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233 lines
8.1 KiB
233 lines
8.1 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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import logging
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import sys
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from typing import Dict, Tuple, Optional
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import numpy as np
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from tqdm.auto import tqdm
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from miplearn import Classifier
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from miplearn.classifiers.counting import CountingClassifier
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from miplearn.components.component import Component
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from miplearn.types import TrainingSample, Features
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logger = logging.getLogger(__name__)
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class LazyConstraint:
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def __init__(self, cid, obj):
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self.cid = cid
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self.obj = obj
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class StaticLazyConstraintsComponent(Component):
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def __init__(
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self,
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classifier=CountingClassifier(),
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threshold=0.05,
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use_two_phase_gap=True,
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large_gap=1e-2,
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violation_tolerance=-0.5,
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):
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assert isinstance(classifier, Classifier)
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self.threshold = threshold
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self.classifier_prototype = classifier
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self.classifiers = {}
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self.pool = []
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self.original_gap = None
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self.large_gap = large_gap
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self.is_gap_large = False
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self.use_two_phase_gap = use_two_phase_gap
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self.violation_tolerance = violation_tolerance
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def before_solve_mip(self, solver, instance, model):
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self.pool = []
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if not solver.use_lazy_cb and self.use_two_phase_gap:
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logger.info("Increasing gap tolerance to %f", self.large_gap)
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self.original_gap = solver.gap_tolerance
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self.is_gap_large = True
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solver.internal_solver.set_gap_tolerance(self.large_gap)
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instance.found_violated_lazy_constraints = []
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if instance.has_static_lazy_constraints():
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self._extract_and_predict_static(solver, instance)
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def after_solve_mip(
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self,
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solver,
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instance,
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model,
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stats,
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training_data,
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):
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pass
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def iteration_cb(self, solver, instance, model):
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if solver.use_lazy_cb:
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return False
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else:
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should_repeat = self._check_and_add(instance, solver)
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if should_repeat:
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return True
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else:
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if self.is_gap_large:
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logger.info("Restoring gap tolerance to %f", self.original_gap)
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solver.internal_solver.set_gap_tolerance(self.original_gap)
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self.is_gap_large = False
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return True
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else:
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return False
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def lazy_cb(self, solver, instance, model):
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self._check_and_add(instance, solver)
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def _check_and_add(self, instance, solver):
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logger.debug("Finding violated lazy constraints...")
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constraints_to_add = []
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for c in self.pool:
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if not solver.internal_solver.is_constraint_satisfied(
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c.obj, tol=self.violation_tolerance
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):
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constraints_to_add.append(c)
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for c in constraints_to_add:
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self.pool.remove(c)
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solver.internal_solver.add_constraint(c.obj)
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instance.found_violated_lazy_constraints += [c.cid]
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if len(constraints_to_add) > 0:
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logger.info(
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"%8d lazy constraints added %8d in the pool"
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% (len(constraints_to_add), len(self.pool))
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)
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return True
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else:
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return False
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def fit(self, training_instances):
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training_instances = [
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t
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for t in training_instances
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if hasattr(t, "found_violated_lazy_constraints")
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]
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logger.debug("Extracting x and y...")
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x = self.x(training_instances)
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y = self.y(training_instances)
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logger.debug("Fitting...")
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for category in tqdm(
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x.keys(), desc="Fit (lazy)", disable=not sys.stdout.isatty()
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):
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if category not in self.classifiers:
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self.classifiers[category] = self.classifier_prototype.clone()
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self.classifiers[category].fit(x[category], y[category])
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def predict(self, instance):
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pass
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def evaluate(self, instances):
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pass
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def _extract_and_predict_static(self, solver, instance):
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x = {}
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constraints = {}
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logger.info("Extracting lazy constraints...")
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for cid in solver.internal_solver.get_constraint_ids():
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if instance.is_constraint_lazy(cid):
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category = instance.get_constraint_category(cid)
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if category not in x:
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x[category] = []
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constraints[category] = []
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x[category] += [instance.get_constraint_features(cid)]
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c = LazyConstraint(
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cid=cid,
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obj=solver.internal_solver.extract_constraint(cid),
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)
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constraints[category] += [c]
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self.pool.append(c)
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logger.info("%8d lazy constraints extracted" % len(self.pool))
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logger.info("Predicting required lazy constraints...")
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n_added = 0
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for (category, x_values) in x.items():
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if category not in self.classifiers:
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continue
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if isinstance(x_values[0], np.ndarray):
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x[category] = np.array(x_values)
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proba = self.classifiers[category].predict_proba(x[category])
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for i in range(len(proba)):
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if proba[i][1] > self.threshold:
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n_added += 1
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c = constraints[category][i]
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self.pool.remove(c)
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solver.internal_solver.add_constraint(c.obj)
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instance.found_violated_lazy_constraints += [c.cid]
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logger.info(
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"%8d lazy constraints added %8d in the pool"
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% (
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n_added,
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len(self.pool),
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)
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)
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def _collect_constraints(self, train_instances):
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constraints = {}
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for instance in train_instances:
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for cid in instance.found_violated_lazy_constraints:
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category = instance.get_constraint_category(cid)
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if category not in constraints:
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constraints[category] = set()
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constraints[category].add(cid)
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for (category, cids) in constraints.items():
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constraints[category] = sorted(list(cids))
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return constraints
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def x(self, train_instances):
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result = {}
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constraints = self._collect_constraints(train_instances)
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for (category, cids) in constraints.items():
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result[category] = []
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for instance in train_instances:
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for cid in cids:
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result[category].append(instance.get_constraint_features(cid))
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return result
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def y(self, train_instances):
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result = {}
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constraints = self._collect_constraints(train_instances)
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for (category, cids) in constraints.items():
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result[category] = []
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for instance in train_instances:
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for cid in cids:
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if cid in instance.found_violated_lazy_constraints:
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result[category].append([0, 1])
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else:
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result[category].append([1, 0])
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return result
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@staticmethod
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def xy_sample(
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features: Features,
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sample: TrainingSample,
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) -> Optional[Tuple[Dict, Dict]]:
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if "LazyStatic: Enforced" not in sample:
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return None
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x: Dict = {}
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y: Dict = {}
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for (cid, cfeatures) in features["Constraints"].items():
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if not cfeatures["Lazy"]:
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continue
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category = cfeatures["Category"]
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if category is None:
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continue
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if category not in x:
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x[category] = []
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y[category] = []
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x[category] += [cfeatures["User features"]]
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if cid in sample["LazyStatic: Enforced"]:
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y[category] += [[False, True]]
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else:
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y[category] += [[True, False]]
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return x, y
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