def server(): args = rForest_args() rand = np.random.randint(10000000) data_train, data_test = pre_processed_data_all(args, rand) label_train, label_test = pre_processed_label_all(args, rand) res = [] for i in range(2, 6): print('===\,n=====Epochs: %d=====\n===' % i) res.append( run_function(random_forest, args.cross_validate, data_train, label_train, data_test, label_test, max_depth=i + 1)) print(res) res = extract_measures(res) print(res) plot_experiment('random_forest_test_9000', 'n estimator', res)
def server(): args = rForest_args() rand = np.random.randint(10000000) data_train, data_test = pre_processed_data_all(args, rand) label_train, label_test = pre_processed_label_all(args, rand) res = [] for i in range(5): print('===\n=====Epochs: %d=====\n===' % i) res.append( run_function(linear_classifier, args.cross_validate, data_train, label_train, data_test, label_test, max_iter=(i + 1))) print(res) res = extract_measures(res) print(res) plot_experiment('linear_classifier_test', 'max iteration ( x 100)', res)
def main(): args = keras_args() rand = np.random.randint(10000000) data_train, data_test = helper.pre_processed_data_all(args, rand) label_train, label_test = helper.pre_processed_label_all(args, rand) res = [] for i in range(10, 25): print('===\n=====Epochs: %d=====\n===' % i) resa = [] for j in range(5): resa.append(helper.run_function(keras_build_and_predict, args.cross_validate, data_train, label_train, data_test, label_test, epochs=i)) res.append(helper.mean_measures(helper.extract_measures(resa))) print(res) res = helper.extract_measures(res) print(res) helper.plot_experiment_server('research', 'epochs', res)
def j48_depth(options): args = j48_args(options).parse_args(options[1:]) rand = np.random.randint(10000000) data_train, data_test = pre_processed_data_all(args, rand) label_train, label_test = pre_processed_label_all(args, rand) res = [] for i in range(24): print('===\n=====Epochs: %d=====\n===' % i) res.append( run_function(j48, args.cross_validate, data_train, label_train, data_test, label_test, depth=i, min_split=2, min_leaf=1, min_weight=0)) print(res) res = extract_measures(res) print(res) plot_experiment_server("j48_" + options[0] + "max_depth", 'max depth', res)