LArMvaHelper.h

Go to the documentation of this file.

 
#ifndef LAR_MVA_HELPER_H
#define LAR_MVA_HELPER_H 1
 
#include "larpandoracontent/LArObjects/LArMvaInterface.h"
 
#include "Api/PandoraContentApi.h"
 
#include "Helpers/XmlHelper.h"
 
#include "Pandora/Algorithm.h"
#include "Pandora/AlgorithmTool.h"
#include "Pandora/PandoraInternal.h"
#include "Pandora/StatusCodes.h"
 
#include <chrono>
#include <ctime>
#include <fstream>
 
namespace lar_content
{
 
template <typename... Ts>
class MvaFeatureTool : public pandora::AlgorithmTool
{
public:
    typedef std::vector<MvaFeatureTool<Ts...> *> FeatureToolVector;
    typedef std::map<std::string, MvaFeatureTool<Ts...> *> FeatureToolMap;
 
    MvaFeatureTool() = default;
 
    virtual void Run(MvaTypes::MvaFeatureVector &featureVector, Ts... args) = 0;
    virtual void Run(MvaTypes::MvaFeatureMap &featureMap, pandora::StringVector &featureOrder, const std::string &featureToolName, Ts...)
    {
        (void)featureMap;
        (void)featureOrder;
        (void)featureToolName;
        return;
    };
};
 
template <typename... Ts>
using MvaFeatureToolVector = std::vector<MvaFeatureTool<Ts...> *>;
 
template <typename... Ts>
using MvaFeatureToolMap = std::map<std::string, MvaFeatureTool<Ts...> *>;
 
//------------------------------------------------------------------------------------------------------------------------------------------
 
class LArMvaHelper
{
public:
    typedef MvaTypes::MvaFeature MvaFeature;
    typedef MvaTypes::MvaFeatureVector MvaFeatureVector;
    typedef std::map<std::string, double> DoubleMap;
 
    typedef MvaTypes::MvaFeatureMap MvaFeatureMap;
    typedef std::map<std::string, pandora::AlgorithmTool *> AlgorithmToolMap; // idea would be to put this in PandoraInternal.h at some point in PandoraSDK
 
    template <typename TCONTAINER>
    static pandora::StatusCode ProduceTrainingExample(const std::string &trainingOutputFile, const bool result, TCONTAINER &&featureContainer);
 
    template <typename TCONTAINER>
    static pandora::StatusCode ProduceTrainingExample(
        const std::string &trainingOutputFile, const bool result, const pandora::StringVector &featureOrder, TCONTAINER &&featureContainer);
 
    template <typename TCONTAINER>
    static bool Classify(const MvaInterface &classifier, TCONTAINER &&featureContainer);
 
    template <typename TCONTAINER>
    static bool Classify(const MvaInterface &classifier, const pandora::StringVector &featureOrder, TCONTAINER &&featureContainer);
 
    template <typename TCONTAINER>
    static double CalculateClassificationScore(const MvaInterface &classifier, TCONTAINER &&featureContainer);
 
    template <typename TCONTAINER>
    static double CalculateProbability(const MvaInterface &classifier, TCONTAINER &&featureContainer);
 
    template <typename TCONTAINER>
    static double CalculateProbability(const MvaInterface &classifier, const pandora::StringVector &featureOrder, TCONTAINER &&featureContainer);
 
    template <typename... Ts, typename... TARGS>
    static MvaFeatureVector CalculateFeatures(const MvaFeatureToolVector<Ts...> &featureToolVector, TARGS &&... args);
 
    template <typename... Ts, typename... TARGS>
    static MvaFeatureMap CalculateFeatures(const pandora::StringVector &featureToolOrder, const MvaFeatureToolMap<Ts...> &featureToolMap,
        pandora::StringVector &featureOrder, TARGS &&... args);
 
    template <typename T, typename... Ts, typename... TARGS>
    static MvaFeatureVector CalculateFeaturesOfType(const MvaFeatureToolVector<Ts...> &featureToolVector, TARGS &&... args);
 
    template <typename... Ts>
    static pandora::StatusCode AddFeatureToolToVector(pandora::AlgorithmTool *const pFeatureTool, MvaFeatureToolVector<Ts...> &featureToolVector);
 
    template <typename... Ts>
    static pandora::StatusCode AddFeatureToolToMap(
        pandora::AlgorithmTool *const pFeatureTool, std::string pFeatureToolName, MvaFeatureToolMap<Ts...> &featureToolMap);
 
    static pandora::StatusCode ProcessAlgorithmToolListToMap(const pandora::Algorithm &algorithm, const pandora::TiXmlHandle &xmlHandle,
        const std::string &listName, pandora::StringVector &algorithToolNameVector, AlgorithmToolMap &algorithmToolMap);
 
    template <typename TLIST, typename... TLISTS>
    static MvaFeatureVector ConcatenateFeatureLists(TLIST &&featureList, TLISTS &&... featureLists);
 
    static MvaFeatureVector ConcatenateFeatureLists();
 
private:
    static std::string GetTimestampString();
 
    template <typename TCONTAINER>
    static pandora::StatusCode WriteFeaturesToFile(std::ofstream &outfile, const std::string &delimiter, TCONTAINER &&featureContainer);
 
    template <typename TCONTAINER>
    static pandora::StatusCode WriteFeaturesToFileImpl(std::ofstream &outfile, const std::string &delimiter, TCONTAINER &&featureContainer);
};
 
//------------------------------------------------------------------------------------------------------------------------------------------
 
template <typename TCONTAINER>
pandora::StatusCode LArMvaHelper::ProduceTrainingExample(const std::string &trainingOutputFile, const bool result, TCONTAINER &&featureContainer)
{
    std::ofstream outfile;
    outfile.open(trainingOutputFile, std::ios_base::app); // always append to the output file
 
    if (!outfile.is_open())
    {
        std::cout << "LArMvaHelper: could not open file for training examples at " << trainingOutputFile << std::endl;
        return pandora::STATUS_CODE_FAILURE;
    }
 
    std::string delimiter(",");
    outfile << GetTimestampString() << delimiter;
 
    PANDORA_RETURN_RESULT_IF(pandora::STATUS_CODE_SUCCESS, !=, WriteFeaturesToFile(outfile, delimiter, featureContainer));
    outfile << static_cast<int>(result) << '\n';
 
    return pandora::STATUS_CODE_SUCCESS;
}
 
//------------------------------------------------------------------------------------------------------------------------------------------
 
template <typename TCONTAINER>
pandora::StatusCode LArMvaHelper::ProduceTrainingExample(
    const std::string &trainingOutputFile, const bool result, const pandora::StringVector &featureOrder, TCONTAINER &&featureContainer)
{
    // Make a feature vector from the map and calculate the features
    LArMvaHelper::MvaFeatureVector featureVector;
 
    for (auto const &pFeatureToolName : featureOrder)
    {
        if (featureContainer.find(pFeatureToolName) == featureContainer.end())
        {
            std::cout << "LArMvaHelper::ProduceTrainingExample "
                      << "- Error: feature tool " << pFeatureToolName << " not found." << std::endl;
            throw pandora::StatusCodeException(pandora::STATUS_CODE_NOT_FOUND);
        }
        featureVector.push_back(featureContainer.at(pFeatureToolName));
    }
 
    return ProduceTrainingExample(trainingOutputFile, result, featureVector);
}
 
//------------------------------------------------------------------------------------------------------------------------------------------
 
template <typename TCONTAINER>
bool LArMvaHelper::Classify(const MvaInterface &classifier, TCONTAINER &&featureContainer)
{
    return classifier.Classify(featureContainer);
}
 
//------------------------------------------------------------------------------------------------------------------------------------------
 
template <typename TCONTAINER>
bool LArMvaHelper::Classify(const MvaInterface &classifier, const pandora::StringVector &featureOrder, TCONTAINER &&featureContainer)
{
    // Make a feature vector from the map and calculate the features
    LArMvaHelper::MvaFeatureVector featureVector;
 
    for (auto const &pFeatureToolName : featureOrder)
    {
        if (featureContainer.find(pFeatureToolName) == featureContainer.end())
        {
            std::cout << "LArMvaHelper::Classify "
                      << "- Error: feature tool " << pFeatureToolName << " not found." << std::endl;
            throw pandora::StatusCodeException(pandora::STATUS_CODE_NOT_FOUND);
        }
        featureVector.push_back(featureContainer.at(pFeatureToolName));
    }
 
    return Classify(classifier, featureVector);
}
 
//------------------------------------------------------------------------------------------------------------------------------------------
 
template <typename TCONTAINER>
double LArMvaHelper::CalculateClassificationScore(const MvaInterface &classifier, TCONTAINER &&featureContainer)
{
    return classifier.CalculateClassificationScore(featureContainer);
}
 
//------------------------------------------------------------------------------------------------------------------------------------------
 
template <typename TCONTAINER>
double LArMvaHelper::CalculateProbability(const MvaInterface &classifier, TCONTAINER &&featureContainer)
{
    return classifier.CalculateProbability(featureContainer);
}
 
//------------------------------------------------------------------------------------------------------------------------------------------
 
template <typename TCONTAINER>
double LArMvaHelper::CalculateProbability(const MvaInterface &classifier, const pandora::StringVector &featureOrder, TCONTAINER &&featureContainer)
{
    // Make a feature vector from the map and calculate the features
    LArMvaHelper::MvaFeatureVector featureVector;
 
    for (auto const &pFeatureToolName : featureOrder)
    {
        if (featureContainer.find(pFeatureToolName) == featureContainer.end())
        {
            std::cout << "LArMvaHelper::CalculateProbability "
                      << "- Error: feature tool " << pFeatureToolName << " not found." << std::endl;
            throw pandora::StatusCodeException(pandora::STATUS_CODE_NOT_FOUND);
        }
        featureVector.push_back(featureContainer.at(pFeatureToolName));
    }
 
    return CalculateProbability(classifier, featureVector);
}
 
//------------------------------------------------------------------------------------------------------------------------------------------
 
template <typename... Ts, typename... TARGS>
LArMvaHelper::MvaFeatureVector LArMvaHelper::CalculateFeatures(const MvaFeatureToolVector<Ts...> &featureToolVector, TARGS &&... args)
{
    LArMvaHelper::MvaFeatureVector featureVector;
 
    for (MvaFeatureTool<Ts...> *const pFeatureTool : featureToolVector)
        pFeatureTool->Run(featureVector, std::forward<TARGS>(args)...);
 
    return featureVector;
}
 
//------------------------------------------------------------------------------------------------------------------------------------------
 
template <typename... Ts, typename... TARGS>
LArMvaHelper::MvaFeatureMap LArMvaHelper::CalculateFeatures(const pandora::StringVector &featureToolOrder,
    const MvaFeatureToolMap<Ts...> &featureToolMap, pandora::StringVector &featureOrder, TARGS &&... args)
{
    LArMvaHelper::MvaFeatureMap featureMap;
 
    for (auto const &pFeatureToolName : featureToolOrder)
    {
        if (featureToolMap.find(pFeatureToolName) == featureToolMap.end())
        {
            std::cout << "LArMvaHelper::CalculateFeatures "
                      << "- Error: feature tool " << pFeatureToolName << " not found." << std::endl;
            throw pandora::StatusCodeException(pandora::STATUS_CODE_NOT_FOUND);
        }
        featureToolMap.at(pFeatureToolName)->Run(featureMap, featureOrder, pFeatureToolName, std::forward<TARGS>(args)...);
    }
 
    return featureMap;
}
 
//------------------------------------------------------------------------------------------------------------------------------------------
 
template <typename T, typename... Ts, typename... TARGS>
LArMvaHelper::MvaFeatureVector LArMvaHelper::CalculateFeaturesOfType(const MvaFeatureToolVector<Ts...> &featureToolVector, TARGS &&... args)
{
    using TD = typename std::decay<T>::type;
    LArMvaHelper::MvaFeatureVector featureVector;
 
    for (MvaFeatureTool<Ts...> *const pFeatureTool : featureToolVector)
    {
        if (TD *const pCastFeatureTool = dynamic_cast<TD *const>(pFeatureTool))
            pCastFeatureTool->Run(featureVector, std::forward<TARGS>(args)...);
    }
 
    return featureVector;
}
 
//------------------------------------------------------------------------------------------------------------------------------------------
 
template <typename... Ts>
pandora::StatusCode LArMvaHelper::AddFeatureToolToVector(pandora::AlgorithmTool *const pFeatureTool, MvaFeatureToolVector<Ts...> &featureToolVector)
{
    if (MvaFeatureTool<Ts...> *const pCastFeatureTool = dynamic_cast<MvaFeatureTool<Ts...> *const>(pFeatureTool))
    {
        featureToolVector.push_back(pCastFeatureTool);
        return pandora::STATUS_CODE_SUCCESS;
    }
 
    return pandora::STATUS_CODE_FAILURE;
}
 
//------------------------------------------------------------------------------------------------------------------------------------------
 
template <typename... Ts>
pandora::StatusCode LArMvaHelper::AddFeatureToolToMap(
    pandora::AlgorithmTool *const pFeatureTool, std::string pFeatureToolName, MvaFeatureToolMap<Ts...> &featureToolMap)
{
    if (MvaFeatureTool<Ts...> *const pCastFeatureTool = dynamic_cast<MvaFeatureTool<Ts...> *const>(pFeatureTool))
    {
        featureToolMap[pFeatureToolName] = pCastFeatureTool;
        return pandora::STATUS_CODE_SUCCESS;
    }
 
    return pandora::STATUS_CODE_FAILURE;
}
 
//------------------------------------------------------------------------------------------------------------------------------------------
 
inline std::string LArMvaHelper::GetTimestampString()
{
    std::time_t timestampNow = std::chrono::system_clock::to_time_t(std::chrono::system_clock::now());
 
    struct tm *pTimeInfo(NULL);
    char buffer[80];
 
    pTimeInfo = localtime(&timestampNow);
    strftime(buffer, 80, "%x_%X", pTimeInfo);
 
    std::string timeString(buffer);
 
    if (!timeString.empty() && timeString.back() == '\n') // last char is always a newline
        timeString.pop_back();
 
    return timeString;
}
 
//------------------------------------------------------------------------------------------------------------------------------------------
 
template <typename TCONTAINER>
inline pandora::StatusCode LArMvaHelper::WriteFeaturesToFile(std::ofstream &outfile, const std::string &delimiter, TCONTAINER &&featureContainer)
{
    static_assert(std::is_same<typename std::decay<TCONTAINER>::type, LArMvaHelper::MvaFeatureVector>::value,
        "LArMvaHelper: Could not write training set example because a passed parameter was not a vector of MvaFeatures");
 
    PANDORA_RETURN_RESULT_IF(pandora::STATUS_CODE_SUCCESS, !=, WriteFeaturesToFileImpl(outfile, delimiter, featureContainer));
    return pandora::STATUS_CODE_SUCCESS;
}
 
//------------------------------------------------------------------------------------------------------------------------------------------
 
template <typename TCONTAINER>
pandora::StatusCode LArMvaHelper::WriteFeaturesToFileImpl(std::ofstream &outfile, const std::string &delimiter, TCONTAINER &&featureContainer)
{
    for (const MvaFeature &feature : featureContainer)
        outfile << feature.Get() << delimiter;
 
    return pandora::STATUS_CODE_SUCCESS;
}
 
//------------------------------------------------------------------------------------------------------------------------------------------
 
template <typename TLIST, typename... TLISTS>
LArMvaHelper::MvaFeatureVector LArMvaHelper::ConcatenateFeatureLists(TLIST &&featureList, TLISTS &&... featureLists)
{
    static_assert(std::is_same<typename std::decay<TLIST>::type, LArMvaHelper::MvaFeatureVector>::value,
        "LArMvaHelper: Could not concatenate feature lists because one or more lists was not a vector of MvaFeatures");
 
    LArMvaHelper::MvaFeatureVector featureVector;
 
    for (const MvaFeature &feature : featureList)
        featureVector.push_back(feature);
 
    LArMvaHelper::MvaFeatureVector newFeatureVector = ConcatenateFeatureLists(std::forward<TLISTS>(featureLists)...);
    featureVector.insert(featureVector.end(), newFeatureVector.begin(), newFeatureVector.end());
 
    return featureVector;
}
 
//------------------------------------------------------------------------------------------------------------------------------------------
 
inline LArMvaHelper::MvaFeatureVector LArMvaHelper::ConcatenateFeatureLists()
{
    return LArMvaHelper::MvaFeatureVector();
}
 
} // namespace lar_content
 
#endif // #ifndef LAR_MVA_HELPER_H