def add_cluster_with_representative(self, genome_id: ObjectId):
cluster = Cluster()
cluster._id = ObjectId("00000000000000000000000"+str(self.next_id))
cluster.representative = genome_id
cluster.fitness = int(str(genome_id))
self.clusters.append(cluster)
self.next_id += 1
def decode_Cluster(document: dict) -> Cluster:
try:
if document['_type'] == 'Cluster':
document.pop('_type')
cluster = Cluster()
cluster.__dict__ = document
return cluster
except KeyError:
return None
def add_cluster_with_representative(
self,
genome_id: ObjectId
) -> ObjectId:
cluster = Cluster()
cluster.representative = genome_id
return self._database_connector.insert_one(
"clusters",
Transformator.encode_Cluster(cluster)
)
def test_selectClusterForCombination(self):
offspring = [10, 10, 10, 10]
cluster = []
counter = 0
for i in offspring:
c = Cluster()
c.offspring = i
c.fitness = float(1/(counter+1))
cluster.append(c)
counter += 1
self.mock_cluster_repository.get_current_clusters = MagicMock(return_value=cluster)
c = Cluster()
d = (c, c)
test_comb = self.genome_selector.select_clusters_for_combination()
self.assertEqual(type(d), type(self.genome_selector.select_clusters_for_combination()))
self.assertFalse(
test_comb[0].__eq__(test_comb[1])
)
def test_selectGenomesForDiscarding(self):
"""
checks if 20% of worst cluster or genomes were selected
:return:
"""
cluster = []
genome = []
for i in range(0, 4):
c = Cluster()
c.offspring = i
cluster.append(c)
for i in range(0, 10):
g = StorageGenome()
g.fitness = float(1/(i+1))
genome.append(g)
self.mock_cluster_repository.get_current_clusters = MagicMock(return_value=cluster)
self.mock_genome_repository.get_genomes_in_cluster = MagicMock(return_value=genome)
self.assertEqual(8, len(self.genome_selector.select_genomes_for_discarding()))
def test_selectGenomesForBreeding_and_selectGenomesForMutation(self):
offspring = [10, 10, 10, 10]
cluster = []
genome = []
for i in offspring:
c = Cluster()
c.offspring = i
cluster.append(c)
for i in range(0, 8):
g = StorageGenome()
g.fitness = float(1/(i+1))
genome.append(g)
g = StorageGenome()
self.mock_cluster_repository.get_current_clusters = MagicMock(return_value=cluster)
self.mock_genome_repository.get_genomes_in_cluster = MagicMock(return_value=genome)
self.assertEqual(8, len(self.genome_selector.select_genomes_for_breeding(0.2)))
self.assertEqual(type(g), type(self.genome_selector.select_genomes_for_breeding(0.2)[0][0]))
self.assertEqual(type(g), type(self.genome_selector.select_genomes_for_mutation(0.2)[0]))
self.assertEqual(8, len(self.genome_selector.select_genomes_for_mutation(0.2)))
def test_selectClusterCombination(self):
offspring = [10, 10]
cluster = []
genome = []
for i in offspring:
c = Cluster()
c.offspring = i
cluster.append(c)
for i in range(0, 8):
g = StorageGenome()
g.fitness = float(1/(i+1))
genome.append(g)
self.mock_genome_repository.get_genomes_in_cluster = MagicMock(return_value=genome)
test_comb = self.genome_selector.select_cluster_combinations(cluster[0], cluster[1], 2)
g = StorageGenome()
g_type = (g, g)
g_list = [g_type]
self.assertEqual(2, len(test_comb))
self.assertEqual(type(g_list), type(test_comb))
self.assertEqual(2, len(test_comb[0]))
self.assertEqual(type(g_type), type(test_comb[0]))
def test_getClusterAreaSortedByFitness(self):
unsorted = [0.0, 1.0, 2.4, 0.4, 0.9, 0.12, 4.32, 3.2, 5.1, 0.55]
chosen = [0.4, 0.55, 0.9, 1.0, 2.4, 3.2]
discard = [0.0, 0.12]
cluster = []
cluster_chosen = []
cluster_discard = []
for i in unsorted:
c = Cluster()
c.fitness = i
cluster.append(c)
if i in chosen:
cluster_chosen.append(c)
if i in discard:
cluster_discard.append(c)
self.mock_cluster_repository.get_current_clusters = MagicMock(return_value=cluster)
cluster_chosen.sort(key=lambda x: x.fitness)
self.assertListEqual(cluster_chosen, self.genome_selector.get_cluster_area_sorted_by_fitness(0.2, 0.8))
self.assertListEqual(cluster_discard, self.genome_selector.select_clusters_for_discarding())