def _run_optimisation(a):
if isinstance(a.parameters, str): a.parameters = parse_list_arg(a.parameters)
if isinstance(a.min_constraints, str): a.min_constraints = parse_list_arg(a.min_constraints)
if isinstance(a.max_constraints, str): a.max_constraints = parse_list_arg(a.max_constraints)
if isinstance(a.target_data, str): a.target_data = parse_dict_arg(a.target_data)
if isinstance(a.weights, str): a.weights = parse_dict_arg(a.weights)
if isinstance(a.known_target_values, str): a.known_target_values = parse_dict_arg(a.known_target_values)
pynml.print_comment_v("=====================================================================================")
pynml.print_comment_v("Starting run_optimisation with: ")
for key,value in a.__dict__.items():
pynml.print_comment_v(" %s = %s%s"%(key,' '*(30-len(key)),value))
pynml.print_comment_v("=====================================================================================")
if a.dry_run:
pynml.print_comment_v("Dry run; not running optimization...")
return
ref = a.prefix
run_dir = "NT_%s_%s"%(ref, time.ctime().replace(' ','_' ).replace(':','.' ))
os.mkdir(run_dir)
my_controller = NeuroMLController(ref,
a.neuroml_file,
a.target,
a.sim_time,
a.dt,
simulator = a.simulator,
generate_dir=run_dir,
num_parallel_evaluations = a.num_parallel_evaluations)
peak_threshold = 0
analysis_var = {'peak_delta': 0,
'baseline': 0,
'dvdt_threshold': 0,
'peak_threshold': peak_threshold}
sim_var = OrderedDict()
#make an evaluator, using automatic target evaluation:
my_evaluator=evaluators.NetworkEvaluator(controller=my_controller,
analysis_start_time=a.analysis_start_time,
analysis_end_time=a.sim_time,
parameters=a.parameters,
analysis_var=analysis_var,
weights=a.weights,
targets=a.target_data)
#make an optimizer
my_optimizer = optimizers.CustomOptimizerA(a.max_constraints,
a.min_constraints,
my_evaluator,
population_size = a.population_size,
max_evaluations = a.max_evaluations,
num_selected = a.num_selected,
num_offspring = a.num_offspring,
num_elites = a.num_elites,
mutation_rate = a.mutation_rate,
seeds = None,
verbose = a.verbose)
start = time.time()
#run the optimizer
best_candidate, fitness = my_optimizer.optimize(do_plot = False,
seed= a.seed,
summary_dir = run_dir)
secs = time.time()-start
reportj = {}
info = "Ran %s evaluations (pop: %s) in %f seconds (%f mins total; %fs per eval)\n\n"%(a.max_evaluations, a.population_size, secs, secs/60.0, (secs/a.max_evaluations))
report = "----------------------------------------------------\n\n"+ info
reportj['comment'] = info
reportj['time'] = secs
for key,value in zip(a.parameters,best_candidate):
sim_var[key]=value
best_candidate_t, best_candidate_v = my_controller.run_individual(sim_var,show=False)
best_candidate_analysis = analysis.NetworkAnalysis(best_candidate_v,
best_candidate_t,
analysis_var,
start_analysis=a.analysis_start_time,
end_analysis=a.sim_time)
best_cand_analysis_full = best_candidate_analysis.analyse()
best_cand_analysis = best_candidate_analysis.analyse(a.weights.keys())
report+="---------- Best candidate ------------------------------------------\n"
report+=pp.pformat(best_cand_analysis_full)+"\n\n"
report+="TARGETS: \n"
report+=pp.pformat(a.target_data)+"\n\n"
report+="TUNED VALUES:\n"
report+=pp.pformat(best_cand_analysis)+"\n\n"
report+="FITNESS: %f\n\n"%fitness
report+="FITTEST: %s\n\n"%pp.pformat(dict(sim_var))
pynml.print_comment_v(report)
reportj['fitness']=fitness
reportj['fittest vars']=dict(sim_var)
reportj['best_cand_analysis_full']=best_cand_analysis_full
reportj['best_cand_analysis']=best_cand_analysis
reportj['parameters']=a.parameters
reportj['analysis_var']=analysis_var
reportj['target_data']=a.target_data
reportj['weights']=a.weights
reportj['analysis_start_time']=a.analysis_start_time
reportj['population_size']=a.population_size
reportj['max_evaluations']=a.max_evaluations
reportj['num_selected']=a.num_selected
reportj['num_offspring']=a.num_offspring
reportj['mutation_rate']=a.mutation_rate
reportj['num_elites']=a.num_elites
reportj['seed']=a.seed
reportj['simulator']=a.simulator
reportj['sim_time']=a.sim_time
reportj['dt']=a.dt
reportj['run_directory'] = run_dir
reportj['reference'] = ref
report_file = open("%s/report.json"%run_dir,'w')
report_file.write(pp.pformat(reportj))
report_file.close()
plot_file = open("%s/plotgens.py"%run_dir,'w')
plot_file.write('from neurotune.utils import plot_generation_evolution\nimport os\n')
plot_file.write('\n')
plot_file.write('parameters = %s\n'%a.parameters)
plot_file.write('\n')
plot_file.write("curr_dir = os.path.dirname(__file__) if len(os.path.dirname(__file__))>0 else '.'\n")
plot_file.write("plot_generation_evolution(parameters, individuals_file_name = '%s/ga_individuals.csv'%curr_dir)\n")
plot_file.close()
if not a.nogui:
added =[]
#print("Plotting saved data from %s which are relevant for targets: %s"%(best_candidate_v.keys(), a.target_data.keys()))
fig = plt.figure()
fig.canvas.set_window_title("Simulation of fittest individual from run: %s"%ref)
for tref in best_candidate_v.keys(): ##################a.target_data.keys():
ref = tref.split(':')[0]
if not ref in added:
added.append(ref)
#pynml.print_comment(" - Adding plot of: %s"%ref)
plt.plot(best_candidate_t,best_candidate_v[ref], label="%s - %i evaluations"%(ref,a.max_evaluations))
plt.legend()
#plt.ylim(-80.0,80.0)
plt.xlim(0.0,a.sim_time)
plt.title("Models %s"%a.prefix)
plt.xlabel("Time (ms)")
plt.ylabel("Membrane potential(mV)")
utils.plot_generation_evolution(sim_var.keys(),
individuals_file_name = '%s/ga_individuals.csv'%run_dir,
target_values=a.known_target_values,
show_plot_already = a.show_plot_already)
if a.show_plot_already:
plt.show()
return reportj
def run_optimisation(prefix,
config,
level,
parameters,
max_constraints,
min_constraints,
weights,
target_data,
sim_time=500,
dt=0.05,
analysis_start_time=0,
population_size=20,
max_evaluations=20,
num_selected=10,
num_offspring=20,
mutation_rate=0.9,
num_elites=1,
seed=12345,
simulator='jNeuroML',
nogui=False,
num_local_procesors_to_use=4):
print("Running optimisation...")
print("parameters: %s" % parameters)
print("max_constraints: %s" % max_constraints)
print("min_constraints: %s" % min_constraints)
print("simulator: %s" % simulator)
ref = prefix + config
run_dir = "NT_%s_%s" % (ref, time.ctime().replace(' ', '_').replace(
':', '.'))
os.mkdir(run_dir)
my_controller = C302Controller(
ref,
level,
config,
sim_time,
dt,
simulator=simulator,
generate_dir=run_dir,
num_local_procesors_to_use=num_local_procesors_to_use)
peak_threshold = -31 if level.startswith('A') or level.startswith(
'B') else (-40)
analysis_var = {
'peak_delta': 0,
'baseline': 0,
'dvdt_threshold': 0,
'peak_threshold': peak_threshold
}
data = ref + '.dat'
sim_var = OrderedDict()
for i in range(len(parameters)):
sim_var[
parameters[i]] = max_constraints[i] / 2 + min_constraints[i] / 2
#make an evaluator, using automatic target evaluation:
my_evaluator = c302Evaluators.EnhancedNetworkEvaluator(
controller=my_controller,
analysis_start_time=analysis_start_time,
analysis_end_time=sim_time,
parameters=parameters,
analysis_var=analysis_var,
weights=weights,
targets=target_data)
#make an optimizer
my_optimizer = optimizers.CustomOptimizerA(max_constraints,
min_constraints,
my_evaluator,
population_size=population_size,
max_evaluations=max_evaluations,
num_selected=num_selected,
num_offspring=num_offspring,
num_elites=num_elites,
mutation_rate=mutation_rate,
seeds=None,
verbose=False)
start = time.time()
#run the optimizer
best_candidate, fitness = my_optimizer.optimize(do_plot=False,
seed=seed,
summary_dir=run_dir)
secs = time.time() - start
reportj = {}
info = "Ran %s evaluations (pop: %s) in %f seconds (%f mins total; %fs per eval)\n\n" % (
max_evaluations, population_size, secs, secs / 60.0,
(secs / max_evaluations))
report = "----------------------------------------------------\n\n" + info
reportj['comment'] = info
reportj['time'] = secs
for key, value in zip(parameters, best_candidate):
sim_var[key] = value
best_candidate_t, best_candidate_v = my_controller.run_individual(
sim_var, show=False)
best_candidate_results = my_controller.last_results
best_candidate_analysis = c302Analysis.Data_Analyser(
best_candidate_v,
best_candidate_t,
analysis_var,
start_analysis=analysis_start_time,
end_analysis=sim_time)
best_cand_analysis_full = best_candidate_analysis.analyse()
best_cand_analysis = best_candidate_analysis.analyse(target_data)
report += "---------- Best candidate ------------------------------------------\n"
report += pp.pformat(best_candidate) + "\n\n"
report += pp.pformat(best_cand_analysis_full) + "\n"
report += pp.pformat(best_cand_analysis) + "\n\n"
report += "FITNESS: %f\n\n" % fitness
print(report)
reportj['fitness'] = fitness
reportj['fittest vars'] = dict(sim_var)
reportj['best_cand_details'] = best_candidate
reportj['best_cand_analysis_full'] = best_cand_analysis_full
reportj['best_cand_analysis'] = best_cand_analysis
reportj['parameters'] = parameters
reportj['analysis_var'] = analysis_var
reportj['target_data'] = target_data
reportj['weights'] = weights
reportj['analysis_start_time'] = analysis_start_time
reportj['population_size'] = population_size
reportj['max_evaluations'] = max_evaluations
reportj['num_selected'] = num_selected
reportj['num_offspring'] = num_offspring
reportj['mutation_rate'] = mutation_rate
reportj['num_elites'] = num_elites
reportj['seed'] = seed
reportj['simulator'] = simulator
reportj['sim_time'] = sim_time
reportj['dt'] = dt
reportj['run_directory'] = run_dir
reportj['reference'] = ref
report_file = open("%s/report.json" % run_dir, 'w')
report_file.write(pp.pformat(reportj))
report_file.close()
plot_file = open("%s/plotgens.py" % run_dir, 'w')
plot_file.write(
'from neurotune.utils import plot_generation_evolution\nimport os\n')
plot_file.write('\n')
plot_file.write('parameters = %s\n' % parameters)
plot_file.write('\n')
plot_file.write(
"curr_dir = os.path.dirname(__file__) if len(os.path.dirname(__file__))>0 else '.'\n"
)
plot_file.write(
"plot_generation_evolution(parameters, individuals_file_name = '%s/ga_individuals.csv'%curr_dir)\n"
)
plot_file.close()
if not nogui:
added = []
for wref in weights.keys():
ref = wref.split(':')[0]
if not ref in added and not "phase_offset" in ref:
added.append(ref)
best_candidate_plot = plt.plot(best_candidate_t,
best_candidate_v[ref],
label="%s - %i evaluations" %
(ref, max_evaluations))
plt.legend()
plt.ylim(-80.0, 80.0)
plt.xlim(0.0, sim_time)
plt.title("Models")
plt.xlabel("Time (ms)")
plt.ylabel("Membrane potential(mV)")
utils.plot_generation_evolution(
sim_var.keys(),
individuals_file_name='%s/ga_individuals.csv' % run_dir)
c302_utils.plot_c302_results(best_candidate_results,
config,
level,
directory=run_dir,
save=True)
def run_optimisation(prefix,
config,
level,
parameters,
max_constraints,
min_constraints,
weights,
target_data,
sim_time = 500,
dt = 0.05,
analysis_start_time = 0,
population_size = 20,
max_evaluations = 20,
num_selected = 10,
num_offspring = 20,
mutation_rate = 0.5,
num_elites = 1,
seed = 12345,
simulator = 'jNeuroML',
nogui = False):
ref = prefix+config
run_dir = "NT_%s_%s"%(ref, time.ctime().replace(' ','_' ).replace(':','.' ))
os.mkdir(run_dir)
my_controller = C302Controller(ref, level, config, sim_time, dt, simulator = simulator, generate_dir=run_dir)
peak_threshold = -31 if level is 'A' or level is 'B' else (-20 if level is 'C1' else 0)
analysis_var = {'peak_delta': 0,
'baseline': 0,
'dvdt_threshold': 0,
'peak_threshold': peak_threshold}
data = ref+'.dat'
sim_var = OrderedDict()
for i in range(len(parameters)):
sim_var[parameters[i]] = max_constraints[i]/2 + min_constraints[i]/2
#make an evaluator, using automatic target evaluation:
my_evaluator=evaluators.NetworkEvaluator(controller=my_controller,
analysis_start_time=analysis_start_time,
analysis_end_time=sim_time,
parameters=parameters,
analysis_var=analysis_var,
weights=weights,
targets=target_data)
#make an optimizer
my_optimizer = optimizers.CustomOptimizerA(max_constraints,
min_constraints,
my_evaluator,
population_size=population_size,
max_evaluations=max_evaluations,
num_selected=num_selected,
num_offspring=num_offspring,
num_elites=num_elites,
mutation_rate=mutation_rate,
seeds=None,
verbose=False)
start = time.time()
#run the optimizer
best_candidate, fitness = my_optimizer.optimize(do_plot=False,
seed=seed,
summary_dir = run_dir)
secs = time.time()-start
reportj = {}
info = "Ran %s evaluations (pop: %s) in %f seconds (%f mins)\n\n"%(max_evaluations, population_size, secs, secs/60.0)
report = "----------------------------------------------------\n\n"+ info
reportj['comment'] = info
reportj['time'] = secs
for key,value in zip(parameters,best_candidate):
sim_var[key]=value
best_candidate_t, best_candidate_v = my_controller.run_individual(sim_var,show=False)
best_candidate_analysis = analysis.NetworkAnalysis(best_candidate_v,
best_candidate_t,
analysis_var,
start_analysis=analysis_start_time,
end_analysis=sim_time)
best_cand_analysis_full = best_candidate_analysis.analyse()
best_cand_analysis = best_candidate_analysis.analyse(weights.keys())
report+="---------- Best candidate ------------------------------------------\n"
report+=pp.pformat(best_cand_analysis_full)+"\n"
report+=pp.pformat(best_cand_analysis)+"\n\n"
report+="FITNESS: %f\n\n"%fitness
print(report)
reportj['fitness']=fitness
reportj['best_cand_analysis_full']=best_cand_analysis_full
reportj['best_cand_analysis']=best_cand_analysis
reportj['parameters']=parameters
reportj['analysis_var']=analysis_var
reportj['target_data']=target_data
reportj['weights']=weights
reportj['analysis_start_time']=analysis_start_time
reportj['population_size']=population_size
reportj['max_evaluations']=max_evaluations
reportj['num_selected']=num_selected
reportj['num_offspring']=num_offspring
reportj['mutation_rate']=mutation_rate
reportj['num_elites']=num_elites
reportj['sim_time']=sim_time
reportj['dt']=dt
report_file = open("%s/report.json"%run_dir,'w')
report_file.write(pp.pformat(reportj))
report_file.close()
plot_file = open("%s/plotgens.py"%run_dir,'w')
plot_file.write('from neurotune.utils import plot_generation_evolution\nimport os\n')
plot_file.write('\n')
plot_file.write('parameters = %s\n'%parameters)
plot_file.write('\n')
plot_file.write("curr_dir = os.path.dirname(__file__) if len(os.path.dirname(__file__))>0 else '.'\n")
plot_file.write("plot_generation_evolution(parameters, individuals_file_name = '%s/ga_individuals.csv'%curr_dir)\n")
plot_file.close()
if not nogui:
added =[]
for wref in weights.keys():
ref = wref.split(':')[0]
if not ref in added:
added.append(ref)
best_candidate_plot = plt.plot(best_candidate_t,best_candidate_v[ref], label="%s - %i evaluations"%(ref,max_evaluations))
plt.legend()
plt.ylim(-80.0,80.0)
plt.xlim(0.0,sim_time)
plt.title("Models")
plt.xlabel("Time (ms)")
plt.ylabel("Membrane potential(mV)")
plt.show()
utils.plot_generation_evolution(sim_var.keys(), individuals_file_name = '%s/ga_individuals.csv'%run_dir)
data_plot = plt.plot(t,v[cell_ref], label="Original data")
best_candidate_plot = plt.plot(best_candidate_t,best_candidate_v[cell_ref], label="Best model - %i evaluations"%max_evaluations)
plt.legend()
plt.ylim(-80.0,80.0)
plt.xlim(0.0,1000.0)
plt.title("Models optimized from data")
plt.xlabel("Time (ms)")
plt.ylabel("Membrane potential(mV)")
plt.savefig("data_vs_candidate.png",bbox_inches='tight',format='png')
plt.show()
utils.plot_generation_evolution(sim_var.keys())
else:
sim_var = OrderedDict([('leak_cond_density', 0.05),
('k_slow_cond_density', 0.5),
('k_fast_cond_density', 0.05),
('ca_boyle_cond_density', 0.5),
('specific_capacitance', 1.05),
('leak_erev', -50),
('k_slow_erev', -60),
('k_fast_erev', -60),
('ca_boyle_erev', 40)])
num_selected = 10
num_offspring = 5
mutation_rate = 0.5
num_elites = 1
#make an optimizer
my_optimizer=optimizers.CustomOptimizerA(max_constraints,
min_constraints,
my_evaluator,
population_size=population_size,
max_evaluations=max_evaluations,
num_selected=num_selected,
num_offspring=num_offspring,
num_elites=num_elites,
mutation_rate=mutation_rate,
seeds=None,
verbose=True)
#run the optimizer
best_candidate, fitness = my_optimizer.optimize(do_plot=False, seed=1234567)
keys = list(sim_vars.keys())
for i in range(len(best_candidate)):
sim_vars[keys[i]] = best_candidate[i]
fit_times, fit_volts = swc.run_individual(sim_vars, showPlots, False)
if showPlots:
utils.plot_generation_evolution(sim_vars.keys(), sim_vars)
def _run_optimisation(a):
if isinstance(a.parameters, str):
a.parameters = parse_list_arg(a.parameters)
if isinstance(a.min_constraints, str):
a.min_constraints = parse_list_arg(a.min_constraints)
if isinstance(a.max_constraints, str):
a.max_constraints = parse_list_arg(a.max_constraints)
if isinstance(a.target_data, str):
a.target_data = parse_dict_arg(a.target_data)
if isinstance(a.weights, str): a.weights = parse_dict_arg(a.weights)
if isinstance(a.known_target_values, str):
a.known_target_values = parse_dict_arg(a.known_target_values)
if isinstance(a.extra_report_info, str):
a.extra_report_info = parse_dict_arg(a.extra_report_info)
pynml.print_comment_v(
"====================================================================================="
)
pynml.print_comment_v("Starting run_optimisation with: ")
keys = sorted(a.__dict__.keys())
for key in keys:
value = a.__dict__[key]
pynml.print_comment_v(" %s = %s%s" % (key, ' ' *
(30 - len(key)), value))
pynml.print_comment_v(
"====================================================================================="
)
if a.dry_run:
pynml.print_comment_v("Dry run; not running optimization...")
return
ref = a.prefix
run_dir = "NT_%s_%s" % (ref, time.ctime().replace(' ', '_').replace(
':', '.'))
os.mkdir(run_dir)
my_controller = NeuroMLController(
ref,
a.neuroml_file,
a.target,
a.sim_time,
a.dt,
simulator=a.simulator,
generate_dir=run_dir,
num_parallel_evaluations=a.num_parallel_evaluations,
cleanup=a.cleanup)
peak_threshold = 0
analysis_var = {
'peak_delta': 0,
'baseline': 0,
'dvdt_threshold': 0,
'peak_threshold': peak_threshold
}
sim_var = OrderedDict()
#make an evaluator, using automatic target evaluation:
my_evaluator = evaluators.NetworkEvaluator(
controller=my_controller,
analysis_start_time=a.analysis_start_time,
analysis_end_time=a.sim_time,
parameters=a.parameters,
analysis_var=analysis_var,
weights=a.weights,
targets=a.target_data)
#make an optimizer
my_optimizer = optimizers.CustomOptimizerA(
a.max_constraints,
a.min_constraints,
my_evaluator,
population_size=a.population_size,
max_evaluations=a.max_evaluations,
num_selected=a.num_selected,
num_offspring=a.num_offspring,
num_elites=a.num_elites,
mutation_rate=a.mutation_rate,
seeds=None,
verbose=a.verbose)
start = time.time()
#run the optimizer
best_candidate, fitness = my_optimizer.optimize(do_plot=False,
seed=a.seed,
summary_dir=run_dir)
secs = time.time() - start
reportj = {}
info = "Ran %s evaluations (pop: %s) in %f seconds (%f mins total; %fs per eval)\n\n" % (
a.max_evaluations, a.population_size, secs, secs / 60.0,
(secs / a.max_evaluations))
report = "----------------------------------------------------\n\n" + info
reportj['comment'] = info
reportj['time'] = secs
for key, value in zip(a.parameters, best_candidate):
sim_var[key] = value
best_candidate_t, best_candidate_v = my_controller.run_individual(
sim_var, show=False, cleanup=False)
best_candidate_analysis = analysis.NetworkAnalysis(
best_candidate_v,
best_candidate_t,
analysis_var,
start_analysis=a.analysis_start_time,
end_analysis=a.sim_time)
best_cand_analysis_full = best_candidate_analysis.analyse()
best_cand_analysis = best_candidate_analysis.analyse(a.weights.keys())
report += "---------- Best candidate ------------------------------------------\n"
report += pp.pformat(best_cand_analysis_full) + "\n\n"
report += "TARGETS: \n"
report += pp.pformat(a.target_data) + "\n\n"
report += "TUNED VALUES:\n"
report += pp.pformat(best_cand_analysis) + "\n\n"
report += "FITNESS: %f\n\n" % fitness
report += "FITTEST: %s\n\n" % pp.pformat(dict(sim_var))
pynml.print_comment_v(report)
reportj['fitness'] = fitness
reportj['fittest vars'] = dict(sim_var)
reportj['best_cand_analysis_full'] = best_cand_analysis_full
reportj['best_cand_analysis'] = best_cand_analysis
reportj['parameters'] = a.parameters
reportj['analysis_var'] = analysis_var
reportj['target_data'] = a.target_data
reportj['weights'] = a.weights
reportj['analysis_start_time'] = a.analysis_start_time
reportj['population_size'] = a.population_size
reportj['max_evaluations'] = a.max_evaluations
reportj['num_selected'] = a.num_selected
reportj['num_offspring'] = a.num_offspring
reportj['mutation_rate'] = a.mutation_rate
reportj['num_elites'] = a.num_elites
reportj['seed'] = a.seed
reportj['simulator'] = a.simulator
reportj['sim_time'] = a.sim_time
reportj['dt'] = a.dt
reportj['run_directory'] = run_dir
reportj['reference'] = ref
if a.extra_report_info:
for key in a.extra_report_info:
reportj[key] = a.extra_report_info[key]
report_file = open("%s/report.json" % run_dir, 'w')
report_file.write(pp.pformat(reportj))
report_file.close()
plot_file = open("%s/plotgens.py" % run_dir, 'w')
plot_file.write(
'from neurotune.utils import plot_generation_evolution\nimport os\n')
plot_file.write('\n')
plot_file.write('parameters = %s\n' % a.parameters)
plot_file.write('\n')
plot_file.write(
"curr_dir = os.path.dirname(__file__) if len(os.path.dirname(__file__))>0 else '.'\n"
)
plot_file.write(
"plot_generation_evolution(parameters, individuals_file_name = '%s/ga_individuals.csv'%curr_dir)\n"
)
plot_file.close()
if not a.nogui:
added = []
#print("Plotting saved data from %s which are relevant for targets: %s"%(best_candidate_v.keys(), a.target_data.keys()))
fig = plt.figure()
fig.canvas.set_window_title(
"Simulation of fittest individual from run: %s" % ref)
for tref in best_candidate_v.keys(
): ##################a.target_data.keys():
ref = tref.split(':')[0]
if not ref in added:
added.append(ref)
#pynml.print_comment(" - Adding plot of: %s"%ref)
plt.plot(best_candidate_t,
best_candidate_v[ref],
label="%s - %i evaluations" %
(ref, a.max_evaluations))
plt.legend()
#plt.ylim(-80.0,80.0)
plt.xlim(0.0, a.sim_time)
plt.title("Models %s" % a.prefix)
plt.xlabel("Time (ms)")
plt.ylabel("Membrane potential(mV)")
utils.plot_generation_evolution(
sim_var.keys(),
individuals_file_name='%s/ga_individuals.csv' % run_dir,
target_values=a.known_target_values,
show_plot_already=a.show_plot_already,
title_prefix=ref)
if a.show_plot_already:
plt.show()
return reportj
best_candidate_plot = plt.plot(best_candidate_t,
best_candidate_v[cell_ref],
label="Best model - %i evaluations" %
max_evaluations)
plt.legend()
plt.ylim(-80.0, 80.0)
plt.xlim(0.0, 1000.0)
plt.title("Models optimized from data")
plt.xlabel("Time (ms)")
plt.ylabel("Membrane potential(mV)")
plt.savefig("data_vs_candidate.png", bbox_inches='tight', format='png')
plt.show()
utils.plot_generation_evolution(sim_var.keys())
else:
sim_var = OrderedDict({
"muscle_k_slow_cond_density": .22,
"muscle_ca_simple_cond_density": .15,
"muscle_leak_cond_density": 0.002,
"k_slow_erev": -93.,
"ca_simple_erev": 60.,
"unphysiological_offset_current": 5
})
example_run_t, example_run_v = my_controller.run_individual(sim_var,
show=True)
def run_optimisation(prefix,
config,
level,
parameters,
max_constraints,
min_constraints,
weights,
target_data,
sim_time = 500,
dt = 0.05,
analysis_start_time = 0,
population_size = 20,
max_evaluations = 20,
num_selected = 10,
num_offspring = 20,
mutation_rate = 0.5,
num_elites = 1,
nogui = False):
ref = prefix+config
my_controller = C302Controller(ref, level, config, sim_time, dt)
peak_threshold = -31 if level is 'A' or level is 'B' else 0
analysis_var = {'peak_delta': 0,
'baseline': 0,
'dvdt_threshold': 0,
'peak_threshold': peak_threshold}
data = ref+'.dat'
sim_var = OrderedDict()
for i in range(len(parameters)):
sim_var[parameters[i]] = max_constraints[i]/2 + min_constraints[i]/2
#make an evaluator, using automatic target evaluation:
my_evaluator=evaluators.NetworkEvaluator(controller=my_controller,
analysis_start_time=analysis_start_time,
analysis_end_time=sim_time,
parameters=parameters,
analysis_var=analysis_var,
weights=weights,
targets=target_data)
#make an optimizer
my_optimizer = optimizers.CustomOptimizerA(max_constraints,
min_constraints,
my_evaluator,
population_size=population_size,
max_evaluations=max_evaluations,
num_selected=num_selected,
num_offspring=num_offspring,
num_elites=num_elites,
mutation_rate=mutation_rate,
seeds=None,
verbose=False)
start = time.time()
#run the optimizer
best_candidate, fitness = my_optimizer.optimize(do_plot=False, seed=123456)
secs = time.time()-start
print("----------------------------------------------------\n\n"
+"Ran %s evaluations (pop: %s) in %f seconds (%f mins)\n"%(max_evaluations, population_size, secs, secs/60.0))
for key,value in zip(parameters,best_candidate):
sim_var[key]=value
best_candidate_t, best_candidate_v = my_controller.run_individual(sim_var,show=False)
best_candidate_analysis = analysis.NetworkAnalysis(best_candidate_v,
best_candidate_t,
analysis_var,
start_analysis=analysis_start_time,
end_analysis=sim_time)
best_cand_analysis_full = best_candidate_analysis.analyse()
best_cand_analysis = best_candidate_analysis.analyse(weights.keys())
print("---------- Best candidate ------------------------------------------")
pp.pprint(best_cand_analysis_full)
pp.pprint(best_cand_analysis)
print("Fitness: %f"%fitness)
if not nogui:
added =[]
for wref in weights.keys():
ref = wref.split(':')[0]
if not ref in added:
added.append(ref)
best_candidate_plot = plt.plot(best_candidate_t,best_candidate_v[ref], label="%s - %i evaluations"%(ref,max_evaluations))
plt.legend()
plt.ylim(-80.0,80.0)
plt.xlim(0.0,sim_time)
plt.title("Models")
plt.xlabel("Time (ms)")
plt.ylabel("Membrane potential(mV)")
plt.show()
utils.plot_generation_evolution(sim_var.keys())
def run_optimisation(
prefix,
neuroml_file,
target,
parameters,
max_constraints,
min_constraints,
weights,
target_data,
sim_time=500,
dt=0.05,
analysis_start_time=0,
population_size=20,
max_evaluations=20,
num_selected=10,
num_offspring=20,
mutation_rate=0.5,
num_elites=1,
seed=12345,
simulator="jNeuroML",
nogui=False,
):
ref = prefix
run_dir = "NT_%s_%s" % (ref, time.ctime().replace(" ", "_").replace(":", "."))
os.mkdir(run_dir)
my_controller = NeuroMLController(
ref, neuroml_file, target, sim_time, dt, simulator=simulator, generate_dir=run_dir
)
peak_threshold = 0
analysis_var = {"peak_delta": 0, "baseline": 0, "dvdt_threshold": 0, "peak_threshold": peak_threshold}
sim_var = OrderedDict()
# make an evaluator, using automatic target evaluation:
my_evaluator = evaluators.NetworkEvaluator(
controller=my_controller,
analysis_start_time=analysis_start_time,
analysis_end_time=sim_time,
parameters=parameters,
analysis_var=analysis_var,
weights=weights,
targets=target_data,
)
# make an optimizer
my_optimizer = optimizers.CustomOptimizerA(
max_constraints,
min_constraints,
my_evaluator,
population_size=population_size,
max_evaluations=max_evaluations,
num_selected=num_selected,
num_offspring=num_offspring,
num_elites=num_elites,
mutation_rate=mutation_rate,
seeds=None,
verbose=False,
)
start = time.time()
# run the optimizer
best_candidate, fitness = my_optimizer.optimize(do_plot=False, seed=seed, summary_dir=run_dir)
secs = time.time() - start
reportj = {}
info = "Ran %s evaluations (pop: %s) in %f seconds (%f mins)\n\n" % (
max_evaluations,
population_size,
secs,
secs / 60.0,
)
report = "----------------------------------------------------\n\n" + info
reportj["comment"] = info
reportj["time"] = secs
for key, value in zip(parameters, best_candidate):
sim_var[key] = value
best_candidate_t, best_candidate_v = my_controller.run_individual(sim_var, show=False)
best_candidate_analysis = analysis.NetworkAnalysis(
best_candidate_v, best_candidate_t, analysis_var, start_analysis=analysis_start_time, end_analysis=sim_time
)
best_cand_analysis_full = best_candidate_analysis.analyse()
best_cand_analysis = best_candidate_analysis.analyse(weights.keys())
report += "---------- Best candidate ------------------------------------------\n"
report += pp.pformat(best_cand_analysis_full) + "\n"
report += pp.pformat(best_cand_analysis) + "\n\n"
report += "FITNESS: %f\n\n" % fitness
report += "FITTEST: %s\n\n" % sim_var
print(report)
reportj["fitness"] = fitness
reportj["fittest vars"] = sim_var
reportj["best_cand_analysis_full"] = best_cand_analysis_full
reportj["best_cand_analysis"] = best_cand_analysis
reportj["parameters"] = parameters
reportj["analysis_var"] = analysis_var
reportj["target_data"] = target_data
reportj["weights"] = weights
reportj["analysis_start_time"] = analysis_start_time
reportj["population_size"] = population_size
reportj["max_evaluations"] = max_evaluations
reportj["num_selected"] = num_selected
reportj["num_offspring"] = num_offspring
reportj["mutation_rate"] = mutation_rate
reportj["num_elites"] = num_elites
reportj["sim_time"] = sim_time
reportj["dt"] = dt
report_file = open("%s/report.json" % run_dir, "w")
report_file.write(pp.pformat(reportj))
report_file.close()
plot_file = open("%s/plotgens.py" % run_dir, "w")
plot_file.write("from neurotune.utils import plot_generation_evolution\nimport os\n")
plot_file.write("\n")
plot_file.write("parameters = %s\n" % parameters)
plot_file.write("\n")
plot_file.write("curr_dir = os.path.dirname(__file__) if len(os.path.dirname(__file__))>0 else '.'\n")
plot_file.write("plot_generation_evolution(parameters, individuals_file_name = '%s/ga_individuals.csv'%curr_dir)\n")
plot_file.close()
if not nogui:
added = []
for wref in weights.keys():
ref = wref.split(":")[0]
if not ref in added:
added.append(ref)
best_candidate_plot = plt.plot(
best_candidate_t, best_candidate_v[ref], label="%s - %i evaluations" % (ref, max_evaluations)
)
plt.legend()
plt.ylim(-80.0, 80.0)
plt.xlim(0.0, sim_time)
plt.title("Models")
plt.xlabel("Time (ms)")
plt.ylabel("Membrane potential(mV)")
plt.show()
utils.plot_generation_evolution(sim_var.keys(), individuals_file_name="%s/ga_individuals.csv" % run_dir)