# 导入必要的库
from sklearn.ensemble import RandomForestRegressor
# from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import numpy as np
import matplotlib.pyplot as plt

import numpy as np
import pandas as pd

# 指定本地数据路径
data_path = "/home/admin-root/haotian/ML/dataset/CaliforniaHousing"

# 加载数据文件
# X = np.loadtxt(f"{data_path}/cal_housing.data", delimiter=" ")
X = np.genfromtxt(f"{data_path}/cal_housing.data", delimiter=',', dtype=float)

# 读取特征名称 (如果 domain 文件是特征的描述)
with open(f"{data_path}/cal_housing.domain", "r") as f:
    feature_names = [line.strip() for line in f.readlines()]

# 显示特征名称，检查是否正确
print("Feature Names:", feature_names)

# 假设目标变量是 `X` 的最后一列，按列拆分
y = X[:, -1]  # 目标变量（房价）
X = X[:, :-1]  # 特征数据

# # 1. 加载数据集（使用加州房价数据集）
# data = fetch_california_housing(data_home="/home/admin-root/haotian/ML/dataset/CaliforniaHousing")
# X = data.data  # 特征
# y = data.target  # 目标变量（房价）

# 2. 划分训练集和测试集（80% 训练，20% 测试）
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 3. 创建随机森林回归模型
rf_regressor = RandomForestRegressor(n_estimators=100, random_state=42)

# 4. 训练模型
rf_regressor.fit(X_train, y_train)

# 5. 进行预测
y_pred = rf_regressor.predict(X_test)

# 6. 评估模型
mae = mean_absolute_error(y_test, y_pred)
mse = mean_squared_error(y_test, y_pred)
rmse = np.sqrt(mse)
r2 = r2_score(y_test, y_pred)

print(f"均绝对误差（MAE）：{mae:.4f}")
print(f"均方误差（MSE）：{mse:.4f}")
print(f"均方根误差（RMSE）：{rmse:.4f}")
print(f"R² 评分（R2 Score）：{r2:.4f}")

# 7. 可视化预测结果
plt.figure(figsize=(8, 6))
plt.scatter(y_test, y_pred, alpha=0.6)
plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], '--r')  # 理想预测线
plt.xlabel("true_label")
plt.ylabel("predict_label")
plt.title("random_forest_regression")
plt.savefig('./output/random_forest_r.png')

# # 8. 特征重要性可视化
# feature_importances = rf_regressor.feature_importances_
# plt.figure(figsize=(10, 6))
# plt.barh(feature_names, feature_importances)
# plt.xlabel("feature importance")
# plt.ylabel("feature name")
# plt.title("random_forest_regression - feature_importance")
# plt.savefig('./output/random_forest_r_feature.png')