141 fTheMethod (
"Fisher" ),
142 fFisherMethod ( kFisher ),
158 const TString& theWeightFile) :
161 fTheMethod (
"Fisher" ),
162 fFisherMethod ( kFisher ),
180 fFisherCoeff =
new std::vector<Double_t>( GetNvar() );
183 SetSignalReferenceCut( 0.0 );
196 DeclareOptionRef( fTheMethod =
"Fisher",
"Method",
"Discrimination method" );
197 AddPreDefVal(
TString(
"Fisher"));
198 AddPreDefVal(
TString(
"Mahalanobis"));
206 if (fTheMethod ==
"Fisher" ) fFisherMethod = kFisher;
207 else fFisherMethod = kMahalanobis;
218 if (fBetw ) {
delete fBetw; fBetw = 0; }
219 if (fWith ) {
delete fWith; fWith = 0; }
220 if (fCov ) {
delete fCov; fCov = 0; }
221 if (fDiscrimPow ) {
delete fDiscrimPow; fDiscrimPow = 0; }
222 if (fFisherCoeff) {
delete fFisherCoeff; fFisherCoeff = 0; }
243 GetCov_WithinClass();
246 GetCov_BetweenClass();
270 const Event * ev = GetEvent();
272 for (
UInt_t ivar=0; ivar<GetNvar(); ivar++)
276 NoErrorCalc(err, errUpper);
288 fMeanMatx =
new TMatrixD( GetNvar(), 3 );
291 fBetw =
new TMatrixD( GetNvar(), GetNvar() );
292 fWith =
new TMatrixD( GetNvar(), GetNvar() );
293 fCov =
new TMatrixD( GetNvar(), GetNvar() );
296 fDiscrimPow =
new std::vector<Double_t>( GetNvar() );
308 const UInt_t nvar = DataInfo().GetNVariables();
313 for (
UInt_t ivar=0; ivar<nvar; ivar++) { sumS[ivar] = sumB[ivar] = 0; }
316 for (
Int_t ievt=0; ievt<Data()->GetNEvents(); ievt++) {
319 const Event * ev = GetEvent(ievt);
323 if (DataInfo().IsSignal(ev)) fSumOfWeightsS += weight;
324 else fSumOfWeightsB += weight;
326 Double_t*
sum = DataInfo().IsSignal(ev) ? sumS : sumB;
331 for (
UInt_t ivar=0; ivar<nvar; ivar++) {
332 (*fMeanMatx)( ivar, 2 ) = sumS[ivar];
333 (*fMeanMatx)( ivar, 0 ) = sumS[ivar]/fSumOfWeightsS;
335 (*fMeanMatx)( ivar, 2 ) += sumB[ivar];
336 (*fMeanMatx)( ivar, 1 ) = sumB[ivar]/fSumOfWeightsB;
339 (*fMeanMatx)( ivar, 2 ) /= (fSumOfWeightsS + fSumOfWeightsB);
354 assert( fSumOfWeightsS > 0 && fSumOfWeightsB > 0 );
359 const Int_t nvar = GetNvar();
360 const Int_t nvar2 = nvar*nvar;
364 memset(sumSig,0,nvar2*
sizeof(
Double_t));
365 memset(sumBgd,0,nvar2*
sizeof(
Double_t));
368 for (
Int_t ievt=0; ievt<Data()->GetNEvents(); ievt++) {
371 const Event* ev = GetEvent(ievt);
379 if (DataInfo().IsSignal(ev)) {
380 Double_t v = ( (xval[
x] - (*fMeanMatx)(
x, 0))*(xval[
y] - (*fMeanMatx)(
y, 0)) )*weight;
383 Double_t v = ( (xval[
x] - (*fMeanMatx)(
x, 1))*(xval[
y] - (*fMeanMatx)(
y, 1)) )*weight;
402 (*fWith)(
x,
y) = sumSig[k]/fSumOfWeightsS + sumBgd[k]/fSumOfWeightsB;
420 assert( fSumOfWeightsS > 0 && fSumOfWeightsB > 0);
427 prodSig = ( ((*fMeanMatx)(
x, 0) - (*fMeanMatx)(
x, 2))*
428 ((*fMeanMatx)(
y, 0) - (*fMeanMatx)(
y, 2)) );
429 prodBgd = ( ((*fMeanMatx)(
x, 1) - (*fMeanMatx)(
x, 2))*
430 ((*fMeanMatx)(
y, 1) - (*fMeanMatx)(
y, 2)) );
432 (*fBetw)(
x,
y) = (fSumOfWeightsS*prodSig + fSumOfWeightsB*prodBgd) / (fSumOfWeightsS + fSumOfWeightsB);
444 (*fCov)(
x,
y) = (*fWith)(
x,
y) + (*fBetw)(
x,
y);
460 assert( fSumOfWeightsS > 0 && fSumOfWeightsB > 0);
464 switch (GetFisherMethod()) {
472 Log() << kFATAL <<
"<GetFisherCoeff> undefined method" << GetFisherMethod() <<
Endl;
478 Log() << kWARNING <<
"<GetFisherCoeff> matrix is almost singular with determinant="
480 <<
" did you use the variables that are linear combinations or highly correlated?"
485 Log() << kFATAL <<
"<GetFisherCoeff> matrix is singular with determinant="
487 <<
" did you use the variables that are linear combinations? \n"
488 <<
" do you any clue as to what went wrong in above printout of the covariance matrix? "
495 Double_t xfact =
TMath::Sqrt( fSumOfWeightsS*fSumOfWeightsB ) / (fSumOfWeightsS + fSumOfWeightsB);
498 std::vector<Double_t> diffMeans( GetNvar() );
500 for (ivar=0; ivar<GetNvar(); ivar++) {
501 (*fFisherCoeff)[ivar] = 0;
503 for (jvar=0; jvar<GetNvar(); jvar++) {
504 Double_t d = (*fMeanMatx)(jvar, 0) - (*fMeanMatx)(jvar, 1);
505 (*fFisherCoeff)[ivar] += invCov(ivar, jvar)*
d;
508 (*fFisherCoeff)[ivar] *= xfact;
514 for (ivar=0; ivar<GetNvar(); ivar++){
515 fF0 += (*fFisherCoeff)[ivar]*((*fMeanMatx)(ivar, 0) + (*fMeanMatx)(ivar, 1));
530 for (
UInt_t ivar=0; ivar<GetNvar(); ivar++) {
531 if ((*fCov)(ivar, ivar) != 0)
532 (*fDiscrimPow)[ivar] = (*fBetw)(ivar, ivar)/(*fCov)(ivar, ivar);
534 (*fDiscrimPow)[ivar] = 0;
544 fRanking =
new Ranking( GetName(),
"Discr. power" );
546 for (
UInt_t ivar=0; ivar<GetNvar(); ivar++) {
547 fRanking->AddRank(
Rank( GetInputLabel(ivar), (*fDiscrimPow)[ivar] ) );
559 Log() << kHEADER <<
"Results for Fisher coefficients:" <<
Endl;
561 if (GetTransformationHandler().GetTransformationList().GetSize() != 0) {
562 Log() << kINFO <<
"NOTE: The coefficients must be applied to TRANFORMED variables" <<
Endl;
563 Log() << kINFO <<
" List of the transformation: " <<
Endl;
564 TListIter trIt(&GetTransformationHandler().GetTransformationList());
566 Log() << kINFO <<
" -- " << trf->GetName() <<
Endl;
569 std::vector<TString> vars;
570 std::vector<Double_t> coeffs;
571 for (
UInt_t ivar=0; ivar<GetNvar(); ivar++) {
572 vars .push_back( GetInputLabel(ivar) );
573 coeffs.push_back( (*fFisherCoeff)[ivar] );
575 vars .push_back(
"(offset)" );
576 coeffs.push_back( fF0 );
582 if (IsNormalised()) {
583 Log() << kINFO <<
"NOTE: You have chosen to use the \"Normalise\" booking option. Hence, the" <<
Endl;
584 Log() << kINFO <<
" coefficients must be applied to NORMALISED (') variables as follows:" <<
Endl;
586 for (
UInt_t ivar=0; ivar<GetNvar(); ivar++)
if (GetInputLabel(ivar).Length() > maxL) maxL = GetInputLabel(ivar).Length();
589 for (
UInt_t ivar=0; ivar<GetNvar(); ivar++) {
591 << std::setw(maxL+9) <<
TString(
"[") + GetInputLabel(ivar) +
"]' = 2*("
592 << std::setw(maxL+2) <<
TString(
"[") + GetInputLabel(ivar) +
"]"
593 << std::setw(3) << (GetXmin(ivar) > 0 ?
" - " :
" + ")
594 << std::setw(6) <<
TMath::Abs(GetXmin(ivar)) << std::setw(3) <<
")/"
595 << std::setw(6) << (GetXmax(ivar) - GetXmin(ivar) )
596 << std::setw(3) <<
" - 1"
599 Log() << kINFO <<
"The TMVA Reader will properly account for this normalisation, but if the" <<
Endl;
600 Log() << kINFO <<
"Fisher classifier is applied outside the Reader, the transformation must be" <<
Endl;
601 Log() << kINFO <<
"implemented -- or the \"Normalise\" option is removed and Fisher retrained." <<
Endl;
602 Log() << kINFO <<
Endl;
612 for (
UInt_t ivar=0; ivar<GetNvar(); ivar++) istr >> (*fFisherCoeff)[ivar];
625 for (
UInt_t ivar=0; ivar<GetNvar(); ivar++) {
628 gTools().
AddAttr( coeffxml,
"Value", (*fFisherCoeff)[ivar] );
639 fFisherCoeff->resize(ncoeff-1);
646 if (coeffidx==0) fF0 = coeff;
647 else (*fFisherCoeff)[coeffidx-1] = coeff;
657 Int_t dp = fout.precision();
658 fout <<
" double fFisher0;" << std::endl;
659 fout <<
" std::vector<double> fFisherCoefficients;" << std::endl;
660 fout <<
"};" << std::endl;
661 fout <<
"" << std::endl;
662 fout <<
"inline void " << className <<
"::Initialize() " << std::endl;
663 fout <<
"{" << std::endl;
664 fout <<
" fFisher0 = " << std::setprecision(12) << fF0 <<
";" << std::endl;
665 for (
UInt_t ivar=0; ivar<GetNvar(); ivar++) {
666 fout <<
" fFisherCoefficients.push_back( " << std::setprecision(12) << (*fFisherCoeff)[ivar] <<
" );" << std::endl;
669 fout <<
" // sanity check" << std::endl;
670 fout <<
" if (fFisherCoefficients.size() != fNvars) {" << std::endl;
671 fout <<
" std::cout << \"Problem in class \\\"\" << fClassName << \"\\\"::Initialize: mismatch in number of input values\"" << std::endl;
672 fout <<
" << fFisherCoefficients.size() << \" != \" << fNvars << std::endl;" << std::endl;
673 fout <<
" fStatusIsClean = false;" << std::endl;
674 fout <<
" } " << std::endl;
675 fout <<
"}" << std::endl;
677 fout <<
"inline double " << className <<
"::GetMvaValue__( const std::vector<double>& inputValues ) const" << std::endl;
678 fout <<
"{" << std::endl;
679 fout <<
" double retval = fFisher0;" << std::endl;
680 fout <<
" for (size_t ivar = 0; ivar < fNvars; ivar++) {" << std::endl;
681 fout <<
" retval += fFisherCoefficients[ivar]*inputValues[ivar];" << std::endl;
682 fout <<
" }" << std::endl;
684 fout <<
" return retval;" << std::endl;
685 fout <<
"}" << std::endl;
687 fout <<
"// Clean up" << std::endl;
688 fout <<
"inline void " << className <<
"::Clear() " << std::endl;
689 fout <<
"{" << std::endl;
690 fout <<
" // clear coefficients" << std::endl;
691 fout <<
" fFisherCoefficients.clear(); " << std::endl;
692 fout <<
"}" << std::endl;
693 fout << std::setprecision(dp);
707 Log() <<
"Fisher discriminants select events by distinguishing the mean " <<
Endl;
708 Log() <<
"values of the signal and background distributions in a trans- " <<
Endl;
709 Log() <<
"formed variable space where linear correlations are removed." <<
Endl;
711 Log() <<
" (More precisely: the \"linear discriminator\" determines" <<
Endl;
712 Log() <<
" an axis in the (correlated) hyperspace of the input " <<
Endl;
713 Log() <<
" variables such that, when projecting the output classes " <<
Endl;
714 Log() <<
" (signal and background) upon this axis, they are pushed " <<
Endl;
715 Log() <<
" as far as possible away from each other, while events" <<
Endl;
716 Log() <<
" of a same class are confined in a close vicinity. The " <<
Endl;
717 Log() <<
" linearity property of this classifier is reflected in the " <<
Endl;
718 Log() <<
" metric with which \"far apart\" and \"close vicinity\" are " <<
Endl;
719 Log() <<
" determined: the covariance matrix of the discriminating" <<
Endl;
720 Log() <<
" variable space.)" <<
Endl;
724 Log() <<
"Optimal performance for Fisher discriminants is obtained for " <<
Endl;
725 Log() <<
"linearly correlated Gaussian-distributed variables. Any deviation" <<
Endl;
726 Log() <<
"from this ideal reduces the achievable separation power. In " <<
Endl;
727 Log() <<
"particular, no discrimination at all is achieved for a variable" <<
Endl;
728 Log() <<
"that has the same sample mean for signal and background, even if " <<
Endl;
729 Log() <<
"the shapes of the distributions are very different. Thus, Fisher " <<
Endl;
730 Log() <<
"discriminants often benefit from suitable transformations of the " <<
Endl;
731 Log() <<
"input variables. For example, if a variable x in [-1,1] has a " <<
Endl;
732 Log() <<
"a parabolic signal distributions, and a uniform background" <<
Endl;
733 Log() <<
"distributions, their mean value is zero in both cases, leading " <<
Endl;
734 Log() <<
"to no separation. The simple transformation x -> |x| renders this " <<
Endl;
735 Log() <<
"variable powerful for the use in a Fisher discriminant." <<
Endl;
739 Log() <<
"<None>" <<
Endl;
#define REGISTER_METHOD(CLASS)
for example
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t result
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t Int_t Int_t Window_t TString Int_t GCValues_t GetPrimarySelectionOwner GetDisplay GetScreen GetColormap GetNativeEvent const char const char dpyName wid window const char font_name cursor keysym reg const char only_if_exist regb h Point_t winding char text const char depth char const char Int_t count const char ColorStruct_t color const char Pixmap_t Pixmap_t PictureAttributes_t attr const char char ret_data h unsigned char height h Atom_t Int_t ULong_t ULong_t unsigned char prop_list Atom_t Atom_t Atom_t Time_t type
TMatrixT< Double_t > TMatrixD
Class that contains all the data information.
Float_t GetValue(UInt_t ivar) const
return value of i'th variable
Double_t GetWeight() const
return the event weight - depending on whether the flag IgnoreNegWeightsInTraining is or not.
Virtual base Class for all MVA method.
Fisher and Mahalanobis Discriminants (Linear Discriminant Analysis)
void ReadWeightsFromStream(std::istream &i)
read Fisher coefficients from weight file
void GetCov_Full(void)
compute full covariance matrix from sum of within and between matrices
void GetHelpMessage() const
get help message text
MethodFisher(const TString &jobName, const TString &methodTitle, DataSetInfo &dsi, const TString &theOption="Fisher")
standard constructor for the "Fisher"
const Ranking * CreateRanking()
computes ranking of input variables
virtual ~MethodFisher(void)
destructor
Double_t GetMvaValue(Double_t *err=nullptr, Double_t *errUpper=nullptr)
returns the Fisher value (no fixed range)
void Train(void)
computation of Fisher coefficients by series of matrix operations
void GetDiscrimPower(void)
computation of discrimination power indicator for each variable small values of "fWith" indicates lit...
virtual Bool_t HasAnalysisType(Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets)
Fisher can only handle classification with 2 classes.
void PrintCoefficients(void)
display Fisher coefficients and discriminating power for each variable check maximum length of variab...
void GetCov_BetweenClass(void)
the matrix of covariance 'between class' reflects the dispersion of the events of a class relative to...
void MakeClassSpecific(std::ostream &, const TString &) const
write Fisher-specific classifier response
void ReadWeightsFromXML(void *wghtnode)
read Fisher coefficients from xml weight file
void ProcessOptions()
process user options
void GetFisherCoeff(void)
Fisher = Sum { [coeff]*[variables] }.
void GetMean(void)
compute mean values of variables in each sample, and the overall means
void AddWeightsXMLTo(void *parent) const
create XML description of Fisher classifier
void DeclareOptions()
MethodFisher options: format and syntax of option string: "type" where type is "Fisher" or "Mahalanob...
void InitMatrices(void)
initialization method; creates global matrices and vectors
void GetCov_WithinClass(void)
the matrix of covariance 'within class' reflects the dispersion of the events relative to the center ...
void Init(void)
default initialization called by all constructors
Ranking for variables in method (implementation)
Singleton class for Global types used by TMVA.
void Print(Option_t *name="") const override
Print the matrix as a table of elements.
Double_t Determinant() const override
Return the matrix determinant.
TMatrixT< Element > & Invert(Double_t *det=nullptr)
Invert the matrix and calculate its determinant.
MsgLogger & Endl(MsgLogger &ml)
Double_t Sqrt(Double_t x)
Returns the square root of x.
Short_t Abs(Short_t d)
Returns the absolute value of parameter Short_t d.
static uint64_t sum(uint64_t i)