File size: 4,000 Bytes
d2a2a12 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 | from sqlalchemy import create_engine, Column, Integer, String, Float, DateTime
from sqlalchemy.sql import text
from sqlalchemy.orm import declarative_base
from datetime import datetime
import os
from dotenv import load_dotenv
Base = declarative_base()
# Connection string
load_dotenv()
DATABASE_URL = os.getenv("DATABASE_URL")
engine = create_engine(DATABASE_URL)
class EmployeeSirh(Base):
__tablename__ = 'employees_sirh'
id_employee = Column(Integer, primary_key=True)
age = Column(Integer)
genre = Column(String)
revenu_mensuel = Column(Integer)
statut_marital = Column(String)
departement = Column(String)
poste = Column(String)
nombre_experiences_precedentes = Column(Integer)
nombre_heures_travaillees = Column(Integer)
annee_experience_totale = Column(Integer)
annees_dans_l_entreprise = Column(Integer)
annees_dans_le_poste_actuel = Column(Integer)
class EmployeeEval(Base):
__tablename__ = 'employees_eval'
eval_number = Column(Integer, primary_key=True)
satisfaction_employee_environnement = Column(Integer)
note_evaluation_precedente = Column(Integer)
niveau_hierarchique_poste = Column(Integer)
satisfaction_employee_nature_travail = Column(Integer)
satisfaction_employee_equipe = Column(Integer)
satisfaction_employee_equilibre_pro_perso = Column(Integer)
note_evaluation_actuelle = Column(Integer)
heure_supplementaires = Column(String)
augementation_salaire_precedente = Column(String)
class EmployeeSondage(Base):
__tablename__ = 'employees_sondage'
code_sondage = Column(Integer, primary_key=True)
a_quitte_l_entreprise = Column(String)
nombre_participation_pee = Column(Integer)
nb_formations_suivies = Column(Integer)
nombre_employee_sous_responsabilite = Column(Integer)
distance_domicile_travail = Column(Integer)
niveau_education = Column(Integer)
domaine_etude = Column(String)
ayant_enfants = Column(String)
frequence_deplacement = Column(String)
annees_depuis_la_derniere_promotion = Column(Integer)
annes_sous_responsable_actuel = Column(Integer)
class PredictionLog(Base):
__tablename__ = 'predictions_log'
id = Column(Integer, primary_key=True, autoincrement=True)
id_employee = Column(Integer)
timestamp = Column(DateTime, default=datetime.utcnow)
# Inputs
genre = Column(String)
statut_marital = Column(String)
departement = Column(String)
poste = Column(String)
domaine_etude = Column(String)
frequence_deplacement = Column(String)
heure_supplementaires = Column(String)
age = Column(Integer)
revenu_mensuel = Column(Integer)
nombre_experiences_precedentes = Column(Integer)
annee_experience_totale = Column(Integer)
annees_dans_l_entreprise = Column(Integer)
annees_dans_le_poste_actuel = Column(Integer)
nb_formations_suivies = Column(Integer)
distance_domicile_travail = Column(Integer)
niveau_education = Column(Integer)
annees_depuis_la_derniere_promotion = Column(Integer)
annes_sous_responsable_actuel = Column(Integer)
satisfaction_employee_environnement = Column(Integer)
note_evaluation_precedente = Column(Float)
satisfaction_employee_nature_travail = Column(Integer)
satisfaction_employee_equipe = Column(Integer)
satisfaction_employee_equilibre_pro_perso = Column(Integer)
note_evaluation_actuelle = Column(Float)
augementation_salaire_precedente = Column(String)
# Outputs
prediction = Column(String)
probability_score = Column(Float)
primary_driver = Column(String)
strong_factor = Column(String)
moderate_factor = Column(String)
contributing_factor = Column(String)
notable_factor = Column(String)
unknown_category_warning = Column(String, nullable=True)
ground_truth = Column(Integer, nullable=True)
if __name__ == "__main__":
Base.metadata.create_all(engine)
print("Tables created successfully!")
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