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62 lines
2.0 KiB
62 lines
2.0 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2022, 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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import numpy as np
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from scipy.stats import uniform, randint
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from miplearn.problems.multiknapsack import (
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MultiKnapsackGenerator,
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MultiKnapsackData,
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build_multiknapsack_model_gurobipy,
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)
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def test_knapsack_generator() -> None:
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np.random.seed(42)
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gen = MultiKnapsackGenerator(
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n=randint(low=5, high=6),
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m=randint(low=3, high=4),
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w=randint(low=0, high=1000),
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K=randint(low=500, high=501),
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u=uniform(loc=0.0, scale=1.0),
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alpha=uniform(loc=0.25, scale=0.0),
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fix_w=True,
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w_jitter=uniform(loc=0.9, scale=0.2),
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p_jitter=uniform(loc=0.9, scale=0.2),
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round=True,
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)
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data = gen.generate(2)
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assert data[0].prices.tolist() == [433.0, 477.0, 802.0, 494.0, 458.0]
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assert data[0].capacities.tolist() == [458.0, 357.0, 392.0]
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assert data[0].weights.tolist() == [
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[111.0, 392.0, 945.0, 276.0, 108.0],
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[64.0, 633.0, 20.0, 602.0, 110.0],
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[510.0, 203.0, 303.0, 469.0, 85.0],
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]
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assert data[1].prices.tolist() == [344.0, 527.0, 658.0, 519.0, 460.0]
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assert data[1].capacities.tolist() == [449.0, 377.0, 380.0]
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assert data[1].weights.tolist() == [
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[92.0, 473.0, 871.0, 264.0, 96.0],
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[67.0, 664.0, 21.0, 628.0, 129.0],
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[436.0, 209.0, 309.0, 481.0, 86.0],
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]
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def test_knapsack_model() -> None:
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data = MultiKnapsackData(
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prices=np.array([344.0, 527.0, 658.0, 519.0, 460.0]),
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capacities=np.array([449.0, 377.0, 380.0]),
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weights=np.array(
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[
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[92.0, 473.0, 871.0, 264.0, 96.0],
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[67.0, 664.0, 21.0, 628.0, 129.0],
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[436.0, 209.0, 309.0, 481.0, 86.0],
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]
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),
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)
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model = build_multiknapsack_model_gurobipy(data)
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model.optimize()
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assert model.inner.objVal == -460.0
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