Implements Example 22: Randomized feature selection with Oscillating Search., see also Example 22 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_RandomRandom<INTERVALLER,IDXTYPE,BINTYPE> SPLITTERRANDRAND; typedef FST::Data_Accessor_Splitting_MemTRN<DATATYPE,IDXTYPE,INTERVALLER> DATAACCESSOR; // uncomment for TRN data format //typedef FST::Data_Accessor_Splitting_MemARFF<DATATYPE,IDXTYPE,INTERVALLER> DATAACCESSOR; // uncomment for ARFF data format typedef FST::Criterion_Normal_Bhattacharyya<RETURNTYPE,DATATYPE,REALTYPE,IDXTYPE,DIMTYPE,SUBSET,DATAACCESSOR> BHATTCRIT; typedef FST::Distance_Euclid<DATATYPE,DIMTYPE,SUBSET> DISTANCE; typedef FST::Classifier_kNN<RETURNTYPE,DATATYPE,IDXTYPE,DIMTYPE,SUBSET,DATAACCESSOR,DISTANCE> CLASSIFIERKNN; typedef FST::Criterion_Wrapper<RETURNTYPE,SUBSET,CLASSIFIERKNN,DATAACCESSOR> WRAPPERKNN; typedef FST::Sequential_Step_Straight<RETURNTYPE,DIMTYPE,SUBSET,BHATTCRIT> EVALUATOR; std::cout << "Starting Example 22: Randomized feature selection with Oscillating Search..." << std::endl; // use randomly chosen 75% of data for training and keep the other 25% for independent testing of final classification performance PSPLITTER dsp_outer(new SPLITTERRANDRAND(1,75,25)); // (there will be one outer randomized split only) // 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_outer); boost::shared_ptr<DATAACCESSOR> da(new DATAACCESSOR("data/sonar_60.trn",splitters,dsc)); da->initialize(); // initiate access to split data parts da->setSplittingDepth(0); if(!da->getFirstSplit()) throw FST::fst_error("75/25 data split failed."); // initiate the storage for subset to-be-selected (+ one more for storing temporaries) boost::shared_ptr<SUBSET> sub(new SUBSET(da->getNoOfFeatures())); boost::shared_ptr<SUBSET> submax(new SUBSET(da->getNoOfFeatures())); // set-up normal Bhattacharyya criterion boost::shared_ptr<BHATTCRIT> cb(new BHATTCRIT); cb->initialize(da); // initialization = normal model parameter estimation on training data on the current split // set-up the standard sequential search step object (option: hybrid, ensemble, etc.) boost::shared_ptr<EVALUATOR> eval(new EVALUATOR); // set-up Oscillating Search procedure with the extent of search specified by delta (admissible values 1..da->getNoOfFeatures()) FST::Search_OS<RETURNTYPE,DIMTYPE,SUBSET,BHATTCRIT,EVALUATOR> srch(eval); srch.set_delta(10); // target subset size must be set because Bhattacharyya is monotonous with respect to subset size (i.e., evaluates full set as the best) DIMTYPE target_subsize=30; // repeat random-initialized OS searches until there is no improvement in 5 consecutive runs std::cout << "Feature selection setup:" << std::endl << *da << std::endl << srch << std::endl << *cb << std::endl << std::endl; RETURNTYPE critval_train, critval_trainmax; int non_improving_runs=-1; // -1 indicates first run const int max_non_improving_runs=5; do { sub->make_random_subset(target_subsize); if(!srch.search(target_subsize,critval_train,sub,cb,std::cout)) throw FST::fst_error("Search not finished."); if(non_improving_runs==-1 || critval_train>critval_trainmax) { critval_trainmax=critval_train; submax->stateless_copy(*sub); non_improving_runs=0; } else non_improving_runs++; } while(non_improving_runs<max_non_improving_runs-1); std::cout << std::endl << "Final search result: " << std::endl << *submax << std::endl << "Criterion value=" << critval_trainmax << std::endl << std::endl; // (optionally) validate result by estimating kNN accuracy on selected feature sub-space on independent test data RETURNTYPE critval_test; boost::shared_ptr<CLASSIFIERKNN> cknn(new CLASSIFIERKNN); cknn->set_k(3); cknn->train(da,submax); 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; }
1.6.1