Github user Ishiihara commented on a diff in the pull request:
https://github.com/apache/spark/pull/2356#discussion_r18117490
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala ---
@@ -284,6 +285,80 @@ class PythonMLLibAPI extends Serializable {
}
/**
+ * Java stub for Python mllib Word2Vec fit(). This stub returns a
+ * handle to the Java object instead of the content of the Java object.
+ * Extra care needs to be taken in the Python code to ensure it gets
freed on
+ * exit; see the Py4J documentation.
+ * @param dataJRDD Input JavaRDD
+ * @return A handle to java Word2VecModel instance at python side
+ */
+ def trainWord2Vec(
+ dataJRDD: JavaRDD[java.util.ArrayList[String]]
+ ): Word2VecModel = {
+ val data = dataJRDD.rdd.map(_.toArray(new
Array[String](0)).toSeq).cache()
--- End diff --
@mengxr @davies Thank you for pointing this out. I am inclined to cache
words RDD inside word2vec.fit as I discovered that words RDD is used twice, the
first time is calling learnVocab(words) and the second time is creating
newSentences RDD. This method will not increate memory as there is no
overlapping between computation on words RDD and newSentences RDD. Thus, we can
unpersist words RDD before caching newSentences.
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