First commit
This commit is contained in:
479
liblinear-2.49/train.c
Normal file
479
liblinear-2.49/train.c
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@@ -0,0 +1,479 @@
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#include <stdio.h>
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#include <math.h>
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#include <stdlib.h>
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#include <string.h>
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#include <ctype.h>
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#include <errno.h>
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#include "linear.h"
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#define Malloc(type,n) (type *)malloc((n)*sizeof(type))
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#define INF HUGE_VAL
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void print_null(const char *s) {}
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void exit_with_help()
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{
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printf(
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"Usage: train [options] training_set_file [model_file]\n"
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"options:\n"
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"-s type : set type of solver (default 1)\n"
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" for multi-class classification\n"
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" 0 -- L2-regularized logistic regression (primal)\n"
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" 1 -- L2-regularized L2-loss support vector classification (dual)\n"
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" 2 -- L2-regularized L2-loss support vector classification (primal)\n"
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" 3 -- L2-regularized L1-loss support vector classification (dual)\n"
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" 4 -- support vector classification by Crammer and Singer\n"
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" 5 -- L1-regularized L2-loss support vector classification\n"
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" 6 -- L1-regularized logistic regression\n"
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" 7 -- L2-regularized logistic regression (dual)\n"
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" for regression\n"
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" 11 -- L2-regularized L2-loss support vector regression (primal)\n"
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" 12 -- L2-regularized L2-loss support vector regression (dual)\n"
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" 13 -- L2-regularized L1-loss support vector regression (dual)\n"
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" for outlier detection\n"
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" 21 -- one-class support vector machine (dual)\n"
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"-c cost : set the parameter C (default 1)\n"
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"-p epsilon : set the epsilon in loss function of SVR (default 0.1)\n"
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"-n nu : set the parameter nu of one-class SVM (default 0.5)\n"
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"-e epsilon : set tolerance of termination criterion\n"
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" -s 0 and 2\n"
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" |f'(w)|_2 <= eps*min(pos,neg)/l*|f'(w0)|_2,\n"
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" where f is the primal function and pos/neg are # of\n"
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" positive/negative data (default 0.01)\n"
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" -s 11\n"
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" |f'(w)|_2 <= eps*|f'(w0)|_2 (default 0.0001)\n"
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" -s 1, 3, 4, 7, and 21\n"
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" Dual maximal violation <= eps; similar to libsvm (default 0.1 except 0.01 for -s 21)\n"
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" -s 5 and 6\n"
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" |f'(w)|_1 <= eps*min(pos,neg)/l*|f'(w0)|_1,\n"
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" where f is the primal function (default 0.01)\n"
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" -s 12 and 13\n"
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" |f'(alpha)|_1 <= eps |f'(alpha0)|,\n"
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" where f is the dual function (default 0.1)\n"
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"-B bias : if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term added (default -1)\n"
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"-R : not regularize the bias; must with -B 1 to have the bias; DON'T use this unless you know what it is\n"
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" (for -s 0, 2, 5, 6, 11)\n"
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"-wi weight: weights adjust the parameter C of different classes (see README for details)\n"
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"-v n: n-fold cross validation mode\n"
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"-C : find parameters (C for -s 0, 2 and C, p for -s 11)\n"
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"-q : quiet mode (no outputs)\n"
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);
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exit(1);
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}
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void exit_input_error(int line_num)
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{
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fprintf(stderr,"Wrong input format at line %d\n", line_num);
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exit(1);
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}
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static char *line = NULL;
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static int max_line_len;
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static char* readline(FILE *input)
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{
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int len;
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if(fgets(line,max_line_len,input) == NULL)
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return NULL;
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while(strrchr(line,'\n') == NULL)
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{
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max_line_len *= 2;
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line = (char *) realloc(line,max_line_len);
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len = (int) strlen(line);
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if(fgets(line+len,max_line_len-len,input) == NULL)
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break;
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}
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return line;
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}
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void parse_command_line(int argc, char **argv, char *input_file_name, char *model_file_name);
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void read_problem(const char *filename);
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void do_cross_validation();
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void do_find_parameters();
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struct feature_node *x_space;
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struct parameter param;
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struct problem prob;
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struct model* model_;
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int flag_cross_validation;
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int flag_find_parameters;
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int flag_C_specified;
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int flag_p_specified;
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int flag_solver_specified;
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int nr_fold;
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double bias;
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int main(int argc, char **argv)
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{
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char input_file_name[1024];
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char model_file_name[1024];
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const char *error_msg;
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parse_command_line(argc, argv, input_file_name, model_file_name);
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read_problem(input_file_name);
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error_msg = check_parameter(&prob,¶m);
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if(error_msg)
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{
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fprintf(stderr,"ERROR: %s\n",error_msg);
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exit(1);
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}
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if (flag_find_parameters)
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{
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do_find_parameters();
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}
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else if(flag_cross_validation)
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{
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do_cross_validation();
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}
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else
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{
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model_=train(&prob, ¶m);
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if(save_model(model_file_name, model_))
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{
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fprintf(stderr,"can't save model to file %s\n",model_file_name);
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exit(1);
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}
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free_and_destroy_model(&model_);
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}
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destroy_param(¶m);
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free(prob.y);
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free(prob.x);
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free(x_space);
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free(line);
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return 0;
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}
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void do_find_parameters()
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{
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double start_C, start_p, best_C, best_p, best_score;
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if (flag_C_specified)
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start_C = param.C;
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else
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start_C = -1.0;
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if (flag_p_specified)
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start_p = param.p;
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else
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start_p = -1.0;
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printf("Doing parameter search with %d-fold cross validation.\n", nr_fold);
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find_parameters(&prob, ¶m, nr_fold, start_C, start_p, &best_C, &best_p, &best_score);
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if(param.solver_type == L2R_LR || param.solver_type == L2R_L2LOSS_SVC)
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printf("Best C = %g CV accuracy = %g%%\n", best_C, 100.0*best_score);
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else if(param.solver_type == L2R_L2LOSS_SVR)
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printf("Best C = %g Best p = %g CV MSE = %g\n", best_C, best_p, best_score);
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}
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void do_cross_validation()
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{
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int i;
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int total_correct = 0;
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double total_error = 0;
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double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
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double *target = Malloc(double, prob.l);
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cross_validation(&prob,¶m,nr_fold,target);
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if(param.solver_type == L2R_L2LOSS_SVR ||
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param.solver_type == L2R_L1LOSS_SVR_DUAL ||
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param.solver_type == L2R_L2LOSS_SVR_DUAL)
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{
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for(i=0;i<prob.l;i++)
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{
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double y = prob.y[i];
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double v = target[i];
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total_error += (v-y)*(v-y);
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sumv += v;
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sumy += y;
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sumvv += v*v;
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sumyy += y*y;
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sumvy += v*y;
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}
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printf("Cross Validation Mean squared error = %g\n",total_error/prob.l);
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printf("Cross Validation Squared correlation coefficient = %g\n",
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((prob.l*sumvy-sumv*sumy)*(prob.l*sumvy-sumv*sumy))/
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((prob.l*sumvv-sumv*sumv)*(prob.l*sumyy-sumy*sumy))
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);
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}
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else
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{
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for(i=0;i<prob.l;i++)
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if(target[i] == prob.y[i])
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++total_correct;
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printf("Cross Validation Accuracy = %g%%\n",100.0*total_correct/prob.l);
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}
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free(target);
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}
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void parse_command_line(int argc, char **argv, char *input_file_name, char *model_file_name)
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{
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int i;
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void (*print_func)(const char*) = NULL; // default printing to stdout
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// default values
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param.solver_type = L2R_L2LOSS_SVC_DUAL;
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param.C = 1;
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param.p = 0.1;
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param.nu = 0.5;
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param.eps = INF; // see setting below
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param.nr_weight = 0;
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param.regularize_bias = 1;
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param.weight_label = NULL;
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param.weight = NULL;
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param.init_sol = NULL;
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param.w_recalc = false;
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flag_cross_validation = 0;
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flag_C_specified = 0;
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flag_p_specified = 0;
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flag_solver_specified = 0;
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flag_find_parameters = 0;
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bias = -1;
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// parse options
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for(i=1;i<argc;i++)
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{
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if(argv[i][0] != '-') break;
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if(++i>=argc)
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exit_with_help();
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switch(argv[i-1][1])
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{
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case 's':
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param.solver_type = atoi(argv[i]);
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flag_solver_specified = 1;
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break;
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case 'c':
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param.C = atof(argv[i]);
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flag_C_specified = 1;
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break;
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case 'p':
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flag_p_specified = 1;
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param.p = atof(argv[i]);
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break;
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case 'n':
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param.nu = atof(argv[i]);
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break;
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case 'e':
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param.eps = atof(argv[i]);
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break;
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case 'B':
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bias = atof(argv[i]);
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break;
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case 'w':
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++param.nr_weight;
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param.weight_label = (int *) realloc(param.weight_label,sizeof(int)*param.nr_weight);
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param.weight = (double *) realloc(param.weight,sizeof(double)*param.nr_weight);
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param.weight_label[param.nr_weight-1] = atoi(&argv[i-1][2]);
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param.weight[param.nr_weight-1] = atof(argv[i]);
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break;
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case 'v':
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flag_cross_validation = 1;
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nr_fold = atoi(argv[i]);
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if(nr_fold < 2)
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{
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fprintf(stderr,"n-fold cross validation: n must >= 2\n");
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exit_with_help();
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}
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break;
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case 'q':
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print_func = &print_null;
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i--;
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break;
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case 'C':
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flag_find_parameters = 1;
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i--;
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break;
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case 'R':
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param.regularize_bias = 0;
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i--;
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break;
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default:
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fprintf(stderr,"unknown option: -%c\n", argv[i-1][1]);
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exit_with_help();
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break;
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}
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}
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set_print_string_function(print_func);
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// determine filenames
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if(i>=argc)
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exit_with_help();
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strcpy(input_file_name, argv[i]);
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if(i<argc-1)
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strcpy(model_file_name,argv[i+1]);
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else
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{
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char *p = strrchr(argv[i],'/');
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if(p==NULL)
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p = argv[i];
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else
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++p;
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sprintf(model_file_name,"%s.model",p);
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}
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// default solver for parameter selection is L2R_L2LOSS_SVC
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if(flag_find_parameters)
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{
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if(!flag_cross_validation)
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nr_fold = 5;
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if(!flag_solver_specified)
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{
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fprintf(stderr, "Solver not specified. Using -s 2\n");
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param.solver_type = L2R_L2LOSS_SVC;
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}
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else if(param.solver_type != L2R_LR && param.solver_type != L2R_L2LOSS_SVC && param.solver_type != L2R_L2LOSS_SVR)
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{
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fprintf(stderr, "Warm-start parameter search only available for -s 0, -s 2 and -s 11\n");
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exit_with_help();
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}
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}
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if(param.eps == INF)
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{
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switch(param.solver_type)
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{
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case L2R_LR:
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case L2R_L2LOSS_SVC:
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param.eps = 0.01;
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break;
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case L2R_L2LOSS_SVR:
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param.eps = 0.0001;
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break;
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case L2R_L2LOSS_SVC_DUAL:
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case L2R_L1LOSS_SVC_DUAL:
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case MCSVM_CS:
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case L2R_LR_DUAL:
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param.eps = 0.1;
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break;
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case L1R_L2LOSS_SVC:
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case L1R_LR:
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param.eps = 0.01;
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break;
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case L2R_L1LOSS_SVR_DUAL:
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case L2R_L2LOSS_SVR_DUAL:
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param.eps = 0.1;
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break;
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case ONECLASS_SVM:
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param.eps = 0.01;
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break;
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}
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}
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}
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// read in a problem (in libsvm format)
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void read_problem(const char *filename)
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{
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int max_index, inst_max_index, i;
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size_t elements, j;
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FILE *fp = fopen(filename,"r");
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char *endptr;
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char *idx, *val, *label;
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if(fp == NULL)
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{
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fprintf(stderr,"can't open input file %s\n",filename);
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exit(1);
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}
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prob.l = 0;
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elements = 0;
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max_line_len = 1024;
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line = Malloc(char,max_line_len);
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while(readline(fp)!=NULL)
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{
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char *p = strtok(line," \t"); // label
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// features
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while(1)
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{
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p = strtok(NULL," \t");
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if(p == NULL || *p == '\n') // check '\n' as ' ' may be after the last feature
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break;
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elements++;
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}
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elements++; // for bias term
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prob.l++;
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}
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rewind(fp);
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prob.bias=bias;
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prob.y = Malloc(double,prob.l);
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prob.x = Malloc(struct feature_node *,prob.l);
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x_space = Malloc(struct feature_node,elements+prob.l);
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max_index = 0;
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j=0;
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for(i=0;i<prob.l;i++)
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{
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inst_max_index = 0; // strtol gives 0 if wrong format
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readline(fp);
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prob.x[i] = &x_space[j];
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label = strtok(line," \t\n");
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if(label == NULL) // empty line
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exit_input_error(i+1);
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prob.y[i] = strtod(label,&endptr);
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if(endptr == label || *endptr != '\0')
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exit_input_error(i+1);
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while(1)
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{
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idx = strtok(NULL,":");
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val = strtok(NULL," \t");
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if(val == NULL)
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break;
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errno = 0;
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x_space[j].index = (int) strtol(idx,&endptr,10);
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if(endptr == idx || errno != 0 || *endptr != '\0' || x_space[j].index <= inst_max_index)
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exit_input_error(i+1);
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else
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inst_max_index = x_space[j].index;
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errno = 0;
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x_space[j].value = strtod(val,&endptr);
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if(endptr == val || errno != 0 || (*endptr != '\0' && !isspace(*endptr)))
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exit_input_error(i+1);
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++j;
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}
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if(inst_max_index > max_index)
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max_index = inst_max_index;
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if(prob.bias >= 0)
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x_space[j++].value = prob.bias;
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x_space[j++].index = -1;
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}
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if(prob.bias >= 0)
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{
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prob.n=max_index+1;
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for(i=1;i<prob.l;i++)
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(prob.x[i]-2)->index = prob.n;
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x_space[j-2].index = prob.n;
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}
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else
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prob.n=max_index;
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fclose(fp);
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}
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