def test_convergence(run_model, chains, saveplots=False):
"""
Uses ipyparallel client to run <chains> model fits, then runs r-hat (gelman-rubin) statistic
on the resulting traces. Finally, the models are concatenated and their posteriors are plotted for assessing
convergence.
"""
from ipyparallel import Client
c = Client()[:] # create the ipython client
jobs = c.map(run_model, range(chains)) # c.map(function, number of CPUs)
models = jobs.get() # run the jobs
# Calculate convergence statistics
from kabuki.analyze import gelman_rubin
rhat = gelman_rubin(models)
# Create a new model that contains all traces concatenated from the individual models
from kabuki.utils import concat_models
combined_model = concat_models(models)
# Plot posteriors
combined_model.plot_posteriors(save=saveplots)
return rhat
# =============================================== #
# For loop to run the grid search and save output
# =============================================== #
model_name = 'stimcoding_z_SW'
def parloop():
# Create a list for the model variables
nEs = list(mydata.columns[10:14])
modelCount = 0
for nE in nEs:
print('***** WORKING ON ELEC ' + nE + ' *****\n\n\n')
modelCount = modelCount + 1
try:
print('We are up to model ', modelCount,'!')
run_model(mypath, model_name, nE, 8000, 2000, 1)
except:
print('Failing to run model ', modelCount,'!')
continue
from ipyparallel import Client
rc = Client(profle='default')
v = Client()[:]
jobs = v.map(parloop, range(4))
"---------------------------------------------------------------------------------------------"
# =============================================== #
# For loop to run the grid search and save output
# =============================================== #
model_name = 'stimcoding_z_SW'
def parloop():
# Create a list for the model variables
nEs = list(mydata.columns[10:73])
modelCount = 0
for nE in nEs:
print('***** WORKING ON ELEC ' + nE + ' *****\n\n\n')
modelCount = modelCount + 1
try:
print('We are up to model ', modelCount, '!')
run_model(mypath, model_name, nE, 8000, 2000, 1)
except:
print('Failing to run model ', modelCount, '!')
continue
from ipyparallel import Client
v = Client()[:]
# Edit this for numer of cpus (x)
jobs = v.map(parloop, range(x))