def test_stab():
graph = nx.cycle_graph(5)
weights = [1., 1., 1., 1., 1.]
instance = MaxWeightStableSetInstance(graph, weights)
solver = LearningSolver()
solver.solve(instance)
assert instance.model.OBJ() == 2.
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 _get_instances():
instances = [
KnapsackInstance(
weights=[23., 26., 20., 18.],
prices=[505., 352., 458., 220.],
capacity=67.,
),
] * 5
models = [inst.to_model() for inst in instances]
solver = LearningSolver()
for i in range(len(instances)):
solver.solve(instances[i], models[i])
return instances, models
def get_training_instances_and_models():
instances = [
KnapsackInstance(
weights=[23., 26., 20., 18.],
prices=[505., 352., 458., 220.],
capacity=67.,
),
KnapsackInstance(
weights=[25., 30., 22., 18.],
prices=[500., 365., 420., 150.],
capacity=70.,
),
]
models = [instance.to_model() for instance in instances]
solver = LearningSolver()
for i in range(len(instances)):
solver.solve(instances[i], models[i])
return instances, models
def _get_instances():
instances = [
KnapsackInstance(
weights=[1., 2., 3.],
prices=[10., 20., 30.],
capacity=2.5,
),
KnapsackInstance(
weights=[3., 4., 5.],
prices=[20., 30., 40.],
capacity=4.5,
),
]
models = [instance.to_model() for instance in instances]
solver = LearningSolver()
for (i, instance) in enumerate(instances):
solver.solve(instances[i], models[i])
return instances, models
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)
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