def main(job_id, params):
print '!!! Entered Main !!!'
print 'Anything printed here will end up in the output directory for job #:', str(
job_id)
print params
f = Branin() # Change this
res = f.objective_function([params['x'], params['y']]) # CHANGE THIS
print res
with open('/home/mansurm/Experiments/brannin/run1.csv',
'a') as csvfile: # CHANGE THIS
writer = csv.writer(csvfile, delimiter=',')
writer.writerow([res['main'][0]])
return res['main'][0]
def run_example(num_points_to_sample=200, verbose=False, **kwargs):
b = Branin()
bounds = b.get_meta_information()['bounds']
dimensions = len(bounds)
lower =np.array([i[0] for i in bounds])
upper =np.array([i[1] for i in bounds])
start_point = (upper-lower)/2
exp = Experiment([lower,upper])
exp.historical_data.append_sample_points([
SamplePoint(start_point, wrapper(start_point,b), 0.6)])
for _ in range(num_points_to_sample):
next_point_to_sample = gp_next_points(exp, **kwargs)[0]
value_of_next_point = wrapper(next_point_to_sample,b)
if verbose:
print "Sampled f({0:s}) = {1:.18E}".format(str(next_point_to_sample), value_of_next_point)
exp.historical_data.append_sample_points([SamplePoint(next_point_to_sample, value_of_next_point, 0.6)])
import numpy as np
from hpolib.benchmarks.synthetic_functions import Branin
# Perform random search on the Branin function
b = Branin()
values = []
cs = b.get_configuration_space()
for i in range(1000):
configuration = cs.sample_configuration()
rval = b.objective_function(configuration)
loss = rval['function_value']
values.append(loss)
print(np.min(values))
import os
from fanova import fANOVA
import numpy as np
from robo.fmin import random_search
from hpolib.benchmarks.synthetic_functions import Branin
import fanova.visualizer
objective_function = Branin()
info = objective_function.get_meta_information()
bounds = np.array(info['bounds'])
config_space = objective_function.get_configuration_space()
# Start Bayesian optimization to optimize the objective function
results = random_search(objective_function,
bounds[:, 0],
bounds[:, 1],
num_iterations=50)
# Creating a fANOVA object
X = np.array([i for i in results['X']])
Y = np.array([i for i in results['y']])
f = fANOVA(X, Y)
print(f.quantify_importance((0, )))
# Visualization
os.makedirs("./plots", exist_ok=True)
vis = fanova.visualizer.Visualizer(f, config_space, "./plots/")
vis.plot_marginal(1)
"""
This example shows how RoBO can be combined with HPOlib.
Before you run it, make sure that you installed it.
For further information have a look here https://github.com/automl/HPOlib2.git
"""
import numpy as np
from hpolib.benchmarks.synthetic_functions import Branin
from robo.fmin import bayesian_optimization
f = Branin()
info = f.get_meta_information()
bounds = np.array(info['bounds'])
# Start Bayesian optimization to optimize the objective function
results = bayesian_optimization(f,
bounds[:, 0],
bounds[:, 1],
num_iterations=50)
print(results["x_opt"])
print(results["f_opt"])
import numpy as np from hpolib.benchmarks.synthetic_functions import Branin from robo.fmin import bayesian_optimization f = Branin() info = f.get_meta_information() bounds = np.array(info['bounds']) # Start Bayesian optimization to optimize the objective function results = bayesian_optimization(f, bounds[:, 0], bounds[:, 1], num_iterations=50) print(results["x_opt"]) print(results["f_opt"])
class Wrapper(UserFunction):
def __init__(self, b):
self.b = b
def evaluate(self, X: np.ndarray):
res = []
for xi in X:
yi = self.b.objective_function(xi)["function_value"]
res.append(UserFunctionResult(xi, np.array([yi])))
return res
if args.benchmark == "branin":
b = Branin()
elif args.benchmark == "hartmann3":
b = Hartmann3()
elif args.benchmark == "hartmann6":
b = Hartmann6()
elif args.benchmark == "bohachevsky":
b = Bohachevsky()
elif args.benchmark == "rosenbrock":
b = Rosenbrock()
elif args.benchmark == "goldsteinprice":
b = GoldsteinPrice()
elif args.benchmark == "forrester":
b = Forrester()
elif args.benchmark == "camelback":
b = Camelback()
elif args.benchmark == "sinone":
from fanova import fANOVA import numpy as np from robo.fmin import random_search from hpolib.benchmarks.synthetic_functions import Branin import fanova.visualizer objective_function = Branin() info = objective_function.get_meta_information() bounds = np.array(info['bounds']) config_space = objective_function.get_configuration_space() # Start Bayesian optimization to optimize the objective function results = random_search(objective_function, bounds[:, 0], bounds[:, 1], num_iterations=50) # Creating a fANOVA object X = np.array([i for i in results['X']]) Y = np.array([i for i in results['y']]) f = fANOVA(X,Y) print(f.quantify_importance((0, ))) # Visualization vis = fanova.visualizer.Visualizer(f, config_space, "./plots/") vis.plot_marginal(1)