def _read_population(self, generation):
file_dir = self._get_configuration_directory()
filename = f'{file_dir}/generation_{generation}_population.json'
_wait_for_file_to_be_available(filename=filename, timeout=60)
genomes = read_json_file_to_dict(filename=filename)
population = {}
for genome_dict in genomes:
genome = Genome.create_from_julia_dict(genome_dict)
population[genome.key] = genome
return population
def main():
ALGORITHM_VERSION = 'bayes-neat'
DATASET = 'mnist_binary'
CORRELATION_ID = 'test'
# execution_id = 'f6d2d5e3-26a3-4069-9071-b74009323761' # 2 hours run
execution_id = 'bf516f54-c29b-4f88-949c-102ab67930b3' # 10 hours run (learning architecture)
# execution_id = '59cbe09c-4ee7-4e7e-9b17-26c866113cfe' # test-run
# execution_id = 'c5551a6c-177b-4c2c-8ecd-a75e79ae0ec2'
execution_id = '1f30c172-9056-4012-9651-0765527bd550' # fitness -0.2
execution_id = 'a91761a0-6201-4a1d-9293-5e713f305fbf' # fitness -0.86
execution_id = '991b275d-6282-4f7d-8e97-3908baf94726'
report_repository = ReportRepository.create(project='neuro-evolution',
logs_path=LOGS_PATH)
report = report_repository.get_report(algorithm_version=ALGORITHM_VERSION,
dataset=DATASET,
correlation_id=CORRELATION_ID,
execution_id=execution_id)
genome_dict = report.data['best_individual']
best_individual_fitness = report.data['best_individual_fitness']
print(f'Fitness of best individual: {best_individual_fitness}')
genome = Genome.create_from_julia_dict(genome_dict)
# config = get_configuration()
print(f'Execution id: {execution_id}')
loss = get_loss(problem_type=config.problem_type)
##### EVALUATE ######
print('Evaluating results')
evaluate_with_parallel(genome, loss, config)
dataset = get_dataset(config.dataset, testing=True)
dataset.generate_data()
# TODO: remove data-loader. If we want to sample the dataset in each generation, the we can create a
# middlelayer between evaluation and dataset
x, y_true, y_pred, loss_value = evaluate_genome(
genome=genome,
dataset=dataset,
loss=loss,
problem_type=config.problem_type,
beta_type=config.beta_type,
batch_size=config.batch_size,
n_samples=config.n_samples,
is_gpu=config.is_gpu,
return_all=True)
y_pred = torch.argmax(y_pred, dim=1)
from sklearn.metrics import confusion_matrix, accuracy_score
print(f'Loss: {loss_value}')
print('Confusion Matrix:')
print(confusion_matrix(y_true, y_pred))
print(f'Accuracy: {accuracy_score(y_true, y_pred) * 100} %')