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[SPARK-12685] [MLlib] [Backport to 1.4]word2vec trainWordsCount gets overflow #10721

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[SPARK-12685] [MLlib] [Backport to 1.4]word2vec trainWordsCount gets overflow #10721

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8 changes: 4 additions & 4 deletions mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -139,21 +139,21 @@ class Word2Vec extends Serializable with Logging {
/** context words from [-window, window] */
private val window = 5

private var trainWordsCount = 0
private var trainWordsCount = 0L
private var vocabSize = 0
@transient private var vocab: Array[VocabWord] = null
@transient private var vocabHash = mutable.HashMap.empty[String, Int]

private def learnVocab(words: RDD[String]): Unit = {
vocab = words.map(w => (w, 1))
.reduceByKey(_ + _)
.filter(_._2 >= minCount)
.map(x => VocabWord(
x._1,
x._2,
new Array[Int](MAX_CODE_LENGTH),
new Array[Int](MAX_CODE_LENGTH),
0))
.filter(_.cn >= minCount)
.collect()
.sortWith((a, b) => a.cn > b.cn)

Expand All @@ -164,7 +164,7 @@ class Word2Vec extends Serializable with Logging {
trainWordsCount += vocab(a).cn
a += 1
}
logInfo("trainWordsCount = " + trainWordsCount)
logInfo(s"vocabSize = $vocabSize, trainWordsCount = $trainWordsCount")
}

private def createExpTable(): Array[Float] = {
Expand Down Expand Up @@ -313,7 +313,7 @@ class Word2Vec extends Serializable with Logging {
val random = new XORShiftRandom(seed ^ ((idx + 1) << 16) ^ ((-k - 1) << 8))
val syn0Modify = new Array[Int](vocabSize)
val syn1Modify = new Array[Int](vocabSize)
val model = iter.foldLeft((syn0Global, syn1Global, 0, 0)) {
val model = iter.foldLeft((syn0Global, syn1Global, 0L, 0L)) {
case ((syn0, syn1, lastWordCount, wordCount), sentence) =>
var lwc = lastWordCount
var wc = wordCount
Expand Down