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Diffstat (limited to 'src/GenSVMtraintest.c')
| -rw-r--r-- | src/GenSVMtraintest.c | 270 |
1 files changed, 270 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; + } +} + |