+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(_.toDouble)) > > >> > > > >> > //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 > > >> > > >> >> > > > >> > > >> >> > > >> > > >> > > > >> > > >> > > > >> > > >> > > >> > > > > > >> > > > > > >> > > > > >> > > > > >> > > > >> > > > > > > > > >
