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34 lines
1.0 KiB
34 lines
1.0 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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from miplearn import LearningSolver, PrimalSolutionComponent
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from miplearn.problems.knapsack import KnapsackInstance
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import numpy as np
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import tempfile
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def _get_instances():
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instances = [
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KnapsackInstance(
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weights=[23., 26., 20., 18.],
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prices=[505., 352., 458., 220.],
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capacity=67.,
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),
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] * 5
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models = [inst.to_model() for inst in instances]
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solver = LearningSolver()
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for i in range(len(instances)):
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solver.solve(instances[i], models[i])
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return instances, models
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def test_predict():
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instances, models = _get_instances()
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comp = PrimalSolutionComponent()
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comp.fit(instances)
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solution = comp.predict(instances[0])
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assert "x" in solution
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for idx in range(4):
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assert idx in solution["x"]
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