Heteroscedastic evolutionary bayesian optimisation
Project description
pip install HEBO
README
Bayesian optimsation library developed by Huawei Noahs Ark Decision Making and Reasoning (DMnR) lab. The winning submission to the NeurIPS 2020 Black-Box Optimisation Challenge.
Summary | Ablation |
---|---|
Results | Results |
Contributors
Alexander I. Cowen-Rivers, Wenlong Lyu, Rasul Tutunov, Zhi Wang, Antoine Grosnit, Ryan Rhys Griffiths, Alexandre Max Maraval, Hao Jianye, Jun Wang, Jan Peters, Haitham Bou Ammar
Installation
You can install HEBO from pypi by
pip install HEBO
You can also build from source code to obtain our latest update:
cd HEBO
pip install -e .
Documentation
Online documentation can be seen here
You can also build the documentation by your self
pip install -r dev-requirements.txt
cd doc
make html
Demo
import pandas as pd
import numpy as np
from hebo.design_space.design_space import DesignSpace
from hebo.optimizers.hebo import HEBO
def obj(params : pd.DataFrame) -> np.ndarray:
return ((params.values - 0.37)**2).sum(axis = 1).reshape(-1, 1)
space = DesignSpace().parse([{'name' : 'x', 'type' : 'num', 'lb' : -3, 'ub' : 3}])
opt = HEBO(space)
for i in range(5):
rec = opt.suggest(n_suggestions = 4)
opt.observe(rec, obj(rec))
print('After %d iterations, best obj is %.2f' % (i, opt.y.min()))
Auto Tuning via Sklearn Estimator
from sklearn.datasets import load_boston
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score, mean_squared_error
from hebo.sklearn_tuner import sklearn_tuner
space_cfg = [
{'name' : 'max_depth', 'type' : 'int', 'lb' : 1, 'ub' : 20},
{'name' : 'min_samples_leaf', 'type' : 'num', 'lb' : 1e-4, 'ub' : 0.5},
{'name' : 'max_features', 'type' : 'cat', 'categories' : ['auto', 'sqrt', 'log2']},
{'name' : 'bootstrap', 'type' : 'bool'},
{'name' : 'min_impurity_decrease', 'type' : 'pow', 'lb' : 1e-4, 'ub' : 1.0},
]
X, y = load_boston(return_X_y = True)
result = sklearn_tuner(RandomForestRegressor, space_cfg, X, y, metric = r2_score, max_iter = 16)
Test
pytest -v test/ --cov ./bo --cov-report term-missing --cov-config ./test/.coveragerc
Reproduce Experimental Results
- See
archived_submissions/hebo
, which is the exact submission that won the NeurIPS2020 Black-Box Optimsation Challenge. - Use
run_local.sh
in bbo_challenge_starter_kit to reproducebayesmark
experiments, you can just droparchived_submissions/hebo
to theexample_submissions
directory. - The
MACEBO
inbo.optimizers.mace
is the same optimiser, with same hyperparameters but a modified interface (bayesmark dependency removed).
Features
- Continuous, integer and categorical design parameters.
- Constrained and multi-objective optimsation.
- Contextual optimsation.
- Multiple surrogate models including GP, RF and BNN.
- Modular and flexible Bayesian Optimisation building blocks.
Cite Us
Cowen-Rivers, Alexander I., et al. "An Empirical Study of Assumptions in Bayesian Optimisation." arXiv preprint arXiv:2012.03826 (2021).
BibTex
@article{Cowen-Rivers2022-HEBO,
author = {Cowen-Rivers, Alexander and Lyu, Wenlong and Tutunov, Rasul and Wang, Zhi and Grosnit, Antoine and Griffiths, Ryan-Rhys and Maravel, Alexandre and Hao, Jianye and Wang, Jun and Peters, Jan and Bou Ammar, Haitham},
year = {2022},
month = {07},
pages = {},
title = {HEBO: Pushing The Limits of Sample-Efficient Hyperparameter Optimisation},
volume = {74},
journal = {Journal of Artificial Intelligence Research}
}
Project details
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