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43 lines
1.6 KiB
43 lines
1.6 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 KnapsackInstance2
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import numpy as np
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def test_solver():
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instance = KnapsackInstance2(weights=[23., 26., 20., 18.],
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prices=[505., 352., 458., 220.],
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capacity=67.)
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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():
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instance = KnapsackInstance2(weights=[23., 26., 20., 18.],
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prices=[505., 352., 458., 220.],
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capacity=67.)
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solver = LearningSolver()
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solver.solve(instance)
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solver.fit()
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solver.save("/tmp/knapsack_train.bin")
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prev_x_train_len = len(solver.x_train)
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prev_y_train_len = len(solver.y_train)
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solver = LearningSolver()
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solver.load("/tmp/knapsack_train.bin")
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assert len(solver.x_train) == prev_x_train_len
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assert len(solver.y_train) == prev_y_train_len
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def test_parallel_solve():
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instances = [KnapsackInstance2(weights=np.random.rand(5),
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prices=np.random.rand(5),
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capacity=3.0)
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for _ in range(10)]
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solver = LearningSolver()
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solver.parallel_solve(instances, n_jobs=3)
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assert len(solver.x_train[0]) == 10
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assert len(solver.y_train[0]) == 10 |