def model_test_eval(self, tests):
"""
Take a model and some tests
Evaluate a test suite over them.
"""
from sciunit import TestSuite
if type(tests) is TestSuite:
not_suite = TSD({t.name: t for t in tests.tests})
OM = OptMan(tests, backend=self._backend)
dtc = DataTC()
dtc.attrs = self.attrs
assert set(self._backend.attrs.keys()) in set(self.attrs.keys())
dtc.backend = self._backend
dtc.tests = copy.copy(not_suite)
dtc = dtc_to_rheo(dtc)
if dtc.rheobase is not None:
dtc.tests = dtc.format_test()
dtc = list(map(OM.elephant_evaluation, [dtc]))
model = dtc.dtc_to_model()
model.SM = dtc.SM
model.obs_preds = dtc.obs_preds
return dtc[0], model
def generate_prediction(self,model = None):
if self.prediction is None:
dtc = DataTC()
dtc.backed = model.backend
dtc.attrs = model.attrs
dtc.rheobase = model.rheobase
dtc.tests = [self]
dtc = three_step_protocol(dtc)
dtc,ephys0 = allen_wave_predictions(dtc,thirty=True)
dtc,ephys1 = allen_wave_predictions(dtc,thirty=False)
if self.name in ephys0.keys():
feature = ephys0[self.name]
self.prediction = {}
self.prediction['value'] = feature
#self.prediction['std'] = feature
if self.name in ephys1.keys():
feature = ephys1[self.name]
self.prediction = {}
self.prediction['value'] = feature
#self.prediction['std'] = feature
return self.prediction
hold_constant_glif = {}
for k,v in gd.items():
if k not in explore_ranges:
hold_constant_glif[k] = v
try:
with open('glif_seeds.p','rb') as f:
seeds = pickle.load(f)
assert seeds is not None
except:
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 = {}