hdbo-b 0.1.0

High-Dimentional Bayesian Benchmark (HDBO-B)

HDBO-B: High Dimensional Bayesian Optimization Benchmark

HDBO-B, a generalized and unified benchmark for High Dimensional Bayesian Optimization, including testing functions, example algorithms and more examples.

:gear: Installation

We recommend using CONDA to isolate Python environments and their packages, this protects you from potential package version conflicts.

To install HDBO-B package, choose one below:

• pip install hdbo-b
• git clone https://github.com/yiyuiii/HDBO-B && cd HDBO-B && pip install -e .

In case we use HEBO 0.3.5 for experiments and there is 0.3.2 on PyPI currently, user should manually install:

• git clone https://github.com/huawei-noah/HEBO && cd HEBO/HEBO && pip install -e .

:rocket: Quick Start

Uncomment lines in 'test.py', and run 'python test.py'.

User may also check codes in folder ‘./example’.

:wrench: Details

Import hand-designed 30 test functions:

from HDBOBenchmark import TestFuncs as func_list

We have designed a stricter framework for functions and algorithms, This brings a lot of convenience to scaling, while there are a bit more difficulties in getting started. Check them out in

• './HDBOBenchmark/base/FunctionBase.py'
• './HDBOBenchmark/AdditiveFunction.py'
• './example/wrapper/hebo_wrapper.py'

Optimizing algorithms under './examples' will save histories in folder './result', which are automatically read by './example/plot_result.py'.

:speech_balloon: Common Issues

TODO List

-[ ] Introducing more realistic test functions

-[ ] Introducing VAE-based algorithms and related datasets needed

Cite Us

:clipboard: Changelog

2023.06.03 > v0.1.0 :fire:

• Initialization.