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import pandas as pd
from app.feature_engineering import transform_fe
def test_transform_fe():
df = pd.DataFrame([{
"age": 30,
"revenu_mensuel": 2500.0,
"statut_marital": "Marié(e)",
"departement": "IT",
"poste": "Développeur",
"annee_experience_totale": 5,
"annees_dans_l_entreprise": 2,
"annees_dans_le_poste_actuel": 2,
"satisfaction_employee_environnement": 3,
"note_evaluation_precedente": 4.2,
"satisfaction_employee_nature_travail": 4,
"satisfaction_employee_equipe": 4,
"satisfaction_employee_equilibre_pro_perso": 3,
"note_evaluation_actuelle": 4.5,
"heure_supplementaires": "Oui",
"augementation_salaire_precedente": "10%",
"nombre_participation_pee": 1,
"frequence_deplacement": "Rare",
"annes_sous_responsable_actuel": 1
}])
out = transform_fe(df)
# 1. La fonction renvoie bien un DataFrame
assert isinstance(out, pd.DataFrame)
# 2. Vérifier que les colonnes calculées existent
assert "revenu_par_anciennete" in out.columns
assert "ratio_exp_entreprise_externe" in out.columns
assert "ratio_manager" in out.columns
# 3. Vérifier que Oui -> 1
assert out.loc[0, "heure_supplementaires"] == 1
# 4. Vérifier que "10%" devient 10.0
assert out.loc[0, "augementation_salaire_precedente"] == 10.0
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