def test_crossover(self):
genome1 = Genome(self.genome_params)
genome2 = Genome(self.genome_params)
genome1.connect()
genome2.connect()
genome1.fitness = 5
genome2.fitness = 20
genome2.mutate()
assert len(genome2.nodes) == 4
assert len(genome2.connection_genes) == 4
assert genome2.connection_genes[3].innovation_number == 4
disabled_gene = None
for idx, cg in enumerate(genome2.connection_genes):
if not cg.enabled:
disabled_gene = idx
break
child = genome2.crossover(genome1, genome2)
# child inherited fittest parent nodes
assert len(child.nodes) == len(genome2.nodes)
# should be of same length despite some being disjoint genes
assert len(child.connection_genes) == len(genome2.connection_genes)
# check child got disabled gene transferred
assert not child.connection_genes[disabled_gene].enabled
pool = Pool(processes=N_PROCESSES, initializer=process_initialization, initargs=(config.dataset, True))
for genome in genomes:
logger.debug(f'Genome {genome.key}: {genome.get_graph()}')
# x = torch.zeros(2, 784).float()
# y = torch.zeros(2).long()
# x=dataset.x.clone().detach()
# y=dataset.y.clone().detach()
loss = get_loss('classification')
beta_type = 'other'
problem_type = 'classification'
batch_size = 100000
n_samples = 20
is_gpu = False
x = (genome.copy(),
# x, y,
loss, beta_type, problem_type,
batch_size, n_samples, is_gpu)
tasks.append(x)
# TODO: fix logging when using multiprocessing. Easy fix is to disable
fitnesses = list(pool.imap(evaluate_genome_parallel, tasks, chunksize=max([len(genomes)//N_PROCESSES, 1])))
pool.close()
for i, genome in enumerate(genomes):
print(fitnesses[i])
genome.fitness = fitnesses[i]
print('finished')