code: def setUp(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 = "cardio.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.detector_list = [LOF(), LOF()]
      self.clf = LSCP(self.detector_list)
      self.clf.fit(self.X_train)
      self.roc_floor = 0.6
  
imports: from __future__ import division
  from __future__ import print_function
  import os
  import sys
  import unittest
  from os import path
  from numpy.testing import assert_allclose
  from numpy.testing import assert_array_less
  from numpy.testing import assert_equal
  from numpy.testing import assert_raises
  from scipy.io import loadmat
  from scipy.stats import rankdata
  from sklearn.base import clone
  from sklearn.metrics import roc_auc_score
  from sklearn.model_selection import train_test_split
  from sklearn.utils.validation import check_X_y
  from pyod.models.lscp import LSCP
  from pyod.models.lof import LOF
  from pyod.utils.data import generate_data
label_desc: Use image node_id 1 for calling the TestLSCP obj's underlying member method code with example usage: obj.setUp() without return types