10#ifndef INCLUDE_GUARD_BELLE2_MVA_FASTBDT_HEADER
11#define INCLUDE_GUARD_BELLE2_MVA_FASTBDT_HEADER
13#include <mva/interface/Options.h>
14#include <mva/interface/Teacher.h>
15#include <mva/interface/Expert.h>
18#include <FastBDT_IO.h>
19#include <Classifier.h>
32 bool isValidSignal(
const std::vector<bool>& Signals);
44 virtual void load(
const boost::property_tree::ptree& pt)
override;
50 virtual void save(boost::property_tree::ptree& pt)
const override;
60 virtual std::string
getMethod()
const override {
return "FastBDT"; }
67 std::vector<unsigned int>
117 virtual std::vector<float>
apply(
Dataset& test_data)
const override;
Abstract base class of all Datasets given to the MVA interface The current event can always be access...
Abstract base class of all Expert Each MVA library has its own implementation of this class,...
Expert for the FastBDT MVA method.
FastBDT::Forest< float > m_expert_forest
Forest Expert -> used in case of no purity transformation.
FastBDT::Classifier m_classifier
Simplified FastBDT interface: classifier combines preprocessing and forest.
virtual std::vector< float > apply(Dataset &test_data) const override
Apply this expert onto a dataset.
bool m_use_simplified_interface
Use the simplified FastBDT interface of version 4.
virtual void load(Weightfile &weightfile) override
Load the expert from a Weightfile.
FastBDTOptions m_specific_options
Method specific options.
Options for the FANN MVA method.
virtual std::string getMethod() const override
Return method name.
std::vector< unsigned int > m_individual_nCuts
Number of cut Levels = log_2(Number of Cuts) for each provided feature.
bool m_sPlot
Activates sPlot sampling.
virtual po::options_description getDescription() override
Returns a program options description for all available options.
double m_randRatio
Fraction of data to use in the stochastic training.
double m_flatnessLoss
Flatness Loss constant.
double m_shrinkage
Shrinkage during the boosting step.
virtual void load(const boost::property_tree::ptree &pt) override
Load mechanism to load Options from a xml tree.
bool m_purityTransformation
Activates purity transformation globally for all features.
unsigned int m_nLevels
Depth of tree.
virtual void save(boost::property_tree::ptree &pt) const override
Save mechanism to store Options in a xml tree.
std::vector< bool > m_individualPurityTransformation
Vector which decided for each feature individually if the purity transformation should be used.
unsigned int m_nCuts
Number of cut Levels = log_2(Number of Cuts)
unsigned int m_nTrees
Number of trees.
Teacher for the FastBDT MVA method.
FastBDTOptions m_specific_options
Method specific options.
virtual Weightfile train(Dataset &training_data) const override
Train a mva method using the given dataset returning a Weightfile.
General options which are shared by all MVA trainings.
Specific Options, all method Options have to inherit from this class.
Abstract base class of all Teachers Each MVA library has its own implementation of this class,...
The Weightfile class serializes all information about a training into an xml tree.
Abstract base class for different kinds of events.