@Dmitriy,@Trevor and @Andrew Sir, I am still stuck at the above problem can you please help me out with it. I am unable to find the proper reference to solve the above issue.
Thanks & Regards Parth Khatwani <https://mailtrack.io/> Sent with Mailtrack <https://mailtrack.io/install?source=signature&lang=en&referral=h2016...@pilani.bits-pilani.ac.in&idSignature=22> On Sat, Apr 15, 2017 at 10:07 AM, KHATWANI PARTH BHARAT < h2016...@pilani.bits-pilani.ac.in> wrote: > @Dmitriy, > @Trevor and @Andrew > > I have tried > Testing this Row Key assignment issue which i have mentioned in the above > mail, > By Writing the a separate code where i am assigning the a default value 1 > to each row Key of The DRM and then taking the aggregating transpose > I have committed the separate test code to the Github Branch > <https://github.com/parth2691/Spark_Mahout/tree/Dmitriy-Lyubimov>. > > The Code is as follows > > val inCoreA = dense((1,1, 2, 3), (1,2, 3, 4), (1,3, 4, 5), (1,4, 5, 6)) > val A = drmParallelize(m = inCoreA) > > //Mapblock > val drm2 = A.mapBlock() { > case (keys, block) => for(row <- 0 until keys.size) { > > * //assigning 1 to each row index* keys(row) = 1 } > (keys, block) } prinln("After New Cluster assignment") > println(""+drm2.collect) val aggTranspose = drm2.t println("Result of > aggregating tranpose") println(""+aggTranspose.collect) > > Out of 1st println After New Cluster assignment should be > This > { > 0 => {0:1.0, 1: 1.0, 2: 1.0, 3: 3.0} > 1 => {0:1.0, 1: 2.0, 2: 3.0, 3: 4.0} > 2 => {0:1.0, 1: 3.0, 2: 4.0, 3: 5.0} > 3 => {0:1.0, 1: 4.0, 2: 5.0, 3: 6.0} > } > (Here zeroth Column is used to store the centriod count and column 1,2 and > 3 Contains Data) > > But Turns out to be this > { > 0 => {} > 1 => {0:1.0,1:4.0,2:5.0,3:6.0} > 2 => {} > 3 => {} > } > And the result of aggregating Transpose should be > { > 0 => {1: 4.0} > 1 => {1: 9.0} > 2 => {1: 12.0} > 3 => {1: 15.0} > } > > > I have referred to the book written by Andrew And Dmitriy Apache Mahout: > Beyond MapReduce > <https://www.amazon.com/Apache-Mahout-MapReduce-Dmitriy-Lyubimov/dp/1523775785> > Aggregating > Transpose and other concepts are explained very nicely over here but i am > unable to find any example where > Row Keys are assigned new Values . Mahout Samsara Manual > http://apache.github.io/mahout/doc/ScalaSparkBindings.html Also Does not > contain any such examples. > It will great if i can get some reference to solution of mentioned issue. > > > Thanks > Parth Khatwani > > > > On Sat, Apr 15, 2017 at 12:13 AM, Andrew Palumbo <ap....@outlook.com> > wrote: > >> +1 >> >> >> >> Sent from my Verizon Wireless 4G LTE smartphone >> >> >> -------- Original message -------- >> From: Trevor Grant <trevor.d.gr...@gmail.com> >> Date: 04/14/2017 11:40 (GMT-08:00) >> To: dev@mahout.apache.org >> Subject: Re: Trying to write the KMeans Clustering Using "Apache Mahout >> Samsara" >> >> Parth and Dmitriy, >> >> This is awesome- as a follow on can we work on getting this rolled in to >> the algorithms framework? >> >> Happy to work with you on this Parth! >> >> Trevor Grant >> Data Scientist >> https://github.com/rawkintrevo >> http://stackexchange.com/users/3002022/rawkintrevo >> http://trevorgrant.org >> >> *"Fortunate is he, who is able to know the causes of things." -Virgil* >> >> >> On Fri, Apr 14, 2017 at 1:27 PM, Dmitriy Lyubimov <dlie...@gmail.com> >> wrote: >> >> > i would think reassinging keys should work in most cases. >> > The only exception is that technically Spark contracts imply that effect >> > should be idempotent if task is retried, which might be a problem in a >> > specific scenario of the object tree coming out from block cache object >> > tree, which can stay there and be retried again. but specifically w.r.t. >> > this key assignment i don't see any problem since the action obviously >> > would be idempotent even if this code is run multiple times on the same >> > (key, block) pair. This part should be good IMO. >> > >> > On Fri, Apr 14, 2017 at 2:26 AM, KHATWANI PARTH BHARAT < >> > h2016...@pilani.bits-pilani.ac.in> wrote: >> > >> > > @Dmitriy Sir, >> > > In the K means code above I think i am doing the following Incorrectly >> > > >> > > Assigning the closest centriod index to the Row Keys of DRM >> > > >> > > //11. Iterating over the Data Matrix(in DrmLike[Int] format) to >> calculate >> > > the initial centriods >> > > dataDrmX.mapBlock() { >> > > case (keys, block) => >> > > for (row <- 0 until block.nrow) { >> > > var dataPoint = block(row, ::) >> > > >> > > //12. findTheClosestCentriod find the closest centriod to >> the >> > > Data point specified by "dataPoint" >> > > val closesetIndex = findTheClosestCentriod(dataPoint, >> > centriods) >> > > >> > > //13. assigning closest index to key >> > > keys(row) = closesetIndex >> > > } >> > > keys -> block >> > > } >> > > >> > > in step 12 i am finding the centriod closest to the current dataPoint >> > > in step13 i am assigning the closesetIndex to the key of the >> > corresponding >> > > row represented by the dataPoint >> > > I think i am doing step13 incorrectly. >> > > >> > > Also i am unable to find the proper reference for the same in the >> > reference >> > > links which you have mentioned above >> > > >> > > >> > > Thanks & Regards >> > > Parth Khatwani >> > > >> > > >> > > >> > > >> > > >> > > On Thu, Apr 13, 2017 at 6:24 PM, KHATWANI PARTH BHARAT < >> > > h2016...@pilani.bits-pilani.ac.in> wrote: >> > > >> > > > Dmitriy Sir, >> > > > I have Created a github branch Github Branch Having Initial Kmeans >> Code >> > > > <https://github.com/parth2691/Spark_Mahout/tree/Dmitriy-Lyubimov> >> > > > >> > > > >> > > > Thanks & Regards >> > > > Parth Khatwani >> > > > >> > > > On Thu, Apr 13, 2017 at 3:19 AM, Andrew Palumbo <ap....@outlook.com >> > >> > > > wrote: >> > > > >> > > >> +1 to creating a branch. >> > > >> >> > > >> >> > > >> >> > > >> Sent from my Verizon Wireless 4G LTE smartphone >> > > >> >> > > >> >> > > >> -------- Original message -------- >> > > >> From: Dmitriy Lyubimov <dlie...@gmail.com> >> > > >> Date: 04/12/2017 11:25 (GMT-08:00) >> > > >> To: dev@mahout.apache.org >> > > >> Subject: Re: Trying to write the KMeans Clustering Using "Apache >> > Mahout >> > > >> Samsara" >> > > >> >> > > >> can't say i can read this code well formatted that way... >> > > >> >> > > >> it would seem to me that the code is not using the broadcast >> variable >> > > and >> > > >> instead is using closure variable. that's the only thing i can >> > > immediately >> > > >> see by looking in the middle of it. >> > > >> >> > > >> it would be better if you created a branch on github for that code >> > that >> > > >> would allow for easy check-outs and comments. >> > > >> >> > > >> -d >> > > >> >> > > >> On Wed, Apr 12, 2017 at 10:29 AM, KHATWANI PARTH BHARAT < >> > > >> h2016...@pilani.bits-pilani.ac.in> wrote: >> > > >> >> > > >> > @Dmitriy Sir >> > > >> > >> > > >> > I have completed the Kmeans code as per the algorithm you have >> > Outline >> > > >> > above >> > > >> > >> > > >> > My code is as follows >> > > >> > >> > > >> > This code works fine till step number 10 >> > > >> > >> > > >> > In step 11 i am assigning the new centriod index to >> corresponding >> > row >> > > >> key >> > > >> > of data Point in the matrix >> > > >> > I think i am doing something wrong in step 11 may be i am using >> > > >> incorrect >> > > >> > syntax >> > > >> > >> > > >> > Can you help me find out what am i doing wrong. >> > > >> > >> > > >> > >> > > >> > //start of main method >> > > >> > >> > > >> > def main(args: Array[String]) { >> > > >> > //1. initialize the spark and mahout context >> > > >> > val conf = new SparkConf() >> > > >> > .setAppName("DRMExample") >> > > >> > .setMaster(args(0)) >> > > >> > .set("spark.serializer", "org.apache.spark.serializer. >> > > >> > KryoSerializer") >> > > >> > .set("spark.kryo.registrator", >> > > >> > "org.apache.mahout.sparkbindings.io.MahoutKryoRegistrator") >> > > >> > implicit val sc = new SparkDistributedContext(new >> > > >> SparkContext(conf)) >> > > >> > >> > > >> > //2. read the data file and save it in the rdd >> > > >> > val lines = sc.textFile(args(1)) >> > > >> > >> > > >> > //3. convert data read in as string in to array of double >> > > >> > val test = lines.map(line => line.split('\t').map(_.toDoubl >> e)) >> > > >> > >> > > >> > //4. add a column having value 1 in array of double this will >> > > >> > create something like (1 | D)', which will be used while >> > calculating >> > > >> > (1 | D)' >> > > >> > val augumentedArray = test.map(addCentriodColumn _) >> > > >> > >> > > >> > //5. convert rdd of array of double in rdd of DenseVector >> > > >> > val rdd = augumentedArray.map(dvec(_)) >> > > >> > >> > > >> > //6. convert rdd to DrmRdd >> > > >> > val rddMatrixLike: DrmRdd[Int] = rdd.zipWithIndex.map { case >> (v, >> > > >> > idx) => (idx.toInt, v) } //7. convert DrmRdd to >> > > >> > CheckpointedDrm[Int] val matrix = drmWrap(rddMatrixLike) >> //8. >> > > >> > seperating the column having all ones created in step 4 and will >> use >> > > >> > it later val oneVector = matrix(::, 0 until 1) //9. >> final >> > > >> > input data in DrmLike[Int] format val dataDrmX = matrix(::, 1 >> > until >> > > >> > 4) //9. Sampling to select initial centriods val >> > > >> > centriods = drmSampleKRows(dataDrmX, 2, false) centriods.size >> > > >> > //10. Broad Casting the initial centriods val broadCastMatrix >> = >> > > >> > drmBroadcast(centriods) //11. Iterating over the Data >> > > >> > Matrix(in DrmLike[Int] format) to calculate the initial centriods >> > > >> > dataDrmX.mapBlock() { case (keys, block) => for (row >> <- >> > 0 >> > > >> > until block.nrow) { var dataPoint = block(row, ::) >> > > >> > //12. findTheClosestCentriod find the closest centriod to >> > the >> > > >> > Data point specified by "dataPoint" val closesetIndex = >> > > >> > findTheClosestCentriod(dataPoint, centriods) >> > //13. >> > > >> > assigning closest index to key keys(row) = closesetIndex >> > > >> > } keys -> block } >> > > >> > >> > > >> > //14. Calculating the (1|D) val b = (oneVector cbind >> > > >> > dataDrmX) //15. Aggregating Transpose (1|D)' val >> > bTranspose >> > > >> > = (oneVector cbind dataDrmX).t // after step 15 bTranspose >> will >> > > >> > have data in the following format /*(n+1)*K where >> n=dimension >> > > >> > of the data point, K=number of clusters * zeroth row will >> contain >> > > >> > the count of points assigned to each cluster * assuming 3d >> data >> > > >> > points * */ >> > > >> > >> > > >> > >> > > >> > val nrows = b.nrow.toInt //16. slicing the count vectors >> out >> > > >> > val pointCountVectors = drmBroadcast(b(0 until 1, ::).collect(0, >> > ::)) >> > > >> > val vectorSums = b(1 until nrows, ::) //17. dividing the >> data >> > > >> > point by count vector vectorSums.mapBlock() { case (keys, >> > > >> > block) => for (row <- 0 until block.nrow) { >> > block(row, >> > > >> > ::) /= pointCountVectors } keys -> block } >> //18. >> > > >> > seperating the count vectors val newCentriods = >> vectorSums.t(::,1 >> > > >> > until centriods.size) //19. iterate over the above >> code >> > > >> > till convergence criteria is meet }//end of main method >> > > >> > >> > > >> > >> > > >> > >> > > >> > // method to find the closest centriod to data point( vec: >> Vector >> > > >> > in the arguments) def findTheClosestCentriod(vec: Vector, >> matrix: >> > > >> > Matrix): Int = { >> > > >> > var index = 0 >> > > >> > var closest = Double.PositiveInfinity >> > > >> > for (row <- 0 until matrix.nrow) { >> > > >> > val squaredSum = ssr(vec, matrix(row, ::)) >> > > >> > val tempDist = Math.sqrt(ssr(vec, matrix(row, ::))) >> > > >> > if (tempDist < closest) { >> > > >> > closest = tempDist >> > > >> > index = row >> > > >> > } >> > > >> > } >> > > >> > index >> > > >> > } >> > > >> > >> > > >> > //calculating the sum of squared distance between the >> > > points(Vectors) >> > > >> > def ssr(a: Vector, b: Vector): Double = { >> > > >> > (a - b) ^= 2 sum >> > > >> > } >> > > >> > >> > > >> > //method used to create (1|D) >> > > >> > def addCentriodColumn(arg: Array[Double]): Array[Double] = { >> > > >> > val newArr = new Array[Double](arg.length + 1) >> > > >> > newArr(0) = 1.0; >> > > >> > for (i <- 0 until (arg.size)) { >> > > >> > newArr(i + 1) = arg(i); >> > > >> > } >> > > >> > newArr >> > > >> > } >> > > >> > >> > > >> > >> > > >> > Thanks & Regards >> > > >> > Parth Khatwani >> > > >> > >> > > >> > >> > > >> > >> > > >> > On Mon, Apr 3, 2017 at 7:37 PM, KHATWANI PARTH BHARAT < >> > > >> > h2016...@pilani.bits-pilani.ac.in> wrote: >> > > >> > >> > > >> > > >> > > >> > > ---------- Forwarded message ---------- >> > > >> > > From: Dmitriy Lyubimov <dlie...@gmail.com> >> > > >> > > Date: Fri, Mar 31, 2017 at 11:34 PM >> > > >> > > Subject: Re: Trying to write the KMeans Clustering Using >> "Apache >> > > >> Mahout >> > > >> > > Samsara" >> > > >> > > To: "dev@mahout.apache.org" <dev@mahout.apache.org> >> > > >> > > >> > > >> > > >> > > >> > > ps1 this assumes row-wise construction of A based on training >> set >> > > of m >> > > >> > > n-dimensional points. >> > > >> > > ps2 since we are doing multiple passes over A it may make >> sense to >> > > >> make >> > > >> > > sure it is committed to spark cache (by using checkpoint api), >> if >> > > >> spark >> > > >> > is >> > > >> > > used >> > > >> > > >> > > >> > > On Fri, Mar 31, 2017 at 10:53 AM, Dmitriy Lyubimov < >> > > dlie...@gmail.com >> > > >> > >> > > >> > > wrote: >> > > >> > > >> > > >> > > > here is the outline. For details of APIs, please refer to >> > samsara >> > > >> > manual >> > > >> > > > [2], i will not be be repeating it. >> > > >> > > > >> > > >> > > > Assume your training data input is m x n matrix A. For >> > simplicity >> > > >> let's >> > > >> > > > assume it's a DRM with int row keys, i.e., DrmLike[Int]. >> > > >> > > > >> > > >> > > > Initialization: >> > > >> > > > >> > > >> > > > First, classic k-means starts by selecting initial clusters, >> by >> > > >> > sampling >> > > >> > > > them out. You can do that by using sampling api [1], thus >> > forming >> > > a >> > > >> k >> > > >> > x n >> > > >> > > > in-memory matrix C (current centroids). C is therefore of >> > Mahout's >> > > >> > Matrix >> > > >> > > > type. >> > > >> > > > >> > > >> > > > You the proceed by alternating between cluster assignments >> and >> > > >> > > > recompupting centroid matrix C till convergence based on some >> > test >> > > >> or >> > > >> > > > simply limited by epoch count budget, your choice. >> > > >> > > > >> > > >> > > > Cluster assignments: here, we go over current generation of A >> > and >> > > >> > > > recompute centroid indexes for each row in A. Once we >> recompute >> > > >> index, >> > > >> > we >> > > >> > > > put it into the row key . You can do that by assigning >> centroid >> > > >> indices >> > > >> > > to >> > > >> > > > keys of A using operator mapblock() (details in [2], [3], >> [4]). >> > > You >> > > >> > also >> > > >> > > > need to broadcast C in order to be able to access it in >> > efficient >> > > >> > manner >> > > >> > > > inside mapblock() closure. Examples of that are plenty given >> in >> > > [2]. >> > > >> > > > Essentially, in mapblock, you'd reform the row keys to >> reflect >> > > >> cluster >> > > >> > > > index in C. while going over A, you'd have a "nearest >> neighbor" >> > > >> problem >> > > >> > > to >> > > >> > > > solve for the row of A and centroids C. This is the bulk of >> > > >> computation >> > > >> > > > really, and there are a few tricks there that can speed this >> > step >> > > >> up in >> > > >> > > > both exact and approximate manner, but you can start with a >> > naive >> > > >> > search. >> > > >> > > > >> > > >> > > > Centroid recomputation: >> > > >> > > > once you assigned centroids to the keys of marix A, you'd >> want >> > to >> > > >> do an >> > > >> > > > aggregating transpose of A to compute essentially average of >> > row A >> > > >> > > grouped >> > > >> > > > by the centroid key. The trick is to do a computation of >> (1|A)' >> > > >> which >> > > >> > > will >> > > >> > > > results in a matrix of the shape (Counts/sums of cluster >> rows). >> > > >> This is >> > > >> > > the >> > > >> > > > part i find difficult to explain without a latex graphics. >> > > >> > > > >> > > >> > > > In Samsara, construction of (1|A)' corresponds to DRM >> expression >> > > >> > > > >> > > >> > > > (1 cbind A).t (again, see [2]). >> > > >> > > > >> > > >> > > > So when you compute, say, >> > > >> > > > >> > > >> > > > B = (1 | A)', >> > > >> > > > >> > > >> > > > then B is (n+1) x k, so each column contains a vector >> > > corresponding >> > > >> to >> > > >> > a >> > > >> > > > cluster 1..k. In such column, the first element would be # of >> > > >> points in >> > > >> > > the >> > > >> > > > cluster, and the rest of it would correspond to sum of all >> > points. >> > > >> So >> > > >> > in >> > > >> > > > order to arrive to an updated matrix C, we need to collect B >> > into >> > > >> > memory, >> > > >> > > > and slice out counters (first row) from the rest of it. >> > > >> > > > >> > > >> > > > So, to compute C: >> > > >> > > > >> > > >> > > > C <- B (2:,:) each row divided by B(1,:) >> > > >> > > > >> > > >> > > > (watch out for empty clusters with 0 elements, this will >> cause >> > > lack >> > > >> of >> > > >> > > > convergence and NaNs in the newly computed C). >> > > >> > > > >> > > >> > > > This operation obviously uses subblocking and row-wise >> iteration >> > > >> over >> > > >> > B, >> > > >> > > > for which i am again making reference to [2]. >> > > >> > > > >> > > >> > > > >> > > >> > > > [1] https://github.com/apache/mahout/blob/master/math-scala/ >> > > >> > > > src/main/scala/org/apache/mahout/math/drm/package.scala#L149 >> > > >> > > > >> > > >> > > > [2], Sasmara manual, a bit dated but viable, >> > http://apache.github >> > > . >> > > >> > > > io/mahout/doc/ScalaSparkBindings.html >> > > >> > > > >> > > >> > > > [3] scaladoc, again, dated but largely viable for the >> purpose of >> > > >> this >> > > >> > > > exercise: >> > > >> > > > http://apache.github.io/mahout/0.10.1/docs/mahout-math- >> > > >> scala/index.htm >> > > >> > > > >> > > >> > > > [4] mapblock etc. http://apache.github.io/mahout >> > > >> /0.10.1/docs/mahout- >> > > >> > > > math-scala/index.html#org.apache.mahout.math.drm.RLikeDrmOps >> > > >> > > > >> > > >> > > > On Fri, Mar 31, 2017 at 9:54 AM, KHATWANI PARTH BHARAT < >> > > >> > > > h2016...@pilani.bits-pilani.ac.in> wrote: >> > > >> > > > >> > > >> > > >> @Dmitriycan you please again tell me the approach to move >> > ahead. >> > > >> > > >> >> > > >> > > >> >> > > >> > > >> Thanks >> > > >> > > >> Parth Khatwani >> > > >> > > >> >> > > >> > > >> >> > > >> > > >> On Fri, Mar 31, 2017 at 10:15 PM, KHATWANI PARTH BHARAT < >> > > >> > > >> h2016...@pilani.bits-pilani.ac.in> wrote: >> > > >> > > >> >> > > >> > > >> > yes i am unable to figure out the way ahead. >> > > >> > > >> > Like how to create the augmented matrix A := (0|D) which >> you >> > > have >> > > >> > > >> > mentioned. >> > > >> > > >> > >> > > >> > > >> > >> > > >> > > >> > On Fri, Mar 31, 2017 at 10:10 PM, Dmitriy Lyubimov < >> > > >> > dlie...@gmail.com >> > > >> > > > >> > > >> > > >> > wrote: >> > > >> > > >> > >> > > >> > > >> >> was my reply for your post on @user has been a bit >> > confusing? >> > > >> > > >> >> >> > > >> > > >> >> On Fri, Mar 31, 2017 at 8:40 AM, KHATWANI PARTH BHARAT < >> > > >> > > >> >> h2016...@pilani.bits-pilani.ac.in> wrote: >> > > >> > > >> >> >> > > >> > > >> >> > Sir, >> > > >> > > >> >> > I am trying to write the kmeans clustering algorithm >> using >> > > >> Mahout >> > > >> > > >> >> Samsara >> > > >> > > >> >> > but i am bit confused >> > > >> > > >> >> > about how to leverage Distributed Row Matrix for the >> same. >> > > Can >> > > >> > > >> anybody >> > > >> > > >> >> help >> > > >> > > >> >> > me with same. >> > > >> > > >> >> > >> > > >> > > >> >> > >> > > >> > > >> >> > >> > > >> > > >> >> > >> > > >> > > >> >> > >> > > >> > > >> >> > Thanks >> > > >> > > >> >> > Parth Khatwani >> > > >> > > >> >> > >> > > >> > > >> >> >> > > >> > > >> > >> > > >> > > >> > >> > > >> > > >> >> > > >> > > > >> > > >> > > > >> > > >> > > >> > > >> > > >> > > >> > >> > > >> >> > > > >> > > > >> > > >> > >> > >
