Thanks again! This is very helpful! Best regards, Iman On Dec 2, 2016 2:49 PM, "Huamin Li" <3eri...@gmail.com> wrote:
> Hi Iman, > > You can get my code from https://github.com/hl475/svd/tree/testSVD. In > additional to fix the index issue for IndexedRowMatrix ( > https://issues.apache.org/jira/browse/SPARK-8614), I have made some the > following changes as well: > > (1) Add tallSkinnySVD and computeSVDbyGram to indexedRowMatrix. > (2) Add shuffle.scala to mllib/src/main/scala/org/apach > e/spark/mllib/linalg/distributed/ (you need this if you want to use > tallSkinnySVD). There was a bug about shuffle method in breeze, and I sent > the pull request to https://github.com/scalanlp/breeze/pull/571. However, > the pull request has been merged to breeze 0.13, whereas the version of > breeze for current Spark is 0.12. > (3) Add partialSVD to BlockMatrix which computes the randomized singular > value decomposition of a given BlockMatrix. > > The new SVD methods (tallSkinnySVD, computeSVDbyGram, and partialSVD) are > in beta version right now. You are totally welcome to test it and share the > feedback with me! > > I implemented these codes for my summer intern project with Mark Tygert, > and we are currently testing the performance of the new codes. > > Best, > Huamin > > On Fri, Dec 2, 2016 at 2:07 PM, Iman Mohtashemi <iman.mohtash...@gmail.com > > wrote: > >> Great thanks! Where can I get the latest with the bug fixes? >> best regards, >> Iman >> >> On Fri, Dec 2, 2016 at 10:54 AM Huamin Li <3eri...@gmail.com> wrote: >> >>> Hi, >>> >>> There seems to be a bug in the section of code that converts the >>> RowMatrix format back into indexedRowMatrix format. >>> >>> For RowMatrix, I think the singular values and right singular vectors >>> (not the left singular vectors U) that computeSVD computes are correct when >>> using multiple executors/machines; Only the R (not the Q) in tallSkinnyQR >>> is correct when using multiple executors/machines. U and Q were being >>> stored in RowMatrix format. There is no index information about RowMatrix, >>> so it does not make sense for U and Q. >>> >>> Others have run into this same problem (https://issues.apache.org/jir >>> a/browse/SPARK-8614) >>> >>> I think the quick solution for this problem is copy and paste the multiply, >>> computeSVD, and tallSkinnyQR code from RowMatrix to IndexedRowMatrix >>> and make the corresponding changes although this would result in code >>> duplication. >>> >>> I have fixed the problem by what I mentioned above. Now, multiply, >>> computeSVD, and tallSkinnyQR are giving the correct results for >>> indexedRowMatrix when using multiple executors or workers. Let me know >>> if I should do a pull request for this. >>> >>> Best, >>> Huamin >>> >>> On Fri, Dec 2, 2016 at 11:23 AM, Iman Mohtashemi < >>> iman.mohtash...@gmail.com> wrote: >>> >>> Ok thanks. >>> >>> On Fri, Dec 2, 2016 at 8:19 AM Sean Owen <so...@cloudera.com> wrote: >>> >>> I tried, but enforcing the ordering changed a fair bit of behavior and I >>> gave up. I think the way to think of it is: a RowMatrix has whatever >>> ordering you made it with, so you need to give it ordered rows if you're >>> going to use a method like the QR decomposition. That works. I don't think >>> the QR method should ever have been on this class though, for this reason. >>> >>> On Fri, Dec 2, 2016 at 4:13 PM Iman Mohtashemi < >>> iman.mohtash...@gmail.com> wrote: >>> >>> Hi guys, >>> Was this bug ever resolved? >>> Iman >>> >>> On Fri, Nov 11, 2016 at 9:59 AM Iman Mohtashemi < >>> iman.mohtash...@gmail.com> wrote: >>> >>> Yes this would be helpful, otherwise the Q part of the decomposition is >>> useless. One can use that to solve the system by transposing it and >>> multiplying with b and solving for x (Ax = b) where A = R and b = Qt*b >>> since the Upper triangular matrix is correctly available (R) >>> >>> On Fri, Nov 11, 2016 at 3:56 AM Sean Owen <so...@cloudera.com> wrote: >>> >>> @Xiangrui / @Joseph, do you think it would be reasonable to have >>> CoordinateMatrix sort the rows it creates to make an IndexedRowMatrix? in >>> order to make the ultimate output of toRowMatrix less surprising when it's >>> not ordered? >>> >>> >>> On Tue, Nov 8, 2016 at 3:29 PM Sean Owen <so...@cloudera.com> wrote: >>> >>> I think the problem here is that IndexedRowMatrix.toRowMatrix does *not* >>> result in a RowMatrix with rows in order of their indices, necessarily: >>> >>> >>> // Drop its row indices. >>> RowMatrix rowMat = indexedRowMatrix.toRowMatrix(); >>> >>> What you get is a matrix where the rows are arranged in whatever order >>> they were passed to IndexedRowMatrix. RowMatrix says it's for rows where >>> the ordering doesn't matter, but then it's maybe surprising it has a QR >>> decomposition method, because clearly the result depends on the order of >>> rows in the input. (CC Yuhao Yang for a comment?) >>> >>> You could say, well, why doesn't IndexedRowMatrix.toRowMatrix return at >>> least something with sorted rows? that would not be hard. It also won't >>> return "missing" rows (all zeroes), so it would not in any event result in >>> a RowMatrix whose implicit rows and ordering represented the same matrix. >>> That, at least, strikes me as something to be better documented. >>> >>> Maybe it would be nicer still to at least sort the rows, given the >>> existence of use cases like yours. For example, at least >>> CoordinateMatrix.toIndexedRowMatrix could sort? that is less surprising. >>> >>> In any event you should be able to make it work by manually getting the >>> RDD[IndexedRow] out of IndexedRowMatrix, sorting by index, then mapping it >>> to Vectors and making a RowMatrix from it. >>> >>> >>> >>> On Tue, Nov 8, 2016 at 2:41 PM Iman Mohtashemi < >>> iman.mohtash...@gmail.com> wrote: >>> >>> Hi Sean, >>> Here you go: >>> >>> sparsematrix.txt = >>> >>> row, col ,val >>> 0,0,.42 >>> 0,1,.28 >>> 0,2,.89 >>> 1,0,.83 >>> 1,1,.34 >>> 1,2,.42 >>> 2,0,.23 >>> 3,0,.42 >>> 3,1,.98 >>> 3,2,.88 >>> 4,0,.23 >>> 4,1,.36 >>> 4,2,.97 >>> >>> The vector is just the third column of the matrix which should give the >>> trivial solution of [0,0,1] >>> >>> This translates to this which is correct >>> There are zeros in the matrix (Not really sparse but just an example) >>> 0.42 0.28 0.89 >>> 0.83 0.34 0.42 >>> 0.23 0.0 0.0 >>> 0.42 0.98 0.88 >>> 0.23 0.36 0.97 >>> >>> >>> Here is what I get for the Q and R >>> >>> Q: -0.21470961288429483 0.23590615093828807 0.6784910613691661 >>> -0.3920784235278427 -0.06171221388256143 0.5847874866876442 >>> -0.7748216464954987 -0.4003560542230838 -0.29392323671555354 >>> -0.3920784235278427 0.8517909521421976 -0.31435038559403217 >>> -0.21470961288429483 -0.23389547730301666 -0.11165321782745863 >>> R: -1.0712142642814275 -0.8347536340918976 -1.227672225670157 >>> 0.0 0.7662808691141717 0.7553315911660984 >>> 0.0 0.0 0.7785210939368136 >>> >>> When running this in matlab the numbers are the same but row 1 is the >>> last row and the last row is interchanged with row 3 >>> >>> >>> >>> On Mon, Nov 7, 2016 at 11:35 PM Sean Owen <so...@cloudera.com> wrote: >>> >>> Rather than post a large section of code, please post a small example of >>> the input matrix and its decomposition, to illustrate what you're saying is >>> out of order. >>> >>> On Tue, Nov 8, 2016 at 3:50 AM im281 <iman.mohtash...@gmail.com> wrote: >>> >>> I am getting the correct rows but they are out of order. Is this a bug >>> or am >>> I doing something wrong? >>> >>> >>> >>> >
