How to use the bonsai.ensemble.gbm.GBM function in bonsai

y[y<0] = 0
    n, m = X.shape
    X_train, X_test, y_train, y_test = train_test_split(X, y, 
                                            test_size=0.5)

    models = {"palobst": PaloBoost(distribution="bernoulli",
                            n_estimators=10, 
                            learning_rate=1.0,
                            max_depth=4,
                            subsample=0.5),
            "palofrst": PaloForest(distribution="bernoulli",
                            n_estimators=10, 
                            learning_rate=1.0,
                            max_depth=4,
                            subsample0=0.5),
            "gbm": GBM(distribution="bernoulli",
                            n_estimators=10, 
                            learning_rate=1.0,
                            max_depth=4,
                            subsample=0.5),
            "sklearn": GradientBoostingClassifier(
                        n_estimators=10, 
                        learning_rate=1.0,
                        max_depth=4, 
                        subsample=0.5)}

    print("\n")
    print("# Test Classification")
    print("-----------------------------------------------------")
    print(" model_name     train_time     predict_time   auc    ")
    print("-----------------------------------------------------")
    print(" {0:12}   {1:12}   {2:12}   {3:.5f}".format(

def test_regression():

    X, y = make_friedman1(n_samples=1000, noise=1.0) 
    #X, y = make_friedman2(n_samples=100000) 
    #X, y = make_friedman3(n_samples=100000) 
    n, m = X.shape
    X_train, X_test, y_train, y_test = train_test_split(X, y, 
                                            test_size=0.2)

    models = {"bonsai-gbm": GBM(distribution="gaussian",
                            n_estimators=100, 
                            learning_rate=0.1,
                            max_depth=3,
                            subsample=0.7),
            "sklearn": GradientBoostingRegressor(
                        n_estimators=100, 
                        learning_rate=0.1,
                        max_depth=3, 
                        subsample=0.7)}

    print("\n")
    print("# Test Regression")
    print("-----------------------------------------------------")
    print(" model_name     train_time     predict_time   rmse   ")
    print("-----------------------------------------------------")
    print(" {0:12}   {1:12}   {2:12}   {3:.5f}".format(

X, y = make_friedman1(n_samples=10000, noise=5) 
    n, m = X.shape
    X_train, X_test, y_train, y_test = train_test_split(X, y, 
                                            test_size=0.5)

    models = {"palobst": PaloBoost(distribution="gaussian",
                            n_estimators=100,
                            learning_rate=1.0,
                            max_depth=4,
                            subsample=0.5),
            "palofrst": PaloForest(distribution="gaussian",
                            n_estimators=100,
                            learning_rate=1.0,
                            max_depth=4,
                            subsample0=0.5),
            "gbm": GBM(distribution="gaussian",
                        n_estimators=100, 
                        learning_rate=1.0,
                        max_depth=4,
                        subsample=0.5),
            "sklearn": GradientBoostingRegressor(
                        n_estimators=100, 
                        learning_rate=1.0,
                        max_depth=4, 
                        subsample=0.5)}

    print("\n")
    print("# Test Regression")
    print("-----------------------------------------------------")
    print(" model_name     train_time     predict_time   rmse   ")
    print("-----------------------------------------------------")
    print(" {0:12}   {1:12}   {2:12}   {3:.5f}".format(

def test_classification():

    X, y = make_hastie_10_2(n_samples=100000) 
    y[y<0] = 0
    n, m = X.shape
    X_train, X_test, y_train, y_test = train_test_split(X, y, 
                                            test_size=0.2)

    models = {"bonsai-gbm": GBM(distribution="bernoulli",
                            n_estimators=10, 
                            learning_rate=1.0,
                            max_depth=3,
                            subsample=0.7),
            "sklearn": GradientBoostingClassifier(
                        n_estimators=10, 
                        learning_rate=1.0,
                        max_depth=3, 
                        subsample=0.7)}

    print("\n")
    print("# Test Classification")
    print("-----------------------------------------------------")
    print(" model_name     train_time     predict_time   auc    ")
    print("-----------------------------------------------------")
    print(" {0:12}   {1:12}   {2:12}   {3:.5f}".format(

def clstask(X, y, n_estimators, learning_rate, max_depth, n_btstrp, 
            has_missing, test_size, add_noise):
    models = {"0. PaloBoost": PaloBoost(distribution="bernoulli",
                        n_estimators=n_estimators,
                        learning_rate=learning_rate,
                        max_depth=max_depth, 
                        subsample=0.7),
        "1. SGTB-Bonsai": GBM(distribution="bernoulli",
                        n_estimators=n_estimators,
                        learning_rate=learning_rate,
                        max_depth=max_depth, 
                        subsample=0.7),
         "2. XGBoost": XGBClassifier(
                    n_estimators=n_estimators, 
                    learning_rate=learning_rate,
                    max_depth=max_depth, 
                    subsample=0.7)}
    if not has_missing:
        models["3. Scikit-Learn"] = GradientBoostingClassifier(
                                        n_estimators=n_estimators,
                                        learning_rate=learning_rate,
                                        max_depth=max_depth,
                                        subsample=0.7)
    perf_df = pd.DataFrame(columns=["model", "value", "n_est", "b_idx"])

def regtask(X, y, n_estimators, learning_rate, max_depth, n_btstrp, 
        has_missing, test_size):
    models = {"0. PaloBoost": PaloBoost(distribution="gaussian",
                        n_estimators=n_estimators,
                        learning_rate=learning_rate,
                        max_depth=max_depth, 
                        subsample=0.7),
        "1. SGTB-Bonsai": GBM(distribution="gaussian",
                        n_estimators=n_estimators,
                        learning_rate=learning_rate,
                        max_depth=max_depth, 
                        subsample=0.7),
        "2. XGBoost": XGBRegressor(
                    n_estimators=n_estimators, 
                    learning_rate=learning_rate,
                    max_depth=max_depth, 
                    subsample=0.7)}
    if not has_missing:
        models["3. Scikit-Learn"] = GradientBoostingRegressor(
                                        n_estimators=n_estimators,
                                        learning_rate=learning_rate,
                                        max_depth=max_depth,
                                        subsample=0.7)