def test_random_search(self):
res = random_search(objective_function=objective,
lower=self.lower, upper=self.upper,
num_iterations=3)
assert len(res["x_opt"]) == 1
assert np.array(res["x_opt"]) >= 0
assert np.array(res["x_opt"]) <= 1
def optimization(self):
if (self.method == 'bayesian_optimization'):
results = bayesian_optimization(self.objective_function,
lower=self.lower_bounds,
upper=self.upper_bounds,
num_iterations=self.num_iterations,
acquisition_func='ei')
print(results['x_opt'], results['f_opt'])
self.results = {
'x_opt': results['x_opt'],
'f_opt': results['f_opt'],
'mse': results['incumbent_values']
}
elif (self.method == 'random_search'):
results = random_search(self.objective_function,
lower=self.lower_bounds,
upper=self.upper_bounds,
num_iterations=self.num_iterations)
print(results['x_opt'], results['f_opt'])
self.results = {
'x_opt': results['x_opt'],
'f_opt': results['f_opt'],
'mse': results['incumbent_values']
}
else:
pass
def test_random_search(self):
res = random_search(objective_function=objective,
lower=self.lower,
upper=self.upper,
num_iterations=3)
assert len(res["x_opt"]) == 1
assert np.array(res["x_opt"]) >= 0
assert np.array(res["x_opt"]) <= 1
def get_res(self):
lower = self.space.bound[0]
upper = self.space.bound[1]
res = random_search(
self.fn,
lower,
upper,
num_iterations=self.params['niter'],
output_path=str(self.output_path),
rng=self.rng,
)
return res
def rand_search():
"""
Random search
"""
print('\n============= START Random Search OPTIMIZATION =============\n')
print("""Optimization parameters:
- lower = {}
- upper = {}
- num_iter = {}""".format(lower, upper,
args.num_iterations))
results = random_search(objective_function,
lower,
upper,
num_iterations=args.num_iterations)
print(results["x_opt"])
print(results["f_opt"])
print('\n============= END OPTIMIZATION =============\n')
def optimization(self):
for optimization_method in self.optimization_methods:
t_start = time.time()
if (optimization_method == 'bayesian_optimization'):
results = bayesian_optimization(
self.objective_function,
lower=self.lower_bounds,
upper=self.upper_bounds,
num_iterations=self.num_iterations,
acquisition_func='ei')
t_end = time.time()
t = round(t_end - t_start)
x_opt = {}
for i in range(len(self.tune_params)):
x_opt[self.tune_params[i]] = results['x_opt'][i]
print(x_opt, results['f_opt'])
self.results['bayesian_optimization'] = {
'method': 'bayesian_optimization',
'x_opt': x_opt,
'f_opt': results['f_opt'],
'mse': results['incumbent_values'],
'time_consume': t
}
elif (optimization_method == 'random_search'):
results = random_search(self.objective_function,
lower=self.lower_bounds,
upper=self.upper_bounds,
num_iterations=self.num_iterations)
t_end = time.time()
t = round(t_end - t_start)
x_opt = {}
print(results['x_opt'])
for i in range(len(self.tune_params)):
x_opt[self.tune_params[i]] = results['x_opt'][i]
print(x_opt, results['f_opt'])
self.results['random_search'] = {
'method': 'random_search',
'x_opt': x_opt,
'f_opt': results['f_opt'],
'mse': results['incumbent_values'],
'time_consume': t
}
else:
pass
import numpy as np
from robo.fmin import random_search
# The optimization function that we want to optimize.
# It gets a numpy array with shape (1,D) where D is the number of input dimensions
def objective_function(x):
y = np.sin(3 * x[0]) * 4 * (x[0] - 1) * (x[0] + 2)
return y
# Defining the bounds and dimensions of the input space
lower = np.array([0])
upper = np.array([6])
# Start Bayesian optimization to optimize the objective function
results = random_search(objective_function, lower, upper, num_iterations=20)
print(results["x_opt"])
print(results["f_opt"])
results = entropy_search(f, bounds[:, 0], bounds[:, 1],
num_iterations=n_iters, n_init=n_init)
elif method == "gp_mcmc":
results = bayesian_optimization(f, bounds[:, 0], bounds[:, 1],
num_iterations=n_iters,
n_init=n_init, model_type="gp_mcmc")
elif method == "gp":
results = bayesian_optimization(f, bounds[:, 0], bounds[:, 1],
num_iterations=n_iters,
n_init=n_init, model_type="gp")
elif method == "rf":
results = bayesian_optimization(f, bounds[:, 0], bounds[:, 1],
num_iterations=n_iters,
n_init=n_init, model_type="rf")
elif method == "random_search":
results = random_search(f, bounds[:, 0], bounds[:, 1],
num_iterations=n_iters)
elif method == "bohamiann":
results = bohamiann(f, bounds[:, 0], bounds[:, 1],
num_iterations=n_iters,
n_init=n_init)
# Offline Evaluation
test_error = []
cum_cost = 0
for i, inc in enumerate(results["incumbents"]):
y = f.objective_function_test(np.array(inc))["function_value"]
test_error.append(y)
# Compute the time it would have taken to evaluate this configuration
results = bayesian_optimization(f,
bounds[:, 0],
bounds[:, 1],
num_iterations=n_iters,
n_init=n_init,
model_type="gp")
elif method == "rf":
results = bayesian_optimization(f,
bounds[:, 0],
bounds[:, 1],
num_iterations=n_iters,
n_init=n_init,
model_type="rf")
elif method == "random_search":
results = random_search(f,
bounds[:, 0],
bounds[:, 1],
num_iterations=n_iters)
elif method == "bohamiann":
results = bohamiann(f,
bounds[:, 0],
bounds[:, 1],
num_iterations=n_iters,
n_init=n_init)
# Offline Evaluation
test_error = []
cum_cost = 0
for i, inc in enumerate(results["incumbents"]):
y = f.objective_function_test(np.array(inc))["function_value"]
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)
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)