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<[email protected]> 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<[email protected]>
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<[email protected]>
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<[email protected]>
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
