test_simple_run.py

import os
import neat


def eval_dummy_genome(genome, config):
    return 1.0


def eval_dummy_genomes(genomes, config):
    for genome_id, genome in genomes:
        genome.fitness = eval_dummy_genome(genome, config)


def test_serial():
    # Load configuration.
    local_dir = os.path.dirname(__file__)
    config_path = os.path.join(local_dir, 'test_configuration')
    config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,
                         neat.DefaultSpeciesSet, neat.DefaultStagnation,
                         config_path)

    # Create the population, which is the top-level object for a NEAT run.
    p = neat.Population(config)

    # Add a stdout reporter to show progress in the terminal.
    p.add_reporter(neat.StdOutReporter())
    stats = neat.StatisticsReporter()
    p.add_reporter(stats)

    # Run for up to 300 generations.
    p.run(eval_dummy_genomes, 300)

    stats.save()


def test_parallel():
    # Load configuration.
    local_dir = os.path.dirname(__file__)
    config_path = os.path.join(local_dir, 'test_configuration')
    config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,
                         neat.DefaultSpeciesSet, neat.DefaultStagnation,
                         config_path)

    # Create the population, which is the top-level object for a NEAT run.
    p = neat.Population(config)

    # Add a stdout reporter to show progress in the terminal.
    p.add_reporter(neat.StdOutReporter())
    stats = neat.StatisticsReporter()
    p.add_reporter(stats)

    # Run for up to 300 generations.
    pe = neat.ParallelEvaluator(4, eval_dummy_genome)
    p.run(pe.evaluate, 300)

    stats.save()


if __name__ == '__main__':
    test_serial()
    test_parallel()