HEPML_HandsOn_BDT.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dhrou/HEPMLtutorials/blob/master/HEPML_HandsOn_BDT.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZT7AoY8sKwp1"
      },
      "source": [
        "# Hands on:introduction to BDT on HEP dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eT7-MMpfrlHR"
      },
      "source": [
        "## Standard Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "QdmaHjz-xCm-"
      },
      "outputs": [],
      "source": [
        "COLAB=True #if running on https://colab.research.google.com/notebooks/welcome.ipynb\n",
        "#COLAB=False #if running on local computer, for example with Anaconda installation https://docs.anaconda.com/anaconda/install/\n",
        "\n",
        "#fullrun=True #if run all modules (slow!)\n",
        "fullrun=False #if run a subset to get started\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "xy-_72FJKwp6"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "pd.set_option('display.max_columns', 100) # to see more columns of df.head()\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "#increase default plot size\n",
        "plt.rcParams['figure.figsize'] = [1.5*i for i in plt.rcParams['figure.figsize']]\n",
        "\n",
        "from IPython.display import display, HTML\n",
        "\n",
        "# Set the height for the scrollable output windo\n",
        "#display(HTML(\"<style>.output_scroll { height: 800px; }</style>\"))\n",
        "\n",
        "import time\n",
        "np.random.seed(31415) # set the np random seed for the reproducibility\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "oF367-yfKwp9",
        "outputId": "822b4c32-e045-4e51-f589-9f85bce5ffef",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.1.4\n",
            "4.5.0\n",
            "1.6.1\n"
          ]
        }
      ],
      "source": [
        "# Fix environment if necessary\n",
        "\n",
        "if COLAB:\n",
        "    #!pip install xgboost --upgrade #  maybe be needed as Colab is not always uptodate\n",
        "    pass\n",
        "else:\n",
        "    # install xgboost and lighgbm, two popular Boosted Decision Tree packages\n",
        "    # the following need to be done only once. To be commented out later\n",
        "    %pip install xgboost\n",
        "    %pip install lightgbm\n",
        "    pass\n",
        "\n",
        "import xgboost\n",
        "print (xgboost.__version__) # Tested with 2.0.3\n",
        "import lightgbm\n",
        "print (lightgbm.__version__) # Tested with 4.1.0\n",
        "import sklearn\n",
        "print (sklearn.__version__) # Tested with 1.2.2\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "IsChk6u-r7fk",
        "scrolled": true
      },
      "outputs": [],
      "source": [
        "if COLAB:\n",
        "    #### Reading files from Google Drive\n",
        "    # one need a google account to be identified\n",
        "    # select a google account, then cut and paste the long password in the pop up field\n",
        "    #!pip install PyDrive\n",
        "    import os\n",
        "    from pydrive2.auth import GoogleAuth\n",
        "    from pydrive2.drive import GoogleDrive\n",
        "    from google.colab import auth\n",
        "    from oauth2client.client import GoogleCredentials\n",
        "    auth.authenticate_user()\n",
        "    gauth = GoogleAuth()\n",
        "    gauth.credentials = GoogleCredentials.get_application_default()\n",
        "    drive = GoogleDrive(gauth)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g14yIjbPKwqC"
      },
      "source": [
        "# Load events"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "kl-4W4Ifs8EV",
        "outputId": "c3b48c6a-6d21-4cb6-9908-0f39be4fa6cd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total 35072\n",
            "drwxr-xr-x 1 root root     4096 Apr  4 13:38 sample_data\n",
            "-rw-r--r-- 1 root root 35905611 Apr  7 21:42 dataWW_d1_600k.csv.gz\n",
            "Will read file: dataWW_d1_600k.csv.gz\n",
            "now : 2025-04-07 21:42:32.728235\n",
            "File loaded with  600000  events \n"
          ]
        }
      ],
      "source": [
        "if COLAB:\n",
        "\n",
        "    #attach dataset from google drive\n",
        "    download = drive.CreateFile({'id': '1nlXp7P-xq_jip4aPE0j0mnPhYnIOcBv4'})\n",
        "    download.GetContentFile(\"dataWW_d1_600k.csv.gz\")\n",
        "\n",
        "\n",
        "    datapath=\"\"\n",
        "\n",
        "\n",
        "    !ls -lrt\n",
        "else :\n",
        "    # make sure the file is available locally.\n",
        "    #Should be downloaded from https://drive.google.com/open?id=1nlXp7P-xq_jip4aPE0j0mnPhYnIOcBv4\n",
        "    %ls -lrt # what is in the local directory\n",
        "    datapath=\"\" # if in same directory (really or soft link), otherwise use proper path\n",
        "    print (\"Will take data from : \",datapath)\n",
        "\n",
        "filename=os.path.join(datapath,\"dataWW_d1_600k.csv.gz\")\n",
        "print(\"Will read file:\",filename)\n",
        "#load data\n",
        "# data was created from ATLAS Open Data see doc\n",
        "# http://opendata.atlas.cern/release/2020/documentation/datasets/intro.html\n",
        "dfall = pd.read_csv(filename)\n",
        "\n",
        "#shuffle the events, already done but just to be safe\n",
        "dfall = dfall.sample(frac=1).reset_index(drop=True)\n",
        "from datetime import datetime\n",
        "print (\"now :\",datetime.now())\n",
        "print (\"File loaded with \",dfall.shape[0], \" events \")\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uf9Gt8g8KwqF"
      },
      "source": [
        "At this point, it should tell you \"File Loaded with 600000 events\". If not, it could not access the datafile, no point going further !"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "67cCYsYIKwqG"
      },
      "source": [
        "# Examine dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "sx1_PB22KwqG",
        "outputId": "595f1371-05e4-4f93-b21c-867e9fe14468",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['eventNumber', 'label', 'met_et', 'met_phi', 'lep_n', 'lep_pt_0',\n",
              "       'lep_pt_1', 'lep_eta_0', 'lep_eta_1', 'lep_phi_0', 'lep_phi_1',\n",
              "       'lep_E_0', 'lep_E_1', 'lep_charge_0', 'lep_charge_1', 'lep_type_0',\n",
              "       'lep_type_1', 'jet_n', 'jet_pt_0', 'jet_pt_1', 'jet_eta_0', 'jet_eta_1',\n",
              "       'jet_phi_0', 'jet_phi_1', 'jet_E_0', 'jet_E_1', 'mcWeight', 'runNumber',\n",
              "       'channelNumber'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "#dump list of feature\n",
        "dfall.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "Kl5MAys-yd0R"
      },
      "outputs": [],
      "source": [
        "dfall.mcWeight*=4 # arbitrary scale to have larger significance (this is a hack)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "j9l7wkkorlHe",
        "outputId": "01737927-196e-4efe-dd87-66c77352177a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   eventNumber  label   met_et  met_phi  lep_n  lep_pt_0  lep_pt_1  lep_eta_0  \\\n",
              "0       402756      1   25.609  0.42452      2    48.295    15.214    0.73991   \n",
              "1       101274      0  196.560  1.31140      2    69.459    21.081   -0.52666   \n",
              "2       468437      1   45.653 -2.76860      2    45.927    22.822   -1.61910   \n",
              "3       272337      1   49.415 -0.57805      2    45.929    14.263    1.55280   \n",
              "4       354546      1   71.988 -2.60390      2    62.029    21.453   -0.51082   \n",
              "\n",
              "   lep_eta_1  lep_phi_0  lep_phi_1   lep_E_0  lep_E_1  lep_charge_0  \\\n",
              "0    2.27420  -2.316400   -1.39410   62129.0  74721.0            -1   \n",
              "1    0.22380   0.023132   -0.67855   79317.0  21611.0            -1   \n",
              "2   -2.00770   1.901700   -0.11248  120480.0  86498.0            -1   \n",
              "3    0.12809   2.254700   -2.54810  113360.0  14380.0            -1   \n",
              "4   -0.38177   0.236200    1.40820   70300.0  23036.0            -1   \n",
              "\n",
              "   lep_charge_1  lep_type_0  lep_type_1  jet_n  jet_pt_0  jet_pt_1  jet_eta_0  \\\n",
              "0             1          13          11      2     26.32    20.064    -1.1350   \n",
              "1             1          13          13      2    192.12    36.217    -1.2643   \n",
              "2             1          11          11      0     -7.00    -7.000    -7.0000   \n",
              "3             1          13          13      0     -7.00    -7.000    -7.0000   \n",
              "4             1          11          13      0     -7.00    -7.000    -7.0000   \n",
              "\n",
              "   jet_eta_1  jet_phi_0  jet_phi_1   jet_E_0  jet_E_1  mcWeight  runNumber  \\\n",
              "0   -2.07540    -2.8092     2.0740   45397.0  81304.0  0.000008     284500   \n",
              "1   -0.69746    -2.2147    -1.9152  367670.0  45733.0  0.001153     284500   \n",
              "2   -7.00000    -7.0000    -7.0000      -7.0     -7.0  0.000116     284500   \n",
              "3   -7.00000    -7.0000    -7.0000      -7.0     -7.0  0.000116     284500   \n",
              "4   -7.00000    -7.0000    -7.0000      -7.0     -7.0  0.000008     284500   \n",
              "\n",
              "   channelNumber  \n",
              "0         345323  \n",
              "1         363492  \n",
              "2         345324  \n",
              "3         345324  \n",
              "4         345323  "
            ],
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>eventNumber</th>\n",
              "      <th>label</th>\n",
              "      <th>met_et</th>\n",
              "      <th>met_phi</th>\n",
              "      <th>lep_n</th>\n",
              "      <th>lep_pt_0</th>\n",
              "      <th>lep_pt_1</th>\n",
              "      <th>lep_eta_0</th>\n",
              "      <th>lep_eta_1</th>\n",
              "      <th>lep_phi_0</th>\n",
              "      <th>lep_phi_1</th>\n",
              "      <th>lep_E_0</th>\n",
              "      <th>lep_E_1</th>\n",
              "      <th>lep_charge_0</th>\n",
              "      <th>lep_charge_1</th>\n",
              "      <th>lep_type_0</th>\n",
              "      <th>lep_type_1</th>\n",
              "      <th>jet_n</th>\n",
              "      <th>jet_pt_0</th>\n",
              "      <th>jet_pt_1</th>\n",
              "      <th>jet_eta_0</th>\n",
              "      <th>jet_eta_1</th>\n",
              "      <th>jet_phi_0</th>\n",
              "      <th>jet_phi_1</th>\n",
              "      <th>jet_E_0</th>\n",
              "      <th>jet_E_1</th>\n",
              "      <th>mcWeight</th>\n",
              "      <th>runNumber</th>\n",
              "      <th>channelNumber</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>402756</td>\n",
              "      <td>1</td>\n",
              "      <td>25.609</td>\n",
              "      <td>0.42452</td>\n",
              "      <td>2</td>\n",
              "      <td>48.295</td>\n",
              "      <td>15.214</td>\n",
              "      <td>0.73991</td>\n",
              "      <td>2.27420</td>\n",
              "      <td>-2.316400</td>\n",
              "      <td>-1.39410</td>\n",
              "      <td>62129.0</td>\n",
              "      <td>74721.0</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>11</td>\n",
              "      <td>2</td>\n",
              "      <td>26.32</td>\n",
              "      <td>20.064</td>\n",
              "      <td>-1.1350</td>\n",
              "      <td>-2.07540</td>\n",
              "      <td>-2.8092</td>\n",
              "      <td>2.0740</td>\n",
              "      <td>45397.0</td>\n",
              "      <td>81304.0</td>\n",
              "      <td>0.000008</td>\n",
              "      <td>284500</td>\n",
              "      <td>345323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>101274</td>\n",
              "      <td>0</td>\n",
              "      <td>196.560</td>\n",
              "      <td>1.31140</td>\n",
              "      <td>2</td>\n",
              "      <td>69.459</td>\n",
              "      <td>21.081</td>\n",
              "      <td>-0.52666</td>\n",
              "      <td>0.22380</td>\n",
              "      <td>0.023132</td>\n",
              "      <td>-0.67855</td>\n",
              "      <td>79317.0</td>\n",
              "      <td>21611.0</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>13</td>\n",
              "      <td>2</td>\n",
              "      <td>192.12</td>\n",
              "      <td>36.217</td>\n",
              "      <td>-1.2643</td>\n",
              "      <td>-0.69746</td>\n",
              "      <td>-2.2147</td>\n",
              "      <td>-1.9152</td>\n",
              "      <td>367670.0</td>\n",
              "      <td>45733.0</td>\n",
              "      <td>0.001153</td>\n",
              "      <td>284500</td>\n",
              "      <td>363492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>468437</td>\n",
              "      <td>1</td>\n",
              "      <td>45.653</td>\n",
              "      <td>-2.76860</td>\n",
              "      <td>2</td>\n",
              "      <td>45.927</td>\n",
              "      <td>22.822</td>\n",
              "      <td>-1.61910</td>\n",
              "      <td>-2.00770</td>\n",
              "      <td>1.901700</td>\n",
              "      <td>-0.11248</td>\n",
              "      <td>120480.0</td>\n",
              "      <td>86498.0</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>-7.00</td>\n",
              "      <td>-7.000</td>\n",
              "      <td>-7.0000</td>\n",
              "      <td>-7.00000</td>\n",
              "      <td>-7.0000</td>\n",
              "      <td>-7.0000</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>0.000116</td>\n",
              "      <td>284500</td>\n",
              "      <td>345324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>272337</td>\n",
              "      <td>1</td>\n",
              "      <td>49.415</td>\n",
              "      <td>-0.57805</td>\n",
              "      <td>2</td>\n",
              "      <td>45.929</td>\n",
              "      <td>14.263</td>\n",
              "      <td>1.55280</td>\n",
              "      <td>0.12809</td>\n",
              "      <td>2.254700</td>\n",
              "      <td>-2.54810</td>\n",
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              "      <td>14380.0</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>-7.00</td>\n",
              "      <td>-7.000</td>\n",
              "      <td>-7.0000</td>\n",
              "      <td>-7.00000</td>\n",
              "      <td>-7.0000</td>\n",
              "      <td>-7.0000</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>0.000116</td>\n",
              "      <td>284500</td>\n",
              "      <td>345324</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>354546</td>\n",
              "      <td>1</td>\n",
              "      <td>71.988</td>\n",
              "      <td>-2.60390</td>\n",
              "      <td>2</td>\n",
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              "      <td>-0.38177</td>\n",
              "      <td>0.236200</td>\n",
              "      <td>1.40820</td>\n",
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              "      <td>23036.0</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>13</td>\n",
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              "      <td>-7.00</td>\n",
              "      <td>-7.000</td>\n",
              "      <td>-7.0000</td>\n",
              "      <td>-7.00000</td>\n",
              "      <td>-7.0000</td>\n",
              "      <td>-7.0000</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>-7.0</td>\n",
              "      <td>0.000008</td>\n",
              "      <td>284500</td>\n",
              "      <td>345323</td>\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-762f9e67-65ca-460b-ab74-15d02c2f94ab button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-762f9e67-65ca-460b-ab74-15d02c2f94ab');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-cc44a5e9-b91f-4d18-baeb-be644943dadd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cc44a5e9-b91f-4d18-baeb-be644943dadd')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-cc44a5e9-b91f-4d18-baeb-be644943dadd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "#examine first few events\n",
        "display(dfall.head())\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "Oz-lWJhgrlHg",
        "outputId": "cbd6807e-0eb3-4dfa-e069-3aa2ce0291cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        eventNumber          label         met_et        met_phi  \\\n",
              "count  6.000000e+05  600000.000000  600000.000000  600000.000000   \n",
              "mean   7.029775e+05       0.666667      60.365356      -0.008556   \n",
              "std    4.568961e+05       0.471405      48.153210       1.812778   \n",
              "min    1.000000e+00       0.000000       0.044211      -3.141600   \n",
              "25%    3.498568e+05       0.000000      33.196000      -1.577600   \n",
              "50%    6.678815e+05       1.000000      50.082000      -0.018694   \n",
              "75%    1.001273e+06       1.000000      73.032250       1.559300   \n",
              "max    1.997995e+06       1.000000    7062.200000       3.141600   \n",
              "\n",
              "               lep_n       lep_pt_0       lep_pt_1      lep_eta_0  \\\n",
              "count  600000.000000  600000.000000  600000.000000  600000.000000   \n",
              "mean        2.005748      60.270328      28.133970       0.000269   \n",
              "std         0.076061      42.676858      20.352272       1.210614   \n",
              "min         2.000000      25.000000       7.000300      -2.700000   \n",
              "25%         2.000000      36.624750      15.316000      -0.899650   \n",
              "50%         2.000000      48.229500      23.644000       0.003413   \n",
              "75%         2.000000      68.979000      34.149000       0.901950   \n",
              "max         5.000000    7041.400000     744.900000       2.699800   \n",
              "\n",
              "           lep_eta_1      lep_phi_0      lep_phi_1       lep_E_0  \\\n",
              "count  600000.000000  600000.000000  600000.000000  6.000000e+05   \n",
              "mean       -0.000664       0.005219       0.008524  1.164008e+05   \n",
              "std         1.235890       1.811177       1.814205  1.083341e+05   \n",
              "min        -2.699900      -3.141600      -3.141600  2.501300e+04   \n",
              "25%        -0.946043      -1.560200      -1.562900  5.221675e+04   \n",
              "50%         0.002974       0.014583       0.018823  8.184450e+04   \n",
              "75%         0.942210       1.573000       1.577300  1.412400e+05   \n",
              "max         2.700000       3.141500       3.141600  7.041600e+06   \n",
              "\n",
              "            lep_E_1   lep_charge_0   lep_charge_1     lep_type_0  \\\n",
              "count  6.000000e+05  600000.000000  600000.000000  600000.000000   \n",
              "mean   5.630948e+04      -0.027027       0.027037      11.907273   \n",
              "std    5.748834e+04       0.999636       0.999635       0.995692   \n",
              "min    7.003800e+03      -1.000000      -1.000000      11.000000   \n",
              "25%    2.329100e+04      -1.000000      -1.000000      11.000000   \n",
              "50%    3.824900e+04      -1.000000       1.000000      11.000000   \n",
              "75%    6.730825e+04       1.000000       1.000000      13.000000   \n",
              "max    2.107900e+06       1.000000       1.000000      13.000000   \n",
              "\n",
              "          lep_type_1          jet_n       jet_pt_0       jet_pt_1  \\\n",
              "count  600000.000000  600000.000000  600000.000000  600000.000000   \n",
              "mean       11.958010       1.334185      59.606114      13.521164   \n",
              "std         0.999119       1.232632      79.586143      38.074439   \n",
              "min        11.000000       0.000000      -7.000000      -7.000000   \n",
              "25%        11.000000       0.000000      -7.000000      -7.000000   \n",
              "50%        11.000000       1.000000      38.744000      -7.000000   \n",
              "75%        13.000000       2.000000      84.291250      26.881000   \n",
              "max        13.000000      13.000000    1658.200000    1429.800000   \n",
              "\n",
              "           jet_eta_0      jet_eta_1      jet_phi_0      jet_phi_1  \\\n",
              "count  600000.000000  600000.000000  600000.000000  600000.000000   \n",
              "mean       -1.934521      -4.402337      -1.939645      -4.401941   \n",
              "std         3.349404       3.491304       3.489197       3.559421   \n",
              "min        -7.000000      -7.000000      -7.000000      -7.000000   \n",
              "25%        -7.000000      -7.000000      -7.000000      -7.000000   \n",
              "50%        -0.891010      -7.000000      -1.210600      -7.000000   \n",
              "75%         0.724705      -0.821005       0.967753      -1.088575   \n",
              "max         2.500000       2.500000       3.141600       3.141600   \n",
              "\n",
              "            jet_E_0       jet_E_1       mcWeight  runNumber  channelNumber  \n",
              "count  6.000000e+05  6.000000e+05  600000.000000   600000.0  600000.000000  \n",
              "mean   1.375410e+05  4.125468e+04       0.000872   284500.0  351379.666667  \n",
              "std    1.980717e+05  8.990068e+04       0.002944        0.0    8564.720183  \n",
              "min   -7.000000e+00 -7.000000e+00      -0.182228   284500.0  345323.000000  \n",
              "25%   -7.000000e+00 -7.000000e+00       0.000008   284500.0  345323.000000  \n",
              "50%    7.501700e+04 -7.000000e+00       0.000116   284500.0  345324.000000  \n",
              "75%    1.766200e+05  5.060500e+04       0.000786   284500.0  363492.000000  \n",
              "max    5.638800e+06  2.784300e+06       0.209680   284500.0  363492.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e77bf107-00c7-4a0e-a7e3-f2d24152c2b9\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>eventNumber</th>\n",
              "      <th>label</th>\n",
              "      <th>met_et</th>\n",
              "      <th>met_phi</th>\n",
              "      <th>lep_n</th>\n",
              "      <th>lep_pt_0</th>\n",
              "      <th>lep_pt_1</th>\n",
              "      <th>lep_eta_0</th>\n",
              "      <th>lep_eta_1</th>\n",
              "      <th>lep_phi_0</th>\n",
              "      <th>lep_phi_1</th>\n",
              "      <th>lep_E_0</th>\n",
              "      <th>lep_E_1</th>\n",
              "      <th>lep_charge_0</th>\n",
              "      <th>lep_charge_1</th>\n",
              "      <th>lep_type_0</th>\n",
              "      <th>lep_type_1</th>\n",
              "      <th>jet_n</th>\n",
              "      <th>jet_pt_0</th>\n",
              "      <th>jet_pt_1</th>\n",
              "      <th>jet_eta_0</th>\n",
              "      <th>jet_eta_1</th>\n",
              "      <th>jet_phi_0</th>\n",
              "      <th>jet_phi_1</th>\n",
              "      <th>jet_E_0</th>\n",
              "      <th>jet_E_1</th>\n",
              "      <th>mcWeight</th>\n",
              "      <th>runNumber</th>\n",
              "      <th>channelNumber</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6.000000e+05</td>\n",
              "      <td>600000.000000</td>\n",
              "      <td>600000.000000</td>\n",
              "      <td>600000.000000</td>\n",
              "      <td>600000.000000</td>\n",
              "      <td>600000.000000</td>\n",
              "      <td>600000.000000</td>\n",
              "      <td>600000.000000</td>\n",
              "      <td>600000.000000</td>\n",
              "      <td>600000.000000</td>\n",
              "      <td>600000.000000</td>\n",
              "      <td>6.000000e+05</td>\n",
              "      <td>6.000000e+05</td>\n",
              "      <td>600000.000000</td>\n",
              "      <td>600000.000000</td>\n",
              "      <td>600000.000000</td>\n",
              "      <td>600000.000000</td>\n",
              "      <td>600000.000000</td>\n",
              "      <td>600000.000000</td>\n",
              "      <td>600000.000000</td>\n",
              "      <td>600000.000000</td>\n",
              "      <td>600000.000000</td>\n",
              "      <td>600000.000000</td>\n",
              "      <td>600000.000000</td>\n",
              "      <td>6.000000e+05</td>\n",
              "      <td>6.000000e+05</td>\n",
              "      <td>600000.000000</td>\n",
              "      <td>600000.0</td>\n",
              "      <td>600000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>7.029775e+05</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>60.365356</td>\n",
              "      <td>-0.008556</td>\n",
              "      <td>2.005748</td>\n",
              "      <td>60.270328</td>\n",
              "      <td>28.133970</td>\n",
              "      <td>0.000269</td>\n",
              "      <td>-0.000664</td>\n",
              "      <td>0.005219</td>\n",
              "      <td>0.008524</td>\n",
              "      <td>1.164008e+05</td>\n",
              "      <td>5.630948e+04</td>\n",
              "      <td>-0.027027</td>\n",
              "      <td>0.027037</td>\n",
              "      <td>11.907273</td>\n",
              "      <td>11.958010</td>\n",
              "      <td>1.334185</td>\n",
              "      <td>59.606114</td>\n",
              "      <td>13.521164</td>\n",
              "      <td>-1.934521</td>\n",
              "      <td>-4.402337</td>\n",
              "      <td>-1.939645</td>\n",
              "      <td>-4.401941</td>\n",
              "      <td>1.375410e+05</td>\n",
              "      <td>4.125468e+04</td>\n",
              "      <td>0.000872</td>\n",
              "      <td>284500.0</td>\n",
              "      <td>351379.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.568961e+05</td>\n",
              "      <td>0.471405</td>\n",
              "      <td>48.153210</td>\n",
              "      <td>1.812778</td>\n",
              "      <td>0.076061</td>\n",
              "      <td>42.676858</td>\n",
              "      <td>20.352272</td>\n",
              "      <td>1.210614</td>\n",
              "      <td>1.235890</td>\n",
              "      <td>1.811177</td>\n",
              "      <td>1.814205</td>\n",
              "      <td>1.083341e+05</td>\n",
              "      <td>5.748834e+04</td>\n",
              "      <td>0.999636</td>\n",
              "      <td>0.999635</td>\n",
              "      <td>0.995692</td>\n",
              "      <td>0.999119</td>\n",
              "      <td>1.232632</td>\n",
              "      <td>79.586143</td>\n",
              "      <td>38.074439</td>\n",
              "      <td>3.349404</td>\n",
              "      <td>3.491304</td>\n",
              "      <td>3.489197</td>\n",
              "      <td>3.559421</td>\n",
              "      <td>1.980717e+05</td>\n",
              "      <td>8.990068e+04</td>\n",
              "      <td>0.002944</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8564.720183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.044211</td>\n",
              "      <td>-3.141600</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>7.000300</td>\n",
              "      <td>-2.700000</td>\n",
              "      <td>-2.699900</td>\n",
              "      <td>-3.141600</td>\n",
              "      <td>-3.141600</td>\n",
              "      <td>2.501300e+04</td>\n",
              "      <td>7.003800e+03</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-7.000000e+00</td>\n",
              "      <td>-7.000000e+00</td>\n",
              "      <td>-0.182228</td>\n",
              "      <td>284500.0</td>\n",
              "      <td>345323.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.498568e+05</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>33.196000</td>\n",
              "      <td>-1.577600</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>36.624750</td>\n",
              "      <td>15.316000</td>\n",
              "      <td>-0.899650</td>\n",
              "      <td>-0.946043</td>\n",
              "      <td>-1.560200</td>\n",
              "      <td>-1.562900</td>\n",
              "      <td>5.221675e+04</td>\n",
              "      <td>2.329100e+04</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-7.000000e+00</td>\n",
              "      <td>-7.000000e+00</td>\n",
              "      <td>0.000008</td>\n",
              "      <td>284500.0</td>\n",
              "      <td>345323.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>6.678815e+05</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>50.082000</td>\n",
              "      <td>-0.018694</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>48.229500</td>\n",
              "      <td>23.644000</td>\n",
              "      <td>0.003413</td>\n",
              "      <td>0.002974</td>\n",
              "      <td>0.014583</td>\n",
              "      <td>0.018823</td>\n",
              "      <td>8.184450e+04</td>\n",
              "      <td>3.824900e+04</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>38.744000</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-0.891010</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>-1.210600</td>\n",
              "      <td>-7.000000</td>\n",
              "      <td>7.501700e+04</td>\n",
              "      <td>-7.000000e+00</td>\n",
              "      <td>0.000116</td>\n",
              "      <td>284500.0</td>\n",
              "      <td>345324.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.001273e+06</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>73.032250</td>\n",
              "      <td>1.559300</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>68.979000</td>\n",
              "      <td>34.149000</td>\n",
              "      <td>0.901950</td>\n",
              "      <td>0.942210</td>\n",
              "      <td>1.573000</td>\n",
              "      <td>1.577300</td>\n",
              "      <td>1.412400e+05</td>\n",
              "      <td>6.730825e+04</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>84.291250</td>\n",
              "      <td>26.881000</td>\n",
              "      <td>0.724705</td>\n",
              "      <td>-0.821005</td>\n",
              "      <td>0.967753</td>\n",
              "      <td>-1.088575</td>\n",
              "      <td>1.766200e+05</td>\n",
              "      <td>5.060500e+04</td>\n",
              "      <td>0.000786</td>\n",
              "      <td>284500.0</td>\n",
              "      <td>363492.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.997995e+06</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7062.200000</td>\n",
              "      <td>3.141600</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>7041.400000</td>\n",
              "      <td>744.900000</td>\n",
              "      <td>2.699800</td>\n",
              "      <td>2.700000</td>\n",
              "      <td>3.141500</td>\n",
              "      <td>3.141600</td>\n",
              "      <td>7.041600e+06</td>\n",
              "      <td>2.107900e+06</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>1658.200000</td>\n",
              "      <td>1429.800000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>3.141600</td>\n",
              "      <td>3.141600</td>\n",
              "      <td>5.638800e+06</td>\n",
              "      <td>2.784300e+06</td>\n",
              "      <td>0.209680</td>\n",
              "      <td>284500.0</td>\n",
              "      <td>363492.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e77bf107-00c7-4a0e-a7e3-f2d24152c2b9')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e77bf107-00c7-4a0e-a7e3-f2d24152c2b9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e77bf107-00c7-4a0e-a7e3-f2d24152c2b9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6725b9d7-66f3-4684-98d3-8e8a44c92822\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6725b9d7-66f3-4684-98d3-8e8a44c92822')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6725b9d7-66f3-4684-98d3-8e8a44c92822 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "#examine feature distribution\n",
        "dfall.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "lQsalTmorlHj",
        "outputId": "28718923-35f4-45fc-d035-d76cbcb8d9ff",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total label weights  B S = (np.float64(498.6491778568), np.float64(24.8002135904))\n",
            "total class number of events B S (200000, 400000)\n"
          ]
        }
      ],
      "source": [
        "label_weights = (dfall[dfall.label==0].mcWeight.sum(), dfall[dfall.label==1].mcWeight.sum() )\n",
        "print(\"total label weights  B S =\",label_weights)\n",
        "\n",
        "\n",
        "label_nevents = (dfall[dfall.label==0].shape[0], dfall[dfall.label==1].shape[0] )\n",
        "print (\"total class number of events B S\",label_nevents)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rtI5u5GErlHq"
      },
      "source": [
        "## Event selection"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "kaO2JM1hrlHr",
        "outputId": "0b8588e5-2268-4215-96be-1c320b508937",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Df shape before selection : (600000, 29)\n",
            "Df shape after selection : (577357, 29)\n"
          ]
        }
      ],
      "source": [
        "print (\"Df shape before selection :\", dfall.shape)\n",
        "# only keep events with exactly two leptons\n",
        "# only keep events with positive weight. This is not correct but mandatory for a tutorial because\n",
        "# many tools choke on negative weight.\n",
        "fulldata=dfall[ (dfall.lep_n==2) & (dfall.mcWeight > 0)]\n",
        "\n",
        "\n",
        "print (\"Df shape after selection :\",fulldata.shape)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nviyIMgerlH3"
      },
      "source": [
        "\n",
        "# DO NOT MODIFY ANYTHING ABOVE\n",
        "... and always rerun from this cell whenever you change something below"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "6e0Hlpv6rlH4",
        "scrolled": false,
        "outputId": "e7811157-658a-44d3-c53f-ff3d0d7def8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 672
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Df shape of dataset to be used : (577357, 6)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "    met_et  met_phi  lep_pt_0  lep_pt_1  lep_phi_0  lep_phi_1\n",
              "0   25.609  0.42452    48.295    15.214  -2.316400   -1.39410\n",
              "1  196.560  1.31140    69.459    21.081   0.023132   -0.67855\n",
              "2   45.653 -2.76860    45.927    22.822   1.901700   -0.11248\n",
              "3   49.415 -0.57805    45.929    14.263   2.254700   -2.54810\n",
              "4   71.988 -2.60390    62.029    21.453   0.236200    1.40820"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-56d6b42a-9999-41ac-8044-885f146b7b61\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>met_et</th>\n",
              "      <th>met_phi</th>\n",
              "      <th>lep_pt_0</th>\n",
              "      <th>lep_pt_1</th>\n",
              "      <th>lep_phi_0</th>\n",
              "      <th>lep_phi_1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>25.609</td>\n",
              "      <td>0.42452</td>\n",
              "      <td>48.295</td>\n",
              "      <td>15.214</td>\n",
              "      <td>-2.316400</td>\n",
              "      <td>-1.39410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>196.560</td>\n",
              "      <td>1.31140</td>\n",
              "      <td>69.459</td>\n",
              "      <td>21.081</td>\n",
              "      <td>0.023132</td>\n",
              "      <td>-0.67855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>45.653</td>\n",
              "      <td>-2.76860</td>\n",
              "      <td>45.927</td>\n",
              "      <td>22.822</td>\n",
              "      <td>1.901700</td>\n",
              "      <td>-0.11248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>49.415</td>\n",
              "      <td>-0.57805</td>\n",
              "      <td>45.929</td>\n",
              "      <td>14.263</td>\n",
              "      <td>2.254700</td>\n",
              "      <td>-2.54810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>71.988</td>\n",
              "      <td>-2.60390</td>\n",
              "      <td>62.029</td>\n",
              "      <td>21.453</td>\n",
              "      <td>0.236200</td>\n",
              "      <td>1.40820</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-56d6b42a-9999-41ac-8044-885f146b7b61')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-56d6b42a-9999-41ac-8044-885f146b7b61 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-56d6b42a-9999-41ac-8044-885f146b7b61');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e66a0584-4c34-407b-a478-642d7c0a74bc')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e66a0584-4c34-407b-a478-642d7c0a74bc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(weights\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"met_et\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 68.37533388218297,\n        \"min\": 25.609,\n        \"max\": 196.56,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          196.56,\n          71.988,\n          45.653\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"met_phi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8115565789894612,\n        \"min\": -2.7686,\n        \"max\": 1.3114,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.3114,\n          -2.6039,\n          -2.7686\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lep_pt_0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.790848585722998,\n        \"min\": 45.927,\n        \"max\": 69.459,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          69.459,\n          62.029,\n          45.927\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lep_pt_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.9281942798186544,\n        \"min\": 14.263,\n        \"max\": 22.822,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          21.081,\n          21.453,\n          22.822\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lep_phi_0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8193907290752032,\n        \"min\": -2.3164,\n        \"max\": 2.2547,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.023132,\n          0.2362,\n          1.9017\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lep_phi_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4731999705335321,\n        \"min\": -2.5481,\n        \"max\": 1.4082,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -0.67855,\n          1.4082,\n          -0.11248\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    0\n",
              "2    1\n",
              "3    1\n",
              "4    1\n",
              "Name: label, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
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              "      <th>2</th>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0    0.000008\n",
              "1    0.001153\n",
              "2    0.000116\n",
              "3    0.000116\n",
              "4    0.000008\n",
              "Name: mcWeight, dtype: float64"
            ],
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              "</style>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mcWeight</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000008</td>\n",
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              "      <td>0.001153</td>\n",
              "    </tr>\n",
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              "      <th>2</th>\n",
              "      <td>0.000116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.000008</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "#hide label and weights in separate vectors\n",
        "#they are not real features\n",
        "\n",
        "#WARNING : there should be no selection nor shuffling later on ! (otherwise misalignement)\n",
        "target = fulldata[\"label\"]\n",
        "weights = fulldata[\"mcWeight\"]\n",
        "\n",
        "\n",
        "# for simplicity of the exercise only keep some features\n",
        "# this is actually making a deep copy from fulldata\n",
        "data=pd.DataFrame(fulldata, columns=[\"met_et\",\"met_phi\",\"lep_pt_0\",\"lep_pt_1\",'lep_phi_0', 'lep_phi_1'])\n",
        "#data=pd.DataFrame(fulldata, columns=[\"met_et\",\"met_phi\",\"lep_pt_0\",\"lep_pt_1\",'lep_eta_0', 'lep_eta_1', 'lep_phi_0', 'lep_phi_1','jet_n','jet_pt_0',\n",
        "#       'jet_pt_1', 'jet_eta_0', 'jet_eta_1', 'jet_phi_0', 'jet_phi_1']\n",
        "\n",
        "print (\"Df shape of dataset to be used :\",data.shape)\n",
        "display(data.head())\n",
        "display(target.head())\n",
        "display(weights.head())\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "IJAmtop6yd0a",
        "scrolled": false,
        "outputId": "2c7e02e2-f57a-421c-b79c-a4c53f344cb7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 655
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Missing Transverse Energy for large lepton Pt'}, ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 960x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#data[data.met_et<1000]['met_et'].plot.hist(title='Missing Transverse Energy')\n",
        "data[data.lep_pt_0+data.lep_pt_1>1000]['met_et'].plot.hist(bins=np.linspace(0,400,100),\n",
        "                                                           title='Missing Transverse Energy for large lepton Pt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "VfrUijp2yd0b",
        "scrolled": false,
        "outputId": "e9967ef5-54b0-4899-8aea-858bb82f8f7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 960x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#fig=plt.figure()\n",
        "ax=data[target==0].plot.scatter(x='met_et', y='lep_pt_0',color=\"b\",label=\"B\")\n",
        "data[target==1].plot.scatter(x='met_et', y='lep_pt_0',color=\"r\",label=\"S\",ax=ax)\n",
        "#plt.legend(loc='best')\n",
        "#ax.set_xlabel('weight*1000')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "px76_qOlyd0c",
        "scrolled": true,
        "outputId": "8acc89f5-7a2f-4eb9-891a-261c89f21314",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        met_et  met_phi  lep_pt_0  lep_pt_1  lep_phi_0  lep_phi_1\n",
              "308053  2755.4   1.8538    2678.7    42.131    -1.2958    0.13151\n",
              "552671  2145.7   0.3056    2180.0    30.531    -2.8257    1.50800"
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>met_et</th>\n",
              "      <th>met_phi</th>\n",
              "      <th>lep_pt_0</th>\n",
              "      <th>lep_pt_1</th>\n",
              "      <th>lep_phi_0</th>\n",
              "      <th>lep_phi_1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>308053</th>\n",
              "      <td>2755.4</td>\n",
              "      <td>1.8538</td>\n",
              "      <td>2678.7</td>\n",
              "      <td>42.131</td>\n",
              "      <td>-1.2958</td>\n",
              "      <td>0.13151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>552671</th>\n",
              "      <td>2145.7</td>\n",
              "      <td>0.3056</td>\n",
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              "        document.querySelector('#df-3ac82d7c-5f81-4b3d-8742-f50c7456126d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3ac82d7c-5f81-4b3d-8742-f50c7456126d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-88f6e16d-cb81-4edb-adc8-ad9804b14985\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-88f6e16d-cb81-4edb-adc8-ad9804b14985')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-88f6e16d-cb81-4edb-adc8-ad9804b14985 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data[data\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"met_et\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 431.1230044894382,\n        \"min\": 2145.7,\n        \"max\": 2755.4,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2145.7,\n          2755.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"met_phi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0947427186330128,\n        \"min\": 0.3056,\n        \"max\": 1.8538,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.3056,\n          1.8538\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lep_pt_0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 352.6341517777311,\n        \"min\": 2180.0,\n        \"max\": 2678.7,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2180.0,\n          2678.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lep_pt_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.202438661763953,\n        \"min\": 30.531,\n        \"max\": 42.131,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          30.531,\n          42.131\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lep_phi_0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.081802664537299,\n        \"min\": -2.8257,\n        \"max\": -1.2958,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          -2.8257,\n          -1.2958\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lep_phi_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9733254132354708,\n        \"min\": 0.13151,\n        \"max\": 1.508,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.508,\n          0.13151\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "data[data.lep_pt_0+data.lep_pt_1>2000].head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bVHrJtodL2wo"
      },
      "source": [
        "## Examine the weights"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "cFD4s616LS0F",
        "outputId": "a15e519a-3f28-471c-fb9c-cba87dd63f17",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 960x720 with 1 Axes>"
            ],
            "image/png": 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PCQCA/cj1118fq1atioKCgjj22GPjiSeeiIMOOqiup7VPqtW4+PDDD2PSpEkxZsyYXZ5CmTFjRlx11VW1OQ0AAPZT/fv3j+XLl9f1NPYbtfZVtB999FF8/etfjyzL4tZbb93luClTpkRZWVluWb9+fW1NCQAAqEW1cuZie1i8/vrr8eijj+72wo/CwsIoLCysjWkAAAB7UfK42B4Wq1evjsceeyzatm2behcAAOyB7b84DZ9Wwy+Q3aUax8XmzZtjzZo1udtr166NkpKSaNOmTXTs2DH+8R//MVasWBFz586NioqKKC0tjYiINm3aREFBQZJJAwBQfQUFBZGfnx9vvvlmtGvXLgoKCmr0I3Ls27Isi7fffjvy8vKicePGn2tbNY6LZcuWxUknnZS7PXHixIiIGDduXFx55ZXxwAMPREREv379qjzusccei2HDhu35TAEA2CP5+fnRtWvX2LBhgx83Zqfy8vLi4IMPjkaNGn2u7dQ4LoYNG7bb0yapTqkAAJBOQUFBdOnSJT7++OOoqKio6+lQzzRu3Phzh0XEXvidCwAA6oftH3v5vB99gV2pta+iBQAA9i/iAgAASEJcAAAASYgLAAAgCXEBAAAkIS4AAIAkxAUAAJCEuAAAAJIQFwAAQBLiAgAASEJcAAAASYgLAAAgCXEBAAAkIS4AAIAkxAUAAJCEuAAAAJIQFwAAQBLiAgAASEJcAAAASYgLAAAgCXEBAAAkIS4AAIAkxAUAAJCEuAAAAJIQFwAAQBLiAgAASEJcAAAASYgLAAAgCXEBAAAkIS4AAIAkxAUAAJCEuAAAAJIQFwAAQBLiAgAASEJcAAAASYgLAAAgCXEBAAAkIS4AAIAkxAUAAJCEuAAAAJIQFwAAQBLiAgAASEJcAAAASYgLAAAgCXEBAAAkIS4AAIAkxAUAAJCEuAAAAJIQFwAAQBLiAgAASEJcAAAASYgLAAAgCXEBAAAkIS4AAIAkxAUAAJCEuAAAAJIQFwAAQBLiAgAASEJcAAAASYgLAAAgCXEBAAAkIS4AAIAkxAUAAJCEuAAAAJIQFwAAQBLiAgAASEJcAAAASYgLAAAgCXEBAAAkIS4AAIAkxAUAAJCEuAAAAJIQFwAAQBI1jovHH388Tj/99OjUqVPk5eXFfffdV+X+LMti2rRp0bFjx2jatGkMHz48Vq9enWq+AABAPVXjuNiyZUv07ds3brnllp3ef91118UvfvGLmDlzZixZsiSaN28eI0aMiA8//PBzTxYAAKi/DqjpA0aOHBkjR47c6X1ZlsVNN90UV1xxRZxxxhkREXHnnXdGhw4d4r777otvfOMbOzxm69atsXXr1tzt8vLymk4JAACoB5Jec7F27dooLS2N4cOH59YVFRXF8ccfH4sXL97pY2bMmBFFRUW5pXPnzimnBAAA7CVJ46K0tDQiIjp06FBlfYcOHXL3fdqUKVOirKwst6xfvz7llAAAgL2kxh+LSq2wsDAKCwvrehoAAMDnlPTMRXFxcUREbNy4scr6jRs35u4DAAD2TUnjomvXrlFcXBzz58/PrSsvL48lS5bEoEGDUu4KAACoZ2r8sajNmzfHmjVrcrfXrl0bJSUl0aZNm+jSpUtMmDAh/vVf/zV69OgRXbt2jalTp0anTp1i9OjRKecNAADUMzWOi2XLlsVJJ52Uuz1x4sSIiBg3blzMmjUrLrvsstiyZUtccMEF8f7778cJJ5wQDz/8cDRp0iTdrAEAgHqnxnExbNiwyLJsl/fn5eXF1VdfHVdfffXnmhgAANCwJL3mAgAA2H+JCwAAIAlxAQAAJCEuAACAJMQFAACQhLgAAACSEBcAAEAS4gIAAEhCXAAAAEmICwAAIAlxAQAAJCEuAACAJMQFAACQhLgAAACSEBcAAEAS4gIAAEhCXAAAAEmICwAAIAlxAQAAJCEuAACAJMQFAACQhLgAAACSEBcAAEAS4gIAAEhCXAAAAEmICwAAIAlxAQAAJCEuAACAJMQFAACQhLgAAACSEBcAAEAS4gIAAEhCXAAAAEmICwAAIAlxAQAAJCEuAACAJMQFAACQhLgAAACSEBcAAEAS4gIAAEhCXAAAAEmICwAAIAlxAQAAJCEuAACAJMQFAACQhLgAAACSEBcAAEAS4gIAAEhCXAAAAEmICwAAIAlxAQAAJCEuAACAJMQFAACQhLgAAACSEBcAAEAS4gIAAEhCXAAAAEmICwAAIAlxAQAAJCEuAACAJMQFAACQhLgAAACSEBcAAEAS4gIAAEhCXAAAAEmICwAAIAlxAQAAJCEuAACAJMQFAACQhLgAAACSEBcAAEAS4gIAAEhCXAAAAEkkj4uKioqYOnVqdO3aNZo2bRqHHXZY/OhHP4osy1LvCgAAqEcOSL3Ba6+9Nm699daYPXt2HHXUUbFs2bI455xzoqioKH74wx+m3h0AAFBPJI+LRYsWxRlnnBGjRo2KiIhDDz00fv/738czzzyTelcAAEA9kvxjUYMHD4758+fHK6+8EhERzz77bDz55JMxcuTInY7funVrlJeXV1kAAICGJ/mZi8mTJ0d5eXkcccQR0ahRo6ioqIhrrrkmxo4du9PxM2bMiKuuuir1NAAAgL0s+ZmLe+65J37729/G7373u1ixYkXMnj07rr/++pg9e/ZOx0+ZMiXKyspyy/r161NPCQAA2AuSn7m49NJLY/LkyfGNb3wjIiKOPvroeP3112PGjBkxbty4HcYXFhZGYWFh6mkAAAB7WfIzF3/9618jP7/qZhs1ahSVlZWpdwUAANQjyc9cnH766XHNNddEly5d4qijjoqVK1fGDTfcEN/+9rdT7woAAKhHksfFL3/5y5g6dWp8//vfj7feeis6deoU3/nOd2LatGmpdwUAANQjyeOiZcuWcdNNN8VNN92UetMAAEA9ljwuANh7rrxyz+4DgNqQ/IJuAABg/yQuAACAJMQFAACQhLgAAACSEBcAAEAS4gIAAEhCXAAAAEmICwAAIAlxAQAAJCEuAACAJMQFAACQhLgAAACSEBcAAEAS4gIAAEhCXAAAAEmICwAAIAlxAQAAJCEuAACAJMQFAACQhLgAAACSEBcAAEAS4gIAAEhCXAAAAEmICwAAIAlxAQAAJCEuAACAJMQFAACQhLgAAACSEBcAAEAS4gIAAEhCXAAAAEmICwAAIAlxAQAAJCEuAACAJMQFAACQhLgAAACSEBcAAEAS4gIAAEhCXAAAAEmICwAAIAlxAQAAJCEuAACAJMQFAACQhLgAAACSEBcAAEAS4gIAAEhCXAAAAEmICwAAIAlxAQAAJCEuAACAJMQFAACQhLgAAACSEBcAAEAS4gIAAEhCXAAAAEmICwAAIAlxAQAAJCEuAACAJMQFAACQhLgAAACSEBcAAEAS4gIAAEhCXAAAAEmICwAAIAlxAQAAJCEuAACAJMQFAACQhLgAAACSEBcAAEAS4gIAAEhCXAAAAEmICwAAIIlaiYs33ngj/vmf/znatm0bTZs2jaOPPjqWLVtWG7sCAADqiQNSb/C9996LIUOGxEknnRQPPfRQtGvXLlavXh0HHnhg6l0BAAD1SPK4uPbaa6Nz587xm9/8Jreua9euqXcDAADUM8k/FvXAAw/EcccdF1/72teiffv20b9//7j99tt3OX7r1q1RXl5eZQEAABqe5HHx2muvxa233ho9evSIRx55JL73ve/FD3/4w5g9e/ZOx8+YMSOKiopyS+fOnVNPCQAA2AuSx0VlZWUcc8wx8eMf/zj69+8fF1xwQZx//vkxc+bMnY6fMmVKlJWV5Zb169ennhIAALAXJI+Ljh07Rq9evaqsO/LII2PdunU7HV9YWBitWrWqsgAAAA1P8rgYMmRIrFq1qsq6V155JQ455JDUuwIAAOqR5HFx8cUXx9NPPx0//vGPY82aNfG73/0ufvWrX8X48eNT7woAAKhHksfFgAEDYs6cOfH73/8+evfuHT/60Y/ipptuirFjx6beFQAAUI8k/52LiIjTTjstTjvttNrYNAAAUE8lP3MBAADsn8QFAACQhLgAAACSEBcAAEAS4gIAAEhCXAAAAEmICwAAIAlxAQAAJCEuAACAJMQFAACQhLgAAACSEBcAAEAS4gIAAEhCXAAAAEmICwAAIAlxAQAAJCEuAACAJMQFAACQhLgAAACSEBcAAEAS4gIAAEhCXAAAAEmICwAAIAlxAQAAJCEuAACAJMQFAACQhLgAAACSEBcAAEAS4gIAAEhCXAAAAEmICwAAIAlxAQAAJCEuAACAJMQFAACQhLgAAACSEBcAAEAS4gIAAEhCXAAAAEmICwAAIAlxAQAAJCEuAACAJMQFAACQhLgAAACSEBcAAEAS4gIAAEhCXAAAAEmICwAAIAlxAQAAJCEuAACAJMQFAACQhLgAAACSEBcAAEAS4gIAAEhCXAAAAEmICwAAIAlxAQAAJCEuAACAJMQFAACQhLgAAACSEBcAAEAS4gIAAEhCXAAAAEmICwAAIAlxAQAAJCEuAACAJMQFAACQhLgAAACSEBcAAEAS4gIAAEhCXAAAAEmICwAAIAlxAQAAJCEuAACAJMQFAACQRK3HxU9+8pPIy8uLCRMm1PauAACAOlSrcbF06dK47bbbok+fPrW5GwAAoB6otbjYvHlzjB07Nm6//fY48MADa2s3AABAPVFrcTF+/PgYNWpUDB8+fLfjtm7dGuXl5VUWAACg4TmgNjZ61113xYoVK2Lp0qWfOXbGjBlx1VVX1cY0AACAvSj5mYv169fHRRddFL/97W+jSZMmnzl+ypQpUVZWllvWr1+fekoAAMBekPzMxfLly+Ott96KY445JreuoqIiHn/88bj55ptj69at0ahRo9x9hYWFUVhYmHoaAADAXpY8Lk4++eR4/vnnq6w755xz4ogjjohJkyZVCQsAAGDfkTwuWrZsGb17966yrnnz5tG2bdsd1gMAAPsOv9ANAAAkUSvfFvVpCxYs2Bu7AQAA6pAzFwAAQBLiAgAASEJcAAAASYgLAAAgCXEBAAAkIS4AAIAkxAUAAJCEuAAAAJIQFwAAQBLiAgAASEJcAAAASYgLAAAgCXEBAAAkIS4AAIAkxAUAAJCEuAAAAJIQFwAAQBLiAgAASEJcAAAASYgLAAAgCXEBAAAkIS4AAIAkxAUAAJCEuAAAAJIQFwAAQBLiAgAASEJcAAAASYgLAAAgCXEBAAAkIS4AAIAkxAUAAJCEuAAAAJIQFwAAQBLiAgAASEJcAAAASYgLAAAgCXEBAAAkIS4AAIAkxAUAAJCEuAAAAJIQFwAAQBLiAgAASEJcAAAASYgLAAAgCXEBAAAkIS4AAIAkxAUAAJCEuAAAAJIQFwAAQBLiAgAASEJcAAAASYgLAAAgCXEBAAAkIS4AAIAkxAUAAJCEuAAAAJIQFwAAQBLiAgAASEJcAAAASYgLAAAgCXEBAAAkIS4AAIAkxAUAAJCEuAAAAJIQFwAAQBLiAgAASEJcAAAASYgLAAAgCXEBAAAkIS4AAIAkxAUAAJCEuAAAAJIQFwAAQBLJ42LGjBkxYMCAaNmyZbRv3z5Gjx4dq1atSr0bAACgnkkeFwsXLozx48fH008/HfPmzYuPPvooTj311NiyZUvqXQEAAPXIAak3+PDDD1e5PWvWrGjfvn0sX748TjzxxNS7AwAA6onkcfFpZWVlERHRpk2bnd6/devW2Lp1a+52eXl5bU8JAACoBbV6QXdlZWVMmDAhhgwZEr17997pmBkzZkRRUVFu6dy5c21OCQAAqCW1Ghfjx4+Pv/zlL3HXXXftcsyUKVOirKwst6xfv742pwQAANSSWvtY1IUXXhhz586Nxx9/PA4++OBdjissLIzCwsLamgYAALCXJI+LLMviBz/4QcyZMycWLFgQXbt2Tb0LAACgHkoeF+PHj4/f/e53cf/990fLli2jtLQ0IiKKioqiadOmqXcHAADUE8mvubj11lujrKwshg0bFh07dswtd999d+pdAQAA9UitfCwKAADY/9Tqt0UBAAD7D3EBAAAkIS4AAIAkxAUAAJCEuAAAAJIQFwAAQBLiAgAASEJcAAAASYgLAAAgCXEBAAAkIS4AAIAkxAUAAJCEuAAAAJIQFwAAQBLiAgAASEJcAAAASRxQ1xMAoHZceWXN1gPA5+XMBQAAkIQzFwD7mT09c+GMBwCfxZkLAAAgCXEBAAAk4WNRAFTL7j4W5SNTAEQ4cwEAACQiLgAAgCTEBQAAkIRrLgD43PxgHwARzlwAAACJiAsAACAJcQEAACQhLgAAgCRc0A1ArfHDewD7F2cuAACAJMQFAACQhLgAAACSEBcAAEAS4gIAAEhCXAAAAEn4KloA6oSvqQXY9zhzAQAAJCEuAACAJMQFAACQhLgAAACSEBcAAEASvi0KgHrHN0kBNEzOXAAAAEmICwAAIAlxAQAAJOGaCwAalF1dc+FaDIC658wFAACQhLgAAACSEBcAAEASrrkAYJ/gtzEA6p4zFwAAQBLiAgAASMLHogDY5/nIFMDe4cwFAACQhLgAAACSEBcAAEASrrkAYL/megyAdMQFAOzCruJCdADsnI9FAQAASYgLAAAgCR+LAoAacp0GwM45cwEAACThzAUAJOSsBrA/ExcAsJcID2BfJy4AoB4QHsC+QFwAQD3n9zaAhsIF3QAAQBLOXABAA7WnZy6c8QBqi7gAgP3MnsSFIAGqQ1wAAJ/JBedAdYgLAOBzSR0XYgUaLnEBANQre3qWxNkVqHu1Fhe33HJL/PSnP43S0tLo27dv/PKXv4yBAwfW1u4AgP3A3ryIXZBAzdVKXNx9990xceLEmDlzZhx//PFx0003xYgRI2LVqlXRvn372tglAEBSDT0uGvr8aZhqJS5uuOGGOP/88+Occ86JiIiZM2fGgw8+GL/+9a9j8uTJtbFLAAA+QVxUj+OUVvK42LZtWyxfvjymTJmSW5efnx/Dhw+PxYsX7zB+69atsXXr1tztsrKyiIgoLy9PPTXY93zi350d7O7foT193C7MmFHjh+zTPvH2V4XjBFD/7Oo9uz6p6zlu/7s8y7LPHpwl9sYbb2QRkS1atKjK+ksvvTQbOHDgDuOnT5+eRYTFYrFYLBaLxWKpx8v69es/swXq/NuipkyZEhMnTszdrqysjHfffTfatm0beXl5dTav8vLy6Ny5c6xfvz5atWpVZ/PYlzimaTme6TmmaTme6TmmaTme6Tmm6dWHY5plWWzatCk6der0mWOTx8VBBx0UjRo1io0bN1ZZv3HjxiguLt5hfGFhYRQWFlZZ17p169TT2mOtWrXyL0dijmlajmd6jmlajmd6jmlajmd6jml6dX1Mi4qKqjUuP/WOCwoK4thjj4358+fn1lVWVsb8+fNj0KBBqXcHAADUE7XysaiJEyfGuHHj4rjjjouBAwfGTTfdFFu2bMl9exQAALDvqZW4OOuss+Ltt9+OadOmRWlpafTr1y8efvjh6NChQ23srlYUFhbG9OnTd/jIFnvOMU3L8UzPMU3L8UzPMU3L8UzPMU2voR3TvCyrzndKAQAA7F7yay4AAID9k7gAAACSEBcAAEAS4gIAAEhCXHzCNddcE4MHD45mzZpV+4f8siyLadOmRceOHaNp06YxfPjwWL16de1OtIF49913Y+zYsdGqVato3bp1nHvuubF58+bdPmbYsGGRl5dXZfnud7+7l2Zc/9xyyy1x6KGHRpMmTeL444+PZ555Zrfj//CHP8QRRxwRTZo0iaOPPjr+9Kc/7aWZNhw1OaazZs3a4fXYpEmTvTjb+u3xxx+P008/PTp16hR5eXlx3333feZjFixYEMccc0wUFhZG9+7dY9asWbU+z4aipsdzwYIFO7w+8/LyorS0dO9MuAGYMWNGDBgwIFq2bBnt27eP0aNHx6pVqz7zcd5Ld25Pjqf30d279dZbo0+fPrkfyBs0aFA89NBDu31MfX99iotP2LZtW3zta1+L733ve9V+zHXXXRe/+MUvYubMmbFkyZJo3rx5jBgxIj788MNanGnDMHbs2HjhhRdi3rx5MXfu3Hj88cfjggsu+MzHnX/++bFhw4bcct111+2F2dY/d999d0ycODGmT58eK1asiL59+8aIESPirbfe2un4RYsWxZgxY+Lcc8+NlStXxujRo2P06NHxl7/8ZS/PvP6q6TGN+Nsvon7y9fj666/vxRnXb1u2bIm+ffvGLbfcUq3xa9eujVGjRsVJJ50UJSUlMWHChDjvvPPikUceqeWZNgw1PZ7brVq1qsprtH379rU0w4Zn4cKFMX78+Hj66adj3rx58dFHH8Wpp54aW7Zs2eVjvJfu2p4czwjvo7tz8MEHx09+8pNYvnx5LFu2LP7hH/4hzjjjjHjhhRd2Or5BvD4zdvCb3/wmKyoq+sxxlZWVWXFxcfbTn/40t+7999/PCgsLs9///ve1OMP678UXX8wiIlu6dGlu3UMPPZTl5eVlb7zxxi4fN3To0Oyiiy7aCzOs/wYOHJiNHz8+d7uioiLr1KlTNmPGjJ2O//rXv56NGjWqyrrjjz8++853vlOr82xIanpMq/teQJZFRDZnzpzdjrnsssuyo446qsq6s846KxsxYkQtzqxhqs7xfOyxx7KIyN577729Mqd9wVtvvZVFRLZw4cJdjvFeWn3VOZ7eR2vuwAMPzO64446d3tcQXp/OXHwOa9eujdLS0hg+fHhuXVFRURx//PGxePHiOpxZ3Vu8eHG0bt06jjvuuNy64cOHR35+fixZsmS3j/3tb38bBx10UPTu3TumTJkSf/3rX2t7uvXOtm3bYvny5VVeW/n5+TF8+PBdvrYWL15cZXxExIgRI/b71+J2e3JMIyI2b94chxxySHTu3Hm3/zWJz+Y1Wjv69esXHTt2jFNOOSWeeuqpup5OvVZWVhYREW3atNnlGK/T6qvO8YzwPlpdFRUVcdddd8WWLVti0KBBOx3TEF6ftfIL3fuL7Z9r/fQvj3fo0GG//8xraWnpDqfmDzjggGjTps1uj80//dM/xSGHHBKdOnWK5557LiZNmhSrVq2Ke++9t7anXK/83//9X1RUVOz0tfXyyy/v9DGlpaVei7uxJ8e0Z8+e8etf/zr69OkTZWVlcf3118fgwYPjhRdeiIMPPnhvTHufsqvXaHl5eXzwwQfRtGnTOppZw9SxY8eYOXNmHHfccbF169a44447YtiwYbFkyZI45phj6np69U5lZWVMmDAhhgwZEr17997lOO+l1VPd4+l99LM9//zzMWjQoPjwww+jRYsWMWfOnOjVq9dOxzaE1+c+HxeTJ0+Oa6+9drdjXnrppTjiiCP20owatuoezz31yWsyjj766OjYsWOcfPLJ8eqrr8Zhhx22x9uFPTFo0KAq//Vo8ODBceSRR8Ztt90WP/rRj+pwZvC3P9p69uyZuz148OB49dVX48Ybb4x///d/r8OZ1U/jx4+Pv/zlL/Hkk0/W9VT2CdU9nt5HP1vPnj2jpKQkysrK4j//8z9j3LhxsXDhwl0GRn23z8fFv/zLv8TZZ5+92zHdunXbo20XFxdHRMTGjRujY8eOufUbN26Mfv367dE267vqHs/i4uIdLpL9+OOP4913380dt+o4/vjjIyJizZo1+1VcHHTQQdGoUaPYuHFjlfUbN27c5fErLi6u0fj9zZ4c009r3Lhx9O/fP9asWVMbU9zn7eo12qpVK2ctEhk4cKA/nnfiwgsvzH2xyGf913LvpZ+tJsfz07yP7qigoCC6d+8eERHHHntsLF26NH7+85/HbbfdtsPYhvD63OevuWjXrl0cccQRu10KCgr2aNtdu3aN4uLimD9/fm5deXl5LFmyZJeflWvoqns8Bw0aFO+//34sX74899hHH300Kisrc8FQHSUlJRERVeJtf1BQUBDHHntslddWZWVlzJ8/f5evrUGDBlUZHxExb968ffa1WFN7ckw/raKiIp5//vn97vWYitdo7SspKfH6/IQsy+LCCy+MOXPmxKOPPhpdu3b9zMd4ne7anhzPT/M++tkqKytj69atO72vQbw+6/qK8vrk9ddfz1auXJldddVVWYsWLbKVK1dmK1euzDZt2pQb07Nnz+zee+/N3f7JT36StW7dOrv//vuz5557LjvjjDOyrl27Zh988EFdPIV65Ytf/GLWv3//bMmSJdmTTz6Z9ejRIxszZkzu/v/93//NevbsmS1ZsiTLsixbs2ZNdvXVV2fLli3L1q5dm91///1Zt27dshNPPLGunkKduuuuu7LCwsJs1qxZ2YsvvphdcMEFWevWrbPS0tIsy7Lsm9/8ZjZ58uTc+Keeeio74IADsuuvvz576aWXsunTp2eNGzfOnn/++bp6CvVOTY/pVVddlT3yyCPZq6++mi1fvjz7xje+kTVp0iR74YUX6uop1CubNm3KvU9GRHbDDTdkK1euzF5//fUsy7Js8uTJ2Te/+c3c+Ndeey1r1qxZdumll2YvvfRSdsstt2SNGjXKHn744bp6CvVKTY/njTfemN13333Z6tWrs+effz676KKLsvz8/OzPf/5zXT2Feud73/teVlRUlC1YsCDbsGFDbvnrX/+aG+O9tPr25Hh6H929yZMnZwsXLszWrl2bPffcc9nkyZOzvLy87L//+7+zLGuYr09x8Qnjxo3LImKH5bHHHsuNiYjsN7/5Te52ZWVlNnXq1KxDhw5ZYWFhdvLJJ2erVq3a+5Ovh955551szJgxWYsWLbJWrVpl55xzTpVQW7t2bZXju27duuzEE0/M2rRpkxUWFmbdu3fPLr300qysrKyOnkHd++Uvf5l16dIlKygoyAYOHJg9/fTTufuGDh2ajRs3rsr4e+65Jzv88MOzgoKC7KijjsoefPDBvTzj+q8mx3TChAm5sR06dMi+9KUvZStWrKiDWddP278K9dPL9mM4bty4bOjQoTs8pl+/fllBQUHWrVu3Ku+n+7uaHs9rr702O+yww7ImTZpkbdq0yYYNG5Y9+uijdTP5empnx/PT/z/uvbT69uR4eh/dvW9/+9vZIYcckhUUFGTt2rXLTj755FxYZFnDfH3mZVmW7YUTJAAAwD5un7/mAgAA2DvEBQAAkIS4AAAAkhAXAABAEuICAABIQlwAAABJiAsAACAJcQEAACQhLgD2Y4ceemjcdNNN1R7/P//zP5GXlxclJSW1NicAGi5xAbAfW7p0aVxwwQVJtzlr1qxo3br1Lu8fNmzYDuvuvffeOPXUU6Nt27a7jJcPP/wwxo8fH23bto0WLVrEmWeeGRs3bqwyZt26dTFq1Kho1qxZtG/fPi699NL4+OOPq4xZsGBBHHPMMVFYWBjdu3ePWbNm7cGzBGBnxAXAfqxdu3bRrFmzWt/P3LlzY8WKFVXW3XXXXfHKK69ERMSWLVvihBNOiGuvvXaX27j44ovjv/7rv+IPf/hDLFy4MN5888346le/mru/oqIiRo0aFdu2bYtFixbF7NmzY9asWTFt2rTcmLVr18aoUaPipJNOipKSkpgwYUKcd9558cgjjyR+xgD7qQyABuO//uu/sqKiouzjjz/OsizLVq5cmUVENmnSpNyYc889Nxs7dmyWZVn2xBNPZCeccELWpEmT7OCDD85+8IMfZJs3b86NPeSQQ7Ibb7wxd/ull17KhgwZkhUWFmZHHnlkNm/evCwisjlz5mRZlmVr167NIiL74x//mA0bNixr2rRp1qdPn2zRokVZlmXZY489lkVElWX69OnZCy+8kJ166qnZtGnTsr59+2Zf+9rXsgsuuCB75513qjy/7dtfuXJllfXvv/9+1rhx4+wPf/hDlblGRLZ48eIsy7LsT3/6U5afn5+Vlpbmxtx6661Zq1atsq1bt2ZZlmWXXXZZdtRRR1XZ9llnnZWNGDGi2v8bALBrzlwANCBf+MIXYtOmTbFy5cqIiFi4cGEcdNBBsWDBgtyYhQsXxrBhw+LVV1+NL37xi3HmmWfGc889F3fffXc8+eSTceGFF+502xUVFTF69Oho1qxZLFmyJH71q1/F5ZdfvtOxl19+eVxyySVRUlIShx9+eIwZMyY+/vjjGDx4cNx0003RqlWr2LBhQ2zYsCEuueSS6NWrVzzyyCOxevXqePbZZ2P48OFx2223RZs2bar1vJcvXx4fffRRDB8+PLfuiCOOiC5dusTixYsjImLx4sVx9NFHR4cOHXJjRowYEeXl5fHCCy/kxnxyG9vHbN8GAJ+PuABoQIqKiqJfv365mFiwYEFcfPHFsXLlyti8eXO88cYbsWbNmhg6dGjMmDEjxo4dGxMmTIgePXrE4MGD4xe/+EXceeed8eGHH+6w7Xnz5sWrr74ad955Z/Tt2zdOOOGEuOaaa3Y6j0suuSRGjRoVhx9+eFx11VXx+uuvx5o1a6KgoCCKiooiLy8viouLo7i4OFq0aBGrVq2KkSNHRvfu3aNv377x5z//Ob7//e/He++9V63nXVpaGgUFBTtcy9GhQ4coLS3NjflkWGy/f/t9uxtTXl4eH3zwQbXmAsCuiQuABmbo0KGxYMGCyLIsnnjiifjqV78aRx55ZDz55JOxcOHC6NSpU/To0SOeffbZmDVrVrRo0SK3jBgxIiorK2Pt2rU7bHfVqlXRuXPnKC4uzq0bOHDgTufQp0+f3D937NgxIiLeeuutXc75lVdeiWuuuSauvvrqaN26ddxzzz3xhS98Id5+++09PQwA1EMH1PUEAKiZYcOGxa9//et49tlno3HjxnHEEUfEsGHDYsGCBfHee+/F0KFDIyJi8+bN8Z3vfCd++MMf7rCNLl26fK45NG7cOPfPeXl5ERFRWVm5y/Gnn376DuvGjBlT7f0VFxfHtm3b4v33369y9mLjxo25GCouLo5nnnmmyuO2f5vUJ8d8+humNm7cGK1atYqmTZtWez4A7JwzFwANzPbrLm688cZcSGyPiwULFuS+6vWYY46JF198Mbp3777DUlBQsMN2e/bsGevXr6/yx/fSpUtrPL+CgoKoqKjY5f2fvD6kuo499tho3LhxzJ8/P7du1apVsW7duhg0aFBERAwaNCief/75KmdQ5s2bF61atYpevXrlxnxyG9vHbN8GAJ+PuABoYA488MDo06dP/Pa3v82FxIknnhgrVqyIV155JRcckyZNikWLFsWFF14YJSUlsXr16rj//vt3eUH3KaecEocddliMGzcunnvuuXjqqafiiiuuiIj//+xEdRx66KGxefPmmD9/fvzf//1f/PWvf/3Mx7z77rtRUlISL774YkT8LRxKSkpy10oUFRXFueeeGxMnTozHHnssli9fHuecc04MGjQo/v7v/z4iIk499dTo1atXfPOb34xnn302Hnnkkbjiiiti/PjxUVhYGBER3/3ud+O1116Lyy67LF5++eX4t3/7t7jnnnvi4osvrvbzA2DXxAVAAzR06NCoqKjIxUWbNm2iV69eUVxcHD179oyIv10XsXDhwnjllVfiC1/4QvTv3z+mTZsWnTp12uk2GzVqFPfdd19s3rw5BgwYEOedd17u26KaNGlS7bkNHjw4vvvd78ZZZ50V7dq1i+uuu+4zH/PAAw9E//79Y9SoURER8Y1vfCP69+8fM2fOzI258cYb47TTToszzzwzTjzxxCguLo577723yvznzp0bjRo1ikGDBsU///M/x7e+9a24+uqrc2O6du0aDz74YMybNy/69u0bP/vZz+KOO+6IESNGVPv5AbBreVmWZXU9CQDqp6eeeipOOOGEWLNmTRx22GF1PR0A6jlxAUDOnDlzokWLFtGjR49Ys2ZNXHTRRXHggQfGk08+WddTA6AB8G1RAORs2rQpJk2aFOvWrYuDDjoohg8fHj/72c/qeloANBDOXAAAAEm4oBsAAEhCXAAAAEmICwAAIAlxAQAAJCEuAACAJMQFAACQhLgAAACSEBcAAEAS/x/7KT3QKNYhtAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig,ax=plt.subplots()\n",
        "#fig=plt.figure()\n",
        "\n",
        "bins=np.linspace(-1,3,101)\n",
        "plt.hist(weights[target==0]*1000,bins=bins,color='b',alpha=0.5,density=True,label='B ackground')\n",
        "plt.hist(weights[target==1]*1000,bins=bins,color='r',alpha=0.5,density=True,label='S ignal')\n",
        "plt.legend(loc='best')\n",
        "ax.set_xlabel('weight*1000')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jrCZdWvqyd0e"
      },
      "source": [
        "# Some weight studies\n",
        "\n",
        "$s=\\sum w$ for signal dataset : predicted number of signal events (luminosity, cross section, efficiencies etc... already includded in the weights). Ditto for b background.\n",
        "\n",
        "\n",
        "Effective number of events fraction : $\\frac{N_{eff}}{N}= \\frac{1}{1+\\frac{Var(w)}{<w>^2}}$ . Example : if 0.2 it means the precision achieved with this dataset is the one which would be achieved with an unweighted dataset of 0.2 x N events (this is a rough estimate, only true for a simple counting)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "YdpzI4-6MVIK",
        "outputId": "8993a09d-f382-447a-c868-ace34b2d4fdd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Weights quantities for background (target==0) and signal (target==1)\n",
            "Weights sum [638.7539676  24.717593 ]\n",
            "N events [180381. 396976.]\n",
            "Weights mean [3.54113775e-03 6.22647037e-05]\n",
            "Weights std [3.41237459e-03 5.42304678e-05]\n",
            "Weights Neff fraction [0.51851137 0.56863978]\n"
          ]
        }
      ],
      "source": [
        "label_n_weights=np.zeros(2)\n",
        "label_sum_weights=np.zeros(2)\n",
        "label_mean_weights=np.zeros(2)\n",
        "label_std_weights=np.zeros(2)\n",
        "label_neff_fraction=np.zeros(2)\n",
        "\n",
        "for i in range(2):\n",
        "  label_n_weights[i]=weights[target==i].size\n",
        "  label_mean_weights[i]=weights[target==i].mean()\n",
        "  label_std_weights[i]=weights[target==i].std()\n",
        "  label_sum_weights[i]=weights[target==i].sum()\n",
        "  label_neff_fraction[i]=1/(1+(label_std_weights[i]/label_mean_weights[i])**2)\n",
        "\n",
        "print (\"Weights quantities for background (target==0) and signal (target==1)\")\n",
        "print (\"Weights sum\",label_sum_weights)\n",
        "print (\"N events\",label_n_weights)\n",
        "print (\"Weights mean\",label_mean_weights)\n",
        "print (\"Weights std\",label_std_weights)\n",
        "print (\"Weights Neff fraction\",label_neff_fraction)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F77Cx9LhKwqc"
      },
      "source": [
        "# Feature engineering\n",
        "To be switched on in a second iteration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "4oe_9ZEEKwqc"
      },
      "outputs": [],
      "source": [
        "if False:\n",
        "    data[\"lep_deltaphi\"]=np.abs(np.mod(data.lep_phi_1-data.lep_phi_0+3*np.pi,2*np.pi)-np.pi)\n",
        "    #data[\"lep_deltaphi\"]=data.lep_phi_1-data.lep_phi_0\n",
        "\n",
        "\n",
        "    print (data.shape)\n",
        "    display(data.head())\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kDp3D2Fkyd0h"
      },
      "source": [
        "# Plot the features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "o2mf1bLVrlH7",
        "outputId": "0a29ad99-b459-486f-8ca8-f8b07f60a6d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 960x720 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1200 with 6 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure()\n",
        "\n",
        "ax=data[target==0].hist(weights=weights[target==0],figsize=(15,12),color='b',alpha=0.5,density=True,label=\"B\")\n",
        "ax=ax.flatten()[:data.shape[1]] # to avoid error if holes in the grid of plots (like if 7 or 8 features)\n",
        "data[target==1].hist(weights=weights[target==1],figsize=(15,12),color='r',alpha=0.5,density=True,ax=ax,label=\"S\")\n",
        "\n",
        "\n",
        "plt.legend(loc=\"best\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IUMC85s4yd0i"
      },
      "source": [
        "### Features correlation matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "Kb-U3Ri5yd0i",
        "scrolled": false,
        "outputId": "5eb2fad7-5ec2-44f7-b239-1e7a88331278",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Signal feature correlation matrix\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 960x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Background feature correlation matrix\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 960x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sn # seaborn for nice plot quicker\n",
        "print (\"Signal feature correlation matrix\")\n",
        "corrMatrix = data[target==1].corr()\n",
        "sn.heatmap(corrMatrix, annot=True)\n",
        "plt.show()\n",
        "\n",
        "print (\"Background feature correlation matrix\")\n",
        "corrMatrix = data[target==0].corr()\n",
        "sn.heatmap(corrMatrix, annot=True)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4kowHjX4rlIC"
      },
      "source": [
        "## Split dataset and transform the features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "Y9j5hdrmrlID",
        "outputId": "7d14cd55-6ea5-44a9-ddcf-8e5635db62fc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(433017, 6)\n",
            "(433017,)\n",
            "(433017,)\n",
            "(144340, 6)\n",
            "(144340,)\n",
            "(144340,)\n",
            "Weights have been normalised to a given number of proton collision\n",
            "Orig : total weight sig 24.717592998400008\n",
            "Orig : total weight bkg 638.7539675983998\n",
            "Test : total weight sig 24.717773318400006\n",
            "Test : total weight bkg 635.6988284976001\n",
            "Train : total weight sig 479.8292604740002\n",
            "Train : total weight bkg 479.82926047399997\n"
          ]
        }
      ],
      "source": [
        "np.random.seed(31415) # set the random seed (used for the train/test splitting)\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "train_size = 0.75 # fraction of sample used for training\n",
        "\n",
        "X_train, X_test, y_train, y_test, weights_train, weights_test = \\\n",
        "    train_test_split(data, target, weights, train_size=train_size)\n",
        "#reset index for dataseries, not needed for ndarray (X_train, X_test)\n",
        "#y_train, y_test, weights_train, weights_test = \\\n",
        "#     y_train.reset_index(drop=True),y_test.reset_index(drop=True), \\\n",
        "#    weights_train.reset_index(drop=True), weights_test.reset_index(drop=True)\n",
        "\n",
        "print (X_train.shape)\n",
        "print (y_train.shape)\n",
        "print (weights_train.shape)\n",
        "print (X_test.shape)\n",
        "print (y_test.shape)\n",
        "print (weights_test.shape)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# scale to mean 0 and variance 1\n",
        "# not really needed for BDT but we never know\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train) #calculate and apply the transformation to training dataset\n",
        "X_test = scaler.transform(X_test)  # apply to test dataset the transformation calculated the line above\n",
        "\n",
        "\n",
        "class_weights_train = (weights_train[y_train == 0].sum(), weights_train[y_train == 1].sum())\n",
        "\n",
        "for i in range(len(class_weights_train)): # loop on B then S target\n",
        "    #training dataset: equalize number of background and signal\n",
        "    weights_train[y_train == i] *= max(class_weights_train)/ class_weights_train[i]\n",
        "    #test dataset : increase test weight to compensate for sampling\n",
        "    weights_test[y_test == i] *= 1/(1-train_size)\n",
        "\n",
        "print (\"Weights have been normalised to a given number of proton collision\")\n",
        "print (\"Orig : total weight sig\", weights[target == 1].sum())\n",
        "print (\"Orig : total weight bkg\", weights[target == 0].sum())\n",
        "\n",
        "\n",
        "print (\"Test : total weight sig\", weights_test[y_test == 1].sum())\n",
        "print (\"Test : total weight bkg\", weights_test[y_test == 0].sum())\n",
        "print (\"Train : total weight sig\", weights_train[y_train == 1].sum())\n",
        "print (\"Train : total weight bkg\", weights_train[y_train == 0].sum())\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yxybCOi-rlIM"
      },
      "source": [
        "# Testing BDT"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AIsMSGl-Kwql"
      },
      "source": [
        "## Load significance function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "qubY3CMNKwql"
      },
      "outputs": [],
      "source": [
        "from math import sqrt\n",
        "from math import log\n",
        "import numpy as np\n",
        "#\n",
        "def amsasimov(s_in,b_in): # asimov significance arXiv:1007.1727 eq. 97 (reduces to s/sqrt(b) if s<<b)\n",
        "        # if b==0 ams is undefined, but return 0 without warning for convenience (hack)\n",
        "        s=np.copy(s_in)\n",
        "        b=np.copy(b_in)\n",
        "        s=np.where( (b_in == 0) , 0., s_in)\n",
        "        b=np.where( (b_in == 0) , 1., b)\n",
        "\n",
        "        ams = np.sqrt(2*((s+b)*np.log(1+s/b)-s))\n",
        "        ams=np.where( (s < 0)  | (b < 0), np.nan, ams) # nan if unphysical values.\n",
        "        if np.isscalar(s_in):\n",
        "            return float(ams)\n",
        "        else:\n",
        "            return  ams\n",
        "# test\n",
        "#ss=np.array([-1,2,4,4,0,10,0]); bb=np.array([12,-23,100,4,10,0,0])\n",
        "#print(amsasimov(ss,bb))\n",
        "#print(amsasimov(4,16))\n",
        "#print(amsasimov(4,1600))\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ifnvM3xMWAo"
      },
      "source": [
        "# Load significance score function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "m_AfSLR_MWAo",
        "outputId": "05b7251e-66e5-4eb4-a18f-1a136d5aba63",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 309 µs, sys: 0 ns, total: 309 µs\n",
            "Wall time: 315 µs\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "# same signature as roc_auc_score\n",
        "def significance_vscore(y_true, y_score, sample_weight=None):\n",
        "    if sample_weight is None:\n",
        "        # Provide a default value of 1.\n",
        "        sample_weight = np.full(len(y_true), 1.)\n",
        "\n",
        "\n",
        "    # Define bins for y_score, adapt the number as needed for your data\n",
        "    bins = np.linspace(0, 1., 101)\n",
        "\n",
        "\n",
        "# Fills s and b weighted binned distributions\n",
        "    s_hist, bin_edges = np.histogram(y_score[y_true == 1], bins=bins, weights=sample_weight[y_true == 1])\n",
        "    b_hist, bin_edges = np.histogram(y_score[y_true == 0], bins=bins, weights=sample_weight[y_true == 0])\n",
        "\n",
        "\n",
        "    # Compute cumulative sums (from the right!)\n",
        "    s_cumul = np.cumsum(s_hist[::-1])[::-1]\n",
        "    b_cumul = np.cumsum(b_hist[::-1])[::-1]\n",
        "\n",
        "    # Compute significance\n",
        "    significance=amsasimov(s_cumul,b_cumul)\n",
        "\n",
        "    # Find the bin with the maximum significance\n",
        "    max_value = np.max(significance)\n",
        "\n",
        "    return significance\n",
        "\n",
        "def significance_score(y_true, y_score, sample_weight=None):\n",
        "    # Find the bin with the maximum significance\n",
        "    max_value = np.max(significance_vscore(y_true, y_score, sample_weight))\n",
        "\n",
        "    return max_value\n",
        "\n",
        "# this can then be used as a scorer in sklearn algs.\n",
        "fsignificance_score = sklearn.metrics.make_scorer(significance_score)\n",
        "\n",
        "# Example usage:\n",
        "# y_test = np.array([...])  # true labels\n",
        "# y_pred = np.array([...])  # predicted scores\n",
        "# weights_test = np.array([...])  # weights\n",
        "#vscore=significance_vscore(y_true=y_test, y_score=y_pred_skgb, sample_weight=weights_test)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F4DfF0ISrlIN"
      },
      "source": [
        "## XGBoost"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "nqMCgvbkrlIN",
        "scrolled": true,
        "outputId": "d77ca209-8e19-4171-af88-58fe18a0ccba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training time: 5.23175573348999\n",
            "auc test: 0.8670325892826282\n",
            "auc train: 0.8754233526495165\n",
            "Z: 1.623242142166869\n",
            "CPU times: user 10.8 s, sys: 52.6 ms, total: 10.8 s\n",
            "Wall time: 6.89 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "np.random.seed(31415) # set the random seed\n",
        "\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.metrics import roc_auc_score # for binary classification if x > 0.5 -> 1 else -> 0\n",
        "\n",
        "# creates the classifier\n",
        "xgb = XGBClassifier()\n",
        "\n",
        "starting_time = time.time()\n",
        "\n",
        "# does the real work\n",
        "\n",
        "xgb.fit(X_train, y_train.values, sample_weight=weights_train) # note that XGB 1.3.X and higher requires positive weight\n",
        "\n",
        "training_time = time.time( ) - starting_time\n",
        "print(\"Training time:\",training_time)\n",
        "\n",
        "y_pred_xgb = xgb.predict_proba(X_test)[:,1]\n",
        "y_pred_train_xgb = xgb.predict_proba(X_train)[:,1]\n",
        "\n",
        "auc_test_xgb = roc_auc_score(y_true=y_test, y_score=y_pred_xgb,sample_weight=weights_test)\n",
        "print(\"auc test:\",auc_test_xgb)\n",
        "\n",
        "print (\"auc train:\",roc_auc_score(y_true=y_train.values, y_score=y_pred_train_xgb,sample_weight=weights_train))\n",
        "\n",
        "vamsasimov_xgb=significance_vscore(y_true=y_test, y_score=y_pred_xgb, sample_weight=weights_test)\n",
        "significance_xgb = np.max(vamsasimov_xgb)\n",
        "\n",
        "print(\"Z:\",significance_xgb)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "Y_1SDqpwyd0m",
        "outputId": "254857c7-9d15-4435-bff4-54fbd94382a2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "auc test 0.8670325892826282\n",
            "auc test without weights 0.8677920698367818\n"
          ]
        }
      ],
      "source": [
        "print(\"auc test\",roc_auc_score(y_true=y_test, y_score=y_pred_xgb,sample_weight=weights_test))\n",
        "print(\"auc test without weights\",roc_auc_score(y_true=y_test, y_score=y_pred_xgb))\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NmadcFRJrlIT"
      },
      "source": [
        "## LightGBM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "9YmfuiwUrlIU",
        "outputId": "6b3cf257-8a59-42d4-ac17-c63cc32d0fa4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Info] Number of positive: 297571, number of negative: 135446\n",
            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.028249 seconds.\n",
            "You can set `force_col_wise=true` to remove the overhead.\n",
            "[LightGBM] [Info] Total Bins 1530\n",
            "[LightGBM] [Info] Number of data points in the train set: 433017, number of used features: 6\n",
            "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n",
            "[LightGBM] [Info] Start training from score -0.000000\n",
            "Training time: 3.7813010215759277\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.11/dist-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "auc test: 0.8296102251691393\n",
            "auc train: 0.8379442181132511\n",
            "Z: 1.421062826327018\n"
          ]
        }
      ],
      "source": [
        "np.random.seed(31415) # set the random seed\n",
        "\n",
        "import lightgbm as lgb\n",
        "from sklearn.metrics import roc_auc_score # for binary classification if x > 0.5 -> 1 else -> 0\n",
        "\n",
        "gbm = lgb.LGBMClassifier()\n",
        "\n",
        "starting_time = time.time( )\n",
        "\n",
        "gbm.fit(X_train, y_train.values,sample_weight=weights_train)\n",
        "\n",
        "training_time = time.time( ) - starting_time\n",
        "print(\"Training time:\",training_time)\n",
        "\n",
        "y_pred_gbm = gbm.predict_proba(X_test)[:,1]\n",
        "y_pred_gbm = y_pred_gbm.ravel()\n",
        "y_pred_train_gbm = gbm.predict_proba(X_train)[:,1]\n",
        "auc_test_gbm = roc_auc_score(y_true=y_test, y_score=y_pred_gbm,sample_weight=weights_test)\n",
        "print(\"auc test:\",auc_test_gbm)\n",
        "print (\"auc train:\",roc_auc_score(y_true=y_train.values, y_score=y_pred_train_gbm,sample_weight=weights_train))\n",
        "\n",
        "#compute significance\n",
        "vamsasimov_gbm=significance_vscore(y_true=y_test, y_score=y_pred_gbm, sample_weight=weights_test)\n",
        "significance_gbm = np.max(vamsasimov_gbm)\n",
        "\n",
        "\n",
        "print(\"Z:\",significance_gbm)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I7HnCErpyd0p"
      },
      "source": [
        "## SKLearn GBDT"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "eriizDZkyd0p",
        "outputId": "58617f59-d488-4813-fd2c-d998db077151",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training time: 6.649728775024414\n",
            "auc test: 0.8473429113208336\n",
            "auc train: 0.8558127934697892\n",
            "Z: 1.5097369723236957\n"
          ]
        }
      ],
      "source": [
        "from sklearn import ensemble\n",
        "\n",
        "skgb=ensemble.HistGradientBoostingClassifier()\n",
        "\n",
        "\n",
        "starting_time = time.time( )\n",
        "\n",
        "skgb.fit(X_train, y_train.values,sample_weight=weights_train)\n",
        "\n",
        "\n",
        "training_time = time.time( ) - starting_time\n",
        "print(\"Training time:\",training_time)\n",
        "\n",
        "y_pred_skgb = skgb.predict_proba(X_test)[:,1]\n",
        "y_pred_skgb = y_pred_skgb.ravel()\n",
        "y_pred_train_skgb = skgb.predict_proba(X_train)[:,1]\n",
        "auc_test_skgb = roc_auc_score(y_true=y_test, y_score=y_pred_skgb,sample_weight=weights_test)\n",
        "print(\"auc test:\",auc_test_skgb)\n",
        "print (\"auc train:\",roc_auc_score(y_true=y_train.values, y_score=y_pred_train_skgb,sample_weight=weights_train))\n",
        "\n",
        "#compute significance\n",
        "vamsasimov_skgb=significance_vscore(y_true=y_test, y_score=y_pred_skgb, sample_weight=weights_test)\n",
        "significance_skgb = np.max(vamsasimov_skgb)\n",
        "\n",
        "print(\"Z:\",significance_skgb)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "3pSxtcTGyd0q",
        "outputId": "672f4c5f-d379-46fe-917c-666d8f4f0188",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best significance found are:\n",
            "XGBoost :  1.623242142166869\n",
            "LightGBM:  1.421062826327018\n",
            "sklearn:  1.5097369723236957\n",
            "\n",
            "Best auc test found are:\n",
            "XGBoost:  0.8670325892826282\n",
            "LightGBM:  0.8296102251691393\n",
            "sklearn:  0.8473429113208336\n"
          ]
        }
      ],
      "source": [
        "print('Best significance found are:')\n",
        "print('XGBoost : ', significance_xgb)\n",
        "print('LightGBM: ', significance_gbm)\n",
        "print('sklearn: ', significance_skgb)\n",
        "print ()\n",
        "\n",
        "print('Best auc test found are:')\n",
        "print('XGBoost: ', roc_auc_score(y_true=y_test.values, y_score=y_pred_xgb,sample_weight=weights_test))\n",
        "print('LightGBM: ', roc_auc_score(y_true=y_test.values, y_score=y_pred_gbm,sample_weight=weights_test))\n",
        "print('sklearn: ', roc_auc_score(y_true=y_test.values, y_score=y_pred_skgb,sample_weight=weights_test))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T-LB9cbErlIb"
      },
      "source": [
        "# Some nice plots"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_nxVz32hKwqz"
      },
      "source": [
        "## load score plotting function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "Ay8QTiKPKwqz"
      },
      "outputs": [],
      "source": [
        "# some utilities\n",
        "from math import sqrt\n",
        "from math import log\n",
        "\n",
        "# Plot score for signal and background, comparing training and testing\n",
        "def compare_train_test(y_pred_train, y_train, y_pred, y_test, high_low=(0,1),\n",
        "                       bins=30,xlabel=\"\", ylabel=\"Arbitrary units\", title=\"\",\n",
        "                       weights_train=np.array([]), weights_test=np.array([]),\n",
        "                       density=True):\n",
        "    if weights_train.size != 0:\n",
        "        weights_train_signal = weights_train[y_train == 1]\n",
        "        weights_train_background = weights_train[y_train == 0]\n",
        "    else:\n",
        "        weights_train_signal = None\n",
        "        weights_train_background = None\n",
        "    plt.hist(y_pred_train[y_train == 1],\n",
        "                 color='r', alpha=0.5, range=high_low, bins=bins,\n",
        "                 histtype='stepfilled', density=density,\n",
        "                 label='S (train)', weights=weights_train_signal) # alpha is transparancy\n",
        "    plt.hist(y_pred_train[y_train == 0],\n",
        "                 color='b', alpha=0.5, range=high_low, bins=bins,\n",
        "                 histtype='stepfilled', density=density,\n",
        "                 label='B (train)', weights=weights_train_background)\n",
        "\n",
        "    if weights_test.size != 0:\n",
        "        weights_test_signal = weights_test[y_test == 1]\n",
        "        weights_test_background = weights_test[y_test == 0]\n",
        "    else:\n",
        "        weights_test_signal = None\n",
        "        weights_test_background = None\n",
        "    hist, bins = np.histogram(y_pred[y_test == 1],\n",
        "                                  bins=bins, range=high_low, density=density, weights=weights_test_signal)\n",
        "    scale = len(y_pred[y_test == 1]) / sum(hist)\n",
        "    err = np.sqrt(hist * scale) / scale\n",
        "\n",
        "    center = (bins[:-1] + bins[1:]) / 2\n",
        "    plt.errorbar(center, hist, yerr=err, fmt='o', c='r', label='S (test)')\n",
        "\n",
        "    hist, bins = np.histogram(y_pred[y_test == 0],\n",
        "                                  bins=bins, range=high_low, density=density, weights=weights_test_background)\n",
        "    scale = len(y_pred[y_test == 0]) / sum(hist)\n",
        "    err = np.sqrt(hist * scale) / scale\n",
        "\n",
        "    center = (bins[:-1] + bins[1:]) / 2\n",
        "    plt.errorbar(center, hist, yerr=err, fmt='o', c='b', label='B (test)')\n",
        "    plt.title(title)\n",
        "    plt.xlabel(xlabel)\n",
        "    plt.ylabel(ylabel)\n",
        "    plt.legend(loc='best')\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "kEQ342s-rlIe",
        "outputId": "8604736b-5072-46f1-90f4-d17b1bacee96",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 960x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 960x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 960x720 with 1 Axes>"
            ],
            "image/png": 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gLbNYpIyM+reGmc2OdvZ58Ynw8HD16NFDPXr00NChQ3X77bertLRU+/fvd3vOmjVrFB4ernPOOUeSVFRUpLFjx0qSunTpIpPJpOnTp0uSxowZo5kzZyozM1PdunXThAkTJEkPP/ywBg0apE6dOikhIUE33HCDqqurna9x/G1j8+bN09ChQ/XSSy8pOTlZ0dHR+u1vf6uq4+ZBXXLJJVq+fLk3fjSN4rYxAACAts5ikVJSHKuKVVU5gkxiIiMuflJdXa2XX35Z/fr1U9euXd32y8/P14gRI5zPExIS9Oabb+qyyy7T9u3bZTab1aFDB+fxF198Uddff70++ugjZ1tISIgeffRR9e7dW7t27dINN9yg2267TU8++aTb1925c6dWrlyp1atX6+DBg8rIyNADDzyg++67z9nnrLPO0n333aeamhqFh4c390dxQoQXAAAAOILKL/Mo4HurV69WZGSkJOnw4cOKi4vT6tWrFdJIYCwuLlbPnj2dz0NDQ3XKKadIkmJjY+vNeenfv78WLVrk0paZmen8Ojk5Wffee6/+/Oc/Nxpe7Ha7srKyFPXL6Nw111yj9evXu4SXnj17qra2VhUVFUpKSmr8mz8JxGkAAADAz8aOHauCggIVFBTos88+04QJE3TRRRepuLjY7Tk//vijIiIimvwax47S1Hnvvfd0wQUXqFevXoqKitI111yjAwcO6IcffnB7neTkZGdwkaS4uDjt27fPpU/diE9j1/EGwgsAAADgZ506dVK/fv3Ur18/nXnmmXruued0+PBhPfvss27P6datmw4ePOjRaxyrqKhIv/71rzV48GC9+eab+vLLL/XEE09IanxCf/v27V2em0wm2e12l7bvv/9ekhQTE9Pk+pqD8AIAAAAEmMlkUkhIiH788Ue3fYYNG6ZvvvnGpS0sLEySZLPZTvgaX375pex2ux566CGdc845OvXUU/Xtt9+eXOG/2Lp1q+Lj49WtWzevXM8dwgsAAADgZzU1NaqoqFBFRYUKCwt10003qbq6WpdcconbcyZMmKCvv/7aZfQlKSlJJpNJq1ev1v79+11WDjtev3799NNPP+mxxx7Trl279NJLL2np0qVe+X7y8/N14YUXeuVajSG8AAAAAH6Wm5uruLg4xcXF6eyzz9bnn3+uN954Q2PGjHF7zqBBgzR8+HBlZ2c723r16qX58+fr9ttvV/fu3TVz5ky35w8ZMkQPP/ywFi5cqIEDB+qVV17RggULTvp7OXLkiFauXKkZM2ac9LVOxGQYhuHzV2lBrFaroqOjVVlZKbPZHOhyAAAAfM+X2837cyv74xw5ckS7d+9W7969PZrIHszefvtt/fWvf9XWrVsbXZnMn5566im99dZbevfdd932aezvypPP5yyVDAAAAASJiy++WP/73/+0Z88eJSQkBLocSY4J/Y899phfXiugce2pp57S4MGDZTabZTabNXLkSL3zzjtu+2dlZclkMrk82krKBgAAACTHXi0tJbhI0nXXXaeUlBS/vFZAR17i4+P1wAMPqH///jIMQy+++KImTZqkTZs26fTTT2/wHLPZrO3btzufm0wmf5ULAAAAIIACGl6OX03hvvvu01NPPaVPPvnEbXgxmUzq0aOHP8oDAAAA0IK0jFk+cqxNvXz5ch0+fFgjR45026+6ulpJSUlKSEjQpEmT9PXXX/uxSgAAAACBEvAJ+1u2bNHIkSN15MgRRUZG6q233tKAAQMa7JuSkqIXXnhBgwcPVmVlpR588EGNGjVKX3/9teLj4xs8p6amRjU1Nc7nVqvVJ98HAAAAAN8K+MhLSkqKCgoK9Omnn+r666/XtGnT6u0cWmfkyJGaOnWqhg4dqtGjRysnJ0cxMTF6+umn3V5/wYIFio6Odj5a0uQmAAAAAE0X8PASFhamfv36acSIEVqwYIGGDBmiJUuWNOnc9u3ba9iwYdqxY4fbPnPmzFFlZaXzUVpa6q3SAQAAAPhRwMPL8ex2u8ttXo2x2WzasmWL4uLi3PYJDw93LsVc9wAAAACcDh+WTCbH4/DhQFeDRgQ0vMyZM0cbNmxQUVGRtmzZojlz5igvL09XX321JGnq1KmaM2eOs//dd9+td999V7t27dJXX32l3/3udyouLtZ1110XqG8BAAAA8Iva2lr169dPGzdu9PtrL126tN5KwYEQ0PCyb98+TZ06VSkpKbrgggv0+eefa+3atRo/frwkqaSkROXl5c7+Bw8e1IwZM2SxWDRx4kRZrVZt3LjR7QR/AAAA4IRstqNfb9jg+twH9u/fr+uvv16JiYkKDw9Xjx49NGHCBH300UeNnrd06VL17t1bo0aNkiQVFRXJZDKpoKDAq/WZTCatXLnSpe0Pf/iDvvrqK+Xn53v1tTwV0NXGnn/++UaP5+XluTx/5JFH9Mgjj/iwIgAAALQpOTnSzTcffT5xohQfLy1ZIk2Z4pOXvOyyy1RbW6sXX3xRffr00d69e7V+/XodOHDA7TmGYejxxx/X3Xff7ZOaTiQsLExXXXWVHn30UaWmpgakBqkFznkBAAAA/CInR0pPl/bscW3fs8fRnpPj9Zc8dOiQ8vPztXDhQo0dO1ZJSUk666yzNGfOHF166aVuz/vyyy+1c+dOXXzxxc623r17S5KGDRsmk8mkMWPGOI8999xzslgsioiI0GmnnaYnn3zSeay2tlYzZ85UXFycIiIilJSUpAULFkiSkpOTJUm/+c1vZDKZnM8lxwbzq1at0o8//uiFn0TzEF4AAADQ9ths0qxZkmHUP1bXlpnp9VvIIiMjFRkZqZUrVzZ5kSpJys/P16mnnqqoqChn22effSZJeu+991ReXq6cX8LWK6+8orvuukv33XefCgsLdf/99+vOO+/Uiy++KEl69NFHtWrVKmVnZ2v79u165ZVXnCHl888/lyQtW7ZM5eXlzueSdMYZZ+jnn3/Wp59+elI/g5MR8E0qAQAAAL/Lz5fKytwfNwyptNTR75gRjZPVrl07ZWVlacaMGVq6dKmGDx+u0aNH67e//a0GDx7s9rzi4mL17NnTpS0mJkaS1LVrV/Xo0cPZPnfuXD300EOa8sttb71799Y333yjp59+WtOmTVNJSYn69++vX/3qVzKZTEpKSqp3zc6dO7tcU5I6duyo6OhoFRcXn9wP4SQw8gIAAIC255hFobzSzwOXXXaZvv32W61atUppaWnKy8vT8OHDlZWV5facH3/8URERESe89uHDh7Vz505de+21zlGeyMhI3Xvvvdq5c6ckafr06SooKFBKSopuvvlmvfvuu02uvUOHDvrhhx+a3N/bCC8AAABoexrZJ7BZ/TwUERGh8ePH684779TGjRs1ffp0zZ07123/bt266eDBgye8bnV1tSTp2WefVUFBgfOxdetWffLJJ5Kk4cOHa/fu3brnnnv0448/KiMjQ+np6U2q+/vvv3eOzgQC4QUAAABtT2qqY1Uxk6nh4yaTlJDg6OcHAwYM0OFGNsgcNmyYtm3bJuOYOTphYWGSHBu31+nevbt69uypXbt2qV+/fi6Pugn+kmQ2m3XFFVfo2Wef1euvv64333xT33//vSSpffv2Ltess3PnTh05ckTDhg076e+3uZjzAgAAgLYnNNSxHHJ6uiOoHDtxvy7QLF7s6OdFBw4c0OWXX64//OEPGjx4sKKiovTFF19o0aJFmjRpktvzxo4dq+rqan399dcaOHCgJCk2NlYdOnRQbm6u4uPjFRERoejoaM2fP18333yzoqOjlZaWppqaGn3xxRc6ePCgZs+erYcfflhxcXEaNmyYQkJC9MYbb6hHjx7q3LmzJMeKY+vXr9e5556r8PBwdenSRZJj0YA+ffqob9++Xv2ZeIKRFwAAALRNU6ZIK1ZIx02EV3y8o90H+7xERkbq7LPP1iOPPKLzzjtPAwcO1J133qkZM2bo8ccfd3te165d9Zvf/EavvPKKs61du3Z69NFH9fTTT6tnz57O8HPdddfpueee07JlyzRo0CCNHj1aWVlZzpGXqKgoLVq0SGeccYbOPPNMFRUVac2aNQoJcUSDhx56SOvWrVNCQoLLKMtrr72mGTNmeP1n4gmTYTS0PlzrZbVaFR0drcrKSpnN5kCXAwAA4Hvz5gXntU/gyJEj2r17t3r37t2kyexuWa1SdLTj6zVrpAsv9PqIizds3rxZ48eP186dOxUZGenX1/766691/vnn67///a+i635WHmjs78qTz+eMvAAAAMC3rFbHrVgmk/TOO17fO+WkHRtUzjuvRQYXSRo8eLAWLlyo3bt3+/21y8vL9c9//rNZwcWbmPMCAAAA38nJkW6++ejziRMdt2UtWeKT27KapVOnhjerbIGmT58ekNcdN25cQF73eIQXAAAANF9jt40VFkrZ2fXby8qkyy6TMjIki8Xz66LNIrzAI630llkAAOBtdruUm9t4n9xcKSVFCmEmA5qGdwoAAAC8r6TEMdelMVarox/QRIQXAAAAeF9VlXf7ASK8AAAAwBeiorzbDxDhBQAAAL6QmCidaE89s9nRD2giwgsAAAC8LyRESktrvE9aWouYrH/48NFtaA4fDnQ1aEzg3y0AAABonSwWx3LIx4/AmM2NL5MMtw4cOKDY2FgVFRX5/bVvv/123XTTTX5/3WMRXgAAAOA7Fos0a5Y0bZpjU8pp0xzPW1BwsdmOfr1hg+tzX5g+fbpMJpPz0bVrV6WlpWnz5s0nPPe+++7TpEmTlJycLEnKy8uTyWTSoUOHvFZfUVGRTCaTCgoKXNpvvfVWvfjii9q1a5fXXstThBcAAAD4VkiIlJwsDRrk+LMF3CpWJydHGjDg6POJEx0l5uT49nXT0tJUXl6u8vJyrV+/Xu3atdOvf/3rRs/54Ycf9Pzzz+vaa6/1bXFudOvWTRMmTNBTTz0VkNeXCC8AAABoo3JypPR0ac8e1/Y9exztvgww4eHh6tGjh3r06KGhQ4fq9ttvV2lpqfbv3+/2nDVr1ig8PFznnHOOJMcIydixYyVJXbp0kclk0vTp0yVJdrtdCxYsUO/evdWhQwcNGTJEK1ascF7r4MGDuvrqqxUTE6MOHTqof//+WrZsmSSpd+/ekqRhw4bJZDJpzJgxzvMuueQSLV++3Js/Co+0C9grAwAAAAFisznuXjOM+scMwzF5PzNTmjRJCg31bS3V1dV6+eWX1a9fP3Xt2tVtv/z8fI0YMcL5PCEhQW+++aYuu+wybd++XWazWR06dJAkLViwQC+//LKWLl2q/v37a8OGDfrd736nmJgYjR49Wnfeeae++eYbvfPOO+rWrZt27NihH3/8UZL02Wef6ayzztJ7772n008/XWFhYc7XPOuss1RWVqaioiLnrWv+RHgBAABAm5OfL5WVuT9uGFJpqaPfMQMPXrN69WpFRkZKkg4fPqy4uDitXr1aIY3cUldcXKyePXs6n4eGhuqUU06RJMXGxqpz586SpJqaGt1///167733NHLkSElSnz599OGHH+rpp5/W6NGjVVJSomHDhumMM86QJJcgEhMTI0nq2rWrevTo4VJD3esXFxcTXgAAAAB/KC/3bj9PjR071jl35ODBg3ryySd10UUX6bPPPlNSUlKD5/z444+KiIg44bV37NihH374QePHj3dpr62t1bBhwyRJ119/vS677DJ99dVXuvDCCzV58mSNGjXqhNeuG9n54YcfTtjXFwgvAAAAaHPi4rzbz1OdOnVSv379nM+fe+45RUdH69lnn9W9997b4DndunXTwYMHT3jt6upqSdLbb7+tXr16uRwLDw+XJF100UUqLi7WmjVrtG7dOl1wwQW68cYb9eCDDzZ67e+//17S0dEZf2PCPgAAANqc1FQpPt4xt6UhJpOUkODo5w8mk0khISHOeScNGTZsmL755huXtrr5KLZj1nceMGCAwsPDVVJSon79+rk8EhISnP1iYmI0bdo0vfzyy1q8eLGeeeYZt9ess3XrVrVv316nn35687/Zk8DIC7yutlZasMDx9Zw50jFzvAAAAFqE0FBpyRLHqmImk+vE/bpAs3ix7ybr19TUqKKiQpLjtrHHH39c1dXVuuSSS9yeM2HCBM2ZM0cHDx5Uly5dJElJSUkymUxavXq1Jk6cqA4dOigqKkq33nqrbrnlFtntdv3qV79SZWWlPvroI5nNZk2bNk133XWXRowYodNPP101NTVavXq1LL/svRMbG6sOHTooNzdX8fHxioiIUHR0tCTHogGpqanO28f8jZEXAAAAtElTpkgrVkjHzIGX5BiRWbHCcdxXcnNzFRcXp7i4OJ199tn6/PPP9cYbb7gsS3y8QYMGafjw4crOzna29erVS/Pnz9ftt9+u7t27a+bMmZKke+65R3feeacWLFggi8WitLQ0vf32285lkMPCwjRnzhwNHjxY5513nkJDQ51LILdr106PPvqonn76afXs2VOTJk1yvt7y5cs1Y8YMH/xEmsZkGA0tENd6Wa1WRUdHq7KyUmazOdDlBJ15807c58gRaeFCx9dXXSX17du0vaiacm0AANAMwfg/2SbUfOTIEe3evVu9e/du0kR2d6xW6ZeBBa1ZI114oe+XR26ut99+W3/961+1devWRlcm84V33nlHf/nLX7R582a1a+fZDVyN/V158vmc28bgVYWF0jvvHH3+6quS2SylpUm/jEQCAAC0KMcGlfPOa7nBRZIuvvhi/e9//9OePXtc5q/4w+HDh7Vs2TKPg4s3EV7gNYWF0jGjmE5Wq6M9I4MAAwAAWp5OnRrerLKlyszMDMjrpqenB+R1j8WcF3iF3S7l5jbeJzfX0Q8AAABoDsILvKKkxDHC0hir1dEPAAAAaA7CC7yiqsq7/QAAAJqqja0/FZS89XdEeIFXREV5tx8AAMCJtG/fXpL0ww8/BLgSnEhtba0kKfQkV0Ngwj68IjHRsapYY7eOmc2OfgAAAN4QGhqqzp07a9++fZKkjh07ylS3wyRaDLvdrv3796tjx44nvVIZ4QVeERLiWA65odXG6qSlNW2/FwAAgKbq0aOHJDkDDFqmkJAQJSYmnnS4JLzAaywWx3LI77zjOreFfV4AAICvmEwmxcXFKTY2Vj/99FOgy4EbYWFhXtlUk/ACr7JYpN69pYULHc+vukrq25cRFwAAvCaYtoP3o9DQ0JOeT4GWj4+U8LqICGnuXMejf3+CCwAAXpOTIw0YcPT5xIlScrKjHWgD+FgJAAAQDHJypPR0ac8e1/Y9exztBBi0AYQXAACAls5mk2bNkhraK6OuLTPT0Q9oxQgvAAAALV1+vlRW5v64YUilpY5+QCtGeAEAAGjpysu92w8IUqw2BgAA0FLMm9dwe1FR085fu1bavt1b1QAtDiMvAAAALV1iomPjtMaYzY5+QCtGeAEAAGjpQkIcOz43Ji2N/QnQ6vEOBwAACAYWi5SRUX8Exmx2tFssgakL8CPmvAAAAAQLi0VKSZFKSqSqKikqynGrGCMuaCMILwAAAMEkJERKTg50FUBAENMBAAAABAXCCwAAAICgQHgBAAAAEBQILwAAAACCAuEFAAAAQFAgvAAAAAAICgENL0899ZQGDx4ss9kss9mskSNH6p133mn0nDfeeEOnnXaaIiIiNGjQIK1Zs8ZP1QIAAAAIpICGl/j4eD3wwAP68ssv9cUXX+j888/XpEmT9PXXXzfYf+PGjbryyit17bXXatOmTZo8ebImT56srVu3+rlyAAAAAP4W0PByySWXaOLEierfv79OPfVU3XfffYqMjNQnn3zSYP8lS5YoLS1Nf/3rX2WxWHTPPfdo+PDhevzxx/1cOQAAAAB/azFzXmw2m5YvX67Dhw9r5MiRDfb5+OOPNW7cOJe2CRMm6OOPP/ZHiQAAAAACqF2gC9iyZYtGjhypI0eOKDIyUm+99ZYGDBjQYN+Kigp1797dpa179+6qqKhwe/2amhrV1NQ4n1utVu8UDgAAAMCvAj7ykpKSooKCAn366ae6/vrrNW3aNH3zzTdeu/6CBQsUHR3tfCQkJHjt2gAAAAD8J+DhJSwsTP369dOIESO0YMECDRkyREuWLGmwb48ePbR3716Xtr1796pHjx5urz9nzhxVVlY6H6WlpV6tP5gdPiyZTI7H4cOBrgYAAABoXMDDy/HsdrvLbV7HGjlypNavX+/Stm7dOrdzZCQpPDzcuRRz3QMONtvRrzdscH0OAAAAtDQBDS9z5szRhg0bVFRUpC1btmjOnDnKy8vT1VdfLUmaOnWq5syZ4+w/a9Ys5ebm6qGHHtK2bds0b948ffHFF5o5c2agvoWglZMjHTu1aOJEKTnZ0Q4AAAC0RAGdsL9v3z5NnTpV5eXlio6O1uDBg7V27VqNHz9eklRSUqKQkKP5atSoUXr11Vd1xx136G9/+5v69++vlStXauDAgYH6FoJSTo6Uni4Zhmv7nj2O9hUrpClTAlMbAAAA4E5Aw8vzzz/f6PG8vLx6bZdffrkuv/xyH1XU+tls0qxZ9YOL5GgzmaTMTGnSJCk01O/lAQAAAG4FfKlk+Ma8eQ23FxVJZWXuzzMMqbRUuvZax21kAAAAQEvR4ibsw7eqqrzbDwAAAPAXwksbExXl3X4AAACAvxBe2pjEROlEq0WbzY5+AAAAQEtCeGljQkKktLTG+6SlOfoBAAAALQkfUdsgi0XKyKh/a5jZ7Gi3WAJTFwAAANAYVhtroywWqXdvaeFCx/OrrpL69mXEBQAAAC0X4aUNi4iQ5s4NdBUAAABA0/B7dgAAAABBgfACAAAAICgQXgAAAAAEBcILAAAAgKBAeAEAAAAQFAgvAAAAAIIC4QUAAABAUCC8AAAAAAgKhBcAAAAAQYHwAgAAACAoEF4AAAAABAXCCwAAAICgQHgBAAAAEBQILwAAAACCAuEFAAAAQFAgvAAAAAAICoQXAAAAAEGhXaALAAAAaHVsNik/Xyovl+LipNRUKTQ00FUBQY/wAgAA4E05OdLNN0t79hxti4+XliyRpkwJXF1AK8BtYwAAAN6SkyOlp7sGF8nxPD3dcRxAsxFeAAAAvMFmk2bNkgyj/rG6tsxMRz8AzUJ4AQAA8Ib8fKmszP1xw5BKSx39ADQLc14AAAA8MW9ew+1btjTt/Mcek/LyvFUN0KYw8gIAAOANUVHe7QegHsILAACANyQmSmZz433MZkc/AM1CeAEAAPCGkBApLa3xPmlpjn4AmoX/egAAALzFYpEyMuqPwJjNjnaLJTB1Aa0EE/YRVA4fliIjHV9XV0udOgW2HgAA6rFYpJQUqaREqqpyzHFJTGTEBfACwguCyrFL42/YIF14oRQaGrh6AABoUEiIlJwc6CqAVodfASBo5ORIAwYcfT5xouP/C2xWDAAA0DYQXhAUcnKk9HRpzx7X9j17HO0EGAAAgNaP8IIWz2aTZs1ybEx8vLq2zEzXW8oAAADQ+hBe0OLl50tlZe6PG4ZUWuroBwAAgNaL8IIWr7zcu/0AAAAQnAgvaPHi4rzbDwAAAMGJ8IIWLzVVio+XTKaGj5tMUkKCox8AAABaL8ILWrzQUGnJEsfXxweYuueLF7PfCwAAQGtHeEFQmDJFWrFC6tnTtT0+3tE+ZUpg6gIAAID/tAt0AUCdefNO3Od3v5MWLnR8fdVVUt++0ubNjsfJXBcAAAAtH+EFQSUiQpo7N9BVAAAAIBC4bQwAAABAUCC8AAAAAAgKhBcAAAAAQYHwAgAAACAoEF4AAAAABAXCCwAAAICgQHgBAAAAEBQILwAAAACCAuEFAAAAQFAgvAAAAAAICgENLwsWLNCZZ56pqKgoxcbGavLkydq+fXuj52RlZclkMrk8IiIi/FQxAAAAgEAJaHj597//rRtvvFGffPKJ1q1bp59++kkXXnihDh8+3Oh5ZrNZ5eXlzkdxcbGfKgYAAAAQKO0C+eK5ubkuz7OyshQbG6svv/xS5513ntvzTCaTevTo4evyAAAAALQgLWrOS2VlpSTplFNOabRfdXW1kpKSlJCQoEmTJunrr79227empkZWq9XlAQAAACD4tJjwYrfblZmZqXPPPVcDBw502y8lJUUvvPCC/vWvf+nll1+W3W7XqFGjVFZW1mD/BQsWKDo62vlISEjw1bcAAAAAwIdaTHi58cYbtXXrVi1fvrzRfiNHjtTUqVM1dOhQjR49Wjk5OYqJidHTTz/dYP85c+aosrLS+SgtLfVF+QAAAAB8LKBzXurMnDlTq1ev1oYNGxQfH+/Rue3bt9ewYcO0Y8eOBo+Hh4crPDzcG2UCAAAACKCAjrwYhqGZM2fqrbfe0vvvv6/evXt7fA2bzaYtW7YoLi7OBxUCAAAAaCkCOvJy44036tVXX9W//vUvRUVFqaKiQpIUHR2tDh06SJKmTp2qXr16acGCBZKku+++W+ecc4769eunQ4cO6R//+IeKi4t13XXXBez7AAAAQcpmk/LzpfJyKS5OSk2VQkMDXRUANwIaXp566ilJ0pgxY1zaly1bpunTp0uSSkpKFBJydIDo4MGDmjFjhioqKtSlSxeNGDFCGzdu1IABA/xVNgAAaA1ycqRZs6RjF/2Jj5eWLJGmTAlcXQDcCmh4MQzjhH3y8vJcnj/yyCN65JFHfFQRAABoNebNc3+ssFDKzq7fXlYmXXaZlJEhWSw+Kw1A87SY1cYAAAD8wm6Xjtsou57cXEc/AC0K4QUAALQtJSXSiTattlod/QC0KIQXAADQtlRVebcfAL8hvAAAgLYlKsq7/QD4DeEFAAC0LYmJktnceB+z2dEPQItCeAEAAG1LSIiUltZ4n7Q0Rz8ALQr/VQIAgLbHYnEsh3z8CIzZzDLJQAsW0H1eAAAAAsZikVJSHKuKVVU55rgkJjLiArRghBcAANB2hYRIycmBrgJAE/GrBQAAAABBgfACAAAAICgQXgAAAAAEBcILAAAAgKBAeAEAAAAQFAgvAAAAAIIC4QUAAABAUCC8AAAAAAgKhBcAAAAAQYHwAgAAACAoEF4AAAAABAXCCwAAAICgQHgBAAAAEBQILwAAAACCAuEFAAAAQFAgvAAAAAAICoQXAAAAAEGB8AIAAAAgKBBeAAAAAAQFwgsAAACAoEB4AQAAABAUCC8AAAAAggLhBQAAAEBQILwAAAAACAqEFwAAAABBwePwUlpaqrKyMufzzz77TJmZmXrmmWe8WhgAAAAAHMvj8HLVVVfpgw8+kCRVVFRo/Pjx+uyzz/T3v/9dd999t9cLBAAAAACpGeFl69atOuussyRJ2dnZGjhwoDZu3KhXXnlFWVlZ3q4PAAAAACQ1I7z89NNPCg8PlyS99957uvTSSyVJp512msrLy71bHQAAAAD8wuPwcvrpp2vp0qXKz8/XunXrlJaWJkn69ttv1bVrV68XCAAAAABSM8LLwoUL9fTTT2vMmDG68sorNWTIEEnSqlWrnLeTAQAAAIC3tfP0hDFjxui7776T1WpVly5dnO1//OMf1alTJ68WBwAAAAB1PB55Of/881VVVeUSXCTplFNO0RVXXOG1wgAAACRJNpuUlye99prjT5st0BUBCBCPR17y8vJUW1tbr/3IkSPKz8/3SlEAAACSpJwcadYs6Zg95hQfLy1ZIk2ZEri6AAREk8PL5s2bnV9/8803qqiocD632WzKzc1Vr169vFsdAABou3JypPR0yTBc2/fscbSvWEGAAdqYJoeXoUOHymQyyWQy6fzzz693vEOHDnrssce8WhwAAGijbDbHiMvxwUVytJlMUmamNGmSFBrq9/IABEaTw8vu3btlGIb69Omjzz77TDExMc5jYWFhio2NVSj/eAAAAG/Iz3e9Vex4hiGVljr6jRnjt7IABFaTw0tSUpIkyW63+6wYAAAASVJTN75mg2ygTWlSeFm1apUuuugitW/fXqtWrWq076WXXuqVwgAAQBsWF+fdfgBahSaFl8mTJ6uiokKxsbGaPHmy234mk0k2li8EAAAnKzXVsarYnj0Nz3sxmRzHU1P9XxuAgGnSPi92u12xsbHOr909CC4AAMArQkMdyyFLjqByrLrnixczWR9oYzzepBIAAMAvpkxxLId8/FYM8fEskwy0UR5vUilJ69ev1/r167Vv3756E/hfeOEFrxQGAADagHnzTtzn97+XSkqkqiopKkpKTJQ2b3Y8ALQpHoeX+fPn6+6779YZZ5yhuLg4mY4fygUAAPCmkBApOTnQVQBoATwOL0uXLlVWVpauueYaX9QDAAAAAA3yeM5LbW2tRo0a5YtaAAAAAMAtj8PLddddp1dffdUXtQAAAACAWx7fNnbkyBE988wzeu+99zR48GC1b9/e5fjDDz/steIAAAAAoI7HIy+bN2/W0KFDFRISoq1bt2rTpk3OR0FBgUfXWrBggc4880xFRUU5N8Dcvn37Cc974403dNpppykiIkKDBg3SmjVrPP02AAAAAAQZj0dePvjgA6+9+L///W/deOONOvPMM/Xzzz/rb3/7my688EJ988036tSpU4PnbNy4UVdeeaUWLFigX//613r11Vc1efJkffXVVxo4cKDXagMAAADQspgMwzACXUSd/fv3KzY2Vv/+97913nnnNdjniiuu0OHDh7V69Wpn2znnnKOhQ4dq6dKlJ3wNq9Wq6OhoVVZWymw2e632lqYpy+a3FfwsAKAF4x9puMN7o83w5PO5xyMvY8eObXRvl/fff9/TSzpVVlZKkk455RS3fT7++GPNnj3bpW3ChAlauXJlg/1rampUU1PjfG61WptdH1o3q1WKjnZ8vWaNdOGFUmhoYGsCAADAUR7PeRk6dKiGDBnifAwYMEC1tbX66quvNGjQoGYXYrfblZmZqXPPPbfR278qKirUvXt3l7bu3buroqKiwf4LFixQdHS085GQkNDsGtF65eRIAwYcfT5xomM/tJycgJUEAACA43g88vLII4802D5v3jxVV1c3u5Abb7xRW7du1YcfftjsazRkzpw5LiM1VquVAAMXOTlSerp0/A2Ue/Y42leskKZMCUxtAAAAOMrjkRd3fve73+mFF15o1rkzZ87U6tWr9cEHHyg+Pr7Rvj169NDevXtd2vbu3asePXo02D88PFxms9nlAdSx2aRZs+oHF+loW2amox8AAAACy2vh5eOPP1ZERIRH5xiGoZkzZ+qtt97S+++/r969e5/wnJEjR2r9+vUubevWrdPIkSM9em1AkvLzpbIy98cNQyotdfQDAABAYHl829iU4+6fMQxD5eXl+uKLL3TnnXd6dK0bb7xRr776qv71r38pKirKOW8lOjpaHTp0kCRNnTpVvXr10oIFCyRJs2bN0ujRo/XQQw/p4osv1vLly/XFF1/omWee8fRbAVRe7t1+AAAA8B2Pw0t03XJMvwgJCVFKSoruvvtuXXjhhR5d66mnnpIkjRkzxqV92bJlmj59uiSppKREISFHB4hGjRqlV199VXfccYf+9re/qX///lq5ciV7vKBZ4uK82w8AAAC+43F4WbZsmddevClbzOTl5dVru/zyy3X55Zd7rQ60bo0tE2+3S2azY5lkd8xm6f33pQbeiixBDwAA4Edem/MCBKOQECktrfE+aWmOfgAAAAgsPpKhzbNYpIwMKSrKtd1sdrRbLIGpCwAAAK48vm0MaI0sFiklRSopkaqqHEEmMZERFwAAgJaE8AL8IiRESk4OdBUAAABwx+PfK3/wwQe+qAMAAAAAGuVxeElLS1Pfvn117733qrS01Bc1AQAAAEA9HoeXPXv2aObMmVqxYoX69OmjCRMmKDs7W7W1tb6oDwAAAAAkNSO8dOvWTbfccosKCgr06aef6tRTT9UNN9ygnj176uabb9Z//vMfX9QJAAAAoI07qbWUhg8frjlz5mjmzJmqrq7WCy+8oBEjRig1NVVff/21t2oEAAAAgOaFl59++kkrVqzQxIkTlZSUpLVr1+rxxx/X3r17tWPHDiUlJenyyy/3dq0AAAAA2jCPl0q+6aab9Nprr8kwDF1zzTVatGiRBg4c6DzeqVMnPfjgg+rZs6dXCwUAAADQtnkcXr755hs99thjmjJlisLDwxvs061bN5ZUBgAAAOBVHt029tNPPykpKUnnnHOO2+AiSe3atdPo0aNPujgAAAAAqONReGnfvr3efPNNX9UCAAAAAG55PGF/8uTJWrlypQ9KAQAAAAD3PJ7z0r9/f91999366KOPNGLECHXq1Mnl+M033+y14gAAAACgjsfh5fnnn1fnzp315Zdf6ssvv3Q5ZjKZCC8AAAAAfMLj8LJ7925f1AEAAAAAjWrWJpUAAAAA4G8ej7xIUllZmVatWqWSkhLV1ta6HHv44Ye9UhgAAAAAHMvj8LJ+/Xpdeuml6tOnj7Zt26aBAweqqKhIhmFo+PDhvqgRAAAAADy/bWzOnDm69dZbtWXLFkVEROjNN99UaWmpRo8ercsvv9wXNQIAAACA5+GlsLBQU6dOlSS1a9dOP/74oyIjI3X33Xdr4cKFXi8QAAAAAKRmhJdOnTo557nExcVp586dzmPfffed9yoDAAAAgGN4POflnHPO0YcffiiLxaKJEyfqL3/5i7Zs2aKcnBydc845vqgRAAAAADwPLw8//LCqq6slSfPnz1d1dbVef/119e/fn5XGAAAAAPiMR+HFZrOprKxMgwcPluS4hWzp0qU+KQwAAAAAjuXRnJfQ0FBdeOGFOnjwoK/qAQAAAIAGeTxhf+DAgdq1a5cvagEAAMHMZpPy8qTXXnP8abMFuiIArYzH4eXee+/VrbfeqtWrV6u8vFxWq9XlAQAA2qCcHCk5WRo7VrrqKsefycmOdgDwEo8n7E+cOFGSdOmll8pkMjnbDcOQyWSSjd+yAADQtuTkSOnpkmG4tu/Z42hfsUKaMiUwtQFoVTwOLx988IEv6gAAAMHIZpNmzaofXCRHm8kkZWZKkyZJoaF+Lw9A6+JxeOndu7cSEhJcRl0kx8hLaWmp1woDAABBID9fKitzf9wwpNJSR78xY/xWFoDWyeM5L71799b+/fvrtX///ffq3bu3V4oCAABBorzcu/0AoBEeh5e6uS3Hq66uVkREhFeKAgAAQSIuzrv9AKARTb5tbPbs2ZIkk8mkO++8Ux07dnQes9ls+vTTTzV06FCvFwgAAFqw1FQpPt4xOb+heS8mk+N4aqr/awPQ6jQ5vGzatEmSY+Rly5YtCgsLcx4LCwvTkCFDdOutt3q/QgAAEFjz5jV+fNQoKTu74WOGIY0cKd1zj9fLAtD2NDm81K0y9vvf/15LliyR2Wz2WVEAACCIWCxSRoaUmysdu+eb2SylpTmOA4AXeLza2LJly3xRBwAACGYWi5SSIpWUSFVVUlSUlJgohXg8vRYA3GpSeJkyZYqysrJkNps15QSbTOWwky4AAG1TSIiUnBzoKgC0Yk0KL9HR0c4VxqKjo31aEAAAAAA0pEnh5dhbxbhtDAAAAEAgeDznpc6+ffu0fft2SVJKSopiY2O9VhQAAAAAHM/jWXRWq1XXXHONevXqpdGjR2v06NHq1auXfve736mystIXNQIAAACA5+FlxowZ+vTTT7V69WodOnRIhw4d0urVq/XFF1/oT3/6ky9qBAAAAADPbxtbvXq11q5dq1/96lfOtgkTJujZZ59VWlqaV4sDAAAAgDoej7x07dq1wRXHoqOj1aVLF68UBQAAAADH8zi83HHHHZo9e7YqKiqcbRUVFfrrX/+qO++806vFAQAAAECdJt02NmzYMOc+L5L0v//9T4mJiUpMTJQklZSUKDw8XPv372feCwAAAACfaFJ4mTx5so/LAAAAAIDGNSm8zJ07V5Jks9n00UcfafDgwercubMv6wIAAAAAFx7NeQkNDdWFF16ogwcP+qoeAAAAAGiQxxP2Bw4cqF27dvmiFgAAAABwy+Pwcu+99+rWW2/V6tWrVV5eLqvV6vIA4MpqlUwmx+OddySbLdAVAQAABCePN6mcOHGiJOnSSy91WYHMMAyZTCbZ+GQGOOXkSDfffPT5xIlSfLy0ZIk0ZUrg6gIAAAhGHoeXDz74wBd1AK1OTo6Uni4Zhmv7nj2O9hUrCDAAAACe8Di8jB492u2xrVu3nlQxQGths0mzZtUPLpKjzWSSMjOlSZOk0FC/lwcAABCUPJ7zcryqqio988wzOuusszRkyBCPzt2wYYMuueQS9ezZUyaTSStXrmy0f15enkwmU71HRUXFSXwHgPfl50tlZe6PG4ZUWuroBwAAgKZpdnjZsGGDpk2bpri4OD344IM6//zz9cknn3h0jcOHD2vIkCF64oknPDpv+/btKi8vdz5iY2M9Oh/wtfJy7/YDAACAh7eNVVRUKCsrS88//7ysVqsyMjJUU1OjlStXasCAAR6/+EUXXaSLLrrI4/NiY2PZJBMtWlycd/sBAADAg5GXSy65RCkpKdq8ebMWL16sb7/9Vo899pgva3Nr6NChiouL0/jx4/XRRx8FpAagMampjlXFjlmQz4XJJCUkOPoBAACgaZo88vLOO+/o5ptv1vXXX6/+/fv7sia34uLitHTpUp1xxhmqqanRc889pzFjxujTTz/V8OHDGzynpqZGNTU1zufsRQN/CA11LIecnu4IKsdO3K8LNIsXM1kfAADAE00OLx9++KGef/55jRgxQhaLRddcc41++9vf+rK2elJSUpSSkuJ8PmrUKO3cuVOPPPKIXnrppQbPWbBggebPn++vEtHGzJvX+PHLL5dycx0bVdaJipLS0qTNmx2P5lwXAACgLWpyeDnnnHN0zjnnaPHixXr99df1wgsvaPbs2bLb7Vq3bp0SEhIUFRXly1obdNZZZ+nDDz90e3zOnDmaPXu287nValVCQoI/SgNksUgpKVJJiVRV5QguiYlSyEmv8wcAAND2ePwRqlOnTvrDH/6gDz/8UFu2bNFf/vIXPfDAA4qNjdWll17qixobVVBQoLhGZj2Hh4fLbDa7PAB/CgmRkpOlQYMcfxJcAAAAmuekPkalpKRo0aJFKisr02uvvebx+dXV1SooKFBBQYEkaffu3SooKFBJSYkkx6jJ1KlTnf0XL16sf/3rX9qxY4e2bt2qzMxMvf/++7rxxhtP5tsAAAAAEAQ8WirZndDQUE2ePFmTJ0/26LwvvvhCY8eOdT6vu71r2rRpysrKUnl5uTPISFJtba3+8pe/aM+ePerYsaMGDx6s9957z+UaAAAAAFonr4SX5hozZoyMY5dhOk5WVpbL89tuu0233Xabj6sCAAAA0BIFNLwAAAA/s9mk/HypvNyxU25qKuu2AwgahBcAANqKnBxp1iyprOxoW3y8Y2OqKVMCVxcANBHrHgEA0Bbk5Dh2zj02uEjSnj2O9pycwNQFAB4gvAAA0NrZbI4Rl4bmmda1ZWY6+gFAC0Z4AQCgtcvPrz/icizDkEpLHf0AoAUjvAAA0NqVl3u3HwAECOEFAIDWLi7Ou/0AIEAILwAAtHapqY5VxUymho+bTFJCgqMfALRgLJUMAEBrMW+e+2OjRknZ2Q0fMwxp5Ejpnnt8UhYAeAsjLwAAtAUWi5SRIZnNru1ms6PdYglMXQDgAUZeAABoKywWKSVFKimRqqqkqCgpMVEK4XeZAIID4QUAgLYkJERKTg50FQDQLPyqBQAAAEBQILwAAAAACAqEFwAAAABBgfACAAAAICgQXgAAAAAEBcILAAAAgKBAeAEAAAAQFAgvAAAAAIIC4QUAAABAUCC8AAAAAAgKhBcAAAAAQYHwAgAAACAotAt0AQAAoAE2m5SfL5WXS3FxUmqqFBoa6KoAIKAILwAAtDQ5OdKsWVJZ2dG2+HhpyRJpypTA1QUAAUZ4AQDAn+bNa/x4YaGUnV2/vaxMuuwyKSNDslh8UhoAtHTMeQEAoKWw26Xc3Mb75OY6+gFAG0R4AQCgpSgpkazWxvtYrY5+ANAGcdsYEMSYzwu0MlVV3u0HAK0M4QVogU50S7zkuC3+nXdcP8OYzVJaWuO3wzfl2gACJCrKu/0AoJXhtjEgCNXN5z3+l69Wq6O9sDAwdQE4SYmJjt9CNMZsdvQDgDaI8AIEGebzAq1YSIhj+LQxaWmOfgDQBvGvHxBkmM8LtHIWi2M55ONHYMxmlkkG0OYx5wUIMsznBdoAi0VKSXH8FqKqyjHHJTGRERcAbR7hBQgyzOcF2oiQECk5OdBVAECLwq9wgCDDfF4AANBWEV6AIMN8XgAA0Fbx8QYIQsznBQAAbRFzXoAgxXxeAADQ1hBegCDGfF4AANCWEF4AAGgum03Kz5fKy6W4OCk1VQoNDXRVANBqEV4AAGiOnBxp1iyprOxoW3y8tGSJNGVK4OoCgFaM8AIAQEPmzXN/rLBQys6u315WJl12GStnAICPMLUXAABP2O1Sbm7jfXJzHf0AAF5FeAEAwBMlJZLV2ngfq9XRDwDgVYQXAAA8UVXl3X4AgCYjvAAA4ImoKO/2AwA0GeEFAABPJCZKZnPjfcxmRz8AgFex2hjQxjS2gFIdu91xu35VleOXx4mJjg0xT/a6QKsQEiKlpTW82lidtLQT/0cDAPAY4QWAi8JC6Z13XG/XN5sdn8VY+RX4hcXiWA45N9d18j7/sQCATxFeADi527rCanW0s3UFcAyLRUpJ8XyYEgDQbIQXAJKavnVFSgqfzQCnkBApOTnQVQBAm8FHEACS2LoCAAC0fIQXAJLYugIAALR8hBcAkti6AgAAtHyEFwCS2LoCAAC0fAENLxs2bNAll1yinj17ymQyaeXKlSc8Jy8vT8OHD1d4eLj69eunrKwsn9cJtAV1W1c0hq0rAABAIAX0Y8jhw4c1ZMgQPfHEE03qv3v3bl188cUaO3asCgoKlJmZqeuuu05r1671caVA21C3dcXxIzBmM8skAwCAwAvoUskXXXSRLrrooib3X7p0qXr37q2HHnpIkmSxWPThhx/qkUce0YQJE3xVJtCm+HrrCptNys+XysuluDgpNVUKDfXOtQEAQOsWVPu8fPzxxxo3bpxL24QJE5SZmRmYgoBWyldbV+TkSDffLO3Zc7QtPl5askSaMsX7rwcAAFqXoAovFRUV6t69u0tb9+7dZbVa9eOPP6pDhw71zqmpqVFNTY3zufVEG1kA8ImcHCk9XTIM1/Y9exztK1YQYNAM8+Y1rZ/d7rvhRAC+0dT/vlvateFTQRVemmPBggWaP39+oMtoNm6xQWtgs0mzZtUPLpKjzWSSMjOlSZN4f8MHCgul3FzXXVjNZscKFEzkAoCgElS/durRo4f27t3r0rZ3716ZzeYGR10kac6cOaqsrHQ+SktL/VGqV+TkOG7dGTtWuuoqx5/JyY52IJjk50tlZe6PG4ZUWuroB3hVYaGUne0aXCTH8+xsx3EAwc1ul4qKpC1bHH/a7YGuCD4UVCMvI0eO1Jo1a1za1q1bp5EjR7o9Jzw8XOHh4b4uzeu4xQatSXm5d/sBTWK3O0ZcGpOb61ihglvIgODEyGqbE9B/raurq1VQUKCCggJJjqWQCwoKVFJSIskxajJ16lRn/z//+c/atWuXbrvtNm3btk1PPvmksrOzdcsttwSifJ850S02kuMWG5vNr2UBzRYX591+QJOUlNQfcTme1eroByD4MLLaJgV05OWLL77Q2LFjnc9nz54tSZo2bZqysrJUXl7uDDKS1Lt3b7399tu65ZZbtGTJEsXHx+u5555rdcske3KLzZgxfisLaFRjcx/tdscvwhr7HGk2S++/L+XleXZtwK2qKu/2A9ByMLLaZgU0vIwZM0ZGQ8MLv8jKymrwnE2bNvmwqsDjFhu0NiEhjhH87Gz3fdLS+P8LvCwqyrv9ALQcnoys+mLtfwQMHxVaIG6xQWtksUgZGY4RlmOZzY52bk2G1yUm1n/DHc9sdvQDEFwYWW2zgmrCfluRmurYuG/PnobnvZhMjuOpqf6vDTgZFotjBJ/tNtAgb68Nz5Af0Hoxstpm8S92CxQa6thxXHIElWPVPV+8mP0wEJxCQhwj+IMGOf7kcyMk+W5teIb8gNaJkdU2i5GXFmrKFMdyyLNmuU7ej493BBeWSQbQavh6bXiG/IKezW5SfkmSyqsiFRdVrdTEYoWGuJ8z21KuDR9iZLXNIrwEUFNWUPr97+v//3bzZscDAILeidaGN5kca8NPmnTyt5AxaTco5RRadPM7F2lP1dHfssebK7UkLVdTLCe3FG5OoUWzctNUZo32+rXhB3Ujq+zz0qYQXlo4/n8L+HapZJZhDjDWhkcjcgotSs/O0PHRdo/VrPTsDK3IyG52yPDlteFHjKy2OfzNAgACh7Xh4YbNbtKs3LRfwoXrBFDjl+eZuWmy2U31zg3ktREATKZsU/jbBYATsNkcm2e+9prjT5st0BW1IqwNDzfyS5J+uZ2r4QBhyKRSa7TyS5Ja1LUB+Ba3jQFoFex239w1kJPT8MIZS5awcIZXsDY83CivivRqP39duw4LAQC+QXgBEPQKC5s/X7OxOS+FhQ0vZFNWJl12WeMr7TKXponq1oZPT3cElWMDDGvDtwnz8sY02F50qHOTzl+7s5+2H+jW8LXH5DXYHhdV3aRrN7Xf8VgIAPAdwguAoOYuYFitjvbmbuVhtzsCUWNycx3zRLm9uglOlOYuv7x+Ao2KciRQllhskxKjD8kcdkTW2nA1fHuXIXN4jRKjD3l87dTEYsWbK7XHanbOcTmWSYbizValJhZ7fG0WAgB8i/ACIGj5MmCUlLh+jm6I1erox4qAXsCKQThOiElK67dD2d+cLsmQa4BxRIO0vjsU0ow59aEhhpak5So9O0MmGS4BxvTLtRen5Xp8m9eJFgIwyVBmbpompWzjFjKgmQgvAIKWLwNGVZV3+/mbzeZYXbi83DHXPTU1CO68Ym14HMcS850yBnyt3B39ZK2NcLabw2uU1neHLDHfNXq+u1vS6lzewLWjfrn25r3dtXlv94av6+Z2tKMLATTs2IUAxiQXNVobgIYRXgAELV8GjKgo7/ZriK8CBosMoDWxxHynlG7fqaSys6pqwxQVVqvE6EPNGnHx9bX9sRAA0NYRXgAELV8GjMREx6T/xkZ2zGZHv+bwVcDIyXHMfT9+4a49exztK1YQYBB8QkxScudDLf7avl4IAAD7vAAIYnUBozHNDRghIY654o1JS2velIy6gHH8xvJ1ASMnx/NrSo6RnFmzGl5xuK4tM5N9agBfqVsIwFRvur6DSYYSzJXNWggAgAMjLwCCVl3AaGi1sTrNDRiSYw55RkbzlmF2t7iW3e4YXWksYPz+91JBgfu63V07P79+IDr++qWljn5jxrjv16ignEwD+IevFgIAcBThBUBQO5mA0dTre3MRLG8sMuAuvGzZ0rQaHntMystr+nWdmEwDnNAUS6FWZGQ3sM+LVYvZ5wU4aYQXAEHP16vsenMRrKBdZIDJNGgCdpV3mGIp1KSUbfwsAB8gvABoFYJlld2WvMiAu5EXk92mzCWzZDYM2RWifKWqXHGKU7lSjXyFyJD195laXDBJRkjDt5C5uTRaEXaVdxUaYrAcMuADTNgHAD8KxkUGkkryFW0t01v6jZJVpLHK01V6TWOVp2QV6S1NVrS1VEkl+Z4XjVahblf5Mqvrm7tuV/mcwpO8fxMubHaT8oqS9dqWgcorSpbN7oV1o4EgwcgLAPhRS15koMGJMJIi961Xjn6jdK2ot4bSHvVSulZohdIV+dl6qcjNtcecuHZf3nLE7Uy+w67y/sUIF9o6wgsA+FmwLTJgbd9Nc7Xklw+nrhcxFCKT7MrUYs1r/1aza/blBzI+7J28xnaqLzrUuUm7yl+7apLP9mrxt8Z+Hid97TF5bo/VjXDV+yXCLyNcKzKyeU+j1SO8AEAABNMiAxv0K5Upwe1xQyEqVaI26FdKlucrDfjyAxkf9nyvqjbMq/3QMEa4AAfmvABAgNQFjEGDHH96K7h4W9VPHbza71gn+kAmSZm5ac26p9+X18ZRUWG1Xu2HhuWXJP0ywtXw+7VuhCu/JMm/hQF+xsgLAKBR3vhw6u42m5O95aixW2yOfthr/Nr5JUmsCnUSEqMPyRx2RNbacDX8wdqQObxGidGH/FxZ61JeFenVfkCwIrwAQCtkstuUVJKvyKpyVUfFqTgx1e0yxifiyw+nvrzlyB8f9lraQgBNmYthN6SSys6qqg1TVFitEqMPKeQkBp9CTFJavx3K/uZ0SYZc3yOOn0Va3x0n9RqQ4qKqvdoPCFaEFwBoZSyFOUrLnaVoa5mzrdIcr9y0JSq0eL6RpC8/nJ7sqM6JJpI3xdqd/bT9QLf6125kVEcKzoUACvd3U+6OfrLWRjjbzGFHlNZvhywx3zX7upaY75Qx4Ov61w6vUVrfk7s2HFITixVvrtQeq9l52+OxTDIUb7YqNbE4ANUB/tNC77AGADSHpTBHGdnpMh8TXCTJbN2jjOx0WQpzmnfdXz6cmsNqXK8bXqOMAV83+8Np3aiO6k2pr2PIHH6kWaM6vry2P/Y18fZeHoX7uyn7m9N/GUE7ylobruxvTlfh/voBzhOWmO8065xPNG1IgaZYvtG0IQWadfYnBBcvCQ0xtCQtV5IjqByr7vnitFwm66PVY+QFAFoJk92mtNxZUgO/lzXJkCGT0nIztS1lUrNuIbPEfKeUbt8FzS1HJ3ttd6M6dkNa8sk5jSwEYOj3/5qkgoruzf7ZeHuExG5IuTv6/fKs/rtDMpS7s59Sun130n+frWU55JZoiqVQKzKyGxjxs2pxCx7xA7yJ8AIArURSSb7LrWLHM8lQtLVUSSX5Kkoe06zX8MWHU1/ecuSLa5dUdna5Vn0mWWsiVFLZuVk/q7oRkuPVjZA0Z6TL1zXDf6ZYCjUpZZtP5lq1tDlcQEMILwDQSkRWlXu1nz/5YlTHV9f25SIDvhohYS+W4OHpBpjbD3RTXlHyia/bCudwoW0ivABAK1EdFefVfv7my1uOvHltX+5r4qsREvZiQWPYzBXBhAn7ANBKFCemqtIc3+BKRJJjPkalOUHFial+rqx18eVCAL4aIfFlzQhubOaKYMPICwAEm7y8BpsNSbnxM5TxzVw309MN5cZfJ2NDvq8rbNVa8tLR7rAXC9xhM1cEG0ZeAKAVKYw5T9kD5ssaFuPSbg2PUfaA+SqMOS9AlbUuwbh0tK9qRnDzx2augDcx8gIArUxhzHna1u1cJVVuUWTtAVWHdVVx9CAZJs+XR4Z7wbZ0tK9qRnCLi6r2aj/A1wgvANAKGaZQFXUeGugyWr1gWzpaYi8WuEpNLFa8uVJ7rOYG58uZZCjebFVqYnEAqgPqI7wAANDCMEICfwkNMbQkLVfp2RnOzWzrmH4Z7Vuclst+L2gxmPMCAEALVDdCMih2n5I7E1zgO1MshVqRka1eZqtLe7zZyjLJaHEYeQEAAGjlmrL55e+Hbqo32rd5b3dt3tu98WufYANMwJsILwAAAGA+FIICt40BAAAACAqEFwAAAABBgfACAAAAICgw5wUAAsRktympJF+RVeWqjopTcWKqjBA2kgQAwB3CCwAEgKUwR2m5sxRtLXO2VZrjlZu2RIWWKQGsDACAlovbxgDAzyyFOcrITpf5mOAiSWbrHmVkp8tSmBOgygAAaNkILwDgRya7TWm5sySXfax/OfbLbtZpuZky2W1+rw0AgJaO28YAwBfy8hpsTjpU4HKr2PFMMhRtLVXSqsdU1Hmob2oDACBIEV4AwI8iaw94tR8ABAOb3aT8kiSVV0UqLqpaqYnFCg0xAl0WghDhBQD8qDqsq1f7AUBLl1No0azcNJVZo51t8eZKLUnL1RRLYQArQzBizgsA+FFx9CBVhsXI3e8bDUmV4TEqjh7kz7IAwCdyCi1Kz85QmdXs0r7HalZ6doZyCi0BqgzBivACAH5kmEKV22+m4+vjj/3yZ27fmTJM7PcCILjZ7CbNyk375d821yVK6pYsycxNk81+/PIlgHuEFwDws8KY85Q9YL6sYTEu7dbwGGUPmK/CmPMCVBkAeE9+SdIvt4o1HE4MmVRqjVZ+SZJ/C0NQY84LAARAYcx52tbtXCVVblFk7QFVh3VVcfQgRlwAtBrlVZFe7QdIhBcACBjDFMpyyABarbioaq/2AyTCCwCckMluU1JJviKrylUdFafixFQZIYyQAEBjUhOLFW+u1B6ruYFteR37WsWbrUpNLA5AdQhWLWLOyxNPPKHk5GRFRETo7LPP1meffea2b1ZWlkwmk8sjIiLCj9UCaEsshTnKXJKs6S+OVXrOVZr+4lhlLkmWpTAn0KUBQIsWGmJoSVquJEdQOVbd88Vpuez3Ao8EPLy8/vrrmj17tubOnauvvvpKQ4YM0YQJE7Rv3z6355jNZpWXlzsfxcUkdgDeZynMUUZ2uszWMpd2s3WPMrLTCTAAcAJTLIVakZGtXmarS3u82aoVGdns8wKPBfy2sYcfflgzZszQ73//e0nS0qVL9fbbb+uFF17Q7bff3uA5JpNJPXr08GeZANoYk92mtNxZUgM3O5hkyJBJabmZ2pYyiVvIAKARUyyFmpSyTfklSSqvilRcVLVSE4sZcUGzBDS81NbW6ssvv9ScOXOcbSEhIRo3bpw+/vhjt+dVV1crKSlJdrtdw4cP1/3336/TTz+9wb41NTWqqalxPrdarQ32A9BG5eU12Jx0qEDRx424HMskQ9HWUiWteoxJ9wBwAqEhhsYkFwW6DLQCAb1t7LvvvpPNZlP37t1d2rt3766KiooGz0lJSdELL7ygf/3rX3r55Zdlt9s1atQolZU1/CFjwYIFio6Odj4SEhK8/n0AaH0iaw94tR8AADh5AZ/z4qmRI0dq6tSpGjp0qEaPHq2cnBzFxMTo6aefbrD/nDlzVFlZ6XyUlpb6uWIAwag6rKtX+wEAgJMX0NvGunXrptDQUO3du9elfe/evU2e09K+fXsNGzZMO3bsaPB4eHi4wsPDT7pWAG1LcfQgVYbFyFy7v8G9oQ1J1vAYFUcP8ndpAAC0WQEdeQkLC9OIESO0fv16Z5vdbtf69es1cuTIJl3DZrNpy5YtiouL81WZANogwxSq3H4zHV8ff+yXP3P7zpRhYrI+AAD+EvDbxmbPnq1nn31WL774ogoLC3X99dfr8OHDztXHpk6d6jKh/+6779a7776rXbt26auvvtLvfvc7FRcX67rrrgvUtwCgBTDZbUouytPALa8puShPJrvtpK9ZGHOesgfMlzUsxqXdGh6j7AHzVRhz3km/BgAAaLqAL5V8xRVXaP/+/brrrrtUUVGhoUOHKjc31zmJv6SkRCEhRzPWwYMHNWPGDFVUVKhLly4aMWKENm7cqAEDBgTqWwAQYJbCHKXlznJZHazSHK/ctCUqtEw5qWsXxpynbd3OVVLlFkXWHlB1WFcVRw9ixAUAgAAwGYbRphbZtlqtio6OVmVlpcxmc0BrmTcvoC8PtAp1G0kevx9L3bPsjBWNBxg3SyUDAAJr3pg8H158nu+uDY958vk84LeNAUBznWgjSUlKy830yi1kAAAg8AgvAIJWUkm+oq1lDa4GJh2zkWRJvl/rAgAAvkF4ARC0IqvKvdoPAAC0bIQXAEGrOqppS6Q3tR8AAGjZCC8AglZxYqoqzfENzHhxMGRSpTlBxYmpfq4MAAD4AuEFQNAyQkKVm7bE8fVxAabueW7aYhkhLGsMAEBrEPB9XgDghBpZzrhQpyh7wDyl7Xhc0bX7ne3W8G7K7TtThXtPkfa6Px8AAAQPwguAoMdGkgAAtA2EFwCtgmEKVVHnoYEuAwAA+BDhBYBfmew2JZXkK7KqXNVRcSpOTGVOCgAAaBLCCwC/sRTmKC13lqKtZc62SnO8ctOWqNAyJYCVAQCAYMBqYwD8wlKYo4zsdJmPCS6SZLbuUUZ2uiyFOQGqDAAABAvCCwCfM9ltSsudpfoLGksmGZKktNxMmew2v9cGAACCB+EFgM8lleQr2lrmZitJR4CJtpYqqSTfr3UBAIDgQngB4HORVeVe7QcAANomJuwD8I5GNpKsPrS3SZeo3rlXOuD+OgAAoG0jvACox9vLGRdHD1JlWIzMtfsbvHXMkGQNj1Fx9KBmvwYAAGj9CC8AXPhiOWPDFKrcfjOV8c1cGZJLgDF++TO370wZJvZ7AQAA7jHnBYCTL5czLow5T9kD5ssaFuPSbg2PUfaA+SqMOa/Z1wYAAG0DIy8AJJ14OWNDJqXlZmpbyqRm30JWGHOetnU7V0mVWxRZe0DVYV1VHD2IERcAANAkhBcAko4uZ+zOscsZFyWPafbrGKZQFXUe2uzzAQBA28VtYwAksZwxAABo+QgvACRJ1VFxXu0HAADgbYQXAJKk4sRUVZrjG5jx4mDIpEpzgooTU/1cGQAAgAPhBYAkyQgJVW7aEsfXxwWYuue5aYtPar8XAACAk0F4AeBUaJmi7IwVspp7ubRbzfHKzljR7H1eAAAAvIHVxoC2Ji+v0cOFOkXbhmbVX854b6i0t/FzAQAAfInwAqAeljMGAAAtEbeNAQAAAAgKhBcAAAAAQYHbxoAgZrLblFSSr8iqclVHxak4MZXVwAAAQKtFeAGClKUwR2m5sxRtLXO2VZrjlZu2hFXBAABAq8RtY0AQshTmKCM7XeZjgoskma17lJGdLkthToAqAwAA8B3CCxBkTHab0nJnqf5WkpJJhiQpLTdTJrvN77UBAAD4EuEFCDJJJfmKtpbVCy51TDIUbS1VUkm+X+sCAADwNcILEGQiq8q92g8AACBYMGEfaIny8tweqj60t0mXqN65Vzrg/joAAADBhvACBJni6EGqDIuRuXZ/g7eOGZKs4TEqjh7k79IAAPA7m92k/JIklVdFKi6qWqmJxQoNMQJdFnyE8AL4mLf3YjFMocrtN1MZ38yVIbkEmLp/qnP7zpRhYr8XAEDrllNo0azcNJVZo51t8eZKLUnL1RRLYQArg68QXgAf8tVeLIUx5yl7wHyl7Xhc0bX7ne3W8Bjl9p2pwpjzTqpuAABaupxCi9KzM3T8GMseq1np2RlakZFNgGmFCC+Aj9TtxaLj/lmt24slO2PFSQeYbd3OVVLlFkXWHlB1WFcVRw9ixAUA0OrZ7CbNyk375f+wrjdRGzLJJEOZuWmalLKNW8haGVYbA3zAX3uxGKZQFXUeqq2xF6io81CCCwCgTcgvSfrlVrGGNw4wZFKpNVr5JUn+LQw+R3gBfIC9WAAA8J3yqkiv9kPwILwAPsBeLAAA+E5cVLVX+yF4MOcFOBlu9mNhLxYAAHwnNbFY8eZK7bGaG7hB23GHQ7zZqtTE4gBUB19i5AX4hcluU3JRngZueU3JRXknNR+lbi8Wd1MEDUmV7MUCAECzhIYYWpKWK+noXNI6dc8Xp+UyWb8VYuQFkPeXNGYvFgAAfGuKpVArMrIb2OfFqsXe2OfFZpPy86XycikuTkpNlUL5/3agEV7Q5vlqSWP2YgEAwLemWAo1KWWb8kuSVF4VqbioaqUmFp/8iEtOjjRrllR29Jeaio+XliyRpjR/mwOcPMIL2rQTLWlsyKS03ExtS5kkI8Tz37awFwsAAL4VGmJoTHKR9y6YkyOlp0vGcQFozx5H+4oVBJgAIrwgqJjsNiWV5CuyqlzVUXEqTkxtVqioU7eksdvXO2ZJ46LkMc16jbq9WAAAQNPNyxvju2u7O2CzOUZcjg8ukqPNZJIyM6VJk7iFLEAILwgazZ6X4mZFMEmK3Le+Sa8d+dl6qaiJhQIAgJZt3ryG24uKXG8VO55hSKWl0rXXSsnJTb8uvIbVxhAU6ualmI8bJambl2IpzGnWdavDunq1HwAACGJVVd7tB68jvKDFO9G8FElKy81s1tLGLGkMAACcoqK82w9eR3hBi1c3L6X+FlQOx85L8VTdksbS8WuNsaQxAABtTmKiZDY33sdsdvRDQBBe4HXe3OxRkiKryr3a73h1Sxpbw2Jc2q3hMcoeMJ8ljQEAaCtCQqS0tMb7pKU5+iEgmLAPr/L2Zo+SVB0V59V+DWFJYwAAIEmyWKSMDCk3V7Jaj7abzY7gYrEErjYQXtoyby87fNKbPbpZFazYsKkyLEbm2v0N3jpmyDFKUrzbJhU1fI2mYEljAAAgSbJYZOt/mvK/6KDy78MVd0qNUs/4UaHt3N3E7iGbTcrPl8rLpbg4KTWVpZebqEWMeT3xxBNKTk5WRESEzj77bH322WeN9n/jjTd02mmnKSIiQoMGDdKaNWv8VKn/efsWrDqWwhxlLknW9BfHKj3nKk1/cawylyQ3e9UuX06qZ14KAADwp5xCi5Ifm62xa2/XVZ/forFrb1fyY7OVU+iFUZecHNmS+ihv7Dy9dtUq5Y2dJ1tSH8fmmCfJVmtT3uICvXbTRuUtLpCt1jufG1uSgIeX119/XbNnz9bcuXP11VdfaciQIZowYYL27dvXYP+NGzfqyiuv1LXXXqtNmzZp8uTJmjx5srZu3ernyn3P2wHj2Ot6e9lhX06ql5iXAgAA/COn0KL07AyVWV0n7u+xmpWenXFyASYnRzmXvaLkPR9qrPJ0lV7TWOUpec+HyrnslZMKMDm3faLkjns19pahuurxURp7y1Ald9yrnNs+aX69LZDJMBraQtR/zj77bJ155pl6/PHHJUl2u10JCQm66aabdPvtt9frf8UVV+jw4cNavXq1s+2cc87R0KFDtXTp0hO+ntVqVXR0tCorK2U+0WoSPtbYPkbH3oJ1bCCoe3bCW7DcMNltylySLLOboGHIJKs5Xotn7W74FjI3t3YN3Lde6YX3nvD1V1ju0NbYCzwr+hgmw8a8FAAAcFLmjclrsN1mNyl5SeYvwaX+JyWTDMWbrdo9a7FCQxr4CN3YhzubTTnd/6z0A0//cufI0TEEk+ySpBVd/6wpe5/y+BaynNs+Ufo/znJ/3b9+pimLzvHomv7kyefzgI681NbW6ssvv9S4ceOcbSEhIRo3bpw+/vjjBs/5+OOPXfpL0oQJE9z2D0a+vAXLVyMk/trssW5eytbYC1TUeSjBBQAAeE1+SZLKrNFqKLhIjk9mpdZo5ZckeXxtW16+Zh24q17AcFzX8TzzwB2y5Xn2GcxWa9OshxMbv+7DCa3mFrKATtj/7rvvZLPZ1L17d5f27t27a9u2bQ2eU1FR0WD/ioqKBvvX1NSopqbG+dx67KoRLVRdwHDn2IBRlDym4U5uRkgi961vUg2Rn62XiprUVdLRzR5POKmezR4BAECAzcsb02D7ln2xTTr/sc/OUl5Rcv3rNjLykv++XWVq+HUlR9AoVaLy5z+nMfl59Tu4uXb+k1tUZhva+HVtvZT/ZIHGZLrvFyxa/WpjCxYs0Pz58wNdRoPcvr9fK5dePPH50yeUS1e6Ozqm4eY8SWNPfHtX+pMXKH2Mm2u4k7NUSk93fH3s3Ygmk0ySol9dqrlTmn/LGAAAgC/l5Uk5Y0/c76YnB2jMmAENHBnj9pxy2zdSEwZVykf/VprX0LXd9N/5g1f7tXQBvW2sW7duCg0N1d69e13a9+7dqx49ejR4To8ePTzqP2fOHFVWVjofpaWl3inel+KauF9JU/sdKzVVio+XTG5uHDOZpIQERz9PTZkirVgh9erl2h4f72if0rx9XgAAAPzBlx+T4sakeLWfs3/fjl7t19IFNLyEhYVpxIgRWr/+6K1Mdrtd69ev18iRIxs8Z+TIkS79JWndunVu+4eHh8tsNrs8Wjxf/pcTGiotWXL0OsdfV5IWL27+WuNTpkhFRdIHH0ivvur4c/duggsAAGjxfPkxKXVMqOK7/uCcRH88k+xK6PqDUsd4dvHUGwYpPvTbxq8bukepN7SOW/cDvlTy7Nmz9eyzz+rFF19UYWGhrr/+eh0+fFi///3vJUlTp07VnDlznP1nzZql3NxcPfTQQ9q2bZvmzZunL774QjNnzgzUt+B9/ggYvhwhCQ2VxoyRrrzS8SebLgEAgCDhq49JoaHSkmc6SjLVCxqO5yYtfqajxx+bQsNCtWR2yTHXOf660uLZpQoNax2fxwK+VLIkPf744/rHP/6hiooKDR06VI8++qjOPvtsSdKYMWOUnJysrKwsZ/833nhDd9xxh4qKitS/f38tWrRIEydObNJrtaSlkk8oJ0eaNUsqO2byfkKCI7h4YySD3V0BAAAa5KuPSY6Pd4bKyo7+gjoh3tDiJaaT+niXc9snmvVwospsPY9eN3SPFs8ubdHLJEuefT5vEeHFn4IqvEgEDAAAgFbGVx/vbLU25T+5ReU7f1Bc345KvWFQUIy4EF4aEXThBQAAAGjFgmaTSgAAAABoKsILAAAAgKBAeAEAAAAQFAgvAAAAAIIC4QUAAABAUCC8AAAAAAgKhBcAAAAAQYHwAgAAACAoEF4AAAAABAXCCwAAAICgQHgBAAAAEBQILwAAAACCAuEFAAAAQFAgvAAAAAAICoQXAAAAAEGB8AIAAAAgKBBeAAAAAASFdoEuwN8Mw5AkWa3WAFcCAAAAoO5zed3n9Ma0ufBSVVUlSUpISAhwJQAAAADqVFVVKTo6utE+JqMpEacVsdvt+vbbbxUVFSWTyRSwOqxWqxISElRaWiqz2RywOtDy8N5AY3h/wB3eG3CH9wbcaSnvDcMwVFVVpZ49eyokpPFZLW1u5CUkJETx8fGBLsPJbDbzDwkaxHsDjeH9AXd4b8Ad3htwpyW8N0404lKHCfsAAAAAggLhBQAAAEBQILwESHh4uObOnavw8PBAl4IWhvcGGsP7A+7w3oA7vDfgTjC+N9rchH0AAAAAwYmRFwAAAABBgfACAAAAICgQXgAAAAAEBcKLDz3xxBNKTk5WRESEzj77bH322WeN9n/jjTd02mmnKSIiQoMGDdKaNWv8VCn8zZP3xrPPPqvU1FR16dJFXbp00bhx4074XkJw8/TfjjrLly+XyWTS5MmTfVsgAsbT98ahQ4d04403Ki4uTuHh4Tr11FP5f0sr5el7Y/HixUpJSVGHDh2UkJCgW265RUeOHPFTtfCXDRs26JJLLlHPnj1lMpm0cuXKE56Tl5en4cOHKzw8XP369VNWVpbP6/QE4cVHXn/9dc2ePVtz587VV199pSFDhmjChAnat29fg/03btyoK6+8Utdee602bdqkyZMna/Lkydq6daufK4evefreyMvL05VXXqkPPvhAH3/8sRISEnThhRdqz549fq4c/uDp+6NOUVGRbr31VqWmpvqpUvibp++N2tpajR8/XkVFRVqxYoW2b9+uZ599Vr169fJz5fA1T98br776qm6//XbNnTtXhYWFev755/X666/rb3/7m58rh68dPnxYQ4YM0RNPPNGk/rt379bFF1+ssWPHqqCgQJmZmbruuuu0du1aH1fqAQM+cdZZZxk33nij87nNZjN69uxpLFiwoMH+GRkZxsUXX+zSdvbZZxt/+tOffFon/M/T98bxfv75ZyMqKsp48cUXfVUiAqg574+ff/7ZGDVqlPHcc88Z06ZNMyZNmuSHSuFvnr43nnrqKaNPnz5GbW2tv0pEgHj63rjxxhuN888/36Vt9uzZxrnnnuvTOhFYkoy33nqr0T633Xabcfrpp7u0XXHFFcaECRN8WJlnGHnxgdraWn355ZcaN26csy0kJETjxo3Txx9/3OA5H3/8sUt/SZowYYLb/ghOzXlvHO+HH37QTz/9pFNOOcVXZSJAmvv+uPvuuxUbG6trr73WH2UiAJrz3li1apVGjhypG2+8Ud27d9fAgQN1//33y2az+ats+EFz3hujRo3Sl19+6by1bNeuXVqzZo0mTpzol5rRcgXD59F2gS6gNfruu+9ks9nUvXt3l/bu3btr27ZtDZ5TUVHRYP+Kigqf1Qn/a85743j/93//p549e9b7xwXBrznvjw8//FDPP/+8CgoK/FAhAqU5741du3bp/fff19VXX601a9Zox44duuGGG/TTTz9p7ty5/igbftCc98ZVV12l7777Tr/61a9kGIZ+/vln/fnPf+a2Mbj9PGq1WvXjjz+qQ4cOAarsKEZegCDywAMPaPny5XrrrbcUERER6HIQYFVVVbrmmmv07LPPqlu3boEuBy2M3W5XbGysnnnmGY0YMUJXXHGF/v73v2vp0qWBLg0BlpeXp/vvv19PPvmkvvrqK+Xk5Ojtt9/WPffcE+jSgBNi5MUHunXrptDQUO3du9elfe/everRo0eD5/To0cOj/ghOzXlv1HnwwQf1wAMP6L333tPgwYN9WSYCxNP3x86dO1VUVKRLLrnE2Wa32yVJ7dq10/bt29W3b1/fFg2/aM6/HXFxcWrfvr1CQ0OdbRaLRRUVFaqtrVVYWJhPa4Z/NOe9ceedd+qaa67RddddJ0kaNGiQDh8+rD/+8Y/6+9//rpAQfrfdVrn7PGo2m1vEqIvEyItPhIWFacSIEVq/fr2zzW63a/369Ro5cmSD54wcOdKlvyStW7fObX8Ep+a8NyRp0aJFuueee5Sbm6szzjjDH6UiADx9f5x22mnasmWLCgoKnI9LL73UuUpMQkKCP8uHDzXn345zzz1XO3bscAZaSfrvf/+ruLg4gksr0pz3xg8//FAvoNSFXMMwfFcsWryg+Dwa6BUDWqvly5cb4eHhRlZWlvHNN98Yf/zjH43OnTsbFRUVhmEYxjXXXGPcfvvtzv4fffSR0a5dO+PBBx80CgsLjblz5xrt27c3tmzZEqhvAT7i6XvjgQceMMLCwowVK1YY5eXlzkdVVVWgvgX4kKfvj+Ox2ljr5el7o6SkxIiKijJmzpxpbN++3Vi9erURGxtr3HvvvYH6FuAjnr435s6da0RFRRmvvfaasWvXLuPdd981+vbta2RkZATqW4CPVFVVGZs2bTI2bdpkSDIefvhhY9OmTUZxcbFhGIZx++23G9dcc42z/65du4yOHTsaf/3rX43CwkLjiSeeMEJDQ43c3NxAfQv1EF586LHHHjMSExONsLAw46yzzjI++eQT57HRo0cb06ZNc+mfnZ1tnHrqqUZYWJhx+umnG2+//bafK4a/ePLeSEpKMiTVe8ydO9f/hcMvPP2341iEl9bN0/fGxo0bjbPPPtsIDw83+vTpY9x3333Gzz//7Oeq4Q+evDd++uknY968eUbfvn2NiIgIIyEhwbjhhhuMgwcP+r9w+NQHH3zQ4GeIuvfDtGnTjNGjR9c7Z+jQoUZYWJjRp08fY9myZX6vuzEmw2B8EAAAAEDLx5wXAAAAAEGB8AIAAAAgKBBeAAAAAAQFwgsAAACAoEB4AQAAABAUCC8AAAAAggLhBQAAAEBQILwAAAAACAqEFwDACU2fPl2TJ092e3zevHkaOnSo3+oBALRNhBcAAAAAQYHwAgAICrW1tYEuoUmCpU4ACEaEFwCAJGnFihUaNGiQOnTooK5du2rcuHE6fPhwg30///xzxcTEaOHChW6v99xzz8lisSgiIkKnnXaannzySZfj//d//6dTTz1VHTt2VJ8+fXTnnXfqp59+ch6vuxXtueeeU+/evRURESFJMplMeu655/Sb3/xGHTt2VP/+/bVq1apGv7cnn3xS/fv3V0REhLp376709HTnMbvdrkWLFqlfv34KDw9XYmKi7rvvPufxLVu26Pzzz3f+XP74xz+qurraebzulrr77rtPPXv2VEpKiiSptLRUGRkZ6ty5s0455RRNmjRJRUVFjdYJAGhcu0AXAAAIvPLycl155ZVatGiRfvOb36iqqkr5+fkyDKNe3/fff19TpkzRokWL9Mc//rHB673yyiu666679Pjjj2vYsGHatGmTZsyYoU6dOmnatGmSpKioKGVlZalnz57asmWLZsyYoaioKN12223O6+zYsUNvvvmmcnJyFBoa6myfP3++Fi1apH/84x967LHHdPXVV6u4uFinnHJKvVq++OIL3XzzzXrppZc0atQoff/998rPz3cenzNnjp599lk98sgj+tWvfqXy8nJt27ZNknT48GFNmDBBI0eO1Oeff659+/bpuuuu08yZM5WVleW8xvr162U2m7Vu3TpJ0k8//eQ8Lz8/X+3atdO9996rtLQ0bd68WWFhYR787QAAnAwAQJv35ZdfGpKMoqKiBo9PmzbNmDRpkpGTk2NERkYay5cvdzk+d+5cY8iQIc7nffv2NV599VWXPvfcc48xcuRItzX84x//MEaMGOFyzfbt2xv79u1z6SfJuOOOO5zPq6urDUnGO++80+B133zzTcNsNhtWq7XeMavVaoSHhxvPPvtsg+c+88wzRpcuXYzq6mpn29tvv22EhIQYFRUVhmE4fjbdu3c3ampqnH1eeuklIyUlxbDb7c62mpoao0OHDsbatWvd/gwAAI1j5AUAoCFDhuiCCy7QoEGDNGHCBF144YVKT09Xly5dnH0+/fRTrV69WitWrGh05bHDhw9r586duvbaazVjxgxn+88//6zo6Gjn89dff12PPvqodu7cqerqav38888ym80u10pKSlJMTEy91xg8eLDz606dOslsNmvfvn0N1jN+/HglJSWpT58+SktLU1pamvOWs8LCQtXU1OiCCy5o8NzCwkINGTJEnTp1crade+65stvt2r59u7p37y5JGjRokMtoyn/+8x/t2LFDUVFRLtc7cuSIdu7c2eBrAQBOjDkvAACFhoZq3bp1eueddzRgwAA99thjSklJ0e7du519+vbtq9NOO00vvPCCy9yU49XNB3n22WdVUFDgfGzdulWffPKJJOnjjz/W1VdfrYkTJ2r16tXatGmT/v73v9eb7H5saDhW+/btXZ6bTCbZ7fYG+0ZFRemrr77Sa6+9pri4ON11110aMmSIDh06pA4dOpz4h9MEx9dZXV2tESNGuHz/BQUF+u9//6urrrrKK68JAG0R4QUAIMkRAM4991zNnz9fmzZtUlhYmN566y3n8W7duun999/Xjh07lJGR4TbAdO/eXT179tSuXbvUr18/l0fv3r0lSRs3blRSUpL+/ve/64wzzlD//v1VXFzss++tXbt2GjdunBYtWqTNmzerqKhI77//vvr3768OHTpo/fr1DZ5nsVj0n//8x2Xhgo8++kghISHOifkNGT58uP73v/8pNja23s/g2NEnAIBnCC8AAH366ae6//779cUXX6ikpEQ5OTnav3+/LBaLS7/Y2Fi9//772rZtm6688kr9/PPPDV5v/vz5WrBggR599FH997//1ZYtW7Rs2TI9/PDDkqT+/furpKREy5cv186dO/Xoo4+6BCVvWr16tR599FEVFBSouLhY//znP2W325WSkqKIiAj93//9n2677Tb985//1M6dO/XJJ5/o+eeflyRdffXVioiI0LRp07R161Z98MEHuummm3TNNdc4bxlryNVXX61u3bpp0qRJys/P1+7du5WXl6ebb75ZZWVlPvk+AaAtILwAAGQ2m7VhwwZNnDhRp556qu644w499NBDuuiii+r17dGjh95//31t2bJFV199tWw2W70+1113nZ577jktW7ZMgwYN0ujRo5WVleUcebn00kt1yy23aObMmRo6dKg2btyoO++80yffW+fOnZWTk6Pzzz9fFotFS5cu1WuvvabTTz9dknTnnXfqL3/5i+666y5ZLBZdccUVzvkzHTt21Nq1a/X999/rzDPPVHp6ui644AI9/vjjjb5mx44dtWHDBiUmJmrKlCmyWCy69tprdeTIkXrzegAATWcyjAbWwQQAAACAFoaRFwAAAABBgfACAAAAICgQXgAAAAAEBcILAAAAgKBAeAEAAAAQFAgvAAAAAIIC4QUAAABAUCC8AAAAAAgKhBcAAAAAQYHwAgAAACAoEF4AAAAABAXCCwAAAICg8P+51Zl3AICisgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "compare_train_test(y_pred_train_xgb, y_train, y_pred_xgb, y_test,\n",
        "                   xlabel=\"XGboost score\", title=\"XGboost\",\n",
        "                   weights_train=weights_train.values, weights_test=weights_test.values)\n",
        "plt.savefig(\"Score_BDT_XGBoost_Hist.pdf\")\n",
        "plt.show()\n",
        "compare_train_test(y_pred_train_gbm, y_train, y_pred_gbm, y_test,\n",
        "                   xlabel=\"LightGBM score\", title=\"LightGBM\",\n",
        "                   weights_train=weights_train.values, weights_test=weights_test.values)\n",
        "plt.savefig(\"Score_BDT_LightGBM.pdf\")\n",
        "plt.show()\n",
        "compare_train_test(y_pred_train_skgb, y_train, y_pred_skgb, y_test,\n",
        "                   xlabel=\"sklearn score\", title=\"sklearn\",\n",
        "                   weights_train=weights_train.values, weights_test=weights_test.values)\n",
        "plt.savefig(\"Score_BDT_sklearn.pdf\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yPv67MTLyd0s"
      },
      "source": [
        "## Plot without renormalising"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "visTZB8eyd0t",
        "outputId": "8616ea71-72e1-4014-ffc2-0fdd0225fef8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 960x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "compare_train_test(y_pred_train_xgb, y_train, y_pred_xgb, y_test,\n",
        "                   xlabel=\"XGboost score\", ylabel=\"Expected number of events\", title=\"XGboost\",\n",
        "                   weights_train=weights_train.values, weights_test=weights_test.values,\n",
        "                   density=False)\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zxGjEcej8PIi"
      },
      "source": [
        "## ROC curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "tUKIWu2orlIg",
        "outputId": "de249eef-a4d4-4c87-fadf-a1c5d31d8fde",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 960x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "lw = 2\n",
        "\n",
        "fpr_xgb,tpr_xgb,_ = roc_curve(y_true=y_test, y_score=y_pred_xgb,sample_weight=weights_test.values)\n",
        "fpr_gbm,tpr_gbm,_ = roc_curve(y_true=y_test, y_score=y_pred_gbm,sample_weight=weights_test.values)\n",
        "fpr_skgb,tpr_skgb,_ = roc_curve(y_true=y_test, y_score=y_pred_skgb,sample_weight=weights_test.values)\n",
        "\n",
        "\n",
        "plt.plot(fpr_xgb, tpr_xgb, color='darkgreen',lw=lw, label='XGBoost (AUC  = {:.3f})'.format(auc_test_xgb))\n",
        "plt.plot(fpr_skgb, tpr_skgb, color='darkblue',lw=lw, label='sklearn (AUC  = {:.3f})'.format(auc_test_skgb))\n",
        "plt.plot(fpr_gbm, tpr_gbm, color='darkorange',lw=lw, label='LightGBM (AUC  = {:.3f})'.format(auc_test_gbm))\n",
        "\n",
        "\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('Background Efficiency')\n",
        "plt.ylabel('Signal Efficiency')\n",
        "plt.title('ROC curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.savefig(\"ROC_comparing.pdf\")\n",
        "plt.show() # blue line = random classification -> maximize true positive rate while miniize false positive rate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ziQzdapn8Tnk"
      },
      "source": [
        "## Significance curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "CGF3k0KJrlIi",
        "outputId": "f7b25d86-6cfa-48a3-a68c-e93d24a2d1ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 960x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "x = np.linspace(0, 1, num=len(vamsasimov_xgb))\n",
        "vmsasimovs = np.column_stack((vamsasimov_xgb, vamsasimov_skgb, vamsasimov_gbm))\n",
        "labels = [\n",
        "    'XGBoost (Z = {:.2f})'.format(significance_xgb),\n",
        "    'sklearn (Z = {:.2f})'.format(significance_skgb),\n",
        "    'LightGBM (Z = {:.2f})'.format(significance_gbm)\n",
        "]\n",
        "\n",
        "plt.plot(x, vmsasimovs,label=labels)\n",
        "\n",
        "\n",
        "plt.title(\"BDT Significance\")\n",
        "plt.xlabel(\"Threshold\")\n",
        "plt.ylabel(\"Significance\")\n",
        "plt.legend()\n",
        "plt.savefig(\"Significance_comparing.pdf\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z1mXF5iTNZMW"
      },
      "source": [
        "# More advanced"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lhsyS1NuNZMW"
      },
      "source": [
        "# Hyper Parameter Optimisation\n",
        "Can be done by hand or with [random search](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html) or [grid search](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) ."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "BapVDsX1NZMW",
        "outputId": "fbae6732-61e9-40b9-c834-d27496ce72a4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "auc test: 0.8670325892826282\n",
            "significance test: 1.623242142166869\n"
          ]
        }
      ],
      "source": [
        "#FIXME : weights are not passed to scoring function\n",
        "#recompute reference\n",
        "# creates the classifier\n",
        "xgb = XGBClassifier()\n",
        "# does the real work\n",
        "xgb.fit(X_train, y_train.values, sample_weight=weights_train) # note that XGB 1.3.X and higher requires positive weight\n",
        "y_pred_xgb=xgb.predict_proba(X_test)[:,1]\n",
        "auc_test_xgb = roc_auc_score(y_true=y_test, y_score=y_pred_xgb,sample_weight=weights_test)\n",
        "print(\"auc test:\",auc_test_xgb)\n",
        "amsasimov_xgb=significance_score(y_true=y_test, y_score=y_pred_xgb, sample_weight=weights_test)\n",
        "print(\"significance test:\",amsasimov_xgb)\n",
        "\n",
        "\n",
        "#RandomSearchCV for advanced HPO\n",
        "import scipy.stats as stats\n",
        "if fullrun or False:\n",
        "    np.random.seed(31416)\n",
        "    from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "    # specify parameters and distributions to sample from\n",
        "    param_dist_XGB = {'max_depth': stats.randint(3, 9), # default 6\n",
        "                      'n_estimators': stats.randint(30, 300), #default 100\n",
        "                      'learning_rate': stats.uniform(0.1, 0.5)} #def 0.3\n",
        "\n",
        "    # default CV is 5 fold, reduce to 2 for speed concern\n",
        "    gsearch = RandomizedSearchCV(estimator = XGBClassifier(),\n",
        "                        param_distributions = param_dist_XGB,\n",
        "                        scoring='roc_auc',n_iter=10,cv=2)\n",
        "#FIXME                        scoring=fsignificance_score,n_iter=10,cv=2)\n",
        "# it looks like the weights are not passed to scoringRandomizedSearchCV\n",
        "    gsearch.fit(X_train,y_train, sample_weight=weights_train)\n",
        "\n",
        "    print (\"Best parameters : \",gsearch.best_params_)\n",
        "    print (\"Best score (on train dataset CV) : \",gsearch.best_score_)\n",
        "\n",
        "\n",
        "    y_pred_gs = gsearch.predict_proba(X_test)[:,1]\n",
        "    print(\"... corresponding score on test dataset AUC: \",roc_auc_score(y_true=y_test, y_score=y_pred_gs, sample_weight=weights_test))\n",
        "    print(\"... corresponding score on test dataset signif: \",significance_score(y_true=y_test, y_score=y_pred_gs, sample_weight=weights_test))\n",
        "\n",
        "\n",
        "    # recompute reference\n",
        "    # creates the classifier\n",
        "    print(gsearch.best_params_)\n",
        "    xgb = XGBClassifier(**gsearch.best_params_)\n",
        "    xgb.fit(X_train, y_train.values, sample_weight=weights_train)\n",
        "    y_pred_xgb=xgb.predict_proba(X_test)[:,1]\n",
        "    auc_test_xgb = roc_auc_score(y_true=y_test, y_score=y_pred_xgb,sample_weight=weights_test)\n",
        "    print(\"auc test:\",auc_test_xgb)\n",
        "    amsasimov_xgb=significance_score(y_true=y_test, y_score=y_pred_xgb, sample_weight=weights_test)\n",
        "    print(\"significance test:\",amsasimov_xgb)\n",
        "\n",
        "\n",
        "\n",
        "    dfsearch=pd.DataFrame.from_dict(gsearch.cv_results_)\n",
        "    display(dfsearch)\n",
        "\n",
        "    dfsearch.plot.scatter(\"param_n_estimators\",\"mean_test_score\")\n",
        "    dfsearch.plot.scatter(\"param_max_depth\",\"mean_test_score\")\n",
        "    dfsearch.plot.scatter(\"param_learning_rate\",\"mean_test_score\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ZtnPtx1NZMW"
      },
      "source": [
        "# Learning Curve\n",
        "This could be done with sklearn  [learning_curve](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.learning_curve.html).\n",
        "However (last time I checked) : it does not handle weights, it does not allow to control testing dataset size\n",
        "\n",
        "Exercise left to the reader : watch impact of complexity of the model on the learning curve\n",
        "\n",
        "Exercise left to the reader : look at significance rather than auc\n",
        "\n",
        "Exercise left to the reader : look separately at performance versus number of signal event and number of background events.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "mcuHrmwTNZMX"
      },
      "outputs": [],
      "source": [
        "#example with sklearn (but without weights and with no fixed test dataset)\n",
        "#from sklearn.model_selection import learning_curve\n",
        "#train_sizes,train_scores,test_scores=learning_curve(\n",
        "#     XGBClassifier(),\n",
        "#     X_train,y_train,\n",
        "#     train_sizes=[0.01,0.05,0.1,0.2,0.5,0.75,1],\n",
        "#     scoring='roc_auc',cv=5)\n",
        "\n",
        "if fullrun or False :\n",
        "    train_sizes=[0.01,0.05,0.1,0.2,0.5,0.75,1]\n",
        "    ntrains=[]\n",
        "    test_aucs=[]\n",
        "    train_aucs=[]\n",
        "    times=[]\n",
        "\n",
        "    #xgb = XGBClassifier()\n",
        "    xgb = XGBClassifier(n_estimators=10) # simpler GBDT, for illustration\n",
        "\n",
        "    for train_size in train_sizes:\n",
        "      ntrain=int(len(X_train)*train_size)\n",
        "      print(\"training with \",ntrain,\" events\")\n",
        "      ntrains+=[ntrain]\n",
        "      starting_time = time.time()\n",
        "\n",
        "      # train using the first ntrain event of the training dataset\n",
        "      xgb.fit(X_train[:ntrain,], y_train[:ntrain], sample_weight=weights_train[:ntrain])\n",
        "      training_time = time.time( ) - starting_time\n",
        "      times+=[training_time]\n",
        "\n",
        "      # score on test dataset (always the same)\n",
        "      y_pred_xgb=xgb.predict_proba(X_test)[:,1]\n",
        "      auc_test_xgb = roc_auc_score(y_true=y_test, y_score=y_pred_xgb,sample_weight=weights_test)\n",
        "      test_aucs+=[auc_test_xgb]\n",
        "\n",
        "      # score on the train dataset\n",
        "      y_train_xgb=xgb.predict_proba(X_train[:ntrain])[:,1]\n",
        "      auc_train_xgb = roc_auc_score(y_true=y_train[:ntrain], y_score=y_train_xgb,sample_weight=weights_train[:ntrain])\n",
        "      train_aucs+=[auc_train_xgb]\n",
        "\n",
        "    dflearning=pd.DataFrame({\"Ntraining\":ntrains,\n",
        "                             \"test_auc\":test_aucs,\n",
        "                             \"train_auc\":train_aucs,\n",
        "                             \"time\":times})\n",
        "    display(dflearning)\n",
        "\n",
        "    dflearning.plot.scatter(\"Ntraining\",\"test_auc\")\n",
        "    # focus on the last point\n",
        "    dflearning[4:].plot.scatter(\"Ntraining\",\"test_auc\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cJI9aimTyd0v"
      },
      "source": [
        "# Feature importance\n",
        "Feature importance allows to display the importance of each feature without rerunnning the training. It is obtained from internal algorithm quantities, like number of time a feature is used to definea leaf. Magnitude is arbitrary. It can be used as a not very reliable indication of which feature is the most discriminant."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "QN2h9y5JrlIm",
        "outputId": "a84a93b3-dcc0-4b0b-b70a-5d4823a59016",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 960x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 960x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.bar(data.columns.values, xgb.feature_importances_)\n",
        "plt.xticks(rotation=90)\n",
        "plt.title(\"Feature importances XGBoost Hist\")\n",
        "#plt.savefig(new_dir + \"/VarImp_BDT_XGBoost_Hist.pdf\",bbox_inches='tight')\n",
        "plt.show()\n",
        "plt.bar(data.columns.values, gbm.feature_importances_)\n",
        "plt.xticks(rotation=90)\n",
        "plt.title(\"Feature importances LightGBM\")\n",
        "#plt.savefig(new_dir + \"/VarImp_BDT_LightGBM.pdf\",bbox_inches='tight')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i7WxH3bDrlIp"
      },
      "source": [
        "# Permutation importance"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "55ZOBmxxyd0w"
      },
      "source": [
        "A better way to show the importance of each feature is Permutation Importance, where each feature in turn is replaced by an instance of an other event (effectively switching it off by randomising).\n",
        "In particular it allows to :\n",
        "   * display directly the loss in whatever criteria (ROC auc, asimov significance) when the feature is switched off\n",
        "   * display the feature importance for a specific subset (for example the most signal like)\n",
        "   * it can even display which feature has the larges impact on systematics\n",
        "\n",
        "\n",
        "However, report can be misleading in case of highly correlated variables.\n",
        "\n",
        "Exercise left to the reader : define a custom metric ( check sklearn.metrics.make_scorer ) for significance\n",
        "   \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "oVCKajODNZMX"
      },
      "outputs": [],
      "source": [
        "if fullrun or False:\n",
        "    xgb = XGBClassifier()\n",
        "    xgb.fit(X_train, y_train.values, sample_weight=weights_train) # note that XGB 1.3.X and higher requires positive weight\n",
        "\n",
        "    from sklearn.inspection import permutation_importance\n",
        "    #WARNING interesting feature in permutation_importance, need to ravel label and test vectors\n",
        "    r = permutation_importance(xgb, X_test.copy(), y_test.ravel(),sample_weight=weights_test.ravel(),\n",
        "                           scoring='roc_auc',n_repeats=1,n_jobs=-1,\n",
        "                            random_state=0)\n",
        "    plt.bar(data.columns,r.importances.mean(axis=1).T,)\n",
        "\n",
        "    plt.xlabel('features')\n",
        "    plt.ylabel('impact on auc')\n",
        "    plt.title('Permutation Importance XGBoost')\n",
        "\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BiGDmMS-yd0x"
      },
      "source": [
        "##  Model serialisation\n",
        "It is useful to be able to save a model in order to apply it without retraining. There are many ways to do it. One is to save the whole python object with joblib (beware this is not safe if the software evolves). Another is to used dedicated serialisation like the one proposed by xgboost.\n",
        "\n",
        "Note that the full model includes StandardScorer which is not serialised here.\n",
        "\n",
        "Exercise left to the reader. Use Pipeline to define the full model and serialise it.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "Nlj3NPpkyd0y"
      },
      "outputs": [],
      "source": [
        "#WARNING : StandardScaler has not been saved, it should be for the complete model to be saved\n",
        "if fullrun or False:\n",
        "    print( \"will print xgb performance directly after fit, after serialising with xgb api, after serialising with joblib. Should be identical\")\n",
        "    import joblib\n",
        "    myxgb = XGBClassifier()\n",
        "    myxgb.fit(X_train, y_train, sample_weight=weights_train)\n",
        "\n",
        "    auc_test_xgb = roc_auc_score(y_true=y_test, y_score=myxgb.predict_proba(X_test)[:,1],sample_weight=weights_test)\n",
        "    print (\"myxgb score\",auc_test_xgb)\n",
        "\n",
        "    # save model with xgb api\n",
        "    myxgb.save_model(\"XGBoost.json\")\n",
        "\n",
        "    # save python object\n",
        "    joblib.dump(myxgb, \"myxgb.dat\")\n",
        "\n",
        "\n",
        "    del myxgb # delete xgb object, just to be sure\n",
        "\n",
        "    # reload model from XGB API\n",
        "    myxgb_reloaded_from_model =XGBClassifier()\n",
        "    myxgb_reloaded_from_model.load_model(\"XGBoost.json\")\n",
        "    print (\"myxgb reloaded from model\",\n",
        "           roc_auc_score(y_true=y_test,\n",
        "                         y_score=myxgb_reloaded_from_model.predict_proba(X_test)[:,1],sample_weight=weights_test)\n",
        "          )\n",
        "\n",
        "\n",
        "    # reload object from joblib\n",
        "    myxgb_reloaded_from_joblib=joblib.load(\"myxgb.dat\")\n",
        "    print (\"myxgb reloaded from object\",\n",
        "           roc_auc_score(y_true=y_test,\n",
        "                         y_score=myxgb_reloaded_from_joblib.predict_proba(X_test)[:,1],sample_weight=weights_test)\n",
        "          )\n",
        "    # dump json file\n",
        "    #!python -m json.tool XGBoost.json\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    }
  ],
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