Find optimal parameters for CatBoost using GridSearchCV for Regression in Python

catsu.py

"""source : https://nilimeshhalder.medium.com/how-to-find-optimal-parameters-for-catboost-using-gridsearchcv-for-regression-in-python-ef778b60d95d"""
def Snippet_199(): 
    print()
    print(format('How to find optimal parameters for CatBoost using GridSearchCV for Regression','*^82'))    
    
    import warnings
    warnings.filterwarnings("ignore")
    
    # load libraries
    from sklearn import datasets
    from sklearn.model_selection import train_test_split
    from sklearn.model_selection import GridSearchCV
    from catboost import CatBoostRegressor

    # load the iris datasets
    dataset = datasets.load_boston()
    X = dataset.data; y = dataset.target
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

    model = CatBoostRegressor()
    parameters = {'depth'         : [6,8,10],
                  'learning_rate' : [0.01, 0.05, 0.1],
                  'iterations'    : [30, 50, 100]
                 }
    grid = GridSearchCV(estimator=model, param_grid = parameters, cv = 2, n_jobs=-1)
    grid.fit(X_train, y_train)    

    # Results from Grid Search
    print("\n========================================================")
    print(" Results from Grid Search " )
    print("========================================================")    
    
    print("\n The best estimator across ALL searched params:\n",
          grid.best_estimator_)
    
    print("\n The best score across ALL searched params:\n",
          grid.best_score_)
    
    print("\n The best parameters across ALL searched params:\n",
          grid.best_params_)
    
    print("\n ========================================================")
Snippet_199()