问LightGBM内件中的若干问题

EN

LGBM_SPACE = {
    'type': 'LGBM',
    'task': hp.choice('lgbm_task', ['train', 'prediction']),
    'boosting_type': hp.choice('lgbm_boosting_type', ['gbdt', 'dart']),
    'objective': hp.choice('lgbm_objective', ['regression']),
    'n_estimators': hp.choice('lgbm_n_estimators', range(10, 201, 5)),
    'learning_rate':  hp.uniform('lgbm_learning_rate', 0.05, 1.0),
    'num_leaves': hp.choice('lgbm_num_leaves', range(2, 7, 1)),
    'tree_learner': hp.choice('lgbm_tree_learner', ['serial', 'feature', 'data']),
    'metric': hp.choice('lgbm_metric', ['l1', 'l2', 'huber', 'fair']),
    'huber_delta': hp.uniform('lgbm_huber_delta', 0.0, 1.0),
    'fair_c': hp.uniform('lgbm_fair_c', 0.0, 1.0),
    'max_depth': hp.choice('lgbm_max_depth', range(3, 11)),
    'min_data_in_leaf': hp.choice('lgbm_min_data_in_leaf', range(0, 6, 1)),
    'min_sum_hessian_in_leaf': hp.loguniform('lgbm_min_sum_hessian_in_leaf', -16, 5),
    'feature_fraction': hp.uniform('lgbm_feature_fractionf', 0.0, 1.0),
    'feature_fraction_seed': hp.choice('lgbm_feature_fraction_seed', [12345]),
    'bagging_fraction': hp.uniform('lgbm_bagging_fraction', 0.0, 1.0),
    'bagging_freq': hp.choice('lgbm_bagging_freq', range(0, 16, 1)),
    'bagging_seed': hp.choice('lgbm_bagging_seed', [12345]),
    'min_gain_to_split': hp.uniform('lgbm_min_gain_to_split', 0.0, 1.0),
    'drop_rate': hp.uniform('lgbm_drop_rate', 0.0, 1.0),
    'skip_drop': hp.uniform('lgbm_skip_drop', 0.0, 1.0),
    'max_drop': hp.choice('lgbm_max_drop', [-1] + range(2, 51, 1)),
    'drop_seed': hp.choice('lgbm_uniform_drop', [12345]),
    'verbose': hp.choice('lgbm_verbose', [-1]),
    'num_threads': hp.choice('lgbm_threads', [2]), 
} 

model = LGBMRegressor()
model.fit(X, Y)
model.predict(XT)