search_seq_sffs.hpp

Go to the documentation of this file.

#ifndef FSTSEARCHSEQSFFS_H
#define FSTSEARCHSEQSFFS_H
 /* 
=========================================================================
Copyright and license:
  * FST4 software (with the exception of any bundled or externally linked
    third-party components) is Copyright (C) 2000-present Institute of
    Information Theory and Automation of the Czech Academy of Sciences (UTIA).
  * FST4 is free software: you can redistribute it and/or modify it under
    the terms of the GNU Affero General Public License as published by the
    Free Software Foundation, version 3. The full text of the license is in
    the file LICENSE in the distribution root and at
    https://www.gnu.org/licenses/agpl-3.0.html.
  * FST4 is distributed in the hope that it will be useful, but WITHOUT ANY
    WARRANTY; without even the implied warranty of MERCHANTABILITY or
    FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public
    License for more details.
  * Closed-source use without the AGPL's copyleft obligations requires a
    commercial license from UTIA; in case of interest write to
    utia@utia.cas.cz. LICENSING.md in the distribution root explains both
    options, the contributor license agreement that every contribution
    requires, and the contacts (technical questions and pull requests:
    somol@utia.cas.cz, with "FST" in the subject line).
========================================================================== */
 
#include <memory>
#include <cassert>
#include <iostream>
#include <sstream>
#include "error.hpp"
#include "global.hpp"
#include "stopwatch.hpp"
#include "search_seq.hpp"
 
/*============== Template parameter type naming conventions ==============
--------- Numeric types: -------------------------------------------------
DATATYPE - data sample values - usually real numbers (but may be integers
          in text processing etc.)
REALTYPE - must be real numbers - for representing intermediate results of 
          calculations like mean, covariance etc.
IDXTYPE - index values for enumeration of data samples - (nonnegative) integers, 
          extent depends on numbers of samples in data
DIMTYPE - index values for enumeration of features (dimensions), or classes (not 
          class sizes) - (nonnegative) integers, usually lower extent than IDXTYPE, 
          but be aware of expressions like _classes*_features*_features ! 
          in linearized representations of feature matrices for all classes
BINTYPE - feature selection marker type - represents ca. <10 different feature 
          states (selected, deselected, sel./desel. temporarily 1st nested loop, 2nd...)
RETURNTYPE - criterion value: real value, but may be extended in future to support 
          multiple values 
--------- Class types: ---------------------------------------------------
SUBSET       - class of class type Subset 
CLASSIFIER   - class implementing interface defined in abstract class Classifier 
EVALUATOR    - class implementing interface defined in abstract class Sequential_Step 
DISTANCE     - class implementing interface defined in abstract class Distance 
DATAACCESSOR - class implementing interface defined in abstract class Data_Accessor 
INTERVALCONTAINER - class of class type Data_Intervaller 
CONTAINER    - STL container of class type Data_Interval  
========================================================================== */
 
namespace FST {

template<class RETURNTYPE, typename DIMTYPE, class SUBSET, class CRITERION, class EVALUATOR>
class Search_SFFS : public Search_Sequential<RETURNTYPE,DIMTYPE,SUBSET,CRITERION,EVALUATOR>{ 
public:
        typedef Search_Sequential<RETURNTYPE,DIMTYPE,SUBSET,CRITERION,EVALUATOR> parent; 
        typedef typename parent::parent grandparent; 
        typedef std::shared_ptr<EVALUATOR> PEvaluator; 
        typedef std::shared_ptr<CRITERION> PCriterion; 
        typedef std::shared_ptr<SUBSET> PSubset; 
        Search_SFFS(const PEvaluator evaluator) : Search_Sequential<RETURNTYPE,DIMTYPE,SUBSET,CRITERION,EVALUATOR>(evaluator), _direction(FORWARD), _delta(0), _n(0) {notify("Search_SFFS constructor.");} 
        virtual ~Search_SFFS() {notify("Search_SFFS destructor.");}
 
        // NOTE: crit is expected to be already initialize()d before use here
        virtual bool search(const DIMTYPE target_d, RETURNTYPE &result, const PSubset sub, const PCriterion crit, std::ostream& os=std::cout) override; 
 
        void set_search_direction(const SearchDirection direction) {_direction=direction;} 
        SearchDirection get_search_direction() const {return _direction;} 
 
        void set_delta(const DIMTYPE delta) {_delta=delta;} 
        DIMTYPE get_delta() const {return _delta;} 
 
        bool get_result(const DIMTYPE d, RETURNTYPE &result, PSubset &sub); 
        
        virtual std::ostream& print(std::ostream& os) const override;
protected:
        SearchDirection _direction; 
        struct OneSubset {
                RETURNTYPE critvalue; 
                std::unique_ptr<SUBSET> sub; 
        };
        DIMTYPE _delta; 
        std::unique_ptr<OneSubset[]> bsubs; 
        DIMTYPE _n; 
        std::unique_ptr<SUBSET> pivotsub; 
        std::unique_ptr<SUBSET> maxcritsub; 
};
 
template<class RETURNTYPE, typename DIMTYPE, class SUBSET, class CRITERION, class EVALUATOR>
std::ostream& Search_SFFS<RETURNTYPE,DIMTYPE,SUBSET,CRITERION,EVALUATOR>::print(std::ostream& os) const 
{
        if(parent::get_generalization_level()>1) os << "(G)";
        if(_direction==FORWARD) os << "SFFS"; else os << "SBFS";
        if(parent::get_generalization_level()>1) os << ", G=" << parent::get_generalization_level();
        os << ", delta=" << _delta;
        os << "  [Search_SFFS()";
        if(parent::_evaluator) os << " with " << *parent::_evaluator; 
        if(grandparent::result_tracker_active()) os << " with " << *grandparent::_tracker; 
        os << "]";
        return os;
}
 
template<class RETURNTYPE, typename DIMTYPE, class SUBSET, class CRITERION, class EVALUATOR>
bool Search_SFFS<RETURNTYPE,DIMTYPE,SUBSET,CRITERION,EVALUATOR>::search(const DIMTYPE target_d, RETURNTYPE &result, const PSubset sub, const PCriterion crit, std::ostream& os) // returns found subset + criterion value
{
        // NOTE: certain parameter combinations (initial sub->get_d() versus target_d versus parent::_generalization_level) 
        //       make it impossible to yield a valid solution. In such case search() just returns false
        // NOTE: so far this is the most original version of SFFS, i.e., it does 
        //       not "correct misleading paths" ("worse-than-before" forward steps after successful backtracking) etc.
        StopWatch swatch;
        if(parent::_evaluator) parent::_evaluator->set_output_detail(grandparent::get_output_detail()); else throw FST::fst_error("Search_Sequential: Missing evaluator.");
        bool track=grandparent::result_tracker_active(); if(track) grandparent::_tracker->set_output_detail(grandparent::get_output_detail());
        assert(sub);
        assert(crit);
        assert(sub->get_n()>0);
        assert(target_d<sub->get_n());
        assert(parent::get_generalization_level()>=1 && parent::get_generalization_level()<=sub->get_n());
 
        const bool _forward=(_direction==FORWARD);
        sub->set_forward_mode(true); // select_all()/deselect_all() write mode-dependent ids - a Subset left in backward mode by a previous search() would otherwise re-initialize empty
        if(!parent::initial_subset_enabled()) {
                if(_forward) sub->deselect_all(); else sub->select_all();
        }
        DIMTYPE _d=sub->get_d();
        if( (!_forward && _d==0) ||
                (target_d>0 && ((target_d>_d)?(target_d-_d):(_d-target_d))%parent::get_generalization_level()>0) ) return false; // no solution reachable under this setting
 
        if(grandparent::output_normal()) {
                msgbuf sos; 
                sos << "---------------------------------------" << std::endl;
                sos << "Starting " << *this << std::endl;
                sos << "with Criterion: " << *crit << std::endl;
                sos << "with initial subset: " << *sub << std::endl;
                if(target_d==0) sos << "Subset size to be optimized." << std::endl; else sos << "Target subset size set to: " << target_d << std::endl;
                sos << std::endl << std::flush;
                this->report(os,sos);
        }
 
        _n=sub->get_n();
        bsubs.reset(new OneSubset[_n]);
        if(!pivotsub || pivotsub->get_n()!=_n) pivotsub.reset(new SUBSET(_n));
        if(!maxcritsub || maxcritsub->get_n()!=_n) maxcritsub.reset(new SUBSET(_n));
        RETURNTYPE maxcritval;
        bool havemax=false;
        DIMTYPE _d_max;
 
        sub->set_forward_mode(_forward); // the search's own direction; Step() flips it per step as needed
        if(_d>0) { // evaluate initial subset if any
                if(!crit->evaluate(result,sub)) return false;
                if(track) grandparent::_tracker->add(result,sub);
                bsubs[_d-1].sub.reset(new SUBSET(_n));
                bsubs[_d-1].sub->stateless_copy(*sub);
                bsubs[_d-1].critvalue=result;
                maxcritsub->stateless_copy(*sub); 
                maxcritval=result; 
                _d_max = maxcritsub->get_d();
                havemax=true;
                if(grandparent::output_normal()) this->report_solution(os, maxcritval, *maxcritsub, msgbuf() << "initial MAXCRIT="<<maxcritval<<", " << *maxcritsub << std::endl << swatch << std::endl << std::endl << std::flush);
        }
 
        // Cardinality window the floating frontier may move in. Without a delta bound it is the
        // full 1.._n range; with one it narrows to target_d-delta .. target_d+delta, clipped to
        // that full range. A delta is meaningless when the cardinality itself is being optimized
        // (target_d==0), so it is ignored then.
        const DIMTYPE _forward_thr=(target_d>0 && _delta>0 && target_d+_delta<_n)?target_d+_delta:_n;
        const DIMTYPE _backward_thr=(target_d>0 && _delta>0 && _delta<target_d)?target_d-_delta:1;
        while( (_forward && _d+parent::get_generalization_level()<=_forward_thr) || (!_forward && _d>=_backward_thr+parent::get_generalization_level()) ) {
                // unconditional step IN THE SEARCH'S OWN DIRECTION (adding a feature in SFFS, removing
                // one in SBFS); the conditional backtracking below always steps the opposite way
                if(!parent::Step(_forward, result, sub, crit, os)) return false;
                if(track) grandparent::_tracker->add(result,sub);
                _d=sub->get_d();
 
                // tie rule: at EQUAL criterion value the smaller subset wins
                if((!havemax) || (result>maxcritval) || (result==maxcritval && _d<_d_max)) {
                        maxcritsub->stateless_copy(*sub); maxcritval=result; _d_max=_d; havemax=true;
                        if(grandparent::output_normal()) this->report_solution(os, maxcritval, *maxcritsub, msgbuf() << "new MAXCRIT="<<maxcritval<<", " << *maxcritsub << std::endl << swatch << std::endl << std::endl << std::flush);
                }
                assert(_d-1<_n);
                if(!bsubs[_d-1].sub) { // first result in that dimensionality
                        bsubs[_d-1].sub.reset(new SUBSET(_n));
                        bsubs[_d-1].sub->stateless_copy(*sub);
                        bsubs[_d-1].critvalue=result;
                        if(grandparent::output_normal()) this->report_solution(os, result, *sub, msgbuf() << "new subresult["<<_d<<"]="<<result<<", " << *sub << std::endl << std::endl << std::flush);
                } else {
                        if(result > bsubs[_d-1].critvalue) { // better than before found for that dimensionality
                                bsubs[_d-1].sub->stateless_copy(*sub);
                                bsubs[_d-1].critvalue=result;
                                if(grandparent::output_normal()) this->report_solution(os, result, *sub, msgbuf() << "new subresult["<<_d<<"]="<<result<<", " << *sub << std::endl << std::endl << std::flush);
                        }
                }
                
                pivotsub->stateless_copy(*sub);
                // Conditional BACKTRACKING: keep stepping back as long as each step still IMPROVES the
                // record of the cardinality it lands on, remembering the last improving subset in
                // pivotsub. The loop ends on the first step that does not improve (backtrack=false) or
                // when the cardinality bound is reached; the outer loop then resumes from pivotsub, so a
                // non-improving excursion is discarded. A record is replaced only on a strictly better
                // value, hence an equally good subset does not prolong the backtracking either.
                bool backtrack=true;
                while(backtrack && ((_forward && _d>=1+parent::get_generalization_level()) || (!_forward && _d+parent::get_generalization_level()<=_n))) {
                        if(!parent::Step(!_forward, result, sub, crit, os)) return false;
                        if(track) grandparent::_tracker->add(result,sub);
                        _d=sub->get_d();
 
                        if((!havemax) || (result>maxcritval) || (result==maxcritval && _d<_d_max)) {
                                maxcritsub->stateless_copy(*sub); maxcritval=result; _d_max=_d; havemax=true;
                                if(grandparent::output_normal()) this->report_solution(os, maxcritval, *maxcritsub, msgbuf() << "new MAXCRIT="<<maxcritval<<", " << *maxcritsub << std::endl << swatch << std::endl << std::endl << std::flush);
                        }
                        assert(_d-1<_n);
                        if(!bsubs[_d-1].sub) { // first result in that dimensionality
                                bsubs[_d-1].sub.reset(new SUBSET(_n));
                                bsubs[_d-1].sub->stateless_copy(*sub);
                                bsubs[_d-1].critvalue=result;
                                if(grandparent::output_normal()) this->report_solution(os, result, *sub, msgbuf() << "new subresult["<<_d<<"]="<<result<<", " << *sub << std::endl << std::endl << std::flush);
                        } else {
                                if(result > bsubs[_d-1].critvalue) { // better than before found for that dimensionality
                                        bsubs[_d-1].sub->stateless_copy(*sub);
                                        bsubs[_d-1].critvalue=result;
                                        pivotsub->stateless_copy(*sub);
                                        if(grandparent::output_normal()) this->report_solution(os, result, *sub, msgbuf() << "new subresult["<<_d<<"]="<<result<<", " << *sub << std::endl << std::endl << std::flush);
                                } else backtrack=false;
                        }
                }
                sub->stateless_copy(*pivotsub);
                _d=sub->get_d();
        }
        if(target_d>0) { // return subset of size target_d
                assert(target_d>=1 && target_d<=_n);
                if(!bsubs[target_d-1].sub) return false;
                result=bsubs[target_d-1].critvalue;
                sub->stateless_copy(*bsubs[target_d-1].sub);
        } else { // find best dimensionality
                if(!havemax) return false;
                sub->stateless_copy(*maxcritsub);
                result=maxcritval;
        }
        if(grandparent::output_normal()) {
                msgbuf sos; 
                sos << "......................................." << std::endl;
                sos << "Search_SFFS() search finished. " << swatch << std::endl;
                sos << "Search result: "<< std::endl << *sub << std::endl << "Criterion value: " << result << std::endl << std::endl << std::flush;
                this->report(os,sos);
        }
        return true;
}
 
template<class RETURNTYPE, typename DIMTYPE, class SUBSET, class CRITERION, class EVALUATOR>
bool Search_SFFS<RETURNTYPE,DIMTYPE,SUBSET,CRITERION,EVALUATOR>::get_result(const DIMTYPE d, RETURNTYPE &result, PSubset &sub) // retrieves temporary solution of cardinality d that had been updated in the course of the last search
{
        assert(sub);
        assert(d>0 && d<=_n);
        if(bsubs && d>0 && d<=_n && bsubs[d-1].sub) {
                result=bsubs[d-1].critvalue;
                sub->stateless_copy(*(bsubs[d-1].sub));
                return true;
        }
        return false;
}
 
} // namespace
#endif // FSTSEARCHSEQSFFS_H ///:~