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46 lines
1.6 KiB
46 lines
1.6 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 BasePyomoSolver, GurobiSolver, InternalSolver, Instance
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from miplearn.problems.knapsack import KnapsackInstance, GurobiKnapsackInstance
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from miplearn.solvers.learning import LearningSolver
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from tests.solvers import _is_subclass_or_instance
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def get_test_pyomo_instances():
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instances = [
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KnapsackInstance(
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weights=[23.0, 26.0, 20.0, 18.0],
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prices=[505.0, 352.0, 458.0, 220.0],
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capacity=67.0,
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),
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KnapsackInstance(
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weights=[25.0, 30.0, 22.0, 18.0],
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prices=[500.0, 365.0, 420.0, 150.0],
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capacity=70.0,
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),
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]
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models = [instance.to_model() for instance 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 get_knapsack_instance(solver: InternalSolver) -> Instance:
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if _is_subclass_or_instance(solver, BasePyomoSolver):
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return KnapsackInstance(
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weights=[23.0, 26.0, 20.0, 18.0],
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prices=[505.0, 352.0, 458.0, 220.0],
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capacity=67.0,
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)
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elif _is_subclass_or_instance(solver, GurobiSolver):
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return GurobiKnapsackInstance(
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weights=[23.0, 26.0, 20.0, 18.0],
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prices=[505.0, 352.0, 458.0, 220.0],
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capacity=67.0,
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)
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else:
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assert False
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