HypervolumeContributionApproximator.h

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/*!
 *
 *
 * \brief       Approximately determines the individual contributing the least
 * hypervolume.
 *
 *
 *
 * \author      T.Voss, O.Krause
 * \date        2010-2016
 *
 *
 * \par Copyright 1995-2017 Shark Development Team
 *
 * <BR><HR>
 * This file is part of Shark.
 * <https://shark-ml.github.io/Shark/>
 *
 * Shark is free software: you can redistribute it and/or modify
 * it under the terms of the GNU Lesser General Public License as published
 * by the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * Shark 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 Lesser General Public License for more details.
 *
 * You should have received a copy of the GNU Lesser General Public License
 * along with Shark.  If not, see <http://www.gnu.org/licenses/>.
 *
 */
#ifndef SHARK_ALGORITHMS_DIRECTSEARCH_HYPERVOLUME_CONTRIBUTION_APPROXIMATOR_H
#define SHARK_ALGORITHMS_DIRECTSEARCH_HYPERVOLUME_CONTRIBUTION_APPROXIMATOR_H
 
#include <boost/cstdint.hpp>
 
#include <shark/Algorithms/DirectSearch/Operators/Domination/ParetoDominance.h>
#include <shark/Algorithms/DirectSearch/Operators/Hypervolume/HypervolumeCalculator.h>
#include <algorithm>
#include <limits>
#include <vector>
#include <cmath>
 
namespace shark {
 
/// \brief Approximately determines the point of a set contributing the least hypervolume.
///
/// See K. Bringmann, T. Friedrich. Approximating the least hypervolume contributor: NP-hard in general, but fast in practice. Proc. of the 5th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2009), Vol. 5467 of LNCS, pages 6-20, Springer-Verlag, 2009.
///
/// The algorithm works by estimating a bounding box for every point that includes all its contribution volume and then draw
/// sample inside the box to estimate which fraction of the box is covered by other points in the set.
///
/// The algorithm only implements the k=1 version of the smallest contribution. For the element A it returns holds the
/// following guarantue: with probability of 1-delta, Con(A) < (1+epsilon)Con(LC)
/// where LC is true least contributor. Note that there are no error guarantuees regarding the returned value of the contribution:
/// the algorithm stops when it is sure that the bound above holds, but depending on the setup, this might be very early.
///
/// Note that, while on average the algorithm performs reasonable well, the upper run-time is not bounded by the number of elements or dimensionality.
/// When two points have nearly the same(or exactly the same) contribution
/// the algorithm will run for many iterations, until the bound above holds. The same holds if the point with the smallest contribution
/// has a very large potential contribution as many samples are required to establish that allmost all of the box is covered.
///
///\tparam random The type of the random for sampling random points.
struct HypervolumeContributionApproximator{
    /// \brief Models a point and associated information for book-keeping purposes.
    template<typename VectorType>
    struct Point {
        Point( VectorType const& point, VectorType const& reference ) 
        : point( point )
        , sample( point.size() )
        , boundingBox( reference )
        , boundingBoxVolume( 0. )
        , approximatedContribution( 0. )
        , contributionLowerBound( 0. )
        , contributionUpperBound( 0. )
        , computedExactly(false)
        , noSamples( 0 )
        , noSuccessfulSamples( 0 )
        {}
 
        VectorType point;
        VectorType sample;
        VectorType boundingBox;
        std::vector< typename std::vector<Point>::const_iterator > influencingPoints;
 
        double boundingBoxVolume;
        double approximatedContribution;
        double contributionLowerBound;
        double contributionUpperBound;
        bool computedExactly;
        
        std::size_t noSamples;
        std::size_t noSuccessfulSamples;
    };
 
    double m_startDeltaMultiplier;
    double m_multiplierDelta;
    double m_minimumMultiplierDelta;
    
    double m_gamma;
    double m_errorProbability; ///<The error probability.
    double m_errorBound;  ///<The error bound
 
    /// \brief C'tor
    HypervolumeContributionApproximator()
    : m_startDeltaMultiplier( 0.1 )
    , m_multiplierDelta( 0.775 )
    , m_minimumMultiplierDelta( 0.2 )
    , m_gamma( 0.25 )
    , m_errorProbability(1.E-2)
    , m_errorBound(1.E-2)
    {}
        
    double delta()const{
        return m_errorProbability;
    }
    double& delta(){
        return m_errorProbability;
    }
    
    double epsilon()const{
        return m_errorBound;
    }
    double& epsilon(){
        return m_errorBound;
    }
    
    template<typename Archive>
    void serialize( Archive & archive, const unsigned int version ) {
        archive & BOOST_SERIALIZATION_NVP(m_startDeltaMultiplier);
        archive & BOOST_SERIALIZATION_NVP(m_multiplierDelta);
        archive & BOOST_SERIALIZATION_NVP(m_minimumMultiplierDelta);
        archive & BOOST_SERIALIZATION_NVP(m_gamma);
        archive & BOOST_SERIALIZATION_NVP(m_errorProbability);
        archive & BOOST_SERIALIZATION_NVP(m_errorBound);
    }
 
    /// \brief Determines the point contributing the least hypervolume to the overall set of points.
    ///
    /// \param [in] points pareto front of points
    /// \param [in] k The number of points to select.
    /// \param [in] reference The reference point to consider for calculating individual points' contributions.
    template<class Set,class VectorType>
    std::vector<KeyValuePair<double,std::size_t> > smallest(Set const& points, std::size_t k, VectorType const& reference)const{
        SHARK_RUNTIME_CHECK(points.size() >= k, "There must be at least k points in the set");
        SHARK_RUNTIME_CHECK(k == 1, "Not implemented for k != 1");
                
        std::vector< Point<VectorType> > front;
        for(auto const& point: points) {
            front.emplace_back( point, reference );
        }
        computeBoundingBoxes( front );
        
        std::vector< typename std::vector< Point<VectorType> >::iterator > activePoints;
        for(auto it = front.begin(); it != front.end(); ++it ) {
            activePoints.push_back( it );
        }
        
        auto smallest = computeSmallest(activePoints, front.size());
        
        std::vector<KeyValuePair<double,std::size_t> > result(1);
        result[0].key = smallest->approximatedContribution;
        result[0].value = smallest-front.begin();
        return result;
    }
        
    
    /// \brief Returns the index of the points with smallest contribution.
    ///
    /// As no reference point is given, the extremum points can not be computed and are never selected.
    ///
    /// \param [in] points The set \f$S\f$ of points from which to select the smallest contributor.
    /// \param [in] k The number of points to select.
    template<class Set>
    std::vector<KeyValuePair<double,std::size_t> > smallest(Set const& points, std::size_t k)const{
        SHARK_RUNTIME_CHECK(points.size() >= k, "There must be at least k points in the set");
        SHARK_RUNTIME_CHECK(k == 1, "Not implemented for k != 1");
        
        //find reference point as well as points with lowest function value
        std::vector<std::size_t> minIndex(points[0].size(),0);
        RealVector minVal = points[0];
        RealVector reference=points[0];
        for(std::size_t i = 1; i != points.size(); ++i){
            noalias(reference) = max(reference,points[i]);
            for(std::size_t j = 0; j != minVal.size(); ++j){
                if(points[i](j)< minVal[j]){
                    minVal[j] = points[i](j);
                    minIndex[j]=i;
                }
            }
        }
        
        std::vector< Point<RealVector> > front;
        front.reserve( points.size() );
        for(auto const& point: points){
            front.emplace_back( point, reference );
        }
        computeBoundingBoxes( front );
        
        std::vector<std::vector< Point<RealVector> >::iterator > activePoints;
        for(auto it = front.begin(); it != front.end(); ++it ) {
            if(std::find(minIndex.begin(),minIndex.end(),it-front.begin()) != minIndex.end())
                continue;
            //~ //check whether point is on the boundary -> least contributor
            //~ for(std::size_t j = 0; j != minVal.size(); ++j){
                //~ if(it->point[j] == reference[j]){
                    //~ return std::vector<KeyValuePair<double,std::size_t> >(1,{0.0,it-front.begin()});
                //~ }
            //~ }
            activePoints.push_back( it );
        
        }
        
        
        auto smallest = computeSmallest(activePoints, front.size());
        std::vector<KeyValuePair<double,std::size_t> > result(1);
        result[0].key = smallest->approximatedContribution;
        result[0].value = smallest-front.begin();
        return result;
    }
 
private:
    
    template<class Set>
    typename Set::value_type computeSmallest(Set& activePoints, std::size_t n)const{
        typedef typename Set::value_type SetIter;
        //compute initial guess for delta
        double delta = 0.;
        for( auto it = activePoints.begin(); it != activePoints.end(); ++it )
            delta = std::max( delta, (*it)->boundingBoxVolume );
        delta *= m_startDeltaMultiplier;
        
        unsigned int round = 0;
        while( true ) {
            round++;
            
            //check whether we spent so much time on sampling that computing the real volume is not much more expensive any more.
            //this guarantuees convergence even in cases that two points have the same hyper volume.
            SHARK_PARALLEL_FOR(int i = 0; i < (int)activePoints.size(); ++i){
                if(shouldCompute(*activePoints[i])){
                    computeExactly(*activePoints[i]);
                }
            }
            
            //sample all active points so that their individual deviations are smaller than delta
            for( auto point: activePoints )
                sample( *point, round, delta, n );
 
            //find the current least contributor
            auto minimalElement = std::min_element(
                activePoints.begin(),activePoints.end(),
                [](SetIter const& a, SetIter const& b){return a->approximatedContribution < b->approximatedContribution;}
            );
 
            //section 3.4.1: push the least contributor: decrease its delta further to have a chance to end earlier.
            if( activePoints.size() > 2 ) {
                sample( **minimalElement, round, m_minimumMultiplierDelta * delta, n );
                minimalElement = std::min_element(
                    activePoints.begin(),activePoints.end(),
                    [](SetIter const& a, SetIter const& b){return a->approximatedContribution < b->approximatedContribution;}
                );
            }
 
            //remove all points whose confidence interval does not overlap with the current minimum any more.
            double erase_level = (*minimalElement)->contributionUpperBound;
            auto erase_start = std::remove_if(
                activePoints.begin(),activePoints.end(),
                [=](SetIter const& point){return point->contributionLowerBound > erase_level;}
            );
            activePoints.erase(erase_start,activePoints.end());
            
            //if the set only has one point left, we are done.
            if(activePoints.size() == 1)
                return activePoints.front();
 
            // stopping conditions: have we reached the desired accuracy? 
            // for this we need to know:
            // 1. contribution for all points are bounded above 0
            // 2. upperBound(LC) < (1+epsilon)*lowerBound(A) for all A
            double d = 0;
            for( auto it = activePoints.begin(); it != activePoints.end(); ++it ) {
                if( it == minimalElement )
                    continue;
                double nom = (*minimalElement)-> contributionUpperBound;
                double den = (*it)->contributionLowerBound;
                if( den <= 0. )
                    d = std::numeric_limits<double>::max();
                else
                    d = std::max(d,nom/den);
            }
            
            //if the stopping condition is fulfilled, return the minimal element
            if(d < 1. + m_errorBound){
                return *minimalElement;
            }
            
            delta *= m_multiplierDelta;
        }
    }
    
    /// \brief Samples in the bounding box of the supplied point until a pre-defined threshold is reached.
    ///
    /// \param [in] point Iterator to the point that should be sampled.
    /// \param [in] r The current round.
    /// \param [in] delta The delta that should be reached.
    /// \param [in] n the total number of points in the front. Required for proper calculation of bounds
    template<class VectorType>
    void sample( Point<VectorType>& point, unsigned int r, double delta, std::size_t n )const{
        if(point.computedExactly) return;//spend no time on points that are computed exactly
        
        double logFactor = std::log( 2. * n * (1. + m_gamma) / (m_errorProbability * m_gamma) );
        double logR = std::log( static_cast<double>( r ) );
        //compute how many samples we need until the bound of the current box is smaller than delta
        //this is formula (3) in the paper when used in an equality < delta and solving for noSamples
        //we add +1 to ensure that the inequality holds.
        double thresholdD= 1.0+0.5 * ( (1. + m_gamma) * logR + logFactor ) * sqr( point.boundingBoxVolume / delta );
        std::size_t threshold = static_cast<std::size_t>(thresholdD);
        //sample points inside the box of the current point
        for( ; point.noSamples < threshold; point.noSamples++ ) {
            //sample a point inside the box
            point.sample.resize(point.point.size());
            for( unsigned int i = 0; i < point.sample.size(); i++ ) {
                point.sample[ i ] =  random::uni(random::globalRng, point.point[ i ], point.boundingBox[ i ] );
            }
            ++point.noSamples;
            //check if the point is not dominated by any of the influencing points
            if( !isPointDominated( point.influencingPoints, point.sample ) )
                point.noSuccessfulSamples++;
        }
 
        //current best guess for volume: fraction of accepted points inside the box imes the volume of the box. (2) in the paper
        point.approximatedContribution = (point.boundingBoxVolume * point.noSuccessfulSamples) /  point.noSamples;
        //lower and upper bounds for the best guess: with high probability it will be in this region. (3) in the paper.
        double deltaReached = std::sqrt( 0.5 * ((1. + m_gamma) * logR+ logFactor ) / point.noSamples ) * point.boundingBoxVolume;
        point.contributionLowerBound = point.approximatedContribution - deltaReached;
        point.contributionUpperBound = point.approximatedContribution + deltaReached;
    }
    
    /// \brief Checks whether a point is dominated by any point in a given set.
    ///
    /// \tparam Set The type of the set of points.
    ///
    /// \param [in] set The set of individuals to check the sampled point against.
    /// \param [in] point Point to test
    ///
    /// \returns true if the point was non-dominated
    template<typename Set, typename VectorType>
    bool isPointDominated( Set const& set, VectorType const& point )const{
        
        for( unsigned int i = 0; i < set.size(); i++ ) {
            DominanceRelation rel = dominance(set[i]->point, point);
            if (rel == LHS_DOMINATES_RHS)
                return true;
        }
        return false;
    }
 
    /// \brief Computes bounding boxes and their volume for the range of points defined by the iterators.
    /// \param [in] set The set of individuals.
    template<class Set>
    void computeBoundingBoxes(Set& set )const{
        for(auto it = set.begin(); it != set.end(); ++it ) {
            auto& p1 = *it;
            //first cut the bounding boxes on sides that are completely covered by other points
            for(auto itt = set.begin(); itt != set.end(); ++itt ) {
 
                if( itt == it )
                    continue;
 
                auto& p2 = *itt;
 
                unsigned int coordCounter = 0;
                unsigned int coord = 0;
                for( unsigned int o = 0; o < p1.point.size(); o++ ) {
                    if( p2.point[ o ] > p1.point[ o ] ) {
                        coordCounter++;
                        if( coordCounter == 2 )
                            break;
                        coord = o;
                    }
                }
 
                if( coordCounter == 1 && p1.boundingBox[ coord ] > p2.point[ coord ] )
                    p1.boundingBox[ coord ] = p2.point[ coord ];
            }
            
            //compute volume of the box
            it->boundingBoxVolume = 1.;
            for(unsigned int i = 0 ; i < it->boundingBox.size(); i++ ) {
                it->boundingBoxVolume *= it->boundingBox[ i ] - it->point[ i ];
            }
 
            //find all points that are partially covering this box
            for(auto itt = set.begin(); itt != set.end(); ++itt ) {
                if( itt == it )
                    continue;
 
                bool isInfluencing = true;
                for( unsigned int i = 0; i < it->point.size(); i++ ) {
                    if( itt->point[ i ] >= it->boundingBox[ i ] ) {
                        isInfluencing = false;
                        break;
                    }
                }
                if( isInfluencing ) {
                    it->influencingPoints.push_back( itt );
                }
            }
        }
    }
    template<class VectorType>
    bool shouldCompute(Point<VectorType> const& point)const{
        //we do not compute if it is already computed
        if(point.computedExactly) return false;
        std::size_t numPoints = point.influencingPoints.size();
        //point is on its own no need to sample.
        if(numPoints == 0) return true;
        std::size_t numObjectives = point.point.size();
        //runtime already spend on point
        double time = (double)point.noSamples * numObjectives;
        
        //estimate of algo run time
        double algoRunTime = 0.03 * numObjectives * numObjectives * std::pow(numPoints, numObjectives * 0.5 );
        return time > algoRunTime;
    }
    
    template<class VectorType>
    void computeExactly(Point<VectorType>& point)const{
        std::size_t numPoints = point.influencingPoints.size();
        //compute volume of the points inside the box
        std::vector<VectorType> transformedPoints(numPoints);
        for(std::size_t j = 0; j != numPoints; ++j){
            transformedPoints[j] = max(point.influencingPoints[j]->point, point.point);
        }
        HypervolumeCalculator vol;
        double volume = point.boundingBoxVolume - vol(transformedPoints, point.boundingBox);
        point.computedExactly = true;
        point.contributionLowerBound = volume;
        point.contributionUpperBound = volume;
        point.approximatedContribution = volume;
    }
};
}
 
#endif