This source file includes following definitions.
- _get_len
- TestTrainPredict
- checkPredictError
- TestSaveLoad
- checkLoadSave
- run
- TEST
#include "test_precomp.hpp"
#if 0
#include <string>
#include <fstream>
#include <iostream>
using namespace std;
class CV_GBTreesTest : public cvtest::BaseTest
{
public:
CV_GBTreesTest();
~CV_GBTreesTest();
protected:
void run(int);
int TestTrainPredict(int test_num);
int TestSaveLoad();
int checkPredictError(int test_num);
int checkLoadSave();
string model_file_name1;
string model_file_name2;
string* datasets;
string data_path;
CvMLData* data;
CvGBTrees* gtb;
vector<float> test_resps1;
vector<float> test_resps2;
int64 initSeed;
};
int _get_len(const CvMat* mat)
{
return (mat->cols > mat->rows) ? mat->cols : mat->rows;
}
CV_GBTreesTest::CV_GBTreesTest()
{
int64 seeds[] = { CV_BIG_INT(0x00009fff4f9c8d52),
CV_BIG_INT(0x0000a17166072c7c),
CV_BIG_INT(0x0201b32115cd1f9a),
CV_BIG_INT(0x0513cb37abcd1234),
CV_BIG_INT(0x0001a2b3c4d5f678)
};
int seedCount = sizeof(seeds)/sizeof(seeds[0]);
cv::RNG& rng = cv::theRNG();
initSeed = rng.state;
rng.state = seeds[rng(seedCount)];
datasets = 0;
data = 0;
gtb = 0;
}
CV_GBTreesTest::~CV_GBTreesTest()
{
if (data)
delete data;
delete[] datasets;
cv::theRNG().state = initSeed;
}
int CV_GBTreesTest::TestTrainPredict(int test_num)
{
int code = cvtest::TS::OK;
int weak_count = 200;
float shrinkage = 0.1f;
float subsample_portion = 0.5f;
int max_depth = 5;
bool use_surrogates = false;
int loss_function_type = 0;
switch (test_num)
{
case (1) : loss_function_type = CvGBTrees::SQUARED_LOSS; break;
case (2) : loss_function_type = CvGBTrees::ABSOLUTE_LOSS; break;
case (3) : loss_function_type = CvGBTrees::HUBER_LOSS; break;
case (0) : loss_function_type = CvGBTrees::DEVIANCE_LOSS; break;
default :
{
ts->printf( cvtest::TS::LOG, "Bad test_num value in CV_GBTreesTest::TestTrainPredict(..) function." );
return cvtest::TS::FAIL_BAD_ARG_CHECK;
}
}
int dataset_num = test_num == 0 ? 0 : 1;
if (!data)
{
data = new CvMLData();
data->set_delimiter(',');
if (data->read_csv(datasets[dataset_num].c_str()))
{
ts->printf( cvtest::TS::LOG, "File reading error." );
return cvtest::TS::FAIL_INVALID_TEST_DATA;
}
if (test_num == 0)
{
data->set_response_idx(57);
data->set_var_types("ord[0-56],cat[57]");
}
else
{
data->set_response_idx(13);
data->set_var_types("ord[0-2,4-13],cat[3]");
subsample_portion = 0.7f;
}
int train_sample_count = cvFloor(_get_len(data->get_responses())*0.5f);
CvTrainTestSplit spl( train_sample_count );
data->set_train_test_split( &spl );
}
data->mix_train_and_test_idx();
if (gtb) delete gtb;
gtb = new CvGBTrees();
bool tmp_code = true;
tmp_code = gtb->train(data, CvGBTreesParams(loss_function_type, weak_count,
shrinkage, subsample_portion,
max_depth, use_surrogates));
if (!tmp_code)
{
ts->printf( cvtest::TS::LOG, "Model training was failed.");
return cvtest::TS::FAIL_INVALID_OUTPUT;
}
code = checkPredictError(test_num);
return code;
}
int CV_GBTreesTest::checkPredictError(int test_num)
{
if (!gtb)
return cvtest::TS::FAIL_GENERIC;
float mean[] = {5.80226f, 12.68689f, 13.49095f, 13.19628f};
float sigma[] = {0.4764534f, 3.166919f, 3.022405f, 2.868722f};
float current_error = gtb->calc_error(data, CV_TEST_ERROR);
if ( abs( current_error - mean[test_num]) > 6*sigma[test_num] )
{
ts->printf( cvtest::TS::LOG, "Test error is out of range:\n"
"abs(%f/*curEr*/ - %f/*mean*/ > %f/*6*sigma*/",
current_error, mean[test_num], 6*sigma[test_num] );
return cvtest::TS::FAIL_BAD_ACCURACY;
}
return cvtest::TS::OK;
}
int CV_GBTreesTest::TestSaveLoad()
{
if (!gtb)
return cvtest::TS::FAIL_GENERIC;
model_file_name1 = cv::tempfile();
model_file_name2 = cv::tempfile();
gtb->save(model_file_name1.c_str());
gtb->calc_error(data, CV_TEST_ERROR, &test_resps1);
gtb->load(model_file_name1.c_str());
gtb->calc_error(data, CV_TEST_ERROR, &test_resps2);
gtb->save(model_file_name2.c_str());
return checkLoadSave();
}
int CV_GBTreesTest::checkLoadSave()
{
int code = cvtest::TS::OK;
ifstream f1( model_file_name1.c_str() ), f2( model_file_name2.c_str() );
string s1, s2;
int lineIdx = 0;
CV_Assert( f1.is_open() && f2.is_open() );
for( ; !f1.eof() && !f2.eof(); lineIdx++ )
{
getline( f1, s1 );
getline( f2, s2 );
if( s1.compare(s2) )
{
ts->printf( cvtest::TS::LOG, "first and second saved files differ in %n-line; first %n line: %s; second %n-line: %s",
lineIdx, lineIdx, s1.c_str(), lineIdx, s2.c_str() );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
}
if( !f1.eof() || !f2.eof() )
{
ts->printf( cvtest::TS::LOG, "First and second saved files differ in %n-line; first %n line: %s; second %n-line: %s",
lineIdx, lineIdx, s1.c_str(), lineIdx, s2.c_str() );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
f1.close();
f2.close();
remove( model_file_name1.c_str() );
remove( model_file_name2.c_str() );
CV_Assert( test_resps1.size() == test_resps2.size() );
vector<float>::const_iterator it1 = test_resps1.begin(), it2 = test_resps2.begin();
for( ; it1 != test_resps1.end(); ++it1, ++it2 )
{
if( fabs(*it1 - *it2) > FLT_EPSILON )
{
ts->printf( cvtest::TS::LOG, "Responses predicted before saving and after loading are different" );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
}
return code;
}
void CV_GBTreesTest::run(int)
{
string dataPath = string(ts->get_data_path());
datasets = new string[2];
datasets[0] = dataPath + string("spambase.data");
datasets[1] = dataPath + string("housing_.data");
int code = cvtest::TS::OK;
for (int i = 0; i < 4; i++)
{
int temp_code = TestTrainPredict(i);
if (temp_code != cvtest::TS::OK)
{
code = temp_code;
break;
}
else if (i==0)
{
temp_code = TestSaveLoad();
if (temp_code != cvtest::TS::OK)
code = temp_code;
delete data;
data = 0;
}
delete gtb;
gtb = 0;
}
delete data;
data = 0;
ts->set_failed_test_info( code );
}
TEST(ML_GBTrees, regression) { CV_GBTreesTest test; test.safe_run(); }
#endif