def test_set_model_attrs(self):
from neuronunit.optimization.model_parameters import model_params
provided_keys = list(model_params.keys())
from bluepyopt.deapext.optimisations import DEAPOptimisation
DO = DEAPOptimisation()
for i in range(1, 10):
for j in range(1, 10):
provided_keys = list(model_params.keys())[j]
DO.setnparams(nparams=i, provided_keys=provided_keys)
def test_set_model_attrs(self):
from neuronunit.optimization.model_parameters import model_params
provided_keys = list(model_params.keys())
from bluepyopt.deapext.optimisations import DEAPOptimisation
DO = DEAPOptimisation()
for i in range(1,10):
for j in range(1,10):
provided_keys = list(model_params.keys())[j]
DO.setnparams(nparams = i, provided_keys = provided_keys)
def test_opt_set_GA_params(self):
'''
Test to check if making the population size bigger, or increasing the run_number
of generations the GA consumes actually increases the fitness.
Note only test for better fitness every 3rd count, because GAs are stochastic,
and by chance occassionally fitness may not increase, just because the GA has
more genes to sample with.
A fairer test of the GA would probably test if the mean of mean fitness is improving
or use a sliding window.
'''
from neuronunit.optimization.model_parameters import model_params
provided_keys = list(model_params.keys())
from bluepyopt.deapext.optimisations import DEAPOptimisation
DO = DEAPOptimisation()
nparams = 5
cnt = 0
provided_keys = list(model_params.keys())[nparams]
DO.setnparams(nparams=nparams, provided_keys=provided_keys)
list_check_increase = []
for NGEN in range(1, 30):
for MU in range(5, 40):
pop, hof, log, history, td, gen_vs_hof = DO.run(
offspring_size=MU,
max_ngen=NGEN,
cp_frequency=4,
cp_filename='checkpointedGA.p')
avgf = np.mean([h.fitness for h in hof])
list_check_increase.append(avgf)
if cnt % 3 == 0:
avgf = np.mean([h.fitness for h in hof])
old_avg = np.mean(list_check_increase)
self.assertGreater(np.mean(list_check_increase), old_avg)
self.assertGreater(avgf, old)
old = avgf
elif cnt == 0:
avgf = np.mean([h.fitness for h in hof])
old = avgf
old_avg = np.mean(list_check_increase)
cnt += 1
print(old, cnt)
def test_opt_set_GA_params(self):
'''
Test to check if making the population size bigger, or increasing the run_number
of generations the GA consumes actually increases the fitness.
Note only test for better fitness every 3rd count, because GAs are stochastic,
and by chance occassionally fitness may not increase, just because the GA has
more genes to sample with.
A fairer test of the GA would probably test if the mean of mean fitness is improving
or use a sliding window.
'''
from neuronunit.optimization.model_parameters import model_params
provided_keys = list(model_params.keys())
from bluepyopt.deapext.optimisations import DEAPOptimisation
DO = DEAPOptimisation()
nparams = 5
cnt = 0
provided_keys = list(model_params.keys())[nparams]
DO.setnparams(nparams = nparams, provided_keys = provided_keys)
list_check_increase = []
for NGEN in range(1,30):
for MU in range(5,40):
pop, hof, log, history, td, gen_vs_hof = DO.run(offspring_size = MU, max_ngen = NGEN, cp_frequency=4,cp_filename='checkpointedGA.p')
avgf = np.mean([ h.fitness for h in hof ])
list_check_increase.append(avgf)
if cnt % 3 == 0:
avgf = np.mean([ h.fitness for h in hof ])
old_avg = np.mean(list_check_increase)
self.assertGreater(np.mean(list_check_increase),old_avg)
self.assertGreater(avgf,old)
old = avgf
elif cnt == 0:
avgf = np.mean([ h.fitness for h in hof ])
old = avgf
old_avg = np.mean(list_check_increase)
cnt += 1
print(old,cnt)
# In[ ]:
# In[ ]:
from neuronunit.optimization import get_neab
#fi_basket = {'nlex_id':'NLXCELL:100201'}
neuron = {'nlex_id': 'nifext_50'}
error_criterion, inh_observations = get_neab.get_neuron_criteria(fi_basket)
print(error_criterion)
from bluepyopt.deapext.optimisations import DEAPOptimisation
DO = DEAPOptimisation(error_criterion=error_criterion)
DO.setnparams(nparams=nparams, provided_keys=provided_keys)
pop, hof, log, history, td, gen_vs_hof = DO.run(offspring_size=MU,
max_ngen=NGEN,
cp_frequency=4,
cp_filename='checkpointedGA.p')
with open('ga_dump.p', 'wb') as f:
pickle.dump([pop, log, history, hof, td], f)
# In[ ]:
# Layer V pyramidal cell
# In[ ]:
# In[ ]: