How to use the ngboost.SurvNGBoost function in ngboost

def run_experiments(df_train_filename, df_test_filename, natural_gradient = False,
                   second_order = False, quadrant_search = False):
    df_train = pd.read_csv(df_train_filename)
    df_test = pd.read_csv(df_test_filename)
    Y = np.array(df_train['Y'])
    C = np.array(df_train['C'])
    X = np.array(df_train.drop(['Y', 'C'], axis=1))
    sb = SurvNGBoost(Base = lambda : DecisionTreeRegressor(criterion='mse'),
                     Dist = LogNormal,
                     Score = CRPS_surv,
                     n_estimators = 1000,
                     learning_rate = 0.1,
                     natural_gradient = natural_gradient,
                     second_order = second_order,
                     quadrant_search = quadrant_search,
                     nu_penalty=1e-5)
    loss_train = sb.fit(X, Y, C)
    
    preds_train = sb.pred_mean(X)
    preds_test = sb.pred_mean(df_test.drop(["Y", "C"], axis=1))
    conc_test = calculate_concordance_naive(preds_test, df_test["Y"], df_test["C"])
    test_true_mean = np.mean(df_test["Y"])
    test_pred_mean = np.mean(preds_test)
    return loss_train, conc_test, test_true_mean, test_pred_mean

natural_gradient = False,
                     second_order = True,
                     quadrant_search = False,
                     nu_penalty=1e-5)

    sb3 = SurvNGBoost(Base = lambda : DecisionTreeRegressor(criterion='friedman_mse', min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3),
                     Dist = HomoskedasticNormal,
                     Score = MLE_surv,
                     n_estimators = 100,
                     learning_rate = 0.1,
                     natural_gradient = True,
                     second_order = False,
                     quadrant_search = False,
                     nu_penalty=1e-5)

    sb4 = SurvNGBoost(Base = lambda : DecisionTreeRegressor(criterion='friedman_mse', min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3),
                     Dist = HomoskedasticNormal,
                     Score = MLE_surv,
                     n_estimators = 100,
                     learning_rate = 0.1,
                     natural_gradient = True,
                     second_order = True,
                     quadrant_search = False,
                     nu_penalty=1e-5)

    gbm = GradientBoostingRegressor(n_estimators = 100, learning_rate = 0.1)

    train, test = sb1.fit(X[:700], Y[:700], C[:700], X[700:], Y[700:], C[700:])
    train, test = sb2.fit(X[:700], Y[:700], C[:700], X[700:], Y[700:], C[700:])
    train, test = sb3.fit(X[:700], Y[:700], C[:700], X[700:], Y[700:], C[700:])
    train, test = sb4.fit(X[:700], Y[:700], C[:700], X[700:], Y[700:], C[700:])

def run_survboost():

    df_train = pd.read_csv("data/simulated/sim_data_train.csv")
    df_test = pd.read_csv("data/simulated/sim_data_test.csv")

    sb = SurvNGBoost(Base = lambda: DecisionTreeRegressor(criterion='mse'),
                     Dist = LogNormal,
                     Score = CRPS_surv,
                     n_estimators = 200)

    sb.fit(df_train.drop(["Y", "C"], axis=1).as_matrix(), 
           df_train["Y"], df_train["C"])
    preds_test = sb.pred_mean(df_test.drop(["Y", "C"], axis=1))
    np.save("data/simulated/sim_preds_survboost.npy", preds_test)

natural_gradient = False,
                     second_order = False,
                     quadrant_search = False,
                     nu_penalty=1e-5)

    sb2 = SurvNGBoost(Base = lambda : DecisionTreeRegressor(criterion='friedman_mse', min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3),
                     Dist = HomoskedasticNormal,
                     Score = MLE_surv,
                     n_estimators = 100,
                     learning_rate = 0.1,
                     natural_gradient = False,
                     second_order = True,
                     quadrant_search = False,
                     nu_penalty=1e-5)

    sb3 = SurvNGBoost(Base = lambda : DecisionTreeRegressor(criterion='friedman_mse', min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3),
                     Dist = HomoskedasticNormal,
                     Score = MLE_surv,
                     n_estimators = 100,
                     learning_rate = 0.1,
                     natural_gradient = True,
                     second_order = False,
                     quadrant_search = False,
                     nu_penalty=1e-5)

    sb4 = SurvNGBoost(Base = lambda : DecisionTreeRegressor(criterion='friedman_mse', min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3),
                     Dist = HomoskedasticNormal,
                     Score = MLE_surv,
                     n_estimators = 100,
                     learning_rate = 0.1,
                     natural_gradient = True,
                     second_order = True,

def main():
    m, n = 1000, 5
    X = np.random.rand(m, n).astype(np.float32) + 1
    #Y = np.random.rand(m).astype(np.float32) * 2 + 1
    Y = np.sum(X, axis=1)
    #Y = (Y - np.mean(Y)) / np.std(Y)
    #Y = Y - np.min(Y) + 1e-2
    C = (np.random.rand(m) > 1.5).astype(np.float32)
    # C = np.zeros_like(Y)
    #X = simulate_X(num_unif=30, num_bi=30, N=1000, num_normal=30, normal_cov_strength=[0.5]*30)
    #Y, C = simulate_Y_C(X)
    print(X.shape, Y.shape, C.shape)
    print('Censoring fraction: %f' % (np.mean(C)))

    sb1 = SurvNGBoost(Base = lambda : DecisionTreeRegressor(criterion='friedman_mse', min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3),
                     Dist = HomoskedasticNormal,
                     Score = MLE_surv,
                     n_estimators = 100,
                     learning_rate = 0.1,
                     natural_gradient = False,
                     second_order = False,
                     quadrant_search = False,
                     nu_penalty=1e-5)

    sb2 = SurvNGBoost(Base = lambda : DecisionTreeRegressor(criterion='friedman_mse', min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3),
                     Dist = HomoskedasticNormal,
                     Score = MLE_surv,
                     n_estimators = 100,
                     learning_rate = 0.1,
                     natural_gradient = False,
                     second_order = True,

#X = simulate_X(num_unif=30, num_bi=30, N=1000, num_normal=30, normal_cov_strength=[0.5]*30)
    #Y, C = simulate_Y_C(X)
    print(X.shape, Y.shape, C.shape)
    print('Censoring fraction: %f' % (np.mean(C)))

    sb1 = SurvNGBoost(Base = lambda : DecisionTreeRegressor(criterion='friedman_mse', min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3),
                     Dist = HomoskedasticNormal,
                     Score = MLE_surv,
                     n_estimators = 100,
                     learning_rate = 0.1,
                     natural_gradient = False,
                     second_order = False,
                     quadrant_search = False,
                     nu_penalty=1e-5)

    sb2 = SurvNGBoost(Base = lambda : DecisionTreeRegressor(criterion='friedman_mse', min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3),
                     Dist = HomoskedasticNormal,
                     Score = MLE_surv,
                     n_estimators = 100,
                     learning_rate = 0.1,
                     natural_gradient = False,
                     second_order = True,
                     quadrant_search = False,
                     nu_penalty=1e-5)

    sb3 = SurvNGBoost(Base = lambda : DecisionTreeRegressor(criterion='friedman_mse', min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3),
                     Dist = HomoskedasticNormal,
                     Score = MLE_surv,
                     n_estimators = 100,
                     learning_rate = 0.1,
                     natural_gradient = True,
                     second_order = False,