def main():
filename = os.path.join('hdf5', 'FiringRate.hdf5')
env = Environment(
trajectory='FiringRate',
comment='Experiment to measure the firing rate '
'of a leaky integrate and fire neuron. '
'Exploring different input currents, '
'as well as refractory periods',
add_time=False, # We don't want to add the current time to the name,
log_stdout=True,
log_config='DEFAULT',
multiproc=True,
ncores=4, #I think my laptop has 4 cores
git_repository='/home/pinolej/th',
wrap_mode='QUEUE',
filename=filename,
overwrite_file=True)
traj = env.trajectory
# Add parameters
add_parameters(traj)
# Let's explore
add_exploration(traj)
# Ad the postprocessing function
env.add_postprocessing(neuron_postproc)
# Run the experiment
env.run(run_neuron)
# Finally disable logging and close all log-files
env.disable_logging()
def main():
filename = os.path.join('hdf5', 'FiringRate.hdf5')
env = Environment(trajectory='FiringRate',
comment='Experiment to measure the firing rate '
'of a leaky integrate and fire neuron. '
'Exploring different input currents, '
'as well as refractory periods',
add_time=False, # We don't want to add the current time to the name,
log_stdout=True,
log_config='DEFAULT',
multiproc=True,
ncores=2, #My laptop has 2 cores ;-)
wrap_mode='QUEUE',
filename=filename,
overwrite_file=True)
traj = env.trajectory
# Add parameters
add_parameters(traj)
# Let's explore
add_exploration(traj)
# Ad the postprocessing function
env.add_postprocessing(neuron_postproc)
# Run the experiment
env.run(run_neuron)
# Finally disable logging and close all log-files
env.disable_logging()
def main():
# pypet environment
env = Environment(trajectory=SIM_NAME,
comment="Experiment on density with binary covariates",
log_config=None,
multiproc=False,
ncores=1,
filename=SIM_PATH + "/results/",
overwrite_file=True)
traj = env.trajectory
# parameters (data generation)
traj.f_add_parameter("data.N", np.int64(500), "Number of nodes")
traj.f_add_parameter("data.K", np.int64(5),
"True number of latent components")
traj.f_add_parameter("data.p_cts", np.int64(0),
"Number of continuous covariates")
traj.f_add_parameter("data.p_bin", np.int64(0),
"Number of binary covariates")
traj.f_add_parameter("data.var_adj", np.float64(1.),
"True variance in the link Probit model")
traj.f_add_parameter("data.var_cov", np.float64(1.),
"True variance in the covariate model (cts and bin)")
traj.f_add_parameter("data.missing_rate", np.float64(0.1), "Missing rate")
traj.f_add_parameter("data.seed", np.int64(1), "Random seed")
traj.f_add_parameter("data.alpha_mean", np.float64(-1.85),
"Mean of the heterogeneity parameter")
# parameters (model)
traj.f_add_parameter("model.K", np.int64(5),
"Number of latent components in the model")
traj.f_add_parameter("model.adj_model", "Logistic", "Adjacency model")
traj.f_add_parameter("model.bin_model", "Logistic",
"Binary covariate model")
# parameters (fit)
traj.f_add_parameter("fit.n_iter", np.int64(20),
"Number of VEM iterations")
traj.f_add_parameter("fit.n_vmp", np.int64(5),
"Number of VMP iterations per E-step")
traj.f_add_parameter("fit.n_gd", np.int64(5),
"Number of GD iterations per M-step")
traj.f_add_parameter("fit.step_size", np.float64(0.01), "GD Step size")
# experiment
explore_dict = {
"data.alpha_mean":
np.array([-3.2, -2.8, -2.4, -2., -1.6, -1.2, -0.8, -0.4, 0.0, 0.4]),
"data.p_bin":
np.array([10, 100, 500]),
"data.seed":
np.arange(0, 100, 1)
}
experiment = cartesian_product(explore_dict, tuple(explore_dict.keys()))
traj.f_explore(experiment)
env.add_postprocessing(post_processing)
env.run(run)
env.disable_logging()
def main(path, name, explore_dict):
comment = "\n".join(
["{}: {}".format(k, v) for k, v in explore_dict.items()])
# pypet environment
env = Environment(trajectory=name,
comment=comment,
log_config=None,
multiproc=False,
ncores=1,
filename=path + name + "/results/",
overwrite_file=True)
traj = env.trajectory
traj.f_add_parameter("path", path + name, "Path")
# parameters (data generation)
traj.f_add_parameter("data.N", np.int64(500), "Number of nodes")
traj.f_add_parameter("data.K", np.int64(5),
"True number of latent components")
traj.f_add_parameter("data.p_cts", np.int64(0),
"Number of continuous covariates")
traj.f_add_parameter("data.p_bin", np.int64(0),
"Number of binary covariates")
traj.f_add_parameter("data.var_cov", np.float64(1.),
"True variance in the covariate model (cts and bin)")
traj.f_add_parameter("data.missing_rate", np.float64(0.1), "Missing rate")
traj.f_add_parameter("data.seed", np.int64(1), "Random seed")
traj.f_add_parameter("data.center", np.int64(1), "Ego-network center")
traj.f_add_parameter("data.alpha_mean", np.float64(-1.85),
"Mean of the heterogeneity parameter")
# parameters (model)
traj.f_add_parameter("model.K", np.int64(5),
"Number of latent components in the model")
# parameters (fit)
traj.f_add_parameter("fit.algo", "MLE", "Inference algorithm")
traj.f_add_parameter("fit.max_iter", np.int64(500),
"Number of VEM iterations")
traj.f_add_parameter("fit.n_sample", np.int64(1),
"Number of samples for VIMC")
traj.f_add_parameter("fit.eps", np.float64(1.0e-6),
"convergence threshold")
traj.f_add_parameter("fit.lr", np.float64(0.01), "GD Step size")
# experiment
experiment = cartesian_product(explore_dict, tuple(explore_dict.keys()))
traj.f_explore(experiment)
env.add_postprocessing(post_processing)
env.run(run)
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, 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-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():
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 _batch_run(dir='unnamed',
batch_id='template',
space=None,
save_data_in_hdf5=False,
single_method=single_run,
process_method=null_processing,
post_process_method=None,
final_process_method=None,
multiprocessing=True,
resumable=True,
overwrite=False,
sim_config=None,
params=None,
optimization=None,
post_kwargs={},
run_kwargs={}):
saved_args = locals()
traj_name = f'{batch_id}_traj'
parent_dir_path = f'{paths.BatchRunFolder}/{dir}'
dir_path = os.path.join(parent_dir_path, batch_id)
filename = f'{dir_path}/{batch_id}.hdf5'
build_new = True
if os.path.exists(parent_dir_path) and os.path.exists(
dir_path) and overwrite == False:
build_new = False
try:
# print('Trying to resume existing trajectory')
env = Environment(continuable=True)
env.resume(trajectory_name=traj_name, resume_folder=dir_path)
print('Resumed existing trajectory')
build_new = False
except:
try:
# print('Trying to load existing trajectory')
traj = load_trajectory(filename=filename,
name=traj_name,
load_all=0)
env = Environment(trajectory=traj, multiproc=True, ncores=4)
traj = config_traj(traj, optimization)
traj.f_load(index=None, load_parameters=2, load_results=0)
traj.f_expand(space)
print('Loaded existing trajectory')
build_new = False
except:
print(
'Neither of resuming or expanding of existing trajectory worked'
)
if build_new:
if multiprocessing:
multiproc = True
resumable = False
wrap_mode = pypetconstants.WRAP_MODE_QUEUE
else:
multiproc = False
resumable = True
wrap_mode = pypetconstants.WRAP_MODE_LOCK
# print('Trying to create novel environment')
env = Environment(
trajectory=traj_name,
filename=filename,
file_title=batch_id,
comment=f'{batch_id} batch run!',
large_overview_tables=True,
overwrite_file=True,
resumable=False,
resume_folder=dir_path,
multiproc=multiproc,
ncores=4,
use_pool=
True, # Our runs are inexpensive we can get rid of overhead by using a pool
freeze_input=
True, # We can avoid some overhead by freezing the input to the pool
wrap_mode=wrap_mode,
graceful_exit=True)
print('Created novel environment')
traj = prepare_traj(env.traj, sim_config, params, batch_id,
parent_dir_path, dir_path)
traj = config_traj(traj, optimization)
traj.f_explore(space)
if post_process_method is not None:
env.add_postprocessing(post_process_method, **post_kwargs)
env.run(single_method,
process_method,
save_data_in_hdf5=save_data_in_hdf5,
save_to=dir_path,
**run_kwargs)
env.disable_logging()
print('Batch run complete')
if final_process_method is not None:
results = final_process_method(env.traj)
# print(results)
return results
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():
env = Environment(trajectory='postproc_deap',
overwrite_file=True,
log_stdout=False,
log_level=50, # only display ERRORS
automatic_storing=True, # Since we us post-processing, we
# can safely enable automatic storing, because everything will
# only be stored once at the very end of all runs.
comment='Using pypet and DEAP with less overhead'
)
traj = env.traj
# ------- Add parameters ------- #
traj.f_add_parameter('popsize', 100, comment='Population size')
traj.f_add_parameter('CXPB', 0.5, comment='Crossover term')
traj.f_add_parameter('MUTPB', 0.2, comment='Mutation probability')
traj.f_add_parameter('NGEN', 20, comment='Number of generations')
traj.f_add_parameter('generation', 0, comment='Current generation')
traj.f_add_parameter('ind_idx', 0, comment='Index of individual')
traj.f_add_parameter('ind_len', 50, comment='Length of individual')
traj.f_add_parameter('indpb', 0.005, comment='Mutation parameter')
traj.f_add_parameter('tournsize', 3, comment='Selection parameter')
traj.f_add_parameter('seed', 42, comment='Seed for RNG')
# Placeholders for individuals and results that are about to be explored
traj.f_add_derived_parameter('individual', [0 for x in range(traj.ind_len)],
'An indivudal of the population')
traj.f_add_result('fitnesses', [], comment='Fitnesses of all individuals')
# ------- Create and register functions with DEAP ------- #
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)
toolbox = base.Toolbox()
# Attribute generator
toolbox.register("attr_bool", random.randint, 0, 1)
# Structure initializers
toolbox.register("individual", tools.initRepeat, creator.Individual,
toolbox.attr_bool, traj.ind_len)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
# Operator registering
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutFlipBit, indpb=traj.indpb)
toolbox.register("select", tools.selTournament, tournsize=traj.tournsize)
# ------- Initialize Population and Trajectory -------- #
random.seed(traj.seed)
pop = toolbox.population(n=traj.popsize)
eval_pop = [ind for ind in pop if not ind.fitness.valid]
traj.f_explore(cartesian_product({'generation': [0],
'ind_idx': range(len(eval_pop)),
'individual':[list(x) for x in eval_pop]},
[('ind_idx', 'individual'),'generation']))
# ----------- Add postprocessing ------------------ #
postproc = Postprocessing(pop, eval_pop, toolbox) # Add links to important structures
env.add_postprocessing(postproc)
# ------------ Run applying post-processing ---------- #
env.run(eval_one_max)
# ------------ Finished all runs and print result --------------- #
print("-- End of (successful) evolution --")
best_ind = tools.selBest(pop, 1)[0]
print("Best individual is %s, %s" % (best_ind, best_ind.fitness.values))
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 _batch_run(dir='unnamed',
batch_id='template',
space=None,
save_data_in_hdf5=False,
single_method=single_run,
process_method=null_processing,
post_process_method=None,
final_process_method=None,
multiprocessing=True,
resumable=True,
overwrite=False,
sim_config=None,
params=None,
config=None,
post_kwargs={},
run_kwargs={}):
saved_args = locals()
# print(locals())
traj_name = f'{batch_id}_traj'
parent_dir_path = f'{BatchRunFolder}/{dir}'
dir_path = os.path.join(parent_dir_path, batch_id)
plot_path = os.path.join(dir_path, f'{batch_id}.pdf')
data_path = os.path.join(dir_path, f'{batch_id}.csv')
filename = f'{dir_path}/{batch_id}.hdf5'
build_new = True
if os.path.exists(parent_dir_path) and os.path.exists(
dir_path) and overwrite == False:
build_new = False
try:
print('Trying to resume existing trajectory')
env = Environment(continuable=True)
env.resume(trajectory_name=traj_name, resume_folder=dir_path)
print('Resumed existing trajectory')
build_new = False
except:
try:
print('Trying to load existing trajectory')
traj = load_trajectory(filename=filename,
name=traj_name,
load_all=0)
env = Environment(trajectory=traj)
traj.f_load(index=None, load_parameters=2, load_results=0)
traj.f_expand(space)
print('Loaded existing trajectory')
build_new = False
except:
print(
'Neither of resuming or expanding of existing trajectory worked'
)
if build_new:
if multiprocessing:
multiproc = True
resumable = False
wrap_mode = pypetconstants.WRAP_MODE_QUEUE
else:
multiproc = False
resumable = True
wrap_mode = pypetconstants.WRAP_MODE_LOCK
# try:
print('Trying to create novel environment')
env = Environment(
trajectory=traj_name,
filename=filename,
file_title=batch_id,
comment=f'{batch_id} batch run!',
large_overview_tables=True,
overwrite_file=True,
resumable=False,
resume_folder=dir_path,
multiproc=multiproc,
ncores=4,
use_pool=
True, # Our runs are inexpensive we can get rid of overhead by using a pool
freeze_input=
True, # We can avoid some overhead by freezing the input to the pool
wrap_mode=wrap_mode,
graceful_exit=True)
traj = env.traj
print('Created novel environment')
fly_params, env_params, sim_params = sim_config[
'fly_params'], sim_config['env_params'], sim_config['sim_params']
if all(v is not None for v in [sim_params, env_params, fly_params]):
traj = load_default_configuration(traj,
sim_params=sim_params,
env_params=env_params,
fly_params=fly_params)
elif params is not None:
for p in params:
traj.f_apar(p, 0.0)
if config is not None:
for k, v in config.items():
traj.f_aconf(k, v)
traj.f_aconf('parent_dir_path',
parent_dir_path,
comment='The parent directory')
traj.f_aconf('dir_path',
dir_path,
comment='The directory path for saving data')
traj.f_aconf('plot_path',
plot_path,
comment='The file path for saving plot')
traj.f_aconf('data_path',
data_path,
comment='The file path for saving data')
traj.f_aconf('dataset_path',
f'{dir_path}/{batch_id}',
comment='The directory path for saving datasets')
traj.f_explore(space)
# except:
# raise ValueError(f'Failed to perform batch run {batch_id}')
if post_process_method is not None:
env.add_postprocessing(post_process_method, **post_kwargs)
env.run(single_method,
process_method,
save_data_in_hdf5=save_data_in_hdf5,
save_to=dir_path,
common_folder=batch_id,
**run_kwargs)
env.disable_logging()
print('Batch run complete')
if final_process_method is not None:
return final_process_method(env.traj)
def main():
# pypet environment
env = Environment(
trajectory="missing_rate",
comment="Test experiment with varying missing rate",
log_config=None,
multiproc=False,
ncores=1,
# use_pool=True,
# freeze_input=True,
# wrap_mode=pypetconstants.WRAP_MODE_QUEUE,
# graceful_exit=True,
filename="./simulations/results/test/",
overwrite_file=True
)
traj = env.trajectory
# parameters (data generation)
traj.f_add_parameter(
"data.N", np.int64(500), "Number of nodes"
)
traj.f_add_parameter(
"data.K", np.int64(5), "True number of latent components"
)
traj.f_add_parameter(
"data.p_cts", np.int64(10), "Number of continuous covariates"
)
traj.f_add_parameter(
"data.p_bin", np.int64(0), "Number of binary covariates"
)
traj.f_add_parameter(
"data.var_adj", np.float64(1.), "True variance in the link Probit model"
)
traj.f_add_parameter(
"data.var_cov", np.float64(1.), "True variance in the covariate model (cts and bin)"
)
traj.f_add_parameter(
"data.missing_rate", np.float64(0.2), "Missing rate"
)
traj.f_add_parameter(
"data.seed", np.int64(1), "Random seed"
)
traj.f_add_parameter(
"data.alpha_mean", np.float64(-1.85), "Mean of the heterogeneity parameter"
)
# parameters (model)
traj.f_add_parameter(
"model.K", np.int64(3), "Number of latent components in the model"
)
traj.f_add_parameter(
"model.adj_model", "Logistic", "Adjacency model"
)
traj.f_add_parameter(
"model.bin_model", "Logistic", "Binary covariate model"
)
# parameters (fit)
traj.f_add_parameter(
"fit.n_iter", np.int64(10), "Number of VEM iterations"
)
traj.f_add_parameter(
"fit.n_vmp", np.int64(10), "Number of VMP iterations per E-step"
)
traj.f_add_parameter(
"fit.n_gd", np.int64(10), "Number of GD iterations per M-step"
)
traj.f_add_parameter(
"fit.step_size", np.float64(0.01), "GD Step size"
)
# experiment
explore_dict = {
"data.missing_rate": np.array([0.05]),
"data.p_cts": np.array([10]),
"data.seed": np.array([1])
}
experiment = cartesian_product(explore_dict, ('data.missing_rate', "data.p_cts", "data.seed"))
traj.f_explore(experiment)
env.add_postprocessing(post_processing)
env.run(run)
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-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-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():
env = Environment(
trajectory='postproc_deap',
overwrite_file=True,
log_stdout=False,
log_level=50, # only display ERRORS
automatic_storing=True, # Since we us post-processing, we
# can safely enable automatic storing, because everything will
# only be stored once at the very end of all runs.
comment='Using pypet and DEAP with less overhead')
traj = env.traj
# ------- Add parameters ------- #
traj.f_add_parameter('popsize', 100, comment='Population size')
traj.f_add_parameter('CXPB', 0.5, comment='Crossover term')
traj.f_add_parameter('MUTPB', 0.2, comment='Mutation probability')
traj.f_add_parameter('NGEN', 20, comment='Number of generations')
traj.f_add_parameter('generation', 0, comment='Current generation')
traj.f_add_parameter('ind_idx', 0, comment='Index of individual')
traj.f_add_parameter('ind_len', 50, comment='Length of individual')
traj.f_add_parameter('indpb', 0.005, comment='Mutation parameter')
traj.f_add_parameter('tournsize', 3, comment='Selection parameter')
traj.f_add_parameter('seed', 42, comment='Seed for RNG')
# Placeholders for individuals and results that are about to be explored
traj.f_add_derived_parameter('individual',
[0 for x in range(traj.ind_len)],
'An indivudal of the population')
traj.f_add_result('fitnesses', [], comment='Fitnesses of all individuals')
# ------- Create and register functions with DEAP ------- #
creator.create("FitnessMax", base.Fitness, weights=(1.0, ))
creator.create("Individual", list, fitness=creator.FitnessMax)
toolbox = base.Toolbox()
# Attribute generator
toolbox.register("attr_bool", random.randint, 0, 1)
# Structure initializers
toolbox.register("individual", tools.initRepeat, creator.Individual,
toolbox.attr_bool, traj.ind_len)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
# Operator registering
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutFlipBit, indpb=traj.indpb)
toolbox.register("select", tools.selTournament, tournsize=traj.tournsize)
# ------- Initialize Population and Trajectory -------- #
random.seed(traj.seed)
pop = toolbox.population(n=traj.popsize)
eval_pop = [ind for ind in pop if not ind.fitness.valid]
traj.f_explore(
cartesian_product(
{
'generation': [0],
'ind_idx': range(len(eval_pop)),
'individual': [list(x) for x in eval_pop]
}, [('ind_idx', 'individual'), 'generation']))
# ----------- Add postprocessing ------------------ #
postproc = Postprocessing(pop, eval_pop,
toolbox) # Add links to important structures
env.add_postprocessing(postproc)
# ------------ Run applying post-processing ---------- #
env.run(eval_one_max)
# ------------ Finished all runs and print result --------------- #
print("-- End of (successful) evolution --")
best_ind = tools.selBest(pop, 1)[0]
print("Best individual is %s, %s" % (best_ind, best_ind.fitness.values))
