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 main():
fit, swc, ref = sys.argv[1:]
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={})
# Innerloop simulator
optimizee = ArbSCOptimizee(traj, fit, swc, ref)
# 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(optimizee=optimizee,
optimizer=optimizer,
optimizer_parameters=parameters,
optimizee_parameters=None)
experiment.end_experiment(optimizer)
def main():
name = 'L2L-FUN-FACE'
experiment = Experiment("../results/")
trajectory_name = name
traj, all_jube_params, = experiment.prepare_experiment(name=name,
trajectory_name=trajectory_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
## Innerloop simulator
optimizee = FunctionGeneratorOptimizee(traj, benchmark_function, seed=optimizee_seed)
## Outerloop optimizer initialization
parameters = FACEParameters(min_pop_size=20, max_pop_size=50, n_elite=10, smoothing=0.2, temp_decay=0,
n_iteration=1,
distribution=Gaussian(), n_expand=5, stop_criterion=np.inf, seed=109)
optimizer = FACEOptimizer(traj, optimizee_create_individual=optimizee.create_individual,
optimizee_fitness_weights=(-0.1,),
parameters=parameters,
optimizee_bounding_func=optimizee.bounding_func)
experiment.run_experiment(optimizer=optimizer, optimizee=optimizee,
optimizer_parameters=parameters,
optimizee_parameters=None)
experiment.end_experiment(optimizer)
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():
from l2l.utils.experiment import Experiment
experiment = Experiment(root_dir_path='../../Data_Produced/L2L')
name = 'L2L-TEST-WholeBrain'
traj, _ = experiment.prepare_experiment(name=name,
log_stdout=True,
multiprocessing=False)
optimizee = WholeBrainOptimizee(traj, {'we': (0, 10)})
traj.individual = sdict(optimizee.create_individual())
class OptimizerTestCase(unittest.TestCase):
def setUp(self):
# Test function
function_id = 14
bench_functs = BenchmarkedFunctions()
(benchmark_name, benchmark_function), benchmark_parameters = \
bench_functs.get_function_by_index(function_id, noise=True)
self.experiment = Experiment(root_dir_path='../../results')
jube_params = {}
self.trajectory, all_jube_params = self.experiment.prepare_experiment(
name='L2L', log_stdout=True, jube_parameter=jube_params)
self.optimizee_parameters = namedtuple('OptimizeeParameters', [])
self.optimizee = FunctionGeneratorOptimizee(self.trajectory,
benchmark_function,
seed=1)
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():
name = 'L2L-FUN-GD'
experiment = Experiment("../results")
traj, all_jube_params = experiment.prepare_experiment(name=name,
trajectory_name=name)
## 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)
## Innerloop simulator
optimizee = FunctionGeneratorOptimizee(traj, benchmark_function,
seed=optimizee_seed)
## Outerloop optimizer initialization
# parameters = ClassicGDParameters(learning_rate=0.01, exploration_step_size=0.01,
# n_random_steps=5, n_iteration=100,
# stop_criterion=np.Inf)
# parameters = AdamParameters(learning_rate=0.01, exploration_step_size=0.01, n_random_steps=5, first_order_decay=0.8,
# second_order_decay=0.8, n_iteration=100, stop_criterion=np.Inf)
# 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=(0.1,),
parameters=parameters,
optimizee_bounding_func=optimizee.bounding_func)
experiment.run_experiment(optimizer=optimizer, optimizee=optimizee,
optimizer_parameters=parameters)
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!!!")
class SetupTestCase(unittest.TestCase):
def setUp(self):
self.experiment = Experiment(root_dir_path='../../results')
jube_params = {}
try:
self.trajectory, _ = self.experiment.prepare_experiment(
name='test_trajectory',
log_stdout=True,
add_time=True,
automatic_storing=True,
jube_parameter=jube_params)
except FileNotFoundError as fe:
self.fail(
"{} \n L2L is not well configured. Missing path file.".format(
fe))
self.paths = self.experiment.paths
def test_paths(self):
self.assertIsNotNone(self.paths)
self.assertIsNotNone(Paths.simulation_path)
def test_environment_trajectory_setup(self):
self.assertIsNotNone(self.trajectory.individual)
def test_trajectory_parms_setup(self):
self.trajectory.f_add_parameter_group("Test_params",
"Contains Test parameters")
self.trajectory.f_add_parameter_to_group("Test_params", "param1",
"value1")
self.assertEqual("value1",
self.trajectory.Test_params.params["param1"])
def test_juberunner_setup(self):
self.experiment = Experiment(root_dir_path='../../results')
self.trajectory, _ = self.experiment.prepare_experiment(
name='test_trajectory',
trajectory='test_trajectory',
filename=".",
file_title='{} data'.format('test_trajectory'),
comment='{} data'.format('test_trajectory'),
add_time=True,
automatic_storing=True,
log_stdout=False,
jube_parameter={})
self.trajectory.f_add_parameter_group("JUBE_params",
"Contains JUBE parameters")
self.trajectory.f_add_parameter_to_group(
"JUBE_params", "exec", "python " + os.path.join(
self.paths.simulation_path, "run_files/run_optimizee.py"))
self.trajectory.f_add_parameter_to_group("JUBE_params", "paths",
self.paths)
## 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 = 1
optimizee = FunctionGeneratorOptimizee(self.trajectory,
benchmark_function,
seed=optimizee_seed)
jube.prepare_optimizee(optimizee, self.paths.root_dir_path)
fname = os.path.join(self.paths.root_dir_path, "optimizee.bin")
try:
f = open(fname, "r")
f.close()
except Exception:
self.fail()
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']}")
import numpy as np
from random import randrange as rand
import sys
from os.path import abspath as expand
from l2l.utils.experiment import Experiment
from l2l.optimizers.evolution import GeneticAlgorithmOptimizer, GeneticAlgorithmParameters
from l2l.optimizees.arbor.SC import ArbSCOptimizee
fit, swc, ref = list(map(expand, sys.argv[1:]))
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,
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})")