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author | Yuhao Yang <hhbyyh@gmail.com> | 2015-07-30 07:49:10 -0700 |
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committer | Xiangrui Meng <meng@databricks.com> | 2015-07-30 07:49:10 -0700 |
commit | d31c618e3c8838f8198556876b9dcbbbf835f7b2 (patch) | |
tree | f0a47803e957f5ea4450e496b21a2b03cc3ff59c /mllib/src/main/scala | |
parent | 6175d6cfe795fbd88e3ee713fac375038a3993a8 (diff) | |
download | spark-d31c618e3c8838f8198556876b9dcbbbf835f7b2.tar.gz spark-d31c618e3c8838f8198556876b9dcbbbf835f7b2.tar.bz2 spark-d31c618e3c8838f8198556876b9dcbbbf835f7b2.zip |
[SPARK-7368] [MLLIB] Add QR decomposition for RowMatrix
jira: https://issues.apache.org/jira/browse/SPARK-7368
Add QR decomposition for RowMatrix.
I'm not sure what's the blueprint about the distributed Matrix from community and whether this will be a desirable feature , so I sent a prototype for discussion. I'll go on polish the code and provide ut and performance statistics if it's acceptable.
The implementation refers to the [paper: https://www.cs.purdue.edu/homes/dgleich/publications/Benson%202013%20-%20direct-tsqr.pdf]
Austin R. Benson, David F. Gleich, James Demmel. "Direct QR factorizations for tall-and-skinny matrices in MapReduce architectures", 2013 IEEE International Conference on Big Data, which is a stable algorithm with good scalability.
Currently I tried it on a 400000 * 500 rowMatrix (16 partitions) and it can bring down the computation time from 8.8 mins (using breeze.linalg.qr.reduced) to 2.6 mins on a 4 worker cluster. I think there will still be some room for performance improvement.
Any trial and suggestion is welcome.
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes #5909 from hhbyyh/qrDecomposition and squashes the following commits:
cec797b [Yuhao Yang] remove unnecessary qr
0fb1012 [Yuhao Yang] hierarchy R computing
3fbdb61 [Yuhao Yang] update qr to indirect and add ut
0d913d3 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into qrDecomposition
39213c3 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into qrDecomposition
c0fc0c7 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into qrDecomposition
39b0b22 [Yuhao Yang] initial draft for discussion
Diffstat (limited to 'mllib/src/main/scala')
-rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala | 8 | ||||
-rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala | 46 |
2 files changed, 53 insertions, 1 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala index 9669c364ba..b416d50a56 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala @@ -25,3 +25,11 @@ import org.apache.spark.annotation.Experimental */ @Experimental case class SingularValueDecomposition[UType, VType](U: UType, s: Vector, V: VType) + +/** + * :: Experimental :: + * Represents QR factors. + */ +@Experimental +case class QRDecomposition[UType, VType](Q: UType, R: VType) + diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala index 1626da9c3d..bfc90c9ef8 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala @@ -22,7 +22,7 @@ import java.util.Arrays import scala.collection.mutable.ListBuffer import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, SparseVector => BSV, axpy => brzAxpy, - svd => brzSvd} + svd => brzSvd, MatrixSingularException, inv} import breeze.numerics.{sqrt => brzSqrt} import com.github.fommil.netlib.BLAS.{getInstance => blas} @@ -498,6 +498,50 @@ class RowMatrix( } /** + * Compute QR decomposition for [[RowMatrix]]. The implementation is designed to optimize the QR + * decomposition (factorization) for the [[RowMatrix]] of a tall and skinny shape. + * Reference: + * Paul G. Constantine, David F. Gleich. "Tall and skinny QR factorizations in MapReduce + * architectures" ([[http://dx.doi.org/10.1145/1996092.1996103]]) + * + * @param computeQ whether to computeQ + * @return QRDecomposition(Q, R), Q = null if computeQ = false. + */ + def tallSkinnyQR(computeQ: Boolean = false): QRDecomposition[RowMatrix, Matrix] = { + val col = numCols().toInt + // split rows horizontally into smaller matrices, and compute QR for each of them + val blockQRs = rows.glom().map { partRows => + val bdm = BDM.zeros[Double](partRows.length, col) + var i = 0 + partRows.foreach { row => + bdm(i, ::) := row.toBreeze.t + i += 1 + } + breeze.linalg.qr.reduced(bdm).r + } + + // combine the R part from previous results vertically into a tall matrix + val combinedR = blockQRs.treeReduce{ (r1, r2) => + val stackedR = BDM.vertcat(r1, r2) + breeze.linalg.qr.reduced(stackedR).r + } + val finalR = Matrices.fromBreeze(combinedR.toDenseMatrix) + val finalQ = if (computeQ) { + try { + val invR = inv(combinedR) + this.multiply(Matrices.fromBreeze(invR)) + } catch { + case err: MatrixSingularException => + logWarning("R is not invertible and return Q as null") + null + } + } else { + null + } + QRDecomposition(finalQ, finalR) + } + + /** * Find all similar columns using the DIMSUM sampling algorithm, described in two papers * * http://arxiv.org/abs/1206.2082 |