def run_pulsar_svm(): tuned_parameters = {"C": [0.1, 1, 10], 'gamma': [0.0001, 0.001, 0.01]} iter_range = np.arange(1, 150, 15) x_train, x_test, y_train, y_test = get_pulsar_data() run_svm("Pulsar", x_train, x_test, y_train, y_test, tuned_parameters, iter_range)
def run_pulsar_boosting(): x_train, x_test, y_train, y_test = get_pulsar_data() tuned_parameters = { "learning_rate": [0.1, 0.15, 0.2, 0.25, 0.3], "max_depth": [2, 3, 4, 5, 6], "n_estimators": [10, 15, 20, 25, 30] } run_boosting("Pulsar", x_train, x_test, y_train, y_test, tuned_parameters)
def run_pulsar_nn(): name = 'Pulsar' x_train, x_test, y_train, y_test = get_pulsar_data() run_nn(name, x_train, x_test, y_train, y_test) run_rhc_nn(name, x_train, x_test, y_train, y_test) run_sa_nn(name, x_train, x_test, y_train, y_test) run_ga_nn(name, x_train, x_test, y_train, y_test)
def run_pulsar_nn(): tuned_parameters = { "C": [0.1, 1, 10], 'gamma': [0.001, 0.01, 0.1] } tuned_parameters = { 'max_iter': [1000], 'alpha': 10.0 ** -np.arange(1, 5), 'hidden_layer_sizes':np.arange(10, 15), 'random_state':[99] } iter_range = np.arange(1,200,10) x_train, x_test, y_train, y_test = get_pulsar_data() run_nn("Pulsar", x_train, x_test, y_train, y_test, tuned_parameters, iter_range)
def run_pulsar_knn(): x_train, x_test, y_train, y_test = get_pulsar_data() tuned_parameters = [{'n_neighbors': list(range(1, 10))}] run_knn("Pulsar", x_train, x_test, y_train, y_test, tuned_parameters)
def run_pulsar_dt(): x_train, x_test, y_train, y_test = get_pulsar_data() run_dt("Pulsar", x_train, x_test, y_train, y_test)