random_forest_model_v1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/kt/92wnzvcj6qq_r6j3j19yp5lw0000gp/T/ipykernel_10129/581540614.py:2: DtypeWarning: Columns (1,2,3,4,5) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  data1 = pd.read_csv(\"data/corona_tested_individuals_ver_006_march_april.english.csv\")\n",
      "/var/folders/kt/92wnzvcj6qq_r6j3j19yp5lw0000gp/T/ipykernel_10129/581540614.py:3: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  data2 = pd.read_csv(\"data/corona_tested_individuals_ver_0083_september_november.english.csv\")\n"
     ]
    }
   ],
   "source": [
    "#read in the data\n",
    "data1 = pd.read_csv(\"data/corona_tested_individuals_ver_006_march_april.english.csv\")\n",
    "data2 = pd.read_csv(\"data/corona_tested_individuals_ver_0083_september_november.english.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Take the processing function from hackathon_lgbm.ipynb to process the 0083 data\n",
    "\n",
    "def prepareData(X):\n",
    "    X = X.dropna()\n",
    "    X = X.copy().query('gender != \"None\"')\n",
    "    X = X.query('corona_result != \"other\"')\n",
    "    X['test_indication'] = X['test_indication'].map({'Contact with confirmed': 1, 'Other': 0, 'Abroad': 0})\n",
    "    X['age_60_and_above'] = X['age_60_and_above'].map({'Yes': 1, 'No': 0})\n",
    "    X['gender'] = X['gender'].map({'male': 1, 'female': 0})\n",
    "    X['corona_result'] = X['corona_result'].map({'positive': 1, 'negative': 0})\n",
    "    X = X.copy().query('cough != \"None\"')\n",
    "    X = X.copy().query('fever != \"None\"')\n",
    "    X = X.copy().query('sore_throat != \"None\"')\n",
    "    X = X.copy().query('shortness_of_breath != \"None\"')\n",
    "    X = X.copy().query('head_ache != \"None\"')\n",
    "    X['cough'] = X['cough'].astype(int)\n",
    "    X['fever'] = X['fever'].astype(int)\n",
    "    X['sore_throat'] = X['sore_throat'].astype(int)\n",
    "    X['shortness_of_breath'] = X['shortness_of_breath'].astype(int)\n",
    "    X['head_ache'] = X['head_ache'].astype(int)\n",
    "    X['gender'] = X['gender'].astype(int)\n",
    "    X = X.drop(['test_date'],axis = 1)\n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/kt/92wnzvcj6qq_r6j3j19yp5lw0000gp/T/ipykernel_10129/3264763642.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X['test_indication'] = X['test_indication'].map({'Contact with confirmed': 1, 'Other': 0, 'Abroad': 0})\n",
      "/var/folders/kt/92wnzvcj6qq_r6j3j19yp5lw0000gp/T/ipykernel_10129/3264763642.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X['age_60_and_above'] = X['age_60_and_above'].map({'Yes': 1, 'No': 0})\n",
      "/var/folders/kt/92wnzvcj6qq_r6j3j19yp5lw0000gp/T/ipykernel_10129/3264763642.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X['gender'] = X['gender'].map({'male': 1, 'female': 0})\n",
      "/var/folders/kt/92wnzvcj6qq_r6j3j19yp5lw0000gp/T/ipykernel_10129/3264763642.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X['corona_result'] = X['corona_result'].map({'positive': 1, 'negative': 0})\n",
      "/var/folders/kt/92wnzvcj6qq_r6j3j19yp5lw0000gp/T/ipykernel_10129/3264763642.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X['test_indication'] = X['test_indication'].map({'Contact with confirmed': 1, 'Other': 0, 'Abroad': 0})\n",
      "/var/folders/kt/92wnzvcj6qq_r6j3j19yp5lw0000gp/T/ipykernel_10129/3264763642.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X['age_60_and_above'] = X['age_60_and_above'].map({'Yes': 1, 'No': 0})\n",
      "/var/folders/kt/92wnzvcj6qq_r6j3j19yp5lw0000gp/T/ipykernel_10129/3264763642.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X['gender'] = X['gender'].map({'male': 1, 'female': 0})\n",
      "/var/folders/kt/92wnzvcj6qq_r6j3j19yp5lw0000gp/T/ipykernel_10129/3264763642.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X['corona_result'] = X['corona_result'].map({'positive': 1, 'negative': 0})\n"
     ]
    }
   ],
   "source": [
    "data_processed1 = prepareData(data1)\n",
    "data_processed2 = prepareData(data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cough                  0\n",
      "fever                  0\n",
      "sore_throat            0\n",
      "shortness_of_breath    0\n",
      "head_ache              0\n",
      "corona_result          0\n",
      "age_60_and_above       0\n",
      "gender                 0\n",
      "test_indication        0\n",
      "dtype: int64\n",
      "cough                  0\n",
      "fever                  0\n",
      "sore_throat            0\n",
      "shortness_of_breath    0\n",
      "head_ache              0\n",
      "corona_result          0\n",
      "age_60_and_above       0\n",
      "gender                 0\n",
      "test_indication        0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#check for nas\n",
    "print(data_processed1.isna().sum())\n",
    "print(data_processed2.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop nas from data_processed1\n",
    "data_processed1.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot a correlation heatmap for all variables in data1 \n",
    "sns.heatmap(data_processed1.corr(), xticklabels=data_processed1.columns, yticklabels=data_processed1.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot a correlation heatmap for all variables in data2\n",
    "sns.heatmap(data_processed2.corr(), xticklabels=data_processed2.columns, yticklabels=data_processed2.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training and Testing for March - April 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a train test split\n",
    "\n",
    "X1 = data_processed1.drop(['corona_result'],axis = 1)\n",
    "y1 = data_processed1.copy()['corona_result']\n",
    "\n",
    "X_train1, X_test1, y_train1, y_test1 = train_test_split(X1, y1, test_size = 0.2,random_state = 2022, stratify = y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create the random forest model\n",
    "rf_model1 = RandomForestClassifier()\n",
    "rf_model1.fit(X_train1, y_train1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEGCAYAAABo25JHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAA0uElEQVR4nO3deXxU1dnA8d9D9oSwhUUEQ1BQdgKkUkAslKJWBa1VKVYrVV/eolZfF6xWqxZbq1WxFbUWFMENULuIS93qLi4ECTGArLIEUUjYErInz/vHvTNMQpK5IZkMyTzfzyefzL1zl+fOJOfcc84954iqYowxJnK1CXcAxhhjwssyAmOMiXCWERhjTISzjMAYYyKcZQTGGBPhosMdQEN17txZ09LSwh2GMca0KCtWrMhT1S61vdfiMoK0tDQyMzPDHYYxxrQoIrK1rvesasgYYyKcZQTGGBPhLCMwxpgIZxmBMcZEOMsIjDEmwoUsIxCR+SKyS0Ry6nhfROQhEdkoItkiMjxUsRhjjKlbKEsEC4Az6nn/x0Bf92c68LcQxmKMMaYOIetHoKofiEhaPZucAzylzjjYn4pIBxHprqo7QxWTMca0BCXllewuKGV3Yanz2/2Z0L8rQ3p2aPLzhbNDWQ9ge8ByrrvusIxARKbjlBpITU1tluCMMaYplVdWsedgWbWE3Z/Qu7/z3PUFpRW1HqNzclyrywg8U9W5wFyAjIwMm0nHGHNUqKpS9hWXByTsJf7XeYVl1RL8PQfLaj1Gclw0XZLj6JwcR/9j23Fq2zi6JMfRxfc7OY7ObeNIaRtLTFRoavPDmRHsAI4LWO7prjPGmLBRVQpLKw67a88rPPxOPr+wjIqqw+9N46Lb+BPxXimJjEjrWC1h9yX0ndvGkRAbFYarrC6cGcFS4GoRWQyMBPZb+4AxJlRqq3evLXHfXVBKaUXVYftHtRE6t431J+L9j2l3WMLuu7NPjotGRMJwlUcmZBmBiCwCxgGdRSQXuAOIAVDVx4DXgDOBjUAR8MtQxWKMaZ2aot69U1KsPxHv1SuxRuIe71bNxNIxMZY2bVpO4t4QoXxqaGqQ9xW4KlTnN8a0TLXVu+cVlB32BM3uwlL2FpWhtbQaHg317i1Ji2gsNsa0bLXVu+fVfDyyldW7tySWERhjjljNeve8Wu7afetLyiOr3r0lsYzAGFNNVZWSV1jKrloaUWsm+AUlVu/eGlhGYEyEKSmvZOf+EnbsLeabfcXk7iv2v96xr5id+4sprzy8aqZavfsx7Ti1r9W7txaWERjTiqgq+4vLyd3rJOrf+BL5/c7vHftKyCssrbaPCHRLjqdHxwSGHteBMwd359gO8XRrF2/17hHCMgJjWhBVZVdBKdv2FLkJu/sTcEdfVFZZbZ+46Db06JhAjw4J9O/ejmM7OK99645pH2938BHOU0YgIm2AocCxQDGQo6q7QhmYMZGqorKKb/aVsHXPQbbkF7Et/yBb84vYml/Etj1FFJdXT+g7JcVybId4ju+SxNi+XTi2Qzw9Oyb4E/xOSbHWyGrqVW9GICInAL8BfgRsAHYD8cCJIlIE/B1YqKqHPw5gjKlTSXkl2/c4ifuW/INs21PkT/Rz9xZXe3wyLroNqZ0S6ZWSxCl9O9MrJZHUTon07JjIsR3iSYy1gr1pnGB/QX/AmSfgf90OYH4i0hW4CLgEWBia8IxpuQ6UlLMtPyCxD0j0vz1QUq0jVHJ8NL1SEhnYoz1nDu5OrxQn4e+Vkki35Hh7ssaEVL0ZQX29g92qob80dUDGtBSqSl5hGdv2HHQTe+eOfotbhVNztMnObZ2OUKNOSKFXpyTSOif67/Q7JsZY9Y0JmyMuU4rIRFV9qymDMeZoU1ml7Nxf7NzZ7wm8s3cS/YMBDbMicGz7BHqlJHL6wGPolZJIWkoiqZ2SSE1JpG2cVeGYo1Nj/jKfAGyWGNPilVVUsX1vkb/qxtcouyX/ILl7iimrPNQEFhvVhp6dEujVKZGRvTu5ib2T0PfsmEBctD1iaVqeYI3FS+t6C0hp+nCMCY2DpRVuAu9U3fhf5xWxc38xgUPbJMVGkZqSxEndkpk4oJtTjZOSSGpKIt3bJxBl9fWmlQlWIhgLXAwU1lgvwMkhiciYI6Cq7CsqP/QETl4RW/ccqsap2YmqU1IsqZ0S+V5aR1JTetKrU6JbZ59E57b2uKWJLMEygk+BIlV9v+YbIrIuNCEZU7uqKqczVeATOFv3FLHVrc6pOe5N9/bxpHZKZEK/rqS6VTi93Dv7dvExYboKY44+wZ4a+nE9753a9OGYSFdeWcWOvcVs3XPoCRynM5Vzpx84c1R0G6FnxwRSU5IYntqR1E6HEvvjOiUSH2P19cZ4YY8xmGZXXFbJtj2HEvctAT1nd+wrpjKgwj4+po37qGUS407qQmqKU1/fq1MSx3aIJ9qGRjCm0SwjMCGxv7jcX2Xj1Nkfqsb57kD1+vp28dGkdU5iSM/2TB56bLVqnK7JcVZfb0yIWUZgjoiqsruw1H8n70v0fYn9vqLyatt3dWeVGtu3C706JVZL7DskxobpKowxYBmBqUdllfLNvmI3gT9YLcHftqeo2iiXbQR6dEygV6ckzqoxREJqp0QbD8eYo5jn/04RuVNV76xr2bRMvsT+67yDbMk/6PzOcxL77XuLqk1QEusb/KxTIqNP6Owm9k6C36NDArHRVl9vTEvUkNu0FUGWzVGqqkr59kAJW/IO8nW+k9B/nXdouITAnrOJsVH0SkmiX/dkTh90DL06HbqzP6adDX5mTGvkOSNQ1ZfrWzbhparsLigNuLMv4uu8Qn/HqsCJw+Oi25CWksQJXZKY0L8rvVOcp3J6d06yxlljIlCwISbmAIdPXupS1WuaPCLjSUl5JZ9syuettd+RtW0fW2sMgBYTJaR2SqR35yTG9u3sT+jTOifR3e7sjTEBgpUIMpslCuPJ3oNlvLtuF2+t+Y731++mqKySxNgoMtI6cXLvTv6EvneKPWNvjPEuWM/iahPOiEiiqhaFNiQTaFt+EW+u+Za31nxH5ta9VFYpXZPjOHdYDyYO6Mao41OsB60xplG8zlk8CmfY6bZAqogMxZm17MpQBheJqqqU7B37ectN/Nd/54z3d1K3ZGb84AQmDujG4B7trWrHGNNkvDYW/wU4HVgKoKqrRMTGGmpChaUV/P39TSxZvp1dBaVEtRG+l9aR3509gIn9u5GakhjuEI0xrVRDnhraXuNpksq6tjXelVdW8Xzmdh58awN5haX8qH83zhx8DD/s19V63BpjmoXXjGC7iIwGVERigGuBtaELq/WrrFJeytrBX97ewLY9RWT06sjjl2aQflyHcIdmjIkwXjOCXwF/BXoA3wBvAFeFKqjWTFV5PedbZr+1ng27Chl4bDuenPY9xp3UxZ7fN8aEhaeMQFXzgJ839OAicgZOBhIFPK6q99R4PxVYCHRwt7lZVV9r6HlaAlXlvfW7eeDNdeTsOMAJXZJ49OfDOWPgMdbwa4wJK69PDR2Pk6B/H6eD2SfAdaq6uZ59ooBHgIlALrBcRJaq6pqAzW4DnlfVv4nIAOA1IO1ILuRo9tnmfO5/cx3Lt+ylZ8cE7r9gKD8Z1sPmvjXGHBW8Vg09h5Oo/8Rd/hmwCBhZzz4nAxt9mYWILAbOAQIzAgXaua/b41Q7tRqrtu/j/jfX8eGGPLq1i+OucwcxJeM4G5zNGHNU8ZoRJKrq0wHLz4jIzCD79AC2ByzncnjGcSfwpoj8GkgCflTbgURkOjAdIDU11WPI4bPu2wIeeHMdb675jo6JMdx6Zn8uGdXLOn4ZY45KwcYa6uS+/I+I3AwsxrmLn4JTjdNYU4EFqvqA22ntaREZpKpVgRup6lxgLkBGRkadYx+F24GScv74ylqeX7GdtrHRXD/xRC47pTdt42wsfmPM0StYCrUCJ+H3VWb/b8B7CtxSz747gOMClnu66wJdDpwBoKqfiEg80BnYFSSuo86yjXnMfDGbnfuLueKU3lw1vo/1AzDGtAjBxhrq3YhjLwf6ikhvnAzgZ8BFNbbZBkwAFohIfyAe2N2Icza74rJK7n39KxYs28LxnZN4ccZohqd2DHdYxhjjWUNmKBsEDMBJrAFQ1afq2l5VK0Tkapw+B1HAfFVdLSKzgExVXQrcAMwTketwShjTVPWorfqpaeW2vdzw/Co25x1k2ug0fnNGPxJirR3AGNOyeH189A5gHE5G8BrwY+AjoM6MAMDtE/BajXW3B7xeA4xpUMRHiSc//pq7XllD9/YJPHfFSEb36RzukIwx5oh4LRGcDwwFVqrqL0WkG/BM6MI6us1+cx0PvbOR0wZ04/4Lh9IuPibcIRljzBHzmhEUq2qViFSISDucxtzjgu3UGj2fuZ2H3tnIlIzjuPu8wdYpzBjT4nnNCDJFpAMwD+dJokKc3sURZcXWPfzu3zmMPiHFMgFjTKvhdawh3wQ0j4nI60A7Vc0OXVhHn427Crl8YSbHdkhgztRhlgkYY1qNYB3Khtf3nqp+0fQhHX2+O1DCpfM/J7qNsPCXJ5PSNi7cIRljTJMJViJ4oJ73FPhhE8ZyVDpQUs6l8z9nX1EZi6ePspnCjDGtTrAOZeObK5CjUWlFJb96egUbdxUyf9r3GNyzfbhDMsaYJmeD4NShqkq58YVslm3KZ/aFQzn1xC7hDskYY0LCxkOuw92vreXlVd9w0xkncd7wnuEOxxhjQsYyglo8/uFmHv/oay4d1YsZPzgh3OEYY0xIecoIxHGxiNzuLqeKyMmhDS08XsrawR9eXcuPBx3D7ZMG2jzCxphWz2uJ4FFgFM78AQAFODOWtSrrvyvgxhdWMbJ3Jx6ckm59BYwxEcFrY/FIVR0uIisBVHWviLS6wfb/9t4mYqLa8LeLR9hsYsaYiOG1RFDuTkavACLSBaiqf5eWZfueIpau+oaLTk6lU1Kry+OMMaZOXjOCh4B/AV1F5I84Q1DfHbKowmDeh5tpI3D52MbMxWOMMS2P17GGnhWRFTiziQlwrqquDWlkzSi/sJQly7dz3rCedG+fEO5wjDGmWXmdmOYhYLGqtroGYoA3Vn9HaUUV08akhTsUY4xpdl6rhlYAt4nIJhG5X0QyQhlUc3t33S56dEig3zHJ4Q7FGGOanaeMQFUXquqZwPeAdcC9IrIhpJE1k9KKSj7emMf4fl2sz4AxJiI1tGdxH6Af0Av4qunDaX4rt+2jqKySH5zYNdyhGGNMWHjtWfxntwQwC8gBMlR1UkgjayarvzkAQPpxHcIbiDHGhInXDmWbgFGqmhfKYMJhzTcH6JocR5dkm2zGGBOZgs1Q1k9VvwKWA6kikhr4fmuYoWzNzgMMOLZduMMwxpiwCVYiuB6YTu0zlbX4GcrKKqrYuKuA8SfZXAPGmMgVbIay6e7LH6tqSeB7IhIfsqiayYZdBZRXqpUIjDERzetTQ8s8rmtR1rgNxQO6W0ZgjIlcwdoIjgF6AAkiMgxneAmAdkCLn8V9Ve4+EmOj6JWSFO5QjDEmbIK1EZwOTAN6ArMD1hcAvw1RTM1CVXk951vGndTF5h0wxkS0YG0EC4GFIvJTVf1HM8XULLbvKSavsIxT+lhDsTEmsgWrGrpYVZ8B0kTk+prvq+rsWnZrEbJ37ANgSM/24Q3EGGPCLFhjsa/yvC2QXMtPvUTkDBFZJyIbReTmOra5UETWiMhqEXmuAbE3ype5+4mNasOJ3WygOWNMZAtWNfR39/fvG3pgd0azR4CJQC6wXESWquqagG36ArcAY9zpL5ttwJ/s3P30755MbHRDh1syxpjWpSFjDbUTkRgR+a+I7BaRi4PsdjKwUVU3q2oZsBg4p8Y2/wM8oqp7AVR1V0Mv4EhUVSk5O/Yz2KqFjDHGcz+C01T1AHA2sAVnFNKZQfbpAWwPWM511wU6EThRRD4WkU9F5IzaDiQi00UkU0Qyd+/e7THkuu3YV0xBaQUDj7WMwBhjvGYEviqks4AXVHV/E50/GugLjAOmAvNEpEPNjVR1rqpmqGpGly6Nf8pnV4HTSbp7+xbfOdoYYxrNa0bwioh8BYwA/isiXYCSIPvsAI4LWO7prguUCyxV1XJV/RpYj5MxhNSuA6UAdE22jMAYY7zOUHYzMBpnHoJy4CCH1/fXtBzoKyK9RSQW+BmwtMY2/8YpDSAinXGqijZ7Df5IfXvAycO6tbOhp40xxuvk9THAxcCp7nSO7wOP1bePqlaIyNXAG0AUMF9VV4vILCBTVZe6750mImuASmCmquYf8dV4tPdgGW0EOiTGhvpUxhhz1PM6Mc3fgBjgUXf5EnfdFfXtpKqvAa/VWHd7wGvFGer6sM5qobSnqIwOibE2tIQxxuA9I/ieqg4NWH5HRFaFIqDmsPdgOR0SY8IdhjHGHBW8NhZXisgJvgUROR6nKqdFOlhWQVKs1zzQGGNaN6+p4UzgXRHZjDMUdS/glyGLKsT2HiyjY5K1DxhjDHjICNxHRffj9BT2DQGxTlVLQxlYKO0qKKVPVxtjyBhjIEjVkIhcAawG5gBZQJqqZrfkTKCqSskrLKWrPTpqjDFA8BLB/wEDVXW32y7wLIf3BWhR9hWXU16pdGlrGYExxkDwxuIyVd0NoKqbgRafevqGl7ASgTHGOIKVCHqKyEN1LavqNaEJK3TyC8sASEmyjMAYYyB4RlBzhNEVoQqkuRwoLgegfYL1IzDGGPA2Z3GrUlhaAUByvPUjMMYYCP7U0DwRGVTHe0kicpmI/Dw0oYVGUZnTDy4pzjICY4yB4FVDjwC3i8hgIAfYDcTjDBXdDpiP8yRRi3GwzCkRJMZGhTkSY4w5OgSrGsoCLhSRtkAG0B0oBtaq6rrQh9f0ikoriWojxNlcxcYYA3gcYkJVC4H3QhtK8zhYVkFibBTucNrGGBPxIu62uKi00gacM8aYABGXERwsqyAxztoHjDHGp0EZgYgkhiqQ5nKw1IagNsaYQJ4yAhEZ7U4n+ZW7PFREHg2y21HpYFmlPTFkjDEBvJYIHgROB/IBVHUVcGqoggqlorIK60NgjDEBPFcNqer2Gqta5AxlRaVWIjDGmEBeb423i8hoQEUkBrgWWBu6sELH9/ioMcYYh9cSwa+Aq4AewA4gHbgyRDGF1MHSSqsaMsaYAF5TxJNUtdqYQiIyBvi46UMKHVW1ieuNMaYGryWCOR7XHdVKyqtQxfoRGGNMgHpvjUVkFDAa6CIi1we81Q5ocalpkTvgnJUIjDHmkGApYizQ1t0uOWD9AeD8UAUVKr4hqBOssdgYY/yCjT76PvC+iCxQ1a3NFFPI+OcisBKBMcb4eU0Ri0TkPmAgznwEAKjqD0MSVYgU2VwExhhzGK+Nxc/iDC/RG/g9sAVYHqKYQsaqhowx5nBeM4IUVX0CKFfV91X1MqBFlQbAqoaMMaY2XlPEcvf3ThE5C/gG6BSakELHVzVkJQJjjDnEa4ngDyLSHrgBuBF4HPi/YDuJyBkisk5ENorIzfVs91MRURHJ8BjPETk0cb1lBMYY4+N1qspX3Jf7gfHg71lcJxGJAh4BJgK5wHIRWaqqa2psl4wzdtFnDQu94XwZQWKMVQ0ZY4xPvSUCEYkSkakicqOIDHLXnS0iy4CHgxz7ZGCjqm5W1TJgMXBOLdvdBdwLlDQ8/IYptqohY4w5TLCqoSeAK4AU4CEReQa4H/izqg4Lsm8PIHDo6lx3nZ+IDAeOU9VX6zuQiEwXkUwRydy9e3eQ09btYFkl0W2E2OiIm6HTGGPqFKyOJAMYoqpVIhIPfAucoKr5jT2xiLQBZgPTgm2rqnOBuQAZGRl6pOcsKa+00oAxxtQQ7Na4TFWrAFS1BNjcgExgB3BcwHJPd51PMjAIeE9EtgDfB5aGssG4tKKKOCsNGGNMNcFKBP1EJNt9LcAJ7rIAqqpD6tl3OdBXRHrjZAA/Ay7yvamq+4HOvmUReQ+4UVUzG3wVHpVVVBEbZRmBMcYECpYR9D/SA6tqhYhcDbyBM1LpfFVdLSKzgExVXXqkxz5SxWWVxFvVkDHGVBNs0LlGDTSnqq8Br9VYd3sd245rzLm8KLJpKo0x5jARVU9SVmlVQ8YYU1NEpYqqENVGwh2GMcYcVTxnBCKSICInhTKYUKuoUtqIZQTGGBPIU0YgIpOALOB1dzldRJq9sbexSiuqiIuxNgJjjAnktURwJ86QEfsAVDULZ26CFqW0vNL6ERhjTA1eU8Vy97n/QEfcwzdcyiqqbHgJY4ypweswnKtF5CIgSkT6AtcAy0IXVmiUVlQRH21VQ8YYE8jr7fGvceYrLgWewxmO+v9CFFPIlFVWERNljcXGGBPIa4mgn6reCtwaymBCraS8knhrLDbGmGq8lggeEJG1InKXb16Clqi0vMoyAmOMqcFTRqCq43FmJtsN/F1EvhSR20IaWROrqlLKKquIj7HGYmOMCeQ5VVTVb1X1IeBXOH0Kah0z6GhVVlkFQIwNMWGMMdV47VDWX0TuFJEvgTk4Twz1DGlkTaygxJmmMjne5is2xphAXlPF+cAS4HRV/SaE8YRMZZXT7cFKBMYYU52njEBVR4U6kFCrqHKqhmzMOWOMqa7ejEBEnlfVC90qocCexF5mKDuqVFQ64VvPYmOMqS5YieBa9/fZoQ4k1Kq0xY2IYYwxzaLe22NV3em+vFJVtwb+AFeGPrymU2FtBMYYUyuvqeLEWtb9uCkDCbWyCnt81BhjahOsjWAGzp3/8SKSHfBWMvBxKANrauVuPwKbqtIYY6oL1kbwHPAf4E/AzQHrC1R1T8iiCgFf1VC0DTpnjDHVBMsIVFW3iMhVNd8QkU4tKTPw9SOIsqkqjTGmGi8lgrOBFTiPjwamogocH6K4mpzvoSGxjMAYY6qpNyNQ1bPd3y1uWkpjjDHeeB1raIyIJLmvLxaR2SKSGtrQjDHGNAevj9D8DSgSkaHADcAm4OmQRRUC2vKmWDbGmGbhNSOoUFUFzgEeVtVHcB4hbTF8bQQ21pAxxlTndfTRAhG5BbgEGCsibYCY0IXV9HxDTLSxnMAYY6rxWiKYgjNx/WWq+i3OXAT3hSyqELASgTHG1M7rVJXfAs8C7UXkbKBEVZ8KaWTGGGOahdenhi4EPgcuAC4EPhOR8z3sd4aIrBORjSJycy3vXy8ia0QkW0T+KyK9GnoBXh0afdSKBMYYE8hrG8GtwPdUdReAiHQB3gZerGsHEYkCHsEZsC4XWC4iS1V1TcBmK4EMVS1yxzX6M041VJPzDToXZ/MRGGNMNV5TxTa+TMCV72Hfk4GNqrpZVcuAxThPHfmp6ruqWuQufkoI50H2lwesQGCMMdV4LRG8LiJvAIvc5SnAa0H26QFsD1jOBUbWs/3lOAPcHUZEpgPTAVJTrR+bMcY0Ja9zFs8UkfOAU9xVc1X1X00VhIhcDGQAP6jj/HOBuQAZGRlH1DNM3TYCsTYCY4ypJth8BH2B+4ETgC+BG1V1h8dj7wCOC1ju6a6reY4f4bRB/EBVSz0eu8H8j49aE4ExxlQTLFmcD7wC/BRnBNI5DTj2cqCviPQWkVjgZ8DSwA1EZBjwd2ByjTaIJmcDTBhjTO2CVQ0lq+o89/U6EfnC64FVtUJErgbeAKKA+aq6WkRmAZmquhSnU1pb4AV3eOhtqjq5wVfhgX9iGutRZowx1QTLCOLdu3Zf6pkQuKyq9WYMqvoaNRqVVfX2gNc/anDER8jfRmCPDRljTDXBMoKdwOyA5W8DlhX4YSiCMsYY03yCTUwzvrkCMcYYEx72DI0xxkQ4ywiMMSbCWUZgjDERzuvoo+LOVXy7u5wqIieHNjRjjDHNwWuJ4FFgFDDVXS7AGVnUGGNMC+d10LmRqjpcRFYCqOpet7dwi3FohjLrR2CMMYG8lgjK3fkFFPzzEVSFLKoQUHyDzhljjAnkNSN4CPgX0FVE/gh8BNwdsqiMMcY0G6/DUD8rIiuACTg31eeq6tqQRtbE1EadM8aYWnnKCEQkFSgCXg5cp6rbQhVYU6t0B52zNgJjjKnOa2PxqzjtAwLEA72BdcDAEMXV5GyqSmOMqZ3XqqHBgcsiMhy4MiQRhYqbE1hGYIwx1R1Rz2J3+On65h8+atkw1MYYU53XNoLrAxbbAMOBb0ISkTHGmGbltY0gOeB1BU6bwT+aPhxjjDHNLWhG4HYkS1bVG5shHmOMMc2s3jYCEYlW1UpgTDPFEzJq09cbY0ytgpUIPsdpD8gSkaXAC8BB35uq+s8QxtakqvxjDYU3DmOMOdp4bSOIB/Jx5ij29SdQoMVkBL6exWKjDRljTDXBMoKu7hNDORzKAHxaVF2Lf9A5yweaVXl5Obm5uZSUlIQ7FGMiQnx8PD179iQmJsbzPsEygiigLbUP2tmyMgJ/icA0p9zcXJKTk0lLS7M+HMaEmKqSn59Pbm4uvXv39rxfsIxgp6rOalxoR4dDQ0xYYtScSkpKLBMwppmICCkpKezevbtB+wXrWdxq/ntVrWooXCwTMKb5HMn/W7CMYMKRhXL0saohY4ypXb0Zgaruaa5AQu1QicCygkgTFRVFeno6gwYNYtKkSezbt69JjrtgwQKuvvrqJjlWWloagwcPJj09nfT0dJYtW9Ykx60pKyuL1157rdq6//znP2RkZDBgwACGDRvGDTfcAMCdd97J/fff32TnHj16tP/1zJkzGThwIDNnzuSxxx7jqaeeatSxV65cyeWXX15t3bnnnsv3v//9auumTZvGiy++WG1d27Zt/a/Xr1/PmWeeSd++fRk+fDgXXngh3333XaNi27NnDxMnTqRv375MnDiRvXv31rrdb37zGwYNGsSgQYNYsmSJf/3XX3/NyJEj6dOnD1OmTKGsrAyAhx9+mPnz5zcqNp8jGnSuJfK3EYQ1ChMOCQkJZGVlkZOTQ6dOnXjkkUfCHVKt3n33XbKyssjKyqqWaNanoqKiQeeomRHk5ORw9dVX88wzz7BmzRoyMzPp06dPg47pVWDmNnfuXLKzs7nvvvv41a9+xS9+8QvPx6ntmu+++26uueYa//K+fftYsWIF+/fvZ/PmzZ6OW1JSwllnncWMGTPYsGEDX3zxBVdeeWWD69truueee5gwYQIbNmxgwoQJ3HPPPYdt8+qrr/LFF1+QlZXFZ599xv3338+BAwcAJ4O47rrr2LhxIx07duSJJ54A4LLLLmPOnDmNis3Haz+CFs8mrw+/37+8mjXfHGjSYw44th13TPI+LcaoUaPIzs4G4PPPP+faa6+lpKSEhIQEnnzySU466SQWLFjA0qVLKSoqYtOmTfzkJz/hz3/+MwBPPvkkf/rTn+jQoQNDhw4lLi4OgC1btnDZZZeRl5dHly5dePLJJ0lNTWXatGkkJCSwcuVKdu3axfz583nqqaf45JNPGDlyJAsWLKgz1vqOGR8fz8qVKxkzZgxXXXUVV111Fbt37yYxMZF58+bRr18/XnjhBX7/+98TFRVF+/btefvtt7n99tspLi7mo48+4pZbbuHVV1/l1ltvpV+/foBTepoxY8ZhscybN4+5c+dSVlZGnz59ePrpp0lMTDzsHB988AGrV6/ml7/8JWVlZVRVVfGPf/yDvn370rZtWwoLC5k8eTKFhYWMGDGCW265hbVr19K2bVtuvPFGNm3aVOu11Lzm2bNn+2MrKCggOzuboUOH+tf985//ZNKkSXTr1o3Fixfz29/+NujfxnPPPceoUaOYNGmSf924ceOC7hfMSy+9xHvvvQfApZdeyrhx47j33nurbbNmzRpOPfVUoqOjiY6OZsiQIbz++utccMEFvPPOOzz33HP+/e+8805mzJhBYmIiaWlpfP7555x88smNijFiSgRV1kgQ8SorK/nvf//L5MmTAejXrx8ffvghK1euZNasWdUSi6ysLJYsWcKXX37JkiVL2L59Ozt37uSOO+7g448/5qOPPmLNmjX+7X/9619z6aWXkp2dzc9//vNqd6d79+7lk08+4cEHH2Ty5Mlcd911rF69mi+//JKsrCz/duPHjyc9PZ2RI0cGPWZubi7Lli1j9uzZTJ8+nTlz5rBixQruv/9+rrzSmSpk1qxZvPHGG6xatYqlS5cSGxvLrFmzmDJlCllZWUyZMoWcnBxGjBgR9LM777zzWL58OatWraJ///7+u9Ka5wB47LHHuPbaa8nKyiIzM5OePXtWO9bSpUv9pbQpU6ZUe6+ua6l5zYEyMzMZNGhQtXWLFi1i6tSpTJ06lUWLFgW9PsDzZ1FQUOCvwqv5E/g34fPdd9/RvXt3AI455phaq5qGDh3K66+/TlFREXl5ebz77rts376d/Px8OnToQHS0c8/es2dPduzY4d8vIyODDz/80NP11SdiSgQ+ViAIn4bcuTel4uJi0tPT2bFjB/3792fixIkA7N+/n0svvZQNGzYgIpSXl/v3mTBhAu3btwdgwIABbN26lby8PMaNG0eXLl0AmDJlCuvXrwfgk08+4Z//dDraX3LJJdx0003+Y02aNAkRYfDgwXTr1o3Bg515ngYOHMiWLVtIT08HnKqhzp07+/er75gXXHABUVFRFBYWsmzZMi644AL/e6WlpQCMGTOGadOmceGFF3Leeec16jPMycnhtttuY9++fRQWFnL66afXeY5Ro0bxxz/+kdzcXM477zz69u3r6Rz1XUvgNde0c+dO/3cCTsK7YcMGTjnlFESEmJgYcnJyGDRoUK1thA1tN0xOTq6WgTeEiNR6vtNOO43ly5czevRounTpwqhRo2q91pq6du3KV199dUSxBAppiUBEzhCRdSKyUURuruX9OBFZ4r7/mYikhSoWKxBELt/d59atW1FVfxvB7373O8aPH09OTg4vv/xytd7PviofcKpLGloXH8h3rDZt2lQ7bps2bY74uElJSQBUVVXRoUMHf9tCVlYWa9euBZw78z/84Q9s376dESNGkJ+ff9hxBg4cyIoVK4Keb9q0aTz88MN8+eWX3HHHHf7PqrZzXHTRRf67/jPPPJN33nnH0zXVdy2B11xTQkJCte/u+eefZ+/evfTu3Zu0tDS2bNniLxWkpKRUa6zds2ePP/P1+lk0tETQrVs3du7cCTiZVteuXWs97q233kpWVhZvvfUWqsqJJ55ISkoK+/bt8/+d5Obm0qNHD/8+vmrNxgpZRuAOX/0I8GNgADBVRAbU2OxyYK+q9gEeBO4lRA4NMWFZQaRKTEzkoYce4oEHHqCiooL9+/f7/6nqq6v3GTlyJO+//z75+fmUl5fzwgsv+N8bPXo0ixcvBuDZZ59l7NixjY7XyzHbtWtH7969/bGoKqtWrQJg06ZNjBw5klmzZtGlSxe2b99OcnIyBQUF/v1nzpzJ3Xff7S/ZVFVV8dhjjx12noKCArp37055eTnPPvusf31t59i8eTPHH38811xzDeecc46/TSaY+q6lPv3792fjxo3+5UWLFvH666+zZcsWtmzZwooVK/yf47hx41iyZIn/yZsFCxYwfvx4AC666CKWLVvGq6++6j/WBx98QE5OTrXz+UoEtf0MGFAziYPJkyezcOFCABYuXMg555xz2DaVlZX+jDo7O5vs7GxOO+00RITx48f7n3Squf/69esPqxY7EqEsEZwMbFTVzapaBiwGan4C5wAL3dcvAhMkRCm1jT5qAIYNG8aQIUNYtGgRN910E7fccgvDhg3zdGfevXt37rzzTkaNGsWYMWPo37+//705c+bw5JNPMmTIEJ5++mn++te/NjpWr8d89tlneeKJJxg6dCgDBw7kpZdeApxEfvDgwQwaNIjRo0czdOhQxo8fz5o1a0hPT2fJkiUMGTKEv/zlL0ydOpX+/fszaNCgWp+yueuuuxg5ciRjxozxNyzXdY7nn3+eQYMGkZ6eTk5OToOeCKrrWurTr18/9u/fT0FBAVu2bGHr1q3VHhvt3bs37du357PPPuPss89m7NixjBgxgvT0dD7++GN/w21CQgKvvPIKc+bMoW/fvgwYMIBHH320WrXTkbj55pt566236Nu3L2+//TY33+xUjmRmZnLFFVcAzphcY8eOZcCAAUyfPp1nnnnG3y5w7733Mnv2bPr06UN+fn61x2Q//vhjf1VnY4jv+fqmJiLnA2eo6hXu8iXASFW9OmCbHHebXHd5k7tNXo1jTQemA6Smpo7YunVrg+N5c/W3vJT1DbOnDCUuOnjdm2kaa9eurZZgGhMKDz74IMnJyf6ENRKsXLmS2bNn8/TTTx/2Xm3/dyKyQlUzajtWi3hqSFXnqmqGqmYcae582sBjeOTnwy0TMKYVmjFjRrX2l0iQl5fHXXfd1STHCuVTQzuA4wKWe7rratsmV0SigfY48x4YY4xn8fHxXHLJJeEOo1k1RZWQTyhLBMuBviLSW0RigZ8BS2tssxS41H19PvCOhqquyoSNfaXGNJ8j+X8LWUagqhXA1cAbwFrgeVVdLSKzRGSyu9kTQIqIbASuBw57xNS0bPHx8eTn51tmYEwz8M1HEB8f36D9QtZYHCoZGRmamZkZ7jCMRzZDmTHNq64ZyuprLI64nsWmecXExDRopiRjTPNrEU8NGWOMCR3LCIwxJsJZRmCMMRGuxTUWi8huoOFdix2dgbygW7Uuds2Rwa45MjTmmnupaq09cltcRtAYIpJZV6t5a2XXHBnsmiNDqK7ZqoaMMSbCWUZgjDERLtIygrnhDiAM7Jojg11zZAjJNUdUG4ExxpjDRVqJwBhjTA2WERhjTIRrlRmBiJwhIutEZKOIHDaiqYjEicgS9/3PRCQtDGE2KQ/XfL2IrBGRbBH5r4j0CkecTSnYNQds91MRURFp8Y8aerlmEbnQ/a5Xi8hzzR1jU/Pwt50qIu+KyEr37/vMcMTZVERkvojscmdwrO19EZGH3M8jW0SGN/qkqtqqfoAoYBNwPBALrAIG1NjmSuAx9/XPgCXhjrsZrnk8kOi+nhEJ1+xulwx8AHwKZIQ77mb4nvsCK4GO7nLXcMfdDNc8F5jhvh4AbAl33I285lOB4UBOHe+fCfwHEOD7wGeNPWdrLBGcDGxU1c2qWgYsBs6psc05wEL39YvABBFpydPaB71mVX1XVYvcxU9xZoxrybx8zwB3AfcCrWEcbC/X/D/AI6q6F0BVdzVzjE3NyzUr0M593R74phnja3Kq+gGwp55NzgGeUsenQAcR6d6Yc7bGjKAHsD1gOdddV+s26kygsx9IaZboQsPLNQe6HOeOoiULes1ukfk4VX21OQMLIS/f84nAiSLysYh8KiJnNFt0oeHlmu8ELhaRXOA14NfNE1rYNPT/PSibjyDCiMjFQAbwg3DHEkoi0gaYDUwLcyjNLRqnemgcTqnvAxEZrKr7whlUiE0FFqjqAyIyCnhaRAapalW4A2spWmOJYAdwXMByT3ddrduISDROcTK/WaILDS/XjIj8CLgVmKyqpc0UW6gEu+ZkYBDwnohswalLXdrCG4y9fM+5wFJVLVfVr4H1OBlDS+Xlmi8HngdQ1U+AeJzB2VorT//vDdEaM4LlQF8R6S0isTiNwUtrbLMUuNR9fT7wjrqtMC1U0GsWkWHA33EygZZebwxBrllV96tqZ1VNU9U0nHaRyarakuc59fK3/W+c0gAi0hmnqmhzM8bY1Lxc8zZgAoCI9MfJCHY3a5TNaynwC/fpoe8D+1V1Z2MO2OqqhlS1QkSuBt7AeeJgvqquFpFZQKaqLgWewCk+bsRplPlZ+CJuPI/XfB/QFnjBbRffpqqTwxZ0I3m85lbF4zW/AZwmImuASmCmqrbY0q7Ha74BmCci1+E0HE9ryTd2IrIIJzPv7LZ73AHEAKjqYzjtIGcCG4Ei4JeNPmcL/ryMMcY0gdZYNWSMMaYBLCMwxpgIZxmBMcZEOMsIjDEmwllGYIwxEc4yggggIpUikhXwk1bPtoVNcL4FIvK1e64v3N6eDT3G4yIywH392xrvLWtsjO5xfJ9Ljoi8LCIdgmyffiQjW4pIdxF5xX09TkT2u+ddKyJ3HMHxJvtG4RSRc32fk7s8y+042Cjud3h+kG3ea0gHPffaX/GwXa2jb4rI/SLyQ6/nM95ZRhAZilU1PeBnSzOcc6aqpgM343RkaxBVvUJV17iLv63x3ujGhwcc+lwG4fQnuSrI9uk4z2831PXAvIDlD93PJgNnjJwGDSOsqktV9R538VycETd9792uqm8fQYxHkwVAbWMkzcH5ezJNzDKCCCQibcWZk+ALEflSRA4btdO9i/0g4I55rLv+NBH5xN33BRFpG+R0HwB93H2vd4+VIyL/565LEpFXRWSVu36Ku/49EckQkXuABDeOZ933Ct3fi0XkrICYF4jI+SISJSL3ichyccZr/18PH8snuAN3icjJ7jWuFJFlInKS26t1FjDFjWWKG/t8Efnc3ba20U8Bfgq8XnOlqh4EVgB93NLGp268/xKRjm4s18iheSQWu+umicjDIjIamAzc58Z0QsBncIaIvBDw2fjvxhv6HYrI7e5nmSMic0WqjdR7ScDfyMnu9l4/l1rVNfqmqm4FUkTkmIYcz3gQjvG27ad5f3B6mGa5P//C6VHezn2vM04PRV/nwkL39w3Are7rKJyxezrjJOxJ7vrfALfXcr4FwPnu6wuAz4ARwJdAEk4P59XAMJxEcl7Avu3d3+/hzh/giylgG1+MPwEWuq9jcUZkTACmA7e56+OATKB3LXEWBlzfC8AZ7nI7INp9/SPgH+7racDDAfvfDVzsvu6AM65PUo1z9AZWBCyPA15xX6cAW4CBQDbwA3f9LOAv7utvgDjfOWrGEfhZBy673/G2gO/qb8DFR/gddgpY/zQwKeA7mue+PhV3/Py6Ppca154BPF7P32watYzHj1Oy+mm4/6da20+rG2LC1KpYnaoIAEQkBrhbRE4FqnDuhLsB3wbssxyY7277b1XNEpEf4FRDfOzeFMbi3EnX5j4RuQ1nzJfLccaC+Zc6d8GIyD+BsTh3yg+IyL04icSHDbiu/wB/FZE4nKqED1S1WEROA4YE1HG3xxl47esa+yeISJZ7/WuBtwK2XygifXGGLIip4/ynAZNF5EZ3OR5IdY/l053Dx70ZKyIrcT77e3AGiuugqu+77y/EyZjAySCeFZF/44wj5Ik6QzO8DkwSkReBs4CbcEad9fod+owXkZuARKATTib+svveIvd8H4hIO3HaWer6XALjywSu8Ho9AXYBxx7BfqYelhFEpp8DXYARqlouzuic8YEbuP/Yp+IkIAtEZDawF3hLVad6OMdMVX3RtyAiE2rbSFXXu3XkZwJ/EJH/quosLxehqiUi8h5wOjAFZ9IScGZu+rWqvhHkEMWqmi4iiThj2VwFPIQzmc27qvoTcRrW36tjf8G5O11X3zmo8dnitBGc7T+ISPt69j8L5257EnCriAyuZ9uaFgNX41SzZKpqgVut4/U7RETigUdxSmfbReROql9PzTFqlDo+FxHp1oDY6xKP85maJmRtBJGpPbDLzQTGA4fNXyzOnMbfqeo84HGcqfM+BcaIiK/OP0lETvR4zg+Bc0UkUUSScKp1PhSRY4EiVX0GZ2C82hpOy92SSW2W4Ay65StdgJOoz/DtIyInuueslTozt10D3CCHhiX3Des7LWDTApwqMp83gF/76szFGeG1pvU41Rx1UtX9wF5x22GAS4D3xZlT4ThVfRenCqc9TrVaoJoxBXof5/P8Hw5lkg39Dn2Jfp7bllDzSSJfm84pOKNg7sfb53KkTgRqncvXHDnLCCLTs0CGiHwJ/AL4qpZtxgGr3CqMKcBfVXU3TsK4SESycaoU+nk5oap+gVPv/DlOm8HjqroSGAx87lbR3AH8oZbd5wLZ4jYW1/AmTnXH2+pMZQhOxrUG+EKcRxD/TpDSrxtLNs4kJ38G/uRee+B+7wIDfI3FOCWHGDe21e5yzeMeBDb5Et56XIpTnZaN83TSLJy2i2fc72kl8JAePsHMYmCm2yh7Qo1zVwKvAD92f9PQ79A93zycxPcNnCrDQCXu5/QYThUgePhcxHkQ4PHazinO6JufACeJSK6IXO6uj8F58KAlDyV+VLLRR40JMRH5CU413G3hjqUlcz/H4ar6u3DH0tpYG4ExIaaq/xKRljwn9tEiGngg3EG0RlYiMMaYCGdtBMYYE+EsIzDGmAhnGYExxkQ4ywiMMSbCWUZgjDER7v8BCCHbDCOgIjEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import RocCurveDisplay\n",
    "rf_disp1 = RocCurveDisplay.from_estimator(rf_model1, X_test1, y_test1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>test_indication</th>\n",
       "      <td>0.466695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>head_ache</th>\n",
       "      <td>0.195909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sore_throat</th>\n",
       "      <td>0.110256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fever</th>\n",
       "      <td>0.092211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shortness_of_breath</th>\n",
       "      <td>0.078955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cough</th>\n",
       "      <td>0.042216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_60_and_above</th>\n",
       "      <td>0.008447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>0.005311</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            0\n",
       "test_indication      0.466695\n",
       "head_ache            0.195909\n",
       "sore_throat          0.110256\n",
       "fever                0.092211\n",
       "shortness_of_breath  0.078955\n",
       "cough                0.042216\n",
       "age_60_and_above     0.008447\n",
       "gender               0.005311"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show the variable importance in the rf model\n",
    "pd.DataFrame(rf_model1.feature_importances_, index=X_train1.columns).sort_values(by=0, ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing our trained model on data from later in the year (September to November 2020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a train test split\n",
    "\n",
    "X2 = data_processed2.drop(['corona_result'],axis = 1)\n",
    "y2 = data_processed2.copy()['corona_result']\n",
    "\n",
    "X_train2, X_test2, y_train2, y_test2 = train_test_split(X2, y2, test_size = 0.2,random_state = 2022, stratify = y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#test the model trained in March - April 2020 for September - November 2020\n",
    "rf_disp2 = RocCurveDisplay.from_estimator(rf_model1, X_test2, y_test2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrain the model for the second half of the year and compare accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create the random forest model\n",
    "rf_model2 = RandomForestClassifier()\n",
    "rf_model2.fit(X_train2, y_train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#test the model trained for September - November 2020\n",
    "rf_disp2 = RocCurveDisplay.from_estimator(rf_model2, X_test2, y_test2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>test_indication</th>\n",
       "      <td>0.466695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>head_ache</th>\n",
       "      <td>0.195909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sore_throat</th>\n",
       "      <td>0.110256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fever</th>\n",
       "      <td>0.092211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shortness_of_breath</th>\n",
       "      <td>0.078955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cough</th>\n",
       "      <td>0.042216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_60_and_above</th>\n",
       "      <td>0.008447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>0.005311</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            0\n",
       "test_indication      0.466695\n",
       "head_ache            0.195909\n",
       "sore_throat          0.110256\n",
       "fever                0.092211\n",
       "shortness_of_breath  0.078955\n",
       "cough                0.042216\n",
       "age_60_and_above     0.008447\n",
       "gender               0.005311"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show the variable importance in the rf model\n",
    "pd.DataFrame(rf_model1.feature_importances_, index=X_train1.columns).sort_values(by=0, ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuning Parameters for our RF Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "#set list of parameters to tune our first model \n",
    "\n",
    "# Number of trees in random forest\n",
    "n_estimators = np.linspace(5, 2000, int((2000-5)/10) + 1, dtype=int)\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [1, 5, 10, 20, 50, 75, 100, 150, 200]\n",
    "# Minimum number of samples required to split a node\n",
    "# min_samples_split = [int(x) for x in np.linspace(start = 2, stop = 10, num = 9)]\n",
    "min_samples_split = [1, 2, 5, 10, 15, 20, 30]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 3, 4]\n",
    "# Method of selecting samples for training each tree\n",
    "bootstrap = [True, False]\n",
    "# Criterion\n",
    "criterion=['gini', 'entropy']\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "#                'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               'bootstrap': bootstrap,\n",
    "               'criterion': criterion}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=1, n_estimators=1588; total time=   0.5s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=1, n_estimators=1588; total time=   0.6s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=1, n_estimators=1588; total time=   0.6s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=1, n_estimators=1588; total time=   0.6s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=20, min_samples_leaf=4, min_samples_split=1, n_estimators=1588; total time=   0.5s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=200, min_samples_leaf=1, min_samples_split=30, n_estimators=676; total time=  15.8s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=200, min_samples_leaf=1, min_samples_split=30, n_estimators=676; total time=  15.9s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=200, min_samples_leaf=1, min_samples_split=30, n_estimators=676; total time=  16.0s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=200, min_samples_leaf=1, min_samples_split=30, n_estimators=676; total time=  16.0s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=200, min_samples_leaf=1, min_samples_split=30, n_estimators=676; total time=  15.8s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=75, min_samples_leaf=3, min_samples_split=2, n_estimators=1328; total time=  30.5s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=75, min_samples_leaf=3, min_samples_split=2, n_estimators=1328; total time=  30.5s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=75, min_samples_leaf=3, min_samples_split=2, n_estimators=1328; total time=  30.4s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=1, min_samples_leaf=3, min_samples_split=1, n_estimators=947; total time=   0.4s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=1, min_samples_leaf=3, min_samples_split=1, n_estimators=947; total time=   0.4s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=1, min_samples_leaf=3, min_samples_split=1, n_estimators=947; total time=   0.4s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=1, min_samples_leaf=3, min_samples_split=1, n_estimators=947; total time=   0.4s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=1, min_samples_leaf=3, min_samples_split=1, n_estimators=947; total time=   0.4s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=75, min_samples_leaf=3, min_samples_split=2, n_estimators=1328; total time=  30.3s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=15, n_estimators=796; total time=  18.5s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=15, n_estimators=796; total time=  18.7s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=75, min_samples_leaf=3, min_samples_split=2, n_estimators=1328; total time=  32.4s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=15, n_estimators=796; total time=  20.6s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=15, n_estimators=796; total time=  20.6s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=20, min_samples_leaf=2, min_samples_split=15, n_estimators=796; total time=  20.7s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=5, min_samples_leaf=3, min_samples_split=20, n_estimators=686; total time=  12.8s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=5, min_samples_leaf=3, min_samples_split=20, n_estimators=686; total time=  12.5s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=5, min_samples_leaf=3, min_samples_split=20, n_estimators=686; total time=  12.4s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=5, min_samples_leaf=3, min_samples_split=20, n_estimators=686; total time=  12.4s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=5, min_samples_leaf=3, min_samples_split=20, n_estimators=686; total time=  12.1s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=1, min_samples_leaf=4, min_samples_split=2, n_estimators=867; total time=  11.6s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=1, min_samples_leaf=4, min_samples_split=2, n_estimators=867; total time=  11.6s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=1, min_samples_leaf=4, min_samples_split=2, n_estimators=867; total time=  11.5s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=100, min_samples_leaf=3, min_samples_split=1, n_estimators=1378; total time=   0.6s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=100, min_samples_leaf=3, min_samples_split=1, n_estimators=1378; total time=   0.6s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=100, min_samples_leaf=3, min_samples_split=1, n_estimators=1378; total time=   0.6s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=100, min_samples_leaf=3, min_samples_split=1, n_estimators=1378; total time=   0.6s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=100, min_samples_leaf=3, min_samples_split=1, n_estimators=1378; total time=   0.6s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=1, min_samples_leaf=4, min_samples_split=2, n_estimators=867; total time=  11.5s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=1, min_samples_leaf=4, min_samples_split=2, n_estimators=867; total time=  11.5s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=666; total time=  15.8s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=666; total time=  15.9s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=666; total time=  16.1s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=666; total time=  16.4s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=666; total time=  16.8s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=100, min_samples_leaf=3, min_samples_split=10, n_estimators=987; total time=  24.2s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=100, min_samples_leaf=3, min_samples_split=10, n_estimators=987; total time=  24.3s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=100, min_samples_leaf=3, min_samples_split=10, n_estimators=987; total time=  23.9s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=100, min_samples_leaf=3, min_samples_split=10, n_estimators=987; total time=  24.4s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=100, min_samples_leaf=3, min_samples_split=10, n_estimators=987; total time=  24.4s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=1278; total time=  32.2s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=1278; total time=  32.5s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=1278; total time=  32.2s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=1278; total time=  32.5s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=1278; total time=  33.5s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=50, min_samples_leaf=3, min_samples_split=2, n_estimators=1248; total time=  32.6s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=50, min_samples_leaf=3, min_samples_split=2, n_estimators=1248; total time=  33.4s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=50, min_samples_leaf=2, min_samples_split=2, n_estimators=35; total time=   1.0s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=50, min_samples_leaf=2, min_samples_split=2, n_estimators=35; total time=   1.0s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=50, min_samples_leaf=2, min_samples_split=2, n_estimators=35; total time=   1.0s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=50, min_samples_leaf=2, min_samples_split=2, n_estimators=35; total time=   1.0s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=50, min_samples_leaf=2, min_samples_split=2, n_estimators=35; total time=   0.9s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=50, min_samples_leaf=3, min_samples_split=2, n_estimators=1248; total time=  33.2s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=50, min_samples_leaf=3, min_samples_split=2, n_estimators=1248; total time=  34.7s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=50, min_samples_leaf=3, min_samples_split=2, n_estimators=1248; total time=  36.1s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=150, min_samples_leaf=1, min_samples_split=15, n_estimators=1358; total time=  37.7s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=150, min_samples_leaf=1, min_samples_split=15, n_estimators=1358; total time=  37.7s\n",
      "[CV] END bootstrap=True, criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   5.8s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=150, min_samples_leaf=1, min_samples_split=15, n_estimators=1358; total time=  36.7s\n",
      "[CV] END bootstrap=True, criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   7.7s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=150, min_samples_leaf=1, min_samples_split=15, n_estimators=1358; total time=  36.9s\n",
      "[CV] END bootstrap=True, criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   6.5s\n",
      "[CV] END bootstrap=True, criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   6.3s\n",
      "[CV] END bootstrap=True, criterion=gini, max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=235; total time=   5.9s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=150, min_samples_leaf=1, min_samples_split=15, n_estimators=1358; total time=  37.7s\n",
      "[CV] END bootstrap=True, criterion=gini, max_depth=150, min_samples_leaf=2, min_samples_split=5, n_estimators=907; total time=  23.1s\n",
      "[CV] END bootstrap=True, criterion=gini, max_depth=150, min_samples_leaf=2, min_samples_split=5, n_estimators=907; total time=  23.2s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=20, min_samples_leaf=1, min_samples_split=1, n_estimators=215; total time=   0.1s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=20, min_samples_leaf=1, min_samples_split=1, n_estimators=215; total time=   0.1s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=20, min_samples_leaf=1, min_samples_split=1, n_estimators=215; total time=   0.1s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=20, min_samples_leaf=1, min_samples_split=1, n_estimators=215; total time=   0.1s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=20, min_samples_leaf=1, min_samples_split=1, n_estimators=215; total time=   0.1s\n",
      "[CV] END bootstrap=True, criterion=gini, max_depth=150, min_samples_leaf=2, min_samples_split=5, n_estimators=907; total time=  23.5s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=10, min_samples_leaf=3, min_samples_split=5, n_estimators=205; total time=   5.8s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=10, min_samples_leaf=3, min_samples_split=5, n_estimators=205; total time=   5.1s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=10, min_samples_leaf=3, min_samples_split=5, n_estimators=205; total time=   4.9s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=10, min_samples_leaf=3, min_samples_split=5, n_estimators=205; total time=   5.0s\n",
      "[CV] END bootstrap=True, criterion=gini, max_depth=150, min_samples_leaf=2, min_samples_split=5, n_estimators=907; total time=  23.1s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=10, min_samples_leaf=3, min_samples_split=5, n_estimators=205; total time=   5.0s\n",
      "[CV] END bootstrap=True, criterion=gini, max_depth=150, min_samples_leaf=2, min_samples_split=5, n_estimators=907; total time=  22.8s\n",
      "[CV] END bootstrap=True, criterion=gini, max_depth=1, min_samples_leaf=1, min_samples_split=10, n_estimators=1699; total time=  22.8s\n",
      "[CV] END bootstrap=True, criterion=gini, max_depth=1, min_samples_leaf=1, min_samples_split=10, n_estimators=1699; total time=  23.0s\n",
      "[CV] END bootstrap=True, criterion=gini, max_depth=1, min_samples_leaf=1, min_samples_split=10, n_estimators=1699; total time=  22.6s\n",
      "[CV] END bootstrap=True, criterion=gini, max_depth=1, min_samples_leaf=1, min_samples_split=10, n_estimators=1699; total time=  23.2s\n",
      "[CV] END bootstrap=True, criterion=gini, max_depth=1, min_samples_leaf=1, min_samples_split=10, n_estimators=1699; total time=  24.2s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=5, min_samples_leaf=3, min_samples_split=15, n_estimators=1358; total time=  26.8s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=5, min_samples_leaf=3, min_samples_split=15, n_estimators=1358; total time=  27.1s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=5, min_samples_leaf=3, min_samples_split=15, n_estimators=1358; total time=  26.3s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=150, min_samples_leaf=3, min_samples_split=15, n_estimators=275; total time=   6.6s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=150, min_samples_leaf=3, min_samples_split=15, n_estimators=275; total time=   6.6s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=150, min_samples_leaf=3, min_samples_split=15, n_estimators=275; total time=   6.5s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=150, min_samples_leaf=3, min_samples_split=15, n_estimators=275; total time=   6.7s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=150, min_samples_leaf=3, min_samples_split=15, n_estimators=275; total time=   6.7s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=5, min_samples_leaf=3, min_samples_split=15, n_estimators=1358; total time=  25.9s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=5, min_samples_leaf=3, min_samples_split=15, n_estimators=1358; total time=  25.5s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=10, min_samples_leaf=3, min_samples_split=5, n_estimators=1258; total time=  29.6s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=10, min_samples_leaf=3, min_samples_split=5, n_estimators=1258; total time=  30.1s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=10, min_samples_leaf=3, min_samples_split=5, n_estimators=1258; total time=  30.3s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=10, min_samples_leaf=3, min_samples_split=5, n_estimators=1258; total time=  30.2s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=200, min_samples_leaf=3, min_samples_split=2, n_estimators=416; total time=  11.3s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=200, min_samples_leaf=3, min_samples_split=2, n_estimators=416; total time=  11.4s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=200, min_samples_leaf=3, min_samples_split=2, n_estimators=416; total time=  11.2s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=200, min_samples_leaf=3, min_samples_split=2, n_estimators=416; total time=  10.3s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=200, min_samples_leaf=3, min_samples_split=2, n_estimators=416; total time=  10.4s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=10, min_samples_leaf=3, min_samples_split=5, n_estimators=1258; total time=  31.2s\n",
      "[CV] END bootstrap=True, criterion=gini, max_depth=1, min_samples_leaf=4, min_samples_split=20, n_estimators=1117; total time=  15.2s\n",
      "[CV] END bootstrap=True, criterion=gini, max_depth=1, min_samples_leaf=4, min_samples_split=20, n_estimators=1117; total time=  15.2s\n",
      "[CV] END bootstrap=True, criterion=gini, max_depth=1, min_samples_leaf=4, min_samples_split=20, n_estimators=1117; total time=  15.1s\n",
      "[CV] END bootstrap=True, criterion=gini, max_depth=1, min_samples_leaf=4, min_samples_split=20, n_estimators=1117; total time=  15.1s\n",
      "[CV] END bootstrap=True, criterion=gini, max_depth=1, min_samples_leaf=4, min_samples_split=20, n_estimators=1117; total time=  15.2s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=1, min_samples_leaf=2, min_samples_split=20, n_estimators=1558; total time=  17.9s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=1, min_samples_leaf=2, min_samples_split=20, n_estimators=1558; total time=  18.0s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=1, min_samples_leaf=2, min_samples_split=20, n_estimators=1558; total time=  18.2s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=1, min_samples_leaf=2, min_samples_split=20, n_estimators=1558; total time=  18.2s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=1, min_samples_leaf=2, min_samples_split=20, n_estimators=1558; total time=  19.3s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=75, min_samples_leaf=4, min_samples_split=30, n_estimators=1799; total time=  45.9s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=75, min_samples_leaf=4, min_samples_split=30, n_estimators=1799; total time=  46.2s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=75, min_samples_leaf=4, min_samples_split=30, n_estimators=1799; total time=  46.1s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=75, min_samples_leaf=4, min_samples_split=30, n_estimators=1799; total time=  44.5s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=100, min_samples_leaf=1, min_samples_split=30, n_estimators=1057; total time=  25.7s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=100, min_samples_leaf=1, min_samples_split=30, n_estimators=1057; total time=  25.9s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=100, min_samples_leaf=1, min_samples_split=30, n_estimators=1057; total time=  26.3s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=5, min_samples_leaf=3, min_samples_split=1, n_estimators=1659; total time=   0.7s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=75, min_samples_leaf=4, min_samples_split=30, n_estimators=1799; total time=  45.2s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=5, min_samples_leaf=3, min_samples_split=1, n_estimators=1659; total time=   0.7s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=5, min_samples_leaf=3, min_samples_split=1, n_estimators=1659; total time=   0.7s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=5, min_samples_leaf=3, min_samples_split=1, n_estimators=1659; total time=   0.8s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=5, min_samples_leaf=3, min_samples_split=1, n_estimators=1659; total time=   0.7s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=75, min_samples_leaf=2, min_samples_split=5, n_estimators=85; total time=   2.2s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=75, min_samples_leaf=2, min_samples_split=5, n_estimators=85; total time=   2.2s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=75, min_samples_leaf=2, min_samples_split=5, n_estimators=85; total time=   2.2s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=75, min_samples_leaf=2, min_samples_split=5, n_estimators=85; total time=   2.1s\n",
      "[CV] END bootstrap=False, criterion=gini, max_depth=75, min_samples_leaf=2, min_samples_split=5, n_estimators=85; total time=   2.1s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=100, min_samples_leaf=1, min_samples_split=30, n_estimators=1057; total time=  25.4s\n",
      "[CV] END bootstrap=True, criterion=entropy, max_depth=100, min_samples_leaf=1, min_samples_split=30, n_estimators=1057; total time=  25.4s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=1, min_samples_leaf=4, min_samples_split=30, n_estimators=1308; total time=  14.8s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=1, min_samples_leaf=4, min_samples_split=30, n_estimators=1308; total time=  14.9s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=1, min_samples_leaf=4, min_samples_split=30, n_estimators=1308; total time=  13.9s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=1, min_samples_leaf=4, min_samples_split=30, n_estimators=1308; total time=  13.5s\n",
      "[CV] END bootstrap=False, criterion=entropy, max_depth=1, min_samples_leaf=4, min_samples_split=30, n_estimators=1308; total time=  11.7s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: \n",
      "25 fits failed out of a total of 150.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "20 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/usr/local/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 450, in fit\n",
      "    trees = Parallel(\n",
      "  File \"/usr/local/lib/python3.9/site-packages/joblib/parallel.py\", line 1043, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/usr/local/lib/python3.9/site-packages/joblib/parallel.py\", line 861, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/usr/local/lib/python3.9/site-packages/joblib/parallel.py\", line 779, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/usr/local/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/usr/local/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/usr/local/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/usr/local/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/usr/local/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 216, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/usr/local/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 187, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=sample_weight, check_input=False)\n",
      "  File \"/usr/local/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 937, in fit\n",
      "    super().fit(\n",
      "  File \"/usr/local/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 250, in fit\n",
      "    raise ValueError(\n",
      "ValueError: min_samples_split must be an integer greater than 1 or a float in (0.0, 1.0]; got the integer 1\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "5 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/usr/local/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 450, in fit\n",
      "    trees = Parallel(\n",
      "  File \"/usr/local/lib/python3.9/site-packages/joblib/parallel.py\", line 1043, in __call__\n",
      "    if self.dispatch_one_batch(iterator):\n",
      "  File \"/usr/local/lib/python3.9/site-packages/joblib/parallel.py\", line 861, in dispatch_one_batch\n",
      "    self._dispatch(tasks)\n",
      "  File \"/usr/local/lib/python3.9/site-packages/joblib/parallel.py\", line 779, in _dispatch\n",
      "    job = self._backend.apply_async(batch, callback=cb)\n",
      "  File \"/usr/local/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 208, in apply_async\n",
      "    result = ImmediateResult(func)\n",
      "  File \"/usr/local/lib/python3.9/site-packages/joblib/_parallel_backends.py\", line 572, in __init__\n",
      "    self.results = batch()\n",
      "  File \"/usr/local/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in __call__\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/usr/local/lib/python3.9/site-packages/joblib/parallel.py\", line 262, in <listcomp>\n",
      "    return [func(*args, **kwargs)\n",
      "  File \"/usr/local/lib/python3.9/site-packages/sklearn/utils/fixes.py\", line 216, in __call__\n",
      "    return self.function(*args, **kwargs)\n",
      "  File \"/usr/local/lib/python3.9/site-packages/sklearn/ensemble/_forest.py\", line 185, in _parallel_build_trees\n",
      "    tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)\n",
      "  File \"/usr/local/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 937, in fit\n",
      "    super().fit(\n",
      "  File \"/usr/local/lib/python3.9/site-packages/sklearn/tree/_classes.py\", line 250, in fit\n",
      "    raise ValueError(\n",
      "ValueError: min_samples_split must be an integer greater than 1 or a float in (0.0, 1.0]; got the integer 1\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "/usr/local/lib/python3.9/site-packages/sklearn/model_selection/_search.py:969: UserWarning: One or more of the test scores are non-finite: [       nan 0.95794011 0.95790343        nan 0.95794011 0.95764663\n",
      " 0.92448296        nan 0.95771083 0.95801348 0.95790343 0.95790343\n",
      " 0.95790343 0.95793094 0.95794011 0.95801348        nan 0.95801348\n",
      " 0.92499656 0.95764663 0.95794011 0.95801348 0.95790343 0.92506993\n",
      " 0.92512496 0.95794011 0.95801348        nan 0.95790343 0.92467556]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=30,\n",
       "                   n_jobs=4,\n",
       "                   param_distributions={'bootstrap': [True, False],\n",
       "                                        'criterion': ['gini', 'entropy'],\n",
       "                                        'max_depth': [1, 5, 10, 20, 50, 75, 100,\n",
       "                                                      150, 200],\n",
       "                                        'min_samples_leaf': [1, 2, 3, 4],\n",
       "                                        'min_samples_split': [1, 2, 5, 10, 15,\n",
       "                                                              20, 30],\n",
       "                                        'n_estimators': array([   5,   15,   25,   35,   45,   55,   65,   75,   85,   95,  105,\n",
       "        115,  125,  135,  145,  155,  165,  175,  1...\n",
       "       1328, 1338, 1348, 1358, 1368, 1378, 1388, 1398, 1408, 1418, 1428,\n",
       "       1438, 1448, 1458, 1468, 1478, 1488, 1498, 1508, 1518, 1528, 1538,\n",
       "       1548, 1558, 1568, 1578, 1588, 1598, 1609, 1619, 1629, 1639, 1649,\n",
       "       1659, 1669, 1679, 1689, 1699, 1709, 1719, 1729, 1739, 1749, 1759,\n",
       "       1769, 1779, 1789, 1799, 1809, 1819, 1829, 1839, 1849, 1859, 1869,\n",
       "       1879, 1889, 1899, 1909, 1919, 1929, 1939, 1949, 1959, 1969, 1979,\n",
       "       1989, 2000])},\n",
       "                   random_state=42, verbose=2)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#perform 5-fold cross validation against all parameter ranges set above to find optimal parameters for the rf model\n",
    "#we will train and test each time on March - April 2020 data as this was just an accurate predictor of September - November 2020 data and easier to train\n",
    "\n",
    "rf_base = RandomForestClassifier()\n",
    "rf_random = RandomizedSearchCV(estimator = rf_base,\n",
    "                               param_distributions = random_grid,\n",
    "                               n_iter = 30, cv = 5,\n",
    "                               verbose=2,\n",
    "                               random_state=42, n_jobs = 4)\n",
    "rf_random.fit(X_train1, y_train1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 987,\n",
       " 'min_samples_split': 10,\n",
       " 'min_samples_leaf': 3,\n",
       " 'max_depth': 100,\n",
       " 'criterion': 'entropy',\n",
       " 'bootstrap': True}"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9580134819094787\n"
     ]
    }
   ],
   "source": [
    "print(rf_random.score(X_train1, y_train1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9586558567812465\n"
     ]
    }
   ],
   "source": [
    "print(rf_random.score(X_test1, y_test1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#test the model trained for March-April 2020\n",
    "rf_disp3 = RocCurveDisplay.from_estimator(rf_random, X_test1, y_test1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#test the model for September-November 2020\n",
    "rf_disp4 = RocCurveDisplay.from_estimator(rf_random, X_test2, y_test2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_best_params = RandomForestClassifier(n_estimators = 987,\n",
    " min_samples_split = 10,\n",
    " min_samples_leaf = 3,\n",
    " max_depth = 100,\n",
    " criterion = 'entropy',\n",
    " bootstrap = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', max_depth=100, min_samples_leaf=3,\n",
       "                       min_samples_split=10, n_estimators=987)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_best_params.fit(X_train1, y_train1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#test the model trained for March-April 2020\n",
    "rf_disp5 = RocCurveDisplay.from_estimator(rf_best_params, X_test1, y_test1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#test the model trained for September-November 2020\n",
    "rf_disp6 = RocCurveDisplay.from_estimator(rf_best_params, X_test2, y_test2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>test_indication</th>\n",
       "      <td>0.453558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>head_ache</th>\n",
       "      <td>0.163782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fever</th>\n",
       "      <td>0.146011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sore_throat</th>\n",
       "      <td>0.085715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cough</th>\n",
       "      <td>0.072916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shortness_of_breath</th>\n",
       "      <td>0.061342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_60_and_above</th>\n",
       "      <td>0.008427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>0.008248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            0\n",
       "test_indication      0.453558\n",
       "head_ache            0.163782\n",
       "fever                0.146011\n",
       "sore_throat          0.085715\n",
       "cough                0.072916\n",
       "shortness_of_breath  0.061342\n",
       "age_60_and_above     0.008427\n",
       "gender               0.008248"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show the variable importance in the rf model\n",
    "pd.DataFrame(rf_best_params.feature_importances_, index=X_train1.columns).sort_values(by=0, ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing Model with best parameters trained on March-April 2020 on March-April 2021 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/kt/92wnzvcj6qq_r6j3j19yp5lw0000gp/T/ipykernel_10129/3302323151.py:2: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  data3 = pd.read_csv('data/corona_tests_2021_mar.csv')\n"
     ]
    },
    {
     "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>test_date</th>\n",
       "      <th>cough</th>\n",
       "      <th>fever</th>\n",
       "      <th>sore_throat</th>\n",
       "      <th>shortness_of_breath</th>\n",
       "      <th>head_ache</th>\n",
       "      <th>corona_result</th>\n",
       "      <th>age_60_and_above</th>\n",
       "      <th>gender</th>\n",
       "      <th>test_indication</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>negative</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>negative</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>negative</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>negative</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-04-30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>negative</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    test_date  cough  fever  sore_throat  shortness_of_breath  head_ache  \\\n",
       "0  2021-04-30      0      0            0                    0          0   \n",
       "1  2021-04-30      0      0            0                    0          0   \n",
       "2  2021-04-30      0      0            0                    0          0   \n",
       "3  2021-04-30      0      0            0                    0          0   \n",
       "4  2021-04-30      0      0            0                    0          0   \n",
       "\n",
       "  corona_result age_60_and_above gender test_indication  \n",
       "0      negative              NaN    NaN           Other  \n",
       "1      negative              NaN    NaN           Other  \n",
       "2      negative              NaN    NaN           Other  \n",
       "3      negative              NaN    NaN           Other  \n",
       "4      negative              NaN    NaN           Other  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load in March-April 2021 data\n",
    "data3 = pd.read_csv('data/corona_tests_2021_mar.csv')\n",
    "data3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>cough</th>\n",
       "      <th>fever</th>\n",
       "      <th>sore_throat</th>\n",
       "      <th>shortness_of_breath</th>\n",
       "      <th>head_ache</th>\n",
       "      <th>corona_result</th>\n",
       "      <th>age_60_and_above</th>\n",
       "      <th>gender</th>\n",
       "      <th>test_indication</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>257533</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257534</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257536</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257537</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257539</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429875</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429876</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429877</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429878</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429879</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>115057 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        cough  fever  sore_throat  shortness_of_breath  head_ache  \\\n",
       "257533      0      0            0                    0          0   \n",
       "257534      0      0            0                    0          0   \n",
       "257536      0      0            0                    0          0   \n",
       "257537      0      0            0                    0          0   \n",
       "257539      0      0            0                    0          0   \n",
       "...       ...    ...          ...                  ...        ...   \n",
       "429875      0      0            0                    0          0   \n",
       "429876      0      0            0                    0          0   \n",
       "429877      0      0            0                    0          0   \n",
       "429878      0      0            0                    0          0   \n",
       "429879      0      0            0                    0          0   \n",
       "\n",
       "        corona_result  age_60_and_above  gender  test_indication  \n",
       "257533            0.0                 0       1                0  \n",
       "257534            0.0                 0       0                0  \n",
       "257536            0.0                 0       1                0  \n",
       "257537            0.0                 0       1                0  \n",
       "257539            0.0                 0       0                0  \n",
       "...               ...               ...     ...              ...  \n",
       "429875            0.0                 0       0                1  \n",
       "429876            0.0                 0       1                0  \n",
       "429877            0.0                 0       0                0  \n",
       "429878            0.0                 0       1                0  \n",
       "429879            0.0                 0       0                0  \n",
       "\n",
       "[115057 rows x 9 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_processed3 = prepareData(data3)\n",
    "data_processed3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop nas from the data\n",
    "data_processed3.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cough                  0\n",
       "fever                  0\n",
       "sore_throat            0\n",
       "shortness_of_breath    0\n",
       "head_ache              0\n",
       "corona_result          0\n",
       "age_60_and_above       0\n",
       "gender                 0\n",
       "test_indication        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check there are no more nas\n",
    "data_processed3.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a train test split for the 2021 data\n",
    "\n",
    "X3 = data_processed3.drop(['corona_result'],axis = 1)\n",
    "y3 = data_processed3.copy()['corona_result']\n",
    "\n",
    "X_train3, X_test3, y_train3, y_test3 = train_test_split(X3, y3, test_size = 0.2,random_state = 2022, stratify = y3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#test the best parameter model on the 2021 data\n",
    "#test the model on March-April 2021 data\n",
    "rf_disp6 = RocCurveDisplay.from_estimator(rf_best_params, X_test3, y_test3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrain the best parameter model on the 2021 data and check results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', max_depth=100, min_samples_leaf=3,\n",
       "                       min_samples_split=10, n_estimators=987)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#train the model again on the 2021 data and check the AUC\n",
    "rf_best_params.fit(X_train3, y_train3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#test the model on March-April 2021 data\n",
    "rf_disp7 = RocCurveDisplay.from_estimator(rf_best_params, X_test3, y_test3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}