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
> > > >> >> >
> > > >> >>
> > > >> >
> > > >> >
> > > >>
> > > >
> > > >
> > >
> > >
> >
>
