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74 lines
2.5 KiB
74 lines
2.5 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, 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 miplearn import LearningSolver
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from miplearn.problems.tsp import TravelingSalesmanGenerator, TravelingSalesmanInstance
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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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def test_generator():
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instances = TravelingSalesmanGenerator(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).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():
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n_cities = 4
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distances = np.array([
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[0., 1., 2., 1.],
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[1., 0., 1., 2.],
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[2., 1., 0., 1.],
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[1., 2., 1., 0.],
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])
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instance = TravelingSalesmanInstance(n_cities, distances)
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for solver_name in ['gurobi']:
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solver = LearningSolver(solver=solver_name)
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solver.solve(instance)
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x = instance.solution["x"]
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assert x[0,1] == 1.0
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assert x[0,2] == 0.0
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assert x[0,3] == 1.0
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assert x[1,2] == 1.0
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assert x[1,3] == 0.0
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assert x[2,3] == 1.0
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assert instance.lower_bound == 4.0
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assert instance.upper_bound == 4.0
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def test_subtour():
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n_cities = 6
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cities = np.array([
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[0., 0.],
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[1., 0.],
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[2., 0.],
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[3., 0.],
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[0., 1.],
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[3., 1.],
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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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for solver_name in ['gurobi']:
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solver = LearningSolver(solver=solver_name)
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solver.solve(instance)
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assert hasattr(instance, "found_violated_lazy_constraints")
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assert hasattr(instance, "found_violated_user_cuts")
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x = instance.solution["x"]
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assert x[0,1] == 1.0
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assert x[0,4] == 1.0
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assert x[1,2] == 1.0
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assert x[2,3] == 1.0
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assert x[3,5] == 1.0
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assert x[4,5] == 1.0
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solver.fit([instance])
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solver.solve(instance) |