def METHOD_NAME(self, Xt, Xs, Ys, Xt2, Yt2):
    '''
    Transfrom Xt and Xs, get Xs_new
    Transform Xt2 with projection matrix created by Xs and Xt, get Xt2_new
    Make predictions on Xt2_new using classifier trained on Xs_new
    :param Xt: ns * n_feature, target feature
    :param Xs: ns * n_feature, source feature
    :param Ys: ns * 1, source label
    :param Xt2: nt * n_feature, new target feature
    :param Yt2: nt * 1, new target label
    :return: Accuracy and predicted_labels on the target domain
    '''
    Xs_new, _ = self.fit(Xs, Xt)
    Xt2_new = self.fit_new(Xs, Xt, Xt2)
    clf = KNeighborsClassifier(n_neighbors=1)
    clf.fit(Xs_new, Ys.ravel())
    y_pred = clf.predict(Xt2_new)
    acc = sklearn.metrics.accuracy_score(Yt2, y_pred)
    return acc, y_pred