added mkl multiclass cross-validation example

shogun-toolbox · Jul 20, 2012 · e2f0d12 · e2f0d12
1 parent fab1560
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diff --git a/examples/undocumented/libshogun/Makefile b/examples/undocumented/libshogun/Makefile
@@ -34,6 +34,7 @@ TARGETS = basic_minimal \
 		  evaluation_cross_validation_regression \
 		  evaluation_cross_validation_locked_comparison \
 		  evaluation_cross_validation_multiclass \
+		  evaluation_cross_validation_multiclass_mkl \
 		  modelselection_parameter_combination_test \
 		  regression_gaussian_process_gaussian \
 		  regression_gaussian_process_sum \

diff --git a/examples/undocumented/libshogun/evaluation_cross_validation_multiclass_mkl.cpp b/examples/undocumented/libshogun/evaluation_cross_validation_multiclass_mkl.cpp
@@ -0,0 +1,154 @@
+/*
+ * This program is free software; you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation; either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * Written (W) 2012 yoo, thereisnoknife@gmail.com
+ * Written (W) 2012 Heiko Strathmann
+ */
+
+#include <shogun/features/StreamingDenseFeatures.h>
+#include <shogun/io/StreamingAsciiFile.h>
+#include <shogun/labels/MulticlassLabels.h>
+#include <shogun/kernel/GaussianKernel.h>
+#include <shogun/kernel/LinearKernel.h>
+#include <shogun/kernel/PolyKernel.h>
+#include <shogun/kernel/CombinedKernel.h>
+#include <shogun/classifier/mkl/MKLMulticlass.h>
+#include <shogun/evaluation/StratifiedCrossValidationSplitting.h>
+#include <shogun/evaluation/CrossValidation.h>
+#include <shogun/evaluation/MulticlassAccuracy.h>
+
+using namespace shogun;
+
+void test_multiclass_mkl_cv()
+{
+	/* stream data from a file */
+	int32_t num_vectors=50;
+	int32_t num_feats=2;
+
+	/* file data */
+	char fname_feats[]="../data/fm_train_real.dat";
+	char fname_labels[]="../data/label_train_multiclass.dat";
+	CStreamingAsciiFile* ffeats_train=new CStreamingAsciiFile(fname_feats);
+	CStreamingAsciiFile* flabels_train=new CStreamingAsciiFile(fname_labels);
+	SG_REF(ffeats_train);
+	SG_REF(flabels_train);
+
+	/* streaming data */
+	CStreamingDenseFeatures<float64_t>* stream_features=
+			new CStreamingDenseFeatures<float64_t>(ffeats_train, false, 1024);
+	CStreamingDenseFeatures<float64_t>* stream_labels=
+			new CStreamingDenseFeatures<float64_t>(flabels_train, true, 1024);
+	SG_REF(stream_features);
+	SG_REF(stream_labels);
+
+	/* matrix data */
+	SGMatrix<float64_t> mat=SGMatrix<float64_t>(num_feats, num_vectors);
+	SGVector<float64_t> vec;
+	stream_features->start_parser();
+
+	index_t count=0;
+	while (stream_features->get_next_example() && count<num_vectors)
+	{
+		vec=stream_features->get_vector();
+		for (int32_t i=0; i<num_feats; ++i)
+			mat(i,count)=vec[i];
+
+		stream_features->release_example();
+		count++;
+	}
+	stream_features->end_parser();
+	mat.num_cols=num_vectors;
+
+	/* dense features from streamed matrix */
+	CDenseFeatures<float64_t>* features=new CDenseFeatures<float64_t>(mat);
+	CMulticlassLabels* labels=new CMulticlassLabels(num_vectors);
+	SG_REF(features);
+	SG_REF(labels);
+
+	/* read labels from file */
+	int32_t idx=0;
+	stream_labels->start_parser();
+	while (stream_labels->get_next_example())
+	{
+		labels->set_int_label(idx++, (int32_t)stream_labels->get_label());
+		stream_labels->release_example();
+	}
+	stream_labels->end_parser();
+
+	/* combined features and kernel */
+	CCombinedFeatures *cfeats=new CCombinedFeatures();
+	CCombinedKernel *cker=new CCombinedKernel();
+	SG_REF(cfeats);
+	SG_REF(cker);
+
+	/** 1st kernel: gaussian */
+	cfeats->append_feature_obj(features);
+	cker->append_kernel(new CGaussianKernel(features, features, 1.2, 10));
+
+	/** 2nd kernel: linear */
+	cfeats->append_feature_obj(features);
+	cker->append_kernel(new CLinearKernel(features, features));
+
+	/** 3rd kernel: poly */
+	cfeats->append_feature_obj(features);
+	cker->append_kernel(new CPolyKernel(features, features, 2, true, 10));
+
+	cker->init(cfeats, cfeats);
+
+	/* create mkl instance */
+	CMKLMulticlass* mkl=new CMKLMulticlass(1.2, cker, labels);
+	SG_REF(mkl);
+	mkl->set_epsilon(0.00001);
+	mkl->parallel->set_num_threads(1);
+	mkl->set_mkl_epsilon(0.001);
+	mkl->set_mkl_norm(1.5);
+
+	/* train to see weights */
+	mkl->train();
+	cker->get_subkernel_weights().display_vector("weights");
+
+	/* cross-validation instances */
+	index_t n_folds=3;
+	index_t n_runs=5;
+	CMulticlassAccuracy* eval_crit=new CMulticlassAccuracy();
+	CStratifiedCrossValidationSplitting* splitting=
+			new CStratifiedCrossValidationSplitting(labels, n_folds);
+	CCrossValidation *cross=new CCrossValidation(mkl, cfeats, labels, splitting,
+			eval_crit);
+	cross->set_autolock(false);
+	cross->set_num_runs(n_runs);
+	cross->set_conf_int_alpha(0.05);
+
+	/* perform x-val and print result */
+	CrossValidationResult* result=(CrossValidationResult*)cross->evaluate();
+	SG_SPRINT("mean of %d %d-fold x-val runs: %f\n", n_runs, n_folds,
+			result->mean);
+
+	/* clean up */
+	SG_UNREF(ffeats_train);
+	SG_UNREF(flabels_train);
+	SG_UNREF(stream_features);
+	SG_UNREF(stream_labels);
+	SG_UNREF(features);
+	SG_UNREF(labels);
+	SG_UNREF(cfeats);
+	SG_UNREF(cker);
+	SG_UNREF(mkl);
+	SG_UNREF(cross);
+	SG_UNREF(result);
+}
+
+int main(int argc, char** argv){
+	shogun::init_shogun_with_defaults();
+
+//	sg_io->set_loglevel(MSG_DEBUG);
+
+	/* performs cross-validation on a multi-class mkl machine */
+	test_multiclass_mkl_cv();
+
+	exit_shogun();
+}
+