def test_minimal():
local_dir = os.path.dirname(__file__)
config_path = os.path.join(local_dir, 'test_configuration')
pop = Population(config_path)
pe = parallel.ParallelEvaluator(4, eval_fitness)
pop.run(pe.evaluate, 400)
def test_minimal():
# sample fitness function
def eval_fitness(population):
for individual in population:
individual.fitness = 1.0
# creates the population
local_dir = os.path.dirname(__file__)
config = Config(os.path.join(local_dir, 'test_configuration'))
config.save_best = True
pop = Population(config)
# run the simulation for up to 20 generations
pop.run(eval_fitness, 20)
# Test save_checkpoint with defaults
pop.save_checkpoint()
# get statistics
best_unique = pop.statistics.best_unique_genomes(1)
best_unique = pop.statistics.best_unique_genomes(2)
avg_fitness = pop.statistics.get_average_fitness()
assert len(avg_fitness) == 1
assert all(f == 1 for f in avg_fitness)
# Change fitness threshold and do another run.
config.max_fitness_threshold = 1.1
pop = Population(config)
# runs the simulation for 20 generations
pop.run(eval_fitness, 20)
# get statistics
avg_fitness = pop.statistics.get_average_fitness()
assert len(avg_fitness) == 20
assert all(f == 1 for f in avg_fitness)
def test_minimal():
# sample fitness function
def eval_fitness(population):
for individual in population:
individual.fitness = 1.0
# creates the population
local_dir = os.path.dirname(__file__)
config = Config(os.path.join(local_dir, 'test_configuration'))
pop = Population(config)
# run the simulation for up to 20 generations
pop.run(eval_fitness, 20)
# get statistics
avg_fitness = pop.statistics.get_average_fitness()
assert len(avg_fitness) == 1
assert all(f == 1 for f in avg_fitness)
# Change fitness threshold and do another run.
config.max_fitness_threshold = 1.1
pop = Population(config)
# runs the simulation for 20 generations
pop.run(eval_fitness, 20)
# get statistics
avg_fitness = pop.statistics.get_average_fitness()
assert len(avg_fitness) == 20
assert all(f == 1 for f in avg_fitness)
def test_checkpoint():
# sample fitness function
def eval_fitness(population):
for individual in population:
individual.fitness = 0.99
# creates the population
local_dir = os.path.dirname(__file__)
config = Config(os.path.join(local_dir, 'test_configuration'))
pop = Population(config)
pop.run(eval_fitness, 20)
t = tempfile.NamedTemporaryFile(delete=False)
t.close()
pop.save_checkpoint(t.name)
pop2 = Population(config)
pop2.load_checkpoint(t.name)
def test_checkpoint():
# sample fitness function
def eval_fitness(population):
for individual in population:
individual.fitness = 0.99
# creates the population
local_dir = os.path.dirname(__file__)
config = Config(os.path.join(local_dir, 'test_configuration'))
pop = Population(config)
pop.run(eval_fitness, 20)
t = tempfile.NamedTemporaryFile(delete=False)
t.close()
pop.save_checkpoint(t.name)
pop2 = Population(config)
pop2.load_checkpoint(t.name)
def test_config_options():
# sample fitness function
def eval_fitness(population):
for individual in population:
individual.fitness = 1.0
local_dir = os.path.dirname(__file__)
config = Config(os.path.join(local_dir, 'test_configuration'))
for hn in (0, 1, 2):
config.hidden_nodes = hn
for fc in (0, 1):
config.fully_connected = fc
for act in activation_functions.functions.keys():
config.allowed_activation = [act]
for ff in (0, 1):
config.feedforward = ff
pop = Population(config)
pop.run(eval_fitness, 250)
def test_config_options():
# sample fitness function
def eval_fitness(population):
for individual in population:
individual.fitness = 1.0
local_dir = os.path.dirname(__file__)
config = Config(os.path.join(local_dir, 'test_configuration'))
for hn in (0, 1, 2):
config.hidden_nodes = hn
for fc in (0, 1):
config.fully_connected = fc
for act in activation_functions.functions.keys():
config.allowed_activation = [act]
for ff in (0, 1):
config.feedforward = ff
pop = Population(config)
pop.run(eval_fitness, 250)
time_out = config.fitness_threshold
proper_game = play(game=proper_tetris, brain=brain, time_out=time_out)
return proper_game.score
evaluator = ParallelEvaluator(
num_workers=CPU_COUNT,
eval_function=fitness_f,
)
if __name__ == '__main__':
print('CPU_COUNT:', CPU_COUNT)
p = Population(MAIN_CONFIG)
p.add_reporter(StdOutReporter(True))
stats = StatisticsReporter()
p.add_reporter(stats)
p.add_reporter(Checkpointer(50))
try:
winner = p.run(evaluator.evaluate, 1000)
except KeyboardInterrupt:
pass
stats.save()
print(stats.best_genome())
