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。
133 lines
4.4 KiB
Python
133 lines
4.4 KiB
Python
"""Decision trace and explanation helpers for risk graph observations."""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from decimal import Decimal
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from typing import Any
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from .models import PeerBaseline, RiskEvidence
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RISK_SCORE_FORMULA = (
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"0.35*S_rule + 0.25*S_anomaly + "
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"0.20*S_graph + 0.15*S_policy + 0.05*S_history"
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)
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@dataclass(slots=True)
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class DecisionTrace:
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formula: str
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algorithm_version: str
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input_scores: dict[str, int]
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output_score: int
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decision_row: str
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feature_contributions_json: list[dict[str, Any]]
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uncertainty_reasons_json: list[str]
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explanation_template_key: str
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metadata: dict[str, Any] = field(default_factory=dict)
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def as_dict(self) -> dict[str, Any]:
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return {
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"formula": self.formula,
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"algorithm_version": self.algorithm_version,
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"input_scores": dict(self.input_scores),
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"output_score": self.output_score,
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"decision_row": self.decision_row,
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"feature_contributions_json": list(self.feature_contributions_json),
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"uncertainty_reasons_json": list(self.uncertainty_reasons_json),
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"explanation_template_key": self.explanation_template_key,
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**self.metadata,
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}
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class DecisionTraceBuilder:
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def build(
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self,
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*,
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algorithm_version: str,
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risk_signal: str,
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risk_level: str,
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raw_risk_score: int,
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risk_score: int,
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contribution_scores: dict[str, int],
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evidence: list[RiskEvidence],
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baseline: PeerBaseline,
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confidence: Decimal,
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metadata: dict[str, Any],
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) -> DecisionTrace:
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return DecisionTrace(
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formula=RISK_SCORE_FORMULA,
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algorithm_version=algorithm_version,
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input_scores=contribution_scores,
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output_score=risk_score,
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decision_row=_decision_row(risk_score=risk_score, risk_level=risk_level),
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feature_contributions_json=_feature_contributions(contribution_scores),
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uncertainty_reasons_json=_uncertainty_reasons(
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raw_risk_score=raw_risk_score,
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risk_score=risk_score,
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evidence=evidence,
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baseline=baseline,
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confidence=confidence,
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metadata=metadata,
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),
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explanation_template_key=f"risk.{risk_signal}.{risk_level}",
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metadata=metadata,
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)
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def _decision_row(*, risk_score: int, risk_level: str) -> str:
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if risk_score >= 90:
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return f"{risk_level}:score>=90"
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if risk_score >= 70:
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return f"{risk_level}:70<=score<90"
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if risk_score >= 45:
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return f"{risk_level}:45<=score<70"
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return f"{risk_level}:score<45"
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def _feature_contributions(scores: dict[str, int]) -> list[dict[str, Any]]:
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weights = {
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"S_rule": Decimal("0.35"),
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"S_anomaly": Decimal("0.25"),
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"S_graph": Decimal("0.20"),
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"S_policy": Decimal("0.15"),
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"S_history": Decimal("0.05"),
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}
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rows = []
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for key, score in scores.items():
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weighted_score = Decimal(int(score or 0)) * weights.get(key, Decimal("0"))
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rows.append(
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{
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"feature": key,
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"score": int(score or 0),
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"weight": str(weights.get(key, Decimal("0"))),
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"weighted_score": float(weighted_score),
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}
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)
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return sorted(rows, key=lambda item: item["weighted_score"], reverse=True)
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def _uncertainty_reasons(
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*,
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raw_risk_score: int,
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risk_score: int,
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evidence: list[RiskEvidence],
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baseline: PeerBaseline,
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confidence: Decimal,
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metadata: dict[str, Any],
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) -> list[str]:
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reasons: list[str] = []
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if risk_score < raw_risk_score:
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reasons.append("score_capped_by_gate")
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if baseline.scope == "insufficient_sample" or baseline.sample_size <= 0:
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reasons.append("peer_baseline_insufficient")
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if confidence < Decimal("0.55"):
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reasons.append("low_confidence")
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if len({item.source for item in evidence if item.source}) < 2:
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reasons.append("single_evidence_source")
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if metadata.get("ontology_gate") == "candidate_only":
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reasons.append("ontology_candidate_only")
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if metadata.get("data_quality_gate") not in {"", "passed", None}:
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reasons.append("data_quality_gate_not_passed")
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return list(dict.fromkeys(reasons))
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