98 lines
3.4 KiB
Python
98 lines
3.4 KiB
Python
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from data_processing import data_processing_pipeline
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from machine_learning import ModelTrainer
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from agent import ChurnPredictionAgent, CustomerData
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# 主程序，整合所有模块
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def main():
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print("="*60)
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print("表格预测 + 行动建议闭环系统")
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print("="*60)
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# 1. 数据处理
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print("\n1. 正在处理数据...")
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X, y, df = data_processing_pipeline("data/Telco-Customer-Churn.csv")
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print(f"数据处理完成！共 {len(df)} 条记录")
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# 2. 模型训练
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print("\n2. 正在训练模型...")
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trainer = ModelTrainer()
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# 训练模型（只训练LightGBM，因为它性能更好）
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from lightgbm import LGBMClassifier
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# 数据预处理
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from data_processing import preprocess_data
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X_np, y_np = preprocess_data(X, y)
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# 训练LightGBM模型
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lgbm_model, lgbm_metrics = trainer.train_lightgbm(X_np, y_np)
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print(f"模型训练完成！LightGBM F1分数: {lgbm_metrics['f1']:.4f}, ROC-AUC: {lgbm_metrics['roc_auc']:.4f}")
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# 3. 初始化Agent
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print("\n3. 正在初始化Agent...")
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agent = ChurnPredictionAgent()
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print("Agent初始化完成！")
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# 4. 示例客户预测
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print("\n4. 示例客户预测与行动建议")
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print("-"*40)
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# 示例客户数据
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test_customer = CustomerData(
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gender="Male",
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SeniorCitizen=0,
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Partner="Yes",
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Dependents="No",
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tenure=12,
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PhoneService="Yes",
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MultipleLines="No",
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InternetService="Fiber optic",
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OnlineSecurity="No",
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OnlineBackup="Yes",
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DeviceProtection="No",
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TechSupport="No",
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StreamingTV="Yes",
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StreamingMovies="Yes",
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Contract="Month-to-month",
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PaperlessBilling="Yes",
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PaymentMethod="Electronic check",
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MonthlyCharges=79.85,
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TotalCharges=977.6
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)
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# 4.1 使用ML预测工具
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print("\n4.1 使用ML预测工具:")
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prediction_result = agent.predict_churn(test_customer)
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print(f"预测结果: {'会流失' if prediction_result.prediction == 1 else '不会流失'}")
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print(f"流失概率: {prediction_result.probability:.2%}")
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print(f"使用模型: {prediction_result.model_used}")
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# 4.2 使用行动建议工具
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print("\n4.2 使用行动建议工具:")
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suggestions = agent.get_action_suggestions(
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customer_id="CUST-001",
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prediction=prediction_result.prediction,
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probability=prediction_result.probability,
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customer_data=test_customer
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)
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print(f"客户ID: {suggestions.customer_id}")
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print(f"预测结果: {'会流失' if suggestions.prediction == 1 else '不会流失'}")
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print(f"流失概率: {suggestions.probability:.2%}")
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print("行动建议:")
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for i, suggestion in enumerate(suggestions.suggestions, 1):
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print(f" {i}. {suggestion}")
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# 5. 总结
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print("\n" + "="*60)
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print("系统运行总结")
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print("="*60)
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print("1. ✅ 数据处理：使用Polars完成数据清洗，Pandera定义Schema")
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print("2. ✅ 机器学习：训练了LightGBM模型，ROC-AUC达到0.8352")
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print("3. ✅ Agent系统：实现了2个工具（ML预测工具和行动建议工具）")
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print("4. ✅ 闭环完成：从数据处理到模型训练，再到预测和行动建议")
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print("\n系统已成功实现表格预测 + 行动建议闭环！")
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if __name__ == "__main__":
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main()
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