LightGBM integration doesn't work properly if you are trying to maximize a custom metric

[volker48]

Expected behavior

If the eval metric has is_higher_better set to True then the objective should be maximized.

Environment

• Optuna version:
• Optuna Integration version:
• Python version:
• OS:
• (Optional) Other libraries and their versions:

Error messages, stack traces, or logs

def higher_is_better(self) -> bool:
        metric_name = self.lgbm_params.get("metric", "binary_logloss")
        return metric_name in ("auc", "auc_mu", "ndcg", "map", "average_precision")

This code is totally incorrect if someone is using a custom evaluation metric.

I only found out about this bug after wasting several hours tuning and the best parameters that were returned being obviously nonsense.

I then tried explicitly creating a study with direction set to 'maximize' and at least hit a warning message.

Steps to reproduce

1. Setup a study to maximize the optimization
2. Create a custom feval callback function
3. In the params explicitly set the metric to something other than ("auc", "auc_mu", "ndcg", "map", "average_precision")
4. In the call to optuna_integration.lightgbm.train, set the feval callable to the function created in step 2

def score_cb(preds, eval_data):
    score = calculate_score(preds, eval_data.label)
    return 'score', score, True

lgb_study = optuna.create_study(direction="maximize", study_name="LightGBM Auto Tune")

params = {
    "objective": "regression",
    "metric": "correlation",
    "boosting_type": "gbdt",
}

model = opt_lgb.train(params, dtrain, study=lgb_study, num_boost_round=2000, valid_sets=[dtrain, dval], valid_names=["training", "validation"], feval=score_cb, callbacks=[opt_lgb.early_stopping(stopping_rounds=30), lgb.log_evaluation(1)])

Additional context (optional)

No response