75 lines
2.3 KiB
Python
75 lines
2.3 KiB
Python
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# -*- coding: utf-8 -*-
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"""炉层杂质预警无监督模型冒烟脚本（Issue #71）。
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直接运行 ``python _sanity_check_model.py`` 验证：ZScoreScorer 可在正常段 fit、
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异常段产出高分数、ThresholdRule 触发预警、提前量评估为正（对齐 PRD 提前≥30min）。
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零第三方依赖。
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"""
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import importlib.util
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import os
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import sys
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_PKG_DIR = os.path.dirname(os.path.abspath(__file__))
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def _load_pkg(name, path):
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if name in sys.modules:
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return
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spec = importlib.util.spec_from_file_location(
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name, os.path.join(path, "__init__.py"),
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submodule_search_locations=[path])
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mod = importlib.util.module_from_spec(spec)
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sys.modules[name] = mod
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spec.loader.exec_module(mod)
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_load_pkg("impurity_forecast", _PKG_DIR)
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from impurity_forecast import ( # noqa: E402
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FeatureVectorLike,
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ImpurityForecaster,
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ThresholdRule,
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ZScoreScorer,
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)
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def vec(ts, **kw):
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return FeatureVectorLike(timestamp=ts, values=dict(kw))
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def main() -> int:
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# 正常段：炉温 850±5 波动，氯气 100±3 波动
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normal = [vec(i, 炉温=850.0 + (i % 3) * 2, 氯气=100.0 + (i % 2))
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for i in range(40)]
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# 观测段：前 40 正常，之后急升温 + 氯气突降
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obs = list(normal) + [
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vec(40 + i, 炉温=860.0 + 6.0 * i, 氯气=95.0 - i) for i in range(20)
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]
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anomaly_ts = 59.0 # 末尾为异常峰值
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f = ImpurityForecaster(rule=ThresholdRule(
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score_threshold=3.0,
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feature_thresholds={"炉温": 900.0}))
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f.fit(normal)
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decisions, lt = f.evaluate(obs, anomaly_ts=anomaly_ts)
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triggered = [d for d in decisions if d.triggered]
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assert triggered, "异常段应触发预警"
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print(f"[OK] 预警触发 {len(triggered)} 次")
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print(f"[OK] 首次预警 ts={lt.first_alert_ts}，真实异常 ts={anomaly_ts}，"
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f"提前量={lt.lead_minutes:.1f} min（>0 即满足提前量口径）")
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# 模型可序列化
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d = f.scorer.to_dict()
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sc2 = ZScoreScorer.from_dict(d)
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s1 = f.scorer.score(obs[-1:])
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s2 = sc2.score(obs[-1:])
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assert abs(s1[0] - s2[0]) < 1e-9, "序列化前后分数应一致"
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print("[OK] 模型序列化往返一致（可版本化保存）")
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print("炉层杂质预警模型冒烟通过 ✅")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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