return np.sin(3 * x) * 4 * (x - 1) * (x + 2)
X_lower = np.array([0])
X_upper = np.array([6])
dims = 1
kernel = GPy.kern.Matern52(input_dim=dims, lengthscale=0.01)
model = GPyModel(kernel, optimize=True, noise_variance=1e-8, num_restarts=10)
acquisition_func = EI(model, X_upper=X_upper, X_lower=X_lower, compute_incumbent=compute_incumbent, par=0.1)
maximizer = DIRECT
bo = BayesianOptimization(acquisition_fkt=acquisition_func,
model=model,
maximize_fkt=maximizer,
X_lower=X_lower,
X_upper=X_upper,
dims=dims,
objective_fkt=objective_function)
bo.run(num_iterations=5)
X, Y = bo.get_observations()
X = X[:-1]
Y = Y[:-1]
model = bo.get_model()
f, (ax1, ax2) = plt.subplots(2, sharex=True)
ax1 = plot_model(model, X_lower, X_upper, ax1)
ax1 = plot_objective_function(objective_function, X_lower, X_upper, X, Y, ax1)
ax1.legend()
from robo.acquisition.Entropy import Entropy
from robo.maximizers.maximize import grid_search
from robo.recommendation.incumbent import compute_incumbent
from robo import BayesianOptimization
def objective_function(x):
return np.sin(3 * x) * 4 * (x - 1) * (x + 2)
X_lower = np.array([0])
X_upper = np.array([6])
dims = 1
kernel = GPy.kern.Matern52(input_dim=dims)
model = GPyModel(kernel, optimize=True, noise_variance=1e-4, num_restarts=10)
proposal_measurement = EI(model, X_upper=X_upper, X_lower=X_lower, compute_incumbent=compute_incumbent, par=0.1)
acquisition_func = Entropy(model, X_lower, X_upper, sampling_acquisition=proposal_measurement)
maximizer = grid_search
bo = BayesianOptimization(acquisition_fkt=acquisition_func,
model=model,
maximize_fkt=maximizer,
X_lower=X_lower,
X_upper=X_upper,
dims=dims,
objective_fkt=objective_function)
bo.run(num_iterations=10)
compute_incumbent=compute_incumbent,
sampling_acquisition=LogEI,
Nb=10,
Np=300,
Nf=3500,
loss_function=logLoss,
)
# ei = EI(model, X_upper=X_upper, X_lower=X_lower, par=0.3)
# pi = PI(model, X_upper= X_upper, X_lower=X_lower, par =0.3)
for acquisition_fkt in [entropy_mc]:
bo = BayesianOptimization(
acquisition_fkt=acquisition_fkt,
model=model,
maximize_fkt=maximize_fkt,
X_lower=X_lower,
X_upper=X_upper,
dims=dims,
objective_fkt=objective_funktion,
save_dir=None,
)
next_x = bo.choose_next(initial_X, initial_Y)
print model.m
Visualization(
bo,
next_x,
X=initial_X,
Y=initial_Y,
show_acq_method=False,
show_obj_method=True,
show_model_method=True,
resolution=1000,
return np.sin(3 * x) * 4 * (x - 1) * (x + 2)
X_lower = np.array([0])
X_upper = np.array([6])
dims = 1
kernel = GPy.kern.Matern52(input_dim=dims)
model = GPyModel(kernel, optimize=True, noise_variance=1e-4, num_restarts=10)
proposal_measurement = EI(model,
X_upper=X_upper,
X_lower=X_lower,
compute_incumbent=compute_incumbent,
par=0.1)
acquisition_func = Entropy(model,
X_lower,
X_upper,
sampling_acquisition=proposal_measurement)
maximizer = grid_search
bo = BayesianOptimization(acquisition_fkt=acquisition_func,
model=model,
maximize_fkt=maximizer,
X_lower=X_lower,
X_upper=X_upper,
dims=dims,
objective_fkt=objective_function)
bo.run(num_iterations=10)
kernel = GPy.kern.RBF(input_dim=dims)
kernel = GPy.kern.Matern52(input_dim=dims)
maximize_fkt = grid_search
model = GPyModel(kernel, optimize=True, noise_variance=1e-4, num_restarts=10)
# entropy = Entropy(model, X_upper= X_upper, X_lower=X_lower, sampling_acquisition= LogEI, Nb=10, Np=600, loss_function = logLoss)
entropy_mc = EntropyMC(model, X_upper=X_upper, X_lower=X_lower, compute_incumbent=compute_incumbent, sampling_acquisition=LogEI, Nb=10, Np=300, Nf=3500, loss_function=logLoss)
#ei = EI(model, X_upper=X_upper, X_lower=X_lower, par=0.3)
# pi = PI(model, X_upper= X_upper, X_lower=X_lower, par =0.3)
for acquisition_fkt in [entropy_mc]:
bo = BayesianOptimization(acquisition_fkt=acquisition_fkt,
model=model,
maximize_fkt=maximize_fkt,
X_lower=X_lower,
X_upper=X_upper,
dims=dims,
objective_fkt=objective_funktion,
save_dir=None)
next_x = bo.choose_next(initial_X, initial_Y)
print model.m
Visualization(bo,
next_x,
X=initial_X,
Y=initial_Y,
show_acq_method=False,
show_obj_method=True,
show_model_method=True,
resolution=1000,
dest_folder="./test_output")
X_lower = np.array([-5,0])
X_upper = np.array([10,15])
alpha=10
beta=10
dims = 2
model=NeuralNet(net,X_train,y_train,alpha,beta)
acquisition_func = EI(model, X_upper=X_upper, X_lower=X_lower, compute_incumbent=compute_incumbent, par=0.1)
maximizer = stochastic_local_search
bo = BayesianOptimization(acquisition_fkt=acquisition_func,
model=model,
maximize_fkt=maximizer,
X_lower=X_lower,
X_upper=X_upper,
dims=dims,
objective_fkt=branin)
start_time=time.time()
bo.run(num_iterations=5,X=X_train,Y=y_train)
end_time=time.time()
X, Y = bo.get_observations()
X = X[:-1]
Y = Y[:-1]
model = bo.get_model()
