# -*- coding: utf-8 -*- """炉层杂质预警无监督模型单元测试（Issue #71）。 覆盖： - ZScoreScorer：fit 估计 μ/σ、score 异常分数（含 σ=0 恒定列、缺失值、未 fit 拒绝）； - ThresholdRule：分数阈值 ∪ 特征 breach 决策； - ImpurityForecaster：fit/predict 端到端； - evaluate_lead_time：提前量评估（对齐 PRD 提前 ≥ 30min）； - 序列化：to_dict/from_dict/save/load 可复现。 """ import math import os import sys import tempfile import unittest sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) import _bootstrap # noqa: F401 挂载 impurity_forecast 包 from impurity_forecast import ( # noqa: E402 AlertDecision, FeatureVectorLike, ImpurityForecaster, ThresholdRule, ZScoreScorer, evaluate_lead_time, ) NAN = float("nan") def _approx(a: float, b: float, eps: float = 1e-6) -> bool: if math.isnan(a) and math.isnan(b): return True return abs(a - b) <= eps def vec(ts: float, **kw) -> FeatureVectorLike: return FeatureVectorLike(timestamp=ts, values=dict(kw)) # --------------------------------------------------------------------------- # 1. ZScoreScorer # --------------------------------------------------------------------------- class ZScoreScorerTest(unittest.TestCase): def test_fit_estimates_mean_std(self): sc = ZScoreScorer().fit([ vec(1, x=10.0), vec(2, x=12.0), vec(3, x=14.0), vec(4, x=12.0), ]) self.assertTrue(sc.fitted) # mean=12, std=sqrt(((10-12)^2+(12-12)^2+(14-12)^2+(12-12)^2)/4)=sqrt(2)=1.414 self.assertTrue(_approx(sc._mean["x"], 12.0)) self.assertTrue(_approx(sc._std["x"], math.sqrt(2.0))) def test_score_normal_is_low(self): sc = ZScoreScorer().fit([vec(i, x=100.0) for i in range(20)]) # 正常段（等于均值）分数应为 0 scores = sc.score([vec(100, x=100.0)]) self.assertTrue(_approx(scores[0], 0.0)) def test_score_anomaly_is_high(self): # 正常段均值 100、std≈1.414；异常值 110 → |110-100|/1.414≈7.07 sc = ZScoreScorer().fit([vec(i, x=100.0 + (i % 3)) for i in range(20)]) scores = sc.score([vec(99, x=110.0)]) self.assertGreater(scores[0], 5.0) def test_score_takes_max_across_features(self): sc = ZScoreScorer().fit([ vec(1, a=0.0, b=0.0), vec(2, a=2.0, b=2.0), vec(3, a=1.0, b=1.0), ]) # a/b 均值=1，std≈0.816；输入 a=1（近均值）、b=10（远）→ 取 b 的偏离 scores = sc.score([vec(4, a=1.0, b=10.0)]) # b 的 z = |10-1|/0.816 ≈ 11.02，应远大于 a 的 z≈0 self.assertGreater(scores[0], 10.0) def test_constant_column_deviation_flagged(self): # 训练段恒定（std=0），推理段偏离 → 用大常数识别为异常 sc = ZScoreScorer().fit([vec(i, c=5.0) for i in range(10)]) scores = sc.score([vec(11, c=5.0), vec(12, c=6.0)]) self.assertTrue(_approx(scores[0], 0.0)) # 不偏离 self.assertGreater(scores[1], 1e5) # 偏离 → 大常数 def test_missing_value_skipped(self): sc = ZScoreScorer().fit([vec(1, x=10.0), vec(2, x=12.0)]) # x 缺失（NaN）不应崩溃，分数按可用特征计算（这里全缺失 → 0） scores = sc.score([vec(3, x=NAN)]) self.assertTrue(_approx(scores[0], 0.0)) def test_not_fitted_raises(self): with self.assertRaises(ValueError): ZScoreScorer().score([vec(1, x=1.0)]) def test_fit_empty_raises(self): with self.assertRaises(ValueError): ZScoreScorer().fit([]) # --------------------------------------------------------------------------- # 2. ThresholdRule # --------------------------------------------------------------------------- class ThresholdRuleTest(unittest.TestCase): def test_score_below_threshold_no_alert(self): rule = ThresholdRule(score_threshold=3.0) d = rule.decide(1.0, {"x": 1.0}, score=2.0) self.assertFalse(d.triggered) def test_score_above_threshold_alerts(self): rule = ThresholdRule(score_threshold=3.0) d = rule.decide(1.0, {"x": 1.0}, score=4.5) self.assertTrue(d.triggered) self.assertTrue(any("异常分数" in r for r in d.reasons)) def test_feature_breach_alerts(self): rule = ThresholdRule(score_threshold=3.0, feature_thresholds={"炉温_ema5": 900.0}) # 分数低，但特征超阈值 → 仍预警 d = rule.decide(1.0, {"炉温_ema5": 950.0}, score=1.0) self.assertTrue(d.triggered) self.assertTrue(any("炉温_ema5" in r for r in d.reasons)) def test_score_threshold_must_be_positive(self): with self.assertRaises(ValueError): ThresholdRule(score_threshold=0) with self.assertRaises(ValueError): ThresholdRule(score_threshold=-1) # --------------------------------------------------------------------------- # 3. ImpurityForecaster 端到端 # --------------------------------------------------------------------------- class ForecasterTest(unittest.TestCase): def test_fit_then_predict(self): f = ImpurityForecaster(rule=ThresholdRule(score_threshold=3.0)) normal = [vec(i, x=100.0 + (i % 3)) for i in range(20)] f.fit(normal) decisions = f.predict(normal + [vec(99, x=200.0)]) # 正常段无预警；最后一条异常值预警 self.assertFalse(any(d.triggered for d in decisions[:-1])) self.assertTrue(decisions[-1].triggered) def test_evaluate_returns_leadtime(self): f = ImpurityForecaster(rule=ThresholdRule(score_threshold=3.0)) f.fit([vec(i, x=100.0) for i in range(10)]) # 构造：ts 0..9 正常，ts 10 起开始异常（递增） samples = [vec(i, x=100.0) for i in range(10)] + \ [vec(i, x=100.0 + 5.0 * (i - 9)) for i in range(10, 20)] decisions, lt = f.evaluate(samples, anomaly_ts=19.0) # 应在 ts=19（峰值）前触发 → 提前量为正 self.assertIsNotNone(lt.first_alert_ts) self.assertGreater(lt.lead_seconds, 0) self.assertGreater(lt.lead_minutes, 0) # --------------------------------------------------------------------------- # 4. evaluate_lead_time # --------------------------------------------------------------------------- class LeadTimeTest(unittest.TestCase): def test_no_alert_returns_none(self): decisions = [AlertDecision(timestamp=t, score=1.0, triggered=False) for t in [1, 2, 3]] lt = evaluate_lead_time(decisions, anomaly_ts=3.0) self.assertIsNone(lt.first_alert_ts) self.assertIsNone(lt.lead_seconds) def test_alert_before_anomaly_positive_lead(self): decisions = [ AlertDecision(timestamp=1, score=1.0, triggered=False), AlertDecision(timestamp=5, score=4.0, triggered=True), AlertDecision(timestamp=10, score=5.0, triggered=True), ] lt = evaluate_lead_time(decisions, anomaly_ts=10.0) self.assertEqual(lt.first_alert_ts, 5) # 提前量 = 10 - 5 = 5s self.assertTrue(_approx(lt.lead_seconds, 5.0)) self.assertTrue(_approx(lt.lead_minutes, 5.0 / 60)) # --------------------------------------------------------------------------- # 5. 序列化 # --------------------------------------------------------------------------- class SerializationTest(unittest.TestCase): def test_roundtrip_dict(self): sc = ZScoreScorer().fit([vec(1, x=10.0), vec(2, x=20.0)]) d = sc.to_dict() sc2 = ZScoreScorer.from_dict(d) self.assertTrue(sc2.fitted) # 复现：同一输入分数一致 s1 = sc.score([vec(3, x=15.0)]) s2 = sc2.score([vec(3, x=15.0)]) self.assertTrue(_approx(s1[0], s2[0])) def test_save_load_file(self): sc = ZScoreScorer().fit([vec(1, x=10.0), vec(2, x=20.0)]) with tempfile.NamedTemporaryFile("w", suffix=".json", delete=False) as fh: path = fh.name try: sc.save(path) sc2 = ZScoreScorer.load(path) self.assertTrue(sc2.fitted) finally: os.unlink(path) if __name__ == "__main__": unittest.main(verbosity=2)