Require a callable as the internal solver

miplearn/problems/tests/test_tsp.py

@@ -36,18 +36,17 @@ def test_instance():
         ]
     )
     instance = TravelingSalesmanInstance(n_cities, distances)
-    for solver_name in ["gurobi"]:
-        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
+    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():
@@ -64,17 +63,16 @@ def test_subtour():
     )
     distances = squareform(pdist(cities))
     instance = TravelingSalesmanInstance(n_cities, distances)
-    for solver_name in ["gurobi"]:
-        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)
+    solver = LearningSolver()
+    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)