删除 src/__pycache__/agent_app.py

src/__pycache__/agent_app.py

@@ -1,184 +0,0 @@
-from pydantic import BaseModel, Field
-import joblib
-import json
-import pandas as pd
-import numpy as np
-import logging
-import os
-import random
-
-# 配置日志
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-logger = logging.getLogger(__name__)
-
-# ==========================================
-# 1. 定义 Pydantic 数据模型
-# ==========================================
-
-class CustomerFeatures(BaseModel):
-    """客户特征输入模型"""
-    age: int = Field(ge=18, le=120, description="客户年龄")
-    job: str = Field(description="职业")
-    marital: str = Field(description="婚姻状况")
-    education: str = Field(description="教育程度")
-    default: str = Field(pattern="^(yes|no)$", description="是否有违约记录")
-    balance: int = Field(description="账户余额")
-    housing: str = Field(pattern="^(yes|no)$", description="是否有住房贷款")
-    loan: str = Field(pattern="^(yes|no)$", description="是否有个人贷款")
-    contact: str = Field(description="联系方式")
-    day: int = Field(ge=1, le=31, description="最后联系日")
-    month: str = Field(description="最后联系月份")
-    campaign: int = Field(ge=1, description="本次活动联系次数")
-    pdays: int = Field(description="距离上次联系天数 (-1代表未联系)")
-    previous: int = Field(ge=0, description="活动前联系次数")
-    poutcome: str = Field(description="上次活动结果")
-    
-    # 注意：我们不包含 duration，因为它是事后变量
-
-class Decision(BaseModel):
-    """Agent 输出的结构化决策"""
-    risk_score: float = Field(ge=0, le=1, description="预测购买概率 (0-1)")
-    customer_segment: str = Field(description="客户分群 (如: 高价值/潜在/沉睡)")
-    decision: str = Field(description="建议策略 (如: 立即致电/邮件触达/放弃)")
-    actions: list[str] = Field(description="可执行动作清单")
-    rationale: str = Field(description="决策依据 (结合模型预测与业务规则)")
-
-# ==========================================
-# 2. 定义 Agent 类
-# ==========================================
-
-class MarketingAgent:
-    def __init__(self, model_path="models/model_artifacts.pkl"):
-        self.model_path = model_path
-        self.artifacts = None
-        self._load_model()
-        
-    def _load_model(self):
-        if os.path.exists(self.model_path):
-            self.artifacts = joblib.load(self.model_path)
-            logger.info(f"Agent 已加载模型: {self.model_path}")
-        else:
-            logger.warning(f"模型文件不存在: {self.model_path}，Agent 将无法进行预测")
-
-    def predict_risk(self, features: CustomerFeatures) -> dict:
-        """
-        Tool 1: 调用 ML 模型预测购买概率
-        """
-        if not self.artifacts:
-            return {"score": 0.0, "reason": "Model not loaded"}
-            
-        # 转换输入为 DataFrame
-        data = features.model_dump()
-        df = pd.DataFrame([data])
-        
-        # 预处理 (使用训练时保存的 encoder)
-        # 注意：这里需要严格复现训练时的预处理逻辑
-        # 训练时我们做了 Label Encoding
-        for col, le in self.artifacts['encoders'].items():
-            if col in df.columns:
-                # 处理未知类别
-                try:
-                    df[col] = le.transform(df[col].astype(str))
-                except:
-                    # 遇到未知类别，这里简单处理为 0 (或者 mode)
-                    logger.warning(f"Unknown category in {col}")
-                    df[col] = 0
-        
-        # 确保列顺序一致
-        # 我们训练时用了 X (df.drop(target))
-        # 这里需要筛选出 numeric_cols + categorical_cols
-        # 简单起见，我们假设 feature names 保存了顺序
-        feature_names = self.artifacts['features']
-        
-        # 补齐可能缺失的列
-        for col in feature_names:
-            if col not in df.columns:
-                df[col] = 0
-                
-        X_input = df[feature_names]
-        
-        # 预测
-        model = self.artifacts['lgb_model'] # 优先使用 LightGBM
-        prob = model.predict_proba(X_input)[0][1]
-        
-        return {
-            "score": float(prob),
-            "top_features": ["balance", "poutcome"] # 这里简化，实际可用 SHAP
-        }
-
-    def get_strategy(self, score: float) -> dict:
-        """
-        Tool 2: 规则引擎/检索工具
-        """
-        if score > 0.6:
-            return {
-                "segment": "高意向 VIP",
-                "action_type": "人工介入",
-                "templates": ["尊贵的客户，鉴于您...", "专属理财经理一对一服务"]
-            }
-        elif score > 0.3:
-            return {
-                "segment": "潜在客户",
-                "action_type": "自动化营销",
-                "templates": ["你好，近期理财活动...", "点击领取加息券"]
-            }
-        else:
-            return {
-                "segment": "低意向群体",
-                "action_type": "静默/邮件",
-                "templates": ["月度财经摘要"]
-            }
-
-    def run(self, features: CustomerFeatures) -> Decision:
-        """
-        Agent 主流程
-        """
-        logger.info(f"Agent 正在处理客户: {features.job}, {features.age}岁")
-        
-        # 1. 感知 (调用 ML 工具)
-        pred_result = self.predict_risk(features)
-        score = pred_result["score"]
-        
-        # 2. 规划 (调用 策略工具)
-        strategy = self.get_strategy(score)
-        
-        # 3. 决策 (模拟 LLM 整合)
-        # 在真实场景中，这里构建 Prompt 发送给 DeepSeek
-        # 这里我们用 Python 逻辑模拟 LLM 的结构化输出能力
-        
-        decision = Decision(
-            risk_score=round(score, 4),
-            customer_segment=strategy["segment"],
-            decision=f"建议采取 {strategy['action_type']}",
-            actions=[f"使用话术: {t}" for t in strategy["templates"]],
-            rationale=f"模型预测概率为 {score:.1%}，属于{strategy['segment']}。该群体对{strategy['action_type']}转化率较高。"
-        )
-        
-        return decision
-
-if __name__ == "__main__":
-    # 测试 Agent
-    agent = MarketingAgent()
-    
-    # 构造一个测试用例
-    test_customer = CustomerFeatures(
-        age=35,
-        job="management",
-        marital="married",
-        education="tertiary",
-        default="no",
-        balance=2000,
-        housing="yes",
-        loan="no",
-        contact="cellular",
-        day=15,
-        month="may",
-        campaign=1,
-        pdays=-1,
-        previous=0,
-        poutcome="unknown"
-    )
-    
-    result = agent.run(test_customer)
-    print("\n=== Agent Decision ===")
-    print(result.model_dump_json(indent=2))