LightGBM_Classifier_forPull.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.signal import find_peaks, peak_prominences\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>HbA1c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13</td>\n",
       "      <td>MALE</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>5.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>MALE</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9</td>\n",
       "      <td>MALE</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>MALE</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>MALE</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>MALE</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>MALE</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID  Gender  HbA1c\n",
       "0   13    MALE    5.7\n",
       "1    1  FEMALE    5.5\n",
       "2    3  FEMALE    5.9\n",
       "3    4  FEMALE    6.4\n",
       "4    5  FEMALE    5.7\n",
       "5    2    MALE    5.6\n",
       "6    6  FEMALE    5.8\n",
       "7    7  FEMALE    5.3\n",
       "8    8  FEMALE    5.6\n",
       "9   10  FEMALE    6.0\n",
       "10   9    MALE    6.1\n",
       "11  11    MALE    6.0\n",
       "12  12    MALE    5.6\n",
       "13  14    MALE    5.5\n",
       "14  15  FEMALE    5.5\n",
       "15  16    MALE    5.5"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dg_df = pd.read_csv('./big-ideas-lab-glycemic-variability-and-wearable-device-data-1.1.2/Demographics.csv')\n",
    "dg_df.head(16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "추후 Feature Enginnering에 이용될 계산 함수 정의"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "###### ACC\n",
    "# 가속도 벡터 magnitude 계산 함수\n",
    "def calculate_mag(acc_x, acc_y, acc_z):\n",
    "    acc_mag = np.sqrt(acc_x ** 2   acc_y ** 2   acc_z ** 2)\n",
    "    return acc_mag\n",
    "\n",
    "###### Activity bouts\n",
    "def exercisepts(acc, hr, time): #acc and hr must be same length, acc must be magnitude\n",
    "    \"\"\"\n",
    "        calculates activity bouts using accelerometry and heart rate\n",
    "        Args:\n",
    "            acc (pandas.DataFrame column or pandas series): accelerometry column\n",
    "            hr (pandas.DataFrame column or pandas series): heart rate column\n",
    "            time (pandas.DataFrame column or pandas series): time column\n",
    "        Returns:\n",
    "            countbouts (IntegerType): the number of acitvity bouts total\n",
    "            returndf (pandas.DataFrame): a pandas dataframe with time and activity bouts (designated as a '1') to easily integrate with your data workflow\n",
    "    \"\"\"  \n",
    "    \n",
    "    exercisepoints = []\n",
    "    for z in range(len(acc)):\n",
    "        if acc.iloc[z] > np.mean(acc.iloc[0:z]):\n",
    "            if hr.iloc[z] > np.mean(hr.iloc[0:z]):\n",
    "                exercisepoints.append(1)\n",
    "            else:\n",
    "                exercisepoints.append(0)\n",
    "        else:\n",
    "            exercisepoints.append(0)\n",
    "            \n",
    "    returndf = pd.DataFrame()\n",
    "    returndf['datetime'] = time\n",
    "    returndf['Activity Bouts'] = exercisepoints\n",
    "    \n",
    "    countbouts = len(exercisepoints)\n",
    "    return countbouts, returndf\n",
    "\n",
    "###### EDA\n",
    "def peak_eda(eda):\n",
    "    peaks_idx, properties = find_peaks(eda, height=0, distance=4, prominence=0.3)\n",
    "    peak_eda = len(peaks_idx)\n",
    "    return peak_eda\n",
    "\n",
    "'''\n",
    "# rolling window에서 피크를 찾는 함수\n",
    "def find_peaks_in_rolling_window(data, window_size):\n",
    "    number_of_windows = 0\n",
    "    peaks_in_window = 0\n",
    "    for window_data in data.rolling(window=window_size):\n",
    "        peaks, _ = find_peaks(window_data[' eda'], height=0, distance=4, prominence=0.3)\n",
    "        if peaks.any():\n",
    "            peaks_in_window  = len(peaks)\n",
    "        number_of_windows  = 1\n",
    "    return number_of_windows, peaks_in_window\n",
    "'''\n",
    "\n",
    "# PersHigh, PersLow, PersNorm 분류 함수\n",
    "def classify_glucose(value, mean, std):\n",
    "    if value > mean   std:\n",
    "        return 'PersHigh'\n",
    "    elif value < mean - std:\n",
    "        return 'PersLow'\n",
    "    else:\n",
    "        return 'PersNorm'\n",
    "    \n",
    "###### IBI\n",
    "def nn50(ibi):\n",
    "    differences = abs(np.diff(ibi))\n",
    "    n = np.sum(differences > 50)\n",
    "    nn50 = round(n,1)\n",
    "    return nn50\n",
    "\n",
    "def pnn50(ibi):\n",
    "    differences = abs(np.diff(ibi))\n",
    "    n = np.sum(differences > 50)\n",
    "    p=0\n",
    "    if len(differences) != 0:\n",
    "        p = (n / len(differences)) * 100\n",
    "    pnn50 = round(p, 1)\n",
    "    return pnn50\n",
    "\n",
    "def rmssd(ibi):\n",
    "    differences = abs(np.diff(ibi))\n",
    "    rmssd = 0\n",
    "    if len(differences) != 0:\n",
    "        rmssd = np.sqrt(np.sum(np.square(differences)) / len(differences))\n",
    "    rmssd = round(rmssd,1)\n",
    "    return rmssd\n",
    "\n",
    "def sdnn(ibi):\n",
    "    if ibi.isnull().any():\n",
    "        return np.nan\n",
    "    #return round(np.sqrt(np.var(ibi, ddof=1)) * 10) / 10\n",
    "    return round(np.sqrt(np.var(ibi, ddof=1)), 1)\n",
    "\n",
    "###### circaidan rhythm\n",
    "def crhythm(time, timeformat='%Y-%m-%d %H:%M:%S'):\n",
    "    \"\"\"\n",
    "        computes 'minutes from midnight' and 'hours from midnight'- these features will allow you to account for circaidan rhythm effects\n",
    "        Args:\n",
    "             time (pandas.DataFrame): time column\n",
    "             timeformat (String): default = '%Y-%m-%d %H:%M:%S.%f'; format of timestamp in time column\n",
    "        Returns:\n",
    "            hourfrommid (ListType): Hours from midnight, the same length as your time column\n",
    "            minfrommid (ListType): Minutes from midnight, the same length as your time column\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    df = pd.DataFrame()\n",
    "    df['Time'] =  pd.to_datetime(time, format=timeformat)\n",
    "    df['Timefrommidnight'] =  df['Time'].dt.time\n",
    "    hourfrommid=[]\n",
    "    minfrommid=[]\n",
    "    \n",
    "    for i in range(0, len(df['Timefrommidnight'])):\n",
    "        if pd.isna(df['Timefrommidnight'].iloc[i]):\n",
    "            minfrommid.append(0)\n",
    "            hourfrommid.append(0)\n",
    "        else:\n",
    "            minfrommid.append(int(df['Timefrommidnight'].iloc[i].strftime('%H:%M:%S')[0:2])*60   int(df['Timefrommidnight'].iloc[i].strftime('%H:%M:%S')[3:5])   round(int(df['Timefrommidnight'].iloc[i].strftime('%H:%M:%S')[6:9])/60))\n",
    "            hourfrommid.append(round((int(df['Timefrommidnight'].iloc[i].strftime('%H:%M:%S')[0:2])*60   int(df['Timefrommidnight'].iloc[i].strftime('%H:%M:%S')[3:5])   round(int(df['Timefrommidnight'].iloc[i].strftime('%H:%M:%S')[6:9])/60))/60))\n",
    "\n",
    "    return hourfrommid, minfrommid\n",
    "\n",
    "# Wake Time을 감지하는 함수를 정의합니다.\n",
    "def detect_wake_time(data, threshold=0.0, post_wake_duration='75m', initial_check_duration='25m'):\n",
    "    wake_times = pd.Series(0, index=data.index)\n",
    "    \n",
    "    for timestamp in data.index:\n",
    "        if timestamp != data.index[0] and data.loc[timestamp, 'rolling_slope'] != data['rolling_slope'].iloc[data.index.get_loc(timestamp) - 1]:\n",
    "            initial_check_end = timestamp   pd.Timedelta(initial_check_duration)\n",
    "            post_wake_end = timestamp   pd.Timedelta(post_wake_duration)\n",
    "            \n",
    "            if all(data.loc[timestamp:initial_check_end, 'rolling_avg'] > data['rolling_avg'].iloc[data.index.get_loc(timestamp) - 1]):\n",
    "                if all(data.loc[timestamp:post_wake_end, 'rolling_avg'] > data['rolling_avg'].iloc[data.index.get_loc(timestamp) - 1]):\n",
    "                    if data.loc[timestamp, 'binary'] == 1:\n",
    "                        wake_times.loc[timestamp] = 1\n",
    "\n",
    "    return wake_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 빈 DataFrame 생성\n",
    "df = pd.DataFrame(columns=[\n",
    "    'calories2hr',\n",
    "    'protein2hr',\n",
    "    'sugar2hr',\n",
    "    'carbs2hr',\n",
    "    'calories8hr',\n",
    "    'protein8hr',\n",
    "    'sugar8hr',\n",
    "    'carbs8hr',\n",
    "    'calories24hr',\n",
    "    'protein24hr',\n",
    "    'sugar24hr',\n",
    "    'carbs24hr',\n",
    "    'eat',\n",
    "    'eatcnt2hr',\n",
    "    'eatcnt8hr',\n",
    "    'eatcnt24hr',\n",
    "    'eatmean2hr',\n",
    "    'eatmean8hr',\n",
    "    'eatmean24hr',\n",
    "    'acc_mean',\n",
    "    'acc_std',\n",
    "    'acc_min',\n",
    "    'acc_max',\n",
    "    'acc_q1g',\n",
    "    'acc_q3g',\n",
    "    'acc_skew',\n",
    "    'acc_mean_2hrs',\n",
    "    'acc_max_2hrs',\n",
    "    'eda_mean',\n",
    "    'eda_std',\n",
    "    'eda_min',\n",
    "    'eda_max',\n",
    "    'eda_q1g',\n",
    "    'eda_q3g',\n",
    "    'eda_skew',\n",
    "    'PeakEDA',\n",
    "    'PeakEDA2hr_sum',\n",
    "    'PeakEDA2hr_mean',\n",
    "    'hr_mean',\n",
    "    'hr_std',\n",
    "    'hr_min',\n",
    "    'hr_max',\n",
    "    'hr_q1g',\n",
    "    'hr_q3g',\n",
    "    'hr_skew',\n",
    "    'maxHRV',\n",
    "    'minHRV',\n",
    "    'medianHRV',\n",
    "    'meanHRV',\n",
    "    'SDNN',\n",
    "    'NN50',\n",
    "    'pNN50',\n",
    "    'RMSSD',\n",
    "    'temp_mean',\n",
    "    'temp_std',\n",
    "    'temp_min',\n",
    "    'temp_max',\n",
    "    'temp_q1g',\n",
    "    'temp_q3g',\n",
    "    'temp_skew',\n",
    "    'Activity_bouts',\n",
    "    'Activity24',\n",
    "    'Activity1hr',\n",
    "    'Minfrommid',\n",
    "    'Hourfrommid'\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Warnings\n",
    "\n",
    "* 003 데이터셋 Food_Log에 칼럼 누락되어 있음 추가 필요 (단, time_end 칼럼은 없어야 하며, 뒷부분의 4개 칼럼은 {calorie,total_carb,sugar,protein}으로 구성됨).\n",
    "* 001 데이터셋의 HR 대해서 datetime 포맷이 다름 (hr_df['datetime']=pd.to_datetime(hr_df['datetime'], format='%m/%d/%y %H:%M')).\n",
    "* 012 데이터셋 Food_Log의 마지막 행에 데이터 누락됨.\n",
    "    ~~~\n",
    "    2020-05-11,07:00:00,,,Boost,,,,654.0,82.0,5.6,40.0,26.0,26.0\n",
    "    -> 2020-05-11,07:00:00, 2020-05-11 07:00:00,,Boost,,,,654.0,82.0,5.6,40.0,26.0,26.0\n",
    "    ~~~\n",
    "* 013 데이터셋 Food_Log의 date와 datetime 포맷이 다름.\n",
    "* 중요 : 007 데이터셋의 Food_Log 날짜가 다른 데이터셋들의 측정 날짜와 전혀 일치하지 않음."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def validate_daily_dfs(daily_dfs):\n",
    "    diff_set = set()\n",
    "    accset = set()\n",
    "    edaset = set()\n",
    "    hrset = set()\n",
    "    ibiset = set()\n",
    "    tempset = set()\n",
    "    dexcomset = set()\n",
    "    for idx in range(len(daily_dfs['acc'])):\n",
    "        accset = accset.union(set(date.strftime(\"%Y-%m-%d\") for date in daily_dfs['acc'][idx].index.date))\n",
    "    for idx in range(len(daily_dfs['eda'])):\n",
    "        edaset = edaset.union(set(date.strftime(\"%Y-%m-%d\") for date in daily_dfs['eda'][idx].index.date))\n",
    "    for idx in range(len(daily_dfs['hr'])):\n",
    "        hrset = hrset.union(set(date.strftime(\"%Y-%m-%d\") for date in daily_dfs['hr'][idx].index.date))\n",
    "    for idx in range(len(daily_dfs['ibi'])):\n",
    "        ibiset = ibiset.union(set(date.strftime(\"%Y-%m-%d\") for date in daily_dfs['ibi'][idx].index.date))\n",
    "    for idx in range(len(daily_dfs['temp'])):\n",
    "        tempset = tempset.union(set(date.strftime(\"%Y-%m-%d\") for date in daily_dfs['temp'][idx].index.date))\n",
    "    for idx in range(len(daily_dfs['dexcom'])):\n",
    "        dexcomset = dexcomset.union(set(date.strftime(\"%Y-%m-%d\") for date in daily_dfs['dexcom'][idx].index.date))\n",
    "    ## 차집합 연산 : 하나라도 예외적인 다른 날짜가 있다면 제외시켜야 함.\n",
    "    diff_set = diff_set.union(dexcomset.difference(accset))\n",
    "    diff_set = diff_set.union(dexcomset.difference(edaset))\n",
    "    diff_set = diff_set.union(dexcomset.difference(hrset))\n",
    "    diff_set = diff_set.union(dexcomset.difference(ibiset))\n",
    "    diff_set = diff_set.union(dexcomset.difference(tempset))\n",
    "    print(diff_set)\n",
    "\n",
    "    if len(diff_set) > 0:\n",
    "        ## diffset의 date값을 갖고 있는 daily_dfs 딕셔너리 내 value 의 리스트 원소(데이터프레임)를 제거\n",
    "        if len(diff_set & accset) > 0:\n",
    "            indexes_to_remove = []\n",
    "            for idx in range(len(daily_dfs['acc'])):\n",
    "                for date in list(date.strftime(\"%Y-%m-%d\") for date in daily_dfs['acc'][idx].index.date):\n",
    "                    if date in diff_set:\n",
    "                        indexes_to_remove.append(idx)\n",
    "            ## 제거 대상을 제거한 리스트를 새로 할당\n",
    "            daily_dfs['acc'] = [daily_dfs['acc'][i] for i in range(len(daily_dfs['acc'])) if i not in indexes_to_remove]\n",
    "\n",
    "        if len(diff_set & edaset) > 0:\n",
    "            indexes_to_remove = []\n",
    "            for idx in range(len(daily_dfs['eda'])):\n",
    "                for date in list(date.strftime(\"%Y-%m-%d\") for date in daily_dfs['eda'][idx].index.date):\n",
    "                    if date in diff_set:\n",
    "                        indexes_to_remove.append(idx)\n",
    "            ## 제거 대상을 제거한 리스트를 새로 할당\n",
    "            daily_dfs['eda'] = [daily_dfs['eda'][i] for i in range(len(daily_dfs['eda'])) if i not in indexes_to_remove]\n",
    "\n",
    "        if len(diff_set & hrset) > 0:\n",
    "            indexes_to_remove = []\n",
    "            for idx in range(len(daily_dfs['hr'])):\n",
    "                for date in list(date.strftime(\"%Y-%m-%d\") for date in daily_dfs['hr'][idx].index.date):\n",
    "                    if date in diff_set:\n",
    "                        indexes_to_remove.append(idx)\n",
    "            ## 제거 대상을 제거한 리스트를 새로 할당\n",
    "            daily_dfs['hr'] = [daily_dfs['hr'][i] for i in range(len(daily_dfs['hr'])) if i not in indexes_to_remove]\n",
    "\n",
    "        if len(diff_set & ibiset) > 0:\n",
    "            indexes_to_remove = []\n",
    "            for idx in range(len(daily_dfs['ibi'])):\n",
    "                for date in list(date.strftime(\"%Y-%m-%d\") for date in daily_dfs['ibi'][idx].index.date):\n",
    "                    if date in diff_set:\n",
    "                        indexes_to_remove.append(idx)\n",
    "            ## 제거 대상을 제거한 리스트를 새로 할당\n",
    "            daily_dfs['ibi'] = [daily_dfs['ibi'][i] for i in range(len(daily_dfs['ibi'])) if i not in indexes_to_remove]\n",
    "\n",
    "        if len(diff_set & tempset) > 0:\n",
    "            indexes_to_remove = []\n",
    "            for idx in range(len(daily_dfs['temp'])):\n",
    "                for date in list(date.strftime(\"%Y-%m-%d\") for date in daily_dfs['temp'][idx].index.date):\n",
    "                    if date in diff_set:\n",
    "                        indexes_to_remove.append(idx)\n",
    "            ## 제거 대상을 제거한 리스트를 새로 할당\n",
    "            daily_dfs['temp'] = [daily_dfs['temp'][i] for i in range(len(daily_dfs['temp'])) if i not in indexes_to_remove]\n",
    "        \n",
    "        if len(diff_set & dexcomset) > 0:\n",
    "            indexes_to_remove = []\n",
    "            for idx in range(len(daily_dfs['dexcom'])):\n",
    "                for date in list(date.strftime(\"%Y-%m-%d\") for date in daily_dfs['dexcom'][idx].index.date):\n",
    "                    if date in diff_set:\n",
    "                        indexes_to_remove.append(idx)\n",
    "            ## 제거 대상을 제거한 리스트를 새로 할당\n",
    "            daily_dfs['dexcom'] = [daily_dfs['dexcom'][i] for i in range(len(daily_dfs['dexcom'])) if i not in indexes_to_remove]\n",
    "\n",
    "        return False, daily_dfs\n",
    "    return True, daily_dfs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "24시간 윈도우"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ibimultiplier = 1000 # Transform ibi to milliseconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID : 1\n",
      "set()\n",
      "len acc == hr : True\n",
      "2020-02-14 17:33:30, 2020-02-14 17:43:30, 2020-02-14 17:48:31, 2020-02-14 19:48:30, 2020-02-14 19:58:30, 2020-02-14 20:03:30, 2020-02-14 21:28:30, 2020-02-14 21:33:31, 2020-02-14 21:38:30, 2020-02-14 21:53:31, 2020-02-14 21:58:30, 2020-02-14 22:03:30, 2020-02-14 22:13:30, 2020-02-14 22:18:30, 2020-02-14 22:23:30, 2020-02-14 22:38:30, 2020-02-14 22:43:30, 2020-02-14 22:48:30, 2020-02-14 22:58:30, 2020-02-14 23:23:30, 2020-02-14 23:58:30, 2020-02-15 00:03:30, 2020-02-15 00:08:31, 2020-02-15 00:13:30, 2020-02-15 00:18:30, 2020-02-15 00:23:31, 2020-02-15 12:48:30, 2020-02-15 12:58:30, 2020-02-16 15:18:29, 2020-02-16 15:23:30, 2020-02-16 15:28:30, 2020-02-16 15:38:30, 2020-02-16 15:43:30, 2020-02-16 15:48:30, 2020-02-16 15:53:30, 2020-02-16 15:58:30, 2020-02-16 16:13:30, 2020-02-16 16:18:30, 2020-02-16 16:33:30, 2020-02-16 16:38:30, 2020-02-16 17:08:29, 2020-02-16 17:13:30, 2020-02-16 17:18:30, 2020-02-16 17:43:29, 2020-02-16 17:48:30, 2020-02-16 17:53:29, 2020-02-16 17:58:29, 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2020-02-22 12:38:24, 2020-02-22 12:48:23, 2020-02-22 15:18:23, 2020-02-22 15:33:23, 2020-02-22 16:08:23, 2020-02-22 16:28:22, 2020-02-22 16:38:22, 2020-02-22 16:43:22, 2020-02-22 16:58:22, 2020-02-22 17:03:23, 2020-02-22 17:08:23, 2020-02-22 17:13:22, 2020-02-22 17:18:22, 2020-02-22 17:23:23, 2020-02-22 17:28:23, 2020-02-22 17:33:23, 2020-02-22 17:38:23, 2020-02-22 17:43:23, 2020-02-22 17:48:22, 2020-02-22 17:53:23\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\smk03\\AppData\\Local\\Temp\\ipykernel_7448\\1059198651.py:383: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, base_df], ignore_index=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID : 2\n",
      "set()\n",
      "len acc == hr : True\n",
      "2020-02-22 11:23:34, 2020-02-22 15:08:33, 2020-02-22 15:38:34, 2020-02-22 15:43:34, 2020-02-22 15:48:33, 2020-02-22 15:53:33, 2020-02-22 16:18:33, 2020-02-22 16:23:33, 2020-02-22 16:28:33, 2020-02-22 16:33:33, 2020-02-22 16:38:33, 2020-02-22 16:43:33, 2020-02-22 16:48:33, 2020-02-22 16:58:33, 2020-02-22 17:18:33, 2020-02-22 17:33:33, 2020-02-22 17:38:33, 2020-02-22 17:43:33, 2020-02-22 17:48:33, 2020-02-22 17:53:33, 2020-02-22 17:58:33, 2020-02-22 18:03:33, 2020-02-22 18:18:33, 2020-02-22 18:28:33, 2020-02-22 18:33:33, 2020-02-22 18:38:34, 2020-02-22 18:58:33, 2020-02-22 19:03:34, 2020-02-22 19:08:34, 2020-02-22 19:48:33, 2020-02-22 19:58:33, 2020-02-22 20:18:33, 2020-02-22 20:58:33, 2020-02-22 22:33:33, 2020-02-23 00:43:34, 2020-02-23 01:23:33, 2020-02-23 01:33:33, 2020-02-23 15:13:32, 2020-02-23 15:18:33, 2020-02-23 15:23:33, 2020-02-23 15:28:32, 2020-02-23 15:38:33, 2020-02-23 15:48:33, 2020-02-23 16:03:33, 2020-02-23 16:08:32, 2020-02-23 16:18:33, 2020-02-23 16:33:31, 2020-02-23 16:38:31, 2020-02-23 16:43:32, 2020-02-23 16:48:31, 2020-02-23 16:53:32, 2020-02-23 16:58:32, 2020-02-23 17:03:32, 2020-02-23 17:08:32, 2020-02-23 17:13:32, 2020-02-23 17:18:32, 2020-02-23 21:08:32, 2020-02-23 21:13:32, 2020-02-23 21:18:31, 2020-02-23 21:23:32, 2020-02-23 21:28:32, 2020-02-23 21:33:32, 2020-02-23 21:38:32, 2020-02-23 21:43:31, 2020-02-23 21:48:32, 2020-02-23 21:53:31, 2020-02-23 21:58:31, 2020-02-24 01:53:32, 2020-02-24 02:08:32, 2020-02-24 02:58:32, 2020-02-24 03:43:32, 2020-02-24 03:58:32, 2020-02-24 04:03:32, 2020-02-24 04:08:32, 2020-02-24 15:58:32, 2020-02-24 16:58:32, 2020-02-24 17:03:32, 2020-02-24 19:13:30, 2020-02-24 19:33:31, 2020-02-24 19:58:30, 2020-02-24 20:13:30, 2020-02-24 20:48:31, 2020-02-24 20:53:30, 2020-02-24 20:58:31, 2020-02-24 21:03:31, 2020-02-24 21:08:31, 2020-02-24 21:13:31, 2020-02-24 21:18:31, 2020-02-24 21:23:31, 2020-02-24 21:28:31, 2020-02-24 21:33:31, 2020-02-24 21:43:31, 2020-02-24 21:48:30, 2020-02-24 21:53:31, 2020-02-24 22:03:30, 2020-02-24 22:08:30, 2020-02-24 22:13:30, 2020-02-25 17:18:30, 2020-02-25 18:48:30, 2020-02-25 18:53:30, 2020-02-25 18:58:30, 2020-02-25 19:03:29, 2020-02-25 19:08:30, 2020-02-25 19:13:30, 2020-02-25 19:18:30, 2020-02-25 19:23:30, 2020-02-25 19:28:30, 2020-02-25 19:33:30, 2020-02-25 19:38:30, 2020-02-25 19:43:30, 2020-02-25 19:48:30, 2020-02-25 19:53:30, 2020-02-25 19:58:30, 2020-02-25 20:03:30, 2020-02-25 20:08:31, 2020-02-25 20:13:30, 2020-02-26 00:08:29, 2020-02-26 15:28:29, 2020-02-26 15:33:29, 2020-02-26 15:38:28, 2020-02-26 16:08:29, 2020-02-26 16:48:28, 2020-02-26 16:58:28, 2020-02-26 17:03:29, 2020-02-26 19:23:28, 2020-02-26 21:13:29, 2020-02-26 21:43:29, 2020-02-26 21:48:29, 2020-02-26 21:53:29, 2020-02-26 22:03:29, 2020-02-26 22:08:29, 2020-02-26 22:13:29, 2020-02-26 22:18:28, 2020-02-26 22:23:29, 2020-02-26 22:43:28, 2020-02-26 23:03:29, 2020-02-26 23:08:28, 2020-02-26 23:28:29, 2020-02-26 23:38:28, 2020-02-26 23:43:28, 2020-02-26 23:48:28, 2020-02-26 23:53:28, 2020-02-27 02:38:28, 2020-02-27 03:13:28, 2020-02-27 03:23:27, 2020-02-27 03:43:28, 2020-02-27 04:08:28, 2020-02-27 04:13:27, 2020-02-27 04:18:28, 2020-02-27 04:23:29, 2020-02-27 04:48:28, 2020-02-27 05:38:28, 2020-02-27 11:43:28, 2020-02-27 11:53:28, 2020-02-27 11:58:28, 2020-02-27 12:03:28, 2020-02-27 12:08:28, 2020-02-27 12:48:27, 2020-02-27 12:53:27, 2020-02-27 12:58:27, 2020-02-27 13:23:27, 2020-02-27 13:33:27, 2020-02-27 13:38:27, 2020-02-27 17:08:27, 2020-02-27 21:13:27, 2020-02-27 21:18:28, 2020-02-27 21:23:27, 2020-02-27 21:28:28, 2020-02-27 21:33:28, 2020-02-27 21:53:28, 2020-02-27 21:58:28, 2020-02-27 22:23:28, 2020-02-27 22:28:28, 2020-02-27 22:43:28, 2020-02-27 22:48:28, 2020-02-27 23:33:28, 2020-02-27 23:38:28, 2020-02-27 23:43:28, 2020-02-27 23:48:27, 2020-02-27 23:53:27, 2020-02-28 00:43:27, 2020-02-28 00:48:27, 2020-02-28 00:53:27, 2020-02-28 01:58:26, 2020-02-28 02:03:26, 2020-02-28 02:08:26, 2020-02-28 02:13:27, 2020-02-28 02:18:26, 2020-02-28 02:23:26, 2020-02-28 02:28:26, 2020-02-28 02:33:26, 2020-02-28 11:48:26, 2020-02-28 14:18:27, 2020-02-28 14:33:27, 2020-02-28 15:48:26, 2020-02-28 16:13:26, 2020-02-28 16:23:26, 2020-02-28 16:58:26, 2020-02-28 17:28:26, 2020-02-28 17:33:26, 2020-02-28 17:43:25, 2020-02-28 17:48:25, 2020-02-28 17:58:25, 2020-02-28 19:53:25, 2020-02-28 21:03:27, 2020-02-28 21:08:27, 2020-02-28 21:18:26, 2020-02-28 21:23:27, 2020-02-28 23:58:26, 2020-02-29 00:33:26, 2020-02-29 01:03:26, 2020-02-29 01:18:26, 2020-02-29 01:23:26, 2020-02-29 01:28:26, 2020-02-29 01:33:26, 2020-02-29 01:38:26, 2020-02-29 01:43:26, 2020-02-29 01:48:26, 2020-02-29 01:53:26, 2020-02-29 01:58:26, 2020-02-29 02:03:26, 2020-02-29 02:08:26, 2020-02-29 02:13:26, 2020-02-29 02:18:26, 2020-02-29 02:23:26, 2020-02-29 02:28:26, 2020-02-29 02:33:26, 2020-02-29 02:38:26, 2020-02-29 02:43:26, 2020-02-29 02:48:26\n",
      "ID : 3\n",
      "set()\n",
      "len acc == hr : True\n",
      "2020-02-23 10:56:44, 2020-02-23 12:06:44, 2020-02-23 12:11:44, 2020-02-23 12:16:44, 2020-02-23 12:21:44, 2020-02-23 12:41:44, 2020-02-23 12:46:44, 2020-02-23 12:51:44, 2020-02-23 13:01:43, 2020-02-23 13:06:43, 2020-02-23 13:11:43, 2020-02-23 13:16:43, 2020-02-23 13:21:43, 2020-02-23 13:26:43, 2020-02-23 13:31:43, 2020-02-23 13:36:43, 2020-02-23 13:41:43, 2020-02-23 13:46:42, 2020-02-23 13:51:42, 2020-02-23 13:56:42, 2020-02-23 14:11:42, 2020-02-23 14:16:42, 2020-02-23 14:21:42, 2020-02-23 14:26:42, 2020-02-23 14:31:42, 2020-02-23 14:41:42, 2020-02-23 14:46:42, 2020-02-23 14:51:42, 2020-02-23 14:56:42, 2020-02-23 15:01:42, 2020-02-23 19:21:41, 2020-02-23 19:31:42, 2020-02-23 20:01:43, 2020-02-23 20:21:43, 2020-02-23 20:26:43, 2020-02-23 20:36:43, 2020-02-23 21:01:43, 2020-02-23 21:11:42, 2020-02-23 21:16:42, 2020-02-23 21:21:42, 2020-02-23 21:26:43, 2020-02-23 21:31:43, 2020-02-23 21:36:43, 2020-02-23 21:41:42, 2020-02-23 21:46:43, 2020-02-23 21:56:43, 2020-02-23 22:01:42, 2020-02-23 22:06:43, 2020-02-23 22:11:42, 2020-02-24 00:01:43, 2020-02-24 02:06:42, 2020-02-24 02:11:42, 2020-02-24 02:16:42, 2020-02-24 02:21:41, 2020-02-24 02:26:42, 2020-02-24 02:46:41, 2020-02-24 02:51:41, 2020-02-24 02:56:41, 2020-02-24 05:01:41, 2020-02-24 18:31:40, 2020-02-24 18:56:40, 2020-02-24 19:21:40, 2020-02-24 19:31:39, 2020-02-24 19:36:40, 2020-02-24 19:41:40, 2020-02-24 19:46:40, 2020-02-24 19:51:39, 2020-02-24 20:31:40, 2020-02-24 20:36:40, 2020-02-24 20:41:40, 2020-02-24 20:46:40, 2020-02-24 20:51:40, 2020-02-24 20:56:40, 2020-02-24 21:01:40, 2020-02-24 21:06:40, 2020-02-24 21:56:40, 2020-02-24 22:06:41, 2020-02-24 22:11:41, 2020-02-24 22:26:41, 2020-02-24 23:11:40, 2020-02-25 00:16:40, 2020-02-25 00:21:40, 2020-02-25 00:26:40, 2020-02-25 00:31:40, 2020-02-25 00:41:40, 2020-02-26 11:21:38, 2020-02-26 14:51:37, 2020-02-26 15:21:37, 2020-02-26 15:41:38, 2020-02-26 15:51:38, 2020-02-26 15:56:37, 2020-02-26 16:01:37, 2020-02-26 16:16:37, 2020-02-26 16:21:37, 2020-02-26 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2020-02-27 21:16:35, 2020-02-27 22:16:35, 2020-02-27 23:26:34, 2020-02-27 23:46:35, 2020-02-28 00:56:35, 2020-02-28 01:01:34, 2020-02-28 01:06:34, 2020-02-28 01:26:35, 2020-02-28 01:31:35, 2020-02-28 01:36:34, 2020-02-28 02:06:35, 2020-02-28 03:36:35, 2020-02-28 03:41:35, 2020-02-28 09:51:35, 2020-02-28 10:01:34, 2020-02-28 10:06:34, 2020-02-28 10:11:34, 2020-02-28 10:16:35, 2020-02-28 10:26:35, 2020-02-28 10:46:34, 2020-02-28 15:01:34, 2020-02-28 15:11:34, 2020-02-28 15:16:34, 2020-02-28 15:21:34, 2020-02-28 15:26:34, 2020-02-28 15:31:34, 2020-02-28 15:41:34, 2020-02-28 15:46:34, 2020-02-28 15:51:34, 2020-02-28 15:56:34, 2020-02-28 16:01:33, 2020-02-28 16:06:34, 2020-02-28 16:26:34, 2020-02-28 16:31:34, 2020-02-28 16:36:34, 2020-02-28 16:41:34, 2020-02-28 16:46:34, 2020-02-28 16:51:34, 2020-02-28 16:56:34, 2020-02-28 17:01:34, 2020-02-28 17:06:34, 2020-02-28 19:16:33, 2020-02-28 21:06:34, 2020-02-28 21:11:34, 2020-02-28 21:16:33, 2020-02-28 21:21:34, 2020-02-28 21:26:34, 2020-02-28 21:31:33, 2020-02-29 01:51:33, 2020-02-29 02:51:33, 2020-02-29 02:56:33, 2020-02-29 03:01:33, 2020-02-29 03:06:33, 2020-02-29 03:11:33, 2020-02-29 03:16:33, 2020-02-29 03:21:32, 2020-02-29 03:26:33, 2020-02-29 03:31:33, 2020-02-29 03:36:33, 2020-02-29 03:41:33, 2020-02-29 03:46:33, 2020-02-29 03:51:33, 2020-02-29 03:56:33, 2020-02-29 04:01:33, 2020-02-29 04:06:32, 2020-02-29 04:11:33, 2020-02-29 04:16:33, 2020-02-29 04:21:33, 2020-02-29 17:06:31, 2020-02-29 18:01:32, 2020-02-29 20:51:32, 2020-02-29 21:31:32, 2020-02-29 22:06:32, 2020-02-29 22:21:32, 2020-02-29 22:26:32, 2020-02-29 22:36:32, 2020-02-29 22:41:32, 2020-02-29 22:46:32, 2020-02-29 22:51:32, 2020-02-29 22:56:32, 2020-02-29 23:01:32, 2020-02-29 23:06:32, 2020-02-29 23:11:32, 2020-03-01 01:01:32, 2020-03-01 01:06:32, 2020-03-01 01:36:32, 2020-03-01 02:41:31, 2020-03-01 02:46:31, 2020-03-01 02:51:31, 2020-03-01 03:06:31, 2020-03-01 03:11:31, 2020-03-01 03:16:31, 2020-03-01 10:01:31, 2020-03-01 10:06:31, 2020-03-01 10:21:31, 2020-03-01 10:26:31, 2020-03-01 10:31:31, 2020-03-01 10:36:32, 2020-03-01 10:41:31, 2020-03-01 10:46:31, 2020-03-01 11:26:31\n",
      "ID : 4\n",
      "set()\n",
      "len acc == hr : True\n",
      "2020-02-29 19:31:16, 2020-02-29 19:46:15, 2020-02-29 20:11:16, 2020-02-29 20:41:15, 2020-02-29 20:56:16, 2020-02-29 21:21:16, 2020-02-29 21:56:16, 2020-02-29 22:06:16, 2020-02-29 22:26:15, 2020-02-29 22:41:16, 2020-02-29 22:46:16, 2020-02-29 22:51:16, 2020-02-29 22:56:15, 2020-02-29 23:06:16, 2020-02-29 23:11:16, 2020-02-29 23:21:16, 2020-02-29 23:26:16, 2020-02-29 23:31:16, 2020-02-29 23:36:16, 2020-02-29 23:46:16, 2020-02-29 23:51:16, 2020-02-29 23:56:16, 2020-03-01 00:01:16, 2020-03-01 00:06:15, 2020-03-01 00:11:16, 2020-03-01 00:16:16, 2020-03-01 00:31:16, 2020-03-01 00:36:16, 2020-03-01 00:41:16, 2020-03-01 00:46:15, 2020-03-01 00:51:16, 2020-03-01 00:56:16, 2020-03-01 01:06:16, 2020-03-01 01:16:15, 2020-03-01 01:21:16, 2020-03-01 01:31:16, 2020-03-01 01:36:16, 2020-03-01 03:11:16, 2020-03-01 09:26:15, 2020-03-01 09:31:15, 2020-03-01 10:06:15, 2020-03-01 10:11:15, 2020-03-01 10:16:15, 2020-03-01 10:21:14, 2020-03-01 10:56:14, 2020-03-01 11:11:15, 2020-03-01 11:31:15, 2020-03-01 11:36:15, 2020-03-01 11:46:15, 2020-03-01 11:51:15, 2020-03-01 11:56:16, 2020-03-01 12:01:15, 2020-03-01 12:26:15, 2020-03-01 12:46:14, 2020-03-01 14:11:14, 2020-03-01 16:21:14, 2020-03-01 18:06:14, 2020-03-01 18:26:14, 2020-03-01 18:31:14, 2020-03-01 18:41:14, 2020-03-01 22:36:13, 2020-03-01 22:41:12, 2020-03-01 23:46:14, 2020-03-01 23:56:14, 2020-03-02 00:01:14, 2020-03-02 00:41:14, 2020-03-02 01:16:14, 2020-03-02 01:31:14, 2020-03-02 02:21:14, 2020-03-02 02:26:14, 2020-03-02 02:31:15, 2020-03-02 02:36:14, 2020-03-02 02:41:14, 2020-03-02 02:46:13, 2020-03-02 09:41:12, 2020-03-02 09:46:13, 2020-03-02 10:51:13, 2020-03-02 11:06:13, 2020-03-02 11:31:13, 2020-03-02 11:46:13, 2020-03-02 12:01:13, 2020-03-02 12:06:14, 2020-03-02 12:11:13, 2020-03-02 12:16:13, 2020-03-02 12:21:13, 2020-03-02 12:31:12, 2020-03-02 12:36:12, 2020-03-02 13:06:13, 2020-03-02 13:41:12, 2020-03-02 13:46:12, 2020-03-02 16:21:12, 2020-03-03 05:11:11, 2020-03-03 15:06:11, 2020-03-03 16:06:10, 2020-03-03 16:21:10, 2020-03-03 16:41:11, 2020-03-03 16:51:11, 2020-03-03 16:56:10, 2020-03-03 17:06:10, 2020-03-03 17:11:10, 2020-03-03 17:21:11, 2020-03-03 17:36:10, 2020-03-03 17:56:10, 2020-03-03 20:31:11, 2020-03-03 20:46:10, 2020-03-03 20:56:10, 2020-03-03 21:16:10, 2020-03-03 21:21:10, 2020-03-04 00:36:10, 2020-03-04 00:41:10, 2020-03-04 00:46:10, 2020-03-04 01:36:09, 2020-03-04 01:56:10, 2020-03-04 02:01:10, 2020-03-04 02:06:10, 2020-03-04 02:11:10, 2020-03-04 02:16:10, 2020-03-04 02:21:10, 2020-03-04 02:26:10, 2020-03-04 02:31:10, 2020-03-04 02:36:10, 2020-03-04 02:41:10, 2020-03-04 02:46:10, 2020-03-04 02:51:10, 2020-03-04 02:56:10, 2020-03-04 03:01:10, 2020-03-04 03:06:10, 2020-03-04 03:16:10, 2020-03-04 03:21:10, 2020-03-04 03:26:09, 2020-03-04 03:31:10, 2020-03-04 04:01:10, 2020-03-04 04:21:10, 2020-03-04 04:26:10, 2020-03-04 04:31:10, 2020-03-04 04:41:10, 2020-03-04 04:51:10, 2020-03-04 15:26:09, 2020-03-04 16:16:08, 2020-03-04 16:36:08, 2020-03-04 16:51:08, 2020-03-04 16:56:09, 2020-03-04 17:06:09, 2020-03-04 17:11:09, 2020-03-04 17:21:09, 2020-03-04 17:26:08, 2020-03-04 17:31:08, 2020-03-04 17:36:09, 2020-03-04 17:46:09, 2020-03-04 17:56:09, 2020-03-04 18:06:08, 2020-03-04 18:11:09, 2020-03-04 18:16:09, 2020-03-04 18:21:09, 2020-03-04 19:56:09, 2020-03-04 20:06:09, 2020-03-04 20:16:09, 2020-03-04 21:11:09, 2020-03-04 21:56:07, 2020-03-04 22:11:08, 2020-03-04 22:56:08, 2020-03-05 04:41:08, 2020-03-05 21:36:07, 2020-03-05 22:01:06, 2020-03-05 23:11:06, 2020-03-05 23:21:06, 2020-03-06 00:11:06, 2020-03-06 00:21:06, 2020-03-06 00:51:06, 2020-03-06 00:56:06, 2020-03-06 01:31:06, 2020-03-06 02:11:06, 2020-03-06 02:21:06, 2020-03-06 02:31:06, 2020-03-06 02:36:07, 2020-03-06 02:41:06, 2020-03-06 02:46:06, 2020-03-06 02:51:06, 2020-03-06 02:56:06, 2020-03-06 03:01:06, 2020-03-06 03:06:06, 2020-03-06 03:11:06, 2020-03-06 03:36:06, 2020-03-06 03:41:06, 2020-03-06 03:46:06, 2020-03-06 03:51:07, 2020-03-06 03:56:06, 2020-03-06 04:06:06\n",
      "ID : 5\n",
      "set()\n",
      "len acc == hr : True\n",
      "2020-02-28 15:15:09, 2020-02-28 15:20:08, 2020-02-28 15:25:09, 2020-02-28 15:30:07, 2020-02-28 16:10:09, 2020-02-28 18:00:09, 2020-02-28 18:40:08, 2020-02-28 18:45:08, 2020-02-28 18:50:08, 2020-02-28 19:05:08, 2020-02-28 19:20:08, 2020-02-28 19:25:08, 2020-02-28 19:45:08, 2020-02-28 20:00:08, 2020-02-28 20:20:07, 2020-02-28 20:25:07, 2020-02-28 20:30:08, 2020-02-28 22:40:07, 2020-02-28 22:45:07, 2020-02-28 22:55:07, 2020-02-28 23:00:08, 2020-02-28 23:15:07, 2020-02-28 23:20:08, 2020-02-28 23:30:07, 2020-02-28 23:45:07, 2020-02-28 23:50:07, 2020-02-28 23:55:06, 2020-02-29 00:05:06, 2020-02-29 00:10:08, 2020-02-29 00:35:07, 2020-02-29 01:05:07, 2020-02-29 01:10:08, 2020-02-29 01:15:07, 2020-02-29 01:20:08, 2020-02-29 01:25:07, 2020-02-29 01:30:08, 2020-02-29 01:35:07, 2020-02-29 01:40:08, 2020-02-29 09:05:05, 2020-02-29 09:20:06, 2020-02-29 09:55:05, 2020-02-29 10:05:04, 2020-02-29 10:10:05, 2020-02-29 10:20:05, 2020-02-29 10:30:06, 2020-02-29 10:35:05, 2020-02-29 10:40:07, 2020-02-29 10:45:06, 2020-02-29 14:40:04, 2020-02-29 14:45:03, 2020-02-29 19:05:04, 2020-02-29 19:15:04, 2020-02-29 19:20:03, 2020-02-29 21:55:03, 2020-02-29 23:20:04, 2020-02-29 23:25:02, 2020-02-29 23:30:03, 2020-02-29 23:35:04, 2020-02-29 23:40:03, 2020-02-29 23:45:04, 2020-02-29 23:55:03, 2020-03-01 00:00:04, 2020-03-01 00:05:03, 2020-03-01 00:10:02, 2020-03-01 00:15:03, 2020-03-01 00:20:04, 2020-03-01 00:25:03, 2020-03-01 00:30:02, 2020-03-01 00:35:03, 2020-03-01 00:40:01, 2020-03-01 00:45:02, 2020-03-01 00:50:01, 2020-03-01 00:55:02, 2020-03-01 01:25:02, 2020-03-01 01:35:02, 2020-03-01 01:45:02, 2020-03-01 02:05:02, 2020-03-01 02:10:01, 2020-03-01 02:15:01, 2020-03-01 02:20:01, 2020-03-01 09:35:01, 2020-03-01 10:05:00, 2020-03-01 10:15:02, 2020-03-01 10:20:01, 2020-03-01 10:30:01, 2020-03-01 10:40:01, 2020-03-01 10:50:01, 2020-03-01 11:20:02, 2020-03-01 11:25:01, 2020-03-01 17:25:00, 2020-03-01 17:50:00, 2020-03-01 17:55:00, 2020-03-01 17:59:59, 2020-03-01 19:09:59, 2020-03-01 19:14:58, 2020-03-01 19:59:59, 2020-03-01 21:44:59, 2020-03-01 21:59:59, 2020-03-01 22:24:57, 2020-03-01 22:35:00, 2020-03-01 23:14:59, 2020-03-01 23:19:58, 2020-03-01 23:54:58, 2020-03-02 00:04:58, 2020-03-02 00:09:59, 2020-03-02 00:14:59, 2020-03-02 00:24:57, 2020-03-02 00:29:59, 2020-03-02 00:34:58, 2020-03-02 00:39:58, 2020-03-02 00:44:58, 2020-03-02 00:49:59, 2020-03-02 00:54:58, 2020-03-02 00:59:59, 2020-03-02 01:29:58, 2020-03-02 01:49:59, 2020-03-02 01:54:58, 2020-03-02 01:59:59, 2020-03-02 02:04:58, 2020-03-02 02:09:58, 2020-03-02 02:39:57, 2020-03-02 02:44:57, 2020-03-02 03:24:58, 2020-03-02 10:34:56, 2020-03-02 10:39:57, 2020-03-02 12:04:57, 2020-03-02 12:44:56, 2020-03-02 12:59:57, 2020-03-02 13:09:56, 2020-03-02 16:59:56, 2020-03-02 17:59:56, 2020-03-02 18:09:56, 2020-03-02 18:14:55, 2020-03-02 18:24:56, 2020-03-02 19:54:55, 2020-03-02 20:09:55, 2020-03-02 21:44:54, 2020-03-02 21:49:54, 2020-03-03 00:19:54, 2020-03-03 00:49:54, 2020-03-03 01:34:52, 2020-03-03 01:39:54, 2020-03-03 01:44:53, 2020-03-03 01:49:54, 2020-03-03 01:54:52, 2020-03-03 01:59:53, 2020-03-03 02:04:52, 2020-03-03 02:09:53, 2020-03-03 02:14:53, 2020-03-03 02:24:54, 2020-03-03 02:44:53, 2020-03-03 02:49:54, 2020-03-03 02:54:53, 2020-03-03 02:59:54, 2020-03-03 03:04:53, 2020-03-03 03:09:53, 2020-03-03 03:14:52, 2020-03-03 03:19:54, 2020-03-03 03:39:54, 2020-03-03 03:44:52, 2020-03-03 04:19:54, 2020-03-03 11:34:52, 2020-03-03 13:24:52, 2020-03-03 13:29:51, 2020-03-03 16:54:51, 2020-03-03 16:59:51, 2020-03-03 17:04:51, 2020-03-03 17:09:51, 2020-03-03 17:14:50, 2020-03-03 17:19:51, 2020-03-03 17:24:51, 2020-03-03 17:34:51, 2020-03-03 17:44:49, 2020-03-03 18:24:51, 2020-03-03 18:29:51, 2020-03-03 18:34:50, 2020-03-03 18:39:51, 2020-03-03 19:09:50, 2020-03-03 22:54:50, 2020-03-03 23:19:51, 2020-03-03 23:44:50, 2020-03-04 01:06:16, 2020-03-04 01:11:16, 2020-03-04 01:16:15, 2020-03-04 01:21:16, 2020-03-04 01:31:16, 2020-03-04 01:41:15, 2020-03-04 02:36:16, 2020-03-04 02:41:14, 2020-03-04 02:51:14, 2020-03-04 02:56:15, 2020-03-04 03:01:15, 2020-03-04 03:06:15, 2020-03-04 03:11:16, 2020-03-04 03:16:15, 2020-03-04 03:21:15, 2020-03-04 03:26:15, 2020-03-04 03:31:16, 2020-03-04 03:36:15, 2020-03-04 03:41:14, 2020-03-04 03:46:17, 2020-03-04 03:51:14, 2020-03-04 03:56:15, 2020-03-04 04:01:15, 2020-03-04 04:31:16, 2020-03-04 04:36:17, 2020-03-04 04:46:15, 2020-03-04 04:51:16, 2020-03-04 16:01:15, 2020-03-04 16:06:14, 2020-03-04 16:11:14, 2020-03-04 16:41:14, 2020-03-04 16:51:14, 2020-03-04 18:01:13, 2020-03-04 18:06:13, 2020-03-04 18:11:12, 2020-03-04 18:26:13, 2020-03-04 20:06:11, 2020-03-04 20:51:13, 2020-03-04 20:56:12, 2020-03-04 21:01:12, 2020-03-04 21:21:12, 2020-03-04 21:26:12, 2020-03-04 21:41:13, 2020-03-04 21:46:11, 2020-03-04 22:01:13, 2020-03-04 22:06:12, 2020-03-04 22:11:13, 2020-03-04 22:16:13, 2020-03-04 22:21:13, 2020-03-04 22:26:11, 2020-03-04 22:31:11, 2020-03-05 01:36:11, 2020-03-05 01:41:11, 2020-03-05 01:46:11, 2020-03-05 01:51:10, 2020-03-05 01:56:11, 2020-03-05 02:01:11, 2020-03-05 03:46:12, 2020-03-05 03:51:12, 2020-03-05 12:56:10, 2020-03-05 13:16:08, 2020-03-05 13:51:08, 2020-03-05 17:06:10, 2020-03-05 18:26:09, 2020-03-05 18:36:08, 2020-03-05 19:16:09, 2020-03-05 19:31:08, 2020-03-05 19:46:08, 2020-03-05 20:51:08, 2020-03-05 20:56:08, 2020-03-05 23:01:08, 2020-03-05 23:06:08, 2020-03-05 23:36:06, 2020-03-05 23:46:07, 2020-03-05 23:51:07, 2020-03-06 00:16:07, 2020-03-06 00:21:07, 2020-03-06 00:31:07, 2020-03-06 00:36:07, 2020-03-06 00:41:07, 2020-03-06 00:51:07, 2020-03-06 00:56:07, 2020-03-06 01:01:07, 2020-03-06 01:06:07, 2020-03-06 01:11:06, 2020-03-06 01:16:07, 2020-03-06 01:21:06, 2020-03-06 01:26:07, 2020-03-06 01:31:07, 2020-03-06 01:36:07, 2020-03-06 01:41:07, 2020-03-06 01:46:06, 2020-03-06 08:56:06, 2020-03-06 15:51:05, 2020-03-06 16:01:06, 2020-03-06 16:26:04, 2020-03-06 16:31:06, 2020-03-06 16:36:04, 2020-03-06 17:11:06, 2020-03-06 17:16:05, 2020-03-06 18:11:03, 2020-03-06 18:16:04, 2020-03-06 23:01:08, 2020-03-06 23:21:09, 2020-03-06 23:41:06, 2020-03-06 23:46:07, 2020-03-06 23:51:08, 2020-03-06 23:56:07, 2020-03-07 00:01:08, 2020-03-07 00:06:08, 2020-03-07 00:11:08, 2020-03-07 00:16:07, 2020-03-07 00:21:07, 2020-03-07 00:36:07, 2020-03-07 01:11:06, 2020-03-07 01:21:06, 2020-03-07 01:26:07, 2020-03-07 01:31:07, 2020-03-07 01:36:07, 2020-03-07 01:41:06, 2020-03-07 01:46:07, 2020-03-07 01:51:07, 2020-03-07 01:56:07, 2020-03-07 02:01:07, 2020-03-07 02:21:07, 2020-03-07 08:41:06, 2020-03-07 08:51:06, 2020-03-07 08:56:07, 2020-03-07 09:21:05, 2020-03-07 09:31:06, 2020-03-07 09:36:05, 2020-03-07 09:41:06, 2020-03-07 10:01:07, 2020-03-07 10:06:06, 2020-03-07 10:11:06, 2020-03-07 10:16:06, 2020-03-07 10:21:06, 2020-03-07 10:26:07, 2020-03-07 10:31:07, 2020-03-07 10:36:06, 2020-03-07 10:41:06, 2020-03-07 10:46:06, 2020-03-07 10:51:05, 2020-03-07 10:56:06, 2020-03-07 11:01:06, 2020-03-07 11:06:06, 2020-03-07 11:11:05\n",
      "ID : 6\n",
      "set()\n",
      "len acc == hr : True\n",
      "2020-02-29 13:03:12, 2020-02-29 13:08:12, 2020-02-29 13:13:11, 2020-02-29 13:18:11, 2020-02-29 13:23:12, 2020-02-29 13:28:12, 2020-02-29 13:33:11, 2020-02-29 13:43:12, 2020-02-29 14:33:11, 2020-02-29 14:38:11, 2020-02-29 14:58:11, 2020-02-29 15:43:12, 2020-02-29 16:38:13, 2020-02-29 16:58:12, 2020-02-29 17:13:12, 2020-02-29 17:23:12, 2020-02-29 17:28:12, 2020-02-29 17:48:12, 2020-02-29 17:53:12, 2020-02-29 18:08:12, 2020-02-29 18:13:12, 2020-02-29 18:18:13, 2020-02-29 18:38:12, 2020-02-29 18:43:12, 2020-02-29 18:53:12, 2020-02-29 20:08:12, 2020-02-29 21:43:12, 2020-03-01 01:48:12, 2020-03-01 01:53:11, 2020-03-01 02:03:12, 2020-03-01 02:08:11, 2020-03-01 02:13:11, 2020-03-01 02:48:11, 2020-03-01 02:53:11, 2020-03-01 03:03:12, 2020-03-01 06:33:11, 2020-03-01 07:28:11, 2020-03-01 07:33:11, 2020-03-01 07:38:11, 2020-03-01 12:03:10, 2020-03-01 14:48:10, 2020-03-01 15:13:10, 2020-03-01 15:38:10, 2020-03-01 15:53:10, 2020-03-01 16:18:10, 2020-03-01 16:23:10, 2020-03-01 20:28:09, 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14:38:01, 2020-03-05 14:48:01, 2020-03-05 14:53:01, 2020-03-05 17:48:00, 2020-03-05 18:23:01, 2020-03-05 18:33:01, 2020-03-05 18:38:01, 2020-03-05 19:08:00, 2020-03-06 03:48:00, 2020-03-06 04:07:59, 2020-03-06 04:18:00, 2020-03-06 04:22:59, 2020-03-06 04:27:59, 2020-03-06 04:33:00, 2020-03-06 04:38:00, 2020-03-06 04:43:00, 2020-03-06 04:48:00, 2020-03-06 04:53:00, 2020-03-06 04:58:00, 2020-03-06 05:03:00, 2020-03-06 05:07:59, 2020-03-06 05:27:59, 2020-03-06 05:33:00, 2020-03-06 05:38:00, 2020-03-06 05:42:59, 2020-03-06 05:47:59, 2020-03-06 05:52:59, 2020-03-06 05:57:59, 2020-03-06 06:03:00, 2020-03-06 06:08:00, 2020-03-06 06:13:00, 2020-03-06 06:18:00, 2020-03-06 06:23:00, 2020-03-06 06:27:59, 2020-03-06 06:33:00, 2020-03-06 06:38:00, 2020-03-06 06:42:59, 2020-03-06 06:47:59, 2020-03-06 06:58:00, 2020-03-06 07:02:59, 2020-03-06 07:08:00, 2020-03-06 15:42:59, 2020-03-06 16:32:59, 2020-03-06 16:37:59, 2020-03-06 16:42:58, 2020-03-06 16:47:59, 2020-03-06 16:52:58, 2020-03-06 16:57:58, 2020-03-06 17:07:59, 2020-03-06 17:12:59, 2020-03-06 17:22:59, 2020-03-06 17:27:58, 2020-03-06 17:32:59, 2020-03-06 17:37:58, 2020-03-06 17:42:57, 2020-03-06 17:47:58, 2020-03-06 17:52:58, 2020-03-06 17:57:57, 2020-03-06 18:02:58, 2020-03-06 18:07:57, 2020-03-06 18:12:57, 2020-03-06 18:17:57, 2020-03-06 18:22:58, 2020-03-06 18:27:57, 2020-03-06 18:32:57, 2020-03-06 18:37:58, 2020-03-06 18:42:57, 2020-03-08 04:07:55, 2020-03-08 04:37:56, 2020-03-08 19:07:54, 2020-03-08 19:22:54, 2020-03-08 19:27:53, 2020-03-08 19:32:53, 2020-03-08 19:37:53, 2020-03-08 19:42:54, 2020-03-08 19:47:53, 2020-03-08 19:57:53, 2020-03-08 20:02:53, 2020-03-08 20:07:54, 2020-03-08 20:12:53, 2020-03-08 20:22:53, 2020-03-08 20:27:53, 2020-03-08 20:37:53, 2020-03-08 20:42:54, 2020-03-08 20:47:53, 2020-03-08 20:52:53, 2020-03-08 20:57:53, 2020-03-08 21:02:53, 2020-03-08 21:07:53, 2020-03-08 21:12:53, 2020-03-08 21:17:53, 2020-03-08 21:22:54, 2020-03-08 21:27:53, 2020-03-08 21:32:53, 2020-03-08 21:37:53, 2020-03-08 21:42:53, 2020-03-08 21:47:53, 2020-03-08 21:52:53, 2020-03-08 21:57:53, 2020-03-08 22:02:54, 2020-03-09 09:17:51\n",
      "ID : 8\n",
      "set()\n",
      "len acc == hr : True\n",
      "2020-03-15 18:09:50, 2020-03-15 18:59:50, 2020-03-15 19:24:49, 2020-03-15 19:29:49, 2020-03-15 19:39:49, 2020-03-15 19:44:49, 2020-03-15 19:49:49, 2020-03-15 19:54:48, 2020-03-15 19:59:49, 2020-03-15 20:04:48, 2020-03-15 20:09:49, 2020-03-15 20:54:49, 2020-03-15 21:09:50, 2020-03-15 23:19:49, 2020-03-15 23:24:49, 2020-03-15 23:29:49, 2020-03-15 23:34:49, 2020-03-15 23:39:49, 2020-03-15 23:44:49, 2020-03-15 23:49:49, 2020-03-15 23:54:49, 2020-03-16 00:04:50, 2020-03-16 02:14:49, 2020-03-16 04:29:49, 2020-03-16 04:34:50, 2020-03-16 04:39:50, 2020-03-16 04:44:49, 2020-03-16 04:49:50, 2020-03-16 04:54:50, 2020-03-16 05:04:49, 2020-03-16 05:09:49, 2020-03-16 13:14:48, 2020-03-16 13:49:48, 2020-03-16 14:59:47, 2020-03-16 15:09:47, 2020-03-16 17:14:47, 2020-03-16 21:54:48, 2020-03-16 21:59:47, 2020-03-16 22:09:47, 2020-03-16 22:19:48, 2020-03-16 22:24:47, 2020-03-16 22:29:47, 2020-03-16 22:34:47, 2020-03-16 22:49:47, 2020-03-16 22:54:47, 2020-03-16 22:59:46, 2020-03-16 23:04:47, 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2020-03-19 00:19:42, 2020-03-19 00:24:42, 2020-03-19 00:29:42, 2020-03-19 00:34:42, 2020-03-19 00:39:42, 2020-03-19 00:44:42, 2020-03-19 00:49:42, 2020-03-19 09:29:41, 2020-03-19 10:44:40, 2020-03-19 11:44:39, 2020-03-19 11:54:39, 2020-03-19 12:39:39, 2020-03-19 13:04:39, 2020-03-19 17:59:39, 2020-03-19 18:04:39, 2020-03-19 18:39:39, 2020-03-19 18:49:40, 2020-03-19 18:54:39, 2020-03-19 18:59:39, 2020-03-19 19:04:39, 2020-03-19 19:09:40, 2020-03-19 19:14:39, 2020-03-19 19:19:39, 2020-03-19 19:29:39, 2020-03-19 19:44:39, 2020-03-19 19:49:39, 2020-03-19 20:09:40, 2020-03-19 20:14:39, 2020-03-19 20:19:40, 2020-03-19 20:24:40, 2020-03-19 20:29:39, 2020-03-19 20:34:39, 2020-03-19 20:39:39, 2020-03-19 20:44:39, 2020-03-19 20:49:40, 2020-03-19 20:54:39, 2020-03-19 20:59:40, 2020-03-19 21:04:39, 2020-03-19 21:14:40, 2020-03-19 21:19:40, 2020-03-19 21:24:40, 2020-03-19 22:04:39, 2020-03-19 22:59:38, 2020-03-19 23:04:39, 2020-03-19 23:09:39, 2020-03-20 09:39:38, 2020-03-20 09:54:39, 2020-03-20 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2020-03-21 15:59:35, 2020-03-21 16:09:35, 2020-03-21 16:14:35, 2020-03-21 16:19:34, 2020-03-21 16:29:34, 2020-03-21 19:59:34, 2020-03-21 20:09:34, 2020-03-21 20:24:35, 2020-03-21 20:49:35, 2020-03-21 20:54:35, 2020-03-21 21:04:34, 2020-03-21 21:44:33, 2020-03-21 21:49:34, 2020-03-21 21:59:33, 2020-03-21 22:04:33, 2020-03-21 22:19:34, 2020-03-21 22:44:34, 2020-03-21 22:49:34, 2020-03-21 22:54:34, 2020-03-22 01:14:33, 2020-03-22 02:59:34, 2020-03-22 06:39:34, 2020-03-22 11:49:32, 2020-03-22 12:24:33, 2020-03-22 12:44:33, 2020-03-22 14:29:32, 2020-03-22 14:49:32, 2020-03-22 14:59:32, 2020-03-22 15:34:32, 2020-03-22 15:39:33, 2020-03-22 15:44:32, 2020-03-22 15:59:32, 2020-03-22 16:09:32, 2020-03-22 16:14:32, 2020-03-22 16:19:33, 2020-03-22 16:49:33, 2020-03-22 16:54:32, 2020-03-22 17:04:32, 2020-03-22 19:34:32, 2020-03-22 19:39:31, 2020-03-22 20:49:32, 2020-03-22 20:54:32, 2020-03-22 20:59:32, 2020-03-22 21:24:32, 2020-03-22 21:34:31, 2020-03-22 21:39:32, 2020-03-22 21:44:32, 2020-03-22 22:44:32, 2020-03-22 22:49:32, 2020-03-22 23:14:32, 2020-03-22 23:49:31, 2020-03-23 01:09:32, 2020-03-23 01:14:31, 2020-03-23 01:19:32, 2020-03-23 01:24:31, 2020-03-23 01:29:32, 2020-03-23 01:34:31, 2020-03-23 01:39:31, 2020-03-23 10:34:31, 2020-03-23 10:39:31, 2020-03-23 10:44:31, 2020-03-23 10:54:30, 2020-03-23 10:59:31, 2020-03-23 11:04:31, 2020-03-23 11:14:31, 2020-03-23 11:19:31, 2020-03-23 11:24:31, 2020-03-23 11:29:31, 2020-03-23 11:34:31\n",
      "ID : 9\n",
      "set()\n",
      "len acc == hr : True\n",
      "2020-03-22 11:11:48, 2020-03-22 11:16:49, 2020-03-22 11:21:48, 2020-03-22 12:51:48, 2020-03-22 15:26:49, 2020-03-22 15:31:48, 2020-03-22 15:41:48, 2020-03-22 16:36:49, 2020-03-22 17:36:48, 2020-03-22 17:41:48, 2020-03-22 19:06:49, 2020-03-22 19:16:49, 2020-03-22 19:21:50, 2020-03-22 19:31:50, 2020-03-22 21:21:49, 2020-03-22 21:36:49, 2020-03-22 22:56:48, 2020-03-22 23:21:48, 2020-03-22 23:26:49, 2020-03-22 23:36:49, 2020-03-22 23:41:48, 2020-03-23 00:06:48, 2020-03-23 00:11:49, 2020-03-23 00:21:48, 2020-03-23 00:36:49, 2020-03-23 01:36:49, 2020-03-23 01:51:49, 2020-03-23 02:51:48, 2020-03-23 02:56:49, 2020-03-23 03:01:48, 2020-03-23 11:21:48, 2020-03-23 11:31:48, 2020-03-23 11:41:48, 2020-03-23 11:46:48, 2020-03-23 12:21:48, 2020-03-23 12:26:48, 2020-03-23 18:06:49, 2020-03-23 18:56:49, 2020-03-23 19:21:48, 2020-03-23 19:51:48, 2020-03-23 21:26:48, 2020-03-23 22:21:48, 2020-03-23 22:41:48, 2020-03-23 22:51:48, 2020-03-23 23:01:46, 2020-03-23 23:06:46, 2020-03-23 23:11:46, 2020-03-23 23:21:47, 2020-03-23 23:26:46, 2020-03-23 23:31:46, 2020-03-23 23:36:46, 2020-03-23 23:51:47, 2020-03-23 23:56:47, 2020-03-24 00:06:47, 2020-03-24 00:11:47, 2020-03-24 00:16:47, 2020-03-24 01:06:46, 2020-03-24 01:11:47, 2020-03-24 01:16:47, 2020-03-24 01:21:47, 2020-03-24 02:46:47, 2020-03-24 10:46:48, 2020-03-24 10:56:48, 2020-03-24 11:01:48, 2020-03-24 11:06:48, 2020-03-24 11:16:48, 2020-03-24 11:31:48, 2020-03-24 11:36:48, 2020-03-24 11:56:48, 2020-03-24 13:01:48, 2020-03-24 13:06:48, 2020-03-24 13:21:48, 2020-03-24 13:31:48, 2020-03-24 14:46:47, 2020-03-24 14:51:47, 2020-03-24 18:36:47, 2020-03-24 20:01:46, 2020-03-24 20:21:46, 2020-03-24 21:51:46, 2020-03-24 22:06:46, 2020-03-24 22:16:46, 2020-03-24 22:26:46, 2020-03-24 22:31:46, 2020-03-24 22:41:46, 2020-03-24 22:46:46, 2020-03-24 22:56:46, 2020-03-24 23:36:46, 2020-03-24 23:41:46, 2020-03-24 23:51:46, 2020-03-24 23:56:46, 2020-03-25 00:01:45, 2020-03-25 00:06:46, 2020-03-25 00:11:46, 2020-03-25 00:16:46, 2020-03-25 00:21:46, 2020-03-25 00:26:46, 2020-03-25 00:46:46, 2020-03-25 01:06:46, 2020-03-25 01:56:46, 2020-03-25 02:16:46, 2020-03-25 11:01:46, 2020-03-25 11:06:45, 2020-03-25 11:11:45, 2020-03-25 11:26:46, 2020-03-25 11:31:45, 2020-03-25 11:36:45, 2020-03-25 14:41:46, 2020-03-25 14:46:45, 2020-03-25 14:51:45, 2020-03-25 15:11:45, 2020-03-25 15:26:45, 2020-03-25 15:36:45, 2020-03-25 15:51:45, 2020-03-25 16:11:45, 2020-03-25 16:31:45, 2020-03-25 16:36:45, 2020-03-25 16:41:46, 2020-03-25 16:46:45, 2020-03-25 16:51:45, 2020-03-25 17:01:45, 2020-03-25 17:06:45, 2020-03-25 17:11:45, 2020-03-25 17:21:45, 2020-03-25 18:06:45, 2020-03-25 20:36:44, 2020-03-25 20:41:44, 2020-03-25 20:56:45, 2020-03-25 21:01:45, 2020-03-25 21:26:44, 2020-03-25 21:51:45, 2020-03-25 22:01:45, 2020-03-25 22:11:45, 2020-03-25 22:26:45, 2020-03-26 15:26:44, 2020-03-26 15:36:45, 2020-03-26 16:21:44, 2020-03-26 16:26:44, 2020-03-26 16:31:44, 2020-03-26 16:36:44, 2020-03-26 16:41:44, 2020-03-26 16:46:45, 2020-03-26 16:51:44, 2020-03-26 16:56:45, 2020-03-26 17:01:45, 2020-03-26 21:16:45, 2020-03-26 21:31:45, 2020-03-26 21:51:44, 2020-03-26 22:01:44, 2020-03-26 22:06:45, 2020-03-26 22:11:44, 2020-03-26 22:16:44, 2020-03-27 00:01:45, 2020-03-27 00:06:45, 2020-03-27 00:11:45, 2020-03-27 02:16:44, 2020-03-27 02:56:43, 2020-03-27 03:01:44, 2020-03-27 03:41:44, 2020-03-27 04:01:44, 2020-03-27 04:51:44, 2020-03-27 05:01:44, 2020-03-27 11:51:42, 2020-03-27 12:16:43, 2020-03-27 12:41:42, 2020-03-27 12:51:42, 2020-03-27 14:01:42, 2020-03-27 14:11:42, 2020-03-27 14:21:43, 2020-03-27 14:26:43, 2020-03-27 18:16:41, 2020-03-27 18:41:42, 2020-03-27 19:01:42, 2020-03-27 19:06:42, 2020-03-27 19:41:42, 2020-03-27 19:46:42, 2020-03-27 19:56:42, 2020-03-27 20:06:42, 2020-03-27 20:11:42, 2020-03-27 20:16:41, 2020-03-27 20:21:42, 2020-03-27 20:26:41, 2020-03-27 20:31:41, 2020-03-27 20:36:41, 2020-03-27 20:41:42, 2020-03-27 20:46:43, 2020-03-27 20:51:42, 2020-03-27 20:56:42, 2020-03-27 21:01:43, 2020-03-27 21:06:43, 2020-03-27 21:11:43, 2020-03-27 22:36:43, 2020-03-28 00:36:43, 2020-03-28 01:31:42, 2020-03-28 01:36:42, 2020-03-28 11:16:40, 2020-03-28 11:26:41, 2020-03-28 11:41:41, 2020-03-28 12:51:41, 2020-03-28 12:56:41, 2020-03-28 13:01:41, 2020-03-28 17:36:41, 2020-03-28 17:41:41, 2020-03-28 19:16:41, 2020-03-28 19:26:40, 2020-03-28 21:21:40, 2020-03-28 21:36:40, 2020-03-28 21:56:40, 2020-03-28 22:26:40, 2020-03-29 00:01:39, 2020-03-29 00:06:40, 2020-03-29 00:11:40, 2020-03-29 00:16:40, 2020-03-29 00:21:40, 2020-03-29 00:31:40, 2020-03-29 00:46:40, 2020-03-29 00:51:40, 2020-03-29 00:56:39, 2020-03-29 02:11:41, 2020-03-29 02:21:40, 2020-03-29 02:26:41, 2020-03-29 02:31:41, 2020-03-29 02:36:41, 2020-03-29 02:41:41, 2020-03-29 09:11:40, 2020-03-29 09:21:40, 2020-03-29 09:31:40, 2020-03-29 09:36:40, 2020-03-29 09:46:40, 2020-03-29 09:56:40, 2020-03-29 10:11:40, 2020-03-29 10:16:40, 2020-03-29 10:21:40, 2020-03-29 10:26:40, 2020-03-29 10:31:40, 2020-03-29 10:36:41, 2020-03-29 10:41:41, 2020-03-29 10:51:41, 2020-03-29 10:56:41, 2020-03-29 11:01:41\n",
      "ID : 10\n",
      "set()\n",
      "len acc == hr : True\n",
      "2020-03-23 11:44:17, 2020-03-23 11:49:16, 2020-03-23 11:54:15, 2020-03-23 13:39:16, 2020-03-23 13:54:17, 2020-03-23 13:59:16, 2020-03-23 14:04:16, 2020-03-23 14:19:16, 2020-03-23 14:24:16, 2020-03-23 14:29:17, 2020-03-23 14:34:16, 2020-03-23 14:44:16, 2020-03-23 14:59:16, 2020-03-23 16:19:17, 2020-03-23 16:24:17, 2020-03-23 19:44:16, 2020-03-23 20:39:15, 2020-03-23 21:09:13, 2020-03-23 21:14:13, 2020-03-23 21:44:15, 2020-03-23 22:24:14, 2020-03-24 05:04:15, 2020-03-24 15:59:15, 2020-03-24 17:39:13, 2020-03-24 18:19:13, 2020-03-24 18:49:13, 2020-03-24 19:09:13, 2020-03-24 19:44:14, 2020-03-24 19:54:14, 2020-03-24 20:04:14, 2020-03-24 20:09:14, 2020-03-24 20:14:15, 2020-03-24 20:19:15, 2020-03-24 20:24:14, 2020-03-24 20:34:13, 2020-03-24 21:34:14, 2020-03-24 21:39:13, 2020-03-24 21:44:12, 2020-03-24 22:04:12, 2020-03-24 22:34:13, 2020-03-24 22:39:12, 2020-03-24 22:44:12, 2020-03-24 22:54:13, 2020-03-24 23:29:14, 2020-03-24 23:44:13, 2020-03-25 00:04:14, 2020-03-25 00:09:14, 2020-03-25 00:19:13, 2020-03-25 00:24:13, 2020-03-25 00:29:14, 2020-03-25 00:54:13, 2020-03-25 11:19:13, 2020-03-25 11:29:13, 2020-03-25 11:39:13, 2020-03-25 14:09:12, 2020-03-25 14:19:13, 2020-03-25 15:09:11, 2020-03-25 15:19:11, 2020-03-25 16:19:11, 2020-03-25 16:39:12, 2020-03-25 16:44:12, 2020-03-25 16:49:13, 2020-03-25 18:34:12, 2020-03-25 18:39:13, 2020-03-25 18:44:12, 2020-03-25 18:49:13, 2020-03-25 20:29:13, 2020-03-25 20:34:12, 2020-03-25 20:44:11, 2020-03-25 22:04:11, 2020-03-25 22:09:11, 2020-03-25 22:39:12, 2020-03-25 22:44:12, 2020-03-25 23:09:12, 2020-03-25 23:14:12, 2020-03-25 23:19:11, 2020-03-25 23:24:12, 2020-03-25 23:29:12, 2020-03-25 23:49:11, 2020-03-25 23:54:11, 2020-03-26 00:59:11, 2020-03-26 01:04:12, 2020-03-26 07:14:11, 2020-03-26 07:34:11, 2020-03-26 07:54:11, 2020-03-26 08:29:11, 2020-03-26 08:34:11, 2020-03-26 09:09:09, 2020-03-26 11:04:10, 2020-03-26 11:19:09, 2020-03-26 11:44:10, 2020-03-26 11:49:10, 2020-03-26 12:44:10, 2020-03-26 13:04:09, 2020-03-26 13:44:09, 2020-03-26 17:49:09, 2020-03-26 17:59:09, 2020-03-26 18:04:09, 2020-03-26 18:09:09, 2020-03-26 20:04:10, 2020-03-26 20:09:10, 2020-03-26 20:14:10, 2020-03-26 20:19:10, 2020-03-26 20:29:10, 2020-03-26 21:29:10, 2020-03-26 21:59:10, 2020-03-26 22:04:10, 2020-03-26 22:14:09, 2020-03-26 22:19:10, 2020-03-26 22:24:09, 2020-03-26 22:29:09, 2020-03-26 22:34:09, 2020-03-26 22:39:10, 2020-03-26 22:44:09, 2020-03-26 22:49:09, 2020-03-26 22:54:09, 2020-03-26 22:59:10, 2020-03-26 23:54:10, 2020-03-27 07:39:08, 2020-03-27 09:09:07, 2020-03-27 09:19:08, 2020-03-27 09:24:08, 2020-03-27 11:34:09, 2020-03-27 13:59:07, 2020-03-27 16:14:07, 2020-03-27 16:44:07, 2020-03-27 16:54:08, 2020-03-27 17:24:07, 2020-03-27 17:34:07, 2020-03-27 17:44:07, 2020-03-27 17:54:07, 2020-03-27 18:14:07, 2020-03-27 18:19:07, 2020-03-27 18:24:07, 2020-03-27 21:09:08, 2020-03-27 21:34:07, 2020-03-27 21:39:08, 2020-03-27 21:49:08, 2020-03-27 21:54:08, 2020-03-27 21:59:07, 2020-03-27 22:04:07, 2020-03-27 22:09:08, 2020-03-27 22:14:07, 2020-03-27 22:19:07, 2020-03-27 22:24:07, 2020-03-27 22:49:08, 2020-03-27 22:54:07, 2020-03-27 22:59:08, 2020-03-27 23:04:07, 2020-03-27 23:09:08, 2020-03-27 23:14:08, 2020-03-27 23:19:07, 2020-03-27 23:29:08, 2020-03-27 23:34:08, 2020-03-27 23:39:07, 2020-03-27 23:44:06, 2020-03-27 23:49:07, 2020-03-28 01:29:07, 2020-03-28 01:39:07, 2020-03-28 08:54:07, 2020-03-28 09:19:06, 2020-03-28 11:19:06, 2020-03-28 11:49:06, 2020-03-28 17:14:05, 2020-03-28 17:19:05, 2020-03-28 20:59:06, 2020-03-28 21:09:06, 2020-03-29 01:39:05, 2020-03-29 08:19:04, 2020-03-29 08:29:03, 2020-03-29 10:59:03, 2020-03-29 11:04:03, 2020-03-29 11:09:03, 2020-03-29 11:14:03, 2020-03-29 11:39:03, 2020-03-29 11:44:04, 2020-03-29 11:49:03, 2020-03-29 15:29:04, 2020-03-29 15:54:04, 2020-03-29 16:04:04, 2020-03-29 16:09:04, 2020-03-29 20:39:05, 2020-03-29 20:44:04, 2020-03-29 20:49:04, 2020-03-29 20:59:04, 2020-03-29 21:19:04, 2020-03-29 21:29:03, 2020-03-29 21:34:03, 2020-03-29 21:39:03, 2020-03-29 21:49:03, 2020-03-29 22:04:03, 2020-03-29 22:09:03, 2020-03-29 22:59:03, 2020-03-29 23:04:03, 2020-03-29 23:09:04, 2020-03-29 23:14:03, 2020-03-29 23:19:03, 2020-03-29 23:24:03, 2020-03-29 23:29:04, 2020-03-29 23:34:03, 2020-03-30 00:39:03, 2020-03-30 00:59:03, 2020-03-30 07:34:02, 2020-03-30 07:49:01, 2020-03-30 08:34:02, 2020-03-30 08:39:02, 2020-03-30 08:54:02, 2020-03-30 09:04:02, 2020-03-30 09:09:03, 2020-03-30 09:14:02\n",
      "ID : 11\n",
      "set()\n",
      "len acc == hr : False\n",
      "acc : 2020-04-06 08:20:00 ~ 2020-04-16 10:10:00 : 2903\n",
      "hr : 2020-04-06 08:25:00 ~ 2020-04-16 10:10:00 : 2902\n",
      "After preprocessing-> len acc == hr : True\n",
      "2020-04-07 10:01:07, 2020-04-07 10:56:06, 2020-04-07 11:06:06, 2020-04-07 14:31:07, 2020-04-07 14:36:07, 2020-04-07 15:26:07, 2020-04-07 16:51:06, 2020-04-07 17:46:07, 2020-04-07 18:01:07, 2020-04-07 18:16:07, 2020-04-07 18:21:07, 2020-04-07 18:36:07, 2020-04-07 18:41:07, 2020-04-07 18:46:07, 2020-04-07 18:56:06, 2020-04-07 19:01:07, 2020-04-07 19:06:07, 2020-04-07 20:26:06, 2020-04-07 20:31:07, 2020-04-07 20:36:07, 2020-04-07 21:16:07, 2020-04-07 21:36:07, 2020-04-07 23:11:07, 2020-04-07 23:21:07, 2020-04-07 23:56:07, 2020-04-08 04:21:06, 2020-04-08 14:46:05, 2020-04-08 14:51:06, 2020-04-08 14:56:05, 2020-04-08 15:01:06, 2020-04-08 15:21:05, 2020-04-08 15:36:05, 2020-04-08 15:46:05, 2020-04-08 15:51:06, 2020-04-08 15:56:05, 2020-04-08 16:01:06, 2020-04-08 16:06:05, 2020-04-08 16:16:06, 2020-04-08 16:26:05, 2020-04-08 16:31:06, 2020-04-08 16:41:06, 2020-04-08 16:46:05, 2020-04-08 16:56:06, 2020-04-08 17:01:05, 2020-04-08 17:11:05, 2020-04-08 21:11:03, 2020-04-08 21:31:03, 2020-04-08 22:16:03, 2020-04-08 22:31:03, 2020-04-08 22:36:04, 2020-04-08 22:41:03, 2020-04-08 22:46:03, 2020-04-08 22:51:04, 2020-04-08 22:56:03, 2020-04-08 23:01:03, 2020-04-08 23:06:04, 2020-04-08 23:11:04, 2020-04-08 23:16:03, 2020-04-08 23:21:03, 2020-04-08 23:26:03, 2020-04-08 23:31:04, 2020-04-08 23:36:04, 2020-04-08 23:41:03, 2020-04-08 23:46:03, 2020-04-08 23:51:03, 2020-04-08 23:56:03, 2020-04-09 04:16:03, 2020-04-09 04:21:03, 2020-04-09 04:26:03, 2020-04-09 12:51:03, 2020-04-09 13:11:02, 2020-04-09 13:26:02, 2020-04-10 00:46:02, 2020-04-10 00:51:02, 2020-04-10 00:56:02, 2020-04-10 01:06:01, 2020-04-10 01:11:02, 2020-04-10 02:01:01, 2020-04-10 02:51:01, 2020-04-10 02:56:01, 2020-04-10 03:01:02, 2020-04-10 03:06:02, 2020-04-10 03:11:01, 2020-04-10 03:16:01, 2020-04-10 03:21:02, 2020-04-10 03:26:01, 2020-04-10 03:31:01, 2020-04-10 03:36:02, 2020-04-10 03:41:02, 2020-04-10 04:11:01, 2020-04-10 04:31:02, 2020-04-10 04:36:02, 2020-04-10 04:41:02, 2020-04-10 04:46:02, 2020-04-10 04:51:01, 2020-04-10 04:56:02, 2020-04-10 05:01:01, 2020-04-11 06:40:59, 2020-04-11 06:50:59, 2020-04-11 07:15:59, 2020-04-11 07:25:59, 2020-04-11 07:41:00, 2020-04-11 08:21:00, 2020-04-11 08:26:00, 2020-04-11 08:41:00, 2020-04-11 11:56:00, 2020-04-11 12:16:00, 2020-04-11 12:21:00, 2020-04-11 12:56:00, 2020-04-11 14:26:00, 2020-04-11 15:30:58, 2020-04-11 16:55:58, 2020-04-11 17:10:58, 2020-04-11 17:15:58, 2020-04-11 18:35:59, 2020-04-11 19:30:58, 2020-04-11 19:50:58, 2020-04-11 19:55:58, 2020-04-11 20:00:59, 2020-04-11 20:55:59, 2020-04-11 21:00:59, 2020-04-11 21:05:59, 2020-04-11 21:10:59, 2020-04-11 21:15:59, 2020-04-11 21:25:59, 2020-04-11 21:30:59, 2020-04-11 22:20:58, 2020-04-11 22:30:58, 2020-04-11 22:40:59, 2020-04-12 00:40:58, 2020-04-12 05:00:57, 2020-04-12 10:50:57, 2020-04-12 11:10:57, 2020-04-12 11:15:57, 2020-04-12 14:50:57, 2020-04-12 14:55:58, 2020-04-12 15:00:58, 2020-04-12 16:45:57, 2020-04-12 18:25:57, 2020-04-12 18:40:58, 2020-04-12 19:05:58, 2020-04-12 19:15:58, 2020-04-12 19:20:58, 2020-04-12 19:25:57, 2020-04-12 19:30:57, 2020-04-12 19:40:56, 2020-04-12 19:55:57, 2020-04-12 20:10:57, 2020-04-12 20:15:57, 2020-04-12 20:25:57, 2020-04-12 20:35:56, 2020-04-12 20:40:57, 2020-04-12 20:45:57, 2020-04-12 20:55:57, 2020-04-12 21:25:56, 2020-04-12 22:15:56, 2020-04-12 22:55:56, 2020-04-12 23:05:56, 2020-04-12 23:25:56, 2020-04-12 23:55:56, 2020-04-13 00:10:56, 2020-04-13 00:15:56, 2020-04-13 00:20:56, 2020-04-13 00:25:56, 2020-04-13 05:15:56, 2020-04-13 10:55:55, 2020-04-13 11:00:55, 2020-04-13 11:10:55, 2020-04-13 11:15:56, 2020-04-13 13:25:56, 2020-04-13 13:30:55, 2020-04-13 13:35:55, 2020-04-13 13:40:56, 2020-04-13 13:45:55, 2020-04-13 14:40:55, 2020-04-13 14:45:56, 2020-04-13 15:05:56, 2020-04-13 19:10:54, 2020-04-13 19:20:55, 2020-04-13 19:25:55, 2020-04-13 19:30:55, 2020-04-13 19:40:55, 2020-04-13 19:45:55, 2020-04-13 19:50:54, 2020-04-13 19:55:55, 2020-04-13 20:00:54, 2020-04-13 20:05:54, 2020-04-13 20:25:55, 2020-04-13 20:30:55, 2020-04-13 20:45:54, 2020-04-13 21:10:55, 2020-04-13 21:15:54, 2020-04-13 21:20:54, 2020-04-13 21:30:55, 2020-04-13 21:35:55, 2020-04-14 02:45:54, 2020-04-14 02:55:55, 2020-04-14 03:00:55, 2020-04-14 03:05:54, 2020-04-14 03:10:54, 2020-04-14 03:25:54, 2020-04-14 03:30:54, 2020-04-14 04:20:55, 2020-04-14 04:25:55, 2020-04-14 04:30:54, 2020-04-14 04:35:55, 2020-04-14 04:40:54, 2020-04-14 04:45:54, 2020-04-14 04:50:54, 2020-04-14 04:55:55, 2020-04-14 05:00:55, 2020-04-14 05:10:55, 2020-04-14 05:15:54, 2020-04-14 05:20:54, 2020-04-14 05:25:54, 2020-04-14 05:30:55, 2020-04-14 05:35:54, 2020-04-14 05:40:54, 2020-04-14 05:45:54, 2020-04-14 06:25:54, 2020-04-14 13:15:54, 2020-04-14 13:25:54, 2020-04-14 13:30:55, 2020-04-14 13:35:54, 2020-04-14 13:40:54, 2020-04-14 14:15:54, 2020-04-14 14:25:54, 2020-04-14 15:00:54, 2020-04-14 15:50:53, 2020-04-14 16:05:53, 2020-04-14 16:10:53, 2020-04-14 16:15:53, 2020-04-14 16:25:53, 2020-04-14 17:15:53, 2020-04-14 17:50:53, 2020-04-14 20:35:53, 2020-04-14 20:45:52, 2020-04-14 20:50:52, 2020-04-14 21:00:53, 2020-04-14 21:10:53, 2020-04-14 21:25:52, 2020-04-14 21:30:53, 2020-04-14 21:35:53, 2020-04-14 21:45:53, 2020-04-14 22:05:52, 2020-04-14 22:20:52, 2020-04-14 22:25:53, 2020-04-14 22:45:53, 2020-04-14 22:50:52, 2020-04-14 23:10:53, 2020-04-15 00:10:53, 2020-04-15 04:30:53, 2020-04-15 05:00:53, 2020-04-15 05:05:53, 2020-04-15 12:35:52, 2020-04-15 12:40:52, 2020-04-15 14:45:53, 2020-04-15 14:55:53, 2020-04-15 15:10:52, 2020-04-15 15:20:53, 2020-04-15 15:25:52, 2020-04-15 15:30:52, 2020-04-15 15:55:52, 2020-04-15 16:00:53, 2020-04-15 16:05:53, 2020-04-15 16:10:52, 2020-04-15 16:15:53, 2020-04-15 16:20:53, 2020-04-15 16:25:52, 2020-04-15 16:30:52, 2020-04-15 16:35:52, 2020-04-15 16:40:52, 2020-04-15 16:45:52, 2020-04-15 16:50:52, 2020-04-15 16:55:52, 2020-04-15 17:00:52, 2020-04-15 17:05:52, 2020-04-15 21:20:51, 2020-04-15 21:35:51, 2020-04-15 21:40:51, 2020-04-15 22:20:52, 2020-04-15 22:35:51, 2020-04-15 22:40:52, 2020-04-15 22:45:51, 2020-04-15 22:50:51, 2020-04-15 23:00:51, 2020-04-15 23:05:51, 2020-04-15 23:10:51, 2020-04-15 23:15:51, 2020-04-15 23:20:51, 2020-04-16 06:40:51, 2020-04-16 06:55:51, 2020-04-16 07:00:51, 2020-04-16 07:05:51, 2020-04-16 07:20:51\n",
      "ID : 12\n",
      "set()\n",
      "len acc == hr : True\n",
      "2020-05-04 19:47:11, 2020-05-04 19:57:11, 2020-05-04 20:02:11, 2020-05-04 20:07:11, 2020-05-04 20:32:11, 2020-05-04 20:57:11, 2020-05-04 21:02:11, 2020-05-04 21:57:11, 2020-05-04 22:02:11, 2020-05-04 22:17:12, 2020-05-04 22:22:12, 2020-05-04 22:47:11, 2020-05-04 22:57:11, 2020-05-04 23:27:11, 2020-05-04 23:47:11, 2020-05-04 23:52:11, 2020-05-04 23:57:11, 2020-05-05 00:32:11, 2020-05-05 00:37:11, 2020-05-05 01:12:10, 2020-05-05 01:17:11, 2020-05-05 01:37:10, 2020-05-05 01:42:11, 2020-05-05 01:47:11, 2020-05-05 02:37:11, 2020-05-05 02:47:11, 2020-05-05 09:47:10, 2020-05-05 14:42:11, 2020-05-05 14:47:11, 2020-05-05 14:57:11, 2020-05-05 15:02:10, 2020-05-05 15:07:10, 2020-05-05 18:37:09, 2020-05-05 20:37:09, 2020-05-05 20:42:10, 2020-05-05 20:47:09, 2020-05-05 20:52:10, 2020-05-05 21:02:09, 2020-05-05 21:07:10, 2020-05-05 21:12:09, 2020-05-05 21:17:09, 2020-05-05 21:22:10, 2020-05-05 21:27:09, 2020-05-05 21:32:10, 2020-05-05 21:52:09, 2020-05-05 22:02:09, 2020-05-05 22:07:09, 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02:07:07, 2020-05-07 02:12:06, 2020-05-07 03:17:06, 2020-05-07 08:27:07, 2020-05-07 11:47:06, 2020-05-07 11:52:06, 2020-05-07 12:27:06, 2020-05-07 13:22:05, 2020-05-07 13:32:05, 2020-05-07 13:42:05, 2020-05-07 13:47:06, 2020-05-07 13:52:05, 2020-05-07 13:57:05, 2020-05-07 14:02:06, 2020-05-07 14:07:05, 2020-05-07 14:12:06, 2020-05-07 14:17:05, 2020-05-07 14:22:06, 2020-05-07 14:27:06, 2020-05-07 14:32:05, 2020-05-07 14:42:06, 2020-05-07 14:47:05, 2020-05-07 16:42:05, 2020-05-07 18:07:05, 2020-05-07 18:37:05, 2020-05-07 18:52:06, 2020-05-07 18:57:06, 2020-05-07 19:02:06, 2020-05-07 19:07:06, 2020-05-07 19:12:05, 2020-05-07 19:17:06, 2020-05-07 19:22:05, 2020-05-07 19:27:06, 2020-05-07 19:32:06, 2020-05-07 19:37:05, 2020-05-07 19:42:06, 2020-05-08 11:27:05, 2020-05-08 11:32:05, 2020-05-08 11:47:05, 2020-05-08 12:02:05, 2020-05-08 12:07:04, 2020-05-08 12:12:05, 2020-05-08 15:22:04, 2020-05-08 15:47:04, 2020-05-08 15:57:04, 2020-05-08 16:07:05, 2020-05-08 16:12:04, 2020-05-08 16:17:05, 2020-05-08 16:27:04, 2020-05-08 16:32:04, 2020-05-08 20:17:03, 2020-05-08 20:32:03, 2020-05-08 20:52:02, 2020-05-08 21:07:03, 2020-05-08 21:22:01, 2020-05-08 22:57:03, 2020-05-08 23:07:03, 2020-05-08 23:12:02, 2020-05-08 23:17:02, 2020-05-08 23:27:03, 2020-05-08 23:32:03, 2020-05-08 23:52:02, 2020-05-09 00:02:02, 2020-05-09 00:07:02, 2020-05-09 01:02:03, 2020-05-09 01:07:03, 2020-05-09 01:12:03, 2020-05-09 01:17:03, 2020-05-09 01:22:03, 2020-05-09 01:27:03, 2020-05-09 01:32:03, 2020-05-09 01:37:03, 2020-05-09 01:42:03, 2020-05-09 01:47:03, 2020-05-09 01:52:03, 2020-05-09 09:27:03, 2020-05-09 10:27:03, 2020-05-09 10:42:03, 2020-05-09 10:47:03, 2020-05-09 11:07:03, 2020-05-09 11:37:04, 2020-05-09 13:57:02, 2020-05-09 14:27:01, 2020-05-09 14:32:02, 2020-05-09 14:57:01, 2020-05-09 15:22:02, 2020-05-09 15:52:02, 2020-05-09 19:07:02, 2020-05-09 19:42:02, 2020-05-09 20:52:02, 2020-05-09 20:57:03, 2020-05-09 21:02:02, 2020-05-09 21:07:02, 2020-05-09 21:17:01, 2020-05-09 21:22:01, 2020-05-09 21:32:02, 2020-05-09 21:42:02, 2020-05-09 21:47:02, 2020-05-09 21:52:02, 2020-05-09 23:07:01, 2020-05-09 23:12:02, 2020-05-09 23:22:01, 2020-05-09 23:27:02, 2020-05-09 23:32:01, 2020-05-09 23:37:01, 2020-05-09 23:42:02, 2020-05-09 23:47:02, 2020-05-09 23:52:01, 2020-05-10 01:27:01, 2020-05-10 01:37:01, 2020-05-10 01:42:01, 2020-05-10 01:47:02, 2020-05-10 01:52:01, 2020-05-10 01:57:01, 2020-05-10 02:02:01, 2020-05-10 10:07:00, 2020-05-10 10:22:01, 2020-05-10 10:27:01, 2020-05-10 10:32:00, 2020-05-10 10:37:00, 2020-05-10 11:12:00, 2020-05-10 11:17:00, 2020-05-10 11:22:00, 2020-05-10 11:47:00, 2020-05-10 21:07:01, 2020-05-10 21:12:01, 2020-05-10 21:17:01, 2020-05-10 21:27:00, 2020-05-10 21:37:00, 2020-05-10 21:42:01, 2020-05-10 21:47:01, 2020-05-10 21:52:01, 2020-05-10 21:57:01, 2020-05-10 22:12:01, 2020-05-10 23:17:01, 2020-05-10 23:22:00, 2020-05-10 23:52:00, 2020-05-11 00:02:00, 2020-05-11 00:07:01, 2020-05-11 00:27:00, 2020-05-11 00:37:00, 2020-05-11 01:06:59, 2020-05-11 01:11:59, 2020-05-11 01:21:58, 2020-05-11 01:26:59, 2020-05-11 01:31:58, 2020-05-11 01:36:59, 2020-05-11 01:41:59, 2020-05-11 01:46:59, 2020-05-11 01:51:59, 2020-05-11 01:56:59, 2020-05-11 02:01:59, 2020-05-11 02:06:59, 2020-05-11 02:11:59, 2020-05-11 02:16:59, 2020-05-11 02:32:00, 2020-05-11 02:37:00, 2020-05-11 02:41:59, 2020-05-11 02:47:00, 2020-05-11 02:51:59, 2020-05-11 03:01:59, 2020-05-11 08:52:00, 2020-05-11 09:07:00\n",
      "ID : 13\n",
      "set()\n",
      "len acc == hr : True\n",
      "2020-05-29 16:26:53, 2020-05-29 16:31:52, 2020-05-29 17:06:53, 2020-05-29 17:21:53, 2020-05-29 18:31:53, 2020-05-29 18:46:53, 2020-05-29 19:01:52, 2020-05-29 19:16:53, 2020-05-29 19:21:52, 2020-05-29 19:26:52, 2020-05-29 19:41:53, 2020-05-29 20:06:52, 2020-05-29 20:16:53, 2020-05-29 20:21:53, 2020-05-29 20:31:53, 2020-05-29 20:36:52, 2020-05-29 20:41:53, 2020-05-29 20:46:53, 2020-05-29 20:51:52, 2020-05-29 20:56:52, 2020-05-29 21:01:52, 2020-05-29 21:06:52, 2020-05-29 21:11:52, 2020-05-29 21:16:52, 2020-05-29 21:21:53, 2020-05-29 21:56:53, 2020-05-30 00:46:52, 2020-05-30 00:51:52, 2020-05-30 02:21:51, 2020-05-30 03:01:51, 2020-05-30 03:16:51, 2020-05-30 03:21:52, 2020-05-30 03:26:52, 2020-05-30 03:31:51, 2020-05-30 03:36:52, 2020-05-30 03:41:52, 2020-05-30 03:46:52, 2020-05-30 13:36:51, 2020-05-30 13:41:51, 2020-05-30 13:46:50, 2020-05-30 13:51:50, 2020-05-30 14:06:51, 2020-05-30 14:11:51, 2020-05-30 14:51:50, 2020-05-30 15:11:51, 2020-05-30 17:36:50, 2020-05-30 17:51:51, 2020-05-30 17:56:50, 2020-05-30 18:06:50, 2020-05-30 19:21:51, 2020-05-30 19:26:50, 2020-05-30 19:31:50, 2020-05-30 19:36:51, 2020-05-30 19:41:51, 2020-05-30 23:26:50, 2020-05-30 23:51:50, 2020-05-31 00:41:49, 2020-05-31 00:46:50, 2020-05-31 00:51:49, 2020-05-31 01:11:49, 2020-05-31 01:16:49, 2020-05-31 01:21:50, 2020-05-31 02:41:49, 2020-05-31 02:46:50, 2020-05-31 02:51:49, 2020-05-31 03:01:49, 2020-05-31 03:06:49, 2020-05-31 03:16:50, 2020-05-31 03:21:50, 2020-05-31 03:26:49, 2020-05-31 03:31:49, 2020-05-31 03:36:49, 2020-05-31 03:41:49, 2020-05-31 04:01:50, 2020-05-31 04:06:49, 2020-05-31 04:11:49, 2020-05-31 15:36:48, 2020-05-31 15:51:49, 2020-05-31 15:56:48, 2020-05-31 16:01:48, 2020-05-31 16:11:48, 2020-05-31 16:41:48, 2020-05-31 16:56:48, 2020-05-31 17:11:48, 2020-05-31 18:51:47, 2020-05-31 20:31:48, 2020-05-31 20:41:47, 2020-05-31 20:51:47, 2020-05-31 21:11:48, 2020-05-31 21:16:47, 2020-05-31 21:21:47, 2020-05-31 21:26:47, 2020-05-31 21:31:47, 2020-05-31 21:36:47, 2020-05-31 21:41:47, 2020-05-31 21:46:47, 2020-06-01 01:41:47, 2020-06-01 01:56:47, 2020-06-01 02:01:47, 2020-06-01 02:06:47, 2020-06-01 02:11:47, 2020-06-01 02:16:48, 2020-06-01 02:21:47, 2020-06-01 02:26:48, 2020-06-01 02:31:47, 2020-06-01 02:36:48, 2020-06-01 02:41:47, 2020-06-01 03:06:47, 2020-06-01 03:31:47, 2020-06-01 03:41:47, 2020-06-01 03:56:47, 2020-06-01 04:01:47, 2020-06-01 04:11:47, 2020-06-01 04:16:47, 2020-06-01 04:21:48, 2020-06-01 04:26:47, 2020-06-01 04:41:48, 2020-06-01 13:01:46, 2020-06-01 13:46:47, 2020-06-01 13:56:47, 2020-06-01 14:01:47, 2020-06-01 14:06:47, 2020-06-01 19:26:46, 2020-06-01 20:16:46, 2020-06-01 20:56:47, 2020-06-01 21:01:46, 2020-06-01 21:11:46, 2020-06-01 21:26:46, 2020-06-01 21:31:46, 2020-06-01 21:36:46, 2020-06-01 21:46:46, 2020-06-01 21:51:46, 2020-06-01 21:56:46, 2020-06-01 22:01:46, 2020-06-01 22:06:46, 2020-06-01 22:11:46, 2020-06-01 22:16:46, 2020-06-01 22:21:46, 2020-06-01 22:26:46, 2020-06-02 01:56:46, 2020-06-02 02:36:45, 2020-06-02 02:41:45, 2020-06-02 02:46:45, 2020-06-02 02:51:45, 2020-06-02 02:56:45, 2020-06-02 03:01:45, 2020-06-02 03:06:45, 2020-06-02 03:11:45, 2020-06-02 03:16:45, 2020-06-02 03:21:45, 2020-06-02 03:26:45, 2020-06-02 04:21:45, 2020-06-02 04:26:45, 2020-06-02 04:31:45, 2020-06-02 15:31:44, 2020-06-02 15:36:44, 2020-06-02 15:41:44, 2020-06-02 15:46:44, 2020-06-02 15:51:44, 2020-06-02 15:56:43, 2020-06-02 16:01:44, 2020-06-02 20:36:43, 2020-06-02 20:51:44, 2020-06-02 21:01:43, 2020-06-02 21:41:44, 2020-06-02 22:01:44, 2020-06-02 22:41:44, 2020-06-02 22:46:44, 2020-06-02 22:56:44, 2020-06-02 23:01:44, 2020-06-02 23:06:44, 2020-06-02 23:11:43, 2020-06-02 23:16:44, 2020-06-02 23:26:43, 2020-06-02 23:31:44, 2020-06-02 23:36:43, 2020-06-03 01:16:43, 2020-06-03 01:21:43, 2020-06-03 01:31:42, 2020-06-03 02:26:43, 2020-06-03 03:46:43, 2020-06-03 03:51:43, 2020-06-03 03:56:43, 2020-06-03 04:01:43, 2020-06-03 04:06:43, 2020-06-03 04:11:43, 2020-06-03 04:16:43, 2020-06-03 04:31:42, 2020-06-03 10:21:42, 2020-06-03 16:51:41, 2020-06-03 16:56:42, 2020-06-03 17:01:41, 2020-06-03 17:11:41, 2020-06-03 17:26:42, 2020-06-03 17:36:41, 2020-06-03 17:56:42, 2020-06-03 18:11:42, 2020-06-03 18:16:41, 2020-06-03 18:26:41, 2020-06-03 21:26:42, 2020-06-03 21:31:42, 2020-06-04 00:01:41, 2020-06-04 01:21:40, 2020-06-04 01:36:41, 2020-06-04 01:46:41, 2020-06-04 01:56:41, 2020-06-04 02:01:40, 2020-06-04 02:06:41, 2020-06-04 02:11:41, 2020-06-04 02:21:40, 2020-06-04 02:26:41, 2020-06-04 02:31:40, 2020-06-04 02:36:41, 2020-06-04 02:41:41, 2020-06-04 02:46:41, 2020-06-04 02:51:40, 2020-06-04 02:56:41, 2020-06-04 03:26:40, 2020-06-04 03:31:41, 2020-06-04 03:36:40, 2020-06-04 03:51:41, 2020-06-04 03:56:40, 2020-06-04 04:01:41, 2020-06-04 04:06:40, 2020-06-04 04:11:41, 2020-06-04 04:16:40, 2020-06-04 04:21:40, 2020-06-04 05:56:40, 2020-06-04 06:06:40, 2020-06-04 12:06:39\n",
      "ID : 14\n",
      "{'2020-06-05'}\n",
      "daily_dfs is modified...\n",
      "len acc == hr : True\n",
      "2020-06-07 12:03:06, 2020-06-07 12:23:06, 2020-06-07 12:28:07, 2020-06-07 12:33:06, 2020-06-07 12:53:07, 2020-06-07 13:13:06, 2020-06-07 13:38:06, 2020-06-07 13:48:06, 2020-06-07 13:53:06, 2020-06-07 13:58:06, 2020-06-07 14:23:06, 2020-06-07 14:33:06, 2020-06-07 14:43:06, 2020-06-07 14:53:06, 2020-06-07 15:43:06, 2020-06-07 15:48:06, 2020-06-07 15:53:06, 2020-06-07 16:03:06, 2020-06-07 16:08:06, 2020-06-07 16:28:06, 2020-06-07 21:03:06, 2020-06-07 21:08:06, 2020-06-07 21:13:06, 2020-06-07 21:18:06, 2020-06-07 21:23:06, 2020-06-07 21:33:06, 2020-06-07 21:48:06, 2020-06-07 22:33:06, 2020-06-07 22:38:05, 2020-06-07 22:43:05, 2020-06-07 22:48:06, 2020-06-07 22:53:05, 2020-06-07 22:58:05, 2020-06-07 23:18:05, 2020-06-07 23:23:06, 2020-06-07 23:28:06, 2020-06-07 23:33:05, 2020-06-07 23:43:06, 2020-06-07 23:48:06, 2020-06-07 23:53:05, 2020-06-07 23:58:06, 2020-06-08 02:03:05, 2020-06-08 02:08:06, 2020-06-08 02:13:06, 2020-06-08 02:38:06, 2020-06-08 02:43:06, 2020-06-08 12:03:05, 2020-06-08 12:08:04, 2020-06-08 16:23:04, 2020-06-08 17:53:04, 2020-06-08 19:13:04, 2020-06-08 19:38:04, 2020-06-08 21:48:03, 2020-06-08 22:03:04, 2020-06-08 22:08:04, 2020-06-08 22:13:03, 2020-06-08 23:43:04, 2020-06-08 23:48:04, 2020-06-08 23:53:03, 2020-06-08 23:58:03, 2020-06-09 00:03:04, 2020-06-09 00:08:03, 2020-06-09 00:13:03, 2020-06-09 00:18:03, 2020-06-09 00:23:04, 2020-06-09 00:28:03, 2020-06-09 00:33:03, 2020-06-09 00:38:03, 2020-06-09 01:43:04, 2020-06-09 02:23:03, 2020-06-09 02:28:03, 2020-06-09 02:33:04, 2020-06-09 02:38:03, 2020-06-09 02:43:03, 2020-06-09 10:13:04, 2020-06-09 18:38:02, 2020-06-09 19:08:02, 2020-06-09 19:53:01, 2020-06-09 20:38:02, 2020-06-09 20:43:01, 2020-06-10 00:23:02, 2020-06-10 00:33:02, 2020-06-10 00:38:02, 2020-06-10 00:43:02, 2020-06-10 00:53:02, 2020-06-10 01:03:02, 2020-06-10 01:08:03, 2020-06-10 01:13:02, 2020-06-10 01:18:02, 2020-06-10 01:48:03, 2020-06-10 01:53:02, 2020-06-10 01:58:02, 2020-06-10 02:03:02, 2020-06-10 02:08:02, 2020-06-10 02:13:02, 2020-06-10 02:18:02, 2020-06-10 02:23:02, 2020-06-10 02:28:02, 2020-06-10 02:33:02, 2020-06-10 02:38:02, 2020-06-10 02:43:01, 2020-06-10 02:48:02, 2020-06-10 02:53:02, 2020-06-10 02:58:02, 2020-06-10 03:03:02, 2020-06-10 03:08:01, 2020-06-10 03:13:01, 2020-06-10 03:18:02, 2020-06-10 03:23:01, 2020-06-10 04:03:02, 2020-06-10 18:18:00, 2020-06-10 20:23:01, 2020-06-10 20:28:01, 2020-06-10 20:33:01, 2020-06-10 20:38:01, 2020-06-10 20:43:00, 2020-06-10 20:48:00, 2020-06-10 20:53:01, 2020-06-10 20:58:01, 2020-06-10 21:03:00, 2020-06-10 21:08:01, 2020-06-10 21:23:01, 2020-06-10 21:28:00, 2020-06-10 21:43:00, 2020-06-10 21:48:01, 2020-06-10 21:53:00, 2020-06-10 21:58:00, 2020-06-10 22:13:00, 2020-06-10 22:18:00, 2020-06-10 22:23:00, 2020-06-10 22:33:00, 2020-06-10 22:38:00, 2020-06-10 22:43:00, 2020-06-10 22:48:00, 2020-06-10 22:58:00, 2020-06-11 00:18:00, 2020-06-11 00:23:00, 2020-06-11 00:38:00, 2020-06-11 00:43:01, 2020-06-11 01:48:00, 2020-06-11 02:03:01, 2020-06-11 02:08:01, 2020-06-11 02:23:00, 2020-06-11 03:17:59, 2020-06-11 10:52:59, 2020-06-11 11:03:00, 2020-06-11 11:22:59, 2020-06-11 11:47:59, 2020-06-11 12:02:59, 2020-06-11 12:08:00, 2020-06-11 12:52:59, 2020-06-11 12:57:58, 2020-06-11 13:02:58, 2020-06-11 13:07:59, 2020-06-11 14:27:59, 2020-06-11 15:22:59, 2020-06-11 15:28:00, 2020-06-11 15:32:59, 2020-06-11 15:37:59, 2020-06-11 15:42:59, 2020-06-11 15:47:59, 2020-06-11 16:17:59, 2020-06-11 16:43:00, 2020-06-11 17:02:59, 2020-06-11 17:13:00, 2020-06-11 23:22:59, 2020-06-11 23:32:59, 2020-06-11 23:37:59, 2020-06-11 23:42:58, 2020-06-11 23:47:58, 2020-06-12 00:12:58, 2020-06-12 01:07:58, 2020-06-12 01:12:58, 2020-06-12 01:17:58, 2020-06-12 01:22:58, 2020-06-12 01:27:58, 2020-06-12 01:32:58, 2020-06-12 01:37:58, 2020-06-12 01:42:58, 2020-06-12 01:47:58, 2020-06-12 01:52:58, 2020-06-12 01:57:58, 2020-06-12 02:02:58, 2020-06-12 02:07:59, 2020-06-12 02:12:58, 2020-06-12 02:17:58, 2020-06-12 02:22:58, 2020-06-12 03:02:58, 2020-06-12 03:07:59, 2020-06-12 10:47:58, 2020-06-12 10:52:57, 2020-06-12 14:42:58, 2020-06-12 14:52:57, 2020-06-12 14:57:58, 2020-06-12 15:07:58, 2020-06-12 15:12:58, 2020-06-12 15:17:57, 2020-06-12 15:22:58, 2020-06-12 15:32:57, 2020-06-12 15:57:57, 2020-06-12 16:07:57, 2020-06-12 16:32:57, 2020-06-12 17:22:57, 2020-06-12 17:27:57, 2020-06-12 19:22:57, 2020-06-12 19:27:57, 2020-06-12 19:32:57, 2020-06-12 19:47:56, 2020-06-12 23:57:57, 2020-06-13 00:02:57, 2020-06-13 00:07:57, 2020-06-13 00:12:57, 2020-06-13 00:17:56, 2020-06-13 00:52:56, 2020-06-13 00:57:57, 2020-06-13 01:02:56, 2020-06-13 01:07:56, 2020-06-13 01:12:57, 2020-06-13 01:17:56, 2020-06-13 01:22:56, 2020-06-13 01:27:56, 2020-06-13 01:32:56, 2020-06-13 01:37:56, 2020-06-13 01:42:56, 2020-06-13 01:47:56, 2020-06-13 01:52:56, 2020-06-13 01:57:56, 2020-06-13 02:02:56, 2020-06-13 02:07:56, 2020-06-13 02:12:56, 2020-06-13 02:17:56, 2020-06-13 02:57:57, 2020-06-13 10:42:56, 2020-06-13 10:52:56, 2020-06-13 10:57:55, 2020-06-13 11:02:56\n",
      "ID : 15\n",
      "{'2020-07-26', '2020-07-27'}\n",
      "daily_dfs is modified...\n",
      "len acc == hr : True\n",
      "2020-07-21 01:28:54, 2020-07-21 02:58:53, 2020-07-21 03:03:54, 2020-07-21 03:08:54, 2020-07-21 03:13:54, 2020-07-21 03:18:54, 2020-07-21 03:23:54, 2020-07-21 03:28:54, 2020-07-21 03:33:54, 2020-07-21 03:38:54, 2020-07-21 03:48:53, 2020-07-21 03:53:54, 2020-07-21 03:58:54, 2020-07-21 04:03:54, 2020-07-21 04:08:54, 2020-07-21 04:13:54, 2020-07-21 04:18:54, 2020-07-21 04:23:54, 2020-07-21 04:28:54, 2020-07-22 05:13:51, 2020-07-22 05:58:52, 2020-07-22 06:03:51, 2020-07-22 06:13:51, 2020-07-23 04:23:51, 2020-07-23 04:28:50, 2020-07-23 04:33:51, 2020-07-23 04:38:51, 2020-07-23 04:43:51, 2020-07-23 05:53:51, 2020-07-23 05:58:50, 2020-07-23 06:03:51, 2020-07-23 06:08:50, 2020-07-23 06:13:51, 2020-07-23 06:18:51, 2020-07-23 06:23:51, 2020-07-23 06:28:51, 2020-07-23 06:33:50, 2020-07-23 06:38:51, 2020-07-23 06:43:51, 2020-07-23 06:48:51, 2020-07-23 06:53:50, 2020-07-23 06:58:51, 2020-07-23 07:03:50, 2020-07-23 07:08:51, 2020-07-23 07:13:50, 2020-07-23 07:18:50, 2020-07-23 07:23:51, 2020-07-24 10:23:49, 2020-07-24 14:08:49, 2020-07-24 14:38:49, 2020-07-24 14:48:49, 2020-07-24 14:53:48, 2020-07-24 18:08:49, 2020-07-24 18:18:49, 2020-07-24 18:28:48, 2020-07-24 18:38:48, 2020-07-24 20:08:50, 2020-07-24 20:13:49, 2020-07-24 20:18:50, 2020-07-24 20:23:50, 2020-07-24 20:38:49, 2020-07-24 22:18:49, 2020-07-24 22:28:49, 2020-07-24 22:33:49, 2020-07-24 22:38:50, 2020-07-24 22:43:50, 2020-07-24 22:48:49, 2020-07-24 22:58:49, 2020-07-24 23:03:50, 2020-07-24 23:38:49, 2020-07-24 23:53:49, 2020-07-24 23:58:50, 2020-07-25 00:03:49, 2020-07-25 00:28:50, 2020-07-25 03:48:50, 2020-07-25 04:18:50, 2020-07-25 04:23:50, 2020-07-25 04:28:50, 2020-07-25 04:33:50, 2020-07-25 04:38:50, 2020-07-25 04:43:50, 2020-07-25 04:48:49, 2020-07-25 06:08:49, 2020-07-25 06:13:50, 2020-07-25 06:43:49, 2020-07-25 08:38:50, 2020-07-25 08:48:49, 2020-07-25 08:53:48, 2020-07-25 09:08:48, 2020-07-25 10:43:48\n",
      "ID : 16\n",
      "{'2020-07-24'}\n",
      "daily_dfs is modified...\n",
      "len acc == hr : True\n",
      "2020-07-17 11:23:22, 2020-07-17 11:28:22, 2020-07-17 11:58:22, 2020-07-17 12:08:22, 2020-07-17 12:13:23, 2020-07-17 12:18:23, 2020-07-17 12:28:22, 2020-07-17 12:38:22, 2020-07-17 12:43:22, 2020-07-17 12:48:22, 2020-07-17 12:53:22, 2020-07-17 12:58:22, 2020-07-17 13:03:22, 2020-07-17 13:08:22, 2020-07-17 14:18:23, 2020-07-17 16:28:23, 2020-07-17 16:38:23, 2020-07-17 16:53:23, 2020-07-17 18:03:22, 2020-07-17 18:08:22, 2020-07-17 20:08:21, 2020-07-17 21:53:21, 2020-07-17 21:58:22, 2020-07-17 22:03:22, 2020-07-17 22:08:22, 2020-07-17 22:13:22, 2020-07-17 22:18:22, 2020-07-17 22:23:22, 2020-07-17 22:28:22, 2020-07-17 22:33:23, 2020-07-17 22:48:22, 2020-07-17 23:58:21, 2020-07-18 00:03:21, 2020-07-18 00:13:21, 2020-07-18 00:18:21, 2020-07-18 02:28:22, 2020-07-18 02:33:22, 2020-07-18 02:38:23, 2020-07-18 03:33:21, 2020-07-18 03:43:22, 2020-07-18 04:08:20, 2020-07-18 05:23:20, 2020-07-18 05:28:20, 2020-07-18 06:03:20, 2020-07-18 15:48:19, 2020-07-18 15:58:19, 2020-07-18 16:18:20, 2020-07-18 17:38:19, 2020-07-18 17:43:19, 2020-07-18 17:58:19, 2020-07-18 20:18:19, 2020-07-18 20:23:20, 2020-07-18 20:33:19, 2020-07-18 22:03:20, 2020-07-18 22:08:19, 2020-07-18 22:13:19, 2020-07-18 22:18:19, 2020-07-18 22:23:18, 2020-07-18 22:28:18, 2020-07-18 23:03:19, 2020-07-18 23:08:18, 2020-07-18 23:13:18, 2020-07-18 23:33:19, 2020-07-18 23:43:19, 2020-07-19 00:38:19, 2020-07-19 00:43:19, 2020-07-19 00:48:19, 2020-07-19 00:53:19, 2020-07-19 00:58:19, 2020-07-19 01:03:19, 2020-07-19 11:18:18, 2020-07-19 11:23:19, 2020-07-19 11:38:19, 2020-07-19 11:53:19, 2020-07-19 12:03:19, 2020-07-19 12:08:19, 2020-07-19 12:13:19, 2020-07-19 12:48:19, 2020-07-19 13:03:19, 2020-07-20 00:53:17, 2020-07-20 01:33:17, 2020-07-20 01:38:17, 2020-07-20 01:43:17, 2020-07-20 01:53:17, 2020-07-20 01:58:17, 2020-07-20 02:03:16, 2020-07-20 02:08:16, 2020-07-20 02:18:17, 2020-07-20 02:23:16, 2020-07-20 02:28:16, 2020-07-20 02:33:16, 2020-07-20 02:38:17, 2020-07-20 02:43:16, 2020-07-20 02:48:16, 2020-07-20 02:53:16, 2020-07-20 02:58:16, 2020-07-20 03:03:16, 2020-07-20 03:08:16, 2020-07-20 03:13:16, 2020-07-20 03:18:16, 2020-07-20 03:23:16, 2020-07-20 03:28:16, 2020-07-20 03:33:16, 2020-07-20 03:38:16, 2020-07-20 03:43:16, 2020-07-20 03:48:16, 2020-07-20 03:53:16, 2020-07-20 12:48:15, 2020-07-20 12:53:15, 2020-07-20 13:18:16, 2020-07-20 13:23:15, 2020-07-20 13:53:15, 2020-07-20 13:58:16, 2020-07-20 14:03:17, 2020-07-20 14:18:17, 2020-07-20 14:23:17, 2020-07-20 14:28:17, 2020-07-20 18:18:14, 2020-07-21 01:28:15, 2020-07-21 02:08:15, 2020-07-21 02:13:15, 2020-07-21 02:18:15, 2020-07-21 02:23:15, 2020-07-21 02:28:15, 2020-07-21 02:33:15, 2020-07-21 02:38:15, 2020-07-21 02:43:15, 2020-07-21 02:48:15, 2020-07-21 02:53:15, 2020-07-21 02:58:15, 2020-07-21 03:03:15, 2020-07-21 03:08:15, 2020-07-21 03:13:15, 2020-07-21 03:18:15, 2020-07-21 03:23:15, 2020-07-21 03:28:15, 2020-07-21 03:33:15, 2020-07-21 03:38:15, 2020-07-21 03:58:14, 2020-07-21 04:03:14, 2020-07-21 12:03:14, 2020-07-21 12:08:13, 2020-07-21 13:03:14, 2020-07-21 17:23:13, 2020-07-21 17:38:13, 2020-07-21 17:48:13, 2020-07-21 17:53:13, 2020-07-21 17:58:12, 2020-07-21 18:03:12, 2020-07-21 18:08:12, 2020-07-21 18:13:11, 2020-07-21 18:18:12, 2020-07-21 18:23:12, 2020-07-21 18:28:12, 2020-07-21 18:33:12, 2020-07-21 18:48:12, 2020-07-21 23:08:12, 2020-07-21 23:13:12, 2020-07-21 23:18:12, 2020-07-21 23:23:12, 2020-07-21 23:28:12, 2020-07-22 01:53:12, 2020-07-22 01:58:12, 2020-07-22 02:03:12, 2020-07-22 02:33:12, 2020-07-22 02:38:12, 2020-07-22 02:43:13, 2020-07-22 02:48:12, 2020-07-22 02:53:12, 2020-07-22 03:53:11, 2020-07-22 03:58:11, 2020-07-22 04:53:11, 2020-07-22 05:23:12, 2020-07-22 13:43:11, 2020-07-22 13:53:11, 2020-07-22 13:58:11, 2020-07-22 14:03:12, 2020-07-22 17:33:11, 2020-07-22 17:38:10, 2020-07-22 17:43:09, 2020-07-22 18:28:11, 2020-07-22 18:43:10, 2020-07-22 18:48:09, 2020-07-22 18:58:10, 2020-07-22 19:23:10, 2020-07-22 19:28:10, 2020-07-22 19:33:10, 2020-07-22 19:43:10, 2020-07-22 19:53:10, 2020-07-22 20:03:10, 2020-07-22 20:48:11, 2020-07-22 22:18:10, 2020-07-22 22:23:10, 2020-07-22 23:43:10, 2020-07-23 00:33:09, 2020-07-23 00:38:10, 2020-07-23 00:43:10, 2020-07-23 00:48:10, 2020-07-23 01:18:10, 2020-07-23 02:08:10, 2020-07-23 02:28:10, 2020-07-23 02:33:10, 2020-07-23 02:38:10, 2020-07-23 02:43:10, 2020-07-23 03:18:09, 2020-07-23 04:08:09, 2020-07-23 04:13:10, 2020-07-23 04:38:09, 2020-07-23 11:18:08, 2020-07-23 11:53:08, 2020-07-23 11:58:08, 2020-07-23 12:03:08, 2020-07-23 12:08:07, 2020-07-23 12:33:09, 2020-07-23 12:38:09, 2020-07-23 12:43:09, 2020-07-23 12:48:09, 2020-07-23 13:43:09, 2020-07-23 13:53:08, 2020-07-23 13:58:09, 2020-07-23 14:08:09, 2020-07-23 18:08:07, 2020-07-23 18:43:08, 2020-07-23 18:48:08, 2020-07-23 18:53:08, 2020-07-23 18:58:08, 2020-07-23 20:48:07, 2020-07-23 22:18:07, 2020-07-23 22:23:07, 2020-07-23 22:53:07, 2020-07-23 22:58:07, 2020-07-23 23:08:07, 2020-07-23 23:28:07\n"
     ]
    }
   ],
   "source": [
    "for id in range(1, len(dg_df) 1):\n",
    "    if id == 7: # Skip ID 7\n",
    "        continue\n",
    "    fl_df = pd.read_csv(f'./big-ideas-lab-glycemic-variability-and-wearable-device-data-1.1.2/{id:03}/Food_Log_{id:03}.csv')\n",
    "    acc_df = pd.read_csv(f'./big-ideas-lab-glycemic-variability-and-wearable-device-data-1.1.2/{id:03}/ACC_{id:03}.csv')\n",
    "    eda_df = pd.read_csv(f'./big-ideas-lab-glycemic-variability-and-wearable-device-data-1.1.2/{id:03}/EDA_{id:03}.csv')\n",
    "    #bvp_df = pd.read_csv(f'./big-ideas-lab-glycemic-variability-and-wearable-device-data-1.1.2/{id:03}/BVP_{id:03}.csv')\n",
    "    hr_df = pd.read_csv(f'./big-ideas-lab-glycemic-variability-and-wearable-device-data-1.1.2/{id:03}/HR_{id:03}.csv')\n",
    "    ibi_df = pd.read_csv(f'./big-ideas-lab-glycemic-variability-and-wearable-device-data-1.1.2/{id:03}/IBI_{id:03}.csv')\n",
    "    temp_df = pd.read_csv(f'./big-ideas-lab-glycemic-variability-and-wearable-device-data-1.1.2/{id:03}/TEMP_{id:03}.csv')\n",
    "    dexcom_df = pd.read_csv(f'./big-ideas-lab-glycemic-variability-and-wearable-device-data-1.1.2/{id:03}/Dexcom_{id:03}.csv')\n",
    "\n",
    "    biological_sex = dg_df.loc[dg_df['ID'] == id, 'Gender'].values[0]\n",
    "    hbA1c = dg_df.loc[dg_df['ID'] == id, 'HbA1c'].values[0]\n",
    "\n",
    "    print(f'ID : {id}')\n",
    "\n",
    "    ## datetime 변환 및 리샘플링 전처리\n",
    "    ## Dexcom Gluecose\n",
    "    dexcom_df.drop(dexcom_df.loc[:11].index, inplace=True)\n",
    "    dexcom_df.rename(columns = {'Timestamp (YYYY-MM-DDThh:mm:ss)': 'datetime'}, inplace = True)\n",
    "    dexcom_df.rename(columns = {'Glucose Value (mg/dL)': 'glucose'}, inplace = True)\n",
    "    dexcom_df = dexcom_df[['datetime', 'glucose']]\n",
    "    dexcom_df['datetime']=pd.to_datetime(dexcom_df['datetime'])\n",
    "    dexcom_df.set_index('datetime', inplace=True)\n",
    "\n",
    "    ## Food Log\n",
    "    if id == 13:\n",
    "        fl_df['time_begin']=pd.to_datetime(fl_df['time_begin'], format='%m/%d/%Y %H:%M')\n",
    "    else:\n",
    "        fl_df['time_begin']=pd.to_datetime(fl_df['time_begin'])\n",
    "    fl_df.index = pd.DatetimeIndex(fl_df[\"time_begin\"])\n",
    "    fl_df = fl_df.sort_index()\n",
    "    \n",
    "    ## Accelerometer\n",
    "    acc_df['datetime']=pd.to_datetime(acc_df['datetime'])\n",
    "    #acc_df = acc_df.interpolate(method='time')\n",
    "    acc_df['vector_mag'] = calculate_mag(acc_df[' acc_x'], acc_df[' acc_y'], acc_df[' acc_z'])\n",
    "    acc_df.drop(columns=[' acc_x', ' acc_y', ' acc_z'], inplace=True)\n",
    "    #acc_resampled = acc_df.set_index('datetime').resample('5Min').mean().interpolate(method='time')\n",
    "    acc_resampled = pd.DataFrame()\n",
    "    acc_resampled['ACC_Mean'] = acc_df.set_index('datetime')['vector_mag'].resample('5Min').mean().interpolate(method='time')\n",
    "    acc_resampled['ACC_Std'] = acc_df.set_index('datetime')['vector_mag'].resample('5Min').std().interpolate(method='time')\n",
    "    acc_resampled['ACC_Min'] = acc_df.set_index('datetime')['vector_mag'].resample('5Min').min().interpolate(method='time')\n",
    "    acc_resampled['ACC_Max'] = acc_df.set_index('datetime')['vector_mag'].resample('5Min').max().interpolate(method='time')\n",
    "    acc_resampled['ACC_Q1G'] = acc_df.set_index('datetime')['vector_mag'].resample('5Min').quantile(0.25).interpolate(method='time')\n",
    "    acc_resampled['ACC_Q3G'] = acc_df.set_index('datetime')['vector_mag'].resample('5Min').quantile(0.75).interpolate(method='time')\n",
    "    acc_resampled['ACC_Skew'] = acc_df.set_index('datetime')['vector_mag'].resample('5Min').apply(lambda x: x.skew()).interpolate(method='time')\n",
    "    \n",
    "    ## EDA\n",
    "    eda_df['datetime']=pd.to_datetime(eda_df['datetime'])\n",
    "    #eda_df[' eda'] = eda_df[' eda'].interpolate(method='time')\n",
    "    #eda_resampled = eda_df.set_index('datetime').resample('5Min').mean().interpolate(method='time')\n",
    "    eda_resampled = pd.DataFrame()\n",
    "    eda_resampled['EDA_Mean'] = eda_df.set_index('datetime')[' eda'].resample('5Min').mean().interpolate(method='time')\n",
    "    eda_resampled['EDA_Std'] = eda_df.set_index('datetime')[' eda'].resample('5Min').std().interpolate(method='time')\n",
    "    eda_resampled['EDA_Min'] = eda_df.set_index('datetime')[' eda'].resample('5Min').min().interpolate(method='time')\n",
    "    eda_resampled['EDA_Max'] = eda_df.set_index('datetime')[' eda'].resample('5Min').max().interpolate(method='time')\n",
    "    eda_resampled['EDA_Q1G'] = eda_df.set_index('datetime')[' eda'].resample('5Min').quantile(0.25).interpolate(method='time')\n",
    "    eda_resampled['EDA_Q3G'] = eda_df.set_index('datetime')[' eda'].resample('5Min').quantile(0.75).interpolate(method='time')\n",
    "    eda_resampled['EDA_Skew'] = eda_df.set_index('datetime')[' eda'].resample('5Min').apply(lambda eda: eda.skew()).interpolate(method='time')\n",
    "    eda_resampled['peakEDA'] = eda_df.set_index('datetime')[' eda'].resample('5Min').apply(lambda eda: peak_eda(eda)).interpolate(method='time')\n",
    "    ## Heart Rate\n",
    "    if id == 1:\n",
    "        hr_df['datetime']=pd.to_datetime(hr_df['datetime'], format='%m/%d/%y %H:%M')\n",
    "    else:\n",
    "        hr_df['datetime']=pd.to_datetime(hr_df['datetime'])\n",
    "    #hr_df[' hr'] = hr_df[' hr'].interpolate(method='time')\n",
    "    #hr_resampled = hr_df.set_index('datetime').resample('5Min').mean().interpolate(method='time')\n",
    "    hr_resampled = pd.DataFrame()\n",
    "    hr_resampled['HR_Mean'] = hr_df.set_index('datetime')[' hr'].resample('5Min').mean().interpolate(method='time')\n",
    "    hr_resampled['HR_Std'] = hr_df.set_index('datetime')[' hr'].resample('5Min').std().interpolate(method='time')\n",
    "    hr_resampled['HR_Min'] = hr_df.set_index('datetime')[' hr'].resample('5Min').min().interpolate(method='time')\n",
    "    hr_resampled['HR_Max'] = hr_df.set_index('datetime')[' hr'].resample('5Min').max().interpolate(method='time')\n",
    "    hr_resampled['HR_Q1G'] = hr_df.set_index('datetime')[' hr'].resample('5Min').quantile(0.25).interpolate(method='time')\n",
    "    hr_resampled['HR_Q3G'] = hr_df.set_index('datetime')[' hr'].resample('5Min').quantile(0.75).interpolate(method='time')\n",
    "    hr_resampled['HR_Skew'] = hr_df.set_index('datetime')[' hr'].resample('5Min').apply(lambda hr: hr.skew()).interpolate(method='time')\n",
    "    \n",
    "    ## Temperature\n",
    "    temp_df['datetime']=pd.to_datetime(temp_df['datetime'])\n",
    "    #temp_df[' temp'] = temp_df[' temp'].interpolate(method='time')\n",
    "    #temp_resampled = temp_df.set_index('datetime').resample('5Min').mean().interpolate(method='time')\n",
    "    temp_resampled = pd.DataFrame()\n",
    "    temp_resampled['TEMP_Mean'] = temp_df.set_index('datetime')[' temp'].resample('5Min').mean().interpolate(method='time')\n",
    "    temp_resampled['TEMP_Std'] = temp_df.set_index('datetime')[' temp'].resample('5Min').std().interpolate(method='time')\n",
    "    temp_resampled['TEMP_Min'] = temp_df.set_index('datetime')[' temp'].resample('5Min').min().interpolate(method='time')\n",
    "    temp_resampled['TEMP_Max'] = temp_df.set_index('datetime')[' temp'].resample('5Min').max().interpolate(method='time')\n",
    "    temp_resampled['TEMP_Q1G'] = temp_df.set_index('datetime')[' temp'].resample('5Min').quantile(0.25).interpolate(method='time')\n",
    "    temp_resampled['TEMP_Q3G'] = temp_df.set_index('datetime')[' temp'].resample('5Min').quantile(0.75).interpolate(method='time')\n",
    "    temp_resampled['TEMP_Skew'] = temp_df.set_index('datetime')[' temp'].resample('5Min').apply(lambda temp: temp.skew()).interpolate(method='time')\n",
    "    \n",
    "    ## IBI\n",
    "    ibi_df['datetime']=pd.to_datetime(ibi_df['datetime'])\n",
    "    #ibi_df[' ibi'] = ibi_df[' ibi'].interpolate(method='time')\n",
    "    ibi_df[' ibi'] = ibi_df[' ibi'] * ibimultiplier # 초 -> 밀리초 단위로\n",
    "    #ibi_resampled = ibi_df.set_index('datetime').resample('5Min').first().interpolate(method='time')\n",
    "    ibi_resampled = pd.DataFrame()\n",
    "    ibi_resampled['minHRV'] = ibi_df.set_index('datetime')[' ibi'].resample('5Min').min().interpolate(method='time')\n",
    "    ibi_resampled['maxHRV'] = ibi_df.set_index('datetime')[' ibi'].resample('5Min').max().interpolate(method='time')\n",
    "    ibi_resampled['medianHRV'] = ibi_df.set_index('datetime')[' ibi'].resample('5Min').median().interpolate(method='time')\n",
    "    ibi_resampled['meanHRV'] = ibi_df.set_index('datetime')[' ibi'].resample('5Min').mean().interpolate(method='time')\n",
    "    ibi_resampled['SDNN'] = ibi_df.set_index('datetime')[' ibi'].resample('5Min').apply(lambda ibi: sdnn(ibi)).interpolate(method='time')\n",
    "    ibi_resampled['NN50'] = ibi_df.set_index('datetime')[' ibi'].resample('5Min').apply(lambda ibi: nn50(ibi)).interpolate(method='time')\n",
    "    ibi_resampled['pNN50'] = ibi_df.set_index('datetime')[' ibi'].resample('5Min').apply(lambda ibi: pnn50(ibi)).interpolate(method='time')\n",
    "    ibi_resampled['RMSSD'] = ibi_df.set_index('datetime')[' ibi'].resample('5Min').apply(lambda ibi: rmssd(ibi)).interpolate(method='time')\n",
    "\n",
    "    ## 날짜를 기준으로 데이터프레임을 분할하여 리스트에 저장\n",
    "    daily_dfs = {'fl':[], 'acc':[], 'eda':[], 'hr':[], 'ibi':[], 'temp':[], 'bvp':[], 'dexcom':[]}\n",
    "    for name, group in dexcom_df.groupby(dexcom_df.index.date):\n",
    "        daily_dfs['dexcom'].append(group)\n",
    "    for name, group in fl_df.groupby(fl_df['time_begin'].dt.date):\n",
    "        daily_dfs['fl'].append(group)\n",
    "    for name, group in acc_resampled.groupby(acc_resampled.index.date):\n",
    "        daily_dfs['acc'].append(group)\n",
    "    for name, group in eda_resampled.groupby(eda_resampled.index.date):\n",
    "        daily_dfs['eda'].append(group)\n",
    "    for name, group in hr_resampled.groupby(hr_resampled.index.date):\n",
    "        daily_dfs['hr'].append(group)\n",
    "    for name, group in ibi_resampled.groupby(ibi_resampled.index.date):\n",
    "        daily_dfs['ibi'].append(group)\n",
    "    for name, group in temp_resampled.groupby(temp_resampled.index.date):\n",
    "        daily_dfs['temp'].append(group)\n",
    "\n",
    "    isok, daily_dfs = validate_daily_dfs(daily_dfs=daily_dfs)\n",
    "    if isok == False:\n",
    "        print(f'daily_dfs is modified...')\n",
    "        \n",
    "    base_df = pd.concat(daily_dfs['dexcom'])\n",
    "    \n",
    "    ## *_df 이름의 데이터프레임으로 리뉴얼\n",
    "\n",
    "    acc_df.drop(acc_df.index, inplace=True)\n",
    "    eda_df.drop(eda_df.index, inplace=True)\n",
    "    hr_df.drop(hr_df.index, inplace=True)\n",
    "    ibi_df.drop(ibi_df.index, inplace=True)\n",
    "    temp_df.drop(temp_df.index, inplace=True)\n",
    "\n",
    "    if len(daily_dfs['acc']) > 0:\n",
    "        acc_df = pd.concat(daily_dfs['acc'])\n",
    "    if len(daily_dfs['eda']) > 0:\n",
    "        eda_df = pd.concat(daily_dfs['eda'])\n",
    "    if len(daily_dfs['hr']) > 0:   \n",
    "        hr_df = pd.concat(daily_dfs['hr'])\n",
    "    if len(daily_dfs['ibi']) > 0:\n",
    "        ibi_df = pd.concat(daily_dfs['ibi'])\n",
    "    if len(daily_dfs['temp']) > 0:\n",
    "        temp_df = pd.concat(daily_dfs['temp'])\n",
    "    \n",
    "    ###### activitybouts\n",
    "    print(f'len acc == hr : {len(acc_df) == len(hr_df)}')\n",
    "    if len(acc_df) != len(hr_df):\n",
    "        print(f\"acc : {acc_df.index[0].strftime('%Y-%m-%d %H:%M:%S')} ~ {acc_df.index[-1].strftime('%Y-%m-%d %H:%M:%S')} : {len(acc_df)}\")\n",
    "        print(f\"hr : {hr_df.index[0].strftime('%Y-%m-%d %H:%M:%S')} ~ {hr_df.index[-1].strftime('%Y-%m-%d %H:%M:%S')} : {len(hr_df)}\")\n",
    "        # Get the intersection of the indices\n",
    "        common_index = acc_df.index.intersection(hr_df.index)\n",
    "        # Filter both dataframes to only include the common indices\n",
    "        acc_df = acc_df.loc[common_index]\n",
    "        hr_df = hr_df.loc[common_index]\n",
    "        print(f'After preprocessing-> len acc == hr : {len(acc_df) == len(hr_df)}')\n",
    "\n",
    "    resultlen , returndf = exercisepts(acc_df['ACC_Mean'], hr_df['HR_Mean'], acc_df.index)\n",
    "    acc_df['Activity_bouts'] = returndf['Activity Bouts'].values\n",
    "    \n",
    "    ## 윈도우 크기 설정\n",
    "    window_size = '24h'\n",
    "\n",
    "    ## 각 타임스탬프에 대해 24시간 롤링 윈도우를 계산\n",
    "    for timestamp in base_df.index:\n",
    "        window_start = timestamp - pd.Timedelta(window_size)\n",
    "        window_end = timestamp \n",
    "        \n",
    "        if window_start < base_df.index[0]:\n",
    "            continue\n",
    "\n",
    "        # df_glucose의 롤링 윈도우 범위 내의 데이터 선택\n",
    "        glucose_window = base_df.loc[window_start:window_end]\n",
    "        # df_other의 동일한 범위 내의 데이터 선택\n",
    "        fl_window = fl_df.loc[window_start:window_end]# - pd.Timedelta('5m')]\n",
    "        acc_window = acc_df.loc[window_start:window_end]# - pd.Timedelta('5m')]\n",
    "        eda_window = eda_df.loc[window_start:window_end]# - pd.Timedelta('5m')]\n",
    "        hr_window = hr_df.loc[window_start:window_end]# - pd.Timedelta('5m')]\n",
    "        ibi_window = ibi_df.loc[window_start:window_end]# - pd.Timedelta('5m')]\n",
    "        temp_window = temp_df.loc[window_start:window_end]# - pd.Timedelta('5m')]\n",
    "\n",
    "\n",
    "        ##### Wake Time\n",
    "        # 1. 현재 값이 Historical 평균보다 작은지 확인\n",
    "        hr_historical = hr_window\n",
    "        acc_historical = acc_window\n",
    "        hr_mean_avg = hr_historical['HR_Mean'].mean()\n",
    "        hr_std_avg = hr_historical['HR_Std'].mean()\n",
    "        acc_mean_avg = acc_historical['ACC_Mean'].mean()\n",
    "        acc_std_avg = acc_historical['ACC_Std'].mean()\n",
    "        '''\n",
    "        base_df.loc[window_end, 'HR_Mean_below_avg'] = (hr_historical.iloc[-1]['HR_Mean'] < hr_mean_avg).astype(int)\n",
    "        base_df.loc[window_end, 'HR_Std_below_avg'] = (hr_historical.iloc[-1]['HR_Std'] < hr_std_avg).astype(int)\n",
    "        base_df.loc[window_end, 'ACC_Mean_below_avg'] = (acc_historical.iloc[-1]['ACC_Mean'] < acc_mean_avg).astype(int)\n",
    "        base_df.loc[window_end, 'ACC_Std_below_avg'] = (acc_historical.iloc[-1]['ACC_Std'] < acc_std_avg).astype(int)\n",
    "        base_df.loc[window_end, 'points'] = base_df.loc[window_end, ['HR_Mean_below_avg', 'HR_Std_below_avg', 'ACC_Mean_below_avg', 'ACC_Std_below_avg']].sum(axis=1)\n",
    "        '''\n",
    "        base_df.loc[window_end, 'points'] = (\n",
    "            (hr_historical.iloc[-1]['HR_Mean'] < hr_mean_avg).astype(int)  \n",
    "            (hr_historical.iloc[-1]['HR_Std'] < hr_std_avg).astype(int)  \n",
    "            (acc_historical.iloc[-1]['ACC_Mean'] < acc_mean_avg).astype(int)  \n",
    "            (acc_historical.iloc[-1]['ACC_Std'] < acc_std_avg).astype(int)\n",
    "        )\n",
    "\n",
    "        # 2. 점수가 2보다 크면 이진 값을 할당합니다.\n",
    "        base_df.loc[window_end, 'binary'] = (base_df.loc[window_end, 'points'] >= 2).astype(int) # 2보다 작으면 0, 2보다 크거나 같으면 1\n",
    "\n",
    "        # 3시간 동안의 데이터를 이동 평균(rolling)으로 계산합니다.\n",
    "        # 데이터가 5분 간격이라고 가정하면, 3시간은 36 interval입니다.\n",
    "        rolling_window3hr = base_df.loc[window_end - pd.Timedelta('3hr'):window_end]\n",
    "        base_df.loc[window_end, 'rolling_avg'] = rolling_window3hr['binary'].mean()\n",
    "                \n",
    "\n",
    "        ##### fl\n",
    "\n",
    "        calories2hr = 0.\n",
    "        protein2hr = 0.\n",
    "        sugar2hr = 0.\n",
    "        carbs2hr = 0.\n",
    "        calories8hr = 0.\n",
    "        protein8hr = 0.\n",
    "        sugar8hr = 0.\n",
    "        carbs8hr = 0.\n",
    "        calories24hr = 0.\n",
    "        protein24hr = 0.\n",
    "        sugar24hr = 0.\n",
    "        carbs24hr = 0.\n",
    "        \n",
    "        flwindow5min = fl_window[window_end - pd.Timedelta('5m') : window_end]# - pd.Timedelta('5m')]\n",
    "        flwindow2hr = fl_window[window_end - pd.Timedelta('2h') : window_end]# - pd.Timedelta('5m')]\n",
    "        flwindow8hr = fl_window[window_end - pd.Timedelta('8h') : window_end]# - pd.Timedelta('5m')]\n",
    "        flwindow24hr = fl_window[window_end - pd.Timedelta('24h') : window_end]# - pd.Timedelta('5m')]\n",
    "\n",
    "        eat = 0\n",
    "        if len(flwindow5min) > 0:\n",
    "            eat = 1\n",
    "        eatcnt2hr = len(flwindow2hr)\n",
    "        eatcnt8hr = len(flwindow8hr)\n",
    "        eatcnt24hr = len(flwindow24hr)\n",
    "\n",
    "        eatmean2hr = eatcnt2hr / (2 * 60 / 5)  # 2시간 동안 5분 간격으로 나눈 시간 간격 수\n",
    "        eatmean8hr = eatcnt8hr / (8 * 60 / 5)  # 8시간 동안 5분 간격으로 나눈 시간 간격 수\n",
    "        eatmean24hr = eatcnt24hr / (24 * 60 / 5)  # 24시간 동안 5분 간격으로 나눈 시간 간격 수\n",
    "        \n",
    "        calories2hr = flwindow2hr['calorie'].values.sum()\n",
    "        protein2hr = flwindow2hr['protein'].values.sum()\n",
    "        sugar2hr = flwindow2hr['sugar'].values.sum()\n",
    "        carbs2hr = flwindow2hr['total_carb'].values.sum()\n",
    "        \n",
    "        calories8hr = flwindow8hr['calorie'].values.sum()\n",
    "        protein8hr = flwindow8hr['protein'].values.sum()\n",
    "        sugar8hr = flwindow8hr['sugar'].values.sum()\n",
    "        carbs8hr = flwindow8hr['total_carb'].values.sum()\n",
    "        \n",
    "        calories24hr = fl_window['calorie'].values.sum()\n",
    "        protein24hr = fl_window['protein'].values.sum()\n",
    "        sugar24hr = fl_window['sugar'].values.sum()\n",
    "        carbs24hr = fl_window['total_carb'].values.sum()\n",
    "        \n",
    "        #print(f\"window : [{(window_end - pd.Timedelta('24h')).strftime('%Y-%m-%d %H:%M:%S')} ~ {(window_end - pd.Timedelta('5m')).strftime('%Y-%m-%d %H:%M:%S')}]\")\n",
    "        #print(f\"[{window24hr.index[0].strftime('%Y-%m-%d %H:%M:%S')} ~ {window24hr.index[-1].strftime('%Y-%m-%d %H:%M:%S')}] ID: {id}, sugar24hr: {sugar24hr}, glucose: {base_df.loc[window_end, 'glucose']}\")\n",
    "\n",
    "        ###### acc\n",
    "        accwindow2hr = acc_window[window_end - pd.Timedelta('2h') : window_end]# - pd.Timedelta('5m')]\n",
    "        accwindow1hr = acc_window[window_end - pd.Timedelta('1h') : window_end]# - pd.Timedelta('5m')]\n",
    "\n",
    "        acc_mean_2hrs = accwindow2hr['ACC_Mean'].values.mean()\n",
    "        acc_max_2hrs = accwindow2hr['ACC_Max'].values.max()\n",
    "\n",
    "        activity24 = acc_window['Activity_bouts'].values.mean()\n",
    "        activity1hr = accwindow1hr['Activity_bouts'].values.sum()\n",
    "\n",
    "        ###### eda\n",
    "\n",
    "        # peak detection\n",
    "        edawindow2hr = eda_window[window_end - pd.Timedelta('2h') : window_end]# - pd.Timedelta('5m')]\n",
    "\n",
    "        peakEDA2hr_sum = edawindow2hr['peakEDA'].values.sum()\n",
    "        peakEDA2hr_mean = edawindow2hr['peakEDA'].values.mean()\n",
    "\n",
    "        ##### results\n",
    "        #print(f\"(end of window): {window_end}, glucose: {glucose_window['glucose']}, sugar24hr: {sugar24hr}\")\n",
    "        # df_glucose의 개인화된 평균 및 표준 편차 계산 -> Class를 나누는 값 계산\n",
    "        #print(f\"[{window_start.strftime('%Y-%m-%d %H:%M:%S')} ~ {window_end.strftime('%Y-%m-%d %H:%M:%S')}] ID: {id}, sugar24hr: {sugar24hr}, glucose: {base_df.loc[window_end, 'glucose']}\")\n",
    "        base_df.loc[window_end, 'datetime'] = window_end.strftime('%Y-%m-%d %H:%M:%S')\n",
    "        base_df.loc[window_end, 'personalMean'] = glucose_window['glucose'].mean()\n",
    "        base_df.loc[window_end, 'personalStd'] = glucose_window['glucose'].std()\n",
    "        base_df.loc[window_end, 'ID']=id\n",
    "        base_df.loc[window_end, 'HbA1c']=hbA1c\n",
    "        base_df.loc[window_end, 'Biological Sex']=biological_sex\n",
    "        base_df.loc[window_end, 'calories2hr']=round(calories2hr,2)\n",
    "        base_df.loc[window_end, 'protein2hr']=round(protein2hr,2)\n",
    "        base_df.loc[window_end, 'sugar2hr']=round(sugar2hr,2)\n",
    "        base_df.loc[window_end, 'carbs2hr']=round(carbs2hr,2)\n",
    "        base_df.loc[window_end, 'calories8hr']=round(calories8hr,2)\n",
    "        base_df.loc[window_end, 'protein8hr']=round(protein8hr,2)\n",
    "        base_df.loc[window_end, 'sugar8hr']=round(sugar8hr,2)\n",
    "        base_df.loc[window_end, 'carbs8hr']=round(carbs8hr,2)\n",
    "        base_df.loc[window_end, 'calories24hr']=round(calories24hr,2)\n",
    "        base_df.loc[window_end, 'protein24hr']=round(protein24hr,2)\n",
    "        base_df.loc[window_end, 'sugar24hr']=round(sugar24hr,2)\n",
    "        base_df.loc[window_end, 'carbs24hr']=round(carbs24hr,2)\n",
    "        base_df.loc[window_end, 'eat']=eat\n",
    "        base_df.loc[window_end, 'eatcnt2hr']=eatcnt2hr\n",
    "        base_df.loc[window_end, 'eatcnt8hr']=eatcnt8hr\n",
    "        base_df.loc[window_end, 'eatcnt24hr']=eatcnt24hr\n",
    "        base_df.loc[window_end, 'eatmean2hr']=eatmean2hr\n",
    "        base_df.loc[window_end, 'eatmean8hr']=eatmean8hr\n",
    "        base_df.loc[window_end, 'eatmean24hr']=eatmean24hr\n",
    "        base_df.loc[window_end, 'acc_mean']=round(acc_window.iloc[-1]['ACC_Mean'],2)\n",
    "        base_df.loc[window_end, 'acc_std']=round(acc_window.iloc[-1]['ACC_Std'],2)\n",
    "        base_df.loc[window_end, 'acc_min']=round(acc_window.iloc[-1]['ACC_Min'],2)\n",
    "        base_df.loc[window_end, 'acc_max']=round(acc_window.iloc[-1]['ACC_Max'],2)\n",
    "        base_df.loc[window_end, 'acc_q1g']=round(acc_window.iloc[-1]['ACC_Q1G'],2)\n",
    "        base_df.loc[window_end, 'acc_q3g']=round(acc_window.iloc[-1]['ACC_Q3G'],2)\n",
    "        base_df.loc[window_end, 'acc_skew']=round(acc_window.iloc[-1]['ACC_Skew'],2)\n",
    "        base_df.loc[window_end, 'acc_mean_2hrs']=round(acc_mean_2hrs,2)\n",
    "        base_df.loc[window_end, 'acc_max_2hrs']=round(acc_max_2hrs,2)\n",
    "        base_df.loc[window_end, 'eda_mean']=round(eda_window.iloc[-1]['EDA_Mean'],2)\n",
    "        base_df.loc[window_end, 'eda_std']=round(eda_window.iloc[-1]['EDA_Std'],2)\n",
    "        base_df.loc[window_end, 'eda_min']=round(eda_window.iloc[-1]['EDA_Min'],2)\n",
    "        base_df.loc[window_end, 'eda_max']=round(eda_window.iloc[-1]['EDA_Max'],2)\n",
    "        base_df.loc[window_end, 'eda_q1g']=round(eda_window.iloc[-1]['EDA_Q1G'],2)\n",
    "        base_df.loc[window_end, 'eda_q3g']=round(eda_window.iloc[-1]['EDA_Q3G'],2)\n",
    "        base_df.loc[window_end, 'eda_skew']=round(eda_window.iloc[-1]['EDA_Skew'],2)\n",
    "        base_df.loc[window_end, 'PeakEDA']=eda_window.iloc[-1]['peakEDA']\n",
    "        base_df.loc[window_end, 'PeakEDA2hr_sum']=round(peakEDA2hr_sum,2)\n",
    "        base_df.loc[window_end, 'PeakEDA2hr_mean']=round(peakEDA2hr_mean,2)\n",
    "        base_df.loc[window_end, 'hr_mean']=round(hr_window.iloc[-1]['HR_Mean'],2)\n",
    "        base_df.loc[window_end, 'hr_std']=round(hr_window.iloc[-1]['HR_Std'],2)\n",
    "        base_df.loc[window_end, 'hr_min']=round(hr_window.iloc[-1]['HR_Min'],2)\n",
    "        base_df.loc[window_end, 'hr_max']=round(hr_window.iloc[-1]['HR_Max'],2)\n",
    "        base_df.loc[window_end, 'hr_q1g']=round(hr_window.iloc[-1]['HR_Q1G'],2)\n",
    "        base_df.loc[window_end, 'hr_q3g']=round(hr_window.iloc[-1]['HR_Q3G'],2)\n",
    "        base_df.loc[window_end, 'hr_skew']=round(hr_window.iloc[-1]['HR_Skew'],2)\n",
    "        base_df.loc[window_end, 'maxHRV']=round(ibi_window.iloc[-1]['maxHRV'],2)\n",
    "        base_df.loc[window_end, 'minHRV']=round(ibi_window.iloc[-1]['minHRV'],2)\n",
    "        base_df.loc[window_end, 'medianHRV']=round(ibi_window.iloc[-1]['medianHRV'],2)\n",
    "        base_df.loc[window_end, 'meanHRV']=round(ibi_window.iloc[-1]['meanHRV'],2)\n",
    "        base_df.loc[window_end, 'SDNN']=round(ibi_window.iloc[-1]['SDNN'],2)\n",
    "        base_df.loc[window_end, 'NN50']=round(ibi_window.iloc[-1]['NN50'],2)\n",
    "        base_df.loc[window_end, 'pNN50']=round(ibi_window.iloc[-1]['pNN50'],2)\n",
    "        base_df.loc[window_end, 'RMSSD']=round(ibi_window.iloc[-1]['RMSSD'],2)\n",
    "        base_df.loc[window_end, 'temp_mean']=round(temp_window.iloc[-1]['TEMP_Mean'],2)\n",
    "        base_df.loc[window_end, 'temp_std']=round(temp_window.iloc[-1]['TEMP_Std'],2)\n",
    "        base_df.loc[window_end, 'temp_min']=round(temp_window.iloc[-1]['TEMP_Min'],2)\n",
    "        base_df.loc[window_end, 'temp_max']=round(temp_window.iloc[-1]['TEMP_Max'],2)\n",
    "        base_df.loc[window_end, 'temp_q1g']=round(temp_window.iloc[-1]['TEMP_Q1G'],2)\n",
    "        base_df.loc[window_end, 'temp_q3g']=round(temp_window.iloc[-1]['TEMP_Q3G'],2)\n",
    "        base_df.loc[window_end, 'temp_skew']=round(temp_window.iloc[-1]['TEMP_Skew'],2)\n",
    "        base_df.loc[window_end, 'Activity_bouts']=acc_window.iloc[-1]['Activity_bouts']\n",
    "        base_df.loc[window_end, 'Activity24']=activity24\n",
    "        base_df.loc[window_end, 'Activity1hr']=activity1hr\n",
    "\n",
    "\n",
    "    ###### crhythm\n",
    "    hrfrommid, minfrommid = crhythm(base_df['datetime'])\n",
    "    base_df['Minfrommid'] = minfrommid\n",
    "    base_df['Hourfrommid'] = hrfrommid\n",
    "\n",
    "\n",
    "    # rolling average의 기울기(slope)를 계산합니다.\n",
    "    base_df['rolling_slope'] = base_df['rolling_avg'].diff()\n",
    "    # 4. 'WakeTime' 칼럼에 값을 저장합니다.\n",
    "    base_df['WakeTime'] = detect_wake_time(base_df)\n",
    "\n",
    "    # WakeTime을 확인하기 위한 출력\n",
    "    wake_time_indices = base_df[base_df['WakeTime'] == 1].index.strftime('%Y-%m-%d %H:%M:%S')\n",
    "    wake_times_str = ', '.join(map(str, wake_time_indices))\n",
    "    print(wake_times_str)\n",
    "\n",
    "    ###### 각 측정값을 PersHigh, PersLow, PersNorm으로 분류\n",
    "    base_df['label'] = base_df.apply(\n",
    "        lambda row: classify_glucose(row['glucose'], row['personalMean'], row['personalStd']), axis=1\n",
    "    )\n",
    "\n",
    "    base_df.drop(columns = ['points', 'binary', 'rolling_slope', 'rolling_avg'], inplace = True)\n",
    "    base_df.drop(columns=['personalMean', 'personalStd'], inplace=True)\n",
    "\n",
    "    df = pd.concat([df, base_df], ignore_index=True)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34165\n"
     ]
    }
   ],
   "source": [
    "print(len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "calories2hr       4103\n",
       "protein2hr        4103\n",
       "sugar2hr          4103\n",
       "carbs2hr          4103\n",
       "calories8hr       4103\n",
       "                  ... \n",
       "ID                4103\n",
       "HbA1c             4103\n",
       "Biological Sex    4103\n",
       "WakeTime             0\n",
       "label                0\n",
       "Length: 72, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna().reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calories2hr</th>\n",
       "      <th>protein2hr</th>\n",
       "      <th>sugar2hr</th>\n",
       "      <th>carbs2hr</th>\n",
       "      <th>calories8hr</th>\n",
       "      <th>protein8hr</th>\n",
       "      <th>sugar8hr</th>\n",
       "      <th>carbs8hr</th>\n",
       "      <th>calories24hr</th>\n",
       "      <th>protein24hr</th>\n",
       "      <th>...</th>\n",
       "      <th>Activity1hr</th>\n",
       "      <th>Minfrommid</th>\n",
       "      <th>Hourfrommid</th>\n",
       "      <th>glucose</th>\n",
       "      <th>datetime</th>\n",
       "      <th>ID</th>\n",
       "      <th>HbA1c</th>\n",
       "      <th>Biological Sex</th>\n",
       "      <th>WakeTime</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2274</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>410.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>3118.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>684</td>\n",
       "      <td>11</td>\n",
       "      <td>107.0</td>\n",
       "      <td>2020-02-22 11:23:34</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersLow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2319</th>\n",
       "      <td>130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>909</td>\n",
       "      <td>15</td>\n",
       "      <td>93.0</td>\n",
       "      <td>2020-02-22 15:08:33</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersLow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2325</th>\n",
       "      <td>130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>939</td>\n",
       "      <td>16</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2020-02-22 15:38:34</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersLow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2326</th>\n",
       "      <td>130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>944</td>\n",
       "      <td>16</td>\n",
       "      <td>102.0</td>\n",
       "      <td>2020-02-22 15:43:34</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersLow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2327</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>949</td>\n",
       "      <td>16</td>\n",
       "      <td>104.0</td>\n",
       "      <td>2020-02-22 15:48:33</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersLow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2328</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>954</td>\n",
       "      <td>16</td>\n",
       "      <td>105.0</td>\n",
       "      <td>2020-02-22 15:53:33</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersLow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2333</th>\n",
       "      <td>130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2430.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>979</td>\n",
       "      <td>16</td>\n",
       "      <td>105.0</td>\n",
       "      <td>2020-02-22 16:18:33</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersLow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2334</th>\n",
       "      <td>130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2430.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>984</td>\n",
       "      <td>16</td>\n",
       "      <td>112.0</td>\n",
       "      <td>2020-02-22 16:23:33</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersNorm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2335</th>\n",
       "      <td>130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2430.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>989</td>\n",
       "      <td>16</td>\n",
       "      <td>124.0</td>\n",
       "      <td>2020-02-22 16:28:33</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersNorm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2336</th>\n",
       "      <td>130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2430.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>994</td>\n",
       "      <td>17</td>\n",
       "      <td>137.0</td>\n",
       "      <td>2020-02-22 16:33:33</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersNorm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2337</th>\n",
       "      <td>130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2430.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>999</td>\n",
       "      <td>17</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2020-02-22 16:38:33</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersNorm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2338</th>\n",
       "      <td>130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2430.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1004</td>\n",
       "      <td>17</td>\n",
       "      <td>156.0</td>\n",
       "      <td>2020-02-22 16:43:33</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersHigh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2339</th>\n",
       "      <td>130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2430.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1009</td>\n",
       "      <td>17</td>\n",
       "      <td>155.0</td>\n",
       "      <td>2020-02-22 16:48:33</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersHigh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2341</th>\n",
       "      <td>130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2430.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1019</td>\n",
       "      <td>17</td>\n",
       "      <td>133.0</td>\n",
       "      <td>2020-02-22 16:58:33</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersNorm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2345</th>\n",
       "      <td>130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2430.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1039</td>\n",
       "      <td>17</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2020-02-22 17:18:33</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersLow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2348</th>\n",
       "      <td>130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2430.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1054</td>\n",
       "      <td>18</td>\n",
       "      <td>103.0</td>\n",
       "      <td>2020-02-22 17:33:33</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersLow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2349</th>\n",
       "      <td>130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2430.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1059</td>\n",
       "      <td>18</td>\n",
       "      <td>106.0</td>\n",
       "      <td>2020-02-22 17:38:33</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersLow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2350</th>\n",
       "      <td>130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2430.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1064</td>\n",
       "      <td>18</td>\n",
       "      <td>109.0</td>\n",
       "      <td>2020-02-22 17:43:33</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersNorm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2351</th>\n",
       "      <td>130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2430.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1069</td>\n",
       "      <td>18</td>\n",
       "      <td>112.0</td>\n",
       "      <td>2020-02-22 17:48:33</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersNorm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2352</th>\n",
       "      <td>605.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>38.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>735.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>72.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>2905.0</td>\n",
       "      <td>84.5</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1074</td>\n",
       "      <td>18</td>\n",
       "      <td>114.0</td>\n",
       "      <td>2020-02-22 17:53:33</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersNorm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>605.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>38.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>735.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>72.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>2905.0</td>\n",
       "      <td>84.5</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1079</td>\n",
       "      <td>18</td>\n",
       "      <td>113.0</td>\n",
       "      <td>2020-02-22 17:58:33</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersNorm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2354</th>\n",
       "      <td>605.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>38.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>735.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>72.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1545.0</td>\n",
       "      <td>32.5</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1084</td>\n",
       "      <td>18</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2020-02-22 18:03:33</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersNorm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2357</th>\n",
       "      <td>475.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>735.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>72.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>1545.0</td>\n",
       "      <td>32.5</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1099</td>\n",
       "      <td>18</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2020-02-22 18:18:33</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersLow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2359</th>\n",
       "      <td>716.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>38.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>976.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>106.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>1786.0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1109</td>\n",
       "      <td>18</td>\n",
       "      <td>87.0</td>\n",
       "      <td>2020-02-22 18:28:33</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersLow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2360</th>\n",
       "      <td>716.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>38.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>976.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>106.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>1786.0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1114</td>\n",
       "      <td>19</td>\n",
       "      <td>87.0</td>\n",
       "      <td>2020-02-22 18:33:33</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersLow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2361</th>\n",
       "      <td>716.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>38.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>976.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>106.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>1786.0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1119</td>\n",
       "      <td>19</td>\n",
       "      <td>90.0</td>\n",
       "      <td>2020-02-22 18:38:34</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersLow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2365</th>\n",
       "      <td>716.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>38.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>976.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>106.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>1786.0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1139</td>\n",
       "      <td>19</td>\n",
       "      <td>123.0</td>\n",
       "      <td>2020-02-22 18:58:33</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersNorm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2366</th>\n",
       "      <td>716.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>38.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>976.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>106.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>1786.0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1144</td>\n",
       "      <td>19</td>\n",
       "      <td>130.0</td>\n",
       "      <td>2020-02-22 19:03:34</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersNorm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2367</th>\n",
       "      <td>716.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>38.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>976.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>106.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>1786.0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1149</td>\n",
       "      <td>19</td>\n",
       "      <td>136.0</td>\n",
       "      <td>2020-02-22 19:08:34</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersNorm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2375</th>\n",
       "      <td>716.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>38.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>976.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>106.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>1786.0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1189</td>\n",
       "      <td>20</td>\n",
       "      <td>120.0</td>\n",
       "      <td>2020-02-22 19:48:33</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>MALE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PersNorm</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      calories2hr  protein2hr  sugar2hr  carbs2hr  calories8hr  protein8hr  \\\n",
       "2274          0.0         0.0       0.0       0.0        410.0        13.0   \n",
       "2319        130.0         0.0      34.0      35.0        130.0         0.0   \n",
       "2325        130.0         0.0      34.0      35.0        130.0         0.0   \n",
       "2326        130.0         0.0      34.0      35.0        130.0         0.0   \n",
       "2327          0.0         0.0       0.0       0.0        130.0         0.0   \n",
       "2328          0.0         0.0       0.0       0.0        130.0         0.0   \n",
       "2333        130.0         0.0      34.0      35.0        260.0         0.0   \n",
       "2334        130.0         0.0      34.0      35.0        260.0         0.0   \n",
       "2335        130.0         0.0      34.0      35.0        260.0         0.0   \n",
       "2336        130.0         0.0      34.0      35.0        260.0         0.0   \n",
       "2337        130.0         0.0      34.0      35.0        260.0         0.0   \n",
       "2338        130.0         0.0      34.0      35.0        260.0         0.0   \n",
       "2339        130.0         0.0      34.0      35.0        260.0         0.0   \n",
       "2341        130.0         0.0      34.0      35.0        260.0         0.0   \n",
       "2345        130.0         0.0      34.0      35.0        260.0         0.0   \n",
       "2348        130.0         0.0      34.0      35.0        260.0         0.0   \n",
       "2349        130.0         0.0      34.0      35.0        260.0         0.0   \n",
       "2350        130.0         0.0      34.0      35.0        260.0         0.0   \n",
       "2351        130.0         0.0      34.0      35.0        260.0         0.0   \n",
       "2352        605.0        15.5      38.0      87.0        735.0        15.5   \n",
       "2353        605.0        15.5      38.0      87.0        735.0        15.5   \n",
       "2354        605.0        15.5      38.0      87.0        735.0        15.5   \n",
       "2357        475.0        15.5       4.0      52.0        735.0        15.5   \n",
       "2359        716.0        26.5      38.0      88.0        976.0        26.5   \n",
       "2360        716.0        26.5      38.0      88.0        976.0        26.5   \n",
       "2361        716.0        26.5      38.0      88.0        976.0        26.5   \n",
       "2365        716.0        26.5      38.0      88.0        976.0        26.5   \n",
       "2366        716.0        26.5      38.0      88.0        976.0        26.5   \n",
       "2367        716.0        26.5      38.0      88.0        976.0        26.5   \n",
       "2375        716.0        26.5      38.0      88.0        976.0        26.5   \n",
       "\n",
       "      sugar8hr  carbs8hr  calories24hr  protein24hr  ...  Activity1hr  \\\n",
       "2274      66.0      83.0        3118.0        106.0  ...         11.0   \n",
       "2319      34.0      35.0        2300.0         69.0  ...         10.0   \n",
       "2325      34.0      35.0        2300.0         69.0  ...          7.0   \n",
       "2326      34.0      35.0        2300.0         69.0  ...          6.0   \n",
       "2327      34.0      35.0        2300.0         69.0  ...          5.0   \n",
       "2328      34.0      35.0        2300.0         69.0  ...          4.0   \n",
       "2333      68.0      70.0        2430.0         69.0  ...          7.0   \n",
       "2334      68.0      70.0        2430.0         69.0  ...          7.0   \n",
       "2335      68.0      70.0        2430.0         69.0  ...          6.0   \n",
       "2336      68.0      70.0        2430.0         69.0  ...          5.0   \n",
       "2337      68.0      70.0        2430.0         69.0  ...          5.0   \n",
       "2338      68.0      70.0        2430.0         69.0  ...          5.0   \n",
       "2339      68.0      70.0        2430.0         69.0  ...          5.0   \n",
       "2341      68.0      70.0        2430.0         69.0  ...          4.0   \n",
       "2345      68.0      70.0        2430.0         69.0  ...          3.0   \n",
       "2348      68.0      70.0        2430.0         69.0  ...          3.0   \n",
       "2349      68.0      70.0        2430.0         69.0  ...          3.0   \n",
       "2350      68.0      70.0        2430.0         69.0  ...          3.0   \n",
       "2351      68.0      70.0        2430.0         69.0  ...          3.0   \n",
       "2352      72.0     122.0        2905.0         84.5  ...          3.0   \n",
       "2353      72.0     122.0        2905.0         84.5  ...          3.0   \n",
       "2354      72.0     122.0        1545.0         32.5  ...          3.0   \n",
       "2357      72.0     122.0        1545.0         32.5  ...          3.0   \n",
       "2359     106.0     158.0        1786.0         43.5  ...          5.0   \n",
       "2360     106.0     158.0        1786.0         43.5  ...          5.0   \n",
       "2361     106.0     158.0        1786.0         43.5  ...          5.0   \n",
       "2365     106.0     158.0        1786.0         43.5  ...          5.0   \n",
       "2366     106.0     158.0        1786.0         43.5  ...          5.0   \n",
       "2367     106.0     158.0        1786.0         43.5  ...          5.0   \n",
       "2375     106.0     158.0        1786.0         43.5  ...          2.0   \n",
       "\n",
       "      Minfrommid  Hourfrommid  glucose             datetime   ID  HbA1c  \\\n",
       "2274         684           11    107.0  2020-02-22 11:23:34  2.0    5.6   \n",
       "2319         909           15     93.0  2020-02-22 15:08:33  2.0    5.6   \n",
       "2325         939           16    101.0  2020-02-22 15:38:34  2.0    5.6   \n",
       "2326         944           16    102.0  2020-02-22 15:43:34  2.0    5.6   \n",
       "2327         949           16    104.0  2020-02-22 15:48:33  2.0    5.6   \n",
       "2328         954           16    105.0  2020-02-22 15:53:33  2.0    5.6   \n",
       "2333         979           16    105.0  2020-02-22 16:18:33  2.0    5.6   \n",
       "2334         984           16    112.0  2020-02-22 16:23:33  2.0    5.6   \n",
       "2335         989           16    124.0  2020-02-22 16:28:33  2.0    5.6   \n",
       "2336         994           17    137.0  2020-02-22 16:33:33  2.0    5.6   \n",
       "2337         999           17    149.0  2020-02-22 16:38:33  2.0    5.6   \n",
       "2338        1004           17    156.0  2020-02-22 16:43:33  2.0    5.6   \n",
       "2339        1009           17    155.0  2020-02-22 16:48:33  2.0    5.6   \n",
       "2341        1019           17    133.0  2020-02-22 16:58:33  2.0    5.6   \n",
       "2345        1039           17     95.0  2020-02-22 17:18:33  2.0    5.6   \n",
       "2348        1054           18    103.0  2020-02-22 17:33:33  2.0    5.6   \n",
       "2349        1059           18    106.0  2020-02-22 17:38:33  2.0    5.6   \n",
       "2350        1064           18    109.0  2020-02-22 17:43:33  2.0    5.6   \n",
       "2351        1069           18    112.0  2020-02-22 17:48:33  2.0    5.6   \n",
       "2352        1074           18    114.0  2020-02-22 17:53:33  2.0    5.6   \n",
       "2353        1079           18    113.0  2020-02-22 17:58:33  2.0    5.6   \n",
       "2354        1084           18    110.0  2020-02-22 18:03:33  2.0    5.6   \n",
       "2357        1099           18     92.0  2020-02-22 18:18:33  2.0    5.6   \n",
       "2359        1109           18     87.0  2020-02-22 18:28:33  2.0    5.6   \n",
       "2360        1114           19     87.0  2020-02-22 18:33:33  2.0    5.6   \n",
       "2361        1119           19     90.0  2020-02-22 18:38:34  2.0    5.6   \n",
       "2365        1139           19    123.0  2020-02-22 18:58:33  2.0    5.6   \n",
       "2366        1144           19    130.0  2020-02-22 19:03:34  2.0    5.6   \n",
       "2367        1149           19    136.0  2020-02-22 19:08:34  2.0    5.6   \n",
       "2375        1189           20    120.0  2020-02-22 19:48:33  2.0    5.6   \n",
       "\n",
       "      Biological Sex  WakeTime     label  \n",
       "2274            MALE       1.0   PersLow  \n",
       "2319            MALE       1.0   PersLow  \n",
       "2325            MALE       1.0   PersLow  \n",
       "2326            MALE       1.0   PersLow  \n",
       "2327            MALE       1.0   PersLow  \n",
       "2328            MALE       1.0   PersLow  \n",
       "2333            MALE       1.0   PersLow  \n",
       "2334            MALE       1.0  PersNorm  \n",
       "2335            MALE       1.0  PersNorm  \n",
       "2336            MALE       1.0  PersNorm  \n",
       "2337            MALE       1.0  PersNorm  \n",
       "2338            MALE       1.0  PersHigh  \n",
       "2339            MALE       1.0  PersHigh  \n",
       "2341            MALE       1.0  PersNorm  \n",
       "2345            MALE       1.0   PersLow  \n",
       "2348            MALE       1.0   PersLow  \n",
       "2349            MALE       1.0   PersLow  \n",
       "2350            MALE       1.0  PersNorm  \n",
       "2351            MALE       1.0  PersNorm  \n",
       "2352            MALE       1.0  PersNorm  \n",
       "2353            MALE       1.0  PersNorm  \n",
       "2354            MALE       1.0  PersNorm  \n",
       "2357            MALE       1.0   PersLow  \n",
       "2359            MALE       1.0   PersLow  \n",
       "2360            MALE       1.0   PersLow  \n",
       "2361            MALE       1.0   PersLow  \n",
       "2365            MALE       1.0  PersNorm  \n",
       "2366            MALE       1.0  PersNorm  \n",
       "2367            MALE       1.0  PersNorm  \n",
       "2375            MALE       1.0  PersNorm  \n",
       "\n",
       "[30 rows x 72 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['WakeTime'] == 1) & (df['ID'] == 2)].head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2115ecbf050>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 날짜 필터링\n",
    "filtered_df = df.iloc[400:1000]\n",
    "\n",
    "# 그래프 그리기\n",
    "plt.figure(figsize=(10, 3))\n",
    "plt.plot(filtered_df.index, filtered_df['glucose'], 'r', label='Glucose')\n",
    "plt.plot(filtered_df.index, filtered_df['sugar24hr'], 'y', label='Sugar24hr')\n",
    "plt.plot(filtered_df.index, filtered_df['sugar8hr'], 'b', label='Sugar8hr')\n",
    "plt.plot(filtered_df.index, filtered_df['sugar2hr'], 'g', label='Sugar2hr')\n",
    "\n",
    "plt.title('Glucose and SugarXhr')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Value')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(r'./features.csv', sep='\\t', encoding='utf-8', header='true')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'./features.csv', sep='\\t', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna().drop(columns=['Unnamed: 0']).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "calories2hr       0\n",
       "protein2hr        0\n",
       "sugar2hr          0\n",
       "carbs2hr          0\n",
       "calories8hr       0\n",
       "                 ..\n",
       "ID                0\n",
       "HbA1c             0\n",
       "Biological Sex    0\n",
       "WakeTime          0\n",
       "label             0\n",
       "Length: 72, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Biological Sex'] = df['Biological Sex'].map({'FEMALE': 0, 'MALE': 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['label'] = df['label'].map({'PersLow': 0, 'PersNorm': 1, 'PersHigh':2})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 상관관계 Matrix Heat map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrdf = df.drop(columns=['datetime']).corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# 그림 사이즈 지정\n",
    "fig, ax = plt.subplots( figsize=(20,20) )\n",
    "\n",
    "mask = np.zeros_like(corrdf)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "# 히트맵을 그린다\n",
    "sns.heatmap(corrdf, \n",
    "            cmap = 'RdYlBu_r', \n",
    "            annot = False,   # 실제 값을 표시한다\n",
    "            mask=mask,      # 표시하지 않을 마스크 부분을 지정한다\n",
    "            linewidths=.5,  # 경계면 실선으로 구분하기\n",
    "            cbar_kws={\"shrink\": .1},# 컬러바 크기 줄이기\n",
    "            vmin = -1,vmax = 1   # 컬러바 범위 -1 ~ 1\n",
    "           )  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004628 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13892\n",
      "[LightGBM] [Info] Number of data points in the train set: 27054, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988962\n",
      "[LightGBM] [Info] Start training from score -0.340895\n",
      "[LightGBM] [Info] Start training from score -1.883682\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004058 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13888\n",
      "[LightGBM] [Info] Number of data points in the train set: 27055, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988729\n",
      "[LightGBM] [Info] Start training from score -0.340932\n",
      "[LightGBM] [Info] Start training from score -1.883719\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003704 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13883\n",
      "[LightGBM] [Info] Number of data points in the train set: 27055, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988729\n",
      "[LightGBM] [Info] Start training from score -0.340932\n",
      "[LightGBM] [Info] Start training from score -1.883719\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003572 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13895\n",
      "[LightGBM] [Info] Number of data points in the train set: 27055, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988729\n",
      "[LightGBM] [Info] Start training from score -0.340932\n",
      "[LightGBM] [Info] Start training from score -1.883719\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003758 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13893\n",
      "[LightGBM] [Info] Number of data points in the train set: 27055, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988729\n",
      "[LightGBM] [Info] Start training from score -0.340932\n",
      "[LightGBM] [Info] Start training from score -1.883719\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004141 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13885\n",
      "[LightGBM] [Info] Number of data points in the train set: 27055, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988729\n",
      "[LightGBM] [Info] Start training from score -0.340932\n",
      "[LightGBM] [Info] Start training from score -1.883719\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005219 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13886\n",
      "[LightGBM] [Info] Number of data points in the train set: 27055, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988729\n",
      "[LightGBM] [Info] Start training from score -0.340932\n",
      "[LightGBM] [Info] Start training from score -1.883719\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003795 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13894\n",
      "[LightGBM] [Info] Number of data points in the train set: 27055, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988999\n",
      "[LightGBM] [Info] Start training from score -0.340880\n",
      "[LightGBM] [Info] Start training from score -1.883719\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003801 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13894\n",
      "[LightGBM] [Info] Number of data points in the train set: 27055, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988999\n",
      "[LightGBM] [Info] Start training from score -0.340880\n",
      "[LightGBM] [Info] Start training from score -1.883719\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005404 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13889\n",
      "[LightGBM] [Info] Number of data points in the train set: 27055, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988999\n",
      "[LightGBM] [Info] Start training from score -0.340880\n",
      "[LightGBM] [Info] Start training from score -1.883719\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004113 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13898\n",
      "[LightGBM] [Info] Number of data points in the train set: 27054, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988962\n",
      "[LightGBM] [Info] Start training from score -0.340895\n",
      "[LightGBM] [Info] Start training from score -1.883682\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004112 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13891\n",
      "[LightGBM] [Info] Number of data points in the train set: 27055, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988729\n",
      "[LightGBM] [Info] Start training from score -0.340932\n",
      "[LightGBM] [Info] Start training from score -1.883719\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004664 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13881\n",
      "[LightGBM] [Info] Number of data points in the train set: 27055, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988729\n",
      "[LightGBM] [Info] Start training from score -0.340932\n",
      "[LightGBM] [Info] Start training from score -1.883719\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004007 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13881\n",
      "[LightGBM] [Info] Number of data points in the train set: 27055, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988729\n",
      "[LightGBM] [Info] Start training from score -0.340932\n",
      "[LightGBM] [Info] Start training from score -1.883719\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004705 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13890\n",
      "[LightGBM] [Info] Number of data points in the train set: 27055, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988729\n",
      "[LightGBM] [Info] Start training from score -0.340932\n",
      "[LightGBM] [Info] Start training from score -1.883719\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005499 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13890\n",
      "[LightGBM] [Info] Number of data points in the train set: 27055, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988729\n",
      "[LightGBM] [Info] Start training from score -0.340932\n",
      "[LightGBM] [Info] Start training from score -1.883719\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004975 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13886\n",
      "[LightGBM] [Info] Number of data points in the train set: 27055, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988729\n",
      "[LightGBM] [Info] Start training from score -0.340932\n",
      "[LightGBM] [Info] Start training from score -1.883719\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005219 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13883\n",
      "[LightGBM] [Info] Number of data points in the train set: 27055, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988999\n",
      "[LightGBM] [Info] Start training from score -0.340880\n",
      "[LightGBM] [Info] Start training from score -1.883719\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004160 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13890\n",
      "[LightGBM] [Info] Number of data points in the train set: 27055, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988999\n",
      "[LightGBM] [Info] Start training from score -0.340880\n",
      "[LightGBM] [Info] Start training from score -1.883719\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004186 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13892\n",
      "[LightGBM] [Info] Number of data points in the train set: 27055, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988999\n",
      "[LightGBM] [Info] Start training from score -0.340880\n",
      "[LightGBM] [Info] Start training from score -1.883719\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004838 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13881\n",
      "[LightGBM] [Info] Number of data points in the train set: 27054, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988962\n",
      "[LightGBM] [Info] Start training from score -0.340895\n",
      "[LightGBM] [Info] Start training from score -1.883682\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004206 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13885\n",
      "[LightGBM] [Info] Number of data points in the train set: 27055, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988729\n",
      "[LightGBM] [Info] Start training from score -0.340932\n",
      "[LightGBM] [Info] Start training from score -1.883719\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005620 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13883\n",
      "[LightGBM] [Info] Number of data points in the train set: 27055, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988729\n",
      "[LightGBM] [Info] Start training from score -0.340932\n",
      "[LightGBM] [Info] Start training from score -1.883719\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005379 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13890\n",
      "[LightGBM] [Info] Number of data points in the train set: 27055, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988729\n",
      "[LightGBM] [Info] Start training from score -0.340932\n",
      "[LightGBM] [Info] Start training from score -1.883719\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005214 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13891\n",
      "[LightGBM] [Info] Number of data points in the train set: 27055, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988729\n",
      "[LightGBM] [Info] Start training from score -0.340932\n",
      "[LightGBM] [Info] Start training from score -1.883719\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004331 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13885\n",
      "[LightGBM] [Info] Number of data points in the train set: 27055, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988729\n",
      "[LightGBM] [Info] Start training from score -0.340932\n",
      "[LightGBM] [Info] Start training from score -1.883719\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004185 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13891\n",
      "[LightGBM] [Info] Number of data points in the train set: 27055, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988729\n",
      "[LightGBM] [Info] Start training from score -0.340932\n",
      "[LightGBM] [Info] Start training from score -1.883719\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004105 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13891\n",
      "[LightGBM] [Info] Number of data points in the train set: 27055, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988999\n",
      "[LightGBM] [Info] Start training from score -0.340880\n",
      "[LightGBM] [Info] Start training from score -1.883719\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.004475 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13891\n",
      "[LightGBM] [Info] Number of data points in the train set: 27055, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988999\n",
      "[LightGBM] [Info] Start training from score -0.340880\n",
      "[LightGBM] [Info] Start training from score -1.883719\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005423 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13889\n",
      "[LightGBM] [Info] Number of data points in the train set: 27055, number of used features: 69\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Start training from score -1.988999\n",
      "[LightGBM] [Info] Start training from score -0.340880\n",
      "[LightGBM] [Info] Start training from score -1.883719\n",
      "Average Accuracy: 81.28% ± 0.48%\n",
      "Average Precision: 81.89% ± 0.66%\n",
      "Average Recall: 81.28% ± 0.48%\n",
      "Average F1 Score: 78.89% ± 0.62%\n",
      "[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.005595 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13896\n",
      "[LightGBM] [Info] Number of data points in the train set: 30061, number of used features: 69\n",
      "[LightGBM] [Info] Start training from score -1.988833\n",
      "[LightGBM] [Info] Start training from score -0.340913\n",
      "[LightGBM] [Info] Start training from score -1.883715\n",
      "Final Model Accuracy: 84.18%\n",
      "Final Model Precision: 85.18%\n",
      "Final Model Recall: 84.18%\n",
      "Final Model F1 Score: 82.44%\n",
      "Confusion Matrix:\n",
      "[[ 1709  2382    23]\n",
      " [  123 20939   315]\n",
      " [   18  1894  2658]]\n"
     ]
    }
   ],
   "source": [
    "#lightGBM classifier 추가한 부분\n",
    "#특이사항: 기존 목표는 훈련 시에는 혈당수치 포함, 실제 모델을 사용한 예측 시에는 혈당수치 없이 예측을 하려 했으나\n",
    "#LightGBM 사용 시 훈련과 예측 시 사용되는 피쳐의 개수가 같아야한다고 하여, 훈련과 예측 모두 혈당수치 피쳐는 빼버림\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import lightgbm as lgb\n",
    "from sklearn.model_selection import StratifiedKFold, RepeatedStratifiedKFold\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
    "\n",
    "# 데이터 불러오기\n",
    "df = pd.read_csv(r'./features.csv', sep='\\t', encoding='utf-8')\n",
    "\n",
    "# 결측값 제거 및 데이터 타입 변환\n",
    "# 데이터 전처리는 앞에서 이미 해줬으므로 대다수 생략 (피쳐엔지니어링)\n",
    "df = df.dropna().drop(columns=['Unnamed: 0']).reset_index(drop=True) #unnamed 열, 결측값 제거\n",
    "df['Biological Sex'] = df['Biological Sex'].map({'FEMALE': 0, 'MALE': 1}) # 성별 숫자형으로 변환\n",
    "df['label'] = df['label'].map({'PersLow': 0, 'PersNorm': 1, 'PersHigh': 2}) #라벨 숫자형으로 변환 (향후 타겟값)\n",
    "\n",
    "# 변수와 라벨 분리 (glucose 제외)\n",
    "X = df.drop(columns=['datetime', 'label', 'glucose'])\n",
    "y = df['label']\n",
    "\n",
    "# LightGBM 파라미터 설정 ( 하이퍼파라미터 변경가능성 O)\n",
    "params = {\n",
    "    'objective': 'multiclass',\n",
    "    'num_class': 3,\n",
    "    'metric': 'multi_logloss',\n",
    "    'num_leaves': 31,\n",
    "    'learning_rate': 0.05,\n",
    "    'feature_fraction': 0.9\n",
    "}\n",
    "\n",
    "# Repeated Stratified K-Fold Cross Validation 설정 (모델 학습)\n",
    "rskf = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=42) #데이터 분할, 학습/테스트 데이터 설정\n",
    "accuracy_list = []\n",
    "precision_list = []\n",
    "recall_list = []\n",
    "f1_list = []\n",
    "\n",
    "for train_index, test_index in rskf.split(X, y):\n",
    "    X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "    y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
    "    \n",
    "    # LightGBM 데이터셋 생성\n",
    "    train_data = lgb.Dataset(X_train, label=y_train, free_raw_data=False)\n",
    "    test_data = lgb.Dataset(X_test, label=y_test, reference=train_data, free_raw_data=False)\n",
    "    \n",
    "    # 모델 훈련 (num_round 변경가능성 O)\n",
    "    num_round = 100\n",
    "    bst = lgb.train(params, train_data, num_round, valid_sets=[test_data])\n",
    "    \n",
    "    # 예측\n",
    "    y_pred = bst.predict(X_test, num_iteration=bst.best_iteration)\n",
    "    y_pred_max = np.argmax(y_pred, axis=1)\n",
    "    \n",
    "    # 성능 평가\n",
    "    accuracy = accuracy_score(y_test, y_pred_max) * 100\n",
    "    precision = precision_score(y_test, y_pred_max, average='weighted') * 100\n",
    "    recall = recall_score(y_test, y_pred_max, average='weighted') * 100\n",
    "    f1 = f1_score(y_test, y_pred_max, average='weighted') * 100\n",
    "    \n",
    "    accuracy_list.append(accuracy)\n",
    "    precision_list.append(precision)\n",
    "    recall_list.append(recall)\n",
    "    f1_list.append(f1)\n",
    "\n",
    "# 평균 성능 출력\n",
    "print(f'Average Accuracy: {np.mean(accuracy_list):.2f}% ± {np.std(accuracy_list):.2f}%')\n",
    "print(f'Average Precision: {np.mean(precision_list):.2f}% ± {np.std(precision_list):.2f}%')\n",
    "print(f'Average Recall: {np.mean(recall_list):.2f}% ± {np.std(recall_list):.2f}%')\n",
    "print(f'Average F1 Score: {np.mean(f1_list):.2f}% ± {np.std(f1_list):.2f}%')\n",
    "\n",
    "# 최종 모델 훈련 (glucose 제외)\n",
    "train_data = lgb.Dataset(X, label=y, free_raw_data=False)\n",
    "bst = lgb.train(params, train_data, num_round)\n",
    "\n",
    "# 최종 예측\n",
    "y_pred = bst.predict(X, num_iteration=bst.best_iteration)\n",
    "y_pred_max = np.argmax(y_pred, axis=1)\n",
    "accuracy = accuracy_score(y, y_pred_max) * 100\n",
    "precision = precision_score(y, y_pred_max, average='weighted') * 100\n",
    "recall = recall_score(y, y_pred_max, average='weighted') * 100\n",
    "f1 = f1_score(y, y_pred_max, average='weighted') * 100\n",
    "cm = confusion_matrix(y, y_pred_max)\n",
    "\n",
    "print(f'Final Model Accuracy: {accuracy:.2f}%')\n",
    "print(f'Final Model Precision: {precision:.2f}%')\n",
    "print(f'Final Model Recall: {recall:.2f}%')\n",
    "print(f'Final Model F1 Score: {f1:.2f}%')\n",
    "print('Confusion Matrix:')\n",
    "print(cm)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Model Accuracy: 84.18%\n",
      "Final Model Precision: 85.18%\n",
      "Final Model Recall: 84.18%\n",
      "Final Model F1 Score: 82.44%\n",
      "Confusion Matrix:\n"
     ]
    }
   ],
   "source": [
    "print(f'Final Model Accuracy: {accuracy:.2f}%')\n",
    "print(f'Final Model Precision: {precision:.2f}%')\n",
    "print(f'Final Model Recall: {recall:.2f}%')\n",
    "print(f'Final Model F1 Score: {f1:.2f}%')\n",
    "print('Confusion Matrix:')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nfrom sklearn.ensemble import RandomForestClassifier\\nfrom sklearn.model_selection import LeaveOneGroupOut\\nfrom sklearn.metrics import accuracy_score, mean_squared_error, mean_absolute_percentage_error\\n\\ndef lopocvRFC(df, groupcol, targetcol, dropcols, fs = 0.005):\\n    logo = LeaveOneGroupOut()\\n    clf = RandomForestClassifier(n_estimators=100, random_state=0)\\n\\n    X = df.drop(columns=[targetcol])\\n    X = X.drop(columns=dropcols)\\n    y = df[targetcol]\\n    groups = np.array(df[groupcol])\\n\\n    importances_df = pd.DataFrame(columns=['value', 'importances', 'id'])\\n\\n    # RMSE와 MAPE를 저장할 리스트 초기화\\n    rmse_list = []\\n    mape_list = []\\n\\n    for train_index, test_index in logo.split(X, y, groups):\\n        X_train, X_test = X.iloc[train_index], X.iloc[test_index]\\n        y_train, y_test = y.iloc[train_index], y.iloc[test_index]\\n\\n        clf.fit(X_train, y_train.values.ravel())\\n        y_pred = clf.predict(X_test)\\n        print(f'Group {groups[test_index][0]} - Accuracy: {accuracy_score(y_test, y_pred)}')\\n\\n        # RMSE와 MAPE 계산\\n        rmse = np.sqrt(mean_squared_error(y_test, y_pred))\\n        mape = mean_absolute_percentage_error(y_test, y_pred)\\n        rmse_list.append(rmse)\\n        mape_list.append(mape)\\n\\n        feature_list = list(X_train.columns)\\n    \\n        # Feature importances를 데이터프레임에 추가\\n        temp_df = pd.DataFrame({\\n            'value': [feature_list[i] for i in range(X.shape[1])],\\n            'importances': clf.feature_importances_,\\n            'id': groups[test_index][0]  # 테스트 그룹의 ID 추가\\n        })\\n        \\n        importances_df = pd.concat([importances_df, temp_df], ignore_index=True)\\n    \\n    # RMSE와 MAPE의 평균 및 표준 편차 계산 및 출력\\n    mean_rmse = np.mean(rmse_list)\\n    std_rmse = np.std(rmse_list)\\n    mean_mape = np.mean(mape_list)\\n    std_mape = np.std(mape_list)\\n\\n    print('Mean RMSE:'   str(mean_rmse))\\n    print('Std RMSE:'   str(std_rmse))\\n    print('Mean MAPE:'   str(mean_mape))\\n    print('Std MAPE:'   str(std_mape))\\n\\n    # 평균 feature importance 계산\\n    mean_importances = importances_df.groupby('value')['importances'].mean().sort_values(ascending=False).reset_index()\\n\\n    return mean_rmse, std_rmse, mean_mape, std_mape, mean_importances\\n\""
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import LeaveOneGroupOut\n",
    "from sklearn.metrics import accuracy_score, mean_squared_error, mean_absolute_percentage_error\n",
    "\n",
    "def lopocvRFC(df, groupcol, targetcol, dropcols, fs = 0.005):\n",
    "    logo = LeaveOneGroupOut()\n",
    "    clf = RandomForestClassifier(n_estimators=100, random_state=0)\n",
    "\n",
    "    X = df.drop(columns=[targetcol])\n",
    "    X = X.drop(columns=dropcols)\n",
    "    y = df[targetcol]\n",
    "    groups = np.array(df[groupcol])\n",
    "\n",
    "    importances_df = pd.DataFrame(columns=['value', 'importances', 'id'])\n",
    "\n",
    "    # RMSE와 MAPE를 저장할 리스트 초기화\n",
    "    rmse_list = []\n",
    "    mape_list = []\n",
    "\n",
    "    for train_index, test_index in logo.split(X, y, groups):\n",
    "        X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "        y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
    "\n",
    "        clf.fit(X_train, y_train.values.ravel())\n",
    "        y_pred = clf.predict(X_test)\n",
    "        print(f'Group {groups[test_index][0]} - Accuracy: {accuracy_score(y_test, y_pred)}')\n",
    "\n",
    "        # RMSE와 MAPE 계산\n",
    "        rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "        mape = mean_absolute_percentage_error(y_test, y_pred)\n",
    "        rmse_list.append(rmse)\n",
    "        mape_list.append(mape)\n",
    "\n",
    "        feature_list = list(X_train.columns)\n",
    "    \n",
    "        # Feature importances를 데이터프레임에 추가\n",
    "        temp_df = pd.DataFrame({\n",
    "            'value': [feature_list[i] for i in range(X.shape[1])],\n",
    "            'importances': clf.feature_importances_,\n",
    "            'id': groups[test_index][0]  # 테스트 그룹의 ID 추가\n",
    "        })\n",
    "        \n",
    "        importances_df = pd.concat([importances_df, temp_df], ignore_index=True)\n",
    "    \n",
    "    # RMSE와 MAPE의 평균 및 표준 편차 계산 및 출력\n",
    "    mean_rmse = np.mean(rmse_list)\n",
    "    std_rmse = np.std(rmse_list)\n",
    "    mean_mape = np.mean(mape_list)\n",
    "    std_mape = np.std(mape_list)\n",
    "\n",
    "    print('Mean RMSE:'   str(mean_rmse))\n",
    "    print('Std RMSE:'   str(std_rmse))\n",
    "    print('Mean MAPE:'   str(mean_mape))\n",
    "    print('Std MAPE:'   str(std_mape))\n",
    "\n",
    "    # 평균 feature importance 계산\n",
    "    mean_importances = importances_df.groupby('value')['importances'].mean().sort_values(ascending=False).reset_index()\n",
    "\n",
    "    return mean_rmse, std_rmse, mean_mape, std_mape, mean_importances\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nfrom sklearn.ensemble import RandomForestRegressor\\nfrom sklearn.model_selection import LeaveOneGroupOut\\nfrom sklearn.metrics import accuracy_score, mean_squared_error, mean_absolute_percentage_error\\n\\ndef lopocvRFR(df, groupcol, targetcol, dropcols, fs = 0.005):\\n    logo = LeaveOneGroupOut()\\n    reg = RandomForestRegressor(n_estimators=100, random_state=0)\\n\\n    X = df.drop(columns=[targetcol])\\n    X = X.drop(columns=dropcols)\\n    y = df[targetcol]\\n    groups = np.array(df[groupcol])\\n\\n    importances_df = pd.DataFrame(columns=['value', 'importances', 'id'])\\n\\n    # RMSE와 MAPE를 저장할 리스트 초기화\\n    rmse_list = []\\n    mape_list = []\\n\\n    for train_index, test_index in logo.split(X, y, groups):\\n        X_train, X_test = X.iloc[train_index], X.iloc[test_index]\\n        y_train, y_test = y.iloc[train_index], y.iloc[test_index]\\n\\n        reg.fit(X_train, y_train.values.ravel())\\n        y_pred = reg.predict(X_test)\\n\\n        # RMSE와 MAPE 계산\\n        rmse = np.sqrt(mean_squared_error(y_test, y_pred))\\n        mape = mean_absolute_percentage_error(y_test, y_pred)\\n        rmse_list.append(rmse)\\n        mape_list.append(mape)\\n\\n        feature_list = list(X_train.columns)\\n    \\n        # Feature importances를 데이터프레임에 추가\\n        temp_df = pd.DataFrame({\\n            'value': [feature_list[i] for i in range(X.shape[1])],\\n            'importances': reg.feature_importances_,\\n            'id': groups[test_index][0]  # 테스트 그룹의 ID 추가\\n        })\\n        \\n        importances_df = pd.concat([importances_df, temp_df], ignore_index=True)\\n\\n        print(f'ID : {groups[test_index][0]} ... done')\\n    \\n    # RMSE와 MAPE의 평균 및 표준 편차 계산 및 출력\\n    mean_rmse = np.mean(rmse_list)\\n    std_rmse = np.std(rmse_list)\\n    mean_mape = np.mean(mape_list)\\n    std_mape = np.std(mape_list)\\n\\n    print('Mean RMSE:'   str(mean_rmse))\\n    print('Std RMSE:'   str(std_rmse))\\n    print('Mean MAPE:'   str(mean_mape))\\n    print('Std MAPE:'   str(std_mape))\\n\\n    # 평균 feature importance 계산\\n    mean_importances = importances_df.groupby('value')['importances'].mean().sort_values(ascending=False).reset_index()\\n\\n    return mean_rmse, std_rmse, mean_mape, std_mape, mean_importances\\n\""
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import LeaveOneGroupOut\n",
    "from sklearn.metrics import accuracy_score, mean_squared_error, mean_absolute_percentage_error\n",
    "\n",
    "def lopocvRFR(df, groupcol, targetcol, dropcols, fs = 0.005):\n",
    "    logo = LeaveOneGroupOut()\n",
    "    reg = RandomForestRegressor(n_estimators=100, random_state=0)\n",
    "\n",
    "    X = df.drop(columns=[targetcol])\n",
    "    X = X.drop(columns=dropcols)\n",
    "    y = df[targetcol]\n",
    "    groups = np.array(df[groupcol])\n",
    "\n",
    "    importances_df = pd.DataFrame(columns=['value', 'importances', 'id'])\n",
    "\n",
    "    # RMSE와 MAPE를 저장할 리스트 초기화\n",
    "    rmse_list = []\n",
    "    mape_list = []\n",
    "\n",
    "    for train_index, test_index in logo.split(X, y, groups):\n",
    "        X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "        y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
    "\n",
    "        reg.fit(X_train, y_train.values.ravel())\n",
    "        y_pred = reg.predict(X_test)\n",
    "\n",
    "        # RMSE와 MAPE 계산\n",
    "        rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "        mape = mean_absolute_percentage_error(y_test, y_pred)\n",
    "        rmse_list.append(rmse)\n",
    "        mape_list.append(mape)\n",
    "\n",
    "        feature_list = list(X_train.columns)\n",
    "    \n",
    "        # Feature importances를 데이터프레임에 추가\n",
    "        temp_df = pd.DataFrame({\n",
    "            'value': [feature_list[i] for i in range(X.shape[1])],\n",
    "            'importances': reg.feature_importances_,\n",
    "            'id': groups[test_index][0]  # 테스트 그룹의 ID 추가\n",
    "        })\n",
    "        \n",
    "        importances_df = pd.concat([importances_df, temp_df], ignore_index=True)\n",
    "\n",
    "        print(f'ID : {groups[test_index][0]} ... done')\n",
    "    \n",
    "    # RMSE와 MAPE의 평균 및 표준 편차 계산 및 출력\n",
    "    mean_rmse = np.mean(rmse_list)\n",
    "    std_rmse = np.std(rmse_list)\n",
    "    mean_mape = np.mean(mape_list)\n",
    "    std_mape = np.std(mape_list)\n",
    "\n",
    "    print('Mean RMSE:'   str(mean_rmse))\n",
    "    print('Std RMSE:'   str(std_rmse))\n",
    "    print('Mean MAPE:'   str(mean_mape))\n",
    "    print('Std MAPE:'   str(std_mape))\n",
    "\n",
    "    # 평균 feature importance 계산\n",
    "    mean_importances = importances_df.groupby('value')['importances'].mean().sort_values(ascending=False).reset_index()\n",
    "\n",
    "    return mean_rmse, std_rmse, mean_mape, std_mape, mean_importances\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Leave-one-person-out cross validation Random forest regression 모델을 이용한 간질 포도당 예측에 대한 기여도 결정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "#meanrmse, stdrmse, meanmape, stdmape, importances_reg = lopocvRFR(df, 'ID', 'glucose', ['label', 'datetime'], fs=0.005)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importances_reg.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\n\\n\\n# 막대그래프로 중요도 출력\\nplt.figure(figsize=(15, 6))\\nplt.bar(importances_reg['value'], importances_reg['importances'])\\nplt.xlabel('Feature')\\nplt.ylabel('Importance')\\nplt.title('Feature Importance')\\nplt.xticks(rotation=45, ha='right')  # x축 라벨을 45도로 회전하여 가독성 높임\\nplt.tight_layout()  # 그래프 간격 조정\\nplt.show()\\n\""
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "\n",
    "\n",
    "# 막대그래프로 중요도 출력\n",
    "plt.figure(figsize=(15, 6))\n",
    "plt.bar(importances_reg['value'], importances_reg['importances'])\n",
    "plt.xlabel('Feature')\n",
    "plt.ylabel('Importance')\n",
    "plt.title('Feature Importance')\n",
    "plt.xticks(rotation=45, ha='right')  # x축 라벨을 45도로 회전하여 가독성 높임\n",
    "plt.tight_layout()  # 그래프 간격 조정\n",
    "plt.show()\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "def LOOCV_featureselection(data, ids, outcomevar, dropcols, idcolumn, numestimators=1000):\n",
    "    \"\"\"\n",
    "        Intermediate function. Please use loocvRF function.\n",
    "            \n",
    "    \"\"\"\n",
    "    # Separate data for leave-one-person-out-cross-validation (LOOCV)\n",
    "    LOOCV_O = ids \n",
    "    data[idcolumn] = data[idcolumn].apply(str)\n",
    "    data_filtered = data[data[idcolumn] != LOOCV_O]\n",
    "    data_cv = data[data[idcolumn] == LOOCV_O]\n",
    "    \n",
    "    print(len(data_filtered))\n",
    "    print(len(data_cv))\n",
    "    \n",
    "    # Test data - the person left out of training\n",
    "    data_test = data_cv.drop(columns=dropcols)\n",
    "    X_test = data_test.drop(columns=[outcomevar])\n",
    "    y_test = data_test[outcomevar] #This is the outcome variable\n",
    "    \n",
    "    # Train data - all other people in dataframe\n",
    "    data_train = data_filtered.drop(columns=dropcols)\n",
    "    X_train = data_train.drop(columns=[outcomevar])\n",
    "    \n",
    "    feature_list = list(X_train.columns)\n",
    "    X_train= np.array(X_train)\n",
    "    y_train = np.array(data_train[outcomevar]) #Outcome variable here\n",
    "\n",
    "    \n",
    "    from sklearn.ensemble import RandomForestRegressor\n",
    "    # Instantiate model with numestimators decision trees\n",
    "    rf = RandomForestRegressor(n_estimators = numestimators, random_state = 0)\n",
    "    # Train the model on training data\n",
    "    print('Now processing... LOOCV_featureselection')\n",
    "    rf.fit(X_train, y_train)\n",
    "    \n",
    "    # Get importances:\n",
    "    importances = list(rf.feature_importances_)# List of tuples with variable and importance\n",
    "    important = pd.DataFrame()\n",
    "    important['value'] = feature_list\n",
    "    important['importances'] = importances\n",
    "    \n",
    "    return important\n",
    "\n",
    "def RFLOOCV(data, ids, outcomevar, dropcols, idcolumn, numestimators=1000, fs=0.02):\n",
    "    \"\"\"\n",
    "        Intermediate function. Please use loocvRF function.\n",
    "            \n",
    "    \"\"\"\n",
    "    # Get important features \n",
    "    listimportances = LOOCV_featureselection(data, ids, outcomevar, dropcols, idcolumn, numestimators)\n",
    "    filteredi = listimportances[listimportances['importances'] < fs]\n",
    "    filteredi = filteredi['value']\n",
    "    \n",
    "    LOOCV_O = str(ids)\n",
    "    data[idcolumn] = data[idcolumn].apply(str)\n",
    "    data_filtered = data[data[idcolumn] != LOOCV_O]\n",
    "    data_cv = data[data[idcolumn] == LOOCV_O]\n",
    "\n",
    "    print(f'LOOCV_O : {LOOCV_O}')\n",
    "   \n",
    "    # Test data - the person left out of training\n",
    "    data_test = data_cv.drop(columns=dropcols)\n",
    "    data_test = data_test.drop(columns=filteredi) #cvf\n",
    "    X_test = data_test.drop(columns=[outcomevar])\n",
    "    y_test = data_test[outcomevar] #This is the outcome variable\n",
    "    \n",
    "    # Train data - all other people in dataframe\n",
    "    data_train = data_filtered.drop(columns=dropcols)\n",
    "    data_train = data_train.drop(columns=filteredi)\n",
    "    X_train = data_train.drop(columns=[outcomevar])\n",
    "    \n",
    "    feature_list = list(X_train.columns)\n",
    "    X_train= np.array(X_train)\n",
    "    y_train = np.array(data_train[outcomevar]) #Outcome variable here\n",
    "\n",
    "    \n",
    "    from sklearn.ensemble import RandomForestRegressor# Instantiate model with 1000 decision trees\n",
    "    rf = RandomForestRegressor(n_estimators = numestimators, random_state = 0)# Train the model on training data\n",
    "    print('Now processing... RFLOOCV')\n",
    "    rf.fit(X_train, y_train)\n",
    "    \n",
    "    # Use the forest's predict method on the test data\n",
    "    predictions = rf.predict(X_test)\n",
    "    errors = abs(predictions - y_test)\n",
    "    \n",
    "    # Root Mean Squared Error (RMSE)\n",
    "    from sklearn import metrics\n",
    "    RMSE = np.sqrt(metrics.mean_squared_error(y_test, predictions))\n",
    "    \n",
    "    # Mean Average Percent Error (MAPE)\n",
    "    MAPerror = np.mean(np.abs((y_test - predictions) / y_test)) * 100 \n",
    "    \n",
    "    # List of tuples with variable and importance\n",
    "    importances = list(rf.feature_importances_)\n",
    "    important = pd.DataFrame()\n",
    "    important['value'] = feature_list\n",
    "    important['importances'] = importances\n",
    "    important['id'] = str(ids)\n",
    "    \n",
    "    return errors, RMSE, MAPerror, important\n",
    "\n",
    "def loocvRF(data, idcolumn, outcomevar, dropcols=[], numestimators=1000, fs=0.02):\n",
    "    \"\"\"\n",
    "        Main loocv RF function that calls other functions to do RF feature selection, training, and testing. \n",
    "\n",
    "        Args:\n",
    "          data (pandas DataFrame): This is a dataframe containing each participant's features and outcome variables\n",
    "          idcolumn (string): This is the column name of your column containing your participant number or ID (case sensitive)\n",
    "          outcomevar (string): This is the column name of your outcome variable (case sensitive)\n",
    "          dropcols (list): This is a list containing strings of each column you wish to drop in your dataframe. Default is empty list [].\n",
    "          numestimators (integer): The number of trees you want built in your RF. Default=1000.\n",
    "          fs (float): The cutoff importance for feature selection. Anything below this importance will be removed for the RF training.\n",
    "          \n",
    "        Returns:\n",
    "            errors (list): This is a list with the absolute error between the predicted value and actual value for each fold.\n",
    "            meanrmse (float): This is the mean root mean squared error (RMSE) over all of the folds\n",
    "            stdrmse (float): This is the standard deviation of the root mean squared error (RMSE) over all of the folds\n",
    "            meanrmse (float): This is the mean mean average percent error (MAPE) over all of the folds\n",
    "            meanrmse (float): This is the standard deviation of the mean average percent error (MAPE) over all of the folds\n",
    "            importances(pandas DataFrame): This is a pandas DataFrame with 3 columns: value (feature), importances (importance of the feature), and id (fold over which this feature importance was derived)\n",
    "            \n",
    "    \"\"\"\n",
    "    \n",
    "    # Make list of all ID's in idcolumn\n",
    "    IDlist = list(set(data[idcolumn].astype(str))) # list(data[idcolumn]) -> list(set(data[idcolumn]))\n",
    "    print(f'IDlist : {IDlist}')\n",
    "    drop = [idcolumn]\n",
    "    drop = drop   dropcols\n",
    "    \n",
    "    # Initialize empty lists and dataframe \n",
    "    errors = []\n",
    "    rmse = []\n",
    "    mape = []\n",
    "    importances = pd.DataFrame(columns=['value', 'importances', 'id'])\n",
    "    \n",
    "    # Run LOOCV Random Forest! \n",
    "    for i in IDlist:\n",
    "        er, rm, ma, imp= RFLOOCV(data, i, outcomevar, drop, idcolumn, numestimators, fs)\n",
    "        errors.append(er)\n",
    "        rmse.append(rm)\n",
    "        mape.append(ma)\n",
    "        importances = pd.concat([importances, imp], ignore_index=True)\n",
    "        idt = str(i)\n",
    "        print('...'   idt   ' processing complete.')\n",
    "\n",
    "    # Compute mean and std RMSE, MAPE\n",
    "    meanrmse = np.mean(rmse)\n",
    "    stdrmse = np.std(rmse)\n",
    "    meanmape = np.mean(mape)\n",
    "    stdmape = np.std(mape)\n",
    "    \n",
    "    # Print RMSE, MAPE\n",
    "    print('Mean RMSE:'   str(meanrmse))\n",
    "    print('Std RMSE:'   str(stdrmse))\n",
    "    print('Mean MAPE:'   str(meanmape))\n",
    "    print('Std MAPE:'   str(stdmape))\n",
    "    \n",
    "    return errors, meanrmse, stdrmse, meanmape, stdmape, importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDlist : ['13.0', '3.0', '10.0', '4.0', '6.0', '11.0', '12.0', '16.0', '1.0', '9.0', '15.0', '5.0', '14.0', '2.0', '8.0']\n",
      "28372\n",
      "1689\n",
      "Now processing... LOOCV_featureselection\n",
      "LOOCV_O : 13.0\n",
      "Now processing... RFLOOCV\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\이경주\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\base.py:458: UserWarning: X has feature names, but RandomForestRegressor was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...13.0 processing complete.\n",
      "28047\n",
      "2014\n",
      "Now processing... LOOCV_featureselection\n"
     ]
    }
   ],
   "source": [
    "errors, RMSE, RMSEstd, MAPE, MAPEstd, importances = loocvRF(data=df, idcolumn='ID', outcomevar='glucose', dropcols=['label', 'datetime'], numestimators=1000, fs=0.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               value  importances    id\n",
      "0           sugar2hr     0.044074   4.0\n",
      "1           carbs2hr     0.094108   4.0\n",
      "2         protein8hr     0.063440   4.0\n",
      "3           carbs8hr     0.083610   4.0\n",
      "4          sugar24hr     0.080125   4.0\n",
      "..               ...          ...   ...\n",
      "202  PeakEDA2hr_mean     0.042167  13.0\n",
      "203          meanHRV     0.071181  13.0\n",
      "204       Activity24     0.087457  13.0\n",
      "205       Minfrommid     0.138622  13.0\n",
      "206            HbA1c     0.046703  13.0\n",
      "\n",
      "[207 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "print(importances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 importances\n",
      "value                       \n",
      "Minfrommid          0.177758\n",
      "Activity24          0.108786\n",
      "acc_mean_2hrs       0.091045\n",
      "acc_max_2hrs        0.083700\n",
      "meanHRV             0.071181\n",
      "carbs2hr            0.068111\n",
      "HbA1c               0.066074\n",
      "acc_std             0.061460\n",
      "carbs8hr            0.060875\n",
      "sugar2hr            0.057821\n",
      "sugar24hr           0.057625\n",
      "protein8hr          0.052821\n",
      "carbs24hr           0.048480\n",
      "protein24hr         0.048113\n",
      "Biological Sex      0.047536\n",
      "PeakEDA2hr_sum      0.045057\n",
      "PeakEDA2hr_mean     0.043560\n",
      "sugar8hr            0.042523\n",
      "calories8hr         0.040513\n",
      "calories24hr        0.034767\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\이경주\\AppData\\Local\\Temp\\ipykernel_21108\\2296911451.py:3: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-<style>'. Alternatively, directly use the seaborn API instead.\n",
      "  plt.style.use('seaborn-white')\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filteredimp = importances.groupby('value').mean(numeric_only=True).sort_values(by='importances', ascending=False)\n",
    "print(filteredimp)\n",
    "plt.style.use('seaborn-white')\n",
    "x_values = list(range(len(filteredimp['importances'])))\n",
    "# Make a bar chart\n",
    "plt.bar(x_values, filteredimp['importances'], orientation = 'vertical')# Tick labels for x axis\n",
    "plt.xticks(x_values, filteredimp.index, rotation='vertical')# Axis labels and title\n",
    "plt.tight_layout()\n",
    "plt.ylabel('Importance'); plt.xlabel('Variable'); plt.title(('Mean Feature Importances'))\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}