test_mails_body, test_labels = get_features('test_mails')
  test_matrix = vectorizer.transform(test_mails_body)

  classifier_names = ['Классификатор MultinomialNB','Классификатор GaussianNB','Классификатор BernoulliNB','Классификатор LinearSVC']
  classifiers = [MultinomialNB(),GaussianNB(),BernoulliNB(),LinearSVC()]



  for name,clf in zip(classifier_names,classifiers):
    train_mtr = train_matrix
    test_mtr = test_matrix
    if 'Gauss' in name:
      train_mtr = train_mtr.toarray()
      test_mtr = test_mtr.toarray()
    clf.fit(train_mtr,mails_labels)
    prediction = clf.predict(test_mtr)
    print(name)
    print(pd.DataFrame(confusion_matrix(test_labels, prediction), columns=['Mails', 'Spam'], index=['Mails', 'Spam']))