def METHOD_NAME(self, Xs, Ys, Xt, Yt):
    '''
    Transform Xs and Xt, then make predictions on target using 1NN
    :param Xs: ns * n_feature, source feature
    :param Ys: ns * 1, source label
    :param Xt: nt * n_feature, target feature
    :param Yt: nt * 1, target label
    :return: Accuracy and predicted_labels on the target domain
    '''
    Xs_new, Xt_new = self.fit(Xs, Xt)
    clf = KNeighborsClassifier(n_neighbors=1)
    clf.fit(Xs_new, Ys.ravel())
    y_pred = clf.predict(Xt_new)
    acc = sklearn.metrics.accuracy_score(Yt, y_pred)
    return acc, y_pred