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-rw-r--r--src/trainMSVMMajdataset.c321
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diff --git a/src/trainMSVMMajdataset.c b/src/trainMSVMMajdataset.c
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-/**
- * @file trainMSVMMajdataset.c
- * @author Gertjan van den Burg
- * @date January, 2014
- * @brief Command line interface for the grid search program
- *
- * @details
- * This is a command line interface to the parameter grid search functionality
- * of the algorithm. The grid search is specified in a separate file, thereby
- * reducing the number of command line arguments. See
- * read_training_from_file() for documentation on the training file.
- *
- * The program runs a grid search as specified in the training file. If
- * desired the grid search can incorporate consistency checks to find the
- * configuration among the best configurations which scores consistently high.
- * All output is written to stdout, unless the quiet mode is specified.
- *
- * For further usage information, see the program help function.
- *
- */
-
-#include <time.h>
-
-#include "crossval.h"
-#include "msvmmaj.h"
-#include "msvmmaj_io.h"
-#include "msvmmaj_init.h"
-#include "msvmmaj_pred.h"
-#include "msvmmaj_train.h"
-#include "msvmmaj_train_dataset.h"
-#include "strutil.h"
-#include "util.h"
-
-#define MINARGS 2
-
-extern FILE *MSVMMAJ_OUTPUT_FILE;
-
-// function declarations
-void exit_with_help();
-void parse_command_line(int argc, char **argv, char *input_filename);
-void read_training_from_file(char *input_filename, struct Training *training);
-
-/**
- * @brief Help function
- */
-void exit_with_help()
-{
- printf("This is MSVMMaj, version %1.1f\n\n", VERSION);
- printf("Usage: trainMSVMMajdataset [options] training_file\n");
- printf("Options:\n");
- printf("-h | -help : print this help.\n");
- printf("-q : quiet mode (no output)\n");
-
- exit(0);
-}
-
-/**
- * @brief Main interface function for trainMSVMMajdataset
- *
- * @details
- * Main interface for the command line program. A given training file which
- * specifies a grid search over a single dataset is read. From this, a Queue
- * is created containing all Task instances that need to be performed in the
- * search. Depending on the type of dataset, either cross validation or
- * train/test split training is performed for all tasks. If specified,
- * consistency repeats are done at the end of the grid search. Note that
- * currently no output is produced other than what is written to stdout.
- *
- * @param[in] argc number of command line arguments
- * @param[in] argv array of command line arguments
- *
- */
-int main(int argc, char **argv)
-{
- char input_filename[MAX_LINE_LENGTH];
-
- struct Training *training = Malloc(struct Training, 1);
- struct MajData *train_data = Malloc(struct MajData, 1);
- struct MajData *test_data = Malloc(struct MajData, 1);
-
- if (argc < MINARGS || msvmmaj_check_argv(argc, argv, "-help")
- || msvmmaj_check_argv_eq(argc, argv, "-h") )
- exit_with_help();
- parse_command_line(argc, argv, input_filename);
-
- training->repeats = 0;
- note("Reading training file\n");
- read_training_from_file(input_filename, training);
-
- note("Reading data from %s\n", training->train_data_file);
- msvmmaj_read_data(train_data, training->train_data_file);
- if (training->traintype == TT) {
- note("Reading data from %s\n", training->test_data_file);
- msvmmaj_read_data(test_data, training->test_data_file);
- }
-
- note("Creating queue\n");
- struct Queue *q = Malloc(struct Queue, 1);
- make_queue(training, q, train_data, test_data);
-
- srand(time(NULL));
-
- note("Starting training\n");
- if (training->traintype == TT)
- start_training_tt(q);
- else
- start_training_cv(q);
- note("Training finished\n");
-
- if (training->repeats > 0) {
- consistency_repeats(q, training->repeats, training->traintype);
- }
-
- free_queue(q);
- free(training);
- msvmmaj_free_data(train_data);
- msvmmaj_free_data(test_data);
-
- note("Done.\n");
- return 0;
-}
-
-/**
- * @brief Parse command line arguments
- *
- * @details
- * Few arguments can be supplied to the command line. Only quiet mode can be
- * specified, or help can be requested. The filename of the training file is
- * read from the arguments. Parsing of the training file is done separately in
- * read_training_from_file().
- *
- * @param[in] argc number of command line arguments
- * @param[in] argv array of command line arguments
- * @param[in] input_filename pre-allocated buffer for the training
- * filename.
- *
- */
-void parse_command_line(int argc, char **argv, char *input_filename)
-{
- int i;
-
- MSVMMAJ_OUTPUT_FILE = stdout;
-
- for (i=1; i<argc; i++) {
- if (argv[i][0] != '-') break;
- if (++i>=argc)
- exit_with_help();
- switch (argv[i-1][1]) {
- case 'q':
- MSVMMAJ_OUTPUT_FILE = NULL;
- i--;
- break;
- default:
- fprintf(stderr, "Unknown option: -%c\n",
- argv[i-1][1]);
- exit_with_help();
- }
- }
-
- if (i >= argc)
- exit_with_help();
-
- strcpy(input_filename, argv[i]);
-}
-
-KernelType parse_kernel_str(char *kernel_line)
-{
- if (str_endswith(kernel_line, "LINEAR\n")) {
- return K_LINEAR;
- } else if (str_endswith(kernel_line, "POLY\n")) {
- return K_POLY;
- } else if (str_endswith(kernel_line, "RBF\n")) {
- return K_RBF;
- } else if (str_endswith(kernel_line, "SIGMOID\n")) {
- return K_SIGMOID;
- } else {
- fprintf(stderr, "Unknown kernel specified on line: %s\n",
- kernel_line);
- exit(1);
- }
-}
-
-/**
- * @brief Read the Training struct from file
- *
- * @details
- * Read the Training struct from a file. The training file follows a specific
- * format specified in @ref spec_training_file.
- *
- * Commonly used string functions in this function are all_doubles_str() and
- * all_longs_str().
- *
- * @param[in] input_filename filename of the training file
- * @param[in] training Training structure to place the parsed
- * parameter grid.
- *
- */
-void read_training_from_file(char *input_filename, struct Training *training)
-{
- long i, nr = 0;
- FILE *fid;
- char buffer[MAX_LINE_LENGTH];
- char train_filename[MAX_LINE_LENGTH];
- char test_filename[MAX_LINE_LENGTH];
- double *params = Calloc(double, MAX_LINE_LENGTH);
- long *lparams = Calloc(long, MAX_LINE_LENGTH);
-
- fid = fopen(input_filename, "r");
- if (fid == NULL) {
- fprintf(stderr, "Error opening training file %s\n",
- input_filename);
- exit(1);
- }
- training->traintype = CV;
- while ( fgets(buffer, MAX_LINE_LENGTH, fid) != NULL ) {
- Memset(params, double, MAX_LINE_LENGTH);
- Memset(lparams, long, MAX_LINE_LENGTH);
- if (str_startswith(buffer, "train:")) {
- sscanf(buffer, "train: %s\n", train_filename);
- training->train_data_file = Calloc(char,
- MAX_LINE_LENGTH);
- strcpy(training->train_data_file, train_filename);
- } else if (str_startswith(buffer, "test:")) {
- sscanf(buffer, "test: %s\n", test_filename);
- training->test_data_file = Calloc(char,
- MAX_LINE_LENGTH);
- strcpy(training->test_data_file, test_filename);
- training->traintype = TT;
- } else if (str_startswith(buffer, "p:")) {
- nr = all_doubles_str(buffer, 2, params);
- training->ps = Calloc(double, nr);
- for (i=0; i<nr; i++)
- training->ps[i] = params[i];
- training->Np = nr;
- } else if (str_startswith(buffer, "lambda:")) {
- nr = all_doubles_str(buffer, 7, params);
- training->lambdas = Calloc(double, nr);
- for (i=0; i<nr; i++)
- training->lambdas[i] = params[i];
- training->Nl = nr;
- } else if (str_startswith(buffer, "kappa:")) {
- nr = all_doubles_str(buffer, 6, params);
- training->kappas = Calloc(double, nr);
- for (i=0; i<nr; i++)
- training->kappas[i] = params[i];
- training->Nk = nr;
- } else if (str_startswith(buffer, "epsilon:")) {
- nr = all_doubles_str(buffer, 8, params);
- training->epsilons = Calloc(double, nr);
- for (i=0; i<nr; i++)
- training->epsilons[i] = params[i];
- training->Ne = nr;
- } else if (str_startswith(buffer, "weight:")) {
- nr = all_longs_str(buffer, 7, lparams);
- training->weight_idxs = Calloc(int, nr);
- for (i=0; i<nr; i++)
- training->weight_idxs[i] = lparams[i];
- training->Nw = nr;
- } else if (str_startswith(buffer, "folds:")) {
- nr = all_longs_str(buffer, 6, lparams);
- training->folds = lparams[0];
- if (nr > 1)
- fprintf(stderr, "Field \"folds\" only takes "
- "one value. Additional "
- "fields are ignored.\n");
- } else if (str_startswith(buffer, "repeats:")) {
- nr = all_longs_str(buffer, 8, lparams);
- training->repeats = lparams[0];
- if (nr > 1)
- fprintf(stderr, "Field \"repeats\" only "
- "takes one value. Additional "
- "fields are ignored.\n");
- } else if (str_startswith(buffer, "kernel:")) {
- training->kerneltype = parse_kernel_str(buffer);
- } else if (str_startswith(buffer, "gamma:")) {
- nr = all_doubles_str(buffer, 6, params);
- if (training->kerneltype == K_LINEAR) {
- fprintf(stderr, "Field \"gamma\" ignored, "
- "linear kernel is used.\n");
- training->Ng = 0;
- break;
- }
- training->gammas = Calloc(double, nr);
- for (i=0; i<nr; i++)
- training->gammas[i] = params[i];
- training->Ng = nr;
- } else if (str_startswith(buffer, "coef:")) {
- nr = all_doubles_str(buffer, 5, params);
- if (training->kerneltype == K_LINEAR ||
- training->kerneltype == K_RBF) {
- fprintf(stderr, "Field \"coef\" ignored with "
- "specified kernel.\n");
- training->Nc = 0;
- break;
- }
- training->coefs = Calloc(double, nr);
- for (i=0; i<nr; i++)
- training->coefs[i] = params[i];
- training->Nc = nr;
- } else if (str_startswith(buffer, "degree:")) {
- nr = all_doubles_str(buffer, 7, params);
- if (training->kerneltype != K_POLY) {
- fprintf(stderr, "Field \"degree\" ignored "
- "with specified kernel.\n");
- training->Nd = 0;
- break;
- }
- training->degrees = Calloc(double, nr);
- for (i=0; i<nr; i++)
- training->degrees[i] = params[i];
- training->Nd = nr;
- } else {
- fprintf(stderr, "Cannot find any parameters on line: "
- "%s\n", buffer);
- }
- }
-
- free(params);
- free(lparams);
- fclose(fid);
-}
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