def run_pension(context, yearleg, time_step='year', to_check=False, output='pension', cProfile=False):
''' run PensionSimulation after having converted the liam context in a PenionData
- note there is a selection '''
sali = context['longitudinal']['sali']
workstate = context['longitudinal']['workstate']
# calcul de la date de naissance au bon format
datesim = context['period']
age_year = context['agem'] // 12
age_month = context['agem'] % 12 + 1
naiss_year = datesim // 100 - age_year
naiss_month = datesim % 100 - age_month + 1
naiss = pd.Series(naiss_year * 100 + naiss_month)
naiss = naiss.map(lambda t: dt.date(t // 100, t % 100, 1))
info_ind = pd.DataFrame({'index':context['id'], 'agem': context['agem'],'naiss': naiss, 'sexe' : context['sexe'],
'nb_enf_all': context['nb_enf'], 'nb_pac': context['nb_pac'], 'nb_enf_RG': context['nb_enf_RG'],
'nb_enf_RSI': context['nb_enf_RSI'], 'nb_enf_FP': context['nb_enf_FP'], 'tauxprime': context['tauxprime']})
info_ind = info_ind.to_records(index=False)
# TODO: filter should be done in liam
if output == 'dates_taux_plein':
# But: déterminer les personnes partant à la retraite avec préselection des plus de 55 ans
#TODO: faire la préselection dans Liam
info_ind = info_ind[(info_ind['agem'] > 55 * 12)]
if output == 'pension':
info_ind = info_ind[context['to_be_retired']] #TODO: filter should be done in yaml
workstate = workstate.loc[workstate['id'].isin(info_ind.index), :].copy()
workstate.set_index('id', inplace=True)
workstate.sort_index(inplace=True)
sali = sali.loc[sali['id'].isin(info_ind.index), :].copy()
sali.set_index('id', inplace=True)
sali.sort_index(inplace=True)
sali.fillna(0, inplace=True)
yearleg = context['period'] // 100
if yearleg > 2009: #TODO: remove
yearleg = 2009
data = PensionData.from_arrays(workstate, sali, info_ind)
param = PensionParam(yearleg, data)
legislation = PensionLegislation(param)
simul_til = PensionSimulation(data, legislation)
if cProfile:
result_til_year = simul_til.profile_evaluate(yearleg, to_check=to_check, output=output)
else:
result_til_year = simul_til.evaluate(yearleg, to_check=to_check, output=output)
if output == 'dates_taux_plein':
# Renvoie un dictionnaire donnant la date de taux plein par régime (format numpy) et l'index associé
return result_til_year
elif output == 'pension':
result_to_liam = output_til_to_liam(output_til=result_til_year,
index_til=info_ind.index,
context_id=context['id'])
return result_to_liam.astype(float)
def get_pension(context, yearleg):
""" return a PensionSimulation """
sali = context["longitudinal"]["sali"]
workstate = context["longitudinal"]["workstate"]
# calcul de la date de naissance au bon format
datesim = context["period"]
datesim_in_month = 12 * (datesim // 100) + datesim % 100
datenaiss_in_month = datesim_in_month - context["agem"]
naiss = 100 * (datenaiss_in_month // 12) + datenaiss_in_month % 12 + 1
naiss = pd.Series(naiss)
naiss = pd.Series(naiss).map(lambda t: dt.date(t // 100, t % 100, 1))
info_ind = pd.DataFrame(
{
"index": context["id"],
"agem": context["agem"],
"naiss": naiss,
"sexe": context["sexe"],
"nb_enf_all": context["nb_enf"],
"nb_pac": context["nb_pac"],
"nb_enf_RG": context["nb_enf_RG"],
"nb_enf_RSI": context["nb_enf_RSI"],
"nb_enf_FP": context["nb_enf_FP"],
"tauxprime": context["tauxprime"],
}
)
info_ind = info_ind.to_records(index=False)
workstate = workstate.loc[workstate["id"].isin(info_ind.index), :].copy()
workstate.set_index("id", inplace=True)
workstate.sort_index(inplace=True)
sali = sali.loc[sali["id"].isin(info_ind.index), :].copy()
sali.set_index("id", inplace=True)
sali.sort_index(inplace=True)
sali.fillna(0, inplace=True)
data = PensionData.from_arrays(workstate, sali, info_ind)
param = PensionParam(yearleg, data)
legislation = PensionLegislation(param)
simulation = PensionSimulation(data, legislation)
simulation.set_config()
return simulation
def load_til_pensipp(pensipp_comparison_path, years, to_print=(None,None,True)):
result_pensipp = load_pensipp_result(pensipp_comparison_path, to_csv=False)
result_til = pd.DataFrame(columns = var_to_check_montant + var_to_check_taux, index = result_pensipp.index)
result_til['yearliq'] = -1
for yearsim in years:
print(yearsim)
data_bounded = load_pensipp_data(pensipp_comparison_path, yearsim, first_year_sal)
param = PensionParam(yearsim, data_bounded)
legislation = PensionLegislation(param)
simul_til = PensionSimulation(data_bounded, legislation)
simul_til.set_config()
vars_to_calculate = dict()
result_til_year = dict()
P = simul_til.legislation.P
for regime in ['FP', 'RG', 'RSI']:
trim_regime = simul_til.calculate('nb_trimesters', regime)
for varname in ['coeff_proratisation', 'DA', 'decote', 'n_trim', 'salref', 'surcote', 'taux', 'pension']:
if varname == 'coeff_proratisation':
result_til_year['CP_' + regime] = simul_til.calculate(varname, regime)*(trim_regime > 0)
elif varname == 'decote':
param_name = simul_til.get_regime(regime).param_name
taux_plein = reduce(getattr, param_name.split('.'), P).plein.taux
calc = simul_til.calculate(varname, regime)
result_til_year[varname + '_' + regime] = taux_plein*calc*(trim_regime > 0)
else:
if varname != 'n_trim':
calc = simul_til.calculate(varname, regime)
result_til_year[varname + '_' + regime] = calc*(trim_regime > 0)
else:
result_til_year[varname + '_' + regime] = simul_til.calculate(varname, regime)
for regime in ['agirc', 'arrco']:
for varname in ['coefficient_age', 'nb_points', 'pension']:
if varname == 'coefficient_age':
result_til_year['coeff_age_' + regime] = simul_til.calculate(varname, regime)
if varname == 'nombre_points':
result_til_year['nb_points_' + regime] = simul_til.calculate(varname, regime)
else:
result_til_year[varname + '_' + regime] = simul_til.calculate(varname, regime)
result_til_year['N_CP_RG'] = simul_til.calculate('N_CP', 'RG')
result_til_year['pension_tot'] = simul_til.calculate('pension', 'all')
result_til_year = pd.DataFrame(result_til_year, index=data_bounded.info_ind['index'])
id_year_in_initial = [ident for ident in result_til_year.index if ident in result_til.index]
assert (id_year_in_initial == result_til_year.index).all()
result_til.loc[result_til_year.index, :] = result_til_year
result_til.loc[result_til_year.index, 'yearliq'] = yearsim
to_compare = (result_til['yearliq']!= -1)
til_compare = result_til.loc[to_compare,:]
pensipp_compare = result_pensipp.loc[to_compare,:]
return til_compare, pensipp_compare , simul_til
def load_til_pensipp(pensipp_comparison_path,
years,
to_print=(None, None, True)):
result_pensipp = load_pensipp_result(pensipp_comparison_path, to_csv=False)
result_til = pd.DataFrame(columns=var_to_check_montant + var_to_check_taux,
index=result_pensipp.index)
result_til['yearliq'] = -1
for yearsim in years:
print(yearsim)
data_bounded = load_pensipp_data(pensipp_comparison_path, yearsim,
first_year_sal)
param = PensionParam(yearsim, data_bounded)
legislation = PensionLegislation(param)
simul_til = PensionSimulation(data_bounded, legislation)
simul_til.set_config()
vars_to_calculate = dict()
result_til_year = dict()
P = simul_til.legislation.P
for regime in ['FP', 'RG', 'RSI']:
trim_regime = simul_til.calculate('nb_trimesters', regime)
for varname in [
'coeff_proratisation', 'DA', 'decote', 'n_trim', 'salref',
'surcote', 'taux', 'pension'
]:
if varname == 'coeff_proratisation':
result_til_year['CP_' + regime] = simul_til.calculate(
varname, regime) * (trim_regime > 0)
elif varname == 'decote':
param_name = simul_til.get_regime(regime).param_name
taux_plein = reduce(getattr, param_name.split('.'),
P).plein.taux
calc = simul_til.calculate(varname, regime)
result_til_year[
varname + '_' +
regime] = taux_plein * calc * (trim_regime > 0)
else:
if varname != 'n_trim':
calc = simul_til.calculate(varname, regime)
result_til_year[varname + '_' +
regime] = calc * (trim_regime > 0)
else:
result_til_year[varname + '_' +
regime] = simul_til.calculate(
varname, regime)
for regime in ['agirc', 'arrco']:
for varname in ['coefficient_age', 'nb_points', 'pension']:
if varname == 'coefficient_age':
result_til_year['coeff_age_' +
regime] = simul_til.calculate(
varname, regime)
if varname == 'nombre_points':
result_til_year['nb_points_' +
regime] = simul_til.calculate(
varname, regime)
else:
result_til_year[varname + '_' +
regime] = simul_til.calculate(
varname, regime)
result_til_year['N_CP_RG'] = simul_til.calculate('N_CP', 'RG')
result_til_year['pension_tot'] = simul_til.calculate('pension', 'all')
result_til_year = pd.DataFrame(result_til_year,
index=data_bounded.info_ind['index'])
id_year_in_initial = [
ident for ident in result_til_year.index
if ident in result_til.index
]
assert (id_year_in_initial == result_til_year.index).all()
result_til.loc[result_til_year.index, :] = result_til_year
result_til.loc[result_til_year.index, 'yearliq'] = yearsim
to_compare = (result_til['yearliq'] != -1)
til_compare = result_til.loc[to_compare, :]
pensipp_compare = result_pensipp.loc[to_compare, :]
return til_compare, pensipp_compare, simul_til
def run_to_print(data, yearsim):
param = PensionParam(yearsim, data)
legislation = PensionLegislation(param)
simul_til = PensionSimulation(data, legislation)
simul_til.profile_evaluate(to_check=False)
sexe = data['sexi'][0] == 2
nb_enf = data['nbenf'][0]
return workstate, sali, sexe, nb_enf
agem = (2009-1954 + 0.5)*12
naiss = dt.date(1954, 6, 1)
info_ind['agem'] = agem
info_ind['naiss'] = naiss
info_ind['tauxprime'] = 0
for i in range(nb_scenarios):
work_i, sali_i, sexe, nb_enf = load_case(i+1) # Attention déclage dans la numérotaiton qui ne commence pas à zeros
sali[i,:] = sali_i
workstate[i,:] = work_i
info_ind.loc[i,['sexe','nb_enf','nb_pac',
'nb_enf_RG','nb_enf_RSI','nb_enf_FP']] = [sexe, nb_enf, nb_enf,
nb_enf, nb_enf, nb_enf]
#TODO: know why nbenf is often NaN and not 0.
info_ind.fillna(0, inplace=True)
data = PensionData.from_arrays(workstate, sali, info_ind, dates)
param = PensionParam(201001, data)
legislation = PensionLegislation(param)
simulation = PensionSimulation(data, legislation)
trim = simulation.profile_evaluate(output='trimesters_wages')
result_til_year = simulation.profile_evaluate(to_check=True)
pdb.set_trace()
