def __init__(self,
model=None,
heuristic_method=inspyred.ec.GA,
objective_function=None,
seed=None,
termination=inspyred.ec.terminators.evaluation_termination,
plot=True,
progress=True,
*args,
**kwargs):
super(HeuristicOptimization, self).__init__(*args, **kwargs)
logger.debug("Seed: %s" % seed)
self.plot = plot
self.progress = progress
if seed is None:
seed = int(time.time())
self.seed = seed
self.observers = []
self.random = Random(seed=seed)
self.model = model
self.termination = termination
self._objective_function = objective_function
self._heuristic_method = None
self.heuristic_method = heuristic_method
self.heuristic_method.terminator = termination
self._generator = None
def setUp(self):
self.model = TEST_MODEL
self.representation = [r.id for r in self.model.reactions]
random = Random(SEED)
args = {"representation": self.representation}
self.solutions = BestSolutionArchive()
for _ in range(10000):
self.solutions.add(set_generator(random, args), random.random(), None, True, 100)
self.decoder = ReactionSetDecoder(self.representation, self.model)
def test_set_indel(self):
individual = OrderedSet([1, 3, 5, 9, 10])
representation = list(range(10))
args = {
"representation": representation,
"indel_rate": 1
}
new_individuals = set_indel(Random(SEED), [individual], args)
self.assertEqual(new_individuals[0], [5, 3, 9, 1])
def test_set_indel(self):
individual = [1, 3, 5, 9, 10]
representation = list(range(10))
args = {
"representation": representation,
"indel_rate": 1
}
new_individuals = set_indel(Random(SEED), [individual], args)
self.assertNotEqual(len(new_individuals[0]), len(individual))
self.assertEqual(new_individuals[0], [1, 3, 5, 6, 9, 10])
def test_do_set_n_point_crossover(self):
representation = ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N"]
int_representation = [representation.index(v) for v in representation]
mom = OrderedSet([representation.index(v) for v in ["A", "B", "E", "K", "L", "M"]])
dad = OrderedSet([representation.index(v) for v in ["A", "C", "I", "J", "K", "L"]])
points = [4]
children = _do_set_n_point_crossover(int_representation, mom, dad, points, Random(), len(mom))
bro = OrderedSet([0, 1, 8, 9, 10, 11])
sis = OrderedSet([0, 2, 4, 10, 11, 12])
self.assertEqual(children[0], bro)
self.assertEqual(children[1], sis)
def test_set_mutation(self):
individual = OrderedSet([1, 3, 5, 9, 10])
representation = list(range(10))
args = {
"representation": representation,
"mutation_rate": 1
}
new_individuals = set_mutation(Random(SEED), [individual], args)
self.assertEqual(len(new_individuals[0]), len(individual))
self.assertNotEqual(new_individuals[0], individual)
self.assertEqual(new_individuals[0], [0, 2, 4, 6, 7])
def test_multiple_chromossome_set_generator(self):
random = Random(SEED)
args = dict(keys=["test_key_1", "test_key_2"],
test_key_1_representation=["a1", "a2", "a3", "a4", "a5"],
test_key_2_representation=["b1", "b2", "b3", "b4", "b5", "b6", "b7"],
test_key_1_max_size=3,
test_key_2_max_size=5,
variable_size=False)
candidate = multiple_chromosome_set_generator(random, args)
self.assertEqual(len(candidate['test_key_1']), 3)
self.assertEqual(len(candidate['test_key_2']), 5)
def test_set_n_point_crossover(self):
mom = OrderedSet([1, 3, 5, 9, 10])
dad = OrderedSet([2, 3, 7, 8])
args = {
"crossover_rate": 1.0,
"num_crossover_points": 1,
"candidate_size": 10
}
children = set_n_point_crossover(Random(SEED), [mom, dad], args)
bro = OrderedSet([1, 3, 5, 8])
sis = OrderedSet([2, 3, 7, 9, 10])
self.assertEqual(bro, children[0])
self.assertEqual(sis, children[1])
def test_multiple_chromosome_set_mutation(self):
genome = MultipleChromosomeGenome(["A", "B"])
genome["A"] = [1, 2, 3, 4]
genome["B"] = [1, 5, 7, 10]
representation = list(range(10))
args = {
"A_representation": representation,
"B_representation": representation,
"A_mutation_rate": 1,
"B_mutation_rate": 1
}
new_individuals = multiple_chromosome_set_mutation(Random(SEED), [genome], args)
self.assertEqual(new_individuals[0]["A"], OrderedSet([0, 6, 7, 8]))
self.assertEqual(new_individuals[0]["B"], OrderedSet([0, 6, 8, 9]))
def test_set_generator(self):
random = Random(SEED)
representation = ["a", "b", "c", "d", "e", "f"]
max_size = 5
variable_size = False
expected = [[0, 1, 2, 4, 5],
[0, 2, 3, 4, 5],
[0, 1, 2, 3, 5],
[1, 2, 3, 4, 5],
[0, 2, 3, 4, 5]]
for i in range(len(expected)):
candidate = set_generator(random, dict(representation=representation,
max_size=max_size,
variable_size=variable_size))
self.assertEqual(candidate, expected[i])
def test_multiple_chromosome_set_indel(self):
genome = MultipleChromosomeGenome(["A", "B"])
genome["A"] = [1, 2, 3, 4]
genome["B"] = [1, 5, 7, 10]
representation = list(range(10))
args = {
"A_representation": representation,
"B_representation": representation,
"A_indel_rate": 1,
"B_indel_rate": 1
}
random = Random(SEED)
new_individuals = multiple_chromosome_set_indel(random, [genome for _ in range(5)], args)
self.assertEqual(new_individuals[0]["A"], OrderedSet([1, 2, 3, 4, 7]))
self.assertEqual(new_individuals[0]["B"], OrderedSet([1, 5, 10]))
self.assertEqual(new_individuals[1]["A"], OrderedSet([2, 3, 4]))
self.assertEqual(new_individuals[1]["B"], OrderedSet([1, 5, 7, 8, 10]))
self.assertEqual(new_individuals[2]["A"], OrderedSet([1, 2, 3, 4, 6]))
self.assertEqual(new_individuals[2]["B"], OrderedSet([1, 5, 7]))
self.assertEqual(new_individuals[3]["A"], OrderedSet([1, 2, 3, 4, 8]))
self.assertEqual(new_individuals[3]["B"], OrderedSet([0, 1, 5, 7, 10]))
self.assertEqual(new_individuals[4]["A"], OrderedSet([1, 2, 3, 4, 7]))
self.assertEqual(new_individuals[4]["B"], OrderedSet([1, 5, 7, 8, 10]))
def random(self):
if self._random is None:
self._random = Random()
return self._random
def setUp(self):
self.population = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
self.random = Random(SEED)
def setUp(self):
self.model = TEST_MODEL
self.args = {}
self.args.setdefault('representation', [r.id for r in self.model.reactions])
self.random = Random()