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https://issues.apache.org/jira/browse/MAHOUT-232?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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zhao zhendong updated MAHOUT-232:
---------------------------------

    Attachment: SequentialSVM_0.2.patch

> 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: 0.1
>            Reporter: zhao zhendong
>         Attachments: SequentialSVM_0.1.patch, SequentialSVM_0.2.patch
>
>
> 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.
>    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.
> -------------------------------------------------------------------------------------------
> TODO:
> -------------------------------------------------------------------------------------------
> 1. HDFS writ function for storing model file to HDFS.
> 2. Parallel testing algorithm based MapReduce framework.
> 3. Regression.
> 4. Multi-classification.
> -------------------------------------------------------------------------------------------
> Usage:
> -------------------------------------------------------------------------------------------
> Training:
> SVMPegasosTraining.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:
> -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
> >>>>>>>
> 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
> -------------------------------------------------------------------------------------------
> Experimental Results:
> -------------------------------------------------------------------------------------------
> 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          |

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