www.pudn.com > svm_multiClass.rar > svm_learn_main.c
/***********************************************************************/
/* */
/* svm_learn_main.c */
/* */
/* Command line interface to the learning module of the */
/* Support Vector Machine. */
/* */
/* Author: Thorsten Joachims */
/* Date: 02.07.02 */
/* */
/* Copyright (c) 2000 Thorsten Joachims - All rights reserved */
/* */
/* This software is available for non-commercial use only. It must */
/* not be modified and distributed without prior permission of the */
/* author. The author is not responsible for implications from the */
/* use of this software. */
/* */
/***********************************************************************/
/* uncomment, if you want to use svm-learn out of C++ */
/* extern "C" { */
# include "svm_common.h"
# include "svm_learn.h"
/* } */
char docfile[200]; /* file with training examples */
char modelfile[200]; /* file for resulting classifier */
char restartfile[200]; /* file with initial alphas */
void read_input_parameters(int, char **, char *, char *, char *, long *,
LEARN_PARM *, KERNEL_PARM *);
void wait_any_key();
void print_help();
int main (int argc, char* argv[])
{
DOC **docs; /* training examples */
long totwords,totdoc,i;
double *target;
double *alpha_in=NULL;
KERNEL_CACHE *kernel_cache;
LEARN_PARM learn_parm;
KERNEL_PARM kernel_parm;
MODEL *model=(MODEL *)my_malloc(sizeof(MODEL));
read_input_parameters(argc,argv,docfile,modelfile,restartfile,&verbosity,
&learn_parm,&kernel_parm);
read_documents(docfile,&docs,&target,&totwords,&totdoc);
if(restartfile[0]) alpha_in=read_alphas(restartfile,totdoc);
if(kernel_parm.kernel_type == LINEAR) { /* don't need the cache */
kernel_cache=NULL;
}
else {
/* Always get a new kernel cache. It is not possible to use the
same cache for two different training runs */
kernel_cache=kernel_cache_init(totdoc,learn_parm.kernel_cache_size);
}
if(learn_parm.type == CLASSIFICATION) {
svm_learn_classification(docs,target,totdoc,totwords,&learn_parm,
&kernel_parm,kernel_cache,model,alpha_in);
}
else if(learn_parm.type == REGRESSION) {
svm_learn_regression(docs,target,totdoc,totwords,&learn_parm,
&kernel_parm,&kernel_cache,model);
}
else if(learn_parm.type == RANKING) {
svm_learn_ranking(docs,target,totdoc,totwords,&learn_parm,
&kernel_parm,&kernel_cache,model);
}
else if(learn_parm.type == OPTIMIZATION) {
svm_learn_optimization(docs,target,totdoc,totwords,&learn_parm,
&kernel_parm,kernel_cache,model,alpha_in);
}
if(kernel_cache) {
/* Free the memory used for the cache. */
kernel_cache_cleanup(kernel_cache);
}
/* Warning: The model contains references to the original data 'docs'.
If you want to free the original data, and only keep the model, you
have to make a deep copy of 'model'. */
/* deep_copy_of_model=copy_model(model); */
write_model(modelfile,model);
free(alpha_in);
free_model(model,0);
for(i=0;ipredfile, "trans_predictions");
strcpy (learn_parm->alphafile, "");
strcpy (restartfile, "");
(*verbosity)=1;
learn_parm->biased_hyperplane=1;
learn_parm->sharedslack=0;
learn_parm->remove_inconsistent=0;
learn_parm->skip_final_opt_check=0;
learn_parm->svm_maxqpsize=10;
learn_parm->svm_newvarsinqp=0;
learn_parm->svm_iter_to_shrink=-9999;
learn_parm->maxiter=100000;
learn_parm->kernel_cache_size=40;
learn_parm->svm_c=0.0;
learn_parm->eps=0.1;
learn_parm->transduction_posratio=-1.0;
learn_parm->svm_costratio=1.0;
learn_parm->svm_costratio_unlab=1.0;
learn_parm->svm_unlabbound=1E-5;
learn_parm->epsilon_crit=0.001;
learn_parm->epsilon_a=1E-15;
learn_parm->compute_loo=0;
learn_parm->rho=1.0;
learn_parm->xa_depth=0;
kernel_parm->kernel_type=0;
kernel_parm->poly_degree=3;
kernel_parm->rbf_gamma=1.0;
kernel_parm->coef_lin=1;
kernel_parm->coef_const=1;
strcpy(kernel_parm->custom,"empty");
strcpy(type,"c");
for(i=1;(ibiased_hyperplane=atol(argv[i]); break;
case 'i': i++; learn_parm->remove_inconsistent=atol(argv[i]); break;
case 'f': i++; learn_parm->skip_final_opt_check=!atol(argv[i]); break;
case 'q': i++; learn_parm->svm_maxqpsize=atol(argv[i]); break;
case 'n': i++; learn_parm->svm_newvarsinqp=atol(argv[i]); break;
case '#': i++; learn_parm->maxiter=atol(argv[i]); break;
case 'h': i++; learn_parm->svm_iter_to_shrink=atol(argv[i]); break;
case 'm': i++; learn_parm->kernel_cache_size=atol(argv[i]); break;
case 'c': i++; learn_parm->svm_c=atof(argv[i]); break;
case 'w': i++; learn_parm->eps=atof(argv[i]); break;
case 'p': i++; learn_parm->transduction_posratio=atof(argv[i]); break;
case 'j': i++; learn_parm->svm_costratio=atof(argv[i]); break;
case 'e': i++; learn_parm->epsilon_crit=atof(argv[i]); break;
case 'o': i++; learn_parm->rho=atof(argv[i]); break;
case 'k': i++; learn_parm->xa_depth=atol(argv[i]); break;
case 'x': i++; learn_parm->compute_loo=atol(argv[i]); break;
case 't': i++; kernel_parm->kernel_type=atol(argv[i]); break;
case 'd': i++; kernel_parm->poly_degree=atol(argv[i]); break;
case 'g': i++; kernel_parm->rbf_gamma=atof(argv[i]); break;
case 's': i++; kernel_parm->coef_lin=atof(argv[i]); break;
case 'r': i++; kernel_parm->coef_const=atof(argv[i]); break;
case 'u': i++; strcpy(kernel_parm->custom,argv[i]); break;
case 'l': i++; strcpy(learn_parm->predfile,argv[i]); break;
case 'a': i++; strcpy(learn_parm->alphafile,argv[i]); break;
case 'y': i++; strcpy(restartfile,argv[i]); break;
default: printf("\nUnrecognized option %s!\n\n",argv[i]);
print_help();
exit(0);
}
}
if(i>=argc) {
printf("\nNot enough input parameters!\n\n");
wait_any_key();
print_help();
exit(0);
}
strcpy (docfile, argv[i]);
if((i+1)svm_iter_to_shrink == -9999) {
if(kernel_parm->kernel_type == LINEAR)
learn_parm->svm_iter_to_shrink=2;
else
learn_parm->svm_iter_to_shrink=100;
}
if(strcmp(type,"c")==0) {
learn_parm->type=CLASSIFICATION;
}
else if(strcmp(type,"r")==0) {
learn_parm->type=REGRESSION;
}
else if(strcmp(type,"p")==0) {
learn_parm->type=RANKING;
}
else if(strcmp(type,"o")==0) {
learn_parm->type=OPTIMIZATION;
}
else if(strcmp(type,"s")==0) {
learn_parm->type=OPTIMIZATION;
learn_parm->sharedslack=1;
}
else {
printf("\nUnknown type '%s': Valid types are 'c' (classification), 'r' regession, and 'p' preference ranking.\n",type);
wait_any_key();
print_help();
exit(0);
}
if((learn_parm->skip_final_opt_check)
&& (kernel_parm->kernel_type == LINEAR)) {
printf("\nIt does not make sense to skip the final optimality check for linear kernels.\n\n");
learn_parm->skip_final_opt_check=0;
}
if((learn_parm->skip_final_opt_check)
&& (learn_parm->remove_inconsistent)) {
printf("\nIt is necessary to do the final optimality check when removing inconsistent \nexamples.\n");
wait_any_key();
print_help();
exit(0);
}
if((learn_parm->svm_maxqpsize<2)) {
printf("\nMaximum size of QP-subproblems not in valid range: %ld [2..]\n",learn_parm->svm_maxqpsize);
wait_any_key();
print_help();
exit(0);
}
if((learn_parm->svm_maxqpsizesvm_newvarsinqp)) {
printf("\nMaximum size of QP-subproblems [%ld] must be larger than the number of\n",learn_parm->svm_maxqpsize);
printf("new variables [%ld] entering the working set in each iteration.\n",learn_parm->svm_newvarsinqp);
wait_any_key();
print_help();
exit(0);
}
if(learn_parm->svm_iter_to_shrink<1) {
printf("\nMaximum number of iterations for shrinking not in valid range: %ld [1,..]\n",learn_parm->svm_iter_to_shrink);
wait_any_key();
print_help();
exit(0);
}
if(learn_parm->svm_c<0) {
printf("\nThe C parameter must be greater than zero!\n\n");
wait_any_key();
print_help();
exit(0);
}
if(learn_parm->transduction_posratio>1) {
printf("\nThe fraction of unlabeled examples to classify as positives must\n");
printf("be less than 1.0 !!!\n\n");
wait_any_key();
print_help();
exit(0);
}
if(learn_parm->svm_costratio<=0) {
printf("\nThe COSTRATIO parameter must be greater than zero!\n\n");
wait_any_key();
print_help();
exit(0);
}
if(learn_parm->epsilon_crit<=0) {
printf("\nThe epsilon parameter must be greater than zero!\n\n");
wait_any_key();
print_help();
exit(0);
}
if(learn_parm->rho<0) {
printf("\nThe parameter rho for xi/alpha-estimates and leave-one-out pruning must\n");
printf("be greater than zero (typically 1.0 or 2.0, see T. Joachims, Estimating the\n");
printf("Generalization Performance of an SVM Efficiently, ICML, 2000.)!\n\n");
wait_any_key();
print_help();
exit(0);
}
if((learn_parm->xa_depth<0) || (learn_parm->xa_depth>100)) {
printf("\nThe parameter depth for ext. xi/alpha-estimates must be in [0..100] (zero\n");
printf("for switching to the conventional xa/estimates described in T. Joachims,\n");
printf("Estimating the Generalization Performance of an SVM Efficiently, ICML, 2000.)\n");
wait_any_key();
print_help();
exit(0);
}
}
void wait_any_key()
{
printf("\n(more)\n");
(void)getc(stdin);
}
void print_help()
{
printf("\nSVM-light %s: Support Vector Machine, learning module %s\n",VERSION,VERSION_DATE);
copyright_notice();
printf(" usage: svm_learn [options] example_file model_file\n\n");
printf("Arguments:\n");
printf(" example_file-> file with training data\n");
printf(" model_file -> file to store learned decision rule in\n");
printf("General options:\n");
printf(" -? -> this help\n");
printf(" -v [0..3] -> verbosity level (default 1)\n");
printf("Learning options:\n");
printf(" -z {c,r,p} -> select between classification (c), regression (r),\n");
printf(" and preference ranking (p) (default classification)\n");
printf(" -c float -> C: trade-off between training error\n");
printf(" and margin (default [avg. x*x]^-1)\n");
printf(" -w [0..] -> epsilon width of tube for regression\n");
printf(" (default 0.1)\n");
printf(" -j float -> Cost: cost-factor, by which training errors on\n");
printf(" positive examples outweight errors on negative\n");
printf(" examples (default 1) (see [4])\n");
printf(" -b [0,1] -> use biased hyperplane (i.e. x*w+b>0) instead\n");
printf(" of unbiased hyperplane (i.e. x*w>0) (default 1)\n");
printf(" -i [0,1] -> remove inconsistent training examples\n");
printf(" and retrain (default 0)\n");
printf("Performance estimation options:\n");
printf(" -x [0,1] -> compute leave-one-out estimates (default 0)\n");
printf(" (see [5])\n");
printf(" -o ]0..2] -> value of rho for XiAlpha-estimator and for pruning\n");
printf(" leave-one-out computation (default 1.0) (see [2])\n");
printf(" -k [0..100] -> search depth for extended XiAlpha-estimator \n");
printf(" (default 0)\n");
printf("Transduction options (see [3]):\n");
printf(" -p [0..1] -> fraction of unlabeled examples to be classified\n");
printf(" into the positive class (default is the ratio of\n");
printf(" positive and negative examples in the training data)\n");
printf("Kernel options:\n");
printf(" -t int -> type of kernel function:\n");
printf(" 0: linear (default)\n");
printf(" 1: polynomial (s a*b+c)^d\n");
printf(" 2: radial basis function exp(-gamma ||a-b||^2)\n");
printf(" 3: sigmoid tanh(s a*b + c)\n");
printf(" 4: user defined kernel from kernel.h\n");
printf(" -d int -> parameter d in polynomial kernel\n");
printf(" -g float -> parameter gamma in rbf kernel\n");
printf(" -s float -> parameter s in sigmoid/poly kernel\n");
printf(" -r float -> parameter c in sigmoid/poly kernel\n");
printf(" -u string -> parameter of user defined kernel\n");
printf("Optimization options (see [1]):\n");
printf(" -q [2..] -> maximum size of QP-subproblems (default 10)\n");
printf(" -n [2..q] -> number of new variables entering the working set\n");
printf(" in each iteration (default n = q). Set n size of cache for kernel evaluations in MB (default 40)\n");
printf(" The larger the faster...\n");
printf(" -e float -> eps: Allow that error for termination criterion\n");
printf(" [y [w*x+b] - 1] >= eps (default 0.001)\n");
printf(" -y [0,1] -> restart the optimization from alpha values in file\n");
printf(" specified by -a option. (default 0)\n");
printf(" -h [5..] -> number of iterations a variable needs to be\n");
printf(" optimal before considered for shrinking (default 100)\n");
printf(" -f [0,1] -> do final optimality check for variables removed\n");
printf(" by shrinking. Although this test is usually \n");
printf(" positive, there is no guarantee that the optimum\n");
printf(" was found if the test is omitted. (default 1)\n");
printf(" -y string -> if option is given, reads alphas from file with given\n");
printf(" and uses them as starting point. (default 'disabled')\n");
printf(" -# int -> terminate optimization, if no progress after this\n");
printf(" number of iterations. (default 100000)\n");
printf("Output options:\n");
printf(" -l string -> file to write predicted labels of unlabeled\n");
printf(" examples into after transductive learning\n");
printf(" -a string -> write all alphas to this file after learning\n");
printf(" (in the same order as in the training set)\n");
wait_any_key();
printf("\nMore details in:\n");
printf("[1] T. Joachims, Making Large-Scale SVM Learning Practical. Advances in\n");
printf(" Kernel Methods - Support Vector Learning, B. Schölkopf and C. Burges and\n");
printf(" A. Smola (ed.), MIT Press, 1999.\n");
printf("[2] T. Joachims, Estimating the Generalization performance of an SVM\n");
printf(" Efficiently. International Conference on Machine Learning (ICML), 2000.\n");
printf("[3] T. Joachims, Transductive Inference for Text Classification using Support\n");
printf(" Vector Machines. International Conference on Machine Learning (ICML),\n");
printf(" 1999.\n");
printf("[4] K. Morik, P. Brockhausen, and T. Joachims, Combining statistical learning\n");
printf(" with a knowledge-based approach - A case study in intensive care \n");
printf(" monitoring. International Conference on Machine Learning (ICML), 1999.\n");
printf("[5] T. Joachims, Learning to Classify Text Using Support Vector\n");
printf(" Machines: Methods, Theory, and Algorithms. Dissertation, Kluwer,\n");
printf(" 2002.\n\n");
}