https://github.com/oxfordcontrol/Bayesian-Optimization
Tip revision: 980a0edb978710fe29529526977d0c14649b3088 authored by nrontsis on 01 May 2018, 15:18:21 UTC
Match merged PR of GPflow.
Match merged PR of GPflow.
Tip revision: 980a0ed
run.py
import os
import numpy as np
import tensorflow as tf
import random
import argparse
import gpflow
from methods.oei import OEI
from methods.random import Random
import time
import pickle
from benchmark_functions import scale_function, hart6
import copy
algorithms = {
'OEI': OEI,
'Random': Random
}
class SafeMatern32(gpflow.kernels.Matern32):
# See https://github.com/GPflow/GPflow/pull/727
def euclid_dist(self, X, X2):
r2 = self.square_dist(X, X2)
return tf.sqrt(tf.maximum(r2, 1e-40))
def run(options, seed, robust=False, save=False):
'''
Runs bayesian optimization on the setup defined in the options dictionary
starting from a predefined seed. Saves results on the folder named 'out' while logging
is saved on the folder 'log'.
'''
options['seed'] = seed
# Set random seed: Numpy, Tensorflow, Python
tf.reset_default_graph()
tf.set_random_seed(seed)
np.random.seed(seed)
random.seed(seed)
# Create bo object which will be called later to perform Bayesian Optimization.
bo = algorithms[options['algorithm']](options)
try:
start = time.time()
# Run BO
X, Y = bo.bayesian_optimization()
end = time.time()
print('Done with:', bo.options['job_name'], 'seed:', seed,
'Time:', '%.2f' % ((end - start)/60), 'min')
except KeyboardInterrupt:
print("Caught KeyboardInterrupt, stopping.")
raise
except:
print('Experiment of', bo.options['job_name'],
'with seed', seed, 'failed')
X, Y = None, None
if not robust:
raise
if save:
save_folder = 'out/' + bo.options['job_name'] + '/'
filepath = save_folder + str(seed) + '.npz'
try:
os.makedirs(save_folder)
except OSError:
pass
try:
os.remove(filepath)
except OSError:
pass
np.savez(filepath, X=X, Y=Y)
def create_options(args):
functions = {
'hart6': hart6()
}
kernels_gpflow = {
'RBF': gpflow.kernels.RBF,
'Matern32': SafeMatern32,
}
options = vars(copy.copy(args))
options['objective'] = functions[options['function']]
options['objective'].bounds = np.asarray(options['objective'].bounds)
# This scales the input domain of the function to [-0.5, 0.5]^n. It's different to the
# normalize option, which scales the output of the function.
options['objective'] = scale_function(options['objective'])
input_dim = options['objective'].bounds.shape[0]
if options['algorithm'] != 'LP_EI':
k = kernels_gpflow[options['kernel']](
input_dim=input_dim, ARD=options['ard'])
if options['priors']:
k.lengthscales.prior = gpflow.priors.Gamma(shape=2, scale=0.5)
k.variance.prior = gpflow.priors.Gaussian(mu=1, var=2)
options['kernel'] = k
options['job_name'] = options['function'] + '_' + options['algorithm']
return options
def main(args):
options = create_options(args)
save_folder = 'out/' + options['job_name'] + '/'
filepath = save_folder + 'arguments.pkl'
try:
os.makedirs(save_folder)
except OSError:
pass
try:
os.remove(filepath)
except OSError:
pass
try:
with open(filepath, 'wb') as file:
pickle.dump(args, file, pickle.HIGHEST_PROTOCOL)
except OSError:
pass
filepath = save_folder + 'fmin.txt'
try:
fmin = options['objective'].fmin
except AttributeError:
fmin = 0
np.savetxt(filepath, np.array([fmin]))
for seed in range(args.seed, args.seed + args.num_seeds):
run(options, seed=seed, save=options['save'])
def create_parser():
parser = argparse.ArgumentParser()
parser.add_argument('--function', default='hart6')
parser.add_argument('--algorithm', default='OEI')
parser.add_argument('--seed', type=int, default=123)
parser.add_argument('--num_seeds', type=int, default=1)
parser.add_argument('--save', type=int, default=1)
parser.add_argument('--batch_size', type=int, default=5)
parser.add_argument('--iterations', type=int, default=10)
parser.add_argument('--initial_size', type=int, default=10)
parser.add_argument('--model_restarts', type=int, default=20,
help='Random restarts when optimizing the Likelihood of the GP.')
parser.add_argument('--opt_restarts', type=int, default=20,
help='Random restarts when optimizing the acquisition function.')
parser.add_argument('--normalize_Y', type=int, default=1,
help='If set to 1, then the outputs of the function under optimization is normalized to have variance 1 and mean 0')
parser.add_argument('--noise', type=float,
help='Used to set the likelihood to a fixed value')
parser.add_argument('--kernel', default='Matern32')
parser.add_argument('--ard', type=int, default=0)
parser.add_argument('--nl_solver', default='knitro')
parser.add_argument('--hessian', type=int, default=1)
parser.add_argument('--priors', type=int, default=0)
return parser
if __name__ == '__main__':
parser = create_parser()
args = parser.parse_args()
main(args)