def nunit_evaluation(tuple_object): #,backend=None):
# Inputs single data transport container modules, and neuroelectro observations that
# inform test error error_criterion
# Outputs Neuron Unit evaluation scores over error criterion
dtc, tests = tuple_object
dtc = copy.copy(dtc)
dtc.model_path = path_params['model_path']
LEMS_MODEL_PATH = path_params['model_path']
assert dtc.rheobase is not None
backend = dtc.backend
if backend == 'glif':
model = glif.GC(
) #ReducedModel(LEMS_MODEL_PATH,name=str('vanilla'),backend=('NEURON',{'DTC':dtc}))
tests[0].prediction = dtc.rheobase
model.rheobase = dtc.rheobase['value']
else:
model = ReducedModel(LEMS_MODEL_PATH,
name=str('vanilla'),
backend=('NEURON', {
'DTC': dtc
}))
model.set_attrs(dtc.attrs)
tests[0].prediction = dtc.rheobase
model.rheobase = dtc.rheobase['value']
for k, t in enumerate(tests[1:-1]):
t.params = dtc.vtest[k]
score = None
score = t.judge(model, stop_on_error=False, deep_error=False)
dtc.score[str(t)] = {}
dtc.score[str(t)]['value'] = copy.copy(score.sort_key)
if hasattr(score, 'prediction'):
if type(score.prediction) is not type(None):
dtc.score[str(t)][str('prediction')] = score.prediction
dtc.score[str(t)][str('observation')] = score.observation
if 'mean' in score.observation.keys(
) and 'mean' in score.prediction.keys():
print(score.observation.keys())
dtc.score[str(t)][str('agreement')] = np.abs(
score.observation['mean'] - score.prediction['mean'])
else:
print(score, type(score))
if score.sort_key is not None:
##
# This probably does something different to what I thought.
##
#dtc.scores.get(str(t), 1.0 - score.sort_key)
dtc.scores[str(t)] = 1.0 - score.sort_key
else:
dtc.scores[str(t)] = 1.0
return dtc
def nunit_evaluation(tuple_object):#,backend=None):
# Inputs single data transport container modules, and neuroelectro observations that
# inform test error error_criterion
# Outputs Neuron Unit evaluation scores over error criterion
dtc,tests = tuple_object
dtc.model_path = path_params['model_path']
LEMS_MODEL_PATH = path_params['model_path']
assert dtc.rheobase is not None
backend = dtc.backend
if backend == 'glif':
model = glif.GC()#ReducedModel(LEMS_MODEL_PATH,name=str('vanilla'),backend=('NEURON',{'DTC':dtc}))
tests[0].prediction = dtc.rheobase
model.rheobase = dtc.rheobase['value']
else:
model = ReducedModel(LEMS_MODEL_PATH,name = str('vanilla'),backend = ('NEURON',{'DTC':dtc}))
model.set_attrs(dtc.attrs)
tests[0].prediction = dtc.rheobase
model.rheobase = dtc.rheobase['value']
for k,t in enumerate(tests[1:-1]):
t.params = dtc.vtest[k]
score = t.judge(model,stop_on_error = False, deep_error = False)
dtc.score.get(str(t), score)
dtc.score[str(t)][str('prediction')] = score.prediction
dtc.score[str(t)][str('observation')] = score.observation
if score.sort_key is not None:
if not hasattr(dtc,'score'):
dtc.score = score
print(dtc.score.prediction)
print(dtc.score.observation)
dtc.scores.get(str(t), 1.0 - score.sort_key)
else:
dtc.scores[str(t)] = 1.0
return dtc
import os
import sys
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from neuronunit.models.interfaces import glif
gc = glif.GC()
from neuronunit.optimization import get_neab #import get_neuron_criteria, impute_criteria
from neuronunit.optimization.model_parameters import model_params
import os
import pickle
electro_path = 'pipe_tests.p'
assert os.path.isfile(electro_path) == True
with open(electro_path, 'rb') as f:
electro_tests = pickle.load(f)
MU = 6
NGEN = 6
CXPB = 0.9
USE_CACHED_GA = False
#provided_keys = list(model_params.keys())
USE_CACHED_GS = False
from bluepyopt.deapext.optimisations import DEAPOptimisation
npoints = 2
nparams = 10