Python训练营---Day31
知识点回顾
- 规范的文件命名
- 规范的文件夹管理
- 机器学习项目的拆分
- 编码格式和类型注解
作业：尝试针对之前的心脏病项目ipynb，将他按照今天的示例项目整理成规范的形式，思考下哪些部分可以未来复用。
拆分后的目录结构如下：
1、src/data/load_data.py
# -*- coding: utf-8 -*-
import sys
import io
# 设置标准输出为 UTF-8 编码
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
import pandas as pddef load_data(file_path: str) -> pd.DataFrame:"""加载数据文件Args:file_path: 数据文件路径Returns:加载的数据框"""return pd.read_csv(file_path)if __name__ == "__main__":# 测试代码data = load_data("testDay31/data/raw/heart.csv")print("数据读取完成！") 2、src/data/preprocessing.py
# -*- coding: utf-8 -*-
import sys
import io
import os
# 设置标准输出为 UTF-8 编码
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')import pandas as pd
import numpy as np
from typing import Tuple, Dictdef load_data(file_path: str) -> pd.DataFrame:"""加载数据文件Args:file_path: 数据文件路径Returns:加载的数据框"""return pd.read_csv(file_path)# 仅以处理缺失值为例
def handle_missing_values(data: pd.DataFrame) -> pd.DataFrame:"""处理缺失值Args:data: 包含缺失值的数据框Returns:处理后的数据框"""data_clean = data.copy()continuous_features = data.select_dtypes(include=['int64', 'float64']).columns.tolist()for feature in continuous_features:mode_value = data[feature].mode()[0]data_clean[feature].fillna(mode_value, inplace=True)return data_cleanif __name__ == "__main__":# 测试代码data = load_data("testDay31/data/raw/heart.csv")# data_encoded, mappings = encode_categorical_features(data)data_clean = handle_missing_values(data)print("数据预处理完成！") 3、models/train.py
# -*- coding: utf-8 -*-
import sys
import os
import io
# 设置标准输出为 UTF-8 编码
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, confusion_matrix
import time
import joblib # 用于保存模型
from typing import Tuple # 用于类型注解from data.preprocessing import load_data,handle_missing_values
# from data.load_data import load_datadef prepare_data() -> Tuple:"""准备训练数据Returns:训练集和测试集的特征和标签"""# 加载和预处理数据data = load_data("testDay31/data/raw/heart.csv")data_clean = handle_missing_values(data)# 分离特征和标签X = data_clean.drop(['target'], axis=1)y = data_clean['target']# 划分训练集和测试集X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)return X_train, X_test, y_train, y_testdef train_model(X_train, y_train, model_params=None) -> RandomForestClassifier:"""训练随机森林模型Args:X_train: 训练特征y_train: 训练标签model_params: 模型参数字典Returns:训练好的模型"""if model_params is None:model_params = {'random_state': 42}model = RandomForestClassifier(**model_params)model.fit(X_train, y_train)return modeldef evaluate_model(model, X_test, y_test) -> None:"""评估模型性能Args:model: 训练好的模型X_test: 测试特征y_test: 测试标签"""y_pred = model.predict(X_test)print("\n分类报告：")print(classification_report(y_test, y_pred))print("\n混淆矩阵：")print(confusion_matrix(y_test, y_pred))def save_model(model, model_path: str) -> None:"""保存模型Args:model: 训练好的模型model_path: 模型保存路径"""os.makedirs(os.path.dirname(model_path), exist_ok=True)joblib.dump(model, model_path)print(f"\n模型已保存至: {model_path}")if __name__ == "__main__":# 准备数据X_train, X_test, y_train, y_test = prepare_data()# 记录开始时间start_time = time.time()# 训练模型model = train_model(X_train, y_train)# 记录结束时间end_time = time.time()print(f"\n训练耗时: {end_time - start_time:.4f} 秒")# 评估模型evaluate_model(model, X_test, y_test)# 保存模型save_model(model, "testDay31/models/random_forest_model.joblib") 