#include "test_precomp.hpp" #if 0 #include #include #include 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 test_resps1; vector 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[] = {5.430247f, 13.5654f, 12.6569f, 13.1661f}; //float sigma[] = {0.4162694f, 3.21161f, 3.43297f, 3.00624f}; 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; // 1. compare files 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(); // delete temporary files remove( model_file_name1.c_str() ); remove( model_file_name2.c_str() ); // 2. compare responses CV_Assert( test_resps1.size() == test_resps2.size() ); vector::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"); /*string("dataset_classification.csv");*/ datasets[1] = dataPath + string("housing_.data"); /*string("dataset_regression.csv");*/ 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 registration ///////////////////////////////////// ///////////////////////////////////////////////////////////////////////////// TEST(ML_GBTrees, regression) { CV_GBTreesTest test; test.safe_run(); } #endif