pipeline_training.py

xgb_pipeline = Pipeline([("st_scaler", StandardScaler()),("xgb_model",xgb.XGBRegressor())])

gbm_param_grid = {
                  'xgb_model__subsample': np.arange(.05, 1, 0.05),
                  'xgb_model__max_depth': np.arange(5,50,5),
                  'xgb_model__colsample_bytree': np.arange(.1,1.05,.05)
                  }

randomized_neg_mse = RandomizedSearchCV(estimator=xgb_pipeline, 
                      param_distributions=gbm_param_grid, n_iter=10,
                      scoring='neg_mean_squared_error', cv=10)
                      
randomized_neg_mse.fit(X, y)
print("Best rmse: ", np.sqrt(np.abs(randomized_neg_mse.best_score_)))