75 lines
2.6 KiB
Python
75 lines
2.6 KiB
Python
# 导入必要的库
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from sklearn.ensemble import RandomForestRegressor
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# from sklearn.datasets import fetch_california_housing
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
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import numpy as np
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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# 指定本地数据路径
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data_path = "/home/admin-root/haotian/ML/dataset/CaliforniaHousing"
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# 加载数据文件
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# X = np.loadtxt(f"{data_path}/cal_housing.data", delimiter=" ")
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X = np.genfromtxt(f"{data_path}/cal_housing.data", delimiter=',', dtype=float)
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# 读取特征名称 (如果 domain 文件是特征的描述)
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with open(f"{data_path}/cal_housing.domain", "r") as f:
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feature_names = [line.strip() for line in f.readlines()]
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# 显示特征名称，检查是否正确
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print("Feature Names:", feature_names)
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# 假设目标变量是 `X` 的最后一列，按列拆分
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y = X[:, -1] # 目标变量（房价）
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X = X[:, :-1] # 特征数据
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# # 1. 加载数据集（使用加州房价数据集）
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# data = fetch_california_housing(data_home="/home/admin-root/haotian/ML/dataset/CaliforniaHousing")
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# X = data.data # 特征
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# y = data.target # 目标变量（房价）
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# 2. 划分训练集和测试集（80% 训练，20% 测试）
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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# 3. 创建随机森林回归模型
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rf_regressor = RandomForestRegressor(n_estimators=100, random_state=42)
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# 4. 训练模型
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rf_regressor.fit(X_train, y_train)
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# 5. 进行预测
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y_pred = rf_regressor.predict(X_test)
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# 6. 评估模型
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mae = mean_absolute_error(y_test, y_pred)
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mse = mean_squared_error(y_test, y_pred)
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rmse = np.sqrt(mse)
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r2 = r2_score(y_test, y_pred)
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print(f"均绝对误差（MAE）：{mae:.4f}")
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print(f"均方误差（MSE）：{mse:.4f}")
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print(f"均方根误差（RMSE）：{rmse:.4f}")
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print(f"R² 评分（R2 Score）：{r2:.4f}")
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# 7. 可视化预测结果
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plt.figure(figsize=(8, 6))
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plt.scatter(y_test, y_pred, alpha=0.6)
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plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], '--r') # 理想预测线
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plt.xlabel("true_label")
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plt.ylabel("predict_label")
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plt.title("random_forest_regression")
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plt.savefig('./output/random_forest_r.png')
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# # 8. 特征重要性可视化
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# feature_importances = rf_regressor.feature_importances_
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# plt.figure(figsize=(10, 6))
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# plt.barh(feature_names, feature_importances)
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# plt.xlabel("feature importance")
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# plt.ylabel("feature name")
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# plt.title("random_forest_regression - feature_importance")
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# plt.savefig('./output/random_forest_r_feature.png')
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