ISF::PunchThroughClassifier Class Reference

#include <PunchThroughClassifier.h>

Inheritance diagram for ISF::PunchThroughClassifier:

Collaboration diagram for ISF::PunchThroughClassifier:

Public Member Functions
	PunchThroughClassifier (const std::string &, const std::string &, const IInterface *)
	Constructor.
virtual	~PunchThroughClassifier ()=default
	Destructor.
virtual StatusCode	initialize () override final
	AlgTool initialize method.
StatusCode	initializeScaler (const std::string &scalerConfigFile)
	input variable MinMaxScaler initialize method
StatusCode	initializeNetwork (const std::string &networkConfigFile)
	neural network initialize method
StatusCode	initializeCalibrator (const std::string &calibratorConfigFile)
	isotonic regressor calibrator initialize method
virtual double	computePunchThroughProbability (const ISF::ISFParticle &isfp, const TFCSSimulationState &simulstate) const override
	interface method to return probability prediction of punch through
std::map< std::string, std::map< std::string, double > >	scaleInputs (std::map< std::string, std::map< std::string, double > > &inputs) const
	scale NN inputs using MinMaxScaler
double	calibrateOutput (double &networkOutput) const
	calibrate NN output using isotonic regressor

Static Public Member Functions
static std::map< std::string, std::map< std::string, double > >	computeInputs (const ISF::ISFParticle &isfp, const TFCSSimulationState &simulstate)
	calcalate NN inputs based on isfp and simulstate

Private Attributes
std::unique_ptr< lwt::LightweightGraph >	m_graph {}
	NN graph.
double	m_scalerMin {}
	input variable MinMaxScaler members
double	m_scalerMax {}
std::map< std::string, double >	m_scalerMinMap
std::map< std::string, double >	m_scalerMaxMap
std::string	m_calibratorConfigFile
	isotonic regressor calibrator members
double	m_calibrationMin {}
double	m_calibrationMax {}
std::map< double, double >	m_calibrationMap
std::string	m_networkConfigFileName
std::string	m_scalerConfigFileName
std::string	m_calibratorConfigFileName

Detailed Description

Definition at line 26 of file PunchThroughClassifier.h.

Constructor & Destructor Documentation

PunchThroughClassifier()

ISF::PunchThroughClassifier::PunchThroughClassifier	(	const std::string &	type,
		const std::string &	name,
		const IInterface *	parent )

Constructor.

Definition at line 28 of file PunchThroughClassifier.cxx.

    : base_class(type, name, parent) {
 
    declareProperty( "ScalerConfigFileName",          m_scalerConfigFileName   );
    declareProperty( "NetworkConfigFileName",          m_networkConfigFileName   );
    declareProperty( "CalibratorConfigFileName",          m_calibratorConfigFileName   );
}

~PunchThroughClassifier()

virtual ISF::PunchThroughClassifier::~PunchThroughClassifier ( )

virtualdefault

Destructor.

Member Function Documentation

calibrateOutput()

double ISF::PunchThroughClassifier::calibrateOutput

(

double &

networkOutput

)

const

calibrate NN output using isotonic regressor

Definition at line 202 of file PunchThroughClassifier.cxx.

                                                                             {
 
    //calibrate output of network using isotonic regressor model
 
    //if network output is outside of the range of isotonic regressor then return min and max values
    if (networkOutput < m_calibrationMin){
        return m_calibrationMin;
    }
    else if (networkOutput > m_calibrationMax){
        return m_calibrationMax;
    }
 
    //otherwise find neighbouring points in isotonic regressor
    auto upper = m_calibrationMap.upper_bound(networkOutput);
    auto lower = upper--;
 
    //Perform linear interpolation between points
    double m = (upper->second - lower->second)/(upper->first - lower->first);
    double c = lower->second - m * lower->first;
    double calibrated = m * networkOutput + c;
 
    return calibrated;
}

computeInputs()

std::map< std::string, std::map< std::string, double > > ISF::PunchThroughClassifier::computeInputs ( const ISF::ISFParticle & isfp, const TFCSSimulationState & simulstate )

static

calcalate NN inputs based on isfp and simulstate

Definition at line 161 of file PunchThroughClassifier.cxx.

                                                                                                                                                        {
 
    //calculate inputs for NN
 
    std::map<std::string, std::map<std::string, double> > networkInputs;
 
    //add initial particle and total energy variables
    networkInputs["node_0"] = {
        {"variable_0", isfp.momentum().mag() },
        {"variable_1", std::abs(isfp.position().eta()) },
        {"variable_2", isfp.position().phi() },
        {"variable_3", simulstate.E()},
    };
 
    //add energy fraction variables
    for (unsigned int i = 0; i < 24; i++){
        networkInputs["node_0"].insert({"variable_" + std::to_string(i + 4), simulstate.Efrac(i)});
    }
 
    return networkInputs;
}

computePunchThroughProbability()

double ISF::PunchThroughClassifier::computePunchThroughProbability ( const ISF::ISFParticle & isfp, const TFCSSimulationState & simulstate ) const

overridevirtual

interface method to return probability prediction of punch through

Definition at line 36 of file PunchThroughClassifier.cxx.

                                                                                                                                        {
 
    std::map<std::string, std::map<std::string, double> > networkInputs = computeInputs(isfp, simulstate); //compute inputs
 
    networkInputs = scaleInputs(networkInputs); //scale inputs
 
    std::map<std::string, double> networkOutputs = m_graph->compute(networkInputs); //call neural network on inputs
 
    double calibratedOutput = calibrateOutput(networkOutputs["out_0"]); //calibrate neural network output
 
    return calibratedOutput;
}

initialize()

StatusCode ISF::PunchThroughClassifier::initialize ( )

finaloverridevirtual

AlgTool initialize method.

Definition at line 50 of file PunchThroughClassifier.cxx.

                                                {
 
    ATH_MSG_VERBOSE( "[ punchthroughclassifier ] initialize()" );
 
    std::string resolvedScalerFileName = PathResolverFindCalibFile (m_scalerConfigFileName);
    if ( initializeScaler(resolvedScalerFileName) != StatusCode::SUCCESS)
    {
        ATH_MSG_ERROR("[ punchthroughclassifier ] unable to load punchthroughclassifier input scaler");
    }
 
    std::string resolvedNetworkFileName = PathResolverFindCalibFile (m_networkConfigFileName);
    if ( initializeNetwork(resolvedNetworkFileName) != StatusCode::SUCCESS)
    {
        ATH_MSG_ERROR("[ punchthroughclassifier ] unable to load punchthroughclassifier network");
    }
 
    std::string resolvedCalibratorFileName = PathResolverFindCalibFile (m_calibratorConfigFileName);
    if ( initializeCalibrator(resolvedCalibratorFileName) != StatusCode::SUCCESS)
    {
        ATH_MSG_ERROR("[ punchthroughclassifier ] unable to load punchthroughclassifier calibrator");
    }
 
    return StatusCode::SUCCESS;
}

initializeCalibrator()

StatusCode ISF::PunchThroughClassifier::initializeCalibrator

(

const std::string &

calibratorConfigFile

)

isotonic regressor calibrator initialize method

Definition at line 130 of file PunchThroughClassifier.cxx.

                                                                                                {
 
    //parse xml that contains config for isotonic regressor used to calibrate the network output
    ATH_MSG_INFO( "[ punchthroughclassifier ] Loading calibrator: " << calibratorConfigFile);
 
    xmlDocPtr doc = xmlParseFile( calibratorConfigFile.c_str() );
 
    for( xmlNodePtr nodeRoot = doc->children; nodeRoot != nullptr; nodeRoot = nodeRoot->next) {
 
        if (xmlStrEqual( nodeRoot->name, BAD_CAST "Transformations" )) {
            for( xmlNodePtr nodeTransform = nodeRoot->children; nodeTransform != nullptr; nodeTransform = nodeTransform->next ) {
 
                //get lower and upper bounds of isotonic regressor
                if (xmlStrEqual( nodeTransform->name, BAD_CAST "LimitValues" )) {
                    m_calibrationMin = GetXmlAttr<double>( nodeTransform, "min" );
                    m_calibrationMax = GetXmlAttr<double>( nodeTransform, "max" );
                }
 
                //get defined points where isotonic regressor knows transform
                if (xmlStrEqual( nodeTransform->name, BAD_CAST "LinearNorm" )) {
                    double orig = GetXmlAttr<double>( nodeTransform, "orig" );
                    double norm = GetXmlAttr<double>( nodeTransform, "norm" );
                    m_calibrationMap.emplace ( orig, norm );
                }
            }
        }
    }
 
    return StatusCode::SUCCESS;
}

initializeNetwork()

StatusCode ISF::PunchThroughClassifier::initializeNetwork

(

const std::string &

networkConfigFile

)

neural network initialize method

Definition at line 109 of file PunchThroughClassifier.cxx.

                                                                                          {
 
    ATH_MSG_INFO( "[ punchthroughclassifier ] Loading classifier: " << networkConfigFile);
 
    std::ifstream input(networkConfigFile);
    if(!input){
        ATH_MSG_ERROR("Could not find json file " << networkConfigFile );
        return StatusCode::FAILURE;
    }
 
    m_graph  = std::make_unique<lwt::LightweightGraph>(lwt::parse_json_graph(input));
    if(!m_graph){
        ATH_MSG_ERROR("Could not parse graph json file " << networkConfigFile );
        return StatusCode::FAILURE;
    }
 
 
    return StatusCode::SUCCESS;
}

initializeScaler()

StatusCode ISF::PunchThroughClassifier::initializeScaler

(

const std::string &

scalerConfigFile

)

input variable MinMaxScaler initialize method

Definition at line 75 of file PunchThroughClassifier.cxx.

                                                                                        {
 
    //parse xml that contains config for MinMaxScaler for each of the network inputs
 
    xmlDocPtr doc = xmlParseFile( scalerConfigFile.c_str() );
 
    ATH_MSG_INFO( "[ punchthroughclassifier ] Loading scaler: " << scalerConfigFile);
 
    for( xmlNodePtr nodeRoot = doc->children; nodeRoot != nullptr; nodeRoot = nodeRoot->next) {
 
        if (xmlStrEqual( nodeRoot->name, BAD_CAST "Transformations" )) {
            for( xmlNodePtr nodeTransform = nodeRoot->children; nodeTransform != nullptr; nodeTransform = nodeTransform->next ) {
 
                //Get min and max values that we normalise values to
                if (xmlStrEqual( nodeTransform->name, BAD_CAST "ScalerValues" )) {
                    m_scalerMin = GetXmlAttr<double>( nodeTransform, "min" );
                    m_scalerMax = GetXmlAttr<double>( nodeTransform, "max" );
                }
 
                //Get values necessary to normalise each input variable
                if (xmlStrEqual( nodeTransform->name, BAD_CAST "VarScales" )) {
                    std::string name = GetXmlAttr<std::string>( nodeTransform, "name" );
                    double min = GetXmlAttr<double>( nodeTransform, "min" );
                    double max = GetXmlAttr<double>( nodeTransform, "max" );
                    m_scalerMinMap.emplace ( name, min );
                    m_scalerMaxMap.emplace ( name, max );
                }
            }
        }
    }
 
    return StatusCode::SUCCESS;
}

scaleInputs()

std::map< std::string, std::map< std::string, double > > ISF::PunchThroughClassifier::scaleInputs

(

std::map< std::string, std::map< std::string, double > > &

inputs

)

const

scale NN inputs using MinMaxScaler

Definition at line 183 of file PunchThroughClassifier.cxx.

                                                                                                                                               {
 
    //apply MinMaxScaler to network inputs
 
    for (auto& var : inputs["node_0"]) {
 
        double x_std;
        if(m_scalerMaxMap.at(var.first) != m_scalerMinMap.at(var.first)){
            x_std = (var.second - m_scalerMinMap.at(var.first)) / (m_scalerMaxMap.at(var.first) - m_scalerMinMap.at(var.first));
        }
        else{
            x_std = (var.second - m_scalerMinMap.at(var.first));
        }
        var.second = x_std * (m_scalerMax - m_scalerMin) + m_scalerMin;
    }
 
    return inputs;
}

Member Data Documentation

m_calibrationMap

std::map<double, double> ISF::PunchThroughClassifier::m_calibrationMap

private

Definition at line 73 of file PunchThroughClassifier.h.

m_calibrationMax

double ISF::PunchThroughClassifier::m_calibrationMax {}

private

Definition at line 72 of file PunchThroughClassifier.h.

{};

m_calibrationMin

double ISF::PunchThroughClassifier::m_calibrationMin {}

private

Definition at line 71 of file PunchThroughClassifier.h.

{};

m_calibratorConfigFile

std::string ISF::PunchThroughClassifier::m_calibratorConfigFile

private

isotonic regressor calibrator members

Definition at line 70 of file PunchThroughClassifier.h.

m_calibratorConfigFileName

std::string ISF::PunchThroughClassifier::m_calibratorConfigFileName

private

Definition at line 78 of file PunchThroughClassifier.h.

m_graph

std::unique_ptr<lwt::LightweightGraph> ISF::PunchThroughClassifier::m_graph {}

private

NN graph.

Definition at line 61 of file PunchThroughClassifier.h.

{};

m_networkConfigFileName

std::string ISF::PunchThroughClassifier::m_networkConfigFileName

private

Definition at line 76 of file PunchThroughClassifier.h.

m_scalerConfigFileName

std::string ISF::PunchThroughClassifier::m_scalerConfigFileName

private

Definition at line 77 of file PunchThroughClassifier.h.

m_scalerMax

double ISF::PunchThroughClassifier::m_scalerMax {}

private

Definition at line 65 of file PunchThroughClassifier.h.

{};

m_scalerMaxMap

std::map<std::string, double> ISF::PunchThroughClassifier::m_scalerMaxMap

private

Definition at line 67 of file PunchThroughClassifier.h.

m_scalerMin

double ISF::PunchThroughClassifier::m_scalerMin {}

private

input variable MinMaxScaler members

Definition at line 64 of file PunchThroughClassifier.h.

{};

m_scalerMinMap

std::map<std::string, double> ISF::PunchThroughClassifier::m_scalerMinMap

private

Definition at line 66 of file PunchThroughClassifier.h.

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The documentation for this class was generated from the following files:

• PunchThroughClassifier.h
• PunchThroughClassifier.cxx