def test_get_ss(self):
from neuronunit.optimization.data_transport_container import DataTC
dtc = DataTC()
self.assertIsNone(dtc.get_ss())
dtc.scores = {'score1': 1}
self.assertEqual(dtc.get_ss(), 1)
dtc.scores = {'score1': 1, 'score2': 2}
self.assertEqual(dtc.get_ss(), 3)
for local_attrs in grid:
store_glif_results[str(local_attrs.values())] = {}
dtc = DataTC()
dtc.tests = use_test
complete_params = {}
dtc.attrs = local_attrs
dtc.backend = 'GLIF'
dtc.cell_name = 'GLIF'
for key, use_test in test_frame.items():
dtc.tests = use_test
dtc = dtc_to_rheo(dtc)
dtc = format_test(dtc)
if dtc.rheobase is not None:
if dtc.rheobase!=-1.0:
dtc = nunit_evaluation(dtc)
print(dtc.get_ss())
store_glif_results[str(local_attrs.values())][key] = dtc.get_ss()
df = pd.DataFrame(store_glif_results)
best_params = {}
for index, row in df.iterrows():
best_params[index] = row == row.min()
best_params[index] = best_params[index].to_dict()
seeds = {}
for k,v in best_params.items():
for nested_key,nested_val in v.items():
if True == nested_val:
seed = nested_key
seeds[k] = seed
with open('glif_seeds.p','wb') as f:
dtc.attrs = v
dtc.backend = 'RAW'
dtc.cell_name = 'vanilla'
for key, use_test in test_frame.items():
dtc.tests = use_test
dtc = dtc_to_rheo(dtc)
dtc = format_test(dtc)
if dtc.rheobase is not None:
if dtc.rheobase!=-1.0:
dtc = nunit_evaluation(dtc)
df[k][key] = int(dtc.get_ss())
# A sparse grid sampling over the parameter space, using the published and well corrobarated parameter points, from Izhikitch publications, and the Open Source brain, shows that without optimization, using off the shelf parameter sets to fit real-life biological cell data, does not work so well.
MU = 6
NGEN = 150
for key, use_test in test_frame.items():
ga_out, _ = om.run_ga(explore_param,NGEN,use_test,free_params=free_params, NSGA = True, MU = MU)
test_opt = {str('multi_objective')+str(ga_out):ga_out}
with open('multi_objective.p','wb') as f:
pickle.dump(test_opt,f)
'''
MU = 6