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50 lines
1.9 KiB
50 lines
1.9 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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from .. import Component, InstanceFeaturesExtractor, ObjectiveValueExtractor
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from sklearn.linear_model import LinearRegression
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from copy import deepcopy
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import numpy as np
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import logging
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logger = logging.getLogger(__name__)
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class ObjectiveValueComponent(Component):
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"""
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A Component which predicts the optimal objective value of the problem.
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"""
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def __init__(self,
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regressor=LinearRegression()):
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self.ub_regressor = None
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self.lb_regressor = None
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self.regressor_prototype = regressor
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def before_solve(self, solver, instance, model):
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if self.ub_regressor is not None:
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lb, ub = self.predict([instance])[0]
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instance.predicted_ub = ub
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instance.predicted_lb = lb
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logger.info("Predicted objective: [%.2f, %.2f]" % (lb, ub))
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def after_solve(self, solver, instance, model):
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pass
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def merge(self, other):
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pass
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def fit(self, training_instances):
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features = InstanceFeaturesExtractor().extract(training_instances)
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ub = ObjectiveValueExtractor(kind="upper bound").extract(training_instances)
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lb = ObjectiveValueExtractor(kind="lower bound").extract(training_instances)
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self.ub_regressor = deepcopy(self.regressor_prototype)
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self.lb_regressor = deepcopy(self.regressor_prototype)
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self.ub_regressor.fit(features, ub)
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self.lb_regressor.fit(features, lb)
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def predict(self, instances):
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features = InstanceFeaturesExtractor().extract(instances)
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lb = self.lb_regressor.predict(features)
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ub = self.ub_regressor.predict(features)
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return np.hstack([lb, ub])
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