def _create_archive(self):
        # pickle the model without its factors
        # then use numpy to save the factors efficiently
        tmp = (self.U,self.V)
        self.U = self.V = None
        m = pickle.dumps(self)
        self.U,self.V = tmp
        return {'model':m,'U':self.U,'V':self.V}
Return fields to be serialized in a numpy archive.
dataset/ETHPy150Open Mendeley/mrec/mrec/mf/recommender.py/MatrixFactorizationRecommender._create_archive