TMVA_DataLoader.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://oproject.org/img/ROOT.png\" height=\"30%\" width=\"30%\">\n",
    "<img src=\"http://oproject.org/img/tmvalogo.png\" height=\"30%\" width=\"30%\">\n",
    "\n",
    "# DataLoader Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Declare Factory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Factory                  : You are running ROOT Version: 6.07/07, Apr 1, 2016\n",
      "--- Factory                  : \n",
      "--- Factory                  : _/_/_/_/_/ _|      _|  _|      _|    _|_|   \n",
      "--- Factory                  :    _/      _|_|  _|_|  _|      _|  _|    _| \n",
      "--- Factory                  :   _/       _|  _|  _|  _|      _|  _|_|_|_| \n",
      "--- Factory                  :  _/        _|      _|    _|  _|    _|    _| \n",
      "--- Factory                  : _/         _|      _|      _|      _|    _| \n",
      "--- Factory                  : \n",
      "--- Factory                  : ___________TMVA Version 4.2.1, Feb 5, 2015\n",
      "--- Factory                  : \n"
     ]
    }
   ],
   "source": [
    "TMVA::Tools::Instance();\n",
    "\n",
    "auto inputFile = TFile::Open(\"https://raw.githubusercontent.com/iml-wg/tmvatutorials/master/inputdata.root\");\n",
    "auto outputFile = TFile::Open(\"TMVAOutputCV.root\", \"RECREATE\");\n",
    "\n",
    "TMVA::Factory factory(\"TMVAClassification\", outputFile,\n",
    "                      \"!V:ROC:!Correlations:!Silent:Color:!DrawProgressBar:AnalysisType=Classification\" ); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Declare DataLoader(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "TMVA::DataLoader loader1(\"dataset1\");\n",
    "\n",
    "loader1.AddVariable(\"var1\");\n",
    "loader1.AddVariable(\"var2\");\n",
    "loader1.AddVariable(\"var3\");\n",
    "\n",
    "TMVA::DataLoader loader2(\"dataset2\"); \n",
    "\n",
    "loader2.AddVariable(\"var1\");\n",
    "loader2.AddVariable(\"var2\");\n",
    "loader2.AddVariable(\"var3\");\n",
    "loader2.AddVariable(\"var4\");\n",
    "loader2.AddVariable(\"var5 := var1-var3\");\n",
    "loader2.AddVariable(\"var6 := var1+var2\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup Dataset(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- DataSetInfo              : Dataset[dataset1] : Added class \"Signal\"\t with internal class number 0\n",
      "--- dataset1                 : Add Tree Sig of type Signal with 6000 events\n",
      "--- DataSetInfo              : Dataset[dataset1] : Added class \"Background\"\t with internal class number 1\n",
      "--- dataset1                 : Add Tree Bkg of type Background with 6000 events\n",
      "--- dataset1                 : Preparing trees for training and testing...\n",
      "--- DataSetInfo              : Dataset[dataset2] : Added class \"Signal\"\t with internal class number 0\n",
      "--- dataset2                 : Add Tree Sig of type Signal with 6000 events\n",
      "--- DataSetInfo              : Dataset[dataset2] : Added class \"Background\"\t with internal class number 1\n",
      "--- dataset2                 : Add Tree Bkg of type Background with 6000 events\n",
      "--- dataset2                 : Preparing trees for training and testing...\n"
     ]
    }
   ],
   "source": [
    "TTree *tsignal, *tbackground;\n",
    "inputFile->GetObject(\"Sig\", tsignal);\n",
    "inputFile->GetObject(\"Bkg\", tbackground);\n",
    "\n",
    "TCut mycuts, mycutb;\n",
    "\n",
    "loader1.AddSignalTree    (tsignal,     1.0);   //signal weight  = 1\n",
    "loader1.AddBackgroundTree(tbackground, 1.0);   //background weight = 1 \n",
    "loader1.PrepareTrainingAndTestTree(mycuts, mycutb,\n",
    "                                   \"nTrain_Signal=1000:nTrain_Background=1000:SplitMode=Random:NormMode=NumEvents:!V\" ); \n",
    "\n",
    "loader2.AddSignalTree    (tsignal,     1.0);   //signal weight  = 1\n",
    "loader2.AddBackgroundTree(tbackground, 1.0);   //background weight = 1   \n",
    "loader2.PrepareTrainingAndTestTree(mycuts, mycutb,\n",
    "                                   \"nTrain_Signal=1000:nTrain_Background=1000:SplitMode=Random:NormMode=NumEvents:!V\" );   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Booking Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Factory                  : Booking method: \u001b[1mBDT\u001b[0m DataSet Name: \u001b[1mdataset1\u001b[0m\n",
      "--- DataSetFactory           : Dataset[dataset1] : Splitmode is: \"RANDOM\" the mixmode is: \"SAMEASSPLITMODE\"\n",
      "--- DataSetFactory           : Dataset[dataset1] : Create training and testing trees -- looping over class \"Signal\" ...\n",
      "--- DataSetFactory           : Dataset[dataset1] : Weight expression for class 'Signal': \"\"\n",
      "--- DataSetFactory           : Dataset[dataset1] : Create training and testing trees -- looping over class \"Background\" ...\n",
      "--- DataSetFactory           : Dataset[dataset1] : Weight expression for class 'Background': \"\"\n",
      "--- DataSetFactory           : Dataset[dataset1] : Number of events in input trees (after possible flattening of arrays):\n",
      "--- DataSetFactory           : Dataset[dataset1] :     Signal          -- number of events       : 6000   / sum of weights: 6000 \n",
      "--- DataSetFactory           : Dataset[dataset1] :     Background      -- number of events       : 6000   / sum of weights: 6000 \n",
      "--- DataSetFactory           : Dataset[dataset1] :     Signal     tree -- total number of entries: 6000 \n",
      "--- DataSetFactory           : Dataset[dataset1] :     Background tree -- total number of entries: 6000 \n",
      "--- DataSetFactory           : Dataset[dataset1] : Preselection: (will NOT affect number of requested training and testing events)\n",
      "--- DataSetFactory           : Dataset[dataset1] :     No preselection cuts applied on event classes\n",
      "--- DataSetFactory           : Dataset[dataset1] : Weight renormalisation mode: \"NumEvents\": renormalises all event classes \n",
      "--- DataSetFactory           : Dataset[dataset1] :  such that the effective (weighted) number of events in each class equals the respective \n",
      "--- DataSetFactory           : Dataset[dataset1] :  number of events (entries) that you demanded in PrepareTrainingAndTestTree(\"\",\"nTrain_Signal=.. )\n",
      "--- DataSetFactory           : Dataset[dataset1] :  ... i.e. such that Sum[i=1..N_j]{w_i} = N_j, j=0,1,2...\n",
      "--- DataSetFactory           : Dataset[dataset1] :  ... (note that N_j is the sum of TRAINING events (nTrain_j...with j=Signal,Background..\n",
      "--- DataSetFactory           : Dataset[dataset1] :  ..... Testing events are not renormalised nor included in the renormalisation factor! )\n",
      "--- DataSetFactory           : Dataset[dataset1] : --> Rescale Signal     event weights by factor: 1\n",
      "--- DataSetFactory           : Dataset[dataset1] : --> Rescale Background event weights by factor: 1\n",
      "--- DataSetFactory           : Dataset[dataset1] : Number of training and testing events after rescaling:\n",
      "--- DataSetFactory           : Dataset[dataset1] : ---------------------------------------------------------------------------\n",
      "--- DataSetFactory           : Dataset[dataset1] : Signal     -- training events            : 1000 (sum of weights: 1000) - requested were 1000 events\n",
      "--- DataSetFactory           : Dataset[dataset1] : Signal     -- testing events             : 5000 (sum of weights: 5000) - requested were 0 events\n",
      "--- DataSetFactory           : Dataset[dataset1] : Signal     -- training and testing events: 6000 (sum of weights: 6000)\n",
      "--- DataSetFactory           : Dataset[dataset1] : Background -- training events            : 1000 (sum of weights: 1000) - requested were 1000 events\n",
      "--- DataSetFactory           : Dataset[dataset1] : Background -- testing events             : 5000 (sum of weights: 5000) - requested were 0 events\n",
      "--- DataSetFactory           : Dataset[dataset1] : Background -- training and testing events: 6000 (sum of weights: 6000)\n",
      "--- DataSetFactory           : Dataset[dataset1] : Create internal training tree\n",
      "--- DataSetFactory           : Dataset[dataset1] : Create internal testing tree\n",
      "--- DataSetInfo              : Dataset[dataset1] : Correlation matrix (Signal):\n",
      "--- DataSetInfo              : --------------------------------\n",
      "--- DataSetInfo              :             var1    var2    var3\n",
      "--- DataSetInfo              :    var1:  +1.000  +0.386  +0.597\n",
      "--- DataSetInfo              :    var2:  +0.386  +1.000  +0.696\n",
      "--- DataSetInfo              :    var3:  +0.597  +0.696  +1.000\n",
      "--- DataSetInfo              : --------------------------------\n",
      "--- DataSetInfo              : Dataset[dataset1] : Correlation matrix (Background):\n",
      "--- DataSetInfo              : --------------------------------\n",
      "--- DataSetInfo              :             var1    var2    var3\n",
      "--- DataSetInfo              :    var1:  +1.000  +0.856  +0.914\n",
      "--- DataSetInfo              :    var2:  +0.856  +1.000  +0.927\n",
      "--- DataSetInfo              :    var3:  +0.914  +0.927  +1.000\n",
      "--- DataSetInfo              : --------------------------------\n",
      "--- DataSetFactory           : Dataset[dataset1] :  \n",
      "--- Factory                  : Booking method: \u001b[1mMLP\u001b[0m DataSet Name: \u001b[1mdataset1\u001b[0m\n",
      "--- MLP                      : Dataset[dataset1] : Create Transformation \"N\" with events from all classes.\n",
      "--- Norm                     : Transformation, Variable selection : \n",
      "--- Norm                     : Input : variable 'var1' (index=0).   <---> Output : variable 'var1' (index=0).\n",
      "--- Norm                     : Input : variable 'var2' (index=1).   <---> Output : variable 'var2' (index=1).\n",
      "--- Norm                     : Input : variable 'var3' (index=2).   <---> Output : variable 'var3' (index=2).\n",
      "--- MLP                      : Building Network\n",
      "--- MLP                      : Initializing weights\n"
     ]
    }
   ],
   "source": [
    "//Boosted Decision Trees\n",
    "factory.BookMethod(&loader1,TMVA::Types::kBDT, \"BDT\",\n",
    "                   \"!V:NTrees=200:MinNodeSize=2.5%:MaxDepth=2:BoostType=AdaBoost:AdaBoostBeta=0.5:UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20\" );\n",
    "\n",
    "//Multi-Layer Perceptron (Neural Network)\n",
    "factory.BookMethod(&loader1, TMVA::Types::kMLP, \"MLP\",\n",
    "                   \"!H:!V:NeuronType=tanh:VarTransform=N:NCycles=100:HiddenLayers=N+5:TestRate=5:!UseRegulator\" );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Second dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Factory                  : Booking method: \u001b[1mBDT\u001b[0m DataSet Name: \u001b[1mdataset2\u001b[0m\n",
      "--- DataSetFactory           : Dataset[dataset2] : Splitmode is: \"RANDOM\" the mixmode is: \"SAMEASSPLITMODE\"\n",
      "--- DataSetFactory           : Dataset[dataset2] : Create training and testing trees -- looping over class \"Signal\" ...\n",
      "--- DataSetFactory           : Dataset[dataset2] : Weight expression for class 'Signal': \"\"\n",
      "--- DataSetFactory           : Dataset[dataset2] : Create training and testing trees -- looping over class \"Background\" ...\n",
      "--- DataSetFactory           : Dataset[dataset2] : Weight expression for class 'Background': \"\"\n",
      "--- DataSetFactory           : Dataset[dataset2] : Number of events in input trees (after possible flattening of arrays):\n",
      "--- DataSetFactory           : Dataset[dataset2] :     Signal          -- number of events       : 6000   / sum of weights: 6000 \n",
      "--- DataSetFactory           : Dataset[dataset2] :     Background      -- number of events       : 6000   / sum of weights: 6000 \n",
      "--- DataSetFactory           : Dataset[dataset2] :     Signal     tree -- total number of entries: 6000 \n",
      "--- DataSetFactory           : Dataset[dataset2] :     Background tree -- total number of entries: 6000 \n",
      "--- DataSetFactory           : Dataset[dataset2] : Preselection: (will NOT affect number of requested training and testing events)\n",
      "--- DataSetFactory           : Dataset[dataset2] :     No preselection cuts applied on event classes\n",
      "--- DataSetFactory           : Dataset[dataset2] : Weight renormalisation mode: \"NumEvents\": renormalises all event classes \n",
      "--- DataSetFactory           : Dataset[dataset2] :  such that the effective (weighted) number of events in each class equals the respective \n",
      "--- DataSetFactory           : Dataset[dataset2] :  number of events (entries) that you demanded in PrepareTrainingAndTestTree(\"\",\"nTrain_Signal=.. )\n",
      "--- DataSetFactory           : Dataset[dataset2] :  ... i.e. such that Sum[i=1..N_j]{w_i} = N_j, j=0,1,2...\n",
      "--- DataSetFactory           : Dataset[dataset2] :  ... (note that N_j is the sum of TRAINING events (nTrain_j...with j=Signal,Background..\n",
      "--- DataSetFactory           : Dataset[dataset2] :  ..... Testing events are not renormalised nor included in the renormalisation factor! )\n",
      "--- DataSetFactory           : Dataset[dataset2] : --> Rescale Signal     event weights by factor: 1\n",
      "--- DataSetFactory           : Dataset[dataset2] : --> Rescale Background event weights by factor: 1\n",
      "--- DataSetFactory           : Dataset[dataset2] : Number of training and testing events after rescaling:\n",
      "--- DataSetFactory           : Dataset[dataset2] : ---------------------------------------------------------------------------\n",
      "--- DataSetFactory           : Dataset[dataset2] : Signal     -- training events            : 1000 (sum of weights: 1000) - requested were 1000 events\n",
      "--- DataSetFactory           : Dataset[dataset2] : Signal     -- testing events             : 5000 (sum of weights: 5000) - requested were 0 events\n",
      "--- DataSetFactory           : Dataset[dataset2] : Signal     -- training and testing events: 6000 (sum of weights: 6000)\n",
      "--- DataSetFactory           : Dataset[dataset2] : Background -- training events            : 1000 (sum of weights: 1000) - requested were 1000 events\n",
      "--- DataSetFactory           : Dataset[dataset2] : Background -- testing events             : 5000 (sum of weights: 5000) - requested were 0 events\n",
      "--- DataSetFactory           : Dataset[dataset2] : Background -- training and testing events: 6000 (sum of weights: 6000)\n",
      "--- DataSetFactory           : Dataset[dataset2] : Create internal training tree\n",
      "--- DataSetFactory           : Dataset[dataset2] : Create internal testing tree\n",
      "--- DataSetInfo              : Dataset[dataset2] : Correlation matrix (Signal):\n",
      "--- DataSetInfo              : --------------------------------------------------------------\n",
      "--- DataSetInfo              :               var1    var2    var3    var4 var1-var3 var1+var2\n",
      "--- DataSetInfo              :      var1:  +1.000  +0.386  +0.597  +0.808    +0.442    +0.826\n",
      "--- DataSetInfo              :      var2:  +0.386  +1.000  +0.696  +0.743    -0.350    +0.839\n",
      "--- DataSetInfo              :      var3:  +0.597  +0.696  +1.000  +0.860    -0.456    +0.778\n",
      "--- DataSetInfo              :      var4:  +0.808  +0.743  +0.860  +1.000    -0.065    +0.931\n",
      "--- DataSetInfo              : var1-var3:  +0.442  -0.350  -0.456  -0.065    +1.000    +0.047\n",
      "--- DataSetInfo              : var1+var2:  +0.826  +0.839  +0.778  +0.931    +0.047    +1.000\n",
      "--- DataSetInfo              : --------------------------------------------------------------\n",
      "--- DataSetInfo              : Dataset[dataset2] : Correlation matrix (Background):\n",
      "--- DataSetInfo              : --------------------------------------------------------------\n",
      "--- DataSetInfo              :               var1    var2    var3    var4 var1-var3 var1+var2\n",
      "--- DataSetInfo              :      var1:  +1.000  +0.856  +0.914  +0.964    +0.122    +0.966\n",
      "--- DataSetInfo              :      var2:  +0.856  +1.000  +0.927  +0.937    -0.248    +0.960\n",
      "--- DataSetInfo              :      var3:  +0.914  +0.927  +1.000  +0.971    -0.290    +0.955\n",
      "--- DataSetInfo              :      var4:  +0.964  +0.937  +0.971  +1.000    -0.101    +0.987\n",
      "--- DataSetInfo              : var1-var3:  +0.122  -0.248  -0.290  -0.101    +1.000    -0.057\n",
      "--- DataSetInfo              : var1+var2:  +0.966  +0.960  +0.955  +0.987    -0.057    +1.000\n",
      "--- DataSetInfo              : --------------------------------------------------------------\n",
      "--- DataSetFactory           : Dataset[dataset2] :  \n",
      "--- Factory                  : Booking method: \u001b[1mMLP\u001b[0m DataSet Name: \u001b[1mdataset2\u001b[0m\n",
      "--- MLP                      : Dataset[dataset2] : Create Transformation \"N\" with events from all classes.\n",
      "--- Norm                     : Transformation, Variable selection : \n",
      "--- Norm                     : Input : variable 'var1' (index=0).   <---> Output : variable 'var1' (index=0).\n",
      "--- Norm                     : Input : variable 'var2' (index=1).   <---> Output : variable 'var2' (index=1).\n",
      "--- Norm                     : Input : variable 'var3' (index=2).   <---> Output : variable 'var3' (index=2).\n",
      "--- Norm                     : Input : variable 'var4' (index=3).   <---> Output : variable 'var4' (index=3).\n",
      "--- Norm                     : Input : variable 'var5' (index=4).   <---> Output : variable 'var5' (index=4).\n",
      "--- Norm                     : Input : variable 'var6' (index=5).   <---> Output : variable 'var6' (index=5).\n",
      "--- MLP                      : Building Network\n",
      "--- MLP                      : Initializing weights\n"
     ]
    }
   ],
   "source": [
    "//Boosted Decision Trees\n",
    "factory.BookMethod(&loader2, TMVA::Types::kBDT, \"BDT\",\n",
    "                   \"!V:NTrees=200:MinNodeSize=2.5%:MaxDepth=2:BoostType=AdaBoost:AdaBoostBeta=0.5:UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20\" );\n",
    "\n",
    "//Multi-Layer Perceptron\n",
    "factory.BookMethod(&loader2, TMVA::Types::kMLP, \"MLP\",\n",
    "                   \"!H:!V:NeuronType=tanh:VarTransform=N:NCycles=100:HiddenLayers=N+5:TestRate=5:!UseRegulator\" );\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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[dataset1] : Create Transformation \"I\" with events from all classes.\n",
      "--- Id                       : Transformation, Variable selection : \n",
      "--- Id                       : Input : variable 'var1' (index=0).   <---> Output : variable 'var1' (index=0).\n",
      "--- Id                       : Input : variable 'var2' (index=1).   <---> Output : variable 'var2' (index=1).\n",
      "--- Id                       : Input : variable 'var3' (index=2).   <---> Output : variable 'var3' (index=2).\n",
      "--- Id                       : Preparing the Identity transformation...\n",
      "--- TFHandler_Factory        : -----------------------------------------------------------\n",
      "--- TFHandler_Factory        : Variable        Mean        RMS   [        Min        Max ]\n",
      "--- TFHandler_Factory        : -----------------------------------------------------------\n",
      "--- TFHandler_Factory        :     var1:   0.088993     1.6612   [    -4.9583     4.3390 ]\n",
      "--- TFHandler_Factory        :     var2:   0.095657     1.5731   [    -4.6474     3.9513 ]\n",
      "--- TFHandler_Factory        :     var3:    0.10551     1.7363   [    -5.0373     4.2785 ]\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        : TMVAOutputCV.root:/dataset1/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 : var1      : 2.898e-01\n",
      "--- IdTransformation         :    2 : var3      : 2.828e-01\n",
      "--- IdTransformation         :    3 : var2      : 2.032e-01\n",
      "--- IdTransformation         : -----------------------------\n",
      "--- Factory                  : Train method: BDT for Classification\n",
      "--- BDT                      : Dataset[dataset1] : Begin training\n",
      "--- BDT                      :  found and suggest the following possible pre-selection cuts \n",
      "--- BDT                      : as option DoPreselection was not used, these cuts however will not be performed, but the training will see the full sample\n",
      "--- BDT                      :  found cut: Bkg if var 0 < -2.99038\n",
      "--- BDT                      :  found cut: Bkg if var 2 < -2.88493\n",
      "--- BDT                      : <InitEventSample> For classification trees, \n",
      "--- BDT                      :  the effective number of backgrounds is scaled to match \n",
      "--- BDT                      :  the signal. Othersise the first boosting step would do 'just that'!\n",
      "--- BDT                      : re-normlise events such that Sig and Bkg have respective sum of weights = 1\n",
      "--- BDT                      :   sig->sig*1ev. bkg->bkg*1ev.\n",
      "--- BDT                      : #events: (reweighted) sig: 1000 bkg: 1000\n",
      "--- BDT                      : #events: (unweighted) sig: 1000 bkg: 1000\n",
      "--- BDT                      : Training 200 Decision Trees ... patience please\n",
      "--- BinaryTree               : The minimal node size MinNodeSize=2.5 fMinNodeSize=2.5% is translated to an actual number of events = 25.7 for the training sample size of 1028\n",
      "--- BinaryTree               : Note: This number will be taken as absolute minimum in the node, \n",
      "--- BinaryTree               :       in terms of 'weighted events' and unweighted ones !! \n",
      "--- BDT                      : <Train> elapsed time: \u001b[1;31m0.118 sec\u001b[0m                              \n",
      "--- BDT                      : <Train> average number of nodes (w/o pruning) : 4\n",
      "--- BDT                      : Dataset[dataset1] : End of training                                              \n",
      "--- BDT                      : Dataset[dataset1] : Elapsed time for training with 2000 events: \u001b[1;31m0.123 sec\u001b[0m         \n",
      "--- BDT                      : Dataset[dataset1] : Create MVA output for Dataset[dataset1] : classification on training sample\n",
      "--- BDT                      : Dataset[dataset1] : Evaluation of BDT on training sample (2000 events)\n",
      "--- BDT                      : Dataset[dataset1] : Elapsed time for evaluation of 2000 events: \u001b[1;31m0.0283 sec\u001b[0m       \n",
      "--- BDT                      : Dataset[dataset1] : Creating weight file in xml format: \u001b[0;36mdataset1/weights/TMVAClassification_BDT.weights.xml\u001b[0m\n",
      "--- BDT                      : Dataset[dataset1] : Creating standalone response class: \u001b[0;36mdataset1/weights/TMVAClassification_BDT.class.C\u001b[0m\n",
      "--- BDT                      : Write monitoring histograms to file: TMVAOutputCV.root:/dataset1/Method_BDT/BDT\n",
      "--- Factory                  : Training finished\n",
      "--- Factory                  : Train method: MLP for Classification\n",
      "--- Norm                     : Preparing the transformation.\n",
      "--- TFHandler_MLP            : -----------------------------------------------------------\n",
      "--- TFHandler_MLP            : Variable        Mean        RMS   [        Min        Max ]\n",
      "--- TFHandler_MLP            : -----------------------------------------------------------\n",
      "--- TFHandler_MLP            :     var1:   0.085752    0.35735   [    -1.0000     1.0000 ]\n",
      "--- TFHandler_MLP            :     var2:    0.10321    0.36589   [    -1.0000     1.0000 ]\n",
      "--- TFHandler_MLP            :     var3:    0.10411    0.37276   [    -1.0000     1.0000 ]\n",
      "--- TFHandler_MLP            : -----------------------------------------------------------\n",
      "--- MLP                      : Dataset[dataset1] : Begin training\n",
      "--- MLP                      : Training Network\n",
      "--- MLP                      : Dataset[dataset1] : End of training                                              \n",
      "--- MLP                      : Dataset[dataset1] : Elapsed time for training with 2000 events: \u001b[1;31m0.955 sec\u001b[0m         \n",
      "--- MLP                      : Dataset[dataset1] : Create MVA output for Dataset[dataset1] : classification on training sample\n",
      "--- MLP                      : Dataset[dataset1] : Evaluation of MLP on training sample (2000 events)\n",
      "--- MLP                      : Dataset[dataset1] : Elapsed time for evaluation of 2000 events: \u001b[1;31m0.00232 sec\u001b[0m       \n",
      "--- MLP                      : Dataset[dataset1] : Creating weight file in xml format: \u001b[0;36mdataset1/weights/TMVAClassification_MLP.weights.xml\u001b[0m\n",
      "--- MLP                      : Dataset[dataset1] : Creating standalone response class: \u001b[0;36mdataset1/weights/TMVAClassification_MLP.class.C\u001b[0m\n",
      "--- MLP                      : Write special histos to file: TMVAOutputCV.root:/dataset1/Method_MLP/MLP\n",
      "--- Factory                  : Training finished\n",
      "--- Factory                  : \n",
      "--- Factory                  : Ranking input variables (method specific)...\n",
      "--- BDT                      : Ranking result (top variable is best ranked)\n",
      "--- BDT                      : --------------------------------------\n",
      "--- BDT                      : Rank : Variable  : Variable Importance\n",
      "--- BDT                      : --------------------------------------\n",
      "--- BDT                      :    1 : var3      : 3.607e-01\n",
      "--- BDT                      :    2 : var1      : 3.428e-01\n",
      "--- BDT                      :    3 : var2      : 2.965e-01\n",
      "--- BDT                      : --------------------------------------\n",
      "--- MLP                      : Ranking result (top variable is best ranked)\n",
      "--- MLP                      : -----------------------------\n",
      "--- MLP                      : Rank : Variable  : Importance\n",
      "--- MLP                      : -----------------------------\n",
      "--- MLP                      :    1 : var1      : 3.074e+00\n",
      "--- MLP                      :    2 : var3      : 2.229e+00\n",
      "--- MLP                      :    3 : var2      : 1.144e+00\n",
      "--- MLP                      : -----------------------------\n",
      "--- Factory                  : \n",
      "--- Factory                  : === Destroy and recreate all methods via weight files for testing ===\n",
      "--- Factory                  : \n",
      "--- MethodBase               : Dataset[dataset1] : Reading weight file: \u001b[0;36mdataset1/weights/TMVAClassification_BDT.weights.xml\u001b[0m\n",
      "--- BDT                      : Dataset[dataset1] : Read method \"BDT\" of type \"BDT\"\n",
      "--- BDT                      : Dataset[dataset1] : MVA method was trained with TMVA Version: 4.2.1\n",
      "--- BDT                      : Dataset[dataset1] : MVA method was trained with ROOT Version: 6.07/07\n",
      "--- MethodBase               : Dataset[dataset1] : Reading weight file: \u001b[0;36mdataset1/weights/TMVAClassification_MLP.weights.xml\u001b[0m\n",
      "--- MLP                      : Dataset[dataset1] : Read method \"MLP\" of type \"MLP\"\n",
      "--- MLP                      : Dataset[dataset1] : MVA method was trained with TMVA Version: 4.2.1\n",
      "--- MLP                      : Dataset[dataset1] : MVA method was trained with ROOT Version: 6.07/07\n",
      "--- MLP                      : Building Network\n",
      "--- MLP                      : Initializing weights\n",
      "--- Factory                  : \n",
      "--- Factory                  : current transformation string: 'I'\n",
      "--- Factory                  : Dataset[dataset2] : Create Transformation \"I\" with events from all classes.\n",
      "--- Id                       : Transformation, Variable selection : \n",
      "--- Id                       : Input : variable 'var1' (index=0).   <---> Output : variable 'var1' (index=0).\n",
      "--- Id                       : Input : variable 'var2' (index=1).   <---> Output : variable 'var2' (index=1).\n",
      "--- Id                       : Input : variable 'var3' (index=2).   <---> Output : variable 'var3' (index=2).\n",
      "--- Id                       : Input : variable 'var4' (index=3).   <---> Output : variable 'var4' (index=3).\n",
      "--- Id                       : Input : variable 'var5' (index=4).   <---> Output : variable 'var5' (index=4).\n",
      "--- Id                       : Input : variable 'var6' (index=5).   <---> Output : variable 'var6' (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        :     var1:   0.088993     1.6612   [    -4.9583     4.3390 ]\n",
      "--- TFHandler_Factory        :     var2:   0.095657     1.5731   [    -4.6474     3.9513 ]\n",
      "--- TFHandler_Factory        :     var3:    0.10551     1.7363   [    -5.0373     4.2785 ]\n",
      "--- TFHandler_Factory        :     var4:    0.27815     2.1526   [    -5.9505     4.6404 ]\n",
      "--- TFHandler_Factory        :     var5:  -0.016515    0.91457   [    -3.1013     3.0035 ]\n",
      "--- TFHandler_Factory        :     var6:    0.18465     3.0371   [    -8.1442     7.2697 ]\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        : TMVAOutputCV.root:/dataset2/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 : var4      : 3.585e-01\n",
      "--- IdTransformation         :    2 : var6      : 3.225e-01\n",
      "--- IdTransformation         :    3 : var1      : 2.898e-01\n",
      "--- IdTransformation         :    4 : var3      : 2.828e-01\n",
      "--- IdTransformation         :    5 : var2      : 2.032e-01\n",
      "--- IdTransformation         :    6 : var5      : 5.558e-02\n",
      "--- IdTransformation         : -----------------------------\n",
      "--- Factory                  : Train method: BDT for Classification\n",
      "--- BDT                      : Dataset[dataset2] : Begin training\n",
      "--- BDT                      :  found and suggest the following possible pre-selection cuts \n",
      "--- BDT                      : as option DoPreselection was not used, these cuts however will not be performed, but the training will see the full sample\n",
      "--- BDT                      :  found cut: Bkg if var 0 < -2.99038\n",
      "--- BDT                      :  found cut: Bkg if var 2 < -2.88493\n",
      "--- BDT                      :  found cut: Bkg if var 3 < -2.54088\n",
      "--- BDT                      :  found cut: Bkg if var 5 < -5.0999\n",
      "--- BDT                      : <InitEventSample> For classification trees, \n",
      "--- BDT                      :  the effective number of backgrounds is scaled to match \n",
      "--- BDT                      :  the signal. Othersise the first boosting step would do 'just that'!\n",
      "--- BDT                      : re-normlise events such that Sig and Bkg have respective sum of weights = 1\n",
      "--- BDT                      :   sig->sig*1ev. bkg->bkg*1ev.\n",
      "--- BDT                      : #events: (reweighted) sig: 1000 bkg: 1000\n",
      "--- BDT                      : #events: (unweighted) sig: 1000 bkg: 1000\n",
      "--- BDT                      : Training 200 Decision Trees ... patience please\n",
      "--- BinaryTree               : The minimal node size MinNodeSize=2.5 fMinNodeSize=2.5% is translated to an actual number of events = 25.7 for the training sample size of 1028\n",
      "--- BinaryTree               : Note: This number will be taken as absolute minimum in the node, \n",
      "--- BinaryTree               :       in terms of 'weighted events' and unweighted ones !! \n",
      "--- BDT                      : <Train> elapsed time: \u001b[1;31m0.107 sec\u001b[0m                              \n",
      "--- BDT                      : <Train> average number of nodes (w/o pruning) : 5\n",
      "--- BDT                      : Dataset[dataset2] : End of training                                              \n",
      "--- BDT                      : Dataset[dataset2] : Elapsed time for training with 2000 events: \u001b[1;31m0.111 sec\u001b[0m         \n",
      "--- BDT                      : Dataset[dataset2] : Create MVA output for Dataset[dataset2] : classification on training sample\n",
      "--- BDT                      : Dataset[dataset2] : Evaluation of BDT on training sample (2000 events)\n",
      "--- BDT                      : Dataset[dataset2] : Elapsed time for evaluation of 2000 events: \u001b[1;31m0.0222 sec\u001b[0m       \n",
      "--- BDT                      : Dataset[dataset2] : Creating weight file in xml format: \u001b[0;36mdataset2/weights/TMVAClassification_BDT.weights.xml\u001b[0m\n",
      "--- BDT                      : Dataset[dataset2] : Creating standalone response class: \u001b[0;36mdataset2/weights/TMVAClassification_BDT.class.C\u001b[0m\n",
      "--- BDT                      : Write monitoring histograms to file: TMVAOutputCV.root:/dataset2/Method_BDT/BDT\n",
      "--- Factory                  : Training finished\n",
      "--- Factory                  : Train method: MLP for Classification\n",
      "--- Norm                     : Preparing the transformation.\n",
      "--- TFHandler_MLP            : -----------------------------------------------------------\n",
      "--- TFHandler_MLP            : Variable        Mean        RMS   [        Min        Max ]\n",
      "--- TFHandler_MLP            : -----------------------------------------------------------\n",
      "--- TFHandler_MLP            :     var1:   0.085752    0.35735   [    -1.0000     1.0000 ]\n",
      "--- TFHandler_MLP            :     var2:    0.10321    0.36589   [    -1.0000     1.0000 ]\n",
      "--- TFHandler_MLP            :     var3:    0.10411    0.37276   [    -1.0000     1.0000 ]\n",
      "--- TFHandler_MLP            :     var4:    0.17623    0.40650   [    -1.0000     1.0000 ]\n",
      "--- TFHandler_MLP            :     var5:   0.010608    0.29962   [    -1.0000     1.0000 ]\n",
      "--- TFHandler_MLP            :     var6:   0.080694    0.39407   [    -1.0000     1.0000 ]\n",
      "--- TFHandler_MLP            : -----------------------------------------------------------\n",
      "--- MLP                      : Dataset[dataset2] : Begin training\n",
      "--- MLP                      : Training Network\n",
      "--- MLP                      : Dataset[dataset2] : End of training                                              \n",
      "--- MLP                      : Dataset[dataset2] : Elapsed time for training with 2000 events: \u001b[1;31m1.17 sec\u001b[0m         \n",
      "--- MLP                      : Dataset[dataset2] : Create MVA output for Dataset[dataset2] : classification on training sample\n",
      "--- MLP                      : Dataset[dataset2] : Evaluation of MLP on training sample (2000 events)\n",
      "--- MLP                      : Dataset[dataset2] : Elapsed time for evaluation of 2000 events: \u001b[1;31m0.00304 sec\u001b[0m       \n",
      "--- MLP                      : Dataset[dataset2] : Creating weight file in xml format: \u001b[0;36mdataset2/weights/TMVAClassification_MLP.weights.xml\u001b[0m\n",
      "--- MLP                      : Dataset[dataset2] : Creating standalone response class: \u001b[0;36mdataset2/weights/TMVAClassification_MLP.class.C\u001b[0m\n",
      "--- MLP                      : Write special histos to file: TMVAOutputCV.root:/dataset2/Method_MLP/MLP\n",
      "--- Factory                  : Training finished\n",
      "--- Factory                  : \n",
      "--- Factory                  : Ranking input variables (method specific)...\n",
      "--- BDT                      : Ranking result (top variable is best ranked)\n",
      "--- BDT                      : --------------------------------------\n",
      "--- BDT                      : Rank : Variable  : Variable Importance\n",
      "--- BDT                      : --------------------------------------\n",
      "--- BDT                      :    1 : var4      : 2.831e-01\n",
      "--- BDT                      :    2 : var1      : 1.620e-01\n",
      "--- BDT                      :    3 : var6      : 1.619e-01\n",
      "--- BDT                      :    4 : var3      : 1.432e-01\n",
      "--- BDT                      :    5 : var2      : 1.391e-01\n",
      "--- BDT                      :    6 : var5      : 1.107e-01\n",
      "--- BDT                      : --------------------------------------\n",
      "--- MLP                      : Ranking result (top variable is best ranked)\n",
      "--- MLP                      : -----------------------------\n",
      "--- MLP                      : Rank : Variable  : Importance\n",
      "--- MLP                      : -----------------------------\n",
      "--- MLP                      :    1 : var4      : 9.713e+00\n",
      "--- MLP                      :    2 : var1      : 4.210e+00\n",
      "--- MLP                      :    3 : var6      : 2.539e+00\n",
      "--- MLP                      :    4 : var3      : 1.827e+00\n",
      "--- MLP                      :    5 : var5      : 1.640e+00\n",
      "--- MLP                      :    6 : var2      : 1.519e+00\n",
      "--- MLP                      : -----------------------------\n",
      "--- Factory                  : \n",
      "--- Factory                  : === Destroy and recreate all methods via weight files for testing ===\n",
      "--- Factory                  : \n",
      "--- MethodBase               : Dataset[dataset2] : Reading weight file: \u001b[0;36mdataset2/weights/TMVAClassification_BDT.weights.xml\u001b[0m\n",
      "--- BDT                      : Dataset[dataset2] : Read method \"BDT\" of type \"BDT\"\n",
      "--- BDT                      : Dataset[dataset2] : MVA method was trained with TMVA Version: 4.2.1\n",
      "--- BDT                      : Dataset[dataset2] : MVA method was trained with ROOT Version: 6.07/07\n",
      "--- MethodBase               : Dataset[dataset2] : Reading weight file: \u001b[0;36mdataset2/weights/TMVAClassification_MLP.weights.xml\u001b[0m\n",
      "--- MLP                      : Dataset[dataset2] : Read method \"MLP\" of type \"MLP\"\n",
      "--- MLP                      : Dataset[dataset2] : MVA method was trained with TMVA Version: 4.2.1\n",
      "--- MLP                      : Dataset[dataset2] : MVA method was trained with ROOT Version: 6.07/07\n",
      "--- MLP                      : Building Network\n",
      "--- MLP                      : Initializing weights\n"
     ]
    }
   ],
   "source": [
    "factory.TrainAllMethods();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test and Evaluate Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Factory                  : Test all methods...\n",
      "--- Factory                  : Test method: BDT for Classification performance\n",
      "--- BDT                      : Dataset[dataset1] : Evaluation of BDT on testing sample (10000 events)\n",
      "--- BDT                      : Dataset[dataset1] : Elapsed time for evaluation of 10000 events: \u001b[1;31m0.153 sec\u001b[0m       \n",
      "--- Factory                  : Test method: MLP for Classification performance\n",
      "--- MLP                      : Dataset[dataset1] : Evaluation of MLP on testing sample (10000 events)\n",
      "--- MLP                      : Dataset[dataset1] : Elapsed time for evaluation of 10000 events: \u001b[1;31m0.014 sec\u001b[0m       \n",
      "--- Factory                  : Test method: BDT for Classification performance\n",
      "--- BDT                      : Dataset[dataset2] : Evaluation of BDT on testing sample (10000 events)\n",
      "--- BDT                      : Dataset[dataset2] : Elapsed time for evaluation of 10000 events: \u001b[1;31m0.135 sec\u001b[0m       \n",
      "--- Factory                  : Test method: MLP for Classification performance\n",
      "--- MLP                      : Dataset[dataset2] : Evaluation of MLP on testing sample (10000 events)\n",
      "--- MLP                      : Dataset[dataset2] : Elapsed time for evaluation of 10000 events: \u001b[1;31m0.0209 sec\u001b[0m       \n",
      "--- Factory                  : Evaluate all methods...\n",
      "--- Factory                  : Evaluate classifier: BDT\n",
      "--- BDT                      : Dataset[dataset1] : Loop over test events and fill histograms with classifier response...\n",
      "--- Factory                  : Write evaluation histograms to file\n",
      "--- TFHandler_BDT            : Plot event variables for BDT\n",
      "--- TFHandler_BDT            : -----------------------------------------------------------\n",
      "--- TFHandler_BDT            : Variable        Mean        RMS   [        Min        Max ]\n",
      "--- TFHandler_BDT            : -----------------------------------------------------------\n",
      "--- TFHandler_BDT            :     var1: 0.00077102     1.6695   [    -5.8991     4.7639 ]\n",
      "--- TFHandler_BDT            :     var2: -0.0063164     1.5765   [    -5.2454     4.8300 ]\n",
      "--- TFHandler_BDT            :     var3:  -0.010870     1.7365   [    -5.3563     4.6430 ]\n",
      "--- TFHandler_BDT            : -----------------------------------------------------------\n",
      "--- TFHandler_BDT            : Create scatter and profile plots in target-file directory: \n",
      "--- TFHandler_BDT            : TMVAOutputCV.root:/dataset1/Method_BDT/BDT/CorrelationPlots\n",
      "--- Factory                  : Evaluate classifier: MLP\n",
      "--- TFHandler_MLP            : -----------------------------------------------------------\n",
      "--- TFHandler_MLP            : Variable        Mean        RMS   [        Min        Max ]\n",
      "--- TFHandler_MLP            : -----------------------------------------------------------\n",
      "--- TFHandler_MLP            :     var1:   0.066774    0.35913   [    -1.2024     1.0914 ]\n",
      "--- TFHandler_MLP            :     var2:   0.079492    0.36669   [    -1.1391     1.2044 ]\n",
      "--- TFHandler_MLP            :     var3:   0.079125    0.37282   [    -1.0685     1.0783 ]\n",
      "--- TFHandler_MLP            : -----------------------------------------------------------\n",
      "--- MLP                      : Dataset[dataset1] : Loop over test events and fill histograms with classifier response...\n",
      "--- Factory                  : Write evaluation histograms to file\n",
      "--- TFHandler_MLP            : Plot event variables for MLP\n",
      "--- TFHandler_MLP            : -----------------------------------------------------------\n",
      "--- TFHandler_MLP            : Variable        Mean        RMS   [        Min        Max ]\n",
      "--- TFHandler_MLP            : -----------------------------------------------------------\n",
      "--- TFHandler_MLP            :     var1:   0.066774    0.35913   [    -1.2024     1.0914 ]\n",
      "--- TFHandler_MLP            :     var2:   0.079492    0.36669   [    -1.1391     1.2044 ]\n",
      "--- TFHandler_MLP            :     var3:   0.079125    0.37282   [    -1.0685     1.0783 ]\n",
      "--- TFHandler_MLP            : -----------------------------------------------------------\n",
      "--- TFHandler_MLP            : Create scatter and profile plots in target-file directory: \n",
      "--- TFHandler_MLP            : TMVAOutputCV.root:/dataset1/Method_MLP/MLP/CorrelationPlots\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                  : dataset1             MLP            : 0.066(03)  0.427(06)  0.809(05)    0.831       0.831 | 0.357    1.043\n",
      "--- Factory                  : dataset1             BDT            : 0.066(03)  0.400(06)  0.802(05)    0.826       0.826 | 0.349    1.004\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                  : dataset1             MLP            : 0.066 (0.067)       0.427 (0.433)      0.809 (0.833)\n",
      "--- Factory                  : dataset1             BDT            : 0.066 (0.095)       0.400 (0.451)      0.802 (0.838)\n",
      "--- Factory                  : -------------------------------------------------------------------------------------------------------------------\n",
      "--- Factory                  : \n",
      "--- Dataset:dataset1         : Dataset[dataset1] : Created tree 'TestTree' with 10000 events\n",
      "--- Dataset:dataset1         : Dataset[dataset1] : Created tree 'TrainTree' with 2000 events\n",
      "--- Factory                  : Evaluate classifier: BDT\n",
      "--- BDT                      : Dataset[dataset2] : Loop over test events and fill histograms with classifier response...\n",
      "--- Factory                  : Write evaluation histograms to file\n",
      "--- TFHandler_BDT            : Plot event variables for BDT\n",
      "--- TFHandler_BDT            : -----------------------------------------------------------\n",
      "--- TFHandler_BDT            : Variable        Mean        RMS   [        Min        Max ]\n",
      "--- TFHandler_BDT            : -----------------------------------------------------------\n",
      "--- TFHandler_BDT            :     var1: 0.00077102     1.6695   [    -5.8991     4.7639 ]\n",
      "--- TFHandler_BDT            :     var2: -0.0063164     1.5765   [    -5.2454     4.8300 ]\n",
      "--- TFHandler_BDT            :     var3:  -0.010870     1.7365   [    -5.3563     4.6430 ]\n",
      "--- TFHandler_BDT            :     var4:    0.14557     2.1608   [    -6.9675     5.0307 ]\n",
      "--- TFHandler_BDT            :     var5:   0.011641    0.92093   [    -3.2430     3.5043 ]\n",
      "--- TFHandler_BDT            :     var6: -0.0055454     3.0494   [    -9.8605     7.9024 ]\n",
      "--- TFHandler_BDT            : -----------------------------------------------------------\n",
      "--- TFHandler_BDT            : Create scatter and profile plots in target-file directory: \n",
      "--- TFHandler_BDT            : TMVAOutputCV.root:/dataset2/Method_BDT/BDT/CorrelationPlots\n",
      "--- Factory                  : Evaluate classifier: MLP\n",
      "--- TFHandler_MLP            : -----------------------------------------------------------\n",
      "--- TFHandler_MLP            : Variable        Mean        RMS   [        Min        Max ]\n",
      "--- TFHandler_MLP            : -----------------------------------------------------------\n",
      "--- TFHandler_MLP            :     var1:   0.066774    0.35913   [    -1.2024     1.0914 ]\n",
      "--- TFHandler_MLP            :     var2:   0.079492    0.36669   [    -1.1391     1.2044 ]\n",
      "--- TFHandler_MLP            :     var3:   0.079125    0.37282   [    -1.0685     1.0783 ]\n",
      "--- TFHandler_MLP            :     var4:    0.15120    0.40805   [    -1.1921     1.0737 ]\n",
      "--- TFHandler_MLP            :     var5:   0.019832    0.30171   [    -1.0464     1.1641 ]\n",
      "--- TFHandler_MLP            :     var6:   0.056015    0.39567   [    -1.2227     1.0821 ]\n",
      "--- TFHandler_MLP            : -----------------------------------------------------------\n",
      "--- MLP                      : Dataset[dataset2] : Loop over test events and fill histograms with classifier response...\n",
      "--- Factory                  : Write evaluation histograms to file\n",
      "--- TFHandler_MLP            : Plot event variables for MLP\n",
      "--- TFHandler_MLP            : -----------------------------------------------------------\n",
      "--- TFHandler_MLP            : Variable        Mean        RMS   [        Min        Max ]\n",
      "--- TFHandler_MLP            : -----------------------------------------------------------\n",
      "--- TFHandler_MLP            :     var1:   0.066774    0.35913   [    -1.2024     1.0914 ]\n",
      "--- TFHandler_MLP            :     var2:   0.079492    0.36669   [    -1.1391     1.2044 ]\n",
      "--- TFHandler_MLP            :     var3:   0.079125    0.37282   [    -1.0685     1.0783 ]\n",
      "--- TFHandler_MLP            :     var4:    0.15120    0.40805   [    -1.1921     1.0737 ]\n",
      "--- TFHandler_MLP            :     var5:   0.019832    0.30171   [    -1.0464     1.1641 ]\n",
      "--- TFHandler_MLP            :     var6:   0.056015    0.39567   [    -1.2227     1.0821 ]\n",
      "--- TFHandler_MLP            : -----------------------------------------------------------\n",
      "--- TFHandler_MLP            : Create scatter and profile plots in target-file directory: \n",
      "--- TFHandler_MLP            : TMVAOutputCV.root:/dataset2/Method_MLP/MLP/CorrelationPlots\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                  : dataset2             MLP            : 0.355(06)  0.790(05)  0.957(02)    0.934       0.933 | 0.597    1.689\n",
      "--- Factory                  : dataset2             BDT            : 0.297(06)  0.731(06)  0.939(03)    0.919       0.919 | 0.547    1.449\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                  : dataset2             MLP            : 0.355 (0.385)       0.790 (0.808)      0.957 (0.961)\n",
      "--- Factory                  : dataset2             BDT            : 0.297 (0.345)       0.731 (0.793)      0.939 (0.942)\n",
      "--- Factory                  : -------------------------------------------------------------------------------------------------------------------\n",
      "--- Factory                  : \n",
      "--- Dataset:dataset2         : Dataset[dataset2] : Created tree 'TestTree' with 10000 events\n",
      "--- Dataset:dataset2         : Dataset[dataset2] : Created tree 'TrainTree' with 2000 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"
     ]
    }
   ],
   "source": [
    "factory.TestAllMethods();\n",
    "factory.EvaluateAllMethods();    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot ROC Curve\n",
    "We enable JavaScript visualisation for the plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%jsroot on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"root_plot_1\"\n",
       "     style=\"width: 696px; height: 472px\">\n",
       "</div>\n",
       "\n",
       "<script>\n",
       " requirejs.config({\n",
       "     paths: {\n",
       "       'JSRootCore' : 'https://root.cern.ch/js/notebook//scripts/JSRootCore',\n",
       "     }\n",
       "   });\n",
       " require(['JSRootCore'],\n",
       "     function(Core) {\n",
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fRed\":9.902000e-01,\"fGreen\":8.416063e-01,\"fBlue\":1.566094e-01,\"fHue\":-1.588187e-23,\"fLight\":-1.588187e-23,\"fSaturation\":-1.588187e-23,\"fAlpha\":1},{\"_typename\":\"TColor\",\"fUniqueID\":0,\"fBits\":50331648,\"fName\":\"\",\"fTitle\":\"\",\"fNumber\":1157,\"fRed\":9.896000e-01,\"fGreen\":8.477625e-01,\"fBlue\":1.521437e-01,\"fHue\":-1.588187e-23,\"fLight\":-1.588187e-23,\"fSaturation\":-1.588187e-23,\"fAlpha\":1},{\"_typename\":\"TColor\",\"fUniqueID\":0,\"fBits\":50331648,\"fName\":\"\",\"fTitle\":\"\",\"fNumber\":1158,\"fRed\":9.890000e-01,\"fGreen\":8.539187e-01,\"fBlue\":1.476781e-01,\"fHue\":-1.588187e-23,\"fLight\":-1.588187e-23,\"fSaturation\":-1.588187e-23,\"fAlpha\":1},{\"_typename\":\"TColor\",\"fUniqueID\":0,\"fBits\":50331648,\"fName\":\"\",\"fTitle\":\"\",\"fNumber\":1159,\"fRed\":9.884000e-01,\"fGreen\":8.600750e-01,\"fBlue\":1.432125e-01,\"fHue\":-1.588187e-23,\"fLight\":-1.588187e-23,\"fSaturation\":-1.588187e-23,\"fAlpha\":1},{\"_typename\":\"TColor\",\"fUniqueID\":0,\"fBits\":50331648,\"fName\":\"\",\"fTitle\":\"\",\"fNumber\":1160,\"fRed\":9.878000e-01,\"fGreen\":8.662313e-01,\"fBlue\":1.387469e-01,\"fHue\":-1.588187e-23,\"fLight\":-1.588187e-23,\"fSaturation\":-1.588187e-23,\"fAlpha\":1},{\"_typename\":\"TColor\",\"fUniqueID\":0,\"fBits\":50331648,\"fName\":\"\",\"fTitle\":\"\",\"fNumber\":1161,\"fRed\":9.872000e-01,\"fGreen\":8.723875e-01,\"fBlue\":1.342812e-01,\"fHue\":-1.588187e-23,\"fLight\":-1.588187e-23,\"fSaturation\":-1.588187e-23,\"fAlpha\":1},{\"_typename\":\"TColor\",\"fUniqueID\":0,\"fBits\":50331648,\"fName\":\"\",\"fTitle\":\"\",\"fNumber\":1162,\"fRed\":9.866000e-01,\"fGreen\":8.785437e-01,\"fBlue\":1.298156e-01,\"fHue\":-1.588187e-23,\"fLight\":-1.588187e-23,\"fSaturation\":-1.588187e-23,\"fAlpha\":1},{\"_typename\":\"TColor\",\"fUniqueID\":0,\"fBits\":50331648,\"fName\":\"\",\"fTitle\":\"\",\"fNumber\":1163,\"fRed\":9.860000e-01,\"fGreen\":8.847000e-01,\"fBlue\":1.253500e-01,\"fHue\":-1.588187e-23,\"fLight\":-1.588187e-23,\"fSaturation\":-1.588187e-23,\"fAlpha\":1},{\"_typename\":\"TColor\",\"fUniqueID\":0,\"fBits\":50331648,\"fName\":\"\",\"fTitle\":\"\",\"fNumber\":1164,\"fRed\":9.854000e-01,\"fGreen\":8.908563e-01,\"fBlue\":1.208844e-01,\"fHue\":-1.588187e-23,\"fLight\":-1.588187e-23,\"fSaturation\":-1.588187e-23,\"fAlpha\":1},{\"_typename\":\"TColor\",\"fUniqueID\":0,\"fBits\":50331648,\"fName\":\"\",\"fTitle\":\"\",\"fNumber\":1165,\"fRed\":9.848000e-01,\"fGreen\":8.970125e-01,\"fBlue\":1.164187e-01,\"fHue\":-1.588187e-23,\"fLight\":-1.588187e-23,\"fSaturation\":-1.588187e-23,\"fAlpha\":1},{\"_typename\":\"TColor\",\"fUniqueID\":0,\"fBits\":50331648,\"fName\":\"\",\"fTitle\":\"\",\"fNumber\":1166,\"fRed\":9.842000e-01,\"fGreen\":9.031687e-01,\"fBlue\":1.119531e-01,\"fHue\":-1.588187e-23,\"fLight\":-1.588187e-23,\"fSaturation\":-1.588187e-23,\"fAlpha\":1},{\"_typename\":\"TColor\",\"fUniqueID\":0,\"fBits\":50331648,\"fName\":\"\",\"fTitle\":\"\",\"fNumber\":1167,\"fRed\":9.836000e-01,\"fGreen\":9.093250e-01,\"fBlue\":1.074875e-01,\"fHue\":-1.588187e-23,\"fLight\":-1.588187e-23,\"fSaturation\":-1.588187e-23,\"fAlpha\":1},{\"_typename\":\"TColor\",\"fUniqueID\":0,\"fBits\":50331648,\"fName\":\"\",\"fTitle\":\"\",\"fNumber\":1168,\"fRed\":9.830000e-01,\"fGreen\":9.154813e-01,\"fBlue\":1.030219e-01,\"fHue\":-1.588187e-23,\"fLight\":-1.588187e-23,\"fSaturation\":-1.588187e-23,\"fAlpha\":1},{\"_typename\":\"TColor\",\"fUniqueID\":0,\"fBits\":50331648,\"fName\":\"\",\"fTitle\":\"\",\"fNumber\":1169,\"fRed\":9.824000e-01,\"fGreen\":9.216375e-01,\"fBlue\":9.855625e-02,\"fHue\":-1.588187e-23,\"fLight\":-1.588187e-23,\"fSaturation\":-1.588187e-23,\"fAlpha\":1},{\"_typename\":\"TColor\",\"fUniqueID\":0,\"fBits\":50331648,\"fName\":\"\",\"fTitle\":\"\",\"fNumber\":1170,\"fRed\":9.818000e-01,\"fGreen\":9.277937e-01,\"fBlue\":9.409063e-02,\"fHue\":-1.588187e-23,\"fLight\":-1.588187e-23,\"fSaturation\":-1.588187e-23,\"fAlpha\":1},{\"_typename\":\"TColor\",\"fUniqueID\":0,\"fBits\":50331648,\"fName\":\"\",\"fTitle\":\"\",\"fNumber\":1171,\"fRed\":9.812000e-01,\"fGreen\":9.339500e-01,\"fBlue\":8.962500e-02,\"fHue\":-1.588187e-23,\"fLight\":-1.588187e-23,\"fSaturation\":-1.588187e-23,\"fAlpha\":1},{\"_typename\":\"TColor\",\"fUniqueID\":0,\"fBits\":50331648,\"fName\":\"\",\"fTitle\":\"\",\"fNumber\":1172,\"fRed\":9.806000e-01,\"fGreen\":9.401063e-01,\"fBlue\":8.515938e-02,\"fHue\":-1.588187e-23,\"fLight\":-1.588187e-23,\"fSaturation\":-1.588187e-23,\"fAlpha\":1},{\"_typename\":\"TColor\",\"fUniqueID\":0,\"fBits\":50331648,\"fName\":\"\",\"fTitle\":\"\",\"fNumber\":1173,\"fRed\":9.800000e-01,\"fGreen\":9.462625e-01,\"fBlue\":8.069375e-02,\"fHue\":-1.588187e-23,\"fLight\":-1.588187e-23,\"fSaturation\":-1.588187e-23,\"fAlpha\":1},{\"_typename\":\"TColor\",\"fUniqueID\":0,\"fBits\":50331648,\"fName\":\"\",\"fTitle\":\"\",\"fNumber\":1174,\"fRed\":9.794000e-01,\"fGreen\":9.524187e-01,\"fBlue\":7.622813e-02,\"fHue\":-1.588187e-23,\"fLight\":-1.588187e-23,\"fSaturation\":-1.588187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dataset1\",\"fTitle\":\"ROC Curve\",\"fDISPLAY\":\"$DISPLAY\",\"fDoubleBuffer\":0,\"fRetained\":true,\"fXsizeUser\":0,\"fYsizeUser\":0,\"fXsizeReal\":20,\"fYsizeReal\":1.428571e+01,\"fWindowTopX\":0,\"fWindowTopY\":0,\"fWindowWidth\":0,\"fWindowHeight\":0,\"fCw\":696,\"fCh\":472,\"fCatt\":{\"_typename\":\"TAttCanvas\",\"fXBetween\":2,\"fYBetween\":2,\"fTitleFromTop\":1.200000e+00,\"fXdate\":2.000000e-01,\"fYdate\":3.000000e-01,\"fAdate\":1},\"kMoveOpaque\":true,\"kResizeOpaque\":true,\"fHighLightColor\":2,\"fBatch\":true,\"kShowEventStatus\":false,\"kAutoExec\":true,\"kMenuBar\":true});\n",
       "       Core.draw(\"root_plot_1\", obj, \"\");\n",
       "     }\n",
       " );\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "auto c1 = factory.GetROCCurve(&loader1);\n",
    "c1->Draw();\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"root_plot_2\"\n",
       "     style=\"width: 696px; height: 472px\">\n",
       "</div>\n",
       "\n",
       "<script>\n",
       " requirejs.config({\n",
       "     paths: {\n",
       "       'JSRootCore' : 'https://root.cern.ch/js/notebook//scripts/JSRootCore',\n",
       "     }\n",
       "   });\n",
       " require(['JSRootCore'],\n",
       "     function(Core) {\n",
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dataset2\",\"fTitle\":\"ROC Curve\",\"fDISPLAY\":\"$DISPLAY\",\"fDoubleBuffer\":0,\"fRetained\":true,\"fXsizeUser\":0,\"fYsizeUser\":0,\"fXsizeReal\":20,\"fYsizeReal\":1.428571e+01,\"fWindowTopX\":0,\"fWindowTopY\":0,\"fWindowWidth\":0,\"fWindowHeight\":0,\"fCw\":696,\"fCh\":472,\"fCatt\":{\"_typename\":\"TAttCanvas\",\"fXBetween\":2,\"fYBetween\":2,\"fTitleFromTop\":1.200000e+00,\"fXdate\":2.000000e-01,\"fYdate\":3.000000e-01,\"fAdate\":1},\"kMoveOpaque\":true,\"kResizeOpaque\":true,\"fHighLightColor\":2,\"fBatch\":true,\"kShowEventStatus\":false,\"kAutoExec\":true,\"kMenuBar\":true});\n",
       "       Core.draw(\"root_plot_2\", obj, \"\");\n",
       "     }\n",
       " );\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "auto c2 = factory.GetROCCurve(&loader2);\n",
    "c2->Draw();"
   ]
  }
 ],
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