Convience template to create a mva filter for a set of variables. More...

#include <MVAFilter.dcl.h>

Inheritance diagram for MVAFilter< AVarSet >:

Public Types
using	Object = typename AVarSet::Object
	Type of the object to be analysed.

Public Member Functions
	MVAFilter (const std::string &defaultTrainingName="", double defaultCut=NAN, const std::string &defaultDBObjectName="")
	Constructor of the filter.

	~MVAFilter ()
	Default destructor.

void	exposeParameters (ModuleParamList *moduleParamList, const std::string &prefix) override
	Expose the set of parameters of the filter to the module parameter list.

void	initialize () override
	Initialize the expert before event processing.

void	beginRun () override
	Signal to load new run parameters.

Weight	operator() (const Object &obj) override
	Function to object for its signalness.

virtual std::vector< float >	operator() (const std::vector< Object * > &objs) override
	Evaluate the MVA method over a vector of objects.

virtual double	predict (const Object &obj)
	Evaluate the mva method.

std::vector< float >	predict (const std::vector< Object * > &objs)
	Evaluate the MVA method over several inputs simultaneously.

bool	needsTruthInformation () override
	Checks if any variables need Monte Carlo information.

std::unique_ptr< AVarSet >	releaseVarSet () &&
	Steal the set of variables form this filter - filter becomes disfunctional afterwards.

Protected Member Functions
AVarSet &	getVarSet () const
	Getter for the set of variables.

void	setVarSet (std::unique_ptr< AVarSet > varSet)
	Setter for the set of variables.

Private Types
using	Super = MVA< Filter< typename AVarSet::Object > >
	Type of the super class.

using	AVarSet = BaseVarSet< Object >
	Type of the variable set.

Private Attributes
std::string	m_identifier
	Database identifier of the expert or weight file name.

double	m_cutValue
	The cut on the MVA output.

std::string	m_DBPayloadName
	Name of the DB payload.

std::unique_ptr< MVAExpert >	m_mvaExpert
	MVA Expert to examine the object.

std::vector< Named< Float_t * > >	m_namedVariables
	named variables, ordered as in the weightFile:

std::unique_ptr< AVarSet >	m_varSet
	Instance of the variable set to be used in the filter.

Detailed Description

template<class AVarSet>
class Belle2::TrackFindingCDC::MVAFilter< AVarSet >

Convience template to create a mva filter for a set of variables.

Definition at line 100 of file MVAFilter.dcl.h.

Member Typedef Documentation

◆ AVarSet

using AVarSet = BaseVarSet<Object>

privateinherited

Type of the variable set.

Definition at line 48 of file MVAFilter.dcl.h.

◆ Object

using Object = typename AVarSet::Object

Type of the object to be analysed.

Definition at line 108 of file MVAFilter.dcl.h.

◆ Super

using Super = MVA<Filter<typename AVarSet::Object> >

private

Type of the super class.

Definition at line 104 of file MVAFilter.dcl.h.

Constructor & Destructor Documentation

◆ MVAFilter()

MVAFilter	(	const std::string &	defaultTrainingName = `""`,
		double	defaultCut = `NAN`,
		const std::string &	defaultDBObjectName = `""`
	)

explicit

Constructor of the filter.

Definition at line 160 of file MVAFilter.icc.h.

      : Super(std::make_unique<AVarSet>(), defaultTrainingName, defaultCut, defaultDBObjectName)
    {
    }

Member Function Documentation

◆ beginRun()

void beginRun

overrideinherited

Signal to load new run parameters.

Make sure that the sequence of columns (features) is correct and follows the one from the weightFile

Definition at line 67 of file MVAFilter.icc.h.

    {
      Super::beginRun();
      m_mvaExpert->beginRun();
      const auto& selectedVars = m_mvaExpert->getVariableNames();
      const std::vector<Named<Float_t*>>& namedVariables = Super::getVarSet().getNamedVariables();
      m_namedVariables.clear();
      for (const auto& name : selectedVars) {
 
        auto itNamedVariable = std::find_if(namedVariables.begin(),
                                            namedVariables.end(),
        [name](const Named<Float_t*>& namedVariable) {
          return namedVariable.getName() == name;
        });
        if (itNamedVariable == namedVariables.end()) {
          B2ERROR("Variable name " << name << " mismatch for MVA filter. " <<
                  "Could not find expected variable '" << name << "'");
        }
        m_namedVariables.push_back(*itNamedVariable);
      }
    }

◆ exposeParameters()

void exposeParameters	(	ModuleParamList *	moduleParamList,
		const std::string &	prefix
	)

overrideinherited

Expose the set of parameters of the filter to the module parameter list.

Definition at line 61 of file MVAFilter.icc.h.

    {
      Super::exposeParameters(moduleParamList, prefix);
      moduleParamList->addParameter(prefixed(prefix, "cut"),
                                    m_cutValue,
                                    "The cut value of the mva output below which the object is rejected",
                                    m_cutValue);
 
      moduleParamList->addParameter(prefixed(prefix, "identifier"),
                                    m_identifier,
                                    "Database identfier of the expert of weight file name",
                                    m_identifier);
 
      moduleParamList->addParameter(prefixed(prefix, "DBPayloadName"),
                                    m_DBPayloadName,
                                    "Name of the DB payload containing weightfile name and the cut value. If a DB payload with both values is available and valid, it will override the values provided by parameters.",
                                    m_DBPayloadName);
    }

◆ getVarSet()

auto getVarSet

protectedinherited

Getter for the set of variables.

Definition at line 80 of file FilterOnVarSet.icc.h.

    {
      return *m_varSet;
    }

◆ initialize()

void initialize

overrideinherited

Initialize the expert before event processing.

Definition at line 64 of file MVAFilter.icc.h.

    {
      Super::initialize();
 
      DBObjPtr<TrackingMVAFilterParameters> mvaParameterPayload(m_DBPayloadName);
      if (mvaParameterPayload.isValid()) {
        m_identifier = mvaParameterPayload->getIdentifierName();
        m_cutValue = mvaParameterPayload->getCutValue();
        B2DEBUG(20, "MVAFilter: Using DBObject " << m_DBPayloadName << " with weightfile " << m_identifier << " and cut value " <<
                m_cutValue << ".");
      } else {
        B2FATAL("MVAFilter: No valid MVAFilter payload with name " + m_DBPayloadName + " was found.");
      }
 
      std::vector<Named<Float_t*>> namedVariables = Super::getVarSet().getNamedVariables();
 
      m_mvaExpert = std::make_unique<MVAExpert>(m_identifier, std::move(namedVariables));
      m_mvaExpert->initialize();
    }

◆ needsTruthInformation()

bool needsTruthInformation

overrideinherited

Checks if any variables need Monte Carlo information.

Definition at line 49 of file FilterOnVarSet.icc.h.

    {
      bool result = Super::needsTruthInformation();
      if (result) return true;
 
      const std::vector<Named<Float_t*>>& namedVariables = m_varSet->getNamedVariables();
      for (const Named<Float_t*>& namedVariable : namedVariables) {
        std::string name = namedVariable.getName();
        // If the name contains the word truth it is considered to have Monte carlo information.
        if (name.find("truth") != std::string::npos) {
          return true;
        }
      }
      return false;
    }

◆ operator()() [1/2]

Weight operator() ( const Object & obj )

overrideinherited

Function to object for its signalness.

Definition at line 71 of file MVAFilter.icc.h.

    {
      double prediction = predict(obj);
      return prediction < m_cutValue ? NAN : prediction;
    }

◆ operator()() [2/2]

std::vector< float > operator() ( const std::vector< Object * > & objs )

overridevirtualinherited

Evaluate the MVA method over a vector of objects.

Definition at line 80 of file MVAFilter.icc.h.

    {
      auto out = predict(objs);
      for (auto& res : out) {
        res = res < m_cutValue ? NAN : res;
      }
      return out;
    }

◆ predict() [1/2]

double predict ( const Object & obj )

virtualinherited

Evaluate the mva method.

Definition at line 74 of file MVAFilter.icc.h.

    {
      Weight extracted = Super::operator()(obj);
      if (std::isnan(extracted)) {
        return NAN;
      } else {
        return m_mvaExpert->predict();
      }
    }

◆ predict() [2/2]

std::vector< float > predict ( const std::vector< Object * > & objs )

inherited

Evaluate the MVA method over several inputs simultaneously.

Definition at line 77 of file MVAFilter.icc.h.

    {
      const int nFeature = m_namedVariables.size();
      const int nRows    = objs.size();
      auto allFeatures = std::unique_ptr<float[]>(new float[nRows * nFeature]);
      size_t iRow = 0;
      for (const auto& obj : objs) {
        if (Super::getVarSet().extract(obj)) {
          for (int iFeature = 0; iFeature < nFeature; iFeature += 1) {
            allFeatures[nFeature * iRow + iFeature] = *m_namedVariables[iFeature];
          }
          iRow += 1;
        }
      }
      return m_mvaExpert->predict(allFeatures.get(), nFeature, nRows);
    }

◆ releaseVarSet()

auto releaseVarSet ( ) &&

inherited

Steal the set of variables form this filter - filter becomes disfunctional afterwards.

Definition at line 75 of file FilterOnVarSet.icc.h.

                                                                      {
      return std::move(m_varSet);
    }

◆ setVarSet()

void setVarSet ( std::unique_ptr< AVarSet > varSet )

protectedinherited

Setter for the set of variables.

Definition at line 86 of file FilterOnVarSet.icc.h.

    {
      m_varSet = std::move(varSet);
    }

Member Data Documentation

◆ m_cutValue

double m_cutValue

privateinherited

The cut on the MVA output.

Definition at line 86 of file MVAFilter.dcl.h.

◆ m_DBPayloadName

std::string m_DBPayloadName

privateinherited

Name of the DB payload.

Definition at line 89 of file MVAFilter.dcl.h.

◆ m_identifier

std::string m_identifier

privateinherited

Database identifier of the expert or weight file name.

Definition at line 83 of file MVAFilter.dcl.h.

◆ m_mvaExpert

std::unique_ptr<MVAExpert> m_mvaExpert

privateinherited

MVA Expert to examine the object.

Definition at line 92 of file MVAFilter.dcl.h.

◆ m_namedVariables

std::vector<Named<Float_t*> > m_namedVariables

privateinherited

named variables, ordered as in the weightFile:

Definition at line 95 of file MVAFilter.dcl.h.

◆ m_varSet

std::unique_ptr<AVarSet> m_varSet

privateinherited

Instance of the variable set to be used in the filter.

Definition at line 71 of file FilterOnVarSet.dcl.h.

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

tracking/trackFindingCDC/filters/base/include/MVAFilter.dcl.h
tracking/trackFindingCDC/filters/base/include/MVAFilter.icc.h

Public Types

Public Member Functions

Protected Member Functions

Private Types

Private Attributes

Detailed Description

Member Typedef Documentation

◆ AVarSet

◆ Object

◆ Super

Constructor & Destructor Documentation

◆ MVAFilter()

Member Function Documentation

◆ beginRun()

◆ exposeParameters()

◆ getVarSet()

◆ initialize()

◆ needsTruthInformation()

◆ operator()() [1/2]

◆ operator()() [2/2]

◆ predict() [1/2]

◆ predict() [2/2]

◆ releaseVarSet()

◆ setVarSet()

Member Data Documentation

◆ m_cutValue

◆ m_DBPayloadName

◆ m_identifier

◆ m_mvaExpert

◆ m_namedVariables

◆ m_varSet