Improve logging

miplearn/components/cuts.py

@@ -32,7 +32,7 @@ class UserCutsComponent(Component):
     def before_solve(self, solver, instance, model):
         logger.info("Predicting violated user cuts...")
         violations = self.predict(instance)
-        logger.info("Enforcing %d cuts..." % len(violations))
+        logger.info("Enforcing %d user cuts..." % len(violations))
         for v in violations:
             cut = instance.build_user_cut(model, v)
             solver.internal_solver.add_constraint(cut)

miplearn/components/lazy.py

@@ -32,7 +32,7 @@ class LazyConstraintsComponent(Component):
     def before_solve(self, solver, instance, model):
         logger.info("Predicting violated lazy constraints...")
         violations = self.predict(instance)
-        logger.info("Enforcing %d constraints..." % len(violations))
+        logger.info("Enforcing %d lazy constraints..." % len(violations))
         for v in violations:
             cut = instance.build_lazy_constraint(model, v)
             solver.internal_solver.add_constraint(cut)

miplearn/components/objective.py

@@ -23,10 +23,11 @@ class ObjectiveValueComponent(Component):
     
     def before_solve(self, solver, instance, model):
         if self.ub_regressor is not None:
+            logger.info("Predicting optimal value...")
             lb, ub = self.predict([instance])[0]
             instance.predicted_ub = ub
             instance.predicted_lb = lb
-            logger.info("Predicted objective: [%.2f, %.2f]" % (lb, ub))
+            logger.info("Predicted values: lb=%.2f, ub=%.2f" % (lb, ub))
     
     def after_solve(self, solver, instance, model, results):
         if self.ub_regressor is not None:

miplearn/components/primal.py

@@ -30,6 +30,7 @@ class PrimalSolutionComponent(Component):
         self.classifier_prototype = classifier
 
     def before_solve(self, solver, instance, model):
+        logger.info("Predicting primal solution...")
         solution = self.predict(instance)
         if self.mode == "heuristic":
             solver.internal_solver.fix(solution)

miplearn/solvers/learning.py

@@ -156,7 +156,7 @@ class LearningSolver:
         self.internal_solver.set_instance(instance, model)
 
         if solve_lp_first:
-            logger.debug("Solving LP relaxation...")
+            logger.info("Solving LP relaxation...")
             results = self.internal_solver.solve_lp(tee=tee)
             instance.lp_solution = self.internal_solver.get_solution()
             instance.lp_value = results["Optimal value"]
@@ -171,6 +171,7 @@ class LearningSolver:
         if relaxation_only:
             return results
 
+        logger.info("Solving MILP...")
         results = self.internal_solver.solve(tee=tee)
         results["LP value"] = instance.lp_value