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TMVAMulticlass.C File Reference
Tutorials » TMVA tutorials

Detailed Description

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This macro provides a simple example for the training and testing of the TMVA multiclass classification

  • Project : TMVA - a Root-integrated toolkit for multivariate data analysis
  • Package : TMVA
  • Root Macro: TMVAMulticlass
==> Start TMVAMulticlass
--- TMVAMulticlass: Using input file: /github/home/ROOT-CI/build/tutorials/tmva/data/tmva_multiclass_example.root
DataSetInfo : [dataset] : Added class "Signal"
: Add Tree TreeS of type Signal with 2000 events
DataSetInfo : [dataset] : Added class "bg0"
: Add Tree TreeB0 of type bg0 with 2000 events
DataSetInfo : [dataset] : Added class "bg1"
: Add Tree TreeB1 of type bg1 with 2000 events
DataSetInfo : [dataset] : Added class "bg2"
: Add Tree TreeB2 of type bg2 with 2000 events
: Dataset[dataset] : Class index : 0 name : Signal
: Dataset[dataset] : Class index : 1 name : bg0
: Dataset[dataset] : Class index : 2 name : bg1
: Dataset[dataset] : Class index : 3 name : bg2
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 TreeS
: Building event vectors for type 2 bg0
: Dataset[dataset] : create input formulas for tree TreeB0
: Building event vectors for type 2 bg1
: Dataset[dataset] : create input formulas for tree TreeB1
: Building event vectors for type 2 bg2
: Dataset[dataset] : create input formulas for tree TreeB2
DataSetFactory : [dataset] : Number of events in input trees
:
:
:
:
: Number of training and testing events
: ---------------------------------------------------------------------------
: Signal -- training events : 1000
: Signal -- testing events : 1000
: Signal -- training and testing events: 2000
: bg0 -- training events : 1000
: bg0 -- testing events : 1000
: bg0 -- training and testing events: 2000
: bg1 -- training events : 1000
: bg1 -- testing events : 1000
: bg1 -- training and testing events: 2000
: bg2 -- training events : 1000
: bg2 -- testing events : 1000
: bg2 -- training and testing events: 2000
:
DataSetInfo : Correlation matrix (Signal):
: ----------------------------------------
: var1 var2 var3 var4
: var1: +1.000 +0.385 +0.621 +0.838
: var2: +0.385 +1.000 +0.698 +0.723
: var3: +0.621 +0.698 +1.000 +0.849
: var4: +0.838 +0.723 +0.849 +1.000
: ----------------------------------------
DataSetInfo : Correlation matrix (bg0):
: ----------------------------------------
: var1 var2 var3 var4
: var1: +1.000 +0.413 +0.612 +0.833
: var2: +0.413 +1.000 +0.728 +0.753
: var3: +0.612 +0.728 +1.000 +0.855
: var4: +0.833 +0.753 +0.855 +1.000
: ----------------------------------------
DataSetInfo : Correlation matrix (bg1):
: ----------------------------------------
: var1 var2 var3 var4
: var1: +1.000 +0.423 +0.619 +0.846
: var2: +0.423 +1.000 +0.705 +0.730
: var3: +0.619 +0.705 +1.000 +0.855
: var4: +0.846 +0.730 +0.855 +1.000
: ----------------------------------------
DataSetInfo : Correlation matrix (bg2):
: ----------------------------------------
: var1 var2 var3 var4
: var1: +1.000 -0.658 +0.032 -0.004
: var2: -0.658 +1.000 -0.000 +0.014
: var3: +0.032 -0.000 +1.000 -0.048
: var4: -0.004 +0.014 -0.048 +1.000
: ----------------------------------------
DataSetFactory : [dataset] :
:
Factory : Booking method: ␛[1mMLP␛[0m
:
MLP : Building Network.
: Initializing weights
Factory : Booking method: ␛[1mPDEFoam␛[0m
:
Factory : Booking method: ␛[1mDL_CPU␛[0m
:
: Parsing option string:
: ... "!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=N:WeightInitialization=XAVIERUNIFORM:Architecture=GPU:Layout=TANH|100,TANH|50,TANH|10,LINEAR:TrainingStrategy=Optimizer=ADAM,LearningRate=1e-3,TestRepetitions=1,ConvergenceSteps=10,BatchSize=100,MaxEpochs=20"
: 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=N:WeightInitialization=XAVIERUNIFORM:Architecture=GPU:Layout=TANH|100,TANH|50,TANH|10,LINEAR:TrainingStrategy=Optimizer=ADAM,LearningRate=1e-3,TestRepetitions=1,ConvergenceSteps=10,BatchSize=100,MaxEpochs=20"
: The following options are set:
: - By User:
: V: "True" [Verbose output (short form of "VerbosityLevel" below - overrides the latter one)]
: VarTransform: "N" [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]
: Layout: "TANH|100,TANH|50,TANH|10,LINEAR" [Layout of the network.]
: ErrorStrategy: "CROSSENTROPY" [Loss function: Mean squared error (regression) or cross entropy (binary classification).]
: WeightInitialization: "XAVIERUNIFORM" [Weight initialization strategy]
: Architecture: "GPU" [Which architecture to perform the training on.]
: TrainingStrategy: "Optimizer=ADAM,LearningRate=1e-3,TestRepetitions=1,ConvergenceSteps=10,BatchSize=100,MaxEpochs=20" [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)]
: InputLayout: "0|0|0" [The Layout of the input]
: BatchLayout: "0|0|0" [The Layout of the batch]
: RandomSeed: "0" [Random seed used for weight initialization and batch shuffling]
: 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%)]
DL_CPU : [dataset] : Create Transformation "N" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'var1' <---> Output : variable 'var1'
: Input : variable 'var2' <---> Output : variable 'var2'
: Input : variable 'var3' <---> Output : variable 'var3'
: Input : variable 'var4' <---> Output : variable 'var4'
␛[31m<ERROR> : CUDA backend not enabled. Please make sure you have CUDA installed and it was successfully detected by CMAKE by using -Dtmva-gpu=On ␛[0m
: Will now use instead the CPU architecture !
: Will now use the CPU architecture with BLAS and IMT support !
Factory : ␛[1mTrain all methods␛[0m
Factory : [dataset] : Create Transformation "I" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'var1' <---> Output : variable 'var1'
: Input : variable 'var2' <---> Output : variable 'var2'
: Input : variable 'var3' <---> Output : variable 'var3'
: Input : variable 'var4' <---> Output : variable 'var4'
Factory : [dataset] : Create Transformation "D" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'var1' <---> Output : variable 'var1'
: Input : variable 'var2' <---> Output : variable 'var2'
: Input : variable 'var3' <---> Output : variable 'var3'
: Input : variable 'var4' <---> Output : variable 'var4'
Factory : [dataset] : Create Transformation "P" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'var1' <---> Output : variable 'var1'
: Input : variable 'var2' <---> Output : variable 'var2'
: Input : variable 'var3' <---> Output : variable 'var3'
: Input : variable 'var4' <---> Output : variable 'var4'
Factory : [dataset] : Create Transformation "G" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'var1' <---> Output : variable 'var1'
: Input : variable 'var2' <---> Output : variable 'var2'
: Input : variable 'var3' <---> Output : variable 'var3'
: Input : variable 'var4' <---> Output : variable 'var4'
Factory : [dataset] : Create Transformation "D" with events from all classes.
:
: Transformation, Variable selection :
: Input : variable 'var1' <---> Output : variable 'var1'
: Input : variable 'var2' <---> Output : variable 'var2'
: Input : variable 'var3' <---> Output : variable 'var3'
: Input : variable 'var4' <---> Output : variable 'var4'
TFHandler_Factory : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: var1: 0.052185 1.0190 [ -4.0592 3.2645 ]
: var2: 0.33312 1.0446 [ -3.6891 3.7877 ]
: var3: 0.10463 1.1205 [ -3.6296 3.9200 ]
: var4: -0.078123 1.2764 [ -4.8486 4.3625 ]
: -----------------------------------------------------------
: Preparing the Decorrelation transformation...
TFHandler_Factory : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: var1: 0.089502 1.0000 [ -3.4349 2.7570 ]
: var2: 0.38543 1.0000 [ -3.3765 3.1055 ]
: var3: 0.052636 1.0000 [ -2.8007 3.1004 ]
: var4: -0.20867 1.0000 [ -3.0012 2.5822 ]
: -----------------------------------------------------------
: Preparing the Principle Component (PCA) transformation...
TFHandler_Factory : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: var1:-2.3297e-10 1.8127 [ -7.2691 6.3617 ]
: var2:-3.1381e-10 0.89464 [ -2.7283 2.6323 ]
: var3:-2.2463e-10 0.73955 [ -2.6363 2.4256 ]
: var4:-9.8869e-11 0.61727 [ -1.7822 2.2327 ]
: -----------------------------------------------------------
: Preparing the Gaussian transformation...
: Preparing the Decorrelation transformation...
TFHandler_Factory : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: var1: 0.0071986 1.0000 [ -2.5427 5.8540 ]
: var2: 0.0087421 1.0000 [ -2.8611 4.9796 ]
: var3: 0.0090897 1.0000 [ -2.9572 5.6365 ]
: var4: 0.0084612 1.0000 [ -3.0233 5.7479 ]
: -----------------------------------------------------------
: Ranking input variables (method unspecific)...
Factory : Train method: BDTG for Multiclass classification
:
: Training 1000 Decision Trees ... patience please
: Elapsed time for training with 4000 events: 5.47 sec
: Dataset[dataset] : Create results for training
: Dataset[dataset] : Multiclass evaluation of BDTG on training sample
: Dataset[dataset] : Elapsed time for evaluation of 4000 events: 1.7 sec
: Creating multiclass response histograms...
: Creating multiclass performance histograms...
: Creating xml weight file: ␛[0;36mdataset/weights/TMVAMulticlass_BDTG.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVAMulticlass_BDTG.class.C␛[0m
: TMVAMulticlass.root:/dataset/Method_BDT/BDTG
Factory : Training finished
:
Factory : Train method: MLP for Multiclass classification
:
: Training Network
:
: Elapsed time for training with 4000 events: 24.4 sec
: Dataset[dataset] : Create results for training
: Dataset[dataset] : Multiclass evaluation of MLP on training sample
: Dataset[dataset] : Elapsed time for evaluation of 4000 events: 0.00923 sec
: Creating multiclass response histograms...
: Creating multiclass performance histograms...
: Creating xml weight file: ␛[0;36mdataset/weights/TMVAMulticlass_MLP.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVAMulticlass_MLP.class.C␛[0m
: Write special histos to file: TMVAMulticlass.root:/dataset/Method_MLP/MLP
Factory : Training finished
:
Factory : Train method: PDEFoam for Multiclass classification
:
: Build up multiclass foam 0
: Elapsed time: 0.764 sec
: Build up multiclass foam 1
: Elapsed time: 0.731 sec
: Build up multiclass foam 2
: Elapsed time: 0.715 sec
: Build up multiclass foam 3
: Elapsed time: 0.538 sec
: Elapsed time for training with 4000 events: 2.91 sec
: Dataset[dataset] : Create results for training
: Dataset[dataset] : Multiclass evaluation of PDEFoam on training sample
: Dataset[dataset] : Elapsed time for evaluation of 4000 events: 0.122 sec
: Creating multiclass response histograms...
: Creating multiclass performance histograms...
: Creating xml weight file: ␛[0;36mdataset/weights/TMVAMulticlass_PDEFoam.weights.xml␛[0m
: writing foam MultiClassFoam0 to file
: writing foam MultiClassFoam1 to file
: writing foam MultiClassFoam2 to file
: writing foam MultiClassFoam3 to file
: Foams written to file: ␛[0;36mdataset/weights/TMVAMulticlass_PDEFoam.weights_foams.root␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVAMulticlass_PDEFoam.class.C␛[0m
Factory : Training finished
:
Factory : Train method: DL_CPU for Multiclass classification
:
TFHandler_DL_CPU : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: var1: 0.12276 0.27828 [ -1.0000 1.0000 ]
: var2: 0.075909 0.27943 [ -1.0000 1.0000 ]
: var3: -0.010745 0.29684 [ -1.0000 1.0000 ]
: var4: 0.035804 0.27714 [ -1.0000 1.0000 ]
: -----------------------------------------------------------
: Start of deep neural network training on CPU using MT, nthreads = 1
:
TFHandler_DL_CPU : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: var1: 0.12276 0.27828 [ -1.0000 1.0000 ]
: var2: 0.075909 0.27943 [ -1.0000 1.0000 ]
: var3: -0.010745 0.29684 [ -1.0000 1.0000 ]
: var4: 0.035804 0.27714 [ -1.0000 1.0000 ]
: -----------------------------------------------------------
: ***** Deep Learning Network *****
DEEP NEURAL NETWORK: Depth = 4 Input = ( 1, 1, 4 ) Batch size = 100 Loss function = C
Layer 0 DENSE Layer: ( Input = 4 , Width = 100 ) Output = ( 1 , 100 , 100 ) Activation Function = Tanh
Layer 1 DENSE Layer: ( Input = 100 , Width = 50 ) Output = ( 1 , 100 , 50 ) Activation Function = Tanh
Layer 2 DENSE Layer: ( Input = 50 , Width = 10 ) Output = ( 1 , 100 , 10 ) Activation Function = Tanh
Layer 3 DENSE Layer: ( Input = 10 , Width = 4 ) Output = ( 1 , 100 , 4 ) Activation Function = Identity
: Using 3200 events for training and 800 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 = 0.700649
: --------------------------------------------------------------
: 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 | 0.618874 0.559888 0.691577 0.0637412 5096.88 0
: 2 Minimum Test error found - save the configuration
: 2 | 0.526186 0.503989 0.726187 0.0669604 4854.18 0
: 3 Minimum Test error found - save the configuration
: 3 | 0.48561 0.473933 0.726082 0.0635995 4830.31 0
: 4 Minimum Test error found - save the configuration
: 4 | 0.455324 0.444917 0.340231 0.0034925 9502.92 0
: 5 Minimum Test error found - save the configuration
: 5 | 0.424437 0.416248 0.0327283 0.00365895 110081 0
: 6 Minimum Test error found - save the configuration
: 6 | 0.397848 0.393743 0.032752 0.00346337 109258 0
: 7 Minimum Test error found - save the configuration
: 7 | 0.378883 0.380547 0.0329487 0.00349081 108630 0
: 8 Minimum Test error found - save the configuration
: 8 | 0.365204 0.367871 0.0340923 0.0035452 104756 0
: 9 Minimum Test error found - save the configuration
: 9 | 0.355158 0.360194 0.0336011 0.00356252 106529 0
: 10 Minimum Test error found - save the configuration
: 10 | 0.346747 0.35544 0.03388 0.00367717 105950 0
: 11 Minimum Test error found - save the configuration
: 11 | 0.339348 0.343643 0.0338756 0.00361888 105762 0
: 12 Minimum Test error found - save the configuration
: 12 | 0.332061 0.337061 0.0355933 0.00362303 100093 0
: 13 Minimum Test error found - save the configuration
: 13 | 0.325297 0.332307 0.0346895 0.0037429 103404 0
: 14 Minimum Test error found - save the configuration
: 14 | 0.319053 0.325747 0.0344056 0.00365759 104072 0
: 15 Minimum Test error found - save the configuration
: 15 | 0.313551 0.321992 0.0349269 0.00373558 102593 0
: 16 Minimum Test error found - save the configuration
: 16 | 0.308599 0.318959 0.034619 0.00367275 103405 0
: 17 Minimum Test error found - save the configuration
: 17 | 0.303026 0.308206 0.035013 0.00369881 102190 0
: 18 Minimum Test error found - save the configuration
: 18 | 0.29959 0.302727 0.0349848 0.00371882 102348 0
: 19 Minimum Test error found - save the configuration
: 19 | 0.295396 0.299165 0.0350024 0.0037265 102315 0
: 20 Minimum Test error found - save the configuration
: 20 | 0.290337 0.296592 0.035195 0.00374556 101751 0
:
: Elapsed time for training with 4000 events: 3.12 sec
: Dataset[dataset] : Create results for training
: Dataset[dataset] : Multiclass evaluation of DL_CPU on training sample
: Dataset[dataset] : Elapsed time for evaluation of 4000 events: 0.0552 sec
: Creating multiclass response histograms...
: Creating multiclass performance histograms...
: Creating xml weight file: ␛[0;36mdataset/weights/TMVAMulticlass_DL_CPU.weights.xml␛[0m
: Creating standalone class: ␛[0;36mdataset/weights/TMVAMulticlass_DL_CPU.class.C␛[0m
Factory : Training finished
:
: Ranking input variables (method specific)...
BDTG : Ranking result (top variable is best ranked)
: --------------------------------------
: Rank : Variable : Variable Importance
: --------------------------------------
: 1 : var4 : 3.060e-01
: 2 : var1 : 2.473e-01
: 3 : var2 : 2.400e-01
: 4 : var3 : 2.067e-01
: --------------------------------------
MLP : Ranking result (top variable is best ranked)
: -----------------------------
: Rank : Variable : Importance
: -----------------------------
: 1 : var4 : 5.440e+01
: 2 : var1 : 2.568e+01
: 3 : var2 : 2.223e+01
: 4 : var3 : 7.204e+00
: -----------------------------
PDEFoam : Ranking result (top variable is best ranked)
: --------------------------------------
: Rank : Variable : Variable Importance
: --------------------------------------
: 1 : var4 : 2.766e-01
: 2 : var1 : 2.682e-01
: 3 : var2 : 2.510e-01
: 4 : var3 : 2.042e-01
: --------------------------------------
: No variable ranking supplied by classifier: DL_CPU
TH1.Print Name = TrainingHistory_DL_CPU_trainingError, Entries= 0, Total sum= 7.48053
TH1.Print Name = TrainingHistory_DL_CPU_valError, Entries= 0, Total sum= 7.44317
Factory : === Destroy and recreate all methods via weight files for testing ===
:
: Reading weight file: ␛[0;36mdataset/weights/TMVAMulticlass_BDTG.weights.xml␛[0m
: Reading weight file: ␛[0;36mdataset/weights/TMVAMulticlass_MLP.weights.xml␛[0m
MLP : Building Network.
: Initializing weights
: Reading weight file: ␛[0;36mdataset/weights/TMVAMulticlass_PDEFoam.weights.xml␛[0m
: Read foams from file: ␛[0;36mdataset/weights/TMVAMulticlass_PDEFoam.weights_foams.root␛[0m
: Reading weight file: ␛[0;36mdataset/weights/TMVAMulticlass_DL_CPU.weights.xml␛[0m
Factory : ␛[1mTest all methods␛[0m
Factory : Test method: BDTG for Multiclass classification performance
:
: Dataset[dataset] : Create results for testing
: Dataset[dataset] : Multiclass evaluation of BDTG on testing sample
: Dataset[dataset] : Elapsed time for evaluation of 4000 events: 0.959 sec
: Creating multiclass response histograms...
: Creating multiclass performance histograms...
Factory : Test method: MLP for Multiclass classification performance
:
: Dataset[dataset] : Create results for testing
: Dataset[dataset] : Multiclass evaluation of MLP on testing sample
: Dataset[dataset] : Elapsed time for evaluation of 4000 events: 0.0103 sec
: Creating multiclass response histograms...
: Creating multiclass performance histograms...
Factory : Test method: PDEFoam for Multiclass classification performance
:
: Dataset[dataset] : Create results for testing
: Dataset[dataset] : Multiclass evaluation of PDEFoam on testing sample
: Dataset[dataset] : Elapsed time for evaluation of 4000 events: 0.115 sec
: Creating multiclass response histograms...
: Creating multiclass performance histograms...
Factory : Test method: DL_CPU for Multiclass classification performance
:
: Dataset[dataset] : Create results for testing
: Dataset[dataset] : Multiclass evaluation of DL_CPU on testing sample
: Dataset[dataset] : Elapsed time for evaluation of 4000 events: 0.0545 sec
: Creating multiclass response histograms...
: Creating multiclass performance histograms...
Factory : ␛[1mEvaluate all methods␛[0m
: Evaluate multiclass classification method: BDTG
: Creating multiclass response histograms...
: Creating multiclass performance histograms...
: Creating multiclass response histograms...
: Creating multiclass performance histograms...
TFHandler_BDTG : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: var1: 0.065615 1.0061 [ -4.0592 3.5808 ]
: var2: 0.29707 1.0658 [ -3.6952 3.7877 ]
: var3: 0.13183 1.1245 [ -4.5727 4.5640 ]
: var4: -0.071010 1.2654 [ -4.8486 5.0412 ]
: -----------------------------------------------------------
: Evaluate multiclass classification method: MLP
: Creating multiclass response histograms...
: Creating multiclass performance histograms...
: Creating multiclass response histograms...
: Creating multiclass performance histograms...
TFHandler_MLP : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: var1: 0.065615 1.0061 [ -4.0592 3.5808 ]
: var2: 0.29707 1.0658 [ -3.6952 3.7877 ]
: var3: 0.13183 1.1245 [ -4.5727 4.5640 ]
: var4: -0.071010 1.2654 [ -4.8486 5.0412 ]
: -----------------------------------------------------------
: Evaluate multiclass classification method: PDEFoam
: Creating multiclass response histograms...
: Creating multiclass performance histograms...
: Creating multiclass response histograms...
: Creating multiclass performance histograms...
TFHandler_PDEFoam : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: var1: 0.065615 1.0061 [ -4.0592 3.5808 ]
: var2: 0.29707 1.0658 [ -3.6952 3.7877 ]
: var3: 0.13183 1.1245 [ -4.5727 4.5640 ]
: var4: -0.071010 1.2654 [ -4.8486 5.0412 ]
: -----------------------------------------------------------
: Evaluate multiclass classification method: DL_CPU
: Creating multiclass response histograms...
: Creating multiclass performance histograms...
: Creating multiclass response histograms...
: Creating multiclass performance histograms...
TFHandler_DL_CPU : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: var1: 0.12643 0.27476 [ -1.0000 1.0864 ]
: var2: 0.066267 0.28510 [ -1.0016 1.0000 ]
: var3: -0.0035395 0.29791 [ -1.2498 1.1706 ]
: var4: 0.037349 0.27475 [ -1.0000 1.1474 ]
: -----------------------------------------------------------
TFHandler_DL_CPU : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: var1: 0.12643 0.27476 [ -1.0000 1.0864 ]
: var2: 0.066267 0.28510 [ -1.0016 1.0000 ]
: var3: -0.0035395 0.29791 [ -1.2498 1.1706 ]
: var4: 0.037349 0.27475 [ -1.0000 1.1474 ]
: -----------------------------------------------------------
:
: 1-vs-rest performance metrics per class
: -------------------------------------------------------------------------------------------------------
:
: Considers the listed class as signal and the other classes
: as background, reporting the resulting binary performance.
: A score of 0.820 (0.850) means 0.820 was acheived on the
: test set and 0.850 on the training set.
:
: Dataset MVA Method ROC AUC Sig eff@B=0.01 Sig eff@B=0.10 Sig eff@B=0.30
: Name: / Class: test (train) test (train) test (train) test (train)
:
: dataset BDTG
: ------------------------------
: Signal 0.967 (0.980) 0.496 (0.616) 0.910 (0.953) 0.994 (0.997)
: bg0 0.908 (0.927) 0.201 (0.331) 0.729 (0.777) 0.924 (0.944)
: bg1 0.945 (0.955) 0.413 (0.429) 0.833 (0.860) 0.970 (0.973)
: bg2 0.974 (0.984) 0.600 (0.677) 0.926 (0.973) 0.995 (0.998)
:
: dataset MLP
: ------------------------------
: Signal 0.975 (0.976) 0.591 (0.609) 0.931 (0.940) 0.997 (0.994)
: bg0 0.930 (0.934) 0.279 (0.389) 0.781 (0.789) 0.960 (0.951)
: bg1 0.963 (0.964) 0.494 (0.462) 0.889 (0.900) 0.990 (0.994)
: bg2 0.971 (0.977) 0.653 (0.697) 0.901 (0.900) 0.994 (1.000)
:
: dataset PDEFoam
: ------------------------------
: Signal 0.923 (0.931) 0.270 (0.365) 0.760 (0.774) 0.957 (0.966)
: bg0 0.843 (0.855) 0.113 (0.166) 0.604 (0.615) 0.830 (0.841)
: bg1 0.903 (0.912) 0.290 (0.293) 0.686 (0.733) 0.922 (0.927)
: bg2 0.972 (0.972) 0.481 (0.477) 0.921 (0.923) 1.000 (0.999)
:
: dataset DL_CPU
: ------------------------------
: Signal 0.962 (0.962) 0.326 (0.406) 0.931 (0.921) 0.989 (0.990)
: bg0 0.923 (0.911) 0.236 (0.318) 0.767 (0.732) 0.947 (0.927)
: bg1 0.952 (0.949) 0.289 (0.260) 0.873 (0.855) 0.982 (0.980)
: bg2 0.911 (0.925) 0.559 (0.525) 0.753 (0.782) 0.895 (0.918)
:
: -------------------------------------------------------------------------------------------------------
:
:
: Confusion matrices for all methods
: -------------------------------------------------------------------------------------------------------
:
: Does a binary comparison between the two classes given by a
: particular row-column combination. In each case, the class
: given by the row is considered signal while the class given
: by the column index is considered background.
:
: === Showing confusion matrix for method : BDTG
: (Signal Efficiency for Background Efficiency 0.01%)
: ---------------------------------------------------
: Signal bg0 bg1 bg2
: test (train) test (train) test (train) test (train)
: Signal - 0.489 (0.430) 0.864 (0.764) 0.784 (0.472)
: bg0 0.311 (0.181) - 0.207 (0.132) 0.694 (0.611)
: bg1 0.830 (0.834) 0.288 (0.339) - 0.668 (0.511)
: bg2 0.708 (0.593) 0.684 (0.593) 0.625 (0.600) -
:
: (Signal Efficiency for Background Efficiency 0.10%)
: ---------------------------------------------------
: Signal bg0 bg1 bg2
: test (train) test (train) test (train) test (train)
: Signal - 0.901 (0.852) 0.996 (0.993) 0.956 (0.892)
: bg0 0.810 (0.763) - 0.643 (0.601) 0.924 (0.904)
: bg1 0.984 (0.984) 0.716 (0.677) - 0.898 (0.843)
: bg2 0.983 (0.928) 0.982 (0.953) 0.948 (0.897) -
:
: (Signal Efficiency for Background Efficiency 0.30%)
: ---------------------------------------------------
: Signal bg0 bg1 bg2
: test (train) test (train) test (train) test (train)
: Signal - 0.981 (0.960) 1.000 (0.999) 0.999 (0.998)
: bg0 0.963 (0.927) - 0.852 (0.814) 0.990 (0.986)
: bg1 0.999 (0.998) 0.915 (0.888) - 0.984 (0.984)
: bg2 0.999 (0.996) 0.998 (0.995) 0.998 (0.993) -
:
: === Showing confusion matrix for method : MLP
: (Signal Efficiency for Background Efficiency 0.01%)
: ---------------------------------------------------
: Signal bg0 bg1 bg2
: test (train) test (train) test (train) test (train)
: Signal - 0.456 (0.481) 0.936 (0.943) 0.645 (0.548)
: bg0 0.421 (0.278) - 0.302 (0.229) 0.604 (0.477)
: bg1 0.913 (0.925) 0.261 (0.400) - 0.566 (0.602)
: bg2 0.675 (0.662) 0.710 (0.669) 0.696 (0.641) -
:
: (Signal Efficiency for Background Efficiency 0.10%)
: ---------------------------------------------------
: Signal bg0 bg1 bg2
: test (train) test (train) test (train) test (train)
: Signal - 0.875 (0.891) 0.999 (1.000) 0.920 (0.894)
: bg0 0.766 (0.755) - 0.696 (0.707) 0.909 (0.905)
: bg1 0.997 (0.992) 0.780 (0.790) - 0.901 (0.867)
: bg2 0.880 (0.890) 0.944 (0.933) 0.891 (0.886) -
:
: (Signal Efficiency for Background Efficiency 0.30%)
: ---------------------------------------------------
: Signal bg0 bg1 bg2
: test (train) test (train) test (train) test (train)
: Signal - 0.974 (0.972) 1.000 (1.000) 0.995 (0.998)
: bg0 0.954 (0.960) - 0.914 (0.914) 0.995 (0.992)
: bg1 0.999 (0.999) 0.958 (0.944) - 0.998 (0.996)
: bg2 0.999 (0.990) 1.000 (0.996) 0.999 (0.989) -
:
: === Showing confusion matrix for method : PDEFoam
: (Signal Efficiency for Background Efficiency 0.01%)
: ---------------------------------------------------
: Signal bg0 bg1 bg2
: test (train) test (train) test (train) test (train)
: Signal - 0.276 (0.123) 0.429 (0.447) 0.415 (0.458)
: bg0 0.156 (0.076) - 0.126 (0.081) 0.522 (0.492)
: bg1 0.481 (0.407) 0.200 (0.222) - 0.420 (0.322)
: bg2 0.478 (0.479) 0.462 (0.490) 0.477 (0.461) -
:
: (Signal Efficiency for Background Efficiency 0.10%)
: ---------------------------------------------------
: Signal bg0 bg1 bg2
: test (train) test (train) test (train) test (train)
: Signal - 0.678 (0.661) 0.829 (0.854) 0.839 (0.825)
: bg0 0.569 (0.563) - 0.521 (0.475) 0.838 (0.816)
: bg1 0.824 (0.802) 0.541 (0.521) - 0.825 (0.790)
: bg2 0.937 (0.921) 0.959 (0.948) 0.885 (0.884) -
:
: (Signal Efficiency for Background Efficiency 0.30%)
: ---------------------------------------------------
: Signal bg0 bg1 bg2
: test (train) test (train) test (train) test (train)
: Signal - 0.904 (0.888) 0.974 (0.966) 0.972 (0.964)
: bg0 0.783 (0.792) - 0.723 (0.735) 0.944 (0.925)
: bg1 0.963 (0.949) 0.839 (0.843) - 0.955 (0.944)
: bg2 0.999 (0.998) 0.999 (1.000) 0.999 (1.000) -
:
: === Showing confusion matrix for method : DL_CPU
: (Signal Efficiency for Background Efficiency 0.01%)
: ---------------------------------------------------
: Signal bg0 bg1 bg2
: test (train) test (train) test (train) test (train)
: Signal - 0.417 (0.516) 0.925 (0.955) 0.298 (0.123)
: bg0 0.356 (0.244) - 0.222 (0.228) 0.463 (0.281)
: bg1 0.914 (0.943) 0.237 (0.300) - 0.192 (0.170)
: bg2 0.507 (0.551) 0.580 (0.564) 0.498 (0.564) -
:
: (Signal Efficiency for Background Efficiency 0.10%)
: ---------------------------------------------------
: Signal bg0 bg1 bg2
: test (train) test (train) test (train) test (train)
: Signal - 0.881 (0.899) 0.991 (0.990) 0.810 (0.763)
: bg0 0.781 (0.814) - 0.654 (0.701) 0.744 (0.795)
: bg1 0.994 (0.998) 0.785 (0.812) - 0.790 (0.760)
: bg2 0.794 (0.768) 0.789 (0.757) 0.769 (0.744) -
:
: (Signal Efficiency for Background Efficiency 0.30%)
: ---------------------------------------------------
: Signal bg0 bg1 bg2
: test (train) test (train) test (train) test (train)
: Signal - 0.982 (0.982) 0.997 (0.997) 0.966 (0.970)
: bg0 0.947 (0.972) - 0.888 (0.905) 0.950 (0.972)
: bg1 0.999 (1.000) 0.948 (0.951) - 0.964 (0.963)
: bg2 0.927 (0.908) 0.941 (0.920) 0.887 (0.862) -
:
: -------------------------------------------------------------------------------------------------------
:
Dataset:dataset : Created tree 'TestTree' with 4000 events
:
Dataset:dataset : Created tree 'TrainTree' with 4000 events
:
Factory : ␛[1mThank you for using TMVA!␛[0m
: ␛[1mFor citation information, please visit: http://tmva.sf.net/citeTMVA.html␛[0m
==> Wrote root file: TMVAMulticlass.root
==> TMVAMulticlass is done!
#include <cstdlib>
#include <iostream>
#include <map>
#include <string>
#include "TFile.h"
#include "TTree.h"
#include "TString.h"
#include "TSystem.h"
#include "TROOT.h"
#include "TMVA/Tools.h"
#include "TMVA/Factory.h"
#include "TMVA/DataLoader.h"
#include "TMVA/TMVAMultiClassGui.h"
using namespace TMVA;
void TMVAMulticlass( TString myMethodList = "" )
{
// This loads the library
TMVA::Tools::Instance();
// to get access to the GUI and all tmva macros
//
// TString tmva_dir(TString(gRootDir) + "/tmva");
// if(gSystem->Getenv("TMVASYS"))
// tmva_dir = TString(gSystem->Getenv("TMVASYS"));
// gROOT->SetMacroPath(tmva_dir + "/test/:" + gROOT->GetMacroPath() );
// gROOT->ProcessLine(".L TMVAMultiClassGui.C");
//---------------------------------------------------------------
// Default MVA methods to be trained + tested
std::map<std::string,int> Use;
Use["MLP"] = 1;
Use["BDTG"] = 1;
#ifdef R__HAS_TMVAGPU
Use["DL_CPU"] = 1;
Use["DL_GPU"] = 1;
#else
Use["DL_CPU"] = 1;
Use["DL_GPU"] = 0;
#endif
Use["FDA_GA"] = 0;
Use["PDEFoam"] = 1;
//---------------------------------------------------------------
std::cout << std::endl;
std::cout << "==> Start TMVAMulticlass" << std::endl;
if (myMethodList != "") {
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) it->second = 0;
std::vector<TString> mlist = TMVA::gTools().SplitString( myMethodList, ',' );
for (UInt_t i=0; i<mlist.size(); i++) {
std::string regMethod(mlist[i]);
if (Use.find(regMethod) == Use.end()) {
std::cout << "Method \"" << regMethod << "\" not known in TMVA under this name. Choose among the following:" << std::endl;
for (std::map<std::string,int>::iterator it = Use.begin(); it != Use.end(); it++) std::cout << it->first << " ";
std::cout << std::endl;
return;
}
Use[regMethod] = 1;
}
}
// Create a new root output file.
TString outfileName = "TMVAMulticlass.root";
TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
TMVA::Factory *factory = new TMVA::Factory( "TMVAMulticlass", outputFile,
"!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=multiclass" );
TMVA::DataLoader *dataloader=new TMVA::DataLoader("dataset");
dataloader->AddVariable( "var1", 'F' );
dataloader->AddVariable( "var2", "Variable 2", "", 'F' );
dataloader->AddVariable( "var3", "Variable 3", "units", 'F' );
dataloader->AddVariable( "var4", "Variable 4", "units", 'F' );
TFile *input(nullptr);
TString fname = gROOT->GetTutorialDir() + "/tmva/data/tmva_multiclass_example.root";
if (!gSystem->AccessPathName( fname )) {
input = TFile::Open( fname ); // check if file in local directory exists
}
if (!input) {
std::cout << "ERROR: could not open data file" << std::endl;
exit(1);
}
std::cout << "--- TMVAMulticlass: Using input file: " << input->GetName() << std::endl;
TTree *signalTree = (TTree*)input->Get("TreeS");
TTree *background0 = (TTree*)input->Get("TreeB0");
TTree *background1 = (TTree*)input->Get("TreeB1");
TTree *background2 = (TTree*)input->Get("TreeB2");
gROOT->cd( outfileName+TString(":/") );
dataloader->AddTree (signalTree,"Signal");
dataloader->AddTree (background0,"bg0");
dataloader->AddTree (background1,"bg1");
dataloader->AddTree (background2,"bg2");
dataloader->PrepareTrainingAndTestTree( "", "SplitMode=Random:NormMode=NumEvents:!V" );
if (Use["BDTG"]) // gradient boosted decision trees
factory->BookMethod( dataloader, TMVA::Types::kBDT, "BDTG", "!H:!V:NTrees=1000:BoostType=Grad:Shrinkage=0.10:UseBaggedBoost:BaggedSampleFraction=0.50:nCuts=20:MaxDepth=2");
if (Use["MLP"]) // neural network
factory->BookMethod( dataloader, TMVA::Types::kMLP, "MLP", "!H:!V:NeuronType=tanh:NCycles=1000:HiddenLayers=N+5,5:TestRate=5:EstimatorType=MSE");
if (Use["FDA_GA"]) // functional discriminant with GA minimizer
factory->BookMethod( dataloader, TMVA::Types::kFDA, "FDA_GA", "H:!V:Formula=(0)+(1)*x0+(2)*x1+(3)*x2+(4)*x3:ParRanges=(-1,1);(-10,10);(-10,10);(-10,10);(-10,10):FitMethod=GA:PopSize=300:Cycles=3:Steps=20:Trim=True:SaveBestGen=1" );
if (Use["PDEFoam"]) // PDE-Foam approach
factory->BookMethod( dataloader, TMVA::Types::kPDEFoam, "PDEFoam", "!H:!V:TailCut=0.001:VolFrac=0.0666:nActiveCells=500:nSampl=2000:nBin=5:Nmin=100:Kernel=None:Compress=T" );
if (Use["DL_CPU"]) {
TString layoutString("Layout=TANH|100,TANH|50,TANH|10,LINEAR");
TString trainingStrategyString("TrainingStrategy=Optimizer=ADAM,LearningRate=1e-3,"
"TestRepetitions=1,ConvergenceSteps=10,BatchSize=100,MaxEpochs=20");
TString nnOptions("!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=N:"
"WeightInitialization=XAVIERUNIFORM:Architecture=GPU");
nnOptions.Append(":");
nnOptions.Append(layoutString);
nnOptions.Append(":");
nnOptions.Append(trainingStrategyString);
factory->BookMethod(dataloader, TMVA::Types::kDL, "DL_CPU", nnOptions);
}
if (Use["DL_GPU"]) {
TString layoutString("Layout=TANH|100,TANH|50,TANH|10,LINEAR");
TString trainingStrategyString("TrainingStrategy=Optimizer=ADAM,LearningRate=1e-3,"
"TestRepetitions=1,ConvergenceSteps=10,BatchSize=100,MaxEpochs=20");
TString nnOptions("!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=N:"
"WeightInitialization=XAVIERUNIFORM:Architecture=GPU");
nnOptions.Append(":");
nnOptions.Append(layoutString);
nnOptions.Append(":");
nnOptions.Append(trainingStrategyString);
factory->BookMethod(dataloader, TMVA::Types::kDL, "DL_GPU", nnOptions);
}
// Train MVAs using the set of training events
factory->TrainAllMethods();
// Evaluate all MVAs using the set of test events
factory->TestAllMethods();
// Evaluate and compare performance of all configured MVAs
factory->EvaluateAllMethods();
// --------------------------------------------------------------
// Save the output
outputFile->Close();
std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl;
std::cout << "==> TMVAMulticlass is done!" << std::endl;
delete factory;
delete dataloader;
// Launch the GUI for the root macros
if (!gROOT->IsBatch()) TMVAMultiClassGui( outfileName );
}
int main( int argc, char** argv )
{
// Select methods (don't look at this code - not of interest)
TString methodList;
for (int i=1; i<argc; i++) {
TString regMethod(argv[i]);
if(regMethod=="-b" || regMethod=="--batch") continue;
if (!methodList.IsNull()) methodList += TString(",");
methodList += regMethod;
}
TMVAMulticlass(methodList);
return 0;
}
main
int main()
Definition Prototype.cxx:12
UInt_t
unsigned int UInt_t
Definition RtypesCore.h:46
TRangeDynCast
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
Definition TCollection.h:358
input
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void input
Definition TGWin32VirtualXProxy.cxx:142
gROOT
#define gROOT
Definition TROOT.h:406
gSystem
R__EXTERN TSystem * gSystem
Definition TSystem.h:572
TFile
A ROOT file is an on-disk file, usually with extension .root, that stores objects in a file-system-li...
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:4094
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::Tools::Instance
static Tools & Instance()
Definition Tools.cxx:71
TMVA::Tools::SplitString
std::vector< TString > SplitString(const TString &theOpt, const char separator) const
splits the option string at 'separator' and fills the list 'splitV' with the primitive strings
Definition Tools.cxx:1199
TMVA::Types::kPDEFoam
@ kPDEFoam
Definition Types.h:94
TString
Basic string class.
Definition TString.h:139
TSystem::AccessPathName
virtual Bool_t AccessPathName(const char *path, EAccessMode mode=kFileExists)
Returns FALSE if one can access a file using the specified access mode.
Definition TSystem.cxx:1296
TTree
A TTree represents a columnar dataset.
Definition TTree.h:79
TMVA
create variable transformations
Definition GeneticMinimizer.h:22
TMVA::gTools
Tools & gTools()
TMVA::TMVAMultiClassGui
void TMVAMultiClassGui(const char *fName="TMVAMulticlass.root", TString dataset="")
Author
Andreas Hoecker

Definition in file TMVAMulticlass.C.