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75 lines
2.8 KiB
75 lines
2.8 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, BranchPriorityComponent
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from miplearn.problems.knapsack import KnapsackInstance
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def _get_instance():
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return 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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def test_solver():
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instance = _get_instance()
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for mode in ["exact", "heuristic"]:
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for internal_solver in ["cplex", "gurobi"]:
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solver = LearningSolver(time_limit=300,
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gap_tolerance=1e-3,
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threads=1,
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solver=internal_solver,
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mode=mode,
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)
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results = solver.solve(instance)
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assert instance.solution["x"][0] == 1.0
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assert instance.solution["x"][1] == 0.0
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assert instance.solution["x"][2] == 1.0
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assert instance.solution["x"][3] == 1.0
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assert instance.lower_bound == 1183.0
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assert instance.upper_bound == 1183.0
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assert round(instance.lp_solution["x"][0], 3) == 1.000
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assert round(instance.lp_solution["x"][1], 3) == 0.923
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assert round(instance.lp_solution["x"][2], 3) == 1.000
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assert round(instance.lp_solution["x"][3], 3) == 0.000
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assert round(instance.lp_value, 3) == 1287.923
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solver.fit()
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solver.solve(instance)
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# def test_solve_save_load_state():
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# instance = _get_instance()
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# components_before = {
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# "warm-start": WarmStartComponent(),
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# }
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# solver = LearningSolver(components=components_before)
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# solver.solve(instance)
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# solver.fit()
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# solver.save_state("/tmp/knapsack_train.bin")
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# prev_x_train_len = len(solver.components["warm-start"].x_train)
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# prev_y_train_len = len(solver.components["warm-start"].y_train)
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# components_after = {
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# "warm-start": WarmStartComponent(),
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# }
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# solver = LearningSolver(components=components_after)
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# solver.load_state("/tmp/knapsack_train.bin")
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# assert len(solver.components.keys()) == 1
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# assert len(solver.components["warm-start"].x_train) == prev_x_train_len
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# assert len(solver.components["warm-start"].y_train) == prev_y_train_len
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def test_parallel_solve():
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instances = [_get_instance() for _ in range(10)]
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solver = LearningSolver()
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results = solver.parallel_solve(instances, n_jobs=3)
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assert len(results) == 10
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for instance in instances:
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assert len(instance.solution["x"].keys()) == 4
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