def objectiveCat(trial):
    param = {}
    param['learning_rate'] = 0.02
    param['depth'] = trial.suggest_int('depth', 13, 15)
    param['l2_leaf_reg'] = trial.suggest_discrete_uniform('l2_leaf_reg', 4.0, 5.5, 0.5)
    param['min_child_samples'] = trial.suggest_categorical('min_child_samples', [4, 6, 8])
    param['grow_policy'] = 'Depthwise'
    param['iterations'] = trial.suggest_int('iterations', 600, 1000)
    param['use_best_model'] = True
    param['eval_metric'] = 'RMSE'
    param['od_type'] = 'iter'
    param['od_wait'] = 20
    param['random_state'] = 1
    param['logging_level'] = 'Silent'
    regressor = CatBoostRegressor(**param)

    regressor.fit(train_X.copy(), train_y.copy(),
                eval_set=[(val_X.copy(), val_y.copy())],
                early_stopping_rounds=10)
    loss = mean_squared_error(val_y, regressor.predict(val_X.copy()))
    return loss

study = optuna.create_study(study_name=f'catboost-seed{1}')
study.optimize(objectiveCat, n_trials=10)