You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Bjarne Fruergaard (JIRA)" <ji...@apache.org> on 2016/09/29 09:23:20 UTC
[jira] [Updated] (SPARK-17721) Erroneous computation in
multiplication of transposed SparseMatrix with SparseVector
[ https://issues.apache.org/jira/browse/SPARK-17721?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Bjarne Fruergaard updated SPARK-17721:
--------------------------------------
Description:
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}
was:
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}
> 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}
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org