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",
)
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")
