def main():
name = 'LTL-MDP-CE_6_8_TD1_New'
try:
with open('path.conf') as f:
root_dir_path = f.read().strip()
except FileNotFoundError:
raise FileNotFoundError(
"You have not set the root path to store your results."
" Write the path to a path.conf text file in the bin directory"
" before running the simulation")
paths = Paths(name, dict(run_no='test'), root_dir_path=root_dir_path)
print("All output logs can be found in directory ", paths.logs_path)
traj_file = os.path.join(paths.output_dir_path, 'data.h5')
# Create an environment that handles running our simulation
# This initializes a PyPet environment
env = Environment(
trajectory=name,
filename=traj_file,
file_title=u'{} data'.format(name),
comment=u'{} data'.format(name),
add_time=True,
freeze_input=True,
multiproc=True,
use_scoop=True,
wrap_mode=pypetconstants.WRAP_MODE_LOCAL,
automatic_storing=True,
log_stdout=False, # Sends stdout to logs
log_folder=os.path.join(paths.output_dir_path, 'logs'))
create_shared_logger_data(logger_names=['bin', 'optimizers'],
log_levels=['INFO', 'INFO'],
log_to_consoles=[True, True],
sim_name=name,
log_directory=paths.logs_path)
configure_loggers()
# Get the trajectory from the environment
traj = env.trajectory
# NOTE: Benchmark function
optimizee = StateActionOptimizee(traj)
# NOTE: Outerloop optimizer initialization
# TODO: Change the optimizer to the appropriate Optimizer class
parameters = CrossEntropyParameters(pop_size=75,
rho=0.2,
smoothing=0.0,
temp_decay=0,
n_iteration=75,
distribution=NoisyGaussian(
noise_magnitude=1,
noise_decay=0.95),
stop_criterion=np.inf,
seed=102)
optimizer = CrossEntropyOptimizer(
traj,
optimizee_create_individual=optimizee.create_individual,
optimizee_fitness_weights=(-1., ),
parameters=parameters,
optimizee_bounding_func=optimizee.bounding_func)
# Add post processing
env.add_postprocessing(optimizer.post_process)
# Add Recorder
recorder = Recorder(trajectory=traj,
optimizee_name='SNN StateAction',
optimizee_parameters=['gamma', 'eta'],
optimizer_name=optimizer.__class__.__name__,
optimizer_parameters=optimizer.get_params())
recorder.start()
# Run the simulation with all parameter combinations
env.run(optimizee.simulate)
# NOTE: Outerloop optimizer end
optimizer.end(traj)
recorder.end()
# Finally disable logging and close all log-files
env.disable_logging()
def main(dependent, optimizer):
opt = optimizer.upper()
identifier = '{:05x}'.format(np.random.randint(16**5))
print('Identifier: ' + identifier)
allocated_id = '07' # dls.get_allocated_board_ids()[0]
board_calibration_map = {
'B291698': {
'dac': 'dac_default.json',
'cap': 'cap_mem_29.json'
},
'07': {
'dac': 'dac_07_chip_20.json',
'cap': 'calibration_20.json'
},
'B201319': {
'dac': 'dac_B201319_chip_21.json',
'cap': 'calibration_24.json'
},
'B201330': {
'dac': 'dac_B201330_chip_22.json',
'cap': 'calibration_22.json'
}
}
dep_name = 'DEP' if dependent else 'IND'
name = 'MAB_ANN_{}_{}_{}'.format(identifier, opt, dep_name)
root_dir_path = os.path.expanduser('~/simulations')
paths = Paths(name, dict(run_no=u'test'), root_dir_path=root_dir_path)
with open(os.path.expanduser('~/LTL/bin/logging.yaml')) as f:
l_dict = yaml.load(f)
log_output_file = os.path.join(paths.results_path,
l_dict['handlers']['file']['filename'])
l_dict['handlers']['file']['filename'] = log_output_file
logging.config.dictConfig(l_dict)
print("All output logs can be found in directory " + str(paths.logs_path))
traj_file = os.path.join(paths.output_dir_path, u'data.h5')
# Create an environment that handles running our simulation
# This initializes a PyPet environment
env = Environment(
trajectory=name,
filename=traj_file,
file_title=u'{} data'.format(name),
comment=u'{} data'.format(name),
add_time=True,
# freeze_input=True,
# multiproc=True,
# use_scoop=True,
wrap_mode=pypetconstants.WRAP_MODE_LOCK,
automatic_storing=True,
log_stdout=False, # Sends stdout to logs
log_folder=os.path.join(paths.output_dir_path, 'logs'))
create_shared_logger_data(logger_names=['bin', 'optimizers', 'optimizees'],
log_levels=['INFO', 'INFO', 'INFO'],
log_to_consoles=[True, True, True],
sim_name=name,
log_directory=paths.logs_path)
configure_loggers()
# Get the trajectory from the environment
traj = env.trajectory
optimizee_seed = 100
with open('../adv/' + board_calibration_map[allocated_id]['cap']) as f:
calibrated_config = json.load(f)
with open('../adv/' + board_calibration_map[allocated_id]['dac']) as f:
dac_config = json.load(f)
class Dummy(object):
def __init__(self, connector):
self.connector = connector
def __enter__(self):
return self.connector
def __exit__(self, exc_type, exc_val, exc_tb):
pass
class Mgr(object):
def __init__(self):
self.connector = None
def establish(self):
return Dummy(self.connector)
max_learning_rate = 1.
mgr = Mgr()
optimizee_parameters = \
BanditParameters(n_arms=2, n_pulls=100, n_samples=40, seed=optimizee_seed,
max_learning_rate=max_learning_rate, learning_rule=ANNLearningRule,
establish_connection=mgr.establish)
optimizee = BanditOptimizee(traj, optimizee_parameters, dp=dependent)
# Add post processing
optimizer = None
pop_size = 200
n_iteration = 60
if opt == 'CE':
ce_optimizer_parameters = CrossEntropyParameters(
pop_size=pop_size,
rho=0.06,
smoothing=0.3,
temp_decay=0,
n_iteration=n_iteration,
distribution=NoisyGaussian(noise_magnitude=.2, noise_decay=.925),
#Gaussian(),#NoisyGaussian(noise_magnitude=1., noise_decay=0.99),
stop_criterion=np.inf,
seed=102)
ce_optimizer = CrossEntropyOptimizer(
traj,
optimizee_create_individual=optimizee.create_individual,
optimizee_fitness_weights=(1, ),
parameters=ce_optimizer_parameters,
optimizee_bounding_func=optimizee.bounding_func)
optimizer = ce_optimizer
elif opt == 'ES':
es_optimizer_parameters = EvolutionStrategiesParameters(
learning_rate=1.8,
learning_rate_decay=.93,
noise_std=.03,
mirrored_sampling_enabled=True,
fitness_shaping_enabled=True,
pop_size=int(pop_size / 2),
n_iteration=n_iteration,
stop_criterion=np.inf,
seed=102)
optimizer = EvolutionStrategiesOptimizer(traj,
optimizee.create_individual,
(1, ),
es_optimizer_parameters,
optimizee.bounding_func)
elif opt == 'GD':
gd_parameters = ClassicGDParameters(learning_rate=.003,
exploration_step_size=.1,
n_random_steps=pop_size,
n_iteration=n_iteration,
stop_criterion=np.inf,
seed=102)
optimizer = GradientDescentOptimizer(traj, optimizee.create_individual,
(1, ), gd_parameters,
optimizee.bounding_func)
elif opt == 'SA':
sa_parameters = SimulatedAnnealingParameters(
n_parallel_runs=pop_size,
noisy_step=.1,
temp_decay=.9,
n_iteration=n_iteration,
stop_criterion=np.inf,
seed=102,
cooling_schedule=AvailableCoolingSchedules.EXPONENTIAL_ADDAPTIVE)
optimizer = SimulatedAnnealingOptimizer(traj,
optimizee.create_individual,
(1, ), sa_parameters,
optimizee.bounding_func)
elif opt == 'GS':
n_grid_points = 5
gs_optimizer_parameters = GridSearchParameters(
param_grid={
'weight_prior': (0, 1, n_grid_points),
'learning_rate': (0, 1, n_grid_points),
'stim_inhibition': (0, 1, n_grid_points),
'action_inhibition': (0, 1, n_grid_points),
'learning_rate_decay': (0, 1, n_grid_points)
})
gs_optimizer = GridSearchOptimizer(
traj,
optimizee_create_individual=optimizee.create_individual,
optimizee_fitness_weights=(1, ),
parameters=gs_optimizer_parameters)
optimizer = gs_optimizer
else:
exit(1)
env.add_postprocessing(optimizer.post_process)
# Add Recorder
recorder = Recorder(trajectory=traj,
optimizee_name='MAB',
optimizee_parameters=optimizee_parameters,
optimizer_name=optimizer.__class__.__name__,
optimizer_parameters=optimizer.get_params())
recorder.start()
# Run the simulation with all parameter combinations
# optimizee.simulate(traj)
# exit(0)
with Connector(calibrated_config, dac_config, 3) as connector:
mgr.connector = connector
env.run(optimizee.simulate)
mgr.connector.disconnect()
## Outerloop optimizer end
optimizer.end(traj)
recorder.end()
# Finally disable logging and close all log-files
env.disable_logging()
def main(path_name, resolution, fixed_delay, state_handling, use_pecevski,
num_trials):
name = path_name
try:
with open('bin/path.conf') as f:
root_dir_path = f.read().strip()
except FileNotFoundError:
raise FileNotFoundError(
"You have not set the root path to store your results."
" Write the path to a path.conf text file in the bin directory"
" before running the simulation")
paths = Paths(name, dict(run_no='test'), root_dir_path=root_dir_path)
traj_file = os.path.join(paths.output_dir_path, 'data.h5')
# Create an environment that handles running our simulation
# This initializes a PyPet environment
env = Environment(
trajectory=name,
filename=traj_file,
file_title='{} data'.format(name),
comment='{} data'.format(name),
add_time=True,
automatic_storing=True,
use_scoop=True,
multiproc=True,
wrap_mode=pypetconstants.WRAP_MODE_LOCAL,
log_stdout=False, # Sends stdout to logs
)
create_shared_logger_data(logger_names=['bin', 'optimizers'],
log_levels=['INFO', 'INFO'],
log_to_consoles=[True, True],
sim_name=name,
log_directory=paths.logs_path)
configure_loggers()
# Get the trajectory from the environment
traj = env.trajectory
# NOTE: Innerloop simulator
optimizee = SAMGraphOptimizee(traj,
n_NEST_threads=1,
time_resolution=resolution,
fixed_delay=fixed_delay,
use_pecevski=use_pecevski,
state_handling=state_handling,
plots_directory=paths.output_dir_path,
num_fitness_trials=num_trials)
# Get bounds for mu and sigma calculation.
param_spec = OrderedDict(sorted(SAMGraph.parameter_spec(4).items()))
names = [k for k, _ in param_spec.items()]
mu = np.array([(v_min + v_max) / 2
for k, (v_min, v_max) in param_spec.items()])
sigma = np.array([(v_max - v_min) / 2
for k, (v_min, v_max) in param_spec.items()])
print("Using means: {}\nUsing stds: {}".format(dict(zip(names, mu)),
dict(zip(names, sigma))))
# NOTE: Outerloop optimizer initialization
parameters = NaturalEvolutionStrategiesParameters(
seed=0,
pop_size=96,
n_iteration=40,
learning_rate_sigma=0.5,
learning_rate_mu=0.5,
mu=mu,
sigma=sigma,
mirrored_sampling_enabled=True,
fitness_shaping_enabled=True,
stop_criterion=np.Inf)
optimizer = NaturalEvolutionStrategiesOptimizer(
traj,
optimizee_create_individual=optimizee.create_individual,
optimizee_fitness_weights=(-1.0, ),
parameters=parameters,
optimizee_bounding_func=optimizee.bounding_func,
fitness_plot_name=path_name)
# Add post processing
env.add_postprocessing(optimizer.post_process)
# Run the simulation with all parameter combinations
env.run(optimizee.simulate)
# NOTE: Outerloop optimizer end
optimizer.end(traj)
# Finally disable logging and close all log-files
env.disable_logging()
def main():
name = 'LTL-MDP-GD_6_8_TD1'
try:
with open('path.conf') as f:
root_dir_path = f.read().strip()
except FileNotFoundError:
raise FileNotFoundError(
"You have not set the root path to store your results."
" Write the path to a path.conf text file in the bin directory"
" before running the simulation"
)
paths = Paths(name, dict(run_no='test'), root_dir_path=root_dir_path)
print("All output logs can be found in directory ", paths.logs_path)
traj_file = os.path.join(paths.output_dir_path, 'data.h5')
# Create an environment that handles running our simulation
# This initializes a PyPet environment
env = Environment(trajectory=name, filename=traj_file, file_title=u'{} data'.format(name),
comment=u'{} data'.format(name),
add_time=True,
# freeze_input=True,
# multiproc=True,
# use_scoop=True,
wrap_mode=pypetconstants.WRAP_MODE_LOCK,
automatic_storing=True,
log_stdout=False, # Sends stdout to logs
log_folder=os.path.join(paths.output_dir_path, 'logs')
)
create_shared_logger_data(logger_names=['bin', 'optimizers'],
log_levels=['INFO', 'INFO'],
log_to_consoles=[True, True],
sim_name=name,
log_directory=paths.logs_path)
configure_loggers()
# Get the trajectory from the environment
traj = env.trajectory
optimizee = DLSMDPOptimizee(traj)
## Outerloop optimizer initialization
parameters = ClassicGDParameters(learning_rate=0.001, exploration_step_size=0.001,
n_random_steps=50, n_iteration=30,
stop_criterion=np.Inf, seed=1234)
#parameters = AdamParameters(learning_rate=0.01, exploration_step_size=0.01, n_random_steps=15, first_order_decay=0.8,
# second_order_decay=0.8, n_iteration=83, stop_criterion=np.Inf, seed=99)
# parameters = StochasticGDParameters(learning_rate=0.01, stochastic_deviation=1, stochastic_decay=0.99,
# exploration_step_size=0.01, n_random_steps=5, n_iteration=100,
# stop_criterion=np.Inf)
#parameters = RMSPropParameters(learning_rate=0.01, exploration_step_size=0.01,
# n_random_steps=5, momentum_decay=0.5,
# n_iteration=100, stop_criterion=np.Inf, seed=99)
optimizer = GradientDescentOptimizer(traj, optimizee_create_individual=optimizee.create_individual,
optimizee_fitness_weights=(-1.,),
parameters=parameters,
optimizee_bounding_func=optimizee.bounding_func,
base_point_evaluations=10)
# Add post processing
env.add_postprocessing(optimizer.post_process)
# Run the simulation with all parameter combinations
env.run(optimizee.simulate)
## Outerloop optimizer end
optimizer.end(traj)
# Finally disable logging and close all log-files
env.disable_logging()
def main(path_name, resolution, fixed_delay, use_pecevski, num_trials):
name = path_name
try:
with open('bin/path.conf') as f:
root_dir_path = f.read().strip()
except FileNotFoundError:
raise FileNotFoundError(
"You have not set the root path to store your results."
" Write the path to a path.conf text file in the bin directory"
" before running the simulation")
paths = Paths(name, dict(run_no='test'), root_dir_path=root_dir_path)
traj_file = os.path.join(paths.output_dir_path, 'data.h5')
# Create an environment that handles running our simulation
# This initializes a PyPet environment
env = Environment(
trajectory=name,
filename=traj_file,
file_title='{} data'.format(name),
comment='{} data'.format(name),
add_time=True,
automatic_storing=True,
use_scoop=True,
multiproc=True,
wrap_mode=pypetconstants.WRAP_MODE_LOCAL,
log_stdout=False, # Sends stdout to logs
)
create_shared_logger_data(logger_names=['bin', 'optimizers'],
log_levels=['INFO', 'INFO'],
log_to_consoles=[True, True],
sim_name=name,
log_directory=paths.logs_path)
configure_loggers()
# Get the trajectory from the environment
traj = env.trajectory
# NOTE: Innerloop simulator
optimizee = SAMOptimizee(traj,
use_pecevski=use_pecevski,
n_NEST_threads=1,
time_resolution=resolution,
fixed_delay=fixed_delay,
plots_directory=paths.output_dir_path,
num_fitness_trials=num_trials)
# NOTE: Outerloop optimizer initialization
parameters = GeneticAlgorithmParameters(seed=0,
popsize=200,
CXPB=0.5,
MUTPB=1.0,
NGEN=20,
indpb=0.01,
tournsize=20,
matepar=0.5,
mutpar=1.0,
remutate=False)
optimizer = GeneticAlgorithmOptimizer(
traj,
optimizee_create_individual=optimizee.create_individual,
optimizee_fitness_weights=(-0.1, ),
parameters=parameters,
optimizee_bounding_func=optimizee.bounding_func,
optimizee_parameter_spec=optimizee.parameter_spec,
fitness_plot_name=path_name)
# Add post processing
env.add_postprocessing(optimizer.post_process)
# Run the simulation with all parameter combinations
env.run(optimizee.simulate)
# NOTE: Outerloop optimizer end
optimizer.end(traj)
# Finally disable logging and close all log-files
env.disable_logging()
def main():
name = 'LTL-MDP-SA_6_8_TD1'
try:
with open('path.conf') as f:
root_dir_path = f.read().strip()
except FileNotFoundError:
raise FileNotFoundError(
"You have not set the root path to store your results."
" Write the path to a path.conf text file in the bin directory"
" before running the simulation"
)
paths = Paths(name, dict(run_no='test'), root_dir_path=root_dir_path)
print("All output logs can be found in directory ", paths.logs_path)
traj_file = os.path.join(paths.output_dir_path, 'data.h5')
# Create an environment that handles running our simulation
# This initializes a PyPet environment
env = Environment(trajectory=name, filename=traj_file, file_title=u'{} data'.format(name),
comment=u'{} data'.format(name),
add_time=True,
# freeze_input=True,
# multiproc=True,
# use_scoop=True,
wrap_mode=pypetconstants.WRAP_MODE_LOCK,
automatic_storing=True,
log_stdout=False, # Sends stdout to logs
log_folder=os.path.join(paths.output_dir_path, 'logs')
)
create_shared_logger_data(logger_names=['bin', 'optimizers'],
log_levels=['INFO', 'INFO'],
log_to_consoles=[True, True],
sim_name=name,
log_directory=paths.logs_path)
configure_loggers()
# Get the trajectory from the environment
traj = env.trajectory
optimizee = DLSMDPOptimizee(traj)
# NOTE: Outerloop optimizer initialization
parameters = SimulatedAnnealingParameters(n_parallel_runs=50, noisy_step=.03, temp_decay=.99, n_iteration=30,
stop_criterion=np.Inf, seed=np.random.randint(1e5), cooling_schedule=AvailableCoolingSchedules.QUADRATIC_ADDAPTIVE)
optimizer = SimulatedAnnealingOptimizer(traj, optimizee_create_individual=optimizee.create_individual,
optimizee_fitness_weights=(-1.,),
parameters=parameters,
optimizee_bounding_func=optimizee.bounding_func)
# Add post processing
env.add_postprocessing(optimizer.post_process)
# Run the simulation with all parameter combinations
env.run(optimizee.simulate)
## Outerloop optimizer end
optimizer.end(traj)
# Finally disable logging and close all log-files
env.disable_logging()
def main():
name = 'LTL-MDP-GS'
try:
with open('path.conf') as f:
root_dir_path = f.read().strip()
except FileNotFoundError:
raise FileNotFoundError(
"You have not set the root path to store your results."
" Write the path to a path.conf text file in the bin directory"
" before running the simulation")
paths = Paths(name, dict(run_no='test'), root_dir_path=root_dir_path)
print("All output logs can be found in directory ", paths.logs_path)
traj_file = os.path.join(paths.output_dir_path, 'data.h5')
# Create an environment that handles running our simulation
# This initializes a PyPet environment
env = Environment(
trajectory=name,
filename=traj_file,
file_title=u'{} data'.format(name),
comment=u'{} data'.format(name),
add_time=True,
freeze_input=True,
multiproc=True,
use_scoop=True,
wrap_mode=pypetconstants.WRAP_MODE_LOCAL,
automatic_storing=True,
log_stdout=False, # Sends stdout to logs
log_folder=os.path.join(paths.output_dir_path, 'logs'))
create_shared_logger_data(logger_names=['bin', 'optimizers'],
log_levels=['INFO', 'INFO'],
log_to_consoles=[True, True],
sim_name=name,
log_directory=paths.logs_path)
configure_loggers()
# Get the trajectory from the environment
traj = env.trajectory
# NOTE: Innerloop simulator
optimizee = StateActionOptimizee(traj)
# NOTE: Outerloop optimizer initialization
n_grid_divs_per_axis = 50
parameters = GridSearchParameters(
param_grid={
'gamma': (optimizee.bound[0], optimizee.bound[1],
n_grid_divs_per_axis),
#'lam': (optimizee.bound[0], optimizee.bound[1], n_grid_divs_per_axis),
'eta': (optimizee.bound[0], optimizee.bound[1],
n_grid_divs_per_axis),
})
optimizer = GridSearchOptimizer(
traj,
optimizee_create_individual=optimizee.create_individual,
optimizee_fitness_weights=(-1.),
parameters=parameters)
# Add post processing
env.add_postprocessing(optimizer.post_process)
# Run the simulation with all parameter combinations
env.run(optimizee.simulate)
# NOTE: Outerloop optimizer end
optimizer.end(traj)
# Finally disable logging and close all log-files
env.disable_logging()
def main():
name = 'LTL-MDP-ES_6_8_TD1'
try:
with open('path.conf') as f:
root_dir_path = f.read().strip()
except FileNotFoundError:
raise FileNotFoundError(
"You have not set the root path to store your results."
" Write the path to a path.conf text file in the bin directory"
" before running the simulation"
)
paths = Paths(name, dict(run_no='test'), root_dir_path=root_dir_path)
print("All output logs can be found in directory ", paths.logs_path)
traj_file = os.path.join(paths.output_dir_path, 'data.h5')
# Create an environment that handles running our simulation
# This initializes a PyPet environment
env = Environment(trajectory=name, filename=traj_file, file_title=u'{} data'.format(name),
comment=u'{} data'.format(name),
add_time=True,
# freeze_input=True,
# multiproc=True,
# use_scoop=True,
wrap_mode=pypetconstants.WRAP_MODE_LOCK,
automatic_storing=True,
log_stdout=False, # Sends stdout to logs
log_folder=os.path.join(paths.output_dir_path, 'logs')
)
create_shared_logger_data(logger_names=['bin', 'optimizers'],
log_levels=['INFO', 'INFO'],
log_to_consoles=[True, True],
sim_name=name,
log_directory=paths.logs_path)
configure_loggers()
# Get the trajectory from the environment
traj = env.trajectory
## Benchmark function
optimizee = DLSMDPOptimizee(traj)
## Innerloop simulator
## Outerloop optimizer initialization
optimizer_seed = 1234
parameters = EvolutionStrategiesParameters(
learning_rate=0.5,
learning_rate_decay=0.95,
noise_std=0.1,
mirrored_sampling_enabled=True,
fitness_shaping_enabled=True,
pop_size=25,
n_iteration=30,
stop_criterion=np.Inf,
seed=optimizer_seed)
optimizer = EvolutionStrategiesOptimizer(
traj,
optimizee_create_individual=optimizee.create_individual,
optimizee_fitness_weights=(-1.,),
parameters=parameters,
optimizee_bounding_func=optimizee.bounding_func)
# Add post processing
env.add_postprocessing(optimizer.post_process)
# Run the simulation with all parameter combinations
env.run(optimizee.simulate)
## Outerloop optimizer end
optimizer.end(traj)
# Finally disable logging and close all log-files
env.disable_logging()
def main():
name = 'LTL-MDP-FACE'
try:
with open('path.conf') as f:
root_dir_path = f.read().strip()
except FileNotFoundError:
raise FileNotFoundError(
"You have not set the root path to store your results."
" Write the path to a path.conf text file in the bin directory"
" before running the simulation")
paths = Paths(name, dict(run_no='test'), root_dir_path=root_dir_path)
print("All output logs can be found in directory ", paths.logs_path)
traj_file = os.path.join(paths.output_dir_path, 'data.h5')
# Create an environment that handles running our simulation
# This initializes a PyPet environment
env = Environment(
trajectory=name,
filename=traj_file,
file_title=u'{} data'.format(name),
comment=u'{} data'.format(name),
add_time=True,
# freeze_input=True,
# multiproc=True,
# use_scoop=True,
wrap_mode=pypetconstants.WRAP_MODE_LOCK,
automatic_storing=True,
log_stdout=False, # Sends stdout to logs
log_folder=os.path.join(paths.output_dir_path, 'logs'))
create_shared_logger_data(logger_names=['bin', 'optimizers'],
log_levels=['INFO', 'INFO'],
log_to_consoles=[True, True],
sim_name=name,
log_directory=paths.logs_path)
configure_loggers()
# Get the trajectory from the environment
traj = env.trajectory
optimizee = DLSMDPOptimizee(traj)
# NOTE: Outerloop optimizer initialization
# TODO: Change the optimizer to the appropriate Optimizer class
parameters = FACEParameters(min_pop_size=25,
max_pop_size=25,
n_elite=10,
smoothing=0.2,
temp_decay=0,
n_iteration=100,
distribution=Gaussian(),
n_expand=5,
stop_criterion=np.inf,
seed=109)
optimizer = FACEOptimizer(
traj,
optimizee_create_individual=optimizee.create_individual,
optimizee_fitness_weights=(-1.),
parameters=parameters,
optimizee_bounding_func=optimizee.bounding_func)
# Add post processing
env.add_postprocessing(optimizer.post_process)
# Run the simulation with all parameter combinations
env.run(optimizee.simulate)
## Outerloop optimizer end
optimizer.end(traj)
# Finally disable logging and close all log-files
env.disable_logging()
def run_best_spigraph(resolution, fixed_delay, min_delay, max_delay,
use_pecevski, num_trials, seed):
'''Runs the best SPI setup in the log file chosen by the user.'''
import logging.config
from sam.optimizee import SPINetworkOptimizee
from ltl import sdict
from pypet import Environment, pypetconstants
from ltl.logging_tools import create_shared_logger_data, configure_loggers
from ltl.paths import Paths
logger = logging.getLogger('bin.ltl-spigraph-ga')
name = "trial"
root_dir_path = "plots"
paths = Paths(name, dict(run_no='test'), root_dir_path=root_dir_path)
traj_file = os.path.join(paths.output_dir_path, 'data.h5')
# Create an environment that handles running our simulation
# This initializes a PyPet environment
env = Environment(
trajectory=name,
filename=traj_file,
file_title='{} data'.format(name),
comment='{} data'.format(name),
add_time=True,
automatic_storing=True,
use_scoop=True,
multiproc=True,
wrap_mode=pypetconstants.WRAP_MODE_LOCAL,
log_stdout=False, # Sends stdout to logs
)
create_shared_logger_data(logger_names=['bin', 'optimizers'],
log_levels=['INFO', 'INFO'],
log_to_consoles=[True, True],
sim_name=name,
log_directory=paths.logs_path)
configure_loggers()
# Get the trajectory from the environment
traj = env.trajectory
print(
"Running with resolution = {}, fixed delay = {}, use_pecevski = {}\n".
format(resolution, fixed_delay, use_pecevski))
fns, hps = process_spigraph_results('/home/krisdamato/LTL-SAM/results/')
print('')
for i, fn in enumerate(fns):
print("{}: {}".format(i, fn))
try:
i = int(input('\nChoose log index: '))
except ValueError:
print("Not a number!")
return
if i >= len(fns):
print("Choose within the range!")
return
# Get best hps in the chosen log file.
params = hps[fns[i]]
params['intrinsic_step_time_fraction'] = 1.0
params['stdp_time_fraction'] = 1.0
params['learning_time'] = 300000
# Create the SAM optimizee.
optimizee = SPINetworkOptimizee(
traj,
use_pecevski=use_pecevski,
n_NEST_threads=1,
time_resolution=resolution,
min_delay=min_delay,
max_delay=max_delay,
fixed_delay=fixed_delay,
plots_directory='/home/krisdamato/LTL-SAM/plots/',
forced_params=params,
plot_all=False,
seed=seed,
num_fitness_trials=num_trials)
# Run simulation with the forced params.
optimizee.simulate(traj)
