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Spark/mllib SVD example
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import org.apache.spark.mllib.linalg.distributed.RowMatrix | |
import org.apache.spark.mllib.linalg._ | |
import org.apache.spark.{SparkConf, SparkContext} | |
// To use the latest sparse SVD implementation, please build your spark-assembly after this | |
// change: https://github.com/apache/spark/pull/1378 | |
// Input tsv with 3 fields: rowIndex(Long), columnIndex(Long), weight(Double), indices start with 0 | |
// Assume the number of rows is larger than the number of columns, and the number of columns is | |
// smaller than Int.MaxValue | |
// sc is a SparkContext defined in the job | |
val inputData = sc.textFile("hdfs://...").map{ line => | |
val parts = line.split("\t") | |
(parts(0).toLong, parts(1).toInt, parts(2).toDouble) | |
} | |
// Number of columns | |
val nCol = inputData.map(_._2).distinct().count().toInt | |
// Construct rows of the RowMatrix | |
val dataRows = inputData.groupBy(_._1).map[(Long, Vector)]{ row => | |
val (indices, values) = row._2.map(e => (e._2, e._3)).unzip | |
(row._1, new SparseVector(nCol, indices.toArray, values.toArray)) | |
} | |
// Compute 20 largest singular values and corresponding singular vectors | |
val svd = new RowMatrix(dataRows.map(_._2).persist()).computeSVD(20, computeU = true) | |
// Write results to hdfs | |
val V = svd.V.toArray.grouped(svd.V.numRows).toList.transpose | |
sc.makeRDD(V, 1).zipWithIndex() | |
.map(line => line._2 + "\t" + line._1.mkString("\t")) // make tsv line starting with column index | |
.saveAsTextFile("hdfs://...output/right_singular_vectors") | |
svd.U.rows.map(row => row.toArray).zip(dataRows.map(_._1)) | |
.map(line => line._2 + "\t" + line._1.mkString("\t")) // make tsv line starting with row index | |
.saveAsTextFile("hdfs://...output/left_singular_vectors") | |
sc.makeRDD(svd.s.toArray, 1) | |
.saveAsTextFile("hdfs://...output/singular_values") |
Hello,
Can you please tell me the article whose method you implemented in particular? I want to go through the theory a little.
Sincerely,
Sayantan
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Hello,
Thank you very much for sharing the code. I am looking for a solution to support matrix inverse, SVD could be one. A couple of questions:
matrix A (m x n) ... we assume n is smaller than m.
Does this mean that computeSVD does not work for a square matrix?
thank you very much
canal