def test_v003():
folder_path = './data/tmp1'
utils.make_dir_if_not_exists(folder_path)
evo = Evolution(
random_seed=utils.random_int(),
population_size=4,
genotype_size=2,
evaluation_function=lambda pop, seeds: np.sum(
pop, axis=1), #np.arange(1,0,-1/len(pop)),
performance_objective=0.5, #MAX MIN ZERO ABS_MAX
fitness_normalization_mode='NONE',
selection_mode='UNIFORM', # UNIFORM, SUS, RWS
reproduce_from_elite=True,
reproduction_mode=
'GENETIC_ALGORITHM', # 'GENETIC_ALGORITHM' 'HILL_CLIMBING'
mutation_variance=0.1,
elitist_fraction=0.5,
mating_fraction=0.5,
crossover_probability=0.5,
crossover_mode='UNIFORM',
max_generation=100,
termination_function=None,
checkpoint_interval=1,
folder_path=folder_path,
timeit=True)
evo.run()
evo.timing.report()
def test_random_genotypes():
# gen_size = 20
# pop_size = 96
a = Evolution.get_random_genotype(RandomState(None), 20)
b = Evolution.get_random_genotype(RandomState(None), 20)
a = linmap(a, (-1, 1), (0, 1))
b = linmap(b, (-1, 1), (0, 1))
dist = np.linalg.norm(a - b)
similarity = 1 - dist
print(a)
print(b)
print(a - b)
print(dist)
print(similarity)
def test_crossover():
evo = Evolution(
population_size=0,
genotype_size=10,
evaluation_function=lambda _: 0,
# crossover_mode = "UNIFORM"
crossover_mode="1-POINT",
crossover_points=[2, 5, 8])
genotype1 = [0] * evo.genotype_size
genotype2 = [1] * evo.genotype_size
a, b = evo.crossover(genotype1, genotype2)
print(''.join(str(int(x)) for x in a))
print(''.join(str(int(x)) for x in b))
def test_random_genotype():
from dol import gen_structure
from pyevolver.evolution import Evolution
from numpy.random import RandomState
num_dim = 1
num_neurons = 2
default_gen_structure = gen_structure.DEFAULT_GEN_STRUCTURE(
num_dim, num_neurons)
gen_size = gen_structure.get_genotype_size(default_gen_structure)
num_brain_neurons = gen_structure.get_num_brain_neurons(
default_gen_structure)
print('Gen size of agent: {}'.format(gen_size))
print('Num brain neurons: {}'.format(num_brain_neurons))
random_genotype = Evolution.get_random_genotype(RandomState(None),
gen_size)
agent = Agent(
num_dim,
num_brain_neurons,
brain_step_size=0.1,
genotype_structure=default_gen_structure,
)
agent.init_params()
agent.genotype_to_phenotype(random_genotype)
for i in range(10):
agent.brain.euler_step()
print(i)
print(' brain output: {}'.format(agent.brain.output))
agent.compute_motor_outputs()
print(' motor output: {}'.format(agent.motors))
def run_random_agents():
genotype_structure=gen_structure.DEFAULT_GEN_STRUCTURE(2)
gen_size = gen_structure.get_genotype_size(genotype_structure)
random_genotype = Evolution.get_random_genotype(RandomState(None), gen_size*2) # pairs of agents in a single genotype
sim = Simulation(
genotype_structure=genotype_structure,
agent_body_radius=4,
agents_pair_initial_distance=20,
agent_sensors_divergence_angle=np.radians(45), # angle between sensors and axes of symmetry
brain_step_size=0.1,
trial_duration=200, # the brain would iterate trial_duration/brain_step_size number of time
num_cores=1
)
trial_index = 0
data_record_list = []
random_seed = utils.random_int()
perf = sim.run_simulation([random_genotype],0, random_seed, data_record_list=data_record_list)
print("random perf: {}".format(perf))
data_record = data_record_list[0]
vis = Visualization(sim)
vis.start_simulation_from_data(trial_index, data_record)
def random_pairs():
dir = "data/2n_rp-3_shannon-dd_neural_social_coll-edge/seed_001"
evo_file = os.path.join(dir, "evo_2000.json")
sim_file = os.path.join(dir, "simulation.json")
output_file = os.path.join(dir, "perf_dist.json")
evo = Evolution.load_from_file(evo_file, folder_path=dir)
sim = Simulation.load_from_file(sim_file)
sim.num_random_pairings = 0 # we build the pairs dynamically
pop_size = len(evo.population)
best_agent = evo.population[0]
if os.path.exists(output_file):
perfomances, distances = read_data_from_file(output_file)
else:
new_population_pairs = []
perfomances = []
distances = []
for j in range(1, pop_size):
b = evo.population[j]
pair = np.concatenate([best_agent, b])
new_population_pairs.append(pair)
distances.append(euclidean_distance(best_agent, b))
for i in tqdm(range(pop_size - 1)):
perf = sim.run_simulation(new_population_pairs, i)
perfomances.append(perf)
write_data_to_file(perfomances, distances, output_file)
plt.scatter(distances, perfomances)
plt.show()
def test_mutate():
np.random.seed(11)
evo = Evolution(
population_size=2,
genotype_size=10,
search_constraint=np.array([True] * 10),
# search_constraint = np.array([False] * 10),
evaluation_function=lambda _: [1],
max_generation=0,
)
genotype = np.array([-1] * 10)
mutant = evo.mutate(genotype)
print(mutant)
print('SUCCESS!')
def test_select_mating_pool_RWS():
from collections import Counter
performances = np.array([1000, 1000, 300, 200, 200, 100, 50, 50, 50, 50])
np.random.seed(11)
evo = Evolution(
population_size=10,
population=np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
genotype_size=1,
fitness_normalization_mode='RANK',
selection_mode='RWS',
evaluation_function=lambda _: performances,
max_generation=0,
elitist_fraction=0.2,
mating_fraction=0.8, # in beer this is 1 - elitist_fraction
)
evo.run()
print("Fitnesses: {}".format(evo.fitnesses))
# assert evo.n_elite == 2 # only in genetic algorithm
# assert evo.n_fillup == 0 # only in genetic algorithm
assert evo.n_mating == evo.population_size
mating_counter = Counter()
repetitions = 10000
for _ in range(repetitions):
mating_pool = evo.select_mating_pool()
for e in mating_pool:
mating_counter[e] += 1
# we check if the number of time each genome is selected is proportionate to the its performance (and fitness)
# not guaranteed to work for fitness_normalization_mode == 'FPS' because of the linear scaling
# not easy to test this further (not implemented in beer)
for i in range(2, 9):
assert mating_counter[i] > mating_counter[i + 1]
print(sorted(mating_counter.items(), key=lambda kv: kv[0]))
print('SUCCESS!')
def single_paired_agents(input_dir='data'):
"""
Test whether individually evolved agents can perform the task together
and calculate their combined neural complexity.
"""
from dol.simulation import Simulation
import json
seed_dir = f'{input_dir}/2n_exc-0.1_zfill/seed_001'
generation = 5000
population_idx = 0
rs = RandomState(1)
sim_json_filepath = os.path.join(seed_dir, 'simulation.json')
evo_json_filepath = os.path.join(seed_dir, 'evo_{}.json'.format(generation))
sim = Simulation.load_from_file(
sim_json_filepath,
switch_agents_motor_control=True, # forcing switch
num_random_pairings=1 # forcing to play with one another
)
evo = Evolution.load_from_file(evo_json_filepath, folder_path=None)
original_populations = evo.population_unsorted
best_two_agent_pop = np.array([
[
original_populations[0][x] for x in
evo.population_sorted_indexes[population_idx][:2]
]
])
data_record_list = []
performance, sim_perfs, _ = sim.run_simulation(
best_two_agent_pop, 0, 0, 0, None,
data_record_list
)
nc = get_sim_agent_complexity(
sim_perfs, sim, data_record_list,
agent_index=None,
sim_idx=None,
analyze_sensors=True,
analyze_brain=True,
analyze_motors=False,
combined_complexity=False,
only_part_n1n2=False,
rs=rs
)
print('performance', performance)
print("Sim agents similarity: ", sim.agents_genotype_distance[0])
print('nc', nc)
def time_evolution():
evo = Evolution(
random_seed=123,
population_size=5000,
genotype_size=100,
evaluation_function=lambda pop, seeds: [1] * len(pop),
fitness_normalization_mode='RANK',
selection_mode='RWS', # SUS, RWS
reproduction_mode=
'GENETIC_ALGORITHM', # 'GENETIC_ALGORITHM' 'HILL_CLIMBING'
mutation_variance=0.1,
elitist_fraction=0.1,
mating_fraction=0.9,
crossover_probability=0.5,
crossover_mode='1-POINT',
max_generation=100,
termination_function=None,
checkpoint_interval=1,
timeit=True)
evo.run()
evo.timing.report()
def test_fitness_RANK():
performances = np.array([1, 50, 15, 21, 100, 23, 88, 45, 44, 76])
evo = Evolution(population_size=10,
genotype_size=0,
fitness_normalization_mode='RANK',
evaluation_function=lambda _: performances,
max_generation=0)
evo.run()
correct_fitness_rank = sorted([
0.11000000000000001, 0.10777777777777779, 0.10555555555555556,
0.10333333333333335, 0.10111111111111111, 0.09888888888888889,
0.09666666666666666, 0.09444444444444444, 0.09222222222222222, 0.09
],
reverse=True)
assert list(evo.fitnesses) == correct_fitness_rank
# print('fitnesses_rank: {}'.format(fitnesses_rank))
print('SUCCESS!')
def test_select_mating_pool_SUS():
np.random.seed(11)
evo = Evolution(
population_size=10,
population=np.array(list(range(1, 11))),
genotype_size=1,
fitness_normalization_mode='FPS',
selection_mode='SUS',
reproduction_mode='GENETIC_ALGORITHM',
evaluation_function=lambda _:
[0.3, 0.2, 0.1, 0.1, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05],
max_generation=0,
elitist_fraction=0.2,
mating_fraction=0.8, # in beer this is 1 - elitist_fraction
)
evo.run()
evo.fitnesses = np.array(
[0.3, 0.2, 0.1, 0.1, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05])
# print("Fitnesses: {}".format(evo.fitnesses))
assert evo.n_mating == 8
assert evo.n_elite == 2
assert evo.n_fillup == 0
mating_pool = evo.select_mating_pool()
print(mating_pool)
assert mating_pool == [1, 1, 1, 2, 3, 4, 6, 8]
print('SUCCESS!')
def test_fitness_FPS():
performances = np.array([1, 50, 15, 21, 100, 23, 88, 45, 44, 76])
evo = Evolution(population_size=10,
genotype_size=0,
fitness_normalization_mode='FPS',
evaluation_function=lambda _: performances,
max_generation=0)
evo.run()
correct_fitness_fps = sorted([
0.09156424581005587, 0.10068901303538176, 0.09417132216014897,
0.09528864059590317, 0.11000000000000001, 0.0956610800744879,
0.10776536312849162, 0.09975791433891994, 0.09957169459962756,
0.10553072625698326
],
reverse=True)
assert list(evo.fitnesses) == correct_fitness_fps
# print('fitnesses_fps: {}'.format(fitnesses_fps))
print('SUCCESS!')
def main_scatter_plot(input_dir='data'):
"""
From a given seed, look at the last generation,
and compute the neural complexity for all agents.
Plot correlation between fitness and complexity.
"""
seed_dir = f'{input_dir}/2n_exc-0.1_zfill/seed_001'
generation = 5000
pop_index = 0
analyze_sensors = True
analyze_brain = True
analyze_motors = False
combined_complexity = False
only_part_n1n2 = True
rs = RandomState(1)
evo_file = sorted([f for f in os.listdir(seed_dir) if 'evo_' in f])[0]
evo_json_filepath = os.path.join(seed_dir, evo_file)
evo = Evolution.load_from_file(evo_json_filepath, folder_path=None)
pop_size = len(evo.population[0])
print('pop_size', pop_size)
perf_data = np.zeros(pop_size)
nc_data = np.zeros(pop_size)
for genotype_idx in tqdm(range(pop_size)):
perf, sim_perfs, evo, sim, data_record_list, sim_idx = run_simulation_from_dir(
seed_dir, generation, genotype_idx, population_idx=pop_index, quiet=True)
agent_index = None # agent_index must be None (to get best agents of the two)
sim_idx = None # sim_idx must be None (to get best sim among randomom pairs)
nc_avg = get_sim_agent_complexity(
sim_perfs, sim, data_record_list, agent_index, sim_idx,
analyze_sensors, analyze_brain, analyze_motors,
combined_complexity, only_part_n1n2, rs
)
perf_data[genotype_idx] = perf
nc_data[genotype_idx] = nc_avg
fig = plt.figure(figsize=(10, 6))
ax = fig.add_subplot(1, 1, 1)
ax.scatter(perf_data, nc_data)
plt.xlabel('performance')
plt.ylabel('nc')
plt.show()
def test_continuation():
eval_func = lambda pop, rand_seed: RandomState(rand_seed).random(len(pop))
print('Loading evolution from json file')
folder_path = './tmp_cont1'
utils.make_dir_if_not_exists(folder_path)
evo1 = Evolution(
random_seed=123,
population_size=1000,
genotype_size=2,
evaluation_function=eval_func,
fitness_normalization_mode='RANK',
selection_mode='RWS',
reproduction_mode=
'GENETIC_ALGORITHM', #'HILL_CLIMBING', 'GENETIC_ALGORITHM'
mutation_variance=0.1,
elitist_fraction=0.1,
mating_fraction=0.9,
crossover_probability=0.5,
crossover_mode='1-POINT',
max_generation=100,
folder_path=folder_path,
termination_function=None,
checkpoint_interval=50)
evo1.run()
print()
new_folder_path = './tmp_cont2'
utils.make_dir_if_not_exists(new_folder_path)
evo2 = Evolution.load_from_file(os.path.join(folder_path,
'evo_00050.json'),
evaluation_function=eval_func,
folder_path=new_folder_path,
max_generation=110)
evo2.run()
def test_random_genotype():
from dyadic_interaction import gen_structure
from pyevolver.evolution import Evolution
from numpy.random import RandomState
default_gen_structure = gen_structure.DEFAULT_GEN_STRUCTURE(2)
gen_size = gen_structure.get_genotype_size(default_gen_structure)
num_brain_neurons = gen_structure.get_num_brain_neurons(default_gen_structure)
print('Gen size of agent: {}'.format(gen_size))
print('Num brain neurons: {}'.format(num_brain_neurons))
random_genotype = Evolution.get_random_genotype(RandomState(None), gen_size)
agent_net = AgentNetwork(
num_brain_neurons,
brain_step_size=0.1,
genotype_structure=default_gen_structure,
genotype = random_genotype
)
agent_net.brain.states = np.array([0., 0.])
agent_net.brain.compute_output()
print('brain output: {}'.format(agent_net.brain.output))
motor_outputs = agent_net.compute_motor_outputs()
print('motor output: {}'.format(motor_outputs))
def same_pairs():
dir = "data/2n_shannon-dd_neural_social_coll-edge/seed_001"
evo_file = os.path.join(dir, "evo_2000.json")
sim_file = os.path.join(dir, "simulation.json")
output_file = os.path.join(dir, "perf_dist.json")
if os.path.exists(output_file):
perfomances, distances = read_data_from_file(output_file)
else:
evo = Evolution.load_from_file(evo_file, folder_path=dir)
sim = Simulation.load_from_file(sim_file)
assert sim.num_random_pairings == 0
pop_size = len(evo.population)
perfomances = []
distances = []
for i in tqdm(range(pop_size)):
perf = sim.run_simulation(evo.population, i)
a, b = np.array_split(evo.population[i], 2)
perfomances.append(perf)
distances.append(euclidean_distance(a, b))
write_data_to_file(perfomances, distances, output_file)
plt.scatter(distances, perfomances)
plt.show()
def run_simulation_from_dir(dir, generation=None, genotype_idx=0, **kwargs):
'''
utitity function to get data from a simulation
'''
random_pos_angle = kwargs.get('random_pos_angle', None)
entropy_type = kwargs.get('entropy_type', None)
entropy_target_value = kwargs.get('entropy_target_value', None)
concatenate = kwargs.get('concatenate', None)
collision_type = kwargs.get('collision_type', None)
ghost_index = kwargs.get('ghost_index', None)
initial_distance = kwargs.get('initial_distance', None)
isolation = kwargs.get('isolation', None)
write_data = kwargs.get('write_data', None)
func_arguments = locals()
from pyevolver.evolution import Evolution
evo_files = [f for f in os.listdir(dir) if f.startswith('evo_')]
assert len(evo_files) > 0, "Can't find evo files in dir {}".format(dir)
file_num_zfill = len(evo_files[0].split('_')[1].split('.')[0])
if generation is None:
# assumes last generation
evo_files = sorted([f for f in os.listdir(dir) if f.startswith('evo')])
evo_json_filepath = os.path.join(dir, evo_files[-1])
else:
generation = str(generation).zfill(file_num_zfill)
evo_json_filepath = os.path.join(dir, 'evo_{}.json'.format(generation))
sim_json_filepath = os.path.join(dir, 'simulation.json')
sim = Simulation.load_from_file(sim_json_filepath)
evo = Evolution.load_from_file(evo_json_filepath, folder_path=dir)
if initial_distance is not None:
print("Forcing initial distance to: {}".format(initial_distance))
sim.agents_pair_initial_distance = initial_distance
sim.set_initial_positions_angles()
if random_pos_angle:
print("Randomizing positions and angles")
random_state = RandomState()
sim.set_initial_positions_angles(random_state)
if entropy_type is not None:
sim.entropy_type = entropy_type
print("Forcing entropy type: {}".format(sim.entropy_type))
if entropy_target_value is not None:
sim.entropy_target_value = entropy_target_value
print("Forcing entropy target value: {}".format(
sim.entropy_target_value))
if concatenate is not None:
sim.concatenate = concatenate == 'on'
print("Forcing concatenation: {}".format(sim.concatenate))
if collision_type is not None:
sim.collision_type = collision_type
sim.init_agents_pair()
print("Forcing collision_type: {}".format(sim.collision_type))
if isolation is not None:
sim.isolation = isolation
print("Forcing isolation to: {}".format(isolation))
data_record_list = []
genotype_idx_unsorted = evo.population_sorted_indexes[genotype_idx]
random_seed = evo.pop_eval_random_seeds[genotype_idx_unsorted]
if ghost_index is not None:
assert ghost_index in [0, 1], 'ghost_index must be 0 or 1'
# get original results without ghost condition and no random
func_arguments['ghost_index'] = None
func_arguments['random_pos_angle'] = False
func_arguments['initial_distance'] = None
func_arguments['write_data'] = None
_, _, original_data_record_list = run_simulation_from_dir(
**func_arguments)
perf = sim.run_simulation(
evo.population_unsorted,
genotype_idx_unsorted,
random_seed,
data_record_list,
ghost_index=ghost_index,
original_data_record_list=original_data_record_list)
print(
"Overall Performance recomputed (non-ghost agent only): {}".format(
perf))
else:
perf = sim.run_simulation(evo.population_unsorted,
genotype_idx_unsorted, random_seed,
data_record_list)
if genotype_idx == 0:
original_perfomance = evo.best_performances[-1]
print("Original Performance: {}".format(original_perfomance))
print("Overall Performance recomputed: {}".format(perf))
if write_data:
for s, data_record in enumerate(data_record_list, 1):
if len(data_record_list) > 1:
outdir = os.path.join(dir, 'data', 'sim_{}'.format(s))
else:
outdir = os.path.join(dir, 'data')
utils.make_dir_if_not_exists(outdir)
for t in range(sim.num_trials):
for k, v in data_record.items():
if v is dict:
# summary
if t == 0: # only once
outfile = os.path.join(outdir, '{}.json'.format(k))
utils.save_json_numpy_data(v, outfile)
elif len(v) != sim.num_trials:
# genotype/phenotype
outfile = os.path.join(outdir, '{}.json'.format(k))
utils.save_json_numpy_data(v, outfile)
elif len(v[0]) == 2:
# data for each agent
for a in range(2):
outfile = os.path.join(
outdir,
'{}_{}_{}.json'.format(k, t + 1, a + 1))
utils.save_json_numpy_data(v[t][a], outfile)
else:
# single data for both agent (e.g., distance)
outfile = os.path.join(outdir,
'{}_{}.json'.format(k, t + 1))
utils.save_json_numpy_data(v[t], outfile)
return evo, sim, data_record_list
def compute_std_from_random_runs(num_cores, num_good_runs,
entropy_target_value):
from dyadic_interaction.simulation import Simulation
assert entropy_target_value in ['neural', 'distance', 'angle']
genotype_structure = gen_structure.DEFAULT_GEN_STRUCTURE(num_neurons)
gen_size = gen_structure.get_genotype_size(genotype_structure)
num_data_points_per_agent_pair = sim_duration * num_trials
num_data_points = num_data_points_per_agent_pair * num_good_runs # 8 million!
if entropy_target_value == 'distance':
num_all_runs = num_good_runs * MULTIPLY_FACTOR
else:
num_all_runs = num_good_runs
all_distances = np.zeros(num_data_points)
rs = RandomState(seed)
sim_array = [
Simulation(
entropy_type='shannon-dd',
genotype_structure=genotype_structure,
) for _ in range(num_cores)
]
random_genotypes = [
Evolution.get_random_genotype(rs, gen_size * 2)
for _ in range(num_all_runs)
]
def run_one_core(r):
data_record_list = []
sim_array[r % num_cores].run_simulation(
random_genotypes, r, data_record_list=data_record_list)
data_record = data_record_list[0]
if entropy_target_value == 'neural':
concat_outputs = np.concatenate([
data_record['brain_output'][t][a] for t in range(4)
for a in range(2)
])
concat_outputs = np.concatenate(
[concat_outputs[:, c] for c in range(num_neurons)])
return concat_outputs
elif entropy_target_value == 'distance':
concat_distances = np.concatenate(data_record['distance'])
if any(concat_distances > MAX_DISTANCE):
return None
return concat_distances
else:
assert entropy_target_value == 'angle'
concat_angles = np.concatenate([
data_record['angle'][t][a] for t in range(4) for a in range(2)
])
return concat_angles
run_distances = Parallel(
n_jobs=num_cores)( # prefer="threads" does not work
delayed(run_one_core)(r) for r in tqdm(range(num_all_runs)))
good_run_distances = [run for run in run_distances if run is not None]
print("Number of good runs: {}".format(len(good_run_distances)))
assert len(good_run_distances) >= num_good_runs
all_distances = np.concatenate(good_run_distances[:num_good_runs]
) # take only the first 1000 good runs
# json.dump(
# all_distances,
# open('data/tmp_distances.json', 'w'),
# indent=3,
# cls=NumpyListJsonEncoder
# )
std = all_distances.std()
print(std)
if args.num_random_pairings == 0:
genotype_size *= 2 # two agents per genotype
evo = Evolution(
random_seed=args.seed,
population_size=args.popsize,
genotype_size=genotype_size,
evaluation_function=sim.evaluate,
performance_objective=args.perf_obj,
fitness_normalization_mode=
'NONE', # 'NONE', FPS', 'RANK', 'SIGMA' -> NO NORMALIZATION
selection_mode='UNIFORM', # 'UNIFORM', 'RWS', 'SUS'
reproduce_from_elite=True,
reproduction_mode=
'GENETIC_ALGORITHM', # 'HILL_CLIMBING', 'GENETIC_ALGORITHM'
mutation_variance=0.1, # mutation noice with variance 0.1
elitist_fraction=0.04, # elite fraction of the top 4% solutions
mating_fraction=0.96, # the remaining mating fraction
crossover_probability=0.1,
crossover_mode='UNIFORM',
crossover_points=None, #genotype_structure['crossover_points'],
folder_path=outdir,
max_generation=args.num_gen,
termination_function=None,
checkpoint_interval=np.ceil(args.num_gen / 100),
)
evo.run()
if args.entropy_type == 'transfer':
# shutdown JVM
def get_simulation_data_from_random_agent(gen_struct, rs, num_dim=1):
from pyevolver.evolution import Evolution
gen_size = gen_structure.get_genotype_size(gen_struct)
random_genotype = Evolution.get_random_genotype(rs, gen_size)
return get_simulation_data_from_agent(gen_struct, random_genotype, rs,
num_dim)
args = parser.parse_args()
dir = args.dir
evo_files = sorted([f for f in os.listdir(dir) if f.startswith('evo_')])
assert len(evo_files) > 0, "Can't find evo files in dir {}".format(dir)
last_generation = evo_files[-1].split('_')[1].split('.')[0]
file_num_zfill = len(last_generation)
sim_json_filepath = os.path.join(dir, 'simulation.json')
evo_json_filepath = os.path.join(dir,
'evo_{}.json'.format(last_generation))
assert args.max_gen > int(last_generation), \
"max_gen is <= of the last available generation ({})".format(last_generation)
sim = Simulation.load_from_file(sim_json_filepath)
if args.cores > 0:
sim.cores = args.cores
evo = Evolution.load_from_file(evo_json_filepath,
evaluation_function=sim.evaluate,
max_generation=args.max_gen)
t = TicToc()
t.tic()
evo.run()
print('Ellapsed time: {}'.format(t.tocvalue()))
def main(raw_args=None):
parser = argparse.ArgumentParser(
description='Run the Division of Labor Simulation'
)
# evolution arguments
parser.add_argument('--seed', type=int, default=0, help='Random seed')
parser.add_argument('--dir', type=str, default=None, help='Output directory')
parser.add_argument('--perf_obj', default='MAX',
help='Performance objective') # 'MAX', 'MIN', 'ZERO', 'ABS_MAX' or float value
parser.add_argument('--gen_zfill', type=bool, default=False,
help='whether to fill geotipes with zeros (True) or random (false - default)')
parser.add_argument('--popsize', type=int, default=96, help='Population size')
parser.add_argument('--max_gen', type=int, default=10, help='Number of generations')
# simulation arguments
parser.add_argument('--num_neurons', type=int, default=2, help='Number of neurons in agent')
parser.add_argument('--num_dim', type=int, choices=[1, 2], default=1, help='Number of dimensions of the simulation')
parser.add_argument('--num_trials', type=int, default=4, help='Number of trials')
parser.add_argument('--trial_duration', type=int, default=50, help='Trial duration')
parser.add_argument('--num_random_pairings', type=int, default=None,
help='None -> agents are alone in the simulation (default). '
'0 -> agents are evolved in pairs: a genotype contains a pair of agents. '
'n>0 -> each agent will go though a simulation with N other agents (randomly chosen).')
parser.add_argument('--switch_agents_motor_control', type=bool, default=False,
help=
'when num_agents is 2 this decides whether the two agents switch control of L/R motors '
'in different trials (switch=True) or not (switch=False) in which case the first agent '
'always control the left motor and the second the right')
parser.add_argument('--exclusive_motors_threshold', type=float, default=None,
help='prevent motors to run at the same time')
parser.add_argument('--dual_population', type=bool, default=False,
help='If to evolve two separate populations, one always controlling the left '
'motor and the other the right')
parser.add_argument('--cores', type=int, default=1, help='Number of cores')
# Gather the provided arguements as an array.
args = parser.parse_args(raw_args)
t = TicToc()
t.tic()
genotype_structure = gen_structure.DEFAULT_GEN_STRUCTURE(args.num_dim, args.num_neurons)
genotype_size = gen_structure.get_genotype_size(genotype_structure)
if args.dir is not None:
# create default path if it specified dir already exists
if os.path.isdir(args.dir):
subdir = '{}d_{}n'.format(args.num_dim, args.num_neurons)
if args.exclusive_motors_threshold is not None:
subdir += '_exc-{}'.format(args.exclusive_motors_threshold)
if args.gen_zfill:
subdir += '_zfill'
if args.num_random_pairings is not None:
subdir += '_rp-{}'.format(args.num_random_pairings)
if args.switch_agents_motor_control:
subdir += '_switch'
if args.dual_population:
subdir += '_dual'
seed_dir = 'seed_{}'.format(str(args.seed).zfill(3))
outdir = os.path.join(args.dir, subdir, seed_dir)
else:
# use the specified dir if it doesn't exist
outdir = args.dir
utils.make_dir_if_not_exists_or_replace(outdir)
else:
outdir = None
checkpoint_interval = int(np.ceil(args.max_gen / 10))
sim = Simulation(
genotype_structure=genotype_structure,
num_dim=args.num_dim,
num_trials=args.num_trials,
trial_duration=args.trial_duration, # the brain would iterate trial_duration/brain_step_size number of time
num_random_pairings=args.num_random_pairings,
switch_agents_motor_control=args.switch_agents_motor_control,
exclusive_motors_threshold=args.exclusive_motors_threshold,
dual_population=args.dual_population,
num_cores=args.cores
)
if outdir is not None:
sim_config_json = os.path.join(outdir, 'simulation.json')
sim.save_to_file(sim_config_json)
if args.num_random_pairings == 0:
genotype_size *= 2 # two agents per genotype
num_populations = 2 if args.dual_population else 1
population = None # by default randomly initialized in evolution
if args.gen_zfill:
# all genotypes initialized with zeros
population = np.zeros(
(num_populations, args.popsize, genotype_size)
)
evo = Evolution(
random_seed=args.seed,
population=population,
num_populations=num_populations,
population_size=args.popsize,
genotype_size=genotype_size,
evaluation_function=sim.evaluate,
performance_objective=args.perf_obj,
fitness_normalization_mode='FPS', # 'NONE', 'FPS', 'RANK', 'SIGMA' -> NO NORMALIZATION
selection_mode='RWS', # 'UNIFORM', 'RWS', 'SUS'
reproduce_from_elite=False,
reproduction_mode='GENETIC_ALGORITHM', # 'HILL_CLIMBING', 'GENETIC_ALGORITHM'
mutation_variance=0.05, # mutation noice with variance 0.1
elitist_fraction=0.05, # elite fraction of the top 4% solutions
mating_fraction=0.95, # the remaining mating fraction (consider leaving something for random fill)
crossover_probability=0.1,
crossover_mode='UNIFORM',
crossover_points=None, # genotype_structure['crossover_points'],
folder_path=outdir,
max_generation=args.max_gen,
termination_function=None,
checkpoint_interval=checkpoint_interval
)
print('Output path: ', outdir)
print('n_elite, n_mating, n_filling: ', evo.n_elite, evo.n_mating, evo.n_fillup)
evo.run()
print('Ellapsed time: {}'.format(t.tocvalue()))
return sim, evo
def run_simulation_from_dir(dir, generation=None, genotype_idx=0, population_idx=0,
random_target_seed=None, random_pairing_seed=None,
isolation_idx=None, write_data=False, **kwargs):
"""
Utitity function to get data from a simulation
"""
evo_files = sorted([f for f in os.listdir(dir) if f.startswith('evo_')])
assert len(evo_files) > 0, "Can't find evo files in dir {}".format(dir)
file_num_zfill = len(evo_files[0].split('_')[1].split('.')[0])
if generation is None:
evo_json_filepath = os.path.join(dir, evo_files[-1])
generation = int(evo_files[-1].split('_')[1].split('.')[0])
else:
generation_str = str(generation).zfill(file_num_zfill)
evo_json_filepath = os.path.join(dir, 'evo_{}.json'.format(generation_str))
sim_json_filepath = os.path.join(dir, 'simulation.json')
sim = Simulation.load_from_file(sim_json_filepath)
evo = Evolution.load_from_file(evo_json_filepath, folder_path=dir)
data_record_list = []
random_seed = evo.pop_eval_random_seed
expect_same_results = isolation_idx is None
# overwriting simulaiton
if random_target_seed is not None:
print("Using random target")
# standard target was initialized in sim.__post_init__
# so this is going to overwrite it
sim.init_target(RandomState(random_target_seed))
expect_same_results = False
if random_pairing_seed is not None:
print("Setting random pairing with seed ", random_pairing_seed)
random_seed = random_pairing_seed
expect_same_results = False
original_populations = evo.population_unsorted
# get the indexes of the populations as they were before being sorted by performance
# we only need to do this for the first population (index 0)
original_genotype_idx = evo.population_sorted_indexes[population_idx][genotype_idx]
performance, sim_perfs, _ = sim.run_simulation(
original_populations,
original_genotype_idx,
random_seed,
population_idx,
isolation_idx,
data_record_list
)
performance = sim.normalize_performance(performance)
verbose = not kwargs.get('quiet', False)
if verbose:
if genotype_idx == 0:
perf_orig = evo.best_performances[generation][population_idx]
perf_orig = sim.normalize_performance(perf_orig)
print("Performace original: {}".format(perf_orig))
print("Performace recomputed: {}".format(performance))
if expect_same_results:
diff_perfomance = abs(perf_orig - performance)
if diff_perfomance > 1e-5:
print(f'Warning: diff_perfomance: {diff_perfomance}')
# assert diff_perfomance < 1e-5, f'diff_perfomance: {diff_perfomance}'
# if performance == perf_orig:
# print("Exact!!")
if write_data:
for s, data_record in enumerate(data_record_list, 1):
if len(data_record_list) > 1:
outdir = os.path.join(dir, 'data', 'sim_{}'.format(s))
else:
outdir = os.path.join(dir, 'data')
utils.make_dir_if_not_exists_or_replace(outdir)
for k, v in data_record.items():
if type(v) is dict:
# summary
outfile = os.path.join(outdir, '{}.json'.format(k))
utils.save_json_numpy_data(v, outfile)
else:
outfile = os.path.join(outdir, '{}.json'.format(k))
utils.save_json_numpy_data(v, outfile)
if kwargs.get('select_sim', None) is None:
# select best one
sim_idx = np.argmax(sim_perfs)
# if sim.num_random_pairings != None and sim.num_random_pairings > 0:
if verbose:
print("Best sim (random pairings)", sim_idx+1)
else:
sim_idx = kwargs['select_sim'] - 1 # zero based
if verbose:
print("Selecting simulation", sim_idx+1)
if verbose:
sim_perf = sim.normalize_performance(sim_perfs[sim_idx])
print("Performance recomputed (sim): ", sim_idx+1, sim_perf)
if sim.num_agents == 2:
print("Sim agents genotype distance: ", sim.agents_genotype_distance[sim_idx])
# print agents signatures
agents_sign = [get_numpy_signature(gt) for gt in data_record_list[sim_idx]['genotypes']]
print('Agent(s) signature(s):', agents_sign)
if kwargs.get('compute_complexity', False):
from dol.analyze_complexity import get_sim_agent_complexity
for a in range(sim.num_agents):
nc = get_sim_agent_complexity(
sim_perfs, sim, data_record_list,
agent_index=a,
sim_idx=sim_idx,
analyze_sensors=True,
analyze_brain=True,
analyze_motors=False,
combined_complexity=False,
only_part_n1n2=True,
rs=RandomState(1)
)
print('TSE', a+1, nc)
return performance, sim_perfs, evo, sim, data_record_list, sim_idx
def test_split_genotypes():
dir = "data/2n_shannon-dd_neural_social_coll-edge/seed_001"
evo_file = os.path.join(dir, "evo_2000.json")
evo = Evolution.load_from_file(evo_file, folder_path=dir)
get_similarity_matrix(evo.population)
get_similarity_split(evo.population)
