# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved. # Released under the modified BSD license. See COPYING.md for more details. import numpy as np from miplearn.extractors import InstanceFeaturesExtractor from miplearn.problems.knapsack import KnapsackInstance from miplearn.solvers.learning import LearningSolver def _get_instances(): instances = [ KnapsackInstance( weights=[1.0, 2.0, 3.0], prices=[10.0, 20.0, 30.0], capacity=2.5, ), KnapsackInstance( weights=[3.0, 4.0, 5.0], prices=[20.0, 30.0, 40.0], capacity=4.5, ), ] models = [instance.to_model() for instance in instances] solver = LearningSolver() for (i, instance) in enumerate(instances): solver.solve(instances[i], models[i]) return instances, models def test_instance_features_extractor(): instances, models = _get_instances() features = InstanceFeaturesExtractor().extract(instances) assert features.shape == (2, 3)