Processing /mnt/build/workspace/root-makedoc-v610/rootspi/rdoc/src/v6-10-00-patches/tutorials/tmva/TMVAMulticlass.C...
==> Start TMVAMulticlass
Creating testdata....
... event: 0 (2000)
... event: 1000 (2000)
======> EVENT:0
var1 = -1.14361
var2 = -0.822373
var3 = -0.395426
var4 = -0.529427
created tree: TreeS
... event: 0 (2000)
... event: 1000 (2000)
======> EVENT:0
var1 = -1.54361
var2 = -1.42237
var3 = -1.39543
var4 = -2.02943
created tree: TreeB0
... event: 0 (2000)
... event: 1000 (2000)
======> EVENT:0
var1 = -1.54361
var2 = -0.822373
var3 = -0.395426
var4 = -2.02943
created tree: TreeB1
======> EVENT:0
var1 = 0.463304
var2 = 1.37192
var3 = -1.16769
var4 = -1.77551
created tree: TreeB2
created data file: tmva_example_multiple_background.root
created tmva_example_multiple_background.root for tests of the multiclass features
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 *InverseBoostNegWeights* does not exist for BoostType=Grad --> change
: to new default for GradBoost *Pray*
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 +0.380 +0.597 +0.819
: var2: +0.380 +1.000 +0.706 +0.744
: var3: +0.597 +0.706 +1.000 +0.853
: var4: +0.819 +0.744 +0.853 +1.000
: ----------------------------------------
DataSetInfo : Correlation matrix (bg0):
: ----------------------------------------
: var1 var2 var3 var4
: var1: +1.000 +0.406 +0.621 +0.837
: var2: +0.406 +1.000 +0.696 +0.727
: var3: +0.621 +0.696 +1.000 +0.853
: var4: +0.837 +0.727 +0.853 +1.000
: ----------------------------------------
DataSetInfo : Correlation matrix (bg1):
: ----------------------------------------
: var1 var2 var3 var4
: var1: +1.000 +0.371 +0.602 +0.831
: var2: +0.371 +1.000 +0.699 +0.721
: var3: +0.602 +0.699 +1.000 +0.847
: var4: +0.831 +0.721 +0.847 +1.000
: ----------------------------------------
DataSetInfo : Correlation matrix (bg2):
: ----------------------------------------
: var1 var2 var3 var4
: var1: +1.000 -0.660 +0.034 -0.012
: var2: -0.660 +1.000 +0.007 -0.004
: var3: +0.034 +0.007 +1.000 -0.037
: var4: -0.012 -0.004 -0.037 +1.000
: ----------------------------------------
DataSetFactory : [dataset] :
:
Factory : Booking method: [1mMLP[0m
:
MLP : Building Network.
: Initializing weights
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: 0.060705 1.0014 [ -4.0592 3.5808 ]
: var2: 0.31440 1.0501 [ -3.6891 3.7877 ]
: var3: 0.12000 1.1225 [ -3.6148 4.5640 ]
: var4: -0.070020 1.2598 [ -4.8486 5.0412 ]
: -----------------------------------------------------------
: Preparing the Decorrelation transformation...
TFHandler_Factory : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: var1: 0.096903 1.0000 [ -3.5985 2.9977 ]
: var2: 0.35671 1.0000 [ -3.3391 3.5408 ]
: var3: 0.070223 1.0000 [ -2.8950 3.1502 ]
: var4: -0.20167 1.0000 [ -3.2998 2.8753 ]
: -----------------------------------------------------------
: Preparing the Principle Component (PCA) transformation...
TFHandler_Factory : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: var1: 1.8262e-10 1.7916 [ -7.2781 7.8235 ]
: var2:-4.0762e-10 0.89644 [ -3.2734 2.6837 ]
: var3:-1.3316e-10 0.74817 [ -2.4103 2.7078 ]
: var4:-1.5119e-10 0.61596 [ -2.2644 1.5471 ]
: -----------------------------------------------------------
: Preparing the Gaussian transformation...
: Preparing the Decorrelation transformation...
TFHandler_Factory : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: var1: 0.0081281 1.0000 [ -2.6178 6.0982 ]
: var2: 0.010257 1.0000 [ -2.8460 6.2789 ]
: var3: 0.0095035 1.0000 [ -3.0077 5.8864 ]
: var4: 0.0074780 1.0000 [ -3.0452 5.6560 ]
: -----------------------------------------------------------
: Ranking input variables (method unspecific)...
Factory : Train method: BDTG for Multiclass classification
:
BDTG : #events: (reweighted) sig: 2000 bkg: 2000
: #events: (unweighted) sig: 1000 bkg: 3000
: Training 1000 Decision Trees ... patience please
: Elapsed time for training with 4000 events: 5.77 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: 1.39 sec
: Creating multiclass response 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_BDTG/BDTG
Factory : Training finished
:
Factory : Train method: MLP for Multiclass classification
:
: Training Network
:
: Elapsed time for training with 4000 events: 20 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: 0.018 sec
: Creating multiclass response 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
:
: Ranking input variables (method specific)...
BDTG : Ranking result (top variable is best ranked)
: --------------------------------------
: Rank : Variable : Variable Importance
: --------------------------------------
: 1 : var4 : 3.063e-01
: 2 : var1 : 2.578e-01
: 3 : var2 : 2.400e-01
: 4 : var3 : 1.959e-01
: --------------------------------------
MLP : Ranking result (top variable is best ranked)
: -----------------------------
: Rank : Variable : Importance
: -----------------------------
: 1 : var4 : 2.946e+01
: 2 : var1 : 1.697e+01
: 3 : var2 : 1.033e+01
: 4 : var3 : 6.599e+00
: -----------------------------
Factory : === Destroy and recreate all methods via weight files for testing ===
:
MLP : Building Network.
: Initializing weights
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: 0.752 sec
: Creating multiclass response 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: 0.0193 sec
: Creating multiclass response histograms...
Factory : [1mEvaluate all methods[0m
: Evaluate multiclass classification method: BDTG
TFHandler_BDTG : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: var1: 0.057094 1.0236 [ -3.6592 3.2749 ]
: var2: 0.31579 1.0607 [ -3.6952 3.7877 ]
: var3: 0.11645 1.1227 [ -4.5727 4.5640 ]
: var4: -0.079113 1.2819 [ -4.7970 4.2221 ]
: -----------------------------------------------------------
: Evaluate multiclass classification method: MLP
TFHandler_MLP : Variable Mean RMS [ Min Max ]
: -----------------------------------------------------------
: var1: 0.057094 1.0236 [ -3.6592 3.2749 ]
: var2: 0.31579 1.0607 [ -3.6952 3.7877 ]
: var3: 0.11645 1.1227 [ -4.5727 4.5640 ]
: var4: -0.079113 1.2819 [ -4.7970 4.2221 ]
: -----------------------------------------------------------
: 1-vs-rest performance metrics per class
: -------------------------------------------------------------------------------------------------------
:
: Considers the listed class as signal and the other classes
: as background, reporting the resulting binary performance.
:
: Dataset MVA Method Sig eff Sig eff Sig eff
: Name: / Class: ROC AUC @B=0.01 @B=0.10 @B=0.30
:
: dataset BDTG
: Signal 0.964 0.419 0.905 0.988
: bg0 0.881 0.144 0.626 0.889
: bg1 0.930 0.413 0.771 0.945
: bg2 0.955 0.555 0.870 0.972
:
: dataset MLP
: Signal 0.972 0.662 0.933 0.993
: bg0 0.933 0.333 0.786 0.954
: bg1 0.965 0.582 0.898 0.991
: bg2 0.978 0.648 0.954 0.996
: -------------------------------------------------------------------------------------------------------
:
:
: 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
: Signal - 0.420 0.691 0.272
: bg0 0.366 - 0.075 0.611
: bg1 0.718 0.267 - 0.475
: bg2 0.690 0.508 0.513 -
:
: (Signal Efficiency for Background Efficiency 0.10%)
: Signal bg0 bg1 bg2
: Signal - 0.872 0.982 0.867
: bg0 0.824 - 0.360 0.880
: bg1 0.971 0.646 - 0.757
: bg2 0.935 0.868 0.810 -
:
: (Signal Efficiency for Background Efficiency 0.30%)
: Signal bg0 bg1 bg2
: Signal - 0.960 0.999 0.998
: bg0 0.960 - 0.696 0.962
: bg1 0.995 0.860 - 0.943
: bg2 0.990 0.974 0.940 -
:
: Showing confusion matrix for method : MLP
: (Signal Efficiency for Background Efficiency 0.01%)
: Signal bg0 bg1 bg2
: Signal - 0.490 0.940 0.662
: bg0 0.400 - 0.175 0.676
: bg1 0.912 0.353 - 0.674
: bg2 0.659 0.634 0.644 -
:
: (Signal Efficiency for Background Efficiency 0.10%)
: Signal bg0 bg1 bg2
: Signal - 0.876 0.993 0.886
: bg0 0.742 - 0.671 0.945
: bg1 0.995 0.767 - 0.911
: bg2 0.952 0.970 0.944 -
:
: (Signal Efficiency for Background Efficiency 0.30%)
: Signal bg0 bg1 bg2
: Signal - 0.966 0.995 0.994
: bg0 0.946 - 0.916 0.992
: bg1 0.996 0.940 - 0.995
: bg2 0.995 0.996 0.996 -
:
: -------------------------------------------------------------------------------------------------------
:
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!