def main():
name = 'L2L-FUN-GS'
experiment = Experiment(root_dir_path='../results')
traj, _ = experiment.prepare_experiment(name=name, log_stdout=True)
## Benchmark function
function_id = 4
bench_functs = BenchmarkedFunctions()
(benchmark_name, benchmark_function), benchmark_parameters = \
bench_functs.get_function_by_index(function_id, noise=True)
optimizee_seed = 100
random_state = np.random.RandomState(seed=optimizee_seed)
function_tools.plot(benchmark_function, random_state)
## Innerloop simulator
optimizee = FunctionGeneratorOptimizee(traj, benchmark_function, seed=optimizee_seed)
## Outerloop optimizer initialization
n_grid_divs_per_axis = 30
parameters = GridSearchParameters(param_grid={
'coords': (optimizee.bound[0], optimizee.bound[1], n_grid_divs_per_axis)
})
optimizer = GridSearchOptimizer(traj, optimizee_create_individual=optimizee.create_individual,
optimizee_fitness_weights=(-0.1,),
parameters=parameters)
# Experiment run
experiment.run_experiment(optimizee=optimizee, optimizer=optimizer,
optimizee_parameters=parameters)
# End experiment
experiment.end_experiment(optimizer)
def main():
# TODO: use the experiment module to prepare and run later the simulation
# define a directory to store the results
experiment = Experiment(root_dir_path='~/home/user/L2L/results')
# TODO when using the template: use keywords to prepare the experiment and
# create a dictionary for jube parameters
# prepare_experiment returns the trajectory and all jube parameters
jube_params = {"nodes": "2",
"walltime": "10:00:00",
"ppn": "1",
"cpu_pp": "1"}
traj, all_jube_params = experiment.prepare_experiment(name='L2L',
log_stdout=True,
**jube_params)
## Innerloop simulator
# TODO when using the template: Change the optimizee to the appropriate
# Optimizee class
optimizee = Optimizee(traj)
# TODO Create optimizee parameters
optimizee_parameters = OptimizeeParameters()
## Outerloop optimizer initialization
# TODO when using the template: Change the optimizer to the appropriate
# Optimizer class and use the right value for optimizee_fitness_weights.
# Length is the number of dimensions of fitness, and negative value
# implies minimization and vice versa
optimizer_parameters = OptimizerParameters()
optimizer = Optimizer(traj, optimizee.create_individual, (1.0,),
optimizer_parameters)
experiment.run_experiment(optimizee=optimizee,
optimizee_parameters=optimizee_parameters,
optimizer=optimizer,
optimizer_parameters=optimizer_parameters)
def run_experiment():
experiment = Experiment("../results/")
name = 'L2L-FUN-ES'
trajectory_name = 'mirroring-and-fitness-shaping'
traj, all_jube_params = experiment.prepare_experiment(
name=name, trajectory_name=trajectory_name, log_stdout=True)
## Benchmark function
function_id = 14
bench_functs = BenchmarkedFunctions()
(benchmark_name, benchmark_function), benchmark_parameters = \
bench_functs.get_function_by_index(function_id, noise=True)
optimizee_seed = 200
## Innerloop simulator
optimizee = FunctionGeneratorOptimizee(traj,
benchmark_function,
seed=optimizee_seed)
## Outerloop optimizer initialization
optimizer_seed = 1234
parameters = EvolutionStrategiesParameters(learning_rate=0.1,
noise_std=1.0,
mirrored_sampling_enabled=True,
fitness_shaping_enabled=True,
pop_size=20,
n_iteration=1000,
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)
# Run experiment
experiment.run_experiment(optimizer=optimizer,
optimizee=optimizee,
optimizer_parameters=parameters)
# End experiment
experiment.end_experiment(optimizer)
def main():
name = 'L2L-FunctionGenerator-SA'
experiment = Experiment("../results/")
traj, all_jube_params = experiment.prepare_experiment(name=name,
log_stdout=True)
## Benchmark function
function_id = 14
bench_functs = BenchmarkedFunctions()
(benchmark_name, benchmark_function), benchmark_parameters = \
bench_functs.get_function_by_index(function_id, noise=True)
optimizee_seed = 100
random_state = np.random.RandomState(seed=optimizee_seed)
function_tools.plot(benchmark_function, random_state)
## Innerloop simulator
optimizee = FunctionGeneratorOptimizee(traj,
benchmark_function,
seed=optimizee_seed)
## Outerloop optimizer initialization
parameters = SimulatedAnnealingParameters(
n_parallel_runs=50,
noisy_step=.03,
temp_decay=.99,
n_iteration=100,
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)
# Run experiment
experiment.run_experiment(optimizer=optimizer,
optimizee=optimizee,
optimizer_parameters=parameters)
# End experiment
experiment.end_experiment(optimizer)
def run_experiment():
name = 'L2L-MNIST-CE'
experiment = Experiment("../results/")
traj, all_jube_params = experiment.prepare_experiment(name=name,
trajectory_name=name,
log_stdout=True)
optimizee_seed = 200
optimizee_parameters = MNISTOptimizeeParameters(n_hidden=10,
seed=optimizee_seed,
use_small_mnist=True)
## Innerloop simulator
optimizee = MNISTOptimizee(traj, optimizee_parameters)
## Outerloop optimizer initialization
optimizer_seed = 1234
optimizer_parameters = CrossEntropyParameters(pop_size=40,
rho=0.9,
smoothing=0.0,
temp_decay=0,
n_iteration=5000,
distribution=NoisyGaussian(
noise_magnitude=1.,
noise_decay=0.99),
stop_criterion=np.inf,
seed=optimizer_seed)
optimizer = CrossEntropyOptimizer(
traj,
optimizee_create_individual=optimizee.create_individual,
optimizee_fitness_weights=(1., ),
parameters=optimizer_parameters,
optimizee_bounding_func=optimizee.bounding_func)
# Run experiment
experiment.run_experiment(optimizer=optimizer,
optimizee=optimizee,
optimizer_parameters=optimizer_parameters,
optimizee_parameters=optimizee_parameters)
# End experiment
experiment.end_experiment(optimizer)
def main():
experiment = Experiment(root_dir_path='../results')
name = 'L2L-FUN-GA'
traj, _ = experiment.prepare_experiment(name=name, log_stdout=True)
## Benchmark function
function_id = 4
bench_functs = BenchmarkedFunctions()
(benchmark_name, benchmark_function), benchmark_parameters = \
bench_functs.get_function_by_index(function_id, noise=True)
optimizee_seed = 100
random_state = np.random.RandomState(seed=optimizee_seed)
function_tools.plot(benchmark_function, random_state)
## Innerloop simulator
optimizee = FunctionGeneratorOptimizee(traj,
benchmark_function,
seed=optimizee_seed)
## Outerloop optimizer initialization
parameters = GeneticAlgorithmParameters(seed=0,
popsize=50,
CXPB=0.5,
MUTPB=0.3,
NGEN=100,
indpb=0.02,
tournsize=15,
matepar=0.5,
mutpar=1)
optimizer = GeneticAlgorithmOptimizer(
traj,
optimizee_create_individual=optimizee.create_individual,
optimizee_fitness_weights=(-0.1, ),
parameters=parameters)
experiment.run_experiment(optimizer=optimizer,
optimizee=optimizee,
optimizee_parameters=parameters)
experiment.end_experiment(optimizer)
def run_experiment():
name = 'L2L-MNIST-ES'
trajectory_name = 'mirroring-and-fitness-shaping'
experiment = Experiment("../results/")
traj, all_jube_params = experiment.prepare_experiment(name=name,
trajectory_name=trajectory_name,
log_stdout=True)
optimizee_seed = 200
optimizee_parameters = MNISTOptimizeeParameters(n_hidden=10, seed=optimizee_seed, use_small_mnist=True)
## Innerloop simulator
optimizee = MNISTOptimizee(traj, optimizee_parameters)
## Outerloop optimizer initialization
optimizer_seed = 1234
optimizer_parameters = EvolutionStrategiesParameters(
learning_rate=0.1,
noise_std=0.1,
mirrored_sampling_enabled=True,
fitness_shaping_enabled=True,
pop_size=20,
n_iteration=2000,
stop_criterion=np.Inf,
seed=optimizer_seed)
optimizer = EvolutionStrategiesOptimizer(
traj,
optimizee_create_individual=optimizee.create_individual,
optimizee_fitness_weights=(1.,),
parameters=optimizer_parameters,
optimizee_bounding_func=optimizee.bounding_func)
# Run experiment
experiment.run_experiment(optimizer=optimizer, optimizee=optimizee,
optimizer_parameters=optimizer_parameters,
optimizee_parameters=optimizee_parameters)
# End experiment
experiment.end_experiment(optimizer)
def prepro():
# Make the neuronal model to work as the DMF model
# neuronalModel.alpha = 0.
# neuronalModel.beta = 0.
# distanceSettings = {'FC': (FC, False), 'swFCD': (swFCD, True), 'GBC': (GBC, False)} # 'phFCD': (phFCD, True)
distanceSettings = {'swFCD': (swFCD, True)}
swFCD.windowSize = 80
swFCD.windowStep = 18
# baseGOptimNames = baseOutPath+"/fitting_we{}.mat"
# step = 0.001
# WEs = np.arange(0, 3.+step, step) # Range used in the original code
# WEs = np.arange(0, 3.+step, 0.05) # reduced range for DEBUG only!!!
# Model Simulations
# ------------------------------------------
BalanceFIC.verbose = True
# balancedParms = BalanceFIC.Balance_AllJ9(C, WEs, baseName=J_fileNames)
# modelParms = [balancedParms[i] for i in balancedParms]
# Now, optimize all we (G) values: determine optimal G to work with
print(
"\n\n###################################################################"
)
print("# Compute optimization with L2L")
print(
"###################################################################\n"
)
experiment = Experiment(root_dir_path='Data_Produced/L2L')
name = 'L2L-DecoEtAl2020-Prepro'
traj, _ = experiment.prepare_experiment(name=name,
log_stdout=True,
multiprocessing=False)
# Setup the WhileBrain optimizee
WBOptimizee.neuronalModel = neuronalModel
WBOptimizee.integrator = integrator
WBOptimizee.simulateBOLD = simulateBOLD
WBOptimizee.measure = distanceSettings['swFCD'][
0] # Measure to use to compute the error
WBOptimizee.applyFilters = distanceSettings['swFCD'][
1] # Whether to apply filters to the resulting signal or not
outEmpFileName = baseOutPath + '/fNeuro_emp_L2L.mat'
WBOptimizee.processedEmp = processEmpiricalSubjects(
tc_transf, distanceSettings, outEmpFileName)[
'swFCD'] # reference values (e.g., empirical) to compare to.
WBOptimizee.N = N # Number of regions in the parcellation
WBOptimizee.trials = NumTrials # Number of trials to try
optimizee_parameters = namedtuple('OptimizeeParameters', [])
filePattern = baseOutPath + '/fitting_{}_L2L.mat'
optimizee = WBOptimizee.WholeBrainOptimizee(traj, {'we': (0., 3.)},
setupFunc=setupFunc,
outFilenamePattern=filePattern)
# =================== Test for debug only
# traj.individual = sdict(optimizee.create_individual())
# testing_error = optimizee.simulate(traj)
# print("Testing error is %s", testing_error)
# =================== end Test
# Setup the GridSearchOptimizer
n_grid_divs_per_axis = 60 # 0.05
optimizer_parameters = GridSearchParameters(
param_grid={'we': (0., 3., n_grid_divs_per_axis)})
optimizer = GridSearchOptimizer(
traj,
optimizee_create_individual=optimizee.create_individual,
optimizee_fitness_weights=(-1., ), # minimize!
parameters=optimizer_parameters)
experiment.run_experiment(optimizee=optimizee,
optimizee_parameters=optimizee_parameters,
optimizer=optimizer,
optimizer_parameters=optimizer_parameters)
experiment.end_experiment(optimizer)
print(f"best: {experiment.optimizer.best_individual['we']}")
# fitting = parmSweep.distanceForAll_Parms(tc_transf, WEs, modelParms, NumSimSubjects=NumTrials,
# distanceSettings=distanceSettings,
# parmLabel='we',
# outFilePath=baseOutPath)
# optimal = {sd: distanceSettings[sd][0].findMinMax(fitting[sd]) for sd in distanceSettings}
# ------------------------------------------
# ------------------------------------------
filePath = baseOutPath + '/DecoEtAl2020_fneuro-L2L.mat'
# sio.savemat(filePath, #{'JI': JI})
# {'we': WEs,
# 'swFCDfitt': fitting['swFCD'], # swFCDfitt,
# 'FCfitt': fitting['FC'], # FCfitt,
# 'GBCfitt': fitting['GBC'], # GBCfitt
# })
# print(f"DONE!!! (file: {filePath})")
plotTrajectory1D(optimizer.param_list['we'],
[v for (i, v) in traj.current_results])
print("DONE!!!")
name = 'ARBOR-FUN'
results_folder = '../results'
trajectory_name = 'ARBOR'
experiment = Experiment(results_folder)
traj, _ = experiment.prepare_experiment(
trajectory_name=trajectory_name,
name=name,
jube_parameter={"exec": "srun -n 1 -c 8 --exclusive python"})
# Innerloop simulator
optimizee = ArbSCOptimizee(traj, fit, swc, ref)
# Outerloop optimizer initialization
parameters = GeneticAlgorithmParameters(seed=0,
popsize=100,
CXPB=0.5,
MUTPB=0.3,
NGEN=10,
indpb=0.02,
tournsize=100,
matepar=0.5,
mutpar=1)
optimizer = GeneticAlgorithmOptimizer(
traj,
optimizee_create_individual=optimizee.create_individual,
optimizee_fitness_weights=(-0.1, ),
parameters=parameters)
experiment.run_experiment(optimizee=optimizee,
optimizer=optimizer,
optimizer_parameters=parameters,
optimizee_parameters=None)
experiment.end_experiment(optimizer)
def main():
experiment = Experiment(root_dir_path='Data_Produced/L2L')
# name = 'L2L-FUN-GA'
name = 'L2L-FUN-GS'
traj, _ = experiment.prepare_experiment(name=name,
log_stdout=True,
multiprocessing=False)
# ---------------------------------------------------------------------------------------------------------
# Benchmark function
"""
Ackley function has a large hole in at the centre surrounded by small hill like regions. Algorithms can get
trapped in one of its many local minima.
reference: https://www.sfu.ca/~ssurjano/ackley.html
:param dims: dimensionality of the function
Note: uses the recommended variable values, which are: a = 20, b = 0.2 and c = 2π.
"""
function_id = 4 # Select Ackley2d
bench_functs = BenchmarkedFunctions()
(benchmark_name, benchmark_function), benchmark_parameters = \
bench_functs.get_function_by_index(function_id, noise=True)
# ---------------------------------------------------------------------------------------------------------
optimizee_seed = 100
random_state = np.random.RandomState(seed=optimizee_seed)
# function_tools.plot(benchmark_function, random_state)
## Innerloop simulator
optimizee = FunctionGeneratorOptimizee(traj,
benchmark_function,
seed=optimizee_seed)
## Outerloop optimizer initialization
# parameters = GeneticAlgorithmParameters(seed=0, pop_size=50, cx_prob=0.5,
# mut_prob=0.3, n_iteration=100,
# ind_prob=0.02,
# tourn_size=15, mate_par=0.5,
# mut_par=1
# )
#
# optimizer = GeneticAlgorithmOptimizer(traj, optimizee_create_individual=optimizee.create_individual,
# optimizee_fitness_weights=(-0.1,),
# parameters=parameters)
# Setup the GridSearchOptimizer
n_grid_divs_per_axis = 30
parameters = GridSearchParameters(param_grid={
'coords': (optimizee.bound[0], optimizee.bound[1],
n_grid_divs_per_axis)
})
optimizer = GridSearchOptimizer(
traj,
optimizee_create_individual=optimizee.create_individual,
optimizee_fitness_weights=(-0.1, ), # minimize!
parameters=parameters)
## Optimization!!!
experiment.run_experiment(optimizer=optimizer,
optimizee=optimizee,
optimizee_parameters=parameters)
experiment.end_experiment(optimizer)
print(f"best: {experiment.optimizer.best_individual['coords']}")
def Fitting():
baseOutPath = 'Data_Produced/DecoEtAl2020'
# %%%%%%%%%%%%%%% Set General Model Parameters
we = 2.1 # Global Coupling parameter, found in the DecoEtAl2018_Prepro_* file...
J_fileName = baseOutPath+"/J_Balance_we2.1.mat" # "Data_Produced/SC90/J_test_we{}.mat"
balancedG = BalanceFIC.Balance_J9(we, C, False, J_fileName)
balancedG['J'] = balancedG['J'].flatten()
balancedG['we'] = balancedG['we']
neuronalModel.setParms(balancedG)
# distanceSettings = {'FC': (FC, False), 'swFCD': (swFCD, True), 'GBC': (GBC, False)} # 'phFCD': (phFCD, True)
distanceSettings = {'swFCD': (swFCD, True)}
swFCD.windowSize = 80
swFCD.windowStep = 18
# J_fileNames = baseOutPath+"/J_Balance_we{}.mat"
# step = 0.05
# Alphas = np.arange(-0.6, 0+step, step) # Range used in the original code for B
# Betas = np.arange(0, 2+step, step) # Range used in the original code for Z
Alphas = np.arange(-0.6, 0+0.1, 0.1) # reduced range for DEBUG only!!!
Betas = np.arange(0, 2+0.2, 0.2) # reduced range for DEBUG only!!!
# grid = np.meshgrid(Alphas, Betas)
# grid = np.round(grid[0],3), np.round(grid[1],3)
# gridParms = [{'alpha': a, 'beta': b} for a,b in np.nditer(grid)]
# Model Simulations
# ------------------------------------------
# Now, optimize all alpha (B), beta (Z) values: determine optimal (B,Z) to work with
print("\n\n###################################################################")
print("# Fitting (B,Z)")
print("###################################################################\n")
experiment = Experiment(root_dir_path='Data_Produced/L2L')
name = 'L2L-DecoEtAl2020-Prepro'
traj, _ = experiment.prepare_experiment(name=name, log_stdout=True, multiprocessing=False)
# Setup the WhileBrain optimizee
WBOptimizee.neuronalModel = neuronalModel
WBOptimizee.integrator = integrator
WBOptimizee.simulateBOLD = simulateBOLD
distanceSettings = {'swFCD': (swFCD, True)} # We need to overwrite this, as L2L only works with ONE observable at a time.
WBOptimizee.measure = distanceSettings['swFCD'][0] # Measure to use to compute the error
WBOptimizee.applyFilters = distanceSettings['swFCD'][1] # Whether to apply filters to the resulting signal or not
outEmpFileName = baseOutPath + '/fNeuro_emp_L2L.mat'
WBOptimizee.processedEmp = processEmpiricalSubjects(tc_transf,
distanceSettings,
outEmpFileName)['swFCD'] # reference values (e.g., empirical) to compare to.
WBOptimizee.N = N # Number of regions in the parcellation
WBOptimizee.trials = NumTrials # Number of trials to try
optimizee_parameters = namedtuple('OptimizeeParameters', [])
filePattern = baseOutPath + '/fitting_{}_L2L.mat'
optimizee = WBOptimizee.WholeBrainOptimizee(traj, {'alpha': (-0.6, 0), 'beta': (0., 2.)}, outFilenamePattern=filePattern) #setupFunc=setupFunc,
# =================== Test for debug only
# traj.individual = sdict(optimizee.create_individual())
# testing_error = optimizee.simulate(traj)
# print("Testing error is %s", testing_error)
# =================== end Test
# Setup the GridSearchOptimizer
optimizer_parameters = GridSearchParameters(param_grid={
'alpha': (-0.6, 0., 6),
'beta': (0., 2., 10)
})
optimizer = GridSearchOptimizer(traj,
optimizee_create_individual=optimizee.create_individual,
optimizee_fitness_weights=(-1.,), # minimize!
parameters=optimizer_parameters)
experiment.run_experiment(optimizee=optimizee,
optimizee_parameters=optimizee_parameters,
optimizer=optimizer,
optimizer_parameters=optimizer_parameters)
experiment.end_experiment(optimizer)
print(f"best: alpha={experiment.optimizer.best_individual['alpha']} & beta={experiment.optimizer.best_individual['beta']}")
# fitting = optim1D.distanceForAll_Parms(tc_transf, grid, gridParms, NumSimSubjects=NumTrials,
# distanceSettings=distanceSettings,
# parmLabel='BZ',
# outFilePath=baseOutPath)
#
# optimal = {sd: distanceSettings[sd][0].findMinMax(fitting[sd]) for sd in distanceSettings}
# ------------------------------------------
# ------------------------------------------
filePath = baseOutPath+'/DecoEtAl2020_fittingBZ.mat'
# sio.savemat(filePath, #{'JI': JI})
# {'Alphas': Alphas,
# 'Betas': Betas,
# 'swFCDfitt': fitting['swFCD'], # swFCDfitt,
# 'FCfitt': fitting['FC'], # FCfitt,
# 'GBCfitt': fitting['GBC'], # GBCfitt
# })
print(f"DONE!!! (file: {filePath})")
