feat: 完成 issue #80 [Ti-2] 跨工序寻优模型训练（纯标准库岭回归+多目标关联建模+R2评估+可解释权重+JSON序列化）
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# -*- coding: utf-8 -*-
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"""Ti-2 跨工序关联寻优模型训练 单元测试（Issue #80）。
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覆盖：
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- RidgeRegression（拟合/预测/正则/权重/序列化、奇异矩阵处理）；
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- 线性求解器；
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- CrossProcessModelConfig（合法性校验）；
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- CrossProcessModel（fit/predict/evaluate R²/特征权重可解释/序列化往返）；
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- 数据门槛（min_samples 拒绝、缺失值过滤）。
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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 tempfile
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import unittest
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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import _bootstrap # noqa: E402
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from recipe_optim.cross_process import ( # noqa: E402
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CrossProcessError,
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CrossProcessModel,
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CrossProcessModelConfig,
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CrossProcessSample,
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RidgeRegression,
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_solve_linear,
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)
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class TestSolveLinear(unittest.TestCase):
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def test_basic(self):
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# x + y = 3, 2x - y = 0 → x=1, y=2
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w = _solve_linear([[1, 1], [2, -1]], [3, 0])
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self.assertAlmostEqual(w[0], 1.0)
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self.assertAlmostEqual(w[1], 2.0)
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def test_singular_raises(self):
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with self.assertRaises(CrossProcessError):
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_solve_linear([[1, 1], [1, 1]], [1, 1])
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class TestRidgeRegression(unittest.TestCase):
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def test_fit_predict_linear(self):
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# y = 2x + 1（精确线性）
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reg = RidgeRegression(alpha=0.0)
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reg.fit([[0], [1], [2], [3]], [1, 3, 5, 7], ["x"])
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self.assertAlmostEqual(reg.predict_one([4]), 9.0)
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d = reg.weights_dict()
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self.assertAlmostEqual(d["x"], 2.0, places=6)
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self.assertAlmostEqual(d["__intercept__"], 1.0, places=6)
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def test_multivariate(self):
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# y = x0 + 2*x1
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reg = RidgeRegression(alpha=0.0)
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reg.fit([[0, 0], [1, 0], [0, 1], [1, 1], [2, 3]],
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[0, 1, 2, 3, 8], ["x0", "x1"])
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self.assertAlmostEqual(reg.predict_one([1, 1]), 3.0, places=5)
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def test_regularization_smooths(self):
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# 强正则下权重被压缩向 0
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X = [[0], [1], [2], [3]]
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y = [1, 3, 5, 7]
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r0 = RidgeRegression(alpha=0.0); r0.fit(X, y, ["x"])
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rbig = RidgeRegression(alpha=1000.0); rbig.fit(X, y, ["x"])
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self.assertLess(abs(rbig.weights_dict()["x"]), abs(r0.weights_dict()["x"]))
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def test_negative_alpha_rejected(self):
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with self.assertRaises(CrossProcessError):
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RidgeRegression(alpha=-1)
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def test_mismatched_columns_rejected(self):
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reg = RidgeRegression()
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with self.assertRaises(CrossProcessError):
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reg.fit([[1, 2]], [3], ["x"])
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def test_predict_before_fit(self):
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with self.assertRaises(CrossProcessError):
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RidgeRegression().predict_one([1])
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def test_roundtrip(self):
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reg = RidgeRegression(alpha=0.5)
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reg.fit([[0], [1], [2]], [1, 3, 5], ["x"])
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reg2 = RidgeRegression.from_dict(reg.to_dict())
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self.assertAlmostEqual(reg2.predict_one([3]), reg.predict_one([3]))
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class TestConfig(unittest.TestCase):
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def test_empty_features_rejected(self):
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with self.assertRaises(CrossProcessError):
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CrossProcessModelConfig(upstream_features=[], downstream_targets=["t"])
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def test_empty_targets_rejected(self):
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with self.assertRaises(CrossProcessError):
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CrossProcessModelConfig(upstream_features=["f"], downstream_targets=[])
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def test_bad_min_samples(self):
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with self.assertRaises(CrossProcessError):
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CrossProcessModelConfig(upstream_features=["f"], downstream_targets=["t"],
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min_samples=1)
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def _gen_samples(n=20, seed=42):
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"""生成 y = 2*x + 3 的合成样本（上游 x，下游 y），用于训练/评估。"""
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import random
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rng = random.Random(seed)
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samples = []
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for i in range(n):
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x = rng.uniform(0, 10)
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samples.append(CrossProcessSample(
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upstream={"TiCl4_purity": x},
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downstream={"sponge_titanium_grade": 2.0 * x + 3.0},
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batch=f"B{i}", timestamp=float(i),
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))
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return samples
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class TestCrossProcessModel(unittest.TestCase):
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def test_fit_predict_evaluate(self):
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cfg = CrossProcessModelConfig(
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upstream_features=["TiCl4_purity"],
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downstream_targets=["sponge_titanium_grade"],
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alpha=0.0, min_samples=10,
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)
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m = CrossProcessModel(cfg)
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train = _gen_samples(15, seed=1)
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m.fit(train)
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# 预测接近真实
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pred = m.predict({"TiCl4_purity": 5.0})
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self.assertAlmostEqual(pred["sponge_titanium_grade"], 2 * 5 + 3, places=3)
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# R² 接近 1（线性可精确拟合）
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report = m.evaluate(_gen_samples(20, seed=2))
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self.assertGreater(report["r2_sponge_titanium_grade"], 0.99)
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self.assertIn("mse_overall", report)
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def test_min_samples_enforced(self):
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cfg = CrossProcessModelConfig(
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upstream_features=["f"], downstream_targets=["t"], min_samples=10)
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m = CrossProcessModel(cfg)
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with self.assertRaises(CrossProcessError):
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m.fit([CrossProcessSample(upstream={"f": 1}, downstream={"t": 2})] * 3)
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def test_missing_values_filtered(self):
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cfg = CrossProcessModelConfig(
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upstream_features=["f1", "f2"], downstream_targets=["t"], min_samples=5)
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samples = []
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for i in range(10):
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s = CrossProcessSample(
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upstream={"f1": float(i), "f2": float(i)},
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downstream={"t": float(i) + float(i)},
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batch=f"B{i}")
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samples.append(s)
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# 给部分样本注入缺失值（应被过滤，但剩余 ≥ min_samples 仍可训练）
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samples[0].upstream["f1"] = float("nan")
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m = CrossProcessModel(cfg)
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m.fit(samples)
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self.assertTrue(m.fitted)
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def test_predict_missing_feature(self):
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cfg = CrossProcessModelConfig(
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upstream_features=["f"], downstream_targets=["t"], min_samples=5)
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m = CrossProcessModel(cfg)
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m.fit([CrossProcessSample(upstream={"f": float(i)},
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downstream={"t": float(i)}) for i in range(6)])
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with self.assertRaises(CrossProcessError):
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m.predict({}) # 缺 f
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def test_feature_weights_explainable(self):
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cfg = CrossProcessModelConfig(
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upstream_features=["f"], downstream_targets=["t"],
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alpha=0.0, min_samples=5)
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m = CrossProcessModel(cfg)
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m.fit([CrossProcessSample(upstream={"f": float(i)},
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downstream={"t": 2 * float(i) + 1})
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for i in range(6)])
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w = m.feature_weights("t")
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self.assertAlmostEqual(w["f"], 2.0, places=4)
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self.assertIn("__intercept__", w)
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with self.assertRaises(CrossProcessError):
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m.feature_weights("ghost")
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def test_evaluate_before_fit(self):
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cfg = CrossProcessModelConfig(
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upstream_features=["f"], downstream_targets=["t"], min_samples=5)
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with self.assertRaises(CrossProcessError):
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CrossProcessModel(cfg).evaluate([])
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def test_save_load_roundtrip(self):
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cfg = CrossProcessModelConfig(
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upstream_features=["TiCl4_purity"],
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downstream_targets=["sponge_titanium_grade"],
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alpha=0.1, min_samples=5)
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m = CrossProcessModel(cfg)
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m.fit(_gen_samples(10, seed=3))
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with tempfile.TemporaryDirectory() as d:
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path = os.path.join(d, "model.json")
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m.save(path)
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m2 = CrossProcessModel.load(path)
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self.assertTrue(m2.fitted)
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p1 = m.predict({"TiCl4_purity": 4.0})
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p2 = m2.predict({"TiCl4_purity": 4.0})
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self.assertAlmostEqual(p1["sponge_titanium_grade"],
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p2["sponge_titanium_grade"], places=6)
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if __name__ == "__main__":
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unittest.main()
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