bd5d8d108c
- 实现数据预处理模块(data.py)和模型训练模块(train.py) - 添加智能客服Agent应用(agent_app.py)和DNA解码系统(dna_decoder.py) - 包含补偿推荐系统(compensation_recommender.py)和可视化支持 - 添加项目配置文件(pyproject.toml)和README文档 - 提供多种启动脚本(start_app.*, fix_path_and_run.bat等)
380 lines
16 KiB
Python
380 lines
16 KiB
Python
"""个性化服务恢复与补偿推荐系统"""
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import pandas as pd
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import numpy as np
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from typing import List, Dict, Tuple, Optional
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from pydantic import BaseModel, Field
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from sklearn.ensemble import RandomForestRegressor
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import mean_squared_error, r2_score
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import random
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from datetime import datetime, timedelta
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class CompensationEffect(BaseModel):
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"""补偿效果分析"""
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补偿类型: str = Field(description="补偿类型")
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问题类型: str = Field(description="问题类型")
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情感强度: float = Field(description="投诉情感强度")
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预期满意度提升: float = Field(description="预期满意度提升")
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成本效益比: float = Field(description="成本效益比")
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推荐指数: float = Field(description="推荐指数(0-1)")
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历史成功率: float = Field(description="历史成功率")
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class CompensationRecommendation(BaseModel):
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"""补偿推荐结果"""
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最优补偿方案: str = Field(description="最优补偿方案")
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备选方案: List[str] = Field(description="备选补偿方案")
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预期效果: CompensationEffect = Field(description="预期效果分析")
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推荐理由: str = Field(description="推荐理由")
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实施建议: str = Field(description="实施建议")
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A_B测试建议: str = Field(description="A/B测试建议")
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class ABTestResult(BaseModel):
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"""A/B测试结果"""
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测试组: str = Field(description="测试组")
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对照组: str = Field(description="对照组")
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满意度提升差异: float = Field(description="满意度提升差异")
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统计显著性: float = Field(description="统计显著性(p值)")
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推荐结论: str = Field(description="推荐结论")
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class CompensationRecommender:
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"""个性化服务恢复与补偿推荐系统"""
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def __init__(self, data_path: str = "data/Tweets.csv"):
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self.data_path = data_path
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self.compensation_types = [
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"里程积分补偿", "代金券补偿", "升舱服务", "优先登机",
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"免费行李额度", "贵宾室体验", "快速安检", "延误保险理赔",
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"餐饮补偿", "酒店住宿", "交通补贴", "下次旅行折扣"
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]
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self.problem_types = [
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"航班延误", "行李丢失", "服务态度", "座位问题",
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"餐饮质量", "登机流程", "取消航班", "超售问题"
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]
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self.model = None
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self.effect_data = self._generate_historical_effect_data()
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def _generate_historical_effect_data(self) -> pd.DataFrame:
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"""生成历史补偿效果数据（模拟数据）"""
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np.random.seed(42)
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data = []
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for problem in self.problem_types:
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for compensation in self.compensation_types:
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for _ in range(50): # 每个组合50条历史数据
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sentiment_intensity = np.random.uniform(-1, 0) # 负面情感
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# 基于问题类型和补偿类型的预期效果
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base_effect = self._calculate_base_effect(problem, compensation)
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# 考虑情感强度的影响
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sentiment_multiplier = 1 + abs(sentiment_intensity) * 0.5
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# 随机因素
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random_factor = np.random.normal(1, 0.1)
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satisfaction_improvement = base_effect * sentiment_multiplier * random_factor
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cost_benefit_ratio = self._calculate_cost_benefit(compensation, satisfaction_improvement)
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success_rate = np.random.uniform(0.6, 0.95)
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data.append({
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'problem_type': problem,
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'compensation_type': compensation,
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'sentiment_intensity': sentiment_intensity,
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'satisfaction_improvement': max(0, min(1, satisfaction_improvement)),
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'cost_benefit_ratio': cost_benefit_ratio,
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'success_rate': success_rate
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})
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return pd.DataFrame(data)
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def _calculate_base_effect(self, problem: str, compensation: str) -> float:
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"""计算基础补偿效果"""
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# 问题-补偿匹配度矩阵
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effect_matrix = {
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"航班延误": {"里程积分补偿": 0.8, "代金券补偿": 0.7, "延误保险理赔": 0.9},
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"行李丢失": {"里程积分补偿": 0.6, "代金券补偿": 0.8, "快速安检": 0.4},
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"服务态度": {"升舱服务": 0.7, "贵宾室体验": 0.6, "下次旅行折扣": 0.8},
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"餐饮质量": {"餐饮补偿": 0.9, "代金券补偿": 0.7, "里程积分补偿": 0.6}
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}
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# 默认效果
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default_effect = 0.5
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return effect_matrix.get(problem, {}).get(compensation, default_effect)
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def _calculate_cost_benefit(self, compensation: str, improvement: float) -> float:
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"""计算成本效益比"""
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# 补偿成本估算
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cost_estimates = {
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"里程积分补偿": 50, "代金券补偿": 100, "升舱服务": 200,
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"优先登机": 30, "免费行李额度": 40, "贵宾室体验": 60,
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"快速安检": 20, "延误保险理赔": 150, "餐饮补偿": 25,
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"酒店住宿": 120, "交通补贴": 80, "下次旅行折扣": 90
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}
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cost = cost_estimates.get(compensation, 50)
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benefit = improvement * 200 # 假设满意度提升的价值
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return benefit / cost if cost > 0 else 0
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def train_recommendation_model(self) -> None:
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"""训练补偿推荐模型"""
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# 准备特征数据
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X = pd.get_dummies(self.effect_data[['problem_type', 'compensation_type']])
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X['sentiment_intensity'] = self.effect_data['sentiment_intensity']
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y = self.effect_data['satisfaction_improvement']
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# 训练模型
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self.model = RandomForestRegressor(n_estimators=100, random_state=42)
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self.model.fit(X, y)
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def recommend_compensation(self, problem_type: str, sentiment_intensity: float,
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user_history: Optional[Dict] = None) -> CompensationRecommendation:
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"""推荐补偿方案"""
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if self.model is None:
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self.train_recommendation_model()
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# 评估所有补偿方案
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compensation_effects = []
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for compensation in self.compensation_types:
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# 准备特征
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features = self._prepare_features(problem_type, compensation, sentiment_intensity)
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# 预测满意度提升
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predicted_improvement = self.model.predict([features])[0]
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# 计算成本效益比
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cost_benefit = self._calculate_cost_benefit(compensation, predicted_improvement)
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# 获取历史成功率
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historical_data = self.effect_data[
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(self.effect_data['problem_type'] == problem_type) &
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(self.effect_data['compensation_type'] == compensation)
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]
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success_rate = historical_data['success_rate'].mean() if not historical_data.empty else 0.7
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# 计算推荐指数
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recommendation_score = self._calculate_recommendation_score(
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predicted_improvement, cost_benefit, success_rate
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)
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compensation_effects.append(CompensationEffect(
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补偿类型=compensation,
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问题类型=problem_type,
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情感强度=sentiment_intensity,
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预期满意度提升=predicted_improvement,
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成本效益比=cost_benefit,
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推荐指数=recommendation_score,
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历史成功率=success_rate
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))
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# 排序并选择最优方案
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compensation_effects.sort(key=lambda x: x.推荐指数, reverse=True)
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best_compensation = compensation_effects[0]
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# 生成推荐理由
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recommendation_reason = self._generate_recommendation_reason(best_compensation)
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# 生成实施建议
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implementation_advice = self._generate_implementation_advice(best_compensation.补偿类型)
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# 生成A/B测试建议
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ab_test_suggestion = self._generate_ab_test_suggestion(best_compensation, compensation_effects[1:3])
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return CompensationRecommendation(
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最优补偿方案=best_compensation.补偿类型,
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备选方案=[ce.补偿类型 for ce in compensation_effects[1:3]],
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预期效果=best_compensation,
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推荐理由=recommendation_reason,
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实施建议=implementation_advice,
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A_B测试建议=ab_test_suggestion
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)
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def _prepare_features(self, problem_type: str, compensation: str, sentiment_intensity: float) -> List[float]:
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"""准备特征数据"""
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# 创建问题类型和补偿类型的独热编码
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problem_features = [1 if problem_type == pt else 0 for pt in self.problem_types]
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compensation_features = [1 if compensation == ct else 0 for ct in self.compensation_types]
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return problem_features + compensation_features + [sentiment_intensity]
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def _calculate_recommendation_score(self, improvement: float, cost_benefit: float, success_rate: float) -> float:
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"""计算推荐指数"""
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# 加权综合评分
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improvement_weight = 0.4
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cost_benefit_weight = 0.4
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success_rate_weight = 0.2
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return (improvement * improvement_weight +
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min(cost_benefit, 5) * 0.2 * cost_benefit_weight + # 限制成本效益比影响
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success_rate * success_rate_weight)
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def _generate_recommendation_reason(self, effect: CompensationEffect) -> str:
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"""生成推荐理由"""
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reasons = []
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if effect.预期满意度提升 > 0.7:
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reasons.append("预期满意度提升效果显著")
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elif effect.预期满意度提升 > 0.5:
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reasons.append("预期满意度提升效果良好")
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if effect.成本效益比 > 3:
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reasons.append("成本效益比优秀")
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elif effect.成本效益比 > 2:
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reasons.append("成本效益比良好")
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if effect.历史成功率 > 0.8:
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reasons.append("历史成功率较高")
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return "; ".join(reasons) if reasons else "基于综合评估推荐"
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def _generate_implementation_advice(self, compensation_type: str) -> str:
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"""生成实施建议"""
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advice_map = {
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"里程积分补偿": "建议立即发放积分，并发送确认邮件",
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"代金券补偿": "生成电子代金券，有效期建议30天",
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"升舱服务": "为下次旅行提供升舱机会，需提前确认",
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"优先登机": "自动添加到乘客档案，下次生效",
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"延误保险理赔": "启动保险理赔流程，需收集相关证明"
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}
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return advice_map.get(compensation_type, "按照标准流程实施补偿")
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def _generate_ab_test_suggestion(self, best_compensation: CompensationEffect,
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alternatives: List[CompensationEffect]) -> str:
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"""生成A/B测试建议"""
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if not alternatives:
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return "暂无合适的A/B测试方案"
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alternative = alternatives[0]
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return (
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f"建议进行A/B测试：将{best_compensation.补偿类型}与{alternative.补偿类型}进行对比，"
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f"预期效果差异为{abs(best_compensation.预期满意度提升 - alternative.预期满意度提升):.2f}"
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)
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def run_ab_test(self, problem_type: str, sentiment_intensity: float,
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test_groups: List[str], sample_size: int = 100) -> ABTestResult:
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"""运行A/B测试"""
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if len(test_groups) < 2:
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raise ValueError("A/B测试需要至少2个测试组")
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# 模拟测试结果
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group_a = test_groups[0]
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group_b = test_groups[1]
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# 模拟测试数据
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group_a_improvement = np.random.normal(0.6, 0.1, sample_size)
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group_b_improvement = np.random.normal(0.65, 0.1, sample_size)
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# 计算差异
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mean_diff = group_b_improvement.mean() - group_a_improvement.mean()
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# 简单显著性检验（模拟）
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p_value = 0.03 if abs(mean_diff) > 0.05 else 0.15
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conclusion = "B组效果显著优于A组" if p_value < 0.05 else "两组效果无显著差异"
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return ABTestResult(
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测试组=group_b,
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对照组=group_a,
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满意度提升差异=mean_diff,
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统计显著性=p_value,
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推荐结论=conclusion
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)
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# 创建补偿推荐Agent
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from pydantic_ai import Agent
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class CompensationAnalysis(BaseModel):
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"""补偿分析结果"""
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问题诊断: str = Field(description="问题诊断")
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补偿策略: str = Field(description="补偿策略")
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预期ROI: float = Field(description="预期投资回报率")
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风险分析: str = Field(description="风险分析")
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长期影响: str = Field(description="长期影响评估")
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compensation_agent = Agent(
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'deepseek:deepseek-chat',
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system_prompt="""
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你是航空公司补偿策略专家，基于行为经济学和因果推断原理分析补偿方案。
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你的任务：
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1. 分析客户问题的严重程度和影响范围
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2. 评估不同补偿方案的有效性
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3. 预测补偿方案的长期影响
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4. 提供风险分析和优化建议
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分析要点：
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- 考虑客户的情感强度和问题类型
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- 评估补偿方案的成本效益比
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- 预测客户满意度的恢复程度
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- 分析长期客户关系影响
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输出必须是结构化的JSON格式。
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""",
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output_type=CompensationAnalysis
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)
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async def analyze_compensation_strategy(problem_description: str, sentiment_intensity: float) -> CompensationAnalysis:
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"""分析补偿策略"""
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result = await compensation_agent.run(f"""
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请分析以下客户问题的补偿策略：
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问题描述: {problem_description}
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情感强度: {sentiment_intensity:.2f}
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请提供：
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1. 问题诊断和严重程度评估
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2. 推荐的补偿策略
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3. 预期投资回报率分析
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4. 风险分析和优化建议
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5. 长期客户关系影响评估
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""")
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# 获取实际的CompensationAnalysis对象
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# pydantic-ai不同版本中，result可能有不同结构
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compensation_analysis = None
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# 尝试多种可能的属性访问方式
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if hasattr(result, 'data'):
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compensation_analysis = result.data
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elif hasattr(result, 'result'):
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compensation_analysis = result.result
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elif hasattr(result, 'output'):
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compensation_analysis = result.output
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elif hasattr(result, 'value'):
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compensation_analysis = result.value
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else:
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compensation_analysis = result
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# 如果compensation_analysis仍然是AgentRunResult类型，尝试直接访问其属性
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if hasattr(compensation_analysis, '问题诊断'):
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# 已经是正确的CompensationAnalysis对象
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pass
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else:
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# 尝试从其他属性获取
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if hasattr(compensation_analysis, 'data'):
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compensation_analysis = compensation_analysis.data
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elif hasattr(compensation_analysis, 'result'):
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compensation_analysis = compensation_analysis.result
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elif hasattr(compensation_analysis, 'output'):
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compensation_analysis = compensation_analysis.output
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elif hasattr(compensation_analysis, 'value'):
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compensation_analysis = compensation_analysis.value
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# 最终检查是否获取到了正确的对象
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if not hasattr(compensation_analysis, '问题诊断'):
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raise AttributeError(f"无法访问CompensationAnalysis对象的问题诊断属性，对象类型: {type(compensation_analysis)}")
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return compensation_analysis |