K.set_session(sess) from sklearn.ensemble import RandomForestClassifier from sklearn.linear_model import LogisticRegression, RidgeClassifier from sklearn.ensemble import VotingClassifier from sklearn.model_selection import cross_val_score from sklearn.ensemble import AdaBoostClassifier from sklearn import svm if __name__ == "__main__": filehandler = open(features_evaluation.SELECTED_FEATURES_CORPUS_CHI2, 'r') corpus = pickle.load(filehandler) dataset = Dataset(corpus=corpus) X = dataset.get_train_x() y = dataset.get_train_y() scores_dict = defaultdict(list) clf1 = LogisticRegression(C=0.05, random_state=1, class_weight='balanced') clf2 = RandomForestClassifier(random_state=1) clf3 = svm.SVC(C=0.35, class_weight='balanced') clf4 = RidgeClassifier(alpha=2.5) clf5 = AdaBoostClassifier(n_estimators=150) eclf = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2), ('svm', clf3), ('rc', clf4), ('ab', clf5)], voting='hard') for clf, label in zip([clf1, clf2, clf3, clf4, clf5, eclf],