FastBDTExpert Class Reference

Expert for the FastBDT MVA method. More...

#include <FastBDT.h>

Inheritance diagram for FastBDTExpert:

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 expert onto a dataset.
virtual std::vector< std::vector< float > >	applyMulticlass (Dataset &test_data) const
	Apply this m_expert onto a dataset.

Protected Attributes
GeneralOptions	m_general_options
	General options loaded from the weightfile.

Private Attributes
FastBDTOptions	m_specific_options
	Method specific options.
bool	m_use_simplified_interface = false
	Use the simplified FastBDT interface of version 4.
FastBDT::Classifier	m_classifier
	Simplified FastBDT interface: classifier combines preprocessing and forest.
FastBDT::Forest< float >	m_expert_forest
	Forest Expert -> used in case of no purity transformation.

Detailed Description

Expert for the FastBDT MVA method.

Definition at line 104 of file FastBDT.h.

Member Function Documentation

apply()

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

overridevirtual

Apply this expert onto a dataset.

Parameters

test_data

dataset

Implements Expert.

Definition at line 251 of file FastBDT.cc.

    {
 
      std::vector<float> probabilities(test_data.getNumberOfEvents());
      for (unsigned int iEvent = 0; iEvent < test_data.getNumberOfEvents(); ++iEvent) {
        test_data.loadEvent(iEvent);
        if (m_use_simplified_interface)
          probabilities[iEvent] = m_classifier.predict(test_data.m_input);
        else
          probabilities[iEvent] = m_expert_forest.Analyse(test_data.m_input);
      }
 
      return probabilities;
 
    }

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 expert

Implements Expert.

Definition at line 192 of file FastBDT.cc.

    {
 
      std::string custom_weightfile = weightfile.generateFileName();
      weightfile.getFile("FastBDT_Weightfile", custom_weightfile);
      std::fstream file(custom_weightfile, std::ios_base::in);
 
      int version = weightfile.getElement<int>("FastBDT_version", 0);
      B2DEBUG(100, "FastBDT Weightfile Version " << version);
      if (version < 2) {
        std::stringstream s;
        {
          std::string t;
          std::fstream file2(custom_weightfile, std::ios_base::in);
          getline(file2, t);
          s << t;
        }
        int dummy;
        // Try to read to integers, if this is successful we have a old weightfile with a Feature Binning before the Tree.
        if (!(s >> dummy >> dummy)) {
          B2DEBUG(100, "FastBDT: I read a new weightfile of FastBDT using the new FastBDT version 3. Everything fine!");
          // New format since version 3
          m_expert_forest = FastBDT::readForestFromStream<float>(file);
        } else {
          B2INFO("FastBDT: I read an old weightfile of FastBDT using the new FastBDT version 3."
                 "I will convert your FastBDT on-the-fly to the new version."
                 "Retrain the classifier to get rid of this message");
          // Old format before version 3
          // We read in first the feature binnings and than rewrite the tree
          std::vector<FastBDT::FeatureBinning<float>> feature_binnings;
          file >> feature_binnings;
          double F0;
          file >> F0;
          double shrinkage;
          file >> shrinkage;
          // This parameter was not available in the old version
          bool transform2probability = true;
          FastBDT::Forest<unsigned int> temp_forest(shrinkage, F0, transform2probability);
          unsigned int size;
          file >> size;
          for (unsigned int i = 0; i < size; ++i) {
            temp_forest.AddTree(FastBDT::readTreeFromStream<unsigned int>(file));
          }
 
          FastBDT::Forest<float> cleaned_forest(temp_forest.GetShrinkage(), temp_forest.GetF0(), temp_forest.GetTransform2Probability());
          for (auto& tree : temp_forest.GetForest()) {
            cleaned_forest.AddTree(FastBDT::removeFeatureBinningTransformationFromTree(tree, feature_binnings));
          }
          m_expert_forest = cleaned_forest;
        }
      } else {
        m_use_simplified_interface = true;
        m_classifier = FastBDT::Classifier(file);
      }
      file.close();
 
      weightfile.getOptions(m_specific_options);
    }

Member Data Documentation

m_classifier

FastBDT::Classifier m_classifier

private

Simplified FastBDT interface: classifier combines preprocessing and forest.

Definition at line 122 of file FastBDT.h.

m_expert_forest

FastBDT::Forest<float> m_expert_forest

private

Forest Expert -> used in case of no purity transformation.

Definition at line 123 of file FastBDT.h.

m_general_options

GeneralOptions m_general_options

protectedinherited

General options loaded from the weightfile.

Definition at line 70 of file Expert.h.

m_specific_options

FastBDTOptions m_specific_options

private

Method specific options.

Definition at line 120 of file FastBDT.h.

m_use_simplified_interface

bool m_use_simplified_interface = false

private

Use the simplified FastBDT interface of version 4.

Definition at line 121 of file FastBDT.h.

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

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