Organize test fixtures; handle infeasibility in DropRedundant

miplearn/components/steps/drop_redundant.py

@@ -32,7 +32,7 @@ class DropRedundantInequalitiesStep(Component):
         classifier=CountingClassifier(),
         threshold=0.95,
         slack_tolerance=1e-5,
-        check_feasibility=False,
+        check_feasibility=True,
         violation_tolerance=1e-5,
         max_iterations=3,
     ):
@@ -208,6 +208,8 @@ class DropRedundantInequalitiesStep(Component):
             return False
         if self.current_iteration >= self.max_iterations:
             return False
+        if solver.internal_solver.is_infeasible():
+            return False
         self.current_iteration += 1
         logger.debug("Checking that dropped constraints are satisfied...")
         constraints_to_add = []

miplearn/solvers/gurobi.py

@@ -342,7 +342,7 @@ class GurobiSolver(InternalSolver):
         self.model.remove(constr)
         return cobj
 
-    def is_constraint_satisfied(self, cobj, tol=1e-5):
+    def is_constraint_satisfied(self, cobj, tol=1e-6):
         lhs, sense, rhs, name = cobj
         if self.cb_where is not None:
             lhs_value = lhs.getConstant()
@@ -378,6 +378,7 @@ class GurobiSolver(InternalSolver):
 
     def relax(self) -> None:
         assert self.model is not None
+        self.model.update()
         self.model = self.model.relax()
         self._update_vars()
 

miplearn/solvers/internal.py

@@ -173,7 +173,7 @@ class InternalSolver(ABC):
         pass
 
     @abstractmethod
-    def is_constraint_satisfied(self, cobj: Constraint) -> bool:
+    def is_constraint_satisfied(self, cobj: Constraint, tol: float = 1e-6) -> bool:
         """
         Returns True if the current solution satisfies the given constraint.
         """

miplearn/solvers/pyomo/base.py

@@ -308,7 +308,7 @@ class BasePyomoSolver(InternalSolver):
     def extract_constraint(self, cid: str) -> Constraint:
         raise Exception("Not implemented")
 
-    def is_constraint_satisfied(self, cobj: Constraint) -> bool:
+    def is_constraint_satisfied(self, cobj: Constraint, tol: float = 1e-6) -> bool:
         raise Exception("Not implemented")
 
     def is_infeasible(self) -> bool:

tests/components/steps/test_drop_redundant.py

@@ -6,13 +6,14 @@ from unittest.mock import Mock, call
 
 import numpy as np
 
-from miplearn import RelaxIntegralityStep, BasePyomoSolver
+from miplearn import RelaxIntegralityStep, GurobiSolver
 from miplearn.classifiers import Classifier
 from miplearn.components.steps.drop_redundant import DropRedundantInequalitiesStep
 from miplearn.instance import Instance
 from miplearn.solvers.internal import InternalSolver
 from miplearn.solvers.learning import LearningSolver
-from tests.solvers import _get_knapsack_instance
+from tests.fixtures.infeasible import get_infeasible_instance
+from tests.fixtures.redundant import get_instance_with_redundancy
 
 
 def _setup():
@@ -30,6 +31,7 @@ def _setup():
     )
     internal.extract_constraint = Mock(side_effect=lambda cid: "<%s>" % cid)
     internal.is_constraint_satisfied = Mock(return_value=False)
+    internal.is_infeasible = Mock(return_value=False)
 
     instance = Mock(spec=Instance)
     instance.get_constraint_features = Mock(
@@ -399,13 +401,18 @@ def test_x_multiple_solves():
 
 
 def test_usage():
+    for internal_solver in [GurobiSolver]:
+        for instance in [
+            get_instance_with_redundancy(internal_solver),
+            get_infeasible_instance(internal_solver),
+        ]:
             solver = LearningSolver(
+                solver=internal_solver,
                 components=[
                     RelaxIntegralityStep(),
                     DropRedundantInequalitiesStep(),
-        ]
+                ],
             )
-    instance = _get_knapsack_instance(BasePyomoSolver)
             # The following should not crash
             solver.solve(instance)
             solver.fit([instance])

tests/fixtures/__init__.py

@@ -0,0 +1,3 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.

tests/fixtures/infeasible.py

@@ -0,0 +1,40 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+from typing import Any
+
+from pyomo import environ as pe
+
+from miplearn.instance import Instance
+from miplearn.solvers.gurobi import GurobiSolver
+from miplearn.solvers.pyomo.base import BasePyomoSolver
+from tests.solvers import _is_subclass_or_instance
+
+
+class InfeasiblePyomoInstance(Instance):
+    def to_model(self) -> pe.ConcreteModel:
+        model = pe.ConcreteModel()
+        model.x = pe.Var([0], domain=pe.Binary)
+        model.OBJ = pe.Objective(expr=model.x[0], sense=pe.maximize)
+        model.eq = pe.Constraint(expr=model.x[0] >= 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 get_infeasible_instance(solver):
+    if _is_subclass_or_instance(solver, BasePyomoSolver):
+        return InfeasiblePyomoInstance()
+    if _is_subclass_or_instance(solver, GurobiSolver):
+        return InfeasibleGurobiInstance()

tests/fixtures/redundant.py

@@ -0,0 +1,39 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+from typing import Any
+
+from miplearn import Instance, BasePyomoSolver, GurobiSolver
+import pyomo.environ as pe
+
+from tests.solvers import _is_subclass_or_instance
+
+
+class PyomoInstanceWithRedundancy(Instance):
+    def to_model(self) -> pe.ConcreteModel:
+        model = pe.ConcreteModel()
+        model.x = pe.Var([0, 1], domain=pe.Binary)
+        model.OBJ = pe.Objective(expr=model.x[0] + model.x[1], sense=pe.maximize)
+        model.eq1 = pe.Constraint(expr=model.x[0] + model.x[1] <= 1)
+        model.eq2 = pe.Constraint(expr=model.x[0] + model.x[1] <= 2)
+        return model
+
+
+class GurobiInstanceWithRedundancy(Instance):
+    def to_model(self) -> Any:
+        import gurobipy as gp
+        from gurobipy import GRB
+
+        model = gp.Model()
+        x = model.addVars(2, vtype=GRB.BINARY, name="x")
+        model.addConstr(x[0] + x[1] <= 1)
+        model.addConstr(x[0] + x[1] <= 2)
+        model.setObjective(x[0] + x[1], GRB.MAXIMIZE)
+        return model
+
+
+def get_instance_with_redundancy(solver):
+    if _is_subclass_or_instance(solver, BasePyomoSolver):
+        return PyomoInstanceWithRedundancy()
+    if _is_subclass_or_instance(solver, GurobiSolver):
+        return GurobiInstanceWithRedundancy()

tests/solvers/__init__.py

@@ -3,11 +3,8 @@
 #  Released under the modified BSD license. See COPYING.md for more details.
 
 from inspect import isclass
-from typing import List, Callable, Any
+from typing import List, Callable
 
-from pyomo import environ as pe
-
-from miplearn.instance import Instance
 from miplearn.problems.knapsack import KnapsackInstance, GurobiKnapsackInstance
 from miplearn.solvers.gurobi import GurobiSolver
 from miplearn.solvers.internal import InternalSolver
@@ -16,27 +13,6 @@ from miplearn.solvers.pyomo.gurobi import GurobiPyomoSolver
 from miplearn.solvers.pyomo.xpress import XpressPyomoSolver
 
 
-class InfeasiblePyomoInstance(Instance):
-    def to_model(self) -> pe.ConcreteModel:
-        model = pe.ConcreteModel()
-        model.x = pe.Var([0], domain=pe.Binary)
-        model.OBJ = pe.Objective(expr=model.x[0], sense=pe.maximize)
-        model.eq = pe.Constraint(expr=model.x[0] >= 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)
@@ -59,12 +35,5 @@ def _get_knapsack_instance(solver):
     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]

tests/solvers/test_internal_solver.py

@@ -14,8 +14,8 @@ from miplearn.solvers.pyomo.base import BasePyomoSolver
 from . import (
     _get_knapsack_instance,
     _get_internal_solvers,
-    _get_infeasible_instance,
 )
+from ..fixtures.infeasible import get_infeasible_instance
 
 logger = logging.getLogger(__name__)
 
@@ -186,7 +186,7 @@ def test_relax():
 
 def test_infeasible_instance():
     for solver_class in _get_internal_solvers():
-        instance = _get_infeasible_instance(solver_class)
+        instance = get_infeasible_instance(solver_class)
         solver = solver_class()
         solver.set_instance(instance)
         stats = solver.solve()