def test_entropy_search(self): res = entropy_search(objective_function=objective, lower=self.lower, upper=self.upper, n_init=2, num_iterations=3) assert len(res["x_opt"]) == 1 assert np.array(res["x_opt"]) >= 0 assert np.array(res["x_opt"]) <= 1
def test_entropy_search(self): res = entropy_search(objective_function=objective, lower=self.lower, upper=self.upper, n_init=2, 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 = entropy_search( self.fn, lower, upper, maximizer=self.params.get('maximizer', 'scipy'), num_iterations=self.params['niter'], output_path=str(self.output_path), rng=self.rng, ) return res
def ent_search(): """ Entropy search """ print('\n============= START Entropy Search OPTIMIZATION =============\n') print("""Optimization parameters: - lower = {} - upper = {} - num_iter = {} - maximizer = {} - model_type = {} - n_init = {} """.format(lower, upper, args.num_iterations, args.maximizer, args.model_type, args.n_init)) results = entropy_search(objective_function, lower, upper, num_iterations=args.num_iterations, maximizer=args.maximizer, model=args.model_type) print(results["x_opt"]) print(results["f_opt"]) print('\n============= END OPTIMIZATION =============\n')
output_path = "./experiments/fabolas/results/cnn_%s/entropy_search_%d" % ( dataset, run_id) elif dataset == "res_net": f = ResidualNeuralNetworkOnCIFAR10(rng=rng) num_iterations = 10 output_path = "./experiments/fabolas/results/res_%s/entropy_search_%d" % ( dataset, run_id) os.makedirs(output_path, exist_ok=True) info = f.get_meta_information() bounds = np.array(info['bounds']) results = entropy_search(f, bounds[:, 0], bounds[:, 1], num_iterations=num_iterations, n_init=2, rng=rng, output_path=output_path) results["run_id"] = run_id results['X'] = results['X'].tolist() results['y'] = results['y'].tolist() test_error = [] current_inc = None current_inc_val = None key = "incumbents" for inc in results["incumbents"]:
f = SurrogateSVM(path="/ihome/kleinaa/devel/git/HPOlib/surrogates/") elif benchmark == "cnn_cifar10": f = SurrogateCNN(path="/ihome/kleinaa/devel/git/HPOlib/surrogates/") elif benchmark == "fcnet_mnist": f = SurrogateFCNet(path="/ihome/kleinaa/devel/git/HPOlib/surrogates/") elif benchmark == "paramnet": dataset = sys.argv[4] f = SurrogateParamNet(dataset, "/ihome/kleinaa/devel/git/HPOlib/surrogates/") benchmark += "_" + dataset info = f.get_meta_information() bounds = np.array(info['bounds']) if method == "entropy_search": 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 benchmark == "fcnet_mnist": f = SurrogateFCNet(path="/ihome/kleinaa/devel/git/HPOlib/surrogates/") elif benchmark == "paramnet": dataset = sys.argv[4] f = SurrogateParamNet(dataset, "/ihome/kleinaa/devel/git/HPOlib/surrogates/") benchmark += "_" + dataset info = f.get_meta_information() bounds = np.array(info['bounds']) if method == "entropy_search": 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,
results_over_runs = dict() ################################################# # Initial data setup ################################################# version = 1 n_init_num = 5 budget_iter = 50 x_init_dir = home_dir + '/IBO_master/experiments_IBO/' + exp_name + '/initial_data/' x_init_name = 'x_init_{}_v{}.pkl'.format(exp_name, version) ################################################# # ES main function ################################################# for it in range(n_runs): results_over_runs[it] = entropy_search(objective_function, lower, upper, n_init=n_init_num, init_data=x_init_dir + x_init_name, num_iterations=budget_iter) ################################################# # Saving the results ################################################# output_main_dir = home_dir + '/IBO_master/experiments_IBO/' + exp_name + '/output_main/' pickle.dump( results_over_runs, open( output_main_dir + "results_{}_{}_init{}_budget{}_v{}.pkl".format( exp_name, method, n_init_num, budget_iter, version), "wb")) print(results_over_runs[0])
output_path = "./experiments/fabolas/results/cnn_%s/entropy_search_%d" % (dataset, run_id) elif dataset == "svhn": f = ConvolutionalNeuralNetworkOnSVHN(rng=rng) num_iterations = 15 output_path = "./experiments/fabolas/results/cnn_%s/entropy_search_%d" % (dataset, run_id) elif dataset == "res_net": f = ResidualNeuralNetworkOnCIFAR10(rng=rng) num_iterations = 10 output_path = "./experiments/fabolas/results/%s/entropy_search_%d" % (dataset, run_id) os.makedirs(output_path, exist_ok=True) info = f.get_meta_information() bounds = np.array(info['bounds']) results = entropy_search(f, bounds[:, 0], bounds[:, 1], num_iterations=num_iterations, n_init=2, rng=rng, output_path=output_path) results["run_id"] = run_id results['X'] = results['X'].tolist() results['y'] = results['y'].tolist() test_error = [] current_inc = None current_inc_val = None key = "incumbents" for inc in results["incumbents"]: print(inc) if current_inc == inc: