09-09-googleflustudy.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting and nowcasting with Google Flu Trends\n",
    "\n",
    "Rather than predicting the future, nowcasting attempts to use ideas from forecasting to measure the current state of the world; it attempts to “predict the present” (Choi and Varian 2012). Nowcasting has the potential to be especially useful to governments and companies that require timely and accurate measures of the world.\n",
    "\n",
    "https://www.bitbybitbook.com/en/1st-ed/observing-behavior/strategies/forecasting/\n",
    "\n",
    "## Predicting the Present with Google Trends\n",
    "\n",
    "HYUNYOUNG CHOI and HAL VARIAN\n",
    "\n",
    "Google, Inc., California, USA\n",
    "\n",
    "> In this paper we show how to use search engine data to forecast near-term values of economic indicators. Examples include automobile sales, unemployment claims, travel destination planning\n",
    "and consumer confidence.\n",
    "\n",
    "Choi, Hyunyoung, and Hal Varian. 2012. “Predicting the Present with Google Trends.” Economic Record 88 (June):2–9. https://doi.org/10.1111/j.1475-4932.2012.00809.x.\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detecting influenza epidemics using search engine query data\n",
    "\n",
    "- Jeremy Ginsberg et al. (2009) Detecting influenza epidemics using search engine query data. Nature. 457, pp:1012–1014 https://www.nature.com/articles/nature07634#Ack1 \n",
    "\n",
    "- **Google Query Data** https://static-content.springer.com/esm/art%3A10.1038%2Fnature07634/MediaObjects/41586_2009_BFnature07634_MOESM271_ESM.xls Query fractions for the top 100 search queries, sorted by mean Z-transformed correlation with CDC-provided ILI percentages across the nine regions of the United States. (XLS 5264 kb)\n",
    "\n",
    "- **CDC’s ILI Data**. We use the weighted version of CDC’s ILI activity level as the estimation target (available at gis.cdc.gov/grasp/fluview/fluportaldashboard.html). The weekly revisions of CDC’s ILI are available at the CDC website for all recorded seasons (from week 40 of a given year to week 20 of the subsequent year). Click **Download Data** to get the data.\n",
    "\n",
    "![image.png](./img/09CDC.png)\n",
    "\n",
    "For example, ILI report revision at week 50 of season 2012–2013 is available at www.cdc.gov/flu/weekly/weeklyarchives2012-2013/data/senAllregt50.htm; ILI report revision at week 9 of season 2014–2015 is available at www.cdc.gov/flu/weekly/weeklyarchives2014-2015/data/senAllregt09.html.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:58:31.335729Z",
     "start_time": "2021-02-23T05:58:31.331731Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import ElasticNetCV, ElasticNet\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T04:54:02.340478Z",
     "start_time": "2021-02-23T04:54:01.713935Z"
    }
   },
   "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>Date</th>\n",
       "      <th>United States</th>\n",
       "      <th>New England Region</th>\n",
       "      <th>Mid-Atlantic Region</th>\n",
       "      <th>East North Central Region</th>\n",
       "      <th>West North Central Region</th>\n",
       "      <th>South Atlantic Region</th>\n",
       "      <th>East South Central Region</th>\n",
       "      <th>West South Central Region</th>\n",
       "      <th>Mountain Region</th>\n",
       "      <th>Pacific Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2003-06-01</td>\n",
       "      <td>0.778</td>\n",
       "      <td>0.979</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.673</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.922</td>\n",
       "      <td>0.809</td>\n",
       "      <td>0.486</td>\n",
       "      <td>0.621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003-06-08</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.932</td>\n",
       "      <td>0.806</td>\n",
       "      <td>0.879</td>\n",
       "      <td>0.839</td>\n",
       "      <td>0.852</td>\n",
       "      <td>0.479</td>\n",
       "      <td>0.800</td>\n",
       "      <td>1.383</td>\n",
       "      <td>1.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003-06-15</td>\n",
       "      <td>0.838</td>\n",
       "      <td>1.018</td>\n",
       "      <td>0.892</td>\n",
       "      <td>0.839</td>\n",
       "      <td>0.568</td>\n",
       "      <td>0.751</td>\n",
       "      <td>1.130</td>\n",
       "      <td>1.111</td>\n",
       "      <td>0.702</td>\n",
       "      <td>0.777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003-06-22</td>\n",
       "      <td>0.828</td>\n",
       "      <td>0.615</td>\n",
       "      <td>1.149</td>\n",
       "      <td>0.676</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.867</td>\n",
       "      <td>1.149</td>\n",
       "      <td>0.498</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003-06-29</td>\n",
       "      <td>0.747</td>\n",
       "      <td>0.896</td>\n",
       "      <td>0.768</td>\n",
       "      <td>0.829</td>\n",
       "      <td>0.612</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.854</td>\n",
       "      <td>0.491</td>\n",
       "      <td>1.081</td>\n",
       "      <td>1.015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  United States  New England Region  Mid-Atlantic Region  \\\n",
       "0  2003-06-01          0.778               0.979                0.990   \n",
       "1  2003-06-08          0.850               0.932                0.806   \n",
       "2  2003-06-15          0.838               1.018                0.892   \n",
       "3  2003-06-22          0.828               0.615                1.149   \n",
       "4  2003-06-29          0.747               0.896                0.768   \n",
       "\n",
       "   East North Central Region  West North Central Region  \\\n",
       "0                      0.838                      0.673   \n",
       "1                      0.879                      0.839   \n",
       "2                      0.839                      0.568   \n",
       "3                      0.676                      0.730   \n",
       "4                      0.829                      0.612   \n",
       "\n",
       "   South Atlantic Region  East South Central Region  \\\n",
       "0                  0.732                      0.922   \n",
       "1                  0.852                      0.479   \n",
       "2                  0.751                      1.130   \n",
       "3                  0.867                      1.149   \n",
       "4                  0.530                      0.854   \n",
       "\n",
       "   West South Central Region  Mountain Region  Pacific Region  \n",
       "0                      0.809            0.486           0.621  \n",
       "1                      0.800            1.383           1.008  \n",
       "2                      1.111            0.702           0.777  \n",
       "3                      0.498            0.936           0.734  \n",
       "4                      0.491            1.081           1.015  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('41586_2009_BFnature07634_MOESM271_ESM.xls', sheet_name=1, header = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T04:29:08.728063Z",
     "start_time": "2021-02-23T04:29:08.621990Z"
    }
   },
   "outputs": [
    {
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vgIIsb+z9/Cwv7RrezuYPWPjcLlzDHIMGOvNWaipo6+mnMNsXe78gy0uHX8Pb0fyBUCyo48XKJlrzVmpSC1mGDn+Q/PiZd6ZHZ95O1xcceooOaNlEqamiMzLDLowL74IsLx29zug20fAegT9gDWkThNNlk34tm8TsOt7OkztO2D0MpZKqrScc3k6teWur4Ah6+0NDOk1Au00Gq+/wc/0P/wzA9asqEJFRPkKp9BAN6cLsQTNvrXk7m1/LJmPyjSf3xN5u7XHGD7VSydDWO8zMO9NLR2/AETsLaniPwB8IDWkTBF0eP9j2Y21k+8Lfp7rWHptHo1TyjDTztgyx1ZZ20vAegT9gDd9t4tVuk6hAyOJkey8XLSgBTu97rNRk0B7Zw2RAt0lWuNLshLq3hvcI/IHEZZPoIp6p7GSbH8vABfPD4X2sRWfeavJoH6ZsEn1bw9vB+oLWsOHtdbvwuV1092t4H42E9YoZBRRkeXXmrSaVtp4A2T53rEkBTs/CndAuqOE9gnDNe/hvT26mh64++3/z2u1YpMY9qziLyqIsrXmrSaVt0NJ4CN+wBJ15O5o/ECLLN3TmDZCb4aHLIdtC2uloSw8el1BREA1vnXmryWPwviZwumzihHZBDe8R9I5Q84ZIePdpeB9r6WFmURZul1BZlE1day/G2N9CpVQytPcMM/OOlU00vB3JGIM/YCUsm3TqzJtjrb3MKsoGoLIoi95AiOZuZ5wyotR4tfcGBrQJAuRF9vVuccDPuYb3MKI93IMPH47K05k3EJ55zyrOAqCiIPz/k21+O4ekVNK09fYPmXm7XEJpro8mBxxCrOE9jL7Y4cMjlE0yNbzbewO0dPczpyQHgBmF4Q3rT7Rr3VulP2MMrd0BinJ8Q/6sLC+Dhk4Nb0eK3ozIyxh+6xe9YQlHmroBmF8aDe/ozFvDW6W/zr4g/SGL0pyMIX9WnpdJYzqFt4hkicjiiRyMU5zqCL/0nxY5/miw3EwPnVN85n2kqQuA+WXh8C7J8eHzuDjZrmUTlf5ausI17eLhZt65GekR3iKSLyK/A+qBL8U9fqeIHBWR/SJy7UQOMtVORQKoYoTwzsvw0B+0pvTmVEcau3EJzCoO37AUESoKMjmh4a0mgebucDiX5A4N7/L8DJq6+gjZvDnVWGbeFvBD4O+iD4jIAuAOYAVwA/AzEfEO/+HpJxre0/JHmHlHyindfVMjvHv6gwRCA/dyqW7qZlZx9oDVZxUFmVo2UZNCc2TmXTJM2aQsLwPLYHvHyajhbYzpMsa8CMTXCW4AHjPGdBpj9gA1wLrBHysit4tIlYhUNTY2JmvME+5Uh59sn5v8zBFq3pFVVlOl7n3zA2/y0R9voiduJ7Ujjd3Mi9S7o2YUZE2assmm6mb+8TfvaN/6FBVteR1u5l2WGw50u0snZ3vDchZQG/d+HVAx+EnGmAeMMeuNMevLysrO8kul3ql2P9PzM0c8WCA6854KHSd9wRC7T3Sw7WgbX31iJxC+E3+kaWh4VxRmcqrDb/vLyWT4w44TPFp1TMtAU1RzpBVwuJp3eX44vBs67f3ZONvw9hEup0RZwKSpIZzq8DN9hHo3QF7m1Anvw43dhCxDeV4Gz+2uJxiyON7WS28gFOs0iaooyCJkGdt/qJMhekN21/F2m0ei7NDc3U9uhmfYduGy3HA2pOvM+yQwM+79SuDY+IfjDNGZ90hOz7ztXyI70Q7UdwJw47pKegMh9td38sahJgDWzy0e8NxYr/ckWKhzuDHcCrn7RIfNI1F2aO7qH7ZkAuGaN2B7r/fZhvfTwM0iki0iy4BiYHvyhmUfyzLUjzLzzo3MvKfCEvmD9V24XcKHzw3/rt5xrJ1X9jcyPT+TpdPzBjx3dnF4Jl4T6QFPV53+QOwf5p4TOvOeilq6+4ctmQBk+dzkZXhsn3mPegCxiOQB24A8IFNErgBuAx4BdgN+4FYzSe7sNHX3EbRM4vCeQjXvA/WdzCnJZmF5LsU5PqpqWvjzwSauXz30sOE5Jdl43cLBhi6bRpscNU3hrW3zMz3sOq4z76moqauPysi+PcMpy8+gvsPhNe9IR8lCY8w0Y0xB5O2XjTH3GGPmGWOWGWPeSMVgU6G+PfzbdExlk6kw827oYnF5HiLC6soCntp5ks6+IJcvLh/yXK/bxbzSHA41dNow0uQ5HKl3v3fldE51+B2xj0UqBEIWj7xZO2ILXMgy3Ppfm/n673ZN+jUOzd39lIww8waYW5ITW2VsF10eP8jJyN4ciWbe2T43IpN/5u0PhKht7mbxtFwALl5YSiBkcePaSt61dGh4Ayyalpf2M+/qyAKk966cDsCBU+n9y2isfr35GHf/bhd3Pbp92BbJx6qO8cLeBh5+s5b//T/v2DDC1LAsQ2v3yDVvgAVl4fC28xR5De9BokvjE4W3iJCbkb7bwjZ29rHxUBOdo2wof6C+E8vAsop8AD5z0VyqvvZu7vvoanwjbJe7qDyXoy09aX3GZ3VjF5VF2SyvKIi9P9n19of44YsHKcz28uqBRp7YenzAn/sDIe770342zC3i4+fP5vk9p+gPTs5DuDv8AYKWGbHmDTC/LJe+YLjzyi4a3oOcavfjccmwG9LES+dtYb/19B5u+elbXPbdlzmR4IdvT6TTYvmMcHh73C5KchN/XxaV52FMegfejmNtnDOzgGn5GeRmeDiU5q8kxuLxrXU0dPZx/yfWMbMwi5f2NQz48+3H2mjq6uf2yxZw6aIy/AGLd+rabBrtxKptDt/zSFTzXlAWfjVq58+5hvcgp9r9TMvPxOUafoFOVEG2z/blsWerurGLJdPy6A2EuPt3u9h7smPYl8l7TnaQm+GJHbgwFosiJZaD9ekZePUdfupae1k7pwgRYUF5LodG+Afa2x/ie88f4IHXqmnrSc+fhahHNx9jWUU+588rZs3sQrYfGxjMm4+0ALBhbhHnzwu3iL4VeWyyiQbywvLcEZ8T3ZAt2lJqBw3vQU51+JmWn3h2CTCvNDstW+KMMdQ293D+/GL+7j2LeWlfA9d+/3W++9z+Ic/de7KDZRV5o/4iizevNIcsr3vIP/50sbW2FYB1c4qAcG1zuJm3PxDic/+1me+/eJB7/riPux5N307Z3Sfa2Xm8nY+tr0REWFNZyPG23gE3at+uaWHp9DwKs30U5fhYOj2PNw832zjqiVPd2IXHJcwpGXnSUpLjoyDLqzNvJznV7o+dCpPIvNIcjrb0DNmwyenaegJ0+oPMLs7mtkvn89hfXsgHVs/g/lerqappIRCyaO8JYFmGvSc7Y/XusfK6XWyYVxxbyJNuttS2kuFxsTxy3QvLc6nv6Bty4Owv3zrKxupm7rtpNV+8ahGvHGiktjn9fpkDPPLmUXweFx+K9PKvqgzX+qNlkWDIYmttKxviFmVdML+EzTUt9Pan772NkVQ3dDO7JBuve+R4FBHml+VoeDuFMSYy8x75ZmXUvNJcgpbhWEtPCkaWPLWR8c4pyUFEOG9eMfd8+BxKczO4/9XD/Msf93HePS/wf57cTVdfMBZiZ+LiBSUcbOiiweY+2LNRVdvK6srC2A3ZhZHaZvzL4/6gxU9eO8z584q5cV0lHz9/Ni4RHnmzdtjP6VT+QIj6Dj9PbK3jxrUzKcwO36BbObMAl8D2Y+EFSm9UN9PdH2LDvNPhffXyafgDFq8eaBj2c6ebvmAodg+rurErVtNOZMm0PPad6rRt8zIN7zgd/iA9/aER9/GOF92Uye5ezzN1NBLes4tPvyTMzfBw3TkVvH6wkSe21QHw0KZa1swq5OoV08/4a1y8sBSAjdXp9bK6qy/IruPtrJ9bFHss+srjBy8e5J4/7uXeZ/bxtd/u5FSHnzuuXAiEtw6+amk5f9hxIq12IfzQj97gku+8RF/Q4nOXzI89npPhYfG0PDZVN1HX2sNdj25nXmnOgPbQ8+YVU5zj4487T9kx9KS7+7e7uP4Hr9MXDFHT3D2m8F49q5C2ngA1zfZM4EZdYTmV1I9ygk68BWVpGt6Rl/bx4Q1w9YppPLixhr6gxf2fWMeaWYVMy88YcWfFRJZX5FOY7eXFfQ2xl+JO1NzVR2NXH0um5bGjrp3mrvDq2ugvHwgfNvHND67g67/fzWsHGjGEF6t8/vIFXLro9PPevWwaf9pTz75TZ15qssOpdj/7TnWyoCyHyxeXD7k598E1M/nOs/v4m19toy8Q4mefvzC2OA3CnUfXrJjGH7afwB8IjXjeazro6Q/y1Dsn6Q2EeHhTLYGQSXizMurc2YUAbD/WOmSHzVTQ8I5zcpQTdOIVZvsoyvZSbePd5rNR29xDeV4GWb6B/9jOm1tMUbaXkGW4cmnZgEMWzpTLJXz43Eoe2lTDyfctHdM9hFRr7OzjI/dv5GS7nzuvWsS/Pref8rwMfB5X7GZl1CcvnMvyGfmxcpo/YA35x335kvCWx6/sb3R0eLf19POLN2pi1/J/P7qG1bMKhzzvI+sque9P+9l+rI273r2Y+cPMRK9eMZ3/fvsYbx9p4bLF6bPl82Av7m2gNxDC53bxnWf3AXDOzIJRP25ReR45PjfbjrZxw7mVEz3MIbRsEqc+Et6JlsbHm1eaE9s6NF3UtvQMexfd43bxlfct42vXLRtXcEd99uK5GOAXb9SM+3Ml2zM7T/LBf/8zDR19WJbhXyOdNg2dfayfUzTsLHLdnGIqi7KpLMoedlY2LT+TZRX5vLLf2TXgbzy1h++/eJBvPb2HLK871sM/WFleBu87p4LS3Aw+d+m8YZ9zwbwSfG4Xrx9Mn4NWhvP77SeYlp/BHVcuJGQZvnvjKpYM2nRtOG6XsKpyaFtlqmh4xznY0InP7RrTDUuAJdPz2X2ig2AadZzUNnfHdv8b7KPrZ/GxDbOT8nVmFWdz9fJpPLG1ztYlxIN1+AN88dfbKMj28cit5/OxDbMAuPu6ZfjcLq5cMvyy/7F497JyNte0sMOhbZK/3VbHE1uPk5vhoac/xOpZBQk7Ku698Rye/dtLB5RL4mX53KyfW8TrB9Ozswhg/6lOXtxXz0fWVfKFdy1k45ev4qORn4mxWDO7kD0nOmzputHwjlNV28o5lQUjLv0e7JKFpXT6g+yoS49tQ7v6gtR39MUWGEy0q1dMo6mrn10O2lb1tQONBEKGb3xwBevmFPHla5fy40+u43OXzOPVL13BZy6ee9af+9ZL51OWl8GXfvOO47YHeHhTDXc9uoP1c4r48SfDJxZuGLQf+2DZPg+lo6yovWRRKftOdabNARwNHX6e31Mfe/97zx8g1+fhtkvn43JJwm0xhnPevGKClmHr0dZkD3VUGt4R/kBoSKfBaC5eWIIIafOyMbqoaEGKwvuyRWWIwMv7nPP9eXFvA0XZXtbODv8952V6uWbFdESEioKshDPR0RRkebn3xlUcaOjkrx7Z4pg1AF19Qf7tTwe4eGEJv7rtAi5aUML9n1jLZy8evhxyJq6I7C7532859yyWXcfbufeZfWysbuKfn9rDbQ9V8YcdJ3h21yme3X2K2y6bH2uTPFMb5hbjdgmbbOis0vCOeKeunUDIsH5O4tlIvMJsH6sqC/lzmrxsPBwJ73mlo99JT4aS3AxWVxbyskPqwMGQxcv7G7hySTnuM1g1eiauXFLOtz60kpf3N/LE1roJ+Rpn6qFNNbT3BvjSNUvxeVyICO9dWZFw46WxWj4jn+tWVfCjlw9x2GH72Rhj+Mlrh3n/v/+Z+1+t5ku/eYcX9tTjdgl//9h27vz1NlZVFvD5yxec9dfIzfBwzswCW1abanhHbK4J79MwuNNgNJctKmXbsTbae5x/JNrhxi5ESLjsN9kuX1zGjro22nvt//68eqCRtp7AWfWun4lbzpvNrOIsntllfw90yDI8tLGWSxeVDttVkgz/9P7leNzCT14/PCGf/2w9v6eeb/9xL9eunM53P7KKutZe+oIWP7rlXD514Vw+tGYmP7pl7ZjLpCO5YH4JO+ra6OlP7UZ1Gt7Axuom/uPlQ6yuLDjj2ciVS8sJWYZX0mCl2ZGmbmYWZqW0J/eC+SUYA1tq7d/E6OE3aynPy+CqZWd/U3IsRIT3rpjOxkPNQ5bVp9obh5o41eHnf52XnLnCfskAAA9OSURBVBvRwynPy+SqZdNiB1Q7gTGGH750iDkl2fzg5nP5yNpK5pflUFmUxTUrpvP165fznY+sYlbx+CcylywsJRAyvHYgta/Ap3x4/8crh/jkz95mRmEWD3xq/Rl//JrKQkpzfbyw1/nhfbixO+WLCc6dXYjXLbbvQLfreDuvHmjk5vNmj6uuPVbXrJhOf8ji5X32/lz8ZksdBVneCf+Fdd05FbR09/PmYft/SQM8s+sUO4+381eXL8DjduFyCb/4zAYe/OyGs1p4lsgF84spzfXx++3HR39yEk3p8N51vJ3vPrufq5dP4/G/vmjMLYLxXC7hyiXlvLK/wTE3qIZjjOFI09iW/SZTptfNqspC3rYxvDdVN3PT/Zsozc3gE+dP3Aw03trZRcwszOLhTfbtd1Ld2MWzu07xgdUzktK7n8gVS8rI8bn57bbUBthw3j7Swl2PbmfFjHw+vPb04pk5JTksLB+9f/tMedwu3r96Bi/ubUhp+XRKh/ePXztMboaHe29cRX6m96w/z7XnTKfTH+T2h6rYerTVUX3NUYebuunqC7KsIvk/vKM5b14xO+vaU14ThPAvrW8+tYeyvAye/sIllJ/FL+iz4XIJt106j6raVlt+cQVCFl95YieZXhdfuGrhhH+9TK+bm9bP4rfb6thv47FxwZDFl594h4qCTB76i/PGXc8eqw+fW0l/yOKhTTUp+XowhcO7rrWHp985wcfPn01B1tkHN4Q7DL7xwRW8Ud3Mh/9jI3/1yy2EHBbgVZEbsutH6e2dCJcuKiVoGVs2MfrTnnr2nOzgb9+9KGXBHfWxDbMpyfHxb8/tT+kv9Nbufj7+07d4+0gLd1+/nPK81Fz3nVctIi/Ty18+XMUPXzxoyyTm8a11HG7s5ivvWzbqqU/JdE5lAe87Zzo/fOlQyn55Tdnw/v32E1gGPnHBnHF/LhHhUxfO5a2vXMXfv2cxz+2u55tP7UnCKJNnc00rxTk+5tuwgc6F80tYOj2Pn7x2OKW77hlj+P4LB5lXmsMHVs9I2deNyvK5+cdrl/J2TUvKZmR9wRC3P1zF9mNt/L+PreGj68e+WnC8inJ83HfTagqyvNz3/AH+89XqlH1tCB/V9q2n9nLu7EKuXj4tpV8b4JsfXElepoe/+dXWlByROCXD2xjDE1vrOG9ucVLuNkcV5fj4wlWL+Nwl83hwYw3P7DyZtM89XltqW1kXOdor1USE2y6dz/76zpTWRJ+PzLr/5sqFeFJwk3I4N62r5IolZdzzx30TvpjLGMOXfvMOm2taue+m1bbs6Pju5dP43R0X8/7VM7jvT/vZlqKVh6fa/Xz6529TlOPjR7esteXnvCQ3gx/+r3Opbuzi67/bNeFfb8qFtz8Q4md/PkJ1Y/eE/XD/43uXsrqygLse2+6IhRpHmro50tTNhjNYPZpsH1gzgw1zi/jy4zt5aV/96B8wTt19Qb773H7mlmTzwTWpn3VHiQjf/9i5zC/L4dM/f5ub7t84YS+rv/f8AX6//QT/cM0S3m/DK40oEeGeG1YyLT+Trzyxc8Jv5AdDFl96/B36gxb/9RfnMaPQvl0sL1pYyh1XLuS3245P+MKdKRXeXX1BPv7Tt/jW03tZXVnA9asrJuTr+DwufvrpDayuLOTvHtvBt5/ew3+8csiWDYuqG7u45Sdvkp/p4b0rJuZ6x8LrdvHTT21gYXkuf/FgFV95Yif7J+AUkv2nOiMhuYnDjV1880MrbZt1RxVke/nVbRfwhXctoqa5hxv/cyP//ORuNlU3J+36f7Oljh+8dIiPrq/kr684+xWDyZKX6eUbH1zJvlOdfOPJPRNWLjvU0MmN/7mR1w408rXrltmyr/Zgf33FQmYWZvG13+6c0IOpJVU1yPXr15uqqqqUfK3hvH2khbt/t5Pqxm6+97E1vH9VxYS/tApG7vj/z5bw7FskfKzWvNIcPn/Fgtj+GhOhqqaFNw838+DGGoyBhz93/ojbf6aSPxDi3mf28au3jtIfsqgsyuITF8zh5g2zznp/iahdx9v55M/eAsLdD7dfNj8p+3ck08n2Xr76xE42VjfTF7RYXpHPF69axBVLys568dTG6iY+/fO3OW9eMQ9+9ryU9LGP1b/8cS8/fu0wly8u4/2rZ/CeZdMoyB5fg0DUL9+q5Z+f3EOOz803P7SS61fZ92pjsI2HmvjMLzazaFouX752KZcsLD3rvBGRLcaYIYtQJn14N3b28S/P7OWJrceZWZjFt25YOa5tP8+UMYbdJzooyvHxyzdrOdzYzVtHmmntCXDJwlI+dO5MLlpQkpSXet19Qf777aMcauji0apjGBM+Mefnn9kwppNBUqmh088Lexp4cscJNh1uJtPrYkZhFu9ZPo3rzqngaEsPc0tymFeaQ7bPzabqZlwu4dzZhRxr6aWlu5/Vswro7Q/x5I4T1DT38D9Vx8jL9PKr285nTon9M7BEouP+95cPcbSlBxHI9Xm4+bxZfHhtJS/tawgfGLGukiXT8wYEsmUZDjd1sftEB3tPdvLQphpmFGbx+F9dNO7OqWSLrnT89dtHOdHux+MSLphfwro5RayZVRhZ9Zg9pr1momfMtvUE+PGr1fxu+wkuW1zGv920KmUdNWfixb31/MNv3qGlu59/v+Xcs/7lkrbh7Q+E2Hm8nYP1Xfg8rtgG8nNLsnl5fwNvHm7hyiXlTMvPoCQng+8+t4+dx9spzc1gWn4GT71zEn8gxK2XzucL71pIts/+w4O6+4I88mYt/7WxhhORAyBmFGSybm4xyyryKMzy4fO4ONrcjdvlojDbS0GWl8bOPt453s6F80soyvZSmO2jLxjizcMtbK1t5XBTF01d/WR6XVx3zgz+6QPLycvw2HLz5kzsPdnBo5uPUdvczcv7h97UK8jyDrs3SrbPTW8ghDGQ5XUzpySbn356PZVFqdu7Zbz6gxYbq5vYerSNI03dPPXOCaL/JH1uF/0hC5/bxaziLJq7+8nxeejqCw74frzvnOncfd1yW2u9ozHGsKOunWd2neSVfY0caOiMXWdBlpfZxdl09weZUxw+8KKlu5+e/iAFWV5yMz3UNPWw+0Q7rZFFMD63i89fsYA7r1o0YZuMJYM/EOLZXad478rpZ/3KKm3D+3//zw5+s2XoTb+8TA+d/qHtOC4JH4B7vLWXurZerl4+jb9992LHzTzh9Kx8c00LVbWtbKlp5VTciesugcGtssU5Plq6B9bRPC5hzaxCyvIyuPXS+We8uZaTvHm4meOtvSyZnsexlh6qG7s42tLD+jnFeD1CbXP4JKAsr4c/H2qkPC+Tdy0tZ+UYjq1KB0ebe9h6tJW5pTnMLs7m5X0NHGjopKapm+KcDPyBEBkeF2vnFLG6spCKwsxxLTCzS1dfkN3H26lt7uGtIy00dfWR5XVT29JDXWsPpbkZ5GZ4aOnup6svyOzibFbMyGfFjHzys8Jb+iazU8zJJiS8ReSjwHeAEHCPMebnIz33bMN729FWGjv7WD4jH8uCzr4A2462se9UBxUFWdxy3my21LbS3hvgcFMXly0q4/z5JUD45aXLwb+Vh9PTH6Qzcor9zMIsXALtvQHaewP4PC5mFmZR3dhNIGTR3NWP1y0srch33MtlpVRyJD28RSQP2ANcQDi8twPnGGOGbWa1+4alUkqlo5HCezy3pa8BXjXGHDfGnAJeAq4ax+dTSik1RuMJ71lA/JZpdcCARmIRuV1EqkSkqrHROUdhKaVUuhtPePuA+KVTFuHySYwx5gFjzHpjzPqysrJxfCmllFLxxhPeJ4H49eWVgHNPIVVKqUlkPOH9HHCNiJSLyHTgIuBPyRmWUkqpRM56xYoxpl5EvgZsijz098aY7uQMSymlVCLjWm5ojHkQeDApI1FKKTVmztnBRiml1JilbHm8iDQysLUwnZUCTXYPwgZT8bqn4jXD1Lxup17zHGPMkHa9lIX3ZCIiVcOteJrspuJ1T8Vrhql53el2zVo2UUqpNKThrZRSaUjD++w8YPcAbDIVr3sqXjNMzetOq2vWmrdSSqUhnXkrpVQa0vBWSqk0pOGtlFJpSMM7QkQyReQBEdkvIrUiclfk8TtF5Gjk8Wvjnn+viNSJyE4RWTfoc10mIpaIXJLq6zhTybhuEblCRDpE5FDkv7vsup6xSNbftYhcIiLviEiNiHzPjms5E0n6u3477u/5qIg02HU9Y5Gkay4Ukaci17xFRJbbdT0DGGP0v/BN2xLgRkAIr7SqBy4HDgB5wHLgBOAF3gX8mfDeMO8Btsd9ngzgdWAzcInd15WK6wauAB60+1pSfM1FwBFgdeT9LLuvK1U/43Gf7zbgPruvKwV/1/8UvU7gL4FH7b4uY4zOvKOMMc3GmMdNWBPhvckvAx4zxnQaY/YANcA64MOEwypojHkeKItsiwtwN/BjIC12WEzidaeNJF3z54BHjDE7Ip+z15aLOQMT8Hd9KzDioeNOkKRr7gOibXmZhH8B2E7DexgispLwX1Ipwx/1NvgIuONAhYisANYaYx5J1ViT6Wyvm/AP9vtEpFpEfi0i5Ska8riN45pXA9mRl9E7ROQ9KRpyUozjuqMfvwqwjDG7J360yTGOa/4+sEREXgSuA76ekgGPQsN7EBEpBR4GPsvIR72N9PgPgC+mZqTJNZ7rNsa8aowpB5YSPmHp/6Zk0OM0zr/rcsKlk/MIlw8eFpG0+Pc0zuuOug2Hz7rjjfOaLwH6gb8GGgnP0G2XFj9sqSIiRcCTwFeNMZsZ+ai3wY/PALKARcDjIrIdWA/8QkSWpGLs4zHO666LvmOMCQA/A1ZO9JjHKwnX3AA8ZYwJGWPeJvyPuyQVYx+PZPxdi0gmcAPw61SMebyScM3fBv7JGLMf+AJwbyrGPSq7i+5O+Q/IJ3yj8fq4x9YBe4BsYFnkbSF8A+RFwE34xsYLw3y+V0iPG5bjvm5gLuGbPAL8C/CA3deVgmu+BfhV5DlrgUN2X1cqrjvyMZ8EfmH39aTw7/o14C8ib78H2Gf3dRljNLzj/kLvJnyT8VDcf/OBrxLuKtgLXBx5rotwiaQW2AosHebzpUt4j/u6gc8QnqFUA48CxXZfVwqu2R15/FDk8fPtvq5U/YxHwuxSu68nhX/Xa4FtkZ/vzcCFdl+XMUb3NlFKqXSkNW+llEpDGt5KKZWGNLyVUioNaXgrpVQa0vBWSqk0pOGtlFJpSMNbKaXSkIa3Ukqlof8PpnjvYt0O6pAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['Date'], df['United States']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T04:29:36.055512Z",
     "start_time": "2021-02-23T04:29:35.949263Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['Date'], df['Mid-Atlantic Region']);"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T04:18:41.205969Z",
     "start_time": "2021-02-23T04:18:41.184275Z"
    }
   },
   "source": [
    "Figure 1: An evaluation of how many top-scoring queries to include in the ILI-related query fraction.\n",
    "\n",
    "![image.png](./img/09gft1.png)\n",
    "\n",
    "Maximal performance at estimating out-of-sample points during cross-validation was obtained by summing the top 45 search queries. A steep drop in model performance occurs after adding query 81, which is ‘oscar nominations’."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T04:55:19.600137Z",
     "start_time": "2021-02-23T04:54:52.270573Z"
    }
   },
   "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>date</th>\n",
       "      <th>query1</th>\n",
       "      <th>query2</th>\n",
       "      <th>query3</th>\n",
       "      <th>query4</th>\n",
       "      <th>query5</th>\n",
       "      <th>query6</th>\n",
       "      <th>query7</th>\n",
       "      <th>query8</th>\n",
       "      <th>query9</th>\n",
       "      <th>...</th>\n",
       "      <th>query36</th>\n",
       "      <th>query37</th>\n",
       "      <th>query38</th>\n",
       "      <th>query39</th>\n",
       "      <th>query40</th>\n",
       "      <th>query41</th>\n",
       "      <th>query42</th>\n",
       "      <th>query43</th>\n",
       "      <th>query44</th>\n",
       "      <th>query45</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2003-06-01</td>\n",
       "      <td>0.778</td>\n",
       "      <td>5.297</td>\n",
       "      <td>6.096</td>\n",
       "      <td>0.893</td>\n",
       "      <td>1.036</td>\n",
       "      <td>1.357</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.366</td>\n",
       "      <td>0.675</td>\n",
       "      <td>...</td>\n",
       "      <td>0.483</td>\n",
       "      <td>0.131</td>\n",
       "      <td>0.633</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.848</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.190</td>\n",
       "      <td>2.027</td>\n",
       "      <td>2.133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003-06-08</td>\n",
       "      <td>0.850</td>\n",
       "      <td>5.348</td>\n",
       "      <td>6.097</td>\n",
       "      <td>1.005</td>\n",
       "      <td>0.899</td>\n",
       "      <td>1.584</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.432</td>\n",
       "      <td>0.574</td>\n",
       "      <td>...</td>\n",
       "      <td>0.527</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.716</td>\n",
       "      <td>0.227</td>\n",
       "      <td>0.257</td>\n",
       "      <td>1.153</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.164</td>\n",
       "      <td>2.245</td>\n",
       "      <td>2.335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003-06-15</td>\n",
       "      <td>0.838</td>\n",
       "      <td>4.961</td>\n",
       "      <td>5.772</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.811</td>\n",
       "      <td>1.515</td>\n",
       "      <td>0.084</td>\n",
       "      <td>0.392</td>\n",
       "      <td>0.563</td>\n",
       "      <td>...</td>\n",
       "      <td>0.509</td>\n",
       "      <td>0.156</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.213</td>\n",
       "      <td>0.234</td>\n",
       "      <td>1.123</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.213</td>\n",
       "      <td>2.338</td>\n",
       "      <td>2.311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003-06-22</td>\n",
       "      <td>0.828</td>\n",
       "      <td>4.480</td>\n",
       "      <td>5.140</td>\n",
       "      <td>0.733</td>\n",
       "      <td>0.883</td>\n",
       "      <td>1.942</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.326</td>\n",
       "      <td>0.478</td>\n",
       "      <td>...</td>\n",
       "      <td>0.417</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.715</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0.192</td>\n",
       "      <td>1.220</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.198</td>\n",
       "      <td>2.231</td>\n",
       "      <td>2.237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003-06-29</td>\n",
       "      <td>0.747</td>\n",
       "      <td>3.910</td>\n",
       "      <td>4.409</td>\n",
       "      <td>0.637</td>\n",
       "      <td>0.726</td>\n",
       "      <td>1.580</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.352</td>\n",
       "      <td>0.364</td>\n",
       "      <td>...</td>\n",
       "      <td>0.429</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.671</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.153</td>\n",
       "      <td>1.114</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.165</td>\n",
       "      <td>2.005</td>\n",
       "      <td>2.085</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  query1  query2  query3  query4  query5  query6  query7  query8  \\\n",
       "0  2003-06-01   0.778   5.297   6.096   0.893   1.036   1.357   0.124   0.366   \n",
       "1  2003-06-08   0.850   5.348   6.097   1.005   0.899   1.584   0.096   0.432   \n",
       "2  2003-06-15   0.838   4.961   5.772   0.868   0.811   1.515   0.084   0.392   \n",
       "3  2003-06-22   0.828   4.480   5.140   0.733   0.883   1.942   0.052   0.326   \n",
       "4  2003-06-29   0.747   3.910   4.409   0.637   0.726   1.580   0.049   0.352   \n",
       "\n",
       "   query9  ...  query36  query37  query38  query39  query40  query41  query42  \\\n",
       "0   0.675  ...    0.483    0.131    0.633    0.173    0.241    0.848    0.138   \n",
       "1   0.574  ...    0.527    0.178    0.716    0.227    0.257    1.153    0.145   \n",
       "2   0.563  ...    0.509    0.156    0.760    0.213    0.234    1.123    0.138   \n",
       "3   0.478  ...    0.417    0.128    0.715    0.198    0.192    1.220    0.146   \n",
       "4   0.364  ...    0.429    0.080    0.671    0.150    0.153    1.114    0.138   \n",
       "\n",
       "   query43  query44  query45  \n",
       "0    0.190    2.027    2.133  \n",
       "1    0.164    2.245    2.335  \n",
       "2    0.213    2.338    2.311  \n",
       "3    0.198    2.231    2.237  \n",
       "4    0.165    2.005    2.085  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine 45 queries\n",
    "dict = {'date': df['Date'].tolist()}\n",
    "for i in range(1, 46):\n",
    "    df = pd.read_excel('41586_2009_BFnature07634_MOESM271_ESM.xls', sheet_name=i, header = 1)\n",
    "    dict['query'+str(i)] = df['United States'].tolist()\n",
    "dat = pd.DataFrame.from_dict(dict)\n",
    "dat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Parable of Google Flu: Traps in Big Data Analysis\n",
    "\n",
    "David Lazer*, Ryan Kennedy, Gary King, Alessandro Vespignani\n",
    "\n",
    "\n",
    "Science 14 Mar 2014: Vol. 343, Issue 6176, pp. 1203-1205 DOI: 10.1126/science.1248506\n",
    "\n",
    "In February 2013, Google Flu Trends (GFT) made headlines but not for a reason that Google executives or the creators of the flu tracking system would have hoped. Nature reported that GFT was predicting more than double the proportion of doctor visits for influenza-like illness (ILI) than the Centers for Disease Control and Prevention (CDC), which bases its estimates on surveillance reports from laboratories across the United States (1, 2). This happened despite the fact that GFT was built to predict CDC reports. Given that GFT is often held up as an exemplary use of big data (3, 4), what lessons can we draw from this error?\n",
    "\n",
    "https://science.sciencemag.org/content/343/6176/1203.summary\n",
    "\n",
    "**Data & Code**\n",
    "\n",
    "https://science.sciencemag.org/content/sci/suppl/2014/03/12/343.6176.1203.DC1/1248506.Lazer.SM.revision1.pdf\n",
    "\n",
    "\n",
    "Lazer, David; Kennedy, Ryan; King, Gary; Vespignani, Alessandro, 2014, \"Replication data for: The Parable of Google Flu: Traps in Big Data Analysis\", https://doi.org/10.7910/DVN/24823, Harvard Dataverse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:01:15.535135Z",
     "start_time": "2021-02-23T05:01:15.510399Z"
    }
   },
   "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>date</th>\n",
       "      <th>query1</th>\n",
       "      <th>query2</th>\n",
       "      <th>query3</th>\n",
       "      <th>query4</th>\n",
       "      <th>query5</th>\n",
       "      <th>query6</th>\n",
       "      <th>query7</th>\n",
       "      <th>query8</th>\n",
       "      <th>query9</th>\n",
       "      <th>...</th>\n",
       "      <th>query37</th>\n",
       "      <th>query38</th>\n",
       "      <th>query39</th>\n",
       "      <th>query40</th>\n",
       "      <th>query41</th>\n",
       "      <th>query42</th>\n",
       "      <th>query43</th>\n",
       "      <th>query44</th>\n",
       "      <th>query45</th>\n",
       "      <th>cflu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2003-09-28</td>\n",
       "      <td>1.853</td>\n",
       "      <td>6.679</td>\n",
       "      <td>7.824</td>\n",
       "      <td>1.072</td>\n",
       "      <td>2.399</td>\n",
       "      <td>1.623</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.606</td>\n",
       "      <td>0.731</td>\n",
       "      <td>...</td>\n",
       "      <td>0.224</td>\n",
       "      <td>0.741</td>\n",
       "      <td>0.324</td>\n",
       "      <td>0.496</td>\n",
       "      <td>1.267</td>\n",
       "      <td>0.294</td>\n",
       "      <td>0.204</td>\n",
       "      <td>2.097</td>\n",
       "      <td>2.905</td>\n",
       "      <td>0.884021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003-10-05</td>\n",
       "      <td>1.976</td>\n",
       "      <td>6.310</td>\n",
       "      <td>8.259</td>\n",
       "      <td>1.194</td>\n",
       "      <td>2.733</td>\n",
       "      <td>1.589</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.607</td>\n",
       "      <td>0.662</td>\n",
       "      <td>...</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.852</td>\n",
       "      <td>0.295</td>\n",
       "      <td>0.442</td>\n",
       "      <td>1.329</td>\n",
       "      <td>0.322</td>\n",
       "      <td>0.225</td>\n",
       "      <td>2.233</td>\n",
       "      <td>2.713</td>\n",
       "      <td>1.027731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003-10-12</td>\n",
       "      <td>2.834</td>\n",
       "      <td>6.911</td>\n",
       "      <td>9.009</td>\n",
       "      <td>1.228</td>\n",
       "      <td>3.304</td>\n",
       "      <td>1.581</td>\n",
       "      <td>0.221</td>\n",
       "      <td>0.664</td>\n",
       "      <td>0.783</td>\n",
       "      <td>...</td>\n",
       "      <td>0.259</td>\n",
       "      <td>0.890</td>\n",
       "      <td>0.306</td>\n",
       "      <td>0.413</td>\n",
       "      <td>1.392</td>\n",
       "      <td>0.353</td>\n",
       "      <td>0.201</td>\n",
       "      <td>2.305</td>\n",
       "      <td>2.874</td>\n",
       "      <td>1.282964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003-10-19</td>\n",
       "      <td>3.501</td>\n",
       "      <td>7.492</td>\n",
       "      <td>9.611</td>\n",
       "      <td>1.291</td>\n",
       "      <td>3.846</td>\n",
       "      <td>1.619</td>\n",
       "      <td>0.326</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.841</td>\n",
       "      <td>...</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.352</td>\n",
       "      <td>0.450</td>\n",
       "      <td>1.357</td>\n",
       "      <td>0.383</td>\n",
       "      <td>0.255</td>\n",
       "      <td>2.279</td>\n",
       "      <td>2.965</td>\n",
       "      <td>1.326605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003-10-26</td>\n",
       "      <td>3.721</td>\n",
       "      <td>7.121</td>\n",
       "      <td>9.352</td>\n",
       "      <td>1.309</td>\n",
       "      <td>3.876</td>\n",
       "      <td>1.640</td>\n",
       "      <td>0.288</td>\n",
       "      <td>0.674</td>\n",
       "      <td>0.762</td>\n",
       "      <td>...</td>\n",
       "      <td>0.260</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.293</td>\n",
       "      <td>0.393</td>\n",
       "      <td>1.367</td>\n",
       "      <td>0.319</td>\n",
       "      <td>0.271</td>\n",
       "      <td>2.317</td>\n",
       "      <td>2.986</td>\n",
       "      <td>1.773040</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  query1  query2  query3  query4  query5  query6  query7  query8  \\\n",
       "0  2003-09-28   1.853   6.679   7.824   1.072   2.399   1.623   0.162   0.606   \n",
       "1  2003-10-05   1.976   6.310   8.259   1.194   2.733   1.589   0.167   0.607   \n",
       "2  2003-10-12   2.834   6.911   9.009   1.228   3.304   1.581   0.221   0.664   \n",
       "3  2003-10-19   3.501   7.492   9.611   1.291   3.846   1.619   0.326   0.698   \n",
       "4  2003-10-26   3.721   7.121   9.352   1.309   3.876   1.640   0.288   0.674   \n",
       "\n",
       "   query9  ...  query37  query38  query39  query40  query41  query42  query43  \\\n",
       "0   0.731  ...    0.224    0.741    0.324    0.496    1.267    0.294    0.204   \n",
       "1   0.662  ...    0.210    0.852    0.295    0.442    1.329    0.322    0.225   \n",
       "2   0.783  ...    0.259    0.890    0.306    0.413    1.392    0.353    0.201   \n",
       "3   0.841  ...    0.260    0.900    0.352    0.450    1.357    0.383    0.255   \n",
       "4   0.762  ...    0.260    0.950    0.293    0.393    1.367    0.319    0.271   \n",
       "\n",
       "   query44  query45      cflu  \n",
       "0    2.097    2.905  0.884021  \n",
       "1    2.233    2.713  1.027731  \n",
       "2    2.305    2.874  1.282964  \n",
       "3    2.279    2.965  1.326605  \n",
       "4    2.317    2.986  1.773040  \n",
       "\n",
       "[5 rows x 47 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge the ILI data\n",
    "# cflu is CDC % ILI\n",
    "dat2 = pd.read_csv('../GFT2.0/parable/ParableOfGFT(Replication).csv')\n",
    "dat3 = dat2[['date', 'cflu']]\n",
    "data = pd.merge(dat, dat3, how='right', on='date')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:19:50.372566Z",
     "start_time": "2021-02-23T05:19:50.358642Z"
    }
   },
   "outputs": [],
   "source": [
    "#data.to_csv('gft_ili_us.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:15:37.048038Z",
     "start_time": "2021-02-23T05:15:37.038628Z"
    }
   },
   "outputs": [],
   "source": [
    "# filter data\n",
    "data = data[data['query1'].notna()]\n",
    "data['date'] = pd.to_datetime(data['date'])\n",
    "data['date'] = data['date'].dt.date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:17:30.809667Z",
     "start_time": "2021-02-23T05:17:30.666108Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data['date'], data['query1'], label = 'query1')\n",
    "plt.plot(data['date'], data['query2'], label = 'query2')\n",
    "plt.plot(data['date'], data['query3'], label = 'query3')\n",
    "plt.plot(data['date'], data['cflu'],  label = 'CDC ILI')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using this ILI-related query fraction as the explanatory variable, we fit a final linear model to weekly ILI percentages between 2003 and 2007 for all nine regions together, thus obtaining a single, region-independent coefficient. The model was able to obtain a good fit with CDC-reported ILI percentages, with a mean correlation of 0.90 (min = 0.80, max = 0.96, n = 9 regions; Fig. 2).\n",
    "\n",
    "**Figure 2**: A comparison of model estimates for the mid-Atlantic region (black) against CDC-reported ILI percentages (red), including points over which the model was fit and validated.\n",
    "\n",
    "![image.png](./img/09gft2.png)\n",
    "\n",
    "A correlation of 0.85 was obtained over 128 points from this region to which the model was fit, whereas a correlation of 0.96 was obtained over 42 validation points. Dotted lines indicate 95% prediction intervals. The region comprises New York, New Jersey and Pennsylvania."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 3**: ILI percentages estimated by our model (black) and provided by the CDC (red) in the **mid-Atlantic region**, showing data available at four points in the 2007-2008 influenza season.\n",
    "\n",
    "![image.png](./img/09gft3.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:52:42.296353Z",
     "start_time": "2021-02-23T05:52:42.285525Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "for i in range(1, 8):\n",
    "    data[\"lag_{}\".format(i)] = data['cflu'].shift(i)\n",
    "print(\"done\")\n",
    "data=data.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:10:15.443855Z",
     "start_time": "2021-02-23T06:10:15.440188Z"
    }
   },
   "outputs": [],
   "source": [
    "y = data['cflu']\n",
    "date = data['date']\n",
    "X = data.drop(['cflu', 'date'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:56:18.565470Z",
     "start_time": "2021-02-23T05:56:18.561045Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "242"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:10:21.993297Z",
     "start_time": "2021-02-23T06:10:21.987554Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.884021\n",
       "1      1.027731\n",
       "2      1.282964\n",
       "3      1.326605\n",
       "4      1.773040\n",
       "         ...   \n",
       "237    1.165197\n",
       "238    1.020348\n",
       "239    0.877607\n",
       "240    0.825107\n",
       "241    0.787315\n",
       "Name: cflu, Length: 242, dtype: float64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:10:29.531513Z",
     "start_time": "2021-02-23T06:10:29.503369Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>query1</th>\n",
       "      <th>query2</th>\n",
       "      <th>query3</th>\n",
       "      <th>query4</th>\n",
       "      <th>query5</th>\n",
       "      <th>query6</th>\n",
       "      <th>query7</th>\n",
       "      <th>query8</th>\n",
       "      <th>query9</th>\n",
       "      <th>query10</th>\n",
       "      <th>...</th>\n",
       "      <th>query43</th>\n",
       "      <th>query44</th>\n",
       "      <th>query45</th>\n",
       "      <th>lag_1</th>\n",
       "      <th>lag_2</th>\n",
       "      <th>lag_3</th>\n",
       "      <th>lag_4</th>\n",
       "      <th>lag_5</th>\n",
       "      <th>lag_6</th>\n",
       "      <th>lag_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.853</td>\n",
       "      <td>6.679</td>\n",
       "      <td>7.824</td>\n",
       "      <td>1.072</td>\n",
       "      <td>2.399</td>\n",
       "      <td>1.623</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.606</td>\n",
       "      <td>0.731</td>\n",
       "      <td>0.145</td>\n",
       "      <td>...</td>\n",
       "      <td>0.204</td>\n",
       "      <td>2.097</td>\n",
       "      <td>2.905</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.976</td>\n",
       "      <td>6.310</td>\n",
       "      <td>8.259</td>\n",
       "      <td>1.194</td>\n",
       "      <td>2.733</td>\n",
       "      <td>1.589</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.607</td>\n",
       "      <td>0.662</td>\n",
       "      <td>0.185</td>\n",
       "      <td>...</td>\n",
       "      <td>0.225</td>\n",
       "      <td>2.233</td>\n",
       "      <td>2.713</td>\n",
       "      <td>0.884021</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.834</td>\n",
       "      <td>6.911</td>\n",
       "      <td>9.009</td>\n",
       "      <td>1.228</td>\n",
       "      <td>3.304</td>\n",
       "      <td>1.581</td>\n",
       "      <td>0.221</td>\n",
       "      <td>0.664</td>\n",
       "      <td>0.783</td>\n",
       "      <td>0.259</td>\n",
       "      <td>...</td>\n",
       "      <td>0.201</td>\n",
       "      <td>2.305</td>\n",
       "      <td>2.874</td>\n",
       "      <td>1.027731</td>\n",
       "      <td>0.884021</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.501</td>\n",
       "      <td>7.492</td>\n",
       "      <td>9.611</td>\n",
       "      <td>1.291</td>\n",
       "      <td>3.846</td>\n",
       "      <td>1.619</td>\n",
       "      <td>0.326</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.841</td>\n",
       "      <td>0.312</td>\n",
       "      <td>...</td>\n",
       "      <td>0.255</td>\n",
       "      <td>2.279</td>\n",
       "      <td>2.965</td>\n",
       "      <td>1.282964</td>\n",
       "      <td>1.027731</td>\n",
       "      <td>0.884021</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.721</td>\n",
       "      <td>7.121</td>\n",
       "      <td>9.352</td>\n",
       "      <td>1.309</td>\n",
       "      <td>3.876</td>\n",
       "      <td>1.640</td>\n",
       "      <td>0.288</td>\n",
       "      <td>0.674</td>\n",
       "      <td>0.762</td>\n",
       "      <td>0.321</td>\n",
       "      <td>...</td>\n",
       "      <td>0.271</td>\n",
       "      <td>2.317</td>\n",
       "      <td>2.986</td>\n",
       "      <td>1.326605</td>\n",
       "      <td>1.282964</td>\n",
       "      <td>1.027731</td>\n",
       "      <td>0.884021</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>2.683</td>\n",
       "      <td>8.227</td>\n",
       "      <td>11.683</td>\n",
       "      <td>1.636</td>\n",
       "      <td>2.690</td>\n",
       "      <td>2.322</td>\n",
       "      <td>0.241</td>\n",
       "      <td>0.858</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.292</td>\n",
       "      <td>...</td>\n",
       "      <td>0.281</td>\n",
       "      <td>2.707</td>\n",
       "      <td>1.569</td>\n",
       "      <td>1.311296</td>\n",
       "      <td>1.652751</td>\n",
       "      <td>2.033656</td>\n",
       "      <td>2.692446</td>\n",
       "      <td>3.215218</td>\n",
       "      <td>3.832486</td>\n",
       "      <td>4.532340</td>\n",
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       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>2.290</td>\n",
       "      <td>7.648</td>\n",
       "      <td>10.502</td>\n",
       "      <td>1.435</td>\n",
       "      <td>2.359</td>\n",
       "      <td>2.239</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.769</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.225</td>\n",
       "      <td>...</td>\n",
       "      <td>0.254</td>\n",
       "      <td>2.568</td>\n",
       "      <td>1.510</td>\n",
       "      <td>1.165197</td>\n",
       "      <td>1.311296</td>\n",
       "      <td>1.652751</td>\n",
       "      <td>2.033656</td>\n",
       "      <td>2.692446</td>\n",
       "      <td>3.215218</td>\n",
       "      <td>3.832486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>1.766</td>\n",
       "      <td>7.375</td>\n",
       "      <td>10.081</td>\n",
       "      <td>1.258</td>\n",
       "      <td>2.031</td>\n",
       "      <td>2.194</td>\n",
       "      <td>0.142</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.214</td>\n",
       "      <td>...</td>\n",
       "      <td>0.237</td>\n",
       "      <td>2.480</td>\n",
       "      <td>1.468</td>\n",
       "      <td>1.020348</td>\n",
       "      <td>1.165197</td>\n",
       "      <td>1.311296</td>\n",
       "      <td>1.652751</td>\n",
       "      <td>2.033656</td>\n",
       "      <td>2.692446</td>\n",
       "      <td>3.215218</td>\n",
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       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>1.446</td>\n",
       "      <td>7.132</td>\n",
       "      <td>9.531</td>\n",
       "      <td>1.097</td>\n",
       "      <td>1.930</td>\n",
       "      <td>2.190</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.654</td>\n",
       "      <td>0.657</td>\n",
       "      <td>0.188</td>\n",
       "      <td>...</td>\n",
       "      <td>0.269</td>\n",
       "      <td>2.436</td>\n",
       "      <td>1.378</td>\n",
       "      <td>0.877607</td>\n",
       "      <td>1.020348</td>\n",
       "      <td>1.165197</td>\n",
       "      <td>1.311296</td>\n",
       "      <td>1.652751</td>\n",
       "      <td>2.033656</td>\n",
       "      <td>2.692446</td>\n",
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       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>1.317</td>\n",
       "      <td>6.807</td>\n",
       "      <td>9.315</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.752</td>\n",
       "      <td>2.551</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.619</td>\n",
       "      <td>0.672</td>\n",
       "      <td>0.168</td>\n",
       "      <td>...</td>\n",
       "      <td>0.259</td>\n",
       "      <td>2.470</td>\n",
       "      <td>1.423</td>\n",
       "      <td>0.825107</td>\n",
       "      <td>0.877607</td>\n",
       "      <td>1.020348</td>\n",
       "      <td>1.165197</td>\n",
       "      <td>1.311296</td>\n",
       "      <td>1.652751</td>\n",
       "      <td>2.033656</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>242 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     query1  query2  query3  query4  query5  query6  query7  query8  query9  \\\n",
       "0     1.853   6.679   7.824   1.072   2.399   1.623   0.162   0.606   0.731   \n",
       "1     1.976   6.310   8.259   1.194   2.733   1.589   0.167   0.607   0.662   \n",
       "2     2.834   6.911   9.009   1.228   3.304   1.581   0.221   0.664   0.783   \n",
       "3     3.501   7.492   9.611   1.291   3.846   1.619   0.326   0.698   0.841   \n",
       "4     3.721   7.121   9.352   1.309   3.876   1.640   0.288   0.674   0.762   \n",
       "..      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "237   2.683   8.227  11.683   1.636   2.690   2.322   0.241   0.858   0.933   \n",
       "238   2.290   7.648  10.502   1.435   2.359   2.239   0.165   0.769   0.850   \n",
       "239   1.766   7.375  10.081   1.258   2.031   2.194   0.142   0.735   0.775   \n",
       "240   1.446   7.132   9.531   1.097   1.930   2.190   0.114   0.654   0.657   \n",
       "241   1.317   6.807   9.315   1.000   1.752   2.551   0.126   0.619   0.672   \n",
       "\n",
       "     query10  ...  query43  query44  query45     lag_1     lag_2     lag_3  \\\n",
       "0      0.145  ...    0.204    2.097    2.905  0.000000  0.000000  0.000000   \n",
       "1      0.185  ...    0.225    2.233    2.713  0.884021  0.000000  0.000000   \n",
       "2      0.259  ...    0.201    2.305    2.874  1.027731  0.884021  0.000000   \n",
       "3      0.312  ...    0.255    2.279    2.965  1.282964  1.027731  0.884021   \n",
       "4      0.321  ...    0.271    2.317    2.986  1.326605  1.282964  1.027731   \n",
       "..       ...  ...      ...      ...      ...       ...       ...       ...   \n",
       "237    0.292  ...    0.281    2.707    1.569  1.311296  1.652751  2.033656   \n",
       "238    0.225  ...    0.254    2.568    1.510  1.165197  1.311296  1.652751   \n",
       "239    0.214  ...    0.237    2.480    1.468  1.020348  1.165197  1.311296   \n",
       "240    0.188  ...    0.269    2.436    1.378  0.877607  1.020348  1.165197   \n",
       "241    0.168  ...    0.259    2.470    1.423  0.825107  0.877607  1.020348   \n",
       "\n",
       "        lag_4     lag_5     lag_6     lag_7  \n",
       "0    0.000000  0.000000  0.000000  0.000000  \n",
       "1    0.000000  0.000000  0.000000  0.000000  \n",
       "2    0.000000  0.000000  0.000000  0.000000  \n",
       "3    0.000000  0.000000  0.000000  0.000000  \n",
       "4    0.884021  0.000000  0.000000  0.000000  \n",
       "..        ...       ...       ...       ...  \n",
       "237  2.692446  3.215218  3.832486  4.532340  \n",
       "238  2.033656  2.692446  3.215218  3.832486  \n",
       "239  1.652751  2.033656  2.692446  3.215218  \n",
       "240  1.311296  1.652751  2.033656  2.692446  \n",
       "241  1.165197  1.311296  1.652751  2.033656  \n",
       "\n",
       "[242 rows x 52 columns]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:57:53.292925Z",
     "start_time": "2021-02-23T05:57:53.069272Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最佳 alpha: 0.06455398\n",
      "最佳 l1_ratio: 0.500\n",
      "迭代次数 63\n"
     ]
    }
   ],
   "source": [
    "N = 50\n",
    "X_train = X.iloc[:N,]\n",
    "X_test = X.iloc[N:,]\n",
    "y_train = y[:N]\n",
    "y_test = y[N:]\n",
    "\n",
    "# 利用弹性网络\n",
    "from sklearn.model_selection import cross_val_score\n",
    "cv_model = ElasticNetCV(l1_ratio=0.5, eps=1e-3, n_alphas=200, fit_intercept=True, \n",
    "                        normalize=True, precompute='auto', max_iter=200, tol=0.006, cv=10, \n",
    "                        copy_X=True, verbose=0, n_jobs=-1, positive=False, random_state=0)\n",
    "\n",
    "# 训练模型              \n",
    "cv_model.fit(X_train, y_train)\n",
    "\n",
    "# 计算最佳迭代次数、alpha和ratio\n",
    "print('最佳 alpha: %.8f'%cv_model.alpha_)\n",
    "print('最佳 l1_ratio: %.3f'%cv_model.l1_ratio_)\n",
    "print('迭代次数 %d'%cv_model.n_iter_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:58:35.598117Z",
     "start_time": "2021-02-23T05:58:35.589316Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train r2 score:  0.899526504124001\n",
      "Test r2 score:  0.8509986476456004\n",
      "Train RMSE: 0.4792\n",
      "Test RMSE: 0.4345\n"
     ]
    }
   ],
   "source": [
    "# 输出结果\n",
    "y_train_pred = cv_model.predict(X_train)\n",
    "y_pred = cv_model.predict(X_test)\n",
    "print('Train r2 score: ', r2_score(y_train_pred, y_train))\n",
    "print('Test r2 score: ', r2_score(y_test, y_pred))\n",
    "train_mse = mean_squared_error(y_train_pred, y_train)\n",
    "test_mse = mean_squared_error(y_pred, y_test)\n",
    "train_rmse = np.sqrt(train_mse)\n",
    "test_rmse = np.sqrt(test_mse)\n",
    "print('Train RMSE: %.4f' % train_rmse)\n",
    "print('Test RMSE: %.4f' % test_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:59:50.425915Z",
     "start_time": "2021-02-23T05:59:50.275688Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datetime\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "plt.rcParams.update({'figure.figsize': (15, 5)})\n",
    "\n",
    "plt.plot(date, y)\n",
    "plt.plot(date[N:], y_pred)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, this apparent success story eventually turned into an embarrassment. \n",
    "\n",
    "1. Google Flu Trends with all its data, machine learning, and powerful computing did not dramatically outperform a simple and easier-to-understand heuristic. This suggests that when evaluating any forecast or nowcast, it is important to compare against a baseline.\n",
    "2. Its ability to predict the CDC flu data was prone to short-term failure and long-term decay because of drift and algorithmic confounding. \n",
    "\n",
    "These two caveats complicate future nowcasting efforts, but they do not doom them. In fact, by using more careful methods, Lazer et al. (2014) and Yang, Santillana, and Kou (2015) were able to avoid these two problems."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "\n",
    "- Goel, Sharad, Jake M. Hofman, Sébastien Lahaie, David M. Pennock, and Duncan J. Watts. 2010. “Predicting Consumer Behavior with Web Search.” Proceedings of the National Academy of Sciences of the USA 107 (41):17486–90. https://doi.org/10.1073/pnas.1005962107.\n",
    "\n",
    "- Yang, Shihao, Mauricio Santillana, and S. C. Kou. 2015. “Accurate Estimation of Influenza Epidemics Using Google Search Data via ARGO.” Proceedings of the National Academy of Sciences of the USA 112 (47):14473–8. https://doi.org/10.1073/pnas.1515373112.\n",
    "\n",
    "- Lazer, David, Ryan Kennedy, Gary King, and Alessandro Vespignani. 2014. “The Parable of Google Flu: Traps in Big Data Analysis.” Science 343 (6176):1203–5. https://doi.org/10.1126/science.1248506."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning by Doing\n",
    "\n",
    "https://github.com/JEstebanMejiaV/The.Analytics.Edge/blob/352d59a27d2c376f268b1dbdf838e9ee77989d36/Unit%202%20-%20Linear%20Regression/Detecting%20Flu%20Epidemics%20via%20Search%20Engine%20Query%20Data.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:18:59.014440Z",
     "start_time": "2021-02-23T05:18:59.001711Z"
    }
   },
   "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>Week</th>\n",
       "      <th>ILI</th>\n",
       "      <th>Queries</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2004-01-04 - 2004-01-10</td>\n",
       "      <td>2.418331</td>\n",
       "      <td>0.237716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2004-01-11 - 2004-01-17</td>\n",
       "      <td>1.809056</td>\n",
       "      <td>0.220452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004-01-18 - 2004-01-24</td>\n",
       "      <td>1.712024</td>\n",
       "      <td>0.225764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004-01-25 - 2004-01-31</td>\n",
       "      <td>1.542495</td>\n",
       "      <td>0.237716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004-02-01 - 2004-02-07</td>\n",
       "      <td>1.437868</td>\n",
       "      <td>0.224436</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Week       ILI   Queries\n",
       "0  2004-01-04 - 2004-01-10  2.418331  0.237716\n",
       "1  2004-01-11 - 2004-01-17  1.809056  0.220452\n",
       "2  2004-01-18 - 2004-01-24  1.712024  0.225764\n",
       "3  2004-01-25 - 2004-01-31  1.542495  0.237716\n",
       "4  2004-02-01 - 2004-02-07  1.437868  0.224436"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat = pd.read_csv('FluTrain.csv')\n",
    "dat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T05:19:12.565639Z",
     "start_time": "2021-02-23T05:19:12.559924Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2004-01-04 - 2004-01-10\n",
       "1      2004-01-11 - 2004-01-17\n",
       "2      2004-01-18 - 2004-01-24\n",
       "3      2004-01-25 - 2004-01-31\n",
       "4      2004-02-01 - 2004-02-07\n",
       "                ...           \n",
       "412    2011-11-27 - 2011-12-03\n",
       "413    2011-12-04 - 2011-12-10\n",
       "414    2011-12-11 - 2011-12-17\n",
       "415    2011-12-18 - 2011-12-24\n",
       "416    2011-12-25 - 2011-12-31\n",
       "Name: Week, Length: 417, dtype: object"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat['Week']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:01:23.913604Z",
     "start_time": "2021-02-23T06:01:23.904355Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "for i in range(1, 8):\n",
    "    dat[\"lag_{}\".format(i)] = dat['ILI'].shift(i)\n",
    "print(\"done\")\n",
    "dat=dat.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:06:52.620092Z",
     "start_time": "2021-02-23T06:06:52.614910Z"
    }
   },
   "outputs": [],
   "source": [
    "y = dat['ILI']\n",
    "week = dat['Week']\n",
    "week = [i[:10] for i in week.tolist()]\n",
    "week = pd.to_datetime(week)\n",
    "X = dat.drop(['ILI', 'Week'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:08:28.271674Z",
     "start_time": "2021-02-23T06:08:28.266786Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2.418331\n",
       "1      1.809056\n",
       "2      1.712024\n",
       "3      1.542495\n",
       "4      1.437868\n",
       "         ...   \n",
       "412    1.465723\n",
       "413    1.518106\n",
       "414    1.663954\n",
       "415    1.852736\n",
       "416    2.124130\n",
       "Name: ILI, Length: 417, dtype: float64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:03:15.386111Z",
     "start_time": "2021-02-23T06:03:15.372378Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Queries</th>\n",
       "      <th>lag_1</th>\n",
       "      <th>lag_2</th>\n",
       "      <th>lag_3</th>\n",
       "      <th>lag_4</th>\n",
       "      <th>lag_5</th>\n",
       "      <th>lag_6</th>\n",
       "      <th>lag_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>1.252586</td>\n",
       "      <td>1.236957</td>\n",
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       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>0.464807</td>\n",
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       "      <td>1.655415</td>\n",
       "      <td>1.462212</td>\n",
       "      <td>1.440892</td>\n",
       "      <td>1.452843</td>\n",
       "      <td>1.305461</td>\n",
       "      <td>1.252586</td>\n",
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       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>0.479416</td>\n",
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       "      <td>1.655415</td>\n",
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       "      <td>1.452843</td>\n",
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       "      <th>415</th>\n",
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       "      <td>1.663954</td>\n",
       "      <td>1.518106</td>\n",
       "      <td>1.465723</td>\n",
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       "      <td>1.440892</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>417 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Queries     lag_1     lag_2     lag_3     lag_4     lag_5     lag_6  \\\n",
       "0    0.237716  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1    0.220452  2.418331  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2    0.225764  1.809056  2.418331  0.000000  0.000000  0.000000  0.000000   \n",
       "3    0.237716  1.712024  1.809056  2.418331  0.000000  0.000000  0.000000   \n",
       "4    0.224436  1.542495  1.712024  1.809056  2.418331  0.000000  0.000000   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "412  0.478088  1.655415  1.462212  1.440892  1.452843  1.305461  1.252586   \n",
       "413  0.464807  1.465723  1.655415  1.462212  1.440892  1.452843  1.305461   \n",
       "414  0.479416  1.518106  1.465723  1.655415  1.462212  1.440892  1.452843   \n",
       "415  0.537849  1.663954  1.518106  1.465723  1.655415  1.462212  1.440892   \n",
       "416  0.618858  1.852736  1.663954  1.518106  1.465723  1.655415  1.462212   \n",
       "\n",
       "        lag_7  \n",
       "0    0.000000  \n",
       "1    0.000000  \n",
       "2    0.000000  \n",
       "3    0.000000  \n",
       "4    0.000000  \n",
       "..        ...  \n",
       "412  1.236957  \n",
       "413  1.252586  \n",
       "414  1.305461  \n",
       "415  1.452843  \n",
       "416  1.440892  \n",
       "\n",
       "[417 rows x 8 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:03:25.130871Z",
     "start_time": "2021-02-23T06:03:24.804493Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最佳 alpha: 0.00034012\n",
      "最佳 l1_ratio: 0.500\n",
      "迭代次数 15\n"
     ]
    }
   ],
   "source": [
    "N = 100\n",
    "X_train = X.iloc[:N,]\n",
    "X_test = X.iloc[N:,]\n",
    "y_train = y[:N]\n",
    "y_test = y[N:]\n",
    "\n",
    "# 利用弹性网络\n",
    "from sklearn.model_selection import cross_val_score\n",
    "cv_model = ElasticNetCV(l1_ratio=0.5, eps=1e-3, n_alphas=200, fit_intercept=True, \n",
    "                        normalize=True, precompute='auto', max_iter=200, tol=0.006, cv=10, \n",
    "                        copy_X=True, verbose=0, n_jobs=-1, positive=False, random_state=0)\n",
    "\n",
    "# 训练模型              \n",
    "cv_model.fit(X_train, y_train)\n",
    "\n",
    "# 计算最佳迭代次数、alpha和ratio\n",
    "print('最佳 alpha: %.8f'%cv_model.alpha_)\n",
    "print('最佳 l1_ratio: %.3f'%cv_model.l1_ratio_)\n",
    "print('迭代次数 %d'%cv_model.n_iter_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:03:28.444991Z",
     "start_time": "2021-02-23T06:03:28.436914Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train r2 score:  0.8704190473192481\n",
      "Test r2 score:  0.9216895094890315\n",
      "Train RMSE: 0.2868\n",
      "Test RMSE: 0.3293\n"
     ]
    }
   ],
   "source": [
    "# 输出结果\n",
    "y_train_pred = cv_model.predict(X_train)\n",
    "y_pred = cv_model.predict(X_test)\n",
    "print('Train r2 score: ', r2_score(y_train_pred, y_train))\n",
    "print('Test r2 score: ', r2_score(y_test, y_pred))\n",
    "train_mse = mean_squared_error(y_train_pred, y_train)\n",
    "test_mse = mean_squared_error(y_pred, y_test)\n",
    "train_rmse = np.sqrt(train_mse)\n",
    "test_rmse = np.sqrt(test_mse)\n",
    "print('Train RMSE: %.4f' % train_rmse)\n",
    "print('Test RMSE: %.4f' % test_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:07:03.727620Z",
     "start_time": "2021-02-23T06:07:03.565619Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(week, y)\n",
    "plt.plot(week[N:], y_pred)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.6"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}