MVA/TMVA_tutorial_regression_tmva.py.nbconvert.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "numerous-prototype",
   "metadata": {},
   "source": [
    "#  T M V A_Tutorial_Regression_Tmva\n",
    "TMVA example, for regression. The input data is a simple TTree file, which containts the needed input variables.\n",
    " with following objectives:\n",
    " * Train a BDT with TMVA\n",
    "\n",
    "\n",
    "Modified from [RegressionKeras.py](https://root.cern/doc/master/RegressionKeras_8py.html) and [TMVARegression.C](https://root.cern/doc/master/TMVARegression_8C.html)\n",
    "\n",
    "\n",
    "**Author:** Lailin XU  \n",
    "<i><small>This notebook tutorial was automatically generated with <a href= \"https://github.com/root-project/root/blob/master/documentation/doxygen/converttonotebook.py\">ROOTBOOK-izer</a> from the macro found in the ROOT repository  on Tuesday, April 27, 2021 at 02:02 PM.</small></i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ambient-garden",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-04-27T06:02:53.140926Z",
     "iopub.status.busy": "2021-04-27T06:02:53.140279Z",
     "iopub.status.idle": "2021-04-27T06:02:53.738181Z",
     "shell.execute_reply": "2021-04-27T06:02:53.738389Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to JupyROOT 6.22/07\n"
     ]
    }
   ],
   "source": [
    "from ROOT import TMVA, TFile, TTree, TCut\n",
    "from subprocess import call\n",
    "from os.path import isfile\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gentle-embassy",
   "metadata": {},
   "source": [
    "Setup TMVA\n",
    "=================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "confident-advertiser",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-04-27T06:02:53.754052Z",
     "iopub.status.busy": "2021-04-27T06:02:53.753741Z",
     "iopub.status.idle": "2021-04-27T06:02:53.861038Z",
     "shell.execute_reply": "2021-04-27T06:02:53.861431Z"
    }
   },
   "outputs": [],
   "source": [
    "TMVA.Tools.Instance()\n",
    "(TMVA.gConfig().GetVariablePlotting()).fMaxNumOfAllowedVariablesForScatterPlots = 5 \n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "massive-horse",
   "metadata": {},
   "source": [
    "Create a new root output file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "sticky-mouse",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-04-27T06:02:53.892212Z",
     "iopub.status.busy": "2021-04-27T06:02:53.891827Z",
     "iopub.status.idle": "2021-04-27T06:02:53.999676Z",
     "shell.execute_reply": "2021-04-27T06:02:54.000178Z"
    }
   },
   "outputs": [],
   "source": [
    "outfileName = 'TMVA_tutorial_reg_1.root'\n",
    "output = TFile.Open(outfileName, 'RECREATE')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dangerous-turner",
   "metadata": {},
   "source": [
    "Create the factory object. Later you can choose the methods whose performance you'd like to investigate. The factory will\n",
    "then run the performance analysis for you.\n",
    "\n",
    "The first argument is the base of the name of all the weightfiles in the directory weight/\n",
    "\n",
    "The second argument is the output file for the training results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "sized-midwest",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-04-27T06:02:54.037882Z",
     "iopub.status.busy": "2021-04-27T06:02:54.037451Z",
     "iopub.status.idle": "2021-04-27T06:02:54.146478Z",
     "shell.execute_reply": "2021-04-27T06:02:54.147148Z"
    }
   },
   "outputs": [],
   "source": [
    "factory = TMVA.Factory('TMVARegression', output, '!V:!Silent:Color:!DrawProgressBar:Transformations=D,G:AnalysisType=Regression')\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "basic-guarantee",
   "metadata": {},
   "source": [
    "Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "romance-franchise",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-04-27T06:02:54.152186Z",
     "iopub.status.busy": "2021-04-27T06:02:54.151474Z",
     "iopub.status.idle": "2021-04-27T06:02:54.261450Z",
     "shell.execute_reply": "2021-04-27T06:02:54.262083Z"
    }
   },
   "outputs": [],
   "source": [
    "trfile = \"example_data/SM_ttbar.root\"\n",
    "if not isfile(trfile):\n",
    "    call(['curl', '-L', '-O', 'https://gitee.com/lailinxu/hepstat-tutorial/raw/master/MVA/example_data/{0}'.format(trfile)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "corrected-embassy",
   "metadata": {},
   "source": [
    "Get the tree file "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "lesbian-split",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-04-27T06:02:54.309217Z",
     "iopub.status.busy": "2021-04-27T06:02:54.308754Z",
     "iopub.status.idle": "2021-04-27T06:02:54.417609Z",
     "shell.execute_reply": "2021-04-27T06:02:54.418383Z"
    }
   },
   "outputs": [],
   "source": [
    "data = TFile.Open(trfile)\n",
    "if not data:\n",
    "  print(\"Error! file not opened\", trfile)\n",
    "trname = \"tree\"\n",
    "tree = data.Get(trname)\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hidden-journalist",
   "metadata": {},
   "source": [
    "Define the input variables that shall be used for the MVA training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "compressed-bracket",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-04-27T06:02:54.443344Z",
     "iopub.status.busy": "2021-04-27T06:02:54.442787Z",
     "iopub.status.idle": "2021-04-27T06:02:54.552789Z",
     "shell.execute_reply": "2021-04-27T06:02:54.553375Z"
    }
   },
   "outputs": [],
   "source": [
    "dataloader = TMVA.DataLoader('dataset')\n",
    "for branch in tree.GetListOfBranches():\n",
    "    name = branch.GetName()\n",
    "    if not 'mtt' in name:\n",
    "        dataloader.AddVariable(name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "relative-fighter",
   "metadata": {},
   "source": [
    "Add the variable carrying the regression target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "known-phrase",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-04-27T06:02:54.565357Z",
     "iopub.status.busy": "2021-04-27T06:02:54.564620Z",
     "iopub.status.idle": "2021-04-27T06:02:54.674448Z",
     "shell.execute_reply": "2021-04-27T06:02:54.675035Z"
    }
   },
   "outputs": [],
   "source": [
    "dataloader.AddTarget('mtt_truth')\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sixth-glasgow",
   "metadata": {},
   "source": [
    "Register the regression tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "requested-grammar",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-04-27T06:02:54.689784Z",
     "iopub.status.busy": "2021-04-27T06:02:54.689168Z",
     "iopub.status.idle": "2021-04-27T06:02:54.799344Z",
     "shell.execute_reply": "2021-04-27T06:02:54.800086Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataSetInfo              : [dataset] : Added class \"Regression\"\n",
      "                         : Add Tree tree of type Regression with 62710 events\n"
     ]
    }
   ],
   "source": [
    "dataloader.AddRegressionTree(tree, 1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "disciplinary-execution",
   "metadata": {},
   "source": [
    "Apply some cuts\n",
    "Set the number of events for training, and use all remaining events in the trees after training for testing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mysterious-gates",
   "metadata": {},
   "source": [
    "If no numbers of events are given, half of the events in the tree are used for training, and the other half for testing:\n",
    "\n",
    "    dataloader->PrepareTrainingAndTestTree( mycut, \"SplitMode=random:!V\" );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "mental-constitutional",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-04-27T06:02:54.812455Z",
     "iopub.status.busy": "2021-04-27T06:02:54.811720Z",
     "iopub.status.idle": "2021-04-27T06:02:54.922114Z",
     "shell.execute_reply": "2021-04-27T06:02:54.922795Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         : Dataset[dataset] : Class index : 0  name : Regression\n"
     ]
    }
   ],
   "source": [
    "dataloader.PrepareTrainingAndTestTree(TCut(''), 'nTrain_Regression=10000:SplitMode=Random:NormMode=NumEvents:!V')\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "korean-conclusion",
   "metadata": {},
   "source": [
    "Generate model\n",
    "=============\n",
    "Book MVA methods \n",
    "BDT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "threaded-israel",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-04-27T06:02:55.083727Z",
     "iopub.status.busy": "2021-04-27T06:02:55.083412Z",
     "iopub.status.idle": "2021-04-27T06:02:55.191152Z",
     "shell.execute_reply": "2021-04-27T06:02:55.191530Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cppyy.gbl.TMVA.MethodBDT object at 0x10e331600>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Factory                  : Booking method: \u001b[1mBDT\u001b[0m\n",
      "                         : \n",
      "                         : Building event vectors for type 2 Regression\n",
      "                         : Dataset[dataset] :  create input formulas for tree tree\n",
      "DataSetFactory           : [dataset] : Number of events in input trees\n",
      "                         : \n",
      "                         : Number of training and testing events\n",
      "                         : ---------------------------------------------------------------------------\n",
      "                         : Regression -- training events            : 10000\n",
      "                         : Regression -- testing events             : 52710\n",
      "                         : Regression -- training and testing events: 62710\n",
      "                         : \n",
      "<WARNING>                : Dataset[dataset] : Variable nlep is constant. Please remove the variable.\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (0, 1) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (0, 2) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (0, 3) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (0, 4) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (0, 5) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (0, 6) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (0, 7) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (0, 8) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (0, 9) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (0, 10) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (0, 11) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (0, 12) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (0, 13) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (0, 14) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (0, 15) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (0, 16) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (0, 17) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (0, 18) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (0, 19) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (0, 20) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (0, 21) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (0, 22) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (0, 23) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (1, 0) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (2, 0) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (3, 0) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (4, 0) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (5, 0) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (6, 0) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (7, 0) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (8, 0) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (9, 0) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (10, 0) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (11, 0) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (12, 0) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (13, 0) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (14, 0) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (15, 0) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (16, 0) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (17, 0) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (18, 0) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (19, 0) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (20, 0) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (21, 0) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (22, 0) = 0\n",
      "<WARNING>                : Dataset[Default] : <GetCorrelationMatrix> Zero variances for variables (23, 0) = 0\n",
      "DataSetInfo              : Correlation matrix (Regression):\n",
      "                         : -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "                         :              nlep lep1_pt lep1_eta lep1_phi  lep1_m lep2_pt lep2_eta lep2_phi  lep2_m   njets jet1_pt jet1_eta jet1_phi  jet1_m jet2_pt jet2_eta jet2_phi  jet2_m jet3_pt jet3_eta jet3_phi  jet3_m     met met_phi\n",
      "                         :     nlep:  +1.000  +0.000   +0.000   +0.000  +0.000  +0.000   +0.000   +0.000  +0.000  +0.000  +0.000   +0.000   +0.000  +0.000  +0.000   +0.000   +0.000  +0.000  +0.000   +0.000   +0.000  +0.000  +0.000  +0.000\n",
      "                         :  lep1_pt:  +0.000  +1.000   -0.002   -0.014  -0.010  +1.000   -0.002   -0.014  -0.010  +0.075  +0.247   -0.006   -0.003  +0.167  +0.139   -0.014   +0.005  +0.106  +0.114   +0.006   +0.000  +0.106  +0.068  +0.019\n",
      "                         : lep1_eta:  +0.000  -0.002   +1.000   +0.013  -0.013  -0.002   +1.000   +0.013  -0.013  +0.004  -0.001   +0.404   -0.000  -0.005  -0.001   +0.371   -0.006  -0.005  +0.002   +0.175   +0.008  +0.006  -0.006  -0.002\n",
      "                         : lep1_phi:  +0.000  -0.014   +0.013   +1.000  +0.021  -0.014   +0.013   +1.000  +0.021  -0.008  -0.010   +0.008   -0.169  -0.006  -0.009   +0.002   -0.059  -0.005  -0.014   -0.001   -0.053  -0.004  -0.015  -0.097\n",
      "                         :   lep1_m:  +0.000  -0.010   -0.013   +0.021  +1.000  -0.010   -0.013   +0.021  +1.000  -0.006  +0.007   -0.018   -0.002  -0.005  +0.009   -0.003   +0.003  +0.023  +0.001   -0.010   +0.010  -0.002  +0.004  +0.002\n",
      "                         :  lep2_pt:  +0.000  +1.000   -0.002   -0.014  -0.010  +1.000   -0.002   -0.014  -0.010  +0.075  +0.247   -0.006   -0.003  +0.167  +0.139   -0.014   +0.005  +0.106  +0.114   +0.006   +0.000  +0.106  +0.068  +0.019\n",
      "                         : lep2_eta:  +0.000  -0.002   +1.000   +0.013  -0.013  -0.002   +1.000   +0.013  -0.013  +0.004  -0.001   +0.404   -0.000  -0.005  -0.001   +0.371   -0.006  -0.005  +0.002   +0.175   +0.008  +0.006  -0.006  -0.002\n",
      "                         : lep2_phi:  +0.000  -0.014   +0.013   +1.000  +0.021  -0.014   +0.013   +1.000  +0.021  -0.008  -0.010   +0.008   -0.169  -0.006  -0.009   +0.002   -0.059  -0.005  -0.014   -0.001   -0.053  -0.004  -0.015  -0.097\n",
      "                         :   lep2_m:  +0.000  -0.010   -0.013   +0.021  +1.000  -0.010   -0.013   +0.021  +1.000  -0.006  +0.007   -0.018   -0.002  -0.005  +0.009   -0.003   +0.003  +0.023  +0.001   -0.010   +0.010  -0.002  +0.004  +0.002\n",
      "                         :    njets:  +0.000  +0.075   +0.004   -0.008  -0.006  +0.075   +0.004   -0.008  -0.006  +1.000  +0.241   +0.005   -0.004  +0.197  +0.314   -0.003   -0.007  +0.255  +0.819   -0.026   +0.005  +0.770  +0.095  -0.013\n",
      "                         :  jet1_pt:  +0.000  +0.247   -0.001   -0.010  +0.007  +0.247   -0.001   -0.010  +0.007  +0.241  +1.000   +0.000   +0.008  +0.712  +0.691   -0.004   -0.018  +0.487  +0.433   +0.003   +0.008  +0.363  +0.328  +0.014\n",
      "                         : jet1_eta:  +0.000  -0.006   +0.404   +0.008  -0.018  -0.006   +0.404   +0.008  -0.018  +0.005  +0.000   +1.000   -0.006  -0.008  -0.001   +0.220   +0.005  -0.003  +0.003   +0.093   +0.002  +0.004  -0.004  -0.007\n",
      "                         : jet1_phi:  +0.000  -0.003   -0.000   -0.169  -0.002  -0.003   -0.000   -0.169  -0.002  -0.004  +0.008   -0.006   +1.000  +0.012  +0.002   +0.010   -0.182  +0.000  +0.002   +0.003   -0.082  -0.000  +0.006  -0.211\n",
      "                         :   jet1_m:  +0.000  +0.167   -0.005   -0.006  -0.005  +0.167   -0.005   -0.006  -0.005  +0.197  +0.712   -0.008   +0.012  +1.000  +0.497   -0.002   -0.026  +0.357  +0.329   -0.007   +0.007  +0.281  +0.237  -0.004\n",
      "                         :  jet2_pt:  +0.000  +0.139   -0.001   -0.009  +0.009  +0.139   -0.001   -0.009  +0.009  +0.314  +0.691   -0.001   +0.002  +0.497  +1.000   +0.001   -0.006  +0.714  +0.563   -0.004   -0.006  +0.484  +0.153  +0.009\n",
      "                         : jet2_eta:  +0.000  -0.014   +0.371   +0.002  -0.003  -0.014   +0.371   +0.002  -0.003  -0.003  -0.004   +0.220   +0.010  -0.002  +0.001   +1.000   -0.010  -0.006  +0.004   +0.091   +0.000  +0.004  +0.007  +0.007\n",
      "                         : jet2_phi:  +0.000  +0.005   -0.006   -0.059  +0.003  +0.005   -0.006   -0.059  +0.003  -0.007  -0.018   +0.005   -0.182  -0.026  -0.006   -0.010   +1.000  -0.002  -0.010   -0.000   -0.021  -0.006  -0.004  -0.081\n",
      "                         :   jet2_m:  +0.000  +0.106   -0.005   -0.005  +0.023  +0.106   -0.005   -0.005  +0.023  +0.255  +0.487   -0.003   +0.000  +0.357  +0.714   -0.006   -0.002  +1.000  +0.436   -0.012   -0.007  +0.371  +0.110  -0.001\n",
      "                         :  jet3_pt:  +0.000  +0.114   +0.002   -0.014  +0.001  +0.114   +0.002   -0.014  +0.001  +0.819  +0.433   +0.003   +0.002  +0.329  +0.563   +0.004   -0.010  +0.436  +1.000   -0.025   +0.000  +0.884  +0.147  -0.002\n",
      "                         : jet3_eta:  +0.000  +0.006   +0.175   -0.001  -0.010  +0.006   +0.175   -0.001  -0.010  -0.026  +0.003   +0.093   +0.003  -0.007  -0.004   +0.091   -0.000  -0.012  -0.025   +1.000   +0.003  -0.023  -0.010  +0.015\n",
      "                         : jet3_phi:  +0.000  +0.000   +0.008   -0.053  +0.010  +0.000   +0.008   -0.053  +0.010  +0.005  +0.008   +0.002   -0.082  +0.007  -0.006   +0.000   -0.021  -0.007  +0.000   +0.003   +1.000  -0.009  +0.009  -0.054\n",
      "                         :   jet3_m:  +0.000  +0.106   +0.006   -0.004  -0.002  +0.106   +0.006   -0.004  -0.002  +0.770  +0.363   +0.004   -0.000  +0.281  +0.484   +0.004   -0.006  +0.371  +0.884   -0.023   -0.009  +1.000  +0.110  -0.010\n",
      "                         :      met:  +0.000  +0.068   -0.006   -0.015  +0.004  +0.068   -0.006   -0.015  +0.004  +0.095  +0.328   -0.004   +0.006  +0.237  +0.153   +0.007   -0.004  +0.110  +0.147   -0.010   +0.009  +0.110  +1.000  -0.004\n",
      "                         :  met_phi:  +0.000  +0.019   -0.002   -0.097  +0.002  +0.019   -0.002   -0.097  +0.002  -0.013  +0.014   -0.007   -0.211  -0.004  +0.009   +0.007   -0.081  -0.001  -0.002   +0.015   -0.054  -0.010  -0.004  +1.000\n",
      "                         : -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "DataSetFactory           : [dataset] :  \n",
      "                         : \n",
      "Factory                  : Booking method: \u001b[1mBDTG\u001b[0m\n",
      "                         : \n",
      "<WARNING>                : Value for option maxdepth was previously set to 3\n",
      "                         : the option NegWeightTreatment=InverseBoostNegWeights does not exist for BoostType=Grad\n",
      "                         : --> change to new default NegWeightTreatment=Pray\n"
     ]
    }
   ],
   "source": [
    "factory.BookMethod( dataloader,  TMVA.Types.kBDT, \"BDT\",\n",
    "  \"!H:!V:NTrees=100:MinNodeSize=1.0%:BoostType=AdaBoostR2:SeparationType=RegressionVariance:nCuts=20:PruneMethod=CostComplexity:PruneStrength=30\" )\n",
    "factory.BookMethod( dataloader,  TMVA.Types.kBDT, \"BDTG\",\n",
    "  \"!H:!V:NTrees=2000::BoostType=Grad:Shrinkage=0.1:UseBaggedBoost:BaggedSampleFraction=0.5:nCuts=20:MaxDepth=3:MaxDepth=4\" )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mental-reason",
   "metadata": {},
   "source": [
    "Neural network (MLP)\n",
    "actory.BookMethod( dataloader,  TMVA.Types.kMLP, \"MLP\","
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "designed-fancy",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-04-27T06:02:55.194462Z",
     "iopub.status.busy": "2021-04-27T06:02:55.193967Z",
     "iopub.status.idle": "2021-04-27T06:02:55.303459Z",
     "shell.execute_reply": "2021-04-27T06:02:55.304181Z"
    }
   },
   "outputs": [],
   "source": [
    "  #\"!H:!V:VarTransform=Norm:NeuronType=tanh:NCycles=20000:HiddenLayers=N+20:TestRate=6:TrainingMethod=BFGS:Sampling=0.3:SamplingEpoch=0.8:ConvergenceImprove=1e-6:ConvergenceTests=15:!UseRegulator\" )\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "junior-macro",
   "metadata": {},
   "source": [
    "Run TMVA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "mounted-associate",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-04-27T06:03:21.314739Z",
     "iopub.status.busy": "2021-04-27T06:03:21.314423Z",
     "iopub.status.idle": "2021-04-27T06:03:21.426526Z",
     "shell.execute_reply": "2021-04-27T06:03:21.426845Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Factory                  : \u001b[1mTrain all methods\u001b[0m\n",
      "Factory                  : [dataset] : Create Transformation \"D\" with events from all classes.\n",
      "                         : \n",
      "                         : Transformation, Variable selection : \n",
      "                         : Input : variable 'nlep' <---> Output : variable 'nlep'\n",
      "                         : Input : variable 'lep1_pt' <---> Output : variable 'lep1_pt'\n",
      "                         : Input : variable 'lep1_eta' <---> Output : variable 'lep1_eta'\n",
      "                         : Input : variable 'lep1_phi' <---> Output : variable 'lep1_phi'\n",
      "                         : Input : variable 'lep1_m' <---> Output : variable 'lep1_m'\n",
      "                         : Input : variable 'lep2_pt' <---> Output : variable 'lep2_pt'\n",
      "                         : Input : variable 'lep2_eta' <---> Output : variable 'lep2_eta'\n",
      "                         : Input : variable 'lep2_phi' <---> Output : variable 'lep2_phi'\n",
      "                         : Input : variable 'lep2_m' <---> Output : variable 'lep2_m'\n",
      "                         : Input : variable 'njets' <---> Output : variable 'njets'\n",
      "                         : Input : variable 'jet1_pt' <---> Output : variable 'jet1_pt'\n",
      "                         : Input : variable 'jet1_eta' <---> Output : variable 'jet1_eta'\n",
      "                         : Input : variable 'jet1_phi' <---> Output : variable 'jet1_phi'\n",
      "                         : Input : variable 'jet1_m' <---> Output : variable 'jet1_m'\n",
      "                         : Input : variable 'jet2_pt' <---> Output : variable 'jet2_pt'\n",
      "                         : Input : variable 'jet2_eta' <---> Output : variable 'jet2_eta'\n",
      "                         : Input : variable 'jet2_phi' <---> Output : variable 'jet2_phi'\n",
      "                         : Input : variable 'jet2_m' <---> Output : variable 'jet2_m'\n",
      "                         : Input : variable 'jet3_pt' <---> Output : variable 'jet3_pt'\n",
      "                         : Input : variable 'jet3_eta' <---> Output : variable 'jet3_eta'\n",
      "                         : Input : variable 'jet3_phi' <---> Output : variable 'jet3_phi'\n",
      "                         : Input : variable 'jet3_m' <---> Output : variable 'jet3_m'\n",
      "                         : Input : variable 'met' <---> Output : variable 'met'\n",
      "                         : Input : variable 'met_phi' <---> Output : variable 'met_phi'\n",
      "Factory                  : [dataset] : Create Transformation \"G\" with events from all classes.\n",
      "                         : \n",
      "                         : Transformation, Variable selection : \n",
      "                         : Input : variable 'nlep' <---> Output : variable 'nlep'\n",
      "                         : Input : variable 'lep1_pt' <---> Output : variable 'lep1_pt'\n",
      "                         : Input : variable 'lep1_eta' <---> Output : variable 'lep1_eta'\n",
      "                         : Input : variable 'lep1_phi' <---> Output : variable 'lep1_phi'\n",
      "                         : Input : variable 'lep1_m' <---> Output : variable 'lep1_m'\n",
      "                         : Input : variable 'lep2_pt' <---> Output : variable 'lep2_pt'\n",
      "                         : Input : variable 'lep2_eta' <---> Output : variable 'lep2_eta'\n",
      "                         : Input : variable 'lep2_phi' <---> Output : variable 'lep2_phi'\n",
      "                         : Input : variable 'lep2_m' <---> Output : variable 'lep2_m'\n",
      "                         : Input : variable 'njets' <---> Output : variable 'njets'\n",
      "                         : Input : variable 'jet1_pt' <---> Output : variable 'jet1_pt'\n",
      "                         : Input : variable 'jet1_eta' <---> Output : variable 'jet1_eta'\n",
      "                         : Input : variable 'jet1_phi' <---> Output : variable 'jet1_phi'\n",
      "                         : Input : variable 'jet1_m' <---> Output : variable 'jet1_m'\n",
      "                         : Input : variable 'jet2_pt' <---> Output : variable 'jet2_pt'\n",
      "                         : Input : variable 'jet2_eta' <---> Output : variable 'jet2_eta'\n",
      "                         : Input : variable 'jet2_phi' <---> Output : variable 'jet2_phi'\n",
      "                         : Input : variable 'jet2_m' <---> Output : variable 'jet2_m'\n",
      "                         : Input : variable 'jet3_pt' <---> Output : variable 'jet3_pt'\n",
      "                         : Input : variable 'jet3_eta' <---> Output : variable 'jet3_eta'\n",
      "                         : Input : variable 'jet3_phi' <---> Output : variable 'jet3_phi'\n",
      "                         : Input : variable 'jet3_m' <---> Output : variable 'jet3_m'\n",
      "                         : Input : variable 'met' <---> Output : variable 'met'\n",
      "                         : Input : variable 'met_phi' <---> Output : variable 'met_phi'\n",
      "                         : Preparing the Decorrelation transformation...\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "<WARNING>                : <GetSQRootMatrix> error in matrix diagonalization; printed S and B\n",
      "                         : Preparing the Gaussian transformation...\n",
      "TFHandler_Factory        :  Variable         Mean         RMS   [        Min         Max ]\n",
      "                         : ----------------------------------------------------------------\n",
      "                         :      nlep:  0.0060303    0.99786   [    -3.1846     5.7307 ]\n",
      "                         :   lep1_pt:  0.0060741    0.99818   [    -3.1847     5.7307 ]\n",
      "                         :  lep1_eta:  0.0066000     1.0009   [    -3.1846     5.7307 ]\n",
      "                         :  lep1_phi:  0.0062637    0.99884   [    -3.1847     5.7307 ]\n",
      "                         :    lep1_m:  0.0060362    0.99789   [    -3.1845     5.7307 ]\n",
      "                         :   lep2_pt:  0.0060741    0.99818   [    -3.1847     5.7307 ]\n",
      "                         :  lep2_eta:  0.0066000     1.0009   [    -3.1846     5.7307 ]\n",
      "                         :  lep2_phi:  0.0062637    0.99884   [    -3.1847     5.7307 ]\n",
      "                         :    lep2_m:  0.0060362    0.99789   [    -3.1845     5.7307 ]\n",
      "                         :     njets:  0.0062806    0.99940   [    -3.1846     5.7307 ]\n",
      "                         :   jet1_pt:  0.0051834     1.0013   [    -3.3201     5.7307 ]\n",
      "                         :  jet1_eta:  0.0063563    0.99942   [    -3.1847     5.7307 ]\n",
      "                         :  jet1_phi:  0.0059446    0.99716   [    -3.1847     5.7307 ]\n",
      "                         :    jet1_m:  0.0063153    0.99929   [    -3.1847     5.7307 ]\n",
      "                         :   jet2_pt:  0.0059550    0.99829   [    -3.1847     5.7307 ]\n",
      "                         :  jet2_eta:  0.0067362     1.0007   [    -3.1847     5.7307 ]\n",
      "                         :  jet2_phi:  0.0065551     1.0000   [    -3.1847     5.7307 ]\n",
      "                         :    jet2_m:  0.0059264    0.99831   [    -3.1847     5.7307 ]\n",
      "                         :   jet3_pt:  0.0059947    0.99823   [    -3.1847     5.7307 ]\n",
      "                         :  jet3_eta:  0.0061759    0.99941   [    -3.1847     5.7307 ]\n",
      "                         :  jet3_phi:  0.0061388    0.99838   [    -3.1846     5.7307 ]\n",
      "                         :    jet3_m:  0.0060735    0.99839   [    -3.1847     5.7307 ]\n",
      "                         :       met:  0.0063530    0.99935   [    -3.1847     5.7307 ]\n",
      "                         :   met_phi:    -5.7307 1.6280e-06   [    -5.7307    -5.7307 ]\n",
      "                         : mtt_truth:     548.93     192.44   [     344.48     2856.9 ]\n",
      "                         : ----------------------------------------------------------------\n",
      "                         : \u001b[32m\n",
      "                         : <PlotVariables> Will not produce scatter plots ==> \n",
      "                         : |  The number of 24 input variables and 1 target values would require 300 two-dimensional\n",
      "                         : |  histograms, which would occupy the computer's memory. Note that this\n",
      "                         : |  suppression does not have any consequences for your analysis, other\n",
      "                         : |  than not disposing of these scatter plots. You can modify the maximum\n",
      "                         : |  number of input variables allowed to generate scatter plots in your\n",
      "                         : |  script via the command line:\n",
      "                         : |  \"(TMVA::gConfig().GetVariablePlotting()).fMaxNumOfAllowedVariablesForScatterPlots = <some int>;\"\u001b[0m\n",
      "                         : \n",
      "                         : Some more output\n",
      "Factory                  : Train method: BDT for Regression\n",
      "                         : \n",
      "                         : Regression Loss Function: Huber\n",
      "                         : Training 100 Decision Trees ... patience please\n",
      "                         : Elapsed time for training with 10000 events: 1.18 sec         \n",
      "                         : Dataset[dataset] : Create results for training\n",
      "                         : Dataset[dataset] : Evaluation of BDT on training sample\n",
      "                         : Dataset[dataset] : Elapsed time for evaluation of 10000 events: 0.227 sec       \n",
      "                         : Create variable histograms\n",
      "                         : Create regression target histograms\n",
      "                         : Create regression average deviation\n",
      "                         : Results created\n",
      "                         : Creating xml weight file: \u001b[0;36mdataset/weights/TMVARegression_BDT.weights.xml\u001b[0m\n",
      "                         : TMVA_tutorial_reg_1.root:/dataset/Method_BDT/BDT\n",
      "Factory                  : Training finished\n",
      "                         : \n",
      "Factory                  : Train method: BDTG for Regression\n",
      "                         : \n",
      "                         : Regression Loss Function: Huber\n",
      "                         : Training 2000 Decision Trees ... patience please\n",
      "                         : Elapsed time for training with 10000 events: 9.33 sec         \n",
      "                         : Dataset[dataset] : Create results for training\n",
      "                         : Dataset[dataset] : Evaluation of BDTG on training sample\n",
      "                         : Dataset[dataset] : Elapsed time for evaluation of 10000 events: 0.983 sec       \n",
      "                         : Create variable histograms\n",
      "                         : Create regression target histograms\n",
      "                         : Create regression average deviation\n",
      "                         : Results created\n",
      "                         : Creating xml weight file: \u001b[0;36mdataset/weights/TMVARegression_BDTG.weights.xml\u001b[0m\n",
      "                         : TMVA_tutorial_reg_1.root:/dataset/Method_BDT/BDTG\n",
      "Factory                  : Training finished\n",
      "                         : \n",
      "Factory                  : === Destroy and recreate all methods via weight files for testing ===\n",
      "                         : \n",
      "                         : Reading weight file: \u001b[0;36mdataset/weights/TMVARegression_BDT.weights.xml\u001b[0m\n",
      "                         : Reading weight file: \u001b[0;36mdataset/weights/TMVARegression_BDTG.weights.xml\u001b[0m\n",
      "Factory                  : \u001b[1mTest all methods\u001b[0m\n",
      "Factory                  : Test method: BDT for Regression performance\n",
      "                         : \n",
      "                         : Dataset[dataset] : Create results for testing\n",
      "                         : Dataset[dataset] : Evaluation of BDT on testing sample\n",
      "                         : Dataset[dataset] : Elapsed time for evaluation of 52710 events: 1.14 sec       \n",
      "                         : Create variable histograms\n",
      "                         : Create regression target histograms\n",
      "                         : Create regression average deviation\n",
      "                         : Results created\n",
      "Factory                  : Test method: BDTG for Regression performance\n",
      "                         : \n",
      "                         : Dataset[dataset] : Create results for testing\n",
      "                         : Dataset[dataset] : Evaluation of BDTG on testing sample\n",
      "                         : Dataset[dataset] : Elapsed time for evaluation of 52710 events: 4.76 sec       \n",
      "                         : Create variable histograms\n",
      "                         : Create regression target histograms\n",
      "                         : Create regression average deviation\n",
      "                         : Results created\n",
      "Factory                  : \u001b[1mEvaluate all methods\u001b[0m\n",
      "                         : Evaluate regression method: BDT\n",
      "                         : TestRegression (testing)\n",
      "                         : Calculate regression for all events\n",
      "                         : Elapsed time for evaluation of 52710 events: 1.13 sec       \n",
      "                         : TestRegression (training)\n",
      "                         : Calculate regression for all events\n",
      "                         : Elapsed time for evaluation of 10000 events: 0.214 sec       \n",
      "<WARNING>                : Variable \"nlep\" has zero, negative, or NaN RMS^2: 0 ==> set to zero. Please check the variable content\n",
      "TFHandler_BDT            :  Variable         Mean         RMS   [        Min         Max ]\n",
      "                         : ----------------------------------------------------------------\n",
      "                         :      nlep:     2.0000     0.0000   [     2.0000     2.0000 ]\n",
      "                         :   lep1_pt:     70.046     41.351   [     4.1578     672.59 ]\n",
      "                         :  lep1_eta: -0.0024155     1.2918   [    -3.9764     3.9982 ]\n",
      "                         :  lep1_phi:   0.011608     1.8159   [    -3.1416     3.1415 ]\n",
      "                         :    lep1_m:   0.058735   0.052292   [ 0.00050974    0.10570 ]\n",
      "                         :   lep2_pt:     70.046     41.351   [     4.1578     672.59 ]\n",
      "                         :  lep2_eta: -0.0024155     1.2918   [    -3.9764     3.9982 ]\n",
      "                         :  lep2_phi:   0.011608     1.8159   [    -3.1416     3.1415 ]\n",
      "                         :    lep2_m:   0.058735   0.052292   [ 0.00050974    0.10570 ]\n",
      "                         :     njets:     2.5349    0.49878   [     2.0000     3.0000 ]\n",
      "                         :   jet1_pt:     98.524     58.509   [     25.074     1136.8 ]\n",
      "                         :  jet1_eta:  0.0052311     1.5400   [    -4.6982     4.7550 ]\n",
      "                         :  jet1_phi:  -0.022144     1.8193   [    -3.1415     3.1414 ]\n",
      "                         :    jet1_m:     10.611     6.8559   [-1.5259e-05     127.67 ]\n",
      "                         :   jet2_pt:     59.905     34.281   [     25.000     787.93 ]\n",
      "                         :  jet2_eta:-0.00097910     1.6902   [    -4.9487     4.9034 ]\n",
      "                         :  jet2_phi:   0.011790     1.8091   [    -3.1414     3.1415 ]\n",
      "                         :    jet2_m:     6.9959     4.3239   [-1.0790e-05     61.173 ]\n",
      "                         :   jet3_pt:     24.593     28.286   [     0.0000     481.16 ]\n",
      "                         :  jet3_eta: -0.0020990     1.3861   [    -4.9051     4.8836 ]\n",
      "                         :  jet3_phi:  0.0067077     1.3260   [    -3.1415     3.1408 ]\n",
      "                         :    jet3_m:     3.0510     3.7428   [-1.5259e-05     74.066 ]\n",
      "                         :       met:     75.637     45.340   [    0.22603     1122.6 ]\n",
      "                         :   met_phi: -0.0044184     1.8080   [    -3.1416     3.1416 ]\n",
      "                         : mtt_truth:     551.03     193.45   [     340.91     3021.4 ]\n",
      "                         : ----------------------------------------------------------------\n",
      "                         : \u001b[32m\n",
      "                         : <PlotVariables> Will not produce scatter plots ==> \n",
      "                         : |  The number of 24 input variables and 1 target values would require 300 two-dimensional\n",
      "                         : |  histograms, which would occupy the computer's memory. Note that this\n",
      "                         : |  suppression does not have any consequences for your analysis, other\n",
      "                         : |  than not disposing of these scatter plots. You can modify the maximum\n",
      "                         : |  number of input variables allowed to generate scatter plots in your\n",
      "                         : |  script via the command line:\n",
      "                         : |  \"(TMVA::gConfig().GetVariablePlotting()).fMaxNumOfAllowedVariablesForScatterPlots = <some int>;\"\u001b[0m\n",
      "                         : \n",
      "                         : Some more output\n",
      "                         : Evaluate regression method: BDTG\n",
      "                         : TestRegression (testing)\n",
      "                         : Calculate regression for all events\n",
      "                         : Elapsed time for evaluation of 52710 events: 4.77 sec       \n",
      "                         : TestRegression (training)\n",
      "                         : Calculate regression for all events\n",
      "                         : Elapsed time for evaluation of 10000 events: 0.903 sec       \n",
      "<WARNING>                : Variable \"nlep\" has zero, negative, or NaN RMS^2: 0 ==> set to zero. Please check the variable content\n",
      "TFHandler_BDTG           :  Variable         Mean         RMS   [        Min         Max ]\n",
      "                         : ----------------------------------------------------------------\n",
      "                         :      nlep:     2.0000     0.0000   [     2.0000     2.0000 ]\n",
      "                         :   lep1_pt:     70.046     41.351   [     4.1578     672.59 ]\n",
      "                         :  lep1_eta: -0.0024155     1.2918   [    -3.9764     3.9982 ]\n",
      "                         :  lep1_phi:   0.011608     1.8159   [    -3.1416     3.1415 ]\n",
      "                         :    lep1_m:   0.058735   0.052292   [ 0.00050974    0.10570 ]\n",
      "                         :   lep2_pt:     70.046     41.351   [     4.1578     672.59 ]\n",
      "                         :  lep2_eta: -0.0024155     1.2918   [    -3.9764     3.9982 ]\n",
      "                         :  lep2_phi:   0.011608     1.8159   [    -3.1416     3.1415 ]\n",
      "                         :    lep2_m:   0.058735   0.052292   [ 0.00050974    0.10570 ]\n",
      "                         :     njets:     2.5349    0.49878   [     2.0000     3.0000 ]\n",
      "                         :   jet1_pt:     98.524     58.509   [     25.074     1136.8 ]\n",
      "                         :  jet1_eta:  0.0052311     1.5400   [    -4.6982     4.7550 ]\n",
      "                         :  jet1_phi:  -0.022144     1.8193   [    -3.1415     3.1414 ]\n",
      "                         :    jet1_m:     10.611     6.8559   [-1.5259e-05     127.67 ]\n",
      "                         :   jet2_pt:     59.905     34.281   [     25.000     787.93 ]\n",
      "                         :  jet2_eta:-0.00097910     1.6902   [    -4.9487     4.9034 ]\n",
      "                         :  jet2_phi:   0.011790     1.8091   [    -3.1414     3.1415 ]\n",
      "                         :    jet2_m:     6.9959     4.3239   [-1.0790e-05     61.173 ]\n",
      "                         :   jet3_pt:     24.593     28.286   [     0.0000     481.16 ]\n",
      "                         :  jet3_eta: -0.0020990     1.3861   [    -4.9051     4.8836 ]\n",
      "                         :  jet3_phi:  0.0067077     1.3260   [    -3.1415     3.1408 ]\n",
      "                         :    jet3_m:     3.0510     3.7428   [-1.5259e-05     74.066 ]\n",
      "                         :       met:     75.637     45.340   [    0.22603     1122.6 ]\n",
      "                         :   met_phi: -0.0044184     1.8080   [    -3.1416     3.1416 ]\n",
      "                         : mtt_truth:     551.03     193.45   [     340.91     3021.4 ]\n",
      "                         : ----------------------------------------------------------------\n",
      "                         : \u001b[32m\n",
      "                         : <PlotVariables> Will not produce scatter plots ==> \n",
      "                         : |  The number of 24 input variables and 1 target values would require 300 two-dimensional\n",
      "                         : |  histograms, which would occupy the computer's memory. Note that this\n",
      "                         : |  suppression does not have any consequences for your analysis, other\n",
      "                         : |  than not disposing of these scatter plots. You can modify the maximum\n",
      "                         : |  number of input variables allowed to generate scatter plots in your\n",
      "                         : |  script via the command line:\n",
      "                         : |  \"(TMVA::gConfig().GetVariablePlotting()).fMaxNumOfAllowedVariablesForScatterPlots = <some int>;\"\u001b[0m\n",
      "                         : \n",
      "                         : Some more output\n",
      "                         : \n",
      "                         : Evaluation results ranked by smallest RMS on test sample:\n",
      "                         : (\"Bias\" quotes the mean deviation of the regression from true target.\n",
      "                         :  \"MutInf\" is the \"Mutual Information\" between regression and target.\n",
      "                         :  Indicated by \"_T\" are the corresponding \"truncated\" quantities ob-\n",
      "                         :  tained when removing events deviating more than 2sigma from average.)\n",
      "                         : --------------------------------------------------------------------------------------------------\n",
      "                         : --------------------------------------------------------------------------------------------------\n",
      "                         : dataset              BDTG           :    -20.4    -1.99     172.     121.  |  0.138  0.173\n",
      "                         : dataset              BDT            :     63.6     95.7     213.     147.  |  0.026  0.022\n",
      "                         : --------------------------------------------------------------------------------------------------\n",
      "                         : \n",
      "                         : Evaluation results ranked by smallest RMS on training sample:\n",
      "                         : (overtraining check)\n",
      "                         : --------------------------------------------------------------------------------------------------\n",
      "                         : DataSet Name:         MVA Method:        <Bias>   <Bias_T>    RMS    RMS_T  |  MutInf MutInf_T\n",
      "                         : --------------------------------------------------------------------------------------------------\n",
      "                         : dataset              BDTG           :    -18.3    -2.84     109.     59.4  |  0.784  0.828\n",
      "                         : dataset              BDT            :     66.0     96.3     204.     144.  |  0.066  0.029\n",
      "                         : --------------------------------------------------------------------------------------------------\n",
      "                         : \n",
      "Dataset:dataset          : Created tree 'TestTree' with 52710 events\n",
      "                         : \n",
      "Dataset:dataset          : Created tree 'TrainTree' with 10000 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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Error in <TDecompLU::DecomposeLUCrout>: matrix is singular\n",
      "Error in <TDecompLU::InvertLU>: matrix is singular, 0 diag elements < tolerance of 2.2204e-16\n",
      "Error in <TH1F::Smooth>: Smooth only supported for histograms with >= 3 bins. Nbins = 2\n"
     ]
    }
   ],
   "source": [
    "factory.TrainAllMethods()\n",
    "factory.TestAllMethods()\n",
    "factory.EvaluateAllMethods()\n",
    "\n",
    "output.Close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "macro-evidence",
   "metadata": {},
   "source": [
    "Draw all canvases "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "impressive-motivation",
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2021-04-27T06:03:21.431113Z",
     "iopub.status.busy": "2021-04-27T06:03:21.430644Z",
     "iopub.status.idle": "2021-04-27T06:03:21.540077Z",
     "shell.execute_reply": "2021-04-27T06:03:21.541141Z"
    }
   },
   "outputs": [],
   "source": [
    "from ROOT import gROOT \n",
    "gROOT.GetListOfCanvases().Draw()"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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   "file_extension": ".py",
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