search_seq_os.hpp

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#ifndef FSTSEARCHSEQOS_H
#define FSTSEARCHSEQOS_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 <vector>
#include "error.hpp"
#include "global.hpp"
#include "stopwatch.hpp"
#include "search_ranking_bif.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_OS : public Search_Sequential<RETURNTYPE,DIMTYPE,SUBSET,CRITERION,EVALUATOR>{ 
public:
        typedef Search_Sequential<RETURNTYPE,DIMTYPE,SUBSET,CRITERION,EVALUATOR> parent; 
        typedef typename parent::parent grandparent; 
        typedef Search_Ranking_BIF<RETURNTYPE,DIMTYPE,SUBSET,CRITERION> SearchBIF; 
        typedef std::shared_ptr<SearchBIF> PSearchBIF; 
        typedef std::shared_ptr<EVALUATOR> PEvaluator; 
        typedef std::shared_ptr<CRITERION> PCriterion; 
        typedef std::shared_ptr<SUBSET> PSubset; 
        Search_OS(const PEvaluator evaluator) : Search_Sequential<RETURNTYPE,DIMTYPE,SUBSET,CRITERION,EVALUATOR>(evaluator), _delta(1), track(false) {parent::enable_initial_subset(); notify("Search_OS constructor.");} 
        virtual ~Search_OS() {notify("Search_OS 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_delta(const DIMTYPE delta) {assert(delta>0); _delta=delta;} 
        DIMTYPE get_delta() const {return _delta;} 
 
        virtual std::ostream& print(std::ostream& os) const override;
protected:
        enum SwingPhase {
                RESTART=0,
                DOWN=1,
                RETURN_UP=2,
                UP=4,
                RETURN_DOWN=8
        };

        virtual bool search_one(RETURNTYPE &result, const PSubset sub, const PCriterion crit, std::ostream& os); 
        virtual bool adjust_initial_subset(const DIMTYPE target_d, const PSubset sub, const PSearchBIF bif, std::ostream& os);
 
        DIMTYPE _delta; 
 
        std::unique_ptr<SUBSET> pivotsub; 
        std::unique_ptr<SUBSET> pivot_top; 
        std::unique_ptr<SUBSET> pivot_bottom; 
 
        std::unique_ptr<SUBSET> inisub; 
        std::shared_ptr<SUBSET> tmpsub; 
private:
        bool track; 
};
 
template<class RETURNTYPE, typename DIMTYPE, class SUBSET, class CRITERION, class EVALUATOR>
std::ostream& Search_OS<RETURNTYPE,DIMTYPE,SUBSET,CRITERION,EVALUATOR>::print(std::ostream& os) const 
{
        if(parent::get_generalization_level()>1) os << "(G)";
        os << "OS";
        if(parent::get_generalization_level()>1) os << ", G=" << parent::get_generalization_level();
        os << ", delta=" << _delta;
        os << "  [Search_OS()";
        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_OS<RETURNTYPE,DIMTYPE,SUBSET,CRITERION,EVALUATOR>::adjust_initial_subset(const DIMTYPE target_d, const PSubset sub, const PSearchBIF bif, std::ostream& os)
{
        assert(sub);
        assert(bif);
        assert(target_d>0 && target_d<=sub->get_n());
        RETURNTYPE value;
        DIMTYPE f;
        
        if(target_d!=0 && sub->get_d()!=target_d) {
                if(grandparent::output_normal()) this->report(os, msgbuf() << "original initial "<< *sub << std::endl << std::flush);
                // collect the BIF ranking (best -> worst) once; an undersized subset grows by
                // selecting best-ranked features, an oversized one shrinks by deselecting
                // WORST-ranked features - hence the reverse iteration in the shrink branch
                std::vector<DIMTYPE> ranked;
                if(!bif->getFirstFeature(value, f)) return false;
                do {
                        assert(f<=sub->get_n());
                        ranked.push_back(f);
                } while(bif->getNextFeature(value, f));
                if(sub->get_d()<target_d) {
                        for(typename std::vector<DIMTYPE>::const_iterator it=ranked.begin();it!=ranked.end() && sub->get_d()!=target_d;++it) sub->select(*it);
                } else {
                        for(typename std::vector<DIMTYPE>::const_reverse_iterator it=ranked.rbegin();it!=ranked.rend() && sub->get_d()!=target_d;++it) sub->deselect(*it);
                }
                if(sub->get_d()!=target_d) return false;
                if(grandparent::output_normal()) this->report(os, msgbuf() << "adjusted initial "<< *sub << std::endl << std::endl << std::flush);
        }
        return true;
}
 
template<class RETURNTYPE, typename DIMTYPE, class SUBSET, class CRITERION, class EVALUATOR>
bool Search_OS<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 _delta versus target_d versus parent::_generalization_level) 
        //       make it impossible to yield a valid solution. In such case search() just returns false
        StopWatch swatch;
        if(parent::_evaluator) parent::_evaluator->set_output_detail(grandparent::get_output_detail()); else throw FST::fst_error("Search_Sequential: Missing evaluator.");
        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());
 
        if(!parent::initial_subset_enabled()) {sub->set_forward_mode(true); sub->deselect_all();} // deselect_all() writes mode-dependent ids: switch to forward mode FIRST, or a Subset left in backward mode by a previous search comes up in the wrong state
 
        if( (_delta<parent::get_generalization_level()) ) return false; // impossible to run the search
 
        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);
        }
 
        PSearchBIF bif;
        DIMTYPE _n=sub->get_n();
 
        if(target_d==0) { // optimize the cardinality: run one full oscillating search per subset size
                          // and keep the best result over all of them (at d==_n there is nothing to
                          // oscillate, so that one subset is merely evaluated)
                if(!inisub || inisub->get_n()!=_n) inisub.reset(new SUBSET(_n));
                if(!tmpsub || tmpsub->get_n()!=_n) tmpsub.reset(new SUBSET(_n));
                RETURNTYPE value;
                if(!bif) {
                        bif.reset(new SearchBIF());
                        if(!bif->evaluate_individuals(_n, crit, os)) return false;
                }
                inisub->stateless_copy(*sub);
                for(DIMTYPE d=1;d<=_n;d++) {
                        tmpsub->stateless_copy(*inisub);
                        if(!adjust_initial_subset(d,tmpsub,bif,os)) return false;
                        if(d<_n) {if(!search_one(value,tmpsub,crit,os)) return false;} else {if(!crit->evaluate(value,tmpsub)) return false;}
                        if(d==1 || value>result) {
                                result=value;
                                sub->stateless_copy(*tmpsub);
                                if(grandparent::output_normal()) this->report_solution(os, result, *sub, msgbuf() << "NEW MAXVAL (d="<<d<<") ="<<result<<", " << *sub << std::endl << swatch << std::endl << std::endl << std::flush);
                        } else
                                if(grandparent::output_normal()) this->report_solution(os, value, *tmpsub, msgbuf() << "result (d="<<d<<") ="<<value<<", " << *tmpsub << std::endl << std::endl << std::flush);
                }
        } else { // evaluate subset of target_d features
                if(sub->get_d()!=target_d) {
                        if(!bif) {
                                bif.reset(new SearchBIF);
                                if(!bif->evaluate_individuals(_n,crit,os)) return false;
                        }
                        if(!adjust_initial_subset(target_d,sub,bif,os)) return false;
                }
                if(!search_one(result,sub,crit,os)) return false;
        }
        if(grandparent::output_normal()) {
                msgbuf sos; 
                sos << "......................................." << std::endl;
                sos << "Search_OS() 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_OS<RETURNTYPE,DIMTYPE,SUBSET,CRITERION,EVALUATOR>::search_one(RETURNTYPE &result, const PSubset sub, const PCriterion crit, std::ostream& os) // returns found subset + criterion value
{
        notify("Search_OS::search_one().");
        assert(sub);
        assert(crit);
        assert(sub->get_n()>0);
        assert(sub->get_d()>0);
        assert(parent::get_generalization_level()>=1 && parent::get_generalization_level()<=sub->get_n());
 
        if( (sub->get_d()==0) ||
                (_delta<parent::get_generalization_level()) ) return false; // impossible to run the search
 
        DIMTYPE _n=sub->get_n();
        if(!pivotsub || pivotsub->get_n()!=_n) pivotsub.reset(new SUBSET(_n));
        if(!pivot_top || pivot_top->get_n()!=_n) pivot_top.reset(new SUBSET(_n));
        if(!pivot_bottom || pivot_bottom->get_n()!=_n) pivot_bottom.reset(new SUBSET(_n));
 
        RETURNTYPE bestval;
        
        // sub is assumed to contain the initial subset
        sub->set_forward_mode(true);
 
        // The initial subset's value seeds the pivot, so this failure MUST be checked: continuing
        // with an unset result would start the oscillation from an indeterminate pivot.
        // NOTE this value comes from crit, while a Sequential_Step_Hybrid with keep_perc==0 selects
        // by its filter criterion - which is why that combination is unsupported (see the class docs).
        if(!crit->evaluate(result,sub)) return false; // the initial subset is the initially best pivot
        if(track) grandparent::_tracker->add(result,sub);
 
        SwingPhase swing=RESTART;
 
        DIMTYPE _d=sub->get_d();
        DIMTYPE _d_piv=_d;
        DIMTYPE _cur_delta=parent::get_generalization_level();
        bool pivot_top_avail=false;
        bool pivot_bottom_avail=false;
 
        bool swinged=false; // whether the current swing managed to take at least one step
 
        // the oscillation itself: a state machine over SwingPhase (documented at its declaration),
        // looping until the cycle depth _cur_delta has grown past the configured maximum _delta
        while(_cur_delta<=_delta)
                switch(swing) {
                case RESTART:
                        assert(_d_piv==sub->get_d());
                        pivotsub->stateless_copy(*sub); bestval=result;
                        pivot_top_avail=pivot_bottom_avail=false;
                        _cur_delta=parent::get_generalization_level();
                        swing=DOWN;
                        if(grandparent::output_normal()) this->report_solution(os, result, *sub, msgbuf() << "new subresult ="<<result<<", " << *sub << std::endl << std::endl << std::flush);
                        break;
                case DOWN:
                        assert(_d==sub->get_d());
                        assert(sub->get_d()!=0);
                        if(pivot_bottom_avail) {assert(pivot_bottom->get_d()!=0); sub->stateless_copy(*pivot_bottom); _d=sub->get_d();}
                        swinged=false;
                        while(_d>parent::get_generalization_level() && _d_piv<_cur_delta+_d){
                                if(!parent::Step(false/*forward*/, result, sub, crit, os)) {if(grandparent::output_detailed()) this->report(os, msgbuf() << "DOWN failure" << std::endl << std::flush); return false;}
                                if(track) grandparent::_tracker->add(result,sub);
                                _d=sub->get_d(); swinged=true;
                        }
                        if(swinged) {
                                assert(_d==sub->get_d());
                                if(_d>parent::get_generalization_level()) {
                                        pivot_bottom_avail=true;
                                        pivot_bottom->stateless_copy(*sub);
                                }
                                swing=RETURN_UP;
                        } else {
                                pivot_bottom_avail=false;
                                swing=UP;
                        }
                        assert(_d==sub->get_d());
                        break;
                case RETURN_UP:
                        assert(_d==sub->get_d());
                        while(_d<_d_piv){
                                assert(_d==sub->get_d());
                                if(!parent::Step(true/*forward*/, result, sub, crit, os)) {if(grandparent::output_detailed()) this->report(os, msgbuf() << "RETURN UP failure" << std::endl << std::flush); return false;}
                                if(track) grandparent::_tracker->add(result,sub);
                                _d=sub->get_d();
                        }
                        if(result>bestval) swing=RESTART; // down-swing successful - reset
                        else {
                                sub->stateless_copy(*pivotsub);
                                swing=UP; swinged=false;
                        }
                        assert(_d==sub->get_d());
                        break;
                case UP:
                        assert(_d==sub->get_d());
                        if(pivot_top_avail) {assert(pivot_top->get_d()!=0); sub->stateless_copy(*pivot_top); _d=sub->get_d();}
                        while(_d+parent::get_generalization_level()<=_n && _d<_cur_delta+_d_piv){
                                assert(_d==sub->get_d());
                                if(!parent::Step(true/*forward*/, result, sub, crit, os)) {if(grandparent::output_detailed()) this->report(os, msgbuf() << "UP failure" << std::endl << std::flush); return false;}
                                if(track) grandparent::_tracker->add(result,sub);
                                _d=sub->get_d(); swinged=true;
                        }
                        if(swinged) {
                                assert(_d==sub->get_d());
                                if(_d+parent::get_generalization_level()<=_n) {
                                        pivot_top_avail=true;
                                        pivot_top->stateless_copy(*sub);
                                }
                                swing=RETURN_DOWN;
                        } else { // the UP swing could not move at all: deepen the cycle and try DOWN again
                                pivot_top_avail=false;
                                swing=DOWN;
                                _cur_delta+=parent::get_generalization_level();
                        }
                        assert(_d==sub->get_d());
                        break;
                case RETURN_DOWN:
                        assert(_d==sub->get_d());
                        while(_d>_d_piv){
                                assert(_d==sub->get_d());
                                if(!parent::Step(false/*forward*/, result, sub, crit, os)) {if(grandparent::output_detailed()) this->report(os, msgbuf() << "RETURN DOWN failure" << std::endl << std::flush); return false;}
                                if(track) grandparent::_tracker->add(result,sub);
                                _d=sub->get_d();
                        }
                        if(result>bestval) swing=RESTART; // up-swing successful - reset
                        else { // neither swing improved at this depth: go back to the pivot and deepen the cycle
                                sub->stateless_copy(*pivotsub);
                                swing=DOWN; swinged=false;
                                _cur_delta+=parent::get_generalization_level();
                        }
                        assert(_d==sub->get_d());
                        break;
                }
 
        // the loop ends once the cycle depth exceeds _delta; the pivot is then the best subset found
        if(pivotsub->get_d()!=0) {
                sub->stateless_copy(*pivotsub);
                result=bestval;
                return true;
        }
        return false; // an empty pivot means no valid subset was ever adopted
}
 
 
} // namespace
#endif // FSTSEARCHSEQOS_H ///:~