lar_content::AdaBoostDecisionTree Class Reference

AdaBoostDecisionTree class. More...

#include <LArAdaBoostDecisionTree.h>

Inheritance diagram for lar_content::AdaBoostDecisionTree:

Classes
class	Node
	Node class used for representing a decision tree. More...
class	StrongClassifier
	StrongClassifier class used in application of adaptive boost decision tree. More...
class	WeakClassifier
	WeakClassifier class containing a decision tree and a weight. More...

Public Member Functions
	AdaBoostDecisionTree ()
	Constructor. More...
	AdaBoostDecisionTree (const AdaBoostDecisionTree &rhs)
	Copy constructor. More...
AdaBoostDecisionTree &	operator= (const AdaBoostDecisionTree &rhs)
	Assignment operator. More...
	~AdaBoostDecisionTree ()
	Destructor. More...
pandora::StatusCode	Initialize (const std::string &parameterLocation, const std::string &bdtName)
	Initialize the bdt model. More...
bool	Classify (const LArMvaHelper::MvaFeatureVector &features) const
	Classify the set of input features based on the trained model. More...
double	CalculateClassificationScore (const LArMvaHelper::MvaFeatureVector &features) const
	Calculate the classification score for a set of input features, based on the trained model. More...
double	CalculateProbability (const LArMvaHelper::MvaFeatureVector &features) const
	Calculate the classification probability for a set of input features, based on the trained model. More...
Public Member Functions inherited from lar_content::MvaInterface
virtual	~MvaInterface ()=default
	Destructor. More...

Private Types
typedef std::map< int, const Node * >	IdToNodeMap
typedef std::vector< const WeakClassifier * >	WeakClassifiers

Private Member Functions
double	CalculateScore (const LArMvaHelper::MvaFeatureVector &features) const
	Calculate score for input features using strong classifier. More...

Private Attributes
StrongClassifier *	m_pStrongClassifier
	Strong adaptive boost tree classifier. More...

Detailed Description

AdaBoostDecisionTree class.

Definition at line 27 of file LArAdaBoostDecisionTree.h.

Member Typedef Documentation

typedef std::map<int, const Node *> lar_content::AdaBoostDecisionTree::IdToNodeMap

private

Definition at line 191 of file LArAdaBoostDecisionTree.h.

typedef std::vector<const WeakClassifier *> lar_content::AdaBoostDecisionTree::WeakClassifiers

private

Definition at line 264 of file LArAdaBoostDecisionTree.h.

Constructor & Destructor Documentation

lar_content::AdaBoostDecisionTree::AdaBoostDecisionTree

(

)

Constructor.

Definition at line 18 of file LArAdaBoostDecisionTree.cc.

                                           : m_pStrongClassifier(nullptr)
{
}

lar_content::AdaBoostDecisionTree::AdaBoostDecisionTree

(

const AdaBoostDecisionTree &

rhs

)

Copy constructor.

Parameters

rhs	the AdaBoostDecisionTree to copy

Definition at line 24 of file LArAdaBoostDecisionTree.cc.

{
    m_pStrongClassifier = new StrongClassifier(*(rhs.m_pStrongClassifier));
}

lar_content::AdaBoostDecisionTree::~AdaBoostDecisionTree

(

)

Destructor.

Definition at line 41 of file LArAdaBoostDecisionTree.cc.

{
    delete m_pStrongClassifier;
}

Member Function Documentation

double lar_content::AdaBoostDecisionTree::CalculateClassificationScore ( const LArMvaHelper::MvaFeatureVector &  features ) const

virtual

Calculate the classification score for a set of input features, based on the trained model.

Parameters

features

the input features

Returns

the classification score

Implements lar_content::MvaInterface.

Definition at line 126 of file LArAdaBoostDecisionTree.cc.

{
    return this->CalculateScore(features);
}

double lar_content::AdaBoostDecisionTree::CalculateProbability ( const LArMvaHelper::MvaFeatureVector &  features ) const

virtual

Calculate the classification probability for a set of input features, based on the trained model.

Parameters

features

the input features

Returns

the classification probability

Implements lar_content::MvaInterface.

Definition at line 133 of file LArAdaBoostDecisionTree.cc.

{
    // ATTN: BDT score, once normalised by total weight, is confined to the range -1 to +1.  This linear mapping places the score in the
    // range 0 to 1 so that it may be interpreted as a probability.
    return (this->CalculateScore(features) + 1.) * 0.5;
}

double lar_content::AdaBoostDecisionTree::CalculateScore ( const LArMvaHelper::MvaFeatureVector &  features ) const

private

Calculate score for input features using strong classifier.

Parameters

features

the input features

Returns

score

Definition at line 142 of file LArAdaBoostDecisionTree.cc.

{
    if (!m_pStrongClassifier)
    {
        std::cout << "AdaBoostDecisionTree: Attempting to use an uninitialized bdt" << std::endl;
        throw StatusCodeException(STATUS_CODE_NOT_INITIALIZED);
    }

    try
    {
        // TODO: Add consistency check for number of features, bearing in mind not all features in a bdt may be used
        return m_pStrongClassifier->Predict(features);
    }
    catch (StatusCodeException &statusCodeException)
    {
        if (STATUS_CODE_NOT_FOUND == statusCodeException.GetStatusCode())
        {
            std::cout << "AdaBoostDecisionTree: Caught exception thrown when trying to cut on an unknown variable." << std::endl;
        }
        else if (STATUS_CODE_INVALID_PARAMETER == statusCodeException.GetStatusCode())
        {
            std::cout << "AdaBoostDecisionTree: Caught exception thrown when classifier weights sum to zero indicating defunct classifier."
                      << std::endl;
        }
        else if (STATUS_CODE_OUT_OF_RANGE == statusCodeException.GetStatusCode())
        {
            std::cout << "AdaBoostDecisionTree: Caught exception thrown when heirarchy in decision tree is incomplete." << std::endl;
        }
        else
        {
            std::cout << "AdaBoostDecisionTree: Unexpected exception thrown." << std::endl;
        }

        throw statusCodeException;
    }
}

bool lar_content::AdaBoostDecisionTree::Classify ( const LArMvaHelper::MvaFeatureVector &  features ) const

virtual

Classify the set of input features based on the trained model.

Parameters

features

the input features

Returns

the classification

Implements lar_content::MvaInterface.

Definition at line 119 of file LArAdaBoostDecisionTree.cc.

{
    return ((this->CalculateScore(features) > 0.) ? true : false);
}

StatusCode lar_content::AdaBoostDecisionTree::Initialize	(	const std::string &	parameterLocation,
		const std::string &	bdtName
	)

Initialize the bdt model.

Parameters

parameterLocation	the location of the model
bdtName	the name of the model

Returns

success

Definition at line 48 of file LArAdaBoostDecisionTree.cc.

{
    if (m_pStrongClassifier)
    {
        std::cout << "AdaBoostDecisionTree: AdaBoostDecisionTree was already initialized" << std::endl;
        return STATUS_CODE_ALREADY_INITIALIZED;
    }

    TiXmlDocument xmlDocument(bdtXmlFileName);

    if (!xmlDocument.LoadFile())
    {
        std::cout << "AdaBoostDecisionTree::Initialize - Invalid xml file." << std::endl;
        return STATUS_CODE_INVALID_PARAMETER;
    }

    const TiXmlHandle xmlDocumentHandle(&xmlDocument);
    TiXmlNode *pContainerXmlNode(TiXmlHandle(xmlDocumentHandle).FirstChildElement().Element());

    while (pContainerXmlNode)
    {
        if (pContainerXmlNode->ValueStr() != "AdaBoostDecisionTree")
            return STATUS_CODE_FAILURE;

        const TiXmlHandle currentHandle(pContainerXmlNode);

        std::string currentName;
        PANDORA_THROW_RESULT_IF(STATUS_CODE_SUCCESS, !=, XmlHelper::ReadValue(currentHandle, "Name", currentName));

        if (currentName.empty() || (currentName.size() > 1000))
        {
            std::cout << "AdaBoostDecisionTree::Initialize - Implausible AdaBoostDecisionTree name extracted from xml." << std::endl;
            return STATUS_CODE_INVALID_PARAMETER;
        }

        if (currentName == bdtName)
            break;

        pContainerXmlNode = pContainerXmlNode->NextSibling();
    }

    if (!pContainerXmlNode)
    {
        std::cout << "AdaBoostDecisionTree: Could not find an AdaBoostDecisionTree of name " << bdtName << std::endl;
        return STATUS_CODE_NOT_FOUND;
    }

    const TiXmlHandle xmlHandle(pContainerXmlNode);

    try
    {
        m_pStrongClassifier = new StrongClassifier(&xmlHandle);
    }
    catch (StatusCodeException &statusCodeException)
    {
        delete m_pStrongClassifier;

        if (STATUS_CODE_INVALID_PARAMETER == statusCodeException.GetStatusCode())
            std::cout << "AdaBoostDecisionTree: Initialization failure, unknown component in xml file." << std::endl;

        if (STATUS_CODE_FAILURE == statusCodeException.GetStatusCode())
            std::cout << "AdaBoostDecisionTree: Node definition does not contain expected leaf or branch variables." << std::endl;

        return statusCodeException.GetStatusCode();
    }

    return STATUS_CODE_SUCCESS;
}

AdaBoostDecisionTree & lar_content::AdaBoostDecisionTree::operator=

(

const AdaBoostDecisionTree &

rhs

)

Assignment operator.

Parameters

rhs	the AdaBoostDecisionTree to assign

Definition at line 31 of file LArAdaBoostDecisionTree.cc.

{
    if (this != &rhs)
        m_pStrongClassifier = new StrongClassifier(*(rhs.m_pStrongClassifier));

    return *this;
}

Member Data Documentation

StrongClassifier* lar_content::AdaBoostDecisionTree::m_pStrongClassifier

private

Strong adaptive boost tree classifier.

Definition at line 325 of file LArAdaBoostDecisionTree.h.

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

• larpandoracontent/larpandoracontent/LArObjects/LArAdaBoostDecisionTree.h
• larpandoracontent/larpandoracontent/LArObjects/LArAdaBoostDecisionTree.cc