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Rename before/after_solve to before/after_solve_mip

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This commit is contained in:
Alinson S. Xavier
2021-03-30 09:00:02 -05:00
parent bcaf26b18c
commit 6ae052c8d0
15 changed files with 42 additions and 42 deletions
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8
miplearn/components/component.py
View File

@@ -21,14 +21,14 @@ class Component(ABC):
strategy.
"""
def before_solve(
def before_solve_mip(
self,
solver: "LearningSolver",
instance: Instance,
model: Any,
) -> None:
"""
Method called by LearningSolver before the problem is solved.
Method called by LearningSolver before the MIP is solved.
Parameters
----------
@@ -42,7 +42,7 @@ class Component(ABC):
return
@abstractmethod
def after_solve(
def after_solve_mip(
self,
solver: "LearningSolver",
instance: Instance,
@@ -51,7 +51,7 @@ class Component(ABC):
training_data: TrainingSample,
) -> None:
"""
Method called by LearningSolver after the problem is solved to optimality.
Method called by LearningSolver after the MIP is solved.
Parameters
----------

8
miplearn/components/composite.py
View File

@@ -21,11 +21,11 @@ class CompositeComponent(Component):
def __init__(self, children):
self.children = children
def before_solve(self, solver, instance, model):
def before_solve_mip(self, solver, instance, model):
for child in self.children:
child.before_solve(solver, instance, model)
child.before_solve_mip(solver, instance, model)
def after_solve(
def after_solve_mip(
self,
solver,
instance,
@@ -34,7 +34,7 @@ class CompositeComponent(Component):
training_data,
):
for child in self.children:
child.after_solve(solver, instance, model, stats, training_data)
child.after_solve_mip(solver, instance, model, stats, training_data)
def fit(self, training_instances):
for child in self.children:

4
miplearn/components/cuts.py
View File

@@ -33,7 +33,7 @@ class UserCutsComponent(Component):
self.classifier_prototype: Classifier = classifier
self.classifiers: Dict[Any, Classifier] = {}
def before_solve(self, solver, instance, model):
def before_solve_mip(self, solver, instance, model):
instance.found_violated_user_cuts = []
logger.info("Predicting violated user cuts...")
violations = self.predict(instance)
@@ -42,7 +42,7 @@ class UserCutsComponent(Component):
cut = instance.build_user_cut(model, v)
solver.internal_solver.add_constraint(cut)
def after_solve(
def after_solve_mip(
self,
solver,
instance,

4
miplearn/components/lazy_dynamic.py
View File

@@ -33,7 +33,7 @@ class DynamicLazyConstraintsComponent(Component):
self.classifier_prototype: Classifier = classifier
self.classifiers: Dict[Any, Classifier] = {}
def before_solve(self, solver, instance, model):
def before_solve_mip(self, solver, instance, model):
instance.found_violated_lazy_constraints = []
logger.info("Predicting violated lazy constraints...")
violations = self.predict(instance)
@@ -54,7 +54,7 @@ class DynamicLazyConstraintsComponent(Component):
solver.internal_solver.add_constraint(cut)
return True
def after_solve(
def after_solve_mip(
self,
solver,
instance,

4
miplearn/components/lazy_static.py
View File

@@ -40,7 +40,7 @@ class StaticLazyConstraintsComponent(Component):
self.use_two_phase_gap = use_two_phase_gap
self.violation_tolerance = violation_tolerance
def before_solve(self, solver, instance, model):
def before_solve_mip(self, solver, instance, model):
self.pool = []
if not solver.use_lazy_cb and self.use_two_phase_gap:
logger.info("Increasing gap tolerance to %f", self.large_gap)
@@ -52,7 +52,7 @@ class StaticLazyConstraintsComponent(Component):
if instance.has_static_lazy_constraints():
self._extract_and_predict_static(solver, instance)
def after_solve(
def after_solve_mip(
self,
solver,
instance,

4
miplearn/components/objective.py
View File

@@ -44,7 +44,7 @@ class ObjectiveValueComponent(Component):
self._predicted_ub: Optional[float] = None
self._predicted_lb: Optional[float] = None
def before_solve(
def before_solve_mip(
self,
solver: "LearningSolver",
instance: Instance,
@@ -63,7 +63,7 @@ class ObjectiveValueComponent(Component):
)
)
def after_solve(
def after_solve_mip(
self,
solver: "LearningSolver",
instance: Instance,

4
miplearn/components/primal.py
View File

@@ -52,7 +52,7 @@ class PrimalSolutionComponent(Component):
self._n_zero = 0
self._n_one = 0
def before_solve(self, solver, instance, model):
def before_solve_mip(self, solver, instance, model):
if len(self.thresholds) > 0:
logger.info("Predicting primal solution...")
solution = self.predict(instance)
@@ -77,7 +77,7 @@ class PrimalSolutionComponent(Component):
else:
solver.internal_solver.set_warm_start(solution)
def after_solve(
def after_solve_mip(
self,
solver: "LearningSolver",
instance: Instance,

4
miplearn/components/steps/convert_tight.py
View File

@@ -46,7 +46,7 @@ class ConvertTightIneqsIntoEqsStep(Component):
self.converted = []
self.original_sense = {}
def before_solve(self, solver, instance, _):
def before_solve_mip(self, solver, instance, _):
logger.info("Predicting tight LP constraints...")
x, constraints = DropRedundantInequalitiesStep._x_test(
instance,
@@ -73,7 +73,7 @@ class ConvertTightIneqsIntoEqsStep(Component):
logger.info(f"Converted {self.n_converted} inequalities")
def after_solve(
def after_solve_mip(
self,
solver,
instance,

4
miplearn/components/steps/drop_redundant.py
View File

@@ -50,7 +50,7 @@ class DropRedundantInequalitiesStep(Component):
self.total_kept = 0
self.total_iterations = 0
def before_solve(self, solver, instance, _):
def before_solve_mip(self, solver, instance, _):
self.current_iteration = 0
logger.info("Predicting redundant LP constraints...")
@@ -79,7 +79,7 @@ class DropRedundantInequalitiesStep(Component):
self.total_kept += 1
logger.info(f"Extracted {self.total_dropped} predicted constraints")
def after_solve(
def after_solve_mip(
self,
solver,
instance,

4
miplearn/components/steps/relax_integrality.py
View File

@@ -14,11 +14,11 @@ class RelaxIntegralityStep(Component):
Component that relaxes all integrality constraints before the problem is solved.
"""
def before_solve(self, solver, instance, _):
def before_solve_mip(self, solver, instance, _):
logger.info("Relaxing integrality...")
solver.internal_solver.relax()
def after_solve(
def after_solve_mip(
self,
solver,
instance,

14
miplearn/solvers/learning.py
View File

@@ -86,7 +86,7 @@ class LearningSolver:
If true, use native solver callbacks for enforcing lazy constraints,
instead of a simple loop. May not be supported by all solvers.
solve_lp_first: bool
If true, solve LP relaxation first, then solve original MILP. This
If true, solve LP relaxation first, then solve original MIP. 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.
@@ -187,9 +187,9 @@ class LearningSolver:
training_sample["LP value"] = 0.0
# Before-solve callbacks
logger.debug("Running before_solve callbacks...")
logger.debug("Running before_solve_mip callbacks...")
for component in self.components.values():
component.before_solve(self, instance, model)
component.before_solve_mip(self, instance, model)
# Define wrappers
def iteration_cb_wrapper() -> bool:
@@ -212,8 +212,8 @@ class LearningSolver:
if self.use_lazy_cb:
lazy_cb = lazy_cb_wrapper
# Solve MILP
logger.info("Solving MILP...")
# Solve MIP
logger.info("Solving MIP...")
stats = cast(
LearningSolveStats,
self.internal_solver.solve(
@@ -238,9 +238,9 @@ class LearningSolver:
training_sample["Solution"] = self.internal_solver.get_solution()
# After-solve callbacks
logger.debug("Calling after_solve callbacks...")
logger.debug("Calling after_solve_mip callbacks...")
for component in self.components.values():
component.after_solve(self, instance, model, stats, training_sample)
component.after_solve_mip(self, instance, model, stats, training_sample)
# Write to file, if necessary
if not discard_output and filename is not None: