How to use the pyod.models.hbos.HBOS function in pyod

if __name__ == "__main__":
    contamination = 0.1  # percentage of outliers
    n_train = 200  # number of training points
    n_test = 100  # number of testing points

    # Generate sample data
    X_train, y_train, X_test, y_test = \
        generate_data(n_train=n_train,
                      n_test=n_test,
                      n_features=2,
                      contamination=contamination,
                      random_state=42)

    # train HBOS detector
    clf_name = 'HBOS'
    clf = HBOS()
    clf.fit(X_train)

    # get the prediction labels and outlier scores of the training data
    y_train_pred = clf.labels_  # binary labels (0: inliers, 1: outliers)
    y_train_scores = clf.decision_scores_  # raw outlier scores

    # get the prediction on the test data
    y_test_pred = clf.predict(X_test)  # outlier labels (0 or 1)
    y_test_scores = clf.decision_function(X_test)  # outlier scores

    # evaluate and print the results
    print("\nOn Training Data:")
    evaluate_print(clf_name, y_train, y_train_scores)
    print("\nOn Test Data:")
    evaluate_print(clf_name, y_test, y_test_scores)

X_train, X_test, y_train, y_test = \
            train_test_split(X, y, test_size=0.4, random_state=random_state)

        # standardizing data for processing
        X_train_norm, X_test_norm = standardizer(X_train, X_test)

        classifiers = {'Angle-based Outlier Detector (ABOD)': ABOD(
            contamination=outliers_fraction),
            'Cluster-based Local Outlier Factor': CBLOF(
                n_clusters=10,
                contamination=outliers_fraction,
                check_estimator=False,
                random_state=random_state),
            'Feature Bagging': FeatureBagging(contamination=outliers_fraction,
                                              random_state=random_state),
            'Histogram-base Outlier Detection (HBOS)': HBOS(
                contamination=outliers_fraction),
            'Isolation Forest': IForest(contamination=outliers_fraction,
                                        random_state=random_state),
            'K Nearest Neighbors (KNN)': KNN(contamination=outliers_fraction),
            'Local Outlier Factor (LOF)': LOF(
                contamination=outliers_fraction),
            'Minimum Covariance Determinant (MCD)': MCD(
                contamination=outliers_fraction, random_state=random_state),
            'One-class SVM (OCSVM)': OCSVM(contamination=outliers_fraction),
            'Principal Component Analysis (PCA)': PCA(
                contamination=outliers_fraction, random_state=random_state),
        }
        classifiers_indices = {
            'Angle-based Outlier Detector (ABOD)': 0,
            'Cluster-based Local Outlier Factor': 1,
            'Feature Bagging': 2,

k_range = [1, 3, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]

        # validate the value of k
        k_range = [k for k in k_range if k < X.shape[0]]

        for k in k_range:
            estimator_list.append(KNN(n_neighbors=k, method='largest'))
            estimator_list.append(KNN(n_neighbors=k, method='mean'))
            estimator_list.append(LOF(n_neighbors=k))
            standardization_flag_list.append(True)
            standardization_flag_list.append(True)
            standardization_flag_list.append(True)

        n_bins_range = [3, 5, 7, 9, 12, 15, 20, 25, 30, 50]
        for n_bins in n_bins_range:
            estimator_list.append(HBOS(n_bins=n_bins))
            standardization_flag_list.append(False)

        # predefined range of nu for one-class svm
        nu_range = [0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.99]
        for nu in nu_range:
            estimator_list.append(OCSVM(nu=nu))
            standardization_flag_list.append(True)

        # predefined range for number of estimators in isolation forests
        n_range = [10, 20, 50, 70, 100, 150, 200, 250]
        for n in n_range:
            estimator_list.append(
                IForest(n_estimators=n, random_state=self.random_state))
            standardization_flag_list.append(False)

        return estimator_list, standardization_flag_list

def __init__(self, n_bins=10, alpha=0.1, tol=0.5, contamination=0.1):
        super(HBOS, self).__init__(contamination=contamination)
        self.n_bins = n_bins
        self.alpha = alpha
        self.tol = tol

        check_parameter(alpha, 0, 1, param_name='alpha')
        check_parameter(tol, 0, 1, param_name='tol')

LOF(n_neighbors=85, contamination=contamination),
        LOF(n_neighbors=90, contamination=contamination),
        LOF(n_neighbors=95, contamination=contamination),
        LOF(n_neighbors=100, contamination=contamination),

        HBOS(contamination=contamination),
        HBOS(contamination=contamination),
        HBOS(contamination=contamination),
        HBOS(contamination=contamination),
        HBOS(contamination=contamination),
        HBOS(contamination=contamination),
        HBOS(contamination=contamination),
        HBOS(contamination=contamination),
        HBOS(contamination=contamination),
        HBOS(contamination=contamination),
        HBOS(contamination=contamination),
        HBOS(contamination=contamination),
        HBOS(contamination=contamination),
        HBOS(contamination=contamination),
        HBOS(contamination=contamination),
        HBOS(contamination=contamination),
        HBOS(contamination=contamination),
        HBOS(contamination=contamination),
        HBOS(contamination=contamination),
        HBOS(contamination=contamination),

        PCA(contamination=contamination),
        PCA(contamination=contamination),
        PCA(contamination=contamination),
        PCA(contamination=contamination),
        PCA(contamination=contamination),
        PCA(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),
        LOF(n_neighbors=5, contamination=contamination),
        LOF(n_neighbors=15, contamination=contamination),
        LOF(n_neighbors=25, contamination=contamination),
        LOF(n_neighbors=35, contamination=contamination),
        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)