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* @Author : yiyuiii
* @Date : 2023-06-03 20:00:00
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<!-- markdownlint-disable MD033 MD036 MD041 -->
<div align="center">
# HDBO-B: High Dimensional Bayesian Optimization Benchmark
</div>
<p align="center">
<a href="https://raw.githubusercontent.com/Yiyuiii/HDBO-B/master/LICENSE"><img src="https://img.shields.io/github/license/Yiyuiii/HDBO-B.svg" alt="license"></a>
<a href="https://pypi.python.org/pypi/HDBO-B"><img src="https://img.shields.io/pypi/v/HDBO-B.svg" alt="pypi"></a>
<a href="https://github.com/psf/black"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"></a>
<img src="https://img.shields.io/badge/python-3.8+-blue.svg" alt="python">
</p>
**HDBO-B**, a generalized and unified benchmark for High Dimensional Bayesian Optimization, is including testing functions, example algorithms and more examples.
## :gear: Installation
We recommend using [CONDA](https://www.anaconda.com/) to isolate Python environments and their packages, this protects you from potential package version conflicts.
To install HDBO-B package, choose one below:
- `git clone https://github.com/yiyuiii/HDBO-B && cd HDBO-B && pip install -e .` (You may get example codes **ONLY** by this way.)
- `pip install hdbo-b`
- `wget https://github.com/Yiyuiii/HDBO-B/releases/download/whl/hdbo_b-0.1.0-py3-none-any.whl && pip install hdbo_b-0.1.0-py3-none-any.whl`
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
### Test Functions
To import hand-designed 30 test functions:
```python
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`
### Bayesian Optimization Algorithms
Algorithms can be found in `./examples/algorithms` (more original) and `./examples/wrapper` (directly imported by `./examples/optimize.py`), including
- GP(Gaussian Process) + UCB(Upper Confidence Bound) Implementation with [BOTorch](https://github.com/pytorch/botorch)
> M. Balandat, B. Karrer, D. R. Jiang, S. Daulton, B. Letham, A. G. Wilson, and E. Bakshy. BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization. Advances in Neural Information Processing Systems 33, 2020.
- GP/GPy(warped GP)/gumbel/... + MACE(Multi-objective ACquisition function Ensemble) with [HEBO](https://github.com/huawei-noah/HEBO/tree/master/HEBO)
> Cowen-Rivers, Alexander I., et al. HEBO: Pushing The Limits of Sample-Efficient Hyperparameter Optimisation. Journal of Artificial Intelligence Research, 2022.
- Python implementation of [Add-GP-UCB](https://github.com/kirthevasank/add-gp-bandits)
> Kandasamy K, Schneider J, Póczos B. High dimensional Bayesian optimisation and bandits via additive models. International conference on machine learning, 2015.
- Python implementation of [REMBO](https://github.com/ziyuw/rembo)
> Wang Z, Hutter F, Zoghi M, et al. Bayesian optimization in a billion dimensions via random embeddings. Journal of Artificial Intelligence Research, 2016.
- Wrapper of [ALEBO](https://github.com/facebookresearch/alebo)
> Letham B, Calandra R, Rai A, et al. Re-examining linear embeddings for high-dimensional Bayesian optimization. Advances in neural information processing systems, 2020.
- Wrapper of [TuRBO](https://github.com/uber-research/TuRBO)
> Eriksson D, Pearce M, Gardner J, et al. Scalable global optimization via local bayesian optimization. Advances in neural information processing systems, 2019.
- Wrapper of [SAASBO](https://github.com/martinjankowiak/saasbo)
> Eriksson D, Jankowiak M. High-dimensional Bayesian optimization with sparse axis-aligned subspaces. Uncertainty in Artificial Intelligence, 2021.
`./examples/optimize.py` saves optimizing histories in folder `./result`, which are automatically read by `./example/plot_result.py`.
## :speech_balloon: Common Issues
## :triangular_flag_on_post: TODO List
- [ ] Introducing more realistic test functions
- [ ] Introducing VAE-based algorithms and related datasets needed
## :microscope: Cite Us
## :clipboard: Changelog
#### 2023.06.06 > v0.1.1 :fire:
- Fixed logging.
#### 2023.06.03 > v0.1.0
- Initialization.
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