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https://issues.apache.org/jira/browse/MAHOUT-232?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13000368#comment-13000368
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Ted Dunning commented on MAHOUT-232:
------------------------------------
Viktor,
I am really sorry if I was demotivating. Please let me withdraw that.
Right now, you are the one with the ball on this. If you think it needs to be
re-implemented in a cleaner fashion, then take the current code as a learning
experience and move forward with what you think is necessary. Sean's comments
are just right. You are the one making progress and that puts you in an
important position.
Go ahead and file a new JIRA while we decide whether to close this one. Take
bits of the current code if you like or the approach or nothing as you see fit.
> Implementation of sequential SVM solver based on Pegasos
> --------------------------------------------------------
>
> Key: MAHOUT-232
> URL: https://issues.apache.org/jira/browse/MAHOUT-232
> Project: Mahout
> Issue Type: New Feature
> Components: Classification
> Affects Versions: 0.4
> Reporter: zhao zhendong
> Assignee: Ted Dunning
> Attachments: 0001-Rename-DatastoreSequenenceFile-class.patch,
> 0002-Renamed-HADOOP_MODLE_PATH-to-HADOOP_MODEL_PATH.patch,
> 0003-Change-MultiClassifierDrivers-type-to-AbstractJob.patch,
> 0004-A-script-for-svm-classification-on-20news-dataset.patch,
> Mahout-232-0.8.patch, SVMDataset.patch, SVMonMahout0.5.1.patch,
> SVMonMahout0.5.patch, SequentialSVM_0.1.patch, SequentialSVM_0.2.2.patch,
> SequentialSVM_0.3.patch, SequentialSVM_0.4.patch, a2a.mvc
>
>
> After discussed with guys in this community, I decided to re-implement a
> Sequential SVM solver based on Pegasos for Mahout platform (mahout command
> line style, SparseMatrix and SparseVector etc.) , Eventually, it will
> support HDFS.
> Sequential SVM based on Pegasos.
> Maxim zhao (zhaozhendong at gmail dot com)
> -------------------------------------------------------------------------------------------
> Currently, this package provides (Features):
> -------------------------------------------------------------------------------------------
> 1. Sequential SVM linear solver, include training and testing.
> 2. Support general file system and HDFS right now.
> 3. Supporting large-scale data set training.
> Because of the Pegasos only need to sample certain samples, this package
> supports to pre-fetch
> the certain size (e.g. max iteration) of samples to memory.
> For example: if the size of data set has 100,000,000 samples, due to the
> default maximum iteration is 10,000,
> as the result, this package only random load 10,000 samples to memory.
> 4. Sequential Data set testing, then the package can support large-scale data
> set both on training and testing.
> 5. Supporting parallel classification (only testing phrase) based on
> Map-Reduce framework.
> 6. Supoorting Multi-classfication based on Map-Reduce framework (whole
> parallelized version).
> 7. Supporting Regression.
> -------------------------------------------------------------------------------------------
> TODO:
> -------------------------------------------------------------------------------------------
> 1. Multi-classification Probability Prediction
> 2. Performance Testing
> -------------------------------------------------------------------------------------------
> Usage:
> -------------------------------------------------------------------------------------------
> >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> Classification:
> >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
> @@ Training: @@
> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
> SVMPegasosTraining.java
> The default argument is:
> -tr ../examples/src/test/resources/svmdataset/train.dat -m
> ../examples/src/test/resources/svmdataset/SVM.model
> ~~~~~~~~~~~~~~~~~~~~~~
> @ For the case that training data set on HDFS:@
> ~~~~~~~~~~~~~~~~~~~~~~
> 1 Assure that your training data set has been submitted to hdfs
> hadoop-work-space# bin/hadoop fs -ls path-of-train-dataset
> 2 revise the argument:
> -tr /user/hadoop/train.dat -m
> ../examples/src/test/resources/svmdataset/SVM.model -hdfs
> hdfs://localhost:12009
> ~~~~~~~~~~~~~~~~~~~~~~
> @ Multi-class Training [Based on MapReduce Framework]:@
> ~~~~~~~~~~~~~~~~~~~~~~
> bin/hadoop jar mahout-core-0.3-SNAPSHOT.job
> org.apache.mahout.classifier.svm.ParallelAlgorithms.ParallelMultiClassifierTrainDriver
> -if /user/maximzhao/dataset/protein -of /user/maximzhao/protein -m
> /user/maximzhao/proteinmodel -s 1000000 -c 3 -nor 3 -ms 923179 -mhs -Xmx1000M
> -ttt 1080
> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
> @@ Testing: @@
> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
> SVMPegasosTesting.java
> I have hard coded the arguments in this file, if you want to custom the
> arguments by youself, please uncomment the first line in main function.
> The default argument is:
> -te ../examples/src/test/resources/svmdataset/test.dat -m
> ../examples/src/test/resources/svmdataset/SVM.model
> ~~~~~~~~~~~~~~~~~~~~~~
> @ Parallel Testing (Classification): @
> ~~~~~~~~~~~~~~~~~~~~~~
> ParallelClassifierDriver.java
> bin/hadoop jar mahout-core-0.3-SNAPSHOT.job
> org.apache.mahout.classifier.svm.ParallelAlgorithms.ParallelClassifierDriver
> -if /user/maximzhao/dataset/rcv1_test.binary -of /user/maximzhao/rcv.result
> -m /user/maximzhao/rcv1.model -nor 1 -ms 241572968 -mhs -Xmx500M -ttt 1080
> ~~~~~~~~~~~~~~~~~~~~~~
> @ Parallel multi-classification: @
> ~~~~~~~~~~~~~~~~~~~~~~
> bin/hadoop jar mahout-core-0.3-SNAPSHOT.job
> org.apache.mahout.classifier.svm.ParallelAlgorithms.ParallelMultiClassPredictionDriver
> -if /user/maximzhao/dataset/protein.t -of
> /user/maximzhao/proteinpredictionResult -m /user/maximzhao/proteinmodel -c 3
> -nor 1 -ms 2226917 -mhs -Xmx1000M -ttt 1080
> Note: the parameter -ms 241572968 is obtained by equation : ms = input files
> size / number of mapper.
> >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> Regression:
> >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> SVMPegasosTraining.java
> -tr ../examples/src/test/resources/svmdataset/abalone_scale -m
> ../examples/src/test/resources/svmdataset/SVMregression.model -s 1
> -------------------------------------------------------------------------------------------
> Experimental Results:
> -------------------------------------------------------------------------------------------
> >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> Classsification:
> >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> Data set:
> name source type class training size testing
> size feature
> -----------------------------------------------------------------------------------------------
> rcv1.binary [DL04b] classification 2 20,242
> 677,399 47,236
> covtype.binary UCI classification 2 581,012
> 54
> a9a UCI classification 2 32,561
> 16,281 123
> w8a [JP98a] classification 2 49,749
> 14,951 300
> >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> Data set | Accuracy | Training Time
> | Testing Time |
> rcv1.binary | 94.67% | 19 Sec
> | 2 min 25 Sec |
> covtype.binary | | 19 Sec
> | |
> a9a | 84.72% | 14 Sec
> | 12 Sec |
> w8a | 89.8 % | 14 Sec
> | 8 Sec |
> >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> Parallel Classification (Testing)
> Data set | Accuracy | Training Time
> | Testing Time |
> rcv1.binary | 94.98% | 19 Sec
> | 3 min 29 Sec (one node)|
> >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> Parallel Multi-classification Based on MapReduce Framework:
> >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> Data set:
> name | source | type | class | training size |
> testing size | feature
> -----------------------------------------------------------------------------------------------
> poker | UCI | classification | 10 | 25,010 | 1,000,000
> | 10
> protein | [JYW02a] | classification | 3 | 17,766
> | 6,621 | 357
> >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> Data set | Accuracy vs. (Libsvm with linear kernel)
> poker | 50.14 % vs. ( 49.952% ) |
> protein | 68.14% vs. ( 64.93% ) |
> >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> Regression:
> >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> Data set:
> name | source | type | class | training size |
> testing size | feature
> -----------------------------------------------------------------------------------------------
> abalone | UCI | regression | 4,177 | | 8
> triazines | UCI | regression | 186 | | 60
> cadata | StatLib | regression | 20,640 | | 8
> >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
> Data set | Mean Squared error vs. (Libsvm with linear
> kernel) | Training Time | Test Time |
> abalone | 6.01 vs. (5.25) | 13 Sec |
> triazines | 0.031 vs. (0.0276) | 14 Sec |
> cadata | 5.61 e +10 vs. (1.40 e+10) | 20 Sec |
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