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Merge pull request #453 from karlnapf/master
added mkl modelselection example
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karlnapf committed Apr 14, 2012
2 parents 2f00592 + d716ae0 commit c12e96c
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153 changes: 153 additions & 0 deletions examples/undocumented/libshogun/modelselection_grid_search_mkl.cpp
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/*
* 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;
}

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