// 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;
}