def optimize_job(self, model_type, score_type=ZScore):
find_sweep_with_n_spikes = 8
model = GenericModel()
model.backend = model_type
model.params = BPO_PARAMS[model_type]
fixed_current = 122 * qt.pA
if model_type == "ADEXP":
NGEN = 55
MU = 16
else:
NGEN = 45
MU = 100
mapping_funct = dask_map_function
cell_evaluator, simple_cell, suite, target_current, spk_count = wrap_setups(
self.specimen_id,
model_type,
find_sweep_with_n_spikes,
template_model=model,
fixed_current=False,
cached=False,
score_type=score_type,
)
final_pop, hall_of_fame, logs, hist = opt_exec(MU, NGEN, mapping_funct,
cell_evaluator)
opt, target, scores, obs_preds, df = opt_to_model(
hall_of_fame, cell_evaluator, suite, target_current, spk_count)
best_ind = hall_of_fame[0]
fitnesses = cell_evaluator.evaluate_with_lists(best_ind)
target.vm_soma = suite.traces["vm_soma"]
return np.sum(fitnesses)
def test_opt_1(self):
cellmodel = "ADEXP"
if cellmodel == "IZHI":
model = model_classes.IzhiModel()
if cellmodel == "MAT":
model = model_classes.MATModel()
if cellmodel == "ADEXP":
model = model_classes.ADEXPModel()
dtc = DataTC()
dtc.backend = cellmodel
dtc._backend = model._backend
dtc.attrs = model.attrs
dtc.params = {
k: np.mean(v)
for k, v in MODEL_PARAMS[cellmodel].items()
}
other_params = BPO_PARAMS[cellmodel]
dtc = dtc_to_rheo(dtc)
assert dtc.rheobase is not None
self.assertIsNotNone(dtc.rheobase)
vm, plt, dtc = inject_and_plot_model(dtc, plotly=False)
self.assertIsNotNone(vm)
model = dtc.dtc_to_model()
self.assertIsNotNone(model)
def test_opt_1(self):
specimen_id = self.ids[1]
cellmodel = "ADEXP"
if cellmodel == "IZHI":
model = model_classes.IzhiModel()
if cellmodel == "MAT":
model = model_classes.MATModel()
if cellmodel == "ADEXP":
model = model_classes.ADEXPModel()
target_num_spikes = 8
dtc = DataTC()
dtc.backend = cellmodel
dtc._backend = model._backend
dtc.attrs = model.attrs
dtc.params = {
k: np.mean(v)
for k, v in MODEL_PARAMS[cellmodel].items()
}
dtc = dtc_to_rheo(dtc)
assert dtc.rheobase is not None
self.assertIsNotNone(dtc.rheobase)
vm, plt, dtc = inject_and_plot_model(dtc, plotly=False)
fixed_current = 122 * qt.pA
model, suite, nu_tests, target_current, spk_count = opt_setup(
specimen_id,
cellmodel,
target_num_spikes,
provided_model=model,
fixed_current=False,
cached=True)
model = dtc.dtc_to_model()
model.seeded_current = target_current['value']
model.allen = True
model.seeded_current
model.NU = True
cell_evaluator, simple_cell = opt_setup_two(model,
cellmodel,
suite,
nu_tests,
target_current,
spk_count,
provided_model=model)
NGEN = 15
MU = 12
mapping_funct = dask_map_function
final_pop, hall_of_fame, logs, hist = opt_exec(MU, NGEN, mapping_funct,
cell_evaluator)
opt, target = opt_to_model(hall_of_fame, cell_evaluator, suite,
target_current, spk_count)
best_ind = hall_of_fame[0]
fitnesses = cell_evaluator.evaluate_with_lists(best_ind)
assert np.sum(fitnesses) < 0.7
self.assertGreater(0.7, np.sum(fitnesses))
#obnames = [obj.name for obj in cell_evaluator.objectives]
gen_numbers = logs.select('gen')
min_fitness = logs.select('min')
max_fitness = logs.select('max')
avg_fitness = logs.select('avg')
plt.plot(gen_numbers, max_fitness, label='max fitness')
plt.plot(gen_numbers, avg_fitness, label='avg fitness')
plt.plot(gen_numbers, min_fitness, label='min fitness')
plt.plot(gen_numbers, min_fitness, label='min fitness')
plt.semilogy()
plt.xlabel('generation #')
plt.ylabel('score (# std)')
plt.legend()
plt.xlim(min(gen_numbers) - 1, max(gen_numbers) + 1)
#model = opt.dtc_to_model()
plt.plot(opt.vm15.times, opt.vm15)
plt.plot(opt.vm15.times, opt.vm15)
target.vm15 = suite.traces['vm15']
plt.plot(target.vm15.times, target.vm15)
target.vm15 = suite.traces['vm15']
check_bin_vm15(target, opt)
if cellmodel == "ADEXP":
model = model_classes.ADEXPModel()
specific_filter_list = [
'ISI_log_slope_1.5x', 'mean_frequency_1.5x', 'adaptation_index2_1.5x',
'first_isi_1.5x', 'ISI_CV_1.5x', 'median_isi_1.5x', 'Spikecount_1.5x',
'all_ISI_values', 'ISI_values', 'time_to_first_spike',
'time_to_last_spike', 'time_to_second_spike', 'spike_times'
]
simple_yes_list = specific_filter_list
target_num_spikes = 8
dtc = DataTC()
dtc.backend = cellmodel
dtc._backend = model._backend
dtc.attrs = model.attrs
dtc.params = {k: np.mean(v) for k, v in MODEL_PARAMS[cellmodel].items()}
dtc = dtc_to_rheo(dtc)
vm, plt, dtc = inject_and_plot_model(dtc, plotly=False)
fixed_current = 122 * qt.pA
model, suite, nu_tests, target_current, spk_count = opt_setup(
specimen_id,
cellmodel,
target_num_spikes,
provided_model=model,
fixed_current=False)
model = dtc.dtc_to_model()
model.seeded_current = target_current['value']
model.allen = True
model.seeded_current
model.NU = True
cell_evaluator, simple_cell = opt_setup_two(model,
def test_opt(self):
ids = [
324257146, 325479788, 476053392, 623893177, 623960880, 482493761,
471819401
]
specimen_id = ids[1]
cellmodel = "MAT"
if cellmodel == "IZHI":
model = model_classes.IzhiModel()
if cellmodel == "MAT":
model = model_classes.MATModel()
if cellmodel == "ADEXP":
model = model_classes.ADEXPModel()
specific_filter_list = [
'ISI_log_slope_1.5x', 'mean_frequency_1.5x',
'adaptation_index2_1.5x', 'first_isi_1.5x', 'ISI_CV_1.5x',
'median_isi_1.5x', 'Spikecount_1.5x', 'all_ISI_values',
'ISI_values', 'time_to_first_spike', 'time_to_last_spike',
'time_to_second_spike', 'spike_times'
]
simple_yes_list = specific_filter_list
target_num_spikes = 8
dtc = DataTC()
dtc.backend = cellmodel
dtc._backend = model._backend
dtc.attrs = model.attrs
dtc.params = {
k: np.mean(v)
for k, v in MODEL_PARAMS[cellmodel].items()
}
dtc = dtc_to_rheo(dtc)
vm, plt, dtc = inject_and_plot_model(dtc, plotly=False)
fixed_current = 122 * qt.pA
model, suite, nu_tests, target_current, spk_count = opt_setup(
specimen_id,
cellmodel,
target_num_spikes,
provided_model=model,
fixed_current=False)
model = dtc.dtc_to_model()
model.seeded_current = target_current['value']
model.allen = True
model.seeded_current
model.NU = True
cell_evaluator, simple_cell = opt_setup_two(model,
cellmodel,
suite,
nu_tests,
target_current,
spk_count,
provided_model=model)
NGEN = 15
MU = 12
mapping_funct = dask_map_function
final_pop, hall_of_fame, logs, hist = opt_exec(MU, NGEN, mapping_funct,
cell_evaluator)
opt, target = opt_to_model(hall_of_fame, cell_evaluator, suite,
target_current, spk_count)
best_ind = hall_of_fame[0]
fitnesses = cell_evaluator.evaluate_with_lists(best_ind)
assert np.sum(fitnesses) < 0.7
self.assertGreater(0.7, np.sum(fitnesses))
#obnames = [obj.name for obj in cell_evaluator.objectives]
gen_numbers = logs.select('gen')
min_fitness = logs.select('min')
max_fitness = logs.select('max')
avg_fitness = logs.select('avg')
plt.plot(gen_numbers, max_fitness, label='max fitness')
plt.plot(gen_numbers, avg_fitness, label='avg fitness')
plt.plot(gen_numbers, min_fitness, label='min fitness')
plt.plot(gen_numbers, min_fitness, label='min fitness')
plt.semilogy()
plt.xlabel('generation #')
plt.ylabel('score (# std)')
plt.legend()
plt.xlim(min(gen_numbers) - 1, max(gen_numbers) + 1)
#model = opt.dtc_to_model()
plt.plot(opt.vm15.times, opt.vm15)
plt.plot(opt.vm15.times, opt.vm15)
target.vm15 = suite.traces['vm15']
plt.plot(target.vm15.times, target.vm15)
target.vm15 = suite.traces['vm15']
check_bin_vm15(target, opt)