def mmi_model_Mood_LTA_gamma_wo_RT_p0_beta(subj_input_dir,subj_out_dir,sid):
# import some useful libraries
import numpy as np # numerical analysis linear algebra
import pandas as pd # efficient tables
#import matplotlib.pyplot as plt # plotting
from scipy import stats
from RunDEMC.density import kdensity
from RunDEMC import Model, Param, dists, calc_bpic, joint_plot
from mmi_mood_LTA_gamma_wo_RT_p0_beta import MMIModel, load_mmi_data
from joblib import Parallel, delayed
import pickle
from pathlib import Path
try:
import scoop
from scoop import futures
except ImportError:
print("Error loading scoop, reverting to joblib.")
scoop = None
# ## Load and examine data
subj_input_dir=Path(subj_input_dir)
subj_out_dir=Path(subj_out_dir)
pattern=f'*{sid}*.xlsx'
input_file=list(subj_input_dir.glob(pattern))
print(input_file[0])
dat = load_mmi_data(input_file[0])
# In[4]:
#dat.at[2, 'Choice']=='Gamble'
# In[5]:
# find the minimum RT for the param range
min_RT = dat.loc[dat.RT>0, 'RT'].min()
print('Min RT:', min_RT)
# ## Use RunDEMC to fit the model to a participant's data
# define model evaluation functions for RunDEMC
def eval_mod(params, param_names, bdat=None):
# use global dat if none based in
if bdat is None:
bdat = dat
# turn param list into dict
mod_params = {x: params[n]
for n, x in enumerate(param_names)}
# try:
# print(mod_params['lambda'])
# except:
# print("Problems")
if mod_params['lambda']<0 or mod_params['lambda']>=1 :
return np.log(0), np.log(0)
if mod_params['gamma']<0 or mod_params['gamma']>=np.inf :
return np.log(0), np.log(0)
# if mod_params['beta']<0 or mod_params['beta']>1 :
# return np.log(0), np.log(0)
# if mod_params['p0']<0 or mod_params['p0']>1 :
# return np.log(0), np.log(0)
if mod_params['w_LTA']<0 or mod_params['w_LTA']>=np.inf :
return np.log(0), np.log(0)
if mod_params['w_RPE']<0 or mod_params['w_RPE']>=np.inf :
return np.log(0), np.log(0)
if mod_params['b']<=-np.inf or mod_params['b']>=np.inf :
return np.log(0), np.log(0)
if mod_params['s_v']<=0 or mod_params['s_v']>=np.inf :
return np.log(0), np.log(0)
if mod_params['c']<0 or mod_params['c']>=np.inf :
return np.log(0), np.log(0)
## calculate the log_likes and mood_log_likes
mod_res = MMIModel(params=mod_params,
ignore_non_resp=True).proc_trials(bdat.copy())
ll = mod_res.log_like.sum()
mood_ll = mod_res.mood_log_like.sum()
return ll,mood_ll
# this is the required def for RunDEMC
def eval_fun(pop, *args):
bdat = args[0]
pnames = args[1]
if scoop and scoop.IS_RUNNING:
res_tups = list(futures.map(eval_mod, [indiv for indiv in pop],
[pnames]*len(pop), [bdat]*len(pop)))
else:
res_tups= Parallel(n_jobs=-1)(delayed(eval_mod)(indiv,pnames, bdat)
for indiv in pop)
likes = np.array([rt[0] for rt in res_tups])
mood_likes = np.array([rt[1] for rt in res_tups])
return likes, mood_likes
def get_best_fit(m, burnin=250, verbose=True):
best_ind = m.weights[burnin:].argmax()
if verbose:
print("Weight:", m.weights[burnin:].ravel()[best_ind])
indiv = [m.particles[burnin:,:,i].ravel()[best_ind]
for i in range(m.particles.shape[-1])]
pp = {}
for p,v in zip(m.param_names,indiv):
pp[p] = v
if verbose:
print('"%s": %f,'%(p,v))
return pp
# ### Base model
# In[20]:
# set up model params
params = [Param(name='gamma',
display_name=r'$\gamma$',
prior=dists.gamma(1.5, 0.5),
),
Param(name='c',
display_name=r'$c$',
prior=dists.gamma(1.5, 0.5),
),
# Param(name='beta',
# display_name=r'$\beta$',
# prior=dists.normal(0, 1.4),
# transform=dists.invlogit
# ),
# Param(name='w',
# display_name=r'$w$',
# prior=dists.normal(0, 1.4),
# transform=dists.invlogit
# ),
# Param(name='a',
# display_name=r'$a$',
# prior=dists.gamma(2.0, 0.5),
# ),
# Param(name='t0',
# display_name=r'$t_0$',
# prior=dists.normal(0, 1.4),
# transform=lambda x: dists.invlogit(x)*min_RT,
# ),
# Param(name='p0',
# display_name=r'$p_0$',
# prior=dists.normal(0, 1.4),
# transform=dists.invlogit
# ),
Param(name='lambda',
display_name=r'$lambda',
prior=dists.normal(0, 1.4),
transform=dists.invlogit
),
# Param(name='lambda',
# display_name=r'$lambda',
# prior=dists.beta(0.5, 0.5)
# ),
Param(name='w_LTA',
display_name=r'w_{LTA}',
prior=dists.normal(0, 1),
transform=np.exp,
inv_transform=np.log),
# Param(name='w_EG',
# display_name=r'w_{EG}',
# prior=dists.normal(0, 1),
# transform=np.exp,
# inv_transform=np.log),
Param(name='w_RPE',
display_name=r'w_{RPE}',
prior=dists.normal(0, 1),
transform=np.exp,
inv_transform=np.log),
Param(name='b',
display_name=r'b',
prior=dists.normal(0, 3)),
Param(name='s_v',
display_name=r's_v',
prior=dists.exp(3))
]
# grab the param names
pnames = [p.name for p in params]
m = Model('mmi', params=params,
like_fun=eval_fun,
like_args=(dat, pnames),
init_multiplier=3,
# num_chains=gsize,
verbose=True)
# do some burnin
times = m.sample(150, burnin=True)
# now map the posterior
times = m.sample(650, burnin=False)
# show the chains are mixing and converging
#plt.plot(m.weights[30:], alpha=.3);
pickle_name=subj_out_dir / f'mWgt_{sid}.pickle'
print(pickle_name)
pickle_out = open(pickle_name,"wb")
pickle.dump(m.weights, pickle_out)
pickle_out.close()
#print("Best fitting params:")
#pp = get_best_fit(m, burnin=250, verbose=True)
print("Best fitting params:")
pp = get_best_fit(m, burnin=250, verbose=True)
pickle_name=subj_out_dir / f'mBFprm_{sid}.pickle'
print(pickle_name)
pickle_out = open(pickle_name,"wb")
pickle.dump(pp, pickle_out)
pickle_out.close()
# In[42]:
#### BPIC calculations
burnin=250
log_like_prior = m.weights - m.log_likes
#print(log_like_prior)
weight_mood = m.posts + log_like_prior #m.posts is log_like_Mood
#print(weight_mood)
print("Mood_BPIC :",calc_bpic(weight_mood[burnin:])['bpic'])
Mood_BPIC=calc_bpic(weight_mood[burnin:])['bpic']
pickle_name=subj_out_dir / f'Mood_BPIC_{sid}.pickle'
print(pickle_name)
pickle_out = open(pickle_name,"wb")
pickle.dump(Mood_BPIC, pickle_out)
pickle_out.close()
log_like_RT = m.log_likes - m.posts
weight_RT = log_like_RT + log_like_prior
print("RT_BPIC :",calc_bpic(weight_RT[burnin:])['bpic'])
RT_BPIC=calc_bpic(weight_RT[burnin:])['bpic']
pickle_name=subj_out_dir / f'RT_BPIC_{sid}.pickle'
print(pickle_name)
pickle_out = open(pickle_name,"wb")
pickle.dump(RT_BPIC, pickle_out)
pickle_out.close()
print(calc_bpic(m.weights[burnin:])['bpic'])
mBPIC=calc_bpic(m.weights[burnin:])['bpic']
pickle_name=subj_out_dir / f'Total_BPIC_{sid}.pickle'
print(pickle_name)
pickle_out = open(pickle_name,"wb")
pickle.dump(mBPIC, pickle_out)
pickle_out.close()
# In[46]:
# plot the param distributions (note, we did not get full posteriors, yet)
# plt.figure(figsize=(12,10))
# burnin=30
# for i in range(len(m.param_names)):
# plt.subplot(3,4,i+1)
# plt.hist(m.particles[burnin:, :, i].flatten(), bins='auto', alpha=.5, density=True);
# plt.title(m.param_names[i])
# In[ ]:
pickle_name=subj_out_dir / f'mParticles_{sid}.pickle'
print(pickle_name)
pickle_out = open(pickle_name,"wb")
pickle.dump(m.particles, pickle_out)
pickle_out.close()
pickle_name=subj_out_dir / f'mParams_{sid}.pickle'
print(pickle_name)
pickle_out = open(pickle_name,"wb")
pickle.dump(m.param_names, pickle_out)
pickle_out.close()
Param(name='a', prior=dists.uniform(-100, 100)),
Param(name='b', prior=dists.uniform(-100, 100)),
Param(name='c', prior=dists.uniform(-100, 100)),
Param(
name='delta',
display_name=r'$\mathbf{\delta}$',
prior=dists.exp(20),
init_prior=dists.uniform(0, 10),
),
]
# set up mod
mod = Model(name='fun',
params=params,
like_fun=eval_fun,
like_args=(xData, yData),
initial_zeros_ok=False,
use_priors=True,
verbose=True)
# run for a burnin with the local_to_best
burnin = 400
mod(200, burnin=True)
# fix the delta error term based on the mean
ind = mod.param_names.index('delta')
fixed_delta = mod.particles[-1, :, ind].mean()
mod._particles[-1][:, ind] = fixed_delta
mod._params[ind].prior = fixed_delta
mod._log_likes[-1], temp_posts = mod._calc_log_likes(mod._particles[-1])
mod._weights[-1][:] = mod._log_likes[-1][:] + \
Param(name='alpha',
display_name=r'$\alpha$',
prior=dists.normal(-1.0, 1.2),
transform=lambda x: dists.invlogit(x) * (30. - 0.0) + (0.0)),
Param(name='t0',
display_name=r'$t_0$',
prior=dists.normal(-.2, 1.2),
transform=lambda x: dists.invlogit(x) * (min_rt - 0.0) + (0.0)),
]
# grab the param names
pnames = [p.name for p in params]
print pnames
mod = Model('subjid',
params=params,
like_fun=eval_fun,
like_args=('subjid', ddat, pnames),
num_chains=90,
init_multiplier=4,
purify_every=5,
verbose=True)
out_file = 'flanker_' + s[:-4] + '.tgz'
for mm in range(16):
mod(25, burnin=True, migration_prob=0.0)
save_results(out_file, mod)
for mm in range(64):
mod(25, burnin=False, migration_prob=0.0)
save_results(out_file, mod)
Param(name='kappa',
prior=dists.normal(0.0, 1.4),
transform=dists.invlogit),
Param(name='beta',
prior=dists.normal(0.0, 1.4),
transform=dists.invlogit),
Param(name='alpha',
prior=dists.trunc_normal(2.5, 10.0, lower=0.0, upper=30.0)),
Param(name='t0', prior=dists.uniform(0., min_rt))
]
pnames = [p.name for p in params]
# instantiate model object
m = Model('urdm_lca',
params=params,
like_fun=eval_fun_lca,
like_args=(s, ),
init_multiplier=4,
verbose=True,
purify_every=5)
# set up the run name
output_name = 'rdm_lca_tcv_both_cb_afrl_mri_subj_' + str(
int(s)) + '.tgz'
# do some burnin
for i in range(5):
times = m(100, burnin=True)
save_results(output_name, m)
# sample the posterior
for i in range(10):
h_beta = HyperPrior(name='h_beta',
dist=dists.normal,
params=[
Param(name='mu', prior=dists.normal(1, .5)),
Param(name='sigma', prior=dists.invgamma(4, 10))
])
# set up lower level (i.e., subject)
def subj_like(pop, *args):
return np.log(dists.beta(pop[:, 0], pop[:, 1]).pdf(args[0]))
submods = [
Model(name='subj_%s' % s,
params=[
Param(name='alpha', prior=h_alpha),
Param(name='beta', prior=h_beta)
],
like_fun=subj_like,
like_args=(sdat[s], ),
verbose=False) for s in subjs
]
hier = Hiearachy(submods)
hier(50, burnin=True)
hier(500)
name='t0',
display_name=r'$t_0$',
prior=dists.uniform(0, min_RT),
#init_prior=dists.trunc_normal(mean=.1,std=.25,lower=0,upper=min_RT)
),
# strength scale
Param(name='scale',
display_name=r'$scale$',
prior=dists.uniform(0, 10)),
]
#grab the param names
pnames = [p.name for p in params]
m = Model(s,
params=params,
like_fun=eval_fun,
like_args=(s, dat[s], pnames),
verbose=True)
m(50, burnin=True)
save_results(out_file, m)
m(25, burnin=True)
save_results(out_file, m)
m(25, burnin=True)
save_results(out_file, m)
m(25, burnin=True)
save_results(out_file, m)
m(25, burnin=True)
save_results(out_file, m)
m(25, burnin=True)
save_results(out_file, m)
lower=0.,
upper=60.)),
Param(name='n_2',
display_name=r'$\n_2$',
prior=dists.trunc_normal(mean=15.0,
std=10.,
lower=0.,
upper=60.))
]
pnames = [p.name for p in params]
#RunDEMC TIME
m = Model(subject,
params=params,
like_fun=eval_fun,
like_args=('subject', pnames, balloon_tensor,
total_money_observed, starting_avg_ticker,
False, bank, 3, targets, ranges),
verbose=True,
purify_every=5)
# file_name = 'fits/sequential_fits/eeg_'+subject+'_block' + str(block) + '.tgz'
print(fits_storage, tag, subject, str(block))
file_name = os.path.join(
'{0}'.format(fits_storage),
'{0}_{1}_block{2}.tgz'.format(tag, subject, str(block)))
# run model
m(400, burnin=True, migration_prob=0.0) #Burnin
m(1000, burnin=False, migration_prob=0.0) #Poseteriors
save_results(file_name, m)
hdf.close()
prior=dists.normal(mean=0, std=1.4),
transform=dists.invlogit),
Param(
name='t0',
display_name=r'$t_0$',
prior=dists.uniform(0, min_RT),
),
]
# grab the param names
pnames = [p.name for p in params]
# initialize the model
m = Model(s,
params=params,
like_fun=eval_fun,
like_args=(s, data_sub, pnames),
num_chains=80,
init_multiplier=4,
verbose=True)
# set number of desired burn-in trials
num_burnin = 400
# interval of how often simulations should be saved
interval = 25
# total number of intervals
num_intervals = int(num_burnin / interval)
# how many simulations have been completed?
done_sims = 0
for i in range(num_intervals):
print(done_sims, 'done in burn-in')
# run this model simulation for the number of simulations in each interval
Param(name='mu', prior=dists.uniform(-50, 50)),
Param(name='sig', prior=dists.invgamma(1, 1))
])
# set up the submodels for each participant
smods = []
for j in range(nsubj):
# Append a new model, note the use of the hyperprior for setting
# up the intercept param, and the fixed beta and sigma across
# participants
smods.append(
Model(name=str(j),
params=[
Param(name='alpha', display_name=r'$\alpha$', prior=halpha),
beta, sigma
],
like_fun=subj_like,
like_args=(data[j], ),
num_chains=gsize,
verbose=False))
# put together into a single model
hmod = Hierarchy(smods)
###################
# Run the inference
###################
# burnin
hmod(50, burnin=True)