def run_test():
print('===== Test Start =====')
# Define Search Space
space = sp.Space()
x1 = sp.Real("x1", -5, 10, default_value=0)
x2 = sp.Real("x2", 0, 15, default_value=0)
space.add_variables([x1, x2])
# Run
try:
max_runs = 10
opt = Optimizer(
branin,
space,
max_runs=max_runs,
time_limit_per_trial=30,
task_id='test_install',
)
history = opt.run()
except Exception:
print(traceback.format_exc())
print('===== Exception in run_test()! Please check. =====')
else:
cnt = history.trial_states.count(SUCCESS)
if cnt == max_runs:
print('===== Congratulations! All trials succeeded. =====')
else:
print('===== %d/%d trials failed! Please check. =====' %
(max_runs - cnt, max_runs))
result = dict()
result['objs'] = [
t1 + t2 + t3,
]
result['constraints'] = [
np.sum((X + 5)**2) - 25,
]
return result
if __name__ == "__main__":
params = {'float': {'x0': (-10, 0, -5), 'x1': (-6.5, 0, -3.25)}}
space = sp.Space()
space.add_variables(
[sp.Real(name, *para) for name, para in params['float'].items()])
opt = Optimizer(
mishra,
space,
num_constraints=1,
num_objs=1,
surrogate_type='gp',
acq_optimizer_type='random_scipy',
max_runs=50,
time_limit_per_trial=10,
task_id='soc',
)
history = opt.run()
print(history)
# License: MIT
import numpy as np
import matplotlib.pyplot as plt
from openbox import Advisor, sp, Observation
# Define Search Space
space = sp.Space()
x1 = sp.Real("x1", -5, 10, default_value=0)
x2 = sp.Real("x2", 0, 15, default_value=0)
space.add_variables([x1, x2])
# Define Objective Function
def branin(config):
x1, x2 = config['x1'], config['x2']
y = (x2 - 5.1 / (4 * np.pi ** 2) * x1 ** 2 + 5 / np.pi * x1 - 6) ** 2 \
+ 10 * (1 - 1 / (8 * np.pi)) * np.cos(x1) + 10
return {'objs': (y, )}
# Run
if __name__ == "__main__":
advisor = Advisor(
space,
# surrogate_type='gp',
surrogate_type='auto',
task_id='quick_start',
)
MAX_RUNS = 50
px2 = 15 * x2
f1 = (px2 - 5.1 / (4 * np.pi ** 2) * px1 ** 2 + 5 / np.pi * px1 - 6) ** 2 \
+ 10 * (1 - 1 / (8 * np.pi)) * np.cos(px1) + 10
f2 = (1 - np.exp(-1 / (2 * x2))) * (2300 * x1 ** 3 + 1900 * x1 ** 2 + 2092 * x1 + 60) \
/ (100 * x1 ** 3 + 500 * x1 ** 2 + 4 * x1 + 20)
result = dict()
result['objs'] = [f1, f2]
return result
if __name__ == "__main__":
# search space
space = sp.Space()
x1 = sp.Real("x1", 0, 1)
x2 = sp.Real("x2", 0, 1)
space.add_variables([x1, x2])
# provide reference point if using EHVI method
ref_point = [18.0, 6.0]
# run
opt = Optimizer(
BraninCurrin,
space,
num_objs=2,
num_constraints=0,
max_runs=50,
surrogate_type='gp',
acq_type='ehvi',
obj1 = x1
obj2 = (1.0 + x2) / x1
c1 = 6.0 - 9.0 * x1 - x2
c2 = 1.0 - 9.0 * x1 + x2
result = dict()
result['objs'] = [obj1, obj2]
result['constraints'] = [c1, c2]
return result
if __name__ == "__main__":
# search space
space = sp.Space()
x1 = sp.Real("x1", 0.1, 10.0)
x2 = sp.Real("x2", 0.0, 5.0)
space.add_variables([x1, x2])
# provide reference point if using EHVI method
ref_point = [10.0, 10.0]
# run
opt = Optimizer(
CONSTR,
space,
num_objs=2,
num_constraints=2,
max_runs=20,
surrogate_type='gp',
acq_type='ehvic',