HEPML_HandsOn_BDT_addendum.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"HEPML_HandsOn_BDT_LearningCurve.ipynb","provenance":[{"file_id":"https://github.com/dhrou/HEPMLtutorials/blob/master/HEPML_HandsOn_BDT.ipynb","timestamp":1621949617714}],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"cells":[{"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","metadata":{"id":"QdmaHjz-xCm-","executionInfo":{"status":"ok","timestamp":1622185218745,"user_tz":-120,"elapsed":868,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}}},"source":["COLAB=True #if running on https://colab.research.google.com/notebooks/welcome.ipynb\n","#COLAB=False #if running on local anaconda installation https://docs.anaconda.com/anaconda/install/"],"execution_count":1,"outputs":[]},{"cell_type":"code","metadata":{"id":"xy-_72FJKwp6","executionInfo":{"status":"ok","timestamp":1622185218746,"user_tz":-120,"elapsed":9,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}}},"source":["import os\n","import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","#from IPython import display USEFUL ???\n","from IPython.display import display, HTML\n","%matplotlib inline\n","import time\n","pd.set_option('display.max_columns', 100) # to see more columns of df.head()\n","np.random.seed(31415) # set the np random seed for the reproducibility\n","#from IPython.core.interactiveshell import InteractiveShell\n","#InteractiveShell.ast_node_interactivity = \"all\" # instead of \"last_expr\", allow all cell output to appear \n"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oF367-yfKwp9","executionInfo":{"status":"ok","timestamp":1622185244462,"user_tz":-120,"elapsed":25724,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"f4bde33e-3327-4277-a858-dc98af4f8b63"},"source":["# Fix environment if necessary\n","if COLAB:\n","    !pip install xgboost --upgrade #  Colab has 0.9.0, not good enough\n","    pass\n","else:    \n","    # install xgboost and lighgbm, two popular Boosted Decision Tree packages\n","    # normally it is just a matter of a few pip install but there might be some glitches\n","    # note : I have better experience with pip install than conda install. \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 1.0.2, version above 1 is recommended.  \n","import lightgbm\n","print (lightgbm.__version__) # Tested with 2.3.1\n","\n"],"execution_count":3,"outputs":[{"output_type":"stream","text":["Collecting xgboost\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/bb/35/169eec194bf1f9ef52ed670f5032ef2abaf6ed285cfadcb4b6026b800fc9/xgboost-1.4.2-py3-none-manylinux2010_x86_64.whl (166.7MB)\n","\u001b[K     |████████████████████████████████| 166.7MB 67kB/s \n","\u001b[?25hRequirement already satisfied, skipping upgrade: scipy in /usr/local/lib/python3.7/dist-packages (from xgboost) (1.4.1)\n","Requirement already satisfied, skipping upgrade: numpy in /usr/local/lib/python3.7/dist-packages (from xgboost) (1.19.5)\n","Installing collected packages: xgboost\n","  Found existing installation: xgboost 0.90\n","    Uninstalling xgboost-0.90:\n","      Successfully uninstalled xgboost-0.90\n","Successfully installed xgboost-1.4.2\n","1.4.2\n","2.2.3\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"IsChk6u-r7fk","scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1622185268230,"user_tz":-120,"elapsed":23773,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"9abe38fb-fe73-4da4-9e41-5909c41d5ebb"},"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 pydrive.auth import GoogleAuth\n","    from pydrive.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)"],"execution_count":4,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: PyDrive in /usr/local/lib/python3.7/dist-packages (1.3.1)\n","Requirement already satisfied: PyYAML>=3.0 in /usr/local/lib/python3.7/dist-packages (from PyDrive) (3.13)\n","Requirement already satisfied: oauth2client>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from PyDrive) (4.1.3)\n","Requirement already satisfied: google-api-python-client>=1.2 in /usr/local/lib/python3.7/dist-packages (from PyDrive) (1.12.8)\n","Requirement already satisfied: six>=1.6.1 in /usr/local/lib/python3.7/dist-packages (from oauth2client>=4.0.0->PyDrive) (1.15.0)\n","Requirement already satisfied: httplib2>=0.9.1 in /usr/local/lib/python3.7/dist-packages (from oauth2client>=4.0.0->PyDrive) (0.17.4)\n","Requirement already satisfied: pyasn1>=0.1.7 in /usr/local/lib/python3.7/dist-packages (from oauth2client>=4.0.0->PyDrive) (0.4.8)\n","Requirement already satisfied: pyasn1-modules>=0.0.5 in /usr/local/lib/python3.7/dist-packages (from oauth2client>=4.0.0->PyDrive) (0.2.8)\n","Requirement already satisfied: rsa>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from oauth2client>=4.0.0->PyDrive) (4.7.2)\n","Requirement already satisfied: google-api-core<2dev,>=1.21.0 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client>=1.2->PyDrive) (1.26.3)\n","Requirement already satisfied: uritemplate<4dev,>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client>=1.2->PyDrive) (3.0.1)\n","Requirement already satisfied: google-auth-httplib2>=0.0.3 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client>=1.2->PyDrive) (0.0.4)\n","Requirement already satisfied: google-auth>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client>=1.2->PyDrive) (1.30.0)\n","Requirement already satisfied: requests<3.0.0dev,>=2.18.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.21.0->google-api-python-client>=1.2->PyDrive) (2.23.0)\n","Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.21.0->google-api-python-client>=1.2->PyDrive) (2018.9)\n","Requirement already satisfied: googleapis-common-protos<2.0dev,>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.21.0->google-api-python-client>=1.2->PyDrive) (1.53.0)\n","Requirement already satisfied: packaging>=14.3 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.21.0->google-api-python-client>=1.2->PyDrive) (20.9)\n","Requirement already satisfied: setuptools>=40.3.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.21.0->google-api-python-client>=1.2->PyDrive) (56.1.0)\n","Requirement already satisfied: protobuf>=3.12.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.21.0->google-api-python-client>=1.2->PyDrive) (3.12.4)\n","Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth>=1.16.0->google-api-python-client>=1.2->PyDrive) (4.2.2)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<2dev,>=1.21.0->google-api-python-client>=1.2->PyDrive) (1.24.3)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<2dev,>=1.21.0->google-api-python-client>=1.2->PyDrive) (2.10)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<2dev,>=1.21.0->google-api-python-client>=1.2->PyDrive) (3.0.4)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<2dev,>=1.21.0->google-api-python-client>=1.2->PyDrive) (2020.12.5)\n","Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=14.3->google-api-core<2dev,>=1.21.0->google-api-python-client>=1.2->PyDrive) (2.4.7)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"g14yIjbPKwqC"},"source":["# Load events"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kl-4W4Ifs8EV","executionInfo":{"status":"ok","timestamp":1622185289802,"user_tz":-120,"elapsed":8087,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"3c0a95ff-3612-4514-cec0-f889e371d668"},"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=\"/Users/rousseau/Google\\ Drive/GD_openData/dataWW_ATLAS_openData13TeV_filtered/\"\n","\n","    !ls -lrt {datapath} # what is in the data directory\n","    datapath=os.path.abspath(datapath).replace(\"\\ \", \" \")  # try to normalise the path (annoyance with the space)\n","    print (\"Will take data from : \",datapath)\n","\n","filename=os.path.join(datapath,\"dataWW_d1_600k.csv.gz\")\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"],"execution_count":5,"outputs":[{"output_type":"stream","text":["total 35076\n","drwxr-xr-x 1 root root     4096 May  6 13:44 sample_data\n","-rw-r--r-- 1 root root      720 May 28 07:01 adc.json\n","-rw-r--r-- 1 root root 35905611 May 28 07:01 dataWW_d1_600k.csv.gz\n","now : 2021-05-28 07:01:33.296306\n","File loaded with  600000  events \n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Uf9Gt8g8KwqF"},"source":["At this point, it should tell you \"File Loaded with XXX 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","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sx1_PB22KwqG","executionInfo":{"status":"ok","timestamp":1622185303549,"user_tz":-120,"elapsed":242,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"96aec4c8-bad3-4a27-9833-aca59df63d75"},"source":["#dump list of feature\n","dfall.columns"],"execution_count":6,"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":{"tags":[]},"execution_count":6}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":411},"id":"j9l7wkkorlHe","executionInfo":{"status":"ok","timestamp":1622185303857,"user_tz":-120,"elapsed":7,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"c30fa0ec-1a93-414d-b3dc-ab1dcf659a67"},"source":["#examine first few events\n","display(dfall.head())\n","display(dfall.head())\n","\n"],"execution_count":7,"outputs":[{"output_type":"display_data","data":{"text/html":["<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>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>0.000002</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>0.000288</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>0.000029</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","      <td>113360.0</td>\n","      <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>0.000029</td>\n","      <td>284500</td>\n","      <td>345324</td>\n","    </tr>\n","    <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","      <td>62.029</td>\n","      <td>21.453</td>\n","      <td>-0.51082</td>\n","      <td>-0.38177</td>\n","      <td>0.236200</td>\n","      <td>1.40820</td>\n","      <td>70300.0</td>\n","      <td>23036.0</td>\n","      <td>-1</td>\n","      <td>1</td>\n","      <td>11</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>0.000002</td>\n","      <td>284500</td>\n","      <td>345323</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"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  0.000002     284500   \n","1   -0.69746    -2.2147    -1.9152  367670.0  45733.0  0.000288     284500   \n","2   -7.00000    -7.0000    -7.0000      -7.0     -7.0  0.000029     284500   \n","3   -7.00000    -7.0000    -7.0000      -7.0     -7.0  0.000029     284500   \n","4   -7.00000    -7.0000    -7.0000      -7.0     -7.0  0.000002     284500   \n","\n","   channelNumber  \n","0         345323  \n","1         363492  \n","2         345324  \n","3         345324  \n","4         345323  "]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/html":["<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>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>0.000002</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>0.000288</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>0.000029</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","      <td>113360.0</td>\n","      <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>0.000029</td>\n","      <td>284500</td>\n","      <td>345324</td>\n","    </tr>\n","    <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","      <td>62.029</td>\n","      <td>21.453</td>\n","      <td>-0.51082</td>\n","      <td>-0.38177</td>\n","      <td>0.236200</td>\n","      <td>1.40820</td>\n","      <td>70300.0</td>\n","      <td>23036.0</td>\n","      <td>-1</td>\n","      <td>1</td>\n","      <td>11</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>0.000002</td>\n","      <td>284500</td>\n","      <td>345323</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"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  0.000002     284500   \n","1   -0.69746    -2.2147    -1.9152  367670.0  45733.0  0.000288     284500   \n","2   -7.00000    -7.0000    -7.0000      -7.0     -7.0  0.000029     284500   \n","3   -7.00000    -7.0000    -7.0000      -7.0     -7.0  0.000029     284500   \n","4   -7.00000    -7.0000    -7.0000      -7.0     -7.0  0.000002     284500   \n","\n","   channelNumber  \n","0         345323  \n","1         363492  \n","2         345324  \n","3         345324  \n","4         345323  "]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":317},"id":"Oz-lWJhgrlHg","executionInfo":{"status":"ok","timestamp":1622185305243,"user_tz":-120,"elapsed":610,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"ddd528f6-e520-4a59-bbe5-c80756918e66"},"source":["#examine feature distribution\n","dfall.describe()"],"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/html":["<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>0.000218</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>0.000736</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>-0.045557</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>0.000002</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>0.000029</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>0.000197</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>0.052420</td>\n","      <td>284500.0</td>\n","      <td>363492.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"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       0.000218   284500.0  351379.666667  \n","std    1.980717e+05  8.990068e+04       0.000736        0.0    8564.720183  \n","min   -7.000000e+00 -7.000000e+00      -0.045557   284500.0  345323.000000  \n","25%   -7.000000e+00 -7.000000e+00       0.000002   284500.0  345323.000000  \n","50%    7.501700e+04 -7.000000e+00       0.000029   284500.0  345324.000000  \n","75%    1.766200e+05  5.060500e+04       0.000197   284500.0  363492.000000  \n","max    5.638800e+06  2.784300e+06       0.052420   284500.0  363492.000000  "]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lQsalTmorlHj","executionInfo":{"status":"ok","timestamp":1622185305477,"user_tz":-120,"elapsed":237,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"a55be6af-8e0a-4c28-e4a4-1ee9f0e7c225"},"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","\n","\n","\n","\n","\n","\n","\n"],"execution_count":9,"outputs":[{"output_type":"stream","text":["total label weights  B S = (124.6622944642, 6.2000533976)\n","total class number of events B S (200000, 400000)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"rtI5u5GErlHq"},"source":["## Event selection"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kaO2JM1hrlHr","executionInfo":{"status":"ok","timestamp":1622185306322,"user_tz":-120,"elapsed":274,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"a75e5167-430e-4709-f0b3-9464ec899c57"},"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 in principle wrong. Many DS tool 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"],"execution_count":10,"outputs":[{"output_type":"stream","text":["Df shape before selection : (600000, 29)\n","Df shape after selection : (577357, 29)\n"],"name":"stdout"}]},{"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","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":425},"id":"6e0Hlpv6rlH4","scrolled":true,"executionInfo":{"status":"ok","timestamp":1622185306797,"user_tz":-120,"elapsed":5,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"86abe420-6ed1-4a09-c925-28df28d13ad5"},"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"],"execution_count":11,"outputs":[{"output_type":"stream","text":["Df shape of dataset to be used : (577357, 6)\n"],"name":"stdout"},{"output_type":"display_data","data":{"text/html":["<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>"],"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"]},"metadata":{"tags":[]}},{"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"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"text/plain":["0    0.000002\n","1    0.000288\n","2    0.000029\n","3    0.000029\n","4    0.000002\n","Name: mcWeight, dtype: float64"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"scrolled":true,"id":"7RbBc9ASuvqF","colab":{"base_uri":"https://localhost:8080/","height":298},"executionInfo":{"status":"ok","timestamp":1622185308065,"user_tz":-120,"elapsed":683,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"3e238579-f770-4c38-ba8d-e3c30f6a495e"},"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),title='Missing Transverse Energy for large lepton Pt')"],"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"scrolled":true,"id":"kEQ5ERycuvqF","colab":{"base_uri":"https://localhost:8080/","height":297},"executionInfo":{"status":"ok","timestamp":1622185314214,"user_tz":-120,"elapsed":5762,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"ae36f6e7-6559-456c-a7d8-680d9be452fd"},"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()"],"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"scrolled":true,"id":"mofOpgfWuvqG","colab":{"base_uri":"https://localhost:8080/","height":111},"executionInfo":{"status":"ok","timestamp":1622185314215,"user_tz":-120,"elapsed":7,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"50eca6a1-673a-4ddc-844b-88ed17c71633"},"source":["data[data.lep_pt_0+data.lep_pt_1>2000].head()"],"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/html":["<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>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","      <td>2180.0</td>\n","      <td>30.531</td>\n","      <td>-2.8257</td>\n","      <td>1.50800</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"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"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"markdown","metadata":{"id":"bVHrJtodL2wo"},"source":["## Examine the weights"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":279},"id":"cFD4s616LS0F","executionInfo":{"status":"ok","timestamp":1622185314725,"user_tz":-120,"elapsed":515,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"d08a8152-37f5-4f37-d3cb-55179b64da74"},"source":["fig,ax=plt.subplots()\n","#fig=plt.figure()\n","\n","bins=np.linspace(-1,1,101)\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]*10000,bins=bins,color='r',alpha=0.5,density=True,label='S ignal (Wx10)')\n","plt.legend(loc='best')\n","ax.set_xlabel('weight*1000')\n","plt.show()"],"execution_count":15,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"yj3GZQs8uvqH"},"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","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YdpzI4-6MVIK","executionInfo":{"status":"ok","timestamp":1622185314979,"user_tz":-120,"elapsed":257,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"16b19568-e8b0-4e79-8efd-6e7697d94442"},"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)"],"execution_count":16,"outputs":[{"output_type":"stream","text":["Weights quantities for background (target==0) and signal (target==1)\n","Weights sum [159.6884919    6.17939825]\n","N events [180381. 396976.]\n","Weights mean [8.85284436e-04 1.55661759e-05]\n","Weights std [8.53093648e-04 1.35576170e-05]\n","Weights Neff fraction [0.51851137 0.56863978]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"F77Cx9LhKwqc"},"source":["# Feature engineering\n","To be switched on in a second iteration"]},{"cell_type":"code","metadata":{"id":"4oe_9ZEEKwqc","executionInfo":{"status":"ok","timestamp":1622185422689,"user_tz":-120,"elapsed":166,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}}},"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","    \n","\n"],"execution_count":18,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"BePeK1UyuvqI"},"source":["# Plot the features"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":733},"id":"o2mf1bLVrlH7","executionInfo":{"status":"ok","timestamp":1622185425642,"user_tz":-120,"elapsed":1779,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"666d968a-f8af-49e2-dc5b-e9e6784ef89e"},"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"],"execution_count":19,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 0 Axes>"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1080x864 with 6 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"eWj7TeTZuvqI"},"source":["### Features correlation matrix"]},{"cell_type":"code","metadata":{"scrolled":false,"id":"zpZy8M38uvqJ","colab":{"base_uri":"https://localhost:8080/","height":629},"executionInfo":{"status":"ok","timestamp":1622185426546,"user_tz":-120,"elapsed":907,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"68577c4c-cabd-4eec-a404-d85210d92f57"},"source":["import seaborn as sn # seaborn for nice plot quicker\n","print (\"Background feature correlation matrix\")\n","corrMatrix = data[target==0].corr()\n","sn.heatmap(corrMatrix, annot=True)\n","plt.show()\n","\n","print (\"Signal feature correlation matrix\")\n","corrMatrix = data[target==1].corr()\n","sn.heatmap(corrMatrix, annot=True)\n","plt.show()\n","\n"],"execution_count":20,"outputs":[{"output_type":"stream","text":["Background feature correlation matrix\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["Signal feature correlation matrix\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"4kowHjX4rlIC"},"source":["## Split dataset and transform the features"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Y9j5hdrmrlID","executionInfo":{"status":"ok","timestamp":1622185427111,"user_tz":-120,"elapsed":379,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"319cfc0b-51f1-4aa1-fbf8-f7dd5bef5749"},"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","from sklearn.preprocessing import StandardScaler\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","\n","scaler = StandardScaler()\n","X_train = scaler.fit_transform(X_train)\n","X_test = scaler.transform(X_test)  # applied 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)):\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 (\"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","    "],"execution_count":21,"outputs":[{"output_type":"stream","text":["(433017, 6)\n","(433017,)\n","(433017,)\n","(144340, 6)\n","(144340,)\n","(144340,)\n","Orig : total weight sig 6.179398249600002\n","Orig : total weight bkg 159.68849189959997\n","Test : total weight sig 6.179443329600001\n","Test : total weight bkg 158.92470712440002\n","Train : total weight sig 119.95731511850002\n","Train : total weight bkg 119.95731511849999\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"yxybCOi-rlIM"},"source":["# Testing BDT"]},{"cell_type":"markdown","metadata":{"id":"AIsMSGl-Kwql"},"source":["## Load significance function"]},{"cell_type":"code","metadata":{"id":"qubY3CMNKwql","executionInfo":{"status":"ok","timestamp":1622185429176,"user_tz":-120,"elapsed":151,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}}},"source":["from math import sqrt\n","from math import log\n","def amsasimov(s,b): # asimov significance arXiv:1007.1727 eq. 97\n","        if b<=0 or s<=0:\n","            return 0\n","        try:\n","            return sqrt(2*((s+b)*log(1+float(s)/b)-s))\n","        except ValueError:\n","            print(1+float(s)/b)\n","            print (2*((s+b)*log(1+float(s)/b)-s))\n","        #return s/sqrt(s+b)\n"],"execution_count":22,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"F4DfF0ISrlIN"},"source":["## XGBoost"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nqMCgvbkrlIN","scrolled":true,"executionInfo":{"status":"ok","timestamp":1622187981196,"user_tz":-120,"elapsed":6730,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"a4324809-ce0e-40f3-c5b7-58491904a119"},"source":["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","# tree_method=\"hist\" is 10 times faster, however less robust against awkwards features (not a bad idea to double check without it)\n","# can even try tree_method=\"gpu_hist\" if proper GPU installation\n","# use_label_encoder and eval_metric to silence warning in 1.3.0\n","xgb = XGBClassifier(tree_method=\"hist\",use_label_encoder=False,eval_metric='logloss')\n","# HPO (==Hyper Parameter Optimization), check on the web https://xgboost.readthedocs.io/ for other parameters\n","#xgb = XGBClassifier(tree_method=\"hist\",use_label_encoder=False,max_depth=10,n_estimators=100) \n","\n","\n","\n","starting_time = time.time()\n","\n","xgb.fit(X_train, y_train.values, sample_weight=weights_train.values) # note that XGB 1.3.X requires positive weight\n","\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_xgb = y_pred_xgb.ravel()\n","y_pred_train_xgb = xgb.predict_proba(X_train)[:,1].ravel()\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","print (\"auc train:\",roc_auc_score(y_true=y_train.values, y_score=y_pred_train_xgb,))\n","int_pred_test_sig_xgb = [weights_test[(y_test ==1) & (y_pred_xgb > th_cut)].sum() for th_cut in np.linspace(0,1,num=50)]\n","int_pred_test_bkg_xgb = [weights_test[(y_test ==0) & (y_pred_xgb > th_cut)].sum() for th_cut in np.linspace(0,1,num=50)]\n","\n","vamsasimov_xgb = [amsasimov(sumsig,sumbkg) for (sumsig,sumbkg) in zip(int_pred_test_sig_xgb,int_pred_test_bkg_xgb)]\n","significance_xgb = max(vamsasimov_xgb)\n","Z = significance_xgb\n","print(\"Z:\",Z)\n","# To save model\n","xgb.save_model(\"XGBoost.model\")"],"execution_count":53,"outputs":[{"output_type":"stream","text":["Training time: 4.758774518966675\n","auc test: 0.8635099168311366\n","auc train: 0.8642336732023654\n","Z: 0.8015178701442962\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Td8mffSm9Y6A"},"source":["##\n","\n","```\n","# Ce texte est au format code\n","```\n","\n"," Learning curve\n","\n"," \n"]},{"cell_type":"markdown","metadata":{"id":"6R606WhZ9lGt"},"source":["##Learning curve\n","Compute the AUC by varying the number of training event. Testing set remains the same."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":385},"id":"P2FGlKG6yjy-","executionInfo":{"status":"ok","timestamp":1622188216132,"user_tz":-120,"elapsed":16934,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"837d1cb6-beea-4951-e18f-826c13a84672"},"source":["#In principle could, use learning_curve in sklearn\n","#However : it does not handle weights, it does not allow to control testing dataset size\n","#from sklearn.model_selection import learning_curve\n","#train_sizes,train_scores,test_scores=learning_curve(\n","#     XGBClassifier(tree_method=\"hist\",use_label_encoder=False,eval_metric='logloss',n_estimators=10),\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","\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","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)"],"execution_count":54,"outputs":[{"output_type":"stream","text":["training with  4330  events\n","training with  21650  events\n","training with  43301  events\n","training with  86603  events\n","training with  216508  events\n","training with  324762  events\n","training with  433017  events\n"],"name":"stdout"},{"output_type":"display_data","data":{"text/html":["<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>Ntraining</th>\n","      <th>test_auc</th>\n","      <th>train_auc</th>\n","      <th>time</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>4330</td>\n","      <td>0.500000</td>\n","      <td>0.500000</td>\n","      <td>0.055916</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>21650</td>\n","      <td>0.757231</td>\n","      <td>0.768778</td>\n","      <td>0.204203</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>43301</td>\n","      <td>0.809673</td>\n","      <td>0.814826</td>\n","      <td>0.406185</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>86603</td>\n","      <td>0.840353</td>\n","      <td>0.846965</td>\n","      <td>0.903981</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>216508</td>\n","      <td>0.859644</td>\n","      <td>0.865656</td>\n","      <td>2.334670</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>324762</td>\n","      <td>0.862258</td>\n","      <td>0.867240</td>\n","      <td>3.558805</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>433017</td>\n","      <td>0.863510</td>\n","      <td>0.867887</td>\n","      <td>4.896082</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Ntraining  test_auc  train_auc      time\n","0       4330  0.500000   0.500000  0.055916\n","1      21650  0.757231   0.768778  0.204203\n","2      43301  0.809673   0.814826  0.406185\n","3      86603  0.840353   0.846965  0.903981\n","4     216508  0.859644   0.865656  2.334670\n","5     324762  0.862258   0.867240  3.558805\n","6     433017  0.863510   0.867887  4.896082"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":296},"id":"05-B7ImAzPR0","executionInfo":{"status":"ok","timestamp":1622187909887,"user_tz":-120,"elapsed":452,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"d1ed2101-79df-4839-c5eb-20be4407f319"},"source":["ax=dflearning.plot.scatter(\"Ntraining\",\"test_auc\",label=\"test\")\n","dflearning.plot.scatter(\"Ntraining\",\"train_auc\",label=\"train\",color=\"r\",ax=ax)"],"execution_count":52,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7fed3590c8d0>"]},"metadata":{"tags":[]},"execution_count":52},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"AEoxOIiCuvqM"},"source":["# Hyper Parameter Optimisation\n","Can be done by hand, 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","metadata":{"scrolled":true,"id":"vSeMklV8uvqM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1622189029882,"user_tz":-120,"elapsed":168,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"1b5b1629-4217-48b9-ef6b-8c4e1fca736d"},"source":["%%time\n","#RandomSearchCV for advanced HPO \n","#TODO plot RMS\n","import scipy.stats as stats\n","if False:\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, 12), # default 6\n","                      'n_estimators': stats.randint(300, 800), #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(tree_method=\"hist\",use_label_encoder=False,eval_metric='logloss'), \n","                        param_distributions = param_dist_XGB, \n","                        scoring='roc_auc',n_iter=10,cv=2)\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 : \",roc_auc_score(y_true=y_test, y_score=y_pred_gs, sample_weight=weights_test))\n","    dfsearch=pd.DataFrame.from_dict(gsearch.cv_results_)\n","    display(dfsearch)\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\")"],"execution_count":57,"outputs":[{"output_type":"stream","text":["CPU times: user 14 µs, sys: 0 ns, total: 14 µs\n","Wall time: 20.3 µs\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"NmadcFRJrlIT"},"source":["## LightGBM"]},{"cell_type":"code","metadata":{"id":"9YmfuiwUrlIU","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1622189281767,"user_tz":-120,"elapsed":6238,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"cc75f63d-3cfd-4a03-b687-a9786c340da2"},"source":["import lightgbm as lgb\n","from sklearn.metrics import roc_auc_score # for binary classification if x > 0.5 -> 1 else -> 0\n","#gbm = lgb.LGBMClassifier()\n","gbm = lgb.LGBMClassifier()\n","# gbm = lgb.LGBMClassifier(max_depth=12) # HPO, check on the web https://lightgbm.readthedocs.io/ for other parameters\n","\n","\n","starting_time = time.time( )\n","\n","gbm.fit(X_train, y_train.values,sample_weight=weights_train.values)\n","#gbm.fit(X_train, y_train.values) #ma\n","\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].ravel()\n","auc_test_gbm = roc_auc_score(y_true=y_test, y_score=y_pred_gbm)\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,))\n","\n","int_pred_test_sig_gbm = [weights_test[(y_test ==1) & (y_pred_gbm > th_cut)].sum() for th_cut in np.linspace(0,1,num=50)]\n","int_pred_test_bkg_gbm = [weights_test[(y_test ==0) & (y_pred_gbm > th_cut)].sum() for th_cut in np.linspace(0,1,num=50)]\n","\n","vamsasimov_gbm = [amsasimov(sumsig,sumbkg) for (sumsig,sumbkg) in zip(int_pred_test_sig_gbm,int_pred_test_bkg_gbm)]\n","significance_gbm = max(vamsasimov_gbm)\n","Z = significance_gbm\n","print(\"Z:\",Z)\n","# To save model\n","gbm.booster_.save_model(\"LightGBM.model\")\n","\n","\n"],"execution_count":61,"outputs":[{"output_type":"stream","text":["Training time: 3.608083963394165\n","auc test: 0.8332713133743304\n","auc train: 0.8382267032466185\n","Z: 0.7316926719571807\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["<lightgbm.basic.Booster at 0x7fed42b59410>"]},"metadata":{"tags":[]},"execution_count":61}]},{"cell_type":"code","metadata":{"id":"zmJxYTUmrlIZ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1622189281768,"user_tz":-120,"elapsed":21,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"a777d6d9-692a-4a29-84a9-bcf1ce7d0d68"},"source":["print('Best significance found are:')\n","print('LightGBM: ', significance_gbm)\n","print('XGBoost : ', significance_xgb)\n","print('Best auc test found are:')\n","print('LightGBM: ', roc_auc_score(y_true=y_test.values, y_score=y_pred_gbm,))\n","print('XGBoost: ', roc_auc_score(y_true=y_test.values, y_score=y_pred_xgb,)) "],"execution_count":62,"outputs":[{"output_type":"stream","text":["Best significance found are:\n","LightGBM:  0.7316926719571807\n","XGBoost :  0.8015178701442962\n","Best auc test found are:\n","LightGBM:  0.8332713133743304\n","XGBoost:  0.8629082862773125\n"],"name":"stdout"}]},{"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","metadata":{"id":"Ay8QTiKPKwqz","executionInfo":{"status":"ok","timestamp":1622189425294,"user_tz":-120,"elapsed":155,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}}},"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), bins=30, xlabel=\"\", ylabel=\"Arbitrary units\", title=\"\", \n","                       weights_train=np.array([]), weights_test=np.array([])):\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=True,\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=True,\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=True, weights=weights_test_signal)\n","    #FIXME INCORRECT\n","    scale = len(y_pred[y_test == 1]) / sum(hist)\n","    err = np.sqrt(hist * scale) / scale\n","\n","    #width = (bins[1] - bins[0])\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=True, weights=weights_test_background)\n","    scale = len(y_pred[y_test == 0]) / sum(hist)\n","    err = np.sqrt(hist * scale) / scale\n","\n","    #width = (bins[1] - bins[0])\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"],"execution_count":67,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":573},"id":"kEQ342s-rlIe","executionInfo":{"status":"ok","timestamp":1622189426103,"user_tz":-120,"elapsed":811,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"4d7cfdaa-33ce-4a75-9721-05cdd9abd23f"},"source":["\n","\n","compare_train_test(y_pred_train_xgb, y_train, y_pred_xgb, y_test, 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, 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()"],"execution_count":68,"outputs":[{"output_type":"display_data","data":{"image/png":"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size 432x288 with 1 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"xm_cI0xhD6EE"},"source":["## Different normalisation"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":281},"id":"mdfQ4LMCBpR1","executionInfo":{"status":"ok","timestamp":1622190173288,"user_tz":-120,"elapsed":382,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"7fa0022c-c013-4dda-e9dd-a1aea74cc5cb"},"source":["\n","\n","density=True   # normalised to 1 (=> probability density function)\n","#density=False   # normalised to one year at LHC\n","\n","_ = plt.hist(y_pred_xgb[y_test == 0],\n","                 color='b', alpha=0.5, \n","                #range=high_low, \n","                bins=30,\n","                 histtype='stepfilled', density=density,\n","                 label='B (test)', weights=weights_test[y_test == 0]) # alpha is transparancy\n","_ = plt.hist(y_pred_xgb[y_test == 1],\n","                 color='r', alpha=0.5, \n","                #range=high_low, \n","                bins=30,\n","                 histtype='stepfilled', density=density,\n","                 label='S (test)', weights=weights_test[y_test == 1]) # alpha is transparancy\n","plt.legend()\n","_ = plt.title (\"XGboost score\")\n","#                 color='b', alpha=0.5, range=high_low, bins=bins,\n","#                 histtype='stepfilled', density=True,\n","#                 label='B (train)', weights=weights_test_background)"],"execution_count":88,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"tUKIWu2orlIg","executionInfo":{"status":"ok","timestamp":1622190360318,"user_tz":-120,"elapsed":828,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"9974a928-94b9-4fa6-8b70-71391388034f"},"source":["from sklearn.metrics import roc_curve\n","lw = 2\n","\n","fpr_gbm,tpr_gbm,_ = roc_curve(y_true=y_test, y_score=y_pred_gbm,sample_weight=weights_test.values)\n","fpr_xgb,tpr_xgb,_ = roc_curve(y_true=y_test, y_score=y_pred_xgb,sample_weight=weights_test.values)\n","\n","plt.plot(fpr_gbm, tpr_gbm, color='darkorange',lw=lw, label='LightGBM (AUC  = {})'.format(np.round(auc_test_gbm,decimals=2)))\n","plt.plot(fpr_xgb, tpr_xgb, color='darkgreen',lw=lw, label='XGBoost (AUC  = {})'.format(np.round(auc_test_xgb,decimals=2)))\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('False Positive Rate')\n","plt.ylabel('True Positive Rate')\n","plt.title('Receiver Operating Characteristic')\n","plt.legend(loc=\"lower right\")\n","#import os\n","#new_dir = \"Plots/Comparing\" \n","#if not os.path.isdir(new_dir):\n","#    os.mkdir(new_dir)\n","plt.savefig(\"ROC_comparing.pdf\")\n","plt.show() # blue line = random classification -> maximize true positive rate while miniize false positive rate"],"execution_count":92,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"CGF3k0KJrlIi","colab":{"base_uri":"https://localhost:8080/","height":295},"executionInfo":{"status":"ok","timestamp":1622189436535,"user_tz":-120,"elapsed":633,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"7d95e109-a835-40b3-aa90-74ce830f9911"},"source":["plt.plot(np.linspace(0,1,num=50),vamsasimov_gbm, label='LightGBM (Z = {})'.format(np.round(significance_gbm,decimals=2)))\n","plt.plot(np.linspace(0,1,num=50),vamsasimov_xgb, label='XGBoost (Z = {})'.format(np.round(significance_xgb,decimals=2)))\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()"],"execution_count":70,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdd3hUZfbA8e9JT0ijBEiFQGhJgNBBRUBAQRQEG+jaldUF/a2uurjFXd21r13WLriuK4hKsSAqggKC1FBCMfQkBEggvZf398cdYoAEksxM6vk8zzyTuXPve08i3nPvW8UYg1JKqZbLpaEDUEop1bA0ESilVAuniUAppVo4TQRKKdXCaSJQSqkWThOBUkq1cJoIlKqCiLwhIn91UFm5ItLF9rO3iHwuIlkiskBEbhSRbxxxHqXqShOBahRE5KCIFNgumhki8qWIhFf6fq6IFItIju21Q0SeEpEA2/d/sh2bKyKFIlJW6XNCNee8Q0R228o7JiJfiYgfgDHmbmPMPxzxuxljfI0x+20frwE6AG2NMdcaYz40xlzqiPMoVVeaCFRjcqUxxhcIBo4Br57x/bPGGD8gCLgNGAqsEZFWxpgnbRdcX+BuYO2pz8aYmDNPJCIjgCeBabYyewHznferVegE/GKMKa2HcylVI5oIVKNjjCkEPgGiq/veGLMBmAi0xUoKtTUIK1lssZV50hjzvjEmByqeQP55amcReVhEUkXkiIjcKSJGRKIq7Tvb9hSTIyI/i0jXSscaEYkSkceAR4HrbU8qd4jIrSKyutK+MSLyrYictD2l/Mm2fbCIrBWRTFscr4mIxxnnuFtEEm37zBYRqfT9XSKyyxbfThHpb9seIiKfikiaiBwQkfvq8LdUTZwmAtXoiIgPcD2w7lz72S7a3wLD63Can4HLROQxEblQRDzPEc844AFgDBAFjKxit6nAY0BrYC/wRBXx/g3rKWS+7Unl3TPO4wd8B3wNhNjOtdz2dRlwP9AOGAaMBn53ximuwEpwfYDrgMts5V4L/B24GfDHSqAnRMQF+BzYCoTayvy9iFxW3d9CNU+aCFRjskhEMoEsYCzwXA2OOQK0qe2JjDGrgClAf+BLrAvjCyLiWsXu1wFzjDEJxph8rIvqmRYaY9bbqnw+BOJqGxPWhfyoMeZ521NPjjHmZ1u8m4wx64wxpcaYg8CbwIgzjn/aGJNpjDkMrKgUw51Y1WobjGWvMeYQVtIIMsY8bowptrVjvI2V1FQL4tbQAShVyVXGmO9sF+NJwA8iEm2MOXqOY0KBk3U5mTFmKbDUdmc8ClgA7MG6yFYWAmys9DmpiuIqx5gP+NYhpHBgX1VfiEh34AVgIOCD9f/uphrGUF25nYAQW/I9xRVYVevIVZOmTwSq0THGlBljPsOqDrmouv1ExBerusauC5cxptwYsxz4HoitYpdUIKzS5/Aq9nGEJKBLNd+9DuwGuhlj/IE/AVLNvlWV27Wa7QeMMYGVXn7GmMtrG7hq2jQRqEZHLJOw6tt3VfG9p4gMABYBGcCcOpxjkohMFZHWtvMNxqpqqapd4mPgNhHpZWu/cMj4gip8AQSLyO9tv6OfiAyxfecHZAO5ItITuKcW5b4DPCgiA2y/a5SIdALWAzki8kexxje4ikisiAxy6G+lGj1NBKox+VxEcrEueE8AtxhjKo8BeFhEcoATwH+wqkYuMMbk1eFcGcBdQKLtfP8FnjPGfHjmjrYqpFew6t338muyKKrDeatla/weC1yJVc2TiFVlBfAgcAOQg1WPX+OursaYBVh/z//Zjl8EtDHGlGG1S8QBB4B0rKQR4IBfRzUhogvTKFU7ItIL2AF46ngA1RzoE4FSNSAik23VNa2BZ4DPNQmo5kITgVI181vgOFbvmzJqV0evVKOmVUNKKdXC6ROBUkq1cE1uQFm7du1M586dGzoMpZRqUjZt2pRujAmq6rsmlwg6d+7Mxo0bz7+jUkqpCiJyqLrvtGpIKaVaOE0ESinVwmkiUEqpFk4TgVJKtXCaCJRSqoVzaiIQkXEiskdE9orIrCq+jxCRFSKyRUS2iYhOf6uUUvXMaYnAtrjIbGA81tqz00TkzDVo/wJ8bIzph7Uq0r+dFY9SSqmqOXMcwWBgr235O0RkHtaqUzsr7WOw1lAFa+rbI06MR6nmp6QQ8o5D7nHIOQq5xyAvHUw5uLiBi4v1Lq7Wu5snuPuAhw+4e4N7K+u9VTvwC7H2Vy2OMxNBKKcv6ZcMDDljn78D34jIvUArrNWmlGoZysuhKBsKM6Eg0/aecfbPRdlQnAfF+VCS9+vPRTlQlOW4eNx9oE1XaBcFbbtBu24Q1APax4Brkxt7qmqhof/rTgPmGmOeF5FhwAciEmuMKa+8k4hMB6YDRERENECYStWQMZCdAmm7rbv0vHTIS4P8E9Z7XtrpF/jT/6mfztUDvFuDpz94tLJerYIgsJPtsy/4BoFvB/DtCL7trZ9btbPu/svLoLwUjO29vAxKC6GkAErybYnF9so5Cif2wYlEOBIPOxf/GpuHL4QNgoih1it0IHjWZUlm1Vg5MxGkcPrarmG2bZXdAYwDMMasFREvoB3WdL8VjDFvAW8BDBw4UKdLVY1DQSYc2wHHdsLxnXB8l/U68y7d1dO6gLdqCz7toG0UeAWCd+Cv796tz/7Z3RukpssSV8HVre538qVFcPKA9fsdXme9Vj4NGKuaKSQO+k6DvlPB06/uMapGwWnTUIuIG/ALMBorAWwAbqi89KCILAXmG2Pm2lZ9Wg6EmnMENXDgQKNzDal6l38SUuMhdat1x5y6FTIO/Pq9V4BVhdIhGtr3gqCe4B9iJQAPX/su6I1FYRYkbYDDayHxGzi6DTz8rGQw6E5o37OhI1TnICKbjDEDq/zOmesR2LqDvgS4Au8ZY54QkceBjcaYJbZeRG8DvlgNxw8bY745V5maCJRTFeVA2h7rzj5t96/v2ZUeZgM7QXBf6664Y1/r4u8X3Dwu9jVlDKRsgvVvQ8JnUFYMnYdbCaHnFdqm0Ag1WCJwBk0EymGKcm13+JshZbP1nnHw1+/dvKzG0qBe1l1+SBx07AM+bRos5EYpLx02/wc2zoGsw9CuO4z+G/Sc0LKSYyOniUApsBpED/xovZI3QvqeXxtEA8IhpB8E94H20VbVTuvO4OLaoCE3KeVlsPsLWP4Pq9E5fAiMeQw6DWvoyBTnTgT6/Kaar8IsOLga9v8AB36wqnjAaogNHwzRkyC0v5UAfNs3bKzNgYur9TftMQHi/wsrnoI546DH5dYTgrYhNFr6RKCah9Jiq4fLqWqelM22C7+x+sdHDIPIi6HLCKt6R+/0na84H35+HVa/BMW5MPB26wlBu542CH0iUM1PeZlVvZO4zLrjP7rNarAEq4tm6ACImQydL4KwgdaIWlW/PHxg+B9gwG3wwzPw85tWb6NJs62krBoNfSJQTUdBBuxdbl1MEr+FgpNWn/awQRA+yLr4hw6w6vu1kbLxObQWFv8OTu6HwdNhzN+tgXGqXugTgWqajIH0RPjla/hlmdV/3ZSBdxvodil0vxS6XmINwFKNX6dhcPcaWP44/PyG7eng39D5woaOrMXTRKAal9JiOLTGuvD/8vWvg7Y69IaLfg/dx1l3/VrH3zR5+MD4p6HXldbTwdzLYejvrMZkd6+Gjq7F0kSgGl5eunV3uGcp7FsBxTlWH/7IEXDBvdD9MggIa+golSN1vhDu+Qm+/Rus+7fVznP1O9bgPFXvNBGo+meM1aNnz1Lrrj9pPWCs0bm9r7bu+iNHWHePqvnyaAUT/mUl+kX3wNujYOw/YPBd2sZTzzQRqPpRnGcN5DrV0Jtlm6E8uC+M+CP0GAfBcXoBaIm6jbWeDhb9DpY+BPuWw8TXrJlVVb3QRKCcJyvFms448Rur3r+s2JqArctIq1th98usidlaAGMMuUWlpOUUUVBSRmmZobS83PZuKCkrx8vdlXa+ngT5eeLv5Ya0pKTo2x5uXADr34Jv/gqvXwCTX4coXaKkPmgiUI6VfxJ2LYFtC6yLPwba9bC6C3a71BrY5ebR0FHaraSsnD1Hc0g8nkNBcTmFJWUUlZ7+npZbRFp2EcdzCjmWbSWAmvJwdaGdrwft/Dxp28qD1j4eBPi4E+jtQetW7gR4u9Pax4MO/l50DPBqHolDBIb81hr78ckd8N9rrHaD3tc0dGTNniYCZb/ifKuuf/sCq9qnvMRa4WrkI9b/xG27NnSEdjHGkHSygC1JGWxNymJrciY7UrIoKq16URkPNxe83Fwq7u57hwUy2s+T9n7WZx8PN9xdBTdXF9xcxHq5ulBYUkZ6bhFpOUWk5RaRnlNMWm4Rx3OKSDyeS1Z+CTlFpVWe09vdlY4BXnTw96SjvxfBgd6EBHgRHOBNcKD13trHvWkkiw4xcNf38OE1sPC31noH3S9r6KiaNR1QpuqmpMC66CcstJJASb7V2Bt7NfS+1qr7bwoXHawLfUZ+CUcyC0jJLCDpZL71yijg8Ml8kjPyKSyxLvpe7i70Dg2gb1ggfcMD6RXsj5+XG55uLni5u+Lh6oKLi/N+75KycrIKSsjML+FkXjHHsgs5ll1IalYhR7MLOZZl/Xwsu5DS8tP/3/ZydyEkwJvQ1t6EBtpetp+7dfCjTatG9qRWmA3/mWgt/PObT3Q0sp109lHlGCWFVkNewkKrx09xLvi0hV4Tf53OoZH2788rKmVfWi57j+eyLy2XI5mFpGYVkGq7cBafcXfv5+lGeBsfItr4EN7Gm8h2vvQND6BHBz/cXBv/Au/l5Yb03CKOZBWSmlnAkaxCjmQWVLxSMgtIzy0+7ZjoYH8u6taOi6LaMahzG7w9GsF/y/yTMGc8ZCXDzYut6UJUnWgiUPY5lmDNN791nrWouncba0BQzGRrMZJGtAhJSVk5icdy2XEki12p2ew9bl38U7MKK/ZxcxE6BngRfKrqJMCr4nNIoDcRbXwI8G4i1Sh2KCwpq0gK25KzWJ2YzqZDGRSXlePh6sKATq0Z0SOIK/oEE9a6AbvyZqdas5gWZMJtX1lVR6rWGnKFsnHAy1grlL1jjHn6jO9fBEbZPvoA7Y0xgecqUxNBPSnKgR2fWgkgZZO1kHrPKyDuRmsGT1f3ho6Q4tJydqVmsy0li4SULBKOZLPnaA7FZdbdvY+HK1Htfeka5Hvae6e2Prg3gbv6hpBfXMqGgxmsTkxj9d4T7ErNBmBwZBsm9wvl8thgAnwa4L99xkF4b5w12eDtXzf5dqeG0CCJQERcsdYsHgskY61ZPM0Ys7Oa/e8F+hljbj9XuZoInKi8HA6thq3zreqfkjxrda4Bt0Cf6xt0Za7KDbbxSZnEJ2WScCS7okqntY87MSEBxIT6W+8h/kS2beXU+vqWIOlkPovjU/hsSwr70/LwcHXhkp7tmdw/lDG9OuBan3/f47utKSncfeCOb8E/uP7O3Qw0VCIYBvzdGHOZ7fMjAMaYp6rZ/yfgb8aYb89VriYCJ0jbY1X7bF9gDfTy8IOYq2DArda8Pg1QRXIit4htyVnEJ2WyNTmTrUmZZOSXAFYPmd6hAcRFBNoabQMIDfRu9lU5DckYw46UbBZuSWHJ1iOk5xYRHezPnyf04sKodvUXyJF4mHM5dOwNt37RKJ5Mm4qGSgTXAOOMMXfaPt8EDDHGzKxi307AOiDMGHNWZ2sRmQ5MB4iIiBhw6NAhp8TcouSfhG0fw9aPIDXems45arR159/j8nqd3qGs3LD7aDabDmWw8aB1x3/4ZD5g5aDu7f2ICw+kT3gAceGBTabBtrkqLSvny+2pPPv1HlIyC7ikZ3seGd+Tbh386ieA7Z/Ap3fAsJlw2RP1c85moClMQz0V+KSqJABgjHkLeAusJ4L6DKxZMcaaynnTXEhYBGVFVjfPy56y+vvX03KN+cWlbDmcyYaDJ9l0KIMthzPJtfWP7+jvRb+IQG4cEkHf8EBiQwPw9Wws/0wVgJurC5PiQrkspiPv/3SQ11bsZdzLq5g6KJzfj+lOkJ+TFwHqfQ0k/QxrX7PWooi5yrnnawGc+X9YChBe6XOYbVtVpgIznBhLy5Z/0qr62TTXWrDd0x/632xV/XSMdfrpcwpL2HQog58PnOTn/SfYlpxFablBBHp29Gdyv1AGdm7NgE6ttYqnCfFyd+W3I7py7cBwXlmeyH/XHWJx/BGemtKbK/s6eeqQS5+wliNdPNPqRdSum3PP18w5s2rIDauxeDRWAtgA3GCMSThjv57A10CkqUEw2kZQCymbYf3bVu+fsiLr7mnArVa3TyevDHUyr5jPNifz+bZUtidnUm6sbpt9wgIY0qUtQyLb0L9Ta/y9tI63udiflsvDn2xj0+EMnr26D9cODD//QfbISoY3hoNvB7hrua52dh4NUjVkjCkVkZnAMqzuo+8ZYxJE5HFgozFmiW3XqcC8miQBVQMlhVaPnw1vW90+PXyh329g4G1WA5sTlZcb1uxLZ96GJL5JOEpJmaFPWAAzRkUxJLIt/TsF4uOh1TzNVZcgXz64Ywh3/WcjD32yjeKycm4c0sl5JwwIg2vehQ+mwBf3w+Q3m8xo9sZGB5Q1F5lJsPFdq99//glo1x0G3QV9p4KXv1NPnZyRz2ebU5i/IYmUzAICfdyZ3C+U6weF07Ojc8+tGp/CkjJ+9+Fmvt99nEeviOb2iyKde8IfnoUVT8CE52HQnc49VxPWFBqLVV0YA4d+stZ/3f2Fta3H5dbCHpEjnHp3lJlfzJfbU1m0JYUNBzMAuCiqHX8c35NLozvg5d4IpidQDcLL3ZU3fjOAez/azONf7KSotJx7RjpxANjwB63Fjb5+BEL6Q2h/552rmdIngqaopBB2fGIlgKPbwSvQqvsfdCcEOq9etrCkjOW7jrMoPoWVe45TUmaIau/LVXEhTIoLJbyNriimflVSVs4DH2/l861H+P2Ybvzf6G7O6wiQfxL+PcxqNL71C+eco4nTJ4LmoiDTuvivfxvy061Rv1e+DL2vc2q//9SsAj5Ye4iP1h8mI7+E9n6e3HpBZybFhRIT4q+9fFSV3F1deOn6ODzdXHjpu0TKDTwwtrtzTubTBi68D5b9CQ6vg4ihzjlPM6WJoCkozIJ1b8Da2VCUBd3Hw9B7rGl5nXgR3nw4g/dWH+DrHUcpM4axvTpw07BOXNC1Xf1OLaCaLFcX4dmr+yDAK8sTCQnwYurgCOecbMCtsOp5+PFf1rTVqsY0ETRmhdnw85vWwJnCTOgxAUbOguA+TjtlSVk5X21P5b01B9malImflxu3XtCZWy7orFU/qk5cXIQnp/TmWE4Rf160g+BAb0Z0d8J6xB6tYNgMWP64NRVFSJzjz9FMaRtBY1SUC+vfhJ9ehYIM6wlg5Cyn/sPOKypl3oYk3lt9gJTMArq0a8WtF3bm6v5htNKRvcoBcotKufaNtRw+kceCuy8gOsQJPcoKs+DF3tDlYrj+v44vvwnTNoKmoqQQNr4Hq1+AvDRrjd+Rjzi1F8TxnELmrjnIf9cdIruwlMGd2/DYxBgu6dleZ+5UDuXr6cacWwcx+d9ruH3uBhbOuIDgAG/HnsQrwFr3+Mdn4fguaN/LseU3U/pE0BiUlcCWD+CH5yDniNX185K/Qvggp50yJbOA175P5NNNKZSUl3NZdEemj+hC/4jWTjunUgC7UrO59o21hLX2ZsHdw/Bz9Ojy/JPwYiz0nABXv+3YspswfSJorMrLramfVz5pLbwRNhimvOnUtVkz8or598q9vL/WmsH12oFh3Dm8C5HtdHi+qh+9gv359439uW3uBn734Wbeu3WQYxcK8mkDg263OleMnKWL2NSAzuXbUE7sg/evhIXTwdMPbvgY7vjGaUmgoLiM2Sv2cvFzK3h39QEm9Q1hxYMjeWJyb00Cqt5d3D2Ipyb3ZlViOo98tp3ycgfXTAy7F1zcYfWLji23mdIngvpWVmr1Alr5FLh6wsRXIe434OKcnFxaVs6CTcm89N0vHMsuYkyv9jx0WU96dKynueOVqsZ1g8I5klXAS98lUlpWznPX9nXck4FfB2tlvY3vwYg/OnWgZXOgiaA+Hd0Oi2dA6lZr/d/L/+XU5fY2H87gLwt3sDM1m/4Rgbw6rT+DIxtuuUmlzvT7Md1xd3XhuWV7yCsu49Vp/Rw3PckF98HGOfDTK3D5c44ps5nSRFAfSousibHWvATereHa9yF6ktMGg53MK+aZpbuZvzGJjv5ezL6hP5f37qgjgFWjNGNUFH5ebjy6OIE739/IWzcPcMwstYHh1qSLm9635iPy62B/mc2UthE42/Fd8PYlsOpf1lQQM9ZbKyo54aJcXm7438+HueT5lXy6OZnpF3fhuz+MYEKfYE0CqlG7eVhn/nVtX37al85N764nq6DEMQVfdD+Ul1jVsapa+kTgLMZYcwJ9+1drTYBp86HHOKedLuFIFn9euIP4pEwGR7bhn1fF0r2+1pBVygGuGRBGKw9X7pu3hWlvreM/dwymna+dy1627Qq9Jlrds0f9Gdy9HBNsM6NPBM6Qexz+dx0sfQg6D4ffrXVaEigqLeNfy/Yw6bU1JGfk88J1fZk/fagmAdUkje8dzDu3DGJ/ei7Xv7mWPNta1nYZcKs1Qn+3zkpaHacmAhEZJyJ7RGSviMyqZp/rRGSniCSIyP+cGU+9+GWZNR3u/h9g/HNw4wKnLQq/+XAGE15ZzWsr9jIpLpTvHhjBlP5hWg2kmrQR3YN45+ZB7EvL480f9tlfYOQICIyAze/bX1Yz5bSqIRFxBWYDY4FkYIOILDHG7Ky0TzfgEeBCY0yGiDjnilkfjLEmu1r9AnSIteZEd9Lw9oLiMv71zR7eW3OAYH8v5t42iJE9mu6fTqkzXdStHVf0CeatVfuZOjiCkEA7pqJwcYF+N8OKf8LJ/dCmi+MCbSac+UQwGNhrjNlvjCkG5gGTztjnLmC2MSYDwBhz3InxOE95OXz1kJUE+t8Cdy53WhJYt/8E417+kXdXH+DGIREsu/9iTQKqWZo1viflBp79erf9hfW7EcQFtuhEdFVxZiIIBZIqfU62bausO9BdRNaIyDoRqbIiXUSmi8hGEdmYlpbmpHDrqLwMPr/XWiz+gnuthWKc0CBVWlbOC9/sYdrb6wCYN30o/7yqt+PnaVGqkQhr7cNdwyNZFH+E+KRM+wrzD7EmcdzyoTWoU52moRuL3YBuwEhgGvC2iASeuZMx5i1jzEBjzMCgICfMY15XZSXw2XTrLmPELBj7D6d0Cz2SWcC0t9fxyvd7uaZ/GEv/bzhDu7R1+HmUamzuGRlFkJ8n//hiJ3ZPkNn/Zsg9ConfOCa4ZsSZiSAFqDyuO8y2rbJkYIkxpsQYcwD4BSsxNH6lRbDgVmvt4DGPwahHnJIEvkk4yviXV7HzSDYvXR/Hc9f2dcxgG6WaAF9PNx68tDubDmXwxbZU+wrrdin4doDN/3FMcM2IMxPBBqCbiESKiAcwFVhyxj6LsJ4GEJF2WFVF+50Yk2MU58O8G6zuaOOfg4t+7/BTFJaU8bfFO5j+wSYi2vjw5X3DuarfmTVrSjV/1wwIJzrYn6eX7qawpKzuBbm6Q9yNkLgMso84LsBmwGmJwBhTCswElgG7gI+NMQki8riITLTttgw4ISI7gRXAQ8aYE86KySFKi2HeNNi7HCa+BkOmO/wUB9PzmPLvn3h/7SHuvCiST++5gM46Q6hqoVxdhL9c0YuUzALeXX3AvsL6/QZMOcR/6JjgmgldmKY2jIElM602gateh7gbHH6Kr3ek8tCCbbi6Ci9c15dLeur8KEoB3PWfjfy0N50VD42kvZ8dHTLmXgGZh+G+eKfN+tsYnWthmpbzV3CENS9bSeDihx2eBErKyvnnFzu5+7+b6dLely/vG65JQKlK/nR5L4rLynnhm1/sK6j/LZB5CA7+6JjAmgFNBDW163P47u8QMwVG/cmhRadmFTD1rXW8s/oAt17QmQW/HUaoPQNolGqGItu14uZhnZm/MYlfjuXUvaBeV4JXoDYaV6KJoCaObIFP74KwgXDVvx3aO2hVYhoTXlnN7tRsXp3Wj79PjMHDTf+zKFWVmaOicHMR5m9IOv/O1XH3sqan3vU55DXuJsn6olec88lKgf9NhVZBMPV/4O6YO/XycsMryxO5+b31tPP1YMm9F3Fl3xCHlK1Uc9W6lQeje3ZgcXwKJWXldS+o301QVgzb5jsuuCZME8G5FOXCR9dDcR7cMN9hk8dl5hdz+/sbeOHbX7gqLpRFMy6ka5CvQ8pWqrm7ekAY6bnFrEq0Y5aBjrEQOkCrh2w0EVTHGPjsLjiWANfOhQ7RDil2e3IWE15ZzZq96fzjqlheuE4HiClVGyO6B9GmlQefbj5zfGot9Z0GabvguAPmMmriNBFUZ+tHsOcruPQJ6DbG7uKMMcxbf5ir3/gJYwwL7r6Am4Z20imjlaolDzcXJvYN4dudx+xbyazXlYDAzsUOi62p0kRQlfyT8M1fIHwIDLnb7uIKS8p4+JNtzPpsO0Mi2/DFfcOJCz9rSiWlVA1N6R9KcWk5X9oz7YRfR4gYpokATQRV++5vUJAJV7xo94CT5Ix8rn79JxZsSua+0d2Ye9tg2rTycFCgSrVMvUMD6Nbel882J9tXUPQkOJ4A6YmOCayJ0kRwpsPrrAakYb+DDjF2FfXTvnQmvraGwyfyefeWgTwwtjuuLloVpJS9RIQp/cPYeCiDQyfy6l5Qryut952LHBNYE6WJoLKyEvjiAfAPs6aVriNjDO+uPsBN766nTSsPFs+8kNG9dJSwUo50Vb8QROAzexqNA0IhbHCLrx7SRFDZutetx8TLnwXPunXnLCwp44GPt/KPL3Yypld7Fs24kC7aNVQphwsO8ObCru34bEsy5eV2zJkWPQmObocTDlgfuYnSRHBKZhKsfAq6j4eeE+pURNJJqz1gUXwKfxjbnddvHICvp3YNVcpZpvQPJelkARsPZdS9kGjbZMi7zpwlv+XQRHDK0j9a75c/W+tDjTF8simZy19eVdEecO/obrhoe4BSTjUutiM+Hq72NRoHRkBI/xZdPaSJAGD3V7DnSxjxR+sfRS2k54j/9R0AACAASURBVBbx2w828eCCrfQK8eer/9NZQ5WqLz4eboyPDebLban2LVoTPcmaUyzjkOOCa0JqnAhEpJOIjLH97C0ifjU4ZpyI7BGRvSJyVuuriNwqImkiEm973Vm78B2gMAuWPgxBvWDYjFod+k3CUS578UdW7knjz5f3Yt5dQwlv4+OkQJVSVbm6fyg5RaV8s/NY3QuJnmS9t9DqoRolAhG5C/gEeNO2KQxrmclzHeMKzAbGA9HANBGpap6G+caYONvrnRpH7gg5R2HOBMhJtcYMuLrX6LDswhIeXLCV6R9somOAF5/fexF3XdxFq4KUagBDu7QlJMDLvuqhNpEQ3LfFVg/VtCVzBjAY+BnAGJMoIuebgW0wsNcYsx9AROYBk4CddYzVsdJ+gf9eDfknYNp86DTsvIckHMliwcZkFsWnkF1Qwr2XRHHvJd102milGpCLizC5fyivr9zH8ZzCuq9eFj0Jlj8OWckQEObYIBu5ml7Biowxxac+iIgbcL7+WqFA5UnDk23bznS1iGwTkU9EJLyG8dgnaT28dymUFsBtX55zLqHM/GLe/+kgE15ZxYRXVvO/9YcZ3i2Ihb+7kD9c2kOTgFKNwOR+YZQbWBJvx6L0vU5VD33umKCakJo+EfwgIn8CvEVkLPA7wBF/rc+Bj4wxRSLyW+B94JIzdxKR6cB0gIiI2jXmnmX3V/DJbeAfAr/5FNp0Oe1rYwz70/NYt/8EqxPTWb7rOMVl5cSG+vP4pBgm9g0h0EeniFCqMYlq70uPDn58v/s4dw7vcv4DqtIuCjrEWtVDQ+9xbICNXE0TwSzgDmA78FvgK+B89fkpQOU7/DDbtgrGmMrLA70DVNl30xjzFvAWWIvX1zDms22cA18+gAmOo+Da/1Hs2Zai7EKyCkrYeDCDdftPsG7/CY7nFAHQwd+TG4ZEcO3AMGJCAup8WqWU843q2Z53Vu0np7AEP6+atfedJXoSrHgSslPBP9ixATZiNU0E3sB7xpi3oaIh2BvIP8cxG4BuIhKJlQCmAqet+C4iwcaYU9MHTgR21SL2Wln3v38y9JfnWFEWx+/2z6Tgmc1n7dPez5NhXdsytEtbhnVpS6e2PjpNtFJNxKgeQbzxwz7W7E1nXGwdL+LRk2DFE7D7Cxh8l2MDbMRqmgiWA2OAXNtnb+Ab4ILqDjDGlIrITGAZ4IqVSBJE5HFgozFmCXCfiEwESoGTwK11+i1qwLPnGNZnHWZL5//jHg8PPNxc8HRzwcPNBR8PV+LCW9NZL/xKNVn9O7XGz8uNFbvT6p4IgnpAUE+rekgTwVm8jDGnkgDGmFwROW+HeWPMV1jVSJW3PVrp50eAR2oYg1369R8K/YcyuD5OppSqd+6uLlzcLYgVe45jjKn7TV30JPjxOchNA98gxwbZSNW0y0ueiPQ/9UFEBgAFzglJKaXqZmSPII7nFLEzNbvuhXS/DEw5HPjBcYE1cjVNBL8HFojIKhFZDcwHZjovLKWUqr2RPazhTSt2H697IcFx4BUI+1c4KKrGr0ZVQ8aYDSLSE+hh27THGGPHYqFKKeV4QX6e9AkLYMWeNGZe0q1uhbi4QuTFsG8lGAMtoN2wNqOhBgF9gP5Y00Xc7JyQlFKq7kb2aM+Wwxlk5BWff+fqdBkJ2cktZo2Cms419AHwL+AirIQwCBjoxLiUUqpORvUIotzAj4lpdS+ky0jrvYVUD9W019BAINoYY8cyQEop5Xx9wgJp08qDlXvSmBRX1aw2NdCmizUl/f6VLaIbaU2rhnYAHZ0ZiFJKOYKrizCyexAr9xynrK5LWIpYTwUHVkG5HescNBE1TQTtgJ0iskxElpx6OTMwpZSqq5E925ORX8LW5My6F9JlJBRlWQvWNHM1rRr6uzODUEopR7q4WztcBFbuPk7/iNZ1KyRyhPW+fwWENe8m0Ro9ERhjfqjq5ezglFKqLgJ9POgf0ZoVe+xoMG7VDjr2gf3N/1JX015DQ0Vkg4jkikixiJSJiB1D95RSyrlG9WzP9pQsjucU1r2QLiPh8DooznNUWI1STdsIXgOmAYlYE87dibUMpVJKNUqjbKOMV9rzVNBlJJSXwKG1DompsarxgDJjzF7A1RhTZoyZA4xzXlhKKWWfXsF+dPD3ZOUeO6abiBgGrh7NfjxBTRuL80XEA4gXkWeBVGo3KlkppeqViDCqR3u+3JZKSVk57q51uGR5+EDE0GbfTlDTv8xNtn1nAnlYK49d7ayglFLKEUb2aE9OUSmbDmXUvZAuI+HYdsi148mikatpIkgHio0x2caYx4CHADtWiVZKKee7qFs73F3FvtlIu4y03g/86IiQGqWaJoLlQOWFaLyB7853kIiME5E9IrJXRGadY7+rRcSISPPurKuUqle+nm70i2jNugMn615IC5iWuqaJ4KwVyjg9MZzFtq7xbGA8EI01Y2l0Ffv5Af8H/FzToJVSqqb6hAawOzWb0rLyuhVw5rTUzZAzVygbDOw1xuw3xhQD84BJVez3D+AZwI7OvkopVbXY0ACKSsvZm5Z7/p2r02Vks56W2pkrlIUCSZU+J9u2VbAll3BjzJfnKkhEpovIRhHZmJZmR59gpVSLExvqD8COFDvGwHYZab030+qhmk4xsQHoCdwD3A30MsZssufEIuICvAD8oQbnf8sYM9AYMzAoqGUsJq2UcozIdr74eLiyIyWr7oW06QIBtmmpm6FzjiMQkUuMMd+LyJQzvuouIhhjPjvH4SlY3UxPCbNtO8UPiAVWirUUXEdgiYhMNMZsrPFvoJRS5+DqIkQH+5NwxI5EIAJdR0LCYigrBdeaDsFqGs73RHCx7f3KKl5XnOfYDUA3EYm0DUabClRMXW2MyTLGtDPGdDbGdAbWAZoElFIOFxsaQMKRbMrruj4BNOtpqc+X1k6NwnjXGLO6NgUbY0pFZCawDHAF3jPGJIjI48BGY4yuZ6CUqhcxIf7kF5dx4EQeXYN861ZIZ9t98aE1ED7IccE1AudLBLcBLwOvYC1aXyvGmK+Ar87Y9mg1+46sbflKKVUTsaEBAOxIyap7IvANgjZdIan59XQ/X9XQLhFJBHqIyLZKr+0isq0+AlRKKXtFtffFw82FhCN2zp4fMdSalrqZjSc45xOBMWaaiHTEqt6ZWD8hKaWUY7m7utCro599PYcAwodA/IeQnghB3R0TXCNw3qZvY8xRoG89xKKUUk4TExrAF1uPYIzB1lOx9iKGWe9J65pVIjhn1ZCIfGx7365VQ0qppiw2JIDswlKSTp5vUoRzaNcNvNvA4ebVTnC+J4L/s72fr6uoUko1ahUjjI9kEdH2nFOlVU/Eqh5KWufAyBreOZ8IjDGptvdDVb3qJ0SllLJfj45+uLmI/e0EEUPgxF7IS3dMYI1ATRevnyIiiSKSJSLZIpKji9crpZoSTzdXunfwY4e9PYfCh1rvh5vPU0FNJ517FmvUb4Axxt8Y42eM8XdmYEop5Wixof4kpGRh7On+GdLPWse4GVUP1TQRHDPG7HJqJEop5WSxoQGcyCvmaLYds967e1nJoBk1GNd05qSNIjIfWAQUndp4nknnlFKqUYkJOTXCOJvgAO+6FxQ+BH5+A0oKrcTQxNX0icAfyAcupeaTzimlVKPSK9gPF8EBDcZDoay42UxAV6MnAmPMbc4ORCmlnM3Hw42uQb72TUkN1hMBwOG10GmY/YE1sBolAhF5pYrNWViziC52bEhKKeU8saEB/LTPzq6frdpB227NZgK6Gi9eD8QBibZXH6yFZu4QkZecFJtSSjlcTIg/x7KLOJ5j5zLpEUOsRFBe7pjAGlBNE0EfYJQx5lVjzKvAGKylKydjtRsopVSTcGpKartnIg0fCgUZcCLRAVE1rJomgtZA5Um8WwFtjDFlVOpFpJRSjV1MiDUEKsERDcbQLAaW1WZAWbyIzBGRucAW4DkRaQV8V91BIjJORPaIyF4RmVXF93fbJrCLF5HVIhJdl19CKaVqys/Lnch2rdiRYucTQdso8GnbLBJBTXsNvSsiXwGDbZv+ZIw5Yvv5oaqOERFXYDYwFkgGNojIEmPMzkq7/c8Y84Zt/4nAC8C42v8aSilVczEh/sQnZdpXiIhVPdQMRhifbxrqnrb3/kAwkGR7dbRtO5fBwF5jzH5jTDEwD5hUeQdjTOWU3ApoXsv+KKUapdjQAJIzCsjML7avoIghcHI/5B53TGAN5HxPBA8A04HnbZ/PvFBfco5jQ7GSxinJwJAzdxKRGbbzeFRXnohMt8VBRETEeUJWSqlziw35tcH4wqh2dS/o1AR0ST9DrysdEFnDOF8bwTsi0tEYM8oYMwp4H8gFdgDXOCIAY8xsY0xX4I/AX6rZ5y1jzEBjzMCgoCBHnFYp1YKdajC2e4RxSBy4ejb5doLzJYI3gGIAEbkYeAorGWQBb53n2BQgvNLnMNu26swDrjpPmUopZbfWrTwIDfRmu72JwM3TNgFd804ErsaYk7afrwfeMsZ8aoz5KxB1nmM3AN1EJFJEPICpwJLKO4hIt0ofJ2ANVlNKKaeLDfW3fywBWO0EqVuhxI4lMBvYeROBiJxqRxgNfF/pu3O2LxhjSoGZwDJgF/CxMSZBRB639RACmCkiCSISj9VOcEutfwOllKqD2JAADqTnkVtUal9BEcOgvARSNjsmsAZwvsbij4AfRCQdKABWAYhIFFb10DkZY74Cvjpj26OVfv6/sw5SSql6EG1rJ9iVms2gzm3qXtCpCeiS1kHnCx0QWf073139EyKyHKvr6Dfm12V9XIB7nR2cUko5y6m1CRJSsuxLBD5tbBPQbXBQZPXvvAPKjDFntYIYY35xTjhKKVU/Ovh70raVh2PaCcIHwy9fgzHWQLMmpqZTTCilVLMiIkSHOKjBOGwg5J+AjAP2l9UANBEopVqsmJAAEo/nUFxq51TSYYOs9+SN9gfVADQRKKVarJgQf0rKDL8cy7GvoPbR4N4KktY7JrB6polAKdVinRphvNPe6iEXVwjtD8lNs8FYE4FSqsXq3LYVrTxc7V/DGKwG42M7oDjf/rLqmSYCpVSL5eIi9Ap2VIPxICgvhdR4+8uqZ5oIlFItWkyIP7tSsykvt3MW/NCB1nsTrB7SRKCUatFiQgLIKy7j0Ek7q3R8g6B1ZJNsMNZEoJRq0U5NNeGQdoKwQdYTgWlaa2xpIlBKtWjdO/jh7iqOG2Gcewyyks6/byOiiUAp1aJ5uLkQ1d7PcSOMocm1E2giUEq1eDEh/uw8koWxt0qnQyy4eTe5EcaaCJRSLV5MiD/pucUczymyryBXd2vFsibWYOzURCAi40Rkj4jsFZFZVXz/gIjsFJFtIrJcRDo5Mx6llKpKxZTUDmkwHghHt0GpnUmlHjktEYiIKzAbGA9EA9NEJPqM3bYAA40xfYBPgGedFY9SSlWnV7AfAAkpDmowLiu2lq9sIpz5RDAY2GuM2W+MKcZanH5S5R2MMSuMMac6767DWuBeKaXqlZ+XO53b+jimwbgJDixzZiIIBSr3oUq2bavOHcBSJ8ajlFLVigkJICHVAVVD/sEQEK6JoLZE5DfAQOC5ar6fLiIbRWRjWlpa/QanlGoRokP8STpZQFZBif2FhQ1qUktXOjMRpADhlT6H2badRkTGAH8GJhpjqmxdMca8ZYwZaIwZGBQU5JRglVItW0ylxeztFjYIspMh+4j9ZdUDZyaCDUA3EYkUEQ9gKrCk8g4i0g94EysJHHdiLEopdU6/9hxyUIMxNJnqIaclAmNMKTATWAbsAj42xiSIyOMiMtG223OAL7BAROJFZEk1xSmllFMF+XnS3s/TMV1IO/YGV48mkwjcnFm4MeYr4Ksztj1a6ecxzjy/UkrVhjXC2AFPBG6eENy3yYwwbhSNxUop1RhYi9nnUlhSZn9hYYPhyBYoLba/LCfTRKCUUjbRIf6UlTtgMXuwRhiXFlrLVzZymgiUUsompmJtgpbVYKyJQCmlbMJb++Dn6eaYBmP/UPALbhIT0GkiUEopGxcXoVeIgxazF4GIoXBoTaNfsUwTgVJKVRIT4s/u1BzK7F3MHqDraMhJbfTtBJoIlFKqkj5hARSUlLEjxQHVQ1G2HvJ7v7O/LCfSRKCUUpVc0qMDHq4uLIo/a0ac2vMPhg69IVETgVJKNRkBPu5c0rM9n289QmlZuf0FRo2GpHVQ6IB2ByfRRKCUUmeY3D+U9NxiViWm219Yt7FQXgoHfrC/LCfRRKCUUmcY1aM9gT7uLNzigOqh8CHg4QeJ39pflpNoIlBKqTN4uLlwRZ9gvtl5lNyiUvsKc3WHLiNg7/JG241UE4FSSlVhcr8wCkvKWbo91f7Cuo211idI221/WU6giUApparQPyKQTm19HFM91Mi7kWoiUEqpKogIV8WFsnb/CVKzCuwrLCAMgno12nYCTQRKKVWNyf1CMQYWbXHAkpPdxsDhtVCUa39ZDubUhWlEZBzwMuAKvGOMefqM7y8GXgL6AFONMZ/U5TwlJSUkJydTWFhob8iqGfPy8iIsLAx3d/eGDkU1EZ3btaJ/RCALtyRz94guiEjdC4saAz+9CgdXQY/xjgvSAZyWCETEFZgNjAWSgQ0issQYs7PSboeBW4EH7TlXcnIyfn5+dO7c2b7/UKrZMsZw4sQJkpOTiYyMbOhwVBMyuX8Yf120g52p2RXrGtdJxDBwb2VVDzWyRODMqqHBwF5jzH5jTDEwD5hUeQdjzEFjzDbAruF7hYWFtG3bVpOAqpaI0LZtW31qVLV2Re9g3F2FhZvtbDR287R1I/220XUjdWYiCAWSKn1Otm2rNRGZLiIbRWRjWlpadfvUpWjVgui/EVUXrVt5MKpHexY7YsqJqNGQeRhO7HVMcA7SJBqLjTFvGWMGGmMGBgUFNXQ4SqkWZnK/UNJyiliz74R9BTXSbqTOTAQpQHilz2G2bc2Sr6/vWdveeOMN/vOf/5zzuLlz5zJz5swqv3vyySdP+3zs2DFuuOEGunTpwoABAxg2bBgLFy4EYOXKlQQEBBAXF0efPn0YM2YMx48frziHiPDdd7/+41u0aBEiwiefVN0+//vf/54ff/yRpKQk4uLiTnv5+/vzxz/+8Zy/V0089dRTREVF0aNHD5YtW1blPsOHD684b0hICFdddRUAixcvpk+fPsTFxTFw4EBWr14NQFpaGuPGjbM7NqUqu6RXe/y93Fi4Odm+glp3hrbdGl83UmOMU15YDdH7gUjAA9gKxFSz71zgmpqUO2DAAHOmnTt3nrWtvrVq1apOx82ZM8fMmDHjvGWWl5eboUOHmtdff71i28GDB80rr7xijDFmxYoVZsKECRXfzZo1yzz66KMV5+jdu7e54447Kr6/7rrrTN++fc2CBQvOOm96eroZMmRIlTFt27bNhIWFmdTU1Fr8lmdLSEgwffr0MYWFhWb//v2mS5cuprS09JzHTJkyxbz//vvGGGNycnJMeXm5McaYrVu3mh49elTsd+utt5rVq1dXWUZj+LeimqZZn24zPf+y1OQWlthX0NJZxjweZExRnmMCqyFgo6nmuuq0XkPGmFIRmQksw+o++p4xJkFEHrcFtEREBgELgdbAlSLymDEmxp7zPvZ5AjsdscxcJdEh/vztytqH9fe//x1fX18efPBBNmzYwB133IGLiwtjx45l6dKl7NhhrVp05MgRxo0bx759+5g8eTLPPvsss2bNoqCggLi4OGJiYrj99tvx8PDg7rvvrii/U6dO3HvvvWed1xhDTk4OUVFRFduGDx/OqlWrKCkpoaioiL179xIXF1dl3J9++mmVd9WFhYXccMMNzJ49m44dO9b671HZ4sWLmTp1Kp6enkRGRhIVFcX69esZNmxYlftnZ2fz/fffM2fOHOD0J7C8vLzT6v+vuuoqPvzwQy688EK7YlSqsin9Q/lo/WE+3pjEbRfa0fMsagys+7e1hGW3sY4L0A5ObSMwxnxljOlujOlqjHnCtu1RY8wS288bjDFhxphWxpi29iaBxuy2227jzTffJD4+HldX19O+i4+PZ/78+Wzfvp358+eTlJTE008/jbe3N/Hx8Xz44YckJCTQv3//c55j1apVxMXFERERwXfffcftt99e8Z2IMGbMGJYtW8bixYuZOHFiteWsWbOGAQMGnLX94Ycf5qKLLqr22Pvvv/+saqS4uDiefvrps/ZNSUkhPPzXmsOwsDBSUqqvOVy0aBGjR4/G39+/YtvChQvp2bMnEyZM4L333qvYPnDgQFatWlVtWUrVxcBOrbmga1te/PYXTuQW1b2gTheCm3ejqh5y6oCyhlCXO3dny8zMJCcnp+Ju94YbbuCLL76o+H706NEEBFj9k6Ojozl06NBpF8mqzJgxg9WrV+Ph4cGGDRsA667/VLnPPPMMDz/8MG+88UbFMVOnTuWVV14hKyuL559//qw2iFNSU1M5s1F+6dKlfPfdd2zatKnamF588cVzxmyPjz76iDvvvPO0bZMnT2by5Mn8+OOP/PWvf61oA2nfvj1HjjhgJKhSlYgIj02MYfzLq3hu2R6evrpP3Qpy94LI4b92I20EvdmaRK+h5s7T07PiZ1dXV0pLz572NiYmhs2bN1d8nj17NsuXL6e67rQTJ07kxx9/PG3b4MGD2b59O+np6XTv3r3aeLy9vU/rb3/8+HF++9vf8uGHH+Lt7V3tcbV5IggNDSUp6dfexcnJyYSGVt27OD09nfXr1zNhwoQqv7/44ovZv38/6enWIiKFhYXnjFOpuurWwY/bLuzM/I1JxCdl1r2g7uPg5H44luC44OygiaAeBAYG4ufnx88//wzAvHnzanScu7s7JSUlAFxyySUUFhby+uuvV3yfn59f7bGrV6+ma9euZ21/+umnq30SOKVXr17s3ftrP+fbb7+de++9l379+p3zuBdffJH4+PizXrNmzTpr34kTJzJv3jyKioo4cOAAiYmJDB48uMpyP/nkE6644gq8vLwqtu3du/dURwM2b95MUVERbdu2BeCXX34hNjb2nLEqVVf3je5GO19PHl28g/LyOg4Mi54E4go76jSrjsNpInCQ/Px8wsLCKl4vvPDCad+/++673HXXXcTFxZGXl1dRFXQu06dPp0+fPtx4442ICIsWLeKHH34gMjKSwYMHc8stt/DMM89U7H+qjaBv37588MEHPP/882eVOX78eEaNGnXO806YMIGVK1cCsHbtWr788kv++9//nnaX/9BDD9Xgr1K9mJgYrrvuOqKjoxk3bhyzZ8+uaDu5/PLLT6vamTdvHtOmTTvt+E8//ZTY2Fji4uKYMWMG8+fPr2gwXrFiRbVPD0rZy8/LnT9f3ottyVl8vDHp/AdUpVU76DoKdnzaKEYZi2kEQdTGwIEDzcaNG0/btmvXLnr16tVAEdVMbm5uRU+Xp59+mtTUVF5++eUGjqp6F110EV988QWBgYENHUqtXXzxxSxevJjWrVuf9V1T+LeiGj9jDNe/uY7E4zmseHAkgT4etS8k/iNYdDfc8S2EV/007EgisskYM7Cq7/SJoJ58+eWXxMXFERsby6pVq/jLX/7S0CGd0/PPP8/hw4cbOoxaS0tL44EHHqgyCSjlKCLCY5NiyCoo4flvfqlbIT0ngJsXbG/46iF9IlAtiv5bUY709yUJ/GftQZbMvIjY0DrMTDr/Jji8Dh7YBa7O7cSpTwRKKeUE94/tTmsfD/62JKFuDce9r4G849YaBQ1IE4FSStVRgLc7fxzfk02HMvisLmsbd7sUPPwavPeQJgKllLLDNf3DGNCpNU9+tYuTecW1O9jdG3pdATs/h1I7RivbSROBUkrZwcVFeHJyb7ILSnjyq121LyD2GijKatCpqTUROEBSUhKRkZGcPHkSgIyMDCIjIzl48CAAiYmJXHHFFXTt2pUBAwYwatSoilG/c+fOJSgoqGJyuWuuueacA8VqKz4+nq+++qra77ds2cIdd9wBWPMhVR4r0LlzZzp06GB3DJs2baJ3795ERUVx3333UVUHhaysLK688kr69u1LTExMxeRyOq20agp6dPRj+sVd+GRTMmtru2ZBlxHg07ZBew9pInCA8PBw7rnnnooRtLNmzWL69Ol07tyZwsJCJkyYwPTp09m3bx+bNm3i1VdfZf/+/RXHX3/99cTHx5OQkICHhwfz5893WGznSwRPPvkk9913HwBz5sypGA28efNmIiIieOKJJ+yO4Z577uHtt98mMTGRxMREvv7667P2mT17NtHR0WzdupWVK1fyhz/8geLiYoKCgggODmbNmjV2x6GUM903uhsRbXz488LtFJaU1fxAV3eImQx7lkJRrvMCPIdmN+kcS2fB0e2OLbNjbxh/9nw5ld1///0MGDCAl156idWrV/Paa68B8OGHHzJs2LDTZuyMjY2tcgqE0tJS8vLyKvrAHzx4kNtvv5309HSCgoKYM2cOERER1W5fsGABjz32GK6urgQEBPDdd9/x6KOPUlBQwOrVq3nkkUe4/vrrK86Xk5PDtm3b6Nu371mxPPnkkwQFBZ010Vttpaamkp2dzdChQwG4+eabWbRoEePHn754t4iQk5ODMYbc3FzatGmDm5v1z1OnlVZNgZe7K09MjuWmd9fz75X7eGBs9fN5nSX2GtjwjpUM+lzrvCCroU8EDuLu7s5zzz3H/fffz0svvYS7uztAjaaPnj9/PnFxcYSGhnLy5EmuvPJKAO69915uueUWtm3bxo033lhx517d9scff5xly5axdetWlixZgoeHB48//njFE0flJACwcePGKhPS+vXreeedd3j77berjHfFihVVTi53wQUXnLVvSkoKYWFhFZ+rm2565syZ7Nq1i5CQEHr37s3LL7+Mi4v1z1OnlVZNxfBuQVwVF8LrK/ey93hOzQ8MHwL+YQ3We6j5PRGc587dmZYuXUpwcDA7duxg7NiqF5yYOECOHAAACwdJREFUPHkyiYmJdO/enc8++wywqoZee+01jDHMmDGD5557jlmzZrF27dqKfW666SYefvhhgGq3X3jhhdx6661cd911TJky5bzxVjXddG5uLr/5zW949913adOmTZXHjRo1ivj4+Br8RWpu2bJlxMXF8f3337Nv3z7Gjh3L8OHD8ff312mlVZPylyuiWbEnjT99toN504fi4lKDaaZdXCB2Cqx7HfJPgk/V/+85i1OfCERknIjsEZG9InLWFJQi4iki823f/ywinZ0ZjzPFx8fz7bffsm7dOl588UVSU1OBs6ePXrhwIXPnzq1oWK5MRLjyyivPmj66pt544w3++c9/kpSUxIABAzhx4tyNVmdONw3W08akSZMYPXp0tcfV5okgNDSU5ORf13mtbrrpOXPmMGXKFESEqKgoIiMj2b17N6DTSqumpZ2vJ3+6vCfrD56s3aR0va+B8hLYtcR5wVXDaYlARFyB2cB4IBqYJiLRZ+x2B5BhjIkCXgSeoQkyxnDPPffw0ksvERERwUMPPcSDDz4IWIvQrFmzhiVLfv2PW9Ppoy+44IL/b+/+Y6uqzziOvz9ioTovuElhhFIpCbI5IU5ANBkyBxkKGUUl/khgljmNLM45NhyLf8ywbMsytzJ1GavTdEyGCpmmCS5sqz8wgzrrFCYKE9RCBQUrGhWDFZ79cb4l1/a299T23su953klTc8593vPfZ7ee/vcc77nfr/Hh6xes2YN06dP73X77t27mTZtGitWrKCiooK9e/eSSqV4773Mh6hdh5tev349W7duzdpB3HlE0PVn8+bN3dqOGjWKoUOH0tzcjJmxevVqampqurWrqqqiqakJgDfffJOdO3cybtw4wIeVdsXnyiljOL/6c/z80Zc4+F7M7wd8flI0sX0Brh7K2VhDki4Ebjez2WH9xwBm9ou0NhtDmy2STgbeACqsl6BOxLGG6uvraWpqOn61z9GjR5k6dSp1dXXMmDGDHTt2sHTpUnbs2MHIkSNJpVLceuutzJo1i4aGBpYtW8bo0aM5duwYlZWVNDQ0MGLECFpbW1m8eHG3TuGetl9++eW8/PLLmBkzZ85k5cqVHDp0iNmzZ9PR0dGtsxhg4sSJbN68mVQqxYQJEzh8+PDxcf07bdmypV+fyFtaWqitreXDDz/k0ksv5a677kLS8dnTbrzxRvbt20dtbS379+/HzFi+fDkLFy4E4I477mDIkCEZ52fuq0K/Vlxy7DrwPnN++xSnn1rGsFPKYt1n4ZG1LDryAHtOqsx4e/vkW5g899NdwNHbWEO5LAQLgEvM7NthfREwzcxuSmvzQmjTFtZ3hzZvddnXDcANAFVVVZNbW1s/8Vj+5v706urqSKVS/b46KJd6G1a6r/y14vJpw7b9bPhv/P6toR+/zWVvraLMMn9DuWzqYibOyN7/l0lvhaAoOovNrB6oh+iIoMDhlJQlS5awbt26QofRIx9W2hWzuZNGMXfSqD7eK/OFJrmUy87i14H0Gdgrw7aMbcKpoWFAH7+W5/qjvLycRYsWFTqMHlVUVDB//vxCh+FcSctlIXgGGC+pWtJg4Gqga3d4I3BtWF4APNZb/0Bvim1eBZd//hpxLrOcFQIz+xi4CdgIvAQ8ZGbbJa2Q1Pk123uBMyTtApYC3Wc5j6G8vJz29nZ/o7semRnt7e2Ul5cXOhTnTjglMUNZR0cHbW1t3a6Jdy5deXk5lZWVx7/17VySFH1ncTZlZWVUV1cXOgznnCtKPtaQc84lnBcC55xLOC8EzjmXcEXXWSzpINCatWFmw4G3srYqLZ5zMnjOydCfnM80s4pMNxRdIegPSS099ZqXKs85GTznZMhVzn5qyDnnEs4LgXPOJVzSCkF9oQMoAM85GTznZMhJzonqI3DOOddd0o4InHPOdeGFwDnnEq4kC4GkSyTtlLRLUrcRTSUNkfRguP1pSWPzH+XAipHzUkkvStomqUnSmYWIcyBlyzmt3RWSTFLRX2oYJ2dJV4bnerukv+Q7xoEW47VdJelxSc+F1/ecQsQ5UCTdJ+lAmMEx0+2SdGf4e2yTdF6/H9TMSuoHGATsBsYBg4GtwNld2nwHWBWWrwYeLHTcecj5YuDUsLwkCTmHdilgE9AMTCl03Hl4nscDzwGfDesjCh13HnKuB5aE5bOB1woddz9zvgg4D3ihh9vnAH8DBFwAPN3fxyzFI4LzgV1m9oqZfQQ8ANR0aVMD/CksrwdmSlIeYxxoWXM2s8fN7HBYbSaaMa6YxXmeAX4K/BIohTHK4+R8PfA7MzsEYGYH8hzjQIuTswFDw/IwIP4kwScgM9sEvN1LkxpgtUWagdMl9XU+zE8oxUIwGtibtt4WtmVsY9EEOu8CZ+QlutyIk3O664g+URSzrDmHQ+YxZrYhn4HlUJzn+SzgLEn/ktQs6ZK8RZcbcXK+HVgoqQ14FPhufkIrmL6+37MqifkIXHySFgJTgBmFjiWXJJ0E/AaoLXAo+XYy0emhrxId9W2SNNHM3iloVLl1DdBgZr+WdCHwZ0nnmNmxQgdWLErxiOB1YEzaemXYlrGNpJOJDifb8xJdbsTJGUmzgNuAeWZ2JE+x5Uq2nFPAOcATkl4jOpfaWOQdxnGe5zag0cw6zOxV4H9EhaFYxcn5OuAhADPbApQTDc5WqmK93/uiFAvBM8B4SdWSBhN1Bjd2adMIXBuWFwCPWeiFKVJZc5b0ZeAPREWg2M8bQ5aczexdMxtuZmPNbCxRv8g8M2vJvLuiEOe1/QjR0QCShhOdKnoln0EOsDg57wFmAkj6IlEhOJjXKPOrEfhmuHroAuBdM9vfnx2W3KkhM/tY0k3ARqIrDu4zs+2SVgAtZtYI3Et0+LiLqFPm6sJF3H8xc/4VcBqwLvSL7zGzeQULup9i5lxSYua8Efi6pBeBo8AyMyvao92YOf8AuEfS94k6jmuL+YOdpLVExXx46Pf4CVAGYGariPpB5gC7gMPA4n4/ZhH/vZxzzg2AUjw15Jxzrg+8EDjnXMJ5IXDOuYTzQuCccwnnhcA55xLOC4FLDElnSHo+/Lwh6fWw/E643HKgH+92ST/s433e72F7g6QFAxOZc5/khcAlhpm1m9m5ZnYusAqoC8vnAlmHIwjfQneu5HghcC4ySNI9YQz/v0s6BUDSE5JWSmoBvidpsqQnJT0raWPnqI+Sbk6b7+GBtP2eHfbxiqSbOzcqmh/ihfBzS9dgwrdG7w7j8P8TGJHj/F2C+Scc5yLjgWvM7HpJDwFXAPeH2wab2RRJZcCTQI2ZHZR0FfAz4FvAcqDazI5IOj1tv18gmgsiBeyU9HtgEtG3QacRjSn/tKQnzey5tPtdBkwgGl9/JPAicF9OMneJ54XAucirZvZ8WH4WGJt224Ph9wSigez+EYbpGAR0jvGyDVgj6RGi8X46bQgD/B2RdIDon/pXgIfN7AMASX8FphNNKNPpImCtmR0F9kl6bECydC4DLwTORdJHYz0KnJK2/kH4LWC7mV2Y4f5zif55fwO4TdLEHvbr7zl3wvE+Aufi2wlUhDHvkVQm6Uth7oMxZvY48COiYc1P62U/TwHzJZ0q6TNEp4Ge6tJmE3CVpEGhH+LigU7GuU7+6cS5mMzso3AJ552ShhG9f1YSjfl/f9gm4E4ze6en2U/N7D+SGoB/h01/7NI/APAw8DWivoE9wJaBzse5Tj76qHPOJZyfGnLOuYTzQuCccwnnhcA55xLOC4FzziWcFwLnnEs4LwTOOZdwXgiccy7h/g8uAj7a2251agAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"1uxRKuMmG2zl"},"source":["#Model serialisation"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"boWL_HmgG80L","executionInfo":{"status":"ok","timestamp":1622191205448,"user_tz":-120,"elapsed":972,"user":{"displayName":"David Rousseau","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghk0iB7lkciixK5KdF7vnoleA_kBfpNi_RSd0Wcxg=s64","userId":"13371246477126231971"}},"outputId":"05313891-874b-485e-915c-4e085a3e1482"},"source":["import joblib\n","myxgb = XGBClassifier(tree_method=\"hist\",use_label_encoder=False,eval_metric='logloss',n_estimators=5)\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","joblib.dump(myxgb, \"myxgb.dat\")\n","\n","print (\"myxgb score\",auc_test_xgb)\n","\n","del myxgb # delete model\n","\n","myxgb_reloaded=joblib.load(\"myxgb.dat\")\n","auc_test = roc_auc_score(y_true=y_test, y_score=myxgb_reloaded.predict_proba(X_test)[:,1],sample_weight=weights_test)\n","\n","print (\"myxgb reloaded score\",auc_test)\n","\n","\n","\n"],"execution_count":95,"outputs":[{"output_type":"stream","text":["myxgb score 0.7784839586860766\n","myxgb reloaded score 0.7784839586860766\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"yjvKWVx3uvqP"},"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","metadata":{"id":"i9aA6WXIG0x-"},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"QN2h9y5JrlIm"},"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"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"i7WxH3bDrlIp"},"source":["# Permutation importance"]},{"cell_type":"markdown","metadata":{"id":"SiPAfpWquvqP"},"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","One possible implementation:\n","https://github.com/aghoshpub/permutationImportancePhysics \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"]},{"cell_type":"code","metadata":{"id":"AZsHARtTuvqQ"},"source":["if False:\n","    !pip install PermutationImportancePhysics\n","    from permutationimportancephysics.PermutationImportance import PermulationImportance # note the delibrate typo PermuLation\n","    PI_xgb = PermulationImportance(model=xgb, X=X_test,y=y_test,weights=weights_test,\\\n","                           n_iterations=1,usePredict_poba=True, scoreFunction=\"amsasimov\", colNames=list(data.columns.values))\n","    #PI_xgb.dislayResults()\n","    plott_xgb = PI_xgb.plotBars()\n","    plott_xgb.show()\n","    PI_gbm = PermulationImportance(model=gbm, X=X_test,y=y_test,weights=weights_test,\\\n","                           n_iterations=1,usePredict_poba=True, scoreFunction=\"amsasimov\", colNames=list(data.columns.values))\n","    #PI_gbm.dislayResults()\n","    plott_gbm = PI_gbm.plotBars()\n","    plott_gbm.show()\n"],"execution_count":null,"outputs":[]}]}