TMVA RMVA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://oproject.org/tiki-download_file.php?fileId=6&display\">\n",
    "<img src=\"http://gfif.udea.edu.co/root/tmva/img/tmva_logo.gif\">\n",
    "## Required header files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#include <cstdlib>\n",
    "#include <iostream>\n",
    "#include <map>\n",
    "#include <string>\n",
    "\n",
    "#include \"TChain.h\"\n",
    "#include \"TFile.h\"\n",
    "#include \"TTree.h\"\n",
    "#include \"TString.h\"\n",
    "#include \"TObjString.h\"\n",
    "#include \"TSystem.h\"\n",
    "#include \"TROOT.h\"\n",
    "\n",
    "\n",
    "#include \"TMVA/Factory.h\"\n",
    "#include \"TMVA/Tools.h\"\n",
    "#include \"TMVA/DataLoader.h\"\n",
    "#include \"TRInterface.h\"\n",
    "#include \"TMVA/MethodC50.h\"\n",
    "#include \"TMVA/MethodRSNNS.h\"\n",
    "#include \"TMVA/MethodRSVM.h\"\n",
    "#include \"TMVA/MethodRXGB.h\"\n",
    "\n",
    "#include<TMVA/efficiencies.h>\n",
    "#include \"TMVA/tmvaglob.h\"\n",
    "#include \"TH2F.h\"\n",
    "#include \"TFile.h\"\n",
    "#include \"TIterator.h\"\n",
    "#include \"TKey.h\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ROOT-R Initialization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ROOT::R::TRInterface::Instance();\n",
    "TMVA::Tools::Instance();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Declare Factory and DataLoader\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Factory                  : You are running ROOT Version: 6.05/03, Sep 14, 2015\n",
      "--- Factory                  : \n",
      "--- Factory                  : _/_/_/_/_/ _|      _|  _|      _|    _|_|   \n",
      "--- Factory                  :    _/      _|_|  _|_|  _|      _|  _|    _| \n",
      "--- Factory                  :   _/       _|  _|  _|  _|      _|  _|_|_|_| \n",
      "--- Factory                  :  _/        _|      _|    _|  _|    _|    _| \n",
      "--- Factory                  : _/         _|      _|      _|      _|    _| \n",
      "--- Factory                  : \n",
      "--- Factory                  : ___________TMVA Version 5.0.0 alpha, Oct 30, 2015\n",
      "--- Factory                  : \n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "TString outfileName( \"RMVAOutput.root\" );\n",
    "TFile* outputFile = TFile::Open( outfileName, \"RECREATE\" );\n",
    "\n",
    "TMVA::Factory *factory = new TMVA::Factory( \"TMVAClassification\", outputFile,\n",
    "                                               \"!V:ROC:Correlations:!Silent:Color:!DrawProgressBar:AnalysisType=Classification\" );\n",
    "\n",
    "TMVA::DataLoader *loader=new TMVA::DataLoader(\"mymc-dataset\");\n",
    "\n",
    "TFile *input = TFile::Open( \"MyMCdataset.root\");\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding variables to DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "loader->AddVariable( \"var0\", 'F' );\n",
    "loader->AddVariable( \"var1\", 'F' );\n",
    "loader->AddVariable( \"var2\", 'F' );\n",
    "loader->AddVariable( \"var3 := var0-var1\", 'F' );\n",
    "loader->AddVariable( \"var4 := var0*var2\", 'F' );\n",
    "loader->AddVariable( \"var5 := var1+var2\", 'F' );   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up dataset from Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- TMVAClassification       : Using input file: MyMCdataset.root\n",
      "--- DataSetInfo              : Dataset[mymc-dataset] : Added class \"Signal\"\t with internal class number 0\n",
      "--- mymc-dataset             : Add Tree MyMCSig of type Signal with 5449 events\n",
      "--- DataSetInfo              : Dataset[mymc-dataset] : Added class \"Background\"\t with internal class number 1\n",
      "--- mymc-dataset             : Add Tree MyMCBkg of type Background with 5449 events\n",
      "--- mymc-dataset             : Preparing trees for training and testing...\n"
     ]
    }
   ],
   "source": [
    "std::cout << \"--- TMVAClassification       : Using input file: \" << input->GetName() << std::endl;\n",
    "   \n",
    "// --- Register the training and test trees\n",
    "TTree *tsignal     = (TTree*)input->Get(\"MyMCSig\");\n",
    "TTree *tbackground = (TTree*)input->Get(\"MyMCBkg\");\n",
    "   \n",
    "Double_t signalWeight     = 1.0;\n",
    "Double_t backgroundWeight = 1.0;\n",
    "   \n",
    "loader->AddSignalTree    ( tsignal,     signalWeight     );\n",
    "loader->AddBackgroundTree( tbackground, backgroundWeight );   \n",
    "    \n",
    "TCut mycuts = \"\"; \n",
    "TCut mycutb = \"\"; \n",
    "\n",
    "loader->fSignalWeight=signalWeight;\n",
    "loader->fBackgroundWeight=backgroundWeight;\n",
    "loader->fTreeS=tsignal;\n",
    "loader->fTreeB=tbackground;\n",
    "loader->PrepareTrainingAndTestTree( mycuts, mycutb,\n",
    "                                        \"nTrain_Signal=3000:nTrain_Background=3000:nTest_Signal=1449:nTest_Background=1449:SplitMode=Random:NormMode=NumEvents:!V\" );\n",
    "                                        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Booking methods\n",
    "Available Booking methods with options for RMVA are\n",
    "\n",
    "C50 Boosted Decision Trees http://oproject.org/tiki-index.php?page=RMVA#C50Booking\n",
    "\n",
    "RMLP Neural Networks http://oproject.org/tiki-index.php?page=RMVA#RSNNSMLP \n",
    "\n",
    "RSVM Support Vector Machines http://oproject.org/tiki-index.php?page=RMVA#E1071RSVM\n",
    "\n",
    "Extreme Gradient Boosted (RXGB) Decision Trees http://oproject.org/tiki-index.php?page=RMVA#RXGBBooking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Factory                  : Booking method: \u001b[1mC50\u001b[0m DataSet Name: \u001b[1mmymc-dataset\u001b[0m\n",
      "--- DataSetFactory           : Dataset[mymc-dataset] : Splitmode is: \"RANDOM\" the mixmode is: \"SAMEASSPLITMODE\"\n",
      "--- DataSetFactory           : Dataset[mymc-dataset] : Create training and testing trees -- looping over class \"Signal\" ...\n",
      "--- DataSetFactory           : Dataset[mymc-dataset] : Weight expression for class 'Signal': \"\"\n",
      "--- DataSetFactory           : Dataset[mymc-dataset] : Create training and testing trees -- looping over class \"Background\" ...\n",
      "--- DataSetFactory           : Dataset[mymc-dataset] : Weight expression for class 'Background': \"\"\n",
      "--- DataSetFactory           : Dataset[mymc-dataset] : Number of events in input trees (after possible flattening of arrays):\n",
      "--- DataSetFactory           : Dataset[mymc-dataset] :     Signal          -- number of events       : 5449   / sum of weights: 5449 \n",
      "--- DataSetFactory           : Dataset[mymc-dataset] :     Background      -- number of events       : 5449   / sum of weights: 5449 \n",
      "--- DataSetFactory           : Dataset[mymc-dataset] :     Signal     tree -- total number of entries: 5449 \n",
      "--- DataSetFactory           : Dataset[mymc-dataset] :     Background tree -- total number of entries: 5449 \n",
      "--- DataSetFactory           : Dataset[mymc-dataset] : Preselection: (will NOT affect number of requested training and testing events)\n",
      "--- DataSetFactory           : Dataset[mymc-dataset] :     No preselection cuts applied on event classes\n",
      "--- DataSetFactory           : Dataset[mymc-dataset] : Weight renormalisation mode: \"NumEvents\": renormalises all event classes \n",
      "--- DataSetFactory           : Dataset[mymc-dataset] :  such that the effective (weighted) number of events in each class equals the respective \n",
      "--- DataSetFactory           : Dataset[mymc-dataset] :  number of events (entries) that you demanded in PrepareTrainingAndTestTree(\"\",\"nTrain_Signal=.. )\n",
      "--- DataSetFactory           : Dataset[mymc-dataset] :  ... i.e. such that Sum[i=1..N_j]{w_i} = N_j, j=0,1,2...\n",
      "--- DataSetFactory           : Dataset[mymc-dataset] :  ... (note that N_j is the sum of TRAINING events (nTrain_j...with j=Signal,Background..\n",
      "--- DataSetFactory           : Dataset[mymc-dataset] :  ..... Testing events are not renormalised nor included in the renormalisation factor! )\n",
      "--- DataSetFactory           : Dataset[mymc-dataset] : --> Rescale Signal     event weights by factor: 1\n",
      "--- DataSetFactory           : Dataset[mymc-dataset] : --> Rescale Background event weights by factor: 1\n",
      "--- DataSetFactory           : Dataset[mymc-dataset] : Number of training and testing events after rescaling:\n",
      "--- DataSetFactory           : Dataset[mymc-dataset] : ---------------------------------------------------------------------------\n",
      "--- DataSetFactory           : Dataset[mymc-dataset] : Signal     -- training events            : 3000 (sum of weights: 3000) - requested were 3000 events\n",
      "--- DataSetFactory           : Dataset[mymc-dataset] : Signal     -- testing events             : 1449 (sum of weights: 1449) - requested were 1449 events\n",
      "--- DataSetFactory           : Dataset[mymc-dataset] : Signal     -- training and testing events: 4449 (sum of weights: 4449)\n",
      "--- DataSetFactory           : Dataset[mymc-dataset] : Background -- training events            : 3000 (sum of weights: 3000) - requested were 3000 events\n",
      "--- DataSetFactory           : Dataset[mymc-dataset] : Background -- testing events             : 1449 (sum of weights: 1449) - requested were 1449 events\n",
      "--- DataSetFactory           : Dataset[mymc-dataset] : Background -- training and testing events: 4449 (sum of weights: 4449)\n",
      "--- DataSetFactory           : Dataset[mymc-dataset] : Create internal training tree\n",
      "--- DataSetFactory           : Dataset[mymc-dataset] : Create internal testing tree\n",
      "--- DataSetInfo              : Dataset[mymc-dataset] : Correlation matrix (Signal):\n",
      "--- DataSetInfo              : ----------------------------------------------------------------\n",
      "--- DataSetInfo              :               var0    var1    var2 var0-var1 var0*var2 var1+var2\n",
      "--- DataSetInfo              :      var0:  +1.000  -0.008  +0.012    +0.857    +0.922    +0.001\n",
      "--- DataSetInfo              :      var1:  -0.008  +1.000  +0.028    -0.521    +0.003    +0.810\n",
      "--- DataSetInfo              :      var2:  +0.012  +0.028  +1.000    -0.004    +0.331    +0.609\n",
      "--- DataSetInfo              : var0-var1:  +0.857  -0.521  -0.004    +1.000    +0.785    -0.416\n",
      "--- DataSetInfo              : var0*var2:  +0.922  +0.003  +0.331    +0.785    +1.000    +0.197\n",
      "--- DataSetInfo              : var1+var2:  +0.001  +0.810  +0.609    -0.416    +0.197    +1.000\n",
      "--- DataSetInfo              : ----------------------------------------------------------------\n",
      "--- DataSetInfo              : Dataset[mymc-dataset] : Correlation matrix (Background):\n",
      "--- DataSetInfo              : ----------------------------------------------------------------\n",
      "--- DataSetInfo              :               var0    var1    var2 var0-var1 var0*var2 var1+var2\n",
      "--- DataSetInfo              :      var0:  +1.000  -0.008  -0.004    +0.652    +0.664    -0.009\n",
      "--- DataSetInfo              :      var1:  -0.008  +1.000  +0.031    -0.764    +0.018    +0.746\n",
      "--- DataSetInfo              :      var2:  -0.004  +0.031  +1.000    -0.026    +0.692    +0.688\n",
      "--- DataSetInfo              : var0-var1:  +0.652  -0.764  -0.026    +1.000    +0.415    -0.572\n",
      "--- DataSetInfo              : var0*var2:  +0.664  +0.018  +0.692    +0.415    +1.000    +0.474\n",
      "--- DataSetInfo              : var1+var2:  -0.009  +0.746  +0.688    -0.572    +0.474    +1.000\n",
      "--- DataSetInfo              : ----------------------------------------------------------------\n",
      "--- DataSetFactory           : Dataset[mymc-dataset] :  \n",
      "--- Factory                  : Booking method: \u001b[1mRMLP\u001b[0m DataSet Name: \u001b[1mmymc-dataset\u001b[0m\n",
      "--- RMLP                     : Dataset[mymc-dataset] : Create Transformation \"N\" with events from all classes.\n",
      "--- Norm                     : Transformation, Variable selection : \n",
      "--- Norm                     : Input : variable 'var0' (index=0).   <---> Output : variable 'var0' (index=0).\n",
      "--- Norm                     : Input : variable 'var1' (index=1).   <---> Output : variable 'var1' (index=1).\n",
      "--- Norm                     : Input : variable 'var2' (index=2).   <---> Output : variable 'var2' (index=2).\n",
      "--- Norm                     : Input : variable 'var3' (index=3).   <---> Output : variable 'var3' (index=3).\n",
      "--- Norm                     : Input : variable 'var4' (index=4).   <---> Output : variable 'var4' (index=4).\n",
      "--- Norm                     : Input : variable 'var5' (index=5).   <---> Output : variable 'var5' (index=5).\n",
      "--- Factory                  : Booking method: \u001b[1mRXGB\u001b[0m DataSet Name: \u001b[1mmymc-dataset\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "//C50 Boosted Decision Trees (BDTs)\n",
    "factory->BookMethod(loader, TMVA::Types::kC50, \"C50\",\"!H:NTrials=5:Rules=kTRUE:ControlSubSet=kFALSE:ControlBands=10:ControlWinnow=kFALSE:ControlNoGlobalPruning=kTRUE:ControlCF=0.25:ControlMinCases=2:ControlFuzzyThreshold=kTRUE:ControlSample=0:ControlEarlyStopping=kTRUE:!V\" );\n",
    "   \n",
    "//Neural Networks using RSNNS package\n",
    "//factory->BookMethod(loader, TMVA::Types::kRSNNS, \"RMLP\",\"!H:VarTransform=N:Size=c(5):Maxit=10:InitFunc=Randomize_Weights:LearnFunc=Std_Backpropagation:LearnFuncParams=c(0.2,0):!V\" );\n",
    "   \n",
    "//Support Vector Machines (SVM) using e1071\n",
    "//factory->BookMethod(loader, TMVA::Types::kRSVM, \"RSVM\",\"!H:Kernel=linear:Type=C-classification:VarTransform=Norm:Probability=kTRUE:Tolerance=0.001:!V\" );\n",
    "\n",
    "//eXtreme Gradient Boosted XGB Decision Trees\n",
    "factory->BookMethod(loader, TMVA::Types::kRXGB, \"RXGB\",\"!V:NRounds=50:MaxDepth=2:Eta=1\" );\n",
    "\n",
    "//TMVA BDTs\n",
    "///factory->BookMethod(loader,TMVA::Types::kBDT, \"BDT\",\"!V:NTrees=10:MinNodeSize=2.5%:MaxDepth=2:BoostType=AdaBoost:AdaBoostBeta=0.5:UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20\" );\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Factory                  :  \n",
      "--- Factory                  : Train all methods for Classification ...\n",
      "--- Factory                  : \n",
      "--- Factory                  : current transformation string: 'I'\n",
      "--- Factory                  : Dataset[mymc-dataset] : Create Transformation \"I\" with events from all classes.\n",
      "--- Id                       : Transformation, Variable selection : \n",
      "--- Id                       : Input : variable 'var0' (index=0).   <---> Output : variable 'var0' (index=0).\n",
      "--- Id                       : Input : variable 'var1' (index=1).   <---> Output : variable 'var1' (index=1).\n",
      "--- Id                       : Input : variable 'var2' (index=2).   <---> Output : variable 'var2' (index=2).\n",
      "--- Id                       : Input : variable 'var3' (index=3).   <---> Output : variable 'var3' (index=3).\n",
      "--- Id                       : Input : variable 'var4' (index=4).   <---> Output : variable 'var4' (index=4).\n",
      "--- Id                       : Input : variable 'var5' (index=5).   <---> Output : variable 'var5' (index=5).\n",
      "--- Id                       : Preparing the Identity transformation...\n",
      "--- TFHandler_Factory        : -----------------------------------------------------------\n",
      "--- TFHandler_Factory        : Variable        Mean        RMS   [        Min        Max ]\n",
      "--- TFHandler_Factory        : -----------------------------------------------------------\n",
      "--- TFHandler_Factory        :     var0:     3.0511     1.6889   [   0.061879     9.9950 ]\n",
      "--- TFHandler_Factory        :     var1:     1.6099     1.4314   [ 9.9424e-05     4.9956 ]\n",
      "--- TFHandler_Factory        :     var2:     3.5015     1.2158   [ 0.00088645     4.9993 ]\n",
      "--- TFHandler_Factory        :     var3:     1.4412     2.2459   [    -3.6483     9.9615 ]\n",
      "--- TFHandler_Factory        :     var4:     10.770     7.6139   [ 0.00088366     48.019 ]\n",
      "--- TFHandler_Factory        :     var5:     5.1113     1.8033   [    0.17973     9.9030 ]\n",
      "--- TFHandler_Factory        : -----------------------------------------------------------\n",
      "--- TFHandler_Factory        : Plot event variables for Id\n",
      "--- TFHandler_Factory        : Create scatter and profile plots in target-file directory: \n",
      "--- TFHandler_Factory        : RMVAOutput.root:/mymc-dataset/InputVariables_Id/CorrelationPlots\n",
      "--- TFHandler_Factory        :  \n",
      "--- TFHandler_Factory        : Ranking input variables (method unspecific)...\n",
      "--- IdTransformation         : Ranking result (top variable is best ranked)\n",
      "--- IdTransformation         : -----------------------------\n",
      "--- IdTransformation         : Rank : Variable  : Separation\n",
      "--- IdTransformation         : -----------------------------\n",
      "--- IdTransformation         :    1 : var3      : 2.799e-01\n",
      "--- IdTransformation         :    2 : var1      : 1.387e-01\n",
      "--- IdTransformation         :    3 : var2      : 1.296e-01\n",
      "--- IdTransformation         :    4 : var0      : 8.644e-02\n",
      "--- IdTransformation         :    5 : var4      : 6.156e-02\n",
      "--- IdTransformation         :    6 : var5      : 6.009e-02\n",
      "--- IdTransformation         : -----------------------------\n",
      "--- Factory                  : Train method: C50 for Classification\n",
      "--- C50                      : Dataset[mymc-dataset] : Begin training\n",
      "--- C50                      : \n",
      "--- C50                      : \u001b[1m--- Saving State File In:\u001b[0mweights/C50Model.RData\n",
      "--- C50                      : \n",
      "--- C50                      : Dataset[mymc-dataset] : End of training                                              \n",
      "--- C50                      : Dataset[mymc-dataset] : Elapsed time for training with 6000 events: \u001b[1;31m1.19 sec\u001b[0m         \n",
      "--- C50                      : Dataset[mymc-dataset] : Create MVA output for Dataset[mymc-dataset] : classification on training sample\n",
      "--- C50                      : Dataset[mymc-dataset] : Evaluation of C50 on training sample (6000 events)\n",
      "--- C50                      : Dataset[mymc-dataset] : Elapsed time for evaluation of 6000 events: \u001b[1;31m32.6 sec\u001b[0m       \n",
      "--- C50                      : Dataset[mymc-dataset] : Creating weight file in xml format: \u001b[0;36mweights/TMVAClassification_C50.weights.xml\u001b[0m\n",
      "--- Factory                  : Training finished\n",
      "--- Factory                  : Train method: RMLP for Classification\n",
      "--- Norm                     : Preparing the transformation.\n",
      "--- TFHandler_RMLP           : -----------------------------------------------------------\n",
      "--- TFHandler_RMLP           : Variable        Mean        RMS   [        Min        Max ]\n",
      "--- TFHandler_RMLP           : -----------------------------------------------------------\n",
      "--- TFHandler_RMLP           :     var0:   -0.39047    0.34232   [    -1.0000     1.0000 ]\n",
      "--- TFHandler_RMLP           :     var1:   -0.37756    0.56933   [    -1.0000     1.0000 ]\n",
      "--- TFHandler_RMLP           :     var2:    0.40316    0.47901   [    -1.0000     1.0000 ]\n",
      "--- TFHandler_RMLP           :     var3:   -0.20970    0.32158   [    -1.0000     1.0000 ]\n",
      "--- TFHandler_RMLP           :     var4:   -0.55506    0.31225   [    -1.0000     1.0000 ]\n",
      "--- TFHandler_RMLP           :     var5:  0.0035159    0.36382   [    -1.0000     1.0000 ]\n",
      "--- TFHandler_RMLP           : -----------------------------------------------------------\n",
      "--- RMLP                     : Dataset[mymc-dataset] : Begin training\n",
      "--- RMLP                     : \n",
      "--- RMLP                     : \u001b[1m--- Saving State File In:\u001b[0mweights/RMLPModel.RData\n",
      "--- RMLP                     : \n",
      "--- RMLP                     : Dataset[mymc-dataset] : End of training                                              \n",
      "--- RMLP                     : Dataset[mymc-dataset] : Elapsed time for training with 6000 events: \u001b[1;31m8.9 sec\u001b[0m         \n",
      "--- RMLP                     : Dataset[mymc-dataset] : Create MVA output for Dataset[mymc-dataset] : classification on training sample\n",
      "--- RMLP                     : Dataset[mymc-dataset] : Evaluation of RMLP on training sample (6000 events)\n",
      "--- RMLP                     : Dataset[mymc-dataset] : Elapsed time for evaluation of 6000 events: \u001b[1;31m116 sec\u001b[0m       \n",
      "--- RMLP                     : Dataset[mymc-dataset] : Creating weight file in xml format: \u001b[0;36mweights/TMVAClassification_RMLP.weights.xml\u001b[0m\n",
      "--- RMLP                     : Dataset[mymc-dataset] : Creating standalone response class: \u001b[0;36mweights/TMVAClassification_RMLP.class.C\u001b[0m\n",
      "--- Factory                  : Training finished\n",
      "--- Factory                  : Train method: RXGB for Classification\n",
      "--- RXGB                     : Dataset[mymc-dataset] : Begin training\n",
      "[0]\ttrain-rmse:0.443114\n",
      "[1]\ttrain-rmse:0.400006\n",
      "[2]\ttrain-rmse:0.389818\n",
      "[3]\ttrain-rmse:0.381229\n",
      "[4]\ttrain-rmse:0.376760\n",
      "[5]\ttrain-rmse:0.373655\n",
      "[6]\ttrain-rmse:0.371800\n",
      "[7]\ttrain-rmse:0.370711\n",
      "[8]\ttrain-rmse:0.370009\n",
      "[9]\ttrain-rmse:0.369051\n",
      "[10]\ttrain-rmse:0.367911\n",
      "[11]\ttrain-rmse:0.367475\n",
      "[12]\ttrain-rmse:0.367146\n",
      "[13]\ttrain-rmse:0.366493\n",
      "[14]\ttrain-rmse:0.365990\n",
      "[15]\ttrain-rmse:0.365687\n",
      "[16]\ttrain-rmse:0.365237\n",
      "[17]\ttrain-rmse:0.364522\n",
      "[18]\ttrain-rmse:0.363864\n",
      "[19]\ttrain-rmse:0.363257\n",
      "[20]\ttrain-rmse:0.362710\n",
      "[21]\ttrain-rmse:0.362192\n",
      "[22]\ttrain-rmse:0.361724\n",
      "[23]\ttrain-rmse:0.361275\n",
      "[24]\ttrain-rmse:0.360775\n",
      "[25]\ttrain-rmse:0.360477\n",
      "[26]\ttrain-rmse:0.360034\n",
      "[27]\ttrain-rmse:0.359801\n",
      "[28]\ttrain-rmse:0.359469\n",
      "[29]\ttrain-rmse:0.359101\n",
      "[30]\ttrain-rmse:0.358483\n",
      "[31]\ttrain-rmse:0.358055\n",
      "[32]\ttrain-rmse:0.357947\n",
      "[33]\ttrain-rmse:0.357393\n",
      "[34]\ttrain-rmse:0.356931\n",
      "[35]\ttrain-rmse:0.356706\n",
      "[36]\ttrain-rmse:0.356244\n",
      "[37]\ttrain-rmse:0.355817\n",
      "[38]\ttrain-rmse:0.355308\n",
      "[39]\ttrain-rmse:0.354779\n",
      "[40]\ttrain-rmse:0.354514\n",
      "[41]\ttrain-rmse:0.354336\n",
      "[42]\ttrain-rmse:0.353752\n",
      "[43]\ttrain-rmse:0.353103\n",
      "[44]\ttrain-rmse:0.352599\n",
      "[45]\ttrain-rmse:0.352415\n",
      "[46]\ttrain-rmse:0.352185\n",
      "[47]\ttrain-rmse:0.351728\n",
      "[48]\ttrain-rmse:0.351179\n",
      "[49]\ttrain-rmse:0.350553\n",
      "--- RXGB                     : \n",
      "--- RXGB                     : \u001b[1m--- Saving State File In:\u001b[0mweights/RXGBModel.RData\n",
      "--- RXGB                     : \n",
      "--- RXGB                     : Dataset[mymc-dataset] : End of training                                              \n",
      "--- RXGB                     : Dataset[mymc-dataset] : Elapsed time for training with 6000 events: \u001b[1;31m0.144 sec\u001b[0m         \n",
      "--- RXGB                     : Dataset[mymc-dataset] : Create MVA output for Dataset[mymc-dataset] : classification on training sample\n",
      "--- RXGB                     : Dataset[mymc-dataset] : Evaluation of RXGB on training sample (6000 events)\n",
      "--- RXGB                     : Dataset[mymc-dataset] : Elapsed time for evaluation of 6000 events: \u001b[1;31m6.69 sec\u001b[0m       \n",
      "--- RXGB                     : Dataset[mymc-dataset] : Creating weight file in xml format: \u001b[0;36mweights/TMVAClassification_RXGB.weights.xml\u001b[0m\n",
      "--- Factory                  : Training finished\n",
      "--- Factory                  : \n",
      "--- Factory                  : Ranking input variables (method specific)...\n",
      "--- Factory                  : No variable ranking supplied by classifier: C50\n",
      "--- Factory                  : No variable ranking supplied by classifier: RMLP\n",
      "--- Factory                  : No variable ranking supplied by classifier: RXGB\n",
      "--- Factory                  : \n",
      "--- Factory                  : === Destroy and recreate all methods via weight files for testing ===\n",
      "--- Factory                  : \n",
      "--- MethodBase               : Dataset[mymc-dataset] : Reading weight file: \u001b[0;36mweights/TMVAClassification_C50.weights.xml\u001b[0m\n",
      "--- C50                      : Dataset[mymc-dataset] : Read method \"C50\" of type \"C50\"\n",
      "--- C50                      : Dataset[mymc-dataset] : MVA method was trained with TMVA Version: 4.2.1\n",
      "--- C50                      : Dataset[mymc-dataset] : MVA method was trained with ROOT Version: 6.05/03\n",
      "--- MethodBase               : Dataset[mymc-dataset] : Reading weight file: \u001b[0;36mweights/TMVAClassification_RMLP.weights.xml\u001b[0m\n",
      "--- RMLP                     : Dataset[mymc-dataset] : Read method \"RMLP\" of type \"RSNNS\"\n",
      "--- RMLP                     : Dataset[mymc-dataset] : MVA method was trained with TMVA Version: 4.2.1\n",
      "--- RMLP                     : Dataset[mymc-dataset] : MVA method was trained with ROOT Version: 6.05/03\n",
      "--- MethodBase               : Dataset[mymc-dataset] : Reading weight file: \u001b[0;36mweights/TMVAClassification_RXGB.weights.xml\u001b[0m\n",
      "--- RXGB                     : Dataset[mymc-dataset] : Read method \"RXGB\" of type \"RXGB\"\n",
      "--- RXGB                     : Dataset[mymc-dataset] : MVA method was trained with TMVA Version: 4.2.1\n",
      "--- RXGB                     : Dataset[mymc-dataset] : MVA method was trained with ROOT Version: 6.05/03\n"
     ]
    }
   ],
   "source": [
    "factory->TrainAllMethods();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Factory                  : Test all methods...\n",
      "--- Factory                  : Test method: C50 for Classification performance\n",
      "--- C50                      : Dataset[mymc-dataset] : Evaluation of C50 on testing sample (2898 events)\n",
      "--- C50                      : \n",
      "--- C50                      : \u001b[1m--- Loading State File From:\u001b[0mweights/C50Model.RData\n",
      "--- C50                      : \n",
      "--- C50                      : Dataset[mymc-dataset] : Elapsed time for evaluation of 2898 events: \u001b[1;31m16.4 sec\u001b[0m       \n",
      "--- Factory                  : Test method: RMLP for Classification performance\n",
      "--- RMLP                     : Dataset[mymc-dataset] : Evaluation of RMLP on testing sample (2898 events)\n",
      "--- RMLP                     : \n",
      "--- RMLP                     : \u001b[1m--- Loading State File From:\u001b[0mweights/RMLPModel.RData\n",
      "--- RMLP                     : \n",
      "--- RMLP                     : Dataset[mymc-dataset] : Elapsed time for evaluation of 2898 events: \u001b[1;31m58.2 sec\u001b[0m       \n",
      "--- Factory                  : Test method: RXGB for Classification performance\n",
      "--- RXGB                     : Dataset[mymc-dataset] : Evaluation of RXGB on testing sample (2898 events)\n",
      "--- RXGB                     : \n",
      "--- RXGB                     : \u001b[1m--- Loading State File From:\u001b[0mweights/RXGBModel.RData\n",
      "--- RXGB                     : \n",
      "--- RXGB                     : Dataset[mymc-dataset] : Elapsed time for evaluation of 2898 events: \u001b[1;31m3.23 sec\u001b[0m       \n",
      "--- Factory                  : Evaluate all methods...\n",
      "--- Factory                  : Evaluate classifier: C50\n",
      "--- C50                      : Testing Classification C50 METHOD  \n",
      "--- C50                      : Dataset[mymc-dataset] : Loop over test events and fill histograms with classifier response...\n",
      "--- Factory                  : Write evaluation histograms to file\n",
      "--- TFHandler_C50            : Plot event variables for C50\n",
      "--- TFHandler_C50            : -----------------------------------------------------------\n",
      "--- TFHandler_C50            : Variable        Mean        RMS   [        Min        Max ]\n",
      "--- TFHandler_C50            : -----------------------------------------------------------\n",
      "--- TFHandler_C50            :     var0:     3.0511     1.6889   [   0.061879     9.9950 ]\n",
      "--- TFHandler_C50            :     var1:     1.6099     1.4314   [ 9.9424e-05     4.9956 ]\n",
      "--- TFHandler_C50            :     var2:     3.5015     1.2158   [ 0.00088645     4.9993 ]\n",
      "--- TFHandler_C50            :     var3:     1.4412     2.2459   [    -3.6483     9.9615 ]\n",
      "--- TFHandler_C50            :     var4:     10.770     7.6139   [ 0.00088366     48.019 ]\n",
      "--- TFHandler_C50            :     var5:     5.1113     1.8033   [    0.17973     9.9030 ]\n",
      "--- TFHandler_C50            : -----------------------------------------------------------\n",
      "--- TFHandler_C50            : Create scatter and profile plots in target-file directory: \n",
      "--- TFHandler_C50            : RMVAOutput.root:/mymc-dataset/Method_C50/C50/CorrelationPlots\n",
      "--- Factory                  : Evaluate classifier: RMLP\n",
      "--- RMLP                     : Testing Classification RMLP METHOD  \n",
      "--- TFHandler_RMLP           : -----------------------------------------------------------\n",
      "--- TFHandler_RMLP           : Variable        Mean        RMS   [        Min        Max ]\n",
      "--- TFHandler_RMLP           : -----------------------------------------------------------\n",
      "--- TFHandler_RMLP           :     var0:   -0.39553    0.33943   [   -0.99628     1.0000 ]\n",
      "--- TFHandler_RMLP           :     var1:   -0.35571    0.57297   [    -1.0001    0.99955 ]\n",
      "--- TFHandler_RMLP           :     var2:    0.40013    0.48671   [    -1.0012    0.99971 ]\n",
      "--- TFHandler_RMLP           :     var3:   -0.22116    0.32283   [   -0.95276     1.0036 ]\n",
      "--- TFHandler_RMLP           :     var4:   -0.55720    0.31347   [    -1.0006    0.97637 ]\n",
      "--- TFHandler_RMLP           :     var5:   0.013099    0.36760   [   -0.99223    0.98990 ]\n",
      "--- TFHandler_RMLP           : -----------------------------------------------------------\n",
      "--- RMLP                     : Dataset[mymc-dataset] : Loop over test events and fill histograms with classifier response...\n",
      "--- Factory                  : Write evaluation histograms to file\n",
      "--- TFHandler_RMLP           : Plot event variables for RMLP\n",
      "--- TFHandler_RMLP           : -----------------------------------------------------------\n",
      "--- TFHandler_RMLP           : Variable        Mean        RMS   [        Min        Max ]\n",
      "--- TFHandler_RMLP           : -----------------------------------------------------------\n",
      "--- TFHandler_RMLP           :     var0:   -0.39553    0.33943   [   -0.99628     1.0000 ]\n",
      "--- TFHandler_RMLP           :     var1:   -0.35571    0.57297   [    -1.0001    0.99955 ]\n",
      "--- TFHandler_RMLP           :     var2:    0.40013    0.48671   [    -1.0012    0.99971 ]\n",
      "--- TFHandler_RMLP           :     var3:   -0.22116    0.32283   [   -0.95276     1.0036 ]\n",
      "--- TFHandler_RMLP           :     var4:   -0.55720    0.31347   [    -1.0006    0.97637 ]\n",
      "--- TFHandler_RMLP           :     var5:   0.013099    0.36760   [   -0.99223    0.98990 ]\n",
      "--- TFHandler_RMLP           : -----------------------------------------------------------\n",
      "--- TFHandler_RMLP           : Create scatter and profile plots in target-file directory: \n",
      "--- TFHandler_RMLP           : RMVAOutput.root:/mymc-dataset/Method_RSNNS/RMLP/CorrelationPlots\n",
      "--- Factory                  : Evaluate classifier: RXGB\n",
      "--- RXGB                     : Testing Classification RXGB METHOD  \n",
      "--- RXGB                     : Dataset[mymc-dataset] : Loop over test events and fill histograms with classifier response...\n",
      "--- Factory                  : Write evaluation histograms to file\n",
      "--- TFHandler_RXGB           : Plot event variables for RXGB\n",
      "--- TFHandler_RXGB           : -----------------------------------------------------------\n",
      "--- TFHandler_RXGB           : Variable        Mean        RMS   [        Min        Max ]\n",
      "--- TFHandler_RXGB           : -----------------------------------------------------------\n",
      "--- TFHandler_RXGB           :     var0:     3.0511     1.6889   [   0.061879     9.9950 ]\n",
      "--- TFHandler_RXGB           :     var1:     1.6099     1.4314   [ 9.9424e-05     4.9956 ]\n",
      "--- TFHandler_RXGB           :     var2:     3.5015     1.2158   [ 0.00088645     4.9993 ]\n",
      "--- TFHandler_RXGB           :     var3:     1.4412     2.2459   [    -3.6483     9.9615 ]\n",
      "--- TFHandler_RXGB           :     var4:     10.770     7.6139   [ 0.00088366     48.019 ]\n",
      "--- TFHandler_RXGB           :     var5:     5.1113     1.8033   [    0.17973     9.9030 ]\n",
      "--- TFHandler_RXGB           : -----------------------------------------------------------\n",
      "--- TFHandler_RXGB           : Create scatter and profile plots in target-file directory: \n",
      "--- TFHandler_RXGB           : RMVAOutput.root:/mymc-dataset/Method_RXGB/RXGB/CorrelationPlots\n",
      "--- Factory                  : \n",
      "--- Factory                  : Dataset[mymc-dataset] : Inter-MVA correlation matrix (signal):\n",
      "--- Factory                  : --------------------------------\n",
      "--- Factory                  :              C50    RMLP    RXGB\n",
      "--- Factory                  :     C50:  +1.000  +0.187  +0.746\n",
      "--- Factory                  :    RMLP:  +0.187  +1.000  +0.141\n",
      "--- Factory                  :    RXGB:  +0.746  +0.141  +1.000\n",
      "--- Factory                  : --------------------------------\n",
      "--- Factory                  : \n",
      "--- Factory                  : Dataset[mymc-dataset] : Inter-MVA correlation matrix (background):\n",
      "--- Factory                  : --------------------------------\n",
      "--- Factory                  :              C50    RMLP    RXGB\n",
      "--- Factory                  :     C50:  +1.000  +0.386  +0.828\n",
      "--- Factory                  :    RMLP:  +0.386  +1.000  +0.452\n",
      "--- Factory                  :    RXGB:  +0.828  +0.452  +1.000\n",
      "--- Factory                  : --------------------------------\n",
      "--- Factory                  : \n",
      "--- Factory                  : Dataset[mymc-dataset] : Correlations between input variables and MVA response (signal):\n",
      "--- Factory                  : --------------------------------\n",
      "--- Factory                  :              C50    RMLP    RXGB\n",
      "--- Factory                  :    var0:  +0.071  -0.096  +0.165\n",
      "--- Factory                  :    var1:  -0.192  -0.957  -0.172\n",
      "--- Factory                  :    var2:  +0.494  +0.056  +0.372\n",
      "--- Factory                  :    var3:  +0.162  +0.430  +0.230\n",
      "--- Factory                  :    var4:  +0.206  -0.064  +0.250\n",
      "--- Factory                  :    var5:  +0.131  -0.744  +0.077\n",
      "--- Factory                  : --------------------------------\n",
      "--- Factory                  : \n",
      "--- Factory                  : Dataset[mymc-dataset] : Correlations between input variables and MVA response (background):\n",
      "--- Factory                  : --------------------------------\n",
      "--- Factory                  :              C50    RMLP    RXGB\n",
      "--- Factory                  :    var0:  -0.146  +0.098  -0.114\n",
      "--- Factory                  :    var1:  -0.411  -0.932  -0.494\n",
      "--- Factory                  :    var2:  +0.358  +0.096  +0.253\n",
      "--- Factory                  :    var3:  +0.216  +0.766  +0.299\n",
      "--- Factory                  :    var4:  +0.145  +0.153  +0.100\n",
      "--- Factory                  :    var5:  -0.058  -0.607  -0.187\n",
      "--- Factory                  : --------------------------------\n",
      "--- Factory                  : \n",
      "--- Factory                  : Dataset[mymc-dataset] : The following \"overlap\" matrices contain the fraction of events for which \n",
      "--- Factory                  : Dataset[mymc-dataset] : the MVAs 'i' and 'j' have returned conform answers about \"signal-likeness\"\n",
      "--- Factory                  : Dataset[mymc-dataset] : An event is signal-like, if its MVA output exceeds the following value:\n",
      "--- Factory                  : --------------------\n",
      "--- Factory                  :  Method:  Cut value:\n",
      "--- Factory                  : --------------------\n",
      "--- Factory                  :     C50:     +0.605\n",
      "--- Factory                  :    RMLP:     +0.926\n",
      "--- Factory                  :    RXGB:     +0.542\n",
      "--- Factory                  : --------------------\n",
      "--- Factory                  : Dataset[mymc-dataset] : which correspond to the working point: eff(signal) = 1 - eff(background)\n",
      "--- Factory                  : \n",
      "--- Factory                  : Dataset[mymc-dataset] : Inter-MVA overlap matrix (signal):\n",
      "--- Factory                  : --------------------------------\n",
      "--- Factory                  :              C50    RMLP    RXGB\n",
      "--- Factory                  :     C50:  +1.000  +0.631  +0.881\n",
      "--- Factory                  :    RMLP:  +0.631  +1.000  +0.614\n",
      "--- Factory                  :    RXGB:  +0.881  +0.614  +1.000\n",
      "--- Factory                  : --------------------------------\n",
      "--- Factory                  : \n",
      "--- Factory                  : Dataset[mymc-dataset] : Inter-MVA overlap matrix (background):\n",
      "--- Factory                  : --------------------------------\n",
      "--- Factory                  :              C50    RMLP    RXGB\n",
      "--- Factory                  :     C50:  +1.000  +0.701  +0.921\n",
      "--- Factory                  :    RMLP:  +0.701  +1.000  +0.711\n",
      "--- Factory                  :    RXGB:  +0.921  +0.711  +1.000\n",
      "--- Factory                  : --------------------------------\n",
      "--- Factory                  : \n",
      "--- Factory                  : Evaluation results ranked by best signal efficiency and purity (area)\n",
      "--- Factory                  : -------------------------------------------------------------------------------------------------------------------\n",
      "--- Factory                  : DataSet              MVA              Signal efficiency at bkg eff.(error):                | Sepa-    Signifi- \n",
      "--- Factory                  : Name:                Method:          @B=0.01    @B=0.10    @B=0.30    ROC-integ    ROCCurve| ration:  cance:   \n",
      "--- Factory                  : -------------------------------------------------------------------------------------------------------------------\n",
      "--- Factory                  : mymc-dataset         RXGB           : 0.083(07)  0.593(12)  0.883(08)    0.879       0.879 | 0.455    1.244\n",
      "--- Factory                  : mymc-dataset         C50            : 0.000(00)  0.581(12)  0.906(07)    0.847       0.869 | 0.466    1.261\n",
      "--- Factory                  : mymc-dataset         RMLP           : 0.060(06)  0.309(12)  0.605(12)    0.691       0.674 | 0.117    0.409\n",
      "--- Factory                  : -------------------------------------------------------------------------------------------------------------------\n",
      "--- Factory                  : \n",
      "--- Factory                  : Testing efficiency compared to training efficiency (overtraining check)\n",
      "--- Factory                  : -------------------------------------------------------------------------------------------------------------------\n",
      "--- Factory                  : DataSet              MVA              Signal efficiency: from test sample (from training sample) \n",
      "--- Factory                  : Name:                Method:          @B=0.01             @B=0.10            @B=0.30   \n",
      "--- Factory                  : -------------------------------------------------------------------------------------------------------------------\n",
      "--- Factory                  : mymc-dataset         RXGB           : 0.083 (0.237)       0.593 (0.674)      0.883 (0.900)\n",
      "--- Factory                  : mymc-dataset         C50            : 0.000 (0.000)       0.581 (0.601)      0.906 (0.903)\n",
      "--- Factory                  : mymc-dataset         RMLP           : 0.060 (0.070)       0.309 (0.286)      0.605 (0.600)\n",
      "--- Factory                  : -------------------------------------------------------------------------------------------------------------------\n",
      "--- Factory                  : \n",
      "--- Dataset:mymc-dataset     : Dataset[mymc-dataset] : Created tree 'TestTree' with 2898 events\n",
      "--- Dataset:mymc-dataset     : Dataset[mymc-dataset] : Created tree 'TrainTree' with 6000 events\n",
      "--- Factory                  :   \n",
      "--- Factory                  : \u001b[1mThank you for using TMVA!\u001b[0m\n",
      "--- Factory                  : \u001b[1mFor citation information, please visit: http://tmva.sf.net/citeTMVA.html\u001b[0m\n",
      "==> TMVAClassification is done!\n"
     ]
    }
   ],
   "source": [
    "factory->TestAllMethods();\n",
    "\n",
    "factory->EvaluateAllMethods();    \n",
    "\n",
    "// Save the output\n",
    "outputFile->Close();\n",
    "\n",
    "//    std::cout << \"==> Wrote root file: \" << outputFile->GetName() << std::endl;\n",
    "std::cout << \"==> TMVAClassification is done!\" << std::endl;\n",
    "\n",
    "delete factory;\n",
    "delete loader;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TColor::TColor:0: RuntimeWarning: color 1367 already defined\n",
      "Info in <TCanvas::Print>: eps file plots/rejBvsS.eps has been created\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Found 3 classifier types\n",
      "--- Found 1 instance(s) of the method Method_C50\n",
      "--- Found 1 instance(s) of the method Method_RSNNS\n",
      "--- Found 1 instance(s) of the method Method_RXGB\n",
      "--- Found 3 classifier types\n",
      "--- Found 1 instance(s) of the method Method_C50\n",
      "--- Found 1 instance(s) of the method Method_RSNNS\n",
      "--- Found 1 instance(s) of the method Method_RXGB\n"
     ]
    },
    {
     "data": {
      "image/png": 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cMTyEYIhYO9B7mOx8QnQOV/qZy+uO0PATgCvQi1kEy4KBy5FkEIEsmqa5dDRt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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TFile *file=TFile::Open(\"RMVAOutput.root\");\n",
    "gDirectory->cd(\"mymc-dataset\");\n",
    "\n",
    "TMVA::plot_efficiencies(file,  2,gDirectory);\n",
    "c->Draw();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ROOT Prompt",
   "language": "c++",
   "name": "root"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}