def fit_sx(mname, project='behav', regress=False):
pth=find_path()
data=defmod.find_data(mname, project)
grp_dict={}; subj_params=[]; aic_list=[]; bic_list=[]; dic_list=[]; ic_dict={}
for subj_idx, subj_data in data.groupby('subj_idx'):
m_sx=defmod.define_sxbayes(mname, subj_data, project=project, regress=regress)
m_sx.sample(1000, burn=500, dbname=str(subj_idx)+"_"+mname+'_traces.db', db='pickle')
sx_df=parse.stats_df(m_sx)
sx_df=sx_df.drop("sub", axis=1)
sx_df['sub']=[subj_idx]*len(sx_df)
subj_params.append(sx_df)
aic_list.append(aic(m_sx)); bic_list.append(bic(m_sx)); dic_list.append(m_sx.dic)
allsx_df=pd.concat(subj_params)
allsx_df.to_csv(mname+"_SxStats.csv", index=False)
ic_dict={'aic':aic_list, 'bic':bic_list, 'dic':dic_list}
ic_df=pd.DataFrame(ic_dict)
ic_df.to_csv(mname+"_IC_Rank.csv")
return allsx_df
def run_models(mname, project, regress=False):
#bayes fit all subject
allsx_df = fit_sx(mname, project=project, regress=regress)
#parse model output
subdf = parse_allsx(allsx_df)
pdict = subdf_to_pdict(subdf)
#simulate and compare with observed data
data = defmod.find_data(mname, project=project)
simdf = vis.predict(pdict,
data,
ntrials=160,
nsims=100,
save=True,
RTname="SimRT_EvT.jpeg",
ACCname="SimACC_EvT.jpeg")
simdf.to_csv("simdf_sxbayes.csv")
#save pdict; can be reloaded and transformed back into
#the original pdict format by the following commands
#1. pdict=pd.read_csv("sxbayes_pdict.csv")
#2. pdict=pdict.to_dict()
params = pd.DataFrame(pdict)
params.to_csv("sxbayes_pdict.csv", index=False)
def run_models(mname, project, regress=False):
#bayes fit all subject
allsx_df=fit_sx(mname, project=project, regress=regress)
#parse model output
subdf=parse_allsx(allsx_df)
pdict=subdf_to_pdict(subdf)
#simulate and compare with observed data
data=defmod.find_data(mname, project=project)
simdf=vis.predict(pdict, data, ntrials=160, nsims=100, save=True, RTname="SimRT_EvT.jpeg", ACCname="SimACC_EvT.jpeg")
simdf.to_csv("simdf_sxbayes.csv")
#save pdict; can be reloaded and transformed back into
#the original pdict format by the following commands
#1. pdict=pd.read_csv("sxbayes_pdict.csv")
#2. pdict=pdict.to_dict()
params=pd.DataFrame(pdict)
params.to_csv("sxbayes_pdict.csv", index=False)
def fit_sx(mname, project='behav', regress=False):
pth = find_path()
data = defmod.find_data(mname, project)
grp_dict = {}
subj_params = []
aic_list = []
bic_list = []
dic_list = []
ic_dict = {}
for subj_idx, subj_data in data.groupby('subj_idx'):
m_sx = defmod.define_sxbayes(mname,
subj_data,
project=project,
regress=regress)
m_sx.sample(1000,
burn=500,
dbname=str(subj_idx) + "_" + mname + '_traces.db',
db='pickle')
sx_df = parse.stats_df(m_sx)
sx_df = sx_df.drop("sub", axis=1)
sx_df['sub'] = [subj_idx] * len(sx_df)
subj_params.append(sx_df)
aic_list.append(aic(m_sx))
bic_list.append(bic(m_sx))
dic_list.append(m_sx.dic)
allsx_df = pd.concat(subj_params)
allsx_df.to_csv(mname + "_SxStats.csv", index=False)
ic_dict = {'aic': aic_list, 'bic': bic_list, 'dic': dic_list}
ic_df = pd.DataFrame(ic_dict)
ic_df.to_csv(mname + "_IC_Rank.csv")
return allsx_df
