jupyter notebook中GBM调参

以下是在Jupyter Notebook中使用LightGBM进行调参的步骤：

1.导入必要的库和数据集

import lightgbm as lgb
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split

data = load_breast_cancer()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42)

2.将数据集转换为LightGBM的数据格式

train_data = lgb.Dataset(X_train, label=y_train)
test_data = lgb.Dataset(X_test, label=y_test)

3.设置参数空间并进行调参

from hyperopt import fmin, tpe, hp, STATUS_OK, Trials

space = {
    'max_depth': hp.quniform('max_depth', 2, 10, 1),
    'learning_rate': hp.loguniform('learning_rate', -5, 0),
    'n_estimators': hp.quniform('n_estimators', 50, 500, 1),
    'subsample': hp.uniform('subsample', 0.1, 1),
    'colsample_bytree': hp.uniform('colsample_bytree', 0.1, 1),
    'reg_alpha': hp.uniform('reg_alpha', 0, 1),
    'reg_lambda': hp.uniform('reg_lambda', 0, 1),
    'min_child_weight': hp.quniform('min_child_weight', 1, 10, 1),
    'objective': 'binary',
    'boosting_type': 'gbdt',
    'metric': 'binary_logloss',
    'num_threads': 4,
    'verbose': -1
}

def objective(params):
    model = lgb.train(params, train_data, valid_sets=[test_data], num_boost_round=1000, early_stopping_rounds=50, verbose_eval=False)
    score = model.best_score['valid_0']['binary_logloss']
    return {'loss': score, 'status': STATUS_OK}

trials = Trials()
best = fmin(fn=objective, space=space, algo=tpe.suggest, max_evals=100, trials=trials)

4.输出最佳参数

print(best)