BayesianOptimization.py

from bayes_opt import BayesianOptimization
from bayes_opt.logger import JSONLogger
from bayes_opt.event import Events

logger = JSONLogger(path="./tau_logs.json")


pbounds = {'tau1': (0.1, 0.2), 'tau2': (0.3, 0.4),
           'tau3': (0.5, 0.6), 'tau4': (0.7, 0.8),
           }

def search(tau1, tau2, tau3, tau4):
    params = {}
    params['tau1'] = tau1
    params['tau2'] = tau2
    params['tau3'] = tau3
    params['tau4'] = tau4
    
    score = Testing(tau1, tau2, tau3, tau4)
    return score

# Run Bayesian Optimization
start = time.time()
bayes_optimizer = BayesianOptimization(search, random_state=1, pbounds=pbounds)
bayes_optimizer.subscribe(Events.OPTIMIZATION_STEP, logger)
bayes_optimizer.maximize(init_points=5, n_iter=25)
print('It takes %s minutes' % ((time.time() - start)/60))
print(optimizer.max)