Implements Example 10: Basic Filter-based feature selection., see also Example 10 source code
/* ========================================================================= Feature Selection Toolbox 3 source code --------------------------------------- */ /* ========================================================================= Copyright: * FST3 software (with exception of any externally linked libraries) is copyrighted by Institute of Information Theory and Automation (UTIA), Academy of Sciences of the Czech Republic. * FST3 source codes as presented here do not contain code of third parties. FST3 may need linkage to external libraries to exploit its functionality in full. For details on obtaining and possible usage restrictions of external libraries follow their original sources (referenced from FST3 documentation wherever applicable). * FST3 software is available free of charge for non-commercial use. Please address all inquires concerning possible commercial use of FST3, or if in doubt, to FST3 maintainer (see http://fst.utia.cz). * Derivative works based on FST3 are permitted as long as they remain non-commercial only. * Re-distribution of FST3 software is not allowed without explicit consent of the copyright holder. Disclaimer of Warranty: * FST3 software is presented "as is", without warranty of any kind, either expressed or implied, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose. The entire risk as to the quality and performance of the program is with you. Should the program prove defective, you assume the cost of all necessary servicing, repair or correction. Limitation of Liability: * The copyright holder will in no event be liable to you for damages, including any general, special, incidental or consequential damages arising out of the use or inability to use the code (including but not limited to loss of data or data being rendered inaccurate or losses sustained by you or third parties or a failure of the program to operate with any other programs). ========================================================================== */ #include <boost/smart_ptr.hpp> #include <exception> #include <iostream> #include <cstdlib> #include <string> #include <vector> #include "error.hpp" #include "global.hpp" #include "subset.hpp" #include "data_intervaller.hpp" #include "data_splitter.hpp" #include "data_splitter_5050.hpp" //#include "data_splitter_cv.hpp" //#include "data_splitter_holdout.hpp" //#include "data_splitter_leave1out.hpp" //#include "data_splitter_resub.hpp" //#include "data_splitter_randrand.hpp" //#include "data_splitter_randfix.hpp" #include "data_scaler.hpp" #include "data_scaler_void.hpp" //#include "data_scaler_to01.hpp" //#include "data_scaler_white.hpp" #include "data_accessor_splitting_memTRN.hpp" #include "data_accessor_splitting_memARFF.hpp" //#include "criterion_normal_bhattacharyya.hpp" #include "criterion_normal_gmahalanobis.hpp" //#include "criterion_normal_divergence.hpp" //#include "criterion_multinom_bhattacharyya.hpp" //#include "criterion_wrapper.hpp" //#include "criterion_wrapper_bias_estimate.hpp" //#include "criterion_subsetsize.hpp" //#include "criterion_sumofweights.hpp" //#include "criterion_negative.hpp" #include "distance_euclid.hpp" //#include "distance_L1.hpp" //#include "distance_Lp.hpp" #include "classifier_knn.hpp" //#include "classifier_normal_bayes.hpp" //#include "classifier_multinom_naivebayes.hpp" //#include "classifier_svm.hpp" //#include "search_bif.hpp" //#include "search_bif_threaded.hpp" //#include "search_monte_carlo.hpp" //#include "search_monte_carlo_threaded.hpp" //#include "search_exhaustive.hpp" //#include "search_exhaustive_threaded.hpp" //#include "branch_and_bound_predictor_averaging.hpp" //#include "search_branch_and_bound_basic.hpp" //#include "search_branch_and_bound_improved.hpp" //#include "search_branch_and_bound_partial_prediction.hpp" //#include "search_branch_and_bound_fast.hpp" #include "seq_step_straight.hpp" //#include "seq_step_straight_threaded.hpp" //#include "seq_step_hybrid.hpp" //#include "seq_step_ensemble.hpp" #include "search_seq_sfs.hpp" //#include "search_seq_sffs.hpp" //#include "search_seq_sfrs.hpp" //#include "search_seq_os.hpp" //#include "search_seq_dos.hpp" //#include "result_tracker_dupless.hpp" //#include "result_tracker_regularizer.hpp" //#include "result_tracker_feature_stats.hpp" //#include "result_tracker_stabileval.hpp" int main() { try{ typedef double RETURNTYPE; typedef double DATATYPE; typedef double REALTYPE; typedef unsigned int IDXTYPE; typedef unsigned int DIMTYPE; typedef short BINTYPE; typedef FST::Subset<BINTYPE, DIMTYPE> SUBSET; typedef FST::Data_Intervaller<std::vector<FST::Data_Interval<IDXTYPE> >,IDXTYPE> INTERVALLER; typedef boost::shared_ptr<FST::Data_Splitter<INTERVALLER,IDXTYPE> > PSPLITTER; typedef FST::Data_Splitter_5050<INTERVALLER,IDXTYPE> SPLITTER5050; typedef FST::Data_Accessor_Splitting_MemTRN<DATATYPE,IDXTYPE,INTERVALLER> DATAACCESSOR; typedef FST::Criterion_Normal_GMahalanobis<RETURNTYPE,DATATYPE,REALTYPE,IDXTYPE,DIMTYPE,SUBSET,DATAACCESSOR> FILTERCRIT; typedef FST::Sequential_Step_Straight<RETURNTYPE,DIMTYPE,SUBSET,FILTERCRIT> EVALUATOR; typedef FST::Distance_Euclid<DATATYPE,DIMTYPE,SUBSET> DISTANCE; typedef FST::Classifier_kNN<RETURNTYPE,DATATYPE,IDXTYPE,DIMTYPE,SUBSET,DATAACCESSOR,DISTANCE> CLASSIFIERKNN; std::cout << "Starting Example 10: Basic Filter-based feature selection..." << std::endl; // in the course of search use the first half of data for feature selection and the second half for testing using 3-NN classifier PSPLITTER dsp(new SPLITTER5050()); // do not scale data boost::shared_ptr<FST::Data_Scaler<DATATYPE> > dsc(new FST::Data_Scaler_void<DATATYPE>()); // set-up data access boost::shared_ptr<std::vector<PSPLITTER> > splitters(new std::vector<PSPLITTER>); splitters->push_back(dsp); boost::shared_ptr<DATAACCESSOR> da(new DATAACCESSOR("data/speech_15.trn",splitters,dsc)); da->initialize(); // initiate access to split data parts da->setSplittingDepth(0); if(!da->getFirstSplit()) throw FST::fst_error("50/50 data split failed."); // initiate the storage for subset to-be-selected boost::shared_ptr<SUBSET> sub(new SUBSET(da->getNoOfFeatures())); sub->deselect_all(); // set-up the normal Generalized Mahalanobis criterion boost::shared_ptr<FILTERCRIT> crit(new FILTERCRIT); crit->initialize(da); // initialization = normal model parameter estimation on training data part // set-up the standard sequential search step object (options: hybrid, ensemble, etc.) boost::shared_ptr<EVALUATOR> eval(new EVALUATOR); // set-up Sequential Forward Floating Selection search procedure FST::Search_SFS<RETURNTYPE,DIMTYPE,SUBSET,FILTERCRIT,EVALUATOR> srch(eval); srch.set_search_direction(FST::FORWARD); // try FST::BACKWARD // run the search std::cout << "Feature selection setup:" << std::endl << *da << std::endl << srch << std::endl << *crit << std::endl << std::endl; RETURNTYPE critval_train, critval_test; const DIMTYPE d=7; // request subset of size d; if set to 0, cardinality will decided in the course of search if(!srch.search(d,critval_train,sub,crit,std::cout)) throw FST::fst_error("Search not finished."); // (optionally) the following line is included here just for illustration because srch.search() reports results in itself std::cout << std::endl << "Search result: " << std::endl << *sub << std::endl << "Criterion value=" << critval_train << std::endl << std::endl; // (optionally) validate result by estimating kNN accuracy on selected feature sub-space on independent test data boost::shared_ptr<CLASSIFIERKNN> cknn(new CLASSIFIERKNN); cknn->set_k(3); da->setSplittingDepth(0); cknn->train(da,sub); cknn->test(critval_test,da); std::cout << "Validated "<<cknn->get_k()<<"-NN accuracy=" << critval_test << std::endl << std::endl; } catch(FST::fst_error &e) {std::cerr<<"FST ERROR: "<< e.what() << ", code=" << e.code() << std::endl;} catch(std::exception &e) {std::cerr<<"non-FST ERROR: "<< e.what() << std::endl;} return 0; }