Execution Time8.74s

Test: tutorial-tmva-envelope-classification (Passed)
Build: master-x86_64-centos7-clang100-opt-no-rt-cxxmodules (olsnba08.cern.ch) on 2020-01-24 14:06:15

Test Timing: Passed
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Test output
Processing /data/sftnight/workspace/root-benchmark-no-rt-cxxmodules/BUILDTYPE/Release/COMPILER/clang_gcc62/LABEL/performance-sandy-cc7/root/tutorials/tmva/envelope/classification.C...
Info in <TFile::OpenFromCache>: using local cache copy of http://root.cern.ch/files/tmva_class_example.root [./files/tmva_class_example.root]
create data set info dataset
DataSetInfo              : [dataset] : Added class "Signal"
                         : Add Tree TreeS of type Signal with 6000 events
DataSetInfo              : [dataset] : Added class "Background"
                         : Add Tree TreeB of type Background with 6000 events
<HEADER>                          : Loading booked method: BDT BDTG
                         : 
                         : 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 Background
                         : Dataset[dataset] :  create input formulas for tree TreeB
<HEADER> DataSetFactory           : [dataset] : Number of events in input trees
                         : 
                         : 
                         : Number of training and testing events
                         : ---------------------------------------------------------------------------
                         : Signal     -- training events            : 1000
                         : Signal     -- testing events             : 5000
                         : Signal     -- training and testing events: 6000
                         : Background -- training events            : 1000
                         : Background -- testing events             : 5000
                         : Background -- training and testing events: 6000
                         : 
<HEADER> DataSetInfo              : Correlation matrix (Signal):
                         : ----------------------------------------
                         :           myvar1  myvar2    var3    var4
                         :  myvar1:  +1.000  -0.039  +0.778  +0.931
                         :  myvar2:  -0.039  +1.000  -0.111  +0.033
                         :    var3:  +0.778  -0.111  +1.000  +0.860
                         :    var4:  +0.931  +0.033  +0.860  +1.000
                         : ----------------------------------------
<HEADER> DataSetInfo              : Correlation matrix (Background):
                         : ----------------------------------------
                         :           myvar1  myvar2    var3    var4
                         :  myvar1:  +1.000  +0.033  +0.784  +0.931
                         :  myvar2:  +0.033  +1.000  -0.014  +0.112
                         :    var3:  +0.784  -0.014  +1.000  +0.863
                         :    var4:  +0.931  +0.112  +0.863  +1.000
                         : ----------------------------------------
<HEADER> DataSetFactory           : [dataset] :  
                         : 
<HEADER>                          : Loading booked method: SVM SVM
                         : 
<HEADER> SVM                      : [dataset] : Create Transformation "Norm" with events from all classes.
                         : 
<HEADER>                          : Transformation, Variable selection : 
                         : Input : variable 'myvar1' <---> Output : variable 'myvar1'
                         : Input : variable 'myvar2' <---> Output : variable 'myvar2'
                         : Input : variable 'var3' <---> Output : variable 'var3'
                         : Input : variable 'var4' <---> Output : variable 'var4'
<HEADER>                          : Loading booked method: BDT BDTB
                         : 
<HEADER>                          : Loading booked method: Cuts Cuts
                         : 
                         : Use optimization method: "Monte Carlo"
                         : Use efficiency computation method: "Event Selection"
                         : Use "FSmart" cuts for variable: 'myvar1'
                         : Use "FSmart" cuts for variable: 'myvar2'
                         : Use "FSmart" cuts for variable: 'var3'
                         : Use "FSmart" cuts for variable: 'var4'
                         : --------------------------------------------------- :
                         : DataSet              MVA                            :
                         : Name:                Method/Title:    ROC-integ     :
                         : --------------------------------------------------- :
                         : dataset              SVM/SVM          0.911         :
                         : dataset              Cuts/Cuts        0.792         :
                         : dataset              BDT/BDTG         0.885         :
                         : dataset              BDT/BDTB         0.854         :
                         : --------------------------------------------------- :
                         : -----------------------------------------------------
<HEADER>                          : Evaluation done.
                         : 
                         : Jobs = 4 Real Time = 7.406342 
                         : -----------------------------------------------------
                         : Evaluation done.