Github user Krimit commented on a diff in the pull request:
https://github.com/apache/spark/pull/17673#discussion_r113600716
--- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
---
@@ -194,6 +232,285 @@ final class Word2Vec @Since("1.4.0") (
@Since("1.4.1")
override def copy(extra: ParamMap): Word2Vec = defaultCopy(extra)
+
+ /**
+ * Similar to InitUnigramTable in the original code. Instead of using an
array of size 100 million
+ * like the original, we size it to be 20 times the vocabulary size.
+ * We sacrifice memory here, to get constant time lookups into this
array when generating
+ * negative samples.
+ */
+ private def generateUnigramTable(normalizedWeights: Array[Double],
tableSize: Int): Array[Int] = {
+ val table = Array.fill(tableSize)(0)
+ var a = 0
+ var i = 0
+ while (a < table.length) {
+ table.update(a, i)
+ if (a.toFloat / table.length >= normalizedWeights(i)) {
+ i = math.min(normalizedWeights.length - 1, i + 1)
+ }
+ a += 1
+ }
+ table
+ }
+
+ private def generateVocab[S <: Iterable[String]](input: RDD[S]):
+ (Int, Long, Map[String, Int], Array[Int]) = {
+ val sc = input.context
+
+ val words = input.flatMap(x => x)
+
+ val vocab = words.map(w => (w, 1L))
+ .reduceByKey(_ + _)
+ .filter{case (w, c) => c >= $(minCount)}
--- End diff --
You should broadcast ``$(minCount)`` to avoid shipping the entire class to
each task. (and make sure to destroy it at the end of the method)
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