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70 lines
2.3 KiB
70 lines
2.3 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 miplearn.problems.knapsack import KnapsackInstance
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from miplearn.extractors import (UserFeaturesExtractor,
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SolutionExtractor,
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CombinedExtractor,
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)
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import numpy as np
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import pyomo.environ as pe
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def _get_instances():
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return [
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KnapsackInstance(weights=[1., 2., 3.],
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prices=[10., 20., 30.],
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capacity=2.5,
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),
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KnapsackInstance(weights=[3., 4., 5.],
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prices=[20., 30., 40.],
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capacity=4.5,
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),
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]
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def test_user_features():
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instances = _get_instances()
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extractor = UserFeaturesExtractor()
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features = extractor.extract(instances)
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assert isinstance(features, dict)
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assert "default" in features.keys()
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assert isinstance(features["default"], np.ndarray)
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assert features["default"].shape == (6, 4)
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def test_solution_extractor():
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instances = _get_instances()
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models = [instance.to_model() for instance in instances]
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for model in models:
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solver = pe.SolverFactory("cbc")
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solver.solve(model)
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extractor = SolutionExtractor()
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features = extractor.extract(instances, models)
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assert isinstance(features, dict)
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assert "default" in features.keys()
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assert isinstance(features["default"], np.ndarray)
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assert features["default"].shape == (6, 2)
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assert features["default"].ravel().tolist() == [
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1., 0.,
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0., 1.,
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1., 0.,
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1., 0.,
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0., 1.,
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1., 0.,
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]
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def test_combined_extractor():
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instances = _get_instances()
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models = [instance.to_model() for instance in instances]
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extractor = CombinedExtractor(extractors=[UserFeaturesExtractor(),
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SolutionExtractor()])
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features = extractor.extract(instances, models)
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assert isinstance(features, dict)
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assert "default" in features.keys()
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assert isinstance(features["default"], np.ndarray)
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assert features["default"].shape == (6, 6)
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