SMO Platt's SVM classifier with Keerthi & Shavade improvements.
Definition at line 61 of file MethodSVM.h.
Public Member Functions | |
MethodSVM (const TString &jobName, const TString &methodTitle, DataSetInfo &theData, const TString &theOption="") | |
standard constructor | |
MethodSVM (DataSetInfo &theData, const TString &theWeightFile) | |
constructor from weight file | |
virtual | ~MethodSVM (void) |
destructor | |
void | AddWeightsXMLTo (void *parent) const |
write configuration to xml file | |
const Ranking * | CreateRanking () |
void | GetMGamma (const std::vector< float > &gammas) |
Produces GammaList string for multigaussian kernel to be written to xml file. | |
Double_t | GetMvaValue (Double_t *err=nullptr, Double_t *errUpper=nullptr) |
returns MVA value for given event | |
const std::vector< Float_t > & | GetRegressionValues () |
std::map< TString, std::vector< Double_t > > | GetTuningOptions () |
GetTuningOptions Function to allow for ranges and number of steps (for scan) when optimising kernel function parameters. | |
virtual Bool_t | HasAnalysisType (Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets) |
SVM can handle classification with 2 classes and regression with one regression-target. | |
void | Init (void) |
default initialisation | |
virtual TClass * | IsA () const |
std::vector< TMVA::SVKernelFunction::EKernelType > | MakeKernelList (std::string multiKernels, TString kernel) |
MakeKernelList Function providing string manipulation for product or sum of kernels functions to take list of kernels specified in the booking of the method and provide a vector of SV kernels to iterate over in SVKernelFunction. | |
virtual std::map< TString, Double_t > | OptimizeTuningParameters (TString fomType="ROCIntegral", TString fitType="Minuit") |
Optimize Tuning Parameters This is used to optimise the kernel function parameters and cost. | |
virtual void | ReadWeightsFromStream (std::istream &)=0 |
void | ReadWeightsFromStream (std::istream &istr) |
virtual void | ReadWeightsFromStream (TFile &) |
void | ReadWeightsFromStream (TFile &fFin) |
TODO write IT. | |
void | ReadWeightsFromXML (void *wghtnode) |
void | Reset (void) |
void | SetCost (Double_t c) |
void | SetGamma (Double_t g) |
void | SetKappa (Double_t k) |
void | SetMGamma (std::string &mg) |
Takes as input a string of values for multigaussian gammas and splits it, filling the gamma vector required by the SVKernelFunction. | |
void | SetMult (Double_t m) |
void | SetOrder (Double_t o) |
void | SetTheta (Double_t t) |
virtual void | SetTuneParameters (std::map< TString, Double_t > tuneParameters) |
Set the tuning parameters according to the argument. | |
virtual void | Streamer (TBuffer &) |
void | StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b) |
void | Train (void) |
Train SVM. | |
void | WriteWeightsToStream (TFile &fout) const |
TODO write IT write training sample (TTree) to file. | |
![]() | |
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) | |
DataSet * | Data () const |
DataSetInfo & | DataInfo () const |
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) |
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 |
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 |
void | InitIPythonInteractive () |
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 | |
void | PrintHelpMessage () const |
prints out method-specific help method | |
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 | 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) |
void | SetupMethod () |
setup of methods | |
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 | |
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 | |
![]() | |
IMethod () | |
virtual | ~IMethod () |
void | StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b) |
![]() | |
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 | |
![]() | |
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) |
![]() | |
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 () |
![]() | |
static TClass * | Class () |
static const char * | Class_Name () |
static constexpr Version_t | Class_Version () |
static const char * | DeclFileName () |
![]() | |
static TClass * | Class () |
static const char * | Class_Name () |
static constexpr Version_t | Class_Version () |
static const char * | DeclFileName () |
![]() | |
static TClass * | Class () |
static const char * | Class_Name () |
static constexpr Version_t | Class_Version () |
static const char * | DeclFileName () |
![]() | |
static TClass * | Class () |
static const char * | Class_Name () |
static constexpr Version_t | Class_Version () |
static const char * | DeclFileName () |
![]() | |
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. | |
Protected Member Functions | |
void | GetHelpMessage () const |
get help message text | |
void | MakeClassSpecific (std::ostream &, const TString &) const |
write specific classifier response | |
![]() | |
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 | MakeClassSpecificHeader (std::ostream &, const TString &="") const |
void | NoErrorCalc (Double_t *const err, Double_t *const errUpper) |
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 |
![]() | |
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 | |
![]() | |
virtual void | DoError (int level, const char *location, const char *fmt, va_list va) const |
Interface to ErrorHandler (protected). | |
void | MakeZombie () |
Private Member Functions | |
void | DeclareCompatibilityOptions () |
options that are used ONLY for the READER to ensure backward compatibility | |
void | DeclareOptions () |
declare options available for this method | |
Double_t | getLoss (TString lossFunction) |
getLoss Calculates loss for testing dataset. | |
void | ProcessOptions () |
option post processing (if necessary) | |
Private Attributes | |
Float_t | fBparm |
free plane coefficient | |
Float_t | fCost |
cost value | |
Int_t | fDataSize |
Float_t | fDoubleSigmaSquared |
for RBF Kernel | |
Float_t | fGamma |
RBF Kernel parameter. | |
std::string | fGammaList |
std::string | fGammas |
std::vector< TMVA::SVEvent * > * | fInputData |
vector of training data in SVM format | |
Float_t | fKappa |
for Sigmoidal Kernel | |
TString | fLoss |
UInt_t | fMaxIter |
max number of iteration | |
TVectorD * | fMaxVars |
for normalization //is it still needed?? | |
std::vector< Float_t > | fmGamma |
vector of gammas for multi-gaussian kernel | |
TVectorD * | fMinVars |
for normalization //is it still needed?? | |
Float_t | fMult |
std::string | fMultiKernels |
UShort_t | fNSubSets |
nr of subsets, default 1 | |
Float_t | fNumVars |
number of input variables for multi-gaussian | |
Int_t | fOrder |
for Polynomial Kernel ( polynomial order ) | |
std::vector< TMVA::SVEvent * > * | fSupportVectors |
contains support vectors | |
SVKernelFunction * | fSVKernelFunction |
kernel function | |
TString | fTheKernel |
kernel name | |
Float_t | fTheta |
for Sigmoidal Kernel | |
Float_t | fTolerance |
tolerance parameter | |
std::string | fTune |
Specify parameters to be tuned. | |
std::vector< TString > | fVarNames |
SVWorkingSet * | fWgSet |
svm working set | |
Additional Inherited Members | |
![]() | |
enum | EWeightFileType { kROOT =0 , kTEXT } |
![]() | |
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 )) } |
![]() | |
Bool_t | fSetupCompleted |
TrainingHistory | fTrainHistory |
![]() | |
enum | { kOnlyPrepStep = (1ULL << ( 3 )) } |
![]() | |
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 |
![]() | |
MsgLogger * | fLogger |
! message logger | |
![]() | |
TString | fName |
TString | fTitle |
#include <TMVA/MethodSVM.h>
TMVA::MethodSVM::MethodSVM | ( | const TString & | jobName, |
const TString & | methodTitle, | ||
DataSetInfo & | theData, | ||
const TString & | theOption = "" |
||
) |
standard constructor
Definition at line 90 of file MethodSVM.cxx.
TMVA::MethodSVM::MethodSVM | ( | DataSetInfo & | theData, |
const TString & | theWeightFile | ||
) |
constructor from weight file
Definition at line 126 of file MethodSVM.cxx.
|
virtual |
destructor
Definition at line 161 of file MethodSVM.cxx.
|
virtual |
write configuration to xml file
Implements TMVA::MethodBase.
Definition at line 398 of file MethodSVM.cxx.
|
static |
|
inlinestaticconstexpr |
Definition at line 165 of file MethodSVM.h.
|
inlinevirtual |
Implements TMVA::MethodBase.
Definition at line 104 of file MethodSVM.h.
|
privatevirtual |
options that are used ONLY for the READER to ensure backward compatibility
Reimplemented from TMVA::MethodBase.
Definition at line 251 of file MethodSVM.cxx.
|
privatevirtual |
declare options available for this method
Implements TMVA::MethodBase.
Definition at line 220 of file MethodSVM.cxx.
|
inlinestatic |
Definition at line 165 of file MethodSVM.h.
|
protectedvirtual |
get help message text
typical length of text line: "|--------------------------------------------------------------|"
Implements TMVA::IMethod.
Definition at line 715 of file MethodSVM.cxx.
getLoss Calculates loss for testing dataset.
The loss function can be specified when booking the method, otherwise defaults to hinge loss. Currently not used however is accesible if required.
Definition at line 1163 of file MethodSVM.cxx.
void TMVA::MethodSVM::GetMGamma | ( | const std::vector< float > & | gammas | ) |
Produces GammaList string for multigaussian kernel to be written to xml file.
Definition at line 1032 of file MethodSVM.cxx.
|
virtual |
returns MVA value for given event
Implements TMVA::MethodBase.
Definition at line 577 of file MethodSVM.cxx.
|
virtual |
Reimplemented from TMVA::MethodBase.
Definition at line 602 of file MethodSVM.cxx.
GetTuningOptions Function to allow for ranges and number of steps (for scan) when optimising kernel function parameters.
Specified when booking the method after the parameter to be optimised between square brackets with each value separated by ;, the first value is the lower limit, the second the upper limit and the third is the number of steps. Example: "Tune=Gamma[0.01;1.0;100]" would only tune the RBF Gamma between 0.01 and 100 steps.
Definition at line 1106 of file MethodSVM.cxx.
|
virtual |
SVM can handle classification with 2 classes and regression with one regression-target.
Implements TMVA::IMethod.
Definition at line 195 of file MethodSVM.cxx.
|
virtual |
|
inlinevirtual |
Reimplemented from TMVA::MethodBase.
Definition at line 165 of file MethodSVM.h.
|
protectedvirtual |
write specific classifier response
Reimplemented from TMVA::MethodBase.
Definition at line 635 of file MethodSVM.cxx.
std::vector< TMVA::SVKernelFunction::EKernelType > TMVA::MethodSVM::MakeKernelList | ( | std::string | multiKernels, |
TString | kernel | ||
) |
MakeKernelList Function providing string manipulation for product or sum of kernels functions to take list of kernels specified in the booking of the method and provide a vector of SV kernels to iterate over in SVKernelFunction.
Example:
"KernelList=RBF*Polynomial" would use a product of the RBF and Polynomial kernels.
Definition at line 1054 of file MethodSVM.cxx.
|
virtual |
Optimize Tuning Parameters This is used to optimise the kernel function parameters and cost.
All kernel parameters are optimised by default with default ranges, however the parameters to be optimised can be set when booking the method with the option Tune.
Example:
"Tune=Gamma[0.01;1.0;100]" would only tune the RBF Gamma between 0.01 and 1.0 with 100 steps.
Reimplemented from TMVA::MethodBase.
Definition at line 760 of file MethodSVM.cxx.
|
privatevirtual |
option post processing (if necessary)
Implements TMVA::MethodBase.
Definition at line 268 of file MethodSVM.cxx.
|
virtual |
Implements TMVA::MethodBase.
|
virtual |
Implements TMVA::MethodBase.
Definition at line 513 of file MethodSVM.cxx.
|
inlinevirtual |
Reimplemented from TMVA::MethodBase.
Definition at line 266 of file MethodBase.h.
|
virtual |
|
virtual |
Implements TMVA::MethodBase.
Definition at line 430 of file MethodSVM.cxx.
|
virtual |
Reimplemented from TMVA::MethodBase.
Definition at line 174 of file MethodSVM.cxx.
|
inline |
Definition at line 108 of file MethodSVM.h.
|
inline |
Definition at line 107 of file MethodSVM.h.
|
inline |
Definition at line 112 of file MethodSVM.h.
void TMVA::MethodSVM::SetMGamma | ( | std::string & | mg | ) |
Takes as input a string of values for multigaussian gammas and splits it, filling the gamma vector required by the SVKernelFunction.
Example: "GammaList=0.1,0.2,0.3" would make a vector with Gammas of 0.1,0.2 & 0.3 corresponding to input variables 1,2 & 3 respectively.
Definition at line 1018 of file MethodSVM.cxx.
|
inline |
Definition at line 113 of file MethodSVM.h.
|
inline |
Definition at line 110 of file MethodSVM.h.
|
inline |
Definition at line 111 of file MethodSVM.h.
Set the tuning parameters according to the argument.
Reimplemented from TMVA::MethodBase.
Definition at line 917 of file MethodSVM.cxx.
|
virtual |
Reimplemented from TMVA::MethodBase.
|
inline |
Definition at line 165 of file MethodSVM.h.
|
virtual |
void TMVA::MethodSVM::WriteWeightsToStream | ( | TFile & | fout | ) | const |
TODO write IT write training sample (TTree) to file.
Definition at line 507 of file MethodSVM.cxx.
|
private |
free plane coefficient
Definition at line 137 of file MethodSVM.h.
|
private |
cost value
Definition at line 133 of file MethodSVM.h.
|
private |
Definition at line 162 of file MethodSVM.h.
|
private |
for RBF Kernel
Definition at line 149 of file MethodSVM.h.
|
private |
RBF Kernel parameter.
Definition at line 138 of file MethodSVM.h.
|
private |
Definition at line 158 of file MethodSVM.h.
|
private |
Definition at line 157 of file MethodSVM.h.
|
private |
vector of training data in SVM format
Definition at line 140 of file MethodSVM.h.
|
private |
for Sigmoidal Kernel
Definition at line 152 of file MethodSVM.h.
|
private |
Definition at line 163 of file MethodSVM.h.
|
private |
max number of iteration
Definition at line 135 of file MethodSVM.h.
|
private |
for normalization //is it still needed??
Definition at line 145 of file MethodSVM.h.
|
private |
vector of gammas for multi-gaussian kernel
Definition at line 154 of file MethodSVM.h.
|
private |
for normalization //is it still needed??
Definition at line 144 of file MethodSVM.h.
|
private |
Definition at line 153 of file MethodSVM.h.
|
private |
Definition at line 160 of file MethodSVM.h.
|
private |
nr of subsets, default 1
Definition at line 136 of file MethodSVM.h.
|
private |
number of input variables for multi-gaussian
Definition at line 155 of file MethodSVM.h.
|
private |
for Polynomial Kernel ( polynomial order )
Definition at line 150 of file MethodSVM.h.
|
private |
contains support vectors
Definition at line 141 of file MethodSVM.h.
|
private |
kernel function
Definition at line 142 of file MethodSVM.h.
|
private |
kernel name
Definition at line 148 of file MethodSVM.h.
|
private |
for Sigmoidal Kernel
Definition at line 151 of file MethodSVM.h.
|
private |
tolerance parameter
Definition at line 134 of file MethodSVM.h.
|
private |
Specify parameters to be tuned.
Definition at line 159 of file MethodSVM.h.
|
private |
Definition at line 156 of file MethodSVM.h.
|
private |
svm working set
Definition at line 139 of file MethodSVM.h.