best_ind_dict = cell_evaluator.param_dict(best_ind)
print(cell_evaluator.evaluate_with_dicts(best_ind_dict))
model = cell_evaluator.cell_model
cell_evaluator.param_dict(best_ind)
model.attrs = {str(k):float(v) for k,v in cell_evaluator.param_dict(best_ind).items()}
opt = model.model_to_dtc()
opt.attrs = {str(k):float(v) for k,v in cell_evaluator.param_dict(best_ind).items()}
check_binary_match(target,opt)
inject_and_plot_passive_model(opt,second=target)
# As you can see the evaluation returns the same values as the fitness values provided by the optimisation output.
# We can have a look at the responses now.
# In[ ]:
#plot_responses(twostep_protocol.run(cell_model=simple_cell, param_values=best_ind_dict, sim=nrn))
# Let's have a look at the optimisation statistics.
# We can plot the minimal score (sum of all objective scores) found in every optimisation.
# The optimisation algorithm uses negative fitness scores, so we actually have to look at the maximum values log.
def instance_opt(experimental_constraints,MODEL_PARAMS,test_key,model_value,MU,NGEN):
cell_evaluator, simple_cell, score_calc, test_names = utils.make_evaluator(
experimental_constraints,
MODEL_PARAMS,
test_key,
model=model_value)
#cell_evaluator, simple_cell, score_calc = make_evaluator(cells,MODEL_PARAMS)
#MU =10
mut = 0.05
cxp = 0.1
optimisation = bpop.optimisations.DEAPOptimisation(
evaluator=cell_evaluator,
offspring_size = MU,
map_function = utils.dask_map_function,
selector_name=diversity,
mutpb=mut,
cxpb=cxp)
final_pop, hall_of_fame, logs, hist = optimisation.run(max_ngen=NGEN)
best_ind = hall_of_fame[0]
st.markdown('---')
st.success("Model best fit to experiment {0}".format(test_key))
st.markdown("Would you like to pickle the optimal model? (Note not implemented yet, but trivial)")
st.markdown('---')
st.markdown('\n\n\n\n')
best_ind_dict = cell_evaluator.param_dict(best_ind)
model = cell_evaluator.cell_model
cell_evaluator.param_dict(best_ind)
model.attrs = {str(k):float(v) for k,v in cell_evaluator.param_dict(best_ind).items()}
opt = model.model_to_dtc()
opt.attrs = {str(k):float(v) for k,v in cell_evaluator.param_dict(best_ind).items()}
opt.tests = experimental_constraints
obs_preds = opt.make_pretty(experimental_constraints)
frame = opt.SA.to_frame()
st.write(frame.T)
obs_preds = opt.obs_preds
st.write(obs_preds.T)
st.markdown("----")
st.markdown("Model behavior at rheobase current injection")
vm,fig = inject_and_plot_model(opt,plotly=True)
st.write(fig)
st.markdown("----")
st.markdown("Model behavior at -10pA current injection")
fig = inject_and_plot_passive_model(opt,opt,plotly=True)
st.write(fig)
#plt.show()
st.markdown("""
-----
The optimal model parameterization is {0}
-----
""".format(opt.attrs))
st.markdown("""
-----
Model Performance Relative to fitting data {0}
-----
""".format(sum(best_ind.fitness.values)/(30*len(experimental_constraints))))
# radio_value = st.sidebar.radtio("Target Number of Samples",[10,20,30])
st.markdown("""
-----
This score is {0} worst score is {1}
-----
""".format(sum(best_ind.fitness.values),30*len(experimental_constraints)))
model = cell_evaluator.cell_model
cell_evaluator.param_dict(best_ind)
model.attrs = {
str(k): float(v)
for k, v in cell_evaluator.param_dict(best_ind).items()
}
opt = model.model_to_dtc()
opt.attrs = {
str(k): float(v)
for k, v in cell_evaluator.param_dict(best_ind).items()
}
check_binary_match(target, opt)
inject_and_plot_passive_model(opt, second=target)
# As you can see the evaluation returns the same values as the fitness values provided by the optimisation output.
# We can have a look at the responses now.
# In[ ]:
#plot_responses(twostep_protocol.run(cell_model=simple_cell, param_values=best_ind_dict, sim=nrn))
# Let's have a look at the optimisation statistics.
# We can plot the minimal score (sum of all objective scores) found in every optimisation.
# The optimisation algorithm uses negative fitness scores, so we actually have to look at the maximum values log.
gen_numbers = logs.select('gen')
min_fitness = logs.select('min')
max_fitness = logs.select('max')