Ameliorations variables erfs fpr

CHANGELOG.md

@@ -1,5 +1,13 @@
 # Changelog
 
+# 1.1.0 [#225](https://github.com/openfisca/openfisca-france-data/pull/225)
+
+
+* Technical changes
+- Ajoute des variables concernant le calcul des aides au logement, des non salariés et du handicap dans le builder de openfisca-france-data
+
+
+
 # 1.0.0 [#224](https://github.com/openfisca/openfisca-france-data/pull/224)
 
 * Breaking changes

openfisca_france_data/erfs_fpr/comparison.py

@@ -45,17 +45,6 @@ class ErfsFprtoInputComparator(AbstractComparator):
        "statut_occupation_logement",
         ]
 
-    from openfisca_france_data.erfs_fpr.get_survey_scenario import menage_projected_variables
-
-    target_menage_projected_variables = [
-        f"{menage_projected_variable}_menage"
-        for menage_projected_variable
-        in menage_projected_variables
-        ]
-
-    default_target_variables += target_menage_projected_variables
-
-
     def compute_test_dataframes(self):
         erfs_fpr_survey_collection = SurveyCollection.load(collection = "erfs_fpr")
         # infer names of the survey and data tables

openfisca_france_data/erfs_fpr/get_survey_scenario.py

@@ -28,10 +28,8 @@
 
 
 menage_projected_variables = [
-    # "rev_financier_prelev_lib_imputes",
-    "revenu_categoriel_foncier",
-    "revenus_capitaux_prelevement_forfaitaire_unique_ir",
-    ]
+
+]
 
 
 class erfs_fpr_plugin(Reform):
@@ -144,7 +142,6 @@ def get_survey_scenario(
     # S'il n'y a pas de données, on sait où les trouver.
     if data is None:
         input_data_table_by_entity = dict(
-            foyer_fiscal = f"foyer_fiscal_{year}",
             individu = f"individu_{year}",
             menage = f"menage_{year}",
             )

openfisca_france_data/erfs_fpr/input_data_builder/__init__.py

@@ -19,7 +19,6 @@
     step_02_menage as menage,
     step_03_variables_individuelles as variables_individuelles,
     step_04_famille as famille,
-    step_05_foyer as foyer,
     step_06_final as final,
     )
 
@@ -50,7 +49,7 @@ def build(year: int, export_flattened_df_filepath: str = None) -> None:
     # - On merge les tables individus / menages
     #
     # Note : c'est ici où on objectivise les hypothèses, step 1
-    log.info('\n [[[ Year {} - Step 1 / 6 ]]] \n'.format(year))
+    log.info('\n [[[ Year {} - Step 1 / 5 ]]] \n'.format(year))
     preprocessing.build_merged_dataframes(year = year)
 
     # Step 02 : Si on veut calculer les allocations logement, il faut faire le matching avec une autre enquête (ENL)
@@ -59,11 +58,11 @@ def build(year: int, export_flattened_df_filepath: str = None) -> None:
     # stata_directory = openfisca_survey_collection.config.get('data', 'stata_directory')
     # stata_file = os.path.join(stata_directory, 'log_men_ERFS.dta')
     # imputation_loyer.merge_imputation_loyer(stata_file = stata_file, year = year)
-    log.info('\n [[[ Year {} - Step 2 / 6 SKIPPED ]]] \n'.format(year))
+    log.info('\n [[[ Year {} - Step 2 / 5 SKIPPED ]]] \n'.format(year))
     menage.build_variables_menage(year = year)
 
     # Step 03 : on commence par les variables indivuelles
-    log.info('\n [[[ Year {} - Step 3 / 6 ]]] \n'.format(year))
+    log.info('\n [[[ Year {} - Step 3 / 5 ]]] \n'.format(year))
     variables_individuelles.build_variables_individuelles(year = year)
 
     # Step 04 : ici on va constituer foyer et famille à partir d'invididu et ménage
@@ -72,18 +71,15 @@ def build(year: int, export_flattened_df_filepath: str = None) -> None:
     # - On va faire des suppositions pour faire les familles
     # - On va faire les foyers fiscaux à partir des familles
     # - On va faire de suppositions pour faire les foyers fiscaux
-    log.info('\n [[[ Year {} - Step 4 / 6 ]]] \n'.format(year))
+    log.info('\n [[[ Year {} - Step 4 / 5 ]]] \n'.format(year))
     famille.build_famille(year = year)
 
-    log.info('\n [[[ Year {} - Step 5 / 6 ]]] \n'.format(year))
-    foyer.build_variables_foyers_fiscal(year = year)
-
     # Affreux ! On injectait tout dans un même DataFrame !!!
     # C'est très moche !
     #
     # On crée une df par entité par période.
     # Elles sont stockées dans un fichier h5
-    log.info('\n [[[ Year {} - Step 6 / 6 ]]] \n'.format(year))
+    log.info('\n [[[ Year {} - Step 5 / 5 ]]] \n'.format(year))
     final.create_input_data_frame(year = year, export_flattened_df_filepath = export_flattened_df_filepath)
 
 

openfisca_france_data/erfs_fpr/input_data_builder/step_02_menage.py

@@ -12,16 +12,25 @@
 
 @temporary_store_decorator(file_name = 'erfs_fpr')
 def build_variables_menage(temporary_store = None, year = None):
+    menages = temporary_store[f'menages_{year}']
+
+    if "loyer" in menages.columns:
+        menages['loyer'] = menages['loyer'] * 12
+
     if year == 2018:
-        menages = temporary_store[f'menages_{year}']
         menages['statut_occupation_logement'] = menages['so'].copy()
 
     if year == 2019:  # SO ne fonctionne pas en 2019 bcp de 0 = non-renseigné
-        menages = temporary_store[f'menages_{year}']
         menages['statut_occupation_logement'] = menages['logt'].copy()
 
     if year >= 2018:
-        menages['zone_apl'] = 2
+        menages['zone_apl'] = 3
+        menages.loc[menages['tau2010'] == 10,'zone_apl'] = 1
+        menages.loc[menages['tau2010'] == 9,'zone_apl'] = 2
+        #menages.loc[menages['tau2010'] == 8,'zone_apl'] = 2
         # pour l'instant on met tout le monde à 2 mais à améliorer, peut être en fonction de la taille de l'aire urbaine ?
         menages.loc[(menages.statut_occupation_logement == 7), 'statut_occupation_logement'] = 2
+        menages['logement_conventionne'] = False
+        menages.loc[menages['statut_occupation_logement'] == 3 ,'logement_conventionne'] = True
         temporary_store['menages_{}'.format(year)] = menages
+

openfisca_france_data/erfs_fpr/input_data_builder/step_03_variables_individuelles.py

@@ -76,7 +76,7 @@ def create_variables_individuelles(individus, year, survey_year = None, revenu_t
     create_activite(individus)
     create_contrat_de_travail(individus, period = period, salaire_type = revenu_type)
     create_categorie_salarie(individus, period = period, survey_year = survey_year)
-
+    create_categorie_non_salarie(individus)
     # inversion des revenus pour retrouver le brut
     # pour les revenus de remplacement on a la csg et la crds dans l'erfs-fpr donc on peut avoir le brut directement
     create_revenus_remplacement_bruts(individus)
@@ -489,6 +489,7 @@ def create_categorie_non_salarie(individus):
     commercant = individus.cstot.isin([22])
     chef_entreprise = individus.cstot.isin([23])
     profession_liberale = individus.cstot.isin([31])
+    individus['categorie_non_salarie'] = 0
     individus.loc[
         agriculteur | artisan,
         'categorie_non_salarie'
@@ -501,6 +502,12 @@ def create_categorie_non_salarie(individus):
         profession_liberale,
         'categorie_non_salarie'
         ] = 3
+    #fix un peu crade : cstot ne semble pas recouvrir tout le champ des personnes qui ont du rpns
+    # on met par défaut ces gens là en chef d'entreprise
+    individus.loc[
+        ((individus['rpns_imposables'] != 0) & (individus['categorie_non_salarie'] == 0)),
+        'categorie_non_salarie'
+        ] = 2  
 
 
 def create_contrat_de_travail(individus, period, salaire_type = 'imposable'):

openfisca_france_data/erfs_fpr/input_data_builder/step_06_final.py

@@ -16,13 +16,13 @@ def create_input_data_frame(temporary_store = None, year = None, export_flattene
 
     individus = temporary_store['individus_{}'.format(year)]
     menages = temporary_store['menages_{}'.format(year)]
-    foyers_fiscaux = temporary_store['foyers_fiscaux_{}'.format(year)]
 
     # ici : variables à garder
     var_individus = [
         'activite',
         'age',
         'categorie_salarie',
+        'categorie_non_salarie',
         'chomage_brut',
         'contrat_de_travail',
         'date_naissance',
@@ -47,12 +47,6 @@ def create_input_data_frame(temporary_store = None, year = None, export_flattene
         "primes_fonction_publique",
         "traitement_indiciaire_brut",
         ]
-    var_foyers_fiscaux = [
-        'idfoy',
-        'rev_financier_prelev_lib_imputes',
-        'revenu_categoriel_foncier',
-        'revenus_capitaux_prelevement_forfaitaire_unique_ir',
-        ]
 
     var_menages = [
         'idmen',
@@ -61,6 +55,8 @@ def create_input_data_frame(temporary_store = None, year = None, export_flattene
         'taxe_habitation',
         'wprm',
         'zone_apl',
+        'logement_conventionne',
+        'prest_precarite_hand' # on récupère la variable de montant de aah / caah pour pouvoir faire une imputation du handicap
     ]
 
     individus = create_ids_and_roles(individus)
@@ -80,14 +76,7 @@ def create_input_data_frame(temporary_store = None, year = None, export_flattene
     idmens = individus.idmen.unique()
     menages = menages.loc[
         menages.idmen.isin(idmens),
-        [
-            'idmen',
-            'loyer',
-            'statut_occupation_logement',
-            'taxe_habitation',
-            'wprm',
-            'zone_apl',
-            ]
+        var_menages
         ].copy()
     survey_name = 'openfisca_erfs_fpr_' + str(year)
 
@@ -101,16 +90,6 @@ def create_input_data_frame(temporary_store = None, year = None, export_flattene
                             how = 'inner',
                             on = 'idmen_original')
 
-    foyers_fiscaux = foyers_fiscaux.rename(columns = {'idfoy':'idfoy_original'})
-    unique_idfoy = individus[['idfoy','idfoy_original']].drop_duplicates()
-    assert len(unique_idmen) == len(menages), "Number of idfoy should be the same individus and foyers tables."
-
-    foyers_fiscaux = foyers_fiscaux.merge(unique_idfoy,
-                                          how = 'inner',
-                                          on = 'idfoy_original')
-
-    foyers_fiscaux = foyers_fiscaux[var_foyers_fiscaux]
-
     if export_flattened_df_filepath:
         supermerge = individus.merge(
             menages,
@@ -131,17 +110,6 @@ def create_input_data_frame(temporary_store = None, year = None, export_flattene
         survey_name = survey_name,
         )
 
-    foyers_fiscaux = foyers_fiscaux.sort_values(by = ['idfoy'])
-    log.debug(f"Saving entity 'foyers fiscaux' in collection 'openfisca_erfs_fpr' and survey name '{survey_name}' with set_table_in_survey")
-    set_table_in_survey(
-        foyers_fiscaux,
-        entity = "foyer_fiscal",
-        period = year,
-        collection = "openfisca_erfs_fpr",
-        survey_name = survey_name,
-        )
-    log.debug("End of create_input_data_frame")
-
     menages = menages.sort_values(by = ['idmen'])
     log.debug(f"Saving entity 'menage' in collection 'openfisca_erfs_fpr' and survey name '{survey_name}' with set_table_in_survey")
     set_table_in_survey(
@@ -255,7 +223,7 @@ def extract_menages_variables_from_store(temporary_store = None, year = None):
 
 def extract_menages_variables(menages):
     variables = ['ident', 'wprm', 'taxe_habitation']
-    external_variables = ['loyer', 'zone_apl', 'statut_occupation_logement']
+    external_variables = ['loyer', 'zone_apl', 'statut_occupation_logement','logement_conventionne', 'prest_precarite_hand']
     for external_variable in external_variables:
         if external_variable in menages.columns:
             log.debug("Found {} in menages table: we keep it".format(external_variable))

openfisca_france_data/erfs_fpr/scenario.py

@@ -11,6 +11,7 @@ class ErfsFprSurveyScenario(AbstractErfsSurveyScenario):
         "activite",
         "autonomie_financiere",
         "categorie_salarie",
+        "categorie_non_salarie",
         "chomage_brut",
         "chomage_imposable",
         "contrat_de_travail",
@@ -19,16 +20,14 @@ class ErfsFprSurveyScenario(AbstractErfsSurveyScenario):
         "effectif_entreprise",
         "f4ba",
         "heures_remunerees_volume",
+        "logement_conventionne",
         "loyer",
         "pensions_alimentaires_percues",
         "pensions_invalidite",
         "primes_fonction_publique",
         "rag",
         "retraite_brute",
         "retraite_imposable",
-        # "rev_financier_prelev_lib_imputes",
-        "revenu_categoriel_foncier",
-        "revenus_capitaux_prelevement_forfaitaire_unique_ir",
         "ric",
         "rnc",
         "rpns_imposables",

openfisca_france_data/surveys.py

@@ -193,9 +193,6 @@ def custom_initialize(self, simulation):
 
 
     def custom_input_data_frame(self, input_data_frame, **kwargs):
-        if "loyer" in input_data_frame:
-            input_data_frame["loyer"] = 12 * input_data_frame.loyer
-
         for variable in ["quifam", "quifoy", "quimen"]:
             if variable in input_data_frame:
                 log.debug(input_data_frame[variable].value_counts(dropna = False))

setup.py

@@ -6,7 +6,7 @@
 
 setup(
     name = "OpenFisca-France-Data",
-    version = "1.0",
+    version = "1.1.0",
     description = "OpenFisca-France-Data module to work with French survey data",
     long_description = long_description,
     long_description_content_type="text/markdown",