def test_solver():
instance = _get_instance()
for mode in ["exact", "heuristic"]:
for internal_solver in ["cplex", "gurobi"]:
solver = LearningSolver(time_limit=300,
gap_tolerance=1e-3,
threads=1,
solver=internal_solver,
mode=mode)
solver.solve(instance)
assert instance.solution["x"][0] == 1.0
assert instance.solution["x"][1] == 0.0
assert instance.solution["x"][2] == 1.0
assert instance.solution["x"][3] == 1.0
assert instance.lower_bound == 1183.0
assert instance.upper_bound == 1183.0
assert round(instance.lp_solution["x"][0], 3) == 1.000
assert round(instance.lp_solution["x"][1], 3) == 0.923
assert round(instance.lp_solution["x"][2], 3) == 1.000
assert round(instance.lp_solution["x"][3], 3) == 0.000
assert round(instance.lp_value, 3) == 1287.923
solver.fit([instance])
solver.solve(instance)
def test_learning_solver():
instance = _get_instance()
for mode in ["exact", "heuristic"]:
for internal_solver in ["cplex", "gurobi", GurobiSolver]:
solver = LearningSolver(time_limit=300,
gap_tolerance=1e-3,
threads=1,
solver=internal_solver,
mode=mode)
solver.solve(instance)
assert instance.solution["x"][0] == 1.0
assert instance.solution["x"][1] == 0.0
assert instance.solution["x"][2] == 1.0
assert instance.solution["x"][3] == 1.0
assert instance.lower_bound == 1183.0
assert instance.upper_bound == 1183.0
assert round(instance.lp_solution["x"][0], 3) == 1.000
assert round(instance.lp_solution["x"][1], 3) == 0.923
assert round(instance.lp_solution["x"][2], 3) == 1.000
assert round(instance.lp_solution["x"][3], 3) == 0.000
assert round(instance.lp_value, 3) == 1287.923
assert instance.found_violations == []
assert len(instance.solver_log) > 100
solver.fit([instance])
solver.solve(instance)
# Assert solver is picklable
with tempfile.TemporaryFile() as file:
pickle.dump(solver, file)
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)
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)