def test_regression_case(self):
config = create_configuration(filename='/regression-siso.json')
config.parallel_evaluation = False
genome = Genome(key=1)
genome.create_random_genome()
dataset = get_dataset(config.dataset,
train_percentage=config.train_percentage,
testing=True)
n_samples = 3
network = ComplexStochasticNetwork(genome=genome)
x, y_true, output_distribution = calculate_prediction_distribution(
network,
dataset=dataset,
problem_type=config.problem_type,
is_testing=True,
n_samples=n_samples,
use_sigmoid=False)
expected_output_distribution_shape = [
len(y_true), n_samples, config.n_output
]
self.assertEqual(list(output_distribution.shape),
expected_output_distribution_shape)
def test_write_and_read_geneme(self):
filename = self.path + '/genome_test.json'
genome = Genome(key=0)
genome.create_random_genome()
genome.save_genome(filename)
genome_read = Genome.create_from_file(filename)
self.assertEqual(len(genome.__dict__), len(genome_read.__dict__))
def initialize_population(self):
population = {}
for i in range(self.pop_size):
key = next(self.genome_indexer)
if self.config.initial_genome_filename is None:
genome = Genome(key=key)
genome.create_random_genome()
else:
filename = self.config.initial_genome_filename
genome = Genome.create_from_file(filename=filename, key=key)
population[key] = genome
self.ancestors[key] = tuple()
return population
def test_classification_case(self):
config = create_configuration(filename='/classification-miso.json')
genome = Genome(key=1)
genome.create_random_genome()
n_samples = 5
estimator = PredictionDistributionEstimatorGenome(genome=genome, config=config, testing=True,
n_samples=n_samples)\
.estimate() \
.enrich_with_dispersion_quantile() \
.calculate_metrics_by_dispersion_quantile()
results = estimator.results
self.assertTrue(isinstance(results, pd.DataFrame))
from neat.representation_mapping.genome_to_network.complex_stochastic_network import ComplexStochasticNetwork
from neat.utils import timeit
from config_files import create_configuration
config = create_configuration(filename='/mnist_binary.json')
LOGS_PATH = f'{os.getcwd()}/'
logger = get_neat_logger(path=LOGS_PATH)
# N_SAMPLES = 10
N_PROCESSES = 16
N_GENOMES = 100
genomes = []
for i in range(N_GENOMES):
genome = Genome(key=i)
genome.create_random_genome()
genomes.append(genome)
def evaluate_genome_parallel(x):
return evaluate_genome(*x)
def process_initialization(dataset_name, testing):
global dataset
dataset = get_dataset(dataset_name, testing=testing)
dataset.generate_data()
@timeit
def evaluate_genome(genome: Genome, loss, beta_type, problem_type,
batch_size=10000, n_samples=10, is_gpu=False):