def test_if_has_all_terminals(self):
filename = path.join('datasets', 'ORLibrary', 'steinb15.txt')
stpg = ReaderORLibrary().parser(filename)
terminals = stpg.terminals
crossover = CrossoverPrimRST(stpg)
parent_a = gen_random_walk(stpg)
parent_b = gen_random_walk(stpg)
offspring = crossover(parent_a, parent_b)
vertices = set([v for v in offspring.vertices])
self.assertTrue(terminals.issubset(vertices))
def test_terminals_are_in(self):
stpg = self.stpg
terminals = set(stpg.terminals)
individual = gen_random_walk(stpg)
vertices = set([v for v in individual.vertices])
self.assertTrue(terminals.issubset(vertices))
def simulation_random_walk(params):
STPG = read_problem("datasets", "ORLibrary", params["dataset"])
crossover = CrossoverRandomWalkRST(STPG)
evaluation = EvaluateEdgeSet(STPG)
mutate = MutationReplaceByRandomEdge(STPG)
output_data_dir = os.path.join("data", "test", "edgeset", STPG.name)
tracker = DataTracker(params['runtrial'], target=output_data_dir)
population = (GPopulation(
chromosomes=[
gen_random_walk(STPG) for _ in range(params["population_size"])
],
eval_function=evaluation,
maximize=True).evaluate().normalize(
norm_function=normalize).callback(update_best))
evol = (Evolution().evaluate().normalize(
norm_function=normalize).callback(update_best).callback(
tracker.log_evaluation).select(selection_func=roullete).crossover(
combiner=crossover).mutate(
mutate_function=mutate,
probability=params['tx_mutation']).callback(
update_generation).callback(display, every=100))
with Stagnation(interval=params["stagnation_interval"]), \
BestKnownReached(global_optimum=params['global_optimum']):
result = population.evolve(evol, n=params["n_iterations"])
tracker.log_simulation(params, STPG, result)
tracker.report()
print(result.stoppedby)
def test_if_return_an_edgeset(self):
filename = path.join('datasets', 'ORLibrary', 'steinb15.txt')
stpg = ReaderORLibrary().parser(filename)
crossover = CrossoverPrimRST(stpg)
parent_a = gen_random_walk(stpg)
parent_b = gen_random_walk(stpg)
self.assertIsInstance(parent_a, EdgeSet)
self.assertIsInstance(parent_b, EdgeSet)
offspring = crossover(parent_a, parent_b)
self.assertIsInstance(offspring, EdgeSet)
self.assertFalse(offspring is parent_a)
self.assertFalse(offspring is parent_b)
self.assertNotEqual(offspring, parent_a)
self.assertNotEqual(offspring, parent_b)
def test_if_is_it_a_tree(self):
filename = path.join('datasets', 'ORLibrary', 'steinb15.txt')
stpg = ReaderORLibrary().parser(filename)
crossover = CrossoverKruskalRST(stpg)
parent_a = gen_random_walk(stpg)
parent_b = gen_random_walk(stpg)
offspring = crossover(parent_a, parent_b)
tree = UGraph()
for edge in offspring:
u, v = edge[0], edge[1]
tree.add_edge(u, v)
_, response = is_steiner_tree(tree, stpg)
self.assertTrue(response['all_terminals_in'])
self.assertFalse(response['has_cycle'])
self.assertTrue(response['all_edges_are_reliable'])
self.assertTrue(response['graph_is_connected'])
def test_is_steiner_tree(self):
stpg = self.stpg
chromosome = gen_random_walk(stpg)
tree = UGraph()
for edge in chromosome:
v, u = edge
tree.add_edge(v, u)
_, response = is_steiner_tree(tree, stpg)
self.assertTrue(response['all_terminals_in'])
self.assertFalse(response['has_cycle'])
self.assertTrue(response['all_edges_are_reliable'])
self.assertTrue(response['graph_is_connected'])
def test_returning_edge_set(self):
stpg = self.stpg
chromosome = gen_random_walk(stpg)
self.assertIsInstance(chromosome, EdgeSet)