Definition at line 57 of file RuleFitParams.h.
|
| | RuleFitParams () |
| | constructor More...
|
| |
| virtual | ~RuleFitParams () |
| | destructor More...
|
| |
| Int_t | FindGDTau () |
| | This finds the cutoff parameter tau by scanning several different paths. More...
|
| |
| UInt_t | GetPathIdx1 () const |
| |
| UInt_t | GetPathIdx2 () const |
| |
| UInt_t | GetPerfIdx1 () const |
| |
| UInt_t | GetPerfIdx2 () const |
| |
| void | Init () |
| | Initializes all parameters using the RuleEnsemble and the training tree. More...
|
| |
| void | InitGD () |
| | Initialize GD path search. More...
|
| |
| 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...
|
| |
| 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...
|
| |
| 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...
|
| |
| void | MakeGDPath () |
| | The following finds the gradient directed path in parameter space. More...
|
| |
| Double_t | Penalty () const |
| | This is the "lasso" penalty To be used for regression. More...
|
| |
| Double_t | Risk (UInt_t ind1, UInt_t ind2, Double_t neff) const |
| | risk asessment More...
|
| |
| Double_t | Risk (UInt_t ind1, UInt_t ind2, Double_t neff, UInt_t itau) const |
| | risk asessment for tau model <itau> More...
|
| |
| Double_t | RiskPath () const |
| |
| Double_t | RiskPerf () const |
| |
| Double_t | RiskPerf (UInt_t itau) const |
| |
| UInt_t | RiskPerfTst () |
| | Estimates the error rate with the current set of parameters. More...
|
| |
| void | SetGDErrScale (Double_t s) |
| |
| void | SetGDNPathSteps (Int_t np) |
| |
| void | SetGDPathStep (Double_t s) |
| |
| void | SetGDTau (Double_t t) |
| |
| void | SetGDTauPrec (Double_t p) |
| |
| void | SetGDTauRange (Double_t t0, Double_t t1) |
| |
| void | SetGDTauScan (UInt_t n) |
| |
| void | SetMsgType (EMsgType t) |
| |
| void | SetRuleFit (RuleFit *rf) |
| |
| Int_t | Type (const Event *e) const |
| |
#include <TMVA/RuleFitParams.h>
| TMVA::RuleFitParams::RuleFitParams |
( |
| ) |
|
| TMVA::RuleFitParams::~RuleFitParams |
( |
| ) |
|
|
virtual |
| Double_t TMVA::RuleFitParams::CalcAverageResponse |
( |
| ) |
|
|
protected |
| Double_t TMVA::RuleFitParams::CalcAverageResponseOLD |
( |
| ) |
|
|
protected |
| Double_t TMVA::RuleFitParams::CalcAverageTruth |
( |
| ) |
|
|
protected |
| void TMVA::RuleFitParams::CalcFStar |
( |
| ) |
|
|
protected |
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 |
( |
| ) |
|
|
inlineprotected |
| void TMVA::RuleFitParams::CalcTstAverageResponse |
( |
| ) |
|
|
protected |
| Double_t TMVA::RuleFitParams::ErrorRateBin |
( |
| ) |
|
|
protected |
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 |
( |
| ) |
|
|
protected |
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 |
( |
| ) |
|
|
protected |
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 |
( |
| ) |
|
|
protected |
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 |
( |
| ) |
|
|
inlineprotected |
| void TMVA::RuleFitParams::EvaluateAveragePerf |
( |
| ) |
|
|
inlineprotected |
| void TMVA::RuleFitParams::FillCoefficients |
( |
| ) |
|
|
protected |
helper function to store the rule coefficients in local arrays
Definition at line 864 of file RuleFitParams.cxx.
| Int_t TMVA::RuleFitParams::FindGDTau |
( |
| ) |
|
This finds the cutoff parameter tau by scanning several different paths.
Definition at line 446 of file RuleFitParams.cxx.
| UInt_t TMVA::RuleFitParams::GetPathIdx1 |
( |
| ) |
const |
|
inline |
| UInt_t TMVA::RuleFitParams::GetPathIdx2 |
( |
| ) |
const |
|
inline |
| UInt_t TMVA::RuleFitParams::GetPerfIdx1 |
( |
| ) |
const |
|
inline |
| UInt_t TMVA::RuleFitParams::GetPerfIdx2 |
( |
| ) |
const |
|
inline |
| void TMVA::RuleFitParams::InitGD |
( |
| ) |
|
| void TMVA::RuleFitParams::InitNtuple |
( |
| ) |
|
|
protected |
| MsgLogger& TMVA::RuleFitParams::Log |
( |
| ) |
const |
|
inlineprivate |
| Double_t TMVA::RuleFitParams::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)
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 |
( |
| ) |
|
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 |
( |
| ) |
|
|
protected |
| void TMVA::RuleFitParams::MakeTstGradientVector |
( |
| ) |
|
|
protected |
| Double_t TMVA::RuleFitParams::Optimism |
( |
| ) |
|
|
protected |
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 |
( |
| ) |
const |
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 |
( |
| ) |
const |
|
inline |
| Double_t TMVA::RuleFitParams::RiskPerf |
( |
| ) |
const |
|
inline |
| UInt_t TMVA::RuleFitParams::RiskPerfTst |
( |
| ) |
|
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 |
( |
Int_t |
np | ) |
|
|
inline |
| void TMVA::RuleFitParams::SetGDTauScan |
( |
UInt_t |
n | ) |
|
|
inline |
| Int_t TMVA::RuleFitParams::Type |
( |
const Event * |
e | ) |
const |
| void TMVA::RuleFitParams::UpdateCoefficients |
( |
| ) |
|
|
protected |
Establish maximum gradient for rules, linear terms and the offset.
Definition at line 1457 of file RuleFitParams.cxx.
| void TMVA::RuleFitParams::UpdateTstCoefficients |
( |
| ) |
|
|
protected |
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 |
|
protected |
| std::vector<Double_t> TMVA::RuleFitParams::fAverageRulePerf |
|
protected |
| std::vector<Double_t> TMVA::RuleFitParams::fAverageSelectorPath |
|
protected |
| std::vector<Double_t> TMVA::RuleFitParams::fAverageSelectorPerf |
|
protected |
| Double_t TMVA::RuleFitParams::fAverageTruth |
|
protected |
| std::vector<Double_t> TMVA::RuleFitParams::fFstar |
|
protected |
| Double_t TMVA::RuleFitParams::fFstarMedian |
|
protected |
| std::vector< std::vector<Double_t> > TMVA::RuleFitParams::fGDCoefLinTst |
|
protected |
| std::vector< std::vector<Double_t> > TMVA::RuleFitParams::fGDCoefTst |
|
protected |
| Double_t TMVA::RuleFitParams::fGDErrScale |
|
protected |
| std::vector<Double_t> TMVA::RuleFitParams::fGDErrTst |
|
protected |
| std::vector<Char_t> TMVA::RuleFitParams::fGDErrTstOK |
|
protected |
| Int_t TMVA::RuleFitParams::fGDNPathSteps |
|
protected |
| UInt_t TMVA::RuleFitParams::fGDNTau |
|
protected |
| UInt_t TMVA::RuleFitParams::fGDNTauTstOK |
|
protected |
| TTree* TMVA::RuleFitParams::fGDNtuple |
|
protected |
| std::vector<Double_t> TMVA::RuleFitParams::fGDOfsTst |
|
protected |
| Double_t TMVA::RuleFitParams::fGDPathStep |
|
protected |
| Double_t TMVA::RuleFitParams::fGDTauPrec |
|
protected |
| UInt_t TMVA::RuleFitParams::fGDTauScan |
|
protected |
| std::vector< Double_t > TMVA::RuleFitParams::fGDTauVec |
|
protected |
| std::vector<Double_t> TMVA::RuleFitParams::fGradVec |
|
protected |
| std::vector<Double_t> TMVA::RuleFitParams::fGradVecLin |
|
protected |
| std::vector< std::vector<Double_t> > TMVA::RuleFitParams::fGradVecLinTst |
|
protected |
| std::vector< std::vector<Double_t> > TMVA::RuleFitParams::fGradVecTst |
|
protected |
| Double_t TMVA::RuleFitParams::fNEveEffPath |
|
protected |
| Double_t TMVA::RuleFitParams::fNEveEffPerf |
|
protected |
| UInt_t TMVA::RuleFitParams::fNLinear |
|
protected |
| UInt_t TMVA::RuleFitParams::fNRules |
|
protected |
| Double_t TMVA::RuleFitParams::fNTCoefRad |
|
protected |
| Double_t TMVA::RuleFitParams::fNTErrorRate |
|
protected |
| Double_t* TMVA::RuleFitParams::fNTLinCoeff |
|
protected |
| UInt_t TMVA::RuleFitParams::fPathIdx1 |
|
protected |
| UInt_t TMVA::RuleFitParams::fPathIdx2 |
|
protected |
| UInt_t TMVA::RuleFitParams::fPerfIdx1 |
|
protected |
| UInt_t TMVA::RuleFitParams::fPerfIdx2 |
|
protected |
| RuleFit* TMVA::RuleFitParams::fRuleFit |
|
protected |
The documentation for this class was generated from the following files:
ROOT 6.07/09 - Reference Guide Generated on Wed Oct 19 2016 02:28:53 using Doxygen 1.8.12.
