84 lines
2.9 KiB
Python
84 lines
2.9 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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测试数据加载和处理的脚本
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"""
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import sys
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import os
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# 添加父目录到路径
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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# 导入数据处理函数
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from cardio_dashboard import load_and_process_data
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def test_data_loading():
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"""测试数据加载和清洗功能"""
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print("开始测试数据加载和清洗...")
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try:
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# 加载数据
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df = load_and_process_data()
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if df.empty:
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print("❌ 数据加载失败：返回空数据框")
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return False
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print(f"✅ 数据加载成功！共 {len(df)} 条记录")
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# 检查必要的列
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required_columns = ['age_years', 'bmi', 'bmi_category', 'cholesterol_str',
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'gluc_str', 'gender_str', 'cardio_str', 'ap_hi', 'ap_lo']
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missing_columns = [col for col in required_columns if col not in df.columns]
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if missing_columns:
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print(f"❌ 缺少必要的列: {missing_columns}")
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return False
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print("✅ 所有必要的列都存在")
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# 检查数据类型
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print("\n数据摘要:")
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print(f"- 年龄范围: {df['age_years'].min()} ~ {df['age_years'].max()} 岁")
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print(f"- BMI范围: {df['bmi'].min():.1f} ~ {df['bmi'].max():.1f}")
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print(f"- 收缩压范围: {df['ap_hi'].min()} ~ {df['ap_hi'].max()} mmHg")
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print(f"- 舒张压范围: {df['ap_lo'].min()} ~ {df['ap_lo'].max()} mmHg")
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# 检查类别转换
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print("\n类别分布:")
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print(f"- 性别: {df['gender_str'].value_counts().to_dict()}")
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print(f"- 心血管疾病: {df['cardio_str'].value_counts().to_dict()}")
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print(f"- BMI分类: {df['bmi_category'].value_counts().to_dict()}")
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print(f"- 胆固醇水平: {df['cholesterol_str'].value_counts().to_dict()}")
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print(f"- 血糖水平: {df['gluc_str'].value_counts().to_dict()}")
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# 检查异常值处理
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invalid_bp = df['ap_lo'] >= df['ap_hi']
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if invalid_bp.any():
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print(f"❌ 仍然存在舒张压 >= 收缩压的记录: {invalid_bp.sum()} 条")
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return False
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else:
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print("✅ 已成功删除舒张压 >= 收缩压的记录")
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# 检查血压范围
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bp_in_range = ((df['ap_hi'] >= 90) & (df['ap_hi'] <= 250) &
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(df['ap_lo'] >= 60) & (df['ap_lo'] <= 150))
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if not bp_in_range.all():
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print(f"❌ 仍然存在血压异常值: {(~bp_in_range).sum()} 条")
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return False
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else:
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print("✅ 所有血压值都在正常范围内")
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print("\n🎉 所有测试通过！")
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return True
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except Exception as e:
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print(f"❌ 测试过程中发生错误: {str(e)}")
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import traceback
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traceback.print_exc()
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return False
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if __name__ == "__main__":
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success = test_data_loading()
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sys.exit(0 if success else 1) |