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Improve logging

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This commit is contained in:
Alinson S. Xavier
2020-09-15 08:46:49 -05:00
parent 42a891276c
commit 6b104433be
5 changed files with 7 additions and 4 deletions
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2
miplearn/components/cuts.py
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@@ -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)

2
miplearn/components/lazy.py
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@@ -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)

3
miplearn/components/objective.py
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@@ -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:

1
miplearn/components/primal.py
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@@ -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)

3
miplearn/solvers/learning.py
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@@ -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