UCL / Bayesian_opt_Pyro

This repo will implement Bayesian optimization using PYRO

Bayesian_opt_Pyro

This repo aims to solve Bayesian optimization using Pyro and pytorch simple. The objective function and the constraints can be defined in numpy format. The repository implements the following:

Unconstrained Optimization

The unconstrained optimization can minimize one of the following acquisition functions: mean, Lower confidence bound or negative expected improvement. The default solver here is BDGS via Pyro.

Constrained Optimization

The constrained optimization can minimize one of the following acquisition functions: mean, Lower confidence bound or negative expected improvement. The constraints can be satisfied with respect to the mean (probabilistic to be included) Casadi is used and ipopt.

Installation

git clone https://github.com/UCL/Bayesian_opt_Pyro.git

Additional packages needed

pip install casadi 
pip3 install pyro-ppl
pip install sobol_seq
pip install pyDOE

Options for solver

The value depited is the default one.

objective: (REQUIRED) Objective to be minimized

xo initial point. It is not required

bounds (REQUIRED) Bounds for the decision variable

maxfun=20 Number of iterations

N_initial=4 Number of initial points

select_kernel='Matern52' Kernel for Gaussian process

acquisition='LCB' Acquisition function

casadi=False Solve the problem via casadi and ipopt (this is used for constrained problems

constraints = None No constraints by defaults

probabilistic=False To be implemented for probabilistic constraints

print_iteration=False Print iterations

Example

from Bayesian_opt_Pyro.utilities_full import BayesOpt
assert pyro.__version__.startswith('1.5.1')
pyro.enable_validation(True)  # can help with debugging
pyro.set_rng_seed(1)


def f1(x):
    return (6 * x[0] - 2)**2 * np.sin(12 * x[0] - 4)
def g1(x):
    return (6 * x[0] - 2)  - 1

lower = np.array([0.0]*1)
upper = np.array([1.]*1)

solution1 = BayesOpt().solve(f1, [0], bounds=(lower,upper), acquisition='LCB', print_iteration=True, constraints=[g1])


print(solution1)