“ML2021Spring_HW1_ipynb”（改进版）.ipynb

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      "name": "“ML2021Spring - HW1.ipynb”（改进版）",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Orange-yy/ML2021/blob/main/%E2%80%9CML2021Spring_HW1_ipynb%E2%80%9D%EF%BC%88%E6%94%B9%E8%BF%9B%E7%89%88%EF%BC%89.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "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",
        "Videos (Mandarin): https://cool.ntu.edu.tw/courses/4793/modules/items/172854  \n",
        "https://cool.ntu.edu.tw/courses/4793/modules/items/172853  \n",
        "Video (English): https://cool.ntu.edu.tw/courses/4793/modules/items/176529\n",
        "\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",
        "If any questions, please contact the TAs via TA hours, NTU COOL, or email.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jx3x1nDkG-Uy"
      },
      "source": [
        "# **Download Data**\n",
        "\n",
        "\n",
        "If the Google drive links are dead, you can download data from [kaggle](https://www.kaggle.com/c/ml2021spring-hw1/data), and upload data manually to the workspace."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tMj55YDKG6ch",
        "outputId": "7b070c8c-5908-4275-afcb-7fbd6779a8d0"
      },
      "source": [
        "tr_path = 'covid.train.csv'  # path to training data\n",
        "tt_path = 'covid.test.csv'   # path to testing data\n",
        "\n",
        "!gdown --id '19CCyCgJrUxtvgZF53vnctJiOJ23T5mqF' --output covid.train.csv\n",
        "!gdown --id '1CE240jLm2npU-tdz81-oVKEF3T2yfT1O' --output covid.test.csv"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=19CCyCgJrUxtvgZF53vnctJiOJ23T5mqF\n",
            "To: /content/covid.train.csv\n",
            "100% 2.00M/2.00M [00:00<00:00, 9.33MB/s]\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1CE240jLm2npU-tdz81-oVKEF3T2yfT1O\n",
            "To: /content/covid.test.csv\n",
            "100% 651k/651k [00:00<00:00, 3.98MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wS_4-77xHk44"
      },
      "source": [
        "# **Import Some Packages**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k-onQd4JNA5H"
      },
      "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)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CPvPmBBI3hSb"
      },
      "source": [
        "# **Data Analysis**\n",
        "可视化数据，筛选有用特征"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "id": "d774vXAS3zd6",
        "outputId": "06ed94f5-5ff0-4cf5-eecf-c3e773a99387"
      },
      "source": [
        "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\n",
        "data_tr.head(3) #数据量很大，看前三行就行，大致浏览下数据类型"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            "text/plain": [
              "   id   AL   AK  ...  worried_become_ill.2  worried_finances.2  tested_positive.2\n",
              "0   0  1.0  0.0  ...             53.991549           43.604229          20.704935\n",
              "1   1  1.0  0.0  ...             54.185521           42.665766          21.292911\n",
              "2   2  1.0  0.0  ...             53.637069           42.972417          21.166656\n",
              "\n",
              "[3 rows x 95 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "id": "6ZQRLy2E4bpq",
        "outputId": "a4a2ee8d-4454-428c-e2b0-c679814c4fbb"
      },
      "source": [
        "data_tt.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<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",
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              "    .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>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>ID</th>\n",
              "      <th>IL</th>\n",
              "      <th>IN</th>\n",
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              "      <th>KS</th>\n",
              "      <th>KY</th>\n",
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              "      <th>MD</th>\n",
              "      <th>MA</th>\n",
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              "      <th>MN</th>\n",
              "      <th>MS</th>\n",
              "      <th>MO</th>\n",
              "      <th>NE</th>\n",
              "      <th>NV</th>\n",
              "      <th>NJ</th>\n",
              "      <th>NM</th>\n",
              "      <th>NY</th>\n",
              "      <th>NC</th>\n",
              "      <th>OH</th>\n",
              "      <th>OK</th>\n",
              "      <th>OR</th>\n",
              "      <th>PA</th>\n",
              "      <th>RI</th>\n",
              "      <th>SC</th>\n",
              "      <th>TX</th>\n",
              "      <th>UT</th>\n",
              "      <th>VA</th>\n",
              "      <th>WA</th>\n",
              "      <th>WV</th>\n",
              "      <th>...</th>\n",
              "      <th>depressed</th>\n",
              "      <th>felt_isolated</th>\n",
              "      <th>worried_become_ill</th>\n",
              "      <th>worried_finances</th>\n",
              "      <th>tested_positive</th>\n",
              "      <th>cli.1</th>\n",
              "      <th>ili.1</th>\n",
              "      <th>hh_cmnty_cli.1</th>\n",
              "      <th>nohh_cmnty_cli.1</th>\n",
              "      <th>wearing_mask.1</th>\n",
              "      <th>travel_outside_state.1</th>\n",
              "      <th>work_outside_home.1</th>\n",
              "      <th>shop.1</th>\n",
              "      <th>restaurant.1</th>\n",
              "      <th>spent_time.1</th>\n",
              "      <th>large_event.1</th>\n",
              "      <th>public_transit.1</th>\n",
              "      <th>anxious.1</th>\n",
              "      <th>depressed.1</th>\n",
              "      <th>felt_isolated.1</th>\n",
              "      <th>worried_become_ill.1</th>\n",
              "      <th>worried_finances.1</th>\n",
              "      <th>tested_positive.1</th>\n",
              "      <th>cli.2</th>\n",
              "      <th>ili.2</th>\n",
              "      <th>hh_cmnty_cli.2</th>\n",
              "      <th>nohh_cmnty_cli.2</th>\n",
              "      <th>wearing_mask.2</th>\n",
              "      <th>travel_outside_state.2</th>\n",
              "      <th>work_outside_home.2</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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              "  </thead>\n",
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              "      <td>3.138238</td>\n",
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              "      <td>17.635395</td>\n",
              "      <td>23.832616</td>\n",
              "      <td>55.692361</td>\n",
              "      <td>38.756539</td>\n",
              "      <td>2.011478</td>\n",
              "      <td>0.413432</td>\n",
              "      <td>0.413432</td>\n",
              "      <td>13.408644</td>\n",
              "      <td>9.240964</td>\n",
              "      <td>89.177999</td>\n",
              "      <td>6.903325</td>\n",
              "      <td>30.309182</td>\n",
              "      <td>58.742461</td>\n",
              "      <td>21.720187</td>\n",
              "      <td>41.375784</td>\n",
              "      <td>9.450179</td>\n",
              "      <td>3.150088</td>\n",
              "      <td>22.075715</td>\n",
              "      <td>17.302077</td>\n",
              "      <td>23.559622</td>\n",
              "      <td>57.015009</td>\n",
              "      <td>38.372829</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>19.377685</td>\n",
              "      <td>26.159011</td>\n",
              "      <td>54.357669</td>\n",
              "      <td>39.335294</td>\n",
              "      <td>1.658765</td>\n",
              "      <td>0.450383</td>\n",
              "      <td>0.430983</td>\n",
              "      <td>13.254896</td>\n",
              "      <td>8.767861</td>\n",
              "      <td>89.109790</td>\n",
              "      <td>6.754043</td>\n",
              "      <td>27.881581</td>\n",
              "      <td>58.287380</td>\n",
              "      <td>19.698013</td>\n",
              "      <td>39.292417</td>\n",
              "      <td>8.563846</td>\n",
              "      <td>2.846935</td>\n",
              "      <td>24.362498</td>\n",
              "      <td>18.798445</td>\n",
              "      <td>25.657059</td>\n",
              "      <td>55.069605</td>\n",
              "      <td>39.451691</td>\n",
              "      <td>2.163458</td>\n",
              "      <td>0.428718</td>\n",
              "      <td>0.440211</td>\n",
              "      <td>12.983094</td>\n",
              "      <td>8.577449</td>\n",
              "      <td>88.323581</td>\n",
              "      <td>7.016447</td>\n",
              "      <td>28.660016</td>\n",
              "      <td>59.109045</td>\n",
              "      <td>20.123959</td>\n",
              "      <td>40.072556</td>\n",
              "      <td>8.781522</td>\n",
              "      <td>2.888209</td>\n",
              "      <td>23.920870</td>\n",
              "      <td>18.342506</td>\n",
              "      <td>24.993341</td>\n",
              "      <td>55.291498</td>\n",
              "      <td>38.907257</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 94 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   id   AL   AK  ...  felt_isolated.2  worried_become_ill.2  worried_finances.2\n",
              "0   0  0.0  0.0  ...        24.747837             66.194950           44.873473\n",
              "1   1  0.0  0.0  ...        23.559622             57.015009           38.372829\n",
              "2   2  0.0  0.0  ...        24.993341             55.291498           38.907257\n",
              "\n",
              "[3 rows x 94 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pU8GIwet4fg2",
        "outputId": "0b67a0af-d09c-4e0b-e185-b64d11db022f"
      },
      "source": [
        "data_tr.columns #查看有多少列特征"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uDF-c-sI4jTS"
      },
      "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)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3EBQR2NZ4m0n",
        "outputId": "408e8f0b-2336-4f74-da03-a08ba1d482b1"
      },
      "source": [
        "cols = list(data_tr.columns)  #拿到特征列名称\n",
        "pp.pprint(data_tr.columns)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yVnGaLFV4qJg",
        "outputId": "a482cee8-0fe6-416b-9518-1e60297a3cfe"
      },
      "source": [
        "pp.pprint(data_tr.info()) #看每列数据类型和大小"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HVrNGuCU4ssO",
        "outputId": "8e6c8fbf-2eaa-4fc4-9ac8-435c5ceba078"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "39"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "yrOyf4kb4xVC",
        "outputId": "151e5726-f027-4fa3-9229-23464bbb6339"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>large_event</th>\n",
              "      <th>public_transit</th>\n",
              "      <th>anxious</th>\n",
              "      <th>depressed</th>\n",
              "      <th>felt_isolated</th>\n",
              "      <th>worried_become_ill</th>\n",
              "      <th>worried_finances</th>\n",
              "      <th>tested_positive</th>\n",
              "      <th>cli.1</th>\n",
              "      <th>ili.1</th>\n",
              "      <th>hh_cmnty_cli.1</th>\n",
              "      <th>nohh_cmnty_cli.1</th>\n",
              "      <th>wearing_mask.1</th>\n",
              "      <th>travel_outside_state.1</th>\n",
              "      <th>work_outside_home.1</th>\n",
              "      <th>shop.1</th>\n",
              "      <th>restaurant.1</th>\n",
              "      <th>spent_time.1</th>\n",
              "      <th>large_event.1</th>\n",
              "      <th>public_transit.1</th>\n",
              "      <th>anxious.1</th>\n",
              "      <th>depressed.1</th>\n",
              "      <th>felt_isolated.1</th>\n",
              "      <th>worried_become_ill.1</th>\n",
              "      <th>worried_finances.1</th>\n",
              "      <th>tested_positive.1</th>\n",
              "      <th>cli.2</th>\n",
              "      <th>ili.2</th>\n",
              "      <th>hh_cmnty_cli.2</th>\n",
              "      <th>nohh_cmnty_cli.2</th>\n",
              "      <th>wearing_mask.2</th>\n",
              "      <th>travel_outside_state.2</th>\n",
              "      <th>work_outside_home.2</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",
              "      <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>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>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>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>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>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>10.352273</td>\n",
              "      <td>2.393285</td>\n",
              "      <td>18.074684</td>\n",
              "      <td>13.075498</td>\n",
              "      <td>19.213321</td>\n",
              "      <td>64.633769</td>\n",
              "      <td>44.519474</td>\n",
              "      <td>16.300893</td>\n",
              "      <td>0.994568</td>\n",
              "      <td>1.019135</td>\n",
              "      <td>29.529305</td>\n",
              "      <td>24.402875</td>\n",
              "      <td>89.736737</td>\n",
              "      <td>8.861371</td>\n",
              "      <td>31.664651</td>\n",
              "      <td>55.198075</td>\n",
              "      <td>16.635440</td>\n",
              "      <td>36.176886</td>\n",
              "      <td>10.304595</td>\n",
              "      <td>2.389372</td>\n",
              "      <td>18.071667</td>\n",
              "      <td>13.067127</td>\n",
              "      <td>19.228457</td>\n",
              "      <td>64.734139</td>\n",
              "      <td>44.544124</td>\n",
              "      <td>16.366695</td>\n",
              "      <td>0.997986</td>\n",
              "      <td>1.022472</td>\n",
              "      <td>29.610807</td>\n",
              "      <td>24.477913</td>\n",
              "      <td>89.790227</td>\n",
              "      <td>8.830759</td>\n",
              "      <td>31.624272</td>\n",
              "      <td>55.119903</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>4.698705</td>\n",
              "      <td>1.053270</td>\n",
              "      <td>2.248750</td>\n",
              "      <td>1.621328</td>\n",
              "      <td>2.706605</td>\n",
              "      <td>6.232239</td>\n",
              "      <td>5.265787</td>\n",
              "      <td>7.637823</td>\n",
              "      <td>0.420114</td>\n",
              "      <td>0.423538</td>\n",
              "      <td>9.082940</td>\n",
              "      <td>8.443146</td>\n",
              "      <td>5.366067</td>\n",
              "      <td>3.389310</td>\n",
              "      <td>4.916168</td>\n",
              "      <td>4.524887</td>\n",
              "      <td>5.660085</td>\n",
              "      <td>6.664218</td>\n",
              "      <td>4.692479</td>\n",
              "      <td>1.053237</td>\n",
              "      <td>2.249864</td>\n",
              "      <td>1.625269</td>\n",
              "      <td>2.707148</td>\n",
              "      <td>6.226622</td>\n",
              "      <td>5.248787</td>\n",
              "      <td>7.627538</td>\n",
              "      <td>0.420205</td>\n",
              "      <td>0.423705</td>\n",
              "      <td>9.070537</td>\n",
              "      <td>8.437044</td>\n",
              "      <td>5.351574</td>\n",
              "      <td>3.377722</td>\n",
              "      <td>4.901857</td>\n",
              "      <td>4.524442</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>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",
              "      <td>0.126321</td>\n",
              "      <td>0.132470</td>\n",
              "      <td>9.961640</td>\n",
              "      <td>6.857181</td>\n",
              "      <td>72.330064</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>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",
              "      <td>0.126321</td>\n",
              "      <td>0.132470</td>\n",
              "      <td>9.961640</td>\n",
              "      <td>6.857181</td>\n",
              "      <td>72.356322</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>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>6.653427</td>\n",
              "      <td>1.720601</td>\n",
              "      <td>16.420485</td>\n",
              "      <td>11.943953</td>\n",
              "      <td>17.292063</td>\n",
              "      <td>59.529326</td>\n",
              "      <td>40.520369</td>\n",
              "      <td>10.200722</td>\n",
              "      <td>0.676205</td>\n",
              "      <td>0.699773</td>\n",
              "      <td>23.264324</td>\n",
              "      <td>18.607342</td>\n",
              "      <td>86.386111</td>\n",
              "      <td>6.168986</td>\n",
              "      <td>28.202745</td>\n",
              "      <td>51.403036</td>\n",
              "      <td>13.248788</td>\n",
              "      <td>30.646955</td>\n",
              "      <td>6.605724</td>\n",
              "      <td>1.715372</td>\n",
              "      <td>16.423140</td>\n",
              "      <td>11.933745</td>\n",
              "      <td>17.303887</td>\n",
              "      <td>59.703583</td>\n",
              "      <td>40.533768</td>\n",
              "      <td>10.251453</td>\n",
              "      <td>0.680065</td>\n",
              "      <td>0.703390</td>\n",
              "      <td>23.307794</td>\n",
              "      <td>18.644297</td>\n",
              "      <td>86.436468</td>\n",
              "      <td>6.159286</td>\n",
              "      <td>28.187875</td>\n",
              "      <td>51.262363</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>9.802380</td>\n",
              "      <td>2.204258</td>\n",
              "      <td>17.685476</td>\n",
              "      <td>12.963659</td>\n",
              "      <td>18.735807</td>\n",
              "      <td>65.688024</td>\n",
              "      <td>43.911769</td>\n",
              "      <td>15.479766</td>\n",
              "      <td>0.917343</td>\n",
              "      <td>0.942587</td>\n",
              "      <td>29.061296</td>\n",
              "      <td>23.905188</td>\n",
              "      <td>90.859943</td>\n",
              "      <td>8.274067</td>\n",
              "      <td>32.108420</td>\n",
              "      <td>55.129326</td>\n",
              "      <td>16.293314</td>\n",
              "      <td>36.169954</td>\n",
              "      <td>9.738629</td>\n",
              "      <td>2.203602</td>\n",
              "      <td>17.684970</td>\n",
              "      <td>12.956723</td>\n",
              "      <td>18.745824</td>\n",
              "      <td>65.783579</td>\n",
              "      <td>43.947131</td>\n",
              "      <td>15.572281</td>\n",
              "      <td>0.920815</td>\n",
              "      <td>0.948001</td>\n",
              "      <td>29.137273</td>\n",
              "      <td>24.010817</td>\n",
              "      <td>90.912271</td>\n",
              "      <td>8.251691</td>\n",
              "      <td>32.051128</td>\n",
              "      <td>54.990445</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>65.932258</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>13.734197</td>\n",
              "      <td>2.745406</td>\n",
              "      <td>19.501218</td>\n",
              "      <td>14.214320</td>\n",
              "      <td>20.665840</td>\n",
              "      <td>69.497484</td>\n",
              "      <td>48.098224</td>\n",
              "      <td>22.503685</td>\n",
              "      <td>1.268148</td>\n",
              "      <td>1.301877</td>\n",
              "      <td>36.233383</td>\n",
              "      <td>30.318671</td>\n",
              "      <td>93.955966</td>\n",
              "      <td>11.525572</td>\n",
              "      <td>35.362666</td>\n",
              "      <td>58.797715</td>\n",
              "      <td>21.333613</td>\n",
              "      <td>41.562070</td>\n",
              "      <td>13.684985</td>\n",
              "      <td>2.734372</td>\n",
              "      <td>19.503419</td>\n",
              "      <td>14.214320</td>\n",
              "      <td>20.693846</td>\n",
              "      <td>69.578458</td>\n",
              "      <td>48.108341</td>\n",
              "      <td>22.527315</td>\n",
              "      <td>1.269136</td>\n",
              "      <td>1.304112</td>\n",
              "      <td>36.345667</td>\n",
              "      <td>30.459044</td>\n",
              "      <td>93.975501</td>\n",
              "      <td>11.477910</td>\n",
              "      <td>35.299957</td>\n",
              "      <td>58.752924</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>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>38.670000</td>\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>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",
              "      <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>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",
              "</div>"
            ],
            "text/plain": [
              "               cli          ili  ...  worried_finances.2  tested_positive.2\n",
              "count  2700.000000  2700.000000  ...         2700.000000        2700.000000\n",
              "mean      0.991587     1.016136  ...           44.568440          16.431280\n",
              "std       0.420296     0.423629  ...            5.232030           7.619354\n",
              "min       0.126321     0.132470  ...           33.113882           2.338708\n",
              "25%       0.673929     0.697515  ...           40.549987          10.327314\n",
              "50%       0.912747     0.940295  ...           43.997637          15.646480\n",
              "75%       1.266849     1.302040  ...           48.118283          22.535165\n",
              "max       2.597732     2.625885  ...           58.433600          40.959495\n",
              "\n",
              "[8 rows x 54 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "AZhqsrVU4zsy",
        "outputId": "2e469a4f-729d-46a3-a11a-5b99b8e2a077"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>large_event</th>\n",
              "      <th>public_transit</th>\n",
              "      <th>anxious</th>\n",
              "      <th>depressed</th>\n",
              "      <th>felt_isolated</th>\n",
              "      <th>worried_become_ill</th>\n",
              "      <th>worried_finances</th>\n",
              "      <th>tested_positive</th>\n",
              "      <th>cli.1</th>\n",
              "      <th>ili.1</th>\n",
              "      <th>hh_cmnty_cli.1</th>\n",
              "      <th>nohh_cmnty_cli.1</th>\n",
              "      <th>wearing_mask.1</th>\n",
              "      <th>travel_outside_state.1</th>\n",
              "      <th>work_outside_home.1</th>\n",
              "      <th>shop.1</th>\n",
              "      <th>restaurant.1</th>\n",
              "      <th>spent_time.1</th>\n",
              "      <th>large_event.1</th>\n",
              "      <th>public_transit.1</th>\n",
              "      <th>anxious.1</th>\n",
              "      <th>depressed.1</th>\n",
              "      <th>felt_isolated.1</th>\n",
              "      <th>worried_become_ill.1</th>\n",
              "      <th>worried_finances.1</th>\n",
              "      <th>tested_positive.1</th>\n",
              "      <th>cli.2</th>\n",
              "      <th>ili.2</th>\n",
              "      <th>hh_cmnty_cli.2</th>\n",
              "      <th>nohh_cmnty_cli.2</th>\n",
              "      <th>wearing_mask.2</th>\n",
              "      <th>travel_outside_state.2</th>\n",
              "      <th>work_outside_home.2</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>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>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>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>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>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>10.356177</td>\n",
              "      <td>2.382769</td>\n",
              "      <td>18.030215</td>\n",
              "      <td>13.020293</td>\n",
              "      <td>19.230715</td>\n",
              "      <td>64.406944</td>\n",
              "      <td>44.379019</td>\n",
              "      <td>15.976544</td>\n",
              "      <td>0.977508</td>\n",
              "      <td>0.997195</td>\n",
              "      <td>29.133016</td>\n",
              "      <td>24.076375</td>\n",
              "      <td>89.715077</td>\n",
              "      <td>8.955668</td>\n",
              "      <td>31.541307</td>\n",
              "      <td>55.360132</td>\n",
              "      <td>16.510614</td>\n",
              "      <td>36.268780</td>\n",
              "      <td>10.309059</td>\n",
              "      <td>2.376621</td>\n",
              "      <td>18.013300</td>\n",
              "      <td>13.007566</td>\n",
              "      <td>19.220921</td>\n",
              "      <td>64.527609</td>\n",
              "      <td>44.386619</td>\n",
              "      <td>15.989196</td>\n",
              "      <td>0.981119</td>\n",
              "      <td>1.000032</td>\n",
              "      <td>29.192015</td>\n",
              "      <td>24.117403</td>\n",
              "      <td>89.765373</td>\n",
              "      <td>8.917700</td>\n",
              "      <td>31.513665</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>4.521531</td>\n",
              "      <td>1.114568</td>\n",
              "      <td>2.235211</td>\n",
              "      <td>1.715389</td>\n",
              "      <td>2.689158</td>\n",
              "      <td>5.721753</td>\n",
              "      <td>4.579553</td>\n",
              "      <td>7.813659</td>\n",
              "      <td>0.413665</td>\n",
              "      <td>0.418835</td>\n",
              "      <td>9.527793</td>\n",
              "      <td>8.920860</td>\n",
              "      <td>4.708376</td>\n",
              "      <td>3.651532</td>\n",
              "      <td>4.734470</td>\n",
              "      <td>4.374390</td>\n",
              "      <td>5.686128</td>\n",
              "      <td>6.195079</td>\n",
              "      <td>4.508985</td>\n",
              "      <td>1.119861</td>\n",
              "      <td>2.210427</td>\n",
              "      <td>1.705901</td>\n",
              "      <td>2.674568</td>\n",
              "      <td>5.694758</td>\n",
              "      <td>4.612057</td>\n",
              "      <td>7.786780</td>\n",
              "      <td>0.413244</td>\n",
              "      <td>0.418081</td>\n",
              "      <td>9.467570</td>\n",
              "      <td>8.865726</td>\n",
              "      <td>4.692231</td>\n",
              "      <td>3.637221</td>\n",
              "      <td>4.733639</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>2.324264</td>\n",
              "      <td>0.785854</td>\n",
              "      <td>12.853772</td>\n",
              "      <td>8.453722</td>\n",
              "      <td>13.269686</td>\n",
              "      <td>50.303447</td>\n",
              "      <td>35.432447</td>\n",
              "      <td>1.339310</td>\n",
              "      <td>0.152059</td>\n",
              "      <td>0.166967</td>\n",
              "      <td>9.207952</td>\n",
              "      <td>5.750693</td>\n",
              "      <td>76.895363</td>\n",
              "      <td>2.009744</td>\n",
              "      <td>18.154712</td>\n",
              "      <td>45.209752</td>\n",
              "      <td>3.549729</td>\n",
              "      <td>21.337579</td>\n",
              "      <td>2.285853</td>\n",
              "      <td>0.839549</td>\n",
              "      <td>12.968745</td>\n",
              "      <td>8.491336</td>\n",
              "      <td>13.177680</td>\n",
              "      <td>49.993134</td>\n",
              "      <td>34.663854</td>\n",
              "      <td>1.351363</td>\n",
              "      <td>0.049938</td>\n",
              "      <td>0.065544</td>\n",
              "      <td>9.459442</td>\n",
              "      <td>6.034050</td>\n",
              "      <td>77.025654</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>18.278377</td>\n",
              "      <td>44.671891</td>\n",
              "      <td>3.837441</td>\n",
              "      <td>21.338425</td>\n",
              "      <td>2.334654</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>6.832898</td>\n",
              "      <td>1.786206</td>\n",
              "      <td>16.463262</td>\n",
              "      <td>11.800174</td>\n",
              "      <td>17.164105</td>\n",
              "      <td>60.070159</td>\n",
              "      <td>40.683578</td>\n",
              "      <td>9.982916</td>\n",
              "      <td>0.667296</td>\n",
              "      <td>0.684726</td>\n",
              "      <td>21.967645</td>\n",
              "      <td>17.602241</td>\n",
              "      <td>86.761239</td>\n",
              "      <td>7.043880</td>\n",
              "      <td>28.713787</td>\n",
              "      <td>51.686774</td>\n",
              "      <td>13.275460</td>\n",
              "      <td>31.324347</td>\n",
              "      <td>6.890855</td>\n",
              "      <td>1.775599</td>\n",
              "      <td>16.502816</td>\n",
              "      <td>11.806194</td>\n",
              "      <td>17.101370</td>\n",
              "      <td>60.267099</td>\n",
              "      <td>40.912076</td>\n",
              "      <td>10.070058</td>\n",
              "      <td>0.677422</td>\n",
              "      <td>0.691911</td>\n",
              "      <td>22.145670</td>\n",
              "      <td>17.687770</td>\n",
              "      <td>86.799638</td>\n",
              "      <td>6.908287</td>\n",
              "      <td>28.730951</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>9.734692</td>\n",
              "      <td>2.173884</td>\n",
              "      <td>17.750598</td>\n",
              "      <td>12.819747</td>\n",
              "      <td>19.154800</td>\n",
              "      <td>64.744199</td>\n",
              "      <td>44.459526</td>\n",
              "      <td>15.435832</td>\n",
              "      <td>0.919533</td>\n",
              "      <td>0.944818</td>\n",
              "      <td>28.177370</td>\n",
              "      <td>23.094211</td>\n",
              "      <td>90.142711</td>\n",
              "      <td>8.727310</td>\n",
              "      <td>31.647384</td>\n",
              "      <td>55.784308</td>\n",
              "      <td>17.064074</td>\n",
              "      <td>36.405847</td>\n",
              "      <td>9.712576</td>\n",
              "      <td>2.155570</td>\n",
              "      <td>17.783846</td>\n",
              "      <td>12.824065</td>\n",
              "      <td>19.094616</td>\n",
              "      <td>64.967013</td>\n",
              "      <td>44.485451</td>\n",
              "      <td>15.381420</td>\n",
              "      <td>0.931789</td>\n",
              "      <td>0.944038</td>\n",
              "      <td>28.137863</td>\n",
              "      <td>23.116177</td>\n",
              "      <td>90.182055</td>\n",
              "      <td>8.682130</td>\n",
              "      <td>31.525946</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>13.637503</td>\n",
              "      <td>2.650302</td>\n",
              "      <td>19.455838</td>\n",
              "      <td>14.153693</td>\n",
              "      <td>21.129580</td>\n",
              "      <td>68.826027</td>\n",
              "      <td>47.217995</td>\n",
              "      <td>21.395513</td>\n",
              "      <td>1.253486</td>\n",
              "      <td>1.264377</td>\n",
              "      <td>36.938802</td>\n",
              "      <td>31.125779</td>\n",
              "      <td>93.464698</td>\n",
              "      <td>10.361459</td>\n",
              "      <td>35.106697</td>\n",
              "      <td>59.127759</td>\n",
              "      <td>20.797971</td>\n",
              "      <td>41.113748</td>\n",
              "      <td>13.465480</td>\n",
              "      <td>2.666154</td>\n",
              "      <td>19.466619</td>\n",
              "      <td>14.129555</td>\n",
              "      <td>21.233162</td>\n",
              "      <td>68.900357</td>\n",
              "      <td>47.244684</td>\n",
              "      <td>21.451636</td>\n",
              "      <td>1.250863</td>\n",
              "      <td>1.275584</td>\n",
              "      <td>36.762927</td>\n",
              "      <td>31.015791</td>\n",
              "      <td>93.482444</td>\n",
              "      <td>10.422368</td>\n",
              "      <td>35.072704</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>22.278912</td>\n",
              "      <td>9.054438</td>\n",
              "      <td>27.926575</td>\n",
              "      <td>19.377685</td>\n",
              "      <td>26.159011</td>\n",
              "      <td>77.227806</td>\n",
              "      <td>56.288410</td>\n",
              "      <td>40.746942</td>\n",
              "      <td>2.509106</td>\n",
              "      <td>2.532059</td>\n",
              "      <td>53.690218</td>\n",
              "      <td>48.916631</td>\n",
              "      <td>97.845669</td>\n",
              "      <td>26.438426</td>\n",
              "      <td>42.639474</td>\n",
              "      <td>63.790457</td>\n",
              "      <td>27.145540</td>\n",
              "      <td>52.621101</td>\n",
              "      <td>22.871782</td>\n",
              "      <td>9.189612</td>\n",
              "      <td>26.832552</td>\n",
              "      <td>18.798445</td>\n",
              "      <td>25.817923</td>\n",
              "      <td>77.476644</td>\n",
              "      <td>56.269653</td>\n",
              "      <td>41.645746</td>\n",
              "      <td>2.491521</td>\n",
              "      <td>2.522978</td>\n",
              "      <td>52.906363</td>\n",
              "      <td>48.000709</td>\n",
              "      <td>97.935455</td>\n",
              "      <td>26.016608</td>\n",
              "      <td>43.105181</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",
              "</div>"
            ],
            "text/plain": [
              "              cli         ili  ...  worried_become_ill.2  worried_finances.2\n",
              "count  893.000000  893.000000  ...            893.000000          893.000000\n",
              "mean     0.972457    0.991809  ...             64.619920           44.411505\n",
              "std      0.411997    0.415468  ...              5.685865            4.605268\n",
              "min      0.139558    0.159477  ...             50.212234           35.072577\n",
              "25%      0.673327    0.689367  ...             60.358203           40.910546\n",
              "50%      0.925230    0.936610  ...             65.148128           44.504010\n",
              "75%      1.251219    1.267463  ...             68.994309           47.172065\n",
              "max      2.488967    2.522263  ...             76.871053           56.442135\n",
              "\n",
              "[8 rows x 53 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "gdFjSwJm49Bx",
        "outputId": "ec2a4bc3-1416-4251-a8a6-e947f952fada"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f2e0be8dc10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "jjv8_ucy49r-",
        "outputId": "9fae198a-82d4-4d5e-ea4a-b7f9952eadfe"
      },
      "source": [
        "plt.scatter(data_tr.loc[:, 'ili'], data_tr.loc[:, 'tested_positive.2'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f2e0b972610>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "LkOvINvt5EBz",
        "outputId": "5ff1386a-65c5-44ad-cc25-d6040160bdab"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f2e0b8de990>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "TyuPU34B5G4b",
        "outputId": "cacc97f6-3785-42e5-dbc8-dd06ea6aa54d"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f2e0b8c7110>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "jDKXxfue5KSY",
        "outputId": "28d33f68-41cc-4623-f1e7-29f5db2ab6aa"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f2e0b836310>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ADm195Lm5M--",
        "outputId": "23fcb269-1ce6-40bd-f5ce-22bab0fde2f3"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>large_event</th>\n",
              "      <th>public_transit</th>\n",
              "      <th>anxious</th>\n",
              "      <th>depressed</th>\n",
              "      <th>felt_isolated</th>\n",
              "      <th>worried_become_ill</th>\n",
              "      <th>worried_finances</th>\n",
              "      <th>tested_positive</th>\n",
              "      <th>cli.1</th>\n",
              "      <th>ili.1</th>\n",
              "      <th>hh_cmnty_cli.1</th>\n",
              "      <th>nohh_cmnty_cli.1</th>\n",
              "      <th>wearing_mask.1</th>\n",
              "      <th>travel_outside_state.1</th>\n",
              "      <th>work_outside_home.1</th>\n",
              "      <th>shop.1</th>\n",
              "      <th>restaurant.1</th>\n",
              "      <th>spent_time.1</th>\n",
              "      <th>large_event.1</th>\n",
              "      <th>public_transit.1</th>\n",
              "      <th>anxious.1</th>\n",
              "      <th>depressed.1</th>\n",
              "      <th>felt_isolated.1</th>\n",
              "      <th>worried_become_ill.1</th>\n",
              "      <th>worried_finances.1</th>\n",
              "      <th>tested_positive.1</th>\n",
              "      <th>cli.2</th>\n",
              "      <th>ili.2</th>\n",
              "      <th>hh_cmnty_cli.2</th>\n",
              "      <th>nohh_cmnty_cli.2</th>\n",
              "      <th>wearing_mask.2</th>\n",
              "      <th>travel_outside_state.2</th>\n",
              "      <th>work_outside_home.2</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",
              "      <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>-0.042033</td>\n",
              "      <td>-0.367103</td>\n",
              "      <td>0.273874</td>\n",
              "      <td>0.098033</td>\n",
              "      <td>0.100928</td>\n",
              "      <td>0.218502</td>\n",
              "      <td>0.537608</td>\n",
              "      <td>0.839122</td>\n",
              "      <td>0.980379</td>\n",
              "      <td>0.976171</td>\n",
              "      <td>0.896211</td>\n",
              "      <td>0.885178</td>\n",
              "      <td>-0.101056</td>\n",
              "      <td>-0.097092</td>\n",
              "      <td>0.087080</td>\n",
              "      <td>-0.367850</td>\n",
              "      <td>-0.147491</td>\n",
              "      <td>-0.216168</td>\n",
              "      <td>-0.051724</td>\n",
              "      <td>-0.371063</td>\n",
              "      <td>0.256712</td>\n",
              "      <td>0.088676</td>\n",
              "      <td>0.099487</td>\n",
              "      <td>0.223326</td>\n",
              "      <td>0.543373</td>\n",
              "      <td>0.839929</td>\n",
              "      <td>0.957059</td>\n",
              "      <td>0.952707</td>\n",
              "      <td>0.898067</td>\n",
              "      <td>0.887103</td>\n",
              "      <td>-0.094664</td>\n",
              "      <td>-0.097903</td>\n",
              "      <td>0.085913</td>\n",
              "      <td>-0.370197</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>-0.043535</td>\n",
              "      <td>-0.356652</td>\n",
              "      <td>0.281974</td>\n",
              "      <td>0.102715</td>\n",
              "      <td>0.107079</td>\n",
              "      <td>0.212931</td>\n",
              "      <td>0.532217</td>\n",
              "      <td>0.829756</td>\n",
              "      <td>0.977225</td>\n",
              "      <td>0.980473</td>\n",
              "      <td>0.892667</td>\n",
              "      <td>0.881292</td>\n",
              "      <td>-0.102606</td>\n",
              "      <td>-0.107662</td>\n",
              "      <td>0.085966</td>\n",
              "      <td>-0.361304</td>\n",
              "      <td>-0.146353</td>\n",
              "      <td>-0.214354</td>\n",
              "      <td>-0.052961</td>\n",
              "      <td>-0.360574</td>\n",
              "      <td>0.264872</td>\n",
              "      <td>0.093371</td>\n",
              "      <td>0.105446</td>\n",
              "      <td>0.217739</td>\n",
              "      <td>0.537874</td>\n",
              "      <td>0.831129</td>\n",
              "      <td>0.954996</td>\n",
              "      <td>0.956979</td>\n",
              "      <td>0.894564</td>\n",
              "      <td>0.883263</td>\n",
              "      <td>-0.096315</td>\n",
              "      <td>-0.107903</td>\n",
              "      <td>0.084708</td>\n",
              "      <td>-0.363795</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>-0.124151</td>\n",
              "      <td>-0.432142</td>\n",
              "      <td>0.336748</td>\n",
              "      <td>0.184739</td>\n",
              "      <td>0.198176</td>\n",
              "      <td>0.344457</td>\n",
              "      <td>0.552431</td>\n",
              "      <td>0.880187</td>\n",
              "      <td>0.887944</td>\n",
              "      <td>0.884020</td>\n",
              "      <td>0.998356</td>\n",
              "      <td>0.995176</td>\n",
              "      <td>-0.030237</td>\n",
              "      <td>-0.069270</td>\n",
              "      <td>0.074972</td>\n",
              "      <td>-0.474799</td>\n",
              "      <td>-0.250349</td>\n",
              "      <td>-0.297071</td>\n",
              "      <td>-0.130729</td>\n",
              "      <td>-0.432765</td>\n",
              "      <td>0.323053</td>\n",
              "      <td>0.182383</td>\n",
              "      <td>0.201034</td>\n",
              "      <td>0.347562</td>\n",
              "      <td>0.557364</td>\n",
              "      <td>0.880416</td>\n",
              "      <td>0.881768</td>\n",
              "      <td>0.877550</td>\n",
              "      <td>0.995396</td>\n",
              "      <td>0.991738</td>\n",
              "      <td>-0.025367</td>\n",
              "      <td>-0.069043</td>\n",
              "      <td>0.069933</td>\n",
              "      <td>-0.476538</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>-0.116761</td>\n",
              "      <td>-0.423773</td>\n",
              "      <td>0.344074</td>\n",
              "      <td>0.190062</td>\n",
              "      <td>0.197661</td>\n",
              "      <td>0.340192</td>\n",
              "      <td>0.524022</td>\n",
              "      <td>0.869674</td>\n",
              "      <td>0.877606</td>\n",
              "      <td>0.873424</td>\n",
              "      <td>0.996165</td>\n",
              "      <td>0.998259</td>\n",
              "      <td>-0.040738</td>\n",
              "      <td>-0.062039</td>\n",
              "      <td>0.093529</td>\n",
              "      <td>-0.467316</td>\n",
              "      <td>-0.241687</td>\n",
              "      <td>-0.284398</td>\n",
              "      <td>-0.123252</td>\n",
              "      <td>-0.424445</td>\n",
              "      <td>0.331791</td>\n",
              "      <td>0.188544</td>\n",
              "      <td>0.200843</td>\n",
              "      <td>0.343024</td>\n",
              "      <td>0.529514</td>\n",
              "      <td>0.870315</td>\n",
              "      <td>0.872292</td>\n",
              "      <td>0.867896</td>\n",
              "      <td>0.993750</td>\n",
              "      <td>0.995093</td>\n",
              "      <td>-0.035759</td>\n",
              "      <td>-0.062137</td>\n",
              "      <td>0.088394</td>\n",
              "      <td>-0.469026</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>-0.894970</td>\n",
              "      <td>0.131350</td>\n",
              "      <td>0.232620</td>\n",
              "      <td>-0.070022</td>\n",
              "      <td>0.422058</td>\n",
              "      <td>0.843990</td>\n",
              "      <td>0.354130</td>\n",
              "      <td>-0.049350</td>\n",
              "      <td>-0.121569</td>\n",
              "      <td>-0.123680</td>\n",
              "      <td>-0.046423</td>\n",
              "      <td>-0.056529</td>\n",
              "      <td>0.998287</td>\n",
              "      <td>-0.220442</td>\n",
              "      <td>-0.737554</td>\n",
              "      <td>-0.688627</td>\n",
              "      <td>-0.787245</td>\n",
              "      <td>-0.805468</td>\n",
              "      <td>-0.892267</td>\n",
              "      <td>0.132301</td>\n",
              "      <td>0.217574</td>\n",
              "      <td>-0.069369</td>\n",
              "      <td>0.424822</td>\n",
              "      <td>0.842499</td>\n",
              "      <td>0.347359</td>\n",
              "      <td>-0.059477</td>\n",
              "      <td>-0.135146</td>\n",
              "      <td>-0.137841</td>\n",
              "      <td>-0.058149</td>\n",
              "      <td>-0.067698</td>\n",
              "      <td>0.995953</td>\n",
              "      <td>-0.219916</td>\n",
              "      <td>-0.739112</td>\n",
              "      <td>-0.685437</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>0.324270</td>\n",
              "      <td>-0.198308</td>\n",
              "      <td>-0.023175</td>\n",
              "      <td>0.058548</td>\n",
              "      <td>-0.376858</td>\n",
              "      <td>-0.136811</td>\n",
              "      <td>-0.096444</td>\n",
              "      <td>-0.113726</td>\n",
              "      <td>-0.091186</td>\n",
              "      <td>-0.102645</td>\n",
              "      <td>-0.063619</td>\n",
              "      <td>-0.055823</td>\n",
              "      <td>-0.220397</td>\n",
              "      <td>0.995838</td>\n",
              "      <td>0.268864</td>\n",
              "      <td>0.252461</td>\n",
              "      <td>0.288160</td>\n",
              "      <td>0.343854</td>\n",
              "      <td>0.322149</td>\n",
              "      <td>-0.201241</td>\n",
              "      <td>-0.011044</td>\n",
              "      <td>0.061782</td>\n",
              "      <td>-0.374146</td>\n",
              "      <td>-0.134507</td>\n",
              "      <td>-0.094679</td>\n",
              "      <td>-0.105467</td>\n",
              "      <td>-0.086332</td>\n",
              "      <td>-0.097991</td>\n",
              "      <td>-0.057164</td>\n",
              "      <td>-0.049281</td>\n",
              "      <td>-0.219423</td>\n",
              "      <td>0.989310</td>\n",
              "      <td>0.275348</td>\n",
              "      <td>0.249670</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>0.767305</td>\n",
              "      <td>-0.110077</td>\n",
              "      <td>0.013537</td>\n",
              "      <td>0.075801</td>\n",
              "      <td>-0.431247</td>\n",
              "      <td>-0.656085</td>\n",
              "      <td>-0.339975</td>\n",
              "      <td>0.025780</td>\n",
              "      <td>0.096755</td>\n",
              "      <td>0.096343</td>\n",
              "      <td>0.089934</td>\n",
              "      <td>0.107979</td>\n",
              "      <td>-0.732848</td>\n",
              "      <td>0.259748</td>\n",
              "      <td>0.991471</td>\n",
              "      <td>0.638500</td>\n",
              "      <td>0.737725</td>\n",
              "      <td>0.700146</td>\n",
              "      <td>0.762592</td>\n",
              "      <td>-0.109727</td>\n",
              "      <td>0.018079</td>\n",
              "      <td>0.075357</td>\n",
              "      <td>-0.430562</td>\n",
              "      <td>-0.654251</td>\n",
              "      <td>-0.328919</td>\n",
              "      <td>0.031094</td>\n",
              "      <td>0.104981</td>\n",
              "      <td>0.104965</td>\n",
              "      <td>0.099741</td>\n",
              "      <td>0.117226</td>\n",
              "      <td>-0.729730</td>\n",
              "      <td>0.258430</td>\n",
              "      <td>0.975017</td>\n",
              "      <td>0.640972</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>0.781862</td>\n",
              "      <td>0.132385</td>\n",
              "      <td>-0.265503</td>\n",
              "      <td>-0.041607</td>\n",
              "      <td>-0.491608</td>\n",
              "      <td>-0.864583</td>\n",
              "      <td>-0.489539</td>\n",
              "      <td>-0.427815</td>\n",
              "      <td>-0.348133</td>\n",
              "      <td>-0.340973</td>\n",
              "      <td>-0.462807</td>\n",
              "      <td>-0.455990</td>\n",
              "      <td>-0.694338</td>\n",
              "      <td>0.261335</td>\n",
              "      <td>0.616394</td>\n",
              "      <td>0.991248</td>\n",
              "      <td>0.816414</td>\n",
              "      <td>0.828992</td>\n",
              "      <td>0.784996</td>\n",
              "      <td>0.131371</td>\n",
              "      <td>-0.246039</td>\n",
              "      <td>-0.034364</td>\n",
              "      <td>-0.493842</td>\n",
              "      <td>-0.865601</td>\n",
              "      <td>-0.482534</td>\n",
              "      <td>-0.419104</td>\n",
              "      <td>-0.331428</td>\n",
              "      <td>-0.323789</td>\n",
              "      <td>-0.452086</td>\n",
              "      <td>-0.445815</td>\n",
              "      <td>-0.696457</td>\n",
              "      <td>0.266438</td>\n",
              "      <td>0.599363</td>\n",
              "      <td>0.977890</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>0.912449</td>\n",
              "      <td>-0.043954</td>\n",
              "      <td>-0.312912</td>\n",
              "      <td>-0.074059</td>\n",
              "      <td>-0.642316</td>\n",
              "      <td>-0.835101</td>\n",
              "      <td>-0.447892</td>\n",
              "      <td>-0.173726</td>\n",
              "      <td>-0.129772</td>\n",
              "      <td>-0.128114</td>\n",
              "      <td>-0.235459</td>\n",
              "      <td>-0.226870</td>\n",
              "      <td>-0.789257</td>\n",
              "      <td>0.286921</td>\n",
              "      <td>0.746680</td>\n",
              "      <td>0.820264</td>\n",
              "      <td>0.997496</td>\n",
              "      <td>0.877660</td>\n",
              "      <td>0.911143</td>\n",
              "      <td>-0.044942</td>\n",
              "      <td>-0.295416</td>\n",
              "      <td>-0.073814</td>\n",
              "      <td>-0.645507</td>\n",
              "      <td>-0.833903</td>\n",
              "      <td>-0.439702</td>\n",
              "      <td>-0.165959</td>\n",
              "      <td>-0.116415</td>\n",
              "      <td>-0.114323</td>\n",
              "      <td>-0.223203</td>\n",
              "      <td>-0.215113</td>\n",
              "      <td>-0.788931</td>\n",
              "      <td>0.285380</td>\n",
              "      <td>0.748185</td>\n",
              "      <td>0.818073</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>0.918504</td>\n",
              "      <td>-0.037282</td>\n",
              "      <td>-0.209830</td>\n",
              "      <td>0.104628</td>\n",
              "      <td>-0.511772</td>\n",
              "      <td>-0.870365</td>\n",
              "      <td>-0.536561</td>\n",
              "      <td>-0.275476</td>\n",
              "      <td>-0.189519</td>\n",
              "      <td>-0.187173</td>\n",
              "      <td>-0.280262</td>\n",
              "      <td>-0.268086</td>\n",
              "      <td>-0.808963</td>\n",
              "      <td>0.352038</td>\n",
              "      <td>0.697270</td>\n",
              "      <td>0.808526</td>\n",
              "      <td>0.877051</td>\n",
              "      <td>0.995393</td>\n",
              "      <td>0.916514</td>\n",
              "      <td>-0.039224</td>\n",
              "      <td>-0.189704</td>\n",
              "      <td>0.105809</td>\n",
              "      <td>-0.514850</td>\n",
              "      <td>-0.869399</td>\n",
              "      <td>-0.529935</td>\n",
              "      <td>-0.264309</td>\n",
              "      <td>-0.170275</td>\n",
              "      <td>-0.167358</td>\n",
              "      <td>-0.265245</td>\n",
              "      <td>-0.253751</td>\n",
              "      <td>-0.809003</td>\n",
              "      <td>0.352962</td>\n",
              "      <td>0.700309</td>\n",
              "      <td>0.800586</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>1.000000</td>\n",
              "      <td>-0.137804</td>\n",
              "      <td>-0.257765</td>\n",
              "      <td>0.058379</td>\n",
              "      <td>-0.555397</td>\n",
              "      <td>-0.875077</td>\n",
              "      <td>-0.391258</td>\n",
              "      <td>-0.073912</td>\n",
              "      <td>-0.024652</td>\n",
              "      <td>-0.025801</td>\n",
              "      <td>-0.111220</td>\n",
              "      <td>-0.104612</td>\n",
              "      <td>-0.896679</td>\n",
              "      <td>0.324122</td>\n",
              "      <td>0.771286</td>\n",
              "      <td>0.777809</td>\n",
              "      <td>0.911946</td>\n",
              "      <td>0.917266</td>\n",
              "      <td>0.997466</td>\n",
              "      <td>-0.138529</td>\n",
              "      <td>-0.235867</td>\n",
              "      <td>0.058109</td>\n",
              "      <td>-0.560147</td>\n",
              "      <td>-0.875092</td>\n",
              "      <td>-0.382278</td>\n",
              "      <td>-0.063393</td>\n",
              "      <td>-0.007143</td>\n",
              "      <td>-0.007783</td>\n",
              "      <td>-0.097153</td>\n",
              "      <td>-0.091332</td>\n",
              "      <td>-0.897079</td>\n",
              "      <td>0.323379</td>\n",
              "      <td>0.775721</td>\n",
              "      <td>0.773843</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>-0.137804</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.056476</td>\n",
              "      <td>-0.168079</td>\n",
              "      <td>0.001651</td>\n",
              "      <td>-0.045535</td>\n",
              "      <td>-0.136948</td>\n",
              "      <td>-0.451438</td>\n",
              "      <td>-0.361893</td>\n",
              "      <td>-0.351305</td>\n",
              "      <td>-0.430665</td>\n",
              "      <td>-0.421817</td>\n",
              "      <td>0.130233</td>\n",
              "      <td>-0.198224</td>\n",
              "      <td>-0.113609</td>\n",
              "      <td>0.131196</td>\n",
              "      <td>-0.046257</td>\n",
              "      <td>-0.036488</td>\n",
              "      <td>-0.137412</td>\n",
              "      <td>0.991382</td>\n",
              "      <td>-0.055710</td>\n",
              "      <td>-0.166971</td>\n",
              "      <td>0.001870</td>\n",
              "      <td>-0.046336</td>\n",
              "      <td>-0.137627</td>\n",
              "      <td>-0.449597</td>\n",
              "      <td>-0.358679</td>\n",
              "      <td>-0.348166</td>\n",
              "      <td>-0.429116</td>\n",
              "      <td>-0.419692</td>\n",
              "      <td>0.129357</td>\n",
              "      <td>-0.197844</td>\n",
              "      <td>-0.118109</td>\n",
              "      <td>0.128893</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>-0.257765</td>\n",
              "      <td>-0.056476</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.594517</td>\n",
              "      <td>0.535627</td>\n",
              "      <td>0.277613</td>\n",
              "      <td>0.199737</td>\n",
              "      <td>0.161826</td>\n",
              "      <td>0.285145</td>\n",
              "      <td>0.293405</td>\n",
              "      <td>0.346069</td>\n",
              "      <td>0.352811</td>\n",
              "      <td>0.238204</td>\n",
              "      <td>-0.025926</td>\n",
              "      <td>0.013529</td>\n",
              "      <td>-0.270760</td>\n",
              "      <td>-0.320063</td>\n",
              "      <td>-0.214328</td>\n",
              "      <td>-0.270192</td>\n",
              "      <td>-0.055029</td>\n",
              "      <td>0.980917</td>\n",
              "      <td>0.567346</td>\n",
              "      <td>0.525281</td>\n",
              "      <td>0.277862</td>\n",
              "      <td>0.209399</td>\n",
              "      <td>0.168780</td>\n",
              "      <td>0.291233</td>\n",
              "      <td>0.299527</td>\n",
              "      <td>0.354593</td>\n",
              "      <td>0.361294</td>\n",
              "      <td>0.244730</td>\n",
              "      <td>-0.026904</td>\n",
              "      <td>0.009454</td>\n",
              "      <td>-0.276209</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>0.058379</td>\n",
              "      <td>-0.168079</td>\n",
              "      <td>0.594517</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.617101</td>\n",
              "      <td>-0.043194</td>\n",
              "      <td>0.031992</td>\n",
              "      <td>0.030221</td>\n",
              "      <td>0.104670</td>\n",
              "      <td>0.109144</td>\n",
              "      <td>0.184914</td>\n",
              "      <td>0.189185</td>\n",
              "      <td>-0.073307</td>\n",
              "      <td>0.059502</td>\n",
              "      <td>0.079328</td>\n",
              "      <td>-0.043558</td>\n",
              "      <td>-0.070157</td>\n",
              "      <td>0.109766</td>\n",
              "      <td>0.061439</td>\n",
              "      <td>-0.167409</td>\n",
              "      <td>0.599963</td>\n",
              "      <td>0.978588</td>\n",
              "      <td>0.605572</td>\n",
              "      <td>-0.050108</td>\n",
              "      <td>0.027120</td>\n",
              "      <td>0.033558</td>\n",
              "      <td>0.107306</td>\n",
              "      <td>0.112050</td>\n",
              "      <td>0.185570</td>\n",
              "      <td>0.189329</td>\n",
              "      <td>-0.075791</td>\n",
              "      <td>0.061079</td>\n",
              "      <td>0.083127</td>\n",
              "      <td>-0.044305</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>-0.555397</td>\n",
              "      <td>0.001651</td>\n",
              "      <td>0.535627</td>\n",
              "      <td>0.617101</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.410344</td>\n",
              "      <td>0.144067</td>\n",
              "      <td>0.093155</td>\n",
              "      <td>0.095158</td>\n",
              "      <td>0.101735</td>\n",
              "      <td>0.189076</td>\n",
              "      <td>0.188219</td>\n",
              "      <td>0.419421</td>\n",
              "      <td>-0.376396</td>\n",
              "      <td>-0.430319</td>\n",
              "      <td>-0.488412</td>\n",
              "      <td>-0.638389</td>\n",
              "      <td>-0.505312</td>\n",
              "      <td>-0.549834</td>\n",
              "      <td>0.005813</td>\n",
              "      <td>0.532070</td>\n",
              "      <td>0.611981</td>\n",
              "      <td>0.990325</td>\n",
              "      <td>0.402841</td>\n",
              "      <td>0.136087</td>\n",
              "      <td>0.087830</td>\n",
              "      <td>0.088167</td>\n",
              "      <td>0.095148</td>\n",
              "      <td>0.179939</td>\n",
              "      <td>0.179002</td>\n",
              "      <td>0.416448</td>\n",
              "      <td>-0.374715</td>\n",
              "      <td>-0.429474</td>\n",
              "      <td>-0.485858</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>-0.875077</td>\n",
              "      <td>-0.045535</td>\n",
              "      <td>0.277613</td>\n",
              "      <td>-0.043194</td>\n",
              "      <td>0.410344</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.502649</td>\n",
              "      <td>0.279780</td>\n",
              "      <td>0.203275</td>\n",
              "      <td>0.196973</td>\n",
              "      <td>0.333275</td>\n",
              "      <td>0.329671</td>\n",
              "      <td>0.844686</td>\n",
              "      <td>-0.137145</td>\n",
              "      <td>-0.655130</td>\n",
              "      <td>-0.862088</td>\n",
              "      <td>-0.833427</td>\n",
              "      <td>-0.870224</td>\n",
              "      <td>-0.873946</td>\n",
              "      <td>-0.044556</td>\n",
              "      <td>0.263788</td>\n",
              "      <td>-0.041215</td>\n",
              "      <td>0.415507</td>\n",
              "      <td>0.996887</td>\n",
              "      <td>0.496683</td>\n",
              "      <td>0.270844</td>\n",
              "      <td>0.189101</td>\n",
              "      <td>0.182175</td>\n",
              "      <td>0.321093</td>\n",
              "      <td>0.318056</td>\n",
              "      <td>0.844619</td>\n",
              "      <td>-0.137755</td>\n",
              "      <td>-0.653743</td>\n",
              "      <td>-0.859742</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>-0.391258</td>\n",
              "      <td>-0.136948</td>\n",
              "      <td>0.199737</td>\n",
              "      <td>0.031992</td>\n",
              "      <td>0.144067</td>\n",
              "      <td>0.502649</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.488931</td>\n",
              "      <td>0.526652</td>\n",
              "      <td>0.520635</td>\n",
              "      <td>0.545356</td>\n",
              "      <td>0.516829</td>\n",
              "      <td>0.356017</td>\n",
              "      <td>-0.093741</td>\n",
              "      <td>-0.348098</td>\n",
              "      <td>-0.492839</td>\n",
              "      <td>-0.449846</td>\n",
              "      <td>-0.537010</td>\n",
              "      <td>-0.394803</td>\n",
              "      <td>-0.139490</td>\n",
              "      <td>0.176998</td>\n",
              "      <td>0.028940</td>\n",
              "      <td>0.143802</td>\n",
              "      <td>0.504792</td>\n",
              "      <td>0.994908</td>\n",
              "      <td>0.482591</td>\n",
              "      <td>0.514752</td>\n",
              "      <td>0.508145</td>\n",
              "      <td>0.537065</td>\n",
              "      <td>0.508739</td>\n",
              "      <td>0.357488</td>\n",
              "      <td>-0.090841</td>\n",
              "      <td>-0.356205</td>\n",
              "      <td>-0.495560</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>-0.073912</td>\n",
              "      <td>-0.451438</td>\n",
              "      <td>0.161826</td>\n",
              "      <td>0.030221</td>\n",
              "      <td>0.093155</td>\n",
              "      <td>0.279780</td>\n",
              "      <td>0.488931</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.829515</td>\n",
              "      <td>0.819290</td>\n",
              "      <td>0.877574</td>\n",
              "      <td>0.866768</td>\n",
              "      <td>-0.045737</td>\n",
              "      <td>-0.115217</td>\n",
              "      <td>0.026935</td>\n",
              "      <td>-0.428698</td>\n",
              "      <td>-0.174339</td>\n",
              "      <td>-0.276841</td>\n",
              "      <td>-0.078836</td>\n",
              "      <td>-0.451824</td>\n",
              "      <td>0.148057</td>\n",
              "      <td>0.025556</td>\n",
              "      <td>0.091067</td>\n",
              "      <td>0.282407</td>\n",
              "      <td>0.492442</td>\n",
              "      <td>0.990998</td>\n",
              "      <td>0.818252</td>\n",
              "      <td>0.806981</td>\n",
              "      <td>0.873880</td>\n",
              "      <td>0.862657</td>\n",
              "      <td>-0.042460</td>\n",
              "      <td>-0.116795</td>\n",
              "      <td>0.027376</td>\n",
              "      <td>-0.428797</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>-0.024652</td>\n",
              "      <td>-0.361893</td>\n",
              "      <td>0.285145</td>\n",
              "      <td>0.104670</td>\n",
              "      <td>0.095158</td>\n",
              "      <td>0.203275</td>\n",
              "      <td>0.526652</td>\n",
              "      <td>0.829515</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.995674</td>\n",
              "      <td>0.893440</td>\n",
              "      <td>0.882504</td>\n",
              "      <td>-0.116011</td>\n",
              "      <td>-0.092130</td>\n",
              "      <td>0.096964</td>\n",
              "      <td>-0.354292</td>\n",
              "      <td>-0.133947</td>\n",
              "      <td>-0.196898</td>\n",
              "      <td>-0.034315</td>\n",
              "      <td>-0.365006</td>\n",
              "      <td>0.272439</td>\n",
              "      <td>0.096959</td>\n",
              "      <td>0.094111</td>\n",
              "      <td>0.207996</td>\n",
              "      <td>0.534853</td>\n",
              "      <td>0.837592</td>\n",
              "      <td>0.980362</td>\n",
              "      <td>0.976220</td>\n",
              "      <td>0.896347</td>\n",
              "      <td>0.885444</td>\n",
              "      <td>-0.109756</td>\n",
              "      <td>-0.093782</td>\n",
              "      <td>0.096738</td>\n",
              "      <td>-0.358005</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>-0.025801</td>\n",
              "      <td>-0.351305</td>\n",
              "      <td>0.293405</td>\n",
              "      <td>0.109144</td>\n",
              "      <td>0.101735</td>\n",
              "      <td>0.196973</td>\n",
              "      <td>0.520635</td>\n",
              "      <td>0.819290</td>\n",
              "      <td>0.995674</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.889592</td>\n",
              "      <td>0.878349</td>\n",
              "      <td>-0.118039</td>\n",
              "      <td>-0.103381</td>\n",
              "      <td>0.096277</td>\n",
              "      <td>-0.346964</td>\n",
              "      <td>-0.132414</td>\n",
              "      <td>-0.194549</td>\n",
              "      <td>-0.035472</td>\n",
              "      <td>-0.354399</td>\n",
              "      <td>0.280783</td>\n",
              "      <td>0.101597</td>\n",
              "      <td>0.100651</td>\n",
              "      <td>0.201701</td>\n",
              "      <td>0.528760</td>\n",
              "      <td>0.828069</td>\n",
              "      <td>0.977015</td>\n",
              "      <td>0.980458</td>\n",
              "      <td>0.892653</td>\n",
              "      <td>0.881465</td>\n",
              "      <td>-0.111738</td>\n",
              "      <td>-0.104618</td>\n",
              "      <td>0.095890</td>\n",
              "      <td>-0.350867</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>-0.111220</td>\n",
              "      <td>-0.430665</td>\n",
              "      <td>0.346069</td>\n",
              "      <td>0.184914</td>\n",
              "      <td>0.189076</td>\n",
              "      <td>0.333275</td>\n",
              "      <td>0.545356</td>\n",
              "      <td>0.877574</td>\n",
              "      <td>0.893440</td>\n",
              "      <td>0.889592</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.997234</td>\n",
              "      <td>-0.040817</td>\n",
              "      <td>-0.063902</td>\n",
              "      <td>0.086705</td>\n",
              "      <td>-0.465404</td>\n",
              "      <td>-0.239324</td>\n",
              "      <td>-0.284723</td>\n",
              "      <td>-0.118792</td>\n",
              "      <td>-0.431668</td>\n",
              "      <td>0.333905</td>\n",
              "      <td>0.182467</td>\n",
              "      <td>0.192332</td>\n",
              "      <td>0.337200</td>\n",
              "      <td>0.551375</td>\n",
              "      <td>0.879283</td>\n",
              "      <td>0.888167</td>\n",
              "      <td>0.884147</td>\n",
              "      <td>0.998334</td>\n",
              "      <td>0.995144</td>\n",
              "      <td>-0.035703</td>\n",
              "      <td>-0.064120</td>\n",
              "      <td>0.082329</td>\n",
              "      <td>-0.467467</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>-0.104612</td>\n",
              "      <td>-0.421817</td>\n",
              "      <td>0.352811</td>\n",
              "      <td>0.189185</td>\n",
              "      <td>0.188219</td>\n",
              "      <td>0.329671</td>\n",
              "      <td>0.516829</td>\n",
              "      <td>0.866768</td>\n",
              "      <td>0.882504</td>\n",
              "      <td>0.878349</td>\n",
              "      <td>0.997234</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.050771</td>\n",
              "      <td>-0.056521</td>\n",
              "      <td>0.104905</td>\n",
              "      <td>-0.458404</td>\n",
              "      <td>-0.230950</td>\n",
              "      <td>-0.272641</td>\n",
              "      <td>-0.112093</td>\n",
              "      <td>-0.422784</td>\n",
              "      <td>0.341646</td>\n",
              "      <td>0.187345</td>\n",
              "      <td>0.191949</td>\n",
              "      <td>0.333406</td>\n",
              "      <td>0.523226</td>\n",
              "      <td>0.868688</td>\n",
              "      <td>0.878054</td>\n",
              "      <td>0.873809</td>\n",
              "      <td>0.996172</td>\n",
              "      <td>0.998238</td>\n",
              "      <td>-0.045506</td>\n",
              "      <td>-0.057139</td>\n",
              "      <td>0.100475</td>\n",
              "      <td>-0.460357</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>-0.896679</td>\n",
              "      <td>0.130233</td>\n",
              "      <td>0.238204</td>\n",
              "      <td>-0.073307</td>\n",
              "      <td>0.419421</td>\n",
              "      <td>0.844686</td>\n",
              "      <td>0.356017</td>\n",
              "      <td>-0.045737</td>\n",
              "      <td>-0.116011</td>\n",
              "      <td>-0.118039</td>\n",
              "      <td>-0.040817</td>\n",
              "      <td>-0.050771</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.221169</td>\n",
              "      <td>-0.735726</td>\n",
              "      <td>-0.692353</td>\n",
              "      <td>-0.789159</td>\n",
              "      <td>-0.808473</td>\n",
              "      <td>-0.895517</td>\n",
              "      <td>0.130088</td>\n",
              "      <td>0.222474</td>\n",
              "      <td>-0.073073</td>\n",
              "      <td>0.422506</td>\n",
              "      <td>0.844969</td>\n",
              "      <td>0.349277</td>\n",
              "      <td>-0.055730</td>\n",
              "      <td>-0.130174</td>\n",
              "      <td>-0.132709</td>\n",
              "      <td>-0.052158</td>\n",
              "      <td>-0.061575</td>\n",
              "      <td>0.998296</td>\n",
              "      <td>-0.221026</td>\n",
              "      <td>-0.737723</td>\n",
              "      <td>-0.689339</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>0.324122</td>\n",
              "      <td>-0.198224</td>\n",
              "      <td>-0.025926</td>\n",
              "      <td>0.059502</td>\n",
              "      <td>-0.376396</td>\n",
              "      <td>-0.137145</td>\n",
              "      <td>-0.093741</td>\n",
              "      <td>-0.115217</td>\n",
              "      <td>-0.092130</td>\n",
              "      <td>-0.103381</td>\n",
              "      <td>-0.063902</td>\n",
              "      <td>-0.056521</td>\n",
              "      <td>-0.221169</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.262586</td>\n",
              "      <td>0.257018</td>\n",
              "      <td>0.287521</td>\n",
              "      <td>0.348482</td>\n",
              "      <td>0.323193</td>\n",
              "      <td>-0.199258</td>\n",
              "      <td>-0.015064</td>\n",
              "      <td>0.062803</td>\n",
              "      <td>-0.374742</td>\n",
              "      <td>-0.135757</td>\n",
              "      <td>-0.092582</td>\n",
              "      <td>-0.108233</td>\n",
              "      <td>-0.087144</td>\n",
              "      <td>-0.098786</td>\n",
              "      <td>-0.057807</td>\n",
              "      <td>-0.050337</td>\n",
              "      <td>-0.220649</td>\n",
              "      <td>0.995846</td>\n",
              "      <td>0.267127</td>\n",
              "      <td>0.252978</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>0.771286</td>\n",
              "      <td>-0.113609</td>\n",
              "      <td>0.013529</td>\n",
              "      <td>0.079328</td>\n",
              "      <td>-0.430319</td>\n",
              "      <td>-0.655130</td>\n",
              "      <td>-0.348098</td>\n",
              "      <td>0.026935</td>\n",
              "      <td>0.096964</td>\n",
              "      <td>0.096277</td>\n",
              "      <td>0.086705</td>\n",
              "      <td>0.104905</td>\n",
              "      <td>-0.735726</td>\n",
              "      <td>0.262586</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.629487</td>\n",
              "      <td>0.743630</td>\n",
              "      <td>0.696654</td>\n",
              "      <td>0.766574</td>\n",
              "      <td>-0.110684</td>\n",
              "      <td>0.021666</td>\n",
              "      <td>0.078505</td>\n",
              "      <td>-0.429578</td>\n",
              "      <td>-0.654236</td>\n",
              "      <td>-0.336724</td>\n",
              "      <td>0.032510</td>\n",
              "      <td>0.106880</td>\n",
              "      <td>0.106623</td>\n",
              "      <td>0.097491</td>\n",
              "      <td>0.115173</td>\n",
              "      <td>-0.732798</td>\n",
              "      <td>0.258633</td>\n",
              "      <td>0.991393</td>\n",
              "      <td>0.635925</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>0.777809</td>\n",
              "      <td>0.131196</td>\n",
              "      <td>-0.270760</td>\n",
              "      <td>-0.043558</td>\n",
              "      <td>-0.488412</td>\n",
              "      <td>-0.862088</td>\n",
              "      <td>-0.492839</td>\n",
              "      <td>-0.428698</td>\n",
              "      <td>-0.354292</td>\n",
              "      <td>-0.346964</td>\n",
              "      <td>-0.465404</td>\n",
              "      <td>-0.458404</td>\n",
              "      <td>-0.692353</td>\n",
              "      <td>0.257018</td>\n",
              "      <td>0.629487</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.819514</td>\n",
              "      <td>0.819231</td>\n",
              "      <td>0.782372</td>\n",
              "      <td>0.132603</td>\n",
              "      <td>-0.251395</td>\n",
              "      <td>-0.036188</td>\n",
              "      <td>-0.490321</td>\n",
              "      <td>-0.863728</td>\n",
              "      <td>-0.486134</td>\n",
              "      <td>-0.421535</td>\n",
              "      <td>-0.338344</td>\n",
              "      <td>-0.330673</td>\n",
              "      <td>-0.455003</td>\n",
              "      <td>-0.448561</td>\n",
              "      <td>-0.694869</td>\n",
              "      <td>0.261958</td>\n",
              "      <td>0.613885</td>\n",
              "      <td>0.991241</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>0.911946</td>\n",
              "      <td>-0.046257</td>\n",
              "      <td>-0.320063</td>\n",
              "      <td>-0.070157</td>\n",
              "      <td>-0.638389</td>\n",
              "      <td>-0.833427</td>\n",
              "      <td>-0.449846</td>\n",
              "      <td>-0.174339</td>\n",
              "      <td>-0.133947</td>\n",
              "      <td>-0.132414</td>\n",
              "      <td>-0.239324</td>\n",
              "      <td>-0.230950</td>\n",
              "      <td>-0.789159</td>\n",
              "      <td>0.287521</td>\n",
              "      <td>0.743630</td>\n",
              "      <td>0.819514</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.877585</td>\n",
              "      <td>0.912477</td>\n",
              "      <td>-0.045290</td>\n",
              "      <td>-0.302435</td>\n",
              "      <td>-0.070335</td>\n",
              "      <td>-0.642225</td>\n",
              "      <td>-0.833459</td>\n",
              "      <td>-0.442064</td>\n",
              "      <td>-0.166938</td>\n",
              "      <td>-0.120357</td>\n",
              "      <td>-0.118444</td>\n",
              "      <td>-0.227415</td>\n",
              "      <td>-0.219465</td>\n",
              "      <td>-0.789448</td>\n",
              "      <td>0.286193</td>\n",
              "      <td>0.746670</td>\n",
              "      <td>0.818945</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>0.917266</td>\n",
              "      <td>-0.036488</td>\n",
              "      <td>-0.214328</td>\n",
              "      <td>0.109766</td>\n",
              "      <td>-0.505312</td>\n",
              "      <td>-0.870224</td>\n",
              "      <td>-0.537010</td>\n",
              "      <td>-0.276841</td>\n",
              "      <td>-0.196898</td>\n",
              "      <td>-0.194549</td>\n",
              "      <td>-0.284723</td>\n",
              "      <td>-0.272641</td>\n",
              "      <td>-0.808473</td>\n",
              "      <td>0.348482</td>\n",
              "      <td>0.696654</td>\n",
              "      <td>0.819231</td>\n",
              "      <td>0.877585</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.918789</td>\n",
              "      <td>-0.037518</td>\n",
              "      <td>-0.194729</td>\n",
              "      <td>0.111829</td>\n",
              "      <td>-0.509611</td>\n",
              "      <td>-0.870532</td>\n",
              "      <td>-0.530574</td>\n",
              "      <td>-0.267019</td>\n",
              "      <td>-0.177467</td>\n",
              "      <td>-0.174577</td>\n",
              "      <td>-0.270753</td>\n",
              "      <td>-0.259381</td>\n",
              "      <td>-0.809536</td>\n",
              "      <td>0.351225</td>\n",
              "      <td>0.695840</td>\n",
              "      <td>0.808827</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>0.997466</td>\n",
              "      <td>-0.137412</td>\n",
              "      <td>-0.270192</td>\n",
              "      <td>0.061439</td>\n",
              "      <td>-0.549834</td>\n",
              "      <td>-0.873946</td>\n",
              "      <td>-0.394803</td>\n",
              "      <td>-0.078836</td>\n",
              "      <td>-0.034315</td>\n",
              "      <td>-0.035472</td>\n",
              "      <td>-0.118792</td>\n",
              "      <td>-0.112093</td>\n",
              "      <td>-0.895517</td>\n",
              "      <td>0.323193</td>\n",
              "      <td>0.766574</td>\n",
              "      <td>0.782372</td>\n",
              "      <td>0.912477</td>\n",
              "      <td>0.918789</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.136693</td>\n",
              "      <td>-0.247837</td>\n",
              "      <td>0.062536</td>\n",
              "      <td>-0.554791</td>\n",
              "      <td>-0.875358</td>\n",
              "      <td>-0.386212</td>\n",
              "      <td>-0.068697</td>\n",
              "      <td>-0.016901</td>\n",
              "      <td>-0.017671</td>\n",
              "      <td>-0.105503</td>\n",
              "      <td>-0.099627</td>\n",
              "      <td>-0.896991</td>\n",
              "      <td>0.323153</td>\n",
              "      <td>0.770730</td>\n",
              "      <td>0.778333</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>-0.138529</td>\n",
              "      <td>0.991382</td>\n",
              "      <td>-0.055029</td>\n",
              "      <td>-0.167409</td>\n",
              "      <td>0.005813</td>\n",
              "      <td>-0.044556</td>\n",
              "      <td>-0.139490</td>\n",
              "      <td>-0.451824</td>\n",
              "      <td>-0.365006</td>\n",
              "      <td>-0.354399</td>\n",
              "      <td>-0.431668</td>\n",
              "      <td>-0.422784</td>\n",
              "      <td>0.130088</td>\n",
              "      <td>-0.199258</td>\n",
              "      <td>-0.110684</td>\n",
              "      <td>0.132603</td>\n",
              "      <td>-0.045290</td>\n",
              "      <td>-0.037518</td>\n",
              "      <td>-0.136693</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.054406</td>\n",
              "      <td>-0.165913</td>\n",
              "      <td>0.005852</td>\n",
              "      <td>-0.046285</td>\n",
              "      <td>-0.139565</td>\n",
              "      <td>-0.450922</td>\n",
              "      <td>-0.360692</td>\n",
              "      <td>-0.350053</td>\n",
              "      <td>-0.430189</td>\n",
              "      <td>-0.420841</td>\n",
              "      <td>0.128971</td>\n",
              "      <td>-0.198886</td>\n",
              "      <td>-0.114411</td>\n",
              "      <td>0.130798</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>-0.235867</td>\n",
              "      <td>-0.055710</td>\n",
              "      <td>0.980917</td>\n",
              "      <td>0.599963</td>\n",
              "      <td>0.532070</td>\n",
              "      <td>0.263788</td>\n",
              "      <td>0.176998</td>\n",
              "      <td>0.148057</td>\n",
              "      <td>0.272439</td>\n",
              "      <td>0.280783</td>\n",
              "      <td>0.333905</td>\n",
              "      <td>0.341646</td>\n",
              "      <td>0.222474</td>\n",
              "      <td>-0.015064</td>\n",
              "      <td>0.021666</td>\n",
              "      <td>-0.251395</td>\n",
              "      <td>-0.302435</td>\n",
              "      <td>-0.194729</td>\n",
              "      <td>-0.247837</td>\n",
              "      <td>-0.054406</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.594177</td>\n",
              "      <td>0.530247</td>\n",
              "      <td>0.264571</td>\n",
              "      <td>0.191871</td>\n",
              "      <td>0.157409</td>\n",
              "      <td>0.283873</td>\n",
              "      <td>0.292503</td>\n",
              "      <td>0.343566</td>\n",
              "      <td>0.350763</td>\n",
              "      <td>0.227949</td>\n",
              "      <td>-0.018054</td>\n",
              "      <td>0.021339</td>\n",
              "      <td>-0.256669</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>0.058109</td>\n",
              "      <td>-0.166971</td>\n",
              "      <td>0.567346</td>\n",
              "      <td>0.978588</td>\n",
              "      <td>0.611981</td>\n",
              "      <td>-0.041215</td>\n",
              "      <td>0.028940</td>\n",
              "      <td>0.025556</td>\n",
              "      <td>0.096959</td>\n",
              "      <td>0.101597</td>\n",
              "      <td>0.182467</td>\n",
              "      <td>0.187345</td>\n",
              "      <td>-0.073073</td>\n",
              "      <td>0.062803</td>\n",
              "      <td>0.078505</td>\n",
              "      <td>-0.036188</td>\n",
              "      <td>-0.070335</td>\n",
              "      <td>0.111829</td>\n",
              "      <td>0.062536</td>\n",
              "      <td>-0.165913</td>\n",
              "      <td>0.594177</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.613455</td>\n",
              "      <td>-0.047574</td>\n",
              "      <td>0.028859</td>\n",
              "      <td>0.029784</td>\n",
              "      <td>0.103255</td>\n",
              "      <td>0.107941</td>\n",
              "      <td>0.182810</td>\n",
              "      <td>0.186714</td>\n",
              "      <td>-0.076585</td>\n",
              "      <td>0.063947</td>\n",
              "      <td>0.081876</td>\n",
              "      <td>-0.037549</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>-0.560147</td>\n",
              "      <td>0.001870</td>\n",
              "      <td>0.525281</td>\n",
              "      <td>0.605572</td>\n",
              "      <td>0.990325</td>\n",
              "      <td>0.415507</td>\n",
              "      <td>0.143802</td>\n",
              "      <td>0.091067</td>\n",
              "      <td>0.094111</td>\n",
              "      <td>0.100651</td>\n",
              "      <td>0.192332</td>\n",
              "      <td>0.191949</td>\n",
              "      <td>0.422506</td>\n",
              "      <td>-0.374742</td>\n",
              "      <td>-0.429578</td>\n",
              "      <td>-0.490321</td>\n",
              "      <td>-0.642225</td>\n",
              "      <td>-0.509611</td>\n",
              "      <td>-0.554791</td>\n",
              "      <td>0.005852</td>\n",
              "      <td>0.530247</td>\n",
              "      <td>0.613455</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.409078</td>\n",
              "      <td>0.138023</td>\n",
              "      <td>0.086887</td>\n",
              "      <td>0.088147</td>\n",
              "      <td>0.095166</td>\n",
              "      <td>0.183325</td>\n",
              "      <td>0.182704</td>\n",
              "      <td>0.419704</td>\n",
              "      <td>-0.374097</td>\n",
              "      <td>-0.429090</td>\n",
              "      <td>-0.487016</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>-0.875092</td>\n",
              "      <td>-0.046336</td>\n",
              "      <td>0.277862</td>\n",
              "      <td>-0.050108</td>\n",
              "      <td>0.402841</td>\n",
              "      <td>0.996887</td>\n",
              "      <td>0.504792</td>\n",
              "      <td>0.282407</td>\n",
              "      <td>0.207996</td>\n",
              "      <td>0.201701</td>\n",
              "      <td>0.337200</td>\n",
              "      <td>0.333406</td>\n",
              "      <td>0.844969</td>\n",
              "      <td>-0.135757</td>\n",
              "      <td>-0.654236</td>\n",
              "      <td>-0.863728</td>\n",
              "      <td>-0.833459</td>\n",
              "      <td>-0.870532</td>\n",
              "      <td>-0.875358</td>\n",
              "      <td>-0.046285</td>\n",
              "      <td>0.264571</td>\n",
              "      <td>-0.047574</td>\n",
              "      <td>0.409078</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.499533</td>\n",
              "      <td>0.273726</td>\n",
              "      <td>0.192776</td>\n",
              "      <td>0.185828</td>\n",
              "      <td>0.325491</td>\n",
              "      <td>0.322393</td>\n",
              "      <td>0.845445</td>\n",
              "      <td>-0.136571</td>\n",
              "      <td>-0.653320</td>\n",
              "      <td>-0.861109</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>-0.382278</td>\n",
              "      <td>-0.137627</td>\n",
              "      <td>0.209399</td>\n",
              "      <td>0.027120</td>\n",
              "      <td>0.136087</td>\n",
              "      <td>0.496683</td>\n",
              "      <td>0.994908</td>\n",
              "      <td>0.492442</td>\n",
              "      <td>0.534853</td>\n",
              "      <td>0.528760</td>\n",
              "      <td>0.551375</td>\n",
              "      <td>0.523226</td>\n",
              "      <td>0.349277</td>\n",
              "      <td>-0.092582</td>\n",
              "      <td>-0.336724</td>\n",
              "      <td>-0.486134</td>\n",
              "      <td>-0.442064</td>\n",
              "      <td>-0.530574</td>\n",
              "      <td>-0.386212</td>\n",
              "      <td>-0.139565</td>\n",
              "      <td>0.191871</td>\n",
              "      <td>0.028859</td>\n",
              "      <td>0.138023</td>\n",
              "      <td>0.499533</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.487482</td>\n",
              "      <td>0.524226</td>\n",
              "      <td>0.517587</td>\n",
              "      <td>0.544000</td>\n",
              "      <td>0.515809</td>\n",
              "      <td>0.351079</td>\n",
              "      <td>-0.090375</td>\n",
              "      <td>-0.344796</td>\n",
              "      <td>-0.489468</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>-0.063393</td>\n",
              "      <td>-0.449597</td>\n",
              "      <td>0.168780</td>\n",
              "      <td>0.033558</td>\n",
              "      <td>0.087830</td>\n",
              "      <td>0.270844</td>\n",
              "      <td>0.482591</td>\n",
              "      <td>0.990998</td>\n",
              "      <td>0.837592</td>\n",
              "      <td>0.828069</td>\n",
              "      <td>0.879283</td>\n",
              "      <td>0.868688</td>\n",
              "      <td>-0.055730</td>\n",
              "      <td>-0.108233</td>\n",
              "      <td>0.032510</td>\n",
              "      <td>-0.421535</td>\n",
              "      <td>-0.166938</td>\n",
              "      <td>-0.267019</td>\n",
              "      <td>-0.068697</td>\n",
              "      <td>-0.450922</td>\n",
              "      <td>0.157409</td>\n",
              "      <td>0.029784</td>\n",
              "      <td>0.086887</td>\n",
              "      <td>0.273726</td>\n",
              "      <td>0.487482</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.827841</td>\n",
              "      <td>0.817491</td>\n",
              "      <td>0.876584</td>\n",
              "      <td>0.865664</td>\n",
              "      <td>-0.052249</td>\n",
              "      <td>-0.110418</td>\n",
              "      <td>0.033584</td>\n",
              "      <td>-0.422507</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>-0.007143</td>\n",
              "      <td>-0.358679</td>\n",
              "      <td>0.291233</td>\n",
              "      <td>0.107306</td>\n",
              "      <td>0.088167</td>\n",
              "      <td>0.189101</td>\n",
              "      <td>0.514752</td>\n",
              "      <td>0.818252</td>\n",
              "      <td>0.980362</td>\n",
              "      <td>0.977015</td>\n",
              "      <td>0.888167</td>\n",
              "      <td>0.878054</td>\n",
              "      <td>-0.130174</td>\n",
              "      <td>-0.087144</td>\n",
              "      <td>0.106880</td>\n",
              "      <td>-0.338344</td>\n",
              "      <td>-0.120357</td>\n",
              "      <td>-0.177467</td>\n",
              "      <td>-0.016901</td>\n",
              "      <td>-0.360692</td>\n",
              "      <td>0.283873</td>\n",
              "      <td>0.103255</td>\n",
              "      <td>0.088147</td>\n",
              "      <td>0.192776</td>\n",
              "      <td>0.524226</td>\n",
              "      <td>0.827841</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.995609</td>\n",
              "      <td>0.893731</td>\n",
              "      <td>0.882959</td>\n",
              "      <td>-0.124673</td>\n",
              "      <td>-0.088621</td>\n",
              "      <td>0.107075</td>\n",
              "      <td>-0.344467</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>-0.007783</td>\n",
              "      <td>-0.348166</td>\n",
              "      <td>0.299527</td>\n",
              "      <td>0.112050</td>\n",
              "      <td>0.095148</td>\n",
              "      <td>0.182175</td>\n",
              "      <td>0.508145</td>\n",
              "      <td>0.806981</td>\n",
              "      <td>0.976220</td>\n",
              "      <td>0.980458</td>\n",
              "      <td>0.884147</td>\n",
              "      <td>0.873809</td>\n",
              "      <td>-0.132709</td>\n",
              "      <td>-0.098786</td>\n",
              "      <td>0.106623</td>\n",
              "      <td>-0.330673</td>\n",
              "      <td>-0.118444</td>\n",
              "      <td>-0.174577</td>\n",
              "      <td>-0.017671</td>\n",
              "      <td>-0.350053</td>\n",
              "      <td>0.292503</td>\n",
              "      <td>0.107941</td>\n",
              "      <td>0.095166</td>\n",
              "      <td>0.185828</td>\n",
              "      <td>0.517587</td>\n",
              "      <td>0.817491</td>\n",
              "      <td>0.995609</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.889809</td>\n",
              "      <td>0.878775</td>\n",
              "      <td>-0.127130</td>\n",
              "      <td>-0.100016</td>\n",
              "      <td>0.106541</td>\n",
              "      <td>-0.336660</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>-0.097153</td>\n",
              "      <td>-0.429116</td>\n",
              "      <td>0.354593</td>\n",
              "      <td>0.185570</td>\n",
              "      <td>0.179939</td>\n",
              "      <td>0.321093</td>\n",
              "      <td>0.537065</td>\n",
              "      <td>0.873880</td>\n",
              "      <td>0.896347</td>\n",
              "      <td>0.892653</td>\n",
              "      <td>0.998334</td>\n",
              "      <td>0.996172</td>\n",
              "      <td>-0.052158</td>\n",
              "      <td>-0.057807</td>\n",
              "      <td>0.097491</td>\n",
              "      <td>-0.455003</td>\n",
              "      <td>-0.227415</td>\n",
              "      <td>-0.270753</td>\n",
              "      <td>-0.105503</td>\n",
              "      <td>-0.430189</td>\n",
              "      <td>0.343566</td>\n",
              "      <td>0.182810</td>\n",
              "      <td>0.183325</td>\n",
              "      <td>0.325491</td>\n",
              "      <td>0.544000</td>\n",
              "      <td>0.876584</td>\n",
              "      <td>0.893731</td>\n",
              "      <td>0.889809</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.997239</td>\n",
              "      <td>-0.046638</td>\n",
              "      <td>-0.058647</td>\n",
              "      <td>0.094164</td>\n",
              "      <td>-0.457631</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>-0.091332</td>\n",
              "      <td>-0.419692</td>\n",
              "      <td>0.361294</td>\n",
              "      <td>0.189329</td>\n",
              "      <td>0.179002</td>\n",
              "      <td>0.318056</td>\n",
              "      <td>0.508739</td>\n",
              "      <td>0.862657</td>\n",
              "      <td>0.885444</td>\n",
              "      <td>0.881465</td>\n",
              "      <td>0.995144</td>\n",
              "      <td>0.998238</td>\n",
              "      <td>-0.061575</td>\n",
              "      <td>-0.050337</td>\n",
              "      <td>0.115173</td>\n",
              "      <td>-0.448561</td>\n",
              "      <td>-0.219465</td>\n",
              "      <td>-0.259381</td>\n",
              "      <td>-0.099627</td>\n",
              "      <td>-0.420841</td>\n",
              "      <td>0.350763</td>\n",
              "      <td>0.186714</td>\n",
              "      <td>0.182704</td>\n",
              "      <td>0.322393</td>\n",
              "      <td>0.515809</td>\n",
              "      <td>0.865664</td>\n",
              "      <td>0.882959</td>\n",
              "      <td>0.878775</td>\n",
              "      <td>0.997239</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.055872</td>\n",
              "      <td>-0.051542</td>\n",
              "      <td>0.111921</td>\n",
              "      <td>-0.451024</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>-0.897079</td>\n",
              "      <td>0.129357</td>\n",
              "      <td>0.244730</td>\n",
              "      <td>-0.075791</td>\n",
              "      <td>0.416448</td>\n",
              "      <td>0.844619</td>\n",
              "      <td>0.357488</td>\n",
              "      <td>-0.042460</td>\n",
              "      <td>-0.109756</td>\n",
              "      <td>-0.111738</td>\n",
              "      <td>-0.035703</td>\n",
              "      <td>-0.045506</td>\n",
              "      <td>0.998296</td>\n",
              "      <td>-0.220649</td>\n",
              "      <td>-0.732798</td>\n",
              "      <td>-0.694869</td>\n",
              "      <td>-0.789448</td>\n",
              "      <td>-0.809536</td>\n",
              "      <td>-0.896991</td>\n",
              "      <td>0.128971</td>\n",
              "      <td>0.227949</td>\n",
              "      <td>-0.076585</td>\n",
              "      <td>0.419704</td>\n",
              "      <td>0.845445</td>\n",
              "      <td>0.351079</td>\n",
              "      <td>-0.052249</td>\n",
              "      <td>-0.124673</td>\n",
              "      <td>-0.127130</td>\n",
              "      <td>-0.046638</td>\n",
              "      <td>-0.055872</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.221655</td>\n",
              "      <td>-0.735766</td>\n",
              "      <td>-0.692859</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>0.323379</td>\n",
              "      <td>-0.197844</td>\n",
              "      <td>-0.026904</td>\n",
              "      <td>0.061079</td>\n",
              "      <td>-0.374715</td>\n",
              "      <td>-0.137755</td>\n",
              "      <td>-0.090841</td>\n",
              "      <td>-0.116795</td>\n",
              "      <td>-0.093782</td>\n",
              "      <td>-0.104618</td>\n",
              "      <td>-0.064120</td>\n",
              "      <td>-0.057139</td>\n",
              "      <td>-0.221026</td>\n",
              "      <td>0.995846</td>\n",
              "      <td>0.258633</td>\n",
              "      <td>0.261958</td>\n",
              "      <td>0.286193</td>\n",
              "      <td>0.351225</td>\n",
              "      <td>0.323153</td>\n",
              "      <td>-0.198886</td>\n",
              "      <td>-0.018054</td>\n",
              "      <td>0.063947</td>\n",
              "      <td>-0.374097</td>\n",
              "      <td>-0.136571</td>\n",
              "      <td>-0.090375</td>\n",
              "      <td>-0.110418</td>\n",
              "      <td>-0.088621</td>\n",
              "      <td>-0.100016</td>\n",
              "      <td>-0.058647</td>\n",
              "      <td>-0.051542</td>\n",
              "      <td>-0.221655</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.261260</td>\n",
              "      <td>0.258033</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>0.775721</td>\n",
              "      <td>-0.118109</td>\n",
              "      <td>0.009454</td>\n",
              "      <td>0.083127</td>\n",
              "      <td>-0.429474</td>\n",
              "      <td>-0.653743</td>\n",
              "      <td>-0.356205</td>\n",
              "      <td>0.027376</td>\n",
              "      <td>0.096738</td>\n",
              "      <td>0.095890</td>\n",
              "      <td>0.082329</td>\n",
              "      <td>0.100475</td>\n",
              "      <td>-0.737723</td>\n",
              "      <td>0.267127</td>\n",
              "      <td>0.991393</td>\n",
              "      <td>0.613885</td>\n",
              "      <td>0.746670</td>\n",
              "      <td>0.695840</td>\n",
              "      <td>0.770730</td>\n",
              "      <td>-0.114411</td>\n",
              "      <td>0.021339</td>\n",
              "      <td>0.081876</td>\n",
              "      <td>-0.429090</td>\n",
              "      <td>-0.653320</td>\n",
              "      <td>-0.344796</td>\n",
              "      <td>0.033584</td>\n",
              "      <td>0.107075</td>\n",
              "      <td>0.106541</td>\n",
              "      <td>0.094164</td>\n",
              "      <td>0.111921</td>\n",
              "      <td>-0.735766</td>\n",
              "      <td>0.261260</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.626952</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>0.773843</td>\n",
              "      <td>0.128893</td>\n",
              "      <td>-0.276209</td>\n",
              "      <td>-0.044305</td>\n",
              "      <td>-0.485858</td>\n",
              "      <td>-0.859742</td>\n",
              "      <td>-0.495560</td>\n",
              "      <td>-0.428797</td>\n",
              "      <td>-0.358005</td>\n",
              "      <td>-0.350867</td>\n",
              "      <td>-0.467467</td>\n",
              "      <td>-0.460357</td>\n",
              "      <td>-0.689339</td>\n",
              "      <td>0.252978</td>\n",
              "      <td>0.635925</td>\n",
              "      <td>0.991241</td>\n",
              "      <td>0.818945</td>\n",
              "      <td>0.808827</td>\n",
              "      <td>0.778333</td>\n",
              "      <td>0.130798</td>\n",
              "      <td>-0.256669</td>\n",
              "      <td>-0.037549</td>\n",
              "      <td>-0.487016</td>\n",
              "      <td>-0.861109</td>\n",
              "      <td>-0.489468</td>\n",
              "      <td>-0.422507</td>\n",
              "      <td>-0.344467</td>\n",
              "      <td>-0.336660</td>\n",
              "      <td>-0.457631</td>\n",
              "      <td>-0.451024</td>\n",
              "      <td>-0.692859</td>\n",
              "      <td>0.258033</td>\n",
              "      <td>0.626952</td>\n",
              "      <td>1.000000</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>0.910579</td>\n",
              "      <td>-0.048799</td>\n",
              "      <td>-0.327660</td>\n",
              "      <td>-0.065903</td>\n",
              "      <td>-0.633869</td>\n",
              "      <td>-0.831439</td>\n",
              "      <td>-0.451124</td>\n",
              "      <td>-0.174815</td>\n",
              "      <td>-0.138355</td>\n",
              "      <td>-0.136904</td>\n",
              "      <td>-0.242962</td>\n",
              "      <td>-0.234893</td>\n",
              "      <td>-0.787873</td>\n",
              "      <td>0.287332</td>\n",
              "      <td>0.737749</td>\n",
              "      <td>0.815317</td>\n",
              "      <td>0.997484</td>\n",
              "      <td>0.876180</td>\n",
              "      <td>0.912015</td>\n",
              "      <td>-0.047397</td>\n",
              "      <td>-0.309644</td>\n",
              "      <td>-0.066076</td>\n",
              "      <td>-0.638136</td>\n",
              "      <td>-0.832038</td>\n",
              "      <td>-0.444050</td>\n",
              "      <td>-0.167639</td>\n",
              "      <td>-0.124823</td>\n",
              "      <td>-0.123005</td>\n",
              "      <td>-0.231610</td>\n",
              "      <td>-0.223909</td>\n",
              "      <td>-0.789539</td>\n",
              "      <td>0.286899</td>\n",
              "      <td>0.743692</td>\n",
              "      <td>0.818509</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>0.913814</td>\n",
              "      <td>-0.035965</td>\n",
              "      <td>-0.218920</td>\n",
              "      <td>0.113934</td>\n",
              "      <td>-0.497951</td>\n",
              "      <td>-0.869933</td>\n",
              "      <td>-0.536959</td>\n",
              "      <td>-0.278257</td>\n",
              "      <td>-0.204750</td>\n",
              "      <td>-0.202412</td>\n",
              "      <td>-0.288664</td>\n",
              "      <td>-0.276769</td>\n",
              "      <td>-0.806218</td>\n",
              "      <td>0.342678</td>\n",
              "      <td>0.698691</td>\n",
              "      <td>0.829363</td>\n",
              "      <td>0.876508</td>\n",
              "      <td>0.995383</td>\n",
              "      <td>0.917360</td>\n",
              "      <td>-0.036646</td>\n",
              "      <td>-0.199497</td>\n",
              "      <td>0.117099</td>\n",
              "      <td>-0.503093</td>\n",
              "      <td>-0.870442</td>\n",
              "      <td>-0.531072</td>\n",
              "      <td>-0.268959</td>\n",
              "      <td>-0.185582</td>\n",
              "      <td>-0.182693</td>\n",
              "      <td>-0.275863</td>\n",
              "      <td>-0.264597</td>\n",
              "      <td>-0.808958</td>\n",
              "      <td>0.347804</td>\n",
              "      <td>0.695202</td>\n",
              "      <td>0.819755</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>0.993111</td>\n",
              "      <td>-0.137080</td>\n",
              "      <td>-0.283515</td>\n",
              "      <td>0.063086</td>\n",
              "      <td>-0.544678</td>\n",
              "      <td>-0.872394</td>\n",
              "      <td>-0.397443</td>\n",
              "      <td>-0.083275</td>\n",
              "      <td>-0.044520</td>\n",
              "      <td>-0.045430</td>\n",
              "      <td>-0.125902</td>\n",
              "      <td>-0.119138</td>\n",
              "      <td>-0.892712</td>\n",
              "      <td>0.321376</td>\n",
              "      <td>0.761755</td>\n",
              "      <td>0.785631</td>\n",
              "      <td>0.911182</td>\n",
              "      <td>0.916829</td>\n",
              "      <td>0.997449</td>\n",
              "      <td>-0.136200</td>\n",
              "      <td>-0.260678</td>\n",
              "      <td>0.065712</td>\n",
              "      <td>-0.549472</td>\n",
              "      <td>-0.874175</td>\n",
              "      <td>-0.389929</td>\n",
              "      <td>-0.073982</td>\n",
              "      <td>-0.027097</td>\n",
              "      <td>-0.027895</td>\n",
              "      <td>-0.113619</td>\n",
              "      <td>-0.107674</td>\n",
              "      <td>-0.895733</td>\n",
              "      <td>0.322521</td>\n",
              "      <td>0.765953</td>\n",
              "      <td>0.783057</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>-0.139139</td>\n",
              "      <td>0.982095</td>\n",
              "      <td>-0.054270</td>\n",
              "      <td>-0.165972</td>\n",
              "      <td>0.009742</td>\n",
              "      <td>-0.043575</td>\n",
              "      <td>-0.141140</td>\n",
              "      <td>-0.451809</td>\n",
              "      <td>-0.369066</td>\n",
              "      <td>-0.358447</td>\n",
              "      <td>-0.432234</td>\n",
              "      <td>-0.423434</td>\n",
              "      <td>0.130892</td>\n",
              "      <td>-0.202010</td>\n",
              "      <td>-0.110337</td>\n",
              "      <td>0.131734</td>\n",
              "      <td>-0.046082</td>\n",
              "      <td>-0.039405</td>\n",
              "      <td>-0.137279</td>\n",
              "      <td>0.991364</td>\n",
              "      <td>-0.053189</td>\n",
              "      <td>-0.164973</td>\n",
              "      <td>0.009842</td>\n",
              "      <td>-0.045239</td>\n",
              "      <td>-0.141796</td>\n",
              "      <td>-0.451397</td>\n",
              "      <td>-0.363815</td>\n",
              "      <td>-0.353209</td>\n",
              "      <td>-0.431142</td>\n",
              "      <td>-0.421805</td>\n",
              "      <td>0.128696</td>\n",
              "      <td>-0.199731</td>\n",
              "      <td>-0.111477</td>\n",
              "      <td>0.132409</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>-0.215598</td>\n",
              "      <td>-0.055799</td>\n",
              "      <td>0.951196</td>\n",
              "      <td>0.599423</td>\n",
              "      <td>0.526345</td>\n",
              "      <td>0.251045</td>\n",
              "      <td>0.152500</td>\n",
              "      <td>0.132802</td>\n",
              "      <td>0.254911</td>\n",
              "      <td>0.263278</td>\n",
              "      <td>0.319697</td>\n",
              "      <td>0.328817</td>\n",
              "      <td>0.208011</td>\n",
              "      <td>-0.003803</td>\n",
              "      <td>0.025430</td>\n",
              "      <td>-0.232298</td>\n",
              "      <td>-0.285147</td>\n",
              "      <td>-0.175019</td>\n",
              "      <td>-0.226287</td>\n",
              "      <td>-0.053367</td>\n",
              "      <td>0.980965</td>\n",
              "      <td>0.599952</td>\n",
              "      <td>0.527040</td>\n",
              "      <td>0.250985</td>\n",
              "      <td>0.168931</td>\n",
              "      <td>0.143395</td>\n",
              "      <td>0.270811</td>\n",
              "      <td>0.279485</td>\n",
              "      <td>0.330882</td>\n",
              "      <td>0.339048</td>\n",
              "      <td>0.212752</td>\n",
              "      <td>-0.007996</td>\n",
              "      <td>0.028803</td>\n",
              "      <td>-0.237570</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>0.055579</td>\n",
              "      <td>-0.167599</td>\n",
              "      <td>0.539596</td>\n",
              "      <td>0.953157</td>\n",
              "      <td>0.604416</td>\n",
              "      <td>-0.038421</td>\n",
              "      <td>0.027382</td>\n",
              "      <td>0.021773</td>\n",
              "      <td>0.088243</td>\n",
              "      <td>0.092825</td>\n",
              "      <td>0.180703</td>\n",
              "      <td>0.186480</td>\n",
              "      <td>-0.071689</td>\n",
              "      <td>0.065990</td>\n",
              "      <td>0.077455</td>\n",
              "      <td>-0.029772</td>\n",
              "      <td>-0.070812</td>\n",
              "      <td>0.111992</td>\n",
              "      <td>0.061477</td>\n",
              "      <td>-0.165462</td>\n",
              "      <td>0.567651</td>\n",
              "      <td>0.978623</td>\n",
              "      <td>0.608896</td>\n",
              "      <td>-0.044886</td>\n",
              "      <td>0.026522</td>\n",
              "      <td>0.025272</td>\n",
              "      <td>0.096270</td>\n",
              "      <td>0.100997</td>\n",
              "      <td>0.180963</td>\n",
              "      <td>0.185514</td>\n",
              "      <td>-0.075599</td>\n",
              "      <td>0.067252</td>\n",
              "      <td>0.080485</td>\n",
              "      <td>-0.031062</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>-0.565014</td>\n",
              "      <td>0.001697</td>\n",
              "      <td>0.516252</td>\n",
              "      <td>0.592656</td>\n",
              "      <td>0.978303</td>\n",
              "      <td>0.419940</td>\n",
              "      <td>0.144230</td>\n",
              "      <td>0.090015</td>\n",
              "      <td>0.093092</td>\n",
              "      <td>0.099412</td>\n",
              "      <td>0.195294</td>\n",
              "      <td>0.195257</td>\n",
              "      <td>0.425830</td>\n",
              "      <td>-0.372008</td>\n",
              "      <td>-0.429387</td>\n",
              "      <td>-0.492524</td>\n",
              "      <td>-0.645411</td>\n",
              "      <td>-0.513196</td>\n",
              "      <td>-0.559791</td>\n",
              "      <td>0.005809</td>\n",
              "      <td>0.519936</td>\n",
              "      <td>0.601982</td>\n",
              "      <td>0.990446</td>\n",
              "      <td>0.414444</td>\n",
              "      <td>0.138286</td>\n",
              "      <td>0.085417</td>\n",
              "      <td>0.087526</td>\n",
              "      <td>0.094463</td>\n",
              "      <td>0.186653</td>\n",
              "      <td>0.186517</td>\n",
              "      <td>0.423325</td>\n",
              "      <td>-0.372366</td>\n",
              "      <td>-0.428880</td>\n",
              "      <td>-0.488979</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>-0.874083</td>\n",
              "      <td>-0.046611</td>\n",
              "      <td>0.280087</td>\n",
              "      <td>-0.055694</td>\n",
              "      <td>0.395606</td>\n",
              "      <td>0.992976</td>\n",
              "      <td>0.506907</td>\n",
              "      <td>0.285052</td>\n",
              "      <td>0.212721</td>\n",
              "      <td>0.206378</td>\n",
              "      <td>0.340223</td>\n",
              "      <td>0.336189</td>\n",
              "      <td>0.843469</td>\n",
              "      <td>-0.133520</td>\n",
              "      <td>-0.652367</td>\n",
              "      <td>-0.864694</td>\n",
              "      <td>-0.832047</td>\n",
              "      <td>-0.869427</td>\n",
              "      <td>-0.875258</td>\n",
              "      <td>-0.047158</td>\n",
              "      <td>0.264619</td>\n",
              "      <td>-0.054501</td>\n",
              "      <td>0.401543</td>\n",
              "      <td>0.996878</td>\n",
              "      <td>0.501713</td>\n",
              "      <td>0.276338</td>\n",
              "      <td>0.197407</td>\n",
              "      <td>0.190436</td>\n",
              "      <td>0.329330</td>\n",
              "      <td>0.326080</td>\n",
              "      <td>0.845721</td>\n",
              "      <td>-0.135255</td>\n",
              "      <td>-0.652395</td>\n",
              "      <td>-0.862711</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>-0.372589</td>\n",
              "      <td>-0.138801</td>\n",
              "      <td>0.217988</td>\n",
              "      <td>0.021274</td>\n",
              "      <td>0.128047</td>\n",
              "      <td>0.490127</td>\n",
              "      <td>0.988123</td>\n",
              "      <td>0.495753</td>\n",
              "      <td>0.540981</td>\n",
              "      <td>0.534751</td>\n",
              "      <td>0.556547</td>\n",
              "      <td>0.528994</td>\n",
              "      <td>0.342057</td>\n",
              "      <td>-0.090896</td>\n",
              "      <td>-0.325294</td>\n",
              "      <td>-0.478978</td>\n",
              "      <td>-0.433497</td>\n",
              "      <td>-0.523476</td>\n",
              "      <td>-0.376785</td>\n",
              "      <td>-0.140425</td>\n",
              "      <td>0.201912</td>\n",
              "      <td>0.024123</td>\n",
              "      <td>0.130005</td>\n",
              "      <td>0.492998</td>\n",
              "      <td>0.994864</td>\n",
              "      <td>0.491043</td>\n",
              "      <td>0.532770</td>\n",
              "      <td>0.526026</td>\n",
              "      <td>0.550290</td>\n",
              "      <td>0.522506</td>\n",
              "      <td>0.343891</td>\n",
              "      <td>-0.089308</td>\n",
              "      <td>-0.333070</td>\n",
              "      <td>-0.482649</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>-0.052473</td>\n",
              "      <td>-0.448360</td>\n",
              "      <td>0.173295</td>\n",
              "      <td>0.037689</td>\n",
              "      <td>0.082182</td>\n",
              "      <td>0.262211</td>\n",
              "      <td>0.475462</td>\n",
              "      <td>0.981165</td>\n",
              "      <td>0.838224</td>\n",
              "      <td>0.829200</td>\n",
              "      <td>0.879438</td>\n",
              "      <td>0.869278</td>\n",
              "      <td>-0.065600</td>\n",
              "      <td>-0.100407</td>\n",
              "      <td>0.037930</td>\n",
              "      <td>-0.412705</td>\n",
              "      <td>-0.159121</td>\n",
              "      <td>-0.255714</td>\n",
              "      <td>-0.058079</td>\n",
              "      <td>-0.449079</td>\n",
              "      <td>0.164537</td>\n",
              "      <td>0.033149</td>\n",
              "      <td>0.081521</td>\n",
              "      <td>0.264816</td>\n",
              "      <td>0.480958</td>\n",
              "      <td>0.991012</td>\n",
              "      <td>0.835751</td>\n",
              "      <td>0.826075</td>\n",
              "      <td>0.878218</td>\n",
              "      <td>0.867535</td>\n",
              "      <td>-0.062037</td>\n",
              "      <td>-0.103868</td>\n",
              "      <td>0.039304</td>\n",
              "      <td>-0.415130</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",
              "</div>"
            ],
            "text/plain": [
              "                             cli  ...  tested_positive.2\n",
              "cli                     1.000000  ...           0.838504\n",
              "ili                     0.995735  ...           0.830527\n",
              "hh_cmnty_cli            0.893416  ...           0.879724\n",
              "nohh_cmnty_cli          0.882322  ...           0.869938\n",
              "wearing_mask           -0.107406  ...          -0.069531\n",
              "travel_outside_state   -0.095964  ...          -0.097303\n",
              "work_outside_home       0.087305  ...           0.034865\n",
              "shop                   -0.364165  ...          -0.410430\n",
              "restaurant             -0.143318  ...          -0.157945\n",
              "spent_time             -0.209020  ...          -0.252125\n",
              "large_event            -0.042033  ...          -0.052473\n",
              "public_transit         -0.367103  ...          -0.448360\n",
              "anxious                 0.273874  ...           0.173295\n",
              "depressed               0.098033  ...           0.037689\n",
              "felt_isolated           0.100928  ...           0.082182\n",
              "worried_become_ill      0.218502  ...           0.262211\n",
              "worried_finances        0.537608  ...           0.475462\n",
              "tested_positive         0.839122  ...           0.981165\n",
              "cli.1                   0.980379  ...           0.838224\n",
              "ili.1                   0.976171  ...           0.829200\n",
              "hh_cmnty_cli.1          0.896211  ...           0.879438\n",
              "nohh_cmnty_cli.1        0.885178  ...           0.869278\n",
              "wearing_mask.1         -0.101056  ...          -0.065600\n",
              "travel_outside_state.1 -0.097092  ...          -0.100407\n",
              "work_outside_home.1     0.087080  ...           0.037930\n",
              "shop.1                 -0.367850  ...          -0.412705\n",
              "restaurant.1           -0.147491  ...          -0.159121\n",
              "spent_time.1           -0.216168  ...          -0.255714\n",
              "large_event.1          -0.051724  ...          -0.058079\n",
              "public_transit.1       -0.371063  ...          -0.449079\n",
              "anxious.1               0.256712  ...           0.164537\n",
              "depressed.1             0.088676  ...           0.033149\n",
              "felt_isolated.1         0.099487  ...           0.081521\n",
              "worried_become_ill.1    0.223326  ...           0.264816\n",
              "worried_finances.1      0.543373  ...           0.480958\n",
              "tested_positive.1       0.839929  ...           0.991012\n",
              "cli.2                   0.957059  ...           0.835751\n",
              "ili.2                   0.952707  ...           0.826075\n",
              "hh_cmnty_cli.2          0.898067  ...           0.878218\n",
              "nohh_cmnty_cli.2        0.887103  ...           0.867535\n",
              "wearing_mask.2         -0.094664  ...          -0.062037\n",
              "travel_outside_state.2 -0.097903  ...          -0.103868\n",
              "work_outside_home.2     0.085913  ...           0.039304\n",
              "shop.2                 -0.370197  ...          -0.415130\n",
              "restaurant.2           -0.151291  ...          -0.160181\n",
              "spent_time.2           -0.222834  ...          -0.258956\n",
              "large_event.2          -0.060308  ...          -0.063709\n",
              "public_transit.2       -0.374071  ...          -0.450436\n",
              "anxious.2               0.237135  ...           0.152903\n",
              "depressed.2             0.081456  ...           0.029578\n",
              "felt_isolated.2         0.098345  ...           0.081174\n",
              "worried_become_ill.2    0.228750  ...           0.267610\n",
              "worried_finances.2      0.550564  ...           0.485843\n",
              "tested_positive.2       0.838504  ...           1.000000\n",
              "\n",
              "[54 rows x 54 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kGU22GuR5Q1C",
        "outputId": "854797c7-4df2-4d18-9e1c-00cee3e3ebd9"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L5kb1Kx-5T2V",
        "outputId": "a074e4e0-5c05-48b3-b3c2-e8cd779806ac"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wi9jeewo5WY7",
        "outputId": "521e523a-9fd0-46d3-b3ab-c6ac13ada5aa"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "huXmprAZ5aVg",
        "outputId": "3eb70fd7-233b-4e83-9b5d-f827b0b3a5de"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[40, 41, 42, 43, 57, 58, 59, 60, 61, 75, 76, 77, 78, 79]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BtE3b6JEH7rw"
      },
      "source": [
        "# **Some Utilities**\n",
        "\n",
        "You do not need to modify this part."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FWMT3uf1NGQp"
      },
      "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()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "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": "3wCxFVEOUpI6"
      },
      "source": [
        "# 新段落"
      ]
    },
    {
      "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",
      "metadata": {
        "id": "0zlpIp9ANJRU"
      },
      "source": [
        "class COVID19Dataset(Dataset):\n",
        "    ''' Dataset for loading and preprocessing the COVID19 dataset '''\n",
        "    def __init__(self,\n",
        "                 path,\n",
        "                 mu,      #mu,std是自己加，baseline代码归一化有问题，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) #去掉表头行和id列\n",
        "        \n",
        "        if not target_only:\n",
        "            feats = list(range(93))#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",
        "        else:\n",
        "            # TODO: Using 40 states & 4 tested_positive features (indices = 57 & 75)\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 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 = 0.1, random_state = 0)\n",
        "            if self.mode == 'train':\n",
        "                indices = indices_tr\n",
        "            elif self.mode == 'dev':\n",
        "                indices = indices_dev\n",
        "\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",
        " \n",
        "        #baseline这段代码数据归一化用的是当前数据归一化\n",
        "        #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",
        "        if self.mode == \"train\": \n",
        "          #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: \n",
        "          #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)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AlhTlkE7MDo3"
      },
      "source": [
        "## **DataLoader**\n",
        "\n",
        "A `DataLoader` loads data from a given `Dataset` into batches.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hlhLk5t6MBX3"
      },
      "source": [
        "def prep_dataloader(path, mode, batch_size, n_jobs=0, target_only=False, mu=None, std=None):\n",
        "    #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"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "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",
      "metadata": {
        "id": "49-uXYovOAI0"
      },
      "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, 64),  #70是我调得最好的， 而且加层很容易过拟和\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(64, 1)\n",
        "        )\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 L1/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, 上面这段代码用的是均方根误差torch.sqrt()，均方根误差和kaggle评测指标一致，而且训练模型也更平稳"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DvFWVjZ5Nvga"
      },
      "source": [
        "# **Train/Dev/Test**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MAM8QecJOyqn"
      },
      "source": [
        "## **Training**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lOqcmYzMO7jB"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0hSd4Bn3O2PL"
      },
      "source": [
        "## **Validation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yrxrD3YsN3U2"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g0pdrhQAO41L"
      },
      "source": [
        "## **Testing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aSBMRFlYN5tB"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "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",
      "metadata": {
        "id": "NPXpdumwPjE7"
      },
      "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",
        "#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",
        "\n",
        "# TODO: How to tune these hyper-parameters to improve your model's performance?\n",
        "# 这里没调\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': 0.001,                 # learning rate of SGD 可换成adam试试\n",
        "        'momentum': 0.9              # 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",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6j1eOV3TOH-j"
      },
      "source": [
        "# **Load data and model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eNrYBMmePLKm",
        "outputId": "45ab09d6-0a93-4480-85f9-81c3049b9054"
      },
      "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)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Finished reading the train set of COVID19 Dataset (2430 samples found, each dim = 54)\n",
            "Finished reading the dev set of COVID19 Dataset (270 samples found, each dim = 54)\n",
            "Finished reading the test set of COVID19 Dataset (2700 samples found, each dim = 54)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FHylSirLP9oh"
      },
      "source": [
        "model = NeuralNet(tr_set.dataset.dim).to(device)  # Construct model and move to device"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sX2B_zgSOPTJ"
      },
      "source": [
        "# **Start Training!**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GrEbUxazQAAZ",
        "outputId": "71760165-cbda-4e93-a95b-83c38e28b5a1"
      },
      "source": [
        "model_loss, model_loss_record = train(tr_set, dv_set, model, config, device)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saving model (epoch =    1, loss = 17.6936)\n",
            "Saving model (epoch =    2, loss = 17.5330)\n",
            "Saving model (epoch =    3, loss = 17.3351)\n",
            "Saving model (epoch =    4, loss = 17.1061)\n",
            "Saving model (epoch =    5, loss = 16.8355)\n",
            "Saving model (epoch =    6, loss = 16.5097)\n",
            "Saving model (epoch =    7, loss = 16.1022)\n",
            "Saving model (epoch =    8, loss = 15.5854)\n",
            "Saving model (epoch =    9, loss = 14.9349)\n",
            "Saving model (epoch =   10, loss = 14.1227)\n",
            "Saving model (epoch =   11, loss = 13.1496)\n",
            "Saving model (epoch =   12, loss = 12.0869)\n",
            "Saving model (epoch =   13, loss = 11.0465)\n",
            "Saving model (epoch =   14, loss = 10.2609)\n",
            "Saving model (epoch =   15, loss = 9.7964)\n",
            "Saving model (epoch =   16, loss = 9.5256)\n",
            "Saving model (epoch =   17, loss = 9.3033)\n",
            "Saving model (epoch =   18, loss = 9.0832)\n",
            "Saving model (epoch =   19, loss = 8.8589)\n",
            "Saving model (epoch =   20, loss = 8.6191)\n",
            "Saving model (epoch =   21, loss = 8.3582)\n",
            "Saving model (epoch =   22, loss = 8.0935)\n",
            "Saving model (epoch =   23, loss = 7.8330)\n",
            "Saving model (epoch =   24, loss = 7.5790)\n",
            "Saving model (epoch =   25, loss = 7.3364)\n",
            "Saving model (epoch =   26, loss = 7.0958)\n",
            "Saving model (epoch =   27, loss = 6.8586)\n",
            "Saving model (epoch =   28, loss = 6.6146)\n",
            "Saving model (epoch =   29, loss = 6.3625)\n",
            "Saving model (epoch =   30, loss = 6.1015)\n",
            "Saving model (epoch =   31, loss = 5.8168)\n",
            "Saving model (epoch =   32, loss = 5.5030)\n",
            "Saving model (epoch =   33, loss = 5.1631)\n",
            "Saving model (epoch =   34, loss = 4.7853)\n",
            "Saving model (epoch =   35, loss = 4.3790)\n",
            "Saving model (epoch =   36, loss = 3.9485)\n",
            "Saving model (epoch =   37, loss = 3.5192)\n",
            "Saving model (epoch =   38, loss = 3.1020)\n",
            "Saving model (epoch =   39, loss = 2.7014)\n",
            "Saving model (epoch =   40, loss = 2.3479)\n",
            "Saving model (epoch =   41, loss = 2.0362)\n",
            "Saving model (epoch =   42, loss = 1.7641)\n",
            "Saving model (epoch =   43, loss = 1.5390)\n",
            "Saving model (epoch =   44, loss = 1.3704)\n",
            "Saving model (epoch =   45, loss = 1.2489)\n",
            "Saving model (epoch =   46, loss = 1.2050)\n",
            "Saving model (epoch =   47, loss = 1.1737)\n",
            "Saving model (epoch =   48, loss = 1.1674)\n",
            "Saving model (epoch =   49, loss = 1.1469)\n",
            "Saving model (epoch =   50, loss = 1.1438)\n",
            "Saving model (epoch =   51, loss = 1.1228)\n",
            "Saving model (epoch =   53, loss = 1.1215)\n",
            "Saving model (epoch =   54, loss = 1.1043)\n",
            "Saving model (epoch =   55, loss = 1.1007)\n",
            "Saving model (epoch =   56, loss = 1.0998)\n",
            "Saving model (epoch =   57, loss = 1.0992)\n",
            "Saving model (epoch =   58, loss = 1.0913)\n",
            "Saving model (epoch =   60, loss = 1.0791)\n",
            "Saving model (epoch =   62, loss = 1.0758)\n",
            "Saving model (epoch =   63, loss = 1.0727)\n",
            "Saving model (epoch =   64, loss = 1.0690)\n",
            "Saving model (epoch =   67, loss = 1.0625)\n",
            "Saving model (epoch =   68, loss = 1.0605)\n",
            "Saving model (epoch =   69, loss = 1.0601)\n",
            "Saving model (epoch =   70, loss = 1.0550)\n",
            "Saving model (epoch =   71, loss = 1.0538)\n",
            "Saving model (epoch =   73, loss = 1.0412)\n",
            "Saving model (epoch =   78, loss = 1.0356)\n",
            "Saving model (epoch =   80, loss = 1.0318)\n",
            "Saving model (epoch =   83, loss = 1.0312)\n",
            "Saving model (epoch =   85, loss = 1.0279)\n",
            "Saving model (epoch =   89, loss = 1.0277)\n",
            "Saving model (epoch =   91, loss = 1.0188)\n",
            "Saving model (epoch =   94, loss = 1.0173)\n",
            "Saving model (epoch =   96, loss = 1.0150)\n",
            "Saving model (epoch =   98, loss = 1.0144)\n",
            "Saving model (epoch =  101, loss = 1.0125)\n",
            "Saving model (epoch =  104, loss = 1.0107)\n",
            "Saving model (epoch =  109, loss = 1.0057)\n",
            "Saving model (epoch =  116, loss = 1.0049)\n",
            "Saving model (epoch =  122, loss = 1.0047)\n",
            "Saving model (epoch =  123, loss = 1.0043)\n",
            "Saving model (epoch =  128, loss = 0.9998)\n",
            "Saving model (epoch =  138, loss = 0.9966)\n",
            "Saving model (epoch =  153, loss = 0.9937)\n",
            "Saving model (epoch =  159, loss = 0.9932)\n",
            "Saving model (epoch =  160, loss = 0.9921)\n",
            "Saving model (epoch =  162, loss = 0.9900)\n",
            "Saving model (epoch =  176, loss = 0.9880)\n",
            "Saving model (epoch =  180, loss = 0.9877)\n",
            "Saving model (epoch =  186, loss = 0.9863)\n",
            "Saving model (epoch =  190, loss = 0.9852)\n",
            "Saving model (epoch =  209, loss = 0.9843)\n",
            "Saving model (epoch =  214, loss = 0.9842)\n",
            "Saving model (epoch =  222, loss = 0.9832)\n",
            "Saving model (epoch =  228, loss = 0.9829)\n",
            "Saving model (epoch =  252, loss = 0.9803)\n",
            "Saving model (epoch =  275, loss = 0.9793)\n",
            "Saving model (epoch =  290, loss = 0.9790)\n",
            "Saving model (epoch =  291, loss = 0.9774)\n",
            "Saving model (epoch =  322, loss = 0.9767)\n",
            "Saving model (epoch =  362, loss = 0.9766)\n",
            "Saving model (epoch =  386, loss = 0.9755)\n",
            "Saving model (epoch =  508, loss = 0.9750)\n",
            "Saving model (epoch =  520, loss = 0.9733)\n",
            "Saving model (epoch =  621, loss = 0.9733)\n",
            "Saving model (epoch =  638, loss = 0.9732)\n",
            "Saving model (epoch =  802, loss = 0.9728)\n",
            "Saving model (epoch =  839, loss = 0.9715)\n",
            "Saving model (epoch =  915, loss = 0.9710)\n",
            "Saving model (epoch =  921, loss = 0.9704)\n",
            "Saving model (epoch = 1067, loss = 0.9701)\n",
            "Finished training after 1268 epochs\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "hsNO9nnXQBvP",
        "outputId": "4c3bd966-5fb9-45fb-b3d6-970ce8958d49"
      },
      "source": [
        "plot_learning_curve(model_loss_record, title='deep model')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xiJ-Lprt_SZc",
        "outputId": "ab54cd56-bd5a-45c4-d24c-10ed47e36ab6"
      },
      "source": [
        "dev(dv_set, model, device)  #验证集损失"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9853214025497437"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "3iZTVn5WQFpX",
        "outputId": "23905a65-b1d9-441c-dabf-082450b7e482"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GGQ29sPrHRNMwjKDYvFkFMztbU4uGDGnoFTUMJpqGYeyXDRtUJLduhQ8/hJNOaugVNRwmmoZh1Mq6dSqYu3bBxx/DwIENvaKGxUTTMIx9CagxX92iL0PevI6Ckhhmz4bjjmvoxTU8Fj03DMNPVpamF+XksCKuL4NeGk1RTinznv/RBNOLiaZhGH4yMyE1laXl3Rk8ZQwVRDH/wmfp+8O0hl5Zo8Hcc8NoytTUzq2m49nZfBd1LKdOvZzoCA9zL59Mj7QiWJIF48aFfdf1ukB0KkXjpn///m7x4sUNvQzDCC98rnZqqr/+OycHhg3TJMvKSi1x3LEDoqPhnnv4+uNdnDbltyTGVjB39GS6ttqtbeCWLoXBg/e9TxMoixSRr51z/Q/kGrM0DSOcqWox9u6tApedDT/9BB06qGhu3aq9LLdvh4ULtSRy40ZtvNGmDeTlsejPb3PmlpdIicpj3vCJdEmthJw8vV+vXv6ORb7nzMywF82DwfY0DSNcCQja0KkT/PijDhdftUrfb9+ugrdsGXzxhTbbaN0acnPhf//Tvpbx8SDCp5zIaWufp3XsHhb+dyddOjt/o40uXeCII/b97jDtul4XmGgaRrjiDdrs7Y6+ebOOk9i0ad9u6f/7n1qU8fFQWgopKTqvJz8fgPm5fTnj+7/TMXYnC467jYwvXldBjI1VC3XdOvjoI9i2zf/dYdp1vS4w0TSMcCU7e9/u6Hl5Kpp5efo+PV2FdPNmFb+dO7W923HH6R5mQQGzdh/L2d8/QufozcwfcCcdt36tlmt0tHYVXrBArcz8fH2/ZUtYd12vC0w0DSNcycjwCySogObn6/O2bbBypVqh8fFQUKCi2a2b7k/268f7njM5d+lDdI3fyPyzH6PdrmW6J5qaqtcmJelj504YNEhff/WVvzcmaER9zBh9DsNxvAeDBYIMI1zp3RvuukutvshISExU97tXL/jhBx1NERcHp5+u+50ius/ZoQNvF5/BqD03cVS7rXw8+CladWsPni5w+OF6b5/V6nvdrp22NNq40S+Qvsh84BzzJhBR3x8mmoYRjmRlweTJOrsnOlqf8/LgkENU7DZv1sh5z54qeK1bq5Bu3sybsb/l0k/GcGyrtXw4+k1SLr1GhW7cOP9cn+RkDRyBfwsgcB8zcD8VmlVE3dxzwwhHMjPVakxP97vchx8OMTE6xOyyy6BvX/+M8bZtoW9fpmbcycWzr2JAu3V8fOVrpBRv8Y/iHTHCv1/Zvbu6+vn5+rrqPmbV/VRoNhF1E03DCEeys9UVj4vzH4uL02PZ2fsKoHdk7suLe/G7uVdwcotv+aDV70j6cpaen5rqtxB9c33KyzWZfdAgfV11xk/V/VRoNhF1c88NIxzJyND8y5ISrezZuRMKC9XSjInxC6A38X3SjuFc+/m5nBb/KW93vZMESmHVerUwu3RRtx6Cn+tT3bTJnBwdy9vEMdE0jHAjK0uj45s3q/ssojmVHo8K5qZNeo5XACdMgBtvhLO7/sh/k+8kriRXXfuoKI2sb9qkeZu+a4KhiiiH8xzzA8VE0zDChawsmDgRZs+GVq1gwADNo8zNVTc7IUGPJyXtdbefeAJuvRXOOw9eTx5PbNyR8PbbKrRRUep6l5VpJP5AgzhNdNrk/jDRNIxwwJfis3IlpKXpsR9/VKGsrNT9zHbtNPl8zRpIT+fhnGu56+n2jBwJU6dC9IMd/NHx4mIV2shIOOwwDSI1gyBOXWCBIMMIB3wpPmVl6lLHx6tQ5uaqxVhaqjmUgIuO4a9bruGup9tz6dk5TJumWUl7g0PJyRpNb91a3fKCAi2TjIlp2J8xTDDRNIxwwJfik5yswR9Q0XROLc2yMoiMxDm4u/AuxhXcxhU9v2TKsU8T5fMnffuQ/fqpRbp1q3Y48lUS+fZCjVox99wwGjtZWdrmbdEidcfz86GoSCedlZfrORERuEoPtxU/wBNF1zC2/Uyeu+BrIjZu2PdeffrAc8/p62++UbFNSIBjj1VLsxkkp/9SQiaaInIIMAVoCzhgknPuKREZB1wD7PCeepdz7v1QrcMwwhrfXmbHjrB7tzYM3rxZ3Wrw+t1qcN7knmZCwWhuaPUfnj53AZIfW3PeZGmplkVGBDibHo/tawZBKC3NCuBW59w3ItIS+FpEZnk/e9I5Nz6E320YTYPAcsUdO2D5cr9gApSX4ymv4HqeY1LJaG6Jeorx8Y8jS9qr6/3QQ9XfNyPDHxTy0UyS038pIRNN59wWYIv3dYGILAc6hur7DKNJkp2tDTFAo+UxMf59TKCSCK7mBV7mSu7kIR6UvyJ7EqAwSUssofp5QM04Of2XUi+BIBHpDBwDfOk9dIOIZInISyKSWuOFhtGc8e1lTp+uvSxzcvS4CAAVRDKaKbzMlYzjPh7kbqS8TEW2dWtNJXruuX27u/u6EYG/ZNLXob0ZdCiqC0I+WE1EWgALgAedc5ki0hbYie5zPgC0d86Nqea6scBYgIyMjGPXr18f0nUaRqPCt5dZWQnff697j6tW6XvnKC/zcBlTeZNRPMSd3Mkj/mvbtNFczlGj4J134OST93XDfW75uHH1/mM1NhrdYDURiQb+C0x1zmUCOOe2BXz+L+Dd6q51zk0CJoFOowzlOg2j3qlphK6PiRM1kb2szJ8/mZAAu3ZRGhHPRUxlBufzOLdwC0/6rxPRR0mJutzO6ev58/V9crJ2LbKAz0ETMvdcRAR4EVjunHsi4Hj7gNOGA0tDtQbDaJRUHYjmc5l9OZLTp8Mbb6hrvnu3Rrrz8yElhZJ2nRkRNYMZnM8z3LCvYMLeaDoiet+uXXX6ZHGxllcWF+t7S2Q/aEJpaf4a+B3wvYgs8R67C7hERPqi7vk64NoQrsEwGh81NfD15U++9ppamImJmo+5cydUVlIU2ZLzI99hVukAnj/pVcb+9DbsiNFUoYgIfaSkaNJ7q1Z6/Vdf6fVRUXq8sFCj8PPnq3te1cI19ksoo+efAlLNR5aTaTRvsrP9g8t8LnPr1rBkib8npq/dW3Q0RESwpzSKc3mDBRzPS/2f5cohO6DnOdqwo6JCRbJvX71+zRq1NGNi/DXp27druWRJib6PimpWIyrqEiujNIz6Jibm5y7z/Pm6/7h5szbRiIlRy7GsjPzSWM7kQxZyMq8k3cCV2fdra7i4OLj4YrVczzrL3yz4kEM0cp6aqpZnYiJ07qzffeih+j4lxW/tZmY25G8j7LAySsOoDwIDP1lZavH5BpeBCl5RkTbiiIxUIRUhl2TOdB+xmGN5rcXVjEz8CMo9WgLZvbu/h2WgpThmjEbQAXr0gC++0H6b+fmau1laqvXn0GxGVNQlZmkaRqipGvgpKlL3uLJShSw+XmeLFxer6xwRAfHx7CaNU5nNNxzD9E5/ZGT379XtjoxU4avJrc7IgNWr1Xr98kt18YuL1XoVgRNO0C5HYFVAB4FZmoYRaqoGftLTtaVbixY6hwc0B3PjRhXSTp3YsamMUz3TWElX3moxmnMO2wIlkbr32auXDlOraR+yd2+YMsU/tzw/XwNAf/oTfPediqfHY1VAB4lZmoYRaqpObmzTRluzffstzJunghkZCbffDs6xtSCRwQUz+VG6M7P33ZzT/huNeMfFqWBGRfmnQlbH0qUwcKDuWxYU6PPAgfraqoB+MWZpGkaoCWyOsW2b1pAnJ6tbvmOHWp333AMXXsimPmcx5JJ0Npa15P3LpnHKrZcDl9eeCF+V7GztxN61q/+Yr4NRMx1RUZeYaBpGqAlsjvHDD7qvGB8PQ4boHmZODixdSvbxFzLk+iPZVgofzYMTT7zCf49AocvK0hzLmkTUOhiFFHPPDSMU+IRtzBi1EocNUxHbvFmtzIEDVTABkpNZu6yIQYM0NXPWLDjxxFruW1s1EVQ785ycnNpdeiNozNI0jLrGJ2ypqX5hmzlT9w/hZ1bgqvUxDPn4VgojYc4cbaJeIzVVEwV2XG/G43XrAxNNw6hLsrLUuly/XoM7nTpB//7+JPIqfSxXrI1lyJvXUS4RzDvjPo5+RyC6lj3LwP6aPqrLtbS9y5BhomkYdUVWFtx1l0bDW7TQCp916zTlp08fLXnMztaKnNJSln7vGPrRjQiVzL9gAr0Oc5CTW3tpo+1XNji2p2kYdUVmpkbDW7TwHystVaF8/32tEe/UCWJiWLKxNYPn30dkpDB/5ER6HVGqSe37K220/coGx0TTMOqK7GwVyXbttEwyN1ePl5f7x+xu387i4l4MmX0nCZ5CFp7xID26lO57n9pKG337lZZr2WCYe24YdUVGBixbpq9jY1UkKyvVgkxKgsJCvnhjA2fmjyctoZh5pz9K516JB+5u235lg2KWpmHUFSNGaLVPfr5aly1barf1hAQoK+OT0uM4Pfd10qNyWJg+ks6t95i7HYaYpWkYB0N14ypABdJX5RMZqY04nGPupu6cu2sqh0RtYW7Pm+hQshVcd0sPCkOCEk0RORTo6pybLSLxQJRzriC0SzOMRkp1eZh33aW13du3ay/L3bthwwZYu5aPyodwfsU0DmcNc+LOp21kig47KyvT+5m7HVbs1z0XkWuA6cDz3kOdgLdDuSjDaNQEJpj7It47dmg7tqQk2LMHtm4Fj4d3i4cwrOK/dGcl8yJPo23lZn8XdUsTCkuC2dP8P3TeTz6Ac24VkB7KRRlGo6Zq1yJQd3zXLhVO76jdt9z5jCCTPmQxl6G0idildefbt8PixbZvGaYEI5qlzrky3xsRiUKHohlG8yQjQyPcPrZuVcH0dVyvrOT1kmGMrPwPx/I1szmVNMnRZHfwD0EzlzwsCUY0F4jIXUC8iJwGvAm8E9plGUYjpmrE+9tvNVKelAQlJbzqLuVSpjGQL/iY00kmXwVTRANFqana5cgIS4IRzT8DO4Dv0XG77wN/CeWiDKNRUzXBvKwMTj8dOnbkpaixjGYKg1jAh5xJS/boNSJqXRYWauf0AQMa9mcwDpr9Rs+dcx7gX96HYRiwb8R73DjIyeGf24ZzffndnB4zj7c8w0mIqABiVFQjI/1TJnv3huuvb8jVG7+A/YqmiKylmj1M59xhIVmRYYQTWVmwdStPT+/AH3bdzTktFjA94zbifioDDyqUrVvr6NyiIp0P9OCDtp8ZxgSTp9k/4HUcMBJIC81yDKMRUzWhvXdvmDmT8WtH8Kdd5zM8aTavVY4ipjRNK4MKClQ027ZVt/yII0wwmwDBuOe7qhz6h4h8DdwbmiUZRgNSXaVPnz7VJ7Q/8AAPchd/yTqfUb2W8urwz4n+6Sz47DPtdJTuzcyrrNR2cB07mmA2AYJxz/sFvI1ALU8rvzSaHj5hrKiATZtg0SJ46y0derZ06T4d011KKuM2X8v9Oy/it32+49/nzSAqwqMDzT75xF+DnpwMPXvq+40bG/gHNOqCYMTv8YDXFcA6YFRIVmMYDUlmpgrmsmU6LrdNG83HfOAB6NJlr5XoHNw551Qe3XkiVyZn8q/zvicywrvtv3q1tofLy1PBLC6Gzz9XVz5wOqQRtgTjnp9SHwsxjAYnO1stzLg4fx5laamOrvjxR/jf/3ADT+DWTbfw5KKBXJf2Os9WXk/ER12hb1+9btkyHfKzaZP21IyL03ssXQp33NGwP59RJ9QomiJyS20XOueeqPvlGEYDkpGhLnmbNvp++3b46SeIioK4ODz5e7jprSE86xnITXHP84+WjyBH9IPly/XRrRukpelMoIwMWLHCb3Gmptp+ZhOhNkuz5S+5sYgcAkwB2qIpS5Occ0+JSBrwOtAZr6vvnMv5Jd9lGHXCiBG6h+kTug0bNCk9MRFPZDTXFj3MC57R3BbxOI8l/R3xxPgHnfkqfnbvhjVr1BX3jeit2mTYCGtqFE3n3F9/4b0rgFudc9+ISEvgaxGZBVwBzHHOPSIif0YrjsxvMRoWX9Q8LQ1++EGFs6ICWrakkkiuynuCyaUXcXfS0zxQfBfSY4DuXxYWQocOKpr5+dCrl7rirVur8K5erS57ly6aBO+LxhthSzDR8zjgKqAXmqcJgHNuTG3XOee2AFu8rwtEZDnQETgPGOw9bTIwHxNNoyEJTCc66SQVwaVLIT+fColmdMVL/Kf4TO5PfmBvd1EAACAASURBVJx7Yh8Didf9yooKtS5B3ycnay5mUZHea8kSWLtWg0CHH64WZ22TJo2wIJja81eAdsAZwAK0n+YBNSAWkc7AMcCXQFuvoAJsRd13w2g4qvbH7NoVBg+mfPBpXFL4Av/JOZOHY8dxT/FdaoH27KkiCf7ORiUlejwvT4NC48bp8+DBer9gJk0aYUEwKUdHOOdGish5zrnJIjIN+CTYLxCRFsB/gZudc/ni+58ZcM45Eam2zZyIjAXGAmRYs1YjWGpKTq8N375kAKWJaYz63xXMLDqGJ1rcyx95EjxR6nYXFsIhh6hYFhfrBQMGaF15To6Oq6jhvrVOmjTCgmBEs9z7nCsivVHrMKgmxCISjQrmVOec77/XbSLS3jm3RUTaA9uru9Y5NwmYBNC/f3/r32nsn+qqdoJxhzMy9gnWFJdHccG0EXyw4UgmnPUe/9c1D1Jv1b6ZK1ZogGjZMrUk27VTF720FNq333e+T5X7AvufNGk0eoJxzyeJSCpwDzAT+AF4dH8XiZqULwLLq6QnzQQu976+HJhxQCs2jJqo6maXlsLKlXD55eouZ2VVf11Af8yi0kiGvTKSD9f3YNLJr/J/q27Wvclt21Qge/bUksiEBBXH2Fi1PG++Wb8jUJxt0mSTRJyr3YgTkUjnXOUB31jkRNSN/x7t9wJwF7qv+QaQAaxHU45213av/v37u8WLFx/oEoxw5GDcax9jxqiFGRGhVuEXX/jnjw8apIJVk9WZlcWe197lN/8ewSfbuvFSn39w+aD1Kph5eRodP+EEzcfMzYWUFN2vBL81OW5c3f48RsgRka+dc/33f6afYNzztSLyIZpbOdftT2W9OOc+BaSGj4cGuT6jOXGg7nVVQYqJUYFLTVU3Os6b7JGS4neRMzOrvVd+5z6c/cHhLNoWxyst/49LN02Hzw7VVKHcXHXBf/hBB6hFRUGPHv6La9untEmTTY5g3PMewGx0wNo6EZngtSINo26pbspjTdFmn8Dm5PgFdtMmTSzPyVGhc84f1Qa/uE2f7o9qDx5MzsszOO3XRXz5XSyvtb+FS2OmQ3Q0rFsH332nlT7JybB5s3Yu6t3bn7gOtk/ZzAim9rwIdaff8O5tPoWmHkWGeG1Gc6OmaPOSJer6Brq4gQIL+nzYYeqK+46JqEvd1pvVlpen43Vvv13n+bRvz64NRZx2dWeWVkYxPfUazktbDAWJUF7u36/csUODPqecot/tE+vkZL1nYMTcaPIEY2kiIoNEZCLwNZrgbl2OjLqn6pRH0IqatWv3tSjHj1chrTpGNzlZgz/jxsGUKdC9u7rsgUGYn35SwUxJYXteLKdkv8wPld2ZEXsR50W+qwGfhARNXAd93r7dH8CpOh8oNdWS1ZsZwVQErQO+Ra3NPznnCkO9KKOZ4rPiwG/FLVum7nCgRQlqdfr2L30Eusk+cfPtecbGqhj++COkpLDFtWXo8gmsq+zEu+ljODX/Q4hJU5EsKtI8zC1bVHDT0/cVRtunbNYEEz1Pcs7l19N6qsWi582IqsGdJUtUoCICnCKPR89LSlLRDHSTb7tNz6lmLAWpqTBjBpt2xzNkzww2uQ68lzCKQXFfqqi2aKENN0Q0AFRQoGWRl1+uZZUWAW9yHEz0fL+i2Rgw0WzGeCc97mNR+t779jYDxQz8EXifmM6fv7cJ8PrZqxjy2f3soA0fRA3j15GLVIQHD/Z3Xd+5UxPVBwyAoUP9gltVnE04w55QpRwZRsNRncvuC7xU5yaPG/fzAFF5OaxYwU+rPZyy+O/k0ZJZccP4VcVnEBWrFuvKlbpHGR0NEybAhReqNXvTTbqnmZ6uUXhfUKmG1CWj6WOiaTRuqu5NZmTsW6pYleoi8PHxrFrp4ZSypyh2UcxtPYp+bil0Plrd8IULdR+zc2cdfjZzpl43c6YKZuvW/rEVJ5ygTYqtfrzZYp3bjcbPgQReqqn3Xp7fkSFF/6IiMpp57S6hT+US8NW47dihrnjVCp8JE+Doo9XCLC72j79Yvlwj8paX2WypLeWopffRH7ge7YXZEbgO6FfLdYbRcFSp9/5+VRyDfvo3LiaG+X3/SJ+WazXQ066dP53I4/EnwINuA2zapM89emiCfHGxBosC04+MZsl+O7eLyEKgn3OuwPt+HPBevazOMKZPV6tv0yZ1nW+4QfcbayLAnf/2Wzht1k3ExpUx94Jn6X5YZ6Cz1qV/+60mwqen633bBrR1zcvTY3l5Kq4DB2pZpm9v04JAzZpg9jTbAmUB78uwxsFGfTB9+j7VO+Tm6nvYr3B+VdKHM56CpDYwd2I2h7++EnK8EfDYWE1896UnVVfhc8MN/r3N9HS9xqLmBsGJ5hTgKxF5y/v+fHRMhWGElgkT9lbvsGePimZ+vgpXt241itfnn8OZZ2r8Zt7E5Ry66HW9Ljtb79W3r4618AWXEhLU6ty4cd9AU7duwQegjGZDUHmaItIPOMn7dqFz7tuQrqoKlqfZTOnaVS3MoiIVtMhIfeTlwXnnVWv1LVwIZ5+tY37mPrucTq88/PMcy2HDLPfSAA4uTzOo2nMgAch3zj0FbBSRLge8OsM4UDp2VAtx504Vy+hof0OOarofzZmjFuYhh8CCBdDps9er75o0YULw3ZQMowr7FU0RuQ+dFnmn91A08GooF2UYgO4r5ufrIzJSo9ilpXD88T/rYfnhh/Cb32hRz/z5aqCSnV19Uw9fZLzqccu9NIIgGEtzODAMKARwzm1GU5EMI7RceCE89pg2E962Tfc1O3SAVq38zTmysnjnzGc57+wyesgK5h17G223ZWk1z08/aTBp/ny9HvaNjAdiPTGNIAkmEFQWODVSRBJDvCbD8NOtG/Trp82FW3r/r54/f28jjcwr3+Gib27nmMQf+ajbTaR+uRlu/J+e26GDNuDIzYXPPoOjjlKLNTAyHrinedJJP+/baXucRhWCsTTfEJHngRQRuQbt4v5CaJdlGF4mTtSqHY9Hp0CuWaN7nFu28No/cxn1zR0c12I5s/reTmpSpUbbV6/Wa7p107LHlBRNZN+0SYM9F174856YvuBQ1b6dNQ1jM5otwXRuHy8ipwH5QHfgXufcrJCvzDCysmD2bEhL07Ztu3ZpNU/r1kxZ+Suu3HMlJ0Yt4t3D/0zLKO8ggbg4jbaXlur7du304fGoQNbUE7O6Rh9gjTmMnxFME+JHnXN3ALOqOWYYB8aBTGfMzNT9S1DBjI2F8nJeXDOYa8qf4ZSYz5kZNYLEjR4oS1exLCyEykp9BLJ6tc74GTOm+u+tadSGBYeMKgTjnp9WzbGz6nohRjOgumFogS5wVpZafGPG6POSJZqInpOjJYy7dvHczpFcXf4cZ8TO590uN5IY6a0L//FHLY/cs0eba+zercc8Hn1etEgDQDW53tWN2rDgkFENtXU5uh74PXC4iARu7LQEPg/1wowmhM+6fPtttRb79fPnR4J+9uOP8MAD2i3d41E3vKhI9yW9PFV2HTe7f3AuM3lTRhO7rkQFsrhYJ09GRWl1T3KyBoo2b1Z3ffNmrR/v2lVvVJ3rXVvfTsMIoMaKIBFJBlKBh4E/B3xU4JzbXQ9r24tVBIUxgbPMFyzQtmqlpf4pkb7RFT/8oC54vneyiog+ysogLY3Him/kjuJxjCCT/8ilxER5NBIuoq54WprWk4OKaFycJm2+9JJarp06/XxkxsaN+nngWoPdOjCaBHXaud05lwfkichTwO6ALkdJIvIr59yXv2y5RrMgcNRuSopf0JYvV9HMy1Px2rZNO6z7/hOvqNgrig/k/4F7K+7l4pj/MiXiSqLLysFF6P1271YxDNzDjIvT6Pkpp+j7anpsVut628A0IwiC2dN8DtgT8H6P95hh7J/Aqhxfb0rnNHfS1/cyP1+j4+XlfmtQBFfp4R75G/dW3Mvv4qfzaqubiU5OUKsyMVGFNSZGRdI3uXLlSvj+e7Vae/fWe1Xpsbn3tfXENA6CYERTXIAP75zzYGMyjGAJDLD4elP6XG/fzPDERH3t3N6Hc44/8zB/89zFVfIS/253J5HF3v+7ndMC89RUFVDQvczCQhVLgP79Ne8yK8tmlRt1SjDi95OI3ITfuvw98FPolmQ0KaoGWAJ7WfpEa8AArfJJSoL8fJzHwx95kqe4mesjn2dCizuJyI/Q61u3Vlc+IUEtzZISdc09HrU6U1O18Py449Sa9AV7zPU26ohgLM3rgBOATcBG4FfA2FAuymhCBGPlXX+9Rrs7dMATn8j/yUSe4mb+EDmBZ5PuIqJTBx1mdvzx2pXj2GNVKLOzNWLetatG0WNi1Jr17W9anqURAoKpCNoOXFwPazGaKvuz8vr0gQcfpPLZf3LthlN40Y3k9ph/8EjaY0hSa7Uu27SBb75RK7JPH9iyRYUyLU3rzBMTNci0das/tcjyLI0QUFue5u3OucdE5BngZ3lJzrmbaruxiLwE/AbY7pzr7T02DrgG2OE97S7n3PsHuXYj3KglpaeyEq6cfSmv5J/IPRmT+Wvsc0hepe59gs70KS7WLsPJyRpIatdOk94TErRyaP16dde7d/cHeyzP0qhjarM0l3ufDzZB8mVgAjouI5AnnXPjD/KeRrgSmK8ZWJUzbBjl3/3A6AnH81rumdzffyb3dH4XZm9X17uoCNau1WYdcXFa8SOi+5lt2+r94uP9Q9Di4jQK3769jacwQkJteZrveJ8Pah6Qc26hiHQ+uGUZTYJAy/Knn7RVmy9XsrQUvv2Wsrff55LyyWSWnMmjLf/G7XlTYG2Sljzu2aPiWlGhAlpe7rc8o6J0j7RbNzj5ZBtZYdQbtbnn71CNW+7DOTfsIL/zBhEZjVqwtzrncmr4/rF4A04Zti8VflS1LBct0kT0pCT9/IsvKN2ym5F7XuYddw5PRt/OzVEvwO4If45lord1a2GhWpwlJf48zspKDfzExv58IJphhJDa3HOfCz0CaId/xMUlwLaD/L7ngAdQMX4AeBwYU92JzrlJwCTQMsqD/D6joQisBAIdg5ubq/PDgeKYZIbnjOcjdxoT427hejcRCio1qFNWppajc+pug9akx8drlVBFhaYY9eypIhlYCmkYIaY293wBgIg8XqU28x0ROah9TufcXrEVkX8B7x7MfYwwoGqrtZ49tXv69u0URiYxbP0zzPMcywuR13JV1DQQb016fr664Fu2aGpRYaG+BrUqW7RQi1NEAz7mhRj1TDB5mokicpjvjXcS5UGNvBCR9gFvhwNLD+Y+RhgQGwsffQQzZmjiOsBRR1GQdihnr53A/IJ+TG5xA1dFebfMfdZjZKSmGJ16qnZaj4hQC7N/f93HLCjQ8/v10/dWCmnUM8FUBP0RmC8iPwECHApcu7+LROQ/wGCgtYhsBO4DBotIX9Q9XxfMfYwwJCsLli7VOvCSEhXETz4hL7EDZ8kHfFXUk6nHPM7FuR9CboJamGVlKoKJiZpS1LWrimdqqgpjZqa+z83VRh19+1oXIqNBCCa5/UMR6Qr08B5a4ZwrDeK6S6o5/OIBrs8IRyZO1OBMWdnesRM5pHBGwX/5lu68fuIzXJC8ECK8deO+JhopKRoo8nVr91X0WAmk0YgIZtxFAnALcKhz7hoR6Soi3Z1zth9pVM+iRWoRevced7o0TmMWP3AkmUlXcm7kRjj8aG26UVio1uSSJf7gT8+eeh+r6DEaIcHsaf4bKAMGet9vAv4WshUZ4U9x8d5mwttJZwhzWU5PZnA+51a+rWKYnKxWqK8u3dflqHdvLZm09m1GIyWYPc3DnXMXicglAM65IhFfhrHRrKmpLDIqCjwettCOocxhHZ15j3MYKvPAE+cfJ5GRsa/rXfV+lndpNEKCEc0yEYnHm+guIocD+93TNJo4vuR13zzxRYtg2jRtoLFiBRvpyBDmspkOfMBZDGKh/g2Ki9Nqn+rqwm3v0ggDghHN+4APgUNEZCrwa+CKUC7KaATsb15OZqYK5rJlKoTx8Zq4vmoV6ziUIcxlF634mNM5gS/0mshIOPpojYxb5NsIU2oVTRGJQIerjQAGoClHf3DO7ayHtRkNRU3NNQLrurOz1cL0eLQp8JYtUFbGGg5jCHPJJ4nZnMpxLNZcy6goHaY2b17D/myG8QupVTSdcx5vi7g3gPfqaU1GQ1O1BDJw5K3v+dtvYd06rcyJioKyMlbSjaHMoZh45jKEY1ii50dEaGONQYPq/UcxjLomGPd8tojcBrwOFPoO1vcYX6MeqVoCCRq8WbIEFi/WSY9FRdqFCCA2lh84kiHMwUME8xnMUYHFXiLQubNFwo0mQTCieZH3+f8CjjngsGrONZoCNY283bhR8yqTkrR35ebNUFREVuHhnMosIqlkPoM5cm8rVi+JifDggzXvYdq8cSOMCKYiqEt9LMRoRAQOQyspUQtz1y7YuROio1VQ4+KgZUu+ccdwWuFbxFPMXIbQjVX++4hoV/UWLWr+rmD2Tw2jEbHf5HYRiRORW0QkU0T+KyI3i0hcfSzOaCB8w9DKymDOHD3Wt68KqK8LUVERX+3owtDCGbRgDwuTzqVbzHo91zeiNypKo+opKf790KoE7p9GRPhf13S+YTQwwVQETQF6Ac+g4yt6Aa+EclFGI6BPHx0ncfzxKnyffupvAJyfz2f5R3FqxQeksZuFsadzmFujbntkpJ6XlqaVPc75rdXqyM7W/dJAbIqk0YgJZk+zt3PuyID380Tkh1AtyGhELFmiYyri41X8EhMhP5/5xb/iN24GHdnEnOiz6NQ7DSJT/LmaeXlqZUZH66yeigqtRa+OmvZPrebcaKQEY2l+IyIDfG9E5Fcc/LA1I5zIzfX3s/TOFZ8dczZnu3c5NHIT8xPOoVNyge5ZxsWp2CUkqMV56KH6iIzUXM6UlOq/Y8QIf525r9uR1ZwbjZhgLM1jgc9FxOcvZQArReR7wDnnbLe+qZKSonN9iouhVSs+WHUEw/dMplvkGmZ3v4H0cm/tQ3GxWqIxMTquokcPTUnyNeY44gj/LPKq+PZPrebcCBOCEc0zQ74Ko/EQmP6Tm6sR7aIiZmb3ZeSex+gVtYJZccNotbNYx1Eccoi68bt3w2mnwdChMHOmutu+xhz7sxyt5twII4JJOVpfHwsxGgGB6T/R0ZqXmZ3N9OiLuaTo7/SLWcaHh15L6ilnaGL70qVqXZ511r65ld26meVoNFmCsTSNpkzV2eQdO2qq0bx5sHMn09wljC56kV/xJR/EjCKp7681uAP+cRTjxv38XhkZcPPNJpZGk8NEszniE7clS2DtWujVS/cdfbPJY2IgN5fJRSO5suJ5To74jHejh9OiolCFtVUrWL5c3XcRv+ttSepGM8BEs7kR6ILn5KjoLVumEW/fbPKNG/lX3iiuLX+aoZHzmZF4GQmUQ6lTkS0v12h5TIxeP368piPV1OTDRNNoQgSTcmQ0JXx9ML/7Tvckc3OhslLzK71R72d3X8LYsgmcKR/xTtQIEhJQgYyI0Eh5bKzeq7QUjjlGBXLRIktSN5oFZmk2RWprgBGYsN6ihYpgcbFajz168OTO33FL+T0Mk3d4g1HEOg+Ue/+apKT4x+2mpKhgtmunvTS3bIHp09Va7dlTq4ksSd1ogphoNjX21wAjMGG9TRvYsEGFcNs2HnmlI3cW3cMF6Z8wLe0vxKyphEqPdjY64ghNVj/kELU6fe73tm2wcKEGhSIi9P6ffQZHHaWJ7VVHWhhGmGPueVNjfw0wUlK08mbXLn9fzIIC7i+8lTuL7uGSFjN5rXQ4MVEeFb70dE0/OvFEeOghuP76fSt4vvlG73viidqZPSXFPzfIgkBGE8QszaaGr4Hwtm0a4c7L0yCPzzLs21dd8cWLwePBRURyT/QjPFj6J0anvctLsf9HpCcKCgq0cXCU969I27Z+AQys4CkthZNPVjcd9Nnj0RxPE0yjCWKi2ZTIytL9yvnz1aVOT9duQ3l56jZnZelc8WeegcJCXEQkt5c+wHjPLVwd8SLPF95IxJ5KFVnfXmdJCQwYsG9AJ7CCZ9w4tToDsb1Mowlj7nlTwbeX2aGDCqZv4Nnu3VoX3qsXPPccTJ4MJSW4qGhuLnuU8Z5b+D3P8nzMTXs7v5GfrxH1+HgYOFDTi2oSQWu4YTQzTDSbCr69zG7d/PuWOTmaSrRjh0a8Fy2C7dvxJKdyvZvI05U38EeeYAI3EFFSpG476J5kXJyK7cKFarn27l399/oabqSmqkuemmp7mUaTxtzzcCYwtejbb7VhMGjUes8ejXKDWo2zZkFqKpXJaVxT9iz/LjqPP/MID3En4rtfZaVeExmpQhsdrRZmp07ahKNbt+rF0BpuGM2IkFmaIvKSiGwXkaUBx9JEZJaIrPI+p9Z2D6MWfO54To6KWkyMWoXbtqnglZf79yQLCsDjoWJPCVesupt/7zqPe6Mf5qHIe/yCGRWlVT0RESqaKSmaYnTKKdrWzUZQGAYQWvf8ZX7eVu7PwBznXFdgjve9cTBUTS065hg9PmuWDkBzzv8oK6PcE8llOc/wavEF/C3+Qf4aeT8S6RVIEbUqQcVWRBt35OX5v8+qewwDCKFoOucWAlVno58HTPa+ngycH6rvb/IEztbZtk33LouKYN06/zlRURAZSVlUAhcVvcQbnpH8veM/uLvzVL9YpqZqAw5Q9zwtTd1wj2ffskiLiBsGUP97mm2dc1u8r7cCbev5+5sGvtSiRYt0vER+vopfRYV/vIRzUFFBCbFcWDGN9/gNT8Xezk0dF0BJtPbAXLlSrznpJHXtQXMu9+yBL76AI4/Ue/kaCVt1j2E0XCDIOedExNX0uYiMBcYCZJiF4ycwtWj3bi2DrKjQJhoFBf6JkUAxcZzP23zMGTzHdVwX/zrEH60f7tihEfHNm9UlHzRILc/SUt3DHD5cG3pYI2HD2If6Fs1tItLeObdFRNoD22s60Tk3CZgE0L9//xrFtdkRuJeZlARvv61CWVCgwaCSEnCOQhI4l3eYz2BeZAxjIqfAHtH9zrQ02L5dxfDpp2sWwwsvrN+fzTDCgPrO05wJXO59fTkwo56/P/wJ3Mts107buXXpopZmWRl4PBTQgrP4gAUMYgqjGcO/9fMWLdT13rlTq4Usn9IwDpiQWZoi8h9gMNBaRDYC9wGPAG+IyFXAemBUqL6/yZKRAT/+qG712rXqopeUqGudkEBuRBpnVc7gfxzHNPktF0W8CRKlQZ6kJN0D7d/fBNMwDpKQiaZz7pIaPhoaqu9sFvTuDVOmqAhu26bHRLMtdxdEc4Z7n+/ow5uRlzCctzQglJio50dEmIVpGL8QK6MMN5Yu1XpwXw5lXBykpLAzrhNDZS5Z7igyk8cwPP5D/TwqSs85+WSNkte2h2kYxn6xMspwIzsbDj9cU4vS06Gigm0FCQwtnMEadxgz4y7ijIvawpIj1YVv21Yrhvr23beDu2EYB4WJZmOn6uiK2Fi1MmNiYMcONpe1ZqjnHbLJ4L2o8xnCQth2xs9nkRuGUSeYaDZmqhtdsWGDJrNHRLChpA1DmMNW2vGhnM1JfA6pbTSq7ptFbhhGnWJ7mo2ZqvXlpaWalL5sGesqOnEyC9hOOh/LmZwUvUibbHTqpJVChmGEBLM0GytZWZq4DiqGbdpoqlFsLKsrOjNkzwwKiGVO8gX0T9kErp2KKuyNphuGUfeYpdkY8bnlsbG6d1lcDJ98ApWVrCjpzKDC9yjyxDGvxTD6Fy7Q6p7duzWtqKBAx1MYhhESTDQbIz63vF8/v/VYWcnS7ekMXj6Riqg45ne4jL6R3/uvKSvTGvI2bXRipGEYIcHc88aIb6JkRISOxV2+nO8qe3Nq7htEx0cx98pX6bFoG5TEah5mixaai5mcrLOALGJuGCHDRLMxkpGhkfLUVGjblq8r+3LaF5eRGLWHuRf+k65pZdo7s21b+PWv9Rn8o3MNwwgZ5p43RgImPC7K7sDQyb8jKbqYhU9+Q9cuFSqM6elw1FF+wQRrFGwY9YBZmg1F1aT1wER074THT/+xmLNeuZS2LYuY+5+dZJxxNnC2/3rfjKDkZGsUbBj1hDjX+FtV9u/f3y1evLihl1F3ZGXB3Xdr1Lu0VKPk6enw4IN7hXPePPjN2R4OSdzNnKEP0TGteO+8n70iCzULb6jWXZ/fZxghRkS+ds71P6BrTDQbgOuv11niSUkawCkp0SqfwYPhuef4+GM47zeVHBaxjjkZV9KuZaGmFPkab8TFqVVZn92KAquTAi1b65hkhDEHI5q2p9kQLFoELVtCfLwmosfH6/tFi3j/fRh2rodukauZf8TVtGsvsHWr5l9GRelcH1+VUH2O1K1andQQazCMRoDtadY3WVnq3hYXq/gkJem8n4gI3s4dzKjz4ajWW/m4xRW0ahWjolpZqS58fr4KJ9T/SF1fGlQgNtbXaIaYaNYnWVlw1126L1lWpnuURUWwcydvRl7EpSV/59ijCvnwqAdJ2eid9xMfr+54ebme26WL3qu+I+WBaVA+LFpvNEPMPa9PMjNh/XrthembO+4cU8tHcnHJywxos5qPj/wjKS0roWNHFc3iYp1LXlSkItu9+950pL3BoPogIA0Kj6dh1mAYjQATzfokO1u7FCUkqBBGRfGyXMnveIWToz7ng6szSWrr3eeMitLqnrg4Fc42beCUU9TiTE2t/wCMNw2K1FTNE22INRhGI8Dc8/okJkb3Jb2vJ3mu5lo3gdMi5/B22tUkxFwOUckqSrfdppZpXJyKZWNI7+nTp+HXYBgNjIlmfZGVBZs2QXQ0lJYyoWgMN5Y/wdnyAf9NvIK4Qw7R83z7hCZQhtEoMdGsLzIz4bDDID6ex9/tzm2lD3CezOT16N8Sm9YKjj3Wv09oVT2G0Wgx0Qw1viqaqVOhQwceLrmZu4qHMzLhXabGXE10RLTOIS8vh/btVTDNwjSMRouJ5i9h/BQhSAAAD/RJREFUf2WFAVU0rn0H7l95MeO2DefShLeYnPwHosodxHgDPffcY2JpGGGAiebBElhWGB0NH3wAr74Kp56qj6VLdVxFbCzumH7cvedOHt52JlfETOMFz1giKxM1Sp6WBmvWwMSJ8M9/NvRPZRjGfjDRPFh8ZYVlZdpdo7BQX7/+OkybBq1bQ1ERLq0Vt/13IE/sPJOxPT/hufXXElFRrknrrVtrA+GiIhuGZhhhguVpHizZ2VpGuHChpgjl5Khw5udr56LiYjwRUdy09mae2DmaGzpk8s+Rc4hIiNMczc6dVTB92DA0wwgLTDQPlowM+PprbaBRVqb14aWlWi1TUYFn526uL36CCSXXcGviP3m69f1IrrcMMTZW9zGd02cbhmYYYYO558FSNejTsiV8/rkKX0SEiqbHA0ClR7jaTeLl3FHc2ep5HuQeJKKTCuZDD8HkydpLMy9PBfSII2wYmmGECSaawRAY9OnUSV3xt97Sfcnycq0R9/YlrSCSy5nMNHcZ41qO596IvyPtO6hQ+qLj3bpZM1/DCFMaRDRFZB1QAFQCFQfaBLTeCewlCfpcXq5lkV26aKVPQQHlRHOZZwpvMoqHWjzEnbFPQkXlz9OJrNrHMMKWhrQ0T3HO7WzA76+Zqq74kiU/F7k2bWDDBnXNO3akdP1WLtrzIjMYxuPpj3JLhzch5jA45hi48MKG+TkMw6hzzD2vSnWu+Nq12pkoORmWL9e9yIoKfXg8lGzP54LiqbzvTueZIydywwWFkHeyXvv73zf0T2QYRh3SUKLpgI9FxAHPO+cmNdA6fk5VV7ysTAM8776r7ni7diqgublQUkJRy7acXzSJWRUDeb7reMaetBo2eoefWUmkYTQ5Gko0T3TObRKRdGCWiKxwzi0MPEFExgJjATLqszt44FiHbds0Qh4fr1U/UVHqkicmQmkpeyKSODf7ORbsOZaXhr3NlYnfwIrN2pjDMIwmSYPkaTrnNnmftwNvAcdXc84k51x/51z/Nm3a1N/iMjLU/QZ1xePiNPE8OtofAMrNJb8khjNzX2PhnmN4Zci/ubL9h1o6uX27360fP17dfcMwmgz1LpoikigiLX2vgdOBpfW9jhoJHOuQm6upRL5ZPatXQ1kZuWUJnF48gy8r+vFa0lguq5wCK1ZoUCg93aY1GkYTpiEszbbApyLyHfAV8J5z7sMGWEf1BI51ENFHt25aHunxsJs0TvV8xDeuL29GXsLImJkqrtu3695nz57+e9m0RsNoctT7nqZz7ifg6Pr+3mqpqbWb7zFihLrYK1dCRAQ7Ittxavn7rKQ7b0WN4pyEeYB3zG56ug5Da9vWf3+b1mgYTY7mW3vuSy3Kyal5D9JndZaVsbW8FYMrZvEj3ZgZdxHnxM5Wtz01Vat9nn5aJ0zatEbDaNI0X9EMTC2qbQ+yTx82nfJbBhV/wDp3KO8nX8rpMfM1RzMyEvr29VumNq3RMJo8zTe5PTC1yEc1e5DZ2TBk+g1sKy/no87XcaLnGyiK0cDQiSfu297NyiMNo8nTfEUzI0PdZ18SO/xsD3LtWhgyBHLyY5l13j8YsPlHKEvTevMePbRDUeD1hmE0eZqvex6YWlTNHuSqVXDyyZC3u4I5o55ngPtC9zB79tQPYmNtz9IwmiHN19L07UEGRs+9ZY/Ll8PQoVBeUsG8k+7j6Pg9en5ioiawFxXpXqaVSRpGs6P5ieZ+JkguXaqCKQLzL36eXtF7/C5416461yc1FcaNa5j1G4bRoDQv93w/aUZL3viRwccXEbUnhwUXPkOvzbM0OBSIJawbRrOmeYlmdWlGFRVw000sHnQrQy5tS4IUseDKyXSPXK2RoDVr9r2HJawbRrOmeYmmb4Kkj61bYelSvljXnqGf/ZXkyD0szPgdR1SsUEHt1Uv9dUtYNwzDS/MSzcAORgArVrCwqD+nb3yR9MjdLOz7Bzon7dbmG6ADz7p0sYR1wzD20rwCQb5acoDkZOau7cK5G54lIzmXOT3+SAfPRm0F5xPWvDyNklvQxzAML83L0gwodfzoy2TO2fhPuiTtZP5Vr9Khb7rmYeblQVKSueKGYVRL87I0Afr04d3sPlzwMPTsWsysPo/QpjxKB6X17q17mL5gkeVhGoZRhWYnmm+9BRddBEcfDR99FE/axmv9eZtdu8Idd5hQGoZRI81KNF9/HS67DI47Dj780BtIT7MmG4ZhBE+z2dN89VW49FIYOBA+/vjnOeuGYRjB0CxE86WXYPRoGDRILcyWLRt6RYZhhCtNXjT/+U+N55x2mo4uT0xs6BUZhhHONGnRfPppuP56OOccmDEDEhIaekWGYYQ7TVY0x4+HP/wBhg/X4HhcXEOvyDCMpkCTFM0HH4Q//QlGjdKIeUxMQ6/IMIymQpMSTefgvvvgL3+B3/4Wpk6F6OiGXpVhGE2JJpOn6RzceSc8+ihceSX86186LNIwDKMuaRKWpnNw660qmNddBy+8YIJpGEZoCHvR9HjgxhvhySfhpptg4kTtL2wYhhEKwto993jg2mvVsrztNnjsMZ3tYxiGESrC1iar/P/2zj5GqqsM47+nlI9a2lSEENQqBQ0tGqS4NNV++IEaxJbSdKNIYqg1EhuwlQQTDMHQJiZYI20jBrONfIgKSLHpirW2RWiDTVooZZePFkpbjBIEm1qEWlZYXv84Z+CynTs7sxvmngnvL5nMueeeM+fZd2feOefeuc/thDvuCAlz3jxPmI7j1IeGnGmePBkui1y1Cu69F+bPL1qR4zjnC4XMNCVNlLRH0j5Jc2vpe+IETJ0aEubChZ4wHcepL3VPmpL6AD8HvgyMBr4uaXQ1fTs6oLkZ1q2DRYuC9aXjOE49KWKmeQ2wz8xeM7P/AauBW7rr9M474ZLI1lZYvBhmzz7nOh3Hcd5FEUnzA8DfM9v/iHW5nDoFkycHW7eWFpg585zqcxzHySXZE0GSZgAzAPr3H8OJE7BsGUyfXrAwx3HOa4qYaR4ALs9sfzDWnYWZtZhZk5k1dXT0ZeVKT5iO4xSPzKy+A0oXAnuBCYRkuQWYZma7KvT5F/A3YDDwRj109oCUtUHa+lLWBq6vN6SsDWCUmdV0L4e6L8/N7KSkWcCfgT7A0koJM/YZAiBpq5k11UFmzaSsDdLWl7I2cH29IWVtEPTV2qeQY5pm9hjwWBFjO47j9IaGvYzScRynCBotabYULaACKWuDtPWlrA1cX29IWRv0QF/dTwQ5juM0Mo0203QcxymUhkiavTH4qAeS9kvaIWl7T87GnQM9SyUdlrQzUzdI0pOSXonP701I2wJJB2L8tkuaVJC2yyVtlLRb0i5Jd8f6VGKXpy+V+A2Q9Lyktqjvnlh/haTn4ud3jaS63+qwgrblkl7PxG5sty9mZkk/CD9LehUYAfQD2oDRRevqonE/MLhoHRk9NwLjgJ2ZuvuAubE8F/hxQtoWAHMSiNswYFwsX0L4PfHohGKXpy+V+AkYGMt9geeAa4HfAVNj/S+AOxPSthxoruW1GmGm2SODj/MZM3sGeLNL9S3AilheAUypq6hIjrYkMLODZrYtlo8CLxF8EVKJXZ6+JLDAsbjZNz4M+DzwcKwvJH4VtNVMIyTNmg0+CsCAJyS9EK+ZT5GhZnYwlv8JDC1STBlmSWqPy/dClr9ZJA0HribMSJKLXRd9kEj8JPWRtB04DDxJWCW+ZWYnY5PCPr9dtZlZKXY/irG7X1L/7l6nEZJmI3C9mY0jeITOlHRj0YIqYWGNktLPJpYAI4GxwEHgp0WKkTQQWAd8z8z+k92XQuzK6EsmfmbWaWZjCZ4S1wBXFqWlK121Sfo48AOCxvHAIKBbl95GSJpVGXwUiZkdiM+HgUcIb5bUOCRpGEB8PlywntOY2aH4hj4FPESB8ZPUl5CQfmNmv4/VycSunL6U4lfCzN4CNgKfAi6LnhOQwOc3o21iPORhZtYBLKOK2DVC0twCfDSegesHTAVaC9Z0GkkXS7qkVAa+BOys3KsQWoGST9R04NECtZxFKSFFbqWg+EkS8EvgJTNblNmVROzy9CUUvyGSLovli4AvEo67bgSaY7NC4pej7eXMl6EIx1q7j12RZ9tqOPM1iXCm8FVgXtF6umgbQTij3wbsSkEfsIqwTDtBOIb0LeB9wAbgFeApYFBC2lYCO4B2QoIaVpC26wlL73Zge3xMSih2efpSid8Y4MWoYyfww1g/Ange2AesBfonpO0vMXY7gV8Tz7BXevgVQY7jODXQCMtzx3GcZPCk6TiOUwOeNB3HcWrAk6bjOE4NeNJ0HMepAU+aTrJE9545ZeqnSBrdg9cbLmlaZvt2SYt7q7PMOJskJXtfHKd3eNJ0ekXmSo96MoXg7vMuutEzHJhWYb/jdIsnTScXSfOjj+lmSatKs744k3ogeofeLWmCpBcVPEWXlkwPFHxGB8dyk6RNsbwgttsk6TVJd2XGnCdpr6TNwKgymj4NTAZ+Ev0PR5bRs1xSc6ZPyd1mIXBD7Dc71r1f0uMKXpn3lRlvoqS1me3PSlofy0skbc36M5bpfyxTbpa0PJaHSFonaUt8XFf5v+GkQiF3o3TSR9J44DbgEwQbrW3AC5km/cysSdIAwpUyE8xsr6RfAXcCD3QzxJXA5wi+kHskLSFctTGVYDxxYZkxMbNnJbUC683s4aj1tJ64vTxnzLkE38mbYrvb41hXAx1Rx8/MLOuq9RTQIuliM3sb+BrBnhDC1V9vSuoDbJA0xszau/m7SzwI3G9mmyV9iHBL66uq7OsUiM80nTyuAx41s+MWvBv/0GX/mvg8CnjdzPbG7RUEo+Hu+KOZdZjZGwQDjKHADcAjZvZfC+49tXgMrOm+SVk2mNkRMzsO7AY+nN1pwdLsceDmuPT/Cmeunf6qpG2Ey/M+Rs4hgxy+ACyOVmWtwKXRvchJHJ9pOj3l7SranOTMF/OALvs6MuVOev9ezOo5Pa6kCwiO/3lUo2M1MItgnrzVzI5KugKYA4w3s3/H2W3XvxHOtpHL7r8AuDYma6eB8Jmmk8dfCbOrAXEGdFNOuz3AcEkfidvfAJ6O5f3AJ2P5tirGfAaYIumi6Bx1c067o4RlfR7ZcScTDi9U0y+Ppwm36Pg2Z5bmlxIS9RFJQwlequU4JOmqmLxvzdQ/AXy3tKFq7k3jJIEnTacsZraFsGxsB/5EcII5UqbdceCbwFpJO4BThPvAANwDPBhP0HRWMeY2wjK7LY65JafpauD78eTTyDL7HwI+I6mN4OdYmoW2A50KN9eaXaZfnq5OYD0hMa6PdW2EZfnLwG8JXzLlmBv7PEtwdypxF9Ck4Bi+G/hOtXqcYnGXIycXSQPN7Jik9xBmgTNiYnOc8xY/pulUoiX+iHwAsMITpuP4TNNxHKcm/Jim4zhODXjSdBzHqQFPmo7jODXgSdNxHKcGPGk6juPUgCdNx3GcGvg/wZIj9oqvHtYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "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",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O8cTuQjQQOon",
        "outputId": "3e52cda9-ff9c-4d68-8d08-3d17a218dac0"
      },
      "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"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saving results to pred.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BQz5qxcR_tUj"
      },
      "source": [
        "# **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（在现在的代码里没改还是10%的验证），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": "nfrVxqJanGpE"
      },
      "source": [
        "# **Hints**\n",
        "\n",
        "## **Simple Baseline**\n",
        "* Run sample code\n",
        "\n",
        "## **Medium Baseline**\n",
        "* Feature selection: 40 states + 2 `tested_positive` (`TODO` in dataset)\n",
        "\n",
        "## **Strong Baseline**\n",
        "* Feature selection (what other features are useful?)\n",
        "* DNN architecture (layers? dimension? activation function?)\n",
        "* Training (mini-batch? optimizer? learning rate?)\n",
        "* L2 regularization\n",
        "* There are some mistakes in the sample code, can you find them?"
      ]
    },
    {
      "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"
      ]
    }
  ]
}