1 files changed, 402 insertions, 0 deletions

diff --git a/src/msvmmaj_train_dataset.c b/src/msvmmaj_train_dataset.c
new file mode 100644
index 0000000..2da8bee
--- /dev/null
+++ b/src/msvmmaj_train_dataset.c
@@ -0,0 +1,402 @@
+#include <math.h>
+#include <time.h>
+
+#include "crossval.h"
+#include "libMSVMMaj.h"
+#include "matrix.h"
+#include "msvmmaj_train.h"
+#include "msvmmaj_train_dataset.h"
+#include "msvmmaj_pred.h"
+#include "MSVMMaj.h"
+#include "util.h"
+#include "timer.h"
+
+extern FILE *MSVMMAJ_OUTPUT_FILE;
+
+void make_queue(struct Training *training, struct Queue *queue, 
+		struct MajData *train_data, struct MajData *test_data)
+{
+	long i, j, k, l, m;
+	long N, cnt = 0;
+	struct Task *task;
+	queue->i = 0;
+
+	N = training->Np;
+	N *= training->Nl;
+	N *= training->Nk;
+	N *= training->Ne;
+	N *= training->Nw;
+
+	queue->tasks = Malloc(struct Task *, N);
+	queue->N = N;
+
+	for (i=0; i<training->Ne; i++)
+		for (j=0; j<training->Nw; j++)
+			for (k=0; k<training->Nk; k++)
+				for (l=0; l<training->Nl; l++)
+					for (m=0; m<training->Np; m++) {
+						task = Malloc(struct Task, 1);
+						task->epsilon = training->epsilons[i];
+						task->weight_idx = training->weight_idxs[j];
+						task->kappa = training->kappas[k];
+						task->lambda = training->lambdas[l];
+						task->p = training->ps[m];
+						task->train_data = train_data;
+						task->test_data = test_data;
+						task->folds = training->folds;
+						task->ID = cnt;
+						queue->tasks[cnt] = task;
+						cnt++;
+					}
+}
+
+struct Task *get_next_task(struct Queue *q)
+{
+	long i = q->i;
+	if (i < q->N) {
+		q->i++;
+		return q->tasks[i];
+	}
+	return NULL;
+}
+
+int tasksort(const void *elem1, const void *elem2)
+{
+	const struct Task *t1 = (*(struct Task **) elem1);
+	const struct Task *t2 = (*(struct Task **) elem2);
+	return (t1->performance > t2->performance);
+}
+
+int doublesort(const void *elem1, const void *elem2)
+{
+	const double t1 = (*(double *) elem1);
+	const double t2 = (*(double *) elem2);
+	return t1 > t2;
+}
+
+
+double prctile(double *values, long N, double p)
+{
+	long i;
+	double pi, pr, boundary;
+	double *local = Malloc(double, N);
+	for (i=0; i<N; i++)
+		local[i] = values[i];
+
+	qsort(local, N, sizeof(double), doublesort);
+	p = p*N + 0.5;
+	pi = maximum(minimum(floor(p), N-1), 1);
+	pr = maximum(minimum(p - pi, 1), 0);
+	boundary = (1 - pr)*local[((long) pi)-1] + pr*local[((long) pi)];
+
+	free(local);
+
+	return boundary;
+}
+
+void consistency_repeats(struct Queue *q, long repeats, TrainType traintype)
+{
+	long i, r, N;
+	double p, pi, pr, boundary, time, *std, *mean, *perf;
+	struct Queue *nq = Malloc(struct Queue, 1);
+	struct MajModel *model = Malloc(struct MajModel, 1);
+	struct Task *task = Malloc(struct Task, 1);
+	clock_t loop_s, loop_e;
+
+	// calculate the percentile (Matlab style)
+	qsort(q->tasks, q->N, sizeof(struct Task *), tasksort);
+	p = 0.95*q->N + 0.5;
+	pi = maximum(minimum(floor(p), q->N-1), 1);
+	pr = maximum(minimum(p - pi, 1), 0);
+	boundary = (1 - pr)*q->tasks[((long) pi)-1]->performance;
+	boundary += pr*q->tasks[((long) pi)]->performance;
+	note("boundary determined at: %f\n", boundary);
+
+	N = 0;
+	for (i=0; i<q->N; i++)
+		if (q->tasks[i]->performance >= boundary)
+			N++;
+	note("Number of items: %li\n", N);
+	std = Calloc(double, N);
+	mean = Calloc(double, N);
+	perf = Calloc(double, N*repeats);
+
+	nq->tasks = Malloc(struct Task *, N);
+	for (i=q->N-1; i>q->N-N-1; i--) 
+		nq->tasks[q->N-i-1] = q->tasks[i];
+	nq->N = N;
+	nq->i = 0;
+
+	for (i=0; i<N; i++) {
+		task = get_next_task(nq);
+		make_model_from_task(task, model);
+
+		model->n = task->train_data->n;
+		model->m = task->train_data->m;
+		model->K = task->train_data->K;
+
+		time = 0;
+		note("(%02li/%02li:%03li)\t", i+1, N, task->ID);
+		for (r=0; r<repeats; r++) {
+			if (traintype == CV) {
+				loop_s = clock();
+				p = cross_validation(model, NULL, task->train_data, task->folds);
+				loop_e = clock();
+				time += elapsed_time(loop_s, loop_e);
+				matrix_set(perf, repeats, i, r, p);
+				mean[i] += p/((double) repeats);
+			} else {
+				note("Only cv is implemented\n");
+				exit(1);
+			}
+			note("%3.3f\t", p);
+		}
+		for (r=0; r<repeats; r++) {
+			std[i] += pow(matrix_get(perf, repeats, i, r) - mean[i], 2);
+		}
+		std[i] /= ((double) repeats) - 1.0;
+		std[i] = sqrt(std[i]);
+		note("(m = %3.3f, s = %3.3f, t = %3.3f)\n", mean[i], std[i], time);
+	}
+
+	note("\nBest overall configuration(s):\n");
+	note("ID\tweights\tepsilon\t\tp\t\tkappa\t\tlambda\t\tmean_perf\tstd_perf\n");
+	p = 0.0;
+	bool breakout = false;
+	while (breakout == false) {
+		pi = prctile(mean, N, (100.0-p)/100.0);
+		pr = prctile(std, N, p/100.0);
+		for (i=0; i<N; i++) 
+			if ((pi - mean[i] < 0.0001) && (std[i] - pr < 0.0001)) {
+				note("(%li)\tw = %li\te = %f\tp = %f\tk = %f\tl = %f\t"
+						"mean: %3.3f\tstd: %3.3f\n",
+						nq->tasks[i]->ID, 
+						nq->tasks[i]->weight_idx,
+						nq->tasks[i]->epsilon, nq->tasks[i]->p,
+						nq->tasks[i]->kappa, nq->tasks[i]->lambda,
+						mean[i], std[i]);
+				breakout = true;
+			}
+		p += 1.0;
+	}
+
+	free(task);
+	free(model);
+	free(perf);
+	free(std);
+	free(mean);
+}
+
+double cross_validation(struct MajModel *model, struct MajModel *seed_model,
+		struct MajData *data, long folds) 
+{
+	FILE *fid;
+
+	bool fs = false;
+	long f, *predy;
+	double total_perf = 0;
+	struct MajModel *fold_model;
+	struct MajData *train_data, *test_data;
+
+	long *cv_idx = Calloc(long, model->n);
+	double *performance = Calloc(double, folds);
+
+	if (seed_model == NULL) {
+		seed_model = Malloc(struct MajModel, 1);
+		seed_model->n = 0; // we never use anything other than V
+		seed_model->m = model->m;
+		seed_model->K = model->K;
+		msvmmaj_allocate_model(seed_model);
+		msvmmaj_seed_model_V(NULL, seed_model);
+		fs = true;
+	}
+
+	train_data = Malloc(struct MajData, 1);
+	test_data = Malloc(struct MajData, 1);
+
+	msvmmaj_make_cv_split(model->n, folds, cv_idx);
+	for (f=0; f<folds; f++) {
+		msvmmaj_get_tt_split(data, train_data, test_data, cv_idx, f);
+
+		fold_model = Malloc(struct MajModel, 1);
+		copy_model(model, fold_model);
+	
+		fold_model->n = train_data->n;
+		fold_model->m = train_data->m;
+		fold_model->K = train_data->K;
+		
+		msvmmaj_allocate_model(fold_model);
+		msvmmaj_initialize_weights(train_data, fold_model);
+		msvmmaj_seed_model_V(seed_model, fold_model);
+	
+		fid = MSVMMAJ_OUTPUT_FILE;
+		MSVMMAJ_OUTPUT_FILE = NULL;
+		msvmmaj_optimize(fold_model, train_data);
+		MSVMMAJ_OUTPUT_FILE = fid;
+
+		predy = Calloc(long, test_data->n);
+		msvmmaj_predict_labels(test_data, fold_model, predy);
+		performance[f] = msvmmaj_prediction_perf(test_data, predy);
+		total_perf += performance[f]/((double) folds);
+
+		msvmmaj_seed_model_V(fold_model, seed_model);
+		
+		free(predy);
+		free(train_data->y);
+		free(train_data->Z);
+		free(test_data->y);
+		free(test_data->Z);
+
+		msvmmaj_free_model(fold_model);
+	}
+
+	if (fs)
+		msvmmaj_free_model(seed_model);
+	free(train_data);
+	free(test_data);
+	free(performance);
+	free(cv_idx);
+
+	return total_perf;
+
+}
+
+void start_training_cv(struct Queue *q)
+{
+	double perf, current_max = 0;
+	struct Task *task = get_next_task(q);
+	struct MajModel *seed_model = Malloc(struct MajModel, 1);
+	struct MajModel *model = Malloc(struct MajModel, 1);
+	clock_t main_s, main_e, loop_s, loop_e;
+
+	model->n = task->train_data->n;
+	model->m = task->train_data->m;
+	model->K = task->train_data->K;
+	msvmmaj_allocate_model(model);
+
+	seed_model->n = 0;
+	seed_model->m = task->train_data->m;
+	seed_model->K = task->train_data->K;
+	msvmmaj_allocate_model(seed_model);
+	msvmmaj_seed_model_V(NULL, seed_model);
+	
+	main_s = clock();
+	while (task) {
+		note("(%03li/%03li)\tw = %li\te = %f\tp = %f\tk = %f\t l = %f\t",
+				task->ID+1, q->N, task->weight_idx, task->epsilon,
+				task->p, task->kappa, task->lambda);
+		make_model_from_task(task, model);
+		
+		loop_s = clock();
+		perf = cross_validation(model, seed_model, task->train_data, task->folds);
+		loop_e = clock();
+		current_max = maximum(current_max, perf);
+
+		note("\t%3.3f%% (%3.3fs)\t(best = %3.3f%%)\n", perf,
+				elapsed_time(loop_s, loop_e),
+				current_max);
+
+		q->tasks[task->ID]->performance = perf;
+		task = get_next_task(q);
+	}
+	main_e = clock();
+
+	note("\nTotal elapsed time: %8.8f seconds\n", 
+			elapsed_time(main_s, main_e));
+
+	free(task);
+	msvmmaj_free_model(seed_model);
+}
+
+void start_training_tt(struct Queue *q)
+{
+	FILE *fid;
+
+	long c = 0;
+	long *predy;
+	double total_perf, current_max = 0;
+
+	struct Task *task = get_next_task(q);
+	struct MajModel *seed_model = Malloc(struct MajModel, 1);
+
+	clock_t main_s, main_e;
+	clock_t loop_s, loop_e;
+
+	seed_model->m = task->train_data->m;
+	seed_model->K = task->train_data->K;
+	msvmmaj_allocate_model(seed_model);
+	msvmmaj_seed_model_V(NULL, seed_model);
+	
+	main_s = clock();
+	while (task) {
+		total_perf = 0;
+		note("(%li/%li)\tw = %li\te = %f\tp = %f\tk = %f\tl = %f\t",
+				c+1, q->N, task->weight_idx, task->epsilon,
+				task->p, task->kappa, task->lambda);
+		loop_s = clock();
+		struct MajModel *model = Malloc(struct MajModel, 1);
+		make_model_from_task(task, model);
+
+		model->n = task->train_data->n;
+		model->m = task->train_data->m;
+		model->K = task->train_data->K;
+
+		msvmmaj_allocate_model(model);
+		msvmmaj_initialize_weights(task->train_data, model);
+		msvmmaj_seed_model_V(seed_model, model);
+
+		fid = MSVMMAJ_OUTPUT_FILE;
+		MSVMMAJ_OUTPUT_FILE = NULL;
+		msvmmaj_optimize(model, task->train_data);
+		MSVMMAJ_OUTPUT_FILE = fid;
+
+		predy = Calloc(long, task->test_data->n);
+		msvmmaj_predict_labels(task->test_data, model, predy);
+		if (task->test_data->y != NULL) 
+			total_perf = msvmmaj_prediction_perf(task->test_data, predy);
+		msvmmaj_seed_model_V(model, seed_model);
+
+		msvmmaj_free_model(model);
+		free(predy);
+		note(".");
+		loop_e = clock();
+		current_max = maximum(current_max, total_perf);
+		note("\t%3.3f%% (%3.3fs)\t(best = %3.3f%%)\n", total_perf,
+				elapsed_time(loop_s, loop_e), current_max);
+		q->tasks[task->ID]->performance = total_perf;
+		task = get_next_task(q);
+	}
+	main_e = clock();
+	
+	note("\nTotal elapsed time: %8.8f seconds\n",
+			elapsed_time(main_s, main_e));
+	free(task);
+	msvmmaj_free_model(seed_model);
+}
+
+void free_queue(struct Queue *q)
+{
+	long i;
+	for (i=0; i<q->N; i++)
+		free(q->tasks[i]);
+	free(q->tasks);
+	free(q);
+}
+
+void make_model_from_task(struct Task *task, struct MajModel *model)
+{
+	model->weight_idx = task->weight_idx;
+	model->epsilon = task->epsilon;
+	model->p = task->p;
+	model->kappa = task->kappa;
+	model->lambda = task->lambda;
+}
+
+void copy_model(struct MajModel *from, struct MajModel *to)
+{
+	to->weight_idx = from->weight_idx;
+	to->epsilon = from->epsilon;
+	to->p = from->p;
+	to->kappa = from->kappa;
+	to->lambda = from->lambda;
+}