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Add Multiclass cross-validation example to libshogun
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examples/undocumented/libshogun/evaluation_cross_validation_multiclass.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 | ||
* Copyright (C) 2012 Berlin Institute of Technology and Max-Planck-Society | ||
*/ | ||
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#include <shogun/base/init.h> | ||
#include <shogun/features/DenseFeatures.h> | ||
#include <shogun/features/Labels.h> | ||
#include <shogun/multiclass/MulticlassLibLinear.h> | ||
#include <shogun/io/StreamingAsciiFile.h> | ||
#include <shogun/io/SGIO.h> | ||
#include <shogun/features/StreamingDenseFeatures.h> | ||
#include <shogun/evaluation/CrossValidation.h> | ||
#include <shogun/evaluation/StratifiedCrossValidationSplitting.h> | ||
#include <shogun/evaluation/MulticlassAccuracy.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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void test_cross_validation() | ||
{ | ||
int32_t num_vectors = 0; | ||
int32_t num_feats = 2; | ||
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// Prepare to read a file for the training 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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CStreamingDenseFeatures< float64_t >* stream_features = | ||
new CStreamingDenseFeatures< float64_t >(ffeats_train, false, 1024); | ||
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CStreamingDenseFeatures< float64_t >* stream_labels = | ||
new CStreamingDenseFeatures< float64_t >(flabels_train, true, 1024); | ||
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SG_REF(stream_features); | ||
SG_REF(stream_labels); | ||
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// Create a matrix with enough space to read all the feature vectors | ||
SGMatrix< float64_t > mat = SGMatrix< float64_t >(num_feats, 1000); | ||
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// Read the values from the file and store them in mat | ||
SGVector< float64_t > vec; | ||
stream_features->start_parser(); | ||
while ( stream_features->get_next_example() ) | ||
{ | ||
vec = stream_features->get_vector(); | ||
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for ( int32_t i = 0 ; i < num_feats ; ++i ) | ||
mat.matrix[num_vectors*num_feats + i] = vec[i]; | ||
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num_vectors++; | ||
stream_features->release_example(); | ||
} | ||
stream_features->end_parser(); | ||
mat.num_cols = num_vectors; | ||
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// Create features with the useful values from mat | ||
CDenseFeatures< float64_t >* features = new CDenseFeatures<float64_t>(mat); | ||
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CLabels* labels = new CLabels(num_vectors); | ||
SG_REF(features); | ||
SG_REF(labels); | ||
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// Read the labels from the 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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/* create svm via libsvm */ | ||
float64_t svm_C=10; | ||
float64_t svm_eps=0.0001; | ||
CMulticlassLibLinear* svm=new CMulticlassLibLinear(svm_C, features, labels); | ||
svm->set_epsilon(svm_eps); | ||
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/* train and output */ | ||
svm->train(features); | ||
CLabels* output=svm->apply(features); | ||
for (index_t i=0; i<num_vectors; ++i) | ||
SG_SPRINT("i=%d, class=%f,\n", i, output->get_label(i)); | ||
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/* evaluation criterion */ | ||
CMulticlassAccuracy* eval_crit = new CMulticlassAccuracy (); | ||
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/* evaluate training error */ | ||
float64_t eval_result=eval_crit->evaluate(output, labels); | ||
SG_SPRINT("training error: %f\n", eval_result); | ||
SG_UNREF(output); | ||
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/* assert that regression "works". this is not guaranteed to always work | ||
* but should be a really coarse check to see if everything is going | ||
* approx. right */ | ||
ASSERT(eval_result<2); | ||
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/* splitting strategy */ | ||
index_t n_folds=5; | ||
CStratifiedCrossValidationSplitting* splitting= | ||
new CStratifiedCrossValidationSplitting(labels, n_folds); | ||
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/* cross validation instance, 10 runs, 95% confidence interval */ | ||
CCrossValidation* cross=new CCrossValidation(svm, features, labels, | ||
splitting, eval_crit); | ||
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cross->set_num_runs(10); | ||
cross->set_conf_int_alpha(0.05); | ||
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/* actual evaluation */ | ||
CrossValidationResult result=cross->evaluate(); | ||
result.print_result(); | ||
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/* clean up */ | ||
SG_UNREF(cross); | ||
SG_UNREF(features); | ||
SG_UNREF(labels); | ||
SG_UNREF(ffeats_train); | ||
SG_UNREF(flabels_train); | ||
SG_UNREF(stream_features); | ||
SG_UNREF(stream_labels); | ||
} | ||
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int main(int argc, char **argv) | ||
{ | ||
init_shogun(&print_message, &print_message, &print_message); | ||
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sg_io->set_loglevel(MSG_DEBUG); | ||
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test_cross_validation(); | ||
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exit_shogun(); | ||
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return 0; | ||
} | ||
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