From ece22dc56450723b8b6f039503e2157104b2352c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E6=9E=97=E5=98=89=E7=83=A8?= <st2311511113@gitea.local>
Date: Fri, 16 Jan 2026 19:34:23 +0800
Subject: [PATCH] =?UTF-8?q?=E5=88=A0=E9=99=A4=20train.py?=
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---
 train.py | 90 --------------------------------------------------------
 1 file changed, 90 deletions(-)
 delete mode 100644 train.py

diff --git a/train.py b/train.py
deleted file mode 100644
index 862a0d1..0000000
--- a/train.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import polars as pl
-import pandas as pd
-import lightgbm as lgb
-from sklearn.linear_model import LogisticRegression
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import classification_report, roc_auc_score, f1_score
-from sklearn.preprocessing import LabelEncoder, StandardScaler
-import joblib
-import logging
-import os
-from src.data import load_and_clean_data
-
-# 配置日志
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-logger = logging.getLogger(__name__)
-
-def train_models(data_path="data/bank.csv", model_dir="models"):
-    # 1. 加载数据
-    df_pl = load_and_clean_data(data_path)
-    df = df_pl.to_pandas() # 转换为 Pandas 以兼容 Sklearn
-    
-    # 2. 特征预处理
-    # 区分分类和数值特征
-    target_col = "target"
-    X = df.drop(columns=[target_col])
-    y = df[target_col]
-    
-    cat_cols = X.select_dtypes(include=['object', 'category']).columns.tolist()
-    num_cols = X.select_dtypes(include=['int64', 'float64']).columns.tolist()
-    
-    # Label Encoding (为了简化，LightGBM 可以直接处理 Category，但 Sklearn 需要编码)
-    encoders = {}
-    for col in cat_cols:
-        le = LabelEncoder()
-        X[col] = le.fit_transform(X[col].astype(str))
-        encoders[col] = le
-        
-    # 3. 数据切分
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-    
-    # 4. 训练基线模型 (Logistic Regression)
-    logger.info("训练基线模型 (Logistic Regression)...")
-    # 逻辑回归需要归一化
-    scaler = StandardScaler()
-    X_train_scaled = X_train.copy()
-    X_test_scaled = X_test.copy()
-    X_train_scaled[num_cols] = scaler.fit_transform(X_train[num_cols])
-    X_test_scaled[num_cols] = scaler.transform(X_test[num_cols])
-    
-    lr_model = LogisticRegression(max_iter=1000, random_state=42)
-    lr_model.fit(X_train_scaled, y_train)
-    
-    lr_pred = lr_model.predict(X_test_scaled)
-    lr_prob = lr_model.predict_proba(X_test_scaled)[:, 1]
-    
-    logger.info(f"Baseline F1: {f1_score(y_test, lr_pred):.4f}")
-    logger.info(f"Baseline AUC: {roc_auc_score(y_test, lr_prob):.4f}")
-    
-    # 5. 训练进阶模型 (LightGBM)
-    logger.info("训练进阶模型 (LightGBM)...")
-    lgb_model = lgb.LGBMClassifier(n_estimators=100, learning_rate=0.05, random_state=42, verbose=-1)
-    lgb_model.fit(X_train, y_train)
-    
-    lgb_pred = lgb_model.predict(X_test)
-    lgb_prob = lgb_model.predict_proba(X_test)[:, 1]
-    
-    logger.info(f"LightGBM F1: {f1_score(y_test, lgb_pred):.4f}")
-    logger.info(f"LightGBM AUC: {roc_auc_score(y_test, lgb_prob):.4f}")
-    
-    # 6. 保存模型与元数据
-    if not os.path.exists(model_dir):
-        os.makedirs(model_dir)
-        
-    artifacts = {
-        "lgb_model": lgb_model,
-        "lr_model": lr_model,
-        "scaler": scaler,
-        "encoders": encoders,
-        "features": list(X.columns),
-        "cat_cols": cat_cols,
-        "num_cols": num_cols
-    }
-    
-    joblib.dump(artifacts, os.path.join(model_dir, "model_artifacts.pkl"))
-    logger.info(f"模型已保存至 {model_dir}/model_artifacts.pkl")
-    
-    return artifacts
-
-if __name__ == "__main__":
-    train_models()