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63 lines
1.4 KiB
63 lines
1.4 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.binpack import (
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build_binpack_model_gurobipy,
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BinPackData,
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BinPackGenerator,
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)
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def test_binpack_generator() -> None:
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np.random.seed(42)
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gen = BinPackGenerator(
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n=randint(low=10, high=11),
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sizes=uniform(loc=0, scale=10),
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capacity=uniform(loc=100, scale=0),
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sizes_jitter=uniform(loc=0.9, scale=0.2),
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capacity_jitter=uniform(loc=0.9, scale=0.2),
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fix_items=True,
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)
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data = gen.generate(2)
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assert data[0].sizes.tolist() == [
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3.39,
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10.4,
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7.81,
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5.64,
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1.46,
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1.46,
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0.56,
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8.7,
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5.93,
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6.79,
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]
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assert data[0].capacity == 102.24
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assert data[1].sizes.tolist() == [
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3.48,
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9.11,
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7.12,
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5.93,
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1.65,
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1.47,
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0.58,
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8.82,
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5.47,
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7.23,
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]
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assert data[1].capacity == 93.41
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def test_binpack() -> None:
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model = build_binpack_model_gurobipy(
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BinPackData(
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sizes=np.array([4, 8, 1, 4, 2, 1]),
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capacity=10,
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)
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)
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model.optimize()
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assert model.inner.objVal == 2.0
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