Logo ROOT  
Reference Guide
MethodBase.h
Go to the documentation of this file.
1// @(#)root/tmva $Id$
2// Author: Andreas Hoecker, Peter Speckmayer, Joerg Stelzer, Helge Voss, Kai Voss, Eckhard von Toerne, Jan Therhaag
3
4/**********************************************************************************
5 * Project: TMVA - a Root-integrated toolkit for multivariate data analysis *
6 * Package: TMVA *
7 * Class : MethodBase *
8 * Web : http://tmva.sourceforge.net *
9 * *
10 * Description: *
11 * Virtual base class for all MVA method *
12 * *
13 * Authors (alphabetical): *
14 * Andreas Hoecker <Andreas.Hocker@cern.ch> - CERN, Switzerland *
15 * Peter Speckmayer <peter.speckmayer@cern.ch> - CERN, Switzerland *
16 * Joerg Stelzer <Joerg.Stelzer@cern.ch> - CERN, Switzerland *
17 * Jan Therhaag <Jan.Therhaag@cern.ch> - U of Bonn, Germany *
18 * Eckhard v. Toerne <evt@uni-bonn.de> - U of Bonn, Germany *
19 * Helge Voss <Helge.Voss@cern.ch> - MPI-K Heidelberg, Germany *
20 * Kai Voss <Kai.Voss@cern.ch> - U. of Victoria, Canada *
21 * *
22 * Copyright (c) 2005-2011: *
23 * CERN, Switzerland *
24 * U. of Victoria, Canada *
25 * MPI-K Heidelberg, Germany *
26 * U. of Bonn, Germany *
27 * *
28 * Redistribution and use in source and binary forms, with or without *
29 * modification, are permitted according to the terms listed in LICENSE *
30 * (http://tmva.sourceforge.net/LICENSE) *
31 **********************************************************************************/
32
33#ifndef ROOT_TMVA_MethodBase
34#define ROOT_TMVA_MethodBase
35
36//////////////////////////////////////////////////////////////////////////
37// //
38// MethodBase //
39// //
40// Virtual base class for all TMVA method //
41// //
42//////////////////////////////////////////////////////////////////////////
43
44#include <iosfwd>
45#include <vector>
46#include <map>
47#include "assert.h"
48
49#include "TString.h"
50
51#include "TMVA/IMethod.h"
52#include "TMVA/Configurable.h"
53#include "TMVA/Types.h"
54#include "TMVA/DataSet.h"
55#include "TMVA/Event.h"
57#include <TMVA/Results.h>
59
60#include <TFile.h>
61
62class TGraph;
63class TTree;
64class TDirectory;
65class TSpline;
66class TH1F;
67class TH1D;
68class TMultiGraph;
69
70/*! \class TMVA::IPythonInteractive
71\ingroup TMVA
72
73This class is needed by JsMVA, and it's a helper class for tracking errors during
74the training in Jupyter notebook. It’s only initialized in Jupyter notebook context.
75In initialization we specify some title, and a TGraph will be created for every title.
76We can add new data points easily to all TGraphs. These graphs are added to a
77TMultiGraph, and during an interactive training we get this TMultiGraph object
78and plot it with JsROOT.
79*/
80
81namespace TMVA {
82
83 class Ranking;
84 class PDF;
85 class TSpline1;
86 class MethodCuts;
87 class MethodBoost;
88 class DataSetInfo;
89 namespace Experimental {
90 class Classification;
91 }
92 class TrainingHistory;
93
95 public:
98 void Init(std::vector<TString>& graphTitles);
99 void ClearGraphs();
100 void AddPoint(Double_t x, Double_t y1, Double_t y2);
101 void AddPoint(std::vector<Double_t>& dat);
102 inline TMultiGraph* Get() {return fMultiGraph;}
103 inline bool NotInitialized(){ return fNumGraphs==0;};
104 private:
106 std::vector<TGraph*> fGraphs;
109 };
110
111 class MethodBase : virtual public IMethod, public Configurable {
112
113 friend class CrossValidation;
114 friend class Factory;
115 friend class RootFinder;
116 friend class MethodBoost;
119
120 public:
121
123
124 // default constructor
125 MethodBase( const TString& jobName,
126 Types::EMVA methodType,
127 const TString& methodTitle,
128 DataSetInfo& dsi,
129 const TString& theOption = "" );
130
131 // constructor used for Testing + Application of the MVA, only (no training),
132 // using given weight file
133 MethodBase( Types::EMVA methodType,
134 DataSetInfo& dsi,
135 const TString& weightFile );
136
137 // default destructor
138 virtual ~MethodBase();
139
140 // declaration, processing and checking of configuration options
141 void SetupMethod();
142 void ProcessSetup();
143 virtual void CheckSetup(); // may be overwritten by derived classes
144
145 // ---------- main training and testing methods ------------------------------
146
147 // prepare tree branch with the method's discriminating variable
149
150 // performs classifier training
151 // calls methods Train() implemented by derived classes
152 void TrainMethod();
153
154 // optimize tuning parameters
155 virtual std::map<TString,Double_t> OptimizeTuningParameters(TString fomType="ROCIntegral", TString fitType="FitGA");
156 virtual void SetTuneParameters(std::map<TString,Double_t> tuneParameters);
157
158 virtual void Train() = 0;
159
160 // store and retrieve time used for training
161 void SetTrainTime( Double_t trainTime ) { fTrainTime = trainTime; }
162 Double_t GetTrainTime() const { return fTrainTime; }
163
164 // store and retrieve time used for testing
165 void SetTestTime ( Double_t testTime ) { fTestTime = testTime; }
166 Double_t GetTestTime () const { return fTestTime; }
167
168 // performs classifier testing
169 virtual void TestClassification();
170 virtual Double_t GetKSTrainingVsTest(Char_t SorB, TString opt="X");
171
172 // performs multiclass classifier testing
173 virtual void TestMulticlass();
174
175 // performs regression testing
176 virtual void TestRegression( Double_t& bias, Double_t& biasT,
177 Double_t& dev, Double_t& devT,
178 Double_t& rms, Double_t& rmsT,
179 Double_t& mInf, Double_t& mInfT, // mutual information
180 Double_t& corr,
182
183 // options treatment
184 virtual void Init() = 0;
185 virtual void DeclareOptions() = 0;
186 virtual void ProcessOptions() = 0;
187 virtual void DeclareCompatibilityOptions(); // declaration of past options
188
189 // reset the Method --> As if it was not yet trained, just instantiated
190 // virtual void Reset() = 0;
191 //for the moment, I provide a dummy (that would not work) default, just to make
192 // compilation/running w/o parameter optimisation still possible
193 virtual void Reset(){return;}
194
195 // classifier response:
196 // some methods may return a per-event error estimate
197 // error calculation is skipped if err==0
198 virtual Double_t GetMvaValue( Double_t* errLower = 0, Double_t* errUpper = 0) = 0;
199
200 // signal/background classification response
201 Double_t GetMvaValue( const TMVA::Event* const ev, Double_t* err = 0, Double_t* errUpper = 0 );
202
203 protected:
204 // helper function to set errors to -1
205 void NoErrorCalc(Double_t* const err, Double_t* const errUpper);
206
207 // signal/background classification response for all current set of data
208 virtual std::vector<Double_t> GetMvaValues(Long64_t firstEvt = 0, Long64_t lastEvt = -1, Bool_t logProgress = false);
209
210
211 public:
212 // regression response
213 const std::vector<Float_t>& GetRegressionValues(const TMVA::Event* const ev){
214 fTmpEvent = ev;
215 const std::vector<Float_t>* ptr = &GetRegressionValues();
216 fTmpEvent = 0;
217 return (*ptr);
218 }
219
220 virtual const std::vector<Float_t>& GetRegressionValues() {
221 std::vector<Float_t>* ptr = new std::vector<Float_t>(0);
222 return (*ptr);
223 }
224
225 // multiclass classification response
226 virtual const std::vector<Float_t>& GetMulticlassValues() {
227 std::vector<Float_t>* ptr = new std::vector<Float_t>(0);
228 return (*ptr);
229 }
230
231 // Training history
232 virtual const std::vector<Float_t>& GetTrainingHistory(const char* /*name*/ ) {
233 std::vector<Float_t>* ptr = new std::vector<Float_t>(0);
234 return (*ptr);
235 }
236
237 // probability of classifier response (mvaval) to be signal (requires "CreateMvaPdf" option set)
238 virtual Double_t GetProba( const Event *ev); // the simple one, automatically calculates the mvaVal and uses the SAME sig/bkg ratio as given in the training sample (typically 50/50 .. (NormMode=EqualNumEvents) but can be different)
239 virtual Double_t GetProba( Double_t mvaVal, Double_t ap_sig );
240
241 // Rarity of classifier response (signal or background (default) is uniform in [0,1])
242 virtual Double_t GetRarity( Double_t mvaVal, Types::ESBType reftype = Types::kBackground ) const;
243
244 // create ranking
245 virtual const Ranking* CreateRanking() = 0;
246
247 // make ROOT-independent C++ class
248 virtual void MakeClass( const TString& classFileName = TString("") ) const;
249
250 // print help message
251 void PrintHelpMessage() const;
252
253 //
254 // streamer methods for training information (creates "weight" files) --------
255 //
256 public:
257 void WriteStateToFile () const;
258 void ReadStateFromFile ();
259
260 protected:
261 // the actual "weights"
262 virtual void AddWeightsXMLTo ( void* parent ) const = 0;
263 virtual void ReadWeightsFromXML ( void* wghtnode ) = 0;
264 virtual void ReadWeightsFromStream( std::istream& ) = 0; // backward compatibility
265 virtual void ReadWeightsFromStream( TFile& ) {} // backward compatibility
266
267 private:
268 friend class MethodCategory;
270 void WriteStateToXML ( void* parent ) const;
271 void ReadStateFromXML ( void* parent );
272 void WriteStateToStream ( std::ostream& tf ) const; // needed for MakeClass
273 void WriteVarsToStream ( std::ostream& tf, const TString& prefix = "" ) const; // needed for MakeClass
274
275
276 public: // these two need to be public, they are used to read in-memory weight-files
277 void ReadStateFromStream ( std::istream& tf ); // backward compatibility
278 void ReadStateFromStream ( TFile& rf ); // backward compatibility
279 void ReadStateFromXMLString( const char* xmlstr ); // for reading from memory
280
281 private:
282 // the variable information
283 void AddVarsXMLTo ( void* parent ) const;
284 void AddSpectatorsXMLTo ( void* parent ) const;
285 void AddTargetsXMLTo ( void* parent ) const;
286 void AddClassesXMLTo ( void* parent ) const;
287 void ReadVariablesFromXML ( void* varnode );
288 void ReadSpectatorsFromXML( void* specnode);
289 void ReadTargetsFromXML ( void* tarnode );
290 void ReadClassesFromXML ( void* clsnode );
291 void ReadVarsFromStream ( std::istream& istr ); // backward compatibility
292
293 public:
294 // ---------------------------------------------------------------------------
295
296 // write evaluation histograms into target file
297 virtual void WriteEvaluationHistosToFile(Types::ETreeType treetype);
298
299 // write classifier-specific monitoring information to target file
300 virtual void WriteMonitoringHistosToFile() const;
301
302 // ---------- public evaluation methods --------------------------------------
303
304 // individual initialization for testing of each method
305 // overload this one for individual initialisation of the testing,
306 // it is then called automatically within the global "TestInit"
307
308 // variables (and private member functions) for the Evaluation:
309 // get the efficiency. It fills a histogram for efficiency/vs/bkg
310 // and returns the one value fo the efficiency demanded for
311 // in the TString argument. (Watch the string format)
312 virtual Double_t GetEfficiency( const TString&, Types::ETreeType, Double_t& err );
313 virtual Double_t GetTrainingEfficiency(const TString& );
314 virtual std::vector<Float_t> GetMulticlassEfficiency( std::vector<std::vector<Float_t> >& purity );
315 virtual std::vector<Float_t> GetMulticlassTrainingEfficiency(std::vector<std::vector<Float_t> >& purity );
317 virtual Double_t GetSignificance() const;
318 virtual Double_t GetROCIntegral(TH1D *histS, TH1D *histB) const;
319 virtual Double_t GetROCIntegral(PDF *pdfS=0, PDF *pdfB=0) const;
320 virtual Double_t GetMaximumSignificance( Double_t SignalEvents, Double_t BackgroundEvents,
321 Double_t& optimal_significance_value ) const;
322 virtual Double_t GetSeparation( TH1*, TH1* ) const;
323 virtual Double_t GetSeparation( PDF* pdfS = 0, PDF* pdfB = 0 ) const;
324
325 virtual void GetRegressionDeviation(UInt_t tgtNum, Types::ETreeType type, Double_t& stddev,Double_t& stddev90Percent ) const;
326 // ---------- public accessors -----------------------------------------------
327
328 // classifier naming (a lot of names ... aren't they ;-)
329 const TString& GetJobName () const { return fJobName; }
330 const TString& GetMethodName () const { return fMethodName; }
333 const char* GetName () const { return fMethodName.Data(); }
334 const TString& GetTestvarName () const { return fTestvar; }
335 const TString GetProbaName () const { return fTestvar + "_Proba"; }
337
338 // build classifier name in Test tree
339 // MVA prefix (e.g., "TMVA_")
340 void SetTestvarName ( const TString & v="" ) { fTestvar = (v=="") ? ("MVA_" + GetMethodName()) : v; }
341
342 // number of input variable used by classifier
343 UInt_t GetNvar() const { return DataInfo().GetNVariables(); }
345 UInt_t GetNTargets() const { return DataInfo().GetNTargets(); };
346
347 // internal names and expressions of input variables
348 const TString& GetInputVar ( Int_t i ) const { return DataInfo().GetVariableInfo(i).GetInternalName(); }
349 const TString& GetInputLabel( Int_t i ) const { return DataInfo().GetVariableInfo(i).GetLabel(); }
350 const char * GetInputTitle( Int_t i ) const { return DataInfo().GetVariableInfo(i).GetTitle(); }
351
352 // normalisation and limit accessors
353 Double_t GetMean( Int_t ivar ) const { return GetTransformationHandler().GetMean(ivar); }
354 Double_t GetRMS ( Int_t ivar ) const { return GetTransformationHandler().GetRMS(ivar); }
355 Double_t GetXmin( Int_t ivar ) const { return GetTransformationHandler().GetMin(ivar); }
356 Double_t GetXmax( Int_t ivar ) const { return GetTransformationHandler().GetMax(ivar); }
357
358 // sets the minimum requirement on the MVA output to declare an event signal-like
361
362 // sets the minimum requirement on the MVA output to declare an event signal-like
365
366 // pointers to ROOT directories
367 TDirectory* BaseDir() const;
368 TDirectory* MethodBaseDir() const;
369 TFile* GetFile() const {return fFile;}
370
371 void SetMethodDir ( TDirectory* methodDir ) { fBaseDir = fMethodBaseDir = methodDir; }
372 void SetBaseDir( TDirectory* methodDir ){ fBaseDir = methodDir; }
373 void SetMethodBaseDir( TDirectory* methodDir ){ fMethodBaseDir = methodDir; }
375
376 //Silent file
377 void SetSilentFile(Bool_t status) {fSilentFile=status;}
379
380 //Model Persistence
381 void SetModelPersistence(Bool_t status){fModelPersistence=status;}//added support to create/remove dir here if exits or not
383
384 // the TMVA version can be obtained and checked using
385 // if (GetTrainingTMVAVersionCode()>TMVA_VERSION(3,7,2)) {...}
386 // or
387 // if (GetTrainingROOTVersionCode()>ROOT_VERSION(5,15,5)) {...}
392
394 {
395 if(fTransformationPointer && takeReroutedIfAvailable) return *fTransformationPointer; else return fTransformation;
396 }
397 const TransformationHandler& GetTransformationHandler(Bool_t takeReroutedIfAvailable=true) const
398 {
399 if(fTransformationPointer && takeReroutedIfAvailable) return *fTransformationPointer; else return fTransformation;
400 }
401
402 void RerouteTransformationHandler (TransformationHandler* fTargetTransformation) { fTransformationPointer=fTargetTransformation; }
403
404 // ---------- event accessors ------------------------------------------------
405
406 // returns reference to data set
407 // NOTE: this DataSet is the "original" dataset, i.e. the one seen by ALL Classifiers WITHOUT transformation
408 DataSet* Data() const { return DataInfo().GetDataSet(); }
409 DataSetInfo& DataInfo() const { return fDataSetInfo; }
410
411 mutable const Event* fTmpEvent; //! temporary event when testing on a different DataSet than the own one
412
413 // event reference and update
414 // NOTE: these Event accessors make sure that you get the events transformed according to the
415 // particular classifiers transformation chosen
416 UInt_t GetNEvents () const { return Data()->GetNEvents(); }
417 const Event* GetEvent () const;
418 const Event* GetEvent ( const TMVA::Event* ev ) const;
419 const Event* GetEvent ( Long64_t ievt ) const;
420 const Event* GetEvent ( Long64_t ievt , Types::ETreeType type ) const;
421 const Event* GetTrainingEvent( Long64_t ievt ) const;
422 const Event* GetTestingEvent ( Long64_t ievt ) const;
423 const std::vector<TMVA::Event*>& GetEventCollection( Types::ETreeType type );
424
426 // ---------- public auxiliary methods ---------------------------------------
427
428 // this method is used to decide whether an event is signal- or background-like
429 // the reference cut "xC" is taken to be where
430 // Int_[-oo,xC] { PDF_S(x) dx } = Int_[xC,+oo] { PDF_B(x) dx }
431 virtual Bool_t IsSignalLike();
432 virtual Bool_t IsSignalLike(Double_t mvaVal);
433
434
435 Bool_t HasMVAPdfs() const { return fHasMVAPdfs; }
440
441 // setter method for suppressing writing to XML and writing of standalone classes
442 void DisableWriting(Bool_t setter){ fModelPersistence = setter?kFALSE:kTRUE; }//DEPRECATED
443
444 protected:
445 // helper variables for JsMVA
447 bool fExitFromTraining = false;
449
450 public:
451
452 // initializing IPythonInteractive class (for JsMVA only)
454 if (fInteractive) delete fInteractive;
456 }
457
458 // get training errors (for JsMVA only)
460
461 // stop's the training process (for JsMVA only)
462 inline void ExitFromTraining(){
463 fExitFromTraining = true;
464 }
465
466 // check's if the training ended (for JsMVA only)
467 inline bool TrainingEnded(){
469 delete fInteractive;
470 fInteractive = nullptr;
471 }
472 return fExitFromTraining;
473 }
474
475 // get fIPyMaxIter
476 inline UInt_t GetMaxIter(){ return fIPyMaxIter; }
477
478 // get fIPyCurrentIter
480
481 protected:
482
483 // ---------- protected accessors -------------------------------------------
484
485 //TDirectory* LocalTDir() const { return Data().LocalRootDir(); }
486
487 // weight file name and directory (given by global config variable)
489
490 const TString& GetWeightFileDir() const { return fFileDir; }
491 void SetWeightFileDir( TString fileDir );
492
493 // are input variables normalised ?
494 Bool_t IsNormalised() const { return fNormalise; }
495 void SetNormalised( Bool_t norm ) { fNormalise = norm; }
496
497 // set number of input variables (only used by MethodCuts, could perhaps be removed)
498 // void SetNvar( Int_t n ) { fNvar = n; }
499
500 // verbose and help flags
501 Bool_t Verbose() const { return fVerbose; }
502 Bool_t Help () const { return fHelp; }
503
504 // ---------- protected event and tree accessors -----------------------------
505
506 // names of input variables (if the original names are expressions, they are
507 // transformed into regexps)
508 const TString& GetInternalVarName( Int_t ivar ) const { return (*fInputVars)[ivar]; }
509 const TString& GetOriginalVarName( Int_t ivar ) const { return DataInfo().GetVariableInfo(ivar).GetExpression(); }
510
511 Bool_t HasTrainingTree() const { return Data()->GetNTrainingEvents() != 0; }
512
513 // ---------- protected auxiliary methods ------------------------------------
514
515 protected:
516
517 // make ROOT-independent C++ class for classifier response (classifier-specific implementation)
518 virtual void MakeClassSpecific( std::ostream&, const TString& = "" ) const {}
519
520 // header and auxiliary classes
521 virtual void MakeClassSpecificHeader( std::ostream&, const TString& = "" ) const {}
522
523 // static pointer to this object - required for ROOT finder (to be solved differently)(solved by Omar)
524 //static MethodBase* GetThisBase();
525
526 // some basic statistical analysis
527 void Statistics( Types::ETreeType treeType, const TString& theVarName,
530
531 // if TRUE, write weights only to text files
532 Bool_t TxtWeightsOnly() const { return kTRUE; }
533
534 protected:
535
536 // access to event information that needs method-specific information
537
539
540 private:
541
542 // ---------- private definitions --------------------------------------------
543 // Initialisation
544 void InitBase();
545 void DeclareBaseOptions();
546 void ProcessBaseOptions();
547
548 // used in efficiency computation
551
552 // ---------- private accessors ---------------------------------------------
553
554 // reset required for RootFinder
556
557 // ---------- private auxiliary methods --------------------------------------
558
559 // PDFs for classifier response (required to compute signal probability and Rarity)
560 void CreateMVAPdfs();
561
562 // for root finder
563 //virtual method to find ROOT
564 virtual Double_t GetValueForRoot ( Double_t ); // implementation
565
566 // used for file parsing
567 Bool_t GetLine( std::istream& fin, char * buf );
568
569 // fill test tree with classification or regression results
574
575 private:
576
577 void AddInfoItem( void* gi, const TString& name,
578 const TString& value) const;
579
580 // ========== class members ==================================================
581
582 protected:
583
584 // direct accessors
585 Ranking* fRanking; // pointer to ranking object (created by derived classifiers)
586 std::vector<TString>* fInputVars; // vector of input variables used in MVA
587
588 // histogram binning
589 Int_t fNbins; // number of bins in input variable histograms
590 Int_t fNbinsMVAoutput; // number of bins in MVA output histograms
591 Int_t fNbinsH; // number of bins in evaluation histograms
592
593 Types::EAnalysisType fAnalysisType; // method-mode : true --> regression, false --> classification
594
595 std::vector<Float_t>* fRegressionReturnVal; // holds the return-values for the regression
596 std::vector<Float_t>* fMulticlassReturnVal; // holds the return-values for the multiclass classification
597
598 private:
599
600 // MethodCuts redefines some of the evaluation variables and histograms -> must access private members
601 friend class MethodCuts;
602
603
604 // data sets
605 DataSetInfo& fDataSetInfo; //! the data set information (sometimes needed)
606
607 Double_t fSignalReferenceCut; // minimum requirement on the MVA output to declare an event signal-like
608 Double_t fSignalReferenceCutOrientation; // minimum requirement on the MVA output to declare an event signal-like
609 Types::ESBType fVariableTransformType; // this is the event type (sig or bgd) assumed for variable transform
610
611 // naming and versioning
612 TString fJobName; // name of job -> user defined, appears in weight files
613 TString fMethodName; // name of the method (set in derived class)
614 Types::EMVA fMethodType; // type of method (set in derived class)
615 TString fTestvar; // variable used in evaluation, etc (mostly the MVA)
616 UInt_t fTMVATrainingVersion; // TMVA version used for training
617 UInt_t fROOTTrainingVersion; // ROOT version used for training
618 Bool_t fConstructedFromWeightFile; // is it obtained from weight file?
619
620 // Directory structure: dataloader/fMethodBaseDir/fBaseDir
621 // where the first directory name is defined by the method type
622 // and the second is user supplied (the title given in Factory::BookMethod())
623 TDirectory* fBaseDir; // base directory for the instance, needed to know where to jump back from localDir
624 mutable TDirectory* fMethodBaseDir; // base directory for the method
625 //this will be the next way to save results
627
628 //SilentFile
630 //Model Persistence
632
633 TString fParentDir; // method parent name, like booster name
634
635 TString fFileDir; // unix sub-directory for weight files (default: DataLoader's Name + "weights")
636 TString fWeightFile; // weight file name
637
638 private:
639
640 TH1* fEffS; // efficiency histogram for rootfinder
641
642 PDF* fDefaultPDF; // default PDF definitions
643 PDF* fMVAPdfS; // signal MVA PDF
644 PDF* fMVAPdfB; // background MVA PDF
645
646 // TH1D* fmvaS; // PDFs of MVA distribution (signal)
647 // TH1D* fmvaB; // PDFs of MVA distribution (background)
648 PDF* fSplS; // PDFs of MVA distribution (signal)
649 PDF* fSplB; // PDFs of MVA distribution (background)
650 TSpline* fSpleffBvsS; // splines for signal eff. versus background eff.
651
652 PDF* fSplTrainS; // PDFs of training MVA distribution (signal)
653 PDF* fSplTrainB; // PDFs of training MVA distribution (background)
654 TSpline* fSplTrainEffBvsS; // splines for training signal eff. versus background eff.
655
656 private:
657
658 // basic statistics quantities of MVA
659 Double_t fMeanS; // mean (signal)
660 Double_t fMeanB; // mean (background)
661 Double_t fRmsS; // RMS (signal)
662 Double_t fRmsB; // RMS (background)
663 Double_t fXmin; // minimum (signal and background)
664 Double_t fXmax; // maximum (signal and background)
665
666 // variable preprocessing
667 TString fVarTransformString; // labels variable transform method
668
669 TransformationHandler* fTransformationPointer; // pointer to the rest of transformations
670 TransformationHandler fTransformation; // the list of transformations
671
672
673 // help and verbosity
674 Bool_t fVerbose; // verbose flag
675 TString fVerbosityLevelString; // verbosity level (user input string)
676 EMsgType fVerbosityLevel; // verbosity level
677 Bool_t fHelp; // help flag
678 Bool_t fHasMVAPdfs; // MVA Pdfs are created for this classifier
679
680 Bool_t fIgnoreNegWeightsInTraining;// If true, events with negative weights are not used in training
681
682 protected:
683
685
686 // for signal/background
687 UInt_t fSignalClass; // index of the Signal-class
688 UInt_t fBackgroundClass; // index of the Background-class
689
690 private:
691
692 // timing variables
693 Double_t fTrainTime; // for timing measurements
694 Double_t fTestTime; // for timing measurements
695
696 // orientation of cut: depends on signal and background mean values
697 ECutOrientation fCutOrientation; // +1 if Sig>Bkg, -1 otherwise
698
699 // for root finder
700 TSpline1* fSplRefS; // helper splines for RootFinder (signal)
701 TSpline1* fSplRefB; // helper splines for RootFinder (background)
702
703 TSpline1* fSplTrainRefS; // helper splines for RootFinder (signal)
704 TSpline1* fSplTrainRefB; // helper splines for RootFinder (background)
705
706 mutable std::vector<const std::vector<TMVA::Event*>*> fEventCollections; // if the method needs the complete event-collection, the transformed event coll. ist stored here.
707
708 public:
709 Bool_t fSetupCompleted; // is method setup
710
711 private:
712
713 // This is a workaround for OSx where static thread_local data members are
714 // not supported. The C++ solution would indeed be the following:
715// static MethodBase*& GetThisBaseThreadLocal() {TTHREAD_TLS(MethodBase*) fgThisBase(nullptr); return fgThisBase; };
716
717 // ===== depreciated options, kept for backward compatibility =====
718 private:
719
720 Bool_t fNormalise; // normalise input variables
721 Bool_t fUseDecorr; // synonymous for decorrelation
722 TString fVariableTransformTypeString; // labels variable transform type
723 Bool_t fTxtWeightsOnly; // if TRUE, write weights only to text files
724 Int_t fNbinsMVAPdf; // number of bins used in histogram that creates PDF
725 Int_t fNsmoothMVAPdf; // number of times a histogram is smoothed before creating the PDF
726
727 protected:
729 ClassDef(MethodBase,0); // Virtual base class for all TMVA method
730
731 };
732} // namespace TMVA
733
734
735
736
737
738
739
740// ========== INLINE FUNCTIONS =========================================================
741
742
743//_______________________________________________________________________
744inline const TMVA::Event* TMVA::MethodBase::GetEvent( const TMVA::Event* ev ) const
745{
747}
748
750{
751 if(fTmpEvent)
752 return GetTransformationHandler().Transform(fTmpEvent);
753 else
754 return GetTransformationHandler().Transform(Data()->GetEvent());
755}
756
758{
759 assert(fTmpEvent==0);
760 return GetTransformationHandler().Transform(Data()->GetEvent(ievt));
761}
762
764{
765 assert(fTmpEvent==0);
766 return GetTransformationHandler().Transform(Data()->GetEvent(ievt, type));
767}
768
770{
771 assert(fTmpEvent==0);
772 return GetEvent(ievt, Types::kTraining);
773}
774
776{
777 assert(fTmpEvent==0);
778 return GetEvent(ievt, Types::kTesting);
779}
780
781#endif
int Int_t
Definition: RtypesCore.h:43
char Char_t
Definition: RtypesCore.h:31
unsigned int UInt_t
Definition: RtypesCore.h:44
const Bool_t kFALSE
Definition: RtypesCore.h:90
bool Bool_t
Definition: RtypesCore.h:61
double Double_t
Definition: RtypesCore.h:57
long long Long64_t
Definition: RtypesCore.h:71
const Bool_t kTRUE
Definition: RtypesCore.h:89
#define ClassDef(name, id)
Definition: Rtypes.h:322
char name[80]
Definition: TGX11.cxx:109
int type
Definition: TGX11.cxx:120
Describe directory structure in memory.
Definition: TDirectory.h:40
A ROOT file is a suite of consecutive data records (TKey instances) with a well defined format.
Definition: TFile.h:53
A TGraph is an object made of two arrays X and Y with npoints each.
Definition: TGraph.h:41
1-D histogram with a double per channel (see TH1 documentation)}
Definition: TH1.h:614
1-D histogram with a float per channel (see TH1 documentation)}
Definition: TH1.h:571
The TH1 histogram class.
Definition: TH1.h:56
Class to perform two class classification.
Class to perform cross validation, splitting the dataloader into folds.
Class that contains all the data information.
Definition: DataSetInfo.h:60
UInt_t GetNVariables() const
Definition: DataSetInfo.h:125
UInt_t GetNTargets() const
Definition: DataSetInfo.h:126
DataSet * GetDataSet() const
returns data set
VariableInfo & GetVariableInfo(Int_t i)
Definition: DataSetInfo.h:103
Class that contains all the data information.
Definition: DataSet.h:69
Long64_t GetNEvents(Types::ETreeType type=Types::kMaxTreeType) const
Definition: DataSet.h:217
Long64_t GetNTrainingEvents() const
Definition: DataSet.h:79
This is the main MVA steering class.
Definition: Factory.h:81
Interface for all concrete MVA method implementations.
Definition: IMethod.h:54
This class is needed by JsMVA, and it's a helper class for tracking errors during the training in Jup...
Definition: MethodBase.h:94
void Init(std::vector< TString > &graphTitles)
This function gets some title and it creates a TGraph for every title.
Definition: MethodBase.cxx:171
IPythonInteractive()
standard constructor
Definition: MethodBase.cxx:148
TMultiGraph * fMultiGraph
Definition: MethodBase.h:103
std::vector< TGraph * > fGraphs
Definition: MethodBase.h:106
~IPythonInteractive()
standard destructor
Definition: MethodBase.cxx:156
TMultiGraph * Get()
Definition: MethodBase.h:102
void ClearGraphs()
This function sets the point number to 0 for all graphs.
Definition: MethodBase.cxx:195
void AddPoint(Double_t x, Double_t y1, Double_t y2)
This function is used only in 2 TGraph case, and it will add new data points to graphs.
Definition: MethodBase.cxx:209
Virtual base Class for all MVA method.
Definition: MethodBase.h:111
TransformationHandler * fTransformationPointer
Definition: MethodBase.h:669
virtual void MakeClassSpecificHeader(std::ostream &, const TString &="") const
Definition: MethodBase.h:521
TString fVerbosityLevelString
Definition: MethodBase.h:675
TDirectory * MethodBaseDir() const
returns the ROOT directory where all instances of the corresponding MVA method are stored
virtual const std::vector< Float_t > & GetRegressionValues()
Definition: MethodBase.h:220
const std::vector< Float_t > & GetRegressionValues(const TMVA::Event *const ev)
Definition: MethodBase.h:213
virtual void Train()=0
virtual Double_t GetKSTrainingVsTest(Char_t SorB, TString opt="X")
MethodBase(const TString &jobName, Types::EMVA methodType, const TString &methodTitle, DataSetInfo &dsi, const TString &theOption="")
standard constructor
Definition: MethodBase.cxx:239
TString fMethodName
Definition: MethodBase.h:613
TFile * GetFile() const
Definition: MethodBase.h:369
virtual Double_t GetSeparation(TH1 *, TH1 *) const
compute "separation" defined as
TString fFileDir
Definition: MethodBase.h:635
Bool_t HasTrainingTree() const
Definition: MethodBase.h:511
virtual void DeclareOptions()=0
void SetSilentFile(Bool_t status)
Definition: MethodBase.h:377
void ReadClassesFromXML(void *clsnode)
read number of classes from XML
TMultiGraph * GetInteractiveTrainingError()
Definition: MethodBase.h:459
void SetWeightFileDir(TString fileDir)
set directory of weight file
void WriteStateToXML(void *parent) const
general method used in writing the header of the weight files where the used variables,...
Bool_t fSilentFile
Definition: MethodBase.h:629
void DeclareBaseOptions()
define the options (their key words) that can be set in the option string here the options valid for ...
Definition: MethodBase.cxx:511
UInt_t GetMaxIter()
Definition: MethodBase.h:476
Double_t fRmsB
Definition: MethodBase.h:662
Double_t GetXmin(Int_t ivar) const
Definition: MethodBase.h:355
Bool_t Verbose() const
Definition: MethodBase.h:501
virtual void TestRegression(Double_t &bias, Double_t &biasT, Double_t &dev, Double_t &devT, Double_t &rms, Double_t &rmsT, Double_t &mInf, Double_t &mInfT, Double_t &corr, Types::ETreeType type)
calculate <sum-of-deviation-squared> of regression output versus "true" value from test sample
Definition: MethodBase.cxx:979
Double_t GetMean(Int_t ivar) const
Definition: MethodBase.h:353
virtual void DeclareCompatibilityOptions()
options that are used ONLY for the READER to ensure backward compatibility they are hence without any...
Definition: MethodBase.cxx:598
TString GetMethodTypeName() const
Definition: MethodBase.h:331
Bool_t DoMulticlass() const
Definition: MethodBase.h:439
virtual Double_t GetSignificance() const
compute significance of mean difference
void DisableWriting(Bool_t setter)
Definition: MethodBase.h:442
Bool_t fTxtWeightsOnly
Definition: MethodBase.h:723
virtual void ReadWeightsFromXML(void *wghtnode)=0
virtual Double_t GetProba(const Event *ev)
const char * GetName() const
Definition: MethodBase.h:333
TString fParentDir
Definition: MethodBase.h:633
virtual const std::vector< Float_t > & GetMulticlassValues()
Definition: MethodBase.h:226
TSpline * fSplTrainEffBvsS
Definition: MethodBase.h:654
Types::EAnalysisType GetAnalysisType() const
Definition: MethodBase.h:437
virtual TMatrixD GetMulticlassConfusionMatrix(Double_t effB, Types::ETreeType type)
Construct a confusion matrix for a multiclass classifier.
UInt_t GetTrainingTMVAVersionCode() const
Definition: MethodBase.h:388
UInt_t fTMVATrainingVersion
Definition: MethodBase.h:616
Bool_t IsModelPersistence() const
Definition: MethodBase.h:382
Double_t fXmax
Definition: MethodBase.h:664
Double_t fMeanS
Definition: MethodBase.h:659
Types::ESBType fVariableTransformType
Definition: MethodBase.h:609
const TString & GetInputVar(Int_t i) const
Definition: MethodBase.h:348
void PrintHelpMessage() const
prints out method-specific help method
void SetMethodDir(TDirectory *methodDir)
Definition: MethodBase.h:371
const TString & GetJobName() const
Definition: MethodBase.h:329
Bool_t IgnoreEventsWithNegWeightsInTraining() const
Definition: MethodBase.h:684
Double_t fTrainTime
Definition: MethodBase.h:693
virtual void WriteEvaluationHistosToFile(Types::ETreeType treetype)
writes all MVA evaluation histograms to file
virtual void TestMulticlass()
test multiclass classification
Bool_t fModelPersistence
Definition: MethodBase.h:631
const TString & GetTestvarName() const
Definition: MethodBase.h:334
const std::vector< TMVA::Event * > & GetEventCollection(Types::ETreeType type)
returns the event collection (i.e.
void SetupMethod()
setup of methods
Definition: MethodBase.cxx:408
TDirectory * BaseDir() const
returns the ROOT directory where info/histograms etc of the corresponding MVA method instance are sto...
const Event * GetTestingEvent(Long64_t ievt) const
Definition: MethodBase.h:775
virtual std::vector< Float_t > GetMulticlassEfficiency(std::vector< std::vector< Float_t > > &purity)
UInt_t GetNTargets() const
Definition: MethodBase.h:345
EMsgType fVerbosityLevel
Definition: MethodBase.h:676
const TString GetProbaName() const
Definition: MethodBase.h:335
UInt_t fIPyMaxIter
Definition: MethodBase.h:448
TransformationHandler fTransformation
Definition: MethodBase.h:670
virtual void ReadWeightsFromStream(std::istream &)=0
void AddInfoItem(void *gi, const TString &name, const TString &value) const
xml writing
TDirectory * fMethodBaseDir
Definition: MethodBase.h:624
virtual void AddClassifierOutputProb(Types::ETreeType type)
prepare tree branch with the method's discriminating variable
Definition: MethodBase.cxx:938
virtual void MakeClassSpecific(std::ostream &, const TString &="") const
Definition: MethodBase.h:518
Double_t fSignalReferenceCutOrientation
Definition: MethodBase.h:608
virtual Double_t GetEfficiency(const TString &, Types::ETreeType, Double_t &err)
fill background efficiency (resp.
Int_t fNbinsMVAoutput
Definition: MethodBase.h:590
TString GetTrainingTMVAVersionString() const
calculates the TMVA version string from the training version code on the fly
TSpline * fSpleffBvsS
Definition: MethodBase.h:650
TString fVariableTransformTypeString
Definition: MethodBase.h:722
TSpline1 * fSplTrainRefB
Definition: MethodBase.h:704
const TString & GetWeightFileDir() const
Definition: MethodBase.h:490
virtual void SetAnalysisType(Types::EAnalysisType type)
Definition: MethodBase.h:436
UInt_t GetCurrentIter()
Definition: MethodBase.h:479
TString fTestvar
Definition: MethodBase.h:615
const TString & GetMethodName() const
Definition: MethodBase.h:330
Bool_t TxtWeightsOnly() const
Definition: MethodBase.h:532
void Statistics(Types::ETreeType treeType, const TString &theVarName, Double_t &, Double_t &, Double_t &, Double_t &, Double_t &, Double_t &)
calculates rms,mean, xmin, xmax of the event variable this can be either done for the variables as th...
virtual void ReadWeightsFromStream(TFile &)
Definition: MethodBase.h:265
void ExitFromTraining()
Definition: MethodBase.h:462
UInt_t GetNEvents() const
temporary event when testing on a different DataSet than the own one
Definition: MethodBase.h:416
Bool_t DoRegression() const
Definition: MethodBase.h:438
std::vector< Float_t > * fRegressionReturnVal
Definition: MethodBase.h:595
std::vector< Float_t > * fMulticlassReturnVal
Definition: MethodBase.h:596
Double_t fRmsS
Definition: MethodBase.h:661
Bool_t GetLine(std::istream &fin, char *buf)
reads one line from the input stream checks for certain keywords and interprets the line if keywords ...
const Event * GetEvent() const
Definition: MethodBase.h:749
TString fVarTransformString
Definition: MethodBase.h:667
void ProcessSetup()
process all options the "CheckForUnusedOptions" is done in an independent call, since it may be overr...
Definition: MethodBase.cxx:425
virtual void ProcessOptions()=0
virtual std::vector< Double_t > GetMvaValues(Long64_t firstEvt=0, Long64_t lastEvt=-1, Bool_t logProgress=false)
get all the MVA values for the events of the current Data type
Definition: MethodBase.cxx:896
virtual Bool_t IsSignalLike()
uses a pre-set cut on the MVA output (SetSignalReferenceCut and SetSignalReferenceCutOrientation) for...
Definition: MethodBase.cxx:856
void RerouteTransformationHandler(TransformationHandler *fTargetTransformation)
Definition: MethodBase.h:402
virtual ~MethodBase()
destructor
Definition: MethodBase.cxx:366
Bool_t HasMVAPdfs() const
Definition: MethodBase.h:435
UInt_t fBackgroundClass
Definition: MethodBase.h:688
Double_t fTestTime
Definition: MethodBase.h:694
virtual Double_t GetMaximumSignificance(Double_t SignalEvents, Double_t BackgroundEvents, Double_t &optimal_significance_value) const
plot significance, , curve for given number of signal and background events; returns cut for maximum ...
virtual Double_t GetTrainingEfficiency(const TString &)
void SetWeightFileName(TString)
set the weight file name (depreciated)
DataSetInfo & DataInfo() const
Definition: MethodBase.h:409
virtual void MakeClass(const TString &classFileName=TString("")) const
create reader class for method (classification only at present)
TString GetWeightFileName() const
retrieve weight file name
void SetTestTime(Double_t testTime)
Definition: MethodBase.h:165
Types::EMVA fMethodType
Definition: MethodBase.h:614
virtual void TestClassification()
initialization
void AddOutput(Types::ETreeType type, Types::EAnalysisType analysisType)
virtual void WriteMonitoringHistosToFile() const
write special monitoring histograms to file dummy implementation here --------------—
UInt_t GetNVariables() const
Definition: MethodBase.h:344
Types::EAnalysisType fAnalysisType
Definition: MethodBase.h:593
virtual void AddRegressionOutput(Types::ETreeType type)
prepare tree branch with the method's discriminating variable
Definition: MethodBase.cxx:746
void InitBase()
default initialization called by all constructors
Definition: MethodBase.cxx:443
std::vector< const std::vector< TMVA::Event * > * > fEventCollections
Definition: MethodBase.h:706
bool TrainingEnded()
Definition: MethodBase.h:467
virtual void GetRegressionDeviation(UInt_t tgtNum, Types::ETreeType type, Double_t &stddev, Double_t &stddev90Percent) const
Definition: MethodBase.cxx:726
void InitIPythonInteractive()
Definition: MethodBase.h:453
void ReadStateFromXMLString(const char *xmlstr)
for reading from memory
Bool_t fSetupCompleted
Definition: MethodBase.h:709
void CreateMVAPdfs()
Create PDFs of the MVA output variables.
TString GetTrainingROOTVersionString() const
calculates the ROOT version string from the training version code on the fly
virtual Double_t GetValueForRoot(Double_t)
returns efficiency as function of cut
UInt_t GetTrainingROOTVersionCode() const
Definition: MethodBase.h:389
void ReadStateFromFile()
Function to write options and weights to file.
void WriteVarsToStream(std::ostream &tf, const TString &prefix="") const
write the list of variables (name, min, max) for a given data transformation method to the stream
void ReadVarsFromStream(std::istream &istr)
Read the variables (name, min, max) for a given data transformation method from the stream.
virtual void AddWeightsXMLTo(void *parent) const =0
virtual void Init()=0
void ReadSpectatorsFromXML(void *specnode)
read spectator info from XML
virtual const std::vector< Float_t > & GetTrainingHistory(const char *)
Definition: MethodBase.h:232
const Event * fTmpEvent
Definition: MethodBase.h:411
virtual Double_t GetMvaValue(Double_t *errLower=0, Double_t *errUpper=0)=0
bool fExitFromTraining
Definition: MethodBase.h:447
void SetNormalised(Bool_t norm)
Definition: MethodBase.h:495
void SetTestvarName(const TString &v="")
Definition: MethodBase.h:340
void ReadVariablesFromXML(void *varnode)
read variable info from XML
Bool_t fConstructedFromWeightFile
Definition: MethodBase.h:618
UInt_t GetNvar() const
Definition: MethodBase.h:343
virtual std::map< TString, Double_t > OptimizeTuningParameters(TString fomType="ROCIntegral", TString fitType="FitGA")
call the Optimizer with the set of parameters and ranges that are meant to be tuned.
Definition: MethodBase.cxx:625
void SetTrainTime(Double_t trainTime)
Definition: MethodBase.h:161
Double_t GetXmax(Int_t ivar) const
Definition: MethodBase.h:356
virtual std::vector< Float_t > GetMulticlassTrainingEfficiency(std::vector< std::vector< Float_t > > &purity)
DataSetInfo & fDataSetInfo
Definition: MethodBase.h:605
Bool_t fHasMVAPdfs
Definition: MethodBase.h:678
void WriteStateToStream(std::ostream &tf) const
general method used in writing the header of the weight files where the used variables,...
virtual Double_t GetRarity(Double_t mvaVal, Types::ESBType reftype=Types::kBackground) const
compute rarity:
virtual void SetTuneParameters(std::map< TString, Double_t > tuneParameters)
set the tuning parameters according to the argument This is just a dummy .
Definition: MethodBase.cxx:646
Double_t GetTrainTime() const
Definition: MethodBase.h:162
void SetBaseDir(TDirectory *methodDir)
Definition: MethodBase.h:372
void ReadStateFromStream(std::istream &tf)
read the header from the weight files of the different MVA methods
void AddVarsXMLTo(void *parent) const
write variable info to XML
Bool_t Help() const
Definition: MethodBase.h:502
TSpline1 * fSplRefB
Definition: MethodBase.h:701
TString fJobName
Definition: MethodBase.h:612
UInt_t fIPyCurrentIter
Definition: MethodBase.h:448
TransformationHandler & GetTransformationHandler(Bool_t takeReroutedIfAvailable=true)
Definition: MethodBase.h:393
Bool_t IsSilentFile() const
Definition: MethodBase.h:378
Types::EMVA GetMethodType() const
Definition: MethodBase.h:332
void AddTargetsXMLTo(void *parent) const
write target info to XML
Results * fResults
Definition: MethodBase.h:728
Double_t fXmin
Definition: MethodBase.h:663
void ReadTargetsFromXML(void *tarnode)
read target info from XML
void SetFile(TFile *file)
Definition: MethodBase.h:374
void ProcessBaseOptions()
the option string is decoded, for available options see "DeclareOptions"
Definition: MethodBase.cxx:542
virtual void Reset()
Definition: MethodBase.h:193
UInt_t fROOTTrainingVersion
Definition: MethodBase.h:617
void ReadStateFromXML(void *parent)
Double_t GetSignalReferenceCutOrientation() const
Definition: MethodBase.h:360
void SetSignalReferenceCut(Double_t cut)
Definition: MethodBase.h:363
std::vector< TString > * fInputVars
Definition: MethodBase.h:586
void NoErrorCalc(Double_t *const err, Double_t *const errUpper)
Definition: MethodBase.cxx:838
Double_t fSignalReferenceCut
the data set information (sometimes needed)
Definition: MethodBase.h:607
Double_t GetTestTime() const
Definition: MethodBase.h:166
UInt_t fSignalClass
Definition: MethodBase.h:687
Double_t fMeanB
Definition: MethodBase.h:660
const TransformationHandler & GetTransformationHandler(Bool_t takeReroutedIfAvailable=true) const
Definition: MethodBase.h:397
Int_t fNsmoothMVAPdf
Definition: MethodBase.h:725
void SetSignalReferenceCutOrientation(Double_t cutOrientation)
Definition: MethodBase.h:364
TDirectory * fBaseDir
Definition: MethodBase.h:623
Bool_t fIgnoreNegWeightsInTraining
Definition: MethodBase.h:680
void WriteStateToFile() const
write options and weights to file note that each one text file for the main configuration information...
void AddClassesXMLTo(void *parent) const
write class info to XML
const TString & GetInputLabel(Int_t i) const
Definition: MethodBase.h:349
ECutOrientation GetCutOrientation() const
Definition: MethodBase.h:550
Ranking * fRanking
Definition: MethodBase.h:585
TrainingHistory fTrainHistory
Definition: MethodBase.h:425
void SetMethodBaseDir(TDirectory *methodDir)
Definition: MethodBase.h:373
virtual void AddClassifierOutput(Types::ETreeType type)
prepare tree branch with the method's discriminating variable
Definition: MethodBase.cxx:870
DataSet * Data() const
Definition: MethodBase.h:408
void AddSpectatorsXMLTo(void *parent) const
write spectator info to XML
void SetModelPersistence(Bool_t status)
Definition: MethodBase.h:381
TString fWeightFile
Definition: MethodBase.h:636
Bool_t IsNormalised() const
Definition: MethodBase.h:494
const TString & GetInternalVarName(Int_t ivar) const
Definition: MethodBase.h:508
const Event * GetTrainingEvent(Long64_t ievt) const
Definition: MethodBase.h:769
Double_t GetSignalReferenceCut() const
Definition: MethodBase.h:359
TSpline1 * fSplTrainRefS
Definition: MethodBase.h:703
TSpline1 * fSplRefS
Definition: MethodBase.h:700
virtual Double_t GetROCIntegral(TH1D *histS, TH1D *histB) const
calculate the area (integral) under the ROC curve as a overall quality measure of the classification
ECutOrientation fCutOrientation
Definition: MethodBase.h:697
virtual const Ranking * CreateRanking()=0
Double_t GetRMS(Int_t ivar) const
Definition: MethodBase.h:354
IPythonInteractive * fInteractive
Definition: MethodBase.h:446
const char * GetInputTitle(Int_t i) const
Definition: MethodBase.h:350
const TString & GetOriginalVarName(Int_t ivar) const
Definition: MethodBase.h:509
virtual void AddMulticlassOutput(Types::ETreeType type)
prepare tree branch with the method's discriminating variable
Definition: MethodBase.cxx:796
Bool_t IsConstructedFromWeightFile() const
Definition: MethodBase.h:538
virtual void CheckSetup()
check may be overridden by derived class (sometimes, eg, fitters are used which can only be implement...
Definition: MethodBase.cxx:435
Class for boosting a TMVA method.
Definition: MethodBoost.h:58
Class for categorizing the phase space.
Virtual base class for combining several TMVA method.
Multivariate optimisation of signal efficiency for given background efficiency, applying rectangular ...
Definition: MethodCuts.h:61
PDF wrapper for histograms; uses user-defined spline interpolation.
Definition: PDF.h:63
Ranking for variables in method (implementation)
Definition: Ranking.h:48
Class that is the base-class for a vector of result.
Definition: Results.h:57
Root finding using Brents algorithm (translated from CERNLIB function RZERO)
Definition: RootFinder.h:48
Linear interpolation of TGraph.
Definition: TSpline1.h:43
Tracking data from training.
Class that contains all the data information.
const Event * Transform(const Event *) const
the transformation
Double_t GetRMS(Int_t ivar, Int_t cls=-1) const
Double_t GetMean(Int_t ivar, Int_t cls=-1) const
Double_t GetMin(Int_t ivar, Int_t cls=-1) const
Double_t GetMax(Int_t ivar, Int_t cls=-1) const
TString GetMethodName(Types::EMVA method) const
Definition: Types.cxx:136
static Types & Instance()
the the single instance of "Types" if existing already, or create it (Singleton)
Definition: Types.cxx:70
@ kBackground
Definition: Types.h:137
EAnalysisType
Definition: Types.h:127
@ kMulticlass
Definition: Types.h:130
@ kRegression
Definition: Types.h:129
@ kTraining
Definition: Types.h:144
@ kTesting
Definition: Types.h:145
const TString & GetLabel() const
Definition: VariableInfo.h:59
const TString & GetExpression() const
Definition: VariableInfo.h:57
const TString & GetInternalName() const
Definition: VariableInfo.h:58
A TMultiGraph is a collection of TGraph (or derived) objects.
Definition: TMultiGraph.h:36
virtual const char * GetTitle() const
Returns title of object.
Definition: TNamed.h:48
Base class for spline implementation containing the Draw/Paint methods.
Definition: TSpline.h:22
Basic string class.
Definition: TString.h:131
const char * Data() const
Definition: TString.h:364
A TTree represents a columnar dataset.
Definition: TTree.h:78
Double_t x[n]
Definition: legend1.C:17
create variable transformations
Definition: file.py:1