svm-predict.c

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#include <stdio.h>
#include <ctype.h>
#include <stdlib.h>
#include <string.h>
#include <errno.h>
#include "svm.h"

int print_null(const char *s,...) {return 0;}

static int (*info)(const char *fmt,...) = &printf;

struct svm_node *x;
int max_nr_attr = 64;

struct svm_model* model;
int predict_probability=0;

static char *line = NULL;
static int max_line_len;

static char* readline(FILE *input)
{
        int len;

        if(fgets(line,max_line_len,input) == NULL)
                return NULL;

        while(strrchr(line,'\n') == NULL)
        {
                max_line_len *= 2;
                line = (char *) realloc(line,max_line_len);
                len = (int) strlen(line);
                if(fgets(line+len,max_line_len-len,input) == NULL)
                        break;
        }
        return line;
}

void exit_input_error(int line_num)
{
        fprintf(stderr,"Wrong input format at line %d\n", line_num);
        exit(1);
}

void predict(FILE *input, FILE *output)
{
        int correct = 0;
        int total = 0;
        double error = 0;
        double sump = 0, sumt = 0, sumpp = 0, sumtt = 0, sumpt = 0;

        int svm_type=svm_get_svm_type(model);
        int nr_class=svm_get_nr_class(model);
        double *prob_estimates=NULL;
        int j;

        if(predict_probability)
        {
                if (svm_type==NU_SVR || svm_type==EPSILON_SVR)
                        info("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=%g\n",svm_get_svr_probability(model));
                else
                {
                        int *labels=(int *) malloc(nr_class*sizeof(int));
                        svm_get_labels(model,labels);
                        prob_estimates = (double *) malloc(nr_class*sizeof(double));
                        fprintf(output,"labels");               
                        for(j=0;j<nr_class;j++)
                                fprintf(output," %d",labels[j]);
                        fprintf(output,"\n");
                        free(labels);
                }
        }

        max_line_len = 1024;
        line = (char *)malloc(max_line_len*sizeof(char));
        while(readline(input) != NULL)
        {
                int i = 0;
                double target_label, predict_label;
                char *idx, *val, *label, *endptr;
                int inst_max_index = -1; // strtol gives 0 if wrong format, and precomputed kernel has <index> start from 0

                label = strtok(line," \t\n");
                if(label == NULL) // empty line
                        exit_input_error(total+1);

                target_label = strtod(label,&endptr);
                if(endptr == label || *endptr != '\0')
                        exit_input_error(total+1);

                while(1)
                {
                        if(i>=max_nr_attr-1)    // need one more for index = -1
                        {
                                max_nr_attr *= 2;
                                x = (struct svm_node *) realloc(x,max_nr_attr*sizeof(struct svm_node));
                        }

                        idx = strtok(NULL,":");
                        val = strtok(NULL," \t");

                        if(val == NULL)
                                break;
                        errno = 0;
                        x[i].index = (int) strtol(idx,&endptr,10);
                        if(endptr == idx || errno != 0 || *endptr != '\0' || x[i].index <= inst_max_index)
                                exit_input_error(total+1);
                        else
                                inst_max_index = x[i].index;

                        errno = 0;
                        x[i].value = strtod(val,&endptr);
                        if(endptr == val || errno != 0 || (*endptr != '\0' && !isspace(*endptr)))
                                exit_input_error(total+1);

                        ++i;
                }
                x[i].index = -1;

                if (predict_probability && (svm_type==C_SVC || svm_type==NU_SVC))
                {
                        predict_label = svm_predict_probability(model,x,prob_estimates);
                        fprintf(output,"%g",predict_label);
                        for(j=0;j<nr_class;j++)
                                fprintf(output," %g",prob_estimates[j]);
                        fprintf(output,"\n");
                }
                else
                {
                        predict_label = svm_predict(model,x);
                        fprintf(output,"%g\n",predict_label);
                }

                if(predict_label == target_label)
                        ++correct;
                error += (predict_label-target_label)*(predict_label-target_label);
                sump += predict_label;
                sumt += target_label;
                sumpp += predict_label*predict_label;
                sumtt += target_label*target_label;
                sumpt += predict_label*target_label;
                ++total;
        }
        if (svm_type==NU_SVR || svm_type==EPSILON_SVR)
        {
                info("Mean squared error = %g (regression)\n",error/total);
                info("Squared correlation coefficient = %g (regression)\n",
                        ((total*sumpt-sump*sumt)*(total*sumpt-sump*sumt))/
                        ((total*sumpp-sump*sump)*(total*sumtt-sumt*sumt))
                        );
        }
        else
                info("Accuracy = %g%% (%d/%d) (classification)\n",
                        (double)correct/total*100,correct,total);
        if(predict_probability)
                free(prob_estimates);
}

void exit_with_help()
{
        printf(
        "Usage: svm-predict [options] test_file model_file output_file\n"
        "options:\n"
        "-b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); for one-class SVM only 0 is supported\n"
        "-q : quiet mode (no outputs)\n"
        );
        exit(1);
}

int main(int argc, char **argv)
{
        FILE *input, *output;
        int i;
        // parse options
        for(i=1;i<argc;i++)
        {
                if(argv[i][0] != '-') break;
                ++i;
                switch(argv[i-1][1])
                {
                        case 'b':
                                predict_probability = atoi(argv[i]);
                                break;
                        case 'q':
                                info = &print_null;
                                i--;
                                break;
                        default:
                                fprintf(stderr,"Unknown option: -%c\n", argv[i-1][1]);
                                exit_with_help();
                }
        }

        if(i>=argc-2)
                exit_with_help();

        input = fopen(argv[i],"r");
        if(input == NULL)
        {
                fprintf(stderr,"can't open input file %s\n",argv[i]);
                exit(1);
        }

        output = fopen(argv[i+2],"w");
        if(output == NULL)
        {
                fprintf(stderr,"can't open output file %s\n",argv[i+2]);
                exit(1);
        }

        if((model=svm_load_model(argv[i+1]))==0)
        {
                fprintf(stderr,"can't open model file %s\n",argv[i+1]);
                exit(1);
        }

        x = (struct svm_node *) malloc(max_nr_attr*sizeof(struct svm_node));
        if(predict_probability)
        {
                if(svm_check_probability_model(model)==0)
                {
                        fprintf(stderr,"Model does not support probabiliy estimates\n");
                        exit(1);
                }
        }
        else
        {
                if(svm_check_probability_model(model)!=0)
                        info("Model supports probability estimates, but disabled in prediction.\n");
        }

        predict(input,output);
        svm_free_and_destroy_model(&model);
        free(x);
        free(line);
        fclose(input);
        fclose(output);
        return 0;
}