added example for mkl modelselection (currently not working, but should

in the future, once bugs are fixed)
shogun-toolbox · Apr 14, 2012 · d716ae0 · d716ae0
1 parent 24bab87
commit d716ae0
Showing 1 changed file with 153 additions and 0 deletions.
diff --git a/examples/undocumented/libshogun/modelselection_grid_search_mkl.cpp b/examples/undocumented/libshogun/modelselection_grid_search_mkl.cpp
@@ -0,0 +1,153 @@
+/*
+ * 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 Heiko Strathmann
+ */
+
+#include <shogun/base/init.h>
+#include <shogun/evaluation/CrossValidation.h>
+#include <shogun/evaluation/ContingencyTableEvaluation.h>
+#include <shogun/evaluation/StratifiedCrossValidationSplitting.h>
+#include <shogun/modelselection/GridSearchModelSelection.h>
+#include <shogun/modelselection/ModelSelectionParameters.h>
+#include <shogun/modelselection/ParameterCombination.h>
+#include <shogun/features/Labels.h>
+#include <shogun/features/SimpleFeatures.h>
+#include <shogun/classifier/mkl/MKLClassification.h>
+#include <shogun/classifier/svm/LibSVM.h>
+#include <shogun/kernel/GaussianKernel.h>
+#include <shogun/kernel/CombinedKernel.h>
+
+using namespace shogun;
+
+void print_message(FILE* target, const char* str)
+{
+	fprintf(target, "%s", str);
+}
+
+CModelSelectionParameters* create_param_tree()
+{
+	CModelSelectionParameters* root=new CModelSelectionParameters();
+
+	CModelSelectionParameters* c1=new CModelSelectionParameters("C1");
+	root->append_child(c1);
+	c1->build_values(-1.0, 1.0, R_EXP);
+
+	CModelSelectionParameters* c2=new CModelSelectionParameters("C2");
+	root->append_child(c2);
+	c2->build_values(-1.0, 1.0, R_EXP);
+
+	CCombinedKernel* kernel1=new CCombinedKernel();
+	kernel1->append_kernel(new CGaussianKernel(10, 2));
+	kernel1->append_kernel(new CGaussianKernel(10, 3));
+	kernel1->append_kernel(new CGaussianKernel(10, 4));
+
+	CModelSelectionParameters* param_kernel1=
+			new CModelSelectionParameters("kernel", kernel1);
+	root->append_child(param_kernel1);
+
+	CCombinedKernel* kernel2=new CCombinedKernel();
+	kernel2->append_kernel(new CGaussianKernel(10, 20));
+	kernel2->append_kernel(new CGaussianKernel(10, 30));
+	kernel2->append_kernel(new CGaussianKernel(10, 40));
+
+	CModelSelectionParameters* param_kernel2=
+			new CModelSelectionParameters("kernel", kernel2);
+	root->append_child(param_kernel2);
+
+	return root;
+}
+
+int main(int argc, char **argv)
+{
+	init_shogun(&print_message, &print_message, &print_message);
+	sg_io->set_loglevel(MSG_INFO);
+
+	int32_t num_subsets=3;
+	int32_t num_vectors=20;
+	int32_t dim_vectors=3;
+
+	/* create some data and labels */
+	float64_t* matrix=SG_MALLOC(float64_t, num_vectors*dim_vectors);
+	CLabels* labels=new CLabels(num_vectors);
+	for (int32_t i=0; i<num_vectors*dim_vectors; i++)
+		matrix[i]=CMath::randn_double();
+
+	/* create num_feautres 2-dimensional vectors */
+	CSimpleFeatures<float64_t>* features=new CSimpleFeatures<float64_t> ();
+	features->set_feature_matrix(matrix, dim_vectors, num_vectors);
+
+	/* create combined features */
+	CCombinedFeatures* comb_features=new CCombinedFeatures();
+	comb_features->append_feature_obj(features);
+	comb_features->append_feature_obj(features);
+	comb_features->append_feature_obj(features);
+
+	/* create labels, two classes */
+	for (index_t i=0; i<num_vectors; ++i)
+		labels->set_label(i, i%2==0 ? 1 : -1);
+
+	/* works */
+//	/* create svm */
+//	CMKLClassification* classifier=new CMKLClassification(new CLibSVM());
+//	classifier->set_interleaved_optimization_enabled(false);
+
+	/* create svm */
+	CMKLClassification* classifier=new CMKLClassification();
+
+	// both fail:
+	//classifier->set_interleaved_optimization_enabled(false);
+	classifier->set_interleaved_optimization_enabled(true);
+
+	/* splitting strategy */
+	CStratifiedCrossValidationSplitting* splitting_strategy=
+			new CStratifiedCrossValidationSplitting(labels, num_subsets);
+
+	/* accuracy evaluation */
+	CContingencyTableEvaluation* evaluation_criterium=
+			new CContingencyTableEvaluation(ACCURACY);
+
+	/* cross validation class for evaluation in model selection */
+	CCrossValidation* cross=new CCrossValidation(classifier, comb_features,
+			labels, splitting_strategy, evaluation_criterium);
+	cross->set_num_runs(1);
+
+	/* print all parameter available for modelselection
+	 * Dont worry if yours is not included, simply write to the mailing list */
+	classifier->print_modsel_params();
+
+	/* model parameter selection, deletion is handled by modsel class (SG_UNREF) */
+	CModelSelectionParameters* param_tree=create_param_tree();
+	param_tree->print_tree();
+
+	/* handles all of the above structures in memory */
+	CGridSearchModelSelection* grid_search=new CGridSearchModelSelection(
+			param_tree, cross);
+
+	bool print_state=true;
+	CParameterCombination* best_combination=grid_search->select_model(
+			print_state);
+	SG_SPRINT("best parameter(s):\n");
+	best_combination->print_tree();
+
+	best_combination->apply_to_machine(classifier);
+
+	/* larger number of runs to have tighter confidence intervals */
+	cross->set_num_runs(10);
+	cross->set_conf_int_alpha(0.01);
+	CrossValidationResult result=cross->evaluate();
+	SG_SPRINT("result: ");
+	result.print_result();
+
+	/* clean up destroy result parameter */
+	SG_UNREF(best_combination);
+	SG_UNREF(grid_search);
+
+	exit_shogun();
+
+	return 0;
+}
+