Python neat.DefaultStagnation() Examples
The following are 30
code examples of neat.DefaultStagnation().
You can vote up the ones you like or vote down the ones you don't like,
and go to the original project or source file by following the links above each example.
You may also want to check out all available functions/classes of the module
neat
, or try the search function
.
Example #1
Source File: test_simple_run.py From neat-python with BSD 3-Clause "New" or "Revised" License | 11 votes |
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()
Example #2
Source File: test_config.py From neat-python with BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_bad_config_unknown_option(): """Check that an unknown option (at least in some sections) raises an exception.""" local_dir = os.path.dirname(__file__) config_path = os.path.join(local_dir, 'bad_configuration2') try: config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction, neat.DefaultSpeciesSet, neat.DefaultStagnation, config_path) except NameError: pass else: raise Exception("Did not get a NameError from an unknown configuration file option (in the 'DefaultSpeciesSet' section)") config3_path = os.path.join(local_dir, 'bad_configuration3') try: config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction, neat.DefaultSpeciesSet, neat.DefaultStagnation, config3_path) except NameError: pass else: raise Exception("Did not get a NameError from an unknown configuration file option (in the 'NEAT' section)")
Example #3
Source File: test_simple_run.py From neat-python with BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_run_ctrnn_bad(): """Make sure ctrnn gives error on bad input.""" # 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) try: p.run(eval_dummy_genomes_ctrnn_bad, 19) except RuntimeError: pass else: raise Exception("Did not get RuntimeError for bad input to ctrnn")
Example #4
Source File: test_simple_run.py From neat-python with BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_run_nn_recurrent_bad(): """Make sure nn.recurrent gives error on bad input.""" # 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) try: p.run(eval_dummy_genomes_nn_recurrent_bad, 19) except Exception: # again, may change to more specific in nn.recurrent pass else: raise Exception("Did not get Exception for bad input to nn.recurrent")
Example #5
Source File: test_simple_run.py From neat-python with BSD 3-Clause "New" or "Revised" License | 6 votes |
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()
Example #6
Source File: test_simple_run.py From neat-python with BSD 3-Clause "New" or "Revised" License | 6 votes |
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()
Example #7
Source File: test_simple_run.py From neat-python with BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_serial_bad_configA(): """Test if bad_configurationA causes a RuntimeError on trying to create the population.""" # Load configuration. local_dir = os.path.dirname(__file__) config_path = os.path.join(local_dir, 'bad_configurationA') config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction, neat.DefaultSpeciesSet, neat.DefaultStagnation, config_path) try: # Create the population, which is the top-level object for a NEAT run. p = neat.Population(config) except RuntimeError: pass else: raise Exception( "Should have had a RuntimeError with bad_configurationA")
Example #8
Source File: test_simple_run.py From neat-python with BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_serial_bad_config(): """Test if bad_configuration1 causes a LookupError or TypeError on trying to run.""" # Load configuration. local_dir = os.path.dirname(__file__) config_path = os.path.join(local_dir, 'bad_configuration1') 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) try: p.run(eval_dummy_genomes_nn, 19) except (LookupError, TypeError): pass else: raise Exception( "Should have had a LookupError/TypeError with bad_configuration1")
Example #9
Source File: test_simple_run.py From neat-python with BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_serial4_bad(): """Make sure no_fitness_termination and n=None give an error.""" # Load configuration. local_dir = os.path.dirname(__file__) config_path = os.path.join(local_dir, 'test_configuration4') 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) try: p.run(eval_dummy_genomes_nn, None) except RuntimeError: pass else: raise Exception( "Should have had a RuntimeError with n=None and no_fitness_termination")
Example #10
Source File: test_simple_run.py From neat-python with BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_run_iznn_bad(): """Make sure iznn gives error on bad input.""" # Load configuration. local_dir = os.path.dirname(__file__) config_path = os.path.join(local_dir, 'test_configuration_iznn') config = neat.Config(neat.iznn.IZGenome, 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) try: p.run(eval_dummy_genomes_iznn_bad, 19) except RuntimeError: pass else: raise Exception("Did not get RuntimeError for bad input to iznn")
Example #11
Source File: test_simple_run.py From neat-python with BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_serial_bad_input(): """Make sure get error for bad input.""" # 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) try: p.run(eval_dummy_genomes_nn_bad, 45) except Exception: # may change in nn.feed_forward code to more specific... pass else: raise Exception("Did not get Exception from bad input")
Example #12
Source File: test_config_save_restore.py From neat-python with BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_config_save_error(self): """Check if get error on saving bad partial configuration""" config_filename_initial = 'test_configuration2' config_filename_save = 'save_bad_configuration' # Get config path local_dir = os.path.dirname(__file__) config_path_initial = os.path.join(local_dir, config_filename_initial) config_path_save = os.path.join(local_dir, config_filename_save) # Load initial configuration from file config_initial = neat.Config(neat.DefaultGenome, neat.DefaultReproduction, neat.DefaultSpeciesSet, neat.DefaultStagnation, config_path_initial) config_initial.genome_config.connection_fraction = 1.5 try: config_initial.save(config_path_save) except RuntimeError: pass else: raise Exception("Did not get RuntimeError on attempt to save bad partial configuration")
Example #13
Source File: run_cartpole.py From Evolutionary-Algorithm with MIT License | 6 votes |
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)
Example #14
Source File: test_population.py From neat-python with BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_valid_fitness_criterion(self): for c in ('max', 'min', 'mean'): # 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.fitness_criterion = c p = neat.Population(config) def eval_genomes(genomes, config): for genome_id, genome in genomes: genome.fitness = 1.0 p.run(eval_genomes, 10)
Example #15
Source File: cont_train.py From super-mario-neat with MIT License | 6 votes |
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)
Example #16
Source File: train.py From super-mario-neat with MIT License | 6 votes |
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)
Example #17
Source File: trainer.py From go_dino with GNU General Public License v3.0 | 6 votes |
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)
Example #18
Source File: cppn.py From poet with Apache License 2.0 | 5 votes |
def __init__(self, cppn_config_path='config-cppn', genome_path=None): self.cppn_config_path = os.path.dirname(__file__) + '/' + cppn_config_path self.genome_path = genome_path self.hardcore = False self.cppn_config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction, neat.DefaultSpeciesSet, neat.DefaultStagnation, self.cppn_config_path) self.cppn_genome = None self.altitude_fn = lambda x: x if genome_path is not None: self.cppn_genome = pickle.load(open(genome_path, 'rb')) else: start_cppn_genome = PrettyGenome('0') start_cppn_genome.configure_new(self.cppn_config.genome_config) start_cppn_genome.nodes[0].activation = 'identity' self.cppn_genome = start_cppn_genome self.reset_altitude_fn()
Example #19
Source File: run.py From super-mario-neat with MIT License | 5 votes |
def main(config_file, file, level="1-1"): # with gzip.open(FILENAME) as f: # config = pickle.load(f)[1] # print(str(config.genome_type.size)) config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction, neat.DefaultSpeciesSet, neat.DefaultStagnation, config_file) genome = pickle.load(open(file, 'rb')) env = gym.make('ppaquette/SuperMarioBros-'+level+'-Tiles-v0') net = neat.nn.FeedForwardNetwork.create(genome, config) info = {'distance': 0} try: while info['distance'] != 3252: state = env.reset() done = False i = 0 old = 40 while not done: state = state.reshape(208) output = net.activate(state) ind = output.index(max(output)) s, reward, done, info = env.step(ACTIONS[ind]) state = s i += 1 if i % 50 == 0: if old == info['distance']: break else: old = info['distance'] print("Distance: {}".format(info['distance'])) env.close() except KeyboardInterrupt: env.close() exit()
Example #20
Source File: main.py From PyTorch-NEAT with Apache License 2.0 | 5 votes |
def run(n_generations): # Load the config file, which is assumed to live in # the same directory as this script. config_path = os.path.join(os.path.dirname(__file__), "neat.cfg") config = neat.Config( neat.DefaultGenome, neat.DefaultReproduction, neat.DefaultSpeciesSet, neat.DefaultStagnation, config_path, ) evaluator = MultiEnvEvaluator( make_net, activate_net, make_env=make_env, max_env_steps=max_env_steps ) def eval_genomes(genomes, config): for _, genome in genomes: genome.fitness = evaluator.eval_genome(genome, config) pop = neat.Population(config) stats = neat.StatisticsReporter() pop.add_reporter(stats) reporter = neat.StdOutReporter(True) pop.add_reporter(reporter) logger = LogReporter("neat.log", evaluator.eval_genome) pop.add_reporter(logger) pop.run(eval_genomes, n_generations)
Example #21
Source File: main.py From TensorFlow-NEAT with Apache License 2.0 | 5 votes |
def run(n_generations): # Load the config file, which is assumed to live in # the same directory as this script. config_path = os.path.join(os.path.dirname(__file__), "neat.cfg") config = neat.Config( neat.DefaultGenome, neat.DefaultReproduction, neat.DefaultSpeciesSet, neat.DefaultStagnation, config_path, ) evaluator = MultiEnvEvaluator( make_net, activate_net, make_env=make_env, max_env_steps=max_env_steps ) def eval_genomes(genomes, config): for _, genome in genomes: genome.fitness = evaluator.eval_genome(genome, config) pop = neat.Population(config) stats = neat.StatisticsReporter() pop.add_reporter(stats) reporter = neat.StdOutReporter(True) pop.add_reporter(reporter) logger = LogReporter("./logs/neat.json", evaluator.eval_genome) pop.add_reporter(logger) pop.run(eval_genomes, n_generations)
Example #22
Source File: run_xor.py From Evolutionary-Algorithm with MIT License | 5 votes |
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(50)) # 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-49') p.run(eval_genomes, 10)
Example #23
Source File: test_simple_run.py From neat-python with BSD 3-Clause "New" or "Revised" License | 5 votes |
def test_run_iznn(): """ Basic test of spiking neural network (iznn). [TODO: Takes the longest of any of the tests in this file, by far. Why?] Was because had population of 290 thanks to too much speciation - too-high compatibility_weight_coefficient relative to range for weights. """ # Load configuration. local_dir = os.path.dirname(__file__) config_path = os.path.join(local_dir, 'test_configuration_iznn') config = neat.Config(neat.iznn.IZGenome, 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(2, 10)) # Run for up to 20 generations. p.run(eval_dummy_genomes_iznn, 20) stats.save() unique_genomes = stats.best_unique_genomes(5) assert 1 <= len(unique_genomes) <= 5, "Unique genomes: {!r}".format(unique_genomes) genomes = stats.best_genomes(5) assert len(genomes) == 5, "Genomes: {!r}".format(genomes) stats.best_genome() p.remove_reporter(stats)
Example #24
Source File: play_winner.py From go_dino with GNU General Public License v3.0 | 5 votes |
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')) with open('winner.pkl', 'rb') as f: winner = pickle.load(f) print('\nBest genome:\n{!s}'.format(winner)) print('\nOutput:') winner_net = neat.nn.FeedForwardNetwork.create(winner, config) print('Score:', dino_api.play_game(GetCommand(winner_net)))
Example #25
Source File: test_distributed.py From neat-python with BSD 3-Clause "New" or "Revised" License | 5 votes |
def run_primary(addr, authkey, generations): """Starts a DistributedEvaluator in primary mode.""" # 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(max(1, int(generations / 3)), 5)) # Run for the specified number of generations. de = neat.DistributedEvaluator( addr, authkey=authkey, eval_function=eval_dummy_genome_nn, mode=MODE_PRIMARY, secondary_chunksize=15, ) print("Starting DistributedEvaluator") sys.stdout.flush() de.start() print("Running evaluate") sys.stdout.flush() p.run(de.evaluate, generations) print("Evaluated") sys.stdout.flush() de.stop(wait=5) print("Did de.stop") sys.stdout.flush() stats.save()
Example #26
Source File: test_distributed.py From neat-python with BSD 3-Clause "New" or "Revised" License | 5 votes |
def run_secondary(addr, authkey, num_workers=1): """Starts a DistributedEvaluator in secondary mode.""" # 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) # Run for the specified number of generations. de = neat.DistributedEvaluator( addr, authkey=authkey, eval_function=eval_dummy_genome_nn, mode=MODE_SECONDARY, num_workers=num_workers, ) try: de.start(secondary_wait=3, exit_on_stop=True) except SystemExit: pass else: raise Exception("DistributedEvaluator in secondary mode did not try to exit!")
Example #27
Source File: test_activation.py From neat-python with BSD 3-Clause "New" or "Revised" License | 5 votes |
def test_bad_add1(): 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) try: config.genome_config.add_activation('1.0', 1.0) except TypeError: pass else: raise Exception("Should have had a TypeError/derived for 'function' 1.0")
Example #28
Source File: test_activation.py From neat-python with BSD 3-Clause "New" or "Revised" License | 5 votes |
def test_bad_add2(): 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) try: config.genome_config.add_activation('dud_function', dud_function) except TypeError: pass else: raise Exception("Should have had a TypeError/derived for dud_function")
Example #29
Source File: neat_es.py From DistributedES with Apache License 2.0 | 5 votes |
def load_neat_config(self): local_dir = os.path.dirname(__file__) config_path = os.path.join(local_dir, 'neat-config/%s.txt' % self.config.task) neat_config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction, neat.DefaultSpeciesSet, neat.DefaultStagnation, config_path) neat_config.fitness_threshold = self.config.target return neat_config
Example #30
Source File: test_config_save_restore.py From neat-python with BSD 3-Clause "New" or "Revised" License | 5 votes |
def test_config_save_restore(self): """Check if it is possible to restore saved config""" config_filename_initial = 'test_configuration' config_filename_save = 'save_configuration' # Get config path local_dir = os.path.dirname(__file__) config_path_initial = os.path.join(local_dir, config_filename_initial) config_path_save = os.path.join(local_dir, config_filename_save) # Load initial configuration from file config_initial = neat.Config(neat.DefaultGenome, neat.DefaultReproduction, neat.DefaultSpeciesSet, neat.DefaultStagnation, config_path_initial) config1 = config_initial.genome_config names1 = [p.name for p in config1._params] for n in names1: assert hasattr(config1, n) # Save configuration to another file config_initial.save(config_path_save) # Obtain configuration from saved file config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction, neat.DefaultSpeciesSet, neat.DefaultStagnation, config_path_save) config2 = config.genome_config names2 = [p.name for p in config2._params] for n in names2: assert hasattr(config2, n) self.assertEqual(names1, names2) for n in names1: v1 = getattr(config1, n) v2 = getattr(config2, n) self.assertEqual(v1, v2)