code: def test_input_df_unmodified():
      np.random.seed(42)
  
      (
          df_boston_train,
          df_boston_test,
      ) = utils.get_boston_regression_dataset()
  
      column_descriptions = {"MEDV": "output", "CHAS": "categorical"}
  
      ml_predictor = Predictor(
          type_of_estimator="regressor",
          column_descriptions=column_descriptions,
      )
  
      df_shape = df_boston_train.shape
      ml_predictor.train(df_boston_train)
  
      training_shape = df_boston_train.shape
      assert training_shape[0] == df_shape[0]
      assert training_shape[1] == df_shape[1]
  
      test_score = ml_predictor.score(
          df_boston_test, df_boston_test.MEDV
      )
  
      print("test_score")
      print(test_score)
  
      assert -3.35 < test_score < -2.8
  
file_path: auto_ml/tests/core_tests/basic_tests.py
imports: import datetime
  import os
  import random
  import sys
  from auto_ml import Predictor
  from auto_ml.utils_models import load_ml_model
  from nose.tools import assert_equal, assert_not_equal, with_setup
  from sklearn.metrics import accuracy_score
  import dill
  import numpy
  import utils_testing
label_desc: Use image node_id 2 for calling a global function with example usage: test_input_df_unmodified() without return types