Python/notebooks/variable_selection_04_RF_drop-column_importances_and_dependence.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variable selection 4: Random Forest drop-column variable importance + variable dependance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook goes with the blog post: [Variable selection in Python, part I](https://mycarta.wordpress.com/2019/04/30/variable-selection-in-python-part-i/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code adapted from the `rfpimp` [package](https://github.com/parrt/random-forest-importances)\n",
    "\n",
    "### Reference article: [Beware Default Random Forest Importances](https://explained.ai/rf-importance/index.html)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preliminaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import preprocessing\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.base import clone\n",
    "from rfpimp import *\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mkdf(columns, importances):\n",
    "    I = pd.DataFrame(data={'Feature':columns, 'Importance':importances})\n",
    "    I = I.set_index('Feature')\n",
    "    I = I.sort_values('Importance', ascending=False)\n",
    "    return I"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data loading, dropping production"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('../data/Table2_Hunt_2013_edit.csv').astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Production</th>\n",
       "      <th>Position</th>\n",
       "      <th>Gross pay</th>\n",
       "      <th>Phi-h</th>\n",
       "      <th>Pressure</th>\n",
       "      <th>Random 1</th>\n",
       "      <th>Random 2</th>\n",
       "      <th>Gross pay transform</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15.10</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>379.0</td>\n",
       "      <td>3.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21.30</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>5.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>19.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>8.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15.72</td>\n",
       "      <td>2.1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>21.7</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>11.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.71</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.1</td>\n",
       "      <td>24.6</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>237.0</td>\n",
       "      <td>10.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Production  Position  Gross pay  Phi-h  Pressure  Random 1  Random 2  \\\n",
       "0       15.10       2.1        0.1    0.5      19.0       5.0     379.0   \n",
       "1       21.30       1.1        1.0    4.0      16.0      13.0     269.0   \n",
       "2       22.75       1.0        1.9   19.0      14.0      12.0     245.0   \n",
       "3       15.72       2.1        3.1   21.7      17.0       6.0     273.0   \n",
       "4        7.71       2.9        4.1   24.6      11.0      10.0     237.0   \n",
       "\n",
       "   Gross pay transform  \n",
       "0                 3.54  \n",
       "1                 5.79  \n",
       "2                 8.51  \n",
       "3                11.52  \n",
       "4                10.16  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.loc[:, ['Production', 'Position', 'Gross pay', 'Phi-h', 'Pressure', 'Random 1', 'Random 2', 'Gross pay transform']]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Built-in scikit importances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How to use sklearn fit_transform with pandas and return dataframe instead of numpy array?\n",
    "\n",
    "https://stackoverflow.com/a/35724176/1034648"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop('Production',axis=1)\n",
    "scaled_X = preprocessing.StandardScaler().fit_transform(X)\n",
    "scaled_X_df = pd.DataFrame(scaled_X, index=X.index, columns=X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.844627219114137\n"
     ]
    }
   ],
   "source": [
    "base_rf = RandomForestRegressor(n_estimators=100,\n",
    "                                min_samples_leaf=1,\n",
    "                                n_jobs=-1,\n",
    "                                oob_score=True)\n",
    "\n",
    "X_train, y_train = scaled_X_df, data['Production']\n",
    "rf = clone(base_rf)\n",
    "rf.fit(X_train, y_train)\n",
    "print(rf.oob_score_)\n",
    "\n",
    "I = mkdf(scaled_X_df.columns,rf.feature_importances_)            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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q5K4drYuCu8l3O9dibtPzlnXq1z+OYN2TyOHF4SlVz+kWEGP4r86zDKnVt/7V7W/U4y28lTxQ5Oy6zE2NsTta5TYhX/QMdLHccj75wvV95OPjORevHdb/VvLF0VkpVXu0vbfrEOuCPBjg+sBuKVXnts3f6Tgcqn6tu+0TmbvfWrq5G3wXsE6M4aXNO/x115uXdTE/5GNrqxjD8h1aYXQ6tro5XhemVki0DvkC+fup8WQP8kCXDwHrNrs0xRj+33ys86vkrgxrpcYTNGIM7d3DhuMP5GN+dfLn+qx6/JhfMLer2vx8h91LHsekfd7XAQ+kVF3O3AFm30wO6g7huQOOfiul6scxhj3Jg2SeFWN4a7N7W5vZdHGuL4Dzc36sSG++/yW1cQwMSdJoaj2C8HOx8QSCGMMK5IEdLyTf3V2BfAH+u7bwYnnyH6zw3ND9aeZtWfFX8v9r6zXmX5ouR45PqbqP/Mf2djGG17e9fQC5+fQ23SxrAXshuRl8U+sufeuC5dL69bAO+3lfcvPnGV2sq32/ti5kftMsFGPYgPzUBhj+zZCfkz+rD8YYlmsscyz5QmiTumvNxeTPc3Jz5pifGvFd8mCFg97RT6m6n/ykl+3IjzVtv3htN9D2jmfu+A2Dbe9A829LvkB7zrxd1O8S8meyX2w8PSLmJ7psNUg9Wi4in1ftrU7+l7ljPAyldWx1+hw+AMxKqbq98VY3x+vCdD75ODmGPHDj2W3vrwjc1xZeLEE+52FkN/tWJJ9z7aHhgfOxzNZTc/ZtTox5AN9dgeUW0HfYbGDV+NynlSxTL/drbWV/RR7csmPLhJSqm8nnaWsw54F0e67P7/k5InVLtJWZG3JL6iFbYEiSRk1KVYoxnE/uarBKzKPwL0l+ssUqwO4pVY/GGH5Lvqv48RjDk+RxDVYjhxet8QOWayz6fmCtmB+pd0tK1QzyhcoHgUtifnTrC8iPxXtO//8h7EN+vOeNMT/B407gzeSnOfyK/PSMha0fOCrGMJHcVHpz8h/rF6VUfRfm2c/XxRguJt/x3ov8R/8u7eMgDOB+YL0Ywz7k5vPXkAdRPSXmRxn+nTxOwIeY2wR7OTo3i++ofgLHvuSL61/EGKaSB+DblTxmRCtwOos8dsHB9QX7NcBLyZ/RGGC/LsciOIe5d4ePHqwgucvHA8ARMT8K8w/kQWY/DIytyyw3wLyQB6b9JPCtGMMp9Xa9lTw45+PA2DjvU1sGrF9K1T0xhmOBw8if60XkMQr2o7suSN8k79cD61ZOPyQfzzuQu6x0o/U57B9jWJ08OOPK5M8B8tgITUMerwtTStX9MYZryAOaPsLcQKblO8Au9b69lvz5vp/8COJnGPzzHsh3yJ/Z1fVyx5IHz3w7+Xwa9jJTqq6qvz/3ry/YryV/h+5HbtlyQl10fr/DriV3D1mXOihIqfpHjOF0YJ8Yw2Xk0G1J8nH9EuYNNpo+Sw7ovhBj+E5K1e87lOnqXI8xzO/5OVL/Uy//mlFYtqRhsgWGJGm07Ub+I3tZcnP5z5AveN/ZamZfP3lkK/K4F7uRn5yxI/liY13yGBJbNJb5OWAW8AXyXXtSqq4lX1jPAY4jj9FwAbmfdlfqp0hsQO6zvmtdj7eTH523eUrVSMfAmB9PkC8o3ki+UHgtcDD5D/6m1n5+CXn7DyD3E984pWpal+s6kNwX/WvAR+rB+bYh33k8lPwYxo3I+781SOAW8y5mcClVl5IvbO8mX+AcDfyb/KjQq+oyT5GPic+RWy98td6+W4C3N5/wMIQryBc9j/DcR1d2qtffyX38f1av62vkLjSnkAcjhEG2t67TzuRj8PPk43MCObD73wHmH7R+KT+OdT9ycPEV8nF5dD3foF1o6oBnm7r8hvX2rEseL6B90MiBltH8HNYifw57kS9035gajxOtdXu8Lkxn16/f6hDk7Q2cRL7IP4m8r2eTxyv5ObBJfQd+OI6q/40nPwL0IOY+hvMyckjYbReepknAp8ldir5GPhamkc+HR2CBfIe1niiySdv0/cnfD2uQvweOIX/W2w72/VJ3Cfk4eSyWbzRbiDXKdHWuz+/5OR9arc3an7YiqQf6+vu7HUNMkiQtTDGG64A3p1SNHaqsFj8xP0Z4idbFadt7VwPrpFSN9Mk4C5zH6+IhxvADYOmUqg16XZcS1APBzk6p6qo7oqTRZQsMSZKkMq0HPBxj+FRzYoxhVfJd4Zt7Uist7r4AvLF+msrzWj1Q6WvI+0RSARwDQ5IkqUw3k8eD+XwdWtxGHn9iL/Kghp/rYd20mEqpqmIM3wUOZ+7gmM9XnyOP33JTrysiKbMFhiRJUoHqsQE2BaaQH4H5dXLf/x8DG6RU3dHD6mnxthew6TAeY73YiTFsTB5PZJ+hykpaeBwDQ+qgr6/vxeQnIDzZ46pIkiRJUqmWBGb39/fPGbLkAmAXEqmz1b73ve/dNn78+F7XQ5IkSZKKdM8997Dlllu+Brh9YazPAEPq7Mnx48czYcKEXtdDkiRJkkq20FqtOwaGJEmSJEkqngGGJEmSJEkqngGGJEmSJEkqngGGJEmSJEkqngGGJEmSJEkqngGGJEmSJEkqngGGJEmSJEkq3pheV0BaFJx7/gU8+tjjva6GJEmSJPXcuKXGsusuOy/09RpgSF149LHH2WybnXpdDUmSJEnquRlXTuvJeu1CIkmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSijem1xXQ0GIMY4E9gPcCrwVeAswB7gRmAGelVN3RuxpKkiRJkjS6bIFRuBjD6sAvgK+TP6+vAXsBk4FbycHG7TGGl/eskpIkSZIkjTJbYBQsxrAU8F1gDeA9KVWXdigzFjgA6B9iWUsCS6RUPT4adZUkSZIkaTQZYJRtT2At4JhO4QVAHUgc25wWYzgCOBx4DfBhYEdgFWBz4Lq6zJ7A3sDawBPAzcBRKVU3tC1ra+BgYF3gxcDfgZ8Dn06pmlWXeQVwZL38lYGHgd8Dp6dUnTPYBrbV9aN1XZcjty45NKXq+23lJwEfANYD/gv4F3AD8LmUqlsb5X4NvBRYLaXqmbZl7AhMB3ZNqTpvsPpJkiRJkspgF5Kyvbd+PWOE838T2Ag4HvgUcC9AjOFLwFTgSeDQ+v11gB/EGN7ZmjnGsDFwBTlQOBbYt55vBWDNuswYIAHvA6aRQ5EvArOAtw2jrucCbwa+VK9rVeB7MYbQVm5fcmuTKcA+dX3eBtwYY3h1o9xU4BVA7LCuPcghy7eHUT9JkiRJUg/ZAqNsrwEeSam6pzkxxrAEuXVB05yUqsfapj0EhJSqpxrzTgQOAm4ENkupeqKefgbwW+CUGMMaKVVPA9uSQ64tUqr+1lju0Y2f1wEmAoekVB03wu0EeAp4W6M+ZwJ3ACeRW4m0bJlSNac5Y4zhXOBX5K40e9eTzyOHIR8GrmmUbYUap3fYX5IkSZKkQtkCo2zLAo90mL42uStH898+Hcqd0AwvatsCfcBxrbAAIKXqr8DZwKuA9evJD9evO9QtLTppldk0xvCyQbdmcF9rq8+fyS1I1ooxrN2YPgcgxtAXY1g2xrAieftnAhs2yj0MXARsW5dp+RD5uP9Gp0r09fXt1dfX93Pg4unTp8/H5kiSJEmSFiQDjLI9Qg4x2t1DbkUQgQMHmX9Wh2nj69fbO7x3W/26ev16MvBL4BTggRjDVTGGT8QYVmrNkFL1B+ALwBbAvTGGW2IMx8UYNhikXp38rsO037bVhxjD+jGGK8ljXzzM3ADntczbKmUK8EJgl3rePnKA8auUqls6VaK/v39Kf3//G4EdJk2aNMxNkCRJkiSNFgOMst0GLBtjGN+cmFI1J6WqSqmqgI4X4rVHO0zr63blKVX/BDYANiV35ViG/BjXWTGGjRrlDgNeDewP3EUefPSn9Vgb3er0FJXn1DXG8ErgenILkaOB7cnBSSQHMs85nlOqfkzehx+uJ20OrMbIxxSRJEmSJPWIY2CU7dvA28mBwGcX0DLvql/Xbfzcsk79endrQj0WxnXMfXrJ68ihyWHA1o1yd5NDjpPqR7teAxwcYzi+bfyMgaxDfvJIU6vrSKs+2wNLA+9OqfpBs2CMYQXgPx2WOxU4McbwJnKQ8Ti5a4okSZIkaRFiC4yynUEeyPKgGMP2A5TpukVF7Qpya4eDYgxLtibGGFYhd6/4A7nbCG1jR7TcATwGLF+XWa65HHj20a6tLiHt3ToGckCM4YWN+qwK7AzMTKlqLevp+rW9ZcZHyI9v7eQ8cmhxEDkAuTil6qEu6yRJkiRJKoQtMAqWUvVYjGFr4ErgkhjDdcC1wH3ksTHWAiaRL+z/1OUyZ8YYvgwcDFwfY5hO7hqyF7l1wwfqVhcAU+sg4VpysLFUvb5lyI89hdy9ZEqM4WLyQJr/Bt5AbjVyc0rVzC43dwzwoxjDhfXyP1av7xONMleTu8WcF2M4GXgQeAvwTnJrknmO55SqB2MM36YeBwO7j0iSJEnSIskWGIWru2a8Adi3nvQp8uCUR5LHpzgDWDelqutHZqRUHUIOLMYCXyS3TriD/FjVqxpFzwPuBXYjdw85DHgSeG9K1al1mV8DlwCbAEcBJ9Y/HwO8YxibuivwU+DT5O4yfwG2Sqm6tlHvu4CtyIOYHlrXfXlgY+DPgyx7Sv36e+CHw6iTJEmSJKkQtsBYBKRUPQZ8vf7XTfkjgCOGKDOVPD7EYGUuIYcTg5W5h9xaYn7NSanaD9hviPVdD7y1w1ubDDJba2yMM1OqOg0WKkmSJEkqnC0w9HywL7nlyFm9rogkSZIkaWRsgaHFUozhxcC7yE9b2QWYklJ1X29rJUmSJEkaKQMMLa5WAi4kDyr6bfKgpZIkSZKkRZQBhnqqm/E6Rrjc2Qz/EbOSJEmSpEI5BoYkSZIkSSqeAYYkSZIkSSqeAYYkSZIkSSqeAYYkSZIkSSqeAYYkSZIkSSqeAYYkSZIkSSqeAYYkSZIkSSqeAYYkSZIkSSqeAYYkSZIkSSqeAYYkSZIkSSqeAYYkSZIkSSqeAYYkSZIkSSqeAYYkSZIkSSqeAYYkSZIkSSqeAYYkSZIkSSqeAYYkSZIkSSqeAYYkSZIkSSqeAYYkSZIkSSqeAYYkSZIkSSqeAYYkSZIkSSqeAYYkSZIkSSqeAYYkSZIkSSqeAYYkSZIkSSqeAYYkSZIkSSqeAYYkSZIkSSqeAYYkSZIkSSqeAYYkSZIkSSqeAYYkSZIkSSqeAYYkSZIkSSqeAYYkSZIkSSremF5XQFoUjFtqLDOunNbrakiSJElSz41bamxP1muAIXVh11127nUVJEmSJOl5zS4kkiRJkiSpeAYYkiRJkiSpeAYYkiRJkiSpeAYYkiRJkiSpeAYYkiRJkiSpeAYYkiRJkiSpeD5GVerCuedfwKOPPd7rakiS5tO4pcb6aGxJkhZRBhhSFx597HE222anXldDkjSfZlw5rddVkCRJI2QXEkmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEkZVq+GAAAgAElEQVSSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDPVEjOGIGEN/jGG1Lsv3xxjOHo1lS5IkSZLKN6bXFdDiJcawCfCDtslzgJnAucDJKVVPL+x6SZIkSZIWbQYYGi0XAlcBfcB/A7sDJwDrAnuNYHlLAQYfkiRJkvQ8ZYCh0fKLlKrzW7/EGE4FfgfsGWOYPNyFpVQ9viArJ0mSJElatBhgaKFIqXokxvATYAdg9cZbL4oxHAPsCqwE3AF8JqXqqub8MYZ+4JyUqt2Hsdquli1JkiRJKp+DeGqhiDH0AWvWv/6j8dY5wNuArwCTyUHDZQtoAM7RXLYkSZIkaSGyBYZGy7gYw4rkMTBWAfYDXg/clFJ1Z4yhVe4fwLtSqvoBYgw/AH4KfBT4zHzWYdjL7uvr24s8RseLpk+fzuTJw+7tIkmSJEkaBbbA0Gg5Evg78Dfg18AewBXAdm3lTmwFDAApVT8D/gW8egHUYdjL7u/vn9Lf3/9GYIdJkyYtgCpIkiRJkhYEW2BotEwBLgL6yY9RnZVS9UCHcnd3mPYAsMJgC48xLA+8sDktpeq+BbFsSZIkSVJ5DDA0Wu5Mqaq6KDfQo1H7hpjvEmDjIeYZ6bIlSZIkSYUxwNCi6lPAS3tdCUmSJEnSwmGAoUVSStUtva6DJEmSJGnhcRBPSZIkSZJUPAMMSZIkSZJUvL7+/v6hS0nPM319fRNmzpw5c8KECQCcNvVMNttmpx7XSpI0v2ZcOY2PfWSPXldDkqTFwqxZs5g4ceLE/v7+WQtjfbbAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxTPAkCRJkiRJxRvT6wpIi4JxS41lxpXTel0NSdJ8GrfU2F5XQZIkjZABhtSFXXfZuddVkCRJkqTnNbuQSJIkSZKk4hlgSJIkSZKk4hlgSJIkSZKk4hlgSJIkSZKk4hlgSJIkSZKk4hlgSJIkSZKk4vkYVakL555/AY8+9nivqyFJi71xS4310dWSJKkjAwypC48+9jibbbNTr6shSYu9GVdO63UVJElSoexCIkmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSijem1xXQ4ivGMA74IrAd8HLgTylVq/W0UpIkSZKkRVLXAUaMYSywB/Be4LXAS4A5wJ3ADOCslKo7RqOSz2cxhk2ATYATUqoe6m1thu0QYD/gK8CtwL96Wx1JkiRJ0qKqqwAjxrA6cCWwNvBD4GvAvcDSwHrkYOPAGMMrU6r+Mkp1fb7aBDgcOBtY1AKMCPwmpeqgXldEkiRJkrRoGzLAiDEsBXwXWAN4T0rVpR3KjAUOAPqHWNaSwBIpVY+PrLoaSmH7eGXgjwt6oTGGZVKqbM0hSZIkSc8j3bTA2BNYCzimU3gBUF8sH9ucFmM4gtxy4DXAh4EdgVWAzYHr6jJ7AnuTW3Y8AdwMHJVSdUPbsrYGDgbWBV4M/B34OfDplKpZdZlXAEfWy18ZeBj4PXB6StU5g21gW10/Wtd1OXK3h0NTqr7fVn4S8AFy65P/IneNuAH4XErVrY1yvwZeCqyWUvVM2zJ2BKYDu6ZUnTdAvc4Gdqt/vSfG0HrryJSqI4bax93Ws17XbGA28DHgeODtwDNAAvZNqbqvUXZ5YDLwbvLYFnPqeaelVH05xrA7cFZdfHyMoRVsHZlSdUS9jO2Ag4DX1+/9GjguperyAep1AHk8jTcDD9TLba0nAG+p98HLyJ/bJ1OqbooxbAx8AVgfeAQ4JaXq6Hl2tiRJkiSpaN08heS99esZI1zHN4GNyBfFnyJ3PSHG8CVgKvAkcGj9/jrAD2IM72zNXF+AXkEOFI4F9q3nWwFYsy4zhnyh/T5gGjkU+SIwC3jbMOp6LvkC+Uv1ulYFvhdjCG3l9iW3NpkC7FPX523AjTGGVzfKTQVeQe5K0W4Pcsjy7UHqczrQCo0OAD5Y/7ukrVzHfTyMera8nBwu/ZEcLlwAvIe8X5ouqpd9NXmMi6OAn5K7uwBcX9fzH8Ad7fWOMexdb9fywOeBo+ufL4sx7NWhXq8kj7Pyh7peJ7W93xoo9ERyiLU6cE0dklwC/Ag4sK7LUTGGXTqsQ5IkSZJUsG5aYLwGeCSl6p7mxBjDEuTWBU1zUqoea5v2EBBSqp5qzDuRfCF6I7BZStUT9fQzgN8Cp8QY1kipehrYlhy0bJFS9bfGcpt30dcBJgKHpFQd18U2DeQp4G2N+pxJvug9idxKpGXLlKo5zRljDOcCvyIHDXvXk88jhyEfBq5plG2FGqd32F/PSqn6SYzhVmB74LKUqtkDFJ1nHw+zni1rApNSqr7VKP8MsHeMYa2UqjtiDMsBmwGnplTtO0C97wbujjF8Hrg/per8xvJeChwH3AVsmFL1SD39VOCXwPExhm+1DVg6HvhIStVAIdoSwJsbn9tvgcvJ4dBGKVU/q6d/gxyC7AOc32lBfX19ewF7AS+aPn06kydPHmCVkiRJkqSFqZsWGMuSm963W5vclaP5b58O5U7ocGG9LdBH7jLwRGtiStVfyYNVvorc5B9yKwWAHeqWFp20ymwaY3jZoFszuK+11efP5NYNa8UY1m5MnwMQY+iLMSwbY1iRvP0zgQ0b5R4mt1bYti7T8iHyvv/GfNS1qdM+7rqeDX9thhe1GfXrmvXrY8B/gA1jDKuNoK6R3A3o/1rhRV3XR8hB0dLkLiFNDzC3S0onpzY/N3KLC4CbWuFFvY4nyC1FOrU+AaC/v39Kf3//G4EdJk2a1MXmSJIkSZIWhm4CjEfIIUa7e8gXo5HcPH8gszpMG1+/3t7hvdvq19Xr15PJd+ZPAR6IMVwVY/hEjGGl1gwpVX8gj3OwBXBvjOGWGMNxMYYNBqlXJ7/rMO23bfUhxrB+jOFK8pgSDzM3wHkt87ZKmQK8ENilnrePHGD8KqXqlmHWbyCd9vFw6wlwd4dp/6xfV4BnQ4D9yS1z7okx3B5jOCnGsHmXdR3OZ99yV90aZyDPqXdK1YP1j/d0KPsg9bZIkiRJkhYd3QQYtwHLxhjGNyemVM1JqapSqipgsAvxRztM6+u2gilV/wQ2ADYl36FfhvwY11kxho0a5Q4j31nfn9w9YU/gp/VYG93q9BSV59Q1xvBK8hgP65O7sWxPDk4i+aL8Ofs0perH5H344XrS5sBqjHxMkU7m2cfDrWdtsJDg2f2QUnUaeRs+AvyCPE5KFWOY1kVdu/7sGzodQ00D1Xuw7ZEkSZIkLUK6GQPj2+QnUuwJfHYBrfeu+nXdxs8t69Svz95Vr+++X8fcp5e8jhyaHAZs3Sh3NznkOKl+tOs1wMExhuPbxs8YyDrkJ1g0tbqOtOqzPbmbw7tTqn7QLBhjWIHcvaLdVODEGMObyEHG4+SuKd0Y9NG0gxhJPbuWUnUvOYQ5ox4P5Tzg/fW+/tkgszY/+++3vTfPZy9JkiRJEnTXAuMM8kCWB8UYth+gzHDvql9BvjA/KMawZGtijGEVcveKP5C7jdA2dkTLHeSxGJavyyzXXA48+2jXVpeQTt0lOjkgxvDCRn1WBXYGZqZUtZbVuqvf3jLjI+THt3ZyHjm0OIgcLFzcNkjlYP5dvy7fZfmWkdRzSDGGcTGGcc1pdcDUCn6GqmciP3Z1vxjDMo3lLkN+osm/6zKSJEmSJD1ryBYYKVWPxRi2Bq4ELokxXAdcC9xHHhtjLWAS+YL5T92sNKVqZozhy8DBwPUxhunkriF7kVsNfKAx5sHUOki4lhxsLFWvbxnmPt5zU2BKjOFi8gCV/wbeQG41cnNK1cxu6kXeHz+KMVxYL/9j9fo+0ShzNblLw3kxhpPJYyq8BXgnuXXBPPs0perBGMO3qcfBYHjdR26qX78UY/gmOQi5LaXqtkHmGVE9uzQB+GGM4VJy15gHya1UPk4ec+JHg8xLStVDMYaDga8DN8cYzq7f2p08UOhH68FPJUmSJEl6VjctMFpdM94AtB6b+Sny4JRHksenOANYN6VqercrTqk6hBxYjAW+SG6dcAf5sapXNYqeB9wL7EbuHnIY8CTw3pSqU+syvwYuATYBjgJOrH8+BnhHt3UCdiU/peLT5O4yfwG2Sqm6tlHvu4CtyBfrh9Z1Xx7YGPjzIMueUr/+HvhhtxVKqboROARYg9wV5ULymBNDzTfSeg7lT8CZwHrkz+JkYLu6bm9JqRpqvApSqk4B3kN+/Ovh9b+HgO1TqqYMNq8kSZIk6fmpr79/pEMsLD5iDEeQL6LHp1TNHqV1vAm4GTg0perY0ViHFpy+vr4JM2fOnDlhwgQATpt6Jptts1OPayVJi78ZV07jYx/Zo9fVkCRJXZg1axYTJ06c2N/f3/HJmAtaVy0wtEDsS245clavKyJJkiRJ0qJmpOMgqAsxhhcD7yI/cWMXYEpK1X29rZUkSZIkSYseA4zRtRJ5zIp/kx9He3BvqyNJkiRJ0qLJAANIqToCOGIUljub4T9iVpIkSZIktXEMDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVLwxva6AtCgYt9RYZlw5rdfVkKTF3rilxva6CpIkqVAGGFIXdt1l515XQZIkSZKe1+xCIkmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSimeAIUmSJEmSiudjVKUunHv+BTz62OO9roZUlHFLjfURw5IkSVpoDDCkLjz62ONsts1Ova6GVJQZV07rdRUkSZL0PGIXEkmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDEmSJEmSVDwDDC0UMYb+GMPZwyg/O8Zw3ejVSJIkSZK0KBnT6wpo4YkxbAL8oG3yHGAmcC5wckrV0wuxPkcAv0qpumxhrVOSJEmStGgywHh+uhC4CugD/hvYHTgBWBfYa5TWuRTQHo4cDpwDdAowJgL9o1QXSZIkSdIixgDj+ekXKVXnt36JMZwK/A7YM8YwOaXq/gW9wpSqx4dZ/j8Lug6SJEmSpEWXAYZIqXokxvATYAdgdeD+GMNqwNHAFsBLgD8D04HPp1Q92po3xrA8MBl4N/BycpeU2cC0lKovN8r1A+ekVO1eL/ue+q3dYgy7NerSV5efDcxOqdqkWdcYw3bAQcDr60m/Bo5Lqbq8rdzsuh4fA44H3g48AyRg35Sq+4a1kyRJkiRJPeUgniLG0AesWf/6jxjDq4CfAjuSu5scANwCfAa4OsbQDL4uAvYFrgb2A46q591kkFX+Hfhg/fOP6p8/2Jg2UD33Bi4Flgc+Tw5YlgcuizF06vrycuA64I/k0OMC4D3k8T4kSZIkSYsQW2A8P42LMaxIHgNjFXLw8HrgppSqO2MM3wRWArZOqbqqnueUGMOXgQOB3YBvxBiWAzYDTk2p2rfbladUzQHOjzGcB9zd7M4ykBjDS4HjgLuADVOqHqmnnwr8Ejg+xvCtlKqHGrOtCUxKqfpWYznPAHvHGNZKqbqjfT19fX17kccBedH06dOZPHlyt5slSZIkSRpFtsB4fjqS3Arib+QuGHsAVwDbxRheQO4O8stGeNFyLLkbxvb1748B/wE2rLuFjKYIvBj4v1Z4Abn7C3ASsDQQ2ub5azO8qM2oX9ekg/7+/in9/f1vBHaYNGnSAqm4JEmSJGn+GWA8P00hBwIB2AhYKaVq23rwzpXIYcDt7TOlVD0A3EseJ4OUqieA/YHXAPfEGG6PMZwUY9h8FOo8vn6dp17AbfXr6m3T7+5Q9p/16woLolKSJEmSpIXDLiTPT3emVFUDvNc3nAWlVJ0WY7gc2BrYGHgvsG+MYXpK1U7zWc8R16vW/tjW+V2eJEmSJKlHbIGhdn8D/gWs2/5GPQ7FKrS1bEipujel6oyUqg8Cq5IH/pwUY9hgAdbrrvp1nnoB69SvnVpcSJIkSZIWAwYYeo6UqmeA7wDrxxi2bHv70+Rj5lKAGMO4GMO4tvmfBm6tf11+iNX9u4syzy6a/IjW/WIMy7Qm1j/vVy8rdbksSZIkSdIixi4k6uRQ8hgZl8UYTgF+D7wdmARcD5xTl5sA/DDGcCl5HIoHgbWBjwP3kB+ROpibgBBjOIT8qNP+lKppnQqmVD0UYzgY+Dpwc4zh7Pqt3ckDcn40perh4W+qJEmSJGlRYAsMzSOl6g/AhsBFwC7ACcCbyE8h2Sql6qm66J+AM4H1gMOAk4HtgKnAW1KqHh1iVXsDNwCfBS4gdz0ZrF6nAO8BHgIOr/89BGyfUjVleFspSZIkSVqU9PX39/e6DlJx+vr6JsycOXPmhAkTADht6plsts2CHJNUWvTNuHIaH/vIHr2uhiRJknpk1qxZTJw4cWJ/f/+shbE+W2BIkiRJkqTiGWBIkiRJkqTiGWBIkiRJkqTiGWBIkiRJkqTiGWBIkiRJkqTiGWBIkiRJkqTiGWBIkiRJkqTiGWBIkiRJkqTiGWBIkiRJkqTiGWBIkiRJkqTiGWBIkiRJkqTiGWBIkiRJkqTiGWBIkiRJkqTiGWBIkiRJkqTiGWBIkiRJkqTiGWBIkiRJkqTiGWBIkiRJkqTiGWBIkiRJkqTiGWBIkiRJkqTiGWBIkiRJkqTiGWBIkiRJkqTiGWBIkiRJkqTiGWBIkiRJkqTiGWBIkv5/e3cf7VlV1w/8fX+BDgP+eOxnYK5IZMbHRWpq2MKQzs6WkY8ViEhqQNgSBEtJk0KKlYAUChI/8CkkndHMJ35a7uMAggpoWJbWDCG4MszwgUoeRPH+/jjfa1+/852ZO9zh3n1nXq+17jrz3WeffT7fWXNZ67zZex8AAGieAAMAAABongADAAAAaJ4AAwAAAGieAAMAAABongADAAAAaJ4AAwAAAGieAAMAAABongADAAAAaJ4AAwAAAGjeTktdACwHK3dZkXWXr1nqMqApK3dZsdQlAACwAxFgwDwcc/RRS10CAADADs0SEgAAAKB5AgwAAACgeQIMAAAAoHkCDAAAAKB5AgwAAACgeQIMAAAAoHleowrzcOll78qdd9291GU0a+UuK7xqFgAAuF8JMGAe7rzr7hx2+JFLXUaz1l2+ZqlLAAAAtnOWkAAAAADNE2AAAAAAzRNgAAAAAM0TYAAAAADNE2AAAAAAzRNgAAAAAM0TYAAAAADNE2AAAAAAzRNgAAAAAM0TYAAAAADNE2AAAAAAzRNgAAAAAM0TYAAAAADNE2AAAAAAzRNgAAAAAM0TYAAAAADNE2AAAAAAzRNgAAAAAM0TYAAAAADNE2AAAAAAzRNgAAAAAM0TYAAAAADNE2AAAAAAzRNgAAAAAM0TYAAAAADNE2AAAAAAzRNgAAAAAM0TYAAAAADNE2AAAAAAzRNgAAAAAM0TYAAAAADNE2AAAAAAzRNgAAAAAM0TYAAAAADNE2Bwn5XS3VJKd+VS1wEAAMD2b6elLoCNldIdmuSKieY7kmxI8s4k59faf2+x61pOSumemeQ5SQ5O8tAk/5nki0neUGv/10tZGwAAAFvPDIy2vTvJC5Mck+R1SXZO8idJLlzKopaJi5P8bJIPJ3l5kvOSPCTJR0vpfm8pCwMAAGDrmYHRthtq7S+b+1BKd2GSf05ybCnd79Xa37Z0pTXvqFr7deMNpXQXJPlckj8opbuw1v5bS1MaAAAAW0uAsYzU2t9RSndtkl9JckCSHwQYpXS/kOQ3kjwxyb5JvpPk+iRn1tpfNT7OaN+K/ZM8Jcm5SX4xyQOSXJPkxFr7DRP9Hzrq9/QkM0muSnLypuospTs2yW8leWSSe5Jcl+SMWvtrJvrNJvnzJJcmOTPJQUm+mWGJzFmldHsmeUOSX06yW5J1SY6vtb91Hn9X66a03VlKd3mSVyRZneTaLY0DAABAGywhWX4OGB2/OdH+oiR7ZQgDTkzypxkChI+X0h0yZZxdk3wiyb1JXpPkzUkOTfLBUrofmetUSrfHqN9zM+y/8btJ7sywR8euk4OW0p2V5JIk3x2Ne26SRyW5opTuGVPqeFyS9ya5MslvJ7kxyetL6V6e5ONJ9kxyepKLMgQtl04ZY2v8+Oj4HwscBwAAgEVkBkbbVpbS7ZNh1sOPJTkhwwP/ZyZnSSQ5rtb+jvGGUrqLknwhyauTXD3Rf58k59Tanz3W/7YkZyfpkvzNqPlVGWZrvKTW/u2jtgtL6c7LsLfE+P1WJ3llkk8mOazW/p5R+1sybKB5YSndAbX2945d9tgkB9faXzfq+9YkX84QwFxQa3/S2PhJckop3epa+/XT/8o2rZTuoAxBzNW19l+a1mdmZub4JMcneeDatWtz2mmnbe1tAAAAuB+YgdG212VYJvIfST6fYVnGXyV55mTH8fCilG63Urq9M8yuuC7Jk6eM/f0kb5pom1t2ceBY27OTfC0bz3w4a8qYz8oQtpw9F16Mars1yTuS/ESGAGbcp+fCi1HfezIsfZmZUt9cCHNgtlIp3Y9m+Lu7K8mxm+o3Ozt78ezs7E8ned4RRxyxtbcBAADgfmIGRtsuzrC8YucMMxVOzbAE4u7JjqV0B2TYR+LpSfaYOD07Zexba+0nx/nG6Lj3WNvDMsz4GJ81kVr7r5bS3T5x/U+Ojl+Ycr9/HBvvs2Pt02ZCzG2uefMm2vfOViil2ytJTbJfkl+aMnsFAACAxgkw2nZjrX0/+vNHS+muybDR5kVJjpzrVEq3W4Z9KnbN8LrQf0jy3xlmWbw6yWFTxr53StucmYnP0wKQaf0mP8/HJuuYDE3uy31G4UWf5BFJnj1tc08AAADaZwnJMlJr/6kMG2keUUr3lLFTP59hdsEptfan19q/r9b+Y6PwY6ONNrfSl5KsGt/YM0lK6fZNsvtE35tGx0dPGedRY+MtitFbTOqonufU2v/1Yt0bAACAbUuAsfz8YYZZC2eMtc3NVPihmQmjV6tO2/9ia3wwyYOTHDPRfuqUvh/KMFvjlaV0O4/VsW+SF2fYnPNzC6xnXkbhRZ/kMUmeV2v/0cW4LwAAAPcPS0iWmVr7fymlW5PkBaV0h9TaX51hWcm/Jzm3lG7/JF9J8lNJXphhOcljF3DLs5McleSSUronZNjf4tAkByf5+kRt60vpzsnw5pJPlNKtTfKgDG/12C3JCzazLGRbq0ken+TdSfYopTt64vynNvUmEgAAANpjBsbydGaG/S3OSJJa+9szbN55XZITk5ybYcnGM5LcsJAb1dp/K8khST6QYRbG2UlWJnlakjum9D81Q2CxIsnrM7xW9Z8zvFb1IwupZSs9YXR8foZlN5M/T13EWgAAAFigmdnZTe3PCDuumZmZVevXr1+/atWqJMlFl7wthx1+5Bau2nGtu3xNTjjuJUtdBgAAsIg2bNiQ1atXr56dnV2UNz2agQEAAAA0T4ABAAAANE+AAQAAADRPgAEAAAA0T4ABAAAANE+AAQAAADRPgAEAAAA0T4ABAAAANE+AAQAAADRPgAEAAAA0T4ABAAAANE+AAQAAADRPgAEAAAA0T4ABAAAANE+AAWIZGRYAABXgSURBVAAAADRPgAEAAAA0T4ABAAAANE+AAQAAADRPgAEAAAA0T4ABAAAANE+AAQAAADRPgAEAAAA0T4ABAAAANE+AAQAAADRPgAEAAAA0T4ABAAAANE+AAQAAADRPgAEAAAA0T4ABAAAANE+AAQAAADRPgAEAAAA0T4ABAAAANE+AAQAAADRvp6UuAJaDlbusyLrL1yx1Gc1aucuKpS4BAADYzgkwYB6OOfqopS4BAABgh2YJCQAAANA8AQYAAADQPAEGAAAA0DwBBgAAANA8AQYAAADQPAEGAAAA0DwBBgAAANC8nZa6AFgOLr3sXbnzrruXuoxtYuUuK3LM0UctdRkAAABbRYAB83DnXXfnsMOPXOoytol1l69Z6hIAAAC2miUkAAAAQPMEGAAAAEDzBBgAAABA8wQYAAAAQPMEGAAAAEDzBBgAAABA8wQYAAAAQPMEGAAAAEDzBBgAAABA8wQYAAAAQPMEGAAAAEDzBBgAAABA8wQYAAAAQPMEGAAAAEDzBBgAAABA8wQYAAAAQPMEGAAAAEDzBBgAAABA8wQYAAAAQPMEGAAAAEDzBBgAAABA8wQYAAAAQPMEGAAAAEDzBBgAAABA8wQYAAAAQPMEGAAAAEDzBBgAAABA8wQYAAAAQPMEGAAAAEDzBBgAAABA8wQYAAAAQPMEGAAAAEDzBBgAAABA8wQYAAAAQPN2WuoC2HZK6Q5NcsVE8x1J1ie5NMkFtfb3LnZdAAAAsFACjO3Tu5N8JMlMkv2SvCjJeUkeneT4pSsLAAAA7htLSLZPN9TaX1Zr/85a+7OSPDnJrUmOLaV78LQLSul2LqVbsahVbmOldA9a6hoAAAC4f5iBsQOotf+vUrpPJ3lekoeV0r00yR8keUyS30jya0n2TfLzSa5MklK6LsmrkjwpyYokG5JcWGt/0fjYpXRPSXJakscl2SPJN5L8fZIzau2vHfXZa9TnmUkekmFZyy1J1tTanzPqc2iG5S8vrrV/x8Q93pHk12vtZ8barkyyf5LDkpw9Ou6ZYdZJSulmkpyQ5Ngkj0xyb5LPjuqaXGYDAABA48zA2AGMHuYfPvr49bFTf5Hk4CTnJvntJF8d9T8+yceS7JbkzCSvSHJTkj8rpTtnbNzVSWqSVUnemOS3krw5yWySg8bu894kL0vy0SQnJjkjyfVJDl3gV9styVVJvpfk95KcPnbunUkuSPIvGYKY05PsnqSW0j1zgfcFAABgkZmBsX1aWUq3T4bZCPtmCA0OSnJtrf2NpXRz/W5P0tXaf2+uoZRu3yRvyjA74qixMS8spXtjkleU0l1Ua39TkqcnWZnk+bX2108rpJRu9wyzI/6s1v5l2/RbJnsnObPW/rUT93xOkhck+c1a+4vH2t+Y5Nokbyyl+3Ct/ezkgDMzM8dn2CfkgWvXrs1pp522jUsGAADgvhBgbJ9eN/qZ8/0kH8rGG3ieNx5ejPxKkgcmeesoBBn34SQnZVhqclOS/xy1P6uU7vO19ndPqeWuJN9J8uRSuv1r7W/Z2i+zBW+Y0nZ0kv9O8oFNfIfTkxyYYVnMD5mdnb04ycUzMzOrjjjiiPXbuFYAAADuIwHG9uniDMs2ZjPsN7Gh1v6bU/pt9ACfYb+IJOk3M/7cRqBrMoQFr0lySindtUn+JsPsjS8nSa39PaV0J2dYYnJzKd0Xk6xL8oFa+49v3dfayG219rdv4js8KMnXtvAdpn1/AAAAGiTA2D7dWGu/uQBizp1T2uY2yjwmoz0xpvhSktTafydJKaV7UoblJE/NsL/F6aV0R9Xav3/U76JSug8m+aUkP5dhlsfLSunW1tofORpzo+UcYzb173Ra/XPf4bYkR23ifJL842bOAQAA0BgBBpNuHB2/Ps8QJKP9L65PklK6hyb5XJI/SvL+sT5fTfKWJG8ppfuRDJtsPr+U7txa+88kmZshsteUWzzsPnyHVRn2/Pj2Vl4LAABAg7yFhEnvybBnxetK6XaZPFlKt3sp3QNHf57cXyJJvpJh9sNeoz4rS+lWjneotb83yedHH+cCi5szvE2kG+87ek3rz2zld7g0w7/tP552spTuwdPaAQAAaJcZGPyQWvuvlNK9NMNsiX8qpXtnki8n+dEkj03y7CSPSnJLkteW0v1CksszBBAzSX45ySOSnD0aclWSq0rp3p9h2ca3MuxR8dLRNVeP7vvtUrp3JDm2lO7dSa7MsNHmizOEHeOvZd3Sd/jLUrq3Z1im8vhRfV9P8uMZXhv78Gz9rA4AAACWkACDjdTav72UbkOS30nym0n2yBAArE9yWpJ/H3X9QIbXtP5ahk0x78qwfOO4JG8d9fnXJG9L8rQM4ccDk/xbkkuSnFVrP76PxSmj43OTPCvJDRkCkeOzFQHG6Du8pJTuitG1r07ygFHdN4w+AwAAsIzMzM5ubu9E2DHNzMysWr9+/fpVq1YlSS665G057PAjt3DV8rDu8jU54biXLHUZAADAMrdhw4asXr169ezs7KK84dEeGAAAAEDzBBgAAABA8wQYAAAAQPMEGAAAAEDzBBgAAABA8wQYAAAAQPMEGAAAAEDzBBgAAABA8wQYAAAAQPMEGAAAAEDzBBgAAABA8wQYAAAAQPMEGAAAAEDzBBgAAABA8wQYAAAAQPMEGAAAAEDzBBgAAABA8wQYAAAAQPMEGAAAAEDzBBgAAABA8wQYAAAAQPMEGAAAAEDzBBgAAABA8wQYAAAAQPMEGAAAAEDzBBgAAABA8wQYAAAAQPMEGAAAAEDzBBgAAABA8wQYAAAAQPMEGAAAAEDzBBgAAABA8wQYAAAAQPN2WuoCYDlYucuKrLt8zVKXsU2s3GXFUpcAAACw1QQYMA/HHH3UUpcAAACwQ7OEBAAAAGieAAMAAABongADAAAAaJ4AAwAAAGieAAMAAABongADAAAAaJ4AAwAAAGjeTktdACwHl172rtx5191b7LdylxU55uijFqEiAACAHYsAA+bhzrvuzmGHH7nFfusuX7MI1QAAAOx4LCEBAAAAmifAAAAAAJonwAAAAACaJ8AAAAAAmifAAAAAAJonwAAAAACaJ8AAAAAAmifAAAAAAJonwAAAAACaJ8AAAAAAmifAAAAAAJonwAAAAACaJ8AAAAAAmifAAAAAAJonwAAAAACaJ8AAAAAAmifAAAAAAJonwAAAAACaJ8AAAAAAmifAAAAAAJonwAAAAACaJ8AAAAAAmifAAAAAAJonwAAAAACaJ8AAAAAAmifAAAAAAJonwAAAAACaJ8AAAAAAmifAAAAAAJonwAAAAACaJ8AAAAAAmifAAAAAAJonwAAAAACaJ8DgPiulu6WU7sqlrgMAAIDt305LXQAbK6U7NMkVE813JNmQ5J1Jzq+1/95i17WclNKtTnJcksePfnZP8rpa+9OXsi4AAADuGwFG296d5CNJZpL8WJJjkvxJkkcmOX4J61oODk7yiiQ3JfnbJIctbTkAAAAshCUkbbuh1v6yWvt31tqfk+RnknwlybGldD+6xLW17kNJ9qq1PzDJqUtdDAAAAAtjBsYyUmt/RyndtUl+JckBSW6bO1dK9wtJfiPJE5Psm+Q7Sa5Pcmat/VXj44z2rdg/yVOSnJvkF5M8IMk1SU6std8w0f+ho35PzzAb5KokJ2+qzlK6Y5P8VoaZIvckuS7JGbX210z0m03y50kuTXJmkoOSfDPDEpmzSun2TPKGJL+cZLck65IcX2t/6zz+rr65pT4AAAAsH2ZgLD8HjI6TD+gvSrJXhjDgxCR/miFA+Hgp3SFTxtk1ySeS3JvkNUnenOTQJB8spfuRuU6ldHuM+j03w/4bv5vkzgx7dOw6OWgp3VlJLkny3dG45yZ5VJIrSumeMaWOxyV5b5Irk/x2khuTvL6U7uVJPp5kzySnJ7koQ9By6ZQxAAAA2M6ZgdG2laV0++R/9sA4IcMD/2cmZ0kkOa7W/o7xhlK6i5J8Icmrk1w90X+fJOfU2p891v+2JGcn6ZL8zaj5VRlma7yk1v7to7YLS+nOS/LyifutTvLKJJ9Mclit/T2j9rck+eLougNq7e8du+yxSQ6utb9u1PetSb6cIYC5oNb+pLHxk+SUUrrVtfbrp/+VAQAAsD0yA6Ntr8uwTOQ/knw+w7KMv0ryzMmO4+FFKd1upXR7Z5hdcV2SJ08Z+/tJ3jTRtm50PHCs7dlJvpaNZz6cNWXMZ2UIW86eCy9Gtd2a5B1JfiJDADPu03PhxajvPRmWvsxMqW8uhDkw95OZmZnjZ2ZmPpvkfWvXrr2/bgMAAMBWMgOjbRdnWF6xc4aZCqcm+fEkd092LKU7IMM+Ek9PssfE6dkpY99aaz85zjdGx73H2h6WYcbH+KyJ1Np/tZTu9onrf3J0/MKU+/3j2HifHWv/0pS+3xodb95E+965n8zOzl6c5OKZmZlVRxxxhFkeAAAAjRBgtO3GWvt+9OePltJdk2GjzYuSHDnXqZRutwz7VOya5Lwk/5DkvzPMsnh1pr9C9N4pbXNmJj5PC0Cm9Zv8PB+brGMyNFngfQAAAFjGLCFZRmrtP5VhI80jSumeMnbq55Psl+SUWvvTa+3fV2v/sVH4sdFGm1vpS0lWjW/smSSldPsm2X2i702j46OnjPOosfEAAABgqwgwlp8/zDBr4YyxtrmZCj80M2H0atVp+19sjQ8meXCSYybaT53S90MZZmu8spRu57E69k3y4gybc35ugfUAAACwA7KEZJmptf+XUro1SV5QSndIrf3VGZaV/HuSc0vp9k/ylSQ/leSFGZaTPHYBtzw7yVFJLimle0KG/S0OTXJwkq9P1La+lO6cDG8u+UQp3dokD0pyfJLdkrxgM8tCtqlSut0zvE42GWanJMlTS+leO/rzh2rtP78YtQAAALBwZmAsT2dm2N/ijCSptb89w+ad12V4aD83w5KNZyS5YSE3qrX/VpJDknwgwyyMs5OsTPK0JHdM6X9qhsBiRZLXZ3it6j9neK3qRxZSy1baM8NslT9M8tJR29PG2h6/iLUAAACwQDOzs5vanxF2XDMzM6vWr1+/ftWqVUmSiy55Ww47/MgtXJWsu3xNTjjuJfd3eQAAAEtuw4YNWb169erZ2dkNi3E/MzAAAACA5gkwAAAAgOYJMAAAAIDmCTAAAACA5gkwAAAAgOYJMAAAAIDmCTAAAACA5gkwAAAAgOYJMAAAAIDmCTAAAACA5gkwAAAAgOYJMAAAAIDmCTAAAACA5gkwAAAAgOYJMAAAAIDmCTAAAACA5gkwAAAAgOYJMAAAAIDmCTAAAACA5gkwAAAAgOYJMAAAAIDmCTAAAACA5gkwAAAAgOYJMAAAAIDmCTAAAACA5gkwAAAAgOYJMAAAAIDmCTAAAACA5gkwAAAAgOYJMAAAAIDmCTAAAACA5gkwAAAAgOYJMAAAAIDm7bTUBcBysHKXFVl3+Zp59QMAAGDbE2DAPBxz9FFLXQIAAMAOzRISAAAAoHkCDAAAAKB5AgwAAACgeQIMAAAAoHkCDAAAAKB5AgwAAACgeV6jCtPtfPPNNy91DQAAAM0aPTPtvFj3m5mdnV2se8GyMTMzs2uS/ZN8d9T0a0nes2QFAQAAtGfnJE+bnZ29YDFuJsCAeZiZmfns7OzsTy91HQAAAC1ZzGcle2AAAAAAzRNgAAAAAM0TYMD8XLzUBQAAADRo0Z6V7IEBAAAANM8MDAAAAKB5AgwAAACgeQIMAAAAoHk7LXUBcH8qpftfSV6e5DeT7J/ktiTvSfL7tfZ3zHOMZyR5bZKDknwnyceTvKrW/uYpfXdP8kdJnptk7yQ3JbkgyUW19jacAQAAmrAtnpUmxntPkl9N8oVa+8dMnDs0yRWbuPT/1dofPp97mIHB9u5Pk/xJki8mOTHJe5OclOTDo1/YzSqle26Sy5PskuSVSc5J8tQknyyl22+i7wOS1CQnJFk7ut/6JBcm+YNt9H0AAAC2hQU9K40rpTs8yfOS3LWFrhcneeHEzxvmex8zMNhuldI9OsMv4l/V2j9vrP3mJG9KcmSSd23m+p2TnJ/kX5McUmv/7VH7R5P8bZLTkxw/dsmxSZ6Y5KRa+/NHbZeU0r0vyWtK6d5ea//lbfT1AAAA7pOFPitNjLVbhv9p++Ykz9xC90/X2l92n4qOGRhs356fZCbJeRPtlyS5M8nRW7j+55Lsl+Qtc+FFktTa/12SK5McMQo55hw1GveSiXHOS7JzkiO2sn4AAID7w0KflcadmWFyxGvn07mUbtdSuhVbMf4PCDDYnj0xyfeTXD/eWGt/d5K/G53f0vVJ8ukp565N8r+TrEp+sH7s8Uk+Nxp/3PWjOrZ0PwAAgMWw0GelJEkp3ZOSvCzJybX2/zWPS96Y5NtJ7iql21BK9/JSupn5Fi3AYHu2X5Kv19p/Z8q5f0uyz2jfis1dP9d32vVJ8pDRcc8M+2Rs1Hd0/2+M9QUAAFhKC31WSindThlmbHys1v49W7jfd5N8KMmrMiwzOSHJ7RlmgLxtvkXbA4Pt2coMbw2Z5u6xPvds5vpsYoy7J/psru9c/5WbOAcAALCYFvqslAwvOTgwyXO2dLNa+08medZ4WyndJUk+kuRFpXRvrbW/ZkvjmIHB9uzOJA/cxLkVY302d302Mcbk9ZvrO9d/c/cCAABYLAt6Viqle3iS309yZq39l+5LAbX230/yx6OPz5jPNQIMtme3Zpj6NO0X8yEZpkxtLlG8dazvtOuT/1ky8q0MrwzaqO/o/ntn+lIUAACAxbbQZ6Vzk3wzyftL6R4+95NhlccDRp/3nUcdt4yO+8ynaAEG27PPZPg3/qTxxtGOtz+V5LPzuD5JDp5y7meS/FeSDckP0sMbkjxuyn8EnjSqY0v3AwAAWAwLfVb6iQz7aHwhyY1jPw/JsKzkxmz8dsZpDhwdvzafogUYbM/WJplNcvJE+3EZ1nP9xVxDKd2+pXSPKKUb36fiqiRfTXLs6N3Gc30PSnJokvfW2n93rP+7R+MeP3G/k5N8L8mWNrYBAABYDAt9VvqdJL865ee2JP86+vMfj42x92QBo//xe/ro44fnU/TM7OzsfPrBslRKd36G1/q8P8MGMY9MclKSTyY5bDRzIqV070jy60meVmt/5dj1v5rhl/vvMySI/zvJKRl+2Z9Qa/9vY30fkORTSQ5K8qYk/5RhLddzkvxRrf1p9+NXBQAAmLeFPittYsxbkny71v4xE+2fybBs5W9Hx/2SHJ1hBsb5tfYnzadmMzDY3p2cIR18dJI3JzkyyflJDp/7hdycWvv3ZnjNz3eSvCHJqUmuTvKz4+HFqO89Sbok/zfJ80f3e0SSEzNscAMAANCKBT0rbaW/TPJ/Mjwb/VmSV2TYI/Co+YYXiRkYAAAAwDJgBgYAAADQPAEGAAAA0DwBBgAAANA8AQYAAADQPAEGAAAA0DwBBgAAANA8AQYAAADQPAEGAAAA0DwBBgAAANC8/w/uUBwynk5Y9AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz = plot_importances(I, title=\"Feature importance via avg drop in variance (sklearn)\", \n",
    "                       label_fontsize=18, minheight=10, width=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examine cost of dropping columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import clone\n",
    "\n",
    "def dropcol_importances(rf, X_train, y_train):\n",
    "    rf_ = clone(rf)\n",
    "    rf_.random_state = 999\n",
    "    rf_.fit(X_train, y_train)\n",
    "    baseline = rf_.oob_score_\n",
    "    imp = []\n",
    "    for col in X_train.columns:\n",
    "        X = X_train.drop(col, axis=1)\n",
    "        rf_ = clone(rf)\n",
    "        rf_.random_state = 999\n",
    "        rf_.fit(X, y_train)\n",
    "        o = rf_.oob_score_\n",
    "        imp.append(baseline - o)\n",
    "    return np.array(imp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_rf = RandomForestRegressor(n_estimators=100,\n",
    "                                min_samples_leaf=1,\n",
    "                                n_jobs=-1,\n",
    "                                oob_score=True,\n",
    "                                random_state = 999) # same boostrapping samples\n",
    "\n",
    "X_train, y_train = scaled_X_df, data['Production']\n",
    "rf = clone(base_rf)\n",
    "\n",
    "imp = dropcol_importances(rf, X_train, y_train)\n",
    "I = mkdf(X_train.columns,imp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "viz = plot_importances(I, title=\"Cost of dropping columns RFPIMP\", \n",
    "                       label_fontsize=18, minheight=10, width=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multicollinearity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('precision', 3)\n",
    "D = feature_dependence_matrix(X_train, sort_by_dependence=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Dependence', 'Position', 'Gross pay', 'Phi-h', 'Pressure', 'Random 1',\n",
       "       'Random 2', 'Gross pay transform'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dependence:\n",
      "Gross pay                0.95\n",
      "Gross pay transform     0.804\n",
      "Phi-h                   0.467\n",
      "Position                0.165\n",
      "Random 2               0.0505\n",
      "Pressure               -0.335\n",
      "Random 1               -0.693\n"
     ]
    }
   ],
   "source": [
    "print('Dependence:')\n",
    "print(D['Dependence'].sort_values(ascending=False).to_string())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D = feature_dependence_matrix(X_train)\n",
    "viz = plot_dependence_heatmap(D, figsize=(9,7))\n",
    "viz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove Gross pay and repeat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Position</th>\n",
       "      <th>Phi-h</th>\n",
       "      <th>Pressure</th>\n",
       "      <th>Random 1</th>\n",
       "      <th>Random 2</th>\n",
       "      <th>Gross pay transform</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.310</td>\n",
       "      <td>-1.551</td>\n",
       "      <td>1.379</td>\n",
       "      <td>-0.826</td>\n",
       "      <td>1.488</td>\n",
       "      <td>-2.042</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.136</td>\n",
       "      <td>-1.472</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.447</td>\n",
       "      <td>-0.409</td>\n",
       "      <td>-1.690</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.281</td>\n",
       "      <td>-1.132</td>\n",
       "      <td>-0.477</td>\n",
       "      <td>0.288</td>\n",
       "      <td>-0.823</td>\n",
       "      <td>-1.264</td>\n",
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       "      <td>0.310</td>\n",
       "      <td>-1.070</td>\n",
       "      <td>0.637</td>\n",
       "      <td>-0.667</td>\n",
       "      <td>-0.340</td>\n",
       "      <td>-0.792</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.466</td>\n",
       "      <td>-1.005</td>\n",
       "      <td>-1.591</td>\n",
       "      <td>-0.030</td>\n",
       "      <td>-0.961</td>\n",
       "      <td>-1.005</td>\n",
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      ],
      "text/plain": [
       "   Position  Phi-h  Pressure  Random 1  Random 2  Gross pay transform\n",
       "0     0.310 -1.551     1.379    -0.826     1.488               -2.042\n",
       "1    -1.136 -1.472     0.265     0.447    -0.409               -1.690\n",
       "2    -1.281 -1.132    -0.477     0.288    -0.823               -1.264\n",
       "3     0.310 -1.070     0.637    -0.667    -0.340               -0.792\n",
       "4     1.466 -1.005    -1.591    -0.030    -0.961               -1.005"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train1=X_train.drop(columns=['Gross pay'])\n",
    "X_train1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('precision', 3)\n",
    "D = feature_dependence_matrix(X_train1, sort_by_dependence=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Dependence', 'Position', 'Phi-h', 'Pressure', 'Random 1', 'Random 2',\n",
       "       'Gross pay transform'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dependence:\n",
      "Phi-h                   0.532\n",
      "Gross pay transform     0.326\n",
      "Position                0.149\n",
      "Random 2               0.0791\n",
      "Pressure               -0.428\n",
      "Random 1               -0.706\n"
     ]
    }
   ],
   "source": [
    "print('Dependence:')\n",
    "print(D['Dependence'].sort_values(ascending=False).to_string())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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     "execution_count": 19,
     "metadata": {},
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    }
   ],
   "source": [
    "D = feature_dependence_matrix(X_train1)\n",
    "viz = plot_dependence_heatmap(D, figsize=(9,7))\n",
    "viz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This time remove Gross pay transform and repeat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Position</th>\n",
       "      <th>Gross pay</th>\n",
       "      <th>Phi-h</th>\n",
       "      <th>Pressure</th>\n",
       "      <th>Random 1</th>\n",
       "      <th>Random 2</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.310</td>\n",
       "      <td>-1.675</td>\n",
       "      <td>-1.551</td>\n",
       "      <td>1.379</td>\n",
       "      <td>-0.826</td>\n",
       "      <td>1.488</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.136</td>\n",
       "      <td>-1.520</td>\n",
       "      <td>-1.472</td>\n",
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      "text/plain": [
       "   Position  Gross pay  Phi-h  Pressure  Random 1  Random 2\n",
       "0     0.310     -1.675 -1.551     1.379    -0.826     1.488\n",
       "1    -1.136     -1.520 -1.472     0.265     0.447    -0.409\n",
       "2    -1.281     -1.365 -1.132    -0.477     0.288    -0.823\n",
       "3     0.310     -1.158 -1.070     0.637    -0.667    -0.340\n",
       "4     1.466     -0.986 -1.005    -1.591    -0.030    -0.961"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train2=X_train.drop(columns=['Gross pay transform'])\n",
    "X_train2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('precision', 3)\n",
    "D = feature_dependence_matrix(X_train2, sort_by_dependence=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Dependence', 'Position', 'Gross pay', 'Phi-h', 'Pressure', 'Random 1',\n",
       "       'Random 2'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dependence:\n",
      "Phi-h        0.622\n",
      "Gross pay    0.593\n",
      "Random 2     0.193\n",
      "Position     0.159\n",
      "Pressure    -0.555\n",
      "Random 1    -0.802\n"
     ]
    }
   ],
   "source": [
    "print('Dependence:')\n",
    "print(D['Dependence'].sort_values(ascending=False).to_string())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 24,
     "metadata": {},
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    }
   ],
   "source": [
    "D = feature_dependence_matrix(X_train2)\n",
    "viz = plot_dependence_heatmap(D, figsize=(9,7))\n",
    "viz"
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  }
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