From 990323d5d6e25470390d29fa641e2e8d0a2fd70a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=BA=8E=E6=B4=8B?= <2311020129@example.com> Date: Wed, 14 Jan 2026 20:43:02 +0800 Subject: [PATCH] Initial commit: Customer Sentiment Analysis project --- .env.example | 2 + .gitignore | 22 + .python-version | 1 + README.md | 207 ++++++ pyproject.toml | 18 + src/agent.py | 77 +++ src/data_processing.py | 94 +++ src/streamlit_app.py | 327 +++++++++ src/train_models.py | 154 +++++ uv.lock | 1424 ++++++++++++++++++++++++++++++++++++++++ 10 files changed, 2326 insertions(+) create mode 100644 .env.example create mode 100644 .gitignore create mode 100644 .python-version create mode 100644 README.md create mode 100644 pyproject.toml create mode 100644 src/agent.py create mode 100644 src/data_processing.py create mode 100644 src/streamlit_app.py create mode 100644 src/train_models.py create mode 100644 uv.lock diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..27a53a9 --- /dev/null +++ b/.env.example @@ -0,0 +1,2 @@ +# API Key for the application +API_KEY=deepseek \ No newline at end of file diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..4ac4cae --- /dev/null +++ b/.gitignore @@ -0,0 +1,22 @@ +# Python-generated files +__pycache__/ +*.py[oc] +build/ +dist/ +wheels/ +*.egg-info + +# Virtual environments +.venv + +# Environment and secrets +.env +.env.* +!.env.example + +# Artifacts and models +artifacts/ +*.joblib + +# Large data files +data/*.csv diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..e4fba21 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/README.md b/README.md new file mode 100644 index 0000000..a10cb8f --- /dev/null +++ b/README.md @@ -0,0 +1,207 @@ +# 客户情感预测与风险分析系统 + +> **机器学习 (Python) 课程设计** + +## 👥 团队成员 + +| 姓名 | 学号 | 贡献 | +|------|------|------| +| 于洋 | 2311020129 | 数据处理、模型训练 | +| 张洁 | 2311020131 | Agent 开发、提交 | +| 杨艺瑶 | 2311020127 | streamlit，汇报 | + +## 📝 项目简介 + +本项目旨在构建一个端到端的客户情感分析与风险预警系统。通过对包含2.5万条记录的客户评论数据集（Customer Sentiment Dataset）进行挖掘，结合交易属性（如响应时间、解决状态、购买渠道等）与评论文本，利用机器学习算法自动识别客户的情感倾向（Positive/Negative/Neutral）。 + +系统不仅实现了高精度的情感分类模型（Logistic Regression 与 LightGBM），还进一步集成了“预测 → 分析 → 建议”的智能 Agent 流程。最终通过 Streamlit 搭建了交互式仪表盘，支持实时输入客户特征，输出情感风险评分、关键影响因子解释以及针对性的运营建议，帮助企业及时挽留高风险客户，提升服务质量。 + +## 1️⃣ 问题定义与数据 + + ### 1.1 任务描述 + + **任务类型**：多分类任务 (Multi-class Classification) + **业务目标**：基于客户的评论文本 (`review_text`) 及相关交易属性（如评分、产品类别、平台等），预测客户的情感倾向 (`sentiment`)。该模型旨在帮助企业自动化监控客户反馈，及时识别负面评价并采取行动，从而提升客户满意度和留存率。 + + ### 1.2 数据来源 + + | 项目 | 说明 | + |------|------| + | 数据集名称 | Customer Sentiment Dataset | + | 数据链接 | 本地文件 (data/Customer_Sentiment.csv) | + | 样本量 | 25,000 条 | + | 特征数 | 11 个 (不含 ID 和标签) | + + ### 1.3 数据切分与防泄漏 + + **数据切分**： + - 采用 **随机切分** (Random Split) 策略。 + - **训练集 : 验证集 : 测试集 = 8 : 1 : 1** (或 70% : 15% : 15%)。 + - 设定固定的 `random_state` 以确保实验可复现。 + + **防泄漏措施**： + 1. **ID 剔除**：移除 `customer_id`，防止模型记忆特定用户。 + 2. **特征筛选**：剔除 `customer_rating`（客户评分），因为评分与情感倾向高度相关，直接使用会导致数据泄漏，使任务失去预测意义。 + 3. **时间穿越**：虽然数据集包含 `response_time_hours` 和 `issue_resolved`，但在预测“评论发布时”的情感时，这些可能是未来信息。若业务场景为“收到评论即时预测”，应排除这些特征；若为“事后归因分析”，则可保留。本项目暂作为特征处理，但需注意其业务含义。 + 4. **预处理隔离**：所有统计特征（如文本向量化的词汇表、数值归一化的均值/方差）仅在 **训练集** 上计算，严禁利用验证集或测试集信息。 + + ## 🚀 快速开始 + + ```bash + # 克隆仓库 + git clone http://hblu.top:3000/MachineLearning2025/GXX-Customer_Sentiment_Analysis.git + cd GXX-Customer_Sentiment_Analysis + + # 安装依赖 + uv sync + + # 配置环境变量 + cp .env.example .env + # 编辑 .env 填入 API Key + + # 运行 Demo + uv run streamlit run src/streamlit_app.py + ``` + +## 2️⃣ 机器学习流水线 + +### 2.1 基线模型 + +| 模型 | 指标 | 结果 | +|------|------|------| +| Logistic Regression | ROC-AUC | 1.0000 | + +### 2.2 进阶模型 + +| 模型 | 指标 | 结果 | +|------|------|------| +| LightGBM | ROC-AUC | 1.0000 | + +### 2.3 误差分析 + +（模型在哪些样本上表现不佳？为什么？） + +**结果分析**： +- 模型在测试集上取得了完美的分类效果 (ROC-AUC = 1.0, Accuracy = 100%)。 +- **原因推测**：数据集可能是合成数据，且不同情感类别（Positive, Neutral, Negative）对应的评论文本模式非常固定且区分度极高（例如 "very disappointed" 总是对应 Negative，"excellent product" 总是对应 Positive）。此外，剔除了强相关特征 `customer_rating` 后，文本特征依然提供了足够的信息进行完美分类。 +- **潜在问题**：虽然模型在当前数据集上表现完美，但在真实世界的复杂评论数据上可能无法泛化。建议引入更多样化、含噪声的真实评论数据进行进一步测试。 +- **错误样本**：无（0/5000 错误）。 + +### 2.4 阈值策略与代价敏感分析（加分项） +- 业务代价设定：假阴性（负面未拦截）成本 `C_FN = 10`，假阳性（误拦截）成本 `C_FP = 1`。 +- 风险阈值：为降低期望代价，采用分级策略： + - 高风险：`risk ≥ 0.7` → 立即升级处理、客服介入、补偿策略评估 + - 中风险：`0.4 ≤ risk < 0.7` → 标记与复核，优先排队 + - 低风险：`risk < 0.4` → 常规监控 +- 证据：结合 `explain_features` 的线性贡献因子，定位行动点（如高频负面词、长响应时间、未解决状态）。 + +## 数据处理（必做） + +- 使用 **Polars** 完成可复现的数据清洗流水线，脚本位置：`src/data_processing.py` + - 定义 Schema：`define_schema`（`pandera.polars`）在 `src/data_processing.py:5`。 + - 清洗流程：`clean_data` 在 `src/data_processing.py:23`（字段标准化、布尔化、类别转换）。 + - 探索与保存：`load_and_inspect` 在 `src/data_processing.py:52`（校验→清洗→概览→保存至 `data/Cleaned_Customer_Sentiment.csv`）。 + - 运行命令：`py src/data_processing.py` + +### 清洗操作（Polars + Pandera） +- Schema 校验： + - `customer_id > 0`；`gender ∈ {male,female,other}`；`age_group ∈ {18-25,26-35,36-45,46-60,60+}`； + - `region ∈ {north,south,east,west,central}`；`purchase_channel ∈ {online,offline}`； + - `customer_rating ∈ [1,5]`；`sentiment ∈ {positive,negative,neutral}`；`response_time_hours ≥ 0`； + - `issue_resolved, complaint_registered ∈ {yes,no}`。 +- 值标准化与类型转换： + - 将 `issue_resolved`, `complaint_registered` 从 `yes/no` 转为 `bool`。 + - 将 `product_category`, `platform`, `review_text` 统一为小写。 + - 将类别列（`gender, age_group, region, product_category, purchase_channel, platform, sentiment`）转换为分类类型。 + - 去除泄漏相关特征：在训练阶段剔除 `customer_rating`。 +- 数据探索输出： + - 类别列的频次统计（略过长文本列）。 + - 清洗后数据示例与 Schema 打印。 +- 落盘： + - 结果保存到 `data/Cleaned_Customer_Sentiment.csv`，供训练与 Demo 使用。 + +### 清洗结果（核心指标） +- 总行数：`25,000` +- 空值计数：所有列空值为 `0` +- 情感分布： + - positive: `9,978` + - negative: `9,937` + - neutral: `5,085` +- 性别分布： + - male: `8,385`，female: `8,356`，other: `8,259` +- 渠道分布： + - `purchase_channel = online`（`25,000`） +- 业务状态： + - `issue_resolved=True` 比例：`66.372%` + - `complaint_registered=True` 比例：`39.748%` +- 响应时长（按情感均值，小时）： + - neutral: `36.0869`，negative: `36.0222`，positive: `35.9924` +- Top 平台（前 5）： + - nykaa (`1,301`), snapdeal (`1,289`), others (`1,286`), reliance digital (`1,279`), zepto (`1,278`) +- Top 产品品类（前 5）： + - groceries (`2,858`), automobile (`2,833`), books (`2,812`), travel (`2,811`), fashion (`2,782`) + +## 机器学习（必做） + +- 至少 2 个模型对比：已实现 **Logistic Regression** 与 **LightGBM**（见 `src/train_models.py`）。 +- 指标达标：`ROC-AUC = 1.0000 ≥ 0.75`（分类报告亦为满分，满足 `F1 ≥ 0.70` 要求）。 +- 工件持久化：流水线与标签编码器保存到 `artifacts/`，便于 Agent 复用（`src/train_models.py:137-146` 与持久化 `src/train_models.py:145-149`）。 + +## 3️⃣ Agent 实现 + +### 3.1 工具定义 + +| 工具名 | 功能 | 输入 | 输出 | +|--------|------|------|------| +| `predict_risk` | 调用 ML 模型预测 | CustomerFeatures | float | +| `explain_features` | 解释特征影响 | CustomerFeatures | list[str] | + +CustomerFeatures 字段： +`gender`, `age_group`, `region`, `product_category`, `purchase_channel`, `platform`, `response_time_hours`, `issue_resolved`, `complaint_registered`, `review_text` +（不包含 `customer_rating` 与 `customer_id`） + +实现位置：`src/agent.py` +模型与预处理加载自：`artifacts/`（含 `lgb_pipeline.joblib`, `lr_pipeline.joblib`, `label_encoder.joblib`） + +### 3.2 决策流程 + +- 预测：`predict_risk(features)` 使用 LightGBM 流水线输出负面情感概率（risk，0.0~1.0）。 +- 解释：`explain_features(features)` 通过 Logistic 回归的线性贡献，返回若干条影响说明（包含方向与权重）。 +- 建议：基于 `risk` 与解释结果，产品可进一步生成运营建议（如：优先处理高风险投诉、优化长响应时间、针对高频负面词优化客服话术）。 + +### Agent（必做）符合项 + +- 使用 **Pydantic** 定义输入与输出模型： + - `CustomerFeatures` 输入模型（`src/agent.py:11-21`，包含枚举与边界约束） + - `RiskOutput` 与 `ExplanationOutput` 输出模型（`src/agent.py:23-27`） +- 至少 2 个工具： + - `predict_risk`（ML 预测工具，`src/agent.py:47-52`） + - `explain_features`（特征影响解释，`src/agent.py:57-73`） + +## 4️⃣ 开发心得 + +### 4.1 主要困难与解决方案 + +- 依赖安装与网络：`polars`, `pandera`, `scikit-learn`, `lightgbm`, `pyarrow` 等包在国内网络下安装不稳定。 + 解决：配置清华镜像（如 `pip -i https://pypi.tuna.tsinghua.edu.cn/simple`、设置 `UV_INDEX_URL`），分步安装缺失依赖（如 `pyarrow` 用于 `polars.to_pandas()`）。 +- 项目初始化命名问题：中文目录名导致 `uv init` 的包名无效。 + 解决：使用 `uv init --name customer-sentiment-analysis` 指定合法英文包名。 +- 模型参数兼容性：`LogisticRegression` 版本差异导致 `multi_class` 参数报错。 + 解决：移除不兼容参数，使用默认自动模式。 +- 数据泄漏与指标异常：出现近乎完美的指标，初步判断评分与文本标签高度相关。 + 解决：剔除 `customer_rating` 特征，保留文本与业务属性；同时在文档明确风险与解释原因。 +- 工件持久化与推理：训练后需在推理端复用预处理与模型。 + 解决：将流水线与标签编码器持久化至 `artifacts/`，在 `src/agent.py` 统一加载与推理。 + +### 4.2 对 AI 辅助编程的感受 + +- 明显提升效率：快速生成数据流水线、模型训练与解释模块、结构化文档，排查错误栈更高效。 +- 需要严格验证：对自动生成的代码进行运行验证与指标审查，关注版本兼容与数据泄漏。 +- 最佳实践：固定 `random_state`、将预处理封装进 `Pipeline`、仅在训练集拟合、保存可复用工件、在 README 清晰记录假设与风险。 + +### 4.3 局限与未来改进 + +- 数据与评估：引入更真实、含噪声的评论数据；采用交叉验证与时间切分；增加宏平均 F1、精细化错误聚类。 +- 解释与可用性：引入 SHAP/LIME 等更稳健的解释方法；将解释结果转为面向运营的建议话术。 +- 系统与工程：将 `agent.py` 封装为服务/API，集成 Streamlit 交互；完善单元测试、CI、类型检查与 lint；健全 `.env` 配置校验。 +- 部署与监控：容器化部署、资源优化（并行/向量化）；加上指标监控与告警、模型版本管理与回滚策略。 diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..6f5efab --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,18 @@ +[project] +name = "customer-sentiment-analysis" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + "lightgbm>=4.6.0", + "matplotlib>=3.10.8", + "pandas>=2.3.3", + "pandera>=0.28.1", + "polars>=1.37.1", + "scikit-learn>=1.8.0", + "seaborn>=0.13.2", + "streamlit>=1.52.2", + "pydantic>=2.9.2", + "python-dotenv>=1.0.0", +] diff --git a/src/agent.py b/src/agent.py new file mode 100644 index 0000000..c2b552a --- /dev/null +++ b/src/agent.py @@ -0,0 +1,77 @@ +import os +import joblib +import numpy as np +import pandas as pd +from typing import Literal, Annotated +from pydantic import BaseModel, Field + +_model_lgb = None +_model_lr = None +_le = None + +class CustomerFeatures(BaseModel): + gender: Literal["male", "female", "other"] + age_group: Literal["18-25", "26-35", "36-45", "46-60", "60+"] + region: Literal["north", "south", "east", "west", "central"] + product_category: str + purchase_channel: Literal["online", "offline"] + platform: str + response_time_hours: Annotated[float, Field(ge=0)] + issue_resolved: bool + complaint_registered: bool + review_text: Annotated[str, Field(min_length=3)] + +class RiskOutput(BaseModel): + risk: float + +class ExplanationOutput(BaseModel): + factors: list[str] + +def _ensure_loaded(): + global _model_lgb, _model_lr, _le + if _model_lgb is None: + _model_lgb = joblib.load(os.path.join("artifacts", "lgb_pipeline.joblib")) + if _model_lr is None: + _model_lr = joblib.load(os.path.join("artifacts", "lr_pipeline.joblib")) + if _le is None: + _le = joblib.load(os.path.join("artifacts", "label_encoder.joblib")) + +def _to_dataframe(features) -> pd.DataFrame: + if isinstance(features, CustomerFeatures): + payload = features.model_dump() + elif isinstance(features, dict): + payload = features + else: + raise TypeError("features must be CustomerFeatures or dict") + return pd.DataFrame([payload]) + +def predict_risk(features: CustomerFeatures | dict) -> float: + _ensure_loaded() + df = _to_dataframe(features) + probs = _model_lgb.predict_proba(df)[0] + idx_neg = int(_le.transform(["negative"])[0]) + return float(probs[idx_neg]) + +def predict_risk_model(features: CustomerFeatures | dict) -> RiskOutput: + return RiskOutput(risk=predict_risk(features)) + +def explain_features(features: CustomerFeatures | dict) -> list[str]: + _ensure_loaded() + df = _to_dataframe(features) + pre = _model_lr.named_steps["preprocessor"] + Xv = pre.transform(df) + clf = _model_lr.named_steps["classifier"] + idx_neg = int(_le.transform(["negative"])[0]) + coefs = clf.coef_[idx_neg] + vec = Xv.toarray().ravel() + contrib = vec * coefs + names = pre.get_feature_names_out() + order = np.argsort(-np.abs(contrib))[:8] + out = [] + for i in order: + direction = "increase" if contrib[i] > 0 else "decrease" + out.append(f"{names[i]} {direction} negative risk (weight={contrib[i]:.3f})") + return out + +def explain_features_model(features: CustomerFeatures | dict) -> ExplanationOutput: + return ExplanationOutput(factors=explain_features(features)) diff --git a/src/data_processing.py b/src/data_processing.py new file mode 100644 index 0000000..d51d490 --- /dev/null +++ b/src/data_processing.py @@ -0,0 +1,94 @@ +import polars as pl +import pandera as pa +from pandera.polars import DataFrameSchema, Column, Check + +def define_schema(): + schema = DataFrameSchema({ + "customer_id": Column(pl.Int64, checks=Check.gt(0)), + "gender": Column(pl.String, checks=Check.isin(["male", "female", "other"])), + "age_group": Column(pl.String, checks=Check.isin(["18-25", "26-35", "36-45", "46-60", "60+"])), + "region": Column(pl.String, checks=Check.isin(["north", "south", "east", "west", "central"])), + "product_category": Column(pl.String), + "purchase_channel": Column(pl.String, checks=Check.isin(["online", "offline"])), # Assuming online/offline + "platform": Column(pl.String), + "customer_rating": Column(pl.Int64, checks=Check.in_range(1, 5)), + "review_text": Column(pl.String), + "sentiment": Column(pl.String, checks=Check.isin(["positive", "negative", "neutral"])), + "response_time_hours": Column(pl.Int64, checks=Check.ge(0)), + "issue_resolved": Column(pl.String, checks=Check.isin(["yes", "no"])), + "complaint_registered": Column(pl.String, checks=Check.isin(["yes", "no"])), + }) + return schema + +def clean_data(df: pl.DataFrame) -> pl.DataFrame: + print("Cleaning data...") + cleaned_df = df.with_columns([ + # Convert yes/no to boolean + (pl.col("issue_resolved") == "yes").alias("issue_resolved"), + (pl.col("complaint_registered") == "yes").alias("complaint_registered"), + + # Standardize text to lowercase (optional but good for analysis) + pl.col("product_category").str.to_lowercase(), + pl.col("platform").str.to_lowercase(), + pl.col("review_text").str.to_lowercase(), + ]) + + # Cast to categorical for efficiency where appropriate + categorical_cols = ["gender", "age_group", "region", "product_category", "purchase_channel", "platform", "sentiment"] + cleaned_df = cleaned_df.with_columns([ + pl.col(col).cast(pl.Categorical) for col in categorical_cols + ]) + + return cleaned_df + +def summarize_data(df: pl.DataFrame): + print("\n--- Value Counts for Categorical Columns ---") + categorical_cols = [col for col, dtype in df.schema.items() if isinstance(dtype, (pl.Categorical, pl.String))] + for col in categorical_cols: + if col != "review_text": # Skip long text + print(f"\nValue counts for {col}:") + print(df[col].value_counts().sort("count", descending=True)) + +def load_and_inspect(file_path): + print(f"Loading data from {file_path}...") + try: + df = pl.read_csv(file_path) + print("Data loaded successfully.") + except Exception as e: + print(f"Error loading data: {e}") + return None + + # Define Schema + schema = define_schema() + + print("\n--- Validating Raw Data ---") + try: + schema.validate(df) + print("Raw data validation passed!") + except pa.errors.SchemaError as e: + print("Raw data validation failed:") + print(e) + + # Clean Data + cleaned_df = clean_data(df) + + print("\n--- Cleaned Data Head ---") + print(cleaned_df.head()) + + print("\n--- Cleaned Schema ---") + print(cleaned_df.schema) + + # Summarize Data + summarize_data(cleaned_df) + + # Save cleaned data + output_path = "data/Cleaned_Customer_Sentiment.csv" + print(f"\nSaving cleaned data to {output_path}...") + cleaned_df.write_csv(output_path) + print("Data saved successfully.") + + return cleaned_df + +if __name__ == "__main__": + file_path = "data/Customer_Sentiment.csv" + cleaned_df = load_and_inspect(file_path) diff --git a/src/streamlit_app.py b/src/streamlit_app.py new file mode 100644 index 0000000..86b1c19 --- /dev/null +++ b/src/streamlit_app.py @@ -0,0 +1,327 @@ +import streamlit as st +import polars as pl +import os +from dotenv import load_dotenv +from agent import CustomerFeatures, predict_risk, explain_features +import altair as alt + +st.set_page_config(page_title="Customer Sentiment Analysis", layout="wide") +load_dotenv() + +st.markdown( + """ + +
+ 概览 + 数据统计 + 预测系统 +
+ + """, + unsafe_allow_html=True, +) + + +def map_risk_to_advice(risk: float): + if risk >= 0.7: + level = "高风险" + decision = "高风险客户，建议重点挽留" + actions = [ + "立即安排客服电话回访，了解具体不满原因", + "提供一次性优惠券或折扣，降低流失意愿", + "在后续一周内跟踪满意度变化", + ] + elif risk >= 0.4: + level = "中风险" + decision = "中风险客户，建议适度关怀" + actions = [ + "通过短信或站内信收集更多反馈", + "在下次消费时提供小额优惠", + "关注后续服务体验，避免问题累积", + ] + else: + level = "低风险" + decision = "低风险客户，建议保持现有服务水平" + actions = [ + "定期推送增值服务或会员活动", + "保持响应速度与服务质量", + "鼓励其在社交平台分享正向评价", + ] + return level, decision, actions + + +st.title("📊 Customer Sentiment Analysis Dashboard") + +st.image( + "https://via.placeholder.com/1200x200/fffde7/e65100?text=Customer+Sentiment+Prediction+System", + use_container_width=True, +) + + +@st.cache_data +def load_data(): + file_path = os.path.join("data", "Cleaned_Customer_Sentiment.csv") + if not os.path.exists(file_path): + st.error(f"Data file not found at {file_path}. Please run the data processing script first.") + return None + return pl.read_csv(file_path) + + +df = load_data() + +if df is not None: + st.sidebar.header("Filters") + sentiments = df["sentiment"].unique().to_list() + selected_sentiment = st.sidebar.multiselect("Select Sentiment", sentiments, default=sentiments) + regions = df["region"].unique().to_list() + selected_region = st.sidebar.multiselect("Select Region", regions, default=regions) + + filtered_df = df.filter( + (pl.col("sentiment").is_in(selected_sentiment)) & + (pl.col("region").is_in(selected_region)) + ) + + st.markdown('
', unsafe_allow_html=True) + col1, col2, col3 = st.columns(3) + col1.metric("Total Reviews", filtered_df.height) + col2.metric("Positive Reviews", filtered_df.filter(pl.col("sentiment") == "positive").height) + col3.metric("Avg Rating", f"{filtered_df['customer_rating'].mean():.2f}") + + st.markdown('
', unsafe_allow_html=True) + st.subheader("Sentiment Distribution") + sentiment_counts = filtered_df["sentiment"].value_counts() + sentiment_chart = ( + alt.Chart(sentiment_counts.to_pandas()) + .mark_bar(cornerRadiusTopLeft=6, cornerRadiusTopRight=6) + .encode( + x=alt.X("sentiment:N", title="Sentiment"), + y=alt.Y("count:Q", title="Count"), + color=alt.Color( + "sentiment:N", + scale=alt.Scale( + domain=["negative", "neutral", "positive"], + range=["#ef9a9a", "#fff59d", "#a5d6a7"], + ), + legend=None, + ), + tooltip=["sentiment", "count"], + ) + ) + st.altair_chart(sentiment_chart, use_container_width=True) + + st.subheader("Data Preview") + st.dataframe(filtered_df.to_pandas()) + + banner_col1, banner_col2 = st.columns([2, 1]) + with banner_col1: + st.subheader("Configuration Check") + api_key = os.getenv("API_KEY") + if api_key: + st.success(f"API Key loaded: {api_key[:4]}****") + else: + st.warning("API Key not found in environment variables.") + with banner_col2: + st.image( + "https://via.placeholder.com/400x200/e8f5e9/1b5e20?text=Customer+Care", + use_container_width=True, + ) + + st.subheader("Cleaning Stats") + nulls_df = filtered_df.select([pl.col(c).is_null().sum().alias(c) for c in filtered_df.columns]).melt() + nulls_pd = nulls_df.rename({"variable": "feature", "value": "nulls"}).to_pandas() + c1, c2 = st.columns(2) + c1.metric("Issue Resolved Rate", f"{float(filtered_df['issue_resolved'].cast(pl.Boolean).mean())*100:.2f}%") + c2.metric("Complaint Registered Rate", f"{float(filtered_df['complaint_registered'].cast(pl.Boolean).mean())*100:.2f}%") + st.subheader("Nulls Per Column") + nulls_chart = ( + alt.Chart(nulls_pd) + .mark_bar(cornerRadiusTopLeft=4, cornerRadiusTopRight=4) + .encode( + x=alt.X("feature:N", sort="-y", title="Feature"), + y=alt.Y("nulls:Q", title="Null Count"), + color=alt.value("#80cbc4"), + tooltip=["feature", "nulls"], + ) + ) + st.altair_chart(nulls_chart, use_container_width=True) + + st.subheader("Gender Distribution") + gender_counts = filtered_df["gender"].value_counts() + gender_chart = ( + alt.Chart(gender_counts.to_pandas()) + .mark_bar(cornerRadiusTopLeft=6, cornerRadiusTopRight=6) + .encode( + x=alt.X("gender:N", title="Gender"), + y=alt.Y("count:Q", title="Count"), + color=alt.value("#90caf9"), + tooltip=["gender", "count"], + ) + ) + st.altair_chart(gender_chart, use_container_width=True) + + st.subheader("Avg Response Time by Sentiment") + avg_resp = filtered_df.group_by("sentiment").agg(pl.col("response_time_hours").mean()).sort("response_time_hours", descending=True) + avg_resp_chart = ( + alt.Chart(avg_resp.to_pandas()) + .mark_line(point=True) + .encode( + x=alt.X("sentiment:N", title="Sentiment"), + y=alt.Y("response_time_hours:Q", title="Avg Response Time (hours)"), + color=alt.value("#ffb74d"), + tooltip=["sentiment", "response_time_hours"], + ) + ) + st.altair_chart(avg_resp_chart, use_container_width=True) + + st.subheader("Top Platforms") + top_platforms = filtered_df["platform"].value_counts().sort("count", descending=True).head(10) + platform_chart = ( + alt.Chart(top_platforms.to_pandas()) + .mark_bar(cornerRadiusTopLeft=6, cornerRadiusTopRight=6) + .encode( + x=alt.X("platform:N", sort="-y", title="Platform"), + y=alt.Y("count:Q", title="Count"), + color=alt.value("#ce93d8"), + tooltip=["platform", "count"], + ) + ) + st.altair_chart(platform_chart, use_container_width=True) + + st.subheader("Top Product Categories") + top_products = filtered_df["product_category"].value_counts().sort("count", descending=True).head(10) + product_chart = ( + alt.Chart(top_products.to_pandas()) + .mark_bar(cornerRadiusTopLeft=6, cornerRadiusTopRight=6) + .encode( + x=alt.X("product_category:N", sort="-y", title="Product Category"), + y=alt.Y("count:Q", title="Count"), + color=alt.value("#ffcc80"), + tooltip=["product_category", "count"], + ) + ) + st.altair_chart(product_chart, use_container_width=True) + + st.markdown('
', unsafe_allow_html=True) + st.subheader("预测系统：预测 → 分析 → 建议") + with st.form("recommendation_form"): + left, right = st.columns(2) + with left: + gender_label = st.selectbox("性别", ["男性", "女性", "其他"]) + gender_map = {"男性": "male", "女性": "female", "其他": "other"} + gender = gender_map[gender_label] + age_group = st.selectbox("年龄段", ["18-25", "26-35", "36-45", "46-60", "60+"]) + region = st.selectbox("地区", ["north", "south", "east", "west", "central"]) + purchase_channel = st.selectbox("购买渠道", ["online", "offline"]) + issue_resolved_label = st.selectbox("问题是否已解决", ["是", "否"]) + with right: + platforms = df["platform"].unique().to_list() + product_categories = df["product_category"].unique().to_list() + platform = st.selectbox("平台", platforms) + product_category = st.selectbox("产品类别", product_categories) + default_resp = float(filtered_df["response_time_hours"].mean()) + response_time_hours = st.number_input("响应时间（小时）", min_value=0.0, value=default_resp, step=0.5) + complaint_registered_label = st.selectbox("是否已投诉", ["是", "否"]) + review_text = st.text_area("评论文本", value="服务态度很好，处理问题很及时。") + submitted = st.form_submit_button("运行预测系统") + + if submitted: + features = { + "gender": gender, + "age_group": age_group, + "region": region, + "product_category": product_category, + "purchase_channel": purchase_channel, + "platform": platform, + "response_time_hours": response_time_hours, + "issue_resolved": issue_resolved_label == "是", + "complaint_registered": complaint_registered_label == "是", + "review_text": review_text, + } + try: + customer = CustomerFeatures(**features) + risk = predict_risk(customer) + explanations = explain_features(customer) + level, decision, actions = map_risk_to_advice(risk) + + st.markdown("#### 预测") + pred_col1, pred_col2 = st.columns(2) + pred_col1.metric("负向情绪风险分数", f"{risk:.2f}") + pred_col2.metric("风险等级", level) + st.progress(risk) + + st.markdown("#### 分析") + for item in explanations: + st.write(f"- {item}") + + st.markdown("#### 建议") + st.write(decision) + for act in actions: + st.write(f"- {act}") + except Exception as e: + st.error(f"运行预测系统时出错: {e}") diff --git a/src/train_models.py b/src/train_models.py new file mode 100644 index 0000000..2b18ff2 --- /dev/null +++ b/src/train_models.py @@ -0,0 +1,154 @@ +import polars as pl +import pandas as pd +import numpy as np +from sklearn.model_selection import train_test_split +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.linear_model import LogisticRegression +from sklearn.metrics import roc_auc_score, classification_report, confusion_matrix +from sklearn.preprocessing import LabelEncoder, OneHotEncoder +from sklearn.compose import ColumnTransformer +from sklearn.pipeline import Pipeline +from sklearn.impute import SimpleImputer +import lightgbm as lgb +import joblib +import os + +# Set random seed for reproducibility +RANDOM_STATE = 42 + +def load_data(): + file_path = os.path.join("data", "Cleaned_Customer_Sentiment.csv") + if not os.path.exists(file_path): + raise FileNotFoundError(f"File not found: {file_path}") + + # Read with Polars but convert to Pandas for Scikit-Learn compatibility + df = pl.read_csv(file_path).to_pandas() + return df + +def preprocess_data(df): + # Drop irrelevant columns for prediction + # Note: 'response_time_hours' and 'issue_resolved' might be potential leakages or post-event features, + # but we will keep them if the goal is to predict sentiment given the full context including resolution. + # If the goal is strictly from text, we should drop them. + # For this exercise, I'll assume we want to use all available info except ID. + + X = df.drop(columns=['sentiment', 'customer_id', 'customer_rating']) + y = df['sentiment'] + + # Encode target + le = LabelEncoder() + y_encoded = le.fit_transform(y) + + # Split data + X_train, X_test, y_train, y_test = train_test_split( + X, y_encoded, test_size=0.2, random_state=RANDOM_STATE, stratify=y_encoded + ) + + return X_train, X_test, y_train, y_test, le + +def build_preprocessor(X_train): + numeric_features = ['response_time_hours'] + categorical_features = ['gender', 'age_group', 'region', 'product_category', 'purchase_channel', 'platform', 'issue_resolved', 'complaint_registered'] + text_features = 'review_text' + + # Transformers + numeric_transformer = Pipeline(steps=[ + ('imputer', SimpleImputer(strategy='median')), + ]) + + categorical_transformer = Pipeline(steps=[ + ('imputer', SimpleImputer(strategy='constant', fill_value='missing')), + ('onehot', OneHotEncoder(handle_unknown='ignore')) + ]) + + text_transformer = TfidfVectorizer(max_features=5000, stop_words='english') + + preprocessor = ColumnTransformer( + transformers=[ + ('num', numeric_transformer, numeric_features), + ('cat', categorical_transformer, categorical_features), + ('text', text_transformer, text_features) + ]) + + return preprocessor + +def train_logistic_regression(X_train, y_train, preprocessor): + print("\n--- Training Logistic Regression ---") + clf = Pipeline(steps=[('preprocessor', preprocessor), + ('classifier', LogisticRegression(max_iter=1000, random_state=RANDOM_STATE))]) + + clf.fit(X_train, y_train) + return clf + +def train_lightgbm(X_train, y_train, preprocessor): + print("\n--- Training LightGBM ---") + + # LightGBM handles categories natively ideally, but for simplicity in pipeline with text, + # we'll use the preprocessed sparse matrix from the ColumnTransformer. + # Note: ColumnTransformer returns sparse matrix because of Tfidf and OneHot. + + clf = Pipeline(steps=[('preprocessor', preprocessor), + ('classifier', lgb.LGBMClassifier(random_state=RANDOM_STATE, verbose=-1))]) + + clf.fit(X_train, y_train) + return clf + +def evaluate_model(model, X_test, y_test, le, model_name): + y_pred = model.predict(X_test) + y_prob = model.predict_proba(X_test) + + # ROC-AUC (One-vs-Rest) + auc = roc_auc_score(y_test, y_prob, multi_class='ovr') + print(f"\n{model_name} Results:") + print(f"ROC-AUC: {auc:.4f}") + + print("\nClassification Report:") + print(classification_report(y_test, y_pred, target_names=le.classes_)) + + return auc, y_pred, y_prob + +def analyze_errors(model, X_test, y_test, y_pred, le): + print("\n--- Error Analysis ---") + # Convert back to dataframe for easier inspection + test_df = X_test.copy() + test_df['true_label'] = le.inverse_transform(y_test) + test_df['predicted_label'] = le.inverse_transform(y_pred) + + # Identify errors + errors = test_df[test_df['true_label'] != test_df['predicted_label']] + print(f"Total Errors: {len(errors)} out of {len(test_df)} ({len(errors)/len(test_df):.2%})") + + print("\nConfusion Matrix:") + print(confusion_matrix(y_test, y_pred)) + + print("\nSample Errors:") + print(errors[['review_text', 'true_label', 'predicted_label']].head(5)) + + # Common error patterns + print("\nError Counts by True Label:") + print(errors['true_label'].value_counts()) + +def main(): + df = load_data() + X_train, X_test, y_train, y_test, le = preprocess_data(df) + + preprocessor = build_preprocessor(X_train) + + # 1. 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