用python实现基于传统机器学习算法估算FR-Pue站点的潜热通量特征选择并画散点图

好的，下面是一份完整的 Python 代码，用于基于传统机器学习算法估算FR-Pue站点的潜热通量特征选择并画散点图。本示例使用的机器学习算法是随机森林。

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score, mean_squared_error

# 加载数据集
data = pd.read_csv("FLX_FR-Pue_FLUXNET2015_FULLSET_HH_2000-2014_2-4.csv")

# 删除无关变量
data = data.drop(['TIMESTAMP_START', 'TIMESTAMP_END', 'RECORD', 'USTAR', 'H', 'NETRAD', 'TA_F', 'PA_F', 'VPD_F', 'SWC_F_MDS', 'RNET', 'GPP_NT_VUT_REF', 'NEE_VUT_REF', 'FC_F_MDS', 'SFC_F_MDS', 'TA_F_MDS', 'PA_F_MDS', 'P_F_MDS', 'WS_F_MDS', 'USTAR', 'TSTAR', 'SW_IN_F_MDS', 'SW_OUT_F_MDS', 'LW_IN_F_MDS', 'LW_OUT_F_MDS', 'SWC_F_MDS_1', 'SWC_F_MDS_2', 'USTAR', 'WD', 'WS', 'USTAR', 'ZL', 'SWC_F_MDS_3', 'SWC_F_MDS_4'], axis=1)

# 提取目标变量
target = data['LE_F_MDS']
data = data.drop(['LE_F_MDS'], axis=1)

# 特征选择
rf = RandomForestRegressor(random_state=42)
rf.fit(data, target)
importances = rf.feature_importances_
indices = np.argsort(importances)[::-1]
features = data.columns[indices][:4]
print("Selected features:", features)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(data[features], target, test_size=0.2, random_state=42)

# 随机森林模型训练
rf = RandomForestRegressor(random_state=42)
rf.fit(X_train, y_train)

# 模型预测
y_pred = rf.predict(X_test)

# 计算模型评估指标
print("R-squared score:", r2_score(y_test, y_pred))
print("Mean squared error:", mean_squared_error(y_test, y_pred))

# 绘制散点图
plt.scatter(y_test, y_pred)
plt.xlabel('True Values')
plt.ylabel('Predictions')
plt.title('FR-Pue Site LE_F_MDS Prediction')
plt.show()

这份代码会输出特征选择结果、模型评估指标以及散点图。您可以根据实际情况对代码进行修改。