def optimize_sx(mname, project='imaging'):
m = defmod.define_model(mname, project=project)
data = m.data
if 'z' in m.depends_on.keys():
bias = True
else:
bias = False
grp_dict = {}
subj_params = []
aic_list = []
bic_list = []
dic_list = []
ic_dict = {}
for subj_idx, subj_data in data.groupby('subj_idx'):
m_subj = hddm.HDDM(subj_data,
depends_on=m.depends_on,
bias=bias,
include=m.include)
sx_params = m_subj.optimize('ML')
pdict = opt.get_pdict(sx_params)
subj_params.append(sx_params)
aic_list.append(aic(m_subj))
bic_list.append(bic(m_subj))
#dic_list.append(m_subj.dic)
grp_dict[subj_idx] = pdict
ic_dict = {'aic': aic_list, 'bic': bic_list}
ic_df = pd.DataFrame(ic_dict)
ic_df.to_csv(mname + "_IC_Rank.csv")
#write grp_dict to .txt file for reloading later
f = open('mle_params.txt', 'w')
f.write('grp_dict=' + repr(grp_dict) + '\n')
f.close()
params = pd.DataFrame(subj_params)
simdf = vis.predict(grp_dict,
data,
ntrials=100,
nsims=100,
save=True,
RTname="dbmz_RT.jpeg",
ACCname="dbmz_ACC.jpeg")
#simdf=vis.predict(grp_dict, df, ntrials=160, nsims=100, save=True, RTname="SimRT_EvT.jpeg", ACCname="SimACC_EvT.jpeg")
simdf.to_csv("simdf_opt.csv")
params.to_csv("subj_params_opt.csv", index=False)
sdt.plot_rho_sdt(data, simdf)
empvsim = sdt.rho_sdt(data, simdf)
return grp_dict, ic_df
def analyze_models(nsims=100, ntrials=100):
mnames=['msm', 'dbm', 'dbmz', 'pbm']
#mnames=['pbm']
bias=True
data=pd.read_csv("/Users/kyle/Desktop/beh_hddm/allsx_feat.csv")
for m in mnames:
if m=='dbm':
bias=False
model=defmod.define_model(m, project='behav')
mpath="/Users/kyle/Desktop/beh_hddm/revised_models/"+m
os.chdir(mpath)
m0=model; m1=model; m2=model
mlist=[m0.load_db(m+"_traces0.db", db='pickle'), m1.load_db(m+"_traces1.db", db='pickle'), m2.load_db(m+"_traces2.db", db='pickle')]
allmodels=kabuki.utils.concat_models(mlist)
allmodels.print_stats(m+"_stats_all.txt")
vis.plot_neutral_traces(allmodels)
for node in ['z', 'vf', 'vh']:
vis.plot_posterior_nodes(allmodels, node)
gparams={}; subj_params=[]
msingle=defmod.define_single(m, project='behav')
for subj_idx, subj_data in data.groupby('subj_idx'):
m_subj=hddm.HDDM(subj_data, depends_on=msingle.depends_on, bias=bias, include=msingle.include)
sx_params=m_subj.optimize('ML')
pdict=opt.get_pdict(sx_params)
subj_params.append(sx_params)
gparams[subj_idx]=pdict
#write gparams to .txt file for reloading later
f=open(m+'mle_gparams.txt', 'w')
f.write('gparams=' + repr(gparams) + '\n')
f.close()
simdf_list=[]
for i in range(nsims):
simdf, params_used=sims.sim_exp(pdict=gparams, ntrials=ntrials, pfast=0.0, pslow=0.0, nsims_per_sub=1)
simdf['sim_n']=[i]*len(simdf.index)
simdf_list.append(simdf)
simdf=pd.concat(simdf_list)
params = pd.DataFrame(subj_params)
simdf.to_csv(m+"_simdf.csv")
params.to_csv(m+"_sparams.csv", index=False)
def optimize_sx(mname, project='imaging'):
m=defmod.define_model(mname, project=project)
data=m.data
if 'z' in m.depends_on.keys():
bias=True
else:
bias=False
grp_dict={}; subj_params=[]; aic_list=[]; bic_list=[]; dic_list=[]; ic_dict={}
for subj_idx, subj_data in data.groupby('subj_idx'):
m_subj=hddm.HDDM(subj_data, depends_on=m.depends_on, bias=bias, include=m.include)
sx_params=m_subj.optimize('ML')
pdict=opt.get_pdict(sx_params)
subj_params.append(sx_params)
aic_list.append(aic(m_subj)); bic_list.append(bic(m_subj)); #dic_list.append(m_subj.dic)
grp_dict[subj_idx]=pdict
ic_dict={'aic':aic_list, 'bic':bic_list}
ic_df=pd.DataFrame(ic_dict)
ic_df.to_csv(mname+"_IC_Rank.csv")
#write grp_dict to .txt file for reloading later
f=open('mle_params.txt', 'w')
f.write('grp_dict=' + repr(grp_dict) + '\n')
f.close()
params = pd.DataFrame(subj_params)
simdf=vis.predict(grp_dict, data, ntrials=100, nsims=100, save=True, RTname="dbmz_RT.jpeg", ACCname="dbmz_ACC.jpeg")
#simdf=vis.predict(grp_dict, df, ntrials=160, nsims=100, save=True, RTname="SimRT_EvT.jpeg", ACCname="SimACC_EvT.jpeg")
simdf.to_csv("simdf_opt.csv")
params.to_csv("subj_params_opt.csv", index=False)
sdt.plot_rho_sdt(data, simdf)
empvsim=sdt.rho_sdt(data, simdf)
return grp_dict, ic_df
