h2o-py/h2o/model/word_embedding.py
def transform(self, words, aggregate_method):
        """
        Transform words (or sequences of words) to vectors using a word2vec model.

        :param str words: An H2OFrame made of a single column containing source words.
        :param str aggregate_method: Specifies how to aggregate sequences of words. If method is `NONE`
               then no aggregation is performed and each input word is mapped to a single word-vector.
               If method is 'AVERAGE' then input is treated as sequences of words delimited by NA.
               Each word of a sequences is internally mapped to a vector and vectors belonging to
               the same sentence are averaged and returned in the result.

        :returns: the approximate reconstruction of the training data.
        """
        j = h2o.api("GET /3/Word2VecTransform", data={'model': self.model_id, 'words_frame': words.frame_id, 'aggregate_method': aggregate_method})
        return h2o.get_frame(j["vectors_frame"]["name"])
def transform(self, words, aggregate_method):
        """
        Transform words (or sequences of words) to vectors using a word2vec model.

        :param str words: An H2OFrame made of a single column containing source words.
        :param str aggregate_method: Specifies how to aggregate sequences of words. If method is `NONE`
               then no aggregation is performed and each input word is mapped to a single word-vector.
               If method is 'AVERAGE' then input is treated as sequences of words delimited by NA.
               Each word of a sequences is internally mapped to a vector and vectors belonging to
               the same sentence are averaged and returned in the result.

        :returns: the approximate reconstruction of the training data.
        """
        j = h2o.api("GET /3/Word2VecTransform", data={'model': self.model_id, 'words_frame': words.frame_id, 'aggregate_method': aggregate_method})
        return h2o.get_frame(j["vectors_frame"]["name"])
def transform ( self , words , aggregate_method ) : j = h2o . api ( "GET /3/Word2VecTransform" , data = { 'model' : self . model_id , 'words_frame' : words . frame_id , 'aggregate_method' : aggregate_method } ) return h2o . get_frame ( j [ "vectors_frame" ] [ "name" ] )
Transform words (or sequences of words) to vectors using a word2vec model.

        :param str words: An H2OFrame made of a single column containing source words.
        :param str aggregate_method: Specifies how to aggregate sequences of words. If method is `NONE`
               then no aggregation is performed and each input word is mapped to a single word-vector.
               If method is 'AVERAGE' then input is treated as sequences of words delimited by NA.
               Each word of a sequences is internally mapped to a vector and vectors belonging to
               the same sentence are averaged and returned in the result.

        :returns: the approximate reconstruction of the training data.
Transform words ( or sequences of words ) to vectors using a word2vec model .