Implementation of sequential SVM solver based on Pegasos

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)

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Currently, this package provides (Features):
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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.

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TODO:
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1. Multi-classification Probability Prediction
2. Performance Testing

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Usage:
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Classification:
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@@ Training: @@
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SVMPegasosTraining.java
The default argument is:

-tr ../examples/src/test/resources/svmdataset/train.dat -m ../examples/src/test/resources/svmdataset/SVM.model

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@ For the case that training data set on HDFS:@
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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

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@ Multi-class Training [Based on MapReduce Framework]:@
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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

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@@ Testing: @@
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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

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@ Parallel Testing (Classification): @
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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

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@ Parallel multi-classification: @
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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.

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Regression:
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SVMPegasosTraining.java
-tr ../examples/src/test/resources/svmdataset/abalone_scale -m ../examples/src/test/resources/svmdataset/SVMregression.model -s 1

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Experimental Results:
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Classsification:
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Data set:
name source type class training size testing size feature
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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

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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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Parallel Classification (Testing)
Data set | Accuracy | Training Time | Testing Time |
rcv1.binary | 94.98% | 19 Sec | 3 min 29 Sec (one node)|

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Parallel Multi-classification Based on MapReduce Framework:
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Data set:
name | source | type | class | training size | testing size | feature
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poker | UCI | classification | 10 | 25,010 | 1,000,000 | 10
protein | [JYW02a] | classification | 3 | 17,766 | 6,621 | 357

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Data set | Accuracy vs. (Libsvm with linear kernel)
poker | 50.14 % vs. ( 49.952% ) |
protein | 68.14% vs. ( 64.93% ) |

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Regression:
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Data set:
name | source | type | class | training size | testing size | feature
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abalone | UCI | regression | 4,177 | | 8
triazines | UCI | regression | 186 | | 60
cadata | StatLib | regression | 20,640 | | 8
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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 |