def __init__(self, config, initial_population=None):
"""
:param config: Either a config.Config object or path to a configuration file.
:param initial_population: Either an initial set of Genome instances to be used
as the initial population, or None, in which case a randomized set of Genomes
will be created automatically based on the configuration parameters.
"""
# If config is not a Config object, assume it is a path to the config file.
if not isinstance(config, Config):
config = Config(config)
# Configure statistics and reporting as requested by the user.
self.reporters = ReporterSet()
if config.collect_statistics:
self.statistics = StatisticsReporter()
self.add_reporter(self.statistics)
else:
self.statistics = None
if config.report:
self.add_reporter(StdOutReporter())
self.config = config
self.reproduction = config.reproduction_type(config, self.reporters)
self.species = SpeciesSet(config)
self.generation = -1
self.total_evaluations = 0
# Create a population if one is not given, then partition into species.
if initial_population is None:
initial_population = self.reproduction.create_new(config.pop_size)
self.species.speciate(initial_population)
def __init__(self, config, initial_population=None):
"""
:param config: Either a config.Config object or path to a configuration file.
:param initial_population: Either an initial set of Genome instances to be used
as the initial population, or None, in which case a randomized set of Genomes
will be created automatically based on the configuration parameters.
"""
# If config is not a Config object, assume it is a path to the config file.
if not isinstance(config, Config):
config = Config(config)
# Configure statistics and reporting as requested by the user.
self.reporters = ReporterSet()
if config.collect_statistics:
self.statistics = StatisticsReporter()
self.add_reporter(self.statistics)
else:
self.statistics = None
if config.report:
self.add_reporter(StdOutReporter())
self.config = config
self.reproduction = config.reproduction_type(config, self.reporters)
self.species = SpeciesSet(config)
self.generation = -1
self.total_evaluations = 0
# Create a population if one is not given, then partition into species.
if initial_population is None:
initial_population = self.reproduction.create_new(config.pop_size)
self.species.speciate(initial_population)
class Population(object):
"""
This class implements the core NEAT algorithm. It maintains a list of Species instances,
each of which contains a collection of Genome instances.
"""
def __init__(self, config, initial_population=None):
"""
:param config: Either a config.Config object or path to a configuration file.
:param initial_population: Either an initial set of Genome instances to be used
as the initial population, or None, in which case a randomized set of Genomes
will be created automatically based on the configuration parameters.
"""
# If config is not a Config object, assume it is a path to the config file.
if not isinstance(config, Config):
config = Config(config)
# Configure statistics and reporting as requested by the user.
self.reporters = ReporterSet()
if config.collect_statistics:
self.statistics = StatisticsReporter()
self.add_reporter(self.statistics)
else:
self.statistics = None
if config.report:
self.add_reporter(StdOutReporter())
self.config = config
self.reproduction = config.reproduction_type(config, self.reporters)
self.species = SpeciesSet(config)
self.generation = -1
self.total_evaluations = 0
# Create a population if one is not given, then partition into species.
if initial_population is None:
initial_population = self.reproduction.create_new(config.pop_size)
self.species.speciate(initial_population)
def add_reporter(self, reporter):
self.reporters.add(reporter)
def remove_reporter(self, reporter):
self.reporters.remove(reporter)
def load_checkpoint(self, filename):
'''Resumes the simulation from a previous saved point.'''
self.reporters.loading_checkpoint(filename)
with gzip.open(filename) as f:
(self.species,
self.generation,
random_state) = pickle.load(f)
random.setstate(random_state)
def save_checkpoint(self, filename=None, checkpoint_type="user"):
""" Save the current simulation state. """
if filename is None:
filename = 'neat-checkpoint-{0}'.format(self.generation)
self.reporters.saving_checkpoint(checkpoint_type, filename)
with gzip.open(filename, 'w', compresslevel=5) as f:
data = (self.species,
self.generation,
random.getstate())
pickle.dump(data, f, protocol=pickle.HIGHEST_PROTOCOL)
def run(self, fitness_function, n):
"""
Runs NEAT's genetic algorithm for at most n generations.
The user-provided fitness_function should take one argument, a list of all genomes in the population,
and its return value is ignored. This function is free to maintain external state, perform evaluations
in parallel, and probably any other thing you want. The only requirement is that each individual's
fitness member must be set to a floating point value after this function returns.
It is assumed that fitness_function does not modify the list of genomes, or the genomes themselves, apart
from updating the fitness member.
"""
# Remember start time for saving timed checkpoints.
last_checkpoint = time.time()
for g in range(n):
self.generation += 1
self.reporters.start_generation(self.generation)
# Collect a list of all members from all species.
population = []
for s in self.species.species:
population.extend(s.members)
# Evaluate all individuals in the population using the user-provided function.
# TODO: Add an option to only evaluate each genome once, to reduce number of
# fitness evaluations in cases where the fitness is known to be the same if the
# genome doesn't change--in these cases, evaluating unmodified elites in each
# generation is a waste of time. The user can always take care of this in their
# fitness function in the time being if they wish.
fitness_function(population)
self.total_evaluations += len(population)
# Gather and report statistics.
best = max(population)
self.reporters.post_evaluate(population, self.species.species, best)
# Save the best genome from the current generation if requested.
if self.config.save_best:
with open('best_genome_' + str(self.generation), 'wb') as f:
pickle.dump(best, f)
# End if the fitness threshold is reached.
if best.fitness >= self.config.max_fitness_threshold:
self.reporters.found_solution(self.generation, best)
break
# Create the next generation from the current generation.
new_population = self.reproduction.reproduce(self.species, self.config.pop_size)
# Check for complete extinction
if not self.species.species:
self.reporters.complete_extinction()
# If requested by the user, create a completely new population,
# otherwise raise an exception.
if self.config.reset_on_extinction:
new_population = self.reproduction.create_new(self.config.pop_size)
else:
raise CompleteExtinctionException()
# Update species age.
for s in self.species.species:
s.age += 1
# Divide the new population into species.
self.species.speciate(new_population)
# Save checkpoints if necessary.
if self.config.checkpoint_time_interval is not None:
timed_checkpoint_due = last_checkpoint + 60 * self.config.checkpoint_time_interval
if time.time() >= timed_checkpoint_due:
self.save_checkpoint(checkpoint_type="timed")
last_checkpoint = time.time()
if self.config.checkpoint_gen_interval is not None \
and self.generation % self.config.checkpoint_gen_interval == 0:
self.save_checkpoint(checkpoint_type="generation")
self.reporters.end_generation()
class Population(object):
"""
This class implements the core NEAT algorithm. It maintains a list of Species instances,
each of which contains a collection of Genome instances.
"""
def __init__(self, config, initial_population=None):
"""
:param config: Either a config.Config object or path to a configuration file.
:param initial_population: Either an initial set of Genome instances to be used
as the initial population, or None, in which case a randomized set of Genomes
will be created automatically based on the configuration parameters.
"""
# If config is not a Config object, assume it is a path to the config file.
if not isinstance(config, Config):
config = Config(config)
# Configure statistics and reporting as requested by the user.
self.reporters = ReporterSet()
if config.collect_statistics:
self.statistics = StatisticsReporter()
self.add_reporter(self.statistics)
else:
self.statistics = None
if config.report:
self.add_reporter(StdOutReporter())
self.config = config
self.reproduction = config.reproduction_type(config, self.reporters)
self.species = SpeciesSet(config)
self.generation = -1
self.total_evaluations = 0
# Create a population if one is not given, then partition into species.
if initial_population is None:
initial_population = self.reproduction.create_new(config.pop_size)
self.species.speciate(initial_population)
def add_reporter(self, reporter):
self.reporters.add(reporter)
def remove_reporter(self, reporter):
self.reporters.remove(reporter)
def load_checkpoint(self, filename):
'''Resumes the simulation from a previous saved point.'''
self.reporters.loading_checkpoint(filename)
with gzip.open(filename) as f:
(self.species, self.generation, random_state) = pickle.load(f)
random.setstate(random_state)
def save_checkpoint(self, filename=None, checkpoint_type="user"):
""" Save the current simulation state. """
if filename is None:
filename = 'neat-checkpoint-{0}'.format(self.generation)
self.reporters.saving_checkpoint(checkpoint_type, filename)
with gzip.open(filename, 'w', compresslevel=5) as f:
data = (self.species, self.generation, random.getstate())
pickle.dump(data, f, protocol=pickle.HIGHEST_PROTOCOL)
def run(self, fitness_function, n):
"""
Runs NEAT's genetic algorithm for at most n generations.
The user-provided fitness_function should take one argument, a list of all genomes in the population,
and its return value is ignored. This function is free to maintain external state, perform evaluations
in parallel, and probably any other thing you want. The only requirement is that each individual's
fitness member must be set to a floating point value after this function returns.
It is assumed that fitness_function does not modify the list of genomes, or the genomes themselves, apart
from updating the fitness member.
"""
# Remember start time for saving timed checkpoints.
last_checkpoint = time.time()
for g in range(n):
self.generation += 1
self.reporters.start_generation(self.generation)
# Collect a list of all members from all species.
population = []
for s in self.species.species:
population.extend(s.members)
# Evaluate all individuals in the population using the user-provided function.
# TODO: Add an option to only evaluate each genome once, to reduce number of
# fitness evaluations in cases where the fitness is known to be the same if the
# genome doesn't change--in these cases, evaluating unmodified elites in each
# generation is a waste of time. The user can always take care of this in their
# fitness function in the time being if they wish.
fitness_function(population)
self.total_evaluations += len(population)
# Gather and report statistics.
best = max(population)
self.reporters.post_evaluate(population, self.species.species,
best)
# Save the best genome from the current generation if requested.
if self.config.save_best:
with open('best_genome_' + str(self.generation), 'wb') as f:
pickle.dump(best, f)
# End if the fitness threshold is reached.
if best.fitness >= self.config.max_fitness_threshold:
self.reporters.found_solution(self.generation, best)
break
# Create the next generation from the current generation.
new_population = self.reproduction.reproduce(
self.species, self.config.pop_size)
# Check for complete extinction
if not self.species.species:
self.reporters.complete_extinction()
# If requested by the user, create a completely new population,
# otherwise raise an exception.
if self.config.reset_on_extinction:
new_population = self.reproduction.create_new(
self.config.pop_size)
else:
raise CompleteExtinctionException()
# Update species age.
for s in self.species.species:
s.age += 1
# Divide the new population into species.
self.species.speciate(new_population)
# Save checkpoints if necessary.
if self.config.checkpoint_time_interval is not None:
timed_checkpoint_due = last_checkpoint + 60 * self.config.checkpoint_time_interval
if time.time() >= timed_checkpoint_due:
self.save_checkpoint(checkpoint_type="timed")
last_checkpoint = time.time()
if self.config.checkpoint_gen_interval is not None \
and self.generation % self.config.checkpoint_gen_interval == 0:
self.save_checkpoint(checkpoint_type="generation")
self.reporters.end_generation()