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133 lines
5.0 KiB
133 lines
5.0 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 sys
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from copy import deepcopy
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from miplearn.classifiers.counting import CountingClassifier
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from .component import Component
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from ..extractors import *
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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__(self,
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classifier=CountingClassifier(),
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threshold=0.05):
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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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def before_solve(self, solver, instance, model):
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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(self, solver, instance, model, results):
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pass
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def after_iteration(self, solver, instance, model):
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logger.debug("Finding violated (static) lazy constraints...")
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n_added = 0
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for c in self.pool:
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if not solver.internal_solver.is_constraint_satisfied(c.obj):
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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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n_added += 1
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if n_added > 0:
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logger.debug(" %d violations found" % n_added)
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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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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(x.keys(),
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desc="Fit (lazy)",
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disable=not sys.stdout.isatty()):
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if category not in self.classifiers:
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self.classifiers[category] = deepcopy(self.classifier_prototype)
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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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for cid in solver.internal_solver.get_constraint_names():
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if instance.is_constraint_lazy(cid):
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category = instance.get_lazy_constraint_category(cid)
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if category not in self.classifiers:
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continue
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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_lazy_constraint_features(cid)]
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c = LazyConstraint(cid=cid,
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obj=solver.internal_solver.extract_constraint(cid))
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constraints[category] += [c]
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self.pool.append(c)
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for (category, x_values) in x.items():
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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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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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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_lazy_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_lazy_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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