XGBoost Hyperopt hyperparameter optimization type error

Question

I am using Hyperopt to optimize the hyperparameters of my XGBRegressor model and I am getting the error:
reg_alpha = int(space['reg_alpha'],min_child_weight=space['min_child_weight'], TypeError: int() takes at most 2 arguments (3 given)

from scipy.constants import hp
from sklearn.metrics import mean_squared_error
from xgboost import XGBRegressor

space={'max_depth': hp.quniform("max_depth", 3, 18, 1),
    'gamma': hp.uniform ('gamma', 1,9),
    'reg_alpha' : hp.quniform('reg_alpha', 40,180,1),
    'reg_lambda' : hp.uniform('reg_lambda', 0,1),
    'colsample_bytree' : hp.uniform('colsample_bytree', 0.5,1),
    'min_child_weight' : hp.quniform('min_child_weight', 0, 10, 1),
    'n_estimators': 180
}
def hyperparameter_tuning(space):
    model = XGBRegressor(
        n_estimators=space["n_estimators"],
        max_depth=int(space["max_depth"]),
        gamma=space["gamma"],
        reg_alpha=int(
            space["reg_alpha"],
            min_child_weight=space["min_child_weight"],
            colsample_bytree=space["colsample_bytree"],
        ),
    )

    evaluation = [(X_train, y_train), (X_valid, y_valid)]

    model.fit(
        X_train,
        y_train,
        eval_set=evaluation,
        eval_metric="rmse",
        early_stopping_rounds=10,
        verbose=False,
    )

    pred = model.predict(X_valid)
    mse = mean_squared_error(y_valid, pred)
    print("SCORE:", mse)
    return {"loss": mse, "status": STATUS_OK, "model": model}

score 0 · Answer 1 · answered Aug 06 '23 at 01:41

You wrote

        reg_alpha=int(
            space["reg_alpha"],
            min_child_weight=space["min_child_weight"],
            colsample_bytree=space["colsample_bytree"],
        ),

It appears that Author's Intent was

        reg_alpha=int(
            space["reg_alpha"]),
        min_child_weight=space["min_child_weight"],
        colsample_bytree=space["colsample_bytree"],

XGBoost Hyperopt hyperparameter optimization type error

1 Answers1