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Posted to dev@mahout.apache.org by "zhao zhendong (JIRA)" <ji...@apache.org> on 2010/01/01 15:35:54 UTC
[jira] Updated: (MAHOUT-232) Implementation of sequential SVM
solver based on Pegasos
[ https://issues.apache.org/jira/browse/MAHOUT-232?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
zhao zhendong updated MAHOUT-232:
---------------------------------
Component/s: Classification
Description:
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 |
was:
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.
The plan of Sequential Pegasos:
1 Supporting the general file system ( almost finished );
2 Supporting HDFS;
> 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
>
>
> 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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