def _finetune_best(self):
best_genome = None
best_fitness = -100000
for specie_key, specie in self.species.items():
best_genome_at_specie = specie.get_fittest()
if best_genome_at_specie.fitness > best_fitness:
best_genome = best_genome_at_specie
best_fitness = best_genome_at_specie.fitness
best_genome_finetuned = self._finetune_genome(best_genome)
self.config.parallel_evaluation = False
evaluation_engine = EvaluationStochasticEngine()
self.species_best_genome = evaluation_engine.evaluate(
population={1: best_genome_finetuned})
def test_happy_path_miso(self):
# Multiple-Input Single-Output
self.config = create_configuration(
filename='/classification-miso.json')
self.config.parallel_evaluation = False
self.config.n_processes = 1
genome = generate_genome_with_hidden_units(
n_input=self.config.n_input, n_output=self.config.n_output)
population = {1: genome}
evaluation_engine = EvaluationStochasticEngine()
population = evaluation_engine.evaluate(population=population)
self.assertEqual(type(population.get(1).fitness), float)
class EvolutionEngine:
def __init__(self,
report: EvolutionReport,
notifier: Notifier,
is_cuda: bool = False):
self.report = report
self.notifier = notifier
self.is_cuda = is_cuda
self.population_engine = PopulationEngine(
stagnation_engine=Stagnation())
# self.speciation_engine = SpeciationEngine()
self.speciation_engine = FixSpeciationEngine()
self.evaluation_engine = EvaluationStochasticEngine()
self.evolution_configuration = get_configuration()
self.n_generations = self.evolution_configuration.n_generations
self.population = None
self.start_time = time.perf_counter()
@timeit
def run(self):
logger.info('Started evolutionary process')
end_condition = 'normal'
# try:
# initialize population
self.population = self.population_engine.initialize_population()
self.speciation_engine.speciate(self.population, generation=0)
self.population = self.evaluation_engine.evaluate(
population=self.population)
# report
self.report.report_new_generation(
generation=0,
population=self.population,
species=self.speciation_engine.species)
for generation in range(1, self.n_generations + 1):
self._run_generation(generation)
elapsed = time.perf_counter() - self.start_time
if elapsed > TIMEOUT_SECONDS:
end_condition = 'timeout'
break
if self.evolution_configuration.is_fine_tuning:
fine_tuner = FineTuner(species=self.speciation_engine.species,
config=self.evolution_configuration,
is_cuda=self.is_cuda,
only_best=False)
fine_tuner.run()
best_genomes = fine_tuner.species_best_genome
# best_genomes = self.evaluation_engine.evaluate(population=best_genomes)
self.report.report_fine_tuning(best_genomes)
self.evaluation_engine.close()
self.report.generate_final_report(end_condition=end_condition)\
.persist_report()
self.report.persist_logs()
# self.notifier.send(str(self.report.get_best_individual()))
self._send_final_message()
logger.info('Finished evolutionary process')
@timeit
def _run_generation(self, generation):
# create new generation's population
self.population = self.population_engine.reproduce(
species=self.speciation_engine.species,
pop_size=self.population_engine.pop_size,
generation=generation)
# evaluate
self.population = self.evaluation_engine.evaluate(
population=self.population)
# create new species based on new population
self.speciation_engine.speciate(self.population, generation=generation)
# generation report
self.report.report_new_generation(
generation=generation,
population=self.population,
species=self.speciation_engine.species)
# schedule parameters
if generation == self.evolution_configuration.generation_fix_architecture:
logger.info('Fixing Architecture')
self.evolution_configuration.fix_architecture = True
def _send_final_message(self):
self.notifier.send(
f'-----------------------------------------------------------------\n'
f'Dataset: {self.report.dataset}. \nCorrelation id: {self.report.correlation_id}.\n'
f'Execution id: {self.report.report.execution_id}')
self.notifier.send(
f'Best Fitness: {self.report.best_individual.fitness}')
if self.report.generic_text is not None:
self.notifier.send(str(self.report.generic_text))
self.notifier.send(str(self.report.metrics_best))