TMVAExpertRegression Class Reference

Expert for the TMVA Regression MVA method. More...

#include <TMVA.h>

Inheritance diagram for TMVAExpertRegression:

Collaboration diagram for TMVAExpertRegression:

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

Protected Attributes
TMVAOptionsRegression	specific_options
	Method specific options.
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 Regression MVA method.

Definition at line 362 of file TMVA.h.

Member Function Documentation

apply()

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

overridevirtual

Apply this m_expert onto a dataset.

Parameters

test_data

dataset

Implements Expert.

Definition at line 542 of file TMVA.cc.

    {
 
      std::vector<float> prediction(test_data.getNumberOfEvents());
      for (unsigned int iEvent = 0; iEvent < test_data.getNumberOfEvents(); ++iEvent) {
        test_data.loadEvent(iEvent);
        for (unsigned int i = 0; i < m_input_cache.size(); ++i)
          m_input_cache[i] = test_data.m_input[i];
        prediction[iEvent] = m_expert->EvaluateMVA(specific_options.m_method);
      }
      return prediction;
 
    }

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 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 m_expert

Reimplemented from TMVAExpert.

Definition at line 476 of file TMVA.cc.

    {
 
      weightfile.getOptions(specific_options);
 
      // TMVA parses the method type for plugins out of the weightfile name, so we must ensure that it has the expected format
      std::string custom_weightfile = weightfile.generateFileName(std::string("_") + specific_options.m_method + ".weights.xml");
      weightfile.getFile("TMVA_Weightfile", custom_weightfile);
 
      TMVAExpert::load(weightfile);
 
      if (specific_options.m_type == "Plugins") {
        auto base = std::string("TMVA@@MethodBase");
        auto regexp1 = std::string(".*_") + specific_options.m_method + std::string(".*");
        auto regexp2 = std::string(".*") + specific_options.m_method + std::string(".*");
        auto className = std::string("TMVA::Method") + specific_options.m_method;
        auto ctor1 = std::string("Method") + specific_options.m_method + std::string("(TMVA::DataSetInfo&,TString)");
        auto ctor2 = std::string("Method") + specific_options.m_method + std::string("(TString&,TString&,TMVA::DataSetInfo&,TString&)");
        auto pluginName = std::string("TMVA") + specific_options.m_method;
 
        gROOT->GetPluginManager()->AddHandler(base.c_str(), regexp1.c_str(), className.c_str(), pluginName.c_str(), ctor1.c_str());
        gROOT->GetPluginManager()->AddHandler(base.c_str(), regexp2.c_str(), className.c_str(), pluginName.c_str(), ctor2.c_str());
        B2INFO("Registered new TMVA Plugin named " << pluginName);
      }
 
      if (!m_expert->BookMVA(specific_options.m_method, custom_weightfile)) {
        B2FATAL("Could not set up expert! Please see preceding error message from TMVA!");
      }
 
    }

Member Data Documentation

m_expert

std::unique_ptr<TMVA::Reader> m_expert

protectedinherited

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

mutableprotectedinherited

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

mutableprotectedinherited

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.

specific_options

TMVAOptionsRegression specific_options

protected

Method specific options.

Definition at line 378 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