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From: =?UTF-8?q?=E6=9E=97=E5=98=89=E7=83=A8?= <st2311511113@gitea.local>
Date: Fri, 16 Jan 2026 19:36:56 +0800
Subject: [PATCH] =?UTF-8?q?=E4=B8=8A=E4=BC=A0=E6=96=87=E4=BB=B6=E8=87=B3?=
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---
 src/__pycache__/agent_app.py     | 184 +++++++++++++++++++++++++++++++
 src/__pycache__/data.py          |  92 ++++++++++++++++
 src/__pycache__/streamlit_app.py | 152 +++++++++++++++++++++++++
 src/__pycache__/train.py         |  90 +++++++++++++++
 4 files changed, 518 insertions(+)
 create mode 100644 src/__pycache__/agent_app.py
 create mode 100644 src/__pycache__/data.py
 create mode 100644 src/__pycache__/streamlit_app.py
 create mode 100644 src/__pycache__/train.py

diff --git a/src/__pycache__/agent_app.py b/src/__pycache__/agent_app.py
new file mode 100644
index 0000000..18ce516
--- /dev/null
+++ b/src/__pycache__/agent_app.py
@@ -0,0 +1,184 @@
+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))
diff --git a/src/__pycache__/data.py b/src/__pycache__/data.py
new file mode 100644
index 0000000..067d120
--- /dev/null
+++ b/src/__pycache__/data.py
@@ -0,0 +1,92 @@
+import polars as pl
+import pandera as pa
+from pandera import Column, Check, DataFrameSchema
+import logging
+
+# 配置日志
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# ==========================================
+# 1. 定义 Pandera Schema (数据契约)
+# ==========================================
+# 原始数据 Schema
+raw_schema = DataFrameSchema({
+    "age": Column(int, checks=Check.ge(18)),
+    "job": Column(str),
+    "marital": Column(str),
+    "education": Column(str),
+    "default": Column(str, checks=Check.isin(["yes", "no"])),
+    "balance": Column(int),
+    "housing": Column(str, checks=Check.isin(["yes", "no"])),
+    "loan": Column(str, checks=Check.isin(["yes", "no"])),
+    "contact": Column(str),
+    "day": Column(int, checks=[Check.ge(1), Check.le(31)]),
+    "month": Column(str),
+    "duration": Column(int, checks=Check.ge(0)),
+    "campaign": Column(int, checks=Check.ge(1)),
+    "pdays": Column(int),
+    "previous": Column(int, checks=Check.ge(0)),
+    "poutcome": Column(str),
+    "deposit": Column(str, checks=Check.isin(["yes", "no"])),
+})
+
+# 清洗后 Schema
+processed_schema = DataFrameSchema({
+    "age": Column(int),
+    "balance": Column(int),
+    "deposit": Column(int, checks=Check.isin([0, 1])),
+    # 其他数值化或保留的特征...
+})
+
+def load_and_clean_data(file_path: str):
+    """
+    使用 Polars 加载并清洗数据
+    """
+    logger.info(f"正在加载数据: {file_path}")
+    
+    # 1. Lazy Load
+    lf = pl.scan_csv(file_path)
+    
+    # 2. 初步清洗计划
+    # - 移除 duration (避免数据泄露)
+    # - 将 deposit (yes/no) 转换为 (1/0)
+    # - 简单的分类变量编码 (为了 LightGBM，我们可以保留分类类型或做 Label Encoding)
+    #   LightGBM 原生支持 Category，但 sklearn 需要数值。
+    #   为了通用性，这里做 Label Encoding 或者 One-Hot。
+    #   但 Polars 的 Label Encoding 比较手动。
+    #   我们这里先只做核心转换。
+
+    processed_lf = (
+        lf.drop(["duration"]) # 移除泄露特征
+          .with_columns([
+              pl.col("deposit").replace({"yes": 1, "no": 0}).cast(pl.Int64).alias("target"),
+              # 简单的特征工程示例：将 pdays -1 处理为 999 或单独一类 (这里保持原样，树模型能处理)
+          ])
+          .drop("deposit") # 移除原始标签列，保留 target
+    )
+
+    # 3. 执行计算 (Collect)
+    df = processed_lf.collect()
+    
+    logger.info(f"数据加载完成，形状: {df.shape}")
+    
+    # 4. Pandera 验证 (转换回 Pandas 验证，因为 Pandera 对 Polars 支持尚在实验阶段或部分支持)
+    # 这里我们验证关键字段
+    try:
+        # 简单验证一下 target 是否只有 0 和 1
+        assert df["target"].n_unique() <= 2
+        logger.info("基础数据验证通过")
+    except Exception as e:
+        logger.error(f"数据验证失败: {e}")
+        raise e
+        
+    return df
+
+if __name__ == "__main__":
+    # 测试代码
+    try:
+        df = load_and_clean_data("data/bank.csv")
+        print(df.head())
+    except Exception as e:
+        print(f"Error: {e}")
diff --git a/src/__pycache__/streamlit_app.py b/src/__pycache__/streamlit_app.py
new file mode 100644
index 0000000..037386f
--- /dev/null
+++ b/src/__pycache__/streamlit_app.py
@@ -0,0 +1,152 @@
+import streamlit as st
+import pandas as pd
+import joblib
+import os
+import sys
+
+# 添加项目根目录到 Path 以便导入 src
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+from src.agent_app import MarketingAgent, CustomerFeatures
+
+# 配置页面
+st.set_page_config(
+    page_title="智能银行营销助手",
+    page_icon="🤖",
+    layout="wide"
+)
+
+# 侧边栏：项目信息
+with st.sidebar:
+    st.title("🏦 智能营销系统")
+    st.markdown("---")
+    st.info("**Day 5 演示版**")
+    st.markdown("""
+    **核心能力：**
+    1. 📊 **LightGBM** 客户购买预测
+    2. 🧠 **Agent** 策略生成
+    3. 📝 **Pydantic** 结构化输出
+    """)
+    st.markdown("---")
+    st.caption("由第 X 组开发")
+
+# 主界面
+st.title("🤖 客户意向预测与决策系统")
+
+# 1. 模拟客户输入
+st.header("1. 录入客户信息")
+
+col1, col2, col3 = st.columns(3)
+
+# 映射字典
+job_map = {
+    "management": "管理人员", "technician": "技术人员", "entrepreneur": "企业家", 
+    "blue-collar": "蓝领", "unknown": "未知", "retired": "退休人员", 
+    "admin.": "行政人员", "services": "服务业", "self-employed": "自雇人士", 
+    "unemployed": "失业", "maid": "家政", "student": "学生"
+}
+education_map = {"tertiary": "高等教育", "secondary": "中等教育", "primary": "初等教育", "unknown": "未知"}
+marital_map = {"married": "已婚", "single": "单身", "divorced": "离异"}
+binary_map = {"yes": "是", "no": "否"}
+contact_map = {"cellular": "手机", "telephone": "座机", "unknown": "未知"}
+month_map = {
+    "jan": "1月", "feb": "2月", "mar": "3月", "apr": "4月", "may": "5月", "jun": "6月",
+    "jul": "7月", "aug": "8月", "sep": "9月", "oct": "10月", "nov": "11月", "dec": "12月"
+}
+poutcome_map = {"unknown": "未知", "failure": "失败", "other": "其他", "success": "成功"}
+
+# 辅助函数：反向查找 key
+def get_key(val, my_dict):
+    for key, value in my_dict.items():
+        if val == value: return key
+    return val
+
+with col1:
+    age = st.number_input("年龄", 18, 100, 30)
+    job_display = st.selectbox("职业", list(job_map.values()))
+    job = get_key(job_display, job_map)
+    
+    education_display = st.selectbox("教育", list(education_map.values()))
+    education = get_key(education_display, education_map)
+    
+    balance = st.number_input("账户余额 (欧元)", -1000, 100000, 1500)
+
+with col2:
+    marital_display = st.selectbox("婚姻", list(marital_map.values()))
+    marital = get_key(marital_display, marital_map)
+    
+    housing_display = st.selectbox("是否有房贷", list(binary_map.values()))
+    housing = get_key(housing_display, binary_map)
+    
+    loan_display = st.selectbox("是否有个人贷", list(binary_map.values()))
+    loan = get_key(loan_display, binary_map)
+    
+    default_display = st.selectbox("是否有违约记录", list(binary_map.values()))
+    default = get_key(default_display, binary_map)
+
+with col3:
+    contact_display = st.selectbox("联系方式", list(contact_map.values()))
+    contact = get_key(contact_display, contact_map)
+    
+    month_display = st.selectbox("最后联系月份", list(month_map.values()))
+    month = get_key(month_display, month_map)
+    
+    day = st.slider("最后联系日", 1, 31, 15)
+    
+    poutcome_display = st.selectbox("上次活动结果", list(poutcome_map.values()))
+    poutcome = get_key(poutcome_display, poutcome_map)
+
+# 隐藏的高级特征
+with st.expander("高级营销特征 (可选)"):
+    campaign = st.number_input("本次活动联系次数", 1, 50, 1)
+    pdays = st.number_input("距离上次联系天数 (-1代表无)", -1, 999, -1)
+    previous = st.number_input("活动前联系次数", 0, 100, 0)
+
+# 2. 触发 Agent
+if st.button("🚀 开始分析与决策", type="primary"):
+    try:
+        # 构造 Pydantic 对象
+        customer = CustomerFeatures(
+            age=age, job=job, marital=marital, education=education,
+            default=default, balance=balance, housing=housing, loan=loan,
+            contact=contact, day=day, month=month,
+            campaign=campaign, pdays=pdays, previous=previous, poutcome=poutcome
+        )
+        
+        # 初始化 Agent
+        with st.spinner("Agent 正在加载模型并思考..."):
+            agent = MarketingAgent()
+            decision = agent.run(customer)
+            
+        # 3. 展示结果
+        st.divider()
+        st.header("2. 智能分析报告")
+        
+        # 结果看板
+        res_col1, res_col2 = st.columns([1, 2])
+        
+        with res_col1:
+            st.metric("预测购买概率", f"{decision.risk_score:.1%}")
+            if decision.risk_score > 0.6:
+                st.success(f"分群：{decision.customer_segment}")
+            elif decision.risk_score > 0.3:
+                st.warning(f"分群：{decision.customer_segment}")
+            else:
+                st.error(f"分群：{decision.customer_segment}")
+                
+        with res_col2:
+            st.subheader("💡 决策建议")
+            st.info(decision.decision)
+            st.markdown(f"**决策依据：** {decision.rationale}")
+            
+        # 行动清单
+        st.subheader("📝 执行清单")
+        for i, action in enumerate(decision.actions, 1):
+            st.write(f"{i}. {action}")
+            
+        # JSON 视图
+        with st.expander("查看原始 JSON 输出 (Traceable)"):
+            st.json(decision.model_dump())
+            
+    except Exception as e:
+        st.error(f"发生错误: {str(e)}")
diff --git a/src/__pycache__/train.py b/src/__pycache__/train.py
new file mode 100644
index 0000000..862a0d1
--- /dev/null
+++ b/src/__pycache__/train.py
@@ -0,0 +1,90 @@
+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()