Github user ygcao commented on a diff in the pull request:
https://github.com/apache/spark/pull/10152#discussion_r47822243
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala ---
@@ -281,17 +295,28 @@ 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,
+ // do cutting only when the sentence is larger than
maxSentenceLength
+ if ((wordIter == null || !wordIter.hasNext) &&
sentenceIter.hasNext) {
+ do {
--- End diff --
This is just for the edge case when the input contains empty sentences, the
do while loop will skip empty sentences instead of generate an empty array as a
result for the empty sentence.
of course, if the sentence splitter will skip empty sentences, this is not
necessary, but that's out of this class's control. So, I just recommend to have
the loop, it won't introduce much latency since condition check is so cheap and
the loop will only run once for 99.999% cases.
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