scala – 是否可以将word2vec预训练的可用矢量加载到spark中？

诀窍是弄清楚如何从一组单词向量构造Word2VecModel,以及在尝试以这种方式创建模型时处理一些陷阱.

首先,将单词向量加载到Map中.例如,我将单词向量保存为镶木地板格式(在名为“wordvectors.parquet”的文件夹中),其中“term”列包含String字,“vector”列将向量作为数组[float]保存,我可以在Java中加载它：

// Loads the dataset with the "term" column holding the word and the "vector" column 
// holding the vector as an array[float] 
Dataset<Row> vectorModel = pSpark.read().parquet("wordvectors.parquet");

//convert dataset to a map.
Map<String,List<Float>> vectorMap = Arrays.stream((Row[])vectorModel.collect())
            .collect(Collectors.toMap(row -> row.getAs("term"),row -> row.getList(1)));

//convert to the format that the word2vec model expects float[] rather than List<Float>
Map<String,float[]> word2vecMap = vectorMap.entrySet().stream()
                .collect(Collectors.toMap(Map.Entry::getKey,entry -> (float[]) Floats.toArray(entry.getValue())));

//need to convert to scala immutable map because that's what word2vec needs
scala.collection.immutable.Map<String,float[]> scalaMap = toScalaImmutableMap(word2vecMap);

private static <K,V> scala.collection.immutable.Map<K,V> toScalaImmutableMap(Map<K,V> pFromMap) {
        final List<Tuple2<K,V>> list = pFromMap.entrySet().stream()
                .map(e -> Tuple2.apply(e.getKey(),e.getValue()))
                .collect(Collectors.toList());

        Seq<Tuple2<K,V>> scalaSeq = JavaConverters.asScalaBufferConverter(list).asScala().toSeq();

        return (scala.collection.immutable.Map<K,V>) scala.collection.immutable.Map$.MODULE$.apply(scalaSeq);
    }

现在您可以从头开始构建模型.由于Word2VecModel如何工作的怪癖,您必须手动设置矢量大小,并以一种奇怪的方式这样做.否则它默认为100,并且在尝试调用.transform()时出错.这是我发现有效的方法,不确定是否一切都是必要的：

//not used for fitting,only used for setting vector size param (not sure if this is needed or if result.set is enough
Word2Vec parent = new Word2Vec();
parent.setVectorSize(300);

Word2VecModel result = new Word2VecModel("w2vmodel",new org.apache.spark.mllib.feature.Word2VecModel(scalaMap)).setParent(parent);
        result.set(result.vectorSize(),300);

现在,您应该能够像使用自我训练的模型一样使用result.transform().

我没有测试过其他Word2VecModel函数,看它们是否正常工作,我只测试过.transform().