Feature Engineering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a3e72e1a",
   "metadata": {},
   "source": [
    "#  FEATURE ENGINEERING\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "80d30dc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "17a36772",
   "metadata": {},
   "outputs": [
    {
     "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>EventId</th>\n",
       "      <th>DER_mass_MMC</th>\n",
       "      <th>DER_mass_transverse_met_lep</th>\n",
       "      <th>DER_mass_vis</th>\n",
       "      <th>DER_pt_h</th>\n",
       "      <th>DER_deltaeta_jet_jet</th>\n",
       "      <th>DER_mass_jet_jet</th>\n",
       "      <th>DER_prodeta_jet_jet</th>\n",
       "      <th>DER_deltar_tau_lep</th>\n",
       "      <th>DER_pt_tot</th>\n",
       "      <th>...</th>\n",
       "      <th>PRI_jet_num</th>\n",
       "      <th>PRI_jet_leading_pt</th>\n",
       "      <th>PRI_jet_leading_eta</th>\n",
       "      <th>PRI_jet_leading_phi</th>\n",
       "      <th>PRI_jet_subleading_pt</th>\n",
       "      <th>PRI_jet_subleading_eta</th>\n",
       "      <th>PRI_jet_subleading_phi</th>\n",
       "      <th>PRI_jet_all_pt</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100000</td>\n",
       "      <td>138.470</td>\n",
       "      <td>51.655</td>\n",
       "      <td>97.827</td>\n",
       "      <td>27.980</td>\n",
       "      <td>0.91</td>\n",
       "      <td>124.711</td>\n",
       "      <td>2.666</td>\n",
       "      <td>3.064</td>\n",
       "      <td>41.928</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>67.435</td>\n",
       "      <td>2.150</td>\n",
       "      <td>0.444</td>\n",
       "      <td>46.062</td>\n",
       "      <td>1.24</td>\n",
       "      <td>-2.475</td>\n",
       "      <td>113.497</td>\n",
       "      <td>0.002653</td>\n",
       "      <td>s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100001</td>\n",
       "      <td>160.937</td>\n",
       "      <td>68.768</td>\n",
       "      <td>103.235</td>\n",
       "      <td>48.146</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>3.473</td>\n",
       "      <td>2.078</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>46.226</td>\n",
       "      <td>0.725</td>\n",
       "      <td>1.158</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>46.226</td>\n",
       "      <td>2.233584</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100002</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>162.172</td>\n",
       "      <td>125.953</td>\n",
       "      <td>35.635</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>3.148</td>\n",
       "      <td>9.336</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>44.251</td>\n",
       "      <td>2.053</td>\n",
       "      <td>-2.028</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>44.251</td>\n",
       "      <td>2.347389</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100003</td>\n",
       "      <td>143.905</td>\n",
       "      <td>81.417</td>\n",
       "      <td>80.943</td>\n",
       "      <td>0.414</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>3.310</td>\n",
       "      <td>0.414</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-0.000</td>\n",
       "      <td>5.446378</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100004</td>\n",
       "      <td>175.864</td>\n",
       "      <td>16.915</td>\n",
       "      <td>134.805</td>\n",
       "      <td>16.405</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>3.891</td>\n",
       "      <td>16.405</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6.245333</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   EventId  DER_mass_MMC  DER_mass_transverse_met_lep  DER_mass_vis  DER_pt_h  \\\n",
       "0   100000       138.470                       51.655        97.827    27.980   \n",
       "1   100001       160.937                       68.768       103.235    48.146   \n",
       "2   100002      -999.000                      162.172       125.953    35.635   \n",
       "3   100003       143.905                       81.417        80.943     0.414   \n",
       "4   100004       175.864                       16.915       134.805    16.405   \n",
       "\n",
       "   DER_deltaeta_jet_jet  DER_mass_jet_jet  DER_prodeta_jet_jet  \\\n",
       "0                  0.91           124.711                2.666   \n",
       "1               -999.00          -999.000             -999.000   \n",
       "2               -999.00          -999.000             -999.000   \n",
       "3               -999.00          -999.000             -999.000   \n",
       "4               -999.00          -999.000             -999.000   \n",
       "\n",
       "   DER_deltar_tau_lep  DER_pt_tot  ...  PRI_jet_num  PRI_jet_leading_pt  \\\n",
       "0               3.064      41.928  ...            2              67.435   \n",
       "1               3.473       2.078  ...            1              46.226   \n",
       "2               3.148       9.336  ...            1              44.251   \n",
       "3               3.310       0.414  ...            0            -999.000   \n",
       "4               3.891      16.405  ...            0            -999.000   \n",
       "\n",
       "   PRI_jet_leading_eta  PRI_jet_leading_phi  PRI_jet_subleading_pt  \\\n",
       "0                2.150                0.444                 46.062   \n",
       "1                0.725                1.158               -999.000   \n",
       "2                2.053               -2.028               -999.000   \n",
       "3             -999.000             -999.000               -999.000   \n",
       "4             -999.000             -999.000               -999.000   \n",
       "\n",
       "   PRI_jet_subleading_eta  PRI_jet_subleading_phi  PRI_jet_all_pt    Weight  \\\n",
       "0                    1.24                  -2.475         113.497  0.002653   \n",
       "1                 -999.00                -999.000          46.226  2.233584   \n",
       "2                 -999.00                -999.000          44.251  2.347389   \n",
       "3                 -999.00                -999.000          -0.000  5.446378   \n",
       "4                 -999.00                -999.000           0.000  6.245333   \n",
       "\n",
       "   Label  \n",
       "0      s  \n",
       "1      b  \n",
       "2      b  \n",
       "3      b  \n",
       "4      b  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('training.csv')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8d6ef83",
   "metadata": {},
   "source": [
    "Lets start by removing `EventId`, popping  `Weight` and `Label` into a separate series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "30ce66b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('EventId',axis=1,inplace=True)\n",
    "Weights=df.pop('Weight')\n",
    "y=df.pop('Label')   ##target values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3a0972e7",
   "metadata": {},
   "outputs": [
    {
     "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>DER_mass_MMC</th>\n",
       "      <th>DER_mass_transverse_met_lep</th>\n",
       "      <th>DER_mass_vis</th>\n",
       "      <th>DER_pt_h</th>\n",
       "      <th>DER_deltaeta_jet_jet</th>\n",
       "      <th>DER_mass_jet_jet</th>\n",
       "      <th>DER_prodeta_jet_jet</th>\n",
       "      <th>DER_deltar_tau_lep</th>\n",
       "      <th>DER_pt_tot</th>\n",
       "      <th>DER_sum_pt</th>\n",
       "      <th>...</th>\n",
       "      <th>PRI_met_phi</th>\n",
       "      <th>PRI_met_sumet</th>\n",
       "      <th>PRI_jet_num</th>\n",
       "      <th>PRI_jet_leading_pt</th>\n",
       "      <th>PRI_jet_leading_eta</th>\n",
       "      <th>PRI_jet_leading_phi</th>\n",
       "      <th>PRI_jet_subleading_pt</th>\n",
       "      <th>PRI_jet_subleading_eta</th>\n",
       "      <th>PRI_jet_subleading_phi</th>\n",
       "      <th>PRI_jet_all_pt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>138.470</td>\n",
       "      <td>51.655</td>\n",
       "      <td>97.827</td>\n",
       "      <td>27.980</td>\n",
       "      <td>0.91</td>\n",
       "      <td>124.711</td>\n",
       "      <td>2.666</td>\n",
       "      <td>3.064</td>\n",
       "      <td>41.928</td>\n",
       "      <td>197.760</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.277</td>\n",
       "      <td>258.733</td>\n",
       "      <td>2</td>\n",
       "      <td>67.435</td>\n",
       "      <td>2.150</td>\n",
       "      <td>0.444</td>\n",
       "      <td>46.062</td>\n",
       "      <td>1.24</td>\n",
       "      <td>-2.475</td>\n",
       "      <td>113.497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>160.937</td>\n",
       "      <td>68.768</td>\n",
       "      <td>103.235</td>\n",
       "      <td>48.146</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>3.473</td>\n",
       "      <td>2.078</td>\n",
       "      <td>125.157</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.916</td>\n",
       "      <td>164.546</td>\n",
       "      <td>1</td>\n",
       "      <td>46.226</td>\n",
       "      <td>0.725</td>\n",
       "      <td>1.158</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>46.226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-999.000</td>\n",
       "      <td>162.172</td>\n",
       "      <td>125.953</td>\n",
       "      <td>35.635</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>3.148</td>\n",
       "      <td>9.336</td>\n",
       "      <td>197.814</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.186</td>\n",
       "      <td>260.414</td>\n",
       "      <td>1</td>\n",
       "      <td>44.251</td>\n",
       "      <td>2.053</td>\n",
       "      <td>-2.028</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>44.251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>143.905</td>\n",
       "      <td>81.417</td>\n",
       "      <td>80.943</td>\n",
       "      <td>0.414</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>3.310</td>\n",
       "      <td>0.414</td>\n",
       "      <td>75.968</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060</td>\n",
       "      <td>86.062</td>\n",
       "      <td>0</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>175.864</td>\n",
       "      <td>16.915</td>\n",
       "      <td>134.805</td>\n",
       "      <td>16.405</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>3.891</td>\n",
       "      <td>16.405</td>\n",
       "      <td>57.983</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.871</td>\n",
       "      <td>53.131</td>\n",
       "      <td>0</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>-999.00</td>\n",
       "      <td>-999.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   DER_mass_MMC  DER_mass_transverse_met_lep  DER_mass_vis  DER_pt_h  \\\n",
       "0       138.470                       51.655        97.827    27.980   \n",
       "1       160.937                       68.768       103.235    48.146   \n",
       "2      -999.000                      162.172       125.953    35.635   \n",
       "3       143.905                       81.417        80.943     0.414   \n",
       "4       175.864                       16.915       134.805    16.405   \n",
       "\n",
       "   DER_deltaeta_jet_jet  DER_mass_jet_jet  DER_prodeta_jet_jet  \\\n",
       "0                  0.91           124.711                2.666   \n",
       "1               -999.00          -999.000             -999.000   \n",
       "2               -999.00          -999.000             -999.000   \n",
       "3               -999.00          -999.000             -999.000   \n",
       "4               -999.00          -999.000             -999.000   \n",
       "\n",
       "   DER_deltar_tau_lep  DER_pt_tot  DER_sum_pt  ...  PRI_met_phi  \\\n",
       "0               3.064      41.928     197.760  ...       -0.277   \n",
       "1               3.473       2.078     125.157  ...       -1.916   \n",
       "2               3.148       9.336     197.814  ...       -2.186   \n",
       "3               3.310       0.414      75.968  ...        0.060   \n",
       "4               3.891      16.405      57.983  ...       -0.871   \n",
       "\n",
       "   PRI_met_sumet  PRI_jet_num  PRI_jet_leading_pt  PRI_jet_leading_eta  \\\n",
       "0        258.733            2              67.435                2.150   \n",
       "1        164.546            1              46.226                0.725   \n",
       "2        260.414            1              44.251                2.053   \n",
       "3         86.062            0            -999.000             -999.000   \n",
       "4         53.131            0            -999.000             -999.000   \n",
       "\n",
       "   PRI_jet_leading_phi  PRI_jet_subleading_pt  PRI_jet_subleading_eta  \\\n",
       "0                0.444                 46.062                    1.24   \n",
       "1                1.158               -999.000                 -999.00   \n",
       "2               -2.028               -999.000                 -999.00   \n",
       "3             -999.000               -999.000                 -999.00   \n",
       "4             -999.000               -999.000                 -999.00   \n",
       "\n",
       "   PRI_jet_subleading_phi  PRI_jet_all_pt  \n",
       "0                  -2.475         113.497  \n",
       "1                -999.000          46.226  \n",
       "2                -999.000          44.251  \n",
       "3                -999.000          -0.000  \n",
       "4                -999.000           0.000  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "145d7002",
   "metadata": {},
   "source": [
    "## Imputations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "854f5ecb",
   "metadata": {},
   "source": [
    "The missing values in `DER_mass_MMC` can be imputed using a Linear Regressor or Random Forest Regressor with independent varaibles `DER_mass_vis` and `DER_deltar_tau_lep` because of their high correlation to the dependent variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "27a84e11",
   "metadata": {},
   "outputs": [],
   "source": [
    "def forest_imputer(DF):\n",
    "    \n",
    "    P=RandomForestRegressor(n_estimators=100,n_jobs=4).fit(X=DF[DF.DER_mass_MMC!=-999][['DER_deltar_tau_lep','DER_mass_vis']],\n",
    "       y=DF[DF.DER_mass_MMC!=-999].DER_mass_MMC).predict(DF[DF.DER_mass_MMC==-999][['DER_deltar_tau_lep','DER_mass_vis']])\n",
    "    \n",
    "    DF1=DF.copy()\n",
    "    DF1.DER_mass_MMC[DF1.DER_mass_MMC==-999]=P\n",
    "    return DF1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6b7c63d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def linear_imputer(DF):\n",
    "    \n",
    "    P=LinearRegression().fit(X=DF[DF.DER_mass_MMC!=-999][['DER_deltar_tau_lep','DER_mass_vis']],\n",
    "       y=DF[DF.DER_mass_MMC!=-999].DER_mass_MMC).predict(DF[DF.DER_mass_MMC==-999][['DER_deltar_tau_lep','DER_mass_vis']])\n",
    "    \n",
    "    DF1=DF.copy()\n",
    "    DF1.DER_mass_MMC[DF1.DER_mass_MMC==-999]=P\n",
    "    return DF1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "94791208",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5739/438418694.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  DF1.DER_mass_MMC[DF1.DER_mass_MMC==-999]=P\n"
     ]
    }
   ],
   "source": [
    "df1=forest_imputer(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7c99e86c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5739/2418058539.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  DF1.DER_mass_MMC[DF1.DER_mass_MMC==-999]=P\n"
     ]
    }
   ],
   "source": [
    "df2=linear_imputer(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "e2e8d367",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.hist(x=df1.DER_mass_MMC,alpha=0.5,bins=50,density=1)\n",
    "plt.hist(x=df2.DER_mass_MMC,alpha=0.5,bins=50,density=1)\n",
    "plt.legend(labels=['Random Forest Imputer','Linear Imputer'])\n",
    "plt.title('Density plots between two Imputation Methods')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c0f15d8",
   "metadata": {},
   "source": [
    "- Both the imputation techiniques appear to produce identical density plots\n",
    "- The Random Forest imputator was computationally expensive\n",
    "- Therefore its pragmatic to go with the Linear Imputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "a2f92c95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DER_mass_MMC', 'DER_mass_transverse_met_lep', 'DER_mass_vis',\n",
       "       'DER_pt_h', 'DER_deltaeta_jet_jet', 'DER_mass_jet_jet',\n",
       "       'DER_prodeta_jet_jet', 'DER_deltar_tau_lep', 'DER_pt_tot', 'DER_sum_pt',\n",
       "       'DER_pt_ratio_lep_tau', 'DER_met_phi_centrality',\n",
       "       'DER_lep_eta_centrality', 'PRI_tau_pt', 'PRI_tau_eta', 'PRI_tau_phi',\n",
       "       'PRI_lep_pt', 'PRI_lep_eta', 'PRI_lep_phi', 'PRI_met', 'PRI_met_phi',\n",
       "       'PRI_met_sumet', 'PRI_jet_num', 'PRI_jet_leading_pt',\n",
       "       'PRI_jet_leading_eta', 'PRI_jet_leading_phi', 'PRI_jet_subleading_pt',\n",
       "       'PRI_jet_subleading_eta', 'PRI_jet_subleading_phi', 'PRI_jet_all_pt'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5c37145",
   "metadata": {},
   "source": [
    "Now let us drop `PRI_jet_leading_phi` and `PRI_jet_subleading_phi` since they does not seem to have any affect of the target and contains too many missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "7c5b2c31",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['PRI_jet_leading_phi','PRI_jet_leading_phi'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc5cd54c",
   "metadata": {},
   "source": [
    "We can also encode `Label` ordinally with 's' = 1 and  'b' = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "87b24533",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=np.where(y.values=='s',1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "798d395c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, ..., 1, 0, 0])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65527182",
   "metadata": {},
   "source": [
    "**All the above steps will be abstracted away to `cust_func.py`**"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}