homework_code/hw1/HW1_local参考代码/HW1_local.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mz0_QVkxCrX3"
   },
   "source": [
    "# **Homework 1: COVID-19 Cases Prediction (Regression)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZeZnPAiwDRWG"
   },
   "source": [
    "Author: Heng-Jui Chang\n",
    "\n",
    "- Slides: https://github.com/ga642381/ML2021-Spring/blob/main/HW01/HW01.pdf  \n",
    "- github https://github.com/wolfparticle/machineLearningDeepLearning \n",
    "- Video: https://www.bilibili.com/video/BV1zA411K7en?p=2\n",
    "\n",
    "Objectives:\n",
    "* Solve a regression problem with deep neural networks (DNN).\n",
    "* Understand basic DNN training tips.\n",
    "* Get familiar with PyTorch.\n",
    "\n",
    "下面代码是基于作者baseline改的，主要改动有以下几方面：\n",
    " - 增加特征分析\n",
    " - 修改损失函数为均方根误差\n",
    " - 增加l2正则\n",
    " - 归一化方法\n",
    " - 修改一些超参\n",
    " - 增加部分代码注释"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wS_4-77xHk44"
   },
   "source": [
    "# **Import Some Packages**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "id": "k-onQd4JNA5H"
   },
   "outputs": [],
   "source": [
    "# PyTorch\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "# For data preprocess\n",
    "import numpy as np\n",
    "import csv\n",
    "import os\n",
    "\n",
    "# For plotting\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "\n",
    "#下面三个包是新增的\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "import pprint as pp\n",
    "\n",
    "myseed = 42069  # set a random seed for reproducibility\n",
    "torch.backends.cudnn.deterministic = True\n",
    "torch.backends.cudnn.benchmark = False\n",
    "np.random.seed(myseed)\n",
    "torch.manual_seed(myseed)\n",
    "if torch.cuda.is_available():\n",
    "    torch.cuda.manual_seed_all(myseed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Jx3x1nDkG-Uy"
   },
   "source": [
    "# **Download Data**\n",
    "数据可以去[kaggle](https://www.kaggle.com/c/ml2021spring-hw1/data)下载, 不过我已经下载放到[github](https://github.com/wolfparticle/machineLearningDeepLearning),在/data/目录下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tMj55YDKG6ch",
    "outputId": "fc40ecc9-4756-48b1-d5c6-c169a8b453b2"
   },
   "outputs": [],
   "source": [
    "tr_path = 'covid.train.csv'  # path to training data\n",
    "tt_path = 'covid.test.csv'   # path to testing data\n",
    "data_tr = pd.read_csv(tr_path) #读取训练数据\n",
    "data_tt = pd.read_csv(tt_path) #读取测试数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Data Analysis**\n",
    "可视化数据，筛选有用特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
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       "      <td>...</td>\n",
       "      <td>23.593983</td>\n",
       "      <td>43.362200</td>\n",
       "      <td>16.159971</td>\n",
       "      <td>1.677523</td>\n",
       "      <td>15.717207</td>\n",
       "      <td>12.355918</td>\n",
       "      <td>16.273294</td>\n",
       "      <td>53.637069</td>\n",
       "      <td>42.972417</td>\n",
       "      <td>21.166656</td>\n",
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       "<p>3 rows × 95 columns</p>\n",
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      "text/plain": [
       "   id   AL   AK   AZ   AR   CA   CO   CT   FL   GA  ...  restaurant.2  \\\n",
       "0   0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...     23.812411   \n",
       "1   1  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...     23.682974   \n",
       "2   2  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...     23.593983   \n",
       "\n",
       "   spent_time.2  large_event.2  public_transit.2  anxious.2  depressed.2  \\\n",
       "0     43.430423      16.151527          1.602635  15.409449    12.088688   \n",
       "1     43.196313      16.123386          1.641863  15.230063    11.809047   \n",
       "2     43.362200      16.159971          1.677523  15.717207    12.355918   \n",
       "\n",
       "   felt_isolated.2  worried_become_ill.2  worried_finances.2  \\\n",
       "0        16.702086             53.991549           43.604229   \n",
       "1        16.506973             54.185521           42.665766   \n",
       "2        16.273294             53.637069           42.972417   \n",
       "\n",
       "   tested_positive.2  \n",
       "0          20.704935  \n",
       "1          21.292911  \n",
       "2          21.166656  \n",
       "\n",
       "[3 rows x 95 columns]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_tr.head(3) #数据量很大，看前三行就行，大致浏览下数据类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>id</th>\n",
       "      <th>AL</th>\n",
       "      <th>AK</th>\n",
       "      <th>AZ</th>\n",
       "      <th>AR</th>\n",
       "      <th>CA</th>\n",
       "      <th>CO</th>\n",
       "      <th>CT</th>\n",
       "      <th>FL</th>\n",
       "      <th>GA</th>\n",
       "      <th>...</th>\n",
       "      <th>shop.2</th>\n",
       "      <th>restaurant.2</th>\n",
       "      <th>spent_time.2</th>\n",
       "      <th>large_event.2</th>\n",
       "      <th>public_transit.2</th>\n",
       "      <th>anxious.2</th>\n",
       "      <th>depressed.2</th>\n",
       "      <th>felt_isolated.2</th>\n",
       "      <th>worried_become_ill.2</th>\n",
       "      <th>worried_finances.2</th>\n",
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       "      <td>...</td>\n",
       "      <td>59.109045</td>\n",
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       "      <td>40.072556</td>\n",
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       "      <td>38.907257</td>\n",
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      "text/plain": [
       "   id   AL   AK   AZ   AR   CA   CO   CT   FL   GA  ...     shop.2  \\\n",
       "0   0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  ...  52.071090   \n",
       "1   1  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  58.742461   \n",
       "2   2  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  59.109045   \n",
       "\n",
       "   restaurant.2  spent_time.2  large_event.2  public_transit.2  anxious.2  \\\n",
       "0      8.624001     29.374792       5.391413          2.754804  19.695098   \n",
       "1     21.720187     41.375784       9.450179          3.150088  22.075715   \n",
       "2     20.123959     40.072556       8.781522          2.888209  23.920870   \n",
       "\n",
       "   depressed.2  felt_isolated.2  worried_become_ill.2  worried_finances.2  \n",
       "0    13.685645        24.747837             66.194950           44.873473  \n",
       "1    17.302077        23.559622             57.015009           38.372829  \n",
       "2    18.342506        24.993341             55.291498           38.907257  \n",
       "\n",
       "[3 rows x 94 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_tt.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'AL', 'AK', 'AZ', 'AR', 'CA', 'CO', 'CT', 'FL', 'GA', 'ID', 'IL',\n",
       "       'IN', 'IA', 'KS', 'KY', 'LA', 'MD', 'MA', 'MI', 'MN', 'MS', 'MO', 'NE',\n",
       "       'NV', 'NJ', 'NM', 'NY', 'NC', 'OH', 'OK', 'OR', 'PA', 'RI', 'SC', 'TX',\n",
       "       'UT', 'VA', 'WA', 'WV', 'WI', 'cli', 'ili', 'hh_cmnty_cli',\n",
       "       'nohh_cmnty_cli', 'wearing_mask', 'travel_outside_state',\n",
       "       'work_outside_home', 'shop', 'restaurant', 'spent_time', 'large_event',\n",
       "       'public_transit', 'anxious', 'depressed', 'felt_isolated',\n",
       "       'worried_become_ill', 'worried_finances', 'tested_positive', 'cli.1',\n",
       "       'ili.1', 'hh_cmnty_cli.1', 'nohh_cmnty_cli.1', 'wearing_mask.1',\n",
       "       'travel_outside_state.1', 'work_outside_home.1', 'shop.1',\n",
       "       'restaurant.1', 'spent_time.1', 'large_event.1', 'public_transit.1',\n",
       "       'anxious.1', 'depressed.1', 'felt_isolated.1', 'worried_become_ill.1',\n",
       "       'worried_finances.1', 'tested_positive.1', 'cli.2', 'ili.2',\n",
       "       'hh_cmnty_cli.2', 'nohh_cmnty_cli.2', 'wearing_mask.2',\n",
       "       'travel_outside_state.2', 'work_outside_home.2', 'shop.2',\n",
       "       'restaurant.2', 'spent_time.2', 'large_event.2', 'public_transit.2',\n",
       "       'anxious.2', 'depressed.2', 'felt_isolated.2', 'worried_become_ill.2',\n",
       "       'worried_finances.2', 'tested_positive.2'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_tr.columns #查看有多少列特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_tr.drop(['id'],axis = 1, inplace = True) #由于id列用不到，删除id列\n",
    "data_tt.drop(['id'],axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['AL', 'AK', 'AZ', 'AR', 'CA', 'CO', 'CT', 'FL', 'GA', 'ID', 'IL', 'IN',\n",
      "       'IA', 'KS', 'KY', 'LA', 'MD', 'MA', 'MI', 'MN', 'MS', 'MO', 'NE', 'NV',\n",
      "       'NJ', 'NM', 'NY', 'NC', 'OH', 'OK', 'OR', 'PA', 'RI', 'SC', 'TX', 'UT',\n",
      "       'VA', 'WA', 'WV', 'WI', 'cli', 'ili', 'hh_cmnty_cli', 'nohh_cmnty_cli',\n",
      "       'wearing_mask', 'travel_outside_state', 'work_outside_home', 'shop',\n",
      "       'restaurant', 'spent_time', 'large_event', 'public_transit', 'anxious',\n",
      "       'depressed', 'felt_isolated', 'worried_become_ill', 'worried_finances',\n",
      "       'tested_positive', 'cli.1', 'ili.1', 'hh_cmnty_cli.1',\n",
      "       'nohh_cmnty_cli.1', 'wearing_mask.1', 'travel_outside_state.1',\n",
      "       'work_outside_home.1', 'shop.1', 'restaurant.1', 'spent_time.1',\n",
      "       'large_event.1', 'public_transit.1', 'anxious.1', 'depressed.1',\n",
      "       'felt_isolated.1', 'worried_become_ill.1', 'worried_finances.1',\n",
      "       'tested_positive.1', 'cli.2', 'ili.2', 'hh_cmnty_cli.2',\n",
      "       'nohh_cmnty_cli.2', 'wearing_mask.2', 'travel_outside_state.2',\n",
      "       'work_outside_home.2', 'shop.2', 'restaurant.2', 'spent_time.2',\n",
      "       'large_event.2', 'public_transit.2', 'anxious.2', 'depressed.2',\n",
      "       'felt_isolated.2', 'worried_become_ill.2', 'worried_finances.2',\n",
      "       'tested_positive.2'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "cols = list(data_tr.columns)  #拿到特征列名称\n",
    "pp.pprint(data_tr.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2700 entries, 0 to 2699\n",
      "Data columns (total 94 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   AL                      2700 non-null   float64\n",
      " 1   AK                      2700 non-null   float64\n",
      " 2   AZ                      2700 non-null   float64\n",
      " 3   AR                      2700 non-null   float64\n",
      " 4   CA                      2700 non-null   float64\n",
      " 5   CO                      2700 non-null   float64\n",
      " 6   CT                      2700 non-null   float64\n",
      " 7   FL                      2700 non-null   float64\n",
      " 8   GA                      2700 non-null   float64\n",
      " 9   ID                      2700 non-null   float64\n",
      " 10  IL                      2700 non-null   float64\n",
      " 11  IN                      2700 non-null   float64\n",
      " 12  IA                      2700 non-null   float64\n",
      " 13  KS                      2700 non-null   float64\n",
      " 14  KY                      2700 non-null   float64\n",
      " 15  LA                      2700 non-null   float64\n",
      " 16  MD                      2700 non-null   float64\n",
      " 17  MA                      2700 non-null   float64\n",
      " 18  MI                      2700 non-null   float64\n",
      " 19  MN                      2700 non-null   float64\n",
      " 20  MS                      2700 non-null   float64\n",
      " 21  MO                      2700 non-null   float64\n",
      " 22  NE                      2700 non-null   float64\n",
      " 23  NV                      2700 non-null   float64\n",
      " 24  NJ                      2700 non-null   float64\n",
      " 25  NM                      2700 non-null   float64\n",
      " 26  NY                      2700 non-null   float64\n",
      " 27  NC                      2700 non-null   float64\n",
      " 28  OH                      2700 non-null   float64\n",
      " 29  OK                      2700 non-null   float64\n",
      " 30  OR                      2700 non-null   float64\n",
      " 31  PA                      2700 non-null   float64\n",
      " 32  RI                      2700 non-null   float64\n",
      " 33  SC                      2700 non-null   float64\n",
      " 34  TX                      2700 non-null   float64\n",
      " 35  UT                      2700 non-null   float64\n",
      " 36  VA                      2700 non-null   float64\n",
      " 37  WA                      2700 non-null   float64\n",
      " 38  WV                      2700 non-null   float64\n",
      " 39  WI                      2700 non-null   float64\n",
      " 40  cli                     2700 non-null   float64\n",
      " 41  ili                     2700 non-null   float64\n",
      " 42  hh_cmnty_cli            2700 non-null   float64\n",
      " 43  nohh_cmnty_cli          2700 non-null   float64\n",
      " 44  wearing_mask            2700 non-null   float64\n",
      " 45  travel_outside_state    2700 non-null   float64\n",
      " 46  work_outside_home       2700 non-null   float64\n",
      " 47  shop                    2700 non-null   float64\n",
      " 48  restaurant              2700 non-null   float64\n",
      " 49  spent_time              2700 non-null   float64\n",
      " 50  large_event             2700 non-null   float64\n",
      " 51  public_transit          2700 non-null   float64\n",
      " 52  anxious                 2700 non-null   float64\n",
      " 53  depressed               2700 non-null   float64\n",
      " 54  felt_isolated           2700 non-null   float64\n",
      " 55  worried_become_ill      2700 non-null   float64\n",
      " 56  worried_finances        2700 non-null   float64\n",
      " 57  tested_positive         2700 non-null   float64\n",
      " 58  cli.1                   2700 non-null   float64\n",
      " 59  ili.1                   2700 non-null   float64\n",
      " 60  hh_cmnty_cli.1          2700 non-null   float64\n",
      " 61  nohh_cmnty_cli.1        2700 non-null   float64\n",
      " 62  wearing_mask.1          2700 non-null   float64\n",
      " 63  travel_outside_state.1  2700 non-null   float64\n",
      " 64  work_outside_home.1     2700 non-null   float64\n",
      " 65  shop.1                  2700 non-null   float64\n",
      " 66  restaurant.1            2700 non-null   float64\n",
      " 67  spent_time.1            2700 non-null   float64\n",
      " 68  large_event.1           2700 non-null   float64\n",
      " 69  public_transit.1        2700 non-null   float64\n",
      " 70  anxious.1               2700 non-null   float64\n",
      " 71  depressed.1             2700 non-null   float64\n",
      " 72  felt_isolated.1         2700 non-null   float64\n",
      " 73  worried_become_ill.1    2700 non-null   float64\n",
      " 74  worried_finances.1      2700 non-null   float64\n",
      " 75  tested_positive.1       2700 non-null   float64\n",
      " 76  cli.2                   2700 non-null   float64\n",
      " 77  ili.2                   2700 non-null   float64\n",
      " 78  hh_cmnty_cli.2          2700 non-null   float64\n",
      " 79  nohh_cmnty_cli.2        2700 non-null   float64\n",
      " 80  wearing_mask.2          2700 non-null   float64\n",
      " 81  travel_outside_state.2  2700 non-null   float64\n",
      " 82  work_outside_home.2     2700 non-null   float64\n",
      " 83  shop.2                  2700 non-null   float64\n",
      " 84  restaurant.2            2700 non-null   float64\n",
      " 85  spent_time.2            2700 non-null   float64\n",
      " 86  large_event.2           2700 non-null   float64\n",
      " 87  public_transit.2        2700 non-null   float64\n",
      " 88  anxious.2               2700 non-null   float64\n",
      " 89  depressed.2             2700 non-null   float64\n",
      " 90  felt_isolated.2         2700 non-null   float64\n",
      " 91  worried_become_ill.2    2700 non-null   float64\n",
      " 92  worried_finances.2      2700 non-null   float64\n",
      " 93  tested_positive.2       2700 non-null   float64\n",
      "dtypes: float64(94)\n",
      "memory usage: 1.9 MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "pp.pprint(data_tr.info()) #看每列数据类型和大小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WI_index = cols.index('WI')  # WI列是states one-hot编码最后一列，取值为0或1，后面特征分析时需要把states特征删掉\n",
    "WI_index #wi列索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cli</th>\n",
       "      <th>ili</th>\n",
       "      <th>hh_cmnty_cli</th>\n",
       "      <th>nohh_cmnty_cli</th>\n",
       "      <th>wearing_mask</th>\n",
       "      <th>travel_outside_state</th>\n",
       "      <th>work_outside_home</th>\n",
       "      <th>shop</th>\n",
       "      <th>restaurant</th>\n",
       "      <th>spent_time</th>\n",
       "      <th>...</th>\n",
       "      <th>restaurant.2</th>\n",
       "      <th>spent_time.2</th>\n",
       "      <th>large_event.2</th>\n",
       "      <th>public_transit.2</th>\n",
       "      <th>anxious.2</th>\n",
       "      <th>depressed.2</th>\n",
       "      <th>felt_isolated.2</th>\n",
       "      <th>worried_become_ill.2</th>\n",
       "      <th>worried_finances.2</th>\n",
       "      <th>tested_positive.2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2700.000000</td>\n",
       "      <td>2700.000000</td>\n",
       "      <td>2700.000000</td>\n",
       "      <td>2700.000000</td>\n",
       "      <td>2700.000000</td>\n",
       "      <td>2700.000000</td>\n",
       "      <td>2700.000000</td>\n",
       "      <td>2700.000000</td>\n",
       "      <td>2700.000000</td>\n",
       "      <td>2700.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2700.000000</td>\n",
       "      <td>2700.000000</td>\n",
       "      <td>2700.000000</td>\n",
       "      <td>2700.000000</td>\n",
       "      <td>2700.000000</td>\n",
       "      <td>2700.000000</td>\n",
       "      <td>2700.000000</td>\n",
       "      <td>2700.000000</td>\n",
       "      <td>2700.000000</td>\n",
       "      <td>2700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.991587</td>\n",
       "      <td>1.016136</td>\n",
       "      <td>29.442496</td>\n",
       "      <td>24.323054</td>\n",
       "      <td>89.682322</td>\n",
       "      <td>8.894498</td>\n",
       "      <td>31.703307</td>\n",
       "      <td>55.277153</td>\n",
       "      <td>16.694342</td>\n",
       "      <td>36.283177</td>\n",
       "      <td>...</td>\n",
       "      <td>16.578290</td>\n",
       "      <td>36.074941</td>\n",
       "      <td>10.257474</td>\n",
       "      <td>2.385735</td>\n",
       "      <td>18.067635</td>\n",
       "      <td>13.058828</td>\n",
       "      <td>19.243283</td>\n",
       "      <td>64.834307</td>\n",
       "      <td>44.568440</td>\n",
       "      <td>16.431280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.420296</td>\n",
       "      <td>0.423629</td>\n",
       "      <td>9.093738</td>\n",
       "      <td>8.446750</td>\n",
       "      <td>5.380027</td>\n",
       "      <td>3.404027</td>\n",
       "      <td>4.928902</td>\n",
       "      <td>4.525917</td>\n",
       "      <td>5.668479</td>\n",
       "      <td>6.675206</td>\n",
       "      <td>...</td>\n",
       "      <td>5.651583</td>\n",
       "      <td>6.655166</td>\n",
       "      <td>4.686263</td>\n",
       "      <td>1.053147</td>\n",
       "      <td>2.250081</td>\n",
       "      <td>1.628589</td>\n",
       "      <td>2.708339</td>\n",
       "      <td>6.220087</td>\n",
       "      <td>5.232030</td>\n",
       "      <td>7.619354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.126321</td>\n",
       "      <td>0.132470</td>\n",
       "      <td>9.961640</td>\n",
       "      <td>6.857181</td>\n",
       "      <td>70.950912</td>\n",
       "      <td>1.252983</td>\n",
       "      <td>18.311941</td>\n",
       "      <td>43.220187</td>\n",
       "      <td>3.637414</td>\n",
       "      <td>21.485815</td>\n",
       "      <td>...</td>\n",
       "      <td>3.637414</td>\n",
       "      <td>21.485815</td>\n",
       "      <td>2.118674</td>\n",
       "      <td>0.728770</td>\n",
       "      <td>12.980786</td>\n",
       "      <td>8.370536</td>\n",
       "      <td>13.400399</td>\n",
       "      <td>48.225603</td>\n",
       "      <td>33.113882</td>\n",
       "      <td>2.338708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.673929</td>\n",
       "      <td>0.697515</td>\n",
       "      <td>23.203165</td>\n",
       "      <td>18.539153</td>\n",
       "      <td>86.309537</td>\n",
       "      <td>6.177754</td>\n",
       "      <td>28.247865</td>\n",
       "      <td>51.547206</td>\n",
       "      <td>13.311050</td>\n",
       "      <td>30.740931</td>\n",
       "      <td>...</td>\n",
       "      <td>13.200532</td>\n",
       "      <td>30.606711</td>\n",
       "      <td>6.532543</td>\n",
       "      <td>1.714080</td>\n",
       "      <td>16.420485</td>\n",
       "      <td>11.914167</td>\n",
       "      <td>17.322912</td>\n",
       "      <td>59.782876</td>\n",
       "      <td>40.549987</td>\n",
       "      <td>10.327314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.912747</td>\n",
       "      <td>0.940295</td>\n",
       "      <td>28.955738</td>\n",
       "      <td>23.819761</td>\n",
       "      <td>90.819435</td>\n",
       "      <td>8.288288</td>\n",
       "      <td>32.143140</td>\n",
       "      <td>55.257262</td>\n",
       "      <td>16.371699</td>\n",
       "      <td>36.267966</td>\n",
       "      <td>...</td>\n",
       "      <td>16.227010</td>\n",
       "      <td>36.041389</td>\n",
       "      <td>9.700368</td>\n",
       "      <td>2.199521</td>\n",
       "      <td>17.684197</td>\n",
       "      <td>12.948749</td>\n",
       "      <td>18.760267</td>\n",
       "      <td>65.932259</td>\n",
       "      <td>43.997637</td>\n",
       "      <td>15.646480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.266849</td>\n",
       "      <td>1.302040</td>\n",
       "      <td>36.109114</td>\n",
       "      <td>30.238061</td>\n",
       "      <td>93.937119</td>\n",
       "      <td>11.582209</td>\n",
       "      <td>35.387315</td>\n",
       "      <td>58.866130</td>\n",
       "      <td>21.396971</td>\n",
       "      <td>41.659971</td>\n",
       "      <td>...</td>\n",
       "      <td>21.207162</td>\n",
       "      <td>41.508520</td>\n",
       "      <td>13.602566</td>\n",
       "      <td>2.730469</td>\n",
       "      <td>19.503419</td>\n",
       "      <td>14.214320</td>\n",
       "      <td>20.713638</td>\n",
       "      <td>69.719651</td>\n",
       "      <td>48.118283</td>\n",
       "      <td>22.535165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.597732</td>\n",
       "      <td>2.625885</td>\n",
       "      <td>56.832289</td>\n",
       "      <td>51.550450</td>\n",
       "      <td>98.087160</td>\n",
       "      <td>18.552325</td>\n",
       "      <td>42.359074</td>\n",
       "      <td>65.673889</td>\n",
       "      <td>28.488220</td>\n",
       "      <td>50.606465</td>\n",
       "      <td>...</td>\n",
       "      <td>28.488220</td>\n",
       "      <td>50.606465</td>\n",
       "      <td>24.496711</td>\n",
       "      <td>8.162275</td>\n",
       "      <td>28.574091</td>\n",
       "      <td>18.715944</td>\n",
       "      <td>28.366270</td>\n",
       "      <td>77.701014</td>\n",
       "      <td>58.433600</td>\n",
       "      <td>40.959495</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               cli          ili  hh_cmnty_cli  nohh_cmnty_cli  wearing_mask  \\\n",
       "count  2700.000000  2700.000000   2700.000000     2700.000000   2700.000000   \n",
       "mean      0.991587     1.016136     29.442496       24.323054     89.682322   \n",
       "std       0.420296     0.423629      9.093738        8.446750      5.380027   \n",
       "min       0.126321     0.132470      9.961640        6.857181     70.950912   \n",
       "25%       0.673929     0.697515     23.203165       18.539153     86.309537   \n",
       "50%       0.912747     0.940295     28.955738       23.819761     90.819435   \n",
       "75%       1.266849     1.302040     36.109114       30.238061     93.937119   \n",
       "max       2.597732     2.625885     56.832289       51.550450     98.087160   \n",
       "\n",
       "       travel_outside_state  work_outside_home         shop   restaurant  \\\n",
       "count           2700.000000        2700.000000  2700.000000  2700.000000   \n",
       "mean               8.894498          31.703307    55.277153    16.694342   \n",
       "std                3.404027           4.928902     4.525917     5.668479   \n",
       "min                1.252983          18.311941    43.220187     3.637414   \n",
       "25%                6.177754          28.247865    51.547206    13.311050   \n",
       "50%                8.288288          32.143140    55.257262    16.371699   \n",
       "75%               11.582209          35.387315    58.866130    21.396971   \n",
       "max               18.552325          42.359074    65.673889    28.488220   \n",
       "\n",
       "        spent_time  ...  restaurant.2  spent_time.2  large_event.2  \\\n",
       "count  2700.000000  ...   2700.000000   2700.000000    2700.000000   \n",
       "mean     36.283177  ...     16.578290     36.074941      10.257474   \n",
       "std       6.675206  ...      5.651583      6.655166       4.686263   \n",
       "min      21.485815  ...      3.637414     21.485815       2.118674   \n",
       "25%      30.740931  ...     13.200532     30.606711       6.532543   \n",
       "50%      36.267966  ...     16.227010     36.041389       9.700368   \n",
       "75%      41.659971  ...     21.207162     41.508520      13.602566   \n",
       "max      50.606465  ...     28.488220     50.606465      24.496711   \n",
       "\n",
       "       public_transit.2    anxious.2  depressed.2  felt_isolated.2  \\\n",
       "count       2700.000000  2700.000000  2700.000000      2700.000000   \n",
       "mean           2.385735    18.067635    13.058828        19.243283   \n",
       "std            1.053147     2.250081     1.628589         2.708339   \n",
       "min            0.728770    12.980786     8.370536        13.400399   \n",
       "25%            1.714080    16.420485    11.914167        17.322912   \n",
       "50%            2.199521    17.684197    12.948749        18.760267   \n",
       "75%            2.730469    19.503419    14.214320        20.713638   \n",
       "max            8.162275    28.574091    18.715944        28.366270   \n",
       "\n",
       "       worried_become_ill.2  worried_finances.2  tested_positive.2  \n",
       "count           2700.000000         2700.000000        2700.000000  \n",
       "mean              64.834307           44.568440          16.431280  \n",
       "std                6.220087            5.232030           7.619354  \n",
       "min               48.225603           33.113882           2.338708  \n",
       "25%               59.782876           40.549987          10.327314  \n",
       "50%               65.932259           43.997637          15.646480  \n",
       "75%               69.719651           48.118283          22.535165  \n",
       "max               77.701014           58.433600          40.959495  \n",
       "\n",
       "[8 rows x 54 columns]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_tr.iloc[:, 40:].describe() #从上面可以看出wi 列后面是cli, 所以列索引从40开始， 并查看这些数据分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cli</th>\n",
       "      <th>ili</th>\n",
       "      <th>hh_cmnty_cli</th>\n",
       "      <th>nohh_cmnty_cli</th>\n",
       "      <th>wearing_mask</th>\n",
       "      <th>travel_outside_state</th>\n",
       "      <th>work_outside_home</th>\n",
       "      <th>shop</th>\n",
       "      <th>restaurant</th>\n",
       "      <th>spent_time</th>\n",
       "      <th>...</th>\n",
       "      <th>shop.2</th>\n",
       "      <th>restaurant.2</th>\n",
       "      <th>spent_time.2</th>\n",
       "      <th>large_event.2</th>\n",
       "      <th>public_transit.2</th>\n",
       "      <th>anxious.2</th>\n",
       "      <th>depressed.2</th>\n",
       "      <th>felt_isolated.2</th>\n",
       "      <th>worried_become_ill.2</th>\n",
       "      <th>worried_finances.2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>893.000000</td>\n",
       "      <td>893.000000</td>\n",
       "      <td>893.000000</td>\n",
       "      <td>893.000000</td>\n",
       "      <td>893.000000</td>\n",
       "      <td>893.000000</td>\n",
       "      <td>893.000000</td>\n",
       "      <td>893.000000</td>\n",
       "      <td>893.000000</td>\n",
       "      <td>893.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>893.000000</td>\n",
       "      <td>893.000000</td>\n",
       "      <td>893.000000</td>\n",
       "      <td>893.000000</td>\n",
       "      <td>893.000000</td>\n",
       "      <td>893.000000</td>\n",
       "      <td>893.000000</td>\n",
       "      <td>893.000000</td>\n",
       "      <td>893.000000</td>\n",
       "      <td>893.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.972457</td>\n",
       "      <td>0.991809</td>\n",
       "      <td>29.075682</td>\n",
       "      <td>24.018729</td>\n",
       "      <td>89.637506</td>\n",
       "      <td>9.001325</td>\n",
       "      <td>31.620607</td>\n",
       "      <td>55.422982</td>\n",
       "      <td>16.554387</td>\n",
       "      <td>36.371653</td>\n",
       "      <td>...</td>\n",
       "      <td>55.268628</td>\n",
       "      <td>16.444916</td>\n",
       "      <td>36.165898</td>\n",
       "      <td>10.248975</td>\n",
       "      <td>2.369115</td>\n",
       "      <td>17.988147</td>\n",
       "      <td>12.993830</td>\n",
       "      <td>19.238723</td>\n",
       "      <td>64.619920</td>\n",
       "      <td>44.411505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.411997</td>\n",
       "      <td>0.415468</td>\n",
       "      <td>9.596290</td>\n",
       "      <td>8.988245</td>\n",
       "      <td>4.733549</td>\n",
       "      <td>3.655616</td>\n",
       "      <td>4.754570</td>\n",
       "      <td>4.366780</td>\n",
       "      <td>5.688802</td>\n",
       "      <td>6.203232</td>\n",
       "      <td>...</td>\n",
       "      <td>4.350540</td>\n",
       "      <td>5.656828</td>\n",
       "      <td>6.192274</td>\n",
       "      <td>4.498845</td>\n",
       "      <td>1.114366</td>\n",
       "      <td>2.207022</td>\n",
       "      <td>1.713143</td>\n",
       "      <td>2.687435</td>\n",
       "      <td>5.685865</td>\n",
       "      <td>4.605268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.139558</td>\n",
       "      <td>0.159477</td>\n",
       "      <td>9.171315</td>\n",
       "      <td>6.014740</td>\n",
       "      <td>76.895278</td>\n",
       "      <td>2.062500</td>\n",
       "      <td>18.299198</td>\n",
       "      <td>44.062442</td>\n",
       "      <td>3.800684</td>\n",
       "      <td>21.487077</td>\n",
       "      <td>...</td>\n",
       "      <td>44.671891</td>\n",
       "      <td>3.837441</td>\n",
       "      <td>21.338425</td>\n",
       "      <td>2.334655</td>\n",
       "      <td>0.873986</td>\n",
       "      <td>12.696977</td>\n",
       "      <td>8.462444</td>\n",
       "      <td>13.476209</td>\n",
       "      <td>50.212234</td>\n",
       "      <td>35.072577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.673327</td>\n",
       "      <td>0.689367</td>\n",
       "      <td>21.831730</td>\n",
       "      <td>17.385490</td>\n",
       "      <td>86.587475</td>\n",
       "      <td>7.055039</td>\n",
       "      <td>28.755178</td>\n",
       "      <td>51.726987</td>\n",
       "      <td>13.314242</td>\n",
       "      <td>31.427591</td>\n",
       "      <td>...</td>\n",
       "      <td>51.594301</td>\n",
       "      <td>13.391769</td>\n",
       "      <td>31.330469</td>\n",
       "      <td>6.802860</td>\n",
       "      <td>1.760374</td>\n",
       "      <td>16.406397</td>\n",
       "      <td>11.777101</td>\n",
       "      <td>17.197313</td>\n",
       "      <td>60.358203</td>\n",
       "      <td>40.910546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.925230</td>\n",
       "      <td>0.936610</td>\n",
       "      <td>28.183014</td>\n",
       "      <td>23.035749</td>\n",
       "      <td>90.123133</td>\n",
       "      <td>8.773243</td>\n",
       "      <td>31.826385</td>\n",
       "      <td>55.750887</td>\n",
       "      <td>17.100556</td>\n",
       "      <td>36.692799</td>\n",
       "      <td>...</td>\n",
       "      <td>55.490325</td>\n",
       "      <td>16.975410</td>\n",
       "      <td>36.213594</td>\n",
       "      <td>9.550393</td>\n",
       "      <td>2.146468</td>\n",
       "      <td>17.719760</td>\n",
       "      <td>12.805424</td>\n",
       "      <td>19.068658</td>\n",
       "      <td>65.148128</td>\n",
       "      <td>44.504010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.251219</td>\n",
       "      <td>1.267463</td>\n",
       "      <td>36.813772</td>\n",
       "      <td>31.141866</td>\n",
       "      <td>93.387952</td>\n",
       "      <td>10.452262</td>\n",
       "      <td>35.184926</td>\n",
       "      <td>59.185350</td>\n",
       "      <td>20.919961</td>\n",
       "      <td>41.265159</td>\n",
       "      <td>...</td>\n",
       "      <td>59.078475</td>\n",
       "      <td>20.584376</td>\n",
       "      <td>41.071035</td>\n",
       "      <td>13.372731</td>\n",
       "      <td>2.645314</td>\n",
       "      <td>19.423720</td>\n",
       "      <td>14.091551</td>\n",
       "      <td>21.205695</td>\n",
       "      <td>68.994309</td>\n",
       "      <td>47.172065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.488967</td>\n",
       "      <td>2.522263</td>\n",
       "      <td>53.184067</td>\n",
       "      <td>48.142433</td>\n",
       "      <td>97.843221</td>\n",
       "      <td>26.598752</td>\n",
       "      <td>42.887263</td>\n",
       "      <td>63.979007</td>\n",
       "      <td>27.438286</td>\n",
       "      <td>53.513289</td>\n",
       "      <td>...</td>\n",
       "      <td>63.771097</td>\n",
       "      <td>27.362321</td>\n",
       "      <td>52.045373</td>\n",
       "      <td>23.305630</td>\n",
       "      <td>9.118302</td>\n",
       "      <td>27.003564</td>\n",
       "      <td>18.964157</td>\n",
       "      <td>26.007557</td>\n",
       "      <td>76.871053</td>\n",
       "      <td>56.442135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              cli         ili  hh_cmnty_cli  nohh_cmnty_cli  wearing_mask  \\\n",
       "count  893.000000  893.000000    893.000000      893.000000    893.000000   \n",
       "mean     0.972457    0.991809     29.075682       24.018729     89.637506   \n",
       "std      0.411997    0.415468      9.596290        8.988245      4.733549   \n",
       "min      0.139558    0.159477      9.171315        6.014740     76.895278   \n",
       "25%      0.673327    0.689367     21.831730       17.385490     86.587475   \n",
       "50%      0.925230    0.936610     28.183014       23.035749     90.123133   \n",
       "75%      1.251219    1.267463     36.813772       31.141866     93.387952   \n",
       "max      2.488967    2.522263     53.184067       48.142433     97.843221   \n",
       "\n",
       "       travel_outside_state  work_outside_home        shop  restaurant  \\\n",
       "count            893.000000         893.000000  893.000000  893.000000   \n",
       "mean               9.001325          31.620607   55.422982   16.554387   \n",
       "std                3.655616           4.754570    4.366780    5.688802   \n",
       "min                2.062500          18.299198   44.062442    3.800684   \n",
       "25%                7.055039          28.755178   51.726987   13.314242   \n",
       "50%                8.773243          31.826385   55.750887   17.100556   \n",
       "75%               10.452262          35.184926   59.185350   20.919961   \n",
       "max               26.598752          42.887263   63.979007   27.438286   \n",
       "\n",
       "       spent_time  ...      shop.2  restaurant.2  spent_time.2  large_event.2  \\\n",
       "count  893.000000  ...  893.000000    893.000000    893.000000     893.000000   \n",
       "mean    36.371653  ...   55.268628     16.444916     36.165898      10.248975   \n",
       "std      6.203232  ...    4.350540      5.656828      6.192274       4.498845   \n",
       "min     21.487077  ...   44.671891      3.837441     21.338425       2.334655   \n",
       "25%     31.427591  ...   51.594301     13.391769     31.330469       6.802860   \n",
       "50%     36.692799  ...   55.490325     16.975410     36.213594       9.550393   \n",
       "75%     41.265159  ...   59.078475     20.584376     41.071035      13.372731   \n",
       "max     53.513289  ...   63.771097     27.362321     52.045373      23.305630   \n",
       "\n",
       "       public_transit.2   anxious.2  depressed.2  felt_isolated.2  \\\n",
       "count        893.000000  893.000000   893.000000       893.000000   \n",
       "mean           2.369115   17.988147    12.993830        19.238723   \n",
       "std            1.114366    2.207022     1.713143         2.687435   \n",
       "min            0.873986   12.696977     8.462444        13.476209   \n",
       "25%            1.760374   16.406397    11.777101        17.197313   \n",
       "50%            2.146468   17.719760    12.805424        19.068658   \n",
       "75%            2.645314   19.423720    14.091551        21.205695   \n",
       "max            9.118302   27.003564    18.964157        26.007557   \n",
       "\n",
       "       worried_become_ill.2  worried_finances.2  \n",
       "count            893.000000          893.000000  \n",
       "mean              64.619920           44.411505  \n",
       "std                5.685865            4.605268  \n",
       "min               50.212234           35.072577  \n",
       "25%               60.358203           40.910546  \n",
       "50%               65.148128           44.504010  \n",
       "75%               68.994309           47.172065  \n",
       "max               76.871053           56.442135  \n",
       "\n",
       "[8 rows x 53 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_tt.iloc[:, 40:].describe() #查看测试集数据分布，并和训练集数据分布对比，两者特征之间数据分布差异不是很大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f2d1f372ca0>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.scatter(data_tr.loc[:, 'cli'], data_tr.loc[:, 'tested_positive.2']) #肉眼分析cli特征与目标之间相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f2d1f47fd90>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.scatter(data_tr.loc[:, 'ili'], data_tr.loc[:, 'tested_positive.2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f2d1f39f9a0>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.scatter(data_tr.loc[:, 'cli'], data_tr.loc[:, 'ili'])  #cli 和ili两者差不多，所以这两个特征用一个就行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f2d0bbab5b0>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Le1ga2SjU3WVh/N7rAz9+88hhPHHwLcxoqcfKrZ+4FA8Orq78vBMDeiibhoioNnawyU8W5myucdswZH9tpObbqVCcCW2HplO9G4UyVuk8TiBYEN48cnjOIc0zqpXbDw6ubukmrLhhbxaiEDn7mwDmsy6dBvtzmLjvejyyoc/3HM4g7MAbtloDuQgqi71Bj4J74uBbrs9l3x/G4dVJxWBOFKIguzBNi55Bd3AG0e5FTyst2P7FvjkLm0GCsF9zr1ZuwoobBnOiEAUJKgq4djD0+l2XzY+RJQAWplO4c+dE5e/0C8J2d0fjc5b/fpYnmjFnThQir17cTnaaYfTY6UozKVP/8FzA54wKRenACmBuq1q3NYHl2cy8PLibzILSvJPliWacmROFyG3zi2lSXSjO4LsHjnv2DxcAKz6QMZ6cEweF4gxE4HpWp11K6Zdeso+ha+TkpKTjzJyoBn4VGabNL3funKgrj60A9r9+OpzBt9GZqeL8RlnlCxIkNeVMo7BLojsGc6KAgpbFVQf0bXuOIttlGc/G7AR2awGn4mypZYFfaopplGCYZiEKYGQ8j7ueOhSoIsOtDO+d96aRJCkAXVaw8JGx0sYqlV9NFjxTU0yjBMeZOZEPOzh7BSQntxxwWDs822Xjml48f+hEZRFzaVfp7M2pci7bSQBkuyxMThUraSZTi4Hl2Uygvizkj8GcyIffAl11WVzSap5TAnz3wPE5C7l+KSO37fteVSjMgzeOaRYiH17B2S2fa6p5zmaseRUdcWB/qAj62cLt72cVSvNxZk5UpbpixbR4mRZxDUhutdAAcMXy9+NPBnpxx86JZg4/NPX2YjEtVnL23VwM5kQObhUrVkpgpQXFmQuhLWOl5wVy55uAm/2vn45NmWHWsMnH7XFLFi1grjsCGMyJHEyLlxkrhdnZCxt77CoWewdndYfEuAsSyDNWGltuuoLBOyIYzClxGul3bZpVF1yqNvKThTntWuMcyIPOxG25quvaiT3Go4bBnBKl0X7Xph4iSRf0b7bSgm1fuHJeeok9xtvPt5pFRC4VkX0i8s8ickREvlK+f4uI5EVkovzv080fLpG3Rvpdj4zn8e75ZG3uCZvbQdPsMR4NQWbm0wDuUtWficj7AYyJyEvln21X1f/WvOER1aaRftfb9hyds8hJ7qqvJXuMR4PvzFxVT6jqz8rf/w7AqwD42YkiyVTjnRLB5cMvuPYRtzH4BFN9jdljPBpq2jQkIisA9AM4WL7ryyLysoh8R0S6Db+zSURGRWT01KlTjY2WyIdbnw+gVIXidVwZwOAThFsduds1Z3Os1gsczEXkfQB2A7hDVX8L4FsAPgygD8AJAF93+z1VfVRVB1R1oKenp/ERE3lw7jQ0MeVzTW8Enc5v1yZ3d0aDaIBTvEXEAvA8gD2q+g2Xn68A8LyqftTreQYGBnR0dLTOoRLVZu3WvcbWqgLgja03ziupu3ZVT6VunEq7XF9/mLUN7SYiY6o64PWYINUsAuDbAF51BnIRudjxsM8DeKXegRI1g1cO3HlcmbNV7e6xPIbWr8SbW29Ed7kzYCdb86FurN2613e9gdovSJplLYA/A7Cuqgzxv4rIYRF5GcC1AO5s5kCJamXKgQtgPK7MmYKZ7JDDJDJWGo9s6MPGNb2V4+nSIlj74WX42fGzc97sTOsN1H6+pYmq+hO4H2P4g/CHQxQeU8OrxVYKo8dOG1Mp9ow+6OHMcZMCACl1Q0yL4JarcpUmWA8Orq48bu3WvcY3O+bDo4ctcCmx7IW56nRJoTg7Zxt+NXtGf+2q5C3YiwDptFTa2s6oYvdY3nW27fdmR9HCYE6JZS9u1nr25rWrerB55LBnwI+jjJXG0sXWvI1RpqPvTJ3XWcIZTezNQolU3S+kFjsOHkeAIq/IScmFgySq2emUHYY3qOpZ+LY9R10bh9nrDRQ9nJlTIvkd9eYlboG8u8tCNmMZAzlQSqfs/H9vYWnGvUJHgDmpFlMqRcHmWVHFYE6J1El53TNTxUBdD4sziuLMrGv6RIE5qRZTKsVrMxa1F9MsFFlePbL9+mcntRKlUe+eN39acb4BulUCcYt+tHFmTpHktqHHrnF2+9kdOyfQd/+LlVQBt+abmWbXztk4t+jHD2fmFEl+G3rc8uGThWLlUAQAkFif/dMY0xF22YwVeNbNA5jjhcGcIsmrR7ZXiC4UZ7Dl2SM4Nz3retRbJ8hYadxyVQ47f/oWio5VUSslc87s5DFvycJgTpFkynlnuyz8tjBdOVjZTSce+2Zzns05cNkyY8DmrDt5GMwpkobWr8TQrkPzNri88553IO90+4fXVb5nwO4sXAClyLErVdyOcCt6FVN3uCULueDbyTgzp7Zy6ye+eyxf94afTpVOCR76/Gr/B1JiMZiTJ7967kaf21lVkZ8sYMeB4x1cg1I7AbiASQAYzMmDW7C1S//CCBxu5YcM5CXOhUzTiUm5bGZOjpw6G4M5GXnVetcSzE2z+3q33FspSWzu3D7Ozom7MSkI32AuIpcC+HsAF6E0cXpUVb8pIssA7ASwAsCbAL6oqmeaN1RqNa9a76C8Zvf1bLm3UnA/KiUhnLswnW+CSzMWFlspTE4VmVYhV0Fm5tMA7lLVn4nI+wGMichLAP4cwA9VdauIDAMYBnB384ZKzVY9g852Wa69wGvpZ+01ux9avxJDTx+qaZZd2geUzFm5c7Zd/SY4WSgiY6WxfUMfgzi5CnJs3AkAJ8rf/05EXgWQA/A5ANeUH/Y4gB+BwTzyTCkPtxm0lRJYaZlTIljrx3vTLD4/WcCWZ48kNl0CmLfUu8lVzbbDSnFR56gpZy4iKwD0AzgI4KJyoAeAX6OUhqEI80p5uAWP4qwim7GwZNGCmqtZ7DcNUzATJHunptdBEdXcFjLDSHFRZwkczEXkfQB2A7hDVX8rciFxqaoqIq7/6YrIJgCbAKC3t7ex0VJDvGZ7piBxtlDExH3X1/Q6fqf81DJjjaMlC9N46POrsW3PUd81AdMnHdN6Ao9sI5NAO0BFxEIpkO9Q1WfKd78tIheXf34xgJNuv6uqj6rqgKoO9PQk74DcOPGa7ZmCRD3Bw+uUn1w2k+hAnstmcOSBGzDYn3Ntw2ulBdmM5dtW1u13WcFCXoJUswiAbwN4VVW/4fjRswBuB7C1/PX7TRkhhcZrthdm+ZvpTUNQ6h1iqptOAuff3kh3QnY2pFqJ+jQtEpFPAfgnAIcB2D1F/walvPlTAHoBHEOpNPG013MNDAzo6Ohoo2OmOrmlPzJWujI7DGu3pylYp0Uwq2qskkmC7i4L4/fWlpYi8iMiY6o64PWYINUsP4G5sveP6hkYtZYzSGe7LCxakMLZwvx65bC67LnN8gFUuh0mNZAD8TsMmpKDO0ATrno2fmaq9nrlembsixakOrJZ1tkEV+hQtLEFbsL5Hb/mx+ssTjebRw7jzp0TiS479MJqE2oXBvOEa6ReeWQ8j7ueOhTozWBkPI/+B17EdxPS9bCejgGsNqF2YjBPuHpLDu0ZuelUn/xkAWu37sXIeL7y2KTkwjeu6cX2DX2BHmsHfZ5eT+3GnHnC1Vty6FUrbrNTLoutZOXHd4/lMXDZMmQzlmu6yK7KYbkgRQmDecLVW68cdNt4oTiTqEAOlP6m+5874vozZyknUZQwmHeAekoO62lPmyRuKaPuLgv3ffYKBnKKJObMyZXbdvJO17VwAQM5RRZn5uSqOj2T5F2bQbl9UmnmGalEtWAwpzncghMA3PXUoTaPrLmyGauyK/ZXZwuuOznT5U6h9jXKTxbmdIAM+4xUolowmFOFW7/zoacPAQJjiWISZKzUnDa/K4ZfcH3cjOq8a1R9VXiABLULc+ZUYTqgwnnSUNKkADx888fm3Jcz1ODnsplAJZs8QILagcG8A42M57F2615cPvxCZeMP0KFByGWrp1cv8SDXiFv6qR0YzDuM3TvFrdfK0ozV7uE1hZU2b86fVcxrTTDYn8PDN69GLpuZd4iEX6Dmln5qF99+5mFiP/P2GhnP446dE64/6+4qLQAm8XzlRzb04c6dE549Yx4J2EXSrSe8vQhafSgzUVhC6WdOybB55DC+e+C48edJLTvMWCkM9udw/3NHPP/GoFUoPAGIoorBvAP4BfIksxc3/T6A1lKFEtYhHkRh8s2Zi8h3ROSkiLziuG+LiORFZKL879PNHSbVw17o7NRAvnFNbyXoBjk0oiMXgCkxgiyAPgbgBpf7t6tqX/nfD8IdFjXKeahEp3pwcHXl+yAVJqxCoTjzDeaq+mMAngc1U/QEqYdOAjEUqlTXivv1mmEVCsVdI6WJXxaRl8tpmG7Tg0Rkk4iMisjoqVOnGng5qkXSUwZWWrBxTS8WuERzKy3zAnN1uWF3l4VsxppXekgUV4FKE0VkBYDnVfWj5dsXAfgNShVZ/xnAxar67/yeh6WJzeHWT2XLs0cSfQ5nNmNhyaIFrmmkbMaasz2fKO6aVpqoqm87XuRvATxfz/NQ49z6qdzzzGFIIk7iNDtbKBoXNYMsdhIlTV1pFhG52HHz8wBeMT2WmsstN14ozmCqONumEbXG8mym7vNNiZLId2YuIk8AuAbAB0XklwDuA3CNiPShlGZ5E8BfNG+IBJj7Zic9N+7GuVhZz/mmREnE7fwx4LWF3NlPO6kWLUghY6Ur/cadOy55OAR1Am7nTwi3VIpWfU2y6RnFxIPuC5rcjUlUwq6JMdAJqRSvGvAkH4xBFBYG84gbGc8jZdoZkyBeG5zSHfD3EzWKwTzC7Fx5p89Mb/3Epe0eAlHkMWceorAX4zplS74tl83g2lU9eOLgW5hRRVoEt37i0jk9VojIHYN5SEybd4DaT2ofGc/79t+Oq2zGgsj8/ul2SeFgf47Bm6gOTLOExLR5p/pIMj8j43kM7TqUyEAuALbcdAXG770ej2zocz2WjYjqw5l5SEwVJ7VWomzbcxTFmeTlyAXAbY7+4iwpJAoXZ+YhCWtreRLKEO3iE7sKpbvLwtKMhR0HjmPt1r0YGc+3cXREycRgHhK3ftmmreX2CUCXD78wL7h1LTTXW8fF8qWl9MnvLV2MjWt68V5xFpOFIhQX1hIY0InCxTRLSIIe9Ou1UAoA756Pd/WKAJW2tPnJAnYcOD5vl2ot520SUTAM5iEKkgc2LZR+9akJz12QcVEduE3Z/ySkk4iihGmWFhoZzxvP5JzV+M/Ka8E2tUTh4sw8JH4bhuz0Sieq7uzINrVE4WML3BB4tajNlQP7tj1HjbPyJMtYadxyVQ77XjvFNrVEdWIL3BbxalFrL3B20rZ8W1qEm4GIWoQ58xD4LebFPZD79Szs7rJgpeY+KmOl8fUvXslATtQivsFcRL4jIidF5BXHfctE5CUR+Xn5a3dzhxltSV/My3ZZxoAuAMbvvR7b/uRKbs8naqMgaZbHAPx3AH/vuG8YwA9VdauIDJdv3x3+8OJhaP1KDD19CMXZ5G3DB4DJqdJxbW45f/uNjNvzidrLd2auqj8GcLrq7s8BeLz8/eMABsMdVnzYHQ6TGsgBVBYtg+5wJaLWq3cB9CJVPVH+/tcALjI9UEQ2AdgEAL29vXW+XDS5VbEkjbM1LeC/w5WI2iNQaaKIrADwvKp+tHx7UlWzjp+fUVXfvHncSxOra8mnzk/HrlVtNmPhbKGIBSmgOOv+mOqySgZsovZqZmni2yJysaqeEJGLAZys83liY/PI4Tl9RuJYM57LZrB/eF3ltv3mlJ8sIC2CGVUGcKKYqjeYPwvgdgBby1+/H9qIImhkPO/aMCpO3PLbXLQkSo4gpYlPAPi/AFaKyC9F5EsoBfHrROTnAP5t+XZibdtzNNaBHABuuYqBmyjJfGfmqnqr4Ud/FPJYIisJHf52j+UxcNkyBnSihOJ2/jK3RllAMmblAHuIEyVdRwVzU2dDtwMjhnYdAhSJqh9PwicMInLXMcHc64Qft0ZZSTxUOeltB4g6WWKCuV8/cdMJP/bvxFnGSuPjvUtx4BdnMKMKAZBKCWYcnyq4W5Mo2RIRzL1m3XZANwVsO/jHpW48aH9wvzc3IkqWRARzr1m3HcBMATvbZcWqUdaiBSkMXLYMDw6u9nwca8iJOksi+pl7zbptQ+tXwkrPb+R6dqqIu3e/HItADgCThSLueeYwRsbz7R4KEUVIIoK5aWHPef9gfw5LFs7/IDIL4Ny0oUlJRNmfOoiIbIkI5m7tWQFg6vz0nBns2UK8mmJ5ifuiLRGFKxHBfLA/h4dvXo1sxppz/5mpuSmJuJTmZTMWusun+6TF/YyfuPwtRNQaiVgABUoBfdueo5ismn07UxJT56fbMbSadHdZGL/3+sptt57pLDMkomqJCeaAOfWQnyzgzp0Tkd+Wn04J7vvsFXPu46EQRBREooK5V7141AM5YM55scyQiPwkImduMy2ERoGVgmtppFNxVlmlQkR1ieXM3LS7cbA/h9Fjp/HEwbcwE+A4vFaa1bnb601YpUJE9YhdMPfaug8AO38avUAOIPCYWKVCRPVoKJiLyJsAfgdgBsC034GjYfDauv/uuenI7uS0z9j0wioVIqpXGDnza1W1rxWBHPDeul9dlhgVVlpw6ycunZfPt9KCbKZUT57LZvDwzau50ElEdYldmsVUsRLN+TiQEmDbF67EYH8OA5ctY4khETWFaAP5ZRF5A8AZlGLp/1LVR10eswnAJgDo7e296tixY3W/HuC+iSbKBMAbW29s9zCIKMZEZMwv+9FomuVTqvpxAH8M4K9E5A+rH6Cqj6rqgKoO9PT0NPhy5q377dTdZSEXoNkXEVGzNBTMVTVf/noSwPcAXB3GoIJ4NyJb8610ademW407FzSJqFXqzpmLyBIAKVX9Xfn76wE8ENrIPGzbc7StZ3SmRTCrajyejjlxImq1RhZALwLwPSl19VsA4B9U9f+EMiof7TziTQB8/YtXugZpbrsnonapO5ir6i8AXBniWAJp9wk7t63pZcAmosiJVWnidd/4EX5+8t22vX42Y/mevUlE1A6xaLQ1Mp7Hv/5PP2hqIBcAG9f0Yu2Hlxkfk6STiogoWSIfzDePHMadOydwvskLngpg32unsOPff5JlhkQUO5EO5iPjeew4cLxluzvtVgEsMySiuIl0znzbnqMt3aZvz7x5ug8RxU2kg3krSxCrZ94sMySiOIl0msV0Mn09vJ4pLcKOhUQUa5EO5mEcMpGx0nhkQx+2b+hDd9f8fi4ZK23cBEREFBeRTrPkPA5oDiKbsbDlpisqgXqwP2c8co6IKM4iHcyH1q/E0NOHaj49SFDaqem2wYe5cCJKokgHczvo3vPMyygUZwP9To6zbSLqQJEO5sCFmfTIeB73P3cEZ6ZKuzAFF04Xqk6nEBF1msgHcxvTI0REZpGuZiEiomAYzImIEoDBnIgoARjMiYgSgMGciCgBREPYMh/4xUROATjWshcM5oMAftPuQQTAcYaL4wwXxxmu6nFepqo9Xr/Q0mAeRSIyqqoD7R6HH44zXBxnuDjOcNUzTqZZiIgSgMGciCgBGMyBR9s9gIA4znBxnOHiOMNV8zg7PmdORJQEnJkTESUAgzkRUQJ0dDAXkTdF5LCITIjIaLvHYxOR74jISRF5xXHfMhF5SUR+Xv7a3c4xlsfkNs4tIpIvX9MJEfl0O8dYHtOlIrJPRP5ZRI6IyFfK90fmmnqMMVLXU0QWi8hPReRQeZz3l++/XEQOisi/iMhOEVkY0XE+JiJvOK5nXzvHaRORtIiMi8jz5ds1X8+ODuZl16pqX8RqTx8DcEPVfcMAfqiq/wrAD8u32+0xzB8nAGwvX9M+Vf1Bi8fkZhrAXar6+wDWAPgrEfl9ROuamsYIROt6ngOwTlWvBNAH4AYRWQPgayiN8yMAzgD4UvuGCMA8TgAYclzPiXYNsMpXALzquF3z9WQwjyBV/TGA01V3fw7A4+XvHwcw2MoxuTGMM3JU9YSq/qz8/e9Q+p8mhwhdU48xRoqWvFO+aZX/KYB1AHaV72/7f58e44wcEbkEwI0A/nf5tqCO69npwVwBvCgiYyKyqd2D8XGRqp4of/9rABe1czA+viwiL5fTMG1PBzmJyAoA/QAOIqLXtGqMQMSuZzklMAHgJICXALwOYFJVp8sP+SUi8EZUPU5Vta/nQ+XruV1EFrVvhBWPAPhrAPbZmB9AHdez04P5p1T14wD+GKWPtX/Y7gEFoaV60kjOMgB8C8CHUfpoewLA19s6GgcReR+A3QDuUNXfOn8WlWvqMsbIXU9VnVHVPgCXALgawKr2jshd9ThF5KMA7kFpvH8AYBmAu9s3QkBEPgPgpKqONfpcHR3MVTVf/noSwPdQ+g8zqt4WkYsBoPz1ZJvH40pV3y7/TzQL4G8RkWsqIhZKQXKHqj5TvjtS19RtjFG9ngCgqpMA9gH4JICsiNjHUF4CIN+ucVVzjPOGcjpLVfUcgL9D+6/nWgA3icibAJ5EKb3yTdRxPTs2mIvIEhF5v/09gOsBvOL9W231LIDby9/fDuD7bRyLkR0cyz6PCFzTcg7y2wBeVdVvOH4UmWtqGmPUrqeI9IhItvx9BsB1KOX39wH4Qvlhbf/v0zDO1xxv3oJSHrqt11NV71HVS1R1BYA/BbBXVW9DHdezY3eAisiHUJqNA6WDrf9BVR9q45AqROQJANeg1AbzbQD3ARgB8BSAXpTaCH9RVdu6+GgY5zUopQQUwJsA/sKRl24LEfkUgH8CcBgX8pJ/g1JOOhLX1GOMtyJC11NEPobSglwapcngU6r6QPn/pydRSl2MA9hYnv1GbZx7AfQAEAATAP7SsVDaViJyDYD/qKqfqed6dmwwJyJKko5NsxARJQmDORFRAjCYExElAIM5EVECMJgTESUAgzkRUQIwmBMRJcD/B2I1XuEk3UqGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data_tr.loc[:,  'tested_positive'], data_tr.loc[:, 'tested_positive.2']) #day1 目标值与day3目标值相关性，线性相关的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f2d1f31f1f0>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.scatter(data_tr.loc[:,  'tested_positive.1'], data_tr.loc[:, 'tested_positive.2']) #day2 目标值与day3目标值相关性，线性相关的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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>cli</th>\n",
       "      <th>ili</th>\n",
       "      <th>hh_cmnty_cli</th>\n",
       "      <th>nohh_cmnty_cli</th>\n",
       "      <th>wearing_mask</th>\n",
       "      <th>travel_outside_state</th>\n",
       "      <th>work_outside_home</th>\n",
       "      <th>shop</th>\n",
       "      <th>restaurant</th>\n",
       "      <th>spent_time</th>\n",
       "      <th>...</th>\n",
       "      <th>restaurant.2</th>\n",
       "      <th>spent_time.2</th>\n",
       "      <th>large_event.2</th>\n",
       "      <th>public_transit.2</th>\n",
       "      <th>anxious.2</th>\n",
       "      <th>depressed.2</th>\n",
       "      <th>felt_isolated.2</th>\n",
       "      <th>worried_become_ill.2</th>\n",
       "      <th>worried_finances.2</th>\n",
       "      <th>tested_positive.2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cli</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.995735</td>\n",
       "      <td>0.893416</td>\n",
       "      <td>0.882322</td>\n",
       "      <td>-0.107406</td>\n",
       "      <td>-0.095964</td>\n",
       "      <td>0.087305</td>\n",
       "      <td>-0.364165</td>\n",
       "      <td>-0.143318</td>\n",
       "      <td>-0.209020</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.151291</td>\n",
       "      <td>-0.222834</td>\n",
       "      <td>-0.060308</td>\n",
       "      <td>-0.374071</td>\n",
       "      <td>0.237135</td>\n",
       "      <td>0.081456</td>\n",
       "      <td>0.098345</td>\n",
       "      <td>0.228750</td>\n",
       "      <td>0.550564</td>\n",
       "      <td>0.838504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ili</th>\n",
       "      <td>0.995735</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.889729</td>\n",
       "      <td>0.878280</td>\n",
       "      <td>-0.109015</td>\n",
       "      <td>-0.106934</td>\n",
       "      <td>0.086355</td>\n",
       "      <td>-0.357443</td>\n",
       "      <td>-0.142082</td>\n",
       "      <td>-0.207210</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.150141</td>\n",
       "      <td>-0.220942</td>\n",
       "      <td>-0.061298</td>\n",
       "      <td>-0.363873</td>\n",
       "      <td>0.245228</td>\n",
       "      <td>0.086229</td>\n",
       "      <td>0.104250</td>\n",
       "      <td>0.222909</td>\n",
       "      <td>0.544776</td>\n",
       "      <td>0.830527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hh_cmnty_cli</th>\n",
       "      <td>0.893416</td>\n",
       "      <td>0.889729</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.997225</td>\n",
       "      <td>-0.035441</td>\n",
       "      <td>-0.069595</td>\n",
       "      <td>0.079219</td>\n",
       "      <td>-0.472746</td>\n",
       "      <td>-0.247043</td>\n",
       "      <td>-0.293775</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.253615</td>\n",
       "      <td>-0.300062</td>\n",
       "      <td>-0.136937</td>\n",
       "      <td>-0.433276</td>\n",
       "      <td>0.307581</td>\n",
       "      <td>0.181497</td>\n",
       "      <td>0.203577</td>\n",
       "      <td>0.350255</td>\n",
       "      <td>0.561942</td>\n",
       "      <td>0.879724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nohh_cmnty_cli</th>\n",
       "      <td>0.882322</td>\n",
       "      <td>0.878280</td>\n",
       "      <td>0.997225</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.046063</td>\n",
       "      <td>-0.061914</td>\n",
       "      <td>0.097756</td>\n",
       "      <td>-0.465374</td>\n",
       "      <td>-0.238106</td>\n",
       "      <td>-0.280916</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.245265</td>\n",
       "      <td>-0.287482</td>\n",
       "      <td>-0.129474</td>\n",
       "      <td>-0.424996</td>\n",
       "      <td>0.317836</td>\n",
       "      <td>0.188467</td>\n",
       "      <td>0.203599</td>\n",
       "      <td>0.345448</td>\n",
       "      <td>0.534711</td>\n",
       "      <td>0.869938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wearing_mask</th>\n",
       "      <td>-0.107406</td>\n",
       "      <td>-0.109015</td>\n",
       "      <td>-0.035441</td>\n",
       "      <td>-0.046063</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.220808</td>\n",
       "      <td>-0.735649</td>\n",
       "      <td>-0.691597</td>\n",
       "      <td>-0.788714</td>\n",
       "      <td>-0.807623</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.785281</td>\n",
       "      <td>-0.802659</td>\n",
       "      <td>-0.889021</td>\n",
       "      <td>0.133487</td>\n",
       "      <td>0.204031</td>\n",
       "      <td>-0.067720</td>\n",
       "      <td>0.427533</td>\n",
       "      <td>0.840528</td>\n",
       "      <td>0.340101</td>\n",
       "      <td>-0.069531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>travel_outside_state</th>\n",
       "      <td>-0.095964</td>\n",
       "      <td>-0.106934</td>\n",
       "      <td>-0.069595</td>\n",
       "      <td>-0.061914</td>\n",
       "      <td>-0.220808</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.264107</td>\n",
       "      <td>0.256911</td>\n",
       "      <td>0.288473</td>\n",
       "      <td>0.349829</td>\n",
       "      <td>...</td>\n",
       "      <td>0.288098</td>\n",
       "      <td>0.336937</td>\n",
       "      <td>0.319736</td>\n",
       "      <td>-0.203611</td>\n",
       "      <td>0.001592</td>\n",
       "      <td>0.064425</td>\n",
       "      <td>-0.370776</td>\n",
       "      <td>-0.131961</td>\n",
       "      <td>-0.093096</td>\n",
       "      <td>-0.097303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>work_outside_home</th>\n",
       "      <td>0.087305</td>\n",
       "      <td>0.086355</td>\n",
       "      <td>0.079219</td>\n",
       "      <td>0.097756</td>\n",
       "      <td>-0.735649</td>\n",
       "      <td>0.264107</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.631958</td>\n",
       "      <td>0.743673</td>\n",
       "      <td>0.698047</td>\n",
       "      <td>...</td>\n",
       "      <td>0.730349</td>\n",
       "      <td>0.705533</td>\n",
       "      <td>0.758575</td>\n",
       "      <td>-0.110176</td>\n",
       "      <td>0.018259</td>\n",
       "      <td>0.075562</td>\n",
       "      <td>-0.430307</td>\n",
       "      <td>-0.652231</td>\n",
       "      <td>-0.317717</td>\n",
       "      <td>0.034865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shop</th>\n",
       "      <td>-0.364165</td>\n",
       "      <td>-0.357443</td>\n",
       "      <td>-0.472746</td>\n",
       "      <td>-0.465374</td>\n",
       "      <td>-0.691597</td>\n",
       "      <td>0.256911</td>\n",
       "      <td>0.631958</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.820916</td>\n",
       "      <td>0.819035</td>\n",
       "      <td>...</td>\n",
       "      <td>0.811055</td>\n",
       "      <td>0.838358</td>\n",
       "      <td>0.787237</td>\n",
       "      <td>0.130046</td>\n",
       "      <td>-0.228007</td>\n",
       "      <td>-0.029168</td>\n",
       "      <td>-0.496368</td>\n",
       "      <td>-0.866789</td>\n",
       "      <td>-0.475304</td>\n",
       "      <td>-0.410430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restaurant</th>\n",
       "      <td>-0.143318</td>\n",
       "      <td>-0.142082</td>\n",
       "      <td>-0.247043</td>\n",
       "      <td>-0.238106</td>\n",
       "      <td>-0.788714</td>\n",
       "      <td>0.288473</td>\n",
       "      <td>0.743673</td>\n",
       "      <td>0.820916</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.878576</td>\n",
       "      <td>...</td>\n",
       "      <td>0.993358</td>\n",
       "      <td>0.876107</td>\n",
       "      <td>0.909089</td>\n",
       "      <td>-0.046081</td>\n",
       "      <td>-0.278715</td>\n",
       "      <td>-0.074727</td>\n",
       "      <td>-0.648631</td>\n",
       "      <td>-0.832131</td>\n",
       "      <td>-0.430842</td>\n",
       "      <td>-0.157945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spent_time</th>\n",
       "      <td>-0.209020</td>\n",
       "      <td>-0.207210</td>\n",
       "      <td>-0.293775</td>\n",
       "      <td>-0.280916</td>\n",
       "      <td>-0.807623</td>\n",
       "      <td>0.349829</td>\n",
       "      <td>0.698047</td>\n",
       "      <td>0.819035</td>\n",
       "      <td>0.878576</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.875365</td>\n",
       "      <td>0.986713</td>\n",
       "      <td>0.912682</td>\n",
       "      <td>-0.040623</td>\n",
       "      <td>-0.169965</td>\n",
       "      <td>0.105281</td>\n",
       "      <td>-0.517139</td>\n",
       "      <td>-0.867460</td>\n",
       "      <td>-0.522985</td>\n",
       "      <td>-0.252125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>large_event</th>\n",
       "      <td>-0.042033</td>\n",
       "      <td>-0.043535</td>\n",
       "      <td>-0.124151</td>\n",
       "      <td>-0.116761</td>\n",
       "      <td>-0.894970</td>\n",
       "      <td>0.324270</td>\n",
       "      <td>0.767305</td>\n",
       "      <td>0.781862</td>\n",
       "      <td>0.912449</td>\n",
       "      <td>0.918504</td>\n",
       "      <td>...</td>\n",
       "      <td>0.910579</td>\n",
       "      <td>0.913814</td>\n",
       "      <td>0.993111</td>\n",
       "      <td>-0.139139</td>\n",
       "      <td>-0.215598</td>\n",
       "      <td>0.055579</td>\n",
       "      <td>-0.565014</td>\n",
       "      <td>-0.874083</td>\n",
       "      <td>-0.372589</td>\n",
       "      <td>-0.052473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>public_transit</th>\n",
       "      <td>-0.367103</td>\n",
       "      <td>-0.356652</td>\n",
       "      <td>-0.432142</td>\n",
       "      <td>-0.423773</td>\n",
       "      <td>0.131350</td>\n",
       "      <td>-0.198308</td>\n",
       "      <td>-0.110077</td>\n",
       "      <td>0.132385</td>\n",
       "      <td>-0.043954</td>\n",
       "      <td>-0.037282</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.048799</td>\n",
       "      <td>-0.035965</td>\n",
       "      <td>-0.137080</td>\n",
       "      <td>0.982095</td>\n",
       "      <td>-0.055799</td>\n",
       "      <td>-0.167599</td>\n",
       "      <td>0.001697</td>\n",
       "      <td>-0.046611</td>\n",
       "      <td>-0.138801</td>\n",
       "      <td>-0.448360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anxious</th>\n",
       "      <td>0.273874</td>\n",
       "      <td>0.281974</td>\n",
       "      <td>0.336748</td>\n",
       "      <td>0.344074</td>\n",
       "      <td>0.232620</td>\n",
       "      <td>-0.023175</td>\n",
       "      <td>0.013537</td>\n",
       "      <td>-0.265503</td>\n",
       "      <td>-0.312912</td>\n",
       "      <td>-0.209830</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.327660</td>\n",
       "      <td>-0.218920</td>\n",
       "      <td>-0.283515</td>\n",
       "      <td>-0.054270</td>\n",
       "      <td>0.951196</td>\n",
       "      <td>0.539596</td>\n",
       "      <td>0.516252</td>\n",
       "      <td>0.280087</td>\n",
       "      <td>0.217988</td>\n",
       "      <td>0.173295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>depressed</th>\n",
       "      <td>0.098033</td>\n",
       "      <td>0.102715</td>\n",
       "      <td>0.184739</td>\n",
       "      <td>0.190062</td>\n",
       "      <td>-0.070022</td>\n",
       "      <td>0.058548</td>\n",
       "      <td>0.075801</td>\n",
       "      <td>-0.041607</td>\n",
       "      <td>-0.074059</td>\n",
       "      <td>0.104628</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.065903</td>\n",
       "      <td>0.113934</td>\n",
       "      <td>0.063086</td>\n",
       "      <td>-0.165972</td>\n",
       "      <td>0.599423</td>\n",
       "      <td>0.953157</td>\n",
       "      <td>0.592656</td>\n",
       "      <td>-0.055694</td>\n",
       "      <td>0.021274</td>\n",
       "      <td>0.037689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>felt_isolated</th>\n",
       "      <td>0.100928</td>\n",
       "      <td>0.107079</td>\n",
       "      <td>0.198176</td>\n",
       "      <td>0.197661</td>\n",
       "      <td>0.422058</td>\n",
       "      <td>-0.376858</td>\n",
       "      <td>-0.431247</td>\n",
       "      <td>-0.491608</td>\n",
       "      <td>-0.642316</td>\n",
       "      <td>-0.511772</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.633869</td>\n",
       "      <td>-0.497951</td>\n",
       "      <td>-0.544678</td>\n",
       "      <td>0.009742</td>\n",
       "      <td>0.526345</td>\n",
       "      <td>0.604416</td>\n",
       "      <td>0.978303</td>\n",
       "      <td>0.395606</td>\n",
       "      <td>0.128047</td>\n",
       "      <td>0.082182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worried_become_ill</th>\n",
       "      <td>0.218502</td>\n",
       "      <td>0.212931</td>\n",
       "      <td>0.344457</td>\n",
       "      <td>0.340192</td>\n",
       "      <td>0.843990</td>\n",
       "      <td>-0.136811</td>\n",
       "      <td>-0.656085</td>\n",
       "      <td>-0.864583</td>\n",
       "      <td>-0.835101</td>\n",
       "      <td>-0.870365</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.831439</td>\n",
       "      <td>-0.869933</td>\n",
       "      <td>-0.872394</td>\n",
       "      <td>-0.043575</td>\n",
       "      <td>0.251045</td>\n",
       "      <td>-0.038421</td>\n",
       "      <td>0.419940</td>\n",
       "      <td>0.992976</td>\n",
       "      <td>0.490127</td>\n",
       "      <td>0.262211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worried_finances</th>\n",
       "      <td>0.537608</td>\n",
       "      <td>0.532217</td>\n",
       "      <td>0.552431</td>\n",
       "      <td>0.524022</td>\n",
       "      <td>0.354130</td>\n",
       "      <td>-0.096444</td>\n",
       "      <td>-0.339975</td>\n",
       "      <td>-0.489539</td>\n",
       "      <td>-0.447892</td>\n",
       "      <td>-0.536561</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.451124</td>\n",
       "      <td>-0.536959</td>\n",
       "      <td>-0.397443</td>\n",
       "      <td>-0.141140</td>\n",
       "      <td>0.152500</td>\n",
       "      <td>0.027382</td>\n",
       "      <td>0.144230</td>\n",
       "      <td>0.506907</td>\n",
       "      <td>0.988123</td>\n",
       "      <td>0.475462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tested_positive</th>\n",
       "      <td>0.839122</td>\n",
       "      <td>0.829756</td>\n",
       "      <td>0.880187</td>\n",
       "      <td>0.869674</td>\n",
       "      <td>-0.049350</td>\n",
       "      <td>-0.113726</td>\n",
       "      <td>0.025780</td>\n",
       "      <td>-0.427815</td>\n",
       "      <td>-0.173726</td>\n",
       "      <td>-0.275476</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.174815</td>\n",
       "      <td>-0.278257</td>\n",
       "      <td>-0.083275</td>\n",
       "      <td>-0.451809</td>\n",
       "      <td>0.132802</td>\n",
       "      <td>0.021773</td>\n",
       "      <td>0.090015</td>\n",
       "      <td>0.285052</td>\n",
       "      <td>0.495753</td>\n",
       "      <td>0.981165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cli.1</th>\n",
       "      <td>0.980379</td>\n",
       "      <td>0.977225</td>\n",
       "      <td>0.887944</td>\n",
       "      <td>0.877606</td>\n",
       "      <td>-0.121569</td>\n",
       "      <td>-0.091186</td>\n",
       "      <td>0.096755</td>\n",
       "      <td>-0.348133</td>\n",
       "      <td>-0.129772</td>\n",
       "      <td>-0.189519</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.138355</td>\n",
       "      <td>-0.204750</td>\n",
       "      <td>-0.044520</td>\n",
       "      <td>-0.369066</td>\n",
       "      <td>0.254911</td>\n",
       "      <td>0.088243</td>\n",
       "      <td>0.093092</td>\n",
       "      <td>0.212721</td>\n",
       "      <td>0.540981</td>\n",
       "      <td>0.838224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ili.1</th>\n",
       "      <td>0.976171</td>\n",
       "      <td>0.980473</td>\n",
       "      <td>0.884020</td>\n",
       "      <td>0.873424</td>\n",
       "      <td>-0.123680</td>\n",
       "      <td>-0.102645</td>\n",
       "      <td>0.096343</td>\n",
       "      <td>-0.340973</td>\n",
       "      <td>-0.128114</td>\n",
       "      <td>-0.187173</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.136904</td>\n",
       "      <td>-0.202412</td>\n",
       "      <td>-0.045430</td>\n",
       "      <td>-0.358447</td>\n",
       "      <td>0.263278</td>\n",
       "      <td>0.092825</td>\n",
       "      <td>0.099412</td>\n",
       "      <td>0.206378</td>\n",
       "      <td>0.534751</td>\n",
       "      <td>0.829200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hh_cmnty_cli.1</th>\n",
       "      <td>0.896211</td>\n",
       "      <td>0.892667</td>\n",
       "      <td>0.998356</td>\n",
       "      <td>0.996165</td>\n",
       "      <td>-0.046423</td>\n",
       "      <td>-0.063619</td>\n",
       "      <td>0.089934</td>\n",
       "      <td>-0.462807</td>\n",
       "      <td>-0.235459</td>\n",
       "      <td>-0.280262</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.242962</td>\n",
       "      <td>-0.288664</td>\n",
       "      <td>-0.125902</td>\n",
       "      <td>-0.432234</td>\n",
       "      <td>0.319697</td>\n",
       "      <td>0.180703</td>\n",
       "      <td>0.195294</td>\n",
       "      <td>0.340223</td>\n",
       "      <td>0.556547</td>\n",
       "      <td>0.879438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nohh_cmnty_cli.1</th>\n",
       "      <td>0.885178</td>\n",
       "      <td>0.881292</td>\n",
       "      <td>0.995176</td>\n",
       "      <td>0.998259</td>\n",
       "      <td>-0.056529</td>\n",
       "      <td>-0.055823</td>\n",
       "      <td>0.107979</td>\n",
       "      <td>-0.455990</td>\n",
       "      <td>-0.226870</td>\n",
       "      <td>-0.268086</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.234893</td>\n",
       "      <td>-0.276769</td>\n",
       "      <td>-0.119138</td>\n",
       "      <td>-0.423434</td>\n",
       "      <td>0.328817</td>\n",
       "      <td>0.186480</td>\n",
       "      <td>0.195257</td>\n",
       "      <td>0.336189</td>\n",
       "      <td>0.528994</td>\n",
       "      <td>0.869278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wearing_mask.1</th>\n",
       "      <td>-0.101056</td>\n",
       "      <td>-0.102606</td>\n",
       "      <td>-0.030237</td>\n",
       "      <td>-0.040738</td>\n",
       "      <td>0.998287</td>\n",
       "      <td>-0.220397</td>\n",
       "      <td>-0.732848</td>\n",
       "      <td>-0.694338</td>\n",
       "      <td>-0.789257</td>\n",
       "      <td>-0.808963</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.787873</td>\n",
       "      <td>-0.806218</td>\n",
       "      <td>-0.892712</td>\n",
       "      <td>0.130892</td>\n",
       "      <td>0.208011</td>\n",
       "      <td>-0.071689</td>\n",
       "      <td>0.425830</td>\n",
       "      <td>0.843469</td>\n",
       "      <td>0.342057</td>\n",
       "      <td>-0.065600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>travel_outside_state.1</th>\n",
       "      <td>-0.097092</td>\n",
       "      <td>-0.107662</td>\n",
       "      <td>-0.069270</td>\n",
       "      <td>-0.062039</td>\n",
       "      <td>-0.220442</td>\n",
       "      <td>0.995838</td>\n",
       "      <td>0.259748</td>\n",
       "      <td>0.261335</td>\n",
       "      <td>0.286921</td>\n",
       "      <td>0.352038</td>\n",
       "      <td>...</td>\n",
       "      <td>0.287332</td>\n",
       "      <td>0.342678</td>\n",
       "      <td>0.321376</td>\n",
       "      <td>-0.202010</td>\n",
       "      <td>-0.003803</td>\n",
       "      <td>0.065990</td>\n",
       "      <td>-0.372008</td>\n",
       "      <td>-0.133520</td>\n",
       "      <td>-0.090896</td>\n",
       "      <td>-0.100407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>work_outside_home.1</th>\n",
       "      <td>0.087080</td>\n",
       "      <td>0.085966</td>\n",
       "      <td>0.074972</td>\n",
       "      <td>0.093529</td>\n",
       "      <td>-0.737554</td>\n",
       "      <td>0.268864</td>\n",
       "      <td>0.991471</td>\n",
       "      <td>0.616394</td>\n",
       "      <td>0.746680</td>\n",
       "      <td>0.697270</td>\n",
       "      <td>...</td>\n",
       "      <td>0.737749</td>\n",
       "      <td>0.698691</td>\n",
       "      <td>0.761755</td>\n",
       "      <td>-0.110337</td>\n",
       "      <td>0.025430</td>\n",
       "      <td>0.077455</td>\n",
       "      <td>-0.429387</td>\n",
       "      <td>-0.652367</td>\n",
       "      <td>-0.325294</td>\n",
       "      <td>0.037930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shop.1</th>\n",
       "      <td>-0.367850</td>\n",
       "      <td>-0.361304</td>\n",
       "      <td>-0.474799</td>\n",
       "      <td>-0.467316</td>\n",
       "      <td>-0.688627</td>\n",
       "      <td>0.252461</td>\n",
       "      <td>0.638500</td>\n",
       "      <td>0.991248</td>\n",
       "      <td>0.820264</td>\n",
       "      <td>0.808526</td>\n",
       "      <td>...</td>\n",
       "      <td>0.815317</td>\n",
       "      <td>0.829363</td>\n",
       "      <td>0.785631</td>\n",
       "      <td>0.131734</td>\n",
       "      <td>-0.232298</td>\n",
       "      <td>-0.029772</td>\n",
       "      <td>-0.492524</td>\n",
       "      <td>-0.864694</td>\n",
       "      <td>-0.478978</td>\n",
       "      <td>-0.412705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restaurant.1</th>\n",
       "      <td>-0.147491</td>\n",
       "      <td>-0.146353</td>\n",
       "      <td>-0.250349</td>\n",
       "      <td>-0.241687</td>\n",
       "      <td>-0.787245</td>\n",
       "      <td>0.288160</td>\n",
       "      <td>0.737725</td>\n",
       "      <td>0.816414</td>\n",
       "      <td>0.997496</td>\n",
       "      <td>0.877051</td>\n",
       "      <td>...</td>\n",
       "      <td>0.997484</td>\n",
       "      <td>0.876508</td>\n",
       "      <td>0.911182</td>\n",
       "      <td>-0.046082</td>\n",
       "      <td>-0.285147</td>\n",
       "      <td>-0.070812</td>\n",
       "      <td>-0.645411</td>\n",
       "      <td>-0.832047</td>\n",
       "      <td>-0.433497</td>\n",
       "      <td>-0.159121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spent_time.1</th>\n",
       "      <td>-0.216168</td>\n",
       "      <td>-0.214354</td>\n",
       "      <td>-0.297071</td>\n",
       "      <td>-0.284398</td>\n",
       "      <td>-0.805468</td>\n",
       "      <td>0.343854</td>\n",
       "      <td>0.700146</td>\n",
       "      <td>0.828992</td>\n",
       "      <td>0.877660</td>\n",
       "      <td>0.995393</td>\n",
       "      <td>...</td>\n",
       "      <td>0.876180</td>\n",
       "      <td>0.995383</td>\n",
       "      <td>0.916829</td>\n",
       "      <td>-0.039405</td>\n",
       "      <td>-0.175019</td>\n",
       "      <td>0.111992</td>\n",
       "      <td>-0.513196</td>\n",
       "      <td>-0.869427</td>\n",
       "      <td>-0.523476</td>\n",
       "      <td>-0.255714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>large_event.1</th>\n",
       "      <td>-0.051724</td>\n",
       "      <td>-0.052961</td>\n",
       "      <td>-0.130729</td>\n",
       "      <td>-0.123252</td>\n",
       "      <td>-0.892267</td>\n",
       "      <td>0.322149</td>\n",
       "      <td>0.762592</td>\n",
       "      <td>0.784996</td>\n",
       "      <td>0.911143</td>\n",
       "      <td>0.916514</td>\n",
       "      <td>...</td>\n",
       "      <td>0.912015</td>\n",
       "      <td>0.917360</td>\n",
       "      <td>0.997449</td>\n",
       "      <td>-0.137279</td>\n",
       "      <td>-0.226287</td>\n",
       "      <td>0.061477</td>\n",
       "      <td>-0.559791</td>\n",
       "      <td>-0.875258</td>\n",
       "      <td>-0.376785</td>\n",
       "      <td>-0.058079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>public_transit.1</th>\n",
       "      <td>-0.371063</td>\n",
       "      <td>-0.360574</td>\n",
       "      <td>-0.432765</td>\n",
       "      <td>-0.424445</td>\n",
       "      <td>0.132301</td>\n",
       "      <td>-0.201241</td>\n",
       "      <td>-0.109727</td>\n",
       "      <td>0.131371</td>\n",
       "      <td>-0.044942</td>\n",
       "      <td>-0.039224</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.047397</td>\n",
       "      <td>-0.036646</td>\n",
       "      <td>-0.136200</td>\n",
       "      <td>0.991364</td>\n",
       "      <td>-0.053367</td>\n",
       "      <td>-0.165462</td>\n",
       "      <td>0.005809</td>\n",
       "      <td>-0.047158</td>\n",
       "      <td>-0.140425</td>\n",
       "      <td>-0.449079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anxious.1</th>\n",
       "      <td>0.256712</td>\n",
       "      <td>0.264872</td>\n",
       "      <td>0.323053</td>\n",
       "      <td>0.331791</td>\n",
       "      <td>0.217574</td>\n",
       "      <td>-0.011044</td>\n",
       "      <td>0.018079</td>\n",
       "      <td>-0.246039</td>\n",
       "      <td>-0.295416</td>\n",
       "      <td>-0.189704</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.309644</td>\n",
       "      <td>-0.199497</td>\n",
       "      <td>-0.260678</td>\n",
       "      <td>-0.053189</td>\n",
       "      <td>0.980965</td>\n",
       "      <td>0.567651</td>\n",
       "      <td>0.519936</td>\n",
       "      <td>0.264619</td>\n",
       "      <td>0.201912</td>\n",
       "      <td>0.164537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>depressed.1</th>\n",
       "      <td>0.088676</td>\n",
       "      <td>0.093371</td>\n",
       "      <td>0.182383</td>\n",
       "      <td>0.188544</td>\n",
       "      <td>-0.069369</td>\n",
       "      <td>0.061782</td>\n",
       "      <td>0.075357</td>\n",
       "      <td>-0.034364</td>\n",
       "      <td>-0.073814</td>\n",
       "      <td>0.105809</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.066076</td>\n",
       "      <td>0.117099</td>\n",
       "      <td>0.065712</td>\n",
       "      <td>-0.164973</td>\n",
       "      <td>0.599952</td>\n",
       "      <td>0.978623</td>\n",
       "      <td>0.601982</td>\n",
       "      <td>-0.054501</td>\n",
       "      <td>0.024123</td>\n",
       "      <td>0.033149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>felt_isolated.1</th>\n",
       "      <td>0.099487</td>\n",
       "      <td>0.105446</td>\n",
       "      <td>0.201034</td>\n",
       "      <td>0.200843</td>\n",
       "      <td>0.424822</td>\n",
       "      <td>-0.374146</td>\n",
       "      <td>-0.430562</td>\n",
       "      <td>-0.493842</td>\n",
       "      <td>-0.645507</td>\n",
       "      <td>-0.514850</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.638136</td>\n",
       "      <td>-0.503093</td>\n",
       "      <td>-0.549472</td>\n",
       "      <td>0.009842</td>\n",
       "      <td>0.527040</td>\n",
       "      <td>0.608896</td>\n",
       "      <td>0.990446</td>\n",
       "      <td>0.401543</td>\n",
       "      <td>0.130005</td>\n",
       "      <td>0.081521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worried_become_ill.1</th>\n",
       "      <td>0.223326</td>\n",
       "      <td>0.217739</td>\n",
       "      <td>0.347562</td>\n",
       "      <td>0.343024</td>\n",
       "      <td>0.842499</td>\n",
       "      <td>-0.134507</td>\n",
       "      <td>-0.654251</td>\n",
       "      <td>-0.865601</td>\n",
       "      <td>-0.833903</td>\n",
       "      <td>-0.869399</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.832038</td>\n",
       "      <td>-0.870442</td>\n",
       "      <td>-0.874175</td>\n",
       "      <td>-0.045239</td>\n",
       "      <td>0.250985</td>\n",
       "      <td>-0.044886</td>\n",
       "      <td>0.414444</td>\n",
       "      <td>0.996878</td>\n",
       "      <td>0.492998</td>\n",
       "      <td>0.264816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worried_finances.1</th>\n",
       "      <td>0.543373</td>\n",
       "      <td>0.537874</td>\n",
       "      <td>0.557364</td>\n",
       "      <td>0.529514</td>\n",
       "      <td>0.347359</td>\n",
       "      <td>-0.094679</td>\n",
       "      <td>-0.328919</td>\n",
       "      <td>-0.482534</td>\n",
       "      <td>-0.439702</td>\n",
       "      <td>-0.529935</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.444050</td>\n",
       "      <td>-0.531072</td>\n",
       "      <td>-0.389929</td>\n",
       "      <td>-0.141796</td>\n",
       "      <td>0.168931</td>\n",
       "      <td>0.026522</td>\n",
       "      <td>0.138286</td>\n",
       "      <td>0.501713</td>\n",
       "      <td>0.994864</td>\n",
       "      <td>0.480958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tested_positive.1</th>\n",
       "      <td>0.839929</td>\n",
       "      <td>0.831129</td>\n",
       "      <td>0.880416</td>\n",
       "      <td>0.870315</td>\n",
       "      <td>-0.059477</td>\n",
       "      <td>-0.105467</td>\n",
       "      <td>0.031094</td>\n",
       "      <td>-0.419104</td>\n",
       "      <td>-0.165959</td>\n",
       "      <td>-0.264309</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.167639</td>\n",
       "      <td>-0.268959</td>\n",
       "      <td>-0.073982</td>\n",
       "      <td>-0.451397</td>\n",
       "      <td>0.143395</td>\n",
       "      <td>0.025272</td>\n",
       "      <td>0.085417</td>\n",
       "      <td>0.276338</td>\n",
       "      <td>0.491043</td>\n",
       "      <td>0.991012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cli.2</th>\n",
       "      <td>0.957059</td>\n",
       "      <td>0.954996</td>\n",
       "      <td>0.881768</td>\n",
       "      <td>0.872292</td>\n",
       "      <td>-0.135146</td>\n",
       "      <td>-0.086332</td>\n",
       "      <td>0.104981</td>\n",
       "      <td>-0.331428</td>\n",
       "      <td>-0.116415</td>\n",
       "      <td>-0.170275</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.124823</td>\n",
       "      <td>-0.185582</td>\n",
       "      <td>-0.027097</td>\n",
       "      <td>-0.363815</td>\n",
       "      <td>0.270811</td>\n",
       "      <td>0.096270</td>\n",
       "      <td>0.087526</td>\n",
       "      <td>0.197407</td>\n",
       "      <td>0.532770</td>\n",
       "      <td>0.835751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ili.2</th>\n",
       "      <td>0.952707</td>\n",
       "      <td>0.956979</td>\n",
       "      <td>0.877550</td>\n",
       "      <td>0.867896</td>\n",
       "      <td>-0.137841</td>\n",
       "      <td>-0.097991</td>\n",
       "      <td>0.104965</td>\n",
       "      <td>-0.323789</td>\n",
       "      <td>-0.114323</td>\n",
       "      <td>-0.167358</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.123005</td>\n",
       "      <td>-0.182693</td>\n",
       "      <td>-0.027895</td>\n",
       "      <td>-0.353209</td>\n",
       "      <td>0.279485</td>\n",
       "      <td>0.100997</td>\n",
       "      <td>0.094463</td>\n",
       "      <td>0.190436</td>\n",
       "      <td>0.526026</td>\n",
       "      <td>0.826075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hh_cmnty_cli.2</th>\n",
       "      <td>0.898067</td>\n",
       "      <td>0.894564</td>\n",
       "      <td>0.995396</td>\n",
       "      <td>0.993750</td>\n",
       "      <td>-0.058149</td>\n",
       "      <td>-0.057164</td>\n",
       "      <td>0.099741</td>\n",
       "      <td>-0.452086</td>\n",
       "      <td>-0.223203</td>\n",
       "      <td>-0.265245</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.231610</td>\n",
       "      <td>-0.275863</td>\n",
       "      <td>-0.113619</td>\n",
       "      <td>-0.431142</td>\n",
       "      <td>0.330882</td>\n",
       "      <td>0.180963</td>\n",
       "      <td>0.186653</td>\n",
       "      <td>0.329330</td>\n",
       "      <td>0.550290</td>\n",
       "      <td>0.878218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nohh_cmnty_cli.2</th>\n",
       "      <td>0.887103</td>\n",
       "      <td>0.883263</td>\n",
       "      <td>0.991738</td>\n",
       "      <td>0.995093</td>\n",
       "      <td>-0.067698</td>\n",
       "      <td>-0.049281</td>\n",
       "      <td>0.117226</td>\n",
       "      <td>-0.445815</td>\n",
       "      <td>-0.215113</td>\n",
       "      <td>-0.253751</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.223909</td>\n",
       "      <td>-0.264597</td>\n",
       "      <td>-0.107674</td>\n",
       "      <td>-0.421805</td>\n",
       "      <td>0.339048</td>\n",
       "      <td>0.185514</td>\n",
       "      <td>0.186517</td>\n",
       "      <td>0.326080</td>\n",
       "      <td>0.522506</td>\n",
       "      <td>0.867535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wearing_mask.2</th>\n",
       "      <td>-0.094664</td>\n",
       "      <td>-0.096315</td>\n",
       "      <td>-0.025367</td>\n",
       "      <td>-0.035759</td>\n",
       "      <td>0.995953</td>\n",
       "      <td>-0.219423</td>\n",
       "      <td>-0.729730</td>\n",
       "      <td>-0.696457</td>\n",
       "      <td>-0.788931</td>\n",
       "      <td>-0.809003</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.789539</td>\n",
       "      <td>-0.808958</td>\n",
       "      <td>-0.895733</td>\n",
       "      <td>0.128696</td>\n",
       "      <td>0.212752</td>\n",
       "      <td>-0.075599</td>\n",
       "      <td>0.423325</td>\n",
       "      <td>0.845721</td>\n",
       "      <td>0.343891</td>\n",
       "      <td>-0.062037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>travel_outside_state.2</th>\n",
       "      <td>-0.097903</td>\n",
       "      <td>-0.107903</td>\n",
       "      <td>-0.069043</td>\n",
       "      <td>-0.062137</td>\n",
       "      <td>-0.219916</td>\n",
       "      <td>0.989310</td>\n",
       "      <td>0.258430</td>\n",
       "      <td>0.266438</td>\n",
       "      <td>0.285380</td>\n",
       "      <td>0.352962</td>\n",
       "      <td>...</td>\n",
       "      <td>0.286899</td>\n",
       "      <td>0.347804</td>\n",
       "      <td>0.322521</td>\n",
       "      <td>-0.199731</td>\n",
       "      <td>-0.007996</td>\n",
       "      <td>0.067252</td>\n",
       "      <td>-0.372366</td>\n",
       "      <td>-0.135255</td>\n",
       "      <td>-0.089308</td>\n",
       "      <td>-0.103868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>work_outside_home.2</th>\n",
       "      <td>0.085913</td>\n",
       "      <td>0.084708</td>\n",
       "      <td>0.069933</td>\n",
       "      <td>0.088394</td>\n",
       "      <td>-0.739112</td>\n",
       "      <td>0.275348</td>\n",
       "      <td>0.975017</td>\n",
       "      <td>0.599363</td>\n",
       "      <td>0.748185</td>\n",
       "      <td>0.700309</td>\n",
       "      <td>...</td>\n",
       "      <td>0.743692</td>\n",
       "      <td>0.695202</td>\n",
       "      <td>0.765953</td>\n",
       "      <td>-0.111477</td>\n",
       "      <td>0.028803</td>\n",
       "      <td>0.080485</td>\n",
       "      <td>-0.428880</td>\n",
       "      <td>-0.652395</td>\n",
       "      <td>-0.333070</td>\n",
       "      <td>0.039304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shop.2</th>\n",
       "      <td>-0.370197</td>\n",
       "      <td>-0.363795</td>\n",
       "      <td>-0.476538</td>\n",
       "      <td>-0.469026</td>\n",
       "      <td>-0.685437</td>\n",
       "      <td>0.249670</td>\n",
       "      <td>0.640972</td>\n",
       "      <td>0.977890</td>\n",
       "      <td>0.818073</td>\n",
       "      <td>0.800586</td>\n",
       "      <td>...</td>\n",
       "      <td>0.818509</td>\n",
       "      <td>0.819755</td>\n",
       "      <td>0.783057</td>\n",
       "      <td>0.132409</td>\n",
       "      <td>-0.237570</td>\n",
       "      <td>-0.031062</td>\n",
       "      <td>-0.488979</td>\n",
       "      <td>-0.862711</td>\n",
       "      <td>-0.482649</td>\n",
       "      <td>-0.415130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restaurant.2</th>\n",
       "      <td>-0.151291</td>\n",
       "      <td>-0.150141</td>\n",
       "      <td>-0.253615</td>\n",
       "      <td>-0.245265</td>\n",
       "      <td>-0.785281</td>\n",
       "      <td>0.288098</td>\n",
       "      <td>0.730349</td>\n",
       "      <td>0.811055</td>\n",
       "      <td>0.993358</td>\n",
       "      <td>0.875365</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.876542</td>\n",
       "      <td>0.912564</td>\n",
       "      <td>-0.046246</td>\n",
       "      <td>-0.292246</td>\n",
       "      <td>-0.067040</td>\n",
       "      <td>-0.641984</td>\n",
       "      <td>-0.831868</td>\n",
       "      <td>-0.435929</td>\n",
       "      <td>-0.160181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spent_time.2</th>\n",
       "      <td>-0.222834</td>\n",
       "      <td>-0.220942</td>\n",
       "      <td>-0.300062</td>\n",
       "      <td>-0.287482</td>\n",
       "      <td>-0.802659</td>\n",
       "      <td>0.336937</td>\n",
       "      <td>0.705533</td>\n",
       "      <td>0.838358</td>\n",
       "      <td>0.876107</td>\n",
       "      <td>0.986713</td>\n",
       "      <td>...</td>\n",
       "      <td>0.876542</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.918931</td>\n",
       "      <td>-0.037616</td>\n",
       "      <td>-0.180294</td>\n",
       "      <td>0.118125</td>\n",
       "      <td>-0.507902</td>\n",
       "      <td>-0.870630</td>\n",
       "      <td>-0.524228</td>\n",
       "      <td>-0.258956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>large_event.2</th>\n",
       "      <td>-0.060308</td>\n",
       "      <td>-0.061298</td>\n",
       "      <td>-0.136937</td>\n",
       "      <td>-0.129474</td>\n",
       "      <td>-0.889021</td>\n",
       "      <td>0.319736</td>\n",
       "      <td>0.758575</td>\n",
       "      <td>0.787237</td>\n",
       "      <td>0.909089</td>\n",
       "      <td>0.912682</td>\n",
       "      <td>...</td>\n",
       "      <td>0.912564</td>\n",
       "      <td>0.918931</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.135339</td>\n",
       "      <td>-0.238586</td>\n",
       "      <td>0.066021</td>\n",
       "      <td>-0.554675</td>\n",
       "      <td>-0.875487</td>\n",
       "      <td>-0.380926</td>\n",
       "      <td>-0.063709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>public_transit.2</th>\n",
       "      <td>-0.374071</td>\n",
       "      <td>-0.363873</td>\n",
       "      <td>-0.433276</td>\n",
       "      <td>-0.424996</td>\n",
       "      <td>0.133487</td>\n",
       "      <td>-0.203611</td>\n",
       "      <td>-0.110176</td>\n",
       "      <td>0.130046</td>\n",
       "      <td>-0.046081</td>\n",
       "      <td>-0.040623</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.046246</td>\n",
       "      <td>-0.037616</td>\n",
       "      <td>-0.135339</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.052253</td>\n",
       "      <td>-0.164079</td>\n",
       "      <td>0.009571</td>\n",
       "      <td>-0.047068</td>\n",
       "      <td>-0.142098</td>\n",
       "      <td>-0.450436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anxious.2</th>\n",
       "      <td>0.237135</td>\n",
       "      <td>0.245228</td>\n",
       "      <td>0.307581</td>\n",
       "      <td>0.317836</td>\n",
       "      <td>0.204031</td>\n",
       "      <td>0.001592</td>\n",
       "      <td>0.018259</td>\n",
       "      <td>-0.228007</td>\n",
       "      <td>-0.278715</td>\n",
       "      <td>-0.169965</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.292246</td>\n",
       "      <td>-0.180294</td>\n",
       "      <td>-0.238586</td>\n",
       "      <td>-0.052253</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.594797</td>\n",
       "      <td>0.525171</td>\n",
       "      <td>0.251509</td>\n",
       "      <td>0.184126</td>\n",
       "      <td>0.152903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>depressed.2</th>\n",
       "      <td>0.081456</td>\n",
       "      <td>0.086229</td>\n",
       "      <td>0.181497</td>\n",
       "      <td>0.188467</td>\n",
       "      <td>-0.067720</td>\n",
       "      <td>0.064425</td>\n",
       "      <td>0.075562</td>\n",
       "      <td>-0.029168</td>\n",
       "      <td>-0.074727</td>\n",
       "      <td>0.105281</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.067040</td>\n",
       "      <td>0.118125</td>\n",
       "      <td>0.066021</td>\n",
       "      <td>-0.164079</td>\n",
       "      <td>0.594797</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.610310</td>\n",
       "      <td>-0.051246</td>\n",
       "      <td>0.026621</td>\n",
       "      <td>0.029578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>felt_isolated.2</th>\n",
       "      <td>0.098345</td>\n",
       "      <td>0.104250</td>\n",
       "      <td>0.203577</td>\n",
       "      <td>0.203599</td>\n",
       "      <td>0.427533</td>\n",
       "      <td>-0.370776</td>\n",
       "      <td>-0.430307</td>\n",
       "      <td>-0.496368</td>\n",
       "      <td>-0.648631</td>\n",
       "      <td>-0.517139</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.641984</td>\n",
       "      <td>-0.507902</td>\n",
       "      <td>-0.554675</td>\n",
       "      <td>0.009571</td>\n",
       "      <td>0.525171</td>\n",
       "      <td>0.610310</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.407931</td>\n",
       "      <td>0.132465</td>\n",
       "      <td>0.081174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worried_become_ill.2</th>\n",
       "      <td>0.228750</td>\n",
       "      <td>0.222909</td>\n",
       "      <td>0.350255</td>\n",
       "      <td>0.345448</td>\n",
       "      <td>0.840528</td>\n",
       "      <td>-0.131961</td>\n",
       "      <td>-0.652231</td>\n",
       "      <td>-0.866789</td>\n",
       "      <td>-0.832131</td>\n",
       "      <td>-0.867460</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.831868</td>\n",
       "      <td>-0.870630</td>\n",
       "      <td>-0.875487</td>\n",
       "      <td>-0.047068</td>\n",
       "      <td>0.251509</td>\n",
       "      <td>-0.051246</td>\n",
       "      <td>0.407931</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.495890</td>\n",
       "      <td>0.267610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worried_finances.2</th>\n",
       "      <td>0.550564</td>\n",
       "      <td>0.544776</td>\n",
       "      <td>0.561942</td>\n",
       "      <td>0.534711</td>\n",
       "      <td>0.340101</td>\n",
       "      <td>-0.093096</td>\n",
       "      <td>-0.317717</td>\n",
       "      <td>-0.475304</td>\n",
       "      <td>-0.430842</td>\n",
       "      <td>-0.522985</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.435929</td>\n",
       "      <td>-0.524228</td>\n",
       "      <td>-0.380926</td>\n",
       "      <td>-0.142098</td>\n",
       "      <td>0.184126</td>\n",
       "      <td>0.026621</td>\n",
       "      <td>0.132465</td>\n",
       "      <td>0.495890</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.485843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tested_positive.2</th>\n",
       "      <td>0.838504</td>\n",
       "      <td>0.830527</td>\n",
       "      <td>0.879724</td>\n",
       "      <td>0.869938</td>\n",
       "      <td>-0.069531</td>\n",
       "      <td>-0.097303</td>\n",
       "      <td>0.034865</td>\n",
       "      <td>-0.410430</td>\n",
       "      <td>-0.157945</td>\n",
       "      <td>-0.252125</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.160181</td>\n",
       "      <td>-0.258956</td>\n",
       "      <td>-0.063709</td>\n",
       "      <td>-0.450436</td>\n",
       "      <td>0.152903</td>\n",
       "      <td>0.029578</td>\n",
       "      <td>0.081174</td>\n",
       "      <td>0.267610</td>\n",
       "      <td>0.485843</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             cli       ili  hh_cmnty_cli  nohh_cmnty_cli  \\\n",
       "cli                     1.000000  0.995735      0.893416        0.882322   \n",
       "ili                     0.995735  1.000000      0.889729        0.878280   \n",
       "hh_cmnty_cli            0.893416  0.889729      1.000000        0.997225   \n",
       "nohh_cmnty_cli          0.882322  0.878280      0.997225        1.000000   \n",
       "wearing_mask           -0.107406 -0.109015     -0.035441       -0.046063   \n",
       "travel_outside_state   -0.095964 -0.106934     -0.069595       -0.061914   \n",
       "work_outside_home       0.087305  0.086355      0.079219        0.097756   \n",
       "shop                   -0.364165 -0.357443     -0.472746       -0.465374   \n",
       "restaurant             -0.143318 -0.142082     -0.247043       -0.238106   \n",
       "spent_time             -0.209020 -0.207210     -0.293775       -0.280916   \n",
       "large_event            -0.042033 -0.043535     -0.124151       -0.116761   \n",
       "public_transit         -0.367103 -0.356652     -0.432142       -0.423773   \n",
       "anxious                 0.273874  0.281974      0.336748        0.344074   \n",
       "depressed               0.098033  0.102715      0.184739        0.190062   \n",
       "felt_isolated           0.100928  0.107079      0.198176        0.197661   \n",
       "worried_become_ill      0.218502  0.212931      0.344457        0.340192   \n",
       "worried_finances        0.537608  0.532217      0.552431        0.524022   \n",
       "tested_positive         0.839122  0.829756      0.880187        0.869674   \n",
       "cli.1                   0.980379  0.977225      0.887944        0.877606   \n",
       "ili.1                   0.976171  0.980473      0.884020        0.873424   \n",
       "hh_cmnty_cli.1          0.896211  0.892667      0.998356        0.996165   \n",
       "nohh_cmnty_cli.1        0.885178  0.881292      0.995176        0.998259   \n",
       "wearing_mask.1         -0.101056 -0.102606     -0.030237       -0.040738   \n",
       "travel_outside_state.1 -0.097092 -0.107662     -0.069270       -0.062039   \n",
       "work_outside_home.1     0.087080  0.085966      0.074972        0.093529   \n",
       "shop.1                 -0.367850 -0.361304     -0.474799       -0.467316   \n",
       "restaurant.1           -0.147491 -0.146353     -0.250349       -0.241687   \n",
       "spent_time.1           -0.216168 -0.214354     -0.297071       -0.284398   \n",
       "large_event.1          -0.051724 -0.052961     -0.130729       -0.123252   \n",
       "public_transit.1       -0.371063 -0.360574     -0.432765       -0.424445   \n",
       "anxious.1               0.256712  0.264872      0.323053        0.331791   \n",
       "depressed.1             0.088676  0.093371      0.182383        0.188544   \n",
       "felt_isolated.1         0.099487  0.105446      0.201034        0.200843   \n",
       "worried_become_ill.1    0.223326  0.217739      0.347562        0.343024   \n",
       "worried_finances.1      0.543373  0.537874      0.557364        0.529514   \n",
       "tested_positive.1       0.839929  0.831129      0.880416        0.870315   \n",
       "cli.2                   0.957059  0.954996      0.881768        0.872292   \n",
       "ili.2                   0.952707  0.956979      0.877550        0.867896   \n",
       "hh_cmnty_cli.2          0.898067  0.894564      0.995396        0.993750   \n",
       "nohh_cmnty_cli.2        0.887103  0.883263      0.991738        0.995093   \n",
       "wearing_mask.2         -0.094664 -0.096315     -0.025367       -0.035759   \n",
       "travel_outside_state.2 -0.097903 -0.107903     -0.069043       -0.062137   \n",
       "work_outside_home.2     0.085913  0.084708      0.069933        0.088394   \n",
       "shop.2                 -0.370197 -0.363795     -0.476538       -0.469026   \n",
       "restaurant.2           -0.151291 -0.150141     -0.253615       -0.245265   \n",
       "spent_time.2           -0.222834 -0.220942     -0.300062       -0.287482   \n",
       "large_event.2          -0.060308 -0.061298     -0.136937       -0.129474   \n",
       "public_transit.2       -0.374071 -0.363873     -0.433276       -0.424996   \n",
       "anxious.2               0.237135  0.245228      0.307581        0.317836   \n",
       "depressed.2             0.081456  0.086229      0.181497        0.188467   \n",
       "felt_isolated.2         0.098345  0.104250      0.203577        0.203599   \n",
       "worried_become_ill.2    0.228750  0.222909      0.350255        0.345448   \n",
       "worried_finances.2      0.550564  0.544776      0.561942        0.534711   \n",
       "tested_positive.2       0.838504  0.830527      0.879724        0.869938   \n",
       "\n",
       "                        wearing_mask  travel_outside_state  work_outside_home  \\\n",
       "cli                        -0.107406             -0.095964           0.087305   \n",
       "ili                        -0.109015             -0.106934           0.086355   \n",
       "hh_cmnty_cli               -0.035441             -0.069595           0.079219   \n",
       "nohh_cmnty_cli             -0.046063             -0.061914           0.097756   \n",
       "wearing_mask                1.000000             -0.220808          -0.735649   \n",
       "travel_outside_state       -0.220808              1.000000           0.264107   \n",
       "work_outside_home          -0.735649              0.264107           1.000000   \n",
       "shop                       -0.691597              0.256911           0.631958   \n",
       "restaurant                 -0.788714              0.288473           0.743673   \n",
       "spent_time                 -0.807623              0.349829           0.698047   \n",
       "large_event                -0.894970              0.324270           0.767305   \n",
       "public_transit              0.131350             -0.198308          -0.110077   \n",
       "anxious                     0.232620             -0.023175           0.013537   \n",
       "depressed                  -0.070022              0.058548           0.075801   \n",
       "felt_isolated               0.422058             -0.376858          -0.431247   \n",
       "worried_become_ill          0.843990             -0.136811          -0.656085   \n",
       "worried_finances            0.354130             -0.096444          -0.339975   \n",
       "tested_positive            -0.049350             -0.113726           0.025780   \n",
       "cli.1                      -0.121569             -0.091186           0.096755   \n",
       "ili.1                      -0.123680             -0.102645           0.096343   \n",
       "hh_cmnty_cli.1             -0.046423             -0.063619           0.089934   \n",
       "nohh_cmnty_cli.1           -0.056529             -0.055823           0.107979   \n",
       "wearing_mask.1              0.998287             -0.220397          -0.732848   \n",
       "travel_outside_state.1     -0.220442              0.995838           0.259748   \n",
       "work_outside_home.1        -0.737554              0.268864           0.991471   \n",
       "shop.1                     -0.688627              0.252461           0.638500   \n",
       "restaurant.1               -0.787245              0.288160           0.737725   \n",
       "spent_time.1               -0.805468              0.343854           0.700146   \n",
       "large_event.1              -0.892267              0.322149           0.762592   \n",
       "public_transit.1            0.132301             -0.201241          -0.109727   \n",
       "anxious.1                   0.217574             -0.011044           0.018079   \n",
       "depressed.1                -0.069369              0.061782           0.075357   \n",
       "felt_isolated.1             0.424822             -0.374146          -0.430562   \n",
       "worried_become_ill.1        0.842499             -0.134507          -0.654251   \n",
       "worried_finances.1          0.347359             -0.094679          -0.328919   \n",
       "tested_positive.1          -0.059477             -0.105467           0.031094   \n",
       "cli.2                      -0.135146             -0.086332           0.104981   \n",
       "ili.2                      -0.137841             -0.097991           0.104965   \n",
       "hh_cmnty_cli.2             -0.058149             -0.057164           0.099741   \n",
       "nohh_cmnty_cli.2           -0.067698             -0.049281           0.117226   \n",
       "wearing_mask.2              0.995953             -0.219423          -0.729730   \n",
       "travel_outside_state.2     -0.219916              0.989310           0.258430   \n",
       "work_outside_home.2        -0.739112              0.275348           0.975017   \n",
       "shop.2                     -0.685437              0.249670           0.640972   \n",
       "restaurant.2               -0.785281              0.288098           0.730349   \n",
       "spent_time.2               -0.802659              0.336937           0.705533   \n",
       "large_event.2              -0.889021              0.319736           0.758575   \n",
       "public_transit.2            0.133487             -0.203611          -0.110176   \n",
       "anxious.2                   0.204031              0.001592           0.018259   \n",
       "depressed.2                -0.067720              0.064425           0.075562   \n",
       "felt_isolated.2             0.427533             -0.370776          -0.430307   \n",
       "worried_become_ill.2        0.840528             -0.131961          -0.652231   \n",
       "worried_finances.2          0.340101             -0.093096          -0.317717   \n",
       "tested_positive.2          -0.069531             -0.097303           0.034865   \n",
       "\n",
       "                            shop  restaurant  spent_time  ...  restaurant.2  \\\n",
       "cli                    -0.364165   -0.143318   -0.209020  ...     -0.151291   \n",
       "ili                    -0.357443   -0.142082   -0.207210  ...     -0.150141   \n",
       "hh_cmnty_cli           -0.472746   -0.247043   -0.293775  ...     -0.253615   \n",
       "nohh_cmnty_cli         -0.465374   -0.238106   -0.280916  ...     -0.245265   \n",
       "wearing_mask           -0.691597   -0.788714   -0.807623  ...     -0.785281   \n",
       "travel_outside_state    0.256911    0.288473    0.349829  ...      0.288098   \n",
       "work_outside_home       0.631958    0.743673    0.698047  ...      0.730349   \n",
       "shop                    1.000000    0.820916    0.819035  ...      0.811055   \n",
       "restaurant              0.820916    1.000000    0.878576  ...      0.993358   \n",
       "spent_time              0.819035    0.878576    1.000000  ...      0.875365   \n",
       "large_event             0.781862    0.912449    0.918504  ...      0.910579   \n",
       "public_transit          0.132385   -0.043954   -0.037282  ...     -0.048799   \n",
       "anxious                -0.265503   -0.312912   -0.209830  ...     -0.327660   \n",
       "depressed              -0.041607   -0.074059    0.104628  ...     -0.065903   \n",
       "felt_isolated          -0.491608   -0.642316   -0.511772  ...     -0.633869   \n",
       "worried_become_ill     -0.864583   -0.835101   -0.870365  ...     -0.831439   \n",
       "worried_finances       -0.489539   -0.447892   -0.536561  ...     -0.451124   \n",
       "tested_positive        -0.427815   -0.173726   -0.275476  ...     -0.174815   \n",
       "cli.1                  -0.348133   -0.129772   -0.189519  ...     -0.138355   \n",
       "ili.1                  -0.340973   -0.128114   -0.187173  ...     -0.136904   \n",
       "hh_cmnty_cli.1         -0.462807   -0.235459   -0.280262  ...     -0.242962   \n",
       "nohh_cmnty_cli.1       -0.455990   -0.226870   -0.268086  ...     -0.234893   \n",
       "wearing_mask.1         -0.694338   -0.789257   -0.808963  ...     -0.787873   \n",
       "travel_outside_state.1  0.261335    0.286921    0.352038  ...      0.287332   \n",
       "work_outside_home.1     0.616394    0.746680    0.697270  ...      0.737749   \n",
       "shop.1                  0.991248    0.820264    0.808526  ...      0.815317   \n",
       "restaurant.1            0.816414    0.997496    0.877051  ...      0.997484   \n",
       "spent_time.1            0.828992    0.877660    0.995393  ...      0.876180   \n",
       "large_event.1           0.784996    0.911143    0.916514  ...      0.912015   \n",
       "public_transit.1        0.131371   -0.044942   -0.039224  ...     -0.047397   \n",
       "anxious.1              -0.246039   -0.295416   -0.189704  ...     -0.309644   \n",
       "depressed.1            -0.034364   -0.073814    0.105809  ...     -0.066076   \n",
       "felt_isolated.1        -0.493842   -0.645507   -0.514850  ...     -0.638136   \n",
       "worried_become_ill.1   -0.865601   -0.833903   -0.869399  ...     -0.832038   \n",
       "worried_finances.1     -0.482534   -0.439702   -0.529935  ...     -0.444050   \n",
       "tested_positive.1      -0.419104   -0.165959   -0.264309  ...     -0.167639   \n",
       "cli.2                  -0.331428   -0.116415   -0.170275  ...     -0.124823   \n",
       "ili.2                  -0.323789   -0.114323   -0.167358  ...     -0.123005   \n",
       "hh_cmnty_cli.2         -0.452086   -0.223203   -0.265245  ...     -0.231610   \n",
       "nohh_cmnty_cli.2       -0.445815   -0.215113   -0.253751  ...     -0.223909   \n",
       "wearing_mask.2         -0.696457   -0.788931   -0.809003  ...     -0.789539   \n",
       "travel_outside_state.2  0.266438    0.285380    0.352962  ...      0.286899   \n",
       "work_outside_home.2     0.599363    0.748185    0.700309  ...      0.743692   \n",
       "shop.2                  0.977890    0.818073    0.800586  ...      0.818509   \n",
       "restaurant.2            0.811055    0.993358    0.875365  ...      1.000000   \n",
       "spent_time.2            0.838358    0.876107    0.986713  ...      0.876542   \n",
       "large_event.2           0.787237    0.909089    0.912682  ...      0.912564   \n",
       "public_transit.2        0.130046   -0.046081   -0.040623  ...     -0.046246   \n",
       "anxious.2              -0.228007   -0.278715   -0.169965  ...     -0.292246   \n",
       "depressed.2            -0.029168   -0.074727    0.105281  ...     -0.067040   \n",
       "felt_isolated.2        -0.496368   -0.648631   -0.517139  ...     -0.641984   \n",
       "worried_become_ill.2   -0.866789   -0.832131   -0.867460  ...     -0.831868   \n",
       "worried_finances.2     -0.475304   -0.430842   -0.522985  ...     -0.435929   \n",
       "tested_positive.2      -0.410430   -0.157945   -0.252125  ...     -0.160181   \n",
       "\n",
       "                        spent_time.2  large_event.2  public_transit.2  \\\n",
       "cli                        -0.222834      -0.060308         -0.374071   \n",
       "ili                        -0.220942      -0.061298         -0.363873   \n",
       "hh_cmnty_cli               -0.300062      -0.136937         -0.433276   \n",
       "nohh_cmnty_cli             -0.287482      -0.129474         -0.424996   \n",
       "wearing_mask               -0.802659      -0.889021          0.133487   \n",
       "travel_outside_state        0.336937       0.319736         -0.203611   \n",
       "work_outside_home           0.705533       0.758575         -0.110176   \n",
       "shop                        0.838358       0.787237          0.130046   \n",
       "restaurant                  0.876107       0.909089         -0.046081   \n",
       "spent_time                  0.986713       0.912682         -0.040623   \n",
       "large_event                 0.913814       0.993111         -0.139139   \n",
       "public_transit             -0.035965      -0.137080          0.982095   \n",
       "anxious                    -0.218920      -0.283515         -0.054270   \n",
       "depressed                   0.113934       0.063086         -0.165972   \n",
       "felt_isolated              -0.497951      -0.544678          0.009742   \n",
       "worried_become_ill         -0.869933      -0.872394         -0.043575   \n",
       "worried_finances           -0.536959      -0.397443         -0.141140   \n",
       "tested_positive            -0.278257      -0.083275         -0.451809   \n",
       "cli.1                      -0.204750      -0.044520         -0.369066   \n",
       "ili.1                      -0.202412      -0.045430         -0.358447   \n",
       "hh_cmnty_cli.1             -0.288664      -0.125902         -0.432234   \n",
       "nohh_cmnty_cli.1           -0.276769      -0.119138         -0.423434   \n",
       "wearing_mask.1             -0.806218      -0.892712          0.130892   \n",
       "travel_outside_state.1      0.342678       0.321376         -0.202010   \n",
       "work_outside_home.1         0.698691       0.761755         -0.110337   \n",
       "shop.1                      0.829363       0.785631          0.131734   \n",
       "restaurant.1                0.876508       0.911182         -0.046082   \n",
       "spent_time.1                0.995383       0.916829         -0.039405   \n",
       "large_event.1               0.917360       0.997449         -0.137279   \n",
       "public_transit.1           -0.036646      -0.136200          0.991364   \n",
       "anxious.1                  -0.199497      -0.260678         -0.053189   \n",
       "depressed.1                 0.117099       0.065712         -0.164973   \n",
       "felt_isolated.1            -0.503093      -0.549472          0.009842   \n",
       "worried_become_ill.1       -0.870442      -0.874175         -0.045239   \n",
       "worried_finances.1         -0.531072      -0.389929         -0.141796   \n",
       "tested_positive.1          -0.268959      -0.073982         -0.451397   \n",
       "cli.2                      -0.185582      -0.027097         -0.363815   \n",
       "ili.2                      -0.182693      -0.027895         -0.353209   \n",
       "hh_cmnty_cli.2             -0.275863      -0.113619         -0.431142   \n",
       "nohh_cmnty_cli.2           -0.264597      -0.107674         -0.421805   \n",
       "wearing_mask.2             -0.808958      -0.895733          0.128696   \n",
       "travel_outside_state.2      0.347804       0.322521         -0.199731   \n",
       "work_outside_home.2         0.695202       0.765953         -0.111477   \n",
       "shop.2                      0.819755       0.783057          0.132409   \n",
       "restaurant.2                0.876542       0.912564         -0.046246   \n",
       "spent_time.2                1.000000       0.918931         -0.037616   \n",
       "large_event.2               0.918931       1.000000         -0.135339   \n",
       "public_transit.2           -0.037616      -0.135339          1.000000   \n",
       "anxious.2                  -0.180294      -0.238586         -0.052253   \n",
       "depressed.2                 0.118125       0.066021         -0.164079   \n",
       "felt_isolated.2            -0.507902      -0.554675          0.009571   \n",
       "worried_become_ill.2       -0.870630      -0.875487         -0.047068   \n",
       "worried_finances.2         -0.524228      -0.380926         -0.142098   \n",
       "tested_positive.2          -0.258956      -0.063709         -0.450436   \n",
       "\n",
       "                        anxious.2  depressed.2  felt_isolated.2  \\\n",
       "cli                      0.237135     0.081456         0.098345   \n",
       "ili                      0.245228     0.086229         0.104250   \n",
       "hh_cmnty_cli             0.307581     0.181497         0.203577   \n",
       "nohh_cmnty_cli           0.317836     0.188467         0.203599   \n",
       "wearing_mask             0.204031    -0.067720         0.427533   \n",
       "travel_outside_state     0.001592     0.064425        -0.370776   \n",
       "work_outside_home        0.018259     0.075562        -0.430307   \n",
       "shop                    -0.228007    -0.029168        -0.496368   \n",
       "restaurant              -0.278715    -0.074727        -0.648631   \n",
       "spent_time              -0.169965     0.105281        -0.517139   \n",
       "large_event             -0.215598     0.055579        -0.565014   \n",
       "public_transit          -0.055799    -0.167599         0.001697   \n",
       "anxious                  0.951196     0.539596         0.516252   \n",
       "depressed                0.599423     0.953157         0.592656   \n",
       "felt_isolated            0.526345     0.604416         0.978303   \n",
       "worried_become_ill       0.251045    -0.038421         0.419940   \n",
       "worried_finances         0.152500     0.027382         0.144230   \n",
       "tested_positive          0.132802     0.021773         0.090015   \n",
       "cli.1                    0.254911     0.088243         0.093092   \n",
       "ili.1                    0.263278     0.092825         0.099412   \n",
       "hh_cmnty_cli.1           0.319697     0.180703         0.195294   \n",
       "nohh_cmnty_cli.1         0.328817     0.186480         0.195257   \n",
       "wearing_mask.1           0.208011    -0.071689         0.425830   \n",
       "travel_outside_state.1  -0.003803     0.065990        -0.372008   \n",
       "work_outside_home.1      0.025430     0.077455        -0.429387   \n",
       "shop.1                  -0.232298    -0.029772        -0.492524   \n",
       "restaurant.1            -0.285147    -0.070812        -0.645411   \n",
       "spent_time.1            -0.175019     0.111992        -0.513196   \n",
       "large_event.1           -0.226287     0.061477        -0.559791   \n",
       "public_transit.1        -0.053367    -0.165462         0.005809   \n",
       "anxious.1                0.980965     0.567651         0.519936   \n",
       "depressed.1              0.599952     0.978623         0.601982   \n",
       "felt_isolated.1          0.527040     0.608896         0.990446   \n",
       "worried_become_ill.1     0.250985    -0.044886         0.414444   \n",
       "worried_finances.1       0.168931     0.026522         0.138286   \n",
       "tested_positive.1        0.143395     0.025272         0.085417   \n",
       "cli.2                    0.270811     0.096270         0.087526   \n",
       "ili.2                    0.279485     0.100997         0.094463   \n",
       "hh_cmnty_cli.2           0.330882     0.180963         0.186653   \n",
       "nohh_cmnty_cli.2         0.339048     0.185514         0.186517   \n",
       "wearing_mask.2           0.212752    -0.075599         0.423325   \n",
       "travel_outside_state.2  -0.007996     0.067252        -0.372366   \n",
       "work_outside_home.2      0.028803     0.080485        -0.428880   \n",
       "shop.2                  -0.237570    -0.031062        -0.488979   \n",
       "restaurant.2            -0.292246    -0.067040        -0.641984   \n",
       "spent_time.2            -0.180294     0.118125        -0.507902   \n",
       "large_event.2           -0.238586     0.066021        -0.554675   \n",
       "public_transit.2        -0.052253    -0.164079         0.009571   \n",
       "anxious.2                1.000000     0.594797         0.525171   \n",
       "depressed.2              0.594797     1.000000         0.610310   \n",
       "felt_isolated.2          0.525171     0.610310         1.000000   \n",
       "worried_become_ill.2     0.251509    -0.051246         0.407931   \n",
       "worried_finances.2       0.184126     0.026621         0.132465   \n",
       "tested_positive.2        0.152903     0.029578         0.081174   \n",
       "\n",
       "                        worried_become_ill.2  worried_finances.2  \\\n",
       "cli                                 0.228750            0.550564   \n",
       "ili                                 0.222909            0.544776   \n",
       "hh_cmnty_cli                        0.350255            0.561942   \n",
       "nohh_cmnty_cli                      0.345448            0.534711   \n",
       "wearing_mask                        0.840528            0.340101   \n",
       "travel_outside_state               -0.131961           -0.093096   \n",
       "work_outside_home                  -0.652231           -0.317717   \n",
       "shop                               -0.866789           -0.475304   \n",
       "restaurant                         -0.832131           -0.430842   \n",
       "spent_time                         -0.867460           -0.522985   \n",
       "large_event                        -0.874083           -0.372589   \n",
       "public_transit                     -0.046611           -0.138801   \n",
       "anxious                             0.280087            0.217988   \n",
       "depressed                          -0.055694            0.021274   \n",
       "felt_isolated                       0.395606            0.128047   \n",
       "worried_become_ill                  0.992976            0.490127   \n",
       "worried_finances                    0.506907            0.988123   \n",
       "tested_positive                     0.285052            0.495753   \n",
       "cli.1                               0.212721            0.540981   \n",
       "ili.1                               0.206378            0.534751   \n",
       "hh_cmnty_cli.1                      0.340223            0.556547   \n",
       "nohh_cmnty_cli.1                    0.336189            0.528994   \n",
       "wearing_mask.1                      0.843469            0.342057   \n",
       "travel_outside_state.1             -0.133520           -0.090896   \n",
       "work_outside_home.1                -0.652367           -0.325294   \n",
       "shop.1                             -0.864694           -0.478978   \n",
       "restaurant.1                       -0.832047           -0.433497   \n",
       "spent_time.1                       -0.869427           -0.523476   \n",
       "large_event.1                      -0.875258           -0.376785   \n",
       "public_transit.1                   -0.047158           -0.140425   \n",
       "anxious.1                           0.264619            0.201912   \n",
       "depressed.1                        -0.054501            0.024123   \n",
       "felt_isolated.1                     0.401543            0.130005   \n",
       "worried_become_ill.1                0.996878            0.492998   \n",
       "worried_finances.1                  0.501713            0.994864   \n",
       "tested_positive.1                   0.276338            0.491043   \n",
       "cli.2                               0.197407            0.532770   \n",
       "ili.2                               0.190436            0.526026   \n",
       "hh_cmnty_cli.2                      0.329330            0.550290   \n",
       "nohh_cmnty_cli.2                    0.326080            0.522506   \n",
       "wearing_mask.2                      0.845721            0.343891   \n",
       "travel_outside_state.2             -0.135255           -0.089308   \n",
       "work_outside_home.2                -0.652395           -0.333070   \n",
       "shop.2                             -0.862711           -0.482649   \n",
       "restaurant.2                       -0.831868           -0.435929   \n",
       "spent_time.2                       -0.870630           -0.524228   \n",
       "large_event.2                      -0.875487           -0.380926   \n",
       "public_transit.2                   -0.047068           -0.142098   \n",
       "anxious.2                           0.251509            0.184126   \n",
       "depressed.2                        -0.051246            0.026621   \n",
       "felt_isolated.2                     0.407931            0.132465   \n",
       "worried_become_ill.2                1.000000            0.495890   \n",
       "worried_finances.2                  0.495890            1.000000   \n",
       "tested_positive.2                   0.267610            0.485843   \n",
       "\n",
       "                        tested_positive.2  \n",
       "cli                              0.838504  \n",
       "ili                              0.830527  \n",
       "hh_cmnty_cli                     0.879724  \n",
       "nohh_cmnty_cli                   0.869938  \n",
       "wearing_mask                    -0.069531  \n",
       "travel_outside_state            -0.097303  \n",
       "work_outside_home                0.034865  \n",
       "shop                            -0.410430  \n",
       "restaurant                      -0.157945  \n",
       "spent_time                      -0.252125  \n",
       "large_event                     -0.052473  \n",
       "public_transit                  -0.448360  \n",
       "anxious                          0.173295  \n",
       "depressed                        0.037689  \n",
       "felt_isolated                    0.082182  \n",
       "worried_become_ill               0.262211  \n",
       "worried_finances                 0.475462  \n",
       "tested_positive                  0.981165  \n",
       "cli.1                            0.838224  \n",
       "ili.1                            0.829200  \n",
       "hh_cmnty_cli.1                   0.879438  \n",
       "nohh_cmnty_cli.1                 0.869278  \n",
       "wearing_mask.1                  -0.065600  \n",
       "travel_outside_state.1          -0.100407  \n",
       "work_outside_home.1              0.037930  \n",
       "shop.1                          -0.412705  \n",
       "restaurant.1                    -0.159121  \n",
       "spent_time.1                    -0.255714  \n",
       "large_event.1                   -0.058079  \n",
       "public_transit.1                -0.449079  \n",
       "anxious.1                        0.164537  \n",
       "depressed.1                      0.033149  \n",
       "felt_isolated.1                  0.081521  \n",
       "worried_become_ill.1             0.264816  \n",
       "worried_finances.1               0.480958  \n",
       "tested_positive.1                0.991012  \n",
       "cli.2                            0.835751  \n",
       "ili.2                            0.826075  \n",
       "hh_cmnty_cli.2                   0.878218  \n",
       "nohh_cmnty_cli.2                 0.867535  \n",
       "wearing_mask.2                  -0.062037  \n",
       "travel_outside_state.2          -0.103868  \n",
       "work_outside_home.2              0.039304  \n",
       "shop.2                          -0.415130  \n",
       "restaurant.2                    -0.160181  \n",
       "spent_time.2                    -0.258956  \n",
       "large_event.2                   -0.063709  \n",
       "public_transit.2                -0.450436  \n",
       "anxious.2                        0.152903  \n",
       "depressed.2                      0.029578  \n",
       "felt_isolated.2                  0.081174  \n",
       "worried_become_ill.2             0.267610  \n",
       "worried_finances.2               0.485843  \n",
       "tested_positive.2                1.000000  \n",
       "\n",
       "[54 rows x 54 columns]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_tr.iloc[:, 40:].corr() #上面手动分析太累，还是利用corr方法自动分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cli                       0.838504\n",
       "ili                       0.830527\n",
       "hh_cmnty_cli              0.879724\n",
       "nohh_cmnty_cli            0.869938\n",
       "wearing_mask             -0.069531\n",
       "travel_outside_state     -0.097303\n",
       "work_outside_home         0.034865\n",
       "shop                     -0.410430\n",
       "restaurant               -0.157945\n",
       "spent_time               -0.252125\n",
       "large_event              -0.052473\n",
       "public_transit           -0.448360\n",
       "anxious                   0.173295\n",
       "depressed                 0.037689\n",
       "felt_isolated             0.082182\n",
       "worried_become_ill        0.262211\n",
       "worried_finances          0.475462\n",
       "tested_positive           0.981165\n",
       "cli.1                     0.838224\n",
       "ili.1                     0.829200\n",
       "hh_cmnty_cli.1            0.879438\n",
       "nohh_cmnty_cli.1          0.869278\n",
       "wearing_mask.1           -0.065600\n",
       "travel_outside_state.1   -0.100407\n",
       "work_outside_home.1       0.037930\n",
       "shop.1                   -0.412705\n",
       "restaurant.1             -0.159121\n",
       "spent_time.1             -0.255714\n",
       "large_event.1            -0.058079\n",
       "public_transit.1         -0.449079\n",
       "anxious.1                 0.164537\n",
       "depressed.1               0.033149\n",
       "felt_isolated.1           0.081521\n",
       "worried_become_ill.1      0.264816\n",
       "worried_finances.1        0.480958\n",
       "tested_positive.1         0.991012\n",
       "cli.2                     0.835751\n",
       "ili.2                     0.826075\n",
       "hh_cmnty_cli.2            0.878218\n",
       "nohh_cmnty_cli.2          0.867535\n",
       "wearing_mask.2           -0.062037\n",
       "travel_outside_state.2   -0.103868\n",
       "work_outside_home.2       0.039304\n",
       "shop.2                   -0.415130\n",
       "restaurant.2             -0.160181\n",
       "spent_time.2             -0.258956\n",
       "large_event.2            -0.063709\n",
       "public_transit.2         -0.450436\n",
       "anxious.2                 0.152903\n",
       "depressed.2               0.029578\n",
       "felt_isolated.2           0.081174\n",
       "worried_become_ill.2      0.267610\n",
       "worried_finances.2        0.485843\n",
       "tested_positive.2         1.000000\n",
       "Name: tested_positive.2, dtype: float64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#锁定上面相关性矩阵最后一列，也就是目标值列，每行是与其相关性大小\n",
    "data_corr = data_tr.iloc[:, 40:].corr()\n",
    "target_col = data_corr['tested_positive.2']\n",
    "target_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cli                  0.838504\n",
       "ili                  0.830527\n",
       "hh_cmnty_cli         0.879724\n",
       "nohh_cmnty_cli       0.869938\n",
       "tested_positive      0.981165\n",
       "cli.1                0.838224\n",
       "ili.1                0.829200\n",
       "hh_cmnty_cli.1       0.879438\n",
       "nohh_cmnty_cli.1     0.869278\n",
       "tested_positive.1    0.991012\n",
       "cli.2                0.835751\n",
       "ili.2                0.826075\n",
       "hh_cmnty_cli.2       0.878218\n",
       "nohh_cmnty_cli.2     0.867535\n",
       "tested_positive.2    1.000000\n",
       "Name: tested_positive.2, dtype: float64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature = target_col[target_col > 0.8] #在最后一列相关性数据中选择大于0.8的行，这个0.8是自己设的超参，大家可以根据实际情况调节\n",
    "feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['cli',\n",
      " 'ili',\n",
      " 'hh_cmnty_cli',\n",
      " 'nohh_cmnty_cli',\n",
      " 'tested_positive',\n",
      " 'cli.1',\n",
      " 'ili.1',\n",
      " 'hh_cmnty_cli.1',\n",
      " 'nohh_cmnty_cli.1',\n",
      " 'tested_positive.1',\n",
      " 'cli.2',\n",
      " 'ili.2',\n",
      " 'hh_cmnty_cli.2',\n",
      " 'nohh_cmnty_cli.2']\n"
     ]
    }
   ],
   "source": [
    "feature_cols = feature.index.tolist()  #将选择特征名称拿出来\n",
    "feature_cols.pop() #去掉test_positive标签\n",
    "pp.pprint(feature_cols) #得到每个需要特征名称列表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[40, 41, 42, 43, 57, 58, 59, 60, 61, 75, 76, 77, 78, 79]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feats_selected = [cols.index(col) for col in feature_cols]  #获取该特征对应列索引编号，后续就可以用feats + feats_selected作为特征值\n",
    "feats_selected"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BtE3b6JEH7rw"
   },
   "source": [
    "# **Some Utilities**\n",
    "\n",
    "You do not need to modify this part."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "id": "FWMT3uf1NGQp"
   },
   "outputs": [],
   "source": [
    "def get_device():\n",
    "    ''' Get device (if GPU is available, use GPU) '''\n",
    "    return 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "\n",
    "def plot_learning_curve(loss_record, title=''):\n",
    "    ''' Plot learning curve of your DNN (train & dev loss) '''\n",
    "    total_steps = len(loss_record['train'])\n",
    "    x_1 = range(total_steps)\n",
    "    x_2 = x_1[::len(loss_record['train']) // len(loss_record['dev'])]\n",
    "    figure(figsize=(6, 4))\n",
    "    plt.plot(x_1, loss_record['train'], c='tab:red', label='train')\n",
    "    plt.plot(x_2, loss_record['dev'], c='tab:cyan', label='dev')\n",
    "    plt.ylim(0.0, 5.)\n",
    "    plt.xlabel('Training steps')\n",
    "    plt.ylabel('MSE loss')\n",
    "    plt.title('Learning curve of {}'.format(title))\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def plot_pred(dv_set, model, device, lim=35., preds=None, targets=None):\n",
    "    ''' Plot prediction of your DNN '''\n",
    "    if preds is None or targets is None:\n",
    "        model.eval()\n",
    "        preds, targets = [], []\n",
    "        for x, y in dv_set:\n",
    "            x, y = x.to(device), y.to(device)\n",
    "            with torch.no_grad():\n",
    "                pred = model(x)\n",
    "                preds.append(pred.detach().cpu())\n",
    "                targets.append(y.detach().cpu())\n",
    "        preds = torch.cat(preds, dim=0).numpy()\n",
    "        targets = torch.cat(targets, dim=0).numpy()\n",
    "\n",
    "    figure(figsize=(5, 5))\n",
    "    plt.scatter(targets, preds, c='r', alpha=0.5)\n",
    "    plt.plot([-0.2, lim], [-0.2, lim], c='b')\n",
    "    plt.xlim(-0.2, lim)\n",
    "    plt.ylim(-0.2, lim)\n",
    "    plt.xlabel('ground truth value')\n",
    "    plt.ylabel('predicted value')\n",
    "    plt.title('Ground Truth v.s. Prediction')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "39U_XFX6KOoj"
   },
   "source": [
    "# **Preprocess**\n",
    "\n",
    "We have three kinds of datasets:\n",
    "* `train`: for training\n",
    "* `dev`: for validation\n",
    "* `test`: for testing (w/o target value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TQ-MdwpLL7Dt"
   },
   "source": [
    "## **Dataset**\n",
    "\n",
    "The `COVID19Dataset` below does:\n",
    "* read `.csv` files\n",
    "* extract features\n",
    "* split `covid.train.csv` into train/dev sets\n",
    "* normalize features\n",
    "\n",
    "Finishing `TODO` below might make you pass medium baseline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "id": "0zlpIp9ANJRU"
   },
   "outputs": [],
   "source": [
    "class COVID19Dataset(Dataset):\n",
    "    ''' Dataset for loading and preprocessing the COVID19 dataset '''\n",
    "    def __init__(self,\n",
    "                 path,\n",
    "                 mu,      #mu,std是我自己加，baseline代码归一化有问题，我重写归一化部分\n",
    "                 std,\n",
    "                 mode='train',\n",
    "                 target_only=False):\n",
    "        self.mode = mode\n",
    "\n",
    "        # Read data into numpy arrays\n",
    "        with open(path, 'r') as fp:\n",
    "            data = list(csv.reader(fp))\n",
    "            data = np.array(data[1:])[:, 1:].astype(float)\n",
    "        \n",
    "        if not target_only:  #target_only 默认是false, 所以用的是全量特征，如果要用自己选择特征，则实例化这个类的时候，设置成True\n",
    "            feats = list(range(93))\n",
    "        else:\n",
    "            # TODO: Using 40 states & 2 tested_positive features (indices = 57 & 75)\n",
    "            # TODO: Using 40 states & 4 tested_positive features (indices = 57 & 75)\n",
    "            \n",
    "            feats = list(range(40)) + feats_selected            # feats_selected是我们选择特征, 40代表是states特征\n",
    "            \n",
    "            #如果用只用两个特征，可以忽略前面数据分析过程,直接这样写\n",
    "            #feats = list(range(40)) + [57, 75]\n",
    "        \n",
    "        if self.mode == 'test':\n",
    "            # Testing data\n",
    "            # data: 893 x 93 (40 states + day 1 (18) + day 2 (18) + day 3 (17))\n",
    "            data = data[:, feats]\n",
    "            self.data = torch.FloatTensor(data)\n",
    "        else:\n",
    "            # Training data (train/dev sets)\n",
    "            # data: 2700 x 94 (40 states + day 1 (18) + day 2 (18) + day 3 (18))\n",
    "            target = data[:, -1]\n",
    "            data = data[:, feats]\n",
    "            \n",
    "            # Splitting training data into train & dev sets  \n",
    "#             if mode == 'train':\n",
    "#                 indices = [i for i in range(len(data)) if i % 10 != 0]\n",
    "#             elif mode == 'dev':\n",
    "#                 indices = [i for i in range(len(data)) if i % 10 == 0]\n",
    "            \n",
    "            #baseline上面这段代码划分训练集和测试集按照顺序选择数据，可能造成数据分布问题，我改成随机选择\n",
    "            indices_tr, indices_dev = train_test_split([i for i in range(data.shape[0])], test_size = 0.3, random_state = 0)\n",
    "            if self.mode == 'train':\n",
    "                indices = indices_tr\n",
    "            elif self.mode == 'dev':\n",
    "                indices = indices_dev\n",
    "            # Convert data into PyTorch tensors\n",
    "            self.data = torch.FloatTensor(data[indices])\n",
    "            self.target = torch.FloatTensor(target[indices])\n",
    "\n",
    "        # Normalize features (you may remove this part to see what will happen)\n",
    "        #self.data[:, 40:] = \\\n",
    "            #(self.data[:, 40:] - self.data[:, 40:].mean(dim=0, keepdim=True)) \\\n",
    "            #/ self.data[:, 40:].std(dim=0, keepdim=True)\n",
    "        #self.data = (self.data - self.data.mean(dim = 0, keepdim = True)) / self.data.std(dim=0, keepdim=True)\n",
    "        \n",
    "        #baseline这段代码数据归一化用的是当前数据归一化，事实上验证集上和测试集上归一化一般只能用过去数据即训练集上均值和方差进行归一化\n",
    "        \n",
    "        if self.mode == \"train\": #如果是训练集，均值和方差用自己数据\n",
    "            self.mu = self.data[:, 40:].mean(dim=0, keepdim=True)\n",
    "            self.std = self.data[:, 40:].std(dim=0, keepdim=True)\n",
    "        else: #测试集和开发集，传进来的均值和方差是来自训练集保存，如何保存均值和方差，看数据dataload部分\n",
    "            self.mu = mu\n",
    "            self.std = std\n",
    "            \n",
    "        self.data[:,40:] = (self.data[:, 40:] - self.mu) / self.std  #归一化\n",
    "        self.dim = self.data.shape[1]\n",
    "\n",
    "        print('Finished reading the {} set of COVID19 Dataset ({} samples found, each dim = {})'\n",
    "              .format(mode, len(self.data), self.dim))\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        # Returns one sample at a time\n",
    "        if self.mode in ['train', 'dev']:\n",
    "            # For training\n",
    "            return self.data[index], self.target[index]\n",
    "        else:\n",
    "            # For testing (no target)\n",
    "            return self.data[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        # Returns the size of the dataset\n",
    "        return len(self.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AlhTlkE7MDo3"
   },
   "source": [
    "## **DataLoader**\n",
    "\n",
    "A `DataLoader` loads data from a given `Dataset` into batches.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "id": "hlhLk5t6MBX3"
   },
   "outputs": [],
   "source": [
    "def prep_dataloader(path, mode, batch_size, n_jobs=0, target_only=False, mu=None, std=None): #训练集不需要传mu,std, 所以默认值设置为None\n",
    "    ''' Generates a dataset, then is put into a dataloader. '''\n",
    "    dataset = COVID19Dataset(path, mu, std, mode=mode, target_only=target_only)  # Construct dataset\n",
    "    if mode == 'train':  #如果是训练集，把训练集上均值和方差保存下来\n",
    "        mu = dataset.mu\n",
    "        std = dataset.std\n",
    "    dataloader = DataLoader(\n",
    "        dataset, batch_size,\n",
    "        shuffle=(mode == 'train'), drop_last=False,\n",
    "        num_workers=n_jobs, pin_memory=True)                            # Construct dataloader\n",
    "    return dataloader, mu, std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SGuycwR0MeQB"
   },
   "source": [
    "# **Deep Neural Network**\n",
    "\n",
    "`NeuralNet` is an `nn.Module` designed for regression.\n",
    "The DNN consists of 2 fully-connected layers with ReLU activation.\n",
    "This module also included a function `cal_loss` for calculating loss.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "id": "49-uXYovOAI0"
   },
   "outputs": [],
   "source": [
    "class NeuralNet(nn.Module):\n",
    "    ''' A simple fully-connected deep neural network '''\n",
    "    def __init__(self, input_dim):\n",
    "        super(NeuralNet, self).__init__()\n",
    "\n",
    "        # Define your neural network here\n",
    "        # TODO: How to modify this model to achieve better performance?\n",
    "        self.net = nn.Sequential(\n",
    "            nn.Linear(input_dim, 68),   #70是我调得最好的， 而且加层很容易过拟和\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(68,1)\n",
    "        )\n",
    "        # Mean squared error loss\n",
    "        self.criterion = nn.MSELoss(reduction='mean')\n",
    "\n",
    "    def forward(self, x):\n",
    "        ''' Given input of size (batch_size x input_dim), compute output of the network '''\n",
    "        return self.net(x).squeeze(1)\n",
    "\n",
    "    def cal_loss(self, pred, target):\n",
    "        ''' Calculate loss '''\n",
    "        # TODO: you may implement L2 regularization here\n",
    "        eps = 1e-6\n",
    "        l2_reg = 0\n",
    "        alpha = 0.0001\n",
    "        #这段代码是l2正则，但是实际操作l2正则效果不好，大家也也可以调，把下面这段代码取消注释就行\n",
    "#         for name, w in self.net.named_parameters():\n",
    "#             if 'weight'  in name:\n",
    "#                 l2_reg += alpha * torch.norm(w, p = 2).to(device)\n",
    "        return torch.sqrt(self.criterion(pred, target) + eps) + l2_reg   \n",
    "        #lr_reg=0, 后面那段代码用的是均方根误差，均方根误差和kaggle评测指标一致，而且训练模型也更平稳"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DvFWVjZ5Nvga"
   },
   "source": [
    "# **Train/Dev/Test**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MAM8QecJOyqn"
   },
   "source": [
    "## **Training**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "id": "lOqcmYzMO7jB"
   },
   "outputs": [],
   "source": [
    "def train(tr_set, dv_set, model, config, device):\n",
    "    ''' DNN training '''\n",
    "\n",
    "    n_epochs = config['n_epochs']  # Maximum number of epochs\n",
    "\n",
    "    # Setup optimizer\n",
    "    optimizer = getattr(torch.optim, config['optimizer'])(\n",
    "        model.parameters(), **config['optim_hparas'])\n",
    "\n",
    "    min_mse = 1000.\n",
    "    loss_record = {'train': [], 'dev': []}      # for recording training loss\n",
    "    early_stop_cnt = 0\n",
    "    epoch = 0\n",
    "    while epoch < n_epochs:\n",
    "        model.train()                           # set model to training mode\n",
    "        for x, y in tr_set:                     # iterate through the dataloader\n",
    "            optimizer.zero_grad()               # set gradient to zero\n",
    "            x, y = x.to(device), y.to(device)   # move data to device (cpu/cuda)\n",
    "            pred = model(x)                     # forward pass (compute output)\n",
    "            mse_loss = model.cal_loss(pred, y)  # compute loss\n",
    "            mse_loss.backward()                 # compute gradient (backpropagation)\n",
    "            optimizer.step()                    # update model with optimizer\n",
    "            loss_record['train'].append(mse_loss.detach().cpu().item())\n",
    "\n",
    "        # After each epoch, test your model on the validation (development) set.\n",
    "        dev_mse = dev(dv_set, model, device)\n",
    "        if dev_mse < min_mse:\n",
    "            # Save model if your model improved\n",
    "            min_mse = dev_mse\n",
    "            print('Saving model (epoch = {:4d}, loss = {:.4f})'\n",
    "                .format(epoch + 1, min_mse))\n",
    "            torch.save(model.state_dict(), config['save_path'])  # Save model to specified path\n",
    "            early_stop_cnt = 0\n",
    "        else:\n",
    "            early_stop_cnt += 1\n",
    "\n",
    "        epoch += 1\n",
    "        loss_record['dev'].append(dev_mse)\n",
    "        if early_stop_cnt > config['early_stop']:\n",
    "            # Stop training if your model stops improving for \"config['early_stop']\" epochs.\n",
    "            break\n",
    "\n",
    "    print('Finished training after {} epochs'.format(epoch))\n",
    "    return min_mse, loss_record"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0hSd4Bn3O2PL"
   },
   "source": [
    "## **Validation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "id": "yrxrD3YsN3U2"
   },
   "outputs": [],
   "source": [
    "def dev(dv_set, model, device):\n",
    "    model.eval()                                # set model to evalutation mode\n",
    "    total_loss = 0\n",
    "    for x, y in dv_set:                         # iterate through the dataloader\n",
    "        x, y = x.to(device), y.to(device)       # move data to device (cpu/cuda)\n",
    "        with torch.no_grad():                   # disable gradient calculation\n",
    "            pred = model(x)                     # forward pass (compute output)\n",
    "            mse_loss = model.cal_loss(pred, y)  # compute loss\n",
    "        total_loss += mse_loss.detach().cpu().item() * len(x)  # accumulate loss\n",
    "    total_loss = total_loss / len(dv_set.dataset)              # compute averaged loss\n",
    "\n",
    "    return total_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g0pdrhQAO41L"
   },
   "source": [
    "## **Testing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "id": "aSBMRFlYN5tB"
   },
   "outputs": [],
   "source": [
    "def test(tt_set, model, device):\n",
    "    model.eval()                                # set model to evalutation mode\n",
    "    preds = []\n",
    "    for x in tt_set:                            # iterate through the dataloader\n",
    "        x = x.to(device)                        # move data to device (cpu/cuda)\n",
    "        with torch.no_grad():                   # disable gradient calculation\n",
    "            pred = model(x)                     # forward pass (compute output)\n",
    "            preds.append(pred.detach().cpu())   # collect prediction\n",
    "    preds = torch.cat(preds, dim=0).numpy()     # concatenate all predictions and convert to a numpy array\n",
    "    return preds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SvckkF5dvf0j"
   },
   "source": [
    "# **Setup Hyper-parameters**\n",
    "\n",
    "`config` contains hyper-parameters for training and the path to save your model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "id": "NPXpdumwPjE7"
   },
   "outputs": [],
   "source": [
    "device = get_device()                 # get the current available device ('cpu' or 'cuda')\n",
    "os.makedirs('models', exist_ok=True)  # The trained model will be saved to ./models/\n",
    "\n",
    "#target_only = False  ## TODO: Using 40 states & 2 tested_positive features\n",
    "target_only = True   # 使用自己的特征，如果设置成False，用的是全量特征\n",
    "# TODO: How to tune these hyper-parameters to improve your model's performance? 这里超参数没怎么调，已经最优的了\n",
    "config = {\n",
    "    'n_epochs': 3000,                # maximum number of epochs\n",
    "    'batch_size': 270,               # mini-batch size for dataloader\n",
    "    'optimizer': 'SGD',              # optimization algorithm (optimizer in torch.optim)\n",
    "    'optim_hparas': {                # hyper-parameters for the optimizer (depends on which optimizer you are using)\n",
    "        'lr': 0.005,                 # learning rate of SGD\n",
    "        'momentum': 0.5              # momentum for SGD\n",
    "    },\n",
    "    'early_stop': 200,               # early stopping epochs (the number epochs since your model's last improvement)\n",
    "    #'save_path': 'models/model.pth'  # your model will be saved here\n",
    "    'save_path': 'models/model_select.path'\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6j1eOV3TOH-j"
   },
   "source": [
    "# **Load data and model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eNrYBMmePLKm",
    "outputId": "fcd4f175-4f7e-4306-f33c-5f8285f11dce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished reading the train set of COVID19 Dataset (1890 samples found, each dim = 54)\n",
      "Finished reading the dev set of COVID19 Dataset (810 samples found, each dim = 54)\n",
      "Finished reading the test set of COVID19 Dataset (2700 samples found, each dim = 54)\n"
     ]
    }
   ],
   "source": [
    "tr_set, tr_mu, tr_std = prep_dataloader(tr_path, 'train', config['batch_size'], target_only=target_only)\n",
    "dv_set, mu_none, std_none = prep_dataloader(tr_path, 'dev', config['batch_size'], target_only=target_only, mu=tr_mu, std=tr_std)\n",
    "tt_set, mu_none, std_none = prep_dataloader(tr_path, 'test', config['batch_size'], target_only=target_only, mu=tr_mu, std=tr_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "id": "FHylSirLP9oh"
   },
   "outputs": [],
   "source": [
    "model = NeuralNet(tr_set.dataset.dim).to(device)  # Construct model and move to device"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sX2B_zgSOPTJ"
   },
   "source": [
    "# **Start Training!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GrEbUxazQAAZ",
    "outputId": "f4f3bd74-2d97-4275-b69f-6609976b91f9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving model (epoch =    1, loss = 17.9404)\n",
      "Saving model (epoch =    2, loss = 17.7630)\n",
      "Saving model (epoch =    3, loss = 17.5780)\n",
      "Saving model (epoch =    4, loss = 17.3766)\n",
      "Saving model (epoch =    5, loss = 17.1467)\n",
      "Saving model (epoch =    6, loss = 16.8744)\n",
      "Saving model (epoch =    7, loss = 16.5441)\n",
      "Saving model (epoch =    8, loss = 16.1399)\n",
      "Saving model (epoch =    9, loss = 15.6454)\n",
      "Saving model (epoch =   10, loss = 15.0346)\n",
      "Saving model (epoch =   11, loss = 14.2869)\n",
      "Saving model (epoch =   12, loss = 13.4345)\n",
      "Saving model (epoch =   13, loss = 12.4895)\n",
      "Saving model (epoch =   14, loss = 11.5351)\n",
      "Saving model (epoch =   15, loss = 10.7122)\n",
      "Saving model (epoch =   16, loss = 10.0944)\n",
      "Saving model (epoch =   17, loss = 9.6843)\n",
      "Saving model (epoch =   18, loss = 9.4040)\n",
      "Saving model (epoch =   19, loss = 9.1836)\n",
      "Saving model (epoch =   20, loss = 8.9869)\n",
      "Saving model (epoch =   21, loss = 8.7931)\n",
      "Saving model (epoch =   22, loss = 8.6067)\n",
      "Saving model (epoch =   23, loss = 8.4216)\n",
      "Saving model (epoch =   24, loss = 8.2429)\n",
      "Saving model (epoch =   25, loss = 8.0665)\n",
      "Saving model (epoch =   26, loss = 7.8979)\n",
      "Saving model (epoch =   27, loss = 7.7286)\n",
      "Saving model (epoch =   28, loss = 7.5635)\n",
      "Saving model (epoch =   29, loss = 7.3977)\n",
      "Saving model (epoch =   30, loss = 7.2324)\n",
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      "Saving model (epoch =   40, loss = 4.9431)\n",
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      "Saving model (epoch =   54, loss = 1.5826)\n",
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      "Saving model (epoch =  160, loss = 0.9987)\n",
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      "Saving model (epoch =  365, loss = 0.9648)\n",
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      "Saving model (epoch =  601, loss = 0.9606)\n",
      "Saving model (epoch =  658, loss = 0.9594)\n",
      "Finished training after 859 epochs\n"
     ]
    }
   ],
   "source": [
    "model_loss, model_loss_record = train(tr_set, dv_set, model, config, device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "hsNO9nnXQBvP",
    "outputId": "1626def6-94c7-4a87-9447-d939f827c8eb"
   },
   "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": [
    "plot_learning_curve(model_loss_record, title='deep model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9632348616917928"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dev(dv_set, model, device)  #验证集损失 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "id": "3iZTVn5WQFpX",
    "outputId": "a2d5e118-559d-45c6-b644-6792af54663d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "del model\n",
    "model = NeuralNet(tr_set.dataset.dim).to(device)\n",
    "ckpt = torch.load(config['save_path'], map_location='cpu')  # Load your best model\n",
    "model.load_state_dict(ckpt)\n",
    "plot_pred(dv_set, model, device)  # Show prediction on the validation set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aQikz3IPiyPf"
   },
   "source": [
    "# **Testing**\n",
    "The predictions of your model on testing set will be stored at `pred.csv`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "O8cTuQjQQOon",
    "outputId": "6bc5de07-4c5a-4e87-9ae3-d09f539c5f2c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving results to pred.csv\n"
     ]
    }
   ],
   "source": [
    "def save_pred(preds, file):\n",
    "    ''' Save predictions to specified file '''\n",
    "    print('Saving results to {}'.format(file))\n",
    "    with open(file, 'w') as fp:\n",
    "        writer = csv.writer(fp)\n",
    "        writer.writerow(['id', 'tested_positive'])\n",
    "        for i, p in enumerate(preds):\n",
    "            writer.writerow([i, p])\n",
    "\n",
    "preds = test(tt_set, model, device)  # predict COVID-19 cases with your model\n",
    "save_pred(preds, 'pred.csv')         # save prediction file to pred.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 最后将测试集结果上传kaggle, 分数为 0.88749，离strong baseline还差0.001\n",
    "---\n",
    "#### 总结：\n",
    "    这个case由于数据量比较小，训练数据与测试数据分布基本一致，而且每列数据的值都是百分数，所以特征之间数据分布差异不是很大，导致数据做完归一化效果也没有多大提升。原文数据归一化方法有问题，我改过以后效果也没有提升。其它超参数如增加层，节点，修改激活函数对结果提升也不是很明显， 提升比较显著的是特征筛选。simple baseline 用了93个特征，通过数据分析，我用了52个特征，一下就过了medium baseline, 然后微调了下训练集和验证集数据划分方式为随机选择，比例为7：3，结果一下接近了strong baseline。simple baseline 损失函数均方方差，我改为均方根误差，模型训练收敛比较快，而且loss下降比较平稳，均方误差训练震荡比较大。最后试图用了l2正则，但训练集上效果反而不是很高，所以把l2去掉。除了以上trick,大家还可以尝试修改优化方法，比如adam, kfold等。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9tmCwXgpot3t"
   },
   "source": [
    "# **Reference**\n",
    "This code is completely written by Heng-Jui Chang @ NTUEE.  \n",
    "Copying or reusing this code is required to specify the original author. \n",
    "\n",
    "E.g.  \n",
    "Source: Heng-Jui Chang @ NTUEE (https://github.com/ga642381/ML2021-Spring/blob/main/HW01/HW01.ipynb)\n"
   ]
  }
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