#%%
terms = LD.term.unique() #set of unique loan terms
for term in terms: #for each possible loan term
#get relevant set of loans
cur_loans = LD.term == term
cur_LD = LD[cur_loans]
(NAR, net_returns, p_csum) = LCH.get_NARs(cur_LD, term)
LD.ix[cur_loans,'ROI'] = NAR #measured performance of each loan
LD.ix[cur_loans,'net_returns'] = net_returns #principal weighted avg monthly returns
LD.ix[cur_loans,'prnc_weight'] = p_csum #principal weighted avg monthly returns
LD.ix[cur_loans,'default_prob'] = LD.ix[cur_loans,'is_observed'].astype(float) #principal weighted avg monthly returns
(exp_NAR, tot_default_prob, exp_num_pymnts, exp_net_returns, exp_csum) = \
LCH.get_expected_NARs(cur_LD, term, all_hazards[term])
LD.ix[cur_loans,'ROI'] = exp_NAR
LD.ix[cur_loans,'default_prob'] = tot_default_prob
LD.ix[cur_loans,'exp_num_pymnts'] = exp_num_pymnts
LD.ix[cur_loans,'net_returns'] = exp_net_returns
LD.ix[cur_loans,'prnc_weight'] = exp_csum
LD.ix[cur_loans, 'best_NAR'] = LCH.get_best_returns(cur_LD, term)
LD.ix[LD.is_observed,'exp_num_pymnts'] = LD.ix[LD.is_observed,'num_pymnts']
#%%
save_columns = ['ROI',
'acc_now_delinq',
'addr_state',
'annual_inc',
for term in terms: #for each possible loan term
#get relevant set of loans
cur_loans = LD.term == term
cur_LD = LD[cur_loans]
(NAR, net_returns, p_csum) = LCH.get_NARs(cur_LD, term)
LD.ix[cur_loans, 'ROI'] = NAR #measured performance of each loan
LD.ix[cur_loans,
'net_returns'] = net_returns #principal weighted avg monthly returns
LD.ix[cur_loans,
'prnc_weight'] = p_csum #principal weighted avg monthly returns
LD.ix[cur_loans, 'default_prob'] = LD.ix[cur_loans, 'is_observed'].astype(
float) #principal weighted avg monthly returns
(exp_NAR, tot_default_prob, exp_num_pymnts, exp_net_returns, exp_csum) = \
LCH.get_expected_NARs(cur_LD, term, all_hazards[term])
LD.ix[cur_loans, 'ROI'] = exp_NAR
LD.ix[cur_loans, 'default_prob'] = tot_default_prob
LD.ix[cur_loans, 'exp_num_pymnts'] = exp_num_pymnts
LD.ix[cur_loans, 'net_returns'] = exp_net_returns
LD.ix[cur_loans, 'prnc_weight'] = exp_csum
LD.ix[cur_loans, 'best_NAR'] = LCH.get_best_returns(cur_LD, term)
LD.ix[LD.is_observed, 'exp_num_pymnts'] = LD.ix[LD.is_observed, 'num_pymnts']
#%%
save_columns = [
'ROI', 'acc_now_delinq', 'addr_state', 'annual_inc', 'annual_inc_joint',
'best_NAR', 'collections_12_mths_ex_med', 'cr_line_dur', 'delinq_2yrs',
'desc_length', 'default_prob', 'dti', 'dti_joint', 'emp_length',
