weights/woutCaloAndMinTrack/TMVAClassification_Cuts.class.C

0001 // Class: ReadCuts
0002 // Automatically generated by MethodBase::MakeClass
0003 //
0004
0005 /* configuration options =====================================================
0006
0007 #GEN -*-*-*-*-*-*-*-*-*-*-*- general info -*-*-*-*-*-*-*-*-*-*-*-
0008
0009 Method         : Cuts::Cuts
0010 TMVA Release   : 4.2.1         [262657]
0011 ROOT Release   : 6.22/02       [398850]
0012 Creator        : cdean
0013 Date           : Tue Apr 20 16:38:02 2021
0014 Host           : Linux cvmfswrite02.sdcc.bnl.gov 3.10.0-957.12.2.el7.x86_64 #1 SMP Tue May 14 15:23:27 CDT 2019 x86_64 x86_64 x86_64 GNU/Linux
0015 Dir            : /gpfs/mnt/gpfs02/sphenix/user/cdean/scripts/HF_trigger_ML/TMVA/Odd/wCalo
0016 Training events: 31922
0017 Analysis type  : [Classification]
0018
0019
0020 #OPT -*-*-*-*-*-*-*-*-*-*-*-*- options -*-*-*-*-*-*-*-*-*-*-*-*-
0021
0022 # Set by User:
0023 V: "False" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
0024 H: "False" [Print method-specific help message]
0025 FitMethod: "MC" [Minimisation Method (GA, SA, and MC are the primary methods to be used; the others have been introduced for testing purposes and are depreciated)]
0026 EffMethod: "EffSel" [Selection Method]
0027 # Default:
0028 VerbosityLevel: "Default" [Verbosity level]
0029 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)"]
0030 CreateMVAPdfs: "False" [Create PDFs for classifier outputs (signal and background)]
0031 IgnoreNegWeightsInTraining: "False" [Events with negative weights are ignored in the training (but are included for testing and performance evaluation)]
0032 CutRangeMin[0]: "-1.000000e+00" [Minimum of allowed cut range (set per variable)]
0033     CutRangeMin[1]: "-1.000000e+00"
0034     CutRangeMin[2]: "-1.000000e+00"
0035     CutRangeMin[3]: "-1.000000e+00"
0036 CutRangeMax[0]: "-1.000000e+00" [Maximum of allowed cut range (set per variable)]
0037     CutRangeMax[1]: "-1.000000e+00"
0038     CutRangeMax[2]: "-1.000000e+00"
0039     CutRangeMax[3]: "-1.000000e+00"
0040 VarProp[0]: "FSmart" [Categorisation of cuts]
0041     VarProp[1]: "FSmart"
0042     VarProp[2]: "FSmart"
0043     VarProp[3]: "FSmart"
0044 ##
0045
0046
0047 #VAR -*-*-*-*-*-*-*-*-*-*-*-* variables *-*-*-*-*-*-*-*-*-*-*-*-
0048
0049 NVar 4
0050 max(track_1_IP,track_2_IP)    maxTrackDCA_3D                maxTrackDCA_3D                track-vertex 3D DCA, max      units                             'F'    [0.00383873376995,4.99581956863]
0051 max(abs(track_1_IP_xy),abs(track_2_IP_xy)) maxTrackDCA_2D                             maxTrackDCA_2D                             track-vertex 2D DCA, max                   units                                          'F'    [9.33057162911e-05,4.8996257782]
0052 track_1_track_2_DCA           track_1_track_2_DCA           track_1_track_2_DCA           track-track 3D DCA            units                             'F'    [1.57269468559e-07,0.0499997623265]
0053 INTT_meanHits                 INTT_meanHits                 INTT_meanHits                 INTT avg. hits                units                             'F'    [0,93]
0054 NSpec 0
0055
0056
0057 ============================================================================ */
0058
0059 #include <array>
0060 #include <vector>
0061 #include <cmath>
0062 #include <string>
0063 #include <iostream>
0064
0065 #ifndef IClassifierReader__def
0066 #define IClassifierReader__def
0067
0068 class IClassifierReader {
0069
0070  public:
0071
0072    // constructor
0073    IClassifierReader() : fStatusIsClean( true ) {}
0074    virtual ~IClassifierReader() {}
0075
0076    // return classifier response
0077    virtual double GetMvaValue( const std::vector<double>& inputValues ) const = 0;
0078
0079    // returns classifier status
0080    bool IsStatusClean() const { return fStatusIsClean; }
0081
0082  protected:
0083
0084    bool fStatusIsClean;
0085 };
0086
0087 #endif
0088
0089 class ReadCuts : public IClassifierReader {
0090
0091  public:
0092
0093    // constructor
0094    ReadCuts( std::vector<std::string>& theInputVars )
0095       : IClassifierReader(),
0096         fClassName( "ReadCuts" ),
0097         fNvars( 4 )
0098    {
0099       // the training input variables
0100       const char* inputVars[] = { "max(track_1_IP,track_2_IP)", "max(abs(track_1_IP_xy),abs(track_2_IP_xy))", "track_1_track_2_DCA", "INTT_meanHits" };
0101
0102       // sanity checks
0103       if (theInputVars.size() <= 0) {
0104          std::cout << "Problem in class \"" << fClassName << "\": empty input vector" << std::endl;
0105          fStatusIsClean = false;
0106       }
0107
0108       if (theInputVars.size() != fNvars) {
0109          std::cout << "Problem in class \"" << fClassName << "\": mismatch in number of input values: "
0110                    << theInputVars.size() << " != " << fNvars << std::endl;
0111          fStatusIsClean = false;
0112       }
0113
0114       // validate input variables
0115       for (size_t ivar = 0; ivar < theInputVars.size(); ivar++) {
0116          if (theInputVars[ivar] != inputVars[ivar]) {
0117             std::cout << "Problem in class \"" << fClassName << "\": mismatch in input variable names" << std::endl
0118                       << " for variable [" << ivar << "]: " << theInputVars[ivar].c_str() << " != " << inputVars[ivar] << std::endl;
0119             fStatusIsClean = false;
0120          }
0121       }
0122
0123       // initialize min and max vectors (for normalisation)
0124       fVmin[0] = 0;
0125       fVmax[0] = 0;
0126       fVmin[1] = 0;
0127       fVmax[1] = 0;
0128       fVmin[2] = 0;
0129       fVmax[2] = 0;
0130       fVmin[3] = 0;
0131       fVmax[3] = 0;
0132
0133       // initialize input variable types
0134       fType[0] = 'F';
0135       fType[1] = 'F';
0136       fType[2] = 'F';
0137       fType[3] = 'F';
0138
0139       // initialize constants
0140       Initialize();
0141
0142    }
0143
0144    // destructor
0145    virtual ~ReadCuts() {
0146       Clear(); // method-specific
0147    }
0148
0149    // the classifier response
0150    // "inputValues" is a vector of input values in the same order as the
0151    // variables given to the constructor
0152    double GetMvaValue( const std::vector<double>& inputValues ) const override;
0153
0154  private:
0155
0156    // method-specific destructor
0157    void Clear();
0158
0159    // common member variables
0160    const char* fClassName;
0161
0162    const size_t fNvars;
0163    size_t GetNvar()           const { return fNvars; }
0164    char   GetType( int ivar ) const { return fType[ivar]; }
0165
0166    // normalisation of input variables
0167    double fVmin[4];
0168    double fVmax[4];
0169    double NormVariable( double x, double xmin, double xmax ) const {
0170       // normalise to output range: [-1, 1]
0171       return 2*(x - xmin)/(xmax - xmin) - 1.0;
0172    }
0173
0174    // type of input variable: 'F' or 'I'
0175    char   fType[4];
0176
0177    // initialize internal variables
0178    void Initialize();
0179    double GetMvaValue__( const std::vector<double>& inputValues ) const;
0180
0181    // private members (method specific)
0182    // not implemented for class: "ReadCuts"
0183 };
0184 inline double ReadCuts::GetMvaValue( const std::vector<double>& inputValues ) const
0185 {
0186    // classifier response value
0187    double retval = 0;
0188
0189    // classifier response, sanity check first
0190    if (!IsStatusClean()) {
0191       std::cout << "Problem in class \"" << fClassName << "\": cannot return classifier response"
0192                 << " because status is dirty" << std::endl;
0193    }
0194    else {
0195          retval = GetMvaValue__( inputValues );
0196    }
0197
0198    return retval;
0199 }