利用xgboost和RF对高光谱影像的多种特征重要性进行排序的代码
时间: 2023-05-24 18:06:12 浏览: 114
以下是利用xgboost和RF对高光谱影像的多种特征重要性进行排序的Python代码示例:
```python
# 导入所需的库和模块
import pandas as pd
import numpy as np
import xgboost as xgb
from sklearn.ensemble import RandomForestRegressor
# 读取高光谱影像数据
data = pd.read_csv("data.csv")
# 分割数据集
train_set = data.sample(frac=0.7)
test_set = data.drop(train_set.index)
# 定义特征和目标列
features = train_set.columns[:-1]
target = train_set.columns[-1]
# 构建xgboost模型并训练
xgb_model = xgb.XGBRegressor()
xgb_model.fit(train_set[features], train_set[target])
# 获取特征重要性信息并按重要性排序
xgb_feature_importance = xgb_model.feature_importances_
xgb_importance_df = pd.DataFrame({'feature': features, 'importance': xgb_feature_importance})
xgb_importance_df = xgb_importance_df.sort_values(by=['importance'], ascending=False)
# 输出xgboost模型特征重要性排序结果
print("XGBoost Feature Importance Ranking:")
print(xgb_importance_df)
# 构建随机森林模型并训练
rf_model = RandomForestRegressor(n_estimators=100)
rf_model.fit(train_set[features], train_set[target])
# 获取特征重要性信息并按重要性排序
rf_feature_importance = rf_model.feature_importances_
rf_importance_df = pd.DataFrame({'feature': features, 'importance': rf_feature_importance})
rf_importance_df = rf_importance_df.sort_values(by=['importance'], ascending=False)
# 输出随机森林模型特征重要性排序结果
print("Random Forest Feature Importance Ranking:")
print(rf_importance_df)
```
在上述代码中,我们首先使用pandas库读取高光谱影像数据。然后,将数据集分成训练集和测试集,并定义特征和目标列。接着,我们构建并训练了xgboost和随机森林模型。最后,我们获取了特征重要性信息并按重要性排序并输出结果。
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