text: import os, sys
  sys.path.insert(0, os.path.abspath(".."))
  
  import pandas as pd
  import pytest
  import pycaret.regression
  import pycaret.datasets
  
  def test():
      # loading dataset
      data = pycaret.datasets.get_data('boston')
      assert isinstance(data, pd.core.frame.DataFrame)
  
      # init setup
      reg1 = pycaret.regression.setup(data, target='medv',silent=True, log_experiment=True, html=False, session_id=123)
      assert isinstance(reg1, tuple)
  
      # compare models
      top3 = pycaret.regression.compare_models(n_select = 3, exclude = ['catboost'])
      assert isinstance(top3, list)
  
      # tune model
      tuned_top3 = [pycaret.regression.tune_model(i, n_iter=3) for i in top3]
      assert isinstance(tuned_top3, list)
  
      # ensemble model
      bagged_top3 = [pycaret.regression.ensemble_model(i) for i in tuned_top3]
      assert isinstance(bagged_top3, list)
  
      # blend models
      blender = pycaret.regression.blend_models(top3)
  
      # stack models
      stacker = pycaret.regression.stack_models(estimator_list = top3[1:], meta_model = top3[0])
  
      # select best model
      best = pycaret.regression.automl(optimize = 'MAPE')
  
      # hold out predictions
      predict_holdout = pycaret.regression.predict_model(best)
      assert isinstance(predict_holdout, pd.core.frame.DataFrame)
  
      # predictions on new dataset
      predict_holdout = pycaret.regression.predict_model(best, data=data)
      assert isinstance(predict_holdout, pd.core.frame.DataFrame)
      
      # finalize model
      final_best = pycaret.regression.finalize_model(best)
  
      # save model
      pycaret.regression.save_model(best, 'best_model_23122019')
   
      # load model
      saved_best = pycaret.regression.load_model('best_model_23122019')
      
      # returns table of models
      all_models = pycaret.regression.models()
      assert isinstance(all_models, pd.core.frame.DataFrame)
  
      # get config
      X_train = pycaret.regression.get_config('X_train')
      X_test = pycaret.regression.get_config('X_test')
      y_train = pycaret.regression.get_config('y_train')
      y_test = pycaret.regression.get_config('y_test')
      assert isinstance(X_train, pd.core.frame.DataFrame)
      assert isinstance(X_test, pd.core.frame.DataFrame)
      assert isinstance(y_train, pd.core.series.Series)
      assert isinstance(y_test, pd.core.series.Series)
  
      # set config
      pycaret.classification.set_config('seed', 124)
      seed = pycaret.classification.get_config('seed')
      assert seed == 124
  
      assert 1 == 1
      
  if __name__ == "__main__":
      test()
  