code: def test_verify_features_finds_missing_prediction_features():
      np.random.seed(0)
  
      (
          df_titanic_train,
          df_titanic_test,
      ) = utils.get_titanic_binary_classification_dataset()
  
      column_descriptions = {
          "survived": "output",
          "sex": "categorical",
          "embarked": "categorical",
          "pclass": "categorical",
      }
  
      ml_predictor = Predictor(
          type_of_estimator="classifier",
          column_descriptions=column_descriptions,
      )
      ml_predictor.train(df_titanic_train, verify_features=True)
  
      file_name = ml_predictor.save(str(random.random()))
  
      with open(file_name, "rb") as read_file:
          saved_ml_pipeline = dill.load(read_file)
      os.remove(file_name)
      try:
          keras_file_name = (
              file_name[:-5] + "_keras_deep_learning_model.h5"
          )
          os.remove(keras_file_name)
      except:
          pass
  
      # Remove the "age" column from our prediction data
      df_titanic_test = df_titanic_test.drop("age", axis=1)
  
      missing_features = saved_ml_pipeline.named_steps[
          "final_model"
      ].verify_features(df_titanic_test)
      print("missing_features")
      print(missing_features)
  
      print("len(missing_features['prediction_not_training'])")
      print(len(missing_features["prediction_not_training"]))
      print("len(missing_features['training_not_prediction'])")
      print(len(missing_features["training_not_prediction"]))
      assert len(missing_features["prediction_not_training"]) == 0
      assert len(missing_features["training_not_prediction"]) == 1
  
file_path: auto_ml/tests/core_tests/user_logging_tests.py
imports: import datetime
  import dill
  from nose.tools import raises
  import numpy
  import os
  import random
  import sys
  import warnings
  from auto_ml import Predictor
  import utils_testing
label_desc: Use image node_id 5 for calling a global function with example usage: test_verify_features_finds_missing_prediction_features() without return types