notebooks/ForSergei.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TMVA in Jupyter notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "current limit exceeds maximum limit",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mValueError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-2fba2a3c1dd1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mROOT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mROOT\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTFile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTMVA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTCut\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/qati/.usr/root/lib/ROOT.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    589\u001b[0m    \u001b[0mip\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    590\u001b[0m    \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mip\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"kernel\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 591\u001b[0;31m       \u001b[0;32mimport\u001b[0m \u001b[0mJupyROOT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    592\u001b[0m       \u001b[0;32mimport\u001b[0m \u001b[0mJsMVA\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    593\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/qati/.usr/root/lib/ROOT.py\u001b[0m in \u001b[0;36m_importhook\u001b[0;34m(name, *args, **kwds)\u001b[0m\n\u001b[1;32m    301\u001b[0m       \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    302\u001b[0m          \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 303\u001b[0;31m    \u001b[0;32mreturn\u001b[0m \u001b[0m_orig_ihook\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    304\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    305\u001b[0m \u001b[0m__builtin__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__import__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_importhook\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/qati/.usr/root/lib/JupyROOT/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'__IPYTHON__'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m__builtins__\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0m__IPYTHON__\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miPythonize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/home/qati/.usr/root/lib/JupyROOT/utils.py\u001b[0m in \u001b[0;36miPythonize\u001b[0;34m()\u001b[0m\n\u001b[1;32m    504\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0miPythonize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    505\u001b[0m     \u001b[0msetStyle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 506\u001b[0;31m     \u001b[0mloadExtensionsAndCapturers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    507\u001b[0m     \u001b[0menableCppHighlighting\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    508\u001b[0m     \u001b[0menhanceROOTModule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/qati/.usr/root/lib/JupyROOT/utils.py\u001b[0m in \u001b[0;36mloadExtensionsAndCapturers\u001b[0;34m()\u001b[0m\n\u001b[1;32m    486\u001b[0m         \u001b[0mextMgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_extension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextName\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    487\u001b[0m     \u001b[0mcppcompleter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_ipython_extension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mip\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 488\u001b[0;31m     \u001b[0mcaptures\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mStreamCapture\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    489\u001b[0m     \u001b[0mcaptures\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCaptureDrawnPrimitives\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    490\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/qati/.usr/root/lib/JupyROOT/utils.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, ip)\u001b[0m\n\u001b[1;32m    249\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrString\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 251\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masyncCapturer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRunner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msyncCapture\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    252\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msyncCapture\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdefout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/qati/.usr/root/lib/JupyROOT/handlers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, function)\u001b[0m\n\u001b[1;32m     89\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mplatform\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'darwin'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 91\u001b[0;31m             \u001b[0msetrlimit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRLIMIT_STACK\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRLIM_INFINITY\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mRLIM_INFINITY\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthread\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: current limit exceeds maximum limit"
     ]
    }
   ],
   "source": [
    "import ROOT\n",
    "from ROOT import TFile, TMVA, TCut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<link rel=\"stylesheet\" href=\"https://rawgit.com/qati/GSOC16/master/src/css/TMVAHTMLOutput.min.css\"></link>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class='tmva_output_table'></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%jsmva on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class='tmva_output_table'><tbody class='tmva_output_tbody_multiple_row'><tr><td rowspan='2' class='tmva_output_header'>Factory                 </td><td><b>You are running ROOT Version: 6.07/07, Apr 1, 2016</b></td></tr><tr><td><img src='https://rawgit.com/qati/GSOC16/master/img/tmva-logo.svg' width='100%' /><br /><center><b>TMVA Version 4.2.1, Feb 5, 2015</b></center></td></tr></tr><tr></tr></tbody></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TMVA.Tools.Instance()\n",
    "\n",
    "factory = TMVA.Factory(JobName=\"TMVAClassification\",\n",
    "                       V=False, Transformations=[\"I\", \"D\", \"P\", \"G\", \"D\"],\n",
    "                       AnalysisType=\"Classification\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class='tmva_output_table'><tbody class='tmva_output_tbody_multiple_row'><tr><td rowspan='2' class='tmva_output_header'>DataSetInfo             </td><td><table class='tmva_output_dataset'><tbody><tr><td rowspan='1'>Dataset: tmva_class_example</td><td>Added class \"Signal\"</td><td class='tmva_output_hidden_td'></td></tr></tbody></table></td></tr><tr><td>Add Tree TreeS of type Signal with 6000 events</td></tr></tbody><tbody class='tmva_output_tbody_multiple_row'><tr><td rowspan='2' class='tmva_output_header'>DataSetInfo             </td><td><table class='tmva_output_dataset'><tbody><tr><td rowspan='1'>Dataset: tmva_class_example</td><td>Added class \"Background\"</td><td class='tmva_output_hidden_td'></td></tr></tbody></table></td></tr><tr><td>Add Tree TreeB of type Background with 6000 events</td></tr></tbody></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if ROOT.gSystem.AccessPathName( \"tmva_class_example.root\" ) != 0: \n",
    "    ROOT.gSystem.Exec( \"wget https://root.cern.ch/files/tmva_class_example.root\")\n",
    "    \n",
    "dataset = \"tmva_class_example\" #the dataset name\n",
    "loader  = TMVA.DataLoader(dataset)\n",
    "\n",
    "loader.AddVariable( \"myvar1 := var1+var2\", 'F' )\n",
    "loader.AddVariable( \"myvar2 := var1-var2\", \"Expression 2\", 'F' )\n",
    "loader.AddVariable( \"var3\",                \"Variable 3\", 'F' )\n",
    "loader.AddVariable( \"var4\",                \"Variable 4\", 'F' )\n",
    "loader.AddSpectator( \"spec1:=var1*2\",  \"Spectator 1\",  'F' )\n",
    "loader.AddSpectator( \"spec2:=var1*3\",  \"Spectator 2\",  'F' )\n",
    "\n",
    "\n",
    "input = TFile.Open( \"tmva_class_example.root\" )\n",
    "\n",
    "# Get the signal and background trees for training\n",
    "signal      = input.Get( \"TreeS\" )\n",
    "background  = input.Get( \"TreeB\" )\n",
    "\n",
    "# Global event weights (see below for setting event-wise weights)\n",
    "signalWeight     = 1.0\n",
    "backgroundWeight = 1.0\n",
    "\n",
    "loader.AddSignalTree(signal, signalWeight)\n",
    "loader.AddBackgroundTree(background, backgroundWeight)\n",
    "\n",
    "loader.fSignalWeight = signalWeight\n",
    "loader.fBackgroundWeight = backgroundWeight\n",
    "loader.fTreeS = signal\n",
    "loader.fTreeB = background"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class='tmva_output_table'></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mycuts = TCut(\"\")\n",
    "mycutb = TCut(\"\")\n",
    "\n",
    "loader.PrepareTrainingAndTestTree(SigCut=mycuts, BkgCut=mycutb,\n",
    "                    nTrain_Signal=0, nTrain_Background=0, SplitMode=\"Random\", NormMode=\"NumEvents\", V=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Book DNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<link rel=\"stylesheet\" href=\"https://rawgit.com/qati/GSOC16/master/src/css/NetworkDesigner.min.css\"></link>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"jstmva_1\" style=\"width: 800px; height:450px\"></div>\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'JsMVA':'https://rawgit.com/qati/GSOC16/master/src/js/JsMVA.min'\n",
       "        }\n",
       "    });\n",
       "    require(['JsMVA'],function(jsmva){\n",
       "        jsmva.NetworkDesigner('jstmva_1','');\n",
       "    });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class='tmva_output_table'></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "factory.BookDNN(loader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Old school DNN booking\n",
    "#### if you didn't use BookDNN for booking, uncomment the commented part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ROOT.TMVA::MethodDNN object (\"DNN\") at 0x5985e90>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<table class='tmva_output_table'><tbody class='tmva_output_tbody_multiple_row'><tr><td rowspan='2' class='tmva_output_header'>Factory                 </td><td>Booking method: <b> DNN</b></td></tr><tr></tr></tbody><tbody><tr><td rowspan='1' class='tmva_output_header'>DNN                     </td><td><table class='tmva_output_dataset'><tbody class='tmva_output_tbody_multiple_row'><tr><td rowspan='2'>Dataset: tmva_class_example</td><td>Create Transformation \"Normalize\" with events from all classes.</td><td class='tmva_output_hidden_td'></td></tr></tbody></table></td></tr></tbody><tbody class='tmva_output_tbody_multiple_row'><tr><td rowspan='5' class='tmva_output_header'>Norm                    </td><td>Transformation, Variable selection : </td></tr><tr><td>Input : variable 'myvar1' <---> Output : variable 'myvar1'</td></tr><tr><td>Input : variable 'myvar2' <---> Output : variable 'myvar2'</td></tr><tr><td>Input : variable 'var3' <---> Output : variable 'var3'</td></tr><tr><td>Input : variable 'var4' <---> Output : variable 'var4'</td></tr></tbody></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainingStrategy = [{\n",
    "        \"LearningRate\": 1e-1,\n",
    "        \"Momentum\": 0.0,\n",
    "        \"Repetitions\": 1,\n",
    "        \"ConvergenceSteps\": 300,\n",
    "        \"BatchSize\": 20,\n",
    "        \"TestRepetitions\": 15,\n",
    "        \"WeightDecay\": 0.001,\n",
    "        \"Regularization\": \"NONE\",\n",
    "        \"DropConfig\": \"0.0+0.5+0.5+0.5\",\n",
    "        \"DropRepetitions\": 1,\n",
    "        \"Multithreading\": True\n",
    "        \n",
    "    }, {\n",
    "        \"LearningRate\": 1e-2,\n",
    "        \"Momentum\": 0.5,\n",
    "        \"Repetitions\": 1,\n",
    "        \"ConvergenceSteps\": 300,\n",
    "        \"BatchSize\": 30,\n",
    "        \"TestRepetitions\": 7,\n",
    "        \"WeightDecay\": 0.001,\n",
    "        \"Regularization\": \"L2\",\n",
    "        \"DropConfig\": \"0.0+0.1+0.1+0.1\",\n",
    "        \"DropRepetitions\": 1,\n",
    "        \"Multithreading\": True\n",
    "        \n",
    "    }, {\n",
    "        \"LearningRate\": 1e-2,\n",
    "        \"Momentum\": 0.3,\n",
    "        \"Repetitions\": 1,\n",
    "        \"ConvergenceSteps\": 300,\n",
    "        \"BatchSize\": 40,\n",
    "        \"TestRepetitions\": 7,\n",
    "        \"WeightDecay\": 0.001,\n",
    "        \"Regularization\": \"L2\",\n",
    "        \"Multithreading\": True\n",
    "        \n",
    "    },{\n",
    "        \"LearningRate\": 1e-3,\n",
    "        \"Momentum\": 0.1,\n",
    "        \"Repetitions\": 1,\n",
    "        \"ConvergenceSteps\": 200,\n",
    "        \"BatchSize\": 70,\n",
    "        \"TestRepetitions\": 7,\n",
    "        \"WeightDecay\": 0.001,\n",
    "        \"Regularization\": \"NONE\",\n",
    "        \"Multithreading\": True\n",
    "        \n",
    "}]\n",
    "\n",
    "#factory.BookMethod(DataLoader=loader, Method=TMVA.Types.kDNN, MethodTitle=\"DNN\", H = False, V=False, \n",
    "#     VarTransform=\"Normalize\", ErrorStrategy=\"CROSSENTROPY\", Layout=[\"TANH|100\", \"TANH|50\", \"TANH|10\", \"LINEAR\"],\n",
    "#                   TrainingStrategy=trainingStrategy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Book MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ROOT.TMVA::MethodMLP object (\"MLP\") at 0x5a79860>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<table class='tmva_output_table'><tbody class='tmva_output_tbody_multiple_row'><tr><td rowspan='2' class='tmva_output_header'>Factory                 </td><td>Booking method: <b> MLP</b></td></tr><tr></tr></tbody><tbody><tr><td rowspan='1' class='tmva_output_header'>MLP                     </td><td><table class='tmva_output_dataset'><tbody class='tmva_output_tbody_multiple_row'><tr><td rowspan='2'>Dataset: tmva_class_example</td><td>Create Transformation \"N\" with events from all classes.</td><td class='tmva_output_hidden_td'></td></tr></tbody></table></td></tr></tbody><tbody class='tmva_output_tbody_multiple_row'><tr><td rowspan='5' class='tmva_output_header'>Norm                    </td><td>Transformation, Variable selection : </td></tr><tr><td>Input : variable 'myvar1' <---> Output : variable 'myvar1'</td></tr><tr><td>Input : variable 'myvar2' <---> Output : variable 'myvar2'</td></tr><tr><td>Input : variable 'var3' <---> Output : variable 'var3'</td></tr><tr><td>Input : variable 'var4' <---> Output : variable 'var4'</td></tr></tbody><tbody class='tmva_output_tbody_multiple_row'><tr><td rowspan='2' class='tmva_output_header'>MLP                     </td><td>Building Network. </td></tr><tr><td>Initializing weights</td></tr></tbody></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "factory.BookMethod( DataLoader=loader, Method=TMVA.Types.kMLP, MethodTitle=\"MLP\", \n",
    "                    H=False, V=False, NeuronType=\"tanh\", VarTransform=\"N\", NCycles=600, HiddenLayers=\"N+5\",\n",
    "                    TestRate=5, UseRegulator=False )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Book BDT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ROOT.TMVA::MethodBDT object (\"BDT\") at 0x5b219a0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<table class='tmva_output_table'><tbody class='tmva_output_tbody_multiple_row'><tr><td rowspan='2' class='tmva_output_header'>Factory                 </td><td>Booking method: <b> BDT</b></td></tr><tr></tr></tbody><tbody class='tmva_output_tbody_multiple_row'><tr><td rowspan='2' class='tmva_output_header'>DataSetFactory          </td><td><table class='tmva_output_dataset'><tbody><tr><td rowspan='1'>Dataset: tmva_class_example</td><td>Number of events in input trees</td><td class='tmva_output_hidden_td'></td></tr></tbody></table></td></tr><tr><td><table class='tmva_output_traintestevents'><tr><td colspan='3'><center>Number of training and testing events</center></td></tr><tr><td rowspan='3'>Signal</td><td>training events</td><td>3000</td><td class='tmva_output_hidden_td'></td></tr><tr><td>testing events</td><td>3000</td><td class='tmva_output_hidden_td'></td></tr><tr><td>training and testing events</td><td>6000</td><td class='tmva_output_hidden_td'></td></tr><tr><td rowspan='3'>Background</td><td>training events</td><td>3000</td><td class='tmva_output_hidden_td'></td></tr><tr><td>testing events</td><td>3000</td><td class='tmva_output_hidden_td'></td></tr><tr><td>training and testing events</td><td>6000</td><td class='tmva_output_hidden_td'></td></tr></table></td></tr></tbody><tbody><tr><td rowspan='1' class='tmva_output_header'>DataSetInfo             </td><div id='jsmva_outputtansformer_events_0_onclick' style='display: none;'>{  \"_typename\" : \"TH2F\",  \"fUniqueID\" : 0,  \"fBits\" : 50332168,  \"fName\" : \"CorrelationMatrixSignal\",  \"fTitle\" : \"Correlation Matrix (Signal)\",  \"fLineColor\" : 602,  \"fLineStyle\" : 1,  \"fLineWidth\" : 3,  \"fFillColor\" : 0,  \"fFillStyle\" : 1001,  \"fMarkerColor\" : 0,  \"fMarkerStyle\" : 8,  \"fMarkerSize\" : 1.500000e+00,  \"fNcells\" : 36,  \"fXaxis\" : {    \"_typename\" : \"TAxis\",    \"fUniqueID\" : 0,    \"fBits\" : 51380232,    \"fName\" : \"xaxis\",    \"fTitle\" : \"\",    \"fNdivisions\" : 510,    \"fAxisColor\" : 1,    \"fLabelColor\" : 1,    \"fLabelFont\" : 42,    \"fLabelOffset\" : 1.100000e-02,    \"fLabelSize\" : 4.000000e-02,    \"fTickLength\" : 3.000000e-02,    \"fTitleOffset\" : 1,    \"fTitleSize\" : 3.500000e-02,    \"fTitleColor\" : 1,    \"fTitleFont\" : 42,    \"fNbins\" : 4,    \"fXmin\" : 0,    \"fXmax\" : 4,    \"fXbins\" : [],    \"fFirst\" : 0,    \"fLast\" : 0,    \"fBits2\" : 3,    \"fTimeDisplay\" : false,    \"fTimeFormat\" : \"\",    \"fLabels\" : {      \"_typename\" : \"THashList\",      \"name\" : \"THashList\",      \"arr\" : [{        \"_typename\" : \"TObjString\",        \"fUniqueID\" : 1,        \"fBits\" : 50331648,        \"fString\" : \"myvar1\"      }, {        \"_typename\" : \"TObjString\",        \"fUniqueID\" : 2,        \"fBits\" : 50331648,        \"fString\" : \"Expression 2\"      }, {        \"_typename\" : \"TObjString\",        \"fUniqueID\" : 3,        \"fBits\" : 50331648,        \"fString\" : \"Variable 3\"      }, {        \"_typename\" : \"TObjString\",        \"fUniqueID\" : 4,        \"fBits\" : 50331648,        \"fString\" : \"Variable 4\"      }],      \"opt\" : [\"\", \"\", \"\", \"\"]    }  },  \"fYaxis\" : {    \"_typename\" : \"TAxis\",    \"fUniqueID\" : 0,    \"fBits\" : 50331656,    \"fName\" : \"yaxis\",    \"fTitle\" : \"\",    \"fNdivisions\" : 510,    \"fAxisColor\" : 1,    \"fLabelColor\" : 1,    \"fLabelFont\" : 42,    \"fLabelOffset\" : 5.000000e-03,    \"fLabelSize\" : 4.000000e-02,    \"fTickLength\" : 3.000000e-02,    \"fTitleOffset\" : 1,    \"fTitleSize\" : 3.500000e-02,    \"fTitleColor\" : 1,    \"fTitleFont\" : 42,    \"fNbins\" : 4,    \"fXmin\" : 0,    \"fXmax\" : 4,    \"fXbins\" : [],    \"fFirst\" : 0,    \"fLast\" : 0,    \"fBits2\" : 3,    \"fTimeDisplay\" : false,    \"fTimeFormat\" : \"\",    \"fLabels\" : {      \"_typename\" : \"THashList\",      \"name\" : \"THashList\",      \"arr\" : [{        \"_typename\" : \"TObjString\",        \"fUniqueID\" : 1,        \"fBits\" : 50331648,        \"fString\" : \"myvar1\"      }, {        \"_typename\" : \"TObjString\",        \"fUniqueID\" : 2,        \"fBits\" : 50331648,        \"fString\" : \"Expression 2\"      }, {        \"_typename\" : \"TObjString\",        \"fUniqueID\" : 3,        \"fBits\" : 50331648,        \"fString\" : \"Variable 3\"      }, {        \"_typename\" : \"TObjString\",        \"fUniqueID\" : 4,        \"fBits\" : 50331648,        \"fString\" : \"Variable 4\"      }],      \"opt\" : [\"\", \"\", \"\", \"\"]    }  },  \"fZaxis\" : {    \"_typename\" : \"TAxis\",    \"fUniqueID\" : 0,    \"fBits\" : 50331648,    \"fName\" : \"zaxis\",    \"fTitle\" : \"\",    \"fNdivisions\" : 510,    \"fAxisColor\" : 1,    \"fLabelColor\" : 1,    \"fLabelFont\" : 42,    \"fLabelOffset\" : 5.000000e-03,    \"fLabelSize\" : 3.500000e-02,    \"fTickLength\" : 3.000000e-02,    \"fTitleOffset\" : 1,    \"fTitleSize\" : 3.500000e-02,    \"fTitleColor\" : 1,    \"fTitleFont\" : 42,    \"fNbins\" : 1,    \"fXmin\" : 0,    \"fXmax\" : 1,    \"fXbins\" : [],    \"fFirst\" : 0,    \"fLast\" : 0,    \"fBits2\" : 0,    \"fTimeDisplay\" : false,    \"fTimeFormat\" : \"\",    \"fLabels\" : null  },  \"fBarOffset\" : 0,  \"fBarWidth\" : 1000,  \"fEntries\" : 32,  \"fTsumw\" : 0,  \"fTsumw2\" : 0,  \"fTsumwx\" : 0,  \"fTsumwx2\" : 0,  \"fMaximum\" : 100,  \"fMinimum\" : -100,  \"fNormFactor\" : 0,  \"fContour\" : [],  \"fSumw2\" : [],  \"fOption\" : \"\",  \"fFunctions\" : {    \"_typename\" : \"TList\",    \"name\" : \"TList\",    \"arr\" : [],    \"opt\" : []  },  \"fBufferSize\" : 0,  \"fBuffer\" : [],  \"fBinStatErrOpt\" : 0,  \"fScalefactor\" : 1,  \"fTsumwy\" : 0,  \"fTsumwy2\" : 0,  \"fTsumwxy\" : 0,  \"fArray\" : [0, 0, 0, 0, 0, 0, 0, 100, 0, 77, 92, 0, 0, 0, 100, -9, 6, 0, 0, 77, -9, 100, 85, 0, 0, 92, 6, 85, 100, 0, 0, 0, 0, 0, 0, 0]}</div><td><a onclick=\"require(['JsMVA'],function(jsmva){jsmva.outputShowCorrelationMatrix('jsmva_outputtansformer_events_0_onclick');});\" class='tmva_output_corrmat_link'>Correlation matrix (Signal)</a></td></tr></tbody><tbody><tr><td rowspan='1' class='tmva_output_header'>DataSetInfo             </td><div id='jsmva_outputtansformer_events_1_onclick' style='display: none;'>{  \"_typename\" : \"TH2F\",  \"fUniqueID\" : 0,  \"fBits\" : 50332168,  \"fName\" : \"CorrelationMatrixBackground\",  \"fTitle\" : \"Correlation Matrix (Background)\",  \"fLineColor\" : 602,  \"fLineStyle\" : 1,  \"fLineWidth\" : 3,  \"fFillColor\" : 0,  \"fFillStyle\" : 1001,  \"fMarkerColor\" : 0,  \"fMarkerStyle\" : 8,  \"fMarkerSize\" : 1.500000e+00,  \"fNcells\" : 36,  \"fXaxis\" : {    \"_typename\" : \"TAxis\",    \"fUniqueID\" : 0,    \"fBits\" : 51380232,    \"fName\" : \"xaxis\",    \"fTitle\" : \"\",    \"fNdivisions\" : 510,    \"fAxisColor\" : 1,    \"fLabelColor\" : 1,    \"fLabelFont\" : 42,    \"fLabelOffset\" : 1.100000e-02,    \"fLabelSize\" : 4.000000e-02,    \"fTickLength\" : 3.000000e-02,    \"fTitleOffset\" : 1,    \"fTitleSize\" : 3.500000e-02,    \"fTitleColor\" : 1,    \"fTitleFont\" : 42,    \"fNbins\" : 4,    \"fXmin\" : 0,    \"fXmax\" : 4,    \"fXbins\" : [],    \"fFirst\" : 0,    \"fLast\" : 0,    \"fBits2\" : 3,    \"fTimeDisplay\" : false,    \"fTimeFormat\" : \"\",    \"fLabels\" : {      \"_typename\" : \"THashList\",      \"name\" : \"THashList\",      \"arr\" : [{        \"_typename\" : \"TObjString\",        \"fUniqueID\" : 1,        \"fBits\" : 50331648,        \"fString\" : \"myvar1\"      }, {        \"_typename\" : \"TObjString\",        \"fUniqueID\" : 2,        \"fBits\" : 50331648,        \"fString\" : \"Expression 2\"      }, {        \"_typename\" : \"TObjString\",        \"fUniqueID\" : 3,        \"fBits\" : 50331648,        \"fString\" : \"Variable 3\"      }, {        \"_typename\" : \"TObjString\",        \"fUniqueID\" : 4,        \"fBits\" : 50331648,        \"fString\" : \"Variable 4\"      }],      \"opt\" : [\"\", \"\", \"\", \"\"]    }  },  \"fYaxis\" : {    \"_typename\" : \"TAxis\",    \"fUniqueID\" : 0,    \"fBits\" : 50331656,    \"fName\" : \"yaxis\",    \"fTitle\" : \"\",    \"fNdivisions\" : 510,    \"fAxisColor\" : 1,    \"fLabelColor\" : 1,    \"fLabelFont\" : 42,    \"fLabelOffset\" : 5.000000e-03,    \"fLabelSize\" : 4.000000e-02,    \"fTickLength\" : 3.000000e-02,    \"fTitleOffset\" : 1,    \"fTitleSize\" : 3.500000e-02,    \"fTitleColor\" : 1,    \"fTitleFont\" : 42,    \"fNbins\" : 4,    \"fXmin\" : 0,    \"fXmax\" : 4,    \"fXbins\" : [],    \"fFirst\" : 0,    \"fLast\" : 0,    \"fBits2\" : 3,    \"fTimeDisplay\" : false,    \"fTimeFormat\" : \"\",    \"fLabels\" : {      \"_typename\" : \"THashList\",      \"name\" : \"THashList\",      \"arr\" : [{        \"_typename\" : \"TObjString\",        \"fUniqueID\" : 1,        \"fBits\" : 50331648,        \"fString\" : \"myvar1\"      }, {        \"_typename\" : \"TObjString\",        \"fUniqueID\" : 2,        \"fBits\" : 50331648,        \"fString\" : \"Expression 2\"      }, {        \"_typename\" : \"TObjString\",        \"fUniqueID\" : 3,        \"fBits\" : 50331648,        \"fString\" : \"Variable 3\"      }, {        \"_typename\" : \"TObjString\",        \"fUniqueID\" : 4,        \"fBits\" : 50331648,        \"fString\" : \"Variable 4\"      }],      \"opt\" : [\"\", \"\", \"\", \"\"]    }  },  \"fZaxis\" : {    \"_typename\" : \"TAxis\",    \"fUniqueID\" : 0,    \"fBits\" : 50331648,    \"fName\" : \"zaxis\",    \"fTitle\" : \"\",    \"fNdivisions\" : 510,    \"fAxisColor\" : 1,    \"fLabelColor\" : 1,    \"fLabelFont\" : 42,    \"fLabelOffset\" : 5.000000e-03,    \"fLabelSize\" : 3.500000e-02,    \"fTickLength\" : 3.000000e-02,    \"fTitleOffset\" : 1,    \"fTitleSize\" : 3.500000e-02,    \"fTitleColor\" : 1,    \"fTitleFont\" : 42,    \"fNbins\" : 1,    \"fXmin\" : 0,    \"fXmax\" : 1,    \"fXbins\" : [],    \"fFirst\" : 0,    \"fLast\" : 0,    \"fBits2\" : 0,    \"fTimeDisplay\" : false,    \"fTimeFormat\" : \"\",    \"fLabels\" : null  },  \"fBarOffset\" : 0,  \"fBarWidth\" : 1000,  \"fEntries\" : 32,  \"fTsumw\" : 0,  \"fTsumw2\" : 0,  \"fTsumwx\" : 0,  \"fTsumwx2\" : 0,  \"fMaximum\" : 100,  \"fMinimum\" : -100,  \"fNormFactor\" : 0,  \"fContour\" : [],  \"fSumw2\" : [],  \"fOption\" : \"\",  \"fFunctions\" : {    \"_typename\" : \"TList\",    \"name\" : \"TList\",    \"arr\" : [],    \"opt\" : []  },  \"fBufferSize\" : 0,  \"fBuffer\" : [],  \"fBinStatErrOpt\" : 0,  \"fScalefactor\" : 1,  \"fTsumwy\" : 0,  \"fTsumwy2\" : 0,  \"fTsumwxy\" : 0,  \"fArray\" : [0, 0, 0, 0, 0, 0, 0, 100, 17, 95, 98, 0, 0, 17, 100, 13, 21, 0, 0, 95, 13, 100, 97, 0, 0, 98, 21, 97, 100, 0, 0, 0, 0, 0, 0, 0]}</div><td><a onclick=\"require(['JsMVA'],function(jsmva){jsmva.outputShowCorrelationMatrix('jsmva_outputtansformer_events_1_onclick');});\" class='tmva_output_corrmat_link'>Correlation matrix (Background)</a></td></tr></tbody><tbody><tr><td rowspan='1' class='tmva_output_header'>DataSetFactory          </td><td><table class='tmva_output_dataset'><tbody class='tmva_output_tbody_multiple_row'><tr><td rowspan='2'>Dataset: tmva_class_example</td><td class='tmva_output_hidden_td'></td></tr></tbody></table></td></tr></tbody></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "factory.BookMethod(DataLoader= loader, Method=TMVA.Types.kBDT, MethodTitle=\"BDT\",\n",
    "                   H=False,V=False,NTrees=850,MinNodeSize=\"2.5%\",MaxDepth=3,BoostType=\"AdaBoost\", AdaBoostBeta=0.5,\n",
    "                   UseBaggedBoost=True,BaggedSampleFraction=0.5, SeparationType=\"GiniIndex\", nCuts=20 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<center><h1>Dataset: tmva_class_example</h1></center>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h2><b>Train method: DNN</b></h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <script type=\"text/javascript\">\n",
       "    require([\"jquery\"], function(jQ){\n",
       "        jQ(\"input.stopTrainingButton\").on(\"click\", function(){\n",
       "            IPython.notebook.kernel.interrupt();\n",
       "            jQ(this).css({\n",
       "                \"background-color\": \"rgba(200, 0, 0, 0.8)\",\n",
       "                \"color\": \"#fff\",\n",
       "                \"box-shadow\": \"0 3px 5px rgba(0, 0, 0, 0.3)\",\n",
       "            });\n",
       "        });\n",
       "    });\n",
       "    </script>\n",
       "    <style type=\"text/css\">\n",
       "    input.stopTrainingButton {\n",
       "        background-color: #fff;\n",
       "        border: 1px solid #ccc;\n",
       "        width: 100%;\n",
       "        font-size: 16px;\n",
       "        font-weight: bold;\n",
       "        padding: 6px 12px;\n",
       "        cursor: pointer;\n",
       "        border-radius: 6px;\n",
       "        color: #333;\n",
       "    }\n",
       "    input.stopTrainingButton:hover {\n",
       "        background-color: rgba(204, 204, 204, 0.4);\n",
       "    }\n",
       "    </style>\n",
       "    <input type=\"button\" value=\"Stop\" class=\"stopTrainingButton\" />\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <style>\n",
       "    #jsmva_progress_0 {\n",
       "        position: relative;\n",
       "        float: left;\n",
       "        height: 30px;\n",
       "        width: 100%;\n",
       "        background-color: #f5f5f5;\n",
       "        border-radius: 3px;\n",
       "        box-shadow: inset 0 3px 6px rgba(0, 0, 0, 0.1);\n",
       "    }\n",
       "    #jsmva_bar_0 {\n",
       "        position: absolute;\n",
       "        width: 1%;\n",
       "        height: 100%;\n",
       "        background-color: #337ab7;\n",
       "    }\n",
       "    #jsmva_label_0 {\n",
       "        text-align: center;\n",
       "        line-height: 30px;\n",
       "        color: white;\n",
       "    }\n",
       "    </style>\n",
       "    <div id=\"jsmva_progress_0\">\n",
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       "<table class='tmva_output_table'><tbody><tr><td rowspan='1' class='tmva_output_header'>TFHandler_DNN           </td><td><table class='tmva_output_varmeanrms'><tr><td>Variable<td>Mean<td>RMS<td>Min<td>Max<td class='tmva_output_hidden_td'></td></tr><tr><td>myvar1<td>0.083989<td>0.36407<td>-1.0000<td>1.0000<td class='tmva_output_hidden_td'></td></tr><tr><td>myvar2<td>0.0094778<td>0.27696<td>-1.0000<td>1.0000<td class='tmva_output_hidden_td'></td></tr><tr><td>var3<td>0.080279<td>0.36720<td>-1.0000<td>1.0000<td class='tmva_output_hidden_td'></td></tr><tr><td>var4<td>0.12986<td>0.39603<td>-1.0000<td>1.0000<td class='tmva_output_hidden_td'></td></tr></table></td></tr></tbody><tbody><tr><td rowspan='1' class='tmva_output_header'>TFHandler_DNN           </td><td><table class='tmva_output_varmeanrms'><tr><td>Variable<td>Mean<td>RMS<td>Min<td>Max<td class='tmva_output_hidden_td'></td></tr><tr><td>myvar1<td>0.083989<td>0.36407<td>-1.0000<td>1.0000<td class='tmva_output_hidden_td'></td></tr><tr><td>myvar2<td>0.0094778<td>0.27696<td>-1.0000<td>1.0000<td class='tmva_output_hidden_td'></td></tr><tr><td>var3<td>0.080279<td>0.36720<td>-1.0000<td>1.0000<td class='tmva_output_hidden_td'></td></tr><tr><td>var4<td>0.12986<td>0.39603<td>-1.0000<td>1.0000<td class='tmva_output_hidden_td'></td></tr></table></td></tr></tbody><tbody class='tmva_output_tbody_multiple_row'><tr><td rowspan='32' class='tmva_output_header'>TFHandler_DNN           </td><td><table class='tmva_output_varmeanrms'><tr><td>Variable<td>Mean<td>RMS<td>Min<td>Max<td class='tmva_output_hidden_td'></td></tr><tr><td>myvar1<td>0.075113<td>0.36776<td>-1.1074<td>1.0251<td class='tmva_output_hidden_td'></td></tr><tr><td>myvar2<td>0.0075595<td>0.27349<td>-0.90663<td>1.0008<td class='tmva_output_hidden_td'></td></tr><tr><td>var3<td>0.070228<td>0.37106<td>-1.0649<td>1.0602<td class='tmva_output_hidden_td'></td></tr><tr><td>var4<td>0.12090<td>0.39854<td>-1.1871<td>1.0199<td class='tmva_output_hidden_td'></td></tr></table></td></tr><tr><td>Add Layer with 100 nodes.</td></tr><tr><td>Add Layer with 50 nodes.</td></tr><tr><td>Add Layer with 10 nodes.</td></tr><tr><td>Add Layer with 1 nodes.</td></tr><tr></tr><tr><td>Total number of Synapses = 6010</td></tr><tr><td>Training with learning rate = 0.1, momentum = 0, repetitions = 1</td></tr><tr><td>Drop configuration</td></tr><tr><td>drop repetitions = 1</td></tr><tr><td>Layer 0 = 0</td></tr><tr><td>Layer 1 = 0.5</td></tr><tr><td>Layer 2 = 0.5</td></tr><tr><td>Layer 3 = 0.5</td></tr><tr></tr><tr></tr><tr><td>Training with learning rate = 0.01, momentum = 0.5, repetitions = 1</td></tr><tr><td>Drop configuration</td></tr><tr><td>drop repetitions = 1</td></tr><tr><td>Layer 0 = 0</td></tr><tr><td>Layer 1 = 0.1</td></tr><tr><td>Layer 2 = 0.1</td></tr><tr><td>Layer 3 = 0.1</td></tr><tr></tr><tr></tr><tr><td>Training with learning rate = 0.01, momentum = 0.3, repetitions = 1</td></tr><tr></tr><tr></tr><tr><td>Training with learning rate = 0.001, momentum = 0.1, repetitions = 1</td></tr><tr></tr><tr></tr><tr><td>Elapsed time for training with 6000 events: 11 sec         </td></tr></tbody><tbody class='tmva_output_tbody_multiple_row'><tr><td rowspan='4' class='tmva_output_header'>DNN                     </td><td><table class='tmva_output_dataset'><tbody><tr><td rowspan='1'>Dataset: tmva_class_example</td><td>Evaluation of DNN on training sample (6000 events)</td><td class='tmva_output_hidden_td'></td></tr></tbody></table></td></tr><tr><td>Elapsed time for evaluation of 6000 events: 0.199 sec       </td></tr><tr><td>Creating xml weight file: tmva_class_example/weights/TMVAClassification_DNN.weights.xml</td></tr><tr><td>Creating standalone class: tmva_class_example/weights/TMVAClassification_DNN.class.C</td></tr></tbody><tbody class='tmva_output_tbody_multiple_row'><tr><td rowspan='4' class='tmva_output_header'>TFHandler_MLP           </td><td><table class='tmva_output_varmeanrms'><tr><td>Variable<td>Mean<td>RMS<td>Min<td>Max<td class='tmva_output_hidden_td'></td></tr><tr><td>myvar1<td>0.083989<td>0.36407<td>-1.0000<td>1.0000<td class='tmva_output_hidden_td'></td></tr><tr><td>myvar2<td>0.0094778<td>0.27696<td>-1.0000<td>1.0000<td class='tmva_output_hidden_td'></td></tr><tr><td>var3<td>0.080279<td>0.36720<td>-1.0000<td>1.0000<td class='tmva_output_hidden_td'></td></tr><tr><td>var4<td>0.12986<td>0.39603<td>-1.0000<td>1.0000<td class='tmva_output_hidden_td'></td></tr></table></td></tr><tr><td>Training Network</td></tr><tr></tr><tr><td>Elapsed time for training with 6000 events: 5.91 sec         </td></tr></tbody><tbody class='tmva_output_tbody_multiple_row'><tr><td rowspan='4' class='tmva_output_header'>MLP                     </td><td><table class='tmva_output_dataset'><tbody><tr><td rowspan='1'>Dataset: tmva_class_example</td><td>Evaluation of MLP on training sample (6000 events)</td><td class='tmva_output_hidden_td'></td></tr></tbody></table></td></tr><tr><td>Elapsed time for evaluation of 6000 events: 0.0109 sec       </td></tr><tr><td>Creating xml weight file: tmva_class_example/weights/TMVAClassification_MLP.weights.xml</td></tr><tr><td>Creating standalone class: tmva_class_example/weights/TMVAClassification_MLP.class.C</td></tr></tbody><tbody class='tmva_output_tbody_multiple_row'><tr><td rowspan='4' class='tmva_output_header'>BDT                     </td><td>#events: (reweighted) sig: 3000 bkg: 3000</td></tr><tr><td>#events: (unweighted) sig: 3000 bkg: 3000</td></tr><tr><td>Training 850 Decision Trees ... patience please</td></tr><tr><td>Elapsed time for training with 6000 events: 2.31 sec         </td></tr></tbody><tbody class='tmva_output_tbody_multiple_row'><tr><td rowspan='4' class='tmva_output_header'>BDT                     </td><td><table class='tmva_output_dataset'><tbody><tr><td rowspan='1'>Dataset: tmva_class_example</td><td>Evaluation of BDT on training sample (6000 events)</td><td class='tmva_output_hidden_td'></td></tr></tbody></table></td></tr><tr><td>Elapsed time for evaluation of 6000 events: 0.764 sec       </td></tr><tr><td>Creating xml weight file: tmva_class_example/weights/TMVAClassification_BDT.weights.xml</td></tr><tr><td>Creating standalone class: tmva_class_example/weights/TMVAClassification_BDT.class.C</td></tr></tbody></table>"
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   "source": [
    "factory.TrainAllMethods()"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<div id=\"jstmva_5\" style=\"width: 800px; height:450px\"></div>\n",
       "<script>\n",
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       "            'JsMVA':'https://rawgit.com/qati/GSOC16/master/src/js/JsMVA.min'\n",
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       "    require(['JsMVA'],function(jsmva){\n",
       "        jsmva.drawNeuralNetwork('jstmva_5','{\"variables\": [\"myvar1\", \"Expression 2\", \"Variable 3\", \"Variable 4\"], \"layout\": {\"layer_1\": {\"nneurons\": 10, \"neuron_0\": {\"nsynapses\": 1, \"weights\": [-2.4439406810337267]}, \"neuron_1\": {\"nsynapses\": 1, \"weights\": [-0.38057819846185703]}, \"neuron_2\": {\"nsynapses\": 1, \"weights\": [0.5624593025882727]}, \"neuron_3\": {\"nsynapses\": 1, \"weights\": [2.895766263818153]}, \"neuron_4\": {\"nsynapses\": 1, \"weights\": [2.1289732999782487]}, \"neuron_5\": {\"nsynapses\": 1, \"weights\": [1.4775962759957002]}, \"neuron_6\": {\"nsynapses\": 1, \"weights\": [2.130821277286365]}, \"neuron_7\": {\"nsynapses\": 1, \"weights\": [-2.3222881547477137]}, \"neuron_8\": {\"nsynapses\": 1, \"weights\": [1.2738227571102456]}, \"neuron_9\": {\"nsynapses\": 1, \"weights\": [-2.8910771794149817]}}, \"layer_0\": {\"nneurons\": 5, \"neuron_0\": {\"nsynapses\": 9, \"weights\": [1.4193670314497908, 1.7754031263410948, 0.785010969951431, 0.25225493126890514, -6.7635488591895925, -3.117218838366916, -2.8355585736647098, 2.1325656142355, -2.4347741314429006]}, \"neuron_1\": {\"nsynapses\": 9, \"weights\": [-1.1481204448101239, -1.6328918853858911, -0.8471868907655511, -1.0808830767527076, -0.9050695993413047, -0.5985963055416706, -0.6534877334239593, 1.2843993596648735, 1.64336957517785]}, \"neuron_2\": {\"nsynapses\": 9, \"weights\": [0.7148726572179892, 1.0540946872243695, -0.5063364461294237, -0.5083834121601586, -0.7889144337753129, -2.5632646113724293, -1.7382117753055621, 0.8543730783396892, 1.2516588039779764]}, \"neuron_3\": {\"nsynapses\": 9, \"weights\": [-5.065412605453391, -2.315113963330441, 0.7628192314325827, 3.3463566224287113, 7.706244860201657, 3.0388780325368834, -1.2653435106936401, -2.3315976937336087, 0.5789009716483675]}, \"neuron_4\": {\"nsynapses\": 9, \"weights\": [-0.7206985173773571, 0.7733552349348609, -0.15694149228880366, 1.19014206627139, -0.28792764459317055, 0.32757902814400003, 3.483945658973197, 1.1528434843050648, -1.4399136291539933]}}, \"nlayers\": \"3\", \"layer_2\": {\"neuron_0\": {\"nsynapses\": 0}, \"nneurons\": 1}}}');\n",
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   "source": [
    "factory.DrawNeuralNetwork(dataset, \"MLP\")"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
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       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'JsMVA':'https://rawgit.com/qati/GSOC16/master/src/js/JsMVA.min'\n",
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       "    require(['JsMVA'],function(jsmva){\n",
       "        jsmva.drawNeuralNetwork('jstmva_6','{\"layers\": [{\"Connection\": \"FULL\", \"OutputMode\": \"1\", \"Nodes\": \"100\", \"ActivationFunction\": \"T\"}, {\"Connection\": \"FULL\", \"OutputMode\": \"1\", \"Nodes\": \"50\", \"ActivationFunction\": \"T\"}, {\"Connection\": \"FULL\", \"OutputMode\": \"1\", \"Nodes\": \"10\", \"ActivationFunction\": \"T\"}, {\"Connection\": \"FULL\", \"OutputMode\": \"2\", \"Nodes\": \"1\", \"ActivationFunction\": \"L\"}], \"variables\": [\"myvar1\", \"Expression 2\", \"Variable 3\", \"Variable 4\"], \"synapses\": {\"NumberSynapses\": \"6010\", \"synapses\": [\"-5.0898569458883858e-01\", \"5.5048531199982351e+00\", \"-7.8166455422760472e-01\", \"-2.5820063087664868e+00\", \"-4.0125976850415385e-01\", \"3.5449253067888407e+00\", \"1.1146455082353521e+00\", \"-7.2810987206006572e-01\", \"-2.2622506576970783e-01\", \"-8.7499961472399890e-01\", \"-1.3120614871598157e+00\", \"-1.7947100875878896e+00\", \"-1.0581265385255705e+00\", 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   "source": [
    "factory.DrawNeuralNetwork(dataset, \"DNN\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BDT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
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    "collapsed": false
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       "<div id=\"jstmva_7\" style=\"width: 800px; height:450px\"></div>\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'JsMVA':'https://rawgit.com/qati/GSOC16/master/src/js/JsMVA.min'\n",
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       "    require(['JsMVA'],function(jsmva){\n",
       "        jsmva.drawDecisionTree('jstmva_7','{\"variables\": [\"var1+var2\", \"var1-var2\", \"var3\", \"var4\"], \"tree\": {\"info\": {\"Cut\": \"-2.2410600185394287e+00\", \"IVar\": \"3\", \"purity\": \"4.4245621562004089e-01\", \"pos\": 0}, \"children\": [{\"info\": {\"Cut\": \"2.2773640155792236e+00\", \"IVar\": \"0\", \"purity\": \"4.7790309786796570e-01\", \"pos\": \"l\"}, \"children\": [{\"info\": {\"Cut\": \"1.1225386857986450e+00\", \"IVar\": \"3\", \"purity\": \"5.2420860528945923e-01\", \"pos\": \"l\"}, \"children\": [{\"info\": {\"Cut\": \"0.0000000000000000e+00\", \"IVar\": \"-1\", \"purity\": \"7.2187876701354980e-01\", \"pos\": \"l\"}, \"children\": []}, {\"info\": {\"Cut\": \"0.0000000000000000e+00\", \"IVar\": \"-1\", \"purity\": \"4.7957077622413635e-01\", \"pos\": \"r\"}, \"children\": []}]}, {\"info\": {\"Cut\": \"-1.0061374902725220e+00\", \"IVar\": \"1\", \"purity\": \"3.4954592585563660e-01\", \"pos\": \"r\"}, \"children\": [{\"info\": {\"Cut\": \"0.0000000000000000e+00\", \"IVar\": \"-1\", \"purity\": \"5.6726294755935669e-01\", \"pos\": \"l\"}, \"children\": []}, {\"info\": {\"Cut\": \"0.0000000000000000e+00\", \"IVar\": \"-1\", \"purity\": \"3.0147132277488708e-01\", \"pos\": \"r\"}, \"children\": []}]}]}, {\"info\": {\"Cut\": \"-3.0450844764709473e+00\", \"IVar\": \"-1\", \"purity\": \"4.0342181921005249e-02\", \"pos\": \"r\"}, \"children\": []}]}}');\n",
       "    });\n",
       "</script>\n"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "output_type": "display_data"
    },
    {
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
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   ],
   "source": [
    "factory.DrawDecisionTree(dataset, \"BDT\") #11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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