SparkML to MLLib conversion to run BisectingKMeans clustering

KMeansSparkMLToMLLib.scala

import org.apache.spark.mllib.clustering.BisectingKMeans
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.linalg.Vector

//std_features col is of type vector
scaledFeatures.select($"std_features").printSchema()

val tempFeatureRdd = scaledFeatures.select($"std_features").rdd

import scala.reflect.runtime.universe._
def getType[T: TypeTag](value: T) = typeOf[T]
println("-------BEFORE")
println("Type of RDD: "+getType(tempFeatureRdd))
println("Type of column: "+getType(tempFeatureRdd.first()))

/**
create a new df of type RDD[org.apache.spark.mllib.linalg.Vector] by mapping
RDD[org.apache.spark.sql.Row] to RDD[org.apache.spark.mllib.linalg.Vector] 
as BisectingKMeans works only with Vector type
**/
val input = scaledFeatures
                .select($"std_features")
                .rdd
                .map(v => Vectors.fromML(v.getAs[org.apache.spark.ml.linalg.Vector](0)))
                .cache() //important for ML algos to run faster
println("-------AFTER")
println("Type of RDD: "+getType(input))
println("Type of column: "+getType(input.first()))

println("Total rows: "+input.count())

// Clustering the data into 9 clusters by BisectingKMeans.
val bkm = new BisectingKMeans().setK(9)
val model = bkm.run(input)

println(s"Compute Cost: ${model.computeCost(input)}")
model.clusterCenters.zipWithIndex.foreach { case (center, idx) =>
  println(s"Cluster Center ${idx}: ${center}")
}