Hello Dmitriy, Thanks for reply.
I see similar discussion on following link where I see your reply. http://www.searchworkings.org/forum/-/message_boards/view_message/517870#_19_message_519704 I do also have same problem, need to apply dimensionality reduction and use clustering algo on reduced features. Seems parameters for ssvd are changed from mentioned in SSVD-CLI.pdf. It no longer shows *-us *as parameter I am using mahout-examples-0.7-job.jar mahout ssvd --input /user/hadoop/t/input-set-vector/ --output /user/hadoop/t/input-set-svd/ -k 200 --reduceTasks 2 -pca true -U true -V false *-us true* -ow -q 1 giving option as "*-pca true*" gives error as at org.apache.mahout.math.hadoop.MatrixColumnMeansJob.run(MatrixColumnMeansJob.java:55) at org.apache.mahout.math.hadoop.MatrixColumnMeansJob.run(MatrixColumnMeansJob.java:55) So I removed it. mahout ssvd --input /user/hadoop/t/input-set-vector/ --output /user/hadoop/t/input-set-svd/ -k 200 --reduceTasks 2 -U true -V false *-us true* -ow -q 1 *>> *with above command *>> Unexpected -us *while processing Job-Specific Options. I tried with "-U false -V false -uhs true" it just generated sigma file as expected however no "Usigma" hadoop fs -lsr /user/hadoop/t/PE_EXE/input-set-svd/ -rw-r--r-- 2 hadoop supergroup 1712 2013-05-24 15:34 /user/hadoop/t/PE_EXE/input-set-svd/sigma Then with *"-U true -V false -uhs true" *output dir U is created. * *drwxr-xr-x - hadoop supergroup 0 2013-05-24 15:39 /user/hadoop/t/PE_EXE/input-set-svd/U -rw-r--r-- 2 hadoop supergroup 1712 2013-05-24 15:39 /user/hadoop/t/PE_EXE/input-set-svd/sigma* * My problem is how to use these U/V/sigma file as input to canopy/kmeans ? How to identify which important features from U/Sigma that are retained in dimensionality reduction ? Thanks in Advance ! Rajesh On Fri, May 24, 2013 at 7:01 AM, Dmitriy Lyubimov <[email protected]> wrote: > > https://cwiki.apache.org/confluence/download/attachments/27832158/SSVD-CLI.pdf?version=17&modificationDate=1349999085000 > : > > "In most cases where you might be looking to reduce > dimensionality while retaining variance, you probably need combination of > options -pca true -U false -V > false -us true. > > See ยง3 for details" > > > On Thu, May 23, 2013 at 6:24 PM, Dmitriy Lyubimov <[email protected]> > wrote: > > > Also, for the dimensionality reduction it is important among other things > > to re-center your input first, which is why you also want "-pca true". > > > > > > On Thu, May 23, 2013 at 6:23 PM, Dmitriy Lyubimov <[email protected] > >wrote: > > > >> did you specify -us option? SSVD by default produces only U, V and > Sigma. > >> but it can produce more, e.g. U*Sigma, U*sqrt(Sigma) etc. if you ask for > >> it. And, alternatively, you can suppress any of U, V (you can't suppress > >> sigma but that doesn't cost anything in space anyway). > >> > >> > >> On Thu, May 23, 2013 at 6:20 PM, Rajesh Nikam <[email protected] > >wrote: > >> > >>> I got all three U, V & sigma from ssvd, however which to use as input > to > >>> canopy? > >>> On May 24, 2013 6:47 AM, "Dmitriy Lyubimov" <[email protected]> wrote: > >>> > >>> > I think you want U*Sigma > >>> > > >>> > What you want is ssvd ... -pca true ... -us true ... see the manual > >>> > > >>> > > >>> > > >>> > > >>> > On Thu, May 23, 2013 at 6:07 PM, Rajesh Nikam <[email protected] > > > >>> > wrote: > >>> > > >>> > > Sorry for confusion. Here number of clusters are decided by canopy. > >>> With > >>> > > data as it has 60 to 70 clusters. > >>> > > > >>> > > My question is which part from ssvd output U, V, Sigma should be > >>> used as > >>> > > input to canopy? > >>> > > On May 24, 2013 3:56 AM, "Ted Dunning" <[email protected]> > >>> wrote: > >>> > > > >>> > > > Rajesh, > >>> > > > > >>> > > > This is very confusing. > >>> > > > > >>> > > > You have 1500 things that you are clustering into more than 1400 > >>> > > clusters. > >>> > > > > >>> > > > There is no way for most of these clusters to have >1 member just > >>> > because > >>> > > > there aren't enough clusters compared to the items. > >>> > > > > >>> > > > Is there a typo here? > >>> > > > > >>> > > > > >>> > > > > >>> > > > > >>> > > > On Thu, May 23, 2013 at 5:34 AM, Rajesh Nikam < > >>> [email protected]> > >>> > > > wrote: > >>> > > > > >>> > > > > Hi, > >>> > > > > > >>> > > > > I have input test set of 1500 instances with 1000+ features. I > >>> want > >>> > to > >>> > > to > >>> > > > > SVD to reduce features. I have followed following steps with > >>> generate > >>> > > > 1400+ > >>> > > > > clusters 99% of clusters contain 1 instance :( > >>> > > > > > >>> > > > > Please let me know what is wrong in below steps - > >>> > > > > > >>> > > > > > >>> > > > > mahout arff.vector --input /mnt/cluster/t/input-set.arff > --output > >>> > > > > /user/hadoop/t/input-set-vector/ --dictOut > >>> > > /mnt/cluster/t/input-set-dict > >>> > > > > > >>> > > > > mahout ssvd --input /user/hadoop/t/input-set-vector/ --output > >>> > > > > /user/hadoop/t/input-set-svd/ -k 200 --reduceTasks 2 -ow > >>> > > > > > >>> > > > > mahout canopy -i */user/hadoop/t/input-set-svd/U* -o > >>> > > > > /user/hadoop/t/input-set-canopy-centroids -dm > >>> > > > > org.apache.mahout.common.distance.TanimotoDistanceMeasure *-t1 > >>> 0.001 > >>> > > -t2 > >>> > > > > 0.002* > >>> > > > > > >>> > > > > mahout kmeans -i */user/hadoop/t/input-set-svd/U* -c > >>> > > > > /user/hadoop/t/input-set-canopy-centroids/clusters-0-final -cl > -o > >>> > > > > /user/hadoop/t/input-set-kmeans-clusters -ow -x 10 -dm > >>> > > > > org.apache.mahout.common.distance.TanimotoDistanceMeasure > >>> > > > > > >>> > > > > mahout clusterdump -dt sequencefile -i > >>> > > > > /user/hadoop/t/input-set-kmeans-clusters/clusters-1-final/ -n > 20 > >>> -b > >>> > 100 > >>> > > > -o > >>> > > > > /mnt/cluster/t/cdump-input-set.txt -p > >>> > > > > /user/hadoop/t/input-set-kmeans-clusters/clusteredPoints/ > >>> --evaluate > >>> > > > > > >>> > > > > Thanks in advance ! > >>> > > > > > >>> > > > > Rajesh > >>> > > > > > >>> > > > > > >>> > > > > > >>> > > > > > >>> > > > > On Wed, May 22, 2013 at 2:18 AM, Dmitriy Lyubimov < > >>> [email protected] > >>> > > > >>> > > > > wrote: > >>> > > > > > >>> > > > > > PPS As far as the tool for arff, i am frankly not sure. but > it > >>> > sounds > >>> > > > > like > >>> > > > > > you've already solved this. > >>> > > > > > > >>> > > > > > > >>> > > > > > On Tue, May 21, 2013 at 1:41 PM, Dmitriy Lyubimov < > >>> > [email protected] > >>> > > > > >>> > > > > > wrote: > >>> > > > > > > >>> > > > > > > ps as far as U, V data "close to zero", yes that's what > you'd > >>> > > expect. > >>> > > > > > > > >>> > > > > > > Here, by "close to zero" it still means much bigger than a > >>> > rounding > >>> > > > > error > >>> > > > > > > of course. e.g. 1E-12 is indeed a small number, and 1E-16 > to > >>> > 1E-18 > >>> > > > > would > >>> > > > > > be > >>> > > > > > > indeed "close to zero" for the purposes of singularity. > >>> > 1E-2..1E-5 > >>> > > > are > >>> > > > > > > actually quite "sizeable" numbers by the scale of IEEE 754 > >>> > > > > arithmetics. > >>> > > > > > > > >>> > > > > > > U and V are orthonormal (which means their column vectors > >>> have > >>> > > > > euclidiean > >>> > > > > > > norm of 1) . Note that for large m and n (large inputs) > they > >>> are > >>> > > also > >>> > > > > > > extremely skinny. The larger input is, the smaller the > >>> element > >>> > of U > >>> > > > > > or/and > >>> > > > > > > V is gonna be. > >>> > > > > > > > >>> > > > > > > > >>> > > > > > > > >>> > > > > > > On Tue, May 21, 2013 at 8:48 AM, Dmitriy Lyubimov < > >>> > > [email protected] > >>> > > > > > >wrote: > >>> > > > > > > > >>> > > > > > >> Sounds like dimensionality reduction to me. You may want > to > >>> use > >>> > > ssvd > >>> > > > > > -pca > >>> > > > > > >> > >>> > > > > > >> Apologies for brevity. Sent from my Android phone. > >>> > > > > > >> -Dmitriy > >>> > > > > > >> On May 21, 2013 6:27 AM, "Rajesh Nikam" < > >>> [email protected]> > >>> > > > > wrote: > >>> > > > > > >> > >>> > > > > > >>> Hello Ted, > >>> > > > > > >>> > >>> > > > > > >>> Thanks for reply. > >>> > > > > > >>> > >>> > > > > > >>> I have started exploring SVD based on its mention of > could > >>> help > >>> > > to > >>> > > > > drop > >>> > > > > > >>> features which are not relevant for clustering. > >>> > > > > > >>> > >>> > > > > > >>> My objective is reduce number of features before passing > >>> them > >>> > to > >>> > > > > > >>> clustering > >>> > > > > > >>> and just keep important features. > >>> > > > > > >>> > >>> > > > > > >>> arff/csv==> ssvd (for dimensionality reduction) ==> > >>> clustering > >>> > > > > > >>> > >>> > > > > > >>> Could you please illustrate mahout props to join above > >>> > pipeline. > >>> > > > > > >>> > >>> > > > > > >>> I think, Lanczos SVD needs to be used for mxm matrix. > >>> > > > > > >>> > >>> > > > > > >>> I have tried check ssvd, I have used arff.vector to > covert > >>> > > arff/csv > >>> > > > > to > >>> > > > > > >>> vector file which is then give as input to ssvd and them > >>> dumped > >>> > > U, > >>> > > > V > >>> > > > > > and > >>> > > > > > >>> sigma using vectordump. > >>> > > > > > >>> > >>> > > > > > >>> I see most of the values dumped are near to 0. I dont > >>> > understand > >>> > > is > >>> > > > > > this > >>> > > > > > >>> correct or not. > >>> > > > > > >>> > >>> > > > > > >>> > >>> > > > > > >>> > >>> > > > > > > >>> > > > > > >>> > > > > >>> > > > >>> > > >>> > {0:0.01066724825049657,1:0.016715498597386844,2:2.0187750952311708E-4,3:3.401020567221039E-4,4:-1.2388403347280688E-4,5:6.41502463540719E-5,6:-1.359187582538833E-4,7:6.329813140445419E-5,8:1.670015585746444E-4,9:3.5415113034592744E-4,10:7.108868213280763E-4,11:0.020553517552052456,12:-0.015118680942548916,13:0.007981746711271956,14:-0.003251236468768259,15:0.0038075014396303053,16:-0.0010925318534013683,17:-0.0026943024876179833,18:-0.001744794617721648,19:-0.0024528466548735714} > >>> > > > > > >>> > >>> > > > > > >>> > >>> > > > > > > >>> > > > > > >>> > > > > >>> > > > >>> > > >>> > {0:0.029978614322360833,1:-0.01431521245087889,2:1.3318592088199427E-4,3:1.495356283071516E-4,4:8.762709213918985E-5,5:1.2765191352425177E- > >>> > > > > > >>> > >>> > > > > > >>> Thanks, > >>> > > > > > >>> Rajesh > >>> > > > > > >>> > >>> > > > > > >>> > >>> > > > > > >>> > >>> > > > > > >>> On Tue, May 21, 2013 at 11:35 AM, Ted Dunning < > >>> > > > [email protected] > >>> > > > > > > >>> > > > > > >>> wrote: > >>> > > > > > >>> > >>> > > > > > >>> > Are you using Lanczos instead of SSVD for a reason? > >>> > > > > > >>> > > >>> > > > > > >>> > > >>> > > > > > >>> > > >>> > > > > > >>> > > >>> > > > > > >>> > On Mon, May 20, 2013 at 4:13 AM, Rajesh Nikam < > >>> > > > > [email protected] > >>> > > > > > > > >>> > > > > > >>> > wrote: > >>> > > > > > >>> > > >>> > > > > > >>> > > Hello, > >>> > > > > > >>> > > > >>> > > > > > >>> > > I have arff / csv file containing input data that I > >>> want to > >>> > > > pass > >>> > > > > to > >>> > > > > > >>> svd : > >>> > > > > > >>> > > Lanczos Singular Value Decomposition. > >>> > > > > > >>> > > > >>> > > > > > >>> > > Which tool to use to convert it to required format ? > >>> > > > > > >>> > > > >>> > > > > > >>> > > Thanks in Advance ! > >>> > > > > > >>> > > > >>> > > > > > >>> > > Thanks, > >>> > > > > > >>> > > Rajesh > >>> > > > > > >>> > > > >>> > > > > > >>> > > >>> > > > > > >>> > >>> > > > > > >> > >>> > > > > > > > >>> > > > > > > >>> > > > > > >>> > > > > >>> > > > >>> > > >>> > >> > >> > > >
