def test_homemade_nbptr_function(
reform_config_base_2020, nbptr_parametres_par_defaut, various_cas_types
):
# Verifie que les resultats de nbptr et irpp sont les mêmes avec la fonction par defaut
period = "2020"
data = dataframe_from_cas_types_description(various_cas_types)
tbs_reforme_sans_nbptr = IncomeTaxReform(
FranceTaxBenefitSystem(), reform_config_base_2020, period
)
tbs_reforme_avec_nbptr = IncomeTaxReform(
FranceTaxBenefitSystem(),
{
"impot_revenu": {
**(reform_config_base_2020["impot_revenu"]),
**nbptr_parametres_par_defaut,
}
},
period,
)
sim_sans_nbptr, _ = simulation(period, data, tbs_reforme_sans_nbptr)
sim_avec_nbptr, _ = simulation(period, data, tbs_reforme_avec_nbptr)
print("sans", sim_sans_nbptr.calculate("nbptr", period))
print("avec", sim_avec_nbptr.calculate("nbptr", period))
assert array_equal(
sim_sans_nbptr.calculate("nbptr", period),
sim_avec_nbptr.calculate("nbptr", period),
)
assert array_equal(
sim_sans_nbptr.calculate("irpp", period),
sim_avec_nbptr.calculate("irpp", period),
)
def test_h5_input(input_h5="./Simulation_engine/dummy_data.h5",
name_variables=("rfr", "irpp", "nbptr"),
aggfunc="sum",
compdic=None,
is_plf=False):
PERIOD = "2018"
TBS = TBS_PLF if is_plf else FranceTaxBenefitSystem()
DUMMY_DATA = pandas.read_hdf(input_h5)
simulation_base_deciles = simulation(PERIOD, DUMMY_DATA, TBS)
df = aggregats_ff(PERIOD, simulation_base_deciles,
name_variables).sort_values(by="rfr")
if aggfunc == "sum": # Pour la somme, on calcule les % d'erreur sur la répartition.
testerrorvalues(df)
aggs_to_compute = ["wprm", "salaire_de_base", "retraite_brute"
] + list(name_variables)
val_donnees_pac_agg = 0
trpac_agg = [
compdic[ag] for ag in ["nbF", "nbG", "nbH", "nbJ", "nbR"]
if compdic is not None and ag in compdic
]
val_reelle_pac_agg = sum(trpac_agg) if len(trpac_agg) else None
for ag in aggs_to_compute:
if aggfunc == "sum":
nom_a_afficher = "Total aggrégé"
if ag != "wprm":
val_donnees = (df[ag] * df["wprm"]).sum()
else:
val_donnees = (df[ag]).sum()
elif aggfunc == "countnonzero":
if ag != "wprm":
nom_a_afficher = "Non nuls"
val_donnees = (df[df[ag] != 0]["wprm"]).sum()
else:
nom_a_afficher = "Nombre FF (c'est comme ça le count sur wprm)"
val_donnees = df[ag].count()
else:
raise (
"Only aggregation functions supported are sum and countnonzero. The rest is not very good if you want my opinion"
)
val_reelle = compdic[
ag] if compdic is not None and ag in compdic else None
print("{} {} : {:.0f} {} {}".format(
nom_a_afficher,
ag,
val_donnees,
val_reelle if val_reelle is not None else "",
"{:.2f}%".format((val_donnees / val_reelle - 1) *
100) if val_reelle is not None else "",
))
if ag in ["nbF", "nbG", "nbH", "nbJ", "nbR"]:
val_donnees_pac_agg += val_donnees
if val_reelle_pac_agg is not None:
print("{} {} : {:.0f} {} {}".format(
nom_a_afficher,
"Enfants cumules",
val_donnees_pac_agg,
val_reelle_pac_agg if val_reelle_pac_agg is not None else "",
"{:.2f}%".format((val_donnees_pac_agg / val_reelle_pac_agg - 1) *
100) if val_reelle_pac_agg is not None else "",
))
def test_coefficient_proratisation_only_contract_periods_wide():
tax_benefit_system = FranceTaxBenefitSystem()
scenario = tax_benefit_system.new_scenario()
scenario.init_single_entity(period='2017', # wide: we simulate for the year
parent1=dict(salaire_de_base={'2017-11':2300},
effectif_entreprise=1,
code_postal_entreprise="75001",
categorie_salarie=u'prive_non_cadre',
contrat_de_travail_debut='2017-11-1',
contrat_de_travail_fin='2017-12-01',
allegement_fillon_mode_recouvrement=u'progressif'))
simulation = scenario.new_simulation()
assert_equal(simulation.calculate('coefficient_proratisation','2017-11'),1)
assert_equal(simulation.calculate('coefficient_proratisation','2017-12'),0)
assert_equal(simulation.calculate('coefficient_proratisation','2017-10'),0)
assert_equal(simulation.calculate_add('coefficient_proratisation','2017'),1)
def test_coefficient_proratisation_only_contract_periods_wide():
tax_benefit_system = FranceTaxBenefitSystem()
scenario = tax_benefit_system.new_scenario()
init_single_entity(scenario, period='2017', # wide: we simulate for the year
parent1=dict(salaire_de_base={'2017-11': 2300},
effectif_entreprise=1,
code_postal_entreprise="75001",
categorie_salarie=u'prive_non_cadre',
contrat_de_travail_debut={2017: '2017-11-01'},
contrat_de_travail_fin={2017: '2017-12-01'},
allegement_fillon_mode_recouvrement=u'progressif'))
simulation = scenario.new_simulation()
assert simulation.calculate('coefficient_proratisation', '2017-11') == 1
assert simulation.calculate('coefficient_proratisation', '2017-12') == 0
assert simulation.calculate('coefficient_proratisation', '2017-10') == 0
assert simulation.calculate_add('coefficient_proratisation', '2017') == 1
def compare_input_data(
input_h5="./Simulation_engine/dummy_data.h5",
input_h5_b="./Simulation_engine/dummy_data.h5",
name_variables=("rfr", "irpp", "nbptr"),
PERIOD=None,
):
if PERIOD is None:
PERIOD = annee_de_calcul
TBS = FranceTaxBenefitSystem()
DUMMY_DATA = pandas.read_hdf(input_h5)
simulation_base_deciles, dictionnaire_datagrouped = simulation(
PERIOD, DUMMY_DATA, TBS)
df = dictionnaire_datagrouped["foyer_fiscal"][["wprm"]]
for nv in name_variables:
df["{}_base".format(nv)] = simulation_base_deciles.calculate(
nv, PERIOD)
isdif = False
data2 = pandas.read_hdf(input_h5_b)
col = "b"
newsim, ddg2 = simulation(PERIOD, data2, TBS)
for nv in name_variables:
df["{}_{}".format(nv, col)] = newsim.calculate(nv, PERIOD)
isdif |= len(df[df["{}_{}".format(nv, col)] -
df["{}_base".format(nv)] > 0.01]) + len(
df[df["{}_{}".format(nv, col)] -
df["{}_base".format(nv)] < -0.01])
return not isdif
def test_decomposition_variables():
tbs = FranceTaxBenefitSystem()
path = Path('openfisca_dash_ui/decomposition.yaml')
yaml = YAML(typ='safe')
decomposition = yaml.load(path)
def check(tree):
for node in tree:
assert node['code'] in tbs.variables
check(node['children'])
def test_coefficient_proratisation_only_contract_periods_narrow():
tax_benefit_system = FranceTaxBenefitSystem()
scenario = tax_benefit_system.new_scenario()
init_single_entity(
scenario,
period='2017-11', # narrow: we simulate for the month
parent1=dict(salaire_de_base={'2017-11': 2300},
effectif_entreprise=1,
code_postal_entreprise="75001",
categorie_salarie='prive_non_cadre',
contrat_de_travail_debut={2017: '2017-11-01'},
contrat_de_travail_fin={2017: '2017-12-01'},
allegement_fillon_mode_recouvrement='progressif'))
simulation = scenario.new_simulation()
assert simulation.calculate('coefficient_proratisation', '2017-11') == 1
assert simulation.calculate('coefficient_proratisation', '2017-12') == 0
assert simulation.calculate('coefficient_proratisation', '2017-10') == 0
assert simulation.calculate_add('coefficient_proratisation', '2017') == 1
def test_zero_nbptr(reform_config_base_2020, nbptr_zero, various_cas_types):
# Verifie que les resultats de nbptr sont bien zero pour tout le monde si tous les param
# sont à zéro
period = "2020"
data = dataframe_from_cas_types_description(various_cas_types)
tbs_reforme_avec_nbptr = IncomeTaxReform(
FranceTaxBenefitSystem(),
{"impot_revenu": {**(reform_config_base_2020["impot_revenu"]), **nbptr_zero}},
period,
)
sim_avec_nbptr, _ = simulation(period, data, tbs_reforme_avec_nbptr)
resultats_nbptr = sim_avec_nbptr.calculate("nbptr", period)
assert not resultats_nbptr.any()
def scenar_values(
minv, maxv, var_brute, var_nette, pourcentage_hausse=0.001, valeur_hausse=100
):
"""
Calcule les valeurs de var_nette pour var_brute dans [minv, maxv]
et exporte dans un CSV avec les colonnes suivantes : var_brute,var_nette
"""
df = calcule_maillage_intervalle(
var_brute, minv, maxv, pourcentage_hausse, valeur_hausse
)
PERIOD = str(annee_de_calcul)
TBS = FranceTaxBenefitSystem()
# définit un ménage par ligne
sim = simulation(PERIOD, df, TBS)
net = var_nette
df[net] = sim[0].calculate_add(net, PERIOD)
return df[[var_brute, var_nette]]
def test_deux_adultes_ancien_combattants_deux_enfants(reform_config_base_2020):
# données
foyer = {
"declarants": [
{
"ancienCombattant": True,
"invalide": False,
"parentIsole": False,
"retraite": False,
"veuf": False
},
{
"ancienCombattant": True,
"invalide": False,
"parentIsole": False,
"retraite": False,
"veuf": False
}
],
"personnesACharge": [
{
"chargePartagee": False,
"invalide": False
},
{
"chargePartagee": False,
"invalide": False
}
],
"residence": "metropole",
"revenuImposable": 120000
}
data = dataframe_from_cas_types_description([foyer])
period = "2020"
# loi française + réforme IR
tbs_reforme_impot_revenu = IncomeTaxReform(
FranceTaxBenefitSystem(), reform_config_base_2020, period
)
built_simulation, _dict_data_by_entity = simulation(
period, data, tbs_reforme_impot_revenu
)
nbptr = built_simulation.calculate("nbptr", period)
assert nbptr == [3.5]
apresy = dfv[bestsol + 1][0]
avantx = dfv[bestsol][1]
apresx = dfv[bestsol + 1][1]
lambda_ = (val_brute - avanty) / (apresy - avanty)
return lambda_ * apresx + (1 - lambda_) * avantx
conversion_variables = {}
conversion_variables["salaire_de_base_to_salaire_imposable"] = scenar_values(
0, 12_000_000, "salaire_de_base", "salaire_imposable")
conversion_variables["retraite_brute_to_retraite_imposable"] = scenar_values(
0, 12_000_000, "retraite_brute", "retraite_imposable")
PERIOD = "2018"
TBS = FranceTaxBenefitSystem()
TBS_PLF = IncomeTaxReform(TBS, reformePLF, PERIOD)
CAS_TYPE = load_data("DCT.csv")
SIMCAT = partial(simulation, period=PERIOD, data=CAS_TYPE)
SIMCAT_BASE = SIMCAT(tbs=TBS)
if not version_beta_sans_simu_pop:
# Initialisation des données utilisées pour le calcul sur la population
DUMMY_DATA = load_data(data_path).sort_values(by="idfoy")
print(
"Dummy Data loaded",
len(DUMMY_DATA),
"lines",
len(DUMMY_DATA["idfoy"].unique()),
"foyers fiscaux",
)
import sys
sys.path.insert(0, '/home/giuliano/Documents/openfisca/openfisca-core')
import openfisca_core
sys.path.insert(0, '/home/giuliano/Documents/openfisca/openfisca-france')
# Call module describing the French System
from openfisca_france import FranceTaxBenefitSystem
# Initialize the legislation
tax_benefit_system = FranceTaxBenefitSystem()
from openfisca_france.reforms import plf2018
reform = plf2018.plf2018(tax_benefit_system)
def init_profile(scenario):
scenario.init_single_entity(
period=2018,
parent1=dict(
age=40,
salaire_de_base=1671 * 12,
),
menage=dict(
loyer=5000, # Annual basis
statut_occupation_logement=3,
taxe_habitation=-600,
),
)
return scenario
from copy import deepcopy
import pprint
from openfisca_france import FranceTaxBenefitSystem
from tests.test_entities import TEST_CASE_AGES
tbs = FranceTaxBenefitSystem()
period = '2020'
test_case = deepcopy(TEST_CASE_AGES)
test_case['period'] = period
# pprint.pprint(test_case)
simulation = tbs.new_scenario().init_from_dict(test_case).new_simulation()
print(simulation.calculate('revenu_disponible', period))
# return list(x)
# return x
# with Path("decomposition.json").open('w') as fd:
# json.dump(decomposition_tree, fd, indent=2, default=serialize)
return decomposition_tree
decomposition_file_path = Path("decomposition.json")
if decomposition_file_path.is_file():
print("Loading decomposition from file...")
with decomposition_file_path.open() as fd:
decomposition_tree = json.load(fd)
else:
print("Initializing France tax and benefit system...")
tbs = FranceTaxBenefitSystem()
print("Pre-calculating decomposition...")
decomposition_tree = precalculate_decomposition_json(tbs)
app = dash.Dash()
server = app.server # Referenced by Procfile
app.layout = html.Div(children=[
html.H1(children='OpenFisca'),
html.P(children=[
"Salaire de base : ",
html.Span(id="salaire-de-base-value"),
" € / an",
]),
dcc.Slider(
id="salaire-de-base",
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'-s',
'--source-dir',
default='yaml-clean',
help='path of source directory containing clean IPP YAML files')
parser.add_argument(
'-t',
'--target',
default='ipp-tax-and-benefit-tables-to-openfisca-parameters.yaml',
help=
'path of generated YAML file containing the association between IPP fields to OpenFisca parameters'
)
parser.add_argument('-v',
'--verbose',
action='store_true',
default=False,
help="increase output verbosity")
args = parser.parse_args()
logging.basicConfig(
level=logging.DEBUG if args.verbose else logging.WARNING,
stream=sys.stdout)
file_system_encoding = sys.getfilesystemencoding()
ipp_infos_by_value = {}
for source_dir_encoded, directories_name_encoded, filenames_encoded in os.walk(
args.source_dir):
directories_name_encoded.sort()
for filename_encoded in sorted(filenames_encoded):
if not filename_encoded.endswith('.yaml'):
continue
filename = filename_encoded.decode(file_system_encoding)
sheet_name = os.path.splitext(filename)[0]
source_file_path_encoded = os.path.join(source_dir_encoded,
filename_encoded)
relative_file_path_encoded = source_file_path_encoded[
len(args.source_dir):].lstrip(os.sep)
relative_file_path = relative_file_path_encoded.decode(
file_system_encoding)
if sheet_name.isupper():
continue
assert sheet_name.islower(), sheet_name
log.info(u'Loading file {}'.format(relative_file_path))
with open(source_file_path_encoded) as source_file:
data = yaml.load(source_file)
rows = data.get(u"Valeurs")
if rows is None:
log.info(u' Skipping file {} without "Valeurs"'.format(
relative_file_path))
continue
for row in rows:
start = row.get(u"Date d'effet")
if start is None:
for date_name in date_names:
start = row.get(date_name)
if start is not None:
break
else:
# No date found. Skip row.
continue
elif not isinstance(start, datetime.date):
start = start[u"Année Revenus"]
for name, child in row.iteritems():
if name in date_names:
continue
for path, value in iter_ipp_values(child):
if isinstance(value, basestring):
split_value = value.split()
if len(split_value) == 2 and split_value[1] in (
u'%',
u'AF', # anciens francs
u'CFA', # francs CFA
u'COTISATIONS',
u'EUR',
u'FRF',
):
value = float(split_value[0])
if isinstance(value, float) and value == int(value):
value = int(value)
full_path = tuple(
relative_file_path.split(os.sep)[:-1]) + (
sheet_name, name) + tuple(path)
ipp_infos_by_value.setdefault(value, []).append(
dict(
path=full_path,
start=start,
))
# print yaml.dump(ipp_infos_by_value, allow_unicode = True, default_flow_style = False, indent = 2, width = 120)
tax_benefit_system = FranceTaxBenefitSystem()
# print yaml.dump(tax_benefit_system.legislation_json, allow_unicode = True, default_flow_style = False, indent = 2,
# width = 120)
# openfisca_infos_by_value = {}
# for path, start, value in iter_openfisca_values(tax_benefit_system.legislation_json):
# openfisca_infos_by_value.setdefault(value, []).append(dict(
# path = tuple(path),
# start = start,
# ))
# print yaml.dump(openfisca_infos_by_value, allow_unicode = True, default_flow_style = False, indent = 2, width = 120)
# ipp_count = {}
# for path, start, value in iter_openfisca_values(tax_benefit_system.legislation_json):
# ipp_infos = ipp_infos_by_value.get(value)
# if ipp_infos is None:
# # OpenFisca parameter doesn't exit in IPP.
# continue
# for ipp_info in ipp_infos:
# if ipp_info['start'] == start:
# ipp_child = ipp_count
# ipp_path = ipp_info['path']
# for name in path:
# ipp_child = ipp_child.setdefault(name, {})
# ipp_child_count = ipp_child.setdefault('count_by_path', {})
# for ipp_index in range(len(ipp_path)):
# ipp_sub_path = ipp_path[:ipp_index + 1]
# ipp_child_count[ipp_sub_path] = ipp_child_count.get(ipp_sub_path, 0) + 1
# print yaml.dump(ipp_count, allow_unicode = True, default_flow_style = False, indent = 2, width = 120)
starts_by_ipp_path_by_openfisca_path = {}
starts_by_openfisca_path_by_ipp_path = {}
for path, start, value in iter_openfisca_values(
tax_benefit_system.legislation_json):
ipp_infos = ipp_infos_by_value.get(value)
if ipp_infos is None:
# OpenFisca parameter doesn't exit in IPP.
continue
same_start_ipp_paths = [
ipp_info['path'] for ipp_info in ipp_infos
if ipp_info['start'] == start
]
if len(same_start_ipp_paths) == 1:
ipp_path = same_start_ipp_paths[0]
starts_by_ipp_path_by_openfisca_path.setdefault(
tuple(path), {}).setdefault(ipp_path, set()).add(start)
starts_by_openfisca_path_by_ipp_path.setdefault(
ipp_path, {}).setdefault(tuple(path), set()).add(start)
# for openfisca_path, starts_by_ipp_path in sorted(starts_by_ipp_path_by_openfisca_path.iteritems()):
## if len(starts_by_ipp_path) == 1:
## print u'.'.join(openfisca_path), '->', u' / '.join(starts_by_ipp_path.keys()[0])
# if len(starts_by_ipp_path) > 1:
# print u'.'.join(openfisca_path), '->', starts_by_ipp_path
# for ipp_path, starts_by_openfisca_path in sorted(starts_by_openfisca_path_by_ipp_path.iteritems()):
# if len(starts_by_openfisca_path) == 1:
# print u' / '.join(ipp_path), '->', u'.'.join(
# unicode(fragment)
# for fragment in starts_by_openfisca_path.keys()[0]
# )
## if len(starts_by_openfisca_path) > 1:
## print u' / '.join(ipp_path), '->', u'.'.join(
## unicode(fragment)
## for fragment in starts_by_openfisca_path.keys()[0]
## )
openfisca_path_by_ipp_tree = collections.OrderedDict()
for ipp_path, starts_by_openfisca_path in sorted(
starts_by_openfisca_path_by_ipp_path.iteritems()):
openfisca_path_by_ipp_sub_tree = openfisca_path_by_ipp_tree
for ipp_name in ipp_path[:-1]:
openfisca_path_by_ipp_sub_tree = openfisca_path_by_ipp_sub_tree.setdefault(
ipp_name, collections.OrderedDict())
ipp_name = ipp_path[-1]
openfisca_path_by_ipp_sub_tree[ipp_name] = [
u'.'.join(unicode(fragment) for fragment in openfisca_name)
for openfisca_name in sorted(starts_by_openfisca_path)
]
with open(args.target, 'w') as target_file:
yaml.dump(openfisca_path_by_ipp_tree,
target_file,
allow_unicode=True,
default_flow_style=False,
indent=2,
width=120)
return 0
def ajustement_h5(
input_h5="./Simulation_engine/dummy_data.h5",
output_h5="./Simulation_engine/dummy_data_ajuste.h5",
distribution_rfr_population="./Simulation_engine/Calib/ResFinalCalibSenat.csv",
PERIOD=None,
):
if PERIOD is None:
PERIOD = annee_de_calcul
ajuste_h5 = output_h5
TBS = FranceTaxBenefitSystem()
DUMMY_DATA = pandas.read_hdf(input_h5)
# Keeping computations short with option to keep file under 1000 FF
# DUMMY_DATA = DUMMY_DATA[DUMMY_DATA["idmen"] < 1000]
simulation_base_deciles = simulation(PERIOD, DUMMY_DATA, TBS)
df = aggregats_ff(PERIOD, simulation_base_deciles).sort_values(by="rfr")
print("{} FF sur {} ont un revenu>0 , donc {:.2f}% ont que dalle ".format(
len(df[df["rfr"] > 0.01]),
len(df),
100 - 100 * len(df[df["rfr"] > 0.01]) / len(df),
))
# Step 1 : Ajustement du nombre de mecs à zéro...
oldweight = 1 - df[df["rfr"] > 0.01]["wprm"].sum() / df["wprm"].sum()
targetweight = 0.06
redweightifrfr0 = targetweight * (1 - oldweight) / oldweight / (
1 - targetweight)
print(
"Non en fait {} FF sur {} ont un revenu>0 , donc {:.2f}% ont que dalle. Je vais les ajuster."
.format(
df[df["rfr"] > 0.01]["wprm"].sum(),
df["wprm"].sum(),
100 - 100 * df[df["rfr"] > 0.01]["wprm"].sum() / df["wprm"].sum(),
))
print("old : {} new : {} adj : {}".format(oldweight, targetweight,
redweightifrfr0))
# Ajustement de réduction du poids
df["adjwstep0"] = 1
df["realwprm"] = df["wprm"]
df.loc[df["rfr"] < 0.01, "adjwstep0"] = redweightifrfr0
df.loc[df["rfr"] < 0.01, "realwprm"] = df["wprm"] * redweightifrfr0
# Calibration du nombre total de foyers fiscaux
target_foyers_fiscaux = 38_332_977
# src : https://www.impots.gouv.fr/portail/statistiques (2018)
adjust_wprm = target_foyers_fiscaux / df["realwprm"].sum()
df["realwprm"] = df["realwprm"] * adjust_wprm
print(
"Non en fait {} FF sur {} ont un revenu>0 , donc {:.2f}% ont que dalle "
.format(
df[df["rfr"] > 0.01]["wprm"].sum(),
df["wprm"].sum(),
100 - 100 * df[df["rfr"] > 0]["wprm"].sum() / df["wprm"].sum(),
))
# Step 1.1 : Ajuster le 1er décile (pour l'instant on fait que dalle, y a pas vraiment d'impact
# Step 2 : PBP (pareto by parts)
# Stats officielles
so = pandas.read_csv(distribution_rfr_population)
# doit contenir :
# Colonne Rk : Revenu Fiscal de référence
# Colonne Nk : Pourcentage de foyers fiscaux ayant un RFR >= à la colonne Rk
# Colonne Ark : RFR moyen des foyers fiscaux ayant un RFR >= à la colonne Rk (utilisée seulement pour la loi du
# plus haut décile
# Je vais désormais déterminer la distribution de tout le monde :
# 2.0 - bon je vais associer le running weight de chaque mec...
totw = df["realwprm"].sum()
df = df.sort_values(by="rfr")
df["nw"] = df["realwprm"] / totw # normalized weight (total = 1)
df["rsnw"] = df["nw"].cumsum(
) - df["nw"] / 2 # somme cumulée des nw. on prend
# 2.1 - dans le premier décile : Les valeurs exactes de l'ERFS * un facteur scalaire qui permet de rendre le premier décile = ce que je veux.
targetFirstDec = so["Rk"][1]
limWeightFirstDec = so["Nk"][1]
limOrigFirstDec = max(df[df["rsnw"] <= 1 - limWeightFirstDec]["rfr"])
df["adjrevstep2"] = 1
df.loc[df["rsnw"] <= 1 - limWeightFirstDec,
"adjrevstep2"] = (targetFirstDec / limOrigFirstDec)
# 2.2 - dans toutes les autres catégories (sauf la dernière) : la distrib restrinte à un intervalle est une loi de Pareto au premier paramètre = le
# debut de l'intervalle et deuxième paramètre : celui qui permet d'obtenir le bon nombre de gens dans l'intervalle
# Détermination de ce paramètre
sonk = so["Nk"].values
# parce que je sais toujours pas itérer ligne à ligne dans un DataFrame
sork = so["Rk"].values
paramsPareto = [-1]
for i in range(1, len(sonk) - 1):
n0 = sonk[i]
n1 = sonk[i + 1]
r0 = sork[i]
r1 = sork[i + 1]
newparam = math.log(n1 / n0) / math.log(r0 / r1)
paramsPareto += [newparam]
# 2.3 - dans la dernière catégorie : je prend le param de la loi de Pareto qui permet d'égaliser la moyenne de la dernière tranche
# OK la moyenne d'une Pareto est : esp = (1 + 1/(k-1)) * xm
# k = 1/(esp/xm - 1) + 1
lastaverage = so["dArk"].values[-1] * 1000
lastthresh = sork[-1]
paramsPareto += [1 / (lastaverage / lastthresh - 1) + 1]
so["paramPareto"] = paramsPareto
df["realrfr"] = df.apply(reverseCDF(so), axis=1)
df["realrfrw"] = df["realrfr"] * df["realwprm"]
# OK now that this great function works (does it? Why not try it? comparing it now to the original function??)
# I can generate the REAL rfr
# End of step 2.
testerrorvalues(df, "rfr", "wprm")
aa = testerrorvalues(df, "realrfr", "realwprm")
print("Aggregated Error % after calibration :", aa)
# OKOK bon maintenant mon df contient le bon rfr et le bon realwprm
df["total_ajust_revenu"] = 1
df.loc[df["rfr"] > 0, "total_ajust_revenu"] = df["realrfr"] / df["rfr"]
df["total_ajust_poids"] = df["realwprm"] / df["wprm"]
# Je vais ajuster le .h5
to_transform = pandas.read_hdf(input_h5)
tt_colonnes = to_transform.columns
df_changes = df[["idfoy", "total_ajust_revenu", "total_ajust_poids"]]
to_transform = to_transform.merge(df_changes, on="idfoy")
colspoids = ["wprm"]
colsrevenus = [
"chomage_brut",
"pensions_alimentaires_percues",
"rag",
"ric",
"rnc",
"salaire_de_base",
"f4ba",
# "loyer",
# "taxe_habitation",
]
colsrevenus = [col for col in colsrevenus if col in to_transform.columns]
for cp in colspoids:
to_transform[cp] = to_transform[cp] * to_transform["total_ajust_poids"]
for cp in colsrevenus:
to_transform[
cp] = to_transform[cp] * to_transform["total_ajust_revenu"]
to_transform = to_transform[tt_colonnes]
to_transform.to_hdf(ajuste_h5, key="input")
from openfisca_core.parameters import ParameterNode, Scale
from openfisca_france import FranceTaxBenefitSystem
tax_benefit_system = FranceTaxBenefitSystem()
parameters = tax_benefit_system.parameters
def get_parameters_by_unit(parameter, parameters_by_unit=None):
"""
Build a dictionnary collecting the legislation parameters according to their units
"""
if parameters_by_unit is None:
parameters_by_unit = dict(
scale_none=list(),
scale_currency=list(),
none=list(),
currency=list(),
rate=list(),
year=list(),
)
for sub_parameter in parameter.children.values():
if isinstance(sub_parameter, ParameterNode):
get_parameters_by_unit(sub_parameter, parameters_by_unit)
else:
if isinstance(sub_parameter, Scale):
unit = sub_parameter.metadata.get('unit')
rate_unit = sub_parameter.metadata.get('rate_unit')
threshold_unit = sub_parameter.metadata.get('threshold_unit')
if unit is not None:
raise ValueError(
from openfisca_france import FranceTaxBenefitSystem
tax_benefit_system = FranceTaxBenefitSystem()
scenario = tax_benefit_system.new_scenario()
scenario.init_single_entity(
period = 2015,
parent1 = dict(
age = 30,
salaire_de_base = 50000,
),
enfants = [
dict(age = 12),
dict(age = 18),
],
)
simulation = scenario.new_simulation()
#Some variable can only be computed on a monthly basis
#simulation.calculate('af', '2015')
print(simulation.calculate('af', '2015-01')) #calculate variable af for January 2015
simulation.calculate_add("af", "2015") #to sum on the whole year
#Some variable can only be computed on a anual basis
simulation.calculate('irpp', period = '2015')
scenario.init_single_entity(
period = 2019,
parent1 = dict(
age = 30,
salaire_de_base = 70000,
),
)
def create_individu_for_inversion(year, revenu_type='net'):
assert revenu_type in ['net', 'imposable']
assert year is not None
# Using data produced by preprocessing.build_merged_dataframes
temporary_store = get_store(file_name='erfs_fpr')
individus = temporary_store['individus_{}_post_01'.format(year)]
if revenu_type == 'net':
old_by_new_variables = {
'chomage_i': 'chomage_net',
'pens_alim_recue_i': 'pensions_alimentaires_percues',
'rag_i': 'rag_net',
'retraites_i': 'retraite_nette',
'ric_i': 'ric_net',
'rnc_i': 'rnc_net',
'salaires_i': 'salaire_net',
}
elif revenu_type == 'imposable':
old_by_new_variables = {
'chomage_i': 'chomage_imposable',
'pens_alim_recue_i': 'pensions_alimentaires_percues',
'rag_i': 'rag_net',
'retraites_i': 'retraite_imposable',
'ric_i': 'ric_net',
'rnc_i': 'rnc_net',
'salaires_i': 'salaire_imposable',
}
for variable in old_by_new_variables:
assert variable in individus.columns.tolist(
), "La variable {} n'est pas présente".format(variable)
individus.rename(
columns=old_by_new_variables,
inplace=True,
)
created_variables = []
create_ages(individus, year)
created_variables.append('age')
created_variables.append('age_en_mois')
create_date_naissance(individus,
age_variable=None,
annee_naissance_variable='naia',
mois_naissance='naim',
year=year)
created_variables.append('date_naissance')
period = periods.period(year)
# create_revenus(individus, revenu_type = revenu_type)
# created_variables.append('taux_csg_remplacement')
create_contrat_de_travail(individus,
period=period,
salaire_type=revenu_type)
created_variables.append('contrat_de_travail')
created_variables.append('heures_remunerees_volume')
create_categorie_salarie(individus, period=period)
created_variables.append('categorie_salarie')
tax_benefit_system = FranceTaxBenefitSystem()
create_salaire_de_base(individus,
period=period,
revenu_type=revenu_type,
tax_benefit_system=tax_benefit_system)
created_variables.append('salaire_de_base')
create_effectif_entreprise(individus, period=period)
created_variables.append('effectif_entreprise')
create_traitement_indiciaire_brut(individus,
period=period,
revenu_type=revenu_type,
tax_benefit_system=tax_benefit_system)
created_variables.append('traitement_indiciaire_brut')
created_variables.append('primes_fonction_publique')
other_variables = ['salaire_{}'.format(revenu_type)]
temporary_store['individu_for_inversion_{}'.format(year)] = individus[
created_variables + other_variables]
def test_useless_variables(
input_h5="./Simulation_engine/dummy_data.h5",
outfile_path=None,
name_variables=("rfr", "irpp", "nbptr"),
PERIOD=None,
):
if PERIOD is None:
PERIOD = annee_de_calcul
pandas.options.mode.chained_assignment = None
list_useless_variables = []
TBS = FranceTaxBenefitSystem()
DUMMY_DATA = pandas.read_hdf(input_h5)
simulation_base_deciles, dictionnaire_datagrouped = simulation(
PERIOD, DUMMY_DATA, TBS)
df = dictionnaire_datagrouped["foyer_fiscal"][["wprm"]]
for nv in name_variables:
df["{}_base".format(nv)] = simulation_base_deciles.calculate(
nv, PERIOD)
for col in DUMMY_DATA.columns:
if col == "wprm": # we don't want to remove this one
continue
isdif = False
data_wo_column = DUMMY_DATA[[
k for k in DUMMY_DATA.columns if k != col
]]
try:
newsim, ddg2 = simulation(PERIOD, data_wo_column, TBS)
resvar = {nv: {} for nv in name_variables}
for nv in name_variables:
df["{}_{}".format(nv, col)] = newsim.calculate(nv, PERIOD)
resvar[nv]["countdif"] = len(df[
df["{}_{}".format(nv, col)] != df["{}_base".format(nv)]])
# print(col,nv,resvar[nv]["countdif"])
# print(df[df["{}_{}".format(nv,col)]!=df["{}_base".format(nv)]],len(df[df["{}_{}".format(nv,col)]!=df["{}_base".format(nv)]]))
isdif |= resvar[nv]["countdif"]
if not isdif:
list_useless_variables += [col]
print(
col,
"is",
"not" if isdif else "",
"useless",
"{}".format([resvar[nv]["countdif"]
for nv in name_variables]) if isdif else "",
)
except Exception:
print(col, "is definitely not useless")
data_wo_useless = DUMMY_DATA[[
k for k in DUMMY_DATA.columns if k not in list_useless_variables
]]
newsim, ddg2 = simulation(PERIOD, data_wo_column, TBS)
isdif = False
for nv in name_variables:
# print(col,nv,resvar[nv]["countdif"])
# print(df[df["{}_{}".format(nv,col)]!=df["{}_base".format(nv)]],len(df[df["{}_{}".format(nv,col)]!=df["{}_base".format(nv)]]))
isdif |= len(
df[df["{}_{}".format(nv, col)] != df["{}_base".format(nv)]])
if isdif:
print(
"Removing all variables at once didn't work, good luck with that")
else:
if outfile_path is None:
outfile_path = input_h5.replace(".h5", "_useful.h5")
data_wo_useless.to_hdf(outfile_path, key="input")
print(
"It seems lots of columns don't do anything. Data with only useful columns was exported to {}"
.format(outfile_path))
return list_useless_variables