HEBO 0.3.6

Heteroscedastic evolutionary bayesian optimisation

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 reproduce bayesmark experiments, you can just drop archived_submissions/hebo to the example_submissions directory.
• The MACEBO in bo.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}
}