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_grid_dimensions(self):
from neuronunit.optimization.model_parameters import model_params
provided_keys = list(model_params.keys())
USE_CACHED_GS = False
from neuronunit.optimization import exhaustive_search
from neuronunit.optimization.optimization_management import map_wrapper
import dask.bag as db
npoints = 2
nparams = 3
for i in range(1, 10):
for j in range(1, 10):
grid_points = exhaustive_search.create_grid(npoints=i,
nparams=j)
b0 = db.from_sequence(grid_points[0:2], npartitions=8)
dtcpop = list(
db.map(exhaustive_search.update_dtc_grid, b0).compute())
self.assertEqual(i * j, len(dtcpop))
self.assertNotEqual(dtcpop, None)
dtcpop_compare = map_wrapper(exhaustive_search.update_dtc_grid,
grid_points[0:2])
self.assertNotEqual(dtcpop_compare, None)
self.assertEqual(len(dtcpop_compare), len(dtcpop))
for i, j in enumerate(dtcpop):
for k, v in dtcpop_compare[i].attrs.items():
print(k, v, i, j)
self.assertEqual(j.attrs[k], v)
return True
def test_grid_dimensions(self):
from neuronunit.optimization.model_parameters import model_params
provided_keys = list(model_params.keys())
USE_CACHED_GS = False
from neuronunit.optimization import exhaustive_search
from neuronunit.optimization.optimization_management import map_wrapper
import dask.bag as db
npoints = 2
nparams = 3
for i in range(1,10):
for j in range(1,10):
grid_points = exhaustive_search.create_grid(npoints = i, nparams = j)
b0 = db.from_sequence(grid_points[0:2], npartitions=8)
dtcpop = list(db.map(exhaustive_search.update_dtc_grid,b0).compute())
self.assertEqual(i*j,len(dtcpop))
self.assertNotEqual(dtcpop,None)
dtcpop_compare = map_wrapper(exhaustive_search.update_dtc_grid,grid_points[0:2])
self.assertNotEqual(dtcpop_compare,None)
self.assertEqual(len(dtcpop_compare),len(dtcpop))
for i,j in enumerate(dtcpop):
for k,v in dtcpop_compare[i].attrs.items():
print(k,v,i,j)
self.assertEqual(j.attrs[k],v)
return True
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)
def grid_points():
npoints = 2
nparams = 10
from neuronunit.optimization.model_parameters import model_params
provided_keys = list(model_params.keys())
USE_CACHED_GS = False
from neuronunit.optimization import exhaustive_search
grid_points = exhaustive_search.create_grid(npoints = npoints,nparams = nparams)
import dask.bag as db
b0 = db.from_sequence(grid_points[0:2], npartitions=8)
dtcpop = list(db.map(exhaustive_search.update_dtc_grid,b0).compute())
assert dtcpop is not None
return dtcpop
def grid_points():
npoints = 2
nparams = 10
from neuronunit.optimization.model_parameters import model_params
provided_keys = list(model_params.keys())
USE_CACHED_GS = False
from neuronunit.optimization import exhaustive_search
grid_points = exhaustive_search.create_grid(npoints=npoints,
nparams=nparams)
import dask.bag as db
b0 = db.from_sequence(grid_points[0:2], npartitions=8)
dtcpop = list(db.map(exhaustive_search.update_dtc_grid, b0).compute())
assert dtcpop is not None
return dtcpop
def test_map_wrapper(self):
npoints = 2
nparams = 3
from neuronunit.optimization.model_parameters import model_params
provided_keys = list(model_params.keys())
USE_CACHED_GS = False
from neuronunit.optimization import exhaustive_search
from neuronunit.optimization.optimization_management import map_wrapper
grid_points = exhaustive_search.create_grid(npoints = npoints,nparams = nparams)
b0 = db.from_sequence(grid_points[0:2], npartitions=8)
dtcpop = list(db.map(exhaustive_search.update_dtc_grid,b0).compute())
assert dtcpop is not None
dtcpop_compare = map_wrapper(exhaustive_search.update_dtc_grid,grid_points[0:2])
for i,j in enumerate(dtcpop):
for k,v in dtcpop_compare[i].attrs.items():
print(k,v,i,j)
self.assertEqual(j.attrs[k],v)
return True
def grid_points():
npoints = 2
nparams = 10
from neuronunit.optimization.model_parameters import model_params
provided_keys = list(model_params.keys())
USE_CACHED_GS = False
electro_path = 'pipe_tests.p'
import pickle
assert os.path.isfile(electro_path) == True
with open(electro_path, 'rb') as f:
electro_tests = pickle.load(f)
from neuronunit.optimization import exhaustive_search
grid_points = exhaustive_search.create_grid(npoints=npoints,
nparams=nparams)
import dask.bag as db
b0 = db.from_sequence(grid_points[0:2], npartitions=8)
dtcpop = list(db.map(exhaustive_search.update_dtc_grid, b0).compute())
assert dtcpop is not None
return dtcpop
def reduce_params(model_params, nparams):
key_list = list(model_params.keys())
reduced_key_list = key_list[0:nparams]
subset = {k: model_params[k] for k in reduced_key_list}
return subset
with open('grid_cell_results.p', 'wb') as f:
pickle.dump(results, f)
with open('iterator_state.p', 'wb') as f:
pickle.dump([sub_pop, test, observation, cnt], f)
cnt += 1
print('done cell: ', cnt)
print('done all')
from neuronunit.optimization import get_neab
from neuronunit.optimization.model_parameters import model_params
from bluepyopt.deapext.optimisations import DEAPOptimisation
from neuronunit.optimization.optimization_management import write_opt_to_nml
from neuronunit.optimization import optimization_management
from neuronunit.optimization import optimization_management as om
key_list = list(model_params.keys())
reduced_key_list = key_list[0:nparams]
subset = {k: smaller[k] for k in reduced_key_list}
DO = DEAPOptimisation(error_criterion=test,
selection=str('selIBEA'),
provided_dict=model_params,
elite_size=3)
package = DO.run(offspring_size=MU,
max_ngen=6,
cp_frequency=1,
cp_filename=str(dic_key) + '.p')
pop, hof_py, pf, log, history, td_py, gen_vs_hof = package
with open('all_ga_cell.p', 'wb') as f:
pickle.dump(package, f)
pickle.dump(pipe,handle)
contents = pickle.load(open('ne_neuron_criteria.p','rb'))
pvis_criterion, inh_criterion = contents
electro_tests = []
contents[0][0].observation
for p in pipe:
p_tests, p_observations = get_neab.get_neuron_criteria(p)
electro_tests.append((p_tests, p_observations))
with open('pipe_tests.p','wb') as f:
pickle.dump(electro_tests,f)
'''
MU = 6; NGEN = 6; CXPB = 0.9
USE_CACHED_GA = False
print(get_neab)
from neuronunit.optimization.model_parameters import model_params
provided_keys = list(model_params.keys())
USE_CACHED_GS = False
from bluepyopt.deapext.optimisations import DEAPOptimisation
npoints = 2
nparams = 10
from dask import distributed
#c = distributed.Client()
#NCORES = len(c.ncores().values())-2
for test, observation in electro_tests:
DO = DEAPOptimisation(error_criterion = test, selection = 'selIBEA')
DO.setnparams(nparams = nparams, provided_keys = provided_keys)
pop, hof_py, log, history, td_py, gen_vs_hof = DO.run(offspring_size = MU, max_ngen = NGEN, cp_frequency=0,cp_filename='ga_dumpnifext_50.p')
#with open('ga_dump_NLXCELL:100201.p','wb') as f:
# pickle.dump([pop, log, history, hof_py, td_py],f)
