Reorganize directories

miplearn/problems/tests/test_tsp.py

@@ -1,70 +0,0 @@
-#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
-#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
-#  Released under the modified BSD license. See COPYING.md for more details.
-
-from miplearn import LearningSolver
-from miplearn.problems.tsp import TravelingSalesmanGenerator, TravelingSalesmanInstance
-import numpy as np
-from numpy.linalg import norm
-from scipy.spatial.distance import pdist, squareform
-from scipy.stats import uniform, randint
-
-
-def test_generator():
-    instances = TravelingSalesmanGenerator(x=uniform(loc=0.0, scale=1000.0),
-                                           y=uniform(loc=0.0, scale=1000.0),
-                                           n=randint(low=100, high=101),
-                                           gamma=uniform(loc=0.95, scale=0.1),
-                                           fix_cities=True).generate(100)
-    assert len(instances) == 100
-    assert instances[0].n_cities == 100
-    assert norm(instances[0].distances - instances[0].distances.T) < 1e-6
-    d = [instance.distances[0,1] for instance in instances]
-    assert np.std(d) > 0
-
-    
-def test_instance():
-    n_cities = 4
-    distances = np.array([
-        [0., 1., 2., 1.],
-        [1., 0., 1., 2.],
-        [2., 1., 0., 1.],
-        [1., 2., 1., 0.],
-    ])
-    instance = TravelingSalesmanInstance(n_cities, distances)
-    for solver_name in ['gurobi', 'cplex']:
-        solver = LearningSolver(solver=solver_name)
-        solver.solve(instance)
-        x = instance.solution["x"]
-        assert x[0,1] == 1.0
-        assert x[0,2] == 0.0
-        assert x[0,3] == 1.0
-        assert x[1,2] == 1.0
-        assert x[1,3] == 0.0
-        assert x[2,3] == 1.0
-        assert instance.lower_bound == 4.0
-        assert instance.upper_bound == 4.0
-
-    
-def test_subtour():
-    n_cities = 6
-    cities = np.array([
-        [0., 0.],
-        [1., 0.],
-        [2., 0.],
-        [3., 0.],
-        [0., 1.],
-        [3., 1.],
-    ])
-    distances = squareform(pdist(cities))
-    instance = TravelingSalesmanInstance(n_cities, distances)
-    for solver_name in ['gurobi', 'cplex']:
-        solver = LearningSolver(solver=solver_name)
-        solver.solve(instance)
-        x = instance.solution["x"]
-        assert x[0,1] == 1.0
-        assert x[0,4] == 1.0
-        assert x[1,2] == 1.0
-        assert x[2,3] == 1.0
-        assert x[3,5] == 1.0
-        assert x[4,5] == 1.0