208 lines
7.8 KiB
Python
208 lines
7.8 KiB
Python
# -*- coding: utf-8 -*-
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"""Ti-2 配方优化求解器集成 单元测试（Issue #79）。
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覆盖：
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- 求解器配置（grid_steps/max_combinations 合法性）；
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- 候选取值生成（bounds 等分/integer 去重/choices 枚举）；
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- 网格求解（全局最优、可行性、target 达成、无可行解降级）；
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- 坐标下降（规模超限降级、收敛）；
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- solve 统一入口（自动选策略、静态校验失败、空问题）；
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- Solution 序列化；
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- 加载 #78 模板后端到端求解。
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"""
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import os
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import sys
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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 挂载 recipe_optim 包
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from recipe_optim.problem import ( # noqa: E402
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ConstraintKind,
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ConstraintSpec,
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DecisionVariable,
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DomainKind,
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ObjectiveSpec,
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ObjectiveTerm,
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OptimizationProblem,
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Sense,
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load_problem,
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)
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from recipe_optim.solver import ( # noqa: E402
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SolverConfig,
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SolverError,
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Solution,
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candidate_values,
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solve,
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)
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CONFIG_PATH = os.path.join(
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os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
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"config", "recipe_optim.template.yaml",
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)
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def _toy_problem() -> OptimizationProblem:
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"""minimize -(x+y)，x∈[0,4] 5 点，y∈[0,4] 5 点 → 网格 25 组合。"""
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return OptimizationProblem(
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variables=[
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DecisionVariable("x", DomainKind.BOUNDS, bounds=(0.0, 4.0)),
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DecisionVariable("y", DomainKind.BOUNDS, bounds=(0.0, 4.0)),
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],
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objective=ObjectiveSpec(Sense.MAXIMIZE,
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terms=[ObjectiveTerm("x", 1.0), ObjectiveTerm("y", 1.0)]),
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)
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class TestSolverConfig(unittest.TestCase):
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def test_defaults(self):
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c = SolverConfig()
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self.assertGreaterEqual(c.grid_steps, 2)
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self.assertGreaterEqual(c.max_combinations, 1)
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def test_invalid_grid_steps(self):
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with self.assertRaises(SolverError):
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SolverConfig(grid_steps=1)
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def test_invalid_max_combinations(self):
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with self.assertRaises(SolverError):
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SolverConfig(max_combinations=0)
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class TestCandidateValues(unittest.TestCase):
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def test_bounds_grid(self):
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v = DecisionVariable("t", DomainKind.BOUNDS, bounds=(0.0, 4.0))
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vals = candidate_values(v, SolverConfig(grid_steps=5))
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self.assertEqual(vals[0], 0.0)
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self.assertEqual(vals[-1], 4.0)
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self.assertEqual(len(vals), 5)
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def test_integer_dedupe(self):
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v = DecisionVariable("n", DomainKind.BOUNDS, bounds=(0.0, 4.0), integer=True)
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vals = candidate_values(v, SolverConfig(grid_steps=5))
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self.assertEqual(vals, [0, 1, 2, 3, 4])
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def test_choices_enum(self):
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v = DecisionVariable("c", DomainKind.CHOICES, choices=["A", "B", "C"])
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self.assertEqual(candidate_values(v, SolverConfig()), ["A", "B", "C"])
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self.assertEqual(candidate_values(v, SolverConfig(enumerate_choices=False)), ["A"])
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class TestSolveGrid(unittest.TestCase):
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def test_global_optimum_maximize(self):
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p = _toy_problem()
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sol = solve(p, SolverConfig(grid_steps=5))
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self.assertTrue(sol.feasible)
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# 最优 x=y=4 → obj=8
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self.assertAlmostEqual(sol.objective_value, 8.0)
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self.assertEqual(sol.assignment["x"], 4.0)
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self.assertEqual(sol.assignment["y"], 4.0)
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self.assertEqual(sol.strategy, "grid")
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def test_minimize(self):
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p = OptimizationProblem(
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variables=[DecisionVariable("x", DomainKind.BOUNDS, bounds=(0.0, 4.0))],
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objective=ObjectiveSpec(Sense.MINIMIZE, terms=[ObjectiveTerm("x", 1.0)]),
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)
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sol = solve(p, SolverConfig(grid_steps=5))
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self.assertTrue(sol.feasible)
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self.assertEqual(sol.assignment["x"], 0.0)
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self.assertAlmostEqual(sol.objective_value, 0.0)
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def test_target_met(self):
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p = OptimizationProblem(
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variables=[DecisionVariable("x", DomainKind.BOUNDS, bounds=(0.0, 10.0))],
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objective=ObjectiveSpec(Sense.MAXIMIZE, target_value=8.0,
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terms=[ObjectiveTerm("x", 1.0)]),
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)
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sol = solve(p, SolverConfig(grid_steps=11))
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self.assertTrue(sol.feasible)
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self.assertTrue(sol.target_met) # x=10 >= 8
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def test_target_not_met(self):
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p = OptimizationProblem(
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variables=[DecisionVariable("x", DomainKind.BOUNDS, bounds=(0.0, 5.0))],
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objective=ObjectiveSpec(Sense.MAXIMIZE, target_value=99.0,
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terms=[ObjectiveTerm("x", 1.0)]),
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)
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sol = solve(p, SolverConfig(grid_steps=6))
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self.assertTrue(sol.feasible)
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self.assertFalse(sol.target_met)
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def test_no_feasible_solution(self):
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# box 收紧到与域不交 → 无可行
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p = OptimizationProblem(
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variables=[DecisionVariable("x", DomainKind.BOUNDS, bounds=(0.0, 1.0))],
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constraints=[ConstraintSpec(ConstraintKind.BOX, variable="x", bounds=(5.0, 6.0))],
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objective=ObjectiveSpec(Sense.MAXIMIZE, terms=[ObjectiveTerm("x", 1.0)]),
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)
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sol = solve(p, SolverConfig(grid_steps=3))
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self.assertFalse(sol.feasible)
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self.assertIn("无可行解", sol.message)
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class TestCoordinateDescent(unittest.TestCase):
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def test_falls_back_when_grid_too_large(self):
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# 三个变量 × grid_steps=5 = 125；设 max_combinations=10 → 降级
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p = OptimizationProblem(
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variables=[
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DecisionVariable(f"v{i}", DomainKind.BOUNDS, bounds=(0.0, 4.0))
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for i in range(3)
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],
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objective=ObjectiveSpec(Sense.MAXIMIZE,
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terms=[ObjectiveTerm(f"v{i}", 1.0) for i in range(3)]),
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)
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sol = solve(p, SolverConfig(grid_steps=5, max_combinations=10))
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self.assertEqual(sol.strategy, "coordinate_descent")
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# 坐标下降应能爬到各维上界附近（贪心可收敛到此线性目标的全局最优）
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self.assertTrue(sol.feasible)
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self.assertAlmostEqual(sol.objective_value, 12.0, places=6)
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class TestSolveEntry(unittest.TestCase):
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def test_validate_failure_returns_infeasible(self):
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p = OptimizationProblem(
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variables=[DecisionVariable("x", DomainKind.BOUNDS, bounds=(0.0, 1.0))],
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objective=ObjectiveSpec(Sense.MAXIMIZE,
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terms=[ObjectiveTerm("ghost", 1.0)]),
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)
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sol = solve(p, SolverConfig())
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self.assertFalse(sol.feasible)
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self.assertEqual(sol.strategy, "validate")
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self.assertIn("校验失败", sol.message)
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def test_empty_problem_trivially_feasible(self):
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p = OptimizationProblem()
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sol = solve(p, SolverConfig())
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self.assertTrue(sol.feasible)
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self.assertEqual(sol.strategy, "empty")
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def test_solution_to_dict(self):
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p = _toy_problem()
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sol = solve(p, SolverConfig(grid_steps=3))
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d = sol.to_dict()
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self.assertIn("assignment", d)
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self.assertIn("objective_value", d)
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self.assertIn("evaluated", d)
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self.assertTrue(d["feasible"])
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class TestEndToEndFromTemplate(unittest.TestCase):
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def test_solve_loaded_problem(self):
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p = load_problem(CONFIG_PATH)
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sol = solve(p, SolverConfig(grid_steps=7, max_combinations=200000))
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# 模板含 4 变量；7^3 * 3 = 1029 组合 < 上限 → 网格
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self.assertEqual(sol.strategy, "grid")
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self.assertTrue(sol.feasible)
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# 取值应满足 box 收紧（clf_temp∈[820,900]）与 forbidden（非 C@910）
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self.assertGreaterEqual(sol.assignment["clf_temp"], 820 - 1e-6)
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self.assertLessEqual(sol.assignment["clf_temp"], 900 + 1e-6)
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self.assertFalse(sol.assignment["catalyst"] == "C"
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and sol.assignment.get("clf_temp") == 910)
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# evaluated>0
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self.assertGreater(sol.evaluated, 0)
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if __name__ == "__main__":
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unittest.main()
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