How to use the pyod.models.iforest.IForest function in pyod

X = mat['X']
y = mat['y'].ravel()
outliers_fraction = np.sum(y) / len(y)
X = StandardScaler().fit_transform(X)

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

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

stat_mat_all = np.zeros([len(classifiers), 10])
report_list = ['train_roc_orig', 'train_p@n_orig', 'train_roc_psa',
               'train_p@n_psa', 
               'test_time_orig', 'test_roc_orig', 'test_p@n_orig', 
               'test_time_psa', 'test_roc_psa', 'test_p@n_psa']

classifier_names = ['ABOD', 'CBLOF', 'FB', 'HBOS', 'IF', 'KNN', 'AKNN', 'LOF',

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 IForest detector
    clf_name = 'IForest'
    clf = IForest()
    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)

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)
    evaluate_print('LOF', y_test, clf.decision_function(X_test))

    clf = IForest()
    clf.fit(X_train)
    evaluate_print('IForest', y_test, clf.decision_function(X_test))

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

random_state = np.random.RandomState(42)
# Define nine outlier detection tools to be compared
classifiers = {
    'Angle-based Outlier Detector (ABOD)':
        ABOD(contamination=outliers_fraction),
    'Cluster-based Local Outlier Factor (CBLOF)':
        CBLOF(contamination=outliers_fraction,
              check_estimator=False, random_state=random_state),
    'Feature Bagging':
        FeatureBagging(LOF(n_neighbors=35),
                       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),
    'Average KNN': KNN(method='mean',
                       contamination=outliers_fraction),
    # 'Median KNN': KNN(method='median',
    #                   contamination=outliers_fraction),
    'Local Outlier Factor (LOF)':
        LOF(n_neighbors=35, contamination=outliers_fraction),
    # 'Local Correlation Integral (LOCI)':
    #     LOCI(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),

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),
    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),

def __init__(self, n_estimators=100,
                 max_samples="auto",
                 contamination=0.1,
                 max_features=1.,
                 bootstrap=False,
                 n_jobs=1,
                 behaviour='old',
                 random_state=None,
                 verbose=0):
        super(IForest, self).__init__(contamination=contamination)
        self.n_estimators = n_estimators
        self.max_samples = max_samples
        self.max_features = max_features
        self.bootstrap = bootstrap
        self.n_jobs = n_jobs
        self.behaviour = behaviour
        self.random_state = random_state
        self.verbose = verbose

# 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,
            'Histogram-base Outlier Detection (HBOS)': 3,
            'Isolation Forest': 4,

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),
        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),