# -*- coding: utf-8 -*- """炉层杂质预警 · 无监督异常评分模型训练与推理（Issue #71 / PRD 5.3 ③）。 承接 #70 的特征工程：把特征向量序列喂给**无监督异常评分模型**，输出每个时刻 的「异常分数」与「预警决策」。PRD 5.3 ③ / 风险表明确：一期数据门槛低，以 **阈值 + 无监督**上线，3 个月后转监督（PRD 4.1 / 风险表 ①③先无监督）。 设计要点 -------- 1. **无监督评分器**（零第三方依赖，纯标准库）： - ``ZScoreScorer``：按特征列在训练段估计均值/方差，推理段算各特征 Z-score， 取绝对值最大者（或均值）为该时刻异常分数。对应 PRD「3σ」阈值口径。 - ``ThresholdRule``：把 #70 的 FeatureSpec 阈值 breach 与分数阈值组合，给出 最终预警决策（避免单一指标误报，对齐误报率 ≤ 8%）。 2. **训练 / 推理分离**：``fit`` 在"正常段"估计分布参数，``score`` 在"观测段"产出 异常分数；可序列化保存（零依赖 JSON）。 3. **提前量评估**：``evaluate_lead_time`` 计算预警首次触发时刻相对真实异常 时刻的提前量（对齐 PRD 提前 ≥ 30min）。 4. **与 #70 解耦**：模型只依赖特征向量的 ``values: Dict[str,float]`` / ``timestamp`` （鸭子类型），不强耦合 FeatureEngine，便于独立测试与换行业复用。 """ from __future__ import annotations import json import math import os from dataclasses import dataclass, field from typing import Dict, List, Optional, Sequence, Tuple NAN = float("nan") def _is_num(x: object) -> bool: return isinstance(x, (int, float)) and not (isinstance(x, float) and math.isnan(x)) def _mean(xs: Sequence[float]) -> float: xs = [x for x in xs if _is_num(x)] return sum(xs) / len(xs) if xs else NAN def _std(xs: Sequence[float]) -> float: xs = [x for x in xs if _is_num(x)] n = len(xs) if n == 0: return NAN m = sum(xs) / n return math.sqrt(sum((x - m) ** 2 for x in xs) / n) @dataclass class FeatureVectorLike: """特征向量鸭子类型（与 #70 FeatureVector 字段兼容）。 模型只读 ``timestamp`` 与 ``values``，不依赖具体类，便于独立测试。 """ timestamp: float values: Dict[str, float] = field(default_factory=dict) class ZScoreScorer: """Z-score（3σ）无监督异常评分器。 训练阶段在"正常段"按特征列估计均值 μ 与标准差 σ；推理阶段对每个时刻 计算各特征 ``|x-μ|/σ``，取**最大值**作为该时刻异常分数（取最显著偏离的 特征，对齐"任一指标异常即预警"的工艺口径）。 新特征列（推理段出现而训练段没有）按需跳过；训练段 σ=0（恒定）的特征 视为"无区分度"，偏离即记为高分数（用大常数代替除零）。 """ LARGE = 1e6 # σ=0 时的等效分数，保证恒定列偏离可被识别 def __init__(self) -> None: self._mean: Dict[str, float] = {} self._std: Dict[str, float] = {} self._fitted = False @property def fitted(self) -> bool: return self._fitted def fit(self, samples: Sequence[FeatureVectorLike]) -> "ZScoreScorer": """在正常段估计各特征列的 μ/σ。""" if not samples: raise ValueError("ZScoreScorer.fit 至少需要 1 条样本") names = set() for s in samples: names.update(k for k, v in s.values.items() if _is_num(v)) self._mean = {n: _mean([s.values[n] for s in samples]) for n in names} self._std = {n: _std([s.values[n] for s in samples]) for n in names} self._fitted = True return self def score(self, samples: Sequence[FeatureVectorLike]) -> List[float]: """对观测段逐时刻输出异常分数（≥0，越大越异常）。""" if not self._fitted: raise ValueError("ZScoreScorer 未 fit，请先在正常段训练") out: List[float] = [] for s in samples: best = 0.0 for name, mu in self._mean.items(): v = s.values.get(name) if not _is_num(v): continue sigma = self._std.get(name, 0.0) if sigma <= 1e-12: # 恒定列：任何偏离都视作异常（用大常数） z = self.LARGE if abs(v - mu) > 1e-9 else 0.0 else: z = abs(v - mu) / sigma if z > best: best = z out.append(best) return out # -- 序列化（零依赖 JSON，便于版本化保存/复现） ---------------------- def to_dict(self) -> Dict[str, object]: return { "kind": "zscore", "mean": self._mean, "std": self._std, "fitted": self._fitted, } @classmethod def from_dict(cls, d: Dict[str, object]) -> "ZScoreScorer": m = cls() m._mean = {k: float(v) for k, v in (d.get("mean") or {}).items()} m._std = {k: float(v) for k, v in (d.get("std") or {}).items()} m._fitted = bool(d.get("fitted", False)) return m def save(self, path: str) -> None: with open(path, "w", encoding="utf-8") as fh: json.dump(self.to_dict(), fh, ensure_ascii=False, indent=2) @classmethod def load(cls, path: str) -> "ZScoreScorer": with open(path, "r", encoding="utf-8") as fh: return cls.from_dict(json.load(fh)) @dataclass class AlertDecision: """单时刻预警决策。""" timestamp: float score: float # 异常分数 triggered: bool # 是否触发预警 reasons: List[str] = field(default_factory=list) # 触发原因（分数超阈/特征 breach） class ThresholdRule: """预警决策规则：异常分数阈值 ∪ FeatureSpec breach（任一满足即预警）。 PRD 5.3 ③：误报率 ≤ 8%。组合两条判据降低单指标误报： - 分数判据：``ZScoreScorer`` 输出 ≥ ``score_threshold``（默认 3σ）； - breach 判据：特征值超 #70 FeatureSpec 声明的 ``threshold``（工艺硬限）。 """ def __init__(self, score_threshold: float = 3.0, feature_thresholds: Optional[Dict[str, float]] = None) -> None: if score_threshold <= 0: raise ValueError("score_threshold 必须 > 0") self.score_threshold = score_threshold # feature_thresholds: 特征名 → 绝对上限（来自 #70 FeatureSpec.threshold） self.feature_thresholds: Dict[str, float] = dict(feature_thresholds or {}) def decide(self, timestamp: float, values: Dict[str, float], score: float) -> AlertDecision: reasons: List[str] = [] if _is_num(score) and score >= self.score_threshold: reasons.append(f"异常分数 {score:.2f} ≥ {self.score_threshold}σ") for name, limit in self.feature_thresholds.items(): v = values.get(name) if _is_num(v) and v > limit: reasons.append(f"{name}={v:.2f} 超阈值 {limit}") return AlertDecision( timestamp=timestamp, score=score, triggered=bool(reasons), reasons=reasons, ) @dataclass class LeadTimeResult: """提前量评估结果（对齐 PRD：提前 ≥ 30min）。""" first_alert_ts: Optional[float] # 首次预警时刻（无则 None） anomaly_ts: Optional[float] # 真实异常时刻 lead_seconds: Optional[float] # 提前量（秒）；负=滞后 @property def lead_minutes(self) -> Optional[float]: return None if self.lead_seconds is None else self.lead_seconds / 60.0 def evaluate_lead_time(decisions: Sequence[AlertDecision], anomaly_ts: float) -> LeadTimeResult: """评估首次预警相对真实异常时刻的提前量。 Args: decisions: 按时间升序的预警决策序列。 anomaly_ts: 真实异常（如人工标注/峰值）发生的时刻。 """ first = None for d in decisions: if d.triggered: first = d.timestamp break if first is None: return LeadTimeResult(first_alert_ts=None, anomaly_ts=anomaly_ts, lead_seconds=None) return LeadTimeResult(first_alert_ts=first, anomaly_ts=anomaly_ts, lead_seconds=anomaly_ts - first) class ImpurityForecaster: """炉层杂质预警统一入口：评分器 + 决策规则 + 提前量评估。 典型用法（配合 #70 FeatureEngine）:: from impurity_forecast import FeatureEngine, load_feature_config eng = FeatureEngine.from_template_config("config/features.template.yaml") vectors = eng.transform(samples) # 特征矩阵 forecaster = ImpurityForecaster() forecaster.fit(vectors[:normal_n]) # 正常段训练 decisions = forecaster.predict(vectors) # 全段预警决策 """ def __init__(self, scorer: Optional[ZScoreScorer] = None, rule: Optional[ThresholdRule] = None) -> None: self.scorer = scorer or ZScoreScorer() self.rule = rule or ThresholdRule() def fit(self, normal_samples: Sequence[FeatureVectorLike]) -> "ImpurityForecaster": self.scorer.fit(normal_samples) return self def predict(self, samples: Sequence[FeatureVectorLike]) -> List[AlertDecision]: scores = self.scorer.score(samples) out: List[AlertDecision] = [] for s, sc in zip(samples, scores): out.append(self.rule.decide(s.timestamp, s.values, sc)) return out def evaluate(self, samples: Sequence[FeatureVectorLike], anomaly_ts: float) -> Tuple[List[AlertDecision], LeadTimeResult]: decisions = self.predict(samples) return decisions, evaluate_lead_time(decisions, anomaly_ts)