7989f3a159
后端新增风险图谱算法模块、风险观察与反馈服务、规则 DSL 校验器和可解释性引擎，完善系统仪表盘和财务仪表盘统计， 优化 agent 运行和编排执行链路，清理旧开发文档，前端新增 系统趋势、负载热力图等多种仪表盘图表组件，完善操作反馈 对话框和工作台日期选择器，优化报销创建和审批详情交互， 补充单元测试覆盖。
78 lines
2.8 KiB
Python
78 lines
2.8 KiB
Python
"""Control effect analysis for risk rules, sampling, and digital employees."""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Any
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HIGH_LEVELS = {"high", "critical"}
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@dataclass(slots=True)
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class ControlEffectSummary:
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before_count: int
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after_count: int
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risk_count_delta: int
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average_score_delta: float
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high_rate_delta: float
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confirmation_rate_delta: float
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false_positive_rate_delta: float
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def as_dict(self) -> dict[str, Any]:
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return {
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"before_count": self.before_count,
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"after_count": self.after_count,
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"risk_count_delta": self.risk_count_delta,
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"average_score_delta": self.average_score_delta,
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"high_rate_delta": self.high_rate_delta,
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"confirmation_rate_delta": self.confirmation_rate_delta,
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"false_positive_rate_delta": self.false_positive_rate_delta,
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}
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class ControlEffectAnalyzer:
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def compare(
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self,
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before: list[dict[str, Any]],
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after: list[dict[str, Any]],
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) -> ControlEffectSummary:
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before_metrics = _metrics(before)
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after_metrics = _metrics(after)
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return ControlEffectSummary(
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before_count=before_metrics["count"],
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after_count=after_metrics["count"],
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risk_count_delta=after_metrics["count"] - before_metrics["count"],
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average_score_delta=round(after_metrics["average_score"] - before_metrics["average_score"], 4),
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high_rate_delta=round(after_metrics["high_rate"] - before_metrics["high_rate"], 4),
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confirmation_rate_delta=round(
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after_metrics["confirmation_rate"] - before_metrics["confirmation_rate"],
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4,
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),
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false_positive_rate_delta=round(
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after_metrics["false_positive_rate"] - before_metrics["false_positive_rate"],
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4,
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),
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)
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def _metrics(items: list[dict[str, Any]]) -> dict[str, Any]:
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count = len(items)
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if count == 0:
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return {
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"count": 0,
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"average_score": 0.0,
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"high_rate": 0.0,
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"confirmation_rate": 0.0,
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"false_positive_rate": 0.0,
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}
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confirmed = sum(1 for item in items if item.get("feedback_status") == "confirmed")
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false_positive = sum(1 for item in items if item.get("feedback_status") == "false_positive")
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reviewed = confirmed + false_positive
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return {
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"count": count,
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"average_score": sum(int(item.get("risk_score") or 0) for item in items) / count,
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"high_rate": sum(1 for item in items if item.get("risk_level") in HIGH_LEVELS) / count,
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"confirmation_rate": confirmed / reviewed if reviewed else 0.0,
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"false_positive_rate": false_positive / reviewed if reviewed else 0.0,
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}
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