svm_learn.c

这是一个采用c++编写的用于机器学习文本分类的SVM算法的实现代码。

/***********************************************************************/
/*                                                                     */
/*   svm_learn.c                                                       */
/*                                                                     */
/*   Learning module of Support Vector Machine.                        */
/*                                                                     */
/*   Author: Thorsten Joachims                                         */
/*   Date: 02.07.02                                                    */
/*                                                                     */
/*   Copyright (c) 2002  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.                                             */
/*                                                                     */
/***********************************************************************/


# include "svm_common.h"
# include "svm_learn.h"


/* interface to QP-solver */
double *optimize_qp(QP *, double *, long, double *, LEARN_PARM *);

/*---------------------------------------------------------------------------*/

/* Learns an SVM classification model based on the training data in
   docs/label. The resulting model is returned in the structure
   model. */

void svm_learn_classification(DOC **docs, double *class, long int
			      totdoc, long int totwords, 
			      LEARN_PARM *learn_parm, 
			      KERNEL_PARM *kernel_parm, 
			      KERNEL_CACHE *kernel_cache, 
			      MODEL *model,
			      double *alpha)
     /* docs:        Training vectors (x-part) */
     /* class:       Training labels (y-part, zero if test example for
                     transduction) */
     /* totdoc:      Number of examples in docs/label */
     /* totwords:    Number of features (i.e. highest feature index) */
     /* learn_parm:  Learning paramenters */
     /* kernel_parm: Kernel paramenters */
     /* kernel_cache:Initialized Cache of size totdoc, if using a kernel. 
                     NULL if linear.*/
     /* model:       Returns learning result (assumed empty before called) */
     /* alpha:       Start values for the alpha variables or NULL
	             pointer. The new alpha values are returned after 
		     optimization if not NULL. Array must be of size totdoc. */
{
  long *inconsistent,i,*label;
  long inconsistentnum;
  long misclassified,upsupvecnum;
  double loss,model_length,example_length;
  double maxdiff,*lin,*a,*c;
  long runtime_start,runtime_end;
  long iterations;
  long *unlabeled,transduction;
  long heldout;
  long loo_count=0,loo_count_pos=0,loo_count_neg=0,trainpos=0,trainneg=0;
  long loocomputed=0,runtime_start_loo=0,runtime_start_xa=0;
  double heldout_c=0,r_delta_sq=0,r_delta,r_delta_avg;
  long *index,*index2dnum;
  double *weights;
  CFLOAT *aicache;  /* buffer to keep one row of hessian */

  double *xi_fullset; /* buffer for storing xi on full sample in loo */
  double *a_fullset;  /* buffer for storing alpha on full sample in loo */
  TIMING timing_profile;
  SHRINK_STATE shrink_state;

  runtime_start=get_runtime();
  timing_profile.time_kernel=0;
  timing_profile.time_opti=0;
  timing_profile.time_shrink=0;
  timing_profile.time_update=0;
  timing_profile.time_model=0;
  timing_profile.time_check=0;
  timing_profile.time_select=0;
  kernel_cache_statistic=0;

  learn_parm->totwords=totwords;

  /* make sure -n value is reasonable */
  if((learn_parm->svm_newvarsinqp < 2) 
     || (learn_parm->svm_newvarsinqp > learn_parm->svm_maxqpsize)) {
    learn_parm->svm_newvarsinqp=learn_parm->svm_maxqpsize;
  }

  init_shrink_state(&shrink_state,totdoc,(long)MAXSHRINK);

  label = (long *)my_malloc(sizeof(long)*totdoc);
  inconsistent = (long *)my_malloc(sizeof(long)*totdoc);
  unlabeled = (long *)my_malloc(sizeof(long)*totdoc);
  c = (double *)my_malloc(sizeof(double)*totdoc);
  a = (double *)my_malloc(sizeof(double)*totdoc);
  a_fullset = (double *)my_malloc(sizeof(double)*totdoc);
  xi_fullset = (double *)my_malloc(sizeof(double)*totdoc);
  lin = (double *)my_malloc(sizeof(double)*totdoc);
  learn_parm->svm_cost = (double *)my_malloc(sizeof(double)*totdoc);
  model->supvec = (DOC **)my_malloc(sizeof(DOC *)*(totdoc+2));
  model->alpha = (double *)my_malloc(sizeof(double)*(totdoc+2));
  model->index = (long *)my_malloc(sizeof(long)*(totdoc+2));

  model->at_upper_bound=0;
  model->b=0;	       
  model->supvec[0]=0;  /* element 0 reserved and empty for now */
  model->alpha[0]=0;
  model->lin_weights=NULL;
  model->totwords=totwords;
  model->totdoc=totdoc;
  model->kernel_parm=(*kernel_parm);
  model->sv_num=1;
  model->loo_error=-1;
  model->loo_recall=-1;
  model->loo_precision=-1;
  model->xa_error=-1;
  model->xa_recall=-1;
  model->xa_precision=-1;
  inconsistentnum=0;
  transduction=0;

  r_delta=estimate_r_delta(docs,totdoc,kernel_parm);
  r_delta_sq=r_delta*r_delta;

  r_delta_avg=estimate_r_delta_average(docs,totdoc,kernel_parm);
  if(learn_parm->svm_c == 0.0) {  /* default value for C */
    learn_parm->svm_c=1.0/(r_delta_avg*r_delta_avg);
    if(verbosity>=1) 
      printf("Setting default regularization parameter C=%.4f\n",
	     learn_parm->svm_c);
  }

  learn_parm->eps=-1.0;      /* equivalent regression epsilon for
				classification */

  for(i=0;i<totdoc;i++) {    /* various inits */
    docs[i]->docnum=i;
    inconsistent[i]=0;
    a[i]=0;
    lin[i]=0;
    c[i]=0.0;
    unlabeled[i]=0;
    if(class[i] == 0) {
      unlabeled[i]=1;
      label[i]=0;
      transduction=1;
    }
    if(class[i] > 0) {
      learn_parm->svm_cost[i]=learn_parm->svm_c*learn_parm->svm_costratio*
	docs[i]->costfactor;
      label[i]=1;
      trainpos++;
    }
    else if(class[i] < 0) {
      learn_parm->svm_cost[i]=learn_parm->svm_c*docs[i]->costfactor;
      label[i]=-1;
      trainneg++;
    }
    else {
      learn_parm->svm_cost[i]=0;
    }
  }
  if(verbosity>=2) {
    printf("%ld positive, %ld negative, and %ld unlabeled examples.\n",trainpos,trainneg,totdoc-trainpos-trainneg); fflush(stdout);
  }

  /* caching makes no sense for linear kernel */
  if(kernel_parm->kernel_type == LINEAR) {
    kernel_cache = NULL;   
  } 

  /* compute starting state for initial alpha values */
  if(alpha) {
    if(verbosity>=1) {
      printf("Computing starting state..."); fflush(stdout);
    }
    index = (long *)my_malloc(sizeof(long)*totdoc);
    index2dnum = (long *)my_malloc(sizeof(long)*(totdoc+11));
    weights=(double *)my_malloc(sizeof(double)*(totwords+1));
    aicache = (CFLOAT *)my_malloc(sizeof(CFLOAT)*totdoc);
    for(i=0;i<totdoc;i++) {    /* create full index and clip alphas */
      index[i]=1;
      alpha[i]=fabs(alpha[i]);
      if(alpha[i]<0) alpha[i]=0;
      if(alpha[i]>learn_parm->svm_cost[i]) alpha[i]=learn_parm->svm_cost[i];
    }
    if(kernel_parm->kernel_type != LINEAR) {
      for(i=0;i<totdoc;i++)     /* fill kernel cache with unbounded SV */
	if((alpha[i]>0) && (alpha[i]<learn_parm->svm_cost[i]) 
	   && (kernel_cache_space_available(kernel_cache))) 
	  cache_kernel_row(kernel_cache,docs,i,kernel_parm);
      for(i=0;i<totdoc;i++)     /* fill rest of kernel cache with bounded SV */
	if((alpha[i]==learn_parm->svm_cost[i]) 
	   && (kernel_cache_space_available(kernel_cache))) 
	  cache_kernel_row(kernel_cache,docs,i,kernel_parm);
    }
    (void)compute_index(index,totdoc,index2dnum);
    update_linear_component(docs,label,index2dnum,alpha,a,index2dnum,totdoc,
			    totwords,kernel_parm,kernel_cache,lin,aicache,
			    weights);
    (void)calculate_svm_model(docs,label,unlabeled,lin,alpha,a,c,
			      learn_parm,index2dnum,index2dnum,model);
    for(i=0;i<totdoc;i++) {    /* copy initial alphas */
      a[i]=alpha[i];
    }
    free(index);
    free(index2dnum);
    free(weights);
    free(aicache);
    if(verbosity>=1) {
      printf("done.\n");  fflush(stdout);
    }   
  } 

  if(transduction) {
    learn_parm->svm_iter_to_shrink=99999999;
    if(verbosity >= 1)
      printf("\nDeactivating Shrinking due to an incompatibility with the transductive \nlearner in the current version.\n\n");
  }

  if(transduction && learn_parm->compute_loo) {
    learn_parm->compute_loo=0;
    if(verbosity >= 1)
      printf("\nCannot compute leave-one-out estimates for transductive learner.\n\n");
  }    

  if(learn_parm->remove_inconsistent && learn_parm->compute_loo) {
    learn_parm->compute_loo=0;
    printf("\nCannot compute leave-one-out estimates when removing inconsistent examples.\n\n");
  }    

  if(learn_parm->compute_loo && ((trainpos == 1) || (trainneg == 1))) {
    learn_parm->compute_loo=0;
    printf("\nCannot compute leave-one-out with only one example in one class.\n\n");
  }    


  if(verbosity==1) {
    printf("Optimizing"); fflush(stdout);
  }

  /* train the svm */
  iterations=optimize_to_convergence(docs,label,totdoc,totwords,learn_parm,
				     kernel_parm,kernel_cache,&shrink_state,model,
				     inconsistent,unlabeled,a,lin,
				     c,&timing_profile,
				     &maxdiff,(long)-1,
				     (long)1);
  
  if(verbosity>=1) {
    if(verbosity==1) printf("done. (%ld iterations)\n",iterations);

    misclassified=0;
    for(i=0;(i<totdoc);i++) { /* get final statistic */
      if((lin[i]-model->b)*(double)label[i] <= 0.0) 
	misclassified++;
    }

    printf("Optimization finished (%ld misclassified, maxdiff=%.5f).\n",
	   misclassified,maxdiff); 

    runtime_end=get_runtime();
    if(verbosity>=2) {
      printf("Runtime in cpu-seconds: %.2f (%.2f%% for kernel/%.2f%% for optimizer/%.2f%% for final/%.2f%% for update/%.2f%% for model/%.2f%% for check/%.2f%% for select)\n",
        ((float)runtime_end-(float)runtime_start)/100.0,
        (100.0*timing_profile.time_kernel)/(float)(runtime_end-runtime_start),
	(100.0*timing_profile.time_opti)/(float)(runtime_end-runtime_start),
	(100.0*timing_profile.time_shrink)/(float)(runtime_end-runtime_start),
        (100.0*timing_profile.time_update)/(float)(runtime_end-runtime_start),
        (100.0*timing_profile.time_model)/(float)(runtime_end-runtime_start),
        (100.0*timing_profile.time_check)/(float)(runtime_end-runtime_start),
        (100.0*timing_profile.time_select)/(float)(runtime_end-runtime_start));
    }
    else {
      printf("Runtime in cpu-seconds: %.2f\n",
	     (runtime_end-runtime_start)/100.0);
    }

    if(learn_parm->remove_inconsistent) {	  
      inconsistentnum=0;
      for(i=0;i<totdoc;i++) 
	if(inconsistent[i]) 
	  inconsistentnum++;
      printf("Number of SV: %ld (plus %ld inconsistent examples)\n",
	     model->sv_num-1,inconsistentnum);
    }
    else {
      upsupvecnum=0;
      for(i=1;i<model->sv_num;i++) {
	if(fabs(model->alpha[i]) >= 
	   (learn_parm->svm_cost[(model->supvec[i])->docnum]-
	    learn_parm->epsilon_a)) 
	  upsupvecnum++;
      }
      printf("Number of SV: %ld (including %ld at upper bound)\n",
	     model->sv_num-1,upsupvecnum);
    }
    
    if((verbosity>=1) && (!learn_parm->skip_final_opt_check)) {
      loss=0;
      model_length=0; 
      for(i=0;i<totdoc;i++) {
	if((lin[i]-model->b)*(double)label[i] < 1.0-learn_parm->epsilon_crit)
	  loss+=1.0-(lin[i]-model->b)*(double)label[i];
	model_length+=a[i]*label[i]*lin[i];
      }
      model_length=sqrt(model_length);
      fprintf(stdout,"L1 loss: loss=%.5f\n",loss);
      fprintf(stdout,"Norm of weight vector: |w|=%.5f\n",model_length);
      example_length=estimate_sphere(model,kernel_parm); 
      fprintf(stdout,"Norm of longest example vector: |x|=%.5f\n",
	      length_of_longest_document_vector(docs,totdoc,kernel_parm));
      fprintf(stdout,"Estimated VCdim of classifier: VCdim<=%.5f\n",
	      estimate_margin_vcdim(model,model_length,example_length,
				    kernel_parm));
      if((!learn_parm->remove_inconsistent) && (!transduction)) {
	runtime_start_xa=get_runtime();
	if(verbosity>=1) {
	  printf("Computing XiAlpha-estimates..."); fflush(stdout);
	}
	compute_xa_estimates(model,label,unlabeled,totdoc,docs,lin,a,
			     kernel_parm,learn_parm,&(model->xa_error),
			     &(model->xa_recall),&(model->xa_precision));
	if(verbosity>=1) {
	  printf("done\n");
	}
	printf("Runtime for XiAlpha-estimates in cpu-seconds: %.2f\n",
	       (get_runtime()-runtime_start_xa)/100.0);
	
	fprintf(stdout,"XiAlpha-estimate of the error: error<=%.2f%% (rho=%.2f,depth=%ld)\n",
		model->xa_error,learn_parm->rho,learn_parm->xa_depth);
	fprintf(stdout,"XiAlpha-estimate of the recall: recall=>%.2f%% (rho=%.2f,depth=%ld)\n",
		model->xa_recall,learn_parm->rho,learn_parm->xa_depth);
	fprintf(stdout,"XiAlpha-estimate of the precision: precision=>%.2f%% (rho=%.2f,depth=%ld)\n",
		model->xa_precision,learn_parm->rho,learn_parm->xa_depth);
      }
      else if(!learn_parm->remove_inconsistent) {
	estimate_transduction_quality(model,label,unlabeled,totdoc,docs,lin);
      }
    }
    if(verbosity>=1) {
      printf("Number of kernel evaluations: %ld\n",kernel_cache_statistic);
    }
  }


  /* leave-one-out testing starts now */
  if(learn_parm->compute_loo) {
    /* save results of training on full dataset for leave-one-out */
    runtime_start_loo=get_runtime();
    for(i=0;i<totdoc;i++) {
      xi_fullset[i]=1.0-((lin[i]-model->b)*(double)label[i]);
      if(xi_fullset[i]<0) xi_fullset[i]=0;
      a_fullset[i]=a[i];
    }
    if(verbosity>=1) {
      printf("Computing leave-one-out");
    }
    
    /* repeat this loop for every held-out example */
    for(heldout=0;(heldout<totdoc);heldout++) {
      if(learn_parm->rho*a_fullset[heldout]*r_delta_sq+xi_fullset[heldout]