Definition at line 57 of file RuleFitParams.h.
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| RuleFitParams () |
| constructor More...
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virtual | ~RuleFitParams () |
| destructor More...
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Int_t | FindGDTau () |
| This finds the cutoff parameter tau by scanning several different paths. More...
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UInt_t | GetPathIdx1 () const |
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UInt_t | GetPathIdx2 () const |
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UInt_t | GetPerfIdx1 () const |
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UInt_t | GetPerfIdx2 () const |
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void | Init () |
| Initializes all parameters using the RuleEnsemble and the training tree. More...
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void | InitGD () |
| Initialize GD path search. More...
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Double_t | LossFunction (const Event &e) const |
| Implementation of squared-error ramp loss function (eq 39,40 in ref 1) This is used for binary Classifications where y = {+1,-1} for (sig,bkg) More...
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Double_t | LossFunction (UInt_t evtidx) const |
| Implementation of squared-error ramp loss function (eq 39,40 in ref 1) This is used for binary Classifications where y = {+1,-1} for (sig,bkg) More...
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Double_t | LossFunction (UInt_t evtidx, UInt_t itau) const |
| Implementation of squared-error ramp loss function (eq 39,40 in ref 1) This is used for binary Classifications where y = {+1,-1} for (sig,bkg) More...
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void | MakeGDPath () |
| The following finds the gradient directed path in parameter space. More...
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Double_t | Penalty () const |
| This is the "lasso" penalty To be used for regression. More...
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Double_t | Risk (UInt_t ind1, UInt_t ind2, Double_t neff) const |
| risk asessment More...
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Double_t | Risk (UInt_t ind1, UInt_t ind2, Double_t neff, UInt_t itau) const |
| risk asessment for tau model <itau> More...
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Double_t | RiskPath () const |
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Double_t | RiskPerf () const |
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Double_t | RiskPerf (UInt_t itau) const |
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UInt_t | RiskPerfTst () |
| Estimates the error rate with the current set of parameters. More...
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void | SetGDErrScale (Double_t s) |
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void | SetGDNPathSteps (Int_t np) |
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void | SetGDPathStep (Double_t s) |
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void | SetGDTau (Double_t t) |
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void | SetGDTauPrec (Double_t p) |
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void | SetGDTauRange (Double_t t0, Double_t t1) |
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void | SetGDTauScan (UInt_t n) |
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void | SetMsgType (EMsgType t) |
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void | SetRuleFit (RuleFit *rf) |
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Int_t | Type (const Event *e) const |
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#include <TMVA/RuleFitParams.h>
TMVA::RuleFitParams::RuleFitParams |
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TMVA::RuleFitParams::~RuleFitParams |
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Double_t TMVA::RuleFitParams::CalcAverageResponse |
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Double_t TMVA::RuleFitParams::CalcAverageResponseOLD |
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Double_t TMVA::RuleFitParams::CalcAverageTruth |
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void TMVA::RuleFitParams::CalcFStar |
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Estimates F* (optimum scoring function) for all events for the given sets.
The result is used in ErrorRateReg(). — NOT USED —
Definition at line 882 of file RuleFitParams.cxx.
void TMVA::RuleFitParams::CalcGDNTau |
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void TMVA::RuleFitParams::CalcTstAverageResponse |
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Double_t TMVA::RuleFitParams::ErrorRateBin |
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Estimates the error rate with the current set of parameters It uses a binary estimate of (y-F*(x)) (y-F*(x)) = (Num of events where sign(F)!=sign(y))/Neve y = {+1 if event is signal, -1 otherwise} — NOT USED —.
Definition at line 1010 of file RuleFitParams.cxx.
Double_t TMVA::RuleFitParams::ErrorRateReg |
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Estimates the error rate with the current set of parameters This code is pretty messy at the moment.
Cleanup is needed. – NOT USED —
Definition at line 965 of file RuleFitParams.cxx.
Double_t TMVA::RuleFitParams::ErrorRateRoc |
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Estimates the error rate with the current set of parameters.
It calculates the area under the bkg rejection vs signal efficiency curve. The value returned is 1-area. This works but is less efficient than calculating the Risk using RiskPerf().
Definition at line 1113 of file RuleFitParams.cxx.
void TMVA::RuleFitParams::ErrorRateRocTst |
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Estimates the error rate with the current set of parameters.
It calculates the area under the bkg rejection vs signal efficiency curve. The value returned is 1-area.
See comment under ErrorRateRoc().
Definition at line 1163 of file RuleFitParams.cxx.
evaluate the average of each variable and f(x) in the given range
Definition at line 205 of file RuleFitParams.cxx.
void TMVA::RuleFitParams::EvaluateAveragePath |
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void TMVA::RuleFitParams::EvaluateAveragePerf |
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void TMVA::RuleFitParams::FillCoefficients |
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helper function to store the rule coefficients in local arrays
Definition at line 864 of file RuleFitParams.cxx.
Int_t TMVA::RuleFitParams::FindGDTau |
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This finds the cutoff parameter tau by scanning several different paths.
Definition at line 446 of file RuleFitParams.cxx.
UInt_t TMVA::RuleFitParams::GetPathIdx1 |
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UInt_t TMVA::RuleFitParams::GetPathIdx2 |
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UInt_t TMVA::RuleFitParams::GetPerfIdx1 |
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UInt_t TMVA::RuleFitParams::GetPerfIdx2 |
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void TMVA::RuleFitParams::InitGD |
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void TMVA::RuleFitParams::InitNtuple |
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MsgLogger& TMVA::RuleFitParams::Log |
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Double_t TMVA::RuleFitParams::LossFunction |
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const Event & |
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Implementation of squared-error ramp loss function (eq 39,40 in ref 1) This is used for binary Classifications where y = {+1,-1} for (sig,bkg)
Definition at line 275 of file RuleFitParams.cxx.
Implementation of squared-error ramp loss function (eq 39,40 in ref 1) This is used for binary Classifications where y = {+1,-1} for (sig,bkg)
Definition at line 287 of file RuleFitParams.cxx.
Implementation of squared-error ramp loss function (eq 39,40 in ref 1) This is used for binary Classifications where y = {+1,-1} for (sig,bkg)
Definition at line 299 of file RuleFitParams.cxx.
void TMVA::RuleFitParams::MakeGDPath |
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The following finds the gradient directed path in parameter space.
More work is needed... FT, 24/9/2006 The algorithm is currently as follows: <***if not otherwise stated, the sample used below is [fPathIdx1,fPathIdx2]***>
- Set offset to -average(y(true)) and all coefs=0 => average of F(x)==0
- FindGDTau() : start scanning using several paths defined by different tau choose the tau yielding the best path
- start the scanning the chosen path
- check error rate at a given frequency data used for check: [fPerfIdx1,fPerfIdx2]
- stop when either of the following onditions are fullfilled: a. loop index==fGDNPathSteps b. error > fGDErrScale*errmin c. only in DEBUG mode: risk is not monotoneously decreasing
The algorithm will warn if: I. the error rate was still decreasing when loop finnished -> increase fGDNPathSteps! II. minimum was found at an early stage -> decrease fGDPathStep III. DEBUG: risk > previous risk -> entered caotic region (regularization is too small)
Definition at line 534 of file RuleFitParams.cxx.
void TMVA::RuleFitParams::MakeGradientVector |
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void TMVA::RuleFitParams::MakeTstGradientVector |
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Double_t TMVA::RuleFitParams::Optimism |
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implementation of eq.
7.17 in Hastie,Tibshirani & Friedman book this is the covariance between the estimated response yhat and the true value y. NOT REALLY SURE IF THIS IS CORRECT! — THIS IS NOT USED —
Definition at line 923 of file RuleFitParams.cxx.
Double_t TMVA::RuleFitParams::Penalty |
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This is the "lasso" penalty To be used for regression.
— NOT USED —
Definition at line 353 of file RuleFitParams.cxx.
Double_t TMVA::RuleFitParams::RiskPath |
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Double_t TMVA::RuleFitParams::RiskPerf |
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UInt_t TMVA::RuleFitParams::RiskPerfTst |
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Estimates the error rate with the current set of parameters.
using the <Perf> subsample. Return the tau index giving the lowest error
Definition at line 1211 of file RuleFitParams.cxx.
void TMVA::RuleFitParams::SetGDNPathSteps |
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Int_t |
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void TMVA::RuleFitParams::SetGDTauScan |
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UInt_t |
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Int_t TMVA::RuleFitParams::Type |
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const Event * |
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void TMVA::RuleFitParams::UpdateCoefficients |
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Establish maximum gradient for rules, linear terms and the offset.
Definition at line 1457 of file RuleFitParams.cxx.
void TMVA::RuleFitParams::UpdateTstCoefficients |
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Establish maximum gradient for rules, linear terms and the offset for all taus TODO: do not need index range!
Definition at line 1340 of file RuleFitParams.cxx.
std::vector<Double_t> TMVA::RuleFitParams::fAverageRulePath |
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std::vector<Double_t> TMVA::RuleFitParams::fAverageRulePerf |
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std::vector<Double_t> TMVA::RuleFitParams::fAverageSelectorPath |
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std::vector<Double_t> TMVA::RuleFitParams::fAverageSelectorPerf |
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Double_t TMVA::RuleFitParams::fAverageTruth |
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std::vector<Double_t> TMVA::RuleFitParams::fFstar |
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Double_t TMVA::RuleFitParams::fFstarMedian |
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std::vector< std::vector<Double_t> > TMVA::RuleFitParams::fGDCoefLinTst |
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std::vector< std::vector<Double_t> > TMVA::RuleFitParams::fGDCoefTst |
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Double_t TMVA::RuleFitParams::fGDErrScale |
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std::vector<Double_t> TMVA::RuleFitParams::fGDErrTst |
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std::vector<Char_t> TMVA::RuleFitParams::fGDErrTstOK |
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Int_t TMVA::RuleFitParams::fGDNPathSteps |
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UInt_t TMVA::RuleFitParams::fGDNTau |
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UInt_t TMVA::RuleFitParams::fGDNTauTstOK |
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TTree* TMVA::RuleFitParams::fGDNtuple |
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std::vector<Double_t> TMVA::RuleFitParams::fGDOfsTst |
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Double_t TMVA::RuleFitParams::fGDPathStep |
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Double_t TMVA::RuleFitParams::fGDTauPrec |
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UInt_t TMVA::RuleFitParams::fGDTauScan |
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std::vector< Double_t > TMVA::RuleFitParams::fGDTauVec |
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std::vector<Double_t> TMVA::RuleFitParams::fGradVec |
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std::vector<Double_t> TMVA::RuleFitParams::fGradVecLin |
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std::vector< std::vector<Double_t> > TMVA::RuleFitParams::fGradVecLinTst |
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std::vector< std::vector<Double_t> > TMVA::RuleFitParams::fGradVecTst |
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Double_t TMVA::RuleFitParams::fNEveEffPath |
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Double_t TMVA::RuleFitParams::fNEveEffPerf |
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UInt_t TMVA::RuleFitParams::fNLinear |
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UInt_t TMVA::RuleFitParams::fNRules |
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Double_t TMVA::RuleFitParams::fNTCoefRad |
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Double_t TMVA::RuleFitParams::fNTErrorRate |
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Double_t* TMVA::RuleFitParams::fNTLinCoeff |
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UInt_t TMVA::RuleFitParams::fPathIdx1 |
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UInt_t TMVA::RuleFitParams::fPathIdx2 |
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UInt_t TMVA::RuleFitParams::fPerfIdx1 |
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UInt_t TMVA::RuleFitParams::fPerfIdx2 |
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RuleFit* TMVA::RuleFitParams::fRuleFit |
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The documentation for this class was generated from the following files: