要比较LightGBM和Scikit-Learn中的特征重要性，可以按照以下步骤进行：

1. 导入所需的库和数据：

import lightgbm as lgb
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import numpy as np

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

2. 使用LightGBM进行特征重要性计算：

lgb_model = lgb.LGBMClassifier()
lgb_model.fit(X_train, y_train)
lgb_feature_importances = lgb_model.feature_importances_

3. 使用Scikit-Learn中的RandomForestClassifier进行特征重要性计算：

rf_model = RandomForestClassifier()
rf_model.fit(X_train, y_train)
rf_feature_importances = rf_model.feature_importances_

4. 打印特征重要性结果：

print("LightGBM Feature Importances:")
for feature_importance, feature_name in zip(lgb_feature_importances, iris.feature_names):
    print(f"{feature_name}: {feature_importance}")

print("\nScikit-Learn Random Forest Feature Importances:")
for feature_importance, feature_name in zip(rf_feature_importances, iris.feature_names):
    print(f"{feature_name}: {feature_importance}")

这样就可以比较LightGBM和Scikit-Learn中的特征重要性了。请注意，这只是一个简单的示例，你可以根据自己的需求进行调整和扩展。