text: from StackedRegressor import *
  from models import *
  
  param2 = {"objective":"reg:linear",
            "min_child_weight": 10, 
            'colsample_bytree': 0.8,
            'subsample': 0.8,
            'learning_rate': 0.1,
            'n_estimators': 50,
            'max_depth': 8, 
            'gamma': 0,       
            'alpha': 0.5,
            'seed': 102022
           }
  
  xg2 = xgb.XGBRegressor(**param2)
  
  stackedreg = StackedRegressor(xg2, models = [knn, knn2, ada, rfr, xg_reg, NN_model], model_names = ["KNN", "KNN2", "ADA", "RFR", "XGB", "NN"], trainx = X_train, trainy = y_train)
  
  cv_results = stackedreg.cv(params = paramS, 
                      nfold = cv_folds,
                      early_stopping_rounds = early_stopping_rounds,
                      metrics = "rmse", 
                      as_pandas = True,
                      verbose_eval = True)
  
  print(cv_results["test-rmse-mean"].tail(1))
  
  cv_results.loc[cv_results['test-rmse-mean'].idxmin()]
  
  res = stackedreg.fit()
  
  generate_predictions(stackedreg)
  
  
  
  
  