代码如下：

def objective(trial,data=data,target=target):
    
    train_x, test_x, train_y, test_y = train_test_split(data, target, test_size=0.15,random_state=42)
    param = {
        'tree_method':'gpu_hist',  # this parameter means using the GPU when training our model to speedup the training process
        'lambda': trial.suggest_loguniform('lambda', 1e-3, 10.0),
        'alpha': trial.suggest_loguniform('alpha', 1e-3, 10.0),
        'colsample_bytree': trial.suggest_categorical('colsample_bytree', [0.3,0.4,0.5,0.6,0.7,0.8,0.9, 1.0]),
        'subsample': trial.suggest_categorical('subsample', [0.4,0.5,0.6,0.7,0.8,1.0]),
        'learning_rate': trial.suggest_categorical('learning_rate', [0.008,0.009,0.01,0.012,0.014,0.016,0.018, 0.02]),
        'n_estimators': 4000,
        'max_depth': trial.suggest_categorical('max_depth', [5,7,9,11,13,15,17,20]),
        'random_state': trial.suggest_categorical('random_state', [24, 48,2020]),
        'min_child_weight': trial.suggest_int('min_child_weight', 1, 300),
    }
    model = xgb.XGBRegressor(**param)  
    
    model.fit(train_x,train_y,eval_set=[(test_x,test_y)],early_stopping_rounds=100,verbose=False)
    
    preds = model.predict(test_x)
    
    rmse = mean_squared_error(test_y, preds,squared=False)
    
    return rmse

然后使用optuna进行study：

使用minimize是因为我们的loss使用了mse，因此需要进行最小化我们的loss。

n_trials代表一共进行执行的次数
study = optuna.create_study(direction='minimize')
study.optimize(objective, n_trials=50)
print('Number of finished trials:', len(study.trials))
print('Best trial:', study.best_trial.params)

 下面是catboost的：

def objective(trial,data=data,target=target):
    
    train_x, test_x, train_y, test_y = train_test_split(data, target, test_size=0.15,random_state=42)
    param = {
        'loss_function': 'RMSE',
        'task_type': 'GPU',
        'l2_leaf_reg': trial.suggest_loguniform('l2_leaf_reg', 1e-3, 10.0),
        'max_bin': trial.suggest_int('max_bin', 200, 400),
        #'rsm': trial.suggest_uniform('rsm', 0.3, 1.0),
        'subsample': trial.suggest_uniform('bagging_fraction', 0.4, 1.0),
        'learning_rate': trial.suggest_uniform('learning_rate', 0.006, 0.018),
        'n_estimators':  25000,
        'max_depth': trial.suggest_categorical('max_depth', [7,10,14,16]),
        'random_state': trial.suggest_categorical('random_state', [24, 48,2020]),
        'min_data_in_leaf': trial.suggest_int('min_data_in_leaf', 1, 300),
    }
    model = CatBoostRegressor(**param)  
    
    model.fit(train_x,train_y,eval_set=[(test_x,test_y)],early_stopping_rounds=200,verbose=False)
    
    preds = model.predict(test_x)
    
    rmse = mean_squared_error(test_y, preds,squared=False)
    
    return rmse