HEBO

Heteroscedastic evolutionary bayesian optimisation, developed by Huawei Noah’s Ark Lab

Quick start

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()))

Tuning sklearn estimators

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)

Features

  • Continuous and categorical design parameters

  • Support creating new parameter type

  • Modular and flexible BO building blocks

  • Multiple probabilistic models including GP, RF and BNN

  • Ready to use optimizers:

    • Constrained and multi-objective optimisation

    • Contextual optimisation

Tutorial