Virtual base Class for all MVA method.
MethodBase hosts several specific evaluation methods.
The kind of MVA that provides optimal performance in an analysis strongly depends on the particular application. The evaluation factory provides a number of numerical benchmark results to directly assess the performance of the MVA training on the independent test sample. These are:
\[ \frac{1}{2} \int \frac{(S(x) - B(x))^2}{(S(x) + B(x))} dx \]
where \( S(x) \) and \( B(x) \) are the signal and background distributions, respectively. The separation is zero for identical signal and background MVA shapes, and it is one for disjunctive shapes.The average, \( \int x \mu (S(x)) dx \), of the signal \( \mu_{transform} \). The \( \mu_{transform} \) of an MVA denotes the transformation that yields a uniform background distribution. In this way, the signal distributions \( S(x) \) can be directly compared among the various MVAs. The stronger \( S(x) \) peaks towards one, the better is the discrimination of the MVA. The \( \mu_{transform} \) is documented here.
The MVA standard output also prints the linear correlation coefficients between signal and background, which can be useful to eliminate variables that exhibit too strong correlations.
Definition at line 111 of file MethodBase.h.
Public Types | |
enum | EWeightFileType { kROOT =0 , kTEXT } |
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enum | { kIsOnHeap = 0x01000000 , kNotDeleted = 0x02000000 , kZombie = 0x04000000 , kInconsistent = 0x08000000 , kBitMask = 0x00ffffff } |
enum | { kSingleKey = (1ULL << ( 0 )) , kOverwrite = (1ULL << ( 1 )) , kWriteDelete = (1ULL << ( 2 )) } |
enum | EDeprecatedStatusBits { kObjInCanvas = (1ULL << ( 3 )) } |
enum | EStatusBits { kCanDelete = (1ULL << ( 0 )) , kMustCleanup = (1ULL << ( 3 )) , kIsReferenced = (1ULL << ( 4 )) , kHasUUID = (1ULL << ( 5 )) , kCannotPick = (1ULL << ( 6 )) , kNoContextMenu = (1ULL << ( 8 )) , kInvalidObject = (1ULL << ( 13 )) } |
Public Member Functions | |
MethodBase (const TString &jobName, Types::EMVA methodType, const TString &methodTitle, DataSetInfo &dsi, const TString &theOption="") | |
standard constructor | |
MethodBase (Types::EMVA methodType, DataSetInfo &dsi, const TString &weightFile) | |
constructor used for Testing + Application of the MVA, only (no training), using given WeightFiles | |
virtual | ~MethodBase () |
destructor | |
void | AddOutput (Types::ETreeType type, Types::EAnalysisType analysisType) |
TDirectory * | BaseDir () const |
returns the ROOT directory where info/histograms etc of the corresponding MVA method instance are stored | |
virtual void | CheckSetup () |
check may be overridden by derived class (sometimes, eg, fitters are used which can only be implemented during training phase) | |
virtual const Ranking * | CreateRanking ()=0 |
DataSet * | Data () const |
DataSetInfo & | DataInfo () const |
virtual void | DeclareCompatibilityOptions () |
options that are used ONLY for the READER to ensure backward compatibility they are hence without any effect (the reader is only reading the training options that HAD been used at the training of the .xml weight file at hand | |
virtual void | DeclareOptions ()=0 |
void | DisableWriting (Bool_t setter) |
Bool_t | DoMulticlass () const |
Bool_t | DoRegression () const |
void | ExitFromTraining () |
Types::EAnalysisType | GetAnalysisType () const |
UInt_t | GetCurrentIter () |
virtual Double_t | GetEfficiency (const TString &, Types::ETreeType, Double_t &err) |
fill background efficiency (resp. | |
const Event * | GetEvent () const |
const Event * | GetEvent (const TMVA::Event *ev) const |
const Event * | GetEvent (Long64_t ievt) const |
const Event * | GetEvent (Long64_t ievt, Types::ETreeType type) const |
const std::vector< TMVA::Event * > & | GetEventCollection (Types::ETreeType type) |
returns the event collection (i.e. | |
TFile * | GetFile () const |
const TString & | GetInputLabel (Int_t i) const |
const char * | GetInputTitle (Int_t i) const |
const TString & | GetInputVar (Int_t i) const |
TMultiGraph * | GetInteractiveTrainingError () |
const TString & | GetJobName () const |
virtual Double_t | GetKSTrainingVsTest (Char_t SorB, TString opt="X") |
virtual Double_t | GetMaximumSignificance (Double_t SignalEvents, Double_t BackgroundEvents, Double_t &optimal_significance_value) const |
plot significance, \( \frac{S}{\sqrt{S^2 + B^2}} \), curve for given number of signal and background events; returns cut for maximum significance also returned via reference is the maximum significance | |
UInt_t | GetMaxIter () |
Double_t | GetMean (Int_t ivar) const |
const TString & | GetMethodName () const |
Types::EMVA | GetMethodType () const |
TString | GetMethodTypeName () const |
virtual TMatrixD | GetMulticlassConfusionMatrix (Double_t effB, Types::ETreeType type) |
Construct a confusion matrix for a multiclass classifier. | |
virtual std::vector< Float_t > | GetMulticlassEfficiency (std::vector< std::vector< Float_t > > &purity) |
virtual std::vector< Float_t > | GetMulticlassTrainingEfficiency (std::vector< std::vector< Float_t > > &purity) |
virtual const std::vector< Float_t > & | GetMulticlassValues () |
Double_t | GetMvaValue (const TMVA::Event *const ev, Double_t *err=nullptr, Double_t *errUpper=nullptr) |
virtual Double_t | GetMvaValue (Double_t *errLower=nullptr, Double_t *errUpper=nullptr)=0 |
const char * | GetName () const |
UInt_t | GetNEvents () const |
UInt_t | GetNTargets () const |
UInt_t | GetNvar () const |
UInt_t | GetNVariables () const |
virtual Double_t | GetProba (const Event *ev) |
virtual Double_t | GetProba (Double_t mvaVal, Double_t ap_sig) |
compute likelihood ratio | |
const TString | GetProbaName () const |
virtual Double_t | GetRarity (Double_t mvaVal, Types::ESBType reftype=Types::kBackground) const |
compute rarity: | |
virtual void | GetRegressionDeviation (UInt_t tgtNum, Types::ETreeType type, Double_t &stddev, Double_t &stddev90Percent) const |
virtual const std::vector< Float_t > & | GetRegressionValues () |
const std::vector< Float_t > & | GetRegressionValues (const TMVA::Event *const ev) |
Double_t | GetRMS (Int_t ivar) const |
virtual Double_t | GetROCIntegral (PDF *pdfS=nullptr, PDF *pdfB=nullptr) const |
calculate the area (integral) under the ROC curve as a overall quality measure of the classification | |
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 | |
virtual Double_t | GetSeparation (PDF *pdfS=nullptr, PDF *pdfB=nullptr) const |
compute "separation" defined as | |
virtual Double_t | GetSeparation (TH1 *, TH1 *) const |
compute "separation" defined as | |
Double_t | GetSignalReferenceCut () const |
Double_t | GetSignalReferenceCutOrientation () const |
virtual Double_t | GetSignificance () const |
compute significance of mean difference | |
const Event * | GetTestingEvent (Long64_t ievt) const |
Double_t | GetTestTime () const |
const TString & | GetTestvarName () const |
virtual Double_t | GetTrainingEfficiency (const TString &) |
const Event * | GetTrainingEvent (Long64_t ievt) const |
virtual const std::vector< Float_t > & | GetTrainingHistory (const char *) |
UInt_t | GetTrainingROOTVersionCode () const |
TString | GetTrainingROOTVersionString () const |
calculates the ROOT version string from the training version code on the fly | |
UInt_t | GetTrainingTMVAVersionCode () const |
TString | GetTrainingTMVAVersionString () const |
calculates the TMVA version string from the training version code on the fly | |
Double_t | GetTrainTime () const |
TransformationHandler & | GetTransformationHandler (Bool_t takeReroutedIfAvailable=true) |
const TransformationHandler & | GetTransformationHandler (Bool_t takeReroutedIfAvailable=true) const |
TString | GetWeightFileName () const |
retrieve weight file name | |
Double_t | GetXmax (Int_t ivar) const |
Double_t | GetXmin (Int_t ivar) const |
Bool_t | HasMVAPdfs () const |
virtual void | Init ()=0 |
void | InitIPythonInteractive () |
virtual TClass * | IsA () const |
Bool_t | IsModelPersistence () const |
virtual Bool_t | IsSignalLike () |
uses a pre-set cut on the MVA output (SetSignalReferenceCut and SetSignalReferenceCutOrientation) for a quick determination if an event would be selected as signal or background | |
virtual Bool_t | IsSignalLike (Double_t mvaVal) |
uses a pre-set cut on the MVA output (SetSignalReferenceCut and SetSignalReferenceCutOrientation) for a quick determination if an event with this mva output value would be selected as signal or background | |
Bool_t | IsSilentFile () const |
virtual void | MakeClass (const TString &classFileName=TString("")) const |
create reader class for method (classification only at present) | |
TDirectory * | MethodBaseDir () const |
returns the ROOT directory where all instances of the corresponding MVA method are stored | |
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. | |
void | PrintHelpMessage () const |
prints out method-specific help method | |
virtual void | ProcessOptions ()=0 |
void | ProcessSetup () |
process all options the "CheckForUnusedOptions" is done in an independent call, since it may be overridden by derived class (sometimes, eg, fitters are used which can only be implemented during training phase) | |
void | ReadStateFromFile () |
Function to write options and weights to file. | |
void | ReadStateFromStream (std::istream &tf) |
read the header from the weight files of the different MVA methods | |
void | ReadStateFromStream (TFile &rf) |
write reference MVA distributions (and other information) to a ROOT type weight file | |
void | ReadStateFromXMLString (const char *xmlstr) |
for reading from memory | |
void | RerouteTransformationHandler (TransformationHandler *fTargetTransformation) |
virtual void | Reset () |
virtual void | SetAnalysisType (Types::EAnalysisType type) |
void | SetBaseDir (TDirectory *methodDir) |
void | SetFile (TFile *file) |
void | SetMethodBaseDir (TDirectory *methodDir) |
void | SetMethodDir (TDirectory *methodDir) |
void | SetModelPersistence (Bool_t status) |
void | SetSignalReferenceCut (Double_t cut) |
void | SetSignalReferenceCutOrientation (Double_t cutOrientation) |
void | SetSilentFile (Bool_t status) |
void | SetTestTime (Double_t testTime) |
void | SetTestvarName (const TString &v="") |
void | SetTrainTime (Double_t trainTime) |
virtual void | SetTuneParameters (std::map< TString, Double_t > tuneParameters) |
set the tuning parameters according to the argument This is just a dummy . | |
void | SetupMethod () |
setup of methods | |
virtual void | Streamer (TBuffer &) |
void | StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b) |
virtual void | TestClassification () |
initialization | |
virtual void | TestMulticlass () |
test multiclass classification | |
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 | |
virtual void | Train ()=0 |
bool | TrainingEnded () |
void | TrainMethod () |
virtual void | WriteEvaluationHistosToFile (Types::ETreeType treetype) |
writes all MVA evaluation histograms to file | |
virtual void | WriteMonitoringHistosToFile () const |
write special monitoring histograms to file dummy implementation here --------------— | |
void | WriteStateToFile () const |
write options and weights to file note that each one text file for the main configuration information and one ROOT file for ROOT objects are created | |
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IMethod () | |
virtual | ~IMethod () |
virtual Bool_t | HasAnalysisType (Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets)=0 |
void | StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b) |
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Configurable (const TString &theOption="") | |
constructor | |
virtual | ~Configurable () |
default destructor | |
void | AddOptionsXMLTo (void *parent) const |
write options to XML file | |
template<class T > | |
void | AddPreDefVal (const T &) |
template<class T > | |
void | AddPreDefVal (const TString &optname, const T &) |
void | CheckForUnusedOptions () const |
checks for unused options in option string | |
template<class T > | |
TMVA::OptionBase * | DeclareOptionRef (T &ref, const TString &name, const TString &desc) |
template<class T > | |
OptionBase * | DeclareOptionRef (T &ref, const TString &name, const TString &desc="") |
template<class T > | |
TMVA::OptionBase * | DeclareOptionRef (T *&ref, Int_t size, const TString &name, const TString &desc) |
template<class T > | |
OptionBase * | DeclareOptionRef (T *&ref, Int_t size, const TString &name, const TString &desc="") |
const char * | GetConfigDescription () const |
const char * | GetConfigName () const |
const TString & | GetOptions () const |
MsgLogger & | Log () const |
virtual void | ParseOptions () |
options parser | |
void | PrintOptions () const |
prints out the options set in the options string and the defaults | |
void | ReadOptionsFromStream (std::istream &istr) |
read option back from the weight file | |
void | ReadOptionsFromXML (void *node) |
void | SetConfigDescription (const char *d) |
void | SetConfigName (const char *n) |
void | SetMsgType (EMsgType t) |
void | SetOptions (const TString &s) |
void | StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b) |
void | WriteOptionsToStream (std::ostream &o, const TString &prefix) const |
write options to output stream (e.g. in writing the MVA weight files | |
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TNamed () | |
TNamed (const char *name, const char *title) | |
TNamed (const TNamed &named) | |
TNamed copy ctor. | |
TNamed (const TString &name, const TString &title) | |
virtual | ~TNamed () |
TNamed destructor. | |
void | Clear (Option_t *option="") override |
Set name and title to empty strings (""). | |
TObject * | Clone (const char *newname="") const override |
Make a clone of an object using the Streamer facility. | |
Int_t | Compare (const TObject *obj) const override |
Compare two TNamed objects. | |
void | Copy (TObject &named) const override |
Copy this to obj. | |
virtual void | FillBuffer (char *&buffer) |
Encode TNamed into output buffer. | |
const char * | GetName () const override |
Returns name of object. | |
const char * | GetTitle () const override |
Returns title of object. | |
ULong_t | Hash () const override |
Return hash value for this object. | |
TClass * | IsA () const override |
Bool_t | IsSortable () const override |
void | ls (Option_t *option="") const override |
List TNamed name and title. | |
TNamed & | operator= (const TNamed &rhs) |
TNamed assignment operator. | |
void | Print (Option_t *option="") const override |
Print TNamed name and title. | |
virtual void | SetName (const char *name) |
Set the name of the TNamed. | |
virtual void | SetNameTitle (const char *name, const char *title) |
Set all the TNamed parameters (name and title). | |
virtual void | SetTitle (const char *title="") |
Set the title of the TNamed. | |
virtual Int_t | Sizeof () const |
Return size of the TNamed part of the TObject. | |
void | Streamer (TBuffer &) override |
Stream an object of class TObject. | |
void | StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b) |
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TObject () | |
TObject constructor. | |
TObject (const TObject &object) | |
TObject copy ctor. | |
virtual | ~TObject () |
TObject destructor. | |
void | AbstractMethod (const char *method) const |
Use this method to implement an "abstract" method that you don't want to leave purely abstract. | |
virtual void | AppendPad (Option_t *option="") |
Append graphics object to current pad. | |
virtual void | Browse (TBrowser *b) |
Browse object. May be overridden for another default action. | |
ULong_t | CheckedHash () |
Check and record whether this class has a consistent Hash/RecursiveRemove setup (*) and then return the regular Hash value for this object. | |
virtual const char * | ClassName () const |
Returns name of class to which the object belongs. | |
virtual void | Delete (Option_t *option="") |
Delete this object. | |
virtual Int_t | DistancetoPrimitive (Int_t px, Int_t py) |
Computes distance from point (px,py) to the object. | |
virtual void | Draw (Option_t *option="") |
Default Draw method for all objects. | |
virtual void | DrawClass () const |
Draw class inheritance tree of the class to which this object belongs. | |
virtual TObject * | DrawClone (Option_t *option="") const |
Draw a clone of this object in the current selected pad with: gROOT->SetSelectedPad(c1) . | |
virtual void | Dump () const |
Dump contents of object on stdout. | |
virtual void | Error (const char *method, const char *msgfmt,...) const |
Issue error message. | |
virtual void | Execute (const char *method, const char *params, Int_t *error=nullptr) |
Execute method on this object with the given parameter string, e.g. | |
virtual void | Execute (TMethod *method, TObjArray *params, Int_t *error=nullptr) |
Execute method on this object with parameters stored in the TObjArray. | |
virtual void | ExecuteEvent (Int_t event, Int_t px, Int_t py) |
Execute action corresponding to an event at (px,py). | |
virtual void | Fatal (const char *method, const char *msgfmt,...) const |
Issue fatal error message. | |
virtual TObject * | FindObject (const char *name) const |
Must be redefined in derived classes. | |
virtual TObject * | FindObject (const TObject *obj) const |
Must be redefined in derived classes. | |
virtual Option_t * | GetDrawOption () const |
Get option used by the graphics system to draw this object. | |
virtual const char * | GetIconName () const |
Returns mime type name of object. | |
virtual char * | GetObjectInfo (Int_t px, Int_t py) const |
Returns string containing info about the object at position (px,py). | |
virtual Option_t * | GetOption () const |
virtual UInt_t | GetUniqueID () const |
Return the unique object id. | |
virtual Bool_t | HandleTimer (TTimer *timer) |
Execute action in response of a timer timing out. | |
Bool_t | HasInconsistentHash () const |
Return true is the type of this object is known to have an inconsistent setup for Hash and RecursiveRemove (i.e. | |
virtual void | Info (const char *method, const char *msgfmt,...) const |
Issue info message. | |
virtual Bool_t | InheritsFrom (const char *classname) const |
Returns kTRUE if object inherits from class "classname". | |
virtual Bool_t | InheritsFrom (const TClass *cl) const |
Returns kTRUE if object inherits from TClass cl. | |
virtual void | Inspect () const |
Dump contents of this object in a graphics canvas. | |
void | InvertBit (UInt_t f) |
Bool_t | IsDestructed () const |
IsDestructed. | |
virtual Bool_t | IsEqual (const TObject *obj) const |
Default equal comparison (objects are equal if they have the same address in memory). | |
virtual Bool_t | IsFolder () const |
Returns kTRUE in case object contains browsable objects (like containers or lists of other objects). | |
R__ALWAYS_INLINE Bool_t | IsOnHeap () const |
R__ALWAYS_INLINE Bool_t | IsZombie () const |
void | MayNotUse (const char *method) const |
Use this method to signal that a method (defined in a base class) may not be called in a derived class (in principle against good design since a child class should not provide less functionality than its parent, however, sometimes it is necessary). | |
virtual Bool_t | Notify () |
This method must be overridden to handle object notification (the base implementation is no-op). | |
void | Obsolete (const char *method, const char *asOfVers, const char *removedFromVers) const |
Use this method to declare a method obsolete. | |
void | operator delete (void *ptr) |
Operator delete. | |
void | operator delete (void *ptr, void *vp) |
Only called by placement new when throwing an exception. | |
void | operator delete[] (void *ptr) |
Operator delete []. | |
void | operator delete[] (void *ptr, void *vp) |
Only called by placement new[] when throwing an exception. | |
void * | operator new (size_t sz) |
void * | operator new (size_t sz, void *vp) |
void * | operator new[] (size_t sz) |
void * | operator new[] (size_t sz, void *vp) |
TObject & | operator= (const TObject &rhs) |
TObject assignment operator. | |
virtual void | Paint (Option_t *option="") |
This method must be overridden if a class wants to paint itself. | |
virtual void | Pop () |
Pop on object drawn in a pad to the top of the display list. | |
virtual Int_t | Read (const char *name) |
Read contents of object with specified name from the current directory. | |
virtual void | RecursiveRemove (TObject *obj) |
Recursively remove this object from a list. | |
void | ResetBit (UInt_t f) |
virtual void | SaveAs (const char *filename="", Option_t *option="") const |
Save this object in the file specified by filename. | |
virtual void | SavePrimitive (std::ostream &out, Option_t *option="") |
Save a primitive as a C++ statement(s) on output stream "out". | |
void | SetBit (UInt_t f) |
void | SetBit (UInt_t f, Bool_t set) |
Set or unset the user status bits as specified in f. | |
virtual void | SetDrawOption (Option_t *option="") |
Set drawing option for object. | |
virtual void | SetUniqueID (UInt_t uid) |
Set the unique object id. | |
void | StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b) |
virtual void | SysError (const char *method, const char *msgfmt,...) const |
Issue system error message. | |
R__ALWAYS_INLINE Bool_t | TestBit (UInt_t f) const |
Int_t | TestBits (UInt_t f) const |
virtual void | UseCurrentStyle () |
Set current style settings in this object This function is called when either TCanvas::UseCurrentStyle or TROOT::ForceStyle have been invoked. | |
virtual void | Warning (const char *method, const char *msgfmt,...) const |
Issue warning message. | |
virtual Int_t | Write (const char *name=nullptr, Int_t option=0, Int_t bufsize=0) |
Write this object to the current directory. | |
virtual Int_t | Write (const char *name=nullptr, Int_t option=0, Int_t bufsize=0) const |
Write this object to the current directory. | |
Static Public Member Functions | |
static TClass * | Class () |
static const char * | Class_Name () |
static constexpr Version_t | Class_Version () |
static const char * | DeclFileName () |
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static TClass * | Class () |
static const char * | Class_Name () |
static constexpr Version_t | Class_Version () |
static const char * | DeclFileName () |
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static TClass * | Class () |
static const char * | Class_Name () |
static constexpr Version_t | Class_Version () |
static const char * | DeclFileName () |
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static TClass * | Class () |
static const char * | Class_Name () |
static constexpr Version_t | Class_Version () |
static const char * | DeclFileName () |
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static TClass * | Class () |
static const char * | Class_Name () |
static constexpr Version_t | Class_Version () |
static const char * | DeclFileName () |
static Longptr_t | GetDtorOnly () |
Return destructor only flag. | |
static Bool_t | GetObjectStat () |
Get status of object stat flag. | |
static void | SetDtorOnly (void *obj) |
Set destructor only flag. | |
static void | SetObjectStat (Bool_t stat) |
Turn on/off tracking of objects in the TObjectTable. | |
Public Attributes | |
Bool_t | fSetupCompleted |
TrainingHistory | fTrainHistory |
Protected Member Functions | |
virtual void | AddWeightsXMLTo (void *parent) const =0 |
virtual std::vector< Double_t > | GetDataMvaValues (DataSet *data=nullptr, Long64_t firstEvt=0, Long64_t lastEvt=-1, Bool_t logProgress=false) |
get all the MVA values for the events of the given Data type | |
const TString & | GetInternalVarName (Int_t ivar) const |
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 | |
const TString & | GetOriginalVarName (Int_t ivar) const |
const TString & | GetWeightFileDir () const |
Bool_t | HasTrainingTree () const |
Bool_t | Help () const |
Bool_t | IgnoreEventsWithNegWeightsInTraining () const |
Bool_t | IsConstructedFromWeightFile () const |
Bool_t | IsNormalised () const |
virtual void | MakeClassSpecific (std::ostream &, const TString &="") const |
virtual void | MakeClassSpecificHeader (std::ostream &, const TString &="") const |
void | NoErrorCalc (Double_t *const err, Double_t *const errUpper) |
virtual void | ReadWeightsFromStream (std::istream &)=0 |
virtual void | ReadWeightsFromStream (TFile &) |
virtual void | ReadWeightsFromXML (void *wghtnode)=0 |
void | SetNormalised (Bool_t norm) |
void | SetWeightFileDir (TString fileDir) |
set directory of weight file | |
void | SetWeightFileName (TString) |
set the weight file name (depreciated) | |
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 they are or for normalised variables (in the range of 0-1) if "norm" is set to kTRUE | |
Bool_t | TxtWeightsOnly () const |
Bool_t | Verbose () const |
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virtual void | GetHelpMessage () const =0 |
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void | EnableLooseOptions (Bool_t b=kTRUE) |
const TString & | GetReferenceFile () const |
Bool_t | LooseOptionCheckingEnabled () const |
void | ResetSetFlag () |
resets the IsSet flag for all declare options to be called before options are read from stream | |
void | WriteOptionsReferenceToFile () |
write complete options to output stream | |
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virtual void | DoError (int level, const char *location, const char *fmt, va_list va) const |
Interface to ErrorHandler (protected). | |
void | MakeZombie () |
Protected Attributes | |
Types::EAnalysisType | fAnalysisType |
UInt_t | fBackgroundClass |
bool | fExitFromTraining = false |
std::vector< TString > * | fInputVars |
IPythonInteractive * | fInteractive = nullptr |
temporary dataset used when evaluating on a different data (used by MethodCategory::GetMvaValues) | |
UInt_t | fIPyCurrentIter = 0 |
UInt_t | fIPyMaxIter = 0 |
std::vector< Float_t > * | fMulticlassReturnVal |
Int_t | fNbins |
Int_t | fNbinsH |
Int_t | fNbinsMVAoutput |
Ranking * | fRanking |
std::vector< Float_t > * | fRegressionReturnVal |
Results * | fResults |
UInt_t | fSignalClass |
DataSet * | fTmpData = nullptr |
temporary event when testing on a different DataSet than the own one | |
const Event * | fTmpEvent |
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MsgLogger * | fLogger |
! message logger | |
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TString | fName |
TString | fTitle |
Private Types | |
enum | ECutOrientation { kNegative = -1 , kPositive = +1 } |
Private Member Functions | |
void | AddClassesXMLTo (void *parent) const |
write class info to XML | |
virtual void | AddClassifierOutput (Types::ETreeType type) |
prepare tree branch with the method's discriminating variable | |
virtual void | AddClassifierOutputProb (Types::ETreeType type) |
prepare tree branch with the method's discriminating variable | |
void | AddInfoItem (void *gi, const TString &name, const TString &value) const |
xml writing | |
virtual void | AddMulticlassOutput (Types::ETreeType type) |
prepare tree branch with the method's discriminating variable | |
virtual void | AddRegressionOutput (Types::ETreeType type) |
prepare tree branch with the method's discriminating variable | |
void | AddSpectatorsXMLTo (void *parent) const |
write spectator info to XML | |
void | AddTargetsXMLTo (void *parent) const |
write target info to XML | |
void | AddVarsXMLTo (void *parent) const |
write variable info to XML | |
void | CreateMVAPdfs () |
Create PDFs of the MVA output variables. | |
void | DeclareBaseOptions () |
define the options (their key words) that can be set in the option string here the options valid for ALL MVA methods are declared. | |
ECutOrientation | GetCutOrientation () const |
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 are found | |
virtual Double_t | GetValueForRoot (Double_t) |
returns efficiency as function of cut | |
void | InitBase () |
default initialization called by all constructors | |
void | ProcessBaseOptions () |
the option string is decoded, for available options see "DeclareOptions" | |
void | ReadClassesFromXML (void *clsnode) |
read number of classes from XML | |
void | ReadSpectatorsFromXML (void *specnode) |
read spectator info from XML | |
void | ReadStateFromXML (void *parent) |
void | ReadTargetsFromXML (void *tarnode) |
read target info from XML | |
void | ReadVariablesFromXML (void *varnode) |
read variable info from XML | |
void | ReadVarsFromStream (std::istream &istr) |
Read the variables (name, min, max) for a given data transformation method from the stream. | |
void | ResetThisBase () |
void | WriteStateToStream (std::ostream &tf) const |
general method used in writing the header of the weight files where the used variables, variable transformation type etc. | |
void | WriteStateToXML (void *parent) const |
general method used in writing the header of the weight files where the used variables, variable transformation type etc. | |
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 | |
Friends | |
class | CrossValidation |
class | Experimental::Classification |
class | Factory |
class | MethodBoost |
class | MethodCategory |
class | MethodCompositeBase |
class | MethodCrossValidation |
class | MethodCuts |
class | RootFinder |
Additional Inherited Members | |
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enum | { kOnlyPrepStep = (1ULL << ( 3 )) } |
#include <TMVA/MethodBase.h>
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private |
Enumerator | |
---|---|
kNegative | |
kPositive |
Definition at line 551 of file MethodBase.h.
Enumerator | |
---|---|
kROOT | |
kTEXT |
Definition at line 122 of file MethodBase.h.
TMVA::MethodBase::MethodBase | ( | const TString & | jobName, |
Types::EMVA | methodType, | ||
const TString & | methodTitle, | ||
DataSetInfo & | dsi, | ||
const TString & | theOption = "" |
||
) |
standard constructor
Definition at line 237 of file MethodBase.cxx.
TMVA::MethodBase::MethodBase | ( | Types::EMVA | methodType, |
DataSetInfo & | dsi, | ||
const TString & | weightFile | ||
) |
constructor used for Testing + Application of the MVA, only (no training), using given WeightFiles
Definition at line 303 of file MethodBase.cxx.
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virtual |
destructor
Definition at line 364 of file MethodBase.cxx.
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private |
write class info to XML
Definition at line 1801 of file MethodBase.cxx.
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privatevirtual |
prepare tree branch with the method's discriminating variable
Definition at line 869 of file MethodBase.cxx.
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privatevirtual |
prepare tree branch with the method's discriminating variable
Definition at line 951 of file MethodBase.cxx.
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private |
xml writing
Definition at line 1306 of file MethodBase.cxx.
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privatevirtual |
prepare tree branch with the method's discriminating variable
Definition at line 794 of file MethodBase.cxx.
void TMVA::MethodBase::AddOutput | ( | Types::ETreeType | type, |
Types::EAnalysisType | analysisType | ||
) |
Definition at line 1315 of file MethodBase.cxx.
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privatevirtual |
prepare tree branch with the method's discriminating variable
Definition at line 744 of file MethodBase.cxx.
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private |
write spectator info to XML
Definition at line 1778 of file MethodBase.cxx.
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private |
write target info to XML
Definition at line 1821 of file MethodBase.cxx.
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private |
write variable info to XML
Definition at line 1762 of file MethodBase.cxx.
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protectedpure virtual |
Implemented in TMVA::MethodPyAdaBoost, TMVA::MethodPyGTB, TMVA::MethodPyKeras, TMVA::MethodPyRandomForest, TMVA::MethodPyTorch, TMVA::MethodC50, TMVA::MethodRSNNS, TMVA::MethodRSVM, TMVA::MethodRXGB, TMVA::MethodANNBase, TMVA::MethodBayesClassifier, TMVA::MethodBDT, TMVA::MethodCategory, TMVA::MethodCFMlpANN, TMVA::MethodCompositeBase, TMVA::MethodCrossValidation, TMVA::MethodCuts, TMVA::MethodDL, TMVA::MethodDNN, TMVA::MethodDT, TMVA::MethodFDA, TMVA::MethodFisher, TMVA::MethodHMatrix, TMVA::MethodKNN, TMVA::MethodLD, TMVA::MethodLikelihood, TMVA::MethodPDEFoam, TMVA::MethodPDERS, TMVA::MethodRuleFit, TMVA::MethodSVM, TMVA::MethodTMlpANN, TMVA::PyMethodBase, and TMVA::RMethodBase.
TDirectory * TMVA::MethodBase::BaseDir | ( | ) | const |
returns the ROOT directory where info/histograms etc of the corresponding MVA method instance are stored
Definition at line 1980 of file MethodBase.cxx.
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virtual |
check may be overridden by derived class (sometimes, eg, fitters are used which can only be implemented during training phase)
Reimplemented in TMVA::MethodBoost, TMVA::MethodCuts, and TMVA::MethodFDA.
Definition at line 433 of file MethodBase.cxx.
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static |
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inlinestaticconstexpr |
Definition at line 731 of file MethodBase.h.
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private |
Create PDFs of the MVA output variables.
Definition at line 2185 of file MethodBase.cxx.
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pure virtual |
Implements TMVA::IMethod.
Implemented in TMVA::MethodPyAdaBoost, TMVA::MethodPyGTB, TMVA::MethodPyKeras, TMVA::MethodPyRandomForest, TMVA::MethodPyTorch, TMVA::MethodC50, TMVA::MethodRSNNS, TMVA::MethodRSVM, TMVA::MethodRXGB, TMVA::MethodANNBase, TMVA::MethodBayesClassifier, TMVA::MethodBDT, TMVA::MethodBoost, TMVA::MethodCategory, TMVA::MethodCFMlpANN, TMVA::MethodCrossValidation, TMVA::MethodCuts, TMVA::MethodDL, TMVA::MethodDNN, TMVA::MethodDT, TMVA::MethodFDA, TMVA::MethodFisher, TMVA::MethodHMatrix, TMVA::MethodKNN, TMVA::MethodLD, TMVA::MethodLikelihood, TMVA::MethodPDEFoam, TMVA::MethodPDERS, TMVA::MethodRuleFit, TMVA::MethodSVM, TMVA::MethodTMlpANN, TMVA::PyMethodBase, TMVA::RMethodBase, and TMVA::MethodCompositeBase.
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inline |
Definition at line 409 of file MethodBase.h.
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inline |
Definition at line 410 of file MethodBase.h.
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private |
define the options (their key words) that can be set in the option string here the options valid for ALL MVA methods are declared.
know options:
Definition at line 509 of file MethodBase.cxx.
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virtual |
options that are used ONLY for the READER to ensure backward compatibility they are hence without any effect (the reader is only reading the training options that HAD been used at the training of the .xml weight file at hand
Reimplemented in TMVA::MethodBDT, TMVA::MethodBoost, TMVA::MethodCrossValidation, TMVA::MethodDT, TMVA::MethodKNN, TMVA::MethodLikelihood, TMVA::MethodPDEFoam, and TMVA::MethodSVM.
Definition at line 596 of file MethodBase.cxx.
|
pure virtual |
Implements TMVA::IMethod.
Implemented in TMVA::MethodPyAdaBoost, TMVA::MethodPyGTB, TMVA::MethodPyKeras, TMVA::MethodPyRandomForest, TMVA::MethodPyTorch, TMVA::MethodC50, TMVA::MethodRSNNS, TMVA::MethodRSVM, TMVA::MethodRXGB, TMVA::MethodANNBase, TMVA::MethodBayesClassifier, TMVA::MethodBDT, TMVA::MethodBoost, TMVA::MethodCategory, TMVA::MethodCFMlpANN, TMVA::MethodCrossValidation, TMVA::MethodCuts, TMVA::MethodDL, TMVA::MethodDNN, TMVA::MethodDT, TMVA::MethodFDA, TMVA::MethodFisher, TMVA::MethodHMatrix, TMVA::MethodKNN, TMVA::MethodLD, TMVA::MethodLikelihood, TMVA::MethodMLP, TMVA::MethodPDEFoam, TMVA::MethodPDERS, TMVA::MethodRuleFit, TMVA::MethodSVM, TMVA::MethodTMlpANN, TMVA::PyMethodBase, TMVA::RMethodBase, and TMVA::MethodCompositeBase.
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inlinestatic |
Definition at line 731 of file MethodBase.h.
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inline |
Definition at line 442 of file MethodBase.h.
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inline |
Definition at line 439 of file MethodBase.h.
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inline |
Definition at line 438 of file MethodBase.h.
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inline |
Definition at line 464 of file MethodBase.h.
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inline |
Definition at line 437 of file MethodBase.h.
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inline |
Definition at line 481 of file MethodBase.h.
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inlineprivate |
Definition at line 552 of file MethodBase.h.
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protectedvirtual |
get all the MVA values for the events of the given Data type
Definition at line 940 of file MethodBase.cxx.
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virtual |
fill background efficiency (resp.
rejection) versus signal efficiency plots returns signal efficiency at background efficiency indicated in theString
Reimplemented in TMVA::MethodCuts.
Definition at line 2302 of file MethodBase.cxx.
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inline |
Definition at line 751 of file MethodBase.h.
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inline |
Definition at line 746 of file MethodBase.h.
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inline |
Definition at line 759 of file MethodBase.h.
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inline |
Definition at line 765 of file MethodBase.h.
const std::vector< TMVA::Event * > & TMVA::MethodBase::GetEventCollection | ( | Types::ETreeType | type | ) |
returns the event collection (i.e.
the dataset) TRANSFORMED using the classifiers specific Variable Transformation (e.g. Decorr or Decorr:Gauss:Decorr)
Definition at line 3347 of file MethodBase.cxx.
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inline |
Definition at line 370 of file MethodBase.h.
Definition at line 350 of file MethodBase.h.
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inline |
Definition at line 351 of file MethodBase.h.
Definition at line 349 of file MethodBase.h.
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inline |
Definition at line 461 of file MethodBase.h.
Definition at line 510 of file MethodBase.h.
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inline |
Definition at line 330 of file MethodBase.h.
Definition at line 3392 of file MethodBase.cxx.
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private |
reads one line from the input stream checks for certain keywords and interprets the line if keywords are found
Definition at line 2142 of file MethodBase.cxx.
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virtual |
plot significance, \( \frac{S}{\sqrt{S^2 + B^2}} \), curve for given number of signal and background events; returns cut for maximum significance also returned via reference is the maximum significance
Definition at line 2886 of file MethodBase.cxx.
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inline |
Definition at line 478 of file MethodBase.h.
Definition at line 354 of file MethodBase.h.
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inline |
Definition at line 331 of file MethodBase.h.
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inline |
Definition at line 333 of file MethodBase.h.
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inline |
Definition at line 332 of file MethodBase.h.
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virtual |
Construct a confusion matrix for a multiclass classifier.
The confusion matrix compares, in turn, each class agaist all other classes in a pair-wise fashion. In rows with index \( k_r = 0 ... K \), \( k_r \) is considered signal for the sake of comparison and for each column \( k_c = 0 ... K \) the corresponding class is considered background.
Note that the diagonal elements will be returned as NaN since this will compare a class against itself.
[in] | effB | The background efficiency for which to evaluate. |
[in] | type | The data set on which to evaluate (training, testing ...). |
Definition at line 2750 of file MethodBase.cxx.
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virtual |
Definition at line 2703 of file MethodBase.cxx.
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virtual |
Definition at line 2715 of file MethodBase.cxx.
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inlinevirtual |
Reimplemented in TMVA::MethodPyAdaBoost, TMVA::MethodPyGTB, TMVA::MethodPyKeras, TMVA::MethodPyRandomForest, TMVA::MethodPyTorch, TMVA::MethodANNBase, TMVA::MethodBDT, TMVA::MethodCategory, TMVA::MethodCrossValidation, TMVA::MethodDL, TMVA::MethodDNN, TMVA::MethodFDA, and TMVA::MethodPDEFoam.
Definition at line 227 of file MethodBase.h.
Double_t TMVA::MethodBase::GetMvaValue | ( | const TMVA::Event *const | ev, |
Double_t * | err = nullptr , |
||
Double_t * | errUpper = nullptr |
||
) |
Definition at line 844 of file MethodBase.cxx.
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pure virtual |
Implements TMVA::IMethod.
Implemented in TMVA::MethodANNBase, TMVA::MethodBayesClassifier, TMVA::MethodBDT, TMVA::MethodBoost, TMVA::MethodCategory, TMVA::MethodCFMlpANN, TMVA::MethodCompositeBase, TMVA::MethodCrossValidation, TMVA::MethodCuts, TMVA::MethodDL, TMVA::MethodDNN, TMVA::MethodDT, TMVA::MethodFDA, TMVA::MethodFisher, TMVA::MethodHMatrix, TMVA::MethodKNN, TMVA::MethodLD, TMVA::MethodLikelihood, TMVA::MethodMLP, TMVA::MethodPDEFoam, TMVA::MethodPDERS, TMVA::MethodRuleFit, TMVA::MethodSVM, TMVA::MethodTMlpANN, TMVA::MethodPyKeras, TMVA::MethodPyTorch, TMVA::MethodPyAdaBoost, TMVA::MethodPyGTB, TMVA::MethodPyRandomForest, TMVA::MethodC50, TMVA::MethodRSNNS, TMVA::MethodRSVM, TMVA::MethodRXGB, TMVA::PyMethodBase, TMVA::RMethodBase, and TMVA::MethodCompositeBase.
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protectedvirtual |
get all the MVA values for the events of the current Data type
Reimplemented in TMVA::MethodPyKeras, TMVA::MethodPyTorch, TMVA::MethodDL, TMVA::MethodPyAdaBoost, TMVA::MethodPyGTB, TMVA::MethodPyRandomForest, TMVA::MethodC50, TMVA::MethodRSNNS, TMVA::MethodRSVM, TMVA::MethodRXGB, TMVA::MethodCategory, and TMVA::PyMethodBase.
Definition at line 898 of file MethodBase.cxx.
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inlinevirtual |
Implements TMVA::IMethod.
Definition at line 334 of file MethodBase.h.
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inline |
Definition at line 416 of file MethodBase.h.
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inline |
Definition at line 346 of file MethodBase.h.
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inline |
Definition at line 344 of file MethodBase.h.
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inline |
Definition at line 345 of file MethodBase.h.
Definition at line 511 of file MethodBase.h.
Definition at line 2247 of file MethodBase.cxx.
compute likelihood ratio
Definition at line 2264 of file MethodBase.cxx.
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inline |
Definition at line 336 of file MethodBase.h.
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virtual |
compute rarity:
\[ R(x) = \int_{[-\infty..x]} { PDF(x') dx' } \]
where PDF(x) is the PDF of the classifier's signal or background distribution
Reimplemented in TMVA::MethodCuts.
Definition at line 2285 of file MethodBase.cxx.
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virtual |
Definition at line 724 of file MethodBase.cxx.
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inlinevirtual |
Reimplemented in TMVA::MethodPyKeras, TMVA::MethodPyTorch, TMVA::MethodANNBase, TMVA::MethodBDT, TMVA::MethodCategory, TMVA::MethodCrossValidation, TMVA::MethodDL, TMVA::MethodDNN, TMVA::MethodFDA, TMVA::MethodKNN, TMVA::MethodLD, TMVA::MethodPDEFoam, TMVA::MethodPDERS, and TMVA::MethodSVM.
Definition at line 221 of file MethodBase.h.
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inline |
Definition at line 214 of file MethodBase.h.
Definition at line 355 of file MethodBase.h.
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virtual |
calculate the area (integral) under the ROC curve as a overall quality measure of the classification
Definition at line 2856 of file MethodBase.cxx.
calculate the area (integral) under the ROC curve as a overall quality measure of the classification
Definition at line 2822 of file MethodBase.cxx.
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virtual |
compute "separation" defined as
\[ <s2> = \frac{1}{2} \int_{-\infty}^{+\infty} { \frac{(S(x) - B(x))^2}{(S(x) + B(x))} dx } \]
Reimplemented in TMVA::MethodCuts.
Definition at line 2800 of file MethodBase.cxx.
compute "separation" defined as
\[ <s2> = \frac{1}{2} \int_{-\infty}^{+\infty} { \frac{(S(x) - B(x))^2}{(S(x) + B(x))} dx } \]
Reimplemented in TMVA::MethodCuts.
Definition at line 2789 of file MethodBase.cxx.
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inline |
Definition at line 360 of file MethodBase.h.
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inline |
Definition at line 361 of file MethodBase.h.
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virtual |
compute significance of mean difference
\[ significance = \frac{|<S> - <B>|}{\sqrt{RMS_{S2} + RMS_{B2}}} \]
Reimplemented in TMVA::MethodCuts.
Definition at line 2776 of file MethodBase.cxx.
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inline |
Definition at line 777 of file MethodBase.h.
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inline |
Definition at line 166 of file MethodBase.h.
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inline |
Definition at line 335 of file MethodBase.h.
Reimplemented in TMVA::MethodCuts.
Definition at line 2528 of file MethodBase.cxx.
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inline |
Definition at line 771 of file MethodBase.h.
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inlinevirtual |
Definition at line 233 of file MethodBase.h.
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inline |
Definition at line 390 of file MethodBase.h.
TString TMVA::MethodBase::GetTrainingROOTVersionString | ( | ) | const |
calculates the ROOT version string from the training version code on the fly
Definition at line 3381 of file MethodBase.cxx.
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inline |
Definition at line 389 of file MethodBase.h.
TString TMVA::MethodBase::GetTrainingTMVAVersionString | ( | ) | const |
calculates the TMVA version string from the training version code on the fly
Definition at line 3369 of file MethodBase.cxx.
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inline |
Definition at line 162 of file MethodBase.h.
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inline |
Definition at line 394 of file MethodBase.h.
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inline |
Definition at line 398 of file MethodBase.h.
returns efficiency as function of cut
Definition at line 3320 of file MethodBase.cxx.
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inlineprotected |
Definition at line 492 of file MethodBase.h.
TString TMVA::MethodBase::GetWeightFileName | ( | ) | const |
retrieve weight file name
Definition at line 2076 of file MethodBase.cxx.
Definition at line 357 of file MethodBase.h.
Definition at line 356 of file MethodBase.h.
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inline |
Definition at line 435 of file MethodBase.h.
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inlineprotected |
Definition at line 513 of file MethodBase.h.
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inlineprotected |
Definition at line 504 of file MethodBase.h.
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inlineprotected |
Definition at line 686 of file MethodBase.h.
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pure virtual |
Implements TMVA::IMethod.
Implemented in TMVA::MethodPyAdaBoost, TMVA::MethodPyGTB, TMVA::MethodPyKeras, TMVA::MethodPyRandomForest, TMVA::MethodPyTorch, TMVA::MethodC50, TMVA::MethodRSNNS, TMVA::MethodRSVM, TMVA::MethodRXGB, TMVA::MethodBoost, TMVA::MethodCategory, TMVA::MethodDL, TMVA::MethodDNN, TMVA::MethodHMatrix, TMVA::MethodLikelihood, TMVA::MethodMLP, TMVA::PyMethodBase, TMVA::RMethodBase, TMVA::MethodBayesClassifier, TMVA::MethodBDT, TMVA::MethodCFMlpANN, TMVA::MethodCrossValidation, TMVA::MethodCuts, TMVA::MethodDT, TMVA::MethodFDA, TMVA::MethodFisher, TMVA::MethodKNN, TMVA::MethodLD, TMVA::MethodPDEFoam, TMVA::MethodPDERS, TMVA::MethodRuleFit, TMVA::MethodSVM, and TMVA::MethodTMlpANN.
|
private |
default initialization called by all constructors
Definition at line 441 of file MethodBase.cxx.
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inline |
Definition at line 455 of file MethodBase.h.
|
inlinevirtual |
Reimplemented from TMVA::IMethod.
Reimplemented in TMVA::MethodPyAdaBoost, TMVA::PyMethodBase, TMVA::RMethodBase, TMVA::MethodANNBase, TMVA::MethodBayesClassifier, TMVA::MethodBDT, TMVA::MethodBoost, TMVA::MethodCategory, TMVA::MethodCFMlpANN, TMVA::MethodCompositeBase, TMVA::MethodCrossValidation, TMVA::MethodCuts, TMVA::MethodDL, TMVA::MethodDNN, TMVA::MethodDT, TMVA::MethodFDA, TMVA::MethodFisher, TMVA::MethodHMatrix, TMVA::MethodKNN, TMVA::MethodLD, TMVA::MethodLikelihood, TMVA::MethodMLP, TMVA::MethodPDEFoam, TMVA::MethodPDERS, TMVA::MethodRuleFit, TMVA::MethodSVM, and TMVA::MethodTMlpANN.
Definition at line 731 of file MethodBase.h.
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Definition at line 540 of file MethodBase.h.
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inline |
Definition at line 383 of file MethodBase.h.
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inlineprotected |
Definition at line 496 of file MethodBase.h.
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virtual |
uses a pre-set cut on the MVA output (SetSignalReferenceCut and SetSignalReferenceCutOrientation) for a quick determination if an event would be selected as signal or background
Definition at line 855 of file MethodBase.cxx.
uses a pre-set cut on the MVA output (SetSignalReferenceCut and SetSignalReferenceCutOrientation) for a quick determination if an event with this mva output value would be selected as signal or background
Definition at line 862 of file MethodBase.cxx.
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inline |
Definition at line 379 of file MethodBase.h.
create reader class for method (classification only at present)
Implements TMVA::IMethod.
Reimplemented in TMVA::MethodCategory, TMVA::MethodC50, TMVA::MethodRXGB, and TMVA::MethodTMlpANN.
Definition at line 3003 of file MethodBase.cxx.
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inlineprotectedvirtual |
Implements TMVA::IMethod.
Reimplemented in TMVA::MethodANNBase, TMVA::MethodBayesClassifier, TMVA::MethodBDT, TMVA::MethodCFMlpANN, TMVA::MethodCrossValidation, TMVA::MethodCuts, TMVA::MethodDNN, TMVA::MethodFDA, TMVA::MethodFisher, TMVA::MethodHMatrix, TMVA::MethodKNN, TMVA::MethodLD, TMVA::MethodLikelihood, TMVA::MethodMLP, TMVA::MethodPDEFoam, TMVA::MethodPDERS, TMVA::MethodRuleFit, TMVA::MethodSVM, and TMVA::MethodTMlpANN.
Definition at line 520 of file MethodBase.h.
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inlineprotectedvirtual |
Reimplemented in TMVA::MethodBDT, TMVA::MethodCrossValidation, TMVA::MethodCFMlpANN, and TMVA::MethodLikelihood.
Definition at line 523 of file MethodBase.h.
TDirectory * TMVA::MethodBase::MethodBaseDir | ( | ) | const |
returns the ROOT directory where all instances of the corresponding MVA method are stored
Definition at line 2020 of file MethodBase.cxx.
Definition at line 837 of file MethodBase.cxx.
|
virtual |
call the Optimizer with the set of parameters and ranges that are meant to be tuned.
Reimplemented in TMVA::MethodBDT, and TMVA::MethodSVM.
Definition at line 623 of file MethodBase.cxx.
|
virtual |
prints out method-specific help method
Implements TMVA::IMethod.
Definition at line 3264 of file MethodBase.cxx.
|
private |
the option string is decoded, for available options see "DeclareOptions"
Definition at line 540 of file MethodBase.cxx.
|
pure virtual |
Implements TMVA::IMethod.
Implemented in TMVA::MethodPyAdaBoost, TMVA::MethodPyGTB, TMVA::MethodPyKeras, TMVA::MethodPyRandomForest, TMVA::MethodPyTorch, TMVA::MethodC50, TMVA::MethodRSNNS, TMVA::MethodRSVM, TMVA::MethodRXGB, TMVA::MethodANNBase, TMVA::MethodBayesClassifier, TMVA::MethodBDT, TMVA::MethodBoost, TMVA::MethodCategory, TMVA::MethodCFMlpANN, TMVA::MethodCrossValidation, TMVA::MethodCuts, TMVA::MethodDL, TMVA::MethodDNN, TMVA::MethodDT, TMVA::MethodFDA, TMVA::MethodFisher, TMVA::MethodHMatrix, TMVA::MethodKNN, TMVA::MethodLD, TMVA::MethodLikelihood, TMVA::MethodMLP, TMVA::MethodPDEFoam, TMVA::MethodPDERS, TMVA::MethodRuleFit, TMVA::MethodSVM, TMVA::MethodTMlpANN, TMVA::PyMethodBase, TMVA::RMethodBase, and TMVA::MethodCompositeBase.
void TMVA::MethodBase::ProcessSetup | ( | ) |
process all options the "CheckForUnusedOptions" is done in an independent call, since it may be overridden by derived class (sometimes, eg, fitters are used which can only be implemented during training phase)
Definition at line 423 of file MethodBase.cxx.
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private |
read number of classes from XML
Definition at line 1917 of file MethodBase.cxx.
|
private |
read spectator info from XML
Definition at line 1877 of file MethodBase.cxx.
void TMVA::MethodBase::ReadStateFromFile | ( | ) |
Function to write options and weights to file.
Definition at line 1426 of file MethodBase.cxx.
void TMVA::MethodBase::ReadStateFromStream | ( | std::istream & | tf | ) |
read the header from the weight files of the different MVA methods
Definition at line 1590 of file MethodBase.cxx.
void TMVA::MethodBase::ReadStateFromStream | ( | TFile & | rf | ) |
write reference MVA distributions (and other information) to a ROOT type weight file
Definition at line 1385 of file MethodBase.cxx.
|
private |
Definition at line 1480 of file MethodBase.cxx.
void TMVA::MethodBase::ReadStateFromXMLString | ( | const char * | xmlstr | ) |
for reading from memory
Definition at line 1469 of file MethodBase.cxx.
|
private |
read target info from XML
Definition at line 1959 of file MethodBase.cxx.
|
private |
read variable info from XML
Definition at line 1837 of file MethodBase.cxx.
|
private |
Read the variables (name, min, max) for a given data transformation method from the stream.
In the stream we only expect the limits which will be set
Definition at line 1725 of file MethodBase.cxx.
|
protectedpure virtual |
Implements TMVA::IMethod.
Implemented in TMVA::MethodPyAdaBoost, TMVA::MethodPyGTB, TMVA::MethodPyKeras, TMVA::MethodPyRandomForest, TMVA::MethodPyTorch, TMVA::MethodC50, TMVA::MethodRSNNS, TMVA::MethodRSVM, TMVA::MethodRXGB, TMVA::MethodDL, TMVA::MethodPyAdaBoost, TMVA::MethodPyGTB, TMVA::MethodPyRandomForest, TMVA::PyMethodBase, TMVA::MethodC50, TMVA::MethodRSNNS, TMVA::MethodRSVM, TMVA::MethodRXGB, TMVA::RMethodBase, TMVA::MethodANNBase, TMVA::MethodBayesClassifier, TMVA::MethodBDT, TMVA::MethodCFMlpANN, TMVA::MethodCompositeBase, TMVA::MethodCrossValidation, TMVA::MethodCuts, TMVA::MethodDL, TMVA::MethodDNN, TMVA::MethodDT, TMVA::MethodFDA, TMVA::MethodFisher, TMVA::MethodHMatrix, TMVA::MethodKNN, TMVA::MethodLD, TMVA::MethodPDEFoam, TMVA::MethodRuleFit, TMVA::MethodSVM, TMVA::MethodTMlpANN, TMVA::MethodCuts, TMVA::MethodDNN, TMVA::MethodFDA, TMVA::MethodFisher, TMVA::MethodLD, TMVA::MethodPDEFoam, TMVA::MethodANNBase, TMVA::MethodBayesClassifier, TMVA::MethodBDT, TMVA::MethodCFMlpANN, TMVA::MethodCompositeBase, TMVA::MethodCrossValidation, TMVA::MethodDT, TMVA::MethodHMatrix, TMVA::MethodKNN, TMVA::MethodLikelihood, TMVA::MethodPDERS, TMVA::MethodRuleFit, TMVA::MethodSVM, and TMVA::MethodTMlpANN.
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inlineprotectedvirtual |
Reimplemented in TMVA::MethodPyAdaBoost, TMVA::MethodPyGTB, TMVA::MethodPyKeras, TMVA::MethodPyRandomForest, TMVA::MethodPyTorch, TMVA::PyMethodBase, TMVA::MethodC50, TMVA::MethodRSNNS, TMVA::MethodRSVM, TMVA::MethodRXGB, TMVA::RMethodBase, TMVA::MethodANNBase, TMVA::MethodBayesClassifier, TMVA::MethodBDT, TMVA::MethodCFMlpANN, TMVA::MethodCompositeBase, TMVA::MethodCrossValidation, TMVA::MethodCuts, TMVA::MethodDL, TMVA::MethodDNN, TMVA::MethodDT, TMVA::MethodFDA, TMVA::MethodFisher, TMVA::MethodHMatrix, TMVA::MethodKNN, TMVA::MethodLD, TMVA::MethodPDEFoam, TMVA::MethodRuleFit, TMVA::MethodSVM, TMVA::MethodTMlpANN, TMVA::MethodSVM, TMVA::MethodLikelihood, TMVA::MethodPDERS, and TMVA::MethodKNN.
Definition at line 266 of file MethodBase.h.
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protectedpure virtual |
Implemented in TMVA::MethodPyAdaBoost, TMVA::MethodPyGTB, TMVA::MethodPyKeras, TMVA::MethodPyRandomForest, TMVA::MethodPyTorch, TMVA::MethodC50, TMVA::MethodRSNNS, TMVA::MethodRSVM, TMVA::MethodRXGB, TMVA::MethodBayesClassifier, TMVA::MethodBDT, TMVA::MethodCrossValidation, TMVA::MethodANNBase, TMVA::MethodCategory, TMVA::MethodCFMlpANN, TMVA::MethodCompositeBase, TMVA::MethodCuts, TMVA::MethodDL, TMVA::MethodDNN, TMVA::MethodDT, TMVA::MethodFDA, TMVA::MethodFisher, TMVA::MethodHMatrix, TMVA::MethodKNN, TMVA::MethodLD, TMVA::MethodLikelihood, TMVA::MethodPDEFoam, TMVA::MethodPDERS, TMVA::MethodRuleFit, TMVA::MethodSVM, TMVA::MethodTMlpANN, TMVA::PyMethodBase, and TMVA::RMethodBase.
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Definition at line 403 of file MethodBase.h.
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Reimplemented in TMVA::MethodPDEFoam, TMVA::MethodBDT, TMVA::MethodCrossValidation, and TMVA::MethodSVM.
Definition at line 193 of file MethodBase.h.
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Definition at line 436 of file MethodBase.h.
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Definition at line 373 of file MethodBase.h.
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Definition at line 375 of file MethodBase.h.
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Definition at line 374 of file MethodBase.h.
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Definition at line 372 of file MethodBase.h.
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Definition at line 382 of file MethodBase.h.
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Definition at line 497 of file MethodBase.h.
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Definition at line 364 of file MethodBase.h.
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Definition at line 365 of file MethodBase.h.
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Definition at line 378 of file MethodBase.h.
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Definition at line 165 of file MethodBase.h.
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Definition at line 341 of file MethodBase.h.
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Definition at line 161 of file MethodBase.h.
set the tuning parameters according to the argument This is just a dummy .
. have a look at the MethodBDT how you could perhaps implement the same thing for the other Classifiers..
Reimplemented in TMVA::MethodBDT, and TMVA::MethodSVM.
Definition at line 644 of file MethodBase.cxx.
void TMVA::MethodBase::SetupMethod | ( | ) |
setup of methods
Definition at line 406 of file MethodBase.cxx.
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set directory of weight file
Definition at line 2059 of file MethodBase.cxx.
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set the weight file name (depreciated)
Definition at line 2068 of file MethodBase.cxx.
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calculates rms,mean, xmin, xmax of the event variable this can be either done for the variables as they are or for normalised variables (in the range of 0-1) if "norm" is set to kTRUE
Definition at line 2942 of file MethodBase.cxx.
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Reimplemented from TMVA::IMethod.
Reimplemented in TMVA::MethodPyAdaBoost, TMVA::PyMethodBase, TMVA::RMethodBase, TMVA::MethodANNBase, TMVA::MethodBayesClassifier, TMVA::MethodBDT, TMVA::MethodBoost, TMVA::MethodCategory, TMVA::MethodCFMlpANN, TMVA::MethodCompositeBase, TMVA::MethodCrossValidation, TMVA::MethodCuts, TMVA::MethodDL, TMVA::MethodDNN, TMVA::MethodDT, TMVA::MethodFDA, TMVA::MethodFisher, TMVA::MethodHMatrix, TMVA::MethodKNN, TMVA::MethodLD, TMVA::MethodLikelihood, TMVA::MethodMLP, TMVA::MethodPDEFoam, TMVA::MethodPDERS, TMVA::MethodRuleFit, TMVA::MethodSVM, and TMVA::MethodTMlpANN.
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Definition at line 731 of file MethodBase.h.
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initialization
Reimplemented in TMVA::MethodPyAdaBoost, TMVA::MethodPyGTB, TMVA::MethodPyKeras, TMVA::MethodPyRandomForest, TMVA::MethodPyTorch, TMVA::MethodC50, TMVA::MethodRSNNS, TMVA::MethodRSVM, TMVA::MethodRXGB, TMVA::MethodBoost, and TMVA::MethodCuts.
Definition at line 1127 of file MethodBase.cxx.
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test multiclass classification
Definition at line 1100 of file MethodBase.cxx.
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calculate <sum-of-deviation-squared> of regression output versus "true" value from test sample
Definition at line 992 of file MethodBase.cxx.
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Implements TMVA::IMethod.
Implemented in TMVA::MethodPyAdaBoost, TMVA::MethodPyGTB, TMVA::MethodPyKeras, TMVA::MethodPyRandomForest, TMVA::MethodPyTorch, TMVA::MethodC50, TMVA::MethodRSNNS, TMVA::MethodRSVM, TMVA::MethodRXGB, TMVA::MethodDL, TMVA::MethodDNN, TMVA::MethodHMatrix, TMVA::MethodLikelihood, TMVA::MethodMLP, TMVA::PyMethodBase, TMVA::RMethodBase, TMVA::MethodANNBase, TMVA::MethodCompositeBase, TMVA::MethodBayesClassifier, TMVA::MethodBDT, TMVA::MethodBoost, TMVA::MethodCategory, TMVA::MethodCFMlpANN, TMVA::MethodCrossValidation, TMVA::MethodCuts, TMVA::MethodDT, TMVA::MethodFDA, TMVA::MethodFisher, TMVA::MethodKNN, TMVA::MethodLD, TMVA::MethodPDEFoam, TMVA::MethodPDERS, TMVA::MethodRuleFit, TMVA::MethodSVM, and TMVA::MethodTMlpANN.
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Definition at line 469 of file MethodBase.h.
void TMVA::MethodBase::TrainMethod | ( | ) |
Definition at line 650 of file MethodBase.cxx.
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Definition at line 534 of file MethodBase.h.
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Definition at line 503 of file MethodBase.h.
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writes all MVA evaluation histograms to file
Reimplemented in TMVA::MethodBoost.
Definition at line 2094 of file MethodBase.cxx.
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write special monitoring histograms to file dummy implementation here --------------—
Implements TMVA::IMethod.
Reimplemented in TMVA::MethodANNBase, TMVA::MethodLikelihood, TMVA::MethodBDT, TMVA::MethodBoost, TMVA::MethodCrossValidation, TMVA::MethodCuts, and TMVA::MethodRuleFit.
Definition at line 2133 of file MethodBase.cxx.
void TMVA::MethodBase::WriteStateToFile | ( | ) | const |
write options and weights to file note that each one text file for the main configuration information and one ROOT file for ROOT objects are created
Definition at line 1404 of file MethodBase.cxx.
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general method used in writing the header of the weight files where the used variables, variable transformation type etc.
is specified
Definition at line 1267 of file MethodBase.cxx.
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general method used in writing the header of the weight files where the used variables, variable transformation type etc.
is specified
Definition at line 1331 of file MethodBase.cxx.
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write the list of variables (name, min, max) for a given data transformation method to the stream
Definition at line 1710 of file MethodBase.cxx.
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Definition at line 113 of file MethodBase.h.
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Definition at line 118 of file MethodBase.h.
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Definition at line 114 of file MethodBase.h.
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Definition at line 116 of file MethodBase.h.
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Definition at line 269 of file MethodBase.h.
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Definition at line 270 of file MethodBase.h.
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Definition at line 117 of file MethodBase.h.
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Definition at line 603 of file MethodBase.h.
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Definition at line 115 of file MethodBase.h.
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Definition at line 595 of file MethodBase.h.
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Definition at line 690 of file MethodBase.h.
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Definition at line 625 of file MethodBase.h.
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Definition at line 620 of file MethodBase.h.
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Definition at line 699 of file MethodBase.h.
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Definition at line 607 of file MethodBase.h.
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default PDF definitions
Definition at line 644 of file MethodBase.h.
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efficiency histogram for rootfinder
Definition at line 642 of file MethodBase.h.
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Definition at line 708 of file MethodBase.h.
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Definition at line 449 of file MethodBase.h.
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Definition at line 628 of file MethodBase.h.
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unix sub-directory for weight files (default: DataLoader's Name + "weights")
Definition at line 637 of file MethodBase.h.
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MVA Pdfs are created for this classifier.
Definition at line 680 of file MethodBase.h.
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help flag
Definition at line 679 of file MethodBase.h.
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If true, events with negative weights are not used in training.
Definition at line 682 of file MethodBase.h.
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Definition at line 588 of file MethodBase.h.
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temporary dataset used when evaluating on a different data (used by MethodCategory::GetMvaValues)
Definition at line 448 of file MethodBase.h.
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Definition at line 450 of file MethodBase.h.
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Definition at line 450 of file MethodBase.h.
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Definition at line 614 of file MethodBase.h.
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mean (background)
Definition at line 662 of file MethodBase.h.
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mean (signal)
Definition at line 661 of file MethodBase.h.
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Definition at line 626 of file MethodBase.h.
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Definition at line 615 of file MethodBase.h.
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Definition at line 616 of file MethodBase.h.
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Definition at line 633 of file MethodBase.h.
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Definition at line 598 of file MethodBase.h.
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background MVA PDF
Definition at line 646 of file MethodBase.h.
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signal MVA PDF
Definition at line 645 of file MethodBase.h.
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Definition at line 591 of file MethodBase.h.
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Definition at line 593 of file MethodBase.h.
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Definition at line 592 of file MethodBase.h.
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Definition at line 726 of file MethodBase.h.
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Definition at line 722 of file MethodBase.h.
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Definition at line 727 of file MethodBase.h.
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method parent name, like booster name
Definition at line 635 of file MethodBase.h.
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Definition at line 587 of file MethodBase.h.
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Definition at line 597 of file MethodBase.h.
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Definition at line 730 of file MethodBase.h.
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RMS (background)
Definition at line 664 of file MethodBase.h.
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RMS (signal)
Definition at line 663 of file MethodBase.h.
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Definition at line 619 of file MethodBase.h.
Bool_t TMVA::MethodBase::fSetupCompleted |
Definition at line 711 of file MethodBase.h.
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Definition at line 689 of file MethodBase.h.
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the data set information (sometimes needed)
Definition at line 609 of file MethodBase.h.
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Definition at line 610 of file MethodBase.h.
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Definition at line 631 of file MethodBase.h.
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PDFs of MVA distribution (background)
Definition at line 651 of file MethodBase.h.
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splines for signal eff. versus background eff.
Definition at line 652 of file MethodBase.h.
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Definition at line 703 of file MethodBase.h.
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Definition at line 702 of file MethodBase.h.
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PDFs of MVA distribution (signal)
Definition at line 650 of file MethodBase.h.
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PDFs of training MVA distribution (background)
Definition at line 655 of file MethodBase.h.
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splines for training signal eff. versus background eff.
Definition at line 656 of file MethodBase.h.
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Definition at line 706 of file MethodBase.h.
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Definition at line 705 of file MethodBase.h.
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PDFs of training MVA distribution (signal)
Definition at line 654 of file MethodBase.h.
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Definition at line 696 of file MethodBase.h.
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Definition at line 617 of file MethodBase.h.
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temporary event when testing on a different DataSet than the own one
Definition at line 446 of file MethodBase.h.
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Definition at line 445 of file MethodBase.h.
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Definition at line 618 of file MethodBase.h.
TrainingHistory TMVA::MethodBase::fTrainHistory |
Definition at line 425 of file MethodBase.h.
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Definition at line 695 of file MethodBase.h.
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the list of transformations
Definition at line 672 of file MethodBase.h.
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pointer to the rest of transformations
Definition at line 671 of file MethodBase.h.
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Definition at line 725 of file MethodBase.h.
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Definition at line 723 of file MethodBase.h.
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Definition at line 611 of file MethodBase.h.
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Definition at line 724 of file MethodBase.h.
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labels variable transform method
Definition at line 669 of file MethodBase.h.
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verbose flag
Definition at line 676 of file MethodBase.h.
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verbosity level
Definition at line 678 of file MethodBase.h.
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verbosity level (user input string)
Definition at line 677 of file MethodBase.h.
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weight file name
Definition at line 638 of file MethodBase.h.
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maximum (signal and background)
Definition at line 666 of file MethodBase.h.
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minimum (signal and background)
Definition at line 665 of file MethodBase.h.