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()