mahout-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Grant Ingersoll <gsing...@apache.org>
Subject Re: Clustering from DB
Date Mon, 27 Jul 2009 13:08:52 GMT

On Jul 27, 2009, at 12:00 AM, nfantone wrote:

> 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.
>

I hope to hit this soon.  I've got some Amazon credits I need to use  
and hope to put them towards this.

As with any project in open source, we need people to kick the tires,  
give feedback (thank you!) and also poke around the code to make it  
better.

Have you tried your data with some other clustering code, perhaps Weka  
or something like that?


> 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;
>  }
>


I'm not sure why testing with Random vectors would be all that useful  
other than it shows it runs.  I wouldn't expect anything useful to  
come out of it, though.


> 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
>>
>>

--------------------------
Grant Ingersoll
http://www.lucidimagination.com/

Search the Lucene ecosystem (Lucene/Solr/Nutch/Mahout/Tika/Droids)  
using Solr/Lucene:
http://www.lucidimagination.com/search


Mime
View raw message