def test_cls(): save_dir = './data/eval_exps/soln-ml' if not os.path.exists(save_dir): os.makedirs(save_dir) time_limit = 60 print('==> Start to evaluate with Budget %d' % time_limit) ensemble_method = 'ensemble_selection' eval_type = 'holdout' iris = load_iris() X, y = iris.data, iris.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1, stratify=y) dm = DataManager(X_train, y_train) train_data = dm.get_data_node(X_train, y_train) test_data = dm.get_data_node(X_test, y_test) clf = Classifier(time_limit=time_limit, output_dir=save_dir, ensemble_method=ensemble_method, enable_meta_algorithm_selection=True, ensemble_size=10, evaluation=eval_type, metric='bal_acc') clf.fit(train_data) print(clf.summary()) pred = clf.predict(test_data) print(accuracy_score(test_data.data[1], pred)) shutil.rmtree(save_dir)
def main(): tmp_dir = './data/eval_exps/soln-ml' if not os.path.exists(tmp_dir): os.makedirs(tmp_dir) time_limit = 60 print('==> Start new AutoML task with budget - %d' % time_limit) ensemble_method = 'ensemble_selection' eval_type = 'holdout' iris = load_iris() X, y = iris.data, iris.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1, stratify=y) dm = DataManager(X_train, y_train) train_data = dm.get_data_node(X_train, y_train) test_data = dm.get_data_node(X_test, y_test) clf = Classifier(time_limit=time_limit, output_dir=tmp_dir, ensemble_method=ensemble_method, enable_meta_algorithm_selection=False, ensemble_size=10, optimizer='random_search', evaluation=eval_type, metric='acc', n_jobs=1) clf.fit(train_data, tree_id=2) print(clf.summary()) pred = clf.predict(test_data) print(accuracy_score(test_data.data[1], pred)) shutil.rmtree(tmp_dir)
eval_type = args.eval_type n_jobs = args.n_jobs ensemble_method = args.ens_method if ensemble_method == 'none': ensemble_method = None print('==> Start to evaluate with Budget %d' % time_limit) boston = load_boston() X, y = boston.data, boston.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=1) dm = DataManager(X_train, y_train) train_data = dm.get_data_node(X_train, y_train) test_data = dm.get_data_node(X_test, y_test) save_dir = './data/eval_exps/soln-ml' if not os.path.exists(save_dir): os.makedirs(save_dir) rgs = Regressor(metric='mse', dataset_name='boston', ensemble_method=ensemble_method, evaluation=eval_type, time_limit=time_limit, output_dir=save_dir, random_state=1, n_jobs=n_jobs)