def alp_lik(alp_trans, cdat, dparams, r, lw, gn):
'''objective function for minimizing alp'''
#get w
pre_w = cdat['exptot'] / float(1000)
w = np.log(pre_w)
#untransform alp
alp = untrans(alp_trans)
#update preference parameters
cdat = calc_gv.get_pp(cdat, alp, r, lw, gn)
#Update observation types
cdat = new_ot.ot_step(cdat, dparams, alp, r, lw, gn)
#update preference parameters
cdat = calc_gv.get_pp(cdat, alp, r, lw, gn)
#get likelihood
lik = new_ot.olik(cdat, dparams, w, gn)
lik_sum = -lik.sum()
print(alp)
print('{0:.6f}'.format(lik_sum))
return lik_sum
def obs_type_lik_loop(data_input):
'''loops through each observation type to get likelihoods'''
(k, cdat, dparams, alp, r, lw, w, gn) = data_input
ot_try = pd.Series([k + 1] * len(cdat))
cdat['ot'] = ot_try # new observation type
cdat = calc_gv.get_pp(cdat, alp, r, lw, gn) # update parameters
out = olik(cdat, dparams, w, gn) + cdat['vin' + str(k + 1)] #add logged vindex
return out
def est_loop(cdat, boot, runs, gn, prepend, dp_file, calc_t):
'''main estimation loop'''
cdat_orig = deepcopy(cdat) #for use with bootstrap
for k in range(runs):
#Get sample from cdat for bootstrap
if boot:
randlist = np.random.randint(0,len(cdat),len(cdat))
cdat = deepcopy(cdat_orig.iloc[randlist].reset_index())
#Timestamp
timestamp = datetime.datetime\
.fromtimestamp(time.time())\
.strftime('%Y_%m_%d_%H_%M_%S')
#Get parameters
alp, r, lw = get_pars()
dparams = pd.read_csv(dp_file)
alp = 0.0329
old_alp = 100
while (old_alp - alp) ** 2 > 1e-12:
# Print information
old_alp = deepcopy(alp)
print(alp)
print('params')
#Infer preference parameters
cdat = calc_gv.get_pp(cdat, alp, r, lw, gn)
print('prefs')
#Calculate distribution parameters
dparams = upd_pd.pref_dist(cdat, gn, dparams, calc_t)
print('ob_types')
#Update observation types
cdat = new_ot.ot_step(cdat, dparams, alp, r, lw, gn)
print('params')
#Infer preference parameters
cdat = calc_gv.get_pp(cdat, alp, r, lw, gn)
print('prefs')
#Calculate distribution parameters
dparams = upd_pd.pref_dist(cdat, gn, dparams, calc_t)
print('alp')
#Update alpha and partial lik
alp, lik = upd_alp.alp_step(cdat, alp, r, lw, dparams, gn)
print(alp)
print(old_alp)
#get the correct folder
if boot:
folder = 'boot'
else:
folder = 'results'
cdat.to_csv(prepend + folder + '/cdat' + timestamp + '.csv')
pd.DataFrame(dparams).T.to_csv(prepend + folder + '/params' + timestamp + '.csv')
f = open(prepend + folder + '/alp' + timestamp + '.csv', 'w')
f.write('{:.6f}'.format(alp))
f.close()
