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  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "TMVA::DataLoader loader(\"dataset\");\n",
    "\n",
    "loader.AddVariable(\"var1\");\n",
    "loader.AddVariable(\"var2\");\n",
    "loader.AddVariable(\"var3\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup Dataset(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "TTree *tsignal, *tbackground;\n",
    "inputFile->GetObject(\"Sig\", tsignal);\n",
    "inputFile->GetObject(\"Bkg\", tbackground);\n",
    "\n",
    "TCut mycuts, mycutb;\n",
    "\n",
    "loader.AddSignalTree    (tsignal,     1.0);   //signal weight  = 1\n",
    "loader.AddBackgroundTree(tbackground, 1.0);   //background weight = 1 \n",
    "loader.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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "//Boosted Decision Trees\n",
    "factory.BookMethod(&loader,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(&loader, TMVA::Types::kMLP, \"MLP\",\n",
    "                   \"!H:!V:NeuronType=tanh:VarTransform=N:NCycles=100:HiddenLayers=N+5:TestRate=5:!UseRegulator\" );"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "factory.TrainAllMethods();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test and Evaluate Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%jsroot on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "auto c1 = factory.GetROCCurve(&loader);\n",
    "c1->Draw();\n"
   ]
  }
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