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