def run_model(args, X, y, ensembler = False):
model = None
if args['model'] == 'logistic':
logistic = Logistic(X,y, model)
model = logistic.train_model()
elif args['model'] == 'knn':
knn = KNN(X,y, model)
model = knn.train_model()
elif args['model'] == 'svm':
svm = SVM(X,y, model)
model = svm.train_model()
elif args['model'] == 'rfa':
rfa = RandomForest(X, y, model)
model = rfa.train_model(ensembler)
elif args['model'] == 'xgb':
xgb = XGB(X, y, model)
model = xgb.train_model(ensembler)
elif args['model'] == 'lgbm':
lgbm = LightGBM(X, y, model)
model = lgbm.train_model(ensembler)
elif args['model'] == 'catboost':
catboost = CatBoost(X, y, model)
model = catboost.train_model(ensembler)
elif len(args['models']) > 1:
models = [('', None)]* len(args['models'])
for i in range(len(args['models'])):
model_name = args['models'][i]
temp_args = copy.deepcopy(args)
temp_args['model'] = model_name
models[i] = (model_name, run_model(temp_args, X, y, True))
ensembler = Ensembler(X, y, model, args['ensembler_type'])
model = ensembler.train_model(models)
return model
else:
print('\nInvalid model name :-|\n')
exit()
return model
