• • Overview
• Links
• • Installation
  • • Installation from PyPI
  • Quick Start
  • Benchmark
  • Contributing
  • License
  • TODO

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.

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Overview | Links |Installation | Quick Start | Benchmark |Contributing | License

For more information and API usages, please refer to our Documentation.

Overview

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
BORE optimizer
Anneal
DE
CMA
NSGA
Regularized EA
PBT
TuRBO
LaMCTS
HyperBand
BOHB
DEHB
MFES-BO
TST-R
TAF
TAF(RGPE)
RMoGP
RGPE(mean)

Links

• Documentation
• Pypi package

Installation

Python >= 3.7 is required.

Installation from PyPI

To install XBBO from PyPI:

pip install xbbo

For detailed instructions, please refer to Installation.md

Quick Start

note:XBBO default minimize black box function. All examples can be found in examples/ folder.

import numpy as np

from xbbo.search_space.fast_example_problem import build_space_hard, rosenbrock_2d_hard
from xbbo.search_algorithm.bo_optimizer import BO
from xbbo.utils.constants import MAXINT

if __name__ == "__main__":
  MAX_CALL = 30
  rng = np.random.RandomState(42)

  # define black box function
  blackbox_func = rosenbrock_2d_hard
  # define search space
  cs = build_space_hard(rng)
  # define black box optimizer
  hpopt = BO(space=cs,
              objective_function=blackbox_func,
              seed=rng.randint(MAXINT),
              suggest_limit=MAX_CALL)

  # ---- Use minimize API ----
  hpopt.optimize()
  best_value, best_config = hpopt.trials.get_best()
  print('Find best value:{}'.format(best_value))
  print('Best Config:{}'.format(best_config))

Please refer to Quick Start.md for more information.

Benchmark

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.

Contributing

We welcome contributions to the library along with any potential issues or suggestions.

Please refer to Contributing.md in our docs for more information.

License

This project is released under the MIT license.

TODO

• 文档完善
• Logger
• parallel