Execution Time4.20s

Test: tutorial-tmva-TMVAMinimalClassification (Passed)
Build: master-x86_64-centos7-clang100-opt-no-rt-cxxmodules (olsnba08.cern.ch) on 2019-11-14 01:39:53
Repository revision: 32b17abcda23e44b64218a42d0ca69cb30cda7e0

Test Timing: Passed
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Processing /data/sftnight/workspace/root-benchmark-no-rt-cxxmodules/BUILDTYPE/Release/COMPILER/clang_gcc62/LABEL/performance-sandy-cc7/root/tutorials/tmva/TMVAMinimalClassification.C...
create data set info dataset
<HEADER> DataSetInfo              : [dataset] : Added class "Signal"
                         : Add Tree  of type Signal with 1000 events
<HEADER> DataSetInfo              : [dataset] : Added class "Background"
                         : Add Tree  of type Background with 1000 events
<HEADER> Factory                  : Booking method: BDT
                         : 
                         : Building event vectors for type 2 Signal
                         : Dataset[dataset] :  create input formulas for tree 
                         : Building event vectors for type 2 Background
                         : Dataset[dataset] :  create input formulas for tree 
<HEADER> DataSetFactory           : [dataset] : Number of events in input trees
                         : 
                         : 
                         : Dataset[dataset] : Weight renormalisation mode: "EqualNumEvents": renormalises all event classes ...
                         : Dataset[dataset] :  such that the effective (weighted) number of events in each class is the same 
                         : Dataset[dataset] :  (and equals the number of events (entries) given for class=0 )
                         : Dataset[dataset] : ... i.e. such that Sum[i=1..N_j]{w_i} = N_classA, j=classA, classB, ...
                         : Dataset[dataset] : ... (note that N_j is the sum of TRAINING events
                         : Dataset[dataset] :  ..... Testing events are not renormalised nor included in the renormalisation factor!)
                         : Number of training and testing events
                         : ---------------------------------------------------------------------------
                         : Signal     -- training events            : 500
                         : Signal     -- testing events             : 500
                         : Signal     -- training and testing events: 1000
                         : Background -- training events            : 500
                         : Background -- testing events             : 500
                         : Background -- training and testing events: 1000
                         : 
<HEADER> DataSetInfo              : Correlation matrix (Signal):
                         : ------------------------
                         :                x       y
                         :       x:  +1.000  -0.001
                         :       y:  -0.001  +1.000
                         : ------------------------
<HEADER> DataSetInfo              : Correlation matrix (Background):
                         : ------------------------
                         :                x       y
                         :       x:  +1.000  -0.093
                         :       y:  -0.093  +1.000
                         : ------------------------
<HEADER> DataSetFactory           : [dataset] :  
                         : 
<HEADER> Factory                  : Train all methods
<HEADER> Factory                  : [dataset] : Create Transformation "I" with events from all classes.
                         : 
<HEADER>                          : Transformation, Variable selection : 
                         : Input : variable 'x' <---> Output : variable 'x'
                         : Input : variable 'y' <---> Output : variable 'y'
<HEADER> TFHandler_Factory        : Variable        Mean        RMS   [        Min        Max ]
                         : -----------------------------------------------------------
                         :        x:     1.0237    0.58881   [ 0.00044777     1.9995 ]
                         :        y:     1.5007    0.73750   [   0.015635     2.9981 ]
                         : -----------------------------------------------------------
                         : Ranking input variables (method unspecific)...
<HEADER> IdTransformation         : Ranking result (top variable is best ranked)
                         : --------------------------
                         : Rank : Variable  : Separation
                         : --------------------------
                         :    1 : y         : 5.033e-01
                         :    2 : x         : 6.234e-02
                         : --------------------------
<HEADER> Factory                  : Train method: BDT for Classification
                         : 
<HEADER> BDT                      : #events: (reweighted) sig: 500 bkg: 500
                         : #events: (unweighted) sig: 500 bkg: 500
                         : Training 800 Decision Trees ... patience please
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                         : Elapsed time for training with 1000 events: 1.03 sec         
<HEADER> BDT                      : [dataset] : Evaluation of BDT on training sample (1000 events)
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                         : Elapsed time for evaluation of 1000 events: 0.131 sec       
                         : Creating xml weight file: dataset/weights/_BDT.weights.xml
                         : Creating standalone class: dataset/weights/_BDT.class.C
                         : out.root:/dataset/Method_BDT/BDT
<HEADER> Factory                  : Training finished
                         : 
                         : Ranking input variables (method specific)...
<HEADER> BDT                      : Ranking result (top variable is best ranked)
                         : -----------------------------------
                         : Rank : Variable  : Variable Importance
                         : -----------------------------------
                         :    1 : x         : 5.149e-01
                         :    2 : y         : 4.851e-01
                         : -----------------------------------
<HEADER> Factory                  : === Destroy and recreate all methods via weight files for testing ===
                         : 
                         : Reading weight file: dataset/weights/_BDT.weights.xml
<HEADER> Factory                  : Test all methods
<HEADER> Factory                  : Test method: BDT for Classification performance
                         : 
<HEADER> BDT                      : [dataset] : Evaluation of BDT on testing sample (1000 events)
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                         : Elapsed time for evaluation of 1000 events: 0.0747 sec       
<HEADER> Factory                  : Evaluate all methods
<HEADER> Factory                  : Evaluate classifier: BDT
                         : 
<HEADER> BDT                      : [dataset] : Loop over test events and fill histograms with classifier response...
                         : 
<HEADER> TFHandler_BDT            : Variable        Mean        RMS   [        Min        Max ]
                         : -----------------------------------------------------------
                         :        x:     1.0128    0.56686   [  0.0011208     1.9999 ]
                         :        y:     1.4873    0.77968   [  0.0054384     2.9933 ]
                         : -----------------------------------------------------------
                         : 
                         : Evaluation results ranked by best signal efficiency and purity (area)
                         : -------------------------------------------------------------------------------------------------------------------
                         : DataSet       MVA                       
                         : Name:         Method:          ROC-integ
                         : dataset       BDT            : 0.888
                         : -------------------------------------------------------------------------------------------------------------------
                         : 
                         : Testing efficiency compared to training efficiency (overtraining check)
                         : -------------------------------------------------------------------------------------------------------------------
                         : DataSet              MVA              Signal efficiency: from test sample (from training sample) 
                         : Name:                Method:          @B=0.01             @B=0.10            @B=0.30   
                         : -------------------------------------------------------------------------------------------------------------------
                         : dataset              BDT            : 0.537 (0.665)       0.646 (0.752)      0.822 (0.871)
                         : -------------------------------------------------------------------------------------------------------------------
                         : 
<HEADER> Dataset:dataset          : Created tree 'TestTree' with 1000 events
                         : 
<HEADER> Dataset:dataset          : Created tree 'TrainTree' with 1000 events
                         : 
<HEADER> Factory                  : Thank you for using TMVA!
                         : For citation information, please visit: http://tmva.sf.net/citeTMVA.html