Implement iteration_cb for LearningSolver; reactivate TSP

miplearn/problems/tests/test_tsp.py

@@ -19,56 +19,56 @@ def test_generator():
     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]
+    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)
-#         assert hasattr(instance, "found_violated_lazy_constraints")
-#         assert hasattr(instance, "found_violated_user_cuts")
-#         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
-#         solver.fit([instance])
-#         solver.solve(instance)
+
+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)
+        assert hasattr(instance, "found_violated_lazy_constraints")
+        assert hasattr(instance, "found_violated_user_cuts")
+        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
+        solver.fit([instance])
+        solver.solve(instance)