reworked nearly all of the OpenCV tests (except for opencv_gpu tests) - they now use the Google Test engine.
This commit is contained in:
Vadim Pisarevsky
275
modules/ml/test/test_gbttest.cpp
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275
modules/ml/test/test_gbttest.cpp
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#include "test_precomp.hpp"
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#include <string>
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#include <fstream>
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#include <iostream>
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using namespace std;
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class CV_GBTreesTest : public cvtest::BaseTest
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{
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public:
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CV_GBTreesTest();
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~CV_GBTreesTest();
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protected:
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void run(int);
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int TestTrainPredict(int test_num);
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int TestSaveLoad();
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int checkPredictError(int test_num);
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int checkLoadSave();
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//string model_file_name1;
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//string model_file_name2;
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char model_file_name1[50];
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char model_file_name2[50];
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string* datasets;
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string data_path;
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CvMLData* data;
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CvGBTrees* gtb;
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vector<float> test_resps1;
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vector<float> test_resps2;
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};
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int _get_len(const CvMat* mat)
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{
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return (mat->cols > mat->rows) ? mat->cols : mat->rows;
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}
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CV_GBTreesTest::CV_GBTreesTest()
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{
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datasets = 0;
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data = 0;
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gtb = 0;
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}
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CV_GBTreesTest::~CV_GBTreesTest()
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{
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if (data)
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delete data;
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delete[] datasets;
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}
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int CV_GBTreesTest::TestTrainPredict(int test_num)
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{
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int code = cvtest::TS::OK;
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int weak_count = 200;
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float shrinkage = 0.1f;
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float subsample_portion = 0.5f;
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int max_depth = 5;
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bool use_surrogates = true;
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int loss_function_type = 0;
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switch (test_num)
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{
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case (1) : loss_function_type = CvGBTrees::SQUARED_LOSS; break;
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case (2) : loss_function_type = CvGBTrees::ABSOLUTE_LOSS; break;
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case (3) : loss_function_type = CvGBTrees::HUBER_LOSS; break;
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case (0) : loss_function_type = CvGBTrees::DEVIANCE_LOSS; break;
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default :
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{
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ts->printf( cvtest::TS::LOG, "Bad test_num value in CV_GBTreesTest::TestTrainPredict(..) function." );
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return cvtest::TS::FAIL_BAD_ARG_CHECK;
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}
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}
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int dataset_num = test_num == 0 ? 0 : 1;
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if (!data)
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{
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data = new CvMLData();
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data->set_delimiter(',');
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if (data->read_csv(datasets[dataset_num].c_str()))
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{
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ts->printf( cvtest::TS::LOG, "File reading error." );
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return cvtest::TS::FAIL_INVALID_TEST_DATA;
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}
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if (test_num == 0)
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{
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data->set_response_idx(57);
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data->set_var_types("ord[0-56],cat[57]");
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}
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else
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{
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data->set_response_idx(13);
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data->set_var_types("ord[0-2,4-13],cat[3]");
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subsample_portion = 0.7f;
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}
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int train_sample_count = cvFloor(_get_len(data->get_responses())*0.5f);
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CvTrainTestSplit spl( train_sample_count );
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data->set_train_test_split( &spl );
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}
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data->mix_train_and_test_idx();
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if (gtb) delete gtb;
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gtb = new CvGBTrees();
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bool tmp_code = true;
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tmp_code = gtb->train(data, CvGBTreesParams(loss_function_type, weak_count,
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shrinkage, subsample_portion,
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max_depth, use_surrogates));
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if (!tmp_code)
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{
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ts->printf( cvtest::TS::LOG, "Model training was failed.");
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return cvtest::TS::FAIL_INVALID_OUTPUT;
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}
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code = checkPredictError(test_num);
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return code;
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}
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int CV_GBTreesTest::checkPredictError(int test_num)
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{
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if (!gtb)
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return cvtest::TS::FAIL_GENERIC;
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float mean[] = {5.430247f, 13.5654f, 12.6569f, 13.1661f};
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float sigma[] = {0.4162694f, 3.21161f, 3.43297f, 3.00624f};
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float current_error = gtb->calc_error(data, CV_TEST_ERROR);
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if ( abs( current_error - mean[test_num]) > 6*sigma[test_num] )
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{
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ts->printf( cvtest::TS::LOG, "Test error is out of range:\n"
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"abs(%f/*curEr*/ - %f/*mean*/ > %f/*6*sigma*/",
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current_error, mean[test_num], 6*sigma[test_num] );
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return cvtest::TS::FAIL_BAD_ACCURACY;
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}
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return cvtest::TS::OK;
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}
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int CV_GBTreesTest::TestSaveLoad()
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{
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if (!gtb)
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return cvtest::TS::FAIL_GENERIC;
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tmpnam(model_file_name1);
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tmpnam(model_file_name2);
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if(model_file_name1[0] == '\\')
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model_file_name1[0] = '_';
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if(model_file_name2[0] == '\\')
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model_file_name2[0] = '_';
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gtb->save(model_file_name1);
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gtb->calc_error(data, CV_TEST_ERROR, &test_resps1);
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gtb->load(model_file_name1);
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gtb->calc_error(data, CV_TEST_ERROR, &test_resps2);
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gtb->save(model_file_name2);
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return checkLoadSave();
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}
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int CV_GBTreesTest::checkLoadSave()
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{
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int code = cvtest::TS::OK;
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// 1. compare files
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ifstream f1( model_file_name1 ), f2( model_file_name2 );
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string s1, s2;
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int lineIdx = 0;
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CV_Assert( f1.is_open() && f2.is_open() );
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for( ; !f1.eof() && !f2.eof(); lineIdx++ )
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{
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getline( f1, s1 );
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getline( f2, s2 );
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if( s1.compare(s2) )
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{
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ts->printf( cvtest::TS::LOG, "first and second saved files differ in %n-line; first %n line: %s; second %n-line: %s",
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lineIdx, lineIdx, s1.c_str(), lineIdx, s2.c_str() );
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code = cvtest::TS::FAIL_INVALID_OUTPUT;
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}
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}
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if( !f1.eof() || !f2.eof() )
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{
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ts->printf( cvtest::TS::LOG, "First and second saved files differ in %n-line; first %n line: %s; second %n-line: %s",
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lineIdx, lineIdx, s1.c_str(), lineIdx, s2.c_str() );
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code = cvtest::TS::FAIL_INVALID_OUTPUT;
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}
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f1.close();
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f2.close();
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// delete temporary files
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remove( model_file_name1 );
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remove( model_file_name2 );
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// 2. compare responses
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CV_Assert( test_resps1.size() == test_resps2.size() );
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vector<float>::const_iterator it1 = test_resps1.begin(), it2 = test_resps2.begin();
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for( ; it1 != test_resps1.end(); ++it1, ++it2 )
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{
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if( fabs(*it1 - *it2) > FLT_EPSILON )
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{
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ts->printf( cvtest::TS::LOG, "Responses predicted before saving and after loading are different" );
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code = cvtest::TS::FAIL_INVALID_OUTPUT;
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}
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}
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return code;
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}
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void CV_GBTreesTest::run(int)
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{
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string data_path = string(ts->get_data_path());
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datasets = new string[2];
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datasets[0] = data_path + string("spambase.data"); /*string("dataset_classification.csv");*/
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datasets[1] = data_path + string("housing_.data"); /*string("dataset_regression.csv");*/
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int code = cvtest::TS::OK;
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for (int i = 0; i < 4; i++)
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{
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int temp_code = TestTrainPredict(i);
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if (temp_code != cvtest::TS::OK)
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{
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code = temp_code;
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break;
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}
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else if (i==0)
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{
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temp_code = TestSaveLoad();
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if (temp_code != cvtest::TS::OK)
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code = temp_code;
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delete data;
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data = 0;
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}
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delete gtb;
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gtb = 0;
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}
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delete data;
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data = 0;
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ts->set_failed_test_info( code );
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}
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/////////////////////////////////////////////////////////////////////////////
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//////////////////// test registration /////////////////////////////////////
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/////////////////////////////////////////////////////////////////////////////
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TEST(ML_GBTrees, regression) { CV_GBTreesTest test; test.safe_run(); }
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