TrainedVertexSelectionAlgorithm.cc

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/**
 *  @file   larpandoracontent/LArVertex/TrainedVertexSelectionAlgorithm.cc
 *
 *  @brief  Implementation of the trained vertex selection algorithm class.
 *
 *  $Log: $
 */

#include "Pandora/AlgorithmHeaders.h"

#include "larpandoracontent/LArHelpers/LArClusterHelper.h"
#include "larpandoracontent/LArHelpers/LArFileHelper.h"
#include "larpandoracontent/LArHelpers/LArGeometryHelper.h"
#include "larpandoracontent/LArHelpers/LArInteractionTypeHelper.h"
#include "larpandoracontent/LArHelpers/LArMCParticleHelper.h"
#include "larpandoracontent/LArHelpers/LArMvaHelper.h"

#include "larpandoracontent/LArVertex/EnergyDepositionAsymmetryFeatureTool.h"
#include "larpandoracontent/LArVertex/EnergyKickFeatureTool.h"
#include "larpandoracontent/LArVertex/GlobalAsymmetryFeatureTool.h"
#include "larpandoracontent/LArVertex/LocalAsymmetryFeatureTool.h"
#include "larpandoracontent/LArVertex/RPhiFeatureTool.h"
#include "larpandoracontent/LArVertex/ShowerAsymmetryFeatureTool.h"

#include "larpandoracontent/LArVertex/TrainedVertexSelectionAlgorithm.h"

#include "larpandoracontent/LArUtility/KDTreeLinkerAlgoT.h"

#include <random>

using namespace pandora;

namespace lar_content
{

TrainedVertexSelectionAlgorithm::TrainedVertexSelectionAlgorithm() :
    VertexSelectionBaseAlgorithm(),
    m_trainingSetMode(false),
    m_allowClassifyDuringTraining(false),
    m_mcVertexXCorrection(0.f),
    m_minClusterCaloHits(12),
    m_slidingFitWindow(100),
    m_minShowerSpineLength(15.f),
    m_beamDeweightingConstant(0.4),
    m_localAsymmetryConstant(3.f),
    m_globalAsymmetryConstant(1.f),
    m_showerAsymmetryConstant(1.f),
    m_energyKickConstant(0.06),
    m_showerClusteringDistance(3.f),
    m_minShowerClusterHits(1),
    m_useShowerClusteringApproximation(false),
    m_regionRadius(10.f),
    m_rPhiFineTuningRadius(2.f),
    m_maxTrueVertexRadius(1.f),
    m_useRPhiFeatureForRegion(false),
    m_dropFailedRPhiFastScoreCandidates(true),
    m_testBeamMode(false),
    m_legacyEventShapes(true),
    m_legacyVariables(true)
{
}

//------------------------------------------------------------------------------------------------------------------------------------------

void TrainedVertexSelectionAlgorithm::CalculateShowerClusterList(const ClusterList &inputClusterList, ShowerClusterList &showerClusterList) const
{
    ClusterEndPointsMap clusterEndPointsMap;
    ClusterList showerLikeClusters;
    this->GetShowerLikeClusterEndPoints(inputClusterList, showerLikeClusters, clusterEndPointsMap);

    const float slidingFitPitch(LArGeometryHelper::GetWireZPitch(this->GetPandora()));
    ClusterList availableShowerLikeClusters(showerLikeClusters.begin(), showerLikeClusters.end());

    HitKDTree2D kdTree;
    HitToClusterMap hitToClusterMap;

    if (!m_useShowerClusteringApproximation)
        this->PopulateKdTree(availableShowerLikeClusters, kdTree, hitToClusterMap);

    while (!availableShowerLikeClusters.empty())
    {
        ClusterList showerCluster;
        showerCluster.push_back(availableShowerLikeClusters.back());
        availableShowerLikeClusters.pop_back();

        bool addedCluster(true);
        while (addedCluster && !availableShowerLikeClusters.empty())
        {
            addedCluster = false;
            for (const Cluster *const pCluster : showerCluster)
            {
                if (!m_useShowerClusteringApproximation)
                {
                    addedCluster = this->AddClusterToShower(kdTree, hitToClusterMap, availableShowerLikeClusters, pCluster, showerCluster);
                }
                else
                {
                    addedCluster = this->AddClusterToShower(clusterEndPointsMap, availableShowerLikeClusters, pCluster, showerCluster);
                }

                if (addedCluster)
                    break;
            }
        }

        unsigned int totHits(0);
        for (const Cluster *const pCluster : showerCluster)
            totHits += pCluster->GetNCaloHits();

        if (totHits < m_minClusterCaloHits)
            continue;

        showerClusterList.emplace_back(showerCluster, slidingFitPitch, m_slidingFitWindow);
    }
}

//------------------------------------------------------------------------------------------------------------------------------------------

void TrainedVertexSelectionAlgorithm::GetShowerLikeClusterEndPoints(
    const ClusterList &clusterList, ClusterList &showerLikeClusters, ClusterEndPointsMap &clusterEndPointsMap) const
{
    for (const Cluster *const pCluster : clusterList)
    {
        if (pCluster->GetNCaloHits() < m_minShowerClusterHits)
            continue;

        if (this->IsClusterShowerLike(pCluster))
            showerLikeClusters.push_back(pCluster);

        CaloHitList clusterCaloHitList;
        pCluster->GetOrderedCaloHitList().FillCaloHitList(clusterCaloHitList);

        CaloHitVector clusterCaloHitVector(clusterCaloHitList.begin(), clusterCaloHitList.end());
        std::sort(clusterCaloHitVector.begin(), clusterCaloHitVector.end(), LArClusterHelper::SortHitsByPosition);

        if (clusterCaloHitVector.empty())
            continue;

        ClusterEndPoints clusterEndPoints(clusterCaloHitVector.front()->GetPositionVector(), clusterCaloHitVector.back()->GetPositionVector());
        clusterEndPointsMap.emplace(pCluster, clusterEndPoints);
    }
}

//------------------------------------------------------------------------------------------------------------------------------------------

void TrainedVertexSelectionAlgorithm::PopulateKdTree(const ClusterList &clusterList, HitKDTree2D &kdTree, HitToClusterMap &hitToClusterMap) const
{
    CaloHitList allCaloHits;

    for (const Cluster *const pCluster : clusterList)
    {
        CaloHitList daughterHits;
        pCluster->GetOrderedCaloHitList().FillCaloHitList(daughterHits);
        allCaloHits.insert(allCaloHits.end(), daughterHits.begin(), daughterHits.end());

        for (const CaloHit *const pCaloHit : daughterHits)
            (void)hitToClusterMap.insert(HitToClusterMap::value_type(pCaloHit, pCluster));
    }

    HitKDNode2DList hitKDNode2DList;
    KDTreeBox hitsBoundingRegion2D(fill_and_bound_2d_kd_tree(allCaloHits, hitKDNode2DList));
    kdTree.build(hitKDNode2DList, hitsBoundingRegion2D);
}

//------------------------------------------------------------------------------------------------------------------------------------------

bool TrainedVertexSelectionAlgorithm::AddClusterToShower(const ClusterEndPointsMap &clusterEndPointsMap,
    ClusterList &availableShowerLikeClusters, const Cluster *const pCluster, ClusterList &showerCluster) const
{
    const auto existingEndPointsIter(clusterEndPointsMap.find(pCluster));
    if (existingEndPointsIter == clusterEndPointsMap.end())
        return false;

    const ClusterEndPoints &existingClusterEndPoints(existingEndPointsIter->second);

    for (auto iter = availableShowerLikeClusters.begin(); iter != availableShowerLikeClusters.end(); ++iter)
    {
        const Cluster *const pAvailableShowerLikeCluster(*iter);
        const auto &newEndPointsIter(clusterEndPointsMap.find(pAvailableShowerLikeCluster));

        if (newEndPointsIter == clusterEndPointsMap.end())
            continue;

        const ClusterEndPoints &newClusterEndPoints(newEndPointsIter->second);
        const float startStartDistance((newClusterEndPoints.first - existingClusterEndPoints.first).GetMagnitude());
        const float startEndDistance((newClusterEndPoints.first - existingClusterEndPoints.second).GetMagnitude());
        const float endStartDistance((newClusterEndPoints.second - existingClusterEndPoints.first).GetMagnitude());
        const float endEndDistance((newClusterEndPoints.second - existingClusterEndPoints.second).GetMagnitude());

        const float smallestDistance(std::min(std::min(startStartDistance, startEndDistance), std::min(endStartDistance, endEndDistance)));

        if (smallestDistance < m_showerClusteringDistance)
        {
            showerCluster.push_back(pAvailableShowerLikeCluster);
            availableShowerLikeClusters.erase(iter); // Now must return, after invalidating current iterator
            return true;
        }
    }

    return false;
}

//------------------------------------------------------------------------------------------------------------------------------------------

bool TrainedVertexSelectionAlgorithm::AddClusterToShower(HitKDTree2D &kdTree, const HitToClusterMap &hitToClusterMap,
    ClusterList &availableShowerLikeClusters, const Cluster *const pCluster, ClusterList &showerCluster) const
{
    ClusterSet nearbyClusters;
    CaloHitList daughterHits;
    pCluster->GetOrderedCaloHitList().FillCaloHitList(daughterHits);

    for (const CaloHit *const pCaloHit : daughterHits)
    {
        KDTreeBox searchRegionHits = build_2d_kd_search_region(pCaloHit, m_showerClusteringDistance, m_showerClusteringDistance);

        HitKDNode2DList found;
        kdTree.search(searchRegionHits, found);

        for (const auto &hit : found)
            (void)nearbyClusters.insert(hitToClusterMap.at(hit.data));
    }

    for (auto iter = availableShowerLikeClusters.begin(); iter != availableShowerLikeClusters.end(); ++iter)
    {
        const Cluster *const pAvailableShowerLikeCluster(*iter);

        if ((pAvailableShowerLikeCluster != pCluster) && nearbyClusters.count(pAvailableShowerLikeCluster))
        {
            showerCluster.push_back(pAvailableShowerLikeCluster);
            availableShowerLikeClusters.erase(iter); // Now must return, after invalidating current iterator
            return true;
        }
    }

    return false;
}

//------------------------------------------------------------------------------------------------------------------------------------------

typename TrainedVertexSelectionAlgorithm::EventFeatureInfo TrainedVertexSelectionAlgorithm::CalculateEventFeatures(
    const ClusterList &clusterListU, const ClusterList &clusterListV, const ClusterList &clusterListW, const VertexVector &vertexVector) const
{
    float eventEnergy(0.f);
    unsigned int nShoweryHits(0), nHits(0);

    this->IncrementShoweryParameters(clusterListU, nShoweryHits, nHits, eventEnergy);
    this->IncrementShoweryParameters(clusterListV, nShoweryHits, nHits, eventEnergy);
    this->IncrementShoweryParameters(clusterListW, nShoweryHits, nHits, eventEnergy);

    const unsigned int nClusters(clusterListU.size() + clusterListV.size() + clusterListW.size());
    const float eventShoweryness((nHits > 0) ? static_cast<float>(nShoweryHits) / static_cast<float>(nHits) : 0.f);

    ClusterList allClusters(clusterListU);
    allClusters.insert(allClusters.end(), clusterListV.begin(), clusterListV.end());
    allClusters.insert(allClusters.end(), clusterListW.begin(), clusterListW.end());

    const ClusterListMap clusterListMap{{TPC_VIEW_U, clusterListU}, {TPC_VIEW_V, clusterListV}, {TPC_VIEW_W, clusterListW}};

    float eventArea(0.f), longitudinality(0.f);
    if (m_legacyEventShapes)
        this->GetLegacyEventShapeFeatures(allClusters, eventArea, longitudinality);
    else
        this->GetEventShapeFeatures(clusterListMap, eventArea, longitudinality);

    return EventFeatureInfo(eventShoweryness, eventEnergy, eventArea, longitudinality, nHits, nClusters, vertexVector.size());
}

//------------------------------------------------------------------------------------------------------------------------------------------

void TrainedVertexSelectionAlgorithm::IncrementShoweryParameters(
    const ClusterList &clusterList, unsigned int &nShoweryHits, unsigned int &nHits, float &eventEnergy) const
{
    for (const Cluster *const pCluster : clusterList)
    {
        if (this->IsClusterShowerLike(pCluster))
            nShoweryHits += pCluster->GetNCaloHits();

        eventEnergy += pCluster->GetElectromagneticEnergy();
        nHits += pCluster->GetNCaloHits();
    }
}

//------------------------------------------------------------------------------------------------------------------------------------------

inline bool TrainedVertexSelectionAlgorithm::IsClusterShowerLike(const Cluster *const pCluster) const
{
    return (pCluster->GetParticleId() == E_MINUS && LArClusterHelper::GetLength(pCluster) < m_minShowerSpineLength);
}

//------------------------------------------------------------------------------------------------------------------------------------------

void TrainedVertexSelectionAlgorithm::GetLegacyEventShapeFeatures(const ClusterList &clusterList, float &eventVolume, float &longitudinality) const
{
    InputFloat xMin, yMin, zMin, xMax, yMax, zMax;

    for (const Cluster *const pCluster : clusterList)
    {
        CartesianVector minPosition(0.f, 0.f, 0.f), maxPosition(0.f, 0.f, 0.f);
        LArClusterHelper::GetClusterBoundingBox(pCluster, minPosition, maxPosition);

        this->UpdateSpanCoordinate(minPosition.GetX(), maxPosition.GetX(), xMin, xMax);
        this->UpdateSpanCoordinate(minPosition.GetY(), maxPosition.GetY(), yMin, yMax);
        this->UpdateSpanCoordinate(minPosition.GetZ(), maxPosition.GetZ(), zMin, zMax);
    }

    const float xSpan(this->GetCoordinateSpan(xMax, xMin));
    const float ySpan(this->GetCoordinateSpan(yMax, zMin));
    const float zSpan(this->GetCoordinateSpan(yMax, zMin));

    // Calculate the volume and longitudinality of the event (ySpan often 0 - to be investigated).
    if ((xSpan > std::numeric_limits<float>::epsilon()) && (ySpan > std::numeric_limits<float>::epsilon()))
    {
        eventVolume = xSpan * ySpan * zSpan;
        longitudinality = zSpan / (xSpan + ySpan);
    }

    else if (ySpan > std::numeric_limits<float>::epsilon())
    {
        eventVolume = ySpan * ySpan * zSpan;
        longitudinality = zSpan / (ySpan + ySpan);
    }

    else if (xSpan > std::numeric_limits<float>::epsilon())
    {
        eventVolume = xSpan * xSpan * zSpan;
        longitudinality = zSpan / (xSpan + xSpan);
    }
}

//------------------------------------------------------------------------------------------------------------------------------------------

void TrainedVertexSelectionAlgorithm::GetEventShapeFeatures(const ClusterListMap &clusterListMap, float &eventArea, float &longitudinality) const
{
    float xSpanU(0.f), zSpanU(0.f), xSpanV(0.f), zSpanV(0.f), xSpanW(0.f), zSpanW(0.f);

    this->Get2DSpan(clusterListMap.at(TPC_VIEW_U), xSpanU, zSpanU);
    this->Get2DSpan(clusterListMap.at(TPC_VIEW_V), xSpanV, zSpanV);
    this->Get2DSpan(clusterListMap.at(TPC_VIEW_W), xSpanW, zSpanW);

    const float xSpan = (xSpanU + xSpanV + xSpanW) / 3.f;
    const float zSpan = (zSpanU + zSpanV + zSpanW) / 3.f;

    if ((xSpan > std::numeric_limits<float>::epsilon()) && (zSpan > std::numeric_limits<float>::epsilon()))
    {
        eventArea = xSpan * zSpan;
        longitudinality = zSpan / (xSpan + zSpan);
    }
}

//------------------------------------------------------------------------------------------------------------------------------------------

void TrainedVertexSelectionAlgorithm::Get2DSpan(const ClusterList &clusterList, float &xSpan, float &zSpan) const
{
    FloatVector xPositions, zPositions;

    for (const Cluster *const pCluster : clusterList)
    {
        const OrderedCaloHitList &orderedCaloHitList(pCluster->GetOrderedCaloHitList());

        for (OrderedCaloHitList::const_iterator iter = orderedCaloHitList.begin(), iterEnd = orderedCaloHitList.end(); iter != iterEnd; ++iter)
        {
            for (CaloHitList::const_iterator hitIter = iter->second->begin(), hitIterEnd = iter->second->end(); hitIter != hitIterEnd; ++hitIter)
            {
                xPositions.push_back((*hitIter)->GetPositionVector().GetX());
                zPositions.push_back((*hitIter)->GetPositionVector().GetZ());
            }
        }
    }

    std::sort(xPositions.begin(), xPositions.end());
    std::sort(zPositions.begin(), zPositions.end());

    if (xPositions.empty())
    {
        xSpan = 0;
        zSpan = 0;
    }
    else
    {
        const int low = std::round(0.05 * xPositions.size());
        const int high = std::round(0.95 * xPositions.size()) - 1;

        xSpan = xPositions[high] - xPositions[low];
        zSpan = zPositions[high] - zPositions[low];
    }
}

//------------------------------------------------------------------------------------------------------------------------------------------

inline void TrainedVertexSelectionAlgorithm::UpdateSpanCoordinate(
    const float minPositionCoord, const float maxPositionCoord, InputFloat &minCoord, InputFloat &maxCoord) const
{
    if (!minCoord.IsInitialized() || minPositionCoord < minCoord.Get())
        minCoord = minPositionCoord;

    if (!maxCoord.IsInitialized() || maxPositionCoord > maxCoord.Get())
        maxCoord = maxPositionCoord;
}

//------------------------------------------------------------------------------------------------------------------------------------------

inline float TrainedVertexSelectionAlgorithm::GetCoordinateSpan(const InputFloat &minCoord, const InputFloat &maxCoord) const
{
    if (minCoord.IsInitialized() && maxCoord.IsInitialized())
        return std::fabs(maxCoord.Get() - minCoord.Get());

    return 0.f;
}

//------------------------------------------------------------------------------------------------------------------------------------------

void TrainedVertexSelectionAlgorithm::AddEventFeaturesToVector(const EventFeatureInfo &eventFeatureInfo, LArMvaHelper::MvaFeatureVector &featureVector) const
{
    featureVector.push_back(static_cast<double>(eventFeatureInfo.m_eventShoweryness));
    featureVector.push_back(static_cast<double>(eventFeatureInfo.m_eventEnergy));
    featureVector.push_back(static_cast<double>(eventFeatureInfo.m_eventArea));
    featureVector.push_back(static_cast<double>(eventFeatureInfo.m_longitudinality));
    if (this->IsBeamModeOn())
    {
        featureVector.push_back(static_cast<double>(eventFeatureInfo.m_nHits));
        featureVector.push_back(static_cast<double>(eventFeatureInfo.m_nClusters));
        featureVector.push_back(static_cast<double>(eventFeatureInfo.m_nCandidates));
    }
}

//------------------------------------------------------------------------------------------------------------------------------------------

void TrainedVertexSelectionAlgorithm::PopulateVertexFeatureInfoMap(const BeamConstants &beamConstants, const ClusterListMap &clusterListMap,
    const SlidingFitDataListMap &slidingFitDataListMap, const ShowerClusterListMap &showerClusterListMap, const KDTreeMap &kdTreeMap,
    const Vertex *const pVertex, VertexFeatureInfoMap &vertexFeatureInfoMap) const
{
    float bestFastScore(-std::numeric_limits<float>::max()); // not actually used - artefact of toolizing RPhi score and still using performance trick

    // ATTN - If beam mode is false GetBeamDeweightingScore will fail, so have a default value that we'll ignore when poplating the feature vector
    double tempBeamDeweight{0.f};
    if (this->IsBeamModeOn())
        tempBeamDeweight = this->GetBeamDeweightingScore(beamConstants, pVertex);

    const double beamDeweighting(tempBeamDeweight);

    const double energyKick(LArMvaHelper::CalculateFeaturesOfType<EnergyKickFeatureTool>(m_featureToolVector, this, pVertex,
        slidingFitDataListMap, clusterListMap, kdTreeMap, showerClusterListMap, beamDeweighting, bestFastScore)
                                .at(0)
                                .Get());

    const double localAsymmetry(LArMvaHelper::CalculateFeaturesOfType<LocalAsymmetryFeatureTool>(m_featureToolVector, this, pVertex,
        slidingFitDataListMap, clusterListMap, kdTreeMap, showerClusterListMap, beamDeweighting, bestFastScore)
                                    .at(0)
                                    .Get());

    const double globalAsymmetry(LArMvaHelper::CalculateFeaturesOfType<GlobalAsymmetryFeatureTool>(m_featureToolVector, this, pVertex,
        slidingFitDataListMap, clusterListMap, kdTreeMap, showerClusterListMap, beamDeweighting, bestFastScore)
                                     .at(0)
                                     .Get());

    const double showerAsymmetry(LArMvaHelper::CalculateFeaturesOfType<ShowerAsymmetryFeatureTool>(m_featureToolVector, this, pVertex,
        slidingFitDataListMap, clusterListMap, kdTreeMap, showerClusterListMap, beamDeweighting, bestFastScore)
                                     .at(0)
                                     .Get());

    //const double rPhiFeature(LArMvaHelper::CalculateFeaturesOfType<RPhiFeatureTool>(m_featureToolVector, this, pVertex,
    //    slidingFitDataListMap, clusterListMap, kdTreeMap, showerClusterListMap, beamDeweighting, bestFastScore).at(0).Get());

    double dEdxAsymmetry(0.f), vertexEnergy(0.f);

    if (!m_legacyVariables)
    {
        dEdxAsymmetry = LArMvaHelper::CalculateFeaturesOfType<EnergyDepositionAsymmetryFeatureTool>(m_featureToolVector, this, pVertex,
            slidingFitDataListMap, clusterListMap, kdTreeMap, showerClusterListMap, beamDeweighting, bestFastScore)
                            .at(0)
                            .Get();

        vertexEnergy = this->GetVertexEnergy(pVertex, kdTreeMap);
    }

    VertexFeatureInfo vertexFeatureInfo(beamDeweighting, 0.f, energyKick, localAsymmetry, globalAsymmetry, showerAsymmetry, dEdxAsymmetry, vertexEnergy);
    vertexFeatureInfoMap.emplace(pVertex, vertexFeatureInfo);
}

//------------------------------------------------------------------------------------------------------------------------------------------

void TrainedVertexSelectionAlgorithm::PopulateInitialScoreList(
    VertexFeatureInfoMap &vertexFeatureInfoMap, const Vertex *const pVertex, VertexScoreList &initialScoreList) const
{
    VertexFeatureInfo vertexFeatureInfo = vertexFeatureInfoMap.at(pVertex);

    const float beamDeweightingScore(vertexFeatureInfo.m_beamDeweighting / m_beamDeweightingConstant);
    const float energyKickScore(-vertexFeatureInfo.m_energyKick / m_energyKickConstant);
    const float localAsymmetryScore(vertexFeatureInfo.m_localAsymmetry / m_localAsymmetryConstant);
    const float globalAsymmetryScore(vertexFeatureInfo.m_globalAsymmetry / m_globalAsymmetryConstant);
    const float showerAsymmetryScore(vertexFeatureInfo.m_showerAsymmetry / m_showerAsymmetryConstant);

    initialScoreList.emplace_back(pVertex, beamDeweightingScore + energyKickScore + localAsymmetryScore + globalAsymmetryScore + showerAsymmetryScore);
}

//------------------------------------------------------------------------------------------------------------------------------------------

void TrainedVertexSelectionAlgorithm::GetBestRegionVertices(VertexScoreList &initialScoreList, VertexVector &bestRegionVertices) const
{
    std::sort(initialScoreList.begin(), initialScoreList.end());

    for (const VertexScore &vertexScore : initialScoreList)
    {
        const Vertex *const pVertex(vertexScore.GetVertex());
        bool farEnoughAway(true);

        for (const Vertex *const pRegionVertex : bestRegionVertices)
        {
            if (pRegionVertex == pVertex)
            {
                farEnoughAway = false;
                break;
            }

            const float distance = (pRegionVertex->GetPosition() - pVertex->GetPosition()).GetMagnitude();

            if (distance <= m_regionRadius)
            {
                farEnoughAway = false;
                break;
            }
        }

        if (farEnoughAway)
            bestRegionVertices.push_back(pVertex);
    }
}

//------------------------------------------------------------------------------------------------------------------------------------------

void TrainedVertexSelectionAlgorithm::ProduceTrainingSets(const VertexVector &vertexVector, const VertexVector &bestRegionVertices,
    VertexFeatureInfoMap &vertexFeatureInfoMap, const LArMvaHelper::MvaFeatureVector &eventFeatureList, const KDTreeMap &kdTreeMap) const
{
    if (vertexVector.empty())
        return;

    // Create a distribution for random coin flips.
    std::random_device device;
    std::mt19937 generator(device());
    std::bernoulli_distribution coinFlip(0.5);

    const std::string interactionType(this->GetInteractionType());

    // Produce training examples for the vertices representing regions.
    const Vertex *const pBestRegionVertex(this->ProduceTrainingExamples(bestRegionVertices, vertexFeatureInfoMap, coinFlip, generator,
        interactionType, m_trainingOutputFileRegion, eventFeatureList, kdTreeMap, m_regionRadius, m_useRPhiFeatureForRegion));

    // Get all the vertices in the best region.
    VertexVector regionalVertices{pBestRegionVertex};
    for (const Vertex *const pVertex : vertexVector)
    {
        if (pVertex == pBestRegionVertex)
            continue;

        if ((pBestRegionVertex->GetPosition() - pVertex->GetPosition()).GetMagnitude() < m_regionRadius)
            regionalVertices.push_back(pVertex);
    }

    this->CalculateRPhiScores(regionalVertices, vertexFeatureInfoMap, kdTreeMap);

    // Produce training examples for the final vertices within the best region.
    if (!regionalVertices.empty())
    {
        this->ProduceTrainingExamples(regionalVertices, vertexFeatureInfoMap, coinFlip, generator, interactionType,
            m_trainingOutputFileVertex, eventFeatureList, kdTreeMap, m_maxTrueVertexRadius, true);
    }
}

//------------------------------------------------------------------------------------------------------------------------------------------

void TrainedVertexSelectionAlgorithm::CalculateRPhiScores(
    VertexVector &vertexVector, VertexFeatureInfoMap &vertexFeatureInfoMap, const KDTreeMap &kdTreeMap) const
{
    float bestFastScore(-std::numeric_limits<float>::max());

    for (auto iter = vertexVector.begin(); iter != vertexVector.end(); /* no increment */)
    {
        VertexFeatureInfo &vertexFeatureInfo = vertexFeatureInfoMap.at(*iter);
        vertexFeatureInfo.m_rPhiFeature = static_cast<float>(LArMvaHelper::CalculateFeaturesOfType<RPhiFeatureTool>(m_featureToolVector, this,
            *iter, SlidingFitDataListMap(), ClusterListMap(), kdTreeMap, ShowerClusterListMap(), vertexFeatureInfo.m_beamDeweighting, bestFastScore)
                                                                 .at(0)
                                                                 .Get());

        if (m_dropFailedRPhiFastScoreCandidates && (vertexFeatureInfo.m_rPhiFeature <= std::numeric_limits<float>::epsilon()))
            iter = vertexVector.erase(iter);

        else
            ++iter;
    }
}

//------------------------------------------------------------------------------------------------------------------------------------------

std::string TrainedVertexSelectionAlgorithm::GetInteractionType() const
{
    // Extract input collections
    const MCParticleList *pMCParticleList(nullptr);
    PANDORA_THROW_RESULT_IF(STATUS_CODE_SUCCESS, !=, PandoraContentApi::GetList(*this, m_mcParticleListName, pMCParticleList));

    const CaloHitList *pCaloHitList(nullptr);
    PANDORA_THROW_RESULT_IF(STATUS_CODE_SUCCESS, !=, PandoraContentApi::GetList(*this, m_caloHitListName, pCaloHitList));

    LArMCParticleHelper::MCContributionMap targetMCParticlesToGoodHitsMap;

    if (m_testBeamMode)
    {
        LArMCParticleHelper::SelectReconstructableMCParticles(pMCParticleList, pCaloHitList, LArMCParticleHelper::PrimaryParameters(),
            LArMCParticleHelper::IsTriggeredBeamParticle, targetMCParticlesToGoodHitsMap);
    }
    else
    {
        // ATTN Assumes single neutrino is parent of all neutrino-induced mc particles
        LArMCParticleHelper::SelectReconstructableMCParticles(pMCParticleList, pCaloHitList, LArMCParticleHelper::PrimaryParameters(),
            LArMCParticleHelper::IsBeamNeutrinoFinalState, targetMCParticlesToGoodHitsMap);
    }

    MCParticleList mcPrimaryList;
    for (const auto &mapEntry : targetMCParticlesToGoodHitsMap)
        mcPrimaryList.push_back(mapEntry.first);
    mcPrimaryList.sort(LArMCParticleHelper::SortByMomentum);

    const LArInteractionTypeHelper::InteractionType interactionType(LArInteractionTypeHelper::GetInteractionType(mcPrimaryList));
    return LArInteractionTypeHelper::ToString(interactionType);
}

//------------------------------------------------------------------------------------------------------------------------------------------

const pandora::Vertex *TrainedVertexSelectionAlgorithm::ProduceTrainingExamples(const VertexVector &vertexVector,
    const VertexFeatureInfoMap &vertexFeatureInfoMap, std::bernoulli_distribution &coinFlip, std::mt19937 &generator,
    const std::string &interactionType, const std::string &trainingOutputFile, const LArMvaHelper::MvaFeatureVector &eventFeatureList,
    const KDTreeMap &kdTreeMap, const float maxRadius, const bool useRPhi) const
{
    const Vertex *pBestVertex(nullptr);
    float bestVertexDr(std::numeric_limits<float>::max());

    LArMvaHelper::MvaFeatureVector bestVertexFeatureList;
    this->GetBestVertex(vertexVector, pBestVertex, bestVertexDr);

    VertexFeatureInfo bestVertexFeatureInfo(vertexFeatureInfoMap.at(pBestVertex));
    this->AddVertexFeaturesToVector(bestVertexFeatureInfo, bestVertexFeatureList, useRPhi);

    for (const Vertex *const pVertex : vertexVector)
    {
        if (pVertex == pBestVertex)
            continue;

        LArMvaHelper::MvaFeatureVector featureList;
        VertexFeatureInfo vertexFeatureInfo(vertexFeatureInfoMap.at(pVertex));
        this->AddVertexFeaturesToVector(vertexFeatureInfo, featureList, useRPhi);

        if (!m_legacyVariables)
        {
            LArMvaHelper::MvaFeatureVector sharedFeatureList;
            float separation(0.f), axisHits(0.f);
            this->GetSharedFeatures(pVertex, pBestVertex, kdTreeMap, separation, axisHits);
            VertexSharedFeatureInfo sharedFeatureInfo(separation, axisHits);
            this->AddSharedFeaturesToVector(sharedFeatureInfo, sharedFeatureList);

            if (pBestVertex && (bestVertexDr < maxRadius))
            {
                if (coinFlip(generator))
                    LArMvaHelper::ProduceTrainingExample(trainingOutputFile + "_" + interactionType + ".txt", true,
                        LArMvaHelper::ConcatenateFeatureLists(eventFeatureList, bestVertexFeatureList, featureList, sharedFeatureList));
                else
                    LArMvaHelper::ProduceTrainingExample(trainingOutputFile + "_" + interactionType + ".txt", false,
                        LArMvaHelper::ConcatenateFeatureLists(eventFeatureList, featureList, bestVertexFeatureList, sharedFeatureList));
            }
        }
        else
        {
            if (pBestVertex && (bestVertexDr < maxRadius))
            {
                if (coinFlip(generator))
                    LArMvaHelper::ProduceTrainingExample(trainingOutputFile + "_" + interactionType + ".txt", true,
                        LArMvaHelper::ConcatenateFeatureLists(eventFeatureList, bestVertexFeatureList, featureList));
                else
                    LArMvaHelper::ProduceTrainingExample(trainingOutputFile + "_" + interactionType + ".txt", false,
                        LArMvaHelper::ConcatenateFeatureLists(eventFeatureList, featureList, bestVertexFeatureList));
            }
        }
    }

    return pBestVertex;
}

//------------------------------------------------------------------------------------------------------------------------------------------

void TrainedVertexSelectionAlgorithm::GetSharedFeatures(
    const Vertex *const pVertex1, const Vertex *const pVertex2, const KDTreeMap &kdTreeMap, float &separation, float &axisHits) const
{
    separation = (pVertex1->GetPosition() - pVertex2->GetPosition()).GetMagnitude();

    this->IncrementSharedAxisValues(LArGeometryHelper::ProjectPosition(this->GetPandora(), pVertex1->GetPosition(), TPC_VIEW_U),
        LArGeometryHelper::ProjectPosition(this->GetPandora(), pVertex2->GetPosition(), TPC_VIEW_U), kdTreeMap.at(TPC_VIEW_U), axisHits);

    this->IncrementSharedAxisValues(LArGeometryHelper::ProjectPosition(this->GetPandora(), pVertex1->GetPosition(), TPC_VIEW_V),
        LArGeometryHelper::ProjectPosition(this->GetPandora(), pVertex2->GetPosition(), TPC_VIEW_V), kdTreeMap.at(TPC_VIEW_V), axisHits);

    this->IncrementSharedAxisValues(LArGeometryHelper::ProjectPosition(this->GetPandora(), pVertex1->GetPosition(), TPC_VIEW_W),
        LArGeometryHelper::ProjectPosition(this->GetPandora(), pVertex2->GetPosition(), TPC_VIEW_W), kdTreeMap.at(TPC_VIEW_W), axisHits);

    axisHits = separation > std::numeric_limits<float>::epsilon() ? axisHits / separation : 0.f;
}

//------------------------------------------------------------------------------------------------------------------------------------------

void TrainedVertexSelectionAlgorithm::IncrementSharedAxisValues(
    const CartesianVector pos1, const CartesianVector pos2, HitKDTree2D &kdTree, float &axisHits) const
{
    if (pos1 == pos2)
        return;

    // Define the axis and perpendicular directions
    const CartesianVector unitAxis = (pos1 - pos2).GetUnitVector();
    const CartesianVector unitPerp(unitAxis.GetZ(), 0, -unitAxis.GetX());

    // Define the corners of the search box
    const CartesianVector point1 = pos1 + unitPerp;
    const CartesianVector point2 = pos1 - unitPerp;
    const CartesianVector point3 = pos2 + unitPerp;
    const CartesianVector point4 = pos2 - unitPerp;

    // Find the total coordinate span these points cover
    const float xMin{std::min({point1.GetX(), point2.GetX(), point3.GetX(), point4.GetX()})};
    const float xMax{std::max({point1.GetX(), point2.GetX(), point3.GetX(), point4.GetX()})};
    const float zMin{std::min({point1.GetZ(), point2.GetZ(), point3.GetZ(), point4.GetZ()})};
    const float zMax{std::max({point1.GetZ(), point2.GetZ(), point3.GetZ(), point4.GetZ()})};

    // Use a kd search to find the hits in the 'wide' area
    KDTreeBox searchBox(xMin, zMin, xMax, zMax);
    HitKDNode2DList found;
    kdTree.search(searchBox, found);

    // Use IsHitInBox method to check the 'wide' area hits for those in the search box
    for (auto f : found)
    {
        const CartesianVector &hitPos = f.data->GetPositionVector();
        bool inBox = this->IsHitInBox(hitPos, point1, point2, point3, point4);

        if (inBox)
            ++axisHits;
    }
}

//------------------------------------------------------------------------------------------------------------------------------------------

bool TrainedVertexSelectionAlgorithm::IsHitInBox(const CartesianVector &hitPos, const CartesianVector &point1,
    const CartesianVector &point2, const CartesianVector &point3, const CartesianVector &point4) const
{
    bool b1 = std::signbit(((point2 - point1).GetCrossProduct(point2 - hitPos)).GetY());
    bool b2 = std::signbit(((point4 - point3).GetCrossProduct(point4 - hitPos)).GetY());

    if (!(b1 xor b2))
        return false;

    bool b3 = std::signbit(((point3 - point1).GetCrossProduct(point3 - hitPos)).GetY());
    bool b4 = std::signbit(((point4 - point2).GetCrossProduct(point4 - hitPos)).GetY());

    return (b3 xor b4);
}

//------------------------------------------------------------------------------------------------------------------------------------------

void TrainedVertexSelectionAlgorithm::GetBestVertex(const VertexVector &vertexVector, const Vertex *&pBestVertex, float &bestVertexDr) const
{
    // Extract input collections
    const MCParticleList *pMCParticleList(nullptr);
    PANDORA_THROW_RESULT_IF(STATUS_CODE_SUCCESS, !=, PandoraContentApi::GetList(*this, m_mcParticleListName, pMCParticleList));

    // Obtain vector: true targets
    MCParticleVector mcTargetVector;

    if (m_testBeamMode)
    {
        LArMCParticleHelper::GetTrueTestBeamParticles(pMCParticleList, mcTargetVector);
    }
    else
    {
        LArMCParticleHelper::GetTrueNeutrinos(pMCParticleList, mcTargetVector);
    }

    for (const Vertex *const pVertex : vertexVector)
    {
        float mcVertexDr(std::numeric_limits<float>::max());
        for (const MCParticle *const pMCTarget : mcTargetVector)
        {
            const CartesianVector &mcTargetPosition(
                (m_testBeamMode && std::fabs(pMCTarget->GetParticleId()) == 11) ? pMCTarget->GetVertex() : pMCTarget->GetEndpoint());
            const CartesianVector mcTargetCorrectedPosition(
                mcTargetPosition.GetX() + m_mcVertexXCorrection, mcTargetPosition.GetY(), mcTargetPosition.GetZ());
            const float dr = (mcTargetCorrectedPosition - pVertex->GetPosition()).GetMagnitude();

            if (dr < mcVertexDr)
                mcVertexDr = dr;
        }

        if (mcVertexDr < bestVertexDr)
        {
            bestVertexDr = mcVertexDr;
            pBestVertex = pVertex;
        }
    }
}

//------------------------------------------------------------------------------------------------------------------------------------------

void TrainedVertexSelectionAlgorithm::AddVertexFeaturesToVector(
    const VertexFeatureInfo &vertexFeatureInfo, LArMvaHelper::MvaFeatureVector &featureVector, const bool useRPhi) const
{
    if (this->IsBeamModeOn())
        featureVector.push_back(static_cast<double>(vertexFeatureInfo.m_beamDeweighting));
    featureVector.push_back(static_cast<double>(vertexFeatureInfo.m_energyKick));
    featureVector.push_back(static_cast<double>(vertexFeatureInfo.m_globalAsymmetry));
    featureVector.push_back(static_cast<double>(vertexFeatureInfo.m_localAsymmetry));
    featureVector.push_back(static_cast<double>(vertexFeatureInfo.m_showerAsymmetry));

    if (!m_legacyVariables)
    {
        featureVector.push_back(static_cast<double>(vertexFeatureInfo.m_dEdxAsymmetry));
        featureVector.push_back(static_cast<double>(vertexFeatureInfo.m_vertexEnergy));
    }

    if (useRPhi)
        featureVector.push_back(static_cast<double>(vertexFeatureInfo.m_rPhiFeature));
}

//------------------------------------------------------------------------------------------------------------------------------------------

void TrainedVertexSelectionAlgorithm::AddSharedFeaturesToVector(
    const VertexSharedFeatureInfo &vertexSharedFeatureInfo, LArMvaHelper::MvaFeatureVector &featureVector) const
{
    featureVector.push_back(static_cast<double>(vertexSharedFeatureInfo.m_separation));
    featureVector.push_back(static_cast<double>(vertexSharedFeatureInfo.m_axisHits));
}

//------------------------------------------------------------------------------------------------------------------------------------------

void TrainedVertexSelectionAlgorithm::PopulateFinalVertexScoreList(const VertexFeatureInfoMap &vertexFeatureInfoMap,
    const Vertex *const pFavouriteVertex, const VertexVector &vertexVector, VertexScoreList &finalVertexScoreList) const
{
    if (pFavouriteVertex)
    {
        const CartesianVector vertexPosition(pFavouriteVertex->GetPosition());

        for (const Vertex *const pVertex : vertexVector)
        {
            if ((pVertex->GetPosition() - vertexPosition).GetMagnitude() < m_rPhiFineTuningRadius)
            {
                const float rPhiScore(vertexFeatureInfoMap.at(pVertex).m_rPhiFeature);
                finalVertexScoreList.emplace_back(pVertex, rPhiScore);
            }
        }
    }
}

//------------------------------------------------------------------------------------------------------------------------------------------
//------------------------------------------------------------------------------------------------------------------------------------------

StatusCode TrainedVertexSelectionAlgorithm::ReadSettings(const TiXmlHandle xmlHandle)
{
    AlgorithmToolVector algorithmToolVector;
    PANDORA_RETURN_RESULT_IF(STATUS_CODE_SUCCESS, !=, XmlHelper::ProcessAlgorithmToolList(*this, xmlHandle, "FeatureTools", algorithmToolVector));

    for (AlgorithmTool *const pAlgorithmTool : algorithmToolVector)
        PANDORA_RETURN_RESULT_IF(STATUS_CODE_SUCCESS, !=, LArMvaHelper::AddFeatureToolToVector(pAlgorithmTool, m_featureToolVector));

    PANDORA_RETURN_RESULT_IF_AND_IF(STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "TrainingSetMode", m_trainingSetMode));

    PANDORA_RETURN_RESULT_IF_AND_IF(STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=,
        XmlHelper::ReadValue(xmlHandle, "AllowClassifyDuringTraining", m_allowClassifyDuringTraining));

    PANDORA_RETURN_RESULT_IF_AND_IF(
        STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "MCVertexXCorrection", m_mcVertexXCorrection));

    PANDORA_RETURN_RESULT_IF_AND_IF(STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=,
        XmlHelper::ReadValue(xmlHandle, "TrainingOutputFileRegion", m_trainingOutputFileRegion));

    PANDORA_RETURN_RESULT_IF_AND_IF(STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=,
        XmlHelper::ReadValue(xmlHandle, "TrainingOutputFileVertex", m_trainingOutputFileVertex));

    if (m_trainingSetMode && (m_trainingOutputFileRegion.empty() || m_trainingOutputFileVertex.empty()))
    {
        std::cout << "TrainedVertexSelectionAlgorithm: TrainingOutputFileRegion and TrainingOutputFileVertex are required for training set "
                  << "mode" << std::endl;
        return STATUS_CODE_INVALID_PARAMETER;
    }

    PANDORA_RETURN_RESULT_IF_AND_IF(
        STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "MCParticleListName", m_mcParticleListName));

    PANDORA_RETURN_RESULT_IF_AND_IF(STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "CaloHitListName", m_caloHitListName));

    if (m_trainingSetMode && (m_mcParticleListName.empty() || m_caloHitListName.empty()))
    {
        std::cout << "TrainedVertexSelectionAlgorithm: MCParticleListName and CaloHitListName are required for training set mode" << std::endl;
        return STATUS_CODE_INVALID_PARAMETER;
    }

    PANDORA_RETURN_RESULT_IF(STATUS_CODE_SUCCESS, !=, XmlHelper::ReadVectorOfValues(xmlHandle, "InputClusterListNames", m_inputClusterListNames));

    PANDORA_RETURN_RESULT_IF_AND_IF(
        STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "MinClusterCaloHits", m_minClusterCaloHits));

    PANDORA_RETURN_RESULT_IF_AND_IF(
        STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "SlidingFitWindow", m_slidingFitWindow));

    PANDORA_RETURN_RESULT_IF_AND_IF(
        STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "MinShowerSpineLength", m_minShowerSpineLength));

    PANDORA_RETURN_RESULT_IF_AND_IF(STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=,
        XmlHelper::ReadValue(xmlHandle, "BeamDeweightingConstant", m_beamDeweightingConstant));

    PANDORA_RETURN_RESULT_IF_AND_IF(
        STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "LocalAsymmetryConstant", m_localAsymmetryConstant));

    PANDORA_RETURN_RESULT_IF_AND_IF(STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=,
        XmlHelper::ReadValue(xmlHandle, "GlobalAsymmetryConstant", m_globalAsymmetryConstant));

    PANDORA_RETURN_RESULT_IF_AND_IF(STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=,
        XmlHelper::ReadValue(xmlHandle, "ShowerAsymmetryConstant", m_showerAsymmetryConstant));

    PANDORA_RETURN_RESULT_IF_AND_IF(
        STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "EnergyKickConstant", m_energyKickConstant));

    PANDORA_RETURN_RESULT_IF_AND_IF(STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=,
        XmlHelper::ReadValue(xmlHandle, "ShowerClusteringDistance", m_showerClusteringDistance));

    PANDORA_RETURN_RESULT_IF_AND_IF(
        STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "MinShowerClusterHits", m_minShowerClusterHits));

    PANDORA_RETURN_RESULT_IF_AND_IF(STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=,
        XmlHelper::ReadValue(xmlHandle, "UseShowerClusteringApproximation", m_useShowerClusteringApproximation));

    PANDORA_RETURN_RESULT_IF_AND_IF(STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "RegionRadius", m_regionRadius));

    PANDORA_RETURN_RESULT_IF_AND_IF(
        STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "RPhiFineTuningRadius", m_rPhiFineTuningRadius));

    PANDORA_RETURN_RESULT_IF_AND_IF(
        STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "MaxTrueVertexRadius", m_maxTrueVertexRadius));

    PANDORA_RETURN_RESULT_IF_AND_IF(STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=,
        XmlHelper::ReadValue(xmlHandle, "UseRPhiFeatureForRegion", m_useRPhiFeatureForRegion));

    PANDORA_RETURN_RESULT_IF_AND_IF(STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=,
        XmlHelper::ReadValue(xmlHandle, "DropFailedRPhiFastScoreCandidates", m_dropFailedRPhiFastScoreCandidates));

    PANDORA_RETURN_RESULT_IF_AND_IF(STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "TestBeamMode", m_testBeamMode));

    PANDORA_RETURN_RESULT_IF_AND_IF(
        STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "LegacyEventShapes", m_legacyEventShapes));

    PANDORA_RETURN_RESULT_IF_AND_IF(STATUS_CODE_SUCCESS, STATUS_CODE_NOT_FOUND, !=, XmlHelper::ReadValue(xmlHandle, "LegacyVariables", m_legacyVariables));

    if (m_trainingSetMode && m_legacyEventShapes)
        std::cout << "TrainedVertexSelectionAlgorithm: WARNING -- Producing training sample using incorrect legacy event shapes, consider turning LegacyEventShapes off"
                  << std::endl;

    return VertexSelectionBaseAlgorithm::ReadSettings(xmlHandle);
}

} // namespace lar_content