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()