svm-train.c

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#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <ctype.h>
#include <errno.h>
#include "svm.h"
#define Malloc(type,n) (type *)malloc((n)*sizeof(type))

void print_null(const char *s) {}

void exit_with_help()
{
        printf(
        "Usage: svm-train [options] training_set_file [model_file]\n"
        "options:\n"
        "-s svm_type : set type of SVM (default 0)\n"
        "       0 -- C-SVC\n"
        "       1 -- nu-SVC\n"
        "       2 -- one-class SVM\n"
        "       3 -- epsilon-SVR\n"
        "       4 -- nu-SVR\n"
        "-t kernel_type : set type of kernel function (default 2)\n"
        "       0 -- linear: u'*v\n"
        "       1 -- polynomial: (gamma*u'*v + coef0)^degree\n"
        "       2 -- radial basis function: exp(-gamma*|u-v|^2)\n"
        "       3 -- sigmoid: tanh(gamma*u'*v + coef0)\n"
        "       4 -- precomputed kernel (kernel values in training_set_file)\n"
        "-d degree : set degree in kernel function (default 3)\n"
        "-g gamma : set gamma in kernel function (default 1/num_features)\n"
        "-r coef0 : set coef0 in kernel function (default 0)\n"
        "-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)\n"
        "-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)\n"
        "-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)\n"
        "-m cachesize : set cache memory size in MB (default 100)\n"
        "-e epsilon : set tolerance of termination criterion (default 0.001)\n"
        "-h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1)\n"
        "-b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)\n"
        "-wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1)\n"
        "-v n: n-fold cross validation mode\n"
        "-q : quiet mode (no outputs)\n"
        );
        exit(1);
}

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

void parse_command_line(int argc, char **argv, char *input_file_name, char *model_file_name);
void read_problem(const char *filename);
void do_cross_validation();

struct svm_parameter param;             // set by parse_command_line
struct svm_problem prob;                // set by read_problem
struct svm_model *model;
struct svm_node *x_space;
int cross_validation;
int nr_fold;

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;
}

int main(int argc, char **argv)
{
        char input_file_name[1024];
        char model_file_name[1024];
        const char *error_msg;

        parse_command_line(argc, argv, input_file_name, model_file_name);
        read_problem(input_file_name);
        error_msg = svm_check_parameter(&prob,&param);

        if(error_msg)
        {
                fprintf(stderr,"Error: %s\n",error_msg);
                exit(1);
        }

        if(cross_validation)
        {
                do_cross_validation();
        }
        else
        {
                model = svm_train(&prob,&param);
                if(svm_save_model(model_file_name,model))
                {
                        fprintf(stderr, "can't save model to file %s\n", model_file_name);
                        exit(1);
                }
                svm_free_and_destroy_model(&model);
        }
        svm_destroy_param(&param);
        free(prob.y);
        free(prob.x);
        free(x_space);
        free(line);

        return 0;
}

void do_cross_validation()
{
        int i;
        int total_correct = 0;
        double total_error = 0;
        double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
        double *target = Malloc(double,prob.l);

        svm_cross_validation(&prob,&param,nr_fold,target);
        if(param.svm_type == EPSILON_SVR ||
           param.svm_type == NU_SVR)
        {
                for(i=0;i<prob.l;i++)
                {
                        double y = prob.y[i];
                        double v = target[i];
                        total_error += (v-y)*(v-y);
                        sumv += v;
                        sumy += y;
                        sumvv += v*v;
                        sumyy += y*y;
                        sumvy += v*y;
                }
                printf("Cross Validation Mean squared error = %g\n",total_error/prob.l);
                printf("Cross Validation Squared correlation coefficient = %g\n",
                        ((prob.l*sumvy-sumv*sumy)*(prob.l*sumvy-sumv*sumy))/
                        ((prob.l*sumvv-sumv*sumv)*(prob.l*sumyy-sumy*sumy))
                        );
        }
        else
        {
                for(i=0;i<prob.l;i++)
                        if(target[i] == prob.y[i])
                                ++total_correct;
                printf("Cross Validation Accuracy = %g%%\n",100.0*total_correct/prob.l);
        }
        free(target);
}

void parse_command_line(int argc, char **argv, char *input_file_name, char *model_file_name)
{
        int i;
        void (*print_func)(const char*) = NULL; // default printing to stdout

        // default values
        param.svm_type = C_SVC;
        param.kernel_type = RBF;
        param.degree = 3;
        param.gamma = 0;        // 1/num_features
        param.coef0 = 0;
        param.nu = 0.5;
        param.cache_size = 100;
        param.C = 1;
        param.eps = 1e-3;
        param.p = 0.1;
        param.shrinking = 1;
        param.probability = 0;
        param.nr_weight = 0;
        param.weight_label = NULL;
        param.weight = NULL;
        cross_validation = 0;

        // parse options
        for(i=1;i<argc;i++)
        {
                if(argv[i][0] != '-') break;
                if(++i>=argc)
                        exit_with_help();
                switch(argv[i-1][1])
                {
                        case 's':
                                param.svm_type = atoi(argv[i]);
                                break;
                        case 't':
                                param.kernel_type = atoi(argv[i]);
                                break;
                        case 'd':
                                param.degree = atoi(argv[i]);
                                break;
                        case 'g':
                                param.gamma = atof(argv[i]);
                                break;
                        case 'r':
                                param.coef0 = atof(argv[i]);
                                break;
                        case 'n':
                                param.nu = atof(argv[i]);
                                break;
                        case 'm':
                                param.cache_size = atof(argv[i]);
                                break;
                        case 'c':
                                param.C = atof(argv[i]);
                                break;
                        case 'e':
                                param.eps = atof(argv[i]);
                                break;
                        case 'p':
                                param.p = atof(argv[i]);
                                break;
                        case 'h':
                                param.shrinking = atoi(argv[i]);
                                break;
                        case 'b':
                                param.probability = atoi(argv[i]);
                                break;
                        case 'q':
                                print_func = &print_null;
                                i--;
                                break;
                        case 'v':
                                cross_validation = 1;
                                nr_fold = atoi(argv[i]);
                                if(nr_fold < 2)
                                {
                                        fprintf(stderr,"n-fold cross validation: n must >= 2\n");
                                        exit_with_help();
                                }
                                break;
                        case 'w':
                                ++param.nr_weight;
                                param.weight_label = (int *)realloc(param.weight_label,sizeof(int)*param.nr_weight);
                                param.weight = (double *)realloc(param.weight,sizeof(double)*param.nr_weight);
                                param.weight_label[param.nr_weight-1] = atoi(&argv[i-1][2]);
                                param.weight[param.nr_weight-1] = atof(argv[i]);
                                break;
                        default:
                                fprintf(stderr,"Unknown option: -%c\n", argv[i-1][1]);
                                exit_with_help();
                }
        }

        svm_set_print_string_function(print_func);

        // determine filenames

        if(i>=argc)
                exit_with_help();

        strcpy(input_file_name, argv[i]);

        if(i<argc-1)
                strcpy(model_file_name,argv[i+1]);
        else
        {
                char *p = strrchr(argv[i],'/');
                if(p==NULL)
                        p = argv[i];
                else
                        ++p;
                sprintf(model_file_name,"%s.model",p);
        }
}

// read in a problem (in svmlight format)

void read_problem(const char *filename)
{
        int elements, max_index, inst_max_index, i, j;
        FILE *fp = fopen(filename,"r");
        char *endptr;
        char *idx, *val, *label;

        if(fp == NULL)
        {
                fprintf(stderr,"can't open input file %s\n",filename);
                exit(1);
        }

        prob.l = 0;
        elements = 0;

        max_line_len = 1024;
        line = Malloc(char,max_line_len);
        while(readline(fp)!=NULL)
        {
                char *p = strtok(line," \t"); // label

                // features
                while(1)
                {
                        p = strtok(NULL," \t");
                        if(p == NULL || *p == '\n') // check '\n' as ' ' may be after the last feature
                                break;
                        ++elements;
                }
                ++elements;
                ++prob.l;
        }
        rewind(fp);

        prob.y = Malloc(double,prob.l);
        prob.x = Malloc(struct svm_node *,prob.l);
        x_space = Malloc(struct svm_node,elements);

        max_index = 0;
        j=0;
        for(i=0;i<prob.l;i++)
        {
                inst_max_index = -1; // strtol gives 0 if wrong format, and precomputed kernel has <index> start from 0
                readline(fp);
                prob.x[i] = &x_space[j];
                label = strtok(line," \t");
                prob.y[i] = strtod(label,&endptr);
                if(endptr == label)
                        exit_input_error(i+1);

                while(1)
                {
                        idx = strtok(NULL,":");
                        val = strtok(NULL," \t");

                        if(val == NULL)
                                break;

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

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

                        ++j;
                }

                if(inst_max_index > max_index)
                        max_index = inst_max_index;
                x_space[j++].index = -1;
        }

        if(param.gamma == 0 && max_index > 0)
                param.gamma = 1.0/max_index;

        if(param.kernel_type == PRECOMPUTED)
                for(i=0;i<prob.l;i++)
                {
                        if (prob.x[i][0].index != 0)
                        {
                                fprintf(stderr,"Wrong input format: first column must be 0:sample_serial_number\n");
                                exit(1);
                        }
                        if ((int)prob.x[i][0].value <= 0 || (int)prob.x[i][0].value > max_index)
                        {
                                fprintf(stderr,"Wrong input format: sample_serial_number out of range\n");
                                exit(1);
                        }
                }

        fclose(fp);
}