Optimizing Branin Function

This example is for optimizing Branin function. It needs to install bayeso-benchmarks, which is included in requirements-optional.txt. First, import some packages we need.

import numpy as np

from bayeso import bo
from bayeso_benchmarks.two_dim_branin import Branin
from bayeso.wrappers import wrappers_bo_function
from bayeso.utils import utils_plotting

Then, declare Branin function we will optimize and a search space for the function.

obj_fun = Branin()
bounds = obj_fun.get_bounds()

def fun_target(X):
    return obj_fun.output(X)

We optimize the objective function with 10 Bayesian optimization rounds and 50 iterations per round with 3 initial random evaluations.

str_fun = 'branin'

num_bo = 10
num_iter = 50
num_init = 3

With BO class in bayeso.bo, optimize the objective function.

model_bo = bo.BO(bounds, debug=False)
list_Y = []
list_time = []

for ind_bo in range(0, num_bo):
    print('BO Round', ind_bo + 1)
    X_final, Y_final, time_final, _, _ = wrappers_bo_function.run_single_round(
        model_bo, fun_target, num_init, num_iter,
        str_initial_method_bo='uniform', str_sampling_method_ao='uniform', num_samples_ao=100,
        seed=42 * ind_bo
    )
    list_Y.append(Y_final)
    list_time.append(time_final)

arr_Y = np.array(list_Y)
arr_time = np.array(list_time)

arr_Y = np.expand_dims(np.squeeze(arr_Y), axis=0)
arr_time = np.expand_dims(arr_time, axis=0)

Plot the results in terms of the number of iterations and time.

utils_plotting.plot_minimum_vs_iter(arr_Y, [str_fun], num_init, True,
    use_tex=True,
    str_x_axis=r'\textrm{Iteration}',
    str_y_axis=r'\textrm{Mininum function value}')
utils_plotting.plot_minimum_vs_time(arr_time, arr_Y, [str_fun], num_init, True,
    use_tex=True,
    str_x_axis=r'\textrm{Time (sec.)}',
    str_y_axis=r'\textrm{Mininum function value}')

Full code:

import numpy as np

from bayeso import bo
from bayeso_benchmarks.two_dim_branin import Branin
from bayeso.wrappers import wrappers_bo_function
from bayeso.utils import utils_plotting

obj_fun = Branin()
bounds = obj_fun.get_bounds()

def fun_target(X):
    return obj_fun.output(X)

str_fun = 'branin'

num_bo = 10
num_iter = 50
num_init = 3

model_bo = bo.BO(bounds, debug=False)
list_Y = []
list_time = []

for ind_bo in range(0, num_bo):
    print('BO Round', ind_bo + 1)
    X_final, Y_final, time_final, _, _ = wrappers_bo_function.run_single_round(
        model_bo, fun_target, num_init, num_iter,
        str_initial_method_bo='uniform', str_sampling_method_ao='uniform', num_samples_ao=100,
        seed=42 * ind_bo
    )
    list_Y.append(Y_final)
    list_time.append(time_final)

arr_Y = np.array(list_Y)
arr_time = np.array(list_time)

arr_Y = np.expand_dims(np.squeeze(arr_Y), axis=0)
arr_time = np.expand_dims(arr_time, axis=0)

utils_plotting.plot_minimum_vs_iter(arr_Y, [str_fun], num_init, True,
    use_tex=True,
    str_x_axis=r'\textrm{Iteration}',
    str_y_axis=r'\textrm{Mininum function value}')
utils_plotting.plot_minimum_vs_time(arr_time, arr_Y, [str_fun], num_init, True,
    use_tex=True,
    str_x_axis=r'\textrm{Time (sec.)}',
    str_y_axis=r'\textrm{Mininum function value}')