MVABackgroundClusterFilter Class Reference

Background cluster detection based on MVA package. More...

#include <MVABackgroundClusterFilter.h>

Inheritance diagram for MVABackgroundClusterFilter:

Public Types
using	Super = TrackingUtilities::MVAFilter<BasicClusterVarSet>
	Type of the base class.
using	Object
	Type of the object to be analysed.
using	Object
	Type of the object to be analysed.

Public Member Functions
	MVABackgroundClusterFilter ()
	Constructor initialising the MVAFilter with standard training name for this filter.
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 dysfunctional 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	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

Background cluster detection based on MVA package.

Definition at line 22 of file MVABackgroundClusterFilter.h.

Member Typedef Documentation

AVarSet

template<class AFilter>

using AVarSet = BaseVarSet<Object>

privateinherited

Type of the variable set.

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

Object [1/2]

using Object

inherited

Type of the object to be analysed.

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

Object [2/2]

using Object

inherited

Type of the object to be analysed.

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

Super

using Super = TrackingUtilities::MVAFilter<BasicClusterVarSet>

Type of the base class.

Definition at line 27 of file MVABackgroundClusterFilter.h.

Constructor & Destructor Documentation

MVABackgroundClusterFilter()

MVABackgroundClusterFilter

(

)

Constructor initialising the MVAFilter with standard training name for this filter.

Definition at line 18 of file MVABackgroundClusterFilter.cc.

  : Super("trackfindingcdc_BackgroundClusterFilter", 0.2, "trackfindingcdc_ClusterFilterParameters")
{
}

Member Function Documentation

beginRun()

template<class AFilter>

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 90 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()

template<class AFilter>

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

overridevirtualinherited

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

Reimplemented from OnVarSet< AFilter >.

Definition at line 49 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 identifier 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()

template<class AFilter>

auto getVarSet ( ) const

protectedinherited

Getter for the set of variables.

Definition at line 81 of file FilterOnVarSet.icc.h.

    {
      return *m_varSet;
    }

initialize()

template<class AFilter>

void initialize ( )

overrideinherited

Initialize the expert before event processing.

Definition at line 69 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()

template<class AFilter>

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]

template<class AFilter>

Weight operator() ( const Object & obj )

overrideinherited

Function to object for its signalness.

Definition at line 114 of file MVAFilter.icc.h.

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

operator()() [2/2]

template<class AFilter>

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

overridevirtualinherited

Evaluate the MVA method over a vector of objects.

Reimplemented in MVAFeasibleAxialSegmentPairFilter, MVAFeasibleSegmentRelationFilter, MVARealisticAxialSegmentPairFilter, and MVARealisticSegmentRelationFilter.

Definition at line 150 of file MVAFilter.icc.h.

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

predict() [1/2]

template<class AFilter>

double predict ( const Object & obj )

virtualinherited

Evaluate the mva method.

Definition at line 121 of file MVAFilter.icc.h.

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

predict() [2/2]

template<class AFilter>

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

inherited

Evaluate the MVA method over several inputs simultaneously.

Definition at line 132 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()

template<class AFilter>

auto releaseVarSet ( ) &&

inherited

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

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

    {
      return std::move(m_varSet);
    }

setVarSet()

template<class AFilter>

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

protectedinherited

Setter for the set of variables.

Definition at line 87 of file FilterOnVarSet.icc.h.

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

Member Data Documentation

m_cutValue

template<class AFilter>

double m_cutValue

privateinherited

The cut on the MVA output.

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

m_DBPayloadName

template<class AFilter>

std::string m_DBPayloadName = ""

privateinherited

Name of the DB payload.

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

m_identifier

template<class AFilter>

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

template<class AFilter>

std::unique_ptr<MVAExpert> m_mvaExpert

privateinherited

MVA Expert to examine the object.

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

m_namedVariables

template<class AFilter>

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

template<class AFilter>

std::unique_ptr<AVarSet> m_varSet

privateinherited

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

Definition at line 74 of file FilterOnVarSet.dcl.h.

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

• tracking/trackFindingCDC/filters/cluster/include/MVABackgroundClusterFilter.h
• tracking/trackFindingCDC/filters/cluster/src/MVABackgroundClusterFilter.cc