Github user ygcao commented on a diff in the pull request:
https://github.com/apache/spark/pull/10152#discussion_r47429372
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala ---
@@ -469,13 +495,13 @@ class Word2VecModel private[spark] (
this(Word2VecModel.buildWordIndex(model),
Word2VecModel.buildWordVectors(model))
}
- private def cosineSimilarity(v1: Array[Float], v2: Array[Float]): Double
= {
- require(v1.length == v2.length, "Vectors should have the same length")
- val n = v1.length
- val norm1 = blas.snrm2(n, v1, 1)
- val norm2 = blas.snrm2(n, v2, 1)
- if (norm1 == 0 || norm2 == 0) return 0.0
- blas.sdot(n, v1, 1, v2, 1) / norm1 / norm2
+ /**
+ * get the built vocabulary from the input
+ * this is useful for getting the whole vocabulary to join with other
data or filtering other data
+ * @return a map of word to its index
+ */
+ def getVocabulary: Map[String, Int] = {
--- End diff --
Another thing I want to raise your attention is that the slice function in
scala (used in getVectors) is super slow for whatever reason. We can output
needed vector for join purpose from getWordVectors's return value by using
indexes 100x faster than getVectors function call for each single word.
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