added new command line application for training and testing

2 files changed, 273 insertions, 0 deletions

diff --git a/src/GenSVMtraintest.c b/src/GenSVMtraintest.c
new file mode 100644
index 0000000..e43d09f
--- /dev/null
+++ b/src/GenSVMtraintest.c
@@ -0,0 +1,270 @@
+/**
+ * @file GenSVMtraintest.c
+ * @author Gertjan van den Burg
+ * @date February, 2015
+ * @brief Command line interface for training and testing with a GenSVM model
+ *
+ * @details
+ * This is a command line program for training and testing on a single model 
+ * with specified model parameters.
+ *
+ */
+
+#include <time.h>
+
+#include "gensvm.h"
+#include "gensvm_io.h"
+#include "gensvm_init.h"
+#include "gensvm_kernel.h"
+#include "gensvm_train.h"
+#include "gensvm_pred.h"
+#include "libGenSVM.h"
+#include "util.h"
+
+#define MINARGS 2
+
+extern FILE *GENSVM_OUTPUT_FILE;
+
+// function declarations
+void exit_with_help();
+void parse_command_line(int argc, char **argv, struct GenModel *model,
+		char **model_inputfile, char **training_inputfile,
+		char **testing_inputfile, char **model_outputfile,
+		char **prediction_outputfile);
+
+void exit_with_help()
+{
+	printf("This is GenSVM, version %1.1f\n\n", VERSION);
+	printf("Usage: ./gensvm [options] training_data [test_data]\n");
+	printf("Options:\n");
+	printf("-c coef : coefficient for the polynomial and sigmoid kernel\n");
+	printf("-d degree : degree for the polynomial kernel\n");
+	printf("-e epsilon : set the value of the stopping criterion\n");
+	printf("-g gamma : parameter for the rbf, polynomial or sigmoid "
+			"kernel\n");
+	printf("-h | -help : print this help.\n");
+	printf("-k kappa : set the value of kappa used in the Huber hinge\n");
+	printf("-l lambda : set the value of lambda (lambda > 0)\n");
+	printf("-s seed_model_file : use previous model as seed for V\n");
+	printf("-m model_output_file : write model output to file\n");
+	printf("-o prediction_output : write predictions of test data to "
+			"file\n");
+	printf("-p p-value : set the value of p in the lp norm "
+			"(1.0 <= p <= 2.0)\n");
+	printf("-q : quiet mode (no output)\n");
+	printf("-r rho : choose the weigth specification (1 = unit, 2 = "
+			"group)\n");
+	printf("-t type: kerneltype (0=LINEAR, 1=POLY, 2=RBF, 3=SIGMOID)\n");
+
+	exit(0);
+}
+
+
+int main(int argc, char **argv)
+{
+	bool with_test = false;
+	long i, *predy = NULL;
+	double performance;
+
+	char *training_inputfile = NULL,
+	     *testing_inputfile = NULL,
+	     *model_inputfile = NULL,
+	     *model_outputfile = NULL,
+	     *prediction_outputfile = NULL;
+
+	struct GenModel *model = gensvm_init_model();
+	struct GenData *traindata = gensvm_init_data();
+	struct GenData *testdata = gensvm_init_data();
+
+	if (argc < MINARGS || gensvm_check_argv(argc, argv, "-help")
+		       	|| gensvm_check_argv_eq(argc, argv, "-h"))
+		exit_with_help();
+	parse_command_line(argc, argv, model, &model_inputfile,
+		       	&training_inputfile, &testing_inputfile,
+		       	&model_outputfile, &prediction_outputfile);
+	if (testing_inputfile != NULL)
+		with_test = true;
+
+	// read data from files
+	gensvm_read_data(traindata, training_inputfile);
+	if (with_test)
+		gensvm_read_data(testdata, testing_inputfile);
+
+	// copy dataset parameters to model
+	model->n = traindata->n;
+	model->m = traindata->m;
+	model->K = traindata->K;
+	model->data_file = training_inputfile;
+
+	// allocate model
+	gensvm_allocate_model(model);
+
+	// run pre/post processing in case of kernels
+	gensvm_kernel_preprocess(model, traindata);
+	if (with_test)
+		gensvm_kernel_postprocess(model, traindata, testdata);
+
+	// reallocate model in case of kernel dimension reduction
+	gensvm_reallocate_model(model, traindata->n, traindata->r);
+
+	// initialize weights
+	gensvm_initialize_weights(traindata, model);
+
+	// seed the random number generator
+	srand(time(NULL));
+
+	// load a seed model from file if it is specified
+	if (gensvm_check_argv_eq(argc, argv, "-s")) {
+		struct GenModel *seed_model = gensvm_init_model();
+		gensvm_read_model(seed_model, model_inputfile);
+		gensvm_seed_model_V(seed_model, model, traindata);
+		gensvm_free_model(seed_model);
+	} else {
+		gensvm_seed_model_V(NULL, model, traindata);
+	}
+
+	// start training
+	gensvm_optimize(model, traindata);
+
+	// if we also have a test set, predict labels and write to predictions 
+	// to an output file if specified
+	if (with_test) {
+		// predict labels
+		predy = Calloc(long, testdata->n);
+		gensvm_predict_labels(testdata, model, predy);
+	
+		if (testdata->y != NULL) {
+			performance = gensvm_prediction_perf(testdata, predy);
+			note("Predictive performance: %3.2f%%\n", performance);
+		}
+
+		// if output file is specified, write predictions to it
+		if (gensvm_check_argv_eq(argc, argv, "-o")) {
+			gensvm_write_predictions(testdata, predy,
+				       	prediction_outputfile);
+			note("Prediction written to: %s\n",
+				       	prediction_outputfile);
+		} else {
+			for (i=0; i<testdata->n; i++)
+				printf("%li ", predy[i]);
+			printf("\n");
+		}
+	}
+
+	// write model to output file if necessary
+	if (gensvm_check_argv_eq(argc, argv, "-m")) {
+		gensvm_write_model(model, model_outputfile);
+		note("Model written to: %s\n", model_outputfile);
+	}
+
+	// free everything
+	gensvm_free_model(model);
+	gensvm_free_data(traindata);
+	gensvm_free_data(testdata);
+	free(predy);
+
+	return 0;
+}
+
+void parse_command_line(int argc, char **argv, struct GenModel *model,
+		char **model_inputfile, char **training_inputfile,
+	       	char **testing_inputfile, char **model_outputfile,
+	       	char **prediction_outputfile)
+{
+	int i;
+	double gamma = 1.0,
+	       degree = 2.0,
+	       coef = 0.0;
+	
+	GENSVM_OUTPUT_FILE = stdout;
+
+	// 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 'c':
+				coef = atof(argv[i]);
+				break;
+			case 'd':
+				degree = atof(argv[i]);
+				break;
+			case 'e':
+				model->epsilon = atof(argv[i]);
+				break;
+			case 'g':
+				gamma = atof(argv[i]);
+				break;
+			case 'k':
+				model->kappa = atof(argv[i]);
+				break;
+			case 'l':
+				model->lambda = atof(argv[i]);
+				break;
+			case 's':
+				(*model_inputfile) = Malloc(char,
+					       	strlen(argv[i])+1);
+				strcpy((*model_inputfile), argv[i]);
+				break;
+			case 'm':
+				(*model_outputfile) = Malloc(char,
+						strlen(argv[i])+1);
+				strcpy((*model_outputfile), argv[i]);
+				break;
+			case 'o':
+				(*prediction_outputfile) = Malloc(char,
+						strlen(argv[i])+1);
+				strcpy((*prediction_outputfile), argv[i]);
+				break;
+			case 'p':
+				model->p = atof(argv[i]);
+				break;
+			case 'r':
+				model->weight_idx = atoi(argv[i]);
+				break;
+			case 't':
+				model->kerneltype = atoi(argv[i]);
+				break;
+			case 'q':
+				GENSVM_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();
+
+	(*training_inputfile) = Malloc(char, strlen(argv[i])+1);
+	strcpy((*training_inputfile), argv[i]);
+	if (i+2 == argc) {
+		(*testing_inputfile) = Malloc(char, strlen(argv[i])+1);
+		strcpy((*testing_inputfile), argv[i+1]);
+	}
+
+	// set kernel parameters
+	switch (model->kerneltype) {
+		case K_LINEAR:
+			break;
+		case K_POLY:
+			model->kernelparam = Calloc(double, 3);
+			model->kernelparam[0] = gamma;
+			model->kernelparam[1] = coef;
+			model->kernelparam[2] = degree;
+			break;
+		case K_RBF:
+			model->kernelparam = Calloc(double, 1);
+			model->kernelparam[0] = gamma;
+			break;
+		case K_SIGMOID:
+			model->kernelparam = Calloc(double, 1);
+			model->kernelparam[0] = gamma;
+			model->kernelparam[1] = coef;
+	}
+}
+
diff --git a/src/gensvm_io.c b/src/gensvm_io.c
index 3de0794..8d32f70 100644
--- a/src/gensvm_io.c
+++ b/src/gensvm_io.c
@@ -283,6 +283,9 @@ void gensvm_write_predictions(struct GenData *data, long *predy,
 				output_filename);
 		exit(1);
 	}
+	
+	fprintf(fid, "%li\n", data->n);
+	fprintf(fid, "%li\n", data->m);
 
 	for (i=0; i<data->n; i++) {
 		for (j=0; j<data->m; j++)