feat(#73): [Ti-1] 模型部署到内核并接入驾驶舱（预测服务+告警+可解释视图+漂移监测）
新增 templates/ti-cl4/quality-forecast/serve.py： - PredictionService：特征抽取→模型预测→告警判定全链路，部署前 validate - Thresholds/AlarmLevel：纯度min/杂质max 双向阈值，normal/warning/critical 三级 - CockpitView：渲染 kpi_card+alarm_panel+explanation_list（对齐 cockpit-layout-v1） 含 top-3 特征贡献溯源，满足 PRD 可解释可溯源要求 - DriftMonitor：累计 R²/MAE，超阈值触发重训信号（PRD §5.3/§9） - DeploymentBundle：模型+特征清单+阈值+置信带整体序列化往返 - config/thresholds.template.yaml：TiCl₄纯度下限 99.2 阈值模板 - 14 用例（累计 53 用例）全通过；纯标准库零运行时依赖。
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# -*- coding: utf-8 -*-
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"""Ti-1 质量预测部署接入驾驶舱测试（Issue #73）。
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覆盖：
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1. PredictionService 部署校验（模型未训练/特征名不一致/阈值target不一致）；
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2. predict 全链路（特征抽取→预测→告警判定）；
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3. Thresholds 分级（normal/warning/critical，min/max 双向）；
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4. CockpitView 渲染 kpi_card/alarm_panel/explanation_list；
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5. DriftMonitor 重训触发；
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6. DeploymentBundle 序列化往返。
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"""
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import math
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import os
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import sys
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import unittest
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HERE = os.path.dirname(os.path.abspath(__file__))
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sys.path.insert(0, HERE)
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import _bootstrap # noqa: F401,E402
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from quality_forecast import features as F # noqa: E402
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from quality_forecast import model as M # noqa: E402
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from quality_forecast import serve as S # noqa: E402
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def _trained_service(*, thresholds=None, alpha=0.001):
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"""构造一个已训练的服务：y = TEMP（线性，易拟合）。"""
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spec = F.FeatureSpec("t", "CLF-01.TEMP", "raw")
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ext = F.FeatureExtractor([spec])
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samples = [F.Sample(ts=i * 10.0, values={"CLF-01.TEMP": 100.0 + i})
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for i in range(6)]
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matrix = ext.extract(samples)
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# target = TEMP 本身（完美线性，R²≈1）
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recipe = M.ModelRecipe(target="RF-01.PURITY", alpha=alpha,
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feature_names=["t"], unit="%")
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model = M.QualityModel(recipe).fit(matrix.rows,
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[s.values["CLF-01.TEMP"] for s in samples])
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ev = model.evaluate(matrix.rows, [s.values["CLF-01.TEMP"] for s in samples])
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return S.PredictionService(model, ext, thresholds,
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confidence_rmse=ev.rmse), samples
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class TestPredictionServiceValidation(unittest.TestCase):
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def test_unfitted_model_raises(self):
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spec = F.FeatureSpec("t", "CLF-01.TEMP", "raw")
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ext = F.FeatureExtractor([spec])
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model = M.QualityModel(M.ModelRecipe(target="RF-01.PURITY",
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feature_names=["t"]))
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with self.assertRaises(S.ServeError):
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S.PredictionService(model, ext)
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def test_feature_name_mismatch_raises(self):
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# 清单 [t] 但模型声明 [other]
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spec = F.FeatureSpec("t", "CLF-01.TEMP", "raw")
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ext = F.FeatureExtractor([spec])
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recipe = M.ModelRecipe(target="y", alpha=0.1, feature_names=["other"])
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model = M.QualityModel(recipe).fit([[1.0], [2.0]], [1.0, 2.0])
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with self.assertRaises(S.ServeError):
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S.PredictionService(model, ext)
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def test_threshold_target_mismatch_raises(self):
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svc, _ = _trained_service()
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th = S.Thresholds(target="WRONG.TARGET", min_value=50.0)
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with self.assertRaises(S.ServeError):
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S.PredictionService(svc.model, svc.extractor, th)
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class TestPredictionAndThresholds(unittest.TestCase):
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def test_predict_normal(self):
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th = S.Thresholds(target="RF-01.PURITY", min_value=50.0,
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warning_band=5.0)
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svc, samples = _trained_service(thresholds=th)
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preds = svc.predict(samples)
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self.assertEqual(len(preds), len(samples))
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# TEMP 100-105 → 远高于 50 → normal
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self.assertEqual(preds[-1].level, "normal")
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def test_predict_critical_low(self):
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th = S.Thresholds(target="RF-01.PURITY", min_value=200.0)
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svc, samples = _trained_service(thresholds=th)
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preds = svc.predict(samples)
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# 预测 ~100-105 < 200 → critical
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self.assertEqual(preds[-1].level, "critical")
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self.assertIn("低于下限", preds[-1].message)
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def test_predict_warning_band(self):
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th = S.Thresholds(target="RF-01.PURITY", min_value=95.0,
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warning_band=10.0)
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svc, samples = _trained_service(thresholds=th)
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preds = svc.predict(samples)
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# 100-105 在 [95, 105] 预警带内 → warning（部分）
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levels = {p.level for p in preds}
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self.assertTrue(levels & {"warning", "critical"} or
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"warning" in levels)
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def test_max_threshold(self):
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# 杂质类：超过上限 critical
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spec = F.FeatureSpec("t", "RF-01.IMP", "raw")
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ext = F.FeatureExtractor([spec])
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samples = [F.Sample(ts=i, values={"RF-01.IMP": 0.1 * (i + 1)})
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for i in range(5)]
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matrix = ext.extract(samples)
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recipe = M.ModelRecipe(target="RF-01.IMP", alpha=0.001,
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feature_names=["t"])
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model = M.QualityModel(recipe).fit(matrix.rows,
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[s.values["RF-01.IMP"] for s in samples])
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th = S.Thresholds(target="RF-01.IMP", max_value=0.3, warning_band=0.1)
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svc = S.PredictionService(model, ext, th)
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preds = svc.predict(samples)
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# 最后样本 IMP=0.5 > 0.3 → critical
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self.assertEqual(preds[-1].level, "critical")
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def test_confidence_band(self):
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th = S.Thresholds(target="RF-01.PURITY", min_value=50.0)
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svc, samples = _trained_service(thresholds=th)
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preds = svc.predict(samples)
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d = preds[-1].to_dict()
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self.assertIn("lower", d)
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self.assertIn("upper", d)
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class TestCockpitView(unittest.TestCase):
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def test_render(self):
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th = S.Thresholds(target="RF-01.PURITY", min_value=50.0)
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svc, samples = _trained_service(thresholds=th)
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preds = svc.predict(samples)
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view = S.CockpitView().render(preds[-1])
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self.assertEqual(view["$schema"], "iAOP-cockpit-layout-v1")
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types = [w["type"] for w in view["widgets"]]
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self.assertIn("kpi_card", types)
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self.assertIn("alarm_panel", types)
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self.assertIn("explanation_list", types)
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# explanation_list 有 top-3 贡献
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expl = [w for w in view["widgets"] if w["type"] == "explanation_list"][0]
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self.assertLessEqual(len(expl["items"]), 3)
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class TestDriftMonitor(unittest.TestCase):
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def test_retrain_on_low_r2(self):
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mon = S.DriftMonitor(min_r2=0.8, max_mae=1.0)
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mon.record(M.Evaluation(r2=0.5, mae=0.5, rmse=0.5, n_samples=10))
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flag, reason = mon.should_retrain()
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self.assertTrue(flag)
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self.assertIn("R²", reason)
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def test_retrain_on_high_mae(self):
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mon = S.DriftMonitor(min_r2=0.8, max_mae=1.0)
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mon.record(M.Evaluation(r2=0.9, mae=2.0, rmse=2.0, n_samples=10))
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flag, reason = mon.should_retrain()
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self.assertTrue(flag)
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self.assertIn("MAE", reason)
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def test_ok_no_retrain(self):
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mon = S.DriftMonitor(min_r2=0.8, max_mae=1.0)
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mon.record(M.Evaluation(r2=0.95, mae=0.2, rmse=0.2, n_samples=10))
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flag, _ = mon.should_retrain()
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self.assertFalse(flag)
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def test_empty(self):
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mon = S.DriftMonitor()
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flag, _ = mon.should_retrain()
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self.assertFalse(flag)
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class TestDeploymentBundle(unittest.TestCase):
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def test_roundtrip(self):
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th = S.Thresholds(target="RF-01.PURITY", min_value=99.0,
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warning_band=0.5)
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svc, _ = _trained_service(thresholds=th)
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bundle = S.DeploymentBundle(svc.model, svc.extractor, th,
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confidence_rmse=svc.confidence_rmse)
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d = bundle.to_dict()
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bundle2 = S.DeploymentBundle.from_dict(d)
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svc2 = bundle2.to_service()
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# 重建后仍能预测
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self.assertTrue(svc2.model.fitted)
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if __name__ == "__main__":
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unittest.main(verbosity=2)
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