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added example for mkl multi class cross-validation
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examples/undocumented/libshogun/evaluation_cross_validation_multiclass_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 yoo, thereisnoknife@gmail.com | ||
* Written (W) 2012 Heiko Strathmann | ||
*/ | ||
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#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> | ||
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using namespace shogun; | ||
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void test_multiclass_mkl_cv() | ||
{ | ||
/* stream data from a file */ | ||
int32_t num_vectors=50; | ||
int32_t num_feats=2; | ||
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/* 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); | ||
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/* 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); | ||
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/* matrix data */ | ||
SGMatrix<float64_t> mat=SGMatrix<float64_t>(num_feats, num_vectors); | ||
SGVector<float64_t> vec; | ||
stream_features->start_parser(); | ||
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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]; | ||
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stream_features->release_example(); | ||
count++; | ||
} | ||
stream_features->end_parser(); | ||
mat.num_cols=num_vectors; | ||
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/* 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); | ||
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/* 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(); | ||
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/* combined features and kernel */ | ||
CCombinedFeatures *cfeats=new CCombinedFeatures(); | ||
CCombinedKernel *cker=new CCombinedKernel(); | ||
SG_REF(cfeats); | ||
SG_REF(cker); | ||
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/** 1st kernel: gaussian */ | ||
cfeats->append_feature_obj(features); | ||
cker->append_kernel(new CGaussianKernel(features, features, 1.2, 10)); | ||
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/** 2nd kernel: linear */ | ||
cfeats->append_feature_obj(features); | ||
cker->append_kernel(new CLinearKernel(features, features)); | ||
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/** 3rd kernel: poly */ | ||
cfeats->append_feature_obj(features); | ||
cker->append_kernel(new CPolyKernel(features, features, 2, true, 10)); | ||
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cker->init(cfeats, cfeats); | ||
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/* 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); | ||
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/* train to see weights */ | ||
mkl->train(); | ||
cker->get_subkernel_weights().display_vector("weights"); | ||
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/* 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); | ||
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/* 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); | ||
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/* assert high accuracy */ | ||
ASSERT(result->mean>0.9); | ||
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/* 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); | ||
} | ||
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int main(int argc, char** argv){ | ||
shogun::init_shogun_with_defaults(); | ||
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// sg_io->set_loglevel(MSG_DEBUG); | ||
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/* performs cross-validation on a multi-class mkl machine */ | ||
test_multiclass_mkl_cv(); | ||
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exit_shogun(); | ||
} | ||
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