Move tests to separate folder

tests/problems/test_tsp.py

@@ -0,0 +1,79 @@
+#  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.
+
+import numpy as np
+from numpy.linalg import norm
+from scipy.spatial.distance import pdist, squareform
+from scipy.stats import uniform, randint
+
+from miplearn.problems.tsp import TravelingSalesmanGenerator, TravelingSalesmanInstance
+from miplearn.solvers.learning import LearningSolver
+
+
+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(
+        [
+            [0.0, 1.0, 2.0, 1.0],
+            [1.0, 0.0, 1.0, 2.0],
+            [2.0, 1.0, 0.0, 1.0],
+            [1.0, 2.0, 1.0, 0.0],
+        ]
+    )
+    instance = TravelingSalesmanInstance(n_cities, distances)
+    solver = LearningSolver()
+    stats = solver.solve(instance)
+    x = instance.training_data[0]["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 stats["Lower bound"] == 4.0
+    assert stats["Upper bound"] == 4.0
+
+
+def test_subtour():
+    n_cities = 6
+    cities = np.array(
+        [
+            [0.0, 0.0],
+            [1.0, 0.0],
+            [2.0, 0.0],
+            [3.0, 0.0],
+            [0.0, 1.0],
+            [3.0, 1.0],
+        ]
+    )
+    distances = squareform(pdist(cities))
+    instance = TravelingSalesmanInstance(n_cities, distances)
+    solver = LearningSolver()
+    solver.solve(instance)
+    assert hasattr(instance, "found_violated_lazy_constraints")
+    assert hasattr(instance, "found_violated_user_cuts")
+    x = instance.training_data[0]["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
+    solver.fit([instance])
+    solver.solve(instance)