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58 lines
1.9 KiB
58 lines
1.9 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2021, 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.features import (
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FeaturesExtractor,
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InstanceFeatures,
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VariableFeatures,
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ConstraintFeatures,
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)
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from miplearn.solvers.gurobi import GurobiSolver
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def test_knapsack() -> None:
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for solver_factory in [GurobiSolver]:
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solver = solver_factory()
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instance = solver.build_test_instance_knapsack()
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model = instance.to_model()
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solver.set_instance(instance, model)
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FeaturesExtractor(solver).extract(instance)
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assert instance.features.variables == {
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"x[0]": VariableFeatures(
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category="default",
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user_features=[23.0, 505.0],
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),
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"x[1]": VariableFeatures(
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category="default",
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user_features=[26.0, 352.0],
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),
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"x[2]": VariableFeatures(
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category="default",
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user_features=[20.0, 458.0],
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),
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"x[3]": VariableFeatures(
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category="default",
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user_features=[18.0, 220.0],
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),
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}
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assert instance.features.constraints == {
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"eq_capacity": ConstraintFeatures(
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lhs={
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"x[0]": 23.0,
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"x[1]": 26.0,
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"x[2]": 20.0,
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"x[3]": 18.0,
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},
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sense="<",
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rhs=67.0,
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lazy=False,
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category="eq_capacity",
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user_features=[0.0],
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)
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}
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assert instance.features.instance == InstanceFeatures(
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user_features=[67.0, 21.75],
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lazy_constraint_count=0,
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)
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