dlib C++ Library - data_io.cpp

// Copyright (C) 2011 Davis E. King (davis@dlib.net)
// License: Boost Software License See LICENSE.txt for the full license.
#include "tester.h"
#include <dlib/svm_threaded.h>
#include <dlib/data_io.h>
#include <dlib/sparse_vector.h>
#include "create_iris_datafile.h"
#include <vector>
#include <sstream>
namespace 
{
 using namespace test;
 using namespace dlib;
 using namespace std;
 dlib::logger dlog("test.data_io");
 class test_data_io : public tester
 {
 /*!
 WHAT THIS OBJECT REPRESENTS
 This object represents a unit test. When it is constructed
 it adds itself into the testing framework.
 !*/
 public:
 test_data_io (
 ) :
 tester (
 "test_data_io", // the command line argument name for this test
 "Run tests on the data_io stuff.", // the command line argument description
 0 // the number of command line arguments for this test
 )
 {
 }
 template <typename sample_type>
 void run_test()
 {
 print_spinner();
 typedef typename sample_type::value_type::second_type scalar_type;
 std::vector<sample_type> samples;
 std::vector<scalar_type> labels;
 load_libsvm_formatted_data("iris.scale",samples, labels);
 save_libsvm_formatted_data("iris.scale2", samples, labels);
 DLIB_TEST(samples.size() == 150);
 DLIB_TEST(labels.size() == 150);
 DLIB_TEST(max_index_plus_one(samples) == 5);
 fix_nonzero_indexing(samples);
 DLIB_TEST(max_index_plus_one(samples) == 4);
 load_libsvm_formatted_data("iris.scale2",samples, labels);
 DLIB_TEST(samples.size() == 150);
 DLIB_TEST(labels.size() == 150);
 DLIB_TEST(max_index_plus_one(samples) == 5);
 fix_nonzero_indexing(samples);
 DLIB_TEST(max_index_plus_one(samples) == 4);
 one_vs_one_trainer<any_trainer<sample_type,scalar_type>,scalar_type> trainer;
 typedef sparse_linear_kernel<sample_type> kernel_type;
 trainer.set_trainer(krr_trainer<kernel_type>());
 randomize_samples(samples, labels);
 matrix<double> cv = cross_validate_multiclass_trainer(trainer, samples, labels, 4);
 dlog << LINFO << "confusion matrix: \n" << cv;
 const scalar_type cv_accuracy = sum(diag(cv))/sum(cv);
 dlog << LINFO << "cv accuracy: " << cv_accuracy;
 DLIB_TEST(cv_accuracy > 0.97);
 {
 print_spinner();
 typedef matrix<scalar_type,0,1> dsample_type;
 std::vector<dsample_type> dsamples = sparse_to_dense(samples);
 DLIB_TEST(dsamples.size() == 150);
 DLIB_TEST(dsamples[0].size() == 4);
 DLIB_TEST(max_index_plus_one(dsamples) == 4);
 one_vs_one_trainer<any_trainer<dsample_type,scalar_type>,scalar_type> trainer;
 typedef linear_kernel<dsample_type> kernel_type;
 trainer.set_trainer(rr_trainer<kernel_type>());
 cv = cross_validate_multiclass_trainer(trainer, dsamples, labels, 4);
 dlog << LINFO << "dense confusion matrix: \n" << cv;
 const scalar_type cv_accuracy = sum(diag(cv))/sum(cv);
 dlog << LINFO << "dense cv accuracy: " << cv_accuracy;
 DLIB_TEST(cv_accuracy > 0.97);
 }
 }
 void test_sparse_to_dense()
 {
 {
 std::map<unsigned long, double> temp;
 matrix<double,0,1> m, m2;
 m = sparse_to_dense(m);
 DLIB_TEST(m.size() == 0);
 m.set_size(2,1);
 m = 1, 2;
 m2 = sparse_to_dense(m);
 DLIB_TEST(m == m2);
 m2 = sparse_to_dense(m,1);
 DLIB_TEST(m2.size() == 1);
 DLIB_TEST(m2(0,0) == 1);
 m2 = sparse_to_dense(m,0);
 DLIB_TEST(m2.size() == 0);
 temp[3] = 2;
 temp[5] = 4;
 m2 = sparse_to_dense(temp);
 m.set_size(6);
 m = 0,0,0,2,0,4;
 DLIB_TEST(m2 == m);
 m2 = sparse_to_dense(temp, 5);
 m.set_size(5);
 m = 0,0,0,2,0;
 DLIB_TEST(m2 == m);
 m2 = sparse_to_dense(temp, 7);
 m.set_size(7);
 m = 0,0,0,2,0,4,0;
 DLIB_TEST(m2 == m);
 std::vector<std::vector<std::pair<unsigned long,double> > > vects;
 std::vector<std::pair<unsigned long,double> > v;
 v.push_back(make_pair(5,2));
 v.push_back(make_pair(3,1));
 v.push_back(make_pair(5,2));
 v.push_back(make_pair(3,1));
 v = make_sparse_vector(v);
 vects.push_back(v);
 vects.push_back(v);
 vects.push_back(v);
 vects.push_back(v);
 DLIB_TEST(max_index_plus_one(v) == 6);
 m2 = sparse_to_dense(v);
 m.set_size(6);
 m = 0,0,0,2,0,4;
 DLIB_TEST_MSG(m2 == m, m2 << "\n\n" << m );
 m2 = sparse_to_dense(v,7);
 m.set_size(7);
 m = 0,0,0,2,0,4,0;
 DLIB_TEST(m2 == m);
 m2 = sparse_to_dense(v,5);
 m.set_size(5);
 m = 0,0,0,2,0;
 DLIB_TEST(m2 == m);
 v.clear();
 m2 = sparse_to_dense(v);
 DLIB_TEST(m2.size() == 0);
 std::vector<matrix<double,0,1> > mvects = sparse_to_dense(vects);
 DLIB_TEST(mvects.size() == 4);
 m.set_size(6);
 m = 0,0,0,2,0,4;
 DLIB_TEST(mvects[0] == m);
 DLIB_TEST(mvects[1] == m);
 DLIB_TEST(mvects[2] == m);
 DLIB_TEST(mvects[3] == m);
 mvects = sparse_to_dense(vects, 7);
 DLIB_TEST(mvects.size() == 4);
 m.set_size(7);
 m = 0,0,0,2,0,4,0;
 DLIB_TEST(mvects[0] == m);
 DLIB_TEST(mvects[1] == m);
 DLIB_TEST(mvects[2] == m);
 DLIB_TEST(mvects[3] == m);
 mvects = sparse_to_dense(vects, 5);
 DLIB_TEST(mvects.size() == 4);
 m.set_size(5);
 m = 0,0,0,2,0;
 DLIB_TEST(mvects[0] == m);
 DLIB_TEST(mvects[1] == m);
 DLIB_TEST(mvects[2] == m);
 DLIB_TEST(mvects[3] == m);
 }
 }
 void perform_test (
 )
 {
 print_spinner();
 create_iris_datafile();
 test_sparse_to_dense();
 run_test<std::map<unsigned int, double> >();
 run_test<std::map<unsigned int, float> >();
 run_test<std::vector<std::pair<unsigned int, float> > >();
 run_test<std::vector<std::pair<unsigned long, double> > >();
 }
 };
 test_data_io a;
}