TMVA_DNN.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%\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr style=\"border-top-width: 4px; border-top-color: #34609b;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DNN Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Declare Factory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "require(['notebook'],\n",
       "  function() {\n",
       "    IPython.CodeCell.config_defaults.highlight_modes['magic_text/x-c++src'] = {'reg':[/^%%cpp/]};\n",
       "    console.log(\"JupyROOT - %%cpp magic configured\");\n",
       "  }\n",
       ");\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to JupyROOT 6.09/01\n"
     ]
    }
   ],
   "source": [
    "from ROOT import TMVA, TFile, TTree, TCut, TString"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "TMVA.Tools.Instance()\n",
    "\n",
    "inputFile = TFile.Open(\"https://raw.githubusercontent.com/iml-wg/tmvatutorials/master/inputdata.root\")\n",
    "outputFile = TFile.Open(\"TMVAOutputDNN.root\", \"RECREATE\")\n",
    "\n",
    "factory = TMVA.Factory(\"TMVAClassification\", outputFile,\n",
    "        \"!V:!Silent:Color:!DrawProgressBar:AnalysisType=Classification\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Declare Variables in DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = TMVA.DataLoader(\"dataset_dnn\")\n",
    "\n",
    "loader.AddVariable(\"var1\")\n",
    "loader.AddVariable(\"var2\")\n",
    "loader.AddVariable(\"var3\")\n",
    "loader.AddVariable(\"var4\")\n",
    "loader.AddVariable(\"var5 := var1-var3\")\n",
    "loader.AddVariable(\"var6 := var1+var2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup Dataset(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataSetInfo              : [dataset_dnn] : Added class \"Signal\"\n",
      "                         : Add Tree Sig of type Signal with 6000 events\n",
      "DataSetInfo              : [dataset_dnn] : Added class \"Background\"\n",
      "                         : Add Tree Bkg of type Background with 6000 events\n",
      "                         : Dataset[dataset_dnn] : Class index : 0  name : Signal\n",
      "                         : Dataset[dataset_dnn] : Class index : 1  name : Background\n"
     ]
    }
   ],
   "source": [
    "tsignal = inputFile.Get(\"Sig\")\n",
    "tbackground = inputFile.Get(\"Bkg\")\n",
    "\n",
    "loader.AddSignalTree(tsignal)\n",
    "loader.AddBackgroundTree(tbackground) \n",
    "loader.PrepareTrainingAndTestTree(TCut(\"\"),\n",
    "        \"nTrain_Signal=1000:nTrain_Background=1000:SplitMode=Random:NormMode=NumEvents:!V\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Configure Network Layout "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'!H:!V:ErrorStrategy=CROSSENTROPY:VarTransform=N:WeightInitialization=XAVIERUNIFORM:Layout=TANH|128,TANH|128,TANH|128,LINEAR:TrainingStrategy=LearningRate=1e-1,Momentum=0.9,Repetitions=1,ConvergenceSteps=2,BatchSize=256,TestRepetitions=10,WeightDecay=1e-4,Regularization=L2,DropConfig=0.0+0.5+0.5+0.5, Multithreading=True|LearningRate=1e-2,Momentum=0.9,Repetitions=1,ConvergenceSteps=2,BatchSize=256,TestRepetitions=10,WeightDecay=1e-4,Regularization=L2,DropConfig=0.0+0.0+0.0+0.0, Multithreading=True'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# General layout\n",
    "layoutString = TString(\"Layout=TANH|128,TANH|128,TANH|128,LINEAR\");\n",
    "\n",
    "# Training strategies\n",
    "training0 = TString(\"LearningRate=1e-1,Momentum=0.9,Repetitions=1,\"\n",
    "                        \"ConvergenceSteps=2,BatchSize=256,TestRepetitions=10,\"\n",
    "                        \"WeightDecay=1e-4,Regularization=L2,\"\n",
    "                        \"DropConfig=0.0+0.5+0.5+0.5, Multithreading=True\")\n",
    "training1 = TString(\"LearningRate=1e-2,Momentum=0.9,Repetitions=1,\"\n",
    "                        \"ConvergenceSteps=2,BatchSize=256,TestRepetitions=10,\"\n",
    "                        \"WeightDecay=1e-4,Regularization=L2,\"\n",
    "                        \"DropConfig=0.0+0.0+0.0+0.0, Multithreading=True\")\n",
    "trainingStrategyString = TString(\"TrainingStrategy=\")\n",
    "trainingStrategyString += training0 + TString(\"|\") + training1\n",
    "\n",
    "# General Options\n",
    "dnnOptions = TString(\"!H:!V:ErrorStrategy=CROSSENTROPY:VarTransform=N:\"\n",
    "        \"WeightInitialization=XAVIERUNIFORM\")\n",
    "dnnOptions.Append(\":\")\n",
    "dnnOptions.Append(layoutString)\n",
    "dnnOptions.Append(\":\")\n",
    "dnnOptions.Append(trainingStrategyString)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Booking Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ROOT.TMVA::MethodDNN object (\"DNN\") at 0x564de70>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Factory                  : Booking method: \u001b[1mDNN\u001b[0m\n",
      "                         : \n",
      "DNN                      : [dataset_dnn] : Create Transformation \"N\" with events from all classes.\n",
      "                         : \n",
      "                         : Transformation, Variable selection : \n",
      "                         : Input : variable 'var1' <---> Output : variable 'var1'\n",
      "                         : Input : variable 'var2' <---> Output : variable 'var2'\n",
      "                         : Input : variable 'var3' <---> Output : variable 'var3'\n",
      "                         : Input : variable 'var4' <---> Output : variable 'var4'\n",
      "                         : Input : variable 'var5' <---> Output : variable 'var5'\n",
      "                         : Input : variable 'var6' <---> Output : variable 'var6'\n"
     ]
    }
   ],
   "source": [
    "# Standard implementation, no dependencies.\n",
    "stdOptions =  dnnOptions + \":Architecture=STANDARD\"\n",
    "factory.BookMethod(loader, TMVA.Types.kDNN, \"DNN\", stdOptions)\n",
    "\n",
    "# CPU implementation, using BLAS\n",
    "#cpuOptions = dnnOptions + \":Architecture=CPU\"\n",
    "#factory.BookMethod(loader, TMVA.Types.kDNN, \"DNN CPU\", cpuOptions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Factory                  : \u001b[1mTrain all methods\u001b[0m\n",
      "DataSetFactory           : [dataset_dnn] : Number of events in input trees\n",
      "                         : \n",
      "                         : \n",
      "                         : Number of training and testing events\n",
      "                         : ---------------------------------------------------------------------------\n",
      "                         : Signal     -- training events            : 1000\n",
      "                         : Signal     -- testing events             : 5000\n",
      "                         : Signal     -- training and testing events: 6000\n",
      "                         : Background -- training events            : 1000\n",
      "                         : Background -- testing events             : 5000\n",
      "                         : Background -- training and testing events: 6000\n",
      "                         : \n",
      "DataSetInfo              : Correlation matrix (Signal):\n",
      "                         : --------------------------------------------------------------\n",
      "                         :               var1    var2    var3    var4 var1-var3 var1+var2\n",
      "                         :      var1:  +1.000  +0.386  +0.597  +0.808    +0.442    +0.826\n",
      "                         :      var2:  +0.386  +1.000  +0.696  +0.743    -0.350    +0.839\n",
      "                         :      var3:  +0.597  +0.696  +1.000  +0.860    -0.456    +0.778\n",
      "                         :      var4:  +0.808  +0.743  +0.860  +1.000    -0.065    +0.931\n",
      "                         : var1-var3:  +0.442  -0.350  -0.456  -0.065    +1.000    +0.047\n",
      "                         : var1+var2:  +0.826  +0.839  +0.778  +0.931    +0.047    +1.000\n",
      "                         : --------------------------------------------------------------\n",
      "DataSetInfo              : Correlation matrix (Background):\n",
      "                         : --------------------------------------------------------------\n",
      "                         :               var1    var2    var3    var4 var1-var3 var1+var2\n",
      "                         :      var1:  +1.000  +0.856  +0.914  +0.964    +0.122    +0.966\n",
      "                         :      var2:  +0.856  +1.000  +0.927  +0.937    -0.248    +0.960\n",
      "                         :      var3:  +0.914  +0.927  +1.000  +0.971    -0.290    +0.955\n",
      "                         :      var4:  +0.964  +0.937  +0.971  +1.000    -0.101    +0.987\n",
      "                         : var1-var3:  +0.122  -0.248  -0.290  -0.101    +1.000    -0.057\n",
      "                         : var1+var2:  +0.966  +0.960  +0.955  +0.987    -0.057    +1.000\n",
      "                         : --------------------------------------------------------------\n",
      "DataSetFactory           : [dataset_dnn] :  \n",
      "                         : \n",
      "Factory                  : [dataset_dnn] : Create Transformation \"I\" with events from all classes.\n",
      "                         : \n",
      "                         : Transformation, Variable selection : \n",
      "                         : Input : variable 'var1' <---> Output : variable 'var1'\n",
      "                         : Input : variable 'var2' <---> Output : variable 'var2'\n",
      "                         : Input : variable 'var3' <---> Output : variable 'var3'\n",
      "                         : Input : variable 'var4' <---> Output : variable 'var4'\n",
      "                         : Input : variable 'var5' <---> Output : variable 'var5'\n",
      "                         : Input : variable 'var6' <---> Output : variable 'var6'\n",
      "TFHandler_Factory        : Variable        Mean        RMS   [        Min        Max ]\n",
      "                         : -----------------------------------------------------------\n",
      "                         :     var1:   0.088993     1.6612   [    -4.9583     4.3390 ]\n",
      "                         :     var2:   0.095657     1.5731   [    -4.6474     3.9513 ]\n",
      "                         :     var3:    0.10551     1.7363   [    -5.0373     4.2785 ]\n",
      "                         :     var4:    0.27815     2.1526   [    -5.9505     4.6404 ]\n",
      "                         :     var5:  -0.016515    0.91457   [    -3.1013     3.0035 ]\n",
      "                         :     var6:    0.18465     3.0371   [    -8.1442     7.2697 ]\n",
      "                         : -----------------------------------------------------------\n",
      "                         : Ranking input variables (method unspecific)...\n",
      "IdTransformation         : Ranking result (top variable is best ranked)\n",
      "                         : -----------------------------\n",
      "                         : Rank : Variable  : Separation\n",
      "                         : -----------------------------\n",
      "                         :    1 : var4      : 3.585e-01\n",
      "                         :    2 : var6      : 3.225e-01\n",
      "                         :    3 : var1      : 2.898e-01\n",
      "                         :    4 : var3      : 2.828e-01\n",
      "                         :    5 : var2      : 2.032e-01\n",
      "                         :    6 : var5      : 5.558e-02\n",
      "                         : -----------------------------\n",
      "Factory                  : Train method: DNN for Classification\n",
      "                         : \n",
      "TFHandler_DNN            : Variable        Mean        RMS   [        Min        Max ]\n",
      "                         : -----------------------------------------------------------\n",
      "                         :     var1:   0.085752    0.35735   [    -1.0000     1.0000 ]\n",
      "                         :     var2:    0.10321    0.36589   [    -1.0000     1.0000 ]\n",
      "                         :     var3:    0.10411    0.37276   [    -1.0000     1.0000 ]\n",
      "                         :     var4:    0.17623    0.40650   [    -1.0000     1.0000 ]\n",
      "                         :     var5:   0.010608    0.29962   [    -1.0000     1.0000 ]\n",
      "                         :     var6:   0.080694    0.39407   [    -1.0000     1.0000 ]\n",
      "                         : -----------------------------------------------------------\n",
      "TFHandler_DNN            : Variable        Mean        RMS   [        Min        Max ]\n",
      "                         : -----------------------------------------------------------\n",
      "                         :     var1:   0.085752    0.35735   [    -1.0000     1.0000 ]\n",
      "                         :     var2:    0.10321    0.36589   [    -1.0000     1.0000 ]\n",
      "                         :     var3:    0.10411    0.37276   [    -1.0000     1.0000 ]\n",
      "                         :     var4:    0.17623    0.40650   [    -1.0000     1.0000 ]\n",
      "                         :     var5:   0.010608    0.29962   [    -1.0000     1.0000 ]\n",
      "                         :     var6:   0.080694    0.39407   [    -1.0000     1.0000 ]\n",
      "                         : -----------------------------------------------------------\n",
      "TFHandler_DNN            : Variable        Mean        RMS   [        Min        Max ]\n",
      "                         : -----------------------------------------------------------\n",
      "                         :     var1:   0.066774    0.35913   [    -1.2024     1.0914 ]\n",
      "                         :     var2:   0.079492    0.36669   [    -1.1391     1.2044 ]\n",
      "                         :     var3:   0.079125    0.37282   [    -1.0685     1.0783 ]\n",
      "                         :     var4:    0.15120    0.40805   [    -1.1921     1.0737 ]\n",
      "                         :     var5:   0.019832    0.30171   [    -1.0464     1.1641 ]\n",
      "                         :     var6:   0.056015    0.39567   [    -1.2227     1.0821 ]\n",
      "                         : -----------------------------------------------------------\n",
      "                         : Using Standard Implementation.Training with learning rate = 0.1, momentum = 0.9, repetitions = 1\n",
      "                         : \n",
      "                         : \n",
      "                         : Elapsed time for training with 2000 events: 29.2 sec         \n",
      "DNN                      : [dataset_dnn] : Evaluation of DNN on training sample (2000 events)\n",
      "                         : Elapsed time for evaluation of 2000 events: 0.154 sec       \n",
      "                         : Creating xml weight file: \u001b[0;36mdataset_dnn/weights/TMVAClassification_DNN.weights.xml\u001b[0m\n",
      "                         : Creating standalone class: \u001b[0;36mdataset_dnn/weights/TMVAClassification_DNN.class.C\u001b[0m\n",
      "Factory                  : Training finished\n",
      "                         : \n",
      "                         : Ranking input variables (method specific)...\n",
      "DNN                      : Ranking result (top variable is best ranked)\n",
      "                         : -----------------------------\n",
      "                         : Rank : Variable  : Importance\n",
      "                         : -----------------------------\n",
      "                         :    1 : var1      : 1.000e+00\n",
      "                         :    2 : var2      : 1.000e+00\n",
      "                         :    3 : var3      : 1.000e+00\n",
      "                         :    4 : var4      : 1.000e+00\n",
      "                         :    5 : var5      : 1.000e+00\n",
      "                         :    6 : var6      : 1.000e+00\n",
      "                         : -----------------------------\n",
      "Factory                  : === Destroy and recreate all methods via weight files for testing ===\n",
      "                         : \n"
     ]
    }
   ],
   "source": [
    "factory.TrainAllMethods()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test and Evaluate Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Factory                  : \u001b[1mTest all methods\u001b[0m\n",
      "Factory                  : Test method: DNN for Classification performance\n",
      "                         : \n",
      "DNN                      : [dataset_dnn] : Evaluation of DNN on testing sample (10000 events)\n",
      "                         : Elapsed time for evaluation of 10000 events: 0.753 sec       \n",
      "Factory                  : \u001b[1mEvaluate all methods\u001b[0m\n",
      "Factory                  : Evaluate classifier: DNN\n",
      "                         : \n",
      "TFHandler_DNN            : Variable        Mean        RMS   [        Min        Max ]\n",
      "                         : -----------------------------------------------------------\n",
      "                         :     var1:   0.066774    0.35913   [    -1.2024     1.0914 ]\n",
      "                         :     var2:   0.079492    0.36669   [    -1.1391     1.2044 ]\n",
      "                         :     var3:   0.079125    0.37282   [    -1.0685     1.0783 ]\n",
      "                         :     var4:    0.15120    0.40805   [    -1.1921     1.0737 ]\n",
      "                         :     var5:   0.019832    0.30171   [    -1.0464     1.1641 ]\n",
      "                         :     var6:   0.056015    0.39567   [    -1.2227     1.0821 ]\n",
      "                         : -----------------------------------------------------------\n",
      "DNN                      : [dataset_dnn] : Loop over test events and fill histograms with classifier response...\n",
      "                         : \n",
      "TFHandler_DNN            : Variable        Mean        RMS   [        Min        Max ]\n",
      "                         : -----------------------------------------------------------\n",
      "                         :     var1:   0.066774    0.35913   [    -1.2024     1.0914 ]\n",
      "                         :     var2:   0.079492    0.36669   [    -1.1391     1.2044 ]\n",
      "                         :     var3:   0.079125    0.37282   [    -1.0685     1.0783 ]\n",
      "                         :     var4:    0.15120    0.40805   [    -1.1921     1.0737 ]\n",
      "                         :     var5:   0.019832    0.30171   [    -1.0464     1.1641 ]\n",
      "                         :     var6:   0.056015    0.39567   [    -1.2227     1.0821 ]\n",
      "                         : -----------------------------------------------------------\n",
      "                         : \n",
      "                         : Evaluation results ranked by best signal efficiency and purity (area)\n",
      "                         : -------------------------------------------------------------------------------------------------------------------\n",
      "                         : DataSet       MVA                       \n",
      "                         : Name:         Method:          ROC-integ\n",
      "                         : dataset_dnn   DNN            : 0.932\n",
      "                         : -------------------------------------------------------------------------------------------------------------------\n",
      "                         : \n",
      "                         : Testing efficiency compared to training efficiency (overtraining check)\n",
      "                         : -------------------------------------------------------------------------------------------------------------------\n",
      "                         : DataSet              MVA              Signal efficiency: from test sample (from training sample) \n",
      "                         : Name:                Method:          @B=0.01             @B=0.10            @B=0.30   \n",
      "                         : -------------------------------------------------------------------------------------------------------------------\n",
      "                         : dataset_dnn          DNN            : 0.350 (0.364)       0.781 (0.793)      0.954 (0.958)\n",
      "                         : -------------------------------------------------------------------------------------------------------------------\n",
      "                         : \n",
      "Dataset:dataset_dnn      : Created tree 'TestTree' with 10000 events\n",
      "                         : \n",
      "Dataset:dataset_dnn      : Created tree 'TrainTree' with 2000 events\n",
      "                         : \n",
      "Factory                  : \u001b[1mThank you for using TMVA!\u001b[0m\n",
      "                         : \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": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%jsroot on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
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dataset_dnn\",\"fTitle\":\"ROC Curve\",\"fNumPaletteColor\":0,\"fNextPaletteColor\":0,\"fDISPLAY\":\"$DISPLAY\",\"fDoubleBuffer\":0,\"fRetained\":true,\"fXsizeUser\":0,\"fYsizeUser\":0,\"fXsizeReal\":20,\"fYsizeReal\":14.28571,\"fWindowTopX\":0,\"fWindowTopY\":0,\"fWindowWidth\":0,\"fWindowHeight\":0,\"fCw\":696,\"fCh\":472,\"fCatt\":{\"_typename\":\"TAttCanvas\",\"fXBetween\":2,\"fYBetween\":2,\"fTitleFromTop\":1.2,\"fXdate\":0.2,\"fYdate\":0.3,\"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": [
    "c = factory.GetROCCurve(loader)\n",
    "c.Draw()"
   ]
  }
 ],
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