186d05b75a91c7ec57b54da68783c2853ccbc706,examples/pruning/lightgbm_integration.py,,objective,#Any#,23

Before Change

    dtrain = lgb.Dataset(train_x, label=train_y)
    dtest = lgb.Dataset(test_x, label=test_y)

    param = {
        "objective": "binary",
        "metric": "auc",
        "verbosity": -1,
        "boosting_type": trial.suggest_categorical("boosting", ["gbdt", "dart", "goss"]),
        "num_leaves": trial.suggest_int("num_leaves", 10, 1000),
        "learning_rate": trial.suggest_loguniform("learning_rate", 1e-8, 1.0)
    }

    if param["boosting_type"] == "dart":
        param["drop_rate"] = trial.suggest_loguniform("drop_rate", 1e-8, 1.0)
        param["skip_drop"] = trial.suggest_loguniform("skip_drop", 1e-8, 1.0)

After Change

    dtrain = lgb.Dataset(train_x, label=train_y)
    dtest = lgb.Dataset(test_x, label=test_y)

    param = {
        "objective": "binary",
        "metric": "auc",
        "verbosity": -1,
        "boosting_type": "gbdt",
        "lambda_l1": trial.suggest_loguniform("lambda_l1", 1e-8, 10.0),
        "lambda_l2": trial.suggest_loguniform("lambda_l2", 1e-8, 10.0),
        "num_leaves": trial.suggest_int("num_leaves", 2, 256),
        "feature_fraction": min(trial.suggest_uniform("feature_fraction", 0.4, 1.0 + EPS), 1.0),
        "bagging_fraction": min(trial.suggest_uniform("bagging_fraction", 0.4, 1.0 + EPS), 1.0),
        "bagging_freq": trial.suggest_int("bagging_freq", 1, 7),
        "min_child_samples": int(trial.suggest_uniform("min_child_samples", 5, 100 + EPS)),
    }

    // Add a callback for pruning.
    pruning_callback = optuna.integration.LightGBMPruningCallback(trial, "auc")
    gbm = lgb.train(

Instances