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Reference Guide
TMVAMulticlass.C File Reference

Detailed Description

View in nbviewer Open in SWAN 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: ./files/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: BDTG
:
: 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 +0.397 +0.623 +0.832
: var2: +0.397 +1.000 +0.716 +0.737
: var3: +0.623 +0.716 +1.000 +0.859
: var4: +0.832 +0.737 +0.859 +1.000
: ----------------------------------------
DataSetInfo : Correlation matrix (bg0):
: ----------------------------------------
: var1 var2 var3 var4
: var1: +1.000 +0.365 +0.592 +0.811
: var2: +0.365 +1.000 +0.708 +0.740
: var3: +0.592 +0.708 +1.000 +0.859
: var4: +0.811 +0.740 +0.859 +1.000
: ----------------------------------------
DataSetInfo : Correlation matrix (bg1):
: ----------------------------------------
: var1 var2 var3 var4
: var1: +1.000 +0.407 +0.610 +0.834
: var2: +0.407 +1.000 +0.710 +0.741
: var3: +0.610 +0.710 +1.000 +0.851
: var4: +0.834 +0.741 +0.851 +1.000
: ----------------------------------------
DataSetInfo : Correlation matrix (bg2):
: ----------------------------------------
: var1 var2 var3 var4
: var1: +1.000 -0.647 -0.016 -0.013
: var2: -0.647 +1.000 +0.015 +0.002
: var3: -0.016 +0.015 +1.000 -0.024
: var4: -0.013 +0.002 -0.024 +1.000
: ----------------------------------------
DataSetFactory : [dataset] :
:
Factory : Booking method: MLP
:
MLP : Building Network.
: Initializing weights
Factory : Booking method: PDEFoam
:
Factory : Booking method: DL_CPU
:
: 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"
: 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"
: 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" [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'
<ERROR> : CUDA backend not enabled. Please make sure you have CUDA installed and it was successfully detected by CMAKE by using -Dtmva-gpu=On 
: Will now use instead the CPU architecture !
: Will now use the CPU architecture with BLAS and IMT support !
Factory : Train all methods
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: 0.047647 1.0025 [ -3.6592 3.2645 ]
: var2: 0.32647 1.0646 [ -3.6891 3.7877 ]
: var3: 0.11493 1.1230 [ -4.5727 4.5640 ]
: var4: -0.076531 1.2652 [ -4.8486 5.0412 ]
: -----------------------------------------------------------
: Preparing the Decorrelation transformation...
TFHandler_Factory : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: var1: 0.082544 1.0000 [ -3.6274 3.1017 ]
: var2: 0.36715 1.0000 [ -3.3020 3.4950 ]
: var3: 0.066865 1.0000 [ -2.9882 3.3086 ]
: var4: -0.20593 1.0000 [ -3.3088 2.8423 ]
: -----------------------------------------------------------
: Preparing the Principle Component (PCA) transformation...
TFHandler_Factory : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: var1: 5.7502e-10 1.8064 [ -8.0344 7.8312 ]
: var2:-1.6078e-11 0.90130 [ -2.6765 2.7523 ]
: var3: 3.0841e-10 0.73386 [ -2.6572 2.2255 ]
: var4:-2.6886e-10 0.62168 [ -1.7384 2.2297 ]
: -----------------------------------------------------------
: Preparing the Gaussian transformation...
: Preparing the Decorrelation transformation...
TFHandler_Factory : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: var1: 0.013510 1.0000 [ -2.6520 6.2074 ]
: var2: 0.0096839 1.0000 [ -2.8402 6.3073 ]
: var3: 0.010397 1.0000 [ -3.0251 5.8860 ]
: var4: 0.0053980 1.0000 [ -3.0998 5.7078 ]
: -----------------------------------------------------------
: 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: 5.61 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: 2.01 sec
: Creating multiclass response histograms...
: Creating multiclass performance histograms...
: Creating xml weight file: dataset/weights/TMVAMulticlass_BDTG.weights.xml
: Creating standalone class: dataset/weights/TMVAMulticlass_BDTG.class.C
: 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: 24.5 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: 0.0196 sec
: Creating multiclass response histograms...
: Creating multiclass performance histograms...
: Creating xml weight file: dataset/weights/TMVAMulticlass_MLP.weights.xml
: Creating standalone class: dataset/weights/TMVAMulticlass_MLP.class.C
: 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: 0.672 sec
: Build up multiclass foam 1
: Elapsed time: 0.673 sec
: Build up multiclass foam 2
: Elapsed time: 0.677 sec
: Build up multiclass foam 3
: Elapsed time: 0.476 sec
: Elapsed time for training with 4000 events: 2.67 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: 0.136 sec
: Creating multiclass response histograms...
: Creating multiclass performance histograms...
: Creating xml weight file: dataset/weights/TMVAMulticlass_PDEFoam.weights.xml
: writing foam MultiClassFoam0 to file
: writing foam MultiClassFoam1 to file
: writing foam MultiClassFoam2 to file
: writing foam MultiClassFoam3 to file
: Foams written to file: dataset/weights/TMVAMulticlass_PDEFoam.weights_foams.root
: Creating standalone class: dataset/weights/TMVAMulticlass_PDEFoam.class.C
Factory : Training finished
:
Factory : Train method: DL_CPU for Multiclass classification
:
TFHandler_DL_CPU : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: var1: 0.070769 0.28960 [ -1.0000 1.0000 ]
: var2: 0.074130 0.28477 [ -1.0000 1.0000 ]
: var3: 0.026106 0.24582 [ -1.0000 1.0000 ]
: var4: -0.034951 0.25587 [ -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: 0.070769 0.28960 [ -1.0000 1.0000 ]
: var2: 0.074130 0.28477 [ -1.0000 1.0000 ]
: var3: 0.026106 0.24582 [ -1.0000 1.0000 ]
: var4: -0.034951 0.25587 [ -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 = 0.688878
: --------------------------------------------------------------
: 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 | 0.616593 0.549121 0.0776719 0.00669624 45085.9 0
: 2 Minimum Test error found - save the configuration
: 2 | 0.516041 0.48593 0.078415 0.00674975 44652 0
: 3 Minimum Test error found - save the configuration
: 3 | 0.47033 0.447313 0.0793933 0.00681482 44090.2 0
: 4 Minimum Test error found - save the configuration
: 4 | 0.438585 0.418428 0.0800627 0.00702274 43811.7 0
: 5 Minimum Test error found - save the configuration
: 5 | 0.4163 0.399387 0.0814806 0.00710437 43024.5 0
: 6 Minimum Test error found - save the configuration
: 6 | 0.399916 0.382509 0.0817633 0.00710732 42863.3 0
: 7 Minimum Test error found - save the configuration
: 7 | 0.387564 0.374503 0.0819609 0.00756086 43010.7 0
: 8 Minimum Test error found - save the configuration
: 8 | 0.377428 0.362864 0.0810872 0.00693004 43151.6 0
: 9 Minimum Test error found - save the configuration
: 9 | 0.368836 0.354989 0.0813507 0.00732808 43230 0
: 10 Minimum Test error found - save the configuration
: 10 | 0.361533 0.349073 0.0815275 0.00707769 42982 0
: 11 Minimum Test error found - save the configuration
: 11 | 0.354875 0.34355 0.0814881 0.00703429 42979.7 0
: 12 Minimum Test error found - save the configuration
: 12 | 0.349032 0.33359 0.0818124 0.00704989 42802.2 0
: 13 Minimum Test error found - save the configuration
: 13 | 0.342397 0.33141 0.0810981 0.00701063 43192.2 0
: 14 Minimum Test error found - save the configuration
: 14 | 0.336499 0.324663 0.0811154 0.00704902 43204.5 0
: 15 Minimum Test error found - save the configuration
: 15 | 0.330661 0.318214 0.081587 0.00715914 42994.7 0
: 16 Minimum Test error found - save the configuration
: 16 | 0.325352 0.31243 0.0814156 0.00705579 43034 0
: 17 Minimum Test error found - save the configuration
: 17 | 0.318979 0.307512 0.0820236 0.0071007 42710.6 0
: 18 Minimum Test error found - save the configuration
: 18 | 0.312906 0.30094 0.0818348 0.00718 42863.9 0
: 19 Minimum Test error found - save the configuration
: 19 | 0.307335 0.296373 0.0817493 0.00708734 42859.9 0
: 20 Minimum Test error found - save the configuration
: 20 | 0.301838 0.291392 0.081794 0.00709366 42837.8 0
: 21 Minimum Test error found - save the configuration
: 21 | 0.29758 0.290195 0.0830224 0.00753117 42389 0
: 22 Minimum Test error found - save the configuration
: 22 | 0.293632 0.284487 0.0833441 0.00731411 42088.6 0
: 23 | 0.291094 0.285274 0.0833213 0.00701159 41934.4 1
: 24 Minimum Test error found - save the configuration
: 24 | 0.286917 0.27887 0.081882 0.00712801 42807.1 0
: 25 Minimum Test error found - save the configuration
: 25 | 0.281748 0.275698 0.0823772 0.00720481 42568.8 0
: 26 Minimum Test error found - save the configuration
: 26 | 0.279328 0.272398 0.082552 0.00721133 42473.7 0
: 27 Minimum Test error found - save the configuration
: 27 | 0.27549 0.265732 0.0822792 0.00715707 42597.3 0
: 28 | 0.272976 0.271119 0.0820768 0.00703936 42645.4 1
: 29 Minimum Test error found - save the configuration
: 29 | 0.270354 0.265398 0.0823096 0.00719497 42601.6 0
: 30 Minimum Test error found - save the configuration
: 30 | 0.268107 0.261525 0.0823287 0.0072242 42607.3 0
: 31 Minimum Test error found - save the configuration
: 31 | 0.265495 0.259463 0.0825226 0.00718885 42477.6 0
: 32 Minimum Test error found - save the configuration
: 32 | 0.263426 0.257603 0.0824386 0.00726536 42568.3 0
: 33 Minimum Test error found - save the configuration
: 33 | 0.261025 0.255732 0.0829805 0.0072265 42242 0
: 34 Minimum Test error found - save the configuration
: 34 | 0.259031 0.254431 0.0827947 0.00718772 42324.1 0
: 35 | 0.257475 0.259367 0.0834608 0.00714458 41930.8 1
: 36 Minimum Test error found - save the configuration
: 36 | 0.256828 0.252227 0.084004 0.00724686 41689.9 0
: 37 Minimum Test error found - save the configuration
: 37 | 0.253997 0.247721 0.0833876 0.00719087 41996.6 0
: 38 | 0.251958 0.250697 0.0855109 0.00709855 40809.9 1
: 39 Minimum Test error found - save the configuration
: 39 | 0.250867 0.245167 0.0838618 0.00720397 41744 0
: 40 | 0.249877 0.253117 0.0827328 0.00708995 42304 1
: 41 | 0.249068 0.248014 0.0828519 0.00713478 42262.6 2
: 42 Minimum Test error found - save the configuration
: 42 | 0.246251 0.24289 0.0842965 0.00749057 41663.5 0
: 43 Minimum Test error found - save the configuration
: 43 | 0.244705 0.239544 0.083777 0.00729413 41839.4 0
: 44 Minimum Test error found - save the configuration
: 44 | 0.243295 0.239305 0.083092 0.00724896 42192.4 0
: 45 | 0.242429 0.244083 0.0829918 0.007119 42175.8 1
: 46 Minimum Test error found - save the configuration
: 46 | 0.243171 0.237725 0.0832041 0.00727958 42147.1 0
: 47 | 0.24201 0.238435 0.0830599 0.00711769 42137.3 1
: 48 Minimum Test error found - save the configuration
: 48 | 0.239411 0.233924 0.0832328 0.00724849 42114 0
: 49 | 0.240041 0.237589 0.0830426 0.00712643 42151.8 1
: 50 | 0.237106 0.23693 0.0832697 0.00714609 42036.9 2
: 51 | 0.235756 0.235554 0.0832804 0.00712231 42017.9 3
: 52 Minimum Test error found - save the configuration
: 52 | 0.235649 0.232536 0.0832416 0.00725561 42113 0
: 53 | 0.235164 0.234057 0.0833032 0.00718801 42041.6 1
: 54 | 0.233445 0.233174 0.0834769 0.00716866 41935.2 2
: 55 Minimum Test error found - save the configuration
: 55 | 0.233423 0.231232 0.0836892 0.00727425 41876.6 0
: 56 | 0.231934 0.235537 0.0832989 0.00714949 42022.6 1
: 57 Minimum Test error found - save the configuration
: 57 | 0.232528 0.229681 0.0835825 0.00724492 41919.1 0
: 58 | 0.23138 0.232172 0.0833185 0.0071497 42012 1
: 59 | 0.23021 0.233556 0.0833233 0.00713922 42003.5 2
: 60 Minimum Test error found - save the configuration
: 60 | 0.228545 0.229014 0.0836293 0.00729668 41921.8 0
: 61 | 0.228573 0.229448 0.0837708 0.0071199 41747.7 1
: 62 Minimum Test error found - save the configuration
: 62 | 0.227093 0.228178 0.0837668 0.0074514 41931.2 0
: 63 Minimum Test error found - save the configuration
: 63 | 0.22739 0.228051 0.0837416 0.00726137 41840.9 0
: 64 Minimum Test error found - save the configuration
: 64 | 0.227556 0.22531 0.0839943 0.00732914 41740 0
: 65 | 0.225612 0.229069 0.0833274 0.00720342 42036.7 1
: 66 | 0.2248 0.231352 0.0843127 0.00713487 41462.7 2
: 67 Minimum Test error found - save the configuration
: 67 | 0.225724 0.223639 0.0838661 0.00741301 41855.7 0
: 68 | 0.224099 0.224641 0.0836197 0.00718343 41864.9 1
: 69 | 0.223708 0.223863 0.0835596 0.00718661 41899.6 2
: 70 | 0.22308 0.230918 0.0835942 0.0071588 41865.4 3
: 71 Minimum Test error found - save the configuration
: 71 | 0.222372 0.222501 0.0849815 0.00731887 41203.8 0
: 72 | 0.22272 0.225329 0.0832987 0.00718291 42041.2 1
: 73 Minimum Test error found - save the configuration
: 73 | 0.221448 0.22076 0.0839257 0.00736245 41795.5 0
: 74 | 0.220458 0.223516 0.0833668 0.00715422 41987.8 1
: 75 | 0.220449 0.224291 0.0832049 0.00716946 42085.6 2
: 76 | 0.220002 0.225791 0.0830908 0.00713134 42127.7 3
: 77 | 0.218669 0.223072 0.083098 0.00715312 42135.8 4
: 78 Minimum Test error found - save the configuration
: 78 | 0.218969 0.219774 0.0831679 0.00727215 42163.1 0
: 79 | 0.218598 0.221696 0.083759 0.00720796 41802.2 1
: 80 | 0.217728 0.223143 0.0835024 0.00718367 41929.4 2
: 81 Minimum Test error found - save the configuration
: 81 | 0.217065 0.219603 0.0836692 0.00731113 41907.8 0
: 82 | 0.21642 0.221006 0.0838347 0.00717521 41743 1
: 83 | 0.216015 0.224749 0.0837295 0.00721517 41822.2 2
: 84 Minimum Test error found - save the configuration
: 84 | 0.214373 0.219546 0.0836291 0.00732278 41936.3 0
: 85 | 0.214975 0.223811 0.0834036 0.00717479 41978.9 1
: 86 | 0.214752 0.223776 0.083546 0.00718405 41905.7 2
: 87 | 0.214187 0.221594 0.0836062 0.00721865 41891.6 3
: 88 | 0.213959 0.22383 0.0836607 0.00719374 41848.1 4
: 89 Minimum Test error found - save the configuration
: 89 | 0.21339 0.218816 0.083602 0.00728261 41929 0
: 90 Minimum Test error found - save the configuration
: 90 | 0.213231 0.21777 0.0840299 0.00732626 41719 0
: 91 | 0.212254 0.21863 0.0835074 0.007181 41925.2 1
: 92 Minimum Test error found - save the configuration
: 92 | 0.213047 0.214469 0.0834922 0.00726866 41981.8 0
: 93 | 0.21136 0.214686 0.0835975 0.00717332 41871.5 1
: 94 | 0.212344 0.217588 0.0834496 0.00716673 41949.1 2
: 95 | 0.210366 0.217826 0.0837995 0.00721742 41785.2 3
: 96 | 0.210354 0.220026 0.0835858 0.00717715 41880 4
: 97 | 0.210302 0.215026 0.0836137 0.00721449 41885.3 5
: 98 Minimum Test error found - save the configuration
: 98 | 0.209529 0.213934 0.0837835 0.00733266 41856.9 0
: 99 | 0.210111 0.225228 0.083784 0.00717684 41771.6 1
: 100 | 0.211011 0.215965 0.0847476 0.00727683 41305.9 2
: 101 Minimum Test error found - save the configuration
: 101 | 0.209088 0.213484 0.0836924 0.00730277 41890.5 0
: 102 | 0.208831 0.214259 0.0837065 0.00717605 41813.4 1
: 103 Minimum Test error found - save the configuration
: 103 | 0.208871 0.213086 0.0838135 0.00738197 41867.6 0
: 104 | 0.20783 0.21869 0.0840386 0.00739706 41752.8 1
: 105 | 0.208471 0.216857 0.0836819 0.00721836 41850 2
: 106 | 0.208249 0.214002 0.0835561 0.00718472 41900.5 3
: 107 | 0.207703 0.219531 0.0838795 0.00733846 41807.6 4
: 108 | 0.207979 0.215695 0.0840959 0.00719945 41614.4 5
: 109 Minimum Test error found - save the configuration
: 109 | 0.207219 0.21102 0.0845479 0.0073064 41428.5 0
: 110 | 0.206723 0.214053 0.0834981 0.00717683 41928 1
: 111 | 0.206193 0.212522 0.08442 0.00719417 41436.9 2
: 112 | 0.20536 0.214914 0.0836597 0.0071784 41840.3 3
: 113 | 0.20553 0.214703 0.0836204 0.00718321 41864.5 4
: 114 | 0.204696 0.215957 0.0837572 0.00719436 41795.7 5
: 115 | 0.204675 0.213806 0.0836037 0.00719984 41882.7 6
: 116 Minimum Test error found - save the configuration
: 116 | 0.205074 0.209029 0.0842016 0.00732693 41626.2 0
: 117 | 0.204053 0.214864 0.0841221 0.00720287 41602.1 1
: 118 | 0.205493 0.215975 0.0837008 0.00719881 41829 2
: 119 | 0.203689 0.209197 0.0841463 0.00720364 41589.4 3
: 120 | 0.202738 0.217781 0.0835884 0.00721217 41897.8 4
: 121 | 0.203843 0.214191 0.0836397 0.00720713 41866.9 5
: 122 | 0.202766 0.211095 0.0836528 0.00719974 41855.8 6
: 123 | 0.202502 0.212425 0.0840906 0.00720794 41621.9 7
: 124 | 0.202284 0.212526 0.0835742 0.0072029 41900.6 8
: 125 | 0.204247 0.214062 0.083911 0.00721563 41723.5 9
: 126 | 0.202358 0.21909 0.0838829 0.00724042 41752.3 10
: 127 | 0.205457 0.209062 0.0838544 0.00723698 41765.9 11
:
: Elapsed time for training with 4000 events: 10.6 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: 0.12 sec
: Creating multiclass response histograms...
: Creating multiclass performance histograms...
: Creating xml weight file: dataset/weights/TMVAMulticlass_DL_CPU.weights.xml
: Creating standalone class: dataset/weights/TMVAMulticlass_DL_CPU.class.C
Factory : Training finished
:
: Ranking input variables (method specific)...
BDTG : Ranking result (top variable is best ranked)
: --------------------------------------
: Rank : Variable : Variable Importance
: --------------------------------------
: 1 : var4 : 3.117e-01
: 2 : var1 : 2.504e-01
: 3 : var2 : 2.430e-01
: 4 : var3 : 1.949e-01
: --------------------------------------
MLP : Ranking result (top variable is best ranked)
: -----------------------------
: Rank : Variable : Importance
: -----------------------------
: 1 : var4 : 6.076e+01
: 2 : var2 : 4.824e+01
: 3 : var1 : 2.116e+01
: 4 : var3 : 1.692e+01
: -----------------------------
PDEFoam : Ranking result (top variable is best ranked)
: --------------------------------------
: Rank : Variable : Variable Importance
: --------------------------------------
: 1 : var4 : 2.991e-01
: 2 : var1 : 2.930e-01
: 3 : var3 : 2.365e-01
: 4 : var2 : 1.714e-01
: --------------------------------------
: No variable ranking supplied by classifier: DL_CPU
TH1.Print Name = TrainingHistory_DL_CPU_trainingError, Entries= 0, Total sum= 32.0457
TH1.Print Name = TrainingHistory_DL_CPU_valError, Entries= 0, Total sum= 31.9695
Factory : === Destroy and recreate all methods via weight files for testing ===
:
: Reading weight file: dataset/weights/TMVAMulticlass_BDTG.weights.xml
: Reading weight file: dataset/weights/TMVAMulticlass_MLP.weights.xml
MLP : Building Network.
: Initializing weights
: Reading weight file: dataset/weights/TMVAMulticlass_PDEFoam.weights.xml
: Read foams from file: dataset/weights/TMVAMulticlass_PDEFoam.weights_foams.root
: Reading weight file: dataset/weights/TMVAMulticlass_DL_CPU.weights.xml
Factory : Test all methods
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: 1.02 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: 0.0178 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: 0.137 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: 0.119 sec
: Creating multiclass response histograms...
: Creating multiclass performance histograms...
Factory : Evaluate all methods
: 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: 0.070153 1.0224 [ -4.0592 3.5808 ]
: var2: 0.30372 1.0460 [ -3.6952 3.7877 ]
: var3: 0.12152 1.1222 [ -3.6800 3.9200 ]
: var4: -0.072602 1.2766 [ -4.8486 4.2221 ]
: -----------------------------------------------------------
: 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: 0.070153 1.0224 [ -4.0592 3.5808 ]
: var2: 0.30372 1.0460 [ -3.6952 3.7877 ]
: var3: 0.12152 1.1222 [ -3.6800 3.9200 ]
: var4: -0.072602 1.2766 [ -4.8486 4.2221 ]
: -----------------------------------------------------------
: 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: 0.070153 1.0224 [ -4.0592 3.5808 ]
: var2: 0.30372 1.0460 [ -3.6952 3.7877 ]
: var3: 0.12152 1.1222 [ -3.6800 3.9200 ]
: var4: -0.072602 1.2766 [ -4.8486 4.2221 ]
: -----------------------------------------------------------
: 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: 0.077270 0.29534 [ -1.1155 1.0914 ]
: var2: 0.068045 0.27981 [ -1.0016 1.0000 ]
: var3: 0.027548 0.24565 [ -0.80459 0.85902 ]
: var4: -0.034157 0.25816 [ -1.0000 0.83435 ]
: -----------------------------------------------------------
TFHandler_DL_CPU : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: var1: 0.077270 0.29534 [ -1.1155 1.0914 ]
: var2: 0.068045 0.27981 [ -1.0016 1.0000 ]
: var3: 0.027548 0.24565 [ -0.80459 0.85902 ]
: var4: -0.034157 0.25816 [ -1.0000 0.83435 ]
: -----------------------------------------------------------
:
: 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 0.968 (0.978) 0.508 (0.605) 0.914 (0.945) 0.990 (0.996)
: bg0 0.910 (0.931) 0.256 (0.288) 0.737 (0.791) 0.922 (0.956)
: bg1 0.947 (0.954) 0.437 (0.511) 0.833 (0.856) 0.971 (0.971)
: bg2 0.978 (0.982) 0.585 (0.678) 0.951 (0.956) 0.999 (0.996)
:
: dataset MLP
: ------------------------------
: Signal 0.970 (0.975) 0.596 (0.632) 0.933 (0.938) 0.988 (0.993)
: bg0 0.929 (0.934) 0.303 (0.298) 0.787 (0.793) 0.949 (0.961)
: bg1 0.962 (0.967) 0.467 (0.553) 0.881 (0.906) 0.985 (0.992)
: bg2 0.975 (0.979) 0.629 (0.699) 0.929 (0.940) 0.998 (0.998)
:
: dataset PDEFoam
: ------------------------------
: Signal 0.916 (0.928) 0.294 (0.382) 0.744 (0.782) 0.932 (0.952)
: bg0 0.837 (0.848) 0.109 (0.147) 0.519 (0.543) 0.833 (0.851)
: bg1 0.890 (0.902) 0.190 (0.226) 0.606 (0.646) 0.923 (0.929)
: bg2 0.967 (0.972) 0.510 (0.527) 0.900 (0.926) 0.993 (0.998)
:
: dataset DL_CPU
: ------------------------------
: Signal 0.978 (0.977) 0.614 (0.658) 0.939 (0.937) 0.993 (0.994)
: bg0 0.928 (0.934) 0.299 (0.336) 0.795 (0.790) 0.941 (0.960)
: bg1 0.965 (0.967) 0.484 (0.569) 0.897 (0.893) 0.991 (0.994)
: bg2 0.984 (0.983) 0.714 (0.698) 0.961 (0.956) 1.000 (0.999)
:
: -------------------------------------------------------------------------------------------------------
:
:
: 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 - 0.497 (0.373) 0.710 (0.693) 0.680 (0.574)
: bg0 0.271 (0.184) - 0.239 (0.145) 0.705 (0.667)
: bg1 0.855 (0.766) 0.369 (0.222) - 0.587 (0.578)
: bg2 0.714 (0.585) 0.705 (0.581) 0.648 (0.601) -
:
: (Signal Efficiency for Background Efficiency 0.10%)
: ---------------------------------------------------
: Signal bg0 bg1 bg2
: test (train) test (train) test (train) test (train)
: Signal - 0.911 (0.853) 0.991 (0.981) 0.945 (0.913)
: bg0 0.833 (0.774) - 0.654 (0.582) 0.930 (0.901)
: bg1 0.971 (0.980) 0.716 (0.681) - 0.871 (0.862)
: bg2 0.976 (0.951) 0.971 (0.973) 0.936 (0.941) -
:
: (Signal Efficiency for Background Efficiency 0.30%)
: ---------------------------------------------------
: Signal bg0 bg1 bg2
: test (train) test (train) test (train) test (train)
: Signal - 0.978 (0.957) 0.999 (1.000) 0.998 (0.997)
: bg0 0.965 (0.926) - 0.874 (0.835) 0.991 (0.976)
: bg1 1.000 (0.999) 0.916 (0.894) - 0.988 (0.985)
: bg2 0.999 (0.999) 0.997 (0.999) 0.996 (0.997) -
:
: === 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 - 0.465 (0.490) 0.974 (0.953) 0.632 (0.498)
: bg0 0.320 (0.269) - 0.224 (0.250) 0.655 (0.627)
: bg1 0.943 (0.920) 0.341 (0.275) - 0.632 (0.687)
: bg2 0.665 (0.642) 0.697 (0.680) 0.706 (0.598) -
:
: (Signal Efficiency for Background Efficiency 0.10%)
: ---------------------------------------------------
: Signal bg0 bg1 bg2
: test (train) test (train) test (train) test (train)
: Signal - 0.865 (0.854) 0.996 (0.994) 0.908 (0.907)
: bg0 0.784 (0.776) - 0.666 (0.655) 0.919 (0.895)
: bg1 0.998 (0.998) 0.791 (0.785) - 0.912 (0.902)
: bg2 0.943 (0.903) 0.946 (0.939) 0.924 (0.928) -
:
: (Signal Efficiency for Background Efficiency 0.30%)
: ---------------------------------------------------
: Signal bg0 bg1 bg2
: test (train) test (train) test (train) test (train)
: Signal - 0.978 (0.964) 0.997 (0.997) 0.993 (0.986)
: bg0 0.952 (0.924) - 0.936 (0.928) 0.992 (0.990)
: bg1 1.000 (1.000) 0.945 (0.936) - 0.998 (0.995)
: bg2 0.994 (0.985) 0.998 (0.998) 0.998 (0.998) -
:
: === 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 - 0.289 (0.233) 0.467 (0.436) 0.421 (0.332)
: bg0 0.100 (0.045) - 0.132 (0.116) 0.540 (0.313)
: bg1 0.209 (0.434) 0.153 (0.092) - 0.347 (0.323)
: bg2 0.560 (0.552) 0.445 (0.424) 0.501 (0.506) -
:
: (Signal Efficiency for Background Efficiency 0.10%)
: ---------------------------------------------------
: Signal bg0 bg1 bg2
: test (train) test (train) test (train) test (train)
: Signal - 0.665 (0.640) 0.854 (0.822) 0.807 (0.790)
: bg0 0.538 (0.520) - 0.415 (0.374) 0.843 (0.833)
: bg1 0.885 (0.886) 0.542 (0.491) - 0.728 (0.646)
: bg2 0.928 (0.890) 0.956 (0.959) 0.847 (0.895) -
:
: (Signal Efficiency for Background Efficiency 0.30%)
: ---------------------------------------------------
: Signal bg0 bg1 bg2
: test (train) test (train) test (train) test (train)
: Signal - 0.898 (0.878) 0.971 (0.950) 0.982 (0.975)
: bg0 0.828 (0.810) - 0.696 (0.676) 0.954 (0.951)
: bg1 0.951 (0.966) 0.803 (0.745) - 0.958 (0.966)
: bg2 0.998 (0.991) 0.998 (0.996) 0.998 (0.993) -
:
: === 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 - 0.525 (0.460) 0.932 (0.937) 0.702 (0.685)
: bg0 0.381 (0.370) - 0.208 (0.227) 0.600 (0.591)
: bg1 0.914 (0.913) 0.370 (0.300) - 0.642 (0.659)
: bg2 0.676 (0.684) 0.735 (0.734) 0.689 (0.724) -
:
: (Signal Efficiency for Background Efficiency 0.10%)
: ---------------------------------------------------
: Signal bg0 bg1 bg2
: test (train) test (train) test (train) test (train)
: Signal - 0.878 (0.889) 0.991 (0.991) 0.935 (0.937)
: bg0 0.788 (0.777) - 0.650 (0.686) 0.905 (0.886)
: bg1 0.996 (0.996) 0.805 (0.796) - 0.863 (0.897)
: bg2 0.960 (0.943) 0.972 (0.973) 0.942 (0.964) -
:
: (Signal Efficiency for Background Efficiency 0.30%)
: ---------------------------------------------------
: Signal bg0 bg1 bg2
: test (train) test (train) test (train) test (train)
: Signal - 0.981 (0.979) 0.998 (1.000) 0.999 (1.000)
: bg0 0.957 (0.927) - 0.921 (0.909) 0.992 (0.980)
: bg1 1.000 (0.999) 0.952 (0.951) - 1.000 (0.999)
: bg2 0.999 (0.999) 0.999 (1.000) 0.999 (1.000) -
:
: -------------------------------------------------------------------------------------------------------
:
Dataset:dataset : Created tree 'TestTree' with 4000 events
:
Dataset:dataset : Created tree 'TrainTree' with 4000 events
:
Factory : Thank you for using TMVA!
: For citation information, please visit: http://tmva.sf.net/citeTMVA.html
==> 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"
using namespace TMVA;
void TMVAMulticlass( TString myMethodList = "" )
{
// This loads the library
// 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(0);
TString fname = "./tmva_example_multiclass.root";
if (!gSystem->AccessPathName( fname )) {
input = TFile::Open( fname ); // check if file in local directory exists
}
else {
input = TFile::Open("http://root.cern.ch/files/tmva_multiclass_example.root", "CACHEREAD");
}
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");
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");
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;
}
Author
Andreas Hoecker

Definition in file TMVAMulticlass.C.

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::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:1211
TFile::SetCacheFileDir
static Bool_t SetCacheFileDir(ROOT::Internal::TStringView cacheDir, Bool_t operateDisconnected=kTRUE, Bool_t forceCacheread=kFALSE)
Definition: TFile.h:324
TMVA::Types::kBDT
@ kBDT
Definition: Types.h:88
TMVA::DataLoader::AddTree
void AddTree(TTree *tree, const TString &className, Double_t weight=1.0, const TCut &cut="", Types::ETreeType tt=Types::kMaxTreeType)
Definition: DataLoader.cxx:351
TTree
A TTree represents a columnar dataset.
Definition: TTree.h:79
DataLoader.h
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:3998
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
TTree.h
TString
Basic string class.
Definition: TString.h:136
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:1295
TString.h
TFile.h
TROOT.h
TMVA::Types::kPDEFoam
@ kPDEFoam
Definition: Types.h:96
TDirectoryFile::Get
TObject * Get(const char *namecycle) override
Return pointer to object identified by namecycle.
Definition: TDirectoryFile.cxx:909
TMVAMultiClassGui.h
TSystem.h
TMVA::TMVAMultiClassGui
void TMVAMultiClassGui(const char *fName="TMVAMulticlass.root", TString dataset="")
TMVA::Factory
This is the main MVA steering class.
Definition: Factory.h:80
TFile
A ROOT file is a suite of consecutive data records (TKey instances) with a well defined format.
Definition: TFile.h:54
unsigned int
gSystem
R__EXTERN TSystem * gSystem
Definition: TSystem.h:559
TMVA::Tools::Instance
static Tools & Instance()
Definition: Tools.cxx:75
TMVA::Factory::BookMethod
MethodBase * BookMethod(DataLoader *loader, TString theMethodName, TString methodTitle, TString theOption="")
Book a classifier or regression method.
Definition: Factory.cxx:352
TMVA::Types::kDL
@ kDL
Definition: Types.h:101
TString::IsNull
Bool_t IsNull() const
Definition: TString.h:407
TMVA::Types::kMLP
@ kMLP
Definition: Types.h:92
TFile::Close
void Close(Option_t *option="") override
Close a file.
Definition: TFile.cxx:880
Factory.h
TMVA::Types::kFDA
@ kFDA
Definition: Types.h:94
Tools.h
TNamed::GetName
virtual const char * GetName() const
Returns name of object.
Definition: TNamed.h:47
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::TrainAllMethods
void TrainAllMethods()
Iterates through all booked methods and calls training.
Definition: Factory.cxx:1114
TMVA::DataLoader::AddVariable
void AddVariable(const TString &expression, const TString &title, const TString &unit, char type='F', Double_t min=0, Double_t max=0)
user inserts discriminating variable in data set info
Definition: DataLoader.cxx:485
TMVA::gTools
Tools & gTools()
TMVA
create variable transformations
Definition: GeneticMinimizer.h:22
main
int main(int argc, char *argv[])
Definition: cef_main.cxx:54
gROOT
#define gROOT
Definition: TROOT.h:404
TMVA::DataLoader
Definition: DataLoader.h:50