Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/10152#discussion_r50675972
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala ---
@@ -289,17 +301,28 @@ 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 =>
+ // each partition is a collection of sentences, will be translated
into arrays of Index integer
+ val sentences: RDD[Array[Int]] = dataset.mapPartitions { sentenceIter
=>
new Iterator[Array[Int]] {
- def hasNext: Boolean = iter.hasNext
+ var wordIter: Iterator[String] = null
+
+ def hasNext: Boolean = sentenceIter.hasNext || (wordIter != null
&& wordIter.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 {
+ // do translation of each word into its index in the vocabulary,
--- End diff --
I understand that this part of the change intends to respect the implied
sentence boundaries in the input. I think it can be simpler? One input sentence
maps to 1 or more arrays, and the result should be flattened. Something like?
```
// Each input sentence will produce 1 or more Array[Int], so
flatMapPartitions
dataset.flatMapPartitions { sentenceIter =>
// Each sentence will map to 1 or more Array[Int], so map
sentenceIter.map { sentence =>
// Sentence of words, some of which map to a hash, so flatMap
val hashes = sentence.flatMap(bcVocabHash.value.get)
// break into sequence of at most maxSentenceLength
hashes.grouped(maxSentenceLength).map(_.toArray)
}
}
```
I haven't tested it but does that seem like the intent?
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