def test_bohamiann(self):
res = bohamiann(objective_function=objective,
lower=self.lower,
upper=self.upper,
n_init=2,
num_iterations=3)
assert len(res["x_opt"]) == 1
assert np.array(res["x_opt"]) >= 0
assert np.array(res["x_opt"]) <= 1
def boham():
"""
Bayesian Networks
"""
print('\n============= START Bohamiann OPTIMIZATION =============\n')
print("""Optimization parameters:
- lower = {}
- upper = {}
- num_iter = {}
- maximizer = {}
- acq_func = {}""".format(lower, upper,
args.num_iterations,
args.maximizer,
args.acquisition_func))
results = bohamiann(objective_function,
lower,
upper,
num_iterations=args.num_iterations,
maximizer=args.maximizer,
acquisition_func=args.acquisition_func)
print(results["x_opt"])
print(results["f_opt"])
print('\n============= END OPTIMIZATION =============\n')
elif method == "rf":
results = bayesian_optimization(f,
bounds[:, 0],
bounds[:, 1],
num_iterations=n_iters,
n_init=n_init,
model_type="rf")
elif method == "random_search":
results = random_search(f,
bounds[:, 0],
bounds[:, 1],
num_iterations=n_iters)
elif method == "bohamiann":
results = bohamiann(f,
bounds[:, 0],
bounds[:, 1],
num_iterations=n_iters,
n_init=n_init)
# Offline Evaluation
test_error = []
cum_cost = 0
for i, inc in enumerate(results["incumbents"]):
y = f.objective_function_test(np.array(inc))["function_value"]
test_error.append(y)
# Compute the time it would have taken to evaluate this configuration
c = f.objective_function(np.array(results["X"][i]))["cost"]
cum_cost += c
import logging
import numpy as np
from hpolib.benchmarks.synthetic_functions import Branin
from robo.fmin import bohamiann
logging.basicConfig(level=logging.INFO)
def objective_function(x):
y = np.sin(3 * x[0]) * 4 * (x[0] - 1) * (x[0] + 2)
return y
# Defining the bounds and dimensions of the input space
lower = np.array([0])
upper = np.array([6])
# Start Bayesian optimization to optimize the objective function
results = bohamiann(objective_function, lower, upper, num_iterations=20)
print(results["x_opt"])
print(results["f_opt"])