old_contents: 
  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)
new_contents: 
  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)
  
subject: fix example