notebooks/week5_solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "industrial-facing",
   "metadata": {},
   "source": [
    "# Week 5 - An introduction to machine learning (Part I) - Solutions\n",
    "\n",
    "## Exercises\n",
    "\n",
    "We will work with the built-in `sklearn.datasets` diabetes dataset.\n",
    "\n",
    "This dataset comprises 442 diabetes patients, each with ten baseline predictors: age, sex, body mass index, average blood pressure, and six blood serum measurements. The response variable is measure of disease progression one year after baseline. (Note that it is not neccessary to transform any of these variables prior to training the model - everything is all ready to go straight into `sklearn`.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "completed-satellite",
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>bp</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.038076</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.061696</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>-0.044223</td>\n",
       "      <td>-0.034821</td>\n",
       "      <td>-0.043401</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.019908</td>\n",
       "      <td>-0.017646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.001882</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.051474</td>\n",
       "      <td>-0.026328</td>\n",
       "      <td>-0.008449</td>\n",
       "      <td>-0.019163</td>\n",
       "      <td>0.074412</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.068330</td>\n",
       "      <td>-0.092204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.085299</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.044451</td>\n",
       "      <td>-0.005671</td>\n",
       "      <td>-0.045599</td>\n",
       "      <td>-0.034194</td>\n",
       "      <td>-0.032356</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.002864</td>\n",
       "      <td>-0.025930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.089063</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.011595</td>\n",
       "      <td>-0.036656</td>\n",
       "      <td>0.012191</td>\n",
       "      <td>0.024991</td>\n",
       "      <td>-0.036038</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>0.022692</td>\n",
       "      <td>-0.009362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.005383</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.036385</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>0.003935</td>\n",
       "      <td>0.015596</td>\n",
       "      <td>0.008142</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>-0.031991</td>\n",
       "      <td>-0.046641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age       sex       bmi        bp        s1        s2        s3  \\\n",
       "0  0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
       "1 -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
       "2  0.085299  0.050680  0.044451 -0.005671 -0.045599 -0.034194 -0.032356   \n",
       "3 -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
       "4  0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
       "\n",
       "         s4        s5        s6  \n",
       "0 -0.002592  0.019908 -0.017646  \n",
       "1 -0.039493 -0.068330 -0.092204  \n",
       "2 -0.002592  0.002864 -0.025930  \n",
       "3  0.034309  0.022692 -0.009362  \n",
       "4 -0.002592 -0.031991 -0.046641  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import plotnine as p9\n",
    "from sklearn import datasets\n",
    "import sklearn.linear_model as linear\n",
    "\n",
    "diabetes_dataset = datasets.load_diabetes()\n",
    "predictors = pd.DataFrame(data=diabetes_dataset.data, columns=diabetes_dataset.feature_names)\n",
    "response = diabetes_dataset.target\n",
    "\n",
    "predictors.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "valued-envelope",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _diabetes_dataset:\n",
      "\n",
      "Diabetes dataset\n",
      "----------------\n",
      "\n",
      "Ten baseline variables, age, sex, body mass index, average blood\n",
      "pressure, and six blood serum measurements were obtained for each of n =\n",
      "442 diabetes patients, as well as the response of interest, a\n",
      "quantitative measure of disease progression one year after baseline.\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "  :Number of Instances: 442\n",
      "\n",
      "  :Number of Attributes: First 10 columns are numeric predictive values\n",
      "\n",
      "  :Target: Column 11 is a quantitative measure of disease progression one year after baseline\n",
      "\n",
      "  :Attribute Information:\n",
      "      - age     age in years\n",
      "      - sex\n",
      "      - bmi     body mass index\n",
      "      - bp      average blood pressure\n",
      "      - s1      tc, T-Cells (a type of white blood cells)\n",
      "      - s2      ldl, low-density lipoproteins\n",
      "      - s3      hdl, high-density lipoproteins\n",
      "      - s4      tch, thyroid stimulating hormone\n",
      "      - s5      ltg, lamotrigine\n",
      "      - s6      glu, blood sugar level\n",
      "\n",
      "Note: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times `n_samples` (i.e. the sum of squares of each column totals 1).\n",
      "\n",
      "Source URL:\n",
      "https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html\n",
      "\n",
      "For more information see:\n",
      "Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) \"Least Angle Regression,\" Annals of Statistics (with discussion), 407-499.\n",
      "(https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)\n"
     ]
    }
   ],
   "source": [
    "print(diabetes_dataset.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "sixth-equity",
   "metadata": {},
   "outputs": [
    {
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PxQASQUyfeRk2bJg+/fTTY27bs2ePhg0bdkJFAQAAHE9M4WX27Nm644479Mc//jFi/auvvqo777xT3/3ud/ulOAAAgKPFdNvogQce0Pbt2/Wd73xH6enpys7O1v79+9Xa2qpvfvObeuCBB6Ke6/Dhw3r88ce1bds2tba2KisrS/PmzdPUqVMlSQ0NDVqzZo3q6+uVk5OjRYsWady4cbGUDQAABoGYwktGRobeffddvfTSS9qyZYuam5uVmZmp888/XxdffLGSkqK/oBMKhZSZmakVK1Zo5MiR2rlzp+655x7l5OTo9NNP14oVKzRjxgytXLlSW7ZsUWVlpX75y19yawoAgCEqpvCyadMmNTY2asGCBZo9e3bEtqeeekr5+fkqKiqKaq7U1FSVlJSEl88880yNHTtWO3bsUHt7uzo7OzVnzhwlJSWpqKhIL774omprazV9+vRYSgcAAIaL+avSx/tcy7///W898cQTqqmpiamgjo4O7dq1S7NmzVJjY6Py8/MjruSMGTNGjY2NEb/j9/vl9/vDy263W6NGjYpqf06nM+InAMQDrzEDYyDPM+8fkRwOx4Cdi5jCy4cffqh77733mNsmTJigysrKmIqxLEsPP/ywzjjjDJ177rn6+OOP5fV6I8Z4vV61tbVFrKuurlZFRUV4uaysrM81pKenx1QzAEQjIyMjLvNeeeWVcZnXVPE6z1+E948jXC7XgOwnpvDicDj03//+95jbmpubFQqF+jynZVl69NFHdfDgQd1zzz1yOBzyeDy9gkpbW5s8Hk/EuoULF0bcvnK73Wpubo5qv06nU+np6WppaYmpbgCIRrSvSTgxA3meef+I5PV6FQgETmiOaMNnTOHlvPPO0yOPPKK5c+dGPKju8wBy3nnn9Wk+y7L0+OOP61//+pfuvfdepaamSpLy8vL0wgsvqLu7O3zrqK6uTjNmzIj4/dzcXOXm5oaXm5qa+txIoVCI5gMQN7y+DAw7zjPvHz0syxqw8xBTeKmoqFBRUZHOPvtsXXfddcrNzdXevXv19NNP6+OPP9Zbb73Vp/mqq6v10UcfacWKFUpLSwuvHz9+vFJSUrRx40bNmjVLtbW12rdvnwoLC2MpGwAADAIxhZfCwkJt2rRJt912m26//fbwlZHP10+cODHquQ4cOKCXX35ZKSkp+v73vx9ef9lll2n+/PkqLy9XVVWV1q9fr+zsbJWVlcnn88VSNgAAGARiCi+SNHnyZNXU1Ki9vV3Nzc0aMWJExFWTaI0cOVIvvvjicbcXFBRo1apVsZYJAAAGmZjDy+c8Hk+vD9ACAADES0x/2wgAAMAuhBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCgn/IRdAMCXKy0ttbsEYNDgygsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRku0uIB5cLpfcbndUYx0OhyTJ6/XKsqx4lgUAiDOfzzdg++L9I1JycvKAnf9BGV6CwaCCwWBUY51Op1wulwKBgEKhUJwrAwDEU2tr64Dti/ePSD6f74TPf7QXHrhtBAAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAog/KvSgMAhqbS0tK4zLt69eq4zIvYcOUFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGCXZ7gJeeuklvfHGG6qvr1dhYaFuvfXW8LaGhgatWbNG9fX1ysnJ0aJFizRu3DgbqwUAAHaz/cpLZmam5s+fr+nTp0es7+rq0ooVK1RYWKgNGzbo0ksvVWVlpQ4dOmRTpQAAIBHYHl4mTZqkiRMnKj09PWL99u3b1dnZqTlz5iglJUVFRUXKzs5WbW2tTZUCAIBEYHt4OZ7Gxkbl5+crKelIiWPGjFFjY6ONVQEAALvZ/pmX42lvb5fX641Y5/V61dbW1mus3++X3+8PL7vdbo0aNSqq/TidzoifAAAc7VjvEbx/RHI4HAN2LhI2vHg8nl5Bpa2tTR6Pp9fY6upqVVRUhJfLyspUWVnZp/0dfdsKAIDP3XTTTXGbe/369XGbe6C5XK4B2U/Chpe8vDy98MIL6u7uDt86qqur04wZM3qNXbhwoWbPnh1edrvdam5ujmo/TqdT6enpamlpUSgU6p/iAQCIUrTvV4nO6/UqEAic0BwZGRlRjbM9vIRCIYVCIXV3d6u7u1vBYFBJSUkaP368UlJStHHjRs2aNUu1tbXat2+fCgsLe82Rm5ur3Nzc8HJTU1Ofg8jndQAAMJAGy3uPZVkDdiy2h5dnn31WzzzzTHi5pqZGxcXFuvnmm1VeXq6qqiqtX79e2dnZKisrk8/ns7FaAABgN4dlWZbdRfS3pqamqMc6nU5lZGSoubl50KTfo5WWltpdAgDgOFavXm13Cf3C5/OptbX1hObIysqKalzCflUaAADgWAgvAADAKIQXAABgFMILAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAotv9tIxzBY/wBAPhyXHkBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFMILAAAwSrLdBcSDy+WS2+2OaqzD4ZAkeb1eWZYVz7IAAOjF5/PZXUK/SE5OHrBjGZThJRgMKhgMRjXW6XTK5XIpEAgoFArFuTIAACK1trbaXUK/8Pl8J3ws0V544LYRAAAwCuEFAAAYhfACAACMQngBAABGIbwAAACjEF4AAIBRBuVXpQEAGOpKS0vjNvfq1avjNnc0uPICAACMQngBAABGIbwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYJRkuwsAAGAoKy0ttbsE43DlBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKIQXAABgFJ7z0kd8Hx8AAHtx5QUAABjFiCsvhw4d0iOPPKKtW7fK4/Fo/vz5mjlzpt1lAQAAGxgRXqqrqxUKhfTkk0/K7/fr7rvv1ujRo3X22WfbXRoAABhgCX/bqKOjQzU1NbrqqquUlpam0047TcXFxXr99dftLg0AANgg4cPLp59+KknKy8sLrzv11FPV0NBgV0kAAMBGCX/bqKOjQx6PJ2Kd1+tVe3t7eNnv98vv94eX3W63Ro0aFdX8Tqcz4icAAPhix3rPdDgcA/ZemvDhJTU1NSKoSFIgEIgINNXV1aqoqAgvl5WVqbKysk/7SU9Pj2rc+vXr+zQvAABDhcvlGpD9JHx4OfnkkyVJu3fv1imnnCJJqqurU35+fnjMwoULNXv27PCy2+1Wc3NzVPM7nU6lp6erpaVFoVCoHys3j9frVSAQsLsM29ETkeiLHvTFEfRED3oiUn/0RUZGRlTjEj68pKamavLkyVq3bp1KS0u1f/9+bdq0Sbfddlt4TG5urnJzc8PLTU1NfW6kUCg05JvPsqwhfw7+Fz3Rg76IRF/QE0ejJ3oMZF8kfHiReq6sVFVV6brrrlNaWppKSkp0zjnn2F0WAACwgRHhZdiwYbrjjjvsLgMAACSAhP+qNAAAwP8ivAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AQAARiG8AAAAoxBeAACAUQgvAADAKA7Lsiy7i7CT3+9XdXW1Fi5cqNzcXLvLQQKgJ3As9AWORk/YZ8hfefH7/aqoqJDf77e7FCQIegLHQl/gaPSEfYZ8eAEAAGYhvAAAAKMM+fCSm5ur5cuXc78SYfQEjoW+wNHoCfsM+Q/sAgAAswz5Ky8AAMAshBcAAGCUZLsLsNOhQ4f0yCOPaOvWrfJ4PJo/f75mzpxpd1mIo5deeklvvPGG6uvrVVhYqFtvvTW8raGhQWvWrFF9fb1ycnK0aNEijRs3Lry9pqZGTz31lP7zn/9o7NixWrJkib7yla/YcRjoR4cPH9bjjz+ubdu2qbW1VVlZWZo3b56mTp0qib4YqqqqqvTXv/5V7e3t8vl8mj59uubPny+JnkgI1hC2atUqq7Ky0goEAtauXbusK6+80tq2bZvdZSGOampqrHfffdd67LHHrPvvvz+8/vDhw9YNN9xgPf/881YwGLTeeOMN63vf+57V2tpqWZZl7d6925o/f771t7/9zero6LAee+wx64477rDrMNCP2tvbrd/85jeW3++3QqGQ9eGHH1qXX365tWPHDvpiCGtoaLA6Ojosy7KsAwcOWIsXL7Y2b95MTySIIXvbqKOjQzU1NbrqqquUlpam0047TcXFxXr99dftLg1xNGnSJE2cOFHp6ekR67dv367Ozk7NmTNHKSkpKioqUnZ2tmprayVJb775piZMmKCvf/3rcrvdKikp0c6dO3k41SCQmpqqkpIS5eTkKCkpSWeeeabGjh2rHTt20BdDWF5entxud3jZ4XBo79699ESCGLLh5dNPP5XU06CfO/XUU9XQ0GBXSbBRY2Oj8vPzlZR05P8SY8aMUWNjo6Sey8RjxowJb/P5fDrppJPol0Goo6NDu3btUn5+Pn0xxK1du1bz5s3T9ddfr46ODhUVFdETCWLIfualo6NDHo8nYp3X61V7e7tNFcFO7e3t8nq9Eeu8Xq/a2tok9fRLWlpar+30y+BiWZYefvhhnXHGGTr33HP18ccf0xdD2LXXXqtrrrlGu3bt0nvvvRf+d0tP2G/IXnlJTU3t1UyBQKBXoMHQ4PF4wi8+n2trawv3Q2pqaq/t9MvgYlmWHn30UR08eFC33XabHA4HfQE5HA6dccYZSk5O1oYNG+iJBDFkw8vJJ58sSdq9e3d4XV1dnfLz8+0qCTbKy8tTQ0ODuru7w+vq6urCtxXz8/NVX18f3nbo0CE1NTXRL4OEZVl6/PHH9a9//Us//elPlZqaKom+wBHd3d3y+/30RIIYsuElNTVVkydP1rp169TW1qa6ujpt2rRJ06ZNs7s0xFEoFFIwGFR3d7e6u7sVDAbV1dWl8ePHKyUlRRs3btThw4f19ttva9++fSosLJQkTZ06Ve+//762bdumzs5OrVu3Tl/72td4LPggUV1drY8++kgVFRURl/zpi6EpEAjozTffVFtbm7q7u/WPf/xDr7zyir7+9a/TEwliSP95gEOHDqmqqkpbt25VWloaz3kZAtavX69nnnkmYl1xcbFuvvlm1dfXq6qqSvX19crOztaiRYt01llnhcdt2bJFa9euVXNzs84880ye3TBIHDhwQDfccINSUlLkdDrD6y+77DLNnz+fvhiC2tra9LOf/UyffPKJuru7lZmZqYsuukhz586Vw+GgJxLAkA4vAADAPEP2thEAADAT4QUAABiF8AIAAIxCeAEAAEYhvAAAAKMQXgAAgFEILwAAwCiEFwAAYBTCCwAAMArhBQAAGIXwAgAAjEJ4AXBclmWps7PT7jIAIALhBUDYddddp7POOksvv/yyzjnnHLndbr344ot69913VVxcLK/Xq+HDh+vKK6/UgQMHIn73vvvu0+mnn67U1FSNHDlSF110kerq6iRJ9fX1cjgcWrt2ra6//noNHz5cmZmZWrZsmbq6uiLm+eCDDzRjxgwNGzZM6enp+u53v6tdu3ZFjHE4HLr//vu1fPlyZWdnKysrSwsWLFAgEAiP+c9//qMbb7xRJ598slJTU3XKKafoiiuuiJhnz549uuqqq5SVlSWPx6MLLrhA77//fn+eUgBxkGx3AQASy969e7VkyRKVl5frlFNOUUpKiqZOnaqZM2fq2WefVSAQUHl5uWbPnq0//elPkqSnn35ad911l+655x4VFhbqv//9rzZv3qyWlpaIucvKyjR9+nT99re/1datW3X33XfL5XLpvvvukyTt3r1bU6ZMUUFBgdauXatQKKTly5drypQp+vvf/66TTjopPFdVVZWmTJmitWvX6qOPPtJtt92m7Ozs8FzLli3TK6+8ovvuu08FBQXy+/165ZVXwr/f3Nys888/X8OGDdOaNWs0fPhwrVmzRsXFxfrnP/+pkSNHxvtUA4iVBQD/79prr7UkWe+991543QUXXGBNmjTJ6u7uDq/74IMPLIfDYf3hD3+wLMuybrrpJmvChAnHnbeurs6SZE2ZMiVifXl5uZWWlmZ99tlnlmVZ1tKlS620tDTrwIED4TH19fVWSkqKtXz58vA6SdY3v/nNiLlKSkqs0047Lbw8btw4a9myZcet6e6777aGDx9u7d+/P7yuo6PDGj16tHXrrbce9/cA2I/bRgAiZGVl6Vvf+pYkqa2tTTU1NZo3b55CoZC6urrU1dWlr33ta8rNzdVf/vIXSdKECRP0t7/9TcuWLdOWLVt0+PDhY849Z86ciOW5c+eqra1N27dvlyRt3rxZxcXFEVdY8vPzNWnSJG3evDnid6dPnx6xfOaZZ2rPnj3h5QkTJuipp57SqlWr9MEHH/Sq5dVXX1VRUZEyMzPDx+V0OjVlypTwcQFITIQXABH+93ZJc3OzQqGQli5dqpSUlIh/9u7dq927d0vq+azMQw89pD/+8Y+aMmWKTjrpJC1ZskTt7e3Hnft/l/1+f3h/OTk5vWrKycnRZ599FrFuxIgREcsulyviw8Vr1qzR1VdfrQcffFDjx49XXl6eHnvssfD2pqYmbdy4sddxbdiwIXxcABITn3kBEMHhcIT/94gRI+RwOFRWVqZLLrmk19isrCxJUlJSkpYsWaIlS5bo008/1TPPPKM77rhDWVlZuuuuu8Ljj/6Q7+fLubm5kqTMzEzt37+/13727dunzMzMPh3H8OHD9Ytf/EK/+MUvtH37dj388MNavHixxo0bpwsuuECZmZmaMWOG7r333l6/63a7+7QvAAOLKy8Ajsvr9aqwsFA7duzQN77xjV7/FBQU9Pqdk08+WT/+8Y919tlna8eOHRHbfve730Usv/DCC0pLS9P48eMlSeeff742bdqkgwcPhsfs3r1btbW1mjJlSszHMX78eD300EOSpJ07d0qSLrroIv3jH//Q2LFjex3X5/UASExceQHwhR544AEVFxfr8ssv1xVXXKGMjAzt2bNHr732mhYsWKCpU6dq4cKFysjI0MSJE5WRkaGamhpt27ZNixcvjpjrk08+0YIFC3TFFVdo69at+vnPf66bb75ZGRkZkqSlS5fqySef1PTp03XnnXeGv22UmZmpm266qU91T548WXPmzNFZZ50lp9Opp59+Wi6XKxyCli1bpnXr1unCCy/UkiVLlJeXp3//+9967733NGrUKC1durR/TiCAfkd4AfCFJk2apC1btmj58uVasGCBgsGgRo8erWnTpun0008Pj3niiSf0xBNPqK2tTaeeeqoeeughXX/99RFzVVZW6q233tK8efPkdDq1ePFiVVZWhrefcsopeuedd3TLLbfo6quvVlJSkoqKivTggw9GfIg3GpMnT9bTTz+turo6JSUlafz48fr973+vsWPHSpK+8pWv6E9/+pPKy8t1++236+DBgxo5cqQmTpzY64PFABKLw7Isy+4iAAxu9fX1GjNmjJ577jlddtlldpcDwHB85gUAABiF8AIAAIzCbSMAAGAUrrwAAACjEF4AAIBRCC8AAMAohBcAAGAUwgsAADAK4QUAABiF8AIAAIxCeAEAAEb5P897HjIi0f9HAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8763777073553)>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response_dataframe = pd.DataFrame({\"response\": response})\n",
    "(\n",
    "    p9.ggplot(response_dataframe, p9.aes('response'))\n",
    "    # specify a histogram\n",
    "    + p9.geom_histogram(bins=20)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tough-guidance",
   "metadata": {},
   "source": [
    "### Exercise 1a\n",
    "\n",
    "Using sklearn, fit a multivariate linear regression using all 442 samples to predict the response variable. Use all 10 predictors, and also score your model using predictions on all 442 samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "japanese-sacrifice",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(152.1334841628965,\n",
       " array([ -10.01219782, -239.81908937,  519.83978679,  324.39042769,\n",
       "        -792.18416163,  476.74583782,  101.04457032,  177.06417623,\n",
       "         751.27932109,   67.62538639]))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate the model\n",
    "model = linear.LinearRegression()\n",
    "# Fit the pipeline\n",
    "model.fit(X=predictors, y=response)\n",
    "\n",
    "# Inspect the fit regression coefficients and intercept\n",
    "model.intercept_, model.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "secret-berkeley",
   "metadata": {},
   "source": [
    "Now score our model using the same dataset we used to fit the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "killing-timer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5177494254132934"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X=predictors, y=response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "vital-falls",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8763775425783)>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "response_predicted = model.predict(X=predictors)\n",
    "residuals = pd.DataFrame({'residuals': np.abs(response - response_predicted)})\n",
    "(\n",
    "# the aes object contains only the variable that will be plotted on the x axis i.e. the one for which we want to generate a distribution\n",
    "p9.ggplot(residuals, p9.aes('residuals'))\n",
    "    # specify a histogram\n",
    "    + p9.geom_histogram(bins=20)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "separate-texas",
   "metadata": {},
   "source": [
    "### Exercise 1b\n",
    "\n",
    "Do a test-train split, and run _Exercise 1a_ again, this time fitting your model with the training set, and scoring it both with the test set and with the training set.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "backed-paper",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'predictors' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-a1c70b7b166c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_selection\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpredictors_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredictors_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mresponse_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'predictors' is not defined"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "predictors_train, predictors_test = train_test_split(predictors, test_size=0.3, random_state=42, shuffle=True)\n",
    "response_train, response_test = train_test_split(response, test_size=0.3, random_state=42, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "latter-adelaide",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(152.68215576594528,\n",
       " array([  -3.89994262, -267.20726163,  547.2428539 ,  279.6840414 ,\n",
       "        -394.02524002,  115.31952954,  -28.18780172,  183.22398716,\n",
       "         627.70958412,  105.8876159 ]))"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate the model\n",
    "model = linear.LinearRegression()\n",
    "# Fit the pipeline\n",
    "model.fit(X=predictors_train, y=response_train)\n",
    "# Inspect the fit regression coefficients and intercept\n",
    "model.intercept_, model.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "vertical-danger",
   "metadata": {},
   "source": [
    "#### Intercept before\n",
    "(152.1334841628965,\n",
    " array([ -10.01219782, -239.81908937,  519.83978679,  324.39042769,\n",
    "        -792.18416163,  476.74583782,  101.04457032,  177.06417623,\n",
    "         751.27932109,   67.62538639]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "smaller-animation",
   "metadata": {},
   "source": [
    "Now score our model using the _test_ set.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "conservative-timber",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5172474671249085"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X=predictors_test, y=response_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "copyrighted-beauty",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8763775605041)>"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response_predicted = model.predict(X=predictors_test)\n",
    "residuals = pd.DataFrame({'residuals': np.abs(response_test - response_predicted)})\n",
    "(\n",
    "# the aes object contains only the variable that will be plotted on the x axis i.e. the one for which we want to generate a distribution\n",
    "p9.ggplot(residuals, p9.aes('residuals'))\n",
    "    # specify a histogram\n",
    "    + p9.geom_histogram(bins=20)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "linear-prototype",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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11lu2Do0oVzhERUREJjw8PLBo0SJbh0GUL+zBISIiItlhgUNERESywwKHiIiIZIcFDhER/ad9+/ahdevWaNiwIcaOHYukpCRbh0T0SpxkTEREr7R//34MGjRIWgDw4cOHuH37Nvbs2QOVynZ/RtLT07Flyxbcv38fVatWRbdu3aBUKm0WD9kXFjhEdurRo0cYNWoUzp8/D1dXV0yYMAHBwcEQBMHWoVEuXbx4EX/99Rc8PT3RunVrmxYFebFgwQKT1Y11Oh0uXryIy5cvo1GjRjaJKS0tDZ06dUJ4eDgEQYDRaMS2bduwbt06k41BqfByrE8ZUSGh1WrRvXt3REVFQa/XIyMjA9OnT4eLiwsGDRpk6/AoF+bMmYPvv/8eGo0Ger0eDRo0wK+//iptXPlPkZGRuHz5Mtzd3dGyZcssz7GFxMREs2MKhaLAh6m0Wi3u3LkDtVqNvXv3Ijw8HDqdTnr+6NGj2LlzJ7p161agcZF9YoFDZIeuXr2KO3fumBwzGAz46aefWOA4kPPnz+P777+HKIrIyMgAAFy5cgWLFy/G//73P5Nzt23bhlGjRkGpVMJgMKBy5crYvXs3SpYsaYvQJc+ePUOFChUQGRlpsoO4RqNB7dq1CyyOyMhI9OzZE/fv3wcAFC9eHHq93uQcpVKJu3fvFlhMZN/Yj0dkh7RabZZDUVqt1gbRUF7duHEDTk5OJsd0Oh2uXLlicuzRo0cYPXo0DAYDtFotDAYDoqOj8dlnnxVgtObCwsLQpEkTHD9+3GR/KmdnZ/z0008oXbp0gcRhNBrRt29fxMbGSsdevHiR5XnlypUrkJjI/rEHh8gO1alTB8WLF0dCQoL0h0WtVqNjx442joxyw9PTM8tehrJly5ocu337tknvCPCyELp06ZLVY8yOKIoYNGgQkpKSpH+DCoUCZcqUwZEjR1CqVKkCi+XJkyeIiooyOZYZU+bQn1KphJ+fH3r06FFgcZF9Y4FDZIfc3d2xefNm9OvXD0+ePAEAdOnSBZ9++qmNI6PcaNu2LerUqYPr169Dp9NBpVLB2dkZY8aMMTmvZMmSJpN4gZcbXBZkEfFvSUlJePDggckxo9GI2NhYuLm5FWgszs7OWR53dXXFuHHj8PDhQ1SpUgVDhgwx2SSUCjcWOER2ql69eggLC8ODBw/g5uZm87kYlHsajQbbt2/HggULcOXKFXh7e2PcuHGoVKmSyXk1a9ZE+/btcejQIeh0OgiCAEEQ8Pnnn9socqBIkSJQqVRmPVDOzs5mw27WVqxYMXTu3Bn79u2TJhWrVCoEBwdjwoQJBRoLOQ7OwSGyYyqVCj4+PixuHJirqys+//xzbNmyBQsXLkTlypXNzhEEAT/99BOCg4Ph4eEBURQhiiI2bdqE9PR0G0T98t/epEmTTNaVUSgU+PTTT22yVMHSpUsxYMAAlC5dGt7e3vj444/xySefFHgc5DjYg0NEZAdUKhXOnDmDtLQ0AC/nwGzbtg1OTk749ttvbRLTxIkTUbp0aWzduhWCIODdd99Fnz59bBKLs7Mz5s2bh3nz5tnk+uR4BPHfA7+FRFxcnFXaVSqVKF68OOLj480mDcqFu7u7rJdpZw4dm6PmLzY2FnXr1jU77urqiujoaLPjzKHjYw7zJqdz0zhERURERLLDAoeIyA6ULVsWfn5+JncBqdVqdO3a1XZBETkwFjhERHZAEASsW7cODRo0kB537twZc+bMsXFkRI6p0M7BSUxMtMqtjoIgQKPRQKvVmq1rIRdZ3ToqJ8yhY5ND/lJTU6FSqaDRaLI9hzl0fMxh3uT0b3ehvYtKq9VaZdl7pVIJjUaDlJQU2U6Ok/PEOIA5dHRyyZ/BYJD2r8oKc+j4mMO8yWmBwyEqIiIikh0WOERERCQ7LHCIiIhIdljgEBERkeywwCEiIiLZYYFDREREslNobxMnIioMEhISsHfvXiQlJaFp06aoV6+erUMiKhAscIiIZOrBgwdo164dnj9/DoVCgYyMDMyfPx8DBw60dWhEVschKiIimfrf//6HuLg4aLVapKenQxRFfPzxx3j06JGtQ7OZ+/fvY8eOHThw4ABSU1NtHQ5ZEXtwiIgcwKVLlxAZGYly5crB398/R6/566+/zLYCEEURkZGRKFu2rDXCtGsHDhzAkCFDIIoijEYjKlasiF27dhXKn0VhwB4cIiI7JooiJk+ejHbt2mH8+PHo2rUrPvjgAxiNxv98rZeXFxQK01/zoigWyj/oiYmJGDp0KLRaLXQ6HQwGA+7fv4+JEyfaOjSyEhY4RER27ODBg/j5558hiqK0ceGePXuwatWq/3ztjBkzoFQqoVQqAbzc3PG9995DlSpVrB223YmMjER6errJMZ1Oh8uXL9soIrI2DlEREdmxK1euQKVSmWxYaDQacfnyZfTu3fuVr23UqBEOHDiAVatWITExES1atMB7771n7ZDtUokSJXJ1nBwfCxwicgiRkZGYNWsW7t27h1q1amHq1KkoVaqUrcOyuhIlSkAURZNjSqUSJUuWzNHra9eujW+//dYaoTkUHx8f9OjRA7t27YJOpwMACIKAzz//3MaRkbWwwCEiuxcTE4PWrVsjLS0NBoMB4eHhOHXqFI4fPw53d3dbh2dVvXv3xtKlS/H48WPodDqoVCq4urrigw8+sHVoDmfJkiXw9fXFwYMH4ebmhg8++ABvv/22rcMiK+EcHCKyKL1ej5kzZ6Ju3bqoW7cuvvzyS+kbc1799NNPSE9Pl4ZpdDodHj9+jB07dlgg4oIliiKWL1+OevXqoXr16hgxYgSSkpKyPd/DwwMHDhzAu+++i0aNGqF79+44cuQIypUrl+X5hw4dwrfffotVq1a9st3CSKVSYfz48fjtt9+wadMmFjcyxx4cIrKoyZMnY926dVJRs3z5ciQlJeGbb77Jc5vPnj0zu91ZoVAgPj4+X7HaQkhICKZPny4Va7t27UJsbCx27NgBQRCyfI2npye+++67/2z7iy++wIoVK6BWqyGKIhYvXowDBw4UiqE8on9jDw4RWYxer8fq1atNemx0Oh3WrFljVqDkxhtvvAGVyvT7mFarRf369fPcpq0sX77cZMKwTqfDmTNncPfu3Xy1e/nyZYSEhMBoNCIjIwNarRaPHj3CnDlz8hsykUNigUNEFqPT6bJcn8VoNEKr1ea53UGDBqFDhw4QBAEajQYAMGHCBLz55pt5btNWMjIysjz+71uYcysiIgJqtdrkmE6nQ3h4eL7aJXJUeSpwTpw4geTk5CyfS05OxokTJ/IVFBE5JhcXF/j5+Zn0tqhUKtStWxdFihTJc7tKpRIrV67Enj17sHTpUhw7dgyffvqpJUIucIGBgSaFiEKhgKenZ77XpilXrpzZXCeVSgUfH598tUvkqPJU4AQGBuKvv/7K8rlbt24hMDAwX0ERkeMKDQ3Fa6+9Jj1+7bXXsHr16ny3KwgCGjdujK5du6JWrVr5bs9W5s2bhyZNmkiPS5YsiU2bNsHZ2Tlf7fr7+yMoKAgqlQqCIECtVsPNzQ2fffZZfkMmckh5mmT87zUZ/iklJQUuLi55DoiIHFu5cuVw4sQJREREAABef/11s6GTwszNzQ3btm1DdHQ00tLSUKVKFTg5OeW7XUEQsGLFCqxbtw5hYWEoUaIE3n//fXh7e1sgaiLHk+MC5+zZszhz5oz0eMOGDTh16pTJOenp6di5cydq1KhhuQiJyOGo1Wr+HngFQRBQqVIli7erVCoxaNAgi7dL5IhyXODs378fM2bMAPDyw7lo0SKzczJ/qS1btsxyERIRERHlUo7n4EybNg1GoxFGoxGiKOLs2bPS48z/MjIycOXKFTRr1syaMRMRERG9Up7m4GR1GygRvVrmF4PHjx+jevXqHMKhXHvx4gXu3r2LUqVKoWbNmrYOh8iu5Wsl4/T0dNy9ezfL9RsaNGiQn6aJZEWn02Hw4ME4ePAg1Go1tFotJk+ejAkTJtg6tBx5/Pgxxo0bh4sXL6Jo0aL47LPP0KNHD1uHVaCuXbuGAwcOAAA6dOhQ4AXq3r178cEHH0jr6AwcOBDz5s2DUqks0DiIHEWeChytVotRo0Zh7dq12a5O+s+VOokKuxUrVuDo0aMQRVFa8G7OnDlo3rw5GjdubOPoXi0tLQ1dunTBvXv3oNPpkJCQgJEjR8LJyQmdOnWydXgF4vfff8fgwYOlYmL+/PlYv349WrVqVSDXj46OxrBhw0zWudmwYQNef/11jBw5skBiIHI0eVoHZ8aMGdi/fz9CQ0MhiiKWLFmCVatW4e2338Zrr72G3bt3WzpOIod24cIFs0XYnJyccOXKFdsElAvnz59HVFSUSfyiKGLFihU2jKrgiKKI0aNHw2AwQKvVQqvVQq/XY/To0QUWw59//mm2T5Ver8fBgwcLLAYiR5OnAmfLli2YPn06evfuDQBo3LgxBg4ciAMHDuDNN99kgUP0L6VKlTIbSjAYDChRooSNIsq59PR0KBTmvypSU1NtEE3Be/HiBRITE82Ox8XF5Xt7hZxycXExm/soCALc3NwK5PpEjihPBc79+/dRrVo1KJVKODs7m+zo279/f2zZssViARLJwYgRI6DRaKQiR61Ww8fHBx06dLBxZP/Nz8/PbKE+tVqNd955x0YRFSwPD48st5koVqxYvlcfzqmAgACULVvWJA+CIGD48OEFcn0iR5SnAsfLywsJCQkAgEqVKuHYsWPSc7dv37ZEXESyUqVKFRw8eBDt2rWDn58f+vbti7179+Zrf6aCUqZMGaxbt86ktyAoKAjjx4+3XVAFSKFQ4Pvvv4cgCFCpVFCr1dKxguLm5obffvsNLVq0QMmSJVGtWjVs3brVITcbJSooeZpkHBAQgJMnTyIoKAjDhw/HpEmTEB4eDo1Ggx07dqBfv36WjpPI7qSkpECj0eR4G4Lq1asjNDTUukFZScuWLXH9+nVERUWhWLFiKF++vK1DkoiiiMePH8NgMMDLy8sqdxV1794d3t7e2Lt3LwRBQFBQEBo2bGjx67yKt7c3Nm/eLD12d3dHUlJSgcZA5EjyVODMnj0bcXFxAIDx48dDFEVs3boVaWlpGDt2LKZOnWrRIInsyYMHDzBw4EBcvXoVCoUC/fr1w9y5c6HRaHLcxokTJ/DXX3/B09MTnTp1ssheRNbm6uqK2rVr2zoMEwkJCRg0aJC0jYyvry82b96M4sWLW/xaTZs2RdOmTS3eLhFZhyC+audMGcss0CxNqVSiePHiiI+Pl+2t8nL/5viqHOr1erRs2RJRUVHSEglqtRrDhg3Dl19+maP2P//8c6xcuRIajQYGgwHVq1fH7t27C3TCqFxyOGDAABw5ckS6w0ulUqF69eq4du0aEhISLPoZ1Ol0uH37NgRBQLVq1aBS5WsZsXyTSw6zUhh+jwLMYV6VKlUqR+flaQ6Opel0OixevBjDhg1Dnz59MHr0aJN5PTExMZg0aRJ69uyJDz/8EDdu3DB5/enTpzF8+HD06tULU6dOxbNnzwr4HVBhcffuXdy5c8dk/SedToetW7fm6PVnzpzBypUrYTQakZ6eDp1Oh1u3bmHhwoXWCtlhiaKI0NBQtG7dGq1atcKSJUtM7iQSRRGHDx82uX1dr9fjxo0bFv8Cc+/ePTRv3hwBAQF466230LJlSzx8+NCi1yAiy8pTgZOWlobJkyejWrVqKFKkCJRKpcl/uf1mk3m77KxZs7Bx40aMHj0ay5cvx82bN6HX6zFr1iz4+/tj48aN6NGjB2bPno3k5GQAL+/oWrRoEUaPHo1169bB29sb8+fPz8vbIrK6W7dumQ1l6XQ6XL9+3UYR2a9Fixbh008/RVhYGK5du4bZs2dj+vTpJudk97smp/OicmrgwIH4+++/pcdRUVEYMmSIRa9BRJaVpz7W0aNHY8OGDejVqxeGDBmSq7kHWXF2dkb//v2lxzVr1kSNGjUQHh6OtLQ0ZGRkoFu3blAoFAgMDMSuXbtw5swZtG3bFkePHkWDBg3g5+cH4OVt6gMHDkRsbCy8vLzyFRfRv1WuXBnVq1dHZGSkyRBVnz59cvT6smXLmi34p1Kp7GrSrj0QRRHfffedSde2Xq/H8uXLMXnyZDg7O0MQBPTv3x9r1qyRfqZqtRqtWrVCsWLFTJavyI/k5GSzXmO9Xo9Lly4hNTXVIe6EIyqM8lTg7N69G9988w3GjBlj6XgAvFxYLCIiAkFBQbh37x58fHxMFhqrVKkS7t27B+Dl8FW1atWk59zd3eHp6YmYmBgWOGRxKpUKmzdvxvvvv4+LFy9CqVSif//+mDx5co5e36ZNGzRq1AgXL16ETqeDSqWCm5sbxo0bZ+XIHYvBYEBaWprZcVEUkZKSIq0/8+WXX0KpVGLDhg0wGo1o164dFixYYNFYNBoNBEFAVtMVP//88wK9XdzeXLp0SdqfrEOHDjmaR5aeno5Dhw4hPj4efn5+qFOnTgFESoVRngocpVKJ6tWrWzoWAC9/gS1cuBBVq1ZF/fr1cfv2bbi6upqc4+rqKq2imp6ebvYNytXV1eyXY2xsLGJjY6XHTk5O8Pb2tnj8mbeoynkDPEEQZP3+/iuHFSpUwMGDB5Geng61Wp2rn4VSqcT27dvxww8/ICwsDF5eXhg1alSB9+DYew6VSiXq1KmDGzduSL04CoUC3t7e8PT0lLYtcHFxwddff42vv/7a5LX//N/8cnFxwXvvvYc1a9aYPbdu3Tp88cUX8PT0fGUber0ejx49QokSJSzW42PrHP7www+YMmUKnJycYDAYMG/ePBw4cOCVP4sXL16gffv2iIiIgFKphFarxVdffYXg4GCT8wrD71HA9jm0JrvIoZgH06ZNEwcMGJCXl76S0WgUlyxZIn788cdiWlqaKIqiuGPHDvGLL74wOW/RokXijz/+KIqiKM6cOVPctGmTyfPDhw8X//jjD7OYAUj/TZ482eLxE5Hl3LlzR6xQoYKoUChEhUIhuri4iF27dhVDQkJEg8FQoLFotVpRrVab/A7J/C88PPyVrz1w4IBYtGhREYCoUCjEmTNnikajsYAiz72bN2+K/v7+YtGiRcUaNWqIBw4cMDvnzp07oiAIJj8HtVot9u/f/5VtDx8+XNRoNCavEwRBvHnzprXeDhViOe7B+e6776T/7+bmhpMnT8Lf3x9t2rRBsWLFTM4VBAETJkzIbaGF5cuX4+7du5g5c6bUBV2xYkVs27YNRqNRGqaKiopCu3btAAA+Pj6Ijo6W2klOTkZcXBx8fHxM2g8ODkbnzp2lx05OThYbo/8npVIJDw8PJCYmyvb2RldXV6SkpNg6DKthDu1DyZIlcfr0afz222/46KOPoNVqsXPnTuzZswf79+/Hjz/+aLYBJWC9/DVp0gRnz541uYPOw8MDRYsWzfZ3SUxMDIKCgpCRkQEAMBqNmDZtGjw9PaW9/PLKGjl8+vQp/P39pZ9dYmIi2rVrh99//91kYcM///wTSqXS7G7CCxcuvPL36tmzZ6HVak2OqVQqnDt3DqVLl5aOFYbPIOAYn8O8smYOc7rOVY4LnEmTJpkdu3fvHs6dO2d2PC8FTkhICG7duoVZs2aZdOHWqVMHarUaO3bsQFBQEM6cOYNHjx7B398fwMtVlSdNmoSwsDD4+vpi/fr1qF69utn8Gy8vL5NjcXFxVv3gGAwG2X4wRVEs8Pf24sULjBs3DocOHYJKpcJ7772HqVOnWvxumX9iDm3PxcUF+/fvl3bwBl4O92zfvh1Dhw5F48aNs32tpfO3ePFidO7cGQ8fPoQgCNBoNFizZg3UanW21/nncheZjEYjdu/ejR49euQrHmvkcM+ePUhJSZHaFUURgiBg7dq1qF+/vnSep6enSXED/N8Q4qtiKlu2LG7cuGFyu79er0eJEiWyfJ2cP4OA43wO88OWOcxxgfPvnWwt6cmTJ9i7dy/UajXef/996XjPnj3Ru3dvTJkyBUuWLMGGDRtQpkwZTJ48Ge7u7gBezocYM2YMlixZgvj4eNSsWTPLYowclyiKGDx4MM6dOwedToeMjAz89NNPEAQhx4vr2Yv09HSEhoYiOjoaPj4+GDx4MFxcXGwdll2LiYkx+2OqVqvx4MGDAo2jfPnyOHnyJM6ePYuMjAy88cYbKFOmzCtfo1KpspycbOtFArOTnp5u1itmNBrNehnq1auHzp07Y+/evdDr9dISIV988cUr2//0009x/Phx6PV6GI1GqNVqvPnmm68sVInyyuorGRuNRrRu3RohISGoWrWqNS+VK1zJOO8KevXNR48eZXmnhZubG6Kioix+PWvlMD09HZ06dcJff/0lDblWq1YN+/btK/Aix5FWUB03bhy2bNlidnv94cOHUbduXbPz85I/URTx/PlzKBQKi27z8OTJEzRr1gzJyclSLIIgYMOGDWjdunW+2rZGDm/cuIFWrVqZfKFVKpVYtGiR2ZCawWDAypUrce7cORQvXhzDhw+Hr69vjq6xbNkyxMXFoUmTJvjwww/NlhopDL9HAcf6HOZWoVjJWBRFHDt2TLZJJOv797f3TNbsVbSGDRs24K+//oJOp4PBYJCW/s/q7hz6P1OmTEGZMmWg0Wjg5OQEhUKBMWPGZFnc5MWTJ0/Qvn17+Pr6olq1aujevTtevHhhkbZLly6NnTt34vXXX4dCoUDJkiWxbNmyfBc31lKrVi0sW7bMZOj3ww8/RK9evczOVSqVCA4Oxs8//4xvv/02R8VN5jWWLl2KTZs2YeLEifleR40oO/bZT0r0D97e3qhWrRru3r1rsrhe27ZtbRxZ7vxzJdxMoihmeVyOMjIy8MMPP+Dq1avS7fHlypX7z9d5enri+PHj+PXXX/H8+XPUr18frVq1slhcgwYNwtWrV6XHZ8+exahRo7B+/XqLtF+rVi2cOnVKms9i73r06IE2bdogJiYGZcqUMZn8S+RIWOCQ3VMoFPjll1/Qr18/3Lx5EwDw5ptvOtwCa5UqVTI7JghClscdgV6vx59//onExETUqVPnletK6fV69OzZ02SBw82bN+Po0aM5WgPIw8PDKlsjJCYm4sKFCybHdDodDh06BIPBYNE1PByhuMnk4eHBBfjI4dnFZptE/6VChQo4ceIErl27hps3b2LTpk0Fuvu2JfTt2xcNGjSAWq2GRqOBWq1G3bp1MWDAAFuHlmtJSUno2LEjunTpgsGDB6Nhw4bYvXt3luf+/fffWLlyJf78809pHo1er0dycrLFNhkVRRExMTH466+/kJ6enuPX/XOF9H8ft0RBcvnyZbz11luoVKkS3nzzTfz555/5bpOIcoY9OOQwBEFA2bJlbR1GnqnVamzfvh2//PILYmJi4OPjgz59+jjkHITp06fj2rVrEEVRGjYMDg5Go0aNpBzpdDqMHDkSO3fuzLINvV6P+/fv5zuW1NRUvP/++zh8+DCAl+vn7Nu3D6+//vp/vtbNzU3a0+6f+1n16NEj2+Inp+7evYvOnTtDq9XCaDTi9u3b6Nq1K44dO2ZXN1wQyRV7cIgKkFqtxnvvvYcpU6bgvffec8jiBgD++OMPs7uajEajyaaU33zzDfbu3ZttG2q1GrVr1853LFOnTsWJEyekx8+fP0f79u2RnJyco9eHhISgQ4cOUKvVcHJyQq9evTBv3rx8x7V9+3YYjUZpMrzRaIQoivj111/z3TYR/bc89eAsWbIEAwYMMFvBmIgKh6xupTYYDCa/Ew4ePGhWBAEvCxtRFFG9enWLbDJ66NAhk+uIoohnz54hPDwcDRo0+M/Xu7m5YeXKldJ6NZaaK5PdUFnmqsZEZF156sH55JNP4O3tjX79+uHIkSOvPFepVCIqKooT1ohkZNKkSSbzVNRqNZo1awY/Pz/pnOw2lRwwYACWLl2K33//3SLzqDK3dfm33G5qKQiCRScCBwQEmBV4er0eb731lsWuQUTZy1OBExsbi++++w6RkZFo3bo1KleujFmzZmU7nu7j42PVJfWJqGAFBgZi06ZNaNasGWrWrIkhQ4Zg48aNJncdBQcHmxQMarUaVatWxcyZM9G9e3c4OTlZJJaRI0eaXFetVqNx48Y5XpfFWpo3b46vv/5amsujUCgwa9YsBAQE2DQuosIi3ysZX79+HT/99BM2bNiA58+fo02bNhg6dCg6d+5s10UNVzLOOzmvvgkwh9lJTU3F9OnTcejQITg7O2PkyJEYMGDAK3s9tmzZgrlz5yIxMRENGzbEggULLL6uiiiKWLVqFRYvXoyUlBS0bNkSq1atAgC7yN+LFy/w4MEDeHt7W3RYP7c5NBqNOHr0KO7fv48qVaqgefPmdnvremH4DALy/l1qDysZW2yrhocPH6Jfv37SZL9SpUph5MiR+PTTT+1yrx0WOHkn5w8lkLMcxsbG4uLFi3B2dkazZs1yPRxiKQaDAStWrMDx48fh4eGBDz74wGTX5+zkNoeiKKJfv344fvy4NOyiVCrx9ddfY/DgwXkN3yoKw2cQyF0ODQYDBg0ahEOHDkGtVkOr1aJPnz5YuHChXRY5zKHjs4cCJ1+3iYuiiN9//x0//fQT9uzZg2LFiuHjjz9Gt27dsHfvXixevBjXr1/nXQMkK0ePHsXAgQNhMBhgNBpRoUIF7N69u8BvYRdFESNGjMDu3bthMBggCAJ27tyJbdu2oXnz5ha9VkxMDA4dOmRyzGAwYOHChXkqcC5fvoyjR4/CyckJQUFBqFixooUipaysXbsWR44cMdnZefPmzWjbti06depk4+iIrCNPBU5kZCR+/vlnrFmzBg8fPkSbNm2wfv16dOnSRdolt2nTpmjYsCHeffddiwZMZEvJyckYMmSIyR0yDx48wLhx47Bp06YCjSU0NBQ7duyQHmd2xk6fPh0HDx606LWyu+X637tM58TmzZsxZswYaQh73rx52LVrF+rVq5evGCl7N27cMPsWrVKp8Ndff7HAIdnKU4FTtWpVlCtXDkOGDMHQoUPh4+OT5Xm+vr5o0qRJvgIksidRUVFmf9R1Oh0uXbpUoHHExMTgs88+MzsuiiKePn0qPX7+/Dk+//xzXLhwAaVKlcLkyZPx5ptv5vp6VapUQfHixZGQkCAVUmq1Gi1atMj2Nc+ePcOBAweQkZGBZs2aoVq1akhOTsaECRNgNBql26UzN8/851o29kAURRw+fBi3bt2Cl5cXOnXqVGDrFp06dQrbtm2DwWBAx44d873vmqenJ1QqFbRarXRMFMUcd/UTOaI8FTg7d+5Ex44d/3Olz2rVquHo0aN5Cozs2/Xr13H79m2ULVsW/v7+djmObw1Zrf8CoMDXhPrzzz+hUCjMvpULgiD1hKSnp6Nz5864e/cudDodYmJi0LNnT+zYsSPXfzBdXFywceNGvPvuu0hISAAA1K5dG999912W50dGRqJjx45ISkqCIAjQ6/VYsWIFfH19Tf7IAi8nv0ZHR+cqHmsTRRETJ07Exo0boVarodfrERISgh07dlh9TuH27dulO9BEUcTGjRsxd+7cfO3FNWTIEISGhiI+Ph56vR5qtRrlypVD7969LRi57YSFhSEiIgLe3t5o2rRpofl9RK+Wp9vEg4KC8r2MOTmuWbNmITAwEGPGjEHXrl3x3nvvScv1y1358uXRq1cvaSgWeFlUTJ48uUDjcHZ2Rlb3Bzg7O2Pu3LkAgBMnTiAiIkKaFCyKIkRRxNKlS/N0zTfeeAOXL1/Gb7/9hsOHD2Pfvn3ZFnZjx47FixcvoNVqkZGRAYPBgFGjRsHNzc3sd4cgCPDy8sr2uuHh4fjiiy8wceJEbN++Pcv3bWlHjhzBxo0bYTAYkJ6eDr1ej+vXr2PZsmVWve6TJ08wceJEiKIorXwsiiI+//zzfH3GPD09ceTIEQwcOBCBgYEYOnQoDhw44HD7uWVlxowZaN26NcaOHYsuXbpg2LBhsp6YTDnHvagoV86cOYPZs2cDgPRN/MiRI1i5ciVGjBhhy9AKzKJFi1C1alUcOHBAmmR85coVlChRosAWcQsICECZMmXw5MkTk8XkVqxYIU12Tk5OhlKpNPllL4qi1AOTF25ubmjcuPF/nhceHm72BzkjIwMJCQmYPn06pk+fbrKw3jfffJNlO3/++Se6du0qbXmwfv16hIeHW72gvHXrFtRqtcnPTqvVIjw83GrXPHjwIN5///0sV0DW6XSIj4+Hp6dnjtqKjIzEhQsXUKRIEQQGBsLNzQ1ly5aVil+5OHr0qFR0Zv4+2rt3L9auXWt3d/dRwWOBQ7ly9epVODk5mfwS1ul0uHDhgg2jKlgqlQoTJkyAv78/evTogWvXrgEAfvjhB3z11VcYNmyY1WNwc3PDnj17MG7cOFy7dg0lSpTA1KlT0a5dO+kcPz8/s2+yarU6T3NwcsvT0zPL2189PT0xcuRIVK5cGYcPH5b2fqpbt26W7Xz22WfQ6/XSfk4A8P3332PIkCGv7PXJLy8vL7MCLXNYxxqePXuWbXEDAB4eHihZsmSO2tq5cyeCg4OhUqlgMBjg7e2N3377zaE3qs3OlStXoFarTba/MBgMuHLliu2CIrvBcSbKleLFi2d5N0ZOv1naUlb7IuXHxIkTodPppP8yhxISExMtep3slC9fHr/++itu376Ns2fPokOHDibPV65cGcuWLYNKpZKGhQIDAzFhwgSrxzZz5kyTrRyUSiVGjx4t/Tt55513MG/ePMycOTPb4gYA7t+/b1LcZHr48KF1Av//OnXqhLp160p3eqnVahQtWhSjRo2yyvXCw8Oz3aNKrVZjxYoVOZoW8Pz5c4waNQoGgwEZGRnQ6/WIjY3FpEmTLB2yXShRooTZvw+VSoUSJUrYKCKyJyxwKFc6deqE1157TfrFr1Qq4eTkhA8++MDGkWXvzJkzqFu3Lry9vVG1alXs3LnTIu3ev3/fbD6I0WjEo0ePLNK+JXTv3h2XL1/Gpk2bcPjwYaxbt65A7gRq27Ytdu7cid69eyMoKAjfffcdpk2bJj0viiJCQ0PRtm1btG7dGiEhIVnOralatarJNgzAy7uurL1ujlqtxo4dOzBp0iR06dIFw4cPx7Fjx1CmTBmrXM/d3T3L9+/t7Y3jx4/j7bffzlE7ERERZpO4dTqdbHs0unfvjrJly0q/j1QqFVxcXPD+++/bODKyBxyiolxxdXXF8ePH8dlnn+H69esoV64cPvvsM1SqVMnWoWXp7t276N27t/RLPyEhAR988AFKly4Nf3//fLVdsWJF3Llzx+QbpEqlsurQSV6ULVvWJsMTTZs2RdOmTbN8bsGCBZg7d67UG3j9+nU8ffoUU6ZMMTnvu+++Q/v27ZGWlgZBEKDVajF37tw89RhevXoVq1atwosXL9CiRQsMHjz4lb0iLi4umDhxYq6vkxd16tRB8+bNcf78eamnURAEfP3116hatWqO28nutm9H6GHNC3d3dxw4cACzZs3CzZs3UbFiRUyePBnly5e3dWhkByy2VYOj4VYNeedIy4v/8MMPmD17tkn3v1KpxKBBg7KdcJnTHJ4/fx49evSQChydTodvv/0W7733nmXfhBXYMoeiKKJChQpmQzIKhQL3798328Pu0aNH2LVrF1JTU9G8eXM0atTole1nlb/z58+jS5cu0mRllUqFPn36YMGCBRZ9b/mRkpKCL7/8EidPnoS7uzsmTJhgMqfq37LKoSiKGD16NLZv3w69Xi9N5N64cSNatWpl7bdgMYXh9yjgWL9Lc8vht2ogsndZzd/IvPU2r54+fYoRI0bgzJkzUCqVqF+/Ppo1a4bAwMB89wplevz4MT755BNcuXIFpUuXxtSpU1+5qJ4jybx1/N+MRiNSU1NRtGhRk+Nly5bN9xDo9OnTYTAYpLzr9XqsX78eH374IV5//XWz8+Pj47FkyRJERUWhatWq+PDDD+Hu7p6vGP6Lq6trvu9yEgQBixcvRq1atXDkyBF4eHhg+PDhaNasmYWiJHIcLHBI1t5++23MmjXL5JgoimYTcnPKYDCgT58+uHnzJvR6PfR6PS5evIg333zTYsVNcnIyOnTogNjYWOh0Ojx8+BA9evTAnj17/rP3whE4OTmhevXqiIiIkL7ZKZVKlCtXDh4eHla5ZmxsrFlRKwgCHj9+bFbgvHjxAm+//TYePXoEnU4HtVqN3bt34+DBg3B1dc1zDJm7nu/duxcuLi4YNGgQWrdunef2spM5oXv06NEWb5vIkXCSMVlUamoq1q1bh++++w779+8vkEXZXsXX1xerV6+Wvn07OTnhu+++Q0BAQJ7ai4qKwrVr10zuyNLr9Vi7dq0lwgUAHD58GA8fPjRboG/FihUWu4athYaGolSpUlAoFFAqlShWrBjWrFljtRVoa9WqZTb0pVAoUKVKlSxje/z4sfTzz1wFeuPGjfmKYerUqZg8eTKOHz+O33//Hf369cP27dvz1WZO/fnnn+jevTuaN2+OsWPHIj4+vkCuS2RL7MEhi0lKSkL79u1x9+5dKBQK6PV69O3bF999951Nl05v27YtIiIi8Pz5cxQrVsxkFeLcym412f8aYw4LC8MPP/yA58+fo2nTpiabTf5b5gJ9/7yWKIoFdvt5QXj99dfxxx9/4MKFCzAajWjYsKHZ0JQlzZs3D+3atUNcXByUSiW0Wi2++eabLCdfx8bGZpnP/Nwd9/z5cyxfvtzkmCiKmD59Orp165bla168eIEDBw4gNTUVTZo0ga+vb56uffnyZXTu3FmafxQVFYVLly7h0KFDcHZ2zlObRI6ABQ5ZzIIFC6R9jzJt2LAB3bp1Q8uWLW0Y2ctv65bYWLBKlSrw8fHB/fv3pT+CarUaQUFB2b7m0qVL6Nixo/QH5tSpU/jzzz+xYcOGLAu/Ro0ama3Zo1KpbP4zzK979+5h/PjxuH79OkqXLo0vv/yywCa+ent74+TJk/j999+RlJSExo0bZ7v+jq+vr9k+X0ajMc8FBvByIb+sZNeTcu/ePXTo0AHPnz+XviwsW7YM3bt3z/W1lyxZIv3bA172SEVERODo0aNo3759rtsjchQcoiKLCQ8PN/vDrNFoEBERYaOILE+tVmPLli0mt8V37NgRX375ZbavmTNnjtkfmEOHDmW7A3m1atWwePFiqFQqqQDq3LmzQ2+FkZCQgA4dOuCPP/5AfHw8bt26hb59++Ls2bMFFkPRokXRp08fDBs27JWLCw4YMADNmzeHSqWCs7MzlEol3nnnnTwVF5kqVKhgtu+TSqVC7dq1szx/4sSJePbsGXQ6nbSX14cffojnz5/n+trPnz/PcjG8Fy9e5LotIkfCHhyyGB8fH6hUKpOhFZ1OB29vbxtGZXmVKlXCmTNn8OTJE6SkpMDNzQ1OTk7Znv/48eMs/8A8ffo029e0bNkSLVq0QFhYGDw9PdG3b1+zBe8cycGDB/H8+XOzYbfQ0NBs18qxFZVKhV9++QX79u3D33//jUqVKuGdd97J1zCrs7MzfvrpJwwcOFCaU1W8eHEsWbIky/Nv3LhhNhyq0+kQFRWV61V6W7RogXPnzpl8+dDpdKhfv37u3wiRA2GBQ/m2c+dObN++HRkZGXBxcZF2X1apVPD390ebNm1sHaLFPX36FAMHDpR6YWrXro3SpUvj6dOn8PPzw9SpU6WdtuvXr2+yqzfw6iGPzLuoMu/iSUhIQJ8+fbBr1y40atQIFy9eRHx8PGrUqIEKFSpY/b1aQlpamtmieqIoIiUlxSbxZGRk4Mcff8Tt27fh5eWF4OBgk8JBqVSiU6dOFr1mq1at8Mcff+Ds2bPQaDQICAjIdt5R6dKl8ezZM7NJ+qVLl871dceMGYMLFy7g0KFDUCqVEEUR3377LapXr56n90HkKFjgUL6EhIRg6tSpUg+FIAgIDAxEmTJlULNmTQwdOtShex6y8/7770ubbAIvV+LNdPPmTZw9exaHDx+Gi4sLpk+fjgsXLuDu3btQKpXQ6XSYO3cuXnvttSzbPnz4MGJjY6Vv8Jl/5EJCQjB37lycPHlS+pkuWrQIvXv3ttK7tJymTZua9UioVCqbLD6n0+nQtWtXhIWFQafTQaPR4JdffsHRo0etvodRhQoVclSUfvnll1JeRVGESqXCe++9l6eCVqPRYMOGDbh8+TLi4uLg6+tr9a0uiOwBCxzKM4PBgC+//NJk+EUURZw9exbR0dE2vXPKmlJSUnDu3Llsn88cSvj999/RrVs3FC9eHIcOHcKRI0eQkJAAPz8/1KpVK9vXZ3cXVVhYGGJjYwH8311bY8eORaNGjex2q4xM1apVw/LlyzF69Ghp24yBAwdi8ODBBR7Lrl27cOXKFennq9Vq8fTpUyxbtsxsqwhbeeutt7Bnzx6EhoYiOTkZAQEBGDRoUJ7bEwQBDRo0sGCERPaPBQ7lWXJystnGfsDLtXAyMjJkewuqUqmEIAivXONHpVIhISFBeuzi4oKOHTvmqP3GjRtn2dthNBrNJnErFAqEhYXZfYEDAF27dkVgYCCioqLg6emJcuXK2SSOBw8emBWQOp0ODx48sEk82WnUqJEsFnYkshXeRUV55uHhAU9PT5OeGoVCgQoVKth9cfP06VNs2rQJ69atQ3R0dK5e6+zsjKCgoGzXsQFezvHw8/PLU2xVq1bFsmXLTNrv3r07fH19zXrFDAaDNNfHERQtWhR+fn42K26Alz/ffxeQGo0mV5taEpH9Yw8O5ZkgCFi1ahV69+4t/cFQq9VYuXKljSN7tfDwcHTu3BmpqalST8y6desQGBiY4zYWL16Mjz/+GLt27QIAlChRAg8fPoRarYZer8e0adPydZdK9+7d0bJlS0RERMDT0xOVK1fG+fPnceTIEQAvh6w0Gg18fX25z1AutWvXDkFBQdi9e7fUM1ajRg2MHDnS1qERkQVxN3ELKwy74P57B9wHDx7g+PHj0gTjrFaHtSdvvfUWbt26ZZIfd3d33Lp1C2q1Ok85NBqNOH/+PJ48eQJfX19Uq1bNKrH/8ccf+OabbxAXF4fGjRtj2rRpedoEsrDvYiyKIn777TfpLqru3bu/8lZ/e1TYcygHzGHe5HTRVhY4FlYYPpiO/qH08vLKcsuFixcvomLFisxhLkRGRuLbb7/Fw4cPUa9ePXz88cdmC9oVtMKQP8DxP4evwhw6PhY4NpSYmGiVb2yCIECj0UCr1dp8o0lr+fdifo7AYDAgLi4OJUuWROXKlfHkyROT5wVBwNOnT+Hm5sYc5lBkZCQaN26M9PR0GAwGaDQa1KhRAydOnLBpb0hhyB/gmJ/DnGIOHZ81c5jT3y+Fdg6OVqvN8g6g/FIqldBoNEhJSZHtNw9H+9Zx4MABjBgxAklJSdBoNOjVqxc2bNggfegUCgUmTZoEURSRlJTEHObQ119/LRU3wMvP1I0bN7B161Z07tzZEmHmSWHIH+B4n8PcYA4dnzVzyAKHCC8X3Rs0aJDJmicbNmzAp59+ihs3bkCn06Fjx44OsVievXn8+LHZLy6VSpXtxpIF5c6dO0hMTETp0qVlt00IEeUcCxyStcOHD5uteSIIAh49eoSffvrJhpE5vgYNGuDw4cMmRU5GRka2G0hamyiK+Pzzz/Hjjz9Kt9NPmzYNo0ePtkk8RGRbLHBI1v69/xHwssCR6yrL/2Q0GrFz507cuXMH3t7e6NWrl0XnxlSrVi3LHpzy5ctb7Bq5sWXLFvz8888A/m97ixkzZsDPzw/Nmze3SUxEZDtc6I9k7Z133gEAk4JGFEV069bNViFZzaNHj7B27VqEhoYiIiICgwYNwqhRo7Bo0SL873//Q8eOHZGWlmax6+3cudPsmCAIOHr0qMWukRtnz541m8yo0Whw/vx5m8RDRLbFHhyStcqVK2PLli0YMWIEHj58iBIlSuCbb75B06ZNbR2aRYWFhaFr167QarUQBAEGgwFGoxFGo1EangsPD8ePP/6IsWPHWuSa/9yDLCfHrc3d3R0KhcJsbzRb37ZORLbBAqeQu3fvHvbt2we9Xo+AgIBXbgLpqPz9/REWFga9Xg+VSp7/5IODg5GamvrK4kKn0yEyMtJi1+zWrRt+++03k53klUolAgICLHaN3Bg4cCB+/vlniKIIg8EAlUoFDw8PWfbWEdF/4xBVIXbp0iW8+eabmDlzJubMmYO3334be/bssXVYViPX4sZoNOLu3bv/2XOiVqstOj8mKCgIM2fOhEajAfBy8a1NmzbZbA5OlSpVsHfvXjRt2hSvvfYaWrVqhd9//z3Hi4IRkbwU2oX+uJIx0LRpU0RFRZn8YXRxccGdO3deORlVzms3AI6Vw0zVq1fH8+fPzY6r1WqpN6Ns2bI4cuQIihYtatEcGgwGJCUloWjRonYxedsa+QsLC8OpU6fg7OyMTp06oUyZMhZpNz/k/Dl0xM9gXjCHeZPTLy3y/EpLORIdHW32rT8tLQ2PHz9GxYoVs3xNIa2H7d6cOXMwYsQIAC9zpFAo8Nlnn8HJyQkREREoU6YMWrVqhRcvXsDDw8Oi11YqlQ61o3lu/fLLLxg3bhzUajVEUcScOXOwd+9eq+03RkSWwQKnECtdujRiY2NNjimVSrPqWBRFLF26FN9//z1SU1PRpEkTLFu2jIuo2ZHu3bujRIkS2Lx5s7R4YdeuXQG87H1499138c033wAAWrdujc2bN9swWseRkJCACRMmwGg0IiMjA8DLHqtx48Zh3759No6OiF6Fc3AKsW+++QYKhQJKpRIKhQKCIGD69OkoUqSIyXmrVq3CrFmzkJiYCL1ej/Pnz6Nbt25IT0+3UeSUlYCAACxbtgw//vijVNykpKSgd+/eJsNXx48fxyeffGKjKB1LTEyM2V5BBoMBd+7csVFERJRTLHAKsXfeeQd79uzBwIED0b9/f6xdu1Ya5vin0NBQkzFUnU6Hu3fvIiwsLNu2nzx5gg8++AD+/v7o3r07rly5YnbOs2fPEBYWZrX5UATcunULz58/NxmK1Ol07H3IIS8vL7NjgiBkeZyI7AuHqAq5Ro0aoVGjRq88R6fTmR0TBCHbXXCTk5PRvn17xMbGSsVQx44dcfDgQdSsWRMAEBISgqlTp8JoNEIQBEydOhUffvhh/t8Qmchusrizs3MBR+KYSpcujY8++gjff/+9NLdJEAR8/fXXtg6NiP4De3DoP3Xp0gVqtVp6rFAoUKJECdStWzfL8/ft2ycVN8DL25gNBgNWrlwJADhx4gS++OILqVdBFEV8+eWXOHz4sJXfiTw9ePAAv/32G44fPy7NE8nk6+uLBg0amORPqVRabLG/wuCTTz7B8uXL8e6772LQoEH4/fffufUDkQNgDw79p0mTJuHRo0dYv349gJfd9mvXroW7u3uW5ycmJkKpVJr0/BgMBsTHxwMATp48CbVaDa1WKz2vUqlw8uRJvP3221Z8J/Kzb98+DB06FMDLn3H16tWxY8cOlChRAsDLYmbTpk2YOHEiTp48CRcXF4wZMwbDhw9HcnKyLUN3GIIgoFu3blwwkMjBsMCh/6RSqbBgwQJ8+eWXSE5Oxuuvv47U1NRsz3/jjTfMehJUKpW0PUJWwyOCIMDFxcWygcvcs2fPMHz4cJNCMiIiAp9++ilWrFghHStWrJi0CWUme1ivhojImjhERTnm4eEBb29vKJXKV57n5+eHr776Slq6H3i56u2wYcMAAD169IBKpZKeUygUUCgU6Nmzp3XfgBWlp6dj06ZNWLx4MQ4dOlQg6wXdunXLrJDU6XTcXJKICOzBISsZNmwY2rRpg9u3b6NMmTKoU6eO1Gvw2muvYffu3fjoo48QExODChUqYP78+ahSpYqNo86b5ORkdOzYEXfu3IFSqYRWq8XAgQOldWesJbtetOLFi1v1ukREjoAFDlmNj48PfHx8snyubt26OHjwYAFHZB0LFy7EnTt3oNPppOGiNWvWoEuXLmjRooVVrpmYmIjx48ebHRcEAR9//DEOHz6MZ8+eoU6dOqhRo4ZVYiAismccoiLKpxs3bpjdSq/RaHD79m2rXfP48eN49uyZ2XE/Pz8sWrQI/fv3x8SJE/HWW2+ZzMexJ0ajEV9//TUqV66M8uXL4913383yPRER5QV7cKjA6HQ6XL9+Henp6dDpdEhOTkb16tUddmgqU8WKFaFSqUzWBdLpdFZdDC4jIwMKhfn3k7i4ODx69AgGg0FanPGLL77Am2++aXc9OQsWLMDChQuln9uJEyfw7rvvYv/+/Vm+t4JgMBhw+fJlJCUloVatWihdurRN4iCi/GMPDhWIZ8+eoW3btmjbti06d+6MHj164P3334e/vz+WLl1q6/DyZdy4cfDw8JDWmlGr1WjatCnatm2br3bj4+MxbNgw1KhRA40aNcIvv/wiPdekSROzO6FUKhUUCoVZb5JKpcKNGzfyFYs1hIaGmhWFV65cwd27d20ST3JyMoKCgtC+fXu8++67eOONN3DgwAGbxEJE+ccChwrE+PHjcevWLZNjBoMBoihixowZuHjxoo0iyz8vLy8cP34cwcHB6NmzJz7//HNs3rz5P+82exW9Xo9evXph7969iIuLQ3R0NMaNG4ctW7YAACpUqIA1a9bA1dVVes3gwYNRt25ds+saDAaULFkyz7FYS3YrYWe1cnZB+OKLL6QtRYxGI9LT0zF06FA8ffrUJvEQUf5wiIoKxJkzZ7L9w+Xk5IRLly7hjTfeKOCoLKds2bKYNm2axdq7evWq2V5fRqMRy5YtQ69evQAArVq1Qnh4OP7++2+UKFECpUqVwvXr17F//34ALwsbtVoNPz8/vPnmmxaLzVI6dOiADRs2SP8ulEolvLy8bDZkefr0abN/oxkZGQgPD4enp6dNYiKivGMPDhUINze3bJ8zGAwoVqxYwQXjANLS0rJcjC8lJcXksYuLC6pVq4ZSpUoBAGrXro39+/ejU6dO8Pf3R3BwMLZu3QqVyv6+y8ycOdNkGM/d3R0ff/wxNBqNTeIpWrSo2TFRFOHh4WGDaIgov1jgUIEYP358lhNHVSoVypUrh/bt29sgKvtVq1YtuLm5mRQ5arU6R1tZ1K5dGytXrsSuXbswbdo0FClSxOwcrVaLvXv3Ys2aNbh8+bJFY88pFxcXzJ07F15eXlCr1UhLS8OECRMwZ84cm8Tz0Ucfmfy8NRoNmjVrhjp16tgkHiLKH/v7WkeyNHjwYKjVaoSEhEh7Vbm6uqJevXqYPn36K3t4rCklJQU7d+5EXFwc6tati4CAAJvE8W/FihXD+vXrMWDAACQmJgIA3nrrLUydOjXfbaekpKBdu3a4cuUK1Go1MjIy8Omnn2LixIn5bju3ZsyYgbi4OJOhoe+++w5dunSRdp4vKO3atcPatWvx/fff48WLF2jZsiWmTp2ar7lURGQ7glgQa8rbobi4OKu0q1QqUbx4ccTHx0u36cqNu7s7kpKSrNJ2QkICDh06hLS0NDRt2hRVq1bNUzubN2/GokWLkJKSgoCAAMycOdOsiHrx4gXatWuHmJgYaQXi4OBgzJ49225ymJycjMjISLi5uaFy5coW2UNqxowZWLFihclmp4Ig4ODBg6hXr16+28+NwMBAXL9+3eRYZiEcFBSUq7ZEUUR0dDTi4+PRqFEjALB5/qzJmp9DWysMv0cB5jCvMofk/wt7cMhuREdHo2PHjoiPj4dCoYBer0dISAi6dOmSq3Y2bdqEsWPHwmg0So/v3r2L7du3mwyTzZs3DzExMSYrEC9fvhxdu3bN9y3eluLm5mbxouPy5csmxQ3wcjjmr7/+KvACp2LFiggPDzf5BajT6eDt7Z2rdvR6PT788EP8+uuvAF5u6Prjjz+iXbt2Fo2XiBwH5+CQ3Rg/fjyeP38OnU6HjIwMGAwGjBo1ShqiyamFCxdKxQ3w8g/mmTNnzG5Tt8UKxPbAy8vLbNhFr9fn+FuRJU2dOhUuLi5Qq9UQBAEqlQo9e/ZEgwYNctXO0qVLsXPnTulxeno63n//fcTExFg6ZCJyEOzBIbtx48YNs7VRtFotoqKictWzkJycnKPjFSpUKPAViO3BxIkTsXfvXqmIVKvVaNCgAQIDAws8lipVquDYsWMIDQ1FfHw83njjDfTv3z/XQ3FHjhwx+7cjCAIuXryY7X5oRCRvLHDI5g4cOIADBw7gxYsXWT6f2+XyW7ZsiW3btpn0zri7u8PX19fkvI8//hj79u1DamoqdDod1Go1WrZsaTcTja2latWqOHv2LObMmYPHjx+jQYMGGDdunM1uJffx8cn3GkKZd5z9c0qhwWAwWQiRiAoXFjhkU1999ZXZkNI/DR06NNc9KnPmzEF0dDTOnTsH4GVxs27dOri7u5ucV7FiRRw/fhwrVqzA06dPUbduXQwbNsxm+yAVpGrVqmHRokW2DsNigoODcejQIanAUavVKFeunNV2cyci+8e7qCysMMz+t9TM/4iICPj7+7/ynDt37uRpEUCj0Yhr164hISEB9evXz9VibcyhYzpw4ABmzZqFZ8+eoUmTJpg7d66sVyCWYw4zFYbPIMAc5hXvoiK7Fx0dDaVSme0/foVCkachBoPBgKlTp+Knn36CwWBA7dq1ERoayrkYMpe5mWth+eNIRK9mNwXOnj17cOTIEURHR8Pf3x8ff/yx9FxMTAwWL16M6OholC1bFiNHjkStWrWk50+fPo3Q0FAkJCSgRo0aGDdunF1uLkimKlasmO0fIJVKhREjRkg7dOfGt99+i59//llq++bNm+jRowdOnz4NJyenXLUliiJu376NpKQkVKtWzabL9qekpODEiRNITU1Fw4YNWbAREb2C3Uw2KFGiBHr37m22/oher8esWbPg7++PjRs3okePHpg9e7Z0R8z9+/exaNEijB49GuvWrYO3tzfmz59vi7dAuVStWjUMGjTI5JggCKhWrRqmTp2KL774IsdtxcTEYP/+/bhw4QI2bdpkckeNXq9HTEwMbt68KR0zGo3ZzvvJlJqaij59+qBFixZo37496tWrh1OnTuU4Jkt69OgRWrZsiffffx9jx46Fv78/9u7da5NYiIgcgd0UOM2aNUPTpk3NviFfu3YNGRkZ6NatG9RqNQIDA1GmTBmcOXMGAHD06FE0aNAAfn5+cHJyQv/+/XHz5k3Exsba4m1QLmRkZOD06dMma7IIgoAPP/wQI0eOzPFk37Vr16Jx48YYPHgw2rdvjydPnmR77osXL/Dee+/B29sb5cuXx5gxY5Cenp7luTNmzDApaJKTkzFgwAA8f/48h+/QciZOnIjY2Fjo9XpotVrodDp88MEHSEhIKPBYiIgcgd0UONm5d+8efHx8TP7YVapUCffu3QPw8pt7pUqVpOfc3d3h6enJBb4cwKVLlxAZGWkyTGU0GrFy5coct3Hr1i1MmjQJRqNR6rXRarUm66ioVCr4+PjA19cXQ4cOxeHDh2EwGKDT6fDrr7/ik08+ybLtI0eOmC0EmJqaihs3buQ4vqSkJBw7dgzHjh3L12TCy5cvm8WSkZGBu3fv5rlNIiI5s5s5ONlJS0szm2jq6uqK1NRUAC9XLP33bsmurq5IS0szORYbG2vSq+Pk5JTr5eBzIrM3Qs4b9AmCYJH3p9frzdYuAV7+4c5p+1evXpU2jMxkNBrh7e2NR48ewWg0onr16li3bh20Wi2OHz9u8nqdToetW7di8eLFUlGUee2sJjiLogh3d/ccxXfr1i106dJFumOvVKlS2LFjh9l6PDlRsmTJLO/8K126dJ5yYakc2iOlUgmdTofY2FgUL14czs7Otg7JKuSew3/+r1wxh9Zl9wWOi4uLVMxkSk1NhYuLC4CXe878+/mUlBTp+UwhISGYMWOG9Hjy5MmYPXu2laKGTSejFgSNRpPvNgICAuDh4YEXL16YrF/So0cPFC9ePEdtlC9f3myiskKhQPPmzbFhwwZkZGRIhcqzZ8+ybEMURRQvXtxs9dzJkyejX79+0lwdjUaDevXqISAgIEeL4g0ePBjPnj2TXv/s2TMMHjzYbMuInJg/fz46d+4sxatWq9G3b1/4+fnluq1MlsihPdq+fTvee+89pKSkQKVSYcGCBRg9erStw7IKueYwk9x/jwLMoTXZfYFTsWJFbNu2DUajURqmioqKkjbR8/HxQXR0tHR+cnIy4uLizO4wCQ4Olv5AAC97cOLj4y0er1KphIeHBxITE2V7i6qrqytSUlIs0tamTZvQr18/qfjo0qULxo0bl+PcNGzYEFWrVkVERAR0Oh0UCgUUCgWCg4OlIaHMjSUFQUDDhg0RFhYmDfeo1Wq0b9/eZC5LZg7bt2+P5cuXY/78+UhKSkLz5s3xzTff5GioKSUlxayQMRgMuH37Nv7++2+znc3/S/PmzbFt2zaEhIQgOTkZrVu3xujRo/P8b9iSObQnN27cQK9evaTPnl6vx5gxY+Dp6Yk2bdrYODrLkmsOgcLxexRgDvMqp1+A7abAMRgMMBgM0t0tWq0WCoUCderUgVqtxo4dOxAUFIQzZ87g0aNH0gJxAQEBmDRpEsLCwuDr64v169ejevXqZqvfenl5mRyLi4uz6gcn8/3IkSiKFntvDRo0wNWrV/H333/D3d1d2pYhp+2r1Wrs2rUL06dPx6VLl1C6dGl88sknqFevXpZtrF69GoMGDcKFCxcAvPz3s2DBgizPNRgM6NatG7p162Z2PCdx/XufK+DlfCCNRpOnn1+LFi3MVubNax4smUN7cujQIahUKpP3JggC9u7di1atWtkwMsuTaw7/Sc6/RwHm0NrspsDZtGkTfvnlF+nx6dOn0apVK4wfPx5TpkzBkiVLsGHDBpQpUwaTJ0+Wlt2vUKECxowZgyVLliA+Ph41a9bEpEmTbPU2KA80Gg2qVKmS59cXK1YMCxYs+M/zdDod1q5dC6VSiZYtW2L48OFST6ClKZVKjBs3zqR4UqlUGDt2rGzH3O1BVkOHmbuUE1Hhwq0aLKwwrKLqCMuLG41G3Lp1CykpKfD19YWrqyuGDx+OvXv3SsNTCoUCa9euNVt7yVI5FEURK1eulAr3Pn36YPjw4bneKdsaHCGHeRETE4MWLVpAq9VKc58UCgX27NmDRo0a2Tg6y5JrDoHC8XsUYA7zKqdbNbDAsbDC8MG09w9lcnIy+vfvL62VVLRoUcybNw/BwcFm59asWdPszirm0LFdvHgRo0ePRlRUFDw9PTF//nyr9dTZkpxzWBg+gwBzmFfci4oKRFpaGu7cuQMXFxdUqVLFLnbinjx5Mv7880/pcWJiIiZMmJDludndWUWOq3HjxoiIiMDz58//c7VqIpIv2/81IocVFhaGBg0a4O2330azZs3QpUsXJCYm2josHD9+3GRRPFEUkZqaaravlVqtxhtvvFHQ4VEBsYehQCKyHRY4lCfp6el49913TbYtuHjxoskmqbaS3Q7k06ZNg1qthlqthlKpRMWKFTFv3jx+yycikiEWOJQnkZGRiIuLMykOdDodjh49asOoXhozZozJUJlarUbTpk0xfPhwnDt3DsuWLcPatWvRpUsXNG7cGF5eXujQoQMePHhgw6iJiMiSWOAQAODkyZNo06YN/Pz8MGTIkFduWAnAbKXoTP/eNiM37ty5g8WLF2PhwoUIDw/Pczt9+/bFggULULVqVXh5eaFHjx7YuHEjFAoFKlSogK5duyIiIgKLFi1CamoqjEYjLl++jB49ephs+UBERI6Lk4wJZ8+eRc+ePSGKIkRRxJMnT3Djxg0cPXo02+GeSpUqoWXLlvjjjz+k+S5KpRKjRo3KUwwnTpzAu+++K/W8fP3111i9erXZLdw51bdvX/Tt2zfb59euXWuyCJ9er0dkZCRu3Lghu9uJiYgKI/bgEEJCQqTiBng51PT333/j2LFj2b5GEASsXr0aPXv2ROnSpVGxYkXMnDkTw4cPz1MMI0eOhF6vR0ZGBjIyMqDX6zFy5EizjTgt5d8rDP/XcSIicizswSGTzS4zKZVKJCcnv/J1bm5uWLRoUb6vn56enuWQWGJiIhISEnK870hudO3aFUuWLDHpfSpVqhRq1apl8WsREVHBYw8OISAgwOwWar1eX2C3UDs5OaFo0aJmx52dnbM8bgkff/wx+vTpIz0uV64ctm7dmu2QHBERORYWOIRRo0ahY8eOAF4OPanVaixbtgyvv/56gVxfEAR8++23UCgUUCqVUCqVEAQB8+fPt9rCgWq1Gt9//z2ioqJw48YNXLhwAb6+vla5FhERFTwOURFUKhVWrFiBSZMmIS4uDlWrVpV29S4oXbp0QZkyZbBr1y4YDAa0a9cOgYGBVr+um5sb3NzcrH4dIiIqWOzBIQAve1GqV6+O5s2bF3hxk6lhw4YQRRFr1qxBnz590L59e8TGxtokFiIicmwscChXYmNjERwcjKCgIHz00Ud4+vSpxdrOvDVcr9dDFEVcuXIFvXr14p1NRESUaxyiohx7+vQpWrVqhRcvXkCn0+HixYs4dOgQjh8/jmLFiuW7/V9++cVkDym9Xo9bt27h7t27qFatWr7bJyKiwoM9OJRja9asQWJiolSE6HQ6xMXF4ZdffrFI+9mtecO9ooiIKLdY4FCOxcXFwWAwmB1/9uyZRdrv0aOHye3qKpUKVapUQZUqVSzSPhERFR4scCjH6tatC0EQTI4ZDAbUrVvXIu1PmTIFvXr1kq7h6+uLLVu2mK3RU5AOHTqETp06oWXLlvj888+Rmppqs1iIiCjnOAeHcqxPnz44duwYduzYAbVaDa1Wi379+qFTp04WaV+j0WDhwoWYN28edDqdzW/fPnDgAPr37y8NkUVERCA8PBxbt2612vo8RERkGSxwKMcUCgWWL1+OESNG4NatW6hUqRKaNGli8es4OTnBycnJ4u3m1ty5c03m/+h0Opw8eRLXrl1DvXr1bBgZERH9FxY4lCuCIKBly5aoX7++rUOxuoSEBLNjgiDgxYsXBR8MERHlCvvZibLRrFkzs/k/Go2GWzoQETkAFjhkV0RRRHR0NG7evImMjAybxjJ79mzUrl0bgiBAqVRCo9Fg5cqVNlvpmYiIco5DVCR59OgR5s+fj6ioKNSoUQMfffQRihcvXmDXT05OxsCBA3Hy5EkAQOnSpbFp0ybUrl27wGL4Jw8PD+zduxcXLlxAYmIi6tati7Jly9okFiIiyh0WOAQAePLkCVq1aoX4+Hjo9Xr88ccf2L9/P44ePVpgdzN98sknOHv2rPQ4Li4Offr0wcWLF3Hv3j1cuHAB7u7uaNWqFVxdXQskJpVKhaZNmxbItYiIyHJY4BAAYNWqVUhISJD2fdLpdHj48CE2b96M999/P9/tP3jwAEePHgUABAQEoHz58mbnHDlyxGSrBqPRiCdPnmDZsmWYO3cu1Go1DAYDKlSogN9++w2enp75jouIiOSJBQ4ByHqVYkEQLLKZ5vnz5002zVSpVNi8ebPZLebZ3Ro+b948GI1GaU7O/fv38dlnn2HlypX5jo2IiOSJk4wJAFC7dm2zxev0en2u5r+kpqZi9+7dWL9+PcLDw6Xjw4YNQ3p6OrRaLbRaLdLT0zF06FCz148YMQJKpVJ6rFarUbNmTbPCS6fT4cqVKzmOi4iICh/24BAAYMCAATh06BAOHDgAtVoNnU6Hvn37okOHDjl6/bNnz9CpUydER0dDqVRCp9Ph22+/RdeuXREbG2tyrtFoxOPHj5GcnGwyvyc4OBiiKOKHH35Aeno6AgMDMXToUHTs2NHk9YIg8E4mIiJ6JRY4BABQKpVYs2YNjh07hvv376Ny5cpo1qyZ2d5T2fniiy8QExMDvV4vDUV99NFHeOutt6BSqaRj/7xekSJFTI4JgoCRI0di5MiR0jFRFNG5c2fs27cPOp0OgiBAoVBgypQp+XzHREQkZyxwSCIIAgIDA82OHzhwAKtXr0ZGRgY6dOiAsWPHmp1z+fJlkwnCme3dvHkz3zGFhIRg2bJlOHnyJIoWLYoRI0agYcOGOW5DFEWkpKTA1dU1xwUbERE5NhY49Epbt27FqFGjIIoiAODUqVOIjY3F559/bnJe6dKlcffuXZO9mwwGA9zd3c16bzKfS01NzdEt6CqVCmPHjs2ysPovv/76KyZNmoTk5GSUKlUKP/zwAwICAnLdDhERORZOMqZXmj59ulTcAC8Lk4ULF5rtxzRlyhQoFApporJarUb79u3RpEkTlC9f3mQCs0KhQLly5ay+vs7p06cxcuRIJCcnA3h5p1i/fv1w584dq16XiIhsjwUOvVJWG06Koojnz5+bHGvUqBH27duHrl27IjAwEJ988gl+/vlnCIKA0NBQuLm5Qa1WQ61Ww83NDaGhoVaPfdeuXWZ3hikUCuzfv9/q1yYiItviEBW9Uq1atXD16lWTYSYPDw+UK1fO7Fw/Pz+EhISYHa9Xrx7Onj2LU6dOAQCaN2+e7V1QKSkpOH36NNLS0tCwYcMsr0NERPRfWODQKy1btgydO3fG8+fPpSGojRs3QqPR5KodT09PdOvW7ZXnxMbGIigoCPfv35euFRoaitatW+cp9qCgIKxatcrkmNFoRNu2bfPUHhEROQ4OUdErValSBadPn0ZISAgWLVqEP/74A2+//bZVrjV+/Hg8ePAABoMBOp0OGRkZGDp0qDSHJrdatGiBpUuXSrejFy9eHOvXr0e1atUsGTYREdkhQfznDNJCJDExMdutAfJDEARoNBpotVrI9Ueb1bo2luDt7W02twcAzp49Cz8/vzy3K4oikpKS4O7unqPbxJlDx1YY8gcwh3LAHOZNTv92F9ohqsxtAyxNqVRCo9EgJSXFbIsBuXB3d0dSUpLF2y1WrFiWBY6Tk1O+rycIQo57gphDx1YY8gcwh3LAHOZNTgscDlGR3ZgyZYpJD4tKpULv3r2z3HmciIjoVQptDw7Zn6CgIKxfvx4rV65ESkoK2rRpgw8//NDWYRERkQNigUN2pU2bNmjTpo2twyAiIgfHISoiIiKSHRY4REREJDsscIiIiEh2WOAQERGR7LDAISIiItlhgUNERESywwKHiIiIZIcFDhEREckOCxwiIiKSHRY4REREJDsscIiIiEh2WOAQERGR7LDAISIiItlhgUNERESywwKHiIiIZIcFDhEREckOCxwiIiKSHRY4REREJDsscIiIiEh2WOAQERGR7LDAISIiItlhgUNERESywwKHiIiIZIcFDhEREckOCxwiIiKSHRY4REREJDsscIiIiEh2WOAQERGR7LDAISIiItlhgUNERESywwKHiIiIZIcFDhEREcmOytYBWEpycjKWLl2KS5cuwcXFBb1790aHDh1sHRYRERHZgGwKnJCQEBgMBqxatQqxsbGYOnUqypcvj7p169o6NCIiIipgshiiSk9Px+nTpzFgwAAUKVIEVapUQatWrXDo0CFbh0ZEREQ2IIsC58GDBwCAihUrSscqV66MmJgYW4VERERENiSLIar09HS4uLiYHHN1dUVaWpr0ODY2FrGxsdJjJycneHt7WzwWpVJp8r9yJAiCrN8fc+jYCkP+AOZQDphD65JFgePs7GxSzABASkqKSdETEhKCGTNmSI8nT56M2bNnWy0mDw8Pq7VtDzQaja1DsDrm0LHJPX8AcygHzKH1yKLAKVeuHADg77//RoUKFQAAUVFR8PHxkc4JDg5G586dpcdOTk6Ij4+3eCxKpRIeHh5ITEyEwWCwePv2wNXVFSkpKbYOw2qYQ8dWGPIHMIdywBzmTfHixXN0niwKHGdnZzRv3hzr16/H2LFj8fjxYxw+fBj/+9//pHO8vLzg5eUlPY6Li7PqB8dgMMj2gymKomzf2z8xh45NzvkDmEM5YA6tSxYFDvCyh2bJkiUYPHgwihQpgv79+6NevXq2DouIiIhsQDYFjpubGz799FNbh0FERER2QBa3iRMRERH9EwscIiIikh0WOERERCQ7LHCIiIhIdljgEBERkeywwCEiIiLZYYFDREREssMCh4iIiGSHBQ4RERHJDgscIiIikh0WOERERCQ7LHCIiIhIdgRRFEVbByEnsbGxCAkJQXBwMLy8vGwdDuUBc+jYmD/Hxxw6PnvIIXtwLCw2NhYzZsxAbGysrUOhPGIOHRvz5/iYQ8dnDzlkgUNERESywwKHiIiIZIcFjoV5eXlh2rRpHDd2YMyhY2P+HB9z6PjsIYecZExERESywx4cIiIikh0WOERERCQ7KlsHICfJyclYunQpLl26BBcXF/Tu3RsdOnSwdVj0/+3ZswdHjhxBdHQ0/P398fHHH0vPxcTEYPHixYiOjkbZsmUxcuRI1KpVS3r+9OnTCA0NRUJCAmrUqIFx48ahZMmStngbhZpOp8Py5csRFhaGpKQklCpVCr169UJAQAAA5tERLFmyBBcuXEBaWhrc3d3Rtm1b9O7dGwDz52gSExMxcuRIeHl5Yf78+QDsLIciWcz8+fPF2bNniykpKWJERITYr18/MSwszNZh0f93+vRp8Y8//hB/+OEHcd68edJxnU4nDhs2TNy6dauo1WrFI0eOiH379hWTkpJEURTFv//+W+zdu7d4+fJlMT09Xfzhhx/ETz/91FZvo1BLS0sT161bJ8bGxooGg0G8ceOG2KdPHzE8PJx5dBAxMTFienq6KIqi+OTJE3HUqFHiyZMnmT8H9P3334uTJ08WP/roI1EU7e93KYeoLCQ9PR2nT5/GgAEDUKRIEVSpUgWtWrXCoUOHbB0a/X/NmjVD06ZN4eHhYXL82rVryMjIQLdu3aBWqxEYGIgyZcrgzJkzAICjR4+iQYMG8PPzg5OTE/r374+bN29yETIbcHZ2Rv/+/VG2bFkoFArUrFkTNWrUQHh4OPPoICpWrAgnJyfpsSAIePjwIfPnYK5du4ZHjx4hMDDQ5Jg95ZAFjoU8ePAAwMsPb6bKlSsjJibGViFRDt27dw8+Pj5QKP7v41CpUiXcu3cPwMsu10qVKknPubu7w9PTk7m1A+np6YiIiICPjw/z6EBWr16NXr16YejQoUhPT0dgYCDz50B0Oh1CQkIwYsQICIIgHbe3HHIOjoWkp6fDxcXF5JirqyvS0tJsFBHlVFpaGlxdXU2Oubq6IjU1FcDL3BYpUsTseebWtkRRxMKFC1G1alXUr18ft2/fZh4dxKBBgzBw4EBERETg3LlzUh6YP8ewZcsW1K9fH6+99hoiIyOl4/aWQ/bgWIizs7NZklJSUsyKHrI/Li4u0gcwU2pqqpQ7Z2dns+eZW9sSRRHLli3Ds2fP8L///Q+CIDCPDkYQBFStWhUqlQobN25k/hzEw4cPcezYMfTr18/sOXvLIQscCylXrhwA4O+//5aORUVFwcfHx1YhUQ5VrFgRMTExMBqN0rGoqChpuNHHxwfR0dHSc8nJyYiLi2NubUQURSxfvhx3797F9OnT4ezsDIB5dFRGoxGxsbHMn4MIDw/Hs2fPMGzYMPTv3x8rVqxAZGQk+vfvjzJlythVDlngWIizszOaN2+O9evXIzU1FVFRUTh8+DDefvttW4dG/5/BYIBWq4XRaITRaIRWq4Ver0edOnWgVquxY8cO6HQ6HD9+HI8ePYK/vz8AICAgABcvXkRYWBgyMjKwfv16VK9encvI20hISAhu3bqFGTNmmHR3M4/2LyUlBUePHkVqaiqMRiP++usv7Nu3D35+fsyfg2jRogVCQkKwcOFCLFy4EP369YOPjw8WLlyIhg0b2lUOuVWDBSUnJ2PJkiW4dOkSihQpwnVw7MyGDRvwyy+/mBxr1aoVxo8fj+joaCxZsgTR0dEoU6YMRo4cidq1a0vnnTp1CqtXr0Z8fDxq1qzJ9Tds5MmTJxg2bBjUajWUSqV0vGfPnujduzfzaOdSU1Px1VdfITIyEkajESVKlEDr1q3RvXt3CILA/Dmgw4cPY9++fdI6OPaUQxY4REREJDscoiIiIiLZYYFDREREssMCh4iIiGSHBQ4RERHJDgscIiIikh0WOERERCQ7LHCIiIhIdljgEBERkeywwCEiIiLZYYFDREREssMCh4iIiGSHBQ4RERHJDgscIrJ7u3btgiAIuHPnjsnxFy9eoEiRIli0aJGNIiMie8UCh4jsXseOHVGuXDn8/PPPJsc3btwIo9GIAQMG2CgyIrJXLHCIyO4plUoMGTIEa9asgcFgkI7//PPP6Nq1K0qUKGHD6IjIHrHAISKHMHToUMTGxuL3338HAFy/fh1//vknhg4dauPIiMgeCaIoirYOgogoJ9555x24urpi27ZtmDBhArZv3467d+9CoeB3NSIyxd8KROQwhg8fjj179uDBgwdYv349hgwZwuKGiLLEHhwichg6nQ7ly5eHr68vTp06haioKFSsWNHWYRGRHeJXHyJyGGq1GoMGDcKJEyfQunVrFjdElC0WOETkULp16wYAnFxMRK/EAoeIHMq+fftQsmRJdOnSxdahEJEdU9k6ACKinLh16xZu3bqFxYsXY/To0XBycrJ1SERkxzjJmIgcQkBAAM6ePYt27dph/fr1cHV1tXVIRGTHWOAQERGR7HAODhEREckOCxwiIiKSHRY4REREJDsscIiIiEh2WOAQERGR7LDAISIiItlhgUNERESywwKHiIiIZOf/AbAlvQoS/BrSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8763775227353)>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the results as a scatter plot\n",
    "predictions = pd.DataFrame({'y': response_test, 'y_hat': response_predicted}) # plotnine works with pd.DataFrame\n",
    "\n",
    "(\n",
    "p9.ggplot(predictions, p9.aes('y', 'y_hat'))\n",
    "    + p9.geom_point()\n",
    "    + p9.scales.xlim(0, 400)\n",
    "    + p9.scales.ylim(0, 400)\n",
    ") # plotnine will emit a `PlotnineWarning` indicating that we have cropped some points from our plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cleared-porcelain",
   "metadata": {},
   "source": [
    "### Exercise 1c\n",
    "\n",
    "As with Exercise 1b, but this time try using the SVM ('linear' and 'rbf') and RandomForestRegressor instead. How do your results compare? Try just using one or two predictors \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "retired-samba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=1000.0, gamma=0.3)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm, ensemble\n",
    "#model = linear.LinearRegression()\n",
    "model = svm.SVR(kernel='rbf', C=1e3, gamma=0.3)\n",
    "#model = ensemble.RandomForestRegressor(n_estimators=100, max_depth=3)\n",
    "model.fit(X=predictors_train, y=response_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "unnecessary-paraguay",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8763774514836)>"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response_predicted = model.predict(X=predictors_test)\n",
    "residuals = pd.DataFrame({'residuals': np.abs(response_test - response_predicted)})\n",
    "(\n",
    "    p9.ggplot(residuals, p9.aes('residuals'))\n",
    "    + p9.geom_histogram(bins=20)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "posted-conservative",
   "metadata": {},
   "source": [
    "A little bit of experimentation with C and gamma means we now beat the linear model - just!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "becoming-liberty",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5170546215560003"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X=predictors_test, y=response_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "weighted-island",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ktmx283/opt/anaconda3/envs/202101-data-science-python/lib/python3.8/site-packages/plotnine/layer.py:467: PlotnineWarning: geom_point : Removed 5 rows containing missing values.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8763777074640)>"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the results\n",
    "predictions = pd.DataFrame({'y': response_test, 'y_hat': response_predicted}) # plotnine works with pd.DataFrame\n",
    "\n",
    "(\n",
    "p9.ggplot(predictions, p9.aes('y', 'y_hat'))\n",
    "    + p9.geom_point()\n",
    "    + p9.scales.xlim(0, 300)\n",
    "    + p9.scales.ylim(0, 300)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "greenhouse-visitor",
   "metadata": {},
   "source": [
    "### Exercise 1d (more technical)\n",
    "\n",
    "If you're feeling so inclined, dive into the documentation of [RandomForestRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html) and try tweaking some of the _hyperparameters_ to see how this affects your model score on the training and test datasets ('n_estimators' and 'max_depth' are good places to start). If you want do delve deeper this [blog post](https://towardsdatascience.com/hyperparameter-tuning-the-random-forest-in-python-using-scikit-learn-28d2aa77dd74) is a nice introduction to the concepts of _cross-validation_ and _hyperparameter tuning_ (more on this next week though!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "noted-tokyo",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_estimators_min, n_estimators_max = 2, 100\n",
    "max_depth_min, max_depth_max = 2, 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "extra-dublin",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_parameters = np.mgrid[n_estimators_min - 1:n_estimators_max + 1:20j,  max_depth_min - 1:max_depth_max + 1:9j]\n",
    "parameters = grid_parameters.reshape((2, -1)).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "serious-hierarchy",
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = np.empty(parameters.shape[0])\n",
    "for i, (n_estimators, max_depth) in enumerate(parameters):\n",
    "    model = ensemble.RandomForestRegressor(n_estimators=int(n_estimators), max_depth=int(max_depth))\n",
    "    model.fit(X=predictors_train, y=response_train)\n",
    "    scores[i] = model.score(X=predictors_test, y=response_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "protecting-relative",
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = np.reshape(scores, (20, 9))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "directed-solomon",
   "metadata": {},
   "source": [
    "Display the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "stone-mountain",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f879c1afca0>"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x324 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize=(6, 4.5))\n",
    "ax = fig.add_subplot()\n",
    "ax.set_ylabel('max_depth')\n",
    "ax.set_xlabel('n_estimators')\n",
    "score_min, score_max = scores.min(), scores.max()\n",
    "plot = ax.imshow(\n",
    "    np.rot90(scores),\n",
    "    vmin=score_min,\n",
    "    vmax=score_max,\n",
    "    aspect='auto',\n",
    "    extent=(n_estimators_min - 1, n_estimators_max, max_depth_min - 1, max_depth_max),\n",
    "    cmap=plt.cm.viridis)\n",
    "\n",
    "fig.colorbar(plot, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "requested-billy",
   "metadata": {},
   "source": [
    "### Exercise 2\n",
    "\n",
    "As a bit of pre-reading for next week, have a read through the [Kaggle Titanic](https://www.kaggle.com/c/titanic) prediction competition blurb (watch the video too). The [discussion forum](https://www.kaggle.com/c/titanic/discussion) is full of interesting beginner material too."
   ]
  }
 ],
 "metadata": {
  "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}