Small fixes to lazy constraints

miplearn/components/lazy_static.py

@@ -28,6 +28,7 @@ class StaticLazyConstraintsComponent(Component):
         self.pool = []
 
     def before_solve(self, solver, instance, model):
+        self.pool = []
         instance.found_violated_lazy_constraints = []
         if instance.has_static_lazy_constraints():
             self._extract_and_predict_static(solver, instance)
@@ -36,21 +37,28 @@ class StaticLazyConstraintsComponent(Component):
         pass
 
     def after_iteration(self, solver, instance, model):
-        logger.debug("Finding violated (static) lazy constraints...")
-        n_added = 0
+        logger.info("Finding violated lazy constraints...")
+        constraints_to_add = []
         for c in self.pool:
             if not solver.internal_solver.is_constraint_satisfied(c.obj):
+                constraints_to_add.append(c)
+        for c in constraints_to_add:
             self.pool.remove(c)
             solver.internal_solver.add_constraint(c.obj)
             instance.found_violated_lazy_constraints += [c.cid]
-                n_added += 1
-        if n_added > 0:
-            logger.debug("    %d violations found" % n_added)
+        if len(constraints_to_add) > 0:
+            logger.info("Added %d lazy constraints back into the model" % len(constraints_to_add))
+            logger.info("Lazy constraint pool has %d constraints" % len(self.pool))
             return True
         else:
+            logger.info("Found no violated lazy constraints")
             return False
                 
     def fit(self, training_instances):
+        training_instances = [t
+                              for t in training_instances
+                              if hasattr(t, "found_violated_lazy_constraints")]
+
         logger.debug("Extracting x and y...")
         x = self.x(training_instances)
         y = self.y(training_instances)
@@ -72,11 +80,10 @@ class StaticLazyConstraintsComponent(Component):
     def _extract_and_predict_static(self, solver, instance):
         x = {}
         constraints = {}
-        for cid in solver.internal_solver.get_constraint_names():
+        logger.info("Extracting lazy constraints...")
+        for cid in solver.internal_solver.get_constraint_ids():
             if instance.is_constraint_lazy(cid):
                 category = instance.get_lazy_constraint_category(cid)
-                if category not in self.classifiers:
-                    continue
                 if category not in x:
                     x[category] = []
                     constraints[category] = []
@@ -85,16 +92,24 @@ class StaticLazyConstraintsComponent(Component):
                                    obj=solver.internal_solver.extract_constraint(cid))
                 constraints[category] += [c]
                 self.pool.append(c)
+        logger.info("Extracted %d lazy constraints" % len(self.pool))
+        logger.info("Predicting required lazy constraints...")
+        n_added = 0
         for (category, x_values) in x.items():
+            if category not in self.classifiers:
+                continue
             if isinstance(x_values[0], np.ndarray):
                 x[category] = np.array(x_values)
             proba = self.classifiers[category].predict_proba(x[category])
             for i in range(len(proba)):
                 if proba[i][1] > self.threshold:
+                    n_added += 1
                     c = constraints[category][i]
                     self.pool.remove(c)
                     solver.internal_solver.add_constraint(c.obj)
                     instance.found_violated_lazy_constraints += [c.cid]
+        logger.info("Added %d lazy constraints back into the model" % n_added)
+        logger.info("Lazy constraint pool has %d constraints" % len(self.pool))
 
     def _collect_constraints(self, train_instances):
         constraints = {}

miplearn/components/tests/test_lazy_static.py

@@ -14,7 +14,7 @@ from miplearn.classifiers import Classifier
 def test_usage_with_solver():
     solver = Mock(spec=LearningSolver)
     internal = solver.internal_solver = Mock(spec=InternalSolver)
-    internal.get_constraint_names = Mock(return_value=["c1", "c2", "c3", "c4"])
+    internal.get_constraint_ids = Mock(return_value=["c1", "c2", "c3", "c4"])
     internal.extract_constraint = Mock(side_effect=lambda cid: "<%s>" % cid)
     internal.is_constraint_satisfied = Mock(return_value=False)
 
@@ -59,7 +59,7 @@ def test_usage_with_solver():
     instance.has_static_lazy_constraints.assert_called_once()
 
     # Should ask internal solver for a list of constraints in the model
-    internal.get_constraint_names.assert_called_once()
+    internal.get_constraint_ids.assert_called_once()
 
     # Should ask if each constraint in the model is lazy
     instance.is_constraint_lazy.assert_has_calls([

miplearn/solvers/gurobi.py

@@ -15,10 +15,11 @@ logger = logging.getLogger(__name__)
 class GurobiSolver(InternalSolver):
     def __init__(self, params=None):
         if params is None:
-            params = {
-                "LazyConstraints": 1,
-                "PreCrush": 1,
-            }
+            params = {}
+            # params = {
+            #     "LazyConstraints": 1,
+            #     "PreCrush": 1,
+            # }
         from gurobipy import GRB
         self.GRB = GRB
         self.instance = None
@@ -83,6 +84,7 @@ class GurobiSolver(InternalSolver):
         }
 
     def solve(self, tee=False, iteration_cb=None):
+        self._apply_params()
         total_wallclock_time = 0
         total_nodes = 0
         streams = [StringIO()]
@@ -122,7 +124,7 @@ class GurobiSolver(InternalSolver):
     def get_variables(self):
         variables = {}
         for (varname, vardict) in self._all_vars.items():
-            variables[varname] = {}
+            variables[varname] = []
             for (idx, var) in vardict.items():
                 variables[varname] += [idx]
         return variables
@@ -161,7 +163,7 @@ class GurobiSolver(InternalSolver):
                 var.lb = value
                 var.ub = value
 
-    def get_constraints_ids(self):
+    def get_constraint_ids(self):
         self.model.update()
         return [c.ConstrName for c in self.model.getConstrs()]
 

miplearn/solvers/internal.py

@@ -145,7 +145,7 @@ class InternalSolver(ABC):
         pass
 
     @abstractmethod
-    def get_constraints_ids(self):
+    def get_constraint_ids(self):
         """
         Returns a list of ids, which uniquely identify each constraint in the model.
         """

miplearn/solvers/learning.py

@@ -40,6 +40,14 @@ class LearningSolver:
     Mixed-Integer Linear Programming (MIP) solver that extracts information
     from previous runs, using Machine Learning methods, to accelerate the
     solution of new (yet unseen) instances.
+
+    Parameters
+    ----------
+    solve_lp_first: bool
+        If true, solve LP relaxation first, then solve original MILP. This
+        option should be activated if the LP relaxation is not very
+        expensive to solve and if it provides good hints for the integer
+        solution.
     """
 
     def __init__(self,
@@ -49,7 +57,8 @@ class LearningSolver:
                  solver="gurobi",
                  threads=None,
                  time_limit=None,
-                 node_limit=None):
+                 node_limit=None,
+                 solve_lp_first=True):
         self.components = {}
         self.mode = mode
         self.internal_solver = None
@@ -59,6 +68,7 @@ class LearningSolver:
         self.gap_tolerance = gap_tolerance
         self.tee = False
         self.node_limit = node_limit
+        self.solve_lp_first = solve_lp_first
 
         if components is not None:
             for comp in components:
@@ -84,21 +94,23 @@ class LearningSolver:
         else:
             solver = self.internal_solver_factory
         if self.threads is not None:
+            logger.info("Setting threads to %d" % self.threads)
             solver.set_threads(self.threads)
         if self.time_limit is not None:
+            logger.info("Setting time limit to %f" % self.time_limit)
             solver.set_time_limit(self.time_limit)
         if self.gap_tolerance is not None:
+            logger.info("Setting gap tolerance to %f" % self.gap_tolerance)
             solver.set_gap_tolerance(self.gap_tolerance)
         if self.node_limit is not None:
+            logger.info("Setting node limit to %d" % self.node_limit)
             solver.set_node_limit(self.node_limit)
         return solver
 
     def solve(self,
               instance,
               model=None,
-              tee=False,
-              relaxation_only=False,
-              solve_lp_first=True):
+              tee=False):
         """
         Solves the given instance. If trained machine-learning models are
         available, they will be used to accelerate the solution process.
@@ -127,13 +139,6 @@ class LearningSolver:
             The corresponding Pyomo model. If not provided, it will be created.
         tee: bool
             If true, prints solver log to screen.
-        relaxation_only: bool
-            If true, solve only the root LP relaxation.
-        solve_lp_first: bool
-            If true, solve LP relaxation first, then solve original MILP. This
-            option should be activated if the LP relaxation is not very
-            expensive to solve and if it provides good hints for the integer
-            solution.
 
         Returns
         -------
@@ -155,7 +160,7 @@ class LearningSolver:
         self.internal_solver = self._create_internal_solver()
         self.internal_solver.set_instance(instance, model)
 
-        if solve_lp_first:
+        if self.solve_lp_first:
             logger.info("Solving LP relaxation...")
             results = self.internal_solver.solve_lp(tee=tee)
             instance.lp_solution = self.internal_solver.get_solution()
@@ -168,9 +173,6 @@ class LearningSolver:
         for component in self.components.values():
             component.before_solve(self, instance, model)
 
-        if relaxation_only:
-            return results
-
         def iteration_cb():
             should_repeat = False
             for component in self.components.values():

miplearn/solvers/pyomo/base.py

@@ -203,7 +203,7 @@ class BasePyomoSolver(InternalSolver):
         key = self._get_gap_tolerance_option_name()
         self._pyomo_solver.options[key] = gap_tolerance
 
-    def get_constraints_ids(self):
+    def get_constraint_ids(self):
         return list(self._cname_to_constr.keys())
 
     def extract_constraint(self, cid):

miplearn/solvers/tests/test_internal_solver.py

@@ -110,7 +110,7 @@ def test_internal_solver():
 
         # New constraint should affect solution and should be listed in
         # constraint ids
-        assert solver.get_constraints_ids() == ["eq_capacity", "cut"]
+        assert solver.get_constraint_ids() == ["eq_capacity", "cut"]
         stats = solver.solve()
         assert stats["Lower bound"] == 1030.0
 
@@ -120,7 +120,7 @@ def test_internal_solver():
 
             # New constraint should no longer affect solution and should no longer
             # be listed in constraint ids
-            assert solver.get_constraints_ids() == ["eq_capacity"]
+            assert solver.get_constraint_ids() == ["eq_capacity"]
             stats = solver.solve()
             assert stats["Lower bound"] == 1183.0
 
@@ -131,7 +131,7 @@ def test_internal_solver():
             solver.add_constraint(cobj)
 
             # Constraint should affect solution again
-            assert solver.get_constraints_ids() == ["eq_capacity", "cut"]
+            assert solver.get_constraint_ids() == ["eq_capacity", "cut"]
             stats = solver.solve()
             assert stats["Lower bound"] == 1030.0