InternalSolver: Better specify and test infeasibility

miplearn/solvers/tests/__init__.py

@@ -3,8 +3,11 @@
 #  Released under the modified BSD license. See COPYING.md for more details.
 
 from inspect import isclass
-from typing import List, Callable
+from typing import List, Callable, Any
 
+from pyomo import environ as pe
+
+from miplearn.instance import Instance, PyomoInstance
 from miplearn.problems.knapsack import KnapsackInstance, GurobiKnapsackInstance
 from miplearn.solvers.gurobi import GurobiSolver
 from miplearn.solvers.internal import InternalSolver
@@ -13,28 +16,55 @@ from miplearn.solvers.pyomo.gurobi import GurobiPyomoSolver
 from miplearn.solvers.pyomo.xpress import XpressPyomoSolver
 
 
-def _get_instance(solver):
-    def _is_subclass_or_instance(obj, parent_class):
-        return isinstance(obj, parent_class) or (
-            isclass(obj) and issubclass(obj, parent_class)
-        )
+class InfeasiblePyomoInstance(PyomoInstance):
+    def to_model(self) -> pe.ConcreteModel:
+        model = pe.ConcreteModel()
+        model.x = pe.Var(domain=pe.Binary)
+        model.OBJ = pe.Objective(expr=model.x, sense=pe.maximize)
+        model.eq = pe.Constraint(expr=model.x >= 2)
+        return model
 
+
+class InfeasibleGurobiInstance(Instance):
+    def to_model(self) -> Any:
+        import gurobipy as gp
+        from gurobipy import GRB
+
+        model = gp.Model()
+        x = model.addVars(1, vtype=GRB.BINARY, name="x")
+        model.addConstr(x[0] >= 2)
+        model.setObjective(x[0])
+        return model
+
+
+def _is_subclass_or_instance(obj, parent_class):
+    return isinstance(obj, parent_class) or (
+        isclass(obj) and issubclass(obj, parent_class)
+    )
+
+
+def _get_knapsack_instance(solver):
     if _is_subclass_or_instance(solver, BasePyomoSolver):
         return KnapsackInstance(
             weights=[23.0, 26.0, 20.0, 18.0],
             prices=[505.0, 352.0, 458.0, 220.0],
             capacity=67.0,
         )
-
     if _is_subclass_or_instance(solver, GurobiSolver):
         return GurobiKnapsackInstance(
             weights=[23.0, 26.0, 20.0, 18.0],
             prices=[505.0, 352.0, 458.0, 220.0],
             capacity=67.0,
         )
-
     assert False
 
 
+def _get_infeasible_instance(solver):
+    if _is_subclass_or_instance(solver, BasePyomoSolver):
+        return InfeasiblePyomoInstance()
+    if _is_subclass_or_instance(solver, GurobiSolver):
+        return InfeasibleGurobiInstance()
+
+
 def _get_internal_solvers() -> List[Callable[[], InternalSolver]]:
     return [GurobiPyomoSolver, GurobiSolver, XpressPyomoSolver]

miplearn/solvers/tests/test_internal_solver.py

@@ -11,7 +11,11 @@ import pyomo.environ as pe
 from miplearn.solvers import RedirectOutput
 from miplearn.solvers.gurobi import GurobiSolver
 from miplearn.solvers.pyomo.base import BasePyomoSolver
-from miplearn.solvers.tests import _get_instance, _get_internal_solvers
+from miplearn.solvers.tests import (
+    _get_knapsack_instance,
+    _get_internal_solvers,
+    _get_infeasible_instance,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -30,7 +34,7 @@ def test_redirect_output():
 def test_internal_solver_warm_starts():
     for solver_class in _get_internal_solvers():
         logger.info("Solver: %s" % solver_class)
-        instance = _get_instance(solver_class)
+        instance = _get_knapsack_instance(solver_class)
         model = instance.to_model()
         solver = solver_class()
         solver.set_instance(instance, model)
@@ -82,7 +86,7 @@ def test_internal_solver():
     for solver_class in _get_internal_solvers():
         logger.info("Solver: %s" % solver_class)
 
-        instance = _get_instance(solver_class)
+        instance = _get_knapsack_instance(solver_class)
         model = instance.to_model()
         solver = solver_class()
         solver.set_instance(instance, model)
@@ -158,10 +162,26 @@ def test_internal_solver():
             assert round(stats["Lower bound"]) == 1179.0
 
 
+def test_infeasible_instance():
+    for solver_class in _get_internal_solvers():
+        instance = _get_infeasible_instance(solver_class)
+        solver = solver_class()
+        solver.set_instance(instance)
+        stats = solver.solve()
+
+        assert solver.get_solution() is None
+        assert stats["Upper bound"] is None
+        assert stats["Lower bound"] is None
+
+        stats = solver.solve_lp()
+        assert solver.get_solution() is None
+        assert stats["Optimal value"] is None
+
+
 def test_iteration_cb():
     for solver_class in _get_internal_solvers():
         logger.info("Solver: %s" % solver_class)
-        instance = _get_instance(solver_class)
+        instance = _get_knapsack_instance(solver_class)
         solver = solver_class()
         solver.set_instance(instance)
         count = 0

miplearn/solvers/tests/test_lazy_cb.py

@@ -5,14 +5,14 @@
 import logging
 
 from miplearn.solvers.gurobi import GurobiSolver
-from miplearn.solvers.tests import _get_instance
+from miplearn.solvers.tests import _get_knapsack_instance
 
 logger = logging.getLogger(__name__)
 
 
 def test_lazy_cb():
     solver = GurobiSolver()
-    instance = _get_instance(solver)
+    instance = _get_knapsack_instance(solver)
     model = instance.to_model()
 
     def lazy_cb(cb_solver, cb_model):

miplearn/solvers/tests/test_learning_solver.py

@@ -9,7 +9,7 @@ import os
 
 from miplearn.solvers.gurobi import GurobiSolver
 from miplearn.solvers.learning import LearningSolver
-from miplearn.solvers.tests import _get_instance, _get_internal_solvers
+from miplearn.solvers.tests import _get_knapsack_instance, _get_internal_solvers
 
 logger = logging.getLogger(__name__)
 
@@ -18,7 +18,7 @@ def test_learning_solver():
     for mode in ["exact", "heuristic"]:
         for internal_solver in _get_internal_solvers():
             logger.info("Solver: %s" % internal_solver)
-            instance = _get_instance(internal_solver)
+            instance = _get_knapsack_instance(internal_solver)
             solver = LearningSolver(
                 solver=internal_solver,
                 mode=mode,
@@ -50,7 +50,7 @@ def test_learning_solver():
 def test_solve_without_lp():
     for internal_solver in _get_internal_solvers():
         logger.info("Solver: %s" % internal_solver)
-        instance = _get_instance(internal_solver)
+        instance = _get_knapsack_instance(internal_solver)
         solver = LearningSolver(
             solver=internal_solver,
             solve_lp_first=False,
@@ -62,7 +62,7 @@ def test_solve_without_lp():
 
 def test_parallel_solve():
     for internal_solver in _get_internal_solvers():
-        instances = [_get_instance(internal_solver) for _ in range(10)]
+        instances = [_get_knapsack_instance(internal_solver) for _ in range(10)]
         solver = LearningSolver(solver=internal_solver)
         results = solver.parallel_solve(instances, n_jobs=3)
         assert len(results) == 10
@@ -76,7 +76,7 @@ def test_solve_fit_from_disk():
         # Create instances and pickle them
         filenames = []
         for k in range(3):
-            instance = _get_instance(internal_solver)
+            instance = _get_knapsack_instance(internal_solver)
             with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as file:
                 filenames += [file.name]
                 pickle.dump(instance, file)
@@ -114,7 +114,7 @@ def test_solve_fit_from_disk():
 
 def test_simulate_perfect():
     internal_solver = GurobiSolver
-    instance = _get_instance(internal_solver)
+    instance = _get_knapsack_instance(internal_solver)
     with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as tmp:
         pickle.dump(instance, tmp)
         tmp.flush()