Python neat.StatisticsReporter方法代码示例

# 需要导入模块: import neat [as 别名]
# 或者: from neat import StatisticsReporter [as 别名]
def test_parallel():
    """Test parallel run using ParallelEvaluator (subprocesses)."""
    # 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(VERBOSE))
    stats = neat.StatisticsReporter()
    p.add_reporter(stats)
    p.add_reporter(neat.Checkpointer(1, 5))

    # Run for up to 19 generations.
    pe = neat.ParallelEvaluator(1 + multiprocessing.cpu_count(), eval_dummy_genome_nn)
    p.run(pe.evaluate, 19)

    stats.save() 

# 需要导入模块: import neat [as 别名]
# 或者: from neat import StatisticsReporter [as 别名]
def test_threaded_evaluation():
    """Tests a neat evolution using neat.threaded.ThreadedEvaluator"""
    # 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(True))
    stats = neat.StatisticsReporter()
    p.add_reporter(stats)
    p.add_reporter(neat.Checkpointer(1, 5))

    # Run for up to 19 generations.
    pe = neat.ThreadedEvaluator(4, eval_dummy_genome_nn)
    p.run(pe.evaluate, 19)

    stats.save() 

# 需要导入模块: import neat [as 别名]
# 或者: from neat import StatisticsReporter [as 别名]
def test_run_nn_recurrent():
    """Basic test of nn.recurrent function."""
    # 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)
    config.feed_forward = False

    # 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(VERBOSE))
    stats = neat.StatisticsReporter()
    p.add_reporter(stats)
    p.add_reporter(neat.Checkpointer(1, 5))

    # Run for up to 19 generations.
    p.run(eval_dummy_genomes_nn_recurrent, 19)

    stats.save() 

# 需要导入模块: import neat [as 别名]
# 或者: from neat import StatisticsReporter [as 别名]
def _run(self, config_file, n):
        config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,
                             neat.DefaultSpeciesSet, neat.DefaultStagnation,
                             config_file)
        # p = neat.Population(config)
        p = neat.Checkpointer.restore_checkpoint(self.file_name)
        p.add_reporter(neat.StdOutReporter(True))
        p.add_reporter(neat.Checkpointer(5))
        stats = neat.StatisticsReporter()
        p.add_reporter(stats)
        print("loaded checkpoint...")
        winner = p.run(self._eval_genomes, n)
        win = p.best_genome
        pickle.dump(winner, open('winner.pkl', 'wb'))
        pickle.dump(win, open('real_winner.pkl', 'wb'))

        visualize.draw_net(config, winner, True)
        visualize.plot_stats(stats, ylog=False, view=True)
        visualize.plot_species(stats, view=True) 

# 需要导入模块: import neat [as 别名]
# 或者: from neat import StatisticsReporter [as 别名]
def _run(self, config_file, n):
        config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,
                             neat.DefaultSpeciesSet, neat.DefaultStagnation,
                             config_file)
        p = neat.Population(config)
        p.add_reporter(neat.StdOutReporter(True))
        p.add_reporter(neat.Checkpointer(5))
        stats = neat.StatisticsReporter()
        p.add_reporter(stats)
        print("loaded checkpoint...")
        winner = p.run(self._eval_genomes, n)
        win = p.best_genome
        pickle.dump(winner, open('winner.pkl', 'wb'))
        pickle.dump(win, open('real_winner.pkl', 'wb'))

        visualize.draw_net(config, winner, True)
        visualize.plot_stats(stats, ylog=False, view=True)
        visualize.plot_species(stats, view=True) 

# 需要导入模块: import neat [as 别名]
# 或者: from neat import StatisticsReporter [as 别名]
def main():
    local_dir = os.path.dirname(__file__)
    config = Config(neat.DefaultGenome, neat.DefaultReproduction,
                    neat.DefaultSpeciesSet, neat.DefaultStagnation,
                    os.path.join(local_dir, 'train_config.txt'))
    config.save_best = True
    config.checkpoint_time_interval = 3

    pop = population.Population(config)
    stats = neat.StatisticsReporter()
    pop.add_reporter(stats)
    pop.add_reporter(neat.StdOutReporter(True))
    pop.add_reporter(neat.StatisticsReporter())
    pop.add_reporter(neat.Checkpointer(2))
    winner = pop.run(eval_fitness, 100)
    with open('winner.pkl', 'wb') as f:
        pickle.dump(winner, f) 

# 需要导入模块: import neat [as 别名]
# 或者: from neat import StatisticsReporter [as 别名]
def run():
    config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,
                         neat.DefaultSpeciesSet, neat.DefaultStagnation, CONFIG)
    pop = neat.Population(config)

    # recode history
    stats = neat.StatisticsReporter()
    pop.add_reporter(stats)
    pop.add_reporter(neat.StdOutReporter(True))
    pop.add_reporter(neat.Checkpointer(5))

    pop.run(eval_genomes, 10)       # train 10 generations

    # visualize training
    visualize.plot_stats(stats, ylog=False, view=True)
    visualize.plot_species(stats, view=True) 

# 需要导入模块: import neat [as 别名]
# 或者: from neat import StatisticsReporter [as 别名]
def run():
    # 128x128 thumbnails, 1500x1500 rendered images, 1100x810 viewer, grayscale images, 4 worker processes.
    pb = PictureBreeder(128, 128, 1500, 1500, 1100, 810, 'gray', 4)

    # Determine path to configuration file.
    local_dir = os.path.dirname(__file__)
    config_path = os.path.join(local_dir, 'interactive_config')
    # Note that we provide the custom stagnation class to the Config constructor.
    config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,
                         neat.DefaultSpeciesSet, InteractiveStagnation,
                         config_path)

    # Make sure the network has the expected number of outputs.
    if pb.scheme == 'color':
        config.output_nodes = 3
    else:
        config.output_nodes = 1

    config.pop_size = pb.num_cols * pb.num_rows
    pop = neat.Population(config)

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

    while 1:
        pb.generation = pop.generation + 1
        pop.run(pb.eval_fitness, 1) 

# 需要导入模块: import neat [as 别名]
# 或者: from neat import StatisticsReporter [as 别名]
def run():
    # Load the config file, which is assumed to live in
    # the same directory as this script.
    local_dir = os.path.dirname(__file__)
    config_path = os.path.join(local_dir, 'config-feedforward')
    config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,
                         neat.DefaultSpeciesSet, neat.DefaultStagnation,
                         config_path)

    pop = neat.Population(config)
    stats = neat.StatisticsReporter()
    pop.add_reporter(stats)
    pop.add_reporter(neat.StdOutReporter(True))

    pe = neat.ParallelEvaluator(multiprocessing.cpu_count(), eval_genome)
    winner = pop.run(pe.evaluate)

    # Save the winner.
    with open('winner-feedforward', 'wb') as f:
        pickle.dump(winner, f)

    print(winner)

    visualize.plot_stats(stats, ylog=True, view=True, filename="feedforward-fitness.svg")
    visualize.plot_species(stats, view=True, filename="feedforward-speciation.svg")

    node_names = {-1: 'x', -2: 'dx', -3: 'theta', -4: 'dtheta', 0: 'control'}
    visualize.draw_net(config, winner, True, node_names=node_names)

    visualize.draw_net(config, winner, view=True, node_names=node_names,
                       filename="winner-feedforward.gv")
    visualize.draw_net(config, winner, view=True, node_names=node_names,
                       filename="winner-feedforward-enabled.gv", show_disabled=False)
    visualize.draw_net(config, winner, view=True, node_names=node_names,
                       filename="winner-feedforward-enabled-pruned.gv", show_disabled=False, prune_unused=True) 

# 需要导入模块: import neat [as 别名]
# 或者: from neat import StatisticsReporter [as 别名]
def run():
    # Load the config file, which is assumed to live in
    # the same directory as this script.
    local_dir = os.path.dirname(__file__)
    config_path = os.path.join(local_dir, 'config-ctrnn')
    config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,
                         neat.DefaultSpeciesSet, neat.DefaultStagnation,
                         config_path)

    pop = neat.Population(config)
    stats = neat.StatisticsReporter()
    pop.add_reporter(stats)
    pop.add_reporter(neat.StdOutReporter(True))

    pe = neat.ParallelEvaluator(multiprocessing.cpu_count(), eval_genome)
    winner = pop.run(pe.evaluate)

    # Save the winner.
    with open('winner-ctrnn', 'wb') as f:
        pickle.dump(winner, f)

    print(winner)

    visualize.plot_stats(stats, ylog=True, view=True, filename="ctrnn-fitness.svg")
    visualize.plot_species(stats, view=True, filename="ctrnn-speciation.svg")

    node_names = {-1: 'x', -2: 'dx', -3: 'theta', -4: 'dtheta', 0: 'control'}
    visualize.draw_net(config, winner, True, node_names=node_names)

    visualize.draw_net(config, winner, view=True, node_names=node_names,
                       filename="winner-ctrnn.gv")
    visualize.draw_net(config, winner, view=True, node_names=node_names,
                       filename="winner-ctrnn-enabled.gv", show_disabled=False)
    visualize.draw_net(config, winner, view=True, node_names=node_names,
                       filename="winner-ctrnn-enabled-pruned.gv", show_disabled=False, prune_unused=True) 

# 需要导入模块: import neat [as 别名]
# 或者: from neat import StatisticsReporter [as 别名]
def run(config_file):
    # Load configuration.
    config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,
                         neat.DefaultSpeciesSet, neat.DefaultStagnation,
                         config_file)

    # 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(True))
    stats = neat.StatisticsReporter()
    p.add_reporter(stats)
    p.add_reporter(neat.Checkpointer(5))

    # Run for up to 300 generations.
    winner = p.run(eval_genomes, 300)

    # Display the winning genome.
    print('\nBest genome:\n{!s}'.format(winner))

    # Show output of the most fit genome against training data.
    print('\nOutput:')
    winner_net = neat.nn.FeedForwardNetwork.create(winner, config)
    for xi, xo in zip(xor_inputs, xor_outputs):
        output = winner_net.activate(xi)
        print("input {!r}, expected output {!r}, got {!r}".format(xi, xo, output))

    node_names = {-1:'A', -2: 'B', 0:'A XOR B'}
    visualize.draw_net(config, winner, True, node_names=node_names)
    visualize.plot_stats(stats, ylog=False, view=True)
    visualize.plot_species(stats, view=True)

    p = neat.Checkpointer.restore_checkpoint('neat-checkpoint-4')
    p.run(eval_genomes, 10) 

# 需要导入模块: import neat [as 别名]
# 或者: from neat import StatisticsReporter [as 别名]
def run(config_file):
    # Load configuration.
    config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,
                         neat.DefaultSpeciesSet, neat.DefaultStagnation,
                         config_file)

    # 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(True))
    stats = neat.StatisticsReporter()
    p.add_reporter(stats)

    # Run for up to 300 generations.
    pe = neat.ParallelEvaluator(multiprocessing.cpu_count(), eval_genome)
    winner = p.run(pe.evaluate, 300)

    # Display the winning genome.
    print('\nBest genome:\n{!s}'.format(winner))

    # Show output of the most fit genome against training data.
    print('\nOutput:')
    winner_net = neat.nn.FeedForwardNetwork.create(winner, config)
    for xi, xo in zip(xor_inputs, xor_outputs):
        output = winner_net.activate(xi)
        print("input {!r}, expected output {!r}, got {!r}".format(xi, xo, output))

    node_names = {-1:'A', -2: 'B', 0:'A XOR B'}
    visualize.draw_net(config, winner, True, node_names = node_names)
    visualize.plot_stats(stats, ylog=False, view=True)
    visualize.plot_species(stats, view=True) 

# 需要导入模块: import neat [as 别名]
# 或者: from neat import StatisticsReporter [as 别名]
def run_secondary(addr, authkey, num_workers=1):
    # Determine path to configuration file. This path manipulation is
    # here so that the script will run successfully regardless of the
    # current working directory.
    local_dir = os.path.dirname(__file__)
    config_path = os.path.join(local_dir, 'test_configuration2')

    # Load 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(True))
    stats = neat.StatisticsReporter()
    p.add_reporter(stats)

    # Run for the specified number of generations.
    de = neat.DistributedEvaluator(
        addr,
        authkey=authkey,
        eval_function=eval_genome_distributed,
        mode=MODE_SECONDARY,
        num_workers=num_workers,
    )
    try:
        de.start(secondary_wait=3, exit_on_stop=True, reconnect=True)
    except SystemExit:
        pass
    else:
        raise Exception("DistributedEvaluator in secondary mode did not try to exit!") 

# 需要导入模块: import neat [as 别名]
# 或者: from neat import StatisticsReporter [as 别名]
def test_serial():
    """Test basic (dummy fitness function) non-parallel run."""
    # 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(True))
    stats = neat.StatisticsReporter()
    p.add_reporter(stats)
    p.add_reporter(neat.Checkpointer(1, 5))

    # Run for up to 19 generations.
    p.run(eval_dummy_genomes_nn, 19)

    stats.save()
    # stats.save_genome_fitness(with_cross_validation=True)

    assert len(stats.get_fitness_stdev())
    stats.best_unique_genomes(5)
    stats.best_genomes(5)
    stats.best_genome()

    p.remove_reporter(stats) 

# 需要导入模块: import neat [as 别名]
# 或者: from neat import StatisticsReporter [as 别名]
def test_serial_random():
    """Test basic (dummy fitness function) non-parallel run w/random activation, aggregation init."""
    # Load configuration.
    local_dir = os.path.dirname(__file__)
    config_path = os.path.join(local_dir, 'test_configuration2')
    config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,
                         neat.DefaultSpeciesSet, neat.DefaultStagnation,
                         config_path)

    if VERBOSE:
        print("config.genome_config.__dict__: {!r}".format(
            config.genome_config.__dict__))

    # 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(VERBOSE))
    stats = neat.StatisticsReporter()
    p.add_reporter(stats)
    p.add_reporter(neat.Checkpointer(15, 1))

    # Run for up to 45 generations.
    p.run(eval_dummy_genomes_nn, 45)

    stats.save()
    # stats.save_genome_fitness(with_cross_validation=True)

    stats.get_fitness_stdev()
    stats.best_unique_genomes(5)
    stats.best_genomes(5)
    stats.best_genome()

    p.remove_reporter(stats)