CODEWITHSUNDEEP
scalaapache-sparkrddscala-collections
EdgeRover
EdgeRover

Reputation: 195

Create a SparseVector from the elements of RDD

Using Spark, I have a data structure of type val rdd = RDD[(x: Int, y:Int), cov:Double] in Scala, where each element of the RDD represents an element of a matrix with x representing the row, y representing the column and cov representing the value of the element:

I need to create SparseVectors from rows of this matrix. So I decided to first convert the rdd to RDD[x: Int, (y:Int, cov:Double)] and then use groupByKey to put all elements of a specific row together like this:

val rdd2 = rdd.map{case ((x,y),cov) => (x, (y, cov))}.groupByKey()

Now I need to create the SparseVectors:

val N = 7     //Vector Size
val spvec = {(x: Int,y: Iterable[(Int, Double)]) => new SparseVector(N.toLong, Array(y.map(el => el._1.toInt)), Array(y.map(el => el._2.toDouble)))}
val vecs = rdd2.map(spvec)

However, this is the error that pops up. 

type mismatch; found :Iterable[Int] required:Int
type mismatch; found :Iterable[Double] required:Double

I am guessing that y.map(el => el._1.toInt) is returning an iterable which Array cannot be applied on. I would appreciate if someone could help with how to do this.

Upvotes: 0

Views: 664

Answers (1)

zero323
zero323

Reputation: 330443

The simplest solution is to convert to RowMatrix:

import org.apache.spark.mllib.linalg.distributed.{CoordinateMatrix, MatrixEntry}

val rdd: RDD[((Int, Int), Double)] = ???

val vs: RDD[org.apache.spark.mllib.linalg.SparseVector]= new CoordinateMatrix(
  rdd.map{
    case ((x, y), cov) => MatrixEntry(x, y, cov)
  }
).toRowMatrix.rows.map(_.toSparse)

If you want to preserve row indices you can use toIndexedRowMatrix instead:

import org.apache.spark.mllib.linalg.distributed.IndexedRow

new CoordinateMatrix(
  rdd.map{
    case ((x, y), cov) => MatrixEntry(x, y, cov)
  }
).toIndexedRowMatrix.rows.map { case IndexedRow(i, vs) => (i, vs.toSparse) }

Upvotes: 0

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