| 1 | #ifndef MARS_MRanForestCalc
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| 2 | #define MARS_MRanForestCalc
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| 3 |
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| 4 | #ifndef MARS_MTask
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| 5 | #include "MTask.h"
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| 6 | #endif
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| 7 |
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| 8 | #ifndef ROOT_TObjArray
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| 9 | #include <TObjArray.h>
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| 10 | #endif
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| 11 |
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| 12 | #ifndef ROOT_TArrayD
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| 13 | #include <TArrayD.h>
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| 14 | #endif
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| 15 |
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| 16 | #ifndef ROOT_TF1
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| 17 | #include <TF1.h>
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| 18 | #endif
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| 19 |
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| 20 | class MDataArray;
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| 21 | class MParameterD;
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| 22 | class MHMatrix;
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| 23 |
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| 24 | class MRanForestCalc : public MTask
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| 25 | {
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| 26 | public:
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| 27 | enum EstimationMode_t
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| 28 | {
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| 29 | kMean,
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| 30 | kMaximum,
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| 31 | kFit
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| 32 | };
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| 33 |
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| 34 | private:
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| 35 | static const TString gsDefName; //! Default Name
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| 36 | static const TString gsDefTitle; //! Default Title
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| 37 | static const TString gsNameOutput; //! Default Output name
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| 38 |
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| 39 | MDataArray *fData; //! Used to store the MDataChains to get the event values
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| 40 | MParameterD *fRFOut; //! Used to store result
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| 41 | MHMatrix *fTestMatrix; //! Test Matrix used in Process (together with MMatrixLoop)
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| 42 | TF1 fFunc; //! Function to apply to the result
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| 43 |
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| 44 | TObjArray fEForests; //! List of forests read or to be written
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| 45 |
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| 46 | Int_t fNumTrees; //! Training parameters
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| 47 | Int_t fNumTry; //! Training parameters
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| 48 | Int_t fNdSize; //! Training parameters
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| 49 |
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| 50 | Int_t fNumObsoleteVariables; //! Training parameters
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| 51 | Bool_t fLastDataColumnHasWeights; //! Training parameters
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| 52 |
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| 53 | TString fFileName; // File name to forest
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| 54 | TString fNameOutput; // Name of output container
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| 55 |
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| 56 | Bool_t fDebug; // Debugging of eventloop while training on/off
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| 57 |
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| 58 | EstimationMode_t fEstimationMode; // Mode of estimation in case of multi random forest regression
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| 59 |
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| 60 | private:
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| 61 | // MTask
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| 62 | Int_t PreProcess(MParList *plist);
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| 63 | Int_t Process();
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| 64 |
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| 65 | // MRanForestCalc
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| 66 | Int_t ReadForests(MParList &plist);
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| 67 | Double_t Eval() const;
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| 68 |
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| 69 | // MParContainer
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| 70 | Int_t ReadEnv(const TEnv &env, TString prefix, Bool_t print);
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| 71 |
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| 72 | // Train Interface
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| 73 | Int_t Train(const MHMatrix &n, const TArrayD &grid, Int_t ver);
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| 74 |
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| 75 | public:
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| 76 | MRanForestCalc(const char *name=NULL, const char *title=NULL);
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| 77 | ~MRanForestCalc();
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| 78 |
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| 79 | // TObject
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| 80 | void Print(Option_t *o="") const; //*MENU*
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| 81 |
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| 82 | // Setter for estimation
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| 83 | void SetFileName(TString filename) { fFileName = filename; }
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| 84 | void SetEstimationMode(EstimationMode_t op) { fEstimationMode = op; }
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| 85 | void SetNameOutput(TString name=gsNameOutput) { fNameOutput = name; }
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| 86 |
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| 87 | // Setter for training
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| 88 | void SetNumTrees(UShort_t n=100) { fNumTrees = n; }
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| 89 | void SetNdSize(UShort_t n=5) { fNdSize = n; }
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| 90 | void SetNumTry(UShort_t n=0) { fNumTry = n; }
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| 91 | void SetDebug(Bool_t b=kTRUE) { fDebug = b; }
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| 92 |
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| 93 | Bool_t SetFunction(const char *name="x");
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| 94 |
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| 95 | void SetNumObsoleteVariables(Int_t n=1) { fNumObsoleteVariables = n; }
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| 96 | void SetLastDataColumnHasWeights(Bool_t b=kTRUE) { fLastDataColumnHasWeights = b; }
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| 97 |
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| 98 | // Train Interface
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| 99 | Int_t TrainMultiRF(const MHMatrix &n, const TArrayD &grid)
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| 100 | {
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| 101 | // One yes/no-classification forest is trained for each bin
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| 102 | return Train(n, grid, 0);
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| 103 | }
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| 104 | Int_t TrainSingleRF(const MHMatrix &n, const TArrayD &grid=TArrayD())
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| 105 | {
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| 106 | // w/o Grid: Last Column contains classifier
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| 107 | // w/ Grid: Last Column will be converted by grid into classifier
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| 108 | return Train(n, grid, grid.GetSize()==0 ? 2 : 1);
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| 109 | }
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| 110 | Int_t TrainRegression(const MHMatrix &n)
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| 111 | {
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| 112 | // Use last column for regression
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| 113 | return Train(n, TArrayD(), 3);
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| 114 | }
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| 115 |
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| 116 | // Test Interface
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| 117 | void SetTestMatrix(MHMatrix *m=0) { fTestMatrix=m; }
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| 118 | void InitMapping(MHMatrix *m=0) { fTestMatrix=m; }
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| 119 |
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| 120 | ClassDef(MRanForestCalc, 1) // Task to calculate RF output and for RF training
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| 121 | };
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| 122 |
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| 123 | #endif
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