I am using optuna to tune xgboost model's hyperparameters. I find it stuck at trial 2 (trial_id=3) for a long time(244 minutes). But When I look at the SQLite database which records the trial data, I find all the trial 2 (trial_id=3) hyperparameters has been calculated except the mean squared error value of trial 2. And the optuna trial 2 (trial_id=3) seems stuck at that step. I want to know why this happened? And how to fix the issue?
Here is the code
def xgb_hyperparameter_tuning():
def objective(trial):
params = {
"n_estimators": trial.suggest_int("n_estimators", 1000, 10000, step=100),
"booster": trial.suggest_categorical("booster", ["gbtree", "gblinear", "dart"]),
"max_depth": trial.suggest_int("max_depth", 1, 20, step=1),
"learning_rate": trial.suggest_float("learning_rate", 0.0001, 0.2, step=0.001),
"min_child_weight": trial.suggest_float("min_child_weight", 1.0, 20.0, step=1.0),
"colsample_bytree": trial.suggest_float("colsample_bytree", 0.1, 1.0, step=0.1),
"subsample": trial.suggest_float("subsample",0.1, 1.0, step=0.1),
"reg_alpha": trial.suggest_float("reg_alpha", 0.0, 11.0, step=0.1),
"reg_lambda": trial.suggest_float("reg_lambda", 0.0, 11.0, step=0.1),
"num_parallel_tree": 10,
"random_state": 16,
"n_jobs": 10,
"early_stopping_rounds": 1000,
}
model = XGBRegressor(**params)
mse = make_scorer(mean_squared_error)
cv = cross_val_score(estimator=model, X=X_train, y=log_y_train, cv=20, scoring=mse, n_jobs=-1)
return cv.mean()
study = optuna.create_study(study_name="HousePriceCompetitionXGB", direction="minimize", storage="sqlite:///house_price_competition_xgb.db", load_if_exists=True)
study.optimize(objective, n_trials=100,)
return None
xgb_hyperparameter_tuning()
Here is the output
[I 2021-11-16 10:06:27,522] A new study created in RDB with name: HousePriceCompetitionXGB
[I 2021-11-16 10:08:40,050] Trial 0 finished with value: 0.03599314763859092 and parameters: {'n_estimators': 5800, 'booster': 'gblinear', 'max_depth': 4, 'learning_rate': 0.1641, 'min_child_weight': 17.0, 'colsample_bytree': 0.4, 'subsample': 0.30000000000000004, 'reg_alpha': 10.8, 'reg_lambda': 7.6000000000000005}. Best is trial 0 with value: 0.03599314763859092.
[I 2021-11-16 10:11:55,830] Trial 1 finished with value: 0.028514652199592445 and parameters: {'n_estimators': 6600, 'booster': 'gblinear', 'max_depth': 17, 'learning_rate': 0.0821, 'min_child_weight': 20.0, 'colsample_bytree': 0.7000000000000001, 'subsample': 0.2, 'reg_alpha': 1.2000000000000002, 'reg_lambda': 7.2}. Best is trial 1 with value: 0.028514652199592445.
Here is the sqlite database trial_values table's data
| trial_value_id | trial_id | objective | value |
|---|---|---|---|
| 1 | 1 | 0 | 0.0359931476385909 |
| 2 | 2 | 0 | 0.0285146521995924 |
Here is the sqlite database trial_params table's data And you can see all the trial 2 (trial_id=3) hyperparameters has been calculated
| param_id | trial_id | param_name | param_value | distribution_json |
|---|---|---|---|---|
| 1 | 1 | n_estimators | 5800.0 | {"name": "IntUniformDistribution", "attributes": {"low": 1000, "high": 10000, "step": 100}} |
| 2 | 1 | booster | 1.0 | {"name": "CategoricalDistribution", "attributes": {"choices": ["gbtree", "gblinear", "dart"]}} |
| 3 | 1 | max_depth | 4.0 | {"name": "IntUniformDistribution", "attributes": {"low": 1, "high": 20, "step": 1}} |
| 4 | 1 | learning_rate | 0.1641 | {"name": "DiscreteUniformDistribution", "attributes": {"low": 0.0001, "high": 0.1991, "q": 0.001}} |
| 5 | 1 | min_child_weight | 17.0 | {"name": "DiscreteUniformDistribution", "attributes": {"low": 1.0, "high": 20.0, "q": 1.0}} |
| 6 | 1 | colsample_bytree | 0.4 | {"name": "DiscreteUniformDistribution", "attributes": {"low": 0.1, "high": 1.0, "q": 0.1}} |
| 7 | 1 | subsample | 0.3 | {"name": "DiscreteUniformDistribution", "attributes": {"low": 0.1, "high": 1.0, "q": 0.1}} |
| 8 | 1 | reg_alpha | 10.8 | {"name": "DiscreteUniformDistribution", "attributes": {"low": 0.0, "high": 11.0, "q": 0.1}} |
| 9 | 1 | reg_lambda | 7.6 | {"name": "DiscreteUniformDistribution", "attributes": {"low": 0.0, "high": 11.0, "q": 0.1}} |
| 10 | 2 | n_estimators | 6600.0 | {"name": "IntUniformDistribution", "attributes": {"low": 1000, "high": 10000, "step": 100}} |
| 11 | 2 | booster | 1.0 | {"name": "CategoricalDistribution", "attributes": {"choices": ["gbtree", "gblinear", "dart"]}} |
| 12 | 2 | max_depth | 17.0 | {"name": "IntUniformDistribution", "attributes": {"low": 1, "high": 20, "step": 1}} |
| 13 | 2 | learning_rate | 0.0821 | {"name": "DiscreteUniformDistribution", "attributes": {"low": 0.0001, "high": 0.1991, "q": 0.001}} |
| 14 | 2 | min_child_weight | 20.0 | {"name": "DiscreteUniformDistribution", "attributes": {"low": 1.0, "high": 20.0, "q": 1.0}} |
| 15 | 2 | colsample_bytree | 0.7 | {"name": "DiscreteUniformDistribution", "attributes": {"low": 0.1, "high": 1.0, "q": 0.1}} |
| 16 | 2 | subsample | 0.2 | {"name": "DiscreteUniformDistribution", "attributes": {"low": 0.1, "high": 1.0, "q": 0.1}} |
| 17 | 2 | reg_alpha | 1.2 | {"name": "DiscreteUniformDistribution", "attributes": {"low": 0.0, "high": 11.0, "q": 0.1}} |
| 18 | 2 | reg_lambda | 7.2 | {"name": "DiscreteUniformDistribution", "attributes": {"low": 0.0, "high": 11.0, "q": 0.1}} |
| 19 | 3 | n_estimators | 7700.0 | {"name": "IntUniformDistribution", "attributes": {"low": 1000, "high": 10000, "step": 100}} |
| 20 | 3 | booster | 2.0 | {"name": "CategoricalDistribution", "attributes": {"choices": ["gbtree", "gblinear", "dart"]}} |
| 21 | 3 | max_depth | 4.0 | {"name": "IntUniformDistribution", "attributes": {"low": 1, "high": 20, "step": 1}} |
| 22 | 3 | learning_rate | 0.1221 | {"name": "DiscreteUniformDistribution", "attributes": {"low": 0.0001, "high": 0.1991, "q": 0.001}} |
| 23 | 3 | min_child_weight | 3.0 | {"name": "DiscreteUniformDistribution", "attributes": {"low": 1.0, "high": 20.0, "q": 1.0}} |
| 24 | 3 | colsample_bytree | 0.5 | {"name": "DiscreteUniformDistribution", "attributes": {"low": 0.1, "high": 1.0, "q": 0.1}} |
| 25 | 3 | subsample | 0.1 | {"name": "DiscreteUniformDistribution", "attributes": {"low": 0.1, "high": 1.0, "q": 0.1}} |
| 26 | 3 | reg_alpha | 10.8 | {"name": "DiscreteUniformDistribution", "attributes": {"low": 0.0, "high": 11.0, "q": 0.1}} |
| 27 | 3 | reg_lambda | 1.1 | {"name": "DiscreteUniformDistribution", "attributes": {"low": 0.0, "high": 11.0, "q": 0.1}} |
Although I am not 100% sure, I think I know what happened.
This issue happens because some parameters are not suitable for certain booster type and the trial will return nan as result and be stuck at the step - calculating the MSE score.
To solve the problem, you just need to delete the "booster": "dart".
In other words, using "booster": trial.suggest_categorical("booster", ["gbtree", "gblinear"]), rather than "booster": trial.suggest_categorical("booster", ["gbtree", "gblinear", "dart"]), can solve the problem.
I got the idea when I tuned my LightGBMRegressor Model. I found many trials fail because these trials returned nan and they all used the same "boosting_type"="rf". So I deleted the rf and all 100 trials were completed without any error. Then I looked for the XGBRegressor issue which I posted above. I found all the trials which were stuck had the same "booster":"dart" either. So I deleted the dart, and the XGBRegressor run normally.