{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "202599bf",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Crossvalidation and Regularization\n",
    "\n",
    "Fall 2022: Peter Ralph\n",
    "\n",
    "https://uodsci.github.io/dsci345"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "733bfdf0",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.rcParams['figure.figsize'] = (15, 8)\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from dsci345 import pretty\n",
    "\n",
    "rng = np.random.default_rng(123)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88ec5c59",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "$$\\renewcommand{\\P}{\\mathbb{P}} \\newcommand{\\E}{\\mathbb{E}} \\newcommand{\\var}{\\text{var}} \\newcommand{\\sd}{\\text{sd}} \\newcommand{\\cov}{\\text{cov}} \\newcommand{\\cor}{\\text{cor}}$$\n",
    "This is here so we can use `\\P` and `\\E` and `\\var` and `\\cov` and `\\cor` and `\\sd` in LaTeX below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e6bfa48",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# A cautionary tale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "38512175",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import datetime\n",
    "full_covid = pd.read_csv(\"data/United_States_COVID-19_Cases_and_Deaths_by_State_over_Time.csv\")[[\"state\", \"tot_cases\", \"submission_date\"]]\n",
    "covid = full_covid.rename(columns={'submission_date': 'date', \"tot_cases\": \"cases\"})\n",
    "covid.date = pd.to_datetime(covid.date)\n",
    "covid = covid[covid.date < np.datetime64(\"2021-01-01\")]\n",
    "covid = covid[covid.date.dt.dayofweek == 0]\n",
    "states = ['AK', 'AL', 'AR', 'AZ', 'CA', 'CO', 'CT', 'DC', 'DE', 'FL',\n",
    "       'GA', 'GU', 'HI', 'IA', 'ID', 'IL', 'IN', 'KS', 'KY', 'LA',\n",
    "       'MA', 'MD', 'ME', 'MI', 'MN', 'MO', 'MS', 'MT', 'NC', 'ND',\n",
    "       'NE', 'NH', 'NJ', 'NM', 'NV', 'NY', 'OH', 'OK', 'OR', 'PA',\n",
    "       'PR', 'RI', 'SC', 'SD', 'TN', 'TX', 'UT', 'VA',\n",
    "       'VT', 'WA', 'WI', 'WV', 'WY']\n",
    "covid = covid[np.isin(covid.state, states)]\n",
    "covid = covid.pivot(index=\"date\", columns=\"state\")\n",
    "covid = covid.diff()[1:]\n",
    "covid.columns = [x[1] for x in covid.columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6408390e",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    " Here are weekly COVID case counts across the US states plus DC, PR, and GU for 2020,\n",
    " a 48 x 53 matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "81101624",
   "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>AK</th>\n",
       "      <th>AL</th>\n",
       "      <th>AR</th>\n",
       "      <th>AZ</th>\n",
       "      <th>CA</th>\n",
       "      <th>CO</th>\n",
       "      <th>CT</th>\n",
       "      <th>DC</th>\n",
       "      <th>DE</th>\n",
       "      <th>FL</th>\n",
       "      <th>...</th>\n",
       "      <th>SD</th>\n",
       "      <th>TN</th>\n",
       "      <th>TX</th>\n",
       "      <th>UT</th>\n",
       "      <th>VA</th>\n",
       "      <th>VT</th>\n",
       "      <th>WA</th>\n",
       "      <th>WI</th>\n",
       "      <th>WV</th>\n",
       "      <th>WY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-02-03</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-10</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-17</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-24</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-02</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-09</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-16</th>\n",
       "      <td>1.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>656.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-23</th>\n",
       "      <td>37.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>1341.0</td>\n",
       "      <td>582.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>...</td>\n",
       "      <td>18.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1212.0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-30</th>\n",
       "      <td>80.0</td>\n",
       "      <td>770.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>904.0</td>\n",
       "      <td>4030.0</td>\n",
       "      <td>1890.0</td>\n",
       "      <td>2156.0</td>\n",
       "      <td>358.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>4408.0</td>\n",
       "      <td>...</td>\n",
       "      <td>73.0</td>\n",
       "      <td>1037.0</td>\n",
       "      <td>2587.0</td>\n",
       "      <td>526.0</td>\n",
       "      <td>766.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>2553.0</td>\n",
       "      <td>805.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-06</th>\n",
       "      <td>73.0</td>\n",
       "      <td>1076.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>1299.0</td>\n",
       "      <td>8573.0</td>\n",
       "      <td>2572.0</td>\n",
       "      <td>4335.0</td>\n",
       "      <td>602.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>7585.0</td>\n",
       "      <td>...</td>\n",
       "      <td>187.0</td>\n",
       "      <td>2117.0</td>\n",
       "      <td>4399.0</td>\n",
       "      <td>910.0</td>\n",
       "      <td>1858.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>3222.0</td>\n",
       "      <td>1219.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-13</th>\n",
       "      <td>86.0</td>\n",
       "      <td>1789.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>1246.0</td>\n",
       "      <td>8012.0</td>\n",
       "      <td>2719.0</td>\n",
       "      <td>6475.0</td>\n",
       "      <td>858.0</td>\n",
       "      <td>921.0</td>\n",
       "      <td>6979.0</td>\n",
       "      <td>...</td>\n",
       "      <td>580.0</td>\n",
       "      <td>1730.0</td>\n",
       "      <td>6633.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>2869.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>2303.0</td>\n",
       "      <td>988.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>161.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-20</th>\n",
       "      <td>44.0</td>\n",
       "      <td>1288.0</td>\n",
       "      <td>554.0</td>\n",
       "      <td>1362.0</td>\n",
       "      <td>8630.0</td>\n",
       "      <td>2390.0</td>\n",
       "      <td>6434.0</td>\n",
       "      <td>972.0</td>\n",
       "      <td>1097.0</td>\n",
       "      <td>5966.0</td>\n",
       "      <td>...</td>\n",
       "      <td>817.0</td>\n",
       "      <td>1721.0</td>\n",
       "      <td>5552.0</td>\n",
       "      <td>867.0</td>\n",
       "      <td>3243.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1899.0</td>\n",
       "      <td>1071.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-27</th>\n",
       "      <td>24.0</td>\n",
       "      <td>1457.0</td>\n",
       "      <td>1091.0</td>\n",
       "      <td>1652.0</td>\n",
       "      <td>12486.0</td>\n",
       "      <td>3779.0</td>\n",
       "      <td>6182.0</td>\n",
       "      <td>965.0</td>\n",
       "      <td>1448.0</td>\n",
       "      <td>5545.0</td>\n",
       "      <td>...</td>\n",
       "      <td>560.0</td>\n",
       "      <td>2556.0</td>\n",
       "      <td>5839.0</td>\n",
       "      <td>1048.0</td>\n",
       "      <td>4545.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1706.0</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-04</th>\n",
       "      <td>25.0</td>\n",
       "      <td>1581.0</td>\n",
       "      <td>414.0</td>\n",
       "      <td>2203.0</td>\n",
       "      <td>11473.0</td>\n",
       "      <td>3364.0</td>\n",
       "      <td>3976.0</td>\n",
       "      <td>1278.0</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>4489.0</td>\n",
       "      <td>...</td>\n",
       "      <td>423.0</td>\n",
       "      <td>3516.0</td>\n",
       "      <td>7035.0</td>\n",
       "      <td>1099.0</td>\n",
       "      <td>5957.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1855.0</td>\n",
       "      <td>2155.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-11</th>\n",
       "      <td>11.0</td>\n",
       "      <td>2053.0</td>\n",
       "      <td>574.0</td>\n",
       "      <td>2461.0</td>\n",
       "      <td>13002.0</td>\n",
       "      <td>2809.0</td>\n",
       "      <td>3792.0</td>\n",
       "      <td>1219.0</td>\n",
       "      <td>1498.0</td>\n",
       "      <td>3991.0</td>\n",
       "      <td>...</td>\n",
       "      <td>946.0</td>\n",
       "      <td>1836.0</td>\n",
       "      <td>7537.0</td>\n",
       "      <td>989.0</td>\n",
       "      <td>5578.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1681.0</td>\n",
       "      <td>2182.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-18</th>\n",
       "      <td>20.0</td>\n",
       "      <td>2175.0</td>\n",
       "      <td>770.0</td>\n",
       "      <td>2790.0</td>\n",
       "      <td>12491.0</td>\n",
       "      <td>2394.0</td>\n",
       "      <td>4351.0</td>\n",
       "      <td>881.0</td>\n",
       "      <td>1104.0</td>\n",
       "      <td>5238.0</td>\n",
       "      <td>...</td>\n",
       "      <td>413.0</td>\n",
       "      <td>2753.0</td>\n",
       "      <td>8824.0</td>\n",
       "      <td>1064.0</td>\n",
       "      <td>6070.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1435.0</td>\n",
       "      <td>2269.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-25</th>\n",
       "      <td>10.0</td>\n",
       "      <td>2870.0</td>\n",
       "      <td>1216.0</td>\n",
       "      <td>2391.0</td>\n",
       "      <td>14128.0</td>\n",
       "      <td>2207.0</td>\n",
       "      <td>2757.0</td>\n",
       "      <td>955.0</td>\n",
       "      <td>820.0</td>\n",
       "      <td>5146.0</td>\n",
       "      <td>...</td>\n",
       "      <td>559.0</td>\n",
       "      <td>2451.0</td>\n",
       "      <td>7278.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>6587.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1455.0</td>\n",
       "      <td>2897.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-01</th>\n",
       "      <td>59.0</td>\n",
       "      <td>3213.0</td>\n",
       "      <td>1414.0</td>\n",
       "      <td>3562.0</td>\n",
       "      <td>18448.0</td>\n",
       "      <td>2087.0</td>\n",
       "      <td>1867.0</td>\n",
       "      <td>632.0</td>\n",
       "      <td>638.0</td>\n",
       "      <td>5239.0</td>\n",
       "      <td>...</td>\n",
       "      <td>448.0</td>\n",
       "      <td>3051.0</td>\n",
       "      <td>8909.0</td>\n",
       "      <td>1517.0</td>\n",
       "      <td>7671.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1863.0</td>\n",
       "      <td>2959.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-08</th>\n",
       "      <td>97.0</td>\n",
       "      <td>2739.0</td>\n",
       "      <td>2297.0</td>\n",
       "      <td>7555.0</td>\n",
       "      <td>18313.0</td>\n",
       "      <td>1578.0</td>\n",
       "      <td>1352.0</td>\n",
       "      <td>532.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>8321.0</td>\n",
       "      <td>...</td>\n",
       "      <td>437.0</td>\n",
       "      <td>3228.0</td>\n",
       "      <td>10736.0</td>\n",
       "      <td>2328.0</td>\n",
       "      <td>5853.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>2495.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-15</th>\n",
       "      <td>101.0</td>\n",
       "      <td>5209.0</td>\n",
       "      <td>3177.0</td>\n",
       "      <td>9033.0</td>\n",
       "      <td>20133.0</td>\n",
       "      <td>1163.0</td>\n",
       "      <td>1143.0</td>\n",
       "      <td>410.0</td>\n",
       "      <td>388.0</td>\n",
       "      <td>13687.0</td>\n",
       "      <td>...</td>\n",
       "      <td>457.0</td>\n",
       "      <td>3793.0</td>\n",
       "      <td>13492.0</td>\n",
       "      <td>2397.0</td>\n",
       "      <td>3635.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2096.0</td>\n",
       "      <td>4442.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-22</th>\n",
       "      <td>99.0</td>\n",
       "      <td>4266.0</td>\n",
       "      <td>3166.0</td>\n",
       "      <td>17888.0</td>\n",
       "      <td>26602.0</td>\n",
       "      <td>1524.0</td>\n",
       "      <td>547.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>397.0</td>\n",
       "      <td>23612.0</td>\n",
       "      <td>...</td>\n",
       "      <td>398.0</td>\n",
       "      <td>4547.0</td>\n",
       "      <td>25773.0</td>\n",
       "      <td>3309.0</td>\n",
       "      <td>3579.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2725.0</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-29</th>\n",
       "      <td>141.0</td>\n",
       "      <td>6797.0</td>\n",
       "      <td>4174.0</td>\n",
       "      <td>19954.0</td>\n",
       "      <td>38496.0</td>\n",
       "      <td>1788.0</td>\n",
       "      <td>580.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>48535.0</td>\n",
       "      <td>...</td>\n",
       "      <td>390.0</td>\n",
       "      <td>5434.0</td>\n",
       "      <td>38130.0</td>\n",
       "      <td>4128.0</td>\n",
       "      <td>3724.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>3204.0</td>\n",
       "      <td>3195.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-06</th>\n",
       "      <td>260.0</td>\n",
       "      <td>7295.0</td>\n",
       "      <td>3996.0</td>\n",
       "      <td>26915.0</td>\n",
       "      <td>55134.0</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>614.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>851.0</td>\n",
       "      <td>59914.0</td>\n",
       "      <td>...</td>\n",
       "      <td>389.0</td>\n",
       "      <td>10877.0</td>\n",
       "      <td>47546.0</td>\n",
       "      <td>3741.0</td>\n",
       "      <td>3913.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4659.0</td>\n",
       "      <td>4197.0</td>\n",
       "      <td>572.0</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-13</th>\n",
       "      <td>369.0</td>\n",
       "      <td>11142.0</td>\n",
       "      <td>4686.0</td>\n",
       "      <td>22390.0</td>\n",
       "      <td>57478.0</td>\n",
       "      <td>2988.0</td>\n",
       "      <td>534.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>891.0</td>\n",
       "      <td>76760.0</td>\n",
       "      <td>...</td>\n",
       "      <td>419.0</td>\n",
       "      <td>11915.0</td>\n",
       "      <td>63756.0</td>\n",
       "      <td>4718.0</td>\n",
       "      <td>5540.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>4881.0</td>\n",
       "      <td>5152.0</td>\n",
       "      <td>871.0</td>\n",
       "      <td>228.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-20</th>\n",
       "      <td>417.0</td>\n",
       "      <td>13038.0</td>\n",
       "      <td>4988.0</td>\n",
       "      <td>21366.0</td>\n",
       "      <td>62376.0</td>\n",
       "      <td>3350.0</td>\n",
       "      <td>545.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>692.0</td>\n",
       "      <td>76510.0</td>\n",
       "      <td>...</td>\n",
       "      <td>419.0</td>\n",
       "      <td>14570.0</td>\n",
       "      <td>68121.0</td>\n",
       "      <td>4362.0</td>\n",
       "      <td>6733.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>5958.0</td>\n",
       "      <td>6293.0</td>\n",
       "      <td>829.0</td>\n",
       "      <td>284.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-27</th>\n",
       "      <td>672.0</td>\n",
       "      <td>11976.0</td>\n",
       "      <td>5520.0</td>\n",
       "      <td>18651.0</td>\n",
       "      <td>69012.0</td>\n",
       "      <td>4095.0</td>\n",
       "      <td>928.0</td>\n",
       "      <td>519.0</td>\n",
       "      <td>767.0</td>\n",
       "      <td>70503.0</td>\n",
       "      <td>...</td>\n",
       "      <td>501.0</td>\n",
       "      <td>16021.0</td>\n",
       "      <td>53489.0</td>\n",
       "      <td>3831.0</td>\n",
       "      <td>7697.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>5513.0</td>\n",
       "      <td>6606.0</td>\n",
       "      <td>912.0</td>\n",
       "      <td>333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-03</th>\n",
       "      <td>707.0</td>\n",
       "      <td>11206.0</td>\n",
       "      <td>5150.0</td>\n",
       "      <td>15677.0</td>\n",
       "      <td>54351.0</td>\n",
       "      <td>3680.0</td>\n",
       "      <td>1079.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>668.0</td>\n",
       "      <td>53915.0</td>\n",
       "      <td>...</td>\n",
       "      <td>576.0</td>\n",
       "      <td>14060.0</td>\n",
       "      <td>56091.0</td>\n",
       "      <td>3238.0</td>\n",
       "      <td>7034.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>5670.0</td>\n",
       "      <td>6120.0</td>\n",
       "      <td>919.0</td>\n",
       "      <td>328.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-10</th>\n",
       "      <td>455.0</td>\n",
       "      <td>9842.0</td>\n",
       "      <td>5431.0</td>\n",
       "      <td>8033.0</td>\n",
       "      <td>47010.0</td>\n",
       "      <td>2986.0</td>\n",
       "      <td>505.0</td>\n",
       "      <td>494.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>44277.0</td>\n",
       "      <td>...</td>\n",
       "      <td>643.0</td>\n",
       "      <td>13014.0</td>\n",
       "      <td>48803.0</td>\n",
       "      <td>2853.0</td>\n",
       "      <td>7643.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>4872.0</td>\n",
       "      <td>5955.0</td>\n",
       "      <td>781.0</td>\n",
       "      <td>194.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-17</th>\n",
       "      <td>527.0</td>\n",
       "      <td>6662.0</td>\n",
       "      <td>3049.0</td>\n",
       "      <td>6489.0</td>\n",
       "      <td>66120.0</td>\n",
       "      <td>2209.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>466.0</td>\n",
       "      <td>511.0</td>\n",
       "      <td>36154.0</td>\n",
       "      <td>...</td>\n",
       "      <td>697.0</td>\n",
       "      <td>11112.0</td>\n",
       "      <td>52133.0</td>\n",
       "      <td>2481.0</td>\n",
       "      <td>6672.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>4193.0</td>\n",
       "      <td>5359.0</td>\n",
       "      <td>878.0</td>\n",
       "      <td>289.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-24</th>\n",
       "      <td>517.0</td>\n",
       "      <td>6911.0</td>\n",
       "      <td>3817.0</td>\n",
       "      <td>4416.0</td>\n",
       "      <td>40584.0</td>\n",
       "      <td>2138.0</td>\n",
       "      <td>744.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>24946.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>10106.0</td>\n",
       "      <td>37434.0</td>\n",
       "      <td>2566.0</td>\n",
       "      <td>6209.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>3913.0</td>\n",
       "      <td>4904.0</td>\n",
       "      <td>680.0</td>\n",
       "      <td>272.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-31</th>\n",
       "      <td>458.0</td>\n",
       "      <td>10366.0</td>\n",
       "      <td>4330.0</td>\n",
       "      <td>3428.0</td>\n",
       "      <td>35470.0</td>\n",
       "      <td>2091.0</td>\n",
       "      <td>868.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>651.0</td>\n",
       "      <td>24465.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2084.0</td>\n",
       "      <td>10075.0</td>\n",
       "      <td>32585.0</td>\n",
       "      <td>2739.0</td>\n",
       "      <td>6964.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3344.0</td>\n",
       "      <td>4949.0</td>\n",
       "      <td>938.0</td>\n",
       "      <td>239.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-07</th>\n",
       "      <td>520.0</td>\n",
       "      <td>5695.0</td>\n",
       "      <td>5056.0</td>\n",
       "      <td>4136.0</td>\n",
       "      <td>31150.0</td>\n",
       "      <td>2159.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>323.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>18770.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1791.0</td>\n",
       "      <td>10229.0</td>\n",
       "      <td>27401.0</td>\n",
       "      <td>2951.0</td>\n",
       "      <td>6977.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>3163.0</td>\n",
       "      <td>6360.0</td>\n",
       "      <td>1325.0</td>\n",
       "      <td>190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-14</th>\n",
       "      <td>541.0</td>\n",
       "      <td>6336.0</td>\n",
       "      <td>4347.0</td>\n",
       "      <td>2768.0</td>\n",
       "      <td>22543.0</td>\n",
       "      <td>2404.0</td>\n",
       "      <td>1530.0</td>\n",
       "      <td>307.0</td>\n",
       "      <td>692.0</td>\n",
       "      <td>18268.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1501.0</td>\n",
       "      <td>8427.0</td>\n",
       "      <td>23034.0</td>\n",
       "      <td>3540.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2717.0</td>\n",
       "      <td>8569.0</td>\n",
       "      <td>1245.0</td>\n",
       "      <td>360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-21</th>\n",
       "      <td>580.0</td>\n",
       "      <td>6609.0</td>\n",
       "      <td>5737.0</td>\n",
       "      <td>5533.0</td>\n",
       "      <td>23916.0</td>\n",
       "      <td>3760.0</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>681.0</td>\n",
       "      <td>18630.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2068.0</td>\n",
       "      <td>10994.0</td>\n",
       "      <td>34983.0</td>\n",
       "      <td>5111.0</td>\n",
       "      <td>6567.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2907.0</td>\n",
       "      <td>13091.0</td>\n",
       "      <td>1351.0</td>\n",
       "      <td>552.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-28</th>\n",
       "      <td>733.0</td>\n",
       "      <td>6905.0</td>\n",
       "      <td>5685.0</td>\n",
       "      <td>3266.0</td>\n",
       "      <td>23569.0</td>\n",
       "      <td>4354.0</td>\n",
       "      <td>1123.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>732.0</td>\n",
       "      <td>16536.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2869.0</td>\n",
       "      <td>9406.0</td>\n",
       "      <td>40835.0</td>\n",
       "      <td>7048.0</td>\n",
       "      <td>5455.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3947.0</td>\n",
       "      <td>15629.0</td>\n",
       "      <td>1341.0</td>\n",
       "      <td>810.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-05</th>\n",
       "      <td>982.0</td>\n",
       "      <td>7010.0</td>\n",
       "      <td>5381.0</td>\n",
       "      <td>3567.0</td>\n",
       "      <td>21521.0</td>\n",
       "      <td>4319.0</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>958.0</td>\n",
       "      <td>15795.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2860.0</td>\n",
       "      <td>8763.0</td>\n",
       "      <td>30081.0</td>\n",
       "      <td>7281.0</td>\n",
       "      <td>5964.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>3829.0</td>\n",
       "      <td>17438.0</td>\n",
       "      <td>1230.0</td>\n",
       "      <td>875.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-12</th>\n",
       "      <td>1258.0</td>\n",
       "      <td>6911.0</td>\n",
       "      <td>6057.0</td>\n",
       "      <td>4987.0</td>\n",
       "      <td>23244.0</td>\n",
       "      <td>5803.0</td>\n",
       "      <td>2257.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>948.0</td>\n",
       "      <td>18209.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4327.0</td>\n",
       "      <td>14216.0</td>\n",
       "      <td>25823.0</td>\n",
       "      <td>8109.0</td>\n",
       "      <td>7013.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>4271.0</td>\n",
       "      <td>18676.0</td>\n",
       "      <td>1539.0</td>\n",
       "      <td>1173.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-19</th>\n",
       "      <td>1322.0</td>\n",
       "      <td>7452.0</td>\n",
       "      <td>6110.0</td>\n",
       "      <td>5854.0</td>\n",
       "      <td>20763.0</td>\n",
       "      <td>8035.0</td>\n",
       "      <td>2644.0</td>\n",
       "      <td>373.0</td>\n",
       "      <td>909.0</td>\n",
       "      <td>21336.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4911.0</td>\n",
       "      <td>13443.0</td>\n",
       "      <td>33401.0</td>\n",
       "      <td>8730.0</td>\n",
       "      <td>7258.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>4557.0</td>\n",
       "      <td>22811.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>1509.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-26</th>\n",
       "      <td>2166.0</td>\n",
       "      <td>11592.0</td>\n",
       "      <td>7130.0</td>\n",
       "      <td>7074.0</td>\n",
       "      <td>30219.0</td>\n",
       "      <td>11308.0</td>\n",
       "      <td>4078.0</td>\n",
       "      <td>417.0</td>\n",
       "      <td>944.0</td>\n",
       "      <td>25682.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5905.0</td>\n",
       "      <td>18277.0</td>\n",
       "      <td>38548.0</td>\n",
       "      <td>10521.0</td>\n",
       "      <td>7447.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>5089.0</td>\n",
       "      <td>28786.0</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>2166.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-02</th>\n",
       "      <td>2716.0</td>\n",
       "      <td>9513.0</td>\n",
       "      <td>6914.0</td>\n",
       "      <td>9211.0</td>\n",
       "      <td>29618.0</td>\n",
       "      <td>15715.0</td>\n",
       "      <td>5759.0</td>\n",
       "      <td>626.0</td>\n",
       "      <td>1127.0</td>\n",
       "      <td>29906.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8109.0</td>\n",
       "      <td>13939.0</td>\n",
       "      <td>110710.0</td>\n",
       "      <td>11623.0</td>\n",
       "      <td>9143.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>6119.0</td>\n",
       "      <td>33000.0</td>\n",
       "      <td>3012.0</td>\n",
       "      <td>2690.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-09</th>\n",
       "      <td>3239.0</td>\n",
       "      <td>10609.0</td>\n",
       "      <td>9170.0</td>\n",
       "      <td>11581.0</td>\n",
       "      <td>41223.0</td>\n",
       "      <td>25028.0</td>\n",
       "      <td>7605.0</td>\n",
       "      <td>649.0</td>\n",
       "      <td>1552.0</td>\n",
       "      <td>34832.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8461.0</td>\n",
       "      <td>21972.0</td>\n",
       "      <td>61179.0</td>\n",
       "      <td>17162.0</td>\n",
       "      <td>10059.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>9421.0</td>\n",
       "      <td>41452.0</td>\n",
       "      <td>3570.0</td>\n",
       "      <td>3843.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-16</th>\n",
       "      <td>4071.0</td>\n",
       "      <td>14028.0</td>\n",
       "      <td>11537.0</td>\n",
       "      <td>17234.0</td>\n",
       "      <td>57384.0</td>\n",
       "      <td>34597.0</td>\n",
       "      <td>11821.0</td>\n",
       "      <td>977.0</td>\n",
       "      <td>2273.0</td>\n",
       "      <td>44342.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9967.0</td>\n",
       "      <td>28019.0</td>\n",
       "      <td>77266.0</td>\n",
       "      <td>20911.0</td>\n",
       "      <td>11160.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>13727.0</td>\n",
       "      <td>48182.0</td>\n",
       "      <td>5655.0</td>\n",
       "      <td>5183.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-23</th>\n",
       "      <td>3932.0</td>\n",
       "      <td>15812.0</td>\n",
       "      <td>11842.0</td>\n",
       "      <td>25433.0</td>\n",
       "      <td>81135.0</td>\n",
       "      <td>37795.0</td>\n",
       "      <td>13456.0</td>\n",
       "      <td>1226.0</td>\n",
       "      <td>2999.0</td>\n",
       "      <td>54578.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7570.0</td>\n",
       "      <td>28054.0</td>\n",
       "      <td>85027.0</td>\n",
       "      <td>23641.0</td>\n",
       "      <td>16401.0</td>\n",
       "      <td>677.0</td>\n",
       "      <td>15029.0</td>\n",
       "      <td>45131.0</td>\n",
       "      <td>6654.0</td>\n",
       "      <td>6238.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-30</th>\n",
       "      <td>4443.0</td>\n",
       "      <td>15860.0</td>\n",
       "      <td>11169.0</td>\n",
       "      <td>24514.0</td>\n",
       "      <td>102598.0</td>\n",
       "      <td>32762.0</td>\n",
       "      <td>10555.0</td>\n",
       "      <td>1262.0</td>\n",
       "      <td>3426.0</td>\n",
       "      <td>53444.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6616.0</td>\n",
       "      <td>27143.0</td>\n",
       "      <td>77175.0</td>\n",
       "      <td>16286.0</td>\n",
       "      <td>16797.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>18941.0</td>\n",
       "      <td>32037.0</td>\n",
       "      <td>6728.0</td>\n",
       "      <td>3874.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-07</th>\n",
       "      <td>4903.0</td>\n",
       "      <td>23381.0</td>\n",
       "      <td>14683.0</td>\n",
       "      <td>39047.0</td>\n",
       "      <td>153467.0</td>\n",
       "      <td>34561.0</td>\n",
       "      <td>18549.0</td>\n",
       "      <td>1767.0</td>\n",
       "      <td>5055.0</td>\n",
       "      <td>64630.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6036.0</td>\n",
       "      <td>32250.0</td>\n",
       "      <td>106372.0</td>\n",
       "      <td>21932.0</td>\n",
       "      <td>21035.0</td>\n",
       "      <td>752.0</td>\n",
       "      <td>17031.0</td>\n",
       "      <td>31659.0</td>\n",
       "      <td>8286.0</td>\n",
       "      <td>3680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-14</th>\n",
       "      <td>3908.0</td>\n",
       "      <td>24904.0</td>\n",
       "      <td>15015.0</td>\n",
       "      <td>54426.0</td>\n",
       "      <td>218609.0</td>\n",
       "      <td>26619.0</td>\n",
       "      <td>18148.0</td>\n",
       "      <td>1719.0</td>\n",
       "      <td>5818.0</td>\n",
       "      <td>69233.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4854.0</td>\n",
       "      <td>55834.0</td>\n",
       "      <td>97337.0</td>\n",
       "      <td>18234.0</td>\n",
       "      <td>26279.0</td>\n",
       "      <td>713.0</td>\n",
       "      <td>23549.0</td>\n",
       "      <td>27896.0</td>\n",
       "      <td>8266.0</td>\n",
       "      <td>2790.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-21</th>\n",
       "      <td>2383.0</td>\n",
       "      <td>27509.0</td>\n",
       "      <td>16050.0</td>\n",
       "      <td>41118.0</td>\n",
       "      <td>307304.0</td>\n",
       "      <td>18576.0</td>\n",
       "      <td>13385.0</td>\n",
       "      <td>1702.0</td>\n",
       "      <td>4735.0</td>\n",
       "      <td>77384.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3720.0</td>\n",
       "      <td>64380.0</td>\n",
       "      <td>109807.0</td>\n",
       "      <td>16911.0</td>\n",
       "      <td>25741.0</td>\n",
       "      <td>648.0</td>\n",
       "      <td>15917.0</td>\n",
       "      <td>23068.0</td>\n",
       "      <td>8943.0</td>\n",
       "      <td>2343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-28</th>\n",
       "      <td>1839.0</td>\n",
       "      <td>21699.0</td>\n",
       "      <td>13421.0</td>\n",
       "      <td>43099.0</td>\n",
       "      <td>263628.0</td>\n",
       "      <td>14576.0</td>\n",
       "      <td>13823.0</td>\n",
       "      <td>1602.0</td>\n",
       "      <td>4504.0</td>\n",
       "      <td>67592.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2583.0</td>\n",
       "      <td>40264.0</td>\n",
       "      <td>91478.0</td>\n",
       "      <td>13807.0</td>\n",
       "      <td>25285.0</td>\n",
       "      <td>544.0</td>\n",
       "      <td>12829.0</td>\n",
       "      <td>15784.0</td>\n",
       "      <td>8099.0</td>\n",
       "      <td>1586.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                AK       AL       AR       AZ        CA       CO       CT  \\\n",
       "date                                                                        \n",
       "2020-02-03     0.0      0.0      0.0      0.0       6.0      0.0      0.0   \n",
       "2020-02-10     0.0      0.0      0.0      0.0       0.0      0.0      0.0   \n",
       "2020-02-17     0.0      0.0      0.0      0.0       2.0      0.0      0.0   \n",
       "2020-02-24     0.0      0.0      0.0      0.0      17.0      0.0      0.0   \n",
       "2020-03-02     0.0      0.0      0.0      0.0      14.0      1.0      0.0   \n",
       "2020-03-09     0.0      0.0      0.0      5.0      94.0     11.0      1.0   \n",
       "2020-03-16     1.0     36.0     17.0     12.0     259.0    135.0     40.0   \n",
       "2020-03-23    37.0    188.0    180.0    235.0    1341.0    582.0    374.0   \n",
       "2020-03-30    80.0    770.0    307.0    904.0    4030.0   1890.0   2156.0   \n",
       "2020-04-06    73.0   1076.0    411.0   1299.0    8573.0   2572.0   4335.0   \n",
       "2020-04-13    86.0   1789.0    495.0   1246.0    8012.0   2719.0   6475.0   \n",
       "2020-04-20    44.0   1288.0    554.0   1362.0    8630.0   2390.0   6434.0   \n",
       "2020-04-27    24.0   1457.0   1091.0   1652.0   12486.0   3779.0   6182.0   \n",
       "2020-05-04    25.0   1581.0    414.0   2203.0   11473.0   3364.0   3976.0   \n",
       "2020-05-11    11.0   2053.0    574.0   2461.0   13002.0   2809.0   3792.0   \n",
       "2020-05-18    20.0   2175.0    770.0   2790.0   12491.0   2394.0   4351.0   \n",
       "2020-05-25    10.0   2870.0   1216.0   2391.0   14128.0   2207.0   2757.0   \n",
       "2020-06-01    59.0   3213.0   1414.0   3562.0   18448.0   2087.0   1867.0   \n",
       "2020-06-08    97.0   2739.0   2297.0   7555.0   18313.0   1578.0   1352.0   \n",
       "2020-06-15   101.0   5209.0   3177.0   9033.0   20133.0   1163.0   1143.0   \n",
       "2020-06-22    99.0   4266.0   3166.0  17888.0   26602.0   1524.0    547.0   \n",
       "2020-06-29   141.0   6797.0   4174.0  19954.0   38496.0   1788.0    580.0   \n",
       "2020-07-06   260.0   7295.0   3996.0  26915.0   55134.0   1982.0    614.0   \n",
       "2020-07-13   369.0  11142.0   4686.0  22390.0   57478.0   2988.0    534.0   \n",
       "2020-07-20   417.0  13038.0   4988.0  21366.0   62376.0   3350.0    545.0   \n",
       "2020-07-27   672.0  11976.0   5520.0  18651.0   69012.0   4095.0    928.0   \n",
       "2020-08-03   707.0  11206.0   5150.0  15677.0   54351.0   3680.0   1079.0   \n",
       "2020-08-10   455.0   9842.0   5431.0   8033.0   47010.0   2986.0    505.0   \n",
       "2020-08-17   527.0   6662.0   3049.0   6489.0   66120.0   2209.0    700.0   \n",
       "2020-08-24   517.0   6911.0   3817.0   4416.0   40584.0   2138.0    744.0   \n",
       "2020-08-31   458.0  10366.0   4330.0   3428.0   35470.0   2091.0    868.0   \n",
       "2020-09-07   520.0   5695.0   5056.0   4136.0   31150.0   2159.0    486.0   \n",
       "2020-09-14   541.0   6336.0   4347.0   2768.0   22543.0   2404.0   1530.0   \n",
       "2020-09-21   580.0   6609.0   5737.0   5533.0   23916.0   3760.0   1129.0   \n",
       "2020-09-28   733.0   6905.0   5685.0   3266.0   23569.0   4354.0   1123.0   \n",
       "2020-10-05   982.0   7010.0   5381.0   3567.0   21521.0   4319.0   1973.0   \n",
       "2020-10-12  1258.0   6911.0   6057.0   4987.0   23244.0   5803.0   2257.0   \n",
       "2020-10-19  1322.0   7452.0   6110.0   5854.0   20763.0   8035.0   2644.0   \n",
       "2020-10-26  2166.0  11592.0   7130.0   7074.0   30219.0  11308.0   4078.0   \n",
       "2020-11-02  2716.0   9513.0   6914.0   9211.0   29618.0  15715.0   5759.0   \n",
       "2020-11-09  3239.0  10609.0   9170.0  11581.0   41223.0  25028.0   7605.0   \n",
       "2020-11-16  4071.0  14028.0  11537.0  17234.0   57384.0  34597.0  11821.0   \n",
       "2020-11-23  3932.0  15812.0  11842.0  25433.0   81135.0  37795.0  13456.0   \n",
       "2020-11-30  4443.0  15860.0  11169.0  24514.0  102598.0  32762.0  10555.0   \n",
       "2020-12-07  4903.0  23381.0  14683.0  39047.0  153467.0  34561.0  18549.0   \n",
       "2020-12-14  3908.0  24904.0  15015.0  54426.0  218609.0  26619.0  18148.0   \n",
       "2020-12-21  2383.0  27509.0  16050.0  41118.0  307304.0  18576.0  13385.0   \n",
       "2020-12-28  1839.0  21699.0  13421.0  43099.0  263628.0  14576.0  13823.0   \n",
       "\n",
       "                DC      DE       FL  ...      SD       TN        TX       UT  \\\n",
       "date                                 ...                                       \n",
       "2020-02-03     0.0     1.0      0.0  ...     0.0      0.0       0.0      0.0   \n",
       "2020-02-10     0.0     2.0      0.0  ...     0.0      0.0       0.0      0.0   \n",
       "2020-02-17     0.0     2.0      0.0  ...     0.0      0.0       0.0      0.0   \n",
       "2020-02-24     0.0     2.0      0.0  ...     0.0      0.0       0.0      0.0   \n",
       "2020-03-02     0.0     0.0      2.0  ...     0.0      0.0       0.0      0.0   \n",
       "2020-03-09     2.0     0.0     16.0  ...     0.0      0.0      12.0      1.0   \n",
       "2020-03-16    20.0     7.0    138.0  ...    10.0     10.0      44.0     28.0   \n",
       "2020-03-23   115.0    58.0   1065.0  ...    18.0    495.0     231.0    251.0   \n",
       "2020-03-30   358.0   245.0   4408.0  ...    73.0   1037.0    2587.0    526.0   \n",
       "2020-04-06   602.0   475.0   7585.0  ...   187.0   2117.0    4399.0    910.0   \n",
       "2020-04-13   858.0   921.0   6979.0  ...   580.0   1730.0    6633.0    676.0   \n",
       "2020-04-20   972.0  1097.0   5966.0  ...   817.0   1721.0    5552.0    867.0   \n",
       "2020-04-27   965.0  1448.0   5545.0  ...   560.0   2556.0    5839.0   1048.0   \n",
       "2020-05-04  1278.0  1310.0   4489.0  ...   423.0   3516.0    7035.0   1099.0   \n",
       "2020-05-11  1219.0  1498.0   3991.0  ...   946.0   1836.0    7537.0    989.0   \n",
       "2020-05-18   881.0  1104.0   5238.0  ...   413.0   2753.0    8824.0   1064.0   \n",
       "2020-05-25   955.0   820.0   5146.0  ...   559.0   2451.0    7278.0   1125.0   \n",
       "2020-06-01   632.0   638.0   5239.0  ...   448.0   3051.0    8909.0   1517.0   \n",
       "2020-06-08   532.0   396.0   8321.0  ...   437.0   3228.0   10736.0   2328.0   \n",
       "2020-06-15   410.0   388.0  13687.0  ...   457.0   3793.0   13492.0   2397.0   \n",
       "2020-06-22   259.0   397.0  23612.0  ...   398.0   4547.0   25773.0   3309.0   \n",
       "2020-06-29   234.0   666.0  48535.0  ...   390.0   5434.0   38130.0   4128.0   \n",
       "2020-07-06   223.0   851.0  59914.0  ...   389.0  10877.0   47546.0   3741.0   \n",
       "2020-07-13   391.0   891.0  76760.0  ...   419.0  11915.0   63756.0   4718.0   \n",
       "2020-07-20   433.0   692.0  76510.0  ...   419.0  14570.0   68121.0   4362.0   \n",
       "2020-07-27   519.0   767.0  70503.0  ...   501.0  16021.0   53489.0   3831.0   \n",
       "2020-08-03   455.0   668.0  53915.0  ...   576.0  14060.0   56091.0   3238.0   \n",
       "2020-08-10   494.0   474.0  44277.0  ...   643.0  13014.0   48803.0   2853.0   \n",
       "2020-08-17   466.0   511.0  36154.0  ...   697.0  11112.0   52133.0   2481.0   \n",
       "2020-08-24   366.0   471.0  24946.0  ...  1065.0  10106.0   37434.0   2566.0   \n",
       "2020-08-31   353.0   651.0  24465.0  ...  2084.0  10075.0   32585.0   2739.0   \n",
       "2020-09-07   323.0   697.0  18770.0  ...  1791.0  10229.0   27401.0   2951.0   \n",
       "2020-09-14   307.0   692.0  18268.0  ...  1501.0   8427.0   23034.0   3540.0   \n",
       "2020-09-21   356.0   681.0  18630.0  ...  2068.0  10994.0   34983.0   5111.0   \n",
       "2020-09-28   286.0   732.0  16536.0  ...  2869.0   9406.0   40835.0   7048.0   \n",
       "2020-10-05   283.0   958.0  15795.0  ...  2860.0   8763.0   30081.0   7281.0   \n",
       "2020-10-12   475.0   948.0  18209.0  ...  4327.0  14216.0   25823.0   8109.0   \n",
       "2020-10-19   373.0   909.0  21336.0  ...  4911.0  13443.0   33401.0   8730.0   \n",
       "2020-10-26   417.0   944.0  25682.0  ...  5905.0  18277.0   38548.0  10521.0   \n",
       "2020-11-02   626.0  1127.0  29906.0  ...  8109.0  13939.0  110710.0  11623.0   \n",
       "2020-11-09   649.0  1552.0  34832.0  ...  8461.0  21972.0   61179.0  17162.0   \n",
       "2020-11-16   977.0  2273.0  44342.0  ...  9967.0  28019.0   77266.0  20911.0   \n",
       "2020-11-23  1226.0  2999.0  54578.0  ...  7570.0  28054.0   85027.0  23641.0   \n",
       "2020-11-30  1262.0  3426.0  53444.0  ...  6616.0  27143.0   77175.0  16286.0   \n",
       "2020-12-07  1767.0  5055.0  64630.0  ...  6036.0  32250.0  106372.0  21932.0   \n",
       "2020-12-14  1719.0  5818.0  69233.0  ...  4854.0  55834.0   97337.0  18234.0   \n",
       "2020-12-21  1702.0  4735.0  77384.0  ...  3720.0  64380.0  109807.0  16911.0   \n",
       "2020-12-28  1602.0  4504.0  67592.0  ...  2583.0  40264.0   91478.0  13807.0   \n",
       "\n",
       "                 VA     VT       WA       WI      WV      WY  \n",
       "date                                                          \n",
       "2020-02-03      0.0    0.0      0.0      0.0     0.0     0.0  \n",
       "2020-02-10      0.0    0.0      0.0      0.0     0.0     0.0  \n",
       "2020-02-17      0.0    0.0      0.0      0.0     0.0     0.0  \n",
       "2020-02-24      0.0    0.0      0.0      0.0     0.0     0.0  \n",
       "2020-03-02      0.0    0.0      0.0      0.0     0.0     0.0  \n",
       "2020-03-09      2.0    1.0    153.0      1.0     0.0     0.0  \n",
       "2020-03-16     49.0    7.0    656.0     52.0     0.0     3.0  \n",
       "2020-03-23    203.0   60.0   1212.0    363.0    20.0    23.0  \n",
       "2020-03-30    766.0  168.0   2553.0    805.0   125.0    69.0  \n",
       "2020-04-06   1858.0  274.0   3222.0   1219.0   200.0   117.0  \n",
       "2020-04-13   2869.0  191.0   2303.0    988.0   281.0   161.0  \n",
       "2020-04-20   3243.0   55.0   1899.0   1071.0   276.0    55.0  \n",
       "2020-04-27   4545.0   31.0   1706.0   1582.0   175.0    92.0  \n",
       "2020-05-04   5957.0   42.0   1855.0   2155.0   147.0    76.0  \n",
       "2020-05-11   5578.0   23.0   1681.0   2182.0   145.0    73.0  \n",
       "2020-05-18   6070.0   12.0   1435.0   2269.0   133.0    97.0  \n",
       "2020-05-25   6587.0   22.0   1455.0   2897.0   280.0    77.0  \n",
       "2020-06-01   7671.0   21.0   1863.0   2959.0   246.0    67.0  \n",
       "2020-06-08   5853.0   84.0   1997.0   2495.0   133.0    50.0  \n",
       "2020-06-15   3635.0   50.0   2096.0   4442.0   161.0   119.0  \n",
       "2020-06-22   3579.0   36.0   2725.0   2358.0   249.0   151.0  \n",
       "2020-06-29   3724.0   31.0   3204.0   3195.0   299.0   220.0  \n",
       "2020-07-06   3913.0   41.0   4659.0   4197.0   572.0   225.0  \n",
       "2020-07-13   5540.0   45.0   4881.0   5152.0   871.0   228.0  \n",
       "2020-07-20   6733.0   54.0   5958.0   6293.0   829.0   284.0  \n",
       "2020-07-27   7697.0   40.0   5513.0   6606.0   912.0   333.0  \n",
       "2020-08-03   7034.0   21.0   5670.0   6120.0   919.0   328.0  \n",
       "2020-08-10   7643.0   31.0   4872.0   5955.0   781.0   194.0  \n",
       "2020-08-17   6672.0   58.0   4193.0   5359.0   878.0   289.0  \n",
       "2020-08-24   6209.0   36.0   3913.0   4904.0   680.0   272.0  \n",
       "2020-08-31   6964.0   50.0   3344.0   4949.0   938.0   239.0  \n",
       "2020-09-07   6977.0   31.0   3163.0   6360.0  1325.0   190.0  \n",
       "2020-09-14   7000.0   44.0   2717.0   8569.0  1245.0   360.0  \n",
       "2020-09-21   6567.0   24.0   2907.0  13091.0  1351.0   552.0  \n",
       "2020-09-28   5455.0   21.0   3947.0  15629.0  1341.0   810.0  \n",
       "2020-10-05   5964.0   64.0   3829.0  17438.0  1230.0   875.0  \n",
       "2020-10-12   7013.0   54.0   4271.0  18676.0  1539.0  1173.0  \n",
       "2020-10-19   7258.0   69.0   4557.0  22811.0  2012.0  1509.0  \n",
       "2020-10-26   7447.0  139.0   5089.0  28786.0  1930.0  2166.0  \n",
       "2020-11-02   9143.0  134.0   6119.0  33000.0  3012.0  2690.0  \n",
       "2020-11-09  10059.0  216.0   9421.0  41452.0  3570.0  3843.0  \n",
       "2020-11-16  11160.0  620.0  13727.0  48182.0  5655.0  5183.0  \n",
       "2020-11-23  16401.0  677.0  15029.0  45131.0  6654.0  6238.0  \n",
       "2020-11-30  16797.0  460.0  18941.0  32037.0  6728.0  3874.0  \n",
       "2020-12-07  21035.0  752.0  17031.0  31659.0  8286.0  3680.0  \n",
       "2020-12-14  26279.0  713.0  23549.0  27896.0  8266.0  2790.0  \n",
       "2020-12-21  25741.0  648.0  15917.0  23068.0  8943.0  2343.0  \n",
       "2020-12-28  25285.0  544.0  12829.0  15784.0  8099.0  1586.0  \n",
       "\n",
       "[48 rows x 53 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6188eae7",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "**Question:** Can we predict, say, Oregon's case counts\n",
    "using the other states?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9e19935e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression as lm\n",
    "other_states = covid.loc[:,covid.columns != \"OR\"]\n",
    "obs_OR = covid.loc[:,\"OR\"]\n",
    "OR_fit = lm().fit(other_states, obs_OR)\n",
    "est_OR = OR_fit.predict(other_states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4404ae71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(covid.index, obs_OR, label=\"observed\")\n",
    "plt.plot(covid.index, est_OR, label=\"estimated\", linestyle=\"--\")\n",
    "plt.legend(); plt.xlabel(\"date\"); plt.ylabel(\"OR case counts\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bd16937",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Gee, we can predict the case counts *perfectly*?\n",
    "Does that seem very likely? What's going on?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f599ead",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Let's think about what we're trying to do here.\n",
    "We're trying to find coefficients $b_1, \\ldots, b_k$\n",
    "so that the the linear combination of the columns of $X$\n",
    "$$  \\hat y = b_1 X_{\\cdot 1} + \\cdots + b_k X_{\\cdot k} $$\n",
    "is as close to $y$ as possible."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d270f78",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Well, when is it possible to find $b$ so that $\\hat y = y$?\n",
    "It is possible if $y$ is in the column space of $X$.\n",
    "\n",
    "Recall that $X$ is an $n \\times k$ matrix.\n",
    "If $n \\le k$ then the columns of $X$ span then *entire* space $\\mathbb{R}^n$\n",
    "(unless for instance some columns are identical)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edd74bd2",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "*Takeaway:* if you have more variables than observations\n",
    "(the problem is *singular* and)\n",
    "it is always possible to *exactly* predict the response.\n",
    "\n",
    "*However,* these predictions are unlikely to be *generalizable*."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40db9c8d",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Crossvalidation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "259b8fcf",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "How to tell how good your model is?\n",
    "\n",
    "*See how well it predicts \"new\" data.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2119178a",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "To do $k$-fold crossvalidation:\n",
    "\n",
    "1. Split your dataset into $k$ chunks (these should be independent!), and\n",
    "2. for each chunk in turn, put it aside for \"testing\"\n",
    "    and train your model on the remaining $k-1$ chunks.\n",
    "3. Compare \"test error\" to \"training error\".\n",
    "\n",
    "Predictions for data used to fit the model (\"training error\")\n",
    "should not be much further off than for data held out (\"test error\")."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0844f33",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "This can be used either as an indication of *overfitting*\n",
    "or to compare different models to each other."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2effe8f5",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Crossvalidation set-up"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24d48de8",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Here's a pretty 'easy' prediction problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aa6d5ddc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 100\n",
    "x = np.array([\n",
    "    rng.uniform(low=0, high=10, size=n),\n",
    "    rng.normal(size=n),\n",
    "]).T\n",
    "y = 2.5 + 1.5 * x[:,0] - 4.2 * x[:,1] + rng.normal(size=n)\n",
    "\n",
    "fig, (ax0, ax1) = plt.subplots(1, 2)\n",
    "ax0.scatter(x[:,0], y); ax0.set_xlabel(\"x0\"); ax0.set_ylabel(\"y\")\n",
    "ax1.scatter(x[:,1], y); ax1.set_xlabel(\"x1\"); ax1.set_ylabel(\"y\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae605053",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Let's (a) fit a linear model\n",
    "and (b) do crossvalidation to look for evidence of overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "64c3c531",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "test     1.121362\n",
       "train    1.071623\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def rmse(X, y, model):\n",
    "    yhat = model.predict(X)\n",
    "    resids = y - yhat\n",
    "    return np.sqrt(np.mean(resids ** 2))\n",
    "\n",
    "def kfold(k, X, y, model):\n",
    "    n  = len(y)\n",
    "    folds = np.repeat(np.arange(k), np.ceil(n / k))[:n]\n",
    "    rng.shuffle(folds)\n",
    "    test_rmse = []\n",
    "    train_rmse = []\n",
    "    for ik in range(k):\n",
    "        test_X = X[folds == ik]\n",
    "        test_y = y[folds == ik]\n",
    "        train_X = X[folds != ik]\n",
    "        train_y = y[folds != ik]\n",
    "        model.fit(train_X, train_y)\n",
    "        test_rmse.append(rmse(test_X, test_y, model))\n",
    "        train_rmse.append(rmse(train_X, train_y, model))\n",
    "    return pd.DataFrame({\n",
    "        \"test\" : test_rmse,\n",
    "        \"train\" : train_rmse,\n",
    "    })\n",
    "\n",
    "crossval = kfold(5, x, y, lm())\n",
    "crossval.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "91dbf6ef",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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>test</th>\n",
       "      <th>train</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.002842</td>\n",
       "      <td>1.102181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.280119</td>\n",
       "      <td>1.030386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.342009</td>\n",
       "      <td>1.017593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.042074</td>\n",
       "      <td>1.093432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.939767</td>\n",
       "      <td>1.114522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       test     train\n",
       "0  1.002842  1.102181\n",
       "1  1.280119  1.030386\n",
       "2  1.342009  1.017593\n",
       "3  1.042074  1.093432\n",
       "4  0.939767  1.114522"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crossval"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95f4dc05",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# When you've got too many variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7f4ca9e",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We're going to *add more variables* -\n",
    "these will be *independent* of everything else, so they should *not*\n",
    "give us meaningful predictive power for $y$.\n",
    "However, by chance each is a little correlated with $y$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "854326c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "crossval = pd.DataFrame()\n",
    "for new_vars in np.linspace(0, 80, 9):\n",
    "    new_x = rng.normal(size=(n, int(new_vars)))\n",
    "    X = np.column_stack([x, new_x])\n",
    "    xval = kfold(5, X, y, lm())\n",
    "    xval[\"new_vars\"] = int(new_vars)\n",
    "    crossval = crossval.append(xval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2a4f739a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "crossval.groupby(\"new_vars\").agg(np.mean).plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fa6bdde",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Regularization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f65fabd2",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Recall that, somewhat mysteriously,\n",
    "scikit-learn's method to fit a Binomial GLM with a logistic link function\n",
    "[has a \"penalty\" option](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html).\n",
    "What's that?\n",
    "\n",
    "Well, it's finding $b$ to maximize the likelihood under the following model:\n",
    "$$ Y_i \\sim \\text{Binomial}(N, p(X_i \\cdot b)), $$\n",
    "where $p(\\cdot)$ is the logistic function.\n",
    "The terms in the log-likelihood that depend on $b$ are\n",
    "$$\n",
    "    \\sum_{i=1}^n \\left\\{\n",
    "        Y_i \\log(p(X_i \\cdot b)) + (N_i - Y_i) \\log(1 - p(X_i \\cdot b))\n",
    "    \\right\\} .\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4713898f",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "The problem we had above\n",
    "was that the variables that didn't matter\n",
    "had small but nonzero estimated parameters;\n",
    "and there were so many of them,\n",
    "that together they added up to something big.\n",
    "\n",
    "*Solution:* \"encourage\" them to be small."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8658c015",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "So, from our log-likelihood\n",
    "$$\n",
    "    \\sum_{i=1}^n \\left\\{\n",
    "        Y_i \\log(p(X_i \\cdot b)) + (N_i - Y_i) \\log(1 - p(X_i \\cdot b))\n",
    "    \\right\\} \n",
    "$$\n",
    "we subtract a \"regularization\" term that does the \"encouraging\".\n",
    "Options:\n",
    "$$\\begin{aligned}\n",
    "  \\sum_j |b_j| \\qquad & \\text{\"L1\" or \"$\\ell_1$\" or \"lasso\"}\\\\\n",
    "  \\sum_j b_j^2 \\qquad & \\text{\"L2\" or \"$\\ell_2$\" or \"ridge\" or \"Tikhonov\"}\n",
    "\\end{aligned}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25ebdf5a",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Let's do it, with [the Lasso](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html),\n",
    "which (since this is a standard least-squares linear model) minimizes:\n",
    "$$ \\sum_i (y_i - X_i \\cdot b)^2 + \\alpha \\sum_j |b_j| . $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dfa99130",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "ridge_crossval = pd.DataFrame()\n",
    "for new_vars in np.linspace(0, 80, 9):\n",
    "    new_x = rng.normal(size=(len(y), int(new_vars)))\n",
    "    X = np.column_stack([x, new_x])\n",
    "    xval = kfold(5, X, y, Lasso(alpha=.3))\n",
    "    xval[\"new_vars\"] = int(new_vars)\n",
    "    ridge_crossval = ridge_crossval.append(xval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "57aafe5d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ridge_crossval.groupby(\"new_vars\").agg(np.mean).plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4173181f",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# An experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3553056e",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Let's see\n",
    "\n",
    "*(a)* how well *coefficients* are estimated with the lasso, and\n",
    "\n",
    "*(b)* how well *predictions* are made with the lasso.\n",
    "\n",
    "To do this, we'll \n",
    "\n",
    "1. write a function to generate a dataset, and\n",
    "2. for each of many datasets, fit a model with 'lasso' regularization, then\n",
    "3. look at the distribution of coefficient estimates, and\n",
    "4. the distribution of residual SDs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68e0ad1e",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "(IN CLASS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bbd3897a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "true_params = {\"slope\": -.15, \"intercept\": 1.2}\n",
    "\n",
    "def mean_cups(sleep):\n",
    "    return np.exp(true_params['intercept'] + true_params['slope'] * sleep)\n",
    "    \n",
    "def sim_coffee(n):\n",
    "    sleep = rng.uniform(low=1, high=10, size=n)  # not really uniform\n",
    "    coffee = rng.poisson(lam=mean_cups(sleep))\n",
    "    return sleep, coffee\n",
    "\n",
    "sleep, coffee = sim_coffee(100)\n",
    "plt.scatter(sleep, coffee)\n",
    "plt.plot(np.linspace(1, 10, 11), mean_cups(np.linspace(1, 10, 11)), label='expected number')\n",
    "plt.xlabel(\"hours of sleep\"); plt.ylabel(\"cups of coffee\"); plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "972ea8de",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import PoissonRegressor as poisson_glm\n",
    "def fit_model(sleep, coffee, alpha):\n",
    "    model = poisson_glm(alpha=alpha)\n",
    "    model.fit(np.array([sleep]).T, coffee)\n",
    "    return model\n",
    "\n",
    "model = fit_model(sleep, coffee, alpha=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6481795c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.11482714727544707, 1.0905232611565594)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_[0], model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1dfe8559",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(sleep, coffee)\n",
    "plt.xlabel(\"hours of sleep\"); plt.ylabel(\"cups of coffee\");\n",
    "coffee_hat = np.exp(model.intercept_ + model.coef_[0] * sleep)\n",
    "plt.scatter(sleep, coffee_hat, label='predicted')\n",
    "mean_coffee = mean_cups(sleep)\n",
    "plt.scatter(sleep, mean_coffee, label='true mean')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2b290600",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.10180883,  0.89003322],\n",
       "       [-0.1223202 ,  1.10556814],\n",
       "       [-0.12684918,  1.10732741],\n",
       "       [-0.13122881,  1.04476602],\n",
       "       [-0.12383794,  1.17677132],\n",
       "       [-0.16069652,  1.16739785],\n",
       "       [-0.16488536,  1.33573878],\n",
       "       [-0.14585712,  1.26087712],\n",
       "       [-0.129308  ,  1.04148244],\n",
       "       [-0.11134622,  0.92374059]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def experiment(n, alpha):\n",
    "    # n times, generate data, fit a model, and return an array of the estimated coefficients\n",
    "    out = np.zeros((n, 2))\n",
    "    for j in range(n):\n",
    "        sleep, coffee = sim_coffee(100)\n",
    "        model = fit_model(sleep, coffee, alpha)\n",
    "        out[j, :] = model.coef_[0], model.intercept_\n",
    "    return out\n",
    "\n",
    "experiment(10, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "969fa4c9",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "exp_results = experiment(300, alpha=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "79ed2def",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'estimated intercepts')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax0, ax1) = plt.subplots(1, 2)\n",
    "ax0.hist(exp_results[:, 0])\n",
    "ax0.set_title(\"estimated slopes\")\n",
    "ax0.axvline(true_params['slope'], color='red')\n",
    "ax1.hist(exp_results[:, 1])\n",
    "ax1.axvline(true_params['intercept'], color='red')\n",
    "ax1.set_title(\"estimated intercepts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a2ab318",
   "metadata": {},
   "source": [
    "Now let's look at the effect of changing alpha:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "400d49af",
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha_vals = np.linspace(0, 30, 11)\n",
    "many_experiments = np.array([experiment(100, alpha=a) for a in alpha_vals])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9c570c56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_estimates = np.min(many_experiments[:,:,0], axis=1)\n",
    "max_estimates = np.max(many_experiments[:,:,0], axis=1)\n",
    "mean_estimates = np.mean(many_experiments[:,:,0], axis=1)\n",
    "\n",
    "for a, x, y, z in zip(alpha_vals, min_estimates, mean_estimates, max_estimates):\n",
    "    plt.plot([a, a], [x, z])\n",
    "    plt.scatter([a], [y])\n",
    "    \n",
    "plt.axhline(true_params['slope'], linestyle=\":\", label='true value')\n",
    "plt.xlabel(\"alpha\"); plt.ylabel(\"slope\"); plt.legend();"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "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": "3.9.9"
  },
  "rise": {
   "scroll": true,
   "transition": "none"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}