execution. Moreover, we also provide a Web UI, through which users can easily track and manage the tasks.

Features

• Ease of use. Minimal user configuration and setup, and necessary visualization for optimization process.
• Performance standards. Host state-of-the-art optimization algorithms; select proper algorithms automatically.
• Cost-oriented management. Give cost-model based suggestions to users, e.g., minimal machines or time-budget.
• Scalability. Scale to dimensions on the number of input variables, objectives, tasks, trials, and parallel evaluations.
• High efficiency. Effective use of parallel resource, speeding up optimization with transfer-learning, and multi-fidelity acceleration for computationally-expensive evaluations.
• Data privacy protection, robustness and extensibility.

Links

• Examples
• Documentations
• Pypi package
• Conda package: to appear soon
• Blog post: to appear soon

Application Tutorials

• Tuning LightGBM with OpenBox | 简体中文
• Tuning XGBoost using OpenBox | 简体中文

Benchmark Results

Single-objective problems

Ackley-4	Hartmann

Single-objective problems with constraints

Mishra	Keane-10

Multi-objective problems

DTLZ1-6-5	ZDT2-3

Multi-objective problems with constraints

CONSTR	SRN

Installation

System Requirements

Installation Requirements:

• Python >= 3.6
• SWIG == 3.0

Make sure to install SWIG correctly before you install OpenBox.

To install SWIG, please refer to SWIG Installation Guide

Installation from PyPI

To install OpenBox from PyPI:

pip install openbox

Manual Installation from Source

To install OpenBox from command line, please type the following commands on the command line:

git clone https://github.com/thomas-young-2013/open-box.git && cd open-box
cat requirements/main.txt | xargs -n 1 -L 1 pip install
python setup.py install

The tips for installing pyrfr on macOS is here. Please make sure you installed pyrfr correctly.

Quick Start

import numpy as np
from openbox.utils.config_space import ConfigurationSpace, UniformFloatHyperparameter
from openbox.optimizer.generic_smbo import SMBO

# Define Configuration Space
config_space = ConfigurationSpace()
x1 = UniformFloatHyperparameter("x1", -5, 10, default_value=0)
x2 = UniformFloatHyperparameter("x2", 0, 15, default_value=0)
config_space.add_hyperparameters([x1, x2])

# Define Objective Function
def branin(config):
    x1, x2 = config['x1'], config['x2']
    y = (x2-5.1/(4*np.pi**2)*x1**2+5/np.pi*x1-6)**2+10*(1-1/(8*np.pi))*np.cos(x1)+10
    return y

# Run
bo = SMBO(branin, config_space, max_runs=50, task_id='quick_start')
history = bo.run()
print(history)

Releases and Contributing

OpenBox has a frequent release cycle. Please let us know if you encounter a bug by filling an issue.

We appreciate all contributions. If you are planning to contribute any bug-fixes, please do so without further discussions.

If you plan to contribute new features, new modules, etc. please first open an issue or reuse an existing issue, and discuss the feature with us.

To learn more about making a contribution to OpenBox, please refer to our How-to contribution page.

We appreciate all contributions and thank all the contributors!

Feedback

• File an issue on GitHub.
• Email us via [e-mail].

Related Projects

Targeting at openness and advancing AutoML ecosystems, we had also released few other open source projects.

• VocalnoML : an open source system that provides end-to-end ML model training and inference capabilities.

License

The entire codebase is under MIT license