LearningSolver: fix node count for non-compatible solvers

miplearn/solvers.py

@@ -29,7 +29,7 @@ class LearningSolver:
                  gap_limit=None,
                  internal_solver_factory=_gurobi_factory,
                  components=None,
-                 mode=None):
+                 mode="exact"):
         self.is_persistent = None
         self.internal_solver = None
         self.components = components
@@ -38,19 +38,18 @@ class LearningSolver:
         self.time_limit = time_limit
         self.gap_limit = gap_limit
         self.tee = False
+        self.mode = mode
         
         if self.components is not None:
             assert isinstance(self.components, dict)
         else:
             self.components = {
                 "warm-start": WarmStartComponent(),
-                #"branch-priority": BranchPriorityComponent(),
             }
             
-        if mode is not None:
-            assert mode in ["exact", "heuristic"]
-            for component in self.components.values():
-                component.mode = mode
+        assert self.mode in ["exact", "heuristic"]
+        for component in self.components.values():
+            component.mode = self.mode
         
     def _create_solver(self):
         self.internal_solver = self.internal_solver_factory()
@@ -82,7 +81,10 @@ class LearningSolver:
         else:
             solve_results = self.internal_solver.solve(model, tee=tee, warmstart=is_warm_start_available)
         
-        solve_results["Solver"][0]["Nodes"] = self.internal_solver._solver_model.getAttr("NodeCount")
+        if hasattr(self.internal_solver, "_solver_model"):
+            solve_results["Solver"][0]["Nodes"] = self.internal_solver._solver_model.getAttr("NodeCount")
+        else:
+            solve_results["Solver"][0]["Nodes"] = 1
         
         for component in self.components.values():
             component.after_solve(self, instance, model)