[jira] [Commented] (SPARK-17721) Erroneous computation in multiplication of transposed SparseMatrix with SparseVector

["Bjarne Fruergaard (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-17721?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15532299#comment-15532299 ] 

Bjarne Fruergaard commented on SPARK-17721:
-------------------------------------------

I already have a suggested fix in the pipeline; expect a PR soon

> Erroneous computation in multiplication of transposed SparseMatrix with SparseVector
> ------------------------------------------------------------------------------------
>
>                 Key: SPARK-17721
>                 URL: https://issues.apache.org/jira/browse/SPARK-17721
>             Project: Spark
>          Issue Type: Bug
>          Components: ML, MLlib
>    Affects Versions: 1.4.0, 1.6.1, 2.0.0
>         Environment: Verified on OS X with Spark 1.6.1 and on Databricks running Spark 1.6.1
>            Reporter: Bjarne Fruergaard
>
> There is a bug in how a transposed SparseMatrix (isTransposed=true) does multiplication with a SparseVector. The bug is present (for v. > 2.0.0) in both org.apache.spark.mllib.linalg.BLAS (mllib) and org.apache.spark.ml.linalg.BLAS (mllib-local) in the private gemv method with signature:
> bq. gemv(alpha: Double, A: SparseMatrix, x: SparseVector, beta: Double, y: DenseVector).
> This bug can be verified by running the following snippet in a Spark shell (here using v1.6.1):
> {code:java}
> import com.holdenkarau.spark.testing.SharedSparkContext
> import org.apache.spark.mllib.linalg._
> val A = Matrices.dense(3, 2, Array[Double](0, 2, 1, 1, 2, 0)).asInstanceOf[DenseMatrix].toSparse.transpose
> val b = Vectors.sparse(3, Seq[(Int, Double)]((1, 2), (2, 1))).asInstanceOf[SparseVector]
> A.multiply(b)
> A.multiply(b.toDense)
> {code}
> The first multiply with the SparseMatrix returns the incorrect result:
> {code:java}
> org.apache.spark.mllib.linalg.DenseVector = [5.0,0.0]
> {code}
> whereas the correct result is returned by the second multiply:
> {code:java}
> org.apache.spark.mllib.linalg.DenseVector = [5.0,4.0]
> {code}



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