/lib/JNI_SVM-light-6.01/src/svmlight-6.01/svm_learn_main.c
C | 476 lines | 412 code | 34 blank | 30 comment | 63 complexity | 280657a42ad83a1ac39df93e2868d016 MD5 | raw file
- /***********************************************************************/
- /* */
- /* 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);
-
- FILE * dump = NULL;
- char* traindump = (char *) my_malloc(sizeof(char)*25);
- sprintf(traindump,"maintraindump%d.dat",1);
-
- int lengthcnt = 20;
- int namecnt=2;
- while((dump = fopen(traindump,"r+")) != NULL) {
- fclose(dump);
- printf("traindump is already there: %s\n",traindump);
- if (strlen(traindump) >= lengthcnt) {
- free(traindump);
- lengthcnt =+ 20;
- traindump = (char *) my_malloc(sizeof(char)*lengthcnt);
- }
- sprintf(traindump,"maintraindump%d.dat",namecnt++);
- }
-
- printf("------------------------------ Writing traindump to file %s",traindump);
- if ((dump = fopen(traindump,"w")) == NULL) {
- perror("Doesnt work!\n");
- exit(1);
- }
-
- printf("\n|||||||||||||||||||||||||||||||||| dumping ..\n");
- long int z = 0;
- long int y = 0;
- fprintf(dump,"totaldocuments: %ld \n",totdoc);
- while(z<(totdoc)) {
- fprintf(dump,"(%ld) (QID: %ld) (CF: %.16g) (SID: %ld) ",docs[z]->docnum,docs[z]->queryid,docs[z]->costfactor,docs[z]->slackid);
- SVECTOR *v = docs[z]->fvec;
- fprintf(dump,"(NORM:%.32g) (UD:%s) (KID:%ld) (VL:%p) (F:%.32g) %.32g ",v->twonorm_sq,(v->userdefined == NULL ? "" : v->userdefined),v->kernel_id,v->next,v->factor,target[z]);
- if (v != NULL && v->words != NULL) {
- while ((v->words[y]).wnum) {
- fprintf(dump,"%ld:%.32g ",(v->words[y]).wnum, (v->words[y]).weight);
- y++;
- }
- } else
- fprintf(dump, "NULL WORTE\n");
- fprintf(dump,"\n");
- y=0;
- z++;
- }
-
-
- fprintf(dump,"---------------------------------------------------\n");
- fprintf(dump,"kernel_type: %ld\n",kernel_parm.kernel_type);
- fprintf(dump,"poly_degree: %ld\n",kernel_parm.poly_degree);
- fprintf(dump,"rbf_gamma: %.32g\n",kernel_parm.rbf_gamma);
- fprintf(dump,"coef_lin: %.32g\n",kernel_parm.coef_lin);
- fprintf(dump,"coef_const: %.32g\n",kernel_parm.coef_const);
- fprintf(dump,"custom: %s\n",kernel_parm.custom);
-
- fprintf(dump,"type: %ld\n",learn_parm.type);
- fprintf(dump,"svm_c: %.32g\n",learn_parm.svm_c);
- fprintf(dump,"eps: %.32g\n",learn_parm.eps);
- fprintf(dump,"svm_costratio: %.32g\n",learn_parm.svm_costratio);
- fprintf(dump,"transduction_posratio: %.32g\n",learn_parm.transduction_posratio);
- fprintf(dump,"biased_hyperplane: %ld\n",learn_parm.biased_hyperplane);
- fprintf(dump,"svm_maxqpsize: %ld\n",learn_parm.svm_maxqpsize);
- fprintf(dump,"svm_newvarsinqp: %ld\n",learn_parm.svm_newvarsinqp);
- fprintf(dump,"epsilon_crit: %.32g\n",learn_parm.epsilon_crit);
- fprintf(dump,"epsilon_shrink: %.32g\n",learn_parm.epsilon_shrink);
- fprintf(dump,"svm_iter_to_shrink: %ld\n",learn_parm.svm_iter_to_shrink);
- fprintf(dump,"remove_inconsistent: %ld\n",learn_parm.remove_inconsistent);
- fprintf(dump,"skip_final_opt_check: %ld\n",learn_parm.skip_final_opt_check);
- fprintf(dump,"compute_loo: %ld\n",learn_parm.compute_loo);
- fprintf(dump,"rho: %.32g\n",learn_parm.rho);
- fprintf(dump,"xa_depth: %ld\n",learn_parm.xa_depth);
- fprintf(dump,"predfile: %s\n",learn_parm.predfile);
- fprintf(dump,"alphafile: %s\n",learn_parm.alphafile);
- fprintf(dump,"epsilon_const: %.32g\n",learn_parm.epsilon_const);
- fprintf(dump,"epsilon_a: %.32g\n",learn_parm.epsilon_a);
- fprintf(dump,"opt_precision: %.32g\n",learn_parm.opt_precision);
- fprintf(dump,"svm_c_steps: %ld\n",learn_parm.svm_c_steps);
- fprintf(dump,"svm_c_factor: %.32g\n",learn_parm.svm_c_factor);
- fprintf(dump,"svm_costratio_unlab: %.32g\n",learn_parm.svm_costratio_unlab);
- fprintf(dump,"svm_unlabbound: %.32g\n",learn_parm.svm_unlabbound);
-
-
- 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);
- }
-
- fprintf(dump,"totwords: %ld\n",learn_parm.totwords);
-
- printf("|||||||||||||||||||||||||||||||||| z: %ld, totdoc: %ld\n",z,totdoc);
-
- fclose(dump);
-
- 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;i<totdoc;i++)
- free_example(docs[i],1);
- free(docs);
- free(target);
-
- return(0);
- }
-
- /*---------------------------------------------------------------------------*/
-
- void read_input_parameters(int argc,char *argv[],char *docfile,char *modelfile,
- char *restartfile,long *verbosity,
- LEARN_PARM *learn_parm,KERNEL_PARM *kernel_parm)
- {
- long i;
- char type[100];
-
- /* set default */
- strcpy (modelfile, "svm_model");
- strcpy (learn_parm->predfile, "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;(i<argc) && ((argv[i])[0] == '-');i++) {
- switch ((argv[i])[1])
- {
- case '?': print_help(); exit(0);
- case 'z': i++; strcpy(type,argv[i]); break;
- case 'v': i++; (*verbosity)=atol(argv[i]); break;
- case 'b': i++; learn_parm->biased_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)<argc) {
- strcpy (modelfile, argv[i+1]);
- }
- if(learn_parm->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_maxqpsize<learn_parm->svm_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<q to prevent\n");
- printf(" zig-zagging.\n");
- printf(" -m [5..] -> 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");
- }
-
-