==> 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: ␛[1mBDTG␛[0m
:
: the option NegWeightTreatment=InverseBoostNegWeights does not exist for BoostType=Grad
: --> change to new default NegWeightTreatment=Pray
: 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: ␛[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"
: 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'
␛[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: 0.047647 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: 5.32 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: 1.64 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: 23.3 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: 0.0161 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: 0.662 sec
: Build up multiclass foam 1
: Elapsed time: 0.67 sec
: Build up multiclass foam 2
: Elapsed time: 0.676 sec
: Build up multiclass foam 3
: Elapsed time: 0.471 sec
: Elapsed time for training with 4000 events: 2.65 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: 0.132 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: 0.070769 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 = 0.703728
: --------------------------------------------------------------
: 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 | 0.602543 0.530965 0.0765624 0.00667393 45787.2 0
: 2 Minimum Test error found - save the configuration
: 2 | 0.500337 0.481107 0.077387 0.00663512 45228.5 0
: 3 Minimum Test error found - save the configuration
: 3 | 0.460873 0.442199 0.077983 0.00670922 44897.3 0
: 4 Minimum Test error found - save the configuration
: 4 | 0.432014 0.417845 0.0788273 0.00678154 44416.2 0
: 5 Minimum Test error found - save the configuration
: 5 | 0.412308 0.397617 0.0787267 0.0067476 44457.3 0
: 6 Minimum Test error found - save the configuration
: 6 | 0.396634 0.383267 0.0789771 0.00680014 44335.5 0
: 7 Minimum Test error found - save the configuration
: 7 | 0.384735 0.371368 0.0791341 0.0067886 44232.2 0
: 8 Minimum Test error found - save the configuration
: 8 | 0.374716 0.36232 0.0794162 0.00684678 44095.7 0
: 9 Minimum Test error found - save the configuration
: 9 | 0.365538 0.352554 0.0795871 0.00684172 43989 0
: 10 Minimum Test error found - save the configuration
: 10 | 0.357954 0.346696 0.0798502 0.00689439 43862.2 0
: 11 Minimum Test error found - save the configuration
: 11 | 0.351841 0.340021 0.0799457 0.00688651 43800.1 0
: 12 Minimum Test error found - save the configuration
: 12 | 0.34469 0.332597 0.0800878 0.00690189 43724.2 0
: 13 Minimum Test error found - save the configuration
: 13 | 0.337969 0.325539 0.0801581 0.00689879 43680.5 0
: 14 Minimum Test error found - save the configuration
: 14 | 0.331836 0.31927 0.0803496 0.00694568 43594.4 0
: 15 Minimum Test error found - save the configuration
: 15 | 0.325504 0.315925 0.0805802 0.00696921 43471.8 0
: 16 Minimum Test error found - save the configuration
: 16 | 0.319578 0.304527 0.0805831 0.00698807 43481.2 0
: 17 Minimum Test error found - save the configuration
: 17 | 0.313719 0.300252 0.0806325 0.00696897 43440.8 0
: 18 Minimum Test error found - save the configuration
: 18 | 0.308053 0.299347 0.0808346 0.00698812 43333.1 0
: 19 Minimum Test error found - save the configuration
: 19 | 0.303628 0.292215 0.080824 0.00697441 43331.3 0
: 20 Minimum Test error found - save the configuration
: 20 | 0.299667 0.286101 0.0808799 0.00698901 43307.1 0
: 21 Minimum Test error found - save the configuration
: 21 | 0.296183 0.285981 0.0809238 0.00699552 43285.2 0
: 22 Minimum Test error found - save the configuration
: 22 | 0.292272 0.284088 0.0810181 0.00701568 43241.9 0
: 23 | 0.28929 0.284112 0.0810808 0.00693667 43159.2 1
: 24 Minimum Test error found - save the configuration
: 24 | 0.286911 0.278846 0.0812182 0.00703193 43134.7 0
: 25 Minimum Test error found - save the configuration
: 25 | 0.283832 0.273295 0.0813126 0.00704967 43090.2 0
: 26 | 0.280572 0.275628 0.083263 0.00896368 43069.1 1
: 27 Minimum Test error found - save the configuration
: 27 | 0.279496 0.272172 0.0813864 0.0070505 43047.9 0
: 28 Minimum Test error found - save the configuration
: 28 | 0.277048 0.26692 0.0814958 0.007055 42987.2 0
: 29 Minimum Test error found - save the configuration
: 29 | 0.274483 0.265217 0.0814684 0.00704652 42998.1 0
: 30 Minimum Test error found - save the configuration
: 30 | 0.272083 0.263768 0.0814683 0.00703328 42990.5 0
: 31 | 0.270201 0.264613 0.0814338 0.0069878 42984.2 1
: 32 Minimum Test error found - save the configuration
: 32 | 0.268147 0.256751 0.0815702 0.00706406 42949.5 0
: 33 | 0.267222 0.258134 0.0816302 0.00706122 42913.3 1
: 34 Minimum Test error found - save the configuration
: 34 | 0.265859 0.256224 0.0817171 0.00708576 42877.4 0
: 35 Minimum Test error found - save the configuration
: 35 | 0.264218 0.253795 0.0818566 0.00710912 42810.8 0
: 36 | 0.263096 0.254593 0.0817262 0.00701235 42830.1 1
: 37 Minimum Test error found - save the configuration
: 37 | 0.260211 0.253466 0.0818662 0.00713013 42817.4 0
: 38 | 0.258224 0.255194 0.0818673 0.00703015 42759.5 1
: 39 Minimum Test error found - save the configuration
: 39 | 0.258046 0.250866 0.0821335 0.00714214 42671.6 0
: 40 | 0.256254 0.252569 0.0820686 0.00704532 42653.4 1
: 41 Minimum Test error found - save the configuration
: 41 | 0.254709 0.247123 0.0822245 0.00715364 42626.4 0
: 42 Minimum Test error found - save the configuration
: 42 | 0.254117 0.245624 0.0837775 0.00877931 42667.7 0
: 43 | 0.251292 0.249004 0.0821482 0.00706053 42616.9 1
: 44 Minimum Test error found - save the configuration
: 44 | 0.250534 0.243478 0.0821494 0.00714087 42661.8 0
: 45 | 0.249159 0.247857 0.0820611 0.00705448 42662.9 1
: 46 | 0.248054 0.246445 0.0821284 0.00706981 42633.4 2
: 47 Minimum Test error found - save the configuration
: 47 | 0.246867 0.243084 0.0823591 0.00716682 42557.5 0
: 48 Minimum Test error found - save the configuration
: 48 | 0.245748 0.23763 0.0823353 0.00718374 42580.6 0
: 49 | 0.243722 0.23928 0.0822608 0.00705966 42552.6 1
: 50 | 0.242844 0.237876 0.0822248 0.00706557 42576.3 2
: 51 | 0.241103 0.238102 0.0821528 0.0070575 42612.5 3
: 52 Minimum Test error found - save the configuration
: 52 | 0.240027 0.234999 0.0823926 0.00715516 42532 0
: 53 Minimum Test error found - save the configuration
: 53 | 0.238143 0.234526 0.082299 0.00714209 42577.6 0
: 54 | 0.239007 0.236147 0.0823578 0.00707793 42508.1 1
: 55 Minimum Test error found - save the configuration
: 55 | 0.237646 0.232037 0.0823425 0.00715761 42561.8 0
: 56 Minimum Test error found - save the configuration
: 56 | 0.235842 0.231919 0.082321 0.00712884 42557.6 0
: 57 | 0.235204 0.240052 0.0822667 0.00707685 42558.9 1
: 58 Minimum Test error found - save the configuration
: 58 | 0.235973 0.229142 0.0823783 0.0071521 42538.4 0
: 59 | 0.233669 0.231255 0.0823681 0.00709384 42511.2 1
: 60 Minimum Test error found - save the configuration
: 60 | 0.233077 0.228486 0.0824572 0.00715844 42497.4 0
: 61 | 0.232196 0.235043 0.0823976 0.00707927 42486.3 1
: 62 | 0.23181 0.231446 0.0824067 0.00709304 42489 2
: 63 Minimum Test error found - save the configuration
: 63 | 0.230837 0.222742 0.0825296 0.00718239 42470.1 0
: 64 | 0.232043 0.226364 0.0825377 0.00709198 42414.6 1
: 65 | 0.229687 0.227546 0.0824348 0.00709978 42476.9 2
: 66 | 0.228991 0.228234 0.0827408 0.00710371 42307.3 3
: 67 | 0.228396 0.227168 0.0825862 0.00710541 42394.9 4
: 68 | 0.227904 0.227223 0.0825446 0.0071079 42419.7 5
: 69 | 0.226129 0.224519 0.0824987 0.00709704 42439.4 6
: 70 | 0.225984 0.225485 0.0825251 0.00710138 42427 7
: 71 | 0.225425 0.227692 0.0826011 0.00713882 42405.3 8
: 72 | 0.225387 0.226601 0.0825933 0.00709772 42386.6 9
: 73 | 0.224324 0.224887 0.082527 0.00709778 42423.9 10
: 74 | 0.22423 0.224157 0.0826319 0.007109 42371.3 11
:
: Elapsed time for training with 4000 events: 6.05 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: 0.115 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 : 3.117e-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= 21.0379
TH1.Print Name = TrainingHistory_DL_CPU_valError, Entries= 0, Total sum= 20.4611
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: 0.976 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: 0.0168 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: 0.135 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: 0.115 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: 0.070153 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 0.973 (0.974) 0.516 (0.631) 0.931 (0.942) 0.991 (0.996)
: bg0 0.925 (0.928) 0.313 (0.304) 0.784 (0.767) 0.939 (0.954)
: bg1 0.960 (0.961) 0.473 (0.484) 0.875 (0.879) 0.987 (0.989)
: bg2 0.971 (0.969) 0.645 (0.655) 0.908 (0.881) 0.995 (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 - 0.397 (0.411) 0.940 (0.943) 0.653 (0.558)
: bg0 0.386 (0.380) - 0.204 (0.171) 0.562 (0.526)
: bg1 0.900 (0.872) 0.256 (0.226) - 0.537 (0.625)
: bg2 0.667 (0.637) 0.643 (0.650) 0.645 (0.626) -
:
: (Signal Efficiency for Background Efficiency 0.10%)
: ---------------------------------------------------
: Signal bg0 bg1 bg2
: test (train) test (train) test (train) test (train)
: Signal - 0.868 (0.888) 0.995 (0.991) 0.888 (0.895)
: bg0 0.794 (0.782) - 0.654 (0.675) 0.867 (0.851)
: bg1 0.992 (0.991) 0.779 (0.780) - 0.840 (0.851)
: bg2 0.891 (0.909) 0.891 (0.916) 0.868 (0.890) -
:
: (Signal Efficiency for Background Efficiency 0.30%)
: ---------------------------------------------------
: Signal bg0 bg1 bg2
: test (train) test (train) test (train) test (train)
: Signal - 0.973 (0.978) 0.999 (1.000) 0.999 (1.000)
: bg0 0.954 (0.928) - 0.914 (0.910) 0.992 (0.986)
: bg1 1.000 (0.999) 0.948 (0.950) - 0.997 (0.991)
: bg2 0.999 (0.992) 0.999 (0.999) 0.990 (0.992) -
:
: -------------------------------------------------------------------------------------------------------
:
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!