Read MLeap and score the LeapFrame

scoreREPL.scala

scala> import ml.combust.bundle.BundleFile
scala> import ml.combust.mleap.runtime.MleapSupport._
scala> import resource._
scala> import ml.combust.mleap.core.types._
scala> import ml.combust.mleap.runtime.frame.{DefaultLeapFrame, Row}
 
// creating LeapFrame equivalent to the DataFrame above
scala> val schema = StructType(StructField("email", ScalarType.String),
     |   StructField("first_name", ScalarType.String)).get
 
scala> val data = Seq(Row("john.doe@gmail.com", "John"), Row("JackieChan234@xyz.com","jack"), Row("ping_pong@missed.org","Al"))
 
scala> val leapFrame = DefaultLeapFrame(schema, data)
 
// importing the MLeap bundle
scala> val bundle = (for(bundleFile <- managed(BundleFile("jar:file:MLeap.zip"))) yield {
     |   bundleFile.loadMleapBundle().get
     | }).opt.get
 
scala> val mleapPipeline = bundle.root
 
// scoring the LeapFrame
scala> val outputLeapFrame = mleapPipeline.transform(leapFrame).get
 
scala> outputLeapFrame.dataset
res1: Seq[ml.combust.mleap.runtime.frame.Row] = List(Row(john.doe@gmail.com,John,0.0,1.0,DenseTensor([D@ca72afb,List(2))), Row(JackieChan234@xyz.com,jack,1.0,1.0,DenseTensor([D@3e4dabcb,List(2))), Row(ping_pong@missed.org,Al,2.0,0.0,DenseTensor([D@2b24fcba,List(2))))