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97 lines
4.1 KiB
97 lines
4.1 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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from miplearn.classifiers.counting import CountingClassifier
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from miplearn.components.component import Component
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from miplearn.components.composite import CompositeComponent
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from miplearn.components.steps.convert_tight import ConvertTightIneqsIntoEqsStep
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from miplearn.components.steps.drop_redundant import DropRedundantInequalitiesStep
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from miplearn.components.steps.relax_integrality import RelaxIntegralityStep
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logger = logging.getLogger(__name__)
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class RelaxationComponent(Component):
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"""
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A Component that tries to build a relaxation that is simultaneously strong and easy
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to solve. Currently, this component is composed by three steps:
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- RelaxIntegralityStep
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- DropRedundantInequalitiesStep
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- ConvertTightIneqsIntoEqsStep
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Parameters
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----------
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redundant_classifier : Classifier, optional
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Classifier used to predict if a constraint is likely redundant. One deep
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copy of this classifier is made for each constraint category.
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redundant_threshold : float, optional
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If the probability that a constraint is redundant exceeds this threshold, the
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constraint is dropped from the linear relaxation.
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tight_classifier : Classifier, optional
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Classifier used to predict if a constraint is likely to be tight. One deep
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copy of this classifier is made for each constraint category.
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tight_threshold : float, optional
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If the probability that a constraint is tight exceeds this threshold, the
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constraint is converted into an equality constraint.
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slack_tolerance : float, optional
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If a constraint has slack greater than this threshold, then the constraint is
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considered loose. By default, this threshold equals a small positive number to
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compensate for numerical issues.
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check_feasibility : bool, optional
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If true, after the problem is solved, the component verifies that all dropped
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constraints are still satisfied, re-adds the violated ones and resolves the
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problem. This loop continues until either no violations are found, or a maximum
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number of iterations is reached.
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violation_tolerance : float, optional
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If `check_dropped` is true, a constraint is considered satisfied during the
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check if its violation is smaller than this tolerance.
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max_check_iterations : int
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If `check_dropped` is true, set the maximum number of iterations in the lazy
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constraint loop.
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"""
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def __init__(
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self,
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redundant_classifier=CountingClassifier(),
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redundant_threshold=0.95,
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tight_classifier=CountingClassifier(),
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tight_threshold=0.95,
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slack_tolerance=1e-5,
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check_feasibility=False,
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violation_tolerance=1e-5,
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max_check_iterations=3,
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):
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self.steps = [
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RelaxIntegralityStep(),
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DropRedundantInequalitiesStep(
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classifier=redundant_classifier,
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threshold=redundant_threshold,
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slack_tolerance=slack_tolerance,
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violation_tolerance=violation_tolerance,
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max_iterations=max_check_iterations,
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check_feasibility=check_feasibility,
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),
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ConvertTightIneqsIntoEqsStep(
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classifier=tight_classifier,
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threshold=tight_threshold,
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slack_tolerance=slack_tolerance,
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),
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]
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self.composite = CompositeComponent(self.steps)
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def before_solve(self, solver, instance, model):
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self.composite.before_solve(solver, instance, model)
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def after_solve(self, solver, instance, model, stats, training_data):
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self.composite.after_solve(solver, instance, model, stats, training_data)
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def fit(self, training_instances):
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self.composite.fit(training_instances)
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def iteration_cb(self, solver, instance, model):
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return self.composite.iteration_cb(solver, instance, model)
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