Naive_Method.ipynb

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      "name": "BG_Prediction_Naive_method.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dongsikchoi/ML_Time-Series_EDA/blob/master/BG_Prediction_Univariate_Filter_X_01_25.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lx196wNwq9qn"
      },
      "source": [
        "### 필요 라이브러리 로드"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:11:17.017681Z",
          "start_time": "2021-01-25T01:11:15.594487Z"
        },
        "id": "-7Ts1kTdq9qr"
      },
      "source": [
        "import warnings\n",
        "import itertools\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "plt.style.use('fivethirtyeight')\n",
        "import pandas as pd\n",
        "import statsmodels.api as sm\n",
        "import matplotlib\n",
        "\n",
        "matplotlib.rcParams['axes.labelsize']=14\n",
        "matplotlib.rcParams['xtick.labelsize']=12\n",
        "matplotlib.rcParams['ytick.labelsize']=12\n",
        "matplotlib.rcParams['text.color']='k'"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0mabAlWoq-4v"
      },
      "source": [
        "# Colab\r\n",
        "#from google.colab import auth\r\n",
        "#auth.authenticate_user()"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YMukYC78rI9D",
        "outputId": "da6507b4-f753-45a2-a7de-337d92f5b690"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:11:17.033638Z",
          "start_time": "2021-01-25T01:11:17.021672Z"
        },
        "id": "v-JGa4Mfq9qs"
      },
      "source": [
        "pd.set_option('display.max_columns',500) \n",
        "#출력시 생략 X"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1DTmoDOkq9qs"
      },
      "source": [
        "### 데이터셋 설정 "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:51:11.992775Z",
          "start_time": "2021-01-25T01:51:11.855142Z"
        },
        "id": "2iPg6upoq9qt"
      },
      "source": [
        "#df=pd.read_excel(\"BG_Dataset/BG_sample27.xlsx\")"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mvp2qFY6rUd3"
      },
      "source": [
        "# Colab\r\n",
        "df = pd.read_excel(\"/content/drive/MyDrive/BG_Dataset/BG_sample14.xlsx\")\r\n",
        "#df = pd.read_csv(\"/content/drive/MyDrive/BG_Dataset/BG_Sample46.csv\")\r\n"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:51:14.576866Z",
          "start_time": "2021-01-25T01:51:14.562904Z"
        },
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        },
        "id": "WR0-r_OUq9qt",
        "outputId": "94fabdf2-2a4d-4c02-8dad-de1def82ef28"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1155, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:51:16.523597Z",
          "start_time": "2021-01-25T01:51:16.508638Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "NUfEWB3Oq9qu",
        "outputId": "e96acfdf-c893-4a06-fce7-961946ff8e29"
      },
      "source": [
        "df.head(13)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>색인</th>\n",
              "      <th>타임스탬프(YYYY-MM-DDThh:mm:ss)</th>\n",
              "      <th>이벤트 유형</th>\n",
              "      <th>이벤트 하위 유형</th>\n",
              "      <th>환자 정보</th>\n",
              "      <th>장치 정보</th>\n",
              "      <th>소스 장치 ID</th>\n",
              "      <th>포도당 값 (mg/dL)</th>\n",
              "      <th>인슐린 값(u)</th>\n",
              "      <th>탄수화물 값 (그램)</th>\n",
              "      <th>기간(hh:mm:ss)</th>\n",
              "      <th>포도당 변화율 (mg/dL/min)</th>\n",
              "      <th>트랜스미터 시간 (배장 정수)</th>\n",
              "      <th>트랜스미터 ID</th>\n",
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              "      <td>이름</td>\n",
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              "      <td>sch</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>성</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>생년월일</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1983-01-24 00:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>장치</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Dexcom G5 Mobile App</td>\n",
              "      <td>Android</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>경고</td>\n",
              "      <td>하락</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Android</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>경고</td>\n",
              "      <td>높음</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Android</td>\n",
              "      <td>250.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>경고</td>\n",
              "      <td>낮음</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Android</td>\n",
              "      <td>70.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>경고</td>\n",
              "      <td>신호 손실</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Android</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>00:30:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>8</th>\n",
              "      <td>9</td>\n",
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              "      <td>경고</td>\n",
              "      <td>상승</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Android</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>경고</td>\n",
              "      <td>긴급하게 낮음</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Android</td>\n",
              "      <td>55.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>2020-01-20T15:41:13</td>\n",
              "      <td>교정</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Android</td>\n",
              "      <td>139.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>42E570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>2020-01-20T15:58:23</td>\n",
              "      <td>교정</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Android</td>\n",
              "      <td>135.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>42E570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>2020-01-20T16:02:23</td>\n",
              "      <td>EGV</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Android</td>\n",
              "      <td>123.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1572289.0</td>\n",
              "      <td>42E570</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "    색인 타임스탬프(YYYY-MM-DDThh:mm:ss) 이벤트 유형 이벤트 하위 유형                환자 정보  \\\n",
              "0    1                        NaN     이름       NaN                  sch   \n",
              "1    2                        NaN      성       NaN                    3   \n",
              "2    3                        NaN   생년월일       NaN  1983-01-24 00:00:00   \n",
              "3    4                        NaN     장치       NaN                  NaN   \n",
              "4    5                        NaN     경고        하락                  NaN   \n",
              "5    6                        NaN     경고        높음                  NaN   \n",
              "6    7                        NaN     경고        낮음                  NaN   \n",
              "7    8                        NaN     경고     신호 손실                  NaN   \n",
              "8    9                        NaN     경고        상승                  NaN   \n",
              "9   10                        NaN     경고   긴급하게 낮음                  NaN   \n",
              "10  11        2020-01-20T15:41:13     교정       NaN                  NaN   \n",
              "11  12        2020-01-20T15:58:23     교정       NaN                  NaN   \n",
              "12  13        2020-01-20T16:02:23    EGV       NaN                  NaN   \n",
              "\n",
              "                   장치 정보 소스 장치 ID  포도당 값 (mg/dL)  인슐린 값(u)  탄수화물 값 (그램)  \\\n",
              "0                    NaN      NaN            NaN       NaN          NaN   \n",
              "1                    NaN      NaN            NaN       NaN          NaN   \n",
              "2                    NaN      NaN            NaN       NaN          NaN   \n",
              "3   Dexcom G5 Mobile App  Android            NaN       NaN          NaN   \n",
              "4                    NaN  Android            NaN       NaN          NaN   \n",
              "5                    NaN  Android          250.0       NaN          NaN   \n",
              "6                    NaN  Android           70.0       NaN          NaN   \n",
              "7                    NaN  Android            NaN       NaN          NaN   \n",
              "8                    NaN  Android            NaN       NaN          NaN   \n",
              "9                    NaN  Android           55.0       NaN          NaN   \n",
              "10                   NaN  Android          139.0       NaN          NaN   \n",
              "11                   NaN  Android          135.0       NaN          NaN   \n",
              "12                   NaN  Android          123.0       NaN          NaN   \n",
              "\n",
              "   기간(hh:mm:ss)  포도당 변화율 (mg/dL/min)  트랜스미터 시간 (배장 정수) 트랜스미터 ID  \n",
              "0           NaN                  NaN               NaN      NaN  \n",
              "1           NaN                  NaN               NaN      NaN  \n",
              "2           NaN                  NaN               NaN      NaN  \n",
              "3           NaN                  NaN               NaN      NaN  \n",
              "4           NaN                  3.0               NaN      NaN  \n",
              "5           NaN                  NaN               NaN      NaN  \n",
              "6           NaN                  NaN               NaN      NaN  \n",
              "7      00:30:00                  NaN               NaN      NaN  \n",
              "8           NaN                  3.0               NaN      NaN  \n",
              "9           NaN                  NaN               NaN      NaN  \n",
              "10          NaN                  NaN               NaN   42E570  \n",
              "11          NaN                  NaN               NaN   42E570  \n",
              "12          NaN                  NaN         1572289.0   42E570  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:51:16.538557Z",
          "start_time": "2021-01-25T01:51:16.525593Z"
        },
        "id": "ffJCYqbfq9qu"
      },
      "source": [
        "#필요없는 열 제거\n",
        "cols = ['색인', '이벤트 하위 유형', '환자 정보', '장치 정보', '소스 장치 ID', '기간(hh:mm:ss)', '포도당 변화율 (mg/dL/min)', '트랜스미터 시간 (배장 정수)', '트랜스미터 ID']\n",
        "df.drop(cols, axis=1, inplace=True)\n"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
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          "start_time": "2021-01-25T01:51:19.711592Z"
        },
        "id": "zBXR3xmWq9qu"
      },
      "source": [
        "df=df.rename({'타임스탬프(YYYY-MM-DDThh:mm:ss)':'Timestamp','포도당 값 (mg/dL)':'Glucose','이벤트 유형':'Event',\n",
        "             '인슐린 값(u)':'Insulin','탄수화물 값 (그램)':'Meal'},axis='columns')"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:51:21.609517Z",
          "start_time": "2021-01-25T01:51:21.594557Z"
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        "id": "P1FIKbkoq9qv",
        "outputId": "3b8690f9-bca0-4c89-a118-0765a44e61e0"
      },
      "source": [
        "df['Event'].value_counts()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "EGV     1117\n",
              "인슐린       12\n",
              "교정         9\n",
              "탄수화물       7\n",
              "경고         6\n",
              "생년월일       1\n",
              "장치         1\n",
              "성          1\n",
              "이름         1\n",
              "Name: Event, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "end_time": "2021-01-25T01:51:21.856856Z",
          "start_time": "2021-01-25T01:51:21.841897Z"
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        "id": "O09mun7lq9qv",
        "outputId": "4d02e8b6-1040-43c9-a6d3-5155c6451371"
      },
      "source": [
        "df['Glucose'].value_counts()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "99.0     16\n",
              "129.0    14\n",
              "134.0    14\n",
              "97.0     13\n",
              "153.0    13\n",
              "         ..\n",
              "271.0     1\n",
              "227.0     1\n",
              "185.0     1\n",
              "270.0     1\n",
              "277.0     1\n",
              "Name: Glucose, Length: 209, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
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    {
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          "end_time": "2021-01-25T01:51:26.308136Z",
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        },
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        "outputId": "965717fe-8a2e-49c8-a55b-cdc9781bd002"
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      "source": [
        "df.tail()"
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      "execution_count": 13,
      "outputs": [
        {
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          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>Event</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>Meal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1150</th>\n",
              "      <td>2020-01-24T13:52:10</td>\n",
              "      <td>EGV</td>\n",
              "      <td>182.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1151</th>\n",
              "      <td>2020-01-24T13:57:10</td>\n",
              "      <td>EGV</td>\n",
              "      <td>175.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1152</th>\n",
              "      <td>2020-01-24T14:02:10</td>\n",
              "      <td>EGV</td>\n",
              "      <td>170.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1153</th>\n",
              "      <td>2020-01-24T14:07:10</td>\n",
              "      <td>EGV</td>\n",
              "      <td>167.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1154</th>\n",
              "      <td>2020-01-24T14:12:10</td>\n",
              "      <td>EGV</td>\n",
              "      <td>151.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Timestamp Event  Glucose  Insulin  Meal\n",
              "1150  2020-01-24T13:52:10   EGV    182.0      NaN   NaN\n",
              "1151  2020-01-24T13:57:10   EGV    175.0      NaN   NaN\n",
              "1152  2020-01-24T14:02:10   EGV    170.0      NaN   NaN\n",
              "1153  2020-01-24T14:07:10   EGV    167.0      NaN   NaN\n",
              "1154  2020-01-24T14:12:10   EGV    151.0      NaN   NaN"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 13
        }
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    {
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          "end_time": "2021-01-25T01:51:28.257217Z",
          "start_time": "2021-01-25T01:51:28.243254Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "37jYwbU0q9qv",
        "outputId": "ee99e40b-8aa4-43a1-defb-becb291e2425"
      },
      "source": [
        "# 인슐린 투여횟수\n",
        "df['Insulin'].value_counts()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0     8\n",
              "44.0    2\n",
              "20.0    1\n",
              "52.0    1\n",
              "Name: Insulin, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:51:30.174549Z",
          "start_time": "2021-01-25T01:51:30.160585Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LmVKw59bq9qv",
        "outputId": "754ac962-18ba-4413-89f6-4470bc47440b"
      },
      "source": [
        "# 식사 횟수\n",
        "df['Meal'].value_counts()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0      5\n",
              "200.0    1\n",
              "100.0    1\n",
              "Name: Meal, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:51:32.245847Z",
          "start_time": "2021-01-25T01:51:32.230887Z"
        },
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          "height": 514
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        "id": "lMmVyYYcq9qw",
        "outputId": "e04b473f-a893-46d4-d191-f180896f266f"
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      "source": [
        "df.head(15)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>2</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>경고</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>경고</td>\n",
              "      <td>250.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>NaN</td>\n",
              "      <td>경고</td>\n",
              "      <td>70.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>NaN</td>\n",
              "      <td>경고</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>NaN</td>\n",
              "      <td>경고</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>NaN</td>\n",
              "      <td>경고</td>\n",
              "      <td>55.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2020-01-20T15:41:13</td>\n",
              "      <td>교정</td>\n",
              "      <td>139.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2020-01-20T15:58:23</td>\n",
              "      <td>교정</td>\n",
              "      <td>135.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2020-01-20T16:02:23</td>\n",
              "      <td>EGV</td>\n",
              "      <td>123.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2020-01-20T16:07:15</td>\n",
              "      <td>EGV</td>\n",
              "      <td>112.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2020-01-20T16:12:15</td>\n",
              "      <td>EGV</td>\n",
              "      <td>103.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Timestamp Event  Glucose  Insulin  Meal\n",
              "0                   NaN    이름      NaN      NaN   NaN\n",
              "1                   NaN     성      NaN      NaN   NaN\n",
              "2                   NaN  생년월일      NaN      NaN   NaN\n",
              "3                   NaN    장치      NaN      NaN   NaN\n",
              "4                   NaN    경고      NaN      NaN   NaN\n",
              "5                   NaN    경고    250.0      NaN   NaN\n",
              "6                   NaN    경고     70.0      NaN   NaN\n",
              "7                   NaN    경고      NaN      NaN   NaN\n",
              "8                   NaN    경고      NaN      NaN   NaN\n",
              "9                   NaN    경고     55.0      NaN   NaN\n",
              "10  2020-01-20T15:41:13    교정    139.0      NaN   NaN\n",
              "11  2020-01-20T15:58:23    교정    135.0      NaN   NaN\n",
              "12  2020-01-20T16:02:23   EGV    123.0      NaN   NaN\n",
              "13  2020-01-20T16:07:15   EGV    112.0      NaN   NaN\n",
              "14  2020-01-20T16:12:15   EGV    103.0      NaN   NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:51:32.261804Z",
          "start_time": "2021-01-25T01:51:32.246845Z"
        },
        "id": "WAanR7Flq9qw"
      },
      "source": [
        "# Timestamp != NaN일 때까지 count하고 \n",
        "# Nan에 해당하는 인스턴스 제거 \n",
        "df=df.dropna(subset=['Timestamp'])"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:51:36.348768Z",
          "start_time": "2021-01-25T01:51:36.334807Z"
        },
        "colab": {
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          "height": 669
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        "id": "Ndq-UYgBq9qw",
        "outputId": "85e14a48-e828-4162-a534-850fbecadd21"
      },
      "source": [
        "df.head(20)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>Meal</th>\n",
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              "  <tbody>\n",
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              "      <th>10</th>\n",
              "      <td>2020-01-20T15:41:13</td>\n",
              "      <td>교정</td>\n",
              "      <td>139.0</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2020-01-20T15:58:23</td>\n",
              "      <td>교정</td>\n",
              "      <td>135.0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2020-01-20T16:02:23</td>\n",
              "      <td>EGV</td>\n",
              "      <td>123.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2020-01-20T16:07:15</td>\n",
              "      <td>EGV</td>\n",
              "      <td>112.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2020-01-20T16:12:15</td>\n",
              "      <td>EGV</td>\n",
              "      <td>103.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2020-01-20T16:17:16</td>\n",
              "      <td>EGV</td>\n",
              "      <td>105.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2020-01-20T16:22:15</td>\n",
              "      <td>EGV</td>\n",
              "      <td>111.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2020-01-20T16:27:15</td>\n",
              "      <td>EGV</td>\n",
              "      <td>133.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2020-01-20T16:32:16</td>\n",
              "      <td>EGV</td>\n",
              "      <td>139.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2020-01-20T16:37:15</td>\n",
              "      <td>EGV</td>\n",
              "      <td>150.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2020-01-20T16:42:16</td>\n",
              "      <td>EGV</td>\n",
              "      <td>155.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2020-01-20T16:47:16</td>\n",
              "      <td>EGV</td>\n",
              "      <td>139.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2020-01-20T16:52:16</td>\n",
              "      <td>EGV</td>\n",
              "      <td>134.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2020-01-20T16:57:16</td>\n",
              "      <td>EGV</td>\n",
              "      <td>137.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2020-01-20T17:02:16</td>\n",
              "      <td>EGV</td>\n",
              "      <td>150.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>2020-01-20T17:07:16</td>\n",
              "      <td>EGV</td>\n",
              "      <td>154.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2020-01-20T17:12:16</td>\n",
              "      <td>EGV</td>\n",
              "      <td>157.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>2020-01-20T17:17:16</td>\n",
              "      <td>EGV</td>\n",
              "      <td>159.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>2020-01-20T17:22:16</td>\n",
              "      <td>EGV</td>\n",
              "      <td>172.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>2020-01-20T17:27:16</td>\n",
              "      <td>EGV</td>\n",
              "      <td>183.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Timestamp Event  Glucose  Insulin  Meal\n",
              "10  2020-01-20T15:41:13    교정    139.0      NaN   NaN\n",
              "11  2020-01-20T15:58:23    교정    135.0      NaN   NaN\n",
              "12  2020-01-20T16:02:23   EGV    123.0      NaN   NaN\n",
              "13  2020-01-20T16:07:15   EGV    112.0      NaN   NaN\n",
              "14  2020-01-20T16:12:15   EGV    103.0      NaN   NaN\n",
              "15  2020-01-20T16:17:16   EGV    105.0      NaN   NaN\n",
              "16  2020-01-20T16:22:15   EGV    111.0      NaN   NaN\n",
              "17  2020-01-20T16:27:15   EGV    133.0      NaN   NaN\n",
              "18  2020-01-20T16:32:16   EGV    139.0      NaN   NaN\n",
              "19  2020-01-20T16:37:15   EGV    150.0      NaN   NaN\n",
              "20  2020-01-20T16:42:16   EGV    155.0      NaN   NaN\n",
              "21  2020-01-20T16:47:16   EGV    139.0      NaN   NaN\n",
              "22  2020-01-20T16:52:16   EGV    134.0      NaN   NaN\n",
              "23  2020-01-20T16:57:16   EGV    137.0      NaN   NaN\n",
              "24  2020-01-20T17:02:16   EGV    150.0      NaN   NaN\n",
              "25  2020-01-20T17:07:16   EGV    154.0      NaN   NaN\n",
              "26  2020-01-20T17:12:16   EGV    157.0      NaN   NaN\n",
              "27  2020-01-20T17:17:16   EGV    159.0      NaN   NaN\n",
              "28  2020-01-20T17:22:16   EGV    172.0      NaN   NaN\n",
              "29  2020-01-20T17:27:16   EGV    183.0      NaN   NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:51:36.364724Z",
          "start_time": "2021-01-25T01:51:36.352757Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ndlieh9Vq9qw",
        "outputId": "e8664c6a-67bc-4f5c-cec7-5b7f55be2ed9"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1145, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:51:38.168923Z",
          "start_time": "2021-01-25T01:51:38.154960Z"
        },
        "id": "UudGLrdmq9qx"
      },
      "source": [
        "# drop한 인스턴스가 있어서 인덱스 리셋\n",
        "df=df.sort_values(by=['Timestamp'],axis=0,ascending=True)\n",
        "df=df.reset_index(drop=True)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:51:42.311539Z",
          "start_time": "2021-01-25T01:51:42.297576Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "RKsqfYvyq9qx",
        "outputId": "4273d47e-b447-4f24-d9aa-78f92335d7a4"
      },
      "source": [
        "df.head(15)"
      ],
      "execution_count": 21,
      "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>Timestamp</th>\n",
              "      <th>Event</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>Meal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-20T15:41:13</td>\n",
              "      <td>교정</td>\n",
              "      <td>139.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-20T15:58:23</td>\n",
              "      <td>교정</td>\n",
              "      <td>135.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-20T16:02:23</td>\n",
              "      <td>EGV</td>\n",
              "      <td>123.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-20T16:07:15</td>\n",
              "      <td>EGV</td>\n",
              "      <td>112.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-20T16:12:15</td>\n",
              "      <td>EGV</td>\n",
              "      <td>103.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2020-01-20T16:17:16</td>\n",
              "      <td>EGV</td>\n",
              "      <td>105.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2020-01-20T16:22:15</td>\n",
              "      <td>EGV</td>\n",
              "      <td>111.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2020-01-20T16:27:15</td>\n",
              "      <td>EGV</td>\n",
              "      <td>133.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2020-01-20T16:32:16</td>\n",
              "      <td>EGV</td>\n",
              "      <td>139.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2020-01-20T16:37:15</td>\n",
              "      <td>EGV</td>\n",
              "      <td>150.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2020-01-20T16:42:16</td>\n",
              "      <td>EGV</td>\n",
              "      <td>155.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2020-01-20T16:47:16</td>\n",
              "      <td>EGV</td>\n",
              "      <td>139.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2020-01-20T16:52:16</td>\n",
              "      <td>EGV</td>\n",
              "      <td>134.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2020-01-20T16:57:16</td>\n",
              "      <td>EGV</td>\n",
              "      <td>137.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2020-01-20T17:02:16</td>\n",
              "      <td>EGV</td>\n",
              "      <td>150.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Timestamp Event  Glucose  Insulin  Meal\n",
              "0   2020-01-20T15:41:13    교정    139.0      NaN   NaN\n",
              "1   2020-01-20T15:58:23    교정    135.0      NaN   NaN\n",
              "2   2020-01-20T16:02:23   EGV    123.0      NaN   NaN\n",
              "3   2020-01-20T16:07:15   EGV    112.0      NaN   NaN\n",
              "4   2020-01-20T16:12:15   EGV    103.0      NaN   NaN\n",
              "5   2020-01-20T16:17:16   EGV    105.0      NaN   NaN\n",
              "6   2020-01-20T16:22:15   EGV    111.0      NaN   NaN\n",
              "7   2020-01-20T16:27:15   EGV    133.0      NaN   NaN\n",
              "8   2020-01-20T16:32:16   EGV    139.0      NaN   NaN\n",
              "9   2020-01-20T16:37:15   EGV    150.0      NaN   NaN\n",
              "10  2020-01-20T16:42:16   EGV    155.0      NaN   NaN\n",
              "11  2020-01-20T16:47:16   EGV    139.0      NaN   NaN\n",
              "12  2020-01-20T16:52:16   EGV    134.0      NaN   NaN\n",
              "13  2020-01-20T16:57:16   EGV    137.0      NaN   NaN\n",
              "14  2020-01-20T17:02:16   EGV    150.0      NaN   NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:51:44.377391Z",
          "start_time": "2021-01-25T01:51:44.364427Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dr0kuMdHq9qx",
        "outputId": "4cc89ee6-abf9-482c-96e8-798927c98ba0"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1145, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:51:46.355104Z",
          "start_time": "2021-01-25T01:51:46.341141Z"
        },
        "id": "qy3fiZhVq9qx"
      },
      "source": [
        "# 높음, 낮음, 교정 인덱스 저장 \n",
        "high_list=[]\n",
        "low_list=[]\n",
        "calib_list=[]\n",
        "insulin_list=[]\n",
        "meal_list=[]"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:51:46.371062Z",
          "start_time": "2021-01-25T01:51:46.356102Z"
        },
        "id": "P537zeVnq9qx"
      },
      "source": [
        "def isNaN(num):\n",
        "    return num==num"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:51:49.502725Z",
          "start_time": "2021-01-25T01:51:49.489761Z"
        },
        "id": "QZi1ugehq9qx"
      },
      "source": [
        "# 혈당 '높음' 빈도 카운트\n",
        "def glucose_count_high(df,col):\n",
        "    count=0\n",
        "    for i, row in df.iterrows():\n",
        "         val = row[col]\n",
        "         if val=='높음':\n",
        "            count=count+1\n",
        "            high_list.append(i)\n",
        "    print('높음 : ',count)\n",
        "    "
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:51:50.251721Z",
          "start_time": "2021-01-25T01:51:50.237759Z"
        },
        "id": "Y3T5yccbq9qx"
      },
      "source": [
        "# 혈당 '낮음' 빈도 카운트\n",
        "def glucose_count_low(df,col):\n",
        "    count=0\n",
        "    for i, row in df.iterrows():\n",
        "         val = row[col]\n",
        "         if val=='낮음':\n",
        "            count=count+1\n",
        "            low_list.append(i)\n",
        "    print('낮음 : ',count)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:51:54.181151Z",
          "start_time": "2021-01-25T01:51:54.167192Z"
        },
        "id": "QPEIPRM8q9qy"
      },
      "source": [
        "# 혈당 '교정' 빈도 카운트\n",
        "def calibration_count_low(df,col):\n",
        "    count=0\n",
        "    for i, row in df.iterrows():\n",
        "         val = row[col]\n",
        "         if val=='교정':\n",
        "            count=count+1\n",
        "            calib_list.append(i)\n",
        "    print('교정 : ',count)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:51:56.097402Z",
          "start_time": "2021-01-25T01:51:56.083270Z"
        },
        "id": "0Mf6s7jAq9qy"
      },
      "source": [
        "# 인슐린 카운트 \n",
        "#def insul_count(df,col):\n",
        "#    count=0\n",
        "#    for i, row in df.iterrows():\n",
        "#         val = row[col]\n",
        "#         if val==0:\n",
        "#            count=count+1\n",
        "#            insulin_list.append(i)\n",
        "#    print('인슐린 : ',count)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:51:57.596418Z",
          "start_time": "2021-01-25T01:51:57.582456Z"
        },
        "id": "hRKoPSWdq9qy"
      },
      "source": [
        "# 인슐린 카운트 \n",
        "def insul_count(df,col):\n",
        "    count=0\n",
        "    for i, row in df.iterrows():\n",
        "         val = row[col]\n",
        "         if isNaN(val)==True:\n",
        "            count=count+1\n",
        "            insulin_list.append(i)\n",
        "    print('인슐린 : ',count)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:51:58.298583Z",
          "start_time": "2021-01-25T01:51:58.286616Z"
        },
        "id": "U33fUKIeq9qy"
      },
      "source": [
        "# 식사 횟수 카운트 \n",
        "#def meal_count(df,col):\n",
        "#    count=0\n",
        "#    for i, row in df.iterrows():\n",
        "#         val = row[col]\n",
        "#         if val==0:\n",
        "#            count=count+1\n",
        "#            meal_list.append(i)\n",
        "#    print('식사 횟수 : ',count)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:52:00.266372Z",
          "start_time": "2021-01-25T01:52:00.253356Z"
        },
        "id": "KIBCtRV_q9qy"
      },
      "source": [
        "# 식사 횟수 카운트 \n",
        "def meal_count(df,col):\n",
        "    count=0\n",
        "    for i, row in df.iterrows():\n",
        "         val = row[col]\n",
        "         if isNaN(val)==True:\n",
        "            count=count+1\n",
        "            meal_list.append(i)\n",
        "    print('식사 횟수 : ',count)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:52:01.682876Z",
          "start_time": "2021-01-25T01:52:01.564191Z"
        },
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t76CauuJq9qy",
        "outputId": "9b7d14ad-77b3-4121-81df-1f0c25b3cd86"
      },
      "source": [
        "glucose_count_high(df,'Glucose')"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "높음 :  0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:52:01.791803Z",
          "start_time": "2021-01-25T01:52:01.778789Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1jCdOODYq9qz",
        "outputId": "11241748-89b5-490a-82e3-62fd0e1d43fc"
      },
      "source": [
        "print('높음 개수 :',len(high_list))"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "높음 개수 : 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:52:02.448796Z",
          "start_time": "2021-01-25T01:52:02.435830Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hSi2WJtbq9qz",
        "outputId": "165ccd01-01aa-4ba4-8ae7-db8be93e781b"
      },
      "source": [
        "print('낮음 개수 :',len(low_list))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "낮음 개수 : 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:52:02.464753Z",
          "start_time": "2021-01-25T01:52:02.449793Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7-Iy3eSdq9qz",
        "outputId": "7620546a-f2bf-43c7-f040-67c3afa8ce1b"
      },
      "source": [
        "print('교정 개수 :',len(calib_list))"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "교정 개수 : 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:52:02.603795Z",
          "start_time": "2021-01-25T01:52:02.465750Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Lsj2H6Rq9qz",
        "outputId": "5c34e3ee-beef-479a-e4cb-dcad6722f2fe"
      },
      "source": [
        "glucose_count_low(df,'Glucose')"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "낮음 :  0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:52:02.619752Z",
          "start_time": "2021-01-25T01:52:02.605789Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YB8-5N--q9qz",
        "outputId": "733b2111-52d9-4927-8171-fc13b2118a21"
      },
      "source": [
        "low_list"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:52:02.823004Z",
          "start_time": "2021-01-25T01:52:02.623742Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W-d0Eh-Mq9qz",
        "outputId": "85f1e2b6-7449-4972-e3fd-e7389de3e712"
      },
      "source": [
        "calibration_count_low(df,'Event')"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "교정 :  9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:52:02.837968Z",
          "start_time": "2021-01-25T01:52:02.826000Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AA4DScIlq9q0",
        "outputId": "b0dd038e-e6c7-4559-a1f0-612c3397e982"
      },
      "source": [
        "calib_list"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1, 60, 175, 360, 468, 757, 932, 1023]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:52:02.994907Z",
          "start_time": "2021-01-25T01:52:02.842954Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WVjnBdLEq9q0",
        "outputId": "023e65ce-8bc3-4862-ec84-c678d3dd6eb6"
      },
      "source": [
        "insul_count(df,'Insulin')"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "인슐린 :  12\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:52:03.010871Z",
          "start_time": "2021-01-25T01:52:02.998901Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "diiNlaPhq9q0",
        "outputId": "220a9cbf-7e10-4ab7-af3b-c9d12e9bad1e"
      },
      "source": [
        "insulin_list"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[26, 257, 258, 495, 554, 620, 771, 837, 889, 1052, 1060, 1119]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:52:03.180415Z",
          "start_time": "2021-01-25T01:52:03.013862Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zz4J0RMcq9q0",
        "outputId": "a97e950e-35c6-4347-b17a-de47b41cbe55"
      },
      "source": [
        "meal_count(df,'Meal')"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "식사 횟수 :  7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:52:26.737154Z",
          "start_time": "2021-01-25T01:52:26.722192Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S5Iommgpq9q0",
        "outputId": "290f3faf-3b16-4d19-bec3-124ed12d0faa"
      },
      "source": [
        "meal_list"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[24, 249, 259, 321, 488, 890, 1061]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:52:27.080235Z",
          "start_time": "2021-01-25T01:52:27.065274Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gcwA8bhDq9q0",
        "outputId": "31a07e6d-2a28-46ac-b988-9dad7eee22db"
      },
      "source": [
        "df['Insulin'].value_counts()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0     8\n",
              "44.0    2\n",
              "20.0    1\n",
              "52.0    1\n",
              "Name: Insulin, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:52:27.096191Z",
          "start_time": "2021-01-25T01:52:27.082229Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EWPmhRFwq9q1",
        "outputId": "d92c6929-1c48-4f7f-e4b6-1fa4d96ad644"
      },
      "source": [
        "df['Meal'].value_counts()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0      5\n",
              "200.0    1\n",
              "100.0    1\n",
              "Name: Meal, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:52:27.112149Z",
          "start_time": "2021-01-25T01:52:27.098187Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yVHexf31q9q1",
        "outputId": "2160f7d8-afbf-438c-9f0c-ad8504b2aa36"
      },
      "source": [
        "isNaN(df['Glucose'][14])"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:52:27.128106Z",
          "start_time": "2021-01-25T01:52:27.114144Z"
        },
        "id": "Tytglb5oq9q1"
      },
      "source": [
        "import math\n",
        "for i in insulin_list:\n",
        "    \n",
        "    if isNaN(df['Glucose'][i+1]):\n",
        "        df['Insulin'][i+1]=0\n",
        "        continue\n",
        "    if isNaN(df['Glucose'][i+2]):\n",
        "        df['Insulin'][i+2]=0\n",
        "        continue\n",
        "    if isNaN(df['Glucose'][i+3]):\n",
        "        df['Insulin'][i+3]=0\n",
        "        continue\n",
        "    if isNaN(df['Glucose'][i+4]):\n",
        "        df['Insulin'][i+4]=0\n",
        "        continue"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:52:27.173984Z",
          "start_time": "2021-01-25T01:52:27.155035Z"
        },
        "id": "JcgRMhGVq9q1"
      },
      "source": [
        "for i in meal_list:\n",
        "    \n",
        "    if isNaN(df['Glucose'][i+1]):\n",
        "        df['Meal'][i+1]=0\n",
        "        continue\n",
        "    if isNaN(df['Glucose'][i+2]):\n",
        "        df['Meal'][i+2]=0\n",
        "        continue\n",
        "    if isNaN(df['Glucose'][i+3]):\n",
        "        df['Meal'][i+3]=0\n",
        "        continue\n",
        "    if isNaN(df['Glucose'][i+4]):\n",
        "        df['Meal'][i+4]=0\n",
        "        continue"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:52:27.188944Z",
          "start_time": "2021-01-25T01:52:27.175978Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pCH1l3thq9q1",
        "outputId": "b0edf900-940e-4da6-cef6-5c969ed45f98"
      },
      "source": [
        "import math\n",
        "math.isnan(df['Glucose'][14])"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:52:27.204901Z",
          "start_time": "2021-01-25T01:52:27.190939Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h-zF0AuEq9q1",
        "outputId": "f4e88ba8-d7e0-41c1-bd10-4dcf57943984"
      },
      "source": [
        "insulin_list"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[26, 257, 258, 495, 554, 620, 771, 837, 889, 1052, 1060, 1119]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:52:33.922994Z",
          "start_time": "2021-01-25T01:52:33.909030Z"
        },
        "id": "cYufVrh9q9q1"
      },
      "source": [
        "SetList1 = set(insulin_list)\n",
        "SetList2 = set(meal_list)\n",
        "\n",
        "C = list((SetList2.difference(SetList1)))\n",
        "\n",
        "insulin_list.extend(C)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:52:40.432733Z",
          "start_time": "2021-01-25T01:52:40.418770Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DrJEh6m3q9q2",
        "outputId": "7d6a9b2b-fce6-4eb0-b279-a07c961ff33b"
      },
      "source": [
        "insulin_list"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[26,\n",
              " 257,\n",
              " 258,\n",
              " 495,\n",
              " 554,\n",
              " 620,\n",
              " 771,\n",
              " 837,\n",
              " 889,\n",
              " 1052,\n",
              " 1060,\n",
              " 1119,\n",
              " 321,\n",
              " 259,\n",
              " 1061,\n",
              " 488,\n",
              " 24,\n",
              " 249,\n",
              " 890]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:52:46.966344Z",
          "start_time": "2021-01-25T01:52:46.952382Z"
        },
        "id": "k_g4Uw8_q9q2"
      },
      "source": [
        "df=df.drop(insulin_list)"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:52:48.819528Z",
          "start_time": "2021-01-25T01:52:48.805566Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "70rucVRyq9q2",
        "outputId": "eee0c2f1-b028-4c83-d979-4f557354fc9a"
      },
      "source": [
        "df[10:20]"
      ],
      "execution_count": 54,
      "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>Timestamp</th>\n",
              "      <th>Event</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>Meal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2020-01-20T16:42:16</td>\n",
              "      <td>EGV</td>\n",
              "      <td>155.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2020-01-20T16:47:16</td>\n",
              "      <td>EGV</td>\n",
              "      <td>139.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2020-01-20T16:52:16</td>\n",
              "      <td>EGV</td>\n",
              "      <td>134.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2020-01-20T16:57:16</td>\n",
              "      <td>EGV</td>\n",
              "      <td>137.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2020-01-20T17:02:16</td>\n",
              "      <td>EGV</td>\n",
              "      <td>150.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2020-01-20T17:07:16</td>\n",
              "      <td>EGV</td>\n",
              "      <td>154.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2020-01-20T17:12:16</td>\n",
              "      <td>EGV</td>\n",
              "      <td>157.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2020-01-20T17:17:16</td>\n",
              "      <td>EGV</td>\n",
              "      <td>159.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2020-01-20T17:22:16</td>\n",
              "      <td>EGV</td>\n",
              "      <td>172.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2020-01-20T17:27:16</td>\n",
              "      <td>EGV</td>\n",
              "      <td>183.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Timestamp Event  Glucose  Insulin  Meal\n",
              "10  2020-01-20T16:42:16   EGV    155.0      NaN   NaN\n",
              "11  2020-01-20T16:47:16   EGV    139.0      NaN   NaN\n",
              "12  2020-01-20T16:52:16   EGV    134.0      NaN   NaN\n",
              "13  2020-01-20T16:57:16   EGV    137.0      NaN   NaN\n",
              "14  2020-01-20T17:02:16   EGV    150.0      NaN   NaN\n",
              "15  2020-01-20T17:07:16   EGV    154.0      NaN   NaN\n",
              "16  2020-01-20T17:12:16   EGV    157.0      NaN   NaN\n",
              "17  2020-01-20T17:17:16   EGV    159.0      NaN   NaN\n",
              "18  2020-01-20T17:22:16   EGV    172.0      NaN   NaN\n",
              "19  2020-01-20T17:27:16   EGV    183.0      NaN   NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V-7fuLiuq9q2",
        "outputId": "489b12f2-a061-43e3-9224-6cab652cb935"
      },
      "source": [
        "df['Glucose'].unique()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([139., 135., 123., 112., 103., 105., 111., 133., 150., 155., 134.,\n",
              "       137., 154., 157., 159., 172., 183., 191., 198., 200., 196., 205.,\n",
              "       225., 238., 236., 237., 239., 233., 248., 257., 262., 259., 254.,\n",
              "       244., 228., 222., 213., 206., 194., 188., 177., 167., 163., 165.,\n",
              "       162., 147., 143., 142., 140., 144., 146., 148., 149., 141., 122.,\n",
              "       129., 145., 166., 171., 176., 193., 192., 208., 240., 246., 251.,\n",
              "       256., 260., 263., 265., 250., 243., 241., 253., 252., 232., 229.,\n",
              "       221., 210., 197., 266., 264., 258., 247., 218., 214., 216., 215.,\n",
              "       212., 209., 207., 204., 190., 226., 261., 170., 156., 152., 138.,\n",
              "       136., 131., 128., 121., 114., 110., 109., 107., 104., 102.,  99.,\n",
              "       101., 100.,  96.,  92.,  94.,  95.,  97.,  93.,  88.,  98., 126.,\n",
              "       160., 173., 179., 158., 153., 132., 116.,  91.,  85.,  84.,  79.,\n",
              "        78.,  80.,  76.,  73.,  70.,  75.,  83., 118., 113., 106.,  89.,\n",
              "        77.,  69.,  67., 125.,  87.,  81.,  90., 117., 115.,  86.,  82.,\n",
              "        74.,  72.,  71., 127., 164., 151., 130., 120., 119., 184., 211.,\n",
              "       203., 199., 187., 180., 124., 195., 186., 181., 169., 108., 224.,\n",
              "       230., 217., 270., 272., 275., 277., 282., 279., 268., 168., 185.,\n",
              "       220., 255., 242., 219., 234., 223., 201., 182., 174., 161., 178.,\n",
              "       231., 283., 288., 280., 271., 189., 227., 235., 175., 202.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:52:50.627338Z",
          "start_time": "2021-01-25T01:52:50.613373Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Wc-F60tq9q2",
        "outputId": "0b312296-633e-4002-e4c3-fb8681c4b13d"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp       0\n",
              "Event           0\n",
              "Glucose         0\n",
              "Insulin      1115\n",
              "Meal         1119\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:52:52.672484Z",
          "start_time": "2021-01-25T01:52:52.659179Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aB-Pr1IUq9q2",
        "outputId": "93de55b2-70e0-4909-a4e6-31d8732b69cb"
      },
      "source": [
        "df['Event'].value_counts()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "EGV    1117\n",
              "교정        9\n",
              "Name: Event, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tD-xVtSIq9q3"
      },
      "source": [
        "### 높음 낮음 고정 (추후 전처리 과정에서 400,408,416,408,400 이런 식으로 선형으로 변환해줄 것)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:52:56.678331Z",
          "start_time": "2021-01-25T01:52:56.666364Z"
        },
        "id": "_QqK9A1xq9q3"
      },
      "source": [
        "def fix_glucose_low(df,col):\n",
        "    for i, row in df.iterrows():\n",
        "        val = row[col]\n",
        "        if val=='낮음':\n",
        "            df.loc[i,col] = 60"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:52:54.688521Z",
          "start_time": "2021-01-25T01:52:54.674559Z"
        },
        "id": "XWZicn9Qq9q3"
      },
      "source": [
        "def fix_glucose(df,col):\n",
        "    for i, row in df.iterrows():\n",
        "        val = row[col]\n",
        "        if val=='높음':\n",
        "            df.loc[i,col] = 400\n",
        "        elif val > 400:\n",
        "            df.loc[i,col]=400"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:53:00.780544Z",
          "start_time": "2021-01-25T01:53:00.640251Z"
        },
        "id": "O55uKXOGq9q3"
      },
      "source": [
        "fix_glucose_low(df,'Glucose')"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:52:58.654752Z",
          "start_time": "2021-01-25T01:52:58.545660Z"
        },
        "id": "BvIUO61Mq9q3"
      },
      "source": [
        "fix_glucose(df,'Glucose')"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:53:13.968378Z",
          "start_time": "2021-01-25T01:53:13.948432Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g2avacA9q9q3",
        "outputId": "4aae21e0-9420-47c5-8f7d-86778224ce98"
      },
      "source": [
        "df['Glucose'].value_counts()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "99.0     16\n",
              "134.0    14\n",
              "129.0    14\n",
              "133.0    13\n",
              "97.0     13\n",
              "         ..\n",
              "279.0     1\n",
              "271.0     1\n",
              "266.0     1\n",
              "235.0     1\n",
              "277.0     1\n",
              "Name: Glucose, Length: 208, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:53:16.132062Z",
          "start_time": "2021-01-25T01:53:16.118100Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MhNKSZyTq9q3",
        "outputId": "54e15880-9458-4918-fa71-a7c7e6e5ea9a"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp       0\n",
              "Event           0\n",
              "Glucose         0\n",
              "Insulin      1115\n",
              "Meal         1119\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:53:18.170395Z",
          "start_time": "2021-01-25T01:53:18.157430Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e8GGHlNyq9q4",
        "outputId": "8fe76e48-fa4e-4e86-92ee-6f6ee0b07d69"
      },
      "source": [
        "df['Event'].unique()"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['교정', 'EGV'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TuLeqo_RxtN1"
      },
      "source": [
        "## 이벤트에 '운동' 있어서 혈당값 비어있음 \r\n",
        "# 모델 안돌아감 \r\n",
        "df=df.dropna(subset=['Glucose'])"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:53:20.114266Z",
          "start_time": "2021-01-25T01:53:20.101301Z"
        },
        "id": "-g9RIuKlq9q4"
      },
      "source": [
        "# drop한 인스턴스가 있어서 인덱스 리셋\n",
        "df=df.sort_values(by=['Timestamp'],axis=0,ascending=True)\n",
        "df=df.reset_index(drop=True)"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:53:22.262371Z",
          "start_time": "2021-01-25T01:53:22.141696Z"
        },
        "id": "3raJFd5Oq9q4"
      },
      "source": [
        "new_insul_list =[]\n",
        "new_meal_list =[]\n",
        "new_insul_and_meal_list=[]\n",
        "\n",
        "for i in range(len(df)):\n",
        "    if (df['Insulin'][i] ==0 and df['Meal'][i]==0):\n",
        "        new_insul_and_meal_list.append(i)\n",
        "    if (df['Insulin'][i]==0 and df['Meal'][i]!=0):\n",
        "        new_insul_list.append(i)\n",
        "    if (df['Insulin'][i]!=0 and df['Meal'][i]==0):\n",
        "        new_meal_list.append(i)"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:53:24.282408Z",
          "start_time": "2021-01-25T01:53:24.268445Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "etuTxppeq9q4",
        "outputId": "d0cb8300-25ec-4fc8-97eb-79976be7da5e"
      },
      "source": [
        "new_meal_list"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[24, 247, 315, 481]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UkkZfG03q9q4"
      },
      "source": [
        ""
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:53:26.333121Z",
          "start_time": "2021-01-25T01:53:26.132655Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "JoFY9Drvq9q4",
        "outputId": "aa58fcaf-3fd3-465e-cae8-c198ef7a8271"
      },
      "source": [
        "df.plot(figsize=(15,6))\n",
        "plt.show()"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:53:28.542527Z",
          "start_time": "2021-01-25T01:53:28.300175Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "iXUdyaOvq9q4",
        "outputId": "fdc497b2-985b-4b85-f667-c6bcc1ff5b0d"
      },
      "source": [
        "df.plot(figsize=(15,6))\n",
        "plt.axhline(y=70,c='r',linestyle='--',linewidth=1)\n",
        "plt.axhline(y=180,c='r',linestyle='--',linewidth=1)\n",
        "plt.axhline(y=400,c='r',linestyle='--',linewidth=1)\n",
        "#plt.annotate(df['Timestamp'][13],xy=(-50,40),xytext=(-50,0),fontsize=20,bbox=dict(boxstyle='square',\n",
        "#                                                               color='white'),arrowprops=dict(facecolor='black'))\n",
        "#plt.annotate(df['Timestamp'][len(df['Timestamp'])],xytext=(-,0),fontsize=20,bbox=dict(boxstyle='square',\n",
        " #                                                              color='white'))\n",
        "plt.text(len(df['Timestamp']),185,'Hyperglycemic Threshold',family='Times New Roman',size=15)\n",
        "plt.text(len(df['Timestamp']),75,'Hypoglycemic Threshold',family='Times New Roman',size=15)\n",
        "plt.text(len(df['Timestamp']),405,'Too High ( Glucose value 400 + )',family='Times New Roman',size=15)\n",
        "\n",
        "#y=70\n",
        "#plt.plot(y,'b--',linestyles='--'),\n",
        "#plt.hlines(70,linestyles='--')\n",
        "plt.show()"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:53:30.332463Z",
          "start_time": "2021-01-25T01:53:30.319500Z"
        },
        "id": "Ff-e2NnRq9q5"
      },
      "source": [
        "#df.drop(0,inplace=True)"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:53:30.704466Z",
          "start_time": "2021-01-25T01:53:30.690504Z"
        },
        "id": "3phxJsnVq9q5"
      },
      "source": [
        "# drop한 인스턴스가 있어서 인덱스 리셋\n",
        "#df=df.sort_values(by=['Timestamp'],axis=0,ascending=True)\n",
        "#df=df.reset_index(drop=True)\n",
        "#df.head(21)"
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:53:34.320799Z",
          "start_time": "2021-01-25T01:53:34.306836Z"
        },
        "id": "GHltXweoq9q5"
      },
      "source": [
        "# 인슐린, 탄수화물 투여시 혈당값 NULL\n",
        "# 그래서 t시점에 t-2 , t-1 크기비교해서 upper면 이렇게 lower면 이렇게 해서 interpolation ( 보간)\n",
        "# 이거 위에 참고사항 drop하고 나서 적용 해줘야됨\n",
        "#insul=list()\n",
        "#import math\n",
        "#def interpolate_glucose(df,col):\n",
        "#    #insul=list()\n",
        "    \n",
        "#    for i, row in df.iterrows():\n",
        "#        val=row[col]\n",
        "#        if math.isnan(val):\n",
        "#            if df['Glucose'][i-2] >= df['Glucose'][i-1]:\n",
        "#                c = (df['Glucose'][i-2] - df['Glucose'][i-1]) / 2 \n",
        "#                df.loc[i,col]= df['Glucose'][i-1] - c\n",
        "#                insul.append(i)\n",
        "#                print(insul)\n",
        "                \n",
        "#            if df['Glucose'][i-2] < df['Glucose'][i-1]:\n",
        "#                c = (df['Glucose'][i-2] - df['Glucose'][i-1]) / 2 \n",
        "#                df.loc[i,col]= df['Glucose'][i-1] - c\n",
        "#                insul.append(i)\n",
        "#                print(insul)\n",
        "        \n",
        "           "
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:53:36.328924Z",
          "start_time": "2021-01-25T01:53:36.313965Z"
        },
        "id": "oO-USeM2q9q6"
      },
      "source": [
        "# 인슐린 탄수화물 인덱스 문제 해결 파트 \n",
        "#insul=list()\n",
        "#meal=list()\n",
        "#import math\n",
        "#def interpolate_glucose(df,col):\n",
        "    #insul=list()\n",
        "    \n",
        "#    for i, row in df.iterrows():\n",
        "#        val=row[col]\n",
        "        #df=df.sort_values(by=['Timestamp'],axis=0,ascending=True)\n",
        "        #df=df.reset_index(drop=True)\n",
        "#        if math.isnan(val):\n",
        " #           if df['Event'][i]=='인슐린':\n",
        "  #              if df['Glucose'][i-1] is None:\n",
        "   #                 insul.append(i-2)\n",
        "                    \n",
        "                    \n",
        "    #            else:\n",
        "     #               insul.append(i-1)\n",
        "                    \n",
        "      #      if df['Event'][i]=='탄수화물':\n",
        "       #         if df['Glucose'][i-1] is None:\n",
        "        #            meal.append(i-2)\n",
        "                    \n",
        "         #       else:\n",
        "          #          meal.append(i-1)\n",
        "                    "
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:53:38.317618Z",
          "start_time": "2021-01-25T01:53:38.303654Z"
        },
        "id": "5ARbHQpIq9q6"
      },
      "source": [
        "#interpolate_glucose(df,'Glucose')"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:53:40.152005Z",
          "start_time": "2021-01-25T01:53:40.139039Z"
        },
        "id": "jwvVWaHTq9q6"
      },
      "source": [
        "#meal"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:53:42.020035Z",
          "start_time": "2021-01-25T01:53:42.007070Z"
        },
        "id": "UbYNhh3-q9q6"
      },
      "source": [
        "#insul"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:53:42.050953Z",
          "start_time": "2021-01-25T01:53:42.023027Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dDhQ76wlq9q6",
        "outputId": "d9123304-0518-422d-b011-dbeefb232b88"
      },
      "source": [
        "df['Glucose'].isnull().sum()"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:53:42.796962Z",
          "start_time": "2021-01-25T01:53:42.785989Z"
        },
        "id": "HgDjhDWAq9q6"
      },
      "source": [
        "insul_glucose_value=list()\n",
        "for i in new_insul_list:\n",
        "    insul_glucose_value.append(df['Glucose'][i])"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:53:44.256058Z",
          "start_time": "2021-01-25T01:53:44.242096Z"
        },
        "id": "PCAHh2D_q9q6"
      },
      "source": [
        "meal_glucose_value=list()\n",
        "for i in new_meal_list:\n",
        "    meal_glucose_value.append(df['Glucose'][i])"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:53:45.908642Z",
          "start_time": "2021-01-25T01:53:45.895678Z"
        },
        "id": "aca-BcHzq9q7"
      },
      "source": [
        "insul_and_meal_glucose_value = list()\n",
        "for i in new_insul_and_meal_list:\n",
        "    insul_and_meal_glucose_value.append(df['Glucose'][i])"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:53:45.924599Z",
          "start_time": "2021-01-25T01:53:45.911634Z"
        },
        "id": "Uqt843lhq9q7"
      },
      "source": [
        "#len = len(df['Timestamp'])\n",
        "#plt.text(-100,-1,df['Timestamp'][0],family='Times New Roman',fontweight='bold',size=17)"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:53:46.264689Z",
          "start_time": "2021-01-25T01:53:45.927590Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "ajebDiOAq9q7",
        "outputId": "719ec50a-5c82-4246-bac1-e0ad5d38a25c"
      },
      "source": [
        "import matplotlib.patches as mpatches\n",
        "import matplotlib.lines as mlines\n",
        "\n",
        "df.plot(figsize=(15,6),color='gray',alpha=0.5)\n",
        "plt.axhline(y=70,c='r',linestyle='--',linewidth=1)\n",
        "plt.axhline(y=180,c='r',linestyle='--',linewidth=1)\n",
        "plt.axhline(y=400,c='r',linestyle='--',linewidth=1)\n",
        "#plt.axhline(y=60,c='black',linestyle='--',linewidth=1)\n",
        "plt.text(len(df['Timestamp']),185,'Hyperglycemic Threshold',family='Times New Roman',size=15)\n",
        "plt.text(len(df['Timestamp']),75,'Hypoglycemic Threshold',family='Times New Roman',size=15)\n",
        "\n",
        "plt.scatter(new_insul_list,insul_glucose_value,edgecolor='black',s=50,label='Insulin',marker='o',c='red')\n",
        "plt.scatter(new_meal_list,meal_glucose_value,edgecolor='black',s=50,label='Carbohydrate',marker='o',c='green')\n",
        "plt.scatter(new_insul_and_meal_list,insul_and_meal_glucose_value,edgecolor='black',s=50,label='Insulin + Carbohydrate',marker='o',c='blue')\n",
        "\n",
        "red_patch = mpatches.Patch(color='red',label='Insulin')\n",
        "\n",
        "red_line = mlines.Line2D([],[],color='red',marker='o',markersize=15,label='Insulin')\n",
        "green_line = mlines.Line2D([],[],color='green',marker='o',markersize=15,label='Carbohydrate')\n",
        "blue_line = mlines.Line2D([],[],color='blue',marker='o',markersize=15,label='Insulin + Carbohydrate')\n",
        "plt.text(len(df['Timestamp']),405,'Too High ( Glucose value 400 + )',family='Times New Roman',size=15)\n",
        "plt.text(len(df['Timestamp']),60,'',family='Times New Roman',size=15)\n",
        "#plt.text(len(df['Timestamp']),-1,df['Timestamp'][len(df['Timestamp'])],family='Times New Roman',\n",
        "#         fontweight='bold',size=20)\n",
        "plt.text(-100,-1,df['Timestamp'][0],family='Times New Roman',fontweight='bold',size=17)\n",
        "plt.text(len(df['Timestamp']),-1,df['Timestamp'][len(df['Timestamp'])-1],family='Times New Roman',fontweight='bold',size=17)\n",
        "plt.legend(['Insulin','Carbohydrate','Insulin + Carbohydrate'],loc='best',fontsize='x-small',handles=[red_line,green_line,blue_line])\n",
        "#plt.text(-100,-1,df['Timestamp'][14],family='Times New Roman',\n",
        "\n",
        "#         fontweight='bold',size=20)\n",
        "\n",
        "#y=70\n",
        "#plt.plot(y,'b--',linestyles='--'),\n",
        "#plt.hlines(70,linestyles='--')\n",
        "plt.show()"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:53:46.389356Z",
          "start_time": "2021-01-25T01:53:46.265685Z"
        },
        "id": "eXer8h_dq9q7"
      },
      "source": [
        "for i in range(len(df)):\n",
        "    if isNaN(df['Insulin'][i]):\n",
        "        df['Insulin'][i]=1\n",
        "    else:\n",
        "        df['Insulin'][i]=0\n",
        "        \n",
        "     "
      ],
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:53:51.043530Z",
          "start_time": "2021-01-25T01:53:50.874984Z"
        },
        "id": "rPq3Eljfq9q8"
      },
      "source": [
        "for i in range(len(df)):\n",
        "    if isNaN(df['Meal'][i]):\n",
        "        df['Meal'][i]=1\n",
        "    else:\n",
        "        df['Meal'][i]=0\n",
        "        "
      ],
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:53:51.075446Z",
          "start_time": "2021-01-25T01:53:51.045526Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "gwSdbaC-q9q8",
        "outputId": "d61228d7-fec6-4287-824f-b5a9a07e0bb7"
      },
      "source": [
        "df[1900:1950]"
      ],
      "execution_count": 86,
      "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>Timestamp</th>\n",
              "      <th>Event</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>Meal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Timestamp, Event, Glucose, Insulin, Meal]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QxwcSjuqtMXO"
      },
      "source": [
        "def fix_glucose_value(df,col):\r\n",
        "    for i, row in df.iterrows():\r\n",
        "        val = row[col]\r\n",
        "        \r\n",
        "        df.loc[i,col] = math.floor(val)"
      ],
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sIkC9Oi-t-Rh"
      },
      "source": [
        "fix_glucose_value(df,'Glucose')"
      ],
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KZ8pAdTfq9q8"
      },
      "source": [
        "## 이제 여기부터 DL 모델 돌려볼 것 \n",
        "+ 일단 모델부터 간단하게 돌려보고 성능 측정\n",
        "+ 그리고 전처리 해가면서 성능 비교"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:53:54.756503Z",
          "start_time": "2021-01-25T01:53:54.742541Z"
        },
        "id": "TDvQ2BGIq9q8"
      },
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import random as rn"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:53:58.949108Z",
          "start_time": "2021-01-25T01:53:58.935145Z"
        },
        "id": "qlRi61Seq9q8"
      },
      "source": [
        "# 재현 가능한 결과를 만들기 위해 시드값 설정 \n",
        "import os \n",
        "import random\n",
        "seed_num = 42\n",
        "os.environ['PYTHONHASHSEED']=str(seed_num)\n",
        "\n",
        "random.seed(seed_num)\n",
        "np.random.seed(seed_num)\n",
        "tf.random.set_seed(seed_num)"
      ],
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:54:00.826286Z",
          "start_time": "2021-01-25T01:54:00.780972Z"
        },
        "id": "pUArI0P3q9q8"
      },
      "source": [
        "from tensorflow.compat.v1.keras import backend as K\n",
        "\n",
        "session_conf = tf.compat.v1.ConfigProto(intra_op_parallelism_threads=1,\n",
        "                             inter_op_parallelism_threads=1)\n",
        "sess=tf.compat.v1.Session(graph=tf.compat.v1.get_default_graph(),config=session_conf)\n",
        "K.set_session(sess)"
      ],
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:54:01.331935Z",
          "start_time": "2021-01-25T01:54:01.317971Z"
        },
        "id": "88Z8zIskq9q8"
      },
      "source": [
        "#from keras import backend as K\n",
        "#session_conf = tf.compat.v1.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)\n",
        "#sess = tf.compat.v1.Session(graph=tf.compat.v1.get_default_graph(), config=session_conf)\n",
        "#tf.compat.v1.keras.backend.set_session(sess)\n"
      ],
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:54:02.810348Z",
          "start_time": "2021-01-25T01:54:02.797382Z"
        },
        "id": "Aqk7ufhwq9q8"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "def RMSE(y_test,y_predict):\n",
        "    return np.sqrt(mean_squared_error(y_test,y_predict))"
      ],
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_b7JkM922JMT"
      },
      "source": [
        "## 나이브 메소드"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZRPsJpyM2os6"
      },
      "source": [
        "### 예를 들어 단순히 t 시점에 t - 6 (sampling rate가 5분이므로 30분), t + 1 시점에 t - 5값을 가져오는 naive한 방법"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d8haBBPH-kwK"
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "ExecuteTime": {
          "end_time": "2021-01-25T01:54:14.533048Z",
          "start_time": "2021-01-25T01:54:14.517090Z"
        },
        "id": "y_NuM1bSq9q9"
      },
      "source": [
        "raw_seq = df['Glucose']\n",
        "#raw_seq=raw_seq.to_numpy(dtype='int64')"
      ],
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N56mfk97embM"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\r\n",
        "def RMSE(y_test,y_predict):\r\n",
        "    return np.sqrt(mean_squared_error(y_test,y_predict))"
      ],
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g0xg8dkjd7dd"
      },
      "source": [
        "### train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "85QVOwUGVkOH"
      },
      "source": [
        "TRAIN_SPLIT=int(len(raw_seq)*0.7)"
      ],
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dxRNiSR9WAZg",
        "outputId": "7099b7c9-5fda-4fa8-d0d4-0530670c4d22"
      },
      "source": [
        "TRAIN_SPLIT"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "788"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kg-_x6aUTqLh"
      },
      "source": [
        "train, test = train_test_split(raw_seq,train_size = TRAIN_SPLIT,shuffle=False,random_state=42)"
      ],
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-wRSbBNPT39y"
      },
      "source": [
        "#test[:5]"
      ],
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7w0cU1f3VS4K"
      },
      "source": [
        "#raw_seq[731:740]"
      ],
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EnNrTu5AT95v"
      },
      "source": [
        "#test[:10]"
      ],
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_2AsoeB0UfKJ",
        "outputId": "4fd1b0c3-0432-4886-ea37-db301b68993b"
      },
      "source": [
        "train.shape"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(788,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jva9P7oMeeOm"
      },
      "source": [
        "#### 15 MIN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ci_E-knRwXx6"
      },
      "source": [
        "new_list = []\r\n",
        "for i in range(len(train)):\r\n",
        "  if i < 3:\r\n",
        "    new_list.append(train[i])\r\n",
        "  else:\r\n",
        "    new_list.append(train[i-3])\r\n",
        "  \r\n",
        "  \r\n",
        "\r\n",
        "\r\n",
        "  "
      ],
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G21gpHK7Zwiy"
      },
      "source": [
        "#test =test.reset_index()"
      ],
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O8EHpSxzemba",
        "outputId": "a3dfe1da-cdd6-4c6e-c170-08fa6f8cbe19"
      },
      "source": [
        "# 15분\r\n",
        "RMSE(new_list,train)"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16.470362553374287"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GBSm0jwPe4RZ"
      },
      "source": [
        "#### 30 MIN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zf1lQvqae5pk"
      },
      "source": [
        "new_list = []\r\n",
        "for i in range(len(train)):\r\n",
        "  if i < 6:\r\n",
        "    new_list.append(train[i])\r\n",
        "  else:\r\n",
        "    new_list.append(train[i-6])\r\n",
        "  "
      ],
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fg_rAVRKe8cd",
        "outputId": "11989641-142a-4f9d-fdb0-f3d26aa0223c"
      },
      "source": [
        "# 30분\r\n",
        "RMSE(new_list,train)"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "27.327655615169768"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ap09_eEgfCDa"
      },
      "source": [
        "#### 60 MIN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i6nzr3LRgSNK"
      },
      "source": [
        "new_list = []\r\n",
        "for i in range(len(train)):\r\n",
        "  if i < 12:\r\n",
        "    new_list.append(train[i])\r\n",
        "  else:\r\n",
        "    new_list.append(train[i-12])\r\n",
        "  "
      ],
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TWCufRHxgUIV",
        "outputId": "f6bdeed5-4fae-45fe-cf7b-b14f82f325a8"
      },
      "source": [
        "# 60분\r\n",
        "RMSE(new_list,train)"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "43.22231809755104"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bn-Qy5U_iryl",
        "outputId": "740c108d-e055-4857-e073-9c266c089b9c"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1126, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ie6pKo0CfSGY"
      },
      "source": [
        "### test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G3-UiMS_fSGZ"
      },
      "source": [
        "TRAIN_SPLIT=int(len(raw_seq)*0.7)"
      ],
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1-dADW8tfSGa",
        "outputId": "3fc81766-acd9-4914-82ba-639930883b69"
      },
      "source": [
        "TRAIN_SPLIT"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "788"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e1l1dwztfSGc",
        "outputId": "16d9019b-3aaf-4592-ea14-2629b5550147"
      },
      "source": [
        "test.shape"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(338,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uRcfu1Z68Kja"
      },
      "source": [
        "test2 = []\r\n",
        "for i in range(len(test)):\r\n",
        "  test2.append(test[i+len(train)])\r\n"
      ],
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pLR9qpEnfSGd"
      },
      "source": [
        "#### 15 MIN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nrgTaU2g-Wrj"
      },
      "source": [
        "new_list = []\r\n",
        "for i in range(len(test)):\r\n",
        "  if i < 3:\r\n",
        "    new_list.append(test2[i])\r\n",
        "  else:\r\n",
        "    new_list.append(test2[i-3])"
      ],
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VmHOV7T5fSGd"
      },
      "source": [
        "#test =test.reset_index()\r\n",
        "#new_list[:15]"
      ],
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WhtKubc4-f8_"
      },
      "source": [
        "#test2[:15]"
      ],
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "78rkymcrfSGe",
        "outputId": "01e9a66e-c94e-4384-da48-94d259d61546"
      },
      "source": [
        "# 15분\r\n",
        "RMSE(new_list,test2)"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17.052390555832314"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fnGpURJ6fSGe"
      },
      "source": [
        "#### 30 MIN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V8PvpSb6-pPO"
      },
      "source": [
        "new_list = []\r\n",
        "for i in range(len(test)):\r\n",
        "  if i < 6:\r\n",
        "    new_list.append(test2[i])\r\n",
        "  else:\r\n",
        "    new_list.append(test2[i-6])"
      ],
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sx2fveThfSGf",
        "outputId": "69dfb64e-9855-4f87-be8b-db83e3297902"
      },
      "source": [
        "# 30분\r\n",
        "RMSE(new_list,test2)"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26.315001233912728"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JsNuh7GkfSGf"
      },
      "source": [
        "#### 60 MIN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e3L23Jab-xeU"
      },
      "source": [
        "new_list = []\r\n",
        "for i in range(len(test)):\r\n",
        "  if i < 12:\r\n",
        "    new_list.append(test2[i])\r\n",
        "  else:\r\n",
        "    new_list.append(test2[i-12])"
      ],
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "frUZ32p-fSGg",
        "outputId": "051e11fd-1056-4702-acd6-c5afb0658a38"
      },
      "source": [
        "# 60분\r\n",
        "RMSE(new_list,test2)"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37.1164009426414"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fre6o30E_ELH"
      },
      "source": [
        ""
      ],
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 633
        },
        "id": "wUci25QZ5-tC",
        "outputId": "ca3aa10e-f0f1-49b2-89e8-4dfeca905566"
      },
      "source": [
        "# 테스트셋\r\n",
        "plt.figure(figsize=(12,9))\r\n",
        "plt.plot(test2,label='actual')\r\n",
        "plt.plot(new_list,label='naive')\r\n",
        "plt.axhline(y=70,c='r',linestyle='--',linewidth=1)\r\n",
        "plt.axhline(y=180,c='r',linestyle='--',linewidth=1)\r\n",
        "plt.axhline(y=400,c='r',linestyle='--',linewidth=1)\r\n",
        "plt.text(len(test),75,'Hypoglycemic Threshold',family='Times New Roman',size=15)\r\n",
        "plt.text(len(test),185,'Hyperglycemic Threshold',family='Times New Roman',size=15)\r\n",
        "#plt.text(len(predict_result),75,'Hypoglycemic Threshold',family='Times New Roman',size=15)\r\n",
        "plt.text(-50,-1,df['Timestamp'][TRAIN_SPLIT],family='Times New Roman',fontweight='bold',size=17)\r\n",
        "plt.text(len(df['Timestamp'])-TRAIN_SPLIT-10,-1,df['Timestamp'][len(df['Timestamp'])-1],family='Times New Roman',fontweight='bold',size=17)\r\n",
        "plt.text(len(test),405,'Too High ( Glucose value 400 + )',family='Times New Roman',size=15)\r\n",
        "plt.title('GRU - Validation set',family='Times New Roman')\r\n",
        "plt.legend(loc='best')\r\n",
        "\r\n",
        "plt.show()"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}