generated from Python-2026Spring/assignment-05-final-project-template
165 lines
7.4 KiB
Python
165 lines
7.4 KiB
Python
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# visualization.py - 客户流失预测模型可视化（直接运行即可）
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import joblib
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import matplotlib.pyplot as plt
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import seaborn as sns
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import pandas as pd
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from sklearn.metrics import confusion_matrix, roc_curve, auc
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# -------------------------- 基础设置（解决中文显示、图表样式）--------------------------
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plt.rcParams['font.sans-serif'] = ['SimHei'] # 中文显示（Windows）
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# 如果是Mac/Linux，替换为：plt.rcParams['font.sans-serif'] = ['Arial Unicode MS']
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plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
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plt.style.use('seaborn-v0_8-whitegrid') # 图表样式（清爽易读）
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# -------------------------- 1. 加载模型和数据（复用已有逻辑）--------------------------
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def load_model_and_data():
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"""加载训练好的模型和测试集数据"""
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# 加载模型（确保模型文件路径正确）
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try:
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model = joblib.load('telco_churn_model.pkl')
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print("✅ 模型加载成功")
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except FileNotFoundError:
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raise FileNotFoundError("❌ 未找到模型文件！请先运行 src/model.py 训练模型")
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# 加载并切分数据（复用 src/data.py 的逻辑，避免重复代码）
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try:
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from src.data import load_data, split_data
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df = load_data()
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X_train, X_test, y_train, y_test = split_data(df)
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print("✅ 测试集数据加载成功（共1409条）")
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return model, X_test, y_test
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except ImportError:
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raise ImportError("❌ 未找到 src/data.py！请确保项目目录结构正确")
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# -------------------------- 2. 特征重要性TOP10可视化（核心业务洞察）--------------------------
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def plot_feature_importance(model):
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"""绘制特征重要性TOP10图表"""
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# 提取预处理后的特征名和重要性得分
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preprocessor = model.named_steps['preprocessor']
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feature_names = preprocessor.get_feature_names_out()
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feature_importance = model.named_steps['classifier'].feature_importances_
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# 整理数据（排序+取TOP10，简化特征名方便显示）
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feature_df = pd.DataFrame({
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'特征名': feature_names,
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'重要性': feature_importance
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}).sort_values('重要性', ascending=False).head(10)
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# 简化特征名（原特征名太长，图表显示优化）
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feature_name_map = {
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'tenure': '客户在网时长',
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'TotalCharges': '总消费金额',
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'MonthlyCharges': '月消费金额',
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'Contract_Two year': '合约期-2年',
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'InternetService_Fiber optic': '网络类型-光纤',
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'PaymentMethod_Electronic check': '支付方式-电子支票',
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'Contract_One year': '合约期-1年',
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'OnlineSecurity_Yes': '在线安全服务-有',
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'TechSupport_Yes': '技术支持-有',
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'PaperlessBilling_Yes': '电子账单-是'
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}
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feature_df['简化特征名'] = feature_df['特征名'].map(lambda x: feature_name_map.get(x, x[:15])) # 兜底避免报错
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# 绘制水平条形图（更易读）
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fig, ax = plt.subplots(figsize=(10, 6))
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sns.barplot(
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x='重要性', y='简化特征名', data=feature_df,
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palette='viridis_r', ax=ax # 颜色渐变（反向，重要性越高颜色越深）
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)
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# 图表美化（标题、标签、数值标注）
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ax.set_title('客户流失预测 - 特征重要性TOP10', fontsize=16, fontweight='bold', pad=20)
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ax.set_xlabel('重要性得分', fontsize=12)
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ax.set_ylabel('特征', fontsize=12)
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ax.tick_params(axis='y', labelsize=10)
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# 在条形图上添加数值（直观展示得分）
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for i, v in enumerate(feature_df['重要性']):
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ax.text(v + 0.002, i, f'{v:.3f}', va='center', fontsize=9)
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# 保存图表（高清，可直接插入PPT）
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plt.tight_layout()
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plt.savefig('特征重要性TOP10.png', dpi=300, bbox_inches='tight')
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print("✅ 特征重要性图表已保存为：特征重要性TOP10.png")
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# -------------------------- 3. 混淆矩阵可视化（模型效果直观展示）--------------------------
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def plot_confusion_matrix(model, X_test, y_test):
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"""绘制混淆矩阵（展示模型预测准确率、漏判/误判情况）"""
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# 生成预测结果
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y_pred = model.predict(X_test)
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# 计算混淆矩阵
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cm = confusion_matrix(y_test, y_pred)
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# 混淆矩阵标签（0=未流失，1=流失）
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labels = ['未流失', '流失']
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# 绘制热力图
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fig, ax = plt.subplots(figsize=(8, 6))
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sns.heatmap(
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cm, annot=True, fmt='d', cmap='Blues', # fmt='d' 显示整数
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xticklabels=labels, yticklabels=labels, ax=ax,
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cbar_kws={'label': '客户数量'} # 颜色条标签
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)
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# 图表美化
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ax.set_title('客户流失预测 - 混淆矩阵', fontsize=16, fontweight='bold', pad=20)
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ax.set_xlabel('预测标签', fontsize=12)
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ax.set_ylabel('真实标签', fontsize=12)
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# 添加统计信息（准确率、流失识别率）
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total = cm.sum()
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accuracy = (cm[0,0] + cm[1,1]) / total
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recall_churn = cm[1,1] / (cm[1,0] + cm[1,1]) # 流失客户识别率
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ax.text(0.5, -0.15, f'准确率：{accuracy:.3f} | 流失识别率：{recall_churn:.3f}',
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ha='center', transform=ax.transAxes, fontsize=11)
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# 保存图表
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plt.tight_layout()
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plt.savefig('混淆矩阵.png', dpi=300, bbox_inches='tight')
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print("✅ 混淆矩阵图表已保存为：混淆矩阵.png")
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# -------------------------- 4. 可选：ROC曲线可视化（进阶模型评估）--------------------------
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def plot_roc_curve(model, X_test, y_test):
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"""绘制ROC曲线（展示模型区分能力，AUC值）"""
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# 生成预测概率
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y_pred_proba = model.predict_proba(X_test)[:, 1] # 取流失（1类）的概率
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fpr, tpr, _ = roc_curve(y_test, y_pred_proba)
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roc_auc = auc(fpr, tpr)
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# 绘制ROC曲线
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fig, ax = plt.subplots(figsize=(8, 6))
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ax.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC曲线 (AUC = {roc_auc:.3f})')
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ax.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--', label='随机猜测')
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# 图表美化
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ax.set_xlim([0.0, 1.0])
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ax.set_ylim([0.0, 1.05])
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ax.set_title('客户流失预测 - ROC曲线', fontsize=16, fontweight='bold', pad=20)
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ax.set_xlabel('假阳性率（误判为流失）', fontsize=12)
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ax.set_ylabel('真阳性率（正确识别流失）', fontsize=12)
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ax.legend(loc="lower right", fontsize=11)
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ax.grid(True, alpha=0.3)
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# 保存图表
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plt.tight_layout()
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plt.savefig('ROC曲线.png', dpi=300, bbox_inches='tight')
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print("✅ ROC曲线图表已保存为：ROC曲线.png")
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# -------------------------- 主函数（一键运行所有可视化）--------------------------
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if __name__ == "__main__":
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print("🚀 开始生成可视化图表...")
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try:
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# 加载模型和数据
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model, X_test, y_test = load_model_and_data()
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# 生成3张图表（特征重要性 + 混淆矩阵 + ROC曲线）
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plot_feature_importance(model)
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plot_confusion_matrix(model, X_test, y_test)
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plot_roc_curve(model, X_test, y_test)
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print("\n🎉 所有图表生成完成！文件保存在项目根目录：")
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print("1. 特征重要性TOP10.png（业务洞察核心）")
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print("2. 混淆矩阵.png（模型效果直观展示）")
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print("3. ROC曲线.png（进阶评估，AUC值）")
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except Exception as e:
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print(f"\n❌ 生成失败：{str(e)}")
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