Add types to remaining files; activate mypy's disallow_untyped_defs

tests/solvers/test_learning_solver.py

@@ -16,7 +16,7 @@ from . import _get_knapsack_instance, get_internal_solvers
 logger = logging.getLogger(__name__)
 
 
-def test_learning_solver():
+def test_learning_solver() -> None:
     for mode in ["exact", "heuristic"]:
         for internal_solver in get_internal_solvers():
             logger.info("Solver: %s" % internal_solver)
@@ -30,17 +30,21 @@ def test_learning_solver():
             assert hasattr(instance, "features")
 
             sample = instance.training_data[0]
+            assert sample.solution is not None
             assert sample.solution["x[0]"] == 1.0
             assert sample.solution["x[1]"] == 0.0
             assert sample.solution["x[2]"] == 1.0
             assert sample.solution["x[3]"] == 1.0
             assert sample.lower_bound == 1183.0
             assert sample.upper_bound == 1183.0
+            assert sample.lp_solution is not None
             assert round(sample.lp_solution["x[0]"], 3) == 1.000
             assert round(sample.lp_solution["x[1]"], 3) == 0.923
             assert round(sample.lp_solution["x[2]"], 3) == 1.000
             assert round(sample.lp_solution["x[3]"], 3) == 0.000
+            assert sample.lp_value is not None
             assert round(sample.lp_value, 3) == 1287.923
+            assert sample.mip_log is not None
             assert len(sample.mip_log) > 100
 
             solver.fit([instance])
@@ -51,7 +55,7 @@ def test_learning_solver():
                 dill.dump(solver, file)
 
 
-def test_solve_without_lp():
+def test_solve_without_lp() -> None:
     for internal_solver in get_internal_solvers():
         logger.info("Solver: %s" % internal_solver)
         instance = _get_knapsack_instance(internal_solver)
@@ -64,7 +68,7 @@ def test_solve_without_lp():
         solver.solve(instance)
 
 
-def test_parallel_solve():
+def test_parallel_solve() -> None:
     for internal_solver in get_internal_solvers():
         instances = [_get_knapsack_instance(internal_solver) for _ in range(10)]
         solver = LearningSolver(solver=internal_solver)
@@ -72,10 +76,11 @@ def test_parallel_solve():
         assert len(results) == 10
         for instance in instances:
             data = instance.training_data[0]
+            assert data.solution is not None
             assert len(data.solution.keys()) == 4
 
 
-def test_solve_fit_from_disk():
+def test_solve_fit_from_disk() -> None:
     for internal_solver in get_internal_solvers():
         # Create instances and pickle them
         instances = []
@@ -108,7 +113,7 @@ def test_solve_fit_from_disk():
             os.remove(instance.filename)
 
 
-def test_simulate_perfect():
+def test_simulate_perfect() -> None:
     internal_solver = GurobiSolver()
     instance = _get_knapsack_instance(internal_solver)
     with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as tmp:
@@ -121,7 +126,7 @@ def test_simulate_perfect():
         assert stats["Lower bound"] == stats["Objective: Predicted lower bound"]
 
 
-def test_gap():
+def test_gap() -> None:
     assert LearningSolver._compute_gap(ub=0.0, lb=0.0) == 0.0
     assert LearningSolver._compute_gap(ub=1.0, lb=0.5) == 0.5
     assert LearningSolver._compute_gap(ub=1.0, lb=1.0) == 0.0