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              (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 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\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);
}