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| import sys | |
| import os | |
| sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| import pandas as pd | |
| from sqlalchemy.orm import sessionmaker | |
| from sqlalchemy import create_engine | |
| db_url = ( | |
| f"postgresql+psycopg2://{os.getenv('DB_USER')}:{os.getenv('DB_PASSWORD')}" | |
| f"@{os.getenv('DB_HOST')}:{os.getenv('DB_PORT')}/{os.getenv('DB_NAME')}" | |
| ) | |
| SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=create_engine(db_url)) | |
| from app.db.models import Employee, Prediction | |
| from ml_model.loader import load_pipeline | |
| def stats_employees(session): | |
| total = session.query(Employee).count() | |
| print(f"\n--- Employés en base ---") | |
| print(f"Total : {total}") | |
| quittes = session.query(Employee).filter(Employee.a_quitte_l_entreprise == True).count() | |
| print(f"Ont quitté : {quittes} ({round(quittes / total * 100, 1)}%)") | |
| def apercu_employees(session, n=5): | |
| rows = session.query(Employee).limit(n).all() | |
| print(f"\n--- Aperçu ({n} premiers employés) ---") | |
| for e in rows: | |
| print(f" id={e.id} | {e.poste} | {e.statut_marital} | quitte={e.a_quitte_l_entreprise}") | |
| def predict_from_db(session, employee_id: int): | |
| pipeline, threshold = load_pipeline() | |
| emp = session.query(Employee).filter(Employee.id == employee_id).first() | |
| if not emp: | |
| print(f"Employé id={employee_id} introuvable.") | |
| return | |
| data = {col: getattr(emp, col) for col in [ | |
| "age", "genre", "statut_marital", "poste", "domaine_etude", | |
| "niveau_education", "departement", "niveau_hierarchique_poste", | |
| "nombre_experiences_precedentes", "annee_experience_totale", | |
| "annees_dans_l_entreprise", "annees_dans_le_poste_actuel", | |
| "annees_sous_responsable_actuel", "annees_depuis_la_derniere_promotion", | |
| "note_evaluation_actuelle", "note_evaluation_precedente", | |
| "augmentation_salaire_precedente", "nb_formations_suivies", | |
| "nombre_participation_pee", "satisfaction_employee_environnement", | |
| "satisfaction_employee_nature_travail", "satisfaction_employee_equipe", | |
| "satisfaction_employee_equilibre_pro_perso", "heure_supplementaires", | |
| "frequence_deplacement", "distance_domicile_travail", "revenu_mensuel", | |
| ]} | |
| df = pd.DataFrame([data]) | |
| proba = float(pipeline.predict_proba(df)[0][1]) | |
| prediction = int(proba >= threshold) | |
| label = "Quitte" if prediction == 1 else "Reste" | |
| print(f"\n--- Prédiction pour l'employé id={employee_id} ---") | |
| print(f" Poste : {emp.poste} | Réel : {'Quitte' if emp.a_quitte_l_entreprise else 'Reste'}") | |
| print(f" Prédit : {label} (probabilité : {round(proba, 4)})") | |
| def stats_predictions(session): | |
| total = session.query(Prediction).count() | |
| print(f"\n--- Prédictions loggées en base ---") | |
| print(f"Total : {total}") | |
| if total > 0: | |
| derniere = session.query(Prediction).order_by(Prediction.id.desc()).first() | |
| print(f"Dernière : id={derniere.id} | {'Quitte' if derniere.prediction == 1 else 'Reste'} | proba={derniere.probabilite} | {derniere.created_at}") | |
| if __name__ == "__main__": | |
| session = SessionLocal() | |
| try: | |
| stats_employees(session) | |
| apercu_employees(session, n=5) | |
| first_employee = session.query(Employee).first() | |
| if first_employee: | |
| predict_from_db(session, employee_id=first_employee.id) | |
| stats_predictions(session) | |
| finally: | |
| session.close() | |