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TMVA_RNN_Classification.C File Reference
Tutorials » TMVA tutorials

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TMVA Classification Example Using a Recurrent Neural Network

This is an example of using a RNN in TMVA. We do classification using a toy time dependent data set that is generated when running this example macro

Running with nthreads = 4
--- RNNClassification : Using input file: time_data_t10_d30.root
DataSetInfo : [dataset] : Added class "Signal"
: Add Tree sgn of type Signal with 2000 events
DataSetInfo : [dataset] : Added class "Background"
: Add Tree bkg of type Background with 2000 events
number of variables is 300
vars_time0[0],vars_time0[1],vars_time0[2],vars_time0[3],vars_time0[4],vars_time0[5],vars_time0[6],vars_time0[7],vars_time0[8],vars_time0[9],vars_time0[10],vars_time0[11],vars_time0[12],vars_time0[13],vars_time0[14],vars_time0[15],vars_time0[16],vars_time0[17],vars_time0[18],vars_time0[19],vars_time0[20],vars_time0[21],vars_time0[22],vars_time0[23],vars_time0[24],vars_time0[25],vars_time0[26],vars_time0[27],vars_time0[28],vars_time0[29],vars_time1[0],vars_time1[1],vars_time1[2],vars_time1[3],vars_time1[4],vars_time1[5],vars_time1[6],vars_time1[7],vars_time1[8],vars_time1[9],vars_time1[10],vars_time1[11],vars_time1[12],vars_time1[13],vars_time1[14],vars_time1[15],vars_time1[16],vars_time1[17],vars_time1[18],vars_time1[19],vars_time1[20],vars_time1[21],vars_time1[22],vars_time1[23],vars_time1[24],vars_time1[25],vars_time1[26],vars_time1[27],vars_time1[28],vars_time1[29],vars_time2[0],vars_time2[1],vars_time2[2],vars_time2[3],vars_time2[4],vars_time2[5],vars_time2[6],vars_time2[7],vars_time2[8],vars_time2[9],vars_time2[10],vars_time2[11],vars_time2[12],vars_time2[13],vars_time2[14],vars_time2[15],vars_time2[16],vars_time2[17],vars_time2[18],vars_time2[19],vars_time2[20],vars_time2[21],vars_time2[22],vars_time2[23],vars_time2[24],vars_time2[25],vars_time2[26],vars_time2[27],vars_time2[28],vars_time2[29],vars_time3[0],vars_time3[1],vars_time3[2],vars_time3[3],vars_time3[4],vars_time3[5],vars_time3[6],vars_time3[7],vars_time3[8],vars_time3[9],vars_time3[10],vars_time3[11],vars_time3[12],vars_time3[13],vars_time3[14],vars_time3[15],vars_time3[16],vars_time3[17],vars_time3[18],vars_time3[19],vars_time3[20],vars_time3[21],vars_time3[22],vars_time3[23],vars_time3[24],vars_time3[25],vars_time3[26],vars_time3[27],vars_time3[28],vars_time3[29],vars_time4[0],vars_time4[1],vars_time4[2],vars_time4[3],vars_time4[4],vars_time4[5],vars_time4[6],vars_time4[7],vars_time4[8],vars_time4[9],vars_time4[10],vars_time4[11],vars_time4[12],vars_time4[13],vars_time4[14],vars_time4[15],vars_time4[16],vars_time4[17],vars_time4[18],vars_time4[19],vars_time4[20],vars_time4[21],vars_time4[22],vars_time4[23],vars_time4[24],vars_time4[25],vars_time4[26],vars_time4[27],vars_time4[28],vars_time4[29],vars_time5[0],vars_time5[1],vars_time5[2],vars_time5[3],vars_time5[4],vars_time5[5],vars_time5[6],vars_time5[7],vars_time5[8],vars_time5[9],vars_time5[10],vars_time5[11],vars_time5[12],vars_time5[13],vars_time5[14],vars_time5[15],vars_time5[16],vars_time5[17],vars_time5[18],vars_time5[19],vars_time5[20],vars_time5[21],vars_time5[22],vars_time5[23],vars_time5[24],vars_time5[25],vars_time5[26],vars_time5[27],vars_time5[28],vars_time5[29],vars_time6[0],vars_time6[1],vars_time6[2],vars_time6[3],vars_time6[4],vars_time6[5],vars_time6[6],vars_time6[7],vars_time6[8],vars_time6[9],vars_time6[10],vars_time6[11],vars_time6[12],vars_time6[13],vars_time6[14],vars_time6[15],vars_time6[16],vars_time6[17],vars_time6[18],vars_time6[19],vars_time6[20],vars_time6[21],vars_time6[22],vars_time6[23],vars_time6[24],vars_time6[25],vars_time6[26],vars_time6[27],vars_time6[28],vars_time6[29],vars_time7[0],vars_time7[1],vars_time7[2],vars_time7[3],vars_time7[4],vars_time7[5],vars_time7[6],vars_time7[7],vars_time7[8],vars_time7[9],vars_time7[10],vars_time7[11],vars_time7[12],vars_time7[13],vars_time7[14],vars_time7[15],vars_time7[16],vars_time7[17],vars_time7[18],vars_time7[19],vars_time7[20],vars_time7[21],vars_time7[22],vars_time7[23],vars_time7[24],vars_time7[25],vars_time7[26],vars_time7[27],vars_time7[28],vars_time7[29],vars_time8[0],vars_time8[1],vars_time8[2],vars_time8[3],vars_time8[4],vars_time8[5],vars_time8[6],vars_time8[7],vars_time8[8],vars_time8[9],vars_time8[10],vars_time8[11],vars_time8[12],vars_time8[13],vars_time8[14],vars_time8[15],vars_time8[16],vars_time8[17],vars_time8[18],vars_time8[19],vars_time8[20],vars_time8[21],vars_time8[22],vars_time8[23],vars_time8[24],vars_time8[25],vars_time8[26],vars_time8[27],vars_time8[28],vars_time8[29],vars_time9[0],vars_time9[1],vars_time9[2],vars_time9[3],vars_time9[4],vars_time9[5],vars_time9[6],vars_time9[7],vars_time9[8],vars_time9[9],vars_time9[10],vars_time9[11],vars_time9[12],vars_time9[13],vars_time9[14],vars_time9[15],vars_time9[16],vars_time9[17],vars_time9[18],vars_time9[19],vars_time9[20],vars_time9[21],vars_time9[22],vars_time9[23],vars_time9[24],vars_time9[25],vars_time9[26],vars_time9[27],vars_time9[28],vars_time9[29],
prepared DATA LOADER
Factory : Booking method: ␛[1mTMVA_DNN␛[0m
:
: Parsing option string:
: ... "!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=None:WeightInitialization=XAVIER:RandomSeed=0:InputLayout=1|1|300:Layout=DENSE|64|TANH,DENSE|TANH|64,DENSE|TANH|64,LINEAR:TrainingStrategy=LearningRate=1e-3,Momentum=0.0,Repetitions=1,ConvergenceSteps=10,BatchSize=256,TestRepetitions=1,WeightDecay=1e-4,Regularization=None,MaxEpochs=20DropConfig=0.0+0.+0.+0.,Optimizer=ADAM:CPU"
: The following options are set:
: - By User:
: <none>
: - Default:
: Boost_num: "0" [Number of times the classifier will be boosted]
: Parsing option string:
: ... "!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=None:WeightInitialization=XAVIER:RandomSeed=0:InputLayout=1|1|300:Layout=DENSE|64|TANH,DENSE|TANH|64,DENSE|TANH|64,LINEAR:TrainingStrategy=LearningRate=1e-3,Momentum=0.0,Repetitions=1,ConvergenceSteps=10,BatchSize=256,TestRepetitions=1,WeightDecay=1e-4,Regularization=None,MaxEpochs=20DropConfig=0.0+0.+0.+0.,Optimizer=ADAM:CPU"
: The following options are set:
: - By User:
: V: "True" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
: 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)"]
: H: "False" [Print method-specific help message]
: InputLayout: "1|1|300" [The Layout of the input]
: Layout: "DENSE|64|TANH,DENSE|TANH|64,DENSE|TANH|64,LINEAR" [Layout of the network.]
: ErrorStrategy: "CROSSENTROPY" [Loss function: Mean squared error (regression) or cross entropy (binary classification).]
: WeightInitialization: "XAVIER" [Weight initialization strategy]
: RandomSeed: "0" [Random seed used for weight initialization and batch shuffling]
: Architecture: "CPU" [Which architecture to perform the training on.]
: TrainingStrategy: "LearningRate=1e-3,Momentum=0.0,Repetitions=1,ConvergenceSteps=10,BatchSize=256,TestRepetitions=1,WeightDecay=1e-4,Regularization=None,MaxEpochs=20DropConfig=0.0+0.+0.+0.,Optimizer=ADAM" [Defines the training strategies.]
: - Default:
: VerbosityLevel: "Default" [Verbosity level]
: 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)]
: BatchLayout: "0|0|0" [The Layout of the batch]
: ValidationSize: "20%" [Part of the training data to use for validation. Specify as 0.2 or 20% to use a fifth of the data set as validation set. Specify as 100 to use exactly 100 events. (Default: 20%)]
: Multi-core CPU backend not enabled. For better performances, make sure you have a BLAS implementation and it was successfully detected by CMake as well that the imt CMake flag is set.
: Will use anyway the CPU architecture but with slower performance
Factory : Booking method: ␛[1mBDTG␛[0m
:
: the option NegWeightTreatment=InverseBoostNegWeights does not exist for BoostType=Grad
: --> change to new default NegWeightTreatment=Pray
: Rebuilding Dataset dataset
: Building event vectors for type 2 Signal
: Dataset[dataset] : create input formulas for tree sgn
: Using variable vars_time0[0] from array expression vars_time0 of size 30
: Using variable vars_time1[0] from array expression vars_time1 of size 30
: Using variable vars_time2[0] from array expression vars_time2 of size 30
: Using variable vars_time3[0] from array expression vars_time3 of size 30
: Using variable vars_time4[0] from array expression vars_time4 of size 30
: Using variable vars_time5[0] from array expression vars_time5 of size 30
: Using variable vars_time6[0] from array expression vars_time6 of size 30
: Using variable vars_time7[0] from array expression vars_time7 of size 30
: Using variable vars_time8[0] from array expression vars_time8 of size 30
: Using variable vars_time9[0] from array expression vars_time9 of size 30
: Building event vectors for type 2 Background
: Dataset[dataset] : create input formulas for tree bkg
: Using variable vars_time0[0] from array expression vars_time0 of size 30
: Using variable vars_time1[0] from array expression vars_time1 of size 30
: Using variable vars_time2[0] from array expression vars_time2 of size 30
: Using variable vars_time3[0] from array expression vars_time3 of size 30
: Using variable vars_time4[0] from array expression vars_time4 of size 30
: Using variable vars_time5[0] from array expression vars_time5 of size 30
: Using variable vars_time6[0] from array expression vars_time6 of size 30
: Using variable vars_time7[0] from array expression vars_time7 of size 30
: Using variable vars_time8[0] from array expression vars_time8 of size 30
: Using variable vars_time9[0] from array expression vars_time9 of size 30
DataSetFactory : [dataset] : Number of events in input trees
:
:
: Number of training and testing events
: ---------------------------------------------------------------------------
: Signal -- training events : 1600
: Signal -- testing events : 400
: Signal -- training and testing events: 2000
: Background -- training events : 1600
: Background -- testing events : 400
: Background -- training and testing events: 2000
:
Factory : ␛[1mTrain all methods␛[0m
Factory : Train method: TMVA_DNN for Classification
:
: Start of deep neural network training on single thread CPU (without ROOT-MT support)
:
: ***** Deep Learning Network *****
DEEP NEURAL NETWORK: Depth = 4 Input = ( 1, 1, 300 ) Batch size = 256 Loss function = C
Layer 0 DENSE Layer: ( Input = 300 , Width = 64 ) Output = ( 1 , 256 , 64 ) Activation Function = Tanh
Layer 1 DENSE Layer: ( Input = 64 , Width = 64 ) Output = ( 1 , 256 , 64 ) Activation Function = Tanh
Layer 2 DENSE Layer: ( Input = 64 , Width = 64 ) Output = ( 1 , 256 , 64 ) Activation Function = Tanh
Layer 3 DENSE Layer: ( Input = 64 , Width = 1 ) Output = ( 1 , 256 , 1 ) Activation Function = Identity
: Using 2560 events for training and 640 for testing
: Compute initial loss on the validation data
: Training phase 1 of 1: Optimizer ADAM (beta1=0.9,beta2=0.999,eps=1e-07) Learning rate = 0.001 regularization 0 minimum error = 0.885185
: --------------------------------------------------------------
: Epoch | Train Err. Val. Err. t(s)/epoch t(s)/Loss nEvents/s Conv. Steps
: --------------------------------------------------------------
: Start epoch iteration ...
: 1 Minimum Test error found - save the configuration
: 1 | 0.75936 0.707844 0.147225 0.0108826 18776.3 0
: 2 Minimum Test error found - save the configuration
: 2 | 0.694475 0.699647 0.146059 0.0108992 18940.6 0
: 3 | 0.69538 0.704432 0.146004 0.0106244 18909.8 1
: 4 Minimum Test error found - save the configuration
: 4 | 0.69016 0.699355 0.145796 0.0108728 18973.8 0
: 5 Minimum Test error found - save the configuration
: 5 | 0.688342 0.695444 0.146247 0.0108546 18908 0
: 6 Minimum Test error found - save the configuration
: 6 | 0.678482 0.688943 0.14693 0.0107778 18802.4 0
: 7 Minimum Test error found - save the configuration
: 7 | 0.676084 0.68643 0.149193 0.0108482 18504.5 0
: 8 | 0.676324 0.690479 0.146521 0.0106112 18836.1 1
: 9 | 0.662099 0.702285 0.146298 0.0107203 18882.2 2
: 10 | 0.659483 0.690256 0.146022 0.0107227 18921 3
: 11 Minimum Test error found - save the configuration
: 11 | 0.657342 0.680298 0.146027 0.0108677 18940.6 0
: 12 | 0.66122 0.693457 0.146308 0.010647 18870.6 1
: 13 | 0.652284 0.689575 0.146367 0.0106127 18857.6 2
: 14 Minimum Test error found - save the configuration
: 14 | 0.65482 0.677382 0.145968 0.0108313 18943.8 0
: 15 | 0.652416 0.686204 0.14602 0.0106567 18912.1 1
: 16 | 0.649096 0.68145 0.146682 0.0107134 18827.8 2
: 17 | 0.660374 0.684611 0.147013 0.0109133 18809.8 3
: 18 | 0.678981 0.678071 0.146826 0.010661 18800.7 4
: 19 | 0.663383 0.678224 0.146606 0.0106413 18828.5 5
: 20 | 0.665097 0.679914 0.146287 0.0107186 18883.5 6
:
: Elapsed time for training with 3200 events: 2.95 sec
: Evaluate deep neural network on CPU using batches with size = 256
:
TMVA_DNN : [dataset] : Evaluation of TMVA_DNN on training sample (3200 events)
: Elapsed time for evaluation of 3200 events: 0.0739 sec
: Creating xml weight file: ␛[0;36mdataset/weights/TMVAClassification_TMVA_DNN.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVAClassification_TMVA_DNN.class.C␛[0m
Factory : Training finished
:
Factory : Train method: BDTG for Classification
:
BDTG : #events: (reweighted) sig: 1600 bkg: 1600
: #events: (unweighted) sig: 1600 bkg: 1600
: Training 100 Decision Trees ... patience please
: Elapsed time for training with 3200 events: 0.772 sec
BDTG : [dataset] : Evaluation of BDTG on training sample (3200 events)
: Elapsed time for evaluation of 3200 events: 0.00881 sec
: Creating xml weight file: ␛[0;36mdataset/weights/TMVAClassification_BDTG.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVAClassification_BDTG.class.C␛[0m
: data_RNN_CPU.root:/dataset/Method_BDT/BDTG
Factory : Training finished
:
: Ranking input variables (method specific)...
: No variable ranking supplied by classifier: TMVA_DNN
BDTG : Ranking result (top variable is best ranked)
: --------------------------------------------
: Rank : Variable : Variable Importance
: --------------------------------------------
: 1 : vars_time8 : 2.092e-02
: 2 : vars_time7 : 1.992e-02
: 3 : vars_time8 : 1.984e-02
: 4 : vars_time9 : 1.972e-02
: 5 : vars_time8 : 1.954e-02
: 6 : vars_time7 : 1.943e-02
: 7 : vars_time9 : 1.859e-02
: 8 : vars_time7 : 1.845e-02
: 9 : vars_time9 : 1.790e-02
: 10 : vars_time7 : 1.733e-02
: 11 : vars_time9 : 1.707e-02
: 12 : vars_time8 : 1.670e-02
: 13 : vars_time6 : 1.651e-02
: 14 : vars_time9 : 1.611e-02
: 15 : vars_time5 : 1.579e-02
: 16 : vars_time0 : 1.577e-02
: 17 : vars_time6 : 1.521e-02
: 18 : vars_time8 : 1.484e-02
: 19 : vars_time5 : 1.458e-02
: 20 : vars_time6 : 1.456e-02
: 21 : vars_time0 : 1.449e-02
: 22 : vars_time8 : 1.417e-02
: 23 : vars_time0 : 1.337e-02
: 24 : vars_time6 : 1.319e-02
: 25 : vars_time7 : 1.284e-02
: 26 : vars_time7 : 1.267e-02
: 27 : vars_time4 : 1.215e-02
: 28 : vars_time9 : 1.205e-02
: 29 : vars_time8 : 1.193e-02
: 30 : vars_time9 : 1.178e-02
: 31 : vars_time4 : 1.153e-02
: 32 : vars_time9 : 1.148e-02
: 33 : vars_time9 : 1.116e-02
: 34 : vars_time0 : 1.089e-02
: 35 : vars_time5 : 1.042e-02
: 36 : vars_time8 : 1.018e-02
: 37 : vars_time6 : 9.867e-03
: 38 : vars_time6 : 9.777e-03
: 39 : vars_time6 : 9.773e-03
: 40 : vars_time9 : 9.556e-03
: 41 : vars_time6 : 9.519e-03
: 42 : vars_time5 : 9.451e-03
: 43 : vars_time0 : 9.399e-03
: 44 : vars_time8 : 9.392e-03
: 45 : vars_time5 : 9.275e-03
: 46 : vars_time7 : 8.845e-03
: 47 : vars_time8 : 8.781e-03
: 48 : vars_time9 : 8.567e-03
: 49 : vars_time5 : 8.148e-03
: 50 : vars_time9 : 8.105e-03
: 51 : vars_time3 : 7.784e-03
: 52 : vars_time5 : 7.781e-03
: 53 : vars_time3 : 7.762e-03
: 54 : vars_time7 : 7.568e-03
: 55 : vars_time8 : 7.378e-03
: 56 : vars_time6 : 7.336e-03
: 57 : vars_time8 : 7.266e-03
: 58 : vars_time5 : 7.265e-03
: 59 : vars_time8 : 7.210e-03
: 60 : vars_time6 : 7.189e-03
: 61 : vars_time7 : 6.944e-03
: 62 : vars_time9 : 6.927e-03
: 63 : vars_time6 : 6.805e-03
: 64 : vars_time7 : 6.778e-03
: 65 : vars_time9 : 6.775e-03
: 66 : vars_time0 : 6.684e-03
: 67 : vars_time1 : 6.645e-03
: 68 : vars_time7 : 6.639e-03
: 69 : vars_time6 : 6.582e-03
: 70 : vars_time1 : 6.575e-03
: 71 : vars_time6 : 6.560e-03
: 72 : vars_time7 : 6.495e-03
: 73 : vars_time6 : 6.466e-03
: 74 : vars_time5 : 6.434e-03
: 75 : vars_time9 : 6.312e-03
: 76 : vars_time0 : 6.296e-03
: 77 : vars_time4 : 6.240e-03
: 78 : vars_time7 : 6.235e-03
: 79 : vars_time9 : 6.223e-03
: 80 : vars_time4 : 6.186e-03
: 81 : vars_time1 : 6.077e-03
: 82 : vars_time3 : 6.023e-03
: 83 : vars_time7 : 5.732e-03
: 84 : vars_time8 : 5.663e-03
: 85 : vars_time3 : 5.429e-03
: 86 : vars_time7 : 5.377e-03
: 87 : vars_time2 : 5.296e-03
: 88 : vars_time7 : 5.266e-03
: 89 : vars_time7 : 5.158e-03
: 90 : vars_time6 : 5.015e-03
: 91 : vars_time6 : 4.821e-03
: 92 : vars_time9 : 4.759e-03
: 93 : vars_time2 : 4.687e-03
: 94 : vars_time1 : 4.675e-03
: 95 : vars_time4 : 4.663e-03
: 96 : vars_time0 : 4.553e-03
: 97 : vars_time5 : 4.499e-03
: 98 : vars_time0 : 4.498e-03
: 99 : vars_time5 : 4.468e-03
: 100 : vars_time1 : 4.458e-03
: 101 : vars_time4 : 4.424e-03
: 102 : vars_time8 : 4.162e-03
: 103 : vars_time1 : 3.767e-03
: 104 : vars_time6 : 3.445e-03
: 105 : vars_time4 : 3.196e-03
: 106 : vars_time5 : 3.007e-03
: 107 : vars_time0 : 0.000e+00
: 108 : vars_time0 : 0.000e+00
: 109 : vars_time0 : 0.000e+00
: 110 : vars_time0 : 0.000e+00
: 111 : vars_time0 : 0.000e+00
: 112 : vars_time0 : 0.000e+00
: 113 : vars_time0 : 0.000e+00
: 114 : vars_time0 : 0.000e+00
: 115 : vars_time0 : 0.000e+00
: 116 : vars_time0 : 0.000e+00
: 117 : vars_time0 : 0.000e+00
: 118 : vars_time0 : 0.000e+00
: 119 : vars_time0 : 0.000e+00
: 120 : vars_time0 : 0.000e+00
: 121 : vars_time0 : 0.000e+00
: 122 : vars_time0 : 0.000e+00
: 123 : vars_time0 : 0.000e+00
: 124 : vars_time0 : 0.000e+00
: 125 : vars_time0 : 0.000e+00
: 126 : vars_time0 : 0.000e+00
: 127 : vars_time0 : 0.000e+00
: 128 : vars_time1 : 0.000e+00
: 129 : vars_time1 : 0.000e+00
: 130 : vars_time1 : 0.000e+00
: 131 : vars_time1 : 0.000e+00
: 132 : vars_time1 : 0.000e+00
: 133 : vars_time1 : 0.000e+00
: 134 : vars_time1 : 0.000e+00
: 135 : vars_time1 : 0.000e+00
: 136 : vars_time1 : 0.000e+00
: 137 : vars_time1 : 0.000e+00
: 138 : vars_time1 : 0.000e+00
: 139 : vars_time1 : 0.000e+00
: 140 : vars_time1 : 0.000e+00
: 141 : vars_time1 : 0.000e+00
: 142 : vars_time1 : 0.000e+00
: 143 : vars_time1 : 0.000e+00
: 144 : vars_time1 : 0.000e+00
: 145 : vars_time1 : 0.000e+00
: 146 : vars_time1 : 0.000e+00
: 147 : vars_time1 : 0.000e+00
: 148 : vars_time1 : 0.000e+00
: 149 : vars_time1 : 0.000e+00
: 150 : vars_time1 : 0.000e+00
: 151 : vars_time1 : 0.000e+00
: 152 : vars_time2 : 0.000e+00
: 153 : vars_time2 : 0.000e+00
: 154 : vars_time2 : 0.000e+00
: 155 : vars_time2 : 0.000e+00
: 156 : vars_time2 : 0.000e+00
: 157 : vars_time2 : 0.000e+00
: 158 : vars_time2 : 0.000e+00
: 159 : vars_time2 : 0.000e+00
: 160 : vars_time2 : 0.000e+00
: 161 : vars_time2 : 0.000e+00
: 162 : vars_time2 : 0.000e+00
: 163 : vars_time2 : 0.000e+00
: 164 : vars_time2 : 0.000e+00
: 165 : vars_time2 : 0.000e+00
: 166 : vars_time2 : 0.000e+00
: 167 : vars_time2 : 0.000e+00
: 168 : vars_time2 : 0.000e+00
: 169 : vars_time2 : 0.000e+00
: 170 : vars_time2 : 0.000e+00
: 171 : vars_time2 : 0.000e+00
: 172 : vars_time2 : 0.000e+00
: 173 : vars_time2 : 0.000e+00
: 174 : vars_time2 : 0.000e+00
: 175 : vars_time2 : 0.000e+00
: 176 : vars_time2 : 0.000e+00
: 177 : vars_time2 : 0.000e+00
: 178 : vars_time2 : 0.000e+00
: 179 : vars_time2 : 0.000e+00
: 180 : vars_time3 : 0.000e+00
: 181 : vars_time3 : 0.000e+00
: 182 : vars_time3 : 0.000e+00
: 183 : vars_time3 : 0.000e+00
: 184 : vars_time3 : 0.000e+00
: 185 : vars_time3 : 0.000e+00
: 186 : vars_time3 : 0.000e+00
: 187 : vars_time3 : 0.000e+00
: 188 : vars_time3 : 0.000e+00
: 189 : vars_time3 : 0.000e+00
: 190 : vars_time3 : 0.000e+00
: 191 : vars_time3 : 0.000e+00
: 192 : vars_time3 : 0.000e+00
: 193 : vars_time3 : 0.000e+00
: 194 : vars_time3 : 0.000e+00
: 195 : vars_time3 : 0.000e+00
: 196 : vars_time3 : 0.000e+00
: 197 : vars_time3 : 0.000e+00
: 198 : vars_time3 : 0.000e+00
: 199 : vars_time3 : 0.000e+00
: 200 : vars_time3 : 0.000e+00
: 201 : vars_time3 : 0.000e+00
: 202 : vars_time3 : 0.000e+00
: 203 : vars_time3 : 0.000e+00
: 204 : vars_time3 : 0.000e+00
: 205 : vars_time3 : 0.000e+00
: 206 : vars_time4 : 0.000e+00
: 207 : vars_time4 : 0.000e+00
: 208 : vars_time4 : 0.000e+00
: 209 : vars_time4 : 0.000e+00
: 210 : vars_time4 : 0.000e+00
: 211 : vars_time4 : 0.000e+00
: 212 : vars_time4 : 0.000e+00
: 213 : vars_time4 : 0.000e+00
: 214 : vars_time4 : 0.000e+00
: 215 : vars_time4 : 0.000e+00
: 216 : vars_time4 : 0.000e+00
: 217 : vars_time4 : 0.000e+00
: 218 : vars_time4 : 0.000e+00
: 219 : vars_time4 : 0.000e+00
: 220 : vars_time4 : 0.000e+00
: 221 : vars_time4 : 0.000e+00
: 222 : vars_time4 : 0.000e+00
: 223 : vars_time4 : 0.000e+00
: 224 : vars_time4 : 0.000e+00
: 225 : vars_time4 : 0.000e+00
: 226 : vars_time4 : 0.000e+00
: 227 : vars_time4 : 0.000e+00
: 228 : vars_time4 : 0.000e+00
: 229 : vars_time5 : 0.000e+00
: 230 : vars_time5 : 0.000e+00
: 231 : vars_time5 : 0.000e+00
: 232 : vars_time5 : 0.000e+00
: 233 : vars_time5 : 0.000e+00
: 234 : vars_time5 : 0.000e+00
: 235 : vars_time5 : 0.000e+00
: 236 : vars_time5 : 0.000e+00
: 237 : vars_time5 : 0.000e+00
: 238 : vars_time5 : 0.000e+00
: 239 : vars_time5 : 0.000e+00
: 240 : vars_time5 : 0.000e+00
: 241 : vars_time5 : 0.000e+00
: 242 : vars_time5 : 0.000e+00
: 243 : vars_time5 : 0.000e+00
: 244 : vars_time5 : 0.000e+00
: 245 : vars_time5 : 0.000e+00
: 246 : vars_time5 : 0.000e+00
: 247 : vars_time6 : 0.000e+00
: 248 : vars_time6 : 0.000e+00
: 249 : vars_time6 : 0.000e+00
: 250 : vars_time6 : 0.000e+00
: 251 : vars_time6 : 0.000e+00
: 252 : vars_time6 : 0.000e+00
: 253 : vars_time6 : 0.000e+00
: 254 : vars_time6 : 0.000e+00
: 255 : vars_time6 : 0.000e+00
: 256 : vars_time6 : 0.000e+00
: 257 : vars_time6 : 0.000e+00
: 258 : vars_time6 : 0.000e+00
: 259 : vars_time6 : 0.000e+00
: 260 : vars_time7 : 0.000e+00
: 261 : vars_time7 : 0.000e+00
: 262 : vars_time7 : 0.000e+00
: 263 : vars_time7 : 0.000e+00
: 264 : vars_time7 : 0.000e+00
: 265 : vars_time7 : 0.000e+00
: 266 : vars_time7 : 0.000e+00
: 267 : vars_time7 : 0.000e+00
: 268 : vars_time7 : 0.000e+00
: 269 : vars_time7 : 0.000e+00
: 270 : vars_time7 : 0.000e+00
: 271 : vars_time7 : 0.000e+00
: 272 : vars_time7 : 0.000e+00
: 273 : vars_time8 : 0.000e+00
: 274 : vars_time8 : 0.000e+00
: 275 : vars_time8 : 0.000e+00
: 276 : vars_time8 : 0.000e+00
: 277 : vars_time8 : 0.000e+00
: 278 : vars_time8 : 0.000e+00
: 279 : vars_time8 : 0.000e+00
: 280 : vars_time8 : 0.000e+00
: 281 : vars_time8 : 0.000e+00
: 282 : vars_time8 : 0.000e+00
: 283 : vars_time8 : 0.000e+00
: 284 : vars_time8 : 0.000e+00
: 285 : vars_time8 : 0.000e+00
: 286 : vars_time8 : 0.000e+00
: 287 : vars_time8 : 0.000e+00
: 288 : vars_time9 : 0.000e+00
: 289 : vars_time9 : 0.000e+00
: 290 : vars_time9 : 0.000e+00
: 291 : vars_time9 : 0.000e+00
: 292 : vars_time9 : 0.000e+00
: 293 : vars_time9 : 0.000e+00
: 294 : vars_time9 : 0.000e+00
: 295 : vars_time9 : 0.000e+00
: 296 : vars_time9 : 0.000e+00
: 297 : vars_time9 : 0.000e+00
: 298 : vars_time9 : 0.000e+00
: 299 : vars_time9 : 0.000e+00
: 300 : vars_time9 : 0.000e+00
: --------------------------------------------
TH1.Print Name = TrainingHistory_TMVA_DNN_trainingError, Entries= 0, Total sum= 13.4752
TH1.Print Name = TrainingHistory_TMVA_DNN_valError, Entries= 0, Total sum= 13.7943
Factory : === Destroy and recreate all methods via weight files for testing ===
:
: Reading weight file: ␛[0;36mdataset/weights/TMVAClassification_TMVA_DNN.weights.xml␛[0m
: Reading weight file: ␛[0;36mdataset/weights/TMVAClassification_BDTG.weights.xml␛[0m
nthreads = 4
Factory : ␛[1mTest all methods␛[0m
Factory : Test method: TMVA_DNN for Classification performance
:
: Evaluate deep neural network on CPU using batches with size = 800
:
TMVA_DNN : [dataset] : Evaluation of TMVA_DNN on testing sample (800 events)
: Elapsed time for evaluation of 800 events: 0.0173 sec
Factory : Test method: BDTG for Classification performance
:
BDTG : [dataset] : Evaluation of BDTG on testing sample (800 events)
: Elapsed time for evaluation of 800 events: 0.00202 sec
Factory : ␛[1mEvaluate all methods␛[0m
Factory : Evaluate classifier: TMVA_DNN
:
TMVA_DNN : [dataset] : Loop over test events and fill histograms with classifier response...
:
: Evaluate deep neural network on CPU using batches with size = 1000
:
: Dataset[dataset] : variable plots are not produces ! The number of variables is 300 , it is larger than 200
Factory : Evaluate classifier: BDTG
:
BDTG : [dataset] : Loop over test events and fill histograms with classifier response...
:
: Dataset[dataset] : variable plots are not produces ! The number of variables is 300 , it is larger than 200
:
: Evaluation results ranked by best signal efficiency and purity (area)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA
: Name: Method: ROC-integ
: dataset BDTG : 0.824
: dataset TMVA_DNN : 0.605
: -------------------------------------------------------------------------------------------------------------------
:
: Testing efficiency compared to training efficiency (overtraining check)
: -------------------------------------------------------------------------------------------------------------------
: DataSet MVA Signal efficiency: from test sample (from training sample)
: Name: Method: @B=0.01 @B=0.10 @B=0.30
: -------------------------------------------------------------------------------------------------------------------
: dataset BDTG : 0.097 (0.365) 0.512 (0.699) 0.790 (0.875)
: dataset TMVA_DNN : 0.050 (0.059) 0.166 (0.208) 0.451 (0.483)
: -------------------------------------------------------------------------------------------------------------------
:
Dataset:dataset : Created tree 'TestTree' with 800 events
:
Dataset:dataset : Created tree 'TrainTree' with 3200 events
:
Factory : ␛[1mThank you for using TMVA!␛[0m
: ␛[1mFor citation information, please visit: http://tmva.sf.net/citeTMVA.html␛[0m
/***
# TMVA Classification Example Using a Recurrent Neural Network
This is an example of using a RNN in TMVA.
We do the classification using a toy data set containing a time series of data sample ntimes
and with dimension ndim that is generated when running the provided function `MakeTimeData (nevents, ntime, ndim)`
**/
#include<TROOT.h>
#include "TMVA/Factory.h"
#include "TMVA/DataLoader.h"
#include "TMVA/DataSetInfo.h"
#include "TMVA/Config.h"
#include "TMVA/MethodDL.h"
#include "TFile.h"
#include "TTree.h"
/// Helper function to generate the time data set
/// make some time data but not of fixed length.
/// use a poisson with mu = 5 and truncated at 10
///
void MakeTimeData(int n, int ntime, int ndim )
{
// const int ntime = 10;
// const int ndim = 30; // number of dim/time
TString fname = TString::Format("time_data_t%d_d%d.root", ntime, ndim);
std::vector<TH1 *> v1(ntime);
std::vector<TH1 *> v2(ntime);
int i = 0;
for (int i = 0; i < ntime; ++i) {
v1[i] = new TH1D(TString::Format("h1_%d", i), "h1", ndim, 0, 10);
v2[i] = new TH1D(TString::Format("h2_%d", i), "h2", ndim, 0, 10);
}
auto f1 = new TF1("f1", "gaus");
auto f2 = new TF1("f2", "gaus");
TFile f(fname, "RECREATE");
TTree sgn("sgn", "sgn");
TTree bkg("bkg", "bkg");
std::vector<std::vector<float>> x1(ntime);
std::vector<std::vector<float>> x2(ntime);
for (int i = 0; i < ntime; ++i) {
x1[i] = std::vector<float>(ndim);
x2[i] = std::vector<float>(ndim);
}
for (auto i = 0; i < ntime; i++) {
bkg.Branch(Form("vars_time%d", i), "std::vector<float>", &x1[i]);
sgn.Branch(Form("vars_time%d", i), "std::vector<float>", &x2[i]);
}
sgn.SetDirectory(&f);
bkg.SetDirectory(&f);
gRandom->SetSeed(0);
std::vector<double> mean1(ntime);
std::vector<double> mean2(ntime);
std::vector<double> sigma1(ntime);
std::vector<double> sigma2(ntime);
for (int j = 0; j < ntime; ++j) {
mean1[j] = 5. + 0.2 * sin(TMath::Pi() * j / double(ntime));
mean2[j] = 5. + 0.2 * cos(TMath::Pi() * j / double(ntime));
sigma1[j] = 4 + 0.3 * sin(TMath::Pi() * j / double(ntime));
sigma2[j] = 4 + 0.3 * cos(TMath::Pi() * j / double(ntime));
}
for (int i = 0; i < n; ++i) {
if (i % 1000 == 0)
std::cout << "Generating event ... " << i << std::endl;
for (int j = 0; j < ntime; ++j) {
auto h1 = v1[j];
auto h2 = v2[j];
h1->Reset();
h2->Reset();
f1->SetParameters(1, mean1[j], sigma1[j]);
f2->SetParameters(1, mean2[j], sigma2[j]);
h1->FillRandom("f1", 1000);
h2->FillRandom("f2", 1000);
for (int k = 0; k < ndim; ++k) {
// std::cout << j*10+k << " ";
x1[j][k] = h1->GetBinContent(k + 1) + gRandom->Gaus(0, 10);
x2[j][k] = h2->GetBinContent(k + 1) + gRandom->Gaus(0, 10);
}
}
// std::cout << std::endl;
sgn.Fill();
bkg.Fill();
if (n == 1) {
auto c1 = new TCanvas();
c1->Divide(ntime, 2);
for (int j = 0; j < ntime; ++j) {
c1->cd(j + 1);
v1[j]->Draw();
}
for (int j = 0; j < ntime; ++j) {
c1->cd(ntime + j + 1);
v2[j]->Draw();
}
gPad->Update();
}
}
if (n > 1) {
sgn.Write();
bkg.Write();
sgn.Print();
bkg.Print();
f.Close();
}
}
/// macro for performing a classification using a Recurrent Neural Network
/// @param nevts = 2000 Number of events used. (increase for better classification results)
/// @param use_type
/// use_type = 0 use Simple RNN network
/// use_type = 1 use LSTM network
/// use_type = 2 use GRU
/// use_type = 3 build 3 different networks with RNN, LSTM and GRU
void TMVA_RNN_Classification(int nevts = 2000, int use_type = 1)
{
const int ninput = 30;
const int ntime = 10;
const int batchSize = 100;
const int maxepochs = 20;
int nTotEvts = nevts; // total events to be generated for signal or background
bool useKeras = true;
bool useTMVA_RNN = true;
bool useTMVA_DNN = true;
bool useTMVA_BDT = false;
std::vector<std::string> rnn_types = {"RNN", "LSTM", "GRU"};
std::vector<bool> use_rnn_type = {1, 1, 1};
if (use_type >=0 && use_type < 3) {
use_rnn_type = {0,0,0};
use_rnn_type[use_type] = 1;
}
bool useGPU = true; // use GPU for TMVA if available
#ifndef R__HAS_TMVAGPU
useGPU = false;
#ifndef R__HAS_TMVACPU
Warning("TMVA_RNN_Classification", "TMVA is not build with GPU or CPU multi-thread support. Cannot use TMVA Deep Learning for RNN");
useTMVA_RNN = false;
#endif
#endif
TString archString = (useGPU) ? "GPU" : "CPU";
bool writeOutputFile = true;
const char *rnn_type = "RNN";
#ifdef R__HAS_PYMVA
TMVA::PyMethodBase::PyInitialize();
#else
useKeras = false;
#endif
#ifdef R__USE_IMT
int num_threads = 4; // use max 4 threads
// switch off MT in OpenBLAS to avoid conflict with tbb
gSystem->Setenv("OMP_NUM_THREADS", "1");
// do enable MT running
if (num_threads >= 0) {
ROOT::EnableImplicitMT(num_threads);
}
#endif
TMVA::Config::Instance();
std::cout << "Running with nthreads = " << ROOT::GetThreadPoolSize() << std::endl;
TString inputFileName = "time_data_t10_d30.root";
bool fileExist = !gSystem->AccessPathName(inputFileName);
// if file does not exists create it
if (!fileExist) {
MakeTimeData(nTotEvts,ntime, ninput);
}
auto inputFile = TFile::Open(inputFileName);
if (!inputFile) {
Error("TMVA_RNN_Classification", "Error opening input file %s - exit", inputFileName.Data());
return;
}
std::cout << "--- RNNClassification : Using input file: " << inputFile->GetName() << std::endl;
// Create a ROOT output file where TMVA will store ntuples, histograms, etc.
TString outfileName(TString::Format("data_RNN_%s.root", archString.Data()));
TFile *outputFile = nullptr;
if (writeOutputFile) outputFile = TFile::Open(outfileName, "RECREATE");
/**
## Declare Factory
Create the Factory class. Later you can choose the methods
whose performance you'd like to investigate.
The factory is the major TMVA object you have to interact with. Here is the list of parameters you need to
pass
- The first argument is the base of the name of all the output
weightfiles in the directory weight/ that will be created with the
method parameters
- The second argument is the output file for the training results
- The third argument is a string option defining some general configuration for the TMVA session.
For example all TMVA output can be suppressed by removing the "!" (not) in front of the "Silent" argument in
the option string
**/
// Creating the factory object
TMVA::Factory *factory = new TMVA::Factory("TMVAClassification", outputFile,
"!V:!Silent:Color:DrawProgressBar:Transformations=None:!Correlations:"
"AnalysisType=Classification:ModelPersistence");
TMVA::DataLoader *dataloader = new TMVA::DataLoader("dataset");
TTree *signalTree = (TTree *)inputFile->Get("sgn");
TTree *background = (TTree *)inputFile->Get("bkg");
const int nvar = ninput * ntime;
/// add variables - use new AddVariablesArray function
for (auto i = 0; i < ntime; i++) {
dataloader->AddVariablesArray(Form("vars_time%d", i), ninput);
}
dataloader->AddSignalTree(signalTree, 1.0);
dataloader->AddBackgroundTree(background, 1.0);
// check given input
auto &datainfo = dataloader->GetDataSetInfo();
auto vars = datainfo.GetListOfVariables();
std::cout << "number of variables is " << vars.size() << std::endl;
for (auto &v : vars)
std::cout << v << ",";
std::cout << std::endl;
int nTrainSig = 0.8 * nTotEvts;
int nTrainBkg = 0.8 * nTotEvts;
// build the string options for DataLoader::PrepareTrainingAndTestTree
TString prepareOptions = TString::Format("nTrain_Signal=%d:nTrain_Background=%d:SplitMode=Random:SplitSeed=100:NormMode=NumEvents:!V:!CalcCorrelations", nTrainSig, nTrainBkg);
// Apply additional cuts on the signal and background samples (can be different)
TCut mycuts = ""; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
TCut mycutb = "";
dataloader->PrepareTrainingAndTestTree(mycuts, mycutb, prepareOptions);
std::cout << "prepared DATA LOADER " << std::endl;
/**
## Book TMVA recurrent models
Book the different types of recurrent models in TMVA (SimpleRNN, LSTM or GRU)
**/
if (useTMVA_RNN) {
for (int i = 0; i < 3; ++i) {
if (!use_rnn_type[i])
continue;
const char *rnn_type = rnn_types[i].c_str();
/// define the inputlayout string for RNN
/// the input data should be organize as following:
//// input layout for RNN: time x ndim
TString inputLayoutString = TString::Format("InputLayout=%d|%d", ntime, ninput);
/// Define RNN layer layout
/// it should be LayerType (RNN or LSTM or GRU) | number of units | number of inputs | time steps | remember output (typically no=0 | return full sequence
TString rnnLayout = TString::Format("%s|10|%d|%d|0|1", rnn_type, ninput, ntime);
/// add after RNN a reshape layer (needed top flatten the output) and a dense layer with 64 units and a last one
/// Note the last layer is linear because when using Crossentropy a Sigmoid is applied already
TString layoutString = TString("Layout=") + rnnLayout + TString(",RESHAPE|FLAT,DENSE|64|TANH,LINEAR");
/// Defining Training strategies. Different training strings can be concatenate. Use however only one
TString trainingString1 = TString::Format("LearningRate=1e-3,Momentum=0.0,Repetitions=1,"
"ConvergenceSteps=5,BatchSize=%d,TestRepetitions=1,"
"WeightDecay=1e-2,Regularization=None,MaxEpochs=%d,"
"Optimizer=ADAM,DropConfig=0.0+0.+0.+0.",
batchSize,maxepochs);
TString trainingStrategyString("TrainingStrategy=");
trainingStrategyString += trainingString1; // + "|" + trainingString2
/// Define the full RNN Noption string adding the final options for all network
TString rnnOptions("!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=None:"
"WeightInitialization=XAVIERUNIFORM:ValidationSize=0.2:RandomSeed=1234");
rnnOptions.Append(":");
rnnOptions.Append(inputLayoutString);
rnnOptions.Append(":");
rnnOptions.Append(layoutString);
rnnOptions.Append(":");
rnnOptions.Append(trainingStrategyString);
rnnOptions.Append(":");
rnnOptions.Append(TString::Format("Architecture=%s", archString.Data()));
TString rnnName = "TMVA_" + TString(rnn_type);
factory->BookMethod(dataloader, TMVA::Types::kDL, rnnName, rnnOptions);
}
}
/**
## Book TMVA fully connected dense layer models
**/
if (useTMVA_DNN) {
// Method DL with Dense Layer
TString inputLayoutString = TString::Format("InputLayout=1|1|%d", ntime * ninput);
TString layoutString("Layout=DENSE|64|TANH,DENSE|TANH|64,DENSE|TANH|64,LINEAR");
// Training strategies.
TString trainingString1("LearningRate=1e-3,Momentum=0.0,Repetitions=1,"
"ConvergenceSteps=10,BatchSize=256,TestRepetitions=1,"
"WeightDecay=1e-4,Regularization=None,MaxEpochs=20"
"DropConfig=0.0+0.+0.+0.,Optimizer=ADAM");
TString trainingStrategyString("TrainingStrategy=");
trainingStrategyString += trainingString1; // + "|" + trainingString2
// General Options.
TString dnnOptions("!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=None:"
"WeightInitialization=XAVIER:RandomSeed=0");
dnnOptions.Append(":");
dnnOptions.Append(inputLayoutString);
dnnOptions.Append(":");
dnnOptions.Append(layoutString);
dnnOptions.Append(":");
dnnOptions.Append(trainingStrategyString);
dnnOptions.Append(":");
dnnOptions.Append(archString);
TString dnnName = "TMVA_DNN";
factory->BookMethod(dataloader, TMVA::Types::kDL, dnnName, dnnOptions);
}
/**
## Book Keras recurrent models
Book the different types of recurrent models in Keras (SimpleRNN, LSTM or GRU)
**/
if (useKeras) {
for (int i = 0; i < 3; i++) {
if (use_rnn_type[i]) {
TString modelName = TString::Format("model_%s.h5", rnn_types[i].c_str());
TString trainedModelName = TString::Format("trained_model_%s.h5", rnn_types[i].c_str());
Info("TMVA_RNN_Classification", "Building recurrent keras model using a %s layer", rnn_types[i].c_str());
// create python script which can be executed
// create 2 conv2d layer + maxpool + dense
TMacro m;
m.AddLine("import tensorflow");
m.AddLine("from tensorflow.keras.models import Sequential");
m.AddLine("from tensorflow.keras.optimizers import Adam");
m.AddLine("from tensorflow.keras.layers import Input, Dense, Dropout, Flatten, SimpleRNN, GRU, LSTM, Reshape, "
"BatchNormalization");
m.AddLine("");
m.AddLine("model = Sequential() ");
m.AddLine("model.add(Reshape((10, 30), input_shape = (10*30, )))");
// add recurrent neural network depending on type / Use option to return the full output
if (rnn_types[i] == "LSTM")
m.AddLine("model.add(LSTM(units=10, return_sequences=True) )");
else if (rnn_types[i] == "GRU")
m.AddLine("model.add(GRU(units=10, return_sequences=True) )");
else
m.AddLine("model.add(SimpleRNN(units=10, return_sequences=True) )");
// m.AddLine("model.add(BatchNormalization())");
m.AddLine("model.add(Flatten())"); // needed if returning the full time output sequence
m.AddLine("model.add(Dense(64, activation = 'tanh')) ");
m.AddLine("model.add(Dense(2, activation = 'sigmoid')) ");
m.AddLine(
"model.compile(loss = 'binary_crossentropy', optimizer = Adam(learning_rate = 0.001), weighted_metrics = ['accuracy'])");
m.AddLine(TString::Format("modelName = '%s'", modelName.Data()));
m.AddLine("model.save(modelName)");
m.AddLine("model.summary()");
m.SaveSource("make_rnn_model.py");
// execute python script to make the model
auto ret = (TString *)gROOT->ProcessLine("TMVA::Python_Executable()");
TString python_exe = (ret) ? *(ret) : "python";
gSystem->Exec(python_exe + " make_rnn_model.py");
if (gSystem->AccessPathName(modelName)) {
Warning("TMVA_RNN_Classification", "Error creating Keras recurrent model file - Skip using Keras");
useKeras = false;
} else {
// book PyKeras method only if Keras model could be created
Info("TMVA_RNN_Classification", "Booking Keras %s model", rnn_types[i].c_str());
factory->BookMethod(dataloader, TMVA::Types::kPyKeras,
TString::Format("PyKeras_%s", rnn_types[i].c_str()),
TString::Format("!H:!V:VarTransform=None:FilenameModel=%s:tf.keras:"
"FilenameTrainedModel=%s:GpuOptions=allow_growth=True:"
"NumEpochs=%d:BatchSize=%d",
modelName.Data(), trainedModelName.Data(), maxepochs, batchSize));
}
}
}
}
// use BDT in case not using Keras or TMVA DL
if (!useKeras || !useTMVA_BDT)
useTMVA_BDT = true;
/**
## Book TMVA BDT
**/
if (useTMVA_BDT) {
factory->BookMethod(dataloader, TMVA::Types::kBDT, "BDTG",
"!H:!V:NTrees=100:MinNodeSize=2.5%:BoostType=Grad:Shrinkage=0.10:UseBaggedBoost:"
"BaggedSampleFraction=0.5:nCuts=20:"
"MaxDepth=2");
}
/// Train all methods
factory->TrainAllMethods();
std::cout << "nthreads = " << ROOT::GetThreadPoolSize() << std::endl;
// ---- Evaluate all MVAs using the set of test events
factory->TestAllMethods();
// ----- Evaluate and compare performance of all configured MVAs
factory->EvaluateAllMethods();
// check method
// plot ROC curve
auto c1 = factory->GetROCCurve(dataloader);
c1->Draw();
if (outputFile) outputFile->Close();
}
f
#define f(i)
Definition RSha256.hxx:104
Info
void Info(const char *location, const char *msgfmt,...)
Use this function for informational messages.
Definition TError.cxx:218
Error
void Error(const char *location, const char *msgfmt,...)
Use this function in case an error occurred.
Definition TError.cxx:185
Warning
void Warning(const char *location, const char *msgfmt,...)
Use this function in warning situations.
Definition TError.cxx:229
x2
Option_t Option_t TPoint TPoint const char x2
Definition TGWin32VirtualXProxy.cxx:70
x1
Option_t Option_t TPoint TPoint const char x1
Definition TGWin32VirtualXProxy.cxx:70
i
Int_t i
Definition TGraphAsymmErrors.cxx:1306
gROOT
#define gROOT
Definition TROOT.h:414
gRandom
R__EXTERN TRandom * gRandom
Definition TRandom.h:62
Form
char * Form(const char *fmt,...)
Formats a string in a circular formatting buffer.
Definition TString.cxx:2489
gSystem
R__EXTERN TSystem * gSystem
Definition TSystem.h:566
gPad
#define gPad
Definition TVirtualPad.h:305
TCanvas
The Canvas class.
Definition TCanvas.h:23
TCut
A specialized string object used for TTree selections.
Definition TCut.h:25
TF1
1-Dim function class
Definition TF1.h:233
TFile
A file, usually with extension .root, that stores data and code in the form of serialized objects in ...
Definition TFile.h:53
TFile::Open
static TFile * Open(const char *name, Option_t *option="", const char *ftitle="", Int_t compress=ROOT::RCompressionSetting::EDefaults::kUseCompiledDefault, Int_t netopt=0)
Create / open a file.
Definition TFile.cxx:4110
TFile::Close
void Close(Option_t *option="") override
Close a file.
Definition TFile.cxx:971
TH1D
1-D histogram with a double per channel (see TH1 documentation)
Definition TH1.h:669
TMVA::Config::Instance
static Config & Instance()
static function: returns TMVA instance
Definition Config.cxx:98
TMVA::DataLoader::AddVariablesArray
void AddVariablesArray(const TString &expression, int size, char type='F', Double_t min=0, Double_t max=0)
user inserts discriminating array of variables in data set info in case input tree provides an array ...
Definition DataLoader.cxx:504
TMVA::DataLoader::AddSignalTree
void AddSignalTree(TTree *signal, Double_t weight=1.0, Types::ETreeType treetype=Types::kMaxTreeType)
number of signal events (used to compute significance)
Definition DataLoader.cxx:371
TMVA::DataLoader::PrepareTrainingAndTestTree
void PrepareTrainingAndTestTree(const TCut &cut, const TString &splitOpt)
prepare the training and test trees -> same cuts for signal and background
Definition DataLoader.cxx:632
TMVA::DataLoader::AddBackgroundTree
void AddBackgroundTree(TTree *background, Double_t weight=1.0, Types::ETreeType treetype=Types::kMaxTreeType)
number of signal events (used to compute significance)
Definition DataLoader.cxx:402
TMVA::DataLoader::GetDataSetInfo
DataSetInfo & GetDataSetInfo()
Definition DataLoader.cxx:137
TMVA::DataSetInfo::GetListOfVariables
std::vector< TString > GetListOfVariables() const
returns list of variables
Definition DataSetInfo.cxx:406
TMVA::Factory
This is the main MVA steering class.
Definition Factory.h:80
TMVA::Factory::TrainAllMethods
void TrainAllMethods()
Iterates through all booked methods and calls training.
Definition Factory.cxx:1114
TMVA::Factory::BookMethod
MethodBase * BookMethod(DataLoader *loader, TString theMethodName, TString methodTitle, TString theOption="")
Book a classifier or regression method.
Definition Factory.cxx:352
TMVA::Factory::TestAllMethods
void TestAllMethods()
Evaluates all booked methods on the testing data and adds the output to the Results in the corresponi...
Definition Factory.cxx:1271
TMVA::Factory::EvaluateAllMethods
void EvaluateAllMethods(void)
Iterates over all MVAs that have been booked, and calls their evaluation methods.
Definition Factory.cxx:1376
TMVA::Factory::GetROCCurve
TGraph * GetROCCurve(DataLoader *loader, TString theMethodName, Bool_t setTitles=kTRUE, UInt_t iClass=0, Types::ETreeType type=Types::kTesting)
Argument iClass specifies the class to generate the ROC curve in a multiclass setting.
Definition Factory.cxx:912
TMVA::PyMethodBase::PyInitialize
static void PyInitialize()
Initialize Python interpreter.
Definition PyMethodBase.cxx:153
TMacro
Class supporting a collection of lines with C++ code.
Definition TMacro.h:31
TString
Basic string class.
Definition TString.h:139
TString::Data
const char * Data() const
Definition TString.h:376
TString::Format
static TString Format(const char *fmt,...)
Static method which formats a string using a printf style format descriptor and return a TString.
Definition TString.cxx:2378
TTree
A TTree represents a columnar dataset.
Definition TTree.h:79
ROOT::VecOps::cos
RVec< PromoteType< T > > cos(const RVec< T > &v)
Definition RVec.hxx:1853
ROOT::VecOps::sin
RVec< PromoteType< T > > sin(const RVec< T > &v)
Definition RVec.hxx:1852
c1
return c1
Definition legend1.C:41
n
const Int_t n
Definition legend1.C:16
h1
TH1F * h1
Definition legend1.C:5
f1
TF1 * f1
Definition legend1.C:11
ROOT::EnableImplicitMT
void EnableImplicitMT(UInt_t numthreads=0)
Enable ROOT's implicit multi-threading for all objects and methods that provide an internal paralleli...
Definition TROOT.cxx:539
ROOT::GetThreadPoolSize
UInt_t GetThreadPoolSize()
Returns the size of ROOT's thread pool.
Definition TROOT.cxx:602
TMVA_RNN_Classification
Definition TMVA_RNN_Classification.py:1
TMVA_SOFIE_GNN_Parser.h2
h2
Definition TMVA_SOFIE_GNN_Parser.py:188
TMath::Pi
constexpr Double_t Pi()
Definition TMath.h:37
v2
@ v2
Definition rootcling_impl.cxx:3690
v
@ v
Definition rootcling_impl.cxx:3687
v1
@ v1
Definition rootcling_impl.cxx:3689
m
TMarker m
Definition textangle.C:8
Author
Lorenzo Moneta

Definition in file TMVA_RNN_Classification.C.