The fix got checked in this afternoon. The problem is that a line in the shell script surrounds mahout-examples-*.job with quotes. This makes it not "glob expand the wildcard" to find the actual job file.
look in the bin/mahout shell script, around line 127 On 7/27/11, Jeffrey <[email protected]> wrote: > erm, is there any workaround to the problem? > > > ----- Original Message ----- >> From: Jeff Eastman <[email protected]> >> To: "[email protected]" <[email protected]> >> Cc: >> Sent: Tuesday, July 26, 2011 1:12 PM >> Subject: RE: fkmeans or Cluster Dumper not working? >> >> Also makes sense that fuzzyk centroids would be completely dense, since >> every >> point is a member of every cluster. My reducer heaps are 4G. >> >> -----Original Message----- >> From: Jeff Eastman [mailto:[email protected]] >> Sent: Monday, July 25, 2011 2:32 PM >> To: [email protected]; Jeffrey >> Subject: RE: fkmeans or Cluster Dumper not working? >> >> I'm able to run fuzzyk on your data set with k=10 and k=50 without >> problems. >> I also ran it fine with k=100 just to push it a bit harder. Runs took >> longer as >> k increased as expected (39s, 2m50s, 5m57s) as did the clustering (11s, >> 45s, >> 1m11s). The cluster dumper is throwing an OME with your data points and >> probably >> also with the larger cluster volumes, suggesting it needs a larger -Xmx >> value >> since it is running locally and not influenced by the cluster vm >> parameters. >> >> I will try some more and keep you updated. >> >> The cluster dumper is throwing an OME trying to inhale all your data >> points. It >> is running locally >> >> -----Original Message----- >> From: Jeffrey [mailto:[email protected]] >> Sent: Sunday, July 24, 2011 12:51 AM >> To: [email protected] >> Subject: Re: fkmeans or Cluster Dumper not working? >> >> Erm, is there any update? is the problem reproducible? >> >> Best wishes, >> Jeffrey04 >> >> >> >>> ________________________________ >>> From: Jeffrey <[email protected]> >>> To: Jeff Eastman <[email protected]>; >> "[email protected]" <[email protected]> >>> Sent: Friday, July 22, 2011 12:40 AM >>> Subject: Re: fkmeans or Cluster Dumper not working? >>> >>> >>> Hi Jeff, >>> >>> >>> lol, this is probably my last reply before i fall asleep (GMT+8 here). >>> >>> >>> First thing first, data file is here: http://coolsilon.com/image-tag.mvc >>> >>> >>> Q: What is the cardinality of your vector data? >>> about 1000+ rows (resources) * 14 000+ columns (tags) >>> Q: Is it sparse or dense? >>> sparse (assuming sparse = each vector contains mostly 0) >>> Q: How many vectors are you trying to cluster? >>> all of them? (1000+ rows) >>> Q: What is the exact error you see when fkmeans fails with k=10? With >>> k=50? >>> i think i posted the exception when k=50, but will post them again here >>> >>> >>> k=10, fkmeans actually works, but cluster dumper returns exception, >>> however, >> if i take out --pointsDir, then it would work (output looks ok, but >> without all >> the points) >>> >>> >>> $ bin/mahout fkmeans --input sensei/image-tag.arff.mvc --output >> sensei/clusters --clusters sensei/clusters/clusters-0 --clustering >> --overwrite >> --emitMostLikely false --numClusters 10 --maxIter 10 --m 5 >>> ... >>> $ bin/mahout clusterdump --seqFileDir sensei/clusters/clusters-1 >> --pointsDir sensei/clusters/clusteredPoints --output >> image-tag-clusters.txt >> Running on hadoop, using >> HADOOP_HOME=/home/jeffrey04/Applications/hadoop-0.20.203.0 >>> HADOOP_CONF_DIR=/home/jeffrey04/Applications/hadoop-0.20.203.0/conf >>> MAHOUT-JOB: >> /home/jeffrey04/Applications/mahout/examples/target/mahout-examples-0.6-SNAPSHOT-job.jar >>> 11/07/22 00:14:50 INFO common.AbstractJob: Command line arguments: >> {--dictionaryType=text, --endPhase=2147483647, >> --output=image-tag-clusters.txt, >> --pointsDir=sensei/clusters/clusteredPoints, >> --seqFileDir=sensei/clusters/clusters-1, --startPhase=0, --tempDir=temp} >>> Exception in thread "main" java.lang.OutOfMemoryError: Java >> heap space >>> at java.lang.Object.clone(Native Method) >>> at >> org.apache.mahout.math.DenseVector.<init>(DenseVector.java:44) >>> at >> org.apache.mahout.math.DenseVector.<init>(DenseVector.java:39) >>> at >> org.apache.mahout.math.VectorWritable.readFields(VectorWritable.java:94) >>> at >> org.apache.mahout.clustering.WeightedVectorWritable.readFields(WeightedVectorWritable.java:55) >>> at >> org.apache.hadoop.io.SequenceFile$Reader.getCurrentValue(SequenceFile.java:1751) >>> at >> org.apache.hadoop.io.SequenceFile$Reader.next(SequenceFile.java:1879) >>> at >> org.apache.mahout.common.iterator.sequencefile.SequenceFileIterator.computeNext(SequenceFileIterator.java:95) >>> at >> org.apache.mahout.common.iterator.sequencefile.SequenceFileIterator.computeNext(SequenceFileIterator.java:38) >>> at >> com.google.common.collect.AbstractIterator.tryToComputeNext(AbstractIterator.java:141) >>> at >> com.google.common.collect.AbstractIterator.hasNext(AbstractIterator.java:136) >>> at >> com.google.common.collect.Iterators$5.hasNext(Iterators.java:525) >>> at >> com.google.common.collect.ForwardingIterator.hasNext(ForwardingIterator.java:43) >>> at >> org.apache.mahout.utils.clustering.ClusterDumper.readPoints(ClusterDumper.java:255) >>> at >> org.apache.mahout.utils.clustering.ClusterDumper.init(ClusterDumper.java:209) >>> at >> org.apache.mahout.utils.clustering.ClusterDumper.run(ClusterDumper.java:123) >>> at >> org.apache.mahout.utils.clustering.ClusterDumper.main(ClusterDumper.java:89) >>> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native >>> Method) >>> at >> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) >>> at >> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) >>> at java.lang.reflect.Method.invoke(Method.java:616) >>> at >> org.apache.hadoop.util.ProgramDriver$ProgramDescription.invoke(ProgramDriver.java:68) >>> at >> org.apache.hadoop.util.ProgramDriver.driver(ProgramDriver.java:139) >>> at >> org.apache.mahout.driver.MahoutDriver.main(MahoutDriver.java:188) >>> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native >>> Method) >>> at >> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) >>> at >> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) >>> at java.lang.reflect.Method.invoke(Method.java:616) >>> at org.apache.hadoop.util.RunJar.main(RunJar.java:156) >>> $ bin/mahout clusterdump --seqFileDir sensei/clusters/clusters-1 >> --output image-tag-clusters.txt Running on hadoop, using >> HADOOP_HOME=/home/jeffrey04/Applications/hadoop-0.20.203.0 >>> HADOOP_CONF_DIR=/home/jeffrey04/Applications/hadoop-0.20.203.0/conf >>> MAHOUT-JOB: >> /home/jeffrey04/Applications/mahout/examples/target/mahout-examples-0.6-SNAPSHOT-job.jar >>> 11/07/22 00:19:04 INFO common.AbstractJob: Command line arguments: >> {--dictionaryType=text, --endPhase=2147483647, >> --output=image-tag-clusters.txt, >> --seqFileDir=sensei/clusters/clusters-1, --startPhase=0, --tempDir=temp} >>> 11/07/22 00:19:13 INFO driver.MahoutDriver: Program took 9504 ms >>> >>> >>> k=50, fkmeans shows exception after map 100% reduce 0%, and would retry >>> (map >> 0% reduce 0%) after the exception >>> >>> >>> $ bin/mahout fkmeans --input sensei/image-tag.arff.mvc --output >> sensei/clusters --clusters sensei/clusters/clusters-0 --clustering >> --overwrite >> --emitMostLikely false --numClusters 50 --maxIter 10 --m 5 >>> Running on hadoop, using >> HADOOP_HOME=/home/jeffrey04/Applications/hadoop-0.20.203.0 >>> HADOOP_CONF_DIR=/home/jeffrey04/Applications/hadoop-0.20.203.0/conf >>> MAHOUT-JOB: >> /home/jeffrey04/Applications/mahout/examples/target/mahout-examples-0.6-SNAPSHOT-job.jar >>> 11/07/22 00:21:07 INFO common.AbstractJob: Command line arguments: >> {--clustering=null, --clusters=sensei/clusters/clusters-0, >> --convergenceDelta=0.5, >> --distanceMeasure=org.apache.mahout.common.distance.SquaredEuclideanDistanceMeasure, >> >> --emitMostLikely=false, --endPhase=2147483647, >> --input=sensei/image-tag.arff.mvc, --m=5, --maxIter=10, >> --method=mapreduce, >> --numClusters=50, --output=sensei/clusters, --overwrite=null, >> --startPhase=0, >> --tempDir=temp, --threshold=0} >>> 11/07/22 00:21:09 INFO common.HadoopUtil: Deleting sensei/clusters >>> 11/07/22 00:21:09 INFO util.NativeCodeLoader: Loaded the >>> native-hadoop >> library >>> 11/07/22 00:21:09 INFO zlib.ZlibFactory: Successfully loaded & >> initialized native-zlib library >>> 11/07/22 00:21:09 INFO compress.CodecPool: Got brand-new compressor >>> 11/07/22 00:21:10 INFO compress.CodecPool: Got brand-new decompressor >>> 11/07/22 00:21:21 INFO kmeans.RandomSeedGenerator: Wrote 50 vectors >>> to >> sensei/clusters/clusters-0/part-randomSeed >>> 11/07/22 00:21:24 INFO fuzzykmeans.FuzzyKMeansDriver: Fuzzy K-Means >> Iteration 1 >>> 11/07/22 00:21:25 INFO input.FileInputFormat: Total input paths to >> process : 1 >>> 11/07/22 00:21:26 INFO mapred.JobClient: Running job: >> job_201107211512_0029 >>> 11/07/22 00:21:27 INFO mapred.JobClient: map 0% reduce 0% >>> 11/07/22 00:22:08 INFO mapred.JobClient: map 1% reduce 0% >>> 11/07/22 00:22:20 INFO mapred.JobClient: map 2% reduce 0% >>> 11/07/22 00:22:33 INFO mapred.JobClient: map 3% reduce 0% >>> 11/07/22 00:22:42 INFO mapred.JobClient: map 4% reduce 0% >>> 11/07/22 00:22:50 INFO mapred.JobClient: map 5% reduce 0% >>> 11/07/22 00:23:00 INFO mapred.JobClient: map 6% reduce 0% >>> 11/07/22 00:23:09 INFO mapred.JobClient: map 7% reduce 0% >>> 11/07/22 00:23:18 INFO mapred.JobClient: map 8% reduce 0% >>> 11/07/22 00:23:27 INFO mapred.JobClient: map 9% reduce 0% >>> 11/07/22 00:23:33 INFO mapred.JobClient: map 10% reduce 0% >>> 11/07/22 00:23:42 INFO mapred.JobClient: map 11% reduce 0% >>> 11/07/22 00:23:45 INFO mapred.JobClient: map 12% reduce 0% >>> 11/07/22 00:23:54 INFO mapred.JobClient: map 13% reduce 0% >>> 11/07/22 00:24:03 INFO mapred.JobClient: map 14% reduce 0% >>> 11/07/22 00:24:09 INFO mapred.JobClient: map 15% reduce 0% >>> 11/07/22 00:24:15 INFO mapred.JobClient: map 16% reduce 0% >>> 11/07/22 00:24:24 INFO mapred.JobClient: map 17% reduce 0% >>> 11/07/22 00:24:30 INFO mapred.JobClient: map 18% reduce 0% >>> 11/07/22 00:24:42 INFO mapred.JobClient: map 19% reduce 0% >>> 11/07/22 00:24:51 INFO mapred.JobClient: map 20% reduce 0% >>> 11/07/22 00:24:57 INFO mapred.JobClient: map 21% reduce 0% >>> 11/07/22 00:25:06 INFO mapred.JobClient: map 22% reduce 0% >>> 11/07/22 00:25:09 INFO mapred.JobClient: map 23% reduce 0% >>> 11/07/22 00:25:19 INFO mapred.JobClient: map 24% reduce 0% >>> 11/07/22 00:25:25 INFO 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00:27:23 INFO mapred.JobClient: map 41% reduce 0% >>> 11/07/22 00:27:28 INFO mapred.JobClient: map 42% reduce 0% >>> 11/07/22 00:27:34 INFO mapred.JobClient: map 43% reduce 0% >>> 11/07/22 00:27:40 INFO mapred.JobClient: map 44% reduce 0% >>> 11/07/22 00:27:49 INFO mapred.JobClient: map 45% reduce 0% >>> 11/07/22 00:27:56 INFO mapred.JobClient: map 46% reduce 0% >>> 11/07/22 00:28:05 INFO mapred.JobClient: map 47% reduce 0% >>> 11/07/22 00:28:11 INFO mapred.JobClient: map 48% reduce 0% >>> 11/07/22 00:28:20 INFO mapred.JobClient: map 49% reduce 0% >>> 11/07/22 00:28:26 INFO mapred.JobClient: map 50% reduce 0% >>> 11/07/22 00:28:35 INFO mapred.JobClient: map 51% reduce 0% >>> 11/07/22 00:28:41 INFO mapred.JobClient: map 52% reduce 0% >>> 11/07/22 00:28:47 INFO mapred.JobClient: map 53% reduce 0% >>> 11/07/22 00:28:53 INFO mapred.JobClient: map 54% reduce 0% >>> 11/07/22 00:29:02 INFO mapred.JobClient: map 55% reduce 0% >>> 11/07/22 00:29:08 INFO mapred.JobClient: map 56% reduce 0% >>> 11/07/22 00:29:17 INFO mapred.JobClient: map 57% reduce 0% >>> 11/07/22 00:29:26 INFO mapred.JobClient: map 58% reduce 0% >>> 11/07/22 00:29:32 INFO mapred.JobClient: map 59% reduce 0% >>> 11/07/22 00:29:41 INFO mapred.JobClient: map 60% reduce 0% >>> 11/07/22 00:29:50 INFO mapred.JobClient: map 61% reduce 0% >>> 11/07/22 00:29:53 INFO mapred.JobClient: map 62% reduce 0% >>> 11/07/22 00:29:59 INFO mapred.JobClient: map 63% reduce 0% >>> 11/07/22 00:30:09 INFO mapred.JobClient: map 64% reduce 0% >>> 11/07/22 00:30:15 INFO mapred.JobClient: map 65% reduce 0% >>> 11/07/22 00:30:23 INFO mapred.JobClient: map 66% reduce 0% >>> 11/07/22 00:30:35 INFO mapred.JobClient: map 67% reduce 0% >>> 11/07/22 00:30:41 INFO mapred.JobClient: map 68% reduce 0% >>> 11/07/22 00:30:50 INFO mapred.JobClient: map 69% reduce 0% >>> 11/07/22 00:30:56 INFO mapred.JobClient: map 70% reduce 0% >>> 11/07/22 00:31:05 INFO mapred.JobClient: map 71% reduce 0% >>> 11/07/22 00:31:15 INFO mapred.JobClient: map 72% reduce 0% >>> 11/07/22 00:31:24 INFO mapred.JobClient: map 73% reduce 0% >>> 11/07/22 00:31:30 INFO mapred.JobClient: map 74% reduce 0% >>> 11/07/22 00:31:39 INFO mapred.JobClient: map 75% reduce 0% >>> 11/07/22 00:31:42 INFO mapred.JobClient: map 76% reduce 0% >>> 11/07/22 00:31:50 INFO mapred.JobClient: map 77% reduce 0% >>> 11/07/22 00:31:59 INFO mapred.JobClient: map 78% reduce 0% >>> 11/07/22 00:32:11 INFO mapred.JobClient: map 79% reduce 0% >>> 11/07/22 00:32:28 INFO mapred.JobClient: map 80% reduce 0% >>> 11/07/22 00:32:37 INFO mapred.JobClient: map 81% reduce 0% >>> 11/07/22 00:32:40 INFO mapred.JobClient: map 82% reduce 0% >>> 11/07/22 00:32:49 INFO mapred.JobClient: map 83% reduce 0% >>> 11/07/22 00:32:58 INFO mapred.JobClient: map 84% reduce 0% >>> 11/07/22 00:33:04 INFO mapred.JobClient: map 85% reduce 0% >>> 11/07/22 00:33:13 INFO mapred.JobClient: map 86% reduce 0% >>> 11/07/22 00:33:19 INFO mapred.JobClient: map 87% reduce 0% >>> 11/07/22 00:33:32 INFO mapred.JobClient: map 88% reduce 0% >>> 11/07/22 00:33:38 INFO mapred.JobClient: map 89% reduce 0% >>> 11/07/22 00:33:47 INFO mapred.JobClient: map 90% reduce 0% >>> 11/07/22 00:33:52 INFO mapred.JobClient: map 91% reduce 0% >>> 11/07/22 00:34:01 INFO mapred.JobClient: map 92% reduce 0% >>> 11/07/22 00:34:10 INFO mapred.JobClient: map 93% reduce 0% >>> 11/07/22 00:34:13 INFO mapred.JobClient: map 94% reduce 0% >>> 11/07/22 00:34:25 INFO mapred.JobClient: map 95% reduce 0% >>> 11/07/22 00:34:31 INFO mapred.JobClient: map 96% reduce 0% >>> 11/07/22 00:34:40 INFO mapred.JobClient: map 97% reduce 0% >>> 11/07/22 00:34:47 INFO mapred.JobClient: map 98% reduce 0% >>> 11/07/22 00:34:56 INFO mapred.JobClient: map 99% reduce 0% >>> 11/07/22 00:35:02 INFO mapred.JobClient: map 100% reduce 0% >>> 11/07/22 00:35:07 INFO mapred.JobClient: Task Id : >> attempt_201107211512_0029_m_000000_0, Status : FAILED >>> org.apache.hadoop.util.DiskChecker$DiskErrorException: Could not find >>> >> any valid local directory for output/file.out >>> at >> org.apache.hadoop.fs.LocalDirAllocator$AllocatorPerContext.getLocalPathForWrite(LocalDirAllocator.java:381) >>> at >> org.apache.hadoop.fs.LocalDirAllocator.getLocalPathForWrite(LocalDirAllocator.java:146) >>> at >> org.apache.hadoop.fs.LocalDirAllocator.getLocalPathForWrite(LocalDirAllocator.java:127) >>> at >> org.apache.hadoop.mapred.MapOutputFile.getOutputFileForWrite(MapOutputFile.java:69) >>> at >> org.apache.hadoop.mapred.MapTask$MapOutputBuffer.mergeParts(MapTask.java:1639) >>> at >> org.apache.hadoop.mapred.MapTask$MapOutputBuffer.flush(MapTask.java:1322) >>> at >> org.apache.hadoop.mapred.MapTask$NewOutputCollector.close(MapTask.java:698) >>> at >> org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:765) >>> at org.apache.hadoop.mapred.MapTask.run(MapTask.java:369) >>> at org.apache.hadoop.mapred.Child$4.run(Child.java:259) >>> at java.security.AccessController.doPrivileged(Native Method) >>> at javax.security.auth.Subject.doAs(Subject.java:416) >>> at >> org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1059) >>> at org.apache.hadoop.mapred.Child.main(Child.java:253) >>> >>> >>> 11/07/22 00:35:09 INFO mapred.JobClient: map 0% reduce 0% >>> ... >>> >>> >>> Q: What are the Hadoop heap settings you are using for your job? >>> I am new to hadoop, not sure where to get those, but got these from >> localhost:50070, is it right? >>> 147 files and directories, 60 blocks = 207 total. Heap Size is 31.57 MB / >>> >> 966.69 MB (3%) >>> >>> >>> p/s: i keep forgetting to include my operating environment, sorry. I >> basically run this in a guest operating system (in a virtualbox virtual >> machine), assigned 1 CPU core, and 1.5GB of memory. Then the host >> operating >> system is OS X 10.6.8 running on alubook (macbook late 2008 model) with >> 4GB of >> memory. >>> >>> >>> $ cat /etc/*-release >>> DISTRIB_ID=Ubuntu >>> DISTRIB_RELEASE=11.04 >>> DISTRIB_CODENAME=natty >>> DISTRIB_DESCRIPTION="Ubuntu 11.04" >>> $ uname -a >>> Linux sensei 2.6.38-10-generic #46-Ubuntu SMP Tue Jun 28 15:05:41 UTC >>> >> 2011 i686 i686 i386 GNU/Linux >>> >>> >>> Best wishes, >>> Jeffrey04 >>> >>>> ________________________________ >>>> From: Jeff Eastman <[email protected]> >>>> To: "[email protected]" <[email protected]>; >> Jeffrey <[email protected]> >>>> Sent: Thursday, July 21, 2011 11:54 PM >>>> Subject: RE: fkmeans or Cluster Dumper not working? >>>> >>>> Excellent, so this appears to be localized to fuzzyk. Unfortunately, the >>>> >> Apache mail server strips off attachments so you'd need another mechanism >> (a >> JIRA?) to upload your data if it is not too large. Some more questions in >> the >> interim: >>>> >>>> - What is the cardinality of your vector data? >>>> - Is it sparse or dense? >>>> - How many vectors are you trying to cluster? >>>> - What is the exact error you see when fkmeans fails with k=10? With >> k=50? >>>> - What are the Hadoop heap settings you are using for your job? >>>> >>>> -----Original Message----- >>>> From: Jeffrey [mailto:[email protected]] >>>> Sent: Thursday, July 21, 2011 11:21 AM >>>> To: [email protected] >>>> Subject: Re: fkmeans or Cluster Dumper not >> working? >>>> >>>> Hi Jeff, >>>> >>>> Q: Did you change your invocation to specify a different -c directory >> (e.g. clusters-0)? >>>> A: Yes :) >>>> >>>> Q: Did you add the -cl argument? >>>> A: Yes :) >>>> >>>> $ bin/mahout fkmeans --input sensei/image-tag.arff.mvc --output >> sensei/clusters --clusters sensei/clusters/clusters-0 --clustering >> --overwrite >> --emitMostLikely false --numClusters 5 --maxIter 10 --m 5 >>>> $ bin/mahout fkmeans --input sensei/image-tag.arff.mvc --output >> sensei/clusters --clusters sensei/clusters/clusters-0 --clustering >> --overwrite >> --emitMostLikely false --numClusters 10 --maxIter 10 --m 5 >>>> $ bin/mahout fkmeans --input sensei/image-tag.arff.mvc --output >> sensei/clusters --clusters sensei/clusters/clusters-0 --clustering >> --overwrite >> --emitMostLikely false --numClusters 50 --maxIter 10 --m 5 >>>> >>>> Q: What is the new CLI invocation for clusterdump? >>>> A: >>>> $ bin/mahout clusterdump --seqFileDir sensei/clusters/clusters-4 >> --pointsDir >> sensei/clusters/clusteredPoints --output image-tag-clusters.txt >>>> >>>> >>>> Q: Did this work for -k 10? What happens with -k 50? >>>> A: works for k=5 (but i don't see the points), but not k=10, fkmeans >> fails when k=50, so i can't dump when k=50 >>>> >>>> Q: Have you tried kmeans? >>>> A: Yes (all tested on 0.6-snapshot) >>>> >>>> k=5: no problem :) >>>> k=10: no problem :) >>>> k=50: no problem :) >>>> >>>> p/s: attached with the test data i used (in mvc format), let me know if >> you guys prefer raw data in arff format >>>> >>>> Best wishes, >>>> Jeffrey04 >>>> >>>> >>>> >>>>> ________________________________ >>>>> From: Jeff Eastman <[email protected]> >>>>> To: "[email protected]" >> <[email protected]>; Jeffrey <[email protected]> >>>>> Sent: Thursday, July 21, 2011 9:36 PM >>>>> Subject: RE: fkmeans or Cluster Dumper not working? >>>>> >>>>> You are correct, the wiki for fkmeans did not mention the -cl >> argument. I've added that just now. I think this is what Frank means in >> his >> comment but you do *not* have to write any custom code to get the cluster >> dumper >> to do what you want, just use the -cl argument and specify clusteredPoints >> as >> the -p input to clusterdump. >>>>> >>>>> Check out TestClusterDumper.testKmeans and .testFuzzyKmeans. These >> show how to invoke the clustering and cluster dumper from Java at least. >>>>> >>>>> Did you change your invocation to specify a different -c directory >> (e.g. clusters-0)? >>>>> Did you add the -cl argument? >>>>> What is the new CLI invocation for clusterdump? >>>>> Did this work for -k 10? What happens with -k >> 50? >>>>> Have you tried kmeans? >>>>> >>>>> I can help you better if you will give me answers to my questions >>>>> >>>>> -----Original Message----- >>>>> From: Jeffrey [mailto:[email protected]] >>>>> Sent: Thursday, July 21, 2011 4:30 AM >>>>> To: [email protected] >>>>> Subject: Re: fkmeans or Cluster Dumper not working? >>>>> >>>>> Hi again, >>>>> >>>>> Let me update on what's working and what's not working. >>>>> >>>>> Works: >>>>> fkmeans clustering (10 clusters) - thanks Jeff for the --cl tip >>>>> fkmeans clustering (5 clusters) >>>>> clusterdump (5 clusters) - so points are not included in the >> clusterdump and I need to write a program for it? >>>>> >>>>> Not Working: >>>>> fkmeans clustering (50 clusters) - same error >>>>> clusterdump (10 >> clusters) - same error >>>>> >>>>> >>>>> so it seems to attach points to the cluster dumper output like the >> synthetic control example does, i would have to write some code as pointed >> by >> @Frank_Scholten ? >> https://twitter.com/#!/Frank_Scholten/status/93617269296472064 >>>>> >>>>> Best wishes, >>>>> Jeffrey04 >>>>> >>>>>> ________________________________ >>>>>> From: Jeff Eastman <[email protected]> >>>>>> To: "[email protected]" >> <[email protected]>; Jeffrey <[email protected]> >>>>>> Sent: Wednesday, July 20, 2011 11:53 PM >>>>>> Subject: RE: fkmeans or Cluster Dumper not working? >>>>>> >>>>>> Hi Jeffrey, >>>>>> >>>>>> It is always difficult to debug remotely, but here are some >> suggestions: >>>>>> - First, you are specifying both an input clusters directory >> --clusters and --numClusters clusters so the job is sampling 10 points >> from your >> input data set and writing them to clusteredPoints as the prior clusters >> for the >> first iteration. You should pick a different name for this directory, as >> the >> clusteredPoints directory is used by the -cl (--clustering) option (which >> you >> did not supply) to write out the clustered (classified) input vectors. >> When you >> subsequently supplied clusteredPoints to the clusterdumper it was >> expecting a >> different format and that caused the exception you saw. Change your >> --clusters >> directory (clusters-0 is good) >> and add a -cl argument and things should go more smoothly. The -cl option >> is not >> the default and so no clustering of the input points is performed without >> this >> (Many people get caught by this and perhaps the default should be changed, >> but >> clustering can be expensive and so it is not performed without request). >>>>>> - If you still have problems, try again with k-means. The >> similarity to fkmeans is good and it will eliminate fkmeans itself if you >> see >> the same problems with k-means >>>>>> - I don't see why changing the -k argument from 10 to 50 >> should cause any problems, unless your vectors are very large and you are >> getting an OME in the reducer. Since the reducer is calculating centroid >> vectors >> for the next iteration these will become more dense and memory will >> increase >> substantially. >>>>>> - I can't figure out what might be causing your second >> exception. It is bombing inside of Hadoop file IO and this causes me to >> suspect >> command argument >> problems. >>>>>> >>>>>> Hope this helps, >>>>>> Jeff >>>>>> >>>>>> >>>>>> -----Original Message----- >>>>>> From: Jeffrey [mailto:[email protected]] >>>>>> Sent: Wednesday, July 20, 2011 2:41 AM >>>>>> To: [email protected] >>>>>> Subject: fkmeans or Cluster Dumper not working? >>>>>> >>>>>> Hi, >>>>>> >>>>>> I am trying to generate clusters using the fkmeans command line >> tool from my test data. Not sure if this is correct, as it only runs one >> iteration (output from 0.6-snapshot, gotta use some workaround to some >> weird bug >> - >> http://search.lucidimagination.com/search/document/d95ff0c29ac4a8a7/bug_in_fkmeans >> >> ) >>>>>> >>>>>> $ bin/mahout fkmeans --input sensei/image-tag.arff.mvc --output >> sensei/clusters --clusters sensei/clusteredPoints --maxIter 10 >> --numClusters 10 >> --overwrite --m 5 >>>>>> Running on hadoop, using >> HADOOP_HOME=/home/jeffrey04/Applications/hadoop-0.20.203.0HADOOP_CONF_DIR=/home/jeffrey04/Applications/hadoop-0.20.203.0/confMAHOUT-JOB: >> >> /home/jeffrey04/Applications/mahout/examples/target/mahout-examples-0.6-SNAPSHOT-job.jar11/07/20 >> >> 14:05:18 INFO common.AbstractJob: Command line arguments: >> {--clusters=sensei/clusteredPoints, --convergenceDelta=0.5, >> --distanceMeasure=org.apache.mahout.common.distance.SquaredEuclideanDistanceMeasure, >> >> --emitMostLikely=true, --endPhase=2147483647, >> --input=sensei/image-tag.arff.mvc, >> --m=5, --maxIter=10, --method=mapreduce, --numClusters=10, >> --output=sensei/clusters, --overwrite=null, --startPhase=0, >> --tempDir=temp, >> --threshold=0}11/07/20 14:05:20 INFO common.HadoopUtil: Deleting >> sensei/clusters11/07/20 >> 14:05:20 INFO common.HadoopUtil: Deleting sensei/clusteredPoints11/07/20 >> 14:05:20 INFO util.NativeCodeLoader: Loaded the native-hadoop >> library11/07/20 >> 14:05:20 INFO zlib.ZlibFactory: Successfully >>>>>> loaded & initialized native-zlib library11/07/20 14:05:20 >> INFO compress.CodecPool: Got brand-new compressor11/07/20 14:05:20 INFO >> compress.CodecPool: Got brand-new decompressor >>>>>> 11/07/20 14:05:29 INFO kmeans.RandomSeedGenerator: Wrote 10 >> vectors to sensei/clusteredPoints/part-randomSeed >>>>>> 11/07/20 14:05:29 INFO fuzzykmeans.FuzzyKMeansDriver: Fuzzy >> K-Means Iteration 1 >>>>>> 11/07/20 14:05:30 INFO input.FileInputFormat: Total input paths >> to process : 1 >>>>>> 11/07/20 14:05:30 INFO mapred.JobClient: Running job: >> job_201107201152_0021 >>>>>> 11/07/20 14:05:31 INFO mapred.JobClient: map 0% reduce 0% >>>>>> 11/07/20 14:05:54 INFO mapred.JobClient: map 2% reduce 0% >>>>>> 11/07/20 14:05:57 INFO >> mapred.JobClient: map 5% reduce 0% >>>>>> 11/07/20 14:06:00 INFO mapred.JobClient: map 6% reduce 0% >>>>>> 11/07/20 14:06:03 INFO mapred.JobClient: map 7% reduce 0% >>>>>> 11/07/20 14:06:07 INFO mapred.JobClient: map 10% reduce 0% >>>>>> 11/07/20 14:06:10 INFO mapred.JobClient: map 13% reduce 0% >>>>>> 11/07/20 14:06:13 INFO mapred.JobClient: map 15% reduce 0% >>>>>> 11/07/20 14:06:16 INFO mapred.JobClient: map 17% reduce 0% >>>>>> 11/07/20 14:06:19 INFO mapred.JobClient: map 19% reduce 0% >>>>>> 11/07/20 14:06:22 INFO mapred.JobClient: map 23% reduce 0% >>>>>> 11/07/20 14:06:25 INFO mapred.JobClient: map 25% reduce 0% >>>>>> 11/07/20 14:06:28 INFO mapred.JobClient: map 27% reduce 0% >>>>>> 11/07/20 14:06:31 INFO mapred.JobClient: map 30% reduce 0% >>>>>> 11/07/20 14:06:34 INFO mapred.JobClient: map 33% reduce >> 0% >>>>>> 11/07/20 14:06:37 INFO mapred.JobClient: map 36% reduce 0% >>>>>> 11/07/20 14:06:40 INFO mapred.JobClient: map 37% reduce 0% >>>>>> 11/07/20 14:06:43 INFO mapred.JobClient: map 40% reduce 0% >>>>>> 11/07/20 14:06:46 INFO mapred.JobClient: map 43% reduce 0% >>>>>> 11/07/20 14:06:49 INFO mapred.JobClient: map 46% reduce 0% >>>>>> 11/07/20 14:06:52 INFO mapred.JobClient: map 48% reduce 0% >>>>>> 11/07/20 14:06:55 INFO mapred.JobClient: map 50% reduce 0% >>>>>> 11/07/20 14:06:57 INFO mapred.JobClient: map 53% reduce 0% >>>>>> 11/07/20 14:07:00 INFO mapred.JobClient: map 56% reduce 0% >>>>>> 11/07/20 14:07:03 INFO mapred.JobClient: map 58% reduce 0% >>>>>> 11/07/20 14:07:06 INFO mapred.JobClient: map 60% reduce 0% >>>>>> 11/07/20 14:07:09 INFO mapred.JobClient: map 63% reduce 0% >>>>>> 11/07/20 14:07:13 INFO >> mapred.JobClient: map 65% reduce 0% >>>>>> 11/07/20 14:07:16 INFO mapred.JobClient: map 67% reduce 0% >>>>>> 11/07/20 14:07:19 INFO mapred.JobClient: map 70% reduce 0% >>>>>> 11/07/20 14:07:22 INFO mapred.JobClient: map 73% reduce 0% >>>>>> 11/07/20 14:07:25 INFO mapred.JobClient: map 75% reduce 0% >>>>>> 11/07/20 14:07:28 INFO mapred.JobClient: map 77% reduce 0% >>>>>> 11/07/20 14:07:31 INFO mapred.JobClient: map 80% reduce 0% >>>>>> 11/07/20 14:07:34 INFO mapred.JobClient: map 83% reduce 0% >>>>>> 11/07/20 14:07:37 INFO mapred.JobClient: map 85% reduce 0% >>>>>> 11/07/20 14:07:40 INFO mapred.JobClient: map 87% reduce 0% >>>>>> 11/07/20 14:07:43 INFO mapred.JobClient: map 89% reduce 0% >>>>>> 11/07/20 14:07:46 INFO mapred.JobClient: map 92% reduce 0% >>>>>> 11/07/20 14:07:49 INFO mapred.JobClient: map 95% reduce >> 0% >>>>>> 11/07/20 14:07:55 INFO mapred.JobClient: map 98% reduce 0% >>>>>> 11/07/20 14:07:59 INFO mapred.JobClient: map 99% reduce 0% >>>>>> 11/07/20 14:08:02 INFO mapred.JobClient: map 100% reduce 0% >>>>>> 11/07/20 14:08:23 INFO mapred.JobClient: map 100% reduce 100% >>>>>> 11/07/20 14:08:31 INFO mapred.JobClient: Job complete: >> job_201107201152_0021 >>>>>> 11/07/20 14:08:31 INFO mapred.JobClient: Counters: 26 >>>>>> 11/07/20 14:08:31 INFO mapred.JobClient: Job Counters >>>>>> 11/07/20 14:08:31 INFO mapred.JobClient: Launched reduce >> tasks=1 >>>>>> 11/07/20 14:08:31 INFO mapred.JobClient: >> SLOTS_MILLIS_MAPS=149314 >>>>>> 11/07/20 14:08:31 INFO mapred.JobClient: Total time spent by >> all reduces waiting after reserving slots (ms)=0 >>>>>> 11/07/20 14:08:31 INFO mapred.JobClient: Total time spent by >> all maps waiting after >> reserving slots (ms)=0 >>>>>> 11/07/20 14:08:31 INFO mapred.JobClient: Launched map >> tasks=1 >>>>>> 11/07/20 14:08:31 INFO mapred.JobClient: Data-local map >> tasks=1 >>>>>> 11/07/20 14:08:31 INFO mapred.JobClient: >> SLOTS_MILLIS_REDUCES=15618 >>>>>> 11/07/20 14:08:31 INFO mapred.JobClient: File Output Format >> Counters >>>>>> 11/07/20 14:08:31 INFO mapred.JobClient: Bytes >> Written=2247222 >>>>>> 11/07/20 14:08:31 INFO mapred.JobClient: Clustering >>>>>> 11/07/20 14:08:31 INFO mapred.JobClient: Converged >> Clusters=10 >>>>>> 11/07/20 14:08:31 INFO mapred.JobClient: FileSystemCounters >>>>>> 11/07/20 14:08:31 INFO mapred.JobClient: >> FILE_BYTES_READ=130281382 >>>>>> 11/07/20 14:08:31 INFO mapred.JobClient: >> HDFS_BYTES_READ=254494 >>>>>> 11/07/20 14:08:31 INFO mapred.JobClient: >> FILE_BYTES_WRITTEN=132572666 >>>>>> 11/07/20 14:08:31 INFO mapred.JobClient: >> HDFS_BYTES_WRITTEN=2247222 >>>>>> 11/07/20 14:08:31 INFO mapred.JobClient: File Input Format >> Counters >>>>>> 11/07/20 14:08:31 INFO mapred.JobClient: Bytes Read=247443 >>>>>> 11/07/20 14:08:31 INFO mapred.JobClient: Map-Reduce Framework >>>>>> 11/07/20 14:08:31 INFO mapred.JobClient: Reduce input >> groups=10 >>>>>> 11/07/20 14:08:31 INFO mapred.JobClient: Map output >> materialized bytes=2246233 >>>>>> 11/07/20 14:08:32 INFO mapred.JobClient: Combine output >> records=330 >>>>>> 11/07/20 14:08:32 INFO mapred.JobClient: Map input >> records=1113 >>>>>> 11/07/20 14:08:32 INFO mapred.JobClient: Reduce shuffle >> bytes=2246233 >>>>>> 11/07/20 14:08:32 INFO mapred.JobClient: Reduce output >> records=10 >>>>>> 11/07/20 14:08:32 INFO >> mapred.JobClient: Spilled Records=590 >>>>>> 11/07/20 14:08:32 INFO mapred.JobClient: Map output >> bytes=2499995001 >>>>>> 11/07/20 14:08:32 INFO mapred.JobClient: Combine input >> records=11450 >>>>>> 11/07/20 14:08:32 INFO mapred.JobClient: Map output >> records=11130 >>>>>> 11/07/20 14:08:32 INFO mapred.JobClient: SPLIT_RAW_BYTES=127 >>>>>> 11/07/20 14:08:32 INFO mapred.JobClient: Reduce input >> records=10 >>>>>> 11/07/20 14:08:32 INFO driver.MahoutDriver: Program took 194096 >> ms >>>>>> >>>>>> if I increase the --numClusters argument (e.g. 50), then it will >> return exception after >>>>>> 11/07/20 14:08:02 INFO mapred.JobClient: map 100% reduce 0% >>>>>> >>>>>> and would retry again (also reproducible using 0.6-snapshot) >>>>>> >>>>>> ... >>>>>> 11/07/20 14:22:25 INFO mapred.JobClient: map 100% reduce >> 0% >>>>>> 11/07/20 14:22:30 INFO mapred.JobClient: Task Id : >> attempt_201107201152_0022_m_000000_0, Status : FAILED >>>>>> org.apache.hadoop.util.DiskChecker$DiskErrorException: Could not >> find any valid local directory for output/file.out >>>>>> at >> org.apache.hadoop.fs.LocalDirAllocator$AllocatorPerContext.getLocalPathForWrite(LocalDirAllocator.java:381) >>>>>> at >> org.apache.hadoop.fs.LocalDirAllocator.getLocalPathForWrite(LocalDirAllocator.java:146) >>>>>> at >> org.apache.hadoop.fs.LocalDirAllocator.getLocalPathForWrite(LocalDirAllocator.java:127) >>>>>> at >> org.apache.hadoop.mapred.MapOutputFile.getOutputFileForWrite(MapOutputFile.java:69) >>>>>> at >> org.apache.hadoop.mapred.MapTask$MapOutputBuffer.mergeParts(MapTask.java:1639) >>>>>> at >> org.apache.hadoop.mapred.MapTask$MapOutputBuffer.flush(MapTask.java:1322) >>>>>> at >> org.apache.hadoop.mapred.MapTask$NewOutputCollector.close(MapTask.java:698) >>>>>> at >> org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:765) >>>>>> at >> org.apache.hadoop.mapred.MapTask.run(MapTask.java:369) >>>>>> at org.apache.hadoop.mapred.Child$4.run(Child.java:259) >>>>>> at java.security.AccessController.doPrivileged(Native >> Method) >>>>>> at javax.security.auth.Subject.doAs(Subject.java:416) >>>>>> at >> org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1059) >>>>>> at org.apache.hadoop.mapred.Child.main(Child.java:253) >>>>>> >>>>>> 11/07/20 14:22:32 INFO >> mapred.JobClient: map 0% reduce 0% >>>>>> ... >>>>>> >>>>>> Then I ran cluster dumper to dump information about the >> clusters, this command would work if I only care about the cluster >> centroids >> (both 0.5 release and 0.6-snapshot) >>>>>> >>>>>> $ bin/mahout clusterdump --seqFileDir sensei/clusters/clusters-1 >> --output image-tag-clusters.txt >>>>>> Running on hadoop, using >> HADOOP_HOME=/home/jeffrey04/Applications/hadoop-0.20.203.0 >>>>>> HADOOP_CONF_DIR=/home/jeffrey04/Applications/hadoop-0.20.203.0/conf >>>>>> MAHOUT-JOB: >> /home/jeffrey04/Applications/mahout/examples/target/mahout-examples-0.6-SNAPSHOT-job.jar >>>>>> 11/07/20 14:33:45 INFO common.AbstractJob: Command line >> arguments: {--dictionaryType=text, --endPhase=2147483647, >> --output=image-tag-clusters.txt, --seqFileDir=sensei/clusters/clusters-1, >> --startPhase=0, --tempDir=temp} >>>>>> 11/07/20 14:33:56 INFO driver.MahoutDriver: Program took 11761 >> ms >>>>>> >>>>>> but if I want to see the degree of membership of each points, I >> get another exception (yes, reproducible for both 0.5 release and >> 0.6-snapshot) >>>>>> >>>>>> $ bin/mahout clusterdump --seqFileDir sensei/clusters/clusters-1 >> --output image-tag-clusters.txt --pointsDir sensei/clusteredPoints >>>>>> Running on hadoop, using >> HADOOP_HOME=/home/jeffrey04/Applications/hadoop-0.20.203.0 >>>>>> HADOOP_CONF_DIR=/home/jeffrey04/Applications/hadoop-0.20.203.0/conf >>>>>> MAHOUT-JOB: >> /home/jeffrey04/Applications/mahout/examples/target/mahout-examples-0.6-SNAPSHOT-job.jar >>>>>> 11/07/20 14:35:08 INFO common.AbstractJob: Command line >> arguments: {--dictionaryType=text, --endPhase=2147483647, >> --output=image-tag-clusters.txt, --pointsDir=sensei/clusteredPoints, >> --seqFileDir=sensei/clusters/clusters-1, --startPhase=0, --tempDir=temp} >>>>>> 11/07/20 14:35:10 INFO util.NativeCodeLoader: Loaded the >> native-hadoop >> library >>>>>> 11/07/20 14:35:10 INFO zlib.ZlibFactory: Successfully loaded >> & initialized native-zlib library >>>>>> 11/07/20 14:35:10 INFO compress.CodecPool: Got brand-new >> decompressor >>>>>> Exception in thread "main" >> java.lang.ClassCastException: org.apache.hadoop.io.Text cannot be cast to >> org.apache.hadoop.io.IntWritable >>>>>> at >> org.apache.mahout.utils.clustering.ClusterDumper.readPoints(ClusterDumper.java:261) >>>>>> at >> org.apache.mahout.utils.clustering.ClusterDumper.init(ClusterDumper.java:209) >>>>>> at >> org.apache.mahout.utils.clustering.ClusterDumper.run(ClusterDumper.java:123) >>>>>> at >> org.apache.mahout.utils.clustering.ClusterDumper.main(ClusterDumper.java:89) >>>>>> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native >> Method) >>>>>> >> at >> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) >>>>>> at >> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) >>>>>> at java.lang.reflect.Method.invoke(Method.java:616) >>>>>> at >> org.apache.hadoop.util.ProgramDriver$ProgramDescription.invoke(ProgramDriver.java:68) >>>>>> at >> org.apache.hadoop.util.ProgramDriver.driver(ProgramDriver.java:139) >>>>>> at >> org.apache.mahout.driver.MahoutDriver.main(MahoutDriver.java:188) >>>>>> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native >> Method) >>>>>> at >> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) >>>>>> at >> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) >>>>>> at java.lang.reflect.Method.invoke(Method.java:616) >>>>>> at org.apache.hadoop.util.RunJar.main(RunJar.java:156) >>>>>> >>>>>> erm, would writing a short program to call the API (btw, >> can't seem to find the latest API doc?) be a better choice here? Or did I >> do >> anything wrong here (yes, Java is not my main language, and I am very new >> to >> Mahout.. and h)? >>>>>> >>>>>> the data is converted from an arff file with about 1000 rows >> (resource) and 14k columns (tag), and it is just a subset of my data. >> (actually >> made a mistake so it is now generating resource clusters instead of tag >> clusters, but I am just doing this as a proof of concept whether mahout is >> good >> enough for the task) >>>>>> >>>>>> Best >> wishes, >>>>>> Jeffrey04 >>>>>> >>>>>> >>>>>> >>>>> >>>>> >>>>> >>>> >>>> >>>> >>> >>> >> > -- Lance Norskog [email protected]
