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This directory includes some useful codes: 1. subset selection tools. 2. parameter selection tools. 3. LIBSVM format checking tools Part I: Subset selection tools Introduction ============ Training large data is time consuming. Sometimes one should work on a smaller subset first. The python script subset.py randomly selects a specified number of samples. For classification data, we provide a stratified selection to ensure the same class distribution in the subset. Usage: subset.py [options] dataset number [output1] [output2] This script selects a subset of the given data set. options: -s method : method of selection (default 0) 0 -- stratified selection (classification only) 1 -- random selection output1 : the subset (optional) output2 : the rest of data (optional) If output1 is omitted, the subset will be printed on the screen. Example ======= > python subset.py heart_scale 100 file1 file2 From heart_scale 100 samples are randomly selected and stored in file1. All remaining instances are stored in file2. Part II: Parameter Selection Tools Introduction ============ grid.py is a parameter selection tool for C-SVM classification using the RBF (radial basis function) kernel. It uses cross validation (CV) technique to estimate the accuracy of each parameter combination in the specified range and helps you to decide the best parameters for your problem. grid.py directly executes libsvm binaries (so no python binding is needed) for cross validation and then draw contour of CV accuracy using gnuplot. You must have libsvm and gnuplot installed before using it. The package gnuplot is available at http://www.gnuplot.info/ On Mac OSX, the precompiled gnuplot file needs the library Aquarterm, which thus must be installed as well. In addition, this version of gnuplot does not support png, so you need to change "set term png transparent small" and use other image formats. For example, you may have "set term pbm small color". Usage: grid.py [-log2c begin,end,step] [-log2g begin,end,step] [-v fold] [-svmtrain pathname] [-gnuplot pathname] [-out pathname] [-png pathname] [additional parameters for svm-train] dataset The program conducts v-fold cross validation using parameter C (and gamma) = 2^begin, 2^(begin+step), ..., 2^end. You can specify where the libsvm executable and gnuplot are using the -svmtrain and -gnuplot parameters. For windows users, please use pgnuplot.exe. If you are using gnuplot 3.7.1, please upgrade to version 3.7.3 or higher. The version 3.7.1 has a bug. If you use cygwin on windows, please use gunplot-x11. Example ======= > python grid.py -log2c -5,5,1 -log2g -4,0,1 -v 5 -m 300 heart_scale Users (in particular MS Windows users) may need to specify the path of executable files. You can either change paths in the beginning of grid.py or specify them in the command line. For example, > grid.py -log2c -5,5,1 -svmtrain c:\libsvm\windows\svm-train.exe -gnuplot c:\tmp\gnuplot\bin\pgnuplot.exe -v 10 heart_scale Output: two files dataset.png: the CV accuracy contour plot generated by gnuplot dataset.out: the CV accuracy at each (log2(C),log2(gamma)) Parallel grid search ==================== You can conduct a parallel grid search by dispatching jobs to a cluster of computers which share the same file system. First, you add machine names in grid.py: ssh_workers = ["linux1", "linux5", "linux5"] and then setup your ssh so that the authentication works without asking a password. The same machine (e.g., linux5 here) can be listed more than once if it has multiple CPUs or has more RAM. If the local machine is the best, you can also enlarge the nr_local_worker. For example: nr_local_worker = 2 Example: > python grid.py heart_scale [local] -1 -1 78.8889 (best c=0.5, g=0.5, rate=78.8889) [linux5] -1 -7 83.3333 (best c=0.5, g=0.0078125, rate=83.3333) [linux5] 5 -1 77.037 (best c=0.5, g=0.0078125, rate=83.3333) [linux1] 5 -7 83.3333 (best c=0.5, g=0.0078125, rate=83.3333) . . . If -log2c, -log2g, or -v is not specified, default values are used. If your system uses telnet instead of ssh, you list the computer names in telnet_workers. Part III: LIBSVM format checking tools Introduction ============ `svm-train' conducts only a simple check of the input data. To do a detailed check, we provide a python script `checkdata.py.' Usage: checkdata.py dataset This tool is written by Rong-En Fan at National Taiwan University. Example ======= > cat bad_data 1 3:1 2:4 > python checkdata.py bad_data line 1: feature indices must be in an ascending order, previous/current features 3:1 2:4 Found 1 lines with error.