Implement TSP generator and LazyConstraintsComponent

miplearn/problems/tests/test_tsp.py

@@ -0,0 +1,68 @@
+#  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)
+    solver = LearningSolver()
+    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)
+    solver = LearningSolver()
+    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