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From nfantone <nfant...@gmail.com>
Subject Re: Clustering from DB
Date Mon, 27 Jul 2009 04:00:23 GMT
Thanks, Grant. I just updated and notice the change.

As a side note: you think someone could run some real tests on kMeans,
in particular, other than the ones already in the project? I bet there
are other naive (or not so naive) problems like that. After much
coding, reading and experimenting in the last weeks with clustering in
Mahout, I am inclined to say something may not fully work with kMeans,
as of now. Or perhaps it just needs some refactoring/performance
tweaks. Jeff have claimed to run the job over gigabytes of data, using
a rather small cluster, in minutes. Have anyone tried to accomplish
this recently (since the hadoop upgrade to 0.20)? Just use
ClusteringUtils to write a file of some (arguably not so) significant
number of random Vectors (say, 800.000+) and let that be the input of
a KMeansMRJob (testKMeansMRJob() could very well serve this purpose
with little change). You'll end up with a file of about ~85MB to
~100MB, which can easily fit into memory in any modern computer. Now,
run the whole thing (I've tried both, locally and using a three
node-cluster setup - which, frankly, seemed like a bit too much
computing power for such small number of items in the dataset). It'll
take forever to complete.

This simple methods could be used to generate any given number of
random SparseVectors for testing's sake, if anyone is interested:

  private static Random rnd = new Random();
  private static final int CARDINALITY = 1200;
  private static final int MAX_NON_ZEROS = 200;
  private static final int MAX_VECTORS = 850000;

  private static Vector getRandomVector() {
	Integer id = rnd.nextInt(Integer.MAX_VALUE);
	Vector v = new SparseVector(id.toString(), CARDINALITY);
	int nonZeros = 0;
	while ((nonZeros = rnd.nextInt(MAX_NON_ZEROS)) == 0);
	for (int i = 0; i < nonZeros; i++) {
		v.setQuick(rnd.nextInt(CARDINALITY), rnd.nextDouble());
	}
	return v;
  }

  private static List<Vector> getVectors() {
	  List<Vector> vectors = new ArrayList<Vector>(MAX_VECTORS);
	  for (int i = 0; i < MAX_VECTORS; i++){
		  vectors.add(getRandomVector());
	  }
	  return vectors;
  }

On Sun, Jul 26, 2009 at 10:30 PM, Grant Ingersoll<gsingers@apache.org> wrote:
> Fixed on MAHOUT-152
>
> On Jul 26, 2009, at 9:19 PM, Grant Ingersoll wrote:
>
>> That does indeed look like a problem.  I'll fix.
>>
>> On Jul 26, 2009, at 2:37 PM, nfantone wrote:
>>
>>> While (still) experiencing performance issues and inspecting kMeans
>>> code, I found this lying around SquaredEuclideanDistanceMeasure.java:
>>>
>>> public double distance(double centroidLengthSquare, Vector centroid,
>>> Vector v) {
>>>  if (centroid.size() != centroid.size()) {
>>>    throw new CardinalityException();
>>>  }
>>>  ...
>>>  }
>>>
>>> I bet someone meant to compare centroid and v sizes and didn't noticed.
>>>
>>> On Fri, Jul 24, 2009 at 12:38 PM, nfantone<nfantone@gmail.com> wrote:
>>>>
>>>> Well, as it turned out, it didn't have anything to do with my
>>>> performance issue but I found out that writing a Cluster (with a
>>>> single vector as its center) to a file and then reading it, requires
>>>> the center to be added as point; otherwise, you won't be able to
>>>> retrieve it as it should. Therefore, one should do:
>>>>
>>>> // Writing
>>>> String id = "someID";
>>>> Vector v = new SparseVector();
>>>> Cluster c = new Cluster(v);
>>>> c.addPoint(v);
>>>> seqWriter.append(new Text(id), c);
>>>>
>>>> // Reading
>>>> Writable key = (Writable) seqReader.getKeyClass().newInstance();
>>>> Cluster value = (Cluster) seqReader.getValueClass().newInstance();
>>>> while (seqReader.next(key, value)) {
>>>> ...
>>>> Vector centroid = value.getCenter();
>>>> ...
>>>> }
>>>>
>>>> This way, 'key' corresponds to 'id' and 'v' to 'centroid'. I think
>>>> this shouldn't happen. Then again, it's not that relevant, I guess.
>>>>
>>>> Sorry for bringing different subjects to the same thread.
>>>>
>>>> On Fri, Jul 24, 2009 at 9:14 AM, nfantone<nfantone@gmail.com> wrote:
>>>>>
>>>>> I've been using RandomSeedGenerator to generate initial clusters for
>>>>> kMeans and while checking its code I stumbled upon this:
>>>>>
>>>>>    while (reader.next(key, value)) {
>>>>>      Cluster newCluster = new Cluster(value);
>>>>>      newCluster.addPoint(value);
>>>>>      ....
>>>>>    }
>>>>>
>>>>> I can see it adds the vector to the newly created cluster, even though
>>>>> it is setting it as its center in the constructor. Wasn't this
>>>>> corrected in a past revision? I thought this was not necessary
>>>>> anymore. I'll look into it a little bit more and see if this has
>>>>> something to do with my lack of performance with my dataset.
>>>>>
>>>>> On Thu, Jul 23, 2009 at 3:45 PM, nfantone<nfantone@gmail.com> wrote:
>>>>>>>>>
>>>>>>>>> Perhaps a larger convergence value might help (-d, I
believe).
>>>>>>>>
>>>>>>>> I'll try that.
>>>>>>
>>>>>> There was no significant change while modifying the convergence value.
>>>>>> At least, none was observed during the first three iterations which
>>>>>> lasted the same amount of time than before, more or less.
>>>>>>
>>>>>>>>> Is there any chance your data is publicly shareable?
 Come to think
>>>>>>>>> of
>>>>>>>>> it,
>>>>>>>>> with the vector representations, as long as you don't
publish the
>>>>>>>>> key
>>>>>>>>> (which
>>>>>>>>> terms map to which index), I would think most all data
is publicly
>>>>>>>>> shareable.
>>>>>>>>
>>>>>>>> I'm sorry, I don't quite understand what you're asking. Publicly
>>>>>>>> shareable? As in user-permissions to access/read/write the
data?
>>>>>>>
>>>>>>> As in post a copy of the SequenceFile somewhere for download,
>>>>>>> assuming you
>>>>>>> can.  Then others could presumably try it out.
>>>>>>
>>>>>> My bad. Of course it is:
>>>>>>
>>>>>> http://cringer.3kh.net/web/user-dataset.data.tar.bz2
>>>>>>
>>>>>> That's the ~62Mb SequenceFile sample I've using, in <Text,
>>>>>> SparseVector> logical format.
>>>>>>
>>>>>>> That does seem like an awfully long time for 62 MB on a 6 node
>>>>>>> cluster. How many >terations are running?
>>>>>>
>>>>>> I'm running the whole thing with a 20 iterations cap. Every iteration
>>>>>> - EXCEPT the first one which, oddly, lasted just two minutes-, took
>>>>>> around 3hs to complete:
>>>>>>
>>>>>> Hadoop job_200907221734_0001
>>>>>> Finished in: 1mins, 42sec
>>>>>>
>>>>>> Hadoop job_200907221734_0004
>>>>>> Finished in: 2hrs, 34mins, 3sec
>>>>>>
>>>>>> Hadoop job_200907221734_0005
>>>>>> Finished in: 2hrs, 59mins, 34sec
>>>>>>
>>>>>>> How did you generate your initial clusters?
>>>>>>
>>>>>> I generate the initial clusters via the RandomSeedGenerator setting
a
>>>>>> 'k' value of 200.  This is what I did to initiate the process for
the
>>>>>> first time:
>>>>>>
>>>>>> ./bin/hadoop dfs -D dfs.block.size=4194304 -put ~/user.data
>>>>>> input/user.data
>>>>>> ./bin/hadoop dfs -D dfs.block.size=4194304 -put ~/user.data
>>>>>> init/user.data
>>>>>> ./bin/hadoop jar ~/mahout-core-0.2.jar
>>>>>> org.apache.mahout.clustering.kmeans.KMeansDriver -i input/user.data
-c
>>>>>> init -o output -r 32 -d 0.01 -k 200
>>>>>>
>>>>>>> Where are the iteration jobs spending most of their time (map
vs.
>>>>>>> reduce)
>>>>>>
>>>>>> I'm tempted to say map here, but their spent time is rather
>>>>>> comparable, actually. Reduce attempts are taking an hour and a half
to
>>>>>> end (average), and so are Map attempts. Here are some representative
>>>>>> examples from the web UI:
>>>>>>
>>>>>> reduce
>>>>>> attempt_200907221734_0002_r_000006_0
>>>>>> 22-Jul-2009 21:15:01 (1hrs, 55mins, 55sec)
>>>>>>
>>>>>> map
>>>>>> attempt_200907221734_0002_m_000000_0
>>>>>> 22-Jul-2009 20:52:27 (2hrs, 16mins, 12sec)
>>>>>>
>>>>>> Perhaps, there's some inconvenient in the way I create the
>>>>>> SequenceFile? I could share the JAVA code as well, if required.
>>>>>>
>>>>>
>>>>
>>
>> --------------------------
>> Grant Ingersoll
>> http://www.lucidimagination.com/
>>
>> Search the Lucene ecosystem (Lucene/Solr/Nutch/Mahout/Tika/Droids) using
>> Solr/Lucene:
>> http://www.lucidimagination.com/search
>>
>
> --------------------------
> Grant Ingersoll
> http://www.lucidimagination.com/
>
> Search the Lucene ecosystem (Lucene/Solr/Nutch/Mahout/Tika/Droids) using
> Solr/Lucene:
> http://www.lucidimagination.com/search
>
>

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