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Merge pull request #453 from karlnapf/master
added mkl modelselection example
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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 | ||
*/ | ||
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#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> | ||
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using namespace shogun; | ||
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void print_message(FILE* target, const char* str) | ||
{ | ||
fprintf(target, "%s", str); | ||
} | ||
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CModelSelectionParameters* create_param_tree() | ||
{ | ||
CModelSelectionParameters* root=new CModelSelectionParameters(); | ||
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CModelSelectionParameters* c1=new CModelSelectionParameters("C1"); | ||
root->append_child(c1); | ||
c1->build_values(-1.0, 1.0, R_EXP); | ||
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CModelSelectionParameters* c2=new CModelSelectionParameters("C2"); | ||
root->append_child(c2); | ||
c2->build_values(-1.0, 1.0, R_EXP); | ||
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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)); | ||
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CModelSelectionParameters* param_kernel1= | ||
new CModelSelectionParameters("kernel", kernel1); | ||
root->append_child(param_kernel1); | ||
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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)); | ||
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CModelSelectionParameters* param_kernel2= | ||
new CModelSelectionParameters("kernel", kernel2); | ||
root->append_child(param_kernel2); | ||
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return root; | ||
} | ||
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int main(int argc, char **argv) | ||
{ | ||
init_shogun(&print_message, &print_message, &print_message); | ||
sg_io->set_loglevel(MSG_INFO); | ||
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int32_t num_subsets=3; | ||
int32_t num_vectors=20; | ||
int32_t dim_vectors=3; | ||
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/* 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(); | ||
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/* create num_feautres 2-dimensional vectors */ | ||
CSimpleFeatures<float64_t>* features=new CSimpleFeatures<float64_t> (); | ||
features->set_feature_matrix(matrix, dim_vectors, num_vectors); | ||
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/* 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); | ||
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/* create labels, two classes */ | ||
for (index_t i=0; i<num_vectors; ++i) | ||
labels->set_label(i, i%2==0 ? 1 : -1); | ||
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/* works */ | ||
// /* create svm */ | ||
// CMKLClassification* classifier=new CMKLClassification(new CLibSVM()); | ||
// classifier->set_interleaved_optimization_enabled(false); | ||
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/* create svm */ | ||
CMKLClassification* classifier=new CMKLClassification(); | ||
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// both fail: | ||
//classifier->set_interleaved_optimization_enabled(false); | ||
classifier->set_interleaved_optimization_enabled(true); | ||
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/* splitting strategy */ | ||
CStratifiedCrossValidationSplitting* splitting_strategy= | ||
new CStratifiedCrossValidationSplitting(labels, num_subsets); | ||
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/* accuracy evaluation */ | ||
CContingencyTableEvaluation* evaluation_criterium= | ||
new CContingencyTableEvaluation(ACCURACY); | ||
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/* 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); | ||
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/* print all parameter available for modelselection | ||
* Dont worry if yours is not included, simply write to the mailing list */ | ||
classifier->print_modsel_params(); | ||
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/* model parameter selection, deletion is handled by modsel class (SG_UNREF) */ | ||
CModelSelectionParameters* param_tree=create_param_tree(); | ||
param_tree->print_tree(); | ||
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/* handles all of the above structures in memory */ | ||
CGridSearchModelSelection* grid_search=new CGridSearchModelSelection( | ||
param_tree, cross); | ||
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bool print_state=true; | ||
CParameterCombination* best_combination=grid_search->select_model( | ||
print_state); | ||
SG_SPRINT("best parameter(s):\n"); | ||
best_combination->print_tree(); | ||
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best_combination->apply_to_machine(classifier); | ||
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/* 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(); | ||
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/* clean up destroy result parameter */ | ||
SG_UNREF(best_combination); | ||
SG_UNREF(grid_search); | ||
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exit_shogun(); | ||
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return 0; | ||
} | ||
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