Github user yu-iskw commented on a diff in the pull request:
https://github.com/apache/spark/pull/6791#discussion_r32797065
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala ---
@@ -465,6 +465,36 @@ private[python] class PythonMLLibAPI extends
Serializable {
}
/**
+ * Java stub for Python mllib LDA.run()
+ */
+ def trainLDAModel(
+ data: JavaRDD[LabeledPoint],
--- End diff --
Umm, it seems that we can't deal with `JavaRDD[(Long, Vector)]` in the
parameter of `trainLDAModel`. After all, I got the error message as follows,
when I run the test. Tuple of Python was recognized as `java.lang.Object` in
Java.
```
java.lang.ClassCastException: [Ljava.lang.Object; cannot be cast to
scala.Tuple2
at
org.apache.spark.mllib.api.python.PythonMLLibAPI$$anonfun$6.apply(PythonMLLibAPI.scala:489)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
...
```
According to `trainFPGrowthModel` and `trainWord2Vec`, dealing with input
data as array would be better. What do you think about this implementation?
https://github.com/yu-iskw/spark/commit/cdb2cf167097dda4f15eb690b7a73cadabdd3313
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