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],
