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from traning_zone.traitement_data.feature_engeneering.data_clearning import *
from traning_zone.traitement_data.feature_engeneering.feature_Engineering import *
from traning_zone.modele_zone.modeles import *
from traning_zone.modele_zone.modeles_gridsearch import *
from traning_zone.modele_zone.model import *
import time
import yaml
# Charger le contenu du fichier YAML
with open('variables.yml', 'r') as file:
data = yaml.load(file, Loader=yaml.FullLoader)
hyper_classes = data['hyper_classes']
for hyper_classe in hyper_classes:
nom = list(hyper_classe.keys())[0]
classes = hyper_classe[nom][0]['classes']
liste_classe = []
for classe in classes :
liste_classe.append(str(list(classe.values())[0]))
start_time = time.time()
data = clearning(*liste_classe)
tv_xtrain, tv_xtest, Ytrain, Ytest = engineering(data, nom)
end_time = time.time()
print(f"Temps d'exécution du pré-traitement est : {end_time - start_time} secondes")
for name in modeles.keys():
try :
start_time = time.time()
trainer(tv_xtrain, Ytrain, tv_xtest, Ytest, modeles[name], name, nom )
#traning_gridsearch(tv_xtrain, Ytrain, tv_xtest, Ytest, modeles[name], name, nom, cv= 5)
end_time = time.time()
print(f"Temps d'exécution du d'apprentissage du modèle {name} est : {end_time - start_time} secondes")
except :
print(f"Erreur lors de l'apprentissage du modèle {name}")
print(f"L'hyper classe {nom} est terminée") |