def test_BO(dim, obj_fun, ftarget, max_FEs, lb, ub, logfile):
sys.path.insert(0, '../')
from bayes_optim import AnnealingBO, BO, ContinuousSpace
from bayes_optim.Surrogate import GaussianProcess, trend
space = ContinuousSpace(list(zip(lb, ub)))
mean = trend.constant_trend(dim,
beta=None) # equivalent to Ordinary Kriging
thetaL = 1e-10 * (ub - lb) * np.ones(dim)
thetaU = 10 * (ub - lb) * np.ones(dim)
theta0 = np.random.rand(dim) * (thetaU - thetaL) + thetaL
model = GaussianProcess(mean=mean,
corr='matern',
theta0=theta0,
thetaL=thetaL,
thetaU=thetaU,
noise_estim=False,
nugget=1e-6,
optimizer='BFGS',
wait_iter=5,
random_start=5 * dim,
likelihood='concentrated',
eval_budget=100 * dim)
return BO(search_space=space,
obj_fun=obj_fun,
model=model,
DoE_size=dim * 5,
max_FEs=max_FEs,
verbose=False,
n_point=1,
minimize=True,
ftarget=ftarget,
logger=logfile)
def test_pickling2():
dim = 5
lb, ub = -1, 5
def fitness(x):
x = np.asarray(x)
return np.sum(x**2)
space = ContinuousSpace([lb, ub]) * dim
mean = trend.constant_trend(dim, beta=None)
thetaL = 1e-10 * (ub - lb) * np.ones(dim)
thetaU = 10 * (ub - lb) * np.ones(dim)
theta0 = np.random.rand(dim) * (thetaU - thetaL) + thetaL
model = GaussianProcess(mean=mean,
corr='squared_exponential',
theta0=theta0,
thetaL=thetaL,
thetaU=thetaU,
nugget=0,
noise_estim=False,
optimizer='BFGS',
wait_iter=3,
random_start=dim,
likelihood='concentrated',
eval_budget=100 * dim)
opt = BO(search_space=space,
obj_fun=fitness,
model=model,
DoE_size=5,
max_FEs=10,
verbose=True,
n_point=1,
logger='log')
opt.save('test')
opt = BO.load('test')
print(opt.run())
os.remove('test')
os.remove('log')
def test_BO(dim, obj_fun, ftarget, max_FEs, lb, ub, logfile):
sys.path.insert(0, "../")
from bayes_optim import BO, AnnealingBO, RealSpace
from bayes_optim.Surrogate import GaussianProcess, trend
space = RealSpace([lb, ub]) * dim
mean = trend.constant_trend(dim, beta=0) # equivalent to Ordinary Kriging
thetaL = 1e-10 * (ub - lb) * np.ones(dim)
thetaU = 10 * (ub - lb) * np.ones(dim)
theta0 = np.random.rand(dim) * (thetaU - thetaL) + thetaL
model = GaussianProcess(
mean=mean,
corr="matern",
theta0=theta0,
thetaL=thetaL,
thetaU=thetaU,
noise_estim=False,
nugget=1e-6,
optimizer="BFGS",
wait_iter=5,
random_start=5 * dim,
likelihood="concentrated",
eval_budget=100 * dim,
)
return BO(
search_space=space,
obj_fun=obj_fun,
model=model,
DoE_size=dim * 5,
max_FEs=max_FEs,
verbose=False,
n_point=1,
minimize=True,
ftarget=ftarget,
logger=logfile,
)
from bayes_optim import BO, ContinuousSpace
from bayes_optim.Surrogate import GaussianProcess, trend
np.random.seed(123)
dim = 5
lb, ub = -1, 5
def fitness(x):
x = np.asarray(x)
return np.sum(x**2)
space = ContinuousSpace([lb, ub]) * dim
mean = trend.constant_trend(dim, beta=None)
thetaL = 1e-10 * (ub - lb) * np.ones(dim)
thetaU = 10 * (ub - lb) * np.ones(dim)
theta0 = np.random.rand(dim) * (thetaU - thetaL) + thetaL
model = GaussianProcess(theta0=theta0,
thetaL=thetaL,
thetaU=thetaU,
nugget=0,
noise_estim=False,
optimizer='BFGS',
wait_iter=3,
random_start=dim,
likelihood='concentrated',
eval_budget=100 * dim)