模型数据完整性验证与异常告警机制

模型数据完整性验证与异常告警机制

核心监控指标配置

输入数据完整性监控：

• 缺失值率：设置阈值为5%，当单个字段缺失率超过此值时触发告警
• 数据类型一致性：验证数值型字段是否为float/int，字符串字段是否为str
• 范围验证：如年龄字段应在[0,150]范围内，超出范围的样本标记为异常

实施步骤

import pandas as pd
import numpy as np
from datetime import datetime

# 数据完整性检查类

class DataIntegrityChecker:
    def __init__(self):
        self.alert_thresholds = {
            'missing_rate': 0.05,
            'data_type_violation': 0.02
        }
        
    def validate_input(self, df):
        results = {}
        
        # 检查缺失值
        missing_rates = df.isnull().sum() / len(df)
        results['missing_rate'] = missing_rates.to_dict()
        
        # 数据类型验证
        for col in df.columns:
            if df[col].dtype == 'object':
                # 字符串类型验证
                if not df[col].apply(lambda x: isinstance(x, str)).all():
                    results['data_type_violation'] = True
                    break
        
        return results

# 告警配置示例
checker = DataIntegrityChecker()

# 监控数据
monitor_data = pd.DataFrame({
    'age': [25, 30, None, 45],
    'name': ['Alice', 'Bob', 'Charlie', 'David']
})

results = checker.validate_input(monitor_data)
print(results)

告警规则配置

告警级别：

• 严重级别：缺失率>5% 或数据类型错误
• 警告级别：单个样本超出正常范围

告警触发条件：

alert_rules:
  - name: "high_missing_rate"
    threshold: 0.05
    condition: "missing_rate > threshold"
    severity: "critical"
    notify_channels: ["slack", "email"]

  - name: "data_type_violation"
    condition: "data_type_violation == True"
    severity: "warning"
    notify_channels: ["slack"]

通过上述配置，可实现对模型输入数据的实时完整性监控和异常告警。