Move tests to separate folder

tests/components/steps/test_convert_tight.py

@@ -0,0 +1,123 @@
+from unittest.mock import Mock
+
+from miplearn.classifiers import Classifier
+from miplearn.components.steps.convert_tight import ConvertTightIneqsIntoEqsStep
+from miplearn.components.steps.relax_integrality import RelaxIntegralityStep
+from miplearn.instance import Instance
+from miplearn.problems.knapsack import GurobiKnapsackInstance
+from miplearn.solvers.gurobi import GurobiSolver
+from miplearn.solvers.learning import LearningSolver
+
+
+def test_convert_tight_usage():
+    instance = GurobiKnapsackInstance(
+        weights=[3.0, 5.0, 10.0],
+        prices=[1.0, 1.0, 1.0],
+        capacity=16.0,
+    )
+    solver = LearningSolver(
+        solver=GurobiSolver,
+        components=[
+            RelaxIntegralityStep(),
+            ConvertTightIneqsIntoEqsStep(),
+        ],
+    )
+
+    # Solve original problem
+    stats = solver.solve(instance)
+    original_upper_bound = stats["Upper bound"]
+
+    # Should collect training data
+    assert instance.training_data[0]["slacks"]["eq_capacity"] == 0.0
+
+    # Fit and resolve
+    solver.fit([instance])
+    stats = solver.solve(instance)
+
+    # Objective value should be the same
+    assert stats["Upper bound"] == original_upper_bound
+    assert stats["ConvertTight: Inf iterations"] == 0
+    assert stats["ConvertTight: Subopt iterations"] == 0
+
+
+class SampleInstance(Instance):
+    def to_model(self):
+        import gurobipy as grb
+
+        m = grb.Model("model")
+        x1 = m.addVar(name="x1")
+        x2 = m.addVar(name="x2")
+        m.setObjective(x1 + 2 * x2, grb.GRB.MAXIMIZE)
+        m.addConstr(x1 <= 2, name="c1")
+        m.addConstr(x2 <= 2, name="c2")
+        m.addConstr(x1 + x2 <= 3, name="c2")
+        return m
+
+
+def test_convert_tight_infeasibility():
+    comp = ConvertTightIneqsIntoEqsStep()
+    comp.classifiers = {
+        "c1": Mock(spec=Classifier),
+        "c2": Mock(spec=Classifier),
+        "c3": Mock(spec=Classifier),
+    }
+    comp.classifiers["c1"].predict_proba = Mock(return_value=[[0, 1]])
+    comp.classifiers["c2"].predict_proba = Mock(return_value=[[0, 1]])
+    comp.classifiers["c3"].predict_proba = Mock(return_value=[[1, 0]])
+
+    solver = LearningSolver(
+        solver=GurobiSolver,
+        components=[comp],
+        solve_lp_first=False,
+    )
+    instance = SampleInstance()
+    stats = solver.solve(instance)
+    assert stats["Upper bound"] == 5.0
+    assert stats["ConvertTight: Inf iterations"] == 1
+    assert stats["ConvertTight: Subopt iterations"] == 0
+
+
+def test_convert_tight_suboptimality():
+    comp = ConvertTightIneqsIntoEqsStep(check_optimality=True)
+    comp.classifiers = {
+        "c1": Mock(spec=Classifier),
+        "c2": Mock(spec=Classifier),
+        "c3": Mock(spec=Classifier),
+    }
+    comp.classifiers["c1"].predict_proba = Mock(return_value=[[0, 1]])
+    comp.classifiers["c2"].predict_proba = Mock(return_value=[[1, 0]])
+    comp.classifiers["c3"].predict_proba = Mock(return_value=[[0, 1]])
+
+    solver = LearningSolver(
+        solver=GurobiSolver,
+        components=[comp],
+        solve_lp_first=False,
+    )
+    instance = SampleInstance()
+    stats = solver.solve(instance)
+    assert stats["Upper bound"] == 5.0
+    assert stats["ConvertTight: Inf iterations"] == 0
+    assert stats["ConvertTight: Subopt iterations"] == 1
+
+
+def test_convert_tight_optimal():
+    comp = ConvertTightIneqsIntoEqsStep()
+    comp.classifiers = {
+        "c1": Mock(spec=Classifier),
+        "c2": Mock(spec=Classifier),
+        "c3": Mock(spec=Classifier),
+    }
+    comp.classifiers["c1"].predict_proba = Mock(return_value=[[1, 0]])
+    comp.classifiers["c2"].predict_proba = Mock(return_value=[[0, 1]])
+    comp.classifiers["c3"].predict_proba = Mock(return_value=[[0, 1]])
+
+    solver = LearningSolver(
+        solver=GurobiSolver,
+        components=[comp],
+        solve_lp_first=False,
+    )
+    instance = SampleInstance()
+    stats = solver.solve(instance)
+    assert stats["Upper bound"] == 5.0
+    assert stats["ConvertTight: Inf iterations"] == 0
+    assert stats["ConvertTight: Subopt iterations"] == 0