TMVAExpert Class Reference

Expert for the TMVA MVA method. More...

#include <TMVA.h>

Inheritance diagram for TMVAExpert:

Public Member Functions
virtual void	load (Weightfile &weightfile) override
	Load the expert from a Weightfile.
virtual std::vector< float >	apply (Dataset &test_data) const =0
	Apply this expert onto a dataset.
virtual std::vector< std::vector< float > >	applyMulticlass (Dataset &test_data) const
	Apply this m_expert onto a dataset.

Protected Attributes
std::unique_ptr< TMVA::Reader >	m_expert
	TMVA::Reader pointer.
std::vector< float >	m_input_cache
	Input Cache for TMVA::Reader: Otherwise we would have to set the branch addresses in each apply call.
std::vector< float >	m_spectators_cache
	Spectators Cache for TMVA::Reader: Otherwise we would have to set the branch addresses in each apply call.
GeneralOptions	m_general_options
	General options loaded from the weightfile.

Detailed Description

Expert for the TMVA MVA method.

Definition at line 284 of file TMVA.h.

Member Function Documentation

apply()

virtual std::vector< float > apply ( Dataset & test_data ) const

pure virtualinherited

Apply this expert onto a dataset.

Parameters

test_data

dataset

Implemented in CombinationExpert, FANNExpert, FastBDTExpert, ONNXExpert, PDFExpert, PythonExpert, RegressionExpert< BaseClassifierExpert, RegressionClassifierOptions >, RegressionExpert< FastBDTExpert, RegressionFastBDTOptions >, RegressionExpert< FastBDTExpert, RegressionFastBDTOptions >, ReweighterExpert, TMVAExpertClassification, TMVAExpertMulticlass, TMVAExpertRegression, and TrivialExpert.

applyMulticlass()

virtual std::vector< std::vector< float > > applyMulticlass ( Dataset & test_data ) const

inlinevirtualinherited

Apply this m_expert onto a dataset.

Multiclass mode. Not pure virtual, since not all derived classes to re-implement this.

Parameters

test_data

dataset.

Returns

vector of size N=test_data.getNumberOfEvents() with N=m_classes.size() scores for each event in the dataset.

Reimplemented in ONNXExpert, PythonExpert, TMVAExpertMulticlass, and TrivialExpert.

Definition at line 56 of file Expert.h.

      {
 
        B2ERROR("Attempted to call applyMulticlass() of the abstract base class MVA::Expert. All methods that support multiclass classification should override this definition.");
        (void) test_data;
 
        return std::vector<std::vector<float>>();
      };

load()

void load ( Weightfile & weightfile )

overridevirtual

Load the expert from a Weightfile.

Parameters

weightfile

containing all information necessary to build the expert

Implements Expert.

Reimplemented in TMVAExpertClassification, TMVAExpertMulticlass, and TMVAExpertRegression.

Definition at line 385 of file TMVA.cc.

    {
 
      // Initialize TMVA and ROOT stuff
      TMVA::Tools::Instance();
 
      m_expert = std::make_unique<TMVA::Reader>("!Color:Silent");
 
      GeneralOptions general_options;
      weightfile.getOptions(general_options);
      m_input_cache.resize(general_options.m_variables.size(), 0);
      for (unsigned int i = 0; i < general_options.m_variables.size(); ++i) {
        m_expert->AddVariable(Belle2::MakeROOTCompatible::makeROOTCompatible(general_options.m_variables[i]), &m_input_cache[i]);
      }
      m_spectators_cache.resize(general_options.m_spectators.size(), 0);
      for (unsigned int i = 0; i < general_options.m_spectators.size(); ++i) {
        m_expert->AddSpectator(Belle2::MakeROOTCompatible::makeROOTCompatible(general_options.m_spectators[i]), &m_spectators_cache[i]);
      }
 
      if (weightfile.containsElement("TMVA_Logfile")) {
        std::string custom_weightfile = weightfile.generateFileName("logfile");
        weightfile.getFile("TMVA_Logfile", custom_weightfile);
      }
 
    }

Member Data Documentation

m_expert

std::unique_ptr<TMVA::Reader> m_expert

protected

TMVA::Reader pointer.

Definition at line 294 of file TMVA.h.

m_general_options

GeneralOptions m_general_options

protectedinherited

General options loaded from the weightfile.

Definition at line 70 of file Expert.h.

m_input_cache

std::vector<float> m_input_cache

mutableprotected

Input Cache for TMVA::Reader: Otherwise we would have to set the branch addresses in each apply call.

Definition at line 296 of file TMVA.h.

m_spectators_cache

std::vector<float> m_spectators_cache

mutableprotected

Spectators Cache for TMVA::Reader: Otherwise we would have to set the branch addresses in each apply call.

Definition at line 298 of file TMVA.h.

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

• mva/methods/include/TMVA.h
• mva/methods/src/TMVA.cc