CourseDesign/src/train.py

import sys
import os

# 修复模块路径问题，让你可以在根目录直接 python src/train.py
sys.path.append(os.getcwd())

import joblib
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, accuracy_score, f1_score

from src.data import generate_data, preprocess_data

MODELS_DIR = "models"
MODEL_PATH = os.path.join(MODELS_DIR, "model.pkl")

def get_pipeline(model_type="rf"):
    """
    构建标准的 Sklearn 处理流水线。
    1. 数值特征 -> 缺失填充 (均值) -> 标准化
    2. 类别特征 -> 缺失填充 (众数) -> OneHot编码
    3. 模型 -> LR 或 RF
    """
    # 定义特征列
    numeric_features = ["study_hours", "sleep_hours", "attendance_rate", "stress_level"]
    categorical_features = ["study_type"]
    
    # 数值处理管道
    numeric_transformer = Pipeline(steps=[
        ("imputer", SimpleImputer(strategy="mean")),
        ("scaler", StandardScaler())
    ])
    
    # 类别处理管道
    categorical_transformer = Pipeline(steps=[
        ("imputer", SimpleImputer(strategy="most_frequent")),
        ("onehot", OneHotEncoder(handle_unknown="ignore"))
    ])
    
    # 组合预处理
    preprocessor = ColumnTransformer(
        transformers=[
            ("num", numeric_transformer, numeric_features),
            ("cat", categorical_transformer, categorical_features)
        ]
    )
    
    # 选择模型
    if model_type == "lr":
        clf = LogisticRegression(random_state=42)
    else:
        clf = RandomForestClassifier(n_estimators=500, max_depth=5, random_state=42)
        
    return Pipeline(steps=[
        ("preprocessor", preprocessor),
        ("classifier", clf)
    ])

def train():
    print(">>> 1. 数据准备")
    df = generate_data(n_samples=2000)
    df = preprocess_data(df)
    
    X = df.drop(columns=["is_pass"])
    y = df["is_pass"]
    
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.2, random_state=42
    )
    print(f"训练集大小: {X_train.shape}, 测试集大小: {X_test.shape}")
    
    print("\n>>> 2. 模型训练与对比")
    # 模型 A: 逻辑回归 (Baseline)
    pipe_lr = get_pipeline("lr")
    pipe_lr.fit(X_train, y_train)
    y_pred_lr = pipe_lr.predict(X_test)
    f1_lr = f1_score(y_test, y_pred_lr)
    print(f"[Baseline - LogisticRegression] F1: {f1_lr:.4f}")
    
    # 模型 B: 随机森林 (Target)
    pipe_rf = get_pipeline("rf")
    pipe_rf.fit(X_train, y_train)
    y_pred_rf = pipe_rf.predict(X_test)
    f1_rf = f1_score(y_test, y_pred_rf)
    print(f"[Target - RandomForest] F1: {f1_rf:.4f}")
    
    print("\n>>> 3. 如果 RF 更好，则进行详细评估")
    best_model = pipe_rf
    print(classification_report(y_test, y_pred_rf))
    
    print("\n>>> 4. 误差分析 (Error Analysis)")
    # 找出模型预测错误的样本
    test_df = X_test.copy()
    test_df["True Label"] = y_test
    test_df["Pred Label"] = y_pred_rf
    
    errors = test_df[test_df["True Label"] != test_df["Pred Label"]]
    print(f"总计错误样本数: {len(errors)}")
    if len(errors) > 0:
        print("典型错误样本预览:")
        print(errors.head(3))
        
    print("\n>>> 5. 保存最佳模型")
    os.makedirs(MODELS_DIR, exist_ok=True)
    joblib.dump(best_model, MODEL_PATH)
    print(f"模型 Pipeline 已完整保存至 {MODEL_PATH}")

if __name__ == "__main__":
    train()