Calculate Cosine Similarity Spark Dataframe

Question

I am using Spark Scala to calculate cosine similarity between the Dataframe rows.

Dataframe format is below

root
    |-- SKU: double (nullable = true)
    |-- Features: vector (nullable = true)

Sample of the dataframe below

    +-------+--------------------+
    |    SKU|            Features|
    +-------+--------------------+
    | 9970.0|[4.7143,0.0,5.785...|
    |19676.0|[5.5,0.0,6.4286,4...|
    | 3296.0|[4.7143,1.4286,6....|
    |13658.0|[6.2857,0.7143,4....|
    |    1.0|[4.2308,0.7692,5....|
    |  513.0|[3.0,0.0,4.9091,5...|
    | 3753.0|[5.9231,0.0,4.846...|
    |14967.0|[4.5833,0.8333,5....|
    | 2803.0|[4.2308,0.0,4.846...|
    |11879.0|[3.1429,0.0,4.5,4...|
    +-------+--------------------+

I tried to transpose the matrix and check the following mentioned links.Apache Spark Python Cosine Similarity over DataFrames, calculating-cosine-similarity-by-featurizing-the-text-into-vector-using-tf-idf But I believe there is a better solution

I am tried the below sample code

val irm = new IndexedRowMatrix(inClusters.rdd.map {
  case (v,i:Vector) => IndexedRow(v, i)


}).toCoordinateMatrix.transpose.toRowMatrix.columnSimilarities

But I got the below error

Error:(80, 12) constructor cannot be instantiated to expected type;
 found   : (T1, T2)
 required: org.apache.spark.sql.Row
      case (v,i:Vector) => IndexedRow(v, i)

I checked the following Link Apache Spark: How to create a matrix from a DataFrame? But can't do it using Scala

Answer 1

• DataFrame.rdd returns RDD[Row] not RDD[(T, U)]. You have to pattern match the Row or directly extract interesting parts.
• ml Vector used with Datasets since Spark 2.0 is not the same as mllib Vector use by old API. You have to convert it to use with IndexedRowMatrix.
• Index has to be Long not string.

import org.apache.spark.sql.Row

val irm = new IndexedRowMatrix(inClusters.rdd.map {
  Row(_, v: org.apache.spark.ml.linalg.Vector) => 
    org.apache.spark.mllib.linalg.Vectors.fromML(v)
}.zipWithIndex.map { case (v, i) => IndexedRow(i, v) })