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MethodLikelihood.h
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1// @(#)root/tmva $Id$
2// Author: Andreas Hoecker, Joerg Stelzer, Helge Voss, Kai Voss, Peter Speckmayer, Eckhard von Toerne, Jan Therhaag
3
4/**********************************************************************************
5 * Project: TMVA - a Root-integrated toolkit for multivariate data analysis *
6 * Package: TMVA *
7 * Class : MethodLikelihood *
8 * Web : http://tmva.sourceforge.net *
9 * *
10 * Description: *
11 * Likelihood analysis ("non-parametric approach") *
12 * Also implemented is a "diagonalized likelihood approach", *
13 * which improves over the uncorrelated likelihood ansatz by transforming *
14 * linearly the input variables into a diagonal space, using the square-root *
15 * of the covariance matrix. This approach can be chosen by inserting *
16 * the letter "D" into the option string. *
17 * *
18 * Authors (alphabetical): *
19 * Andreas Hoecker <Andreas.Hocker@cern.ch> - CERN, Switzerland *
20 * Peter Speckmayer <Peter.Speckmazer@cern.ch> - CERN, Switzerland *
21 * Joerg Stelzer <Joerg.Stelzer@cern.ch> - CERN, Switzerland *
22 * Helge Voss <Helge.Voss@cern.ch> - MPI-K Heidelberg, Germany *
23 * Kai Voss <Kai.Voss@cern.ch> - U. of Victoria, Canada *
24 * Jan Therhaag <Jan.Therhaag@cern.ch> - U of Bonn, Germany *
25 * Eckhard v. Toerne <evt@uni-bonn.de> - U of Bonn, Germany *
26 * *
27 * Copyright (c) 2005-2011: *
28 * CERN, Switzerland *
29 * U. of Victoria, Canada *
30 * MPI-K Heidelberg, Germany *
31 * U. of Bonn, Germany *
32 * *
33 * Redistribution and use in source and binary forms, with or without *
34 * modification, are permitted according to the terms listed in LICENSE *
35 * (http://tmva.sourceforge.net/LICENSE) *
36 **********************************************************************************/
37
38#ifndef ROOT_TMVA_MethodLikelihood
39#define ROOT_TMVA_MethodLikelihood
40
41//////////////////////////////////////////////////////////////////////////
42// //
43// MethodLikelihood //
44// //
45// Likelihood analysis ("non-parametric approach") //
46// Also implemented is a "diagonalized likelihood approach", //
47// which improves over the uncorrelated likelihood ansatz by //
48// transforming linearly the input variables into a diagonal space, //
49// using the square-root of the covariance matrix //
50// //
51//////////////////////////////////////////////////////////////////////////
52
53#include "TMVA/MethodBase.h"
54#include "TMVA/PDF.h"
55
56class TH1D;
57
58namespace TMVA {
59
61
62 public:
63
64 MethodLikelihood( const TString& jobName,
65 const TString& methodTitle,
66 DataSetInfo& theData,
67 const TString& theOption = "");
68
70 const TString& theWeightFile);
71
72 virtual ~MethodLikelihood();
73
75 UInt_t numberClasses, UInt_t numberTargets );
76
77 // training method
78 void Train();
79
80 // write weights to file
81 void WriteWeightsToStream( TFile& rf ) const;
82 void AddWeightsXMLTo( void* parent ) const;
83
84 // read weights from file
85 void ReadWeightsFromStream( std::istream& istr );
86 void ReadWeightsFromStream( TFile& istr );
87 void ReadWeightsFromXML( void* wghtnode );
88 // calculate the MVA value
89 // the argument is used for internal ranking tests
90 Double_t GetMvaValue( Double_t* err = 0, Double_t* errUpper = 0 );
91
92 // write method specific histos to target file
93 void WriteMonitoringHistosToFile() const;
94
95 // ranking of input variables
96 const Ranking* CreateRanking();
97
98 virtual void WriteOptionsToStream ( std::ostream& o, const TString& prefix ) const;
99
100 protected:
101
103
104 // make ROOT-independent C++ class for classifier response (classifier-specific implementation)
105 void MakeClassSpecific( std::ostream&, const TString& ) const;
106
107 // header and auxiliary classes
108 void MakeClassSpecificHeader( std::ostream&, const TString& = "" ) const;
109
110 // get help message text
111 void GetHelpMessage() const;
112
113 private:
114
115 // returns transformed or non-transformed output
117
118 // the option handling methods
119 void Init();
120 void DeclareOptions();
121 void ProcessOptions();
122
123 // options
124 Double_t fEpsilon; // minimum number of likelihood (to avoid zero)
125 Bool_t fTransformLikelihoodOutput; // likelihood output is sigmoid-transformed
126
127 Int_t fDropVariable; // for ranking test
128
129 std::vector<TH1*>* fHistSig; // signal PDFs (histograms)
130 std::vector<TH1*>* fHistBgd; // background PDFs (histograms)
131 std::vector<TH1*>* fHistSig_smooth; // signal PDFs (smoothed histograms)
132 std::vector<TH1*>* fHistBgd_smooth; // background PDFs (smoothed histograms)
133
134 PDF* fDefaultPDFLik; // pdf that contains default definitions
135 std::vector<PDF*>* fPDFSig; // list of PDFs (signal)
136 std::vector<PDF*>* fPDFBgd; // list of PDFs (background)
137
138 // default initialisation called by all constructors
139
140 // obsolete variables kept for backward combatibility
141 Int_t fNsmooth; // number of smooth passes
142 Int_t* fNsmoothVarS; // number of smooth passes
143 Int_t* fNsmoothVarB; // number of smooth passes
144 Int_t fAverageEvtPerBin; // average events per bin; used to calculate fNbins
145 Int_t* fAverageEvtPerBinVarS; // average events per bin; used to calculate fNbins
146 Int_t* fAverageEvtPerBinVarB; // average events per bin; used to calculate fNbins
147 TString fBorderMethodString; // the method to take care about "border" effects (string)
148 Float_t fKDEfineFactor; // fine tuning factor for Adaptive KDE
149 TString fKDEiterString; // Number of iterations (string)
150 TString fKDEtypeString; // Kernel type to use for KDE (string)
151 TString* fInterpolateString; // which interpolation method used for reference histograms (individual for each variable)
152
153 ClassDef(MethodLikelihood,0); // Likelihood analysis ("non-parametric approach")
154 };
155
156} // namespace TMVA
157
158#endif // MethodLikelihood_H
int Int_t
Definition: RtypesCore.h:41
unsigned int UInt_t
Definition: RtypesCore.h:42
bool Bool_t
Definition: RtypesCore.h:59
double Double_t
Definition: RtypesCore.h:55
float Float_t
Definition: RtypesCore.h:53
#define ClassDef(name, id)
Definition: Rtypes.h:326
int type
Definition: TGX11.cxx:120
A ROOT file is a suite of consecutive data records (TKey instances) with a well defined format.
Definition: TFile.h:48
1-D histogram with a double per channel (see TH1 documentation)}
Definition: TH1.h:614
Class that contains all the data information.
Definition: DataSetInfo.h:60
Virtual base Class for all MVA method.
Definition: MethodBase.h:111
Likelihood analysis ("non-parametric approach")
const Ranking * CreateRanking()
computes ranking of input variables
void Train()
create reference distributions (PDFs) from signal and background events: fill histograms and smooth t...
virtual Bool_t HasAnalysisType(Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets)
FDA can handle classification with 2 classes.
virtual void WriteOptionsToStream(std::ostream &o, const TString &prefix) const
write options to stream
void WriteMonitoringHistosToFile() const
write histograms and PDFs to file for monitoring purposes
void DeclareCompatibilityOptions()
options that are used ONLY for the READER to ensure backward compatibility they are hence without any...
void MakeClassSpecific(std::ostream &, const TString &) const
write specific classifier response
std::vector< TH1 * > * fHistSig
virtual ~MethodLikelihood()
destructor
void Init()
default initialisation called by all constructors
std::vector< TH1 * > * fHistBgd
void ReadWeightsFromStream(std::istream &istr)
read weight info from file nothing to do for this method
std::vector< PDF * > * fPDFSig
MethodLikelihood(const TString &jobName, const TString &methodTitle, DataSetInfo &theData, const TString &theOption="")
standard constructor
void GetHelpMessage() const
get help message text
Double_t GetMvaValue(Double_t *err=0, Double_t *errUpper=0)
returns the likelihood estimator for signal fill a new Likelihood branch into the testTree
void ReadWeightsFromXML(void *wghtnode)
read weights from XML
std::vector< TH1 * > * fHistSig_smooth
void MakeClassSpecificHeader(std::ostream &, const TString &="") const
write specific header of the classifier (mostly include files)
Double_t TransformLikelihoodOutput(Double_t ps, Double_t pb) const
returns transformed or non-transformed output
std::vector< TH1 * > * fHistBgd_smooth
std::vector< PDF * > * fPDFBgd
void ProcessOptions()
process user options reference cut value to distinguish signal-like from background-like events
void WriteWeightsToStream(TFile &rf) const
write reference PDFs to ROOT file
void AddWeightsXMLTo(void *parent) const
write weights to XML
void DeclareOptions()
define the options (their key words) that can be set in the option string
PDF wrapper for histograms; uses user-defined spline interpolation.
Definition: PDF.h:63
Ranking for variables in method (implementation)
Definition: Ranking.h:48
EAnalysisType
Definition: Types.h:127
Basic string class.
Definition: TString.h:131
static constexpr double ps
create variable transformations