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42 lines
1.5 KiB
42 lines
1.5 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 WarmStartComponent, LearningSolver
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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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return [
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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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] * 2
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def test_warm_start_save_load():
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state_file = tempfile.NamedTemporaryFile(mode="r")
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solver = LearningSolver(components={"warm-start": WarmStartComponent()})
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solver.parallel_solve(_get_instances(), n_jobs=2)
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solver.fit()
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comp = solver.components["warm-start"]
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assert comp.x_train["default"].shape == (8, 6)
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assert comp.y_train["default"].shape == (8, 2)
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assert ("default", 0) in comp.predictors.keys()
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assert ("default", 1) in comp.predictors.keys()
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solver.save_state(state_file.name)
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solver.solve(_get_instances()[0])
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solver = LearningSolver(components={"warm-start": WarmStartComponent()})
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solver.load_state(state_file.name)
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comp = solver.components["warm-start"]
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assert comp.x_train["default"].shape == (8, 6)
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assert comp.y_train["default"].shape == (8, 2)
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assert ("default", 0) in comp.predictors.keys()
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assert ("default", 1) in comp.predictors.keys()
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