You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
140 lines
4.4 KiB
140 lines
4.4 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
|
# Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
|
|
# Released under the modified BSD license. See COPYING.md for more details.
|
|
|
|
import logging
|
|
from typing import Dict, List, TYPE_CHECKING, Hashable, Tuple, Any, Optional, Set
|
|
|
|
import numpy as np
|
|
from overrides import overrides
|
|
|
|
from miplearn.classifiers import Classifier
|
|
from miplearn.classifiers.counting import CountingClassifier
|
|
from miplearn.classifiers.threshold import MinProbabilityThreshold, Threshold
|
|
from miplearn.components.component import Component
|
|
from miplearn.components.dynamic_common import DynamicConstraintsComponent
|
|
from miplearn.features import Sample
|
|
from miplearn.instance.base import Instance
|
|
from miplearn.types import LearningSolveStats
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
if TYPE_CHECKING:
|
|
from miplearn.solvers.learning import LearningSolver
|
|
|
|
|
|
class DynamicLazyConstraintsComponent(Component):
|
|
"""
|
|
A component that predicts which lazy constraints to enforce.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
classifier: Classifier = CountingClassifier(),
|
|
threshold: Threshold = MinProbabilityThreshold([0, 0.05]),
|
|
):
|
|
self.dynamic: DynamicConstraintsComponent = DynamicConstraintsComponent(
|
|
classifier=classifier,
|
|
threshold=threshold,
|
|
attr="lazy_enforced",
|
|
)
|
|
self.classifiers = self.dynamic.classifiers
|
|
self.thresholds = self.dynamic.thresholds
|
|
self.known_cids = self.dynamic.known_cids
|
|
self.lazy_enforced: Set[Hashable] = set()
|
|
|
|
@staticmethod
|
|
def enforce(
|
|
cids: List[Hashable],
|
|
instance: Instance,
|
|
model: Any,
|
|
solver: "LearningSolver",
|
|
) -> None:
|
|
assert solver.internal_solver is not None
|
|
for cid in cids:
|
|
instance.enforce_lazy_constraint(solver.internal_solver, model, cid)
|
|
|
|
@overrides
|
|
def before_solve_mip(
|
|
self,
|
|
solver: "LearningSolver",
|
|
instance: Instance,
|
|
model: Any,
|
|
stats: LearningSolveStats,
|
|
sample: Sample,
|
|
) -> None:
|
|
self.lazy_enforced.clear()
|
|
logger.info("Predicting violated (dynamic) lazy constraints...")
|
|
cids = self.dynamic.sample_predict(instance, sample)
|
|
logger.info("Enforcing %d lazy constraints..." % len(cids))
|
|
self.enforce(cids, instance, model, solver)
|
|
|
|
@overrides
|
|
def after_solve_mip(
|
|
self,
|
|
solver: "LearningSolver",
|
|
instance: Instance,
|
|
model: Any,
|
|
stats: LearningSolveStats,
|
|
sample: Sample,
|
|
) -> None:
|
|
assert sample.after_mip is not None
|
|
assert sample.after_mip.extra is not None
|
|
sample.after_mip.extra["lazy_enforced"] = set(self.lazy_enforced)
|
|
|
|
@overrides
|
|
def iteration_cb(
|
|
self,
|
|
solver: "LearningSolver",
|
|
instance: Instance,
|
|
model: Any,
|
|
) -> bool:
|
|
assert solver.internal_solver is not None
|
|
logger.debug("Finding violated lazy constraints...")
|
|
cids = instance.find_violated_lazy_constraints(solver.internal_solver, model)
|
|
if len(cids) == 0:
|
|
logger.debug("No violations found")
|
|
return False
|
|
else:
|
|
self.lazy_enforced |= set(cids)
|
|
logger.debug(" %d violations found" % len(cids))
|
|
self.enforce(cids, instance, model, solver)
|
|
return True
|
|
|
|
# Delegate ML methods to self.dynamic
|
|
# -------------------------------------------------------------------
|
|
@overrides
|
|
def sample_xy(
|
|
self,
|
|
instance: Optional[Instance],
|
|
sample: Sample,
|
|
) -> Tuple[Dict, Dict]:
|
|
return self.dynamic.sample_xy(instance, sample)
|
|
|
|
def sample_predict(
|
|
self,
|
|
instance: Instance,
|
|
sample: Sample,
|
|
) -> List[Hashable]:
|
|
return self.dynamic.sample_predict(instance, sample)
|
|
|
|
@overrides
|
|
def fit(self, training_instances: List[Instance]) -> None:
|
|
self.dynamic.fit(training_instances)
|
|
|
|
@overrides
|
|
def fit_xy(
|
|
self,
|
|
x: Dict[Hashable, np.ndarray],
|
|
y: Dict[Hashable, np.ndarray],
|
|
) -> None:
|
|
self.dynamic.fit_xy(x, y)
|
|
|
|
@overrides
|
|
def sample_evaluate(
|
|
self,
|
|
instance: Instance,
|
|
sample: Sample,
|
|
) -> Dict[Hashable, Dict[str, float]]:
|
|
return self.dynamic.sample_evaluate(instance, sample)
|