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Reference Guide
MethodPyTorch.h
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1// @(#)root/tmva/pymva $Id$
2// Author: Anirudh Dagar, 2020
3
4/**********************************************************************************
5 * Project: TMVA - a Root-integrated toolkit for multivariate data analysis *
6 * Package: TMVA *
7 * Class : MethodPyTorch *
8 * Web : http://tmva.sourceforge.net *
9 * *
10 * Description: *
11 * Interface for PyTorch python based scientific package supporting *
12 * automatic differentiation for machine learning. *
13 * *
14 * Authors (alphabetical): *
15 * Anirudh Dagar <anirudhdagar6@gmail.com> - IIT, Roorkee *
16 * *
17 * Copyright (c) 2020: *
18 * CERN, Switzerland *
19 * IIT, Roorkee *
20 * *
21 * Redistribution and use in source and binary forms, with or without *
22 * modification, are permitted according to the terms listed in LICENSE *
23 * (http://tmva.sourceforge.net/LICENSE) *
24 **********************************************************************************/
25
26#ifndef ROOT_TMVA_MethodPyTorch
27#define ROOT_TMVA_MethodPyTorch
28
29#include "TMVA/PyMethodBase.h"
30#include <vector>
31
32namespace TMVA {
33
34 class MethodPyTorch : public PyMethodBase {
35
36 public :
37
38 // constructors
39 MethodPyTorch(const TString &jobName,
40 const TString &methodTitle,
41 DataSetInfo &dsi,
42 const TString &theOption = "");
44 const TString &theWeightFile);
46
47 void Train();
48 void Init();
49 void DeclareOptions();
50 void ProcessOptions();
51
52 // Check whether the given analysis type (regression, classification, ...)
53 // is supported by this method
55 // Get signal probability of given event
56 Double_t GetMvaValue(Double_t *errLower, Double_t *errUpper);
57 std::vector<Double_t> GetMvaValues(Long64_t firstEvt, Long64_t lastEvt, Bool_t logProgress);
58 // Get regression values of given event
59 std::vector<Float_t>& GetRegressionValues();
60 // Get class probabilities of given event
61 std::vector<Float_t>& GetMulticlassValues();
62
63 const Ranking *CreateRanking() { return 0; }
64 virtual void TestClassification();
65 virtual void AddWeightsXMLTo(void*) const{}
66 virtual void ReadWeightsFromXML(void*){}
67 virtual void ReadWeightsFromStream(std::istream&) {} // backward compatibility
68 virtual void ReadWeightsFromStream(TFile&){} // backward compatibility
69 void ReadModelFromFile();
70
71 void GetHelpMessage() const;
72
73
74 private:
75
76 TString fFilenameModel; // Filename of the previously exported PyTorch model
77 UInt_t fBatchSize {0}; // Training batch size
78 UInt_t fNumEpochs {0}; // Number of training epochs
79 Int_t fNumThreads {0}; // Number of CPU threads (if 0 uses default values)
80
81 Bool_t fContinueTraining; // Load weights from previous training
82 Bool_t fSaveBestOnly; // Store only weights with smallest validation loss
83 TString fLearningRateSchedule; // Set new learning rate at specific epochs
84
85 TString fNumValidationString; // option string defining the number of validation events
86
87 TString fUserCodeName; // filename of the user script that will be executed before loading the PyTorch model
88
89 bool fModelIsSetup = false; // flag whether model is loaded, needed for getMvaValue during evaluation
90 float* fVals = nullptr; // variables array used for GetMvaValue
91 std::vector<float> fOutput; // probability or regression output array used for GetMvaValue
92 UInt_t fNVars {0}; // number of variables
93 UInt_t fNOutputs {0}; // number of outputs (classes or targets)
94 TString fFilenameTrainedModel; // output filename for trained model
95
96 void SetupPyTorchModel(Bool_t loadTrainedModel); // setups the needed variables, loads the model
97 UInt_t GetNumValidationSamples(); // get number of validation events according to given option
98
100 };
101
102} // namespace TMVA
103
104#endif // ROOT_TMVA_MethodPyTorch
int Int_t
Definition: RtypesCore.h:45
unsigned int UInt_t
Definition: RtypesCore.h:46
bool Bool_t
Definition: RtypesCore.h:63
double Double_t
Definition: RtypesCore.h:59
long long Long64_t
Definition: RtypesCore.h:80
#define ClassDef(name, id)
Definition: Rtypes.h:325
int type
Definition: TGX11.cxx:121
A ROOT file is a suite of consecutive data records (TKey instances) with a well defined format.
Definition: TFile.h:54
Class that contains all the data information.
Definition: DataSetInfo.h:62
Bool_t HasAnalysisType(Types::EAnalysisType type, UInt_t numberClasses, UInt_t)
virtual void AddWeightsXMLTo(void *) const
Definition: MethodPyTorch.h:65
virtual void ReadWeightsFromXML(void *)
Definition: MethodPyTorch.h:66
std::vector< Float_t > & GetMulticlassValues()
const Ranking * CreateRanking()
Definition: MethodPyTorch.h:63
std::vector< float > fOutput
Definition: MethodPyTorch.h:91
MethodPyTorch(const TString &jobName, const TString &methodTitle, DataSetInfo &dsi, const TString &theOption="")
virtual void TestClassification()
initialization
virtual void ReadWeightsFromStream(std::istream &)
Definition: MethodPyTorch.h:67
std::vector< Double_t > GetMvaValues(Long64_t firstEvt, Long64_t lastEvt, Bool_t logProgress)
get all the MVA values for the events of the current Data type
TString fNumValidationString
Definition: MethodPyTorch.h:85
UInt_t GetNumValidationSamples()
Validation of the ValidationSize option.
void GetHelpMessage() const
TString fLearningRateSchedule
Definition: MethodPyTorch.h:83
std::vector< Float_t > & GetRegressionValues()
TString fFilenameTrainedModel
Definition: MethodPyTorch.h:94
virtual void ReadWeightsFromStream(TFile &)
Definition: MethodPyTorch.h:68
void SetupPyTorchModel(Bool_t loadTrainedModel)
Double_t GetMvaValue(Double_t *errLower, Double_t *errUpper)
Ranking for variables in method (implementation)
Definition: Ranking.h:48
EAnalysisType
Definition: Types.h:128
Basic string class.
Definition: TString.h:136
create variable transformations