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)
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))
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)