Convert Rdd[Vector] to Rdd[Double]

Question

How do I convert csv to Rdd[Double]? I have the error: cannot be applied to (org.apache.spark.rdd.RDD[Unit]) at this line:

val kd = new KernelDensity().setSample(rows) 

My full code is here:

   import org.apache.spark.mllib.linalg.Vectors
    import org.apache.spark.mllib.linalg.distributed.RowMatrix
    import org.apache.spark.mllib.stat.KernelDensity
    import org.apache.spark.rdd.RDD
    import org.apache.spark.{SparkContext, SparkConf}

class KdeAnalysis {
  val conf = new SparkConf().setAppName("sample").setMaster("local")
  val sc = new SparkContext(conf)

  val DATAFILE: String = "C:\\Users\\ajohn\\Desktop\\spark_R\\data\\mass_cytometry\\mass.csv"
  val rows = sc.textFile(DATAFILE).map {
    line => val values = line.split(',').map(_.toDouble)
      Vectors.dense(values)
  }.cache()



  // Construct the density estimator with the sample data and a standard deviation for the Gaussian
  // kernels
  val rdd : RDD[Double] = sc.parallelize(rows)
  val kd = new KernelDensity().setSample(rdd)
    .setBandwidth(3.0)

  // Find density estimates for the given values
  val densities = kd.estimate(Array(-1.0, 2.0, 5.0))
}

Answer 1

Since rows is a RDD[org.apache.spark.mllib.linalg.Vector] following line cannot work:

val rdd : RDD[Double] = sc.parallelize(rows)

parallelize expects Seq[T] and RDD is not a Seq .

Even if this part worked as you expect your input is simply wrong. A correct argument for KernelDensity.setSample is either RDD[Double] or JavaRDD[java.lang.Double] . It looks like it doesn't support a multivariate data at this moment.

Regarding a question from the tile you can flatMap

rows.flatMap(_.toArray)

or even better when you create rows

val rows = sc.textFile(DATAFILE).flatMap(_.split(',').map(_.toDouble)).cache()

but I doubt it is really what you need.

Answer 2

已经准备好此代码，请评估它是否可以帮助您->

val doubleRDD = rows.map(_.toArray).flatMap(x => x)