如何使Python LightGBM代码接受列表

import lightgbm as lgb
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# 加载数据集
iris = load_iris()
X, y = iris.data, iris.target

# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 将列表转换为 LightGBM 可接受的格式（通常是 numpy 数组）
# 如果你的数据已经是列表形式，可以使用 numpy 的 array 函数进行转换
X_train = np.array(X_train)
y_train = np.array(y_train)
X_test = np.array(X_test)
y_test = np.array(y_test)

# 创建 LightGBM 数据集
lgb_train = lgb.Dataset(X_train, y_train)
lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)

# 设置参数
params = {
    'objective': 'multiclass',
    'num_class': 3,
    'metric': 'multi_logloss',
    'verbose': -1
}

# 训练模型
num_round = 100
bst = lgb.train(params, lgb_train, num_round, valid_sets=[lgb_eval], early_stopping_rounds=10)

# 预测
y_pred = bst.predict(X_test)
y_pred = [list(x).index(max(x)) for x in y_pred]  # 将概率转换为类别

# 计算准确率
accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy: {accuracy}')