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94 lines
2.8 KiB
94 lines
2.8 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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from tempfile import NamedTemporaryFile
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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.setcover import (
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SetCoverData,
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build_setcover_model_gurobipy,
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SetCoverGenerator,
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build_setcover_model_pyomo,
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)
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from miplearn.solvers.abstract import AbstractModel
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def test_set_cover_generator() -> None:
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np.random.seed(42)
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gen = SetCoverGenerator(
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n_elements=randint(low=3, high=4),
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n_sets=randint(low=5, high=6),
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costs=uniform(loc=0.0, scale=100.0),
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costs_jitter=uniform(loc=0.95, scale=0.10),
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density=uniform(loc=0.5, scale=0),
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K=uniform(loc=25, scale=0),
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fix_sets=False,
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)
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data = gen.generate(2)
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assert data[0].costs.round(1).tolist() == [136.8, 86.2, 25.7, 27.3, 102.5]
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assert data[0].incidence_matrix.tolist() == [
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[1, 0, 1, 0, 1],
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[1, 1, 0, 0, 0],
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[1, 0, 0, 1, 1],
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]
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assert data[1].costs.round(1).tolist() == [63.5, 76.6, 48.1, 74.1, 93.3]
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assert data[1].incidence_matrix.tolist() == [
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[1, 1, 0, 1, 1],
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[0, 1, 0, 1, 0],
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[0, 1, 1, 0, 0],
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]
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def test_set_cover_generator_with_fixed_sets() -> None:
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np.random.seed(42)
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gen = SetCoverGenerator(
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n_elements=randint(low=3, high=4),
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n_sets=randint(low=5, high=6),
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costs=uniform(loc=0.0, scale=100.0),
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costs_jitter=uniform(loc=0.95, scale=0.10),
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density=uniform(loc=0.5, scale=0.00),
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fix_sets=True,
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)
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data = gen.generate(3)
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assert data[0].costs.tolist() == [136.75, 86.17, 25.71, 27.31, 102.48]
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assert data[1].costs.tolist() == [135.38, 82.26, 26.92, 26.58, 98.28]
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assert data[2].costs.tolist() == [138.37, 85.15, 26.95, 27.22, 106.17]
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print(data[0].incidence_matrix)
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for i in range(3):
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assert data[i].incidence_matrix.tolist() == [
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[1, 0, 1, 0, 1],
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[1, 1, 0, 0, 0],
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[1, 0, 0, 1, 1],
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]
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def test_set_cover() -> None:
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data = SetCoverData(
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costs=np.array([5, 10, 12, 6, 8]),
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incidence_matrix=np.array(
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[
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[1, 0, 0, 1, 0],
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[1, 1, 0, 0, 0],
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[0, 0, 1, 1, 1],
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],
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),
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)
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for model in [
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build_setcover_model_pyomo(data),
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build_setcover_model_gurobipy(data),
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]:
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assert isinstance(model, AbstractModel)
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with NamedTemporaryFile() as tempfile:
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with H5File(tempfile.name) as h5:
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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") == 11.0
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