// Class: ReadRuleFit // Automatically generated by MethodBase::MakeClass // /* configuration options ===================================================== #GEN -*-*-*-*-*-*-*-*-*-*-*- general info -*-*-*-*-*-*-*-*-*-*-*- Method : RuleFit::RuleFit TMVA Release : 4.2.1 [262657] ROOT Release : 6.41/01 [403713] Creator : root Date : Tue May 19 20:08:07 2026 Host : Linux d4f37374721b 4.18.0-553.117.1.el8_10.x86_64 #1 SMP Sun Apr 5 23:14:32 EDT 2026 x86_64 GNU/Linux Dir : /github/home/master/notebooks Training events: 2000 Analysis type : [Classification] #OPT -*-*-*-*-*-*-*-*-*-*-*-*- options -*-*-*-*-*-*-*-*-*-*-*-*- # Set by User: V: "False" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)] H: "True" [Print method-specific help message] GDTau: "-1.000000e+00" [Gradient-directed (GD) path: default fit cut-off] GDTauPrec: "1.000000e-02" [GD path: precision of tau] GDStep: "1.000000e-02" [GD path: step size] GDNSteps: "10000" [GD path: number of steps] GDErrScale: "1.020000e+00" [Stop scan when error > scale*errmin] fEventsMin: "1.000000e-02" [Minimum fraction of events in a splittable node] fEventsMax: "5.000000e-01" [Maximum fraction of events in a splittable node] nTrees: "20" [Number of trees in forest.] RuleMinDist: "1.000000e-03" [Minimum distance between rules] MinImp: "1.000000e-03" [Minimum rule importance accepted] Model: "modrulelinear" [Model to be used] RuleFitModule: "rftmva" [Which RuleFit module to use] # Default: VerbosityLevel: "Default" [Verbosity level] VarTransform: "None" [List of variable transformations performed before training, e.g., "D_Background,P_Signal,G,N_AllClasses" for: "Decorrelation, PCA-transformation, Gaussianisation, Normalisation, each for the given class of events ('AllClasses' denotes all events of all classes, if no class indication is given, 'All' is assumed)"] CreateMVAPdfs: "False" [Create PDFs for classifier outputs (signal and background)] IgnoreNegWeightsInTraining: "False" [Events with negative weights are ignored in the training (but are included for testing and performance evaluation)] LinQuantile: "2.500000e-02" [Quantile of linear terms (removes outliers)] GDPathEveFrac: "5.000000e-01" [Fraction of events used for the path search] GDValidEveFrac: "5.000000e-01" [Fraction of events used for the validation] ForestType: "adaboost" [Method to use for forest generation (AdaBoost or RandomForest)] RFWorkDir: "./rulefit" [Friedman's RuleFit module (RFF): working dir] RFNrules: "2000" [RFF: Mximum number of rules] RFNendnodes: "4" [RFF: Average number of end nodes] ## #VAR -*-*-*-*-*-*-*-*-*-*-*-* variables *-*-*-*-*-*-*-*-*-*-*-*- NVar 4 var1+var2 myvar1 myvar1 myvar1 'F' [-9.23118686676,7.07192516327] var1-var2 myvar2 myvar2 Expression 2 'F' [-3.70671987534,4.02912044525] var3 var3 var3 Variable 3 units 'F' [-5.15695810318,4.15070819855] var4 var4 var4 Variable 4 units 'F' [-6.31600189209,4.52105665207] NSpec 2 var1*2 spec1 spec1 Spectator 1 units 'F' [-9.63254642487,9.05203056335] var1*3 spec2 spec2 Spectator 2 units 'F' [-14.4488201141,13.578045845] ============================================================================ */ #include #include #include #include #include #ifndef IClassifierReader__def #define IClassifierReader__def class IClassifierReader { public: // constructor IClassifierReader() : fStatusIsClean( true ) {} virtual ~IClassifierReader() {} // return classifier response virtual double GetMvaValue( const std::vector& inputValues ) const = 0; // returns classifier status bool IsStatusClean() const { return fStatusIsClean; } protected: bool fStatusIsClean; }; #endif class ReadRuleFit : public IClassifierReader { public: // constructor ReadRuleFit( std::vector& theInputVars ) : IClassifierReader(), fClassName( "ReadRuleFit" ), fNvars( 4 ) { // the training input variables const char* inputVars[] = { "var1+var2", "var1-var2", "var3", "var4" }; // sanity checks if (theInputVars.size() <= 0) { std::cout << "Problem in class \"" << fClassName << "\": empty input vector" << std::endl; fStatusIsClean = false; } if (theInputVars.size() != fNvars) { std::cout << "Problem in class \"" << fClassName << "\": mismatch in number of input values: " << theInputVars.size() << " != " << fNvars << std::endl; fStatusIsClean = false; } // validate input variables for (size_t ivar = 0; ivar < theInputVars.size(); ivar++) { if (theInputVars[ivar] != inputVars[ivar]) { std::cout << "Problem in class \"" << fClassName << "\": mismatch in input variable names" << std::endl << " for variable [" << ivar << "]: " << theInputVars[ivar].c_str() << " != " << inputVars[ivar] << std::endl; fStatusIsClean = false; } } // initialize min and max vectors (for normalisation) fVmin[0] = 0; fVmax[0] = 0; fVmin[1] = 0; fVmax[1] = 0; fVmin[2] = 0; fVmax[2] = 0; fVmin[3] = 0; fVmax[3] = 0; // initialize input variable types fType[0] = 'F'; fType[1] = 'F'; fType[2] = 'F'; fType[3] = 'F'; // initialize constants Initialize(); } // destructor virtual ~ReadRuleFit() { Clear(); // method-specific } // the classifier response // "inputValues" is a vector of input values in the same order as the // variables given to the constructor double GetMvaValue( const std::vector& inputValues ) const override; private: // method-specific destructor void Clear(); // common member variables const char* fClassName; const size_t fNvars; size_t GetNvar() const { return fNvars; } char GetType( int ivar ) const { return fType[ivar]; } // normalisation of input variables double fVmin[4]; double fVmax[4]; double NormVariable( double x, double xmin, double xmax ) const { // normalise to output range: [-1, 1] return 2*(x - xmin)/(xmax - xmin) - 1.0; } // type of input variable: 'F' or 'I' char fType[4]; // initialize internal variables void Initialize(); double GetMvaValue__( const std::vector& inputValues ) const; // private members (method specific) // not implemented for class: "ReadRuleFit" }; void ReadRuleFit::Initialize(){} void ReadRuleFit::Clear(){} double ReadRuleFit::GetMvaValue__( const std::vector& inputValues ) const { double rval=3.619441438; // // here follows all rules ordered in importance (most important first) // at the end of each line, the relative importance of the rule is given // if ((inputValues[1]<-0.0229863897)&&(inputValues[3]<-0.1233970076)) rval+=-0.6778870924; // importance = 0.446 if ((-0.6914615035& inputValues ) const { // classifier response value double retval = 0; // classifier response, sanity check first if (!IsStatusClean()) { std::cout << "Problem in class \"" << fClassName << "\": cannot return classifier response" << " because status is dirty" << std::endl; } else { retval = GetMvaValue__( inputValues ); } return retval; }