spark 1.5, ML Pipeline Decision Tree Dataframe Problem

[Yasemin Kaya]

Hi,

I am using *spark 1.5, ML Pipeline Decision Tree
<http://spark.apache.org/docs/latest/ml-decision-tree.html#output-columns>*
to get tree's probability. But I have to convert my data to Dataframe type.
While creating model there is no problem but when I am using model on my
data there is a problem about converting to data frame type. My data type
is *JavaPairRDD<String, Vector>* , when I am creating dataframe

DataFrame production = sqlContext.createDataFrame(
unlabeledTest.values(), Vector.class);

*Error says me: *
Exception in thread "main" java.lang.ClassCastException:
org.apache.spark.mllib.linalg.VectorUDT cannot be cast to
org.apache.spark.sql.types.StructType

I know if I give LabeledPoint type, there will be no problem. But the data
have no label, I wanna predict the label because of this reason I use model
on it.

Is there way to handle my problem?
Thanks.


Best,
yasemin
-- 
hiç ender hiç