model/Tripadv_preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# StampTour dataset preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "use_table = 'tripadvisor'    # 'tripadvisor' or 'review'\n",
    "use_filtering = True   # False or True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import sys\n",
    "\n",
    "import numpy as np\n",
    "from scipy import sparse\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set directory of the stamptour dataset\n",
    "DATA_DIR = \"./raw_data/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>place</th>\n",
       "      <th>uid</th>\n",
       "      <th>title</th>\n",
       "      <th>content</th>\n",
       "      <th>rating</th>\n",
       "      <th>likes</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>경복궁</td>\n",
       "      <td>George_Korean</td>\n",
       "      <td>대한민국의 역사</td>\n",
       "      <td>대한민국의 역사가 잠들어 있는 곳. 서울을 방문했다면 꼭 방문해야 되는 곳. 경복궁...</td>\n",
       "      <td>50</td>\n",
       "      <td>39</td>\n",
       "      <td>작성했습니다. 2020년 10월</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>경복궁</td>\n",
       "      <td>hshdsc408</td>\n",
       "      <td>국민이 공감하는 장소</td>\n",
       "      <td>경복궁은 국민들이 자주 찾는곳으로 작성자는 주말에 자주 가족들과 방문\\n하고 있음....</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>작성했습니다. 2020년 9월</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>경복궁</td>\n",
       "      <td>Expedition705263</td>\n",
       "      <td>산책하기 좋은 경복궁</td>\n",
       "      <td>날씨 좋은 날 종종 산책하러 경복궁에 가는데 마음이 편온해지는 기분이라고 할까요? ...</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>리뷰를 작성했습니다. 2020년 4월</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>경복궁</td>\n",
       "      <td>guuming</td>\n",
       "      <td>Good</td>\n",
       "      <td>Goooooood 다 좋습니다 다음에 또 오고 싶네요 근처 관광지도많고 먹을거리도많...</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>작성했습니다. 2020년 4월</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>경복궁</td>\n",
       "      <td>ruffy1977</td>\n",
       "      <td>가족단위로 방문하기 좋은곳</td>\n",
       "      <td>요새 더더욱 코로나로 인해 사람 방문이 적음. 두자녀 동반시 성인 입장무료. 지금 ...</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>리뷰를 작성했습니다. 2020년 4월</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id place               uid           title  \\\n",
       "0   1   경복궁     George_Korean        대한민국의 역사   \n",
       "1   2   경복궁         hshdsc408     국민이 공감하는 장소   \n",
       "2   3   경복궁  Expedition705263     산책하기 좋은 경복궁   \n",
       "3   4   경복궁           guuming            Good   \n",
       "4   5   경복궁         ruffy1977  가족단위로 방문하기 좋은곳   \n",
       "\n",
       "                                             content  rating likes  \\\n",
       "0  대한민국의 역사가 잠들어 있는 곳. 서울을 방문했다면 꼭 방문해야 되는 곳. 경복궁...      50    39   \n",
       "1  경복궁은 국민들이 자주 찾는곳으로 작성자는 주말에 자주 가족들과 방문\\n하고 있음....      50     0   \n",
       "2  날씨 좋은 날 종종 산책하러 경복궁에 가는데 마음이 편온해지는 기분이라고 할까요? ...      50     0   \n",
       "3  Goooooood 다 좋습니다 다음에 또 오고 싶네요 근처 관광지도많고 먹을거리도많...      50     0   \n",
       "4  요새 더더욱 코로나로 인해 사람 방문이 적음. 두자녀 동반시 성인 입장무료. 지금 ...      50     0   \n",
       "\n",
       "                   time  \n",
       "0     작성했습니다. 2020년 10월  \n",
       "1      작성했습니다. 2020년 9월  \n",
       "2  리뷰를 작성했습니다. 2020년 4월  \n",
       "3      작성했습니다. 2020년 4월  \n",
       "4  리뷰를 작성했습니다. 2020년 4월  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_json(os.path.join(DATA_DIR, 'tripadvisor.json'), encoding='utf-8')\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 데이터 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "review = raw_data\n",
    "total = len(review)\n",
    "threshold = 30\n",
    "xlabel = ['positive','negative']\n",
    "pos = len(review[review['rating']>=threshold])\n",
    "neg = len(review[review['rating']<threshold])\n",
    "y = [pos, neg]\n",
    "x = np.arange(len(y))\n",
    "\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.axis('equal')\n",
    "colors = ['yellowgreen', 'gold']\n",
    "explode = (0.1, 0)\n",
    "plt.title(f'total : {total}')\n",
    "plt.pie(y, explode=explode, labels=xlabel, colors=colors,\n",
    "        autopct='%1.1f%%', shadow=True, startangle=90)\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "threshold = 30\n",
    "xlabel = ['positive','negative','natural']\n",
    "pos = len(review[review['rating']>threshold])\n",
    "neg = len(review[review['rating']<threshold])\n",
    "natural = len(review[review['rating']==threshold])\n",
    "y = [pos, neg, natural]\n",
    "x = np.arange(len(y))\n",
    "\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.axis('equal')\n",
    "colors = ['yellowgreen', 'gold', 'cyan']\n",
    "explode = (0.1, 0,0)\n",
    "plt.title(f'total : {total}')\n",
    "plt.pie(y, explode=explode, labels=xlabel, colors=colors,\n",
    "        autopct='%1.1f%%', shadow=True, startangle=90)\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xlabel = review['rating'].value_counts().index.tolist()\n",
    "y = review['rating'].value_counts().tolist()\n",
    "x = np.arange(len(y))\n",
    "\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.axis('equal')\n",
    "colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral','cyan']\n",
    "explode = (0.1, 0, 0, 0,0)\n",
    "plt.title(f'total : {total}')\n",
    "plt.pie(y, explode=explode, labels=xlabel, colors=colors,\n",
    "        autopct='%1.1f%%', shadow=True, startangle=90)\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xlabel = review['rating'].value_counts().index.tolist()\n",
    "y = review['rating'].value_counts().tolist()\n",
    "x = sorted(np.arange(len(y)),reverse=True)\n",
    "\n",
    "fig = plt.figure(figsize=(6, 6))\n",
    "bar = plt.bar(x,y)\n",
    "plt.xticks(x, xlabel)\n",
    "plt.title('Triadvisor Rating')\n",
    "plt.xlabel('Rating')\n",
    "plt.ylabel('Count')\n",
    "for rect in bar:\n",
    "    height = rect.get_height()\n",
    "    percent = height/total*100\n",
    "    plt.text(rect.get_x() + rect.get_width()/2.0, height/2.0, f'{percent:.2f}%', ha='center', va='bottom')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xlabel = review['place'].value_counts().index.tolist()\n",
    "y = review['place'].value_counts().tolist()\n",
    "x = np.arange(len(y))\n",
    "\n",
    "fig = plt.figure(figsize=(15, 15))\n",
    "plt.bar(x,y)\n",
    "#plt.xticks(x, xlabel)\n",
    "plt.title('Triadvisor dataset')\n",
    "plt.xlabel('Place')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xlabel = review['uid'].value_counts().index.tolist()\n",
    "y = review['uid'].value_counts().tolist()\n",
    "x = np.arange(len(y))\n",
    "\n",
    "fig = plt.figure(figsize=(15, 15))\n",
    "plt.bar(x,y)\n",
    "#plt.xticks(x, xlabel)\n",
    "plt.title('Triadvisor dataset')\n",
    "plt.xlabel('Place')\n",
    "plt.ylabel('Count')\n",
    "plt.ylim(bottom=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(56974, 9)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 데이터 전처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 전체 사용자: 11595\n",
      " 전체 관광지: 3081\n",
      " 리뷰 수: 57974\n",
      "\n",
      " 희소성: 0.162%\n"
     ]
    }
   ],
   "source": [
    "total_user = len(set(raw_data.uid))\n",
    "total_item = len(set(raw_data.place))\n",
    "n_review = len(raw_data)\n",
    "\n",
    "print(f\" 전체 사용자: {total_user}\")\n",
    "print(f\" 전체 관광지: {total_item}\")\n",
    "print(f\" 리뷰 수: {n_review}\")\n",
    "print()\n",
    "sparsity = n_review/(total_user*total_item)*100\n",
    "print(f\" 희소성: {sparsity:.3f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "raw_data = raw_data.drop_duplicates(['uid', 'place'], keep='first').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\170544\\Miniconda3\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    },
    {
     "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>uid</th>\n",
       "      <th>place</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>George_Korean</td>\n",
       "      <td>경복궁</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hshdsc408</td>\n",
       "      <td>경복궁</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Expedition705263</td>\n",
       "      <td>경복궁</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>guuming</td>\n",
       "      <td>경복궁</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ruffy1977</td>\n",
       "      <td>경복궁</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                uid place  rating\n",
       "0     George_Korean   경복궁       1\n",
       "1         hshdsc408   경복궁       1\n",
       "2  Expedition705263   경복궁       1\n",
       "3           guuming   경복궁       1\n",
       "4         ruffy1977   경복궁       1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold=20\n",
    "\n",
    "feature = ['uid','place','rating']\n",
    "raw_data = raw_data[feature]\n",
    "raw_data['rating'][raw_data['rating']<=threshold]=0\n",
    "raw_data['rating'][raw_data['rating']>threshold]=1\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'F9761WE_': 0,\n",
       "  'Voyage749354': 1,\n",
       "  'Seaside806550': 2,\n",
       "  'ychan1949': 3,\n",
       "  'binsjins': 4,\n",
       "  'beachseeker14': 5,\n",
       "  'KangMyeongHyeon': 6,\n",
       "  'Bongtravel890': 7,\n",
       "  'oriwife': 8,\n",
       "  'capsilver8013': 9,\n",
       "  'Venture770794': 10,\n",
       "  'Ambassador805831': 11,\n",
       "  'MyeongSeongK1': 12,\n",
       "  'lovehanra': 13,\n",
       "  'kimmolla': 14,\n",
       "  'sia3107': 15,\n",
       "  'hope601116': 16,\n",
       "  '392jihyunh': 17,\n",
       "  'Explore807187': 18,\n",
       "  'tahoch': 19,\n",
       "  'hajilee': 20,\n",
       "  'BoardingPass810964': 21,\n",
       "  'Escape807710': 22,\n",
       "  'KKungD': 23,\n",
       "  'KimM5794': 24,\n",
       "  'mrpres461': 25,\n",
       "  'MariaP0nce': 26,\n",
       "  'Odyssey807940': 27,\n",
       "  'herenow365': 28,\n",
       "  'FarAway808787': 29,\n",
       "  'wonderp2017': 30,\n",
       "  'shim7138': 31,\n",
       "  'ChungkyuCho': 32,\n",
       "  'Storyteller808720': 33,\n",
       "  'ksh8240': 34,\n",
       "  'HarryK1568': 35,\n",
       "  'JayJang7604': 36,\n",
       "  'Fearless692679': 37,\n",
       "  'Sunshine812620': 38,\n",
       "  'eommefatale': 39,\n",
       "  'gogogo7738': 40,\n",
       "  'Kaaarinkim': 41,\n",
       "  'YounWooY': 42,\n",
       "  'yoonisky': 43,\n",
       "  'HanNaL1652': 44,\n",
       "  'Tourist413154': 45,\n",
       "  'Travel702954': 46,\n",
       "  'bona01': 47,\n",
       "  'kim3rich': 48,\n",
       "  'Sightsee754914': 49,\n",
       "  'fomerna': 50,\n",
       "  'nanuoury': 51,\n",
       "  'Explorer665058': 52,\n",
       "  '42bryant': 53,\n",
       "  'komkomi': 54,\n",
       "  'Inspire645393': 55,\n",
       "  'bonghwa': 56,\n",
       "  'jyo845': 57,\n",
       "  'Anzimba': 58,\n",
       "  '_D7221QO': 59,\n",
       "  'saengsuy': 60,\n",
       "  'lihhwa2018': 61,\n",
       "  'Curiosity808485': 62,\n",
       "  'giftj258': 63,\n",
       "  'okdabang': 64,\n",
       "  'Resort590639': 65,\n",
       "  'YoungSukHan': 66,\n",
       "  '_D9435TT': 67,\n",
       "  'hopetotrip': 68,\n",
       "  'HYUNGDONGL3': 69,\n",
       "  'gasina7070': 70,\n",
       "  'Trail807706': 71,\n",
       "  'yeolk2016': 72,\n",
       "  'tnswud': 73,\n",
       "  'Pioneer807265': 74,\n",
       "  'min_ajju': 75,\n",
       "  'Departure804677': 76,\n",
       "  'kidhan': 77,\n",
       "  'austinted': 78,\n",
       "  'Nana8302': 79,\n",
       "  'Ninano77': 80,\n",
       "  'Paradise651307': 81,\n",
       "  'Escape803485': 82,\n",
       "  'kiring2016': 83,\n",
       "  'Discover803280': 84,\n",
       "  'Sugardgu': 85,\n",
       "  'jinustar': 86,\n",
       "  'FellowTraveler599641': 87,\n",
       "  'Nomad59786347587': 88,\n",
       "  'jungpil': 89,\n",
       "  'green727': 90,\n",
       "  'Navigator488975': 91,\n",
       "  'WonI1': 92,\n",
       "  'kanto199': 93,\n",
       "  'jmoon0414': 94,\n",
       "  'tlepddkf_0': 95,\n",
       "  'summerellahee': 96,\n",
       "  'Trail810428': 97,\n",
       "  '79jihyuny': 98,\n",
       "  'Connector810387': 99,\n",
       "  'eunjung_bae': 100,\n",
       "  'Relax806340': 101,\n",
       "  'Expedition712961': 102,\n",
       "  'ggobong': 103,\n",
       "  'Excursion805844': 104,\n",
       "  'jsh6136': 105,\n",
       "  'hyunsuklee': 106,\n",
       "  'Mylian30': 107,\n",
       "  'Adventurer803625': 108,\n",
       "  'spacewiz1': 109,\n",
       "  'yukib973': 110,\n",
       "  'jieun32462': 111,\n",
       "  'diakonosJ': 112,\n",
       "  'Trip809063': 113,\n",
       "  'Sunshine726160': 114,\n",
       "  'RoyKang1': 115,\n",
       "  'Roving809355': 116,\n",
       "  'Dreamer663194': 117,\n",
       "  'JiHwanK50': 118,\n",
       "  'peekaboo2': 119,\n",
       "  '368suhyuns': 120,\n",
       "  'CHK155': 121,\n",
       "  'Companion804133': 122,\n",
       "  'Stay744590': 123,\n",
       "  'eefree': 124,\n",
       "  'cbodedmd': 125,\n",
       "  'Companion808435': 126,\n",
       "  'Relax657530': 127,\n",
       "  'JunghyeLee': 128,\n",
       "  'Compass810963': 129,\n",
       "  'LVMABP': 130,\n",
       "  'YWmom-0307': 131,\n",
       "  'y0ngkukk': 132,\n",
       "  'JohnY2147': 133,\n",
       "  'army9595': 134,\n",
       "  'Connector810029': 135,\n",
       "  'burnellkim': 136,\n",
       "  'tuyosinoie': 137,\n",
       "  '696eunmij': 138,\n",
       "  'unkn0wny': 139,\n",
       "  'BrandyK161': 140,\n",
       "  'hyojeongLim505': 141,\n",
       "  'EGoroK': 142,\n",
       "  'Jet805539': 143,\n",
       "  'JY_Life': 144,\n",
       "  'xgench': 145,\n",
       "  'jinmommy': 146,\n",
       "  'almacreative': 147,\n",
       "  'FellowTraveler809984': 148,\n",
       "  'TheKorean': 149,\n",
       "  'Adventure792104': 150,\n",
       "  'Kolaxia': 151,\n",
       "  'tourboys': 152,\n",
       "  'LIIBO': 153,\n",
       "  'Roam809495': 154,\n",
       "  'GoPlaces807713': 155,\n",
       "  'Daeunbob': 156,\n",
       "  'dhl9809': 157,\n",
       "  'minjic2018': 158,\n",
       "  'Louis_JO_de_le_est': 159,\n",
       "  'snowcalla': 160,\n",
       "  'Discover803448': 161,\n",
       "  'lwk198': 162,\n",
       "  'janeeooy': 163,\n",
       "  'Seaside804448': 164,\n",
       "  'whitepipi': 165,\n",
       "  'joinstar': 166,\n",
       "  'aphrodite012': 167,\n",
       "  'wony0426': 168,\n",
       "  'se0ye0nj': 169,\n",
       "  'kyoungsuks': 170,\n",
       "  'gregorydo': 171,\n",
       "  'oppaes': 172,\n",
       "  'Culture803978': 173,\n",
       "  'fattydk': 174,\n",
       "  'jaejoon2': 175,\n",
       "  'amykw': 176,\n",
       "  'Dreamer807584': 177,\n",
       "  'Josephine0617': 178,\n",
       "  'Nomad809254': 179,\n",
       "  'sidia1': 180,\n",
       "  'kiyoon': 181,\n",
       "  'wiredfrog': 182,\n",
       "  'Hoduya': 183,\n",
       "  'MyNarae': 184,\n",
       "  'harumikim': 185,\n",
       "  'ativan6mg': 186,\n",
       "  'BackPacker808041': 187,\n",
       "  'Expedition766527': 188,\n",
       "  'Compass28882892270': 189,\n",
       "  'sadong0727': 190,\n",
       "  'kimheesoo': 191,\n",
       "  'hyey0uns': 192,\n",
       "  '_V2318EB': 193,\n",
       "  'eheverydayisaholiday': 194,\n",
       "  'dongyull2016': 195,\n",
       "  '_X9826HH': 196,\n",
       "  'Trek807802': 197,\n",
       "  'FellowTraveler654320': 198,\n",
       "  'Go808603': 199,\n",
       "  'MirafloresLover': 200,\n",
       "  'hyojinj2017': 201,\n",
       "  'smkml': 202,\n",
       "  'Relax746111': 203,\n",
       "  'Climber719563': 204,\n",
       "  'hayul13': 205,\n",
       "  'Getaway807488': 206,\n",
       "  'djune99': 207,\n",
       "  '_E8201DA': 208,\n",
       "  'Vacationer624052': 209,\n",
       "  'H914ZM_': 210,\n",
       "  'mango1030': 211,\n",
       "  'Leej3057': 212,\n",
       "  'jonghee_choi': 213,\n",
       "  'sweetchocho': 214,\n",
       "  'haebchi': 215,\n",
       "  'Trail656652': 216,\n",
       "  'V4033TK_': 217,\n",
       "  'GrandTour811121': 218,\n",
       "  'kanu0520': 219,\n",
       "  'Pathfinder682455': 220,\n",
       "  'rumpkins': 221,\n",
       "  'Doran-PARK': 222,\n",
       "  'mmom661': 223,\n",
       "  'soho201': 224,\n",
       "  'sincere18': 225,\n",
       "  'Road702016': 226,\n",
       "  'rewere': 227,\n",
       "  'Passenger786578': 228,\n",
       "  'avecs27': 229,\n",
       "  'GrandTour807985': 230,\n",
       "  'Sunshine668529': 231,\n",
       "  'yangcho0311': 232,\n",
       "  'hmin912': 233,\n",
       "  'SooyunS3': 234,\n",
       "  'Tour807348': 235,\n",
       "  'kittygirl-97': 236,\n",
       "  'Vacationer804139': 237,\n",
       "  'Mygd': 238,\n",
       "  'dddaisy1': 239,\n",
       "  'sonority237': 240,\n",
       "  'GlobeTrotting807655': 241,\n",
       "  'DREAMING_dream': 242,\n",
       "  'PrettyE59': 243,\n",
       "  'wisemint': 244,\n",
       "  'Roam686041': 245,\n",
       "  '_T5104TA': 246,\n",
       "  'Excursion802864': 247,\n",
       "  'cms6731': 248,\n",
       "  'Trail681352': 249,\n",
       "  'hogeuns25': 250,\n",
       "  'injunb': 251,\n",
       "  'Camper807364': 252,\n",
       "  'Gypsy7627': 253,\n",
       "  'PeterandBruce': 254,\n",
       "  'Trek724895': 255,\n",
       "  'Exploration809239': 256,\n",
       "  'Tour807219': 257,\n",
       "  'yuri_usen': 258,\n",
       "  'cocomo5': 259,\n",
       "  'GlobeTrotting806324': 260,\n",
       "  'Trail757185': 261,\n",
       "  'Ambassador810308': 262,\n",
       "  'kunkunmaron': 263,\n",
       "  'FMoz': 264,\n",
       "  'MozartUC': 265,\n",
       "  'Venture41421548613': 266,\n",
       "  'Inspire807948': 267,\n",
       "  'Stay805704': 268,\n",
       "  'Lyla-Sujin': 269,\n",
       "  'miyeon999': 270,\n",
       "  'jjw2111': 271,\n",
       "  'Explorer804469': 272,\n",
       "  'Go826803': 273,\n",
       "  'gnawhaoos': 274,\n",
       "  'ran98kr': 275,\n",
       "  'HO102': 276,\n",
       "  'Gongtok': 277,\n",
       "  'Luka79_Kim': 278,\n",
       "  'ab961004': 279,\n",
       "  'Roam658113': 280,\n",
       "  'hersorry': 281,\n",
       "  'ssbe1216': 282,\n",
       "  'Expedition739988': 283,\n",
       "  'EmyH245': 284,\n",
       "  'mysticblue70': 285,\n",
       "  'Extraordinary773965': 286,\n",
       "  'N2329UZrachelk': 287,\n",
       "  'T2463NG_': 288,\n",
       "  'Pinkrose0707': 289,\n",
       "  'cheryl0319': 290,\n",
       "  'hurricane8393': 291,\n",
       "  'clock100001': 292,\n",
       "  'LightPacker806980': 293,\n",
       "  'pieterin': 294,\n",
       "  'honnne': 295,\n",
       "  'Wlrtk': 296,\n",
       "  'yhjfe': 297,\n",
       "  'jbyongsu': 298,\n",
       "  'y895538': 299,\n",
       "  'harrymartin2580': 300,\n",
       "  'yhsuk02': 301,\n",
       "  'hslove0908': 302,\n",
       "  'Voyage810112': 303,\n",
       "  'jieuni0409': 304,\n",
       "  'y0ung02': 305,\n",
       "  '325yec': 306,\n",
       "  'Derek_KOR': 307,\n",
       "  'guseralee': 308,\n",
       "  'Sunshine620868': 309,\n",
       "  'OnAir811318': 310,\n",
       "  'Inspiration807174': 311,\n",
       "  'Rokkomaru': 312,\n",
       "  'HyunYoungK29': 313,\n",
       "  'Lauralee627': 314,\n",
       "  'Getaway808557': 315,\n",
       "  'couldntbebetterrr': 316,\n",
       "  'yeeun90': 317,\n",
       "  'Scenic807698': 318,\n",
       "  'yeonsook2015': 319,\n",
       "  'Geumok': 320,\n",
       "  'cdtime': 321,\n",
       "  'Daydream676504': 322,\n",
       "  'Road807248': 323,\n",
       "  'muyoungk': 324,\n",
       "  'A318KArichardc': 325,\n",
       "  '_S3309UK': 326,\n",
       "  'swmmmmm': 327,\n",
       "  'hallower': 328,\n",
       "  'saein87': 329,\n",
       "  'kyw403': 330,\n",
       "  'seoulpeace': 331,\n",
       "  'Trip805420': 332,\n",
       "  'Sassa_Sunny': 333,\n",
       "  'leemossi': 334,\n",
       "  'hj16hj': 335,\n",
       "  'Roam807144': 336,\n",
       "  'Joicechia': 337,\n",
       "  'AlexJ3782': 338,\n",
       "  'fromruby': 339,\n",
       "  'NorthStar809543': 340,\n",
       "  '123SDF': 341,\n",
       "  'MelonH7': 342,\n",
       "  'JwL63': 343,\n",
       "  'Dreamer804115': 344,\n",
       "  'Inspire697954': 345,\n",
       "  'N6292NZ_': 346,\n",
       "  'Dreamer808173': 347,\n",
       "  'Cosmopolitan807493': 348,\n",
       "  'Antonio9ark': 349,\n",
       "  'Lonnie533': 350,\n",
       "  'LOTTEHOTELJEJUHRD': 351,\n",
       "  'TheGoodReviews': 352,\n",
       "  'NorthStar764004': 353,\n",
       "  'Go804056': 354,\n",
       "  'tastebeer': 355,\n",
       "  'Z1715FX_': 356,\n",
       "  'JGOOYOUN': 357,\n",
       "  'Safari762123': 358,\n",
       "  'BluebirdHong': 359,\n",
       "  'hrc91': 360,\n",
       "  'migui8585': 361,\n",
       "  'YsS48': 362,\n",
       "  'Sherpa807171': 363,\n",
       "  'SeojunB1': 364,\n",
       "  'bomiyaa': 365,\n",
       "  'yoon2': 366,\n",
       "  'B5322IH_': 367,\n",
       "  'CaffriA': 368,\n",
       "  'spencerseungkang': 369,\n",
       "  'trave1er_saa': 370,\n",
       "  'Coastal550143': 371,\n",
       "  'Cosmopolitan808511': 372,\n",
       "  'mhh10003': 373,\n",
       "  'Passenger806160': 374,\n",
       "  'kiwee96': 375,\n",
       "  'Climber762394': 376,\n",
       "  '186hyunchulk': 377,\n",
       "  'reembom': 378,\n",
       "  'Sightsee737397': 379,\n",
       "  '_B6121DM': 380,\n",
       "  'Flyer803451': 381,\n",
       "  'doordono': 382,\n",
       "  'kubrick123': 383,\n",
       "  'sanghah': 384,\n",
       "  'GrandTour805468': 385,\n",
       "  'Discover807415': 386,\n",
       "  'mkang08': 387,\n",
       "  'Pioneer557011': 388,\n",
       "  'FellowTraveler807092': 389,\n",
       "  'Eun-byeol-nam': 390,\n",
       "  'sososoh': 391,\n",
       "  'Climber807095': 392,\n",
       "  'jameskW3648ZE': 393,\n",
       "  'kye0ngtaek': 394,\n",
       "  '_G2732SP': 395,\n",
       "  'Hayuco': 396,\n",
       "  'Footprints807318': 397,\n",
       "  'GlobeTrotting809706': 398,\n",
       "  'yoon2jin': 399,\n",
       "  'HyungDonKim': 400,\n",
       "  'SusieROK': 401,\n",
       "  'Vacationer540631': 402,\n",
       "  'sihyunh': 403,\n",
       "  'S2travelS2': 404,\n",
       "  'DYC44': 405,\n",
       "  'Bella_yim': 406,\n",
       "  'cintia3787': 407,\n",
       "  'chloe_ljh': 408,\n",
       "  'GrandTour716218': 409,\n",
       "  '5pizza': 410,\n",
       "  'megmin': 411,\n",
       "  '661seris': 412,\n",
       "  'Stay805535': 413,\n",
       "  'Tourist808100': 414,\n",
       "  'dal0310': 415,\n",
       "  'jos_ricardog': 416,\n",
       "  'Duls2': 417,\n",
       "  'LightPacker807235': 418,\n",
       "  'FlyingMami': 419,\n",
       "  'HyeNaK14': 420,\n",
       "  'misuk1239': 421,\n",
       "  'kozakurainko5150': 422,\n",
       "  'Relax807372': 423,\n",
       "  'Damwoosul': 424,\n",
       "  'Germanyguide': 425,\n",
       "  '48samuely': 426,\n",
       "  'qndjddl': 427,\n",
       "  'Trek815296': 428,\n",
       "  'Experience669140': 429,\n",
       "  'EunseopKim': 430,\n",
       "  'HeesuJung': 431,\n",
       "  'simonmun': 432,\n",
       "  'Sightsee726019': 433,\n",
       "  'WonyoungJ12': 434,\n",
       "  'lhjgo14': 435,\n",
       "  'cosmic_ride': 436,\n",
       "  'Happiness807440': 437,\n",
       "  'Travel825691': 438,\n",
       "  'gracelam0828': 439,\n",
       "  'DayTrip810090': 440,\n",
       "  'BackPacker717327': 441,\n",
       "  'Trip810055': 442,\n",
       "  '868jenahc': 443,\n",
       "  'RosaLinda629': 444,\n",
       "  'curucuru89': 445,\n",
       "  'Inspire803248': 446,\n",
       "  'ant908': 447,\n",
       "  'Guide811201': 448,\n",
       "  'ytwolf': 449,\n",
       "  'hanyfive': 450,\n",
       "  'FarAway709917': 451,\n",
       "  'KateL3091': 452,\n",
       "  'jiyoung47': 453,\n",
       "  'asamijo': 454,\n",
       "  'kim971024': 455,\n",
       "  'Wanderer44907243694': 456,\n",
       "  'Trail811129': 457,\n",
       "  'Coastal744167': 458,\n",
       "  'Seaside804247': 459,\n",
       "  'SeokEH': 460,\n",
       "  'OnAir807284': 461,\n",
       "  '682suntaek': 462,\n",
       "  'Flyer699791': 463,\n",
       "  'Vacationer808014': 464,\n",
       "  'holimoliqucamoli': 465,\n",
       "  'Exploration805193': 466,\n",
       "  'JulianaDC': 467,\n",
       "  'iamjuheeee': 468,\n",
       "  'mumu0505': 469,\n",
       "  'doraora': 470,\n",
       "  'LoveTravel711755': 471,\n",
       "  877888778: 472,\n",
       "  'olkdsybg': 473,\n",
       "  'Curious807612': 474,\n",
       "  'FrequentFlier659038': 475,\n",
       "  'ilikeelsa': 476,\n",
       "  'j0nggyuj': 477,\n",
       "  'P1604TT_': 478,\n",
       "  'LilyTLa': 479,\n",
       "  'Excursion807178': 480,\n",
       "  'jay2kilo': 481,\n",
       "  'jh4324han': 482,\n",
       "  'Excursion745683': 483,\n",
       "  'younsoooo': 484,\n",
       "  'boogie_on_n_oon': 485,\n",
       "  'HyerimP5': 486,\n",
       "  'ksn0w': 487,\n",
       "  '2013tessie': 488,\n",
       "  'AstinP5': 489,\n",
       "  'Resort711711': 490,\n",
       "  'nimalution': 491,\n",
       "  'moo_khao': 492,\n",
       "  'romi438': 493,\n",
       "  'kinscop': 494,\n",
       "  'ahngui': 495,\n",
       "  'TrailBlazer808867': 496,\n",
       "  'Discover807179': 497,\n",
       "  'cari0510': 498,\n",
       "  'vstellav': 499,\n",
       "  'GlobeTrotting807122': 500,\n",
       "  'HappyPapawolf': 501,\n",
       "  'Ambassador681047': 502,\n",
       "  'Yongseokchoe': 503,\n",
       "  'sangyeonj': 504,\n",
       "  'Camille102': 505,\n",
       "  'Flyer707362': 506,\n",
       "  'coolboy45': 507,\n",
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       "  'Footprints807285': 509,\n",
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       "  '화성 공룡알 화석지': 10,\n",
       "  '봉산문화거리': 11,\n",
       "  '테딘워터파크': 12,\n",
       "  'Jangansa': 13,\n",
       "  '파주장릉': 14,\n",
       "  '덕평자연휴게소': 15,\n",
       "  '남이섬': 16,\n",
       "  '부산 시민공원': 17,\n",
       "  '진양호 동물원': 18,\n",
       "  '부산커피박물관': 19,\n",
       "  '부산 아시아드 주 경기장': 20,\n",
       "  '칠천량해전공원': 21,\n",
       "  '다랭이마을': 22,\n",
       "  '울릉도': 23,\n",
       "  '사명암': 24,\n",
       "  '오세암': 25,\n",
       "  '탄금호': 26,\n",
       "  '교동읍성': 27,\n",
       "  '봉산문화회관': 28,\n",
       "  '동구어린이영어도서관': 29,\n",
       "  '팔공산케이블카': 30,\n",
       "  '입곡 군립공원': 31,\n",
       "  '제주 월드컵 경기장': 32,\n",
       "  '코코몽 에코 파크': 33,\n",
       "  '태화산': 34,\n",
       "  '옛터민속박물관': 35,\n",
       "  '숙정문': 36,\n",
       "  '낙산공원': 37,\n",
       "  '대흥동 카페거리': 38,\n",
       "  '안목 해수욕장': 39,\n",
       "  '김포유리박물관': 40,\n",
       "  '선릉과 정릉': 41,\n",
       "  '천자봉': 42,\n",
       "  '진해보타닉뮤지엄': 43,\n",
       "  '세병관': 44,\n",
       "  '부산 근대 역사관': 45,\n",
       "  '골굴사': 46,\n",
       "  '관음사': 47,\n",
       "  '이중섭 문화거리': 48,\n",
       "  '경주월드': 49,\n",
       "  '선비촌': 50,\n",
       "  '가나 아트파크': 51,\n",
       "  '주상절리 전망대': 52,\n",
       "  '수도국산 달동네박물관': 53,\n",
       "  '임랑해수욕장': 54,\n",
       "  'Olympus Hall': 55,\n",
       "  '산막이 옛길': 56,\n",
       "  '패션월드': 57,\n",
       "  '조랑말 체험공원': 58,\n",
       "  '아모레퍼시픽미술관': 59,\n",
       "  '산림박물관': 60,\n",
       "  '낙성대공원': 61,\n",
       "  '청학동 마을': 62,\n",
       "  '영천 만불사': 63,\n",
       "  '또나따목장': 64,\n",
       "  '세계자동차제주박물관': 65,\n",
       "  '여의도 샛강 생태 공원': 66,\n",
       "  '환선굴': 67,\n",
       "  '개항장 역사문화의 거리': 68,\n",
       "  '청담동 패션 거리': 69,\n",
       "  '연세대학교 글로벌캠퍼스': 70,\n",
       "  '정자해수욕장': 71,\n",
       "  '금마저수지': 72,\n",
       "  '동삼동패총전시관': 73,\n",
       "  '돌할매': 74,\n",
       "  '유달산 조각 공원': 75,\n",
       "  '현충사': 76,\n",
       "  '베어트리 파크': 77,\n",
       "  '코엑스 센터': 78,\n",
       "  '구러시아 공사관': 79,\n",
       "  '롯데 영 플라자 - 울산점': 80,\n",
       "  '성밖숲': 81,\n",
       "  '창원시 시립 마산 박물관': 82,\n",
       "  '잠실 야구 경기장': 83,\n",
       "  '비선대': 84,\n",
       "  '일산 호수 공원': 85,\n",
       "  '365 세이프타운': 86,\n",
       "  '사직공원 전망타워': 87,\n",
       "  '문수산산림욕장': 88,\n",
       "  '대한민국 역사 박물관': 89,\n",
       "  '탐라국유일레저타운': 90,\n",
       "  '에델바이스': 91,\n",
       "  '남한산성 역사관': 92,\n",
       "  '광복동 먹자골목': 93,\n",
       "  'Woori Financial Art Hall': 94,\n",
       "  '오죽헌': 95,\n",
       "  '무상사': 96,\n",
       "  '청사포 다릿돌전망대': 97,\n",
       "  '서울 한양 도성': 98,\n",
       "  '효원공원': 99,\n",
       "  '장충단공원': 100,\n",
       "  '철원 평화전망대': 101,\n",
       "  '나혜석거리': 102,\n",
       "  '하슬라 아트 월드': 103,\n",
       "  '제주 여미지 식물원': 104,\n",
       "  '부석사': 105,\n",
       "  '바이나흐튼 크리스마스 박물관': 106,\n",
       "  '넓은들 학원농장': 107,\n",
       "  '계명산 자연휴양림': 108,\n",
       "  '덕평공룡수목원': 109,\n",
       "  '북한산둘레길': 110,\n",
       "  '돈암문화스포츠센터': 111,\n",
       "  '혼불문학관': 112,\n",
       "  '초곡 용굴 촛대바위길': 113,\n",
       "  '종각젊음의거리': 114,\n",
       "  '이케아 - 광명점': 115,\n",
       "  '선암사성보박물관': 116,\n",
       "  '보신각': 117,\n",
       "  '수성아쿠아수영장': 118,\n",
       "  '산방산': 119,\n",
       "  '동대문 밀리오레': 120,\n",
       "  '광주남구문화예술회관': 121,\n",
       "  '합천영상테마파크': 122,\n",
       "  '엔터식스 - 파크에비뉴 한양대점': 123,\n",
       "  '대전전통나래관': 124,\n",
       "  '광화문': 125,\n",
       "  '좌구산휴양림': 126,\n",
       "  '티라이트 공원': 127,\n",
       "  '연화사': 128,\n",
       "  '선두5리항': 129,\n",
       "  '부전시장': 130,\n",
       "  '나주 배박물관': 131,\n",
       "  '신창 풍차해안도로': 132,\n",
       "  '성산패총': 133,\n",
       "  '위양지': 134,\n",
       "  '국립 고궁 박물관': 135,\n",
       "  '남해편백휴양림': 136,\n",
       "  '북악스카이웨이': 137,\n",
       "  '선유도 해수욕장': 138,\n",
       "  '태양의후예 태백세트장': 139,\n",
       "  '아차산': 140,\n",
       "  '은이성지': 141,\n",
       "  '관음도': 142,\n",
       "  '마천시장': 143,\n",
       "  '국립산악박물관': 144,\n",
       "  '국립중앙박물관 어린이박물관': 145,\n",
       "  '더 마 파크': 146,\n",
       "  '경주 동궁원': 147,\n",
       "  '희망대공원': 148,\n",
       "  '오웬기념각': 149,\n",
       "  '목포생활도자박물관': 150,\n",
       "  '한국민속촌': 151,\n",
       "  '한국 금융사 박물관': 152,\n",
       "  '동문 재래시장': 153,\n",
       "  '공작산 수타사': 154,\n",
       "  '열쇠전망대': 155,\n",
       "  '청와대': 156,\n",
       "  '절두산 순교 성지': 157,\n",
       "  '울산향교': 158,\n",
       "  '정심화홀': 159,\n",
       "  '금오름': 160,\n",
       "  '초콜릿박물관': 161,\n",
       "  '광복로 문화 패션 거리': 162,\n",
       "  '낭만포차로': 163,\n",
       "  '연홍도': 164,\n",
       "  '부여서고': 165,\n",
       "  '포석정지': 166,\n",
       "  '마장호수': 167,\n",
       "  '공룡발자국공원': 168,\n",
       "  '충북교육박물관': 169,\n",
       "  '서울 월드컵 경기장 (상암 월드컵 경기장)': 170,\n",
       "  '문장대': 171,\n",
       "  '대산미술관': 172,\n",
       "  '의정부시 청소년회관': 173,\n",
       "  '양평휴게소': 174,\n",
       "  '펄벅기념관': 175,\n",
       "  '신흥사': 176,\n",
       "  '양재 시민 공원': 177,\n",
       "  '재미난박물관': 178,\n",
       "  '우포늪': 179,\n",
       "  '황리단길': 180,\n",
       "  '용주사': 181,\n",
       "  '단국대학교석주선기념박물관': 182,\n",
       "  '명재고택': 183,\n",
       "  '보문호': 184,\n",
       "  'Busan Premium Outlet': 185,\n",
       "  '문동폭포': 186,\n",
       "  '선도리 갯벌체험장': 187,\n",
       "  '온양민속박물관': 188,\n",
       "  '거창박물관': 189,\n",
       "  '대전통일관': 190,\n",
       "  '삼랑진 트윈터널': 191,\n",
       "  '이천 도자기축제': 192,\n",
       "  '은해사': 193,\n",
       "  '전라병영성지': 194,\n",
       "  '남산호국공원': 195,\n",
       "  '커먼그라운드': 196,\n",
       "  '커피커퍼': 197,\n",
       "  '수원제일교회': 198,\n",
       "  '일현미술관': 199,\n",
       "  '덕수궁 중명전': 200,\n",
       "  '화천민속박물관': 201,\n",
       "  '천당 폭포': 202,\n",
       "  '월정리해변': 203,\n",
       "  '마린파크': 204,\n",
       "  '기생충박물관': 205,\n",
       "  '강화평화전망대': 206,\n",
       "  '삼락생태공원': 207,\n",
       "  '국립세종도서관': 208,\n",
       "  '화명야외수영장': 209,\n",
       "  '킴스 아트 필드': 210,\n",
       "  '태안 신두리 해안사구': 211,\n",
       "  '스펀지': 212,\n",
       "  '시인이상화고택': 213,\n",
       "  '보령 충청수영성': 214,\n",
       "  '만장굴': 215,\n",
       "  '유성온천 야외 족욕체험장': 216,\n",
       "  '서울랜드': 217,\n",
       "  '전주동물원': 218,\n",
       "  '안동 임청각': 219,\n",
       "  '한림공원': 220,\n",
       "  '목은선생영당': 221,\n",
       "  '태종대': 222,\n",
       "  '회덕향교': 223,\n",
       "  '쁘띠프랑스': 224,\n",
       "  '소수서원': 225,\n",
       "  '삼년산성': 226,\n",
       "  '서울 어린이 대공원': 227,\n",
       "  '광교호수공원': 228,\n",
       "  '첨성대': 229,\n",
       "  '여수고흥연륙교': 230,\n",
       "  '제주항공우주박물관': 231,\n",
       "  '진해시립박물관': 232,\n",
       "  '천리포수목원': 233,\n",
       "  '대연수목전시원': 234,\n",
       "  '공항철도 트래블 센터': 235,\n",
       "  '명동관광정보센터': 236,\n",
       "  '대둔산': 237,\n",
       "  '화폐박물관': 238,\n",
       "  '창경궁': 239,\n",
       "  '동해문화원': 240,\n",
       "  '별마당 도서관': 241,\n",
       "  '청운공원': 242,\n",
       "  '명동지하상가': 243,\n",
       "  '박물관은 살아있다': 244,\n",
       "  '주교좌 남천성당': 245,\n",
       "  '국순당주향로': 246,\n",
       "  '일광 해수욕장': 247,\n",
       "  '대구삼성라이온즈파크': 248,\n",
       "  '한국미술관': 249,\n",
       "  '캐니빌리지': 250,\n",
       "  '예천박물관': 251,\n",
       "  '도림사': 252,\n",
       "  '진해군항제': 253,\n",
       "  '오죽헌시립박물관': 254,\n",
       "  '축령산편백숲': 255,\n",
       "  '암사동선사주거지 원시생활전시관': 256,\n",
       "  '오색 탄산온천': 257,\n",
       "  '지동벽화마을': 258,\n",
       "  '배다리 성냥마을 박물관': 259,\n",
       "  '창녕박물관': 260,\n",
       "  '마비정 벽화마을': 261,\n",
       "  '청량산 등산로': 262,\n",
       "  '치악산 국립공원': 263,\n",
       "  '김영갑 갤러리 두모악': 264,\n",
       "  '둥지박물관': 265,\n",
       "  '경주 서라벌문화회관': 266,\n",
       "  '보성강골마을': 267,\n",
       "  '성산 일출봉': 268,\n",
       "  'First Garden': 269,\n",
       "  '함주공원': 270,\n",
       "  '혼인지': 271,\n",
       "  '베니스스퀘어': 272,\n",
       "  '우리꽃 식물원': 273,\n",
       "  '새연교': 274,\n",
       "  '송정해수욕장': 275,\n",
       "  '코버월드 화폐박물관': 276,\n",
       "  '덕유산 곤도라': 277,\n",
       "  '순천만정원': 278,\n",
       "  '한라 수목원': 279,\n",
       "  '만옥정공원': 280,\n",
       "  '금오산 도립공원': 281,\n",
       "  '임진각 평화 누리 공원': 282,\n",
       "  '갤러리이': 283,\n",
       "  '플레이아쿠아리움 부천': 284,\n",
       "  '노동당사': 285,\n",
       "  '전주 월드컵 경기장': 286,\n",
       "  '대구제일교회': 287,\n",
       "  '63시티': 288,\n",
       "  '광양장도박물관': 289,\n",
       "  '서울 정릉': 290,\n",
       "  '대원사 티벳박물관': 291,\n",
       "  '혜화문 (홍화문)': 292,\n",
       "  '곤지암 도자공원': 293,\n",
       "  '구로시장': 294,\n",
       "  '경의선숲길': 295,\n",
       "  '방짜유기박물관': 296,\n",
       "  '왜관지구전적기념관': 297,\n",
       "  '화개산': 298,\n",
       "  'Mokin Museum': 299,\n",
       "  '청평 자연휴양림': 300,\n",
       "  '꼭두 박물관': 301,\n",
       "  '진도 신비의바닷길축제': 302,\n",
       "  '불국사': 303,\n",
       "  '동피랑': 304,\n",
       "  '당남리섬': 305,\n",
       "  '속초 등대전망대': 306,\n",
       "  '통일 공원': 307,\n",
       "  '광명전통시장': 308,\n",
       "  '천보산': 309,\n",
       "  '아라리오뮤지엄 탑동시네마': 310,\n",
       "  '월출산 국립공원': 311,\n",
       "  '한화기념관': 312,\n",
       "  '신장 쇼핑몰': 313,\n",
       "  '40계단 문화관광테마거리': 314,\n",
       "  '백운산자연휴양림': 315,\n",
       "  'Yongdoosan Misool Jeonsigwan': 316,\n",
       "  '별마로 천문대': 317,\n",
       "  '국립국악박물관': 318,\n",
       "  '맹씨행단·고불맹사성기념관': 319,\n",
       "  '정남진전망대': 320,\n",
       "  '회원현성지': 321,\n",
       "  '삼성미술관 리움': 322,\n",
       "  '테미오래': 323,\n",
       "  '갑사': 324,\n",
       "  '엔터식스 - 왕십리역점': 325,\n",
       "  '서면 지하도상가': 326,\n",
       "  '익산 왕궁리 오층석탑': 327,\n",
       "  'N 서울 타워': 328,\n",
       "  '한국이민사박물관': 329,\n",
       "  '서도역': 330,\n",
       "  '대흥사': 331,\n",
       "  '장자호수공원': 332,\n",
       "  '자유공원': 333,\n",
       "  '서해 수호관': 334,\n",
       "  '부산 민주공원': 335,\n",
       "  '보문사': 336,\n",
       "  '대포항': 337,\n",
       "  '강릉단오문화관': 338,\n",
       "  'e-스포츠 스타디움': 339,\n",
       "  '환기 미술관': 340,\n",
       "  '성남시립식물원': 341,\n",
       "  '안동호반자연휴양림': 342,\n",
       "  '대전선사박물관': 343,\n",
       "  '흥정계곡': 344,\n",
       "  '임해자연휴양림': 345,\n",
       "  '전주공예품전시관': 346,\n",
       "  '천주교 순교성지 새남터 기념성당': 347,\n",
       "  '박찬호 기념관': 348,\n",
       "  '청사포': 349,\n",
       "  '방림원 (세계 야생화 박물관)': 350,\n",
       "  '얼음나라화천 산천어축제': 351,\n",
       "  '스카이타워': 352,\n",
       "  '다랑쉬오름': 353,\n",
       "  '밧개 해수욕장': 354,\n",
       "  '엉또 폭포': 355,\n",
       "  '주문진 방사제': 356,\n",
       "  '세종탁구장': 357,\n",
       "  '배재학당역사박물관': 358,\n",
       "  '함평향교': 359,\n",
       "  '금정 산성': 360,\n",
       "  'G타워': 361,\n",
       "  '난지미술창작스튜디오': 362,\n",
       "  '메타프로방스': 363,\n",
       "  '거금대교': 364,\n",
       "  '국립여성사전시관': 365,\n",
       "  '경주 사찰': 366,\n",
       "  '명동 성당': 367,\n",
       "  '해망굴': 368,\n",
       "  '왕궁리유적전시관': 369,\n",
       "  '용평 리조트 곤돌라': 370,\n",
       "  '춘천 마임축제': 371,\n",
       "  '은파 호수 공원': 372,\n",
       "  '국립 현대 미술관': 373,\n",
       "  '덕바위 농촌체험마을': 374,\n",
       "  '수성동 계곡': 375,\n",
       "  '남해유배문학관': 376,\n",
       "  '주실마을': 377,\n",
       "  '성산포 조가비 박물관': 378,\n",
       "  '영평사': 379,\n",
       "  '동백동산습지센터': 380,\n",
       "  '영실': 381,\n",
       "  '노무현대통령생가': 382,\n",
       "  '서라벌관광정보센터': 383,\n",
       "  '국망봉 자연휴양림': 384,\n",
       "  '꿈의 숲': 385,\n",
       "  '국립춘천박물관': 386,\n",
       "  '정선 아라리촌': 387,\n",
       "  '칼봉산자연휴양림': 388,\n",
       "  '제이드가든 수목원': 389,\n",
       "  '헤이리 예술 마을': 390,\n",
       "  '브이센터': 391,\n",
       "  '고모산성': 392,\n",
       "  '보롬왓': 393,\n",
       "  '신구대학 식물원': 394,\n",
       "  '삼성궁': 395,\n",
       "  '신영 영화 박물관 무비스타': 396,\n",
       "  '라페스타': 397,\n",
       "  '송정자연휴양림': 398,\n",
       "  '정선아리랑시장': 399,\n",
       "  '삼척장미공원': 400,\n",
       "  '포항운하': 401,\n",
       "  '아름다운정원 화수목': 402,\n",
       "  '육군 사관학교': 403,\n",
       "  '담양 대나무축제': 404,\n",
       "  '수암골전망대': 405,\n",
       "  'NC백화점 - 서면점': 406,\n",
       "  '마니산': 407,\n",
       "  '기당 미술관': 408,\n",
       "  '세계 민속 악기 박물관': 409,\n",
       "  '최승효 가옥': 410,\n",
       "  '밀양 한천박물관': 411,\n",
       "  '제주 곶자왈 도립공원': 412,\n",
       "  '화담숲': 413,\n",
       "  '영천시장': 414,\n",
       "  '전등사': 415,\n",
       "  '장생포 고래문화특구': 416,\n",
       "  '자재암': 417,\n",
       "  '김해관광안내센터': 418,\n",
       "  '북촌 생활사박물관': 419,\n",
       "  '남도전통미술관': 420,\n",
       "  '오도산자연휴양림': 421,\n",
       "  '죽전 카페거리': 422,\n",
       "  '호암 미술관': 423,\n",
       "  '양동 민속 마을': 424,\n",
       "  '보성율포해수풀장': 425,\n",
       "  '삼화사': 426,\n",
       "  '석빙고': 427,\n",
       "  '광주천': 428,\n",
       "  '안면암': 429,\n",
       "  '봉곡사': 430,\n",
       "  '걸포중앙공원': 431,\n",
       "  '공룡체험전시관': 432,\n",
       "  '달아공원': 433,\n",
       "  '만천하스카이워크': 434,\n",
       "  '하나개해수욕장': 435,\n",
       "  '플라토': 436,\n",
       "  '식장산': 437,\n",
       "  '배미꾸미조각공원': 438,\n",
       "  '석탄역사체험관': 439,\n",
       "  '엑스포탁구장': 440,\n",
       "  '익산 보석 박물관': 441,\n",
       "  '영은미술관': 442,\n",
       "  '제부도': 443,\n",
       "  '유등연지': 444,\n",
       "  '킨텍스': 445,\n",
       "  '청풍호반케이블카': 446,\n",
       "  '장흥댐휴게소': 447,\n",
       "  '그리스신화 박물관': 448,\n",
       "  '호평성당': 449,\n",
       "  '덕수궁': 450,\n",
       "  '공항겔러리': 451,\n",
       "  '서귀포매일올레시장': 452,\n",
       "  '기청산식물원': 453,\n",
       "  '화폐 금융 박물관': 454,\n",
       "  '대구근대역사관': 455,\n",
       "  '천곡천연동굴': 456,\n",
       "  '아트선재센터': 457,\n",
       "  '서대문 형무소 역사관': 458,\n",
       "  '사려니숲길': 459,\n",
       "  '성공회강화성당': 460,\n",
       "  '조선통신사역사관': 461,\n",
       "  '하우현 성당': 462,\n",
       "  '삼진미술관': 463,\n",
       "  '정남진 편백숲우드랜드': 464,\n",
       "  '서울시립미술관 남서울분관': 465,\n",
       "  '렛츠런팜 제주': 466,\n",
       "  '불정자연휴양림': 467,\n",
       "  '스카이라인 루지 통영': 468,\n",
       "  '삼성산': 469,\n",
       "  '신풍 신천 바다목장': 470,\n",
       "  '설악산 국립공원': 471,\n",
       "  '교룡산성': 472,\n",
       "  '영남제일관': 473,\n",
       "  '이태원어린이공원': 474,\n",
       "  '유치환 우체통 전망대': 475,\n",
       "  '독산성': 476,\n",
       "  '청양 다락골성지': 477,\n",
       "  '수도국산달동네박물관': 478,\n",
       "  '베어스 타운 리조트': 479,\n",
       "  '동대문 쇼핑몰': 480,\n",
       "  '보광사': 481,\n",
       "  '회덕동춘당': 482,\n",
       "  '기림사': 483,\n",
       "  '갓바위': 484,\n",
       "  '파계사': 485,\n",
       "  '경애왕릉': 486,\n",
       "  '귀족호도박물관': 487,\n",
       "  '김해공항관광안내소': 488,\n",
       "  '바보사거리 디자인거리': 489,\n",
       "  '장태산자연휴양림': 490,\n",
       "  '용인농촌테마파크': 491,\n",
       "  '충주 라이트월드': 492,\n",
       "  '법주사': 493,\n",
       "  '원강우주지구박물관': 494,\n",
       "  '성사진관': 495,\n",
       "  '마사박물관': 496,\n",
       "  '흥부골 자연휴양림': 497,\n",
       "  '김해구지봉': 498,\n",
       "  '구인사': 499,\n",
       "  '휴애리 자연 생활 공원': 500,\n",
       "  '능파대': 501,\n",
       "  '아라리오 갤러리': 502,\n",
       "  '궷물오름': 503,\n",
       "  '고령 대가야유적지': 504,\n",
       "  '산청 한방테마파크': 505,\n",
       "  '영천 전투메모리얼파크': 506,\n",
       "  '신성리 갈대밭': 507,\n",
       "  '제주 민속자연사 박물관': 508,\n",
       "  '대금산': 509,\n",
       "  '아쿠아플라넷 여수': 510,\n",
       "  '무등산 증심사': 511,\n",
       "  ' 해동 용궁사': 512,\n",
       "  '지산포레스트리조트': 513,\n",
       "  '파주 삼릉': 514,\n",
       "  '수종사': 515,\n",
       "  '고려궁지': 516,\n",
       "  '증권박물관 - 부산': 517,\n",
       "  '전주 역사 박물관': 518,\n",
       "  '경인 미술관': 519,\n",
       "  '안동태사묘': 520,\n",
       "  '테마파크 프시케월드': 521,\n",
       "  '충무로 애완동물 거리': 522,\n",
       "  '수원종합운동장': 523,\n",
       "  '러브뮤지엄': 524,\n",
       "  '경북대학교 자연사박물관': 525,\n",
       "  '풍납토성': 526,\n",
       "  '제주 올레길': 527,\n",
       "  '미리내 성지': 528,\n",
       "  '아쿠아 플라넷 제주': 529,\n",
       "  '롯데워터파크': 530,\n",
       "  'Cheongju Art Hall': 531,\n",
       "  '역전시장': 532,\n",
       "  '윤선생생활탁구교실': 533,\n",
       "  '당진항만관광공사': 534,\n",
       "  '선운정사': 535,\n",
       "  '달기약수탕': 536,\n",
       "  '평촌아트홀': 537,\n",
       "  '메세나폴리스몰': 538,\n",
       "  '중미산 휴양림': 539,\n",
       "  '쌍용계곡': 540,\n",
       "  '잠월미술관': 541,\n",
       "  '여수교동시장': 542,\n",
       "  '자갈치 시장': 543,\n",
       "  '좌동재래시장': 544,\n",
       "  '거여공원': 545,\n",
       "  '안동 민속촌': 546,\n",
       "  'Jong Ie Nara Paper Art Museum': 547,\n",
       "  '엑스포 과학공원': 548,\n",
       "  '김포시청': 549,\n",
       "  '경북대학교 박물관': 550,\n",
       "  '옥정호 구절초테마공원': 551,\n",
       "  '이순신영상관': 552,\n",
       "  '태안사': 553,\n",
       "  'BIFF 광장': 554,\n",
       "  'KTX': 555,\n",
       "  '5·18 기념공원': 556,\n",
       "  '은평공원': 557,\n",
       "  '평화의댐 - 화천': 558,\n",
       "  '함덕 서우봉 해변': 559,\n",
       "  '백운사': 560,\n",
       "  '한국차박물관': 561,\n",
       "  '가로수길': 562,\n",
       "  '한과 문화박물관 한가원': 563,\n",
       "  '정방사': 564,\n",
       "  '5.18 Archives': 565,\n",
       "  '안중근의사 기념관': 566,\n",
       "  '남산타운문화체육센터': 567,\n",
       "  '옹기 민속 박물관': 568,\n",
       "  '세계꽃 식물원': 569,\n",
       "  '광주 시립 민속 박물관': 570,\n",
       "  '부산 박물관': 571,\n",
       "  '은평역사한옥박물관': 572,\n",
       "  '창신동 네팔 음식거리': 573,\n",
       "  'KT스퀘어': 574,\n",
       "  '속초해수욕장': 575,\n",
       "  '안산식물원': 576,\n",
       "  '원창역사': 577,\n",
       "  '국립일제강제동원역사관': 578,\n",
       "  '계산노인문화센터': 579,\n",
       "  '오슬로파크': 580,\n",
       "  '제황산공원': 581,\n",
       "  '대전예술가의집': 582,\n",
       "  '전주 한지문화축제': 583,\n",
       "  '향호': 584,\n",
       "  '경리단길': 585,\n",
       "  '북부시장': 586,\n",
       "  '고려대학교': 587,\n",
       "  '통도사': 588,\n",
       "  '의정부 실내빙상장': 589,\n",
       "  '진도개테마파크': 590,\n",
       "  'Trunk Gallery': 591,\n",
       "  '조선대학교박물관': 592,\n",
       "  '신안갯벌센터 슬로시티센터': 593,\n",
       "  '서문 시장': 594,\n",
       "  '부산어린이대공원': 595,\n",
       "  '대전도시철도': 596,\n",
       "  '서울 석촌동 고분군': 597,\n",
       "  '김녕사굴': 598,\n",
       "  '문수야구장': 599,\n",
       "  '경주 월성(반월성)': 600,\n",
       "  '마애여래입상': 601,\n",
       "  '대관령 양떼목장': 602,\n",
       "  '경상북도수목원': 603,\n",
       "  '문무대왕릉': 604,\n",
       "  '경주 남산': 605,\n",
       "  '신촌': 606,\n",
       "  '민락수변공원': 607,\n",
       "  '주문진항': 608,\n",
       "  '만천하 알파인코스터': 609,\n",
       "  '장호항': 610,\n",
       "  '익산쌍릉': 611,\n",
       "  '여수 해양 레일바이크': 612,\n",
       "  '김해시민의종': 613,\n",
       "  '청간정': 614,\n",
       "  '천안시 홍대용과학관': 615,\n",
       "  '유관순열사기념관': 616,\n",
       "  '따라비 오름': 617,\n",
       "  '한려 해상 국립공원': 618,\n",
       "  '제주 돌문화 공원': 619,\n",
       "  '이순신 광장': 620,\n",
       "  '담양식영정': 621,\n",
       "  '국립해양박물관': 622,\n",
       "  '울릉도 등대(태하등대)': 623,\n",
       "  '아쿠아월드': 624,\n",
       "  '피아노 폭포': 625,\n",
       "  '매미성': 626,\n",
       "  '곽지해수욕장': 627,\n",
       "  '삼전도비': 628,\n",
       "  '동묘': 629,\n",
       "  '수리산 성지': 630,\n",
       "  '주렁주렁 - 타임스퀘어점': 631,\n",
       "  '금오산 저수지': 632,\n",
       "  '한벽당': 633,\n",
       "  '항공우주박물관': 634,\n",
       "  '백련사': 635,\n",
       "  '상록리조트 아쿠아피아': 636,\n",
       "  '감자꽃스튜디오': 637,\n",
       "  '식민지역사박물관': 638,\n",
       "  '신라역사과학관': 639,\n",
       "  '법왕사': 640,\n",
       "  '고투 몰': 641,\n",
       "  '해군사관학교박물관': 642,\n",
       "  '서울 시청': 643,\n",
       "  '이기대 섭자리': 644,\n",
       "  '눈스퀘어': 645,\n",
       "  '해운대 달맞이길': 646,\n",
       "  '비로봉': 647,\n",
       "  '경희대학교 혜정박물관': 648,\n",
       "  '광릉': 649,\n",
       "  'High1 Ski Resort': 650,\n",
       "  '안동하회마을': 651,\n",
       "  '캘리포니아 비치': 652,\n",
       "  '샤롯데 가든 - 롯데백화점 센텀시티': 653,\n",
       "  '고창,화순,강화 고인돌유적': 654,\n",
       "  '통영활어시장': 655,\n",
       "  '해운대 영화의거리': 656,\n",
       "  '화계사': 657,\n",
       "  '전주 자연생태관': 658,\n",
       "  '춘천막국수체험박물관': 659,\n",
       "  '부산종합관광안내소': 660,\n",
       "  '안산와스타디움': 661,\n",
       "  '강화산성': 662,\n",
       "  '양산시립박물관': 663,\n",
       "  '서울빛초롱축제': 664,\n",
       "  '제주국제평화센터': 665,\n",
       "  '신세계 백화점 본점': 666,\n",
       "  '새섬': 667,\n",
       "  '고남패총박물관': 668,\n",
       "  '아르코미술관': 669,\n",
       "  '단양 구낭굴 유적': 670,\n",
       "  '무령왕릉과 송산리 고분군': 671,\n",
       "  '이화벽화마을': 672,\n",
       "  '우정박물관': 673,\n",
       "  '대전 곤충생태관': 674,\n",
       "  '강진만생태공원': 675,\n",
       "  '도솔암': 676,\n",
       "  '유교문화박물관': 677,\n",
       "  '의병박물관': 678,\n",
       "  '지니어스 로사이': 679,\n",
       "  '도원유원지': 680,\n",
       "  '주문진 관광안내센터': 681,\n",
       "  '돌하르방 공원': 682,\n",
       "  '철암 탄광역사촌': 683,\n",
       "  '탄도항': 684,\n",
       "  '통고산자연휴양림': 685,\n",
       "  '라뜰리에': 686,\n",
       "  '부평역사박물관': 687,\n",
       "  '커피박물관': 688,\n",
       "  '지리산 천왕봉': 689,\n",
       "  '꽃시계': 690,\n",
       "  '현산공원': 691,\n",
       "  '문묘': 692,\n",
       "  '남선공원종합체육관': 693,\n",
       "  '이응노 미술관': 694,\n",
       "  '문암해변': 695,\n",
       "  '채미정': 696,\n",
       "  '의암호 스카이워크': 697,\n",
       "  '신매시장': 698,\n",
       "  '휘닉스 스노우 파크': 699,\n",
       "  '대진해수욕장': 700,\n",
       "  '분재박물관': 701,\n",
       "  '남평화 상가': 702,\n",
       "  '동화사': 703,\n",
       "  '여수 수산시장': 704,\n",
       "  '진해 드림 파크': 705,\n",
       "  '용머리 해안': 706,\n",
       "  '쌍계사': 707,\n",
       "  '쇳대박물관': 708,\n",
       "  '신화테마파크': 709,\n",
       "  '천마총': 710,\n",
       "  '이기대도시자연공원': 711,\n",
       "  '책테마파크': 712,\n",
       "  '통도환타지아': 713,\n",
       "  '하이원추추파크': 714,\n",
       "  '대관령 삼양목장': 715,\n",
       "  '흔들바위': 716,\n",
       "  '도선사': 717,\n",
       "  '금오민속박물관': 718,\n",
       "  '청주시청': 719,\n",
       "  '예당호 출렁다리': 720,\n",
       "  '영남대학교박물관': 721,\n",
       "  '광주향교': 722,\n",
       "  '정와': 723,\n",
       "  '솔뫼 성지': 724,\n",
       "  '은행나무길': 725,\n",
       "  '동촌유원지': 726,\n",
       "  '연홍미술관': 727,\n",
       "  '웅천 도요지 전시관': 728,\n",
       "  '오륙도': 729,\n",
       "  '안산갈대습지': 730,\n",
       "  '유.스퀘어 문화관': 731,\n",
       "  '중문 마린파크 퍼시픽랜드': 732,\n",
       "  '양백폭포': 733,\n",
       "  '동강사진박물관': 734,\n",
       "  '김해읍성 북문': 735,\n",
       "  '마산 아구찜거리': 736,\n",
       "  '증권박물관': 737,\n",
       "  '엄마아빠 어렸을적에': 738,\n",
       "  '한국 전쟁 기념관': 739,\n",
       "  '이호테우해변': 740,\n",
       "  '정선레일바이크': 741,\n",
       "  '운주산성': 742,\n",
       "  '간송 미술관': 743,\n",
       "  '주상절리 출렁다리': 744,\n",
       "  '테디베어 박물관 제주': 745,\n",
       "  '삼청각': 746,\n",
       "  '용궁사': 747,\n",
       "  '울산종합운동장': 748,\n",
       "  '한얼문예박물관': 749,\n",
       "  '나주곰탕거리': 750,\n",
       "  '표충사': 751,\n",
       "  '척산 족욕공원': 752,\n",
       "  '독락당': 753,\n",
       "  '칠십리 시 공원': 754,\n",
       "  '창원컨벤션센터': 755,\n",
       "  '추전역': 756,\n",
       "  '중앙탑사적공원': 757,\n",
       "  '세곡문화센터': 758,\n",
       "  '원주 소금산 출렁다리': 759,\n",
       "  '국립중앙과학관': 760,\n",
       "  'Busan Air Cruise': 761,\n",
       "  '화명생태공원': 762,\n",
       "  '초지진': 763,\n",
       "  '세계파충류체험박물관': 764,\n",
       "  '용인자연휴양림': 765,\n",
       "  '양주 온릉': 766,\n",
       "  '낙화암': 767,\n",
       "  '수덕사': 768,\n",
       "  '팔영산자연휴양림': 769,\n",
       "  '국립국악원': 770,\n",
       "  '감은사지': 771,\n",
       "  '고석정': 772,\n",
       "  '강주해바라기마을': 773,\n",
       "  '파크 써던랜드': 774,\n",
       "  '김일기념체육관': 775,\n",
       "  '평화공원': 776,\n",
       "  '타워갤러리': 777,\n",
       "  '영주 인삼 박물관': 778,\n",
       "  '다이노스타 - 용인점': 779,\n",
       "  '도산 안창호 기념관': 780,\n",
       "  '강촌 레일 파크': 781,\n",
       "  '전포카페거리': 782,\n",
       "  '월미테마파크': 783,\n",
       "  '수안보 벚꽃길': 784,\n",
       "  '호조벌': 785,\n",
       "  '부천 원미공원': 786,\n",
       "  '광희 패션몰': 787,\n",
       "  '광주-기아 챔피언스 필드': 788,\n",
       "  '익산 교도소 세트장': 789,\n",
       "  '걸매생태공원': 790,\n",
       "  '대정오일시장': 791,\n",
       "  '수도사': 792,\n",
       "  '정자동카페골목': 793,\n",
       "  '송지호해수욕장': 794,\n",
       "  '바람의언덕': 795,\n",
       "  '한려수도 조망 케이블카': 796,\n",
       "  '장등 해수욕장': 797,\n",
       "  '울산소리체험관': 798,\n",
       "  '부천향토역사관': 799,\n",
       "  '인천대학교': 800,\n",
       "  '장복산': 801,\n",
       "  '동학혁명기념관': 802,\n",
       "  '무학시장': 803,\n",
       "  '삼척 해상케이블카': 804,\n",
       "  '부천물박물관': 805,\n",
       "  '여주도자기축제': 806,\n",
       "  '화왕산': 807,\n",
       "  '칠포해수욕장': 808,\n",
       "  '한탄강 하늘다리': 809,\n",
       "  '초량이바구길': 810,\n",
       "  '남강 의암': 811,\n",
       "  '국립진주박물관': 812,\n",
       "  '도도불가마스포츠센터': 813,\n",
       "  '장생포고래문화마을': 814,\n",
       "  '대구 옻골마을': 815,\n",
       "  '테미공원': 816,\n",
       "  '수락산': 817,\n",
       "  '직지문화공원': 818,\n",
       "  '모래 시계 공원': 819,\n",
       "  '사춤 시어터': 820,\n",
       "  '매물도': 821,\n",
       "  '예천곤충연구소 곤충생태원': 822,\n",
       "  '비발디파크 오션월드': 823,\n",
       "  '숭례문': 824,\n",
       "  '포항여객터미널': 825,\n",
       "  '영일대해수욕장': 826,\n",
       "  '칠성시장': 827,\n",
       "  '안성 남사당공연장': 828,\n",
       "  '장산': 829,\n",
       "  '대백프라자': 830,\n",
       "  '성진골 벽화마을': 831,\n",
       "  '효원의 종': 832,\n",
       "  '설악 워터피아': 833,\n",
       "  '세븐럭카지노 강북힐튼점': 834,\n",
       "  '슬도등대': 835,\n",
       "  '계룡산자연사박물관': 836,\n",
       "  '만인의총': 837,\n",
       "  '고삼호수': 838,\n",
       "  '약령시관광안내소': 839,\n",
       "  '융릉과 건릉': 840,\n",
       "  '마산어시장': 841,\n",
       "  '이화 벽화마을': 842,\n",
       "  '충무아트센터': 843,\n",
       "  '보성 비봉공룡공원': 844,\n",
       "  '신지명사십리 해수욕장': 845,\n",
       "  '준상이네집 (겨울연가 촬영지)': 846,\n",
       "  '아이파크몰': 847,\n",
       "  '해저터널': 848,\n",
       "  '남해보물섬전망대 & 스카이워크': 849,\n",
       "  '잠실종합운동장': 850,\n",
       "  '진도 팽목항': 851,\n",
       "  '허브랑야생화': 852,\n",
       "  '파주나비나라 박물관': 853,\n",
       "  '영월종교미술박물관': 854,\n",
       "  '울산암각화박물관': 855,\n",
       "  '시지MBC문화센터': 856,\n",
       "  '봉래폭포': 857,\n",
       "  '국립 과천 과학관': 858,\n",
       "  '순천 아랫장 시장': 859,\n",
       "  '경산시립박물관': 860,\n",
       "  'IFC 몰': 861,\n",
       "  '거제 씨월드': 862,\n",
       "  '태백산맥문학관': 863,\n",
       "  '백범광장': 864,\n",
       "  '계명산자연휴양림': 865,\n",
       "  '의성마늘박물관': 866,\n",
       "  '거제어촌민속전시관': 867,\n",
       "  '동대문 디자인 플라자': 868,\n",
       "  '강암 서예관': 869,\n",
       "  '김시식지': 870,\n",
       "  '해수바이오유황온천': 871,\n",
       "  '두꺼비생태문화관': 872,\n",
       "  '한화생명이글스파크': 873,\n",
       "  '보탑사': 874,\n",
       "  '한상수자수박물관': 875,\n",
       "  '신세계 백화점 센텀시티': 876,\n",
       "  '이중섭 미술관': 877,\n",
       "  '동국사': 878,\n",
       "  '전곡리 선사유적지': 879,\n",
       "  '왕인박사 유적지': 880,\n",
       "  '이화장': 881,\n",
       "  '산악박물관': 882,\n",
       "  '교동향교': 883,\n",
       "  '만휴정': 884,\n",
       "  '국립극장': 885,\n",
       "  '청평호': 886,\n",
       "  '석불사': 887,\n",
       "  '직지사': 888,\n",
       "  '나주 학생독립운동 기념관': 889,\n",
       "  '우전해수욕장': 890,\n",
       "  '부산과학체험관': 891,\n",
       "  '보물섬마늘나라전시관': 892,\n",
       "  '강천산 군립공원': 893,\n",
       "  '서울 시내버스': 894,\n",
       "  '용마랜드': 895,\n",
       "  '달아공원': 896,\n",
       "  '장충체육관': 897,\n",
       "  '지혜의숲': 898,\n",
       "  '문화공간 주차': 899,\n",
       "  '종로타워': 900,\n",
       "  '용화해수욕장': 901,\n",
       "  '부평아트센터': 902,\n",
       "  '동대문운동장기념관': 903,\n",
       "  '김경숙해바라기농장': 904,\n",
       "  '너븐숭이4.3기념관': 905,\n",
       "  '안동 군자마을': 906,\n",
       "  '상족암 군립공원': 907,\n",
       "  '대구 미술관': 908,\n",
       "  '팔용산': 909,\n",
       "  '진해 내수면 환경생태공원': 910,\n",
       "  '한국 지역정보 센터': 911,\n",
       "  '한국 대나무 박물관': 912,\n",
       "  '우일선선교사사택': 913,\n",
       "  '강화풍물시장': 914,\n",
       "  '오목대 이목대': 915,\n",
       "  '청담삼익시장': 916,\n",
       "  '괴산고추축제': 917,\n",
       "  '장충동 족발골목': 918,\n",
       "  '에스알티': 919,\n",
       "  '정지용 생가': 920,\n",
       "  '헬로에이피엠': 921,\n",
       "  '국제현대미술관': 922,\n",
       "  '뮤지엄김치간': 923,\n",
       "  '엑스포다리': 924,\n",
       "  '쌍용굴': 925,\n",
       "  '약현성당': 926,\n",
       "  '영산포 등대': 927,\n",
       "  '뿌리공원': 928,\n",
       "  '뚝섬 한강공원 수영장': 929,\n",
       "  '문화비축기지': 930,\n",
       "  '의성 금성산 고분군': 931,\n",
       "  '제주 유리의 성': 932,\n",
       "  '동탄 센트럴 파크': 933,\n",
       "  '몽마르뜨 공원': 934,\n",
       "  '관광 통역 안내소': 935,\n",
       "  '서울스카이': 936,\n",
       "  '송악산': 937,\n",
       "  '국립생물자원관': 938,\n",
       "  '마리오 아울렛': 939,\n",
       "  '휘닉스 블루캐니언': 940,\n",
       "  '부천 식물원': 941,\n",
       "  '화방사': 942,\n",
       "  '불사리 탑사': 943,\n",
       "  '상해거리': 944,\n",
       "  '동명동 카페거리': 945,\n",
       "  '표선해비치해변': 946,\n",
       "  '개화예술공원': 947,\n",
       "  '돌산 공원': 948,\n",
       "  '홍대앞 예술시장 프리마켓': 949,\n",
       "  '40계단 문화관': 950,\n",
       "  '을왕리해수욕장': 951,\n",
       "  '여의도 공원': 952,\n",
       "  '강남스타일 말춤 무대': 953,\n",
       "  '한강 공원': 954,\n",
       "  '대전근현대사전시관': 955,\n",
       "  '백사실 계곡': 956,\n",
       "  '이태원 앤틱 가구 거리': 957,\n",
       "  '아라리오뮤지엄 탑동바이크샵': 958,\n",
       "  '창원의 집': 959,\n",
       "  '도피안사': 960,\n",
       "  '학암포해수욕장': 961,\n",
       "  '울산바위': 962,\n",
       "  '도당공원': 963,\n",
       "  '정동심곡 바다부채길': 964,\n",
       "  '줄포만갯벌생태공원': 965,\n",
       "  '소공지하도상가': 966,\n",
       "  '팔용산 돌탑': 967,\n",
       "  '주산지': 968,\n",
       "  '태평소금세상': 969,\n",
       "  '내연산 군립 공원': 970,\n",
       "  '천은사': 971,\n",
       "  '이천시립박물관': 972,\n",
       "  '감악산 출렁다리': 973,\n",
       "  '신정호': 974,\n",
       "  '부평시장': 975,\n",
       "  '김포 장릉': 976,\n",
       "  '서대문 자연사 박물관': 977,\n",
       "  '분황사': 978,\n",
       "  '유림공원': 979,\n",
       "  '진포해양테마공원': 980,\n",
       "  '금관총': 981,\n",
       "  '옥연정사': 982,\n",
       "  '국립 등대 박물관': 983,\n",
       "  '고양 아람 누리 예술 센터': 984,\n",
       "  '짜장면 박물관': 985,\n",
       "  '전라병영성 하멜기념관': 986,\n",
       "  '한탄강 관광지': 987,\n",
       "  '환경재생조형박물관': 988,\n",
       "  '국립 해양 문화재 연구소 해양 박물관': 989,\n",
       "  '조곡동 철도문화마을': 990,\n",
       "  '전주 한지 박물관': 991,\n",
       "  '면앙정': 992,\n",
       "  '피크아일랜드': 993,\n",
       "  '앞산공원': 994,\n",
       "  '홍대앞 거리': 995,\n",
       "  '목포대교': 996,\n",
       "  '아라리오뮤지엄 인 스페이스': 997,\n",
       "  '서소문 성지역사박물관': 998,\n",
       "  '한천농악전수회관': 999,\n",
       "  ...})"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_dict = dict()\n",
    "i=0\n",
    "for uid in list(set(raw_data.uid)):\n",
    "    user_dict[uid] = i\n",
    "    i+=1\n",
    "    \n",
    "place_dict = dict()\n",
    "i=0\n",
    "for place in list(set(raw_data.place)):\n",
    "    place_dict[place] = i\n",
    "    i+=1\n",
    "    \n",
    "user_dict, place_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\170544\\Miniconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\170544\\Miniconda3\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(raw_data)):\n",
    "    raw_data.uid[i] = user_dict[raw_data.uid[i]]\n",
    "    \n",
    "for i in range(len(raw_data)):\n",
    "    raw_data.place[i] = place_dict[raw_data.place[i]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data splitting procedure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_count(tp, id):\n",
    "    playcount_groupbyid = tp[[id]].groupby(id, as_index=False)\n",
    "    count = playcount_groupbyid.size()\n",
    "    return count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_triplets(tp, min_uc=5, min_sc=0):\n",
    "    # Only keep the triplets for items which were clicked on by at least min_sc users. \n",
    "    if min_sc > 0:\n",
    "        itemcount = get_count(tp, 'place')\n",
    "        tp = tp[tp['place'].isin(itemcount.index[itemcount >= min_sc])]\n",
    "    \n",
    "    # Only keep the triplets for users who clicked on at least min_uc items\n",
    "    # After doing this, some of the items will have less than min_uc users, but should only be a small proportion\n",
    "    if min_uc > 0:\n",
    "        usercount = get_count(tp, 'uid')\n",
    "        tp = tp[tp['uid'].isin(usercount.index[usercount >= min_uc])]\n",
    "    \n",
    "    # Update both usercount and itemcount after filtering\n",
    "    usercount, itemcount = get_count(tp, 'uid'), get_count(tp, 'place') \n",
    "    return tp, usercount, itemcount"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only keep users who have watched at least 5 movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_user_count = 5 if use_filtering else 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data, user_activity, item_popularity = filter_triplets(raw_data, min_uc=min_user_count, min_sc=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After filtering, there are 42679 watching events from 2697 users and 2974 movies (sparsity: 0.532%)\n"
     ]
    }
   ],
   "source": [
    "sparsity = 1. * raw_data.shape[0] / (user_activity.shape[0] * item_popularity.shape[0])\n",
    "\n",
    "print(\"After filtering, there are %d watching events from %d users and %d movies (sparsity: %.3f%%)\" % \n",
    "      (raw_data.shape[0], user_activity.shape[0], item_popularity.shape[0], sparsity * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_uid = user_activity.index\n",
    "\n",
    "np.random.seed(98765)\n",
    "idx_perm = np.random.permutation(unique_uid.size)\n",
    "unique_uid = unique_uid[idx_perm]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "if use_table == 'tripadvisor':\n",
    "    if use_filtering:\n",
    "        n_heldout_users = int(user_activity.shape[0]*0.1)\n",
    "    else:\n",
    "        n_heldout_users = 1200\n",
    "elif use_table == 'review':\n",
    "    if use_filtering:\n",
    "        n_heldout_users = int(user_activity.shape[0]*0.1)\n",
    "    else:\n",
    "        n_heldout_users = 80\n",
    "else:\n",
    "    n_heldout_users = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create train/validation/test users\n",
    "n_users = unique_uid.size\n",
    "\n",
    "tr_users = unique_uid[:(n_users - n_heldout_users * 2)]\n",
    "vd_users = unique_uid[(n_users - n_heldout_users * 2): (n_users - n_heldout_users)]\n",
    "te_users = unique_uid[(n_users - n_heldout_users):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_plays = raw_data.loc[raw_data['uid'].isin(tr_users)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_sid = pd.unique(train_plays['place'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "show2id = dict((sid, i) for (i, sid) in enumerate(unique_sid))\n",
    "profile2id = dict((pid, i) for (i, pid) in enumerate(unique_uid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "if use_table == 'tripadvisor':\n",
    "    if use_filtering:\n",
    "        pro_dir = \"./tripadvisor_relation_use_filtering/\"\n",
    "    else:\n",
    "        pro_dir = \"./tripadvisor_relation_not_filtering/\"\n",
    "elif use_table == 'review':\n",
    "    if use_filtering:\n",
    "        pro_dir = \"./tripadvisor_review_existence_use_filtering/\"\n",
    "    else:\n",
    "        pro_dir = \"./tripadvisor_review_existence_not_filtering/\"\n",
    "else:\n",
    "    pro_dir = \"./\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(pro_dir):\n",
    "    os.makedirs(pro_dir)\n",
    "\n",
    "with open(os.path.join(pro_dir, 'unique_sid.txt'), 'w') as f:\n",
    "    for sid in unique_sid:\n",
    "        f.write('%s\\n' % sid)\n",
    "\n",
    "with open(os.path.join(pro_dir, 'unique_uid.txt'), 'w') as f:\n",
    "    for uid in unique_uid:\n",
    "        f.write('%s\\n' % uid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_train_test_proportion(data, test_prop=0.2):\n",
    "    data_grouped_by_user = data.groupby('uid')\n",
    "    tr_list, te_list = list(), list()\n",
    "\n",
    "    np.random.seed(98765)\n",
    "\n",
    "    for i, (_, group) in enumerate(data_grouped_by_user):\n",
    "        n_items_u = len(group)\n",
    "        idx = np.zeros(n_items_u, dtype='bool')\n",
    "        te_size = 1\n",
    "        if n_items_u >= 5:\n",
    "            te_size = int(test_prop * n_items_u)\n",
    "            \n",
    "        idx[np.random.choice(n_items_u, size=te_size, replace=False).astype('int64')] = True\n",
    "        tr_list.append(group[np.logical_not(idx)])\n",
    "        te_list.append(group[idx])\n",
    "\n",
    "        if i % 1000 == 0:\n",
    "            print(\"%d users sampled\" % i)\n",
    "            sys.stdout.flush()\n",
    "\n",
    "    data_tr = pd.concat(tr_list)\n",
    "    data_te = pd.concat(te_list)\n",
    "    \n",
    "    return data_tr, data_te"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "vad_plays = raw_data.loc[raw_data['uid'].isin(vd_users)]\n",
    "vad_plays = vad_plays.loc[vad_plays['place'].isin(unique_sid)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "vad_plays, _, _ = filter_triplets(vad_plays, min_uc=min_user_count, min_sc=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 users sampled\n"
     ]
    }
   ],
   "source": [
    "vad_plays_tr, vad_plays_te = split_train_test_proportion(vad_plays)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_plays = raw_data.loc[raw_data['uid'].isin(te_users)]\n",
    "test_plays = test_plays.loc[test_plays['place'].isin(unique_sid)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 users sampled\n"
     ]
    }
   ],
   "source": [
    "test_plays_tr, test_plays_te = split_train_test_proportion(test_plays)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_plays, _, _ = filter_triplets(test_plays, min_uc=min_user_count, min_sc=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(269, 269)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(test_plays_tr.uid)), len(set(test_plays_te.uid))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the data into (user_index, item_index) format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def numerize(tp):\n",
    "    uid = list(map(lambda x: profile2id[x], tp['uid']))\n",
    "    sid = list(map(lambda x: show2id[x], tp['place']))\n",
    "    value = list(map(lambda x: x, tp['rating']))\n",
    "    return pd.DataFrame(data={'uid': uid, 'sid': sid, 'value': value}, columns=['uid', 'sid', 'value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = numerize(train_plays)\n",
    "train_data.to_csv(os.path.join(pro_dir, 'train.csv'), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "vad_data_tr = numerize(vad_plays_tr)\n",
    "vad_data_tr.to_csv(os.path.join(pro_dir, 'validation_tr.csv'), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "vad_data_te = numerize(vad_plays_te)\n",
    "vad_data_te.to_csv(os.path.join(pro_dir, 'validation_te.csv'), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_tr = numerize(test_plays_tr)\n",
    "test_data_tr.to_csv(os.path.join(pro_dir, 'test_tr.csv'), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_te = numerize(test_plays_te)\n",
    "test_data_te.to_csv(os.path.join(pro_dir, 'test_te.csv'), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "tr = list()\n",
    "for i, (_, group) in enumerate(test_data_tr.groupby('uid')):\n",
    "    n_items_u = len(group)\n",
    "    tr.append(n_items_u)\n",
    "    \n",
    "te = list()\n",
    "for i, (_, group) in enumerate(test_data_te.groupby('uid')):\n",
    "    n_items_u = len(group)\n",
    "    te.append(n_items_u)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "17 4\n",
      "5 1\n",
      "4 1\n",
      "5 1\n",
      "13 3\n",
      "9 2\n",
      "7 1\n",
      "12 2\n",
      "8 1\n",
      "17 4\n",
      "5 1\n",
      "7 1\n",
      "25 6\n",
      "5 1\n",
      "24 5\n",
      "4 1\n",
      "13 3\n",
      "5 1\n",
      "103 25\n",
      "32 7\n",
      "52 13\n",
      "5 1\n",
      "6 1\n",
      "6 1\n",
      "14 3\n",
      "17 4\n",
      "10 2\n",
      "20 4\n",
      "4 1\n",
      "10 2\n",
      "10 2\n",
      "6 1\n",
      "16 4\n",
      "8 1\n",
      "9 2\n",
      "9 2\n",
      "4 1\n",
      "4 1\n",
      "16 4\n",
      "8 1\n",
      "5 1\n",
      "12 2\n",
      "7 1\n",
      "8 2\n",
      "9 2\n",
      "5 1\n",
      "4 1\n",
      "8 2\n",
      "4 1\n",
      "12 2\n",
      "9 2\n",
      "23 5\n",
      "80 19\n",
      "8 2\n",
      "14 3\n",
      "19 4\n",
      "6 1\n",
      "20 4\n",
      "39 9\n",
      "6 1\n",
      "7 1\n",
      "8 2\n",
      "4 1\n",
      "16 3\n",
      "13 3\n",
      "4 1\n",
      "32 7\n",
      "19 4\n",
      "6 1\n",
      "12 2\n",
      "5 1\n",
      "4 1\n",
      "12 3\n",
      "24 5\n",
      "8 2\n",
      "6 1\n",
      "5 1\n",
      "8 1\n",
      "5 1\n",
      "7 1\n",
      "6 1\n",
      "4 1\n",
      "4 1\n",
      "4 1\n",
      "40 9\n",
      "12 3\n",
      "9 2\n",
      "5 1\n",
      "27 6\n",
      "5 1\n",
      "4 1\n",
      "39 9\n",
      "4 1\n",
      "4 1\n",
      "5 1\n",
      "4 1\n",
      "8 1\n",
      "24 6\n",
      "20 5\n",
      "9 2\n",
      "4 1\n",
      "16 3\n",
      "17 4\n",
      "6 1\n",
      "7 1\n",
      "11 2\n",
      "12 2\n",
      "7 1\n",
      "12 3\n",
      "14 3\n",
      "14 3\n",
      "7 1\n",
      "6 1\n",
      "47 11\n",
      "5 1\n",
      "4 1\n",
      "8 1\n",
      "10 2\n",
      "10 2\n",
      "11 2\n",
      "6 1\n",
      "76 19\n",
      "35 8\n",
      "44 11\n",
      "12 3\n",
      "6 1\n",
      "26 6\n",
      "32 7\n",
      "8 1\n",
      "13 3\n"
     ]
    }
   ],
   "source": [
    "for i,j in zip(tr,te):\n",
    "    print(i,j)"
   ]
  }
 ],
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