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-rw-r--r--src/trainMSVMMajdataset.c322
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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]);
+}
+
+/**
+ * @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:")) {
+ nr = all_longs_str(buffer, 7, lparams);
+ if (nr > 1)
+ fprintf(stderr, "Field \"kernel\" only takes "
+ "one value. Additional "
+ "fields are ignored.\n");
+ switch (lparams[0]) {
+ case 0:
+ training->kerneltype = K_LINEAR;
+ break;
+ case 1:
+ training->kerneltype = K_POLY;
+ break;
+ case 2:
+ training->kerneltype = K_RBF;
+ break;
+ case 3:
+ training->kerneltype = K_SIGMOID;
+ break;
+ }
+ } 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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