synthesized_programs = int(out.decode("utf-8"))
print(synthesized_programs)
return synthesized_programs
domainMax = None
try:
domainMax = int(os.environ['DOMAIN_MAX'])
acquisition_type = os.environ['ACQUISITION']
except KeyError:
domainMax = 2 if domainMax is None else domainMax
acquisition_type = 'EI'
domain = [{
'name': "var-" + gadget,
'type': 'discrete',
'domain': tuple(range(0, domainMax))
} for gadget in vocab]
print(domain, acquisition_type)
myBopt = BayesianOptimization(f,
domain,
maximize=True,
exact_feval=True,
acquisition_optimizer_type='lbfgs',
acquisition_type=acquisition_type)
myBopt.run_optimization(max_iter=35)
myBopt.save_report("report")
myBopt.save_evaluations("evals")
myBopt.plot_convergence("convergence.png")
Y_init = np.reshape(np.array(array_y), (-1, 1))
for ind, cost in enumerate(array_cost):
list_sampled_x.append(array_sampled_x[ind, :])
list_y.append(array_y[ind])
list_cost.append(array_cost[ind])
max_iter = max_iter_ - len(list_y)
obj_count = len(list_y) - 1
myBO = BayesianOptimization(f=obj_val,
domain=domain,
constraints=constraints,
X=X_init,
Y=Y_init,
initial_design_numdata=initial_design_numdata,
initial_design_type='latin',
acquisition_type=acq,
maximize=is_max)
myBO.run_optimization(max_iter=max_iter)
myBO.save_report(report_file=txt_path + '_report.txt')
myBO.save_evaluations(evaluations_file=txt_path + '_evaluations.txt')
myBO.save_models(txt_path + '_models.txt')
result = {
'sampled_x': np.array(list_sampled_x),
'observed_y': np.array(list_y),
'cost': np.array(list_cost),
'random_seeds': random_seeds
}
with open(txt_path + '_result.txt', "w") as file:
file.write(str(result))
with open(txt_path + '_result.pickle', "wb") as file:
pickle.dump(result, file)
def trained_val_score(parameters):
parameters = parameters[0]
print("The current parameters are: ",
str(np.around(parameters[0], decimals=6)) + ' ',
str(np.around(parameters[1], decimals=6)) + ' ',
str(np.around(parameters[2], decimals=6)) + ' ',
str(int(parameters[3])))
siamese_net = build_siamese_net(parameters[0], parameters[1],
parameters[2])
score = train_siamese_net(siamese_net, int(parameters[3]))
SAVE_PATH = MODELS_DIR + '/model' + '_' + \
str(np.around(parameters[0], decimals=6)) + '_' + \
str(np.around(parameters[1], decimals=6)) + '_' + \
str(np.around(parameters[2], decimals=6)) + '_' + \
str(int(parameters[3])) + '_' + \
'score_' + str(score) + '.h5'
siamese_net.save(SAVE_PATH)
K.clear_session()
return score
optimizer = BayesianOptimization(f=trained_val_score,
domain=bds,
model_type='GP',
initial_design_numdata=10,
acquisition_type='EI',
maximize=True)
optimizer.run_optimization(max_iter=args.max_iter)
optimizer.save_report(report_file=MODELS_DIR + '/optimization_report')
