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https://github.com/ANL-CEEESA/MIPLearn.git
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90 lines
3.0 KiB
Python
90 lines
3.0 KiB
Python
# 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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from tempfile import TemporaryDirectory
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import numpy as np
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from scipy.stats import randint, uniform
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from miplearn.h5 import H5File
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from miplearn.problems.maxcut import (
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MaxCutGenerator,
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build_maxcut_model_gurobipy,
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build_maxcut_model_pyomo,
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)
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from miplearn.solvers.abstract import AbstractModel
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def _set_seed() -> None:
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random.seed(42)
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np.random.seed(42)
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def test_maxcut_generator() -> 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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)
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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()) == [
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(0, 2),
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(0, 3),
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(0, 4),
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(2, 3),
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(2, 4),
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(3, 4),
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]
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assert data[0].weights.tolist() == [-1, 1, -1, -1, -1, 1]
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def test_maxcut_model() -> None:
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_set_seed()
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data = MaxCutGenerator(
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n=randint(low=10, high=11),
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p=uniform(loc=0.5, scale=0.0),
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).generate(1)[0]
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for model in [
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build_maxcut_model_gurobipy(data),
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build_maxcut_model_pyomo(data),
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]:
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assert isinstance(model, AbstractModel)
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with TemporaryDirectory() as tempdir:
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with H5File(f"{tempdir}/data.h5", "w") as h5:
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model.extract_after_load(h5)
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obj_lin = h5.get_array("static_var_obj_coeffs")
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assert obj_lin is not None
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assert obj_lin.tolist() == [
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3.0,
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1.0,
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3.0,
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1.0,
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-1.0,
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0.0,
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-1.0,
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0.0,
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-1.0,
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0.0,
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]
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obj_quad = h5.get_array("static_var_obj_coeffs_quad")
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assert obj_quad is not None
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assert obj_quad.tolist() == [
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[0.0, 0.0, -1.0, 1.0, -1.0, 0.0, 0.0, 0.0, -1.0, -1.0],
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[0.0, 0.0, 1.0, -1.0, 0.0, -1.0, -1.0, 0.0, 0.0, 1.0],
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[0.0, 0.0, 0.0, 0.0, 0.0, -1.0, 0.0, 0.0, -1.0, -1.0],
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[0.0, 0.0, 0.0, 0.0, 0.0, -1.0, 1.0, -1.0, 0.0, 0.0],
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[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0],
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[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
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[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0],
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[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, -1.0],
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[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0],
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[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
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]
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model.optimize()
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model.extract_after_mip(h5)
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assert h5.get_scalar("mip_obj_value") == -4
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