/lib/JNI_SVM-light-6.01/src/svmlight-6.01/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);



	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");

}