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58 lines
2.0 KiB
58 lines
2.0 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2025, 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 random
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import numpy as np
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from miplearn.problems.maxcut import MaxCutGenerator, build_maxcut_model_gurobipy
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from scipy.stats import randint, uniform
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def _set_seed():
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random.seed(42)
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np.random.seed(42)
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def test_maxcut_generator_not_fixed() -> None:
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_set_seed()
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gen = MaxCutGenerator(
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n=randint(low=5, high=6),
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p=uniform(loc=0.5, scale=0.0),
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fix_graph=False,
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)
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data = gen.generate(3)
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assert len(data) == 3
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assert list(data[0].graph.nodes()) == [0, 1, 2, 3, 4]
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assert list(data[0].graph.edges()) == [(0, 2), (0, 3), (0, 4), (2, 3), (2, 4), (3, 4)]
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assert data[0].weights.tolist() == [-1, 1, -1, -1, -1, 1]
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assert list(data[1].graph.nodes()) == [0, 1, 2, 3, 4]
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assert list(data[1].graph.edges()) == [(0, 1), (0, 3), (0, 4), (1, 4), (3, 4)]
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assert data[1].weights.tolist() == [-1, -1, -1, 1, -1]
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def test_maxcut_generator_fixed() -> None:
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random.seed(42)
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np.random.seed(42)
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gen = MaxCutGenerator(
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n=randint(low=5, high=6),
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p=uniform(loc=0.5, scale=0.0),
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fix_graph=True,
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)
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data = gen.generate(3)
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assert len(data) == 3
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assert list(data[0].graph.nodes()) == [0, 1, 2, 3, 4]
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assert list(data[0].graph.edges()) == [(0, 2), (0, 3), (0, 4), (2, 3), (2, 4), (3, 4)]
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assert data[0].weights.tolist() == [-1, 1, -1, -1, -1, 1]
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assert list(data[1].graph.nodes()) == [0, 1, 2, 3, 4]
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assert list(data[1].graph.edges()) == [(0, 2), (0, 3), (0, 4), (2, 3), (2, 4), (3, 4)]
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assert data[1].weights.tolist() == [-1, -1, -1, 1, -1, -1]
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def test_maxcut_model():
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_set_seed()
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data = MaxCutGenerator(
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n=randint(low=20, high=21),
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p=uniform(loc=0.5, scale=0.0),
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fix_graph=True,
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).generate(1)[0]
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model = build_maxcut_model_gurobipy(data)
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model.optimize()
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assert model.inner.ObjVal == -26
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