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63 lines
2.1 KiB
63 lines
2.1 KiB
# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
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# Written by Alinson S. Xavier <axavier@anl.gov>
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from miplearn import LearningSolver
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from miplearn.problems.knapsack import KnapsackInstance
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from miplearn.branching import BranchPriorityComponent
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from miplearn.warmstart import WarmStartComponent
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import numpy as np
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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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solver = LearningSolver()
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solver.solve(instance)
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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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"branch-priority": BranchPriorityComponent(),
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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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assert len(solver.components["warm-start"].x_train["default"]) == 40
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assert len(solver.components["warm-start"].y_train["default"]) == 40
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def test_solver_random_branch_priority():
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instance = _get_instance()
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components = {
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"warm-start": BranchPriorityComponent(),
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}
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solver = LearningSolver(components=components)
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solver.solve(instance)
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solver.fit() |