def METHOD_NAME(self):
    # Define data file and read X and y
    # Generate some data if the source data is missing
    this_directory = path.abspath(path.dirname(__file__))
    mat_file = 'pima.mat'
    try:
        mat = loadmat(path.join(*[this_directory, 'data', mat_file]))
    except TypeError:
        print('{data_file} does not exist. Use generated data'.format(
            data_file=mat_file))
        X, y = generate_data(train_only=True)  # load data
    except IOError:
        print('{data_file} does not exist. Use generated data'.format(
            data_file=mat_file))
        X, y = generate_data(train_only=True)  # load data
    else:
        X = mat['X']
        y = mat['y'].ravel()
        X, y = check_X_y(X, y)
    self.X_train, self.X_test, self.y_train, self.y_test = \
        train_test_split(X, y, test_size=0.4, random_state=42)
    self.clf = XGBOD(random_state=42)
    self.clf.fit(self.X_train, self.y_train)
    self.roc_floor = 0.75