// 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 "create_iris_datafile.h"
#include <vector>
#include <map>
#include <sstream>
namespace
{
using namespace test;
using namespace dlib;
using namespace std;
dlib::logger dlog("test.svm_multiclass_trainer");
class test_svm_multiclass_trainer : 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_svm_multiclass_trainer (
) :
tester (
"test_svm_multiclass_trainer", // the command line argument name for this test
"Run tests on the svm_multiclass_linear_trainer stuff.", // the command line argument description
0 // the number of command line arguments for this test
)
{
}
void test_prior ()
{
print_spinner();
typedef matrix<double,4,1> sample_type;
typedef linear_kernel<sample_type> kernel_type;
std::vector<sample_type> samples;
std::vector<int> labels;
for (int i = 0; i < 4; ++i)
{
if (i==2)
++i;
for (int iter = 0; iter < 5; ++iter)
{
sample_type samp;
samp = 0;
samp(i) = 1;
samples.push_back(samp);
labels.push_back(i);
}
}
svm_multiclass_linear_trainer<kernel_type,int> trainer;
multiclass_linear_decision_function<kernel_type,int> df = trainer.train(samples, labels);
//cout << "test: \n" << test_multiclass_decision_function(df, samples, labels) << endl;
//cout << df.weights << endl;
//cout << df.b << endl;
std::vector<sample_type> samples2;
std::vector<int> labels2;
int i = 2;
for (int iter = 0; iter < 5; ++iter)
{
sample_type samp;
samp = 0;
samp(i) = 1;
samples2.push_back(samp);
labels2.push_back(i);
samples.push_back(samp);
labels.push_back(i);
}
trainer.set_prior(df);
trainer.set_c(0.1);
df = trainer.train(samples2, labels2);
matrix<double> res = test_multiclass_decision_function(df, samples, labels);
dlog << LINFO << "test: \n" << res;
dlog << LINFO << df.weights;
dlog << LINFO << df.b;
DLIB_TEST((unsigned int)sum(diag(res))==samples.size());
}
void test_prior_sparse ()
{
print_spinner();
typedef std::map<unsigned long,double> sample_type;
typedef sparse_linear_kernel<sample_type> kernel_type;
std::vector<sample_type> samples;
std::vector<int> labels;
for (int i = 0; i < 4; ++i)
{
if (i==2)
++i;
for (int iter = 0; iter < 5; ++iter)
{
sample_type samp;
samp[i] = 1;
samples.push_back(samp);
labels.push_back(i);
}
}
svm_multiclass_linear_trainer<kernel_type,int> trainer;
multiclass_linear_decision_function<kernel_type,int> df = trainer.train(samples, labels);
//cout << "test: \n" << test_multiclass_decision_function(df, samples, labels) << endl;
//cout << df.weights << endl;
//cout << df.b << endl;
std::vector<sample_type> samples2;
std::vector<int> labels2;
int i = 2;
for (int iter = 0; iter < 5; ++iter)
{
sample_type samp;
samp[i] = 1;
samp[i+10] = 1;
samples2.push_back(samp);
labels2.push_back(i);
samples.push_back(samp);
labels.push_back(i);
}
trainer.set_prior(df);
trainer.set_c(0.1);
df = trainer.train(samples2, labels2);
matrix<double> res = test_multiclass_decision_function(df, samples, labels);
dlog << LINFO << "test: \n" << res;
dlog << LINFO << df.weights;
dlog << LINFO << df.b;
DLIB_TEST((unsigned int)sum(diag(res))==samples.size());
}
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);
DLIB_TEST(samples.size() == 150);
DLIB_TEST(labels.size() == 150);
typedef sparse_linear_kernel<sample_type> kernel_type;
svm_multiclass_linear_trainer<kernel_type> trainer;
trainer.set_c(100);
trainer.set_epsilon(0.000001);
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);
typedef linear_kernel<dsample_type> kernel_type;
svm_multiclass_linear_trainer<kernel_type> trainer;
trainer.set_c(100);
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 perform_test (
)
{
print_spinner();
create_iris_datafile();
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_prior();
test_prior_sparse();
}
};
test_svm_multiclass_trainer a;
}