波士顿房价预测工具 - XGBoost实现

波士顿房价预测工具 - XGBoost实现

项目概述

这是一个基于PyQt5和XGBoost的波士顿房价预测桌面应用程序。该工具提供了友好的图形界面，允许用户调整XGBoost模型参数，并实时查看训练结果和预测效果。

开发过程详解

1. 项目架构设计

项目采用模块化的MVC架构，将代码按功能分为以下几个主要模块：

boston_housing_predictor/
├── main.py              # 程序入口
├── run.py               # 启动脚本
├── path_config.py       # 路径配置
├── ui/                  # UI界面模块
│   ├── __init__.py
│   ├── main_window.py   # 主窗口
│   └── widgets/         # UI组件
│       ├── __init__.py
│       ├── canvas.py
│       ├── control_panel.py
│       ├── data_table_widget.py
│       ├── prediction_widget.py
│       └── visualization_panel.py
├── models/              # 模型相关
│   ├── __init__.py
│   ├── base_model.py
│   └── xgboost_model.py
├── utils/               # 工具函数
│   ├── __init__.py
│   ├── data_loader.py
│   ├── validators.py
│   └── visualizers.py
├── config/              # 配置管理
│   ├── __init__.py
│   └── settings.py
└── threads/             # 多线程支持├── __init__.py└── training_thread.py

2. 核心功能实现

2.1 数据加载与验证

数据加载模块支持多种数据源：

• CSV文件（支持多种编码）
• sklearn内置数据集
• 自动生成示例数据

2.2 XGBoost模型封装

将XGBoost模型封装为类，提供统一的接口：

• 训练方法（支持早停法）
• 预测方法
• 参数管理
• 特征重要性获取

2.3 可视化功能

提供多种可视化图表：

• 数据探索（分布图、相关性热力图）
• 特征重要性排序
• 预测结果对比
• 残差分析

2.4 预测功能

• 数据预测功能：支持手动输入特征值和CSV批量预测
• GPU加速支持：自动检测并使用GPU加速训练
• 数据表格展示：在第一个标签页显示原始数据

3. 完整代码实现

3.1 程序入口 (main.py)

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
波士顿房价预测工具 - 主程序入口
"""
import sys
from PyQt5.QtWidgets import QApplication
from ui.main_window import BostonHousingApp
from utils.visualizers import setup_chinese_fontdef main():"""主程序入口"""# 设置中文字体setup_chinese_font()# 创建应用app = QApplication(sys.argv)app.setApplicationName("波士顿房价预测工具")app.setOrganizationName("ML Tools Inc.")# 创建主窗口window = BostonHousingApp()window.show()# 运行应用sys.exit(app.exec_())if __name__ == '__main__':main()

3.2 路径配置 (path_config.py)

"""
路径配置模块 - 确保所有模块都能正确导入
"""
import sys
import osdef setup_path():"""设置项目路径"""# 获取当前文件所在目录current_dir = os.path.dirname(os.path.abspath(__file__))# 将项目根目录添加到Python路径if current_dir not in sys.path:sys.path.insert(0, current_dir)# 自动执行路径设置
setup_path()

3.3 配置文件 (config/settings.py)

"""
配置文件 - 存储所有默认参数和常量
"""# XGBoost默认参数
DEFAULT_XGBOOST_PARAMS = {'n_estimators': 100,'max_depth': 6,'learning_rate': 0.1,'subsample': 0.8,'colsample_bytree': 0.8,'reg_alpha': 0.0,'reg_lambda': 1.0,'random_state': 42,'n_jobs': -1,'tree_method': 'hist'
}# 滑块参数范围
SLIDER_RANGES = {'train_test_split': {'min': 50, 'max': 90, 'default': 80},'n_estimators': {'min': 10, 'max': 500, 'default': 100},'max_depth': {'min': 1, 'max': 20, 'default': 6},'learning_rate': {'min': 1, 'max': 50, 'default': 10},  # 实际值需要除以100'subsample': {'min': 50, 'max': 100, 'default': 80},  # 实际值需要除以100'colsample_bytree': {'min': 50, 'max': 100, 'default': 80},  # 实际值需要除以100'reg_alpha': {'min': 0, 'max': 100, 'default': 0},  # 实际值需要除以100'reg_lambda': {'min': 0, 'max': 100, 'default': 10}  # 实际值需要除以10
}# 数据文件配置
DATA_FILE_NAMES = ['波士顿房价数据集.csv', 'boston_housing.csv']
DATA_ENCODINGS = ['utf-8', 'gbk', 'gb2312']# UI配置
WINDOW_TITLE = '波士顿房价预测工具 - XGBoost'
WINDOW_SIZE = (1400, 900)# 可视化配置
FIGURE_DPI = 100
FIGURE_SIZE = (8, 6)# 数据显示配置
MAX_DISPLAY_ROWS = 100  # 最多显示100行数据

3.4 基础模型类 (models/base_model.py)

"""
基础模型类
"""
from abc import ABC, abstractmethod
import numpy as np
from typing import Tuple, Dict, Anyclass BaseModel(ABC):"""模型基类"""def __init__(self):self.model = Noneself.is_trained = False@abstractmethoddef train(self, X_train: np.ndarray, y_train: np.ndarray,X_val: np.ndarray = None, y_val: np.ndarray = None,**kwargs) -> None:"""训练模型"""pass@abstractmethoddef predict(self, X: np.ndarray) -> np.ndarray:"""预测"""pass@abstractmethoddef get_params(self) -> Dict[str, Any]:"""获取模型参数"""pass@abstractmethoddef set_params(self, **params) -> None:"""设置模型参数"""passdef evaluate(self, X_test: np.ndarray, y_test: np.ndarray) -> Dict[str, float]:"""评估模型性能Args:X_test: 测试集特征y_test: 测试集标签Returns:Dict[str, float]: 评估指标"""from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_errorif not self.is_trained:raise ValueError("模型还未训练")y_pred = self.predict(X_test)return {'mse': mean_squared_error(y_test, y_pred),'mae': mean_absolute_error(y_test, y_pred),'r2': r2_score(y_test, y_pred)}

3.5 XGBoost模型实现 (models/xgboost_model.py)

"""
XGBoost模型实现
"""
import xgboost as xgb
import numpy as np
from typing import Dict, Any, Optional, List
from models.base_model import BaseModelclass XGBoostModel(BaseModel):"""XGBoost回归模型"""def __init__(self, **params):super().__init__()self.params = paramsself.model = Noneself.feature_names = Nonedef train(self, X_train: np.ndarray, y_train: np.ndarray,X_val: Optional[np.ndarray] = None,y_val: Optional[np.ndarray] = None,early_stopping: bool = False,feature_names: Optional[List[str]] = None) -> None:"""训练XGBoost模型Args:X_train: 训练集特征y_train: 训练集标签X_val: 验证集特征y_val: 验证集标签early_stopping: 是否使用早停法feature_names: 特征名称列表"""# 保存特征名称self.feature_names = feature_names# 如果使用早停法，设置相关参数if early_stopping and X_val is not None and y_val is not None:self.params['early_stopping_rounds'] = 10self.params['eval_metric'] = 'rmse'# 创建模型self.model = xgb.XGBRegressor(**self.params)# 设置特征名称if feature_names:self.model.feature_names = feature_names# 训练模型if early_stopping and X_val is not None and y_val is not None:eval_set = [(X_train, y_train), (X_val, y_val)]self.model.fit(X_train, y_train, eval_set=eval_set, verbose=False)else:self.model.fit(X_train, y_train)self.is_trained = Truedef predict(self, X: np.ndarray) -> np.ndarray:"""预测"""if not self.is_trained:raise ValueError("模型还未训练")return self.model.predict(X)def get_params(self) -> Dict[str, Any]:"""获取模型参数"""return self.params.copy()def set_params(self, **params) -> None:"""设置模型参数"""self.params.update(params)if self.model is not None:self.model.set_params(**params)def get_feature_importance(self) -> Optional[np.ndarray]:"""获取特征重要性"""if self.is_trained and hasattr(self.model, 'feature_importances_'):return self.model.feature_importances_return Nonedef get_best_iteration(self) -> Optional[int]:"""获取最佳迭代次数（早停法）"""if self.is_trained and hasattr(self.model, 'best_iteration'):return self.model.best_iterationreturn None

3.6 训练线程 (threads/training_thread.py)

"""
训练线程模块
"""
from PyQt5.QtCore import QThread, pyqtSignal
from typing import Optional
import numpy as npclass TrainingThread(QThread):"""训练线程，避免界面卡顿"""progress = pyqtSignal(str)finished = pyqtSignal(object)error = pyqtSignal(str)def __init__(self, model, X_train: np.ndarray, y_train: np.ndarray,X_val: Optional[np.ndarray] = None,y_val: Optional[np.ndarray] = None,early_stopping: bool = False,feature_names: Optional[list] = None):super().__init__()self.model = modelself.X_train = X_trainself.y_train = y_trainself.X_val = X_valself.y_val = y_valself.early_stopping = early_stoppingself.feature_names = feature_namesdef run(self):"""运行训练"""try:self.progress.emit("开始训练模型...")# 训练模型self.model.train(self.X_train, self.y_train,self.X_val, self.y_val,early_stopping=self.early_stopping,feature_names=self.feature_names)if self.early_stopping and self.X_val is not None:self.progress.emit("训练完成（使用早停法）！")else:self.progress.emit("训练完成！")self.finished.emit(self.model)except Exception as e:error_msg = f"训练出错：{str(e)}"self.progress.emit(error_msg)self.error.emit(error_msg)self.finished.emit(None)

3.7 主窗口 (ui/main_window.py)

[主窗口代码已在文档中，这里省略以节省空间]

3.8 UI组件 - 画布 (ui/widgets/canvas.py)

"""
Matplotlib画布组件
"""
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
from utils.visualizers import setup_chinese_fontclass MplCanvas(FigureCanvas):"""Matplotlib画布类"""def __init__(self, parent=None, width=5, height=4, dpi=100):# 确保中文字体设置setup_chinese_font()self.fig = Figure(figsize=(width, height), dpi=dpi, tight_layout=True)super(MplCanvas, self).__init__(self.fig)self.setParent(parent)

3.9 UI组件 - 控制面板 (ui/widgets/control_panel.py)

"""
控制面板组件
"""
from PyQt5.QtWidgets import *
from PyQt5.QtCore import Qt
from PyQt5