code: def test_calibrate_final_model_missing_X_test_y_test_classification():
      np.random.seed(0)
  
      (
          df_titanic_train,
          df_titanic_test,
      ) = utils.get_titanic_binary_classification_dataset()
  
      # Take a third of our test data (a tenth of our overall data) for calibration
      df_titanic_test, df_titanic_calibration = train_test_split(
          df_titanic_test, test_size=0.33, random_state=42
      )
  
      column_descriptions = {
          "survived": "output",
          "sex": "categorical",
          "embarked": "categorical",
          "pclass": "categorical",
      }
  
      ml_predictor = Predictor(
          type_of_estimator="classifier",
          column_descriptions=column_descriptions,
      )
  
      # This should still work, just with warning printed
      ml_predictor.train(df_titanic_train, calibrate_final_model=True)
  
      test_score = ml_predictor.score(
          df_titanic_test, df_titanic_test.survived
      )
  
      print("test_score")
      print(test_score)
  
      assert -0.14 < test_score < -0.12
  
file_path: auto_ml/tests/core_tests/calibrate_classifier_tests.py
imports: import os
  import sys
  from auto_ml import Predictor
  import dill
  from nose.tools import assert_equal, assert_not_equal, with_setup
  import numpy
  from sklearn.model_selection import train_test_split
  import utils_testing
label_desc: Use image node_id 2 for calling a global function with example usage: test_calibrate_final_model_missing_X_test_y_test_classification() without return types