def do_cross_validation(X, Y):
X, Y = shuffle(X, Y)
folds = 10
kf = KFold(n_splits=folds, shuffle=True)
cc = ClassifierChain()
zero_one_score = 0
hamming_score = 0
accuracy = 0
for train, test in kf.split(X):
X_train, y_train = X[train], Y[train]
X_test, y_test = X[test], Y[test]
cc.fit(X_train, y_train)
zero_one_score += cc.zero_one_loss_score(X_test, y_test)
hamming_score += cc.hamming_loss_score(X_test, y_test)
accuracy += cc.accuracy_score(X_test, y_test)
zero_one_score /= folds
hamming_score /= folds
accuracy /= folds
#print "0/1 Loss:", zero_one_score
#print "Hamming Loss:", hamming_score
#print "Accuracy:", accuracy
#print
return [zero_one_score, hamming_score, accuracy]
