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]
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))
obj = Wrapper(b)
f_opt = b.get_meta_information()["f_opt"]
cs = b.get_configuration_space()
list_params = []
for h in cs.get_hyperparameters():
list_params.append(ContinuousParameter(h.name, h.lower, h.upper))
space = ParameterSpace(list_params)
init_design = RandomDesign(space)
X_init = init_design.get_samples(2)
Y_init = np.array([b.objective_function(xi)["function_value"] for xi in X_init])[:, None]
if args.model_type == "bnn":
model = Bohamiann(X_init=X_init, Y_init=Y_init, verbose=True)
elif args.model_type == "rf":
model = RandomForest(X_init=X_init, Y_init=Y_init)
with_gradients = False
elif args.model_type == "dngo":
model = DNGO(X_init=X_init, Y_init=Y_init)
with_gradients = False
elif args.model_type == "gp":
model = BOGP(X_init=X_init, Y_init=Y_init)