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From MLnick <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-12153][SPARK-7617][MLlib]add support of...
Date Mon, 08 Feb 2016 08:29:28 GMT
Github user MLnick commented on a diff in the pull request:

    https://github.com/apache/spark/pull/10152#discussion_r52141782
  
    --- Diff: mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala ---
    @@ -289,26 +301,24 @@ class Word2Vec extends Serializable with Logging {
         val expTable = sc.broadcast(createExpTable())
         val bcVocab = sc.broadcast(vocab)
         val bcVocabHash = sc.broadcast(vocabHash)
    -
    -    val sentences: RDD[Array[Int]] = words.mapPartitions { iter =>
    -      new Iterator[Array[Int]] {
    -        def hasNext: Boolean = iter.hasNext
    -
    -        def next(): Array[Int] = {
    -          val sentence = ArrayBuilder.make[Int]
    -          var sentenceLength = 0
    -          while (iter.hasNext && sentenceLength < MAX_SENTENCE_LENGTH) {
    -            val word = bcVocabHash.value.get(iter.next())
    -            word match {
    -              case Some(w) =>
    -                sentence += w
    -                sentenceLength += 1
    -              case None =>
    +    // each partition is a collection of sentences,
    +    // will be translated into arrays of Index integer
    +    val sentences: RDD[Array[Int]] = dataset.mapPartitions {
    +      // Each sentence will map to 0 or more Array[Int]
    +      sentenceIter =>
    --- End diff --
    
    Generally I prefer the style:
    
    ```
    dataset.mapPartitions { sentenceIter =>
      sentenceIter.flatMap { sentence =>
        ...
    ```
    
    I don't think it's a hard rule for Spark but most other code follows this convention.


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