# # ("extra_trees_clf", extra_trees_clf),
# ("sgd_clf", sgd_clf),
# # ("log_clf", log_clf),
# ("svm_clf", svm_clf),
# ("mlp_clf", mlp_clf)
#]
named_estimators = [
("rand_clf_tt", rand_clf_tt),
("mlp_clf_tt", mlp_clf_tt),
("svc_clf_tt", mlp_clf_tt),
]
voting_clf = VotingClassifier(named_estimators)
voting_clf.fit(x_embeddings_tf_train, strat_train_set_y)
sc = voting_clf.f1_score(x_embeddings_tf_test, strat_test_y)
print("F1 score voting", sc)
y_pred = voting_clf.predict(x_embeddings_tf_test)
from sklearn.metrics import f1_score
scor = f1_score(y_pred, strat_test_y)
print("F1 voting", scor)
#proviamo ad eliminare l'SVM
#del voting_clf.estimators_[2]
#del voting_clf.estimators_[3]
#sc = voting_clf.score(strat_test_set, strat_test_y)
#print("Score voting senza SVM",sc)
#Settiamo il voting a soft
