All/C_CPP/001/svm/svm-train.c

#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		(multi-class classification)\n"
            "	1 -- nu-SVC		(multi-class classification)\n"
            "	2 -- one-class SVM\n"
            "	3 -- epsilon-SVR	(regression)\n"
            "	4 -- nu-SVR		(regression)\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 max_index, inst_max_index, i;
    size_t elements, 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\n");
        if (label == NULL) // empty line
            exit_input_error(i + 1);

        prob.y[i] = strtod(label, &endptr);
        if (endptr == label || *endptr != '\0')
            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);
}