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90 lines
2.5 KiB
90 lines
2.5 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2021, 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 numpy.linalg import norm
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from scipy.spatial.distance import pdist, squareform
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from scipy.stats import uniform, randint
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from miplearn.problems.tsp import TravelingSalesmanGenerator, TravelingSalesmanInstance
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from miplearn.solvers.learning import LearningSolver
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def test_generator() -> None:
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instances = TravelingSalesmanGenerator(
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x=uniform(loc=0.0, scale=1000.0),
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y=uniform(loc=0.0, scale=1000.0),
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n=randint(low=100, high=101),
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gamma=uniform(loc=0.95, scale=0.1),
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fix_cities=True,
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).generate(100)
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assert len(instances) == 100
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assert instances[0].n_cities == 100
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assert norm(instances[0].distances - instances[0].distances.T) < 1e-6
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d = [instance.distances[0, 1] for instance in instances]
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assert np.std(d) > 0
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def test_instance() -> None:
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n_cities = 4
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distances = np.array(
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[
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[0.0, 1.0, 2.0, 1.0],
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[1.0, 0.0, 1.0, 2.0],
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[2.0, 1.0, 0.0, 1.0],
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[1.0, 2.0, 1.0, 0.0],
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]
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)
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instance = TravelingSalesmanInstance(n_cities, distances)
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solver = LearningSolver()
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solver.solve(instance)
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assert len(instance.get_samples()) == 1
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sample = instance.get_samples()[0]
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assert sample.get_vector("mip_var_values") == [1.0, 0.0, 1.0, 1.0, 0.0, 1.0]
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assert sample.get_scalar("mip_lower_bound") == 4.0
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assert sample.get_scalar("mip_upper_bound") == 4.0
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def test_subtour() -> None:
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n_cities = 6
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cities = np.array(
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[
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[0.0, 0.0],
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[1.0, 0.0],
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[2.0, 0.0],
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[3.0, 0.0],
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[0.0, 1.0],
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[3.0, 1.0],
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]
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)
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distances = squareform(pdist(cities))
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instance = TravelingSalesmanInstance(n_cities, distances)
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solver = LearningSolver()
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solver.solve(instance)
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samples = instance.get_samples()
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assert len(samples) == 1
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sample = samples[0]
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lazy_enforced = sample.get_set("mip_constr_lazy_enforced")
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assert lazy_enforced is not None
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assert len(lazy_enforced) > 0
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assert sample.get_vector("mip_var_values") == [
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1.0,
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0.0,
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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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0.0,
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0.0,
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1.0,
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0.0,
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0.0,
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0.0,
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1.0,
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1.0,
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]
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solver.fit([instance])
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solver.solve(instance)
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