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svm_classify.c
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svm_classify.c
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/***********************************************************************/
/* */
/* svm_classify.c */
/* */
/* Classification 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"
void svm_classify_read_input_parameters(int, char **, char *, char *, char *, long *,
long *);
void svm_classify_print_help(void);
int _svm_classify (int argc, char* argv[])
{
char docfile[200];
char modelfile[200];
char predictionsfile[200];
DOC *doc; /* test example */
WORD *words;
long max_docs,max_words_doc,lld;
long totdoc=0,queryid,slackid;
long correct=0,incorrect=0,no_accuracy=0;
long res_a=0,res_b=0,res_c=0,res_d=0,wnum,pred_format;
long j;
double t1,runtime=0;
double dist,doc_label,costfactor;
char *line,*comment;
FILE *predfl,*docfl;
MODEL *model;
svm_classify_read_input_parameters(argc,argv,docfile,modelfile,predictionsfile,
&verbosity,&pred_format);
nol_ll(docfile,&max_docs,&max_words_doc,&lld); /* scan size of input file */
max_words_doc+=2;
lld+=2;
line = (char *)my_malloc(sizeof(char)*lld);
words = (WORD *)my_malloc(sizeof(WORD)*(max_words_doc+10));
model=read_model(modelfile);
if(model->kernel_parm.kernel_type == 0) { /* linear kernel */
/* compute weight vector */
add_weight_vector_to_linear_model(model);
}
if(verbosity>=2) {
printf("Classifying test examples.."); fflush(stdout);
}
if ((docfl = fopen (docfile, "r")) == NULL)
{ perror (docfile); exit (1); }
if ((predfl = fopen (predictionsfile, "w")) == NULL)
{ perror (predictionsfile); exit (1); }
while((!feof(docfl)) && fgets(line,(int)lld,docfl)) {
if(line[0] == '#') continue; /* line contains comments */
parse_document(line,words,&doc_label,&queryid,&slackid,&costfactor,&wnum,
max_words_doc,&comment);
totdoc++;
if(model->kernel_parm.kernel_type == LINEAR) {/* For linear kernel, */
for(j=0;(words[j]).wnum != 0;j++) { /* check if feature numbers */
if((words[j]).wnum>model->totwords) /* are not larger than in */
(words[j]).wnum=0; /* model. Remove feature if */
} /* necessary. */
}
doc = create_example(-1,0,0,0.0,create_svector(words,comment,1.0));
t1=get_runtime();
if(model->kernel_parm.kernel_type == LINEAR) { /* linear kernel */
dist=classify_example_linear(model,doc);
}
else { /* non-linear kernel */
dist=classify_example(model,doc);
}
runtime+=(get_runtime()-t1);
free_example(doc,1);
if(dist>0) {
if(pred_format==0) { /* old weired output format */
fprintf(predfl,"%.8g:+1 %.8g:-1\n",dist,-dist);
}
if(doc_label>0) correct++; else incorrect++;
if(doc_label>0) res_a++; else res_b++;
}
else {
if(pred_format==0) { /* old weired output format */
fprintf(predfl,"%.8g:-1 %.8g:+1\n",-dist,dist);
}
if(doc_label<0) correct++; else incorrect++;
if(doc_label>0) res_c++; else res_d++;
}
if(pred_format==1) { /* output the value of decision function */
fprintf(predfl,"%.8g\n",dist);
}
if((int)(0.01+(doc_label*doc_label)) != 1)
{ no_accuracy=1; } /* test data is not binary labeled */
if(verbosity>=2) {
if(totdoc % 100 == 0) {
printf("%ld..",totdoc); fflush(stdout);
}
}
}
fclose(predfl);
fclose(docfl);
free(line);
free(words);
free_model(model,1);
if(verbosity>=2) {
printf("done\n");
/* Note by Gary Boone Date: 29 April 2000 */
/* o Timing is inaccurate. The timer has 0.01 second resolution. */
/* Because classification of a single vector takes less than */
/* 0.01 secs, the timer was underflowing. */
printf("Runtime (without IO) in cpu-seconds: %.2f\n",
(float)(runtime/100.0));
}
if((!no_accuracy) && (verbosity>=1)) {
printf("Accuracy on test set: %.2f%% (%ld correct, %ld incorrect, %ld total)\n",(float)(correct)*100.0/totdoc,correct,incorrect,totdoc);
printf("Precision/recall on test set: %.2f%%/%.2f%%\n",(float)(res_a)*100.0/(res_a+res_b),(float)(res_a)*100.0/(res_a+res_c));
}
return(0);
}
void svm_classify_read_input_parameters(int argc, char **argv, char *docfile,
char *modelfile, char *predictionsfile,
long int *verbosity, long int *pred_format)
{
long i;
/* set default */
strcpy (modelfile, "svm_model");
strcpy (predictionsfile, "svm_predictions");
(*verbosity)=2;
(*pred_format)=1;
for(i=1;(i<argc) && ((argv[i])[0] == '-');i++) {
switch ((argv[i])[1])
{
case 'h': svm_classify_print_help(); exit(0);
case 'v': i++; (*verbosity)=atol(argv[i]); break;
case 'f': i++; (*pred_format)=atol(argv[i]); break;
default: printf("\nUnrecognized option %s!\n\n",argv[i]);
svm_classify_print_help();
exit(0);
}
}
if((i+1)>=argc) {
printf("\nNot enough input parameters!\n\n");
svm_classify_print_help();
exit(0);
}
strcpy (docfile, argv[i]);
strcpy (modelfile, argv[i+1]);
if((i+2)<argc) {
strcpy (predictionsfile, argv[i+2]);
}
if(((*pred_format) != 0) && ((*pred_format) != 1)) {
printf("\nOutput format can only take the values 0 or 1!\n\n");
svm_classify_print_help();
exit(0);
}
}
void svm_classify_print_help(void)
{
printf("\nSVM-light %s: Support Vector Machine, classification module %s\n",VERSION,VERSION_DATE);
copyright_notice();
printf(" usage: svm_classify [options] example_file model_file output_file\n\n");
printf("options: -h -> this help\n");
printf(" -v [0..3] -> verbosity level (default 2)\n");
printf(" -f [0,1] -> 0: old output format of V1.0\n");
printf(" -> 1: output the value of decision function (default)\n\n");
}