ConvertTight: Always check feasibility

miplearn/components/steps/convert_tight.py

@@ -20,8 +20,10 @@ class ConvertTightIneqsIntoEqsStep(Component):
     """
     Component that predicts which inequality constraints are likely to be binding in
     the LP relaxation of the problem and converts them into equality constraints.
-    Optionally double checks that the conversion process did not affect feasibility
-    or optimality of the problem.
+
+    This component always makes sure that the conversion process does not affect the
+    feasibility of the problem. It can also, optionally, make sure that it does not affect
+    the optimality, but this may be expensive.
 
     This component does not work on MIPs. All integrality constraints must be relaxed
     before this component is used.
@@ -32,13 +34,13 @@ class ConvertTightIneqsIntoEqsStep(Component):
         classifier=CountingClassifier(),
         threshold=0.95,
         slack_tolerance=0.0,
-        check_converted=False,
+        check_optimality=False,
     ):
         self.classifiers = {}
         self.classifier_prototype = classifier
         self.threshold = threshold
         self.slack_tolerance = slack_tolerance
-        self.check_converted = check_converted
+        self.check_optimality = check_optimality
         self.converted = []
         self.original_sense = {}
 
@@ -66,8 +68,10 @@ class ConvertTightIneqsIntoEqsStep(Component):
                     solver.internal_solver.set_constraint_sense(cid, "=")
                     self.converted += [cid]
                     self.n_converted += 1
+                    print(cid)
                 else:
                     self.n_kept += 1
+
         logger.info(f"Converted {self.n_converted} inequalities")
 
     def after_solve(self, solver, instance, model, results):
@@ -173,10 +177,6 @@ class ConvertTightIneqsIntoEqsStep(Component):
         return classifier_evaluation_dict(tp, tn, fp, fn)
 
     def iteration_cb(self, solver, instance, model):
-        if not self.check_converted:
-            return False
-
-        logger.debug("Checking converted inequalities...")
         is_infeasible, is_suboptimal = False, False
         restored = []
 
@@ -206,7 +206,7 @@ class ConvertTightIneqsIntoEqsStep(Component):
                 if abs(pi) > 0:
                     is_infeasible = True
                     restore(cid)
-        else:
+        elif self.check_optimality:
             for cid in self.converted:
                 pi = solver.internal_solver.get_dual(cid)
                 csense = self.original_sense[cid]

miplearn/components/steps/drop_redundant.py

@@ -32,7 +32,7 @@ class DropRedundantInequalitiesStep(Component):
         classifier=CountingClassifier(),
         threshold=0.95,
         slack_tolerance=1e-5,
-        check_dropped=False,
+        check_feasibility=False,
         violation_tolerance=1e-5,
         max_iterations=3,
     ):
@@ -41,7 +41,7 @@ class DropRedundantInequalitiesStep(Component):
         self.threshold = threshold
         self.slack_tolerance = slack_tolerance
         self.pool = []
-        self.check_dropped = check_dropped
+        self.check_feasibility = check_feasibility
         self.violation_tolerance = violation_tolerance
         self.max_iterations = max_iterations
         self.current_iteration = 0
@@ -175,7 +175,7 @@ class DropRedundantInequalitiesStep(Component):
         return classifier_evaluation_dict(tp, tn, fp, fn)
 
     def iteration_cb(self, solver, instance, model):
-        if not self.check_dropped:
+        if not self.check_feasibility:
             return False
         if self.current_iteration >= self.max_iterations:
             return False

miplearn/components/steps/tests/convert_tight_test.py

@@ -30,10 +30,12 @@ def test_convert_tight_usage():
 
     # Fit and resolve
     solver.fit([instance])
-    solver.solve(instance)
+    stats = solver.solve(instance)
 
     # Objective value should be the same
     assert instance.upper_bound == original_upper_bound
+    assert stats["ConvertTight: Inf iterations"] == 0
+    assert stats["ConvertTight: Subopt iterations"] == 0
 
 
 class TestInstance(Instance):
@@ -51,6 +53,21 @@ class TestInstance(Instance):
         return m
 
 
+class TestInstanceMin(Instance):
+    def to_model(self):
+        import gurobipy as grb
+        from gurobipy import 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(
         check_converted=True,
@@ -70,8 +87,10 @@ def test_convert_tight_infeasibility():
         solve_lp_first=False,
     )
     instance = TestInstance()
-    solver.solve(instance)
+    stats = solver.solve(instance)
     assert instance.lower_bound == 5.0
+    assert stats["ConvertTight: Inf iterations"] == 1
+    assert stats["ConvertTight: Subopt iterations"] == 0
 
 
 def test_convert_tight_suboptimality():
@@ -93,5 +112,32 @@ def test_convert_tight_suboptimality():
         solve_lp_first=False,
     )
     instance = TestInstance()
-    solver.solve(instance)
+    stats = solver.solve(instance)
+    assert instance.lower_bound == 5.0
+    assert stats["ConvertTight: Inf iterations"] == 0
+    assert stats["ConvertTight: Subopt iterations"] == 1
+
+
+def test_convert_tight_optimal():
+    comp = ConvertTightIneqsIntoEqsStep(
+        check_converted=True,
+    )
+    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(params={}),
+        components=[comp],
+        solve_lp_first=False,
+    )
+    instance = TestInstance()
+    stats = solver.solve(instance)
     assert instance.lower_bound == 5.0
+    assert stats["ConvertTight: Inf iterations"] == 0
+    assert stats["ConvertTight: Subopt iterations"] == 0