How to use the suod.models.base.SUOD function in suod

y = mat['y']

    # split dataset into train and test
    X_train, X_test, y_train, y_test = \
        train_test_split(X, y, test_size=0.4, random_state=42)

    # standardize data to be digestible for most algorithms
    X_train, X_test = standardizer(X_train, X_test)

    contamination = y.sum() / len(y)

    # get estimators for training and prediction
    base_estimators = get_estimators(contamination=contamination)

    ##########################################################################
    model = SUOD(base_estimators=base_estimators, rp_flag_global=True,
                 approx_clf=approx_clf,
                 n_jobs=n_jobs, bps_flag=True, contamination=contamination,
                 approx_flag_global=True)

    start = time.time()
    model.fit(X_train)  # fit all models with X
    print('Fit time:', time.time() - start)
    print()

    start = time.time()
    model.approximate(X_train)  # conduct model approximation if it is enabled
    print('Approximation time:', time.time() - start)
    print()

    start = time.time()
    predicted_labels = model.predict(X_test)  # predict labels

LOF(n_neighbors=45, contamination=contamination),
        HBOS(contamination=contamination),
        PCA(contamination=contamination),
        OCSVM(contamination=contamination),
        KNN(n_neighbors=5, contamination=contamination),
        KNN(n_neighbors=15, contamination=contamination),
        KNN(n_neighbors=25, contamination=contamination),
        KNN(n_neighbors=35, contamination=contamination),
        KNN(n_neighbors=45, contamination=contamination),
        IForest(n_estimators=50, contamination=contamination),
        IForest(n_estimators=100, contamination=contamination),
        LSCP(detector_list=[LOF(contamination=contamination),
                            LOF(contamination=contamination)])
    ]

    model = SUOD(base_estimators=base_estimators, n_jobs=6, bps_flag=True,
                 contamination=contamination, approx_flag_global=True)

    model.fit(X_train)  # fit all models with X
    model.approximate(X_train)  # conduct model approximation if it is enabled
    predicted_labels = model.predict(X_test)  # predict labels
    predicted_scores = model.decision_function(X_test)  # predict scores
    predicted_probs = model.predict_proba(X_test)  # predict scores

    ###########################################################################
    # compared with other approaches
    evaluate_print('majority vote', y_test, majority_vote(predicted_labels))
    evaluate_print('average', y_test, average(predicted_scores))
    evaluate_print('maximization', y_test, maximization(predicted_scores))

    clf = LOF()
    clf.fit(X_train)

LSCP(detector_list=[LOF(contamination=contamination),
                            LOF(contamination=contamination)]),
        LSCP(detector_list=[LOF(contamination=contamination),
                            LOF(contamination=contamination)]),
        LSCP(detector_list=[LOF(contamination=contamination),
                            LOF(contamination=contamination)]),
        LSCP(detector_list=[LOF(contamination=contamination),
                            LOF(contamination=contamination)]),
        LSCP(detector_list=[LOF(contamination=contamination),
                            LOF(contamination=contamination)]),
    ]

    # model = SUOD(base_estimators=base_estimators, rp_flag_global=True,
    #              n_jobs=6, bps_flag=False, contamination=contamination,
    #              approx_flag_global=True)
    model = SUOD(base_estimators=base_estimators, rp_flag_global=True,
                 n_jobs=6, bps_flag=True, contamination=contamination,
                 approx_flag_global=True)

    start = time.time()
    model.fit(X)  # fit all models with X
    print('Fit time:', time.time() - start)
    print()

    start = time.time()
    model.approximate(X)  # conduct model approximation if it is enabled
    print('Approximation time:', time.time() - start)
    print()

    start = time.time()
    predicted_labels = model.predict(X)  # predict labels
    print('Predict time:', time.time() - start)

X = mat['X']
    y = mat['y']


    X_train, X_test, y_train, y_test = \
        train_test_split(X, y, test_size=0.4, random_state=42)
        
    # standardize data to be digestible for most algorithms
    X_train, X_test = standardizer(X_train, X_test)

    contamination = y.sum() / len(y)

    base_estimators = deepcopy(get_estimators(contamination=contamination))

    ##########################################################################
    model = SUOD(base_estimators=base_estimators, rp_flag_global=True, 
                 approx_clf=RandomForestRegressor(),
                 n_jobs=n_jobs, bps_flag=True, contamination=contamination,
                 approx_flag_global=True)

    start = time.time()
    model.fit(X_train)  # fit all models with X
    print('Fit time:', time.time() - start)
    print()

    start = time.time()
    model.approximate(X_train)  # conduct model approximation if it is enabled
    print('Approximation time:', time.time() - start)
    print()

    start = time.time()
    predicted_labels = model.predict(X_test)  # predict labels