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101 lines
2.7 KiB
101 lines
2.7 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 json
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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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from miplearn.solvers.tests import assert_equals
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def test_generator() -> None:
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data = 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(data) == 100
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assert data[0].n_cities == 100
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assert norm(data[0].distances - data[0].distances.T) < 1e-6
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d = [d.distances[0, 1] for d in data]
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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_equals(sample.get_array("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_encoded = sample.get_scalar("mip_constr_lazy")
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assert lazy_encoded is not None
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lazy = json.loads(lazy_encoded)
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assert lazy == {
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"st[0,1,4]": [0, 1, 4],
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"st[2,3,5]": [2, 3, 5],
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}
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assert_equals(
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sample.get_array("mip_var_values"),
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[
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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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)
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solver.fit([instance])
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solver.solve(instance)
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