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XBBO is an an effective, modular, reproducible and flexible black-box optimization (BBO) codebase, which aims to provide a common framework and benchmark for the BBO community.
This project is now supported by PengCheng Lab.
Overview | Links |Installation | Quick Start | Benchmark |Contributing | License
For more information and API usages, please refer to our Documentation.
XBBO decouples the search algorithm from the search space and provides a unified search space interface, allowing developers to focus on the search algorithm.
We provide these black box optimization algorithms as follows:
Search Algorithm | Docs | Official Links | multi-fideility | transfer | multi-obj |
---|---|---|---|---|---|
Random | Random | ||||
Bayesian Optimization | BO | ||||
TPE | TPE | hyperopt | |||
BORE | BORE | ltiao/bore | |||
Anneal | Anneal | ||||
CEM | CEM | ||||
Diffenential Evolution | DE | ||||
CMA-ES | CMA-ES | CMA-ES/pycma | |||
NSGA-II | √ | ||||
Regularized EA | REA | Google-Research | |||
PBT | |||||
TuRBO | TuRBO | ||||
LaMCTS | LaMCTS | facebookresearch | |||
HyperBand | √ | ||||
BOHB | √ | ||||
DEHB | √ | ||||
MFES-BO | √ | ||||
TST-R | √ | ||||
TAF | √ | ||||
TAF(RGPE) | √ | ||||
RMoGP | √ | ||||
RGPE(mean) | √ | ||||
PSO | |||||
XNES | |||||
LFBO | LFBO | lfbo-ml/lfbo |
Python >= 3.7
is required.
To install XBBO from PyPI:
pip install xbbo
For detailed instructions, please refer to Installation.md
XBBO uses ConfigSpace as a tool to define search space. Please see ConfigSpace for how to define a search space.
note:
XBBO default minimize black box function. All examples can be found in examples/
folder.
Here we take optimizing a quadratic function as a toy example:
from ConfigSpace import ConfigurationSpace
from ConfigSpace.hyperparameters import \
CategoricalHyperparameter, UniformFloatHyperparameter, UniformIntegerHyperparameter
from xbbo.search_algorithm.bo_optimizer import BO
def custom_black_box_func(config):
'''
define black box function:
y = x^2
'''
return config['x'] ** 2
def custom_search_space():
'''
define search space
'''
configuration_space = ConfigurationSpace()
configuration_space.add_hyperparameter(UniformFloatHyperparameter('x', -10, 10, default_value=-3))
return configuration_space
if __name__ == "__main__":
MAX_CALL = 30
cs = custom_search_space()
# specify black box optimizer
hpopt = BO(space=cs, suggest_limit=MAX_CALL)
# ---- Begin BO-loop ----
for i in range(MAX_CALL):
# suggest
trial_list = hpopt.suggest() # defalut suggest one trial
# evaluate
obs = custom_black_box_func(trial_list[0].config_dict)
# observe
trial_list[0].add_observe_value(obs)
hpopt.observe(trial_list=trial_list)
print(obs)
print('find best (value, config):{}'.format(hpopt.trials.get_best()))
Please refer to Quick Start.md for more information.
XBBO provides an easy-to-use benchmark tool, users can easily and quickly test the performance of the variety black-box algorithms on each test problem. Clik here for more information.
We welcome contributions to the library along with any potential issues or suggestions.
Please refer to Contributing.md in our docs for more information.
This project is released under the MIT license.
超参搜索(黑盒优化)框架
Python other
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